It used to be that Vijay Pande was better known in academic circles than investor circles. That changed pretty abruptly a dozen years ago, when Marc Andreessen and Ben Horowitz — who’d spent their firm’s first five years explicitly avoiding healthcare and life sciences — decided the category was worth betting on after all and handed the keys to Pande. At the time, he was a Stanford chemistry professor who was best known for building Folding@home, the distributed-computing project that turned millions of home PCs into a supercomputer for disease research. Over the next decade-plus, he grew a16z’s bet into a practice managing close to $4 billion.

So it was somewhat unexpected when in June of last year, Pande walked away from it all to start something much smaller. In fact, his new firm, VZVC, co-founded with longtime investor Zach Werner, is built around a handful of concentrated bets a year rather than dozens, it has no associates, and it relies heavily on AI for its day-to-day operations.

To learn more about Pande’s hard pivot, we talked with him this week about why he’s making just a handful of concentrated bets rather than spreading himself thin in the current market — and about one of the more interesting conundrums in AI-driven biotech: unlike text, biological data can’t be scraped off the internet, so nearly every company ends up building its own walled-off dataset. What does that mean for all the advances AI in medicine has promised, and who actually gets access to them?

This conversation has been edited for length and clarity. You can also listen to the fuller conversation (below).

You’ve said biology is moving from a “science of discovery” to something you can engineer. What does that mean?

For a lot of the way drugs have been developed, there was very much a fortuitous aspect to it. I think what’s shifted is that AI and machine learning allow computers to wrap their type of understanding around something very, very complicated… to try to figure out what targets you want your drugs to hit, for specific diseases, to be able to make those drugs, and now even to help in the clinical trials — which are the most expensive part of the process.

I thought clinical trials were getting cheaper because drug developers are using more synthetic data, so not as many people are needed for these trials.

That’s, I think, very much an aspiration.

The cost and time to get to clinical trials has been shrinking, especially with AI, but it could still cost hundreds of millions of dollars to run a trial, which is why drugs are very expensive. The probability of a drug going successfully from the first trial to the end of the third trial is just 20%. If 8 out of 10 fail, and these things cost hundreds of millions of dollars, the amortized cost gets really high. The reason they fail typically is not that the biologist did something wrong; it’s that all the experiments these drugs were designed on were on animal models like mice, and in the end, animal models are just not very predictive of humans. The AI model is not going to be perfect, but it’s going to be way better than any animal model would be, and once it crosses that bar, that’s where it gets really exciting.

[The phase after that is]: Is the drug the right drug for me?

You mean personalized medicine. . .

The jargon here is so-called precision medicine. If you go to a doctor with something not trivial, they have to guess what’s going on, because there’s only so much they can tell. Then they give you a drug — and if that doesn’t work, they give you another drug, then another drug. This happens in cancer, it happens in lots of different areas. We would all be much better off if the first drug was the right one. Typically, your blood test values are compared to population averages. But really, they should be compared to: is this [result] weird for you? What we’re starting to do also on the medicine side is [the ability] to just understand what would be right for the individual.

Would you say the path to this moment has been slow and steady, or did it spike more recently?

I think it’s lots of different things [coming together]. So for instance, precision medicine for the longest time was based on genomics. But the reality is your genome is kind of like the blueprint for your house on day one, but your house is fairly different now compared with the moment it was built. So there are many other things that people can now measure in proteomics and so on that are much more relevant for understanding disease and where your body is now. There has also been [a lot of] automation in robotic measurements that is naturally tied into AI, and those two go hand in hand really well. 

Over the last decade, there’s been this steady clip fforin both AI for biology and AI for chemistry. The biology part is like, how can we treat this disease? And then the chemistry part is, how can we come up with a drug to go after that specific protein? There have actually been very significant advances over those 10 years. 

You mentioned that biology is one of the few places AI can’t just scrape data off the internet. What does that mean for how the field develops?

It’s a place where you don’t have any of this data that people can just all train the same thing, and your data can’t be distilled from one model to another. It’s a really interesting play from just the pure AI sense.

Doesn’t that echo a familiar problem in medicine, though — doctors operating in [territorial, often competitive] silos?

You’re onto something really big here. Let’s say [someone] has some type of cancer, and it’s both an issue in oncology and endocrinology — those two doctors really don’t sync together very well. What is really intriguing about AI is that it can, in principle, be a specialist in everything, and it can start to see things that really any single human being couldn’t. It would be equivalent to having a team of the very best doctors all clamoring together in that moment.

But is there enough data sharing for that vision to actually be realized? I understand why founders and investors want to protect their [respective findings], but . . .

