The hidden cost of AI coding

> And while it is easier to become good at coding than at singing - for professional purposes at least - I believe that the effect still holds.

I can't reconcile this with my own view of things but I think "for professional purposes at least" is doing a lot of work in your sentence and I get the feeling you intend to say "good enough" by adding that bit.

Most programmers are very bad at programming (and problem solving) and only if they were compared to absolute beginners with zero insight could they be said to be "good" (and most of that comes down to them at least knowing the names of maybe a couple of concepts, etc., which makes for at least a partial map of the knowledge space). Most of them will never become good at what they do either, but will stay middling because they basically just glue libraries together and learn surface level things over and over.

If all you've ever done is learn a language, a backend framework in that language, learn how to use SQL, learn JavaScript, learn a couple of frontend frameworks for JavaScript, you've just basically learned a bunch of trivia that at best could be considered table stakes for a certain part of the industry.

If you're not actually doing free form problem solving, implementing things from scratch, reading code you didn't have to read and building and reinforcing your own fundamentals in other ways you won't ever be a good programmer.

I've worked with people who've spent 10+ years in the industry only to be essentially useless without frameworks and libraries and it regularly showed in how poor their output was. It wasn't any better when they did have frameworks and libraries to use, but they could pretend it was because at least a solution was reached. The truth is that in most of those cases a much better version could've been reached by simply re-implementing only the parts that were needed, but these types of programmers don't have the capability to do so.

> Why should one be entitled to do one thing for their entire career?

Because if you pursue a career and invest time and money into it over the course of your life, especially in the highly Americanized way which involves both a substantial time and MORE substantial outlay of money to achieve that, and you then go on to live in a society that demands you earn a living or starve/freeze to death, it is unconscionably immoral to fuck with other people's means to earn that living for your own profit.

And to be clear, that doesn't mean "you can never change your career," that's nonsense. People do that all the time, out of necessity or just desire. However currently the ability to do that is heavily gate-kept via the income of the person wanting to do the switching, and for a lot of the people most likely to be automated, they barely make enough to survive right now, where in the hell are they getting capital to retrain themselves on a new career?

And that's not even going into the physiological challenges. You objectively, factually, learn better when you are younger, and it's also worth noting that society at large benefits more from you learning at a younger age like we traditionally do, because you spend the first few years rolling around your house annoying your mother, you spend up to age 18 (usually) in mandatory K-12 education, you spend between 2 and 8 years in college after that if you decide to go or alternatively, a few years learning a trade or something, and then you have what we generally aim for about 40 years of being a productive member of society. If you have to retrain in the middle of that, that's just less efficient on every front. It costs you money and time, it costs society your productivity for however long you leave the workforce and return to education, and of course that's assuming you can afford to do it at all, and don't end up just lurching from one job that's about to nix you to the next, being miserable and depressed, until you can't find another and then you end up in the homeless system.

In my mind at least, the only way I can comprehend anyone being in favor of this sort of system is they imagine themselves one day being on the top of it, because that's the only group of people who are actually benefiting from this sort of instability. The looming threat of and execution of job automation has massive boons for employers; fewer employees can produce at higher rates, the job market is flooded with recently laid off people which lowers the value of labor and lets employers pay the ones who are hired on after less money, and the overall instability "vibes" present makes employees more willing to tolerate bullshit from their employer because they know how much the job market sucks and they don't want to find another, or, who knows, maybe they are trying and literally can't for the previous reasons.

Like in my mind, this just comes down to whether you think you're more likely to be the boot or the neck under the boot, and that's going to decide how you feel about this.

I think at this point we're at an impasse, so agree to disagree. I'm glad there is another person who is wanting to help these people, even if I disagree with their solution :)

> How is using Claude over Llama benefitting corporations over workers?

I'm not sure why you took the very general statement about AI being corp-over-worker to mean paid models vs free models? The technology itself heavily favors corporations, because the resources needed to create and operate the technology is massive, and is largely not available to individuals. Most of the freely downloadable models (and certainly the most popular ones) were trained by corporations. And there are many more models that are not available to individuals, only to corps.

> I work with AI every day and sum total of my token spend across all providers is less than a single NVidia H100 card I'd have to buy (from a pretty big corporation!), at the very least, for comparable purpose?

You don't need an H100 to run SLMs. You don't need LLMs that require commercial-grade memory capacities.

> How are self-hosted LLMs not copying the work of millions without compensating them for it?

Most are, especially when they're training data is closed-source. The ones that have open-source training data tend not to contain infringed-upon copywritten data. There is a huge issue right now with ML models being falsely called "open source", when in fact the source material needed to recreate them is not even known, much less provided publicly, which is probably the cause of the confusion.

> How is the push for more productivity through better technology somehow bad?

It's not, inherently. In practice though, it almost always means that workers are expected to be additionally productive, without being additionally compensated.

Those tokens won't come up during training, but LLMs are capable of In-Context Learning. If you give it some examples of how to create new words/characters in this manner as a part of the prompt, they will be able to use those tokens at inference time. Show it some examples of how to compose a Thai or Chinese sentence out of byte tokens, and give them a description of the hypothetical Unicode range of a custom alphabet, and a sufficiently strong LLM will be able to just output bytes despite those codepoints not technically existing.

And like I said, single-byte tokens very much are a part of word tokenisers, or to be precise, their token selection. "Word tokeniser" is a misnomer in any case - they are word piece tokenisers. English is simple enough that word pieces can be entire words. With languages where you have numerous suffixes, prefixes, and even in-fixes as a part of one "word" (as defined by "one or more characters preceded or followed by a space" - because the truth is more complicated than that), you have not so much "word tokenisers" as "subword tokenisers". A character tokeniser is just a special case of a subword tokeniser where the length of each subword is exactly 1.