They've said there are limits, and increased limits for those on iCloud+ ... so it seems that Apple is in the selling LLM access game now. I don't think there are any details yet on the nature of those limits, and whether they can be increased as required etc.
I am also under the impression that the LLM tech is plateauing before bringing the promised productivity. Great as a coding assistant, great a summarizing a text, translating, great a helping plan a trip...
But for the rest, e.g. act as a life assistant, it is still far off with no hope to reach the desired performance level.
I would not be surprisd to see OpenAI and the likes to start reverting to Siri v1 strategies, i.e. "if this then that" kind of agent routing.
Why this is surprising? LLM-s are good in text generation on the base of the stuff they were trained on. Software is text generation, translation is text generation, LLMs can answer questions since billions were spent on tuning foundation models, that is people were collecting in (semi)automatic way questions with answers to the point we might think that LLM-s are "thinking".
Now people want to handle car rental. What are the relevant data that models were trained on for this kind of application? For Python code there is kirjillion examples on Github, for mathematical proofs there is endless stream of papers, books, etc. But for car rental? Mostly adds in the internet that want to trick you into a bad deal. So yes, LLM will be a disappointment, as it tries, well, to trick you into a bad deal. In addition, data are rather scarce so there will be a lot of hallucination, as it gets mixed up with yacht rental, bikes rental, ski equipment rental, etc.
The performance of specific tasks will depend on either those tasks having been included in the training (which Apple could work on), or added by ways of fine tuning, and context sourced from userland.
For any category of tasks, there's a ton to be gained still in terms of how context is populated more effectively (relevance) and efficiently (token use). See software engineering harnesses and the skills architecture of OpenClaw for example. SWE harnesses make all the difference in how well Claude Code and OpenAI Codex perform. OpenClaw can't do shit without loading skills from the filesystem into context JIT.
I'll be very curious to find out how Apple is feeding context in their new AI approach. Part of it appears to be an 'index' that my iPhone started building (visible in main Settings screen) after installing the iOS 27 Developer Beta.
Going from crypto to fiat and back is an extremely monitored and regulated route. It might be an easy way to settle between counterparties, but a difficult one to launder.
This kind of company does not want "juniors who could independently own products end to end without handholding", they want do-it-all people on the cheap. They just don't know what it takes to do things the right way. It's not unusual that the organization is a mess as well, because management is unable to organize it.
Get out of it, this kind of companies have a dysfunctional management. After the initial learning, they will be unable to recognize your value and contribution.
Also, don't feel guilty about getting burnt out. It's not your fault and you got tougher.
Some may criticize regulations, but the EU-mandated cyber-resilience act (CRA) actually forced companies to have a clear contact point for vulnerabilities reporting, and to act upon it.
2026-09-11, save the date folks. That's when all companies selling products with digital elements in the EU have to have a reporting pipeline for actively exploited vulnerabilities and severe incidents.
I second this idea: LLMs will plateau. They are already pretty good. Plus, scientists struggle to actually score their performance accurately (esp. when it comes to reasoning).
With that said, they are now hitting the walls of energy costs and memory shortages. You brain uses 20W -- don't take it as an insult. There are orders of magnitude to gain from producing energy-efficient models (or model runners).
So I am expecting same performance at lower costs for the coming years.
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