I think one of the bigger trends is that we’re starting to see a shift toward building these atlases of biological information — which, from a technology standpoint, are typically foundation models. And as they become more common, I think we’ll see the same thing that’s happened with open-source LLMs, which do very well against the corporate ones: open-source foundation models in biology having a very broad impact.

You’re involved with Genesis Therapeutics, which came out of your lab at Stanford, and Insitro, the drug-discovery company launched by Daphne Koller, a former colleague at Stanford. You say you’re also incubating a company with a founder you’ve known for 20 years. What are you looking for in founders, and in what areas?

There are two areas that I’ve been spending most of my time on. One is AI for healthcare delivery, which I did a ton at a16z as well, and then AI for clinical trials.

One of the things that’s most important to me [about founders] is that we can really trust each other — founders that have high integrity, that do what they say they’re gonna do… I’m expecting this relationship to be 5, 10 years plus into, ideally, their next company. I want to work with people who are thinking long term like that. Ideally, these are people who are not just trying to win and beat other people, but really thinking about the question: how do we win together?

What have you gotten right and wrong in your investing career so far?

When I started talking about AI and machine learning and technology and medicine and bio 10 plus years ago, there was a lot of resistance and a lot of people saying, ‘Oh, that’s never going to happen. That’s never going to be useful,’ and so on. That resistance is largely gone and seeing this arc is very fulfilling.

I think it took me some time to really appreciate that as seductive as the coolest technologies are, it really always comes back to go-to-market. I tell my founders, especially the ones who are coming from the science or the product side, for them to take all their brilliance and creativity and really apply it to the go-to-market side, that the go-to-market part is at least as hard or harder than the technology side.

Help us understand how you’re designing this new firm differently, compared with what you were running at a16z.

Right now, we’re doing something really quite different… VZ is named after me, Vijay, and my co-founder, Zach Werner — he’s the Z. We’re intentionally really quite small… on the investment side, it’s really just the two of us. We were actually intending on hiring associates, but it turned out, with the agents that we’ve built up, not to be something that we need to do.

How concentrated is “concentrated”?

We’re [not] driving 30 bets per year… we’re talking about probably five, not a lot of investments — very concentrated. Adding a company at a typical fund is like adding a Facebook friend — that’s something you do pretty quickly. For Zach and I, it’s more like . . . wanting to have another child. This is a big deal for us.

With that structure, who are you competing against for deals?

The funny thing about this model is that typically we’re not trying to compete for a hot round — people make room for us. It’s a very different thing than trying to get the hot Series A or Series B. Largely, people want us as investors because of what Zach and I can do, and how hands-on we can be. When I look at people who are inspirations, I look at someone like Antonio Gracias at Valor — he’s well-known now because of the SpaceX deal, but he’s been doing what he’s been doing for 20 years. What Thrive has done, with a more concentrated portfolio, is also a real inspiration. Obviously, a16z is sort of in my DNA as well, but I think those other ones are new additions to how we think about things.

What’s overhyped right now in AI and biotech?

The reality is that AI can find insights that we can’t get from just humans alone. The thing that always gets tricky is when there’s this call that AI is going to cure all everything. The reason for hesitance there is not because of any doubt about AI — it’s about doubt of the data. LLMs work because there’s so much data to learn from. When the data is just simply not there, then AI can’t magically solve that problem.

About 1,600 federal employees who work at the General Services Administration have been forced to relocate from their office on F Street in Washington, DC, to the Theodore Roosevelt Federal Building, roughly two blocks away. Workers say the new building, which also houses staff from the Office of Personnel Management has mice, a caved-in bathroom ceiling, and drinking water that needs to be run for five to 10 seconds before it is clear.

The federal workforce has been rocked by changes made by Elon Musk’s so-called Department of Government Efficiency (DOGE) since January 2025. DOGE effectively fired tens of thousands of workers and forced those who remained to commit to in-person schedules and, in some cases, zero-based budgeting. GSA, the agency in charge of the federal government’s IT services and real estate assets, was hit early by DOGE, and in addition to major workforce cuts also rolled out plans to sell hundreds of federal office buildings. A year after the cuts, GSA announced it planned to hire about 400 workers.

“We’ve all been through a lot, but this really made me finally understand that they don’t care about us,” says one GSA employee who asked to remain anonymous, as they aren’t authorized to speak to the press. “We’re left wondering what else is going to collapse or break—do we need to look at the ceiling before going to the bathroom? Can we actually trust the water is safe to drink?”

