Modern LLMs could be like the equivalent of those steam powered toys the Romans had in their time. Steam tech went through a long winter before finally being fully utilized for the Industrial Revolution. We’ll probably never see the true AI revolution in our lifetime, only glimpses of what could be, through toys like LLMs.
What we should underscore though, is that even if there is a new AI winter, the world isn’t going back to what it was before AI. This is it, forever.
Generations ahead will gaslight themselves into thinking this AI world is better, because who wants to grow up knowing they live in a shitty era full of slop? Don’t believe it.
This has convinced many non-programmers that they can program, but the results are consistently disastrous, because it still requires genuine expertise to spot the hallucinations.
I've been programming for 30+ years and now a people manager. Claude Code has enabled me to code again and I'm several times more productive than I ever was as an IC in the 2000s and 2010s. I suspect this person hasn't really tried the most recent generation, it is quite impressive and works very well if you do know what you are doing
Last week I gave antigravity a try, with the latest models and all, it generated subpar code that did the job very quickly for sure, but no one would have ever accepted this code in a PR, it took me 10x more time to clean it up than to have gemini shit it out.
The only thing I learned is that 90% of devs are code monkeys with very low expectations which basically amount to "it compiles and seems to work then it's good enough for me"
Well, the original "AI winter" was caused by defense contracts running out without anything to show for it -- turns out, the generals of the time could only be fooled by Eliza clones for so long...
The current AI hype is fueled by public markets, and as they found out during the pandemic, the first one to blink and acknowledge the elephant in the room loses, bigly.
So, even in the face of a devastating demonstration of "AI" ineffectiveness (which I personally haven't seen, despite things being, well, entirely underwhelming), we may very well stuck in this cycle for a while yet...
> despite things being, well, entirely underwhelming
People use LLMs for all kinds of things, but for coding it is absolutely not underwhelming. Can you treat it as a real independent developer, one that doesn't need supervision? No. Can it save you hours and hours of work? Yes.
>Expect OpenAI to crash, hard, with investors losing their shirts.
Lol someone doesn't understand how the power structure system works "the golden rule". There is a saying if you owe the bank 100k you have a problem. If you owe the bank ten million the bank has a problem. OpenAI and the other players have made this bubble so big that there is no way the power system will allow themselves to take the hit. Expect some sort of tax subsided bailout in the near future.
When the hype is infinite (technological singularity and utopia), any reality will be a let down.
But there is so much real economic value being created - not speculation, but actual business processes - billions of dollars - it’s hard to seriously defend the claim that LLMs are “failures” in any practical sense.
Doesn’t mean we aren’t headed for a winter of sobering reality… but it doesn’t invalidate the disruption either.
This article uses the computational complexity hammer way too hard, discounts huge progress in every field of AI outside of the hot trend of transformers and LLMs. Nobody is saying the future of AI is autoregressive and this article pretty much ignores any of the research that has been posted here around diffusion based text generation or how it can be combined with autoregressive methods… discounts multi-modal models entirely. He also pretty much discounts everything that’s happened with AlphaFold, Alpha Go etc. reinforcement learning etc.
The argument that computational complexity has something to do with this could have merit but the article certainly doesn’t give indication as to why. Is the brain NP complete? Maybe maybe not. I could see many arguments about why modern research will fail to create AGI but just hand waving “reality is NP-hard” is not enough.
The fact is: something fundamental has changed that enables a computer to pretty effectively understand natural language. That’s a discovery on the scale of the internet or google search and shouldn’t be discounted… and usage proves it. In 2 years there is a platform with billions of users. On top of that huge fields of new research are making leaps and bounds with novel methods utilizing AI for chemistry, computational geometry, biology etc.
I don't think anybody expects AI development to stop. A winter is defined by a relative drying-up of investment and, importantly, it's almost certain that any winter will eventually be followed by another summer.
The pace of investment in the last 2 years has been so insane that even Altman has claimed that it's a bubble.
> that enables a computer to pretty effectively understand natural language
I'd argue that it pretty effectively mimics natural language. I don't think it really understands anything, it is just the best madlibs generator that the world has ever seen.
