It’s capital intensive, doesn’t easily scale without linear costs, is basically a commodity, seemingly has negative cashflow forever, and customers aren’t willing to pay what it actually costs. It’s basically the worst tech business model and yet somehow VCs caught a spell and threw every available penny into the fire.
Watching this all unravel is going to be interesting.
That's a big "if".
Even for coding these things are not ready to take away real decision making and it's not clear they will ever get there.
What are some VC funded companies that come to mind that do not fit this profile?
First all the money went into the browsers like Mosaic then Netscape. Then the browser got commoditized, but that didn't mean the value went away. A better business model came along, and lo and behold, the Chrome browser went open source. The browser became free, and the scale of adoption meant a lot of value could still be extracted.
A free browser product didn't mean the value of Chrome went away. Rather the opposite. It's a gateway for buying stuff through Google search.
Marc Andressen was the "Mosaic browser" guy before he was the investor guy, so the folks in the early days did not lose all their money. Quite the opposite. Many are now billionaire oligarchs.
LLMs will likely go the way of the browser... commoditized, then free, then appropriated to shove ads into your face. I predict a lot of safety and interpretability research will be leveraged to one day push ads, just like "Golden Gate Claude" could be manipulated to think of itself as the Golden Gate Bridge.
Depends on how old you are. This analogy makes sense if you were a teenager in the mid 90s. I am older than that so to me, this same pattern had happened when PCs were becoming mainstream. That was 40 years ago so memory of it might be faint or lost. Also VCs weren't really a huge deal back then either but the fundamentals were the same.
We are all overreacting over a nascent industry, upset that it's increasing prices for everyone and pushing humans out of jobs. This is understandable but the conclusion is wrong. We are letting emotions drive decisions.
On the datacenter side the closest analogy is the fiber buildout of the .com bust. While that fiber ended up being useful decades later it’s unclear what value a bunch of half-completed datacenters will bring if their owners go bust.
And yes, I'm talking about the landslide of NSFW stuff that had to be culled to turn it into an acquisition-friendly target.
Don't have data... I'm guessing.
Am I understanding this correctly as an idiomatic reference to an enchantment or curse? If so, I'd be interested to know where the expression "catching a spell" comes from.
(I'm really interested in the phrasing and I hope you don't take this the wrong way. For me personally, the metaphorical idea that someone with inexplicable behavior has been bewitched is a normal thing to express, but I'd be more likely to say they "fell under" a spell than that they "caught" it. I believe in Mandarin the usual idiom uses 中 "be struck [by a projectile]".)
Perhaps it's more that "consumer startups that want to become a unicorn" are hard.
So did they, they just left it for the conclusion
> It’s not clear whether AI giants like OpenAI and Anthropic, or the AI industry in general have gotten too big or too important to fail, but Character’s bankruptcy shows that the runway for second-tier players is over.
But the current title definitely baits more people into the article
It is difficult to make a company in a fast-growing sector work when all of your technical staff and leadership are working for Google, and your remaining staff can see the writing on the wall and are checking out.
But I think that it is dubious whether that is a good proxy for “the economics of AI” broadly.
Which I am not saying are good—I just don't think Character.ai has much probative value on the broad question on way or the other.
If companies that are basing X% of their future earnings on new AI based products don't pay up a bunch of those data centers are not gonna be re-leased. It's gonna be a problem. An seasoned analyst can take educated guesses as to what that X is: investments made in pure chips by Nvidia/AmD vs how much capital investment— not VC — Faang(i.e ones that are just using the chips for building software) are putting into them. The difference between investment devoted to pure DC build out minus what they are spending is a good proxy to understand the overinvestment.
Not worth much. Hasn’t Burry called a number of bubbles since the great financial crisis, none of which have come to pass? Seems like a broken clock to me (he’ll still be right twice a day.)
The trade surplus of Taiwan, China, Korea, Japan etc including chips has been sky rocketing. This is like Bill of materials(BoM) and it's at least 3-4 trillion in chips for AI alone, summing it up over the past 3 years.
Elsewhere on this page I argued how this reminds me of the PC wars of the mid 90s.
We are all overreacting over a nascent industry, upset that it's increasing prices for everyone and pushing humans out of jobs. This is understandable but the conclusion is wrong. We are letting emotions drive decisions.
Kind of speculative, and a very bold speculation at that. I don’t have anything else to say, but one company that was obviously mismanaged doesn’t really get my attention.
A better report would have been how the "Economics of AI Wrappers Don't Work".
