Patel says chip orders take a minimum of three months from order to delivery from TSMC.
“the moment you even put an order in, even if TSMC started working on it immediately, you could not get a chip out for three more months.”
Patel says Chinese firms offer TSMC engineers five hundred thousand dollars versus eighty to one-fifty thousand in Taiwan.
“In TSMC, they're getting paid, you know, only like 80 to 150 k. Right? Like USD. Right?”
Patel explains TSMC and Intel achieved seven nanometer manufacturing using older DUV lithography with multi-patterning technique.
“TSMC and Intel were able to do it with what's called multi patterning DUV, deep ultraviolet. Right? So that just means they use the DUV lithography many times, right, for simplicity's sake.”
Patel says TSMC still uses DUV for seven nanometer despite having fifty EUV tools because DUV proved superior.
“So TSMC, to this day, even though they've been use they have over 50 EUV tools and they make hundreds of thousands of wafers a month with EUV, still uses DUV for their seven nanometer.”
Patel explains TSMC found DUV superior to EUV for seven nanometer on yield, cost, and performance after trying both.
“TSMC tried both. Right? And so at six nanometer and five nanometer, they use EUV, but they think cost efficiency wise, yield, right, which is how good the product's coming out, how many are good, how many need to be thrown away, the yield is better, the cost is better, the performance is better with DUV rather than EUV.”
Patel argues the semiconductor supply chain is highly fragmented with critical monopolies in countries like Austria.
“People don't understand how fragmented the semiconductor supply chain really is and how many monopolies there are. The US could absolutely shut shut down the Chinese semiconductor supply chain.”
Patel argues AI demand is what makes continued semiconductor node advancement economically viable today.
“funding the next node would not be economically viable anymore if it weren't for AI taking off. Right? And then generating all this humongous demand for the most leading edge chip.”
Patel argues TSMC's $100 billion US investment still relies on Taiwan R&D, not securing supply chain.
“Even if they do fully do the full $100,000,000,000 they commit to, completely still relying on R and D in Taiwan.”
Patel argues TSMC's $100 billion US investment won't secure R&D supply chain or new node development.
“The R and D fab they announced is not going to secure R and D supply chain, right? It's not going to secure new node development, right?”
Patel says TSMC approaches 20% of Taiwan's power consumption, projecting 30-40% within five years.
“And if they were to continue to build out what we expect them to build out in the next four or five years, it's going get to 30%, 40%.”
Patel argues competing with NVIDIA requires leaping forward beyond supply chain advantages across networking, memory, manufacturing and components.
“They're gonna have better negotiations with whether it's TSMC or SK Hynix and the memory and silicon side or all the rack people or, like, copper cables, everything, they're gonna have better cost efficiency.”
Patel lists NVIDIA's structural advantages across networking, HBM, process nodes, speed to market, and supplier negotiations.
“NVIDIA's gonna have better networking than you. They're gonna have better, HBM. They're gonna have better process node. They're gonna come to market faster.”
Patel reveals Huawei acquired nearly 3 million chips from TSMC through shell companies resulting in billion-dollar TSMC fine.
“But they were able to acquire 3,000,000 chips, 2,900,000 chips from TSMC through these other entities. Right?”
Patel says Huawei brought seven nanometer AI chips to market first in 2020 with minimal gap to NVIDIA.
“In 2020, they released an Ascend chip and submit it to impartial public benchmarks, and they were the first to bring seven nanometer AI chips to market.”
Patel reports Huawei acquired 2.9 million chips worth $500 million from TSMC through shell companies before getting caught.
“they were able to acquire 3,000,000 chips, 2,900,000 chips from TSMC through these other entities. Right?”
Patel predicts semiconductor shortages will return as the primary constraint in 2027 after power constraints ease.
“But it will fully beach semiconductors again in '27. Right? And so we see this across the entire space of the ecosystem. It's not just TSMC.”
Patel says memory makers have not built new fabs since 2022 due to cyclical nature.
“The memory makers, in fact, have just not expanded capacity. Basically, new they've not built new fabs since 2022. Yeah. Because their their cycle their cycle is so undulating.”
Patel predicts semiconductor shortages will return as primary constraint in 2027 affecting both TSMC and memory manufacturers.
“But it will fully beach semiconductors again in '27. Right? And so we see this across the entire space of the ecosystem. It's not just TSMC. It's also memory both.”
