Is AI causing a repeat of frontend’s lost decade?

I don't think it was true at first. From what I understand it was mostly about ideology first.

1. Scoping. I give juniors projects that are big enough to be valuable, but not so big that they get lost.

2. Instruction. I give juniors frequent, bite-sized lessons related to their tasks. In this, I invest in their skills.

3. Linting. Anything that could be considered a “nit” in a code review is relegated to machine lints. For example, I will never talk about formatting in a code review. That is solved by tooling.

This article makes me wonder if there’s some sort of machine-readable lint for costly abstraction? In a TS project, is there some way to say, “the shadow DOM under this component is too deep.” Or “this object’s inheritance tree is too long.”

Or for zero/cost abstraction languages, is it feasible/productive to lint “this packages dependency tree is too deep”?

How do you identify abstractions that are too leaky to be helpful?

It most definitely is not.

I agree with you on a technical level and even in a non-cynical "humanity really can rally and change things that seem insurmountable" but you have read way more history than I have. All I know is you have such a frantic geopolitical aspect to this, and such a staggering amount of funding and addressable market, which means unlike blockchain this is both powered by business _and_ government (no government would give up control of the money supply, shocking to me that people believe this), that I see zero path to winding anything down.

> I think the current LLM economy will collapse, leaving behind a few survivors. There will be widespread adoption of cheap OSS LLMs and of more limited, economically viable functionality provided by people with deep pockets like Google. As LLM economics start making more sense, LLMs will be everywhere, once the hardware becomes cheaper and more available.

Cheap OSS LLMs are used everywhere, all the time. They are great, and with subsidies from say China, they could even be competitive with frontier models, but that model of the world requires this mysterious OSS development running at a big big loss. It takes almost a billion dollars to train a frontier model. For many many cases, you do not need frontier model performance. When I do say video captioning, I use small OSS VL models.

Is your theory predicated on OSS models filling some sort of performance gap between the frontier? Or a compromise for less spend at a lower performance? What to you doesn't make sense about LLM economics? Like, LLM's are everywhere. If you think "oh people will just settle for slightly less performance for cheaper" that should have already played out, we've had the same dynamics at every scale: frontier performance is expensive, but then that same performance will cost roughly 10x less in 1 years time. But you don't see people stopping at like GPT-4 and not adopting the frontier models of today.

I think you're right about the value of OSS LLMs but I don't see what would change the calculus to make frontier models somehow less important. It's like in the 90's when we were like "1 GIGABYTE OF RAM? how will that ever be necessary!?!?" and sure, you don't need 1 GB of ram for everything! We have embedded systems. But it's not like there isn't a booming market for >> 1 GB memory modules.

> Regarding lawsuits, do you think Disney & co will take this lying down? The freaking DMCA - an American law - is enforced >. It will take a long time but LLMs will be domesticated.

Not saying lawsuits will be fruitless, I'm sure they will chip something off of the industry, but by now it just won't matter. We're talking about trillions in spend, multiple countries, a government that sees this as a non-negotiable game to win from a geopolitical and military standpoint, and our government knows that they can't execute this themselves despite what I imagine is their distaste for Silicon Valley tech CEOs and their grandstanding. Maybe a lawsuit kneecaps someone, which would be huge, but that doesn't matter for AI generally. Maybe a lawsuit restricts data use, that's fine, these companies have deep pockets for licensing and commissioning datasets; they have opt-in-by-default user flywheels.