For many tasks, this is accurate 99+% of the time, and the failure cases may not matter. Most humans don't perform any better, and arguably regurgitate words without understanding as well.
But if the failure cases matter, then there is no actual understanding and the language the model is generating isn't ever getting "marked to market/reality" because there's no mental world model to check against. That isn't going to be usable if there are real-world consequences of the LLM getting things wrong, and they can wind up making very basic mistakes that humans wouldn't make--because we can innately understand how the world works and aren't always just stringing words together that sound good.
Break the aspects of language understanding and language generation apart. While I would agree that generative LLMs are understanding-free madlibs for writing text, embedding vector spaces and LLM latent spaces seem are a pretty genuine understanding of natural language. High dimensional vector spaces seem like the best machine representation we currently have for meaning and LLMs are using it effectively.
> I could see many arguments about why modern research will fail to create AGI
Why is AGI even necessary? If the loop between teaching the AI something, and it being able to repeat similar enough tasks; if that loop becomes short enough, days or hours instead of months, who cares if some ill-defined bar of AGI is met?
Technologically, I believe that you're right. On the other hands, the previous AI winters happened despite novel, useful technologies, some of which proved extremely useful and actually changed the world of software. They happened because of overhype, then investor moving on to the next opportunity.
Here, the investors are investing in LLMs. Not in AlphaFold, AlphaGo, neurosymbolic, focus learning, etc. If (when) LLMs prove insufficient to the insane level of hype and if (when) experience shows that there is only so much money that you can make with LLMs, it's possible that the money will move on to other types of AI, but there are chances that it will actually go to something entirely different, perhaps quantum, leaving AI in winter.
> The fact is: something fundamental has changed that enables a computer to pretty effectively understand natural language.
I have commented elsewhere but this bears repeating
LLM's do not think, understand, reason, reflect, comprehend and they never shall.
If you had enough paper and ink and the patience to go through it, you could take all the training data and manually step through and train the same model. Then once you have trained the model you could use even more pen and paper to step through the correct prompts to arrive at the answer. All of this would be a completely mechanical process. This really does bear thinking about. It's amazing the results that LLM's are able to acheive. But let's not kid ourselves and start throwing about terms like AGI or emergence just yet. It makes a mechanical process seem magical (as do computers in general).
I should add it also makes sense as to why it would, just look at the volume of human knowledge (the training data). It's the training data with the mass quite literally of mankind's knowledge, genius, logic, inferences, language and intellect that does the heavy lifting.
Blog posts like this make me think model adoption and appropriate use case for the model is...lumpy at best. Every time I read something like it I wonder what tools they are using and how? Modern systems are not raw transformers. A raw transformer will “always output something,” they're right, but nobody deploys naked transformers. This is like claiming CPUs can’t do long division because the ALU doesn’t natively understand decimals. Also, a model is stat aprox trained on the empirical distribution of human knowledge work. It is not trying to compute the exact solution to NP complete problems? Nature does not require worst case complexity, real world cognitive tasks are not worst case NP hardness instances...
AlexNet was only released in 2011. The progress made in just 14 years has been insane. So while I do agree that we are approaching a "back to the drawing board" era, calling the past 14 years a "failure" is just not right.
LLMs are an amazing advancement. The tech side of things is very impressive. Credit where credit is due.
Where the current wave all falls apart is on the financials. None of that makes any sense and there’s no obvious path forward.
Folks say handwavy things like “oh they’ll just sell ads” but even a cursory analysis shows that math doesn’t ad up relative to the sums of money being invested at the moment.
Tech wise I’m bullish. Business wise, AI is setting up to be a big disaster. Those that aimlessly chased the hype are heading for a world of financial pain.
The business trajectory will be like Uber. A few big companies (Google, OpenAI) will price their AI services at a loss until consumers find it to be indispensable and competitors run out of money, then they'll steadily ramp up the pricing to the point where they're gouging consumers (and raking in profits) but still a bit cheaper or better than alternatives (humans in this case).
>None of that makes any sense and there’s no obvious path forward.
The top end models with their high compute requirements probably don't but there is value in lower end models for sure.
