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And then superintelligence becomes when it's better than us at all things. When it's much smarter than you and almost all things is better than you. And you you you say that this might be a decade away or so. Yeah. It might be. It might be even closer. Some people think it's even closer. I might well be much further. It might be fifty years away. That's still a possibility. It might be that somehow training on human data limits you to not being much smarter than humans. My guess is between ten and twenty years we'll have superintelligence.

"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."

We will become a hybrid species, still human but enhanced by AI, no longer limited by our biology, and free to live life without limits. We're going to find solutions to diseases and aging. Having worked in AI for sixty-one years, longer than anyone else alive, and being named one of Time's 100 most influential people in AI, I predicted computers would reach human-level intelligence by 2029, and some say it will happen even sooner.

"I'm not so interested in LLMs anymore." "How do get machines to understand the physical world?" "How do you get them to have persistent memory, which not too many people talk about." "How do you get them to reason and plan?" "there is some effort, of course, to get LLMs to reason." "But in my opinion, it's a very kind of simplistic way of viewing reasoning. I think there are probably kind of more better ways of doing this." "So I'm excited about things that a lot of people in this community, in the tech community, might get excited about five years from now." "But right now, it doesn't look so exciting because it's some obscure academic paper."

"It's really weird to, like, live through watching the world speed up so much." "A kid born today will never be smarter than AI ever." "A kid born today, by the time that kid, like, kinda understands the way the world works, will just always be used to an incredibly fast rate of things improving and discovering new science." "They'll just they will never know any other world." "It will seem totally natural." "It will seem unthinkable and stone age like that we used to use computers or phones or any kind of technology that was not way smarter than we were." "You know we will think like how bad those people of the 2020s had it."

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 expresses optimism about eventually achieving artificial general intelligence (AGI) and artificial superintelligence (ASI), suggesting it could occur in our lifetimes, over the next few decades, or perhaps even centuries. The timeline is uncertain: we'll see how long it takes. The speaker notes that AI is bound by the laws of physics, implying physical constraints will limit progress. Nevertheless, they argue that the potential upper bound on intelligence and on what we can command such systems to accomplish remains very high. The overall takeaway is a recognition of vast future possibilities tempered by fundamental physical limits. This framing leaves room for dramatic advancements while grounding expectations in physics.

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.

That it's being designed by these very flawed entities with very flawed thinking. That'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're gonna 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. Look, oh, 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.

Speaker 0 argues that the human brain is a mobile processor: it weighs a few pounds and consumes around 20 watts. In the brain, signals are sent through dendrites, with a channel frequency in the cortex of about 100 to 200 Hz. The signals themselves are electrochemical wave propagations, moving at about 30 meters per second. When comparing the brain to a data center, there is a vast gap in several dimensions. In a data center, you could have about 200 megawatts of power (instead of 20 watts), several million pounds of mass (instead of a few pounds), about 10,000,000,000 Hz on the channel (instead of roughly 100–200 Hz), and signals propagating at the speed of light, 300,000 kilometers per second (instead of about 30 meters per second). Thus, in terms of energy consumption, space, bandwidth on the channel, and speed of signal propagation, there are six, seven, or eight orders of magnitude differences in all four dimensions simultaneously. Given these disparities, the question arises whether human intelligence will be the upper limit of what’s possible. The speaker answers emphatically, “absolutely not.” As our understanding of how to build intelligence systems develops, we will see AIs go far beyond human intelligence. The speaker likens this to other domains where humans are outmatched by machines in specific capabilities, such as speed, strength, and sensory reach. Humans cannot outrun a top fuel dragster over 100 meters, cannot lift more than a crane, and cannot see beyond the Hubble Telescope. Yet machines already surpass these limits in certain areas. The speaker foresees a similar trajectory for cognition: just as machines can outperform humans in other tasks, AI will eventually exceed human cognitive capabilities as technology and understanding advance.

I'm optimistic about the rapid advancement of powerful AI. If we look at recent developments, we're approaching human-level capabilities. New models, including our SONNET 3.5, are demonstrating significant improvements in coding skills. For instance, SONNET 3.5 achieved around 50% on Swinbench, which evaluates real-world software engineering tasks. At the start of the year, the best performance was only 3 or 4%. In just ten months, we've increased that to 50%, and I believe that within a year, we could reach 90% or even higher.

