Jason Wei

ai researcher @meta, past: openai, google 🧠

[Jason Wei @_jasonwei]

Becoming an RL diehard in the past year and thinking about RL for most of my waking hours inadvertently taught me an important lesson about how to live my own life.

One of the big concepts in RL is that you always want to be “on-policy”: instead of mimicking other people’s successful trajectories, you should take your own actions and learn from the reward given by the environment. Obviously imitation learning is useful to bootstrap to nonzero pass rate initially, but once you can take reasonable trajectories, we generally avoid imitation learning because the best way to leverage the model’s own strengths (which are different from humans) is to only learn from its own trajectories. A well-accepted instantiation of this is that RL is a better way to train language models to solve math word problems compared to simple supervised finetuning on human-written chains of thought.

Similarly in life, we first bootstrap ourselves via imitation learning (school), which is very reasonable. But even after I graduated school, I had a habit of studying how other people found success and trying to imitate them. Sometimes it worked, but eventually I realized that I would never surpass the full ability of someone else because they were playing to their strengths which I didn’t have. It could be anything from a researcher doing yolo runs more successfully than me because they built the codebase themselves and I didn’t, or a non-AI example would be a soccer player keeping ball possession by leveraging strength that I didn’t have.

The lesson of doing RL on policy is that beating the teacher requires walking your own path and taking risks and rewards from the environment. For example, two things I enjoy more than the average researcher are (1) reading a lot of data, and (2) doing ablations to understand the effect of individual components in a system. Once when collecting a dataset, I spent a few days reading data and giving each human annotator personalized feedback, and after that the data turned out great and I gained valuable insight into the task I was trying to solve. Earlier this year I spent a month going back and ablating each of the decisions that I previously yolo’ed while working on deep research. It was a sizable amount of time spent, but through those experiments I learned unique lessons about what type of RL works well. Not only was leaning into my own passions more fulfilling, but I now feel like I’m on a path to carving a stronger niche for myself and my research.

In short, imitation is good and you have to do it initially. But once you’re bootstrapped enough, if you want to beat the teacher you must do on-policy RL and play to your own strengths and weaknesses :)

[Jason Wei @_jasonwei]

@DrSuneelDhand I don't think AI will replace radiologists like you think
https://t.co/JLPlP5adA2

[Jason Wei @_jasonwei]

AI will not automate radiologists like you think

We have been talking for years now about AI automating jobs. Radiology has been a prime target, with Geoff Hinton once claiming, “people should stop training radiologists now, it’s just completely obvious that within five years, deep learning is going to do better than radiologists”, which is funny since he said that in 2016. There is now more demand for radiologists than ever, so clearly things did not turn out the way many thought. Here I will make a prediction that while radiology will change in the coming decade, radiologists will not be automated away any time soon. I will use radiology as an example but I think these principles will broadly apply to any field where demand can be elastic and quality can scale with increased intelligence.

First of all, we have seen in the short history of language models that there is a “jagged edge of intelligence”, where AI excels at verifiable tasks with a lot of data but is underwhelming on less common tasks. One thing most AI researchers underestimate about radiology is that it is actually extremely multi-task. There are at least a hundred conditions that can be diagnosed from x-rays alone, and thousands using CT and MRI. AI will first master common and easy-to-see conditions and it will not be as good as experts at less-common diseases just because there is less data for that. This jagged edge will certainly improve over time but this means that AI will certainly be used as a triage tool at first, rather than full automation in one go. I would guess that this process from solid triage tool to full automation would take 3-5 years in the best case scenario, because training AI to get all the edge cases right will be bottlenecked by data collection of rare diseases, and also because radiology isn’t currently a focus for frontier labs.

Second, there will be elastic demand for radiology as it gets cheaper per unit. I think elastic demand (“Jevons paradox”) is the most underrated phenomenon when people think about AI automation. In software, it is obvious that I have way more demand than before because Claude Code makes it so easy. These days I ask Claude Code to build a custom three-thousand line html viewer to help me visualize every little RL run that I do. The same thing will happen in radiology. If there is essentially no cost or latency of interpreting an MRI, a physician can order many scans preemptively to better diagnose a case. So with AI tools, a radiologist might have to read only 20% of inbound scans, but there might be 5x more scans inbound. Many developing countries have fewer than one radiologist per million people and they will also want to send a lot of radiology to be interpreted.

