If datacenter production sees significant headwind then it’s very bad for ANT. Securing compute is listed as one of their major risks to the SEC.
It seems like much of the tulip mania has become the stuff of legend.
From what I have read, it seems closer to baseball card trading than AI or dot com.
You can find baseball and pokemon cards trading for thousands of dollars right now on ebay.
This would seem insane to a culture just valueing the cards as pieces of cardboard.
It would be like hundred of years from now people viewing our society as wanting to buy Mickey Mantle cards instead of houses. It is a very small number of people involved with this. It is not a widespread movement to hold your retirement money in Mickey Mantle cards.
In an interesting way it shows even better though he we are susceptible to these type of mythologies. People don't even question the absurd givens of the narratives of the tulip mania. We just repeat these narratives to each other as if they are historic fact.
Your Railroads and the Internet parallels are so very apt. We certainly are very productive even in 2026 off those but almost no one remembers how many large behemoths were wiped out in the U.S. because of them.
Yes, it does seem to me that the AI of 2026 will be like the Railroads and the Internet - fundamentally transformative technology that changes how things are done into the future but there will be blood in the short term, which is why I would not be participating in the IPOs directly
The unemployment figures is mostly a political projection, like CPI.
In 2026 everything fron sewage to chickens to oil to education is now tied to AI. The entire economy is now, for good or bad tied to AI.
What most people either didn't notice or understand is that the U.S. was entering a recession and would have been in one if it hadn't been AI
Is it maybe more accurate to say that the US is in a recession, outside of the industries that are fueling AI?
Life's pretty recession-y for everyone else imo
Tied how? How strong is the corellation between these? I'd say very weak, other than everything having to compete with AI for resources.
The same is true of any technology tied to powerful personal computers or the internet - people built and designed beatiful houses, great cars, airliners, wrote books and ran restaurants without computers or the internet.
It has even become a meme how putting smart features and lcd screens into things like washing machines have made them strictly worse.
Even if it takes them 3-6 Month to open up a new field, its going to happen steadily now and were it started, the AI investments will be recuped by layoffs.
Artists -> Translators -> Journalists -> Software engineers -> mathematicians -> lawyers -> Accountants -> ...
Its already disruptive
At best some layers will work faster, but automating their work or people not requiring them? Usually when you contact one there's a lot at stake. Same with accountants.
Software engineering and translators is a different story. Most of the times if something goes wrong nobody cares.
The avg Lawyers might do more complex but again a lot of 'normal' things. Day to day things the next SaaS AI company wants to 'fix' and they might also accept basic liability.
Then you have the 'tv lawyers' the ones who do non normal things, they have A LOT of apprentices and young lawyers going through papers, research etc.
They will for sure have the expert as a human, but that person will need a lot less support.
You shouldn't need an accountant today already. If you have an digital order and a digital bill and a digital signed delivery note, it can be booked automatically. AI will find risks, summarize them etc.
AI is already helping here closing the gap with better OCR etc.
I really think, that when big companies introduce the agentic layer to their products, there will be less people needed and if you need people, they get less paid because they will need to understand less.
We're not paid just for what we do, we get paid because we accept liability for our mistakes. Professional liability insurance is a large part of why licensed professionals charge so much money for their services.
If a lawyer needs less people preparing everything, the industry will also shrink.
Lawyer as SaaS companies will just introduce some insurance and will clean up from the bottom until AI can do all of it.
We will see if we will have one expert ai lawyer per topic or type of lawsuite or if they will get generic good enough that it will not matter.
Even people defending or suing by (successful) themselves became a thing already last year.
Lawyers already need less people to prepare things. That revolution happened several decades ago when computers and word processors became normal.
Lawyers as SaaS companies has been a thing for 2 decades, and remains a tiny niche of the market because it's not the kind of lawyering most clients need. A lawyer SaaS is basically just the equivalent of an underpaid, underresourced public defender and will yield about the same quality of work and results.
