I was lazy and didn’t want to specify the constraints based on the specific economic good.

The market of people who have been burned and value support is <<< everyone else who is just getting stuff done and don’t care, or don’t have the money to care.

I’ve seen multiple conference submissions that read like English, but take hours of effort to disentangle and figure out what they mean.

There is a limited number of known interesting problems that are worthwhile to engage in. New ones are found and understood, but it is a slow process and it is dependent on understanding and verification, not generation of content.

Saying AI will create new ones, strengthens the strip mining analogy. The idea that AI will find other problems parallels the idea that firms can cut down a forest and plant them elsewhere.

The conversation on HN is driven by non mathematicians, to whom math is a subject, like “nature”. It’s not their core business. The original post by Tao is a better source of information on what he means.

You would get banned from 80% of subs for being off topic.

You would get banned because repeating dog whistles of hate groups is the fastest way to get identified with hate groups.

I have seen what happens multiple times when these statements are posted. To the point that those communities got mentioned in Time magazine as examples of hate groups.

If you think this is a tight fist, consider that the communities that did leave Reddit and go to Parler/whatever, reintroduced moderation rules themselves.

Trust and safety is a cost center, and not valued in Tech.

Lets people forget, theDonald created entirely new ways of trolling r/all and by extension everyone who never wanted anything to do with them.

This included having multiple posts trend one beneath the other to let people know what they thought.

This is ignoring the stuff that happened within the subreddit. When they got booted and went to whatever the other site was they were overrun with hate speech so bad that even their own mods had to reintroduce bans.

Looking at people who are happiest leveraging it, it’s people who want to build something.

The people who are maximizing its utility are those with (deep) pre existing expertise.

For people who want to understand and think deeply about things, it’s … less useful. It can be used to create prototypes, or explain terms.

This is the path that is most precarious. Its utility is entirely dependent on being self motivated learners.

As the article showed, at some point people who want to just build, and don’t care about the learning, will just use ChatGPT to build their homework.

The situation seems like we are consuming the seed stock of expertise.

If evidence turns up of students and new entrants to the labour market finding ways to grow, learn skills or just find employment, the picture would clear up.

If this works for kids, and then adults want it, it can then be considered.

There can be no alternative when Amazon is able to buy them out, or dominate the market through network effects.

I’d be interested to know how you used it, and I suspect the element of self directed and self-motivate learning are a good part of it.

Furthermore, courts are paid for primarily by states. The tech solution would run into the same resource constraint that is already present.

All of this ignores a more fundamental aspect of the legal system, having a chance to have your case heard by a competent authority. Institutional sinews are built by doing the hard things and investing in it.

I’m sure people are building things that have specific KPIs based on client preferences. As long as those preferences are being met, then their question becomes “why does it matter what the code quality is?”

Perhaps it’s simply liability and penalties? If getting something wrong means your client is going to sue you into oblivion, then perhaps that is where people are focused on making sure its right, as opposed to making sure its fast?

The ratio of verification capacity to generation capacity, V/G, has broken with LLMs. It’s not simply an issue of more generation or less review.

The impression seems to be that individuals are more productive, but that productivity is someone else’s review burden. So the team/firm as a whole is not better off.

The cheap generation of content does mean that reviewer capacity is now a limited resource.

Unless your firm is aware and is measuring time spent on reviewing slop, there is no incentive or structure to ensure that time is respected and valued.

This is a management and awareness problem since the typical response is “use a bot to review it.”

We have tailors around the world, but Zara and other brands do the lion's share of business.

Automation in physical labour resulted in less work for physical labour. There are still some people producing hand crafted work, but the market doesn't need that many of them.

A much smaller group of people can serve the needs of an entire state.

There are many things about our modern world which make less intuitive sense than the lives of hunter gatherers. People are trying to figure out how to survive in the conditions they are placed in.

Physical labour was weakened and then had to compete with machines, till we got lights out factories.

We were left with the service economy to find roles that allowed us to thrive. Now that is being threatened as well. It is unlikely that LLMs are going to make us all into entrepreneurs and capital owners.

Not to mention, the service sector required far fewer workers than manufacturing.

This isn't quite the same kind of moment we see over and over. It's not simply cars taking out horse drawn carriages.

In general the OP is right, the people who get the most value out of LLMs are veterans.