Drawing on firsthand experience building and scaling more than 20 vertical SaaS platforms, the guide offers practical insights for founders, operators, and investors looking to maximize the value of embedded payments. Download the free guide. Heading to the Vertical Software Summit, November 4–5? Xplor Pay will be there. Let’s talk about how to get more value from your embedded payments program.

Alright, let’s get to it…

Every industry is going to pick its AI platform over the next few years.
For most of them it will be a pick that lasts for a very long time.

There are only three potential winners in every industry:
The frontier labs. The incumbents. The AI-native upstarts.

In every vertical one of them wins. Which one it is depends on the industry, the team, the strategy, and the execution. Not on who has the best model.

Nobody can name the winners yet. Anyone who says they can is selling something. What you can do is learn what each one is good at, where each one breaks, and which conditions inside an industry push the outcome one way and leverage that know-how to emerge the winner.

That’s this week…

The three-way race for the industry platform.

For years, the vertical SaaS pitch was pretty straightforward. Build the system of record. Get the customer to put their business in it. Layer on payments, payroll, and other products. Become very hard to rip out.

AI opens another door. Instead of asking someone to buy a better database, you can offer to do the work.

Scenario #1: The frontier labs suck up the industry

This one usually starts in industries where most customers already have ChatGPT or Claude open. Typically those are industries with heavy knowledge work and a lot of screen time in the day to day. The model gets better. It gets access to the customer’s files and tools. Then it starts completing the industry-specific tasks they used to buy separate software for.

At some point, the customer asks a very uncomfortable question: why am I paying for another application?

The lab does not need to rebuild every legacy database on day one. It can become the work surface above those databases. If the customer starts every task there, it has a shot at owning the relationship.

But the missing pieces are industry-specific UX/UI, industry-specific go-to-market, and an industry-specific brand. A model that can do the work is not the same thing as a product the customer knows how to use, knows how to buy, and trusts with their business.

There is a big assumption underneath this entire ecosystem: the model companies have to leave room for the companies building on top of them to make money.

If they decide the application market is winner-take-all, that assumption gets shaky. Your supplier becomes your competitor. It controls what you pay for the model while deciding what to charge the customer for its own product.

Theoretically, a lab could give its competing application away for free to win the market. You still have an API bill, support costs, and a business to run. Matching free can mean losing money on every customer.

Free does not mean the lab has no costs. It means it can choose to absorb them or subsidize the product from another part of its business. It does not even need to raise your API price to squeeze you. Cutting its own application price can do the job.

That is why it matters so much how the labs see this market. If they want a healthy ecosystem, their partners need workable economics. If they want every customer relationship for themselves, the companies renting their intelligence could find it impossible to make money.

Scenario #2: The incumbents use AI to keep the customer

The incumbent already has the records, the permissions, the billing relationship, and the customer’s trust. Those are real advantages. Especially in industries where moving systems is painful and mistakes are expensive.

But installed distribution is only useful if you turn it into a better product. A chat button sitting beside the same old workflow is not a defense.

I think the better move is to spin up a separate AI product. Keep the data advantage. Leave the historical pricing, packaging, and go-to-market behind. The new product should not need permission from the old revenue model to win.

Or become the platform other agents build on. Give them permissioned access to the industry’s data, build an agent app store, and charge a toll-road tax. You do not have to build every agent if you own the road they need to travel.

Scenario #3: The AI natives take the industry

The starting edge is speed plus zero anchor. No existing pricing to protect. No packaging to preserve. No old go-to-market motion telling you how the new product has to be sold.

Workflow depth is something the upstart has to earn. Its advantage on day one is the freedom to move faster and build the business around what AI can do now.

That industry agent is the wedge. The system of record gets built underneath it as customers hand over more of the workflow.

First you draft the document. Then you manage the matter. Then you own the client record and the invoice. Not every company gets through that sequence. But that is the platform ambition.

There is another version of the upstart: the AI-native services business. It does not sell software seats. It sells the completed job, with software doing more of the work behind the scenes.

That company can win the customer’s budget without owning every record. A great specialist can also build a very good business without becoming the whole platform. This is not a prediction that every other company disappears.

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How each bucket wins.

If I were building in one of these industries, I would start with the customer, not with a model comparison.

Where does the work begin? Who holds the useful context? Who gets blamed when the answer is wrong? And how much pain will the customer tolerate to switch?

If you are an incumbent, keep the data and lose the anchors

I would spin up a separate AI product. Not another tier bolted onto the old pricing page. A product with its own pricing, packaging, and go-to-market, built around the work AI can now complete.

Your historical business should not dictate how the new one gets sold. If an agent does the work of five seats, charging for five seats is not automatically the right answer. Let the new product find its own economics.

What you absolutely should carry over is the data advantage. Years of industry-specific records, context, and outcomes can make the AI dramatically more useful. The ambition should be to use that dataset to make the AI 100x better for your customer, not to decorate the old product with a chatbot. That is the target, not a measured claim.

The other opportunity is an agent app store. Let specialists build agents on top of your permissioned data and integrations. They build the applications. You own the rails and charge a toll-road tax.

That could be a fee on paid agent usage or a share of transactions. The point is to get paid when work moves through your platform, even when someone else builds the agent.

Keep customer permissions and data boundaries intact. A data advantage does not mean handing every developer every customer’s records.

The failure mode is dragging historical pricing and go-to-market into the new business until it moves like the old one. You have the dataset the upstart wishes it had. Do not attach the anchors it is happy to avoid.

If you are an AI native, go narrow before you go wide

Use the speed. Use the lack of anchors. Test a new price, a new package, or a new way to reach the customer without checking whether it cannibalizes a decade-old business.

Find the job people hate doing and already spend real money on. Own it end to end. Not the first draft. Not the demo. The finished, checked result.

Measure what the customer cares about: time saved, work completed, money collected, errors avoided. Get close enough to the workflow that you learn the exceptions nobody put in the spec.

Then expand into the adjacent work. Capture the records your agent needs. Become harder to replace because you understand the business, not because you made the export button hard to find.

If you take the services route, own the delivery too. The customer is buying the result. Your margin depends on what it costs to produce a reliable one, including the human review.

The upstart path is strongest where existing software barely touches the actual work and the labor bill is much bigger than the software bill.

Another path: build an industry-specific LLM

You do not have to outbuild OpenAI/Claude at everything.
You can build a model that is better suited to the work your industry actually needs done.

That might mean adapting an open-weight model, fine-tuning a model with a partner, or training a specialist for a narrow, valuable task. The inputs matter: industry data you have the right to use, expert feedback, and tests that reflect the real job.

The goal is not to win a general benchmark. It is to produce a reliable result at a cost that leaves you a margin.

But be honest about what you own. A custom model built with a frontier lab is not automatically an independent model you can run wherever you want. Hosting, licensing, and inference costs still matter. Owning more of the model layer only reduces supplier leverage if you actually control how it gets deployed and what it costs to serve.

I would test the business against two scenarios: your model bill goes up, and your model supplier launches a competing product at zero dollars. Can you switch models? Can you serve some work yourself? Does the customer still pay for the industry product, the context, and the result?

None of this requires every upstart to train its own frontier model. But your business has to survive the next model release and the next pricing decision. Those are two different risks.

Legal is the first vertical where we are watching this play out.

You have the frontier labs: OpenAI and Anthropic moving into legal work.
You have the AI native upstarts: Harvey, Legora, Spellbook, and plenty of others.
You have the incumbent: Clio.

Three starting points.
An increasingly overlapping ambition.

The labs have a straightforward opening. Legal work is full of documents, language, research, and reasoning. Those are exactly the tasks frontier models are getting better at. The more useful the general tool becomes, the more work a lawyer may try to do there first.

The AI natives are betting that a model alone is not the product.

Harvey’s platform spans legal research, drafting, review, document analysis, and workflows. Legora is building around connected legal work, including document understanding, organizational knowledge, and intake. Spellbook has a particularly clear wedge in contract drafting and review inside Microsoft Word.

Harvey is also an example of the industry-specific model path. OpenAI has documented its work with Harvey on a custom-trained case law model. This is not just a legal interface sitting on a general-purpose model. It is an effort to specialize the model itself for the work. [1]

Harvey has also described co-building a tax assistant with PwC and OpenAI using custom models and expert feedback. The direction is clear: industry knowledge can shape the intelligence, not just the screen around it. [5]

That does not mean Harvey has built an independent foundation model or eliminated its dependence on model partners. It means the upstart has another place to compete: the industry-specific model layer, alongside the product, brand, and go-to-market.

These are not interchangeable companies. A contracts tool, a platform for a large firm, and a practice-management system can serve different buyers and different parts of the day. But each successful wedge puts its owner in a position to ask for more of the workflow.

Then there is Clio. It starts on the other side of the problem. Matter management. Client information. Time tracking. Billing. The operational record of the firm.

Clio’s October 2025 announcement of Clio Work tied legal intelligence to matter and practice data. That shows why the incumbent’s dataset matters. My bet on the next move is a separate AI product that uses that context without inheriting all the old commercial rules. Or an agent ecosystem that pays Clio for access to those rails. That is the opportunity I see, not a claim that Clio has already done either.

The frontier labs can also supply models to the upstarts. An incumbent can buy or partner with a specialist. These buckets compete for the customer while doing business with each other.
That makes the race more interesting, not less.

Here is what I am watching.

Not which company has the best answer to a legal question in a demo. Where does the lawyer go when a new matter arrives? Where does the work get assigned? Where does the approved document live? What system sends the bill?

If the lab owns the place lawyers do the work and everything else becomes a connected tool, the lab has the leverage.

If the AI native owns the workflow, the context, and eventually the record, the upstart has the leverage.

If Clio turns its data into a better standalone AI product, or the rails an ecosystem of agents pays to use, the incumbent has the leverage. The advantage is the data. The old pricing and go-to-market do not have to come with it.

Legal has not produced a single, settled winner.
And the winner for a solo attorney may not be the winner for a global firm.

That is the point. In every vertical, one of these groups becomes the platform of choice. Which one wins depends on the industry, the team, the strategy, and the execution. Not on who has the best model.

And if you are building on someone else’s API, watch their economics as closely as their capabilities.
You need a better product and room to make money selling it.

Someone is going to become the platform your industry runs on.
Do not assume it is the company that owns it today.

That is the question I would be asking in every vertical right now.
And man, I would be planning strategy accordingly…

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

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Low-Tech Markets.
High-Trust Moats.

Every founder is asking some version of the same question right now: if AI makes software easier to build, what stops my customer — or the next startup — from replacing me?

But there’s a big difference between a person who spends all day at a desk and an outfitter who’s running a lodge, coordinating guides, and scouting from a truck. The second customer doesn’t want to become a software builder. They want the business to run.

That doesn’t make low-tech markets immune to AI. It makes the opportunity different. The model labs are teaching customers what’s possible. The vertical platform has to turn that curiosity into actual work: a booking, an invoice, a report, a follow-up.

This week on Verticals, Nic and I sat down with Joel Moreau, co-founder of Mallard Bay, to talk about what that looks like in one of the most overlooked corners of vertical software: outfitters, fishing charters, and adventure lodges.

We dug into the moat beyond the software, onboarding that no longer takes weeks for some operators, the shift from software budgets to operating outcomes, and why a room full of guides eating crawfish might be a better distribution strategy than another polished sales demo.

The throughline wasn’t a better model. It was trust. In low-tech markets, you don’t win by asking customers to become more technical. You win by making the technology useful on their terms.

Watch the episode

This Weeks Vertical Titan:
Joel Moreau (Co-Founder @ Mallard Bay)

Joel grew up on a ranch in West Texas, watching his uncle operate an outfitter. His co-founder ran a duck hunting lodge in Louisiana. Mallard Bay started while the team was at LSU, initially as a marketplace for outdoor trips. In the conversation, Joel describes it as a roughly five-year-old business with a roughly three-year-old SaaS motion.

That background matters. The team isn’t trying to learn this customer from a spreadsheet. Joel says many of their account managers and account executives have outdoor-industry experience themselves. One runs a fishing charter in Louisiana.

Today, Joel says Mallard Bay serves about 600 outfitters across North America. Its platform brings together payments, calendars, resource management, CRM, and the pricing and packaging these businesses need. These aren’t always simple one-hour activities. They’re often multi-day experiences with rooms, meals, guides, boats, vehicles, and groups that all have to line up.

The customer can be running something that looks like a boutique hotel with an activity business layered on top — without ever owning a laptop. That’s why mobile access and purpose-built workflows aren’t cosmetic product choices. They’re the difference between software that works in this market and software that doesn’t.

Mallard Bay’s in-app assistant, Suzy, is becoming another way to get work done: help build listings, understand availability, create reports, and support sales and marketing. Joel says it is some operators’ first real interaction with AI.

His answer to the defensibility question is layered: build the complete operating suite, earn the industry’s trust, and add network benefits that help customers grow. A competitor can match a feature. Matching five years of relationships is a different problem.

Watch the episode

Joel’s 15 Playbooks:

Sixteen founder takeaways drawn from the conversation — an editorial synthesis, not a numbered framework presented by the speakers. Reported results, hypotheses, and experiments are kept distinct.

01. Don’t confuse AI curiosity with a customer building your replacement

Joel is seeing more operators use ChatGPT and Claude for research, graphics, and marketing. He isn’t seeing his customer base vibe-code its own operating systems. His informal estimate is that around 30% of roughly 500 active customers — possibly fewer — are using those tools in a business-beneficial way beyond basic experimentation.

That is a data point from this market, not permission to stop worrying. Joel is already seeing smaller competitors attack narrow slices of the industry. Separate the risk of customer-built tools from the risk of new point solutions. They are not the same threat.

02. Let the model labs educate the market. You make AI useful

Nic’s point: this wave is consumer-first. Operators encounter AI in their personal lives before they buy it for the business. Joel sees the major labs’ advertising warming up customers for an assistant inside the app they already use.

That’s an opening, not a moat on its own. Don’t compete with a general-purpose search box. Become the place an operator goes when they need something done — send an invoice, understand resources, build a listing. Familiarity creates demand. Execution captures it.

03. Attack the onboarding valley of despair first

For Mallard Bay’s first few years, signing a customer wasn’t the same as activating one. Operators still had to enter prices, packages, resources, and photos before they could actually book through the system. That gap consumed time and human help.

Joel says Suzy can use an existing website, a short interview, or uploaded documents to build listings. The reported change: from two to three weeks of onboarding to as little as 15 minutes for some operators, depending on complexity. This isn’t a universal promise. It’s an example of AI removing the work that stands between the sale and the value.

04. Build for the actual resource graph, not a generic booking slot

A multi-day lodge experience can require a room, selected meals, a guide, a blind, a group, and a vehicle. Every piece has its own availability. Every package can have its own price. A clean calendar alone doesn’t solve that.

Joel’s product distinction is the combination of these workflows, accessible from a phone while the operator is out in the field. If you’re building in a niche, map what must be true for the transaction to happen. That operational detail is where a horizontal tool often falls short.

05. Use AI where the permutations break the old product model

A fly-fishing guide in Montana and a one-boat charter in Florida can need different reports for different agencies. Building an endless menu of standard reports isn’t an elegant answer to that complexity.

Joel describes letting the operator explain what they want to extract, then having Suzy return a report they can download or save for future use. The lesson is to turn repeated bespoke requests into a flexible workflow. The operator still needs to check that the output meets the requirements of whoever will receive it.

06. Turn labor-heavy services into a product expansion wedge

Mallard Bay previously offered a sales-engine service staffed by people who called and followed up with an outfitter’s leads. Customers liked having an outsourced sales team. Scaling it meant hiring more people.

Joel says agents now automate much of that follow-up process. This is a more interesting AI opportunity than another writing feature: take a service customers already value and make it less dependent on adding headcount. The outcome to measure is the work completed and the business generated, not the number of messages an agent produces.

07. Change the budget comparison — then prove the result

Joel’s standard-plan example is $399 a month, historically justified by consolidating several separate tools. That’s a software-versus-software comparison.

In one marina conversation, the buyer instead described five full-time people paid $60,000 each to answer phones and asked whether that team could become two. That was a buyer’s question, not a demonstrated staffing reduction. But it shows how the comparison changes when the product does work rather than just organizes it.

Nic argues that willingness to pay can expand with outcomes. I agree with the direction, but the labor-budget thesis has to be proven. Don’t turn a hypothetical reduction or Nic’s illustrative revenue-lift example into a customer result.

08. Don’t make your customer learn token economics

Joel is actively thinking about outcome-based pricing and a credit-based layer on top of subscriptions. He says Mallard Bay is tracking token usage, but hasn’t fully figured out or rolled out that new pricing model.

Operators understand a cut of payments because it’s familiar in the industry. Usage pricing for an AI assistant may be a different conversation. Before passing through a new unit of cost, make the unit of value understandable. The company still has to protect its margins; the customer shouldn’t need to understand a model provider’s bill to understand yours.

09. Stress-test the business at true feature parity

I pushed Joel on a future where anyone can describe the software they want and have AI build it. His answer wasn’t just that the full suite would be hard to reproduce. It was that, even at parity, Mallard Bay has relationships with associations, brands, guides, and operators built over years.

Nic makes a related point: a database or an interface is a weaker foundation for defensibility than it used to be. Ask what survives if a competitor copies your screens. Trust, distribution, a working operating suite, and real customer benefits are better answers than a prettier UI.

10. Give the network a reason to keep growing

Joel describes referrals between outfitters inside Mallard Bay. A tuna charter in South Louisiana can refer a customer who wants to fish for tarpon in Florida to another operator on the platform.

The software is no longer only a way to save time. The network can help generate business. That’s the distinction worth testing: does being part of your platform create value that another standalone tool cannot? Build the benefit, then measure whether customers actually use it.

11. Borrow trust from the people your customer already listens to

Mallard Bay works with industry associations, including the Montana Outfitter and Guides Association, and Joel describes warm introductions through those relationships. He also points to partnerships in Alaska and Michigan.

His advice is straightforward: identify the people and organizations your buyer already trusts, then find an incentive that makes the relationship work for them too. In an offline market, the introduction can matter as much as the pitch. You don’t get to skip earning credibility just because your product is good.

12. Distinguish demand creation from demand capture

Joel says roughly 90% of Mallard Bay’s performance marketing goes through Facebook, where many outfitters already spend time and source customers. The creative speaks directly to them, often using other outfitters.

But form fills aren’t the whole problem. Some operators don’t know a purpose-built solution exists. Mallard Bay is testing connected TV through Vibe, including targeting based on CRM lists. Joel describes starting around $50 a day, with more demanding creative and harder attribution. Treat it as an experiment with a learning goal, not a universally proven acquisition channel.

13. Sometimes the best sales motion is showing up

Joel describes early gatherings in places like Venice, Louisiana: free crawfish, beer, gear, and a short introduction to Mallard Bay. The founders met operators in person, and the operators left knowing who was behind the product.

At trade shows, the team has also shifted some budget from a booth toward after-event parties with other brands. This isn’t a recipe for every market. It’s a reminder that industry familiarity can turn a cold software pitch into a credible conversation. As Joel puts it: “Trust is the value to solve for.”

14. Reward activation with something the customer actually values

Joel’s retention example is gifting an outdoor speaker when an operator reaches a meaningful booking milestone. He contrasts the value of a customer doing $10,000 versus $200,000 in bookings through the platform with the much smaller cost of the gift.

Don’t confuse a gift with product retention. The point is to recognize behavior that deepens the customer’s use of the system, with something that fits their world. Joel also mentions direct mail and an iPad sent with the app as experiments or anecdotes — not scaled, proven programs.

15. Make the supply discoverable — and keep the human taste

Joel wants to get previously hard-to-list outdoor supply into more distribution channels. The difficulty isn’t just finding another marketplace. It’s expressing the operator’s dates, availability, lodging, and unusual pricing in a way a channel can use.

He also sees consumers asking more detailed travel questions and argues for consistent, answer-ready information across websites and social channels. But faster content production doesn’t eliminate editorial judgment. Joel describes a 10–80–10 process: shape the inputs, let AI do much of the work, then apply human taste to the final pass.

His sharpest creative boundary: outdoor customers notice fake imagery. Don’t substitute synthetic ducks or scenery for authentic photography of the experience you’re selling. Our illustrations here are explicitly editorial concepts, not photographs of Mallard Bay, its team, or its customers.

To Wrap It Up…

The low-tech market isn’t a place where AI doesn’t matter. It’s a place where the last mile matters enormously.

The operator may already know ChatGPT exists. They may even use it. But they still need someone to understand the business, turn messy information into a working system, and earn the trust required to act on their behalf.

That’s the opportunity Joel is building toward: a complete operating platform with AI that makes the work easier, industry relationships that make adoption possible, and network benefits that help customers grow.

My takeaway for vertical founders: don’t wait for every customer to become an AI expert. Remove the onboarding work. Solve the weird workflow. Meet the customer where they already are. Prove the outcome. And keep earning the right to do more.

Trust is the value to solve for.

— Luke

Watch the episode

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

Drawing on firsthand experience building and scaling more than 20 vertical SaaS platforms, the guide offers practical insights for founders, operators, and investors looking to maximize the value of embedded payments. Download the free guide. Heading to the Vertical Software Summit, November 4–5? Xplor Pay will be there. Let’s talk about how to get more value from your embedded payments program.

Alright, let’s get to it…

Three things I’ve been chewing on this week:

1. Why the two-week sprint is dead and what’s replacing it

2. How to actually transition out of founder-led sales

3. How to add an agent marketplace into your system of record (and why to do it now)

Let’s dig in.

The old world of software development was a game of telephone.

Customer → CEO’s head → PM → engineer → shipped product.

If you were lucky, there was an actual customer at the start of that chain. More often it was the CEO’s gut, or a sales/cs rep’s war story, or something a board member said last Tuesday.

Ever played telephone as a kid? Think about how much got lost between the first whisper and the last. Now imagine that game played over six weeks, across three Jira tickets, two sprint planning meetings, and a PRD nobody reads to the end.

That’s how most vertical SaaS companies still build product.

The whole Agile religion made sense in 2015. Engineering was the bottleneck. Shipping was slow. The ceremony was worth the overhead. In 2026, engineering is not the bottleneck anymore. Context is. And the sprint does nothing to close the context gap. It just dresses up guesswork in ritual.

What’s actually working now

The best teams I’m seeing have blown this up.

They’ve handed the first draft of the product to the person who actually knows the customer.

Head of CS owns the industry-specific module. She’s on calls every day. She knows which workflows matter. She prototypes the first working version herself using modern AI build tools. Not a Figma mock. A working thing.

A sales leader owns the CRM module. He knows every field that matters and every one that doesn’t. He prototypes it.

Head of ops owns the back-office. Head of implementations owns onboarding. Each one ships a working first draft of the thing they live in every day.

I know “vibe coding” is the term that caught on, but if you’re a vertical SaaS CEO saying that to your board or your customers, you sound unserious. These leaders aren’t vibing. They’re prototyping a solution to a problem they understand better than anyone in the building. It feels more like operator engineering than vibe coding.

What engineers actually do now

Engineering becomes the industrialization layer. They take the operator-built prototype and make it production-grade, secure, scalable, multi-tenant, and commercially viable for 4,000 customers instead of 4.

The prototype replaces the PRD. It’s a working spec. It answers 90% of the questions a traditional PRD leaves open.

Comp follows responsibility. Engineers get paid on uptime, bug count, scalability, infra cost per customer. Domain leaders get paid on adoption and outcomes inside their function. Nobody gets paid on story points. Nobody gets paid on hitting a sprint commitment that was made two weeks before anyone understood the problem.

You know how many engineers are probably coding their sprint committment in a few hours and then working on side projects to fill the day? At non ai-native companies, it’s a lot. That should scare you…

The bar for your leaders

Here’s the uncomfortable part. Every leader on your exec team now needs to be able to prototype the system they run.

Not maintain it. Prototype it.

If your head of CS can’t put together a working prototype of her module in a weekend, you’ve hired the wrong head of CS for 2026. If your head of sales can’t prototype the feature he continues to hear about on sales calls that the customers actually want to use, he can’t explain it clearly enough for anyone else to build it either. If your CEO can’t prototype the dashboard enterprise customers keep telling him about, the context is still trapped in his head and the telephone game starts all over again.

The prototype is the point. It’s how you kill the telephone game. The leader builds a basic working version of what they actually want (even if its crappy its fine). Engineering takes that and makes it real.

The two-week sprint is dead. The telephone game is dead. What’s replacing them is faster, closer to the customer, and honestly a lot more fun.

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Transitioning From Founder-Led Sales to a Real GTM Machine

Founder-led sales works until it doesn’t.

For most of us, it breaks somewhere between $2M and $5M in ARR.

You’re the bottleneck on every deal, every pricing exception, every renewal. Pipeline that used to feel abundant starts feeling fragile.

Here’s the thing: if you’re reading this and nodding, you’ve already done the hard part. You know at least one channel that’s working. Maybe it’s cold outbound to a specific persona. Maybe it’s conferences. Maybe it’s warm intros from your advisor network. Maybe it’s inbound from your Twitter presence.

You don’t need to go find your motion. You already found it. Now you need to scale it.

Here’s the step-by-step.

Step 1: Pick your ONE channel and ignore everything else.

Not two. Not three. One.

The temptation at this stage is to say “we’re going to do outbound AND inbound AND partnerships AND events.” Don’t. You don’t have the bandwidth or the data to run multiple channels well. Pick the one with the clearest signal and double down.

Everything else goes on the “later” list.

Step 2: Document EVERYTHING. This is the single most important thing you’ll do all year.

The founder who skips this step is the founder who hires reps, watches them fail, blames the reps, and starts over 12 months later.

What to document:

• Every stage of your sales cycle. First touch to closed-won. Name each stage. Define what moves a deal from one stage to the next.

• The pitch, verbatim. Record 20 of your own calls. Transcribe them. Pull out the exact words you use. The three objections you hear every time and exactly how you handle each one. The five questions prospects ask when they’re close to buying.

• The demo script. Not an outline. The actual script. What you say in the first 30 seconds. What you click first. Which feature you lead with for which persona. Where you pause for questions.

• A recorded demo video. You, giving the demo, start to finish. This becomes the gold standard every rep is measured against.

• A screen recording of your actual workflow. Hit record, do a day of calls, let your future reps watch it. What you click, where you go, how you take notes, how you follow up, how you handle the no-show.

• The pricing conversation. The exact words. Where you anchor, when you discount, what you refuse to concede.

• The follow-up cadence. Email templates. Timing. Subject lines that get opens. Subject lines that die.

This takes two to three weeks of real work. Do it anyway. The founder who refuses to document is the founder who stays trapped in the sales seat forever.

Step 3: Hire in pairs. Always. Two AEs, not one.

Do NOT hire a head of sales yet. Do NOT hire a player-coach yet. You are the player-coach. This is your job for the next 12 to 18 months.

Hire two AEs. Not one. One rep always fails. If you hire one and they wash out, you have no idea what happened. Was it the rep? The playbook? The ICP? The comp plan? You can’t tell. If you hire two running the same playbook and one succeeds, you’ve got your answer.

If you can only afford one, wait until you can afford two.

Your job the moment those two AEs start: onboard them against the playbook you built in Step 2, run their deal reviews, listen to their calls, close with them when needed. You are their manager. That’s the job.

Step 4: Iterate the playbook weekly, not quarterly.

