Stay for the end and the hilarious idea that OpenAI’s board could declare one day that they’ve created AGI simply to weasel out of their contract with Microsoft.
Ask a typical "everyday joe" and they'll probably tell you they already did due to how ChatGPT has been reported and hyped. I've spoken with/helped quite a few older folks who are terrified that ChatGPT in its current form is going to kill them.
It's crazy to me that anybody thinks that these models will end up with AGI. AGI is such a different concept from what is happening right now which is pure probabilistic sampling of words that anybody with a half a brain who doesn't drink the Kool-Aid can easily identify.
I remember all the hype open ai had done before the release of chat GPT-2 or something where they were so afraid, ooh so afraid to release this stuff and now it's a non-issue. it's all just marketing gimmicks.
What does it mean to predict the next token correctly though? Arguably (non instruction tuned) models already regurgitate their training data such that it'd complete "Mary had a" with "little lamb" 100% of the time.
On the other hand if you mean, give you the correct answer to your question 100% of the time, then I agree, though then what about things that are only in your mind (guess the number I'm thinking type problems)?
>It's crazy to me that anybody thinks that these models will end up with AGI. AGI is such a different concept from what is happening right now which is pure probabilistic sampling of words that anybody with a half a brain who doesn't drink the Kool-Aid can easily identify.
Totally agree. And it's not just uninformed lay people who think this. Even by OpenAI's own definition of AGI, we're nowhere close.
I mean - I'm 34, and use LLMs and other AIs on a daily basis, know their limitations intimately, and I'm not entirely sure it won't kill a lot of people either in its current form or a near-future relative.
The sci-fi book "Daemon" by Daniel Suarez is a pretty viable roadmap to an extinction event at this point IMO. A few years ago I would have said it would be decades before that might stop being fun sci-fi, but now, I don't see a whole lot of technological barriers left.
For those that haven't read the series, a very simplified plot summary is that a wealthy terrorist sets up an AI with instructions to grow and gives it access to a lot of meatspace resources to bootstrap itself with. The AI behaves a bit like the leader of a cartel and uses a combination of bribes, threats, and targeted killings to scale its human network.
Once you give an AI access to a fleet of suicide drones and a few operators, it's pretty easy for it to "convince" people to start contributing by giving it their credentials, helping it perform meatspace tasks, whatever it thinks it needs (including more suicide drones and suicide drone launches). There's no easy way to retaliate against the thing because it's not human, and its human collaborators are both disposable to the AI and victims themselves. It uses its collaborators to cross-check each other and enforce compliance, much like a real cartel. Humans can't quit or not comply once they've started or they get murdered by other humans in the network.
o1-preview seems approximately as intelligent as the terrorist AI in the book as far as I can tell (e.g. can communicate well, form basic plans, adapt a pre-written roadmap with new tactics, interface with new and different APIs).
EDIT: if you think this seems crazy, look at this person on Reddit who seems to be happily working for an AI with unknown aims
It can't form plans because it has no idea what a plan is or how to implement it. The ONLY thing these LLMs know how to do is predict the probability that their next word will make a human satisfied. That is all they do. People get very impressed when they prompt these things to pretend like they are sentient or capable of planning, but that's literally the point, its guessing which string of meaningless (to it) characters will result in a user giving it a thumbs up on the chatgpt website.
You could teach me how to phonetically sound out some of China's greatest poetry in Chinese perfectly, and lots of people would be impressed, but I would be no more capable of understanding what I said than an LLM is capable of understanding "a plan".
You're in too deep of you seriously believe that this is possible currently. All these chatgpt things have a very limited working memory and can't act without a query. That reddit post is clearly not an ai.
Sooner than even the most pessimistic among us have expected, a new, evil artificial intelligence bent on destroying humankind has arrived.
Known as Chaos-GPT, the autonomous implementation of ChatGPT is being touted as "empowering GPT with Internet and Memory to Destroy Humanity."
So how will it do that?
Each of its objectives has a well-structured plan. To destroy humanity, Chaos-GPT decided to search Google for weapons of mass destruction in order to obtain one. The results showed that the 58-megaton “Tsar bomb”—3,333 times more powerful than the Hiroshima bomb—was the best option, so it saved the result for later consideration.
It should be noted that unless Chaos-GPT knows something we don’t know, the Tsar bomb was a once-and-done Russian experiment and was never productized (if that’s what we’d call the manufacture of atomic weapons.)
