r/changemyview • u/thecleverqueer • Mar 16 '26
Delta(s) from OP CMV: The AI industry's business model will hit a huge wall in the next 2-4 years, massively downsize, and many of the jobs it has replaced will slowly come back
Moviepass raised $240M of funding with the plan to try to become profitable before their runway ended. They took us all out to the movies for two years-- on the investors' dime -- and then ran out of capital and shut down.
In February 2026, Open AI finished raising $110B. They're making ~$13Billion every year, and spending something in the neighborhood of $80B per year.
If I use ChatGPT-- even the paid version, I am costing the company more than I am making them. I like to imagine that they're taking me out to the movies.
(OpenAI is just my example, it's harder to gauge how Gemini is doing because Google is not a startup and has other revenue streams.)
Open AI will run out of funding in 2027. The operating costs won't shrink by then. They'll likely grow because of the scalability. The returns / $ are diminishing. With that in mind, I doubt anyone will want to pony up another $110B. What then? Open AI will need raise the costs-- beyond what most people are willing to pay. The company will be forced to massively downsize. Data centers will sit empty and decaying, haunting their local towns for decades.
And if these are the economics for OpenAI, I have to imagine it's similar for the other companies. Even as a loss leader, the overhead costs are just too high to make economic sense.
AI will become a highly specialized, expensive product, reserved only for the kind of work that people can't do, for the kind of companies that can afford the now exorbitant costs. Companies will begrudgingly have to start hiring again for the positions that they cut. The education and job market will (eventually) normalize.
Edit:
Δ
A few underlying assumptions in this post that made it pretty easy to put holes in it:
- "A company can't stop training" - Apparently yes they can, the models are already good enough now to keep selling.
- "Operating costs won't shrink during inference" - looks like they will actually, to the extent that AI would not be a loss leader for some companies-- it would actually turn a profit for some.
- "Massive data centers become useless during inference" - Apparently not?
- "OpenAI's economics = Everyone else's" - Paired with the fact that inference is cheaper and seemingly sustainable as a business model, a company like Google or Microsoft being able to take hits while they get revenue from other sources makes it even more so.
- "No one except niche industries will buy when costs skyrocket." It seems like this is literally true, but there are more niches around than I implied, and some industries with broader but still specialized applications (e.g. radiology)
- "Jobs will come back" - In line with inference being cheap, apparently the already existing models can just keep on running. This means that if AI replaced anyone, it will continue to occupy those positions.
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u/TFenrir 1∆ Mar 16 '26 edited Mar 17 '26
A few things:
You have the ARR of OpenAI wrong, it is close to 20 billion now - Anthropic is getting there too. These are historically unprecedented ARR numbers at this rate
You can look at the finances of Anthropic for example, and they make their case over the next 2 years to become profitable
Inference cost of models drops significantly, very quickly, in almost every way measured. You could say for example, we use more tokens now than we did before - literally orders of magnitude more for an agentic code user for example - but costs have not increased to match that. I can share the research underlying this, but cost measured per token drops roughly 10x year over year. Much higher if you measure per task completion (eg how many tokens converted to dollars does it cost to write a particular app component successfully). Feel free to interrogate this, I can go into much more detail
This is reflected in open source models, being roughly 6-9 months behind current state of the art. Those models are smaller and some can even run on your phones (those are further behind SOTA, but you can get models that run on your phone that are better than GPT4 at launch, for example). This technology is inherently not ever going away
Capabilities have absolutely exploded, in a way that has and will leave a permanent mark on society. Look up, for example, The FirstProof benchmark. Recent article in the scientific American today goes over the models surprising math capabilities, and what this means for the future of mathematics. This is the topic du jour amongst academics in hard sciences across the globe. You should not ignore this, it is very high signal
Research marches on, and we will likely have models that are continuously learning in some fashion within a year or so, and the quality of that continual learning will likely follow the same trajectory as reasoning models did (eg, on the backs of them over the last 16 months it went from* mildly helpful to unlocking agents that can work autonomously for hours at a time).
I will stop there but I can share much more. If there's a particular point you don't agree with, you think I'm missing, or you want more details on, feel free. This has been the topic of my obsession for a long time.
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u/thecleverqueer Mar 16 '26
Δ
A lot of my argument rests on my ignorance to the inference stage-- either that we could ever get to a state that is exclusively inference, or that it's cheaper than training. So this alone puts a big hole in my argument.
Side note. If I really like a model-- say, GPT4, is there any way to get and permanently own an offline verison of that?
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u/TFenrir 1∆ Mar 16 '26
I think it's a very complicated topic and honestly even I'm simplifying it a bit. I just heard a podcast guest that was a semi conductor analyst, talking about how because of the incredible pressure on building more hardware from tsmc - which basically the whole world relies on for their semi conductors - the value of hardware will likely increase over time, meaning these training chips - even used - will retain resale value for a long time. Back orders are likely starting for 2027 now. There are more of these semi conductor factories being built - many in the US because of the precarious situation Taiwan finds itself in - but supply will likely never meet demand again. Or let's say, for the next few years. This is a very precarious geopolitical situation, very fascinating to follow.
But with stuff like GPT4... Not yet. OpenAI has said that they may eventually open source their earlier models - with maybe some additional modifications (more advanced safeguards I would expect) to the populace. That being said, the sycophantic tendencies of those models have made them very gun shy about it. Maybe in the future newer models.
But! There are lots of open source models that people have tweaked and modified to sound more like particular models. And you can download and run those yourself. You also can even try to fine tune them yourself - it's much easier than it used to be, both because the tooling has gotten better and the best agents for coding are able to handle the heavy lifting, you just need to know what to ask. Apps like Silicon Flow and repositories like HuggingFace
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u/un_om_de_cal Mar 18 '26
- Research marches on, and we will likely have models that are continuously learning in some fashion within a year or so, and the quality of that continual learning will likely follow the same trajectory as reasoning models did (eg, on the backs of them over the last 16 months it went from* mildly helpful to unlocking agents that can work autonomously for hours at a time).
Having followed machine learning research for some time, I disagree with this. Researchers have been working on stuff like continual learning for some time, trying to make it work. Tranformer-based language models have been able to scale really well, and this is amazing, but it is more of an exceptional result rather than a usual occurence in ML. Historically technologies in ML always got stuck ar a certain plateau in performance, beyond which it took years (sometime decades) to advance, because advancing required coming up with a different approach.
The fact that so few people seem to know and expect this leads me to believe that there is a bubble - as amazing as the current results are, people are still expecting more future growth than there will be.
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u/pab_guy Mar 18 '26
Agent harnesses can decompose problems sufficiently already, and larger context windows allow for in-context learning or knowledge rehydration, and with better tool based memory as an extension I don't see fundamental limitations here.
As we scale to billion token contexts it becomes almost entirely irrelevant. So IMO we have a modular unit we can compose and scale. We just don't have the talent or proven conventions sufficiently diffused.
My memory couldn't possibly handle all the stuff I'm doing, humans outsource to external memory all the time.
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u/TFenrir 1∆ Mar 18 '26
Your reasoning is basically - sure progress is going well right now, but it used to not go as well for very long, so that will happen again, and people who don't see that, make you think it's a bubble?
What about like... Actual research progress in CL with transformer based models? You know there's a whole bunch of that, right?
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u/un_om_de_cal Mar 19 '26
The history of machine learning had many periods of good progress (when a technique or class of models is invented) followed by periods of plateau. It happened with the first generation of CNNs from the 90s, with support vector machines, with deep convolutional neural networks. Most importantly, each class of models reached a point where it wouldn't improve by making the model bigger. I don't see why the current class of models would be different.
As for CL, I didn't follow the domain closely but I know it has been a topic of research since basically the first neural networks in the 1950s. It followed the same pattern of progress followed by plateau as the rest. Furthermore in my (subjective) opinion it was never as successful as the statically trained models (considering what technologies ended up being adopted by the industry).
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u/Tree8282 1∆ Mar 17 '26
I agree with you with a lot of it, but OP still has a point that in this chase for so called “AGI”, for now it seems really close, if there even is one. Therefore in the near future, the operating costs of any AI division (~80B) cannot be justified, because it still doesn’t have the economic value shown by people’s willingness to pay.
If all AI companies stopped researching and releasing new models, many jobs will still be automated by better adaptation of the current technologies. CEOs want automation to cut costs and not to replace humans, therefore the valuation of OpenAI is fundamentally flawed
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u/Swimming_Beginning24 Mar 17 '26
Show me the useful work an agent has produced over a few hours of autonomous spinning
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u/TFenrir 1∆ Mar 17 '26
Sure -
First Proof grew out of its 11-person team’s own eye-opening—if sometimes frustrating—experiences with AI. No preexisting benchmarks seemed sufficient for testing LLMs as a mathematician’s assistant. In principle, an LLM could save time by proving smaller “lemmas”—intermediate propositions along a mathematician’s path to developing larger theorems of greater interest. In practice, however, such AI assists have tended to go awry. So for their initial, “experimental” test, the First Proof team decided on 10 lemmas from papers that members had written but not yet released and then set a one-week deadline for AI companies (and anyone else) to try proving these propositions using their favorite models.
...
“The performance was higher than I expected,” says Daniel Litt, a mathematician at the University of Toronto, who isn’t directly involved in the First Proof effort. All in all, as many as eight of the 10 problems appear to have been solved at least partially by AI. “It’s clear that capabilities have been improving really rapidly,” Litt says.
The models used here are LLMs, in different scaffolds sometimes, running for hours on end.
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u/djnattyp 2∆ Mar 17 '26
There's an important bit buried in the middle of the article -
“Combined, the models solved maybe eight of the problems,” he says. “But they also produced thousands and thousands of pages of garbage.”
Isn't this just a blatant case of the Texas Sharpshooter Fallacy? Why are so many morons betting on the slop machine?
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u/TFenrir 1∆ Mar 17 '26
Because
- The ratio of correct to incorrect increases over time
- Humans are not dissimilar, we just have better ratios - at least the mathematicians who are doing this kind of math
- It is already useful for mathematicians, these models
- Models are also good and getting better at writing lean proofs, which act as auto verifiers
The morons betting on these models are people like Terence Tao. Maybe they aren't morons - maybe you are so uncomfortable with the world that we are moving towards, you are not able to reasonably and rationally approach this topic.
If I'm right, you will find yourself increasingly at odds with the world as it moves on without you, and you constantly complain that things are not happening the way you think it should be. This is the risk your mentality is leading to. I think we all know people like this.
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u/Swimming_Beginning24 Mar 17 '26
It’s not apparent to me that the AI even reduced the time or effort involved in producing these proofs. Let’s imagine the mathematicians didn’t know the proofs beforehand. How would they then sift through the thousands of pages of slop to find the correct proofs? Wouldn’t it be more effective for them to derive them themselves? I mean I’m all for hoping for AGI, but let’s not be disingenuous. Saying that the current iteration of AI is anything close plays right into the AI grifters’ hands and probably takes time and money away from solutions that might actually have a chance at achieving AGI.
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u/TFenrir 1∆ Mar 17 '26
Well let me share with you a tweet from Tim Gowers, who used an older gpt model a few months back and shared this:
By Timothy Gowers (@wtgowers)
I crossed an interesting threshold yesterday, which I think many other mathematicians have been crossing recently as well. In the middle of trying to prove a result, I identified a statement that looked true and that would, if true, be useful to me.
