Linear #172.5: The Best Vertical AI Founders Will Build Systems Of Love W/ Alex Niehenke (Partner @ Scale)
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There’s a specific kind of startup worth paying attention to right now: the one that goes straight at a category everyone else assumes is locked up.
Not glamorous markets. Not greenfield software. Markets with incumbents. Markets with procurement. Markets with ancient systems of record. Markets where customers have been overpaying for mediocre software for years.
That’s the backdrop for this episode’s conversation with Alex Niehenke of Scale Venture Partners. The core idea is simple: vertical AI is having its moment not because incumbents are small, but because many of them are slow, bloated, and structurally unable to reinvent themselves fast enough. When the dominant players have low customer love, rising prices, and little appetite to disrupt their own revenue streams, the opening for startups gets real very quickly…
This Weeks Titan: Alex Niehenke (Partner at Scale)
Alex Niehenke is a Partner at Scale Venture Partners, where he focuses on early investments in vertical markets where incumbents have failed to invest in advanced technology. He has been at Scale since 2012 and has backed companies including Motive, Root Insurance, Scout RFP, Range, Proscia, and Archipelago. His investing lens was shaped early by watching his father’s transportation businesses intersect with the rest of the economy, which gave him a long-running curiosity about how real industries actually work.
That background matters because Alex has spent years leaning into sectors many investors historically dismissed as “boring”: insurance, logistics, construction, procurement, financial services, and other operationally dense industries. In the episode, he makes the case that these are exactly the kinds of markets where startups can win big, because the software is often deeply entrenched but poorly loved. The old venture knock on vertical software was always that TAMs were too small and buyers were too hard. Alex’s view is almost the inverse: those markets often only look unattractive until someone finally builds something customers actually want.
Scale’s own positioning maps cleanly to that worldview. The firm describes itself as backing early-stage AI companies on the journey from founder-led growth to a go-to-market machine. In other words, Scale is not just underwriting raw technical novelty; it is looking for companies that can turn product advantage into durable commercial momentum. That framing shows up all over Alex’s comments in the episode, especially in his emphasis on growth velocity, category timing, and whether a startup can become indispensable before the incumbent fully wakes up.
The most useful way to understand Alex is that he is not simply bullish on AI. He is bullish on what happens when AI meets neglected vertical markets. He is looking for wedges where legacy vendors have spent years extracting price instead of reinvesting in product, where user NPS is poor, and where a startup can build what he calls a “system of love” rather than just another system of record. That distinction is the heart of the episode.
1. Build a “system of love,” not just a better workflow
Alex’s most important point is that the best vertical AI companies do not just replace software. They replace resentment. In many verticals, the legacy leader still owns the system of record, but it no longer owns user trust. Customers are stuck with bloated tools, poor UX, and annual price increases that feel disconnected from product improvement. That creates the opening. The startup wins by building the product users actually want to use — the one that feels faster, smarter, and more aligned with how work gets done. That is what Alex means by a “system of love.” It is not a cute phrase. It is a wedge strategy. If the incumbent has spent years training customers to feel captive, then delight itself becomes disruptive.
The best way to frame this in the piece is that vertical AI is often entering markets with structural dissatisfaction already baked in. Alex points to sectors like legal and insurance, where incumbents such as Westlaw, LexisNexis, and Vertafore have long benefited from limited competition, low NPS, and pricing power. In those markets, the startup does not need to convince buyers that the old experience is broken. Buyers already know. The founder’s job is to convert that frustration into adoption by making the new tool feel indispensable almost immediately.
You could sharpen the blog language here by saying: the first product advantage in vertical AI is emotional, not technical. Users do not switch because they got a slightly better dashboard. They switch because the new product respects their time, reduces pain, and gives them leverage against a vendor they never really loved in the first place. That is how small startups start to destabilize giant categories.
