Cook’s re-engineering of Apple started in 1998, more than a decade before he took the CEO mantle from Jobs. He had an impact almost immediately, but there are many misunderstandings about what Cook actually changed about Apple’s operating model.

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As senior VP of worldwide operations, Cook was not the man who brought outsource manufacturing to Apple. Nor was he the man who brought China into the company’s supply chain. Both moves predated both Cook, and even Steve Jobs’ return to the company he founded.

Ireland, Singapore, Japan, and South Korea had been sources of Apple product and components since at least the early 1990s, both for inhouse production and also external component supply.

Outsource contract manufacturing was already well underway, not from Asia but via American companies such as SCI Systems and Solectron who pioneered the model. Foxconn founder Terry Gou, at the encouragement of a senior Apple executive, embraced that electronic manufacturing services (EMS) framework.

Seeing the future, Gou slowly oriented his Taiwanese empire away from mere plastics and mechanical components — like metal PC cases — and dove deep into integrated vertical manufacturing. Dell and Compaq had signed on as clients well before Apple came along.

In other words, outsourcing to Asia was underway by the time Cook arrived. It was already in Apple’s future. Cook embraced it.

Yet, Cook and his team brought important operational innovations that remain significant a quarter of a century later. Among them, Apple went deeper into the little-understood but hugely impactful practice of equipment consignment. I wrote about this 16 years ago when I profiled Gou and Foxconn for Bloomberg Businesweek. Patrick McGee covered it in his recent book “Apple in China.”

Made to Measure

Apple often pushed the boundaries of what was physically possible, and that usually meant the need to use new and unique equipment. Its suppliers rarely had the funds to buy such expensive kit, so Apple would write the check with a very specific stipulation: it could only be used to make Apple’s products. This allowed the manufacturer to get the tooling needed to satisfy Apple’s niche requirements, but also locked up factory capacity and resources. Cupertino still uses this strategy today.

There are numerous examples of Apple building unusual relationships with its supply chain, down to insisting that an off-the-shelf component be tweaked just for them.

But beyond the minutiae of manufacturing, Tim Cook’s Apple changed the world in bigger ways.

In my opinion, three foreigners have done more for the development of the modern PRC than anyone else. The first is Henry Kissinger (iykyk). The second is Terry Gou. And Tim Cook is the third.

Gou’s impact was already apparent in the mid-1990s as he connected foreign clients with Chinese workers in the former farmlands of Shenzhen. He was the ultimate salesman, middleman, and logistics chief. Cook signed on.

As Apple factories around the world were shuttered, and contracts started disappearing from Japanese and Korean factories, Cook went all-in on China. By the early 2000s, Foxconn was China’s biggest exporter and a big chunk of that was for Apple.

By 2010, Apple was Foxconn’s single-largest customer. Through its partnership with mostly Taiwanese factories in China, Apple fed, trained, and nurtured more Chinese people than any non-government enterprise on the planet. The continuous income stream Apple provided became an important foundation for the technology-manufacturing hub that China enjoys today.

Cook’s Compromise

But it also made Apple overly reliant on a single country, one that is increasingly at odds with the values of human rights, privacy, and freedom which Apple claims to embrace. To keep China as both a supplier and buyer of its goods, Tim Cook made compromises.

Apple has booted products from its App Store used by pro-democracy movements and bolted shut technological loopholes which allowed people to communicate directly rather than via restricted communications networks. And while industry peers slowly and quietly reduced their China footprint, Apple hung on.

For each of these decisions, the buck stopped at Tim Cook. Such choices have attracted accusations that Apple is a hypocrite, or even downright evil. Maybe, but that’s also a little simplistic.

Consider its 2012 decision to join the Fair Labor Association, an industry group that had counted Nike among its members. There’s no doubt this decision was prompted by the spate of suicides at Foxconn two years prior. Yet, having spent more time in and around Foxconn campuses than most, I can tell you that conditions there are far better than many were led to believe. I can also tell you that trying to paint a more nuanced picture quickly attracts allegations of being an Apple apologist.

Whatever the supposed sins of Apple and Foxconn, the truth is that both companies — and the rest of the iPhone-maker’s suppliers — were quick to embrace change. To seek to do better. That came from Cook.

He backed up his directive for better conditions with the money needed to do so. Foxconn doubled — then tripled — wages and passed the bill to Apple. I have not seen or heard anything to suggest Steve Jobs ever noticed, nor cared about suppliers or their workers. Cook visited regularly and genuinely seemed interested in their plight.

Planet Possible

The biggest impacts from Cook’s leadership span the globe. In 2013 he hired Lisa Jackson, the former head of the US Environmental Protection Agency. Jackson wasn’t brought on board as a marketing ploy or to tick a box, it was a recognition that Apple is truly serious about cleaning up the planet.

In today’s political climate, environmental initiatives are branded mere wokeness. But Apple’s long-term strategies in areas such as pollution control, use of sustainable materials, and connecting to renewable energy are bringing about real change. Because it has the money to pay for these costly measures, both directly and through funding its suppliers, the world now enjoys a cleaner planet than had Apple not even bothered.

Apple’s massive spending power means companies can afford to invest in offshore windfarms and solar arrays, recycle commodity products and rare earths, and develop innovative chemicals and textiles. In a world where caring for others and the planet has been made to seem weak, solutions to protect the environment fell out of favor. But now, at a time when rare earths are hard to come by and energy security is of increasing concern, these strategies seem more relevant than ever.