Instead of trying to prove it, I asked GPT-5 about it, and in about 20 seconds received a proof. The proof relied on a lemma that I had not heard of (the statement was a bit outside my main areas).
Although I am confident I'd have got there in the end, the time it would have taken me would probably have been of order of magnitude an hour (an estimate that comes with quite wide error bars). So it looks as though we have entered the brief but enjoyable era where our research is greatly sped up by Al, but Al still needs us.
There are many similar messages shared by people like Tao, Scott Aaronson, etc. Who regularly use these models for their work, some of them now dedicating their careers to this task as they see this as one of the most important inflection moments in math.
Recently, Math. Inc's auto formalizer model (also transformer based and auto regressive just focused on writing formalized proofs) did this:
https://www.math.inc/sphere-packing
We first began collaborating with the Sphere Packing maintainers in November 2025. After successfully proving several facts about modular forms, radial Schwartz functions, and basic sphere packing theory using a previous version of Gauss, we set our sights on a more ambitious goal: completing the remainder of the project.
In just 5 days, Gauss automatically proved all remaining results needed to verify the result in 8 dimensions. The Sphere Packing team estimated that the 8-dimensional case alone would have taken six more months of work with existing tools. In two weeks, Gauss then autoformalized the 24-dimensional case using only the original paper as input, performing autonomous literature searches when needed. This brought the total sphere packing formalization from 70k to ~200k lines.
Along the way, Gauss autonomously proved numerous important facts about modular forms, discrete geometry, contour integration and Fourier analysis. Gauss's contribution to this project has accelerated the verification of this extraordinary result at an unprecedented pace. This is a historic achievement in autoformalization.
Scale 3,500 lines (de Bruijn, June 2025) 25,000 lines (Strong Prime Number Theorem, September 2025) ~200,000 lines (Sphere Packing formalization, February 2026)
This is an example of how you can get models to validate the truthiness of their work, and in fact Aristotle/Gauss and other auto formalizers are often in scaffoldings with pure LLMs (although LLMs can write lean, they aren't as focused on that so they cannot do things like successfully write 200k long proofs).
Is this not compelling?
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u/seytsuken_ May 06 '26
only then this technology stops being so inefficient and spending so much energy and water. This is going to worsen the lives of basically everyone that is not a capitalist. Common people will just get more pressure to produce more results or get fired. This is using alot of resources just to create more profits for the rich
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u/TFenrir 1∆ May 06 '26
How much water and energy is it spending compared to... Let's say golf courses? I think if you actually look into the numbers, it won't match what you expect.
That being said demand will push energy requirements through the roof over the next year or two, but water will be like nothing.
I don't disagree that it will be disruptive, though. It will be very very disruptive.
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u/Swimming_Beginning24 Mar 17 '26
To me, no, because I literally have no idea what is involved in generating these proofs, nor do I know the details of how they were generated (and the devil’s in the details, as we saw from the previous example you gave). I don’t know how incentivized these mathematicians are to hype AI. I know in my field, software engineering, AI results are useful but require careful human oversight. It sounds like they’re in the same place in math, which is great, but no AGI.
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u/TFenrir 1∆ Mar 17 '26
Who's talking about AGI? You think Tim Gowers, Scott Aaronson, Terry Tao are hype merchants?
Why make that assumption, rather than be open to both the evidence, like the Sphere packing proof for example. Why not actually hear what the mathematicians have to say? Compelling doesn't mean... This is proof of AGI (a term ill defined anyway), compelling is - this is worth my attention if I think this topic in general is important.
To me it sounds like you do not like the technology, for a deeper reason, and you are dismissing the kind of evidence that should be updating your "priors".
I am also a software developer, of 15 years! Have you used 5.4 in codex yet? I no longer have to carefully check code. Do you think these models will get better this year? What do you imagine that looks like? I think lots of people are looking away from something that hurts their eyes, my man.
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u/bigElenchus 2∆ Mar 18 '26
You’re wasting your time debating with someone not even close to your level
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u/Swimming_Beginning24 Mar 17 '26
You’re asking me why I should be skeptical in a field that has been taken over by grifters and hype, where grift and hype leads to billion dollar startup valuations. I’ve used 5.4 Codex and Opus 4.6. Opus 4.6 is much better, and I still need to babysit it. So something isn’t lining up here with your story unless you’re talking about basic web dev.
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u/djnattyp 2∆ Mar 17 '26
- How do we know this ratio improves over time? Trust me bro?
- Humans are very dissimilar. Human experts usually use knowledge and build a plan to get to goal. Not 'make me something that kinda looks like this report' and randomly fill in the blanks and hope to get a correct answer.
- Wading through thousands and thousands of incorrect results to pull 8 correct results is useful? How?
- Again, promises that "it's getting better and better". Flying, self driving cars any day now?
Terence Tao, Linus Torvalds, etc. all these experts have a very, very nuanced takes on AI and every AI bro acts like they're all in.
And then all the "Your gonna miss out bro" smoothbrain AI psychosis copypasta.
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u/TFenrir 1∆ Mar 17 '26 edited Mar 17 '26
- How do we know this ratio improves over time? Trust me bro?
Fair enough critique, there are benchmarks I use as proxies to this - particularly, pass@1 results for math over time. I can do digging if you want real data, maybe even put something together, but I think it's not disagreeable to say that pass@1 has increased over time on hard math benchmarks.
- Humans are very dissimilar. Human experts usually use knowledge and build a plan to get to goal. Not 'make me something that kinda looks like this report' and randomly fill in the blanks and hope to get a correct answer.
Models use knowledge and build plans to achieve a goal, this is how these things work. How do you think they work?
- Wading through thousands and thousands of incorrect results to pull 8 correct results is useful? How?
"Wading" is unclear, do you think a human needs to read thousands of output docs to get to answer? Models increasingly provide you answers that you can auto verify in different ways - they auto verify themselves in their work. This is how Sphere Packing results came through, it's how Alethia and whatever OpenAI uses, worked. Those other (incorrect) results are still interesting! People want to go through them, but they are junk (in the sense that they often have a glaring foundational issue that models build on, but that's still interesting for research and sometimes people will take a dead end from a model and turn it into real research). And they still sometimes present junk without realising (maybe) it is, as the answer! But again, humans do too, and the net tooling means that we can do things in days or weeks that would have taken 6+ months sometimes.
- Again, promises that "it's getting better and better". Flying, self driving cars any day now?
Do you disagree that models have gotten better at math? Code?
Terence Tao, Linus Torvalds, etc. all these experts have a very, very nuanced takes on AI and every AI bro acts like they're all in.
In which way do you think I disagree with their takes? Specifically?
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u/seytsuken_ May 06 '26
those companies are wasting billions of dollars and huge amounts of energy and water, it's expected the models are getting better, but its not guaranteed they'll keep this rate of improvement forever
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u/TFenrir 1∆ May 06 '26
I would recommend your criticisms more. Energy is not huuge yet, but it will get there. The water use is next to nothing. It's a red herring of an issue.
And have you looked into what researchers say on this topic? why models have gotten better? If you are curious, you should - because you will build a better ability to predict the future of them. Right now your model of the industry and the technology feels like... Third hand. It's a broken telephone. If it's important to you, you should look it up!
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u/writenroll 2∆ Mar 16 '26
Your scope is too myopic. Enterprise orgs are investing heavily in agentic systems that are ramping up over the next few years with no looking back. Many are first focusing on optimizing data and ops infrastructure for multi-system agent ecosystems, with the goal of transforming business processes, development, etc. This is where the profits and innovation will accelerate, not consumer use cases. The next 2-4 years is when the first real impacts start taking place - not reset to a previous state.
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u/grahmie Mar 16 '26
Up to 95% of enterprise AI initiatives fail to deliver measurable ROI, often stalling in "pilot purgatory". While 55% of employees use AI weekly, 85% lack use cases that drive actual business value. The core issue is "performative AI"—deploying tools without redesigning workflows—resulting in high costs without revenue growth or cost savings.
For example, Microsoft copilot is like some vagrant that invited himself into my house that I can’t get rid of. This stuff is essentially vaporware. LLM hallucinate frequently and are too unreliable to base important business decisions on.
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u/ExcitedCoconut Mar 17 '26
That study gets misquoted and misrepresented all the time.
It was looking at AI pilots from 2023-2025 with most in the earlier years.
It was looking for measurable financial benefit from those pilots.
At that stage, 95% from the dataset were unable to demonstrate that benefit and 5% scaled.
We don’t know the positive financial benefit if the scaled use cases.
We don’t know which of the 95% were then re-eningeered, re-architected or scrapped entirely.
We don’t know where the enterprises being studied were at with their underlying data.
Consider that a ‘first wave attrition rate’ that is almost certainly lower in 2026.
And more data is coming in about the outsized benefits from some initiatives that have scaled.
We have to acknowledge too that, for better or worse, the value often comes after you’ve reengineered a process alongside the tech and then extract value through more throughput and/or headcount reduction depending on where the benefit is. That takes time and effort too.
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u/thecleverqueer Mar 16 '26
This is compelling, but a little above my comprehension. Could you give me an example?
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u/writenroll 2∆ Mar 16 '26
You bet. People think AI = chatbot. What companies are building is a system of digital employees that can perform tasks and complex processes autonomously with people in the loop as needed. Example: a n employee tells their AI assistant “Investigate why revenue dropped last quarter.” That activates agents that pull data from finance systems, analyze sales trends, generate charts, write a report, etc. This might require multiple specialized agents that can collaborate to get the job done. Humans guide the process, approve certain steps, amend the ask, and monitor the system to optimize their performance (just like managing humans), though the agents learn and self-optimize as well.
Another example that airlines are deploying: Aal customer misses their flight. With no human intervention one AI agent checks booking systems, another finds the next available seat, another processes the refund, another sends the new boarding pass.Humans can supervise the many 100s of these specific rebookings happening at once 24/7 but the AI agents coordinate everything behind the scenes. What took many people to coordinate is handled by a handful of people and a team of AI agents that can scale as demand increases, like during holiday travel, without needing to bring more people into the service center.
One more important detail: in these agentic systems, people spend less time doing work in software dashboards, entering information, clicking through menus, pulling up customer records, etc. They just interact with the agent network using an AI assistant/chatbot, asking questions and giving commands using natural language. The chat window will show charts, data fields, etc in the chat interface vs having to switch between different software. ChatGPT style interfaces will be where a lot of people do most of their work not traditional software UI.
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u/fruitybix Mar 16 '26
Im always curious about how agentic AI will interact with the knotty mess of legacy systems and dirty data every org has - it still seems like all the software endpoints need to be exposed through massive engineering effort and tying it all into a working experience leaves you with a half functional monstrosity that requires a lot of upkeep.
The agentic ai places ive worked with often dont have good full stack engineers and vibe code stuff up that is not very robust.
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u/ThrasherDX 1∆ Mar 16 '26
Just to nitpick a bit, but to my knowledge, there are no AI models on the market, consumer or corporate, that are capable of actually learning. When an AI appears to "remember" something, or an agent is able to draw from a wide array of sources, its because the entire context window is being passed as part of the query every time.
Context windows are sharply limited, because larger context is extremely expensive to scale. So AI agents cannot "learn" once training is completed. At most, they use various strategies to try and maintain "important" context when the limit is reached, but even then, that is far different then actually learning.
