The AI Fad Just Burned to the Waterline | Hacker News Reader

The AI Fad Just Burned to the Waterline

charleshughsmith.blogspot.com

48 pointsdxs66 comments

So, a cuttting edge AI model turned out to be much cheaper and easier to produce than we thought. Weird reason to call something a "fad". Here's to hope nobody invents a way to produce much cheaper and faster cars, or this whole Car Fad will be over too.

From M-W dictionary: Fad : a practice or interest followed for a time with exaggerated zeal : CRAZE

Books are also a fad, if "for a time" stretches across lifetimes; advanced technology that requires some infrastructure, dependent on a complex system, but not nearly as much as computers do, nevermind AI, and which is popular amongst a subset of people. Ignore earth-systems collapse and the underlying technology that keeps these fads afloat will cease to function.

Computers are a cool diversion but not essential for human "survival and thrival", nor is our widespread embracing of the technology without consequences for life on earth.

I would far rather talk with other biased humans than the regurgitations of some biased amalgam-bot made with stolen data, even if it can act syncretically. My bias is as a high-school science teacher who likes helping students gain understanding about the world, and hopefully wisdom, a sense of awe and responsibility, and their own sense of purpose.

The fad pertains to the huge valuations and market hype - which a much cheaper and easier model crashes

“Bubble” is probably more apt.

Remember in the mid-90s when people talked about the internet being a fad? I think AI is a fad in the same way, which is to say not a fad at all no matter how much certain entrenched interests wish it were so.

You're agreeing with the author. The Internet lasted. Webvan and pets.com not so much.

Now we have Instacart and Chewy so the concepts lasted but early adopters not so much.

> Remember in the mid-90s when people talked about the internet being a fad?

People keep bringing this up and it is pure bullshit.

I lived through the 90's and the Internet was never seen as a fad back then. It was hyped through the whole decade as the future, in many ways rightfully so. The hype was so strong that it culminated in the dotcom bubble early next decade.

You may think that AI skepticism is due to "entrenched interests" that want it to be a fad, I argue that AI hype is due to "entrenched interests" wishing that all overprimises are real.

I regularly use AI - Mosltly local models with either Ollama or Stable Diffusion.

I find it mildly useful in some specific scenarios, but very far from being comparable to the internet in terms of how ubiquitous and necessary it might become.

Fad wasn't the word used, but the Internet was described as a "bubble" widely and often.

Well, it was a bubble. There was a major burst in the turn of the century that proved it, with many consequences.

A bubble just meant that the valuation of internet companies at the time were overinflated and detached from reality, not that the Internet as a technology was useless.

I think comparing AI to the internet in terms of usefulness is absolute wishful thinking thinking. The Internet was a major inflection point in the history of the world, maybe in the same magnitude of the advent of computers or the industrial revolution.

AI (and we should be clear that we are actually talking about Generative AI in this context) is an interesting tech, may be pretty useful in some contexts, but it is not in the same league of the previous examples.

Can you specify which are these "entrenched interests"?

> Can you specify which are these "entrenched interests"?

Labor (meaning anyone who works for a living) and anyone who's not prioritizing shareholder value above all else.

Good luck with that. It's not your fault (hopefully). No really we all wanna pat your head, but not until we see results. Bring it fucker.

In the meantime you should expect everything to fall apart for reasons you're completely ignorant of and disconnected from. Maybe your fault? Who knows and who cares? hahahahahah

You can roll over your investments as we all do. You gotta think about number one. Act quickly. You're supposed to be smart money not dumb money...

I mean, just because something is a fad doesn't mean it's not _real_. "There was a fad for X in the 1980s" doesn't necessarily, or even usually, mean that X doesn't happen today, just that it is no longer such a big deal. You could call the late 19th century railway bubble a fad of sorts; railways obviously still exist and are very important, but speculative railway building is no longer a double-digit percentage of the global economy, say.

Like, in ten years it is likely that LLMs will be used for some things. It is less likely that people will be talking about spending literally trillions of dollars on LLM arms races, however.

The fad part is that LLMs don’t work. People keep mistaking simplistic demos for practical applications.

They don’t provide good summaries. They don’t help non-experts simulate expert work. They don’t provide reliable search results.

If someone promoted a calculator that gets 90% of the digits correct in its answers and 90% of the time those digits were in the right order, that would be a useless calculator.

I have not spoken with any AI fanboy who can substantiate his claims about the usefulness of LLMs. I have used Deepseek twice, now, and both times its results were unusable for engineering purposes but would have impressed tipsy people at a party.

I have heard credible reports that Co-pilot is helpful. And I routinely use ChatGPT for prototyping tools— but that makes it one more interesting tool, not a revolution.

