Anthropic may not secure investment-grade rating after IPO, Blackstone says
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Setting fire to cash to have the best model that anyone else can also do isn't a business model. They're never going to be able to stop setting fire to cash or they'll fall behind immediately. Their user base will not pay the bills. It's an obviously bad investment.
dog food is heavy, and a repeat purchase that often requires its own store and brings no joy. Its the ideal thing to get delivered to your house on a schedule.
books are something you choose differently everytime, and the people that like them read recreationally.
Bezos was on another level, launching a successful company(online books) that was effectively just a way to boot strap into an everything store.
In 1999. To ship a book using media mail cost of $1.13 for the first pound and $0.45 for each pound up to 7lbs. It got cheaper per pound above that.
This meant even heavy books were relatively cheap to ship. Way cheaper than random goods.
But the counterpoint still translates here: Amazon did not start with 1000 warehouses (i.e. Anthropic's debt obligations) and _then_ race to build a marketplace that would fill them with everything under the sun. It started lean and mean, and I suspect the Anthropic-killer will do the same.
what I really push back on is the idea that there are magically going to be all sorts of entrants into the space that meaningfully compete with Anthropic or OpenAI in the near or even medium term. for as much as the reddit set continues to put their collective heads in the sand while screaming "there's no moat"... there is a moat, and one so prominent as to be self-evident. not once has an open-source model come even close to genuinely reaching frontier performance for anything other than cost, and cost is by definition the easiest thing to improve on-- just distill bigger model into smaller model: boom, cheaper!
Myspace's peak popularity (in terms of monthly unique pageviews) came in 2007. Yahoo was ahead of Google for several years, though I am struggling to find good data on the specifics there. Your understanding seems to be based in your own personal experience of those sites, which is fine, but I don't think it quite matches the big picture.
Your proposal that there won't be any new entrants is puzzling to me because, in the grand scheme of things, OpenAI and Anthropic ARE new entrants. The entire space is new. It feels like ChatGPT became public eons ago, but it hasn't even been 4 years. Imagine saying in 2008 that no photo-sharing site would ever surpass Flickr - it's been dominant for 4 years! Why would you go to something new when everyone's photos are already on there?
One absolutely enormous advantage new entrants have in the AI space is that they can start on third base. Anthropic had to spend billions and billions of dollars to get to Opus 4.6. A new entrant could distill its way into a model roughly on par with that far more cheaply, and spend its VC cash on things like marketing and price cutting rather than burning it on actually creating a good model. The existing players need to service mountains of debt and are locked into deals for compute based on outdated assumptions about efficiency. I'm not saying it would be EASY for a new entrant to waltz in, especially to the enterprise market. But it's not impossible especially on the consumer side: Meta was essentially a non-factor until Muse came out, and now most people think of Muse when they think of an AI agent (as opposed to Claude, which is just a chatbot in the public imagination, because that's what it was when it became popular).
The winning business model may be 1 or 2 abstractions removed from "build a really good LLM model", and yet to be revealed in the marketplace.
i do agree that there is a good chance that one of these post- OpenAI/Anthropic competitors probably will get some sort of unanticipated foothold, if only because enough talent will drain out over time. what i push back on is the idea that being 90% as good as frontier models is actually good enough to matter in even the slightest. especially since we know that the "real" internal models are so much further advanced than what we get access to publicly.
i absolutely agree that it isn't impossible-- if the only thing stopping a new entrant is capital (as in: they have the trillion-dollar idea necessary to be competitive) that will absolutely never be a true impediment to a worthy competitor.
Yeah, I was with the case bring made, until that point. A very bizarre claim, especially given the questions around safety:
https://www.malwarebytes.com/blog/privacy/2026/10/metas-muse...
When it comes to AI there simply isn't a moat - Anthropic would be my best guess out of the current entrants due to them trying to construct one via value-adding code assistance tools that might create enough friction to prevent customers from jumping ship but the models themselves are a commodity that depreciates extremely quickly. If you want to retain the frontier premium you need to constantly be improving due to the ease of distillation.
It doesn't need to be the best. It just needs to be good enough. I'm not smart enough, but if I was I'd be working on a locally ran interface engine. That's where you charge say 100$ a year. Said engine would allow you to use a large open source model locally to the point where it's good enough to rival Haiku.