2. Use customer hatred as a GTM advantage
One of the hidden advantages in vertical software is that bad incumbents create their own demand for disruption. Alex’s observation is that in a lot of these categories, founders are not selling into neutral markets. They are selling into markets where the customer already feels overcharged, underserved, and trapped. That means the go-to-market motion can be much more direct. The message is not “here is a new tool.” The message is “here is your exit.”
This is especially powerful in categories where incumbents have used contract lock-in, proprietary data, or integration bottlenecks to preserve share. Alex’s point is that once the buyer believes an alternative is real, dissatisfaction turns into urgency. In legal tech, for example, he notes that newer AI-native entrants are benefiting from years of pent-up frustration with legacy research and workflow vendors. In insurance, the same logic applies where multi-owner, PE-shaped incumbents have often optimized for extraction over product improvement. The emotional state of the customer becomes part of the distribution strategy.
A clean way to write this in the post: customer dissatisfaction compresses the sales cycle. When a founder is entering a market where users already hate the default option, education is less important than credibility. The question is not whether the pain exists. The question is whether the startup is trustworthy enough to become the next default.
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3. Don’t fight incumbents in court. Route around them
Alex is very clear on this: founders should not waste their best years trying to litigate their way through a blocked market. His advice is blunt — get the lawyers out of the room. For startups, legal warfare is usually a trap. Incumbents have more money, more patience, and far more ability to absorb distraction. Even when the startup is morally right, the process can still kill momentum.
The better response is tactical. If an incumbent blocks access, restricts integrations, or tries to use data captivity as a moat, the startup should look for ways to become useful anyway. That can mean workflow-based entry, lightweight implementation, partial automation, partner-led adoption, or what Alex loosely refers to as working around the old system rather than begging permission from it. In other words: do not challenge the castle head-on if you can quietly build the road users actually prefer.
This matters because many of the most attractive vertical AI markets are also integration-heavy markets. Alex’s broader framework in his writing is that faster breakout categories tend to require less integration upfront, while slower but potentially more defensible categories require more of it. That means founders need to be strategic about where they need permission and where they do not. The startup that can deliver real ROI before a heavyweight integration is often the startup that earns the right to go deeper later.
4. Win the wedge before you win the platform
A lot of founders talk like they are replacing the incumbent suite on day one. Alex’s worldview is much more practical. The real winners usually start by owning a narrow, painful, high-frequency workflow — something acute enough that users will adopt quickly, even if the broader system stays in place for a while.
That logic is reinforced by his public framework on vertical AI adoption. The earliest breakouts often happen in categories where value is obvious without deep integration and where the sector has historically underinvested in modern software. Legal is a prime example: immediate ROI, text-heavy work, and relatively low implementation friction compared with deeply entangled back-office systems. By contrast, sectors like insurtech may offer huge opportunity, but adoption is slower because the workflows are intertwined with legacy systems and downstream dependencies.
In the post, I’d phrase this as: great vertical AI companies earn expansion through usefulness. First they solve one painful job better than anyone else. Then they become the preferred interface. Then they accumulate context, trust, and data. Only after that do they start to look like a platform. Founders lose when they pitch total replacement too early; they win when they become impossible to remove.
5. Exploit the incumbent’s structure, not just its product weakness
Alex is not just saying incumbents are slow. He is saying many of them are structurally unable to respond. In the episode, he emphasizes that a meaningful number of vertical software leaders are private-equity-owned, heavily levered, and managed for extraction rather than invention. His phrase is that in many of these businesses, “the ambition has left the room.” That is a devastating condition in an AI transition.
This is why the startup’s real edge is often organizational, not merely technical. Founders can take pricing risk. Founders can ship half-finished but highly valuable products. Founders can cannibalize future revenue in order to win the present. A debt-loaded incumbent often cannot do any of those things. It may know the threat is real and still be unable to answer it with the necessary urgency. That is the kind of mismatch vertical AI founders should be looking for: not categories where the incumbent is stupid, but categories where the incumbent is constrained.
