why does the article used words like burn and incinerate, implying that OpenAI is somehow making money disappear or something? They’re spending it; someone is profiting here, even if it’s not OpenAI. Is it all Nvidia?
The fact is nobody has any idea what OpenAI's cash burn is. Measuring how much they're raising is not an adequate proxy.
For all we know, they could be accumulating capital to weather an AI winter.
It's also worth noting that OpenAI has not trained a new model since gpt4o (all subsequent models are routing systems and prompt chains built on top of 4), so the idea of OpenAI being stuck in some kind of runaway training expense is not real.
> It's also worth noting that OpenAI has not trained a new model since gpt4o (all subsequent models are routing systems and prompt chains built on top of 4), so the idea of OpenAI being stuck in some kind of runaway training expense is not real.
This isn't really accurate.
Firstly, GPT4.5 was a new training run, and it is unclear how many other failed training runs they did.
Secondly "all subsequent models are routing systems and prompt chains built on top of 4" is completely wrong. The models after gpt4o were all post-trained differently using reinforcement learning. That is a substantial expense.
Finally, it seems like GPT5.2 is a new training run - or at least the training cut off date is different. Even if they didn't do a full run it must have been a very large run.
i'm sure openai and their investors know what the cash burn is. it's also been well reported by The Information with no pushback from the company or investors. they have also reported that openai is forecasting $9b in training compute spending for 2025, up from $3b last year. this more or less lines up with Epoch's estimate that training compute has reliably grown by ~4x per year. the vast majority of that is just from building bigger data centers rather than chip performance improvements. you obviously need to grow revenue pretty quickly to absorb that.
This article doesn’t add anything to what we know already. It’s still an open question what happens with the labs this coming year, but I personally think Anthropic’s focus on coding represents the clearest path to subscriber-based success (typical SaaS) whereas OpenAI has a clear opportunity with advertising. Both of these paths could be very lucrative. Meanwhile I expect Google will continue to struggle with making products that people actually want to use, irrespective of the quality of its models.
There are other avenues of income. You can invade other industries which are slow on AI uptake and build an AI-from-ground competitor with large advantages over peers. There are hints of this (not AI-from-ground but with more AI) with deepmind's drug research labs. But this can be a huge source of income. You can kill entire industries which inevitably cannot incorporate AI as fast as AI companies can internally.
There is no doubt that OpenAI is taking a lot of risks by betting that AI adoption will translate into revenues in the very short term. And that could really happen imo (with a low probability sure, but worth the risk for VCs? Probably).
On the radio they mentioned that the total global chocolate market is ~100B, I googled it when I was home and it seems to be about ~135B. Apparently that is ... all chocolate, everywhere.. OpenAI's valuation is about 500B. Maybe going up to like 835B.
I'd love to see the rationale that OpenAI (not "AI" everywhere) is more valuable than chocolate globally.
Burn rate often gets treated as a hard signal, but it is mostly about expectations. Once people get used to the idea of cheap intelligence, any slowdown feels like failure, even if the technology is still moving forward. That gap is usually where bubbles begin.
Because the general idea here is that image and video models, when scaled way up, can generalize like text models did[1], and eventually be treated as "world models"[2]; models that can accurately model real world processes. These "world models" then could be used to train embodied agents with RL in an scalable way[3]. The video-slop and image-slop generators is just a way to take advantage of the current research in world models to get more out of it.
I get the allure of the hypothetical future of video slop. Imagine if you could ask the AI to redo lord of the rings but with magneto instead of gandalf. Imagine watching shawshank redemption but in the end we get a "hot fuzz" twist where andy fights everyone. Imagine a dirty harry style police movie but where the protagonist is a xenomorph which is only barely acknowledged.
You could imagine an entirely new cultural engine where entire genres are born off of random reddit "hey have you guys every considered" comments.
However, the practical reality seems to be that you get tick toc style shorts that cost a bunch to create and have a dubious grasp on causality that have to compete with actual tick toc, a platform that has its endless content produced for free.
