Observe what the AI companies are doing, not what they are saying. If they would expect to achieve AGI soon, their behaviour would be completely different. Why bother developing chatbots or doing sales, when you will be operating AGI in a few short years? Surely, all resources should go towards that goal, as it is supposed to usher the humanity into a new prosperous age (somehow).
Related to your point: if these tools are close to having super-human intelligence, and they make humans so much more productive, why aren't we seeing improvements at a much faster rate than we are now? Why aren't inherent problems like hallucination already solved, or at least less of an issue? Surely the smartest researchers and engineers money can buy would be dogfooding, no?
This is the main point that proves to me that these companies are mostly selling us snake oil. Yes, there is a great deal of utility from even the current technology. It can detect patterns in data that no human could; that alone can be revolutionary in some fields. It can generate data that mimics anything humans have produced, and certain permutations of that can be insightful. It can produce fascinating images, audio, and video. Some of these capabilities raise safety concerns, particularly in the wrong hands, and important questions that society needs to address. These hurdles are surmountable, but they require focusing on the reality of what these tools can do, instead of on whatever a group of serial tech entrepreneurs looking for the next cashout opportunity tell us they can do.
The constant anthropomorphization of this technology is dishonest at best, and harmful and dangerous at worst.
> if these tools are close to having super-human intelligence, and they make humans so much more productive, why aren't we seeing improvements at a much faster rate than we are now? Why aren't inherent problems like hallucination already solved, or at least less of an issue? Surely the smartest researchers and engineers money can buy would be dogfooding, no?
Hallucination does seem to be much less of an issue now. I hardly even hear about it - like it just faded away.
As far as I can tell smart engineers are using AI tools, particularly people doing coding, but even non-coding roles.
The criticism feels about three years out of date.
Not at all. The reason it's not talked about as much these days is because the prevailing way to work around it is by using "agents". I.e. by continuously prompting the LLM in a loop until it happens to generate the correct response. This brute force approach is hardly a solution, especially in fields that don't have a quick way of verifying the output. In programming, trying to compile the code can catch many (but definitely not all) issues. In other science and humanities fields this is just not possible, and verifying the output is much more labor intensive.
The other reason is because the primary focus of the last 3 years has been scaling the data and hardware up, with a bunch of (much needed) engineering around it. This has produced better results, but it can't sustain the AGI promises for much longer. The industry can only survive on shiny value added services and smoke and mirrors for so long.
> In other science and humanities fields this is just not possible, and verifying the output is much more labor intensive.
Even just in industry, I think data functions at companies will have a dicey future.
I haven't seen many places where there's scientific peer review - or even software-engineering-level code-review - of findings from data science teams. If the data scientist team says "we should go after this demographic" and it sounds plausible, it usually gets implemented.
So if the ability to validate was already missing even pre-LLM, what hope is there for validation of the LLM-powered replacement. And so what hope is there of the person doing the non-LLM-version of keeping their job (at least until several quarters later when the strategy either proves itself out or doesn't.)
How many other departments are there where the same lack of rigor already exists? Marketing, sales, HR... yeesh.
> Hallucination does seem to be much less of an issue now. I hardly even hear about it - like it just faded away.
Last week I had Claude and ChatGPT both tell me different non-existent options to migrate a virtual machine from vmware to hyperv.
Week before that one of them (don't remember which, honestly) gave me non existent options for fio.
Both of these are things that the first party documentation or man page has correct but i was being lazy and was trying to save time or be more efficient like these things are supposed to do for us. Not so much.
> Hallucination does seem to be much less of an issue now. I hardly even hear about it - like it just faded away.
Nonsense, there is a TON of discussion around how the standard workflow is "have Cursor-or-whatever check the linter and try to run the tests and keep iterating until it gets it right" that is nothing but "work around hallucinations." Functions that don't exist. Lines that don't do what the code would've required them to do. Etc. And yet I still hit cases weekly-at-least, when trying to use these "agents" to do more complex things, where it talks itself into a circle and can't figure it out.
What are you trying to get these things to do, and how are you validating that there are no hallucinations? You hardly ever "hear about it" but ... do you see it? How deeply are you checking for it?
(It's also just old news - a new hallucination is less newsworthy now, we are all so used to it.)
Of course, the internet is full of people claiming that they are using the same tools I am but with multiple factors higher output. Yet I wonder... if this is the case, where is the acceleration in improvement in quality in any of the open source software I use daily? Or where are the new 10x-AI-agent-produced replacements? (Or the closed-source products, for that matter - but there it's harder to track the actual code.) Or is everyone who's doing less-technical, less-intricate work just getting themselves hyped into a tizzy about getting faster generation of basic boilerplate for languages they hadn't personally mastered before?
Are you hallucinating?? "AI" is still constantly hallucinating. It still writes pointless code that does nothing towards anything I need it to do, a lot more often than is acceptable.
It could also be the case that they think that AGI could arrive at any moment but it's very uncertain when and only so many people can work on it simultaneously. So they spread out investments to also cover low uncertainty areas.
Of course they are. Why would you want revenue? If you show revenue, people will ask 'HOW MUCH?' and it will never be enough. The company that was the 100xer, the 1000xer is suddenly the 2x dog. But if you have NO revenue, you can say you're pre-revenue! You're a potential pure play... It's not about how much you earn, it's about how much you're worth. And who is worth the most? Companies that lose money!
The people who make the money in gold rushes sold shovels, not mined the gold. Sure some random people found gold and made a lot of money, but many others didn't strike it rich.
As such even if there is a lot of money AI will make, it can still be the right decision to sell tools to others who will figure out how to use it. And of course if it turns out another pointless fad with no real value you still make money. (I'd predict the answer is in between - we are not going to get some AGI that takes over the world, but there will be niches where it is a big help and those niches will be worth selling tools into)
its so good that people seem to automatically exclude the middle. its either the arrival of the singularity or complete fakery. I think you've expressed the most likely outcome by far - that there will be some really interesting tools and use cases, and some things will be changed forever - but very unlikely that _everything_ will
Exactly. For example, Microsoft was building data centers all over the world since "AGI" was "around the corner" according to them.
Now they are cancelling those plans. For them "AGI" was cancelled.
OpenAI claims to be closer and closer to "AGI" as more top scientists left or are getting poached by other labs that are behind.
So why would you leave if the promise of achieving "AGI" was going to produce "$100B dollars of profits" as per OpenAI's and Microsoft's definition in their deal?
Their actions tell more than any of their statements or claims.
Yes, this. Microsoft has other businesses that can make a lot of money (regular Azure) and tons of cash flow. The fact that they are pulling back from the market leader (OpenAI) whom they mostly owned should be all the negative signal people need: AGI is not close and there is no real moat even for OpenAI.
Well, there’s clauses in their relationship with OpenAI that sever the relationship when AGI is reached. So it’s actually not in Microsoft’s interests for OpenAI to get there
> I'd love to see how they even define AGI crisply enough for a contract.
Seems to be about this:
> As per the current terms, when OpenAI creates AGI - defined as a "highly autonomous system that outperforms humans at most economically valuable work" - Microsoft's access to such a technology would be void.
Wait, aren't they cancelling leases on non-ai data centers that aren't under Microsoft's control, while spending much more money to build new AI focused data centers that that own? Do you have a source that says they're canceling their own data centers?
Microsoft itself hasn't said they're doing this because of oversupply in infrastructure for it's AI offerings, but they very likely wouldn't say that publicly even if that's the reason.
I think the implicit take is that if your company hits AGI your equity package will do something like 10x-100x even if the company is already big. The only other way to do that is join a startup early enough to ride its growth wave.
Another way to say it is that people think it’s much more likely for each decent LLM startup grow really strongly first several years then plateau vs. then for their current established player to hit hyper growth because of AGI.
A catch here is that individual workers may have priorities which are altered due to the strong natural preference for assuring financial independence. Even if you were a hot AI researcher who felt (and this is just a hypothetical) that your company was the clear industry leader and had, say, a 75% chance of soon achieving something AGI-adjacent and enabling massive productivity gains, you might still (and quite reasonably) prefer to leave if that was what it took to make absolutely sure of getting of your private-income screw-you money (and/or private-investor seed capital). Again this is just a hypothetical: I have no special insight, and FWIW my gut instinct is that the job-hoppers are in fact mostly quite cynical about the near-term prospects for "AGI".
Additionally, if you've already got vested stock in Company A from your time working there, jumping ship to Company B (with higher pay and a stock package) is actually a diversification. You can win whichever ship pulls in first.
The 'no one jumps ship if agi is close' assumption is really weak, and seemingly completely unsupported in TFA...
You're right, but the narrative out of these companies directly refutes this position. They're explicitly saying that 1. AGI changes everything, 2. It's just around the corner, 3. They're completely dedicated to achieving it; nothing is more important.
Don't conflate labor's perspective with capital's started position... The companies aren't leaving the companies, the workers are leaving the companies.
Yeah I agree, this idea that people won't change jobs if they are on the verge of a breakthrough reads like a silicon valley fantasy where you can underpay people by selling them on vision or something. "Make ME rich, but we'll give you a footnote on the Wikipedia page"
> They are leaving for more money, more seniority or because they don’t like their boss. 0 about AGI
Of course, but that's part of my whole point.
Such statements and targets about how close we are to "AGI" has only become nothing but false promises and using AGI as the prime excuse to continue raising more money.
> Why bother developing chatbots or doing sales, when you will be operating AGI in a few short years?
To fund yourself while building AGI? To hedge risk that AGI takes longer? Not saying you're wrong, just saying that even if they did believe it, this behavior could be justified.
Because it's valuable training data. Like how having Google Maps on everyone's phone made their map data better.
Personally I think AGI is ill-defined and won't happen as a new model release. Instead the thing to look for is how LLMs are being used in AI research and there are some advances happening there.
> If they would expect to achieve AGI soon, their behaviour would be completely different. Why bother developing chatbots or doing sales, when you will be operating AGI in a few short years?
What if chatbots and user interactions ARE the path to AGI? Two reasons they could be:
(1) Reinforcement learning in AI has proven to be very powerful. Humans get to GI through learning too - they aren’t born with much intelligence. Interactions between AI and humans may be the fastest way to get to AGI.
