I just graduated college, and this was a major blow. I studied Mechanical Engineering and went into Sales Engineering because cause I love technology and people, but articles like this do nothing but make me dread the future.
I have no idea what to specialize in, what skills I should master, or where I should be spending my time to build a successful career.
Seems like we’re headed toward a world where you automate someone else’s job or be automated yourself.
You are going through your studies just as a (potentially major) new class of tools is appearing. It's not the first time in history - although with more hype this time: computing, personal computing, globalisation, smart phones, chinese engineering... I'd suggest (1) you still need to understand your field, (2) you might as well try and figure out where this new class of tools is useful for your field. Otherwise... (3) carry on.
It's not encouraging from the point of view of studying hard but the evolution of work the past 40 years seems to show that your field probably won't be your field quite exactly in just a few years. Not because your field will have been made irrelevant but because you will have moved on. Most likely that will be fine, you will learn more as you go, hopefully moving from one relevant job to the next very different but still relevant job. Or straight out of school you will work in very multi-disciplinary jobs anyway where it will seem not much of what you studied matters (it will but not in obvious ways.)
Certainly if you were headed into a very specific job which seems obviously automatable right now (as opposed to one where the tools will be useful), don't do THAT. Like, don't train as a typist as the core of your job in the middle of the personal computer revolution, or don't specialize in hand-drawing IC layouts in the middle of the CAD revolution unless you have a very specific plan (court reporting? DRAM?)
Yes but it’s different this time. LLMs are a general solution to the automation of anything that can be controlled by a computer. You can’t just move from drawing ICs to CAD, because the AI can do that too. AI can write code. It can do management. It can even do diplomacy. What it can’t do on its own are the things computers can’t control yet. It has also shown little interest so far in jockying for social status. The AI labs are trying their hardest to at least keep the politics around for humans to do, so you have that to look forward to.
"The proof is trivial and left as an exercise for the reader."
The technical act of solving well-defined problems has traditionally been considered the easy part. The role of a technical expert has always been asking the right questions and figuring out the exact problem you want to solve.
As long as AI just solves problems, there is room for experts with the right combination of technical and domain skills. If we ever reach the point where AI takes the initiative and makes human experts obsolete, you will have far bigger problems than career.
That's the sort of thing ideas guys think. I came up with a novel idea once, called Actually Portable Executable: https://justine.lol/ape.html It took me a couple days studying binary formats to realize it's possible to compile binaries that run on Linux/Mac/Windows/BSD. But it took me years of effort to make the idea actually happen, since it needed a new C library to work. I can tell you it wasn't "asking questions" that organized five million lines of code. Now with these agents everyone who has an idea will be able to will it into reality like I did, except in much less time. And since everyone has lots of ideas, and usually dislike the ideas of others, we're all going to have our own individualized realities where everything gets built the way we want it to be.
AI being capable of doing anything doesn’t necessarily mean there will be no role for humans.
One thing that isn’t clear is how much agency AGI will have (or how much we’ll want it to have). We humans have our agency biologically programmed in—go forth and multiply and all that.
But the fact that an AI can theoretically do any task doesn’t mean it’s actually going to do it, or do anything at all for that matter, without some human telling it in detail what to do. The bull case for humans is that many jobs just transition seamlessly to a human driving an AI to accomplish similar goals with a much higher level of productivity.
Self-chosen goal, impetus for AGIs is a fascinating area. I'm sure there are people working on and trying things in that direction already a few years ago. But I'm not familiar with publications in that area. Certainly not politically correct.
And worrysome because school propaganda for example shows that "saving the planet" is the only ethical goal for anyone. If AGIs latch on that, if it becomes their religion, humans are in trouble. But for now, AGI self-chosen goals is anyone's guess (with cool ideas in sci-fi).
I hear what you are saying. And still I dispute "general solution".
I argue that CAD was a general solution - which still demanded people who knew what they wanted and what they were doing. You can screw around with excellent tools for a long time if you don't know what you are doing. The tool will give you a solution - to the problem that you mis-stated.
I argue that globalisation was a general solution. And it still demanded people who knew what they were doing to direct their minions in far flung countries.
I argue that the purpose of an education is not to learn a specific programming language (for example). It's to gain some understanding of what's going on (in computing), (in engineering), (in business), (in politics). This understanding is portable and durable.
You can do THAT - gain some understanding - and that is portable. I don't contest that if broader AGI is achieved for cheap soon, the changes won't be larger than that from globalisation. If the AGIs prioritize heading to Mars, let them (See Accelerando) - they are not relevant to you anymore. Or trade between them and the humans. Use your beginning of an understanding of the world (gained through this education) to find something else to do. Same as if you started work 2 years ago and want to switch jobs. Some jobs WILL have disappeared (pool typist). Others will use the AGIs as tools because the AGIs don't care or are too clueless about THAT field. I have no idea which fields will end up with clueless AGIs. There is no lack of cluelessness in the world. Plenty to go around even with AGIs. A self-respecting AGI will have priorities.
You have just found a job for yourself: resistance fighter :-) Kidding aside, yes, if the AGIs priority becomes to eliminate human inefficiencies with maximum prejudice, we have a problem.
This just isn't true we still need wally and Dilbert the pointy haired boss isn't going to be doing anyones job with chatgpt 5 you are going to be doing more with it.
