Billion-Dollar AI Startups With No Products, No Revenues, Eager Investors

We may have entered the final, truly absurd AI end-game. Think dotcom sock puppets. Dozens of startups with no product or even prospect of revenues are being expansively funded by investors - some of them ostensible professionals from the venture community - who believe that AI's biggest challenges can now be overcome by researchers in their early 20s because - and here's where we may agree - leading models like ChatGPT and Claude have hit a dead end. 

The new entities are called neolabs. Their proposition is that pure AI research is where the really big payoff is going to be delivered. And, who knows, maybe they are right. But it feels more like the AI wealth creation window is beginning to close, that both young techies and investors are realizing it and that they are all trying to cash in before the ridiculously easy money dries up. JL

Kate Clark reports in the Wall Street Journal:

A new wave of startups some have dubbed “neolabs,” which give priority to long-term research and developing new AI models over immediate profits. Interest in neolabs has skyrocketed as investors hunt for the next OpenAI, which began as a research lab and became one of the world’s most valuable startups. A large subset of top AI researchers believe that models like ChatGPT and Claude have hit a dead end and will never reach a level of intelligence that matches or exceeds humans. At the same time, researchers are realizing that the opportunity to raise big VC dollars quickly and easily may not last long. Some neolabs have seen their valuations soar into the tens of billions of dollars, prompting critics to suggest that most of them will have slim odds of turning a profit or launching a winning product.
Ben Spector had an unusual pitch for investors last fall.

A Ph.D. student at Stanford University with a highly prized artificial-intelligence background, Spector had no near-term plans to make money and no traditional pitch deck. He didn’t even have an idea for a hit AI product.

What he did have was a lab, called Flapping Airplanes, a novel idea for training AI models and a zeal to hire talented young researchers eager to tackle AI’s biggest problems.

Venture-capital firms jumped at the chance to back him.

“Small teams of brilliant young people that are able to look at the problem in a new way—those are the kinds of organizations that actually win,” said Spector, 25. 

Flapping Airplanes—a reference to the biological cues future AI should take from nature—is part of a new wave of startups some have dubbed “neolabs,” which give priority to long-term research and developing new AI models over immediate profits.

The interest in neolabs has skyrocketed as investors hunt for the next OpenAI, which began as a r esearch lab and later became one of the world’s most valuable startups. A large subset of top AI researchers believe that models like ChatGPT and Claude have effectively hit a dead end and will never reach a level of intelligence that matches or exceeds that of humans (top AI companies dispute that view).

While there are more than a thousand startups with valuations of $1 billion or higher, the number of neolabs is generally believed to be in the dozens, according to researchers and investors.

Some of the neolabs have seen their valuations soar into the tens of billions of dollars, prompting critics to suggest that most of them will have slim odds of turning a profit or launching a winning product. The labs have created a recruiting frenzy among academics in ways that are drawing promising students away from academia.

This month, Flapping Airplanes raised $180 million at a valuation of $1.5 billion from investors including GV, Sequoia Capital, Index Ventures and Menlo Ventures. Spector took leave from his Ph.D. program in September.

Humans& in January raised $480 million at a $4.48 billion valuation to build AI systems that help people collaborate. Reflection AI raised $2 billion in October at an $8 billion valuation to build an open-source model. And Periodic Labs, which aims to develop AI to automate scientific research, launched with $300 million in funding in September. 

There is also Safe Superintelligence, the AI lab founded by Ilya Sutskever, a co-founder and former chief scientist at OpenAI largely credited with inventing ChatGPT.

Ilya Sutskever, founder of Safe Superintelligence, said AI is returning to an ‘age of research.’ Kimberly White/Getty Images

In June 2024, Sutskever said he was starting a new company with one goal: building safe superintelligence. He has raised $3 billion so far, most recently at a $32 billion valuation, and has been unusually direct with investors about his intentions.

“The way I would describe it is that there are some ideas that I think are promising and I want to investigate them,” Sutskever said on a November episode of the Dwarkesh Podcast, making no promise that such ideas would lead to a breakthrough, a product or revenue. He also said AI is returning to an “age of research” after scaling up from 2020 to 2025.

In the past, the most ambitious AI research happened inside academic institutions or corporate research arms like Google’s DeepMind. Startups focused on finding applications of that research that could make money. The AI boom has pushed investors toward funding research itself.

“A venture-backed lab—this is a new thing,” said Pete Sonsini, co-founder of Laude Ventures. “It’s not traditional venture capital.”

U.S. AI startups raised a record $222 billion last year, according to research firm PitchBook. Investors say they are seeing a rising number of researchers pitching neolabs.

Not everyone is convinced these researchers can generate financial returns. 

“The technical chasm to cross for each of these neolabs is very substantial and I think that risk is very real,” said Ashu Garg, a general partner at Foundation Capital. “The vast majority of them will not cross that at all. They will end up with something that is just incrementally better. And if you’re incrementally better than alternatives, you don’t matter.”

One of the biggest challenges neolabs face is talent retention. In an era when CEOs of the largest tech companies are offering more than $300 million to hire AI experts, it is difficult for startups to hold on to their prized researchers. It is a reality Thinking Machines Lab recently highlighted in dramatic fashion.

Co-founded by former OpenAI executive Mira Murati, Thinking Machines lost two of its founders, Barret Zoph and Luke Metz, to OpenAI in January. In October, another one of its founders, Andrew Tulloch, departed for Meta. Thinking Machines has sought additional capital in recent months that could value the company at $50 billion. 

These losses have rattled investors, who have sought to be more probing with AI founders about their incentives.

“Is it a financial motivation or is it a motivation to really drive an impact?” said Dave Munichiello, a managing partner at GV and an investor in Flapping Airplanes. “Are they in it for 10 years? Or do they have four houses that they need to pay?”

Flapping Airplanes’ strategy to compete in the brutal talent war is to not try to hire the most famous researchers. Instead, they are recruiting newcomers who would have ordinarily pursued a Ph.D. or a role at a quant firm. They have tapped AI legends Andrej Karpathy, an OpenAI founding member, and Jeff Dean, chief scientist at Google DeepMind, as an adviser and an angel investor, respectively. One early area of interest for Spector and others is to train AI models with less data.

They have 11 employees so far, including Spector’s brother and co-founder Asher Spector, who recently completed his Ph.D. in statistics at Stanford, and co-founder Aidan Smith, a 21-year-old Thiel Fellow, a program that pays college students to drop out and pursue their own projects, as well as an 18-year-old high-school student.

Investors love the young hires.

“I am very interested in today’s 22-year-old who’s going to spend the next 10 years trying to find AGI,” said Sequoia Capital partner David Cahn, a Flapping Airplanes investor. “The best science has historically been done by people in their mid-20s,” he added, referencing Albert Einstein’s “miracle year,” when he published a series of influential papers at the age of 26. The rush of younger AI startup talent means fewer purely academic researchers. Stefano Ermon, a computer-science professor at Stanford University, said this is the most turnover he has seen in academia in the decade he has been teaching.

“There will be fewer people going into academic positions and maybe it will be harder to train the next generation,” Ermon said.

At the same time, researchers are realizing that the opportunity to raise big VC dollars quickly and easily may not last long. In November, Ermon announced he raised $50 million for a neolab called Inception focused on developing diffusion models to generate text and code.

“This is the first time I felt like, yeah, the upside is so big and we are so uniquely positioned to go after this,” said Ermon. “It’s now or never.