What will be left for us to work on?

protocolture

We will be sent in to clean up the fallout.

chopete3

Key point: - Work is shifting from building/doing to evaluating, judging, and steering — that's where human value will concentrate. Other supporting points. ------ - No lab milestone or "RSI breakthrough" will suddenly eliminate jobs — economic impact unfolds gradually over decades. - Reliability, not raw capability, is the real bottleneck holding back AI automation today. - Historically, making work cheaper/faster (ATMs, radiology, coding) has grown employment, not destroyed it. - Superintelligence claims misunderstand human intelligence, which is itself amplified by tools like AI ("co-superintelligence"). It is not a good idea to compress articles like this but there are many of these opinions to read and trying to get to the point quickly to uncover new viewpoints.

ProofHouse

'metaverse' aka the spatial internet (prolly by a new name).

subygan

I like the narrative but the key point > A battle of two narratives > Build wealth before AI obviates our skills > Build skills, agency, taste, judgement both narratives are portrayed as being odds with each other but, I can't come up with a single "build wealth" scenario that doesn't involve building skills, agency, taste and judgement. what am I missing ?

franze

Question: Does anybody here yet personally has less (to) work 'cause of AI?

Metricon

I have been writing software for over 40 years and have had a long time interest in and some work in AI over that time. I wouldn’t say this gives me any more prognosticating power about how all of this is ultimately going to go, but I believe we're soon nearing an area of plateauing; whether that's because the science itself is plateauing or the intervention of governments is going to force plateau it. So if things continue as they are today, I think in the near future, being a software developer is going to be more analogous to the medical field, where in the medical field you have different levels of professional expertise. Some will be like nurses, and some will be closer to a medic and a smaller set will be like doctors. Each with increasingly required knowledge and experience to fulfill a needed role. Those who used to be actual software developers are going to be (or have to become) more in the doctor role with years of internship and practical experience to be the architects guiding the overall AI implementation of software development in organizations. The medics are going to be people who are semi-technical, where they have some technical understanding but they don't dedicate themselves to it, like say product managers, where they jump in to help development along, but don't need to have many years of experience or very deep technical knowledge. At the nurse level, it's probably going to be similar to what people would do in the past with no code tools, where somebody in marketing who knows very little to nothing about coding at all is just going to directly converse with AI systems, but they'll never be likely to get anything more advanced than the tools they could think up for themselves. Of course, it's so hard to tell what the next big discovery or changes to the nature of world society might push things in one direction or another.

CuriouslyC

The answer to this question after a lot of reflection: games. AI can slop fork or clone existing software well, but a clone of an existing game is pointless, it's basically guaranteed to be derivative and worse than the original game, and games aren't so expensive that you can't just buy the original. AI can't know if new mechanics or angles to an existing genre will feel good to play, or if a new genre is fun, that requires a human to experience the game in its totality. Games are also very resilient to sloppy AI coding, and if an indy game crashes nobody is getting paged.

asdfsa32

Remember when they created "COBOL" so that everyone could write programs? This is just round two. If you think it is different, just think of how many people write books professionally, or even publish online. Once the noise settles down a bit and boardroom shakes off their delusions as you can see in rehiring in Ford and Zuck who was very bull on AI remark about "not being it". It will be just the same, but different. Ford: https://www.bbc.com/news/articles/cgrkd41n2v9o IBM: https://qz.com/companies-rehiring-workers-ai-layoffs-automat...

ebb_earl_co

I like this article because it seems to go into decent depth on the “framework” that the author comes up with. However, this following quote has a simple reason that I don’t see anywhere in the article or framework: “”” Why is there a huge gap between what people in various occupations could be using AI for and what they’re actually using it for? One reason could be that people are slow to adopt technology, and that’s certainly part of our framework. “”” I would like to add a reason: that the Silicon Valley companies who developed the LLMs are brigands: cognizant of their actions, they have stolen (and continue to steal) the world’s copyrighted material and are selling it back to the masses and the politicians as if they are the arbiters of information itself. Specifically responding to the quoted question, I could be using Claude or ChatGPT or Grok or DeepSeek or any other to have come up with this comment, or to write emails, or to implement my Python for me, etc., but I use none of them for anything. Doing business with brigands is a choice, and a choice that I hope becomes less and less palatable so that the financial, political, social, and moral fever that is our zeitgeist finally breaks.

burningChrome

The other issue is Gen Z and Gen A are now very much opposed to AI. I'm wondering with those two sets of generations who already have a very negative view of AI, how AI can survive that coming tsunami of change. According to WRITER’s 2026 Enterprise Adoption Survey, 44% of Gen Z employees admit to sabotaging their company's AI strategy in at least one way compared to 29% of employees overall. Sabotage behaviours include entering proprietary information into AI tools, using non-approved AI tools, refusing to use AI tools or outputs, ignoring guidelines or best practices, intentionally generating low-quality outputs, refusing to take AI training and tampering with performance metrics to make AI appear to underperform.

cleandreams

We'll be cleaning up tech debt from over-reliance on AI.

uwagar

a professor of computer science at princeton comes up with slop like this. he supposed to be computing not a keynote of woo.