If I had to guess someone is working on this
Two observations - one, there is some set of tasks where intelligence is already saturated, and sure, that threshold is rising by the month. So, sure, for some tasks you don’t need the best model. But there are other tasks that need models smarter than what we have now - like “make a cure for <disease>” that will yield billions of dollars of value, but likely require quintillions of tokens and higher IQ/token than anything we have now.
As long as we have non-saturated economically valuable tasks there will be incentive to train new and smarter models, and the frontier will always be on racks of GPUs not local distilled versions.
Two, it’s not clear that you can distill all the way down to 32GB even for the current frontier models. IIRC Karpathy has speculated that you can get an AGI into GPT-3 size, if you drop all the memorized facts and keep the reasoning core. But this is highly speculative! The best 32GB models right now are substantially dumber than even the large open-weight models, let alone the frontier closed models.
Perhaps I am missing something, what are you imagining of “a locally ran inference engine” that doesn’t already exist in a DGX workstation, Framework desktop, MacBook, etc?
Someone much smarter than me would figure this out.
One option would be an alternative bare metal OS which could allocate ram more carefully.
I'm sure with billions upon billions in VC funding a solution could be found.
Imagine if we had some kind of really intelligent AI model that we could pose problems like this to. It's obviously not the kind of thing an open weight model running on your local machine could do, but if we had some kind of really large, closed source model running in the cloud that could solve really difficult problems like this, I wonder if there'd be a market for it...
I think it's important to qualify that while I will very much appreciate such an innovation and I think it'll provide significant value to society... I remain doubtful of the ability for any company to actually capture such an astronomical yield. Either there's enough of an investment required by the company to help get that to market that the yield will be significantly lower or everyone and their brother will just launch competing products two days later. And, as a society, I don't think it's logical for someone writing "Hey Alex, cure cancer" with billions of dollars so I doubt there's going to be much appetite for creating such a moat via legislation.
This gets to the same point of "If ChatGPT can make you a successful million dollar business in a few prompts why is OpenAI sharing it" paradox. It very well may be that AI tools can accomplish the labor that used to be required to create such a business, and I'm sure a few people will land a jackpot and get rich in the transitional phase but if it's so cheap to build it why would someone pay you money for yours instead of just running the same prompting themselves? For most people that's because they lack the knowledge that they can but you'll still have a significant number of instant impersonators.
I agree fully, to be clear o was using that prompt as a shorthand, I don’t think _you_ can type that prompt and get $1B back.
It can’t do this now, obviously. You need a lot of scaffolding and domain expertise to get the most out of these models. (Eg Harvey.)
But eventually, the labs can just run the business themselves.
I’ve long held that as we see models become more capable, they will be less available to the public. It seems pretty obvious that vertical integration on private models (perhaps a per-industry fine-tune, eg a legal model) licensed to subsidiaries will let the labs capture more value.
Why would you publish your model, enable competitors, and enable distillation of your hard-won intelligence? Only antitrust law can prevent this outcome IMO, the business pressure is extremely strong.
Building the iPhone-platform equivalent of this would be interesting.
I'm far from an expert, but I don't think it's impossible. Future models won't give out vacation ideas, they'll focus on one or two things. This might allow us to get the size down.
Regardless, looking back even a year at SOTA local models that fit on a non-absurd system creates a crazy trendline toward the future. And I think model routing is the gap filler that can make/break now vs. Oct 2027
I suspect Anthropic is of this general thinking as well; they've clearly switched from a thoughtful progression toward a natural IPO to "omg sprint for it". Along with the regulatory capture attempts, it's hard to imagine what is breathing down their neck so hard outside of this.
Totally agree local inference of any major scale is a nerd's wet dream short of many stacked improvements in efficiency. But again, auto-routing smooths over the rough edges if you are ok with a little bit of API spend when needed.
they invented mcp, terminal coding agent, context files like claude/agents.md.
I have no real clue whos going to win the model race but they have repeatedly shown that their internal taste is on point, reminds me a lot of early google.
By any sane normal sense of business and financials these AI labs look terrible on paper.
Any loan would be speculative at best, and the rating would simply reflect that.
Investing in these companies is like going wild at the casino. Sure you might make a fortune but strong odds are you’re gonna lose your shirt.
So...why hasn't that "essentially free" competition taken them down yet?
Everything is very unstable right now. This is still a very new tool and we’re very much experimenting on a product level.
And, when they do, it’s because AI can write code. Take for example financial analysis. AI sucks ass at that, but it’s good at writing code. You’re using it to write code to do the analysis.
