Archimedes

Nothing is cheap in semis and everything has run. Theres no undiscovered bottleneck

Out of this, bottleneck & supply crunch can be interchangeable sometimes In CCL, we are seeing rapid innovation in copper foil and glass fiber (the high end market) and this has turned into a bottleneck But at the lagging edge, there is a geniune supply crunch for T-glass FR4 foil This also happened to lagging edge memory

It's clear that growth for coding tools such as Claude Code has decelerated from the pace it was since the start of the year. It might be compute- constrain related or due to many clients blowing their full-year AI budgets. Monitoring this trend very closely with all the alt data. I will provide regular updates.

I'm happy to report that my AccumulateAlgo.exe has returned back from the repair shop and is now back online. Please keep the market makers in your thoughts and prayers for the remainder of Q2

Haters will say it's AI

Here's some receipts for all the panicans & doubters

🦔Microsoft canceled its internal Claude Code licenses this week after token-based billing made the cost untenable, even for a company with effectively infinite cloud resources. Uber's CTO sent an internal memo warning the company burned through its entire 2026 AI budget in just four months. American AI software prices have jumped 20% to 37%, and GitHub (owned by Microsoft) is dropping flat-rate plans for usage-based billing across its products. My Take The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested. This ties directly to my Gemini Flash post yesterday. Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown. Hedgie🤗