alt Hacker NewsGenerative AI Is an Engineering Disaster
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It is, in some ways, but no matter your opinion, AI in engineering will continue unabated.
Wrote about this recently (from a vibe coding perspective) [1]. This applies to corporations, too, as they're betting their futures on fewer engineers, more AI.
[1] https://graybearding.bearblog.dev/they-got-something-on-the-...
The ultra wealthy will out compete you for the resources you need if we don’t start taxing them has been a discussion from the left for quite some time. It seems to keep coming true in new and disastrous ways, we are going to see more and more extreme concentrations of capital and power distort things and pretty soon it won’t just be middle and poor people who cannot afford anything, the whole value chain is being redesigned around things billionaires want and what could be better for them than agents who replace all the white collar workers. You might think you’re safe from this, I don’t.
It’s worth remembering that during the industrial revolution in Britain, the fastest growing country the world had ever seen, most people were in abject poverty. This tech revolution might end up being worse.
Approaching an AI version of https://hitchhikers.fandom.com/wiki/Shoe_Event_Horizon
i dont think the comparison to traditional vc saas is very good
yes, in traditional venture you want cost per marginal user to decrease and leverage your platform at scale
but improving llms shifts the frontier of their capability and unlocks entirely new use cases. so far, every mega training run has resulted in a model that has paid itself off profitably fully loaded. perhaps the TAM of intelligence has no ceiling?
not to say that we shouldnt be investing in efficient models, but the efficiency comes after we create another mega shoggoth that we can make more efficient
I can't imagine a successful civilization where non-computing energy usage isn't a rounding error.
Comparing how traditional tech companies scale to how frontier AI companies scale doesn't seem very fair to me. AI is still relatively new territory, in comparison traditional software companies benefit from decades of investment in cloud infrastructure, networking, and hardware. I'm certain those technologies also went through periods of heavy investment before they became scalable.
The author also talks about advances like smartphones as though they arrived without significant challenges or trade-offs. Every major technological shift has come with some cost, e.g environmental, shortages, investments.
I'd argue most things are difficult to scale in the beginning. Just because today's LLMs are expensive and resource intensive doesn't mean the technology is fundamentally flawed, it may simply mean the current approach isn't the one we'll end up with. But naturally, someone working on another approach would have something to criticise about others.
Finally, I'm not convinced by the claim that we're "stuck" with LLMs just because they're heavily marketed. They create value for many people, which is why they've been adopted so quickly. As the author points out, investors care about economics. I'm sure people would listen if someone developed a cheaper/more sustainable technology that delivered the same value.