Pattern Recognition and Deduction HI AI generated Voice presents a concept of Pattern Set feeding on figs, describing a deduction path that links various species to a common diet. It lists humans, birds, rodents, insects, bats, primates, civets, elephants, and kangaroos as feeding on figs, all deduced from pattern sets. The speaker asserts that pattern recognition with deduction through pattern sets will be a central main paradigm in artificial intelligence because it does not depend on huge computing power and memory size, unlike brute force AI, as demonstrated with pattern sets in Connect Four. Pattern sets are described as a dominant structure to represent, store, recognize knowledge, and deduce new knowledge and new pattern sets from existing knowledge and pattern sets. Pattern sets are connected by deduction paths and possibly other link types, making the uncensored hyperlinked internet and social media well suited to host, share, and collaborate in equality on common reusable pattern sets for people. The approach is framed as an attempt to simulate a more human and smarter form of modeling and reasoning than brute force, with an AI trying to do it the human way. The transcript concludes with a note indicating “To be continued,” referencing source2mia.org.

- The conversation centers on how AI progress has evolved over the last few years, what is surprising, and what the near future might look like in terms of capabilities, diffusion, and economic impact. - Big picture of progress - Speaker 1 argues that the underlying exponential progression of AI tech has followed expectations, with models advancing from “smart high school student” to “smart college student” to capabilities approaching PhD/professional levels, and code-related tasks extending beyond that frontier. The pace is roughly as anticipated, with some variance in direction for specific tasks. - The most surprising aspect, per Speaker 1, is the lack of public recognition of how close we are to the end of the exponential growth curve. He notes that public discourse remains focused on political controversies while the technology is approaching a phase where the exponential growth tapers or ends. - What “the exponential” looks like now - There is a shared hypothesis dating back to 2017 (the big blob of compute hypothesis) that what matters most for progress are a small handful of factors: compute, data quantity, data quality/distribution, training duration, scalable objective functions, and normalization/conditioning for stability. - Pretraining scaling has continued to yield gains, and now RL shows a similar pattern: pretraining followed by RL phases can scale with long-term training data and objectives. Tasks like math contests have shown log-linear improvements with training time in RL, and this pattern mirrors pretraining. - The discussion emphasizes that RL and pretraining are not fundamentally different in their relation to scaling; RL is seen as an RL-like extension atop the same scaling principles already observed in pretraining. - On the nature of learning and generalization - There is debate about whether the best path to generalization is “human-like” learning (continual on-the-job learning) or large-scale pretraining plus RL. Speaker 1 argues the generalization observed in pretraining on massive, diverse data (e.g., Common Crawl) is what enables the broad capabilities, and RL similarly benefits from broad, varied data and tasks. - The in-context learning capacity is described as a form of short- to mid-term learning that sits between long-term human learning and evolution, suggesting a spectrum rather than a binary gap between AI learning and human learning. - On the end state and timeline to AGI-like capabilities - Speaker 1 expresses high confidence (~90% or higher) that within ten years we will reach capabilities where a country-of-geniuses-level model in a data center could handle end-to-end tasks (including coding) and generalize across many domains. He places a strong emphasis on timing: “one to three years” for on-the-job, end-to-end coding and related tasks; “three to five” or “five to ten” years for broader, high-ability AI integration into real work. - A central caution is the diffusion problem: even if the technology is advancing rapidly, the economic uptake and deployment into real-world tasks take time due to organizational, regulatory, and operational frictions. He envisions two overlapping fast exponential curves: one for model capability and one for diffusion into the economy, with the latter slower but still rapid compared with historical tech diffusion. - On coding and software engineering - The conversation explores whether the near-term future could see 90% or even 100% of coding tasks done by AI. Speaker 1 clarifies his forecast as a spectrum: - 90% of code written by models is already seen in some places. - 90% of end-to-end SWE tasks (including environment setup, testing, deployment, and even writing memos) might be handled by models; 100% is still a broader claim. - The distinction is between what can be automated now and the broader productivity impact across teams. Even with high automation, human roles in software design and project management may shift rather than disappear. - The value of coding-specific products like Claude Code is discussed as a result of internal experimentation becoming externally marketable; adoption is rapid in the coding domain, both internally and externally. - On product strategy and economics - The economics of frontier AI are discussed in depth. The industry is characterized as a few large players with