Longer term, cheap and fast radiology interpretation will also drive incentives to build cheaper radiology scanners. If an MRI had essentially no latency for interpretation, medical devices companies will want to build much cheaper MRI machines that can be used more frequently, perhaps even in the middle of a surgery. AI will accelerate the development of scanning technology, too. If a full body MRI was only $100 dollars I would totally want to get one every month instead of every two years. I can imagine a world where people get 10 or 100x the number of scans they do today, largely for preventative care, if radiology were 10-100x cheaper.

Third, even if we did have a model that could outperform the world’s best radiologists today, that would not be the end of all work on radiology. Medicine is a field of change. We may develop new treatment protocols that require reading radiology images in a way that radiologists currently aren’t used to. New scanners may reveal diseases that we cannot currently diagnose today with radiology alone. Some radiologists will change their work from reading scans to doing frontier research to advance our understanding of radiology and translate new knowledge into updated models.

Moreover, I am sure that we will ask for more out of our AI models than ever before. With a good flywheel, we may one day train AI models that can use radiology images to predict disease probabilities far into the future. With good prediction algorithms, cancer may be detected months earlier than we can detect it now. Society will invest a lot into this research and this will contribute even more to increased demand for radiology and the best AI around it.

On top of all of this, the medical system is typically slower to adopt new technology and has more regulation compared to technology companies. There is legacy infrastructure and medical centers need to constantly support patients which makes it hard to integrate new technology. This is even more true in developing countries. Moreover data access tends to be limited due to HIPAA, deidentification, IRBs, etc. So I think radiologists will be in demand for a while.

While some worry about radiology being totally automated, I think there is a more exciting future for radiology than ever before—one where there is more demand and we ask for more predictive power from our intelligence. This is similar to how many of us feel that programming will thrive more than ever before despite drastic change in the field. More generally, any domain where demand can be elastic and quality can scale with increased intelligence will, like radiology, similarly flourish with the development of AI. This is certainly a more exciting and nuanced future than what Hinton described in 2016!

(Finally, to be fair to Hinton, he did acknowledge in a recent podcast that he was wrong and also agrees with the elastic demand argument.)

Thanks @JayRothschild and @engyziedan for feedback on this post.

[Jason Wei @_jasonwei]

excited for you stephanie! really like the idea of betting on conviction in new technology before market fit. as ai improves faster than ever and has an increasingly outsized impact, the way we fund the ecosystem should evolve with it

[Stephanie Zhan @stephzhan]

I’m starting a new early-stage AI fund, doing what I love: partnering with founders from company formation on bold bets, long before consensus.

The fund’s LPs will include Sequoia, Jeff Dean, Sachin Katti, Philip Wong (my Stanford professor and Chief Scientist adviser to TSMC), and others whose work has shaped our industry and how I think about AI’s future.

I’ll remain a partner at Sequoia and keep serving on my boards on Sequoia’s behalf.

Sequoia has been my home for the past 11 years. I joined as the youngest investor the firm had ever hired. I grew up here. Through years of intense debate and shared work, I found lifelong mentors and friends who took me under their wing and taught me this craft. They taught me to aim high and take big swings. I’ll carry that into what I build: outsized ambition, relentless truth-seeking and curiosity, and a refusal to become complacent.

Working alongside founders has made me want to build something of my own: a firm shaped from day one around the convictions I’ve developed over the past decade.

Life has taught me not to take time for granted. This is how I want to spend mine.

Why now

We’re seeing a new way of building companies in today’s AI era. Research and products often advance together. A breakthrough can open a market or accelerate a business far faster than expected. Some companies require extraordinary capital and compute early on. Others reach meaningful scale with remarkably small teams. The rules for how big these businesses can get, and how fast, are being rewritten.