For what I use them for, the LLM market has become a two player game, and the players are Anthropic and Google. So I find it quite interesting that OpenAI is still the default assumption of the leader.
Even if there is a bailout. Will it happen in time? Once the confidence is lost it is lost and valuations have dropped. Bailout would just mean that who ever gave money would end up as bag holder of something now worth lot less.
Banks needed bailout to keep lending money. Auto industry needed one to keep employing lot of people. AI doesn't employ that many.
I just don't believe bailout can happen before it is too late for it to be effective in saving the market.
OpenAI has #5 traffic levels globally. Their product-market fit is undeniable. The question is monetization.
Their cost to serve each request is roughly 3 orders of magnitude higher than conventional web sites.
While it is clear people see value in the product, we only know they see value at today’s subsidized prices. It is possible that inference prices will continue their rapid decline. Or it is possible that OAI will need to raise prices and consumers will be willing to pay more for the value.
AI is going to be a highly-competitive, extremely capital-intensive commodity market that ends up in a race to the bottom competing on cost and efficiency of delivering models that have all reached the same asymptotic performance in the sense of intelligence, reasoning, etc.
The simple evidence for this is that everyone who has invested the same resources in AI has produced roughly the same result. OpenAI, Anthropic, Google, Meta, Deepseek, etc. There's no evidence of a technological moat or a competitive advantage in any of these companies.
The conclusion? AI is a world-changing technology, just like the railroads were, and it is going to soon explode in a huge bubble - just like the railroads did. That doesn't mean AI is going to go away, or that it won't change the world - railroads are still here and they did change the world - but from a venture investment perspective, get ready for a massive downturn.
People seem to have the assumption that OpenAI and Anthropic dying would be synonymous with AI dying, and that's not the case. OpenAI and Anthropic spent a lot of capital on important research, and if the shareholders and equity markets cannot learn to value and respect that and instead let these companies die, new companies will be formed with the same tech, possibly by the same general group of people, thrive, and conveniently leave out the said shareholders.
Google was built on the shoulders of a lot of infrastructure tech developed by former search engine giants. Unfortunately the equity markets decided to devalue those giants instead of applaud them for their contributions to society.
Um meta didn't achieve the same results yet. And does it matter if they can all achieve the same results if they all manage high enough payoffs? I think subscription based income is only the beginning. Next stage is AI-based subcompanies encroaching on other industries (e.g. deepmind's drug company)
Google’s surface area to apply AI is larger than any other company’s. And they have arguably the best multimodal model and indisputably the best flash model?
This is why I think China will win the AI race. As once it becomes a commodity no other country is capable of bringing down manufacturing and energy costs the way China is today. I am also rooting for them to get on parity with node size for chips for the same reason as they can crash the prices PC hardware.
As a loan officer in Japan who remembers the 1989 bubble, I see the same pattern.
In the traditional "Shinise" world I work with, Cash is Oxygen. You hoard it to survive the inevitable crash.
For OpenAI, Cash is Rocket Fuel. They are burning it all to reach "escape velocity" (AGI) before gravity kicks in.
In 1989, we also bet that land prices would outrun gravity forever.
But usually, Physics (and Debt) wins in the end.
When the railway bubble bursts, only those with "Oxygen" will survive.
This is different because now the cats out of the bag: AI is big money!
I don't expect AGI or Super intelligence to take that long but I do think it'll happen in private labs now. There's an AI business model (pay per token) that folks can use also.
I think we'll find that that asymptote only holds for cases where the end user is not really an active participant in creating the next model:
- take your data
- make a model
- sell it back to you
Eventually all of the available data will have been squeezed for all it's worth the only way to differentiate oneself as an AI company will be to propel your users to new heights so that there's new stuff to learn. That growth will be slower, but I think it'll bear more meaningful fruit.
I'm not sure if today's investors are patient enough to see us through to that phase in any kind of a controlled manner, so I expect a bumpy ride in the interim.
