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And Berkshire Hathaway $BRK.B is writing a $10 billion check to get in. Here's the full breakdown: THE DEAL: - $30B in underwritten public offerings - $40B through an at-the-market stock program starting Q3 2026 - $10B private placement to Berkshire Hathaway THE BERKSHIRE PIECE: - $5B in Class A Common Stock at $351.81 per share - $5B in Class C Capital Stock at $348.20 per share - Berkshire has been building this position since Q3 2025 THE PURPOSE: - Scale AI compute infrastructure to meet "unprecedented customer demand" - Approximately $30B of the ATM proceeds will cover 2026 employee equity tax obligations - Remaining proceeds go directly to AI infrastructure buildout Source: Evan

The AI boom that funneled more than $250 billion into OpenAI and Anthropic ahead of their expected mega-IPOs this year has left hundreds of startups built before ChatGPT’s arrival in 2022 stranded — effectively cut off from venture funding because of their inflated valuations and outdated technology, yet not profitable enough for the public markets. More than 220 companies that had reached billion-dollar valuations in the venture boom are now fallen unicorns, according to PitchBook, which provided a list of the companies exclusively to CNBC. The estimates are based on factors including head count growth and comparisons with public companies. Source: CNBC

AI users are creating agents and code, leading to exponential growth in AI usage. It's just starting. Source: a16z, Jim Bianco

A report from Entelligence AI across 2,444 companies shows that for every $1 spent on AI tokens, $0.44 goes to bug fixes, $0.27 to rewriting AI-generated code, and $0.11 disappears into review and merge delays. Companies spending $100,000 on AI tokens and only $18,000 worth is reaching production. The other $82,000 is overhead generated by the tool itself. Lightrun's 2026 report found that 43% of all AI-generated code still requires manual debugging in production even after passing every quality test. Not a single engineering leader surveyed said they were fully confident AI code would behave correctly once deployed. Wall Street is pricing AI as a productivity tool and the data says 82% of the spend never reaches the actual product. SOURCE: @Aiswarya_Sankar Sam Boboev

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