Engine efficiency, chess rating, AI cap ex. One example is not like the other. Is there steady progress in AI? To me it feels like it’s little progress followed by the occasional breakthrough but I might be totally off here.
I think it's a cool perspective, but the not-so-hidden assumption is that for any given domain, the efficiency asymptote peaks well above the alternative.
And that really is the entire question at this point: Which domains will AI win in by a sufficient margin to be worth it?
This is a fun piece... but what killed off the horses wasn't steady incremental progress in steam engine efficiency, it was the invention of the internal combustion engine.
Sure, but if you look at more complex picture of engine development you could just as easily support the proposition that programmers are currently not in any danger (by pointing out that the qualitative differences between IC and steam engines were decisive when it comes to replacing horses, and the correct analogy is that much like a steam engine could never replace a horse, a transformer model can never replace a human).
Not detracting from the article, I think it's a fun way to shake your brain into the entirely appropriate space of "rapid change is possible"!
People are not simple machines or animals. Unless AI becomes strictly better than humans and humans + AI, from the perspective of other humans, at all activities, there will still be lots of things for humans to do to provide value for each other.
The question is how do our individuals, and more importantly our various social and economic systems handle it when exactly what humans can do to provide value for each other shifts rapidly, and balances of power shift rapidly.
If the benefits of AI accrue to/are captured by a very small number of people, and the costs are widely dispersed things can go very badly without strong societies that are able to mitigate the downsides and spread the upsides.
Banks used to have rooms full of bank clerks who manually did double-entry bookkeeping for all the bank's transactions. For most people, this was a very boring job, and it made bank transactions slow and expensive. In the 50's and 60's we replaced all these people with computers. An entire career of "bank clerk" vanished, and it was a net good for humanity. The cost of bank transactions came down (by a lot!), banks became more responsive and served their customers better. And the people who had to do double-entry bookkeeping all day long got to do other, probably more interesting, jobs.
There are a ton of current careers that are just email + meetings + powerpoint + spreadsheet that can go the same way. They're boring jobs (for most people doing them) and having humans do them makes administration slow and expensive. Automating them will be a net good for humanity. Imagine if "this meeting could have been an email" actually moves to "this meeting never happened at all because the person making the decision just told the LLM and it did it".
You are right that the danger is that most of the benefits of this automation will accrue to capital, but this didn't happen with the bank clerk automation - bank customers accrued a lot of the benefits too. I suspect the same will be true with this automation - if we can create and scale organisations easier and cheaper without employing all the admin staff that we currently do, then maybe we create more agile, responsive, organisations that serve their customers better.
"In 1920, there were 25 million horses in the United States, 25 million horses totally ambivalent to two hundred years of progress in mechanical engines.
And not very long after, 93 per cent of those horses had disappeared.
I very much hope we'll get the two decades that horses did."
I'm reminded of the idiom "be careful what you wish for, as you might just get it." Rapid technogical change has historically lead to prosperity over the long term but not in the short term. My fear is that the pace of change this time around is so rapid that the short term destruction will not be something that can be recovered from even over the longer term.
Wait till the robots arrive. That they will know how to do a vast range of human skills, some that people train their whole lives for, will surprise people the most. The future shock I get from Claude Code, knowing how long stuff takes the hard way, especially niche difficult to research topics like the alternate applicable designs of deep learning models to a modeling task, is a thing of wonder. Imagine now that a master marble carver shows up at an exhibition and some sci-fi author just had robots make a perfect beautiful equivalent of a character from his novel, equivalent in quality to Michaelangelo's David, but cyberpunk.
This tracks with my own AI usage over just this year. There have been two releases that caused step changes in how much I actually use AI:
1. The release of Claude Code in February
2. The release of Opus 4.5 two weeks ago
In both of these cases, it felt like no big new unlocks were made. These releases aren’t like OpenAI’s o1, where they introduced reasoning models with entirely new capabilities, or their Pro offerings, which still feel like the smartest chatbots in the world to me.
Instead, these releases just brought a new user interface, and improved reliability. And yet these two releases mark the biggest increases in my AI usage. These releases caused the utility of AI for my work to pass thresholds where Claude Code became my default way to get LLMs to read my code, and then Opus 4.5 became my default way to make code changes.
It's astounding how subtly anti-AI HN has become over the past year, as the models keep getting better and better. It's now pervasive across nearly every AI thread here.