The railroads provided something of enduring value. They did something materially better than previous competitors (horsecarts and canals) could. Even today, nothing beats freight rail for efficient, cheap modest-speed movement of goods.
If we consider "AI" to be the current LLM and ImageGen bubble, I'm not sure we can say that.
We were all wowed that we could write a brief prompt and get 5,000 lines of React code or an anatomically questionable deepfake of Legally Distinct Chris Hemsworth dancing in a tutu. But once we got past the initial wow, we had to look at the finished product and it's usually not that great. AI as a research tool will spit back complete garbage with a straight face. AI images/video require a lot of manual cleanup to hold up to anything but the most transient scrutiny. AI text has such distinct tones that it's become a joke. AI code isn't better than good human-developed code and is prone to its own unique fault patterns.
It can deliver a lot of mediocrity in a hurry, but how much of that do we really need? I'd hope some of the post-bubble reckoning comes in the form of "if we don't have AI to do it (vendor failures or pricing-to-actual-cost makes it unaffordable), did we really need it in the first place?" I don't need 25 chatbots summarizing things I already read or pleading to "help with my writing" when I know what I want to say.
Something nobody's talking about: OpenAI's losses might actually be attractive to certain investors from a tax perspective.
Microsoft and other corporate investors can potentially use their share of OpenAI's operating losses to offset their own taxable income through partnership tax treatment. It's basically a tax-advantaged way to fund R&D - you get the loss deductions now while retaining upside optionality later. This is why the "cash burn = value destruction" framing misses the mark. For the right investor base, $10B in annual losses at OpenAI could be worth $2-3B in tax shields (depending on their bracket and how the structure works). That completely changes the return calculation.
The real question isn't "can OpenAI justify its valuation" but rather "what's the blended tax rate of its investor base?" If you're sitting on a pile of profitable cloud revenue like Microsoft, suddenly OpenAI's burn rate starts looking like a pretty efficient way to minimize your tax bill while getting a free option on the AI leader. This also explains why big tech is so eager to invest at nosebleed valuations. They're not just betting on AI upside, they're getting immediate tax benefits that de-risk the whole thing.
Anthropic is building moat around theirs models with claude code, Agent SDK, containers, programmatic tool use, tool search, skills and more. Once you fully integrate you will not switch. Also being capital intensive is a form of moat.
I think we will end up with market similar to cloud computing. Few big players with great margins creating cartel.
> AI is a world-changing technology, just like the railroads were
This comparison keeps popping up, and I think it's misleading. The pace of technology uptake is completely different from that of railroads: the user base of ChatGPT alone went from 0 to 200 million in nine months, and it's now- after just three years- around 900 million users on a weekly basis. Even if you think that railroads and AI are equally impactful (I don't, I think AI will be far more impactful) the rapidity with which investments can turn into revenue and profit makes the situation entirely different from an investor's point of view.
> The simple evidence for this is that everyone who has invested the same resources in AI has produced roughly the same result.
I think this conflates together a lot of different types of AI investment - the application layer vs the model layer vs the cloud layer vs the chip layer.
It's entirely possible that it's hard to generate an economic profit at the model layer, but that doesn't mean that there can't be great returns from the other layers (and a lot of VC money is focused on the application layer).
Eh, I wouldn't be so sure, chips with brain matter and or light are on its way and or quantum chips, one of those or even a combination will give AI a gigantic boost in performance. Finally replacing a lot more humans and whoever implements it first will rule the world.
Like railroads, internet, electricity, aviation or car industries before: they've all been indeed the future, and they all peaked (in relative terms), at the very early stages of these industries future.
And among them the overwhelming majority of companies in the sectors died. Out of the 2000ish car-related companies that existed in 1925 only 3 survived to today. And none of those 3 ended up a particularly good long term investment.
> The simple evidence for this is that everyone who has invested the same resources in AI has produced roughly the same result. OpenAI, Anthropic, Google, Meta, Deepseek, etc. There's no evidence of a technological moat or a competitive advantage in any of these companies.
