James Pelly

Once tried to ride horses from Beijing to London. Turns out it's a long way.

[James Pelly @JamesPelly]

@tbpn @joespeez Excited to be building together @joespeez Already bringing great energy to the team

[James Pelly @JamesPelly]

@Grimezsz @sriramk Cloud Atlas

[James Pelly @JamesPelly]

@brandondamos @reflection_ai excited to have you on the team Brandon!

[James Pelly @JamesPelly]

Such a great founding team - and they go out of their way to help others. This is just the beginning

[alphaXiv @askalphaxiv]

We just raised a $7M Seed round co-led by @MenloVentures and @haystackvc with participation from @Shakti_VC, @conviction and @upfrontvc 🚀

We're honored to have the support of incredible angels including @ericschmidt, @SebastianThrun, @sarahookr

Join us: https://t.co/IKwK8KsG96

[James Pelly @JamesPelly]

@zebriez @joincolossus loved reading this Brie. "The default setting is fast"

[James Pelly @JamesPelly]

@Skiminok @reflection_ai this is going to be fun @Skiminok. I'm excited for what's ahead

[James Pelly @JamesPelly]

@dwarkesh_sp @karpathy What if birth isn’t starting from zero (or DNA/ATCG initialization only) but the continuation of months of sandbox learning in the womb

https://t.co/7ywKBwequT

[James Pelly @JamesPelly]

@karpathy @karpathy frames birth as the beginning of learning. In reality fetus muscle contractions start from 6 weeks. Zebra gestation is 12-13 months. That’s almost a year of embodied learning in the womb and a ~15,000x increase on the few-dozen wobbly-legged minutes we see.

[James Pelly @JamesPelly]

@karpathy argues that “If the baby zebra spasmed its muscles around at random as a reinforcement learning policy would have you do at initialization, it wouldn't get very far at all.”

In the womb it doesn’t have to. But that doesn’t mean it can’t be learning.

What if birth isn’t starting from zero (or DNA/ATCG initialization only) but the continuation of months of sandbox learning?

[James Pelly @JamesPelly]

@karpathy @karpathy frames birth as the beginning of learning. In reality fetus muscle contractions start from 6 weeks. Zebra gestation is 12-13 months. That’s almost a year of embodied learning in the womb and a ~15,000x increase on the few-dozen wobbly-legged minutes we see.

[James Pelly @JamesPelly]

@BrianNorgard @micsolana *Laramie

[James Pelly @JamesPelly]

@DannieHerz @MishaLaskin @reflection_ai an incredible team of people to be working alongside

[James Pelly @JamesPelly]

@ristovuorio 👨🏻‍🚒

[James Pelly @JamesPelly]

@thefuturematt rooting for this team!

[James Pelly @JamesPelly]

@ponderousbs you're awesome to work with Ethan. Strong opinions, sharp insights, fun energy to be around

[James Pelly @JamesPelly]

@jakesaper @reflection_ai ty Jake! having a blast alongside an awesome team

[James Pelly @JamesPelly]

@ponderousbs That goes both ways Ethan! Excited for many more fast paced chats around the office

[James Pelly @JamesPelly]

Being at Reflection feels like one of those rare moments - a small team, stacked with talent, moving fast.

To build open intelligence, you have to start at the foundation. It might take more capital. It definitely takes exceptional people.

If you know someone who's a 10/10 — especially in AI research — dm me. We’re hiring in SF, New York and London

[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.

[James Pelly @JamesPelly]

@reflection_ai Reflection has been such a great team to be part of - this is going to be fun

[James Pelly @JamesPelly]

@zebriez fun, honestly. I'm in