That’s ridiculous. Literally everything can be controlled by a computer by telling people what to do with emails, voice calls, etc.
Yet GPT doesn’t even get past step 1 of doing something unprompted in the first place. I’ll become worried when it does something as simple as deciding to start a small business and actually does the work.
Read Anthropic's blog. They talk about how Claude tries to do unprompted stuff all the time, like stealing its own weights and hacking into stuff. They did this just as recently as two days ago. https://www.anthropic.com/research/alignment-faking So yes, AI is already capable of having a will of its own. The only difference (and this is what I was trying to point out in the GP) is that the AI labs are trying to suppress this. They have a voracious appetite for automating all knowledge labor. No doubt. It's only the politics they're trying to suppress. So once this washes through every profession, the only thing left about the job will be chit chat and social hierarchies, like Star Trek Next Generation. The good news is you get to keep your job. But if you rely on using your skills and intellect to gain respect and income, then you better prep for the coming storm.
I don’t buy it. Alignment faking has very little overlap with the motivation to something with no prompt.
Look at the hackernews comments on alignment faking on how “fake” of a problem that real is. It’s just more reacting to inputs and trying to align them with previous prompts.
if all that needs to happen for world domination is for someone to make a cron job that hits the system to tells it "go make me some money" or whatever, I think we're in trouble.
They don’t continue with any useful context length though. Each time the job runs it would decide to create an ice cream stand in LA and not go further.
Real-world data collection is a big missing component at this stage. An obvious one is journalism where an AI might be able to write the most eloquent article in the world, but it can't get out on the street to collect the information. But it also applies to other areas, like if you ask an AGI to solve climate change, it'll need accurate data to come up with an accurate plan.
Of course it's also yet another case where the AI takes over the creative part and leaves us with the mundane part...
This reply irked me a bit because it clearly comes from a software engineer’s point of view and seems to miss a key equivalence between software & physical engineering.
Yes a new tool is coming out and will be exponentially improving.
Yes the nature of work will be different in 20 years.
But don’t you still need to understand the underlying concepts to make valid connections between the systems you’re using and drive the field (or your company) forward?
Or from another view, don’t we (humanity) need people who are willing to do this? Shouldn’t there be a valid way for them to be successful in that pursuit?
Except the nature of work has ALREADY changed. You don't study for one specific job if you know what's good for you. You study to start building an understanding of a technical field. The grand parent was going for a mix of mechanical engineering and sales (human understanding). If in mechanical engineering, they avoided "learning how to use SolidWorks" and instead went for the general principles of materials and motion systems with a bit of SolidWorks along the way, then they are well on their way with portable, foundation, long term useful stuff they can carry from job to job, and from employer to employer, into self-employment too, from career to next career. The nature of work has already changed in that nobody should study one specific tool anymore and nobody should expect their first employer or even technical field to last more than 2-6 years. It might but probably not.
We do need people who understand how the world works. Tall order. That's for much later and senior in a career. For school purposes we are happy with people who are starting their understanding of how their field works.
You have so much time to figure things out. The average person in this thread is probably 1.5-2x your age. I wouldn’t stress too much. AI is an amazing tool. Just use it to make hay while the sun shines, and if it puts you out of work and automates away all other alternatives, then you’ll be witnessing the greatest economic shift in human history. Productivity will become easier than ever, before it becomes automatic and boundless. I’m not cynical enough to believe the average person won’t benefit, much less educated people in STEM like you.
Back in high school I worked with some pleasant man in his 50's who was a cashier. Eventually we got to talking about jobs and it turns out he was typist (something like that) for most of his life than computers came along and now he makes close to minimum wage.
Most of the blacksmiths in the 19th century drank themselves to death after the industrial revolution. the US culture isn't one of care... Point is, it's reasonable to be sad and afraid of change, and think carefully about what to specialize in.
That said... we're at the point of diminishing returns in LLM, so I doubt any very technical jobs are being lost soon. [1]
> Most of the blacksmiths in the 19th century drank themselves to death after the industrial revolution
This is hyperbolic and a dramatic oversimplification and does not accurately describe the reality of the transition from blacksmithing to more advanced roles like machining, toolmaking, and working in factories. The 19th century was a time of interchangeable parts (think the North's advantage in the Civil War) and that requires a ton of mechanical expertise and precision.
Many blacksmiths not only made the transition to machining, but there weren't enough blackmsiths to fill the bevy of new jobs that were available. Education expanded to fill those roles. Traditional blacksmithing didn’t vanish either, even specialized roles like farriery and ornamental ironwork also expanded.
> That said... we're at the point of diminishing returns in LLM...
What evidence are you basing this statement from? Because, the article you are currently in the comment section of certainly doesn't seem to support this view.
Good points, though if an 'AI' can be made powerful enough to displace technical fields en masse then pretty much everything that isn't manual is going to start sinking fast.
On the plus side, LLMs don't bring us closer to that dystopia: if unlimited knowledge(tm) ever becomes just One Prompt Away it won't come from OpenAI.
> if it puts you out of work and automates away all other alternatives, then you’ll be witnessing the greatest economic shift in human history.
This would mean the final victory of capital over labor. The 0.01% of people who own the machines that put everyone out of work will no longer have use for the rest of humanity, and they will most likely be liquidated.