I think this is analysis is too surface level. We are seeing Google Gemini pull away in terms of image generation, and their access to billions of organic user images gives them a huge moat. And in terms of training data, Google also has a huge advantage there.
The moat is the training data, capital investment, and simply having a better AI that others cannot recreate.
I like to tell people that all the AI stuff happening right now is capitalism actually working as intended for once. People competing on features and price where we arent yet in a monopoly/duopoly situation yet. Will it eventually go rotten? Probably — but it's nice that right now for the first time in a while it feels like companies are actually competing for my dollar.
As stated in TFA, this simply has not been demonstrated , nor are there any artifacts of proof. It's reasonable to suspect that there is no special apparatus behind the curtain in this Oz.
From TFA: "One vc [sic] says discussion of cash burn is taboo at the firm, even though leaked figures suggest it will incinerate more than $115bn by 2030."
> The conclusion? AI is a world-changing technology, just like the railroads were, and it is going to soon explode in a huge bubble - just like the railroads did.
Why "soon"? All your arguments may be correct, but none of them imply when the pending implosion will happen.
Have you thought that there was a massive physical infrastructure left behind by the original railroad builders, all compatible with future vehicles? Other companies were able to buy the railroads for low prices and use.
Large Language Models change their power consumption requirements monthly, the hardware required to run them is replaced at a rapid rate too. If it were to stop tomorrow, what would you be left with? Out of date hardware, massively wasted power, and a gigantic hole in your wallet.
You could argue you have the blueprints for LLM building, known solutions, and it could all be rebuilt. The thing is, would you want to rebuild, and invest so much again for arguably little actual, tangible output? There isn't anything you can reuse, like others that came after could reuse the railroads.
> The simple evidence for this is that everyone who has invested the same resources in AI has produced roughly the same result. OpenAI, Anthropic, Google, Meta, Deepseek, etc. There's no evidence of a technological moat or a competitive advantage in any of these companies.
Practically, what I'm finding is that whenever I ask Claude to search stuff on Reddit, it can't but Gemini can. So I think the practical advantages are where certain organizations have unfair data advantages. What I found out is that LLMs work a lot better when they have quality data.
The best case I can see is they integrate shopping and steal the best high-intent cash cow commercial queries from G. It's not really about AI, it's about who gets to be the next toll road.
Google already puts AI summaries at the top of search. It would be trivial for them to incorporate shopping. And they have infinitely more traffic than OpenAI does. I just don’t see how OpenAI could possibly compete with that. What are you seeing that I’m not?
What does it mean for the AI bubble to pop? Everyone stops using AI en masse and we go back to the old ways? Cloud based AI no longer becomes an available product?
I think it mostly just means a few hundred billion dollars of value wiped from the stock market - all the models that have been trained will still exist, as well as all the datacentres, even if the OpenAI entity itself and some of the other startups shut down and other companies else get their assets for pennies on the dollar.
But it might mean that LLMs don't really improve much from where they are today, since there won't be the billions of dollars to throw at training for small incremental improvements that consumers mostly don't care to pay anything for.
AI is turning into the worst possible business setup for AI startups. A commodity that requires huge capital investment and ongoing innovation to stay relevant. There’s no room for someone to run a small but profitable gold mine or couple of oil wells on the side. The only path to survival is investing crazy sums just to stay relevant and keep up. Meanwhile customers have virtually zero brand loyalty so if you slip behind just a bit folks will swap API endpoints and leave you in the dust. It’s a terrible setup business wise.
There’s also no real moat with all the major models converging to be “good enough” for nearly all use cases. Far beyond a typical race to the bottom.
Those like Google with other products will just add AI features and everyone else trying to make AI their product will just get completely crushed financially.
I think I super important aspect that people are overlooking, is that every VC wants to invest in the next "big" AI company, and the probability is in your favor to only give funding to AI companies, bc any one of them could be the next big thing. I think, with a downturn of VC investment, we will see some more investment in companies that arent AI native, but use AI as a tool in the toolbox to deliver insights.
