Nic and I sat down with Nikhil Basu Trivedi, co-founder and GP of Footwork VC and a 16-year VC veteran (early Canva, Lattice, Frame.io), on the question every operator is quietly asking: in a market where the model companies eat horizontal categories overnight and 20 funded competitors crowd every obvious idea, where are the parts of the map you can actually survive on? We map the kill zones to avoid, the moats that still compound (brand, networks, workflow trust), the unloved low-NPS verticals quietly ripe for AI-native challengers, and the consumer-meets-enterprise zone where the biggest companies of the cycle are already being built.

Most of the map is a kill zone.
Find the opportunity.

The honest version of building in vertical AI today is that most of the obvious map is hostile territory. The model companies eat horizontal workflows on a quarterly cadence. The consensus categories are auctions with 20 funded competitors before the round even closes. The old SaaS moats — features, integrations, a tidy ICP — get re-priced overnight. Nikhil’s frame for cutting through it isn’t a thesis, it’s a survival map: a small set of zones on the board where a vertical AI company can actually compound, and a much larger set you should refuse to play on at all.

The first survival question is the kill zone: how badly does your product overlap with what Anthropic or OpenAI is going to ship horizontally next year? Pure document review, generic Q&A, white-collar workflows that live happily in a Claude tab — the labs will eat. The survivors are products that need proprietary data, complicated integrations, or workflows that simply do not fit a chat box. If a one-pager titled “why a Claude tab can’t do this in 12 months” writes itself, you’re on the safe side of the map. If it doesn’t, change the product before you raise.

The second is the moats that still compound: in this era, very few do. Brand is the one technologists chronically underrate and the one Nikhil is most bullish on — the iPhone effect, where users simply do not switch off the products they trust, even when something technically better ships. Real network effects are the rarest and most valuable; the next LinkedIn, the next ACV Auctions. And there is workflow trust in traditional industries — the quiet truth that, once a credit union or a pharma company actually likes you, they do not want to re-evaluate every six months. Build one of these or you’re racing on execution speed alone, which is a brutal place to live.

The third is where on the map to actually build: the unloved low-NPS verticals everyone else ignored. CPG (a $3T+ category with 10,000 brands doing $100M+, almost no software at scale). Credit unions. Blood testing. Pharma research. Accounting back-offices. Antifragile demand, terrible incumbents, no real software heritage — and the buyer is finally ready, because their staff is using ChatGPT at home and asking why their forty-year-old EHR can’t do the same. And quietly underneath all of it, the founders Nikhil keeps backing share one trait: years of having lived in or studied the buyer. Domain depth, not pedigree, is the unlock. The rest of this issue is the full map.

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This Weeks Vertical Titan:
Nikhil Basu Trivedi
(Co-Founder & GP @ Footwork VC)

Nikhil started his VC career nearly two decades ago. He co-founded Footwork five years ago with one partner and built it deliberately small: just the two GPs as the entire investment team, leading the seed or Series A in roughly five companies a year. Footwork is now investing its second fund — $225M, following a $175M first fund — and has made 25 investments to date.

Before Footwork, Nikhil was an early backer of Canva (led the seed in 2014), Lattice, and Frame.io. The Footwork portfolio today reads as a deliberate refusal of the consumer/enterprise binary — Elicit (AI for scientific research, co-led with Spot), Fuse (AI-native loan origination for credit unions, banks, auto lenders), Confido (the source of truth for ops, sales and finance at $100M+ CPG brands), plus stealth investments in blood testing and AI-native services in pharma and financial services.

The frame he keeps coming back to: roughly three-quarters of Footwork’s portfolio self-identifies as both consumer and enterprise. That isn’t a market-sizing trick. It’s a bet that the very biggest companies of this decade will be the ones with the audacity to conquer multiple markets — and that picking a side too early is quietly the most expensive mistake a founder can make.

And the line every founder should write on a wall: Nikhil is looking for teams with “years of having studied the buyer.” In the unsexy verticals where the customer is skeptical and the AI thesis isn’t pre-sold, that domain depth — not pedigree — is the unlock. Several of his best founders are first-time entrepreneurs in their 20s who simply know the back office cold

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Ten coordinates on the survival map.

Operator-grade lessons for the things you’re actually deciding this week — which part of the map is safe to build on, which moats actually compound, which “stupid” vertical to attack, and which categories to walk away from before the model companies eat them — written for founders shipping on Monday morning, not the memo author a year later.

#01: Step one on the map: refuse the consumer/enterprise box

For twenty years the industry trained founders and investors to pick a side. Consumer team or enterprise team. PLG funnel or top-down sales motion. Nikhil’s contrarian read: the biggest companies in the world serve both, and almost always became both over time. Canva did $4B+ of ARR organically spreading from consumers to prosumers to the enterprise — $600M+ of that ARR now sits in the enterprise. SpaceX is a consumer subscription business (Starlink) renting B2B capacity to Colossus. OpenAI, Anthropic, Mercor, Open Evidence — every defining company of this cycle quietly carries both DNAs.

On the survival map, the binary itself is a trap. Picking one side caps your ceiling and quietly hands the other side to a competitor who didn’t. Three-quarters of Footwork’s portfolio refuses the box on purpose — not as a market-size argument but as a product-quality one. The bar set by ChatGPT and Claude at home is now the bar inside the enterprise. Build for one audience only and you’ll be out-magicked by someone who built for both.

Action item: Stop fighting over the consumer vs. enterprise label in your seed deck. Map both wedges. Pick the one you ship first — but design product, brand and pricing as if the other is two years away, not never.

#02: Earn the end user — it’s the cheapest path through the map

Elicit started as a self-serve AI product for individual researchers. Today it has enterprise-wide licenses with multiple top-20 pharma companies — none of which were sold top-down. Open Evidence didn’t pitch hospital systems; it earned physicians one at a time and let the bottoms-up demand pull the contract through. Both quietly went around the procurement playbook every legacy vertical software company is still running.

On the survival map, end-user love is the cheapest distribution you can buy. When the software is doing the work alongside a human — not just recording it — it has to feel personal, immediate, and trustworthy in the first 90 seconds. That’s a consumer bar, not a buying-committee one. Founders who internalize that ship a fundamentally different product, and quietly route around the incumbent sales motion entirely.

Action item: Make the first-time experience self-serve and unforgettable, even for an enterprise-bound product. Track activated end-users per company before you track logos.

#03: Plan for the B2B2C act — it’s a free moat on the map

Fuse is building an AI-native loan origination system for credit unions, banks, auto lenders. It is not, on the tin, a consumer product. But the byproduct of doing the job well is a dramatically better experience for the consumer applying for the loan — and that consumer is otherwise being courted by a neobank with a much sharper UI. Toast is the cautionary tale: a vertical software darling that never built a real consumer brand and is now vulnerable to DoorDash owning the moment of intent.

Almost every vertical SaaS company at scale ends up B2B2C — through embedded payments, financing, marketplaces, or just by being the touchpoint where the end customer experiences your buyer’s business. The founders who plan for that arc early get an extra moat for free. The ones who ignore it leave it for someone else to take.

Action item: Audit where the end consumer touches your product even when you sell B2B. Build for that surface as if it’s a standalone consumer brand — because eventually it will be one.

#04. Brand is the most underrated AI moat — start compounding it now

Nikhil’s most contrarian take: when the post-mortem of this AI era is written, brand will be the moat that quietly held everything together. People don’t switch off the products they trust with their data and their workflow, even when a technically superior competitor launches. iPhone is the case study — the flip phones and folding phones are objectively cool, and almost no one moves. The second Apple ships something, the entire market shifts back. That’s brand doing the work.

In vertical AI this is enormous, because the incumbents you’re attacking — your LabCorps, your 40-year-old EHRs, your legacy ERPs — have terrible NPS but have brand inertia. The opening is real, but only if you build a brand operators love. Capital can help; what compounds faster is a small set of early customers who are extraordinarily happy and tell their friends. That word-of-mouth in a small, tight-knit vertical is the hardest thing for a better-funded latecomer to replicate.

Action item: Treat early NPS, customer love and word-of-mouth in your vertical as a balance-sheet asset. Measure them. Resource them. Don’t outsource them.

#05. Stay out of the LLM kill zone — pick the workflows chat can’t do

Nobody can prognosticate where the model companies are in five years, but you can absolutely choose not to stand in their path. Footwork’s underwriting filter: is this product fundamentally different from what Anthropic or OpenAI will ship horizontally? Is there a real reason for stickiness — proprietary data, complicated integrations, workflows that simply do not fit a chat interface? If not, it’s a thin bet no matter how strong the team.

It’s even worse if your core value prop overlaps with what the LLM already does well. Pure document review, generic Q&A, anything that lives comfortably in a Claude tab — the labs will eat. The work for founders is to find the parts of the job where a chat box is structurally the wrong UI, where the data is hard to get, and where the integration cost is the moat. That’s where defensibility quietly lives.

Action item: Write a one-pager titled ‘Why a Claude tab can’t do this in 12 months.’ If you can’t fill the page, change the product before you raise.

#06. Hunt the legacy verticals with terrible NPS and no software heritage

Ask a room of founders to name a venture-backed software company at scale selling into the CPG industry. Long pause. CPG is a top-six GDP category — $3T+ of spend — with 10,000 brands in the US doing more than $100M of revenue each, almost none of them well-served by software. Footwork’s portfolio company Confido is building the source of truth across ops, sales and finance for those brands, integrating with every retailer, doing trade promotion management and forecasting that has been manual until literally now.

These categories are ‘unloved’ for a reason: they’re harder to sell into, the buyer is more skeptical, the AI tailwind isn’t obvious to the customer yet. But they are also antifragile — people will keep eating, drinking, getting blood drawn, applying for credit-union loans, taking GLP-1s — and they are wildly underpenetrated by software. The boring vertical with low NPS incumbents and a real labor problem is, in 2026, often a far better venture bet than the obvious one.

Action item: Make a list of the ‘no one would build a SaaS company there’ verticals you actually understand. Rank them by NPS of the incumbent and willingness to pay against labor. Go where the score is worst.

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#07. Network effects are still rare, still real, and worth raising your hand for

Nikhil’s 16-year dream company is the one that finally unseats LinkedIn — because LinkedIn is a brutal case study in just how durable a real network effect is. The Mercor / Handshake / data-labeling space is the closest thing this cycle has to that opening: experts and talent on one side, businesses on the other. Whoever actually builds the next professional network — the one where people show their skills, not just their resumes — is the real long-term threat in that category.

The broader point: network effects in this AI era are quietly some of the most powerful moats still available. ACV Auctions in car auctions, a category nobody would have predicted to be network-effect driven, is the archetype. If you have a real one — or sit on a latent network like Handshake did — it is one of the very few assets that compounds against falling model prices and accelerating competition.

Action item: Map the two-sided dynamics inside your vertical. If you can credibly seed both sides with the same product, you have a moat the model companies cannot replicate by shipping a better LLM.

#08. Earn the right to do more than one thing — but only when the team can

Focus still works. Anthropic’s relentless focus on coding agents is what’s powering its run inside the enterprise; OpenAI’s split attention across consumer and enterprise arguably cost them the developer mindshare they’re now chasing back. So the playbook isn’t ‘do everything from day one.’ It’s that at some point — sooner than the SaaS playbook would suggest — the very best companies are forced to do multiple things really, really well, and the founders who built a team capable of that compound faster than the ones who didn’t.

Nikhil isn’t the board member walking in saying ‘focus less.’ He’s the one watching whether the founder is openly thinking about the second and third act, hiring leaders who can shoulder them when the time comes, and staying flexible in a market that re-prices itself every quarter. That mindset is the real differentiator between a good company and a generational one.

Action item: Keep this quarter’s focus brutal. But sketch the second act on a single page and revisit it monthly. Hire one leader this year who could credibly run it.

#09. Back domain depth — especially in the unsexy verticals

The pattern across Confido (CPG), Fuse (credit unions), Elicit (pharma research): the founders are not Silicon Valley generalists parachuting in. Several are first-time entrepreneurs in their 20s. What they share is years of having lived in or studied their buyer. In categories where the customer is more skeptical and the AI thesis isn’t pre-sold, that domain depth is the unlock. You cannot fake your way into the trust of a CPG ops leader or a credit union CIO.

The corollary for operators: if you’re attacking a hairy vertical with no software heritage, your sharpest weapon is the depth of your relationships and the specificity of your insight into how the work actually gets done. The flashy go-to-market deck loses to the founder who can sit in the back office for a week and come back with a roadmap.

Action item: If you don’t have years of domain depth, hire it into the founding team — and spend a month embedded with your buyer before you ship v2.

The map, drawn live — which obvious bets are kill zones, and which are quietly the safe ones.

We closed the episode by putting Nikhil — and Nic — in the seat and pricing the map in real time: which of the most highly valued vertical AI bets are sitting inside the kill zone, and which still have a clean moat, a real network, or an unloved vertical underneath them. Below: where on the map each lands, and the operator-grade reasoning underneath.

The pattern is uncomfortable but consistent. The hype-cycle category of any given year almost never produces the biggest outcome of that year. The biggest outcome comes from the category everyone agreed was dead, niche, or already won. If your whole thesis is in the consensus column, you’re not actually playing for power-law returns — you’re playing for second place.

The operator takeaway from the rapid-fire round: don’t build inside the kill zone, no matter how loud the category is. Spend your time where the moat is brand, network, or workflow depth the LLM cannot replicate — that’s the only stretch of the map that actually compounds.

The most uncomfortable line of the conversation: selling AI into white-collar industries where the user already has Claude open is structurally harder than selling AI into a back office that has nothing today. The unloved legacy vertical with a 40-year-old EHR and no business network is, in 2026, often the better square on the survival map than the keynote-friendly one.

Survive the map first.
Then build something the labs can’t follow.

If you’re a founder or operator shipping vertical AI right now, four moves separate the companies that survive the map from the ones that don’t:

1. Map the kill zone before you map the market. If a Claude tab can credibly do your core value prop inside 12 months, you don’t have a company — you have a feature waiting to be eaten. Pick workflows that need proprietary data, hairy integrations, or a UI that isn’t chat.

2. Compound a real moat from day one. In this era only three actually hold: brand (the iPhone effect), real network effects (the next LinkedIn, the next ACV), and workflow trust earned inside a traditional industry. If you can’t name which one you’re building, execution speed is your only moat — and that’s a brutal place to live.

3. Build in the unloved, antifragile verticals. CPG, credit unions, blood testing, pharma research, accounting back-offices. Terrible incumbents, no real software heritage, demand that isn’t going anywhere. The unsexy squares on the map are quietly the biggest ones — and the consensus auction never shows up.

4. Refuse the binaries that cap your ceiling. Consumer or enterprise. Focused or expansive. Software or services. The defining companies of this cycle — Canva, SpaceX, Anthropic, Open Evidence — refused all three. Plan for the second and third acts early, even if you only ship the first one this year.

The operators who win this cycle are the ones who treat vertical AI like a map, not a thesis. Most of the board is hostile. A small set of squares — unloved verticals, real moats, workflows the labs can’t reach — is where the next generational companies are quietly being built. Find those squares, plant your flag, and compound for a decade.

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MaintainX Acquired By AutoDesk for $3.6B (!!) Let’s dig into the company…

Most people will look at MaintainX and say: “It’s just maintenance software.”

That is technically correct. And strategically useless. Because the real story is not “maintenance software.”

The real story is:

MaintainX built a mobile-first system of record and action for the people who keep the physical world running — then layered data, AI, and enterprise rollout on top of it.

That is a much more interesting company.

And it helps explain why Autodesk agreed to acquire it for approximately $3.6B in an all-cash deal. Autodesk said MaintainX is expected to exceed $135M of ARR in calendar 2026 while growing more than 50%. They paid 26x EOY ARR. Holy sh*t…

The founding story

MaintainX started in 2018 with a very simple observation:

The software given to frontline and industrial workers was stuck in the past.

MaintainX’s own framing is that the tools keeping the world moving were outdated, and that maintenance teams were still relying on antiquated systems for critical workflows. Chris Turlica and his cofounders had already built and sold a company together, and Turlica later said the team wanted to build something as easy to understand as Slack or Asana, but designed specifically for frontline maintenance, safety, and operations.

BCV tells the founding story even better.

Their version is that Turlica’s earlier consumer chat app was being used by factory workers to photograph clipboards and discuss work orders. That is one of those great startup moments where the market tells you what the actual product should be. Not a generic workflow app. Not another dashboard. A purpose-built operating layer for physical operations.

That wedge mattered.

Because if you start with the technician, the supervisor, the plant manager, and the actual job to be done, you don’t build “software for maintenance.” You build software that becomes embedded in the daily motion of keeping equipment online.

The early journey and fundraising history

The company moved quickly.

In March 2019, MaintainX announced a $3.8M seed round led by August Capital and Amity Ventures. At that point the pitch was straightforward: digitize procedures, inspections, audits, checklists, and work orders for deskless teams who were still operating on paper, radios, spreadsheets, and email. The company said more than 1,600 businesses had joined the platform and were ready to start using it daily.

In June 2021, MaintainX announced a $39M Series B, and disclosed a previously unannounced $11M Series A from 2019. That brought total funding at the time to $54M. The company said revenue had grown 12x from early 2020, retention was 98%, and customers across 20+ countries were using the platform to manage hundreds of thousands of physical assets.

By December 2023, MaintainX had raised a $50M Series C at a $1B valuation. Turlica wrote that the company had grown to 6,500+ customers, processed 15M work orders, and managed 2.5M assets.

Then in July 2025, MaintainX raised a $150M Series D at a $2.5B valuation, bringing total funding to $254M. The company said it served 11,000+ companies, managed 11M+ assets, and was using the new capital to expand AI, machine health monitoring, predictive maintenance, and broader enterprise asset management capabilities.

Then came the Autodesk deal.

From seed in 2019 to a $3.6B exit agreement in 2026.

That is a serious run.

What the product actually became

This is the part I think people miss. MaintainX did not stay a simple CMMS.

It kept widening the surface area.

Today the core stack includes:

• work order creation, assignment, and tracking

• preventive maintenance scheduling

• condition-based work triggers

• asset health and reliability insights

• parts and inventory management

• inspections, safety procedures, and compliance workflows

• analytics and reporting

• AI copilots and natural-language querying for maintenance teams

That is how vertical software gets more valuable.

You start with the obvious pain and you solve it with a STUPIDLY simple capability.
Then you absorb adjacent workflow.
Then you absorb data.
Then you absorb decisioning.

At some point, you stop being a tool.

You become the operating layer.

MaintainX’s own homepage now frames the company around work intelligence, asset intelligence, AI-powered reporting, predictive parts needs, and compliance digitization. That is no longer “just maintenance.” That is workflow ownership around uptime.

The GTM motion was sneakily powerful

This may be my favorite part of the story.

BCV describes MaintainX as a rare industrial software company that behaved almost like a PLG wedge inside a traditionally top-down market.

Frontline workers liked the app.
Factories adopted it because it was easy to use.
Regional managers noticed that certain sites were outperforming.
COOs saw the pattern and standardized around it.

That is an elite motion.

You get bottoms-up love and top-down expansion.

You get usability as the wedge.
You get operational data as the moat.
You get enterprise standardization as the scale unlock.

Autodesk explicitly called out MaintainX’s pre-built integrations and scalable go-to-market growth motion in operations as part of the rationale for the acquisition.

That line is doing a lot of work.

Because it tells you Autodesk was not just buying a product.

It was buying a repeatable distribution engine into operations.

The post-sales motion mattered too

A lot of software companies win the deal and then lose the deployment.

MaintainX seems to have taken implementation seriously.

Its implementation page says the company has completed 3,000+ implementations with a stated 98% adoption rate, and offers tailored onboarding led by specialists with engineering and operational backgrounds. The messaging is very clear: get sites live in weeks, not years; standardize, optimize, and build a center of excellence.

For enterprise customers, MaintainX highlights a structured three-week onboarding process, a dedicated implementation specialist, support for multi-site rollouts, ERP/IoT/BI integrations, SSO, MFA, custom permissions, unlimited custom dashboards, and dedicated account management.

And the case studies back it up.

At US LBM, the rollout happened in waves of 3-5 sites at a time, with early focus on the 20% of the tool that would drive 80% of the impact. The company standardized asset naming and reporting, but still allowed sites enough flexibility to adapt local procedures. That is smart implementation. Not rigid. Not chaotic. Structured enough to scale, loose enough to spread best practices.

The customer success motion also looks mature.

MaintainX’s enterprise CSM role includes onboarding, product demos, training sessions, webinars, quarterly business reviews, adoption monitoring, churn mitigation at lagging facilities, and upsell/cross-sell identification with the sales team. That tells you a lot about how the company expanded inside large accounts.

This is what good vertical software looks like.

Not just software people love.

Software that gets implemented, adopted, expanded, and embedded.

Why Autodesk paid up

This is where the story gets really interesting.

Autodesk is trying to connect design, make, and operate.

MaintainX gives them a much stronger position in the last part.

Autodesk said the acquisition strengthens its ability to connect operations workflows with the broader lifecycle, brings richer data on asset history and maintenance patterns, and helps extend Autodesk’s relationship with assets from years to decades.

The investor presentation sharpens that further.

Autodesk frames the deal as a way to add roughly $40B of Operations TAM to its existing $78B Design & Make TAM, while extending its duration with assets and systems from a few years to 50–60+ years.

The idea is simple: design software knows how an asset was supposed to work; MaintainX knows how it actually performs in the field. Put those together, and you get a much more valuable loop.

BCV put it well too.

For decades, design tools captured how an asset should work, while operations platforms captured how it actually worked, and those datasets rarely talked to each other. Autodesk + MaintainX closes that loop.

That is what Autodesk is buying:

Not just CMMS revenue.

A frontline data asset.
A workflow foothold in operations.
A long-duration relationship with the physical world.
And eventually, a better foundation for system-level AI.

Big kudos to the team

This is a huge win.

Chris Turlica deserves a lot of credit for seeing a very unsexy problem clearly and then building with unusual intensity around it.

Same goes for Hugo Dozois-Caouette and the broader team.

They picked a market where the pain was real, the buyer need was persistent, the workflow was mission-critical, and the software incumbents were ancient.

Then they did the hard part:

They actually executed.

They built something workers used.
They made the data useful.
They made implementation real.
They expanded into enterprise.
And they gave a strategic buyer a very clean reason to pay a premium.

That’s the playbook.

Find a workflow the world cannot live without.
Build the system of action around it.
Turn usage into data.
Turn data into intelligence.
Turn intelligence into strategic value.

MaintainX did that.

And a $3.6B outcome is what that can look like when you do it really, really well.

The Hidden Ceiling in Embedded Payments

A lot of vertical SaaS founders think embedded payments is about choosing the “right” model. Referral, hybrid, or PayFac. But I’m starting to think that framing misses the bigger issue entirely.

Because the real mistake usually isn’t choosing the wrong model. It’s locking into one too early.

Early-stage SaaS companies naturally optimize for speed. They want the fastest path to launch, the simplest onboarding experience, and the least operational friction possible. Totally reasonable. Nobody wants payments turning into a six-month operational project while they’re still trying to grow the core platform.

The problem is that what works early often becomes limiting later, because SaaS businesses don’t stay static for very long.

As companies scale:

• Customer segments become more diverse

• Enterprise deals introduce new requirements

• Sales teams need more flexibility

• Payments revenue becomes materially important to company economics

That’s where rigid payments strategies start creating problems. Not obvious failures, but hidden ceilings.

At first, everything still looks healthy on the surface. Merchants onboard. Transactions process. Revenue grows. But underneath, the same payments structure is getting applied across completely different customer types. SMBs get treated like enterprise accounts. Enterprise accounts get forced into SMB workflows. Sales teams lose flexibility, and adoption slows because the process no longer fits every segment equally well.

Most founders don’t notice this immediately because nothing technically “breaks.” Growth just becomes harder than it should be.

That’s why the strongest SaaS platforms don’t treat payments like a fixed infrastructure decision. They treat it like an evolving revenue strategy that matures alongside the business itself.

The priorities change over time:

• Early on, the goal is speed and adoption

• Later, the focus shifts toward improving revenue per customer

• Eventually, the focus becomes margin optimization and enterprise monetization

Those are completely different operational priorities, and they rarely fit neatly into one permanent model.

The best operators understand this intuitively. What works at 100 customers usually breaks at 1,000. What works for SMBs often doesn’t work for enterprise. What feels operationally simple early on can quietly suppress long-term revenue later.

That’s why more sophisticated SaaS platforms are starting to structure payments differently based on the customer segment itself. SMBs might prioritize simplicity and fast onboarding. Mid-market customers may require a more balanced approach. Enterprise accounts can often support higher-control, higher-margin structures.

Not because complexity is the goal, but because maximizing long-term revenue usually requires flexibility.

Honestly, I think this is where the embedded payments conversation is heading. The future probably isn’t about choosing between referral, hybrid, or PayFac. It’s about building the ability to move between them as the business evolves.

That’s part of why I’ve been paying attention to what Xplor Pay is doing lately. They seem to think about embedded payments more as an evolving growth system than a static infrastructure decision, which honestly feels much closer to how SaaS businesses actually operate.

Because at a certain stage, payments stops being just infrastructure. It becomes part of the growth strategy itself.

Your strongest AI Users are probably worth 10x your other employees

One strong AI-native Employee can now do the work that used to require a researcher, a designer, a coordinator, a writer, and a developer.

That changes everything, and we’re really not talking about it enough.

The right question is:

Who in our org has rebuilt their entire workflow around AI?

That’s who matters.

Because once someone figures this out, they stop behaving like a normal employee inside a normal function.

They become a throughput machine.

They learn faster.
They iterate faster.
They ship faster.
They document better.
They unblock other people.
They make management look smarter.
They make average teams feel more coordinated than they really are.

And most importantly:

They create leverage that compounds.

A strong AI user does not just produce more output. They create prompts, templates, automations, operating procedures, and internal playbooks that other people can reuse.

So their value is not just their own output.

It’s the new system of work they drag into the company. That is why I think these people are worth way more than most org charts suggest.

If you have an IC who is implementing AI into every part of their workflow, you should think about them differently.

Not as a normal employee. As infrastructure. As a force multiplier.

I think a lot of companies are massively underestimating this.

Not 10% better.

Not “a bit more productive.”

10x more valuable.

What I would do

1. Identify your top 5 AI-native operators immediately.

Not the people talking about AI the most.

The people quietly using it to get absurd amounts done.

You probably already know who they are.

They are the ones sending better memos faster.
The ones with cleaner thinking.
The ones who come to meetings with the first draft already done.
The ones who somehow keep eliminating steps.

2. Turn them into internal teachers.

Ask them to document exactly how they work.

What tools they use.
What prompts they rely on.
What gets automated.
What still requires human judgment.
What a “good” AI workflow looks like inside your company.

If you do this well, one great AI operator can upgrade ten others.

3. Give them harder problems, not more busywork.

Do not turn your best AI users into cleanup crews.

Use them on strategy, customer insight, GTM experiments, internal systems, and cross-functional bottlenecks.

Point them at the highest-friction parts of the business.

Let them redesign the work.

4. Promote for leverage creation.

In a normal org, we reward ownership.

In an AI-native org, we should reward scalable ownership.

Who is building repeatable systems?
Who is making other people faster?
Who is changing the slope of execution?

That is the game now.