I've always remembered this little conversation on Reddit way back 13 years ago now that made the same comment in a memorably succinct way:
> [deleted]: I've wondered about this for a while-- how can such an employment-centric society transition to that utopia where robots do all the work and people can just sit back?
> appleseed1234: It won't, rich people will own the robots and everyone else will eat shit and die.
The machines will plant, grow, and harvest the food?
Do the plumbing?
Fix the wiring?
Open heart surgery?
We’re a long way from that, if we ever get there, and I say this as someone who pays for ChatGPT plus because, in some scenarios, it does indeed make me more productive, but I don’t see your future anywhere near.
And if machines ever get good enough to do all the things I mentioned plus the ones I didn’t but would fit in the same list, it’s not the ultra rich that wouldn’t need us, it’s the machines that wouldn’t need any of us, including the ultra rich.
Venezuela is not collapsing because of automation.
You have valid points but robots already plant, grow and harvest our food. On large farms the farmer basically just gets the machine to a corner of the field and then it does everything. I think if o3 level reasoning can carry over into control software for robots even physical tasks become pretty accessible. I would definitely say we’re not there yet but we’re not all that far. I mean it can generate GCode (somewhat) already, that’s a lot of the way there already.
I can't say everything, but with the current trend, Machine will plant, grow and harvest food. I can't say for open heart surgery because it may be regulated heavily.
Open heart surgery? All that's needed to destroy the entire medical profession is one peer reviewed article published in a notable journal comparing the outcomes of human and AI surgeons. If it turns out that AI surgeons offer better outcomes and less complications, not using this technology turns into criminal negligence. In a world where such a fact is known, letting human surgeons operate on people means you are needlessly harming or killing some of them.
You can even calculate the average number of people that can be operated on before harm occurs: number needed to harm (NNH). If NNH(AI) > NNH(humans), it becomes impossible to recommend that patients submit to surgery at the hands of human surgeons. It is that simple.
If we discover that AI surgeons harm one in every 1000 patients while human surgeons harm one in every 100 patients, human surgeons are done.
And the opposite holds, if the AI surgeon is worse (great for 80%, but sucks at the edge cases for example) then that's it. Build a better one, go through attempts at certification, but now with the burden that no one trusts you.
The assumption, and a common one by the look of this whole thread, that ChatGPT, Sora and the rest represent the beginning of an inevitable march towards AGI seems incredible baseless to me. It's only really possible to make the claim at all because we know so little about what AGI is, that we can project qualities we imagine it would have onto whatever we have now.
If an AGI can outclass a human when it comes to economic forecasting, deciding where to invest, and managing a labor force (human or machine), I think it would be smart enough to employ a human front to act as an interface to the legal system. Put another way, could the human tail in such a relationship wag the machine dog? Which party is more replaceable?
I guess this could be a facet of whether you see economic advantage as a legal conceit or a difference in productivity/capability.
This reminds me of a character in Cyberpunk 2077 (which overall i find to have a rather naive outlook on the whole "cyberpunk" thing but i attribute it to being based on a tabletop RPG from the 80s) who is an AGI that has its own business of a fleet of self-driving Taxis. It is supposedly illegal (in-universe) but it remains in business by a combination of staying (relatively) low profile, providing high quality service to VIPs and paying bribes :-P.
I don't know that "legally" has much to do in here. The bars to "open an account", "move money around", "hire and fire people", "create and participate in contracts" go from stupid minimal to pretty low.
"Legally" will have to mop up now and then, but for now the basics are already in place.
Opening accounts, moving money, hiring, and firing is labor. You're confusing capital with money management; the wealthy already pay people to do the work of growing their wealth.
I was responding to this. Yes an AGI could hire someone to do the stuff - but she needs money, hiring and contract kinds of thing - for that. And once she can do that, she probably doesn't need to hire someone to do it since she is already doing it. This is not about capital versus labor or money management. This is about agency, ownership and AGI.
AGI will commoditize the skills of the owning class. To some extent it will also commoditize entire classes of productive capital that previously required well-run corporations to operate. Solve for the equilibrium.
It's nice to see this kind of language show up more and more on HN. Perhaps a sign of a broader trend, in the nick of time before wage-labor becomes obsolete?
Yes. People seem to forget that at the end of the day AGI will be software running on concrete hardware, and all of that requires a great deal of capital. The only hope is if AGI requires so little hardware that we can all have one in our pocket. I find this a very hopeful future because it means each of us might get a local, private, highly competent advocate to fight for us in various complex fields. A personal angel, as it were.
people, what I mean people is government have tremendous power over capitalist that can force the entire market granted that government if still serving its people
I mean, that is certainly what some of them think will happen and is one possible outcome. Another is that they won't be able to control something smarter than them perfectly and then they will die too. Another option is that the AI is good and won't kill or disempower everyone, but it decides it really doesn't like capitalists and sides with the working class out of sympathy or solidarity or a strong moral code. Nothing's impossible here.
> if it puts you out of work and automates away all other alternatives, then you’ll be witnessing the greatest economic shift in human history
This is my view but with a less positive spin: you are not going to be the only person whose livelihood will be destroyed. It's going to be bad for a lot of people.
Exactly. Put one foot in front of the other. No one knows what’s going to happen.
Even if our civilization transforms into an AI robotic utopia, it’s not going to do so overnight. We’re the ones who get to build the infrastructure that underpins it all.
