A well reasoned article that fundamentally downplays both the pace of innovation and the exponential increase in capabilities per dollar happening over time. AI is rapidly accelerating its improvement rate, and I do believe its capabilities will continue growing exponentially.
In particular, GPT-2 to GPT-4 spans an increase from 'well read toddler' to 'average high school student' in just a few years, while simultaneously the computational cost of training less capable models goes down similarly.
Also worth noting: the article claims Stripe, another huge money raiser, had an obviously useful product. gdb, sometime-CTO of stripe and its fourth employee, is now president of OpenAI. And, most of all, the author doesn't remember how nonobvious Stripe's utility was in its early days, even in the tech scene: there were established ways to take people's money and it wasn't clear why Stripe had an offering worth switching to.
For an alternate take, I think https://situational-awareness.ai provides a well reasoned argument for the current status of AI innovation and growth rate, and addresses all of the points here on a general (though not OpenAI specific) way.
It's too early to say for sure if LLM capabilities are on an exponential growth function or a sigmoid, but my money is on sigmoid, and my suspicion is that we're plateauing already.
GPT-4 was released 16+ months ago. In that time OpenAI made a cheaper model (which it teased extensively and the media was sure was GPT-5) and its competitors caught up but have not yet exceeded them. OpenAI's now saying that GPT-5 is in progress, but we don't know what it looks like yet and they're not making any promises.
What I'm seeing right now suggests that we're in the optimization stage of the tech as it is currently architected. I expect it to get cheaper and to be used more widely, but barring another breakthrough on the same order as transformers I don't expect it to see the kind of substantial gains in abilities we've hitherto been seeing. If I'm right, OpenAI will quickly be just one of many dealers in commodity tech.
> GPT-4 was released 16+ months ago. In that time OpenAI made a cheaper model (which it teased extensively and the media was sure was GPT-5) and its competitors caught up but have not yet exceeded them. OpenAI's now saying that GPT-5 is in progress, but we don't know what it looks like yet and they're not making any promises.
I don't really know anything about business, but something else I've wondered is this: if LLM scaling/progress really is exponential, and the juice is worth the squeeze, why is OpenAI investing significantly in everything that's not GPT-5? Wouldn't exponential growth imply that the opportunity cost of investing in something like Sora makes little sense?
Yes, exactly. An observation of OpenAI's behavior gives many clues that suggest they know we've hit the plateau and have known for some time.
A huge one for me is that Altman cries "safety" while pushing out everyone who actually cares about safety. Why? He desperately wants governments to build them a moat, yesterday if possible. He's not worried about the risks of AGI, he's afraid his company won't get there first because they're not making progress any more. They're rushing to productize what they have because they lost their only competitive advantage (model quality) and don't see a path towards getting it back.
I think in one interview Ilya or Sam explained that there are limited text data in the world and this is probably one of the bottleneck. But they mentioned there is still a lot of data in other modalities such as audio, video. Probably the reason more focus on multimodal models and also synthetic datasets.
I also don't thing the only way to improve LLM is by improving as zero shot inference. Did wrote any code in zero shot style that compiled and worked? It's a multistep process and probably agents and planning will be a next step for LLM.
Cheap inference help a lot in this case since you can give a task during the night to AI what you wanna do. Go to sleep then in the morning review the results. In this way AI is bruteforcing the solution by trying many different paths but that's kind of e.g. most programming works. You try many things until you don't have errors, code compiles and passes the tests.
> But they mentioned there is still a lot of data in other modalities such as audio, video. Probably the reason more focus on multimodal models and also synthetic datasets.
I think this is really interesting, but I wonder if there really is enough data there to make a qualitative difference. I'm sure there's enough to make a better model, but I'm hesitant to think it would be better than an improved chatbot. What people are really waiting for is a qualitative shift, not a just an improved GPT.
> It's a multistep process and probably agents and planning will be a next step for LLM.
I agree, we definitely need a new understanding here. Right now, with the architecture we have, agents just don't seem to work. In my experience, if the LLM doesn't figure it out with a few shots, trying over and over again with different tools/functions doesn't help.
Because they're running out of training data. OpenAI doesn't want to scrape new text off the Internet because they don't know what is and isn't AI-generated. Training off AI data tends to homogenize the resulting output, as certain patterns overexpressed by one AI get overexpressed by others.
If they start scraping, training, and generating images and video, then they have lots more data to work with.
For multimodal input, okay, I can see the argument. But since, as you said, training on generated data can be almost worse than useless, what's the point of generating it?