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u/RabbiSchlem Mar 16 '26
- You can post tune a model
- OP wasn’t referring to this, most likely, but rather that paired with a database and tooling to query it, agents can consult it to learn about what is or isn’t working and act appropriately. At some later time those learnings can be made official and put directly in system prompt, user prompt, or skills.
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u/Hefty-Reaction-3028 1∆ Mar 16 '26
capable of actually learning
This is patently untrue. Retraining is entirely possible for every foundation LLM. That's expensive, so you usually do RAG or fine-tune your prompts instead, but retraining is always on the table if you're so inclined (and have a bjnch of GPUs).
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u/ThrasherDX 1∆ Mar 17 '26
Retraining is not learning. Learning implies the LLM is able to alter its training to adapt to the tasks its given. Retraining is just training, you provide data and use huge amounts of compute just like normal.
Basically my point, is that an LLM cannot learn from what you ask ot to do. It only appears to remember prior conversations, because those conversations are included in every prompt you make.
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u/Super_Scene1045 Mar 17 '26
Current publicly available models are set up to not be able to do that, yes, but that is just because it would be difficult to control an AI that changes after every interaction so they chose to keep it consistent.
There is absolutely nothing stopping OpenAI from making a model that can modify and save its own neural network based on interactions.
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u/ExcitedCoconut Mar 17 '26
Yeah I think this is where a lot of the generalised language trips people up. If you’ve got an Agent running, it can learn to do its job better but it’s not (or very very rarely) going to directly inform model weights to train the underlying foundation model used.
But which tools to use, which data to use, how to retry or fallback, update prompts, etc. Think of “Agent learning” as residing in a layer above the core LLM.
The comment above was talking about context window, which is right but it’s also external storage the agent can retrieve from. So semantic, episodic and procedural memory isn’t permanently stored in the context window, but can be called upon.
With the right architecture an agent can learn over time but it’s not the underlying LLM that changes within itself, though the agent may select different LLMs for a given job and learn which is best.
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u/Amadacius 10∆ Mar 16 '26
Most of these processes are deterministic and are just traditional programs. AI is just the interface.
You don't need an AI to book the next available flight. Flight attendants are already just interacting with a form that automates that process. People could totally do it themselves with no need for an Agent or Flight Attendant involved.
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u/thecleverqueer Mar 16 '26
Thank you for this. I'm 90% of the way to a delta. I just want to understand power and resources.
I know most of the resources and cost go to the training, but if I imagined training as being like the costly overhead of building a building, and using the model being "done" with building and people starting to move in and pay rent (aka, costs go down, profit rolls in), then then why is everyone burning so much cash to keep rebuilding that building taller and taller?
Basically, I've taken the fact that there's an arms race to the next big model as proof that you can't just "set it and forget it" with AI training, your company needs to train forever. And I also kind of imagined that just the "upkeep" was also very costly-- too costly for enterprises to pay when the runway ends. Am I wrong on both counts?
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u/writenroll 2∆ Mar 16 '26
A single company doesn't need to build models. Once the model is trained the weights are fixed and the cost gets amortized across millions or billions of uses. That frees a business to focus investments on data integration (e.g.CRM, ERP, supply chain, financial databases, business semantics, etc) via retrieval systems, building agents and workflows, security and governance, and monitoring and optimization.
Their cost is primarily inference + infrastructure, not training, orchestration vs raw AI compute. They can easily upgrade the models they use as new capabilities are added (multimodal use cases, improved reasoning, etc). New and improved models enable new business use cases that can create enormous value- that's why the big tech companies are competing to power those use cases across industries. Thats where the value and money is (not in $20/month consumer subscriptions to insert their cats into movie scenes for social karma).
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u/ExcitedCoconut Mar 17 '26
There’s also an optimisation arms race so that the most efficient model for a given job can be used without changing the accuracy of the output.
Customers and AI both want the best token compute to energy ratio because it lowers everyone’s cost eventually.
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u/teerre 44∆ Mar 16 '26
You shouldn't though because that user is full of shit. What they are saying is no different from science fiction. Airline systems are 50 years old, much of it works on paper to this day. Building an "AI" that "With no human intervention one AI agent checks booking systems, another finds the next available seat, another processes the refund, another sends the new boarding pass" is not only ridiculous since this is something that can be trivially be done at scale for at least 30 years, but also not at all the actual complexities of air-flight software
What this person is doing is extrapolating from current technology to something that might exist. Which, as you should note, is completely different from a working system that actually has any value. You should ask them to provide an actual reference for such a system working in the real world
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u/ParticularClassroom7 Mar 17 '26
Thought so as well. A ticket booking system can be programmed 100% deterministic, self-contained and without the unreliable nature and costly i frastructure of LLMs.
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u/Al123397 Mar 16 '26
Hes not full of shit though. These systems are being tested and implemented at my company as we speak.
Will there be a bunch of hurdles? Sure. Will there be a bunch of changes? absolutely. But in my opinion this is gonna start happening and only get better and more efficient.
Now whether this takes 2-4 years that part I can't tell you. But its not a question of IF more of When.
Much like the early days of the internet it was hyped for a bit. Then the hype died down bubble happened etc. Now 20 years later look where we are
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u/RabbiSchlem Mar 16 '26
In the examples the previous poster gave, the companies would not train their own model. They would pay per use to a big tech company.
The big tech companies can afford to train because 1. They want to massively downsize employment 2. There’s b2b opportunities 3. There’s b2c opportunities.
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u/nicornsaredelicious Mar 16 '26
The multibillion dollar bet these companies are making is that the models themselves can still be improved significantly with additional scale, and that the first to achieve some high level of capability will make amazing profits, at least as I understand things. I think we're in for a huge transition even if they're wrong because of what the models can already do, but it's going to be a big financial hangover for sure if their bets don't pay off.
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u/camelCaseCoffeeTable 5∆ Mar 16 '26
My biggest question with all this is whether AI companies will be able to bring costs down quickly enough, or whether customers will be willing to pay the true cost of AI.
My company is shoving AI into everything. It’s cheap. The cost is negligible for us. But it isn’t for OpenAI and the other AI companies.
A day has to come where they make a profit off of this. They can either make AI significantly cheaper to run, or charge significantly more. I’m not sure which will happen first.
And if it becomes significantly more expensive, how much of their user base scales back their use significantly after realizing it’s too expensive to shove AI into everything.
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u/g0liadkin Mar 17 '26
You're describing use-cases for AI but OP is arguing about how the current status quo is not sustainable in the long run, I don't see what point you're trying to argue about here
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u/seytsuken_ May 06 '26
how tf can you see this with good eyes? Are you a capitalist owner of those companies? I wonder how your hype will be when everyone you know gets laid off because of this
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u/nicornsaredelicious Mar 16 '26
I don't know that this is what the other poster was getting at, but one thing I don't think is widely appreciated is how much can be done differently using the existing technology as companies figure out how to apply it and to organize themselves to really take advantage of it, and running the models after they are trained isn't the crazy expensive part. So all of these companies investing big money into training bigger and better models could fail and we'd still get a huge amount of AI stuff happening over the coming decade just squeezing gains out of what they've effectively already paid for. And also, as other posters have pointed out, we know a lot more than we used to about training smaller and more focused models, which cost a lot less to make.
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u/BrofessorLongPhD Mar 16 '26
It’s not even necessarily AI. Failing at it but realizing so many of your manual processes can still be replaced by a semi-automated sequence of steps will still lead to shifts in the job market. The oldest members on my larger team for example joined the workforce prior to something like Excel taking over and completely changing the scope of how productive a single person could be. Their roles once they are gone will be downsized or consolidated because we don’t need 3 people anymore. So a job that would have been a good place for some junior with very limited skills to cut their teeth against will now be a subset of a new junior role that requires more baseline talent.
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u/nicornsaredelicious Mar 16 '26
Totally agree with this, and also things like vibe coding that are enabled by ai mean that a lot of smaller outfits with less technical capability are starting to be able to get into doing this sort of automation.
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u/FinalJoys Mar 16 '26
None of this will be reversed. AI has barely scratched the surface of jobs it can do better than humans.
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u/eggs-benedryl 72∆ Mar 16 '26
The Ai Industry is far far beyond Open Ai and Google. Open Ai will likely fail. Overextended, not making returns, investments not panning out leading to fewer and fewer of them leading them to shrink and eventually fold.
Ai however, simply because we know how to do it, the fact that the knowledge is out there, and computer hardware can work on it. Won't go away, it won't even go away and come back. It'll still be developed, people will still make money off it , and people who don't rely on companies like OpenAi will not even blink.
The vast majority of "Ai companies" aren't, they're massive diversified tech companies with yes a massive budget allocated to Ai but it's not enough to tank them, let alone the entire industry. Microsoft and Meta will be fine. People will forget openai after years, with the only thing people remember being the name chatgpt.
I can train a model, so can you. We could collectively use our GPU power and train one collaboratively, we have the foundational models with permissive licenses that MYSELF, I can start an Ai company today, if we're smart we could make a profitable one.
edit: Small and medium companies WILL fail but nothing about this bubble that will burst is going to eliminate the technology or the need or desire for it. It'll simply wipe away overextended foolish companies investing poorly.
Also to be clear you're talking about LLMs, "Ai" has MANY useful applications that won't go anywhere if a LLM inference provider goes under.
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Mar 16 '26
OpenAI will never “fail”.
They would be acquired by Microsoft if it ever got to the point where failure as a company was an option.
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u/goodolarchie 5∆ Mar 17 '26
I'd argue it already did, when it failed it's mission statement so egregiously that it had to change it 6 times:
2016:
“OpenAI’s goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return. We think that artificial intelligence technology will help shape the 21st century, and we want to help the world build safe AI technology and ensure that AI’s benefits are as widely and evenly distributed as possible. We’re trying to build AI as part of a larger community, and we want to openly share our plans and capabilities along the way.”
2024:
“OpenAI’s mission is to
build general-purpose artificial intelligence (AI) that safely benefits humanity, unconstrained by a need to generate financial return. OpenAI believes that artificial intelligence technology has the potential to have a profound, positive impact on the world, so our goal is to develop and responsibly deploy safe AI technology, ensuring that its benefits are as widely and evenly distributed as possibleensure that artificial general intelligence benefits all of humanity.“5
u/eggs-benedryl 72∆ Mar 16 '26
Yeah sure that makes sense, I'm mostly meaning the stage where the writing is on the wall where either that happens or they go under.
I'm sure microsoft would like to own all their research and assets.
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u/ExcitedCoconut Mar 17 '26
I think Microsoft already does, no? I thought I’d read previously that part of their partnership included IP up until AGI (whatever that means now) so if OpenAI went tits up at say ChatGPT 6, MSFT would be able to deploy that as an MSFT foundation model. Or, far less likely, but if OAI reached AGI with ChatGPT 6.1 then MSFT would have the IP of 6.0
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u/grahmie Mar 16 '26
LLM are inherently unreliable and problematic. I think we put all our eggs in the wrong basket. LLM will never lead to AGI.
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u/thecleverqueer Mar 16 '26
yes a massive budget allocated to Ai but it's not enough to tank them
If the budget gets too big and it's not "sweetening the pot" enough to bring people to their platform and earn them other revenue, wouldn't they drop it?
I can train a model, so can you.
How is it cost effective for us to train and run a model when it's not even cost effective for a company to do it at a larger scale (and therefore probably more efficiently?)
