> The critical difference between AI and a tool like a calculator, to me, is that a calculator's output is accurate, deterministic and provably true.
In current situation, by vibing and YOLOing most problems, we are losing the very ability we still need and can't replace with AI or other tools.
I think past successes have led to a category error in the thinking of a lot of people.
For example, the internet, and many constituent parts of the internet, are built on a base of fallible hardware.
But mitigated hardware errors, whether equipment failures, alpha particles, or other, are uncorrelated.
If you had three uncorrelated calculators that each worked 99.99% of the time, and you used them to check each other, you'd be fine.
But three seemingly uncorrelated LLMs? No fucking way.
This is why I differentiated "uncorrelated" from "seemingly uncorrelated." Sorry if that wasn't clear.
In point of fact, most technological revolutions have fairly immediately benefited a significant number of people in addition to those in the top 1% -- either by increasing demand for labor, or reducing the price of goods, or both.
The promise of LLMs is that they benefit people in the top 1% (investors and highly paid specialists) by reducing the demand for labor to produce the same stuff that was already being produced. There is an incidental initial increase in (or perhaps just reallocation of) labor to build out infrastructure, but that is possibly quite short-lived, and simultaneously drives a huge increase in the cost of electricity, buildings, and computer-related goods.
But the benefits of new technologies are never spread evenly.
When the technology of travel made remote destinations more accessible, it created tourist traps. Some well placed individuals and companies do well out of this, but typically, most people living near tourist traps suffer from the crowds and increased prices.
When power plants are built, neighbors suffer noise and pollution, but other people can turn their lights on.
We haven't yet begun to be able to calculate all the negative externalities of LLMs.
I would not be surpised if the best negative externality comparisons were to the work of Thomas Midgley, who gifted the world both leaded gasoline and CFC refrigerants.
I'm not saying this is ideal, but maybe there's another perspective to consider as well, which is lowering barriers to entry and increased ownership.
Many people can't/won't/don't do what it takes to build things, be it a house or an app, if they're starting from zero knowledge. But if you provide a simple guide they can follow, they might end actually building something. They'll learn a little along the way, make it theirs, and end up with ownership of their thing. As an owner, change comes from you, and so you learn a bit more about your thing.
Obviously whatever gets built by a noob isn't likely to be of the same caliber as a professional who spent half their life in school and job training, but that might be ok. DIY is a great teacher and motivator to continue learning.
Contrast to high barriers to entry, where nothing gets built and nothing gets learned, and the user is left dependent on the powers that be to get what he wants, probably overpriced, and with features he never wanted.
If you're a rocket surgeon and suddenly outsource all your thinking to a new and unpredictable machine, while you get fat and lazy watching tv, that's on you. But for a lot of people who were never going to put in years of preparation just to do a thing, vibing their idea may be a catalyst for positive change.
When slop becomes easier, there are a lot more people ready to push it to others than people that tries to produce guenuine work. Especially when theh are hard to distinguish superficially.
I fear for trying to get it to unlearn code from the last building code cycle when there's changes
The critical difference between AI and a tool like a calculator, to me, is that a calculator's output is accurate, deterministic and provably true. We don't usually need to worry that a calculator might be giving us the wrong result, or an inferior result. It simply gives us an objective fact. Whereas the output of LLMs can be subjectively considered good or bad - even when it is accurate.
So imagine teaching an architecture student to draw plans for a house, with a calculator that spit out incorrect values 20% of the time, or silently developed an opinion about the height of countertops. You'd not just have a structurally unsound plan, you'd also have a student who'd failed to learn anything useful.