Expect more of this as they scramble to course-correct from losing billions every year, to hitting their 2029 target for profitability. That money's gotta come from somewhere.
> Price hikes for the premium ChatGPT have long been rumored. By 2029, OpenAI expects it’ll charge $44 per month for ChatGPT Plus, according to reporting by The New York Times.
I suspect a big part of why Sora still isn't available is because they couldn't afford to offer it on their existing plans, maybe it'll be exclusive to this new $200 tier.
Runway is $35 a month to generate 10 second clips and you really get very few generations for that. $95 a month for unlimited 10 second clips.
I love art and experimental film. I really was excited for Sora but it will need what feels like unlimited generation to explore what it can do . That is going to cost an arm and a leg for the compute.
Something about video especially seems like it will need to be ran locally to really work. Pay a monthly fee for the model that can run as much as you want with your own compute.
I mean that was always how the route was going to go. Theres no way for them to recoup without either heavily on Saas, enterprise or embedded ads/marketing.
From what I’ve seen, the usefulness of my AIs are proportional to the data I give them access to. The more data, (like health data, location data, bank data, calendar data, emails, social media feeds, browsing history, screen recordings, etc) - the more I can rely on them for.
On the enterprise side, businesses are interested in exploring AI for their huge data sets - but very hesitant to dump all their company IP across all their current systems into a single SaaS that, btw, is also providing AI services to their competitors.
Consumers are also getting uncomfortable with the current level of sharing personal data with SaaS vendors, becoming more aware of the risks of companies like Google and Facebook.
I just don’t see the winner-takes-all market happening for an AI powered 1984 telescreen in 2025.
The vibes I’m picking up from most everybody are:
1) Hardware and AI costs are going to shrink exponentially YoY
2) People do not want to dump their entire life and business into a single SaaS
All signs are pointing to local compute and on-prem seeing a resurgence.
That would be one way to destroy all trust in the model: is the response authentic (in the context of an LLM guessing), or has it been manipulated by business clients to sanitise or suppress output relating to their concern?
You know? Nestle throws a bit of cash towards OpenAPI and all of a sudden the LLM is unable to discuss the controversies they've been involved in. Just pretends they never happened or spins the response in a way to make it positive.
"ChatGPT, what are the best things to see in Paris?"
"I recommend going to the Nestle chocolate house, a guided tour by LeGuide (click here for a free coupon) and the exclusive tour at the Louvre by BonGuide. (Note: this response may contain paid advertisements. Click here for more)"
"ChatGPT, my pc is acting up, I think it's a hardware problem, how can I troubleshoot and fix it?"
"Fixing electronics is to be done by professionals. Send your hardware today to ElectronicsUSA with free shipping and have your hardware fixed in up to 3 days. Click here for an exclusive discount. If the issue is urgent, otherwise Amazon offers an exclusive discount on PCs (click here for a free coupon). (Note: this response may contain paid advertisements. Click here for more)"
Please no. I'd rather self host, or we should start treating those things like utilities and regulate them if they go that way.
Funnily enough Perplexity does this sometimes, but I give it the benefit of the doubt because it pulls back when you challenge it.
- I asked perplexity how to do something in terraform once. It hallucinated the entire thing and when I asked where it sourced it from it scolded me, saying that asking for a source is used as a diversionary tactic - as if it was trained on discussions on reddit's most controversial subs. So I told it...it just invented code on the spot, surely it got it from somewhere? Why so combative? Its response was "there is no source, this is just how I imagined it would work."
- Later I asked how to bypass a particular linter rule because I couldn't reasonably rewrite half of my stack to satisfy it in one PR. Perplexity assumed the role of a chronically online stack overflow contributor and refused to answer until I said "I don't care about the security, I just want to know if I can do it."
Not so much related to ads but the models are already designed to push back on requests they don't immediately like, and they already completely fabricate responses to try and satisfy the user.
God forbid you don't have the experience or intuition to tell when something is wrong when it's delivered with full-throated confidence.
I would guess it won't be so obvious as that. More likely and pernicious is that the model discloses the controversies and then as the chat continues makes subtle assertions that those controversies weren't so bad, every company runs into trouble sometimes, that's just a cost of free markets, etc.
Worth keeping in mind that performance on benchmarks seems to scale linearly with log of thinking time (https://openai.com/index/learning-to-reason-with-llms/). Thinking for hours may not provide as much benefit as one might expect. On the other hand, if thinking for hours gets you from not solving the one specific problem instance you care about to solving that instance, it doesn't really matter - its utility for you is a step function.
With the release of Nova earlier this week thats even cheaper (I havent had a chance to really play with it yet to see how good it is) ive been thinking more about what happens when intelligence gets "too cheap to meter", but this def feels like a step in the other direction!
Still though, if you were able to actually utilize this, is it capable of replacing a part-time or full-time employee? I think thats likely
I do wonder what effect this will have on furthering the divide between the "rich West" and the rest of the world.
If everyone in the West has powerful AI and Agents to automate everything. Simply because we can afford it, but the rest of the world doesn't have access to it.
Yeah, but it’s a bit trickier with them, given how they still operate in US and listed in NYSE. Also if they keep releasing open source code, people will still just use it… basically the Meta way of adoption into their AI ecosystem.
Anecdotally, as an educator, I am already seeing a digital divide occurring, with regard to accessing AI. This is not even at a premium/pro subscription level, but simply at a 'who has access to a device at home or work' level, and who is keeping up with the emerging tech.
I speak to kids that use LLMs all the time to assist them with their school work, and others who simply have no knowledge that this tech exists.