As the potential of AI technical agents has gone from an interesting discussion to extraordinarily obvious as to what the outcome is going to be, HN has comically shifted negative in tone on AI. They doth protest too much.
I think it's a very clear case of personal bias. The machines are rapidly coming for the lucrative software jobs. So those with an interest in protecting lucrative tech jobs are talking their book. The hollowing out of Silicon Valley is imminent, as other industrial areas before it. Maybe 10% of the existing software development jobs will remain. There's no time to form powerful unions to stop what's happening, it's already far too late.
I’m not anti-AI; I use it every day. But I also think all this hand-wringing is overblown and unbalanced. LLMs, because of what they are, will never replace a thoughtful engineer. If you’re writing code for a living at the level of an LLM then your job was probably already expendable before LLMs showed up.
I worked for a company that was starting to shove AI incentives down the throat of every engineer as our product got consistently worse and worse due to layoffs and the perceived benefits of AI which were never realized. When you look at the companies that have shifted to 'AI first' and see them shoveling out garbage that barely works, it should be no surprised that people both aware of how the sausage is made and not are starting to hate it.
It's not merely cost per word, but it is even more bizarre: "cost per word thought", whatever that is. Most of these "word thoughts" from LLMs of today are just auto-completed large dumps of text.
There was a time when these models were novel that if use it to write for me. After a year or so the verboseness and lack of personality got old. Now all I have is a decent proofreader. Maybe they'll take over my job but I'm finding the trend going the other way right now.
funny how we have all of this progress yet things that actually matter (sorry chess fans) in the real world are more expensive: health care, housing, cars. and what meager gains there are seem to be more and more concentrated in a smaller group of people.
plenty of charts you can look at - net productivity by virtually any metric vs real adjusted income. the example I like are kiosks and self checkout. who has encountered one at a place where it is cheaper than its main rival and is directly attributable to (by the company or otherwise) to lower prices?? in my view all it did was remove some jobs. that's the preview. that's it. you will lose jobs and you will pay more. congrats.
even with year 2020 tech you could automate most work that needs to be done, if our industry wouldn't endlessly keep disrupting itself and have a little bit of discipline.
so once ai destroys desk jobs and the creative jobs, then what? chill out? too bad anyone who has a house won't let more be built.
Housing is a funny old one and speaks to it being a human problem. One thing a lot of people dont truly engage with with the housing issue is that its a massive issue of distribution. Too many people want to live in too few places. Yes, central banks & interest rates (being too low and also now being relatively too high), nimbyism, and rent seeking play an important role too but solving the "too many people live in too few places" issue actually fixes that problem (slowly, and possibly unpalatably slow for some, but a fix nonetheless)
The key issue upstream is that too many good jobs are concentrated in too few places, and that leads to consumerism stimulating those places and making them further more attractive. Technology, through Covid, actually gave governments a get out of jail free card by allowing remote work to become more mainstream. Only to just not grasp the golden egg they were given. Pivot economies more to remote working more actively helps distribute people to other places with more affordable home. Over time, and again slowly, those places become more attractive because people now actually live there.
Existing homeowners can still wrap themselves in the warm glow of their high house prices which only loses "real" value through inflation which people tend not to notice as much.
But we decided to try to go back to the status quo so oh well
Food and clothes are much cheaper. People used to have to walk or hitchhike a lot more. People died younger, or were trapped with abusive spouses and/or parents. Crime was high. There was little economic mobility. It really sucked if you weren’t a straight white man. Houses had one bathroom. Power went out regularly. Travel was rare and expensive; people rarely flew anywhere. There was limited entertainment or opportunities to learn about the world.
It's inflation, simple as that. The US left the gold standard at the exact same time that productivity diverged from wages. Coincidence? No.
Pretty much everything gets more expensive, with the outliers being tech which has gotten much cheaper, mostly because the rate at which it progresses is faster than the rate at which governments can print money. But everything we need to survive, like food, housing, etc, keeps getting more expensive. And the asset class get richer as a result.
> Back then, me and other old-timers were answering about 4,000 new-hire questions a month.
> Then in December, Claude finally got good enough to answer some of those questions for us.
> … Six months later, 80% of the questions I'd been being asked had disappeared.
Interesting implications for how to train juniors in a remote company, or in general:
> We find that sitting
near teammates increases coding feedback by 18.3% and improves code quality. Gains
are concentrated among less-tenured and younger employees, who are building human capital. However, there is a tradeoff: experienced engineers write less code when
sitting near colleagues.
