I can't be the only one who thinks this version is no better than the previous one, and that LLMs have basically reached a plateau, and all the new releases "feature" are more or less just gimmicks.
In all reality, I have zero clue how any of these companies remain sustainable. I've tried to host some inference on cloud GPUs and its seems like it would be extremely cost prohibitive with any sort of free plan.
They don't, they have a big bag of money they are burning through, and working to raise more. Anthropic is in a better position cause they don't have the majority of the public using their free-tier. But, AFAICT, none of the big players are profitable, some might get there, but likely through verticals rather than just model access.
Or do these people just bet on the post money world of AI?
But now, every $50B+ company seems to have their own model. Chinese companies have an edge in local models and the big tech seems to be fighting each other like cats and dogs for a tech which has failed to generate any profit while masses are draining the cash out from the companies with free usage and ghiblis.
What is the concrete business model here? Someone at google said "we have no moat" and i guess he was right, this is becoming more and more like a commodity.
I am someone who works professionally in ML (though not LLM development itself) and deploys multiple RAG- and MCP-powered LLM apps in side businesses. I code with Copilot, Gemini, and Claude and read and listen to most AI-industry outputs, be they company events, papers, articles, MSM reports, the Dwarkesh podcast, MLST, etc. While I acknowledge some value, having closely followed the field and extensively used LLMs, I find the company's projections and visions deeply unconvincing and cannot identify the trillion-dollar value.
While I never bet for money and don't think everything has to be transactional or competitive, I would bet on defining terms and recognizing if I'm wrong. What do you mean by taking the positive side? Do you think OpenAI's revenue projections are realistic and will be achieved or surpassed by competing in the open market (i.e., excluding purely political capture)?
Betting on the survival of the legal entity would likely not be the right endpoint because OpenAI could likely be profitable with a small team if it restricted itself to serving only GPT 4.1 mini and did not develop anything new. They could also be acquired by companies with deeper pockets that have alternative revenue streams.
But I am highly convinced that OpenAI will not have a revenue of > 100 billion by 2029 while being profitable [1] and willing to take my chances.
1: https://www.reuters.com/technology/artificial-intelligence/o...
Do I think OpenAI’s revenue projections are realistic? I’m aware of leaks that say $12.5bn in 2025 and $100+bn in 2029. Order of magnitude, yes, I think they’re realistic. Well, let me caveat that. I believe they will be selling $100+bn at today’s prices in 2029.
Is this based only/largely on political capture? Don’t know or care really, I’m just tired of (formerly called lazy) journalism that gets people confidently wrong about the future telling everyone OpenAI is doomed.
On the Reuters story — to be clear, OpenAI’s current plans mean that being cash flow positive in 2029 would be a bad thing for the company. It would mean they haven’t gotten investment to the level they think is needed for a long term winning play, and will have been forced to rely on operating cash flow for growth. In this market, which they postulated was winner take all, and now propose is “winner take most” or “brand matters and TAM is monstrous”, they need to raise money to compete with the monstrous cash flow engines arrayed against them: Meta, Google, and likely some day Apple and possibly Oracle. On the flip side, they offer a pretty interesting investment: If you believe most of the future value of GOOG or META will come from AI (and I don’t necessarily believe this, but a certain class of investors may), then you could buy that same value rise for cheap investing in OpenAI. Unusually for such a pitch they have a pretty fantastic track record so far.
For reference, there are roughly 20mm office jobs in USA alone. Chat is currently 65% or so of all chatbot usage. The US is < 1/6 of oAi’s customer base. 10mm people currently pay for chat. OpenAI projects chat to be about 1/3 of income with no innovations beyond Agentic tool calling.
To wit: in 2029 will we be somewhere in the following band of scenarios:
Low Growth in Customers but increased model value: Will 10mm people pay $3.6k a year for chat ($300/month) worldwide in 2029, and API and Agent use each cover a similar amount of usage?
High Growth in Customers with moderate increased model value: Will 100mm people pay $360 a year for o5, which is basically o4 high but super fast and tool-connected to everything?
Ending somewhere in this band seems likely to me, not crazy. The reasons to fall out of this band are: they get beat hard and lose their research edge thoroughly to Google and Anthropic, so badly that they cannot deliver a product that can be backed by their brand and large customer base, or an Open Weights model achieves true AGI ahead of / concurrent with OpenAi and they decide not to become an inference providing company, or the world decides they don’t want to use these tools (hah), or the world’s investors stop paying for frontier model training and everyone has to move to cashflow positive behavior.