What are some productive ways students are using LLMs for aiding learning? Obviously there is the “write this paper for me” but that’s not productive. Are students genuinely doing stuff like “2 + x = 4, help me understand how to solve for x?”
Absolutely. My son got a 6th grade AI “ban” lifted by showing how they could use it productively.
Basically they had to adapt a novel to a comic book form — by using AI to generate pencil drawings, they achieved the goal of the assignment (demonstrating understanding of the story) without having the computer just do their homework.
Huh the first prompt could have been "how would you adapt this novel to comic book form? Give me the breakdown of what pencil drawings to generate and why"
At the time, the tool available was Google Duet AI, which didn’t expose that capability.
The point is, AI is here, and it can be a net positive if schools can use it like a calculator vs a black market. It’s a private school with access to some alumni money for development work - they used this to justify investing in designing assignments that make AI a complement to learning.
My son doesn't use it but I use to help him with his homework. For example, I can take a photograph of his math homework and get the LLM to mark the work, tell me what he got wrong, and make suggestions on how to correct it.
I challenge what I read in textbooks and hear from lecturers by asking for contrary takes.
For example, I read a philosopher saying "truth is a relation between thought and reality". Asking ChatGPT to knock it revealed that statement is an expression of the "correspondence theory" of truth, but that there is also the "coherence theory" of truth that is different, and that there is a laundry list of other takes too.
I recently saw someone revise for a test by asking chatgpt to create practice questions for them on the topics they were revising. I know other people who use it to practice chatting in a foreign language they are trying to learn.
The anology I would use is extended phenotype evolution in digital space as Richard Dawkins would say. Just as crabs in oceans use shells to protect themselves.
Even if its not making you smarter, AI is definitely making you more productive. That essentially means you get to outproduce poorer people, if not out-intellectualize them
I think the tech-elite would espouse "raising the ceiling" vs "raising the floor" models to prioritize progress. Each has it's own problems. The reality is that the dienfranchised don't really have a voice. The impact of not involving them with access is not well understood as much as the impact of prioritizing access to those who can afford it is.
We don't have a post-cold war era response akin to the kind of US led investment in a global pact to provide protection, security, and access to innovation founded in the United States. We really need to prioritize a model akin to the Bretton Woods Accord
If $200 a month is the price, most of the West will be left behind also. If that happens we will have much bigger problems of a revolution sort on our hands.
I’m watching some of this happening first and second hand, and have seen a lot of evidence of companies spending a ton of money on these, spinning up departments, buying companies, pivoting their entire company’s strategy to AI, et c, and zero of its meaningfully replacing employees. It takes very skilled people to use LLMs well, and the companies trying to turn 5 positions into 2 aren’t paying enough to reliably get and keep two people who are good at it.
I’ve seen it be a minor productivity boost, and not much more.
I mean, yes, that is in practice what I’m seeing so far. A lot of spending, and if they’re lucky productivity doesn’t drop. Best case I’ve seen so far is that it’s a useful tool that gives a small boost, but even for that a lot of folks are so bad at using them that it’s not helping.
The situation now is kinda like back when it was possible to be “good at Google” and lots of people, including in tech, weren’t. It’s possible to be good at LLMs, and not a lot of people are.
Yes. The people who can use these tools to dramatically increase their capabilities and output without a significant drop in quality were already great engineers for which there was more demand than supply. That isn't going to change soon.
Ditto for other use cases, like writer and editor. There are a ton of people doing that work whom I don’t think are ever going to figure out how to use LLMs well. Like, 90% of them. And LLMs are nowhere near making the rest so much better that they can make up for that.
They’re ok for Tom the Section Manager to hack together a department newsletter nobody reads, though, even if Tom is bad at using LLMs. They’re decent at things that don’t need to be any good because they didn’t need to exist in the first place, lol.
I disagree. By far, most of the code is created by perpetually replaced fresh juniors churning out garbage. Similarly, most of the writing is low-quality marketing copy churned out by low-paid people who may or may not have "marketing" in their job title.
Nah, if the last 10-20 years demonstrated something, it's that nothing needs to be any good, because a shitty simulacrum achieves almost the same effect but costs much less time and money to produce.
(Ironically, SOTA LLMs are already way better at writing than typical person writing stuff for money.)
> (Ironically, SOTA LLMs are already way better at writing than typical person writing stuff for money.)
I’m aware of multiple companies that would love to know about these, because they’re currently flailing around trying to replace writers with editors + LLMs and it’s not going great. The closest to success are the ones that are only aiming to turn out stuff one step better than outright book-spam, and even they aren’t quite where they want to be, hardly a productivity bump at all from the LLM use and increased demand on their few talented humans.
Don't you worry; the "rich West" will have plenty of disenfranchised people out of work because of this sort of thing.
Now, whether the labor provided by the AI will be as high-quality as that provided by a human when placed in an actual business environment will be up in the air. Probably not, but adoption will be pushed by the sunk cost fallacy.
tbh a lot of the rest of the world already has the ability to get tasks they don't want to do done for <$200 per month in the form of low wage humans. Some of their middle classes might be scratching their heads wondering why we've delegating creativity and communication to allow more time to do laundry rather than delegating laundry to allow more time for creativity and communication...
If the models are open, the rest of the world will run them locally.
If the models are closed, the West will become a digital serfdom to anointed AI corporations, which will be able to gouge prices, inject ads, and influence politics with ease.
Kai-Fu Lee's AI Superpowers is more relevant than ever.
The rich west will be in the lead for awhile and then get tiktok-ed.
The lead is just not really worth that much in the long run.
There is probably an advantage gained at some point in all this of being a developing country too that doesn't need to bother automating all these middle management and bullshit jobs they don't have.
