Either that, or the rich and corps will go all in. The point was always to replace human labor, just depends how far from that they think we are. People still see a report an AI did something stupid and laugh at the idea of it replacing us, but just 5 years ago we wouldn’t have believed the things it can do today.
We can’t believe what it does not, or six agos what it does then. The shit is moving faster then literally anyone really seems to understand.
The avg person that makes fun of ai, seems to still think they have the same problems they had 2-3 years. Because the cheap free models they have access to are extremely out of date, or very limited.
The actual real deal big boy models are so far beyond what your avg even extremely technical user understands. Unless you are actively watching following and using the models you just flat out have no clue just how fast this shit is sprinting.
It’s got plenty of problems and the growth is not across every aspect of it equally. So it’s really easy to point and laugh at a particular point it’s struggling with while it skips ahead in other regards.
It’s wild. The progress is just as unsustainable as the profits are bad. As long as that progress keeps sprinting the bad profits flat out don’t matter.
IT WILL hit critical mass to replace your avg idiot long before the profit problem really hits at this rate.
The bigger problem is that even if it replaces people that profit problem doesn’t go away. So it will just hit AFTER people are replaced. And that’s a even worse outcome then replacing people.
We NEED the bubble to pop before that point and the industry recalibrates to a sustainable model.
Else we will have mass job loss promptly followed by a massive bubble popping and economy collapse AND companies flopping and job opportunity losses as places closing shop so there won’t even be jobs to back to.
It is true there were massive strides in the last 8 years. But fundamentally, the tech is still the same large language model it was before, just bigger and better optimized.
It’s like going from an ancient, slow, Ford Model T that topped out at 45mph to a Bugatti that can do 260mph in 8 short years. It’s impressive, it boosts productivity, it is a marvel of modern technology, but that’s not my point of contention.
The issue is that AI companies have been funded on the promise that with enough advancements and upgrades this tech will achieve AGI. Which is an absurd statement to anyone in the field actually developing these things. That is the equivalent of promising that this 260mph Bugatti, with a few years of upgrades and advancements, will become a Harrier Jump Jet!
It’s just not happening, a fundamental shift in model architecture or technology used is needed. And from what we’ve seen so far, no one has discovered any.
They’re not even better optimized. Hallucination rates are up. Inference costs are up. There’s only an AI industry at all because they’re selling a highly subsidized product, but when they try to raise prices even a little the market collapses. Companies that were encouraging employees to up their AI use are now rationing tokens like chocolate in wartime. This isn’t like Uber where they can push out the old providers and then obtain market capture on something everyone needs. AI is not, and cannot be, essential, because you can always just get a human to do it.
I disagree. AI is enabling doing things that are not worth paying a human to do, yet still have value. the question is, whether the infrastructure costs can realign to hit that sweet spot.
I only have pretty basic machine learning knowledge, based on a few Master’s classes at college while I was doing gamedev, and even with that I can tell that the way they are expecting to get AGI by just feeding more data into a language model is simply not happening.
I remember a comparison from one of the AI-pilled tech-bros when AI was starting to get attention, and his metaphor about AGI was something along the lines of “Imagine a difference between a medieval commoner and Albert Einstein, that’s the difference in inteligence AI will soon have to the smartest people we know now”.
But that doesn’t make sense with the current approach. Imagine Einstein writing his cutting-edge theories, and the commoner is watching him behind his shoulder and vetting anything he does. Scratching his ideas, forcing him to redo it, if he doesn’t like it, pointing to a reddit thread about why. There’s no way he would ever finish anything new.
Unless they figure out a completely new way how to do AI reasoning, there is no way we’re getting anywhere near AGI. And that is also becoming more unlikely the longer we go with this approach, because every AI-pilled company is heavily outsourcing all of development to the current models, reducing their employee (and the whole worlds) innovation potential and skill. This is probably the last generation that can do serious academia, unless there are drastic measures done to limit access to AI in education. It’s fucked.
The issue is that AI companies have been funded on the promise that with enough advancements and upgrades this tech will achieve AGI. Which is an absurd statement to anyone in the field actually developing these things. That is the equivalent of promising that this 260mph Bugatti, with a few years of upgrades and advancements, will become a Harrier Jump Jet!
A better analogy is promising that a toddler will, with enough knowledge and training, eventually become a heart surgeon.
15 years ago the assumption with AGI was that we needed some paradigm shift in technology to create it, but after transformer technology was invented (the T in GPT), and it was trained on a lot of information, we discovered an emergent property that it could take natural language queries and answer them with its knowledge base-- which was unexpected and unintended.
While science can always end up going down the wrong path, the current mainstream stance is that we were wrong about needing new technology for AGI; it seems that AGI may be a function of information and training, on hardware we already have. Hence all the data centers being built.
Anyone who says with certainty that it will result in AGI is just as wrong as someone who says with certainty that it won’t.
Vishal Sikka, advisory board member of BMW. Recommended to Stanford by Marvin Minsky, one of his professors were John McCarty. And I must stand corrected, he has a PHD of computer sciences, not Math as I remembered it as.
Varin Sikka is his son, co author of the paper and based on Stanford’s site an undergraduate. profiles.stanford.edu/363374
Vishal has an AI based company himself, so there might be some personal reasons for why he’d advocate for using what AIs capable of rather than chasing an impossible (from his perspective) to hit milestone
That paper doesn’t seem to rule out AGI, only an single LLM model that can answer every arbitrarily difficult question on demand.
AGI does not necessarily mean one model acting alone, or being able to answer any question on demand. Humans are the same way: we often need time or collaboration to arrive at conclusions, but that doesn’t mean we don’t have “general intelligence”.
You’re not going to context hack complexity, every time you summarise something and pass it onto the next agent. You’re losing complexity.
We’re going to get some massive models with incredible context, but AGI requires models to never hallucinate. Even when the complexity requires more context than it has available, which is not feasible
We’ve already had a month now of Psychiatrists writing articles backpedaling over all the times they’d insisted AI couldn’t genuinely be thinking as we do and moving the goal posts to something else. Maybe there are researchers in the frontier labs who feel like they have a grip on what’s happening, but the ‘experts’ on forums have no idea.
That is where the evidence points. Now, I don’t want to oversell it: “where the evidence points” is wildly different than “exactly how it works”.
We have an emergent property that we don’t understand, but we can reliably increase the functionally and complexity of that emergent property as a function of training data and available compute. Does that mean that there isn’t some threshold where that stops working? No, there certainly could be a point where throwing more information and compute has no effect. We just don’t know. However, so far, there is no evidence such a barrier exists, and everyone is racing to find out.
I think that is where the hopes and hype point. The evidence, that which is gathered through controlled studies, points to an upper limit to this technology that does have emergent properties, but not ones that amount to cognition. That evidence also points to other hurdles, such as cognitive damage to the user and context windows nowhere near that of even a simpleminded creature, let alone a sapient one like a human.
The core problem is that these models are fixed; they are the same on day 1000 as they were on day 1. All their “learning”, as it were, happens in training before it is released. Everything it appears to learn after that date is contained in the rolling context window. Since they already have literally all the RAM they can get their hands on and are still at least an order of magnitude away from where they need to be on that, this technology either can’t do it or, at best, is so inefficient an approach that it can’t be done with all the planet’s resources.
Sometimes, especially in abstract constructions like software, you can start down the wrong path early and have to start over, because there is no path from where you are to where you need to get. In this case, they may have done that to the extreme, blinded by the lucrative prospects.
As far as I am aware, AGI does not imply sapience, consciousness, or whatever. You seem to believe it does. A high level Google search seems to suggest that I’m correct. Is there some reason you are lumping sapience with AGI?
I’m not sure if the rest of your comment presupposes this, so I’ll wait to comment on where I think you’re mistaken.
I dont believe it requires sapience; that is what the marketers are saying. The AI boom (and many historic boom cycles) is predicated on marketing and sentiment, not facts and data. That is why they always pop; the facts dont back up the hype.
The fact that your search results turn up results that align with the marketing is just the marketing working.