5. Weed out the Non AI People

Can you imagine if your entire organization was made up of these people? I’m seeing it first hand. I’m truly living it. I’m involved in companies with a few AI native IC’s (across every type of role BTW) and companies where Claude is the legit operating system of EVERY employee.

There is a serious difference between the two. And I don’t want to be in organizations that are the former. So If your a little hardcore like me, you may want to start thinking about weeding out the people that are still criticizing the use of AI. If they haven’t got it now, will they ever? Do you want to pay for them to catch up? If they ever do?

In an AI-native org, we should reward scalable ownership.

That is the game now.

The takeaway

The companies that win over the next few years will not just “adopt AI.”

They will identify the people who are best at using it, then reorganize around them.

That’s the opportunity.

And that’s also the warning.

If you have a few of these people in your company, treat them like gold.

If you don’t, go find them.

Fast.

If you made it thus far! You’ve found the easter egg.
Snag a free ticket to the Vertical Software Summit. Just use code FREE4FOUNDERS!

Grab Your Free Ticket

Vertical Software/AI Founders, Operators, Investors only please.
If you’re not your submission won’t be accepted.

Do me a solid and forward to a friend :-)

Nic and I sat down with Kyle Liu, General Partner at Bling Capital — built around helping founders find, define and scale PMF — on the two shifts every operator is feeling but few are naming: enterprise buyers are behaving like consumers (they’ll try anything, they’ll churn on a dime, they want one bill not ten), and the old PMF signals — $1M ARR, logo lists, a clean Series A deck — quietly stopped meaning what they used to mean.

The line between consumer and
enterprise is collapsing.

For twenty years there was a clean story. Consumer products were tried in seconds, loved or churned. Enterprise products were sold over quarters, integrated for months, and stuck around for years. That story is dead. In AI, your enterprise buyer behaves like a consumer — they’ll spin you up on a Tuesday and rip you out the following Tuesday — and the old PMF signals never accounted for that.

Kyle’s seen this from the inside. Bling’s product council — 100+ heads of product and growth at top tech and AI companies — used to be a hiring and advisory bench. Now they’re acting like consumer users for portfolio companies: trying everything, switching constantly, telling you within a week if your product earns a permanent seat in their stack. Buying committees flattened. Procurement got bypassed. The “champion + economic buyer + IT” map you learned at the last enterprise SaaS company doesn’t necessarily describe the buyer room anymore.

That collapse cuts both ways. The good news for operators: a single product-led demo can land a six-figure pilot in days. The bad news: a Series A AI company can go from a $30M customer to $0 the next year. Kyle’s seen it. The cost to build collapsed, the speed to market collapsed with it, and the feedback loops shortened — so 20 funded competitors now sit where three used to. $1M in ARR no longer proves anything. Defensibility, the thing every founder used to worry about later, is on the agenda at the seed pitch.

So what does PMF actually look like in the AI age — for founders shipping right now, not investors writing memos a year later?

That’s the lens Kyle brought in.

Earlier signal. Cohort discipline at week ten. A “press release at Series A” you can defend line by line. Gross profit, not ARR, as the honest scorecard.

And a gut-check on whether the enterprise customer who just signed will treat you like an indispensable system — or like a Claude tab they’ll close next quarter.

Watch on Youtube

Listen To Episode Here

This Weeks Vertical Titan: Kyle Lui (Partner @ Bling Capital)

Kyle was an operator first. He founded Choice Passenger, a sales performance management company that was acquired by Salesforce and grew inside it into the Salesforce HR Cloud — roughly $40M in ARR across 1,000+ enterprise, mid-market and SMB customers. From there, eight years investing at DCM, and in 2022 he joined his first-ever angel — Ben Ling — as GP at Bling Capital.

Bling is a product-led seed fund with a 100+ strong product council of heads of product and growth at top tech and AI companies — increasingly doubling as early design partners for portfolio startups. Kyle’s portfolio includes Spellbook (vertical AI for legal), Alma (AI-native immigration law firm), Miniva (vertical AI for high-volume manufacturing), and PartBay (used auto parts — the “is this market even big enough?” company).

The frame he keeps coming back to: cohort data over storytelling. Even ten weeks of data is signal worth pulling — not to declare PMF, but to find the bright spot inside a semi-horizontal product and aim the whole company at it.

And the line every founder should write on a wall: he’s looking for “learn-it-alls, not know-it-alls.” Meet a founder on a Monday, meet them again the next Monday — if they’ve already talked to half the people you introduced them to and re-shaped the plan around what they heard, you’re probably looking at a generational team in formation.

Watch on YouTube

Listen To Episode Here

What PMF actually looks like in the AI age.

Nine lessons every operator should re-internalize before they trust their next ARR chart — including the consumer-style behavior now showing up in every enterprise deal.

#01. Size the company, not the market — in gross profit, not TAM

Bling’s market-sizing exercise doesn’t ask for a TAM slide. It asks how the company gets to $100M and $500M in gross profit: how many customers, what percentage of the market, over what timeline. The numbers aren’t arbitrary — $100M GP is roughly what a seed fund needs from an 8–10% stake to return the fund, and $500M is the multi-stage equivalent. It’s an ICP and willingness-to-pay question dressed as market sizing.

In the AI era, Kyle now adds a third number: $1B. Multiples have come down, growth rates have gone up, competition has gone up, and the power law got even more extreme. A billion in gross profit is the bar a multi-stage fund needs to see to be excited. If your model can’t credibly find that ceiling, your cap table tops out before you’d like it to.

Action item: Run the $100M, $500M and $1B GP exercise for your business. If you can’t credibly hit the second number, you’re not seed-VC-fundable. If you can’t see the third, you’re not multi-stage-fundable.

#02. PMF as the Amazon press release you can’t write yet

At seed, true PMF is unknowable. What’s knowable is the early signal of it. Bling’s exercise: imagine you just raised your Series A 12–18 months from today and write the Amazon press release that announces it. What claims are in there? Customer love, engagement acceleration, early distribution proof points. Then break each claim into OKRs and work backwards.

Every generational company starts as a wedge — Uber was ‘five times the cost of a taxi and people can’t get enough.’ Stripe was a clean API. The vision can be enormous, but the wedge has to be brutally specific, and the press release forces the conversation about which wedge actually proves itself first.

Action item: Draft the press release. Break each claim into measurable proof points. Treat the doc as your PMF roadmap, not a marketing exercise.

#03. Cohorts at week ten — find the bright spot, don’t prove PMF

Whenever there’s data — even ten weeks of it — Bling pulls cohorts. Not to certify PMF, which would be premature, but to find the one or two slices where engagement or retention is meaningfully ahead. That’s especially powerful for companies with semi-horizontal products debating between adjacent verticals.

The job is to cut through the noise of a market with 20 funded competitors and find the customer profile where urgency is real. Once you see it, the whole company should bend toward it: positioning, hiring plan, design partners, the next product surface. Sound discipline beats storytelling — and at seed, that discipline is most of the moat you have.

Action item: Pull cohort retention, expansion and engagement on every customer slice. Pick the bright spot. Re-orient the company around it.

#04. Your enterprise buyer is a consumer now — and ARR is hiding it

In vertical SaaS, hitting $1M ARR meant something — it usually came from roughly twenty discerning customers with 80–95% likelihood of retention. In vertical AI, Kyle has seen a Series A company go from $30M in one customer to $0 the next year. The reason: enterprise buyers now behave like consumers. They trial fast, they champion fast, and they churn fast when the next demo is shinier — exactly the way an individual flips between Claude, ChatGPT and whatever they read about on Twitter that morning.

That’s why ARR has stopped being the honest scorecard. For operators, the numbers that actually predict survival are gross profit, gross-margin trajectory, customer concentration, and second-cohort retention. If a single customer is more than 20% of revenue, that’s not a logo — it’s a runway risk.

Action item: Don’t celebrate ARR until you’ve stress-tested concentration and second-cohort retention. Track GP, not bookings.

#05. Moats fall first — then rebuild around workflow and trust

Kyle is blunt: moats are falling. In the early days founders shouldn’t worry about defensibility because they have nothing to defend — what matters is execution speed and getting people to pay attention. But over time, the durable moats start to look familiar: data, workflow lock-in, and trust earned inside traditional industries that don’t actually want to switch once they’re happy.

Harvey, Legora, Spellbook didn’t out-model Claude — they out-executed everyone else and embedded into workflows the foundational labs aren’t going to touch. Developers are actually the hardest customer to retain, because they care about marginal performance. Traditional industries, once they trust you, are the opposite — they don’t want to re-evaluate every six months.

Action item: Year one, optimize for speed of learning. Year two, consciously pay down workflow depth and switching cost — not just ship features.

#6. Be the router, not the reseller

Users will pay a ~20% tax for a product that picks the right model for each task. Cursor is the canonical horizontal example; every vertical AI company has the same opening. The router is a clean wedge — high frequency, immediately valuable, and a beachhead for the workflow you actually want to own.

But if your core value prop is the same thing the LLM does well — pure document review, for instance — the labs can eat you. Routing is a wedge; it can’t be the whole company. The constellation of features around it is what makes ‘just use Claude’ a non-option twelve months in.

Action item: Start as a great router. Use that to capture workflow, data, and trust. Then layer the features that make a single-LLM fallback genuinely worse.

A quick word from our incredible partner,
Parafin - the leading embedded capital partner.

• 4.8 star rating on Trustpilot, with over 500 reviews [source]

• 71% CSAT rating across all partners

• 84 NPS

• Serving top Fortune 500 companies, including Amazon, Walmart, and DoorDash

Check Out Parafin

#07. Sell against the labor budget — the biggest unlock of the cycle

Kyle has watched board conversations rewire in real time. R&D line items now combine token spend and headcount in the same row — and traditional industries are even further along. They feel labor replacement viscerally. Alma, an AI-native immigration law firm in Kyle’s portfolio, compressed 40 attorney-hours of visa work into roughly 4. Miniva, in high-volume manufacturing, is targeting roles factories literally cannot find people for.

That changes what counts as a venture-scale market. Vertical AI for private aviation brokers sounds laughably niche — until you realize the early contracts are seven figures because the budget is coming out of payroll, not the software stack. The categories that didn’t pencil under SaaS economics often pencil beautifully when the comparison is loaded labor cost.

Action item: Price against fully-loaded labor cost. If buyers are still benchmarking you against SaaS seats, you haven’t told the right story yet.

#08. Gross margin still matters — eventually

Negative gross margin from compute is forgivable; everyone assumes inference costs trend down. Negative gross margin from a giant services bench is not — that’s just BPO with a venture multiple on top. Compute costs come down. People costs don’t.

You can’t invest a dollar to make 80 cents forever. There has to be a clear inflection where the business model becomes viable on its own — and a credible path from today’s gross margin to a steady-state target you can actually defend in a board meeting.

Action item: Map the path from today’s GM to a steady-state target. If you can’t draw that line, you don’t have a business yet — you have a subsidy.

#09. Hunt narrative violations — the obvious bets are already crowded

Big funds coming downstream has created consensus vortices around a few hot categories. Meanwhile the biggest outcomes of this decade — SpaceX, Anthropic, OpenAI — were all unloved at the moments that mattered most. The ‘stupid’ vertical that 10 VCs pass on may actually buy you the longest stretch of low competition and a clean shot at owning the names that matter in your market.

If everyone you respect is already in your category, your moat is now execution speed and very little else — a brutal place to live. If they all think it’s stupid, do the work twice. That’s where the real outcomes hide.

Action item: Pressure-test your category against the consensus list. If you’re on it, ask what would have to be true for you to win on execution alone.

Where vertical AI is already a consensus trade — and where the real outcomes still hide.

Big funds coming downstream has created consensus vortices around a handful of categories. Kyle’s read on where the auction is hot — and where the next category-defining companies are most likely to be quietly built.

Kyle’s most provocative line:

The pattern is uncomfortable but consistent. The hype-cycle category of any given year almost never produces the biggest outcome of that year. The biggest outcome comes from the category everyone agreed was dead, niche, or already won. If your whole thesis is in the consensus column, you’re not actually playing for power-law returns — you’re playing for second place.

Watch on YouTube

Listen To Episode Here

Thanks for reading LINEAR. I reply to every email…
Have any questions, feedback, comments? Let us know, we work for you!

Shaped by lessons from decades of building and scaling 20+ SaaS platforms, it turns payments into a true growth lever. Read the guide to see how top platforms get it right. Read Now.

An Exciting Announcement…

Today I’m excited to announce the second annual Vertical Software Summit.

We spent the last six months pulling together the BEST in vertical software and vertical AI — founders and executives who have actually built, scaled, and in some cases monopolized their corners of software…

• Fred Voccola: The only guy (that I know of) who runs TWO billion dollar vertical software businesses, Kaseya (IT services companies) and Simpro (Trades)

• Founder/CEO of OpenGov: A 15 year journey building the OS for local government and he just sold for $1.8B to Cox

• The Founder/CEO of GCAI: One of the fastest growing Vertical AI companies out there. I’ve never seen growth quite like them…

• The Founder/CEO of GlossGenius - probably worth $1B+. Massive business in the beauty software space.

• The Founder/CEO of Valsoft - He’s bought 100+ vertical SaaS co’s and is approaching $1B of ARR. Monster business.

• The Chief Business Officer of Service Titan. $1B+ of ARR. Self explanatory :-)

• The Founder of a $5B+ vertical marketplace.

• 30+ other Founder/CEO speakers from $1M-$1B in ARR

• Leading CxO’s on AI, FinTech, Customer Success, GTM, Product, and more

The founders who have actually dominated categories don’t speak in abstractions. They talk about the ugly parts:

• The pricing decisions that looked wrong until they worked.

• The distribution channels everyone ignored.

• The product lines they killed.

• The implementation bottlenecks they underestimated.

• The one integration that mattered more than ten features.

• The part of the workflow they thought was peripheral that ended up becoming the real wedge.

That’s where the value is.

Not in hearing polished startup advice you could get anywhere else. In hearing how category leaders actually built businesses that became hard to dislodge…

The conference is built entirely around the folks actually shaping their categories: the founders building vertical businesses and the operators scaling them.

And when you get enough of those people in one place, the conversation is something special…

If you’re a founder, you’ll leave with a better sense for what actually separates a good vertical business from a category-defining one.

If you’re an operator, you’ll hear how leading CxOs think about expansion, implementation, org design, AI adoption, and maintaining advantage as a company scales.

And then there’s the other part that makes this room unusually valuable: it’s not just founders and operators showing up…

The entire vertical investment and M&A community shows up wanting to meet YOU.

We will have ~100 ultra high quality investors and acquirers in the room that know and believe in Vertical Software and Vertical AI.

That’s why this is more than a conference.

It’s a place where the people building the category, scaling the category, and funding the category all collide in the same place.

And that’s when patterns emerge.
That’s when the quality of conversation changes.
That’s when you stop getting generic content and start getting real context.

If you’re serious about vertical software or vertical AI, that’s the kind of room that can change how you build.

So, today’s newsletter is short and sweet, but I encourage you to spend your usual time reading it to check out the Vertical AI event of the year!!

Hope to see in Miami November 4-5th, 2026…

PS - it will sell out (last year we had to turn away ~100 folks who waited until last minute to grab a ticket. Don’t do that!)

Check out the conference

Mike Droesch of Bessemer Venture Partners sits down with Nic to discuss where Voice AI is working, where it’s overcrowded, where the blue oceans still are, and why voice may become the next great application layer in vertical software, not just "AI that answers the phone."

Voice is an interface shift.

Voice isn’t a slicker chatbot with audio. It’s a much higher-bandwidth interface than typing, clicking, or filling out forms — and when you increase the bandwidth of data flowing in, the quality of the output changes dramatically too.

Mike’s argument: voice becomes interesting when it stops being a feature and starts becoming a serious way to gather context and take action. We’re not talking about AI that answers the phone — we’re talking about a new layer in software that captures more nuance, makes better decisions, and eventually owns entire workflows that used to require humans.

There is a lot changing in AI right now. New models. New wrappers. New infrastructure. New noise. But if you forced Mike to pick one category with the potential to reshape software more than people realize, it would be this one. Not because it’s trendy. Not because the demos are cool. Because voice changes the amount and quality of information that can flow into a machine — and that quietly changes everything downstream of it.

That’s the lens Mike brought into this conversation. Not “AI that answers the phone.” Not a better IVR. A new context layer for software, and eventually a new action layer on top of it. If he’s right, voice ends up being one of the most important new wedges into vertical software we’ve seen in a long time.

Watch Episode Here

Listen To Episode Here

This Weeks Vertical Titan: Mike Droesch (Partner @ Bessemer)

Mike came up as an engineer before venture, joined Bessemer close to nine years ago, and got pulled into vertical software at exactly the moment the category started proving itself. Since then he’s built investment roadmaps across marketplaces, supply chain, vertical SaaS, and now vertical AI.

He’s had a front-row seat to the rise of companies like Abridge, Rilla, and Vapi — meaning he’s thought about vertical voice AI as much as almost anyone in the market. The difference in his framing is that he doesn’t treat voice as a narrow subcategory. He treats it as an interface shift.

One of his biggest surprises of the AI era: the fastest-adopting sectors have been regulated ones — healthcare, insurance, financial services, legal. They care deeply about workflow structure and control, and when voice gives them tight gates, identity validation, and rigorous agent monitoring, they actually move faster than the markets you’d expect to.

And the line every founder in this category should write on a wall: “The infrastructure stuff is table stakes. Distribution matters more than it ever has because it’s so easy to get to feature parity with other products.” Latency and reliability are the price of admission. Distribution, workflow ownership, trust and evals are the moat.

Watch Episode Here

Listen To Episode Here

Voice AI changes the economics of software when it actually works. Traditional SaaS made labor more productive. The best voice companies start to replace it — and that moves the budget conversation from the IT line item to payroll and broader opex.

When voice actually works, the economics of software change.

A few observations from Mike on where Voice AI stops looking like a demo and starts looking like a category:

From IT line item to payroll line item.

In the traditional SaaS world, a lot of companies sold tools that made labor more productive. In the Voice AI world, the best companies don’t just support labor — they start replacing parts of it. That changes where the budget comes from. Instead of selling into an IT line item, you’re selling against payroll and broader operating expense.

Mike gave the example of a workflow that might support a roughly $30K ACV in its SaaS form but something closer to $150K when an AI agent actually performs the job end to end. That’s a massive unlock if it proves durable across categories — and it’s why the comp set for voice companies starts to look more like BPO and labor spend than next-seat-license SaaS.

Don’t build voice unless you can close the loop.

Mike said it directly: he would not want to spend his time building a voice agent company if he didn’t think it could do an entire job end to end. There are going to be plenty of products that sound great in a demo, have pleasant conversations, and then hand off to a human the moment things get complicated. Those might become features.

The monster companies are far more likely to be built by owning a full workflow, not a moment inside it. The frame for founders is simple: if the realistic ceiling of your product is “assistive in a slice of the job,” you’re building a feature for someone else’s platform.

The limit isn’t capability anymore. It’s permission.

Mike made a subtle but important distinction: the limiting factor is increasingly not whether the model can handle the interaction. It’s whether buyers trust the model enough to let it actually act.

We’re approaching a world where agents understand more than people expect. But connecting them to core systems, letting them move money, resolve a claim, complete a booking, or change a mission-critical record requires a different level of confidence. That confidence is not built by vibes. It’s built by governance, observability, and repetition. The buyer’s CISO is on your roadmap whether you invited them or not.

Every call is product data.

Mike’s view is that the compounding advantage comes from seeing real-time evals and tests run in production. Every interaction becomes more than a service event — it becomes product data. Every conversation generates signals. Every signal can be measured against expected performance.

Over time, the companies best at instrumenting, interpreting, and improving from those signals build a very real flywheel around quality. A lot of founders still talk as if the moat is the model. Increasingly, it’s the eval system wrapped around the model.

Escalation to a human isn’t failure.

Some people still talk about handoff like it represents failure. Mike’s framing is more practical. In real-world workflows, people usually call when something has already gone wrong, or when they’re in an edge case. The job isn’t to fantasize about 100% automation on day one.

The job is to know exactly where the boundaries are, solve what the system can solve confidently, and fail over elegantly when it can’t. In many verticals, that handoff is the difference between trust being built and trust being destroyed.

Platform + usage today. Outcomes when they’re measurable.

Outcome-based pricing gets attention, but the market today still mostly looks like some version of platform fee plus usage bundle. It’s legible to buyers, maps well to current products, and doesn’t require perfect agreement on what a “successful outcome” actually means. Outcomes will arrive first in use cases where they’re standardized and easy to verify.

One other line that stuck with us: what “fast” looks like today is crazy relative to five or ten years ago. Categories are forming in real time. Feature parity emerges in weeks. Speed itself becomes a meaningful — if temporary — moat. Teams that learn fastest, instrument fastest, and improve fastest buy themselves the time needed to build something deeper.

Nine lessons for building in voice AI.

Frameworks for founders and investors deciding what to build, what to back, and what to walk away from.

#1. Don’t build voice unless you can own the whole job

Mike said it directly: he would not want to spend his time building a voice agent company if he didn’t think it could do an entire job end to end. Pleasant-sounding demos that hand off to a human the second things get hard become features. Companies are built by owning a full workflow, not a moment inside it.

Action item: Map the job from first ring to final action. If your product can’t credibly close the loop, you’re building a feature for someone else’s platform.

#2. Sell against payroll, not the IT line item

Traditional SaaS made labor more productive. Voice AI, when it works, replaces parts of it. That changes where the budget comes from. A workflow that supported a $30K SaaS ACV can support something closer to $150K when an AI agent actually performs the job end to end.

Action item: Price against the loaded cost of the human doing the work today — not against the seat-license comp you’d benchmark to in 2021.

#3. Infrastructure is table stakes. Distribution is the moat.

Low latency, reliability, model quality — all required, none differentiating. Feature parity is reached in weeks now, not years. The deeper moat comes from distribution, workflow ownership, trust, evals, and the feedback loops generated by real calls happening in production.

Action item: Audit how much of your roadmap is infra plumbing vs. distribution and workflow depth. Rebalance accordingly.

A quick word from our incredible partner,
Parafin - the leading embedded capital partner.

• 4.8 star rating on Trustpilot, with over 500 reviews [source]

• 71% CSAT rating across all partners

• 84 NPS

• Serving top Fortune 500 companies, including Amazon, Walmart, and DoorDash

Check Out Parafin

#4. The limiting factor is trust to act, not model capability

The models can already understand more than buyers expect. The real question is whether buyers trust the agent enough to let it move money, resolve a claim, complete a booking, or change a mission-critical record. That confidence isn’t built by vibes — it’s built by governance, observability, and repetition.

Action item: Treat permissions, audit trails, and rollback as product surface area.

#5. Evals are the compounding moat

Mike’s view: the compounding advantage comes from real-time evals and tests running in production. Every interaction becomes product data. Every signal can be measured against expected performance. The companies best at instrumenting, interpreting, and improving from those signals build a real flywheel around quality.

Action item: Ship eval infrastructure on day one. The team that learns fastest from production calls compounds faster than the team with the shinier model.

#6. Graceful handoff is part of the product

Escalation to a human isn’t failure. People usually call when something has already gone wrong or they’re in an edge case. The job isn’t 100% automation on day one. It’s to know where the boundaries are, solve what the system can solve confidently, and fail over elegantly when it can’t.

Action item: Design the handoff like a feature, not an apology. The smoothness of the human transfer is where trust is built — or destroyed.

#7. Pricing is platform + usage — for now

Outcome-based pricing gets the attention, but today’s market mostly looks like a platform fee plus usage bundle. It’s legible to buyers, maps to current products, and doesn’t require perfect agreement on what a ‘successful outcome’ is. Outcome pricing arrives first in use cases where the outcome is standardized and easy to verify.

Action item: Don’t force outcome pricing before the outcome is measurable. Use platform + usage as the bridge from the SaaS era into the agent era.

#8. Regulated industries adopt faster than you’d guess

Counter-intuitive but true: healthcare, insurance, and financial services have been among the fastest-adopting sectors. They care deeply about workflow structure, control, and compliance. If voice lets them gate conversations, validate identity before releasing information, and monitor agent behavior rigorously, they move faster than less-regulated markets.

Action item: Lead with controls, not capability. In regulated verticals, the compliance story is the sales story.

#9. Hunt the messy categories, not the obvious ones

Scheduling, debt collection, recruiting — already crowded. The interesting opportunities are where voice enables something previously too nuanced, messy, or expensive to scale: legal intake, expert-network conversations, mixed-modality workflows combining voice with screen or imagery, field inspections in industrial, roofing, construction.

Action item: If your category has a clean ROI deck and ten well-funded competitors, you picked the easy wedge. Go find the operationally dense one.

Where voice is already a knife fight — and where the real opportunity still hides.

One of our favorite parts of the conversation. Mike’s read on where founders are piling in, and where the next category-defining companies are most likely to be quietly built.

Mike’s most provocative line:

That sounds extreme until you think about how bad most current service experiences are. If the agent is faster, more accurate, always available, and actually resolves the issue, the old assumption that humans are always preferred quietly breaks down.

Voice AI isn’t a feature added to software.
It’s a new chapter in software itself.

If you’re building — or backing — voice AI today, Mike’s worldview comes down to four commitments:

1. Pick an economically meaningful job. Not a demo, not a feature. A workflow with real labor cost attached, where ownership of the job rewrites the budget.

2. Build for trust to act. Governance, observability, permissions, audit trails. The buyer’s confidence to hand over the keys is the actual product.

3. Instrument great evals. Treat every call as product data. The compounding advantage is the eval system, not the underlying model.

4. Earn the right to own the workflow. Start with a wedge you can credibly close, expand into adjacent steps, and let graceful handoff carry you until the system can carry itself.

Not every voice company will matter. Not every demo will become a business. But the founders who pick economically meaningful jobs, build real trust, instrument great evals, and earn the right to own the workflow end to end — those are the ones with a shot at building very big companies.

Watch Episode Here

Listen To Episode Here

Thanks for reading LINEAR. I reply to every email…
Have any questions, feedback, comments? Let us know, we work for you!

Shaped by lessons from growing and scaling 20+ vertical SaaS platforms, this guide explores what AI readiness actually means for software providers. Read the guide.

Most software is going headless

For the last 20 years, most software value was packaged inside the UI.

That was the bundle.

You sold the workflow.
You sold the dashboard.
You sold the reporting layer.
You sold the place where the user logged in and did the work.

The product was the screen.

And that worked because humans were the operators.

Humans had to know where things lived. They had to click through tabs, navigate record pages, update fields, run reports, approve tasks, move work from one system to another, and string together a process using the interface as the coordination layer. That made the UI incredibly important, because it wasn’t just presentation — it was behavior-shaping infrastructure.

But agentic software changes that.

If an agent can understand intent, retrieve context, call the right tools, reason across multiple systems, and execute a workflow on the user’s behalf, then the software no longer needs to be primarily optimized for a human operator sitting inside the product. It increasingly needs to be optimized for machine access, orchestration, permissions, and reliability. OpenAI’s ChatGPT agent is explicitly built to move across connectors, websites, browsers, APIs, and terminals to complete tasks. Anthropic’s computer use capability similarly shows models operating software by looking at the screen, clicking, typing, and navigating interfaces designed for people.

That’s the core headless thesis.

A lot of software won’t necessarily die.
It will just lose its head.

Meaning: the software still exists, but the human-facing interface is no longer the primary source of leverage.