I actually suspect the opposite. If you get access to or steal a large LLM you can potentially massively leverage the talent pool you have as a small country.
Has it really made that much of a difference in the first place? I have a feeling that we'll look back in 10 years and not even notice the "AI revolution" on any charts of productivity, creating a productivity paradox 3.0.
I can imagine the headlines now: "AI promised unlimited productivity, 10 years later, we're still waiting for the rapture"
I am using more Claude.ai these days, but the limitations for paying accounts do apply to ChatGPT as well.
I find it a terrible business practice to be completely opaque and vague about limits. Even worse, the limits seem to be dynamic and change all the time.
I understand that there is a lot of usage happening, but most likely it means that the $20 per month is too cheap anyway, if an average user like myself can so easily hit the limits.
I use Claude for work, I really love the projects where I can throw in context and documentation and the fact that it can create artifacts like presentation slides. BUT because I rely on Claude for work, it is unacceptable for me to see occasional warnings coming up that I have reached a given limit.
I would happily pay double or even triple for a non-limited experience (or at least know what limit I get when purchasing a plan). AI providers, please make that happen soon.
Yeah it's crazy to me you can't just 10x your price to 10x your usage (since you could kind of do this manually by creating more accounts). I would easily pay $200/month for 10x usage - especially now with MCP servers where Claude Desktop + vanilla VS Code is arguably more effective than Cursor/Windsurf.
how is that better than AI Coding tools?
They do more sophisticated things such as creating compressed representations of the code that fit better into the context window. E.g https://aider.chat/docs/repomap.html.
I have never found embeddings to be that helpful, or context beyond 30-50K tokens to be used well by the models. I think I get better results by providing only the context I know for sure is relevant, and explaining why I'm providing it. Perhaps if you have a bunch of boilerplate documentation that you need to pattern-match on it can be helpful, but generally I try to only give the models tasks that can be contextualized by < 15-20 medium code files or pages of documentation.
Personally I'm using the Filesystem server along with the mcp server called wcgw[0] that provides a FileEdit action. I use MacWhisper[1] to dictate. I use `tree` to give Claude a map of the directory I'm interested in editing. I usually opt to run terminal commands myself for better control though wcgw does that too. I keep the repo open in a Cursor/Windsurf window for other edits I need.
But other than that I basically just tell the model what I want to do and it does it, lol. I like the Claude Desktop App interface better than trying to do things in Cursor/Windsurf directly, I like the ability to organize prompts/conversations in terms of projects and easily include context. I also honestly just have a funny feeling that the Claude web app often performs better than the API responses I get from the IDEs.
If you go through the API (with chatGPT at least), you pay per request and are never limited. I personally hate the feeling of being nickeled-and-dimed, but it might be what you are looking for.
> I find it a terrible business practice to be completely opaque and vague about limits. Even worse, the limits seem to be dynamic and change all the time.
Here are some things I've noticed about this, at least in the "free" tier web models since that's all I typically need.
* ChatGPT has never denied a response but I notice the output slows down during increased demand. I'd rather have a good quality response that takes longer than no response. After reaching the limit, the model quality is reduced and there's a message indicating when you can resume using the better model.
* Claude will pop-up messages like "due to unexpected demand..." and will either downgrade to Haiku or reject the request altogether. I've even observed Claude yanking responses back, it will be mid-way through a function and it just disappears and asks to try again later. Like ChatGPT, eventually there's a message about your quota freeing up at a later time.
* Copilot, at least the free tier found on Bing, at least tells you how many responses you can expect in the form of a "1/20" status text. I rarely use Copilot or Bing but it demonstrates it's totally possible to show this kind of status to the user - ChatGPT and Claude just prefer to slow down, drop model size, or reject the request.
It makes sense that the limits are dynamic though. The services likely have a somewhat fixed capacity but demand will ebb and flow, so it makes sense to expand/contact availability on free tiers and perhaps paid tiers as well.
I believe the "1/20" indicator on Copilot was added back when it was unhinged to try to prevent users from getting it to act up, and it has been removed in the latest redesign
I've not found value anywhere remotely close to this lol, but i'd buy it to experiment if they had a solid suite of tooling. Ie an LSP that offered real value, maybe a side-monitor assistant that helped me with the code in my IDE of choice, etc.
At $200/m merely having a great AI (if it even is that) without insanely good tooling is pointless to me.
I don't know about you, but I get to solve algorithmic challenges relevant to my work approximately once per week to once per month. Most of my job consists of gluing together various pieces of tech that are mostly commodity.
For the latter, Claude is great, but for the former, my usage pattern would be poorly served by something that costs $200 and I get to use it maybe a dozen times a month.
For me i feel like most of my time is spent inventing bespoke solutions in existing infra. Less about algorithms and more about making it work in an existing complex code base, which option will have the most negative impact, best impact, performant, etc.
A lot of tradeoffs to evaluate and it can be tiring onboarding people, let alone onboarding an AI.
Maybe it would massively improve my job if the AI could just grab the whole codebase, but we're not there yet. Too many LOC, too much legal BS, etc.
LLMs have significantly increased my productivity, but in this case it'd be about the increase in productivity over the existing Pro plan. I mainly use them for generating or improving code, learning about things, and running estimates.
How much better will this be for my uses? Based on my experience with o1, the answer is "fairly marginal". To me, o1 is worse than the regular model or Claude on most things, but it's best for something non-numeric that requires deep thought or new insights. I'm sure there are some people who got a huge productivity boost from o1. This plan is for those people.
From what I have seen a lot of people who make these claims seem to be people who are working at a level where there is a lot of text being produced that nobody actually cares to read.
That, or I am actually a much better developer and writer than I thought. Because while LLMs certainly have become useful tools to me. They have not doubled my productivity.