That same logic also explains why some of these markets are newly attractive to venture. If the old winner is financially engineered rather than founder-driven, a modern AI-native company can look small in revenue and still be large in strategic threat. The founder is not just shipping better software. They are attacking an institution whose operating model was optimized for a previous era.
6. Growth matters more than neatness
Alex is unusually candid about the market here: growth is trumping everything. Investors are rewarding speed, acceleration, and signs that a company is escaping into category leadership. That does not mean business quality does not matter. It means that in an AI transition, velocity is being read as evidence of inevitability.
He also talks about “elephant accounts” — very large customer deals that can make young companies look lopsided or concentrated, but also signal that serious buyers are willing to place meaningful bets early. For vertical AI founders, this is important. The market is often less interested in perfectly balanced metrics than in whether the startup is becoming strategically important to major buyers. A weird-looking company with explosive pull can be more investable than a tidy one growing at a merely rational pace.
In blog form, the takeaway is: in frontier categories, momentum is a moat. Once a startup becomes the company everyone in a vertical is hearing about, recruiting improves, fundraising improves, customer urgency improves, and incumbent anxiety increases. In these markets, speed does not just create growth. It compounds strategic leverage.
7. Commoditization is coming, so own the workflow, not the feature
Alex is also clear-eyed about the other side of the story. AI lowers the cost of building. What looks novel this quarter may look generic next quarter. He warns that if your company is basically a thin layer on top of model access, pricing pressure will come fast. The consequence is that vertical AI founders need to build defensibility somewhere other than surface-level novelty.
His broader writing offers a useful extension of this idea. In “LLMs and Sneaky Big Markets,” Alex argues that AI changes the economics of vertical software because tools are no longer just helping workers do their jobs — they are beginning to automate pieces of the work itself. That expands TAM, but it also raises the standard for differentiation. The winners will not simply provide software seats; they will capture meaningful economic value by automating real labor, owning critical workflows, and embedding into domain-specific processes.
So the right way to write this playbook is: features commoditize, embeddedness does not. The moat is not that your demo is impressive. The moat is that your product sits in the flow of work, touches proprietary data, understands the regulation, and improves with every customer interaction. That is how vertical AI turns from a fast product into a durable company.
8. The biggest markets are often the ones that looked too small before AI
One of the subtler but more important threads in Alex’s thinking is that AI changes what counts as a venture-scale category. Traditional vertical SaaS math often made certain markets look capped because pricing was seat-based and buyer willingness was constrained. But once software begins automating the work itself, the economic ceiling rises. The market is no longer bounded by software budget alone; it starts to pull against labor budget and workflow value.
That matters for the blog because it reframes why vertical AI is so exciting. It is not just that neglected markets are finally getting better software. It is that categories long dismissed as “too small” can become very large when a product directly absorbs meaningful work. Alex’s point is that founders should stop thinking only in legacy SaaS TAM logic and start asking how much human effort, time, and coordination the product can actually replace.
If you’re building in vertical AI, this episode does not stay at the level of slogans. Alex gets specific about where incumbents are weak, why customer dissatisfaction is a real wedge, how PE-owned software companies lose their appetite for reinvention, and what actually makes a vertical AI company defensible when everyone is worried about commoditization. More importantly, the conversation gives founders a sharper way to think about competition: you do not have to outspend the giants, you have to understand where they stopped serving the customer and move faster than they can respond.
It is also interesting because Alex is not talking about vertical AI as a vague trend. He is talking from the vantage point of someone who has spent years investing in sectors like logistics, insurance, fintech, and construction, and who has backed companies such as Motive, Root Insurance, and Scout RFP through Scale Venture Partners. That makes the discussion feel grounded in pattern recognition rather than hype.
So if you’re a founder, operator, or investor trying to understand where vertical AI is actually going — and how startups can win against entrenched legacy players — go watch the episode. There are a lot of conversations right now about AI tools. This one is about strategy. And that’s what makes it useful.
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