The playbook you documented in Step 2 is version 1.0. Every week, update it based on what your reps are hearing. New objection? Add it. New way of framing the problem that landed well? Add it. Something stopped working? Delete it.

The playbook is a living document. The founder who writes it once and files it away gets stale in 90 days.

Step 5: Only hire a head of sales once you have two AEs hitting quota. And then slowly turn the dial.

This is the step everyone screws up. Two different ways.

First screwup: hiring the head of sales too early. Do not hire a head of sales to figure out the motion. Do not hire one to “build the team.” Do not hire one because your board asked when you’re hiring one. A head of sales cannot build a system that doesn’t exist yet. 95% of sales leaders have spent their careers scaling systems other people built. Hire them before the system is proven and they’ll spend their first year building something you’ll rebuild in year two.

Two AEs hitting quota is the signal. Not before.

Second screwup: once the head of sales is in the seat, letting them go on a hiring spree. Any head of sales (or CEO) who says “let’s hire 20 AEs now” is in for a rude awakening. I did it. It doesn’t work. Painful mistake.

This is a marathon, not a sprint. The right move is to slowly turn the dial. Add two AEs. Make sure the economics still work. Add two more. Check again. If pipeline velocity drops, if win rates slip, if ramp time stretches, pause. The channel that supported two reps may not support ten. The channel that supported ten may collapse at twenty. The only way to find out is to add capacity gradually and watch the numbers.

The right head of sales understands this. The wrong one wants to build an empire in six months.

Step 6: While the head of sales scales channel one, YOU go find channel two.

Here’s the mistake that haunts founders. You scale your first channel. You hit the plan. You exhale. You start working on product strategy, or fundraising, or your next hire.

Then one day, 18 months later, the channel that got you here stops working.

Channels change. What works today usually doesn’t work forever. Outbound email that printed money in 2022 was dead by 2024. SEO plays that minted companies in 2019 got crushed by AI overviews in 2025. Conference pipelines dry up when your category gets crowded. Warm intro networks saturate.

The moment you have a head of sales scaling your first channel, your job is to go hunt for the second one. Not to delegate it. Not to put it on next quarter’s roadmap. To actually go do it. You, or someone you deeply trust, should always be exploring the next channel.

Test outbound in a new segment. Test a partnership motion with an adjacent software company. Test paid acquisition. Test a conference you’ve never been to. Test an industry influencer play. Document what you learn.

You don’t want to wake up one day with 20 reps and a channel that no longer works. That’s an existential threat. The founders who survive channel decay are the ones who were already hunting for the next one 12 months before they needed it.

The common theme

You already found what works.
The job now isn’t discovery.
It’s documentation, duplication, and discipline.

Document everything. Hire in pairs. Stay in the sales seat until two AEs are hitting quota. Then and only then bring in the head of sales who scales it, slowly, one dial-turn at a time. And the whole time they’re scaling channel one, you’re out hunting for channel two.

Founder-led sales isn’t something to escape as fast as possible.
It’s the raw material you use to build the machine that eventually replaces you.

Adding an Agent Marketplace Into Your System of Record (And Why to Do It Now)

If you run an established vertical SaaS, you’re the system of record for your industry. The data lives in you. The workflows run through you. The seat licenses have worked.

That’s not the question.

The question is whether you still hold that position in 2030.

Because here’s what’s happening. AI agents are showing up in every vertical, and every one of them needs context to be useful. That context lives inside your platform.

Right now, whether you know it or not, third parties are starting to scrape it through your API, build agents outside your walls, and charge your customers directly. Every one of those agents is quietly accumulating the customer relationship on top of your database.

Do nothing for three years and here’s the range of outcomes.

Best case: you wake up as the Oracle underneath someone else’s workflow layer. Still a real business. No longer the center of gravity.

Worst case: someone slowly makes you irrelevant as they harvest your data for years until you’re no longer needed at all. The agents become the system of record. You become the legacy headless database nobody opens anymore.

Neither one is a future worth sleepwalking into.

The move is to open the platform and let agents build inside your walls, with your blessing, under your governance, running on your rails. You stay the system of record. You add a transactional marketplace on top. The moat gets deeper, not shallower.

Here’s the step-by-step.

Step 1: Study app stores like a religion.

Before you build anything, go to school on the companies that got this right.

Apple’s App Store. Google Play. Shopify. Salesforce AppExchange. HubSpot. Atlassian Marketplace. Chrome Web Store. Figma. Even the failed ones like BlackBerry World and Windows Phone Store, because the failures teach you more than the wins.

Understand what worked. Understand what didn’t. Understand the rev share decisions, the submission processes, the discovery mechanics, the quality controls, the developer relations motions, the pricing models, the dispute processes.

Then replicate the parts that fit your vertical. This is a solved problem at the pattern level. You don’t need to reinvent anything. You need to adapt proven playbooks to your industry.

Any exec team that tries to build a marketplace without first spending a month studying every app store that exists is going to make expensive, obvious mistakes.

Step 2: Build the agent rails.

Nothing happens until you have the infrastructure.

You need:

• Clean APIs/MCPs with real documentation, versioning, and sandbox environments

• A permissions layer so agents only access the data a customer explicitly authorizes

• A metering and billing layer that tracks every agent action and handles rev share automatically

• An audit trail so every agent action is logged, attributable, and reversible

• A security review process so you decide which agents are allowed on the platform

If you launch a marketplace without this infrastructure, one bad agent leaks customer data and your entire marketplace is dead on arrival. The incumbents who nail this become platforms. The ones who don’t become cautionary tales.

Step 3: Find 5-10 companies to launch it with.

Pick developers who already understand your vertical. Give them early access, white-glove support, co-marketing, and a direct line to your product team. Help them succeed.

Why this matters: the first 10 agents on your marketplace define what the marketplace is. If they’re great, you attract great developers. If they’re mediocre, you attract mediocre developers and the marketplace never recovers. The hand-picked phase is how you set the quality bar.

Keep this phase quiet. No press release. No “marketplace is live.” Just a working ecosystem of 10 serious developers shipping real agents to real customers.

Step 4: Require every customer to use YOUR interface to access agents.

This is the step that separates platforms from pipes.

Think about iOS. You don’t install apps on an iPhone by going to a developer’s website. You go to the App Store. The interface is Apple’s. The payment is Apple’s. The discovery is Apple’s. The developer builds the app, Apple controls the entry point. That’s how Apple captured the economics and the customer relationship.

You need the same thing in your vertical.

Build a Claude-esque interface inside your product where customers can discover, install, authorize, and run agents, both the ones you build and the ones third parties build. The user experience is yours. The data access runs through your permissions layer. The billing runs through your rails. The audit trail is yours.

If customers can bypass your interface to use third-party agents on your data, you’ve built a pipe, not a platform. Make the interface so good and the integration so clean that nobody wants to go around it.

Step 5: Do a massive agent app store launch.

Think Steve Jobs on stage announcing the App Store in 2008.

This is not a quiet “we have a marketplace now” blog post. This is the biggest launch moment in your company’s history. You’ve been building the rails. You’ve got 10+ real agents with real customer traction. You’ve got the interface that makes it all work. You’ve got the economics figured out.

Now you tell the world.

A keynote-style launch event in your vertical. Press. Partner announcements. Customer testimonials. A pre-populated marketplace with real agents your customers can turn on tomorrow. Developer sign-ups open. Marketing budget turns on. Sales enabled.

The reason to make it a huge moment: you want every developer in your vertical to know, on the same day, that your platform is where serious agents get built. You want every customer to know their system of record is now also their agent command center. You want every competitor to realize you just redefined the category.

Soft launches lose this moment. Blog posts lose this moment. Big launches own it

To wrap it up

You already have the hard asset. You own the system of record. The question is whether you turn it into a platform or let someone else build the platform on top of you.

Study the companies that got this right. Build the rails. Pilot with a handful of serious developers. Own the interface. Launch like Apple.

The incumbents who make this move in 2026 will look like category-defining platforms in 2030. They’ll be the Shopify of their vertical.

The ones who don’t will spend the back half of the decade watching third parties monetize the data they spent 20 years collecting.

Pick your story.

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What’s Still There If Everyone Has Your Code?

Over the last few weeks I’ve gotten more panic calls from founders than I have in a long time.

They all sound the same: “Luke, the model companies are getting really good. I’m getting a little scared.”

It’s a fair fear. Here’s a story that makes it concrete. The founder of a multi-billion-dollar vertical SaaS company was negotiating to buy a smaller vertical software company in his space. The seller kept dragging it out, pushing for a bit more leverage here and a bit more there. Then a big model release dropped. The buyer sat down over the weekend and coded up a pretty passable version of the target’s app.

He went back to the seller and said, more or less: your customer base is still valuable to us. Your company is worth less now. You could have closed six weeks ago.

That’s where vertical software is right now. Code that used to take years to build can get roughly copied in a weekend. So the real question for every founder in this space is no longer “what did we build?” It’s “what’s still ours when somebody else has the code?”

Nobody I know has thought about that question more than Coyne Lloyd. He was on the founding team at SnapDocs, a company that charged 5–10x what incumbents charged before AI and would have kept that premium even if a competitor had the whole codebase. He’s incubated companies at Fractal, built two embedded companies, and angel invests across the category.

Coyne and I trade notes in private a lot. This was our first public jam session, and we covered a lot: data moats, marketplace dynamics, group purchasing, process power, embedded, AI rollups, selling your data to the labs, and the coming flood of competitors in every vertical.

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This Weeks Vertical Titan:
Coyne Lloyd (Founding Team @ Snapdocs, Layer, Reach)

Coyne is the rare person in vertical software who has seen the category from almost every angle: early operator, incubator, founder, and investor.

SnapDocs: the AI company before AI. Coyne was on the founding team at SnapDocs, a mortgage closing product. The problem sounds simple until you live it. Mortgages keep moving online, but somebody still has to put pen to paper with a notary or attorney. Say you live in New Jersey, your husband is on an oil rig in Alaska, and your lender and title company are both remote. Someone has to get packages and notaries to every location and get the thing signed before the deadline.

SnapDocs started by managing that workflow. In Coyne’s words, it was only “marginally better” than the existing tools. What set it apart is how it was built. Coyne puts it like this: before AI, SnapDocs was a workflow tool that sold per unit of work, ingested the knowledge of knowledge workers, and ultimately replaced those knowledge workers. That’s the pitch half of today’s vertical AI companies are making.

Over time SnapDocs became a marketplace. Notaries and attorneys had their own profiles, and they spent more time on SnapDocs than on any other platform. It also built a data moat: with visibility into almost every notary and attorney in the country, it knew who the bad actors were. That’s why it could sell against legacy competitors at 5x and sometimes 10x their price.

Fractal: underwriting economies of scale. Coyne later became a partner at Fractal, where he incubated and launched vertical software companies. He’s candid that the thesis back then leaned on software’s own kind of economies of scale. If you sell the software that runs a big hospital system, it’s valuable because it has so many screens, buttons, and complex logic that it takes forever to rebuild. Like a GM assembly plant, but in code.

He believes that moat is going away, and he’s been saying so for a while.

Layer and Reach: betting on embedded when VCs said not to. After Fractal, Coyne co-founded Layer Financial, which he describes as the only embedded bookkeeping platform, and Reach (embedreach.com), which offers embedded marketing and AI go-to-market tools: ad buying, AI voice, email and SMS. Vertical software platforms are the customers. Skimmer, the pool-cleaning software company, uses both.

He started around the time ChatGPT launched. A lot of VCs told him he was an idiot: you don’t own the end customer, and once the platform thinks it’s paying you too much, it’ll cut you out. Three years on, the models keep getting better and that keeps not happening. Better models have let Layer and Reach offer more, not less.

Why Coyne matters right now. Most people asking “is my vertical software company defensible?” are guessing. Coyne has an actual answer from a company that commanded a premium for reasons that had nothing to do with code. He applies that lens to everything: incumbents, embedded, rollups, data deals. And he’s honest when he’s been wrong, which in this market is worth a lot.

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Coyne’s 20 Voice AI Hacks

Below are the biggest playbooks Coyne surfaced, plus a few from my side of the table.

1. Defensibility in the Weekend-Clone Era

Run the “stolen codebase” test every quarter

Coyne’s thought exercise is the most useful thing you’ll read this week:

If your competitors had a complete copy of your codebase, but not your data or your network, what would they be worth compared to you?

At SnapDocs the answer was “a lot less.” Even with every line of code, a competitor still would have sold at a big discount to SnapDocs. If your honest answer is “about the same,” your moat was the time it took to build, and that moat is gone.

My addition: don’t run this once. Run it with your exec team every quarter. Assume the model companies let anyone spin up your exact software tomorrow. What’s still valuable?

The move: Put this on next quarter’s exec agenda. List everything you have that survives a full codebase leak. If the list is short, that’s your strategy problem.

2. Lock-in protects your cash flows. A moat wins the next customer. Know which one you have.

I brought up hardware lock-in as a moat. Think Alex from Senson, who has physical point-of-sale hardware on laundry machines. Coyne pushed back, and he’s right.

Hardware lock-in keeps your cash flows. Nobody wants to rip out installed hardware. But it doesn’t build an advantage over time. Tech companies are valued on expected growth. An iPad you bought from China doesn’t help you win the next account.

Lock-in is something Constellation would pay for. If you’re running a business to protect cash flow, that’s fine. If you’re running a venture-backed growth business, it’s not enough.

The move: Sort your defenses into two buckets: “keeps current customers” and “wins new ones.” Most companies are heavy on the first and light on the second.

3. The only question that matters: does everything you have help you win customer #1,001?

Coyne’s example: a camera on sports fields that makes and reviews game tape. On day one, the AI can tag every tackle. Now picture a much smarter model. If one system has seen every game on every field, home and away, it can tell you this quarterback struggles in the rain.

That’s when the hardware becomes a real moat. If a competitor is on 10 fields and you’re on 1,000, then with the same code and the same hardware, you’re worth more to the 1,001st customer.

Coyne’s claim is bold: that dynamic is the only category of value he can see holding up in these verticals. Every vertical has some version of it. Your job is to find yours.

The move: Find the asset that becomes more valuable as models get better. Data that compounds with scale is the target. Software that models can rebuild is not.

4. Start as a tool, then become the marketplace

SnapDocs didn’t launch as a marketplace. It launched as a workflow tool for offices that each managed their own list of notaries and attorneys.

The change came when notaries and attorneys got their own profiles. Those profiles let them work with everyone on the other side of the deal in one place. Suppose a notary spends more time on your platform than any other. A competitor who copies your software has nothing to offer them.

The move: Find the other side of your customer’s workflow: contractors, vendors, providers, freelancers. Give them their own identity and reason to log in. That’s how a tool turns into a network.

5. Build your data moat on rare disasters

My favorite story of the episode. Every so often, a notary agrees to a $200 signing, gets the signed documents, and then says: “I don’t remember saying $200. I think it was $1,000.” The deal has to close on time or it’s dead. Now everyone is scrambling over who pays.

These blowups are rare. That’s exactly why scale matters. If you can’t see nearly every notary and attorney in the country, you can’t know who the bad actors are. SnapDocs could, so it knew.

Avoiding disasters is a moat that gets stronger with every transaction, and a new entrant can’t recreate it.

The move: List the rare, expensive failures in your vertical: fraud, no-shows, holdups, compliance misses. Ask if you have enough volume to predict them. If so, make that the product.

6. Your price premium should come from the network, not features

SnapDocs charged 5–10x what legacy competitors charged. Coyne’s view is that it held because most of the value came from network effects, marketplace dynamics, and data, none of which a competitor gets by copying the code.

This is important because a lot of founders think pricing power comes from a better UI. In a world where a better UI takes a weekend, it doesn’t.

The move: If your pricing rests on features, plan for it to compress. If it rests on network or data, you’ve earned it.

7. Complexity isn’t a moat anymore. Regulation and a sign-off requirement still are, for now.

Coyne’s line on complexity is brutal: a person built that complex thing. That person had a bad night’s sleep and got COVID three months ago. An agent with none of those problems can probably work through the complexity.

Same goes for “thorny integrations.” Some say the model companies will never do the integration grind. Coyne thinks integrations will just keep getting easier. My view: they’ve gotten much easier where both sides cooperate, mostly in horizontal developer tools and MCPs. In most verticals that hasn’t happened yet. Plenty of PE-backed CEOs are going into “lockdown mode.” But computer-use agents are getting good, so I don’t know how long lockdown holds.

What still holds: regulation and a required human sign-off. If an accountant or lawyer has to bless the output, someone has to be the responsible party. That buys time. Coyne’s caveat is that culture and the law could change too.

The move: Stop listing “complex product” or “hard integrations” as moats in your board deck. Replace them with something that still holds when an agent has unlimited patience.

8. Group purchasing is an economy of scale that lasts

We talked about the “digital franchise” model: wrapping your system of record with services, supplies, and real-world economics. Coyne added a sharper point. Selling the goods the work requires gives you an economy of scale that actually lasts, because you can become the biggest buyer.

The example is Moxie (Coyne is an investor, and Dan from Moxie has been on the show). Coyne believes Moxie is the largest purchaser of injectables in the country. That buying rate can’t come from anywhere else, and it cancels out what Moxie charges its customers. In Coyne’s words: Moxie is a company where you could copy the codebase and it doesn’t matter.

Same logic as Slice with physical supplies. The tradeoff: physical goods get a much lower multiple than software. It still adds real defensibility, and I think people underrate it.

The move: Find the biggest recurring input your customers buy. Can you pool their purchasing so the savings beat your price?

9. Process power is the AI-era moat to watch

Coyne brought up 7 Powers (his signed copy from the Acquired guys is on his shelf). We’ve covered several of those powers already: scale, network effects, brand. The one he’s most excited about is process power: like TSMC, where people just don’t know how to make the chips.

As AI starts doing the work, Coyne expects a version of process power to appear in different verticals: your AI knows how to do things other AI doesn’t.

His test: in which fields is someone with 20 years of experience much better than someone with 2? That’s where process power lives. His long-running belief is that a vertical platform should capture the value of everyone in the industry who doesn’t wear boots or gloves, and maybe one day, when robots arrive, the boots-and-gloves work too.

The move: List the jobs in your vertical where 20 years beats 2 years. Those are where your AI should learn first.

10. The threat isn’t OpenAI. It’s the swarm.

Most founders assume the model companies are coming for their vertical. In most verticals, I don’t think so. They’re too small, or the labs would rather sell picks and shovels to whoever goes after them. The real risk is white-collar, screen-heavy workflows where you’re “just a database” or “just a workflow tool.”

Coyne said the real pressure is somewhere else. A friend who runs a venture incubator told him vertical incubation is much harder than it used to be. Seven years ago, you could find a dusty incumbent Constellation had bought, build something cleaner with a mobile app, and take the market. Now, building is so easy that there might be three companies in each of the next four YC batches, all with $10M raised, all bidding you down.

And it might not even be venture-backed. It could be a 23-year-old whose mom or dad works in the industry and charges 90% less.

The move: Plan your competition as “a dozen well-funded clones plus a bootstrapped kid,” not “OpenAI.” Then go back to Playbook #1.

A few rapid fire hot takes from Coyne to close us out

Most bullish public vSaaS (5-year view): Toast. Coyne is a shareholder. There’s a lot to do for restaurants (Owner has shown that), and Toast “does not seem to be asleep at the wheel.”

Most bearish: ServiceTitan, a long Toast/short ServiceTitan bet “with a very low degree of confidence.” His reasons: lots of smart people he knows are coming after it, its customers vary a lot and skew upmarket in ways that make it vulnerable, and he thinks much of its best talent has left or lost its edge. (ServiceTitan folks, he invited you to prove him wrong.)

Most bullish private, growth-stage: Vesta (Coyne is an investor). Mortgage is huge, with a lot of work that AI will eventually do. Technically Series B, but heavily funded by big names, and up against what Coyne called basically a monopoly last valued around $20B at acquisition.

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See you Sunday,

— Luke

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A roadmap to owning the outcome

For most of the history of vertical SaaS, we’ve built software that helps people do their jobs better. We digitized workflows, replaced spreadsheets, reduced clicks, automated repetitive tasks, and pulled fragmented processes into one system.

Those things created enormous value.
But I think AI is pushing us toward a different question:

What happens when the software doesn’t just help customers do the work, but starts taking responsibility for getting some of it done?

A few weeks ago, I had a conversation with Mark Passifione and Daniel Burton from Xplor about where vertical SaaS is headed with AI. I kept coming back to how differently we may need to think about product value. For years, we’ve asked questions like: How do we save customers time? How do we make the product easier to use? How do we increase efficiency?

Those questions still matter. But what if we could start asking how our platforms help customers grow revenue, improve profitability, or produce a better business outcome? That’s a very different product conversation.

I don’t think the answer is to automate every workflow we can find. The more important question is which outcomes are worth taking on, and more specifically, how do you actually get there.

If I were running a vertical SaaS company today, I’d be looking for the intersection of four things: an outcome that materially improves the customer’s business, a workflow my platform deeply understands, proprietary context that helps me make better decisions, and enough control over the workflow to actually take action.

Here are the steps I’d take to pull this off:

Step #1: Map the Work

The first step toward becoming more agentic isn't choosing an AI model or building an agent. It's understanding the work. Map an important customer process from Point A to Point Z. Then look beyond the screens and features and ask what is happening at every stage.

Look for:

Step #2: Choose the right place to start

Once you’ve mapped the work, resist the urge to automate everything. The strongest starting point usually isn’t the biggest or most complicated process. It’s a clearly defined piece of work where an agent can create meaningful value, the outcome can be measured, and mistakes can be managed.

Bessemer calls this approach progressive delegation: start with a manageable portion of the workflow, keep humans involved where judgment or risk requires it, and expand the agent’s responsibilities as reliability and trust increase.

Look For Work That Is:

Step #3: Build the context before you delegate

An agent is only as useful as the context it has and the actions it can take. A general-purpose AI model may be able to answer questions about an industry. But performing meaningful work inside that industry requires much more: customer data, workflow context, business rules, integrations, permissions, and the ability to interact with the systems where the work actually happens.

This is where Vertical SaaS platforms may have an important advantage. They already sit inside the workflows of a specific industry. They understand how customers operate, what information matters, where processes break down, and what needs to happen next. The opportunity is to turn that context into action.

Understand what the agent needs to do the work successfully:

Step #4: Increase Autonomy as Trust Grows

Moving toward agentic workflows doesn’t mean handing an entire process to AI on day one. Autonomy can increase over time. The right level depends on the work, the risk involved, the quality of the available context, and how reliably the agent performs.

A simple way to think about the progression is:

Crawl | Agent Assists
The agent understands the context and helps determine what should happen next. It might analyze information, surface insights, make recommendations, prepare an action, draft a response.

The human decides and acts.

Walk | Agent Acts
The agent begins executing clearly defined portions of the work. It might complete repetitive tasks, update systems, coordinate defined actions, initiate an approved process, escalate exceptions.

The human provides oversight, approves higher-risk actions, and handles exceptions.

Run | Agent Orchestrates
The agent coordinates multiple actions across a workflow toward a defined outcome. It might interpret intent, determine the next best action, work across multiple systems, execute within defined permissions, adapt based on results, escalate when confidence, authority, or context is insufficient.

The human sets the boundaries and remains accountable for the process.

Step #5: Connect Commerce to the Outcome

Many business workflows eventually reach a moment when something has to be purchased, paid, refunded, billed, renewed, or otherwise transacted. If an agent is responsible for helping complete the workflow, stopping at the transaction creates a new handoff.

That’s where agentic commerce enters the picture. Agentic Commerce Isn’t “AI Making Payments” It’s the ability for commerce to become one of the actions available to an agent as it works toward an authorized outcome.

Think about the progression:

And once the platform can act, the economics get interesting. If you’re no longer just selling a tool someone uses to perform a task, but actually helping produce the outcome, what customers are paying you for starts to change. In some cases, the opportunity may extend beyond traditional software budgets into work historically performed through services or labor.

I think that’s the bigger strategic shift. The next great vertical SaaS companies may not be defined only by the workflows they digitize or the features they build. They may increasingly be defined by the outcomes their platforms can reliably deliver.

So here’s the question I’d be asking:

If your platform could reliably deliver one customer outcome instead of simply giving customers the tools to achieve it, which one would fundamentally change the value of your business?

Want to go deeper? Watch my discussion with Mark and Daniel and get Xplor’s practical roadmap for moving from vertical SaaS to agentic.

Watch the Discussion + Get the Practical Roadmap →

How to understand the value of your company

My friends at L40 dropped a great report on software valuations, and it has some incredible data in it I thought would be valuable to you all.

Vertical Software IS the Anti-Hype Bet

Most founders want to work on something their friends think is cool.

Nick Evans picked lab software.

Before QBench, Evans was the 5th employee at Reverb, the marketplace for guitars and gear that eventually sold to Etsy. Reverb was not a category any generalist founder would have picked. Hobbyists trading used amps. Niche inventory. A buyer base most tech people did not understand and did not want to. But the workflow was deep, the community was real, and the business compounded into a meaningful outcome inside a niche the broader tech world mostly dismissed.

That experience taught him something that defines every real vertical software founder. The work is not glamorous. The work is the opposite of glamorous. And the people who thrive in it are the ones who stop needing it to be.

So when Evans looked at where to spend the next decade of his career, he did not chase AI infrastructure or developer tools or whatever was trending that quarter. He went to LIMS. Laboratory Information Management Systems. Cloud software for contract testing labs running cannabis panels, environmental samples, food safety workflows, and clinical research. A category that gets almost no coverage, almost no venture attention, and almost no competition from generalist founders who want to be on a podcast.

That is the trade.

Being a vertical software founder means spending years learning a workflow nobody at your dinner table cares about. It means getting excited about sample chain of custody and instrument integrations and regulatory audit trails. It means your TAM slide will never impress a generalist VC, your category will never trend on X, and your conference will have fluorescent lighting and a buffet instead of a rooftop in Cabo. The upside is real, but the day to day is unglamorous in a way most founders are not actually built for.

That is why the category stays open. Not because the opportunity is hidden. Because the work filters out everyone who needs external validation to keep going.

The lesson for anyone thinking about building in vertical software is worth sitting with.

The anti-hype bet is a real bet. It is not a guaranteed one. Plenty of boring verticals stay boring because the economics do not work or the buyer will not pay. But the founders who win in vertical software almost always share one trait. They stopped caring whether the category sounded impressive and started caring whether the workflow mattered to the customer.

Evans has done this twice now. Reverb inside a hobbyist marketplace nobody respected. QBench inside a lab software category nobody notices.

The pattern is not the vertical. The pattern is the willingness.

Willingness to go somewhere unsexy. Willingness to learn a workflow most people would find tedious. Willingness to build inside a category that will never make you look cool at a party.

So here is to celebrating the boring. Because it is in the boring where you can compound for years and years, and you may just end up with something quite special.

QBench is an example of that.

///

See you Wednesday,

Luke

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Is the phone your platforms
next territory to own?

If you’re building vertical SaaS, you already know the game: own more of your customer’s workflow, own more of their wallet, own more of their data. That’s how you go from a $200 ACV tool to a $20K platform. That’s how you build a moat the horizontals can’t touch.

So here’s the uncomfortable question for every vertical SaaS founder reading this: why don’t you own the phone yet?

In most of the verticals we care about — healthcare, home services, auto, dental, legal, trades, death care, financial services, fitness — the phone call is still the #1 revenue surface. It’s how new customers come in. It’s how existing ones get served. It’s where quotes get given, appointments get set, bills get paid, and complaints get handled. The call is the business.