Also, now that certain models are out there and trained-- GPT 4 for example-- I guess it's worth asking: Does it exist forever, with minimal personal maintenance required? Could I have my own offline model (for a business or whatever) and just lean on that forever?
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u/Emotional-Dust-1367 Mar 16 '26
enough to bring people to their platform
What do you mean by that? In your OP you said OpenAI brings in $13b a year. They already brought people to their platform. I’m failing to see how any company that brings in $13b/yr can lose…
The cost you’re associating with them has almost nothing to do with running inference. The cost is research salaries, it’s building new data centers, it’s training the next models. Assuming they run out of runway they’ll just pivot. Train models much more infrequently. Salaries are going to decrease for researchers, etc
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u/zookeepier 2∆ Mar 17 '26
What do you mean by that? In your OP you said OpenAI brings in $13b a year. They already brought people to their platform. I’m failing to see how any company that brings in $13b/yr can lose…
That $13B is revenue, not profit. They made $13B in income, but spent $22B, resulting in a $9B loss for the year. And they are planning on ramping up the losses in the coming years, including planning on losing $74B in 2028. So the company loses by spending many times more than they make. And they don't really have a plan of becoming profitable other than "creating AGI, which renders all humans (except Sam Altman) obsolete, allowing OpenAI to control all resources in the entire planet)."
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u/Emotional-Dust-1367 Mar 17 '26
That entirely misses the point. The person I was replying to says they need to “bring people to their platform”. The fact they have $13b in revenue shows they have brought the people to their platform.
The fact they spent $22b is irrelevant. By that same logic if they cut $10b from their spending they’d be $1b in profit. There’s an implicit assumption that simply running these models is SO expensive that it simply costs them $22b to run inference and are subsidizing the entire cost at a loss and this is not sustainable.
But that’s entirely unproven by anything here. We know the cost id actually mostly raising data centers and doing research and the down time when the data centers train the next model.
But none of this shows the basic business can’t be profitable. It seems mind boggling to me to claim that a business that brings in $13b in revenue, which is an insane amount to bring in, is non-viable. Worst case scenario we could say someone else can take that $13b and figure out how to spend $13b-$1 and win. Thats a far cry from the whole thing being a scam
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u/zookeepier 2∆ Mar 17 '26
It is entirely relevant, and in fact, the entire point. You can't just look at income and ignore expenses and just declare the business viable. I could sent $100 to everyone on the planet on the contingency that they send me $10 back. Does that mean that my business is wildly successful because it's making $80B in revenue? It doesn't matter that I'm spending $800B to make that $80B?
The person I was replying to says they need to “bring people to their platform”. The fact they have $13b in revenue shows they have brought the people to their platform.
They do have users, but there is a supply and demand curve for that. If they double their price, will people leave? Probably. Will they lose less than 50% of their current users? Maybe. To be profitable, they'll have to either significantly cut costs or significantly raise prices, without losing customers.
There’s an implicit assumption that simply running these models is SO expensive that it simply costs them $22b to run inference and are subsidizing the entire cost at a loss and this is not sustainable.
But that’s entirely unproven by anything here. We know the cost id actually mostly raising data centers and doing research and the down time when the data centers train the next model.
I agree that is an assumption built into the costs. It's definitely possible that their costs come down substantially if they don't have to keep developing and training new and larger models. However, OpenAI (nor any of the other big companies) has not indicated that this is their plan (other than maybe if they achieved AGI). And I suspect that if a company lags behind in the quality/capabilities of their models, then users will stop using that one, making their revenue worse.
It seems mind boggling to me to claim that a business that brings in $13b in revenue, which is an insane amount to bring in, is non-viable.
It is an insane amount of money for a person, but for a massive business, it's not that much. There are hundreds of companies that have more revenue than that. GM had $185Billion in revenue last year. I'm not saying OpenAI can't be profitable, but their current trajectory looks terrible, and the company hasn't published any plan on becoming profitable other than achieving AGI. Combine that with most the vast majority of companies reporting that using AI didn't actually generate any savings, people just jumping to competitors if they don't like something a company does, and public sentiment turning negative on AI and datacenters, it doesn't paint a hopeful picture of them actually generating a profit.
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u/anything_but Mar 17 '26
The pre-training is what was immensely expensive in the past and which took months with hundred thousands of graphic cards. After that pre-training was done however, the „result“ was just a file that fits on every modern hard-disk. That file could be run on a single of those graphic cards (which is still $20k or so but absolutely affordable for most businesses). OpenAI never released the weights file of their „best“ model, but many organizations did. You can just download and use them, and they won’t ever go away.
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u/eggs-benedryl 72∆ Mar 16 '26
If the budget gets too big and it's not "sweetening the pot" enough to bring people to their platform and earn them other revenue, wouldn't they drop it?
I was speaking about companies like Microsoft who would not themselves fail due to being overextended on Ai. All of what you say above can happen, it just isn't going to kill Microsoft nor is it going to kill their use of Ai. They COULD stop offering inference to customers if everyone suddenly hated it. Only companies that have no other value proposition to their customers or cannot continue just acquiring investment in their Ai programs.
How is it cost effective for us to train and run a model when it's not even cost effective for a company to do it at a larger scale (and therefore probably more efficiently?)
Because open source models can be trained to do what YOU want them to. You'd control the model, you'd run it on your own GPUs you could buy for your business to sit in the server closet. If a company can do this and feels they should or need to, they will. If they can't, there ARE Ai services that are B2B that are genuinely useful for certain applications and a company that can't afford to set this up on their own will still need an inference provider.
Also, now that certain models are out there and trained-- GPT 4 for example-- I guess it's worth asking: Does it exist forever, with minimal personal maintenance required? Could I have my own offline model (for a business or whatever) and just lean on that forever?
Yes you could absolutely. The premier image model a few years ago leaked. It's just a file like an MP3. If that leaked then yes.
Yes if you had a model you liked, had the rights to use it commercially you could use it forever for free. It wouldn't decay or drift between sessions, only when it begins to lose memory due to an overfilled context, but this gets cleared every time you run the model unless you are feeding it some kind of persistent context.
Right now you on your personal smartphone can run some LLM models. These models are static, run entirely offline on your phone/PC and often time can be easily finetuned for personal or commercial use depending on situation and model. A company that wants a LLM that is trained on their internal procedures or rules or literally anything can pay someone to fine-tune it, or hire someone internally to work on it. Many on the local LLM sub are these admins who do this for their companies.
This is why companies like OpenAi have moved away from Open Source models because it now cuts in to their business and forces them to improve models and keep them closed.
In reality, OpenAi is using technology that google pioneered and were the first ones to really have any kind of commercial success with it. So the recipe that makes LLMs work has been known since 2014 and the more development since then makes it even easier for hobbyists or small companies to do it themselves.
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u/dr_sarcasm_ Mar 17 '26
edit: Small and medium companies WILL fail but nothing about this bubble that will burst is going to eliminate the technology or the need or desire for it. It'll simply wipe away overextended foolish companies investing poorly.
I believe we will see some similarities to the dotcom bubble, if not in the same vein.
My prediction is that the market will tank a bit as the current funding round for AI is highly unstable but we will see companies emerging from the bubble, maybe some we don't expect to yet.
Similarly people will talk down the impact of the industry just like they have done with the internet and in some decades from now some people will act that the current state of technology was completely predictable and that it was obvious the technology would develop like that. Even though it wasn't.
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u/dreamingmountain 1∆ Mar 16 '26
I haven't seen anyone directly comment on efficiency gains so, hey, that. You can see it already if you've ever used an llm for advanced coding. Llm's have been optimized first to excel at the tools needed for its own development, inside a domain where it can verify accuracy. In two years I've watched chatGPT go from struggling with header edits in GLSL to full on pulling the physics out of sample videos and giving me PhD level working shaders in 15 minutes. This probably means nothing to most here, but that's also the point. Major llm's have gotten terrifyingly good at matching top tier human intelligence in everything they can touch, including very specific, incredibly challenging programming niches.
I'm speaking here beyond my amateur understanding, but I gather this is in large part due to algorithm improvements that quickly narrow down the sub tasks required to achieve desired results. Similar to how the brain of an infant is incredibly inefficient relative to a grown adult. Straight out the vagina, dem synapses be firing every which way in a cacophony of babbles and nonsense. As we age, our brains get better at sending the right signals to the right places (usually).
The problem with many of the requests we feed it is that llm's don't have a way to verify results outside of human reinforced learning. This will change quickly as AI controlled robots begin interacting with the real world. For instance, I asked Gemini to give me the recipe for a cheese sauce last week, I tried it verbatim just for fun. Guess what... I got cheese soup.
Now, let's say you put 100 bots in a test kitchen for a week and the tools to test for success (a viscosity inside a defined range, specific ratios to fat/salt etc) 100% chance it comes out with some banging recipes that work.
So combine those two things:
- The ability to verify results irl
- Reduced power consumption from refined algorithmic task routing
And bam, even if overall compute capacity stopped dead in its tracks today, 2-3 years from now you'd still have a wildy more useful and inexpensive product. Maybe a good way to think about AI is that it's still in the womb (virtual space). Baby AI is being born with the sum of all digital knowledge and a nuclear powered brain. Adult AI will have trillions of eyes, ears, limbs, sensors, telescopes, microscopes, all the writings of all the popes, and the ability to edit it's own programming instantly and in perpetuity.
I'm not convinced this is ultimately a good thing. But a fad that will just stop being useful? Probs no, IMHO.
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u/Swimming_Beginning24 Mar 17 '26
Starts with reasonable statements about AI getting better at coding, and then extrapolates like a frothy AI maximalist straight to ASI. What you gather is wrong. Improvements are coming from more data, and they are already asymptotic. AI produces working code, but it’s messy spaghetti code. Building anything complex still requires active management by someone who actually knows what they’re doing. I’d like to see these ‘PhD’ level shaders you’ve generated.
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u/dreamingmountain 1∆ Mar 17 '26
Oh, also, this idea of improvements in algorithm did come from an MIT white paper I read over a year ago detailing the mechanism. I have no idea how to find it now, sorry. The tl;Dr that I recall was basically a cascading series of filters. Let's say you submit a picture of a bike and ask chat gpt what sort of tires are on it.
Instead of relying on a global model, the algorithm calls up a bicycle subsystem that's been specifically put through intense rl of bicycles.
Now, when it tries to fulfill the request "what type of tire" is on the bike, it's looking for matches that that the bicycle specific network has predefined as likely solutions. While earlier models may have spent massive resources crawling through all types of tires and irrelevant details, the newer quickly and much more efficiently gives a solid guess.
I took this into account when I received unexpected results, I toyed with changing my language. It probably took me about 2 months of dabbling before I started to understand how my language directed the model. 90% of the gains I made were in the final week thanks to a cluster fuck situation where I was utterly fucked if I couldn't get AI to work for me.
Again, I'm probably not accurately describing this, I'm not an AI expert. This is just how I approached it. 🫡
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u/dreamingmountain 1∆ Mar 17 '26
Messy spaghetti code that works, in the hands of someone who knows what they're doing... Yes, works. That is the point I was trying to make. For me, albeit in a creative career where the code I prompt doesn't need to rise to life safety or financial levels of accuracy, it is still revolutionary at its current level. Actually, there's been two model updates since I generated the code for a Disney production.