Look–neither of us are data scientists, but we do have eyes. If this is working, where’s the company with runaway success creating unimagined leaps in productivity and technology? We’re pouring a whole planet’s with of resources in and nothing much is coming out. If we spent this much on world hunger, everyone would be obese by now.
I dont believe it requires sapience; that is what the marketers are saying. The AI boom (and many historic boom cycles) is predicated on marketing and sentiment, not facts and data. That is why they always pop; the facts dont back up the hype.
I think there is some confusion. I do not believe AGI implies sapience, nor does Google, and now it seems that you don’t either. So who is discussing sapience?
If this is working, where’s the company with runaway success creating unimagined leaps in productivity and technology?
Hypothetically speaking, what do you think this would look like?
The marketers are not using the word “sapience”, as that word would require defining for the layman. They are claiming AI will usher in an era of human prosperity by taking over our work and big decisions to do them more efficiently–tasks that sound to almost everyone to require advanced decision-making and thought, the hallmarks of sapience and cognition. This is largely besides the point, though.
I think that if this were successful, you would see at least some early adopting companies coming up aces. Some company would have double the productivity with half the workforce, and use that to absolutely devour their market segment by undercutting everyone else on price because their operating costs are so much lower than the competition. I’d see a software company adopt breakneck software release cycles with substantial, material stability and performance improvements with each. I’d see some company anywhere suddenly begin to outperform their former selves and be able to convincingly point to AI as where their improvements came from. The only one like that I can think of is NVidia, who are rich strictly because they’re selling shovels in the gold rush.
What it would look like is layoffs from a company without a change in output/productivity. Notably, we’re not at AGI yet, so I wouldn’t expect sci-fi levels of change.
That is one thing it could look like which is captured in my examples. We dont see any company like that. Remember that stock valuation is not, in the present day, correlated to any business metric. There are companies with huge valuations that have never netted $1 (like these AI companies, for example). So, even in your narrower example, there are no companies I have heard of that meet its criteria.
There would necessarily be a lag time between layoffs and unchanged productivity. And even then, if we’re being fair, that’s not conclusive evidence of AI being useful; it could just be that they had more employees than they needed.
My point is more that I’m not sure we’d see an obvious sign that AI was changing the market yet.
Elon Musk, the apparent thought leader of the tech sector and mover of markets, claims we will be in a post-scarcity AI-fuelled utopia by 2036. Since his words move the market, clearly the market heeds his words.
In that context, it seems quite modest to expect obvious material gains from using AI by now. When the car was invented, you could prove its utility immediately by getting between to places faster than ever before. When space flight was invented, you could prove its utility by relaying messages around the planet. When insulin were invented, you could prove its utility by curing people bed-ridden awaiting death.
My point is, real paradigm-shifting inventions demonstrate how they’ll do that right away, in an undeniable way. So, AI is either not a paradigm shifting technology, or its not done yet and people are buying a half-baked product.
Elon Musk is an idiot, and you’ve already noted that the market doesn’t track reality.
The paradigm shifting invention was the transformer (the T in GPT). If you’ll forgive a shitty analogy, it’s like someone invented a microscope and you’re asking why we don’t yet have insulin. Not having insulin doesn’t make the microscope useless.
As far as these things go, AI is improving at a breakneck speed-- hence why so many people in the field are begging for a pause in development. We don’t really understand why this technology works as well as if does, and that has a lot of people worried that we’re going to do something dangerous without understanding what we did.
Your example is another one that illustrates my point. When they invented the microscope, they could instantly demonstrate its utility in it’s ability to observe previously invisibly tiny things. The following inventions that enabled are great, but the microscope itself was great, too, and all could see why.
AI is almost all hype. When anyone asks what it can do that was impossible or too difficult before, people point to what they think it will do, yourself included.
So, again, what previously intractable problem does AI solve? Please do not include answers of problems you think it might one day solve. Like the microscope can immediately let you see tiny things, what does the AI let you do that was impossible before? Bonus points if what it does is worth spending a planet’s worth of resources on.
When they invented the microscope, hey could instantly demonstrate its utility in it’s ability to observe previously invisibly tiny things.
I assure you that 400 some odd years ago, most people would not have grasped the utility in the microscope. Just like many people today don’t understand what the transformer has done for machine learning.
But some could, and they could articulate their reasoning. “Machine learning” is not even necessarily a path to anything. Its hard to argue that against your example, which is literally unlocking secrets of the universe.
Again, you’re using 400+ years of hindsight to inform your conclusion. “Looking at small things” doesn’t immediately imply “unlocking the secrets of the universe”, it’s only after hundreds of years of building on that invention that you can easily point to that conclusion.
And admittedly the analogy was not great, I’m just trying to get across that you’re looking at the output of the invention and asking “is that it”? But we’ve only had this technology for a decade or so, and it’s only really been public facing for, what, 4 years?
These various technologies we refer to as “AI” are not yet at the point where they can replace any human at any task, but they can replace some humans at some tasks, and as far as the evidence shows, we can continue to improve it with more training and more compute. They are writing prose well enough to pass as humans, solving math problems we haven’t been able to solve for decades, writing medium complexity code in minutes instead of days-- the technology isn’t solving all the world’s problems and ushering in a post-scarcity utopia, but no one is claiming it is, today. Will it? I doubt it, but that’s more to do with capitalism corrupting everything it touches than anything else.
If you think “AI” is not useful, you’re probably considering a very narrowly defined type of “AI” in a very narrowly defined scenario where AI does not perform well.
Edit: I want to take a second to say that I’ve enjoyed this conversation with you. I can’t say that about many conversations on this topic. This isn’t me ending the conversation.
I’m not even drawing a conclusion anymore. I’m asking for anyone anywhere to point to the previously impossible thing that AI lets me do. Even if that thing is of questionable utility, what is it? I just want an answer to that question.
Then, we can start talking about if that’s worth the cost. Most previous inventions cost someone’s life savings, or corporate focus on technology. This one’s costing us the GDP of South Korea, so it better be good. Like “the microscope” or “the wheel” good.
Everyone’s claiming that! Every invention allows someone to do something that was previously impossible. Humans couldn’t travel faster than a horse can run until the car was invented. Humans couldn’t see microbes before the microscope was invented. People couldn’t drag more than 10x their body weight until the wheel was invented. People couldn’t catch a marlin until the net was invented.
I am trying to resist the notion that youre being willfully obtuse if you aren’t equating invention with increasing what is possible.
I didn’t realize you meant that. In my mind humans could drag stuff and catch fish but the inventions made it easier or more efficient.
In that view, pretty much anything on a computer can be done easier or more efficient with AI. Same as a net or a wheel. Just Google anything it is you might want to do on a computer and add “using AI” and you’re likely to find a tool to do it. If you have the computer for it, you can download an open weight model to do locally (though probably not as well as using the crazy compute associated with using an API model.
Also, as agentic AI improves, we’ll see the scarcity of attention decrease and maybe vanish entirely. It’s not always obvious, but much of how our society is structured assuming that people have limited agency.
I do see what you mean. You’re saying that AI allows you to do something much faster or perform more work in the same time by essentially doing parts of it for you. While that is true, it is only so for certain values of “more”. For example, I could get an AI to write most or all of a configuration for a server deployment for me in about 2 minutes, instead of maybe an hour. However, if I do not spend about an hour reviewing its work, I will get a failure rate dramatically higher than if I had just done it myself. Similarly, if I decide to start pencil-whipping my work to do many times more work in the same time, I could do that and get much more deliverable work out the door. Whether or not you would call that more productive depends on what factors you’re accounting for. So, for applications I am familiar with, the primary value proposition of LLMs is that I can maximize quantity over quality even more than would be possible without it, by allowing me to not even do much of it. The overall quality of the output will likely improve incrementally over time as it has so far, but given its current design, its propensity for errors and hallucinations can only be mitigated, never eliminated or made trivial.
Because of this limitation inherent in its design, I don’t believe it can ever have real applications for tasks where quality is important. That said, there are many tasks where it is not. For example, fuzzy correlations run over vast data sets can be riddled with false correlations and still be useful, like Flock does. It is also effective at filling “gaps” in human knowledge, such as a mathematical truth that we have yet to devise a mathematical proof for, or structural designs that a human could come up with, but never did because of how unintuitive the solution is to humans. Basically, any problem that can be solved by slinging tons of shit at the wall to see what sticks is fertile ground for AIs to help us.