How about we stop trying the analogy clothing on and just tell it like it is? AI is unlike any other technology to date. Just like predicting the weather, we don't know what it will be like in 20 months. Everything is a guesstimate.
Against that you have the Moore's law like predictions that AI would be getting to around human levels around now from Moravec and the like that have proved fairly spot on. I think you may find it's more like the AI chess ranking graph than the weather.
Probably the point is to think whether the horse or chess engine analogy is a good one. The premise being there will come a certain point when technology reaches a level that makes the alternative obselete suddenly. I don't have good reasons to think that AI will not be able to automate simple jobs with an acceptable error rate eventually, once that happens whole categories of jobs will evaporate. Probably dealing with more people type job, making excel models, transactions based, same thing day in day out, those teams may be gone and only a person or two to do a final review
I think that you're on to something here, though I agree more with your first sentence than the second.
AI is not identical to, as the article compares, mechanical power.
But your weather-forecasting comment suggests a possible similarity (though not the one you go to): for all the millions-fold increase in compute power, and the increased density and specificity of meterological measurements, our accurate weather-forecasting window has only extended by a factor or so of two (roughly five days to ten). That is, there are applications for which vastly more information-processing capacity provides fairly modest returns.
And there are also those in which it's transformative. I'd put reusable rockets in that category, where we can now put sufficiently-reliable compute (and a whole bunch of rocket-related hardware) on a boost-phase rocket such that it can successfully soft-land.
For some years I've been thinking of the notion of technology not as some general principle ("efficiency" is the classic economics formulation), but as a set of specific mechanisms each of which has specific capabilities and limitations.[1] I've held pretty constant with nine of these:
1. Fuels. Applying more (or more useful) energy to a process.
2. Energy transmission and transformation.
3. Materials. Specific properties, abundance, costs, effects, limitations.
4. Process knowledge --- how to do things. What's generally described as "technical knowledge", here considered as a specific mechanism of technology.
5. Structural or causal knowledge --- why things work. What's generally described as "scientific knowledge".
6. Networks. Interactions between nodes via links, physical or virtual, over which matter, energy, information, or some mix flow. Transport, comms, power, information.
7. Systems. Constructs including sensing, processing, action, and feedback. Ranging from conceptual to mechanical to human and social.
8. Information. Sensing, perceiving, processing, storing, retrieving, and transmitting. Ranging from our natural senses to augmented ones, from symbolic systems (language, maths) to algorithms.
9. Hygiene. Sinks and unintended consequences, affecting the function and vitality of systems, and their mitigations or limits.
AI / AGI falls into the 8th category: information, specifically information processing. And as such, getting back to my original point, we can compare it with other information-related technological innovations: speech, writing, maths, boolean logic, switches (valves, transistors, etc.), information storage/retrieval, etc. And, yes, human thought processes. We do have some priors we can look at here, and they might help guide us in what a true AGI might be able to accomplish, and what its limitations may be.
It's often noted (including in this thread) that AGI would not presently be able to persist without copious human assistance, in that it's predicated on a vast technological infrastructure only a small portion of which it would be capable of substituting for. It's quite likely that AGI would be both competitive with and complementary to much human activity. In the horse analogy, it's worth noting that the first stage of mechanised transport development, with steam shipping and rail technology, horses were strongly complementary in that they fulfilled the last-mile delivery role which steamships and locomotives couldn't furnish. Horse drayage populations actually boomed during this period. It was development of ICE-powered lorries which finally out-competed the horse-drawn cart for intra-urban delivery. AGI-as-augmenting-humans is an already highly-utilised model, and will likely persist for some time. Experiments in AGI replacing humans will no doubt occur, some successful, others not. I'd suggest that my 9th category, hygiene, and specifically failure modes of AGI...
> And not very long after, 93 per cent of those horses had disappeared.
> I very much hope we'll get the two decades that horses did.
> But looking at how fast Claude is automating my job, I think we're getting a lot less.
This "our company is onto the discovery that will put you all out of work (or kill you?)" rhetoric makes me angry.
Something this powerful and disruptive (if it is such) doesn't need to be owned or controlled by a handful of companies. It makes me hope the Chinese and their open source models ultimately win.
I've seen Anthropic and OpenAI employees leaning into this rhetoric on an almost daily basis since 2023. Less so OpenAI lately, but you see it all the time from these folks. Even the top leadership.