Instead, the software gets reduced — or elevated, depending on how you look at it — into its underlying components:

• structured data

• workflow logic

• permissions

• audit trails

• compliance controls

• APIs, MCP tools, and commands agents can call

That’s not a small shift. That is a fundamental reordering of where value sits in the software stack.

The other problem is if the AI UI layer wants to do what your headless software does, they will do it, and you will die….

That’s why becoming the AI UI layer in verticals is where i’m spending my time.

The cleanest example of this showing up in the market is Salesforce’s Headless 360 announcement.

Salesforce said the quiet part out loud: instead of burying capabilities behind a UI, expose them so the platform is programmable and accessible from anywhere. They describe Headless 360 as the capabilities agents need most, exposed as an API, MCP tool, or CLI command, across the breadth of Salesforce workflows. They also say plainly that if your platform requires humans to click through UIs to make progress, it is not ready for the agentic enterprise.

That is a massive statement.

Because Salesforce is one of the most important systems-of-record companies in enterprise software. For decades, “using Salesforce” meant working inside Salesforce. Open the app. Navigate the object. Update the record. Run the workflow. Now they are telling the market that the future is not just humans working inside the application — it is agents acting on the system directly. Trailhead makes the same point even more clearly: in the agentic enterprise, agents don’t click through pages, buttons, and tabs; they call APIs, invoke MCP tools, and run CLI commands directly.

This is why I think so many point solutions and horizontal SaaS tools are about to get pushed down the stack.

If your product’s main value proposition is that a human logs in and manually performs a repeatable workflow, that position is getting weaker. If the steps can be abstracted, coordinated, and executed by an agent, then the UI becomes less of a moat. The value migrates below the interface into the quality of the data model, the richness of the workflow graph, the trust and permissioning layers, and the ability to safely support machine execution. a16z’s framing is useful here: in the agentic era, defensibility shifts downward into data models, permissions, workflow logic, and compliance, and upward into networks, proprietary data generation, and real-world execution.

That doesn’t mean every software category gets commoditized overnight.

Some products will remain highly human-centric for a long time. Some workflows will stay collaborative, exception-heavy, or emotionally nuanced enough that the interface still matters a lot. And many companies will live in a hybrid world where humans and agents work side by side for years. Showpad’s response to Salesforce is instructive here: they explicitly say the future is both humans and agents, and they’re building dual-track — strong UX for people while enabling agent access in parallel.

That’s probably the pragmatic transition path for most of the market.

But even in that hybrid state, the power structure changes.

Because once users stop starting in the native app, the real battle is no longer just over the system of record.

It’s over the AI UI layer.

Who owns the place where the user expresses intent?

Who owns the conversational surface?
Who owns the orchestration layer?
Who decides which tools get called, in what sequence, with what context, and with what permissions?

That is the new choke point.

And everyone serious in software can see it.

OpenAI is pushing toward a world where ChatGPT becomes a layer that can act across apps and workflows. Microsoft is explicitly combining agents and workflows so AI can reason where processes are ambiguous and workflows can execute where consistency matters. Salesforce is trying to make its system programmable for agents while also shipping an Experience Layer that lets rich interactive components render across Slack, mobile, ChatGPT, Claude, Gemini, Teams, and other clients.

That Experience Layer point is especially important.

Salesforce describes it as a UI service that separates what an agent does from how it appears, so interactive components can render natively across multiple surfaces. In plain English: build the logic once, but let the interface show up wherever the user already works. That matters because it means the fight is no longer “which app do users log into?” It becomes “which layer owns the interaction, even if execution is happening elsewhere?”

That’s why “headless software” is not just an architecture idea.

It’s a market power idea.

In the old model, the best product often won by becoming the place where work happened.

In the new model, the winners may be the ones that:

• own the deepest context

• control the critical workflow

• enforce the trust layer

• and sit closest to the point where intent becomes action

That is a very different game.

And it is bad news for thin point solutions that are mostly UI wrappers around relatively generic functionality.

If your wedge is “we have a slightly better dashboard,” you should be nervous.

If your moat is proprietary context, embedded workflow ownership, regulated execution, payments, domain-specific data, or a mission-critical system of record, you are in a much better position. Because in a headless world, the closer you are to the trusted execution layer, the more durable you become.

So my take is pretty simple:

Nearly all software is moving toward some degree of headlessness.

Not because interfaces vanish completely.
Not because apps stop mattering.
Not because every workflow becomes fully autonomous.

But because the center of gravity is shifting.

From screens to systems.
From navigation to orchestration.
From UI to execution.
From software you use… to software agents use for you.

And the fiercest battle in software over the next few years may be the battle to own that new AI UI layer.

The Story of AIDoc & Their Massive Series E Round

How a radiology wedge became a clinical AI operating system

Aidoc is one of the clearest examples of what a real vertical AI winner looks like.

Not a generic copilot. Not a horizontal wrapper. Not “AI for healthcare” in the abstract.

It started in one painful, high-stakes workflow, earned trust where mistakes actually matter, and then expanded into platform, workflow, and enterprise infrastructure. That’s the playbook.

1) Founding story: start where the pain is brutal

Aidoc was established in 2016. The company says its founding team came out of deep AI and defense-tech backgrounds, with Talpiot-trained founders bringing algorithmic, computational, medical, and research expertise into one company. CEO and co-founder Elad Walach says he began in the elite Israeli Defense Force technology program Talpiot and later led AI research in the Israeli Air Force; co-founder and CTO Michael Braginsky brought a medical-image-processing background and helped define a roadmap covering more than 75% of common acute CT pathologies. In other words: this was not “let’s bolt AI onto healthcare.” This was a technical founding team choosing a vertical where accuracy, speed, and workflow integration actually change outcomes.

The original wedge was smart too. Aidoc first focused on helping radiologists reduce turnaround time and improve quality by flagging acute anomalies in real time. That’s important. They did not start by promising to reinvent all of medicine. They started with one clear buyer pain: overloaded imaging workflows, rising scan volume, and costly delays in diagnosis. That’s classic vertical software strategy—begin with a sharp wedge inside a mission-critical workflow, then expand once trust is earned.

2) Product: from point solution to operating layer

Aidoc’s first chapter was essentially workflow-aware clinical AI for radiology. But the current product story is much bigger.

Today the company is built around two core assets:

1. CARE — its clinical foundation model

2. aiOS — its enterprise AI operating system for health systems

That product evolution is the whole story.

A lot of AI companies get stuck selling a single model for a single use case. Aidoc appears to have used the initial radiology wedge to climb into infrastructure. aiOS is described as the layer that runs, orchestrates, and governs clinical AI across the health system. It integrates with PACS, EHR, mobile, worklists, and care tools; supports urgency-based triage; provides validation, drift detection, override tracking, and analytics; and is positioned as the way hospitals move from pilot projects to enterprise-scale deployment without “sprawl.” That’s a huge jump up the value chain.

The most important recent product milestone is FDA clearance for what Aidoc calls healthcare’s first comprehensive foundation-model AI triage solution. The company says CARE now supports a workflow combining 11 newly cleared indications with 3 previously cleared ones, with mean sensitivity of 97% and mean specificity of 98% in the FDA-reviewed pivotal study. More strategically, Aidoc says this is the first FDA clearance of a comprehensive set of double-digit acute indications powered by a single foundation model. That matters because it suggests the company is no longer just shipping separate narrow models. It’s building a scalable clinical intelligence layer.

This is what I like most about the product.

They are not selling “AI magic.” They are selling:

• earlier detection

• faster triage

• cleaner workflow routing

• better care-team coordination

• governance

• enterprise deployment

That is how vertical AI becomes durable.

3) GTM: enterprise, multi-stakeholder, and trust-heavy

Aidoc’s go-to-market motion is exactly what you’d expect in a serious healthcare vertical AI company: slow at first, then powerful if you get it right.

This is not bottoms-up PLG. This is not swipe-a-card SaaS. This is enterprise clinical workflow software with regulatory weight.

The company’s public materials point to health-system sales centered on measurable operational outcomes: improved efficiency, shorter length of stay, faster diagnosis, reduced silos, and ROI. It also appears to sell into multiple service lines—radiology, neurology, vascular, cardiology, emergency care—which is important because it expands ACV after the initial wedge. Once you are trusted in one workflow, you can use the same platform and integration layer to expand across adjacent clinical domains.

The integration strategy is a big GTM advantage. Aidoc says aiOS is vendor-agnostic and connects with PACS, VNA, worklists, EHR, scheduling, and communication interfaces. It also says it is available within Epic’s App Orchard and can integrate with Radiant to provide acuity-based feedback in radiologists’ workflows. In healthcare, integration is distribution. If your product lives inside the clinical systems teams already use, adoption friction drops and your ROI story gets easier to prove.

I also like the partner strategy. Aidoc has both reselling partners and AI application partners. That means it is not just selling a closed suite; it is positioning aiOS as the orchestration and governance layer through which other clinical AI applications can be deployed. That is a much more ambitious GTM posture than “buy our algorithm.” It says: make us the control plane for AI inside the health system.

4) Customer success: this is where the company seems to separate itself

Most AI companies talk model performance.

Aidoc talks rollout, workflow, governance, and speed to value.

That’s a tell.

On the aiOS page, Aidoc highlights several scaled deployments:

• Advocate Health: 69 hospitals, 8M+ annual imaging studies, 22-site rollout, nearly 63K patients/year projected to benefit

• Hartford HealthCare: 500+ care locations, first sites live in 3 weeks, 17 FDA-cleared algorithms deployed plus care coordination and patient management tools

• Mercy: 1,000+ facilities, 12 use cases deployed system-wide in 3 months, urgent and incidental findings triaged in under 5 minutes, down from 20

Those are not vanity metrics. Those are customer-success metrics.

They show that Aidoc understands a truth most AI founders miss:

In the enterprise, the model is not the product. The implementation is the product. The workflow change is the product. The internal buy-in is the product.

The Hoag case study reinforces this. Aidoc highlights how radiology leaders built internal buy-in by starting with pulmonary embolism and vascular workflows, demonstrating shorter stays, improved outcomes, and cost savings, then expanding once the ROI case was clear. That is textbook vertical expansion: prove value in one high-pain workflow, create champions, then widen the footprint.

If I were underwriting this business, that’s what I’d focus on.

Not just “does the model work?” But:

• how fast can they go live?

• how many use cases can they layer in?

• how sticky is the workflow once deployed?

• how much internal political capital does the champion need?

Aidoc’s public stories suggest they’ve gotten very good at that layer.

5) Revenue: likely large ACVs, land-and-expand economics

Aidoc does not publicly disclose revenue in its own materials, so any revenue discussion has to be intellectually honest.

What we do know is this:

• the company says it is deployed across nearly 2,000 hospitals

• supports decision-making for roughly 70 million patients each year

• analyzes more than 110 million patient cases

• and is clearly selling enterprise software into major health systems

A third-party database, Prospeo, estimates annual revenue at roughly $95M, but I would treat that as directional rather than verified fact. Still, even if you haircut that estimate, the public customer scale suggests this is no tiny experiment. This is a real enterprise vertical AI business with meaningful commercial traction.

My read on the revenue architecture is that Aidoc likely benefits from a classic vertical AI compounding motion:

• initial wedge into one service line

• expansion into adjacent use cases

• platform value from aiOS

• additional monetization through broader orchestration/governance footprint across the health system

That tends to create larger ACVs, better net revenue retention, and more strategic importance over time. That last part matters most. Once the product becomes the layer managing multiple clinical AI tools and workflows, ripping it out gets much harder. That’s when revenue quality improves.

6) Fundraising: massive round, but the more important signal is why it got done

Aidoc announced a $150M Series E in April 2026 led by Growth Equity at Goldman Sachs Alternatives, with participation from General Catalyst, SoftBank Vision Fund 2, and NVentures. The round brought total funding to over $500M.

That’s obviously a big round.

But the deeper signal is the narrative behind it.

Aidoc is not pitching “we have one impressive model.” It is pitching:

• a clinical foundation model

• an enterprise operating layer

• regulatory rigor

• scaled deployment

• measurable customer ROI

• and a path toward end-to-end workflow automation, including draft reporting

Elad Walach framed the round as “the end of the beginning” for clinical AI and said the capital will go heavily into R&D: expanding CARE to cover hundreds of diseases, upgrading aiOS to support scale, and investing in AI-generated draft reporting. That is exactly the kind of post-wedge capital allocation you want to see. Use the first phase to prove trust and workflow fit. Use the next phase to widen disease coverage, deepen infrastructure, and automate more of the value chain.

Also worth noting: Goldman’s quote emphasizes efficiency, shorter length of stay, and measurable financial returns. That’s investor-speak meeting buyer-speak. The company isn’t being funded on vibes. It’s being funded on the idea that clinical AI is moving from pilot project to operating necessity.

7) Why Aidoc matters

Aidoc matters because it shows what the best vertical AI companies are becoming.

They don’t stay point solutions.

They start as a wedge. Then they become workflow. Then they become system layer. Then they become control plane.

That’s the climb.

Aidoc started with radiology anomaly detection. Now it is trying to become the enterprise operating system for clinical AI across the health system. That is a much bigger business than “AI for scan reading.”

And there’s another reason this is important.

In vertical AI, the real moat is not just the model. It’s the combination of:

• workflow ownership

• trust

• deployment muscle

• integrations

• regulatory credibility

• and customer success at scale

Aidoc seems to understand that better than most.

Be intellectually honest about job security in an agentic world

Now let’s talk about an uncomfortable truth.

Too many companies are still speaking about AI as if it’s only a productivity enhancement.

That framing is becoming less credible by the month.

Agents are not just helping people write faster or summarize documents.

They are increasingly capable of doing real operational work:

• gathering information

• navigating systems

• coordinating tools

• executing repeatable workflows

• updating systems of record

• producing standard outputs

• handing work from one step to the next

And if that’s true, then companies need to be much more intellectually honest about labor.

Not every role goes away.
Not every function gets automated.
Not every org can move at the same speed.

But a lot of companies are keeping roles in place that they no longer really need in the same form.

They know it.

They just aren’t pulling the trigger.

I’ve seen so many companies with staff that they really don’t need anymore.

Why? Because saying “we can eliminate this role” is much harder than saying “we’re experimenting with AI productivity tools.” One sounds innovative. The other forces a real org redesign conversation. But sooner or later, that conversation is coming.

If a role is heavily concentrated in:

• repetitive information gathering

• system-to-system coordination

• standard process execution

• routine output generation

• structured follow-up and admin work

Then leadership should be asking whether that work should still live on the org chart, or whether it should now live inside an agent-enabled workflow.

That’s not fear-mongering.

That’s just operational honesty.

The companies that move first will redesign teams around agents and reallocate human talent toward judgment, escalation, relationship-building, and high-leverage decision-making. The companies that move slowly will keep carrying labor costs for work that the market increasingly expects software to handle.

So what should people do?

Don’t be the person defending the old workflow.
Be the person redesigning the new one.

Map where work is repetitive.
Identify where agents can already perform.
Figure out where humans still add real value.
Learn how to orchestrate tools, prompts, data, permissions, and workflows.
Show your org what AI-native execution actually looks like.

In short:

become the AI whisperer of your organization.

Because in an agentic world, job security won’t come from pretending disruption is exaggerated.

It will come from being the person most capable of helping the company turn that disruption into leverage.

Do me a solid and forward to a friend :-)

This week’s episode was a fun one. We flipped the format and put Nic Poulos in the hot seat, with Sierra Ventures stepping in to run the interview. And honestly, it made for one of the sharper conversations we’ve had yet — less founder storytelling, more raw investing frameworks, category formation, and what actually matters when you’re underwriting vertical AI at day zero.

Nic is a GP at Euclid, but the arc here matters: banking, startup/operator reps, AOL Ventures, co-founding Bowery Capital, then spending the last decade developing real pattern recognition around vertical software before leaning hard into what he sees as the next chapter: vertical AI. His core belief is simple: AI is not killing software — it’s creating a new generation of software winners.

And that’s what makes this episode so good. It’s not fluffy “AI is changing everything” content. It’s a practical conversation about where the wedges are, what actually creates defensibility, how the best founders validate demand before the product is done, and why fund size is quietly reshaping founder outcomes.

Watch Episode Here

Listen To Episode Here

This Weeks Vertical Titan: Nic Poulos (Co-Founder & GP @ Euclid Ventures)

Nic has spent 10+ years backing early-stage vertical software companies, with prior stops including Bowery Capital and now Euclid, which is explicitly built to invest pre-product, pre-traction, pre-everything with a focus on founder-market fit and inception-stage conviction. That background matters because his lens isn’t coming from theory — it’s coming from a decade of seeing how category leaders in vertical software actually get built.

What I liked most is that Nic is not chasing AI because it’s hot. He is chasing it because he thinks LLMs unlock new product wedges that vertical SaaS simply couldn’t access before: voice intake, document abstraction, automated drafting, workflow launch points, and products that can finally go after the much larger services budget sitting next to software spend.

That’s also why the Sierra setup worked so well. Sierra Ventures has been around for four decades, manages more than $2B in AUM, and has a long history in early-stage enterprise investing. So instead of a typical founder interview, you get a real investor-to-investor conversation on how vertical AI is being underwritten right now.

The core thesis: the next big vertical AI companies won’t just be “AI wrappers.” They’ll sit at the point where work actually begins, own a critical workflow, expand from wedge to system, and capture dollars that historically went to labor, BPOs, or fragmented back-office services.

Watch Episode Here

Listen To Episode Here

Below — lessons from Nic:

1) Underwrite the team and market, not fake pre-seed precision

At inception, Nic’s point is brutal and correct: $100K ARR is not meaningfully different from zero if you’re trying to build a category-defining company. The real underwriting variables are the founder, the market, and whether the team has the right to win.

Action item: Stop optimizing your deck for revenue. Make the case for why this team, in this market, at this moment, is uniquely positioned to win.

2) “Earned insight” is still the cheat code

The best founders are not tourists. They’ve lived the pain, sold into the buyer, built adjacent tools, or grew up around the problem. That earned insight is what lets them spot a wedge that outsiders miss.

Action item: Audit your unfair advantage in plain language. If you can’t explain why you specifically should build this, neither can your customer.

3) Start at the authoring layer

This was probably the cleanest framework in the whole episode. The best vertical AI products often start where a record is first created: a phone call, an intake form, a contract hitting the inbox, a claim entering the system. Own that moment, and you can control everything downstream.

Action item: Map your industry’s workflow back to the very first keystroke. Build there, not where the dashboards live.

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4) Don’t build “the agentic command center for X”

That’s the solution-first trap. If your product mostly vacuums data out of systems and summarizes it, you are building directly into the teeth of OpenAI, Anthropic, and every horizontal platform. That’s not great territory.

Action item: Pressure-test your wedge against ‘Claude 14.’ If a better base model flattens you, your moat is positioning, not product.

5) Sell before the product is ready

One of my favorite parts: Nic loves founders who can create commercial pull before the product is fully built. If customers will become design partners, sign LOIs, or start paying early, you’ve proven urgency — and urgency is gold.

Action item: Run a paid pilot or signed LOI before you write the second sprint of code. If the market won’t pull, the product won’t push.

6) Use cold discovery as a market stress test

Talking to 50–100 potential users is not a “nice to have.” It’s the work. And if a founder can convert cold outreach into active design partners quickly, that is often a stronger signal than polished demos or fancy decks.

Action item: Add up the labor and services dollars sitting next to the software line in your customer’s P&L. That’s your real TAM.

7) Go after services budgets, not just software budgets

This is one of the biggest insights in vertical AI. In many industries, services spend is multiples larger than software spend. If AI can replace headcount, manual coordination, outsourced processing, or consultants, the TAM expands dramatically.

Action item: Be ruthless about your wedge. Cut every adjacent feature until the one that earns the next conversation is undeniable.

8) Wedge first, then expand across workflow

The first product does not need to be the whole platform. It needs to be the right entry point. Once the wedge works, you expand into adjacent workflow, pull more actions into the system, and earn the right to become the operating layer.

Action item: Inventory the things that are obvious to you and unknowable to a horizontal team. Build the product around those.

9) Speed is a tool, not a moat

This is the nuance most people miss. Yes, speed matters. But no, speed alone is not defensibility. Speed is how you get to insight faster, ship the second product faster, and lock in workflow ownership before others catch up.

Action item: Before you sign, model the outcome your lead investor needs to be happy. If it isn’t the same as yours, walk.

10) Vertical nuance beats horizontal breadth

Nic’s implicit test is great: imagine a future where frontier models get much better. If your entire company gets flattened by “Claude 14,” you never had enough differentiation. Real moats live in vertical nuance, messy workflow logic, domain-specific edge cases, and system-level integration.

11) Become internal, not external

If the customer sees you as an outside service they can swap out, you’re vulnerable. If you become embedded in first-party workflow, connected to operational data, and tied to daily execution, you start to look like infrastructure.

12) Avoid the permanent services trap

Human-in-the-loop is fine early. Permanent dependency is not. Founders can absolutely use services to bridge the gap while models improve, but if the long-term model never escapes labor-heavy delivery, the ceiling gets lower fast.

13) The first technical hires matter disproportionately

The early engineering team doesn’t just write code — they set the culture, quality bar, and velocity norms for everything that follows. One great early hire compounds. One weak one lingers.

14) Don’t raise just because you can

This one should be printed and taped to every founder’s wall. More capital is not always better capital. Too much money too early can hide problems, delay hard decisions, and reduce the discipline that actually creates product-market fit.

15) Fund size is strategy

Nic’s critique here is sharp: if your investor only wins when you become a $5B+ company, that may not be aligned with what a great founder outcome actually looks like. Bigger funds don’t just change check size — they change incentives, exit math, and boardroom behavior.

The Anti-Playbook

A few traps from the episode that are worth calling out directly:

• Thinking “AI-native” means slapping a chatbot on old SaaS. That’s not reimagining workflow — that’s feature dressing.

• Believing speed alone will save you. It won’t. Speed only matters if it helps you discover and lock in something durable.

• Starting too far downstream in a workflow. If another system owns the first touch, you’re always negotiating from behind.

• Building for consensus instead of alpha. The biggest winners rarely look obvious when they start.

• Assuming a giant fund is automatically the best cap table addition. Sometimes it is. Sometimes it quietly destroys alignment.

We’re not watching the death of SaaS.
We’re watching category creation reopen.

If you’re underwriting — or building — vertical AI today, Nic’s playbook comes down to three commitments:

1. Understand the market deeply. Earned insight beats AI-native positioning every time. Tourists get filtered out by month three.

2. Wedge at the authoring layer. Own the moment the record is created. Everything downstream becomes negotiation from a position of strength.

3. Go after the services budget. The biggest outcomes will eat labor and BPO spend — not fight for another seat license next to the incumbent.

The founders who win here won’t be the ones with the fanciest AI positioning. They’ll be the ones who understand a market deeply, wedge into the workflow at the exact right point, create ROI fast, and then expand with ruthless focus.

Watch Episode Here

Listen To Episode Here

Thanks for reading LINEAR. I reply to every email…
Have any questions, feedback, comments? Let us know, we work for you!

Shaped by lessons from growing and scaling 20+ vertical SaaS platforms, this guide explores what AI readiness actually means for software providers. Read the guide.

Vori: Vori just raised a Series B — and it’s a very important kind of vSaaS company

Most people will look at Vori and say: “grocery software.”

That is technically correct and strategically useless.

What Vori is really building is an operating system for independent grocers — one that sits across the front store, the back office, and increasingly the money flow itself. That matters because grocery is one of those giant, messy, low-glamour industries where software has historically lagged, margins are thin, labor is constant, and the best companies win by helping operators make dozens of tiny decisions better every single day.

The founder story

The company began in 2019 from an idea sparked by Brandon Hill, whose parents spent roughly 40 years in grocery and consumer products and saw firsthand how little the industry had modernized. That is exactly the kind of founder-market fit I pay attention to: not “we discovered a market in a spreadsheet,” but “we lived close enough to the pain to know the software was broken.”

And the early timeline has the right flavor.

The initial iOS app was built in 12 days. The first product was scanned in October 2019. The first wholesale order submitted through Vori was just $200 of kombucha. I love stories like that because they remind you that every vertical software companies rarely begin with giant visions in practice. They begin with one small workflow that matters deeply to one specific operator.

The wedge

Vori did not try to become “the everything platform for grocery” on day one.

The early heartbeat of the business was very operational: products getting scanned, orders getting written, inventory getting understood, stores getting a modern interface for core retail tasks. That is what good wedge products do. They enter through a daily pain point. They get adopted because they are useful, not because the founder has a grand theory of industry transformation.

In grocery, that makes even more sense.

Independent grocers do not have time for bloated implementation cycles or category theory. They need software that helps them order, stock, price, sell, and protect margin. Vori’s product stack reflects that reality: POS, integrated payments, hardware, shopper engagement, order management, pricing automation, inventory management, shrink tracking, and reporting. That is not just a software menu. That is a blueprint for owning the store’s operational surface area.

Going deep: the full-stack play

This is where the company gets interesting.

A lot of vertical software companies win one workflow and then stall. Vori appears to be doing the more ambitious thing: moving from a point solution into a full operating layer. On the front end, it handles checkout, payments, hardware, and shopper engagement. On the back end, it handles ordering, pricing, inventory, shrink, and reporting. And it has started positioning itself around business outcomes, not just features: grow sales, protect margins, optimize labor, expand market share, and modernize with confidence.

That framing is important.

The best vertical software companies eventually stop selling software categories and start selling business outcomes. Grocers do not wake up wanting “better reporting software.” They want fewer stockouts, faster checkout, better margins, less labor waste, and a stronger shot at competing with chains that have more scale. Vori seems to understand that the real product is not a dashboard. The real product is a better run store.

Why grocery is such a compelling vertical

The grocery market is enormous, but more importantly, it is operationally dense.

This is not a niche where a store manager touches software once a week. Grocery operators live inside pricing, replenishment, shrink, checkout, labor, assortment, customer traffic, and vendor relationships. The U.S. grocery market is roughly $1.5 trillion, with tens of thousands of supermarkets, and Vori is going after the independent side of that market — the operators that do not have Walmart-scale internal tooling but still have all the same operational complexity.

That is a classic vSaaS opportunity.

Big market. Fragmented customers. High workflow complexity. Real switching pain. Constant transactions. Embedded financial flows. Low tolerance for bad software. Those are the ingredients that tend to produce very durable vertical winners.

The payments angle is a big deal

The other reason this company matters: it is not just selling software.

Vori has integrated payments at the center of its offering, and that matters because the best vertical SaaS businesses eventually realize the same thing: once you are close enough to the transaction, software alone is not the full prize. One of the most important data points in Vori’s story is that payments reportedly represent roughly 60–70% of revenue, with more than $500 million processed across 55 cities and more than 1 million consumers served. If that continues, this becomes a very different kind of business than a plain subscription software company.

That is the part many founders still underestimate.

The software gets you into the workflow.

The payments layer monetizes the motion.

And once the money starts moving through your rails, your understanding of the business gets deeper, your margin profile improves, and your ability to launch adjacent products gets stronger.

The capital efficiency story

Vori’s new $22 million Series B was led by Cherryrock Capital, following a prior $10 million Series A in 2022. The company now has roughly $50 million in total funding and, based on recent reporting, expects very aggressive growth over the next two years.

I think this is exactly the right kind of company for this moment.

Not a generic AI wrapper.

Not a horizontal tool pretending to be vertical.

Not a software business with no path to owning payments, labor, or economic throughput.