I can see this being bad for OpenAI, Microsoft, and Anthropic, for sure, or at least their current valuations, but I just don't understand why people think this is popping the AI bubble.

This makes a bad assumption that the state of the art is not progressing, that we have exhausted all ideas about how to make models better, that the only way to make models better is to throw more GPUs to it, that there won't be a significant market for actually running the LLMs, and most importantly, that we have somehow exhausted all of the applications for AI.

Even if Deepseek / Deepseek-equivalent models were the limit of what we can do with models, and AI and Anthropic completely busted, we still have ten years at least of developing the most effective applications of them and combining them with other tools to improve productivity.

This feels like some people are too high on it, and some people are overreacting.

https://blogs.windows.com/windowsdeveloper/2025/01/29/runnin...

Microsoft added DeepSeek to Azure and released a NPU optimized version for offline use

I feel most people are rooting for it to be a fad than actually believing that it is. We've barely started to hook these things up on our business processes and companies are investing heavily right now in doing so. Give it a few years.

As for stock prices, no comments from me.

What business process do you want to hook to?

None. But companies are doing regardless of what we think. If it works, it works.

Verification of customer documents, monitoring and interpreting regulatory changes, matching resumes to open positions, support ticket triage, better automated customer support, ...

> If it works, it works.

For very small values of "works".

> If it works, it works.

This is the critical bit, though.

> Verification of customer documents

There are quite serious consequences for getting this wrong, especially systematically (making a few mistakes, you may get away with, but you don't want to be the bank of choice for the North Korean secret service, say), so, if it turns out not to work well, "but the magic robot said so" will not be seen as an excuse, and there will be fines and maybe prison sentences.

> monitoring and interpreting regulatory changes

As above. "We're too cheap to ask a lawyer so we had a magic robot tell us if it was okay" _absolutely_ will not fly.

> matching resumes to open positions

"Half of the people we've hired for the last two years are totally useless, how are we screening these resumes again?"

The customer support stuff is the only thing you've listed where a certain amount of failure is tolerable for many businesses (most customer support is already quite bad and often already uses questionable automation; if you have _good_ customer support you should be more cautious, but most companies don't). But using current LLMs for the important stuff? Nah, that's not going to go well.

I think you're misreading the situation by treating it as 'all or nothing'.

If I'm not mistaken, it's common for a legal team to analyze documents in an hierarchical fashion. A less senior person reads and highlights part of a document, and later a more senior one carefully analyze those points. The 'easy' part of the job that is the target for automation.

As for the verification of customer documents, oh boy, I've seem some stuff first hand and I can tell you that they are definitely not that careful.

What dont you?

1. Report generation

2. Knowledge search

3. Email / Message drafting and auto response

4. Support

5. Small bug fixes

6. Minor design changes

7. Wireframe first drafts

8. Research assistant

9. Executive assistant

10. Outreach

11. User interviews

12. Personalized ads

13. Voice to code

I mean these arent even the theoretical stuff. These (and more) are all happening right now.

1. yes, with reservations

2. yes! but I'd like specific things that a lot of models don't do well quite yet, like accurate, complete citing and contextualizing with quotes across sources, etc.

3. Eh, pretty unexcited but I can see some limited utility.

4. 100% no.

5. Skeptical but there's definitely some good use cases here: smaller bug fixes and linting/vuln/anit-pattern scanning could be good applications, but I'm not sure if its better than the existing non-ai tools.

6. Maybe? If you mean code, existing refactor tools are pretty darn good and are deterministic, they do the thing or they don't. If the tool can't make strong guarantees about that, I'm not interested.

7. Yeah I think that's pretty good. Related: I think there's an application for detecting consistency issues between products/pages/etc that should have a unified design and UX.

8. That's basically #2?

9. I think execs will dump the tool the first time it makes the wrong decision, and I think people the tool reaches out to on behalf of the exec won't take those communications as seriously.

10. No. That's spam, full-stop.

11. No. User's aren't going to take it seriously and aren't going to respond in the same way. Will also be a reputational risk. "Would you mind taking a survey about your experience with our product?" followed by a chat conversation with an AI is going to be dragged online.

12. Absolutely no one wants this.

13. You mean as an accessibility feature? Because I could definitely see that.

I'll bring up that the original question was what ways would you want AI integrated into products. Not "what ways are people integrating AI in a desperate chance to hop on the boat" (to borrow the metaphor from the article).

>> 12. Absolutely no one wants this.

And yet, absolutely everyone will get this.

I agree, but I think there's good reason to be skeptical of the effectiveness of current targeted online ads (in terms of actually getting people to buy products and services that are advertised), and I don't see how AI could make that better.