Patel claims China has the most vertical semiconductor stack and could run fabs independently unlike TSMC.
“China has the most vertical stack in semiconductors today and they're the best at semiconductors in the world because their fabs could still run somewhat on a lot of things because they have built some of these chemical supply chains. Right? Like TSMC for certain kinds of chemicals 100% share from Japan.”
Patel identifies semiconductors as the returning bottleneck, with chip fabs being most complicated buildings requiring multi-year timelines.
“Semiconductors are extremely cyclical. The the the buildings that chips are made in are the most complicated buildings people make. You know, they are they are know, they have multi year timelines.”
Patel explains TSMC must follow US export restrictions regardless of Taiwan's government due to banking and equipment dependencies.
“The way American export restrictions are upheld is that Taiwan utilizes American banking systems, American equipment industries. And so they'll still have to uphold any US export restrictions.”
Patel calculates a gigawatt of Rubin requires 55,000 three-nanometer wafers and 170,000 DRAM wafers.
“You need about 55,000 wafers of three nanometer. You need about 6,000 wafers of five nanometer, and then you need about a 170,000 wafers of DRAM, right, memory.”
Patel details that producing one gigawatt of Rubin chips requires specific wafer volumes across multiple manufacturing nodes.
“a gigawatt of, you know, NVIDIA's Rubin chips. Right? So Rubin is announced at GTC, I believe, the week this podcast goes live.”
Patel argues NVIDIA is setting up supply chain to manufacture tens of millions of AI chips.
“I can't buy millions, tens of millions, which is what Jensen's, setting his supply chain up for.”
Patel reports NVIDIA is negotiating over $250 billion in supply contracts across components this year.
“And as you look at what they're negotiating in the market today, there's over 250,000,000,000 of wafers, of memory, of substrates, of PCBs, of networking equipment that they're going to sign this year,”
Patel lists AMD MI350, Amazon Trainium 3, and Google TPU v7 as AI chips now on 3nm process node.
“MI three fifty from late last year from AMD or Tranium three and TPU v seven from late last year, or slash early this year from Amazon and Google”
Patel reveals NVIDIA acquired Grok partly because Grok chips are made on Samsung, accessing non-TSMC capacity.
“Part of it was because they want to have really fast inference, but like part of it is that Grok is manufactured on Samsung. Right?”
Patel says AI chips consuming all TSMC 3nm and 2nm capacity is forcing companies to alternative fabs.
“More importantly is that AI is buying all the capacity on three nanometer and two nanometer in a couple years that, people are having to turn to other directions.”
Patel says TSMC is forcing Apple, Qualcomm, and MediaTek off three-nanometer, pushing them toward Intel.
“They're telling Qualcomm and MediaTek to get off and all three of these companies are like, oh, maybe we should use Intel because they're not telling us to get off”
Patel explains AI chips are crowding onto three nanometer nodes, displacing mobile chip production.
“Right? Because all the AI chips are on three nanometer and it takes time, Small mobile chips are easier to make than big AI chips, so all the AI chips are moving to three nanometer now, right? MI three fifty from”
Patel argues NVIDIA acquired Grok partly because it's manufactured on Samsung, avoiding TSMC capacity constraints.
“Part of it was because they want to have really fast inference, but like part of it is that Grok is manufactured on Samsung.”
Patel says AI chips are consuming all three-nanometer capacity, forcing others to alternative fabs.
“AI is buying all the capacity on three nanometer and two nanometer in a couple years that, people are having to turn to other directions.”
Patel explains NVIDIA acquired Grok partly because it manufactures on Samsung due to no TSMC 3nm capacity available.
“Because there's no three nanometer capacity for them at TSMC, they need to chip somewhere else, and if you just if AI is as crazy as like we believe it is, and demand is as crazy as we believe it is, it's gonna be even crazier next year,”
Patel says AI chips are moving to three nanometer, squeezing out mobile chip manufacturers.
“Right? Because all the AI chips are on three nanometer and it takes time, Small mobile chips are easier to make than big AI chips, so all the AI chips are moving to three nanometer now, right?”
Patel estimates five to eight years of global tool manufacturing would be needed to replace Taiwan's chip capacity.
“It would take five to eight years of all of the world's tool manufacturing capability Yeah. To even replace the capacity in Taiwan.”