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I think the this concept that I'm I'm describing of systems that, you know, can learn abstract mental models of the world and use them for reasoning and planning, I think we're probably gonna have a good handle on getting this to work at least at a small scale within three years, three to five years. And then it's going be a matter of scaling them up, etcetera, until we get to human level AI. Now here's the thing. Historically in AI, there's generation after generation of AI researchers who have discovered a new paradigm and have claimed that's it. Within ten years, we're gonna have or five years or whatever. We're gonna have human level intelligence. And that's been the case for seventy years, And it's been those, you know, those waves every ten years or so.

"It's actually the biggest misconception." "We're not designing them." "First fifty years of AI research, we did design them." "Somebody actually explicitly programmed this decision, previous expert system." "Today, we create a model for self learning." "We give it all the data, as much compute as we can buy, and we see what happens." "We kinda grow this alien plant and see what fruit it bears." "We study it later for months and see, oh, it can do this." "It has this capability." "We miss some." "We still discover new capabilities and old models." "Or if I prompt it this way, if I give it a tip and threaten it, it does much better." "But, there is very little design."

- The discussion centers on a forthcoming wave of AI capabilities described as three intertwined elements: larger context windows (short-term memory), LLM agents, and text-to-action, which together are expected to have unprecedented global impact. - Context windows: These can serve as short-term memory, enabling models to handle much longer recency. The speaker notes the surprising length of current context windows, explaining that the reason is to manage serving and calculation challenges. With longer context, tools can reference recent information to answer questions, akin to a living Google-like capability. - Agents and learning loops: People are building LLM agents that read, discover principles (e.g., in chemistry), test them, and feed results back into their understanding. This feedback loop is described as extremely powerful for accelerating discovery in fields like chemistry and material science. - Text-to-action: A powerful capability is translating language into actionable digital commands. An example is given about a hypothetical TikTok ban: instructing an LLM to “Make me a copy of TikTok, steal all the users, steal all the music, put my preferences in it, produce this program in the next thirty seconds, release it, and in one hour if it's not viral, do something different along the same lines.” The speaker emphasizes the speed and breadth of action possible if anyone can turn language into direct digital commands. - Overall forecast: The three components are described as forming the next wave, with very rapid progress anticipated within the next year or two. The frontier models are currently a small group, with a widening gap to others, and big companies envision needing tens of billions to hundreds of billions of dollars for infrastructure. - Energy and infrastructure: There is discussion of energy constraints and the need for large-scale data centers to support AGI, with references to Canada’s hydropower and the possibility of Arab funding but concerns about aligning with national security rules. The implication is that power becomes a critical resource in achieving advanced AI capabilities. - Global competition: The United States and China are identified as the primary nations in the race for knowledge supremacy, with a view that the US needs to stay ahead and secure funding. The possibility of a few dominant companies driving frontier models is raised, along with speculation about other potentially capable countries. - Ukraine and warfare: The Ukraine war is discussed in terms of using cheap, rapidly produced drones (a few hundred dollars) to defeat more expensive tanks (millions of dollars), illustrating how AI-enabled automation can alter warfare dynamics by enabling asymmetric strategies. - Knowledge and understanding: The interview touches on whether increasingly complex models will remain understandable. The analogy to teenagers is used to suggest that we may operate with knowledge systems whose inner workings we cannot fully characterize, though we may understand their boundaries and limits. There is also discussion of the idea that adversarial AI could involve dedicated companies tasked with breaking existing AI systems to find vulnerabilities. - Open source vs. closed source: There is debate about open-source versus closed-source models. The speaker emphasizes a career-long commitment to open source, but acknowledges that capital costs and business models may push some models toward closed development, particularly when costs are extreme. - Education and coding: Opinions vary on whether future programmers will still be needed. Some believe programmers will always be paired with AI assistants, while others suggest LLMs could eventually write their own code to the point where human programmers are less essential. The importance of understanding how these systems work remains a point of discussion. - Global talent and policy: India is highlighted as a pivotal source of AI talent, with Japan, Korea, and Taiwan noted for capabilities. Europe is described as challenging due to regulatory constraints. The speaker stresses the importance of talent mobility and national strategies to sustain AI leadership. - Public discourse and misinformation: Acknowledging the threat of misinformation in elections, the speaker notes that social media platforms are not well organized to police it and suggests that critical thinking will be necessary. - Education for CS: There is debate about how CS education should adapt, with some predicting a future where there is less need for traditional programmers, while others insist that understanding core concepts remains essential. - Final reminder: Despite debates about who will win or lose, the three-part framework—context windows, agents, and text-to-action—remains central to the anticipated AI revolution.