That being said, so far the people who buy online ads don't seem to actually care if they're effective or a worthwhile investment, so that may not actually matter.

> but I'd like specific things that a lot of models don't do well quite yet, like accurate, complete citing and contextualizing with quotes across sources, etc.

Er, yeah, good luck with that. You're not going to get than from an LLM.

Some of them do citation okay-ish (which isn't sufficient, but suggests some ability).

But I more or less agree with your skepticism. If you looked over my comments on HN, you'd probably think I was a die-hard AI skeptic, but I'm really not, I just think the vast majority of people's understanding of and expectations of AI are disconnected from reality. I think people are viewing AI (and products, tools, innovations built with/around AI) as they hope it will be in 5 years, and not as it is right now.

1. Generate reports nobody will read, or will feed to another LLM to pretend they read.

2. Sorta, with people taking both the accurate parts and the hallucinations as truth

3. See 1

4. Yet another regression from talking to actual humans for support

5. And small bug introductions

6. Code design (ie architecture)? Hopefully not

7. For things their creator never really loved and nobody wants

8. See 2

10. God, please no!

11. See 10

12. See 10 and 11

13. Alright

>>God, please no!

This is the part which will be implemented for sure as soon as possible and exploited as much as possible.

This is the thing. Many of these Ive tried successfully. You need human oversight but as of today Ive been able to consistently:

1. Debug k8s and implement niche fixes

2. Write code faster

3. Fill out a slack update template from just talking to an LLM

4. Made a system for my partner to generate the first draft of her standardized report (required for assessment)

5. Draft email responses to clients, customers, strata council, lawyers.

6. Turn docs into mermaid diagrams

7. POC new features

8. Write api integration code from a docs url

The obvious caveat is you do need to still act as QA and know what your doing.

But the general improvements, productivity gains, and new possibilities are all real. I think your (hard earned) engineer cynicism is now getting in your way.

None of that is really contributing to revenue, and AI isn’t cheap to run. How is any of that worth it?

AI for the majority of these tasks is pretty damn cheap to run

Well i don't know the future, if it was a fad, this is how i would expect it to work out. Businesses start investing heavily in integrating into processes. Then the truth comes out - either it works, and that is great, or the businesses get burned. If they get burned then sentiment becomes negative and it becomes a past fad.

You're right its too early to know, but i think we are right on the cusp of finding out.

I think there is both fad and substance to AI. Some investments in AI are bad ideas that won't work out and so are a waste. However AI is still useful some some activities and those investments will change the business. In short like every other time some part of AI became the fad and went mainstream.

Part of the problem is the hopelessly broad umbrella term "AI".

Sure, ChatGPT is AI. StableDiffusion is AI. But...

A computer-controlled Zergling in StarCraft: Brood War is AI.

The OCR tools in your phone are AI.

The imminent collision detection in your car is AI.

Skynet is a (fictional) AI.

Etc.

This is a confusion being willfully fostered by companies like OpenAI (particularly the conflation of LLMs like ChatGPT with fictional "strong" AIs, or AGIs), and cavalierly ignored by the media, both tech-focused and mainstream media alike.

Some of these subsets of AI are 100% successful products already out there today—or even 20 years ago! Some have interesting possibilities. Some are most likely fads. But as long as we're talking about them all with the same label, and not drawing explicit distinctions in discussions like this, we're going to continue to have people arguing past each other because one of them is talking about OCR and the other is talking about Skynet.

Reasons for fad status IMHO, things companies are wanting to charge for that I'm willing to pay for: none. Features added to existing products that have made the product worse: actually more than zero.

Maybe this tech is great for people who aren't smart to make decisions and research for them. Maybe it's great for companies that plan to massively decimate labor costs. Maybe it's ...

The jury may still out as to what if any actual long lasting changes this tech is having to change our world for the better, but what I hope is abundantly clear by now is the stratospheric valuations people have slapped on this industry are unlikely to be justified any time soon.

What's happening right now is a fad, even if some aspects of it end up making long term impacts. The earlier AI fads of the 60s and 70s, the expert systems of the 80s, and deep learning of the early 2000s died off but left a few bits of useful stuff. Similar to much of the CASE and UML hype, we didn't get everything promised but we still got some useful IDE features and a recognizable way to express code diagrammatically as a documentation tool.

>We've barely started to hook these things up on our business processes and companies are investing heavily right now in doing so.

They said the same thing for ML, VR, and lots of other things, and they were fads...

But they didn't manage to shift the priority of the entire microchip industry just to support it...

I take this as a sign that AI is here to stay.