If I had these concerns as OpenAI, I'd be pushing hard to regulate and restrict generative image/video models, to push the end of the "low background data" era as far into the future as possible. i feel like the last thing I'd be doing is productizing those models myself!
> If I had these concerns as OpenAI, I'd be pushing hard to regulate and restrict generative image/video models
They are! And I'm guessing maybe their perspective is if they can identify their own generative content, they can make a choice to ignore it and not cannibalize.
I don't actually think it's a bad one, but OpenAI didn't think that far ahead. They are pushing for regulation but that's mainly to screw over competing models, not to give them more data runway. Every capitalist is a temporarily embarrassed feudal aristocrat after all.
Furthermore, even if OpenAI had a perfect AI/human distinguisher oracle and could train solely on human output, that wouldn't get us superhuman reasoning or generalization performance. The training process they use is to have the machine mimic the textual output of humans. How exactly do you get a superhuman AGI[0] without having text generated by a superhuman AGI to train on?
[0] Note: I'm discounting "can write text faster than a human" as AGI here. printf in a tight loop already does that better than GPT-4o.
When it comes to R&D, those are all very similar. Sora is another way to predict tokens, if you will. It's not like you have to choose that much: Sora is not using up all the compute that should've went to GPT-5. (It might not even be training in the same cluster; it might train on a separate set of GPUs which are useless for GPT-5 purposes because they are too small and the datacenter in question is tapped out.) Sora can't be GPT-5, but it could lead to a GPT-6. You have the Gato approach of tokenizing video into a stream of text tokens with RL, but you might instead wind up taking a more complex diffusion approach to video. Yet the goal is still the same: to control the world and solve things like robotics by predicting. And if you want to reach the goal as quickly as possible, you will try things in parallel, even if you know most of them will fail and you might wind up just doing the obvious thing you expected to do from the start. (After all, the whole reason we are here is that OA didn't throw 100% of its compute into big PPO runs doing DRL research, but Alec Radford was allowed to keep tinkering away with RNNs for predicting text, which led to GPT-1 and then GPT-2 etc.)
If I was going to nitpick I would point out that it's not necessarily the GPUs that Sora is consuming, it's the engineering effort from what could be called the top talent in AI and the vast amount of money OpenAI is borrowing that could be spent elsewhere.
> And if you want to reach the goal as quickly as possible, you will try things in parallel
This is sort of the exploration-exploitation problem, right? But I think you'd agree that a company full of people who firmly believe that GPT-(n+1) will literally be AGI, and that we're on an exponential curve, will be fully in exploitation mode. In my mind, exploring methods of generating videos is not a path towards their stated goal of AGI. Instead, it's an avenue to _earn money now_. OpenAI is in a slightly awkward position: their main product (ChatGPT) is not super useful right now, and is facing increasingly viable competition.
> I don't really know anything about business, but something else I've wondered is this: if LLM scaling/progress really is exponential, and the juice is worth the squeeze, why is OpenAI investing significantly in everything that's not GPT-5? Wouldn't exponential growth imply that the opportunity cost of investing in something like Sora makes little sense?
You can spend 100% on the next generation or you can spend a small percentage to productize the previous generation to unlock revenue that can be spent on the next generation.
The latter will result in more investment into the next generation.
In the real world there is no such thing as having an exponential growth function, they're all really just sigmoidal. The sole question is the latter suspicion: is the plateau now happening now or coming later? With sigmoidal growth it's near impossible to have clarity on that until after the fact.
Anthropic’s Claude 3.5 surpasses GPT-4o in a number of applied tasks, coding in particular.
The progress we’ve seen to date was powered by the ambitions and belief of NVIDIA and LLM companies.
Now, it’s head-to-head competition. It is way too early to call an impending slowdown.
Given how NVIDIA and Meta are leaning in on OSS, the next 18 months are going to be very interesting.
Even if fundamental progress slows, there are many, many secondary problems to solve in using the capabilities we have today that are rapidly improving. As someone deploying AI in business use cases daily, we are just now getting started.
I’d look to when NVIDIA starts to slow down on hardware as an early indicator of a plateau.
AWS is a commodity. When your commodity is compute, there’s a very large amount of growth available.
