"First, you can’t (or at least shouldn’t) use this technology for mission-critical work; only for low stakes tasks, or questions to which a clever (and significantly more energy efficient) human can recognize a wrong answer.
Second, that the idea that scaling will make for better models is nonsense: no amount of compute chucked at an LLM will make it a less-hallucinogenic product. Creating AI that rewires itself and creates new information the same way humans do and avoids the kinds of catastrophic errors we see at the moment needs a full fresh start (something Marecki and many others are already working on).
And third, that the massive spending by the hyperscalers (much of it via debt) on giant data centers might be one of the the greatest misallocations of capital of all time. It just isn’t required. That’s particularly the case given there are already free LLM models you can download to a laptop (no data center needed, and better still, your privacy guaranteed) that do what the very large models do. If the paid-for versions have already hit their ceiling and just aren’t going to get any better (it looks like they aren’t), why pay for them? Quite."