After all, its the AWS approach. Most of AWS services is stuff you can easily get for cheaper if you just rent an EC2 and set it up yourself. But because AWS offers very simple setup, companies don't mind paying for it.
Most of the researchers outside big tech only have access to a handful of consumer GPUs at best. They are under a lot of pressure to invent efficient algorithms. The cost coming down by orders of magnitude seems like a good bet.
The poster is right. LLMs are Gish Gallop machines that produce convincing sounding output.
People have figured it out by now. Generative "AI" will fail, other forms may continue, though it it would be interesting to hear from experts in other fields how much fraud there is. There are tons of material science "AI" startups, it is hard to believe they all deliver.
Interesting take. His argument is basically that LLMs have hit their architectural ceiling and the industry is running on hype and unsustainable economics. I’m not fully convinced, but the points about rising costs and diminishing returns are worth paying attention to. The gap between what these models can actually do and what they’re marketed as might become a real problem if progress slows.
claude code + opus4.5 does exactly what it says on the box.
Today I pasted a screenshot of frontend dropdown menu with a prompt "add a an option here to clear the query cache". Claude found the relevant frontend files, figured out the appropriate backend routes / controllers to edit, and submitted a PR.
"The technology is essentially a failure" is in the headline of this article. I have to disagree with that. For the first time in the history of the UNIVERSE, an entity exists that can converse in human language at the same level that humans can.
But that's me being a sucker. Because in reality this is just a clickbait headline for an article basically saying that the tech won't fully get us to AGI and that the bubble will likely pop and only a few players will remain. Which I completely agree with. It's really not that profound.
I agree, I have over 20 years of software engineering experience and after vibe coding/engineering/architecting (or whatever you want to call it) for a couple months, I also don't see the technology progressing further. LLMs are more or less the same as 6 months ago, incremental improvements, but no meaningful progress. And what we have is just bad. I can use it because I know the exact code I want generated and I will just re-prompt if I don't get what I want, but I'm unconvinced that this is faster than a good search engine and writing code myself.
I think I will keep using it while it's cheap, but once I have to pay the real costs of training/running a flagship modell I think I will quit. It's too expensive as it is for what it does.
> LLMs are more or less the same as 6 months ago, incremental improvements, but no meaningful progress.
Go back to a older version of a LLM and then say the same. You will notice that older LLM versions do less, have more issues, write worse code etc...
There have been large jumps in the last 2 years but because its not like we go from a LLM to AGI, that people underestimate the gains.
Trust me, try it, try Claude 3.7 > 4.0 > 4.5 > Opus 4.5 ...
> I'm unconvinced that this is faster than a good search engine and writing code myself.
What i see is mostly somebody who is standoffish on LLMs ... Just like i was. I tried to shoehorn their code generation into "my" code, and while it works reasonably, you tend to see the LLM working on "your code", as a invasion. So you never really use its full capabilities.
LLMs really work the best, if you plan, plan, plan, and then have them create the code for you. The moment you try to get the LLMs work inside existing code, that is especially structured how YOU like it, people tend to be more standoffish.
> It's too expensive as it is for what it does.
CoPilot is like 27 Euro/month(year payment + dollar/euro) for 1500 requests here. We pay just for basic 100Mbit internet 45 Euro per month. I mean ... Will it get more expensive in the future, o yes, for sure. But we may also have alternatives by then. Open Source/Open Weight models are getting better and better, especially with MoE.
Pricing is how you look at it... If i do the work what takes me a months in a few days, what is the price then? If i do work that i normally need to outsource. Or code that is some monotoon repeating end me %@#$ ... in a short time by paying a few cents to a LLM, ...
Reality is, thing change, and just like the farmers that complained about the tractor, while their neighbor now did much more work thanks to that thing, LLMs are the same.
I often see the most resistance from us older programmer folks, who are set in our ways, when its us who actually are the best at wrangling LLMs the best. As we have the experience to fast spot where a LLM goes wrong, and guide it down the right path. Tell it, the direction is debugging is totally wrong and where the bug more likely is ...
