Yup, because they’re still losing hundreds of dollars for every dollar spent. Their economics still rely on a delusion that the hardware outlay is a fixed cost that can be recovered after model training by selling cheaper compute. Instead, models continue to be released not only to match competition, but because models still do not do a fraction of what they are promised and being sold to do. So capital costs are not fixed.
So AI hyperscalers have a lose/lose/lose/lose choice of either growing at a rate that requires unsustainable subsidies, stopping growth and admitting models can’t do what they and a thousand other startups have been selling them for, charging actual costs, or any combination of the three.
If people were charged anything near the actual costs, much less with a profit margin, demand would implode fast enough to create a sonic boom.
Add to this that incremental improvement comes at exponentially more and more cost. So they spend ungodly amounts more in hopes that the next incremental improvement will push them over the magic line beyond where the are.
Some impressive feats but the issues holding it back from the ‘replace the whole economy’ sort of ambition they imagine are very stubborn. Notably it still confidently declares incorrect information (a significant volume of software developement works around by rigging auto-retries until it stumbles into ‘correctness’, and of course the people pumping out slop art/books aren’t bound by ‘correctness’) and they still have no clear path to autonomous control of humanoid robotics (as crazy advanced as the online GenAI experience, they are still struggling to demonstrate even the most basic manual labor tasks).
Yup, because they’re still losing hundreds of dollars for every dollar spent. Their economics still rely on a delusion that the hardware outlay is a fixed cost that can be recovered after model training by selling cheaper compute. Instead, models continue to be released not only to match competition, but because models still do not do a fraction of what they are promised and being sold to do. So capital costs are not fixed.
So AI hyperscalers have a lose/lose/lose/lose choice of either growing at a rate that requires unsustainable subsidies, stopping growth and admitting models can’t do what they and a thousand other startups have been selling them for, charging actual costs, or any combination of the three.
If people were charged anything near the actual costs, much less with a profit margin, demand would implode fast enough to create a sonic boom.
Add to this that incremental improvement comes at exponentially more and more cost. So they spend ungodly amounts more in hopes that the next incremental improvement will push them over the magic line beyond where the are.
Some impressive feats but the issues holding it back from the ‘replace the whole economy’ sort of ambition they imagine are very stubborn. Notably it still confidently declares incorrect information (a significant volume of software developement works around by rigging auto-retries until it stumbles into ‘correctness’, and of course the people pumping out slop art/books aren’t bound by ‘correctness’) and they still have no clear path to autonomous control of humanoid robotics (as crazy advanced as the online GenAI experience, they are still struggling to demonstrate even the most basic manual labor tasks).