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My company writes software to manage youth soccer tournaments. We constantly get hammered to provide a “one button” push to poop out a schedule. Should be easy enough, right? Recently, I fed all the parameters for a sample division into ChatGPT and it popped out an entirely unusable mess. I kept tweeting the prompts and after about four hours of trying to ask it to do what I needed it to figure out and it kept giving me generic, really real-world unusable schedules, I gave up. I could have scheduled an entire 200 team tournament event in the time I wasted on AI on just one division!

I don’t think people who know how to schedule soccer tournaments are going anywhere soon. Maybe it’s too complex for AI, maybe the niche is just way too small for AI to bother learning ... maybe soccer scheduling is just a human skills by humans, for humans.

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This all makes sense, but/and I do think that the concern about the stratification of society into people who can afford the luxury of human interaction is a huge concern. We’re already at heightened levels of social inequality, and AI seems poised to only push that further and harder.

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I do think software has "eaten the world" but mostly from within. Every large company has been revolutionized by software. You apply for jobs online. Grocery stores have internet-enabled cash registers that hook straight into inventory management systems. Clothing stores allow you to order clothes online and then make returns in person. Even the plumbing company has dispatch-management software and you pay on a tablet the plumber brings with them.

What software has not done is allowed for technology-only companies to *replace* companies in other sectors.

I imagine the exact same future for AI. Every single large company will have an AI strategy. Many will become much more efficient. But why should OpanAI open a storefront when Home Depot and Lowe's will both buy their products?

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Very interesting post. It's worth noting that Marc Andreessen recently tweeted something about the impact of AI on labor, in which he came to more or less the same conclusion as you--that its impact on jobs was, at present, overstated. He argued the point on the basis of regulations: regulated industries (law, education, medicine, etc.) will be resistant to the allure of AI technologies, *even if* AI technology produces a superior product.

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What’s the best evidence the tech layoffs are related to copilot?

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Hard to say! I will be thinking about this for sure.

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I don't think I can endorse the "software is eating the world" critique. Software is progressing slowly and can't yet replace the human brain - let alone 8 billion human brains. However, these are clearly early days. Machines really are eating the world. They are just taking a while to digest some parts of it.

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"elder care workers, physical therapists, psychologists, nurses, and workers in similar jobs should be insulated from AI-related disruption because most people are going to prefer human caregivers over robots." You mean that we will keep producing services that cannot have productivity increases, or not in the same way as we produce goods. So we can expect the goods sector's share of the economy getting smaller while the service sector's getting bigger because of the productivity gap. It means that in advanced economies the service sector is slowing down the whole economy because the service sector is growing thanks to labour expensive resource. How can we solve this conundrum in advanced economies?.

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I'd note 4 things and conclude:

1) Human population will soon decline. This trend will possibly accelerate thanks to AI. That's because as you say, humans will always need human interactions, while AGI won't. So, thinking of your convincing idea of a potential two-tier society, where the lower tier will interact much more often with robots, the more we replace humans with AI/robots the less humans will procreate.

2) It's clear that plumbers will be the last jobs to be taken over by AI/machines. It is less clear to me that a white-collar person like you and me will love to drop their current job and start repairing sinks. It's just not what we chose to do and we would probably be bad at that too.

3) ATM example: I can't see any company in today's environment / business-mindset following that example. Simplifying it badly, if you save costs, you either distribute the savings to shareholders or invest in productive business development, but you don't reduce the margins of your slowly shrinking cash cow business.

4) What is missing here is the recognition of the hyperbolic speed and compounded nature of tech's evolution: "But in material terms, our lives aren’t much different than they were 30 years ago". That might be true (I disagree) but from a tech point of view, we are in a very different place today than 30 years ago. Not only the tech stack is much more advanced, but the speed of tech development is orders of magnitude faster.

Concluding, I can't predict the future, but it definitively won't be a repetition of the past or a rhyme of it. Humans have reached the tech inflection point, the point of no return.

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What about those concerned with AI existential risk like your old colleague Kelsey Piper?

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I’ve been thinking about this a bit lately and my take has been much the same. I use Github Copilot daily and it’s been a definite boon to productivity - Copilot knows what I’m doing, knows where I’m going with a block of code and is super quick to offer suggestions on ways to not only achieve it but make it better. But would the end product be better if Copilot just wrote the program? Nah, maybe someday but not today.

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Loved how you described the position in so much detail. I wrote something similar on my Substack - about whether AI will replace legal jobs, again with a resounding NO.

This fear mongering is as common as boom and bust cycles.

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I didn't find the "robotics is hard" section very compelling. You give a few anecdotes about specific robotics projects that have progressed slowly, but you don't really explain the *why*.

All the newer tools (large GPU clusters, SGD, dropout, pre-training, transformers, relu units, etc. etc.) have all been applied to reinforcement learning problems, with some success. So why do these problems still appear so much harder?

I reckon it has something to do with the difficulty of producing an adequate physical model to perform training in a simulated setting, and the high cost of tuning the physical model (or training the whole network) on input from actual physical sensors.

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In the early 1980s I was evaluating one of a number of "create code from English Language" products (I think it was called Rescue); older colleagues predicted then that I wouldn't have much of a software career before these new products took over ... I'm still creating software nearly 40 years later ...

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