Monday December 15 2025

Hacker Times

OpenAI declares 'code red' as Google catches up in AI race

Discussion

But ... I don't understand why this is supposedly such a big deal. Look into it, calculate, and a very different picture comes forward, nVidia reportedly makes about 70% margin on their sales (which is COGS, in other words nVidia still pays about $1400 for chips and memory to produce a $4500 RTX5090 card, and that cost is rising fast).

When you include research for current and future cards, that margin drops to 55-60%.

When you include everything on their cash flow statement it drops to about 50%.

And this is disregarding what Michael Burry pointed out: you really should subtract their stock dilution which is due to stock-based compensation, or about 0.2% of 4.6 trillion dollars per year. Michael Burry's point is of course that this makes for slightly negative shareholders' equity, ie. brings the margin to just under 0, which is mathematically true. But for this argument let's very generously say it eats about another 10% out of that margin. As opposed to the 50% it mathematically eats.

Google and Amazon will have to be less efficient than nVidia, because they're making up ground. Let's very generously say that's another 10%, maybe 20%.

So really, for Google making their own chips saves them at best 30% to 40% on the price, generously. And let's again ignore that Google's claim is that they're 30% to 50% less efficient than nVidia chips, which for large training runs translates directly to dollars.

So for Google, TPUs are just about revenue neutral. It probably allows them to have more chips, more compute than they'd otherwise have, but it doesn't save them money over buying nVidia chips. Frankly, this conclusion sounds "very Google" to me.

It's exactly the sort of thing I'd expect Google to do. VERY impressive technical accomplishment ... but can be criticized for being beside the point. It doesn't actually matter. As an engineer I applaud that they do it, please keep doing it, but it's not building a moat, not building revenue or profit, so the finance guy in me is screaming "WHY????????"

At best, for Google, TPUs mean certainty of supply, relative to nVidia (whereas supplier contracts could build certainty of supply down the chain)

AdieuToLogic•13 days ago

>> Support engineer ran customer query through Claude (trained on our public and internal docs) and it very, very confidently made a bunch of stuff up in the response.

> Yeah, LLMs are not really good about things that can't be done.

From the GP's description, this situation was not a case of "things that can't be done", but instead was the result of a statistically generated document having what should be the expected result:

  It was quite plausible sounding and it would have been 
  great if it worked that way, but it didn't.

wahnfrieden•13 days ago

Yes these are horrible pain points. I can only hope Apple improves this stuff if it's true that they're adding MCP support throughout the OS which should require better multi-agent handling

You can use worktrees to have multiple copies building or testing at once

I'm a solo dev so I rarely use some git features like rebase. I work out of trunk only without branches (if I need a branch, I use a feature flag). So I can't help with that

What I did is build an Xcode MCP server that controls Xcode via AppleScript and the simulator via accessibility & idb. For running, it gives locks to the agent that the agent releases once it's done via another command (or by pattern matching on logs output or scripting via JS criteria for ending the lock "atomically" without requiring a follow-up command, for more typical use). For testing, it serializes the requests into a queue and blocks the MCP response.

This works well for me because I care more about autonomous parallelization than I do eliminating waiting states, as long as I myself am not ever waiting. (This is all very interesting to me as a former DevOps/Continuous Deployment specialist - dramatically different practices around optimizing delivery these days...)

Once I get this tool working better I will productize it. It runs fully inside the macOS sandbox so I will deploy it to the Mac App Store and have an iOS companion for monitoring & managing it that syncs via iCloud and TailScale (no server on my end, more privacy friendly). If this sounds useful to you please let me know!

In addition to this, I also just work on ~3 projects at the same time and rotate through them by having about 20 iTerm2 tabs open where I use the titles of each tab (cmd-i to update) as the task title for my sake.

I've also started building more with SwiftWASM (with SQLite WASM, and I am working on porting SQLiteData to WASM too so I can have a unified data layer that has iCloud sync on Apple platforms) and web deployment for some of my apps features so that I can iterate more quickly and reuse the work in the apps.

rglullis•12 days ago

It's not the open models that suck, it's the infrastructure around them. None of current "open weights providers" have:

   - good tools for agentic workflows
   - no tools for context management
   - infrastructure for input token caching

These are solvable without having to pay anything to OpenAI/Anthropic/Google.

alephnerd•12 days ago

> it’s just that you don’t usually see the CCP publicly making arguments that they need to sacrifice the long term for the short term

They do.

These kinds of statements and discussions happen all the time - in Chinese. The "long-termism" trope is largely an English language one because outsiders either severely degrade or severely fawn Chinese policymaking. Additionally, because most outsiders don't speak or understand Chinese, the spectre of China is often used as a rhetorical device to help drive decisionmaking and using "long-termism" is an easy device for that. A similar thing used to be used with Japan in the 1980s and Germany in the 2000s.

And what actually is the long term value of investing tens of billions in (eg.) AGI versus a similar amount in subsidized healthcare expansion in China? Applications based usescases and domain specific usecases of AI/ML have shown the most success from an outcomes perspective for both National Security and Economic usecases.

AI/ML has a lot of value, but a large amount of the promise is unrealistic for the valuations provided in both the US and China. THe issue is in China, an AI bubble bursting risks leaving local and regional governments holding the bag like during the real estate crisis because the vast majority of capital deployed in subsidizes came from regional and local government's budgets, and takes a large amount of capital away from social service expansion.

For a lot of Chinese leadership, the biggest worry is Japanification, which itself set itself due to the three-way punch of the 1985 Endaka recession, the 1990 Asset Bubble bust, and the 1997 Asian Financial Crisis. Much of China's financial leadership and regulators started their careers managing the blowback of these crises in China during that era or were scholars on them. As such, Chinese regulators are increasingly trying to pop bubbles sooner rather than later especially after the past experiences dealing with the 2015-16 market crash and the Evergrande crisis. Irrational exuberance around AI is increasingly being viewed through that lens as well.