The comparison to railroad bubble economics is apt. OpenAI's infrastructure costs are astronomical - training runs, inference compute, and scaling to meet demand all burn through capital at an incredible rate.
What's interesting is the strategic positioning. They need to maintain leadership while somehow finding a sustainable business model. The API pricing already feels like it's in a race to the bottom as competition intensifies.
For startups building on top of LLM APIs, this should be a wake-up call about vendor lock-in risks. If OpenAI has to dramatically change their pricing or pivot their business model to survive, a lot of downstream products could be impacted. Diversifying across multiple model providers isn't just good engineering - it's business risk management.
I don't see a bubble, I see a rapidly growing business case.
MS Office has about 345 million active users. Those are paying subscriptions. IMHO that's roughly the totally addressable market for OpenAI for non coding users. Coding users is another few 20-30 million.
If OpenAI can convert double digit percentages of those onto 20$ and 50$ per month subscriptions by delivering good enough AI that works well, they should be raking in cash by the billions per month adding up to close to the projected 2030 cash burn per year. That would be just subscription revenue. There is also going to be API revenue. And those expensive models used for video and other media creation are going to be indispensable for media and advertising companies which is yet more revenue.
The office market at 20$/month is worth about 82 billion per year in subscription revenue. Add maybe a few premium tiers to that at 50$/month and 100$/month and that 2030 130 billion per year in cash burn suddenly seems quite reasonable.
I've been quite impressed with Codex in the last few months. I only pay 20$/month for that currently. If that goes up, I won't loose sleep over it as it is valuable enough to me. Most programmers I know are on some paid subscription to that, Anthropic's Claude, or similar. Quite a few spend quite a bit more than that. My Chat GPT Plus subscription feels like really good value to me currently.
Agentic tooling for business users is currently severely lacking in capability. Most of the tools are crap. You can get models to generate text. But forget about getting them to format that text correctly in a word processor. I'm constantly fixing bullets, headings and what not in Google docs for my AI assisted writings. Gemini is close to ff-ing useless both with the text and the formatting.
But I've seen enough technology demos of what is possible to know that this is mostly a UX and software development problem, not a model quality problem. It seems companies are holding back from fully integrating things mainly for liability reasons (I suspect). But unlocking AI value like that is where the money is. Something similarly useful as codex for business usage with full access to your mail, drive, spread sheets, slides, word processors, CRMs, and whatever other tools you use running in YOLO mode (which is how I use codex in a virtual machine currently, --yolo). That would replace a shit ton of manual drudgery for me. It would be valuable to me and lots of other users. Valuable as in "please take my money".
Currently doing stuff like this is a very scary thing to do because it might make expensive/embarrassing mistakes. I do it for code because I can contain the risk to the vm. It actually seems to be pretty well behaved. The vm is just there to make me feel good. It could do all sorts of crazy shit. It mostly just does what I ask it to. Clearly the security model around this needs work and instrumentation. That's not a model training problem though.
Something like this for business usage is going to be the next step in agent powered utility that people will pay for at MS office levels of numbers of users and revenue. Google and MS could do it technically but they have huge legal exposure via their existing SAAS contracts and they seem scared shitless of their own lawyers. OpenAI doing something aggressive in this space in the next year or so is what I'm expecting to happen.
Anyway, the bubble predictors seem to be ignoring the revenue potential here. Could it go wrong for OpenAI? Sure. If somebody else shows up and takes most of the revenue. But I think we're past the point where that revenue is not looking very realistic. Five years is a long time for them to get to 130 billion per year in revenue. Chat GPT did not exist five years ago. OpenAI can mess this up by letting somebody else take most of that revenue. The question is who? Google, maybe but I'm underwhelmed so far. MS, seems to want to but unable to. Apple is flailing. Anthropic se...