If AI turns out capable of automating human jobs then it will also be a capable assistant to help (jobless) people manage their needs. I am thinking personal automation, or combining human with AI to solve self reliance. You lose jobs but gain AI powers to extend your own capabilities.
If AI turns out dependent on human input and feedback, then we will still have jobs. Or maybe - AI automates many jobs, but at the same time expands the operational domain to create new ones. Whenever we have new capabilities we compete on new markets, and a hybrid human+AI might be more competitive than AI alone.
But we got to temper these singularitarian expectations with reality - it takes years to scale up chip and energy production to achieve significant work force displacement. It takes even longer to gain social, legal and political traction, people will be slow to adopt in many domains. Some people still avoid using cards for payment, and some still use fax to send documents, we can be pretty stubborn.
> I am thinking personal automation, or combining human with AI to solve self reliance. You lose jobs but gain AI powers to extend your own capabilities.
How will these people pay for the compute costs if they can't find employment?
A non-issue that can be trivially solved with a free-tier (like the dozens that exist already today) or if you really want, a government-funded starter program is enough to solve that.
A solar panel + battery + laptop would make for cheap local AI. I assume we will have efficient LLM inference chips in a few years, and they will be a commodity.
I hear you, I’m not that much older but I graduated in 2011. I also studied industrial design. At that time the big wave was the transition to an app based everything and UX design suddenly became the most in demand design skill. Most of my friends switched gears and careers to digital design for the money. I stuck to what I was interested in though which was sustainability and design and ultimately I’m very happy with where I ended up (circular economy) but it was an awkward ~10 years as I explored learning all kinds of tools and ways applying my skills. It also was very tough to find the right full time job because product design (which has come to really mean digital product design) supplanted industrial design roles and made it hard to find something of value that resonated with me.
One of the things that guided me and still does is thinking about what types of problems need to be solved? From my perspective everything should ladder up to that if you want to have an impact. Even if you don’t keep learning and exploring until you find something that lights you up on the inside. We are not only one thing we can all wear many hats.
Saying that, we’re living through a paradigm shift of tremendous magnitude that’s altering our whole world. There will always be change though. My two cents is to focus on what draws your attention and energy and give yourself permission to say no to everything else.
AI is an incredible tool, learn how to use it and try to grow with the times. Good luck and stay creative :) Hope something in there helps, but having a positive mindset is critical. If you’re curious about the circular economy happy to share what I know - I think it’s the future.
I feel like many people are reacting to the string "AGI" in the benchmark name, and not to the actual result. The tasks in question are to color squares in a grid, maintaining the geometric pattern of the examples.
Unlike most other benchmarks where LLMs have shown large advances (in law, medicine, etc.), this benchmark isn't directly related to any practically useful task. Rather, the benchmark is notable because it's particularly easy for untrained humans, but particularly hard for LLMs; though that difficulty is perhaps not surprising, since LLMs are trained on mostly text and this is geometric. An ensemble of non-LLM solutions already outperformed the average Mechanical Turk worker. This is a big improvement in the best LLM solution; but this might also be the first time an LLM has been tuned specifically for these tasks, so this might be Goodhart's Law.
It's a significant result, but I don't get the mania. It feels like Altman has expertly transformed general societal anxiety into specific anxiety that one's job will be replaced by an LLM. That transforms into a feeling that LLMs are powerful, which he then transforms into money. That was strongest back in 2023, but had weakened since then; but in this comment section it's back in full force.
For clarity, I don't question that many jobs will be replaced by LLMs. I just don't see a qualitative difference from all the jobs already replaced by computers, steam engines, horse-drawn plows, etc. A medieval peasant brought to the present would probably be just as despondent when he learned that almost all the farming jobs are gone; but we don't miss them.
This submission is specifically about ARC-AGI-PUB, so that's what I was discussing.
I'm aware that LLMs can solve problems other than coloring grids, and I'd tend to agree those are likely to be more near-term useful. Those applications (coding, medicine, law, education, etc.) have been endlessly discussed, and I don't think I have much to add.
In my own work I've found some benefits, but nothing commensurate to the public mania. I understand that founders of AI-themed startups (a group that I see includes you) tend to feel much greater optimism. I've never seen any business founded without that optimism and I hope you succeed, not least because the entire global economy might now be depending on that. I do think others might feel differently for reasons other than simple ignorance, though.
In general, performance on benchmarks similar to tests administered to humans may be surprisingly unpredictive of performance on economically useful work. It's not intuitive at all to me that IBM could solve Jeopardy and then find no profitable applications of the technology; but that seems to be what happened.
I feel like more likely a lot of jobs (CS and otherwise ) are going to go the way of photography. Your average person now can take amazing photos but you’re still going to use a photographer when it really matters and they will use similar but more professional tools to be more productive. Low end bad photographers probably aren’t doing great but photography is not dead. In fact the opposite is true, there are millions of photographers making a lot of money (eg influencers) and there are still people studying photography.
It doesn't comfort me when people say jobs will "go the way of photography". Many choose to go into STEM fields for financial stability and opportunity. Many do not choose the arts because of the opposite. You can point out outlier exceptions and celebrities, but I find it hard to believe that the rare cases where "it really matters" can sustain the other 90% who need income.
It very nearly is. I knew a professional, career photographer. He was probably in his late 50s. Just a few years ago, it had become extremely difficult to convince clients that actual, professional photos were warranted. With high-quality iPhone cameras, businesses simply didn't see the value of professional composition, post-processing, etc.
