What if useful AI is a fantasy?

edit: I had already looked up the error code the "old fashioned" way using Google to find the boiler manual, so I would have stepped in if needed, but it was literally flawless and I can't think of any time anything like that hasn't worked when I've used it for similar diagnostics.

If someone spent a lifetime mastering woodworking with hand tools, and then was shown a couple very early rudimentary power tools (lacking safety features, crude features, etc) they would rightly conclude they weren't useful. The artisan can do better work faster with less risk of dismemberment without them.

Prior to LLMs, the world's demand for good software was bottlenecked by access to competent software engineers. The people who want software just want it, they don't care about the craft. They have a different job to be done than the engineer.

An example this reminds me of is a jobs to be done theory thought exercise:

Two different first time home owners need to store yard working tools in their backyard, and determine they need a shed. The first one cares most about minimizing the time it takes to get the shed. The second one has some special constraints to deal with AND also wants to start developing their amateur construction skills. They both need sheds, but they have different values, so:

- the first one buys a shed-kit made of plastic panels that can easily be assembled in 20 minutes.

- the second one buys a power saw, power drill, tool belt, saw horses, lumber, screws, metal roofing, etc and builds a custom shed from relative scratch over a few weekends.

Another example is getting take out vs cooking the meal yourself. There are many many examples.

LLMs are already useful to many. They are also not useful to many others. To assume they aren't useful to anyone just because they aren't useful to you is a sign of absent cognitive empathy. Not acknowledging that other people have other priorities and values they are equally valid to your own.

Literally nobody thought this.

AI is not going to go away and it's not going to stop improving. That would go against the entire history of computing.

We have levels of improvements in the R&D pipeline in every area: hardware, software, model architecture and training. The models will get larger, architecture more sophisticated, computation dramatically more efficient. New materials, paradigms, more efficient nano-devices, scaling up manufacturing for better devices that are already out of the lab, etc. are in progress pointed towards multiple orders of magnitude efficiency gains. And by the way, that is not at all unusual -- we have been making large and small innovations in computing efficiency for decades.

There are still some things lacking in AI -- it still is jagged intelligence. Give it a few years, people will be nostalgic about the time when humans could still point to some victories here and there. That doesn't require any major breakthroughs -- just continuing to increase the size of the models and improving the training.

Practically speaking, is there really a difference between prompting an LLM to create a CMS for a website and installing something like WordPress or Ghost? The setup requires no understanding of the underlying software, but you'll have to build up a mental model from scratch if you want to extend or modify it later.

Of course, it's not going to please anyone that likes the process of programming or knows enough to tell that the output is mediocre at best. But it's very useful for people that don't know how something works and want a 'workable' solution that functionally does what they need. It's basically the next iteration of the WYSIWYG web design tool, the CMS, the site builder, etc. A professional software engineer would be horrified by the code they output, but a non-technical manager type wouldn't notice or care.

I find it baffling that some people had to find this out the hard way. You already knew that it's — if not more work — then at least more tedious to study existing code than write your own.

People have been choosing greenfield rewrites over grokking legacy code since forever.

I think one of the issues with LLMs that confuses people is that they're such a broad open-ended tool that people don't know what to do with them and so conclude they're not sure if they are useful, but in a meta way you could just keep asking the AI questions like "how do I make good use of LLMs in this case" and so on

They seem like an upgraded kind of search engine for text prompts, if nothing else

If you don't have a useful mental model of the architecture that's a communication / review / documentation problem, not a problem with the entire idea of codegen.

Even if the worst happens in the AI market some uses will still remain. Far fewer, I suspect, than will be demand the computational capacity of all the DCs popping up everywhere.

it usually helps if you know how something is to be done, you just want to save time not doing it yourself. this way you can give the LLM intermediate structs and transitions to work with, and constrain it this way not to do something stupid.

Yes. Like working in a team. It can be hard to work on a team and to have to understand what your colleagues did, and how to fix or extend it. What if working in teams is a fantasy?

It's like saying a calculator can do your taxes.

Doesn't work that way.

But grifters gonna grift

It's a psychotic delusion.

Useful LLM is not.