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dicroce | 10 months ago

Doesn't even matter. The capabilities of the AI that's out NOW will take a decade or more to digest.

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EA-3167|10 months ago

I feel like it's already been pretty well digested and excreted for the most part, now we're into the re-ingestion phase until the bubble bursts.

jdross|10 months ago

I am tech founder, who spends most of my day in my own startup deploying LLM-based tools into my own operations, and I'm maybe 1% of the way through the roadmap I'd like to build with what exists and is possible to do today.

dicroce|10 months ago

Not even close. Software can now understand human language... this is going to mean computers can be a lot more places than they ever could. Furthermore, software can now understand the content of images... eventually this will have a wild impact on nearly everything.

kokanee|10 months ago

To push this metaphor, I'm very curious to see what happens as new organic training material becomes increasingly rare, and AI is fed nothing but its own excrement. What happens as hallucinations become actual training data? Will Google start citing sources for their AI overviews that were in turn AI-generated? Is this already happening?

I figure this problem is why the billionaires are chasing social media dominance, but even on social media I don't know how they'll differentiate organic content from AI content.

tough|10 months ago

maybe silicon valley and the world move at basically different rates

idk AI is just a speck outside of the HN and SV info-bubbles

still early to mass adoption like the smartphone or the internet, mostly nerds playing w it

827a|10 months ago

Agreed. A hot take I have is that I think AI is over-hyped in its long-term capabilities, but under-hyped in its short-term ones. We're at the point today or in the next twelve months where all the frontier labs could stop investing any money into research, they'd still see revenue growth via usage of what they've built, and humanity will still be significantly more productive every year, year-over-year, for quite a bit, because of it.

The real driver of productivity growth from AI systems over the next few years isn't going to be model advancements; it'll be the more traditional software engineering, electrical engineering, robotics, etc systems that get built around the models. Phrased another way: If you're an AI researcher thinking you're safe but the software engineers are going to lose their jobs, I'd bet every dollar on reality being the reverse of that.