For the last 2 years i paid just the basic cheap $10 subscription, and use it but never strongly. It helped with those monotoon tasks etc. Until ... a months ago, i needed a specific new large project and decided to just agent / vibe code it, just to try at first. And THEN i realized, how much i was missing out off. Yes, that was not "my" code, and when the click came in my head that "i am the manager, not the programmer", you suddenly gain a lot.
Its that click that is hard for most seasoned veterans. And its ironically often the seasoned guys that complain the most about AI ... when its the same folks that can get the most out of LLMs.
what a curious coincidence that a soft-hard AI landing would happen to begin at the exact same time as the US government launches a Totally Not a Bailout Strategic Investment Plan Bro I Promise. who could have predicted this?
I am of a belief that upcoming winter will look more like normalization than collapse.
The reason is hype deflation and technical stagnation don't have to arrive together. Once people stop promising AGI by Christmas and clamp down on infinite growth + infinite GPU spend, things will start to look more normal.
At this point, it feels more like the financing story was the shaky part not the tech or the workflows. LLMs’ve changed workflows in a way that’s very hard to unwind now.
I think the author is onto something. but (s)he didn’t highlight that there are some scenarios where factual accuracy is unimportant, or maybe even a detractor.
for example, fictional stories. If you want to be entertained and it doesn’t matter if it’s true or not, there’s no downsides to “hallucinations”. you could argue that stories ARE hallucinations.
another example is advertisements. what matters is how people perceive them, not what’s actually true.
or, content for a political campaign.
the more i think about it, genAI really is a perfect match for social media companies
The winters are the best part, economic harm aside.
Winters are when technology falls out of the vice grip of Capital and into the hands of the everyman.
Winters are when you’ll see folks abandon this AIaaS model for every conceivable use case, and start shifting processing power back to the end user.
Winters ensure only the strongest survive into the next Spring. They’re consequences for hubris (“LLMs will replace all the jobs”) that give space for new things to emerge.
So, yeah, I’m looking forward to another AI winter, because that’s when we finally see what does and does not work. My personal guess is that agents and programming-assistants will be more tightly integrated into some local IDEs instead of pricey software subscriptions, foundational models won’t be trained nearly as often, and some accessibility interfaces will see improvement from the language processing capabilities of LLMs (real-time translation, as an example, or speech-to-action).
That, I’m looking forward to. AI in the hands of the common man, not locked behind subscription paywalls, advertising slop, or VC Capital.
But if you actually filter it out, instead of (over) reacting to it in either direction, progress has been phenomenal and the fact there is visible progress in many areas, including LLMs, in the order of months demonstrates no walls.
Visible progress doesn’t mean astounding progress. But any tech that is improving year to year is moving at a good speed.
Huge apparent leaps in recent years seem to have spoiled some people. Or perhaps desensitized them. Or perhaps, created frustration that big leaps don’t happen every week.
I can’t fathom anyone not using models for 1000 things. But we all operate differently, and have different kinds of lives, work and problems. So I take claims that individuals are not getting much from models at face value.
But that some people are not finding the value isn’t an argument that those of us getting value, increasing value isn’t real.
There was value in leaded gas and asbestos insulation too, nobody denies that.
You're blind to all the negative side effects, AI generated slop ads, engagement traps, political propaganda, scams, &c. The amount of pollution is incredible, search engines are dead, blogs are dead, YouTube is dead, social medias are dead, it's virtually impossible to find non slop content, the ratio is probably already 50:1 by now
And these are only the most visible things, I know a few companies losing hundreds of hours every month replying to support tickets that are fully llm generated an more often than not don't make any sense. Another big topic is education.
I think it primarily comes from people being entrenched in their early opinions that AI is shit and they're now moving goalposts without actually giving it a shot.
I have pretty negative feelings about all this stuff and how the future will be but also have to admit it's crazy how good it is at so many things I would have considered safe a few years ago before chatgpt.
There are a couple really disingenuous bloggers out there who have big audiences themselves and are "experts" for others audiences who really push hard this narrative that AI is a joke and will never progress by where it is today, it is actually completely useless and just a scam. This is comforting for those of us that worry more than are excited about AI so some eat it up while barely trying it for themselves
While both are unlikely, if I have to choose one I would bet on AGI than AI winter in the next five years.