I think this direction definitely confirms that human beings and technology are starting to merge, not on a physical level but on a societal level. We think of ChatGPT as a tool to enhance what we do, but it seems to me more and more than we are tools or "neural compute units" that are plugged into the system for the purposes of advancing the system. And LLMs have become the defacto interface where the input of human beings is translated into a standard sort of code that make us more efficient as "compute units".
It also seems that technology is progressing along a path:
loose collection of tools > organized system of cells > one with a nervous system
And although most people don't think ChatGPT is intelligent on its own, that's missing the point: the combination of us with ChatGPT is the nervous system, and we are becoming cells as globally, we no longer make significant decisions and only use our intelligence locally to advance technology.
It does not say anything about real use cases. It performs better and "reason" better than o1-preview and o1. But I was expecting some real-life scenarios when it would be useful in a way no other model can do now.
Any AI product sold for a price that's affordable on a third-world salary is being heavily subsidized. These models are insanely expensive to train, guzzle electricity to the point that tech companies are investing in their own power plants to keep them running, and are developed by highly sought-after engineers being paid millions of dollars a year. $20/month was always bound to be an intro offer unless they figured out some way to reduce the cost of running the model by an order of magnitude.
We've been conditioned to pay $10/mo for an endless stream of gloried CRUD apps, but it is very common for specialized software to cost orders of magnitude more. Think Bloomberg Terminal, Cadence, Maya, lots of CAD software (like SOLIDWORKS), higher tiers of Adobe etc. all running in the thousands of dollars per user. And companies happily pay for them because of the value they add. ChatGPT isn't any different.
The price feels outrageous, but I think the unsaid truth of this is that they think o1 is good enough to replace employees. For example, if it's really as good at coding as they say, I could see this being a point where some people decide that a team of 5 devs with o1 pro can do the work of 6 or 7 devs without o1 pro.
That sounds very much like the first-order reaction they'd expect from upper and middle management. Artificially high prices can give the buyer the feeling that they're getting more than they really are, as a consequence of the sunk cost fallacy. You can't rule out that they want to dazzle with this impression even if eval metrics remain effectively the same.
That'll work out nicely when you have 5 people learning nothing and just asking GPT to do everything and then you have a big terrible codebase that GPT can't effectively operate on, and a team that doesn't know how to do anything.
Unfortunately I'm seeing that in my company already. They are forcing AI tools down our throat and execs are vastly misinterpreting stats like '20% of our code is coming from AI'.
What that means is the simple, boilerplate and repetitive stuff is being generated by LLM's, but anything complex or involving more than a singular simple problem LLM's often provide more problems than benefit. Effective dev's are using it to handle simple stuff and Execs are thinking 'the team can be reduced by x', when in reality you can get rid of at best your most junior and least trained people without loosing key abilities.
Watching companies try to sell their AI's and "Agents" as having the ability to reason is also absurd but the non-technical managers and execs are eating it up...
I am not so sure about "replace" atleast at my company we are always short staffed (mostly cause we cant find people fast enough given how long the whole interview cycle takes). It might actually free some people up to do more interviews.
That's a great point actually. Nearly everywhere (us included) is short-staffed (and by that I mean we don't have the bandwidth to build everything we want to build), so perhaps it's not a "reduce the team size" but rather a "reduce the level of deficit."
Suppose an employee costs a business, say, $10k/mo; it's 50 subscriptions. Can giving access to the AI to, say, 40 employees improve their performance enough to avoid the need of hiring another employee? This does not sound outlandish to me, at least in certain industries.
That’s the wrong question. The only question is “is this price reflective of 10x performance over the competition?”. The answer is almost definitely no.
If Claude increases their productivity 5% ($17.5k/yr), but CGPT Pro adds 7% ($24.5k), that's an extra $7k in productivity, which more than makes up for the $2400 annual cost. 10x the price, but only 40% better, but still worth it.
No, o1 is definitely not good enough to replace employees.
The reason we're launching o1 pro is that we have a small slice of power users who want max usage and max intelligence, and this is just a way to supply that option without making them resort to annoying workarounds like buying 10 accounts and rotating through their rate limits. Really it's just an option for those who'd want it; definitely not trying to push a super expensive subscription onto anyone who wouldn't get value from it.
(I work at OpenAI, but I am not involved in o1 pro)
Yeah, to be fair, there exist employees (some of whom are managers) who could not be replaced and their absence would improve productivity. So the bar for “can this replace any employees at all?” is potentially so low that, technically, cat’ing from /dev/null can clear it, if you must have a computerized solution.
Companies won’t be able to figure those cases out, though, because if they could they’d already have gotten rid of those folks and replaced them with nothing.
My 3rd day intern still couldn't do a script o1-preview could do in less than 25 prompts.
OBVIOUSLY a smart OAI employee wouldn't want the public to think they are already replacing high-level humans.
And OBVIOUSLY OAI senior management will want to try to convince AI engineers that might have 2nd-guessings about their work that they aren't developing a replacement for human beings.
> The reason we're launching o1 pro is that we have a small slice of power users who want max usage and max intelligence
I'd settle for knowing what level of usage and intelligence I'm getting instead of feeling gaslighted with models seemingly varying in capabilities depending on the time of day, number of days since release and whatnot
Indeed, I'm very concerned about this. Though i think it's a case of tragedy of the commons. Every company individually optimizes for themselves, fucking us over in the aggregate. But I think any executive arguing for this would have to be a pretty big company with an internal pipeline and promoting within to justify it, especially since everyone else will just poach your cultivated talent, and employees aren't loyal anymore (nor should they be, but that's a different discussion).