When you ask AI a math question, it’s not answering the question. It’s writing a program to answer the question. Because AI is basically only useful for writing code.
I guess the mathematicians are all freaking out over nothing, since AI can’t do math. Weird how it’s been solving these problems that have stumped humans for decades or more…just coincidence I guess.
I’m also quite sure that when AI is doing someone’s job by writing code to do it, they’ll be comforted that it’s not really doing it, it’s just creating and using tools from scratch to do it.
The desperate grasping for any kind of position to hang onto that AI isn’t already more capable than most experts in many fields is…sad to watch.
Your scenario is certainly possible, that doesn’t make both predictions equally (un)likely.
If they're actually that confident I hope they short the stock long term with basically all their money.
So yes, I believe it's going to leave a smoking hole in the ground, but I won't be betting money on it.
-Upton Sinclair
But how do you know that? (And no I'm not shocked by this statement, it's just odd to say it with conviction.)
I don't understand how anyone could argue that doesn't crater with any conviction.
Google is a bit oblivious (though is probably the smarter money based on diversification alone IMHO). OpenAI has folks wondering just how publicly Mr. Altman can blunder his wording before he gets ousted (today was along the lines of "you will own nothing" with his take that personal RAM ownership being an affront to efficiency).
All three are trying to convince enterprise buyers that central tokenmaxxing is the best thing since sliced bread; many of the circles I run in occasionally have huge (emphasis: huge in the 2010s ZIRP sense) buyers that are begging for more edge AI.
I'm indifferent either way myself. It's just shocking the amount of ignorance.
But guys, if your companies weren't run by psycopaths and weren't promoted by assholes who appear to be the exact same guys behind the NFT craze, (this is the future of creative expression! Don't like it? Your a luddite. Cope.) and weren't predicting horrible nightmare scenarios based on their tech maaaaaaybe people would like AI and be more interested in using it?
I personally see that generative AI is a very powerful and useful technology but I hate everyone involved in it and do not want to see them succeed because they're icky people.
Its like if the nazis had discovered how to harness atomic energy before anyone else. Fuck that.
Note that both Sam Altman and Dario have conveniently watered down AGI, and moved away from "trying to achieve AGI" essentially.
Question is: Who is gonna be the Carnegie that builds libraries vs the assholes who just hoarded
Structurally they’re in a terrible position on what’s unfolding now with the completely collapse of model valuations. With truly “open” models gaining more and more momentum models are basically worth nothing and “token” prices are collapsing to the cost of compute. That doesn’t leave anything on the table for the Anthropic and Open AI’s of the world.
I think the only way out for Anthropic is to buy a few neoclouds as they go under, but even then it's a coin flip as to if they survive the next 3 years.
Where they quite reasonably might be making a loss is their marginal costs + amortised fixed costs (training).
I predict that eventually the training expenditure is going to slow down. At that point, the large commercial providers are likely hoping they can keep inference demand up, and recoup their investment.
The driver for the training is an arms race between providers and open weights labs. But several dynamics could play out:
* Models will be seen as good enough, with only fine-tuning required, for most use cases, and demand for expensive training of foundation models drops off, stopping the arms race. * Most of the participants cannot raise capital due to lack of market confidence in them, and there is a 'winner takes all' dynamic with the remaining provider, who can then raise costs.
I personally think the first end game is more likely than the second.
> I predict that eventually the training expenditure is going to slow down. At that point, the large commercial providers are likely hoping they can keep inference demand up, and recoup their investment.
The big labs can't do this that's their edge over open models. The stickiness is critical for them to keep up. They might try again but it's going to finish badly.
Is that potential source of revenue? Maybe but folks are verbally very negative on AI ads.
Is that a viable business plan for Open AI? No.
Google search ads are highly converting because they are camouflaged.
OpenAI ads are nothing of the sort!
If I'm searching for red widgets, it's a fairly strong indicator I'm way more likely than the average to want to BUY red widgets, so a well-placed ad for redwidgets-cheap.com didn't feel hostile.
Now when it's "I am clearly searching for red widgets but you want to railroad me into blue doohickeys because they're paying more per click", you're one step above a Taboola chumbox.
Do I think it's dystopian and shitty? Yes. Doesn't mean it ain't gonna happen and doesn't mean it's not the right move for the business.
It's not just kids in my observation.
Honestly, having written that out... I'm not sure that's an outcome that's generally avoidable.
