Usefulness depends on intended job to be done. A hair dryer isn't very useful at drying clothes.

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.

Anecdotally, my 81 year old father was able to point his phone at the boiler and ask Gemini what the error message meant. It correctly identified the boiler model, identified the error code being displayed on the screen, explained what it meant, provided him with a way to confirm the problem by checking the water pressure guage and then asked to be shown the underside. It again correctly identified and described to him the position and colour of the filling loop lever and how to adjust it to start and stop the water flow. He was able to do all this instead of spending hundreds on getting a plumber out at night because it was during a cold spell and he needed the heating on. That kind of thing is no fantasy and was amazing to witness.

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.

It's so interesting that people are so divided on this. He even said he generated multiple applications. Yet he has found a way to just about dismiss it somehow.

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.

I seriously don’t understand the naysayers around here. Have they just not used anything past gpt4? You can’t just outsource all of your thinking to them, but they’re obviously useful.

Personally, I've found that understanding what the AI generates well enough to make substantial changes to it hasn't been too difficult, though it'll obviously depend on how complex the project you're working on is. Effectively speaking, it's like using any sort of off-the-shelf solution and then modifying that solution; you don't need to understand it to use it for your project, but you'll need to do so to make significant changes or add extra functionality.

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.

> At first this seems far-fetched, but consider what happened to Facebook’s Metaverse. For a brief window of time it actually seemed reasonable to believe that we would all be spending most of our waking hours with high-tech ski goggles strapped to our heads. That we would work, relax, and socialize with these bulky headsets tricking our brains into thinking they were in a different world.

Literally nobody thought this.

The problem he's describing comes up any time you have a team of developers. That doesn't mean the usefulness of teams is a fantasy because you didn't write all the code yourself.

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.

IMHO the most durable way to make sure you understand the architecture is to specify it.

> What if <common sense>?

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.

The hypothetical is that everyone will eventually agree with the author that AI is not useful. I don't see what could possibly change everyone's minds at this point. People have too much direct experience with it. At best, it might remain controversial.

It's obviously useful - we have to figure out best practices.

> I didn’t have a mental model for the thing that was in front of me. If there is a bug, or if a new feature needed to be added my mind was precisely where it was before I started prompting, and I couldn’t even begin to make changes until I had built a thorough understanding of the code.

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?

To me, it is as practical and concrete as it can be. Keep coping

Definitely a fantasy, just one that people try to realize at the moment.

So-called "agentic" AI is definitely a fantasy.

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

Doesn't work that way.

But grifters gonna grift

We can already see (net) useful so-called AI is more than a fantasy.

It's a psychotic delusion.

Useful AI is.

Useful LLM is not.