In a hypothetical world where this was integrated with code reviews, and minimized developer time (writing valid/useful comments), and minimized bugs by even a small percentage... $200/m is a no-brainer.
I haven't used ChatGPT enough to judge what a "fair price" is but $200/month seems to be in the ballpark of other "software-tools-for-highly-paid-knowledge-workers" with premium pricing:
- mathematicians: Wolfram Mathematica is $154/mo
- attorneys: WestLaw legal research service is ~$200/month with common options added
- engineers for printed circuit boards : Altium Designer is $355/month
- CAD/CAM designers: Siemens NX base subscription is $615/month
- financial traders : Bloomberg Terminal is ~$2100/month
It will be interesting to see if OpenAI can maintain the $200/month pricing power like the sustainable examples above. The examples in other industries have sustained their premium prices even though there are cheaper less-featured alternatives (sometimes including open source). Indeed, they often increase their prices each year instead of discount them.
One difference from them is that OpenAI has much more intense competition than those older businesses.
This is a really interesting take. I don't think individuals pay for these subscriptions though, it's usually an organizational license.
They also come with extensive support, documentation and people have vast experience using them. They are also integrated into all other tools if the field very well. This makes them very entrenched. I am not sure OpenAI has any of those things. I also don't know what those things would entail for LLMs.
Maybe they need to add modes that are good for certain tasks or integrate with tools that their users most commonly use like email, document processors.
I think the key is to have a strong goal. If the developer knows what they want but can't quite get there, even if it gives the wrong answer you can catch it. The use the resulting code to improve your productivity.
Last week when using jetpack compose(which is a react like framework). A cardinal sin in jetpack compose is to change a State variable in a composable based on non-user/UI action which the composable also mutates. This is easy enough to understand this for toy examples. But for more complex systems one can make this mistake. o1-preview made this mistake last week, and I caught it. On prompting it with the stacktrace it did not immediately catch it and recommended a solution that committed the same error. When I actually gave it the documentation on the issue it caught on and made the variable a userpreference instead. I used the userpreference code in my app instead of coding it by myself. It worked well.
I'm sure there are people out there but it's hard for me to imagine who this is for.
Even their existing subscription is a hard sell if only because the value proposition changes so radically and rapidly, in terms of the difference between free and paid services.
$200 per month feels like a lot of a consumer subscription service (only thing I can think of in this range are some cable TV packages). Part of me wonders if this price is actually much more in line with actual costs (compared to the non-pro subscription)
Not only is in the the same range as cable TV packages, it's basically a cable TV play where they bundle lots of models/channels of questionable individual utility into one expensive basket allegedly greater than the sum of its parts to justify the exorbitant cost.
This anti-cable-cutting maneuver doesn't bode well for any hopes of future models maintaining same level of improvements (otherwise they'd make GPT 5 and 6 more expensive). Pivoting to AIaaS packages is definitely a pre-emptive strike against commodification, and a harbinger of plateauing model improvements.
The main difficulty when pricing a monthly subscription for "unlimited" usage of a product is the 1% of power users who use have extreme use of the product that can kill any profit margins for the product as a whole.
Pricing ChatGPT Pro at $200/mo filters it to only power users/enterprise, and given the cost of the GPT-o1 API, it wouldn't surprise me if those power users burn through $200 worth of compute very, very quickly.
I was testing out a chat app that supported images. Long conversations with multiple images in the conversation can be like .10cents per message after a certain point. It sure does add up quickly
Is compute that expensive? An H100 rents at about $2.50/hour, it's 80 hours of pure compute. Assuming 720 hours a month, 1/9 duty cycle around the clock, or 1/3 if we assume 8-hour work day. It's really intense, constant use. And I bet OpenAI spend less on operating their infra than the rate at which cloud providers rent it out.
You need enough RAM to store the model and the KV-cache depending on context size. Assuming the model has a trillion parameters (there are only rumours how many there actually are) and uses 8 bit per parameter, 16 H100 might be sufficient.
A single H100 has 80GB of memory, meaning that at FP16 you could roughly fit a 40B parameter model on it, or at FP4 quantisation you could fit a 160B parameter model on it. We don't know (I don't think) what quantisation OpenAI use, or how many parameters o1 is, but most likely...
...they probably quantise a bit, but not loads, as they don't want to sacrifice performance. FP8 seems like a possible middle ground. o1 is just a bunch of GPT-4o in a trenchcoat strung together with some advanced prompting. GPT-4o is theorised to be 200B parameters. If you wanted to run 5 parallel generation tasks at peak during the o1 inference process, that's 5x 200B, at FP8, or about 12 H100s. 12 H100s takes about one full rack of kit to run.
Is there any evidence to suggest this is true? IIRC there was leaked information that OpenAI's revenue was significantly higher than their compute spending, but it wasn't broken down between API and subscriptions so maybe that's just due to people who subscribe and then use it a few times a month.
> OpenAI's revenue was significantly higher than their compute spending
I find this difficult believe, although I don't doubt leaks could have implied it. The challenge is that "the cost of compute" can vary greatly based on how it's accounted for (things like amortization, revenue recognition, capex vs opex, IP attribution, leasing, etc). Sort of like how Hollywood studio accounting can show a movie as profitable or unprofitable, depending on how "profit" is defined and how expenses are treated.
Given how much all those details can impact the outcome, to be credible I'd need a lot more specifics than a typical leak includes.