Right now the market is expecting something a lot more than "sells a lot of tokens" out of these things to justify their valuation.
Adobe sits on a treasure trove of:
- Copyrighted multimedia
- Years of telemetry on content creation
Zeiss sits on decades of data:
- Medical imaging, and surgical footage.
- Silicon waffer inspection.
These embody a training corpus that is de facto (lawfully) out of reach of the crawler scrapper approaches of Google/Meta/OpenAI/Anthropic (even if they pirate the seas for torrents like before).
Neither Adobe or Zeiss are an AI power house but the barrier to entry has also dropped with all the movement in the field.
At current prices, it makes sense to self host, but if the price goes down and the demand grows across different verticals, there is room for this business.
https://www.businessinsider.com/companies-hoarding-unused-ai...
Like most “scarcity” scenarios things flip from under supply to massive oversupply real quick. Remember during COVID when lumber got stupidly expensive and then almost overnight folks couldn’t give wood away.
Point is, the number of tokens is a silly metric to proxy these companies' growth.
Anthropic could be super profitable right now if they stopped developing new models. But then they'd quickly fall behind because there's not much of a moat. Maybe the only ones who will survive this are huge companies like Google with separate incomes who can swallow the cost of staying on top.
And these are quite old cards by now. But yeah "all of OpenRouters traffic" is gonna take more than a rack
https://recipes.vllm.ai/deepseek-ai/DeepSeek-V4.1-Flash?hard...
What I’m trying to say here is, what if, they are staying closed strategically so they keep their most advanced models close to their chest until they make an incredible discovery. Say they cure cancer. In this case, they are taking that IP collaborating with big pharma and cashing in on the upside of such discovery.
So, what if on top of being just another enterprise and consumer product that accelerates the way we all work and live, it also becomes this factory of breakthroughs and that’s where open source models can’t compete with them.
Just speculating…
They're not coming from LLMs. They're coming from mRNA-based treatments and applying standard science to their basic principles.
And just to reinforce what djierardi said, you can't make successful new drugs and treatments for diseases just by thinking about it really hard. Practical tests are an absolute, unavoidable necessity—and not even just the final clinical tests; you have to be able to see how the molecules you're synthesizing interact with living tissue of various types, among other things, before you can even hope to test on humans.
Delegate that entirely to an LLM, and you'll end up giving clinical test subjects something that will melt their flesh, or coagulate their blood, or give them turbo-cancer. Because there is no comprehensive dataset that they can use to figure that stuff out purely by computation. We, as a civilization, simply do not yet understand biology and chemistry well enough to model it at that level.
So stop trying to figure out just how OpenAI and Anthropic are playing 12D chess and actually this situation is fantastic and will result in them saving the world. They're just greedy, selfish companies trying desperately not to be left standing when the music stops and all the chairs have disappeared.
You just made me think of LLMs as useful translators. I know they are not going to be the ones leading to the discoveries other ML mechanisms can. But LLMs are a great way to communicate though to a machine. Before it was only for those able to code, but now that problem is being resolved.
That is not to say that computer scientists are no longer useful, but it is now possible for everyone to talk to the computer and viceversa.
Even given the significant advances in coding models, everything I've seen described and experienced myself with them tells me that non-coders simply aren't going to be able to build and maintain nontrivial projects with them unless they get still better at it. By a lot.
And in the realm of medicine, the kinds of mistakes they absolutely will make, and defend, confidently, will get people killed.
Maybe like with problems in maths: they've solved 700+ open conjectures, but how many did they try? Are the ones that they solved merely those that they put $10M effort into where humans just gave up because there wasn't any concrete benefit in putting in that sort of effort?
But can they actually pull it off? Since the open models are kind of able to pull off the same things.
Maybe they can brute force faster, but not releasing allows another to scoop it.
Now, consider they have one, two or three generations of models being tested and toyed around before being released.
Those would be the ones used for breakthroughs.
And I’m not proposing anything. I’m simply using my imagination here.
A pet peeve of mine.
People keep mouthing this same fantasy. But cancer is so many different cancers. And "the cure(s)" aren't just 1000 different drugs, but maybe 10,000 different candidates for each that pass into lab tests, of which maybe 1% make it to phase 1 trials, and of those maybe 10% make it all the way through phase 3, each after 5-10 years of clinical trials and $100s of millions invested each. For the most part, AI-related biotechs are largely focussing on accelerating the discovery of good candidates. But that's a tiny fraction of the pipeline, which includes lots of lab work (which can be automated) and clinical trials (which OTOH require actual human guinea pigs and years of observation and assessment). How do you meaningfully accelerate that? Presumably by improving the odds of success in phase 3 trials from your initial candidates.