Instead, this looks like a company attacking a massive and messy industry through real workflows, then expanding into deeper operational and financial control. That tends to age well.

The moat

If I had to summarize Vori’s moat in one sentence, it would be this:

They are not trying to be a prettier dashboard for grocers. They are trying to become the operating layer an independent grocer trusts to run core parts of the business.

And moats in vertical software usually get stronger when a company can do four things at once:

• win a painful operational wedge

• expand across adjacent workflows

• sit close to the transaction

• earn trust with a specific customer type over time

Vori checks a lot of those boxes already.

It also helps that the company is clearly built for specific grocery formats — supermarkets, meat markets, natural and organic stores, produce markets, and specialty food stores. That kind of vertical specificity matters. Generic software often breaks down in edge cases. Vertical software wins because it understands that the edge cases are the business.

What could happen next

The obvious next chapter is not “more features.”

It is deeper control over store economics.

If Vori keeps expanding across pricing, payments, inventory, shrink, reporting, and shopper engagement, it can move from software vendor to decision engine. That is where vertical software becomes very powerful. The more of the operational loop you touch, the more insight you gain into where margin leaks, where labor gets wasted, and where automation can actually improve outcomes.

And that is what makes Vori especially interesting in the AI era.

Grocery is full of “work,” not just “workflow.” Pricing decisions. Replenishment choices. Exception handling. Labor tradeoffs. Promotions. Shrink. Shopper behavior. If Vori becomes the place where those decisions get surfaced, tracked, and improved, it has a real shot at becoming far more valuable than “grocery POS software.”

Why this story matters for every founder reading this

There is a bigger lesson here.

The best vertical companies rarely start by announcing they are building an empire. They start by solving one ugly, high-frequency problem inside a market that outsiders overlook. Then they use that wedge to move deeper into the economic core of the customer.

That is the Vori playbook as I see it:

1. Start close to the operator

2. Win a painful daily workflow

3. Expand into adjacent systems

4. Own the transaction layer

5. Become the operating system for the vertical

It is a very textbook vSaaS move.

In Closing…

Vori is interesting for three reasons:

• it is attacking a huge, fragmented, operationally complex market

• it has expanded beyond software into payments and core business economics

• it seems to understand that the real goal is not selling tools to grocers, but helping grocers run better stores

That is exactly the kind of company I want to study.

Because in vertical software, the biggest winners usually do not look flashy at first.

They just quietly become essential.

PS/quick disclosure — I am a tiny investor in Vori, but would write this the same regardless :-)

If you’re building in vertical software or vertical AI, we’re going to co-build your GTM playbook live on next week’s workshop.

Raj Bhaskar, Founder & CEO at Tight, and I are doing a live workshop with the CRO whisperer, Martin Roth, former CRO at Levelset (massive exit to Procore), where we’ll get into what actually works when you’re building GTM in a vertical market.

Martin has spent his 10,000 hours thinking deeply about vertical GTM. Not generic SaaS GTM. Not “copy what worked in PLG last year.” Vertical GTM.

The practical stuff.

The stuff that actually matters when you’re trying to sell into a market with weird buyer behavior, long trust cycles, fragmented channels, and a reality that doesn’t fit the standard playbook.

A few topics we plan to get into:

• How to build a repeatable sales motion around the realities of your market instead of forcing a generic one onto it

• Where most teams get stuck as they try to scale from founder-led sales into a real GTM engine

• How great vertical companies shape their sales process around workflow, urgency, and buyer context instead of vanity SaaS rules

• What founders and operators should be watching for as they try to create durable distribution in niche markets

If you’re a founder, operator, or investor thinking seriously about how vertical companies actually win distribution, this should be a good one.

Registration link: https://lnkd.in/eTnD_ETm

Limited to the first 25 vertical founders/operators.

Selling Seats vs. Selling Work

This framework is about to split the software market in two. In the old world, software sold into a software budget. It made a person faster. It gave the team better visibility. It organized the work. But at the end of the day, the human still did the job. In the new world, more and more products will move out of “helping” the worker and into actually doing meaningful chunks of the work itself.

Here’s the cleanest way I know to frame it:

• the software budget = “we help your team work faster”

• the labor budget = “we do the work”

That is the split. And it is a very big one.

If your product still needs the customer’s team to do the actual job, you will probably get priced like software. That means seats, modules, access, dashboards, admin controls, and maybe some nice upsell expansion if you execute well. Good business. Familiar playbook. Still very real.

If your product is reading the intake, structuring the mess, routing the case, drafting the output, escalating the exceptions, and improving over time, you are not really selling software anymore.

You are swallowing labor.

That is why some of the biggest AI companies are not going to look like normal SaaS businesses. They will look like strange hybrids: software on the surface, operations companies underneath. The UI may still look like software. The buyer may still think they are buying software. But the economic engine underneath is much closer to replacing labor than selling access.

And when you start looking at the world this way, the founder questions change.

The old question was: How much does this industry spend on software?

The new question is: How much labor spend exists around this workflow, and what percent of that can AI absorb?

This is where it gets VERY interesting.

Because seat-selling companies and work-selling companies optimize for very different things.

Seat sellers care about:

• deployment

• adoption

• training

• logins

• expansion

• renewal

Work sellers care about:

• throughput

• turnaround time

• accuracy

• exception handling

• labor savings

• margin creation

• customer outcomes

The best AI-native companies will not win because they added a chatbot to the old workflow. They will win because they rebuilt the workflow around doing the work itself. In a lot of categories, the old moat was being the system of record. The new moat is becoming the system that helps the customer make money, save labor, and move faster.

So no, the prize is not “better seat expansion.”

The real prize is owning the work

Do me a solid and forward to a friend :-)

That’s where trillion-dollar capital meets a workflow that hasn’t changed since 2005.

In 2005 the United States built 200 grid-scale solar projects. Today it builds 10,000 every year — with the same overworked analysts, the same spreadsheets, the same hope. 60% of projects get delayed. 30% are killed outright.

That gap between the volume and the tooling is the entire opportunity. Jacob’s argument is that the next iconic vertical companies won’t sell software seats into this world — they’ll sell outcomes into the CapEx of every project getting financed, built and operated.

Watch Episode Here

Listen To Episode Here

This Weeks Vertical Titan: Jacob Sandry (Founder & CEO @ Euclid Power)

Jacob got into renewables ten years ago as a self-described hippie kid working at a solar crowdfunding startup. He spent the next six years on the finance side — the last three helping build Goldman Sachs’s renewables fund from $0 to $3B deployed across wind, solar and storage.

From inside the investment committee he watched the same pattern repeat hundreds of times: brilliant developers, real projects, billions of dollars of capital — all bottlenecked by analysts in Excel reconciling PDFs at 2am. 60% of projects slipped. 30% died outright. Always at the same mile.

He left to build the thing that should have existed. Euclid Power — recently named one of Fast Company’s Most Innovative Companies for 2026 — is now the system of record for the messiest mile of energy: development, financing, M&A and the hand-off into operations. It is software, services and AI, sold into the CapEx of the project itself rather than as a per-seat tool the procurement team will haggle over for nine months.

Customers run the gamut from independent developers shepherding a single interconnection through PJM, to the largest IPPs and infrastructure funds in the world closing nine-figure portfolio deals on Euclid’s deal rooms. The common thread: they all need the numbers to be right the first time.

Watch Episode Here

Listen To Episode Here

Below — nine lessons from Jacob on how to actually build, sell and defend an AI-native company in a high-stakes vertical.

1) In high-stakes industries, 99% is the same as wrong

When you’re underwriting a $100M solar project at 6% unlevered returns, you can’t ‘fail half the time’ the way VC does. A single missing line in a permit, a stale interconnection number, a misread tax credit clause — any of them can sink the deal. Vertical AI for this world isn’t about generating plausible answers. It’s about being defensibly right, every time.

Action item: Define the failure mode before the feature. If a wrong answer is unforgivable, design accuracy in from day one.

2) Sell into the CapEx, not the SaaS line item

Half the spend on a renewable project goes to admin, lawyers, IE reports, appraisers and project managers — and all of it gets capitalized into the deal. That’s the budget Euclid sells into. Per-seat pricing caps you at a tiny pool of analysts. Outcome-priced services tap into the actual project budget that the industry has already accepted.

Action item: Find the budget your customer doesn’t think of as ‘software.’ That’s usually where the real money lives.

3) In CapEx-heavy verticals, lead with services

Euclid started as a spreadsheet. Customers said: ‘we’d pay you 10× more if you’d just do the work.’ That accident became the business model. In capital-intensive industries, going in with a service and a solution is often a better wedge than trying to license a platform — because the customer is buying outcomes, not tools.

Action item: Don’t be afraid to look like a services business on day one. Software is what makes the margin scale later.

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4) Build an exoskeleton, not an autopilot

Pure ‘point Claude at the data room’ tools hit a ceiling fast — customers still have to redo the work. Euclid’s pitch is the opposite: experts who already know the right answer, accelerated 10× by AI that surfaces, suggests, and cites. Investment committees can stand behind that. They cannot stand behind ‘sorry, Claude did it.’

Action item: Pair AI suggestions with a human accountable for the outcome. That combination clears the trust bar.

5) Win the moment the data has to be right

Every vertical has a control point — a moment where information must be accurate and naturally flows into a system. For Euclid it’s diligence. For others it’s intake, claims, or close. Own that moment and you become the multiplayer source of truth for everyone downstream: lawyers, engineers, contractors, investors.

Action item: Map your industry’s workflow and find the one moment where messy data must become clean data. Win it.

6) Structured data is the bridge LLMs still need

Even as models get better, the gap between unstructured documents and reliable outcomes doesn’t close on its own. The verticals where SaaS never quite landed weren’t unworkable — extraction was just too expensive. AI collapses that cost. But the durable value sits in the structured layer in between: the same fields, captured the same way, across thousands of projects.

Action item: Treat your schema as the moat. Models change every quarter; a clean structured graph of your industry compounds.

7) Trust is a moat — even if Silicon Valley pretends it isn’t

Outside the Valley, people don’t tweet benchmarks. They buy the brand they trust, the way they buy the expensive car. Many vertical buyers are still skeptical of AI itself. Some aren’t even allowed to use Claude. The companies that win aren’t the ones with the best model — they’re the ones that earn the right to be trusted with a $100M decision.

Action item: Invest in brand, references and accountability earlier than feels rational. In high-stakes verticals it compounds.

8) Regulation is a moat — but only sometimes

If a third party has to physically stamp the work — an IE, an appraiser, a tax-credit auditor — the foundation model can’t disintermediate that role. You can. If the regulation is just rules a model can read, that’s not a moat: any competitor can wrap the same AI around it. Be honest about which one you actually have.

Action item: Map every regulatory checkpoint in your vertical. Look for the ones that require a human signature — those are the wedges.

9) Be the Schlumberger of your industry

In oil and gas, the Fortune 200 names you’ve never heard of — Schlumberger, Baker Hughes — are the picks-and-shovels for every well drilled. Renewables, electrification and AI infrastructure are about to need the same. Software, services and field expertise wrapped together. Not just better dashboards. The operating system that makes the projects actually get built.

Action item: Pick a vertical going through a generational build-out. Become the indispensable solutions provider, not a tool vendor.

The next great energy companies
won’t sell software. They’ll sell outcomes.

If you’re building vertical AI for a high-stakes industry, Jacob’s playbook comes down to three commitments:

1. Take the problem seriously. Bring in people who have lived it. In trust-driven industries, naive outsiders don’t get a second meeting.

2. Be defensibly accurate. Pair AI with experts accountable for the answer. Investment committees back people, not autocomplete.

3. Sell into the real budget. CapEx, services line items, project budgets — not the SaaS line. That’s where the order of magnitude lives.

In oil and gas, the indispensable companies are names like Schlumberger and Baker Hughes — the picks-and-shovels nobody outside the industry can name. The next renewable build-out will need its own. Euclid is betting it can be that company.

Watch Episode Here

Listen To Episode Here

Thanks for reading LINEAR. I reply to every email…
Have any questions, feedback, comments? Let us know, we work for you!

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Your vSaaS Company Is Probably Not Worth 5x ARR (& how to overcome that)

Ok, I know none of you want to hear this.

I sure didn’t during the SaaS Crash. I anchored my mind that my company was always going to be worth 10x ARR. It was painful when I realized….. It wasn’t.

So let’s talk about it. And what you can do to get back to a higher multiple.

This week’s issue is about valuation reality.

Not the founder-group-chat version.
Not what the bankers tell you.
The real one.

Public SaaS comps have compressed. Private buyers have gotten stricter. AI-native companies are still getting paid differently. And a huge number of founders still have 10x ARR stuck in their head like it’s a law of physics.

It isn’t.

The most important skill for a founder in this market is intellectual honesty: knowing your real downside value (and being GOOD with you / your teams situation if that is indeed what happens) all while still building toward a much better future state.

How to understand the actual value of your company

Most founders start with the wrong question.

They ask:
What multiple should I get?

The better question is:
What is a buyer actually paying for?

Because buyers are not paying for your ARR in the abstract.

1) Start with public reality, not private mythology

As of March 2026, Aventis shows the median public SaaS EV/revenue multiple at 3.4x. That is a brutal reset from the old market. And even strong public software names are not all floating around on fantasy numbers. On the BVP cloud index, recent EV revenue multiples were roughly 4.8x for ServiceNow, 2.7x for HubSpot, 4.9x for Procore, 5.2x for Veeva, 1.2x for monday.com, and 8.3x for Shopify. The spread is wide, but the lesson is obvious: the market is rewarding only a narrow band of truly exceptional assets.

So if elite public software companies are trading in the low-to-mid single digits on forward revenue, your private company does not deserve 5-10x ARR by default just because you are software.

The best companies in the WORLD are trading <5X ARR..

That number is not a principle.
It is our current data.

2) Understand the actual buyer buckets

The cleanest way to think about the market right now is by buyer type and asset quality.

Here’s the rough menu based on what I’m seeing RIGHT NOW:

• Value Buyers (Constellation, Banyan, etc.) often closer to 1–2x ARR / low-teens EBITDA logic

• Private equity-quality SaaS businesses: often 2–6x ARR

• Growth equity-quality assets: often 4–8x ARR

• Venture investors: often 8x ARR+ but these folks are almost exclusively funding AI-Native companies.

• Strategics can land 7–12x ARR, with occasional higher outcomes for standout companies but this is only if they can really sell your product into a massive base and quickly deliver an ROI. A lot of strategics are incredibly worried right now about M&A. Especially public ones. If the market doesn’t like what they paid, they will get hammered. And they can’t afford that right now. They really only want to buy AI.

That doesn’t mean every founder should accept the low end.

It means every founder should understand what market they are actually in.

3) Ask the most important valuation question: what meat is left on the bone?

This is the question buyers actually care about.

Not “how hard did you work?”
Not “what did SaaS trade at in 2021?”
Not “what did your friend get?”

They care about what value is still available after they buy the company.

That usually means they are underwriting some combination of price increases, better packaging, margin improvement, sales efficiency, lower churn, embedded payments attach, AI-driven labor reduction, cross-sell, better lead conversion, or tighter operational control.

If you have already optimized everything, there is less upside for the buyer.

And if there is less upside for the buyer, there is less price for the seller. That is why some founders overestimate value. They confuse “good company” with “high-upside acquisition target.”

Those are not the same thing.

You HAVE to package for a buyer exactly how they will crush it. It took me years to realize this…

4) Separate quality from scarcity

A business can be solid without being scarce.

Scarcity is what drives price.

Scarcity comes from:

• proprietary data,

• embedded workflows,

• trust with a niche customer set,

• measurable AI leverage,

• or a monetization wedge competitors do not have.

That is one reason AI matters so much. If your company is still just workflow software with a few automations, you will usually be valued like software. If your company is becoming the decision layer, action layer, or revenue layer for a vertical, you can start to access a different conversation.

5) Get comfortable with your downside number

This is the part founders avoid.

You should know:

• your fundraising story value,

• your strategic premium value,

• and your downside private-market value.

You do not need to love the downside number.

You just need to know it.

Because once you know it, you can make sharper decisions about whether to raise, wait, hire, cut, reposition, or push harder into AI. Intellectual honesty is not bearish. It is what allows you to move with precision.

This Is Why You Need To Become an AI Company, Not a SaaS Company…

If you are building vertical software right now, you should be racing to become an AI company.

Not because “AI” is trendy.
Not because your deck needs new words.

Because the market is still telling you, very clearly, that AI companies are being priced differently from traditional software companies. Carta found that seed-stage AI companies carried a 42% higher median pre-money valuation than non-AI startups in 2024, while Series A AI companies were valued 30% higher and Series B AI companies 50% higher. In Carta’s 2025 review, the AI premium persisted, with Series A AI valuations 38% higher than non-AI peers and late-stage premiums reaching 193% at Series E+.

But the most useful way to frame this is: the AI premium is real, but it is not automatic.

A lot of that premium is happening in fundraising markets, not necessarily in full-company sale processes. Aventis puts the median revenue multiple for AI companies in fundraising comps at 29.7x, while explicitly warning that full M&A outcomes are usually significantly lower. That distinction matters.

Founders keep confusing “what venture investors will mark up” with “what a buyer will wire.”

Those are VERY DIFFERENT different numbers.

That’s why founders need two prices in their head at all times:

• the story price

• the downside price

The story price is what you market.
The downside price is what you plan around.

The story price is what gets people excited.
The downside price is what keeps you from making bad strategic decisions.

And if you want that downside price to move, you need to become the kind of vertical company that deserves AI repricing.

That usually means some combination of:

• owning a higher-value workflow,

• using proprietary vertical data,

• automating labor-heavy steps,

• getting closer to revenue or margin outcomes,

• and becoming harder to rip out of the customer’s daily operation.

• This is probably the most important one, it’s showing AI-level growth. It’s not just re-branding as an AI company. And that my friends, is a lot harder than X.com makes it seem…

That is the real shift.
The old moat was being the system of record.
The new moat is becoming the system that helps the customer make money, save labor, and move faster.

Avoca: the home-services AI company everyone should study

Avoca announced on April 27 that it had raised more than $125 million across Seed, Series A, and Series B at a $1 billion valuation, backed by Kleiner Perkins, Meritech, General Catalyst, Amplify Partners, Nexus Venture Partners, and Y Combinator. The company says it is building AI agents for the services economy, starting in home services and expanding into roofing, restoration, auto, and adjacent verticals.

That alone makes it notable.

But the more important part is why investors paid attention.

What Avoca actually does

Avoca calls itself “the AI front office for home services.” That is the right framing. Not general AI for contractors. Not “copilot for SMB.” A front-office system built to answer every call, book more jobs, fill open capacity, and give operators visibility into how the revenue engine is performing.

On the product side, Avoca is building a pretty coherent stack:

• Inbound AI agents that answer calls, texts, and chats 24/7

• AI booking that can schedule jobs and sync them into the customer’s CRM

• Emergency escalation and routing when calls require humans or urgent handling

• Outbound campaigns across SMS and calls to re-engage customers, revive old estimates, renew memberships, and fill the board

• Call scoring / analytics / coaching to surface missed opportunities and monitor CSR performance

That matters because most home-service companies do not just have a “phone problem.”

They have a front-office capacity problem.

They are bleeding revenue through:

• missed inbound demand,

• slow speed-to-lead,

• after-hours gaps,

• weak estimate follow-up,

• inconsistent booking quality,

• and underutilized schedules.

Avoca is attacking that whole surface area.

The insight behind the company

The founding story here is unusually good.

Fortune reported that Avoca’s founders were originally thinking about restaurants. Then they met an HVAC company in Texas and realized something important: when a restaurant misses a call, maybe it loses a $30 or $40 order. When a home-service company misses a call, it may be losing a $30,000 or $40,000 HVAC install.

That is the kind of insight I always pay attention to.

It is not abstract.
It is not narrative-driven.
It is economic.

They found a vertical where one operational failure point had an absurdly high dollar consequence.

That is how you build a category.

How Avoca says it works

One of the most interesting things about Avoca is that it is not just selling a model. It is selling deployment.

The company describes itself as an omnichannel AI platform for home services, and its docs and integrations make it clear that the product is meant to sit at the customer communication and intake layer: handling inbound calls, chat, web inquiries, qualification, booking, and syncing clean job records into the underlying system.

That last point is important.

On its Aitto integration page, Avoca says: “Aitto runs operations exactly as it does today” and “Avoca adds a front-end intake layer.” In other words, Avoca is often not asking the contractor to rip out the core operating system. It is asking to own the front door.

That is a very strong wedge.

Because in vertical software, the company that controls the front office often controls:

• lead capture,

• booking quality,

• board fill,

• response speed,

• revenue attribution,

• and the first customer impression.

That is not a side feature.
That is the growth engine.

Why their implementation model matters

Most AI vendors talk about product. Avoca also talks about deployment.

The company has written about using Forward Deployed Engineers who sit with customer teams, learn how dispatch boards are actually used, understand their edge cases, and then tune the deployment around how the office really runs. Avoca says this shortens feedback loops from weeks to hours and allows highly customized logic for things like emergency handling, routing rules, or local operating nuances.

That tells you something important about how they see the market:

They are not treating the front office as generic.
They are treating it as operational software.

And that is the right instinct.

In home services, a Phoenix HVAC operator and a New Jersey plumber do not run the same business. If you want to automate the real workflow, not just the demo version of the workflow, someone has to care about the details.

The product suite is broader than just “AI phone answering”

This is where Avoca gets more interesting than the simple headline.

Yes, the company started around the missed-call problem.

But if you look at the site, case studies, and outbound pages, it is already moving into a broader revenue-ops layer:

• after-hours and overflow call handling,

• hybrid AI + human call coverage,

• outbound reactivation campaigns,

• capacity-aware board filling,

• AI-led qualification,

• direct CRM booking,

• and manager analytics tied to bookings and revenue.

That is an important transition.

Phone-answering is a wedge.
Revenue orchestration is the real opportunity.
It’s basically building Gong for Home Services companies.

The customer evidence is stronger than most early AI companies

The case studies on Avoca’s site are worth reading because they show a consistent pattern: this is not being sold as novelty software. It is being sold as a very clear ROI system.

Aire Serv says replacing a traditional live answering service with Avoca led to:

• after-hours bookings increasing from 58 to 208,

• a 90% booking rate on after-hours calls,

• an 8.6% increase in overall booked calls,

• and 41% of eligible callers signing up for subscription plans.

Call Dad (great name for a home services biz btw) says Avoca’s hybrid model let AI handle 78% of all calls, achieve a 90%+ AI call resolution rate, and drive a mix where 70%+ of booked jobs were repair and service work, which they identify as the highest-margin category.

Reliable Comfort says Avoca enabled 100% after-hours coverage, handled 150+ simultaneous calls, and allowed the business to achieve true 24/7 responsiveness without adding staff.

That pattern is what investors are paying for.

Not AI theatre.
Operational leverage.

So how does Avoca stack up against ServiceTitan and Housecall Pro?

This is where the story gets interesting.

Because Avoca is not entering an empty market. This is a brutally competitive space.

The incumbents have noticed the same thing.

ServiceTitan is the incumbent system-of-record giant

ServiceTitan is pushing hard with its Pro Products suite. Its positioning is broad: automate the entire customer journey using AI powered by ServiceTitan data. The stack includes Contact Center Pro, Marketing Pro, Scheduling Pro, Dispatch Pro, and Phones Pro. Contact Center Pro includes AI call summaries, sentiment analysis, second-chance lead identification, and AI voice agents that can book jobs, reschedule appointments, and escalate to live agents. Phones Pro is fully integrated into ServiceTitan’s software and built around giving office staff a native call-booking workflow.

ServiceTitan’s core advantage is obvious:

• it owns the operating system,

• it has the data,

• it has the incumbent distribution,

• and it can bundle AI into a larger suite.

If you are already a deep ServiceTitan customer, its native AI products will be very attractive.

Housecall Pro is building the SMB-friendly all-in-one

Housecall Pro is coming from a different angle. It positions itself as the simpler “run and grow your business” platform for home-service pros, and increasingly layers AI across that experience. Its AI Team includes:

• CSR AI for answering calls and chats and booking jobs 24/7,

• Analyst AI for reports and trend analysis,

• Coach AI for business recommendations,

• and Marketing AI for generating customer-facing content.

Housecall Pro’s advantage is simplicity and breadth for smaller operators. It is not just selling AI answering. It is selling an easier operating stack with AI embedded inside.

If ServiceTitan is the enterprise incumbent, Housecall Pro is the friendlier SMB operating system with an increasingly opinionated AI layer.

Where Avoca is different

Avoca’s wedge is narrower, but in some ways sharper.

It is not trying to be the whole back office.
It is trying to own the front-office revenue layer.

That means its differentiation looks something like this:

The key strategic distinction is this:

ServiceTitan and Housecall Pro are extending from the system of record outward.
Avoca is attacking from the front office inward.

That is a real opening.

Because many vertical markets get disrupted first at the workflow edge, not at the data core.

The real question: can Avoca become more than a wedge?

This is what I’d be watching.

Avoca clearly has the wedge:

• high-value missed calls,

• after-hours capture,

• faster booking,

• better outbound follow-up,

• stronger board utilization.

That is enough to build a meaningful company.

But the really big outcome happens if the company turns that wedge into a deeper system:

• more ownership of customer communication,

• more attribution,

• more customer lifecycle automation,

• more revenue optimization,

• more manager tooling,

• and more leverage across adjacent service verticals.

The reason this matters is simple:

If Avoca stays “AI phone answering,” it is a feature-rich point solution.

If Avoca becomes the AI revenue layer for service businesses, it becomes something much more durable.

And the site already hints in that direction. Outbound board-fill workflows, estimate recovery, membership renewals, CRM syncing, analytics, and hybrid staffing all push it closer to revenue infrastructure than pure communication software.

My take

I think Avoca is a very good example of where vertical AI gets interesting.

Not when it tries to replace the entire incumbent stack on day one.
Not when it sells “agents” as a vague concept.
But when it finds one painful, high-ROI, high-frequency problem and starts owning an increasingly valuable layer of the workflow.

That is what this looks like:

• missed calls become booked jobs,

• booked jobs become board utilization,

• board utilization becomes revenue,

• and revenue intelligence becomes product expansion.

That is how vertical AI creeps from wedge into platform.

And that is exactly why the round happened.

Do me a solid and forward to a friend :-)

If AI can actually do the work, why are we still valuing it like a tool? A conversation with David Haber on what happens when software stops helping humans do the work — and starts doing the work itself.

Most founders and investors are still looking at AI through a SaaS lens — copilots, productivity gains, better UX. But the real shift is this: software is starting to do the work itself. That isn’t a feature upgrade. That’s a business-model shift.

In vertical markets especially, that shift is going to create some very, very large companies. The economic prize moves from IT budgets to labor budgets — and once you see that, vertical AI starts to look completely different.

Watch The Episode

Listen To Episode Here

This Weeks Vertical Titan: David Haber (GP at a16z)

This week on Verticals, we brought on David Haber.

David is a General Partner at a16z, where he spends a lot of time around AI applications, software, and the broader shift happening across vertical markets. Before that, he was an investor at Spark Capital, then went and built Bond Street, a small business lending company, sold it to Goldman Sachs, and later joined a16z to help build out the firm’s New York office.

So he has seen this world from a few different angles: investor, founder, operator, strategist.

That matters, because the most interesting part of David’s perspective is that it is not some generic “AI is transformative” take. Everyone says that now. His actual argument is much sharper.