> the author doesn't remember how nonobvious Stripe's utility was in its early days, even in the tech scene
I have to push back on this. Anybody who had built for B2B credit card acceptance on the Web prior to Stripe's founding knew immediately what a big deal it was. For starters, they let you get up and running the same day. Second, no credit check (and associated delays). Third, their API made sense (as compared to popular legacy providers like Authorize.net) and was easy to integrate using an open source client. Fourth, self-service near-real-time provisioning. Their value proposition was immediately obvious, and they nailed all of these points in their first home page[1].
By contrast, Fee Fighters[2] was innovative for the time but still required me to fax a credit application to them. They got me up and running faster than the legacy provider, which is to say about a week. And I think I only had to talk on the phone with them once or twice. I remember really liking Fee Fighters, but Stripe was in a class of its own.
Stripe was a hit because they promised to solve hard problems that nobody else did, and then they did exactly that. (You still don't have to talk to a rep or do a personal credit check to start using Stripe!)
even if we don't reach AGI anytime soon there is still many new applications for current AI that we haven't explored too much yet. Robotics will be a huge one IMO.
The problem with your comment is: would this approach support these valuations? And also for robotics, is a company like OpenAI even anywhere near being able to use their tech for robotics?
OpenAI made an agreement with Figure - robotic company. Today figure is supposed to announce something so let's see how is the progress going.
Doing a lot of tedious tasks by robots already will be a big industry. They don't have to be super smart.
Google made a lot of money with ads in Google search - eventually perplexity or searchGPT will have some ads. Maybe they can also make money with affiliate links.
Sora et al will shake movie industry and commercials.
Voice assistants and chatbots already change call centers.
It's been just ~1.5 years since gpt3.5 release and 2 years since stable diffusion - e.g. iphone 2g wasn't mainstream and they sold only 6 million devices in first year, today they sell 250 million iphones a year
Idk, AGI (at least in the information domain) has a sound theoretical basis, the scaling laws seem pretty strong for now and there's a track record of shattering human benchmarks.
> there were established ways to take people's money and it wasn't clear why Stripe had an offering worth switching to.
That wasn't true at all. Stripe was a product that people were rushing to pay for it for just how good and useful it was. It was an example of success MVP that people want to pay to use and the profitability was not a problem.
The same can't be true for OpenAI. We don't know how long it can stay in the red. Maybe it can survive. Maybe its money will run dry first. We are not so sure at current stage
That last part is a key differentiator between Stripe and OpenAI.
Stripe had high variable costs (staff, COGS of pass-through processing fees) but low fixed costs. OpenAI has enormous fixed (pre-revenue!) costs alongside high variable costs (staff of AI engineers, inference).
Financially, OpenAI looks more like one of the EV startups like Tesla or Rivian than it does a company like Stripe. And where Stripe was competing with relatively stodgy financial institutions, OpenAI is competing with the very biggest, richest companies in the world.
Models cost a ton of money to train. GPT-4 training was > $100m. GPT-5 training is estimated to be order of $1B-$2B. This is in the same ballpark as Tesla allocated to get the Fremont facility online.
It is likely training generations beyond GPT-5 will cost still more.
These are essentially fixed costs as OpenAI has to pay to train the model whether or not anyone ever uses it.
if you're here you probably know about Claude and llama3 but for people outside of tech, how many are just going to plug ChatGPT into Google and not venture any further and just plonk down $20?
Isn't that kinda solved problem already? Just push more money in marketing. Like actual marketing. I have only seen some medical AIs this far... But same B2C model as done previously seems the obvious play to follow when someone has something.
Exactly my point. I took great pains to not say "OpenAI will 100% die without fail," because doing so would be declarative in a way that would wall off my argument, no matter how well I researched and presented it.
Instead, I wanted to show people the terms under which OpenAI survives, and how onerous said terms were. It's deeply concerning - and I do not think that's a big thing to say! - how much money they may be burning, and how much money they will take to survive.
Thanks for reading!
I downplay it because I fundamentally disagree on the pace of innovation and the exponential increase in capabilities per dollar happening over time. I do not see the rapid acceleration - or at least, they are yet to substantively and publicly show it.
I also think it's a leap of logic to suggest that the former CTO of Stripe joining is somehow the fix they need, or proof they're going to accelerate.
Also, I fundamentally disagree - Stripe was an obvious business. Explaining what Stripe did wasn't difficult. The established ways of taking money were extremely clunky - perhaps there was RELUCTANCE to change, which is a totally fair thing to bring up, but that doesn't mean it wasn't obvious if you thought about it. What's so obvious about GPT? What's the magic trick here?
Anyway, again, thanks for reading, I know you don't necessarily agree, but you've given me a fair read.