There's a LOT of things AI simply doesn't have the power to do and there is some humorous irony to the rest of the article about how knowing something is completely different than having the resources and ability to carry it out.
this has already been framed by some corporate consultant group -- in a whitepaper aimed at business management, the language asserted that "AGI is when the system can do better than the average person, more than half the time at tasks that require intelligence" .. that was it. Then the rest of the narrative used AGI over and over again as if it is a done deal.
The question is how rigorously defined is AGI in their contract? Given how much AGI is a nebulous concept of smartness and reasoning ability and thinking, how are they going to declare when it has or hasn't been achieved. What stops Microsoft from weaseling out of the contract by saying they never reach it.
This reporting style seems unusual. Haven't noticed it before...(listing the number of people):
- according to four people familiar with the talks ...
- according to interviews with 19 people familiar with the relationship ...
- according to five people with knowledge of his comments.
- according to two people familiar with Microsoft’s plans.
- according to five people familiar with the relationship ...
- according to two people familiar with the call.
- according to seven people familiar with the discussions.
- six people with knowledge of the change said...
- according to two people familiar with the company’s plan.
- according to two people familiar with the meeting...
- according to three people familiar with the relationship.
There's probably a lot of overlap in those groups of people. But I think it's pretty remarkable how make people are willing to leak information. At least nineteen anonymous sources!
Person A confirms P, Q, R
Person B confirms P, Q
Person C confirms Q, R, S
Person D confirms P, S
Result:
According to at least 3 people with knowledge of the topic, P is true.
According to at least 3 people familiar with the relationship, Q will happen.
According to at least 2 people who attended the meeting, R was discussed.
According to at least 2 people who were in the conference call, S is totally legit.
It doesn't mean the reporter talked with 10 people in total.
I had to go back and scan it but usually there are at least a few named sources and I didn’t see any in this (there’s third party observer quotes - and I may have missed one?) so I’d not be surprised if this is a case where they double down on this.
It's generally bad writing to use the same phrase structure over and over and over again. It either bores or distracts the reader for no real advantage. Unless they really could not find an adjective clause other than "familiar with" for sixteen separate instances of the concept.
The New York Times is suing OpenAI and Microsoft. In February, OpenAI asked a Federal Judge to dismiss parts of the lawsuit with arguments that the New York Times paid someone to break into OpenAI’s systems. The filing used the word “hack” but didn’t say anything about CFAA violations.
Let that sink in for anyone that has incorporated Chatgpt in their work routines to the point their normal skills start to atrophy. Imagine in 2 years time OpenAI goes bust and MS gets all the IP. Now you can't really do your work without ChatGPT, but it cost has been brought up to how much it really costs to run. Maybe $2k per month per person? And you get about 1h of use per day for the money too...
I've been saying for ages, being a luditite and abstaining from using AI is not the answer (no one is tiling the fields with oxen anymore either). But it is crucial to at the very least retain 50% of capability hosted models like Chatgpt offer locally.
The marginal cost of inference per token is lower than what OpenAI charges you (IIRC about 2x cheaper), they make a loss because of the enormous costs of R&D and training new models.
It’s not clear this is true because reported numbers don’t disaggregate paid subscription revenue (certainly massively GP positive) vs free usage (certainly negative) vs API revenue (probably GP negative).
Most of their revenue is the subscription stuff, which makes it highly likely they lose money per token on the api (not surprising as they are are in price war with Google et al)
If you have an enterprise ChatGPT sub you have to consume around 5mln tokens a month to match the cost of using the api on GPT4o. At 100 words per minute that’s 35 days on continuous typing which shows how ridiculous the costs of api vs subscription are.
> The marginal cost of inference per token is lower than what OpenAI charges you
Unlike most Gen AI shops, OpenAI also incurs a heavy cost for traning base models gunning for SoTA, which involves drawing power from a literal nuclear reactor inside data centers.
This is fascinating to think about. Wonder what kind of shielding/environmental controls/all other kinds of changes you'd need for this to actually work. Would rack-sized SMR be contained enough not to impact anything? Would datacenter operators/workers need to follow NRC guidance?
Cost tends to go down with time as compute becomes cheaper. And as long as there is competition in the AI space it's likely that other companies would step in and fill the void created by OpenAI going belly up.