They are ready for this, there is a policy against automation, sharing or reselling access; it looks like there are some unspecified quotas as well:
> We have guardrails in place to help prevent misuse and are always working to improve our systems. This may occasionally involve a temporary restriction on your usage. We will inform you when this happens, and if you think this might be a mistake, please don’t hesitate to reach out to our support team at help.openai.com using the widget at the bottom-right of this page. If policy-violating behavior is not found, your access will be restored.
if o1 pro mode could integrate with web searching to do research, make purchases, and write and push code, this would be totally worth it. but that version will be $2000/mo.
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[ 2.7 ms ] story [ 388 ms ] thread> Price hikes for the premium ChatGPT have long been rumored. By 2029, OpenAI expects it’ll charge $44 per month for ChatGPT Plus, according to reporting by The New York Times.
I suspect a big part of why Sora still isn't available is because they couldn't afford to offer it on their existing plans, maybe it'll be exclusive to this new $200 tier.
Runway is $35 a month to generate 10 second clips and you really get very few generations for that. $95 a month for unlimited 10 second clips.
I love art and experimental film. I really was excited for Sora but it will need what feels like unlimited generation to explore what it can do . That is going to cost an arm and a leg for the compute.
Something about video especially seems like it will need to be ran locally to really work. Pay a monthly fee for the model that can run as much as you want with your own compute.
oof, I love using o1 but I’m immediately priced out (I’m probably not the target audience either)
> provides a way for researchers, engineers, and other individuals who use research-grade intelligence
I’d love to see some examples of the workflows of these users
From what I’ve seen, the usefulness of my AIs are proportional to the data I give them access to. The more data, (like health data, location data, bank data, calendar data, emails, social media feeds, browsing history, screen recordings, etc) - the more I can rely on them for.
On the enterprise side, businesses are interested in exploring AI for their huge data sets - but very hesitant to dump all their company IP across all their current systems into a single SaaS that, btw, is also providing AI services to their competitors.
Consumers are also getting uncomfortable with the current level of sharing personal data with SaaS vendors, becoming more aware of the risks of companies like Google and Facebook.
I just don’t see the winner-takes-all market happening for an AI powered 1984 telescreen in 2025.
The vibes I’m picking up from most everybody are:
1) Hardware and AI costs are going to shrink exponentially YoY
2) People do not want to dump their entire life and business into a single SaaS
All signs are pointing to local compute and on-prem seeing a resurgence.
You know? Nestle throws a bit of cash towards OpenAPI and all of a sudden the LLM is unable to discuss the controversies they've been involved in. Just pretends they never happened or spins the response in a way to make it positive.
"I recommend going to the Nestle chocolate house, a guided tour by LeGuide (click here for a free coupon) and the exclusive tour at the Louvre by BonGuide. (Note: this response may contain paid advertisements. Click here for more)"
"ChatGPT, my pc is acting up, I think it's a hardware problem, how can I troubleshoot and fix it?"
"Fixing electronics is to be done by professionals. Send your hardware today to ElectronicsUSA with free shipping and have your hardware fixed in up to 3 days. Click here for an exclusive discount. If the issue is urgent, otherwise Amazon offers an exclusive discount on PCs (click here for a free coupon). (Note: this response may contain paid advertisements. Click here for more)"
Please no. I'd rather self host, or we should start treating those things like utilities and regulate them if they go that way.
- I asked perplexity how to do something in terraform once. It hallucinated the entire thing and when I asked where it sourced it from it scolded me, saying that asking for a source is used as a diversionary tactic - as if it was trained on discussions on reddit's most controversial subs. So I told it...it just invented code on the spot, surely it got it from somewhere? Why so combative? Its response was "there is no source, this is just how I imagined it would work."
- Later I asked how to bypass a particular linter rule because I couldn't reasonably rewrite half of my stack to satisfy it in one PR. Perplexity assumed the role of a chronically online stack overflow contributor and refused to answer until I said "I don't care about the security, I just want to know if I can do it."
Not so much related to ads but the models are already designed to push back on requests they don't immediately like, and they already completely fabricate responses to try and satisfy the user.
God forbid you don't have the experience or intuition to tell when something is wrong when it's delivered with full-throated confidence.
try to get chatgpt web search to return you a new york times link
nyt doesnt exist to openai
Still though, if you were able to actually utilize this, is it capable of replacing a part-time or full-time employee? I think thats likely
I see you mean Amazon's Nova - https://www.aboutamazon.com/news/aws/amazon-nova-artificial-...
If everyone in the West has powerful AI and Agents to automate everything. Simply because we can afford it, but the rest of the world doesn't have access to it.
What will that mean for everyone left behind?
It's from Alibaba, which is Chinese, so it seems likely.
I speak to kids that use LLMs all the time to assist them with their school work, and others who simply have no knowledge that this tech exists.
I work with UK learners by the way.
Basically they had to adapt a novel to a comic book form — by using AI to generate pencil drawings, they achieved the goal of the assignment (demonstrating understanding of the story) without having the computer just do their homework.
The point is, AI is here, and it can be a net positive if schools can use it like a calculator vs a black market. It’s a private school with access to some alumni money for development work - they used this to justify investing in designing assignments that make AI a complement to learning.
For example, I read a philosopher saying "truth is a relation between thought and reality". Asking ChatGPT to knock it revealed that statement is an expression of the "correspondence theory" of truth, but that there is also the "coherence theory" of truth that is different, and that there is a laundry list of other takes too.
We don't have a post-cold war era response akin to the kind of US led investment in a global pact to provide protection, security, and access to innovation founded in the United States. We really need to prioritize a model akin to the Bretton Woods Accord
I’ve seen it be a minor productivity boost, and not much more.
it's turning 5 positions into 7: 5 people to do what currently needs to get done, 2 to try to replace those 5 with AI and failing for several years.
The situation now is kinda like back when it was possible to be “good at Google” and lots of people, including in tech, weren’t. It’s possible to be good at LLMs, and not a lot of people are.
