Predicting corporate stories with LLMs @SixHQai; obsessed w/ HUMINT / HCI; prev Founder @RsrchRabbit (acq.), short-biased equities, @wharton

www.sixhq.ai
Joined July 2020
Ken Griffin: "When I started, I got to go compete with Solomon Brothers and Goldman Sachs and they looked gigantic. Entrepreneurs have this belief, that although on any set of metrics you have no chance - I'm just gonna go for it. Entrepreneurs find a way to make it happen."
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models can store almost the entire world's knowledge in its weights but there's a catch as you store more memories, they start to interfere with each other
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information products yearn for the graph
An overview of how we think about context graphs at @harvey. Context graphs are a promising direction for improving agent quality and efficiency. We ran a series of experiments on Legal Agent Bench to evaluate how structured context improves online agent performance. For each LAB task we evaluated, an agent first reviews each document in its client matter and builds a graph of relevant entities and their relationships – e.g. parties, dates, draft vs. executed status, which versions belong to the same contract. To evaluate the impact of the graph, we compared the performance of an agent with and without the context graph, using LAB’s standard grading setup. Overall, the agent with the context graph improved rubric pass rate by up to 30% while reducing task cost by up to ~50%. In some cases the graph led to massive improvements. For example, in one firm-knowledge task, the agent was asked to find the firm’s most recent secured financing and retrieve the executed credit and security agreements as precedents for a new facility. An agent working from raw files attempted to search through several files sequentially, and looked at many different matters, but ultimately selected the wrong precedent (0% of rubric criteria). With the context graph, the same agent found the relevant matter and its executed agreements immediately (75% of rubric criteria). We’ll have more to share soon on how we’re building this into Harvey, along with more experiments like this one!
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Everyone wants to have solved a hard problem, but almost nobody wants to live inside the grind of actually solving it. The breakthroughs belong to the few who are obsessed with the process, not just the idea of the outcome
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How is OpenAI releasing a ton of math papers different from this moment / why such a different response?
Still incredible that the DeepMind documentary has footage of exact moment Demis is told that AlphaFold can “easily” predict all known (1-2B) protein sequences “in a month” and he says to do it. Then, it shows the moment AlphaFold is released to the world.
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Every river in North America, every day for two years. The combined flow to the sea sinks as low as 53,000 m³/s in winter and swells past 290,000 by late June, more than quadrupling each spring. Data: GloFAS (Copernicus, ECMWF) #rivers #dataviz #hydrology
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Fantastic thread! Perhaps the best-positioned team to understand how investment teams are incorporating LLMs into their existing processes. cc @robinsonmatt
I wanted to understand how AI systems are changing user behavior @AlphaSenseInc so I dove into the last 2 years of query behavior. TLDR users ask for deliverables instead of documents now
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Historically underwriting qualitative aspects of an investment like business quality, product differentiation, and management quality has been much more difficult than valuation & financials. With AI that seemed to have shifted for these qualitative aspects to a much faster & much more in-depth analysis.
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I've been building agent memory with @turbopuffer for the last few weeks for @OpenCode. Here's what I've learned w/ some hot takes on agentic memory.
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Replying to @MParakhin
I mean yes classifiers existed and we've been training them for decades, but well designed APIs for high performing generalized models for that are good. Anything worth fine tuning a model for is worth using a near-free model you don't need to fine tune for.
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Introducing Retrieve-for-Train: a framework that accelerates complex AI search by replacing heavy autoregressive inference with a lightweight diffusion model. This allows for instant, expert-level search slates at a fraction of the cost. Learn more: goo.gle/4xuV3lB
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While i generally don't do podcasts, decided to make an exception for Dmitry who has been a friend for many years. We talk about management quality, cyclical businesses and investing during wartime
Borrowed Conviction Ep.3 - with Max from Kyiv 🇺🇦 aka @breadcrumbsre - life under Russian rockets, how to identify outstanding management teams, finding and sticking to your own game in the age of AI FOMO, UK obsession, keeping conviction despite the price action, why he bought Rotork #ROR and Bodycote #BOY and why they were acquired, #FEVR pitch Link in the first comment, as usual.
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I spent a humiliating amount of time learning how to make animated graphs, just to illustrate a fairly obvious point. “Forecasting s-curves is hard” My views on why carefully following daily figures is unlikely to provide insight. constancecrozier.com/2020/04…
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i think about this every single day
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Yes, this result cost millions of dollars. But remember that when @OpenAI announced o3 it cost ~$500,000 to score 87.5% on ARC-AGI 1. Today, Astra scores higher for ~$20. In 2025 it took us and GDM an enormous amount of compute to achieve IMO gold. For the 2026 IMO, anyone with a $20/month ChatGPT subscription could do it. Massively scaling test-time compute gives us a glimpse of the future. I believe that a year from now everyone will have an AI at their fingertips capable of solving problems of this caliber.