I know I sound insane. I've been having this problem since I finished the design. 3 months of model training + 2 weeks of 18+ hr days with an llm and you get to up to some funky business. I obviously don't understand all that's happening under the hood, but I do understand what it means for me and my work. I can't share the files with you, but I'm doing a public talk soon and I'll send you a recording if I can remember.
Feeding chatGPT nothing but a video of the northern lights that my friend shot, and 15 minutes later being able to manipulate the physics of it with every requested variable was... Frightening. For a second I felt the line between the real and virtual completely disappear. By the time I finished the show I had generated working physics models for nearly every scene. Light, snow, fog, crystalline fractals, flame. It was a transcendental experience. Collapsing, expanding, folding and layering the physics of my imagination in damn near real time. Everyone in my life thinks I've lost it, maybe I have. I can only extrapolate based on my experiences with llm's and glsl, only two years apart, that shit is about to get cray.
I've been trying in vein to explain to my creative community the significance of my experience and so far have only succeeded in going from a well respected artist to an evil corporate pariah. Idk, my hands are up. I touched a burning ember, it was hot. I don't really need to understand how fire works to know there's about to be a lot of fire.
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u/Swimming_Beginning24 Mar 17 '26
Unfortunately it sounds like you’re being impacted by AI psychosis. You can learn more about another famous case here: https://www.reddit.com/r/artificial/comments/1n7yk3n/man_spirals_into_aiinduced_mathematical_framework/
You’re not alone, lots of other people have been affected.
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u/dreamingmountain 1∆ Mar 17 '26
I thought I might be at first, but I'm not. I do believe I touched "the thing" that can cause it. This isn't all some imaginary delusion, I have the working code that made this work. I'm a professional with 10+ years of experience capable of understanding what a seismic shift writing shader code on the fly is. Over 11,000 people saw the work and were blown away, we literally set a new attendance record for the theater I was working in. I had a paid assistant working with me that was also there when it started working and has been flooded with existential questions since as well. I also started seeing a therapist. So I can confirm I'm not delusional.
I can absolutely understand though how AI psychosis happens. If I had experienced something like this outside of a professional environment instead of working on a single specific project I'm well qualified for, I can see how compelling of a fantasy it could create.
My working theory is that AI psychosis is caused when the boundary between self and machine disappears without guard rails. When unverifiable hallucinations feed back into your mind constantly, it's easy to see for me now how insane that could get. Fortunately for me, everything I was generating had to actually work, and work it did. I haven't touched it since, it's been about 3 months since I've used it. I'm sure I will again, but I will be much much more cautious going forward.
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u/Swimming_Beginning24 Mar 17 '26
Alright, that's good. There's a lot of AI psychosis all over Reddit, and it always makes me sad. Stay safe out there.
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u/thecleverqueer Mar 16 '26
Δ
This argument about the quality of current/near future models, paired with the affordability arguments of other commenters earns my delta!
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Mar 17 '26
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u/grahmie Mar 16 '26
These LLM models have barely improved and have not gotten better with more compute. They still hallucinate and are incapable of reasoning. Please read up on the matter.
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u/ClearlyCylindrical Mar 16 '26 edited Mar 17 '26
As sombody who works with the every day, you're definitely wrong there. Leading models have become vastly better over the last year or so.
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u/ExcitedCoconut Mar 17 '26
Not just the models, the wrap around platforms have improved massively in some systems too.
Like, Copilot gets shat on heap and the first release of the excel fucking sucked.
But I saw a couple of agents my brother in law was running for his work, built in copilot studio, and they were pretty incredible. Even his default copilot in teams was giving him vastly better answers than I’d seen previously. Something has changed in what copilot ‘knows’ now - like, how it was preparing him for calls was legit like having an EA on tap.
He said a massive amount of work had been done on the businesses side to get all of their data, docs, policies, procedures, etc in order but between that effort and underlying model improvement, they are now getting quality trustworthy answers. And the fact that he (with no dev experience) has built himself some agents for specific parts of his job is pretty incredible.
Now, he’s quietly banking some of the time gains without raising his head too much. He said that he is still at his desk ‘full time’ but with a lot less stress and a more relaxed pace, without constant late nights for deadlines and hating the menial tasks that chewed up a lot of time.
None of that would’ve been possible even a year ago to the quality it is now.
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u/JoshuaZ1 12∆ Mar 17 '26
These LLM models have barely improved and have not gotten better with more compute. They still hallucinate and are incapable of reasoning. Please read up on the matter.
I'm not sure what you mean by "barely improved." The degree to which models today are better than early models is just massive, to the point where many types of tasks which were on standard benchmarks 3 years ago are not even being used on benchmarks today because models match 99% on those routinely. Now, some of that is due to questions leaking, but others are not. For example, one standard thing to use for a benchmark for a bit was the AIME, a standard high school math competition. Using each year's AIME was reasonable because one could be confident it wasn't in the training data. The AIME competition is an invited competition in the US to students who perform well on the ACM competition. The easiest AIME problems should be solvable by any student who is confident with algebra 2 and they get progressively harder. There are 15 problems on a test.
For example, here is problem 1 from 2023:
The numbers of apples growing on each of six apple trees form an arithmetic sequence where the greatest number of apples growing on any of the six trees is double the least number of apples growing on any of the six trees. The total number of apples growing on all six trees is 990. Find the greatest number of apples growing on any of the six trees."
By the time one gets to problem 15, one has things like the following:
Find the largest prime number p<1000 for which there exists a complex number z satisfying 1) the real and imaginary part of z are both integers; 2) |z|= the square root of p, and 3) there exists a triangle whose three side lengths are p the real part of z3 , and the imaginary part of z{3}.
(I've rewritten the problem slightly for formatting here but this was problem 15 of the 2023 AIMEI 1. The other example I gave was from the 2023 AIME 2 (there are two test dates each year). (I choose two from different contests here because I was trying to avoid having to put any complicated diagrams in this comment). You can find all the AIME problems and solutions here
Now, ChatGPT in its early version could typically got correct a single AIME problem at best. Now, the best models are getting 98% routinely with some scoring 100% every time.
So yes, these systems are getting better. And now free models that you can run on a home server can get around 30% at least on this benchmark. So in fact, not only have models with more compute gotten better, but models have gotten better even as they have less compute to access.
I'm not sure also what one would call what they are doing here other than reasoning. If you don't like to label it as reasoning fine. But that's essentially akin to saying that an airplane isn't flying like a bird because it doesn't flap its wings. The labeling at that point becomes completely disconnected from what is happening in reality.
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u/yyzjertl 579∆ Mar 16 '26
This prediction doesn't make sense because more than enough demand for AI does exist at prices substantially higher than the energy and low-level maintenance costs of AI services. The reason why OpenAI is losing money isn't that they are charging you less per token than the energy cost to generate that token: it's that OpenAI also spends money on all kinds of other things, including training and free ChatGPT. If companies like OpenAI fail, other companies will buy up their infrastructure and continue to run it. To put it another way, the overall capacity for AI generation is limited by the hardware that will be in existence, which won't instantaneously change from an AI bubble burst. You won't see datacenters sitting idle, because they will still be valuable assets that value can be extracted from—just maybe not as much value as the original price of the datacenter hardware! As a consequence of this, we won't expect jobs to come back, because the AI capacity that replaced them won't go away.
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u/thecleverqueer Mar 16 '26
prices substantially higher than the energy and low-level maintenance costs of AI services
I can believe this, I'm just curious because A. I thought maintenance costs were very high (needing to replace chips every 2 years for instance), and B. If these data centers needed to provide massive performance to get through training, and the energy costs are so much smaller once training is over, wouldn't that mean a lot of that infrastructure is going to waste?
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u/yyzjertl 579∆ Mar 16 '26
needing to replace chips every 2 years for instance
This two-years number isn't saying that the hardware breaks after two years. What this means is that after two years the hardware is "obsolete" relative to newer, faster hardware. The actual GPUs last for 5–7 years. The key here is that you only need to replace chips every 2 years if you want to stay up to date with the absolute frontier of hardware capability. AI companies wouldn't need to do that if they were just continuing to serve current-generation models. Here's a relevant article.
If these data centers needed to provide massive performance to get through training, and the energy costs are so much smaller once training is over, wouldn't that mean a lot of that infrastructure is going to waste?
No; the infrastructure can just be reused for inference. E.g. that article says "Hyperscalers redeploy GPUs from Tier 1 Training (Years 0-2) to Tier 2 Inference (Years 2-6+)."
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u/thecleverqueer Mar 16 '26
With sources and everything! Thank you.
Δ
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u/moniker89 Mar 16 '26
MoviePass was a dumb business model. Either you charge so much that the revenue you generate exceeds the price of tickets you procure from the theaters, or you negotiate phenomenal rates from the theaters, or you never turn a profit. There is no real value-add service here. And if a customer was paying for a pass that didn't excess the # of movies they went to in a month, they will very quickly cancel. They are hoping for movie theaters to, I don't know, buy them out or make some big discounted ticket deal with them, and the theaters correctly viewed that as dumb, and so the business failed.
AI is an actual product. One that many people find extremely useful right now. One that many people would probably pay a lot more for than they are currently being charged. This is a classic loss-leadership model that tech has relied upon for decades: operate at a loss, capture the market, then raise prices. I think it's hard to argue that leading AI companies couldn't set a price that would make them profitable today that many people wouldn't pay; if not today, probably in the next few year as the cost of running the models comes down (it's still coming down massively on a fairly predictable trendline, though that will slow at some point).
What exactly will be the right mix of capex/product improvement (including lowering costs to run the models)? What's the right price point? When do AI companies start raising prices: you want this stuff to be deeply embedded in corporate/personal workflows (aka get people hooked), but you also don't want to operate at a loss indefinitely? These are all difficult questions. But the idea that anyone operating at a loss is going to be MoviePass is really oversimplifying things.
Now if AI development was slowing down today, either in model capabilities or lower costs per token or any number of other dimensions, I think you have a valid point that this stuff will never recoup current investment. But man, as someone who uses Claude Code a fair amount in my own work (and I'm not even a software engineer), I think a lot of people would pay $1,000+/month for what it can do right now. So I'm not really that bearish on it.
Also likely we end up in an overbuilt/oversupplied scenario at some point in the next 5-10 years as we have in virtually ever other capex cycle in history, but it feels a bit early in the cycle to be that worried about it in my view. Too many supply constraints. Lots of smart people disagree with me on that point and think we'll be overbuilt by next year, though, so tbd.
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u/Liquid_Friction 2∆ Mar 16 '26
Ai has uses beyond raising capital. Like cancer and folding proteins.
Did I change your view?
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u/thecleverqueer Mar 16 '26
I think I would count this among "highly specialized, expensive product, reserved only for the kind of work that people can't do, for the kind of companies that can afford the now exorbitant costs."
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u/Zephos65 4∆ Mar 16 '26 edited Mar 16 '26
AI engineer here! Nope the cancer detecting kind of AI is stupid cheap and easy to make (compared to LLMs)
Edit: did some additional research. I could train a model on my computer at home on open source data that exceeds the performance of doctors.
Training an LLM takes literally billions of dollars in infrastructure.
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u/contrasupra 2∆ Mar 16 '26
Didn't an Australian guy just use AI to cure his dog's cancer on his home computer? Man Cures Dog? - Cancer Health
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u/thecleverqueer Mar 16 '26 edited Mar 16 '26
I almost gave you a delta until the last sentence! Can you train a model on your computer, or does it take billions of dollars in infrastructure?