And yet most vertical SaaS platforms have ceded that entire surface to RingCentral, Nextiva, AT&T, Vonage — carriers who are now using the voice AI wave as a pretext to push their products into your accounts, with your customer’s data flowing into their models.

This week on Verticals, we sat down with Mike Sharp, Founder & CEO of Dialstack, to talk about why the phone stack is the next territory every vSaaS founder should be annexing, and the specific playbook for doing it without becoming a regulated phone company yourself.

Mike has done the full lap — he built the carrier side, then operated on the vSaaS side, then built the infrastructure he wished existed when he was the operator. If you’re running a vertical SaaS platform and the phone isn’t on your roadmap, this edition is for you.

Watch this weeks episode

This Weeks Vertical Titan:
Mike Sharp (CEO @ Dialstack)

Mike has spent ~20 years at the exact seam where this issue lives: telco meets vertical SaaS.

He co-founded Jive Communications in the mid-2000s, selling VoIP directly to SMBs alongside the early innings of RingCentral and Dialpad. He grew Jive to ~$100M ARR and sold it to LogMeIn (now GoTo), continuing to grow it inside. That gave him a decade-plus of operator reps on how dependent small businesses are on the phone — and how rich the data running through it actually is.

Then he did the thing that makes Dialstack sharp: he spent a few years leading a vertical SaaS healthcare rollup — ~$200M in revenue across 17 brands. And in every single one of those portfolio companies, the same thing kept happening — the platform wanted the phone system. Not a voice AI agent duct-taped on top. The whole thing: the lines, the hardware, the extensions, the 911 stack, the data, the workflow.

The options for actually owning it were brutal:

1. Build it yourself → become a regulated phone company, chase 42,000 US tax and fee jurisdictions, handle FCC filings, 911 compliance, carrier interconnects, and an infinite corner-case roadmap your investors do not want to hear about.

2. Refer out to RingCentral → watch them price into your account, upsell your customer, and eventually launch their voice AI directly against you.

Neither option expands your platform. Both shrink it.

Dialstack is the third door. It’s the embedded phone stack for vertical SaaS — telephony, hardware, 911, regulatory, number porting, and voice AI orchestration — all packaged behind your brand, inside your product. The analogy Mike uses is the one that lands: Stripe did this for payments. Dialstack is doing it for voice.

The reason it matters right now is timing. Carriers are no longer sleepy incumbents — they are actively building voice AI and selling it directly into the SMBs your platform serves. Every quarter a vertical SaaS founder waits to own this surface, more of their customer’s data, revenue, and workflow gets quietly annexed by a carrier that will eventually compete with them head-on.

Mike’s whole thesis for vSaaS founders is simple: the phone is the most valuable unowned territory inside your customer’s business. Go take it.

Mike’s 20 Voice AI Hacks

These are truly a masterclass, carve out the time to read them and you won’t regret it…

1. Treat the phone stack as a territory, not a feature

Every vertical SaaS has a “surface area map” of their customer — the workflows they own and the ones they don’t. The phone is almost always the biggest unowned surface, and in call-heavy verticals (healthcare, home services, auto, dental, trades, legal, death care), it’s often the #1 revenue-generating workflow in the entire business. If you don’t own it, someone else is monetizing your customer’s most important channel. The frame isn’t “should I add voice AI?” The frame is: “this is my customer’s primary revenue surface — why don’t I own it yet?”

2. Voice AI without owning the phone system = half pregnant

This is the sharpest line Mike delivered, and vSaaS founders need to internalize it. If you launch a voice AI agent but your customer’s phone system still lives at RingCentral, you only get the calls that get forwarded to you. Everything else — direct dials, outbound calls, the receptionist’s cell, the extension that bypasses your agent — is invisible to your platform. Your reporting is fragmented, your data set is partial, and your customer experience is split between two vendors. Half the calls = half the data = half the moat.

3. If you don’t own the phone system, you’ll be competing with the phone company inside a year

Read the RingCentral, AT&T, and Vonage investor decks and earnings calls. Voice AI is their #1 growth narrative. They are not sitting still. Every vSaaS platform that is “integrated with” or “forwarding to” a traditional carrier is, in Mike’s words, “just creating pipeline for RingCentral.” The incumbent carriers will use your integration to learn your customer, then launch their own vertical voice AI into that account. Own the stack, or feed the people who will replace you.

4. Don’t become a phone company — rent the regulated layer

Owning the phone experience ≠ owning the regulatory burden. There are 42,000 US tax and fee jurisdictions that want a cut of every phone call. There’s 911 compliance, FCC filings, state PUC licensing, carrier interconnects, and an endless tail of corner cases. Your investors don’t care that you built it. Your customers don’t care that you built it. Rent that layer, brand it as yours, keep the data, keep the revenue, skip the regulated misery.

5. The real moat is “curation of the experience,” not the model

Mike was blunt: “There’s nothing magic about voice AI.” The moat for a vSaaS platform isn’t the LLM — your customers can pick Retell, ElevenLabs, VAPI, or Gemini Live and it won’t change your defensibility. The moat is that you own the full voice surface, you have the customer context, you have the system of record, and you curate the entire experience inside your vertical. The carriers can match the model. They cannot match the vertical workflow depth.

6. There is a Maslow’s Hierarchy of voice AI use cases — start at the bottom

Mike’s stack, from table-stakes to frontier, is a useful roadmap for every vSaaS deciding where to deploy voice AI first:

• Level 1 — “Better voicemail.” Voice AI picks up what humans don’t. Lowest risk, highest adoption. Start here.

• Level 2 — Virtual receptionist / smart routing. Agent triages the call and routes to the right human.

• Level 3 — Transactional task completion. Agent pays bills, answers account questions, triggers texts. Shakiest today — this is where consumer frustration lives.

• Level 4 — Augmentation (the “embedded Gong” use case). Agent listens to human-to-human calls and surfaces answers, SKUs, documentation, and compliance prompts in real time. This is the use case Mike is most excited about, and the one most vSaaS founders are sleeping on.

  

7. Augmentation beats replacement in SMB verticals

In SMB verticals, direct labor replacement is rarely the right wedge. The receptionist is often the owner’s neighbor, cousin, or spouse. Owners don’t want to fire her — they want her to be better at her job. Deploy voice AI as an augmentation layer first: screen pops, call logging, inventory callouts (”that furnace is on 6-week backorder — here are two alternatives”), real-time compliance prompts. You get adoption, you get data, you earn the right to automate more over time.

8. “Who is this voice AI actually serving?” is the eval question that matters

The consumer sniff test is instant. If voice AI makes the caller’s life faster and easier (IRS verification, appointment confirmation, drive-thru ordering), they love it. If it’s obviously deployed to save the business money at the customer’s expense, they hate it and start pounding “zero, zero, human, human.” Build evals around sentiment, call duration, and repetition count, but anchor every deployment decision in: who does this actually serve?

9. Match voice AI to the emotional weight of the call

Not every vertical should be automated the same way. Mike’s example: in death care (funeral homes, crematoriums), every call needs a human — people are grieving. Scheduling a dental cleaning? Automate it. Tax filing confirmation? Automate it. Grief, major medical news, legal defense? Keep the human. Mapping the emotional weight of each call type inside your vertical is table-stakes product work for any vSaaS deploying voice.

10. Your customer’s data is your Jerry Jones oil field — don’t lease it

Luke’s line in the episode is the one every vSaaS founder should tattoo somewhere: “Vertical SaaS founders are sitting on Jerry Jones oil fields and most of them don’t know it yet.” The call transcripts, the system-of-record data, the vertical-specific context — this is the asset that makes your voice AI 30 points better than a generic model. Don’t pipe that data into RingCentral. Don’t pipe it into a third-party voice vendor with conflicting incentives. Keep it, own it, train on it.

11. The same base model + vertical-specific harness = ~30-point performance swing

The benchmark data is clear: take any frontier model and wrap it in a vertical-specific context layer (your system-of-record data + domain content), and you get a ~30% performance improvement over the same model deployed generically. The implication for vSaaS founders is huge — you don’t need to train your own model to win. You need to own the data and the deployment context. That is a game vertical SaaS platforms are structurally positioned to win and horizontals are not.

12. Use voice as a product expansion lever, not a feature

Most vSaaS founders Mike talks to haven’t deployed voice yet, especially at the $5M–$20M ARR band. The ones who have are seeing real revenue lift, not just stickiness. Replacing a $300/month RingCentral line with your own branded phone stack at a competitive price is a new revenue line, a new margin pool, and a new reason your platform is impossible to rip out. Voice is not one product — it can be 5 modules (telephony, voice AI receptionist, ambient scribe, embedded copilot, outbound campaigns). Price it that way.

13. The compliance tailwind is coming — be the vendor that handles it for your customer

TCPA, state-level consumer protection, FCC enforcement on robocalls — all of this is getting more aggressive, not less. Your SMB customer has zero chance of navigating it alone. If your platform owns the phone stack, compliance becomes a wedge, not a liability. You handle consent, opt-outs, recording disclosures, and jurisdictional nuance. They sleep at night. You charge for it.

14. The surprise frontier: Big B-to-Big B voice

Everyone is chasing SMB and consumer voice AI. Mike’s early signal: massive B2B voice flows (insurer-to-insurer, distributor-to-distributor, broker-to-carrier) are still shockingly analog, with billions of dollars moving across phone calls that look like 1998. For vSaaS founders serving industries with heavy inter-company phone workflows (insurance, logistics, wholesale distribution, benefits, healthcare payer-provider), this is an underpriced wedge.

15. The timing argument: three years ago voice was impossible. Today it’s inevitable.

Mike doesn’t blame any vSaaS founder for not pulling voice in 3–5 years ago. It was too hard, too regulated, too crappy. All three of those things have changed simultaneously:

• Voice AI is good enough for a growing set of use cases (and improving monthly).

• Embedded voice infrastructure now exists (Dialstack and peers abstract the regulated layer).

• The economics have inverted — a voice AI call is ~40¢, a human call is $7–$12.

The cost-benefit math on owning the phone has flipped. Three years ago, “don’t own it” was the right call for most vSaaS founders. Today, it’s negligence.

Closing

If you’re a vertical SaaS founder, write down every workflow inside your customer’s business. Circle the ones you own. Draw a square around the phone stack.

That square is almost certainly the biggest unowned, most data-rich, highest-revenue surface left on the board — and the carriers are coming for it with voice AI as the wedge. You can own it, or you can watch RingCentral own your customer.

Go watch the full episode with Mike Sharp on VERTICALS below:

Watch the episode

Reply to this email with what you’re building. I read every one.

— Luke

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

Luke Sophinos, Mark Passifione, and Daniel Burton explore what it takes to move from vertical SaaS to agentic, where software begins doing more of the work and driving outcomes. Watch the discussion on demand and get the practical roadmap for identifying where to start, proving value, and expanding from there.

Watch the Discussion + Get the Roadmap →

Every vertical SaaS founder is sitting on a gold mine and most of them do not know it yet.

Here is the setup. You spent years building software for one industry.

Ugly workflow.
Boring category.
Real customers.
Real revenue.
The kind of company nobody in Silicon Valley (other than me probably :-)) writes about until it exits for a billion dollars.

Here is the part most founders miss: for nearly all of of you, the model companies have no interest in building an industry-specific solution for your market.

OpenAI is not going to build a in dentistry. Anthropicis not going to launch a product for Trade Schools. These are tiny markets to them. Fragmented. Regulated. Hard to sell into. The economics do not work for a horizontal company that would rather sell you tokens to you than hire a specialty sales team.

That is the entire opening.

The model companies built something extraordinary and priced it per token. Your job is to take that raw horsepower and wrap it in the one thing they will never build for your industry: scaffolding that turns a general model into something your customers cannot live without.

Findustry AI ran a benchmark on what this looks like in practice. GPT-5.5 tested on chargeback responses. Alone, the model scored 55%. Wrapped in Findustry’s vertical scaffolding — industry data, workflow tools, expert rules, permissions, customer context — it scored 96.7%. Same model. 41.7 points of difference. Not from a better model. From better scaffolding.

That is not a payments story. That is the pattern that is starting to show up across every serious vertical AI company.

OpenEvidence beats the next best frontier model on a physician-graded benchmark by 12.1 points. Over 350,000 clinicians use it.

Hippocratic AI took clinical accuracy from 80% to 99.38% across four model versions. Evaluated by 6,234 licensed clinicians on 307,000 real patient calls.

Harvey beat the human lawyer baseline on Document Q&A 94.8% to 70.1% in a third-party benchmark. Beat lawyers. Not GPT. Lawyers.

Abridge cut word error rate on clinical conversations 24% below Google Medical ASR. Google. A $2 trillion company. Losing on medical transcription to a startup because the startup built better scaffolding.

Four verticals. Same pattern. The companies building scaffolding are running away with their categories, and they are doing it on top of the same models available to everyone else.

Here is the part vertical SaaS founders need to hear.

You have every raw material needed to build this scaffolding. Ten years of screens. Ten years of edge cases. Ten years of your best customers teaching your product how their industry actually works. Nobody else in the world has that. Not OpenAI. Not Anthropic. Not any horizontal AI startup with a great demo and $50M in seed funding.

The horsepower is rented. The rest is yours. You just need to build the scaffolding. And the founders who do it first are going to dominate their industries in ways that would have been impossible five years ago.

The scaffolding is what turns a horizontal model into an industry-specific gold mine.

The Findustry image above lays out the five components cleanly. Vertical knowledge. Customer context. Data. Tools and integrations. Permissions and controls. That is what turned a 55% model into a 96.7% product. That is the shape of the scaffolding.

The model companies will never build any of these for your industry. They do not want to. Every dollar you spend on their API is a dollar they get without hiring a single salesperson who understands your buyers. That trade is fine for them. It is a massive opening for you.

Here is how each layer plays out in real vertical AI companies right now.

Vertical knowledge and data. OpenEvidence locked up exclusive multi-year deals with NEJM, JAMA, Nature, NCCN, and Cochrane. Trained on 35 million peer-reviewed papers a general model cannot legally touch. Your version is the industry data your customers generate every day that nobody outside your product ever sees.

Customer context. Hippocratic has 6,234 nurses on retainer grading every weird patient interaction their AI has ever had. Every mistake feeds back. That is how they went from 80% accuracy to 99.38%. Your version is sitting in your support tickets, escalation logs, and the heads of your best CSMs.

Tools and integrations. A model that can draft is a copilot. A model that can execute is an agent. The model companies will never wire tools into your customer’s stack. That is your job. Every workflow your product already runs is a callable tool. Harvey turned legal workflows into tools that beat human lawyers on four of seven tasks.

Permissions and controls. Compliance rules. Approval thresholds. State regulations. Payer quirks. Denial codes. Your best operators know every one of them. A horizontal AI company has ML engineers who have never worked a day in your industry. That gap does not close with more compute. It only closes with operator judgment encoded into product, and that only exists inside companies like yours.

Nail one layer and you build a nice AI feature. Nail all five and you build a company that owns the category.

The model companies gave you the intelligence. They will not give you the scaffolding. That gap is the entire opportunity.

How to actually build the scaffolding that dominates your industry.

models, pick a use case that demos well, and ship a copilot. Six months later they have a nice feature and no real advantage.

Wrong sequence. Here is the one that actually wins.

One. Pick one job worth owning.
Not a feature. Not a chatbot. One job in your industry that is expensive, painful, frequent, and measurable. The kind of job your customers currently pay people to do. That is where the economics work in your favor. Everything else is a distraction.

Two. Extract what your best operators know.
Sit down with the smartest domain experts in your company. Interview them on the messiest cases they have ever handled. Record it. Write it down. Turn it into structured examples the model can learn from. This is the single most valuable data engineering work you will ever do. Most of it has never been written down anywhere.

Three. Feed the model your industry, not the internet.
The general model already knows the language. It does not know your industry. Give it access to your corpus, your customer history, your workflow logs, your regulatory documents. Nothing else. That is what separates a copilot from a category winner.

Four. Give the model real tools to act.
Every workflow already in your product is a candidate tool. Expose them one at a time. Start narrow. Prove reliability. Then expand. The moment your model can actually complete a job instead of just describing it, you have a different kind of company.

Five. Keep humans in the loop until trust is earned.
Progressive autonomy beats maximum autonomy every time. A model that can do one job with approval is a feature. A model that can do that same job at scale without approval is a business. The path between those two is measured in months of proving reliability inside real workflows.

Six. Build the eval loop early.
Your best operators become your graders. Real cases become your benchmark. Every failure gets logged and fed back into the system. This is how Findustry proved a 41.7 point lift. This is how OpenEvidence, Hippocratic, Harvey, and Abridge all compound their lead. Without it, you are guessing.

The model is 1% of the work. The scaffolding is 99%. It does not demo well. It does not fundraise well. It is exactly what separates a real vertical AI company from a wrapper on top of ChatGPT.

The good news: your existing product is doing most of the work already. You are not starting from zero. You are turning ten years of built-up context into the one thing the model companies will never build for your vertical.

If this newsletter resonates, the Vertical Software Summit is the room in person.
Every serious acquirer, founder, and investor in vSaaS is coming.
Register today and reserve your spot before it sells out!

Four vertical AI companies. Four different industries. One pattern.

And every one of them built their advantage on top of the same models available to everyone else.

OpenEvidence. Built the data layer nobody else could get. Exclusive content deals with NEJM, JAMA, Nature, NCCN, and Cochrane. Trained a specialized model on 35 million peer-reviewed papers. Scores 82.7% on HealthBench Professional. That is 12.1 points above the next best frontier model. Over 350,000 clinicians use it. OpenAI could not build this. Not because they lack the engineering talent. Because the licensing deals took years to negotiate and cost more than any horizontal player is willing to spend on a single vertical.

Hippocratic AI. Built the expert labeling infrastructure nobody else would build. 6,234 licensed clinicians on retainer, averaging 11.5 years of experience each. Evaluated 307,000 real patient calls. Clinical accuracy went from 80% pre-Polaris to 99.38% at Polaris 3.0. Severe harm errors driven from 0.06% to 0.00%. Anthropic is not going to hire 6,000 nurses. That is not a horizontal business. It is a vertical business built on top of horizontal models.

Harvey. Built the workflow tools no horizontal model can wire into a law firm. Beat the human lawyer baseline on four of seven tasks in the independent Vals Legal AI Report. Document Q&A: 94.8% vs 70.1%. Document Summarization: 72.1% vs 50.3%. Transcript Analysis: 77.8% vs 53.7%. Vertical AI beating trained human professionals. The model companies did not do that. Harvey did that, on top of models the model companies built.

Abridge. Built the medical-specific accuracy layer that a horizontal ASR could never justify. 24% relative reduction in word error rate on clinical conversations vs Google Medical ASR. 83% reduction in transcription errors on new medications. 15% improvement on accented English. Google has been working on medical speech recognition for over a decade with unlimited resources. Abridge beats them because Google does not care enough about clinical conversations specifically to build the last mile. Abridge does.

Four companies. Four verticals. Not one of them tried to compete with the model companies.

They used the model companies. They rented the horsepower and built the scaffolding the model companies would never build. And in doing so, they turned commodity intelligence into industry-specific gold mines.

That is the play. The model companies gave you an unbelievable gift. They spent tens of billions of dollars building something extraordinary and then priced it per token. Your only job is to wrap that horsepower in ten years of industry context nobody else in the world has.

The horsepower is rented. The scaffolding is yours. The industry is waiting.

See you Wednesday,

Luke

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

Grab Your Ticket

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The Art of Vertical Go-To-Market

Watch this weeks episode

Most founders think they’re ready to scale sales the moment they close a Series A. The board wants growth. A competitor just raised. The plan says “hire 10 AEs.” So they do.

And then nothing happens.

This week, I sat down with Kevin Wilson, the CRO at Skimmer, a vertical SaaS company serving pool cleaning businesses. Kevin has built go-to-market engines across three different vertical software companies—Fiix (maintenance teams), Supermove (moving and storage), and now Skimmer—taking them from pre-revenue to exits and multi-million dollar ARR runs.

What makes Kevin different is that he’s lived the entire vertical SaaS GTM playbook: founder-led sales, SMB motions, enterprise motions, PLG vs. sales-led, multi-product strategies, and M&A integration. He’s also built sales teams in some of the most overlooked, blue-collar industries in the world—the kinds of markets where a case of beer moves deals faster than a ritzy campaign.

If you’re building vertical software and struggling to figure out when to scale, how to hire, or what channels actually work, this conversation is a masterclass. Kevin doesn’t deal in theory. He deals in what works—backed by years of doing it, breaking it, and doing it again better.

This Weeks Vertical Titan:
Kevin Wilson (CRO @ Skimmer)

Kevin Wilson didn’t start in SaaS. He started in his dad’s landscaping business, typing up customer quotes on WordPerfect late at night.

He grew up in a blue-collar family—his dad ran a small landscaping company, and most of his extended family were small-town farmers. He watched his dad risk everything: years of his life, the family’s savings, his physical health. Kevin saw two things clearly: how hard small business owners work, and how badly they’re underserved by technology.

That shaped his entire career.

Kevin was the first person in his family to go to university. He went into finance and banking, hated it, and decided to bet on software. He Googled “largest Canadian tech company,” found OpenText in his backyard in Waterloo, Ontario, walked over his resume, and took a 75% pay cut to become a BDR.

From there, he spent time in true enterprise sales at D2L (EdTech), where he dealt with million-dollar quotas, six-figure deals, multi-year RFPs, and selling to government buyers. It was brutal, multi-threaded, and heavy. But it taught him process.

Then he joined Fiix, a blue-collar SaaS company serving maintenance teams in discrete manufacturing. He helped grow that business from $1M to $30M ARR before they sold to Rockwell, a Fortune 500 company. Post-acquisition, Fiix became a $50M revenue business unit. It was a top-ten Canadian exit at the time.

After that, he joined Supermove pre-revenue—vertical SaaS for moving and storage companies. He built the sales motion from scratch, lived through founder-led sales, and took the company to $10M ARR before the Series B.

Now he’s at Skimmer, vertical software for pool servicing companies. Skimmer has 8,000 customers across the U.S. and runs the full operational stack: marketing, lead management, scheduling, routing, servicing, billing, and accounting. They’re building the OS for pool service businesses. They run both a strong PLG motion and a sales-led motion. They recently moved from one product to four products. And they just acquired a pool construction software company, letting them own the workflow from pool build all the way through ongoing service.

Kevin is building Skimmer because he understands the people he serves. He knows what it’s like to work in a business where you go home physically exhausted, where technology feels out of reach, and where software companies don’t speak your language.

That’s why he’s credible. That’s why he wins.
And that’s why his approach to go-to-market is different.

Kevins 20 VERTICAL GTM Hacks

These are truly a masterclass, carve out the time to read them and you won’t regret it…

1. Don’t Scale Sales Because the Board Wants Revenue—Scale When You Have Product-Market Fit AND Go-To-Market Fit

Kevin subscribes to the Mark Roberge school of thought: you’re ready to scale when you have both product-market fit and go-to-market fit.

Product-market fit means customers are succeeding consistently. GDR (Gross Dollar Retention) is the truth. You need to know your leading indicator of early retention—if X happens within Y timeframe, you have a customer for life. Make it scientific. Track what percentage of customers by cohort are hitting that early sign of retention.

Go-to-market fit means you can acquire and retain them scalably. LTV/CAC should be greater than 3. Payback should be less than 12 months. Magic number should be better than 1.

Most founders scale when the board wants more revenue, right after a big Series A, or when a competitor ramps up. That’s emotional. Don’t do it. Scale when the math works.

2. Scale is a Pace, Not an Event

Kevin has been handed plans that say: “We just raised a big round. Go hire 10 AEs.”

That almost never works.

Instead, hire two AEs in month one. Always hire in twos, never just one. When you hire two, you can tell if it’s the rep or the process or the systems that’s broken.

Then watch the speedometer. Track retention, onboarding completion, leading indicators of success, and unit economics. If things are breaking, slow down and fix it. If things are holding, accelerate. Go hire two, then four, then eight.

Also think about where to scale: by product, by market, or by channel. At Skimmer, pool service is highly seasonal. It’s different in the sunbelt states versus the non-sunbelt states. That shapes where they deploy resources.

If you’re running an SMB motion and testing an enterprise motion, separate them. Scale what’s working first. Don’t try to make it all work at the same time.

3. The Founder Should Own Go-To-Market Fit Discovery, Not the First Sales Hire

Too many founders outsource channel testing to their first sales hire. That’s a mistake.

Kevin’s philosophy: the founder should literally test a bunch of channels before hiring a salesperson. Build a spreadsheet. Cold calls are a channel. Ads are a channel. Conferences are a channel. Test two at a time. Track reach, conversion, and funnel metrics.

Almost always, nine out of ten channels suck and one works. That’s normal. You only need one to work.

And that one channel changes over time. Pre-COVID, during COVID, and post-COVID all had different winning channels. GTM fit is not static.

Once you know the math works—once you can prove that a fully-loaded AE costing $10-15K/month can generate $30-50K/month in ARR—then you hand it off to a sales leader.

4. Founders Should Never Get Out of Sales

Kevin sees this mistake constantly: founders hire an AE or a sales leader, and then they disappear. They think, “Great, I don’t have to worry about this anymore. I can go focus on product.”

That’s wrong.

Even at Skimmer, approaching serious revenue, Kevin still has the CEO join every single enterprise customer first call. Not mid-cycle. Not at the end when negotiating. The very first call.

The CEO tells the founder story. The CEO creates a direct line so the deal can move faster if needed. The CEO adds credibility.

The key is doing it in a way that’s respectful of your sales leader. You don’t want them to feel like you’re always peering over their shoulder or parachuting in. Have that conversation early in the hiring process. Find a way to keep the founder in the deal without undermining the sales leader.

5. Hire Two or Three AEs at Once, Not One

Jason Lemkin says hire two AEs—two in the bag. Some people now say three.

Kevin agrees. If you hire two, you can tell if it’s the rep or the process. If you hire three, that third person can really separate themselves from the rest, and you start to see patterns more clearly.

Never hire just one. You won’t know what’s broken.

6. Only Scale What’s Working—Test Everything Else

In later-stage growth (Series B, Series C), Kevin’s rule is simple: only scale what’s working.

Dissect your business by product, by market, by channel. Scale the motion that’s working. Keep testing the things that aren’t working, but don’t invest heavily until they start working.

When those other motions start to work, apply the same disciplined approach: hire two AEs, track velocity metrics, and then scale.

7. Good Sales Leaders Have “Leader-Market Fit”

The number one mistake founders make when hiring a head of sales is hiring someone who hasn’t done their stage.

Kevin calls this “leader-market fit.” Ideally, this person has done your stage and one or two stages bigger. Don’t hire the Salesforce executive who ran a $100M+ revenue team and bring them into a product-market fit or go-to-market fit phase. That doesn’t work. They can’t operate at that altitude.

If you can’t find someone who’s done the next stage, hire someone who’s done your stage well, and then bring in a consultant, a mentor, or a board member who’s seen the next stage. Give them a couple basis points of equity. Use them for feedback.

Also, make sure they’ve grown teams with your velocity metrics. If you’re SMB vertical SaaS, don’t hire someone from horizontal mid-market or enterprise. The motions are completely different.