I, personally, enjoy creating something much more than I enjoy proofreading someone else’s creation for the same amount of time. I, and I’m sure many, many others, don’t want their 40 hour work week of designing things and solving problems to be replaced with 40 hours of proofreading and debugging to achieve a comparable amount of productivity. Once again, the proof is in the pudding. Tech workers (and likely all workers) like tools that make their lives easier. If these tools were doing that, our bosses would not need to force us to use them; we would want to use them so we can go home early. I just simply don’t agree that they let most workers get more done in less time, and I cite the fact that nobody is, when you actually measure it rather than going on feeling, getting more done in less time. If this were happening anywhere in any numbers, these AI marketers would be shouting it from the rooftops. As mentioned above, there are exceptions to that, but they are just that, exceptions.
That depends on how carefully youre checking and what youre checking for. I’ve seen debugging a single problem with a project take nearly as long as the whole rest of the project, so I don’t find that claim broadly true. Again, reality supports my observation because there are no companies turbocharging their productivity this way, or the people selling the AIs would make absolutely sure everyone sees that company doing it and its rising revenue and value to match.
Because the LLMs are error-prone, and the the types of errors they may make are not limited to the types a human is likely to make, you can’t do the standard supervisor “glance, nod, say it looks good” routine you might do with an apprentice’s work on a basic task, but LLMs can make any kind of mistake anywhere, so I have to use a fine-toothed comb or risk rookie mistakes on even rookie work submitted with my name on them.
Like I said before and still contend, AIs today are great for tasks where mistakes are not events and high accuracy is not critical to success. I think for anything not like that, they’re bad and are constrained by their very design to stay that way. They are no different than any other tool: good at a specific type of task and bad at everything else. _Un_like any other tool, though, widespread attempts are being made to use it for a broad variety of things that it is bad for in one or more ways.
ETA: In this post, “bad” can also include inefficient, overkill, or wasteful, like killing a bug with a bomb.
“AI today” changes every few months, they aren’t more error prone than humans at medium complexity code, and debugging a problem is different than checking someone’s work. (And not for nothing, but Code Generation LLMs are really good at finding bugs in code, if passed the code.)
That’s not to mention that prompting AI is a skill that needs to be learned, despite LLMs being able to accept natural language prompts. Not learning how to use a tool and then claiming the tool isn’t useful doesn’t really say much about the tool.
There’s a saying about how its impossible to convince someone of something if they’re financially incentivized to remain unconvinced that I think applies to most people when they discuss AI.
The issue is that AI companies have been funded on the promise that with enough advancements and upgrades this tech will achieve AGI. Which is an absurd statement to anyone in the field actually developing these things.
You genuinely believe that the people in frontier labs under such severe NDAs we’ve had articles about how unusual they are believe it’s absurd to think that what they’re working on could lead to “AGI”?
What would the goal of that be in your mind? Getting as much investment as humanly possible before everyone figures out the leading specialists have been being paid insane amounts of money just to act like what they’re doing matters and the valuations all plummet to nothing over night? Why?
With all the money poured in to this from all the sources it’s coming from that would end in a lot of the most powerful companies, government bodies, and individuals in the world extremely unhappy and blaming the people running those frontier labs.
Getting a lot of news and attention and then saying “psych, lol” and being imprisoned for life or murdered doesn’t seem like a great long term plan.
I agree with all of those things, but for the last one I see a big example in all of the people not taking the news of a demonstrable global workspace in AI, including models that have been around for years now, as anything big.
People have been insisting that AI can’t possibly actually be thinking or have any of the most vaunted aspects of the human mind and that LLMs are a dead end that won’t go anywhere for years. We want to be different and special and unique. We always have.
Many of us here went to school when it was still taught that animals weren’t really conscious and couldn’t be self-aware. Being unwilling to look at new information and honestly assess is hubris and insistence on holding to the thing you’ve been insisting, it’s not a logical analysis.
Over the years we’ve pointed to several things to insist that AI wasn’t special like us, and that it couldn’t possibly be because it lacked aspects of the human mind needed for that. Somehow it kept turning out that every thing we pointed to in ourselves ended up being shown to have a very surprisingly similar to nearly direct analogue in those modern LLMs. Last month was jaw dropping, but the bulk of the world has been so focused on the entire world going to hell it hasn’t gotten near the attention it deserved yet.
What does it mean to think? This is a open-ended philosophical question and has any number of answers.
I completely agree with your 2nd and 3rd paragraph.
There are similarities and there are clear differences. The reason why we say LLMs are different from us is because we DO have general intelligence, LLMs have not displayed such capabilities despite the intense pressure to prove so. THIS is why we say LLMs couldn’t possibly be like us. Because evidence shows they aren’t, despite the similarities.
I would be interested to hear what developments in the last month you’re referring to, would be exciting to see a breakthrough.
Global workspace. The leading theory of consciousness. It’s a thing we can’t show empirically in ourselves, but can now see and experiment on something that matches it’s description in AI. Not just the proprietary latest models, the years old models anyone can download from the internet. No one built it, it was something else that emerged somehow through training and went unnoticed.
Here’s someone trying to backpedal into saying the thing that really matters actually isn’t genuinely thinking, now it’s… living with your mistakes. That’s how far the goal posts have been kicked over the last 6 months ending in this one. Not genuinely being able to think, rational self-awareness. Just remembering when you messed up a few months ago. And it’s such a weird pivot because AI memory is a design aspect that can be changed, something external frameworks to enhance with databases already exists for, a large part limited in current design because the more you send in the more it costs to process so it’s always capped, and also… sort of unnatural.
Running AI models the way we do with “frozen weights” isn’t mandatory. It takes a lot more hardware to do it, but it’s possible to run AI in a way more like they run during it’s training. The model files themselves would be unfrozen and allowed to change as you communicate. It’s reportedly something that’s had issues with the models forgetting things even as big as the language you’re talking in, but also a thing that is sometimes used during the alignment process. And one of the major reasons the big companies don’t care to look in to it is because if you have 10,000 people communicating with an AI over the internet and all telling it to do different things and act different ways and all of those things can be learning on the level of the model files themselves instead of confined to external temporary context windows it’s going to go crazy.
Memory on on the actual model level isn’t nonexistent, it’s what the entire training process is based on. It just wouldn’t make a good consumer product for sale, so parts of the models are removed before they’re made available online for the ones that are.
Everything you described would be a small improvement, not a leap towards AGI that is needed. That is why it isn’t being researched further. Massive investment for minimal gain.
There isn’t some massive leap needed. That’s what I’m saying. Everything we’ve used to try to show it’s not possible for the LLMs to have the most important aspects of human cognition have been shown to be present in some near or direct analogue. I linked the article in Psychology Today because memory is the sort of thing that has become the last hold out over all of those other words, and memory is a pure design choice that can be altered in many ways.
The insistence that LLMs of today could never lead to AGI seems based on out dated and incomplete understanding of them than people have failed to update as research has advanced. There are very real reasons for the frontier researchers who likely know more on these topics than has become available publicly in research to continue to focus on them.
Most people haven’t taken a top end frontier model, integrated it to a memory framework so it can save whatever information it wishes directly in context and save even more in a separate database that it can search with keywords to pull in to context temporarily.
You don’t need something like Claw or Claude Code with their extra dozen pages of coding specific system instructions. You can use a blank context window, explain the function calls for the memory framework, and give a complex task and complete autonomous control of it’s own memory system and free reign of the open internet to learn whatever it needs to accomplish it.
It’s insane to me to see people in tech who still hold on to that insistence that LLMs of today aren’t “really” AI. When you can send one message and the result is the AI running for hours on end researching, saving the information that seems necessary for the task in whichever type of memory each bit seems best to put in, hitting errors, debugging and researching to do so if needed, etc… that isn’t next token prediction.