He believes vertical AI is not simply another software wave. It is a shift in what software is able to capture.

In the old world, software sold into IT budgets. It made a person more productive. It gave a team better visibility. It organized the work. But at the end of the day, the human still did the job.

In the new world, AI-native software increasingly performs the job itself. It reads the intake, structures the mess, makes the decision, routes the case, drafts the document, handles the workflow, and improves over time. Which means the economic prize starts to move away from software budgets and toward labor budgets.

That is a very big deal.

For years, most software founders have been trained to think in terms of seat expansion, ACV expansion, and software category TAM. David’s point is that the more interesting market may not be the software budget at all. It may be the payroll attached to the workflow.

And once you see that, a lot of vertical AI starts to look different.

The great vertical AI companies may not look like traditional SaaS companies. They may look like software on the surface, but underneath, what they are really doing is swallowing labor, one workflow at a time.

That is why David is so focused on vertical markets in particular. These industries are full of messy handoffs, fragmented systems, tribal knowledge, ugly operational workarounds, and expensive human labor hiding between the lines of the official workflow. In other words: exactly the kind of environment where AI-native companies can wedge in and become incredibly valuable.

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David dropped a bunch of frameworks that every founder and investor in vertical AI should internalize…

1) Stop thinking about IT budgets. Start thinking about labor budgets.

This is the first and biggest shift.

If your product is just helping someone work faster, you are still in software-land.

If your product is actually doing the work, you are now competing for labor dollars.

That changes the TAM math dramatically.

The right founder question is no longer:

How much does this industry spend on software?

It is:

How much labor spend exists around this workflow, and what percent of that can AI absorb?

That is a much more interesting question.

A lot of founders are still pitching “better software for X.”

The stronger version is usually: we remove headcount, compress cycle time, improve throughput, and own the output.

That is where the really large companies get built.

2) The best wedges are often the messiest inboxes

One of my favorite ideas from the conversation was David’s point that email, fax, and voice are not just annoying legacy surfaces.

They are wedges.

That is such a useful heuristic.

Why?

Because incumbents usually own the clean database. They usually do not own the ugly, chaotic, upstream intake layer where the work actually starts.

In healthcare, legal, construction, financial services, and other traditional verticals, the highest-value workflows often begin in some gross mix of:

• fax

• voicemail

• email

• PDFs

• attachments

• half-complete forms

• conversations that never make it into the system of record

That mess is exactly where AI can shine.

If you can ingest the chaos, structure it, and trigger downstream action, you can own the workflow before the incumbent even knows the work exists.

That is an incredibly powerful place to start.

A good founder question here is:

Where does the workflow actually begin?

Not where it gets recorded.

Where it begins.

Those are often two very different places.

3) There are 3 credible ways to attack incumbents

David laid out three broad strategies for how AI-native companies can win.

A. Replace the system of record

This is the hardest path, but often the biggest upside.

You build the new core platform from the ground up and try to become the new default operating system for the vertical.

Hard to do. Great if you can pull it off.

Usually works best when the customer is still on primitive tools, spreadsheets, or lightweight software and is ready to graduate.

B. Wrap the incumbent and own the work around it

This is the “wrapper” strategy.

Instead of ripping out the incumbent immediately, you integrate with it and automate the ugly workflows around it.

This is often a much better entry point because customers do not need to take replacement risk on day one.

You get adoption first.

Then maybe you earn the right to become the system of record later.

C. Turn a services business into a software machine

This is the killer one.

Take a low-margin, labor-heavy services category.

Insert AI aggressively.

Use software to operationalize the service.

Let margin accrue to you as automation improves.

That is not just a good software business.

That can become an insane business.

Especially in verticals where customers are already used to paying for outcomes, not licenses.

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4) Market structure matters more than market size

This is a subtle point, but an important one.

David basically argued that market structure matters more than market size.

I agree.

A huge market with a deeply entrenched incumbent, painful switching costs, and locked-up distribution can be far less attractive than a smaller market with fragmented systems, bad UX, and lots of workflow leakage.

Founders love headline TAM.

But smart founders spend more time on:

• fragmentation

• incumbent weakness

• workflow gaps

• distribution access

• speed to embed

• switching friction

• whether they can wedge in upstream

That is what actually matters.

The best vertical markets are not always the biggest.

They are the ones where the structure gives you a path in.

5) Moats still matter. Maybe more than ever.

There is a lazy take floating around that AI kills moats.

I do not buy it.

David does not either.

The interface layer may get easier to copy.

The workflow layer does not.

The real defensibility still comes from the stuff that has always mattered:

• workflow embedding

• proprietary data

• distribution

• trust

• customer intimacy

• operational excellence

• unique outcomes data

But AI introduces a new layer too:

Latent context

This was one of the most important ideas in the whole episode.

Most businesses generate valuable context that never gets captured:

• meetings

• calls

• edge-case discussions

• negotiation language

• verbal decisions

• judgment calls

• exceptions

• customer nuance

Historically, that context just disappeared.

Now it can be recorded, structured, and used.

That is a very big deal.

If you are the company capturing voice, intake interactions, workflow exceptions, and downstream outcomes, you can build a context layer the incumbent never had.

That can absolutely become a moat.

A really good founder question is:

What important information is created during the workflow but never stored anywhere?

That may be your future data advantage.

6) The system of record might become free

This was the most contrarian part of the conversation.

And maybe the most interesting.

If AI owns the workflow, owns the data, and owns the outcome… do you even need to make money on the system of record?

Wild question.

But a real one.

In classic SaaS, the system of record was the castle.

In vertical AI, the system of record may just be the front gate.

If the real money is in the automated work, claims flow, revenue collection, underwriting, financing layer, or some other high-value outcome, then the database itself may just become a distribution asset.

That is a very different world than vertical SaaS 1.0.

A lot of founders are still trying to monetize the software seat.

The more interesting opportunity may be monetizing the work performed through the software.

That changes pricing, bundling, and business model design in a big way.

7) The best AI services businesses get better margins as the model gets better

This is where things get really fun.

Traditional services firms add people to grow.

AI-native services businesses add software.

That means every automation gain can drop to margin.

That is the dream.

You start with a workflow customers already pay humans for.

You operationalize it as a service.

You automate aggressively.

You capture the spread.

You turn a human-heavy operation into something with software-like economics.

This is why so many great vertical AI companies will look weird in the early days.

They may not look like clean SaaS companies.

They may look operationally heavy, messy, or services-inflected.

That is okay.

The real question is not whether it starts as a service.

The real question is whether the workflow is softwareable.

If it is, that can become an incredible business.

The Takeaway

A lot of people are still trying to build better software when they should be trying to build better businesses.

Those are not the same thing.

The best vertical AI founders are not just asking how to add intelligence to an existing workflow. They are asking where the labor sits, where the workflow begins, where context gets lost, and where margin can be captured if the machine starts doing more of the job.

That is a much better set of questions.

If I were building in vertical AI right now, I would spend far less time obsessing over whether the category is “big enough” in the traditional software sense and far more time looking for three things.

First, I would want a workflow with real labor intensity. Not something nice-to-have. Not something cosmetic. I would want a workflow where expensive humans are spending real time on repetitive, rules-heavy, process-heavy work every single day.

Second, I would want messy intake. I would want the part of the workflow that still lives in email, voice, fax, PDFs, attachments, half-complete forms, and ugly communication patterns. Because that is often where the incumbents are weakest and where the wedge is most available.

Third, I would want closed-loop outcomes. I would want to be able to see not just that the work happened, but whether it worked. Did the claim get paid? Did the case settle? Did the loan perform? Did the patient convert? Did the workflow complete successfully? That feedback loop matters enormously, because that is how the system gets smarter and how the business gets stronger.

That combination is deadly. Labor-heavy work, messy intake, and visible outcomes.

If you find all three, you may have the foundation for a very large company.

Because once you own the workflow and the feedback loop, you are not just selling software anymore. You are selling speed. You are selling accuracy. You are selling throughput. You are selling outcomes. And ultimately, you are capturing value that used to belong to labor.

That is the real punchline here.

Vertical AI is not interesting because it makes software cooler.

Vertical AI is interesting because it may let software companies go after a much larger prize than software ever could on its own.

And if that is right, then some of the biggest companies of this cycle are going to look a lot less like traditional SaaS businesses and a lot more like AI-native operating companies hiding inside software wrappers.

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Why One-Size-Fits-All Payments Models Break as You Scale

Here’s something I think a lot of SaaS founders underestimate:

The biggest payments mistake usually isn’t choosing the wrong provider.

It’s choosing one based entirely on what you need today.

Because the reality is, no vertical SaaS company stays simple for long.

Early on, most teams optimize for speed. They want the fastest path to launch, the easiest integration, the fewest moving parts. Totally reasonable. When you’re trying to get product to market, nobody wants payments becoming a six-month side quest.

So, founders pick the provider that feels simplest.

• Fast onboarding.

• Plug-and-play setup.

• Standard workflows.

• Clean API docs.

All good things.

Until the business starts getting more sophisticated.

Because what a lot of founders realize too late is that most “simple” payments providers only

feel simple because they force everyone into the same box.

Same onboarding flow.
Same underwriting rules.
Same operational process.
Same merchant experience.

And that works… until it doesn’t.

Because if your company is growing, your payments needs are going to evolve fast.

Your first enterprise customer wants a custom workflow. A strategic prospect needs underwriting flexibility. Product wants tighter control over UX. Finance starts realizing payments isn’t just a side revenue stream anymore.

And that last part matters.

In a lot of strong vertical SaaS businesses, payments can become 30–40% of total revenue. Once that happens, it stops being “just infrastructure” and starts becoming a major part of the company’s economics.

That’s when rigidity becomes a real problem.

Because suddenly your team starts hearing: “

• We can’t support that.”

• “That’s outside our standard process.”

• “That feature isn’t on the roadmap.”

And before long, you’re no longer building around what’s best for your customers.

You’re building around what your payments provider will allow. That’s a dangerous place to be. A lot of founders don’t realize how limiting that can become until they’re deep enough into scaling that switching providers feels painful, expensive, and operationally miserable.

By then, you’re stuck trying to grow on infrastructure that was built for an earlier version of your company.

That’s why smart founders don’t just ask, “Can this provider get us live?”

They ask bigger questions:

• Can this partner adapt as we grow?

• Can they support complexity as our business matures?

• Does their roadmap actually move?

Will they still make sense when our needs look completely different three years from now? Because the best payments partners understand that scaling SaaS businesses don’t stay static.

What works at 100 merchants usually breaks at 1,000. What works for SMBs often doesn’t work for enterprise. What feels like an “edge case” today becomes normal once you scale enough.

It’s one of the reasons I’ve found Xplor Pay interesting. They’re one of the few providers I’ve seen that seem to understand this dynamic because they’ve actually operated vertical SaaS businesses themselves. They know firsthand how quickly payments needs evolve once a software company starts growing.

And that operator perspective matters.

Because payments isn’t just something you bolt onto your platform.

It becomes part of how your business scales.

And the best founders know this:

You’re not just choosing a payments provider for where your company is today.

You’re choosing one for where you want it to be three years from now.

Dockwa: Vertical Software for Marinas

Dockwa started from a very real, very vertical pain point: Mike Melillo and friends wanted to take a boating trip and couldn’t even get a marina to respond so they could give them money. That’s a great startup smell.

Broken communication, fragmented supply, obvious willingness to pay, and an industry still running on pen-and-paper workflows. Melillo then spent time on the docks in Newport, talked to marina operators, and realized the core problem wasn’t just reservations.

It was the lack of communication and the total absence of modern operating software connecting marinas and boaters. Dockwa was founded in 2014 and launched in May 2015, initially framed as the “OpenTable for boating.”

That’s a good founding story because it starts with a consumer pain point but immediately points toward a much larger operator workflow…

Early customers

The early wedge was geographically tight and operationally smart. Dockwa launched into New England in 2015 with a handful of marinas, then expanded across the East Coast before moving into the Midwest, Caribbean, Great Lakes, and beyond.

By mid-2016, the company said it had grown to more than 400 marinas across 22 states and four countries, and had facilitated more than 12,000 reservations in its first twelve months.

By late 2016, Melillo said the platform served more than 20,000 users and 450 participating marinas. What matters here is not just the raw count. It’s that Dockwa appears to have found early density in a place where operators talk to each other, seasonal demand is concentrated, and success stories travel fast.

One example they highlighted from that period was Edgartown, where the harbormaster reportedly doubled guests served after adopting the software. That’s exactly the kind of early-customer proof that helps a vertical SaaS company compound trust.

Product suite

This is where the company gets more interesting.

Dockwa did not stay a reservation tool.

Marinas are complex. They can be a grocery store, a gas station, a restaurant, a traffic coordinator, are parking lot operator, etc. A lot of tooling has to get built to be successful here.

On the boater side, the product is free and centered around discovery, booking, messaging, storage, payments, and wallet functionality.

On the marina side, the suite has expanded into reservations management, digital contracts, billing, POS, fuel dock workflows, slip assignments and maps, CRM, reporting, storage, and service management.

It also acquired Marinas.com in 2017, which added a large marine directory and a demand/discovery layer on top of the core workflow software.

That expansion path is textbook vertical SaaS.

Start with the narrow interaction that naturally sits between buyer and supplier. Then move into the operating layer underneath it. Then keep expanding until you increasingly look like the system of record.

Business Model

Dockwa’s GTM is what I’d call a two-sided vertical SaaS + marketplace hybrid.

The boater side is free, which helps reduce friction and build demand density. The marina side gets software, bookings, payments, communication tools, and operational leverage. That is a really nice motion because the marketplace does not just generate leads. It also creates workflow gravity. If boaters are already discovering, booking, and paying through Dockwa, marinas have a strong reason to adopt the software layer more deeply.

Early on, Dockwa also monetized the marina side through software subscriptions. In 2016 it launched Dockwa Connect at $299 per month for marinas. That tells you a lot. This wasn’t just a transactional marketplace hoping to live on take-rate. It was already moving toward recurring B2B software revenue. The Marinas.com acquisition reinforces that too. It suggests the company understood that discovery traffic, demand aggregation, and operator software all reinforce each other.

So the GTM flywheel looks something like this: free consumer adoption drives boater demand, marina adoption improves inventory and workflow coverage, better software deepens retention, and the marketplace layer keeps bringing in traffic and bookings.

Funding

In 2015, Dockwa announced $1.1M in additional seed funding, bringing total capital raised at that point to $1.4M. In 2016, it announced an additional $2M seed round. Newport Daily News later described the company as having raised $3.5M in venture capital by late 2016, which lines up directionally with those early reported rounds. Investors mentioned in the early coverage include David Skok of Matrix Partners, Brian Halligan, Mike Volpe, and Elias Torres, among others.

That matters because it implies Dockwa built a pretty substantial footprint without needing a massive venture balance sheet. In other words, this is not a burn-the-earth, hyperfunded vertical. It looks much more like a capital-efficient category builder.

Potential AI opportunities

Dockwa already sits on top of a lot of operational data: bookings, occupancy, boater preferences, contracts, payments, communications, timing, and service requests. That creates several obvious AI wedges.

The first is an AI dockmaster or marina operations copilot that handles inbound boater communication, answers availability questions, suggests optimal slip assignments, manages waitlists, and triages service requests. That alone could remove a lot of seasonal labor.

The second is revenue management. Dockwa already talks about helping marinas improve occupancy, and dynamic pricing is an obvious fit here. AI could push that much further by forecasting demand based on local events, weather, historical occupancy, vessel type, seasonality, and competitive supply.

The third is workflow automation across contracts, billing, collections, and service coordination. Marinas are exactly the kind of business where there’s a lot of repetitive admin wrapped around a physical asset. That is fertile ground for AI-native software.

And the biggest opportunity may be turning Dockwa from a booking-and-management layer into a real marina operator system that helps answer questions like: which slips are underpriced, which service requests drive the most margin, which transient guests should be converted into seasonal relationships, and where labor is being wasted.

Outlook for the future

I think Dockwa has the shape of a very good vertical software company because it followed a pattern I trust.

It started with a narrow wedge that was painfully obvious. It inserted itself into the transaction. It expanded into operator workflows. It built a two-sided network around the category. And it kept moving closer to the system of record.

That’s usually a good place to be.

It’s not a giant horizontal market. But it does look like the kind of category where one company can become the default infrastructure layer.

And if they successfully layer AI on top of bookings, pricing, communication, and operations, Dockwa could get a lot closer to being the operating brain for marinas rather than just the front door for reservations.

That is the real opportunity.

The Three AI Wedges That Are Working

The below was buried in a recent newsletter but I’ve been asked so much about it that I wanted to highlight it specifically…

The first is AI services.

This is maybe the most practical wedge because it goes straight at labor. In a lot of vertical industries, the software exists, but humans are still doing all the real work around it. They are checking, correcting, reconciling, interpreting, and pushing the workflow forward manually. So instead of starting with a better interface, AI services starts with a better way to do the work itself.

Camber is a great example of this. Healthcare reimbursement has historically required huge amounts of specialized human effort layered on top of bad systems and fragmented processes. Claims have to be submitted, fixed, chased, and reworked. Entire services businesses grew up around that pain. What Camber does is turn that labor-heavy reimbursement problem into a software problem. That is why the wedge is so strong. They are not just selling a nicer billing tool. They are going after work that used to require armies of people.

And that is a useful lens for rethinking something like the Toast map. In restaurants, a surprising amount of operational drag still looks like services work hiding inside the business. Catering intake. Guest recovery. AP reconciliation. payroll exception handling. Supplier coordination. These are all places where people are still acting like an internal services layer because the software never really absorbed the work.

The second wedge — and I think the most exciting — is the industry-specific GPT.

This is where things start to get really interesting.

Because now you are no longer just automating a step in the workflow.

You are building an intelligence layer for the vertical itself.

That is a much bigger idea.

An industry-specific GPT is not just a chatbot with a little fine-tuning. It is not just generic AI wearing the clothes of a vertical. At its best, it starts to understand the actual language, logic, edge cases, constraints, and judgment patterns of the domain. It behaves less like a feature and more like an emerging operating intelligence for the industry.

OpenEvidence is a great example of this. The reason it is powerful is not that it is “ChatGPT for doctors.” That framing is too shallow. What makes it compelling is that it is becoming a medical intelligence layer grounded in the realities of clinical decision-making. It is useful because it understands the domain well enough to help practitioners reason inside it.

And once you start thinking this way, the workflow map changes dramatically.

Because now the question is not just: where can I automate labor?

The question becomes: where does this industry need a brain?

What would the restaurant equivalent of that look like?

It would not just answer questions about menu items or write a promo email.

It would understand the operating reality of restaurants. It would understand labor pressure, table turns, reservation pacing, kitchen bottlenecks, menu mix, guest recovery, promo strategy, inventory risk, staffing tradeoffs, refund behavior, and the way all of those things interact.

That is a completely different category of software.

At that point, you are not just building around the workflow. You are building a domain-native intelligence layer that can increasingly guide the workflow.

That is why I think this wedge is the most interesting one.

If the AI services wedge helps you do the work, the industry-specific GPT wedge helps you understand the work at a much deeper level than old software ever could.

And in the long run, that may be the more powerful position.

The third wedge is building agents on top of the system of record.

This one is a little more tactical, but it is incredibly important because it acknowledges a simple truth: in many verticals, the incumbent system still has the data gravity.

It may be hated. It may be clunky. But it is embedded. It is where the records live. It is where the workflow officially lands. That means one of the smartest ways to wedge in is not to replace it immediately, but to start from it.

Use the system of record as the foothold. Read from it. Write back to it. Make it smarter. Reduce the labor around it. Turn it from a passive database into something that feels more action-oriented. You essentially build Agents on top of it.

Datagrid is a great example of this pattern. It does not begin by asking the construction company to replace the system of record (IE Procore). It starts from the fact that the CMS is already embedded in the workflow and already the source of truth. The wedge is to capture the conversation, structure the documentation, and make the surrounding workflow dramatically more usable while integrating into the existing record system. That is a very different strategy from trying to rip out the incumbent on day one.

There is a very obvious parallel in restaurant software too. If you were attacking the Toast map this way, you might not begin by replacing Toast or the broader stack. You might start by asking what AI can do when it sits on top of the systems that already exist. Can it absorb the labor around them? Can it turn raw operational data into clear actions? Can it surface exceptions instead of forcing operators to dig? Can it make the existing stack feel much more intelligent without forcing a painful rip-and-replace?

That is wedge number three.

And the important thing is that these wedges are not mutually exclusive. In fact, the best companies will probably move through them over time.

They may start with AI services, because that is where the urgency and labor budget live.

Then they may deepen into an industry-specific GPT, because that is where the real intelligence advantage gets built.

Then they may sit on top of — or increasingly absorb — the system of record, because that is where the workflow gravity and defensibility live.

That progression feels right to me.

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Software isn’t dying.
But it is being re-rated.

A weird thing is happening…

Public software has been absolutely smoked. Multiples have compressed. The market is acting like the old SaaS playbook is 100% broken. And yet — enterprise buyers still need software. They still don’t want to build everything in-house. And AI isn’t killing software; it’s changing what good software looks like.

That’s why this conversation with Morgan Livermore matters. He’s spent ~15 years investing across enterprise and infrastructure software at Accel, Geodesic, and Quiet Capital. Now he’s building SuperCruise — a fund focused on a very specific wedge: profitable, bootstrapped enterprise and infrastructure companies with a few million in revenue, very high growth, and founders who want aligned capital — not high risk rocket fuel that could blow the engine up.

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This Weeks Vertical Titan: Morgan Livermore (Founder & GP Supercruise Capital)

Morgan is one of those investors who has seen a few software cycles up close.

He started in growth at Accel, where he looked at the kinds of businesses most people now view as canonical software outcomes — profitable, compounding, infrastructure-heavy companies like Atlassian-style businesses that didn’t need to light money on fire to matter. He then sharpened that lens at Geodesic and Quiet Capital.

What he’s building now with SuperCruise is a reaction to the last decade of venture excess.

His core belief is simple: there’s a large and undercapitalized class of software businesses doing $2M–$7M in ARR, growing fast, already showing signs of efficiency, and being pushed toward venture models that don’t actually fit them. Too much capital, taken too early, can create what he calls internal combustion — the company grows headcount faster than it grows product-market fit, and the whole thing becomes structurally misaligned.

So SuperCruise is designed around a different promise: be the first real institutional capital for founders who have already proven viability, help them reach escape velocity, and do it without forcing them into a growth-at-all-costs script that no longer maps to reality.

That worldview also explains why Morgan is so interesting right now.

He’s not just underwriting software companies. He’s underwriting the transition from classic SaaS to AI-shaped software economics — where margins, pricing, distribution, and defensibility all get re-written.

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Sixteen ideas for building
the next era of software.

Not just for investors — for founders building the next generation of vertical software and vertical AI businesses.

Below are the biggest playbooks Morgan surfaced — not just for investors, but for founders building the next generation of vertical software and vertical AI businesses.

1) Don’t confuse capital with product-market fit

A lot of founders still operate as if fundraising is validation.

Morgan’s view is the opposite: prove the business can stand on its own first. Capital should supercharge a working engine, not become the engine.

When money gets used to paper over weak product fit or sloppy execution, it creates internal combustion.

Action item: Ask a brutal question: if you couldn’t raise for 24 months, would your current product and GTM still survive?

2) Optimize for escape velocity, not vanity growth

Morgan isn’t looking for companies growing “fast” in the abstract.

He’s looking for businesses growing fast enough to become undeniable — fast enough to become IPO-able, strategically valuable, or structurally durable.

That’s a much better lens than generic growth worship.

Action item: Replace “how do we grow faster?” with “what growth rate gets us to inevitability?”

3) SaaS isn’t dead — but old valuation math is

One of Morgan’s most important points: software may be oversold, even if it deserved a repricing.

The market is treating many software names as if the whole category is melting.

But enterprises still rely on these tools, and ripping them out is far harder than Twitter makes it sound.

Action item: Separate multiple compression from category collapse. They are not the same thing.

4) ARR is getting more slippery

For years, investors had a clean north star: ARR.

But ARR gets much fuzzier when pricing shifts from seats to usage, billing frequency changes, and revenue becomes tied to outcomes.

In AI, the unit of value is moving.

That means the unit of valuation eventually moves too.

Action item: If you’re an operator, define your real value metric now — before the market defines it for you.

5) The next moat might be… documentation

This was one of my favorite parts of the whole conversation.

Morgan shared an example of a company winning because its documentation is easily accessible to AI agents.

Not because it had the flashiest brand.

Not because it had the biggest sales team.

Because agents could actually find it, understand it, and implement it.

In a world where software gets discovered and evaluated by machines, documentation becomes distribution.

Action item: Treat docs like a GTM surface, not a support artifact.

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6) AI agents change who the buyer is

Historically, software was sold to a human buyer through demos, decks, and relationships.

Increasingly, software will be selected, tested, or recommended by agents upstream of the human decision-maker.

That shifts the game toward accessibility, interoperability, and clarity.

Action item: Ask: can an agent understand what we do, how to use it, and why we’re differentiated?

7) The incumbent tax is real

Legacy software companies aren’t just fighting technical debt.

They’re fighting incentive debt.

If you run a large seat-based software business, moving to an AI-native or outcome-based model means attacking your own cash cow.

It might mean lower prices, fewer seats, and layoffs.

Founder-led challengers can make that leap far more easily than professionally managed incumbents.

Action item: If you’re a startup, go where incumbents are least willing to cannibalize themselves.

8) Outcome-based pricing is coming — slowly, then all at once

Seat-based pricing made perfect sense when software was basically licensed access to workflow.

But AI changes the customer question from “how many users?” to “what work got done?”

Morgan believes software increasingly gets paid on units of labor or business outcomes — tickets resolved, meetings booked, tasks completed.

Action item: Start testing pricing around completed work, not just access.

9) Deep domain insight matters more as code gets cheaper

If everyone can build faster, the bar for “we built software” drops dramatically.

That means defensibility shifts away from raw code and toward earned insight — the kind that comes from understanding the strange, specific, ugly details of a workflow better than anyone else.

Action item: Write down the 10 non-obvious workflow truths your team knows that a generic AI team does not. How can you build around that?

10) Vertical beachheads will compress

The old playbook was to dominate a niche for 7–8 years and then expand.

In AI, that window may shrink to 1–2 years.

Products can be built faster. Competitors arrive faster. Expansion matters sooner.

Action item: Plan adjacency earlier. Your wedge still matters — but your expansion clock starts sooner.

11) The compound startup is becoming the norm

Win a foothold, then sell more and more functions into the same customer.

That matters even more now because distribution is precious and context is compounding.

Once you own trust, workflow, and data exhaust, adjacent products get much easier to layer in.

Action item: Map your second and third products before your first one fully matures.

12) The long tail gets more interesting in AI

Historically, many software companies couldn’t profitably serve small customers because implementation and service costs were too high.

AI changes that.

Lower build costs and lower service costs mean more of the SMB and long-tail market becomes economically viable.

Action item: Re-open segments you previously ignored because ACV looked too small.

13) Free wedges still work

In AI and infrastructure, giving people a “taste” of the product still works extremely well.

Let users adopt something cheap or free, then monetize enterprise security, governance, scale, and support.

That playbook isn’t dead.

It may actually get stronger.

Action item: Identify what complement you can commoditize to pull adoption forward.

14) Gross margins may go down — and that’s okay

One of the most under-discussed AI realities: some of the best new software businesses may not look like classic 80%+ gross margin SaaS.

