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The biggest use of professional AIs will offset the majority of big brain jobs, possibly leaving the majority of jobs left for humans to do being physical back-

The biggest use of professional AIs will offset the majority of big brain jobs, possibly leaving the majority of jobs left for humans to do being physical back-breaking labor at some point before long.

hnlmorg 2y ago
We’ve long since proven that machines are better for back-breaking labor. I also disagree that AI will offset the majority of big brain jobs. What’s actually happening is AI is offsetting the majority of narrowly defined queries. The more open ended problems require an actual understanding of problem solving rather than a really clever text prediction engine. Some examples to illustrate my point: If you wanted to build a mobile app, you’d need to first: - understand the problem you’re trying to solve (is it a game? A medical app? A productivity app? Etc) - choose a frontend stack - create a design language - create wireframes - decide where to host the backend stack (eg self hosted? Or public cloud? If so, which?) - decide which cloud services to use (if public cloud) - decide on how to approach IaC - decide on the backend stack - decide on how to implement your CI/CD pipelines (or even if you want continuous delivery) - understand what privacy and security concerns you have and identify what risks you’re willing to accept - define testing strategy’s - define release cadences And so on and so forth. Writing the code is often actually the easiest part of software development because we already understand the problem by that point. The rest of the work is figuring out all questions that need to be answered — and I don’t even mean answering the questions, I mean understanding what questions to ask. AI can’t do that. GenAI requires human input and much of software development is actually figuring out what those inputs should be rather than generating that output. So that’s what I mean by “big brain jobs”. It’s not writing code, because that’s easy. It’s understanding and defining those requirements to begin with.
- tuckerman 2y ago
  > We’ve long since proven that machines are better for back-breaking labor. This is contrary to my perception of current trends (and to a perhaps generous reading of Moravec's Paradox). Dexterous manipulation is still quite hard which is why a task like laundry folding is so impressive despite being relatively simple for many humans: https://www.physicalintelligence.company/blog/pi0 https://www.physicalintelligence.company/blog/pi0 Perhaps we disagree on what "back breaking" entails but I'd much rather spend all day coding than folding laundry!
  - hnlmorg 2y ago
    Yeah I definitely wouldn’t class laundry folding as “back breaking”. But I agree it is manual labor that’s difficult for current robotics. There have been some advancements in this specific field though. So we might see a mass-produced robot that can do this in our life time. If not for homes then certainly for industrial workflows (dry cleaning services, new workflows for clothes manufacturing, etc)
- Philpax 2y ago
  There's no reason AI couldn't do that too. It will continue moving up the cognitive ladder: there's nothing special about our ability to ask those questions. For example, try asking Claude "what decisions do I have to make when building a mobile app?" It gave me a list that looked quite a lot like yours, which I then asked it to compact into a single paragraph: > When building a mobile app, you'll need to decide between native or cross-platform development (React Native, Flutter); which platforms to prioritize; backend infrastructure; offline capabilities; data storage approach; UI/UX design system; monetization strategy (free, freemium, subscription); launch approach; ASO strategy; budget allocation across development, design, and marketing; timeline with clear milestones; team composition requirements; and legal compliance needs including privacy policy and relevant regulations like GDPR. and of course, I can ask it to answer those questions for a given app. People have written about this, but the ratio of "input amplification" is only getting larger: in the future, you will be able to ask for a multiplayer 3D chess game with native Android and iOS clients with branching timelines, and it'll write every bit of code for you, while asking you about important decisions. (See also Claude Code and similar agents.)
  - hnlmorg 2y ago
    Sure people have written about it but the LLM is only returning text that’s already been written with zero reasoning. What solution architects, devops engineers, etc get paid for is their experience. There is a saying that goes “the devil is in the detail” and that applies here. Everytime someone says “I created x using AI” it’s because they’ve been able to feed the LLM pointed questions and have been able to understand when the LLM was rattling off nonsense. And your counter argument there still falls back to the “writing every bit of code” which I’ve already said is the easy part of software development. I’m sure at some point AI will be able to reason about problems but that’s when they shift from being specialised AI to AGI. And I don’t for one second believe we are as nearly as close to AGI as Sam Altman wants his investors to believe.
    - Philpax 2y ago
      I would encourage you to try out a recent reasoning model like Claude 3.7 Sonnet / o3-mini / R1. No, it's not perfect, but it can very much architect and design things at a higher level than you might think. This is already taken advantage of by Aider, which has a mode that splits the tasks: https://aider.chat/2024/09/26/architect.html https://aider.chat/2024/09/26/architect.html My point isn't that it's perfect today, but it's already further along the trajectory than one might think.

