Since LLMs are sometimes wonderfully useful, and sometimes not, I'd suggest effective use involves figuring out in which cases it's likely to succeed, which it's likely to fail.
For example, the mentioned graph has "initial prompt with iterative tweaks", followed by iterations of 'starting from scratch'. -- I don't understand why you'd think "this is an ineffective way of doing things", and then keep doing it.
Describing LLMs as "slot machines" seems like the author has no curiosity about the shape of what LLMs can/can't do.
Answer is useful as a suggestion, and doesn't need to be factually correct: Good
Answer is useful as is, and needS to be factually correct: Bad
But I think the benefits of AI usage will accumulate with the person doing the prompting and their employers. Every AI usage is contextualized, every benefit or loss is also manifested in the local context of usage. Not at the AI provider.
If I take a photo of my skin sore and put it on ChatGPT for advice, it is not OpenAI that is going to get its skin cured. They get a few cents per million tokens. So the AI providers are just utilities, benefits depend on who sets the prompts and and how skillfully they do it. Risks also go to the user, OpenAI assumes no liability.
Users are like investors - they take on the cost, and support the outcomes, good or bad. AI company is like an employee, they don't really share in the profit, only get a fixed salary for work
The remaining 99% had become a significant challenge to the greatest human achievement in distribution of knowledge.
If people used LLMs, knowing that all output is statistical garbage made to seem plausible (i.e. "hallusinations"), and that it just sometimes overlaps with reality, it would be a lot less dangerous.
There is not a single case of using LLMs that has lead to a news story, that isn't handily explained by conflating a BS-generator with Fact-machine.
Does this sound like I'm saying LLMs are bad? Well, in every single case where you need factual information, it's not only bad, it's dangerous and likely irresponsible.
But there are a lot of great uses when you don't need facts, or by simply knowing it isn't producing facts, makes it useful. In most of these cases, you know the facts yourself, and the LLM is making the draft, the mundane statistically inferable glue/structure. So, what are these cases?
- Directing attention in chaos: Suggest where focus needs attention from a human expert. (useful in a lot of areas, medicine, software development). - Media content: music, audio (fx, speech), 3d/2d art and assets and operations. - Text processing: drafting, contextual transformation, etc
Don't trust AI if the mushroom you picked is safe to eat. But use its 100% confident sounding answer for which mushroom it is, as a starting point to look up the information. Just make sure that the book about mushrooms was written before LLMs took off....