But early on, the technology is unproven, the resources required enormous, and the vision seemingly implausible.

This is what I love most: seeing the potential right from the beginning, and helping founders realize their full scale of ambition.

More than ever, it starts with an extreme willingness to believe early, with bold, independent conviction in a founder’s technical insight before any product or market validation. Then, it means helping founders take the biggest swings, giving the rare, long-term patience to see those bets through uncertainty, ambiguity, and turbulence, and serving as a level head when the stakes are highest.

I learned this kind of partnership at Sequoia. It’s the foundation of the firm I’m building.

Believing before consensus

My fascination with AI’s potential took root at Stanford in the early-to-mid 2010s, studying computer science and working in Andrew Ng’s lab. The friendships I formed connected me to a community whose people and ideas would help shape modern AI and build some of our generation’s defining companies. What stayed with me was the gap between what those people saw was possible and what the wider world could imagine.

I’m drawn to founders pursuing ambitions that sound impossible today.

I start with a simple question: “What if everything goes right?”

Then we work backward together to make it happen.

In 2023, Deepak and Abhinav set out to build a general-purpose robot brain at @SkildAI, before physical AI was a consensus bet. Their sim-to-real breakthroughs gave me conviction where earlier robotics approaches had fallen short. But the data flywheel required deployment at scale. We made commercialization as urgent as research and brought in strategic partners like NVIDIA and SoftBank to support the ambition. Three years in, Skild is advancing the frontier of physical AI with nine figures in annualized revenue.

In 2024, Ioannis and Misha founded @reflection_ai with an insight into scaling reinforcement learning. As that grew into the ambition to build a Western frontier lab for open models, the hard question was how a startup competes with labs spending tens of billions. An introduction to NVIDIA and many compute calls and recruiting events later, the team has built Beam, now one of the leading US open models.

In 2025, Anna and Azalia, the co-creators of AlphaChip, founded @RicursiveAI to design models and hardware together. From day one, we made early customers and ecosystem partners a priority. Today, they are showing order-of-magnitude improvements on real workloads, working with some of the industry’s most consequential players, and reaching a defining inflection point.

The ambition keeps growing as these companies scale.

@linear has always had a kind of magic to it. Now it’s the system a new generation of AI companies builds on, for teams and agents alike.

@tavus saw early that we’d one day talk to AI the way we talk to each other. Today, it’s building the human computing interface for a world of AI colleagues.

@Mach_Industries began as hydrogen experiments in an MIT dorm room. It has since become one of the most exciting next-generation defense companies.

Making the ambition possible

Being a first believer is only the beginning. Compute falls through. A deep-pocketed incumbent makes your mission its top priority. A breakthrough changes the opportunity and the capital required overnight.

These are stomach-churning moments, and early belief carries a responsibility to help make the bet work.

This is the partnership I love: turning the full ambition into concrete steps. Raising capital, securing compute and strategic partners, recruiting exceptional people, and winning the first customers together. Thinking several moves ahead while solving what’s in front of us. And as breakthroughs open new possibilities, helping founders dream even bigger.

Thank you

To the founders I’ve worked with: thank you for trusting me and Sequoia to be your first partner and first believer. It’s been the joy of a lifetime, and I can’t wait to keep building together.

Thank you to Alfred and Pat for your leadership of Sequoia and your support of my next chapter. To Roelof, for bringing me into Sequoia and believing in me from the beginning. Alfred and Roelof, you have been mentors in investing and in life. So much of what I know, and the investor I’ve become, comes from your guidance and generosity over the years. To Bill, for the close partnership and wealth of knowledge, always wrapped in humility. To Shaun, for showing me how to think deeply and imaginatively. To Michael Moritz, for inspiring 22-year-old me to dream big. To Doug, for teaching me to keep things simple. To Bogie and Jess, for bringing joy to the everyday and perspective on what matters. And to all my Sequoia teammates, for the friendship, camaraderie, and lessons I’ll carry with me.

To my family, especially my husband and my brother, who have each started companies of their own: thank you for giving me the courage to take this leap.