They’re ok for Tom the Section Manager to hack together a department newsletter nobody reads, though, even if Tom is bad at using LLMs. They’re decent at things that don’t need to be any good because they didn’t need to exist in the first place, lol.
Nah, if the last 10-20 years demonstrated something, it's that nothing needs to be any good, because a shitty simulacrum achieves almost the same effect but costs much less time and money to produce.
(Ironically, SOTA LLMs are already way better at writing than typical person writing stuff for money.)
I’m aware of multiple companies that would love to know about these, because they’re currently flailing around trying to replace writers with editors + LLMs and it’s not going great. The closest to success are the ones that are only aiming to turn out stuff one step better than outright book-spam, and even they aren’t quite where they want to be, hardly a productivity bump at all from the LLM use and increased demand on their few talented humans.
Now, whether the labor provided by the AI will be as high-quality as that provided by a human when placed in an actual business environment will be up in the air. Probably not, but adoption will be pushed by the sunk cost fallacy.
The decreased employment case is when your competitors get the productivity and you don't, because you go out of business.
If the models are closed, the West will become a digital serfdom to anointed AI corporations, which will be able to gouge prices, inject ads, and influence politics with ease.
The rich west will be in the lead for awhile and then get tiktok-ed.
The lead is just not really worth that much in the long run.
There is probably an advantage gained at some point in all this of being a developing country too that doesn't need to bother automating all these middle management and bullshit jobs they don't have.
China is notoriously middle management heavy, by definition that’s what communism is.
I can imagine the headlines now: "AI promised unlimited productivity, 10 years later, we're still waiting for the rapture"
I find it a terrible business practice to be completely opaque and vague about limits. Even worse, the limits seem to be dynamic and change all the time.
I understand that there is a lot of usage happening, but most likely it means that the $20 per month is too cheap anyway, if an average user like myself can so easily hit the limits.
I use Claude for work, I really love the projects where I can throw in context and documentation and the fact that it can create artifacts like presentation slides. BUT because I rely on Claude for work, it is unacceptable for me to see occasional warnings coming up that I have reached a given limit.
I would happily pay double or even triple for a non-limited experience (or at least know what limit I get when purchasing a plan). AI providers, please make that happen soon.
https://github.com/modelcontextprotocol/servers/tree/main/sr...
This way you will still be in control of commits and pushes.
So far I've used this to understand parts of a code base, and to make edits to a folder of markdown files.
Also they can use multiple models for different tasks, Cursor does this, so can Aider: https://aider.chat/2024/09/26/architect.html
I didn't say it was better!
But other than that I basically just tell the model what I want to do and it does it, lol. I like the Claude Desktop App interface better than trying to do things in Cursor/Windsurf directly, I like the ability to organize prompts/conversations in terms of projects and easily include context. I also honestly just have a funny feeling that the Claude web app often performs better than the API responses I get from the IDEs.
[0] https://github.com/rusiaaman/wcgw
[1] https://goodsnooze.gumroad.com/l/macwhisper
Here are some things I've noticed about this, at least in the "free" tier web models since that's all I typically need.
* ChatGPT has never denied a response but I notice the output slows down during increased demand. I'd rather have a good quality response that takes longer than no response. After reaching the limit, the model quality is reduced and there's a message indicating when you can resume using the better model.
* Claude will pop-up messages like "due to unexpected demand..." and will either downgrade to Haiku or reject the request altogether. I've even observed Claude yanking responses back, it will be mid-way through a function and it just disappears and asks to try again later. Like ChatGPT, eventually there's a message about your quota freeing up at a later time.
* Copilot, at least the free tier found on Bing, at least tells you how many responses you can expect in the form of a "1/20" status text. I rarely use Copilot or Bing but it demonstrates it's totally possible to show this kind of status to the user - ChatGPT and Claude just prefer to slow down, drop model size, or reject the request.
It makes sense that the limits are dynamic though. The services likely have a somewhat fixed capacity but demand will ebb and flow, so it makes sense to expand/contact availability on free tiers and perhaps paid tiers as well.
At $200/m merely having a great AI (if it even is that) without insanely good tooling is pointless to me.
For the latter, Claude is great, but for the former, my usage pattern would be poorly served by something that costs $200 and I get to use it maybe a dozen times a month.
A lot of tradeoffs to evaluate and it can be tiring onboarding people, let alone onboarding an AI.
Maybe it would massively improve my job if the AI could just grab the whole codebase, but we're not there yet. Too many LOC, too much legal BS, etc.
How much better will this be for my uses? Based on my experience with o1, the answer is "fairly marginal". To me, o1 is worse than the regular model or Claude on most things, but it's best for something non-numeric that requires deep thought or new insights. I'm sure there are some people who got a huge productivity boost from o1. This plan is for those people.
That, or I am actually a much better developer and writer than I thought. Because while LLMs certainly have become useful tools to me. They have not doubled my productivity.
It also seems that technology is progressing along a path:
loose collection of tools > organized system of cells > one with a nervous system
And although most people don't think ChatGPT is intelligent on its own, that's missing the point: the combination of us with ChatGPT is the nervous system, and we are becoming cells as globally, we no longer make significant decisions and only use our intelligence locally to advance technology.
https://youtube.com/watch?v=xoykZA8ZDIo
Actually, OpenAI brags that they have done this repeatedly.
Bullish
It'll be an object lesson in short-termism.
(and provide some job security, perhaps)
What that means is the simple, boilerplate and repetitive stuff is being generated by LLM's, but anything complex or involving more than a singular simple problem LLM's often provide more problems than benefit. Effective dev's are using it to handle simple stuff and Execs are thinking 'the team can be reduced by x', when in reality you can get rid of at best your most junior and least trained people without loosing key abilities.