Tokens cost money. Inference costs money.

But if the market gets dramatically larger and the product does more labor, a slightly lower-margin business can still be much bigger and much better.

Action item: Don’t benchmark an AI-native business only against legacy SaaS margin expectations.

15) Taste becomes a moat when intelligence gets commoditized

If intelligence becomes cheap, then knowing what to build, how to package it, and how to make it feel trustworthy becomes more valuable.

In a world full of generic AI output, taste starts to matter more, not less.

Action item: Invest in product sharpness and user judgment, not just model capability.

16) Human trust still matters

This might be the most important counterweight in the whole conversation.

Yes, AI automates.

Yes, agents will mediate decisions.

But in vertical markets especially, buyers still want trusted partners.

They want someone who understands the workflow, the risk, the weird edge cases, and the consequences of getting it wrong.

In a world flooded with AI-generated slop, trust becomes easier to lose and more valuable to own.

Action item: Build your company so customers experience you as a partner, not just a vendor.

The old software era was built on seats.
The next one isn’t.

It will be built by founders who stay lean longer.

By companies that understand a domain better than generalist competitors.

By products that are legible not just to humans, but to agents.

By pricing models that track outcomes more closely than access.

And by teams that understand that trust, taste, and workflow insight may outlast almost every other moat.

So no — software isn’t dead. Bad software, bloated software, and mispriced software are getting re-rated. The next iconic vertical software companies will be born from this exact moment — when everyone else is busy confusing chaos for collapse.

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Thanks for reading LINEAR. I reply to every email…
Have any questions, feedback, comments? Let us know, we work for you!

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I think I’m finally starting to see where software goes from here…

Here’s the short version of the thesis: a small handful of horizontal AI Operating Systems are going to own the general workflows of consumers and businesses, and they’re going to become the most valuable companies in the world, by far. Everything underneath them gets redrawn — category by category, workflow by workflow.

At the same time, a new wave of Vertical AI Operating Systems is going to win the industries where context, depth, regulation, and services actually matter. The horizontal players won’t go all the way down into every market, they will be constantly fighting off crown seekers.

As that plays out, a massive amount of software becomes headless — still useful, but no longer the thing the customer opens. The value moves up to whoever owns the interface, the workflow, and eventually the work itself.

That’s the map. Could I be wrong on parts of it? Definitely. But the more I look at it, the more it lines up. And once you see it, it’s hard to unsee.

The way I think about it now, the new world breaks into seven distinct kingdoms:

The Seven Kingdoms, In Detail

Here’s how I’d break the new stack down. Seven kingdoms. Each one with a different shape, a different set of winners, and a very different game to play.

Kingdom I: General Consumer AI Operating Systems
OpenAI · Meta · Grok
GoT Parallel: Most like Dragonstone — strategic, well-defended, and built to command the serious campaigns.

This is not just another app people open. It’s the place they start and never leave. Where they ask questions, make decisions, create things, shop, research, plan, and increasingly take action.

Right now, ChatGPT is winning in consumer. It’s the closest thing we have to a default AI Operating System for the consumer internet — the place a growing share of people start when they want to ask, decide, create, research, plan, and increasingly take action.

But this throne isn’t settled. Meta has the distribution, the social graph, and the hardware ambitions to push their assistant in front of billions overnight. Grok has Elon, X, and a willingness to play by different rules. Google has Search, Android, YouTube, and Gemini sitting in front of literally billions of people every day, and they’re absolutely going to fight for the consumer surface too, as will Apple. All of them are coming for OpenAI, and the next few years are going to be a real fight.

Whoever ends up owning the consumer interface owns the relationship. This kingdom is going to be small in number and absurdly valuable.

Kingdom II: General Business AI Operating Systems
Anthropic · Google · Microsoft
GoT Parallel: Most like King’s Landing — whoever controls the capital controls attention, distribution, and power.

On the business side, Anthropic/Claude looks really well positioned to become the general business AI OS — the thing that starts to eat the horizontal business suite market the way Google and Microsoft did in prior eras. And don’t count Google and Microsoft out either. They’re going to fight like hell here. OpenAI is also pushing hard into the business layer with ChatGPT Enterprise, Teams, and a growing stack of work-focused features — they want this kingdom too, not just the consumer one.

Additionally, we are seeing all the horizontal productivity software try to win here - think ClickUp, Monday, Asana, etc.

In this new world, Claude (or whoever wins) isn’t a chatbot sitting next to your work. It is the workspace. It’s where teams write, analyze, summarize, retrieve knowledge, make decisions, and increasingly execute workflows across the general business stack.

Same playbook as the consumer side: integrate with everything, learn what matters, then absorb the most strategic categories natively.

Kingdom III: Headless Consumer Software
Bannermen to Kingdom I
GoT Parallel: Most like the Reach — productive, and valuable, but ultimately downstream from whoever rules the center.

Whoever ends up owning the majority of the consumer interface (ChatGPT, Grok, Meta, etc.), a lot of the consumer software you use today gets pushed underneath it. Some of it gets eaten entirely. A lot more of it survives, but as headless utilities sitting behind the AI OS — useful, even necessary, but no longer the front door.

These can still be good businesses. Think booking vacations, shopping, weddings, etc. The catch is that the platform above you can come for your category whenever it decides the margin pool is interesting enough. You’re useful. You’re just not safe.

Kingdom IV: Headless General Business Software
Vassals to Kingdom II
GoT Parallel: Most like the Westerlands — full of assets and infrastructure, but expected to serve the larger war machine above them.

Same dynamic, business edition. CRM, project management, internal docs, reporting, ticketing, payments, payroll, analytics, collaboration — a lot of these tools will keep mattering, but they’ll matter less as front-end destinations if the user lives inside Claude (or Google or Microsoft’s version of it).

Plenty of real billion-dollar companies will still get built here. They’ll just be built knowing the platform above them can integrate, learn, and eventually absorb. The ones that survive will be deeply technical, painful to rebuild, and embedded enough that the platform would rather work with them than redo their work.

Kingdom V: Vertical AI Operating Systems
Health · Law · Build · Coin
GoT Parallel: Most like the North — too large, too complex, and too specific for any southern ruler to fully control from afar.

This is the kingdom I’m most excited about, and the one I’m spending most of my time on.

On the surface, a Vertical AI OS will often look a lot like Claude or ChatGPT. Conversational. Intelligent. Feels like working alongside a really capable AI. Underneath the hood it’s a totally different product. It’s trained on industry data, fine-tuned for industry workflows, wrapped in compliance and human services, and built to compress complex industry work into a few clicks.

Why this works: the horizontal players have an endless roadmap just chasing the most valuable headless tools. They aren’t going to go all the way down into healthcare, legal, construction, insurance, financial ops, or logistics with the depth those markets need. That leaves real room for vertical winners with industry context, regulatory depth, services, GTM, and trust.

And unlike horizontal, vertical isn’t winner-take-all. Some markets get one monopoly. Some get two or three real winners. Some support four or five strong players for a long time. That structure creates room for a huge number of $10M–$1B companies, not just a handful of trillion-dollar outcomes.

Kingdom VI: Headless Vertical Software
Bannermen of Kingdom V
GoT Parallel: Most like the banner houses of the North — not glamorous, but absolutely essential to keeping the realm running.

Underneath every Vertical AI OS, there’s a stack of tools doing the unglamorous work — industry-specific compliance, scheduling, communications, data, embedded finance, point solutions for very specific parts of an industry.

I think this is where thousands of really good bootstrapped and lifestyle businesses get built. They’re not flashy end-user brands. They solve ugly, essential, highly specific problems for the vertical AI OS layer. And because the vertical players don’t have horizontal-scale resources, they’re far less likely to absorb you. This is a much safer place to live than headless horizontal.

Kingdom VII: AI Services
Trade routes to every land
GoT Parallel: Most like Braavos — the wealthy Free City built across islands and lagoons, lending its services to anyone who can pay.

The last kingdom is services — and I think it’s going to be way bigger and way more defensible than people expect.

The AI OS companies want software-esque margins. They want to own the interface, the intelligence, and the highest-leverage parts of the stack. What they don’t want to do is own every messy, exception-heavy, labor-intensive workflow in the real world. That creates massive room for services companies that bring human expertise and actual work completion to both horizontal and vertical AI systems.

These businesses should be much better than old-school services companies. Better margins. Better workflows. Better software leverage. Better operating discipline. But they’re still services in the most important sense: they help the customer actually get the work done when software alone isn’t enough. That’s a great place to be.

How To Survive & Thrive In The AI OS Era

Okay — if that’s the map, the more useful question is where should you actually try to play? Here’s how I’d think about it, kingdom by kingdom.

I. Most of us should probably avoid trying to build the top-level AI OS

If I’m being honest, I think most founders should probably stay away from trying to build one of the giant horizontal AI OS companies at this point.

That market is likely going to be won by a tiny number of players, and most of them will either be today’s leaders or tech giants that come in late with absurd advantages in capital, compute, talent, and distribution. There may be a few late entrants that matter. History usually leaves some room for that. But if you are going after this layer, you need to understand what game you are actually signing up for.

You are not really trying to build a normal startup.

You are trying to build one of the most important companies in the world.

And if you do go after it, you better be prepared to spend something that looks closer to $1T+ than most people are comfortable admitting.

That’s just not the lane I’d advise most people to pick.

II. Headless horizontal software can absolutely work — but it’s a dangerous place to live

I do think there will be real billion-dollar companies built underneath the horizontal AI OS layer.

The problem is not whether they can become valuable. The problem is how much extinction risk comes with the territory.

If you are building headless software or AI that serves the horizontal giants, you have to assume the platform can come for you at any point. If your category matters enough, they can absorb it. If your margin pool looks attractive enough, they can pressure it. If your functionality feels strategic enough, they can decide it should just be native.

So if you want to build here, you need to be more than useful.

You need to be necessary.

The best companies in this layer will own something technical, painful, deeply embedded, and annoying enough that the platform would rather work with them than rebuild them. That can become a fantastic business. But it is not a relaxing place to live. You need to know that going in.

III. Communication tools can still matter — but I don’t think this is some giant greenfield for new entrants

I do think a handful of communication tools remain large and important for a long time.

Video conferencing matters. Team communication matters. There are some categories like that which are just too core to how people work to disappear overnight.

But I don’t really look at this and think, “Wow, what a wide-open new frontier for startups.”

Maybe a few products show up late and plug into the AI OS systems better than what exists today. That can absolutely happen. But if you want to play here, I think you need to be a truly exceptional product person. The bar on product quality, taste, and user love has to be incredibly high. And even then, you are going to spend your life watching your back.

The dynamic here feels much more like Snapchat vs. Facebook than some peaceful little software niche.

You can build something people love. You can carve out real value. You can even get big.

But if the giants decide your product matters, you should assume they are coming.

IV. Vertical AI OS is where I’m spending my time

This is the bucket I’m most focused on.

This is where I’m spending my time, money, and my energy because I think this is where some of the most important and durable companies of the next decade will get built.

The opportunity, in my mind, is to build the Claude Cowork for a specific industry.

On the surface, the interface may look similar to the horizontal AI giants. It will feel conversational. It will feel intelligent. It will feel like you are working alongside an incredibly capable AI. But underneath the hood, it is doing something very different. It is built on the industry dataset, the industry language, the industry workflows, the industry edge cases, and the industry realities. And because of that, it can distill very complex industry work down into just a few clicks.

That’s the unlock.

The defensibility here comes from wrapping the Vertical AI OS in the things the horizontal players are much less likely to go deep on: human services layers, compliance and regulatory depth, embedded workflow knowledge, operational expertise, trust with the customer, industry GTM, industry brand, and the kind of real-world feedback loops you only get by living inside ONE industry.

That combination is what makes this market so interesting to me.

In a lot of verticals, software alone is not enough. A model alone will not be enough either. Even a beautiful interface alone is not enough. What matters is whether the product can actually help the customer get the work done in a way that is faster, more compliant, more trusted, and more complete than the alternatives.

I also like that this market probably will not be winner-take-all in the same way horizontal will be.

Some verticals may end up with one clear monopoly. Some may have two real winners. Some may support four or five strong options for a long time. Those are competitive dynamics I can live with. In fact, I prefer them.

Because that kind of market structure creates room for a lot more outcomes. It creates room for a bunch of billion-dollar companies, but it also creates room for an extraordinary number of businesses in the $10M to $999M range. That is a huge opportunity set. A lot of great companies will get built there. A lot of private equity firms will live and die here too.

V. There will be thousands of great businesses built as headless software tools (but most will be small)

I also think there will be thousands of great bootstrapped and lifestyle businesses built in this era.

My assumption is that most of these are not flashy end-user brands.

They are headless software tools that power the AI OS layer, whether horizontal or vertical.

They sit underneath the interface. They handle narrow but important jobs. They solve ugly, essential, highly specific problems. And they become part of the stack that the AI OS relies on in order to actually function well.

Some will be data providers. Some will handle payments. Some will manage compliance. Some will power execution in very specific parts of a workflow. Some will connect fragmented systems that the AI OS needs in order to actually get work done.

A lot of these will become very good businesses.

In many cases, they will be built by founders who understand one annoying part of the stack better than anyone else and quietly turn that into a durable, profitable company.

VI. AI services will be huge — and much more defensible than people think

I also think there will be an explosion of massive AI services companies.

Why?

Because the AI OS companies will want software-esque margins. They will want to own the interface, the intelligence layer, and the highest-leverage parts of the stack. What they will not want to do is own every messy, labor-intensive, exception-heavy workflow in the real world. That creates a lot of room for services companies that bring human expertise and work completion to both horizontal and vertical AI systems.

These businesses should be much better than old-school services businesses.

Better margins. Better workflows. Better software leverage. Better labor models. Better operating discipline.

But they will still be services businesses in the sense that they help the customer actually get the work done when software alone is not enough.

That is a very good place to be. A defensible place to be. And a very large place to be.

VC’s probably will spend a fair amount of time here.

VII. The companies that move too slowly are in serious danger

The harsh reality is that a lot of companies are just not going to make this transition.

Some won’t see it clearly enough. Some will see it, but move too slowly. Some will keep pretending that adding a few AI features to an old product is the same thing as adapting to a new era.

It isn’t.

A lot of SaaS companies are going to find out the hard way that their position in the stack was weaker than they thought. A lot of services companies are going to realize too late that customers no longer want the old model of labor delivery. A lot of businesses are going to get caught in the middle: not differentiated enough to win, not lean enough to survive, and not fast enough to reposition.

That culling feels inevitable to me.

To Wrap It Up…

This isn’t me about telling you what type of company to build…

It’s about recognizing that the stack is getting redrawn in real time.

A few AI OS companies will sit at the top.
A huge amount of software will get pushed down, abstracted away, or turned headless.

Some companies will become core infrastructure.
Some will become vertical systems.
Some will become service layers.

And a lot of companies will realize too late that the value already moved.

That’s the real question now:

Or are you the company that wakes up one day and realizes it was never as defensible as it thought?

That’s why this moment matters.

Because this isn’t some slow transition that plays out over 20 years.

It’s happening now.

And the people who understand where the value is moving — before everyone else does — are the ones who get to matter in what comes next.

Godspeed.

Hear ye, hear ye—

Unto the brave founders, the bold builders, and all who would dare carve a kingdom from the new software realm — greetings.

If thou hast journeyed thus far down this chronicle, know that the gates are quietly opened. We do hereby summon thee to the

Vertical Software Summit in Miami on November 4th and 5th, 2026.

The formidable lords of every great vertical realm — founders and sovereigns of the mightiest houses — shall gather to share the chronicles of their conquests, their scars, and their unspoken strategies.

It shall be the vertical AI gathering of the year.
None like it shall come again.

Come, then. Take thy place among the worthy.

Sealed this day,

The Council of Vertical

Alex Jekowsky built one of the most defensible companies in vertical SaaS by doing what most investors told him not to do: he went all-in on hardware.

What if the best moat in software isn’t software at all...?

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This Weeks Titan: Alex Jekowsky (Founder & CEO of Cents) & His Laundromat SaaS Empire

Alex Jekowsky is the co-founder and CEO of Cents, and he’s just pulled off one of the most impressive rounds I’ve seen in vertical SaaS this year: A $140 million Series C.

The numbers tell the story: Cents today powers over 4,500 locations, processes over $1 billion in payments annually, and operates in nearly 1 in 6 laundromats across the United States. In an industry that most tech investors wouldn’t even look at, Alex has captured massive market share by doing something most founders are afraid to do: betting on hardware, going deep on operations, and building for the long game.

The journey started after Alex sold his previous company, Ulyngo, in the higher ed tech space back in 2019. During his earnout period, he started looking into buying laundromats as a side investment and discovered something wild: 70-80% of the laundry industry was still coin-operated.

While dozens of venture-backed consumer laundry apps were trying to be the “Uber for laundry” and failing spectacularly, Alex saw the real opportunity. Those delivery companies were just bringing orders to laundromats anyway—the operators were the backbone with a 99% success rate. Alex made a different bet entirely: build for the operators, not the consumers.

What I admire most about Alex’s journey is the conviction to make contrarian bets when everyone said he was wrong. Going deep on laundromats when consumer apps were hot. Betting on hardware when every VC said it would kill his margins. Burning over $1 million per month in the early days—double what software-only peers were spending—because he knew hardware was the foundation.

The result? A $140 million war chest, dominant market share, and a business model that gets stronger with every customer. The hardware moat that VCs laughed at five years ago is now what makes Cents nearly impossible to disrupt in the AI era. That’s vertical SaaS done right.

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Playbook #1: Should You Build Hardware? Alex’s Selection Framework

The answer depends on whether hardware solves the core problem in your vertical. For Alex, hardware wasn’t optional—it was the only way to get laundromats off coins and onto digital payments. But building hardware as a startup is expensive and full of landmines. Here’s how Alex did it without destroying his burn rate.

Bring Hardware Development In-House

Almost all founders outsource hardware development to contract manufacturers or agencies. You’ll get taken for a ride, find out about problems too late, and burn capital you can’t afford. Alex brought hardware development fully in-house from day one.

But how do you hire world-class hardware engineers when you’re pre-revenue? Alex’s solution: hire recent graduates from top hardware companies, give them meaningful equity, and let them build from scratch. He found Cents’s head of hardware through a mutual friend—a recent Apple grad. Tiny contractor fee, meaningful equity stake.

Here’s the recruiting framework: Don’t try to poach senior engineers from Apple or Google. Instead, make friends with those senior engineers and ask them to refer junior talent they’ve worked with. Junior engineers at great companies are well-trained, hungry for equity, and want the zero-to-one experience. Alex used this to build his entire early hardware team.

Accept the 2x Burn (And Fundraise Accordingly)

Hardware will burn roughly double what software-only peers burn. Cents was burning over $1 million monthly while comparable startups burned $400K-$600K. Why? Industrial design, supply chain infrastructure, QA testing, inventory management, PCI compliance, field testing—these costs are unavoidable.

But not all burn is equal. Alex framed hardware investment as the foundation of everything else. Without controlling the payment terminal, you can’t capture transaction data. Without transaction data, you can’t build the operating system. Without the operating system, you’re just another POS system that gets ripped out.

Investors who understood deep vertical SaaS got it. Investors looking for software-only multiples passed. That’s fine—you want investors who understand your strategy.

Use M&A to Accelerate (Not Pivot)

By Series B, Alex had deployed thousands of devices. But hardware development cycles are long, and the market opportunity was NOW. So after 16 months of relationship-building, he acquired Laundry Works in January 2023—a company with proven hardware in thousands of locations.

This wasn’t a pivot. It was strategic acceleration to de-risk execution on a pre-existing roadmap. Building equivalent scale organically would have taken 36 months. He didn’t have that time.

Alex’s M&A framework:

• Does this accelerate our existing roadmap or distract from it?

• What would it cost to build this organically in time and capital?

• Can we integrate quickly without a multi-year nightmare?

• Does this de-risk a critical part of our strategy?

Most investors push back on early M&A. Alex countered: “We’re not changing strategy—we’re accelerating what we already sold you on.”

Hardware as Your AI-Era Moat

Five years ago, VCs told Alex hardware would hurt his valuation. Today, in the AI era where software features can be replicated instantly, hardware is physical defensibility. You can’t ChatGPT your way to controlling payment terminals or building supply chains.

Software is infinitely replicable. Hardware is not. Even if competitors build identical software, they can’t easily steal customers. Switching requires ripping out terminals FROM EVERY Laundry machine, installing new hardware, retraining staff, migrating data, and dealing with downtime. That’s massive friction that software-only products don’t have.

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Playbook #2: The Distributor-Led GTM That Scales

Early on, Cents tried the traditional SaaS playbook: hire salespeople, run ads, do outbound. It worked for initial traction but didn’t scale. Laundromat owners aren’t on LinkedIn or browsing SaaS directories. They’re busy operators running cash businesses.

Then Alex had a revelation: In hyper-verticalized markets, the person your customer already buys from is their most trusted advisor. For laundromats, that’s the equipment distributor. These distributors have decades-long relationships with store owners. They sold them their equipment, service it when it breaks, provide financing, give business advice. They’re consultants, advisors, friends. When a distributor recommends something, operators act on it.

Partner With the Channel, Don’t Disrupt It

Most tech companies see distributors as legacy middlemen to disrupt. Alex made them his core GTM strategy. Today, Cents’s hardware is often sold through laundry equipment distributors. The distributors are a massive sales force. Cents provides product, training, marketing materials, and support.

This gives you instant access to every operator without building a massive sales team. It leverages trust distributors spent decades building. It aligns incentives—distributors make money when operators upgrade. It creates geographic density organically.

But you can’t just show up with a product and expect distributors to care. They’ve seen startups come and go. You have to prove you’re committed for the long haul.

So Alex did something extreme: his team attended 60 distributor trade shows in 2 months. Constantly on the road, setting up booths, doing demos, answering questions, shaking hands. The goal was proving Cents wasn’t some fly-by-night tech company.

It worked. Distributors started seeing them everywhere. Today, the distributor network is one of Cents’s primary growth engines, scaling in a way direct sales never could.

The CAC vs. Channel Economics Trade-Off

Yes, you share economics with distributors. They take margin on hardware sales and sometimes small rev share on software or payments. But here’s what Alex understood: the alternative to paying distributor margins is a brutal CAC.

If you’re doing direct sales in fragmented SMB markets, your CAC might be $5K-$15K per customer. Plus you’re paying sales rep salaries, benefits, tools, travel. With distributors, you’re essentially outsourcing that entire function to people who already have the relationships and trust. Your “CAC” becomes the margin you share, but it’s variable (only when deals close) and you get instant scale.

This is why Cents has a larger Customer Success team than Sales team. The distributors handle acquisition. CS handles retention and expansion across multiple locations. In low-churn verticals, growth comes from expansion, not just new logos.

Playbook #3: The Fundraising Framework That Eliminates Negotiation

Alex’s Series C fundraising strategy is one of the smartest approaches I’ve heard. Most fundraising turns into negotiation about valuation. You justify why you’re worth X, investors try to knock you to Y. Alex flipped the script.

The “Non-Negotiable” Valuation Model

Instead of negotiating, Alex did the math FOR investors. He built a sum-of-the-parts valuation model projecting monthly out to 2031. Cents has three revenue streams: Software (SaaS), Payments (transaction processing), Hardware (equipment sales). He applied market multiples to each, added them up for total enterprise value, then built a Share Price Calculator showing exactly what investors get at a given price and their expected 5-7 year return. He also included every possible company comp in the model to drive the average multiple for each category.

Then he picked a price delivering a 3-5x return for growth equity investors—the standard expectation for that asset class.

When he walked into investor meetings, he walked them through the math. “Here’s our model. Here’s the market data. Here’s the formula. Here’s the price. We’re not negotiating valuation—we’re looking for the right partner.”

He eliminated negotiation and focused on fit. Investors either got it or didn’t. The ones who got it moved fast.

Why This Works

You look sophisticated. Investors respect founders who understand their numbers. You set the tone—you’re not begging for capital, you’re offering a calculated opportunity. You save time—no months of back-and-forth on valuation.

Pro tip: This only works if your numbers are rock-solid. Alex learned this the hard way. Early on, he reported “cARR” (Contracted Annual Revenue). Later, he switched to “Recognized GAAP SaaS Revenue”—the most conservative metric possible.

The Employee Tender Strategy

Here’s a killer recruiting hack: As part of the Series C, Cents ran an employee tender where employees with 2+ years tenure could sell up to 25% of their vested equity. This was $30 million of the $140 million round.

Why? Because Alex competes for talent with AI startups and Big Tech offering $500K+ packages. By offering early liquidity, he can tell recruits: “You don’t have to wait 10 years for life-changing money. If we hit milestones, you can take chips off the table in 2-3 years.”

It rewards early team members who took risk. And it’s a massive recruiting tool when competing with companies that offer higher cash comp but no near-term liquidity.

To Close It Out…

Alex’s journey with Cents is one of the best examples of contrarian thinking in vSaaS. He went into an unsexy vertical. He bet on hardware when everyone said don’t. He partnered with distributors instead of disrupting them.

The result? $140M Series C, 1-in-6 market share, over $1 billion in annual payment processing, and a business that’s nearly impossible to disrupt.

Here’s what is worth taking away:

Hardware can be your moat. If customers have a physical pain point, solve it with hardware and own the value chain.

Partner with the channel, don’t disrupt it. In hyper-verticalized markets, the existing trusted advisors are your best GTM.

Do the math for investors. In later-stage rounds, stop selling vision and start showing math.

You’ve got this. Now back to building!!!

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Thanks for reading LINEAR. I reply to every email…
Have any questions, feedback, comments? Let us know, we work for you!

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This week I went back and looked at one of my favorite old vertical SaaS visuals: the Toast graphic from their S-1.

It still hits…

Not because it’s polished. Not because it says “platform.” And not because it has a bunch of product boxes hanging off a restaurant. It hits because it captures something most software companies miss:

A vertical business is not a collection of features. It’s a collection of workflows.

That’s what Toast understood.

They didn’t just map restaurant software. They mapped the restaurant itself.

They mapped the way guests move through the space. The way staff move through shifts. The way money moves through the register. The way orders move through the kitchen. The way labor, inventory, reservations, reporting, loyalty, payroll, and payments all connect into one living system.

That’s why the visual has held up so well.

It wasn’t really a product diagram.
It was an operating diagram.

And I think that matters even more now than it did back then, because if we’re being honest, most vertical SaaS founders are still doing the old exercise. They’re still mapping the business as software categories. POS. Payroll. Scheduling. CRM. Analytics. Reporting. Maybe a little fintech layered in.

But AI changes the exercise.

Not a little.
Completely.

Because old software mostly helped you record work.
AI-native software can increasingly help you do the work.

That means the map itself has to change…

Toast didn’t just sell restaurant software.
They mapped the whole restaurant.

The easiest way to misunderstand Toast is to say they built restaurant POS software.

That’s true in the same way that saying Amazon is an online bookstore is true.

It’s technically correct and strategically useless.

What Toast really did was much more important: they took a fragmented, chaotic, deeply operational vertical and mapped it as a connected system. They understood that a restaurant is not just a place where transactions happen. It’s a place where dozens of small workflows collide all day long.

A table gets seated. An order gets modified. A ticket backs up. A shift changes. A supplier is late. A payment fails. A line cook doesn’t show up. A guest leaves a bad review. A manager comps a meal. A server picks up another section. A catering lead comes in. Inventory drops faster than expected. Payroll closes at the end of the week whether you’re ready or not.