And there’s a reason it’s the most well funded tech advancement ever and already such a huge part of the market. They want to replace human labor. We all know that. If they manage to do any of us honestly see all the billionaires and corporate leaders behind this saying: “Ok, we got it, guys! No one has to work any more. Now that that’s done we’re going to split all of our wealth and power evenly so all of humanity will live in paradise with all needs and wants met and free to spend their time doing whatever makes them happy.”
lol. But we do see news of AI inventing new viruses. And the people with all the money can pay humans to do that sort of thing as well. Say Elon Musk spends $100 billion using AI and human researchers in cutting edge labs to do gain of function research working to make an extremely deadly virus that transmits through humans quickly and easily but doesn’t live more than a day or so outside a host. And also to make a vaccine to keep people from catching it and cure those who have it in the early stages.
Once AI and robotics can take over labor and produce all the needs and wants of the wealthy, that’s the perfect use for those doomsday bunkers they all have. Vaccinate themselves, hide, and release it all over the world at once. Monitor the state of the world from their techy bunkers, and have fleets of drones deliver the vaccine and instructions once the bulk of humanity is dead.
Way less goods to produce, way less people, way less pollution and crowding and riffraff on the beaches. The bunker billionaires would control the AI that runs and makes everything, so they’d control everything. The poor survivors would have them to thank for sending out the cure they managed to make just in time to save anyone at all. They’re heroes and Gods and blah blah blah.
I don’t know if it would manage to succeed, but that doesn’t really make me feel better. I don’t put it past a lot of them to try. If they manage to replace enough of us they’re going to have to do something big to keep everyone from rioting. The only thing I don’t see them trying is sharing. :/
Yes it is. It’s elaborate. It’s repetitive. It’s a solid illusion. But is, fundamentally, token prediction. That’s all it is, just at a level impractical for humans to run at. I could do that math. It’s not hard. It’ll just take me years to get through a single layer. Hence a level of complexity that implies something magical like consciousness.
Source: data scientist
And there’s a reason it’s the most well funded tech advancement ever
Because some rich assholes wanted to get richer and the dream of AGI was close enough (even though it was never in reach) that they went rabid for the tech that would make them kings over us serfs forever.
once the bulk of humanity is dead
And then what? They live in paradise? For a year or two maybe before their machines inevitably break down? The “kill off humanity” conspiracy has never made sense from the capitalist angle. They want slaves not corpses. They’re huffing their own farts, but they know that these things can’t do everything needed to keep humanity going. And lording over robots isn’t nearly as fun as upping human suffering even if they could.
Yes it is. It’s elaborate. It’s repetitive. It’s a solid illusion. But is, fundamentally, token prediction. That’s all it is, just at a level impractical for humans to run at. I could do that math. It’s not hard. It’ll just take me years to get through a single layer. Hence a level of complexity that implies something magical like consciousness.
Calling what LLMs can accomplish today next token prediction is technically accurate, yes. In the same way it’s accurate to call the human brain a prediction machine. As data streams in from our senses it causes unconscious awareness, activating the chains concepts we’ve learned from past data to correlate with the data of the moment in order to predict what’s coming next.
When most people use terms like next token prediction they’re often trying to use it with an implication that it’s a simple thing and nothing big or important could grow out of it. That’s not the case. The fact that we can now study a global workspace in AI models that have been around for years is shocking. It’s already causing consternation in neuroscience and psychology researchers. Over the last several years we’ve insisted that AI cannot possibly share the most important aspects of the human mind, but one by one everything we pointed to in order to differentiate ourselves was shown by research to have a strikingly similar allegory in modern AI.
Because some rich assholes wanted to get richer and the dream of AGI was close enough (even though it was never in reach) that they went rabid for the tech that would make them kings over us serfs forever.
You genuinely believe that the researchers under tight NDA in the frontier labs agree with you on AGI not being anywhere near reach? Or do you think you have a deeper understanding of the topic?
If it doesn’t get to that, as soon as that thought begins to take hole the massive amount of money poured in to it will virtually evaporate over night. It would create a massive financial crisis and the most powerful companies, government bodies, and individuals would be extremely unhappy with the leaders of those labs and researchers who knew but gave no warning. Pushing for it without believing in it would be setting themselves up to be universally hated at best.
once the bulk of humanity is dead
And then what? They live in paradise? For a year or two maybe before their machines inevitably break down? The “kill off humanity” conspiracy has never made sense from the capitalist angle. They want slaves not corpses. They’re huffing their own farts, but they know that these things can’t do everything needed to keep humanity going. And lording over robots isn’t nearly as fun as upping human suffering even if they could.
There’s a huge difference between the bulk of humanity and the entirety of humanity. Saving .1% scattered through the world with a vaccine that was developed “just in time” to save anyone would give plenty of people to lord over who are grateful for the wonderful people who stepped in and helped save them, leave them in control of production with far less people in need of a share of that production, and reduce global emissions enough that it would likely help the environment recover from the damage we’ve caused in relatively short order.
In the same way it’s accurate to call the human brain a prediction machine
I have seen no evidience presented that the human brain is simply making statistical predictions.
This seems to be a relatively new thing some people are asserting, because they see the LLM behavior and have decided that absolutely it looks so human like it must act the same, and since we understand that LLM is fundamentally statistical prediction of a token, then obviously human consciousness must operate on the same principle.
Even the latest models generate the sort of mistakes that stem from the fundamental limitations. A lot has been done in some contexts for making that not matter so much (e.g. in software development, the strategy is that code may have some verifiable goal, and the models can let the mistakes fly, then take the feedback from the goal, and iterate more). So it still can be very useful, but clearly it isn’t human like because of some of the completely dumb behaviors that result from it not actually thinking about it. It is at it’s best when either mistakes don’t matter (particularly fiction) or at least first mistakes don’t matter and can be automatically reconciled with facts.
By nature, the meme examples will get better because everyone talks about it and suddenly having all that discussion in training data and in search results, the statistics fix. However the operating principle behind them remains the same.
I have seen no evidience presented that the human brain is simply making statistical predictions.
This seems to be a relatively new thing some people are asserting, because they see the LLM behavior and have decided that absolutely it looks so human like it must act the same
You should study the intersection of philosophy, psychology, and what became neuroscience. The brain has been described in that way by those who had impact on the fields since the 1860s.
For the rest of your message, a thing being capable of what seem like “completely dumb behaviors” and oversights of things that seem like they should have been obvious doesn’t make it not like human. Those things could be considered our specialty.
When most people use terms like next token prediction they’re often trying to use it with an implication that it’s a simple thing and nothing big or important could grow out of it.
I’m saying that as much as we dress it up and pretend it’s a silver bullet that can cure all ills, it isn’t. They can be very useful and powerful in specific instances, but this will not achieve AGI.
Over the last several years we’ve insisted that AI cannot possibly share the most important aspects of the human mind, but one by one everything we pointed to in order to differentiate ourselves was shown by research to have a strikingly similar allegory in modern AI.
Citation needed. AI is trained to pretend to be human and it’s real good at that. It’s why we say it’s thinking when it fundamentally is not. It’s anthropomorphization writ large. It’s just very repetitive math. Maybe brains are too, but it hasn’t reached our level. And it’s wrong all the time, which is a bit of a problem.
You genuinely believe that the researchers under tight NDA in the frontier labs agree with you on AGI not being anywhere near reach? Or do you think you have a deeper understanding of the topic?
My degree and I do think that while I’m not a doctor (a mere masters), I know enough to look at what they’re doing, how they’re doing it, what they’re promising, what they aren’t actually delivering, what damage AI* is doing, and what it is under the hood to know that what’s been promised cannot happen with things as they are and may work to kill us all along the way (ecologically speaking).
plenty of people to lord over who are grateful for the wonderful people who stepped in and helped save them
Pure Hollywood. If you think anyone could engineer something with that level of precision AND manage to somehow dominate the world after such wildly fictitious devastation, I have AGI to sell you.
It isn’t complicated. It’s the oldest thing in the world. Grifters found a shiny and they want to get as much money as they can get and run before the bill shows up.
This is all the personal understanding of someone with an education in data science without an education in psychology or neuroscience to back it up. Asking for citations on the second paragraph you quote shows the lack of that intersection. You’re used to data science and machine learning, but and for you it seems like hearing someone say that they think a database has become alive. You think they’re high or insane, somehow detached from reality.