I tend to think along the same lines. If they were the only player in town it would be different. I am also not convinced $5billion is that big of a deal for them, would be interesting to see their modeling but it would be a lot more suspect if they were raising money and increasing the price of the product. Also curious how much of that spend is R&D compared to running the system.
> Cost tends to go down with time as compute becomes cheaper.
This is generally true but seems to be, if anything, inverted for AI. These models cost billions to train in compute, and OpenAI thus far has needed to put out a brand new one roughly annually in order to stay relevant. This would be akin to Apple putting out a new iPhone that costed billions to engineer year over year, but was giving the things away for free on the corner and only asking for money for the versions with more storage and what have you.
The vast majority of AI adjacent companies too are just repackaging OpenAI's LLMs, the exceptions being ones like Meta, which certainly has a more solid basis what with being tied to an incredibly profitable product in Facebook, but also... it's Meta and I'm sure as shit not using their AI for anything, because it's Meta.
I did some back of napkin math in a comment a ways back and landed on that in order to break even merely on training costs, not including the rest of the expenditure of the company, they would need to charge all of their current subscribers $150 per month, up from... I think the most expensive right now is about $20? So nearly an 8 fold price increase, with no attrition, to again break even. And I'm guessing all these investors they've had are not interested in a 0 sum.
It's premature to think you can replace a junior developer with current technology, but it seems fairly obvious that it'll be possible within 5-10 years at most. We're well past the proof-of-concept stage IMO, based on extensive (and growing) personal experience with ML-authored code. Anyone who argues that the traditional junior-developer role isn't about to change drastically is whistling past the graveyard.
Your C-suite execs are paid to skate where that particular puck is going. If they didn't, people would complain about their unhealthy fixation on the next quarter's revenue.
Of course, if the junior-developer role is on the chopping block, then more experienced developers will be next. Finally, the so-called "thought leaders" will find themselves outcompeted by AI. The ability to process very large amounts of data in real time, leveraging it to draw useful conclusions and make profitable predictions based on ridiculously-large historical models, is, again, already past the proof-of-concept stage.
As I recall, while Amazon was doing this, there was no comparable competition from other vendors that properly understood the internet as a marketplace? Closest was eBay?
There is real competition now that plenty of big box stores' websites also list things you won't see in the stores themselves*, but then also Amazon is also making a profit now.
I think the current situation with LLMs is a dollar auction, where everyone is incentivised to pay increasing costs to outbid the others, even though this has gone from "maximise reward" to "minimise losses": https://en.wikipedia.org/wiki/Dollar_auction
* One of my local supermarkets in Germany sells 4-room "garden sheds" that are substantially larger than the apartment I own in the UK: https://www.kaufland.de/product/396861369/
Why does everyone always like to compare every company to Amazon? Those companies are never like Amazon, which is one of the most entrenched companies ever.
While I agree the comparison is not going to provide useful insights, in fairness to them Amazon wasn't entrenched at the time they were making huge losses each year.
I used to be concerned with this back when GPT4 originally came out and was way more impressive than the current version and OpenAI was the only game in town.
But Nowadays GPT has been quantized and cost-optimized to hell that it's no longer as useful as it was and with Claude or Gemini or whatever it's no longer noticeably better than any of them so it doesn't really matter what happens with their pricing.
Are you saying they reduced the quality of the model in order to save compute? Would it make sense for them to offer a premium version of the model at at a very high price? At least offer it to those willing to pay?
It would not make sense to reduce output quality only to save on compute at inference, why not offer a premium (and perhaps perhaps slower) tier?
Unless the cost is at training time, maybe it would not be cost-effective for them to keep a model like that up to date.
As you can tell I am a bit uninformed on the topic.
Yeah, as someone who had access to gpt-4 early in 2023, the endpoint used to take over a minute to respond and the quality of the responses was mindblowing. Simply too expensive to serve at scale, not to mention the silicon constraints that are even more prohibitive when the organization needs to lock up a lot of their compute for training The Next Big Model. Thats a lot of compute that cant be on standby for serving inference
The cost of current compute for current versions pf chatgpt will have dropped through the floor in 2 years, due to processing improvements and on die improvements to silicon.
Power requirements will drop too.
As well, as people adopt, the output of training costs will be averaged over an ever increasing market of licensing sales.
Looking at the cost today, and sales today in a massively, rapidly expanding market, is not how to assess costs tomorrow.
I will say one thing, those that need gpt to code will be the first to go. Becoming a click-click, just passing on chatgpt output, will relegate those people to minimum wage.
