[1]. https://www.technologyreview.com/2022/10/10/1060982/china-pa...

Could you point me towards something that corrects me where I'm wrong i.e. not that they both don't do it but that they are doing mass surveillance at similar scales?

Like they have much lower cost of capital than OpenAI or Anthropic for example and far more direct to consumer muscle, if they could've gotten their models into a better position it's very possible they could've beaten out OpenAI on consumer AI. Probably a smaller prize to win than B2B AI but still huge.

And then if the case where AI is panning out but they are failing to execute (where we appear to be now) they have the option of becoming a SpaceX style NeoCloud that is pretty solid, even though most of their moves were pretty bad along the way.

And intuitively it makes sense we’re talking about groups of people who are skeptical of main stream institutional health recommendations but trust specific personal sources for medical advice.

I’m vibing but it feels like there is a pretty clear intersection of peptides and the fringe science health community no?

The US is certainly inching in that direction but it’s not like someone from the US government sits at Anthropic’s HQ reading chats from state people of interest.

* Software security - I.e. Mozilla blog post on using Mythos for software security [1]

* Algorithms - AlphaEvolve makes real progress on matrix multiplication [2]

* Open source - Backlog crushing in openvpn [3]

* Corporate - CloudFlare Oauth workers [4]

People are clearly using AI to ship real work that people end up using directly or indirectly. Projects like Godot are also in a weird spot though.

[1]. https://blog.mozilla.org/en/privacy-security/ai-security-zer...

[2] https://arxiv.org/abs/2506.13131

[3] https://stanislas.blog/2025/12/claude-code-opus-open-source-...

[4] https://github.com/cloudflare/workers-oauth-provider

I feel like your conflating some general skepticism around the trillion dollar valuation of the US majors and their business model with the topic at hand - large scale C to Rust conversions and similar.

Let's take something like GML 5.2 that is open source - anyone can run it on NVIDIA or AMD chips. People are currently making money by running GLM 5.2 Nvidia and AMD chips and selling inference on aggregators like openRouter.

So the cost to serve is like cost of Chip or financing for Chip + power and rackspace somewhere. We basically know that each year you will be able to server exponentially more inference per $ on Nvidia and AMD hardware as it gets to newer generations, so why would you expect the cost of inference on open models to also increase?

Like let's say not through a Chinese subsidized provider but like BaseTen https://www.baseten.co/pricing/ why would that get more expensive year over year?

I was thinking for all of these that the end goal is to take some existing technology and add some novel features rather than just X in Rust so what I have so far.

1. The Lua project bundles Lua 5.1 - 5.5 in one binary and one npm package so it's easy to run in the browser or CloudFlare Worker etc.

2. The Valkey (Redis) port builds something called EdgeStash - lets you run Valkey with Lua scripting in a CloudFlare Durable Object programmable with Lua scripting.

https://edgestash-valdr.ianmclaughlin1398.workers.dev/ that's a demo of the Edge Valkey node running.

I've been meaning to take take it and do something like yours that is sweet!

> if AI can reproduce such research it would be capable of doing such research itself

Well there is a big distinction between research validation and research generation, it is generally much easier to verify that a math proof is true or false than to find a truly novel proof.

But yes in the long run I’d think AI will be doing tons of research and it will by default reproducible. So maybe we’re aligned after all?

The Chinese open source community has made a lot of incentive to make research reproducible for example. The most reproducible works from I.e. deepseek get widely cited and adopted.

I don’t think we can just say “AI” and it’s fixed but with deliberate effort there’s reason to be optimistic.

Between 2016 and 2021 the share of ML/ robotics/ AI researchers being reproducible (ie contianing code and similar instructions to reproduce) doubled [1].

The major US labs have gone largely closed source (I.e. they no longer publish frontier research) but the Chinese ecosystem has incredibly reproducible code.

This is field dependent obviously but I think it atleast gives reason to be optimistic.

Yes people will churn out fake slop research, but it feels like that can be categorized and then ignored.

[1] https://arxiv.org/pdf/2308.10008

In the long run conceivable we could use AI to hold papers to a much higher standard, audit all the data and code that is associated etc.

Opus 4.8/ GPT 5.6 level models with the right workflows/ data/ access are still good enough to do huge amounts of economically valueable work.

I would naively expect Microsoft’s to be the highest since they are probably mainly just selling access to their capex through cloud since they aren’t seriously pursuing frontier AI, I’d imagine Google to be in the middle (selling TPUs, general cloud GPUs, Gemini, revenue lift on ads from better AI) but also spending heavily on infra to compete with OAI/ Anthropic, and then Meta to be on the low end since they are likely getting serious revenue lift from AI but not monetizing their models by API.