F

[Brais Moure @MoureDev]

Por favor, difusión 🙏

Hi @AnthropicAI, @ClaudeDevs, @claudeai and @DarioAmodei, please help.

Our friend @miriamgonp needs access to your models to keep fighting breast cancer. She has built her own team of AI agents to help study her case, and credits to use your models would be a tremendous help.

The Dana-Farber Cancer Institute itself has acknowledged that the information uncovered by the agents can be very helpful.

It’s a small thing for you, but it would mean so much to her. Thank you.

[F @FernandoLe34378]

@sci_fi_queen Bio requires real world experiments (at least with our current models) and AI models cannot run those experiments autonomously yet. I think much of wet lab work will get automated and accelerated

[F @FernandoLe34378]

@alexrives Do you plan to automate the real-world experiments?

[F @FernandoLe34378]

We are trying trying to develop a benchmark for this, if you have ideas, we are open

[Nicolas Bustamante @nicbstme]

Some people are anti-AI because they can already see the downside, potentially losing their job or watching the craft they spent years learning get automated, while the promised land, curing cancer and treating diseases, still feels very far away.

AI gets incredibly useful when it has good data and a way to test whether it’s right. But biology has an enormous space of possibilities, much of the relevant data hasn’t been generated yet, and you can’t answer every question by thinking harder. At some point you have to run the experiment.

So here’s something I’d love to see someone build: a giant warehouse, basically a data center for biology, filled with lab equipment and eventually tens of thousands of robots running experiments around the clock. Liquid handlers, plate readers, microscopes, incubators, all connected to models that propose experiments, look at the results and decide what to try next.

I call the idea bounded serendipity.

A lot of discovery comes from exploring something and finding something you weren’t looking for. CRISPR came out of research into how bacteria defend themselves against viruses. RNA interference also emerged from unexpected experimental observations. These were years of careful science, but the surprise mattered. You can’t schedule the breakthrough, you can create more opportunities to encounter one.

So why wouldn’t we massively scale those opportunities? Pick a scientifically meaningful area, define what can be tested and measured reliably, then explore far more of it than a human team could. Give the models room to try unusual ideas, follow surprising results and update their understanding, with scientists deciding which questions matter and checking what the results actually mean.

Two things make me think the economics could change.

1/ Robotics. Lab automation already exists, but integrating different instruments and workflows often takes expensive custom engineering. My bet is that increasingly capable general-purpose robots, including humanoids where they make sense, could reduce how much bespoke hardware you need by using equipment designed for humans. Keep specialized machines for the tasks they do best, and use flexible robots to connect everything and handle the work between them.

We’re not yet at the point where you can buy 10,000 humanoids and expect them to run a biology lab reliably. But that’s the direction I’d bet on. Once a workflow works, you can replicate it and run shifts around the clock. Scaling that capacity could become much easier than recruiting and training thousands of lab technicians for every new facility.

2/ The data itself can be a product. Historically, the economics of an experiment often depended on whether it helped produce a valuable discovery. Now there’s another potential buyer: someone training a biology model. A well-run experiment that shows something doesn’t work can still be useful training data. A broken experiment is different, but a clean negative result tells you something about the world.

That creates a possible business model where you sell high-quality experimental datasets while also using them to build better models and pursue your own discoveries. Companies are already selling biological data for AI training, so this part is more than a hypothetical.

And parts of the loop already work. OpenAI and Ginkgo connected GPT-5 to an automated lab, tested more than 36,000 protein-synthesis reaction compositions and reported a 40% reduction in production cost. That’s a narrow result, but it’s a concrete example of a model proposing experiments, getting real measurements back and improving through iteration.

The flywheel is more experiments, more useful data, better models, smarter experiments, and data revenue funding more capacity. We’re building enormous data centers for compute. I’d love to see us build the equivalent for biology, so the promised land gets here faster.

Bounded serendipity at industrial scale.

[Nicolas Bustamante @nicbstme]

Some people are anti-AI because they can already see the downside, potentially losing their job or watching the craft they spent years learning get automated, while the promised land, curing cancer and treating diseases, still feels very far away.