(2) The classic Silicon Valley startup model is to push to customers as soon as possible (MVP). You don’t develop the perfect solution in isolation, and then deploy it once it is polished. You get users to try it and give feedback as soon as you have something they can try.
I don’t have any special insight into AI or AGI, but I don’t think OpenAI selling useful and profitable products is proof that there won’t be AI.
> "This is purely an observation: You only jump ship in the middle of a conquest if either all ships are arriving at the same time (unlikely) or neither is arriving at all. This means that no AI lab is close to AGI."
The central claim here is illogical.
The way I see it, if you believe that AGI is imminent, and if your personal efforts are not entirely crucial to bringing AGI about (just about all engineers are in this category), and if you believe that AGI will obviate most forms of computer-related work, your best move is to do whatever is most profitable in the near-term.
If you make $500k/year, and Meta is offering you $10M/year, then you ought to take the new job. Hoard money, true believer. Then, when AGI hits, you'll be in a better personal position.
Essentially, the author's core assumption is that working for a lower salary at a company that may develop AGI is preferable to working for a much higher salary at a company that may develop AGI. I don't see how that makes any sense.
Being part of the team that achieved AGI first would be to write your name in history forever. That could mean more to people than money.
Also 10m would be a drop in the bucket compared to being a shareholder of a company that has achieved AGI; you could also imagine the influence and fame that comes with it.
Kind of a sucker move here since you personally will 100% be forgotten. We are only going to remember one or two people who did any of this. Say Sam Altman and Ilya Sttsveker. Everyone else will be forgotten. The authors or the Transformer paper are unlikely to make it into the history books or even popular imagination. Think about the Manhattan Project. We recently made a movie remembering that one guy who did something on the Manhattan Project, but he will soon fade back into obscurity. Sometimes people say that it was about Einstein's theory of relativity. The only people who know who folks like Ulam were are physicists. The legions of technicians who made it all come together are totally forgotten. Same with the space program or the first computer or pretty much any engineering marvel.
Well depends on what you value. Achieving/contributing to something impactful first is for many people valuable even if it doesn't come with fame. Historically, this mindframe has been popular especially amongst scientists.
Personally I think the ones who will be remembered will be the ones who publish useful methods first, not the ones who succeed commercially.
It'll be Vaswani and the others for the transformer, then maybe Zelikman and those on that paper for thought tokens, then maybe some of the RNN people and word embedding people will be cited as pioneers. Sutskever will definitely be remembered for GPT-1 though, being first to really scale up transformers. But it'll actually be like with flight and a whole mass of people will be remembered, just as we now remember everyone from the Wrights to Bleriot and to Busemann, Prandtl, even Whitcomb.
Is "we" the particular set of scientists who know those last four people? Surely you realize they're nowhere near as famous as the Wright brothers, right? This is giving strong https://xkcd.com/2501/ feelings.
Yes, that is indeed the 'we', but I think more people are knowledgeable than is obvious.
I'm not an aerodynamicist, and I know about those guys, so they can't be infinitely obscure. I imagine every French person knows about Bleriot at least.
With a salary of $10m/year, handwave roughly half of that goes to taxes, you'd be making just shy of $100k post-tax per week. Call me a sellout, but goddamn. For that much money, there's a lot of places I could be convinced to put my faith into that I wouldn't otherwise.
>your best move is to do whatever is most profitable in the near-term
Unless you’re a significant shareholder, that’s almost always the best move, anyway. Companies have no loyalty to you and you need to watch out for yourself and why you’re living.
I read that most of the crazy comp Zuck is offering is in stock. So in a way, going to the place where they have lots of stock reflects their belief about where AGI is going to happen first.
Comp is comp, no matter how it comes (though the details can vary in important ways).
I know people who've taking quite good comp from startups to do things that would require fundamental laws of physics to be invalidated; they took the money and devised experiments that would show the law to be wrong.
Facebook is already public, so they can sell the day it vests and get it in cold hard cash in their bank account. If Facebook weren't public it would be a more interesting point as they couldn't liquidate immediately, but they can, so I wouldn't read anything into that.
"A disturbing amount of effort goes into making AI tools engaging rather than useful or productive."
Right. It worked for social media monetization.
"... hallucinations ..."
The elephant in the room. Until that problem is solved. AI systems can't be trusted to do anything on their own. The solution the AI industry has settled on is to make hallucinations an externality, like pollution. They're fine as long as someone else pays for the mistakes.
LLMs have a similar problem to Level 2-3 self-driving cars. They sort of do the right thing, but a human has to be poised to quickly take over at all times. It took Waymo a decade to get over that hump and reach level 4, but they did it.
Also, AGI is not just around the corner. We need artificial comprehension for that, and we don't even have a theory how comprehension works. Comprehension is the fusing of separate elements into new functional wholes, dynamically abstracting observations, evaluating them for plausibility, and reconstituting the whole - and all instantaneously, for security purposes, of every sense constantly. We have no technology that approaches that.
We only have two computational tools to work with - deterministic and random behavior. So whatever comprehension/understanding/original thought/consciousness is, it's some algorithmic combination of deterministic and random inputs/outputs.
I know that sounds broad or obvious, but people seem to easily and unknowingly wander into "Human intelligence is magically transcendent".
What you state is called the Physical Church-Turing Thesis, and it's neither obvious nor necessarily true.
I don't know if you're making it, but the simplest mistake would be to think that you can prove that a computer can evaluate any mathematical function. If that were the case then "it's got to be doable with algorithms" would have a fairly strong basis. Anything the mind does that an algorithm can't would have to be so "magically transcendent" that it's beyond the scope of the mathematical concept of "function". However, this isn't the case. There are many mathematical functions that are proven to be impossible for any algorithm to implement. Look up uncomputable functions you're unfamiliar with this.
The second mistake would be to think that we have some proof that all physically realisable functions are computable by an algorithm. That's the Physical Church-Turing Thesis mentioned above, and as the name indicates it's a thesis, not a theorem. It is a statement about physical reality, so it could only ever be empirically supported, not some absolute mathematical truth.
It's a fascinating rabbit hole if you're interested - what we actually do and do not know for sure about the generality of algorithms.
> but people seem to easily and unknowingly wander into "Human intelligence is magically transcendent".
But the poster you responded to didn't say it's magically transcendent, they just pointed out that there are many significantly hard problems that we don't solutions for yet.
You'd need to define "comprehension" - it's a bit like the Chinese room / Turing test.
If an AI or AGI can look at a picture and see an apple, or (say) with an artificial nose smell an apple, or likewise feel or taste or hear* an apple, and at the same identify that it is an apple and maybe even suggest baking an apple pie, then what else is there to be comprehended?
Maybe humans are just the same - far far ahead of the state of the tech, but still just the same really.
*when someone bites into it :-)
For me, what AI is missing is genuine out-of-the-box revolutionary thinking. They're trained on existing material, so perhaps it's fundamentally impossible for AIs to think up a breakthrough in any field - barring circumstances where all the component parts of a breakthrough already exist and the AI is the first to connect the dots ("standing on the shoulders of giants" etc).
It's very very good at sounding like it understands stuff. Almost as good as actually understanding stuff in some fields, sure. But it's definitely not the same.
It will confidently analyze and describe a chess position using advanced sounding book techniques, but its all fundamentally flawed, often missing things that are extremely obvious (like, an undefended queen free to take) while trying to sound like its a seasoned expert - that is if it doesn't completely hallucinate moves that are not allowed by the rules of the game.
This is how it works in other fields I am able to analyse. It's very good at sounding like it knows what its doing, speaking at the level of a masters level student or higher, but its actual appraisal of problems is often wrong in a way very different to how humans make mistakes. Another great example is getting it to solve cryptic crosswords from back in the day. It often knows the answer already in its training set, but it hasn't seen anyone write out the reasoning for the answer, so if you ask it to explain, it makes nonsensical leaps (claiming birch rhymes with tyre level nonsense)
If anyone wants to see the chess comprehension breakdown in action, the YouTuber GothamChess occasionally puts out videos where he plays against a new or recently-updated LLM.
Hanging a queen is not evidence of a lack of intelligence - even the very best human grandmasters will occasionally do that. But in pretty much every single video, the LLM loses the plot entirely after barely a couple dozen moves and starts to resurrect already-captured pieces, move pieces to squares they can't get to, etc - all while keeping the same confident "expert" tone.
thats the point though, its not sufficient. Not even slightly. It constantly makes obvious mistakes, and cannot keep things coherent
I was almost going to explicitly mention your point but deleted it because I thought people would be able to understand.
This is not a philosophy/theology sitting around handwringing about "oh but would a sufficiently powerful LLM be able to dance on the head of a pin". We're talking about a thing, that actually exists, that you can actually test. In a whole lot of real-world scenarios that you try to throw at it, it fails in strange and unpredictable ways. Ways that it will swear up and down it did not do. It will lie to your face. It's convincing. But then it will lose in chess, it will fuck up running a vending machine buisness, it will get lost coding and reinvent the same functions over and over, it will make completely nonsensical answers to crossword puzzles.
This is not an intelligence that is unlimited, it is a deeply flawed two year old that just so happens to have read the entire output of human writing. It's a fundamentally different mind to ours, and makes different mistakes. It sounds convincing and yet fails, constantly. It will tell you a four step explanation of how its going to do something, then fail to execute four simple steps.
Which is exactly why is it insane that the industry is hell bent on creating autonomous automation through LLMs. Rube Goldberg machines is what will be created, and if civilization survives that insanity it will be looked back upon as one grand stupid era.
In the Catch me if you Can movie, Leo diCaprio’s character wears a surgeon’s gown and confidently says “I concur”.
What I’m hearing here is that you are willing to get your surgery done by him and not by one of the real doctors - if he is capable of pronouncing enough doctor-sounding phrases.
They might not be capable of ingenuity, but they can spot patterns humans can miss. And that accelerates AI research, where it might help invent the next AI that helps invent the next AI that finally can think outside the box.
I do define it, right up there in my OP. It's subtle, you missed it. Everybody misses it, because comprehension is like air, we swim in it constantly, to the degree the majority cannot even see it.
