In this episode, Ioannis Antonoglou, co-founder and CTO @ReflectionAI (ex-DeepMind, AlphaGo/AlphaZero/MuZero) explains what they are building: a frontier open-weight “general agent model” trained end-to-end with pretraining plus reinforcement learning.

And I’ll be honest: I left this conversation more skeptical than I expected. They raised $2 billion last year. But where the results?

Reflection’s thesis is huge – build the missing Western open base model, then use RL to push it to the frontier. The problem is that this is also the slowest path in the game. “All hands on deck building the model” means no clear wedge product yet, few concrete proof points, and a lot of execution risk while closed labs keep shipping.

Am I missing something? Watch the video and leave your opinion in the comments

Chapters:

0:00 Building AGI and the Mission Behind Reflection

0:25 From AlphaGo to Today: How AI Progress Really Happens

2:11 Breakthroughs vs. Engineering: What Still Matters Most

3:10 Defining AGI and Why It May Not Need Huge Breakthroughs

3:41 Why Reflection Shifted from Coding Agents to Frontier Models

5:15 The New Focus: Open Frontier Models and General Agents

6:33 Bottlenecks in Building Frontier AI: Team, Compute, and Scale

7:48 AI Tools, Internal Workflows, and Model-First Strategy

8:24 Can Open Models Catch Closed Labs?

10:34 Reinforcement Learning, Research Priorities, and Advice for Young Builders

14:01 Joining DeepMind, Open Science, and the Book That Shaped Him

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*Guest:*

Ioannis Antonoglou, Co-Founder, President & CTO at Reflection AI https://x.com/real_ioannis https://www.linkedin.com/in/ioannis-alexandros-antonoglou-45393253 https://reflection.ai/

📰 Transcript: https://www.turingpost.com/nathan

*Turing Post* – AI stories from labs the Valley doesn't cover.

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Tags: #reflectionai #opensource #deepmind #ai #openclaw #aisafety

What Reflection AI offers to beat closed labs

Turing Post

What Reflection AI offers to beat closed labs