Courtesy of GSA employee

Photos obtained by WIRED show a toilet in a men’s room on the third floor covered in debris from the ceiling, a gaping hole in said ceiling, and a dead mouse caught in a trap. Workers said the mouse wasn’t “entirely shocking” since many federal buildings allegedly have vermin, but the ceiling pushed them over the edge. An additional sign in the office recommends workers filling up water bottles to “run the water without your bottle for 5-10 seconds and wait for the water to run clear.”

GSA and OPM did not immediately respond to a request for comment.

Courtesy of GSA employee

Anthropic is permanently increasing Claude Code’s standard weekly usage limits by 25% for Pro, Max, Team, and seat-based Enterprise plans, but it’s not as good as it sounds.

If you use Claude, you’re actually getting a 17% reduction compared to what you have today.

Claude Code currently has a temporary 50% increase in weekly limits, which Anthropic says will remain in place until September 14.

“Starting September 14, we’re permanently raising standard weekly limits in Claude Code by 25% for Pro, Max, Team, and seat-based Enterprise plans,” Anthropic wrote on X. “Until then, the current 50% increase will be in place.”

It’s a clever way to frame words, as it almost sounds like you’re winning as a customer, but you’re not, and it’s a downgrade.

If Claude Code’s original weekly allowance was 100, the temporary boost gives you 150 today. On September 14, that drops to 125.

In other words, you will have 25% more Claude Code usage than you did before the temporary promotion, but about 17% less than you have right now.

Anthropic says more Claude Code usage changes are coming

Anthropic later deleted the original thread and posted a clarification, where it clearly admits the reduction.

“Compared to today, this works out to a 17% reduction in weekly limits on Claude Code,” the company said.

“We’re working on exciting changes that will make it feel like you’re getting more from Claude, while having more visibility and control of your usage. Can’t wait to share them.”

Anthropic says Claude usage can vary based on factors including conversation length, model choice, tool usage, and effort level, so the weekly allowance does not translate into a fixed number of Claude Code prompts.

The current 50% temporary increase remains available through September 13, with the new permanent limits taking effect on September 14.

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At TechBBQ in Copenhagen, the conversation among investors, founders, and operators from across Europe wasn’t just about what to build with AI, but who should control it. No matter where you were during the annual Nordic conference, from onstage to cocktail hours and after-parties, the chatter kept coming back to how Europe can gain more control over the technology powering AI.

That question of sovereignty — which fittingly matched this year’s theme of “Emerging from Agency” — was especially timely after Anthropic’s AI models Mythos and Fable became unavailable to users outside Europe earlier this year.

The incident led many in the European ecosystem to think seriously about what it means to own the models and the infrastructure underpinning this AI wave, instead of renting that power from the U.S. and China.

One startup exec told me the Mythos and Fable incident seriously disrupted his software team, while another shrugged it off. Sure, the startup exec said, relying on two other geopolitical powers for AI might one day cause Europe severe headaches, but for now, everything is still pretty much OK. 

“TechBBQ’s theme of agency felt particularly timely this year as the conversation shifted from what AI can do to what we’re actually willing to let it do,” Ellen de Brever, an angel investor and head of partnerships at the Novo Nordisk Foundation Cellerator, told me during the conference that wrapped up August 27.

There were panels about the future of women’s health, a summit on how the Nordic and African ecosystems can collaborate on innovation and investment, and talks about how to get more growth capital into Europe. AI was an underlying theme in many of the conversations, but as de Brever pointed out, “the debates weren’t about smarter machines, but about human judgment and who gets to stay in the driver’s seat.” 

“The age of AI agents isn’t really a story about machines gaining agency,” she continued. “It’s a story about humans deciding what to give up.” 

An attendee told me that one of the most refreshing sessions at the event was the panel with Signal President Meredith Whittaker, which I moderated and focused on whether privacy and AI can coexist. Whittaker didn’t mince words, and spoke out against AI assistants and agents being integrated into operating systems, like what ChatGPT is doing with iMessage.

She said this AI era is creating a “data collection apparatus” and that AI labs are using clever marketing to make people forget “about the collateral consequences” of acquiring so much data. 

“There is still a huge market need for privacy,” she said. “And particularly with the sovereignty concerns, we will have customers here.” 

Elsewhere, Emad Mostaque, co-founder of Stability AI and Intelligent Internet, sat on a panel with me about how an agentic workforce will impact the economy and what that means for personal sovereignty.

“Sovereignty is the ability to resist power being exerted over you,” Mostaque said. He spoke about the concentration of power in the hands of a few AI labs and said, “Inevitably, every country will be run by AI and that “the person that controls the AI controls the country.” 

Mia Negru, the advocacy and engagement director for the nonprofit Life With Artificials, sent me a message after the panel about what crossed her mind as she listened.