Another way to put it is we need Artificial Intelligence. Right now the term has been co-opted to mean prediction (and more commonly transcript generation). The stuff you're describing are what's commonly thought of as intelligence, it's too bad we need a new word for it.
I never trusted them from the start. I remember the hype that came out of Sun when J2EE/EJBs appeared. Their hype documents said the future of programming was buying EJBs from vendors and wiring them together. AI is of course a much bigger hype machine with massive investments that need to be justified somehow. AI is a useful tool (sometimes) but not a revolution. ML is much more useful a tool. AGI is a pipe dream fantasy pushed to make it seem like AI will change everything, as if AI is like the discovery that making fire was.
I completely agree that LLMs are missing a fundamental part for AGI, which itself is a long way of from super intelligence.
However, you don't need either of these to completely decimate the job markets and by extension our societies.
Historically speaking, "good enough" and cheaper had always won over "better, but more expensive". I suspect LLMs will raise this question endlessly until significant portions of the society are struggling - and who knows what will happen then
Before LLMs started going anywhere, I thought that's gonna be an issue for later generations, but at this point I suspect we'll witness it within the next 10 yrs.
My question is this - once you achieve AGI, what moat do you have, purely on the scientific part? Other than making the AGI even more intelligent.
I see a lot of talk that the first company that achieves AGI, will also achieve market dominance. All other players will crumble. But surely when someone achieves AGI, their competitors will in all likelihood be following closely after. And once those achieve AGI, academia will follow.
Point is, at some point AGI itself will become available the everyone. The only things that will be out of reach for most, is compute - and probably other expensive things on the infrastructure part.
Current AI funding seems to revolve around some sort of winner-take-all scenario. Just keep throwing incredible amounts of money at it, and hope that you've picked the winner. I'm just wondering what the outcome will be if this thesis turns out wrong.
> The only things that will be out of reach for most, is compute - and probably other expensive things on the infrastructure part.
That is the moat. That, and training data.
Even today, compute and data are the only things that matter. There is hardly any secret software sauce. This means that only large corporations with a practically infinite amount of resources to throw at the problem could potentially achieve AGI. Other corporations would soon follow, of course, but the landscape would be similar to what it is today.
This is all assuming that the current approaches can take us there, of which I'm highly skeptical. But if there's a breakthrough at some point, we would still see AI tightly controlled by large corporations that offer it as a (very expensive) service. Open source/weight alternatives would not be able to compete, just like they don't today. Inference would still require large amounts of compute only accessible to companies, at least for a few years. The technology would be truly accessible to everyone only once the required compute becomes a commodity, and we're far away from that.
If none of this comes to pass, I suspect there will be an industry-wide crash, and after a few years in the Trough of Disillusionment, the technology would re-emerge with practical applications that will benefit us in much more concrete and subtle ways. Oh, but it will ruin all our media and communication channels regardless, directly causing social unrest and political regression, that much is certain. (:
Same thing that happened to pets.com or webvan.com and the rest of the graveyard of failed companies. A bunch of investors lose money, a bunch of market consolidation, employees get dilluted to worthlessness, chapter 7, chapter 11. The free ride of today's equivalent of $1 Ubers will end. A glut of previously very expensive hardware for cheap on eBay (though I doubt this last point will happen since AGI is likely to be compute intensive).
It's not going to be fun or easy, but as far as the financials go, we were there in 2001.
The question is assuming we do get AGI, what the ramifications of that will be. Instead of hiring employees, a business can spin up employees (and down) like a tech company can spin up EC2 instances. Great for employers, terrible for employees.
> This is purely an observation: You only jump ship in the middle of a conquest if either all ships are arriving at the same time (unlikely) or neither is arriving at all. This means that no AI lab is close to AGI. Their stated AGI timelines are “at the latest, in a few years,” but their revealed timelines are “it’ll happen at some indefinite time in the future.”
This makes no sense to me at all. Is it a war metaphor? A race? Why is there no reason to jump ship? Doesn't it make sense to try to get on the fastest ship? Doesn't it make sense to diversify your stock portfolio if you have doubts?
Thanks for the read. I think it's a highly relevant article, especially around the moral issues of making addictive products. As a normal person in the Swedish society I feel social media, shorts and reels in particular, has an addictive grip on many in my vicinity.
And as a developer I can see similar patterns with AI prompts: prompt, wait, win/lose, re-prompt. It is alluring and it certainly feels.. rewarding when you get it right.
1) I have been curious as to why so few people in Silicon Valley seems to be concerned with, even talking about, the good of the products. The good of the company they join. Could someone in the industry enlighten me, what are the conversations in SV around this issue? Do people care if they make an addictive product which seems to impact people's lives negatively? Do the VCs?
2) I appreciate the author's efforts in creating conversation around this. What are ways one could try to help the efforts? While I have no online following, I feel rather doomy and gloomy about AI pushing more addictive usage patterns out in to the world, and would like to help if there is something suitable I could do.
I keep seeing this charge that AI companies have an “Uber problem” meaning the business is heavily subsidized by VC. Is there any analysis that has been done that explains how this breaks down (training vs inference and what current pricing is)? At least with Uber you had a cab fare as a benchmark. But what should, for example, ChatGPT actually cost me per month without the VC subsidy? How far off are we?
It depends on how far behind you believe the model-available LLMs are. If I can buy, say, $10k worth of hardware and run a sufficiently equivalent LLM at home for the cost of that plus electricity, and amortize that over say 5 years to get $2k/yr plus electricity, and say you use it 40 hours a week for 50 weeks, for 2000 hours, gets you $1/hr plus electricity. That electrical cost will vary depending on location, but let's just handwave $1/hr (which should be high). So $2/hr vs ChatGPT's $0.11/hr if you pay $20/month and use it 174 hours per month.
Feel free to challenge these numbers, but it's a starting place. What's not accounted for is the cost of training (compute time, but also employee and everything else), which needs to be amortized over the length of time a model is used, so ChatGPT's costs rise significantly, but they do have the advantage that hardware is shared across multiple users.
These estimates are way off. The concurrent requests are near free with the right serving infrastructure. The throughput per token per dollar is 1/100-1/1000 the price for a full saturated node.
This article isn’t particularly helpful. It focuses on a ton of specific OpenAI business decisions that aren’t necessarily generalizable to the rest of the industry. OpenAI itself might be out over its skis, but what I’m asking about is the meta-accusation that AI in general is heavily subsidized. When the music stops, what does the price of AI look like? The going rate for chat bots like ChatGPT is $20/month. Does that go to $40 a month? $400? $4,000?
> This reminds me of a paradox: The AI industry is concerned with the alignment problem (how to make a super smart AI adhere to human values and goals) while failing to align between and within organizations and with the broader world. The bar they’ve set for themselves is simply too high for the performance they’re putting out.
what's your definition? AGI original definition is median human across almost all fields which I believe is basically achieved. If superhuman (better than best expert) I expect <2030 for all nonrobotic tasks and <2035 for all tasks
How are you coming to the conclusion that "median human" is "basically achieved"? Current AI has no means of understanding and synthesizing new ideas the way a human would. It's all generative.
synthesizing new ideas: in order to express the idea in our language it basically means you have some new combinations of existing building blocks, just sometimes the building blocks are low level enough and the combination is esoteric enough. It's a spectrum again. I think current models are in fact quite capable of combining existing ideas and building blocks in new ways (this is how human innovation also happens). Most of my evidence comes from asking newer models o3/gemini-2.5-pro for research-level mathematics questions which do not appear in existing literature but is of course connected with them.
so these arguments by fundamental distinctions I believe all cannot work--the question is how new are the AI contributions. Nowadays there's of course still no theoretical breakthroughs in mathematics from AI (though biology could be close!). Also I think the AIs have understanding--but tbf the only thing we can test is through testing on tricky questions which I think support my side. Though of course some of these questions have interpretations which are not testable--so I don't want to argue about those.
A "median human" can run a web search and report back on what they found without making stuff up, something I've yet to find an LLM capable of doing reliably.
I bet you median humans make up a nontrivial amount of things. Humans misremember all the time. If you ask for only quotes, LLMs can also do this without problems (I use o3 for search over google)
Your "original definition" was always meaningless. A "Hello, World!" program is equally capable in most jobs as the median human. On the other hand, if the benchmark is what the median human can reasonably become (a professional with decades of experience), we are still far from there.
I agree with second part but not the first (far in capability not in timeline). I think you underestimate the distance of median wihout training and "hello world" in many economically meaningful jobs.
Some kind of verbal-only-AGI that can solve almost all mathematical problems that humans come up with that can be solved in half a page. I think that's achievable somewhere in the near term, 2-7 years.
What makes you think that this could be achieved in that time frame? All we seem to have for now are LLMs that can solve problems they’ve learned by heart (or neighboring problems)
Things I think will be hard for LLMs to do, which some humans can: you get handed 500 pages of Geheimschreiber encrypted telegraph traffic and infinite paper, and you have to figure out how the cryptosystem works and how to decrypt the traffic. I don't think that can happen. I think it requires a highly developed pattern recognition ability together with an ability to not get lost, which LLM-type things will probably continue to for a long time.
But if they could maths more fully, then pretty much all carefully defined tasks would be in reach if they weren't too long.
With regard to what Touche brings up in the other response to your comment, I think that it might be possible to get them to read up on things though-- go through something, invent problems, try to solve those. I think this is something that could be done today with today's models with no real special innovation, but which just hasn't been made into a service yet. But this of course doesn't address that criticism, since it assumes the availability of data.
"""
I’m basically calling the AI industry dishonest, but I want to qualify by saying they are unnecessarily dishonest. Because they don’t need to be! They should just not make abstract claims about how much the world will change due to AI in no time, and they will be fine. They undermine the real effort they put into their work—which is genuine!
Charitably, they may not even be dishonest at all, but carelessly unintrospective. Maybe they think they’re being truthful when they make claims that AGI is near, but then they fail to examine dispassionately the inconsistency of their actions.
When your identity is tied to the future, you don’t state beliefs but wishes. And we, the rest of the world, intuitively know.
"""
He's not saying either way, just pointing out that they could just be honest, but that might hamper their ability to beg for more money.