That is the business.

And what made Toast so strong is that they didn’t look at those as isolated problems. They looked at them as parts of the same operating system.

That’s such a big lesson for vertical SaaS.

The best companies in our world do not start by asking, “What software category can we sell into this industry?”

They start by asking, “How does this business actually run?”

That sounds obvious, but it’s not how most people build.

Most people start with a wedge that looks like a screen. Toast’s insight was that the real wedge was the workflow. Once you understand the workflow, you can keep expanding through it. Once you sit close enough to the operational heartbeat, you get access to more use cases, more data, more trust, and eventually more monetization opportunities.

That’s why companies like Toast become more than software companies. They become the infrastructure layer for the vertical.

And, of course, once you really own the workflow, you don’t just sell software anymore. You start participating in the money movement underneath it. Payments. Capital. Payroll. Financial services. That’s where vertical SaaS got really interesting.

So when I look at that old Toast visual, what I see is not a company that won because it had a better interface.

I see a company that won because it had a better map.

And here’s the question I keep coming back to:

If we were doing that mapping exercise today, in a world where AI can actually interpret, route, recommend, and increasingly act, would we draw the same map?

I don’t think we would.

Not even close…

Old Vertical Software Mapped Workflows
But AI Should Map the Work

This is the real idea.

For the last twenty years or so, most vertical software was built around the same basic logic. Someone opens a screen, enters information, saves it, and then that information sits in a database until someone else needs it later. Maybe it gets turned into a report. Maybe it triggers a basic rule. Maybe it just lives there forever.

That model created an enormous amount of value. It took industries that were running on paper, memory, spreadsheets, and manual coordination and gave them structure. That mattered. It still matters.

But it also created a generation of software that mostly recorded the output of human labor instead of reducing the need for that labor in the first place.

That’s the subtle but important distinction.

The software became very good at storing decisions after a human made them. It was much less good at helping make the decision, moving the decision forward, or resolving the ambiguity that created the work in the first place.

That’s why so much software still feels passive.

It stores. It displays. It reports.

But it doesn’t really orchestrate.

AI changes that.

Now software can take in messy information, figure out what it means, structure it, compare it against context, recommend a next step, and in some cases just do the next step. That’s not a minor product improvement. That’s a different category of software behavior.

Which means if you’re a founder mapping a vertical today, you shouldn’t just be asking where data gets entered.

You should be asking where work begins.

That’s a much more interesting question.

Where does the actual workflow start? Is it an inbound phone call? A voicemail? An email? A PDF invoice? A reservation change? A catering request? A guest complaint? A no-show? A chargeback? A schedule swap? A missing item from a supplier? A manager text at 6:12 a.m. because somebody called out?

That’s the map now.

Not modules. Not menus. Not navigation tabs.

Work.

And if you really follow that thread, the whole architecture of the business starts to look different.

A restaurant, for example, is no longer just POS, payroll, scheduling, inventory, and marketing.

It becomes a chain of workflows.

Demand comes in from all over the place. Some of it is structured, some of it is messy. That demand has to be interpreted, converted, routed, fulfilled, measured, and followed up on. Labor has to flex around it. Inventory has to support it. Payments have to capture it. Exceptions have to get resolved. Managers have to step in only when the situation becomes ambiguous, high-risk, or economically meaningful.

That is a far more AI-native way to see the business.

And this is exactly why I think the “messy inbox” idea David Haber penned is such a strong thinking framework for vertical AI.

Most industries still have some version of this problem: too much unstructured stuff coming in at the top of the funnel, too many humans needed to interpret it, and too many downstream workflows that don’t start until somebody manually turns chaos into structure.

That’s where AI gets really powerful.

Not when it’s bolted onto the end of a workflow as a chat feature.

But when it sits at the beginning of the workflow and turns ambiguity into action.

That’s a very different thing.

And it’s why, if I were redrawing the Toast map for today, I wouldn’t organize it around product modules. I’d organize it around moments of operational gravity.

Where does demand enter the system?

Where does labor get burned?

Where do handoffs happen?

Where does money leak?

Where does a human still spend time interpreting something that software can now reasonably understand?

And maybe most importantly: where should the human still stay in the loop?

Because I don’t think the right AI map is one where everything gets automated. That’s fantasy. The right AI map is one where the software handles the clean, repetitive, high-frequency parts of the workflow and then pushes the weird, high-stakes, low-confidence moments into an exception layer.

That, to me, is the future UI of vertical SaaS.

Not the dashboard.

The exception queue.

A manager does not want another screen full of analytics. They want to know what needs attention right now, what was already handled, what is blocked, and what matters financially.

That’s the new operating system.

And honestly, once you see it that way, it becomes hard to unsee.

Old vertical SaaS mapped where information got entered.

Vertical AI should map where decisions get made, where work gets stuck, and where software can now carry more of the load.

That’s the exercise.

That’s the opportunity.

And I think that’s where the next great vertical companies are going to come from.

How To Re-Do The Exercise in the Age of AI

Part of what pushed me to write this came out of a conversation I had with David Haber, a GP at a16z.

We were talking about old vertical software maps, why some of them were so powerful, and why they now feel incomplete. That conversation sharpened something for me that I had been circling for a while: in the AI era, it is not enough to map the workflows.

You have to map the work.

That sounds like a small distinction, but I think it changes everything.

A workflow is the clean version of the business. It is how the business gets described in a deck, an implementation plan, or a software menu. Orders flow here. Payroll goes there. Scheduling sits over here. Reservations feed into table management. Inventory connects to purchasing. Reporting rolls up on the back end.

That is useful. But it is also sanitized.

Work is messier than workflow.

Work is the manager reading a long email from an angry guest and deciding whether to comp a meal.
Work is someone listening to a voicemail about a catering request and figuring out whether it is real, urgent, and worth following up on.
Work is a back-office employee opening an invoice PDF, checking whether it matches expectations, routing it to the right person, and cleaning up the edge cases.
Work is somebody noticing that a reservation change is going to affect table flow, staffing pressure, and probably ticket times.
Work is somebody acting as the human bridge between a messy real-world event and a structured software system.

That is the piece most old software never really touched.

The old software map showed you where information ended up.

It did not show you how much labor was required to get the information from here to there.

That is why I think the first principle for re-doing the Toast exercise in the age of AI is this:

Don’t just map the workflows — map the work required to keep the workflows moving.

That means asking a different set of questions.

Not just: what are the modules?

But: where is labor being spent on interpretation? Where is labor being spent on coordination? Where is labor being spent on routing, exception handling, follow-up, checking, clarifying, escalating, and cleaning up?

Because that is where AI enters.

If you were doing this for restaurants, you would still want to understand the big functional areas. You would still care about reservations, ordering, kitchen operations, labor, payments, and guest engagement.

But then you would go a level deeper.

You would ask: what does the human actually do inside each of those areas?

What are all the jobs that exist within the company? What are they doing? How much are they paid?

And to go a layer deeper:

Where do they read things?
Where do they listen to things?
Where do they decide what something means?
Where do they move information from one place to another?
Where do they act as the operating glue holding the whole system together?

That is the work map.

And once you have mapped the work, the next question is: what wedge do you use to attack it?

This is where I think David’s framing is especially helpful, because we are not seeing one single AI wedge work across every market. We are seeing a few recurring patterns.

The first is AI services.

This is maybe the most practical wedge because it goes straight at labor. In a lot of vertical industries, the software exists, but humans are still doing all the real work around it. They are checking, correcting, reconciling, interpreting, and pushing the workflow forward manually. So instead of starting with a better interface, AI services starts with a better way to do the work itself.

Camber is a great example of this. Healthcare reimbursement has historically required huge amounts of specialized human effort layered on top of bad systems and fragmented processes. Claims have to be submitted, fixed, chased, and reworked. Entire services businesses grew up around that pain. What Camber does is turn that labor-heavy reimbursement problem into a software problem. That is why the wedge is so strong. They are not just selling a nicer billing tool. They are going after work that used to require armies of people.

And that is a useful lens for rethinking something like the Toast map. In restaurants, a surprising amount of operational drag still looks like services work hiding inside the business. Catering intake. Guest recovery. AP reconciliation. payroll exception handling. Supplier coordination. These are all places where people are still acting like an internal services layer because the software never really absorbed the work.

The second wedge — and I think the most exciting — is the industry-specific GPT.

This is where things start to get really interesting.

Because now you are no longer just automating a step in the workflow.
You are building an intelligence layer for the vertical itself.

That is a much bigger idea.

An industry-specific GPT is not just a chatbot with a little fine-tuning. It is not just generic AI wearing the clothes of a vertical. At its best, it starts to understand the actual language, logic, edge cases, constraints, and judgment patterns of the domain. It behaves less like a feature and more like an emerging operating intelligence for the industry.

OpenEvidence is a great example of this. The reason it is powerful is not that it is “ChatGPT for doctors.” That framing is too shallow. What makes it compelling is that it is becoming a medical intelligence layer grounded in the realities of clinical decision-making. It is useful because it understands the domain well enough to help practitioners reason inside it.

And once you start thinking this way, the workflow map changes dramatically.

Because now the question is not just: where can I automate labor?

The question becomes: where does this industry need a brain?

What would the restaurant equivalent of that look like?

It would not just answer questions about menu items or write a promo email.

It would understand the operating reality of restaurants. It would understand labor pressure, table turns, reservation pacing, kitchen bottlenecks, menu mix, guest recovery, promo strategy, inventory risk, staffing tradeoffs, refund behavior, and the way all of those things interact.

That is a completely different category of software.

At that point, you are not just building around the workflow. You are building a domain-native intelligence layer that can increasingly guide the workflow.

That is why I think this wedge is the most interesting one.

If the AI services wedge helps you do the work, the industry-specific GPT wedge helps you understand the work at a much deeper level than old software ever could.

And in the long run, that may be the more powerful position.

The third wedge is building agents on top of the system of record.

This one is a little more tactical, but it is incredibly important because it acknowledges a simple truth: in many verticals, the incumbent system still has the data gravity.

It may be hated. It may be clunky. But it is embedded. It is where the records live. It is where the workflow officially lands. That means one of the smartest ways to wedge in is not to replace it immediately, but to start from it.

Use the system of record as the foothold. Read from it. Write back to it. Make it smarter. Reduce the labor around it. Turn it from a passive database into something that feels more action-oriented. You essentially build Agents on top of it.

Datagrid is a great example of this pattern. It does not begin by asking the construction company to replace the system of record (IE Procore). It starts from the fact that the CMS is already embedded in the workflow and already the source of truth. The wedge is to capture the conversation, structure the documentation, and make the surrounding workflow dramatically more usable while integrating into the existing record system. That is a very different strategy from trying to rip out the incumbent on day one.

There is a very obvious parallel in restaurant software too. If you were attacking the Toast map this way, you might not begin by replacing Toast or the broader stack. You might start by asking what AI can do when it sits on top of the systems that already exist. Can it absorb the labor around them? Can it turn raw operational data into clear actions? Can it surface exceptions instead of forcing operators to dig? Can it make the existing stack feel much more intelligent without forcing a painful rip-and-replace?

That is wedge number three.

And the important thing is that these wedges are not mutually exclusive. In fact, the best companies will probably move through them over time.

They may start with AI services, because that is where the urgency and labor budget live.

Then they may deepen into an industry-specific GPT, because that is where the real intelligence advantage gets built.

Then they may sit on top of — or increasingly absorb — the system of record, because that is where the workflow gravity and defensibility live.

That progression feels right to me.

And it also gives us a much better way to redo the original Toast exercise.

The old version of the exercise asked: what are all the categories make up the restaurant?

The new version should ask: where is the labor, what kind of work is being done, how much does it cost, and what wedge is best suited to attack it?

Is this work best attacked as an AI service, because humans are still doing it manually at scale?

Is it a place where an industry-specific GPT can become the intelligence layer for the vertical?

Or is it best attacked by building an agent on top of the system of record, because the workflow gravity is already there and the wedge is to make it dramatically more useful?

That is the strategic map I would want to draw now.

Not just where the workflows are.

But where the work is.
How the work gets done.
And what kind of AI wedge can most naturally absorb it.

That feels like the real AI-era version of the Toast map.

And I think founders who do this well are not going to build a handful of AI features around old vertical software.

They are going to build the new operating layer for the vertical itself.

Do me a solid and forward to a friend :-)

There’s a specific kind of startup worth paying attention to right now: the one that goes straight at a category everyone else assumes is locked up.

Not glamorous markets. Not greenfield software. Markets with incumbents. Markets with procurement. Markets with ancient systems of record. Markets where customers have been overpaying for mediocre software for years.

That’s the backdrop for this episode’s conversation with Alex Niehenke of Scale Venture Partners. The core idea is simple: vertical AI is having its moment not because incumbents are small, but because many of them are slow, bloated, and structurally unable to reinvent themselves fast enough. When the dominant players have low customer love, rising prices, and little appetite to disrupt their own revenue streams, the opening for startups gets real very quickly…

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This Weeks Titan: Alex Niehenke (Partner at Scale)

Alex Niehenke is a Partner at Scale Venture Partners, where he focuses on early investments in vertical markets where incumbents have failed to invest in advanced technology. He has been at Scale since 2012 and has backed companies including Motive, Root Insurance, Scout RFP, Range, Proscia, and Archipelago. His investing lens was shaped early by watching his father’s transportation businesses intersect with the rest of the economy, which gave him a long-running curiosity about how real industries actually work.

That background matters because Alex has spent years leaning into sectors many investors historically dismissed as “boring”: insurance, logistics, construction, procurement, financial services, and other operationally dense industries. In the episode, he makes the case that these are exactly the kinds of markets where startups can win big, because the software is often deeply entrenched but poorly loved. The old venture knock on vertical software was always that TAMs were too small and buyers were too hard. Alex’s view is almost the inverse: those markets often only look unattractive until someone finally builds something customers actually want.

Scale’s own positioning maps cleanly to that worldview. The firm describes itself as backing early-stage AI companies on the journey from founder-led growth to a go-to-market machine. In other words, Scale is not just underwriting raw technical novelty; it is looking for companies that can turn product advantage into durable commercial momentum. That framing shows up all over Alex’s comments in the episode, especially in his emphasis on growth velocity, category timing, and whether a startup can become indispensable before the incumbent fully wakes up.

The most useful way to understand Alex is that he is not simply bullish on AI. He is bullish on what happens when AI meets neglected vertical markets. He is looking for wedges where legacy vendors have spent years extracting price instead of reinvesting in product, where user NPS is poor, and where a startup can build what he calls a “system of love” rather than just another system of record. That distinction is the heart of the episode.

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1. Build a “system of love,” not just a better workflow

Alex’s most important point is that the best vertical AI companies do not just replace software. They replace resentment. In many verticals, the legacy leader still owns the system of record, but it no longer owns user trust. Customers are stuck with bloated tools, poor UX, and annual price increases that feel disconnected from product improvement. That creates the opening. The startup wins by building the product users actually want to use — the one that feels faster, smarter, and more aligned with how work gets done. That is what Alex means by a “system of love.” It is not a cute phrase. It is a wedge strategy. If the incumbent has spent years training customers to feel captive, then delight itself becomes disruptive.

The best way to frame this in the piece is that vertical AI is often entering markets with structural dissatisfaction already baked in. Alex points to sectors like legal and insurance, where incumbents such as Westlaw, LexisNexis, and Vertafore have long benefited from limited competition, low NPS, and pricing power. In those markets, the startup does not need to convince buyers that the old experience is broken. Buyers already know. The founder’s job is to convert that frustration into adoption by making the new tool feel indispensable almost immediately.

You could sharpen the blog language here by saying: the first product advantage in vertical AI is emotional, not technical. Users do not switch because they got a slightly better dashboard. They switch because the new product respects their time, reduces pain, and gives them leverage against a vendor they never really loved in the first place. That is how small startups start to destabilize giant categories.

2. Use customer hatred as a GTM advantage

One of the hidden advantages in vertical software is that bad incumbents create their own demand for disruption. Alex’s observation is that in a lot of these categories, founders are not selling into neutral markets. They are selling into markets where the customer already feels overcharged, underserved, and trapped. That means the go-to-market motion can be much more direct. The message is not “here is a new tool.” The message is “here is your exit.”

This is especially powerful in categories where incumbents have used contract lock-in, proprietary data, or integration bottlenecks to preserve share. Alex’s point is that once the buyer believes an alternative is real, dissatisfaction turns into urgency. In legal tech, for example, he notes that newer AI-native entrants are benefiting from years of pent-up frustration with legacy research and workflow vendors. In insurance, the same logic applies where multi-owner, PE-shaped incumbents have often optimized for extraction over product improvement. The emotional state of the customer becomes part of the distribution strategy.

A clean way to write this in the post: customer dissatisfaction compresses the sales cycle. When a founder is entering a market where users already hate the default option, education is less important than credibility. The question is not whether the pain exists. The question is whether the startup is trustworthy enough to become the next default.

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3. Don’t fight incumbents in court. Route around them

Alex is very clear on this: founders should not waste their best years trying to litigate their way through a blocked market. His advice is blunt — get the lawyers out of the room. For startups, legal warfare is usually a trap. Incumbents have more money, more patience, and far more ability to absorb distraction. Even when the startup is morally right, the process can still kill momentum.

The better response is tactical. If an incumbent blocks access, restricts integrations, or tries to use data captivity as a moat, the startup should look for ways to become useful anyway. That can mean workflow-based entry, lightweight implementation, partial automation, partner-led adoption, or what Alex loosely refers to as working around the old system rather than begging permission from it. In other words: do not challenge the castle head-on if you can quietly build the road users actually prefer.

This matters because many of the most attractive vertical AI markets are also integration-heavy markets. Alex’s broader framework in his writing is that faster breakout categories tend to require less integration upfront, while slower but potentially more defensible categories require more of it. That means founders need to be strategic about where they need permission and where they do not. The startup that can deliver real ROI before a heavyweight integration is often the startup that earns the right to go deeper later.

4. Win the wedge before you win the platform

A lot of founders talk like they are replacing the incumbent suite on day one. Alex’s worldview is much more practical. The real winners usually start by owning a narrow, painful, high-frequency workflow — something acute enough that users will adopt quickly, even if the broader system stays in place for a while.

That logic is reinforced by his public framework on vertical AI adoption. The earliest breakouts often happen in categories where value is obvious without deep integration and where the sector has historically underinvested in modern software. Legal is a prime example: immediate ROI, text-heavy work, and relatively low implementation friction compared with deeply entangled back-office systems. By contrast, sectors like insurtech may offer huge opportunity, but adoption is slower because the workflows are intertwined with legacy systems and downstream dependencies.

In the post, I’d phrase this as: great vertical AI companies earn expansion through usefulness. First they solve one painful job better than anyone else. Then they become the preferred interface. Then they accumulate context, trust, and data. Only after that do they start to look like a platform. Founders lose when they pitch total replacement too early; they win when they become impossible to remove.

5. Exploit the incumbent’s structure, not just its product weakness

Alex is not just saying incumbents are slow. He is saying many of them are structurally unable to respond. In the episode, he emphasizes that a meaningful number of vertical software leaders are private-equity-owned, heavily levered, and managed for extraction rather than invention. His phrase is that in many of these businesses, “the ambition has left the room.” That is a devastating condition in an AI transition.

This is why the startup’s real edge is often organizational, not merely technical. Founders can take pricing risk. Founders can ship half-finished but highly valuable products. Founders can cannibalize future revenue in order to win the present. A debt-loaded incumbent often cannot do any of those things. It may know the threat is real and still be unable to answer it with the necessary urgency. That is the kind of mismatch vertical AI founders should be looking for: not categories where the incumbent is stupid, but categories where the incumbent is constrained.

That same logic also explains why some of these markets are newly attractive to venture. If the old winner is financially engineered rather than founder-driven, a modern AI-native company can look small in revenue and still be large in strategic threat. The founder is not just shipping better software. They are attacking an institution whose operating model was optimized for a previous era.

6. Growth matters more than neatness

Alex is unusually candid about the market here: growth is trumping everything. Investors are rewarding speed, acceleration, and signs that a company is escaping into category leadership. That does not mean business quality does not matter. It means that in an AI transition, velocity is being read as evidence of inevitability.

He also talks about “elephant accounts” — very large customer deals that can make young companies look lopsided or concentrated, but also signal that serious buyers are willing to place meaningful bets early. For vertical AI founders, this is important. The market is often less interested in perfectly balanced metrics than in whether the startup is becoming strategically important to major buyers. A weird-looking company with explosive pull can be more investable than a tidy one growing at a merely rational pace.

In blog form, the takeaway is: in frontier categories, momentum is a moat. Once a startup becomes the company everyone in a vertical is hearing about, recruiting improves, fundraising improves, customer urgency improves, and incumbent anxiety increases. In these markets, speed does not just create growth. It compounds strategic leverage.

7. Commoditization is coming, so own the workflow, not the feature

Alex is also clear-eyed about the other side of the story. AI lowers the cost of building. What looks novel this quarter may look generic next quarter. He warns that if your company is basically a thin layer on top of model access, pricing pressure will come fast. The consequence is that vertical AI founders need to build defensibility somewhere other than surface-level novelty.

His broader writing offers a useful extension of this idea. In “LLMs and Sneaky Big Markets,” Alex argues that AI changes the economics of vertical software because tools are no longer just helping workers do their jobs — they are beginning to automate pieces of the work itself. That expands TAM, but it also raises the standard for differentiation. The winners will not simply provide software seats; they will capture meaningful economic value by automating real labor, owning critical workflows, and embedding into domain-specific processes.

So the right way to write this playbook is: features commoditize, embeddedness does not. The moat is not that your demo is impressive. The moat is that your product sits in the flow of work, touches proprietary data, understands the regulation, and improves with every customer interaction. That is how vertical AI turns from a fast product into a durable company.

8. The biggest markets are often the ones that looked too small before AI

One of the subtler but more important threads in Alex’s thinking is that AI changes what counts as a venture-scale category. Traditional vertical SaaS math often made certain markets look capped because pricing was seat-based and buyer willingness was constrained. But once software begins automating the work itself, the economic ceiling rises. The market is no longer bounded by software budget alone; it starts to pull against labor budget and workflow value.

That matters for the blog because it reframes why vertical AI is so exciting. It is not just that neglected markets are finally getting better software. It is that categories long dismissed as “too small” can become very large when a product directly absorbs meaningful work. Alex’s point is that founders should stop thinking only in legacy SaaS TAM logic and start asking how much human effort, time, and coordination the product can actually replace.

If you’re building in vertical AI, this episode does not stay at the level of slogans. Alex gets specific about where incumbents are weak, why customer dissatisfaction is a real wedge, how PE-owned software companies lose their appetite for reinvention, and what actually makes a vertical AI company defensible when everyone is worried about commoditization. More importantly, the conversation gives founders a sharper way to think about competition: you do not have to outspend the giants, you have to understand where they stopped serving the customer and move faster than they can respond.

It is also interesting because Alex is not talking about vertical AI as a vague trend. He is talking from the vantage point of someone who has spent years investing in sectors like logistics, insurance, fintech, and construction, and who has backed companies such as Motive, Root Insurance, and Scout RFP through Scale Venture Partners. That makes the discussion feel grounded in pattern recognition rather than hype.

So if you’re a founder, operator, or investor trying to understand where vertical AI is actually going — and how startups can win against entrenched legacy players — go watch the episode. There are a lot of conversations right now about AI tools. This one is about strategy. And that’s what makes it useful.

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Alright, let’s get to it…

A question I got recently was…

How much time/effort would you say your onboarding takes for your vSaaS?
What about normal support?
Does that vary by plan/price tier and approximate ACV?

I love this question because most founders obsess over CAC and ACV on the way in…

But they don’t spend enough time thinking about the delivery burden after the contract is signed.

And in vertical SaaS, that’s where a lot of the bodies are buried.

Here’s the benchmark they sent me:

• ACV: ~$5K

• Sales process: 1–3 hours on Zoom calls to close

• Onboarding: 5–10 hours of training + 5–10 hours of data migration

• Total onboarding: 10–20 hours per logo

• Ongoing support: ~1 hour per month

• Observation: for every 100% increase in ACV, onboarding time only goes up about 20%

Honestly? That’s pretty healthy.

And it highlights something a lot of founders miss:

Implementation should not scale linearly with ACV.

If your ACV doubles and your onboarding doubles too, you’re not building leverage.
You’re just selling a nicer version of services.

The goal is not to eliminate implementation. The goal is to make sure that as ACV goes up, implementation effort grows much more slowly than revenue.

That is exactly what this persons data suggested.

At roughly $5K ACV, spending 10–20 total hours to get a customer live can make sense if:

• the customer retains well,

• support stays contained,

• and onboarding creates a durable moat rather than a recurring headache.

What would worry me?

If the same customer also needed endless custom setup, weekly handholding, or bespoke migration work that never got productized.

That’s where people fool themselves.
They call it “white glove.”
What it really means is “we haven’t built the product yet.”

So how do I think about it?

ACV doesn’t just shape GTM.
It also shapes implementation architecture.

Low ACV often pushes one toward PLG or marketing-led motions; mid-range ACV leans inside sales; high ACV usually forces higher-touch selling. This isn’t law it’s just what I typically see. The same basic logic applies after the sale. Higher ACV generally supports more human touch — but if you’re doing it right, the effort should rise sublinearly, not one-for-one with revenue.

Here’s the simple mental model I’d use:

Tier 1: Low ACV / light-touch

Think small accounts, simpler workflows, lower switching cost.

• Standardized onboarding

• Template migration

• Group training / office hours

• Async support first

• Product does most of the work

If these customers need lots of calls, custom data cleanup, and one-off workflows, your margins are going to get cooked.

Tier 2: Mid ACV / guided onboarding

This is roughly where your example sits.

• A few sales calls are fine

• 10–20 onboarding hours can work

• Some migration help is fine

• Monthly support can stay lean if the product is clear

• The key is turning repeated onboarding work into product, docs, automations, and checklists

This is often the sweet spot in vertical SaaS.

The customer is paying enough to justify real help.
But not so much that you need a mini-consulting firm attached to every new logo.

Tier 3: Higher ACV / high-stakes onboarding

Once ACV climbs, the implementation burden usually shifts from “training users” to “changing operations.”

Now you’re talking about:

• integrations,

• data mapping,

• policy design,

• stakeholder alignment,

• executive reporting,

• and internal change management.

That’s not just onboarding anymore. That’s operational transformation.

And yes, higher ACV accounts absolutely deserve more touch.

But the best vertical SaaS companies still keep control of the process by making implementation modular, repeatable, and tiered.

That’s the magic.

Not “no humans.”
Precise humans, in the right places.

There’s some good external support for this idea too.

One good onboarding playbook I reviewed recommends using ACV thresholds as routing logic: accounts above roughly $5K estimated ACV can qualify for more human-guided onboarding, while larger accounts may justify even more strategic support. It also suggests very different CS coverage models by segment — roughly 1:20 for high-touch enterprise, 1:100 for hybrid accounts, and automation-first for true self-serve cohorts.

That same playbook had one nugget I really liked:

When accounts stall, speed to human help matters a lot. Customers who got help within 4 hours had a 3–4x higher recovery probability than those that waited 72 hours. That tracks with what most of us have seen in practice. Not every customer needs high-touch onboarding — but the right customer, at the right stuck moment, absolutely does.