Character.ai was likely paying frontier LLM API rates (which are known to be ridiculously expensive at 90% margins) and then had their own R&D plus lawsuit overhead. Most people can use a $20 Claude or ChatGPT sub to build a completely custom Character.ai. Once Character.ai employed MBA Strategy #3 (aka enshittification), churn killed the company (as it should)
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What doesn't work is attempting to build an AI Wrapper. Those companies had a good run 2022-2025, but with how good both open weights and cheap frontier models have become, AI Wrappers can no longer demand fat margins.
I'm sure people will now argue what exactly AI Wrappers are in the comments below, but for now I'm more interested in understanding what the HN hivemind's take is on the topic as a whole.
[0]: https://blog.character.ai/optimizing-ai-inference-at-charact...
Is that known?
Known to someone who doesn't trade on a heavily AI indexed market (in 2026 all companies, even Tyson or Chevron is an AI play but not as heavily AI indexed as those say 2 degrees away from Anthropic or OpenAI) - likely not.
So if the point you're making is your typical barber doesn't know about this, I am guilty.
If you want to do your own research into this, there are a lot of private reports and publications on the topic.
If you don't want to spend any money, there is even a recent public analysis using a 92% API gross margin.
Anthropic's October 5, 2026 analysis also points to a 92% API gross margin that SemiAnalysis based their report on.
OpenAI reported paid-user compute margin reached 70% in October 2025 - a year ago.
DeepSeek has similar reports. The frontier labs are comfortably in the fat inference margin territory.
Do you have information to the contrary?
Character.ai has initiated voluntary Chapter 11 bankruptcy
A simple grammatical mistakes like this should have been caught on review/edit phase.
Either that or writers are relying too much on AI.
(Edit: fixed a grammatical mistake. See?)
"eventually" is doing a lot of work here. Like sure, yes, eventually. But that could be later this year or it could be 50 years from now. We don't know the answer to that. Assuming it's going to happen in a timeline short enough to matter for this conversation isn't worthwhile. It's like saying eventually we'll have cold fusion, so why is everyone skeptical?
Durable storage + sufficiently performant local LLM hardware + appropriate models [last two are wildcards and hard to predict the forward curve imho] = this experience locally. Remember when mobile video hardware acceleration wasn't good? And now, it is very good.
Specifically with regard to AI, the consumers aren't even aware of the "grocery store" either. They're being served from a silver platter. What matters most is the quality of results, not the methods. You can disagree all you want for whatever (good and compelling) reasons, but it doesn't change the fact that many people do not even buy groceries now and doordash everything.
If it turns out that you can put a mobile kitchen in their backyard that cooks up everything without them realizing, it makes no difference to the consumer.
Realistically, yes there's a finite amount of training data, hence the plateau in capability we've seen for at least a year. I think we're likely to see a consolidation and compression of all of this into local models. The economics don't support the expected growth of the current arrangement either. There simply isn't that much distinct data out there as initially assumed.
No-one's going to want to talk to a 7B model for more than 3 minutes
Significant investment is being spent on producing hardware that is more suitable for running mid-sized (100B-ish) models. Starting with flash chips with wide enough interfaces to provide the weights (NAND flash can be ~5x more energy-efficient for reads than DRAM), and going into more exotic solutions like compute-in-memory.
My assumptions are that inference costs continue to decline (and/or the difficulty level of routing requests declines) and hardware acceleration continues into the future. These assumptions might be wrong, but that is the idea the thesis is based around.
(have built workflows and harnesses that currently target LLM provider APIs under the assumption I'll be able to point "locally-ish" eventually, my career had a strong focus on distributed computing about 10-15 years ago, think managing data and work clusters for LHC detector data processing, and I grew up on hobbyist distributed.net and BOINC projects, all the lenses I see this problem through; queues, jobs, sync vs async performance optimization, etc; recovered sysadmin|infra/devops engineer a lifetime ago)
Holding value is contingent on whether a company can manipulate the regulators to create a lasting monopoly or expand the market dramatically. My suspicion is that at some point the investors will gravitate towards the latter by turning LLMs into more active participants in the economy
It's interesting to read this data. A lot of the "common wisdom" was that subscriptions were heavily subsidized, but in actuality they're just not quite as outrageously profitable.
> Character.ai's Bankruptcy Documents Show the Economics of AI Don't Work
My comments:
"I don't agree, here is why."
This specific model doesn't appear to be economically sustainable and/or commercially viable. That is a reasonable statement and assertion, based on the evidence presented. Does not mean the economics of AI in general do not work. That's a very broad statement! Nuance, words mean things.