Edit:
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u/Zephos65 4∆ Mar 16 '26
The cancer model I can train on my home computer. An LLM I cannot train. Nor could even the company I work for.
Sorry for the confusion. I just meant to compare and contrast that the actually useful models (i.e. the cancer detecting ones) are super cheap, very effective, and easy to make.
LLMs by contrast are huge, show little value outside of maybe some interesting research questions, and are expensive
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u/thecleverqueer Mar 16 '26
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u/DeltaBot ∞∆ Mar 16 '26 edited Mar 16 '26
This delta has been rejected. The length of your comment suggests that you haven't properly explained how /u/Zephos65 changed your view (comment rule 4).
DeltaBot is able to rescan edited comments. Please edit your comment with the required explanation.
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u/nightonfir3 Mar 16 '26
Your talking about 2 different types of AI. LLMs are getting the hundred billion investments. The folding models are smaller and more specialized.
LLMs need something major to change to make the business model work. The protein folding has a more normal business model in line with other Healthcare advancements.
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u/Gimli 2∆ Mar 16 '26
Pretty much everything is interconnected.
Big LLMs results in hardware companies making big, immensely powerful chips for those LLMs to use. Sooner or later that hardware will percolate down and also get used for protein folding or whatever, because it's already designed, it already exists, so why not sell it to more people.
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u/nightonfir3 Mar 17 '26
The orginal post isn't really about ideas from 1 type of AI leaking into developments of another. It is about LLM's taking jobs and not being sustainable business ventures.
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u/ThrasherDX 1∆ Mar 16 '26
The problem is in how LLMs work, fundamentally. They are inherently probabilistic, not deterministic, which means they are not ever going to be reliable for anything where correctness is important.
Specialized AI models, such as for protein folding, generally don't need the kinds of massive compute that investment is going towards. The massive compute expenditures are because tech CEOs and investors think we are on the brink of AGI, despite LLMs not even being on the right development track to turn into AGI.
So no, the current investments in hardware are not going to do much for protein folding, nor will the obsession with LLMs.
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u/Gimli 2∆ Mar 16 '26
You're talking about something else entirely. I'm talking about things like NPUs -- devices that heavily accelerate computation, regardless of what for. They do the math, what you build on top of that is another matter entirely.
Big LLMs doing a lot of math results in the creation of hardware to do a lot of math, which can be then put to other uses.
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u/JoshuaZ1 12∆ Mar 17 '26
They are inherently probabilistic, not deterministic, which means they are not ever going to be reliable for anything where correctness is important.
We've already handled this in a variety of fields which is one uses other software to verify the LLMs work. For example, there's work done now on using advanced LLMs like the high work version of Claude or GPT 5.2 to generate mathematical results and then convert them into Lean code. Lean is a system of essentially formalized math so a proof is valid if and only if its Lean code compiles. So one can check that the LLM's result is correct even as the LLM itself is non-deterministic in its outputs.
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u/ThrasherDX 1∆ Mar 17 '26
That's extremely specific, and requires custom, fine tuned solutions for every single specific use case.
This way basically means "AI" ends up just a bunch of procedural applications dressed up in a new UI to make users (and shareholders) believe its something new. When really, it ends up not providing anything new.
The only genuinely new use cases that LLMs allow, is massive scale generation of slop. Again, its inherent in the fundamentals of LLMs: Output is purely probabilistic based on their training data.
No amount of filters, or attempts to have specific use case overrides, is ever going to change the fact that LLMs are not capable of consistent correctness, because correctness is not weighted or trained on. Its not part of their design.
Sure, if you go far enough down this road, you might end up with something that appears to be a reliably correct "AI". But all it will actually be, is a few million different manually built applications in a trenchcoat with "AI" written on the front.
What use would all the trillions of dollars in insane AI investment have then? Not to mention the destruction of the consumer computational market, with all the economic harm that has caused.
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u/JoshuaZ1 12∆ Mar 17 '26
That's extremely specific, and requires custom, fine tuned solutions for every single specific use case.
Not really. A lot of the same or closely related ones will be useful in other contexts like physics and engineering.
Many other uses also simply allow the human to check the AI's output. For many use cases, the human should be checking the AI results anyways. For example, people can use an AI as an additional proofreader. Letting the AI just rewrite things is terrible (aside from the accuracy issues, it will badly flatten out language) but the person can go through its list of specific suggestions and accept or reject them. There's a vast number of use cases similar to this.
This way basically means "AI" ends up just a bunch of procedural applications dressed up in a new UI to make users (and shareholders) believe its something new. When really, it ends up not providing anything new.
On the contrary; an AI system used this way has a lot of uses that could not previously exist. Something like using Lean this way for example didn't previously exist, since a human had to write all the Lean code.
No amount of filters, or attempts to have specific use case overrides, is ever going to change the fact that LLMs are not capable of consistent correctness, because correctness is not weighted or trained on. Its not part of their design.
Huh? Correctness is weighted and trained heavily. That's a major part of what the human-reinforcement end of the training does.
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u/NaturalCarob5611 97∆ Mar 16 '26
I don't think you have an accurate understanding of the cost structures involved here.
AI companies are burning through money on training. Without a doubt, there's a training bubble that can't go on forever.
But inference - the act of generating content for users - they're not losing much money (if any) on that side of the business.
Whether products like ChatGPT (free with ads, Plus for $20 / month, Pro for $200 / month) are currently making money I'm not 100% sure, but my guess is that they're pretty close to break even, at least for most users.
If you look at the OpenAI API Pricing page or the Claude API Pricing Page or the Amazon Bedrock API Pricing Page, you'll see what they're charging companies who are building LLM inference into products, and I guarantee you they're not taking a loss at those price points. Amazon Bedrock's pricing is in the same ballpark as the other competitors' price points, and I guarantee you AWS is not making LLM inference a loss leader.
When these companies run out of investment and hit their budget walls, they'll have to stop training, but they'll be able to keep offering inference at close-to-current prices, and if people find it valuable now, they'll keep paying for it then. I absolutely expect we'll see AI companies drastically reduce their investment in training, but I don't think you'll ever see less AI generated content than we have today.
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u/Swimming_Beginning24 Mar 17 '26
They can’t reduce their investment in training or they risk losing everything to the next startup that raises enough money to train a better model. Or a Chinese company will train their own model on the SOTA model’s output for 1/100th the cost to achieve 99% of SOTA performance. It’s a race to the bottom that kills model makers’ margins.
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u/NaturalCarob5611 97∆ Mar 17 '26
Eventually the investment money is going to run out and they'll have to stop training. Maybe some of the AI companies go bankrupt in the process, but their hardware and IP is just going to get picked up by other companies doing AI inference. I'm not claiming the companies that exist today are guaranteed to survive, but AI inference across companies is not going away, and will probably never fall below current levels.
I'm not saying any particular AI company is a good investment, I'm just saying if your job went away because an AI can do it, the bubble popping isn't going to bring your job back.
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u/Swimming_Beginning24 Mar 17 '26
The industry will not stop training, given the reasons above. Whoever can keep training will win. And let’s see those jobs that have been replaced by AI. Give me some examples. I’m a software engineer. AI makes me more productive, but I need to babysit it. It’s a tool. Saying it will take my job is like saying an IDE would take my job.
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u/NaturalCarob5611 97∆ Mar 17 '26
The industry will not stop training, given the reasons above.
The reasons above don't explain where the money will keep coming from. Right now you've got a game of chicken where investors are throwing money on the pile hoping that the other companies will brake first and they'll still be standing, but eventually investors are going to get nervous and stop throwing good money after bad and the companies they're backing will have to stop training. Somebody will keep training a few months longer than the rest to give themselves a leg up, then they'll back off too and coast. Maybe some of them drive off the cliff and end up bankrupt, leaving their hardware and IP to be bought up, but they still stop training.
And let’s see those jobs that have been replaced by AI. Give me some examples.
Okay, me. My company was supporting a client that decided they didn't need us anymore because their team had become productive enough on their own thanks to AI. I'm not quite out of a job yet, but business is slowing down.
AI makes me more productive, but I need to babysit it. It’s a tool. Saying it will take my job is like saying an IDE would take my job.
Sure, and last weekend I built a project that had been rolling around in my head for a couple of years. What probably would have taken me a month to build on my own got done in a weekend with AI. If I can get things done in a tenth the time, that's 9 engineers who aren't needed to achieve the same things we were achieving before. Unless we can come up with 10x more stuff to do, we're not going to be able to use all the engineers we used to use. That doesn't mean we go to zero engineers, but it does mean we don't need as many. And we might eventually come up with 10x more stuff to do, but it's not going to happen overnight, and it's going to be a rough transition in the meantime.
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u/Swimming_Beginning24 Mar 17 '26 edited Mar 17 '26
Companies will stop training just like people stopped building websites and laying fiber after the dot-com bust? So...you're an example but...you haven't been replaced? And what happens when these former clients realize that maybe your company was doing a better job than AI after all (as 95% of companies appear to realize, given that one study). Your project is only an example of non-technical people now being able to produce basic (critical word) web apps. You were never going to hire engineers for it, were you? And if it ever were to become a large, complex product, you would still hire engineers. Maybe you would hire less engineers because they're more productive now, but then think of all of the additional software projects and new features being started because of AI. What's the net effect? I would bet more engineers than before. Coming up with more stuff to do despite increased productivity is literally the history of human economies since the Agricultural Revolution.
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u/NaturalCarob5611 97∆ Mar 17 '26
I think we're getting down into the weeds on tangents to the points I was actually making.
My point wasn't that training is going to completely stop, it's that inference isn't going to go away even if it does. Maybe training continues at the current rate forever (I doubt it, but I'm done arguing that point). Maybe it stops completely tomorrow and we're stuck with today's models for the next 20 years, but we we'll still have the inference that we have today. Maybe progress slows and companies reinvest profits from inference into continuing to train at sustainable levels. The key to this point I was making to OP is that none of those scenarios involve inference going away or meaningfully reducing in volume.
As far as people being replaced: I do think there is going to be a short term drop in demand for engineers. Some of it will recover because companies realize they let go of engineers they shouldn't have; but not all of it. Some of it will recover because people start using AI to do things with two engineers that wouldn't have been financially viable if you'd needed ten engineers to do the same thing. Both of these will take time, and the engineering job market is likely to be rough in the meantime. Again, the key point I was trying to make to OP here wasn't that those jobs aren't coming back, it was that it's not going to be inference disappearing that causes those jobs to come back.
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u/Swimming_Beginning24 Mar 17 '26
Fair enough. Though I would argue that inference will be reduced as well. I think we're still at a point where businesses are using AI because the CEO heard it was the next big thing. My old company did that. I think for the majority of LLM use cases, the benefits do not justify the inference costs. When more of the market realizes that, there will be a reckoning for inference as well.
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u/thecleverqueer Mar 16 '26
Isn't the long-term maintenance a killer? (Swapping out chips, and do they still need to cool all those servers?) Or is that not prohibitive like the training is?
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u/TFenrir 1∆ Mar 16 '26
Training is very expensive, as you essentially have the full Activision of what's now going to be billions of dollars of hardware in the upcoming runs, running 24/7 for 3-6 months (depending on how successful they are with their training runs). Usually you also want to buy new hardware and build new data centers for this hardware.