AI just got better and better. People thought it couldn't solve math problems without some human formalizes them first. Then it did. People thought it couldn't generate legible text. Then it did.
All while people swore it had reached a "plateau," "architecture ceiling," "inherent limit," or whatever synonym of the goalpost.
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[ 2.8 ms ] story [ 102 ms ] threadWhat we should underscore though, is that even if there is a new AI winter, the world isn’t going back to what it was before AI. This is it, forever.
Generations ahead will gaslight themselves into thinking this AI world is better, because who wants to grow up knowing they live in a shitty era full of slop? Don’t believe it.
Really? I derive a ton of value from it. For me it’s a phenomenal advancement and not a failure at all.
I've been programming for 30+ years and now a people manager. Claude Code has enabled me to code again and I'm several times more productive than I ever was as an IC in the 2000s and 2010s. I suspect this person hasn't really tried the most recent generation, it is quite impressive and works very well if you do know what you are doing
The only thing I learned is that 90% of devs are code monkeys with very low expectations which basically amount to "it compiles and seems to work then it's good enough for me"
The current AI hype is fueled by public markets, and as they found out during the pandemic, the first one to blink and acknowledge the elephant in the room loses, bigly.
So, even in the face of a devastating demonstration of "AI" ineffectiveness (which I personally haven't seen, despite things being, well, entirely underwhelming), we may very well stuck in this cycle for a while yet...
People use LLMs for all kinds of things, but for coding it is absolutely not underwhelming. Can you treat it as a real independent developer, one that doesn't need supervision? No. Can it save you hours and hours of work? Yes.
Lol someone doesn't understand how the power structure system works "the golden rule". There is a saying if you owe the bank 100k you have a problem. If you owe the bank ten million the bank has a problem. OpenAI and the other players have made this bubble so big that there is no way the power system will allow themselves to take the hit. Expect some sort of tax subsided bailout in the near future.
But there is so much real economic value being created - not speculation, but actual business processes - billions of dollars - it’s hard to seriously defend the claim that LLMs are “failures” in any practical sense.
Doesn’t mean we aren’t headed for a winter of sobering reality… but it doesn’t invalidate the disruption either.
The argument that computational complexity has something to do with this could have merit but the article certainly doesn’t give indication as to why. Is the brain NP complete? Maybe maybe not. I could see many arguments about why modern research will fail to create AGI but just hand waving “reality is NP-hard” is not enough.
The fact is: something fundamental has changed that enables a computer to pretty effectively understand natural language. That’s a discovery on the scale of the internet or google search and shouldn’t be discounted… and usage proves it. In 2 years there is a platform with billions of users. On top of that huge fields of new research are making leaps and bounds with novel methods utilizing AI for chemistry, computational geometry, biology etc.
It’s a paradigm shift.
The pace of investment in the last 2 years has been so insane that even Altman has claimed that it's a bubble.
I'd argue that it pretty effectively mimics natural language. I don't think it really understands anything, it is just the best madlibs generator that the world has ever seen.
For many tasks, this is accurate 99+% of the time, and the failure cases may not matter. Most humans don't perform any better, and arguably regurgitate words without understanding as well.
But if the failure cases matter, then there is no actual understanding and the language the model is generating isn't ever getting "marked to market/reality" because there's no mental world model to check against. That isn't going to be usable if there are real-world consequences of the LLM getting things wrong, and they can wind up making very basic mistakes that humans wouldn't make--because we can innately understand how the world works and aren't always just stringing words together that sound good.
Why is AGI even necessary? If the loop between teaching the AI something, and it being able to repeat similar enough tasks; if that loop becomes short enough, days or hours instead of months, who cares if some ill-defined bar of AGI is met?
Here, the investors are investing in LLMs. Not in AlphaFold, AlphaGo, neurosymbolic, focus learning, etc. If (when) LLMs prove insufficient to the insane level of hype and if (when) experience shows that there is only so much money that you can make with LLMs, it's possible that the money will move on to other types of AI, but there are chances that it will actually go to something entirely different, perhaps quantum, leaving AI in winter.
