I’m being reminded of “journey before destination”, quite a bit since AI has come out.

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.

God I miss Reader’s Digest.

Because the development and ability of minors to push back against adults is poor, and the ability to push back against corporations is abysmal.

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

Helpful thanks.

In all seriousness, yes - competition is the answer.

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

Generally speaking you are the exception.

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.

True, but have you seen the state of education in the year of our Ai-ssiah, 2026?

Cheque bounce cases are counted as criminal cases in India and make up a sizeable chunk of the backlog. However it hasn’t been changed to a civil matter, even though it is on policy makers and the legal fraternity’s radar.

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.

Would disagree on the terminology, but not the spirit.

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?

Poor code quality degrades your ability to make future changes without breaking things. Maintenance is the common and I think correct metaphor: the precise location of all the bolts in a machine won’t be part of even the most detailed requirements spec, but if there’s any components you might want to replace in the future you’d better make sure you have non-destructive access to them.

Review burden is increasingly higher for many people, with Tech likely being patient 0 for the rest of the economy.

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.”

I believe this is most clearly visible in mathematics. Who is going to review the 700 proofs put out by OpenAI in a single day ? And even if mathematicians could, how will they handle the exponential growth of LLM-generated proofs ?

Not to say these proofs are slop, even if the LLM-generated work is of good quality, what happens when no one knows how it works anymore ? Even if you ask the LLM to explain, which it does quite badly, the time to understand the explanation is incompressible.

So in the end, productivity will probably reach a ceiling that we can estimate as the product of humans, their cognitive capacity and their time. And that ceiling may be lower than what AI companies valuation expect, regardless of the compute and they can pump out and the RSI level they can reach.

The issue is how much demand.

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.