8. The Four Attributes of a Great Sales Leader

Kevin’s sales leader scorecard has four core areas:

1. Great Coach
They have to develop people quickly. Kevin’s bar: 20 calls reviewed per week. Not just reviewed—coached. Synchronously or asynchronously. If the leader can’t coach, the team won’t ramp.

Interview tip: Have them show you coaching plans and results. Or send them call recordings from your team and have them give you written feedback.

2. Hiring
They have a nose for talent. They can hire the best people inside or outside the org. They’re not afraid to hire people better than them. Junior sales leaders often make the mistake of hiring down because they’re scared of being overshadowed.

Interview tip: Have them name the AEs they would bring with them. Look at those people’s LinkedIn profiles. Call them. It’s a great back-channel reference and shows you how they lead.

3. Strong Operator
They know the business inside and out. They understand how to control their business. If I pull this lever, this outcome happens. In SMB vertical SaaS, feedback loops are tight. You have to be able to tinker with the machine.

Interview tip: Ask them to screen-share their current calendar. Have them walk you through their operating rhythm. What do MBRs look like? Team meetings? One-on-ones?

4. Collaborative Peer Relationships
They build trust with functional leaders across the business. They optimize for the entire business, not just their sales team. They play well with the head of customer success, the head of product, and the head of marketing.

Interview tip: Don’t just call the references they give you. Go find the director of demand gen, the director of onboarding, the director of CS. Call those people. You’ll learn way more about who this person actually is.

9. The DNA of a Great AE: PhD, Curiosity, Adaptability, Intelligence

Kevin has a detailed scorecard for hiring AEs. The highest-weighted category is core attributes—the DNA.

He calls it PhD: Persistency, Heart, and Desire.
If their parents couldn’t change them in the first 18 years of their life, you’re not going to change them either.

Interview tip: Have them talk through the most difficult thing they’ve had to do in their life. If they give a fluffy answer like “adjusting to university,” call bullshit. Go deeper. You’re testing vulnerability and grit.

Curiosity: Do they genuinely care about solving the customer’s pain, or are they just asking the disco questions they’re told to ask?

Interview tip: Leave ample room in the interview for them to ask you questions. Do they ask second- and third-layer questions based on your answers?

Adaptability: This used to be “coachability” for Kevin, but he’s refined it. The best reps have the shortest delta between receiving coaching and implementing it.

Interview tip: Do a case study. Have them pitch their current solution (if they’re a BDR) or Skimmer (if they’re not). Pause the role play. Ask them how they think they did. Give them one piece of coaching. Then have them run it back. Did they implement the coaching immediately, or did they do it the exact same way?

Intelligence (Pattern Recognition): Can they convey complex ideas simply? Skimmer sells to pool pros. Some of the technology is complex. If you just show them every feature, they’re overwhelmed. Great reps distill it down to punchy, simple value statements.

Interview tip: Have them talk about something they’re passionate about and explain it to you like you’re 10 years old.

Beyond DNA, Kevin looks at skill set (sales craft) and previous experience (industry knowledge). But DNA is the most heavily weighted.

10. Industry Experience is Overrated in Sales Roles—Use It in CS Roles Instead

Kevin has hired a lot of industry people. Most of the time, it doesn’t work.

If someone has 10 years in the pool industry but scores low on DNA and skill set, it’s a no-hire. The exception: if they score high on everything and have industry expertise, that’s the holy grail.

But if they only have industry expertise, Kevin will hire them as a consultant for a few months to accelerate his own learning. Or he’ll put them in support, onboarding, or customer success. He’s seen industry vets thrive in those roles.

The mistake: hiring them as AEs. Their Rolodex runs out after the first quarter, and then they can’t generate new pipeline.

11. Ramp Based on Sales Cycle Time

Kevin’s ramping philosophy is tied to average sales cycle.

For a 30-day sales cycle (most SMB vertical SaaS):

• Month 1: 100% quota relief. Onboard intentionally.

• Month 2: 50% quota relief. Build pipeline.

• Month 3 (day 61-90): Full productivity. They should hit 80%+ of quota every month.

If your sales cycle is six months, your ramp might be 6-9-12 months.

Kevin’s philosophy: “Hire fast, onboard intentionally, fire fast.”

12. You Can Tell if a Rep Will Make It in Their First Week on the Phones

At Skimmer, the goal is to get a rep on the phones by week four.

Kevin says you can usually tell if they’ll make it in that first week. The best reps close a deal on their first day. In week one, they’re already pacing toward their monthly target.

If they don’t close a deal in the first week, if they’re not pacing in the first or second week, they typically don’t make it.

Your gut feel is usually right. Kevin has made the mistake of holding on to reps or leaders way too long. You should almost always move on.

13. Fire Fast: <50% Quota in Month Three = PIP

Once a rep is out of ramp (month three), if they hit lower than 50% of quota, they’re put on a PIP immediately.

If they hit 50% or lower again the following month, they’re managed out.
If they hit 80% or more during the PIP, they’re removed from the PIP.
If they hit between 50-80%, the PIP is extended one more month.

Kevin’s approach to coaching out is fact-based, not emotional: “This isn’t working out. We both know it. Here’s where you stand relative to your peers. This isn’t the right environment for you. Here’s a package. Let me know if I can help you find your next company.”

He’s placed reps in other businesses where they’ve thrived. It’s not personal. It’s about fit.

14. Niche Down on ICP—The Riches Are in the Niches

Kevin’s career has one through line: size down on the ICP. Get really tight on your Ideal Customer Profile. Start with the perfect accounts—your serviceable, obtainable market, not your full TAM.

You’ll grow much faster if you niche down.

At Fiix, they were in 25 different verticals at one point. When COVID hit, food manufacturing exploded, so they went hard on just food manufacturing. That’s when they started ripping. The product got better. Marketing got sharper. Sales messaging got more dialed in. All velocity metrics went up.

Don’t go attack your entire TAM right now. Niche down, especially in the early days. Then as you penetrate that slice of the market, start expanding.

15. Don’t Worry About Burning Through Your TAM Too Fast—Focus on Rigor

At Skimmer, there are roughly 50,000 pool servicing companies in the U.S. Kevin only wants to serve a specific segment with the outbound motion. PLG handles the lower end of the market.

For mid-market and enterprise (which is still small business at the end of the day), Kevin had a target list of 5,000 accounts. He deployed two BDRs against that list. He knew that if they worked the list rigorously, they’d get through it in nine months.

He was okay with that. He wanted to grab all the low-hanging fruit early. Then widen.

Kevin’s philosophy: as long as you’re not touching the full market every 30 days or every 3 months, you’re fine. Every six months is probably okay. And it also depends on the nuances of the business. At Skimmer, new business goes down in summer months because pool pros are in the field and don’t have time to take a cold call. But in the off-season, when pool season closes, it fires back up.

The key: get through your targeted TAM rigorously, not spray-and-pray. Then recycle those accounts with new messages and new angles.

16. Use AI to Enrich Third-Party and First-Party Data—That’s the Biggest Needle Mover

Kevin says there’s a lot of “AI theater” on LinkedIn. But the biggest needle-mover AI play at Skimmer has been enriching third-party and first-party data.

Third-party data: Information on your target accounts and contacts. At Skimmer, they target owner-operators. But the key is finding unique, competitive, moat-building information about your ICP that you can scrape on the internet. Kevin credits Jordan Crawford with helping them here.

First-party data: Skimmer’s reps are on 4-5 customer calls a day. All that information is in call recordings. They use a tool called Attention to populate that data back into the CRM.

The results:

• 100% CRM accuracy. No deals go missing.

• 90% forecast accuracy on the first day of the month, every month.

• 190 minutes saved per rep per week in manual CRM work. That’s one or two more customer calls per week.

• Pipeline is up. Close rates are up. Average quota attainment is up.

This is table stakes now. If your CRM is a mess, your sales team is flying blind.

17. Use Permissionless Value Props (PvP) to Break Through

Kevin credits Jordan Crawford with the concept of Permissionless Value Props (PvP).

The idea: deliver a piece of information—via email, text, or call—that is so valuable the prospect would pay to receive it.

At Skimmer, they have 8,000 customers across the U.S. They have all the pricing information. They know what the average zip code charges for pool cleaning.

So they send a message to a prospect: “Do you know you could charge $45 more per month? Here’s the average in your zip code. Here’s what your competitors are charging. Go raise your prices.”

No ask. Just value.

Then the BDR follows up two days later.

This has been game-changing for Skimmer’s outbound results. And it’s all powered by that third-party and first-party data collection.

18. The “Case of Beer” Campaign—A Play on Sam Blond’s Champagne Strategy

Kevin’s favorite GTM motion is the case of beer campaign.

It’s a play on Sam Blond’s champagne campaign, but adapted for blue-collar verticals.

Skimmer ships a case of beer to the pool shop. Technicians come back after a tough shift, punch out, and there’s a case of beer waiting for them in the lobby. There are a few marketing slicks in there, but no CTA, no ask.

The message: “Congrats on another peak season. Have a beer on us.”

It’s communal. The owner comes in, sees their technicians having a few beers, joins in.

A day or two later, the BDR follows up. Subject line: “Hope you enjoyed the beers.” Then they make the ask.

Conversion rates: 70-75% conversion to demo. 70-75% to close.

Why does it work? Because these are blue-collar business owners. Send them a case of beer, and it goes a long way.

19. Conferences Are Where Blue-Collar Small Business Owners Go to Learn

Kevin is a big believer in conferences for blue-collar vertical SaaS.

These are small business owners who are spending days not cleaning pools to go learn something new to elevate their business. They’re growth-minded.

Skimmer goes to all the major conferences. They do dinners with select prospects. They invite a customer to the dinner, because the customer sells it for them.

Kevin asked one of his customer advisory board members if the “steak dinner thing” was overplayed. Should they do something more exciting, like a bayou tour in New Orleans?

The customer looked at him and said: “Dude, burger and beer. That’s all it takes.”

In-person is back. With AI, people want to connect belly-to-belly, eye-to-eye. Blue-collar buyers want to shake your hand before doing a deal. For any deal size over a certain level, Skimmer goes on-site. They spend a day with the customer and their people.

20. Hire a Go-To-Market Engineer to Standardize AI Workflows

Skimmer recently hired their first go-to-market engineer, who reports into the director of RevOps.

Before this, Skimmer ran AI plays in a very decentralized way. Everyone got a license of OpenAI, Claude, and other tools. The result: slop bombs. At one point, there were five different ROI calculators circulating.

The GTM engineer runs all the simulations, runs all the tests, and builds production-grade AI tools. Then they ship them standardized across go-to-market.

The stack: Clay-heavy. Orbital. Jordan Crawford’s tools for third-party data. Attention for AI-based coaching and first-party data population.

This role has been huge for Skimmer. It’s the difference between AI theater and AI that actually moves the business forward.

If you want to hear more from Kevin, he’ll be speaking at the Vertical Software Summit this year. He’s the sales wizard of vertical SaaS, and he’ll talk to whoever wants to listen.

Watch this weeks episode

See you Sunday.
— Luke Sophinos

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

Luke Sophinos, Mark Passifione, and Daniel Burton explore what it takes to move from vertical SaaS to agentic, where software begins doing more of the work and driving outcomes. Watch the discussion on demand and get the practical roadmap for identifying where to start, proving value, and expanding from there.

Watch the Discussion + Get the Roadmap →

The Summit is six weeks away. Last year we turned away 100+ people who waited too long. This year the room is bigger, the speakers are unbelievable, and the investor list is heavy. If you build in vertical software or vertical AI, this is the place to be.

Below: the founder who met his Series A lead in that building — and closed $16M a few months later — the complete playbook for raising your next round while you are there, and who is actually on stage at the Vertical Software Summit in 2026.

Mark Hoadley met his Series A lead at the Summit. Four months later, Frontier Growth wired $16M.

Act I: The ServiceTitan education

Before he founded anything, Mark Hoadley spent nearly four years in sales at ServiceTitan — watching, up close, what a billion-dollar vertical software company actually looks like from the inside. He learned how the trades buy software, why embedded payments matter, and what a real vertical go-to-market motion feels like. But he also saw the limits: he watched ServiceTitan struggle to expand into adjacent recurring-services trades and ultimately have to acquire its way in. Most employees would file that under “not my problem.” Hoadley filed it under “never let that happen to my company.”

Act II: Software from the museum

Meanwhile, his brother-in-law Ben Sikma was working M&A in waste management, and every operator he diligenced was running software that belonged in a museum. In Mark’s words: “Sometimes we’d talk about how this one reminds us of the Oregon Trail, this reminds us of the cell phone Michael Douglas used in Wall Street.” Clunky. On-prem. Written in the 90s. A $145 billion industry — the one that keeps every community in America functioning — running on paper route sheets and gut instinct, with two dominant incumbents that later merged, leaving haulers with almost no real alternative.

So in November 2020, they built one. Hauler Hero is the all-in-one operating system for waste haulers: CRM, billing, routing, dispatch, customer portal, fleet visibility. And — the ServiceTitan lesson applied — deliberately multi-product from day one, rather than a point solution with expansion dreams. It is harder to build. It is also much harder to rip out.

Act III: The AI dividend

Here is the part of the story that matters most in 2026. Because Hauler Hero built a modern cloud system of record from the start — a clean, proprietary data model of every customer, route, truck, and pickup — it became the perfect substrate for AI when the wave arrived. While legacy incumbents are trying to bolt AI onto 90s on-prem databases, Hauler Hero ships agents that sit natively on the data.

The proof is already in production. Their customer service AI agent — think Intercom’s Fin, but for waste haulers — now handles roughly 60% of inbound support interactions. And their camera product pulls images from third-party cameras on garbage trucks straight into a command center, automatically reviewing the exceptions that break a normal day: cans that are overloaded, damaged, or simply not where they are supposed to be. That is not a demo. That is AI doing physical work in a physical industry, powered by a data model the incumbents cannot replicate without rebuilding from zero.

Act IV: The room

Now the part that matters for this newsletter.

In October 2024, Mark showed up at the inaugural Vertical Software Summit in Miami. Dave Pandullo from Frontier sat on the VSaaS investor panel. They connected in person — not over a cold email, not on a Zoom screen, but in the room, with context, surrounded by people who already understood what a good vertical software business looks like.

Four months later, in February 2026, Frontier Growth led Hauler Hero’s $16M Series A, with Disruptive Founders Fund, Somersault Ventures, and several ServiceTitan executives participating. Total funding past $27M. When asked why Frontier, Mark’s answer was simple: of every investor at the table, Frontier had the deepest vertical SaaS expertise.

That is not a coincidence. That is what happens when the right founders and the right funds share a room and real conversation replaces cold outreach.

Mark is registered for the 2026 Summit too — and he is not the only one with that story. Multiple founders from year one told us they met the financier who led their next round in that building.

The lesson is simple. The best vertical software rounds do not close on Zoom. They close after a real conversation between people who actually understand the category. The Summit is one of the few rooms in the world where enough of those people show up at once for that to happen naturally.

If you build in vertical software or vertical AI — and you plan to raise, hire, sell, or learn in the next 18 months — the ROI of two days in Miami is hard to beat.

Grab Your Ticket

(and remember if you need a free ticket we will get you one!)

I’m hosting a workshop about selling your vSaaS business with Ignacio Villanueva Martin, founder of L40, on September 29th. We’ll cover valuation drivers, buyer types, and how to position your business for exit. Click the link right here to register.

How to raise your next round at the Summit

Hoadley’s story is the proof. Here is the repeatable part — the playbook any vertical software founder can run to turn two days in Miami into a closed round. The investor list this year is heavy. These are the funds that have already secured their tickets — not a wishlist, not a “likely to attend.” Confirmed, paid, and walking into the Rubell on November 4:

All actively looking to write checks into vertical software and vertical AI. Most founders will waste this. Here is how to not waste it — before, during, and after.

Before Miami: build the pipeline

1. Treat it like a curated pipeline, not a conference.

Build a short list of the 10 to 15 funds that actually match your stage, ARR, and vertical. Not the biggest names — the most relevant ones. A $3M ARR construction SaaS should not be chasing Thoma Bravo; a growth equity firm writing $50M checks cannot help you, and the hour you spend trying is an hour you did not spend with the seed-to-A fund that can. Know who leads your round and who follows. Write the list down. Rank it.

2. Warm intros beat cold walkups.

LinkedIn every partner attending. Ask for 15 minutes before Miami — a specific ask, referencing their vertical thesis, with your one-liner and ARR in the first message. If they say yes there, they will say yes in person. The founders who get meetings on-site are the ones who booked them three weeks earlier.

3. Prepare your mechanism sentence.

Every founder shows up calling themselves the AI-native leader in their space. Every investor tunes that out by 10am on day one. What cuts through is a single sentence explaining why your workflow is hard to displace — data, payments, compliance, distribution. “We process the hauler’s payments and own their route data” lands. “We’re building AI for waste” does not. Pick one mechanism and own it.

On the ground: run a real process

4. Book coffee meetings

Thirty-minute blocks. The panels feed the coffee conversations — reference what the investor just said on stage and you are instantly differentiated from the 19 other founders they met.

5. Qualify fast and honestly.

Ask directly in the first five minutes: do they lead, what check size, what ARR floor, have they done your vertical before. A fast no is the second-best outcome — it buys your time back. The worst outcome in Miami is a polite maybe that eats three follow-up calls and dies in December.

After: where rounds actually close

The Summit gives you the top of funnel; only your process converts it. Founders who close rounds from the Summit are almost always the ones who ran the tightest post-conference process.

The five mistakes that burn investor trust in Miami

• Chasing logo funds over fit. A brand-name firm that does not do your stage will politely waste your afternoon. Relevance beats prestige, every time.

• Leading with AI adjectives. “AI-native” is table stakes vocabulary in 2026. Mechanism — data, payments, compliance, distribution — is the differentiator.

• Showing up without numbers. This crowd reads vertical SaaS metrics fluently. “We’re growing fast” without ARR, NRR, and margins reads as “not ready.”

The Summit does not raise your round. But it does compress six months of investor outreach into two days — if you show up ready.

Grab Your Ticket

(and remember if you need a free ticket we will get you one!)

Who is actually speaking at the Vertical Software Summit

The playbook only works if the room is full of the right people. Here is who is actually on stage in 2026. Most conferences over-index on people who talk about vertical software. This one over-indexes on people who have built it. That distinction matters more than ever in 2026: the questions that decide whether your company survives the AI transition — do you own the workflow, the data, the payment rail, the compliance burden — are not theoretical to anyone on this stage. They answered them with a balance sheet.

Here is some folks on stage, and the specific reason each person is worth your two days:

• Fred Voccola · Simpro & Kaseya

  The only operator running two separate billion-dollar vertical software businesses at once — Kaseya for IT services, Simpro for the trades. Most people treat each vertical as a one-off. Voccola is the living proof that vertical software is a repeatable skill: the playbook for winning one ugly, fragmented, workflow-heavy industry transfers to the next. If you want to understand how the category compounds, he is the case study.

• Cecilia Ziniti · Founder & CEO · GCAI

  One of the fastest-growing vertical AI companies around. The growth curve does not look like SaaS — it looks like software plus embedded outcomes. If you are trying to figure out what “AI-native vertical software” actually means in dollars and retention rather than in pitch-deck adjectives, this is the talk to sit in the front row for.

• Danielle & Leah Cohen-Shohet · Co-Founders · GlossGenius

  Beauty software done right — a category most VCs ignored for a decade, they just raised at $1B+. The classic ugly-workflow-hides-a-great-business story, plus a masterclass in payments-led vertical SaaS economics.

• Sam Youssef · Founder & CEO · Valsoft

  100+ vertical SaaS acquisitions, approaching $1B of ARR. If AI roll-ups are the trending narrative, Sam has quietly run the actual playbook for years — sourcing, pricing, integrating, and holding vertical software at a scale almost nobody else has attempted.

• Andreas Huber · Founder & CEO · First Due

  Founder & CEO of First Due — the Vertical Software platform for Fire, EMS, and Law Enforcement agencies. Built a $1B+ company without venture capital to redefine how public safety operates.

Plus 30+ founder and CEO speakers running businesses from $1M to $1B in ARR, and leading CxOs on AI, FinTech, Customer Success, GTM, and Product.

The pattern hiding in the lineup

Read the list again and one thing jumps out: nobody on that stage got there by writing a good pitch deck. Every one of them got there by figuring out something ugly about their vertical that everyone else missed — a workflow so unglamorous that talent ignored it, a buyer so fragmented that sales teams gave up, an incumbent so stale that customers had forgotten software could be good.

That is the real curriculum of the Summit. Not “what is AI doing to SaaS” panels. The specific, unglamorous mechanics: how Bookman priced against government procurement, how GlossGenius attached payments, how Valsoft integrates an acquisition in 90 days, how Voccola decides which adjacent vertical is winnable. These are operator details you cannot get from a podcast, because the people who know them rarely talk in public.

The ugliest workflows hide the best businesses.
This stage is a room full of people who proved it.

Grab Your Ticket

(and remember if you need a free ticket we will get you one!)

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

Watch this weeks episode

Everyone spent the first half of 2025 screaming SaaSpocalypse. Then the software indexes quietly clawed their way back — apps and vertical apps roughly 60% off the lows, infrastructure ripping close to 90% on the back of consumption-based tailwinds — and the conversation shifted overnight.

The real question was never whether SaaS was dying. The real question was who inherits each vertical from here.

Because underneath the recovery narrative, something much more interesting is happening. In every vertical, there are now three sets of hands reaching for the same set of workflows: the legacy system of record that has been embedded for a decade, the industry-specific GPTs that are growing faster than anything we have ever seen in vertical software, and the AI-native services companies quietly stealing labor budgets one enterprise at a time.

That is the fight of the next five years. And it is going to be uneven, messy, and enormously lucrative for the founders who read it correctly.

This week’s edition is a debate — Nic and I sat down and stress-tested how we would actually play this from each seat: the insurgent attacking an SOR, the incumbent trying not to get flanked, and the operator trying to figure out where the M&A money is finally going to move. Below is the founder profile of who is winning right now, and the playbooks we pulled out of the conversation.

Vertical Software Summit
We are already 80% sold out so if you’re considering coming to the Vertical
Software Summit this November, now is the time to grab your ticket.

All information at the link below.

Check out the conference

It’s truly the best few days of the year in vertical software and vertical AI.

This Weeks Vertical Titan:
The AI-Native Operating Company

This week is a little different. Instead of a single founder profile, the “Titan” is the new archetype emerging across every vertical — the AI-native operator company that is quietly reshaping how incumbents get displaced.

Think of EvenUp in personal injury law, Abridge in clinical documentation, Harvey in legal, Magic School and Lagora in K-12, Camber in healthcare reimbursement, OpenEvidence in clinical decision support, Datagrid in construction, Brellium in payer/provider compliance, Ninja in MSP, and Hanover Park in fund administration.

The reason this archetype matters is that these companies are proving something the incumbent SaaS world has not fully internalized yet: in the AI era, whoever owns the work owns the workflow, and whoever owns the workflow eventually owns the system of record.

“These are not point solutions. They are the front door to their verticals.”

EvenUp started with demand letters and is now going full stack. Abridge started with documentation and is now creeping into the Epic footprint. Harvey started as, in Nic’s words, “a wrapper” — and just released Tenet at a $15B valuation, worth more than LexisNexis. The founders building them are five years ahead of the incumbents they are attacking.

Four ways through the incumbent

Ten moves for the next chapter (post-SaaSpocalypse)

01. Post-SaaSpocalypse, retention is still a vertical story

Horizontal application-layer software is where net retention is under the most pressure right now. Vertical still has a structural retention advantage, and the consumption-based businesses at every layer — Mongo, RingCentral, and anyone whose model scales as agents scale — are the ones benefiting most from the AI cycle. If you are building vertical, do not accept horizontal churn dynamics as your benchmark. Your industry knowledge should compound into stickier revenue than the SaaSpocalypse discourse suggests.

02. There are only four ways to beat an entrenched SOR — pick one honestly

Nic laid these out cleanly. Every insurgent should be able to state which one they are running: go straight at a weak or distracted incumbent; build a point solution customers refuse to give up; come in as services and own the execution layer; or build a data moat the incumbent cannot replicate. If you cannot clearly identify which of the four you are running, you probably do not have a real strategy against the incumbent yet.

03. AI-native services is a wedge into becoming the next SOR — not a lesser business

The most under-appreciated move in vertical AI right now: go in as forward-deployed services, look like an employee of the customer, automate every workflow you touch with AI, and quietly build your own system of record in the background. By the time you come out commercially, you have already migrated the customer. This is the Palantir move, retooled for vertical. Hanover Park is running it in fund admin. EvenUp ran it in plaintiff law. It is a legitimate path to becoming the incumbent — not just competing with one.

04. Pick a market where the SOR barely matters

In some verticals, nobody logs into the system of record. Fund admin is one. A lot of flame-more-services categories are like this. If the buyer does not care about the underlying record system, the services-forward player can win the whole thing without ever fighting a UI battle.

05. The M&A wave is coming — build for it now

There has not been much M&A yet, but the setup is here: vertical AI is still mostly Series A, incumbents are just starting to realize they cannot build fast enough internally, and the successful precedent is now on the board (EvenUp trajectory, Datagrid → Procore, Evolution IQ’s ~$800M exit at what appeared to be a 50x-plus multiple after only a few years). The next 18–24 months are going to be a strategic buyer’s market for vertical AI companies with real customer traction. If you are a founder who wants to sell into an incumbent, now is the time to make your AI horsepower visible and legible.

We’re doing an initmate webinar on ‘How To Sell Your Vertical Software Company’ on September 29th from 1-2pm MST. Only allowing 15 founders in.

Sign up for webinar

07. Losing the “reselling tokens” argument is worth it to keep the real estate

Yes, being in the business of marking up OpenAI credits is not glamorous. But losing the daily interface — the screen your users open every morning — is worse. A 5% spread on AI usage while retaining the operational surface area you spent a decade building is a trade every incumbent should be willing to make. The interface is the moat. The margin will follow.

08. Every incumbent needs a new wedge — even if they graduated from “wedge thinking” a decade ago

This one is subtle. Most incumbent CEOs think of “wedge product” as an early-stage exercise they finished years ago. That is wrong. In the AI era, incumbents have to run the wedge playbook again — pick one product, one workflow, one net-new experience, and launch it standalone. Chapter one is starting over whether you like it or not.

09. Most PE-backed incumbents will not adapt — and that is the opportunity

Blunt take from Luke: many PE-backed vertical SaaS CEOs are so good at the classic SaaS playbook — four-week sprints, capacity planning, roll-up math — that they are structurally incapable of rebuilding around AI. It is not that they have not woken up. It is that their board incentives, their install base, and their operating muscle memory all point away from reinvention. If you are a founder attacking one of these categories, this is the window.

10. Hire a model researcher — the ROI is asymmetric

Cursor built Composer in coding. Harvey released Tenet at a $15B valuation. In verticals where data is specialized (medical coding, claims, regulated documentation), open-weight models plus a real model researcher can get you close enough to frontier parity to matter. If your business justifies it, paying $1M+ for a serious researcher is probably one of the highest-leverage hires you can make right now.

The next chapter of vertical software is not going to look like the last one…

The incumbents will fight, some will wake up, most will not. The insurgents will keep chipping away with services, GPTs, and agents until one morning a strategic acquirer looks up and realizes the company they should have built already exists — and it is now three years down the road with real customers and real data.

Pick your seat.
Pick your wedge.
And build like the window is closing, because it is.