I have commented elsewhere but this bears repeating
LLM's do not think, understand, reason, reflect, comprehend and they never shall.
If you had enough paper and ink and the patience to go through it, you could take all the training data and manually step through and train the same model. Then once you have trained the model you could use even more pen and paper to step through the correct prompts to arrive at the answer. All of this would be a completely mechanical process. This really does bear thinking about. It's amazing the results that LLM's are able to acheive. But let's not kid ourselves and start throwing about terms like AGI or emergence just yet. It makes a mechanical process seem magical (as do computers in general).
I should add it also makes sense as to why it would, just look at the volume of human knowledge (the training data). It's the training data with the mass quite literally of mankind's knowledge, genius, logic, inferences, language and intellect that does the heavy lifting.
Go ahead and double check when the LLM craze started and perhaps reconsider making things up.
Where the current wave all falls apart is on the financials. None of that makes any sense and there’s no obvious path forward.
Folks say handwavy things like “oh they’ll just sell ads” but even a cursory analysis shows that math doesn’t ad up relative to the sums of money being invested at the moment.
Tech wise I’m bullish. Business wise, AI is setting up to be a big disaster. Those that aimlessly chased the hype are heading for a world of financial pain.
The top end models with their high compute requirements probably don't but there is value in lower end models for sure.
After all, its the AWS approach. Most of AWS services is stuff you can easily get for cheaper if you just rent an EC2 and set it up yourself. But because AWS offers very simple setup, companies don't mind paying for it.
People have figured it out by now. Generative "AI" will fail, other forms may continue, though it it would be interesting to hear from experts in other fields how much fraud there is. There are tons of material science "AI" startups, it is hard to believe they all deliver.
Today I pasted a screenshot of frontend dropdown menu with a prompt "add a an option here to clear the query cache". Claude found the relevant frontend files, figured out the appropriate backend routes / controllers to edit, and submitted a PR.
We should say: most rapidly adopted ... speculation.
Because this is what it is: not a technology, but speculation.
Hint: technology has repeatable results.
QED ;)
But that's me being a sucker. Because in reality this is just a clickbait headline for an article basically saying that the tech won't fully get us to AGI and that the bubble will likely pop and only a few players will remain. Which I completely agree with. It's really not that profound.
I think I will keep using it while it's cheap, but once I have to pay the real costs of training/running a flagship modell I think I will quit. It's too expensive as it is for what it does.
Go back to a older version of a LLM and then say the same. You will notice that older LLM versions do less, have more issues, write worse code etc...
There have been large jumps in the last 2 years but because its not like we go from a LLM to AGI, that people underestimate the gains.
Trust me, try it, try Claude 3.7 > 4.0 > 4.5 > Opus 4.5 ...
> I'm unconvinced that this is faster than a good search engine and writing code myself.
What i see is mostly somebody who is standoffish on LLMs ... Just like i was. I tried to shoehorn their code generation into "my" code, and while it works reasonably, you tend to see the LLM working on "your code", as a invasion. So you never really use its full capabilities.
LLMs really work the best, if you plan, plan, plan, and then have them create the code for you. The moment you try to get the LLMs work inside existing code, that is especially structured how YOU like it, people tend to be more standoffish.
> It's too expensive as it is for what it does.
CoPilot is like 27 Euro/month(year payment + dollar/euro) for 1500 requests here. We pay just for basic 100Mbit internet 45 Euro per month. I mean ... Will it get more expensive in the future, o yes, for sure. But we may also have alternatives by then. Open Source/Open Weight models are getting better and better, especially with MoE.
Pricing is how you look at it... If i do the work what takes me a months in a few days, what is the price then? If i do work that i normally need to outsource. Or code that is some monotoon repeating end me %@#$ ... in a short time by paying a few cents to a LLM, ...
Reality is, thing change, and just like the farmers that complained about the tractor, while their neighbor now did much more work thanks to that thing, LLMs are the same.
