I feel using these tools make me think a lot actually. It's to the point that the more I'm digging a research or code subject, the answers are so dense that I sometimes need hours to get around why this or that (a good example is designing ML systems, losses, theoretical and statistical stuff).

It might not be deep thinking, but it's definitely like being back on a PhD with an always on advisor that splits your brains every few thoughts.

I feel one could make a similar argument about any technological advancement and subsequent abstraction. A farmer pulling a cultivator with a tractor certainly has less of a “feel” for the details of the land and climate than someone doing the same with a handheld implement, yet cultivating manually is not economically viable at scale.

Not to say there isn’t a loss; there absolutely is. But, for better or worse, it has long been the case that the individual is responsible for balancing the beauty of psychological flow and craftsmanship against the literal economic output expected of them.

I think posts like this that frame this issue as exclusively a product of the rise of AI are missing the more fundamental piece: we use the tools, and in turn the tools change us. Just as we are what we eat, we are the tools we use. For better or worse.

Thinking has always been a luxurious privilege. In the renaissance many of the scientists were rich people who did not need to carve out their existence by working day and night.

Nowadays, I estimate that there is little active thinking in my life, and even less of it is enjoyable. The only time that I could think in an enjoyable way is to work on my own projects, which unfortunately is kinda rare these days due to work and family and health issues.

Whilst I agree that AI costs are falling and AIs have practically infinite uptime, I fail to see how/why the current AIs will perform better overall in the work place.

The example of AIs solving mathematics is all fun and games, but it assumes that mastery of mathematics implies that everything else will also be mastered eventually. But instead we're seeing that LLMs exhibit this thing called jagged intelligence, which means that they can be extremely good in one area, but completely fail in another. In theory the LLMs could be improved if data would be available for other areas and if there are good verifiability criteria. Fact is that many fields just don't have those and would not be willing to give up their data.

i think (heh) it depends on what you call thinking. if it is what Deleuze calls 'substance based thinking', which is what the machine is trained on, then we will increasingly find that we are majorly outgunned. if not, then we will need to build another kind of machine before we are made fully redundant

despite what the proverbial 'they' would have you believe, there is not only one type of thought. perhaps this perceived redundancy will be the catalyst that helps us to transcend the self-destructive and limited patterns that have dominated post-enlightenment efforts. perhaps.

Always has been, if you think about it.

I agree that’s what happened to hunting and riding (they became hobbies). But it’s hard to imagine something as broad as thinking will go the same way. Not saying it won’t, just that it’s hard to imagine and that the implications will be far more far reaching.

In the future we may need to go to the thinknasium to stay sharp. There was a time when, for almost everyone, it would have sounded silly to go to a special place to stay fit/strong.

If it becomes "just a hobby", it will become a privilege, too.

Someone here linked Graeber essay https://davidgraeber.org/articles/value-as-the-importance-of.... I read it with interest, although it's a bit abstract. I wish he was still alive, he would have a field day with AI.

> Machines transformed much of physical activity from necessity into choice; similarly, AI will decouple human thinking from its economic purpose.

This could not be more decoupled from the truth.

Yes, industrialization automated a lot of physical activity but I think the author severely underestimates the value that blue collar workers produce to this day. At least this is how I perceive it.

I would not place a lot of emphasis on economic insights from software engineering academics tbh.