Meanwhile Google, apart from perhaps Kilpatrick, is just silent.
Ironically, you could use the sigmoid function instead of horses. The training stimulus slowly builds over multiple iteration and then suddenly, flip: the wrong prediction reverses.
An engine performs a simple mechanical operation. Chess is a closed domain. An AI that could fully automate the job of these new hires, rather than doing RAG over a knowledge base to help onboard them, would have to be far more general than either an engine or a chessbot. This generality used to be foregrounded by the term "AGI." But six months to a year ago when the rate of change in LLMs slowed down, and those exciting exponentials started to look more like plateauing S-curves, executives conveniently stopped using the term "AGI," preferring weasel-words like "transformative AI" instead.
I'm still waiting for something that can learn and adapt itself to new tasks as well as humans can, and something that can reason symbolically about novel domains as well as we can. I've seen about enough from LLMs, and I agree with the critique that som type of breakthrough neuro-symbolic reasoning architecture will be needed. The article is right about one thing: in that moment AI will overtake us suddenly! But I doubt we will make linear progress toward that goal. It could happen in one year, five, ten, fifty, or never. In 2023 I was deeply concerned about being made obsolete by AI, but now I sleep pretty soundly knowing the status quo will more or less continue until Judgment Day, which I can't influence anyway.
I think a lot about how much we altered our environment to suit cars. They're not a perfect solution to transport, but they've been so useful we've built tons more road to accommodate them.
So, while I don't think AGI will happen any time soon, I wonder what 'roads' we'll build to squeeze the most out of our current AI. Probably tons of power generation.
"Tons of power generation?" Perhaps we will go in that direction (as OpenAI projects), but it assumes the juice will be worth the squeeze, i.e., that scaling laws requiring much more power for LLM training and/or inference will deliver a qualitatively better product before they run out. The failure of GPT 4.5, while not a definitive end to scaling, was a pretty discouraging sign.
> An engine performs a simple mechanical operation
It only appears “simple” because you're used to see working engines everywhere without never having to maintain them, but neither the previous generations nor the engineers working on modern engines would agree with you on that.
An engine performs “a simple mechanical operation” the same way an LLM performs a “simple computation”.
I mean it's hard to argue that if we invented a human in a box (AGI) human work would be irrelevent. But I don't know how we could watch current AI and anyone can say we have that.
The big thing this AI boom has showed us that we can all be thankful to have seen is what a human in a box will eventually look like. The first generation of humans to be able to see that is a super lucky experience to have.
Maybe it's one massive breakthrough away or maybe it's dozens away. But there is no way to predict when some massive breakthrough will occur Illya said 5-20 that really just means we don't know.
Why a human in a box and not an android? A lot of jobs will require advanced robotics to fully automate. And then there are jobs where customer preference is for human interaction or human entertainment. It's like how superior chess engines have not reduced the profession of chess grandmasters, because people remain more interested in human chess competition.
126 comments
[ 2.9 ms ] story [ 89.9 ms ] threadAnd that really is the entire question at this point: Which domains will AI win in by a sufficient margin to be worth it?
[0] https://pmc.ncbi.nlm.nih.gov/articles/PMC7023172/
Not detracting from the article, I think it's a fun way to shake your brain into the entirely appropriate space of "rapid change is possible"!
The question is how do our individuals, and more importantly our various social and economic systems handle it when exactly what humans can do to provide value for each other shifts rapidly, and balances of power shift rapidly.
If the benefits of AI accrue to/are captured by a very small number of people, and the costs are widely dispersed things can go very badly without strong societies that are able to mitigate the downsides and spread the upsides.
Banks used to have rooms full of bank clerks who manually did double-entry bookkeeping for all the bank's transactions. For most people, this was a very boring job, and it made bank transactions slow and expensive. In the 50's and 60's we replaced all these people with computers. An entire career of "bank clerk" vanished, and it was a net good for humanity. The cost of bank transactions came down (by a lot!), banks became more responsive and served their customers better. And the people who had to do double-entry bookkeeping all day long got to do other, probably more interesting, jobs.
There are a ton of current careers that are just email + meetings + powerpoint + spreadsheet that can go the same way. They're boring jobs (for most people doing them) and having humans do them makes administration slow and expensive. Automating them will be a net good for humanity. Imagine if "this meeting could have been an email" actually moves to "this meeting never happened at all because the person making the decision just told the LLM and it did it".
