Jonathan Lee

MTS @ Stealth

[Jonathan Lee @jonsjlee]

@HenryYin_ Congrats on the launch!!

[Henry Yin✈️ICML @HenryYin_]

Most AI investing happens downstream of the frontier: a capability emerges, a category gets named, and capital rushes in.

But by the time a category earns a clean box on a market map, the best builders have usually been living in the messy version for months.

Agents. Reasoning. RL environments. World models. AI for Science. Recursive self-improvement.

I call this frontier proximity: the ability to see what is becoming possible before it becomes consensus.

My frontier proximity ladder:

L0 Wrapper: uses today’s models.
L1 Reactor: reacts fast to releases, but roadmap is downstream.
L2 Anticipator: builds for where capabilities are going.
L3 Native: depends on a non-obvious frontier bet.
L4 Shaper: helps move the frontier itself.

The point is not that every company needs to train models.

Apps can have high frontier proximity if they understand what models will make possible next.

Infra can have high frontier proximity if it knows what future agents, multimodal systems, robotics stacks, or scientific workflows will need.

That is why we’re launching MoE Capital.

MoE stands for Mixture of Experts.

The idea is simple: build an AI fund around people closest to the frontier: frontier researchers, technical founders, AI-native builders, and seasoned operators.

We don’t want to be another AI fund with a newsletter-level understanding of the frontier.

We want to build the AI fund closest to the frontier.

More in The Information: https://t.co/CXWJAy34zi

[Henry Yin✈️ICML @HenryYin_]

World model" can mean a video that looks like Sora, a robot brain, or a set of abstract embeddings. These are completely different things.

Two AI research lineages quietly merged in the last two years to produce this confusion. New piece on what happened:

https://t.co/bSPCj7wTy2

[Jonathan Lee @jonsjlee]

@HenryYin_ Great write up Henry!!

[Reflection @reflection_ai]

Reflection is partnering with Shinsegae Group to build a 250-megawatt sovereign AI factory for the Republic of Korea.

Open intelligence. Built on trust between allies. Owned by the nations that need it most.

The future of sovereign AI. Read more in the @WSJ.

[Jonathan Lee @jonsjlee]

@brandondamos @reflection_ai Welcome to the team!!

[Irwan Bello @IrwanBello]

Headed to #NeurIPS with the Reflection team this week! 👋

Keen to chat about LLMs, RL, agents, open research & science.

We have a few open roles: https://t.co/KOesKmY8W2

[Jonathan Lee @jonsjlee]

@caitlinxyz @scho_026 Congrats!! Beautiful wedding

[Ioannis Antonoglou @real_ioannis]

We're bringing the open-model frontier back to the U.S. to advance the science and engineering of agentic intelligence for the world.

Grateful to our investors for powering this vision: NVIDIA, Disruptive, DST, 1789, B Capital, Lightspeed, GIC, Eric Yuan, Eric Schmidt, Citi, Sequoia, CRV, and others.

[Turner Novak 🍌🧢 @TurnerNovak]

Wow. $2B to build more American open source

[Misha Laskin @MishaLaskin]

We are bringing the open model frontier back to the US to build a thriving AI ecosystem globally.

Thankful for the support of our investors including NVIDIA, Disruptive, DST, 1789, B Capital, Lightspeed, GIC, Eric Yuan, Eric Schmidt, Citi, Sequoia, CRV, and others.

[Francis McCann @francismccann14]

The combination of conviction, talent, and resources we’ve assembled at Reflection makes this moment feel special. A rare chance to do something that matters, and to do it in the open.

[Jonathan Lee @jonsjlee]

RT @achowdhery: I've spent years pushing the boundaries of pretraining—first as lead author on PaLM, then as a lead contributor on Gemini p…

[Catherine Cang @catherine_cang]

Excited to be working with this incredibly talented team on building open intelligence!!

[Sabina Kim @skangkim]

Big milestone at Reflection AI ! 🧠✨

We’re tackling some of the toughest research and infra challenges. It’s intense work and so much fun.

