Published on : March 10, 2026
Reflecting on Products That Count’s 2025 Q4 Product Guide: The End of “AI as a Feature”, and what it means for founders:
If you are raising a seed or Series A round for your AI startup in the US right now, you need to know something: investors have stopped being impressed by the letters “AI” in your pitch deck. Over 75% of the 9,000+ nominees in the 2025 Products That Count Product Awards were AI companies. When everyone is an AI company, the label itself is worthless. What matters is whether you have built something that cannot be replicated the week after your demo day.
TL;DR
Two years ago, a founder could walk into a pitch meeting, show a GPT wrapper with a clean UI, and walk out with a term sheet. That window is closed. The 2025 Products That Count analysis found three categories of AI companies winning awards and, by extension, winning investor attention: product-led innovators, AI-led platforms, and vertical specialists. The split matters because each type builds defensibility differently.
The pattern is clear: what investors look for at seed stage and Series A alike is a company that has already found its narrow wedge and started generating proprietary signal from real users.
The old playbook said more data equals better AI equals bigger moat. That playbook is dead. According to analysis shared through the Products That Count awards process, AI models can deliver strong reasoning with or without massive datasets. The differentiator is unique data combined with specialized domain expertise applied in the right context.
This has enormous implications for founders deciding what to build. You do not need a petabyte of training data. You need data that is specific to the problem you are solving, difficult for competitors to acquire, and structured in a way that compounds over time. Early stage funding trends in the US confirm this: Menlo Ventures reported that vertical AI solutions hit $3.5 billion in 2025, tripling from $1.2 billion the year before. Healthcare alone captured $1.5 billion of that total, more than the next four verticals combined.
Capital concentration in AI is staggering. AI startups captured roughly 50% of all global venture funding in 2025, with over $200 billion deployed. But here is the part early-stage founders miss: 58% of that funding went into megarounds of $500 million or more. The top of the market is eating most of the capital.
That does not mean seed and Series A are dead. It means early-stage investors are being more precise about what they fund. The best VCs for product-led growth companies and the top b2b SaaS investors are zeroing in on startups that demonstrate defensibility before they demonstrate scale. Recommended investors for AI Series A rounds increasingly require proof of a data moat or a workflow integration moat before they will lead.
For founders not in the megaround category, this concentration is actually your advantage. You are building a profitable, defensible business in a real vertical while the giants burn cash on infrastructure.
The Products That Count analysis surfaced a theme that most early-stage founders undervalue: interoperability. The winning products were not standalone tools. They were connective tissue between larger systems. Companies that built in isolation, ignoring how their product fits into the broader stack their customers use, were flagged as having limited long-term potential.
This maps directly to what VCs help with in go-to-market post-investment. The best firms push their portfolio companies toward ecosystem thinking from day one. AI agents are accelerating this trend. Startups that view agents as specialized workers collaborating within optimized value chains are building something much harder to displace than those treating agents as isolated features.
The temptation at seed and Series A is to build horizontally. Serve everyone. Cast a wide net. This is almost always wrong for AI startups. The Products That Count founder put it bluntly: the moat in AI is personalization, and personalization demands vertical integration. That means combining unique high-quality data, context-aware user preferences, and privacy-by-design architecture into a product that serves a specific buyer with uncommon depth.
VC funds focused on enterprise software in the US have caught on. The companies commanding premium valuations are those embedded in daily workflows within a defined vertical, not those competing for attention across ten industries. Developer tools, legal tech, and healthcare AI are all commanding 30x to 50x revenue multiples when they demonstrate daily use, strong net revenue retention, and clear workflow lock-in.
The math is straightforward. A vertical AI company with $1 million ARR and deep workflow integration in a regulated industry is more fundable than a horizontal AI tool with $3 million ARR and no switching costs.
Defensibility affects your terms. Founders who walk into a seed or Series A negotiation with a clear moat narrative get better economics. Those who cannot articulate their moat face more aggressive structures: higher liquidation preferences, broader participation rights, and tighter control provisions.
The best defense against bad terms is a moat you can explain in two sentences.
Sources: Products That Count 2025 Product Guide: The AI & Data Issue, Crunchbase 2025 year-end analysis, Menlo Ventures 2025 State of Generative AI in the Enterprise report