You are right that the danger is that most of the benefits of this automation will accrue to capital, but this didn't happen with the bank clerk automation - bank customers accrued a lot of the benefits too. I suspect the same will be true with this automation - if we can create and scale organisations easier and cheaper without employing all the admin staff that we currently do, then maybe we create more agile, responsive, organisations that serve their customers better.
And not very long after, 93 per cent of those horses had disappeared.
I very much hope we'll get the two decades that horses did."
I'm reminded of the idiom "be careful what you wish for, as you might just get it." Rapid technogical change has historically lead to prosperity over the long term but not in the short term. My fear is that the pace of change this time around is so rapid that the short term destruction will not be something that can be recovered from even over the longer term.
1. The release of Claude Code in February
2. The release of Opus 4.5 two weeks ago
In both of these cases, it felt like no big new unlocks were made. These releases aren’t like OpenAI’s o1, where they introduced reasoning models with entirely new capabilities, or their Pro offerings, which still feel like the smartest chatbots in the world to me.
Instead, these releases just brought a new user interface, and improved reliability. And yet these two releases mark the biggest increases in my AI usage. These releases caused the utility of AI for my work to pass thresholds where Claude Code became my default way to get LLMs to read my code, and then Opus 4.5 became my default way to make code changes.
As the potential of AI technical agents has gone from an interesting discussion to extraordinarily obvious as to what the outcome is going to be, HN has comically shifted negative in tone on AI. They doth protest too much.
I think it's a very clear case of personal bias. The machines are rapidly coming for the lucrative software jobs. So those with an interest in protecting lucrative tech jobs are talking their book. The hollowing out of Silicon Valley is imminent, as other industrial areas before it. Maybe 10% of the existing software development jobs will remain. There's no time to form powerful unions to stop what's happening, it's already far too late.
plenty of charts you can look at - net productivity by virtually any metric vs real adjusted income. the example I like are kiosks and self checkout. who has encountered one at a place where it is cheaper than its main rival and is directly attributable to (by the company or otherwise) to lower prices?? in my view all it did was remove some jobs. that's the preview. that's it. you will lose jobs and you will pay more. congrats.
even with year 2020 tech you could automate most work that needs to be done, if our industry wouldn't endlessly keep disrupting itself and have a little bit of discipline.
so once ai destroys desk jobs and the creative jobs, then what? chill out? too bad anyone who has a house won't let more be built.
The key issue upstream is that too many good jobs are concentrated in too few places, and that leads to consumerism stimulating those places and making them further more attractive. Technology, through Covid, actually gave governments a get out of jail free card by allowing remote work to become more mainstream. Only to just not grasp the golden egg they were given. Pivot economies more to remote working more actively helps distribute people to other places with more affordable home. Over time, and again slowly, those places become more attractive because people now actually live there.
Existing homeowners can still wrap themselves in the warm glow of their high house prices which only loses "real" value through inflation which people tend not to notice as much.
But we decided to try to go back to the status quo so oh well
Pretty much everything gets more expensive, with the outliers being tech which has gotten much cheaper, mostly because the rate at which it progresses is faster than the rate at which governments can print money. But everything we need to survive, like food, housing, etc, keeps getting more expensive. And the asset class get richer as a result.
> Then in December, Claude finally got good enough to answer some of those questions for us.
> … Six months later, 80% of the questions I'd been being asked had disappeared.
Interesting implications for how to train juniors in a remote company, or in general:
> We find that sitting near teammates increases coding feedback by 18.3% and improves code quality. Gains are concentrated among less-tenured and younger employees, who are building human capital. However, there is a tradeoff: experienced engineers write less code when sitting near colleagues.
https://pallais.scholars.harvard.edu/sites/g/files/omnuum592...
AI is not identical to, as the article compares, mechanical power.
But your weather-forecasting comment suggests a possible similarity (though not the one you go to): for all the millions-fold increase in compute power, and the increased density and specificity of meterological measurements, our accurate weather-forecasting window has only extended by a factor or so of two (roughly five days to ten). That is, there are applications for which vastly more information-processing capacity provides fairly modest returns.
And there are also those in which it's transformative. I'd put reusable rockets in that category, where we can now put sufficiently-reliable compute (and a whole bunch of rocket-related hardware) on a boost-phase rocket such that it can successfully soft-land.
