>My challenge ahead is to move from making a living by thinking, to making thinking part of a life worth living.
Our challenge ahead is making a living at all. Who is going to be having hobbies if the economy is 100% automated? We're not going to sleepwalk our way into a post-scarcity automated economy where everyone's needs are provided for and they can just spend all their time on hobbies. If we believe AI can replace all cognitive labor, then it follows that it'll be able to replace all physical labor with robotics fairly quickly, and we're left with no labor left for humans. We need to plan for that future or else billions will suffer from the results.
I like to imagine walking into colonial Jamestown and trying to explain to them
"it'll be able to replace all physical labor with robotics fairly quickly, and we're left with no labor left for humans. We need to plan for that future or else billions will suffer from the results."
"And it and those robots will be owned by people who don't care if you starve or freeze to death."
And unlike those in Jamestown, who could just ignore the robots and continue working the land to survive, we will be forced to allow the robots to take over our roles.
Enclosure was well underway in England, so they probably understood the dilemma of modernization simultaneously increasing total productivity and lowering the living standards for a large population.
From my understanding of how LLMs work, I'd argue thinking will become the most valuable attribute of humans (as it has been for some time). LLMs will inevitably surpass humans at functions that we know how to do, and likely through cross-domain knowledge and connecting disparate concepts will surpass us at many functions. But LLMs haven't shown they are capable of truly novel thinking, because it's next token prediction, which is inherently based on existing sources.
I think we'll see a shift towards arts, creativity, research, etc. where true novel creation and an understanding of the human experience exists at a premium.
I agree that’s what happened to hunting and riding (they became hobbies). But it’s hard to imagine something as broad as thinking will go the same way. Not saying it won’t, just that it’s hard to imagine and that the implications will be far more far reaching.
"I hate this damn machine"
"I wish they would just sell"
"It never does what I want"
"Only what I tell it."
I've yet to be convinced we will evolve these systems to a point where we can just click the "Magic Button" thoughtlessly and get results we are satisfied with.
We may have reduced the loss of data between thought and action, but nothing suggests these systems will be able to intuit our every want and desire.
If you have responsibility for things, you want to make the best choices. At some level, this involves thinking. It is responsibility that should not be delegated to AI. The thinking required of the responsible human changes, but is not eliminated.
Such a wild take to argue that current tier LLMs do any “thinking” at all. Was chatting with a friend last week and I was saying that sure AI writes much of my code these days but my cognitive load is HIGHER, as I have to constantly make sure it’s not doing something dumb.
I have to constantly make sure it’s building things in a way that will fit neatly together, in a way doesn’t over (or under) -generalize. In a way that doesn’t waste time and energy on idiotic tests that aren’t really testing anything at all.
LLMs are powerful autocomplete with vast and broad memory, they do not think. And thinking is just a part of the human condition.
> LLMs are powerful autocomplete with vast and broad memory, they do not think.
This is really only true insofar as they are designed such that they output language tokens and their intermediate "chain of thought" is also done in language tokens, but now that's really only done for auditability. There are much more abstract layers before the model output layer of a single token prediciton and they could be designed to to "think" using these more abstract layers as intermediates instead like how many people think in concepts rather than words. There are even some research models on this (see Next Latent Models)[1]. Humans need to communicate in language because we don't have telepathic abilities but these models need not have such a constraint.
I fail to understand, in its current state, the mass hysteria around this revolution. Maybe I'm doing this wrong, but I constantly need to review and manage my agents, are we really headed to a reality where engineering mindsets are no longer needed?
I think it’s just part of the zeitgeist. Like, I’m actually pretty AI-forward, but my post is getting downvoted. Not that I care, it’s just an interesting sign of the times.
By using the models you're training them to be better at these tasks, it's where a good number of improvements have already come from. Frontier labs just feed it back in.
> Such a wild take to argue that current tier LLMs do any “thinking” at all.
It does not matter whether LLMs do any thinking, or "thinking." The only thing that matters is whether they produce similar results to human thinking, even if it's through a different process (or no process at all). When a company decides to deploy an AI-powered customer service representative, it is not because the AI bot goes through the same thinking process as a human when answering a customer's request.
My initial knee-jerk reaction was to list out a whole bunch of reasons why this take is wrong, or shortsighted, or gives too much credit to LLMs as a “revolution”, or any of the other go-tos in my satchel of counterpoints to the usual ad nauseam AI discourse.
Except those don’t really fit, here, because the author is making a far more critical error in ascribing far too much to current human cognition. On the daily I see so-called “knowledge workers” failing to read basic signs, refusing to learn basic skills, and all too eager to cast off the slightest bit of critical thinking to a black box they can subscribe to - be it replacing curating a content library with streaming services and algorithms, or swapping a life of hard-won expertise for a swarm of chatbots and an API bucket. As someone with the uniquely privileged POV to be able to ingest vast sums of technical data but struggle with social data, I look at my fellow Americans and genuinely fret for what the next few decades hold for a cohort that seemingly cannot effectively communicate which direction they’re going but is all too content to hitch their accountability to an LLM they pay for.
