AI isn’t close to curing cancer. Vivodyne says it knows what it will take; plus, TerraPower’s nuclear reactor has a secret weapon for powering AI data centers

0:01This is TechCrunch.

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1:00data that today mostly comes from animal testing or studies of single cells or proteins, not living tissue. Vividyne's CEO and co-founder, Andre Giorgescu, said, absent human testing, what are these AI models going to do? They're going to cure cancer in mice. Even Anthropic CEO Dario Amadei wrote over the weekend that claims that AI will cure cancer have become more cliché than credible, or as he put it, the thing that will work is actually curing cancer. To be fair, the idea that AI will cure cancer is something Amadei himself has tossed out in previous essays. Sam Altman has repeatedly cited curing cancer as a justification for OpenAI's push toward AGI and ever-larger compute build-outs.

1:50And Google DeepMind's Demis Hassabis said last year that AI could potentially cure all disease within a decade. Now, the actual results remain tepid. A handful of AI-designed drugs have proceeded into human trials, one as far as phase three. But the reality is that the roadblocks are not necessarily ones that AI can solve today. Nobel Prize-winning AlphaFold was a big advance for understanding the building blocks of life, but it has yet to actually produce a new drug. Isomorphic Labs, founded to build on AlphaFold, is expecting its first trials, originally planned for last year by the end of this year.

2:32In February, the company wrote that true drug discovery will require highly accurate predictive models across an expansive range of biochemical properties and interactions. Georgescu says the space needs a sanity check that existing models do not have the data to capture the complexity of human biology. It is a challenge already facing the pharmaceutical industry, where 90 % of drugs that are effective enough in animal testing to enter clinical trials do not receive regulatory approval for humans. Vividine's plan is different. The company was spun out of the University of Pennsylvania in 2021, after Georgescu received a PhD in bioengineering there.

3:16The company says its tissues closely match the behavior of real human organs, that its liver cells have 94 % predictive accuracy compared to human trials that test for toxicity. Its airway tissue matches the behavior of real human tissue 96 % of the time, and its bone marrow has achieved 100 % concordance in tests of 20 different chemotherapy drugs. Last week, the company, which has raised just under$80 million across two rounds, led by Coastal Adventures, opened what it calls the world's largest human data center, just outside of San Francisco, and Georgescu says his team is already achieving twice the throughput of all the animal trials being held in the U.S.

4:01The idea is to accelerate the path of drug candidates by having a better idea of what will work before going through the expense of a clinical trial, which typically costs tens of millions of dollars. Though it will not name its partners publicly, Vividyne says it is working with multiple major pharma companies to solve a problem that Georgescu compares to automotive crash tests. An automaker is typically confident its car will pass NHTSA requirements before testing it. But drug makers rarely have that same confidence going into a clinical trial, where the vast majority of drugs fail to win FDA approval.

4:40But there is a larger vision. Georgescu sees his autonomous biology labs as key to generating the kind of causal data that can be used to train new models on human biology. He points to a study published in Nature Methods last month that found no clear data scaling laws when training generative AI models on existing cellular data. He said, all the training is done on static snapshots of these cells, and the models are not conditioned at all by the how a cell got to that state. In other words, the model learns this is cell state A, this is cell state B, but never, cell state B is the effect of inflaming cell state A.

5:23So, Vividine's machines, called Hive, are tracking hundreds of thousands of ongoing experiments where diseased tissue is exposed to some stimulus, which Georgescu expects to provide the kind of reinforcement learning that will produce AI models that understand human biology enough to make more meaningful progress in healthcare. He believes that will be the key not just for today's medicine challenges, but also for a future where complex diseases require drugs that, unlike the majority of those available today target multiple pathways. Georgescu said, if we want combination therapies, the space that has to be searched explodes.

6:04It cannot be an experimental approach. You have to say, I want this effect to happen, so what cause should I invoke? Establishing causality in human biology is the basis of all of this.

6:21Nuclear power startups have been pitching themselves as the antidote to what ails AI data centers, power that's always available. Bill Gates founded TerraPower as one of the latest to throw its hat into that ring, with Bloomberg reporting that the startup plans to announce its first data center project this year. TerraPower did not say who the customer will be, though in January it announced that Meta had agreed to buy eight of its Natrium power plants, the data center project expected to break ground in 2027 would be the company's second power plant, with its first already under construction in Wyoming.

7:00Not every nuclear reactor is suited to data center duty, but TerraPower possesses one key advantage, energy storage, that promises to give it an edge over competitors. And it's all thanks to renewable power sources like wind and solar. Nuclear reactors, Terra powers included, work best when they're running at full tilt. Of all the different types of power plants, nuclear reactors have the highest capacity factor. 92.5 % of the time, they generate at maximum power in the U.S. But in a way, they need to be. Existing reactors are slow to ramp up and down, capable of increasing or decreasing only about 5 % of their total rated output per minute.

7:45That's according to the National Laboratory of the Rockies. New small modular reactors, or SMRs, which many startups are pursuing, can react faster, about 10 % of their rated output per minute per NRL. But running at reduced capacity is not ideal. it's hard to make money when you're not generating electrons. That's true of any power plant, but it's especially true of nuclear, which has the highest capital expenditures of any generating technology. Startups are hoping that mass manufacturing of SMRs will bring CAPEX down, but that has yet to be proven. And if it does work, it could take a decade or more to reap the benefits.

8:27Every startup acknowledges that its early power plants will be expensive, So it makes sense to operate them at peak capacity as often as possible. Now, for data centers, especially those that rely on behind-the-meter power, that poses a challenge. Their loads, especially when training AI or responding to prompts, can sink and soar quickly as GPUs respond to the tasks. The swings are so demanding that natural gas turbines have been breaking under the stress. To smooth out the curve, they need to use large banks of batteries, which increase costs further. TerraPower designed its 345-megawatt molten salt-cooled reactor to work around those challenges.

9:11One of the key considerations was ensuring the reactor could complement intermittent sources of electricity like wind and solar. The power plant needed to ramp up and down quickly. While TerraPower had renewable power, not data centers in mind when it sketched its plans, the two are similarly intermittent, just on different sides of the equation. To ramp quickly, TerraPower does not increase or decrease the power output of its reactor. Rather, it keeps on splitting atoms, and the extra heat gets stored in a giant vat of molten sodium. When power demand spikes, the power plant can tap that reservoir to generate more steam to spend the turbines.

9:53The expensive equipment keeps working even when demand is low, allowing the company to amortize its investment over more operational hours. The approach takes the best of nuclear power, high-capacity factor, and pairs it with an energy storage technology that allows TerraPower to play nicely on a renewable heavy grid or when connected to a data center. It is a flexible approach that could give the startup an advantage and the race to power AI. That's all for now. For the latest in startup tech news, go to TechCrunch.com.

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