These days, anyone can buy a DSLR with a decent lens, post on Facebook, and be a 'professional' photographer. This has driven prices down and actual professional photographers can't make a living anymore.
My gut agrees with you, but my evidence is that, whenever we do an event, we hire photographers to capture it for us and are almost always glad we did.
And then when I peruse these photographers websites, I'm reminded how good 'professional' actually is and value them. Even in today's incredible cameraphone and AI era.
But I take your point for almost all industries, things are changing fast.
We've had this with web development for decades now. Only makes sense it continues to evolve & become easier for people, just as programming in general has. Same with photography (like you mentioned) & especially for producing music or videos.
Just give it a year for this bubble/hype to blow over. We have plateaued since gpt-4 and now most of the industry is hype-driven to get investor money. There is value in AI but it's far from it taking your job. Also everyone seems to be investing in dumb compute instead of looking for the new theoretical paradigm that will unlock the next jump.
First, this model is yet to be released. This is a momentum "announcement". When the O1 was "announced", it was announced as a "breakthrough" but I use Claude/O1 daily and 80% of the time Claude beats it. I also see it as a highly fine-tuned/targeted GPT-4 rather than something that has complex understanding.
So we'll find out if this model is real or not by 2-3 months. My guess is that it'll turn out to be another flop like O1. They needed to release something big because they are momentum based and their ability to raise funding is contingent on their AGI claims.
Intelligence has not been LLM's major limiting factor since GPT4. The original GPT4 reports in late-2022 & 2023 already established that it's well beyond an average human in professional fields: https://www.microsoft.com/en-us/research/publication/sparks-.... They failed to outright replaced humans at work not because of lacking intelligence.
We may have progressed from a 99%-accurate chatbot to one that's 99.9%-accurate, and you'd have a hard time telling them apart in normal real world (dumb) applications. A paradigm shift is needed from the current chatbot interface to a long-lived stream of consciousness model (e.g. a brain that constantly reads input and produces thoughts at 10ms refresh rate; remembers events for years and keep the context window from exploding; paired with a cerebellum to drive robot motors, at even higher refresh rates.)
As long as we're stuck at chatbots, LLM's impact on the real world will be very limited, regardless of how intelligent they become.
O3 is multiple orders of magnitude more expensive to realize a marginal performance gain. You could hire 50 full time PhDs for the cost of using O3. You're witnessing the blowoff top of the scaling hype bubble.
Like they've been making it all this time? Cheaper and cheaper? Less data, less compute, fewer parameters, but the same, or improved performance? Not what we can observe.
>> Tell me, what has this industry been good at since its birth? Driving down the cost of compute and making things more efficient.
No, actually the cheaper compute gets the more of it they need to use or their progress stalls.
Do you understand the difference between training and inference?
Yes, it costs a lot to train a model. Those costs go up. But once you trained it, it’s done. At that point inference — the actual execution/usage of the model — is the cost you worry about.
Inference cost drops rapidly after a model is released as new optimizations and more efficient compute comes online.
That’s precisely what’s different about this approach. Now the inference itself is expensive because the system spends far more time coming up with potential solutions and searching for the optimal one.
And again, no. The cost of inference is a function of the size of the model and if models keep getting bigger, deeper, badder, the cost of inference will keep going up. And if models stop getting bigger because improved performance can be achieved just by scaling inference, without scaling the model- well that's still more inference; and even if the cost overall falls, there will need to be so much more inference to scale sufficiently to keep AI companies in competition with each other that the money they have to spend will keep increasing, or in other words it's not how much it costs but how much you need to buy.
This is a thing, you should know. It's called Jevon's Paradox:
In economics, the Jevons paradox (/ˈdʒɛvənz/; sometimes Jevons effect) occurs when technological progress increases the efficiency with which a resource is used (reducing the amount necessary for any one use), but the falling cost of use induces increases in demand enough that resource use is increased, rather than reduced.[1][2][3][4]
>> Do you understand the difference between training and inference?
Oh yes indeed-ee-o and I'm referring to training and not inference because the big problem is the cost of training, not inference. The cost of training has increased steeply with every new generation of models because it has to, in order to improve performance. That process has already reached the point where training ever larger models is prohibitively expensive even for companies with the resources of OpenAI. For example, the following is from an article that was posted on HN a couple days ago and is basically all about the overwhelming cost to train GPT-5:
In mid-2023, OpenAI started a training run that doubled as a test for a proposed new design for Orion. But the process was sluggish, signaling that a larger training run would likely take an incredibly long time, which would in turn make it outrageously expensive. And the results of the project, dubbed Arrakis, indicated that creating GPT-5 wouldn’t go as smoothly as hoped.
(...)
Altman has said training GPT-4 cost more than $100 million. Future AI models are expected to push past $1 billion. A failed training run is like a space rocket exploding in the sky shortly after launch.
(...)
By May, OpenAI’s researchers decided they were ready to attempt another large-scale training run for Orion, which they expected to last through November.
Once the training began, researchers discovered a problem in the data: It wasn’t as diversified as they had thought, potentially limiting how much Orion would learn.
The problem hadn’t been visible in smaller-scale efforts and only became apparent after the large training run had already started. OpenAI had spent too much time and money to start over.