In that sense, thinking has already become a hobby to the masses, in that it’s entirely optional to exist in current society at large. Autopay apps manage your bills, finance apps keep you on budget, fitness apps tell you to keep running, social media apps tell you who your friends (and enemies) are, video apps tell you what to watch, music apps tell you what to listen to, and cars increasingly tell you not just where to go but also to let them get there for you. I would argue that in that context, thinking is a critically endangered hobby we sorely need to sow new seeds for, lest we risk losing it entirely; AI does not seem keen to do that.
Now to be fair, so much of this is hyperspecialization knowledge overflowing into daily life and making existence a sort of cognitohazard in and of itself. We skip through hundreds of pages of legal boilerplate because we’re not lawyers, and then complain about all the rights we willingly signed away because of the volume of said agreements. Non-technical people engaging with modern computing are either addicts or miserable, unaware that this was both entirely optional and also inevitable in a populace that didn’t think for itself. Finance has become so hyperspecialized that it’s impossible for the layman to tell of something is good or bad, or even why the number went up or down on their portfolio in a given time period. LLM boosters say their tools will solve this complexity nightmare, yet the tools themselves grow increasingly complex just to eke out single-point gains on hyperspecific problems.
Which brings me to the OP again, their highly uncritical claims that AI will bring about a new revolution of thought itself, and that somehow this tool will both restore thought to an elevated pedestal while also making it nearly impossible to meter (at least on American currency; let’s not think too hard about how developing countries might be able to afford it). I don’t see any of that, but for the same reasons the OP feels it’s somehow inevitable. The only difference I can readily figure is our opinions of our fellow humans: I view us as in dire straits and able to do far better than we give ourselves credit for, while they view us as replaceable with servile digital labor for the better.
For anyone like me who started reading science fictions as a kid, that story is a very old hat. So far, AI only seems to solve problems that you tell it to solve, and you have to carefully guide it to get results. So for now the claim seems exaggerated and still in the realm of science fiction.
By the way, there is another problem. For humans we have the saying "garbage in, garbage out." If you read a lot of trash, you will only come up with trash. If you give powerful tools to morons, they will not become geniuses. That's not just true for humans, it's also the case for AI. If you teach your AI trash, it's going to be some trashy AI. Since people do that all the time for various reasons, we're going to have a lot of artificial stupidity, too.
>My challenge ahead is to move from making a living by thinking, to making thinking part of a life worth living.
Our challenge ahead is making a living at all. Who is going to be having hobbies if the economy is 100% automated? We're not going to sleepwalk our way into a post-scarcity automated economy where everyone's needs are provided for and they can just spend all their time on hobbies. If we believe AI can replace all cognitive labor, then it follows that it'll be able to replace all physical labor with robotics fairly quickly, and we're left with no labor left for humans. We need to plan for that future or else billions will suffer from the results.
I like to imagine walking into colonial Jamestown and trying to explain to them
"it'll be able to replace all physical labor with robotics fairly quickly, and we're left with no labor left for humans. We need to plan for that future or else billions will suffer from the results."
"And it and those robots will be owned by people who don't care if you starve or freeze to death."
And unlike those in Jamestown, who could just ignore the robots and continue working the land to survive, we will be forced to allow the robots to take over our roles.
Enclosure was well underway in England, so they probably understood the dilemma of modernization simultaneously increasing total productivity and lowering the living standards for a large population.
Even if we were to figure out the economic side: if thinking becomes a hobby, we're fucked. Democracy won't be sustainable.
Thinking was a hobby in the ancient Athens wasn't it?
From my understanding of how LLMs work, I'd argue thinking will become the most valuable attribute of humans (as it has been for some time). LLMs will inevitably surpass humans at functions that we know how to do, and likely through cross-domain knowledge and connecting disparate concepts will surpass us at many functions. But LLMs haven't shown they are capable of truly novel thinking, because it's next token prediction, which is inherently based on existing sources.
I think we'll see a shift towards arts, creativity, research, etc. where true novel creation and an understanding of the human experience exists at a premium.
I agree that’s what happened to hunting and riding (they became hobbies). But it’s hard to imagine something as broad as thinking will go the same way. Not saying it won’t, just that it’s hard to imagine and that the implications will be far more far reaching.
Always has been, if you think about it.
If you have responsibility for things, you want to make the best choices. At some level, this involves thinking. It is responsibility that should not be delegated to AI. The thinking required of the responsible human changes, but is not eliminated.
For most people, thinking was always a hobby. AI is actually doing them good.
Everyone is always so negative. Cheer up a bit. The "bicycle of the mind" is now the "dirtbike of the mind"... have fun, pop a wheelie.
I like this, thanks
Such a wild take to argue that current tier LLMs do any “thinking” at all. Was chatting with a friend last week and I was saying that sure AI writes much of my code these days but my cognitive load is HIGHER, as I have to constantly make sure it’s not doing something dumb.
I have to constantly make sure it’s building things in a way that will fit neatly together, in a way doesn’t over (or under) -generalize. In a way that doesn’t waste time and energy on idiotic tests that aren’t really testing anything at all.