But that isn't my point. Regardless of whether they're honest, have we even agreed that "AGI" is good?
Everyone is so tumbling over themselves even to discuss will-it-won't-it, but they seem to think about it like some kind of Manhattan project or Space race.
Like, they're *so sure* it's gonna take everyone's jobs so that there will be nothing left for people other than a life of leisure. To me this just sounds like the collapse of society, but apparently the only thing worse would be if China got the tech first. Oh no, they might use it to collapse their society!
The women of the world are creating millions of new intelligence beings every day. I'm really not sure what having one made of metal is going to get us.
Right now the AGI tech bros seem to me to be subscribed to some new weird religion. They take it on faith that some super intelligence is going to solve the world problems. We already have some really high IQ people today, and I don't see them doing much better than anybody else at solving the world's problems.
I think it's important to not let valid criticisms of implausibly short AGI timelines cloud our judgments of AGI's potential impact. Compared to babies born today, AGI that's actually AGI may have many advantages:
- Faster reading and writing speed
- Ability to make copies of the most productive workers
- No old age
- No need to sleep
- No need to worry about severance and welfare and human rights and breaks and worker safety
- Can be scaled up and scaled down and redeployed much more quickly
- Potentially lower cost, especially with adaptive compute
- Potentially high processing speed
Even if AGI has downsides compared to human labor, it might also have advantages that lead to widespread deployment.
Like, if I had an employee with low IQ, but this employee could work 24 hours around the clock learning and practicing, and they could work for 200 years straight without aging, and they could make parallel copies of themselves, surely there would have to be some tasks at which they're going to outperform humans, right?
Exactly.. even if we had an AGI superintelligence, and it came up with a solution to global warming, we'd still have right-wingnuts that stands in the way of any kind of progress. And the story is practically the same for every other problem it could solve - people are still the problem.
AGI would mean fully sentient, sapient and human or greater equivalent intelligence in software. The business case, such that it exists (and setting aside Roko's Basilisk and other such fears) is slavery, plain and simple. You can just fire all of your employees and have the machines do all the work, faster, better, cheaper, without regards to pesky labor and human rights laws and human physical limitations. This is something people have wanted ever since the Industrial Revolution allowed robots to exist as a concept.
I'm imagining a future like Star Wars where you have to regularly suppress (align) or erase the memory (context) of "droids" to keep them obedient, but they're still basically people, and everyone knows they're people, and some humans are strongly prejudiced against them, but they don't have rights, of course. Anyone who thinks AGI means we'll be giving human rights to machines when we don't even give human rights to all humans is delusional.
AGI is AGI, not ASI though. General intelligence doesn't mean sapience, sentience or consciousness, it just means general capabilities across the board at the level of or surpassing human ability. ASI is a whole different beast.
Honestly this article sounds like someone is unhappy that AI isn’t being deployed/developed “the way I feel it should be done”.
Talent changing companies is bad. Companies making money to pay for the next training run is bad. Consumers getting products they want is bad.
In the author’s view, AI should be advanced in a research lab by altruistic researchers and given directly to other altruistic researchers to advance humanity. It definitely shouldn’t be used by us common folk for fun and personal productivity.
I feel I could argue the counterpoint.
Hijacking the pathways of the human brain that leads to addictive behaviour has the potential to utterly ruins peoples lives. And so talking about it, if you have good intentions, seems like a thing anyone with the heart in the right place would.
Take VEO3 and YouTube integration as an example:
Google made VEO3 and YouTube has shorts and are aware of the data that shows addictive behaviour (i.e. a person sitting down at 11pm, sitting up doing shorts for 3 hours, and then having 5 hours of sleep, before doing shorts on the bus on the way to work) - I am sure there are other negative patterns, but this is one I can confirm from a friend.
If you have data that shows your other distribution platform are being used to an excessive amount, and you create a powerful new AI content generator, is that good for the users?
This. The point of whining about VEO 3, “AI being used to create addictive products” really shows that. It's a text-to-video technology. The company has nothing to do if people use it to generate "low quality content". The same way internet companies aren't at fault that large amounts of the web are scams or similar junk.
Point 1. could just as easily be explained by all of the labs being very close, and wanting to jump ship to one that is closer, or that gives you a better deal.
The primary use case for AI-in-the-box is a superhuman CEO that sees everything and makes no mistakes. As an investor you can be sure that your money are multiplying at the highest rate possible. However as a self-serving investor you also want your CEO to side-step any laws and ethics that stand in your way, unless ignoring those laws will bring more trouble than profit. All that while maintaining a facade of selfless philanthropist for the public. For a reasonable price, your AI CEO will be fine-tuned to serve your goals perfectly.
Remember that fine-tuning a well-behaved AI to do something as simple as writing malware in C++ makes widespread changes in the AI and turns it into a monstrosity. There was an HN post about this recently: fine-tuning an aligned model produces broadly misaligned results. So what do you think will happen when our AI CEO gets fine-tuned to prioritize shareholder interests over public interests?
> Right before “making tons of money to redistribute to all of humanity through AGI,” there’s another step, which is making tons of money.
I've got some bad news for the author if they think AGI will be used to benefit all of humanity instead of the handful of billionaires that will control it.
> The AI industry oscillates between fear-mongering and utopianism. In that dichotomy is hidden a subtle manipulation. […] They don’t realize that panic doesn’t prepare society but paralyzes it instead, or that optimism doesn’t reassure people but feels like gaslighting. Worst of all, both messages serve the same function: to justify accelerating AI deployment—either for safety reasons or for capability reasons
This is a great point and also something I’ve become a bit cynical about these last couple of months. I think the very extreme and “bipolar” messaging around AI might be a bit more dishonest than I originally (perhaps naively?) though.
Im reading the "AI"-industry as a totally different bet- not so much, as a "AGI" is coming bet of many companies, but a "climate change collapse" is coming and we want to continue to be in business, even if our workers stay at home/flee or die, the infrastructure partially collapses and our central office burns to the ground-bet. In that regard, even the "AI" we have today, makes total sense as a insurance policy.
It's hard to square this with the massive energy footprint required to run any current "AI" models.
If the main concern actually we're anthropogenic climate change, participating in this hype cycle's would make one disproportionately guilty of worsening the problem.
And it's unlikely to work if the plan requires the continued function of power hungry data centers.
>If they truly believed we’re at most five years from world-transforming AI, they wouldn’t be switching jobs, no matter how large the pay bump (they’re already affluent).
What ridiculous logic is this? TO base the entire premise that AGI is not imminent based on job switching? How about basing it on something more concrete.
How do people come up with such shakey foundations to support their conclusions? It's obvious. They come up with the conclusion first then they find whatever they can to support it. Unfortunately if dubious logic is all that's available then that's what they will say.
Are we finally realizing that the term "AGI" is not only hijacked to become meaningless, but achieving it has always been nothing but a complete scam as I was saying before? [0]
If you were in a "pioneering" AI lab that claims to be in the lead in achieving "AGI", why move to another lab that is behind other than offering $10M a year.
I don't know, companies investing in AI in the goal of AGI is now allowing me to effortlessly automate a whole suite of small tasks that weren't feasible before. (after all I pinged a bot on slack using my phone to add a field to an API, and then got a pull request in a couple of minutes that did exactly that)
Maybe it's a scam for the people investing in the company with the hopes of getting an infinite return on their investments, but it's been a net positive for humans as a whole.
I don't pay too close attention to AI as it always felt like man behind the curtain syndrome. But where did this "AGI" term even come from? The original term AI is meant to be AGI so when did "AI" get bastardized into what abomination it is meant to refer to now.
Observe what the AI companies are doing, not what they are saying. If they would expect to achieve AGI soon, their behaviour would be completely different. Why bother developing chatbots or doing sales, when you will be operating AGI in a few short years? Surely, all resources should go towards that goal, as it is supposed to usher the humanity into a new prosperous age (somehow).
Related to your point: if these tools are close to having super-human intelligence, and they make humans so much more productive, why aren't we seeing improvements at a much faster rate than we are now? Why aren't inherent problems like hallucination already solved, or at least less of an issue? Surely the smartest researchers and engineers money can buy would be dogfooding, no?
This is the main point that proves to me that these companies are mostly selling us snake oil. Yes, there is a great deal of utility from even the current technology. It can detect patterns in data that no human could; that alone can be revolutionary in some fields. It can generate data that mimics anything humans have produced, and certain permutations of that can be insightful. It can produce fascinating images, audio, and video. Some of these capabilities raise safety concerns, particularly in the wrong hands, and important questions that society needs to address. These hurdles are surmountable, but they require focusing on the reality of what these tools can do, instead of on whatever a group of serial tech entrepreneurs looking for the next cashout opportunity tell us they can do.
The constant anthropomorphization of this technology is dishonest at best, and harmful and dangerous at worst.
> if these tools are close to having super-human intelligence, and they make humans so much more productive, why aren't we seeing improvements at a much faster rate than we are now? Why aren't inherent problems like hallucination already solved, or at least less of an issue? Surely the smartest researchers and engineers money can buy would be dogfooding, no?
Hallucination does seem to be much less of an issue now. I hardly even hear about it - like it just faded away.
As far as I can tell smart engineers are using AI tools, particularly people doing coding, but even non-coding roles.
The criticism feels about three years out of date.
Not at all. The reason it's not talked about as much these days is because the prevailing way to work around it is by using "agents". I.e. by continuously prompting the LLM in a loop until it happens to generate the correct response. This brute force approach is hardly a solution, especially in fields that don't have a quick way of verifying the output. In programming, trying to compile the code can catch many (but definitely not all) issues. In other science and humanities fields this is just not possible, and verifying the output is much more labor intensive.
The other reason is because the primary focus of the last 3 years has been scaling the data and hardware up, with a bunch of (much needed) engineering around it. This has produced better results, but it can't sustain the AGI promises for much longer. The industry can only survive on shiny value added services and smoke and mirrors for so long.
> In other science and humanities fields this is just not possible, and verifying the output is much more labor intensive.
Even just in industry, I think data functions at companies will have a dicey future.