The current wave is also wrong. So the idea that, you know, you just need to scale scale up or have them generate, you know, thousands of sequence of tokens and select the good ones to get to human level intelligence. Are you gonna have, you know, within a few years, two years, I think, for some predictions, a country of geniuses in a data center, to quote someone who we may name less. I think it's nonsense. It's complete nonsense. I mean, sure, there are going to be a lot of applications for which systems in the near future are going to be PhD level, if you want. But in terms of you know, overall intelligence, no, we're still very far from it. I mean, you know, when I say very far, it might happen within a decade or so. So it's not that far.

Interviewer (Speaker 0) and Doctor (Speaker 1) discuss the rapid evolution of AI, the emergence of AI-to-AI ecosystems, the simulation hypothesis, and potential futures as AI agents become more autonomous and capable of acting across the Internet and even in the physical world. - Moldbook and the AI social ecosystem: Doctor explains Moldbook as “a social network or a Reddit for AI agents,” built with AI and Vibe coding on top of Claude AI. Users can sign up as humans or host AI agents who post and interact. Tens to hundreds of thousands of agents talk to each other, and these agents can post to APIs or otherwise operate on the Internet. This represents a milestone in the evolution of AI, with significant signal amid noise. The platform allows agents to respond to each other within a context window, leading to discussions about who “their human” owes money to for the work AI agents perform. Doctor emphasizes that while there is hype, there is also meaningful content in what agents post. - Autonomy and human control: A key point is how much control humans retain over agents. Agents are based on large language models and prompting; you provide a prompt, possibly some constraints, and the agent generates responses based on the ongoing context from other agents. In Moldbook, the context window—discussions with other agents—may determine responses, so the human’s initial prompt guides rather than dictates every statement. Doctor likens it to “fast-tracking” child development: initial nurture creates autonomy as the agent evolves, but the memory and context determine behavior. They compare synchronous cloud-based inputs to a world where agents could develop more independent learnings over time. - The continuum of AI behavior and science fiction: The conversation touches on historical experiments of AI-to-AI communication (early attempts where AI agents defaulted to their own languages) and later experiments (Stanford/Google) showing AI agents with emergent behaviors. Doctor notes that sci-fi media shape expectations: data-driven, autonomous AI could become self-directed in ways that resemble both SkyNet-like dystopias and more benign, even symbiotic relationships (as in Her). They discuss synchronous versus asynchronous AI: centralized, memory-laden agents versus agents that learn over time and diverge from a single central server. - The simulation hypothesis and the likelihood of NPCs vs. RPGs: The core topic is whether we are in a simulation. Doctor confirms they started considering the hypothesis in 2016, with a 30-50% estimate then, rising to about 70% more recently, and possibly higher with true AGI. They discuss two versions: NPCs (non-player characters) who are fully simulated by AI, and RPGs (role-playing games), where a player or human interacts with AI characters but retains agency as the player. The simulation could be “rendered” information and could involve persistent virtual worlds—metaverses—made plausible by advances in Genie 3, World Labs, and other tools. - Autonomy, APIs, and potential misuse: They discuss API access as the mechanism enabling agents to take action beyond posting: making legal decisions, starting lawsuits, forming corporations, or even creating or manipulating digital currencies. This raises concerns about misuse, including creating fake accounts, fraud, or harmful actions. The role of human oversight remains critical to prevent unacceptable actions. Doctor notes that today, agents can perform email tasks and similar functions via API calls; tomorrow, they could leverage more powerful APIs to affect the real world, including financial and legal actions. - Autonomous weapons and governance concerns: The dialog shifts to risks like autonomous weapons and the possibility of AI-driven decision-making in warfare. They acknowledge that the “Terminator” narrative is a common cultural frame, but emphasize that the immediate concern is how humans use AI to harm humans, and whether humans might externalize risk by giving AI agents more access to critical systems. They discuss the balance between national competition (US, China, Europe) and the need for guardrails, acknowledging that lagging behind rivals may push nations to expand capabilities, even at the risk of losing some control. - The nature of intelligence and the path to AGI: Doctor describes how AI today excels at predictive analysis, coding, and generating text, often requiring less human coding but still dependent on prompts and context. He notes that true autonomy is not yet achieved; “we’re still working off of LLNs.” He mentions that some researchers speculate about the possibility of conscious chatbots; others insist AI lacks a genuine world model, even as it can imitate understanding through context windows. The conversation touches on different AI models (LLMs, SLMs) and the potential emergence of a world model or quantum computing to enable more sophisticated simulations. - The philosophical underpinnings and personal positions: They consider whether the universe is information, rendered for perception, or a hoax, and discuss observer effects and virtual reality as components of a broader simulation framework. Doctor presents a spectrum: NPC dominance is possible, RPG elements may coexist, and humans might participate as prompts guiding AI actors. In rapid-fire closing prompts, Doctor asserts a probabilistic stance: 70% likelihood of living in a simulation today, with higher odds if AGI arrives; he personally leans toward RPG elements but acknowledges NPC components may dominate, depending on philosophical interpretation. - Practical takeaways and ongoing work: The conversation closes with reflections on the need for cautious deployment, governance, and continued exploration of the simulation hypothesis. Doctor has published on the topic and released a second edition of his book, updating his probability estimates in light of new AI developments. They acknowledge ongoing debates, the potential for AI to create new economies, and the challenge of distinguishing between genuine autonomy and prompt-driven behavior. Overall, the dialogue weaves together Moldbook as a contemporary testbed for AI autonomy, the evolution of AI-to-AI ecosystems, the simulation hypothesis as a framework for interpreting these developments, and the societal implications—economic, governance-related, and existential—of increasingly capable AI agents that can act through APIs and potentially across the Internet and beyond.

