The reality might just be that most technology is slow to spread? But it also depends on what you mean by slow. The name ChatGPT became part of popular culture extremely quickly.
That's a whole lot of twisting to avoid admitting "it usually doesn't work, and even when it does work, it's usually not cost-effective even at the heavily-subsidized prices."
Or maybe it's more about refusing to admit that executives are out of touch with concrete reality and are just blindly chasing trends instead.
reminds me of crypto a bit. most people i know are apathetic or dismissive.
when i see normies use it - its to make selfies with celebrities.
in 5-10 years AI will everywhere. a massive inequality creator.
those who know how to use it and those who can afford the best tools.
the biggest danger is dependency on AI. i really see people becoming dumber and dumber as they outsource more basic cognitive functions and decisions to AI.
and business will use it like any other tool. to strengthen their monopolies and extract more and more value out of less and less resources.
> such as datasets that are not properly integrated into the cloud
I believe this is a core issue that needs to be addressed. I believe companies will need tools to make their data "AI ready" beyond things like RAG. I believe there needs to be a bridge between companies data-lakes and the LLM (or GenAI) systems. Instead of cutting people out of the loop (which a lot of systems seem to be attempting) I believe we need ways to expose the data in ways that allow rank-and-file employees to deploy the data effectively. Instead of threatening to replace the employees, which leads them to be intransigent in adoption, we should focus on empowering employees to use and shape the data.
Very interesting to see the Economist being so bullish on AI though.
not sufficiently useful
not sufficiently trustworthy.
It is my ongoing experience that AI + My Oversight requires more time than not using AI.
Sometimes AI can answer slightly complex things in a helpful way. But for most of the integration troubleshooting I do, AI guidance varies between no help at all and fully wasting my time.
Conversely, I support folks who have the complete opposite experience. AI is of great benefit to them and has hugely increased their productivity.
Both our experiences are valid and representative.
In contrast to the Economist blaming inefficient workers sabotaging the spread of this wonderful technology, make sure to check out https://pivot-to-ai.com/ where David Gerard has been questioning whether people are prompting it wrong or AI is just not that smart.
From where I stand, AI seems to enjoy lightning fast adoption. Personal computers became a viable technology in the eighties, slowly penetrated workplace in the nineties, and supposedly registered in labor productivity in the early 2000s. The large language models became practical less then three years ago, and already plenty of businesses boast how they will integrate AI and cut their workforce, while everyone else feel behind and obsolete.
There was an article on HN a few days back on how it’s very hard to convey all the context in your head about a codebase to solve a problem, and that’s partly why it’s actually hard to use AI for non-trivial implementations. That’s not just limited to code.
I don’t use AI for most of my product work because it doesn’t know any of the nuances of our product, and just like doing code review for AI is boring and tedious, it’s also boring and tedious to exhaustively explain that stuff in a doc, if it can even be fully conveyed, because it’s a combination of strategy, hearsay from customers, long-standing convos with coworkers…
I’d rather just do the product work. Also, I’ve self-selected by survivorship bias to be someone who likes doing the product work too, which means I have even less desire to give it up.
Smarter LLMs could solve this maybe. But the difficulty of conveying information seems like a hard thing to solve.
> With its fantastic capabilities, ai represents hundred-dollar bills lying on the street. Why, then, are firms not picking them up? Economics may provide an answer.
Which science is responsible for the answer that if you can't establish the veracity of the premise for the question, economics can't help you find the missing outcome that shouldn't be there?
One reason is that humans have a strong tendency to optimize for the short term.
I witness it with my developer friends. Most of them try for 5 minutes to get AI to code something that takes them an hour. Then they are annoyed that the result is not good. They might try another 5 minutes, but then they write the code themselves.
My thinking is: Even if it takes me 2 hours to get AI to do something that would take me 1 hour it is worth it. Because during those 2 hours I will make my code base more understandable to help the AI cope with it. I will write better general prompts about how AI should code. Those will be useful beyond this single task. And I will get to know AI better and learn how to interact with it better. This process will probably lead to a situation where in a year, it will take me 30 minutes with AI to do a task that would have taken me an hour otherwise. A doubling of my productivity with just a year of work. Unbelievable.
I see very few other developers share this enthusiasm. They don't like putting a year of work into something so intangible.
Among non-technical friends, after the initial wow factor, even limited expectations were not met. Then it was tainted by the fact that everyone is promoting it as a human replacement technology which is then a tangible threat to their existence. That leads to not just lack of adoption but active sabotage.
And then there’s the large body of people who just haven’t noticed it at all because they don’t give a shit. Stuff just gets done how it always has.
On top of that, it's worth considering that growth is a function of user count and retention. The AI companies only promote count which suggests that the retention numbers are not good or they’d be promoting it. YMMV but people probably aren’t adopting it and keeping it.
