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Misha Laskin

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@MishaLaskin

co-founder, ceo @reflection_ai
NYC reflection.ai Joined August 2013
235 Following 21K Followers 905 Posts

Latest posts 5

Misha Laskin Verified account @MishaLaskin
Stephanie understands AI at a level most investors don’t. Contrarian when it’s uncomfortable. Shows up when things get hard. Helped unblock compute for us when it mattered. Founders building hard, deeply technical companies are lucky to have her. Congrats on the new fund.
Stephanie Zhan Verified account @stephzhan Oct 22
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!
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Misha Laskin Verified account @MishaLaskin
Beam weights are out today (Apache 2.0 license).

We integrated Beam across 40+ different partners.

And are also open sourcing Mirror CLI - the harness Beam was trained against.

Happy building.
Reflection @reflection_ai Oct 16
Beam is now available across platforms, with open weights under the Apache 2.0 license - in collaboration with an ecosystem of 40+ partners.

We're releasing:
- Weights, tokenizer, and reference implementation
- BF16, FP8 and NVFP4 weights and a DFlash2 drafter
- The open source harness we built and trained Beam on: Mirror CLI

Weights and model card on Hugging Face: huggingface.co/reflection
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Misha Laskin Verified account @MishaLaskin
We are introducing Beam today, a 500b open model that excels in coding, agentic, and scientific workloads.

Beam is very token-efficient, pre-trained from scratch, and scaled with the largest RL run documented openly we are aware of.

Getting here was challenging, rewarding, and personal.

I immigrated to the USA during childhood when my parents got jobs as scientists at PNNL, a national lab located in a rural town in Washington state.

Growing up, I developed an interest in physics because it was grounded in simple principles, was foundational, and had long-term impact.

Physics was the foundational science of the 20th century. The first general computer (ENIAC), the transistor, GPS, and much of modern technology had its roots in physics.

In 2016, during my graduate studies, I noticed the birth of a new foundational science - Artificial Intelligence.

AlphaGo, the superintelligent Go agent developed by DeepMind, had come out and made me feel what was about to come.

A neural network, trained to imitate human players, and then improve itself through trial and error, mastered the most intellectually challenging board game in the world.

What surprised me about AlphaGo is how it was conceptually simple, general, and scalable. The system could in principle improve itself forever, it was just a function of compute.

This is when I became “AGI pilled” as they say.

I switched from physics to AI because I thought this would become the root node science of the 21st century, much like physics was the root node science of the 20th century.

I believe that working on foundational science is not enough on its own. Foundational science must also remain open and widely accessible.

We remember Newton for his contributions to physics, but he was also an alchemist.

Alchemy was done in secret, because the artificial production of gold was deemed lucrative but also dangerous with the potential do destroy entire economies. Newton even wrote his alchemy notebooks in cypher.

On the other hand, Principia was published in the Philosophical Transactions of the Royal Society, the knowledge was made widely available, passed down for generations in what we now call Newton’s laws.

The computer, transistor, GPS were possible to build because physics as a science was open. Openness creates an ecosystem of innovation.

The notion of openness, which is a core tenet of scientific discovery, has also become a guiding principle for how foundational technology gets built and distributed.

The internet was built on open protocols. The most widely used operating systems are open. The field of cybersecurity exists because strong encryption protocols were made open in the 1990s.

Open also means safer. The best defence is a distributed one. Commonly used open source software is robust and battle-tested because it is inspected and patched by a community of developers.

AI is different but also similar to software in many ways, though with a much larger surface area of vulnerabilities. It is impossible for a small group of safety researchers working in secrecy to remediate all of the potential issues regardless of their intentions. It is like entrusting a handful of white blood cells to protect your body.

AI is both foundational as a science and a technology, and the safest way that also ensures the benefits of AI are distributed evenly is by making it open.

I’m excited for what developers and scientists build and learn from this model, and for Beam to play a small part in ensuring that AI is made widely accessible, safe, and beneficial to all.
Reflection @reflection_ai Oct 5
Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active.

- Frontier reasoning efficiency
- Advances the Western open frontier on coding & agentic tasks
- Trained end-to-end from scratch

Full weights release this month.

Learn more about Beam: reflection.ai/beam
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Misha Laskin Verified account @MishaLaskin
Linus's law in open source software states that with enough eyeballs all bugs are shallow

The same is true for safety in AI. The more people involved the easier it is to find and patch the long tail of misaligned behaviors
Jensen Huang Verified account @JensenHuang Sep 28
Today, with over 100 industry partners, we introduced the NVIDIA Open Agent Safety Platform, bringing together OpenShell and Sentry.

Artificial intelligence is extraordinary technology that will advance discovery, productivity, security, health, and prosperity for generations to come.

But its full promise can only be realized when people have confidence that AI is being built to be safe and deployed with wisdom and responsibility.

This is bigger than a single product. It's the beginning of an open ecosystem to build the trust layer for safe agent systems.

Together, we are building the foundation of the AI economy.

Trust and innovation are not in conflict. Safety is how trust is earned. We must build not only the most capable AI, but the most trusted AI, so that this extraordinary technology can realize its enormous promise for the world. nvda.ws/4hcoq7m
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