Watch this weeks episode

See you Sunday.
— Luke Sophinos

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

Luke Sophinos, Mark Passifione, and Daniel Burton explore what it takes to move from vertical SaaS to agentic, where software begins doing more of the work and driving outcomes. Watch the discussion on demand and get the practical roadmap for identifying where to start, proving value, and expanding from there.

Watch the Discussion + Get the Roadmap →

I have been running interviews across a few companies I am involved in, and the AI gap is worse than I expected.

Not among the 22 year olds. Among the seasoned ones. Fifteen, twenty years of SaaS wisdom. Sharp resumes. Great references. And they are answering interview questions with playbooks that stopped working eighteen months ago.

Everything has changed. Not most things. Everything.

The product function looks different. Customer success looks different. Engineering looks different. GTM looks different. G&A looks different. Even the shape of the org chart is different.

Most of the market has not caught up. That is a hiring opportunity if you know what to look for. It is a hiring landmine if you do not.

The old world versus the new world, spelled out.

The old world versus the new world, spelled out.

Old world: a customer files a bug. CS logs it. PM triages it. Engineering scopes it. It lands on a roadmap. Timeline gets estimated. Customer sees a fix in six weeks if they are lucky.

New world: a forward deployed engineer joins the call, replicates the issue live, ships a patch before the day ends. Customer thanks you before hanging up.

Old world: marketing needs a real time dashboard. Data team gets a ticket. Thirty days later there is a Looker board that already needs updates.

New world: an operator prompts it into existence, connects it to the warehouse by end of day, iterates on it. Shipped the same week.

Old world: a customer asks for a custom report. CS says we will pass it to product. It gets denied because it is not on the roadmap.

New world: CS builds it themselves. Ships it in an afternoon. Sets up a weekly email to the customer to see what happened. Customer becomes a reference.

Old world: onboarding is a six week implementation project with a services PM and a Gantt chart.

New world: an agent ingests the customer’s legacy data, maps it to your schema, and stands up their instance overnight. CS shows up with their old data nearly ready to go in the system.

Old world: PMs write specs. Engineers estimate. Designers mock. Two week sprint. Retro. Repeat.

New world: CS person or whoever the resident expert is prototypes the actual feature. Engineer reviews the prototype. Ships a scalable version in three days.

Old world: content marketing hires an agency and pays five thousand dollars a month to write blogs.

New world: an operator writes better content themselves in ninety minutes because they know the customer and the model does the drafting.

The pitfall is real. Moving this fast breaks things. Bad prompts ship bad code. AI generated dashboards can lie. Agents onboard customers into the wrong plan tier. Speed without judgment is worse than the old world.

But that is not the argument against AI native operating. That is the argument for hiring people who have already broken things and learned. Not people who are still asking permission to try.

The scary part is how many candidates in the market still think the old cadence is normal. They quote two week sprints as a feature. They say they own the roadmap. They talk about cross functional alignment meetings like it is a virtue. They have not picked their head out of the sand. Process and procedures are in place as guard rails. But they also slow really great people down.

If your interview process is not filtering for this, you are hiring like it is 2019.

I’m hosting a workshop about selling your vSaaS business with Ignacio Villanueva Martin, founder of L40, on September 29th. We’ll cover valuation drivers, buyer types, and how to position your business for exit. Click the link right here to register.

How To Hire In The AI-Native New World

How to hire in the AI native era.

Stop asking about past accomplishments. Start asking what they built last weekend.

Ask candidates to walk you through their personal AI stack. Not the tools their company gave them. The ones they use on their own time. If the answer is a shrug or a single ChatGPT tab, you have your signal.

Ask them to show you the last thing they made. A dashboard, a prototype, a workflow, a piece of research, a landing page, an internal tool. Real artifacts. If they cannot produce one, they are not building anything on their own.

Give them a live task in the interview. Thirty minutes. Tools of their choice. Watch how they work. Do they prompt in full sentences and pray? Or do they orchestrate multiple tools, verify outputs, iterate? The difference is enormous.

Test for experimentation velocity. Ask how many new tools they have tried in the last thirty days. Ask which ones they abandoned and why. Curiosity is not a personality trait anymore. It is a job requirement.

Screen for taste. Anyone can generate output now. Very few can tell what is good. Show them three drafts of something and ask which is best and why. If they cannot articulate the difference, they will flood your product with mediocre AI output.

Look for people who use AI as their operating system. Not as a tool they open occasionally. As the layer that sits between them and every task they do. Email, docs, research, analysis, coding, design, hiring, planning. All of it running through AI by default.

You need people in 2026 who are all about experimentation. They are not stuck in their ways. They are all for relearning how to work in this new age. That is what I am looking for. There are no playbooks right now. Marc Benioff’s school of SaaS looks like most other institutions of higher education. They do not translate to the real world. Or at least the world of the vertical AI hypergrowers.

Now think about what happens when the whole org looks like this.

Every function moves at the speed of the fastest AI native person you have ever worked with. Decisions that used to require three meetings happen in a Slack thread. Prototypes replace specs. Customer requests get answered the same day. Roadmaps get shorter because the cycle time is shorter.

David Senra just interviewed Luca Ferrari from Bending Spoons. Ferrari does not use job titles anymore. He runs one of the most efficient software companies in the world, roughly a fifty percent EBITDA margin on billions in revenue, and he has decided that traditional roles are a legacy artifact.

I think he is directionally right. The future org chart might just be four things. GTM, Product and Engineering, CS, G&A. That is it. A small number of extremely hardcore A players who build inside those four functions. No middle management layer. No coordination tax. No specialists for things that AI now does better than humans. They are running AOL and Evernote with like thirty people per. Even with products that have millions of users.

You do not get there by retrofitting your current team. You get there by hiring differently starting now.

If this newsletter resonates, the Vertical Software Summit is the room in person.
Every serious acquirer, founder, and investor in vSaaS is coming.
Register today and reserve your spot before it sells out!

Playground got acquired by Procare Solutions last week.

That is the headline.
The story underneath it is why anyone building in a small vertical should be paying attention.

Rewind to 2020. Daniel Andrews is a software engineer with a Berkeley degree. His brother Josh and their longtime friend Sasha Reiss are figuring out what to build. The Andrews brothers grew up inside a preschool. Their mom ran one. They watched her drown in paperwork every night for twenty years. Billing spreadsheets. Attendance sheets. Enrollment forms. Subsidy paperwork. Parent updates. Payroll. All done by hand or on software that felt like it was built when Bush was president.

Their first customer was their mom’s school.

That is a wedge most founders miss. They chase venture scale problems and end up with venture scale competition. Playground started with one center, one operator, one deeply personal user. The product got shaped by the actual mess of running a preschool, not by a strategy deck.

They raised three million from Human Capital and Bedrock in 2021. And then they did the thing that most founders in vertical software cannot do. They stopped raising. Total capital raised across the life of the company: roughly three and a half million dollars. That is it.

Compare that to the typical vertical SaaS trajectory. Seed. A. B. C. Growth. A hundred plus million dollars burned before anyone knows if the model works. Playground grew to more than five thousand child care programs on less than one round.

The product itself is a study in workflow depth. Not a point solution. Not a bolted together stack. One platform that runs the operational spine of a center. Billing. Attendance. Enrollment. Payroll. Parent communication. Food programs. Learning. Subsidy management. Everything a director touches in a day.

That is what people miss about vertical SaaS. The category looks small from the outside. Child care software. Five thousand centers. A boring niche. But when you own that much of a customer’s daily workflow, the average revenue per account grows, the retention compounds, and the competitive angle becomes almost impossible to attack from the outside. Horizontal players cannot go deep enough. Legacy players cannot ship fast enough.

Then Camber happened. And this is the part that most operators reading this need to sit with.

A year ago, when every vSaaS company was rushing to slap a chatbot on a marketing page, Playground built a real workflow layer. Camber is not a writing assistant. It is a voice agent that answers inquiry calls from prospective families. It handles daily admin. It writes notes. It works around the clock. Playground called it an AI employee for child care, and that framing is exactly right.

Think about what that means for a preschool director. Enrollment calls used to eat two or three hours of the day. Now Camber picks them up, qualifies the family, and drops the lead into the CRM. Administrative work that used to require an office manager gets absorbed by the software. The center runs leaner without cutting people who touch kids.

That is the vertical AI thesis in practice. Not AI features. AI labor. Priced against the cost of a headcount, not the cost of a software seat.

Now consider the acquirer. Procare Solutions has been in child care software for thirty years. Forty thousand centers on their platform. Roughly two hundred sixty million in revenue and ninety five million in EBITDA when Roper Technologies acquired them for one point seventy five billion in early twenty twenty four. That is six point seven times revenue and eighteen times EBITDA for a vertical software business in a market most investors have never heard of.

Roper is a Nasdaq 100, S&P 500, Fortune 500 serial acquirer of niche vertical software. They own dozens of these businesses. They do not buy for growth. They buy for durable cash flow inside markets nobody else wants to compete in. Procare fit the pattern perfectly. Sticky. Regulated. Mission critical. Fragmented customer base of small operators who never switch.

But even Roper and Procare have a problem. They are not going to build Camber. Thirty year old organizations with forty thousand customers do not ship AI voice agents. They cannot recruit the engineers. They cannot move at the cadence. The technical debt alone would kill it.

So they did the smart thing. They bought the company that already had.

This is the pattern to internalize. In almost every legacy vertical software category right now, there is the same setup. A thirty year incumbent with distribution and a boring balance sheet. A three to five year old challenger with the AI product the incumbent needed to ship two years ago. The incumbent cannot out ship the challenger. Consolidation is a natural ending in a lot of cases. It has already happened in child care. It is going to happen in dozens more categories over the next thirty six months.

Is an M&A tidal wave in the verticals finally incoming?

Three founders. Three and a half million dollars raised. One mom’s preschool as customer zero. Five years to an exit into one of the most acquisitive software holding companies on public markets.

Boring vertical. Personal wedge. Capital efficient. AI native at exactly the moment the incumbent needed a partner. That is the whole playbook.

Stay sharp.

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

Watch this weeks episode

Dom Dinardo has had the unusual advantage of arriving early to three software eras. He joined Salesforce in the UK in 2002, before “cloud” was the category. He later went to Veeva and saw why a company built for one industry can be harder to dislodge than a horizontal giant. Then he founded Aforza around consumer goods, a vast industry whose frontline still runs through stores, bars, coolers, distributors, photographs, promotions, and unreliable connections.

That arc gives him a clean thesis for this decade: vertical AI will not win by putting a chat box beside vertical software. It will win by coupling intelligence to an industry’s data, process, interface, commercial model, and moment of decision.

The distinction sounds small until you picture the user. A field representative doing 20 visits does not want to explain the store to ChatGPT. The useful product already knows the route, account, promotion, shelf, and history. It sees the new photograph, recommends the next action, and gets out of the way.

This is not a story about a better model. It is a story about where intelligence becomes valuable. The model is a component. The compounding advantage is everything wrapped around it: vertical intimacy.

Vertical Software Summit
We are already 80% sold out so if you’re considering coming to the Vertical
Software Summit this November, now is the time to grab your ticket.

All information at the link below.

Check out the conference

It’s truly the best few days of the year in vertical software and vertical AI.

This Weeks Vertical Titan:
Dom Dinardo (Founder & CEO @ Aforza)

Dom’s career is almost a controlled experiment in software strategy. At Salesforce, he learned the horizontal playbook at Marc Benioff’s School of SaaS as employee 3 at Salesforce UK. At Veeva, working in a company engineered around life sciences, he learned what he calls vertical intimacy directly from Founder/CEO Peter Gassner. At Aforza, he is applying those lessons to the frontline of consumer goods in the new era of vertical ai.

Aforza began with complex offline order capture and retail execution. Today its vertical AI product, Ava, is embedded into that operating system. The company describes the product less like software bought and more like an employee hired—a framing that forces the ROI question into plain language.

The scale is already global: roughly 70 countries, with customers named in the conversation including L’Oréal, Lego, A.G. Barr, Asahi, and Edrington. The point is not the logo wall. It is that one vertical data model can travel widely when local process variation is designed into the system.

Ten moves for building vertical AI that matters.

The episode’s lessons extend well beyond consumer goods. They form a practical operating system for founders building intelligence into any industry where work, data, and context resist generic software.

01. Pick the vertical where the hard work still happens off-screen.

Aforza did not begin with a category everyone already understood. Dom saw a large CPG software market served mostly by broad horizontal vendors, but no obvious vertical winner. The opening was not glamorous: representatives taking orders in shops, bars, basements, and places where connectivity fails.

That difficulty was the attraction. Offline mobile sync, country-by-country operating differences, promotions, coolers, shelves, and local distributors create a market that looks inconvenient from the outside and inevitable once you know it from within.

Action item: list the workflows in your industry that break when the signal drops, the employee leaves the desk, or the standard data model runs out. Investors: ask whether the ugly implementation detail is temporary friction or a durable barrier to entry.

02. Build vertical intimacy before you build vertical intelligence.

Dom learned the contrast across two eras. Horizontal SaaS rewards breadth: one product, many industries, a repeatable sales motion. Veeva taught him the inverse. In a vertical company, the product, sales team, implementation, vocabulary, and customer relationships all deepen around one industry’s reality.

AI makes that intimacy more valuable, not less. A general model can know a lot about CPG. It cannot automatically own the customer’s data model, approvals, route plan, commercial rules, or the trust required to change how thousands of people work.

Action item: turn customer knowledge into product objects, workflow rules, and implementation methods—not just sales collateral. Investors: diligence whether vertical expertise lives in the system or only in the founders’ heads.

03. Put AI in the interface, not in place of the interface.

The dominant AI product pattern is still a blank box. That works for knowledge workers who can form the question and evaluate an open-ended answer. It is a poor fit for a field representative expected to make 20 visits, take photographs, check a cooler, place an order, and keep moving.

Dom’s phrase is the useful distinction: not AI is the UI, but AI in the UI. The intelligence should appear at a decision point with the right context already assembled. The user should not have to become a prompt engineer to receive the next best action.

Action item: identify the exact screen and exact second where a better decision changes the outcome, then put the intelligence there. Investors: ask to see AI complete a real job without a carefully staged prompt.

04. Sell a use-case library, not an empty copilot.

Aforza built a library of more than 100 CPG-specific AI use cases, each tied to a job and an expected source of value. That is the opposite of shipping a general assistant and waiting for customers to discover ROI themselves.

The library changes the enterprise conversation. A buyer can choose a known problem, understand the workflow it changes, and measure whether the intervention worked. Product discovery becomes a catalog of outcomes rather than a tour of model capabilities.

Action item: create a written inventory of narrow, repeatable decisions your product can improve and attach a measurable value hypothesis to each. Investors: ask how many use cases have moved from demo to repeatable deployment.

05. Price against the customer’s unit of value.

Tokens are a cost input. They are not a buying language. Dom is blunt about this: customers do not want messages, tokens, or fairy dust. They want a commercial model they can budget, defend, and compare with the economic result.

That is why one pricing model is not enough. A field-sales workflow can price per user. Promotion matching can price per claim. Trade-promotion planning can scale with the size of the business. The metric follows the value architecture of the job.

Action item: map every product line to the unit the buyer already uses to describe value. Investors: test whether gross margin improves with model efficiency while customer pricing remains stable and legible.

We’re doing an initmate webinar on ‘How To Sell Your Vertical Software Company’ on September 29th from 1-2pm MST. Only allowing 15 founders in. Reply to this email and we will get you registered.

06. Make real-time context the product advantage.

One prospect described a next-best-action workflow built from data that was already a week old. For a field team, that is not intelligence; it is a postmortem. The store, shelf, price, stock position, and conversation have already changed.

The better pattern closes the distance between observation and action. A photograph of a shelf becomes a recommendation while the representative is still standing there. That speed is not merely a nicer experience—it changes whether the insight can affect the visit at all.

Action item: measure the age of the context behind every recommendation. Investors: ask what decision becomes possible only because the product acts now rather than in the next dashboard refresh.

07. Design for the worker’s environment, not the demo room.

Frontline software lives under different constraints than white-collar software. Hands are occupied. Connections disappear. Lighting is bad. The employee may have seconds, not minutes. The right input might be a photograph or voice; the right output might be one sentence.

This creates a real vertical advantage. The model may be horizontal, but the interaction design, process timing, and exception handling are not. The product that respects the environment can outperform a more powerful model wrapped in the wrong experience.

Action item: shadow users where the work occurs and remove every tap, field, and prompt that does not survive that environment. Investors: insist on seeing the product used in the field, not only in a conference-room demo.

Watch the episode

08. Use AI to compress implementation, not just the product experience.

Vertical software companies often lose time after the contract is signed. Discovery notes become requirements, requirements become user stories, and local exceptions multiply. Aforza is using AI inside that implementation work, including turning discovery conversations into structured user stories.

This matters because implementation speed is part of the product. If intelligence can shorten the path from agreement to adoption, the company improves time to value, deployment capacity, and the economics of serving complex customers at once.

Action item: map the manual translation steps between signed deal and live workflow, then automate the highest-volume handoffs first. Investors: treat implementation throughput as a core product metric, not a services footnote.

09. Go global by designing for variation from day one.

Aforza’s first customers were in Portugal and Kenya; the product is now used across roughly 70 countries. Edrington reportedly reached 40 markets in under 18 months. That path is possible because country and operating-company variation was treated as a product requirement, not a later localization project.

Vertical does not have to mean geographically narrow. A strong industry data model can provide the common spine while configurable processes absorb local routes, distributors, languages, and commercial rules.

Action item: separate the industry’s universal objects from each market’s configurable rules before international expansion forces the distinction. Investors: ask whether every new country creates reusable product capability or another custom branch.

10. Keep executive sponsorship inside the operating system.

The last mile of vertical AI is organizational. New intelligence changes decisions, responsibilities, and the way managers coach teams. It can disappear into corporate bureaucracy even when the software works exactly as promised.

Dom stays close to steering calls on important rollouts because change management cannot be delegated to a login screen. The best vertical founders do not only understand the workflow; they understand who must sponsor it, who must trust it, and how proof travels through the organization.

Action item: require an executive owner, a quantitative North Star, and a recurring impact review for every major deployment. Investors: ask who inside the customer will still defend the rollout when adoption gets politically difficult.

The moat gets deeper as intelligence moves from answer to action.

This map is qualitative, not a performance score. It translates Dom’s framework across four layers: interface, workflow, data, and commercial model. Move right by becoming more specific to the industry. Move down by owning more of how work is done and value is captured.

The model companies will keep making intelligence cheaper. That does not erase the vertical company. It raises the value of knowing exactly where intelligence belongs.

Watch the episode

See you next Sunday.
— Luke Sophinos

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

Join Xplor Pay and Luke Sophinos, Founder of Vertical SaaS Group, for an executive discussion exploring how AI, workflow ownership, platform strategy, and financial capabilities are reshaping the future of vertical SaaS. Register today and reserve your spot.

Alright, let’s get to it…

Vertical systems of record are about to get bigger. Horizontal tools are getting eaten.

That is the whole AI story if you strip away the hype. Two years of “AI is coming for everything” produced a lot of noise, but the actual shape of the market is becoming clear. AI is the greatest expander of vertical software ever built, and the worst thing that has ever happened to generic horizontal tools and point solutions.

Most founders are reading this wrong. They think AI is a threat to their vertical business. In most cases it is not. It is the force multiplier that makes the system of record or vertical ai company the most valuable asset in software (behind the labs).

I wrote the full version of this in The Seven AI Kingdoms.
Here is the stripped-down map:

There are seven layers to the AI world:

1. Consumer AI operating systems. OpenAI, Meta, Grok. Where people start, ask, shop, create, and never leave.

2. Business AI operating systems. Anthropic, Google, Microsoft. The workspace itself, not a chatbot sitting next to your work.

3. Headless consumer software. Every app that used to be a consumer destination, now running underneath layer one.

4. Headless general business software. CRM, project management, docs, analytics, ticketing. Still necessary (Salesforce, Hubspot, etc.). No longer the front door.

5. Vertical AI operating systems. Health, law, construction, finance. Conversational on the surface, entirely different underneath. Trained on industry data, tuned for industry workflows, wrapped in compliance and human services.

6. Headless vertical software. Payments, payroll, scheduling, regulatory tooling. The unglamorous stack under every vertical AI OS.

7. AI Native Services. The exception-heavy, human-completed work that sits on top of everything.

Now collapse that map into the three buckets that actually matter.

Horizontal AI labs. Layers one and two. A handful of companies, probably fewer than five that matter. They own the general intelligence and the interface, and they will absorb every generic workflow tool in their path.

Headless. Layers three, four, and six. These survive as rails. They still process transactions, store data, handle compliance. But they lose the relationship. The user no longer opens them. Someone else owns the front door, and the front door is where the economics live.

Vertical. Layer five, Systems of Record, Vertical GPT’s, Vertical AI Native Services. This is where the expansion happens.

So if we can agree on that, the important focus point for this weeks edition is the war thats unfolding inside every vertical…

The War Inside the Vertical Bucket

Once you collapse the map, the real fight is not between the three buckets. It is inside the vertical bucket. Three armies are fighting for the same territory: the vertical AI operating system.

Army One: The Vertical System of Record.

The incumbent. It owns the record: the patient, the claim, the project, the job, the student. A decade of workflow data, embedded in the customer’s operations. It has distribution, trust, compliance wrappers, and an integration surface that makes switching painful.

Its weakness is that it thinks in modules and seats. AI gets treated as a feature to bolt on. Pricing is per user, not per outcome. The data moat is real, but it is only worth defending if the company ships AI faster than the insurgents attack.

Army Two: The Vertical-Specific Agents and/or GPT’s.

The insurgent. It offers Agents but underneath it is trained on industry data and tuned for industry workflows. It attacks the highest-value knowledge work first: legal research, clinical notes, claims, quoting, compliance. It often prices with credits/outcomes and moves like a startup. No legacy, no seat-based thinking historical weights holding them down.

Its weakness is fatal if it does not evolve: it has no record. It sees the questions, but not the answers, not the transactions, not the exceptions. The models it rides on are rented and commoditizing. If a SoR ships something similar, cuts off data access, the vertical GPT’s interface is at-risk.

Army Three: The Vertical AI-Native Services Firm.

The shadow. It does not sell software. It sells the outcome: claims processed, denials overturned, reports filed, jobs quoted. It blends AI with human-in-the-loop labor, charges per transaction or per result, and owns the P&L line instead of the software budget.

This is the most underrated army. It can start in the messiest verticals, where software penetration is low and labor is the dominant cost. And as a byproduct of doing the work, it collects the operational record: every claim, every denial reason, every exception.

But it is fighting against commoditization. The work itself is replicable. The labor margin is thin. If this army is going for venture scale, it has to build something incredibly proprietary — a workflow, a data asset, a compliance wrapper, or a feedback loop that nobody else can replicate. That’s the only way the service it is offering continues to exceed every other service company out there.

What Actually Will Decide The Fight

The interface is commoditizing. Every army will have a great chat UI within two years. The model is commoditizing. Everybody rents the same frontier intelligence. What does not commoditize: the record of what actually happened, and the ability to take responsibility for the outcome.

So the war is a fight for the record, fought with three different weapons.

The SoR fights with the data it already owns.

The Vertical GPT fights with the interface and the speed to build a new record from the front door.

The AI-native services firm fights with the outcome data it collects as a byproduct of doing the work.

Three endings are possible. The one you get depends on the shape of the vertical.

Absorption. The SoR wins. It ships Agents, an AI interface, prices on outcomes, etc. And it’s as good as ChatGPT but for their vertical. The services firm becomes a channel partner. This happens in deep, regulated verticals where the incumbent has a real record and the guts to move.

Displacement. The insurgent wins. The GPT or Agents becomes the new record. Fragmented, under-software’d verticals with heavy labor and weak incumbents. The new system of record gets built from the front end or from the operations, and the old tools are forced to go headless or face a slow and agonizing death.

Fusion. The endgame. Record, assistant, and service layer merge into one vertical AI operating system that sells outcomes, not just seats. Every army converges here. The question is whose DNA dominates: the SoR’s data, the Ai Agent/GPT’s interface, or the services firm’s operational grip.

The Tell

Watch who accumulates the exceptions. The GPT sees the questions. The SoR sees the transactions. The services firm sees the denials, the edge cases, the human judgment calls. Exception data is the training data for the next decade of vertical AI. Whoever owns it builds the moat.

The founder implication is uncomfortable.

If you have a system of record, you do not win by bolting on a chatbot. You win by becoming the outcome owner and letting AI compress the work until the record is the only thing left.

If you are a vertical GPT, you are racing to become a record or own the LLM itself before the model or the SoR commoditizes you.

If you are AI-native services, and you are going for venture scale, build something incredibly proprietary — because services alone will not get you there. You risk commodotization like every other service business.

You’re Running Out Of Time :-)

November 4 and 5. Miami. The second annual Vertical Software Summit.

This is the one event where the entire map I just described walks into the same room in person. And if you have been following the posts, you have seen the list. It reads like a market map of the whole category.

Healthcare founders building systems of record for clinics and practices. Construction software operators who have spent a decade in the dirt and have the scars to show for it. Legal tech, insurance, trades, field services, automotive, property management, restaurants, logistics, payments. The entire investment community comes looking to fund you.

Think about what that attendance actually means. In that room, all three armies are present. The SoR founders defending their data. The vertical agent founders racing to build a record. The AI-native services firms quietly collecting outcome data on the back of the work. And the investors who are trying to figure out which one wins.

Here is the honest truth about newsletters and podcasts. You can read every Sunday edition, listen to every episode, and still miss the compound effect of spending two days in a room with three hundred people who live in your exact world. The deal that changes your company’s trajectory gets discussed between sessions. The hire that fixes your hardest problem gets introduced in the hallway. That is not marketing. That is how this community actually works.

The event will sell out. It is that simple. If you have not registered by October 1, you are gambling that a seat will still be there, and that is a bad bet.

The speakers include the people who actually built the playbooks, not the people who talk about them. The attendees are the people executing them. If you build vertical software, vertical AI, or the services layer underneath either, this is your room.

Grab your ticket here.
If you need a free one we will get you one just reply to this email :-)

How To Make Your Merchants Stop Caring About Their Take Rate (& Increase Revenue)

Right now your merchants obsess over their processing rate. 2.2%. 2.3%. They shop it. They threaten to leave over ten basis points.

Why? Because it is their money.

That single fact is the whole opportunity.

Price sensitivity in payments is a function of whose money is being spent. When the merchant pays the fee out of their own margin, every basis point hurts. When the customer pays the fee as a line item, the merchant stops caring. It sounds counterintuitive but hang on with me for a second…

The switch is cash discounting or surcharging. Move the fee from the merchant to the customer line item.

Cash discounting: the merchant posts a cash price and adds a service fee when a card is used. Low friction. Signage and disclosure required.

Surcharging: the merchant adds a surcharge to card transactions. More regulated. Credit cards only, debit cards are off limits, state caps vary.

Now the math that matters.

Typical blended rates runs around 2-2.5%. Under merchant-pay, you earn a thin slice on top, and the merchant watches every basis point. Under customer-pay, you set your program rate at 3.0%. The merchant no longer pays it, so they do not object. The customer pays it, and 9/10 customers just don’t really care anymore…

That 80 basis point spread on the merchant’s total card volume becomes your revenue. On a merchant doing $1 million a year in cards, that is $8,000 a year. Across 1,000 merchants, $8 million a year, collected off a rate your merchants no longer care about.