Maybe I'm naive, but .. aren't businesses slow to adopt anything new, unless possibly when it's giving their competition a large, obvious advantage, or when it's some sort of plug-and-play, black box improvement where you just trade dollars for efficiency ? "AI" tools may be very promising for lots of things but they require people to do things differently, which people are slow to do. (Even as someone who works in the tech industry, it's not like my workflow has changed all that much with these new tools, and my employer has to be much faster than average)
Considering that the main value of ai today is coding bots, I think that traditional companies struggle to get value from it as they are in the hands of consultancies and cannot assess “developer efficiency” or change ways of working for developers. The consultancies aren’t interested at selling fewer man-hours.
A wall-text just to say: "People do not find AI that useful so they are not adopting it even that top executives are extremely excited about it because do not understand nor care about what AI can really do but about hype and share prices."
I have been using it for coding for some time, but I don't think I'm getting much value out of it. It's useful for some boilerplate generation, but for more complex stuff I find that it's more tedious to explain to the AI what I'm trying to do. The issue, I think, is lack of big picture context in a large codebase. It's not useless, but I wouldn't trade it for say access to StackOverflow.
My non-technical friends are essentially using ChatGPT as a search engine. They like the interface, but in the end it's used to find information. I personally just still use a search engine, and I almost always go to straight to Wikipedia, where I think the real value is. Wikipedia has added much more value to the world than AI, but you don't see it reflected in stock market valuations.
My conclusion is that the technology is currently very overhyped, but I'm also excited for where the general AI space may go in the medium term. For chat bots (including voice) in particular, I think it could already offer some very clear improvements.
It is over hyped for sure. This is among the biggest hype cycles we've seen yet. When it bursts, it'll be absolutely devastating. Make no mistake. Many companies will go out of business, many people affected.
However. It doesn't mean AI will go away. AI is really useful. It can do a lot actually. It is a slow adoption because it's somehow not the most intuitive to use. I think that may have a lot to do with tooling and human communication style - or the way we use it.
Once people learn how to use it, I think it'll just become ubiquitous. I don't see it taking anyone's job. The doomers who like to say that are people pushing their own agenda, trolling, or explaining away mass layoffs that were happening BEFORE AI. The layoffs are a result of losing a tax credit for R&D, over hiring, and the economy. Forgetting the tax thing for a moment, is anyone really surprised that companies over hired?? I mean come on. People BARELY do any work at all at large companies like Google, Apple, Amazon, etc. I mean that not quite fair. Don't get me wrong, SOME people there do. They work their tails off and do great things. That's not all of the company's employees though. So what do you expect is going to happen? Eventually the company prunes. They go and mass hire again years later, see who works out, and they prune again. This strategy is why hiring is broken. It's a horrible grind.
Sorry, back to AI adoption. AI is now seen by some caught in this grind as the "enemy." So that's another reason for slow adoption. A big one.
It does work though. I can see how it'll help and I think it's great. If you know how everything gets put together then you can provide the instructions for it to work well. If you don't, then you're not going to get great results. Sorry, if you don't know how software is built, what good code looks like, AND you don't "rub it the right way." Or as people say "prompt engineering."
I think for writing blog posts, getting info, it's easier. Though there's EXTREME dangers with it for other use cases. It can give incredibly dangerous medical advice. My wife is a psychiatrist and she's been keeping an eye on it, testing it, etc. To date AI has done more to harm people than it has help them in terms of mental health. It's also too inaccurate to use for mental health as well. So that field isn't adopting it so quickly. BUT they are trying and experimenting. It's just going to take some time and rightfully so. They don't want to rush start using something that hasn't been tested and validated. That's an understaffed field though, so I'm sure they will love any productivity gain and help they can get.
All said, I don't know what "slow" means for adoption. It feels like it's progressing quickly.
I spent a bit of time reviewing and cleaning up the mess of someone who had taken text that I'd carefully written, put it through an AI to make it more "impactful", and revising it to remove the confusions and things I hadn't said. The text the AI wrote was certainly impactful, but it was also exaggerated and wrong in several places.
So did AI add value here? It seems to me that it wasted a bunch of my time.
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[ 2.9 ms ] story [ 46.4 ms ] threadOr maybe it's more about refusing to admit that executives are out of touch with concrete reality and are just blindly chasing trends instead.
when i see normies use it - its to make selfies with celebrities.
in 5-10 years AI will everywhere. a massive inequality creator.
those who know how to use it and those who can afford the best tools.
the biggest danger is dependency on AI. i really see people becoming dumber and dumber as they outsource more basic cognitive functions and decisions to AI.
and business will use it like any other tool. to strengthen their monopolies and extract more and more value out of less and less resources.
I believe this is a core issue that needs to be addressed. I believe companies will need tools to make their data "AI ready" beyond things like RAG. I believe there needs to be a bridge between companies data-lakes and the LLM (or GenAI) systems. Instead of cutting people out of the loop (which a lot of systems seem to be attempting) I believe we need ways to expose the data in ways that allow rank-and-file employees to deploy the data effectively. Instead of threatening to replace the employees, which leads them to be intransigent in adoption, we should focus on empowering employees to use and shape the data.