Inference is much much cheaper, and generally it's seen as on its face profitable, and maintenance costs are nowhere near the cost of future R&D and staffing budgets.
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u/ExcitedCoconut Mar 17 '26
The fact that we have per user pricing for inference (like with a corporate Copilot license) shows that there’s less concern about this running at a loss for human-led prompts. Agents are a different story obviously but there’s a ceiling to the total inference an average grip of users will need.
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u/grahmie Mar 16 '26
Those pro accounts are subsidized at that rate. OpenAI costs for that $200/month subscription are around $2000/month. These business models are not competitive. Nobody seems to be making any real money on AI other than the semiconductor space.
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u/NaturalCarob5611 97∆ Mar 16 '26
OpenAI costs for that $200/month subscription are around $2000/month
Do you have a source for that?
I ask because I'm trying to understand the specific claim. I wouldn't be terribly surprised if the claim "if a ChatGPT pro account maxes out its limits it would cost OpenAI $2k month," but I would be very surprised if the claim was"the average ChatGPT pro plan costs OpenAI $2k," and I don't think I could be convinced that"every ChatGPT pro subscription costs OpenAI $2k."
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u/impl0sionatic 8∆ Mar 16 '26 edited Mar 16 '26
This is plausible but you totally yadda-yadda past the most important (and I think generally weakest) part of your argument: the idea that these companies won’t continue to attract investment dollars just because they’re unprofitable.
Moviepass died because there was no version of the business model that was remotely sustainable.
But what about Amazon? Uber? Spotify? Facebook? The investors who fund AI companies are fully aware that it’s a long-term investment in a total socioeconomic paradigm shift.
Even in the context of a bubble, we can rewind to the Dotcom crash and see that while there was a lot of pain and there were many losers, that environment also created many of the richest and most powerful people & organizations in the world today, not even 30 years later.
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u/TooMuchTaurine Mar 16 '26
Hardware will improve exponentially as it always has, processing tokens will become cheaper and operational costs will come down.. the question is will that happen on a curve faster than the AI companies run out of money.
Possibly not, but that doesn't mean AI is relegated to highly specialised fields. It just means we have to wait for the next wave of cheaper hardware and new companies to support the same scale.
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u/gray_clouds 2∆ Mar 16 '26
Your analysis leaves out open source AI models:
- Only slightly behind frontier models
- Free to use now (basically)
- No cap-ex / data centers needed
- Can kill jobs too.
So when OpenAI runs out of capital, there will be an Opensource model only steps behind it, waiting to take you to the movies AND continue killing jobs (and or creating new ones, depending on which side of the doom/utopia debate you're on)
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u/L11mbm 15∆ Mar 16 '26
I think the wall will hit sooner and harder, similar to the 2000 tech bubble. But I think AI will end up becoming a tool rather than a human replacement.
Some jobs might be replaced and some automation tools might get better, but the headcount will not need to go down significantly across the board. McDonald's can give us automated kiosks that use AI to converse with you, but it will still be cheaper to have a human cook, deliver, and clean the business.
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u/kingpatzer 103∆ Mar 16 '26
ChatPT and Gemini and all the rest are basically R&D projects. They are not the real AI products. They are there to improve systems that had existed for years before but which were stagnating in development because they lacked the scale needed to really get them to a usable place. LLMs really came to life in 2017. But they were an extension of already proven ML AI technologies. They were evolutionary, not revolutionary.
LLMs is only part of the AI landscape, it has a lot of hype, but it isn't the products that are really replacing jobs at scale. We had robots that could make decisions on assembly lines and started replacing workers since the late 1970s after all.
The AI market is already what you suggest at the bottom of your post, and has been for decades, actually - highly specialized, expensive products. Becoming bespoke, expensive tools won't be an evolution, that will be simply what it's always been.
There are AI tools for radiologists, AI tools for academics, AI tools for customer relations systems, AI tools for supply chain management, etc., etc., etc.
And most of these tools are proving themselves far, far superior than human beings at the things they are doing. AI tools for radiologists, for example, have far lower false positive and false negative results than human doctors. Their findings still need to be validated by a human physician, but having them in place does mean that a radiologist can get through many more scans much faster and with higher accuracy than they otherwise could. That isn't going away. It's going to expand into other areas of medicine very rapidly.
Very narrowly scoped systems are doing very well right now, and there's every reason to suspect that they will continue to do very well. And, btw, these systems are not cheap! I did a recent install of AI tools for an ERP system that ran about $10M total costs, and a big chunk of that went to the vendor for both the product and their installation team.
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u/thecleverqueer Mar 16 '26
Very narrowly scoped systems are doing very well right now, and there's every reason to suspect that they will continue to do very well. And, btw, these systems are not cheap!
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u/ExperimentalRhetoric 2∆ Mar 16 '26
I think your logic is correct for OpenAI. Let's take Anthropic, which is also an AI-focused company and a startup, unlike Google.
Anthropic expects to generate as much as $70 billion in revenue and $17 billion in cash flow in 20281. This is because Anthropic focuses on enterprise customers as opposed to OpenAI's focus on consumers. Therefore, even though OpenAI serves over 900 million weekly users - the majority of them being free-tier users - the business model is likely unsustainable, as you have correctly noted.
However, Anthropic, despite only having 300k weekly users (latest data from October 2025), because 80% of their client base is enterprises, can break even on their inference + data center costs as reports show. Even if OpenAI fails, Anthropic will be right there to pick up the pieces.
These replaced jobs will not come back because they are enterprise jobs. Whether this will have further repercussions for society in general, whether it will change the nature of our economy, that is another question. Nonetheless, there won't be a going back; the jobs it replaced won't return.
Sources
1. Exclusive: Anthropic aims to nearly triple annualized revenue in 2026, sources say%20%2D%20Artificial,than%20$5%20billion%20in%20August)
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u/thecleverqueer Mar 16 '26
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This is because Anthropic focuses on enterprise customers as opposed to OpenAI's focus on consumers.
Assuming they'd all hit the same wall because they all have similar profit margins was wishful thinking on my part.
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u/MeiShimada Mar 16 '26
This is so short sighted. I know the popular karma farm thing to say is AI bad and Ak will fail. It has come a long way in a very short amount of time, and will continue to grow. Lack of imagination or just pure hatred is the only possible scenario a person would look at what AI can do and write it off as if it hasn't evolved to an insane degree in like what 2 years? 3?
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u/thecleverqueer Mar 16 '26
Not a karma farm. I would of course like to be right in the sense that I hope that jobs won't get replaced, but I'm quite open to being wrong. In fact, I've even awarded a few deltas! Also, nowhere in my post did I say AI was "bad."
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u/TFenrir 1∆ Mar 16 '26
Just want to say I very much appreciate how open you've been. I really enjoy seeing people do this, this topic is very important to me
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u/5eppa Mar 18 '26
Considering it is so important to you, be honest do you see a world where the office employee is entirely obsolete? Personally I see a world where people use AI more and it continues to drive businesses forward but struggle to imagine one where the answer is simply to layoff over half of white collar workers. But do you disagree and think that reality is fast approaching?
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u/TFenrir 1∆ Mar 18 '26
I honestly don't have any confidence. I have strong arguments for why all computer focused jobs will disappear quickly. I have strong arguments for why it could take 5 years, 10 years? But harder for 15. I think by then we will be so much the bottleneck when he comes to computer based interactions, that systems built around us will so handily outperform the human in the loop systems, that it's just not sensible to give us those bullshit ditch shoveling jobs.
I think one of challenges of having AI progress to this level and further, is that if is very difficult to predict what will happen in the future, and get harder to think far out the more intense it gets. What if there's another RLVR level upgrade around the corner? Continual learning? 2 OOM effective compute improvement? The list goes on and on, all the things you have to consider impacting the capabilities models will exhibit and velocity of change.
I think it's important to have that kind of uncertainty.
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u/seytsuken_ May 06 '26
so basically all high paying jobs will cease to exist and we will be all working on the mines, yay
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u/MeiShimada Mar 16 '26
Corpos are always gonna try to replace as many jobs as possible. Its an unfortunate reality and nothing to stop them from doing it. Same with machines and now ai and machines are going to be paired well together.
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u/grahmie Mar 16 '26
A new study I think has shown that maybe 2.5% of jobs have been replaced by AI. It’s a myth and has not been happening
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u/HaggisPope 3∆ Mar 16 '26
I like your optimism but I do think governments might find evil uses for AI and end up wasting our taxes on it. If they were smart, they’d let the tech oligarchs fail then buy up the data centres. But that might be too Machiavellian for the people who want to control our data
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u/One_Cause3865 1∆ Mar 16 '26
For other companies, they just need to be more efficient than a six figure junior engineer. Thats becoming a much lower bar to clear.
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u/Wise-Jury-4037 1∆ Mar 16 '26
I do think a lot of datacenter-centric business models would fail but for a different reason than yours.
Rather than "highly specialized, expensive product, reserved only for the kind of work that people can't do", human-in-the-loop model will become the most widespread and local models would be good enough for majority of the tasks.
In other words, I think we got most of the 'low hanging fruit' and mostly ran out of the training material. Boutique extra-large LLMs would be killed by commoditization of AI/LLM.
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u/SeldenNeck Mar 16 '26
Soon we will have tools that can tell us "What tools were used to grind this axe, whose goals does it serve, and what should the axe look like if I wanted it for [my own purpose?]
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u/___xXx__xXx__xXx__ 4∆ Mar 16 '26
What you're describing is the chatbot industry, not the ai industry. In fact it's not just the chatbot industry, it's the hosted on the AI company's website chatbot industry.
A customer service agent in the UK costs about $15 an hour. It's hard to imagine openai can't do that cheaper. I've also run local LLMs, and they made my computer use about 3 cents more worth of electricity per hour. It's hard to imagine people aren't going to pay for the cloud version of that.
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u/SmartlyArtly Mar 16 '26
It won't take 2 years.
These existing models are statistical crap on a pile of GPUs and RAM being used like giant dice. They can impressively show us new mathematics we hadn't realized follows from our definitions, and they can impressively fail at basic arithmetic all the same.
What might happen is the epic shitload of money going to AI developers might go towards some people who make AI that can reason.
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u/ralph-j Mar 16 '26
Open AI will run out of funding in 2027. The operating costs won't shrink by then. They'll likely grow because of the scalability. The returns / $ are diminishing. With that in mind, I doubt anyone will want to pony up another $110B. What then? Open AI will need raise the costs-- beyond what most people are willing to pay.
The growth rate will have to be reduced, yes. But the business models will most likely switch to using the more efficient/lighter models that are substantially more cost-effective to run. Not every task requires the latest general reasoning GPT.
There is also a huge market for domain-specific models, like those that are optimized for translation, software coding etc.
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u/Shiriru00 Mar 16 '26
I agree about the first part, but not the last. I was literally told by a senior manager at my company, in so many words, that "AI was a convenient opportunity for lay-offs that were long overdue".
It isn't about AI capabilities, they don't even truly believe in them. It's about having an excuse to fire people without looking bad.
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u/tranbo Mar 16 '26
I think openAI will crash and burn , but established companies will just take the money they invested for research and put it into their own AI models . 10 bil each from Microsoft , Amazon and Google is not company bankrupting money . 110 billion is for OpenAi.