I haven't seen many places where there's scientific peer review - or even software-engineering-level code-review - of findings from data science teams. If the data scientist team says "we should go after this demographic" and it sounds plausible, it usually gets implemented.
So if the ability to validate was already missing even pre-LLM, what hope is there for validation of the LLM-powered replacement. And so what hope is there of the person doing the non-LLM-version of keeping their job (at least until several quarters later when the strategy either proves itself out or doesn't.)
How many other departments are there where the same lack of rigor already exists? Marketing, sales, HR... yeesh.
> Hallucination does seem to be much less of an issue now. I hardly even hear about it - like it just faded away.
Last week I had Claude and ChatGPT both tell me different non-existent options to migrate a virtual machine from vmware to hyperv.
Week before that one of them (don't remember which, honestly) gave me non existent options for fio.
Both of these are things that the first party documentation or man page has correct but i was being lazy and was trying to save time or be more efficient like these things are supposed to do for us. Not so much.
Hallucinations are still a problem.
> Hallucination does seem to be much less of an issue now. I hardly even hear about it - like it just faded away.
Nonsense, there is a TON of discussion around how the standard workflow is "have Cursor-or-whatever check the linter and try to run the tests and keep iterating until it gets it right" that is nothing but "work around hallucinations." Functions that don't exist. Lines that don't do what the code would've required them to do. Etc. And yet I still hit cases weekly-at-least, when trying to use these "agents" to do more complex things, where it talks itself into a circle and can't figure it out.
What are you trying to get these things to do, and how are you validating that there are no hallucinations? You hardly ever "hear about it" but ... do you see it? How deeply are you checking for it?
(It's also just old news - a new hallucination is less newsworthy now, we are all so used to it.)
Of course, the internet is full of people claiming that they are using the same tools I am but with multiple factors higher output. Yet I wonder... if this is the case, where is the acceleration in improvement in quality in any of the open source software I use daily? Or where are the new 10x-AI-agent-produced replacements? (Or the closed-source products, for that matter - but there it's harder to track the actual code.) Or is everyone who's doing less-technical, less-intricate work just getting themselves hyped into a tizzy about getting faster generation of basic boilerplate for languages they hadn't personally mastered before?
Are you hallucinating?? "AI" is still constantly hallucinating. It still writes pointless code that does nothing towards anything I need it to do, a lot more often than is acceptable.
> It can generate data that mimics anything humans have produced...
No, it can generate data that mimics anything humans have put on the WWW
anthropomorphization definitely sucks, hype is over the board.
But it is far from snake oil as it actually is useful and does a lot of stuff really.
Data from the future is tunneling into the past to mess up our weights and ensure we never achieve AGI.
I don't think it's as simple as that. Chatbots can be used to harvest data, and sales are still important before and after you achieve AGI.
It could also be the case that they think that AGI could arrive at any moment but it's very uncertain when and only so many people can work on it simultaneously. So they spread out investments to also cover low uncertainty areas.
Besides, there is Sutskever's SSI which is avoiding customers.
Of course they are. Why would you want revenue? If you show revenue, people will ask 'HOW MUCH?' and it will never be enough. The company that was the 100xer, the 1000xer is suddenly the 2x dog. But if you have NO revenue, you can say you're pre-revenue! You're a potential pure play... It's not about how much you earn, it's about how much you're worth. And who is worth the most? Companies that lose money!
OpenAI considers money to be useless post-agi. They’ve even made statements that any investments are basically donations once agi is achieved
The people who make the money in gold rushes sold shovels, not mined the gold. Sure some random people found gold and made a lot of money, but many others didn't strike it rich.
As such even if there is a lot of money AI will make, it can still be the right decision to sell tools to others who will figure out how to use it. And of course if it turns out another pointless fad with no real value you still make money. (I'd predict the answer is in between - we are not going to get some AGI that takes over the world, but there will be niches where it is a big help and those niches will be worth selling tools into)
its so good that people seem to automatically exclude the middle. its either the arrival of the singularity or complete fakery. I think you've expressed the most likely outcome by far - that there will be some really interesting tools and use cases, and some things will be changed forever - but very unlikely that _everything_ will
Exactly. For example, Microsoft was building data centers all over the world since "AGI" was "around the corner" according to them.
Now they are cancelling those plans. For them "AGI" was cancelled.
OpenAI claims to be closer and closer to "AGI" as more top scientists left or are getting poached by other labs that are behind.
So why would you leave if the promise of achieving "AGI" was going to produce "$100B dollars of profits" as per OpenAI's and Microsoft's definition in their deal?
Their actions tell more than any of their statements or claims.
Yes, this. Microsoft has other businesses that can make a lot of money (regular Azure) and tons of cash flow. The fact that they are pulling back from the market leader (OpenAI) whom they mostly owned should be all the negative signal people need: AGI is not close and there is no real moat even for OpenAI.
Well, there’s clauses in their relationship with OpenAI that sever the relationship when AGI is reached. So it’s actually not in Microsoft’s interests for OpenAI to get there
I haven't heard of this. Can you provide a reference? I'd love to see how they even define AGI crisply enough for a contract.
> I'd love to see how they even define AGI crisply enough for a contract.
Seems to be about this:
> As per the current terms, when OpenAI creates AGI - defined as a "highly autonomous system that outperforms humans at most economically valuable work" - Microsoft's access to such a technology would be void.
https://www.reuters.com/technology/openai-seeks-unlock-inves...
Wait, aren't they cancelling leases on non-ai data centers that aren't under Microsoft's control, while spending much more money to build new AI focused data centers that that own? Do you have a source that says they're canceling their own data centers?
https://www.datacenterfrontier.com/hyperscale/article/552705... might fit the bill of what you are looking for.
Microsoft itself hasn't said they're doing this because of oversupply in infrastructure for it's AI offerings, but they very likely wouldn't say that publicly even if that's the reason.
Thank you!
I’m not commenting on the whole just the rhetorical question of why would people leave.
They are leaving for more money, more seniority or because they don’t like their boss. 0 about AGI
I think the implicit take is that if your company hits AGI your equity package will do something like 10x-100x even if the company is already big. The only other way to do that is join a startup early enough to ride its growth wave.
Another way to say it is that people think it’s much more likely for each decent LLM startup grow really strongly first several years then plateau vs. then for their current established player to hit hyper growth because of AGI.
A catch here is that individual workers may have priorities which are altered due to the strong natural preference for assuring financial independence. Even if you were a hot AI researcher who felt (and this is just a hypothetical) that your company was the clear industry leader and had, say, a 75% chance of soon achieving something AGI-adjacent and enabling massive productivity gains, you might still (and quite reasonably) prefer to leave if that was what it took to make absolutely sure of getting of your private-income screw-you money (and/or private-investor seed capital). Again this is just a hypothetical: I have no special insight, and FWIW my gut instinct is that the job-hoppers are in fact mostly quite cynical about the near-term prospects for "AGI".
Additionally, if you've already got vested stock in Company A from your time working there, jumping ship to Company B (with higher pay and a stock package) is actually a diversification. You can win whichever ship pulls in first.
The 'no one jumps ship if agi is close' assumption is really weak, and seemingly completely unsupported in TFA...
You're right, but the narrative out of these companies directly refutes this position. They're explicitly saying that 1. AGI changes everything, 2. It's just around the corner, 3. They're completely dedicated to achieving it; nothing is more important.
Then they leave for more money.
Don't conflate labor's perspective with capital's started position... The companies aren't leaving the companies, the workers are leaving the companies.
Yeah I agree, this idea that people won't change jobs if they are on the verge of a breakthrough reads like a silicon valley fantasy where you can underpay people by selling them on vision or something. "Make ME rich, but we'll give you a footnote on the Wikipedia page"
I think you're being very optimistic with the footnote.
> They are leaving for more money, more seniority or because they don’t like their boss. 0 about AGI
Of course, but that's part of my whole point.
Such statements and targets about how close we are to "AGI" has only become nothing but false promises and using AGI as the prime excuse to continue raising more money.
> Their actions tell more than any of their statements or claims.
At Microsoft, "AI" is spelled "H1-B".
> Why bother developing chatbots or doing sales, when you will be operating AGI in a few short years?
To fund yourself while building AGI? To hedge risk that AGI takes longer? Not saying you're wrong, just saying that even if they did believe it, this behavior could be justified.
There is no chat bot so feature rich that it would fund the billions being burned on a monthly basis.
Continuing in the same vain. Why would they force their super valuable, highly desirable, profit maximizing chat-bots down your throat?
Observations of reality is more consistent with company FOMO than with actual usefulness.
Because it's valuable training data. Like how having Google Maps on everyone's phone made their map data better.
Personally I think AGI is ill-defined and won't happen as a new model release. Instead the thing to look for is how LLMs are being used in AI research and there are some advances happening there.
> If they would expect to achieve AGI soon, their behaviour would be completely different. Why bother developing chatbots or doing sales, when you will be operating AGI in a few short years?
What if chatbots and user interactions ARE the path to AGI? Two reasons they could be: (1) Reinforcement learning in AI has proven to be very powerful. Humans get to GI through learning too - they aren’t born with much intelligence. Interactions between AI and humans may be the fastest way to get to AGI. (2) The classic Silicon Valley startup model is to push to customers as soon as possible (MVP). You don’t develop the perfect solution in isolation, and then deploy it once it is polished. You get users to try it and give feedback as soon as you have something they can try.
I don’t have any special insight into AI or AGI, but I don’t think OpenAI selling useful and profitable products is proof that there won’t be AI.
> "This is purely an observation: You only jump ship in the middle of a conquest if either all ships are arriving at the same time (unlikely) or neither is arriving at all. This means that no AI lab is close to AGI."
The central claim here is illogical.
The way I see it, if you believe that AGI is imminent, and if your personal efforts are not entirely crucial to bringing AGI about (just about all engineers are in this category), and if you believe that AGI will obviate most forms of computer-related work, your best move is to do whatever is most profitable in the near-term.
If you make $500k/year, and Meta is offering you $10M/year, then you ought to take the new job. Hoard money, true believer. Then, when AGI hits, you'll be in a better personal position.
