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Honestly, I think it's pretty much always like this. This is why tackling these ethical issues is so difficult. You can't expect most people to engage thoughtfully. And if we're blocked on that, we can't take action to protect them anyway.

there's a certain set of people here who believe that if you are part of an imperfect system, you can never be trusted.

Yeah I get it. I've definitely been the person on the outside as well.

the crypto part of your thread really resonated with me because i am 99% "yes crypto is bad." the 1% is the trans folks i know who describe it as a good way to, for example, get black market hrt. but i also didn't come to the "it's bad" stance without trying to actually learn about + engage with it

yeah and my worry is that 1% of folks don't understand exactly how hard it is to preserve opsec while using it for the purpose they think it's good for, which is going to land them in jail for fincrimes. cash is a better option a vast majority of the time for even that use case.

People who actually get value out of it are going to reach for the tech. That's just how it is. My issue with crypto was that it wasn't just bad tech. But the way it is designed was primarily to enable graft and fraud. The positive cases can be solved with other tech that isn't inherently scammy.

People don’t realize how many places it exists already that were not marketed as “AI”. Image search and general search (think “did you mean…?”) are places LLMs are used heavily.

The only generative case I’ve found useful is code completion. With local context it can save some typing. One problem with most generative uses of AI is people believe they can avoid *thinking*. They want to believe they can gain a skill with no effort, which is dangerous and wrong.

yeah, the de-skilling is pretty scary. I mean, if the abstraction is non-leaky, it's great, but I've been biased by many many years of abstractions promising to save work and then causing arcane failures after the fact.

A friend was a TA for an intro programming class that was heavy on the AI, and students didn’t understand why their code worked at the end, even though they had complete programs. Scary to think about a generation of programmers working in this way.

Okay so the real talk here is that people used to say this about assembly when it was new. “They don’t even understand the machine code they generate anymore. Look how bad and wasteful their programs are.” Yet, we continued.

I remember when C/C++ was the devil that would kill "real" programming skill.

I think it’s a mistake to compare this to C++/assembly. In those cases we got better abstractions, but people still had to understand what they were doing. Now we are saying “you can just keep typing prompts in until it does what you want”. IMO that is not a new abstraction.

i think the devil is in the "understand what they were doing" bit

something I’ve been nibbling on the edges of: there’s (at least some) work as imagined of programming that leads to a mental model of “write instructions, computer executes them” that, in modern practice, isn’t quite true. It’s already a bit like plinko as code descends through abstraction layers

not _really_ plinko, but from a code authoring standpoint, unless I am one of the most sophisticated and well versed in the field, imagining how my lines of code become processor instructions… is a lot, and not really my primary mode of thinking

yes, exactly! and even then: do you think about the way that your processor's cache coherency works, at all times? no! or how the electricity makes the hardware function to do those jobs? not at all. it's always been a fuzzy understanding

Compilers translate from one form of code into another. Saying “give me a program to count the most frequent words in a string” is doing something else.

do you consider code not a language? that is, what is the distinction you're drawing between english and javascript (or whatever)? we use the same tools to talk about both: grammar, structure, semantics...

Yeah, there're some tools in common, but parts of speech haven't proven useful for analyzing code, and PLs' twin audiences, people and engines, break mutual intelligibility as a way to chart language community boundaries.