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Dear Readers,
Noam Brown is one of the most important AI scientists of our time and the outstanding genius behind OpenAI's reasoning models. He rarely gives such deep insights into his work as he does in this interview with Latent.space. So I'm taking this opportunity to summarize and analyze the most important points from the interview. Enjoy!
All the best,
Noam Brown's interview with Latent.space: An Analysis
The TLDR
In an interview with Latent.space, OpenAI's Noam Brown argues that the next major AI breakthroughs are moving beyond just pre-training. The new frontier involves "test-time compute," where models "think" longer about problems at runtime to achieve far better results. This is combined with the development of multi-agent systems, creating an "AI civilization" where multiple AIs interact, collaborate, and compete to develop emergent strategies and intelligence that far exceed what a single model can achieve.
In a world where artificial intelligence is increasingly becoming part of everyday life – from autonomous driving to highly complex strategy games – the question arises: How far can AI actually think – and how will it become even smarter? Noam Brown, a prominent researcher at OpenAI, provides fascinating insights into precisely this question in his latest interview with Latent.Space. It's about the exciting shift from pure pre-training to what is known as “test-time compute” – the use of computing power at runtime to make AI systems smarter and more versatile. Another key topic is the future of multi-agent systems – AIs that interact, compete, and cooperate with each other. The following article examines whether these approaches will bring about the next major shift in AI development – and whether the combination of longer thinking and networked interaction is indeed the key to a true AI civilization.
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Main Section
From “fast thinking” to longer thinking
Brown explains that although modern LLMs respond in milliseconds, they increasingly benefit from longer thinking processes. Instead of delivering results in 100 ms, AI delivers results on the level of a model thousands of times larger through extended thinking, realized by search methods such as “Tree of Thought.”
Noam Brown already described this dynamic in earlier poker and diplomacy projects (Pluribus, Cicero) – and it demonstrates the power of test-time compute.
Self-play beyond boundaries
Historically, self-play has led to game theory-optimal behavior in two-player zero-sum games such as poker. But Brown emphasizes that in games with more agents (e.g., Diplomacy), this works differently and presents new challenges.
“If you go outside a two-player zero-sum game... you don't want to just have this very defensive policy... you're going to end up with really weird behavior.”
This complexity requires advanced techniques – in particular, a combination of chained logic (reasoning) and interactive learning.
Multi-agent and AI civilization
Noam Brown heads the multi-agent team at OpenAI and sees enormous potential here:
Collaboration vs. competition: Through repeated interaction, systems develop emergent strategies – from poker to the global level.
Exploit vs. robustness: Just as important as minimizing losses is the targeted exploitation of opponents' weaknesses. Learning such strategies is reminiscent of human dynamics.
Scalability: Brown's vision: An AI civilization acts like a computing colony in which autonomous agents interact – the more complex the interactions, the stronger and more diverse the intelligence of this AI civilization becomes.
Sustainability: Why routers and scaffolds are disappearing
Currently, models are often combined using router layers or tool scaffolds, for example to switch between fast and deep processes. Brown predicts that such auxiliary layers will become superfluous as individual models become smarter:
“... eventually the models are going to be... a single unified model. And in that world, you shouldn't need a router...”
Limits and prospects
Nevertheless, test-time compute also has its limits – such as costs and runtime. Brown is quite candid: Some systems are already “in an awkward state.” Progress is huge, but limited by time and computing resources. Achievable goal: Model assistance that reflects over longer periods of time – ideal in assistants that solve complex tasks independently over days.
Interim result
Test-time computing – especially when supplemented by multi-agent systems – opens up a new dimension of scalability for AI. These systems can not only think more, but also act in contexts for which local pre-training does not prepare them. They are therefore at the heart of the next AI revolution.
Conclusion
The move toward longer thinking (test-time compute) and networked, multi-agent-based systems marks a paradigm shift. Noam Brown is certain: We have overcome the pre-training plateau – now the next breakthroughs will follow through inference time and agent collaboration. Router frameworks or specialized tools will become dispensable in the long term as individual models become increasingly flexible and deeply reflective.
Answer to our key question: Yes – test-time compute combined with multi-agent systems forms the foundation of an AI civilization that grows beyond the boundaries of its initial models in terms of quality.
Outlook: Imagine a network of self-thinking agents – each specialized in problem solving, globally connected, and capable of tackling complex tasks independently. This opens up not only technical perspectives, but also ethical ones: Who decides what such systems are allowed to do? And how do we teach them human values?
The future will be shaped by agent ecosystems – and by the question of how we design them responsibly.
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Chubby’s Opinion Corner
What Noam Brown outlines in this interview is nothing less than a vision for the next evolutionary leap in AI: a world in which intelligence no longer consists of a single, statically trained model, but of a living, dynamically acting civilization of agents. Optimistically speaking, this means that we are on the cusp of an era in which artificial intelligence will become not only smarter, but also more cooperative.
The potential is enormous. If systems such as Cicero are already demonstrating that AI is capable of forging long-term alliances, negotiating, and building trust, then we are not far from experiencing AI assistants that help shape projects like real partners – over hours, days, or even weeks. Humans will not be replaced, but rather enhanced. Cognitive horizons are expanding, not only in the breadth of information, but also in the depth of thought. The great opportunity lies in designing these new systems to be not only efficient, but also wise – just as a society is judged not only by its gross domestic product, but also by its sense of community. AI civilizations could become our digital mirror image – a reflection of our best cooperative abilities.