We will see if we will have one expert ai lawyer per topic or type of lawsuite or if they will get generic good enough that it will not matter
LLMs are only as good as their training set. In order to be a topic expert, they'd have to be retrained at least every week to account for changes in statutory law and case law. It would be trivial for a human lawyer to outmanuever an outdated LLM when the laws changed...and in the real world this happens all the time between human lawyers when one side hasn't done its research on changes to the applicable laws.
Even people defending or suing by (successful) themselves became a thing already last year.
People successfully defending or suing without a lawyer has been a thing for over a century. It's fewer than 0.1% of legal proceedings. Most people don't handle their own legal cases for the same reason most people hire a plumber, or electrician, or order DoorDash instead of cooking their own dinner. Also...almost all of the people using LLMs in the past 2 years lost their cases, because their LLMs hallucinated laws or facts and they didn't know enough to catch it. Even lawyers dumb enough to use LLMs to draft their documents got caught because they were too lazy to review the output. Courts were willing to forgive those mistakes a few years ago before it was common knowledge that LLMs make things up, but now they're no longer willing to give the benefit of the doubt.
Whatever we had before on full text search, LLMs are a lot better. And i'm not just feeling this, i read plenty of articles about exactly this.
A LLM doesn't need to be retrained every week, it needs to have a base corpus of the field and strategies on how to find current text. They will load it into context and argue.
Then another LLM will take this and formats it for the court.
We will see but i think you overestimate how much automatisation and co current lawyers have and use.
Mine already has been.
You really don't know what we have here, do you? Let me illustrate what you can do with Qwen3.8@q4 locally with about 2TB of storage and a small cloud server to host a website.
Locally you scrap and process every elected official and start tracking what they're doing, what they're promising, and what they're not doing. You build embeddings and make them all geotagged in a database and just start with the largest cities.
The website provides public facing search. User drops in a zipcode, city, state, specific rep's name and gets what's relevant to their results.
All of this is to say, we literally have the technology to hold accountable our representatives and then show everyone objectively who's doing good and bad for their constituents.
It wont take much to solve the problem of bad representation after these last two years. The hard part is literally making sure people going to a polling station can immediately know who the heck the 10 - 30 different people are they're voting for, and track them efficiently within their own specific parameters.
Regulation is coming for more than just the inference providers.
That said, I'm also somewhat skeptical that there is even much room for growth left unless somebody finds a new, important, and token-hungry use case.
If Anthropic manages to start producing new pharmaceuticals with their internal models or something that thesis could make sense but not if 90% of their revenue comes from enterprise API sales which necessarily have to be accessible to the public.
[1] I don't, I think China is primarily limited by compute and will be at least for the next couple of years, probably longer.
They are also (like other models we know about) experimenting with the architecture. [2]
[1] https://arxiv.org/abs/2501.12948
[2] https://sebastianraschka.com/blog/2026/kimi-k3-architecture-...
I am highly confident that Anthropic will 100X their token sales volume. I am also confident that token prices will continue dropping through the floor due to competition. So to 100X their revenue they will have to 1000X or 10000X their token sales. That seems a stretch.
However this effect could just serve to make those who own the compute ridiculous money, while crushing those who need to lease compute.
I now do like 90% of my routine software dev/sysadmin stuff with Luna on Medium by default, some proportion of which could itself be offloaded to a small Qwen model.
Indeed, 2026 was basically that: the LLM providers increased token costs and companies cut LLM spend to the bone. That is why Anthropic's IPO is struggling and MS and Google have failed to meet AI-related revenue targets for 3 straight years.
Using the models for data processing (a lot of input/output tokens) and for role-playing will use more tokens and thus cost more. However, those use cases are not common when comparing the percentage of the user base.
The money maker is agentic coding where people get the models to perform long-running tasks. This is why Open AI and Anthropic have been pushing these hard. However, there is pushback on this, especially among businesses looking for value for money/cut costs [2] like Apple [3], [4].
[1] https://aws.amazon.com/bedrock/pricing/
[2] https://www.ibm.com/think/insights/tokenmaxxing-dead-long-li...
