China’s domestic semiconductor industry landscape has changed considerably. The Biden administration has continued to impose export control restrictions on Chinese firms, and the October 7, 2022, package of controls targeted not only advanced semiconductors (such as GPUs used for running artificial intelligence and machine learning workloads) but also expanded significantly on controls over semiconductor manufacturing equipment (SME). One goal of the U.S. controls is to prevent Chinese firms from moving into nonplanar technology processes, such as FinFET and eventually Gate All Around (GAA). The new restrictions included novel end-use controls and controls on U.S. persons, posing major new challenges...
Again, a strangely confrontational near total agreement with my conclusions that China will be able to easily produce consumer-centered microchips and have difficulty closing the gap to cutting edge microchips.
Two differences of opinion but I can see:
You argue that progressively more advanced microchips don’t matter, I cannot see how having more advanced thinking machines is going to be a less important to automation, AI, national security going forward than they already are.
you believe it’s only a matter of “throwing manpower at the situation” for the Chinese to catch up to TSMC, which does not bear out.
If restaurant A has one cook that has created the most popular omelet through a set of interdependent recipes and complex creative cooking methods, and restaurant B next door hires a new cook every week, instructs them how to create an adequate omelette, and asks them to create a better omelet than the cook at A, restaurant B could easily be stuck with 100 cooks who have learned how to create an adequate omelette and are continuing that set of processes without ever finding a better recipe.
It does not automatically follow that having more cooks is going to result in a better omelette.
Culturally, Taiwan thrives on innovation and creativity. China survives by hierarchy, tradition and established processes.
Again, it isn’t impossible that those one hundred cooks will come up with the perfect omelette, although it’s illogical to think all it takes is hiring more cooks and teaching them the recipe for an adequate omelette to create a better omelette then the cook at restaurant A.
More efficient chips do not have emergent behaviours (outside of, say, mobile and autonomous vehicles). More efficient chips make things more economical. Total compute capability is a function of manufacturing capability (which reflects in capital cost), electricity (which reflects in operating cost), and efficiency (which also reflects in operating cost). If your manufacturing capability is obscene and your electricity output is obscene, then you can handwave a lot of efficiency concerns by just scaling the number of chips you have in a system. In terms of aggregate computing capability, 5nm is more than sufficient to keep pace given enough scale.
There’s an interesting figure that I saw a while ago: China’s % of electricity generation dedicated to data centers is lower than both the US and EU, and due to top line electricity generation growth this proportion is basically not expected to move in the next decade. China has a LOT of freedom to tank efficiency losses that other regions simply do not.
There’s a small condition here that scaling usually has some degree of losses, but for LLM training it’s basically non-existent and for supercomputing it’s supposed to be around 10% losses due to networking/etc.
Again, a strangely confrontational near total agreement with my conclusions that China will be able to easily produce consumer-centered microchips and have difficulty closing the gap to cutting edge microchips.
Two differences of opinion but I can see:
You argue that progressively more advanced microchips don’t matter, I cannot see how having more advanced thinking machines is going to be a less important to automation, AI, national security going forward than they already are.
you believe it’s only a matter of “throwing manpower at the situation” for the Chinese to catch up to TSMC, which does not bear out.
If restaurant A has one cook that has created the most popular omelet through a set of interdependent recipes and complex creative cooking methods, and restaurant B next door hires a new cook every week, instructs them how to create an adequate omelette, and asks them to create a better omelet than the cook at A, restaurant B could easily be stuck with 100 cooks who have learned how to create an adequate omelette and are continuing that set of processes without ever finding a better recipe.
It does not automatically follow that having more cooks is going to result in a better omelette.
Culturally, Taiwan thrives on innovation and creativity. China survives by hierarchy, tradition and established processes.
Again, it isn’t impossible that those one hundred cooks will come up with the perfect omelette, although it’s illogical to think all it takes is hiring more cooks and teaching them the recipe for an adequate omelette to create a better omelette then the cook at restaurant A.
More efficient chips do not have emergent behaviours (outside of, say, mobile and autonomous vehicles). More efficient chips make things more economical. Total compute capability is a function of manufacturing capability (which reflects in capital cost), electricity (which reflects in operating cost), and efficiency (which also reflects in operating cost). If your manufacturing capability is obscene and your electricity output is obscene, then you can handwave a lot of efficiency concerns by just scaling the number of chips you have in a system. In terms of aggregate computing capability, 5nm is more than sufficient to keep pace given enough scale.
There’s an interesting figure that I saw a while ago: China’s % of electricity generation dedicated to data centers is lower than both the US and EU, and due to top line electricity generation growth this proportion is basically not expected to move in the next decade. China has a LOT of freedom to tank efficiency losses that other regions simply do not.
There’s a small condition here that scaling usually has some degree of losses, but for LLM training it’s basically non-existent and for supercomputing it’s supposed to be around 10% losses due to networking/etc.
That is interesting, do you recall where you saw that data about electricity generation growth in different countries?