My read is that the author is saying it would have been really nice if there had been a really good protocol for storing data in a rich semantically structured way and everyone had been really really good at adhering to that standard.

Is that the main thrust of it?

It's very easy to imagine a world where all these things are solved, but it is a worse world to live in overall.

I don't think it is "bad" to be sincerely worried that the current trajectory of AI progress represents this trade.

To be clear, I'm pretty sure the half-trillion figure is the projected combined investment between SoftBank, OpenAI, Oracle, and MGX. Not public, US tax-payer dollars.

If that's not what you meant my apologies. Reason I'm quick to point this out is I think some of the writing/headlines around are suggestive of this misconception.

No one said anything about taxpayer dollars in this thread until you.

Sounds like there's not enough tax revenue!

This is one of the many many experiences in the tapestry of people figuring out how to use this new tool.

There will be many such cases of engineers losing their edge.

There will be many cases of engineers skillfully wielding LLMs and growing as a result.

There will be many cases of hobbyists becoming empowered to build new things.

There will be many cases of SWEs getting lazy and building up huge, messy, intractable code bases.

I enjoy reading from all these perspectives. I am tired of sweeping statements like "AI is Making Developers Dumb."

Exactly. If the whole "deep research" thing pans out, and we have models that can reliably produce proper literature reviews in 10 minutes...that alone will be an enormous boon to research.

Then add all the practical/mundane tasks that you mentioned, and you've got quite the multiplier.

Crucially, this doesn't just require noise but it requires "taste."

I tend to fall back on music creation as an example of this notion. Lots of innovation in music is experimentation/exploration of "noise," (not necessarily literal white noise) but requires the ear of a discerning musician who ultimately goes "Ooh! I liked that" or passes a "generated sample" by.

This is where I wonder if LLMs can ever innovate. I'm not sure they can develop "taste" for things outside of their distribution. However, I could just as easily be convinced that humans can't either, and sophisticated "taste" is just the exploration of obscure regions of the combinatorial space generated from previously observed samples!

Especially with the proliferation of generative AI, I anticipate something of a tech backlash in the next decade, and performatively NOT looking at one's phone will be part of it.

I'm sure that this already exists to some extant in certain subgroups, but I'd bet a small amount of money that this will grow to be a visible trend.

Just a fun thought!

I expect the generative AI to improve enough that everyone's tiny bubble becomes infinite.

The final moments of kali yuga

Definitely interesting, but I'm not so sure that such a study can yet make strong claims about AI-based work in general.

These are scientists that have cultivated a particular workflow/work habits over years, even decades. To a significant extent, I'm sure their workflow is shaped by what they find fulfilling.

That they report less fulfillment when tasked with working under a new methodology, especially one that they feel little to no mastery over, is not terribly surprising.

What is dogfooding?

Eat your own dog food, i.e. use your own product