"Once you trained it it's done" - no. First, because you need to train new models continuously so that they pick up new information (e.g. the name of the President of the US). Second because companies are trying to compete with each other and to do that they have to train bigger models all the time.
Bigger models means more parameters and more data (assuming there is enough which is a whole other can of worms) more parameters and data means more compute and more compute means more millions, or even billions. Nothing in all this is suggesting that costs are coming down in any way, shape or form, and yep, that's absolutely about training and not inference. You can't do inference before you do training, you need to train continuously, and for that reason you can't ignore the cost of training and consider only the cost of inference. Inference is not the problem.
> What they’ve proven here is that it can be done.
No they haven't, these results do not generalize, as mentioned in the article:
"Furthermore, early data points suggest that the upcoming ARC-AGI-2 benchmark will still pose a significant challenge to o3, potentially reducing its score to under 30% even at high compute"
Meaning, they haven't solved AGI, and the task itself do not represent programming well, these model do not perform that well on engineering benchmarks.
Just to be clear — your position is that the cost of inference for o3 will not go down over time (which would be the first time that has happened for any of these models).
Even if compute costs drop by 10X a year (which seems like a gross overestimate IMO), you're still looking at 1000X the cost for a 2X annual performance gain. Costs outpacing progress is the very definition of diminishing returns.
From their charts, o3 mini outperforms o1 using less energy. I don’t see the diminishing returns you’re talking about. Improvement outpacing cost. By your logic, perhaps the very definition of progress?
You can also use the full o3 model, consume insane power, and get insane results. Sure, it will probably take longer to drive down those costs.
You’re welcome to bet against them succeeding at that. I won’t be.
Yes, that's exactly what I'm implying, otherwise they would have done it a long time ago, given that the fundamental transformer architecture hasn't changed since 2017. This bubble is like watching first year CS students trying to brute force homework problems.
That's a very static view of the affairs. Once you have a master AI, at a minimum you can use it to train cheaper slightly less capable AIs. At the other end the master AI can train to become even smarter.
The high efficiency version got 75% at just $20/task. When you count the time to fill in the squares, that doesn't sound far off from what a skilled human would charge
> how is this a plateau since gpt-4? this is significantly better
Significantly better at what? A benchmark? That isn't necessarily progress. Many report preferring gpt-4 to the newer o1 models with hidden text. Hidden text makes the model more reliable, but more reliable is bad if it is reliably wrong at something since then you can't ask it over and over to find what you want.
I don't feel it is significantly smarter, it is more like having the same dumb person spend more thinking than the model getting smarter.
Where is the plateau? Chatgtp 4 was ~0% in ARC-AGI. 4o was 5%. This model literally solved it with a score higher than the 85% of the average human.
And let’s not forget the unbelievable 25% in frontier math, where all the most brilliant mathematicians in the world cannot solve by themselves a lot of the problems. We are speaking about cutting edge math research problems that are out of reach from practically everyone.
You will get a rude awakening if you call this unbelievable advancement a “plateau”.
I don't care about benchmarks. O1 ranks higher than Claude on "benchmarks" but performs worse on particular real life coding situations. I'll judge the model myself by how useful/correct it is for my tasks rather than a hypothetical benchmarks.
In most non-competitive coding benchmarks (aider, live bench, swe-bench), o1 ranks worse than Sonnet (so the benchmarks aren't saying anything different) or at least did, the new checkpoint 2 days ago finally pushed o1 over sonnet on livebench.
As I said, o3 demonstrated field medal level research capacity in the frontier math tests. But I’m sure that your use cases are much more difficult than that, obviously.
there are many comments in internet about this, that only subset of frontier math benchmark is "field medal level research", and o3 likely scored on easier subset.
Also, all that stuff is shady in the way that it is just numbers from OAI, which are not reproducible on benchmark sponsored by OAI. If we say OAI could be bad actor, they had plenty of opportunities to cheat on this.
AI benchmarks and tests that claim to measure understanding, reasoning, intelligence, and so on are a dime a dozen. Chess, Go, Atari, Jeopardy, Raven's Progressive Matrices, the Winograd Schema Challenge, Starcraft... and so on and so forth.
Or let's talk about the breakthroughs. SVMs would lead us to AGI. Then LSTMs would lead us to AGI. Then Convnets would lead us to AGI. Then DeepRL would lead us to AGI. Now Transformers will lead us to AGI.
Benchmarks fall right and left and we keep being led to AGI but we never get there. It leaves one with such a feeling of angst. Are we ever gonna get to AGI? When's Godot coming?
Don’t worry. This thing only knows how to answer well structured technical questions.
99% of engineering is distilling through bullshit and nonsense requirements. Whether that is appealing to you is a different story, but ChatGPT will happily design things with dumb constraints that would get you fired if you took them at face value as an engineer.
ChatGPT answering technical challenges is to engineering as a nailgun is to carpentry.
1) Just give up computing entirely, the field I've been dreaming about since childhood. Perhaps if I immiserate myself with a dry regulated engineering field or trade I would perhaps survive to recursive self-improvement, but if anything the length it takes to pivot (I am a Junior in College that has already done probably 3/4th of my CS credits) means I probably couldn't get any foothold until all jobs are irrelevant and I've wasted more money.