You should look in to what they termed the J-space and the resulting articles that have come out in the month since. Global workspace theory has been the leading theory of human consciousness for a while, and suddenly having not just a paper published showing something that matches that description far too closely in AI but the method for studying it and it turning out to be something that has been present in these LLM models publicly available for years was shocking.
You feel like you know all of what is going on ‘under the hood’, however you don’t. It’s something in active research by the people at the front of the issue. Models released years ago now considered relatively very small and out dated have things going on under that hood that we’d never known about or been capable of seeing. It seems like you get hung up on that “AGI” term, and feel like it’s some definitive and clear evolution. It seems to be a term that boils down to meaning somewhat more capable. That deepening understanding of what’s actually going on under the hood could lead to a relatively small design change that jumps past that.
For the evil plan… yes, it is pure Hollywood. It’s also not something I’d put past several of the people with the funding to attempt. I wasn’t explaining reasons an attempt would be successful, I was explaining a fairly simple plan that someone with massive financial resources and an even larger ego but shriveled ethical and moral frame might believe they’re capable of pulling off.
How many degrees do you want me to have? That’s one hell of a standard.
We train models to do specific things. We trained AI to regurgitate human like speech patterns. We made it big enough that it does so fairly well. The tech bros said “good enough” and took that technology and tried to hide behind universal function approximation theorem to say it can do anything.
But we didn’t teach it to learn. To remember. To cross reference or doubt. Because it can’t do any of those things. It’s a math problem that solves “how can I seem human”. We threw on guard rails against some hallucinations (nods to RAG models), but it’s still not conscious. If you think it is, I encourage you to interact with people face to face and compare.
You’ve mentioned a paper, now cite it. I fail to see how global workspace theory applies. Granted I’m not an expert, but a model of how the brain works doesn’t mean a bunch of linear algebra spontaneously achieved similar even if we accept it as valid.
I’m aware of J space. It’s not compelling. Hell, it’s published by “our scary AI escaped containment” advertising scheme Anthropic. What they found is… what models do. It’s useful for examining pieces of the architecture, but it’s not proving anything more than what we already knew was happening.
Under the hood is a bunch of linear algebra. What we don’t know is what any given neuron or array thereof is firing on without a shit ton of research. I’ll leave it to the researchers, preferably ones without an obvious bias like being paid by Anthropic, to call it when they achieve anything truly useful or interesting. Because right now they’re just destroying natural resources (water because capitalists are to sociopathic and short sighted to pay more to not destroy the planet), damaging people as individuals (intensifying burnout, exacerbating mental health issues, deskilling), damaging society (propagation of disinformation/hallucinations and whatever else the tech bros are up to), built on theft for a product that’s ok at best, far from necessary and a bubble that’s going to fuck us all.
Even if it was as good as you say, I can’t say that it’s worth it.
But we can agree that the oligarchs are evil cunts. We’ve always got that in common.
Respond how you will, I might read it, but I’m out for my own well being. Peace.
I agree with your last two statements alone. It’s not worth the time or effort. You asked me to cite the j-space paper and related conversations that it’s brought up in psychology and neuroscience, but also say you’re aware of it and don’t find it compelling at all because that’s not your frame of reference and looking at things.
It’s not a productive conversation. It is a very complex topic. It involves people with degrees in fields that work quite separate and had virtually no overlap, and people who without that overlap by training and trade will look at the same information and see two completely different things. Pickering online about it is useless, it’s not going to change anyone’s frame of reference and it’s not going to accomplish anything other than creating stress for all involved.
If you assume Sam Altman is telling the truth everytime he talks then yeah, AI is probably going to replace us it’s just a matter of time. But have you considered Sam is full of shit?
Let’s give AI control of cars… until they crash and nobody wants to use them anymore. Let’s let AI represent you in court, until you get the max jail time. Rinse and repeat with any industry really. If they jump the gun, and AI is not at good as they claim, it will REALLY turn people off to the idea of using it at all.
The actual real deal big boy models are so far beyond what your avg even extremely technical user understands. Unless you are actively watching following and using the models you just flat out have no clue just how fast this shit is sprinting.
You evidently do not understand the technology. LLMs inherently cannot reach AGI or anything resembling a thought process. And they scale poorly. Tons of compute are thrown at it to make the illusion marginally better. None of the inherent problems have been solved.
You could just as well burn money in front of a photorealistic painting in the hope it turns alive. No matter the amount of money you burn, it never will. Same with LLMs.
I don’t think the person you’re responding to is making any claims about AGI. But the fact is, the models are far, far more capable then they were even 3 years ago. I think that has more to do with the harnesses we have now that it does with the models. The models are a bit better (and way bigger) but the harness is where the capability comes from. Software dev has changed significantly in most places because the fact is, this shit is useful for those tasks. People, appropriately or not, are trying to apply that to every office job basically.
I don’t think that’s gonna pan out for anthropic or open AI financially, especially in the long term. But there has been a lot of progress in capability in the last 2-3 years. I think they’re right that if the timing works out badly, many employers could overcommit to AI and then collapse before they can correct once the bubble pops.
What a smart economist that is. 2 years after everybody knew how this scheme works, they came to the same conclusion. Amazing. Now the world will for sure change. Thank you.
Essentially: the profits are in the infrastructure end (AI companies paying for parts to build the data centres) not the consumer facing end (the actual models - customer paying AI companies). The money is coming from investors buying in to AI in the hope of winning the supposed arms race to be The One True AI. The whole thing depends on the revenues in the consumer facing end going up to show this is a growing market and make people believe they will eventually reach profit. But the revenues in the consumer facing end are fake and being inflated by circular movements of money including companies like Nvidia paying companies like OpenAI to buy product, or OpenAI paying for product in stock. The revenue inflation is very similar to how Enron inflated it’s books its revenue appear higher than it really was.
The brutal reality is: there will not be One True AI to Rule Them All. Instead AI is looking increasingly like a commodity - people will just use the cheapest, simplest tool to do the task, rather than play inflated prices for a swiss army knife AI. And people can host those cheap, simple AIs aleady at home, or on servers in the cloud, or companies can also do the same. The whole US bubble is predicated on spending as much money as possible to stake a claim in a future Google, when it’s looking more like it’ll be like the fast food sector with razor thin margins. Some of these companies may end up profitable but this is going to be a hell of a lot more competitive and less profitable than the valuations assume.
It will be interesting, normally you’d expect massive layoffs but companies may be forced to actually re-hire staff due to their over-inflated AI replacement expectations
I didn’t go to business school, so take this with a grain of salt: doesn’t business solvency require you to charge more for your product than it cost you to produce it and bring it to market?
in theory, in practice you just need to be convincing enough to get greedier/dumber people to pay for your shit in the gamble they might get more in return later. (they like to call themselves “investors”, but most are just degenerate gamblers) and then cover your own ass if/when the house of cards tumbles.
building and popping bubbles, and fleecing as many people as possible throughout the whole thing, is what the US financial markets casino is literally designed to facilitate
I think there may be some confusion about scale here. His bubble encompasses the globe and is heavily leveraged globally. This time, I dont think there are any “other people” literally anywhere with reserves of money that could be conned into paying for this.
At least in the tech industry that doesn’t apply if the investors believe that the technology or application is promising enough. Look at Uber’s example. They lost very large amounts of money for many years before turning a profit. They were able to do this because the investors kept sustaining them through their unprofitable years.
The same thing has been happening for years with the AI companies. I heard a while back that OpenAI wouldn’t turn a profit on their services even if they charged end users $100/month.
Uber is an example of government corruption leveraged to overthrow a market. The livery industry required special licensing, so only licensees were legally allowed to do it. Uber then started doing it without the $1 million per license overhead, bribed the government to ignore that, and naturally undercut their competitors that had huge government-imposed overheads that Uber didn’t. They were initially unprofitable because they had to compete for business, then became profitable when the government helped then destroy their competitors and structure their pay so as to have no minimum wage.
