AI's real superpower: consuming, not creating

Do you have more resources on that? I'd love to read about the methodology.

> And therefore it's impossible to test the accuracy if it's consuming your own data.

Isn't it only if it's hard to verify the result? If it's a result that's hard to produce but easy to verify, a class which many problems fall into, you'd just need to look at the synthetized results.

If you ask it "given these arbitrary metrics, what is the best business plan for my company?" It'd be really hard to verify the result. I'd be hard to verify the result from anyone for that matter, even specialists.

So I think it's less about expecting the LLM to do autonomous work and more about using LLMs to more efficiently help you search the latent space for interesting correlations, so that you and not the LLM come up with the insights.

This does mean that, useful as e.g. Claude Code is, for any business with NDA-type obligations, I don't think I could recommend it over a locally hosted model, even though the machine needed to run a decent local model might cost €10k (with current price increases due to demand exceeding supply), that the machine is still slower than what hosts the hosted models, that the rapid rate of improvement means a 3-month delay between SOTA in open-weights and private-weights is enough to matter*.

But until then? If I'm vibe coding a video game I'd give away for free anyway, or copy-editing a blog post that's public anyway, or using it to help with some short stories that I'd never be able to charge money for, or uploading pictures of the plants in my garden right by the public road… that's fine.

* When the music (money for training) stops, it could be just about any provider whose model is best, whatever that is is likely to still get distilled down fairly cheaply and/or some 3-month-old open-weights model is likely to get fine-tuned for each task fairly cheaply; independently of this, without the hyper-scalers the supply chains may shift back from DCs to PCs and make local models much more affordable.

This isn't strictly a case against AI, just a case that we have a contradiction on the definition of "well informed". We value over-consumption, to the point where we see learning 3 things in 5 minutes as better than learning 1 thing in 5 minutes, even if that means being fully unable to defend or counterpoint what we just read.

I'm speficially referring to what you said: "the speaker used some obscure technical terminology I didn't know" this is due to lack of assumed background knowledge, which makes it hard to verify a summary on your own.

I looked up a medical term, that is frequently misused (eg. "retarded"), and asked the Gemini to compare it with similar conditions.

Because I have enough of a background in the subject matter, I could tell what it had construed by its mixing the many incorrect references with the much fewer correct references in the training data.

I asked it for sources, and it failed to provide anything useful. But once I am looking at sources, I would be MUCH better off searching and only reading the sources might actually be useful.

I was sitting with a medical professional at the time (who is not also a programmer) and he completely swallowed what Gemini was feeding him. He commented that he appreciates that these summaries let him know when he is not up to date with the latest advances, and he learnt alot from the response.

As an aside, I am not sure I appreciate that Google's profile would now associate me with that particular condition.

Scary!