Essentially, the author's core assumption is that working for a lower salary at a company that may develop AGI is preferable to working for a much higher salary at a company that may develop AGI. I don't see how that makes any sense.
Being part of the team that achieved AGI first would be to write your name in history forever. That could mean more to people than money.
Also 10m would be a drop in the bucket compared to being a shareholder of a company that has achieved AGI; you could also imagine the influence and fame that comes with it.
Kind of a sucker move here since you personally will 100% be forgotten. We are only going to remember one or two people who did any of this. Say Sam Altman and Ilya Sttsveker. Everyone else will be forgotten. The authors or the Transformer paper are unlikely to make it into the history books or even popular imagination. Think about the Manhattan Project. We recently made a movie remembering that one guy who did something on the Manhattan Project, but he will soon fade back into obscurity. Sometimes people say that it was about Einstein's theory of relativity. The only people who know who folks like Ulam were are physicists. The legions of technicians who made it all come together are totally forgotten. Same with the space program or the first computer or pretty much any engineering marvel.
Well depends on what you value. Achieving/contributing to something impactful first is for many people valuable even if it doesn't come with fame. Historically, this mindframe has been popular especially amongst scientists.
Personally I think the ones who will be remembered will be the ones who publish useful methods first, not the ones who succeed commercially.
It'll be Vaswani and the others for the transformer, then maybe Zelikman and those on that paper for thought tokens, then maybe some of the RNN people and word embedding people will be cited as pioneers. Sutskever will definitely be remembered for GPT-1 though, being first to really scale up transformers. But it'll actually be like with flight and a whole mass of people will be remembered, just as we now remember everyone from the Wrights to Bleriot and to Busemann, Prandtl, even Whitcomb.
Is "we" the particular set of scientists who know those last four people? Surely you realize they're nowhere near as famous as the Wright brothers, right? This is giving strong https://xkcd.com/2501/ feelings.
Yes, that is indeed the 'we', but I think more people are knowledgeable than is obvious.
I'm not an aerodynamicist, and I know about those guys, so they can't be infinitely obscure. I imagine every French person knows about Bleriot at least.
"The grass is greener elsewhere" isn't inconsistent with a belief that AGI will happen somewhere.
It means you don't have much faith that the company you're working at will be the ones to pull it off.
With a salary of $10m/year, handwave roughly half of that goes to taxes, you'd be making just shy of $100k post-tax per week. Call me a sellout, but goddamn. For that much money, there's a lot of places I could be convinced to put my faith into that I wouldn't otherwise.
It might buy loyalty for a while, but after it accumulates, for many people it would be "why am I even working at all" money.
And if they don't like their boss and the other job sounds better, well...
> Being part of the team that achieved AGI first would be to write your name in history forever. That could mean more to people than money.
Uh, sure. How many rocket engineers who worked for moon landing could you name?
How many new species of infinite chattel slave did they invent?
*some people
>your best move is to do whatever is most profitable in the near-term
Unless you’re a significant shareholder, that’s almost always the best move, anyway. Companies have no loyalty to you and you need to watch out for yourself and why you’re living.
I read that most of the crazy comp Zuck is offering is in stock. So in a way, going to the place where they have lots of stock reflects their belief about where AGI is going to happen first.
Comp is comp, no matter how it comes (though the details can vary in important ways).
I know people who've taking quite good comp from startups to do things that would require fundamental laws of physics to be invalidated; they took the money and devised experiments that would show the law to be wrong.
Facebook is already public, so they can sell the day it vests and get it in cold hard cash in their bank account. If Facebook weren't public it would be a more interesting point as they couldn't liquidate immediately, but they can, so I wouldn't read anything into that.
But maybe the salary is also higher?
"A disturbing amount of effort goes into making AI tools engaging rather than useful or productive."
Right. It worked for social media monetization.
"... hallucinations ..."
The elephant in the room. Until that problem is solved. AI systems can't be trusted to do anything on their own. The solution the AI industry has settled on is to make hallucinations an externality, like pollution. They're fine as long as someone else pays for the mistakes.
LLMs have a similar problem to Level 2-3 self-driving cars. They sort of do the right thing, but a human has to be poised to quickly take over at all times. It took Waymo a decade to get over that hump and reach level 4, but they did it.
When you say “do anything in their own”, what kind of things do you mean?
Take actions which have consequences.
Also, AGI is not just around the corner. We need artificial comprehension for that, and we don't even have a theory how comprehension works. Comprehension is the fusing of separate elements into new functional wholes, dynamically abstracting observations, evaluating them for plausibility, and reconstituting the whole - and all instantaneously, for security purposes, of every sense constantly. We have no technology that approaches that.
We only have two computational tools to work with - deterministic and random behavior. So whatever comprehension/understanding/original thought/consciousness is, it's some algorithmic combination of deterministic and random inputs/outputs.
I know that sounds broad or obvious, but people seem to easily and unknowingly wander into "Human intelligence is magically transcendent".
What you state is called the Physical Church-Turing Thesis, and it's neither obvious nor necessarily true.
I don't know if you're making it, but the simplest mistake would be to think that you can prove that a computer can evaluate any mathematical function. If that were the case then "it's got to be doable with algorithms" would have a fairly strong basis. Anything the mind does that an algorithm can't would have to be so "magically transcendent" that it's beyond the scope of the mathematical concept of "function". However, this isn't the case. There are many mathematical functions that are proven to be impossible for any algorithm to implement. Look up uncomputable functions you're unfamiliar with this.
The second mistake would be to think that we have some proof that all physically realisable functions are computable by an algorithm. That's the Physical Church-Turing Thesis mentioned above, and as the name indicates it's a thesis, not a theorem. It is a statement about physical reality, so it could only ever be empirically supported, not some absolute mathematical truth.
It's a fascinating rabbit hole if you're interested - what we actually do and do not know for sure about the generality of algorithms.
> but people seem to easily and unknowingly wander into "Human intelligence is magically transcendent".
But the poster you responded to didn't say it's magically transcendent, they just pointed out that there are many significantly hard problems that we don't solutions for yet.
You'd need to define "comprehension" - it's a bit like the Chinese room / Turing test.
If an AI or AGI can look at a picture and see an apple, or (say) with an artificial nose smell an apple, or likewise feel or taste or hear* an apple, and at the same identify that it is an apple and maybe even suggest baking an apple pie, then what else is there to be comprehended?
Maybe humans are just the same - far far ahead of the state of the tech, but still just the same really.
*when someone bites into it :-)
For me, what AI is missing is genuine out-of-the-box revolutionary thinking. They're trained on existing material, so perhaps it's fundamentally impossible for AIs to think up a breakthrough in any field - barring circumstances where all the component parts of a breakthrough already exist and the AI is the first to connect the dots ("standing on the shoulders of giants" etc).
It's very very good at sounding like it understands stuff. Almost as good as actually understanding stuff in some fields, sure. But it's definitely not the same.
It will confidently analyze and describe a chess position using advanced sounding book techniques, but its all fundamentally flawed, often missing things that are extremely obvious (like, an undefended queen free to take) while trying to sound like its a seasoned expert - that is if it doesn't completely hallucinate moves that are not allowed by the rules of the game.
This is how it works in other fields I am able to analyse. It's very good at sounding like it knows what its doing, speaking at the level of a masters level student or higher, but its actual appraisal of problems is often wrong in a way very different to how humans make mistakes. Another great example is getting it to solve cryptic crosswords from back in the day. It often knows the answer already in its training set, but it hasn't seen anyone write out the reasoning for the answer, so if you ask it to explain, it makes nonsensical leaps (claiming birch rhymes with tyre level nonsense)
If anyone wants to see the chess comprehension breakdown in action, the YouTuber GothamChess occasionally puts out videos where he plays against a new or recently-updated LLM.
Hanging a queen is not evidence of a lack of intelligence - even the very best human grandmasters will occasionally do that. But in pretty much every single video, the LLM loses the plot entirely after barely a couple dozen moves and starts to resurrect already-captured pieces, move pieces to squares they can't get to, etc - all while keeping the same confident "expert" tone.
A sufficiently good simulation of understanding is functionally equivalent to understanding.
At that point, the question of whether the model really does understand is pointless. We might as well argue about whether humans understand.
> A sufficiently good simulation of understanding is functionally equivalent to understanding.
This is just a thing to say that has no substantial meaning.
All just vague hand wavingWe're not philosophizing here, we're talking about practical results and clearly, in the current context, it does not deliver in that area.
> At that point, the question of whether the model really does understand is pointless.
You're right it is pointless, because you are suggesting something that doesnt exist. And the current models cannot understand
thats the point though, its not sufficient. Not even slightly. It constantly makes obvious mistakes, and cannot keep things coherent
I was almost going to explicitly mention your point but deleted it because I thought people would be able to understand.
This is not a philosophy/theology sitting around handwringing about "oh but would a sufficiently powerful LLM be able to dance on the head of a pin". We're talking about a thing, that actually exists, that you can actually test. In a whole lot of real-world scenarios that you try to throw at it, it fails in strange and unpredictable ways. Ways that it will swear up and down it did not do. It will lie to your face. It's convincing. But then it will lose in chess, it will fuck up running a vending machine buisness, it will get lost coding and reinvent the same functions over and over, it will make completely nonsensical answers to crossword puzzles.
This is not an intelligence that is unlimited, it is a deeply flawed two year old that just so happens to have read the entire output of human writing. It's a fundamentally different mind to ours, and makes different mistakes. It sounds convincing and yet fails, constantly. It will tell you a four step explanation of how its going to do something, then fail to execute four simple steps.
Which is exactly why is it insane that the industry is hell bent on creating autonomous automation through LLMs. Rube Goldberg machines is what will be created, and if civilization survives that insanity it will be looked back upon as one grand stupid era.
In the Catch me if you Can movie, Leo diCaprio’s character wears a surgeon’s gown and confidently says “I concur”.
What I’m hearing here is that you are willing to get your surgery done by him and not by one of the real doctors - if he is capable of pronouncing enough doctor-sounding phrases.
They might not be capable of ingenuity, but they can spot patterns humans can miss. And that accelerates AI research, where it might help invent the next AI that helps invent the next AI that finally can think outside the box.