But nobody has proposed yet how LLMs can be a magic bullet here. At best they'll give you those 10,000 candidates per cancer type faster. But then you have all that lab work and clinical work to slog through, and all the likely failures you'll see in clinical trials. And there's just not enough data out there to train ML on to meaningfully improve the odds of success in clinical trials, since you'd really have to understand how your drugs work with all proteins and systems in every type of human body, e.g. "in silico" clinical trials. And we're only merely scratching the surface of that with stuff like OpenADMET.
That is, we can't even model well "how a human body works", much less "how those mechanisms go wrong in each type of cancer". But even if you could do all that, for the cures you'd need to figure out what novel, unusual, out-of-distribution thing (relative to your human model) you can throw into the system to avoid the bad behavior, that doesn't kill the person or interfere with other systems or get degraded too quickly or ... tons of other stuff.
Highly non-trivial. Maybe lower-hanging fruit is with a novel peptide or antibody, since those are better modeled with ML. But small drug-like molecules are galactic in number and not well modeled yet. ("Hey, what happens if I introduce this weird platinum-based drug into a body?" Well yeah, cisplatin is used for chemotherapy, but the body of knowledge needed to assess platinum-based drugs in a human body just doesn't exist, and ML isn't gonna intuit it from known data, nor "learn it" since that would require going down to QM, and then integrating that into its model of all human peptides and pathways.)
This is not a side-project for a chatbot company. Its a (multi-)generational project that requires buy-in from labs that can produce the requisite data.
(And okay so maybe I'm fast-and-loose above. Ideally a single "cure" would be to fix the apoptosis pathway, i.e. repair the built-in cell-death path for fast reproducing cells. But cancer cells mutate quickly and evade known avenues. Perhaps there's a generic fix? Plausible but same reasoning applies: ML doesn't have enough data to intuit a solution, if one exists. And there's no reason, a prior, to assume that one does exist. Only hope.)
I just feel like your taking it all as a given/fact. And maybe you’re right, but I’m not that well versed into it to say things for sure.
What I'm trying to get at is that if we’re considering a black swan event, why not consider both the bull and the bear case or even the neutral one and at least accept that we don’t know which one it will be and it may well be a coin toss…
Perhaps using “cancer” deviated things into a specific subject, but it could be anything. A drug discovery, quantum computing, etc.
Again, I’m no quantum physicist or chemist, but as a world citizen, these feel within the realm of possibilities. Just like it destroying humanity or simply just being yet another technological advancement like the computer or the internet was.
—-
I guess the people working on computers could have argued there was a 10% chance it was going to end humanity and so the people working on the internet because AI was going to come about.
—
I’d say that my final conclusion is that we don’t really know and the signals we’re getting from founders, investors, the market, here, and other people we talk to is that NOBODY KNOWS. All we can do is to continue to speculate and make bets based on hunches.
From OAI we're hearing only about 700+ successes. Relative to what failure rate? The cost of success isn't merely the cost of each success, but rather the overall cost of all experiments, of which -- I'd guess -- a huge fraction failed.
Now, imagine having a mechanism that will constantly improve, so you can go from 1% success rate to 5% success rate in say a year.
I see your point. What I think could be different is the speed at which things can be done compared to conventional ways of discovery.
Think of a COVID/MRNA like solution for other areas.
I'm all for the notion that ML can improve 1% to 5%. But I haven't seen anyone suggesting HOW that happens. So far, it looks like taking 1000s of candidates to 10,000 candidates, which just further clogs the pipeline. Using ML to automate lab tests seems like a win there. But still, pushing those through phase 1, phase 2, phase 3 clinical trials is the bottleneck. 90% of what enters that pipeline dies, and each involves many years and many $100s of millions of investment.
But as I noted: the quicker path would seem to be through peptides rather than small drug molecules. ML does pretty well with proteins/peptide chains and mRNA provides a way to introduce them (or injection, like GLP1's). Still requires all the testing, though, but presumably we know more about how they degrade, etc. over time.
Could there be more ChatGPT moments down the line in other areas like science, genetics, space and something we haven’t considered? I think there’s a chance.