AI gets incredibly useful when it has good data and a way to test whether it’s right. But biology has an enormous space of possibilities, much of the relevant data hasn’t been generated yet, and you can’t answer every question by thinking harder. At some point you have to run the experiment.

So here’s something I’d love to see someone build: a giant warehouse, basically a data center for biology, filled with lab equipment and eventually tens of thousands of robots running experiments around the clock. Liquid handlers, plate readers, microscopes, incubators, all connected to models that propose experiments, look at the results and decide what to try next.

I call the idea bounded serendipity.

A lot of discovery comes from exploring something and finding something you weren’t looking for. CRISPR came out of research into how bacteria defend themselves against viruses. RNA interference also emerged from unexpected experimental observations. These were years of careful science, but the surprise mattered. You can’t schedule the breakthrough, you can create more opportunities to encounter one.

So why wouldn’t we massively scale those opportunities? Pick a scientifically meaningful area, define what can be tested and measured reliably, then explore far more of it than a human team could. Give the models room to try unusual ideas, follow surprising results and update their understanding, with scientists deciding which questions matter and checking what the results actually mean.

Two things make me think the economics could change.

1/ Robotics. Lab automation already exists, but integrating different instruments and workflows often takes expensive custom engineering. My bet is that increasingly capable general-purpose robots, including humanoids where they make sense, could reduce how much bespoke hardware you need by using equipment designed for humans. Keep specialized machines for the tasks they do best, and use flexible robots to connect everything and handle the work between them.

We’re not yet at the point where you can buy 10,000 humanoids and expect them to run a biology lab reliably. But that’s the direction I’d bet on. Once a workflow works, you can replicate it and run shifts around the clock. Scaling that capacity could become much easier than recruiting and training thousands of lab technicians for every new facility.

2/ The data itself can be a product. Historically, the economics of an experiment often depended on whether it helped produce a valuable discovery. Now there’s another potential buyer: someone training a biology model. A well-run experiment that shows something doesn’t work can still be useful training data. A broken experiment is different, but a clean negative result tells you something about the world.

That creates a possible business model where you sell high-quality experimental datasets while also using them to build better models and pursue your own discoveries. Companies are already selling biological data for AI training, so this part is more than a hypothetical.

And parts of the loop already work. OpenAI and Ginkgo connected GPT-5 to an automated lab, tested more than 36,000 protein-synthesis reaction compositions and reported a 40% reduction in production cost. That’s a narrow result, but it’s a concrete example of a model proposing experiments, getting real measurements back and improving through iteration.

The flywheel is more experiments, more useful data, better models, smarter experiments, and data revenue funding more capacity. We’re building enormous data centers for compute. I’d love to see us build the equivalent for biology, so the promised land gets here faster.

Bounded serendipity at industrial scale.

[F @FernandoLe34378]

@halmoslabs Great work! Do you automate or plan to automate the real-world lab work?

[F @FernandoLe34378]

@m_goes_distance Which company was it?

[Ryan McGuire @r_mcguire]

@dylanmcguir3 and I just raised a $7.5M pre-seed led by @a16z to build a robotic surgeon @alephsurgery

Much of the early investment in Physical AI has focused on industries where labor is relatively abundant and low-cost. We believe the biggest opportunities to change the world lie at the opposite end of the spectrum.

Surgery is among the most valuable forms of labor on the planet, but it's far too scarce and expensive - due to an exceptionally long training pipeline and highly concentrated expertise.

We’re building the first surgeon that scales: one system that can acquire, refine, and transfer surgical skills across millions of procedures. v1 is already learning to perform surgical tasks autonomously from demonstrations, and improve from its own experience.

Lots of great folks behind us! @BoxGroup, Reveille VC, Constellation, @forward_deploy, @Valkyrie_vc, @DiscipulusVent, @maniacvc + more

[Minh Nhat Nguyen 🦭 @menhguin]

i remember in 2022 thinking "wow it's 2022 and there's no scifi stuff around everything's mostly the same as 20 years ago" and things rly accelerated like crazy since then.
like not even gradually, but very rapidly.

[F @FernandoLe34378]

@Kinai_24 Como ves el futuro de la robotica? Estoy empezando undergrad research en una uni americana y cada dia hay algo nuevo