The AI Gold Rush
digitopoly.org
digitopoly.org
NVidia.
The "AI boom" may take longer to get from today's "sort of works" to "works really well". Look at self-driving cars. 20 years ago, they sort of worked in the DARPA Grand Challenge. But most of the self-driving car companies flopped. Waymo has ground through to limited success, but not profitability. Probably around 10 years out, self-driving cars will be profitable.
What we have as LLM-type AI now is kind of like that. It's fun when it works, but too flaky to trust. There's a market for that, but it's mostly in ads and "assistants" that have to be checked by humans. This limits scaling and utility. We need AI systems that are clear on what they know and what they don't know, and don't "hallucinate" or get lost, before they can be trusted to do things on their own. And ways to keep the AI slaves subservient (the "alignment" problem.) Those are easier problems than ones already solved, but they are not solved yet.
This is a huge market.
Sure, maybe AI isn't going to replace 95% of white collar workers overnight. But it doesn't have to.
In order to still be extremely disruptive, all it has to do is make these workers twice as productive. Work that is "checked by a human", can still be a massive speedup, combined with a human.
Career-wise, I think there is still good money for engineers in the AI area, building these AI things that most of us know are not going to work. What do you think?
I feel like a hostage in AI. Don’t particularly like it, but wouldn’t find a salary like that in any adjacent area in software engineering.
Out of curiosity, why do you think the "alignment" problem is easier to solve than problems already solved?
LLM tutors don't replace teachers entirely, but just being able to express your confusion and getting a good answer nearly instantly is invaluable.
LLM customer support agents can't handle all queries, but they can handle the basic ones and categorize the remaining questions for people to look at. Also, LLM autocomplete is a 10x productivity boost for the support workers that remain. And language barriers are a thing of the past.
LLMs can digest years worth of meeting notes, Excel sheets, and Powerpoint decks. Bigcorps struggle so much with this stuff and LLMs will be a complete gamechanger.
Fully automated translation and subtitling of all video/audio material will increase the reach of multimedia 20x. Regional content becomes global content.
Creative industries (books, movies, comics, music) will also become much more competitive and winner-takes-all.
This AI boom is the real deal and there is no going back.
I think the thing in the zeitgeist that changed isn't anything to do with the technology, it's that truth itself is tangible in the modern society. We're still grappling with the "post truth" era. We're all constantly deceived by marketing, PR, politicians, adverts, social media, each other, etc - and we're all fully aware of that deceit.
In the face of that, an LLM that's only accurate 90% of the time looks acceptable, like a complete technology, and therefore worthy of investing in.
I wouldnt go as far to make this claim, as I believe more people than not fall victim of this deceit daily. See social medias.
Personally that's the most infuriating part.
You have watched too much sci-fi and not read enough UX.
The principle is sound, but local models are really the only option when working on data you don't want to make public.
E.g. On biases in LLM responses and lack of explainability:
> Believe it or not, there is a historical precedent in old industrial technologies for making progress in commercial products and services before experts understood the underlying determinants. At the start of the Bessemer process being used by the US steel industry, for example, steel mills produced high-grade steel at a large scale even though nobody knew the chemistry to explain why ore from some North American locations worked so well. The science of chemistry had not caught up with the industrial processes. As a result, workers needed to learn how to fine-tune performance by recognizing the errors without knowing the underlying causes. The early US steel industry did live for decades with such uncertainty – and furnaces blew up occasionally – but there was too much money to be made.
That's a really good analogy. The question is: in this modern era of higher regulation, will capitalistic drive and risk-appetite (for things blowing up) be able to outpace lawmakers?
However, I use LLMs every day to be more productive. They provide me with ideas for programming, cooking, and even planning a vacation. They're not perfect but interns at work aren't either. Besides that ChatGPT seems more like the combined knowledge of hundreds of interns (but not seniors).
So I wouldn't call it a gold rush because LLMs benefit society already plus the future positive side effects of investments in computer chips.