I also think AI is here to stay and there may be some confirmation bias from the community regarding open source models. It’s like someone saying back then that Linux was going to take over… (?) Open source software seems to be served for technical people. Not for the average Joe looking for an answer or help generating a social media post or help writing an email.
I'm thinking here of LLMs as a lossy compression of all our published knowledge. And then agents doing some smart/heuristic search relative to that. They can surely find things that we have missed -- just because they'll tireless try all avenues of investigation that humans might have given up on -- but if the underlying data is not there they can't manufacture it.
LeCun has made this point elsewhere wrt world models and more ordinary physics. This issue is exaggerated in the case of biophysics and quantum mechanics.
But what they can do with them can make a difference. Especially with the access to compute and resources they have compared to anyone else. So, they still have a competitive advantage.
The one current example I can think of is Google. They have Alpha Fold and Quantum Computing. I believe they’ve seen advancements to the way they work and an improvement to the speed of progress. It’s just that it’s not what it’s talked about.
These are averages, but still!
You do understand how individual slices decrease in size as you divide the pie more ways, right...?
It just, y'know, isn't a good argument for having everyone work for Anthropic ^_^
In contrast, back when RedHat went public they reached out to everyone on the open source mailing lists, and gave everyone a few shares as thanks for the work they were benefiting from. I got some shares even though all I had done was post some free video format converters.
I've had several people I trusted destroy our relationship this way over the years.
An extremely hypothetical experiment but what if this is some sort of MAD (Mutually Assured Destruction) and in our timeline, we have accepted it with AI.
Deepseek can do MAD on Anthropic and release open weights model (which means that they might not earn the most or basically burn millions/billions to then give us all essentially the same thing and we would all be on equal footing, oh they also released their inference engine right now.)
This might explain why the stock market got into an bubble and is fearing itself right now.
Mutually Assured Commoditization. (Credit where it's due I pasted my comment to Chatgpt and it gave me this term ironically and it seems to fit well to the comment that I am writing, I might write a blog about it someday.)
Where it falls apart is when you get companies that take open models, rent compute and sell tokens at a profit, without heavy investment into research or training. They spend way less, and if they can keep distilling the frontier models, they can produce reasonable products without the massive losses of subscription plans. In your example, DeepSeek et al aren't nearly so vulnerable. There's nothing mutually assured.
It is much more of a classic race to the bottom. Every new frontier model shifts the goal posts a bit, but eventually the house of cards will crumble - or at least, that's what the market seems to be thinking will happen.
They are simply less profitable than API pricing. They are on par with what sellers of open model tokens sell for.
Are you trying to say "the Anthropic API surely cannot be highly marked up! Therefore subscriptions surely are selling at a loss!"? If not, I'm not sure how your comment is particularly relevant to the discussion?
And once their models start to accelerate their progress, which may already be happening, this would further solidify their lead.
Infact they’re going to end up in price competition unless they field an army of ‘FDE’ who go to every large firm and reorganise the operations around their models and displace labour.
Anthropic can’t raise their prices without loosing market share of Claude code
Amazon also did not go from ~$1B revenue to $16B in one year.
Same Amazon to develop AWS to profitable business spent only 30B in all capex (including e-commerce) over 12 years between 2003-15 .
But that won't stop them from issuing bonds or more convertible notes as they have and the market may or may not bite.
It does add pressure to their IPO proceedings being as high as possible as their funding mechanism tho. But reduces pressure on them if shares crash a bit after that, still high risk borrowers.
Or, you can trade $0.03 of tokens that cost you $0.02 for $1.00 in ANTHROPIC SHARES, which makes the thing that everyone uses for like, math discoveries and curing cancer."
Average Hacker News commenter (not you): "Nooooooo it's not profitable Anthropic is going to crater nobody will take their convertible notes"
Let's say that the house of cards crashes down and takes the market with it who will shutdown, survive or have no choice but to get acquired for a 90% discount.
Some will be just alive long enough for the early investors to pull out enough before the market crashes.
Now, I'm not saying that's going to happen -- but if you take analogies from the dot-com era and assume they're going to play out in the same way, you can't just cherry-pick the bad ones.
Anthropic had valuation of $550M at their first venture round, and assuming it IPOs at roughly $2T, it will take them 5 years to give this 3500x return to their initial investors.
So you're absolutely correct about the comparison.
