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Releasing the weights of AI models eliminates the main barrier to their use. Training a large model costs hundreds of millions of dollars, putting it out of reach for smaller groups. The speaker compares the weights of AI models to fissile material for nuclear weapons, arguing that making them available is dangerous. If fissile material were easily obtainable, more countries would have nuclear weapons. Similarly, releasing AI model weights allows malicious actors to fine-tune them for harmful purposes at a fraction of the original cost.

The speaker introduces Web, a tool built to allow natural-language conversations with an entire document set (specifically mentioning the Epstein files and expanding to other datasets, including items like the dancing Israeli files and Israeli art students files). Web enables users to ask normal questions, for example: “show me examples of his foundations, charities, and businesses interacting with Israelis or organizations based in Israel.” The tool analyzes the documents based on the user’s natural-language prompt and returns results with sources cited. Key features demonstrated: - When a query is run, Web pulls back all relevant documents, which can be clicked to turn red and opened as primary sources. Users can see the work the tool is doing, including entities such as Ehud Barak and the network of Ehud Barak, Wexner, and Epstein, as it compiles the research. - The response is written in natural language for easy understanding, with sources cited. The primary sources remain accessible on the left in their original organizational structure, allowing users to read documents in their original form. - The tool will not browse the internet or conduct external research to answer questions; it references only the files in the user’s document set and provides citations that can be checked. The speaker presents the current usage experience: - It’s possible to ask follow-up questions and expand the chat, using suggested questions or generating new ones. - The user interface shows both the generated explanation and its sources (with links to the documents). Operational and access details: - The speaker endorses Web as “the absolute shit” and encourages people to try it. After a period without a password gate, it’s offered in an open beta to anyone who wants to try. - The speaker has personally funded the tokens for the beta so users can access it for free during this phase; beta testers aren’t required to pay. - He notes that running AI tools costs money due to compute resources, and, after the open beta, Web will transition to a subscription model with access to additional datasets. - Plans include open-sourcing the project later, allowing people to download and run it themselves and examine the code (with a caveat: selling it would not be allowed). - The goal expressed is to enable broad accessibility so that “any old person can understand these documents” and to clearly show who Epstein worked for and what was in the files, with all content retained even if DOJ deletes files from the public domain, as “we’ve already got them all and they’re not being deleted from our database.”

Could you imagine if QN came out and only worked on non American tech stack? Could you imagine if Kimi came out and it only worked on non American tech stack? And these are the top three open models in the world today. It is downloaded hundreds of millions of times. So the fact of the matter is American tech stack all over the world, being the world's standard, is vital to the future of winning the AI race. You can't do it any other way. We've got to be, you know, as you know, any computing platform wins because of developers. Yeah. And half of the world's developers are

"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.

Speaker believes that China and the United States are competing at more than a peer level in AI. They argue China isn’t pursuing crazy AGI strategies, partly due to hardware limitations and partly because the depth of their capital markets doesn’t exist; they can’t raise funds to build massive data centers. As a result, China is very focused on taking AI and applying it to everything, and the concern is that while the US pursues AGI, everyone will be affected and we should also compete with the Chinese in day-to-day applications—consumer apps, robots, etc. The speaker notes the Shanghai robotics scene as evidence: Chinese robotics companies are attempting to replicate the success seen with electric vehicles, with incredible work ethic and solid funding, but without the same valuations seen in America. While they can’t raise capital at the same scale, they can win in these applied areas. A major geopolitical point is emphasized: the mismatch in openness between the two countries. The speaker’s background is in open source, defined as open code and weights and open training data. China is competing with open weights and open training data, whereas the US is largely focused on closed weights and closed data. This dynamic means a large portion of the world, akin to the Belt and Road Initiative, is likely to use Chinese models rather than American ones. The speaker expresses a preference for the West and democracies, arguing they should support the proliferation of large language models learned with Western values. They underline that the path China is taking—open weights and data—poses a significant strategic and competitive challenge, especially given the global tilt toward Chinese models if openness remains constrained in the US.

I think that's the model of the future. Foundation models will be open source, will be trained in a distributed fashion with various data centers around the world, having access to different subsets of data, and basically training kind of a consensus model, if you want. And so that's the way that's what makes open source platforms completely inevitable. And proprietary platform, I think, are gonna disappear. And and it also makes sense both for the diversity of languages and things, but also for applications. So a given company can download LaMa and then fine tune it on proprietary data that they wouldn't wanna upload. Well, that's what's happening now. I mean, most the the business model of most AI startups basically is around this. Right? This, you know, build Yeah. Specialized system for vertical applications.

When something becomes a common platform, it becomes open source. This applies to the internet's software infrastructure and has led to faster progress and increased safety. The rapid advancement of AI in the past decade is a result of open research and sharing of code. Open sourcing allows for collaboration and reuse, with common platforms like PyTorch benefiting the entire field. If open source is legislated out of existence due to fears, progress will be significantly slowed down.