I’m curious now, what areas of computer science (as opposed to copy paste react development) consider 100ms low latency? Were you working with very big writes? Very old storage?
No, sorry, didn’t mean to come across rude. I wasn’t arguing, I trust that for whatever you were working on, that was indeed low latency and take your word for it. I just can’t think of what field or general area of computing you’re talking about and I’m curious to read up on something I’m unfamiliar with.
Meta makes money on advertising. They are not paying 30% fees on ad spend.
Meta does lose revenue when Apple and Google introduce privacy features and decrease ad tracking efficacy. They also hate that unlike the web, where their cookies and tracking pixels provided near 100% coverage of your behaviour, on your phone you might be doing something that they can’t log and analyse.
Constant hallucinations. OpenAI:s latest on max settings. If you are to naively feed say, a short corpus of text to turn it into a parallel corpus in a few different languages, the original text gets subtly mangled and no longer matches the original. Say you have several hundred annotated sentences. Without hand-coding some regex to make sure that each sentence in the source column occurs in the original corpus you’re bound to get hallucinated sentences with an error rate that exceeds 1:100. Whatever you use as the output, JSON or XML, you will end up with columns that just repeat the original instead of translating it, especially for languages that are very close to each other or represent the same language.
Yes, LLMs can be SOTA for NLP, but you’re going to have to use them to write software or workflows that are more deterministic.
We are either living in different worlds, or squabbling over different meanings of words.
Models have absolutely acquired agency as of 2025. Developers are no longer copy-pasting code from ChatGPT into their text editor, they're working with agents like Claude Code and Codex that can edit code, run terminal commands, do web searches, manage their own context windows, sift through gigabytes of logs with datadog MCP, etc.
Self-improvement is also being worked on. Claude Tag learns over time in slack convos. My company also has an agent that updates its own skill files after every conversation so that we don't need to keep reminding it about the same workflows every time. Is it clunky as hell? Yes. Are the labs plowing billions of dollars into "continual learning" and "recursive self improvement"? Also yes.
What you call a model acquiring agency I call plain old software with productivity workflows designed by humans, with deliberate goals. We must separate “model” and an execution environment using a model. [Model] ≠ [A glorified shell script doing API calls in a control flow based on heuristics]. Agents are not AI, they are plain old software. The weights are the model, and that very much remains a static artifact (and pre-post training models haven’t improved much over the last few years).
What you call self improvement is a duck tape hack to imitate persistence and save on inference. Every time you do an API call, anything that needs to be processed is sent to the model. Narrowing that context down saves money. Finding clever ways to do that improves apparent performance and value. The cleverness is still human.
These are all useful innovations on top of LLMs, which remain models that generate text and symbols based on static weights, which in turn represent training data and the provider’s preferences.
To be useful they’re gonna have to be capable and powerful. If they’re good enough to be useful, they will be dangerous.
Advanced household robots are closer to fully autonomous cars and airplanes than roombas. The code is safety critical. Imagine something capable of flooding or burning down a building with hundreds of people if left to the vibe coders?
Privilege enables you to rent competence, historically by paying other people. The slop companies will now sell you a simulacrum of competence by the token.
The fact that competence can (could?) only be acquired through sustained effort over a long period of time is (was?) levelling the field.
Selling simulated competence perpetuates privilege, instead of dismantling it like you seem to claim.
On TSMC: what is your reasoning or source of belief that ”extracting as much valie as they can” is against TSMC’s nature? Is there some charity charter or non-profit governance arrangement I’m not aware of? Or are you saying that geopolitics affects their pricing power?