hnlmorg 2y ago

We’ve long since proven that machines are better for back-breaking labor. I also disagree that AI will offset the majority of big brain jobs. What’s actually happening is AI is offsetting the majority of narrowly defined queries. The more open ended problems require an actual understanding of problem solving rather than a really clever text prediction engine. Some examples to illustrate my point: If you wanted to build a mobile app, you’d need to first: - understand the problem you’re trying to solve (is it a game? A medical app? A productivity app? Etc) - choose a frontend stack - create a design language - create wireframes - decide where to host the backend stack (eg self hosted? Or public cloud? If so, which?) - decide which cloud services to use (if public cloud) - decide on how to approach IaC - decide on the backend stack - decide on how to implement your CI/CD pipelines (or even if you want continuous delivery) - understand what privacy and security concerns you have and identify what risks you’re willing to accept - define testing strategy’s - define release cadences And so on and so forth. Writing the code is often actually the easiest part of software development because we already understand the problem by that point. The rest of the work is figuring out all questions that need to be answered — and I don’t even mean answering the questions, I mean understanding what questions to ask. AI can’t do that. GenAI requires human input and much of software development is actually figuring out what those inputs should be rather than generating that output. So that’s what I mean by “big brain jobs”. It’s not writing code, because that’s easy. It’s understanding and defining those requirements to begin with.

tuckerman 2y ago
> We’ve long since proven that machines are better for back-breaking labor. This is contrary to my perception of current trends (and to a perhaps generous reading of Moravec's Paradox). Dexterous manipulation is still quite hard which is why a task like laundry folding is so impressive despite being relatively simple for many humans: https://www.physicalintelligence.company/blog/pi0 https://www.physicalintelligence.company/blog/pi0 Perhaps we disagree on what "back breaking" entails but I'd much rather spend all day coding than folding laundry!
- hnlmorg 2y ago
  Yeah I definitely wouldn’t class laundry folding as “back breaking”. But I agree it is manual labor that’s difficult for current robotics. There have been some advancements in this specific field though. So we might see a mass-produced robot that can do this in our life time. If not for homes then certainly for industrial workflows (dry cleaning services, new workflows for clothes manufacturing, etc)
Philpax 2y ago
There's no reason AI couldn't do that too. It will continue moving up the cognitive ladder: there's nothing special about our ability to ask those questions. For example, try asking Claude "what decisions do I have to make when building a mobile app?" It gave me a list that looked quite a lot like yours, which I then asked it to compact into a single paragraph: > When building a mobile app, you'll need to decide between native or cross-platform development (React Native, Flutter); which platforms to prioritize; backend infrastructure; offline capabilities; data storage approach; UI/UX design system; monetization strategy (free, freemium, subscription); launch approach; ASO strategy; budget allocation across development, design, and marketing; timeline with clear milestones; team composition requirements; and legal compliance needs including privacy policy and relevant regulations like GDPR. and of course, I can ask it to answer those questions for a given app. People have written about this, but the ratio of "input amplification" is only getting larger: in the future, you will be able to ask for a multiplayer 3D chess game with native Android and iOS clients with branching timelines, and it'll write every bit of code for you, while asking you about important decisions. (See also Claude Code and similar agents.)
- hnlmorg 2y ago
  Sure people have written about it but the LLM is only returning text that’s already been written with zero reasoning. What solution architects, devops engineers, etc get paid for is their experience. There is a saying that goes “the devil is in the detail” and that applies here. Everytime someone says “I created x using AI” it’s because they’ve been able to feed the LLM pointed questions and have been able to understand when the LLM was rattling off nonsense. And your counter argument there still falls back to the “writing every bit of code” which I’ve already said is the easy part of software development. I’m sure at some point AI will be able to reason about problems but that’s when they shift from being specialised AI to AGI. And I don’t for one second believe we are as nearly as close to AGI as Sam Altman wants his investors to believe.
  - Philpax 2y ago
    I would encourage you to try out a recent reasoning model like Claude 3.7 Sonnet / o3-mini / R1. No, it's not perfect, but it can very much architect and design things at a higher level than you might think. This is already taken advantage of by Aider, which has a mode that splits the tasks: https://aider.chat/2024/09/26/architect.html https://aider.chat/2024/09/26/architect.html My point isn't that it's perfect today, but it's already further along the trajectory than one might think.