Here is the part that feels illegal but is not. The merchant loves you for this. You did not raise their cost. You moved their cost to someone else. You are the software company that “got rid of processing fees.” Churn drops. Renewals close themselves. And your payment revenue expands by the full spread.

The objections you will hear, answered:

“Customers will hate it.” Some will. That is why disclosure and signage matter. But consumers are conditioned to paying these fees now. Thank you Toast (vSaaS for restaraunts)!

“Is it legal?” Yes, in most states, with rules. Credit cards yes, debit cards no. State caps vary. Check it before you roll out. Cash discounting IS legal in every state.

“Will merchants accept 3%?” They already accept 2.2% and resent it. They will accept 3.0% and never notice, because it is not their money. That is not a joke. That is behavioral economics. They will push back but when you lean into their own consumer experience, most of them simply can’t argue with the fact that we as consumers are paying these fees EVERYWHERE today.

To close this week out, if you have experience doing this successfully respond and let me know. It’s pretty amazing and kind of counter-intuitive but I’ve just seen it work so many times across so many verticals. And boy is it powerful…

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

Watch the episode

There’s a reason the Sriracha shortage became a national crisis. David Tran built Huy Fong Foods on a handshake — 28 years, tens of millions of dollars in pepper procurement, and no contract. When that relationship broke, the supply chain broke with it, and we all felt it on our grocery shelves. The uncomfortable truth Nic Poulos opened this week’s episode with: most B2B commerce still runs closer to Sriracha than to Amazon. It isn’t frictionless. It’s fundamentally constrained by human relationships.

That’s about to change. AI commerce is coming; not as a slightly nicer shopping cart, but as something that rewires how transactions get brokered, matched, and monetized. That makes it the perfect moment to put Mike Duboe on the show.

Mike is a rare animal in venture: a marketplace philosopher who came up as an operator. He was the first growth hire at Stitch Fix, where he built the growth team from zero all the way through the IPO. Today he’s a general partner at Greylock, investing in network businesses, marketplaces, and consumer — and, in his words, “accidentally” became a vertical software and application investor along the way.

In this week’s episode we covered what an “AI marketplace” actually means, why the take rate is exposed, where the value is moving after the match, and why embedded ads might be the most underrated monetization story in vertical AI. Here’s what I took from the conversation.

Vertical Software Summit
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This Weeks Vertical Titan:
Mike Duboe (GP @ Greylock)

Mike Duboe is the kind of investor you want when the model itself is in flux, because he’s not a thesis guy, he’s a mechanics guy. He doesn’t defend marketplaces on faith; he can take one apart and tell you which gears still turn.

That operator pedigree matters. Mike ran growth at Stitch Fix from pre-scale all the way through its public debut, meaning he lived the two hardest problems in network businesses at ground level: manufacturing liquidity when no one knows you exist, and turning that liquidity into a compounding machine rather than a series of one-off campaigns. He left about a year after the IPO and joined Greylock, where he focused on “network businesses — so marketplaces and consumer networks,” which he says “led me to accidentally becoming more of a vertical software and application investor.”

That accident is our gain. People who think seriously about market structure are rare. People who have actually operated one are rarer. Mike is both, and it shows every time the conversation drifts toward the parts of the model everyone else takes for granted.

His core view on what’s happening right now: AI isn’t creating “AI-native marketplaces.” It’s making more markets addressable to the marketplace model than ever before. Transactions that were previously human-to-human, high-complexity negotiations — the kind that lended themselves to brokerages rather than streamlined marketplaces — are now things agents can handle. And in verticals where no online catalog has ever existed, AI creates the ability to build one that didn’t exist before. That reframe — addressability expansion, not marketplace replacement — is the most useful lens I’ve heard on this topic in a while.

He’s also refreshingly honest about where the model is exposed. His take: “Nowadays, it’s going to be insufficient to just charge for the match.” That line drives the whole playbook section below.

1. The match fee is exposed — move downstream of the transaction

For decades, the core value of a marketplace was the match, and the match is what you got paid for. Mike’s argument is that agents quietly threaten that: if agents can match supply and demand on both sides without a marketplace interface, then charging for the match itself gets compressed. Expect take-rate compression in some categories.

But this is an opening, not a death spiral. The monetization moves downstream of the transaction — credit guarantees, exception handling, verification, traceability. Those are areas where agents are actually smart, and where the marketplace can own something agents can’t easily replicate.

The takeaway: If you’re building a marketplace or a vertical platform that touches commerce, stop building your P&L around match fees. Build it around what happens after two parties find each other — because that’s where durable value (and defensible revenue) is heading.

2. The proprietary catalog is the new system of record

Mike’s most specific investment insight came from High Stock, a liquidation marketplace for excess inventory. The unlock wasn’t the technology — it was the discovery that a whole “dark market” of cross-border distributors quietly buy near-expiration US beauty products and move them overseas. These buyers have no websites, no online catalogs — they exist entirely offline, found through conferences and phone calls. By onboarding them, High Stock ended up with proprietary SKUs in a market that had no legible catalog.

That’s the pattern Mike looks for in B2B: “How are you actually going and creating the catalog versus just piggybacking one that exists?” If the catalog is already legible to agents, agents can bypass you. If you built the catalog nobody had, you’re the system of record. As I put it in the conversation, that’s a proprietary data moat in the agentic era.

The takeaway: Pick verticals where the supply side has never been digitized. The messy, offline, relationship-based parts of a market aren’t a problem to route around — they’re the wedge. If agents can already see the catalog, you don’t have a business; you have a UI.

3. Differentiated logistics beats liquidity when you’re the challenger

Palm Street built a consumer marketplace in categories Whatnot overlooked — plants first, then reptiles. On paper, Whatnot had the liquidity lead. What pulled sellers over? Fulfillment. Shipping a live plant — let alone a reptile — is genuinely non-trivial, and Palm Street’s specialized logistics expertise was enough incentive for sellers to move despite the liquidity gap.

Mike’s framework: what comes downstream of the transaction ends up being a reason to use a marketplace instead of going direct through agents. Liquidity gets you in the door; operational specialization keeps you in the building.

The takeaway: If you’re going second into a marketplace where the winner has liquidity, don’t fight the match — fight the fulfillment, the exception handling, the compliance, the physical weirdness the winner doesn’t want to deal with. Those are moats agents can’t talk their way around.

4. Start with vertical SaaS on one side, add the marketplace later

The classic marketplace-to-vertical-SaaS pivot is well-trodden. Mike lived the data point: Faire, Pepper, Bori, Resnovie — most of the B2B marketplace theses from his early investing days ended up eschewing the marketplace model entirely, building vertical software first, and monetizing on SaaS and payments rather than the match. Pepper eventually layered on an ad network at real scale. But the core model is SaaS, not match fees.

The interesting question the three of us landed on: does AI change the odds? My answer in the episode was that the two models start to blend into one. You get one side of the ecosystem with classic vertical AI tooling, and then over time you spin up the other side — the way a law-firm tool could eventually push clients to lawyers and take a commission on top of SaaS fees. The line between “vertical AI product” and “vertical marketplace product” is going to dissolve, because at some point you’re both the workflow and the transaction.

The takeaway: Don’t feel obligated to solve two-sided liquidity on day one. Enter with the easier side and a valuable tool, build trust and data, and treat the marketplace as an expansion product — not the founding premise. The cold start problem isn’t gone; AI just made it optional to confront immediately.

5. Consumers won’t hand over the transactions that feel good

Here’s the counterweight to all the agentic-commerce hype. Mike thought about this deeply during his Stitch Fix years, and his conclusion: a lot of consumers don’t actually want frictionless shopping. The instinct to automate everything misses that shopping itself is the product for a meaningful slice of purchases.

His split: where the objective is clear, where search and matching is easy, and where shopping feels like work — agents should and will take it. Think replenishment, commodities, the stuff you automate on Amazon anyway. But where the process of shopping is expressive and fun — fashion, discovery, surprise — AI’s role is to make the experience better, not to remove it. That’s why Whatnot became the outlier marketplace of the past decade: it was fun.

The takeaway: When you design for agentic buying, sort your catalog into “chore” and “joy.” Automate the first. For the second, build AI that delights rather than transacts — because the moment you turn a joy purchase into a utility purchase, you’ve destroyed the value you were supposed to capture.

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6. Trust systems were built for humans. Rebuild them for agents.

One of the sharpest one-liners in the conversation: “Most marketplace trust systems — I think about eBay stars — were built for humans. What’s the equivalent built for agents?” The answer is probably verified outcome data — not star ratings, but auditable performance: did the thing arrive, did it match spec, did the counterparty perform? That’s the currency agents will trust.

This has a practical implication for the “dark marketplace” dream — agents transacting with agents while you sleep. The real constraint isn’t technology; it’s that the agent needs to think like you’d think. Abstraction of buyer judgment is the hardest and most valuable asset in the whole system, and you can only build it with deep engagement over time.

The takeaway: If your marketplace or vertical platform has a reputation layer, start asking what the agent-native version of it looks like. Verified outcome data, performance guarantees, and exception-handling records will matter more than human star ratings in an agent-mediated world.

7. Embeds ads are the vertical AI monetization story

This was the section of the conversation where everyone leaned in. Mike’s example: Open Evidence, the clinical AI company, which converted point-of-time physician usage into massive ad revenue essentially overnight — because pharma has huge ad budgets and there has never been a better moment to reach a doctor than while they’re actively reasoning through a case at the keyboard.

I’ve been sold on this since Uber turned on embedded ads and added roughly a billion to the bottom line. And generative AI is the perfect vehicle for it. You’re serving one industry. There are a finite number of companies desperate to reach those users. The user is mid-workflow, talking to an expert (the AI), with intent visible in every token. An ad served there isn’t a banner — it’s contextually relevant information, delivered at exactly the right moment, tied to what the user is already asking about. It’s a no-brainer.

8. Clear ad labeling is a trust feature, not a betrayal

There’s a reflexive worry that ads inside AI products will break user trust. Mike takes the opposite view, and I think he’s right: when it’s clear what is and isn’t an ad, it builds confidence that the default responses are unbiased. Transparency becomes a feature.

The open question, as he framed it, is whether anyone has built the right native ad unit — the one that enhances engagement rather than trading it away. He points out that Instagram ads arguably make Instagram more engaging, which is an absurdly high bar. Right now, most AI ads are effectively search ads (which is why Open Evidence works). The unlock is a native unit nobody has invented yet — creative, bespoke, per-publisher. My favorite idea from this stretch was Luke floating an API key anyone could drop into a vertical AI app to turn on embedded ads in ten seconds flat. The SSP layer for consumer AI apps is probably early. The B2B version is lower volume and higher value — and, for now, inherently consultative.

The takeaway: Vertical AI founders should treat embedded ads as a first-class revenue line, not a sellout. Ship contextually relevant, clearly labeled placements; watch the engagement data; and expect that the first five ad formats you try aren’t the right ones.

9. Growth talent is the scarcest resource in the AI era

Mike grew up in the early-2010s school of systematic growth thinkers — people who engineered compounding loops instead of running tactics. His observation: in the AI era, basic products are trivial to ship, so what differentiates winners is structural distribution, network effects, and durable brand. That makes systematic growth talent more valuable than ever — and there’s a shortage, because technical founders historically under-valued distribution, so the incentive to train the next generation of growth thinkers collapsed.

The current wave of AI-native marketers are scrappy and fast, but “lean more tacticians versus systematic growth thinkers.” And since the tactics are changing faster than ever, being a tactician without principles atrophies quickly.

The takeaway: If you're a vertical AI founder, the best headcount decision you'll make this year is hiring someone who thinks in systems about distribution — not someone who can spin up channels fast. And if you're a growth person: the window for tacticians is closing. The people who can abstract the principles will own this era.

10. The marketplace underwriting checklist, rewritten for agents

Mike ended with the most useful segment of the whole episode: the Bill Gurley / Jeff Jordan evergreen marketplace attributes — and what needs rewriting for the agent era.

Still evergreen:

• High fragmentation on both sides

• High-frequency transactions (or a credible path to frequency)

• Getting into the payment flow

• A founder with an edge on aggregating demand — typically spiking on industry know-how or growth

• A point of view on disintermediation — which in this era means “going to agents”

Getting rewritten:

• Take rate assumptions — the match fee is exposed (playbook #1)

• Reputation systems as lock-in — less confident; agent-native trust isn’t proven yet

• The 10x interface experience — matters less; he’d rather underwrite a founder’s understanding of market structure and their ability to spin up liquidity fast

That last shift is worth sitting with. For a decade, the marketplace bar was product magic. Mike is saying the new bar is market-structure literacy: do you understand the shape of the market, where the supply hides, who holds the inventory, why they’ll move? Can you generate liquidity faster than anyone else? Because interfaces are replaceable now. Market understanding is not.

That’s the episode. As always, the whole conversation is worth your time — Mike learns in public, which is the best kind of guest. If you’re building in vertical AI, vertical SaaS, or anything touching B2B commerce, this one has quite a few frameworks to steal!

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See you next Wednesday.
— Luke Sophinos

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It took Procore 14 years to reach $100M in ARR. It took Harvey three.

Legora went from zero to $100M in ARR in eighteen months. OpenEvidence hit $150M in five. MagicSchool put up $10M in its first year of monetization. GC AI went from $1M to $10M in under twelve months.

Meanwhile, the last generation of vertical winners, the ones we all study, needed a decade on average to do what this generation is doing in thirty-six months…

This is not a rounding error. It is a structural change in how fast vertical software businesses can compound, and it changes how you should think about every vertical market on earth…

The Two Clocks: Legacy Vertical SaaS vs Vertical AI

Twenty years of vertical software history produced a clear tempo. Build the system of record, grind through multi-year implementations, expand seat by seat, attach payments, cross $100M sometime around year ten, go public a few years after that.

Here is what that clock actually looked like:

Veeva was the sprinter of its generation: seven years to $100M with fewer than 200 customers and only $12M raised. That was considered one of the greatest executions in software history.

Procore was the grinder: a decade to reach $5M, fourteen years to $100M, twenty-two to a billion. Clio needed 14 years to hit $100M and crossed $500M in May of this year, eighteen years after founding. Doximity, a genuinely elite business, took ten years to cross $100M.

Median time to $100M ARR for the vertical software greats: roughly a decade.
Median time to $500M for the ones that got there: well into the teens.

Now look at the clock the vertical AI generation is running…

Harvey crossed $100M in ARR in August 2025, three years after founding. By this summer it was at $350M. If the line holds, Harvey gets to $500M in under five years. Clio just did it in eighteen.

And then Legora broke the clock entirely: $100M of ARR in eighteen months from founding, in the same vertical as Harvey, with half the ramp. Eighteen months is faster than Wiz, faster than Deel, faster than nearly any software company ever measured — and it happened in law, a category the last generation needed a decade and a half to crack.

OpenEvidence is the outer edge: $50M to $150M in ARR in five months, faster than three quarters of every SaaS company ever measured. It reached 300,000 doctors in about a year. Doximity needed a decade to assemble that same audience.

That is the part worth internalizing. The audience was compounding for years while the revenue line sat flat at zero, and monetization was not a build — it was a switch. When a vertical AI product owns the daily habit of a licensed, high-value professional, the revenue ramp is not a function of go-to-market. It is a function of the day someone decides to charge.

Distribution is pre-built.
The wallet got bigger.
Adoption friction collapsed.

The three mechanisms

Distribution is pre-built. The vertical SaaS generation spent twenty years dragging laggard industries onto cloud and mobile. The AI generation inherits buyers who already purchase software, already hold budget, and already know their workflows are broken. Harvey never had to convince a law firm that technology matters. A decade of legal tech did that for them.

The wallet got bigger. Vertical SaaS sold seats, and seat budgets are small. Vertical AI sells completed work: a drafted motion, a resolved claim, a documented encounter. That spend comes out of labor and outsourced services budgets, which dwarf software budgets in every vertical. The AI company is not competing for the incumbent’s line item. It is eating the line item above it.

Adoption friction collapsed. The legacy winners fought implementations measured in years. The AI winners ride an interface that feels like a conversation and returns value in the first session. MagicSchool signed up six million educators without a sales force worth mentioning. That motion did not exist in 2011.

Two honest caveats, because they matter.

Speed is not margin. Harvey’s revenue carries real model costs underneath it in a way Veeva’s never did. A $100M AI ARR and a $100M SaaS ARR are not the same business, and the market will eventually price the difference.

And the vertical still sets the floor. GC AI compounds 23% a month because in-house legal budgets are centralized and decisions are fast. The model does not override the market’s metabolism. It removes the excuses.

That is not a faster horse. It is a different race.

The best vertical software businesses of the last cycle were decade-long builds, and the very best of them, Clio included, needed the better part of two decades to reach $500M. The best vertical AI businesses of this cycle are compressing that into three to five years.

Let’s look at the chart in aggregate…

This may be my favorite chart in newsletter history.
What a time to be alive…

The Map Was Already Drawn

In 2007, Peter Gassner left Salesforce to build software for one industry: pharmaceuticals. Everyone told him the market was too small. Veeva now does $3.4B in revenue and trades around $33B.

In 2011, Aman Narang and his co-founders started building point of sale for restaurants, a category investors had written off as a services-heavy graveyard. Toast now runs at roughly $5B in annual revenue.

In 2012, Ara Mahdessian and Vahe Kuzoyan started building software for plumbers and HVAC shops out of a garage, because their parents ran trades businesses. ServiceTitan crossed $1B in revenue this year.

Each of these stories has the same shape. A founder picked a market that looked boring or broken from the outside, spent a decade proving the wallet existed, educating the buyer, and wiring up the workflow. Then the business compounded for another decade.

Here is the part nobody is pricing in: every one of those stories is also a prospectus for the next one.

Every public vertical software company above a billion in revenue is a proof statement. It proves the demand exists. It proves the buyer can be trained. It proves the workflow is budgeted. Those are the three hardest things to establish in any vertical, and the incumbent already paid for all of them.

Which means the correct way to map vertical AI opportunity is not to chase what is trending. It is to look at where vertical software already produced massive winners and ask: who is the AI-native successor here, and does one exist yet?

Run the scoreboard:

Now notice where the vertical AI winners showed up first: medicine and law.

Doctors got OpenEvidence. Lawyers got Harvey, Legora, and GC AI. Why those two? Because Doximity spent a decade assembling the physician graph and proving doctors monetize, and Clio spent eighteen years proving legal budgets are real, real enough to build a $500M ARR business on top of them. When the AI products arrived, those markets were pre-sold.

The software winner is not the competition.
It is the proof of wallet.

And the successor’s ceiling is higher than the incumbent’s. Veeva sold software, a tool budget. Whoever builds the Veeva of AI sells labor and outcomes, an operating expense budget. The second wallet is five to ten times the size of the first.

Gassner proved pharma pays. Narang proved restaurants pay. Mahdessian proved the trades pay. Twenty years of vertical software history drew the map, marked the wallets, and trained the buyers.

The vertical AI generation does not need a better map. It needs to move faster on the one it was handed. And as this week’s numbers show, it already is…

The Best Value Creation Levers Don’t Stand Alone

The best Vertical SaaS companies I’ve worked with all have one thing in common: they don’t try to do everything.

Instead, they’re remarkably disciplined about where they invest. They evaluate every product decision, strategic initiative, and investment not only by its immediate return, but by what it makes possible next.

That’s a different way of thinking about strategy. If you spend enough time talking with software founders and private equity investors, you’ll notice a common pattern. Nearly every strategic conversation eventually turns into the same question:

Where should we invest next?

The answers are familiar. AI. Embedded payments. Pricing optimization. International expansion. New financial products. Services. Each has a compelling business case, and each has the potential to create meaningful enterprise value.

But I think we’re starting to ask the wrong question. Instead of asking which initiative will deliver the biggest return, leaders should ask:

Which investment will make every future investment more valuable?

Every Vertical SaaS company has more opportunities than it has engineering resources, investment capital, or organizational capacity. The challenge isn’t identifying good ideas. It’s deciding which investments create the greatest strategic optionality over the next five years.

One thing I’ve noticed is that the strongest SaaS companies rarely chase trends. They make a handful of strategic investments that continue paying dividends as the business evolves. Looking back, those decisions often seem obvious. At the time, they usually required saying no to a dozen other attractive opportunities.

Some initiatives create value. Others strengthen the platform in ways that make future initiatives more valuable. The best ones do both. Those are the investments that compound over time.

Take AI as an example. AI can automate workflows, improve customer support, and increase productivity. But its impact changes dramatically when it’s built on top of rich operational data, well-defined workflows, and years of customer context.

The technology matters. The foundation matters even more.

The same principle applies across many of the strategic decisions software companies are making today. Better operational data improves AI outcomes. Deeper workflow ownership strengthens customer retention. Financial infrastructure creates opportunities for embedded finance. Transaction data unlocks new insights.

Viewed independently, these look like separate initiatives competing for budget. Viewed strategically, they’re connected capabilities that reinforce one another.

Embedded payments is one example. For years, we’ve talked about payments as a value creation lever, and I think that’s still true. It can generate new revenue, strengthen customer relationships, and contribute to enterprise value.

But perhaps the bigger opportunity isn’t payments alone.

It’s what becomes possible once payments become part of the platform. Transaction data becomes more accessible. Financial workflows become more connected. Future financial products become easier to introduce. Each new capability increases the value of the next.

That’s why I believe the most successful vertical SaaS companies will increasingly think less about individual initiatives and more about building platforms that create future opportunities.

These are exactly the kinds of conversations I’ll be having with Mark Passifione and Daniel Burton of Xplor Pay during our executive discussion, Building More Valuable Vertical SaaS Companies, on September 10. If these questions are on your mind too, I hope you’ll join us.

→ Reserve Your Spot

The best value creation levers don’t stand alone. Neither do the conversations that shape them.

The next generation of Vertical SaaS leaders won’t win because they identified more value creation levers than everyone else.

They’ll win because they invested in capabilities that made every future lever more powerful.

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

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Every vertical AI company with a seed round is suddenly “forward deployed.” Palantir’s own executives made fun of the copycats on an earnings call: sales engineers in costume. And there is a reason everyone claims it — the model works. But it works like a chainsaw. Great in the right hands, a disaster in the wrong ones.

This week Nick and I sat down with Quinn Litherland, founder and CEO of Revin. They build verticalized automation for home services: HVAC, plumbing, roofing, remodeling, pool services. Think AI Native Services for Mid-Market/Enterprise Field Services. Three years old, just over $10M raised, out of New York. Their agents answer calls at 9pm, qualify homeowners, run a $30,000 roof sale across a four-week omnichannel campaign, and book the job back into ServiceTitan. Their FDEs sit in a random state in the U.S. every week running $250M remodeling businesses. And Quinn has thought harder about how to make forward-deployed actually scale — and how to avoid the two traps that kill companies that try it — than almost anyone we have had on the show.

This episode is a masterclass in the FDE motion: who you hire, how you pay them, where they sit, how you templatize the role, and how you keep from becoming a services shop with venture funding and a prayer.

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We are already 80% sold out so if you’re considering coming to the Vertical
Software Summit this November, now is the time to grab your ticket.

All information at the link below.

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It’s truly the best few days of the year in vertical software and vertical AI.

This Weeks Vertical Titan:
Quinn Litherland
(Founder/CEO @ Revin)

Quinn walked into one of the most fragmented markets in software: home services, a $650B-plus industry with 2.5 million providers running systems older than the startups selling to them.

Revin targets mid-market to enterprise: $25–30M family businesses that scaled regionally, plus PE platforms like Vertex doing $600M+ across 25–26 brands. ACVs run $70K to $250K. A roofing company pays that because a single new job is worth $50,000, and Revin can close five new customers in month one that would have leaked through the funnel.

The strategic insight worth studying is the reframe. Revin is not selling software. They are selling labor. Their workflows and agents are employees to these businesses. And that changes everything downstream — because a remote employee who is onboarded badly and doesn’t understand the business will never run operations effectively. You cannot just ship a dashboard and wait.

Revin is not selling software. We are selling labor.

Quinn Litherland, Revin

So Revin embeds. Deep, fast, from day one of a customer going live. At some point they are essentially running your business, and ripping them out becomes nearly impossible. The product’s atomic unit isn’t the UI. It is the agents, learning directly from every conversation, improving constantly. Quinn calls the endgame “headless companies”: a $500M roofing business run by five people and a stack of agents.

The core product insight is more interesting than the product itself. A roofing job isn’t a transaction; it’s a $30,000 sale that happens over a four-week omnichannel campaign. Someone has to answer the phone at 9pm, qualify the homeowner, follow up while they’re deciding, maintain context across SMS, email, and voice, and then hand off a warm, fully-qualified lead. Historically that “action layer” was people — call center reps doing the same motions for decades, leaking lead after lead. Revvin replaced that with agents that own the workflow end to end: inbound speed to lead, qualification, job-type matching, booking back into ServiceTitan or whatever the system of record is.

That is the vision. The engine that makes it real — and the reason this episode matters — is the forward-deployed motion. That is what this newsletter is about.

01. The Forward-Deployed Motion
Deploy, maintain, expand — in that order.
Renewals are the scoreboard.

That is the Revin mantra. And I love it.

Deploy: go live as fast as possible with the highest quality outcomes.

Maintain: don’t vanish at month three, which is where most FDE-heavy deals die.

Expand: if a customer is buying more workflows, they are happy, and “you crush these three workflows, now do these six” becomes the natural motion. When LTV goes from $70K to $250K to $1M, the renewal stops being a negotiation and becomes a planning session.

Hire founder-shaped operators, not sales engineers.

The profile is the story. Paul, one of Revin’s best FDEs, ran a bootstrapped car detailing business in Austin doing about $500K a month, built his own tools, exited, and joined. Today he manages a couple billion dollars of enterprise value in roofing and works with the C-suites of these businesses. Quinn’s line: Paul could leave tomorrow and run a roofing roll-up better than 95% of the private equity folks he meets. Implementation skills, expansion instincts, industry fluency, and deep empathy for operators because he was one. This is not a sales engineer. This is a founder who chose your company instead of starting his own.

Pay that person like an owner — because they are one.

Quinn points to Decagon as the model. An FDE who can make a million dollars this year if every account renews, plus a cut of expansion, will act like the CEO of their book of business. Renewal-based commission turns your most expensive cost center into a revenue engine. And the economics work: hire one of these people instead of the old pod of PM, engineer, CSM, and sales rep. Pay $250–300K plus commission and you replaced six heads with one. But you have to get the hire right, because a miss is more expensive and more painful than anything we have seen in software.

Put forward-deployed under product, not engineering.

The field is where the signal lives. Your FDE should be able to say, “I have seen this five times today, we need to solve it.” Under engineering, you build what you think is right instead of what the trenches are telling you. Revin’s FDEs are the product org’s nervous system. They are in middle-of-nowhere Ohio every week, understanding these operators in a way a PM staring at a dashboard never will. Handled well, the FDE motion forces you to actually understand the businesses you serve — stickier product, more durable value. Handled wrong, you are screwed.

Watch for the two traps, because both will get you.

This is the most important part of the conversation. Trap one: you become a services factory — running a services playbook on VC money, praying you will eventually automate the humans. Trap two: everyone is “forward deployed” and nobody is building repeatable systems — chickens with their heads cut off. Quinn’s hard truth: forward-deployed is often an excuse not to build great product, and a lot of teams will be screwed because no one is coming to save them.