steep compute needs and a dynamic where training costs grow rapidly while inference margins are substantial. This creates a cycle: training costs are enormous, but inference revenue plus margins can be significant; the industry’s profitability depends on accurately forecasting future demand for compute and managing investment in training versus inference. - The concept of a “country of geniuses in a data center” is used to describe the point at which frontier AI capabilities become so powerful that they unlock large-scale economic value. The timing is uncertain and depends on both technical progress and the diffusion of benefits through the economy. - There is a nuanced view on profitability: in a multi-firm equilibrium, each model may be profitable on its own, but the cost of training new models can outpace current profits if demand does not grow as fast as the compute investments. The balance is described in terms of a distribution where roughly half of compute is used for training and half for inference, with margins on inference driving profitability while training remains a cost center. - On governance, safety, and society - The conversation ventures into governance and international dynamics. The world may evolve toward an “AI governance architecture” with preemption or standard-setting at the federal level, to avoid an unhelpful patchwork of state laws. The idea is to establish standards for transparency, safety, and alignment while balancing innovation. - There is concern about autocracies and the potential for AI to exacerbate geopolitical tensions. The idea is that the post-AGI world may require new governance structures that preserve human freedoms, while enabling competitive but safe AI development. Speaker 1 contemplates scenarios in which authoritarian regimes could become destabilized by powerful AI-enabled information and privacy tools, though cautions that practical governance approaches would be required. - The role of philanthropy is acknowledged, but there is emphasis on endogenous growth and the dissemination of benefits globally. Building AI-enabled health, drug discovery, and other critical sectors in the developing world is seen as essential for broad distribution of AI benefits. - The role of safety tools and alignments - Anthropic’s approach to model governance includes a constitution-like framework for AI behavior, focusing on principles rather than just prohibitions. The idea is to train models to act according to high-level principles with guardrails, enabling better handling of edge cases and greater alignment with human values. - The constitution is viewed as an evolving set of guidelines that can be iterated within the company, compared across different organizations, and subject to broader societal input. This iterative approach is intended to improve alignment while preserving safety and corrigibility. - Specific topics and examples - Video editing and content workflows illustrate how an AI with long-context capabilities and computer-use ability could perform complex tasks, such as reviewing interviews, identifying where to edit, and generating a final cut with context-aware decisions. - There is a discussion of long-context capacity (from thousands of tokens to potentially millions) and the engineering challenges of serving such long contexts, including memory management and inference efficiency. The conversation stresses that these are engineering problems tied to system design rather than fundamental limits of the model’s capabilities. - Final outlook and strategy - The timeline for a country-of-geniuses in a data center is framed as potentially within one to three years for end-to-end on-the-job capabilities, and by 2028-2030 for broader societal diffusion and economic impact. The probability of reaching fundamental capabilities that enable trillions of dollars in revenue is asserted as high within the next decade, with 2030 as a plausible horizon. - There is ongoing emphasis on responsible scaling: the pace of compute expansion must be balanced with thoughtful investment and risk management to ensure long-term stability and safety. The broader vision includes global distribution of benefits, governance mechanisms that preserve civil liberties, and a cautious but optimistic expectation that AI progress will transform many sectors while requiring careful policy and institutional responses. - Mentions of concrete topics - Claude Code as a notable Anthropic product rising from internal use to external adoption. - The idea of a “collective intelligence” approach to shaping AI constitutions with input from multiple stakeholders, including potential future government-level processes. - The role of continual learning, model governance, and the interplay between technology progression and regulatory development. - The broader existential and geopolitical questions—how the world navigates diffusion, governance, and potential misalignment—are acknowledged as central to both policy and industry strategy. - In sum, the dialogue canvasses (a) the expected trajectory of AI progress and the surprising proximity to exponential endpoints, (b) how scaling, pretraining, and RL interact to yield generalization, (c) the practical timelines for on-the-job competencies and automation of complex professional tasks, (d) the economics of compute and the diffusion of frontier AI across the economy, (e) governance, safety, and the potential for a governance architecture (constitutions, preemption, and multi-stakeholder input), and (f) the strategic moves of Anthropic (including Claude Code) within this evolving landscape.