“If intelligent agents perform cognitive work and robots increasingly perform physical work, we may be approaching something much bigger than another technological revolution,” she said in the message. “We may have to rethink ownership, work, democracy, economic participation and eventually even who gets to participate in society.” 

But when the panels weren’t in session, people let loose at happy hours and rooftop soirées, where more lighter conversations eventually took over.

Lovable, Nvidia, OpenAI, AWS Startups, and HSBC came together to host a rooftop barbecue where I spoke to founders about Denmark’s growing tech hubs.

London’s Ada Ventures hosted a gathering at the cocktail bar Bird where I chatted with one health tech founder about increased innovation in the women’s brain health space. Then, there was the speakers’ dinner on an island in the middle of Copenhagen where the international press sat together and watched fire dancers. The after-party continued upstairs, where everyone was tempted to stay for one more drink until the bar tab closed.

“In a room full of conversations about machines, the most memorable moments still come from the simplest thing,” de Brever said. “Human connection, face-to-face, building relationships, sharing ideas, and reminding each other of what technology can never quite replace.” 

Sony Music Publishing, Warner Chappell and numerous other music publishers have sued Anthropic and co-founders Dario Amodei and Benjamin Mann, alleging the AI lab conducted a “brazen campaign of illegally torrenting, scraping, and downloading copyrighted works.”

The lawsuit, which was filed late Friday in the U.S. District Court for the Northern District of California, was first reported by Music Business Worldwide. The publishers accuse Anthropic of “blatant theft” by using thousands of copyrighted works to train its AI model Claude.

“We disagree with the publishers’ claims and we intend to defend ourselves robustly in court,” an Anthropic spokesperson wrote in an emailed statement sent to TechCrunch.

This isn’t the first intellectual property lawsuit Anthropic has faced. Some of the same lawyers behind this lawsuit also represent Concord Music Group and Universal Music Group in a case filed in January and led the Bartz v. Anthropic case, in which a group of authors accused Anthropic of using copyrighted works to train products like Claude. Anthropic was ordered to pay $1.5 billion in the landmark Bartz case after a judge ruled that while it was legal for the AI lab to use copyrighted works, it was not legal to acquire that content through piracy.

While the cases make similar arguments, there are key differences. This latest lawsuit is particularly broad and builds off the other cases, including by accusing Anthropic of “flagrant piracy” through illegal torrenting to obtain millions of copies of books, including those that contain lyrics and sheet music.

Article was updated to include Anthropic’s comment.

Under the influence of psychedelics, the usual organization of the human brain loosens. That’s what scientists have generally thought, with several studies showing that when we take these substances, the signals pinging between our neurons become more diverse and unpredictable. This increased entropy in our gray matter may underlie the strange, random, and often memorable experiences of psychedelic trips.

But in one of the largest neuroimaging studies on the effects of psilocybin to date, researchers uncovered a hidden order in the apparent chaos. The study, published in Nature, indicates that while psilocybin blurs some of the boundaries within the brain, our neurological reactions to what we’re experiencing in that moment become even more distinct.

The researchers tracked moment-by-moment changes across 332 regions of the brain using functional magnetic resonance imaging and electroencephalography, two methods of measuring brain activity. The 62 participants, who had no prior experience with psychedelics, took 19 milligrams of psilocybin in an oral capsule and then meditated, listened to music, or watched a movie.

With the help of artificial intelligence, the researchers identified patterns in the brain for each kind of activity the participants were engaged in. But surprisingly, the more intense the psilocybin experience, the clearer those neural signals were. Rather than becoming lost in the midst of an increasingly unpredictable trip, reality seemed to be leaving a more distinct imprint.

These findings weren’t just apparent in the biological measurements but also in participants’ personal experiences. Those who exhibited the most defined patterns of brain activity also tended to describe more intense experiences of immersion and unity with the world around them, the boundaries between themselves and the environment sometimes feeling blurred.

“Under psilocybin, brain networks that help us process our inner world and the world around us became less distinct, but reorganized into patterns that reflected what people were experiencing,” explained Devon Stoliker, the study’s lead author and a researcher at Monash University’s School of Psychological Sciences in Melbourne, Australia.

In addition to providing a biological explanation for the sense of oneness that many people describe during a psychedelic trip, the findings also provide nuance to the idea that psilocybin simply makes the brain “chaotic.” This understanding is especially important in the emerging field of psychedelic therapy.

“This shows that context is not just background to the psychedelic experience,” Adeel Razi, a professor at Monash and the senior author of the study, emphasized in a press release. “For treatment, that reinforces why the environment and what a person does and experiences during the psychedelic session matter.”

This story originally appeared on WIRED en Español and has been translated from Spanish.

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