That said, I bet whatever happens over the next 20 years goes beyond what any of us can imagine. I wouldn't be surprised if the winners of this paradigm are worth hundreds of trillions. Sounds ridiculous until you look backwards and valuation a and consider this is an exponential curve and time is compressing.
The way we measure inflation is focused on consumption goods that everyone depends on. It doesn't take into account the stock market.
In other words we could stay around 3% in measured inflation but see ~30% annual equity inflation...
So my point is that the most valuable companies in 20 years could be worth hundreds of trillions while the cost of a can of beans is like $5, not $200.
Zimbabwe type inflation happens when the economy doesn't get more efficient at producing stuff as fast as the government prints money. If the economy increases efficiency very quickly and the government prints money to peg at 2% inflation, the products we consume only increase a little bit while scarce assets increase a lot.
This is why in the time I took a can of beans to increase in price 3x a house increased 20x.
And the winners may be not be the obvious choice.
Almost certainly nobody if you're tracking post-IPO growth.
And not because there won't be survivors/winners when the next crash comes (there probably will be) but because of the market changes (in the wake of legislation like the JOBS act) that result in these companies having their significant growth periods prior to IPO instead of after it.
IPOs used to be a launch pad to growth, now they are just a way for private investors to finally cash out when the company is already close to peak valuation.
Companies are staying private longer, raising many orders of magnitude more private funding, and IPOing straight into "top 10 biggest companies in history" size.
If you IPO at $1.5T you are already equivalent to ~1.5% of World GDP.
Thus is just for scale, as comparing GDP vs a company market cap doesn't make total sense since GDP is 1 year economic activity and market caps represent discounted future cashflows of future economic activity...
Let's say Anthropic IPOs at $1.5T and grows even 1000x over 20 years, it would be worth $1.5P (petabillion?). If it had a 20x earnings multiple like google this would imply Net Income of $75T. Mag7 total Net Income for 2025 was $500B or $0.5B.
World GDP is $125T, and has grown less than 5%/year before/thru/after DotCom. So lets say in 20 years its $250B and Anthropic is capturing 30% of world GDP in earnings (not revenue, that would be even higher)!!
So the math doesn't math.
Note that I'm not saying OAI/Anthropic are destined for success - because the rate of change combined with competition with cheap Chinese models is pretty novel in the business case history. But I am saying that pessimistic dot-com-boom comparisons don't seem to be all that deep.
But this is further to the point that the economics are more Amazon than Pets.com. If the plans are losers, great, when they need to be profitable they kill the plans and focus on metered tokens.
Isn’t the difference that ai labs can’t stop the R&D spend? They have to continue training new models.
B) nobody is IPOing at $400 million like Amazon did except SPAC share dilution scams, that you have to be willing to pick up at $30 million a year later
C) private secondary markets solve that, for some and are pretty great
D) tokenized synthetic and SPV backed private secondary markets in crypto ecosystems like Hyperliquid give you that exposure too, so now everyone can replicate the 90s
Exactly You can't wait to IPO until you're already one of the 10 biggest companies ever AND also grow 2500x post-IPO.
Retail is now exit liquidity for Big VC.
They are so going to get disrupted. Chinese open weights models will have no such limitations, and there's no way we'll let Anthropic outlaw or ban them. (And even if they did, they can't police the whole world.)
There is no moat. Anthropic should be playing nice, not trying to create a god while holding engineers under their thumb. We will route around it.
If the people can't access god, then we are living under a dictatorship with aims to enslave us.
Wait, what?
Sci-hub doesn't work anymore but I can at least ask GPT what a paper says
If you can think a thing, you should be able to prompt a thing.
I realize that SpaceX only did slightly better. But not sure what that says
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This line is certainly eyebrow-raising to the educated layman.
Apparently it means that anthropic is leasing a Google data center whose constituent servers draw 1.5 GW of power running representative workloads.
Blackstone told investors Anthropic may not receive an investment-grade rating immediately after going public, a reversal from its previous guidance that would complicate the AI lab’s ability to finance its massive spending.
The private capital giant told investors in Anthropic’s latest $63bn chip financing deal that the start-up is expected to receive a “high” junk rating from major credit rating agencies after its initial public offering, according to people familiar with the matter.
Blackstone is acting as an anchor investor on the deal, known as “Project North Star”, and is working with banks that stumped up financing on selling the debt on to other investors.
Anthropic filed to go public on Monday, setting up what could be the largest listing of all time next month. The AI lab’s prospectus sketched out its “bold but sane” plans to spend more than $800bn on computing infrastructure between now and 2045.