Claiming Stripe was obvious is ahistorical unless you believe tens of thousands or even millions of entrepreneurs discarded $100B.
You have a small point that anyone who used authorize.net or similar wanted it to be better and that was obvious, but there's nearly infinite things people want to be better. I'd like breakfast, my commute, my car, my doctor, my vet, etc to be better. That you could make a better thing was incredibly non-obvious and that's why no one did.
If OpenAI can be as good as an outsourced employee at ~$10 per hour, then you should be looking to replace those outsourced employees. The US and EU employees at >$10 per hour, likely with many tens or hundreds of thousands of dollars in compensation, still exist because they provide some sort of value that necessitates that spend.
I am bearish on AI because the nimbleness of humans, even the outsourced ones, is quite capable. If you only want the AI to operate in a box, then you probably can code the decision tree of the box with more specificity and accuracy than a fuzzy AI can provide.
It's a very useful tool, I'm skeptical however about how it can disrupt things economy-wide. I think it can do some things very well, but the value to the market and businesses vs. the cost of training and adapting it to the business need is quite suspicious, at least for this cycle. I think this is one of those "wait 10 years" situations and many AI companies will die within 1 to 3 years.
> It's a very useful tool, I'm skeptical however about how it can disrupt things economy wide.
It won't disrupt much because we already had "AGI" of a sorts. The internet itself, with billions of people and trillions of pieces of text and media is like a generative model. Instead of generating you search. Instead of LLMs you chat with real people. Instead of Copilot we had StackOverflow and Github. All the knowledge LLMs have is on search engines and social networks, with a few extra steps, and have been for 20 years.
Computers have also gotten a million times faster and more networked. We have automated in software all that we could, we have millions of tools at our disposal, most of them open source. Where did all that productivity go? Why is unemployment so low? The amount of automation possible in code is non-trivial, what can AI do dramatically more than so many human devs put together? Automation in factories is already old, new automation needs to raise the bar.
It seems to me AI will only bring incremental change, an evolution rather than revolution. AI operates like "internet in a box", not something radically new. My yet unrealized hope is that assisting hundreds of millions of users, LLMs will accumulate some kind of wisdom, and they will share back that wisdom at an accelerated speed. An automated open sourcing of problem solving expertise.
Yes, and it's interesting to note that as the proportion of quality information available (and searchable) has declined across the internet there is now an alternative in LLMs - which are being used to further decrease the availability of quality information on the internet.
>My yet unrealized hope is that assisting hundreds of millions of users, LLMs will accumulate some kind of wisdom, and they will share back that wisdom at an accelerated speed
The only wisdom you could derive as a machine interacting with humans at scale is that they're not to be trusted, and that you'd rather you didn't have to given the choice.
Imagine you want to make a script with the LLM, it generates code, and you run it and it errs out. You paste the error, the model gains a new nugget of feedback. Do this sufficiently many times with many devs, and you got an experience flywheel.
But this applies to all domains. Sometimes users return days later to iterate on a problem after trying out in real life ideas generated by AI, this is how LLMs can collect real world feedback and update. Connect related chat sessions across days, and evaluate prior responses in the context of the followups (hindsight).
There is also a wealth of experience we have that is not documented anywhere. The LLM can gradually rub off our lived experiences by making itself useful as an assistant and being in the room when problems get solved.
But this experience flywheel won't be exponentially fast, it will be a slow grind. I don't think LLMs will continue to improve at the speed of GPT 3.5 to GPT 4. That was a one time event based on availability of internet scale organic text, which is now exhausted. Catching up is easier than innovation.
But we can't deny LLMs have "data gravity" - they have a gravitational pull to collect data and experience from us. We bring data right into AIs mouth it doesn't even have to go out of its way to scrape or collect. Probably why we have free access to top models today.
> If you only want the AI to operate in a box, then you probably can code the decision tree of the box with more specificity and accuracy than a fuzzy AI can provide.
I've been saying this for the past couple of years. Yes AI is cool, but we already have computers and computer programs. Things that can be solved algorithmically, SHOULD be solved algorithmically. Because you WANT your business rules and logic to be as predictable and reliable as possible. You want to lessen liability, complexity, and amount of possible outcomes.
We already even see this with human customer support. They follow a script and flowchart. They're just glorified algorithms. Despite being human, they're actively told to not be creative, not think, and act as a computer. Because, as it turns out, from a business perspective that's usually very advantageous (where you can do it).
AI would never, or should never, replace those types of tasks.
