So basically it works out if they become a Microsoft level company

It works out if they can keep up basically one more quarter of their previous growth rate and then plateau for the rest of time.

The challenge there is competition. If other providers can match or beat their model quality at a lower cost that revenue could dry up pretty quickly.

Or as Anthropic put it on page 42 of their S-1: https://www.sec.gov/Archives/edgar/data/2130357/000162828026...

> If we are unable to differentiate our models, platform, and applications from competing offerings through enhanced functionality, superior performance, better safety features and reliability, lower cost, or other factors, demand for our models, platform, and applications may decline. The rapid pace of technological change in AI means that models, platforms, and applications can quickly become outdated. We must continuously invest in research and development to maintain our competitive position, but there is no guarantee that our investments will result in models, platforms, and applications that customers prefer over competing alternatives.

Thank you for the reply!

My thought is that any competition to Anthropic is going to need to actually have the compute to serve all of Anthropics customers. Chinese labs certainly do not. It seems to be the crux of all these compute agreements.

Even then, Anthropic has the luxury of knowing exactly what hardware (even what datacenter!) will serve their models. This gives them the ability to co-design their model for the exact chips it will run on, something that Open Weights models are completely unable to do - even to the point that Deepseek fully open-sourced their inference engine, revealing that they're still running on only H100s and Huawei Ascend chips (https://github.com/deepseek-ai/DeepInfer/blob/main/README.md).

Anthropic will be running their 2027 Opus and Fable models on Vera Rubin, a chip which produces tokens for about 1/100th the cost of the H200. Seriously. 100x more efficient on GLM 5.3: https://inferencex.semianalysis.com/inference/glm-5-3 compared to what DeepSeek is able to use for inference.

Combined with Kimi K3.1's underperformance I'm honestly not too bullish on Chinese AI being able to catch back up. As an example, according to https://artificialanalysis.ai/, Kimi K 3.1 would need to be run 10x cheaper than its current inference cost in order to hit the Paraeto Frontier (matching 6.1 Sol Medium)

That's a really good point, I hadn't thought about that. If Anthropic have tied up almost a trillion dollars in compute commitments, that's going to make it a lot harder for their competitors to steal their customers (though OpenAI seem to be fine on that front, they've been hoarding compute for years.)

One of Anthropic's most interesting technical moats is that they can serve their models on NVIDIA hardware or Google TPUs or AWS Trainium - they talked about that in an incident postmortem last September: https://www.anthropic.com/research/a-postmortem-of-three-rec...

You mean East India Company

When I order from m5, box is postmarked from California

I have a tiny devboard collection habit, and gotta say coding agents work great with these. I've got a stick and a few dials running various hyper specific UIs for home assistant.

I was working on adding better WASM support to ghidra, but now I can just point Claude to binaryen

Well, that's too bad; as the main developer of https://github.com/nneonneo/ghidra-wasm-plugin it would have been really nice to get better support for all the new features that have been added into the spec over the last few years.

I also think that, for larger projects, it's still useful to have real decompilation support; for example, Il2CppDumper works in tandem with ghidra-wasm-plugin to enable decompilation of Unity Il2Cpp projects, which are often enormous (100MB wasm files are not uncommon) and for which the symbols + types are invaluable.

I wonder, how many such interesting and universally beneficial developments did emergence of tools like Claude Code kill?