The escape hatch is obsessive templatization. A year ago, onboarding meant flying to the customer and building from scratch. Today it is: “You are a $50M HVAC business, here are the semantics you will run into. 99% of the time we already know your qualification process, your job-type matching, your system of record.” The FDE handles the residual 1%. Every new customer must take measurably less human effort than the last. Otherwise you are not building a company. You are building a nonprofit consulting firm with venture backing.

The FDE role is getting less technical — and that is fine.

Teams are already using the term “AI deployment strategist,” and Revin’s newest FDEs are less and less technical. The core requirements: really smart product thinkers who can talk hand in hand with a customer, because they are embedded in the trenches. Quinn expects a wave of startups building internal tooling for this motion, which eventually lets the role migrate toward a CSM shell. The jobs-to-be-done don’t change — deployment, trust-building, feeding signal back to product — but the profile is shifting from engineer to operator.

Sell the person, not just the product.

Buyers of vertical AI are starting to demand it: “I don’t want to be your guinea pig. Who am I working with day to day?” Operators want to meet their deployment counterpart before signing, because they will change and improve these workflows together. Your FDE is now a sales asset. Show the operator the exact person who will sit in their trenches and perfect their workflows. If the motion is good, it sells itself. If it isn’t, your sales cycle will find out.

Stake out the action layer, and don’t stab the system of record bear.

For 20 years in home services, humans were the action layer. They interacted with a system of record, performed a task, and either automation pumped the data back or they copy-pasted it themselves. In the agentic era, the agent is the system of action. Quinn’s read: own the workflow and you win. It is the lifeblood of the business, and it matters relatively less where the data lives. And critically: do not announce you are now a field service management tool. The Podium/ServiceTitan blowup is the cautionary tale — Podium spent 18 months building a competitive FSM product, refused marketplace terms, and got shut off. Revin’s stance: be a great integration partner, pipe data back into the system of record, stay clear of the bear. If you own the action, you own the monetizable moment.

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In low-ACV markets, the product has to be the answer — not more FDEs.

The uncomfortable question for anyone with a $5K ACV product: why does this need humans to embed it into companies that won’t pay enough to make the math work? If the buyer can’t pay for the deployment, the product has to get to a place where it doesn’t need one. Quinn’s first question to low-ACV teams is always, “Why is it so low, and what else could you provide that increases their outcomes?” And Nick’s corollary: the gap between “handles 90% of the workflow but you still log in” and “handles it soup to nuts” is enormous — $10–20K versus $100K, because offloading an entire process carries a major premium. If software ACV can’t grow, count the payments and transactional revenue attached to actions. The revenue is there; it’s just not in the SaaS line item.

The throughline of this conversation is that the hard part of vertical AI was never the model. It’s the trust, the buy-in, and the dirty work of embedding your people into businesses that have run the same way for 30 years — and then converting all of that into repeatable product before the services trap swallows you.

Quinn’s bet is that the teams who do that painful work properly are the ones who win the next decade of vertical software. I think he is right for AI Native Services Companies. Handled well, the FDE motion isn’t a cost of doing business. It’s the moat.

If you are building anywhere the action layer is still run by humans, this episode is required listening. Watch it on The Verticals YouTube channel, and subscribe to the newsletter right here.

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See you next Wednesday.
— Luke Sophinos

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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A Guide For When To Go Headless & When To Own The UI

The most dangerous assumption in vertical software right now is that owning the interface is always the prize. It was the correct assumption for twenty years. The company with the login page won.

The AI era inverts that logic, and most founders have not caught up.

A small number of horizontal AI platforms are going to own the general interfaces of the world. Everything else gets pushed into one of two camps: vertical AI operating systems that own deep industry workflows, or headless software that runs underneath everyone else’s screen. Both can be enormous. The difference is whether you choose your camp before the kings arrive, or get assigned one after.

This week: the framework for surrendering the UI layer, when to do it, when to fight like hell to keep it, and three vantage points on the same decision.

The Seven Kingdoms: where the interface is actually going

I wrote about this in Linear #174: The Seven AI Kingdoms. The short version: the AI software stack is reorganizing into seven distinct layers, not the flat SaaS landscape we came from.

Here is the mechanism worth sitting with. The horizontal kings are not going to go all the way down into every market. They cannot. Their economics forbid it. A platform worth a trillion dollars does not deploy its best model, its brand, and its distribution into a workflow that produces a few hundred million in software spend. The marginal cost of entering a big new market is basically a compliance wrapper, an industry landing page, and a sales team. For a small vertical, the same effort does not move their needle at all.

So the world filters cleanly. Healthcare, education, legal, financial services: the giants are already building the general UI there. Elevator repair, fire departments, art galleries, cemeteries: they will never come.

That means the interface layer of large white-collar verticals is being claimed by the kings. The interface layer of everything else belongs to the vertical AI operating systems: the industry-specific “Claude Cowork” that looks conversational on the surface but runs on real workflow depth, regulatory knowledge, and trust. And underneath both sits Kingdom VI, headless vertical software, quietly becoming a compounding infrastructure business.

Headless is not a consolation prize. It is a position.

Surrendering the UI Layer: When to Do It and Why

First, the distinction. The UI layer is the product your end user logs into. The interface owns the relationship, the daily habit, the data, the trust. Headless means you never see the end user. Your customer is a developer. They embed your engine inside their product, and their users never know you exist.

Examples everyone has seen from underneath: Plaid is headless despite every fintech app showing its logo on a bank selection screen. Twilio is headless. Stripe started as an API and only later earned the right to touch merchants directly. Shippo is headless. Pretty much everything in the e-commerce checkout flow you touch is someone else’s engine wearing the storefront’s clothes.

This is not new. Point Nine’s essay The Rise of Vertical APIs catalogued this a decade ago: Onfleet for delivery operations, Eligible for healthcare payments, Infermedica for diagnostic triage, Retsly for MLS data, Aftership for tracking, Commerce.js for storefront infrastructure. It split vertical APIs into two buckets: data APIs and infrastructure APIs. That essay aged perfectly. It just called the category too early.

One thing up front so nobody over-rotates: these are not prerequisites. Not every point below has to be true for headless to be the right call. But at least some of them should be, and the more that hold, the stronger your position. If you have one of these in full force, that can be enough. If you have three, you are building infrastructure on purpose.

Here is when you should go headless.

1. You serve a massive, broad vertical, especially white-collar with heavy screen time.

That is where the giants go. And we can learn exactly who by looking at who they have already targeted.

If your vertical is on that map, the giants are building the general UI for your customer as you read this. Do not fight that at the surface. You do not out-brand OpenAI inside a hospital. You win underneath them.

But there is a caveat worth stating plainly, because this is where the real openings hide. This point only applies if you are serving the broad vertical at the same altitude they are. The moment you go deeper, the game changes. Be the AI for chiropractors, not the AI for healthcare. Be the AI for behavioral health practices, not the AI for hospitals. Be the AI for a specific specialty, a specific workflow, a specific back office, and suddenly you are not competing with the horizontal kings at all. You are operating in a category they will never care about.

The rule for that move is simple and unforgiving: your slice has to be different enough, and ideally much more complex, that the broad tool is 10x worse than your solution. If a general model with a landing page gets you 80% of the way, the giant wins, because they have distribution and you do not. If your depth makes their general tool look like a toy, you win, because no amount of model quality replaces workflow specificity. Depth is the exception that beats scale.

2. The math does not work for the small spaces.

Here is a number worth sitting with. A $100B+ company needs multi-billion dollar outcomes to move its own needle. Not a billion. Multiple billions. A billion-dollar outcome is the same to them as your smallest ACV customer churning is to you. Which is quite wild to think about.

That one sentence explains more about the AI landscape than any model benchmark. It is why the kings go after healthcare and education and legal and not elevator repair. It is why their roadmap is finite and yours is not. And it is the quiet promise underneath the entire “own the UI” argument. They cannot chase you if you are too small to matter to them.

3. Your primary end customer is a developer.

If the buyer is an engineer who wants capability embedded, you are headless whether you admit it or not. The Point Nine essay is the playbook: data APIs, infrastructure APIs, compliance as a service. When your users are other products, you are not in a UI business, so stop spending like you are. No marketing site can fix a product that was built to be embedded.

4. You can solve one problem incredibly well, and it is big enough to still be a massive business.

Shippo. One multi-carrier shipping API with 40+ carriers underneath: rates, labels, tracking, address validation, all over a single REST interface. It never tried to be the storefront. It made shipping disappear as a problem, and every e-commerce platform became more valuable by embedding it. One problem, infrastructure depth, enormous TAM. That is the template. The mistake founders make is assuming that going headless means going narrow in ambition. It does not. It just means the surface is someone else’s and the engine is yours.

5. The trojan horse rule: a dominant industry ERP already owns the workflow.

If the entire industry runs on one system and the operators open it every morning, you do not win the login. You go headless, integrate into their screen, and become the layer they cannot live without. You win by being impossible to remove from inside the incumbent, not by being the second tab nobody opens.

And the trojan horse is just the starting point. There is a longer game underneath it. You integrate to get as much data as possible. You learn the workflows from the inside, the ones that never made it into any requirements doc. You map every inefficiency and every user complaint. And then, when the knowledge is complete and the moment is right, you build those workflows 10x better and launch the competing UI layer, this time from a position of total information. Every roll-up in software history is this play. The headless phase is how you earn the right to eventually own the screen.

Now the other side…

When to not go headless.
When to push hard and own the interface.

1. Your market is too small to move the needle for a $100B+ company.

If the kings will never come, the interface is the biggest prize on the board and it is yours for the taking. Niche is not a limitation here. It is a moat. The very math that pushes the giants toward healthcare is the math that guarantees they leave your vertical alone forever.

2. There is no dominant industry-specific vSaaS yet.

Open field. The interface is the prize. Take it before someone else does.

And let me be direct here, because I know a lot of you do not believe this anymore: this is the classic vertical software play, and it still works. There are still a ton of markets with four or five players that each own maybe ten percent. The other half of the industry is still running on paper, spreadsheets, or frankensteining together horizontal tools that were never built for them. That is not a mature market. That is an open market wearing a mature market’s clothes. The AI layer makes this easier, not harder, because you can reach the paper-based half of the market without building a twenty-person onboarding team.

3. The dominant vSaaS is showing wobbliness.

Private equity bought it and squeezed it. Customers hate it. Data can be pried out of it. R&D has stalled. That is an incumbent that stopped compounding, and the UI is winnable. Entrenched is not the same as strong. Some of the best businesses in this category were built by watching the category leader get hollowed out by financial engineering and simply walking in the front door.

4. You are already the market-leading end-to-end system.

Do not surrender what you already own. Put the AI layer into your own interface and widen the gap. The headless argument is for people who do not have the login page. If you already have it, you are not a utility. You are the throne.

That is the full framework. Now three vantage points on the same decision.

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Real Examples In The Wild

Metriport: the headless checkmark

Metriport is an open-source, universal API for healthcare data. One API for complete patient data, built on FHIR, providing real-time access to medical records for more than 300 million Americans. Your customer is a developer building a health product, and what they buy is access to every patient record in the country without building 40 EHR integrations themselves.

Now check it against the when-to-go-headless list.

Healthcare is the purest example of a massive, white-collar, high-screen-time vertical. Clinicians live in EHRs all day. And the giants are already here: OpenAI, Google, and Microsoft are all shipping healthcare products, while the clinicians’ actual screen is owned by Epic and the entrenched EHR incumbents. You are not winning that UI, and it would be incredibly difficult to try. And that is assuming you are serving healthcare broadly. Go a slice deeper, behavioral health, therapy practices, a specific clinical specialty, and the calculus shifts again, because now depth can beat scale. Metriport is not trying to be that slice. It is making itself useful to every slice at once.

The primary customer is a developer. The one problem solved incredibly well: access longitudinal medical records from every provider, instantly. The trojan horse: integrate underneath whatever the clinician already uses, learn the workflows from the inside, and become the data layer every health product is built on. And the dominant industry layer, the EHRs, is so entrenched that the UI war is not just hard, it is pointless.

The evidence it works: Sollis Health uses Metriport to pull prior EKGs, operative reports, imaging, and lab results in seconds, which keeps patients out of the emergency room. The infrastructure runs at 99.999% uptime and processes over 100 million requests a day. SOC 2, HITRUST, HIPAA. A YC company building the data layer for healthcare. Open source as a trust wedge with exactly the developer buyer it needs.

Metriport is building the engine of healthcare, not the screen. In a market the giants are entering, that is the right side of the table.

First Due: the UI checkmark

First Due is the fastest-growing end-to-end platform for Fire and EMS. It consolidates NERIS incident reporting, ePCR, fire prevention, pre-incident planning, scheduling, assets and inventory, hydrants, training, incident command, and mobile response into a single application with one login. Their own words: the last piece of software your agency needs to buy. More than 2,800 agencies use it, including Charlotte, Mesa, Seattle, and FDNY.

Now check it against the when-to-own-the-interface list.

The market is too small for a $100B+ company to ever care. Fire departments are thousands of fragmented agencies with mission-critical needs and small budgets. OpenAI is never building a fire records management system. The math is definitive. Even a great outcome in this market would be a rounding error on their P&L, which means the interface stays available for whoever earns it.

There was no dominant end-to-end vSaaS in the category. The legacy tools are siloed, disconnected, and universally complained about. That wobbliness is the opening, and First Due is consolidating the field with one modern, cloud-native platform that replaces the patchwork module by module. The fragmented incumbents each hold their little slice, and the other half of the market is still doing this on paper. Classic vertical software play, still working, exactly as described above.

And it is becoming, or already is, the market-leading end-to-end system. Once the fire chief logs into First Due for incident reporting, scheduling, training, and hydrant testing, that interface is the defensibility. The data lives there. The daily habit lives there. The AI layer gets added there, on top of a workflow the company already owns end to end.

First Due is building the screen on purpose. In a market the kings will never attack, the interface is not a battleground. It is a fortress.

Field Services: the interface is a red ocean, the engine is a blue ocean

Field services is the most visible vertical AI battlefield on earth right now. And that is exactly why you should think carefully before joining the fight for the login.

The market is enormous and proven. ServiceTitan went public with roughly $772 million in ARR and a market cap north of $6 billion. Beneath it: Housecall Pro for the small shops, Jobber for the middle, and a long tail of Workiz, Kickserv, FieldEdge, Service Fusion, mHelpDesk, FieldPulse, ServiceTrade, Fieldy. Above it, Salesforce Field Service and ServiceNow FSM push down from the enterprise. The software market alone is $6B+ and growing double digits.

And now the AI layer is piling on. Hundreds, probably thousands of AI upstarts, all with different logins, all pitching themselves as the AI-native operating system for the trades. Every single one trying to own the interface.

To be clear, I am not arguing the incumbents are unbeatable. I think they get beaten. ServiceTitan is a public company now, and public market pressure has a way of pulling category leaders toward financial engineering over innovation. The trades are exactly the kind of market where an AI-native upstart eventually takes the crown.

But here is the question nobody wants to answer: what are your odds of being that upstart?

If there are a thousand startups aiming at this interface, one or two win. Maybe. The other 998 get decimated. The category is enormous, the prize is enormous, and the competition is a massacre. That is not a thesis. That is a lottery ticket with extra steps.

Now flip it. Instead of fighting for the login, go headless. Hook into the model layer: build on Claude or GPT and become the vertical intelligence nobody else bothers to build. Or hook into the workflow layer: integrate into ServiceTitan, Housecall Pro, or Jobber and become the AI capability they cannot live without.

That is a genuinely blue ocean. Why? Because the people fighting the interface war have no time to build the engine. Because the incumbents, even the ones who will eventually lose, need AI depth right now and cannot build it all themselves. Because the horizontal platforms want vertical reach and cannot do it alone. Because the one or two winners who eventually take the UI will need the best engine available, and it does not have to be theirs.

And here is the beauty of the trojan horse. You integrate into whoever owns the screen today. You get the data. You learn the workflow. You become structurally important. And when the UI war finally shakes out, whether the winner is ServiceTitan, an upstart, or a horizontal king, you are already inside. You do not care who wins the surface, because you own the layer every winner has to build on.

Everyone is fighting to be login number one thousand and one. The smarter play is to be the engine inside whichever login wins.

Metriport surrendered the screen and became the engine of healthcare.

First Due is building the screen on purpose in a market the kings will never enter.

And in field services, thousands of startups are fighting to be the one login that survives, when the smarter play is to be the engine that every login, winner or loser, has to build on.

The interface is not always the throne. Sometimes the throne is underneath.

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

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SaaS was stupidly easy to model. Recurring revenue, 80% gross margins, land-and-expand, negative churn. Investors could underwrite it in their sleep. And that, says Yoni Rechtman, is exactly why it’s over.

Here’s the uncomfortable math underneath the whole conversation: a normal business spends 1–3% of its revenue on software. It spends the other 97% on work; labor, contractors, BPOs, services. For fifty years that imbalance held because software was small but hard to make, and being small was survivable.

Two things just changed at once. Software got easy to build. And software got able to do things, to execute the work instead of merely representing it. You can now sell the hammered nail instead of the hammer, and the market for hammered nails is 30 to 100 times bigger than the market for hammers.

In the episode, Yoni walks through the five models he thinks actually work going forward:

1. Service to differentiate software (FDE) You take responsibility for the outcome, not just the tool. “I’m going to change my software around your business process, and I’m going to guarantee that it’s going to do that — and I’ll create that guarantee through a level of service.” This decommodifies the value proposition and lets you sell expertise and “AI transformation” through a different line item, not just a web app. It’s the forward-deployed engineer (FDE) model.

2. Software to differentiate a service (neo-firms) The inverse: you’re running a service business, but software is what makes you faster, cheaper, and higher quality than every other provider of that service. You own the delivery and the liability — which is why Yoni says he’d rather underwrite one of these than a “post-trained model in a nice UI.”

3. AI rollups: Buying existing services businesses and using software + AI to compress their cost structure and standardize delivery — a “growth buyout” wrapper. This is the model Slow has written decks about, where the software isn’t the product; the service company is the product and AI is the margin expansion tool.

4. Agent networks Start in single-player mode — do a unit of work for money, get paid to acquire customers — then flip into multiplayer mode once you own a population. Phoebe (home care) is the example: it starts as labor orchestration filling shifts, and the network of caregivers becomes the asset it can resell to new demand. The end state Yoni calls a “for-profit union.” This one, he says, simply “wasn’t possible before agentic AI” — the 2019 “come for the tool, stay for the network” attempts failed because there was no platform shift underneath.

5. Hardware to differentiate software Software moated by a physical device — the hardware is the wedge that keeps competitors (who only have API keys) out. This is the Metropolis-style play he references: physical infrastructure that software then operates and monetizes.

The throughline on all five: each one escapes the old SaaS trap of selling a tool into the 1–3% of revenue companies spend on software, and instead sells or owns an outcome in the other 97% — the labor and services line items. As he puts it, you can sell the hammered nail instead of the hammer, and the market for nails is 30–100x bigger.

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This Weeks Vertical Titan:
Yoni Rechtman (Partner @ Slow)

Yoni Rechtman is a partner at Slow Ventures, a seed and pre-seed fund. Ask him to describe the firm and he’ll say “generalist,” but that undersells it. Slow’s actual filter is narrower and more interesting: they’re basically hunting for new business models and capital structure wrappers for software, which, as Yoni puts it, “often winds up leading us towards vertical software or vertical AI, in the broadest sense.”

The portfolio proves the thesis. Slow was a seed investor in Metropolis, which used acquisitions to build its way into parking and is now a $5 billion AI company. They backed Recurrence, maker of Phoebe, the home-care platform positioning itself as a three-sided agent network. They led Ando’s seed round in restaurant staffing. The throughline isn’t an industry — it’s structure. Every one of these companies is trying to escape the software business model and own an outcome instead.

What led him here is one observation with violent implications: no one has ever woken up and wanted to buy workflow software. People want invoices sent, claims filed, shifts filled. The only software businesses that ever got to sell outcomes — Visa selling transactions, Uber selling rides, Google selling clicks, Facebook selling customers; were network-effects businesses. As he puts it, “I am starting with a recognition of what most people and businesses in the world want to spend money on, which is not software.” Everything else in this conversation follows from that.

01. Sell the hammered nail, not the hammer.

The core mechanic behind the entire episode: software is priced against the 1–3% of revenue companies spend on software, but the value of an outcome lives in the labor line item. The nth AI receptionist for dental clinics isn’t ripping because it’s better software — it’s ripping because the buyer compares it to a human they’d otherwise pay, not to their practice management system. And here’s the underrated reason that’s an easy sale: businesses love firing contractors. “You pay 7% of your revenue to a medical billing firm — I’ll do the same work for 5%, better and faster.” That drops straight to the bottom line.

Action item: Recalculate your TAM (and your pitch) against the labor or services line item you replace, not the software line item you improve. Position yourself as the contractor replacement, not the software upgrade.

#02. The income statement gets worse. Take the trade anyway.

Yoni is brutally honest about the new model’s economics. Inference cost hits gross margin. Switching costs collapse when the user is an agent swapping API calls instead of a human learning a UI. You can’t amortize R&D as long because competitive pressure is relentless. “Along basically every line of the income statement kind of gets a little bit worse — but the markets get a lot better.” His prediction: software companies get much bigger, run lower margins, and produce far more absolute dollars of revenue and cash flow.

Action item: Stop underwriting your business — and stop letting investors underwrite it — on SaaS-style margin profiles. Model it against a labor-sized market and be upfront that the margin structure is different. It’s the on-prem-to-cloud trade, on a bigger magnitude.

#03. Service to differentiate software — FDE as a pricing unlock.

The first model in the framework. The reason forward-deployed engineers have come into vogue isn’t Palantir’s cultural shadow — it’s that FDE “is allowing people to make bigger promises and therefore charge more money.” Most industries are reticent buyers because they don’t trust the promises: sales says nice things, post-sales says “we’ll do a training.” That’s why ACVs cap out at $50K instead of $500K. When you say “I’ll change my software around your business process and guarantee it works,” you decommodify the value proposition — and you sell into a different line item entirely, because right now enterprises are buying “AI transformation,” expertise, and future-proofing, not web apps.

Action item: If your ACV is stuck because buyers don’t trust the software alone to deliver the outcome, add a service layer that guarantees it — and charge for the guarantee, not the features.

#04. Ownership is the dividing line between software and a service.

Yoni’s test for any AI company: are you responsible for doing the work, or not? There’s no blurry middle. “Harvey could not simply decide to be a law firm — because then it would be a law firm.” He’d rather underwrite a company that owns delivery, quality, and liability — even at a lower likelihood of success — than “a post-trained model presented to you in a nice UI.” His comparison: Crosby definitionally adds value above its inference relative to Heartbeat, because Crosby owns the outputs and the quality of its work.

Action item: Ask which side of the ownership line you’re on. If you’re on the UI side, remember the customer is buying the model’s capability — and the margin accrues to whoever owns the outcome.

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#05. A BPO with tokens is a wedge, not a terminus — and the burden of proof is on you.

Yoni’s honest read: most vertical AI right now is “building BPOs with tokens instead of people.” That’s a great wedge and a murky end state, because someone can always come along and do the work cheaper, “either because their cost structure is better, or they’re just willing to tolerate lower revenues than you are.” His framing for founders and investors alike: “the burden of proof is on you for why there’s moat and terminal margin and terminal value, not on me to demonstrate that there isn’t.”

Action item: If your only moat is price, write the plan for what happens when the undercutting starts — self-improving systems that hit escape velocity, trust and brand, network effects, or distribution. “We do it cheaper” is not a plan.

#06. Everyone has the same API keys — a better prompt is a bet against model progress.

Yoni’s one piece of religion: “We all have access to the same API keys, so no matter what you do, you have to substantially differentiate tokens and code.” Better prompts are not a long-term differentiator. His exhibit A is Jasper, which was “selling a series of skills and prompts for copywriting” and got eaten the moment models absorbed the skill. Even “proprietary data” is fragile — the model labs are actively buying that data to fold into the weights. The PowerPoint corpora people called proprietary a few years ago? “The labs just bought it.”

Action item: Assume any differentiation expressed as a prompt, a thin fine-tune, or a dataset a lab could purchase has a short shelf life. Durable value has to live in workflow ownership, process, distribution, or network position.

#07. Get paid to acquire customers — the single-player to multiplayer motion.

The most novel model in the framework: agent networks. Start in single-player mode with a clear, legible value prop — doing a unit of work for money — and use that revenue and customer access to acquire a large population, then flip into multiplayer mode. The last generation of network businesses spent their way through the cold start problem (free pizza, subsidized rides, signup cash). The new generation gets paid to acquire customers. Yoni’s answer for why “come for the tool, stay for the network” failed in 2019: there was no platform shift underneath — “no profound emerging capability, just two separate kinds of capabilities put together.” Agents that can actually do the work are the shift.

Action item: Before you start, be specific about the second thing you’ll be able to do once you own a population of customers. If you don’t have a conviction about it, you’re just running a services business with extra steps.

#08. The for-profit union.

Slow’s portfolio company Recurrence (the Phoebe product) is the model in action. It starts as labor orchestration for home care — filling shifts for agencies. In doing so, it gets in front of all the caregivers, the entire supply side. The obvious next step is a talent agent: resell that supply to new sources of demand. The agency pays them to build the network; then the network becomes the business. Yoni calls the end state a for-profit union — “if you can serve a valuable universe of supply and control the attention of that valuable universe of supply, there’s a huge amount to do with it.” And one thing agents are genuinely good at: managing attention and negotiating.

Action item: Look for businesses where doing paid work plants you in front of a concentrated supply pool. The network asset you unlock is worth more than the work you’re billing for.

#09. What the customer thinks they’re buying is what you’re selling.

When Nic argued the service/software line is blurry and useless, Yoni pushed back hard: “It’s not unclear for the customers. Whatever they think they’re buying is what you’re selling.” If they think they bought software, you get priced and sold like software. If they think they bought a service, they expect ownership, liability, and someone to call. The customer’s belief dictates your promises, your channel, and how you charge — “all of those things are really dictated by what the customer thinks they’re buying.”

Action item: Audit every touchpoint — sales collateral, pricing page, onboarding — for what the customer would conclude they bought. Fix the mismatch between what you think you’re selling and what they’d say they bought.

#10. The compounding asset is the entire game.

Yoni’s test for whether any of this holds up: from customer 1 to customer 20, something has to get better. “You want to say every customer we get, the product is better and more valuable.” If every engagement is de novo — “every customer is its own snowflake and butterfly” — there’s no compounding asset and you don’t have a venture business; you have a high-end consulting shop. His most contrarian suggestion for where that asset can live: productize the process, not what flows through it. The first scaled consulting businesses — BCG, Bain, McKinsey — were selling a new technology of scientific management, not outputs. “Process and product are distinct until they’re not.”

Action item: For every new customer, ask what the engagement teaches you that makes the next one cheaper, better, or faster. If the honest answer is "nothing," you're not building an asset — and the process you use to deliver may be more productizable than the thing you hand over.

Sell the hammered nail, not the hammer.
Own the work, not the workflow.
Get paid to acquire customers.

Four things worth taping to the wall — whether you’re building the next vertical AI company or underwriting one:

1. The money is in the labor line item, not the software line item. Businesses spend 1–3% of revenue on software and the other 97% on work. Software can finally do that work, which means the addressable market just got 30 to 100 times bigger. Position against the contractor you replace, not the incumbent software you improve — businesses love firing contractors.