Therefore, we should ask ourselves and discuss, "Under what circumstances could|would the economics work? Are there any? Or are there none?" We are discussing the problem space! Be more curious.
The type of investment called out by the start of this thread sounds exactly like the type of speculative "we don't even know if this is technically possible, much less if there's even a market for it" that Silicon Valley was founded on.
yes and that's the entire point of Silicon Valley.
It's also why you can't have another Silicon Valley in Europe or Asia where almost no one is willing to suspend belief and reality like this.
I've been invited to meetings and conferences from places in Sweden, Amsterdam to Thailand, and investors in all of those places balk at the kind of mindset you need to invest in startups. They are more comfortable with loans than investing. They lose confidence faster or require a higher ROI than would make sense. SV is really special and the reason why the best talent from all the over the world gather there.
VCs become rich not because they are smarter and better than Warren Buffet but because they are willing to place bets that Buffet would never take on.
I don't think this model is inherently bad. If an idea is good, is it not saner to get a loan instead of dilluting ownership?
This, of course, is predicated that parties are operating in good faith. SV has been for quite a while a sort of grift. Money is dumped on whatever can reach the exit event, no matter how unhealthy or senseless the business is in general.
I call it a grift even if it indirectly benefitted me. I avoid startups like the plague (both as labor and as user), but this grift did soak up engineers and raised salaries across the board. Alas, sometimes it's important to call a spade a spade.
Uber and Lyft literally "sell rides for a fraction of what all this costs them" upto early 2025. I don't know how old you are but I started to use them back in 2012. I was one of their early users when you could sit next to the driver. From then upto 2025, Uber and Lyft used to literally cost me less than what just gas would cost me to drive the same distance.
This is true too: "It would then claim it’s worth 100x the valuation of the taxi service" - there was literally a website (might be up even now) that literally made this argument and why Uber was doomed to fail anytime now.
Your first two points are also true for Uber Lease - a product that was rolled out for drivers - they could choose to lease specific models and Uber in turn signed agreements with specific automakers. An exact parallel for these points, esp. the circular financing is hard to make because they are not even the same product but I imagine one would agree, on the whole, we are in the conception stage of LLMs and this industry is brand new and figuring things out.
This same pattern had happened when PCs were becoming mainstream. That was 40 years ago so memory of it might be faint or lost. Also VCs weren't really a huge deal back then either but the fundamentals were the same.
We are all overreacting over a nascent industry, upset that it's increasing prices for everyone and pushing humans out of jobs. This is understandable but the conclusion is wrong. We are letting emotions drive decisions.
> It’s capital intensive, doesn’t easily scale without linear costs, is basically a commodity, and customers aren’t willing to pay what it actually costs
As I said, it may happen within a year. But maybe life takes a different direction. The person to whom I responded said "eventually", and yes, "eventually" it will happen. but that's not a useful time horizon to plan around because it could be a lot of things.
The hard part is refining the UX, imho, while supporting pluggable LLM support on the backend. If the future never arrives of cheap on device inference, still works if you pay as you go and for those users willing to pay for their usage directly through their cloud inference provider of choice or through the app (unless the future is "cloud inference is unsustainable at consumer price points and on prem inference never arrives"). Just my two cents.
This is fundamentally what Apple did with new Siri, powered by Gemini today. They will eventually flip the switch to an in house solution. Agree the wildcard that still remains is "can this be done on devices eventually?"
https://www.apple.com/newsroom/2026/09/siri-ai-a-profoundly-...
https://www.macrumors.com/2025/11/05/apple-siri-google-gemin...
> Apple weighed using its own AI models for the LLM version of Siri , and also tested options from OpenAI and Anthropic, but it decided to go with Gemini after deciding Anthropic's fees were too high. Apple already has a partnership with Google for search results, with Google paying Apple around $20 billion per year to be the default search engine option on Apple devices.
> Though Apple is planning to rely on Google AI for now, it plans to continue working on its own models and will transition to an in-house solution when its LLMs are capable enough. Apple is already working on a 1 trillion parameter cloud-based model that could be ready as soon as 2026. Apple is unlikely to publicize its arrangement with Google while it develops in-house models.
I would say you are either very young or unfamiliar with Uber as a business. Uber was and continues to be extremely capital intensive and you need to qualify your "while". Uber burned cash for 14 years.
The one argument I can agree with is Uber didn't burn as much cash as Anthropic or OpenAI does but they are very different businesses with very different impact on humanity.