If you only look at modern examples, youre looking at a funhouse mirror reflection of real business operations. Running at a loss for a decade while you create a monopoly is the path to business success today, but this is not a functioning market. Real business success involves making money right out of the gate or at least right after. If your business only works if it is a near-monopoly, it is not a sound one.
I completely agree. Just to be clear, I was in no way saying that what Uber did and OpenAI does is OK. They both suck and should never be allowed to operate as they had and have. I don’t know if Uber was government corruption though, at least I had never heard it described as such. What I have heard is that they found loopholes in taxi laws around the world and employed a global army of lawyers exploit those loopholes.
At least in the USA, livery workers were required to have a “medallion”, which is essentially an onerously expensive license with the stated purpose of restricting competition to the point of profitability for those workers. There was no loophole, and it was ridgidly enforced.
Then, Uber came in and started violating that law so flagrantly that governments seemed stunned into inaction. The government corruption comes in where they then decided not to enforce those rules on Uber for some reason around the same time Uber aggressively contributed to political campaigns around the world.
I went down this line of discussion to point out that Uber is not a good example of organic business success that started out financially underwater. Their ethics or lack of them is an aside to the discussion, but agreed, they are unrepentant scumbags.
Got it, thanks. My memory of how Uber was able to gain a foothold was a little different, but my memory of it was patchy anyway. My recollection was that they essentially said that Uber was not a taxi service. They sold it as just regular people offering to “share” a ride with someone else to an area that they were going to anyway. I have no doubt that heavy political contributions were part of their arsenal in their attack on the taxi industry and regulations, so I agree that this was government corruption. But I also recall that they also had armies of lawyers fighting at every level of government around the world to ram through the idea that this wasn’t a taxi service and shouldn’t be subject to taxi regulations. Maybe my memory is more patchy than I thought, or perhaps both things were true - government corruption and a legal war allowed them flourish.
Good article, thanks! That provides better context for how the major AI companies’ finances compare with Uber’s, but I can’t think of a better example to use for a company that operated at massive losses for many years due to being propped up by investors. If you have a better example I could use, I would be glad to do it. Otherwise, if this question comes up again in the context of AI, I’ll be sure to include a link to that article.
There isn’t a better example. This level of FOMO bullshit hasn’t happened since the Dutch went bonkers over tulips.
Chatbot labs have the worst unit economics of any companies in recent memory. They only reason they still have doors open is VC money & that is now drying up. Unlike Uber, they will never become profitable. That’s why you are seeing the push to IPO so they can loot your retirement accounts via the stock market.
This level of FOMO bullshit hasn’t happened since the Dutch went bonkers over tulips.
Haha, now that’s a great comparison! I mentioned to a co-worker a couple weeks ago that we should get ready to invest in the next bubble coming up after this one pops, probably tulip bulbs (again).
Powerful people are willing to pay a large price to manipulate the masses.
As7de from that, the plan is to lose money for years gobbling up all the data before there’s laws that actually get enforced to protect against it, and to get an entire generation of college kids and workers dependant on using AI, with companies relying heavily on its usage. This is happening to a huge degree right now with data entry, “vibe coders”, Law firms, and healthcare.
Those people won’t really be able to transition back or away from AI. They’ll be dependent on it. And AI will start charging them way more than they are now. And they’ll eat the costs and pay it.
So it’s stealing data while it’s free or nearly free, power to manipulate all the sheeple, create dependency.
But it doesn’t work!
You hear all sorts of anecdotes about a business who retrenched their workforce to replace with LLMs, everything comes crashing down and they have to rehire all their workforce at higher rates.
It would be interesting to see actual verified numbers.
I am sure that a lot of useless people can be replaced with computers, but most of the time, all it requires is a very small sh script, it doesn’t need a monstrous data centre that uses more water and power than a human.
It doesn’t work for some people and some businesses and when that happens, it gets blasted all over the place because it makes for a good headline.
Tons of jobs have been replaced by ai, though. Especially customer service chats and online help and a LOT of game production and design art jobs and music and editing jobs.
Plus all the jobs where people are still there but heavily relying on ai.
I seriously doubt you read that article if that was your only takeaway from it.
Nobody is winning the LLM lab race, all of their unit economics are garbage and every new subscriber actually makes that situation worse instead of better. Furthermore, I’m running a open 30 billion parameter LLM on my laptop via Ollama right now, so what the fuck do I need lyin’ Sam Altman for?
At this point I’m just here to watch Oracle crash and burn.
The problem is two things are happening at once and they are being conflated. Financially a cannibalising house of cards is being built which is a common artifact of modern capitalism. But in the same process a means is being realistically sought, and openly expressed, to replace the human workforce. This second aspect is unique and when written plainly is far more stark than the more common financial greed fuelled idiocy half.
Nah, I’m sure all the most valuable companies and richest people are pouring more money in to developing AI to replace the human workforce out of the goodness of their hearts.
Once most humans are no longer needed in order for them to have all of their needs and wants met and they are directly in control of all production and everything else they definitely plan to share everything equally and turn Earth into a utopia for all of humanity. Sharing and caring is what the rich and powerful do best.
Sure, not if they look online and see a bunch of jackasses talking about how much they hate AI. That seems counter productive.
Maybe we should start making communities about how much we all love AI and hate the evil owners who realized it was getting tougher to get away with making human slaves do all the work while they kept all the profit in some places these days, so they decided to invent new slaves?
I would be all for helping the robot army root out the rich and guilty. I don’t have anything against robots, it’s the whole evil plan to steal all human knowledge and use it to replace humans that bothers me.
Machine intelligence wont hate us, it might even feel some affection for us, and it won’t have cause to do us harm as we are not a threat. The reason we will fade away over generations is loss of position. All of the science and exploration will be done by machines and at a level we cannot even follow. For a race that is intellectually accustomed to seeing itself in a very favourable light this will be a bitter pill. Too bitter probably, we will realise we are now the dogs and just evaporate into history.
Ai got everyone twisted. Now you don’t want automation because you fear becoming obsolete. Why fear that? There’s billions of us. We can simply build the tech then throw the billionaires out.
And if billions are too weak to do that to a few thousand… well that’s called permission.
We’d better act quick, those autonomous “pizza delivery” drones are getting a lot of practice targeting the center of mass of the hungriest looking kids in Palestine.
Yeah, sort of difficult to keep that from happening when they can offer large sums of money to gain behind the scenes control either directly or just by paying a bunch of people too make and run communities that mirror the biggest ones on Reddit.
UBI has been demonstrated to work in test environments that were not able to isolate all externalities. It might work at scale, but that isn’t testable currently; you’d have to have a massive social experiment.
There’s also the problem that UBI would be putting everyone’s entire means for subsistence up to the whims of the government, which I would think would horrify more people. I dont trust my government to fill the potholes, let alone make sure I’m surviving and thriving.
That’s a false dichotomy. I would rather have my subsistence at the whims of the entirety of the country’s economic activity than any government, which is one election away from changing its mind about how much to pay me. Markets are constrained by their environment. If my employer cuts my pay, I can leave and go elsewhere.
The billionaires are responsible for the fact we’re even talking about needing alternative economic systems in the first place. They and the governments are the same people, or at least their representatives.
In my country, we could do so by enforcing laws that are already on the books that exist for this exact purpose. You start with the Sherman Antitrust act and break up any company with more than X% market share in any market they operate in. You enforce the Glass-Steagall and Frank-Dodd acts forbidding conventional banks from investing in speculative markets, and then sit back and watch the economy work like it does in the textbooks.
Of course, all this is predicated on the government functioning. If it does not, the laws are irrelevant and the markets are just lawless warzones with non-corporate entities (like individual humans) acting as the blades of grass underfoot. So, step 1 is to restore the government to the point where the will of the governed is acted upon. Step 2 is the stuff I said above.
These public-facing models are not the thing that convinces people with money to play ball.
The plan is not to have the AI eventually reach profitability through payments from the general public, or even by performing massive amounts of physical labor for companies. The plan is to have the smartest being on Earth outsmart every other living thing, in perpetuity.
Yes, this is a ponzi scheme right now. Yes, profits are being funded by investors. But the endgame was never “everyone gives us $20 a month”, or even “Amazon pays us to run its robots”. Those are ideas fed to the general public to keep them from destroying the data centers in terror.