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[ 3.2 ms ] story [ 77.6 ms ] threadFor all we know, they could be accumulating capital to weather an AI winter.
It's also worth noting that OpenAI has not trained a new model since gpt4o (all subsequent models are routing systems and prompt chains built on top of 4), so the idea of OpenAI being stuck in some kind of runaway training expense is not real.
This isn't really accurate.
Firstly, GPT4.5 was a new training run, and it is unclear how many other failed training runs they did.
Secondly "all subsequent models are routing systems and prompt chains built on top of 4" is completely wrong. The models after gpt4o were all post-trained differently using reinforcement learning. That is a substantial expense.
Finally, it seems like GPT5.2 is a new training run - or at least the training cut off date is different. Even if they didn't do a full run it must have been a very large run.
https://www.theinformation.com/articles/openai-says-business...
https://epoch.ai/blog/training-compute-of-frontier-ai-models...
I'd love to see the rationale that OpenAI (not "AI" everywhere) is more valuable than chocolate globally.
... so crash early 2026?
The entertainment industry is by far the easiest way to tap into global discretionary income.
[1] https://arxiv.org/pdf/2509.20328
[2] https://deepmind.google/blog/genie-3-a-new-frontier-for-worl...
[3] https://arxiv.org/pdf/2509.24527
You could imagine an entirely new cultural engine where entire genres are born off of random reddit "hey have you guys every considered" comments.
However, the practical reality seems to be that you get tick toc style shorts that cost a bunch to create and have a dubious grasp on causality that have to compete with actual tick toc, a platform that has its endless content produced for free.
GPT5.2 Codex is the best coding model right now in benchmarks. I use it exclusively now.
2026: US AI companies pump stocks -> market correction -> taxpayer bailout
Mark my words. OpenAI will be bailed out by US taxpayers.
Banks needed bailout to keep lending money. Auto industry needed one to keep employing lot of people. AI doesn't employ that many.
I just don't believe bailout can happen before it is too late for it to be effective in saving the market.
Elon owns a competitor and bought the White House.
Their cost to serve each request is roughly 3 orders of magnitude higher than conventional web sites.
While it is clear people see value in the product, we only know they see value at today’s subsidized prices. It is possible that inference prices will continue their rapid decline. Or it is possible that OAI will need to raise prices and consumers will be willing to pay more for the value.
The simple evidence for this is that everyone who has invested the same resources in AI has produced roughly the same result. OpenAI, Anthropic, Google, Meta, Deepseek, etc. There's no evidence of a technological moat or a competitive advantage in any of these companies.
The conclusion? AI is a world-changing technology, just like the railroads were, and it is going to soon explode in a huge bubble - just like the railroads did. That doesn't mean AI is going to go away, or that it won't change the world - railroads are still here and they did change the world - but from a venture investment perspective, get ready for a massive downturn.
Google was built on the shoulders of a lot of infrastructure tech developed by former search engine giants. Unfortunately the equity markets decided to devalue those giants instead of applaud them for their contributions to society.
Try “@gmail” in Gemini
Google’s surface area to apply AI is larger than any other company’s. And they have arguably the best multimodal model and indisputably the best flash model?
As a loan officer in Japan who remembers the 1989 bubble, I see the same pattern. In the traditional "Shinise" world I work with, Cash is Oxygen. You hoard it to survive the inevitable crash. For OpenAI, Cash is Rocket Fuel. They are burning it all to reach "escape velocity" (AGI) before gravity kicks in.
In 1989, we also bet that land prices would outrun gravity forever. But usually, Physics (and Debt) wins in the end. When the railway bubble bursts, only those with "Oxygen" will survive.
I don't expect AGI or Super intelligence to take that long but I do think it'll happen in private labs now. There's an AI business model (pay per token) that folks can use also.
- take your data
- make a model
- sell it back to you
Eventually all of the available data will have been squeezed for all it's worth the only way to differentiate oneself as an AI company will be to propel your users to new heights so that there's new stuff to learn. That growth will be slower, but I think it'll bear more meaningful fruit.