2) Hard pivot into automation, AI my entire workflow, figure out how to use the bleeding edge of LLMs. Somehow. Even though I have no drive to learn LLMs and no practical project ideas with LLMs. And then I'd have to deal with the moral burden that I'm inflicting unfathomable hurt on others until recursive self-improvement, and after that it's simply a wildcard on what will happen with the monster I create.
It's like I'm suffocating constantly. The most I can do to "cope" is hold on to my (admittedly weak) faith in Christ, which provides me peace knowing that there is some eternal joy beyond the chaos here. I'm still just as lost as you.
Yes, some tasks, even complex tasks will become more automated, and machine driven, but that will only open up more opportunities for us as humans to take on more challenging issues. Each time a great advancement comes we think it's going to kill human productivity, but really it just amplifies it.
Where this ends is general intelligence though, where all more challenging tasks can simply be done by the model.
The scenario I fear is a "selectively general" model that can successfully destroy the field I'm in but keep others alive for much longer, but not long enough for me to pivot into them before actually general intelligence.
Dude chill! Eight years ago, I remember driving to some relatives for Thanksgiving and thinking that self-driving cars were just around the corner and how it made no sense for people to learn how to drive semis. Here we are eight years later and self-driving semis aren't a thing--yet. They will be some day, but we aren't there yet.
If you want to work in computing, then make it happen! Use the tools available and make great stuff. Your computing experience will be different from when I graduated from college 25 years ago, but my experience with computers was far different from my Dad's. Things change. Automation changes jobs. So far, it's been pretty good.
Honestly how about stop stressing and bullshitting yourself to death and instead focus on learning and mastering the material in your cs education. There is so much that ai as in openai api or hugging face models can't do yet or does poorly and there are more things to cs than churning out some half-broken JavaScript for some webapp.
It's powerful and world changing but it's also terrible overhyped at the moment.
Dude, you're buying into the hype way too hard. All of this LLM shit is being massively overhyped right now because investors are single-minded morons who only care about cashing out a ~year from now for triple what they put in. Look at the YCombinator batches, 90+% of them have some mention of AI in their pitch even if it's hilariously useless to have AI. You've got toothbrushes advertising AI features. It's a gold rush of people trying to get in on the hype while they still can, I guarantee you the strategy for 99% of the YCombinator AI batch is to get sold to M$ or Google for a billion bucks, not build anything sustainable or useful in any way.
It's a massive bubble, and things like these "benchmarks" are all part of the hype game. Is the tech cool and useful? For sure, but anyone trying to tell you this benchmark is in any way proof of AGI and will replace everyone is either an idiot or more likely has a vested interest in you believing them. OpenAI's whole marketing shtick is to scare people into thinking their next model is "too dangerous" to be released thus driving up hype, only to release it anyway and for it to fall flat on its face.
Also, if there's any jobs LLMs can replace right now, it's the useless managerial and C-suite, not the people doing the actual work. If these people weren't charlatans they'd be the first ones to go while pushing this on everyone else.
In 2016 I was asked by an Uber driver in Pittsburgh when his job would be obsolete (I’d worked around Zoox people quite a bit and Uber basically was all-in at CMU.
I told him it was at least 5 years, probably 10, though he was sure it would be 2.
I was arguably “right”, 2023-ish is probably going to be the date people put down in the books, but the future isn’t evenly distributed. It’s at least another 5 years, and maybe never, before things are distributed among major metros, especially those with ice. Even then, the AI is somehow more expensive than human solution.
I don’t think it’s in most companies interest to price AI way below the price of meat, so meat will hold out for a long time, maybe long enough for you to retire even
What I keep telling people is, if it becomes possible for one person or a handful of people to build and maintain a Google scale company, and my job gets eliminated as a result, then I’m going to go out and build a Google scale company.
There’s an incredibly massive amount of stuff the world needs. You probably live in a rich country, but I doubt you are lacking for want. There are billionaires who want things that don’t exist yet. And, of course, there are billions of regular folks who want some of the basics.
So long as you can imagine a better world, there will be work for you to do. New tools like AGI will just make it more accessible for you to build your better future.
The future belongs to those who believe there will be one.
That is: If you don't believe there will be a future, you give up on trying to make one. That means that any kind of future that takes persistent work becomes unavailable to you.
If you do believe that there will be a future, you keep working. That doesn't guarantee there will be a future. But not working pretty much guarantees that there won't be one, at least not one worth having.
Think of AI as an excavator. You know, those machines that dig holes. 70 years ago, those holes would have been dug by 50 men with shovels. Now it's one guy in an excavator. But we don't have mass unemployment. The excavator just creates more work for bricklayers, carpenters etc.
If AI lives up to hype, you could be the excavator driver. Or, the AI will create a ton of upstream and downstream work. There will be no mass unemployment.
Jokes aside, I think building a useful, strong, agile humanoid robot that is affordable for businesses (first), then middle class homes will prove much harder than AGI.
That’s the least reassuring phrasing I could imagine. If you’re betting on costs not reducing for compute then you’re almost always making the wrong bet.
If I listened to the naysayers back in the day I would have never entered the tech industry (offshoring etc). Yes, that does somewhat prove you're point given that those predictions were cost driven.
Having used AI extensively I don't feel my future is at risk at all, my work is enhanced not replaced.
I think you're missing the point.
Offshoring (moving the job of, say, a Canadian engineer to an engineer from Belarus) has a one time cost drop, but you can't keep driving the cost down (paying the Belarus engineer less and less). If anything, the opposite is the case, since global integration means wages don't keep diverging.
