For funding timelines, I think the main question increasingly becomes: how much of the economical pie could be eaten by narrowly superhuman AI tooling? It doesn’t take hitting an infinity/singularity/fast takeoff for plausible scenarios under this bearish reality to nevertheless squirm through the economy at Cowen-approved diffusion rates and gradually eat insane $$$ worth of value, and therefore, prop up 100b+ buildouts. OAI’s latest sponsored pysop leak today seems right in line with bullet point numero uno under real world predictions, that they are going to try and push 100 billion market eaters on us whether we, ahem, high taste commentators like it or not.
Perhaps I am biased by years of seeing big-numbers-detached-from-reality in FAANG, but I see the centaurized Senior SWE Thane alluded too easily eating up a 100 billion chunk[1] worldwide (at current demand, not even adjusting for the marginal cost of software → size of software market relation!) Did anyone pay attention to the sharp RLable improvements in the O3-in-disguise Deep Research model card, vs O1? We aren’t getting the singularity, yes, but scaling RL on every verifiable code PR in existence (plus 10^? of synthetic copies) seems increasingly likely to get us the junior/mid level API (I hesitate to call it agent), that will write superhuman commits for the ~90% of PRs that have well-defined and/or explicitly testable objectives. Perhaps then we will finally start seeing some of that productivity 10xing that Thane is presently and correctly skeptical off; only Senior+ need apply of course.
(Side note: in the vein of documenting predictions, I currently predict that in the big tech market, at-scale Junior hiring is on its waning and perhaps penultimate cycle, with senior and especially staff compensation likewise soon skyrocketing as every ~1 mil/year USD quartet of supporting Juniors is replaced with a 300k/year Claude Pioneer subscription straight into an L6′s hands.)
I think the main danger is race-to-bottom dynamics and commoditization self-cannibalizing sufficient funding before it could plausibly take off to a N+1 paradigm, with all the requisite scaling in tow.
Amazon alone has in the tens of thousands of US-based L4/Junior engineers, which with TC averaging ~160k * ~1.4 all-in cost of 225k a head, gives a solid 2 billion+ just from this one company, in one country, for one level of one job category.
That’s why I used the “no new commercial breakthroughs” clause, $300bn training systems by 2029 seem in principle possible both technically and financially without an intelligence explosion, just not with the capabilities legibly demonstrated so far. On the other hand, pre-training as we know it will end[1] in any case soon thereafter, because at ~current pace a 2034 training system would need to cost $15 trillion (it’s unclear if manufacturing can be scaled at this pace, and also what to do with that much compute, because there isn’t nearly enough text data, but maybe pre-training on all the video will be important for robotics).
This is of course a quote from Sutskever’s talk. It was widely interpreted as saying it has just ended, in 2024-2025, but he never put a date on it. I don’t think it will end before 2027-2028.
For funding timelines, I think the main question increasingly becomes: how much of the economical pie could be eaten by narrowly superhuman AI tooling? It doesn’t take hitting an infinity/singularity/fast takeoff for plausible scenarios under this bearish reality to nevertheless squirm through the economy at Cowen-approved diffusion rates and gradually eat insane $$$ worth of value, and therefore, prop up 100b+ buildouts. OAI’s latest
sponsored pysopleak today seems right in line with bullet point numero uno under real world predictions, that they are going to try and push 100 billion market eaters on us whether we, ahem, high taste commentators like it or not.Perhaps I am biased by years of seeing big-numbers-detached-from-reality in FAANG, but I see the centaurized Senior SWE Thane alluded too easily eating up a 100 billion chunk[1] worldwide (at current demand, not even adjusting for the marginal cost of software → size of software market relation!) Did anyone pay attention to the sharp RLable improvements in the O3-in-disguise Deep Research model card, vs O1? We aren’t getting the singularity, yes, but scaling RL on every verifiable code PR in existence (plus 10^? of synthetic copies) seems increasingly likely to get us the junior/mid level API (I hesitate to call it agent), that will write superhuman commits for the ~90% of PRs that have well-defined and/or explicitly testable objectives. Perhaps then we will finally start seeing some of that productivity 10xing that Thane is presently and correctly skeptical off; only Senior+ need apply of course.
(Side note: in the vein of documenting predictions, I currently predict that in the big tech market, at-scale Junior hiring is on its waning and perhaps penultimate cycle, with senior and especially staff compensation likewise soon skyrocketing as every ~1 mil/year USD quartet of supporting Juniors is replaced with a 300k/year Claude Pioneer subscription straight into an L6′s hands.)
I think the main danger is race-to-bottom dynamics and commoditization self-cannibalizing sufficient funding before it could plausibly take off to a N+1 paradigm, with all the requisite scaling in tow.
Amazon alone has in the tens of thousands of US-based L4/Junior engineers, which with TC averaging ~160k * ~1.4 all-in cost of 225k a head, gives a solid 2 billion+ just from this one company, in one country, for one level of one job category.
That’s why I used the “no new commercial breakthroughs” clause, $300bn training systems by 2029 seem in principle possible both technically and financially without an intelligence explosion, just not with the capabilities legibly demonstrated so far. On the other hand, pre-training as we know it will end[1] in any case soon thereafter, because at ~current pace a 2034 training system would need to cost $15 trillion (it’s unclear if manufacturing can be scaled at this pace, and also what to do with that much compute, because there isn’t nearly enough text data, but maybe pre-training on all the video will be important for robotics).
How far RL scales remains unclear, and even at the very first step of scaling o3 doesn’t work as clear evidence because it’s still unknown if it’s based on GPT-4o or GPT-4.5 (it’ll become clearer once there’s an API price and more apples-to-apples speed measurements).
This is of course a quote from Sutskever’s talk. It was widely interpreted as saying it has just ended, in 2024-2025, but he never put a date on it. I don’t think it will end before 2027-2028.