[DesertVarnish]

They really aren't the same.

Crypto didn't and still doesn't have the same immediate utility. The value proposition just wasn't there to justify the money and attention it was getting. Bitcoin in particular was a prototype that got mythologized into being "digital gold" despite it's many, many technical limitations.

Diffusion models and LLMs work today and make possible things that were science fiction five years ago, and have shown tremendous and exciting progress in the past 18 months.

[ok_dad]

Your last paragraph is the hype.

I haven’t seen any effective uses of the current AI tech that couldn’t have been done for the same cost by humans so far. Images, text, code; I haven’t seen anything but toys built yet. The coding tools might work okay for your average HTTP API, but it’s not going to develop novel algorithms to control building HVAC systems to reduce energy or demand, for example. It’s not going to code much more efficient search algorithms, or faster compression. Maybe someday, but so far everything produced by AI seems to have huge problems, whether it be drawing realistic hands, knowing the factual truth of certain questions, or introducing subtle bugs in complex code.

[tomohelix]

That is because you are comparing it to the cost of a professional.

I personally look at it in a different way. Now, a rando on the street knowing nothing about everything can pop out arts rivaling an experienced illustrator. A completely clueless wet lab scientist can coerce copilot or GPT4 to cobble together an automated data analysis pipeline in a language that they know nothing about.

To a professional, those applications are toys, easily made and take little effort. But to someone who does not know anything about the work, it is amazingly useful and open up many possibilities. That is the power and the use cases for AI right now. They are tools to augment productivity, not replacing it. And in that regard, it is very successful imo.

Whether it will progress to the point where it can outright handle everything from start to finish or not is another question.

[Hermitian909]

I'm working on integrating AI into a product right now: IMO you want to look at what's happening now as more a shift in cost to develop and maintain - which itself is going to create qualitative differences.

I can now have 1-2 developers stand up ML backed services at a level of quality that a few years ago would have required an ML + engineering team to build along with an ongoing tuning burden. Now that the AI is "good enough" out of the box time-to-value has dropped, which also allows for more exploration.

One area I'm seeing a lot of traction at my company and amongst other developers: onboarding flows for complex products. LLMs are really great at taking a small amount of input from or about a user, walking down a decision tree, and creating some initial dummy data relevant to them to more quickly demonstrate value. You might not ever know chatGPT is involved but it doing wonders for quite a few companies' conversion rates.

[vidarh]

Just last night I was adding and OIDC provider to a website for a friend, and GPT4 did most of the job for me. As in, I mostly cut and pasted code and filled in the actual integration with the login. It saved me time.

Most development is far closer to that than developing more efficient search algorithms, or better compression,something most human developers couldn't do if their lives depended on it.

Could I have hired.someone to do it? Sure, but finding someone and turnaround time would have taken longer than doing it myself, while GPT4 spat out a solution in seconds.

[Charon77]

How about alphafold? Prior usage requires quite literally weeks to months on supercomputers with results often not so comparable with the X ray crystallography.

Or the use of AI for real time upscaling that can be done in real time on low to medium GPU.

I think you only see chat gpt which don have its own draw back, but AI does not equal gpt, does not even has to be generative.