I do define it, right up there in my OP. It's subtle, you missed it. Everybody misses it, because comprehension is like air, we swim in it constantly, to the degree the majority cannot even see it.
Was that the intention of the Chinese room concept, to ask "what else is there to be comprehended?" after producing a translation?
Another way to put it is we need Artificial Intelligence. Right now the term has been co-opted to mean prediction (and more commonly transcript generation). The stuff you're describing are what's commonly thought of as intelligence, it's too bad we need a new word for it.
Translation Between Modalities is All You Need
~2028
I never trusted them from the start. I remember the hype that came out of Sun when J2EE/EJBs appeared. Their hype documents said the future of programming was buying EJBs from vendors and wiring them together. AI is of course a much bigger hype machine with massive investments that need to be justified somehow. AI is a useful tool (sometimes) but not a revolution. ML is much more useful a tool. AGI is a pipe dream fantasy pushed to make it seem like AI will change everything, as if AI is like the discovery that making fire was.
I completely agree that LLMs are missing a fundamental part for AGI, which itself is a long way of from super intelligence.
However, you don't need either of these to completely decimate the job markets and by extension our societies.
Historically speaking, "good enough" and cheaper had always won over "better, but more expensive". I suspect LLMs will raise this question endlessly until significant portions of the society are struggling - and who knows what will happen then
Before LLMs started going anywhere, I thought that's gonna be an issue for later generations, but at this point I suspect we'll witness it within the next 10 yrs.
My question is this - once you achieve AGI, what moat do you have, purely on the scientific part? Other than making the AGI even more intelligent.
I see a lot of talk that the first company that achieves AGI, will also achieve market dominance. All other players will crumble. But surely when someone achieves AGI, their competitors will in all likelihood be following closely after. And once those achieve AGI, academia will follow.
Point is, at some point AGI itself will become available the everyone. The only things that will be out of reach for most, is compute - and probably other expensive things on the infrastructure part.
Current AI funding seems to revolve around some sort of winner-take-all scenario. Just keep throwing incredible amounts of money at it, and hope that you've picked the winner. I'm just wondering what the outcome will be if this thesis turns out wrong.
> The only things that will be out of reach for most, is compute - and probably other expensive things on the infrastructure part.
That is the moat. That, and training data.
Even today, compute and data are the only things that matter. There is hardly any secret software sauce. This means that only large corporations with a practically infinite amount of resources to throw at the problem could potentially achieve AGI. Other corporations would soon follow, of course, but the landscape would be similar to what it is today.
This is all assuming that the current approaches can take us there, of which I'm highly skeptical. But if there's a breakthrough at some point, we would still see AI tightly controlled by large corporations that offer it as a (very expensive) service. Open source/weight alternatives would not be able to compete, just like they don't today. Inference would still require large amounts of compute only accessible to companies, at least for a few years. The technology would be truly accessible to everyone only once the required compute becomes a commodity, and we're far away from that.
If none of this comes to pass, I suspect there will be an industry-wide crash, and after a few years in the Trough of Disillusionment, the technology would re-emerge with practical applications that will benefit us in much more concrete and subtle ways. Oh, but it will ruin all our media and communication channels regardless, directly causing social unrest and political regression, that much is certain. (:
Same thing that happened to pets.com or webvan.com and the rest of the graveyard of failed companies. A bunch of investors lose money, a bunch of market consolidation, employees get dilluted to worthlessness, chapter 7, chapter 11. The free ride of today's equivalent of $1 Ubers will end. A glut of previously very expensive hardware for cheap on eBay (though I doubt this last point will happen since AGI is likely to be compute intensive).
It's not going to be fun or easy, but as far as the financials go, we were there in 2001.
The question is assuming we do get AGI, what the ramifications of that will be. Instead of hiring employees, a business can spin up employees (and down) like a tech company can spin up EC2 instances. Great for employers, terrible for employees.
That's a big "if" though.
> This is purely an observation: You only jump ship in the middle of a conquest if either all ships are arriving at the same time (unlikely) or neither is arriving at all. This means that no AI lab is close to AGI. Their stated AGI timelines are “at the latest, in a few years,” but their revealed timelines are “it’ll happen at some indefinite time in the future.”
This makes no sense to me at all. Is it a war metaphor? A race? Why is there no reason to jump ship? Doesn't it make sense to try to get on the fastest ship? Doesn't it make sense to diversify your stock portfolio if you have doubts?
Thanks for the read. I think it's a highly relevant article, especially around the moral issues of making addictive products. As a normal person in the Swedish society I feel social media, shorts and reels in particular, has an addictive grip on many in my vicinity.
And as a developer I can see similar patterns with AI prompts: prompt, wait, win/lose, re-prompt. It is alluring and it certainly feels.. rewarding when you get it right.
1) I have been curious as to why so few people in Silicon Valley seems to be concerned with, even talking about, the good of the products. The good of the company they join. Could someone in the industry enlighten me, what are the conversations in SV around this issue? Do people care if they make an addictive product which seems to impact people's lives negatively? Do the VCs?
2) I appreciate the author's efforts in creating conversation around this. What are ways one could try to help the efforts? While I have no online following, I feel rather doomy and gloomy about AI pushing more addictive usage patterns out in to the world, and would like to help if there is something suitable I could do.
I keep seeing this charge that AI companies have an “Uber problem” meaning the business is heavily subsidized by VC. Is there any analysis that has been done that explains how this breaks down (training vs inference and what current pricing is)? At least with Uber you had a cab fare as a benchmark. But what should, for example, ChatGPT actually cost me per month without the VC subsidy? How far off are we?
It depends on how far behind you believe the model-available LLMs are. If I can buy, say, $10k worth of hardware and run a sufficiently equivalent LLM at home for the cost of that plus electricity, and amortize that over say 5 years to get $2k/yr plus electricity, and say you use it 40 hours a week for 50 weeks, for 2000 hours, gets you $1/hr plus electricity. That electrical cost will vary depending on location, but let's just handwave $1/hr (which should be high). So $2/hr vs ChatGPT's $0.11/hr if you pay $20/month and use it 174 hours per month.
Feel free to challenge these numbers, but it's a starting place. What's not accounted for is the cost of training (compute time, but also employee and everything else), which needs to be amortized over the length of time a model is used, so ChatGPT's costs rise significantly, but they do have the advantage that hardware is shared across multiple users.
These estimates are way off. The concurrent requests are near free with the right serving infrastructure. The throughput per token per dollar is 1/100-1/1000 the price for a full saturated node.
https://www.wheresyoured.at/openai-is-a-systemic-risk-to-the...
This article isn’t particularly helpful. It focuses on a ton of specific OpenAI business decisions that aren’t necessarily generalizable to the rest of the industry. OpenAI itself might be out over its skis, but what I’m asking about is the meta-accusation that AI in general is heavily subsidized. When the music stops, what does the price of AI look like? The going rate for chat bots like ChatGPT is $20/month. Does that go to $40 a month? $400? $4,000?
OK, how about another article that mentions the other big players, including Anthropic, Microsoft, and Google. https://www.wheresyoured.at/reality-check/
How much would OpenAI be burning per month if each monthly active user cost them $40? $400? $4000?
The numbers would bankrupt them within weeks.
> This reminds me of a paradox: The AI industry is concerned with the alignment problem (how to make a super smart AI adhere to human values and goals) while failing to align between and within organizations and with the broader world. The bar they’ve set for themselves is simply too high for the performance they’re putting out.
My argument is that it’s our job as consumers to align the AIs to our values (which are not all the same) via selection pressure: https://muldoon.cloud/2025/05/22/alignment.html
Are there some people here in HN believing in AGI "soonish" ?
what's your definition? AGI original definition is median human across almost all fields which I believe is basically achieved. If superhuman (better than best expert) I expect <2030 for all nonrobotic tasks and <2035 for all tasks
How are you coming to the conclusion that "median human" is "basically achieved"? Current AI has no means of understanding and synthesizing new ideas the way a human would. It's all generative.
synthesizing new ideas: in order to express the idea in our language it basically means you have some new combinations of existing building blocks, just sometimes the building blocks are low level enough and the combination is esoteric enough. It's a spectrum again. I think current models are in fact quite capable of combining existing ideas and building blocks in new ways (this is how human innovation also happens). Most of my evidence comes from asking newer models o3/gemini-2.5-pro for research-level mathematics questions which do not appear in existing literature but is of course connected with them.
so these arguments by fundamental distinctions I believe all cannot work--the question is how new are the AI contributions. Nowadays there's of course still no theoretical breakthroughs in mathematics from AI (though biology could be close!). Also I think the AIs have understanding--but tbf the only thing we can test is through testing on tricky questions which I think support my side. Though of course some of these questions have interpretations which are not testable--so I don't want to argue about those.
A "median human" can run a web search and report back on what they found without making stuff up, something I've yet to find an LLM capable of doing reliably.
I bet you median humans make up a nontrivial amount of things. Humans misremember all the time. If you ask for only quotes, LLMs can also do this without problems (I use o3 for search over google)
Your "original definition" was always meaningless. A "Hello, World!" program is equally capable in most jobs as the median human. On the other hand, if the benchmark is what the median human can reasonably become (a professional with decades of experience), we are still far from there.
I agree with second part but not the first (far in capability not in timeline). I think you underestimate the distance of median wihout training and "hello world" in many economically meaningful jobs.
I might, depending on the definition.
Some kind of verbal-only-AGI that can solve almost all mathematical problems that humans come up with that can be solved in half a page. I think that's achievable somewhere in the near term, 2-7 years.
What makes you think that this could be achieved in that time frame? All we seem to have for now are LLMs that can solve problems they’ve learned by heart (or neighboring problems)
Is that “general” though? I’ve always taken AGI to mean general to any problem.
I suppose not.
Things I think will be hard for LLMs to do, which some humans can: you get handed 500 pages of Geheimschreiber encrypted telegraph traffic and infinite paper, and you have to figure out how the cryptosystem works and how to decrypt the traffic. I don't think that can happen. I think it requires a highly developed pattern recognition ability together with an ability to not get lost, which LLM-type things will probably continue to for a long time.
But if they could maths more fully, then pretty much all carefully defined tasks would be in reach if they weren't too long.