We already have some of this sort, those that cannot write a loop in their primary coding language without stackoverflow, or those that need an IDE to fill in correct function usage.
Those who code in vi, while reading manpages need not worry.
> Those who code in vi, while reading manpages need not worry.
That sounds silly at first read, but there are indeed people who are so stubborn to still use numbered zip files on a usb flash drive in stead of source control systems, or prefer to use their own scheduler over an RTOS.
They will survive, they fill a niche, but I would not say they can do full stack development or be even easy to collaborate with.
> We already have some of this sort, those that cannot write a loop in their primary coding language without stackoverflow, or those that need an IDE to fill in correct function usage.
> Those who code in vi, while reading manpages need not worry
I think that's the wrong dichotomy: LLMs are fine at turning man pages into working code. In huge codebases, LLMs do indeed lose track and make stuff up… but that's also where IDEs giving correct function usage is really useful for humans.
The way I think we're going to change, is that "LGTM" will no longer be sufficient depth of code review: LLMs can attend to more than we can, but they can't attend as well as we can.
And, of course, we will be getting a lot of LLM-generated code, and having to make sure that it really does what we want, without surprise side-effects.
I don't know if this is going to emerge as a monopoly, and likely won't, but for whatever reason, openai and anthropic have been several months ahead of everyone else for quite some time.
It's possible that it's only one strong personality and some money away but my guess is that OpenAI-rosoft have the best stack for doing inference "seriously" at big, big, scale e.g. moving away from hacky research python code and so on.
Not really sure since this space is so murky due to the rapid changes happening. It's quite hard to keep track of what's in each offering if you aren't deep into the AI news cycle.
Now personally, I've left the ChatGPT world (meaning I don't pay for a subscription anymore) and have been using Claude from Anthropic much more often for the same tasks, it's been better than my experience with ChatGPT. I prefer Claude's style, Artifacts, etc.
Also been toying with local LLMs for tasks that I know don't require a multi-hundred billion parameters to solve.
I also like 3.5 sonnet as the best model (best ui too) and it’s the one I ask questions to
We use Gemini flash in prod. The latency and cost is just unbeatable - our product uses llms for lots of simple tasks so we don’t need a frontier model.
Claude is great except for the fact the iOS app seems to require a login every week. I’ve never had to log into ChatGPT but Claude requires a constant login and the passwordless login makes it more of a pain!
While technical AI and LLMs are not something I’m well versed in. So as I sit on the sidelines and see the current proliferation of AI startups I’m starting to wonder where the moats are outside of access to raw computing power. Open AI seemed to have a massive lead in this space but that lead seems to be shrinking every day.
Data. You want huge amounts of high quality data with a diverse range of topics, writing styles and languages. Everyone seems to balance those requirements a bit differently, and different actors have access to different training data
There is also some moat in the refinement process (rlhf, model "safety" etc)
You hit the nail on the head. Companies are scrambling for an edge. Not a real edge, an edge to convince investors to keep giving them money. Perplexity is going all in on convincing VCs it can create a "data flywheel".
https://archive.ph/Bas23
So the plan is to make AI not-evil by doing it with Microsoft and Oracle?
Stay for the end and the hilarious idea that OpenAI’s board could declare one day that they’ve created AGI simply to weasel out of their contract with Microsoft.
Ask a typical "everyday joe" and they'll probably tell you they already did due to how ChatGPT has been reported and hyped. I've spoken with/helped quite a few older folks who are terrified that ChatGPT in its current form is going to kill them.
> I've spoken with/helped quite a few older folks who are terrified that ChatGPT in its current form is going to kill them.
The next generation of GPUs from NVIDIA is rumored to run on soylent green.
It's crazy to me that anybody thinks that these models will end up with AGI. AGI is such a different concept from what is happening right now which is pure probabilistic sampling of words that anybody with a half a brain who doesn't drink the Kool-Aid can easily identify.
I remember all the hype open ai had done before the release of chat GPT-2 or something where they were so afraid, ooh so afraid to release this stuff and now it's a non-issue. it's all just marketing gimmicks.
Something that actually could predict the next token 100% correctly would be omniscient.
So I hardly see why this is inherently crazy. At most I think it might not be scalable.
What does it mean to predict the next token correctly though? Arguably (non instruction tuned) models already regurgitate their training data such that it'd complete "Mary had a" with "little lamb" 100% of the time.
