Weird but true: without crypto, the current AI boom would not have been possible. Today's data center buildout would've been impossible without Bitcoin miners spending a decade building the knowhow around power, sites, and scale in remote locations. Nvidia could not moved beyond gaming to specialized compute without crypto mining as the bridge. And the biggest neoclouds are almost all ex-crypto talent: @CoreWeave, @CrusoeAI, @togethercompute, @nscale, etc.

This argument by @deanwball is being badly misunderstood. It's OK to disagree with it, but first you have to actually understand what he's saying. He's saying: releasing the weights for a frontier-level model is effectively dumping. Dumping is when you sell a product at significantly below cost in order to corner market share. It's illegal. The reason: dumping results in short-term consumer surplus, but long-term it prevents the formation of a competitive market and discourages capex outside of the dumper. Standard Oil famously did this in order to consolidate the oil market before it was broken up. So why is he claiming releasing the weights of a frontier level model is basically dumping? Isn't he just describing open source? His argument: it's not financially sustainable to train a frontier model and release the weights. In the long run, you will not be able to internalize enough of the gains given the cost of training a frontier model, because neoclouds and other inference providers will be able to outcompete you at actually serving the model. It costs an astronomical amount of money to train frontier models, and if everyone else can serve them, you don't capture enough of the surplus to pay for the training and R&D. It's not like normal open source when you build some software and then release it and sell services on top of it. The amount of capex required for frontier-level models is an order of magnitude higher than normal software, which is why doing this at frontier level is so economically irrational. Right now the Hong Kong stock market is ebullient enough that Chinese AI companies are not getting punished for the fact that they're all deeply, deeply unprofitable. Releasing model weights is great marketing, intellectually appealing, and strikes fear into the hearts of their opponents. We can assume the status quo continues for a while because of the AI supercycle. But eventually the AI market will correct, the Hong Kong market will dump, and suddenly these Chinese labs won't be able to afford to training super expensive models without internalizing more of the gains. But what if China, seeing that this strategy is successfully kneecapping the US lead (by discouraging further capex and lowering valuations), says no--don't stop. And so the Chinese government starts buying up the shares of these companies and demanding that they continue releasing frontier-level weights, profitable or not. In that case, it becomes a genuine space race. For-profit companies cannot continue to compete on either side. US labs valuations fall, and the White House realizes that to keep their advantage in the AI race, they cannot rely on the free market to maintain their lead. They nationalize the labs and fund them off government subsidies. Now you have government-controlled and distributed models on both sides. That's what Dean is calling the "dystopian hellscape." The best analogy is drug development: if China were to sell American drugs back to us really cheaply, that would result in a large short-term consumer surplus. Cheap Viagra and Ozempic is obviously great. But in the long run, this would discourage investment in developing new drugs. That's the sense that Dean is saying it's long-term "decel." Now, I happen to disagree with Dean. I think the consumer surplus of having frontier-level open weight models is huge, even at the current capabilities. I also think China is going to defect from this strategy soon (there's been reporting along these lines, that Beijing will stop allowing large models to be open-weight; I think there are other reasons for this aside from competition). I also suspect that nationalization of labs is inevitable as they take on more geopolitical and cyber capabilities. But he's not wrong--releasing frontier-level weight models is weird. The question of how long this market will remain profit-driven is a very coherent question to ask.

It's been two months since @maraoz made this DeFi doom call. Since then, GLM 5.2, Fable, and GPT 5.6 have all shipped and are all being used in the wild by attackers. The data is in. It's time to call it: the DeFi "hackpocalypse" was a false alarm. It's more than half-way through the year and annualized $ hacked in DeFi in 2026 is lower than 2025 year, and well within historical range. The deeper story is that while the NUMBER of hacks has spiked, the SIZE of hacks fell even more. This means attackers are picking off small protocols and abandonware, the ones that can't afford to AI-harden their code. But large protocols have done the AI-hardening, and they're actually pretty secure now. Lesson: the average dollar in DeFi is as safe as it was a year ago. If you keep your money in large protocols that can afford to harden themselves, you'll likely be fine.

CLOBs are not going to take us to the RWA promised land. Today Hyperliquid owns the liquidity for a handful of RWA macro names. But outside the top 10 traded assets (which are ~90% of volume) liquidity falls off a cliff. When there's enough retail demand, order books can work. But "perps on everything" is a different problem, and TradFi solved it decades ago. The answer isn't every venue rebuilding its own order book. That's not what Robinhood does, it's not what Schwab does, and it's not what DeFi should be doing either. Building your own book for every asset means bootstrapping demand ticker by ticker, renting liquidity with subsidies, and ending up with thin markets that blow out 200x the moment news hits. It's like sucking the ocean of TradFi liquidity through a straw. Variational skips all of it via the RFQ model. RFQ is how institutions like Dragonfly actually trade. In RFQ, dealers quote just-in-time and hedge on the primary venue as orders come in. This lets Variational mainline TradFi liquidity directly and mirror it on-chain. Margin in smart contracts, settlement in stablecoins, liquidity aggregated from the people who already trade on the biggest underlying markets, like the CME and NYSE. It makes it permissionless to access the same depth and spreads the big boys get. With the cold start problem gone, new markets can ship at the speed of software. By next year I expect RWA perps to be the biggest contract class on-chain, bigger than BTC and ETH perps combined. That's how crypto truly becomes the market for everything. I believe the platform that wins that won't look like a traditional exchange. Proud to lead Variational's $50M Series A. Watch this space.

we built the first sane way to debug your agent locally. you can see your traces. codex/claude code can too. this lets them write evals and test your agents automatically. best part: it's completely free and open source. install with 1 line. (github below)

Today, we’re announcing Route 66 (a new initiative co-led by @coinbase to make it faster and cheaper to connect to specialized blockchains) and a strategic investment from @cbventures.

Hermes Agent has been so much more delightful than OpenClaw was. It adapts much better across sessions. OpenClaw felt like it had dementia. I was constantly trying to direct it how to edit its skills and memory, while Hermes just does it on its own.

Following this case with fascination. Judge has allowed Arbitrum DAO to transfer the funds to AAVE to "relieve the Good Samaritan" of the restraining order. But judge says the restraining order is now applied to AAVE until a final judgment. Good sign, but such a weird case.

Worst-to-best reasons to be known as an influencer: F: Meme status - Hawk Tuah girl D: Lifestyle - Bryan Johnson C: Looks - Sydney Sweeney B: Content - Lex Fridman A: Talent - Magnus Carlsen S: Ideas - Steven Pinker