An incredible time to be here! Hard problems, fast progress, and a team that genuinely loves to work. DM me if you're interested. :)

[Ethan Hellman @EthanHellman]

Couldn't be more excited about the next chapter of this company. Feel extremely lucky to be part of this team and to be working on such an important mission.

[Richie Steigerwald @richie_internet]

I’m incredibly proud of what our team has assembled: a seamless environment for doing reinforcement learning at scale, putting together the best of open source, our vendors, and our own in-house tech. Now we get to go after the frontier.

[drubinstein @dsrubinstein]

I joined Reflection a year ago to take a big swing at working on the cutting edge of artificial superintelligence. It’s been an incredible year and looking back, I’m extremely grateful for the opportunity.

Day after day, our ambition grows and now we are venturing onto our next goal with some great strategic partners (including NVIDIA 😉): building open-weight frontier models. Really looking forward to bringing the frontier to the public!

[Jonathan Lee @jonsjlee]

In startups, you’re going from 0 to 1. However, it’s rare that the 1 you’re building towards is to become a frontier AI lab.

If being an early employee at a frontier lab excites you, feel free to reach out. We’re hiring!

[Reflection @reflection_ai]

Today we're sharing the next phase of Reflection.

We're building frontier open intelligence accessible to all.

We've assembled an extraordinary AI team, built a frontier LLM training stack, and raised $2 billion.

Why Open Intelligence Matters

Technological and scientific progress is driven by values of openness and collaboration.

The internet, Linux, and the protocols and standards that underpin modern computing are all open. This isn't a coincidence. Open software is what gets forked, customized, and embedded into systems worldwide. It's what universities teach, what startups build on, what enterprises deploy.

Open science enables others to learn from the results, be inspired by them, interrogate them, and build upon them in order to push the frontier of human knowledge and scientific advancement. AI got to where it is today through scaling ideas (e.g. self-attention, next token prediction, reinforcement learning) that were shared and published openly.

Now AI is becoming the technology layer that everything else runs on top of. The systems that accelerate scientific research, enhance education, optimize energy usage, supercharge medical diagnoses, and run supply chains will all be built on AI infrastructure.

But the frontier is currently concentrated in closed labs. If this continues, a handful of entities will control the capital, compute, and talent required to build AI, creating a runaway dynamic that locks everyone else out. There's a narrow window to change this trajectory. We need to build open models so capable that they become the obvious choice for users and developers worldwide, ensuring the foundation of intelligence remains open and accessible rather than controlled by a few.

What We've Built

Over the last year, we've been preparing for this mission.

We’ve assembled a team who have pioneered breakthroughs including PaLM, Gemini, AlphaGo, AlphaCode, AlphaProof, and contributed to ChatGPT and Character AI, among many others.

We built something once thought possible only inside the world’s top labs: a large-scale LLM and reinforcement learning platform capable of training massive Mixture-of-Experts (MoEs) models at frontier scale. We saw the effectiveness of our approach first-hand when we applied it to the critical domain of autonomous coding. With this milestone unlocked, we're now bringing these methods to general agentic reasoning.

We've raised significant capital and identified a scalable commercial model that aligns with our open intelligence strategy, ensuring we can continue building and releasing frontier models sustainably. We are now scaling up to build open models that bring together large-scale pretraining and advanced reinforcement learning from the ground up.

Safety and Responsibility

Open intelligence also changes how we think about safety. It enables the broader community to participate in safety research and discourse, rather than leaving critical decisions to a few closed labs. Transparency allows independent researchers to identify risks, develop mitigations, and hold systems accountable in ways that closed development cannot.

But openness also requires confronting the challenges of capable models being widely accessible. We're investing in evaluations to assess capabilities and risks before release, security research to protect against misuse, and responsible deployment standards. We believe the answer to AI safety is not “security through obscurity” but rigorous science conducted in the open, where the global research community can contribute to solutions rather than a handful of companies making decisions behind closed doors.

Join Us

There is a window of opportunity today to build frontier open intelligence, but it is closing and this may be the last. If this mission resonates, join us.

[Jonathan Lee @jonsjlee]

@reflection_ai So proud of the team. Excited to be on this journey!