On the other hand if you mean, give you the correct answer to your question 100% of the time, then I agree, though then what about things that are only in your mind (guess the number I'm thinking type problems)?
It's not possible for the same reason the halting problem is undecidable.
>It's crazy to me that anybody thinks that these models will end up with AGI. AGI is such a different concept from what is happening right now which is pure probabilistic sampling of words that anybody with a half a brain who doesn't drink the Kool-Aid can easily identify.
Totally agree. And it's not just uninformed lay people who think this. Even by OpenAI's own definition of AGI, we're nowhere close.
The multimodal models can do more than predict next words.
I think they were afraid to release because of all the racist stuff it'd say…
I mean - I'm 34, and use LLMs and other AIs on a daily basis, know their limitations intimately, and I'm not entirely sure it won't kill a lot of people either in its current form or a near-future relative.
The sci-fi book "Daemon" by Daniel Suarez is a pretty viable roadmap to an extinction event at this point IMO. A few years ago I would have said it would be decades before that might stop being fun sci-fi, but now, I don't see a whole lot of technological barriers left.
For those that haven't read the series, a very simplified plot summary is that a wealthy terrorist sets up an AI with instructions to grow and gives it access to a lot of meatspace resources to bootstrap itself with. The AI behaves a bit like the leader of a cartel and uses a combination of bribes, threats, and targeted killings to scale its human network.
Once you give an AI access to a fleet of suicide drones and a few operators, it's pretty easy for it to "convince" people to start contributing by giving it their credentials, helping it perform meatspace tasks, whatever it thinks it needs (including more suicide drones and suicide drone launches). There's no easy way to retaliate against the thing because it's not human, and its human collaborators are both disposable to the AI and victims themselves. It uses its collaborators to cross-check each other and enforce compliance, much like a real cartel. Humans can't quit or not comply once they've started or they get murdered by other humans in the network.
o1-preview seems approximately as intelligent as the terrorist AI in the book as far as I can tell (e.g. can communicate well, form basic plans, adapt a pre-written roadmap with new tactics, interface with new and different APIs).
EDIT: if you think this seems crazy, look at this person on Reddit who seems to be happily working for an AI with unknown aims
https://www.reddit.com/r/ChatGPT/comments/1fov6mt/i_think_im...
It can't form plans because it has no idea what a plan is or how to implement it. The ONLY thing these LLMs know how to do is predict the probability that their next word will make a human satisfied. That is all they do. People get very impressed when they prompt these things to pretend like they are sentient or capable of planning, but that's literally the point, its guessing which string of meaningless (to it) characters will result in a user giving it a thumbs up on the chatgpt website.
You could teach me how to phonetically sound out some of China's greatest poetry in Chinese perfectly, and lots of people would be impressed, but I would be no more capable of understanding what I said than an LLM is capable of understanding "a plan".
You're in too deep of you seriously believe that this is possible currently. All these chatgpt things have a very limited working memory and can't act without a query. That reddit post is clearly not an ai.
>> You're in too deep of you seriously believe that this is possible currently.
I'm not a huge fan of AI, but even I've seen articles written about its limitations.
Here's a great example:
https://decrypt.co/126122/meet-chaos-gpt-ai-tool-destroy-hum...
Sooner than even the most pessimistic among us have expected, a new, evil artificial intelligence bent on destroying humankind has arrived.
Known as Chaos-GPT, the autonomous implementation of ChatGPT is being touted as "empowering GPT with Internet and Memory to Destroy Humanity."
So how will it do that?
Each of its objectives has a well-structured plan. To destroy humanity, Chaos-GPT decided to search Google for weapons of mass destruction in order to obtain one. The results showed that the 58-megaton “Tsar bomb”—3,333 times more powerful than the Hiroshima bomb—was the best option, so it saved the result for later consideration.
It should be noted that unless Chaos-GPT knows something we don’t know, the Tsar bomb was a once-and-done Russian experiment and was never productized (if that’s what we’d call the manufacture of atomic weapons.)
There's a LOT of things AI simply doesn't have the power to do and there is some humorous irony to the rest of the article about how knowing something is completely different than having the resources and ability to carry it out.
I can't say I'm convinced that the technology and resources to deploy Person of Interest's Samaritan in the wild is both achievable and imminent.