[3] https://www.bloomberg.com/news/articles/2026-11-04/apple-cut...
[4] https://9to5mac.com/2026/11/04/report-apple-reduces-employee...
Something like 99% of the people who have interacted with an LLM chatbot lose interest after a month.
100% of people who have to interact with an LLM chatbot taking the place of a human customer sales or service agent jump to a competitor.
Outside of the tech bubble, the LLM hype is already over.
This is not remotely true. It literally couldn't be true, considering Open AI have 1.2 Billion weekly active ChatGPT users.
The issue is, at some point for AI to grow more in usage it will need to decrease in cost.
We are already seeing this with some companies reducing AI usage or moving to open models due to high costs.
AI will surely boom there's no doubt on that, but it won't necessarily produce the revenue that Anthropic investors hope for.
many companies are now using both, it's only been a few months since it really picked up
Do we know if current pricing is sustainable?
I'm cautiously bullish on AI in general, and I wouldn't touch an AI stock with a 100m pole, because there are way too many uncautiously bullish investors in the market.
We have, for example, no idea what the long-term implications of AI effectively training on its own output is. Recursive self-improvement is not a law of nature. If anything, the opposite: the overwhelming majority of species are extinct. Evolution is as much a story of dead ends as survival of the fittest. So betting on any one narrow path of AI development to survive seems highly suspect.
I care about whether it's going to grow in value/price from that point on or not.
And it's hard for me to imagine it growing even higher than the current valuation.
What's you're point there? People laid off because of AI will immediately shift into AI-centric small business, and start hyping AI?
That doesn't make sense to me. People being laid off are likely to be bitter about the cause of their layoff, and even if they all start their own businesses (which is unlikely), most of those will fail, leading to more bitterness.
If these giant monopolies couldn't squeeze revenue out of these employees using AI, why would we think that they will be able to do it by themselves?
The vast majority of them if they want to be entrepreneurs will be better off starting a new lawn care business, or a bicycle repair shop. They can use AI to help them run that. Getting paid $175K per to write ruby CRUD was a moment that is over, hope you didn't spend all your salary on toys and rent.
> If these giant monopolies couldn't squeeze revenue out of these employees using AI, why would we think that they will be able to do it by themselves?
You assume they are competent. In my experience a monopoly's profit margins makes it very easy for incompetence to flourish.
> The vast majority of them if they want to be entrepreneurs will be better off starting a new lawn care business, or a bicycle repair shop.
Really? Experience engineers are better off starting a lawn care business or a bicycle repair shop? I do not believe this for a second. There are many areas engineering skills can be used in.
Agree. these valuations will hold. Pundits made the same prediction after Facebook's IPO in 2012 that everything would soon crash. here we are...
Wouldn't they tell us before IPO? Unless it's not that promising...
> AI is also gaining a much wider audience and acceptance. It's likely over the next few years, AI demand will grow, not stagnate.
That does not mean the current players profit from this and the infrastructure investments were necessary at that scale. There is competition, there seem to be enormous efficiency gains and it's questionable that demand for even bigger models is large enough.
I find myself wondering what "less sympathetic" and "significant headwinds" mean in the minds of people in the upper echelons of the AI industry.
Rules for training frontier models? Requirements for renewables to power data centers? Actually having to pay people for the content you train on? Data center rules that would end them going behind the backs of local electorates to get governments to approve the building of data centers?
Arguments are being made that, with the midterms over and 2028 just around the corner, a less sympathetic administration would create significant headwinds, adjusting the valuation of Anthropic and OpenAI down.
What we don't know are what Anthropic and OpenAI have planned and in the works.
A large population of extremely skilled and talented people are being laid off by their employers to make space for AI investments. It's likely a significant number of them will start their own business with AI.
AI is also gaining a much wider audience and acceptance. It's likely over the next few years, AI demand will grow, not stagnate.
That said, IPOs are usually liquidity events for LPs and for something really disruptive and unpredictable as AI, I won't be engaging in their IPOs. Only time will tell if this was a good decision, or bad.