An investment-grade credit rating is central to those ambitions, enabling Anthropic to borrow at lower rates to finance its AI infrastructure projects. The group has cash and equivalents of more than $112bn, and investors anticipate it could raise about $100bn more in the IPO. Sign reading "Blackstone" outside the Blackstone headquarters in New York, with pedestrians and city lights reflected in the glass. Blackstone’s funds have invested in $9bn of the junior debt at a lucrative yield of more than 10% © Angus Mordant/Bloomberg
While Blackstone had told its investment partners in recent months that it was expecting an investment-grade rating for Anthropic before year-end, it has recently signalled that a rating in the double B range or above is more likely given the guidance it has received from major credit agencies, the people said.
Such a designation would limit the pool of capital that Anthropic could tap because many insurance and pension funds are restricted from taking large positions in junk-rated borrowers. As a result, the company might need to continue to rely on its suppliers with high credit ratings to support its debt-raising efforts.
US tech stocks slid to session lows on Friday following the FT’s report on Anthropic’s rating, with the Nasdaq 100 down 3.7 per cent in afternoon trading. Markets were sharply lower prior to the report after earnings from Amazon and Intel worsened fears about the sustainability of the AI boom.
“It’s all about access to credit, it really matters . . . if [Anthropic] isn’t investment grade, the cost of credit goes higher, that’s not good for anyone,” said a US tech-focused portfolio manager.
“It’s a bloodbath out there,” they added, referring to the sell-off in stocks.
Wall Street banks earlier this week closed the North Star deal, which provides the financing for Anthropic to lease 1.5 gigawatts of Google’s semiconductors. More than $42bn of the senior loans is guaranteed by Broadcom, which co-develops the chips and has an investment-grade rating, while $18bn of the junior debt is backed by the AI start-up’s lease payments.
Institutional investors covet senior debt because it takes priority over junior loans in the case of a default. Broadcom’s high A-tier credit rating was an important factor in allowing Anthropic to secure the financing.
Blackstone’s funds invested in $9bn of the junior debt at a lucrative yield of more than 10 per cent, the FT previously reported. Bankers for Anthropic and rival OpenAI have also lobbied the largest rating agencies to grant the AI labs investment-grade ratings after their IPOs.
Project North Star’s $18bn junior debt, which exposes lenders more acutely to Anthropic’s credit risk, has been assigned a double B plus rating with positive outlook from Kroll Bond Rating Agency, a slight improvement from stable outlook in a similar deal known as Project “Big Sky” four months ago. It also received a double B rating from Morningstar DBRS, the people added.
The big three credit agencies — Fitch, Moody’s and S&P — which have bigger influence on companies’ borrowing costs, have not officially issued any credit ratings for Anthropic.
Anthropic, Blackstone, KBRA, Morningstar DBRS and S&P declined to comment. Fitch and Moody’s did not respond to requests for comment.
I think 2027 will be when a lot of companies revisit their AI spend and ROI. Have there been measurable business benefits reported by the media outside of programming?
My suspicion (and many others as well) is that AI is used as a way to cover for large corporate restructuring in the face of money no longer being available for “free” due to interest rates.
No, they're not. Maybe you are?
> A significant portion of people don't want AI in a lot of these things
Not true. People love AI. Specifically Android and iPhone AI. I doubt they even know or care about Anthropic or OpenAI.
> the ones that are eager to adopt it can use AI to bootstrap AI tools they use through a token router
Absolutely microscopic share of the overall market for "AI" users. We are hobbyists.
Personally I view it like this - did ant and OAI push the industry forward yup? Will they fail as businesses though? Yup. Why? Business economics - as long as cost efficient Chinese replica’s exist it’s fine. Unless the us govt artificially makes the frontier market large then I see no chance of a large enough market that sustains current valuations.
More intelligence alone != more progress. And more intelligence in the context of business != more productivity that shows up in financial performance.
Life is more nuanced.
Realistically they need to 3x their revenue to pay for their compute. While that's plausible, I'm not convinced that it can all come from developers. Many many developers are already using Claude. I think they need consumer, or some other market too.
https://www.apple.com/newsroom/2026/09/siri-ai-a-profoundly-...
> These new capabilities are powered by the next generation of Apple Foundation Models, custom-built in collaboration with Google and its Gemini models for deeply integrated Apple Intelligence experiences.