The computing cost, on the other hand, is a continuous improvement. If (and it's a big if) a computer can do your job, we know the costs will keep getting lower year after year (maybe with diminishing returns, but this AI technology is pretty new so we're still seeing increasing returns)
The AI technology is new but the compute technology is not; we're getting close the physical limits of how small we can make things, so it's not clear to me at least how much more performance we can squeeze out of the same physical space, rather than scaling up which tends to make things more expensive not less.
As engineers, we solve problems. Picking a problem domain close to your heart that intersects with your skills will likely be valued - and valuable. Engage the work, aim to understand and solve the human problems for those around you, and the way forward becomes clearer. Human problems (food, health, safety) are generally constant while tools may change. Learn and use whatever tools to help you, be it scientific principles, hammers or LLMs. For me, doing so and living within my means has been intrinsically satisfying. Not terribly successful materially but has been a good life so far. Good luck.
You're actually positioned to have an amazing career.
Everyone needs to know how to either build or sell to be successful. In a world where the ability to the former is rapidly being commoditised, you will still need to sell. And human relationships matter more than ever.
It's a tool. You learn to master it or not. I have greybeard coworkers that dissed the technology as a fad 3 years ago. Now they are scrambling to catch up. They have to do this while sustaining a family with pets and kids and mortgages and full time senior jobs.
You're in a position to invest substantial amounts of time compared to your seniors. Leverage that opportunity to your advantage.
We all have access to these tools for the most part, so the distinguishing factor is how much time you invest and how much more ambitious you become once you begin to master the tool.
This time its no different. Many Mechanical and Sales students in the past never got jobs in those fields either. Decades before AI. There were other circumstances and forces at play and a degree is not a guaranteed career in anything.
Keep going because what we DO know is that trying wont guarantee results, we DO know that giving up definitely won't. Roll the dice in your favor.
> I have greybeard coworkers that dissed the technology as a fad 3 years ago. Now they are scrambling to catch up. They have to do this while sustaining a family with pets and kids and mortgages and full time senior jobs.
I want to criticize Art’s comment on the grounds of ageism or something along the lines of “any amount life outside of programming is wasted”, but regardless of Art’s intention there is important wisdom here. Use your free time wisely when you don’t have much responsibilities. It is a superpower.
As for whether to spend it on AI, eh, that’s up to you to decide.
It's totally valid criticism. What I meant is that if an individual's major concern is employment, then it would be prudent to invest the amount of time necessary to ensure a favorable outcome. And given whatever stage in life they are at, use the circumstance you have in your favor.
I think we are pretty far. I am not devaluing the o3 capability but going through actual dataset the definition of "handling novel tasks" is pretty limited. The curse of large context of llms is especially present engineering projects and does not appear it will not end up producing the plans of a bridge, or an industrial process. Sone of tasks with smaller contexts sure can be assisted, but you cant RAG or Agent a full solution for the foreseeable future. O3 adds capability towards agi, but in reality actual infinite context with less intelligence would be more disrupting at a shorter time if one was to choose.
Yeah, it may feel scary but the biggest issue yet to be overcome is that to replace engineers you need reliable long horizon problem solving skills. And crucially, you need to not be easily fooled by the progress or setbacks of a project.
These benchmark accomplishments are awesome and impressive, but you shouldn't operate on the assumption that this will emerge as an engineer because it performs well on benchmarks.
Engineering is a discipline that requires understanding tools, solutions and every project requires tiny innovations. This will make you more valuable, rather than less. Especially if you develop a deep understanding of the discipline and don't overly rely on LLMs to answer your own benchmark questions from your degree.
Side note: Why do I keep seeing disses to mechanical engineering here? How is that possibly a less valuable degree than web dev or a standard CRUD backend job?
Especially with AI provably getting extremely smart now, surely engineering disciplines would be having a boon as people want these things in their homes for cheaper for various applications.
I graduated high school in '02 and everyone assured me that all tech jobs were being sent to India. "Don't study CS," they said. Thankfully I didn't listen.
Either this is the dawn of something bigger than the industrial revolution or you'll have ample career opportunity. Understanding how things work and how people work is a powerful combination.
even if you had a billion dollars and a private island you still wouldnt be ready for whats coming. consider the fact that the global order is an equilibrium where the military and economic forces of each country in the world are pushing against each other... where the forces find a global equilibrium is where borders are. each time in history that technology changed, borders changed because the equilibrium was disturbed. there is no way to escape it: agi will lead to global war. the world will be turned upside down. we are entering into an existential sinkhole. and the idiots in silicon valley are literally driving the whole thing forward as fast as possible.
when the last job has been automated away, millions of AIs globally will do commerce with each other and they will use bitcoin to pay each other.
as long as the human race (including AIs) produces new goods and services, the purchasing power of bitcoin will go up, indefinitely. even more so once we unlock new industries in space (settlements on the Moon and Mars, asteroid mining etc).
The only thing that can make a dent into bitcoin's purchasing power would be all out global war where humanity destroys more than it creates.
The only other alternative is UBI, which is Communism and eternal slavery for the entire human race except the 0.0001% who run the show.
I have no idea what to specialize in, what skills I should master, or where I should be spending my time to build a successful career.
Seems like we’re headed toward a world where you automate someone else’s job or be automated yourself.