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Starting a company has never cost less. A founder with the right AI tools can ship a working product in a weekend, stand up a website in an afternoon, and fill out an accelerator application before lunch. But that speed hasn鈥檛 made it easier to get funded.听

Fewer seed-funded startups are graduating to Series A than just a few years ago, and startup funding has been in a downturn so far in 2026. Investors are concentrating capital in fewer, stronger bets. The question is what “stronger” means now.

Every generation of technology resets what investors should expect from founders. Twenty years ago, a founder who wasn鈥檛 internet-native was at a structural disadvantage. Forty years ago, it was computer literacy. Today, AI-native fluency is the baseline 鈥� the ability to build, test, and iterate using AI copilots, APIs, and low-code tools at a speed that would have required a full engineering team just a few years ago.听

Founders who haven鈥檛 embraced these tools in their daily operations aren鈥檛 even at the table. They鈥檙e new-aged dinosaurs. Technical expertise still matters, but when everyone can build, thanks to AI, it no longer differentiates. And that forces a harder question for investors: If the product isn鈥檛 the moat, what is?

Finding the fit

The answer is founder-market fit. Investors are shifting their attention from what a team can build to whether the founder has domain expertise that predates the startup, has done real customer discovery, and can articulate a path to market that competitors can鈥檛 easily replicate.

AI can help a founder build anything, but it鈥檚 what customers have a need for that tells them what鈥檚 worth building. That judgment is steeped in industry knowledge, customer relationships, and a clear-eyed view of what people will actually pay for. That is the scarce resource these days.

That鈥檚 not to say AI can鈥檛 help build a company the right way. It has implications for how early teams should be composed., the average seed-stage company last year had just over six employees, down from more than 10 in 2021.听

With teams that lean, every hire has to pull disproportionate weight. The highest-leverage early additions are a product-minded builder who can ship fast with AI tools, someone who owns the customer relationship and drives early revenue, and someone who can position the product and generate demand. A bench of engineers no longer tops the list.

The investor鈥檚 harder job

Knowing what to look for is one thing. Finding it is another, because AI has made it easier to fake the signals investors rely on.听

There鈥檚 an entrepreneurial equivalent to the that so many people are talking about. Instead of a tidal wave of empty marketing copy about 鈥渆ver-evolving landscapes,鈥� there鈥檚 startup slop that creates a serious evaluation problem for investors. Dealflow volume has become a vanity metric. There鈥檚 a surge of submissions that are pure noise, especially from software startups that can fabricate credibility in a single afternoon.

Deep tech is harder to fake. Building a therapeutics company still requires real science, real key opinion leaders, and real partnerships. The same is true for hardware and advanced manufacturing. There鈥檚 an actual moat in those sectors, which may help explain why investor interest in deep tech has been growing steadily.听

Investors can weed out the startup slop by asking more specific questions. For instance, our accelerator, AlphaLab, is based in Pittsburgh, and we always ask founders why this city is the right place for them to grow their businesses. You can sense how genuine someone is based on their answer. Same goes for asking about the customer discovery process. Even more telling is why someone started their company in the first place, whether the answer reflects real conviction or a market opportunity they read about.听

AI can鈥檛 manufacture what investors are really looking for. The signals that matter most at the early stage are coachability, hustle, and genuine conviction. There are details in an application that suggest someone has actually lived the problem they鈥檙e solving. Investors don鈥檛 want to write a check to someone who has vibe-coded a company they aren鈥檛 passionate about, and the tells are easier to spot than founders think.听

However, AI has reallocated where founders should spend their energy. Because it can help with some of the technical aspects of creating a company, founders should devote more effort to refining their strategy through higher-order skills like judgment, creativity, storytelling, and relationship-building. Speed of communication has become a revealing signal. There is no longer any excuse for taking four days to respond to an email, skipping a weekly investor update, or failing to follow up after a meeting. AI has eliminated the friction in all of those tasks. A founder who is still slow is telling investors something about how they鈥檒l run a company, and investors are paying attention to those soft interactions more than ever.

While the cost of building companies has dropped, the burden of earning investment has risen. And for investors, the evaluation itself has gotten harder, with more noise, more polish, and fewer of the old signals to rely on. The founders worth funding will stand out the same way they always have: by knowing something the rest of the market doesn鈥檛.

听brings 20 years of experience in venture capital, accelerator leadership and strategic operations to his role as director of accelerator programs at听听in Pittsburgh. He oversees听, AlphaLab Gear, AlphaLab Health and Robotics Factory Accelerate, programs that support early-stage startups with mentorship, resources and capital. His leadership has helped create a connected AlphaLab ecosystem that empowers founders across industries and stages of growth. Earlier in his career, Tainter held roles at听听and听听where he led cross-functional initiatives and evaluated early-stage investments. He also teaches at听, where his work focuses on funding entrepreneurial ventures.

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