There’s no shortage of hype around AI right now. Every pitch deck mentions it. Every company claims to be using it. Every conference has a keynote about how it’s going to change everything. Plenty of hype, but also plenty of real work getting done daily by those truly leveraging the power of AI tools.
Like every venture firm, we’ve been deep in this at Foundry. We’ve been working with our portfolio companies on how they’re integrating AI into their products, their workflows, and their operations. It’s been a major area of collaboration and learning across our portfolio. And what we’re seeing across startups (at Foundry and beyond) - not in the hype but in the actual numbers - is a striking contrast between AI adoption in startups and what’s happening in large enterprises.
Is AI the startup superpower? At the moment it appears to be so.
The Enterprise Struggle Is Real
The data on enterprise AI adoption is stark. A widely cited MIT study from 2025 found that roughly 95% of enterprise AI pilots fail to deliver measurable ROI. That specific number has been debated - the sample was small, and the definition of success was narrow - but even the more conservative research tells a similar story. RAND found that over 80% of AI projects fail, which is twice the failure rate of non-AI IT projects. BCG reported that 74% of companies have seen no tangible value from their AI investments, and only 4% have achieved enterprise-wide AI capabilities. S&P Global found that 42% of companies abandoned most of their AI initiatives in 2025 - up from 17% the year before.
The reasons are consistent across every study. Enterprises pick the wrong problems to solve with AI - choosing projects based on technical novelty instead of business value. Their data is siloed and messy. Projects that work in sandboxes stall when they hit the reality of compliance workflows, authentication systems, and organizational processes that weren’t designed for this. And critically, they try to bolt AI onto existing processes rather than rethinking how work gets done. That rarely works.
None of this is surprising if you’ve watched large organizations try to adopt any transformative technology. What makes this moment different is the other side of the equation.
Startups Are Running With It
This is where it gets interesting. The top AI-native startups now average $3.48 million in revenue per employee - roughly 5.7 times higher than leading traditional SaaS companies. Cursor, the AI code editor, hit $300 million in ARR with about 20 employees. Lovable reached $17 million in ARR with 15 people, three months after launch. And across the board - if Foundry’s portfolio is representative (and I believe that it is) - startups of all kinds are seeing massive leverage from their AI investments.
Nimbleness Has Always Been a Superpower. AI Amplifies It.
Startups have always had the edge when it comes to speed and adaptability. Fewer layers of approval. No legacy systems to work around. A willingness to rethink how things are done rather than protecting how things have always been done. That’s been the case through every technology cycle I’ve watched over 20+ years in venture.
What’s different about AI is how dramatically it amplifies that advantage.
AI doesn’t reward scale the way traditional software does. You don’t need a large engineering team to build a product. You don’t need a large marketing or go-to-market team to sell. You don’t need layers of management. Individuals and small teams can be as productive as entire divisions of larger companies. Powerful models are available to everyone willing to take the time to learn how to leverage them. The APIs are the same whether you’re a Fortune 500 company or three people in a coffee shop. What matters is how quickly you can integrate AI into your workflow, how willing you are to rethink how work gets done, and how fast you can iterate. Startups win on all three counts.
Enterprises, meanwhile, are stuck in what I’d call pilot purgatory. They run a proof of concept, it shows promise, and then it dies in the gap between “this demo looks great” and “this is now part of how we actually operate.” That MIT I cite above tells an important part of the story - companies are choosing projects based on technical novelty rather than business value. They’re solving interesting problems instead of important ones.
The World Economic Forum noted that AI is making resources that were once exclusive to large companies - advanced technology, specialized expertise, sophisticated analysis - broadly accessible to small teams. OECD data shows AI uptake in small firms grew 72% in a single year, with 91% of adopters reporting efficiency gains. Mid-market companies are getting to value in 6 to 9 months, while large enterprises take 12 to 18.
In previous technology cycles - cloud, mobile, SaaS - startups had advantages, but large companies could eventually catch up by throwing resources at the problem. With AI, the traditional advantages of large companies - vast data, deep pockets, big teams - seem to matter less (or, at least, most seem unable to harness these to their advantage). In some cases they’re actually liabilities, because they come with organizational complexity that makes it harder to adapt.
What We’re Watching
The companies in our portfolio that use AI most effectively aren’t treating it as a bolt-on feature. They’re using it as an operating model. They’re building companies that are structurally leaner and faster than what came before - generating more value per person than would have been possible even a few years ago.
That’s not hype. That’s what’s actually happening on the ground, in real companies, with real revenue. And it’s happening while many of their larger competitors are still trying to get their pilots to work.
Startups have always punched above their weight. AI is making the punch a lot harder.