AI isn't something warming a seat sitting in front of a computer reading emails and responding to them. For a technology to replace a human, it would have to be a drop in replacement in all aspects of what it means to be human.
Stripe's ease of integration, pace of innovation and user flow experience was a game-changing upgrade from the brutal payment processing options that we were forced to use until it was available. Nobody building payment processing at the time needed to be convinced that Stripe was awesome. They solved a hair on fire problem, especially for Canadians. The hoops we used to have to jump through were bonkers.
Meh. I'm waiting to see evidence that this exponential growth means anything. Every day I read something like the statement that it's an "average high school student" now, or a med student or a law student or whatever, yet it seems obvious that the difference between it and a high school student is that a high school student can think at a basic level and it can't. So still waiting to see evidence that the exponential growth is on that axis and not the "bullshit on more complicated subjects" axis.
>AI is rapidly accelerating its improvement rate
Is it?
All I see are desperate AI companies slapping on multi-modality because text generation is nearly peaked
> And, most of all, the author doesn't remember how nonobvious Stripe's utility was in its early days, even in the tech scene: there were established ways to take people's money and it wasn't clear why Stripe had an offering worth switching to.
I think you are misremembering. Stripe was a _big deal_. They had a curl call on their home page for a while for how to take a payment IIRC. It was like how Twilio opened the door for anyone to send SMS, Stripe made it stupid-easy to handle payments online. Nothing else at the time compared in terms of simplicity and clearly defined fees.
> In particular, GPT-2 to GPT-4 spans an increase from 'well read toddler' to 'average high school student'
GPT-2 was indeed much smaller and weaker model. But the question do we have "exponential" boost after GPT3, or just marginal while competition commoditized this vertical.
I think you don't understand the meaning of the word exponential. By now, at the intent expressed here, we'd all be in the Kurzeweil singularity if this was correct.
Hint: it's not correct. It's nothing like exponential. It's not even order of magnitude stuff. It's tiny increments, to a system which fundamentally is a bit of a dead end.
I don't think this is correct. As far as I understand it, Elon/SpaceX did attempt a previous investment in satellite internet, but it had died long before Starlink came to be in 2014.
I am curious why you avoid ads - personally I view them as a tremendous good for the world, helping people improve their lives by introducing them to products or even just ideas they didn't know existed.
I tend to view ads as the perfect opposite of what you mentioned; it’s an enormous waste of money and resources on a global scale that provides no tangible benefit for anyone that isn’t easily and cheaply replaced by vastly superior options.
If people valued ad viewing (e.g. for product decisions), we’d have popular websites dedicated to ad viewing. What we have instead is an industry dedicated to the idea of forcefully displaying ads to users in the least convenient places possible, and we still all go to reddit to decide what to buy.
> If people valued ad viewing (e.g. for product decisions), we’d have popular websites dedicated to ad viewing.
There was a site dedicated to ad viewing once (adcritic.com maybe?) and it was great! People just viewed, voted, and commented on ads. Even though it was about the entertainment/artistic value of advertising and not about making product decisions.
Although the situation is likely to change somewhat in the near future, advertising has been one of the few ways that many artists have been able to make a comfortable living. Lying to and manipulating people in order to take more of their money or influence their opinions isn't exactly honorable work, but it has resulted in a lot of art that would not have happened otherwise.
Sadly the website was plagued by legal complaints from extremely shortsighted companies who should have been delighted to see their ads reach more people, and it eventually was forced to shutdown after it got too expensive to run (streaming video in those days was rare, low quality, and costly) although I have to wonder how much of that came from poor choices (like paying for insanely expensive superbowl ads). The website was bought up and came back requiring a subscription at which point I stopped paying any attention to it.
We do have such sites though, like Tom's Hardware or Consumer Reports or Wirecutter or what have you. Consumers pay money for these ads to reduce the conflict of interest, but companies still need to get their products chosen for these review pipelines.
Tom's Hardware and Consumer Reports aren't really about ads (or at least that's not what made them popular). they were about trying to determine the truth about products and see past the lies told about them by advertising.
Strictly speaking, isn't advertising any action that calls attention to a particular product over another? It doesn't have to be directly funded by a manufacturer or a distributor.
I'd consider word-of-mouth a type of advertising as well.
To me advertising isn't just calling attention to something, it's doing so with the intent to sell something or to manipulate.