Upshot: I’d say OpenAI will be cashflow positive or $100bn+ in CF in 2029.
This is the new stochastic parrots meme. Just a few hours ago there was a story on the front page where an LLM based "agent" was given 3 tools to search e-mails and the simple task "find my brother's kid's name", and it was able to systematically work the problem, search, refine the search, and infer the correct name from an e-mail not mentioning anything other than "X's favourite foods" with a link to a youtube video. Come on!
That's not to mention things like alphaevolve, microsoft's agentic test demo w/ copilot running a browser, exploring functionality and writing playright tests, and all the advances in coding.
But sure, it managed to find a name buried in some emails after being told to... Search through emails. Wow. Such magic
[1] https://news.ycombinator.com/item?id=44050152 [2] https://news.ycombinator.com/item?id=44056530
What’s special about it is that it required no handholding; that is new.
My impression is that the base models have not improved dramatically in the last 6 months and incremental improvements in those models is becoming extremely expensive.
Tooling has improved, and the models have. The combo is pretty powerful.
https://aider.chat/docs/leaderboards/ will give you a flavor of the last six months of improvements. Francois Cholet (ARC AGI: https://arcprize.org/leaderboard) has gone from “No current architecture will ever beat ARC” to “o3 has beaten ARC and now we have designed ARC 2”.
At the same time, we have the first really useful 1mm token context model available with reasonably good skills across the context window (Gemini Pro 2.5), and that opens up a different category of work altogether. Reasoning models got launched to the world in the last six months, another significant dimension of improvement.
TLDR: Massive, massive increase in quality for coding models. And o3 is to my mind over the line people had in mind for generally intelligent in, say, 2018 — o3 alone is a huge improvement launched in the last six months. You can now tell o3 something like: “research the X library and architect a custom extension to that library that interfaces with my weird garage door opener; after writing the architecture implement the extension in (node/python/go) and come back in 20 minutes with something that almost certainly compiles and likely largely interfaces properly, leaving touch-up work to be done.
What I haven't seen is any LLM model consistently being able to fully implement new features or make refactors in a large existing code base (100k+ LOC, which are the code bases that most businesses have). These code bases typically require making changes across multiple layers (front end, API, service/business logic layer, data access layer, and the associated tests, even infrastructure changes). LLMs seem to ignore the conventions of the existing code and try to do their own thing, resulting in a mess.
Dumping it at Claude 3.7 with no instructions will 100% get random rewriting - very annoying.
We're watching innovation move into the use and application of LLMs.
So even if the plateau is real (which I doubt given the pace of new releases and things like AlphaEvolve) and we'd only expect small fundamental improvements some "better applications" could still mean a lot of untapped potential.
We'll continue to see incremental improvements as training sets, weights, size, and compute improve. But they're incremental.
At least I personally liked it better.
LLM’s in a generic use sense are done since already earlier this year. OpenAI discovered this when they had to cancel GPT-5 and later released the ”too costly for gains” GPT-4.5 that will be sunset soon.
I’m not sure the stock market has factored all this in yet. There needs to be a breakthrough to get us past this place.
That's nowhere near enough reason to think we've hit a plateau - the pace has been super fast, give it a few more months to call that...!
I think the opposite about the features - they aren't gimmicks at all, but indeed they aren't part of the core AI. Rather it's important "tooling" that adjacent to the AI that we need to actually leverage it. The LLM field in popular usage is still in it's infancy. If the models don't improve (but I expect they will), we have a TON of room with these features and how we interact, feed them information, tool calls, etc to greatly improve usability and capability.
I didn't want to lose the work I had done, and I knew it would be a pain to do it manually with git. The model did a fantastic job of iterating through the git commits and deciding what to put into each branch. It got everything right except for a single test that I was able to easily move to the correct branch myself.
“AI does badly on my test therefore it’s bad”.
The correct question to ask is, of course, what is it good at? (For bonus points, think in terms of $/task rather than simply being dominant over humans.)
“I used an 8088 CPU to whisk egg whites, then an Intel core 9i-12000-vk4*, and they were equally mediocre meringues, therefore the latest Intel processor isn’t a significant improvement over one from 50 years ago”
* Bear with me, no idea their current naming
Spot the problem now?
AI capabilities are highly jagged, they are clearly superhuman in many dimensions, and laughably bad compared to humans in others.
They just need to put out a simple changelog for these model updates, no need to make a big announcement everytime to make it look like it's a whole new thing. And the version numbers are even worse.
Brilliant!
I am pretty much ready to be done talking to human idiots on the internet. It is just so boring after talking to these models.