I'm excited to join @DragonflyVC on the investment team For years crypto has been the driving force in my life I've become a clearer thinker, because permisionless capital aggregates around the best ideas and what drives attention I've met countless new people both online and off, some of whom I'm lucky to call my closest friends Crypto represents the most pure, reflexive form of capital markets, and I strongly believe the most transformational companies, products and protocols will follow Working with @HadickM @hosseeb @tomhschmidt @KawageboF47481 @0xsudogm @0xelton and the rest of the Dragonfly team is an honor, one which I am very grateful for

There are a number of incredible low-margin consumer services that have massively grown their operating profit (and their valuation) through an in-product advertising business: - Amazon: ~$80B - Uber: ~$2B - Doordash: ~$1B - Instacart: ~$1B It'll be interesting to see if this creates a modern form of the innovator's dilemma, i.e., enabling agentic use (which is unequivocally pro-customer) cuts deeply to their bottom line, creating opportunity for those that don't rely on ads for their operating profit (e.g., Shopify).

If you add up the equity appreciation in the last 2 yrs for SPCX, OAI, Ant it’s effectively the largest “stimulus” in US history, and it’s all concentrated in the top 1%. Very little benefit to the other 99% We can apply the same logic to the market. For example…

Couple random thoughts... Frontier labs appear to be 6-7 months ahead with internal models, and are reportedly seeing signs of accelerating their own cycle times, potentially pulling further away. Open weights models are becoming highly capable and the range of tasks they can reliably accomplish is rapidly increasing the surface areas within enterprises that they can be used for The vast majority of token spend is accruing to frontier model providers The price per token and price per task for an increasing number of use cases is plummeting AI potentially represents a China shock for white collar employment, except instead of concentrated losses in a handful of industries and geos its widespread throughout most companies and touches almost every geographic region. Technological diffusion is about more than capability and cost. The spread of FDEs shows the challenge of scaling implementation. Gross margins on inference are much healthier than people expected 12-18 months ago. The AI industry is fundamentally making long term fixed dollar commitments and funding them via selling tokens in the spot market. Yes at certain levels of the stack certain parties are duration matched via contracted revenue, but the industry in aggregate is selling spot tokens against long term fixed costs. The balance sheets of the core funding sources for the AI buildout remain healthy, if less so than a year or two ago. And increasingly third party capital is being mobilized in quantities never before seen. Because the buildout is now financed out of risk capital and not internal funds, it is subject to the vicissitudes of human emotions, not simply math. A surplus of capital today at attractive prices does not imply an availability of any capital at any price tomorrow. This means there is path dependency risk from reflexivity and exogenous shocks.

Josh and Thrive is the next iteration of Buffett/Munger in the AI age “Technological optimism and investment discipline are not opposites. In fact, the more optimistic we are about the size of a paradigm shift, the more disciplined we must be about where value will be captured.”

Tokenization has been a hard-to-invest-in narrative because it intrinsically implies 2 competing forces: 1. Value creation at the offchain layer by cutting costs and friction 2. Those same cost cuts erode the margin the onchain layer could capture Tokenization can win while the onchain layer barely accrues any value.

Today we are introducing Dyna-2, a world-action model pre-trained on one million hours of human video. At this scale, for the first time, we discovered several new scaling laws: • world-action models exhibit scaling law on human data across four orders of magnitude, from 1000 to 1,000,000 hours, • this human data scaling law implied a scaling law on never seen robot data, • both data and objective matter; world modeling and scaling on video data are essential for cross-embodiment scaling transfer to emerge 🧵

Not only that, but when you get the same unbounded optimism and demand narratives, AI momentum stocks trade quite similarly to crypto 20-22. Just more explosive given the technology and baseline fundamental values