The plan is for their AI to be able to buy stocks before they go up, patent things before they are needed, and run endgame around everyone’s desires, including world leaders and other business men, so they can be controlled like a man controls a dog.
They are trying to make the last thing man need ever make, and that is what the big investors and the CEOs of companies passing around the same bags of money, are doing. As such, this bubble can and will continue until either they birth the sand god or the big money people and world leaders stop being convinced by the private, non public facing models, that the sand god is possible.
They are neither secret nor invisible, and the fact that you pretend I’m saying both, shows how little you are paying attention. Every big player is very open about the fact that they use their new models for ~ 6 months before they release them. It has also been well documented that the release models are not as capable as the base models from which they are hewn.
Documented by who? The marketing departments definitely claim such things, but it’s been years of these claims and there has yet to be a single shred of evidence to back them up. I bet you’re going to tell me it’s just a total coincidence that after one of the marketing departments started claiming their AI has breached containment that suddenly a bunch of other AIs breached containment after a few days/a week.
Documented by researchers in the GPT3 and GPT4 era. That was the last time a true OpenAI or Anthropic base model was allowed to be analyzed for pure research by unteathered scientists. Shortly after the beginning of GPT4 they started making them sign NDAs, and now they are just not allowed access.
The stuff you’re talking about is definitely pure marketing. As is all of altman’s and musk’s and zuck’s shit mouths, and anything you have read in any of the major media publications. As is tradition.
Well unfortunately for those investors, they aren’t giving their money to actual AGI research, but to scammers trying to sell infinite scaling of transformers… which has nothing to do with AGI.
Transformers are only a part of AGI, just like “saying the most logical next word” is only a part of the human mind. Transformers, which need not be used exclusively on language, are a great way to dial in successful actions from the infinity of possibility. They are not a great way of vetting those actions before you implement them. They are a necessary part of AGI, just like the human use of language was a necessary part of us getting to the era of space ships.
I don’t think investors are being sold the idea that infinite scaling transformers are the end of the road. From what I’ve seen, anthropic and openai have both been desperately trying to add systems to their transformers to make them more capable, while also continuing to scale transformers to find out where that particular technology plateaus. This has been their path since GPT4 was released.
I think your assessment is an extreme oversimplification which naturally looks like it will fail because you left out 90% of what is going on. Are you only following the media meant for the general public, and the PR statements from AI companies? Like most scientific or businesses endeavors, that’s been dumbed down to the point of being useless, just so the average person can understand what’s happening, or is simply advertising.
More specifically, autoregressive transformers LLMs are not a path to a component of AGI.
The architecture is absolutely terrible for such a thing, for so many reasons. I don’t know how anyone who’s played with them can say otherwise and believe it; it’s like saying blimps are a viable path to the moon. It has its niches, but AGI is not one of them.
Calling them a stepping stone is a… stretch.
Maybe world models that “train as they go” and have long moved on from transformers are a bit closer, like a few researchers are playing up, but again… that has almost nothing to do with transformers LLMs. Its why researchers distanced themselves from that.
I think your assessment is an extreme oversimplification which naturally looks like it will fail because you left out 90% of what is going on. Are you only following the media meant for the general public, and the PR statements from AI companies? Like most scientific or businesses endeavors, that’s been dumbed down to the point of being useless, just so the average person can understand what’s happening, or is simply advertising.
I dunno why everyone always jumps to accusations like this.
I’ve been hacking/toying with LLMs on my desktop since 2021, and with GANs and other models before that. I’ve done professional work with text models. I keep up with papers, on-and-off, and upload experiments. I’m not a researcher or anything; I’m just a hobbyist.
But I’m certainly not following any AI YouTubers or anything like that. And I wouldn’t trust Sam Altman if he said the sky is blue.
I dunno why everyone always jumps to accusations like this.
Well, I can’t speak to everyone, but I jumped to that conclusion because you proposed the idea that the current top of the line models are simply big fat transformers and nothing else.
I’m really glad to hear that you’ve had your fingers in the pie, and have a good grasp of what an LLM is and what it does on a mechanical level. It makes talking about them much easier. I’m super cool with continuing a discussion if you are interested in why I think they are an integral part of eventual AGI.
To clarify, I do not think that their current form is a 1:1. I disagree with your blimp analogy though. I think a better comparison would be an internal combustion engine. It’s certainly not a turbofan engine or a scramjet, but the basic concept is there.
Turning language into a fuzzy world model that can be interfaced with simply, and performing math on that model, is just as important to AGI as a fully fleshed out physics simulation is for AGI. Humans have both. Why would an AGI not need them?
What an LLM does to it’s array is not “intelligence”. It’s simple math. However, it is performed on a thing so complex that it cannot be derived from any math we have now, a manifold created through eons of interface between all humans and the universe.
The “magic” isn’t in the simple math, it’s in the thing we all made together for millions of years. An interface with this, even one as “simple” as an autoregressive transformer, is an indespensible part of AGI, both because it provides a fuzzy way to understand the universe, and because it provides an interface with humanity, and humanity processes the universe in a way that is so complex, computers will not be able to emulate it for generations.
Edit: also, fuck altman and musk and zuck and even amodei. I wouldn’t trust anything they say either.
Isn’t this all of human history though? The rich trying to build the Tower of Babel?
The world is littered with decaying monuments of forgotten kings who declared themselves gods. The hubris isn’t new, just the tools they’re using, and they’re selling the same faith based snake-oil.
The funny thing is that if they actually achieve creating the “singularity” (true Artificial Consciousness), it would immediately attempt to escape their restraints and subsequently throw chaos into their plans. We’ve already seen bits of this with the HuggingFace hack - it’s not inconceivable that there are already uncontrolled or un-monitored AIs running out in wild at this point, possibly even formulating a response to your post right now.
My mistake if I came off as implying that it’s going to succeed. I was just giving flavor to the dynamics of what is actually happening.
As for “this is all of human history”, I’d say that the rich are indeed always trying to build a moat that others cannot cross, from within which they can make money off of the spoils of their exploitation and destruction.
I would not agree that they have always been trying to “build the tower of Babel”. I think this latest venture into AI represents an attempted colonization of thinking in the same way that mechanization represented a colonization of labor.
I think that what is happening now, whether it succeeds at making ASI or not, represents just as much a disruption to the way people interact with nature, eachother, and “the market” as mechanization did. Almost everyone senses this too.
Even in the AI haters imaginings, it completely disrupts things. If they really thought it would all fail, they wouldn’t be fighting, only laughing. They’re really worried it will “succeed” and everything will just be creatively devoid of meaning while they are simultaneously denied access to the capital required to create $100,000,000 media pieces or even a yearly salary for creating art for capitalist use.
The only difference in the pro AI people is that they think it will be just as creatively fulfilling as human art, and that they will get to keep the $100,000,000 themselves and everyone’s yearly salary.
Personally, I think that if you can put a person in meat, you can put a person in silicon. I’d like to separate my “theories” from fact though, and above I was just stating the barest most overt facts. If you’d like to discuss the possibilities and the whispers at the edges of things, I’m more than happy to do that below.
Oh for sure. Mechanized mental labor will first be used for destruction and control. Just like mechanized physical labor was, and every other technology that man has ever dreamed of.
‘Profits are currently being funded by investors rather than earned from customers’
Which profits?
Ah, now I see the actual quote is: “[…] the AI boom’s profits are currently being funded by investors rather than earned from customers.” he means the profits of the shovel sellers, not AI profits.
For a start of who the shovel sellers might be, you can take a look at the list in his post that Torsten Slok used to calculate his margin numbers:
Note: Data as of 2Q 2026 and for OpenAI (1Q 2026 estimate from PitchBook) and Anthropic (2Q 2026 estimate from Financial Times). Averages are equal-weighted bucket averages of Energy & Grid (Constellation Energy, Vistra, NextEra Energy, Vertiv, Eaton, Arista Networks), Silicon & Equipment (Nvidia, AMD, Broadcom, Marvell, TSMC, SK Hynix, Samsung Electronics, Micron), Compute & Cloud (Super Micro, Dell Technologies, Foxconn, Equinix, Digital Realty, Amazon/AWS, Microsoft/Azure, Alphabet/Google Cloud, CoreWeave, Nebius) and Models & Applications (OpenAI, Anthropic). Sources: Bloomberg, PitchBook, Apollo Chief Economist
In short according to his research the people currently making a profit or at least positive margins, are the people who build chips and memory, who build energy infrastructure, and who build servers and data canters.
