devolving-dev

Why were we doing math in the first place? We should be happy that math problems were being solved, since presumably they were blockers for other problems in science and the like. But it feels like math was really more about seeking enlightenment, like a form of mental yoga or something. If so, we can just ignore AI proofs and continue on maybe?

If they pay market rate, the clearing price will go up making things more expensive for everyone. If they go off-grid they make the machines needed to produce water or electricity or whatever more expensive. If they borrow money, they make government bond yields go up and cause governments to start thinking about cutting pensions and stuff, not to mention increased mortgage rates for whoever wants to buy a home or is on a variable rate mortgage. There's no world in which this AI stuff doesn't increase costs for the rest of us.

I think that what's needed is a separation of concerns. Instead of a monolithic institution that is responsible for research, student enlightenment, self-discovery, vocational training, athletic competition, etc, we could move towards a micro-institution architecture and scale the different micro-institutions according to demand. Importantly, we wouldn't cross-subsidize the institutions, so if someone only wanted self-discovery for example, they could just pay for that and get a cheaper price than someone who wanted both self-discovery and vocational training. People could also decide how much of each they need according to their circumstances.

When autonomy is solved, they could put a bed or office or dining table in the car. But I agree, this halfway autonomy where you have to watch everything is kind of useless.

Like you said, I think skills are quickly subsumed by model abilities. I leave relevant notes about my own projects in agents.md and local comments around tricky bits of code that need them, but otherwise I just use vanilla codex.

I don't think Arabic script is that hard. It's nearly phonetic, unlike English. It's not super broad and hard to memorized like kanji. I don't know, maybe I'm just removed from the struggles of people learning it for the first time.

Models keep on improving though, so doesn't fine tuning become an ongoing task with ongoing maintenance burden?

What's the benefit of SSEs here?

I guess this ethics stuff is cool, but I'm more interested in how good it is at running a business and dealing with adversarial humans like in previous vending machine experiments. I hope they release something on that soon.

You'll make more money when you downsize or move to a cheaper city or country for retirement if your house goes up in price. If you own a house in San Francisco you can sell, move to a vlcol city, buy a new house, invest the remainder, and retire. You probably will want a smaller house once the kids move out too.

Is this a situation where AI will go away and we will regret the loss of skills? At worse, we will be forced to use open weight models instead of the cutting edge, so I don't think it's a big deal. I'm sure people got worse at arithmetic after the invention of the calculator.

He means that he's working 10 times faster so the manual things that he does once per task have to be done 10 times, and so he starts automating those too. I'm not claiming that he's actually 10x faster, just explaining.

But it kind of fits my experience too. Less time coding, and more time gathering requirements, testing, and doing knowledge transfer etc. Then you start thinking about how to make those parts automatic via automated tests or automated documentation generation that you review etc.

I think that AI is here to stay, and that you should focus most of your efforts on the parts that AI can't do. It's like with calculators, you still know how to add and multiply and stuff, and such low level things are even more pertinent with AI because it's not deterministic and can make mistakes, but generally you can rely on it for low level coding and debugging and what you really need to focus on is making sure that the artifacts it produces are aligned with your requirements, both functional and non-functional. Think about automated testing, scalability, security, etc. A single developer can produce code of much higher quality with AI because they have the bandwidth to work on such beneficial but optional things that they might have somewhat neglected before when implementation was more time-consuming and expensive.

Even if they don't benefit economically, there's some value in having them feel that they are valued and have a voice at the table. Such policies can provide the popular support needed to implement other more economically optimal policies, and we're seeing this happen for example with the mayor of New York.