Syz the moment

Michael Burry reported that the upcoming public listings for SpaceX, OpenAI, and Anthropic are going to pull more capital out of the market than the entire dot-com wave of 2000. Adjusted for inflation, just these three companies will raise more money than the hundreds of tech firms that flooded the market at the peak of the 2000 bubble. The historical data from 2000 shows exactly why this is dangerous for stocks. That year, the market saw 446 IPOs raise a record $108.15 billion. The Nasdaq peaked on March 10, 2000, at the exact moment this massive supply of new shares hit the market, right before crashing 80%. The crash happened because of a simple liquidity drain. When giant companies go public, big institutional funds need cash to buy the new shares. To get that cash, they have to sell their existing stock positions. This creates immediate selling pressure on the most expensive tech stocks. Today, the setup is identical but much more concentrated. Instead of hundreds of small startups spreading out the drain, just three mega companies are absorbing the market's capital. This directly impacts current market leaders. Microsoft has 49% of its $627 billion cloud backlog tied to OpenAI, and Oracle has 54% of its pipeline dependent on it. The same big funds that need to buy the new IPOs are the ones currently holding these tech giants. In the first quarter of 2000, the average IPO nearly doubled on its first trading day because cash was easily available. By the fourth quarter, capital markets dried up. Gross IPO proceeds collapsed 63% in a single quarter, and average first-day gains dropped to just 14% as companies rushed into layoffs and bankruptcies. When an unprecedented amount of money is pulled out of existing stocks to fund a single massive IPO wave, the broader market historically runs out of the liquidity needed to sustain its peak. Source: Bull Theory

$640 billion has been wiped out from Nvidia's market cap over the same period. Huawei's new chip architecture is now directly challenging the hardware scarcity narrative that supported Nvidia's $5 trillion valuation. Source: Bull Theory

You don't pour billions into massive forward-deployed joint ventures if you think the next model release is going to take care of it. In the cloud supercycle, semis led and software followed (and you didn't need Qualcomm or ARM to tell you the value was migrating up the stack). In AI, the infra layer itself is telling us the application layer is a separate, massive opportunity they can't fully capture. Source: a16z @a16z

Chinese domestic demand accounts for most of that growth. Source: Azeem Azhar

DeepSeek’s massive price cuts have made its AI token costs up to 50x cheaper than OpenAI and Anthropic, reshaping enterprise AI economics. Since hashtag#AI costs scale with token usage, companies running coding agents or reasoning-heavy models can spend millions—or even billions—annually. More advanced models consume huge hidden “reasoning” tokens, dramatically increasing compute costs. This is pushing firms toward cheaper, optimized models and tools, with companies like Microsoft and Uber already feeling budget pressure. The key competitive advantage in AI may shift from having the smartest model to delivering “good enough” AI at the lowest scalable cost. Source: Bull Theory

Monthly tokens processed across Google surfaces: May 2024: 9.7T May 2025: ~480T May 2026: 3.2Q+ That is 7x Y/Y growth. Source: Wall St Engine

Monthly tokens processed across Google surfaces: May 2024: 9.7T May 2025: ~480T May 2026: 3.2Q+ That is 7x Y/Y growth. Source: Wall St Engine

Here are the 19 rules I follow when I use Claude: Source: AI evolution