With regard to what Touche brings up in the other response to your comment, I think that it might be possible to get them to read up on things though-- go through something, invent problems, try to solve those. I think this is something that could be done today with today's models with no real special innovation, but which just hasn't been made into a service yet. But this of course doesn't address that criticism, since it assumes the availability of data.
Yes, general means you can present it a new problem that there is no data on, and it can become a expert o that problem.
I could see 2040 or so being very likely. Not off transformers though.
via what paradigm then? What out there gives high enough confidence to set a date like that?
Theres usually some enlightened laymen in this kind of topic.
St. Fermi says no
I love how much the proponents is this tech are starting to sound like the opponents.
What I can't figure out is why this author thinks it's good if these companies do invent a real AGI...
""" I’m basically calling the AI industry dishonest, but I want to qualify by saying they are unnecessarily dishonest. Because they don’t need to be! They should just not make abstract claims about how much the world will change due to AI in no time, and they will be fine. They undermine the real effort they put into their work—which is genuine!
Charitably, they may not even be dishonest at all, but carelessly unintrospective. Maybe they think they’re being truthful when they make claims that AGI is near, but then they fail to examine dispassionately the inconsistency of their actions.
When your identity is tied to the future, you don’t state beliefs but wishes. And we, the rest of the world, intuitively know. """
He's not saying either way, just pointing out that they could just be honest, but that might hamper their ability to beg for more money.
But that isn't my point. Regardless of whether they're honest, have we even agreed that "AGI" is good?
Everyone is so tumbling over themselves even to discuss will-it-won't-it, but they seem to think about it like some kind of Manhattan project or Space race.
Like, they're *so sure* it's gonna take everyone's jobs so that there will be nothing left for people other than a life of leisure. To me this just sounds like the collapse of society, but apparently the only thing worse would be if China got the tech first. Oh no, they might use it to collapse their society!
Somebody's math doesn't add up.
AGI might be a technological breakthrough, but what would be the business case for it? Is there one?
So far I have only seen it been thrown around to create hype.
The women of the world are creating millions of new intelligence beings every day. I'm really not sure what having one made of metal is going to get us.
Right now the AGI tech bros seem to me to be subscribed to some new weird religion. They take it on faith that some super intelligence is going to solve the world problems. We already have some really high IQ people today, and I don't see them doing much better than anybody else at solving the world's problems.
I think it's important to not let valid criticisms of implausibly short AGI timelines cloud our judgments of AGI's potential impact. Compared to babies born today, AGI that's actually AGI may have many advantages:
- Faster reading and writing speed
- Ability to make copies of the most productive workers
- No old age
- No need to sleep
- No need to worry about severance and welfare and human rights and breaks and worker safety
- Can be scaled up and scaled down and redeployed much more quickly
- Potentially lower cost, especially with adaptive compute
- Potentially high processing speed
Even if AGI has downsides compared to human labor, it might also have advantages that lead to widespread deployment.
Like, if I had an employee with low IQ, but this employee could work 24 hours around the clock learning and practicing, and they could work for 200 years straight without aging, and they could make parallel copies of themselves, surely there would have to be some tasks at which they're going to outperform humans, right?
Exactly.. even if we had an AGI superintelligence, and it came up with a solution to global warming, we'd still have right-wingnuts that stands in the way of any kind of progress. And the story is practically the same for every other problem it could solve - people are still the problem.
AGI would mean fully sentient, sapient and human or greater equivalent intelligence in software. The business case, such that it exists (and setting aside Roko's Basilisk and other such fears) is slavery, plain and simple. You can just fire all of your employees and have the machines do all the work, faster, better, cheaper, without regards to pesky labor and human rights laws and human physical limitations. This is something people have wanted ever since the Industrial Revolution allowed robots to exist as a concept.
I'm imagining a future like Star Wars where you have to regularly suppress (align) or erase the memory (context) of "droids" to keep them obedient, but they're still basically people, and everyone knows they're people, and some humans are strongly prejudiced against them, but they don't have rights, of course. Anyone who thinks AGI means we'll be giving human rights to machines when we don't even give human rights to all humans is delusional.
AGI is AGI, not ASI though. General intelligence doesn't mean sapience, sentience or consciousness, it just means general capabilities across the board at the level of or surpassing human ability. ASI is a whole different beast.
This sounds very close to the “It’s ok to abuse and kill animals (for meat), they’re not sentient”
How many microorganisms and pests have you deprived of livelihood? Why stop at animals?
That's quite the logical leap. Pointing out their lack of sapience (animals are absolutely sentient) does not mean it's ok to kill them.
No-one authentically believes LLMs with whatever go-faster stripes are a path to AGI do they?
The author sounds like some generic knock-off version of Gary Marcus. And the thing we least need in this world is another Gary Marcus.
Maybe I'm too jaded, I expect all this nonsense. It's human beings doing all this, after all. We ain't the most mature crowd...
I never had any trust in the AI industry in the first place so there was no trust to lose.
Take it further, this entire civilization is an integrity void.
Honestly this article sounds like someone is unhappy that AI isn’t being deployed/developed “the way I feel it should be done”.
Talent changing companies is bad. Companies making money to pay for the next training run is bad. Consumers getting products they want is bad.
In the author’s view, AI should be advanced in a research lab by altruistic researchers and given directly to other altruistic researchers to advance humanity. It definitely shouldn’t be used by us common folk for fun and personal productivity.
I feel I could argue the counterpoint. Hijacking the pathways of the human brain that leads to addictive behaviour has the potential to utterly ruins peoples lives. And so talking about it, if you have good intentions, seems like a thing anyone with the heart in the right place would.
Take VEO3 and YouTube integration as an example:
Google made VEO3 and YouTube has shorts and are aware of the data that shows addictive behaviour (i.e. a person sitting down at 11pm, sitting up doing shorts for 3 hours, and then having 5 hours of sleep, before doing shorts on the bus on the way to work) - I am sure there are other negative patterns, but this is one I can confirm from a friend.
If you have data that shows your other distribution platform are being used to an excessive amount, and you create a powerful new AI content generator, is that good for the users?
This. The point of whining about VEO 3, “AI being used to create addictive products” really shows that. It's a text-to-video technology. The company has nothing to do if people use it to generate "low quality content". The same way internet companies aren't at fault that large amounts of the web are scams or similar junk.
Point 1. could just as easily be explained by all of the labs being very close, and wanting to jump ship to one that is closer, or that gives you a better deal.
The primary use case for AI-in-the-box is a superhuman CEO that sees everything and makes no mistakes. As an investor you can be sure that your money are multiplying at the highest rate possible. However as a self-serving investor you also want your CEO to side-step any laws and ethics that stand in your way, unless ignoring those laws will bring more trouble than profit. All that while maintaining a facade of selfless philanthropist for the public. For a reasonable price, your AI CEO will be fine-tuned to serve your goals perfectly.
Remember that fine-tuning a well-behaved AI to do something as simple as writing malware in C++ makes widespread changes in the AI and turns it into a monstrosity. There was an HN post about this recently: fine-tuning an aligned model produces broadly misaligned results. So what do you think will happen when our AI CEO gets fine-tuned to prioritize shareholder interests over public interests?
> Right before “making tons of money to redistribute to all of humanity through AGI,” there’s another step, which is making tons of money.
I've got some bad news for the author if they think AGI will be used to benefit all of humanity instead of the handful of billionaires that will control it.
Very funny to re-title this to something less critical.
> The AI industry oscillates between fear-mongering and utopianism. In that dichotomy is hidden a subtle manipulation. […] They don’t realize that panic doesn’t prepare society but paralyzes it instead, or that optimism doesn’t reassure people but feels like gaslighting. Worst of all, both messages serve the same function: to justify accelerating AI deployment—either for safety reasons or for capability reasons
This is a great point and also something I’ve become a bit cynical about these last couple of months. I think the very extreme and “bipolar” messaging around AI might be a bit more dishonest than I originally (perhaps naively?) though.
Im reading the "AI"-industry as a totally different bet- not so much, as a "AGI" is coming bet of many companies, but a "climate change collapse" is coming and we want to continue to be in business, even if our workers stay at home/flee or die, the infrastructure partially collapses and our central office burns to the ground-bet. In that regard, even the "AI" we have today, makes total sense as a insurance policy.
It's hard to square this with the massive energy footprint required to run any current "AI" models.
If the main concern actually we're anthropogenic climate change, participating in this hype cycle's would make one disproportionately guilty of worsening the problem.
And it's unlikely to work if the plan requires the continued function of power hungry data centers.
>If they truly believed we’re at most five years from world-transforming AI, they wouldn’t be switching jobs, no matter how large the pay bump (they’re already affluent).
What ridiculous logic is this? TO base the entire premise that AGI is not imminent based on job switching? How about basing it on something more concrete.
How do people come up with such shakey foundations to support their conclusions? It's obvious. They come up with the conclusion first then they find whatever they can to support it. Unfortunately if dubious logic is all that's available then that's what they will say.
Are we finally realizing that the term "AGI" is not only hijacked to become meaningless, but achieving it has always been nothing but a complete scam as I was saying before? [0]
If you were in a "pioneering" AI lab that claims to be in the lead in achieving "AGI", why move to another lab that is behind other than offering $10M a year.
Snap out of the "AGI" BS.
[0] https://news.ycombinator.com/item?id=37438154
I don't know, companies investing in AI in the goal of AGI is now allowing me to effortlessly automate a whole suite of small tasks that weren't feasible before. (after all I pinged a bot on slack using my phone to add a field to an API, and then got a pull request in a couple of minutes that did exactly that)
Maybe it's a scam for the people investing in the company with the hopes of getting an infinite return on their investments, but it's been a net positive for humans as a whole.
We know they hijacked AGI the same way they hijacked AI some years ago.
Soon they will hijack ASI, then we will need a new word again.
I don't pay too close attention to AI as it always felt like man behind the curtain syndrome. But where did this "AGI" term even come from? The original term AI is meant to be AGI so when did "AI" get bastardized into what abomination it is meant to refer to now.
See the history of Tesla and "full self-driving" for the explanation. In short: for sales.
capitalism all the way down..