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<\/a><\/figure>\n\n\n\nThe Repricing of Expertise<\/h2>\n\n\n\n
We are watching a repricing of expertise, a slow one, with uneven edges.<\/p>\n\n\n\n
Not at the foundational layer. Paradigm-shifting breakthroughs still matter. The rare intellectual leap that unlocks a new architecture or a new computational primitive remains valuable and durable. But most companies are not those breakthroughs. Most companies sit on top of them.<\/p>\n\n\n\n
I have written before about how AI is repricing skill at the individual level<\/a>, injecting liquidity into what was once a slow-moving market for technical expertise. What is happening at the venture level is the same dynamic playing out across entire product categories. When fifty startups can build near-equivalent products in twelve months, product differentiation compresses. Expertise becomes assisted. Execution becomes accelerated. Barriers to entry fall.<\/p>\n\n\n\n It is worth being direct about what that means. AI does not just flatten products. It flattens people. The scarcity that once justified premium human expertise, the advisor with the rare insight, the consultant who had seen this problem before, is narrowing. That edge does not disappear, but it compresses fast unless the expertise is embedded in distribution, in relationships and customer context that cannot be replicated from a prompt.<\/p>\n\n\n\n There is an important exception. In data-rich verticals, proprietary datasets create compounding advantages that AI amplifies rather than erodes. Healthcare, finance, legal, infrastructure – in these markets the data is not just an asset, it is a moat that gets stronger as it grows. AI makes that data more useful, not less defensible. The dynamic in these verticals is different. The scarcity is not building capability or even distribution in the generic sense. It is the data itself, and the domain-specific judgment required to use it correctly. This connects to a broader point worth sitting with: when you rent the capability layer, you rent the moat<\/a>. In AI-native verticals, whoever owns the model behavior owns the product – and that is a different kind of lock-in than anything cloud computing created.<\/p>\n\n\n\n The result is predictable. A wave of companies will launch in every attractive AI-adjacent category. Many will grow quickly. Many will look venture-scale in their first 24 to 36 months. Most will not become venture-scale businesses.<\/p>\n\n\n\n They will explode and then flatten.<\/p>\n\n\n\n Not because they were poorly run. Not because the founders lacked talent. But because it became too inexpensive to create what they created. The winner-take-most dynamic compresses margins and growth for everyone except the few that secure durable control.<\/p>\n\n\n\n Cheap building creates crowded categories. Crowded categories destroy the middle of the return distribution.<\/p>\n\n\n\n The venture math here deserves to be stated plainly. Cheap building means more competitors. More competitors cap market power. Capped market power caps exit multiples. In a crowded AI category where any competent team can replicate the core product, the venture model itself compresses. Not because the market is small, but because structural dominance becomes harder to achieve and sustain. Many of these companies are structurally unlikely to become venture-scale businesses. The category economics will not support multiple large players once replication costs collapse, and most founders do not have the distribution infrastructure to be the one that survives. Asymmetric outcomes remain possible. They are just harder to achieve and harder to sustain in categories where the product itself can be reproduced quickly.<\/p>\n\n\n\n This has structural consequences for venture capital, though they will play out over years, not quarters.<\/p>\n\n\n\n If building is cheap and competition is abundant, returns concentrate harder and faster. You get more rockets. Fewer reach orbit.<\/p>\n\n\n\n Investors will demand signal sooner. Growth becomes the proxy for distribution dominance. Capital is deployed to test whether the company can win quickly, not whether it can build elegantly. The tolerance for long, patient build cycles without distribution proof shrinks. Capital releases in stages tied to evidence of emerging control.<\/p>\n\n\n\n This is reshaping round structure too. When building is cheap, large upfront rounds are harder to justify – you no longer need $20M to construct the product. Seed rounds compress because the build cost does not warrant more. But growth rounds are becoming larger and more heavily tranched, with capital tied to distribution milestones rather than product ones. Channel proof. Embedded customer cohorts. Pipeline velocity. The structure of the round starts to reflect the new scarcity. Capital flows in proportion to what is actually hard, and what is actually hard is no longer building the thing.<\/p>\n\n\n\n The traditional power-law model assumed a long tail of moderate outcomes. In a world of rapid replication, the moderate outcome becomes harder to sustain.<\/p>\n\n\n\n Meanwhile, IPO pathways have narrowed. The regulatory intent was investor protection. The outcome was exclusion. By making it harder for companies to go public early, regulators locked retail investors out of the steepest part of the value curve, the years when a company moves from promising to dominant. Secondary markets expanded to fill the gap, but access to those markets is not democratic. Private capital captures what public markets used to offer to a broader population. Venture starts to look less like broad-based growth capital and more like concentrated private allocation, closer to family offices, less like 1990s expansion funds. AI will likely accelerate that dynamic. The companies creating the most value will stay private longer, and the people with access to them will be a narrower group than before.