I'm not sure if today's investors are patient enough to see us through to that phase in any kind of a controlled manner, so I expect a bumpy ride in the interim.
If we consider "AI" to be the current LLM and ImageGen bubble, I'm not sure we can say that.
We were all wowed that we could write a brief prompt and get 5,000 lines of React code or an anatomically questionable deepfake of Legally Distinct Chris Hemsworth dancing in a tutu. But once we got past the initial wow, we had to look at the finished product and it's usually not that great. AI as a research tool will spit back complete garbage with a straight face. AI images/video require a lot of manual cleanup to hold up to anything but the most transient scrutiny. AI text has such distinct tones that it's become a joke. AI code isn't better than good human-developed code and is prone to its own unique fault patterns.
It can deliver a lot of mediocrity in a hurry, but how much of that do we really need? I'd hope some of the post-bubble reckoning comes in the form of "if we don't have AI to do it (vendor failures or pricing-to-actual-cost makes it unaffordable), did we really need it in the first place?" I don't need 25 chatbots summarizing things I already read or pleading to "help with my writing" when I know what I want to say.
They only lasted a couple of decades as the main transportation method. I'd say the internal combustion engine was a lot more transformative.
I think we will end up with market similar to cloud computing. Few big players with great margins creating cartel.
This comparison keeps popping up, and I think it's misleading. The pace of technology uptake is completely different from that of railroads: the user base of ChatGPT alone went from 0 to 200 million in nine months, and it's now- after just three years- around 900 million users on a weekly basis. Even if you think that railroads and AI are equally impactful (I don't, I think AI will be far more impactful) the rapidity with which investments can turn into revenue and profit makes the situation entirely different from an investor's point of view.
I think this conflates together a lot of different types of AI investment - the application layer vs the model layer vs the cloud layer vs the chip layer.
It's entirely possible that it's hard to generate an economic profit at the model layer, but that doesn't mean that there can't be great returns from the other layers (and a lot of VC money is focused on the application layer).
And among them the overwhelming majority of companies in the sectors died. Out of the 2000ish car-related companies that existed in 1925 only 3 survived to today. And none of those 3 ended up a particularly good long term investment.
I may add that investors are mostly US-centric, and so will the bubble-bursting chaos that ensues.
I think this is analysis is too surface level. We are seeing Google Gemini pull away in terms of image generation, and their access to billions of organic user images gives them a huge moat. And in terms of training data, Google also has a huge advantage there.
The moat is the training data, capital investment, and simply having a better AI that others cannot recreate.
I don't see how Google doesn't win this thing.
As stated in TFA, this simply has not been demonstrated , nor are there any artifacts of proof. It's reasonable to suspect that there is no special apparatus behind the curtain in this Oz.
From TFA: "One vc [sic] says discussion of cash burn is taboo at the firm, even though leaked figures suggest it will incinerate more than $115bn by 2030."
Why "soon"? All your arguments may be correct, but none of them imply when the pending implosion will happen.
The other, highly invested, companies (if openai and anthropic) may be in for a free fall.
You never wake to be left in the wake of "the next big thing".
Have you thought that there was a massive physical infrastructure left behind by the original railroad builders, all compatible with future vehicles? Other companies were able to buy the railroads for low prices and use.
Large Language Models change their power consumption requirements monthly, the hardware required to run them is replaced at a rapid rate too. If it were to stop tomorrow, what would you be left with? Out of date hardware, massively wasted power, and a gigantic hole in your wallet.
You could argue you have the blueprints for LLM building, known solutions, and it could all be rebuilt. The thing is, would you want to rebuild, and invest so much again for arguably little actual, tangible output? There isn't anything you can reuse, like others that came after could reuse the railroads.
Practically, what I'm finding is that whenever I ask Claude to search stuff on Reddit, it can't but Gemini can. So I think the practical advantages are where certain organizations have unfair data advantages. What I found out is that LLMs work a lot better when they have quality data.