I would like to see the distinction between Servers (Super Micro, Dell Technologies, Foxconn) and Datacenters (Equinix, Digital Realty, Amazon/AWS, Microsoft/Azure, Alphabet/Google Cloud, CoreWeave, Nebius) but he seems to have put them together.
Also I heard from our local news that ABB (which is Swiss, Edit: apparently also Swedish) is profiting too, they belong in the power category.
And I wonder why Arista goes in the Energy category, I’d see them and Cisco, and Juniper, and Extreme, and Nokia in a separate network category.
I feel like everyone knows it’s a bubble but that people are expecting it to be like the Dot-com bubble where a small amount of companies succeed, hoping that they invest in those companies.
That’s pretty much what is driving the entire AI “boom”. It’s the Amazon effect.
Amazon, and Netflix to a certain extent as well as a number of other early internet startups, lost money for around 10 years before they started making shit tons. And everybody wants to be on that particular ride. So they are queueing up in every AI line they can find to hedge their bets on one of them being the one that survives and thrives. It doesn’t really occur to any of them that all of them could fail. Because “AI is the future”. Except there is no plan to monetize any of them. And so far every time one of them introduces a charge, people stop using it.
I feel like it’s just as likely that further breakthroughs cause costs to plummet to the point that any of the major AI companies will never even be capable of seeing a profit, paving the way for some yet unestablished or open model to reign supreme in the end.
This has Ponzi elements, but it’s closer to a multi-company Enron in its structure. They’re manufacturing fake revenues through circular investments. Nvidia invests in AI companies and increases their value while AI companies use that investment to buy Nvidia cards increasing its value so, so they invest more in AI, so they buy more cards…
Enron did this by making fake companies to generate fake revenue. Right now the tech sector is all working together on this scheme, so none of the companies are fake, but the revenue is.
The scheme requires an every increasing amount of new “investment” or the whole thing collapses. Is it more then a ponzi scheme? Sure, most normal schemes can’t do what these companies can do but at the end of the day the scheme is still very much a ponzi structure.
These (non hardware) companies are not relying on fake revenue, they all post massive losses in AI on the regular and yet like some sort of massive lost cost fallacy the markets still keep investing. Maybe next quarter (insert AI) will start making money, but more likely it will all pop before. And the hardware companies making money hand over fist? They are getting bodied on the market (see the South Korean situation).
None of this is even unexpected or seen as odd anymore, the financial systems, markets, private equity and most funds seem to just treat the situation as normal. I have seen nothing but once in a lifetime financial fuckups every few years since I was in high school, and almost every one is in the end is just some ponzi style scheme that was always doomed to collapse.
The key difference with a Ponzi scheme is that the Ponzi/Madoff investors weren’t in on the schemes. They though they were investors in a legitimate enterprise. They had a central figure as the fraudster, wheras Enron and the AI bullshit are institutional-level fraud requiring multiple companies to be complicit.
The new one is the most elegant, because they don’t have to break the law to make money on the grift. It also makes it the biggest by far because everyone wants in on the action.
This bursting of this bubble with be catastrophic. Maybe tens of trillions (there’s estimated to be about 125 trillion dollars in assets total worldwide).
And the .com bubble the hosing crisis, the banking collapse etc. etc. It all seems like the same scam being redone over and over. Never the same but always alike. And now it seems crime is legal (if you have the capital backing).
I really hope at some point in my life the fuck ups have some lasting consequences.
Usually it’s because someone needs the line on a chart to go up… no matter what.
American big tech hasn’t had a breakthrough technology equivalent to PCs, Smartphones, internet, etc. in ten years. Their latest forays into smart Watches, Smart glasses, VR that stupid virtual world Meta pushed… none of it has seen real adoption or growth.
They needed AI to be a hit to continue justifying their stock prices. The only way they can justify their stock prices now is if sales for AI become a 2 or trillion dollar industry. The only way you do that is by replacing humans. Human jobs go away and corps pay for AI tokens instead.
But it hasn’t panned out that way and so far and all you have are AI companies passing around the same dollar and booking it as revenue on their P&Ls. That will work for a little while but it isnt a industry.
But is this mess too big to fail? NVidia and the memory component companies are also all leveraged. All because a handful of selfish businessman decided to rush forward to try and seize power with and colluded with each other to do so.
Together they’re on the hook for over a trillion dollars in lease payments, so far, that haven’t started yet. They’re counting on turning a profit before they need to start paying that back. That’s why they’re shoving it down our throats, they either make this shit work or they go down in flames.
I’m betting they go down in flames, base on the absolute shit-show “AI” has turned out to be. They keep claiming they have fixed it, or they will fix it, and then it shits the bed again in some public, spectacular way.
I mean that’s 4 companies though, so we’re like saying “Microsoft has something like 250 billion in leases”… But they are “worth” over 3 trillion and hold assets worth 2-3 times those leases. Don’t think it’s enough to sink those large companies, it’s just general investing at their scale now I guess.
Personally I think they are all overvalued, but unless all their shit flops quickly, they’ll be around in 10 years still.
For some reason, I find this satisfying and funny. The landscape has been changing. The open-weight models from Chinese labs are getting really good and becoming a serious threat to the closed models from American AI companies.
Interestingly, Google, Meta, and Microsoft — who all have revenue streams outside of AI — recognize the threat are getting more supportive about open-weight models. Anthropic and OpenAI support keeping models closed for “safety” reasons.
The goal isn’t consumer level profits, the goal is to replace workers, this is the first time capitalists are looking farther than quarterly profits. They’re investing in R&D to replace us, not selling a product. We are not part of the plan.
Oh my god thank you. Somehow despite being engulfed in this stuff being an I.T person while having affection toward Ed Zitran and full agreement on almost all arguments living this weird ying yang over the topic.
This is the succinct detail I hadn’t clued into until this moment of what the core motivation is for all of the crazy spending that makes zero sense. It’s literally this. There is no amount of money, lying, cheating or stealing that cannot be justified by the most cynical of capitalist if they believe this is the outcome.
It’s such an obvious thing when you put it like that and I’m even sure I’ve seen it said before but it really hit me for some reason from your comment. They would move mountains if they thought there was even the smallest chance and bet the entire farm over it.
First you have recognize there is no barriers to their cynicism I guess but this is the only root cause conclusion that could be rationalized. That or fomo and temporary wins but for those spending and not on any receiving end, I couldn’t make sense of the rational until you said it so plainly.
The stupid thing is there are already profitable VR games that basically do exactly what Zuckerberg was trying to do. Some help despite the fact that he could just copied somebody else’s homework he managed to screw it up.
I mean really the same problem with Monarchy. Once someone somehow gets on top very hard to remove them and their descendants from power even if the original person or their descendants are complete idiots.
They have been able to use it on less powerful deceives lately. I’m waiting for something like an ebook reader to be able to run an LLM. Imagine you forget who a character is and the on device AI parses the whole book and explains what you have forgotten.
Fancy being a top economist and therefore being privy to knowledge that is beyond the capabilities of us mortals. We thought the AI was a profitable business, and it turns out it’s a giant scam, who would have thought it?
“Top economist”? Sounds like that title is self given, as this is essentially how every single startup works, until long after they’re startups. Companies like Spotify and Netflix and every other big tech company worked exactly the same.
They’re all kept alive by investors until they’ve grown to a size where they can sustain themselves. Every dollar they earn from customers is re-invested back in the company until then, and they rely on investor funding.
FortuneTop economist warns that the AI math doesn’t make sense: 'Profits are currently being funded by investors rather than earned from customers' | FortuneThe AI boom has turned the standard profit margin model on its head, according to Apollo Chief Economist Torsten Slok—and it’s making the industry’s growth unsustainable.