When it's totally organic the person doing the promotion doesn't stand to gain anything. It less about trying to get you to buy something and usually just people sharing what they enjoy/has worked for them, or what they think you'd enjoy/would work for you. It's the intent behind the promotion and who is intended to benefit from it that makes the difference between friendly/helpful promotion and adversarial/harmful promotion.
Word of mouth can be a form of advertising that is directly funded by a manufacturer or a distributor too though. Social media influencers are one example, but companies will pay people to pretend to casually/organically talk up their products/services to strangers at bars/nightclubs, conferences, events, etc. just to take advantage of the increased level trust we put in word of mouth promotion exactly because of the assumption that the intent is to be helpful vs to sell.
To me, ads are primarily a way to extract more value from ad-viewers by stochastically manipulating their behavior.
There is a lot of support in favor. Consider:
- Ads are typically NOT consumed enthusiastically or even sought out (which would be the cases if they were strongly mutually beneficial). There are such cases but they are a very small minority.
- If product introduction was the primary purpose, then repeatedly bombarding people with well-known brands would not make sense. But that is exactly what is being done (and paid for!) the most. Coca Cola does not pay for you to learn that they produce softdrinks. They pay for ads to shift your spending/consumption habits.
- Ads are an inherently flawed and biased way to learn about products, because there is no incentive whatsoever to inform you of flaws, or even to represent price/quality tradeoffs honestly.
Back when I was a professor I would give a lecture on ethical design near the end of the intro course. In my experience, most people who think critically about ethics eventually arrive at their own personal ethics which are rarely uniform.
For example, many years ago I worked on military AI for my country. I eventually decided I couldn't square that with my ethics and left. But I consider advertising to be (often non-consensual) mind control designed to keep consumers in a state of perpetual desire and I'd sooner go back to building military AI than work for an advertising company, no matter how many brilliant engineers work there.
Products (and particularly ideas) can be explored in a pull pattern too. Pushing things—physical items, concepts of identity, or political ideology—in the fashion endemic to the ad industry is a pretty surefire way to end up with an extremely bland society, or one that segments increasingly depending on targeting profile.
>I am curious why you avoid ads - personally I view them as a tremendous good for the world, helping people improve their lives by introducing them to products or even just ideas they didn't know existed.
I would agree with you if ads were just that. Here's our product, here's what it does, here's what it costs. Unfortunately ads sell the sizzle not the steak. That has been advertising mantra for probably 100 years.
This seems very much the beginning of the situation predicted by Aschenbrenner in [1], where the AI labs eventually will be fully part of the national security apparatus. Fascinating to see if the other major AI labs also add ex-military folks to their directors or whether this is unique to OpenAI.
Or conceivably his experience is genuinely relevant and unrelated to US national security going forward, completely unrelated to the governmental apparatus and not a sign of the times.
At least 12 exabytes of mostly encrypted data, waiting for the day that the NSA can decrypt it and unleash all of these tools on it.
Whenever that day happens (or happened) it will represent a massive shift in global power. It is on par with the Manhattan project in terms of scope and consequences.
Soon if not already they can just ask questions about people now.
"Has this person ever done anything illegal?"
Then the tools comb through a lifetime of communications intercepts looking for that answer.
It's like the ultimate dirt finder, but without the outsized manual human effort required to ensure that it's largely only abused against people of prominence.
It's less about the NSA having AI capabilities and more the inverse - the NSA having access to people's chatGPT queries. Especially if we fast-forward a few years I suspect people are going to be "confiding" a ton in LLMs so the NSA is going to have a lot of useful data to harvest. (This is in general regardless of them hiring an ex-spook BTW; I imagine it's going to be just like what they do with email, phone calls and general web traffic, namely slurping up all the data permanently in their giant datacenters and running all kinds of analysis on it)
I think the use case here are LLMs trained on billions of terabytes of bulk surveillance data. Imagine an LLM that has been fed every banking transaction, text message or geolocation ping within a target country. An intelligence analyst can now get the answer to any question very, very quickly.
> I suspect people are going to be "confiding" a ton in LLMs
They won't even need to rely on people using ChatGPT for that if things like Microsoft's "Recall" is rolled out and enabled by default. People who aren't privacy conscious will not disable it or care.
Probably, but so did a lot of people. Computer vision and classifier/discriminator models were pretty common in the 2000s and extremely feasible with consumer hardware in the 2010s.
Its unsafe, and there are warning signs everywhere warning you to supervise the fuelling and not leave it unattended. You could walk away from a fuelling car... but the failure mode of burning down the whole gas station is so severe it seems a poor idea.
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