But it might mean that LLMs don't really improve much from where they are today, since there won't be the billions of dollars to throw at training for small incremental improvements that consumers mostly don't care to pay anything for.
There’s also no real moat with all the major models converging to be “good enough” for nearly all use cases. Far beyond a typical race to the bottom.
Those like Google with other products will just add AI features and everyone else trying to make AI their product will just get completely crushed financially.
What's interesting is the strategic positioning. They need to maintain leadership while somehow finding a sustainable business model. The API pricing already feels like it's in a race to the bottom as competition intensifies.
For startups building on top of LLM APIs, this should be a wake-up call about vendor lock-in risks. If OpenAI has to dramatically change their pricing or pivot their business model to survive, a lot of downstream products could be impacted. Diversifying across multiple model providers isn't just good engineering - it's business risk management.
MS Office has about 345 million active users. Those are paying subscriptions. IMHO that's roughly the totally addressable market for OpenAI for non coding users. Coding users is another few 20-30 million.
If OpenAI can convert double digit percentages of those onto 20$ and 50$ per month subscriptions by delivering good enough AI that works well, they should be raking in cash by the billions per month adding up to close to the projected 2030 cash burn per year. That would be just subscription revenue. There is also going to be API revenue. And those expensive models used for video and other media creation are going to be indispensable for media and advertising companies which is yet more revenue.
The office market at 20$/month is worth about 82 billion per year in subscription revenue. Add maybe a few premium tiers to that at 50$/month and 100$/month and that 2030 130 billion per year in cash burn suddenly seems quite reasonable.
I've been quite impressed with Codex in the last few months. I only pay 20$/month for that currently. If that goes up, I won't loose sleep over it as it is valuable enough to me. Most programmers I know are on some paid subscription to that, Anthropic's Claude, or similar. Quite a few spend quite a bit more than that. My Chat GPT Plus subscription feels like really good value to me currently.
Agentic tooling for business users is currently severely lacking in capability. Most of the tools are crap. You can get models to generate text. But forget about getting them to format that text correctly in a word processor. I'm constantly fixing bullets, headings and what not in Google docs for my AI assisted writings. Gemini is close to ff-ing useless both with the text and the formatting.
But I've seen enough technology demos of what is possible to know that this is mostly a UX and software development problem, not a model quality problem. It seems companies are holding back from fully integrating things mainly for liability reasons (I suspect). But unlocking AI value like that is where the money is. Something similarly useful as codex for business usage with full access to your mail, drive, spread sheets, slides, word processors, CRMs, and whatever other tools you use running in YOLO mode (which is how I use codex in a virtual machine currently, --yolo). That would replace a shit ton of manual drudgery for me. It would be valuable to me and lots of other users. Valuable as in "please take my money".
Currently doing stuff like this is a very scary thing to do because it might make expensive/embarrassing mistakes. I do it for code because I can contain the risk to the vm. It actually seems to be pretty well behaved. The vm is just there to make me feel good. It could do all sorts of crazy shit. It mostly just does what I ask it to. Clearly the security model around this needs work and instrumentation. That's not a model training problem though.
Something like this for business usage is going to be the next step in agent powered utility that people will pay for at MS office levels of numbers of users and revenue. Google and MS could do it technically but they have huge legal exposure via their existing SAAS contracts and they seem scared shitless of their own lawyers. OpenAI doing something aggressive in this space in the next year or so is what I'm expecting to happen.
Anyway, the bubble predictors seem to be ignoring the revenue potential here. Could it go wrong for OpenAI? Sure. If somebody else shows up and takes most of the revenue. But I think we're past the point where that revenue is not looking very realistic. Five years is a long time for them to get to 130 billion per year in revenue. Chat GPT did not exist five years ago. OpenAI can mess this up by letting somebody else take most of that revenue. The question is who? Google, maybe but I'm underwhelmed so far. MS, seems to want to but unable to. Apple is flailing. Anthropic se...