Let’s get to work!

[Jason Wei @_jasonwei]

@joejanizek youre right, the interpretation is only a fraction of total cost. but maybe latency goes down and you get higher accuracy and some predictive power too, which helps. and agree with your point that ai can help us make cheaper radiology equipment

[Jason Wei @_jasonwei]

AI will not automate radiologists like you think

We have been talking for years now about AI automating jobs. Radiology has been a prime target, with Geoff Hinton once claiming, “people should stop training radiologists now, it’s just completely obvious that within five years, deep learning is going to do better than radiologists”, which is funny since he said that in 2016. There is now more demand for radiologists than ever, so clearly things did not turn out the way many thought. Here I will make a prediction that while radiology will change in the coming decade, radiologists will not be automated away any time soon. I will use radiology as an example but I think these principles will broadly apply to any field where demand can be elastic and quality can scale with increased intelligence.

First of all, we have seen in the short history of language models that there is a “jagged edge of intelligence”, where AI excels at verifiable tasks with a lot of data but is underwhelming on less common tasks. One thing most AI researchers underestimate about radiology is that it is actually extremely multi-task. There are at least a hundred conditions that can be diagnosed from x-rays alone, and thousands using CT and MRI. AI will first master common and easy-to-see conditions and it will not be as good as experts at less-common diseases just because there is less data for that. This jagged edge will certainly improve over time but this means that AI will certainly be used as a triage tool at first, rather than full automation in one go. I would guess that this process from solid triage tool to full automation would take 3-5 years in the best case scenario, because training AI to get all the edge cases right will be bottlenecked by data collection of rare diseases, and also because radiology isn’t currently a focus for frontier labs.

Second, there will be elastic demand for radiology as it gets cheaper per unit. I think elastic demand (“Jevons paradox”) is the most underrated phenomenon when people think about AI automation. In software, it is obvious that I have way more demand than before because Claude Code makes it so easy. These days I ask Claude Code to build a custom three-thousand line html viewer to help me visualize every little RL run that I do. The same thing will happen in radiology. If there is essentially no cost or latency of interpreting an MRI, a physician can order many scans preemptively to better diagnose a case. So with AI tools, a radiologist might have to read only 20% of inbound scans, but there might be 5x more scans inbound. Many developing countries have fewer than one radiologist per million people and they will also want to send a lot of radiology to be interpreted.

Longer term, cheap and fast radiology interpretation will also drive incentives to build cheaper radiology scanners. If an MRI had essentially no latency for interpretation, medical devices companies will want to build much cheaper MRI machines that can be used more frequently, perhaps even in the middle of a surgery. AI will accelerate the development of scanning technology, too. If a full body MRI was only $100 dollars I would totally want to get one every month instead of every two years. I can imagine a world where people get 10 or 100x the number of scans they do today, largely for preventative care, if radiology were 10-100x cheaper.

Third, even if we did have a model that could outperform the world’s best radiologists today, that would not be the end of all work on radiology. Medicine is a field of change. We may develop new treatment protocols that require reading radiology images in a way that radiologists currently aren’t used to. New scanners may reveal diseases that we cannot currently diagnose today with radiology alone. Some radiologists will change their work from reading scans to doing frontier research to advance our understanding of radiology and translate new knowledge into updated models.

Moreover, I am sure that we will ask for more out of our AI models than ever before. With a good flywheel, we may one day train AI models that can use radiology images to predict disease probabilities far into the future. With good prediction algorithms, cancer may be detected months earlier than we can detect it now. Society will invest a lot into this research and this will contribute even more to increased demand for radiology and the best AI around it.

On top of all of this, the medical system is typically slower to adopt new technology and has more regulation compared to technology companies. There is legacy infrastructure and medical centers need to constantly support patients which makes it hard to integrate new technology. This is even more true in developing countries. Moreover data access tends to be limited due to HIPAA, deidentification, IRBs, etc. So I think radiologists will be in demand for a while.

