Intel, AMD, Nvidia, Lam Research, Applied Materials, KLA, GlobalFoundries, Broadcom, Apple, Xilinx, Maxim Integrated (now part of Analog Devices), Linear Technology (now part of Analog Devices), Marvell, Cisco, Cypress, Google, Cadence, Synopsys, Western Digital, Seagate.
1) government support - i.e. the national government saying "we want this sector to succeed" and providing funding via grants/loans/subsidies, (or by fiat w/r to PRC)
2) actual labor costs?
3) talent, i.e. American-trained Chinese/Taiwan/Korean talent coming home, then generation 2.0 starting up research at Chineses/Taiwan/korean universities?
Assuming a long-term visionary US government wanted to compete; would we have to fund fab research, development, and capital i.e. similar to the pentagon funding Newport News shipyard to keep strategic know-how alive in the US market?
2) labor cost: probably very insignificant. While they employ high-paying high-tech workers, it's a capital-intensive, not labor-intensive, business. Both TSMC and Samsung spend over $10+B annually on CAPEX.
3) brain drain?: Morris Chang worked for TI for decades before founding TSMC. He pretty much started the outsourcing business in the late 80 single-handedly.
Sure. I don't agree with Trump on a lot of things, but he's at least doing something to bring them back.
also, why would this make sense:
> of course, excepting Apple
Apple is probably the single largest customer of TSMC, and their products depend on their semiconductor design team (especially true now that they are moving to in-house CPUs for laptop/desktop). Why throw them out from a discussion of companies that care about fabs?
But if you ask the question of which sectors the Bay Area has the largest investment and footprint in, then semiconductors would (unfortunately) be only a small part of that. That uncomfortable truth (which is exactly what I'm trying to point out in the above comment) is that market valuations today seem to be driven by the things built on top (in particular, the "data economy" and the effectiveness of personalization-driven consumption increases), rather than (for eg.) the semiconductor engg which is crucial to build those platforms.
An analogue of this is when the Ford CEO speculated in 2018 to the effect that it might be more profitable to sell customer data derived by selling cars, than from selling cars in the first place.
However it is utterly inconceivable that anyone would “go back” to using some old social media platform.
I haven’t reacted my compuserve account, but I’m fine using email, IRC, phpBB, mailing lists...
I was really talking about, for example, Vine. You'd rather use TikTok than Vine. There might not actually be any social networks from late 2016 no one uses today that I've heard of, so it's hard to give an example that looks back to when 14nm was the typical node size for phones. Which is sort of my point, it is in some strict sense of the word literally inconceivable to go back. Whereas I remember what an iPhone 7 was like, and it's not that big of a change.
Anyway, my point is that having faster and more energy efficient chips would make the training of stuff like GPT-3 cheaper.