ML is used extensively in business and I don’t see any indication that it’s fading. I don’t think I saw any real effort to put VR into business processes. Hype around AR has popped up and fizzled quickly a few times, but still no real push.

>ML is used extensively in business

For useless things that were tacked on during the hype because everything "needed to have" VR

>and I don’t see any indication that it’s fading

The hype cycle and huge ML valuations and "this is the new huge industry" and job demand had faded since years

"Google, whats the difference between a Compressor and a Gate?"

> A gate swings outwards, a compressor is generally used to reduce the volume of air, generally used to provide air flow through hoses to pneumatic tools.

Something that is wrong 90% of the time is useless.

Right. The fundamental difference is that LLMs are a technical achievement that almost no one expected to come this fast. Neural networks were long considered a dead approach—not anymore. In 2015, most of us thought we’d be where we are today in NLP circa 2040.

The business fad will no doubt end in a fiery crash as people discover the hard way the limitations of LLMs, but the underlying achievement is still real. This is more like 2001. Lots of dot-coms died either because they were silly or tragically ahead of their time, but the Internet never went away. (Unfortunately, it does seem to be evolving into a worse version of itself due to platform decay, but that’s another topic entirely.)

> there is no monopolistic moat to guarantee profits, and no revenue stream to milk. Rather, like deploying billions of dollars on data farms to improve search, there is no revenue at all. Nobody's paying a single dollar for AI enhanced search.

Not only is nobody going to pay for "AI-enhanced search", people are actively trying to avoid it. This also applies to several other areas where AI slop is basically damaging any business it touches.

I'm really tired of the word "moat".

I'm bullish on AI long term but it still seems over sold currently.

So, you think Amara's law applies?

Yep. Nails it. Nobody wants to be late to the gold rush so we get this hype cycle but it's undercooked at the moment.

“We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.”

https://www.computer.org/publications/tech-news/trends/amara...

I can’t wait to see how this article ages.

Not defending or opposing the thesis of the essay, but this person's blog has (roughly) one post every day... it makes one wonder how much thought and editing goes into writing at that pace.

Most serious writing I've seen is released at a slower cadence. A large volume of content makes me equate its source with the 24/7 news cycle -- more is better, keep pressing the viewer/reader's buttons (outrage, moral indignation, etc.) to keep 'em coming back.

the internet is a fax machine - Paul Krugman

Nvidia got lucky - Intel

Is AI a fad? Who knows? However, I'm pretty sure we can build on top of this AI wave and use Nvidia's chips or any AI chips for other applications

What a strange take. It's like saying the indoor lighting fad is over now they're pulling oil out of Pennsylvania.

Sure, whaling may be in trouble, but that's just an input to the technology

There's a difference between "revolutionary new technology" and "guaranteed way to make excessive profits".

Can we please FFS stop adding AI to everything? Crikey.

More use of the work "crikey" is needed

:) At the rate we're using up names for AI-assistants that we bolt onto every darn thing, I'm sure we'll get down to a "CrikeyAI" offered by some B-tier SaaS company very shortly.

I guess https://www.krikey.ai/ is close enough :)

Another know-it-all calling AI a bubble. Lets revisit this, not in 10 years, but in 5. Things the article misses: "Bubbles" happen for more reasons than hype. There are moats, just not in the obvious or same old places. Funny thing is, an LLM can probably write a better TL;DR, that captures it better, not to mention a more insightful article.

Agreed. I consider myself an AI skeptic, but this reads like the first-draft ramblings of someone who's never heard of the Gartner Hype Cycle.

https://en.m.wikipedia.org/wiki/Gartner_hype_cycle

Yah. It might write a better TL;DR, but it will always be a tool engineers use.

AI is a bubble if you consider the end goal of AI to be a 1:1 replacement for humans.

AI is not a bubble if consider the more likely end goal of AI being just another tool. A really good tool but just a tool real indulgence will use. We have seen this many times in the past with tech. While some people did loose jobs over time it evened out and eventually made more jobs. Just look at how many jobs today exist simply because computers exist, a hardware device that was to then replace the modern worker.

It will be as much a ‘tool’ as human intelligence is a tool.

This article seems to completely misread what the DeepSeek release actually represents. The author uses it as evidence of the AI bubble bursting, but DeepSeek demonstrates exactly the opposite: AI becoming more efficient and practical, not less valuable.

A small company just released a model that matches leading proprietary systems at 1/30th the running costs and fraction of the training costs ($6M vs $60M+). Yes, this challenges the "moat" of big tech companies, but that's not a bubble bursting - it's a technology becoming more accessible and practical. The author conflates "big tech losing its monopoly" with "AI being a fad."

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