While some worry about radiology being totally automated, I think there is a more exciting future for radiology than ever before—one where there is more demand and we ask for more predictive power from our intelligence. This is similar to how many of us feel that programming will thrive more than ever before despite drastic change in the field. More generally, any domain where demand can be elastic and quality can scale with increased intelligence will, like radiology, similarly flourish with the development of AI. This is certainly a more exciting and nuanced future than what Hinton described in 2016!

(Finally, to be fair to Hinton, he did acknowledge in a recent podcast that he was wrong and also agrees with the elastic demand argument.)

Thanks @JayRothschild and @engyziedan for feedback on this post.

[Jason Wei @_jasonwei]

spotted: andrew and muse girl on first date

[Michelle Ko @michelleekokoko]

hiiii 👋🏻

[Jason Wei @_jasonwei]

@RuiboLiu thanks bro!

[Jason Wei @_jasonwei]

@openpcma thanks 帅哥

[Manana Hakobyan @MananaHakobyan]

Cheering for @_jasonwei and his team today! Not only because I know how passionate they are about AI for Health research, but also, at a personal level, growing up with a sibling who was never diagnosed, the actual impact of this acceleration in health research is so hopeful

[Jason Rothschild @JayRothschild]

Really proud to be part of this group and of what everyone built in a few weeks.

I'm amazed how much it's just about the thinking now. I can have an idea and test it without all the tedious speed bumps in between. Research feels purer.

Excited to see where this goes!

[Paul Javid @pauljavid]

👊@_jasonwei accelerating the frontier for health research, and championing health within meta.

[Hyung Won Chung @hwchung27]

In a lecture I gave last year (https://t.co/gyBq2vRbvD), I shared a mental model for AI: the convex hull of humanity’s collective knowledge.

Human expertise is highly specialized and fragmented across people and fields. I hypothesized that there might be a huge overhang of new knowledge accessible through “interpolation”: connecting the dots in this high-dimensional knowledge space.

@_jasonwei and the team are showing what connecting the dots can look like with an existing trove of health data. Exciting to see AI helping researchers turn that data into new scientific insights!

[Jason Wei @_jasonwei]

@engyziedan thanks and happy to partner on the data for this work!

[Engy Ziedan @engyziedan]

Congratulations @_jasonwei and team. What I am most excited about here is the distance between the doctor who sees you and the researcher who studies you will go to zero. One mind. Reducing the marginal cost of research to minutes means you can see patients at the bed side and test hypotheses in data much faster. So its an accelerator to both research and clinical care. Onward!

[Kritika Singh, PhD @kritika_singh24]

Thrilled to have worked with this team! I really enjoyed having the Muse Wonder scaffold as a collaborator to bounce ideas off of. It reduced the admin overhead of research, sped up my workflow from idea to manuscript, and helped me learn a lot along the way. Super proud of what we put together, and excited to keep exploring how AI can support scientific research!

[Xilun Chen @ccsasuke]

Excited to share our work on using AI to accelerate health science research! Health matters to everyone, and I'm personally pumped to see what AI can do to push the science forward. This is just the beginning, and I'm very excited to keep exploring this direction.

[Jason Wei @_jasonwei]

Love this quote about Muse Wonder:
"What I appreciate about Muse Wonder is that it isn't sycophantic. Some other models I've used will make an idea sound great in the moment, and only later do I realize it didn't hold up. Muse Wonder is willing to call me out and steer me toward a better approach, often before I've spotted the problem myself. That saves a lot of time, and it's a big part of why I trust it." — Jason Rothschild, MD, Musculoskeletal radiologist

[Cosmo Du @Answeror]

real world work, publicly available

[Alexandr Wang @alexandr_wang]

10 new papers in health science research. all written with muse models in under 4 weeks.

health is one of the areas where we believe muse is going to have the greatest positive impact on humanity ❤️

[Arturo LoAIza-Bonilla, MD MSEd @DrArturoAI]

https://t.co/QCvXmxgWAt @AIatMeta @alexandr_wang

[Grégoire Mialon @mialon_gregoire]

The ambition is contagious. The prognosis looks good