There Was Never An Easy AI Era, And Investors Are Raising The Bar
By Maor Farid
In 2021, putting AI on a slide could get you funded. Small and mid-sized businesses were eager to be early adopters, and they bought fast. I watched companies secure customers, raise money, then struggle to keep anyone using their products. Retention fell off a cliff.
Many of those products were generic: They did what traditional software already did, with AI fairy dust sprinkled on top. Others saved a bit of time on something nobody’s job depended on. A 20% improvement on a noncritical task makes for a nice demo and a dead renewal.
There were never easy AI wins, even if there was easy money going around. The market is now correcting for that. Companies delivering meaningful value are growing faster than anything I have seen in enterprise software. For investors, the question is which of these companies can keep their customers — and whether the coming IPOs will expose the difference.
What makes an AI business defensible today
Before my co-founder and I wrote a single line of code, we interviewed more than 900 mechanical engineers, from juniors to VPs. We asked where their hours went and which of those hours they would pay to get back. Those 900 conversations shaped how I think about building an AI business.
First: the improvement has to justify the investment. Saving 10% on an occasional task will rarely change how a company operates. Reducing a business-critical process from weeks to minutes, or saving serious money, will.
Second: domain expertise. Frontier models are becoming infrastructure. Access to a good LLM is available to almost anyone with an API. The commercial opportunity lies in understanding an industry well enough to solve problems that generic tools cannot.
Third: proprietary context. In terms of engineering examples, every company that builds physical products has accumulated decades of engineering knowledge. Much of it remains buried in old drawings or locked away in the heads of experienced employees. That knowledge is absent from a foundation model’s training data. An AI product that cannot access it will struggle to become essential for the business. The advantage grows as the product becomes embedded in the customer’s workflows and replacing it becomes costly.
What this means for funding and IPOs
The upcoming AI IPOs will put these business models under much greater scrutiny. Public investors will examine net revenue retention and gross margins, assessing whether rapid growth is able to translate into a sustainable business. They will also want to know whether each deployment becomes cheaper to support as the company grows. The standards they establish will influence what private investors expect from earlier-stage AI companies.
I can already see the change in my own fundraising conversations. A few years ago, $1 million in ARR was a milestone. In the AI era, a new product can reach $1 million in ARR in about a year, and shut down a year later. Now ARR alone no longer tells investors enough.
What investors keep asking about is expansion. They want to know whether customers are increasing their spending after the initial deployment. Expansion is the closest thing we have to proof that a product has changed how an organization works: customers have experienced its value and committed more of their own budget to it.
Over the next 12 to 24 months, I expect capital to keep flowing to companies that combine deep industry expertise with significant improvements in business-critical work and access to customers’ proprietary knowledge. They may grow through hands-on deployment rather than viral self-service, but they can expand inside accounts that trust them.
Companies whose products are thin layers over someone else’s model, with impressive customer lists and retention charts nobody wants to show, will find their next funding round much harder than the last one. I would rather grow fast and steady for five years than spectacularly for five quarters.
Maor Farid is the founder and CEO of Leo AI, the first AI for mechanical engineering — a large mechanical model for physical product design. He conducted AI and mechanical engineering research at MIT as a Fulbright postdoctoral fellow and became the youngest Ph.D. graduate in the history of the Technion – Israel Institute of Technology. Farid has built a community of more than 60,000 engineers and supports underserved youth through his nonprofit initiative.
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Illustration: Dom Guzman
