I think the tech would continue to exist since there is already a market for it and I do believe it will increase productivity. I just get concerned about the concentration of value (for example, 30ish percent of the S&P 500s value is tied to 7 companies) and the shear size of the capex going on (conservatively, hundreds of billions). This increases the risk significantly. Even a normal correction (20% decline) of the stock prices of those 7, will wipe out trillions of dollars in market value and hit everyone, because everyone is essentially invested in it already and the repercussions of that could be brutal (deep recession).

Additionally, new tech could make all the CAPEX in data centers or businesses useless in the fairly near future, but this can be hard to predict (black swan). I think of quantum computers or advancements like DeepSeek's LLM reinforcement learning style, but it will likely be something that isn't in anyone's mind yet. There is also a vast opportunity cost to all this investment.

Those with wealth will be fine (why do you think they're building bunkers), everyone else will pay the price.

Im 2000 and 2008 dot com stocks and house prices stopped going up and fell, new investor money dried up and companies that relied on it or homeowners relying on new finance went bust. Tech advances and housing went on though.

I guess similarly in an AI bust some companies that rely on new investor money would go bust but things would go on.

Possibly AI hysteria heads into the plateau and people can finally start using AI as an amazing instrument rather than hyping about it. I think local models will become productive and cheaper to run but clearly I don't see people abandoning tech completely :)

The correct analogy is IMO the railway mania.

LLM research will go back to (government funded) research labs with government funded supercomputers. All AI investment will need to be written off.

Running the LLMs the research generated will of course be possible, e.g. via AWS bedrock or alternatives. Initially there will be no "flat rate" subscriptions like currently (similar to early Internet), those will come once the prices are low enough further out. Running the LLMs is a low-margin business not justifying high multiples.

Your assumption is wrong.

The LLM companies are profitable on the current gen models. Inference is profitable, rather than subsidized.

They are raising the biggest chunk of capital to buy data center compute that will come online ~2 years from now and be an order of magnitude larger.

The bear case for the labs is that they're Cisco, not Pets.com.

The gartner hype cycle is still as relevant now as it was during the dot com boom.

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

At the moment we’re at the peak of inflated expectations - we might stay there for a quite a while.

And to confuse things more, different people/groups go through the cycle at different times/pace.

I think even if AI can help developer productivity, there are a lot of other elements to software delivery and speeding up developers isn't necessarily going to speed up overall project delivery. A hard question to answer then is, what is the ROI if an organization is spending $10k/month on AI tools but project deliveries are mostly the same as before?

If the AI tool companies increase their rates 2x, 5x, 10x, is it worth it? They aren't going to lower prices.

Consumer AI tool usage isn't going to get a lot of adopters that will pay, people outside of a work environment will see it as a fun toy, much like social media and will be fine with being served ads and letting their loss of privacy be the cost.

The idea of the "AI bubble" popping is a collective delusion driven by a desire to see AI fail.

API access to AI models is not "subsidized", AI companies make a profit on inference. They are only losing money because they spend a lot on training the next generation of models.

It would make sense to claim the bubble pops if AI companies' valuations were pure speculation, but revenues are increasing exponentially for OpenAI/Anthropic. They are selling a product that people are buying at a price which is profitable on margin.

You can run smaller models locally that are pretty good at code gen.

Give it a try.

To get started: https://simonwillison.net/2024/Nov/12/qwen25-coder/

or https://simonwillison.net/2024/Dec/9/llama-33-70b/

AI is here to stay but not in the way big corporations dream of it. People will continue using AI but when the AI bubble pops, sooner than later, things will stabilize and adapt to real usage with a different business model.

As you correctly state, the cost of AI as a Service (AIaaS) will increase for end users, but this isn't necessarily a bad thing. It will allow the "real" users to continue having access to it and sieve out the ones who are just playing around. Prices for RAM, GPUs, SSDs will normalize a lot and more people will move towards local models.

Similarly to what happened with the dot-com bubble (I saw it happening), it doesn't mean that everything will disappear, but that it will change/adapt. All of us AI realists are currently being treated like technophobes when we say things like that ;-)

When the dot com bubble popped, the internet and websites did not go away. (I'm posting this to a website ending in .com) When the mortgage crisis happened, mortgages certainly didn't go away.

When the AI/LLM bubble pops, LLMs will still exist and be used. They just won't be hyped and pushed everywhere.

Clearly we all get on the next train, robotics or quantum.

I think the “AI bubble burst” framing is too simplistic. Bubbles don’t erase technology. They erase mispriced capital.

In 2000, internet stocks collapsed. The internet didn’t. What disappeared were business models built on fantasy unit economics. The same distinction matters here.

If AI funding compresses, here’s what likely happens:

• Speculative AI wrappers die fast • Subsidized API pricing rises toward real compute cost • Consolidation around a few model and infrastructure providers • Enterprises shift from experimentation to strict ROI enforcement

Right now a lot of AI usage is artificially cheap. Growth is being prioritized over margin. If that flips, casual usage drops. That doesn’t mean the tools disappear. It means only usage that creates measurable value survives.

If a tool suddenly costs $1,000 per month, most hobbyists won’t pay. But that’s irrelevant. The real question is whether it replaces or amplifies enough labor to justify the price.