So if I were answering the original question directly, I’d say this:

My take

For a vSaaS business at around $5K ACV, 10–20 hours of onboarding per new logo plus ~1 hour/month of ongoing support is very reasonable — if retention is strong and the work is becoming more repeatable over time.

And yes, this should vary by plan and ACV.

But the variation should look more like this:

• Higher ACV = more structured onboarding

• Not necessarily dramatically more onboarding hours

• Higher ACV = more strategic support moments

• Not necessarily infinite reactive support

In other words:

Revenue should scale faster than service load.

That’s the game.

Because once implementation effort starts rising faster than ACV, the software model starts leaking.

And when founders tell me they have a $5K ACV product that takes 40–60 hours to onboard every customer, I usually have the same reaction:

That’s not a SaaS margin profile.
That’s a services margin profile wearing a SaaS costume.

Quick PSA:

Hadrius & Why More Vertical Start-Ups Need To Focus On Solving Compliance

Compliance is one of those categories that most founders avoid because it looks painfully boring from the outside.

But if you’ve been around vertical software long enough, you know that “boring” is often where the monster businesses live.

Hadrius is a great example of that.

They’re building AI-native compliance infrastructure for financial institutions — RIAs, broker-dealers, private funds, and compliance consultants. The wedge is straightforward: automate the ugly stuff that nobody wants to do manually anymore — communications review, marketing review, archiving, employee oversight, firm oversight, and trade surveillance — and pull it all into one system of record.

That market is not small.

YC’s company page says 30,000+ financial firms spend a combined $16B+ a year on ongoing SEC compliance, while still relying on consultants, law firms, and legacy software glued together with spreadsheets and manual reviews. That’s the kind of market that looks sleepy until you realize how much pain is buried under the surface.

And the founding story is exactly the kind of thing I love in vertical SaaS.

The team behind Hadrius didn’t just “discover” compliance from a market map. They ran an SEC-registered robo-advisor themselves, Quantbase, and felt the pain firsthand. Out of that pain they built Hadrius in 2023. They later raised a $2M seed round with participation from Y Combinator, Lynett Capital, Singularity Capital, Dorm Room Fund, Unpopular Ventures, and a handful of fintech founders/operators.

This is textbook vSaaS behavior:

You start with a painful workflow.
You solve the thing that is both mandatory and miserable.
Then you expand into adjacent workflows that already touch the same data, the same users, and the same compliance clock.

That’s exactly what Hadrius appears to be doing.

Their product suite now spans Marketing Oversight, Communications Oversight, Employee Oversight, Firm Oversight, and Transaction Oversight. In other words: they didn’t stop at one point solution. They’re trying to become the compliance OS.

And there are some real proof points showing up.

On its current site, Hadrius says it serves 500+ client institutions, covers $5T+ in AUM across clients, saves 19+ hours per user per week, reduces false positives by 90%+, and claims a 10x reduction in marketing review cycle times. In a regulated vertical, those are not vanity claims. Those are “I can justify this purchase to my boss and to an auditor” claims.

The other thing I like here?

They’re not pitching “AI magic.” They’re pitching regulator-ready evidence, zero-data-retention AI, WORM-compliant storage, SOC 2 Type II compliance, RBAC, SSO, and audit-readiness. That matters. In vertical software, especially in regulated markets, the winner usually isn’t the company with the flashiest demo. It’s the one the buyer trusts enough to put in the workflow.

This is the broader lesson.

AI in vertical SaaS gets way more interesting when it stops being a chatbot and starts becoming a compliance operator, a claims operator, a dispatch operator, a billing operator, a credentialing operator.

That’s where the value piles up.
That’s where the data moat gets deeper.
That’s where the expansion revenue starts to show up.

Hadrius is interesting not just because it’s an AI company.

It’s interesting because it’s attacking a vertical where the pain is mandatory, the workflow is recurring, the switching costs can get real, and the trust layer matters a lot.

And if they keep moving from wedge to workflow suite, you can start to see the shape of a very serious business.

Early Steps In The Transformation To An AI Company

A lot of software companies are still treating AI like a feature.

Add a copilot.
Add a summarizer.
Add a “generate with AI” button somewhere in the UI.

Cool. Fine. Better than nothing.

But that is not the same thing as becoming AI-native. And I think this is where a lot of incumbents are going to get smoked.

The companies that win this next wave won’t just sprinkle AI on top of old workflows. They’ll rebuild the workflow itself.

That’s the big idea behind Intercom’s transformation, which Eoghan McCabe and the team have talked about pretty openly. Intercom moved from legacy SaaS assumptions toward an AI-first operating model by reorganizing teams, re-architecting parts of the codebase, building rigorous internal eval systems, and shifting pricing toward outcomes.

So if you’re running a vertical SaaS company today and you’re not AI-first yet, here’s the playbook I’d follow.

Step 1: Pick one workflow where labor is still doing the work

Not one feature. One workflow.
That’s the key.

Find the place in your product where your customer is still paying with labor:

• reviewing claims

• checking compliance

• writing notes

• calling patients

• routing jobs

• reconciling invoices

• answering repetitive messages

• moving data from one system to another

That is your wedge.

Hadrius didn’t start with “let’s add AI to finance.”
They attacked recurring compliance work that was already mandatory and already painful. That’s why the wedge makes sense.

Step 2: Create a small AI strike team with a single owner

One of the smartest lessons from Intercom is organizational, not technical.

They centralized AI talent, created focused workstreams, and assigned a Directly Responsible Individual to each mission-critical effort. No mushy committee ownership. No “everyone owns it.”

If you want to become AI-native, do not bury AI inside your normal roadmap grooming process.

Spin up a team.
Give it a workflow.
Give it a number.
Give it a boss.

Step 3: Rebuild the system so AI can actually operate

This is where most companies quit.

They want AI-native output from a legacy stack that has:

• messy data,

• brittle workflows,

• poor instrumentation,

• weird permissions,

• and no clean system of record.

Intercom’s team has talked about re-architecting core systems and even modernizing parts of the stack because AI leverage depends on the underlying foundation. Their point was simple: AI is not an add-on. The foundation matters.

In vertical SaaS, this usually means:

• cleaning workflow data,

• tightening permissions,

• exposing the right actions through APIs,

• building evaluation infrastructure,

• and defining what “good” actually looks like.

No clean data, no real AI operator.

Step 4: Prototype internally first, then with design partners

Another Intercom lesson I like: prototype early, test internally, then go out to market with real conviction.

They built internal eval systems, tested against historical data, and ran large-scale experiments before opening things up more broadly. They also used internal teams as early users before putting unstable systems in front of customers.

This is how I’d do it in vertical SaaS:

• Phase 1: internal dogfooding on your own workflows

• Phase 2: 3–5 design partners

• Phase 3: narrow commercial rollout

• Phase 4: broader launch with clear success metrics

Do not skip the eval layer.

If you can’t measure resolution quality, speed, accuracy, hallucinations, or downstream impact, you don’t have an AI product yet.

You have a demo.

Step 5: Price the outcome, not the seat

This is one of the most important mindset shifts.

Intercom’s AI product, Fin, pushed outcome-based pricing — charging for a successful resolution rather than a seat. If the AI escalates and doesn’t finish the job, the customer doesn’t pay for the resolution. That changes the internal culture. Suddenly everyone is aligned around actual customer value. This is a lot easier to say than do, but give it a shot…

In vertical SaaS, that idea gets very interesting.

What if you charged for:

• claims resolved,

• compliance reviews completed,

• invoices reconciled,

• messages handled,

• appointments booked,

• or dispatches completed?

Not every product can move all the way there.
But every founder should at least ask the question.

Because AI-native businesses are often much closer to outcome economics than legacy SaaS businesses.

Step 6: Change the org, not just the product

This is the part people don’t want to hear.

Becoming AI-native usually means changing:

• hiring,

• team structure,

• success metrics,

• product rituals,

• and what “good” looks like across engineering and GTM.

Intercom described this as a real operating-model shift, not a feature launch. They centralized talent, emphasized speed and experimentation, and hired for versatility while still building deep specialist capability where it mattered. BVP Atlas

That’s the opportunity for vertical SaaS founders.

You already have the domain context.
You already understand the workflow.
You already know where the labor is hiding.

Now you need to rebuild your company around eliminating that labor in a trustworthy way.

That’s the difference between “AI-enabled” and “AI-native.”

One gets you a press release.
The other gets you a new company.

And if you’re feeling behind on this, don’t panic.

The good news is vertical SaaS founders usually have a few huge advantages over horizontal teams:

We know the workflow better.
We know where the bodies are buried.
We know what “good enough” actually means in the real world.
We know where the human still has to stay in the loop.

That matters a lot.

So if you’re not AI-native yet, start with one painful workflow, one great team, one measurable outcome, and one real design partner set.

Do that well and you’ll be way ahead of the founders still shipping AI glitter.

You’ve got this.

Do me a solid and forward to a friend :-)

There’s a certain kind of vertical SaaS company that looks small from the outside and massive once you get inside the workflow. This weeks focus company is one of those.

If you looked at it lazily, you’d probably call it booking software for racquet clubs.

That’s technically true, but it completely misses the point. It’s built is much closer to operational infrastructure for an entire category than simple scheduling software.

Let’s dive in…

Listen To Episode Here

This Weeks Titan: Andres Robelo (Founder & CEO at PlayByPoint)

What makes this story interesting to me is not just the market. It’s the shape of the problem.

When Andres started the business, a huge chunk of the racquet sports world was still running on pen and paper, disconnected systems, and generic tools that didn’t really understand how clubs worked. And that’s usually where great vertical SaaS companies are born. They start in markets that outsiders assume are “simple,” but operators know are full of ugly, painful, edge-case-heavy workflows.

That’s exactly what happened here.

A court booking sounds simple until you actually follow the workflow. One player books. Two more get added. Somebody pays later. Somebody else cancels. Refund logic changes based on club policy. Booking windows vary by location. Residential clubs operate differently than country clubs. Municipal facilities have different rule sets than private operators. What sounds like a clean little scheduling problem turns into a pretty deep operating system once you zoom in.

That’s the wedge.

Not “we built booking software.”

More like: we built around a messy, nuanced, rules-heavy operational layer that generic software never cared enough to get right.

And that’s why I think Playbypoint is such a good example of how real vertical titans get built. They don’t win by telling the prettiest story. They win by getting absurdly close to the actual workflow and solving the stuff that looks too annoying for everyone else.

The other thing I really like here is the customer alignment.

Andres is very clear that the company is built around the club, not the end consumer. Yes, there’s a B2B2C dynamic. Yes, there are millions of player touchpoints. But the strategic center of gravity is still the operator. And that matters. Because in vertical SaaS, the operator is the one trusting you with revenue, scheduling, customer data, payments, and the day-to-day functioning of the business.

That trust is what earns you the right to become the system of record.

And once you become the system of record, a whole lot opens up. Payments. Messaging. Retention. Analytics. Marketing automation. AI agents. The expansion paths get a lot more interesting when you own the workflow that actually matters.

One thing that really stood out to me was Andres saying that the business today is still primarily SaaS, not just payments. I expected the mix to skew much more heavily toward payments, but it hasn’t. That tells me there’s real discipline in the model. A lot of founders would be tempted to over-rotate into payments because it looks like the easiest monetization lever. But building a business where your whole moat is take rate compression is dangerous. If somebody better capitalized comes in and buys down the economics, what’s left?

That’s why this company feels thoughtful to me.

It’s not just monetization plumbing.

It’s software depth, operator trust, and category knowledge.

And then there’s the AI angle, which I think is where this story gets even more relevant.

Andres said something that I think a lot of vertical SaaS founders know deep down but don’t always want to say out loud: the old “it took us five years to build this” moat just doesn’t hit the same anymore. That doesn’t mean product knowledge doesn’t matter. It matters a lot. But when building software gets easier, the durable advantage shifts away from code volume and toward workflow understanding, trust, speed, and judgment.

In other words, the question is no longer just “can someone build this?”

The question is “will they know what to build, in what order, with what customer trust, and with what distribution?”

That’s a much better question.

And honestly, it’s why I’m still very bullish on vertical SaaS.

Not lazy vertical SaaS.

Not surface-level vertical SaaS.

But the kind that lives so close to the customer that it understands the business better than anyone else in software possibly could.

Playbypoint feels like that kind of company to me.

It also fits a pattern I’ve seen over and over again in vertical software: the market always looks too small until it doesn’t. Racquet sports, padel, club software — all of that sounds niche until you realize you’re sitting inside a growing category with passionate users, complex operations, embedded payments, physical infrastructure, and plenty of room to layer new products on top.

That’s usually where the best opportunities are.

Not in the categories everyone already agrees are huge.

But in the ones that feel narrow until the workflow and expansion vectors reveal themselves.

That’s what this one feels like.

And I think there are a lot more like it.

Listen To Episode Here

If I were pulling lessons from this story and applying them to vertical SaaS more broadly, the first one would be simple: go where the workflow is ugly.

Too many founders still want markets where the product demo looks clean. That’s usually the wrong instinct. The best vertical opportunities often live inside businesses where the workflow is full of exceptions, weird rules, messy handoffs, and operator pain that doesn’t show up on a clean website screenshot. The uglier the workflow, the more likely it is that generic software has failed.

That’s where you want to be.

The second playbook is to stay obsessively clear about who the customer actually is.

This sounds obvious, but a lot of B2B2C companies lose the plot here. They start by serving the operator, then get distracted by the prettier consumer narrative, and eventually end up building a product strategy that doesn’t serve either side particularly well. The sharper move is to decide who writes the check, who feels the pain, and who decides if you stay or go. In most vertical SaaS markets, the answer is still the operator.

If you get that right, everything gets cleaner. Product. Positioning. Sales. Expansion. Even AI roadmap.

The third playbook is that AI is not killing vertical SaaS. It’s raising the bar.

That’s a very different thing.

If your only moat was that software used to be hard to build, then yes, you should be nervous. But if your moat is real customer intimacy, trust, workflow understanding, and speed of execution, AI can actually widen the gap between strong companies and weak ones. It exposes who really knows the market and who was just benefitting from slower product cycles.

That’s why I think the winners in this next era won’t just be the companies with AI features slapped onto their nav bar. They’ll be the companies that use AI to rethink the actual operation of the customer they serve.

And that leads to the next playbook: build your agent roadmap from the customer’s org chart.

This is one of the cleanest ways to think about AI in vertical SaaS. Don’t start by asking what flashy AI feature you can launch next quarter. Start by asking who works inside the business, what they do all day, what tasks are repetitive, what breaks all the time, and where labor is being wasted on work that should be automated or augmented.

That’s where the real product roadmap lives.

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In a business like a racquet club, you can already see it. Failed payment recovery. Retention campaigns. Match recommendations. Reactivation flows. Member outreach. Operator alerts. Staffing coordination. There are dozens of little workflows that used to feel too expensive or too custom to build. Now they don’t.

That changes the game.

Another playbook here is self-cannibalization.

I think this is one of the hardest muscles for software companies to build. When the market shifts, the natural instinct is to protect the old pricing model, the old packaging, the old way value gets sold. But the companies that survive platform transitions are usually the ones willing to break their own model before the market forces them to.

That mindset really matters right now.

If you know your customers are going to care more about outcomes, automation, and proactive workflows than they care about seats and dashboards, then you need to move there before someone else does. You can’t cling to a shrinking definition of value just because it was once the right one.

Only the paranoid survive.

And finally, maybe the most timeless playbook of all: stay embarrassingly close to the customer.

Andres talked about keeping direct WhatsApp groups with customers, and I loved that. It may not be infinitely scalable, but that’s not the point. The point is proximity. Founders lose touch long before they realize they’ve lost touch. The moment you stop hearing the rough edges in real time, you start building for internal narratives instead of real customer pain.

I’ve seen this happen up close.

It’s subtle at first. You still think you know the customer. You still reference old conversations. You still talk like you’re close to the workflow. But little by little, the sharpness disappears. The intuition dulls. And eventually you’re making roadmap decisions from a conference room instead of from the front lines.

That’s when the rot starts.

The best vertical SaaS founders I know all have some version of unreasonable customer proximity. They stay in the pain longer. They keep the feedback loop tighter. They learn faster because they’re still willing to be uncomfortable.

That never goes out of style.

So if I had to boil this week’s playbook down into one line, it would be this:

Find a category outsiders underestimate, earn deep trust with the operator, and move fast enough that your product evolves before the market gets a vote.

That’s the job.

And in this environment, that’s the whole job.

You’ve got this.

Listen To Episode Here

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Alright, let’s get to it…

InKind: The Most Creative Business Model I’ve Seen In A While…

InKind Is Rewriting The Restaurant Funding Playbook
(And It’s Absolutely Wild)

You know what makes me absolutely giddy about vertical SaaS? When someone takes the most beaten-down, capital-starved, traditionally un-investable industry... and figures out a financial model so creative that it makes VCs who previously ran screaming suddenly start writing checks.

Meet inKind. And buckle up, because this model is chef’s kiss brilliant.

The Founders & The “Why Now” Moment

Johann Moonesinghe and Andrew Harris launched inKind in 2016 in Austin, alongside Johann’s late brother Rajan Moonesinghe and product designer Marcus Triest. But here’s the thing—Johann wasn’t some tech bro trying to “disrupt” restaurants from the outside. The dude owns FOUR restaurants between Austin, Scottsdale, and Las Vegas. He’s lived the pain.

And that pain? It’s restaurant financing. Or more accurately, the complete absence of reasonable restaurant financing.

Think about it: restaurants have historically thin margins, high failure rates, and unpredictable cash flow. Banks don’t want to lend to them. VCs won’t touch them (too asset-heavy, no venture-scale outcome). And traditional equity investors want ownership stakes that most operators aren’t willing to give up.

So what did Johann and Andrew do? They literally cashed out their home and retirement accounts to bootstrap this thing in the early days. Say whaaaaaaaat. That’s conviction, folks.

What It Is & How It Works

Here’s where it gets interesting. InKind isn’t a lender. They’re not taking equity. They’re not even touching your credit card processor.

They’re buying your future food.

Let me break it down: Let’s say you’re a restaurant that needs $100,000 for a kitchen renovation or to smooth out operations. InKind will give you that $100K in cash TODAY—but in exchange, you’re giving them $200,000 worth of food and beverage credit that they can sell to diners through their app.

Then inKind turns around and sells that $200K in credit to consumers through their mobile app—often around $150K, pocketing the spread as their margin. Diners get access to these credits PLUS a 20% cashback reward to use at any other inKind restaurant. So consumers are getting 20 cents back on every dollar spent. Pretty compelling value prop.

For the restaurant, the math works because food cost is typically only 20-30% of menu price. So even selling credits at 50 cents on the dollar, the restaurant can still be profitable—assuming they manage their other costs well and drive enough non-inKind traffic.

The Product Suite: Way More Than Just Financing

But wait, there’s more. (I promise this isn’t an infomercial, though it kinda feels like one.)

InKind has evolved into a full restaurant commerce platform:

1. Sherlock AI - This is their secret sauce. Named perfectly, this system analyzes a restaurant’s menu, local spending behavior, and customer trends to determine EXACTLY how much credit a restaurant can absorb without tanking their cash flow. In Johann’s words from year one: “I lost 50% of the money I funded because I bought $100,000 in donut credits from some donut place in Michigan. It was impossible to sell.” Ouch. But they learned, and now Sherlock prevents that.

2. Labor Management Software - Johann built this internally for his own restaurants and saw a 10% margin improvement. Now they’re rolling it out across their 6,000+ restaurant network.

3. Margin Monitoring Tools - Helping operators actually understand their unit economics in real-time.

4. POS Integrations - They’ve partnered with Toast and other major players so payment processing is seamless.

5. InKind Pass - A monthly subscription for power diners that unlocks premium perks.

The Numbers: Holy Schnikes

Here’s where I had to do a double-take:

• $600M+ deployed to 6,000+ restaurants nationwide

• $450M just raised in February 2026 (Series B led by Magnetar Capital)

• Investors include: Jay-Z’s MarcyPen Capital, ex-Yahoo CEO Jerry Yang, all four members of Metallica (!), and dozens of restaurant owners

• 2% failure rate among funded restaurants (compared to industry average of 60% in first 3 years)

• 4 million+ app users who’ve received $175M in dining rewards

• Partners include José Andrés Group, MINA Group, 20 Michelin-starred restaurants, 50 James Beard nominees

• Estimated revenue: ~$350M (2025 estimates from CB Insights)

• Founders still own 75%+ of the company despite the massive funding

That last stat is WILD. Most founders are diluted to single digits by Series B. But because Johann and Andrew took a relationship-driven funding approach and didn’t need to spray-and-pray with VCs, they maintained control.

Business Model: The Spread Is The Whole Game

InKind makes money on the spread between:

• What they pay restaurants for credit (say, $100K for $200K in credit = 50 cents per dollar)

• What they sell that credit to consumers for (say, $150K = 75 cents per dollar)

• Their gross margin = 25 cents on every dollar of credit purchased

The risk? If restaurants close before the credits are redeemed, inKind eats the loss. That’s why Sherlock AI is so critical—it’s essentially their underwriting engine.

But with a 2% failure rate vs. the industry’s 60%, they’ve clearly figured something out. Part of it is selection (they only fund restaurants with solid fundamentals). Part of it is the inKind effect itself—by driving more customers through the door with that 20% cashback, they’re actually improving the restaurant’s odds of survival.

Capital Raised vs. Capital Deployed

This is fascinating. They’ve raised roughly $600M+ in total funding, and they’ve deployed... $600M to restaurants. They’re essentially a capital-as-a-service business. The funding they raise from investors goes almost directly into restaurant financing, and they recoup it by selling dining credits over time.

Early on, Johann said: “Venture investors hated our business because we’re so balance sheet heavy, we require so much money to give the restaurants. And the debt partners didn’t want to lend us, because they’re like, restaurants are the most risky.”

But now? With a decade of proof, a 2% default rate, and Jay-Z writing checks? The fundraising is relationship-driven, and Johann’s turning investors away.

Outlook For The Future

With that fresh $450M, they’re planning to fund 10,000 additional restaurants in the next year. That would bring them to 16,000+ restaurants.

They’re also expanding the tech platform—more AI-driven tools for labor optimization, margin management, and operational efficiency. Johann’s not just financing restaurants; he’s building the operating system for independent restaurant success.

And here’s the vision: inKind Day. They’re doing a year-long celebration of their 10th birthday with exclusive events in NYC, LA, Chicago, SF, Austin, Miami, and Vegas. They’re building not just a financing platform, but a community and lifestyle brand for restaurant operators and food lovers.

Other Industries That Could Benefit From This Model

Okay, this is where my brain goes into overdrive. Because this model—buying future inventory at a discount, selling it to consumers at a markup, everyone wins—could work in SO MANY verticals:

1. Fitness Studios / Boutique Gyms - ClassPass already does something adjacent, but imagine providing capital to SoulCycle-style studios in exchange for class credits. Sell them to consumers at a discount. Studios get cash flow, consumers get deals, you take the spread.

2. Salons & Spas - Same deal. Buy $200K in haircut/massage credits for $100K cash, sell them for $150K. Built-in customer acquisition for the salon.

3. Auto Repair Shops - This is HUGE. Buy $50K in future service credits, sell them to local car owners. Shops get working capital, car owners lock in discounted maintenance.

4. Veterinary Clinics - Pet owners would LOVE to pre-buy discounted vet visits. Clinics get capital for equipment.

5. Home Services (HVAC, Plumbing, Electrical) - Seasonal businesses with lumpy cash flow. Imagine buying $100K in HVAC credits during winter (slow season), selling them to homeowners before summer (peak season).

6. Dental Practices - Dentists need capital for equipment. Patients want cheaper cleanings and procedures. Match made in heaven.

7. Child Care Centers - Always strapped for cash, parents always looking for deals. Buy tuition credits at a discount, resell them.

The pattern? High-frequency, repeat-purchase businesses with predictable inventory costs in traditionally underfinanced industries. That’s the sweet spot.

The key constraints are:

• Margin structure needs to support the discount (ideally <30% COGS)

• Customer frequency needs to be high enough to sell the credits

• The service/product needs to be appealing enough that consumers will prepay

The Bottom Line

InKind is proof that the best vSaaS businesses don’t just sell software—they solve the existential problems of an industry. For restaurants, that problem isn’t marketing or POS systems (plenty of those). It’s access to non-predatory capital.

By reimagining the entire financing model and building a consumer app to support it, Johann and team created something genuinely new. And with 75% founder ownership at $450M+ raised? They’re playing the long game.

This is what I mean when I say vertical SaaS is about depth, not breadth. InKind went ALL IN on restaurants. They learned the unit economics, built underwriting AI, created a consumer marketplace, integrated with POS systems, and became an indispensable partner to 6,000+ operators.

Could this model work in other industries? Absolutely. But it won’t be easy. You need domain expertise, patient capital, a two-sided marketplace, and a willingness to be balance-sheet heavy.

But if you pull it off? You’re not just building a SaaS company. You’re building the financial backbone of an entire industry.

You’ve got this.

We’re hosting a workshop on
AI-Powered GTM.

This won’t be a theory session. This won’t be a ppt deck full of generic AI takes.

Our guest is going to get into the actual tools and build everything out from scratch in 60 minutes.

Our guest, Ryan Vanshur, VP GTM Intelligence & AI at Handle.com,
and he is one of the most advanced AI pilled folks I’ve met…

If you’re thinking about how AI can actually improve execution across your sales org,
this session will be worth your time.

April 9th, 2026 at 2:30 PM EST

Limited to the first 25 founders/operators.

Sign Up For Free Workshop

Or access our last workshop, Building AI Agents That Actually Work here.

The Best Moat for Legacy Vertical SaaS Systems in the AI Age (And I’ve Seen NO ONE do it yet)

The best moat for legacy vertical SaaS in the AI era might be an agent app store...

If you’re the system of record in a vertical, you already have the thing every AI agent startup wants:

The data, the workflow, the permissions, and the distribution. So why are so few legacy vertical SaaS companies leaning all the way in here? The obvious move isn’t to try to build every AI feature yourself.

It’s to let the market build on top of you.

Give developers and startups governed access to your data + workflow layer
as long as they launch inside your agent app store.

That’s a monster moat.
Why?

Because it means:
— You don’t have to compete with every AI agent upstart
— You don’t have to build an agent for every use case
— You get more innovation than your internal roadmap could ever ship
— You become the distribution layer for the whole category
— You keep the customer relationship and system-of-record status
— You can monetize via rev share, payments, and platform fees

That’s the part I think people are underestimating. In AI, the product moat is getting thinner. But workflow control + proprietary context + embedded distribution? That still compounds.

If I’m a legacy vertical SaaS company, especially a big one,

I’m not only asking:

“Which agents should we build?”

I’m asking:

“How do we become the place where all the best agents get built, discovered, bought, and used?”

That changes the game.

Now every startup building in your vertical is no longer just a threat.
They can become supply.

You don’t need to out-innovate the entire market.
You need to orchestrate it.

And if you do this right, you get the upside of AI innovation without carrying all the product risk yourself. That’s a way better business.

The incumbents already have the ingredients:
-customer trust
-workflow ownership
-historical data
-embedded payments potential
-implementation footprint
-distribution into the exact end user

So again, the question is:
Why haven’t more legacy vertical SaaS brands built AI agent app stores yet?

Feels like one of the clearest moats of this era.

I'm asking you Veeva Systems, Procore Technologies, Yardi, Toast, nCino, etc. (!)