Shitty-kitty

I would take Anthropics "Theoretical limits of A.i" sales brochure, with a very large grain of salt.

bsenftner

Come on now: we translate vague ambitions into communications for non-living entities to do human bidding. Until we have recreated humanity as mythic gawds, there is a ton of work to do.

jambalaya8

Mr. Narayanan seems to be trying to be a bit more positive than the vibe I am getting off of his presentation. Or maybe it is just sort of meant to make us all experience our shoved-down anxiety about being phased out with nowhere else to go. A lot of adaptation to do sounds not so fun. So I kind of think that is not a terrible point, if true. I sort of worry about things like AI figuring out scripts so well that even multi-tier support work is gone. And learning how to write fiction or create foods so in accordance to our tastes (sugar, fat, etc with food, exactly what each of us is interested in, with writing) that we even lose those truly human creative jobs. Might not ever wanna leave those bubbles. So much of the human drive is exploration and why and what if. Assuming everyone in the world can have no money problems, what will AI not be able to figure out? Will we enjoy the equivalent of a major breakthrough if an AI solves it in five minutes, or just the outcome? Why learn things? AI could be a horrible jailor. And better at cancelling than any perhaps sager Gen Z or millenial. Bears some caution to be wary of this and where that power sinkhole will go. But then, I still think the previous AI winters were more a result of sense and caution than most of us know, and we cannot fathom our species' ways of reasoning/thought processes the way we did as a species thirty, fifty, eighty years ago. Erring on the side of caution is not a terrible thing. I mean, I have worked and work with AI, but it seems weird for us as a species not to have placed guardrails to prevent us from wiping one anothers' careers and relationships out. What will we talk about? If our generative AIs should be allowed to date? Again, I am assuming a fast, though not sudden, acceleration that would compound, and sooner than most probably think.

mrcwinn

If we were more connected to all the problems that exist in the world, we’d become acutely aware of just how much work there is to do, and we’d eagerly reach for any tool that could help us do more, faster.

eucryphia

If AI is used to solve the socialist calculation problem, most of you will die. If AI is subject to private ownership in a competitive market between competing suppliers, it will be like better cars, we’ll just drive faster. Power consumption will be a limiting factor in those countries relying on intermittent, weather dependent power generation with no base load. Especially if users prefer Apple’s privacy first AI on edge devices. Hopefully in western countries it can encourage more young women to bear three children before they turn 35. Young men have to pick up their game and create an environment to redirect their suicidal empathy into more productive pursuits. “Where can you find another non-linear servo-mechanism weighing only 150 pounds and having great adaptability, that can be produced so cheaply by completely un-skilled labour?” - Albert Crossfield 1954

zkmon

You need to first address 1)What is work? 2)Why we need to work? Animals don't "work". Not atleast for their own sake. If there is enough green pasture and water around, they don't even migrate to other places. So if work is meant to provide food and shelter and if machines can ensure that, humans don't need to "work". Wealth is only a reserve capacity to help future generations so that they don't need to work for their basic needs. But if machines ensure that too, then wealth itself, as a reserve, is unnecessary.

jppope

I wrote a related article here: https://jonpauluritis.com/articles/good-soldiers-find-wars/ There will always be new, hard problems to work on. AI will not, and can not eliminate that.

ilaksh

He's basically saying that even though AI capability is high and rapidly increasing, it is not reliable, creative or tasteful enough to replace humans. Further he implies that it will take decades before this is the case. But we already do have have some kind of measurement of most of these types of side factors, and they actually aren't at zero and are increasing rapidly. So the implication that they will not be human level until decades from now is just (hopeful?) speculation or fuzzy thinking. To me this looks like a really academic and official sounding version of the same quasi-religious hopium that usually defends the sanctity of the human. He is essentially saying that there is just something so special about humans that it will never be reproduced in a machine. It's very similar to dualism (and in many people actually is religious dualism). No AI is going to have human creativity or judgement. Not anytime soon. Why? Well, we all just _know_ that's not possible. Okay, maybe in a couple of decades (but they don't necessarily believe that anyway). Why would that take decades? Well we all can just _tell_ it's no where close, right? Because AI of today just isn't special like humans. Aside from that worldview issue, I think that people still are not taking seriously or internalizing the concept of exponential improvement. Computing efficiency gains can actually level off. In fact, they have many, many times before. But they always tilt back up again when we invent the next approach to get beyond the current level. This is how it has been for 90 years. There are multiple ways that we continue to see huge gains in AI software, architecture, and hardware. There are huge efficiency gains available still as we move towards more radical fully compute in memory and/or analog approaches and other options like models implemented in hardware.