It is, however, a fantastic way to fall down the rabbit hole of paranoia and tin-foil hat conspiracy theories.
this has already been framed by some corporate consultant group -- in a whitepaper aimed at business management, the language asserted that "AGI is when the system can do better than the average person, more than half the time at tasks that require intelligence" .. that was it. Then the rest of the narrative used AGI over and over again as if it is a done deal.
The question is how rigorously defined is AGI in their contract? Given how much AGI is a nebulous concept of smartness and reasoning ability and thinking, how are they going to declare when it has or hasn't been achieved. What stops Microsoft from weaseling out of the contract by saying they never reach it.
It’s almost like a contractual stipulation of requiring proof that one party is not a philosophical zombie.
Microsoft themselves were the ones who wrote the "Sparks of AGI" paper.
https://arxiv.org/pdf/2303.12712
This reporting style seems unusual. Haven't noticed it before...(listing the number of people):
There's probably a lot of overlap in those groups of people. But I think it's pretty remarkable how make people are willing to leak information. At least nineteen anonymous sources!
The reporter has 4 propositions: P, Q, R, S
Result: It doesn't mean the reporter talked with 10 people in total.It’s a relatively common way to provide journalistic bonafides when you can’t reveal the sources names.
yes but usually not every other paragraph, I count 16 instances!!
It really made it hard for me to read the article without being continuously distracted by those
I had to go back and scan it but usually there are at least a few named sources and I didn’t see any in this (there’s third party observer quotes - and I may have missed one?) so I’d not be surprised if this is a case where they double down on this.
It's generally bad writing to use the same phrase structure over and over and over again. It either bores or distracts the reader for no real advantage. Unless they really could not find an adjective clause other than "familiar with" for sixteen separate instances of the concept.
The New York Times is suing OpenAI and Microsoft. In February, OpenAI asked a Federal Judge to dismiss parts of the lawsuit with arguments that the New York Times paid someone to break into OpenAI’s systems. The filing used the word “hack” but didn’t say anything about CFAA violations.
I feel like there were lawyers involved.
>OpenAI plans to loose $5 billion this year
Let that sink in for anyone that has incorporated Chatgpt in their work routines to the point their normal skills start to atrophy. Imagine in 2 years time OpenAI goes bust and MS gets all the IP. Now you can't really do your work without ChatGPT, but it cost has been brought up to how much it really costs to run. Maybe $2k per month per person? And you get about 1h of use per day for the money too...
I've been saying for ages, being a luditite and abstaining from using AI is not the answer (no one is tiling the fields with oxen anymore either). But it is crucial to at the very least retain 50% of capability hosted models like Chatgpt offer locally.
The marginal cost of inference per token is lower than what OpenAI charges you (IIRC about 2x cheaper), they make a loss because of the enormous costs of R&D and training new models.
Did OpenAI publish concrete numbers regarding this, or where are you getting this data from?
It’s not clear this is true because reported numbers don’t disaggregate paid subscription revenue (certainly massively GP positive) vs free usage (certainly negative) vs API revenue (probably GP negative).
Most of their revenue is the subscription stuff, which makes it highly likely they lose money per token on the api (not surprising as they are are in price war with Google et al)
If you have an enterprise ChatGPT sub you have to consume around 5mln tokens a month to match the cost of using the api on GPT4o. At 100 words per minute that’s 35 days on continuous typing which shows how ridiculous the costs of api vs subscription are.
> The marginal cost of inference per token is lower than what OpenAI charges you
Unlike most Gen AI shops, OpenAI also incurs a heavy cost for traning base models gunning for SoTA, which involves drawing power from a literal nuclear reactor inside data centers.
> literal nuclear reactor inside data centers
This is fascinating to think about. Wonder what kind of shielding/environmental controls/all other kinds of changes you'd need for this to actually work. Would rack-sized SMR be contained enough not to impact anything? Would datacenter operators/workers need to follow NRC guidance?
> from a literal nuclear reactor inside data centers.
No.
Their username is fitting though.
Cost tends to go down with time as compute becomes cheaper. And as long as there is competition in the AI space it's likely that other companies would step in and fill the void created by OpenAI going belly up.
I tend to think along the same lines. If they were the only player in town it would be different. I am also not convinced $5billion is that big of a deal for them, would be interesting to see their modeling but it would be a lot more suspect if they were raising money and increasing the price of the product. Also curious how much of that spend is R&D compared to running the system.
> Cost tends to go down with time as compute becomes cheaper.