I often see the most resistance from us older programmer folks, who are set in our ways, when its us who actually are the best at wrangling LLMs the best. As we have the experience to fast spot where a LLM goes wrong, and guide it down the right path. Tell it, the direction is debugging is totally wrong and where the bug more likely is ...
For the last 2 years i paid just the basic cheap $10 subscription, and use it but never strongly. It helped with those monotoon tasks etc. Until ... a months ago, i needed a specific new large project and decided to just agent / vibe code it, just to try at first. And THEN i realized, how much i was missing out off. Yes, that was not "my" code, and when the click came in my head that "i am the manager, not the programmer", you suddenly gain a lot.
Its that click that is hard for most seasoned veterans. And its ironically often the seasoned guys that complain the most about AI ... when its the same folks that can get the most out of LLMs.
The reason is hype deflation and technical stagnation don't have to arrive together. Once people stop promising AGI by Christmas and clamp down on infinite growth + infinite GPU spend, things will start to look more normal.
At this point, it feels more like the financing story was the shaky part not the tech or the workflows. LLMs’ve changed workflows in a way that’s very hard to unwind now.
for example, fictional stories. If you want to be entertained and it doesn’t matter if it’s true or not, there’s no downsides to “hallucinations”. you could argue that stories ARE hallucinations.
another example is advertisements. what matters is how people perceive them, not what’s actually true.
or, content for a political campaign.
the more i think about it, genAI really is a perfect match for social media companies
Winters are when technology falls out of the vice grip of Capital and into the hands of the everyman.
Winters are when you’ll see folks abandon this AIaaS model for every conceivable use case, and start shifting processing power back to the end user.
Winters ensure only the strongest survive into the next Spring. They’re consequences for hubris (“LLMs will replace all the jobs”) that give space for new things to emerge.
So, yeah, I’m looking forward to another AI winter, because that’s when we finally see what does and does not work. My personal guess is that agents and programming-assistants will be more tightly integrated into some local IDEs instead of pricey software subscriptions, foundational models won’t be trained nearly as often, and some accessibility interfaces will see improvement from the language processing capabilities of LLMs (real-time translation, as an example, or speech-to-action).
That, I’m looking forward to. AI in the hands of the common man, not locked behind subscription paywalls, advertising slop, or VC Capital.
Yes, there is hype.
But if you actually filter it out, instead of (over) reacting to it in either direction, progress has been phenomenal and the fact there is visible progress in many areas, including LLMs, in the order of months demonstrates no walls.
Visible progress doesn’t mean astounding progress. But any tech that is improving year to year is moving at a good speed.
Huge apparent leaps in recent years seem to have spoiled some people. Or perhaps desensitized them. Or perhaps, created frustration that big leaps don’t happen every week.
I can’t fathom anyone not using models for 1000 things. But we all operate differently, and have different kinds of lives, work and problems. So I take claims that individuals are not getting much from models at face value.
But that some people are not finding the value isn’t an argument that those of us getting value, increasing value isn’t real.
You're blind to all the negative side effects, AI generated slop ads, engagement traps, political propaganda, scams, &c. The amount of pollution is incredible, search engines are dead, blogs are dead, YouTube is dead, social medias are dead, it's virtually impossible to find non slop content, the ratio is probably already 50:1 by now
And these are only the most visible things, I know a few companies losing hundreds of hours every month replying to support tickets that are fully llm generated an more often than not don't make any sense. Another big topic is education.
There are a couple really disingenuous bloggers out there who have big audiences themselves and are "experts" for others audiences who really push hard this narrative that AI is a joke and will never progress by where it is today, it is actually completely useless and just a scam. This is comforting for those of us that worry more than are excited about AI so some eat it up while barely trying it for themselves
Mixing them up doesn’t help with understanding or fixing anything.
Spontaneously projecting that confusion onto others, who agree with you on the latter problem, is even less helpful.
AI just got better and better. People thought it couldn't solve math problems without some human formalizes them first. Then it did. People thought it couldn't generate legible text. Then it did.
All while people swore it had reached a "plateau," "architecture ceiling," "inherent limit," or whatever synonym of the goalpost.