LLMs are powerful autocomplete with vast and broad memory, they do not think. And thinking is just a part of the human condition.
> LLMs are powerful autocomplete with vast and broad memory, they do not think.
This is really only true insofar as they are designed such that they output language tokens and their intermediate "chain of thought" is also done in language tokens, but now that's really only done for auditability. There are much more abstract layers before the model output layer of a single token prediciton and they could be designed to to "think" using these more abstract layers as intermediates instead like how many people think in concepts rather than words. There are even some research models on this (see Next Latent Models)[1]. Humans need to communicate in language because we don't have telepathic abilities but these models need not have such a constraint.
[1] https://arxiv.org/abs/2511.05963
I fail to understand, in its current state, the mass hysteria around this revolution. Maybe I'm doing this wrong, but I constantly need to review and manage my agents, are we really headed to a reality where engineering mindsets are no longer needed?
Even if they did exactly what you wanted, you need to want something. To decide what you want, the thinking shifts from how and when to what and why.
I think it’s just part of the zeitgeist. Like, I’m actually pretty AI-forward, but my post is getting downvoted. Not that I care, it’s just an interesting sign of the times.
By using the models you're training them to be better at these tasks, it's where a good number of improvements have already come from. Frontier labs just feed it back in.
over time you'll need to check it less and less. do we still check if calculators compute large sums properly?
Are calculators now or have they ever been non-deterministic?
Calculators don’t do any creative work, building products is creative. Apples and oranges.
> Such a wild take to argue that current tier LLMs do any “thinking” at all.
It does not matter whether LLMs do any thinking, or "thinking." The only thing that matters is whether they produce similar results to human thinking, even if it's through a different process (or no process at all). When a company decides to deploy an AI-powered customer service representative, it is not because the AI bot goes through the same thinking process as a human when answering a customer's request.
My initial knee-jerk reaction was to list out a whole bunch of reasons why this take is wrong, or shortsighted, or gives too much credit to LLMs as a “revolution”, or any of the other go-tos in my satchel of counterpoints to the usual ad nauseam AI discourse.
Except those don’t really fit, here, because the author is making a far more critical error in ascribing far too much to current human cognition. On the daily I see so-called “knowledge workers” failing to read basic signs, refusing to learn basic skills, and all too eager to cast off the slightest bit of critical thinking to a black box they can subscribe to - be it replacing curating a content library with streaming services and algorithms, or swapping a life of hard-won expertise for a swarm of chatbots and an API bucket. As someone with the uniquely privileged POV to be able to ingest vast sums of technical data but struggle with social data, I look at my fellow Americans and genuinely fret for what the next few decades hold for a cohort that seemingly cannot effectively communicate which direction they’re going but is all too content to hitch their accountability to an LLM they pay for.
In that sense, thinking has already become a hobby to the masses, in that it’s entirely optional to exist in current society at large. Autopay apps manage your bills, finance apps keep you on budget, fitness apps tell you to keep running, social media apps tell you who your friends (and enemies) are, video apps tell you what to watch, music apps tell you what to listen to, and cars increasingly tell you not just where to go but also to let them get there for you. I would argue that in that context, thinking is a critically endangered hobby we sorely need to sow new seeds for, lest we risk losing it entirely; AI does not seem keen to do that.
Now to be fair, so much of this is hyperspecialization knowledge overflowing into daily life and making existence a sort of cognitohazard in and of itself. We skip through hundreds of pages of legal boilerplate because we’re not lawyers, and then complain about all the rights we willingly signed away because of the volume of said agreements. Non-technical people engaging with modern computing are either addicts or miserable, unaware that this was both entirely optional and also inevitable in a populace that didn’t think for itself. Finance has become so hyperspecialized that it’s impossible for the layman to tell of something is good or bad, or even why the number went up or down on their portfolio in a given time period. LLM boosters say their tools will solve this complexity nightmare, yet the tools themselves grow increasingly complex just to eke out single-point gains on hyperspecific problems.
Which brings me to the OP again, their highly uncritical claims that AI will bring about a new revolution of thought itself, and that somehow this tool will both restore thought to an elevated pedestal while also making it nearly impossible to meter (at least on American currency; let’s not think too hard about how developing countries might be able to afford it). I don’t see any of that, but for the same reasons the OP feels it’s somehow inevitable. The only difference I can readily figure is our opinions of our fellow humans: I view us as in dire straits and able to do far better than we give ourselves credit for, while they view us as replaceable with servile digital labor for the better.
For anyone like me who started reading science fictions as a kid, that story is a very old hat. So far, AI only seems to solve problems that you tell it to solve, and you have to carefully guide it to get results. So for now the claim seems exaggerated and still in the realm of science fiction.
By the way, there is another problem. For humans we have the saying "garbage in, garbage out." If you read a lot of trash, you will only come up with trash. If you give powerful tools to morons, they will not become geniuses. That's not just true for humans, it's also the case for AI. If you teach your AI trash, it's going to be some trashy AI. Since people do that all the time for various reasons, we're going to have a lot of artificial stupidity, too.