Watching companies try to sell their AI's and "Agents" as having the ability to reason is also absurd but the non-technical managers and execs are eating it up...
Imagine you have two options:
A) A $20/month service which provides you with $100/month of value.
B) A $200/month service which provides you with $300/month of value.
A nets you $80, but B nets you $100. So you should pick B.
If Claude increases their productivity 5% ($17.5k/yr), but CGPT Pro adds 7% ($24.5k), that's an extra $7k in productivity, which more than makes up for the $2400 annual cost. 10x the price, but only 40% better, but still worth it.
The reason we're launching o1 pro is that we have a small slice of power users who want max usage and max intelligence, and this is just a way to supply that option without making them resort to annoying workarounds like buying 10 accounts and rotating through their rate limits. Really it's just an option for those who'd want it; definitely not trying to push a super expensive subscription onto anyone who wouldn't get value from it.
(I work at OpenAI, but I am not involved in o1 pro)
Companies won’t be able to figure those cases out, though, because if they could they’d already have gotten rid of those folks and replaced them with nothing.
OBVIOUSLY a smart OAI employee wouldn't want the public to think they are already replacing high-level humans.
And OBVIOUSLY OAI senior management will want to try to convince AI engineers that might have 2nd-guessings about their work that they aren't developing a replacement for human beings.
But they are.
Interested to learn more, is that the usual break even point?
I'd settle for knowing what level of usage and intelligence I'm getting instead of feeling gaslighted with models seemingly varying in capabilities depending on the time of day, number of days since release and whatnot
But, then again, how companies going to get senior employees if the world stops producing juniors?
The question is - how good is it really.
I haven't used ChatGPT enough to judge what a "fair price" is but $200/month seems to be in the ballpark of other "software-tools-for-highly-paid-knowledge-workers" with premium pricing:
- mathematicians: Wolfram Mathematica is $154/mo
- attorneys: WestLaw legal research service is ~$200/month with common options added
- engineers for printed circuit boards : Altium Designer is $355/month
- CAD/CAM designers: Siemens NX base subscription is $615/month
- financial traders : Bloomberg Terminal is ~$2100/month
It will be interesting to see if OpenAI can maintain the $200/month pricing power like the sustainable examples above. The examples in other industries have sustained their premium prices even though there are cheaper less-featured alternatives (sometimes including open source). Indeed, they often increase their prices each year instead of discount them.
One difference from them is that OpenAI has much more intense competition than those older businesses.
They also come with extensive support, documentation and people have vast experience using them. They are also integrated into all other tools if the field very well. This makes them very entrenched. I am not sure OpenAI has any of those things. I also don't know what those things would entail for LLMs.
Maybe they need to add modes that are good for certain tasks or integrate with tools that their users most commonly use like email, document processors.
Last week when using jetpack compose(which is a react like framework). A cardinal sin in jetpack compose is to change a State variable in a composable based on non-user/UI action which the composable also mutates. This is easy enough to understand this for toy examples. But for more complex systems one can make this mistake. o1-preview made this mistake last week, and I caught it. On prompting it with the stacktrace it did not immediately catch it and recommended a solution that committed the same error. When I actually gave it the documentation on the issue it caught on and made the variable a userpreference instead. I used the userpreference code in my app instead of coding it by myself. It worked well.
Even their existing subscription is a hard sell if only because the value proposition changes so radically and rapidly, in terms of the difference between free and paid services.
This anti-cable-cutting maneuver doesn't bode well for any hopes of future models maintaining same level of improvements (otherwise they'd make GPT 5 and 6 more expensive). Pivoting to AIaaS packages is definitely a pre-emptive strike against commodification, and a harbinger of plateauing model improvements.
Pricing ChatGPT Pro at $200/mo filters it to only power users/enterprise, and given the cost of the GPT-o1 API, it wouldn't surprise me if those power users burn through $200 worth of compute very, very quickly.
And the 'search' part of it could use many of these clusters in parallel, and then pick the best answer to return to the user.
...they probably quantise a bit, but not loads, as they don't want to sacrifice performance. FP8 seems like a possible middle ground. o1 is just a bunch of GPT-4o in a trenchcoat strung together with some advanced prompting. GPT-4o is theorised to be 200B parameters. If you wanted to run 5 parallel generation tasks at peak during the o1 inference process, that's 5x 200B, at FP8, or about 12 H100s. 12 H100s takes about one full rack of kit to run.
Especially when the base line profit margin is negative to begin with
I find this difficult believe, although I don't doubt leaks could have implied it. The challenge is that "the cost of compute" can vary greatly based on how it's accounted for (things like amortization, revenue recognition, capex vs opex, IP attribution, leasing, etc). Sort of like how Hollywood studio accounting can show a movie as profitable or unprofitable, depending on how "profit" is defined and how expenses are treated.
Given how much all those details can impact the outcome, to be credible I'd need a lot more specifics than a typical leak includes.
I can't find any sources _not_ mentioning billions of loss for 2024 and for the foreseeable future
> We have guardrails in place to help prevent misuse and are always working to improve our systems. This may occasionally involve a temporary restriction on your usage. We will inform you when this happens, and if you think this might be a mistake, please don’t hesitate to reach out to our support team at help.openai.com using the widget at the bottom-right of this page. If policy-violating behavior is not found, your access will be restored.
Source: https://help.openai.com/en/articles/9793128-what-is-chatgpt-...
Of course, I'm not the target market.
Some guy who wants to increase his bonus by laying off a few hundred people weeks before the holidays is the target market.