2. Ownership is the dividing line. Either you’re responsible for doing the work or you’re not, and the customer always knows which one they bought. Harvey can’t simply decide to be a law firm, because then it would be a law firm. A company that owns delivery, quality, and liability is worth more than a post-trained model in a nice UI — because the margin accrues to whoever owns the outcome.

3. The burden of proof is on you. Most vertical AI right now is a BPO with tokens instead of people. That’s a wedge, not a terminus — someone can always do the work cheaper. Assume there’s no moat, terminal margin, or terminal value until you prove otherwise. Everyone has the same API keys; a better prompt is a bet against model progress. Take the uglier income statement if it buys you a labor-sized market.

4. Networked verticals get built by people who get paid to build them. The last generation burned capital through the cold-start problem. This one inverts it: do a unit of work, earn the customers, then flip single-player into multiplayer. Recurrence’s home-care story is the template — get in front of all the caregivers, and the end state is a for-profit union. But it only compounds if every new customer makes the product better. If every engagement is de novo, you’re a consulting shop with a data-lake accessory.

If there’s a single through-line to steal from Yoni this week, it’s this: the best software companies of the next decade sell outcomes into the 97%, structure themselves as services where ownership is the moat, get paid to acquire the networks that make them defensible, and never mistake access to API keys for a business. That’s the compounding formula.

Big thanks to Yoni for opening up the Slow Ventures thinking to the rest of us. If you’re building or backing the software layer, the services layer, or the agent-network layer of a vertical, this episode is required listening. Forward it to a founder or an investor in vertical AI. If you’re building something interesting in the space, my inbox is open.

Watch the episode

See you next Wednesday.
— Luke Sophinos

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

Join Xplor Pay and Luke Sophinos, Founder of Vertical SaaS Group, for an executive discussion exploring how AI, workflow ownership, platform strategy, and financial capabilities are reshaping the future of vertical SaaS. Register today and reserve your spot.

Vertical-Specific GPT’s

A lot of vertical AI companies are not losing to incumbents.
They are losing to ChatGPT.

The founders who understand this are quietly building something different. They are not shipping features. They are shipping answers. Better, deeper, more specific answers than any horizontal model can produce, for the exact questions their customer asks every single day.

If your vertical AI product cannot out-answer GPT on the top 1000 questions your customer actually asks, you do not have a moat. You have a wrapper with a login screen.

This week is about how to actually build that moat, why the Vertical Software Summit is the only room where operators are talking about it honestly, and which vertical GPTs are already proving the model works at scale.

Prompting Your Way to an Epic Vertical AI Use Case

Most vertical AI products die at the prompt layer. The team ships a chat interface, wraps a horizontal model, adds a domain-specific system message, and calls it a product. Six months later the same customer asks the same question in ChatGPT and gets a comparable answer. The wrapper is exposed. Retention collapses. The team blames distribution.

Distribution is not the problem. The answer is the problem.

Here is what the operators actually winning in vertical AI are doing. This is the full playbook.

Step 1: Map the top 1000 questions in order of importance.

Not 10. Not 100. One thousand.

The number is not arbitrary. Below 100 you have a demo. Below 500 you have a feature. At 1000 you have a product. Above 1000 you start covering the long tail where retention actually lives.

How to build the list:

• Pull two years of support tickets. Every ticket is a question in disguise.

• Transcribe every sales call, onboarding call, and QBR from the last 12 months. Extract the questions the customer asked, not the ones your team answered.

• Sit inside the customer for a full week. Not a demo day. A real operating week. Watch what they Google. Watch what they ask their coworker. Watch what they paste into ChatGPT when they think nobody is looking.

• Scrape the industry subreddits, the private Slack groups, the closed Facebook groups, and the trade forums. That is where the real questions live.

• Interview 25 customers with one prompt: what did you ask an expert this week that you wish software could answer.

Then rank them. Two axes. Frequency and economic weight. A question asked 40 times a week by a billing manager beats a question asked once a quarter by the CFO. Order the list. That order is your build sequence.

This list is not a document. It is your product roadmap. Everything downstream of this list is execution.

Step 2: Build a massively detailed prompt for each question.

Twenty pages is not a joke. It is the floor.

The best vertical prompts in production today are structured like operating manuals written by a senior domain expert who is also a systems thinker. Every one of them contains, at minimum:

• Domain framework. The mental model an expert uses to approach this question. Not the answer. The way of thinking.

• Vocabulary layer. The exact terms of art, the acronyms, the regional variations, and the terms that mean different things in different sub-verticals.

• Regulatory context. What the customer is legally allowed to do with this answer. What disclaimers must appear. What jurisdictions apply.

• Workflow context. Where in the customer’s day this question gets asked. What tool they are in when they ask it. What the answer needs to plug into next.

• Output format. Text, table, filled form, diagram, deck, doc, cited citation list. Different questions want different artifacts.

• Edge cases. The top 20 ways this question can be asked incorrectly, incompletely, or with hidden assumptions.

• Graceful degradation. What the answer looks like when 30 percent of the required data is missing. What it looks like at 60 percent missing. What triggers a human handoff.

• Source hierarchy. Which sources are authoritative, which are supporting, which are disqualified.

• Anti-hallucination rules. The specific claim types this prompt is not allowed to make without a citation.

• Tone and register. How a senior person in this vertical actually talks.

If your prompt fits on one screen, you have not done the work. You have written a system message.

Step 3: Test every model against every prompt.

GPT, Claude, Gemini, open source frontier models, image models, deck generators, doc generators, spreadsheet generators, form fillers. Do not have a favorite model. Have a favorite outcome.

For every question in the 1000, run the prompt through every viable model. Score the output on:

• Factual accuracy against a domain-expert answer key.

• Format fidelity to what the customer actually needs.

• Latency at the point of use.

• Cost per answer at scale.

• Failure mode when the input is broken.

Some questions want Claude for reasoning. Some want GPT for structured output. Some want Gemini for long context. Some want an image model to produce an annotated diagram. Some want a deck generator to produce a client-ready output in 30 seconds. Match the model to the answer, not to the vendor relationship.

The customer does not care which model ran. The customer cares that the answer was right.

Step 4: Benchmark every answer against horizontal AI.

For each of the 1000 questions, paste the raw question into ChatGPT, Claude, and Gemini as a naive user would. Capture that answer. That is your baseline.

Your product must produce an answer that is 10x better than the baseline. Not 20 percent better. Not 2x better. 10x.

Ten times better means one or more of the following:

• Cited where the baseline is not.

• Structured for the workflow where the baseline is prose.

• Compliant where the baseline is generic.

• Current where the baseline is stale.

• Personalized to the customer’s data where the baseline is universal.

• Delivered inside the tool where the baseline requires a copy paste.

If you cannot articulate the 10x on a per-question basis, that question is not yet a moat. It is a coin flip. Coin flips lose to whoever has the better distribution, and horizontal AI has better distribution than you do.

Step 5: Build the master spreadsheet.

This is the artifact that separates operators who talk about vertical AI from operators who ship it.

Columns:

• A: Question ID

• B: The question in the customer’s actual words

• C: Ranked importance (1 to 1000)

• D: Frequency per customer per month

• E: Economic weight (dollars touched by this question)

• F: The full prompt (link to the 20 plus page doc)

• G: All similar questions that route to this same prompt (typically 5 to 30 variants)

• H: The winning model for this question

• I: Output format

• J: Latency target

• K: Cost per answer

• L: Horizontal AI baseline answer (link)

• M: Your answer (link)

• N: The specific 10x delta (one sentence)

• O: Score against baseline (1 to 10)

• P: Last tested date

• Q: Owner

• R: Retest cadence

That spreadsheet is your real product spec. The UI is downstream of it. The pricing is downstream of it. The GTM is downstream of it. The moat is inside it.

Step 6: Retest on a cadence.

The horizontal models get better every quarter. Your 10x lead compresses if you do not defend it. Set a mandatory retest cadence. The top 100 questions retest monthly. The next 400 retest quarterly. The bottom 500 retest twice a year. If any question drops below 10x against the current frontier, it moves back into the active build queue.

This is not a project. It is an operating discipline.

The uncomfortable truth: being in the workflow is not a moat anymore. Answering domain questions better than any horizontal model, on a per-question basis, benchmarked and defended, is the moat. If a horizontal model can match your answer, your customer will eventually notice, and your product will collapse into a login screen on top of someone else’s intelligence.

The work is boring. The 1000 questions are boring. The 20 page prompts are boring. The spreadsheet is boring. That is exactly why it compounds. Nobody else is willing to do it.

The Vertical Software Summit 2026

There is a reason we built this thing.

For a decade the vertical software conversation has been fragmented. Scattered X threads. A handful of newsletters. One or two tracks buried inside larger horizontal conferences. A lot of private group chats and off-the-record dinners. The category that quietly produces most of the durable software value in the world had no home.

The Vertical Software Summit is that home.

What makes the format work is what it refuses to be.

It is not a payments conference masquerading as a vertical software confernece.
It is not an investor conference not allowing other investors in.
It is the only conference by vertical software and ai founders for vertical software and ai founders.

The Summit is founders, operators, investors, and all the companies that support vertical businesses talking to each other, in one room, without the noise.

The lineup tells the story:

• Founders who built systems of record in industries most tier one VCs would not touch. Pool cleaning software. Woodworking software. Pizza shop software. Laundromat software. The unglamorous categories that produce 40 percent EBITDA and 110 percent net revenue retention.

• Acquirers & Investors who understand that a boring vertical with those numbers is worth more than a horizontal darling burning cash at scale.

• Operators who have survived the SaaS Crash, the SVB bank crisis, the SaaSpocalaypse and are now walking through the software to AI transition in real time. This is the room where founders learn what the second transition actually costs, whats working, from the people currently paying for it.

• AI-Native companies that are showing us how fast growth can look when you launch a net-new vertical AI product.

The programming is centered around:

1. War stories and hard lessons learned from the folks that have monopolized their verticals.

2. Playbooks we can all take into our own businesses that are already proving to work in adjacent industries.

3. CxO’s who can go deep in particular areas — AI-powered sales enablement, GTM, Customer Success, etc.

The bet underneath the event is simple. Vertical software is not a niche of the tech industry. It is the majority of durable software value creation. The market has priced this in at the incumbent level. It has not priced it in at the emerging company level. That gap is where the next decade of returns lives.

The people already in the room know this. The Summit is where they compare notes.

If you build, invest in, or acquire vertical software companies, this is the room you should be in. I hope to see you there! Grab your ticket, come enjoy Miami in November, and do it before tickets sell out!

Check Out The Summit

The Vertical GPTs That Are Actually Winning

The fastest growing AI companies in the world right now are not horizontal chat products. They are vertical-specific GPTs that decided, early, to out-answer the horizontal models on a narrow, high-value domain. Every single one of them, whether they call it that or not, has been running some version of the 1000 question playbook.

Harvey did it for elite law firms first. The product is not a chat interface. The product is an internal framework that reflects how BigLaw associates actually draft, review, cite, and defend legal work. Horizontal models produce plausible legal text. Harvey produces text a senior partner will sign. That difference is worth every dollar the top firms pay for it, and it is why Harvey now sits inside a large share of the AmLaw 100. The moat is not the model. The moat is the encoded understanding of how legal work actually gets produced inside a firm, and the citation and review infrastructure that makes the output defensible.

MagicSchool did it for K-12 teachers. The horizontal models can technically write a lesson plan. What they cannot do is write a lesson plan that maps to a specific state standard, differentiates for three reading levels in the same classroom, aligns with the district’s approved curriculum, and outputs in the exact format the teacher’s LMS expects. MagicSchool encoded that specificity across dozens of daily teacher workflows and became one of the fastest adopted education products of the AI era, now used by millions of educators across tens of thousands of schools. Teachers do not stay because the model is better. They stay because every answer lands inside their actual workflow.

OpenEvidence did it for practicing physicians. Founder Daniel Nadler had already sold Kensho to S&P for around 700M. He did not need to build this. He built it because he saw that the highest frequency, highest stakes questions in American medicine were being answered either by memory, by outdated UpToDate articles, or by physicians pasting patient scenarios into ChatGPT under the desk. OpenEvidence mapped those questions. Cited every answer to peer-reviewed medical literature. Made it free at the point of use. Monetized, quietly, through embedded pharma advertising to a captive audience of prescribers. The result is one of the fastest ARR ramps in software history, roughly 150M in run rate, reached faster than almost any software company on record. A meaningful percentage of practicing US physicians now use it daily. The horizontal models cannot compete on this domain because they were never given the citation infrastructure, the regulatory awareness, or the workflow context that OpenEvidence encodes by default.

ChipAgents did it for semiconductor design. This is the vertical where horizontal AI fails most visibly. The vocabulary alone requires years of encoded context. RTL, verification, timing closure, DFT, tapeout. A general purpose model produces confident nonsense the moment you push past surface level questions. ChipAgents encoded the domain, integrated with the EDA toolchain, and gave semiconductor engineers an answer layer that actually understands what they are asking. The company raised 21M to keep expanding that answer surface, and its wedge tells you exactly where vertical AI has structural advantage: the more specialized the vocabulary and the more expensive the mistake, the wider the moat.

TrunkTools did it for commercial construction. Construction is the perfect vertical AI target and the perfect vertical AI graveyard. Every project generates thousands of pages of specifications, drawings, RFIs, submittals, and change orders. Every question a superintendent asks in the field has a definitive answer buried somewhere in that document set, and no horizontal model can find it because horizontal models were not trained on a specific project’s document universe, do not understand the specification hierarchy, and cannot cite the exact page a general contractor needs to defend a decision. TrunkTools built the answer layer on top of that document set. A super in the field asks a question. TrunkTools returns the exact spec section, the exact drawing detail, the exact RFI response, cited to the page. The 1000 questions in construction are things like: what is the required fire rating for this partition, what submittal governs this fixture, what is the latest revision of this drawing, which RFI answered this clash. Horizontal AI cannot touch any of that. TrunkTools can, and the general contractors and subcontractors adopting it are compounding a data advantage horizontal models will never replicate.

The pattern under every one of them is identical. They did not win because they had access to a better base model. They won because they took the boring, unglamorous work of mapping their customer’s real questions, building the answer infrastructure around them, and defending that infrastructure with citations, workflow integration, and continuous benchmarking against the horizontal frontier.

The horizontal models are impressive. The vertical products are indispensable.

That is the difference between a demo and a business, and it is the difference between an AI company that will still exist in 2030 and one that already quietly does not.

See you next Sunday.

Luke

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

Ticket prices go up September 1st, and may even run out by then. We are already 75% sold out so if you’re considering coming to the Vertical Software Summit this November, now is the time to grab your ticket!

All information at the link below.
It’s truly the best few days of the year in vertical software and vertical AI.

Check out the conference

Underwrite the platform.
Own what nobody wrote down.

Most VCs in the country have a thesis on vertical software and vertical AI. Which translates to them essentially making educated bets. Sahil Poddar isn’t. As the co-founder and CEO of Parafin, he powers embedded capital inside some of the largest platforms on earth — DoorDash, Worldpay, Mindbody, Amazon, Gusto, and dozens more. That gives him one of the rarest seats in tech: he’s underwriting vertical software companies from the inside. He sees which platforms compound, which stall, which have real merchant lock-in, and which are one AI cycle away from being replaced.

This week’s conversation is the closest thing you’ll get to a live scan of the vertical SaaS ecosystem — what separates the great platforms from the median ones, why sequencing into financial services matters more than ambition, how AI is quietly rewriting underwriting and moats, and why “blue-collar” vertical software might be the most defensible category left standing. If you build, invest in, or sell into vertical software, this one’s essential reading.

Three things in this conversation should change how founders and investors work:

First, distribution — the thing that decides whether a vertical fintech line ever pencils: direct-to-SMB is a graveyard and embedded is the unlock. Marketing a loan selects for the borrowers you least want. Pushing a pre-approved offer through the platform a merchant already opens every morning selects for the ones you do — and hands you granular transaction data to underwrite with instead of a personal credit score.

Second, the separator between best-in-class vertical SaaS and the median: the great platforms act as collective bargaining agents for their merchants. They negotiate payments and capital on behalf of thousands of small businesses and repackage the result as a core service. The merchant data shows the payoff — a hard split between the one-to-two feature cohort and the six-plus feature cohort, with financing very often the stepping stone across.

Third, the moat question every vertical software board is asking wrong. If everything worth knowing about your industry is written in a document an AI can read, AI is coming for it. Legal, finance, accounting — exposed. Pizza shops, home services, anything running on knowledge that only exists in practice — defensible, and even more so once you move past pure software into capital, supply and physical goods.

Watch the Episode

This Weeks Vertical Titan:
Sahil Poddar (Co-Founder & CEO @ Parafin)

Sahil’s path is not the standard fintech founder arc. He grew up in a small-business family in India, then went deep on the sciences — bachelor’s, master’s, and eventually a PhD in particle physics. That work pulled him into data science, which pulled him into Meta, and then — somewhat serendipitously — into Robinhood, where he built and led major pieces of the growth, revenue, payments, and risk infrastructure. It’s also where he met the co-founders he’d eventually start Parafin with.

What Sahil saw at Robinhood was a renaissance in consumer finance. You could download five or six apps from the App Store, move money instantly, and get a best-in-class experience. But he noticed the sector driving ~40% of the U.S. economy — small and medium businesses — was quietly being left behind. Payments had been digitized by Square and Stripe. Credit had not. SMBs had exactly two ways to fund growth: retained earnings or the debt markets. Equity wasn’t a realistic option. And the debt markets, in Sahil’s words, were unfair.

That’s the wedge Parafin was built on. But Sahil’s insight wasn’t just “lend to SMBs.” It was the opposite of the conventional playbook. He looked at the graveyard of direct-to-SMB lenders — Kabbage, BlueVine, OnDeck — and concluded the whole model was structurally broken: marketing-led acquisition attracts adverse selection, you end up competing on ad creative, you have zero proprietary data to underwrite on, and you lead with none of the trust that lending requires.

So Parafin flipped it. Instead of going direct, they went through the platforms — embedding capital inside the software that SMBs already trust, already live inside, and that already has asymmetric transaction data on them. DoorDash knows exactly how much Nic’s restaurant sold last quarter. Mindbody knows exactly how a yoga studio is trending. That data becomes the underwriting engine, and the platform becomes the distribution engine. The result: zero CAC, higher conversion, lower loss rates, and enterprise churn that has stayed at effectively zero since day one.

Today Parafin runs three products — working capital, BNPL, and platform-embedded financing — has built its own transformer model (Paraformer) for time-series credit analysis, and just shipped a bank-transaction AI system that can produce a credit limit from a single Plaid token. Their BNPL line — starting with Gusto for payroll deferral — is growing 3x faster than anything they’ve ever launched. He’s building the underwriting layer for the entire vertical software economy. And because of the seat he sits in, he can see the pattern behind every great vertical SaaS company operating today.

Live Workshop Tomorrow @ 4pm EST.
How to Build An Epic Customer Conference

If you aren’t yet doing a customer conference you should. No matter your size.
I would add seven figures of ARR every time we did it and I’m bringing on the exact guy who organized mine to teach us all how to knock it out of the park. Hope you’ll join us!

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01. Act as the collective bargaining agent for your customers.

Across Parafin’s entire portfolio of platform partners, the single strongest predictor of a great vertical software company is this: it aggressively negotiates with vendors on behalf of its customer base — payments, lending, insurance, supplies, marketing. The harder the platform bargains, the more valuable it becomes to the SMB.

This is what makes a vertical platform look and feel like a franchise. The median platform negotiates to maximize its own take rate. The best ones negotiate to maximize what their customers get, and take the compounding retention that follows. It is the Costco instinct applied to vertical software.

Action item: Founders: in your next vendor negotiation, write down what your customer gets before what you get. If that list is shorter, you’re monetizing your base instead of representing it. Investors: this instinct is the cleanest qualitative separator between best-in-class vertical software and the median.

#02. The wedge gets you in. The feature stack keeps you there.

Every great vertical software company gets its foot in the door with one aha moment — one workflow, one wedge, one feature. But durability comes from stacking features over time until you are the operating system for that business.

Sahil’s data shows two clusters of customers forming on every platform: those using one or two features, and those using six or more. The 6+ cluster is where nearly all the LTV lives, and churn there is a fraction of the rest of the base. Growth is not a funnel problem — it’s a feature-depth problem.

Action item: pull your own feature-count histogram this week and manage to the 6+ cluster, not to logo count. Investors: ask any vertical platform for that distribution. The shape of it is the growth model.

#03. Sequence financial services with discipline, not ambition.

Sahil is blunt: if a platform has less than roughly $1B in payments volume, layering in lending usually doesn’t make sense yet. Parafin routinely turns platforms down and tells them to go make the core product better first.

Payments, then lending, then deeper embedded services is a ladder, not a menu. Your platform has to be sticky and scaled before it can be a distribution channel for capital. Otherwise a financing line is a press release attached to a thin base — and one that consumes the team’s attention at exactly the wrong moment.

Action item: don’t bolt on financial products until your core has the volume and retention to make them a channel rather than a distraction. Investors: ask for the sequencing ladder, not the ambition slide.

#04. Financial products are the on-ramp to your highest-LTV cohort.

Once a customer takes capital through the platform, they adopt more products across the ecosystem — fast. Financing is one of the strongest gateway features in vertical software, not because of the spread, but because of what it does to the rest of the account.

The mental model shift is to stop treating embedded finance as a revenue line and start treating it as the fastest known path into the 6+ feature cluster. Priced that way, the decision changes: you optimize for adoption and trust, not take rate.

Action item: measure your financial products by downstream feature adoption and retention, not by their standalone margin. Investors: ask what happens to net revenue retention after a customer’s first capital event.

A quick word from our sponsor on the Verticals podcast, Parafin. Fresh off the 2026 Forbes Fintech 50 and a new credit facility led by Goldman Sachs, they’ve now extended over $35B in offers to small businesses through platforms like DoorDash, Gusto, and TikTok Shop.

Purpose-built embedded capital for vertical platforms.

Check Out Parafin

#05. Sell the MVP. Then delight into the expansion.

One of the sharpest lessons from Parafin’s early enterprise wins: always sell the smallest version of the product that still delivers real value. If the buyer will pay for the MVP, everything you build on top becomes delight instead of obligation.

Luke shared the inverse — an early $1M ACV deal where he oversold, promised the world, and spent years rebuilding trust. Overselling a whale is a debt that compounds at a punishing rate, and you pay it back in the exact quarters you needed for building.

Action item: strike every unbuilt feature from the enterprise deck. If the deal dies without them, you have a hypothesis with a contract attached, not a wedge. Investors: ask what shipped on the day of signature, not what’s on the roadmap.

#06. Enterprise sales works best when it’s cerebral, not aggressive.

Parafin has won some of the biggest logos on earth with what Sahil calls a ‘very cerebral, non-salesy’ motion: lead with data, lead with analytics, and be willing to say ‘you might not be ready for this yet.’

That intellectual honesty compounds. In enterprise, being the party willing to walk away wins more deals than being the party willing to promise anything. In a market saturated with AI-generated outreach, the scarce asset is a human who will tell a buyer the truth about their own numbers.

Action item: put one honest disqualifying sentence in your next enterprise pitch — the deals you lose to it were never going to activate anyway. Investors: signed logos are not the milestone in vertical software. Activation is.

#07. When your first whale asks you to jump, jump — but never go single-threaded.

Parafin’s first partner was a public DoorDash. On the deciding call, DoorDash asked how big the team was. Sahil answered, ‘Everyone’s on the call.’ Five people. They won by moving faster than any incumbent could.

In parallel they landed partners across other verticals so they were never dependent on one relationship. Balance founder-mode intensity with the discipline of a second and third thread — big partners go quiet for reasons that have nothing to do with you: a reorg, a roadmap freeze, a new CFO.

Action item: pull out every stop for the whale, and keep two other flagship accounts in flight so no single partner’s pause becomes your pause. Investors: concentration isn’t automatically a red flag — an idle roadmap caused by concentration is.

#08. Increase your surface area for luck.

Every early-stage vertical business has a cold-start problem. Sahil’s solution was volume of exposure: talk to as many prospective partners as humanly possible, show up with the most compelling product flow in the room, and give them zero reasons to say no.

Not a deck — a working flow. Absorb the feedback, tighten it, and run it again. Luck in a vertical market is mostly a function of pipeline density and the quality of what you put in front of people.

Action item: count your live prospect conversations this quarter. Under twenty and your problem isn’t conversion, it’s exposure. Investors: at inception stage, surface area and follow-through are the two signals you can actually underwrite.

#09. Your moat is what AI can’t learn from a document.

Sahil’s test is the sharpest moat question in vertical software right now: is your industry’s knowledge written down? If the expertise lives in manuals, statutes, and documentation, a model can absorb it and the software layer gets commoditized. If it lives in practice, relationships, and undocumented judgment, you have time.

Screen time is the second axis. Verticals where the operator lives in software all day are the most exposed to a generalized interface; verticals where the work happens off-screen are harder to reach. Score yourself honestly on both.

Action item: ask whether your value is institutionalized in documents or embedded in practice — if it’s written down, you need a layer beyond software. Investors: this is the best one-question screen for a vertical portfolio today.

#10. Insights are cheap. Actions are the product.

Parafin explicitly declined to build an ‘AI financial insights’ product. Their view: an insight without an action isn’t enough. What changes with modern AI is that the model can now take the action — tool calling, agentic payments, computer use — and that’s the moment there’s a real product instead of a dashboard.

Their filter for every AI idea is two gates: does it save the customer time or money by completing a task, and does the company have a genuine right to win in that category? Most ideas fail one or both. Meanwhile the highest-return AI work has been pointed at the core — underwriting, fraud, reconciliation, projections.

Action item: run every AI roadmap item through both gates and kill anything that clears only one; build the action, not the dashboard. Investors: ‘right to win’ is the question most vertical AI decks never answer.

Underwrite the platform, not just the merchant.
Do the task, not the dashboard. Own what nobody wrote down.

Four things worth taping to the wall — whether you’re operating a vertical platform or underwriting one:

1. Distribution through the workflow owner changes the business. Founders get a better borrower pool, better conversion, better loss rates and data no direct competitor can see. Investors should treat that as a different business model, not a cheaper CAC line.

2. Embedding means data flows in. If the user has to leave, apply cold and maybe get rejected, you built a worse version of direct inside someone else’s product. It’s the fastest way to tell a real integration from a partnership press release.

3. AI has to complete the task, and you need a right to win it. Insight is table stakes and worth nothing on its own. Most vertical AI roadmaps clear the first gate and quietly fail the second.

4. The moat test is whether the knowledge is written down. Documented industries are exposed. Practice-based, physical, off-screen industries are defensible — and get more defensible the further past pure software you go. Score every vertical in your portfolio or your roadmap on that single axis.

If there’s a single through-line to steal from Sahil this week, it’s this: the best vertical software companies act like franchises, negotiate like collectives, sequence financial services with discipline, and build moats in the parts of the business AI can’t read from a document. That’s the compounding formula.

Big thanks to Sahil for opening up the Parafin learnings to the rest of us. If you’re building or backing the workflow layer, the capital layer, or the AI layer of a vertical, this episode is required listening. Forward it to a founder or an investor in vertical software — and if you’re building something interesting in the space, my inbox is open.

Watch the Episode

See you next Wednesday.
— Luke Sophinos

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