In this example a person is looking at code they can't legally copy, learning from it, and re-implementing the same functionality. Someone's definitely making money off of that. That person, that person's employer, clients and vendors, lots of people.
People get upset about AI because 1) the scale is much bigger because no human can read and generally remember all the code on GitHub while a sufficiently large model can, 2) it's a lot easier to prompt an AI into giving you a passable MVP than it is to code one from scratch, ESPECIALLY as a junior or even mid level, 3) there are unlikeable billionaires making money now where there weren't before.
> 1) the scale is much bigger because no human can read and generally remember all the code on GitHub while a sufficiently large model can,
Semi-True but it often hits an uncanny valley of either leaving enough comments in to know it was copied, or missing enough context that I'd rather the thing give me actual permalinks to whatever it thinks is relevant (i.e. like a search engine but better.)
> 2) it's a lot easier to prompt an AI into giving you a passable MVP than it is to code one from scratch, ESPECIALLY as a junior or even mid level
how do we define 'passable'? I've already run into a few cases where a jr/mid is doing 'passable' MVPs that, again, hit that 'uncanny valley' where subtle stuff is broken in an important way but it's hard to detect.
> 3) there are unlikeable billionaires making money now where there weren't before.
IDK 'Eyeball scans' are a bit much for me.
That said, this completely hand-waves over the knock-on effects.
All of the 'hype' generated about this, all the resulting Gartner reports, every organization latching on to the concept the same way I once was asked if I had any thoughts on how to integrate 'blockchain' into an enterprise that literally had no reason to short of attracting investment.
The problem this time, is they have something 'closer' to a product.
And we are seeing the result of that product more and more.
- Layoffs
- People having to deal with impacts of layoffs in their org and surprise surprise, the AI tools didn't replace the lost heads well enough.
- Consumers dealing with the pain of these tools being applied. For example, my insurance provider shortened their online chat staff in lieu of an 'AI bot' that couldn't help me connect to one of the few humans left when all I was trying to do was add my wife to my auto policy. Or my bank that randomly decided one morning that spending <5$ for eggs and bacon was enough to hard-lock my card without even a text prompt -and- invalidate my password so that I had to call in to their line. (The eggs and bacon were purchased at my work's cafeteria, which I had previously purchased from.)
- People sick of AI 'spam' that shows that uncanny valley. e.x. for ungodly reasons I sometimes see clickbait about cars and take it. And then they get very obvious facts completely wrong, that any human being actually writing the article, would have at least checked Wikipedia first for how many years it was produced...
- People sick of getting work from colleagues/superiors where it's obvious an AI generated it and they didn't take the time to make sure it was right. Or maybe they did and it was below their skill, because again, that uncanny valley is a good bullshit generator. I've seen plenty of 'procedure documents' and 'technical requirements' that were obviously AI generated, yet actually catching the -subtlety- of the (still very important!) errors was difficult due to it's capabilities. The problem of course, is it's now someone -else's- problem to make sense of it, and frankly it's a proof of brandolini's law.
People get upset about AI because 1) the scale is much bigger because no human can read and generally remember all the code on GitHub while a sufficiently large model can, 2) it's a lot easier to prompt an AI into giving you a passable MVP than it is to code one from scratch, ESPECIALLY as a junior or even mid level, 3) there are unlikeable billionaires making money now where there weren't before.