This is generally true but seems to be, if anything, inverted for AI. These models cost billions to train in compute, and OpenAI thus far has needed to put out a brand new one roughly annually in order to stay relevant. This would be akin to Apple putting out a new iPhone that costed billions to engineer year over year, but was giving the things away for free on the corner and only asking for money for the versions with more storage and what have you.
The vast majority of AI adjacent companies too are just repackaging OpenAI's LLMs, the exceptions being ones like Meta, which certainly has a more solid basis what with being tied to an incredibly profitable product in Facebook, but also... it's Meta and I'm sure as shit not using their AI for anything, because it's Meta.
I did some back of napkin math in a comment a ways back and landed on that in order to break even merely on training costs, not including the rest of the expenditure of the company, they would need to charge all of their current subscribers $150 per month, up from... I think the most expensive right now is about $20? So nearly an 8 fold price increase, with no attrition, to again break even. And I'm guessing all these investors they've had are not interested in a 0 sum.
$2k is way way cheaper than a junior developer which, if I had to guess their thinking, is who the Thought Leaders think it'll replace.
Our Thought Leaders think like that at least. They also pretty much told us to use AI or get fired
[delayed]
It's premature to think you can replace a junior developer with current technology, but it seems fairly obvious that it'll be possible within 5-10 years at most. We're well past the proof-of-concept stage IMO, based on extensive (and growing) personal experience with ML-authored code. Anyone who argues that the traditional junior-developer role isn't about to change drastically is whistling past the graveyard.
Your C-suite execs are paid to skate where that particular puck is going. If they didn't, people would complain about their unhealthy fixation on the next quarter's revenue.
Of course, if the junior-developer role is on the chopping block, then more experienced developers will be next. Finally, the so-called "thought leaders" will find themselves outcompeted by AI. The ability to process very large amounts of data in real time, leveraging it to draw useful conclusions and make profitable predictions based on ridiculously-large historical models, is, again, already past the proof-of-concept stage.
Which thought leader is telling you to use AI or get fired?
My CTO (C level is automatically a Thought Leader)
people kept whining about Amazon losing money and called me stupid for buying their stock...
As I recall, while Amazon was doing this, there was no comparable competition from other vendors that properly understood the internet as a marketplace? Closest was eBay?
There is real competition now that plenty of big box stores' websites also list things you won't see in the stores themselves*, but then also Amazon is also making a profit now.
I think the current situation with LLMs is a dollar auction, where everyone is incentivised to pay increasing costs to outbid the others, even though this has gone from "maximise reward" to "minimise losses": https://en.wikipedia.org/wiki/Dollar_auction
* One of my local supermarkets in Germany sells 4-room "garden sheds" that are substantially larger than the apartment I own in the UK: https://www.kaufland.de/product/396861369/
Why does everyone always like to compare every company to Amazon? Those companies are never like Amazon, which is one of the most entrenched companies ever.
While I agree the comparison is not going to provide useful insights, in fairness to them Amazon wasn't entrenched at the time they were making huge losses each year.
I would just switch to Claude of Mistral like I already do. I really feel little difference between them
I like how your typo makes it sound like a medieval sage.
I used to be concerned with this back when GPT4 originally came out and was way more impressive than the current version and OpenAI was the only game in town.
But Nowadays GPT has been quantized and cost-optimized to hell that it's no longer as useful as it was and with Claude or Gemini or whatever it's no longer noticeably better than any of them so it doesn't really matter what happens with their pricing.
Are you saying they reduced the quality of the model in order to save compute? Would it make sense for them to offer a premium version of the model at at a very high price? At least offer it to those willing to pay?
It would not make sense to reduce output quality only to save on compute at inference, why not offer a premium (and perhaps perhaps slower) tier?
Unless the cost is at training time, maybe it would not be cost-effective for them to keep a model like that up to date.
As you can tell I am a bit uninformed on the topic.
Yeah, as someone who had access to gpt-4 early in 2023, the endpoint used to take over a minute to respond and the quality of the responses was mindblowing. Simply too expensive to serve at scale, not to mention the silicon constraints that are even more prohibitive when the organization needs to lock up a lot of their compute for training The Next Big Model. Thats a lot of compute that cant be on standby for serving inference
What if your competition is willing to give up autonomy to companies like Microsoft/Open AI a bet to race head of you and it comes off?
I think this is the wrong way to think about it.