<\/p>\n\n\n\n Selectivity increases. Portfolio sizes shrink or become more strategically concentrated. The “grow at all costs, you’ll get more later” model becomes harder to justify when many fast-growing companies are structurally incapable of sustaining dominance. Capital no longer buys uniqueness. It buys speed – the time and resources to build a distribution funnel, execute against it, and reach durable entrenchment before a competitor replicates the product and races to the same buyers.<\/p>\n\n\n\n There is another dynamic that becomes more visible in this environment. Some startups are designed not to become category winners, but to slot perfectly into one specific incumbent. Not strategic fit in the abstract sense. Deliberate adjacency to a single buyer. The product is built to complete a portfolio gap. The roadmap mirrors a specific weakness in a specific acquirer’s product line. Some founders are not optimizing for market dominance. They are optimizing for perfect adjacency to one buyer, and shaping every decision around what makes that buyer say yes.<\/p>\n\n\n\n This is not new. But the calculus around it is shifting.<\/p>\n\n\n\n When technology is easier to replicate, the premium for strategic fit increases relative to the premium for raw IP. At the same time, the value of acquiring technology alone diminishes. If a product can be rebuilt internally in 12 to 18 months, the acquisition multiple compresses. The technology becomes a starting point for an internal conversation, not a reason to write a check.<\/p>\n\n\n\n What remains valuable in M&A is harder to replicate. Embedded distribution. Contractual entrenchment. Regulatory positioning. Customer relationships. Data gravity.<\/p>\n\n\n\n In regulated verticals, this goes further. A company that has already navigated the compliance requirements to operate in a market – secured the certifications, built the audit trails, established the regulatory relationships – has compressed years of a buyer’s time to market into something acquirable. Compliance readiness is not a cost center. It is a distribution accelerator.<\/a> Vertical access and compliance readiness are part of the distribution story, not separate from it. For an acquirer trying to enter a regulated market, the fastest path is often not to build the product. It is to buy the company that already has permission to operate. That shifts what gets priced into an acquisition and why some targets command premiums that pure technology analysis cannot explain.<\/p>\n\n\n\n Technology without distribution is just an expensive prototype.<\/p>\n\n\n\n But what gets lost in that clean analysis is that acquisition decisions are not made by spreadsheets. They are made by people, in rooms, often under time pressure, with incomplete information and competing organizational interests.<\/p>\n\n\n\n A founder who has built real relationships inside a strategic buyer has a fundamentally different acquisition outcome than one who has not, even if the products are comparable. The internal champion who has watched you execute, who trusts your judgment, who has gone to bat for you in internal budget conversations, is not a nice-to-have. They are often the reason a deal happens at all.<\/p>\n\n\n\n Perception compounds this. Acquirers pay for confidence as much as capability. A company perceived as the category leader, even in a crowded category, commands a premium that may not be fully justified by its metrics. Market positioning, analyst coverage, conference presence, and the quality of your reference customers, these shape the narrative in an acquirer’s boardroom. The story they can tell internally about why they did this deal matters enormously. Acquisitions have to survive internal politics.<\/p>\n\n\n\n Timing is almost never purely rational either. Companies get acquired when a buyer is scared, or ambitious, or has capital to deploy, or is about to lose a competitive advantage they can feel slipping. Being visible and credible at that moment, not just when you need a buyer, is what closes deals.<\/p>\n\n\n\n None of this means product and metrics do not matter. They do. But they matter as the floor. Above the floor, acquisition outcomes are determined by relationships, reputation, and the story someone is willing to tell on your behalf inside an organization that does not know you.<\/p>\n\n\n\n Customer management is one of the domains AI is aggressively trying to automate. AI SDRs. AI account managers. Synthetic personalization. Automated follow-up. Generated relationship intelligence.<\/p>\n\n\n\n In a world where distribution is the scarce resource and relationships drive acquisition outcomes, the industry is racing to replace human relationship infrastructure with synthetic substitutes.<\/p>\n\n\n\n This is not irrational. Automation increases efficiency. Most sales and account management processes have enormous amounts of low-value activity that could and should be automated.<\/p>\n\n\n\n But in high-value markets, buyers are not just purchasing functionality. They are purchasing risk reduction. They are purchasing accountability. They are purchasing confidence. And confidence is built through consistent human judgment over time, through the accumulation of trust that comes from someone showing up, delivering, and being present when things go wrong.<\/p>\n\n\n\n<\/a><\/figure>\n\n\n\nWhat This Does to Venture Capital<\/h2>\n\n\n\n
Built for Acquisition, But It Is Not a Spreadsheet Decision<\/h2>\n\n\n\n
The Irony of Automating Your Own Moat<\/h2>\n\n\n\n