For some years I've been thinking of the notion of technology not as some general principle ("efficiency" is the classic economics formulation), but as a set of specific mechanisms each of which has specific capabilities and limitations.[1] I've held pretty constant with nine of these:
1. Fuels. Applying more (or more useful) energy to a process.
2. Energy transmission and transformation.
3. Materials. Specific properties, abundance, costs, effects, limitations.
4. Process knowledge --- how to do things. What's generally described as "technical knowledge", here considered as a specific mechanism of technology.
5. Structural or causal knowledge --- why things work. What's generally described as "scientific knowledge".
6. Networks. Interactions between nodes via links, physical or virtual, over which matter, energy, information, or some mix flow. Transport, comms, power, information.
7. Systems. Constructs including sensing, processing, action, and feedback. Ranging from conceptual to mechanical to human and social.
8. Information. Sensing, perceiving, processing, storing, retrieving, and transmitting. Ranging from our natural senses to augmented ones, from symbolic systems (language, maths) to algorithms.
9. Hygiene. Sinks and unintended consequences, affecting the function and vitality of systems, and their mitigations or limits.
AI / AGI falls into the 8th category: information, specifically information processing. And as such, getting back to my original point, we can compare it with other information-related technological innovations: speech, writing, maths, boolean logic, switches (valves, transistors, etc.), information storage/retrieval, etc. And, yes, human thought processes. We do have some priors we can look at here, and they might help guide us in what a true AGI might be able to accomplish, and what its limitations may be.
It's often noted (including in this thread) that AGI would not presently be able to persist without copious human assistance, in that it's predicated on a vast technological infrastructure only a small portion of which it would be capable of substituting for. It's quite likely that AGI would be both competitive with and complementary to much human activity. In the horse analogy, it's worth noting that the first stage of mechanised transport development, with steam shipping and rail technology, horses were strongly complementary in that they fulfilled the last-mile delivery role which steamships and locomotives couldn't furnish. Horse drayage populations actually boomed during this period. It was development of ICE-powered lorries which finally out-competed the horse-drawn cart for intra-urban delivery. AGI-as-augmenting-humans is an already highly-utilised model, and will likely persist for some time. Experiments in AGI replacing humans will no doubt occur, some successful, others not. I'd suggest that my 9th category, hygiene, and specifically failure modes of AGI...
> I very much hope we'll get the two decades that horses did.
> But looking at how fast Claude is automating my job, I think we're getting a lot less.
This "our company is onto the discovery that will put you all out of work (or kill you?)" rhetoric makes me angry.
Something this powerful and disruptive (if it is such) doesn't need to be owned or controlled by a handful of companies. It makes me hope the Chinese and their open source models ultimately win.
I've seen Anthropic and OpenAI employees leaning into this rhetoric on an almost daily basis since 2023. Less so OpenAI lately, but you see it all the time from these folks. Even the top leadership.
Meanwhile Google, apart from perhaps Kilpatrick, is just silent.
I'm still waiting for something that can learn and adapt itself to new tasks as well as humans can, and something that can reason symbolically about novel domains as well as we can. I've seen about enough from LLMs, and I agree with the critique that som type of breakthrough neuro-symbolic reasoning architecture will be needed. The article is right about one thing: in that moment AI will overtake us suddenly! But I doubt we will make linear progress toward that goal. It could happen in one year, five, ten, fifty, or never. In 2023 I was deeply concerned about being made obsolete by AI, but now I sleep pretty soundly knowing the status quo will more or less continue until Judgment Day, which I can't influence anyway.
So, while I don't think AGI will happen any time soon, I wonder what 'roads' we'll build to squeeze the most out of our current AI. Probably tons of power generation.
Remember when "AGI" was the weasel word because 1980s AI kept on not delivering?
That's highly irrelevant because if it were otherwise, we would already be replaced. The article was talking about the future.
It only appears “simple” because you're used to see working engines everywhere without never having to maintain them, but neither the previous generations nor the engineers working on modern engines would agree with you on that.
An engine performs “a simple mechanical operation” the same way an LLM performs a “simple computation”.
The big thing this AI boom has showed us that we can all be thankful to have seen is what a human in a box will eventually look like. The first generation of humans to be able to see that is a super lucky experience to have.
Maybe it's one massive breakthrough away or maybe it's dozens away. But there is no way to predict when some massive breakthrough will occur Illya said 5-20 that really just means we don't know.
https://www.folklore.org/Negative_2000_Lines_Of_Code.html
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