Very interesting to see the Economist being so bullish on AI though.
Sometimes AI can answer slightly complex things in a helpful way. But for most of the integration troubleshooting I do, AI guidance varies between no help at all and fully wasting my time.
Conversely, I support folks who have the complete opposite experience. AI is of great benefit to them and has hugely increased their productivity.
Both our experiences are valid and representative.
It’s not meeting the expectations, probably because of this aggressive advertising. But I would in no way say that it’s spreading slow. It is fast.
I don’t use AI for most of my product work because it doesn’t know any of the nuances of our product, and just like doing code review for AI is boring and tedious, it’s also boring and tedious to exhaustively explain that stuff in a doc, if it can even be fully conveyed, because it’s a combination of strategy, hearsay from customers, long-standing convos with coworkers…
I’d rather just do the product work. Also, I’ve self-selected by survivorship bias to be someone who likes doing the product work too, which means I have even less desire to give it up.
Smarter LLMs could solve this maybe. But the difficulty of conveying information seems like a hard thing to solve.
Which science is responsible for the answer that if you can't establish the veracity of the premise for the question, economics can't help you find the missing outcome that shouldn't be there?
I witness it with my developer friends. Most of them try for 5 minutes to get AI to code something that takes them an hour. Then they are annoyed that the result is not good. They might try another 5 minutes, but then they write the code themselves.
My thinking is: Even if it takes me 2 hours to get AI to do something that would take me 1 hour it is worth it. Because during those 2 hours I will make my code base more understandable to help the AI cope with it. I will write better general prompts about how AI should code. Those will be useful beyond this single task. And I will get to know AI better and learn how to interact with it better. This process will probably lead to a situation where in a year, it will take me 30 minutes with AI to do a task that would have taken me an hour otherwise. A doubling of my productivity with just a year of work. Unbelievable.
I see very few other developers share this enthusiasm. They don't like putting a year of work into something so intangible.
And then there’s the large body of people who just haven’t noticed it at all because they don’t give a shit. Stuff just gets done how it always has.
On top of that, it's worth considering that growth is a function of user count and retention. The AI companies only promote count which suggests that the retention numbers are not good or they’d be promoting it. YMMV but people probably aren’t adopting it and keeping it.
My non-technical friends are essentially using ChatGPT as a search engine. They like the interface, but in the end it's used to find information. I personally just still use a search engine, and I almost always go to straight to Wikipedia, where I think the real value is. Wikipedia has added much more value to the world than AI, but you don't see it reflected in stock market valuations.
My conclusion is that the technology is currently very overhyped, but I'm also excited for where the general AI space may go in the medium term. For chat bots (including voice) in particular, I think it could already offer some very clear improvements.
However. It doesn't mean AI will go away. AI is really useful. It can do a lot actually. It is a slow adoption because it's somehow not the most intuitive to use. I think that may have a lot to do with tooling and human communication style - or the way we use it.
Once people learn how to use it, I think it'll just become ubiquitous. I don't see it taking anyone's job. The doomers who like to say that are people pushing their own agenda, trolling, or explaining away mass layoffs that were happening BEFORE AI. The layoffs are a result of losing a tax credit for R&D, over hiring, and the economy. Forgetting the tax thing for a moment, is anyone really surprised that companies over hired?? I mean come on. People BARELY do any work at all at large companies like Google, Apple, Amazon, etc. I mean that not quite fair. Don't get me wrong, SOME people there do. They work their tails off and do great things. That's not all of the company's employees though. So what do you expect is going to happen? Eventually the company prunes. They go and mass hire again years later, see who works out, and they prune again. This strategy is why hiring is broken. It's a horrible grind.
Sorry, back to AI adoption. AI is now seen by some caught in this grind as the "enemy." So that's another reason for slow adoption. A big one.
It does work though. I can see how it'll help and I think it's great. If you know how everything gets put together then you can provide the instructions for it to work well. If you don't, then you're not going to get great results. Sorry, if you don't know how software is built, what good code looks like, AND you don't "rub it the right way." Or as people say "prompt engineering."
I think for writing blog posts, getting info, it's easier. Though there's EXTREME dangers with it for other use cases. It can give incredibly dangerous medical advice. My wife is a psychiatrist and she's been keeping an eye on it, testing it, etc. To date AI has done more to harm people than it has help them in terms of mental health. It's also too inaccurate to use for mental health as well. So that field isn't adopting it so quickly. BUT they are trying and experimenting. It's just going to take some time and rightfully so. They don't want to rush start using something that hasn't been tested and validated. That's an understaffed field though, so I'm sure they will love any productivity gain and help they can get.
All said, I don't know what "slow" means for adoption. It feels like it's progressing quickly.
So did AI add value here? It seems to me that it wasted a bunch of my time.