build a team of experts in every function for building a business, including finance, legal, operations, comms, platform and proprietary tech - supporting an expanding investment team of technical generalists with divergent passions and investing styles converging under the banner of Unum Utopia or One Utopia, to compete and earn the right to win with founders, believing before others understand. We have the opportunity to position not on singular breakthroughs but on the convergence of computational and concrete capabilities across previously disconnected domains, to transform atoms as fluidly as bits. We unearth this by charting [[Directional Arrows of Progress]] across a variety of sectors: - cheaper computation - higher energy density per unit of raw material - human computer interfaces exchanging tactile keyboards for gestures captured by reading neuromuscular signals - declining payload costs to access space - rapidly improving and asymmetrically inexpensive autonomous and attritable defence systems ... and then heading to where these arrows converge... We work to earn the "leverage of the long" - a [[Five year psychological bias]] - Everyone wants to be invested today where they should have been looking five years ago. We orient capital towards 2030-scale inflections, will help us harvest an edge that other might not or won't bankroll. > Conviction compounds by trading the panic of the moment for the patience of the decade --- ### Infrastructure layer The capex surge underwriting AI models is already soaking up roughly 5% of US GDP, placing it shoulder to shoulder with the fiber-optic and router binge of the 2000 tech boom (5.2%), and only a short step below the sheet-rock bonanza of the 2005 housing bubble (6.7%). Capital today is being wired into actively self-improving, power-hungry nervous systems that learn, iterate and compound - even as they depreciate. History suggests euphoric investors will overspend on AI capex - and it is most like that the more prudent investors, companies or customer will capture the derivative value. [[The infrastructure layer and AI capex]] [[model layer and above]] [[interesting AI areas]] ---- ### Model Layer Closed models: OpenAI, Anthropic, Google - charging for access and generating billions of follards Open models: (Meta's Llama, Mistral) that perform nearly as well as their pricier cousins. What's the investment calculus: the closed players enjoy 50-70% gross margins, but rapidly burn their revenues and new capital raises on training their models to capture every possible edge case. Open source platforms are more limited in their revenue potential but scale efficiently with good enough performance and unbeatable cost-per-token inference > Smart money may own both sides: equity in the walled catherals and positions in the open bazaar In the current frenzy for AI, there is a great irony. What is being acquired is human intelligence, not hardware or contracts. Ai hands humans enormous leverage: two engineers with these tools might outmaneuver, a hundred-lawyer firm or a legacy consultancy. > The playing field now depends more on implementation skill than headcount. Every white-collar profession faces the same reckoning: yesterday's moats may be today's liabilities and tomorrow belongs to whoever best orchestrates the machines. The frontier of agentic AI capability today isn't defined by model size - but between what we can verify and what we can't. We can train an AI model to solve differential equations because every answer resolves to binary truth: correct or incorrect. It can fumble at tasks of taste. It's not that verification is hard, but it's the rate limiting step for AI progress. Despite 5% of American GDP devoted to AI data infrastructure, our AI agents still can't book a flight from JFK to SFO with precision. Think of the preferences involved: which time of day, aisle or window, how much to gamble on delays, cash or frequent flyer points, the likelihood of that elusive first-class upgrade. Those preferences aren't isolated either: would we prefer a later flight if we could have an aisle seat and better chance of an upgrade? The math is brutal - even a 99% reliable agent degrades to 60.5% accuracy after just 50 sequential decision tasks, each error cascading through the system like spidering cracks in tempered glass. In domains, where we can construct tight feedback loops - code that compiles, games with win conditions, molecules - AI can move to mastery. But in the vast expanse of human endeavor where "good" is contextual or cultural, there is still space for humans. > Tomorrow's edge will be measured in insight per token, note teraflops per second. Multi-agent reinforcement learning verifiers now grade everything from courtroom prose to manufacturing workflows, turning the subjective into back-propagatable currency. This evolution from static data to agentic learning environments marks a critical shift in the directional arrow of future progress in AI. Multi-modality is crushing the latency between imagination and incarnation. Instead of just spending more on compute in the pursuit of ever-bigger models, there is arbitrage in transmuting the subjective into the scorable to help models attend to verified truth. #### [[Friction Frontier]] Friction is the thief of efficiency that steals energy from every moving part. It makes engines run hot and our transmission grind. We spend fortunes on ball bearings and lubricants, on streamlining and smoothing, on fiber optic cables that carry light itself because electrons aren't fast enough. In war, friction kills - every second of delay between order and execution is measured in lives. Money's greatest luxury isn't what it can buy but what it can bypass. E.g. the private jet that leapfrogs TSA lines, the conceirge doctor who answers on the first ring, the Disney plaid-vested guide who spirits you past the sweating masses. It's not mere snobery that drives this parallel infrastructure of privilgie; it's the recognition that friction is modernity's hidden tax, levied in time and aggravation. That's why the ultimate arbitrage lies in friction, owning the bottlenecks imposed by physics and funding the hacks that beat them. There’s a paradox though: the most elegant physical systems often depend on friction as their saving grace. Nuclear reactors use control rods to slow neutron flow—too little friction and Chernobyl burns; the right amount and cities glow with light. Our neurons fire with built-in delays, synaptic gaps that prevent our thoughts from becoming electrical storms of epileptic seizure. In our very cells, DNA’s checkpoint proteins pause replication to proofread and mend molecular misprints, because without that brake, every miswrite might script a tumor. **The question isn’t whether we need friction, but which kind, where and how much we can bear before we pay someone to help us escape it—what we call the Friction Frontier.** If friction is sometimes a virtue, then we must be surgical about where we apply the brake. Our investment instinct is thus two-fold: **own both the bottleneck and the hack by backing the jailbreakers.** Take artificial intelligence. The Friction Frontier lies in mapping AI’s metabolism, finding where silicon speed meets carbon constraint. AI can dream up chip architectures in microseconds but TSMC’s fabs run on geological time, their extreme ultraviolet lithography crawling through wafers at the pace of physics. AI can generate perfect building blueprints, but concrete still cures at its own cadence. The rate-limited steps are hiding in plain sight: rare-earth mining that feeds the hunger for GPUs, the electric grid that can’t be coded into existence, the submarine cables that must be unspooled from a vessel one meter at a time. One can be long Caterpillar’s earthmovers, ASML’s ultraviolet monopoly or whatever can’t be software-defined in the cloud. But it’s also important to be long the lubricants—the companies building the tools that let AI route around reality’s roadblocks, the robotics ventures that give AI hands to match its mind. **Future fortunes lie at the Friction Frontier: profiting from what can’t be rushed and also what makes rushing possible.** [[lifecording]] [[Sep 25 Market Gauge]] [[bitotech gauge]] [[robotics gauge]]