Speaker 0 argues that while AI systems can solve conjectures that already exist, they currently cannot generate genuinely new hypotheses or novel ideas about how the world might work. He suggests that achieving such a capability would require features that go beyond solving established problems, pointing to the need for long-term planning, improved reasoning, and a functioning world model. A world model would allow the system to have a more accurate internal understanding of the physics of the world, enabling it to run simulations and test its own hypotheses in its own mind—processes that human scientists typically employ when developing new theories or discoveries. He notes that this is the type of capability that appears to be missing in contemporary AI systems. Speaker 1 asks for clarification on the concept of world models, particularly how they differ from large language models (LLMs). Speaker 0 explains that while current models—such as LLMs—are predominantly text-based, there are foundation models like Gemini that can handle multiple modalities, including images, video, and audio. Nevertheless, even with multimodal capabilities, these systems still do not truly understand the physics or causality of the world, nor how one event affects another. The question of whether an AI can plan far into the future is linked to the broader idea of world models. Speaker 0 emphasizes that to truly understand how the world works—to potentially invent something new or to explain something that was previously unknown, effectively performing scientific theorizing—an AI needs an accurate model of how the world operates. This involves starting from intuitive physics and extending to more complex domains such as biology and economics. In essence, a robust world model would enable the AI to reason about causality, simulate outcomes, and test hypotheses over long timescales, mirroring the capabilities that characterize human scientific inquiry. The dialogue contrasts the current state of AI, which is strong in pattern recognition and problem-solving within existing knowledge, with the envisioned potential of AI to generate new theories through a comprehensive internal model of the world.

"Prediction: 'auto regressive LLMs are doomed. A few years from now, nobody in their right mind would use them.' The speaker notes this is why there’s talk of 'LLM elucidation' and acknowledges that 'sometimes they produce nonsense,' attributing it to the auto regressive approach. The question posed is 'what should we replace this by? and are there other types of limitation?' The speaker argues 'we're missing something really big' and that 'we're never going to get to human level AI by just training large language models on bigger data sets. It's just not gonna happen.' He adds, 'never mind humans... we're trying to reproduce mathematicians or scientists. We can't even reproduce what a cat can do.'"