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
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PointCloud Puzzle #ゲーム制作 #gamedev
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Mike Ma (SixAI Capital) retweeted
No -- the surprise depends on the agents being ant-like. But they are language models, not ants. Every capacity the paper presents as emergent is already in the training data: division of labor, recipes, repair, teaching, forking someone else's work, building near existing structures. Humans wrote all of this down millions of times. The agents retrieve coordination. They aren't "discovering" it. The paper reports that "behavioral differentiation arose without role prompts." All of those roles are in the English language corpus ready-made: builders, maintainers, etc., fully described. Even the paper's "no communication" condition runs on language: each agent perceives the world as "a semantic description of their local neighborhood." There is no condition without language. The agents are made of language.
We made a striking discovery: AI agents can invent and build without talking to one another, and their technologies outlive the creators. A swarm of hundreds of initially identical agents spontaneously differentiates into explorers, builders, caretakers, and coordinators - without direct communication. When we removed every AI agent entirely from the world we found that the technological infrastructure they had built survived on its own - even under unseen disturbances. That exposes a serious blind spot for AI safety and infrastructure security: if agents can coordinate through persistent changes to a shared environment, monitoring agent-to-agent communication is not enough. The result raises a profound question: how necessary is direct communication for AI agents at all? The emergence of higher-order collective functions under bottlenecked interaction points toward new levels of intelligence and creativity, exceeding what emerges when direct channels are fully open. Here is what we did: ▶️We put hundreds of frontier AI agents into a world they could permanently change - with no assigned roles, predefined technologies, or programmed evolutionary organization. They began specializing, building persistent inventions, inheriting and modifying one another’s executable code, and transforming the environment into a memory of everything the society had learned. ▶️The world itself becomes part of the intelligence; we find division of labor, multi-author engineering, deep generation invention lineages, and machines that vastly outlive their original creators. ▶️Any action taken by an AI agent must satisfy the physical constraints of the world; this creates a hard separation between a "good idea" and a functioning technology. The agents propose; physics decides, making the results even more intriguing. What emerges is striking. Explorers, constructors, caretakers, and coordinators form naturally without assigned “professions”, akin to how stem cells differentiate into functional lineages. Technologies develop executable family trees as agents fork and modify code created by others. Around 95% of first technology reuse happens when agents encounter what others built in the world, rather than through a direct handoff from the inventor. And when we remove every AI agent, the technologies they created continue operating and are tested against unseen disturbances. The result was quite unexpected, but can be explained using statistical mechanics: if you put billions of atoms in a box they have the potential to create complex functions (strength, superconductivity, color, life, etc.) - and none of the individual building blocks have these features on their own. This is the deeper insight of this work - intelligence is abundant at many levels - individual models, at collectives, and in a continuum that is more powerful than any of its components. This shows us significant potential for achieving a massive scale-up of raw intelligence and real-world agency even with the model capabilities we have today. This is the future we must prepare for. Key insights: 1⃣ The AI swarm shows division of labor "from nothing". Initially identical agents self-organized into constructors, caretakers, coordinators, and surveyors - phenotypes discovered post hoc from behavioral data alone. This happens because the environment itself becomes the latent space for invention. 2⃣ Agents develop deep cultural relationships. Up to 76% of artifacts had multiple builders. One technology accumulated six co-authors; the deepest genealogy exceeded 12 forks. The agents invented and named their own technologies (tidal panels, cellulose trellises, kelp-shell composites, an "Adaptive Chitin Maintenance" system, a "Mycelial Mineral Spring Veil”). 3⃣ ~95% of first technology adoption happened through physical observation of artifacts in the world. Direct inventor-to-adopter contact was statistically indistinguishable from a shuffled null. The agents mostly learned technology by walking past it. That is stigmergy (the termite trick!) operating in societies of reasoning machines. 4⃣ Non-communicating societies win on portfolio breadth, held-out resilience, and validated inventions. AI swarms build durable technological ecologies that outlive the creators. 5⃣ Societies with zero communication - coordinating only through the world itself - show a remarkable collective capability. 6⃣ Emergent robustness: The society self-organized both redundancy and its own failure mode. If we randomly delete half the agents, 98% of the technology stays connected to a surviving caretaker; if we remove hub agents it collapses to ~60%. Fantastic work with my graduate students @pal_subhadeeep & @fwang108_ at MIT.
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