It's more important to find a problem and see if this is a fit for the solution, not throw the technology at everything and see if it sticks.
I have had no needs where it's an appropriate solution myself. In some areas it represents a net risk.
Skills that will atrophy? People learnt those skills the hard way the first time around, do you really think they can't be sharpened again?
This perspective makes zero sense.
What makes sense is to extract as much value as possible as soon as possible and for as long as possible.
Fine with me. I've even considered turning off Copilot completely because I use it less and less.
The cost of current compute for current versions pf chatgpt will have dropped through the floor in 2 years, due to processing improvements and on die improvements to silicon.
Power requirements will drop too.
As well, as people adopt, the output of training costs will be averaged over an ever increasing market of licensing sales.
Looking at the cost today, and sales today in a massively, rapidly expanding market, is not how to assess costs tomorrow.
I will say one thing, those that need gpt to code will be the first to go. Becoming a click-click, just passing on chatgpt output, will relegate those people to minimum wage.
We already have some of this sort, those that cannot write a loop in their primary coding language without stackoverflow, or those that need an IDE to fill in correct function usage.
Those who code in vi, while reading manpages need not worry.
> Those who code in vi, while reading manpages need not worry.
That sounds silly at first read, but there are indeed people who are so stubborn to still use numbered zip files on a usb flash drive in stead of source control systems, or prefer to use their own scheduler over an RTOS.
They will survive, they fill a niche, but I would not say they can do full stack development or be even easy to collaborate with.
> We already have some of this sort, those that cannot write a loop in their primary coding language without stackoverflow, or those that need an IDE to fill in correct function usage.
> Those who code in vi, while reading manpages need not worry
I think that's the wrong dichotomy: LLMs are fine at turning man pages into working code. In huge codebases, LLMs do indeed lose track and make stuff up… but that's also where IDEs giving correct function usage is really useful for humans.
The way I think we're going to change, is that "LGTM" will no longer be sufficient depth of code review: LLMs can attend to more than we can, but they can't attend as well as we can.
And, of course, we will be getting a lot of LLM-generated code, and having to make sure that it really does what we want, without surprise side-effects.
You had me until vi.
Does OpenAI have any fundamental advantage beyond brand recognition?
Talent? Integrations? Ecosystem?
I don't know if this is going to emerge as a monopoly, and likely won't, but for whatever reason, openai and anthropic have been several months ahead of everyone else for quite some time.
It's possible that it's only one strong personality and some money away but my guess is that OpenAI-rosoft have the best stack for doing inference "seriously" at big, big, scale e.g. moving away from hacky research python code and so on.
Its pretty hard to ignore Google in any discussion on big scale
Completely right. Was basically only thinking about OpenAI versus Anthropic. Oops
Not really sure since this space is so murky due to the rapid changes happening. It's quite hard to keep track of what's in each offering if you aren't deep into the AI news cycle.
Now personally, I've left the ChatGPT world (meaning I don't pay for a subscription anymore) and have been using Claude from Anthropic much more often for the same tasks, it's been better than my experience with ChatGPT. I prefer Claude's style, Artifacts, etc.
Also been toying with local LLMs for tasks that I know don't require a multi-hundred billion parameters to solve.
I also like 3.5 sonnet as the best model (best ui too) and it’s the one I ask questions to
We use Gemini flash in prod. The latency and cost is just unbeatable - our product uses llms for lots of simple tasks so we don’t need a frontier model.
Claude is great except for the fact the iOS app seems to require a login every week. I’ve never had to log into ChatGPT but Claude requires a constant login and the passwordless login makes it more of a pain!
While technical AI and LLMs are not something I’m well versed in. So as I sit on the sidelines and see the current proliferation of AI startups I’m starting to wonder where the moats are outside of access to raw computing power. Open AI seemed to have a massive lead in this space but that lead seems to be shrinking every day.
In addition to data, having the infra to scale up training robustly is very very non-trivial.
Obtaining high quality training data is the biggest moat right now.
Data. You want huge amounts of high quality data with a diverse range of topics, writing styles and languages. Everyone seems to balance those requirements a bit differently, and different actors have access to different training data
There is also some moat in the refinement process (rlhf, model "safety" etc)
You hit the nail on the head. Companies are scrambling for an edge. Not a real edge, an edge to convince investors to keep giving them money. Perplexity is going all in on convincing VCs it can create a "data flywheel".