If it saves a team $8,000 to $20,000 in monthly productivity or headcount cost, it survives. If it’s just a nice-to-have, it dies.

The bigger risk isn’t foundation models disappearing. It’s the application layer collapsing. A lot of AI startups exist purely because inference is subsidized. If API pricing normalizes, many evaporate overnight.

A post-bubble AI world probably looks less magical and less overfunded, but more disciplined. Fewer demos. More boring enterprise contracts. More focus on margins.

The internet after 2000 didn’t disappear. It matured and consolidated into utilities. AI likely follows the same path.

The real question isn’t whether AI vanishes. It’s who was relying on subsidies instead of economics.

Well, in 2000, I lived in Atlanta with 4 years of experience. 2000 wasn’t bad outside of Silicon Valley. If you were an enterprise developer targeting banks, insurance companies in a 2nd tier city, there was no affect. I had no problem finding a job.

I also was looking for a job in 2008 - again in Atlanta, I was 34 and again had no trouble finding your regular old dev job.

The AI bubble busting means absolutely nothing to me as far as career prospects. While every project I do now is related to AI working in cloud consulting + app dev, AI is just another tool in my tool belt.

“All of the spending” means nothing to Amazon, Google, Meta, and Microsoft.

They all are spending out of free cash flow and the hardware they are buying have a high failure rate and will be worthless in 3 years anyway. That just means they won’t replenish the hardware.

For the most part, I don’t deal with VC funded unprofitable companies - see how the bubble busting didn’t affect me.

If Claude Code becomes more expensive - which I doubt, compute gets cheaper over time and if the bubble does burst, that means compute gets cheaper because of excessive capacity.

On the other hand, I don’t pay for Claude Code myself, if the company thinks it does add value, the company I work for will up my monthly allowance from $1000 a month. If I were paying myself, I would just buy a computer that could run a local model.

I think this time is different.

Yeah, words that rarely age well. But AI isn’t like the web. It is more like the transistor. Or the internet (i.e. the protocol). Or like the first printing press.

Or even cells going multi-cellular, emergence of nerve cells, ganglions, brains.

It is a step change in the life of information. Non-substrate locked information, approaching an ability to improve itself isn’t a phase. it isn’t an adoption S-curve.

It is too fundamental, and has too many paths leading forward, by organizations and individuals, to ever be as simple as a bubble.

There may well be turbulence. But any computing power overhang created by over investment in one segment will get quickly absorbed by another segment. So there may be train wrecks, but the industry as a whole is going to thrive.

The problems I see are the unlimited downstream disruption of AI on everything else. Everything else. But for AI itself, the future is bright.

They are already increasing the pricing gradually.

I am not sure if AI is a complete bubble. To me, it is similar to Amazon, Uber etc in their early days when they were just burning cash but were providing something of great value to consumers. Ultimately, they figured out a way to get cash positive.

I think once the dust settles n next 2-5 years, few clear winners will remain who will figure out a way to become cash positive.

The entire US economy stands on AI hype. There is nothing else. Once it pops, USA will finally go into stagflation. The markets were rigged for way too long and the dollar has overstayed its welcome. Things will get bad, very bad. 2008 was nothing compared to what is coming. If you have any savings, DO NOT KEEP CASH. Go and buy precious metals to preserve whatever purchasing power you have. Or, if you want to catch the best ride of your life - go and buy shares of mining stocks. That's all, there is no reason to go more into details on HN as this is too broad of a topic on technical, financial and geopolitical side.

If such a bubble popping means that only coding agents survive and we can get away from talking baby videos, I'm all for it.

> if a tool like Claude Code (or any other LLM) suddenly cost $1,000 a month to reflect what it actually costs to run, would people keep paying for it out of pocket? Would their companies?

Probably. If you're not gaining at least $1000 a month in productivity now, then you're doing it wrong. I suspect however they may become an enterprise only offering, with limited availability to "normies"

> I’m especially curious to hear from anyone who lived through 2000 or 2008. Does a postbubble world mean we just abandon the tech entirely or is it a move toward expensive solutions?

In the late 90s you could get 100k a year for being able to spell HTML, and then the bubble pop pushed all the grifters back to whatever they were doing before. Those with real skill stuck around, even though it did suppress salaries for a while.

We have a bunch of compute built out that we can use to build actually good products and services again

:)

> Because if a tool like Claude Code (or any other LLM) suddenly cost $1,000 a month to reflect what it actually costs to run, would people keep paying for it out of pocket? Would their companies?

I wouldn't find it hard to personally justify $200/month or $300/month for the single best LLM tool available to me. Right now I have $100/month spread out over a few different tools and it's a bargain.

I think, the cost of running and training models is going to fall due to Moore's law and friends.

That also meas that the worth of today's computers declines rapidly and all the money invested in compute power with it.

Compute is basically what the AI VC money is spend on. That money will be gone in a few years due to the hardware being worthless.

On the other side running a model (locally) will become cheaper and cheaper to the point that Ai stuff becomes a everyday commodity running on cheapo devices everywhere.

Then there are optimizations too. Which lowers the cost.

So it's not going away and it's not going to be expensive for the consumer in the long run.

My 5 year old rtx 2027 runs models those output would have been state of the art a couple of years ago. In a few years something running on the level of today's top models might run under your desk if that progress goes on at this pace.