Put a price on research breakthroughs

Neolabs today are being asked to pull off two moonshots at once. Their investors want both a research breakthrough and a venture-scale business, even though each of those alone is a 1 in 100 outcome. Asking for both makes their chances 1 in 10,000.

I think there’s a better alternative, one that borrows from the way pharma handles risky R&D and lets frontier labs, neolabs, researchers, and investors all come out ahead.

Frontier labs want to scale up what works

Today, OpenAI, Anthropic, and Google DeepMind are caught in a neck-to-neck race on model quality and intelligence. Every release needs to exceed expectations, or at least meet what competitors are doing. Logically, this means they prefer to spend their resources scaling things that are proven to work, like improving data pipelines, building infrastructure for larger runs, and buying ever-more compute. However, researchers often want to work on “the next big thing”, new paradigms that could lead to a discontinuous jump in intelligence. As a result, there’s often tension between what the market needs them to ship and what researchers want to focus on.

While it’s true that scaling gets you continuous, reliable progress, discontinuous jumps come from new ideas. Reasoning started out as a high-conviction but unproven bet within OpenAI, and it led to the big improvements in agent reliability we expect today from every company’s models. Unfortunately, greatness can’t be planned.

Neolabs are asked to do two things at once

In the past year, many senior researchers who want to pursue these bets have started neolabs: a nascent AI startup focused on big research bets, usually funded with a lot of money before it has a product or revenue, so it can pay for compute and salaries. This ensure they can chase the big idea with venture-scale resources.

On the surface, investors are betting that a neolab becomes the next OpenAI or Anthropic. They remind themselves that Anthropic was once a neolab too, started a group of researchers who left OpenAI and raised hundreds of millions of dollars before they had a product. Look how big it’s gotten! Privately though, most of them will admit the odds of a repeat are next to none, even with world-class researchers and engineers.

Running a research team is a different job from scaling a company. Researchers usually aren’t great at product, go-to-market, operations, logistics, etc…, all the things that traditionally matter for a sustainable company. (Ali Ghodsi at Databricks is a good exception.)

However, neolabs are expected to do both: make a breakthrough and scale a product. Sometimes this splits a company in two, with half the people wanting to build and sell and the other half wanting to do research and build ASI. Focus and alignment are the only real advantages a startup has over better-funded incumbents, but this expectation can take a toll on both.

Investors who expect their neolab to do both will likely be disappointed when it does neither.

One radical move is to admit that a neolab is just going to do world-class, groundbreaking research. Expect close to zero revenue and let them cook. If they succeed, they get acquired or acquihired.

The catch is that this doesn’t work for investors. A VC needs companies that can return the entire fund, and it’s hard to promise LPs that an acquihire will do that, especially at the skyhigh multibillion valuations neolabs raise at today.

Pharma already solved a version of this

Coincidentally, the drug development business has faced this similar problem before. Companies regularly risk millions in R&D on a drug that might pay off billions. A drug can pass early trials and still fail before FDA approval, sometimes because of side effects and sometimes because the manufacturing facility wasn’t up to standard.

Pharma handles this by splitting the work. Smaller biotech companies take on the risk of discovery and development. If a drug works, a big pharma company acquires the startup or buys the drug as IP. The big company doubles down on what it’s good at, which is producing and distributing drugs at scale. The small company gets to do the science without having to invent a business model. For the small companies, their business model is one question: can we make this drug work or not?

The division of specialization works. Of the drugs the FDA approved from 2013 to 2022, small companies with under \$500M in revenue originated 52%, while the biggest companies, with over \$10B in revenue, originated 36%¹.

Pre-registered acquisitions

What’s missing today is a promise. The people who start, join, or fund a neolab need to know, before they take the risk, that a big payoff is waiting if the research works.

Big labs could make that promise ahead of time through pre-registered acquisitions. Economists call this kind of promise “pull funding”. Push funding pays for research up front, like a grant or a VC round. Pull funding promises to pay for the result once it exists. In 1714, the British government promised £20,000 to anyone who could find a ship’s longitude at sea, and a self-taught clockmaker named John Harrison responded by inventing a clock that kept accurate time on a rolling ship. In 2009, five countries and the Gates Foundation promised \$1.5B to any company that could supply poor countries with pneumococcal vaccines at \$3.50 a dose or less, and vaccine makers responded with enough supply to immunize more than 150 million children.

I can see this playing out a few ways:

1. A big lab publishes a concrete benchmark or research question and commits to acquiring whoever beats it, at a set price. OpenAI’s Parameter Golf challenge is a small version of this, with job interviews for standout entrants instead of an acquisition.
2. A big lab publishes a concrete ask, like a research breakthrough, specific-data access, or a regulatory approval it needs, and commits to acquiring whoever delivers it, at a set price. RL environment startups are already kind of doing this.
3. A big lab and a neolab agree on a scoped project up front, with an acquisition at a set price if the project succeeds. SpaceX’s option to buy Cursor has this structure: \$60B if SpaceX buys, or \$10B for the work if it doesn’t. SpaceX bought it.

A win-win-win solution

Pre-registered acquisitions let everyone come out ahead.

The frontier labs get to:

• Keep scaling what works.
• Get exposure to 0-to-1 breakthroughs without funding every long shot themselves.
• Avoid the internal tension between people who want to ship products and people who want to do fundamental research.

Neolabs and their researchers get to:

• Chase unproven but high-potential bets that could lead to a breakthrough, with funding behind them.
• Skip product-market fit and go-to-market, and spend their time on the research they’re best at and most excited about.
• Stay aligned, because the target is written down and everyone knows what winning looks like.
• Capture some of the value they create through a real payoff.
• Form a team, or go solo, and take a shot at a published target. If the payoff is \$100M, spending \$100k on compute to try is a reasonable bet.

Investors get to:

• Take on less liquidity risk, because there’s at least one committed buyer and a clear path to a return.
• Value a neolab against the acquisition price, which makes it easier to get their partners and LPs to back the round.

A pre-registered acquisition is also just a suggested floor, not a ceiling. If a neolab hits a target and would rather build a business around what it made, it can always turn the offer down.

Making the implicit explicit

Some version of this already exists. Every past acquisition and acquihire, like Google’s \$2.4B Windsurf deal, sets an implicit anchor for what a research team can exit for, and neolabs point to those anchors to justify their valuations. A pre-registered acquisition just makes that explicit, reducing the uncertainty for everyone.

For a budding neolab, putting a price on research breakthroughs improves its odds of success. It no longer has to win at research and at business. It only has to make the research work, which takes the 1 in 10,000 bet back down to 1 in 100.