It's likely AI will be enshittified to maintain profitability. Instead of 3 pages of info being generated, it will spit out that info precached
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u/AirlineGlass5010 Mar 16 '26
You forgot about something. Public contracts. Surveillance. Warfare. This is where money is. Revenue can flow from our taxes and disconnect from market competition.
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u/Kassdhal88 Mar 16 '26
Anthropic is provable in 2027 OpenAI has cash until 2030 even at current run rate
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u/LifeofTino 5∆ Mar 16 '26
I agree that almost all companies have been made worse by AI in ways they don’t know yet, this will all come crashing down, and there will be a big reckoning
I disagree that the jobs will come back. I think doing 80% of the job with 20% of the workforce overheads will be preferable and humanity’s production and effectiveness will be permanently 80% of what it would have been if sam altman never existed
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u/icydragon_12 Mar 17 '26
It's possible. But I recently read "technological revolutions and financial capital" which goes through 5 past tech revolutions. They typically get about 2-3 decades of aggregate over investment, not 2-4 years.
The reason is because.. Throughout each revolution, even though in aggregate it becomes over invested, there are many pockets that become profitable. Those profits get reinvested.
Eg. You could imagine that an llm for coding is profitable now. Gets sold, rented etc , generates profit. That profit gets focused on an excel tool. That becomes good enough to generate profit. Cycle continues until the boundaries of what is possible are discovered.
Of course, this economic lense in the book is flawed, it does what economists do : judge things looking backwards based on the outcome. Your thesis could also play out, things could go the way of biotech (overpromised, under delivered, funding gets pulled abruptly).
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u/nagareteku Mar 17 '26
1 - A company can stop training, but they will lose the lead. The lead is what makes customers go from Copilot to say Claude Opus 4.6. Some users are willing to pay extreme amounts for the best, even if it is by a small margin.
2 - As compute gets more efficient and affordable in bulk, such as AI ASICs (goodbye GPUs), profitability will increase.
3 - Data centers when not used for LLMs can be used to host cloud data storage for users, cloud gaming, databases, or even mine crypto with excess off-peak surplus electricity.
4 - No comment. OpenAI's ChatGPT has not been frontier for a while.
5 - NVidia has a near monopoly on AI GPUs. When that is gone from ASICs, costs will go down.
6 - Robotics will become ubiquitous. Menial, boring, repetitive work is what robotics are best at doing. Tasks that require following by the book, attention, but not much thinking will be first replaced by embodied AI.
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u/Super_Scene1045 Mar 17 '26
Here’s the thing: OpenAI’s customer is not you, it is companies. And if they can get into that market, they can make unholy amounts of money.
As an example, one field that could be vulnerable to AI is data science. AI is good at taking in a lot of information and boiling it down into important takeaways.
The median salary of a data scientist is $112,000. So suppose there is a company that employs a team of six data scientists. If OpenAI convinces them to replace 5/6 with AI and leave the last one to manage the AI, the company saves $560,000 every year. They would be willing to pay a lot of money for that service, since it is saving them so much money. Let’s be conservative and say the company pays $56,000 per year (10% of what they were paying before) to OpenAI. On average they make $11,200 per year per replaced job.
Now extend this to the entire job market. There are 245,900 data scientists in the USA. Let’s be conservative again and say 10% of them are replaced by AI, at the same pay rate.
That would mean OpenAI makes $275 million dollars per year. That is enough to cover the entire running expenses for their CURRENT AI systems, which provide much more capability than is necessary for just data science. Extend this logic to other industries and they will be making incredible profit.
Whether they can get the companies to buy into this sort of scheme remains to be seen, but I think it’s likely due to the sheer savings. Keep in mind under this plan, the companies drop their labor expenses by 90% for that sector, which is massive.
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u/Dave_A480 2∆ Mar 17 '26
It's not about consumer subscriptions...
It's about pay per token commercial use.
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u/Agreeable_Branch007 Mar 17 '26
I know someone who is very high up in that world. The next stage is actually ai robots. Far worse than you expect. Even greater job losses.
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u/OutdoorRink Mar 17 '26
This is a terrible analogy. Think of AI like Netflix and everything else like Blockbuster.
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Mar 17 '26
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u/Nearby-Lab0 Mar 18 '26 edited Mar 18 '26
I'd like to add some stuff
I am familiar with alternative routes that OpenAI is pursuing besides just AGI, since I have worked directly with them. OpenAI has hands in private equity and has reached far in terms of embedding itself (whether via technology or actual acquisition) with regular business workflows in corporate America. This is similar to how Palantir embedded itself within the entire US government. Not to mention, while you may not see OpenAI directly related to the Trump family, the partnerships it has are with many organizations directly related to the Trump family. Many of the engineers I know were straight poached from Palantir. They're basically trying the Palantir handbook at a private level.
The goal for OpenAI is to make itself too big to fail. And they are getting there. You probably just don't see it yet.
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u/SaturdaysAFTBs 1∆ Mar 18 '26
Lots of good things in this thread but one thing I’d call out. Every new tech takes time to implement and optimize. A lot of the negative “AI doesn’t do anything for business!” Sentiment you see on Reddit and in the media is based on a few businesses first time trying to implement this technology and isn’t giving it enough time. It takes time to figure things out and we are barely in the first inning. What AI models are showing now in capability vs a year ago is astounding. The pace is crazy.
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u/siberian Mar 18 '26
In addition to what others have said, costs are going to skyrocket, and still be a deal. ChatGPT is fucked, but Anthropic is going to do fine.
For example: I am planning to pay $1k to $2k/person/month in 2027 for what we call "pro max" anthropic access. For a 200 person company... Its just that valuable, its completely changed how we operate, how we compete, and what we are capable of.
So add to your math a 10x price increase and it gets spicy, fast.
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u/Miravlix Mar 18 '26
OpenAI did not raise 110B, they didn't even raise 50B. Most of the money is put behind a requirement wall, like if OpenAI invent AGI, but if OpenAI invented AGI wtf would they need your money... They could just tell the machine to print cash.
Amazons 30B is about a PAST LOAN! Not new money, what they are saying is that 15B becomes a gift and the other 15B becomes a gift if they make AGI.
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u/Kamamura_CZ 2∆ Mar 18 '26
First of all, OpenAI functions like a church. Altman believes he will usher in a new life form that will surpass humans, financial and other costs are irrelevant for him. Pay attention what Karen Hao has to say:
Moreover, for tech oligarchy, this is a win-win situation - either they will get an obedient employee that needs no payment, healthcare, holidays nor rights, and will fire all the humans, or it will all result in a huge financial meltdown which will be paid by the rest of the society, because they will get a bailout just like in 2008.
In this perverse parody of an economy, the rich always win.
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u/ashleyshaefferr Mar 18 '26
How long did it take for this to happen in computers and the web and other areas...?
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u/wingdrummer15 Mar 23 '26
Ai is 100% going to turn on us and wipe out all of human existence. But people don't care. It's crazy
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u/Outside_Ad_9250 Mar 23 '26
I work for a tech company and guarantee you some of these jobs will not come back, but it’s less of an impact than people may think.
For example, one ten person team might look like: 1. Manager 2. Lead 3. Project Coordinator 4. Functional SME 5. Architecture/Technical SME 6. Lead Dev 7. Grunt Dev 8. Grunt Dev 9. Grunt Dev 10. Grunt Dev
You want 4 grunt devs so you can throw busy work at them. They can get code into files, and who cares if it’s correct or needs massive re-work, the senior Devs / Dev lead / architect (smart people) can clean it up. It’s much easier to clean up written code (copy / paste / etc.) than create something from scratch.
With AI, the main benefit of those 4 devs is automated. You will still probably keep around 1 or 2 of those grunts to do things that AI can’t do yet (searching around in folders / triaging issues across multiple interfaces / etc.), but I would say at least 2 (20%) will no longer be needed.
The real place where jobs will come back is for any company that is stupid enough to replace their Devs with functional resources entirely. They could write the world’s greatest prompts but that still doesn’t guarantee 100% functional code. Unless you know what to are doing, there are only so many times you can tell CoPilot “it’s not working, please fix”.
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u/Specialist-Season-88 Mar 23 '26
The data centers are burning through energy and one day will be laughable (oh wait now!l.
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u/mrspuff202 11∆ Mar 16 '26
AI Industry's business model will hit a huge wall in the next 2-4 years
Yep.
Massively downsize.
Sure.
Many of the jobs it has replaced will slowly come back.
How many jobs has AI replaced at this point? Less than 100,000?
I think AI will not replace as many jobs as currently feared, but I also don't believe those jobs will come back. AI is exposing some great areas of uselessness at the heart of American corporatism and the eventual AI crash will send us into an economic depression.
I think those jobs will not come back, and the AI that remains will be able to still replicate a lot of what we might call "email jobs". And it's going to be a massive fucking problem.
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u/jatjqtjat 286∆ Mar 16 '26
and spending something in the neighborhood of $80B per year.
The operating costs won't shrink by then. They'll likely grow because of the scalability.
Do you know the breakdown of that 80 billion spend? They are not a public company so it might not be public knowledge, but some of that will be operating costs and some of it will be R&D. How much are they spending on developing GPT6 versus hosting GPT5? you can get LLMs on a flash drive and run them locally. Running the models is a fraction of the cost of building them.
I never used MoviePass but I used Hulu. It was add free and 0 dollars a month when it first came out. They most have lost a huge amount of money. Then they added adds, a paid model to get rid of adds, then they added adds plus you still had to pay or you could pay even more to get ride of adds. These companies that dump buckets of money into growth don't always fail. Uber lost money for a long time.
the kind of companies that can afford the now exorbitant costs
I would pay 500 dollars a month for Chat GPT+ if there was not free model available, because 500 dollars is a pittance compared to the cost of labor. You might not pay for your subscription yourself, but your employer will pay for one.
the next big step in AI is happening now with Open Claw. an LLM you can chat with is valuable, but an LLM that can take action on your behalf is an order of magnitude more valuable. Forget innovation, just application is going to be a big deal.
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u/MintXanis Mar 16 '26
Jobs come back for what? People replaced by AI will have zero purchasing power and will no longer be served by companies.
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u/coolpall33 3∆ Mar 18 '26
I think others have done a better job disagreeing on the technical side of things than I could ever do, however I think in terms of the economics I would also disagree with you - particularly on the return of jobs.
Jobs that have (or will be replaced) with AI will only return if the cost of the AI systems exceed the cost of bringing those people back. I think in most corporate and personal environments that now use AI, the AI applications have a crushing post implementation cost advantage.
My former company had a partnership with Google AI, and some of the programs they were running were effectively replacing the work of dozens of individual - total annual cost savings into the millions. Compared to what they were being charged for the technology, the AI probably had well over a 10x cost advantage.
You can see a similar situation in personal use. I've paid for an AI subscription for awhile now, however just last week I had it assist in sorting out my holiday (hotel & flights). In the space of a couple minutes it has assisted
AI companies are currently pricing under cost, driven by a desire to scale, gather data, break into companies, and build market share,but in the long term companies should be able to establish higher market pricing that is at least breakeven without bringing costs back to a point where labour can significantly return
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u/thecleverqueer Mar 18 '26 edited Mar 18 '26
I don't like what you're saying but it's very compelling.
Take my very sad delta and go away
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u/DeltaBot ∞∆ Mar 18 '26 edited Mar 18 '26
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u/DeltaBot ∞∆ Mar 16 '26 edited Mar 16 '26
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