The Ternus Take

John Ternus, who joined Apple just three years after Cook, looks set to embrace some of his predecessor’s philosophy. In a commencement speech to the graduating class of 2024 at his Almer mater, the University of Pennsylvania, he spoke of “a deep desire to do what we can to protect the environment.”

Yet, he also urged graduates to “build what interests you. Build what excites you. But above all else, build it in a way that aligns with your values.” Like Cook before him, Ternus’s values probably include labor rights and environmental protection. But let’s hope he also embraces human rights and freedom of speech for everyone, not just Apple’s Western hemisphere.

It was always understood that Cook would run Apple very differently to Steve Jobs. Now, upon his retirement, it’s abundantly clear that Tim Cook has indeed built Apple in his own image.

Thanks for reading.

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TSMC’s first-quarter earnings were a display of records across the board: revenue, gross margin, operating profit, net income, wafer shipments, and even ASP. Investors ought to be delighted by what they see.

But I can imagine clients are disappointed.

Not only did TSMC take more of the semiconductor value chain for themselves than ever before, the Taiwanese giant also failed to deliver an upgraded spending forecast for 2026. The best CFO Wendell Huang could offer was to narrow TSMC’s outlook to the “upper end” of the $52 billion to $56 billion capex range it announced in January.

There’s a chance this figure will get a bump later in the year, but the $11.1 billion it spent in 1Q — lower than the $11.5 billion it forked out the prior quarter — means the already backend-loaded spending plan doesn’t leave a lot of room to purchase and install a significantly larger amount of capacity this year.

It’s not all TSMC’s fault. The company needs to secure land, build shells, install utilities, kit out clean rooms, and spin up very complicated equipment. It doesn’t do any of this alone.

Despite the challenges, TSMC wafer shipments grew 28.1% year-over-year in the March quarter, the fastest pace in six years. Sedate growth in recent quarters was due more to supply constraints than demand, so this jump is an indication that new capacity really is coming online.

In addition, TSMC has amended its long-time business model of building new fabs instead of retooling existing ones. Holding onto and operating a chip factory with old equipment and technology is highly profitable because the major cost has already been depreciated. An old fab is a cashcow, a new one is a money sink.

Yet to meet customer needs, TSMC has shuttered 6-inch and 8-inch lines to make room for leading-edge capacity. On paper, that’s not a financially optimal move, but CEO CC Wei likely sees it as a strategically savvy one because it’s the fastest option that he has available to quickly boost supply given his many constraints.

Still, frustration with what many blame as TSMC’s inability or unwillingness to expand has attracted accusations that the Taiwanese company alone is the major speed brake on the world’s AI future. I’ve written before that I think this is untrue and unfair.

Today, I’d note that I haven’t heard a peep out of those same critics with regards to the global shortage of memory chips. Dell CEO and founder Michael Dell was recently cited as saying that “a structure is forming where total memory demand increases by approximately 625 times.” Yikes!

The desire to do an end-run around “the TSMC problem” recently spurred Elon Musk to start moving on plans to get into the chip-fabrication game, and has roped in Intel to help. Details are hazy, but I suspect Musk/Tesla will license technology from Intel and become an important financial backer of Intel’s future expansion plans. Heck, Musk should go all the way and just buy Intel, I say.

Wei seems almost amused at the prospect of Elon Musk soon discovering what he already knows. Something Intel knows, and Samsung knows, and GlobalFoundries knows. And that IBM and Motorola once knew: Making chips is hard.

“There are no shortcuts. The fundamental rules of the foundry game have never changed,” Wei told investors Thursday when asked his thoughts on Tesla and Intel’s plans for Terafab. “They need technology leadership, manufacturing excellence, and customer trust. And most of all service.”

Wei is currently overseeing capacity expansion in four countries across three continents, making him the world’s most preeminent expert on constructing semiconductor factories.

It takes two to three years to build a new fab, and another year or two to ramp up capacity, Wei noted — “no shortcuts.”

Of course, Musk’s move might be a massive headfake, designed to pressure TSMC to expand even quicker so that he can get on with the task of launching AI data centers in space. If so, Wei isn’t buying it, and he’s certainly not about to accelerate TSMC’s growth trajectory to just to stave off a possible loss of orders to Intel and Terafab.

“We repeatedly say that we prepare capacity to meet customers’ demand, not because of our competitor and not because of other considerations,” he said. “The most important one is our customers’ demand, and they work with TSMC so that we plan our capacity and our capital expense.”

Of course, this might be TSMC’s own psyops. The flipside to the “expand now, pay for it later” thesis is that extra industry capacity would give clients more leverage when a slowdown comes, especially since a chipmaker’s capital costs are baked in to its balance sheet and income statement. Seeding doubts into Musk’s mind, Inception style, could pay off bigtime if the world’s richest man later gets cold feet.

Musk’s plan is to scale from an initial output of 100,000 wafers per month to an eventual capacity of one million. Although he didn’t give a timeline, the urgency of his galactic mission suggests that he’d want to do so in under a decade. It took TSMC 37 years to reach the same point. That might make the incumbent chipmaker a bit of a tortoise, but in a few years the hare may learn for himself that there truly are no shortcuts.

Thanks for reading.

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More from Culpium:

• 135.7 points, the highest on record

• +0.2pts for the 7th month of growth, accelerating from +0.1pts in February

• The CASCI cycle is in its 19th month of positive territory (strong or firm)

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Upstream:

Upstream suppliers in semiconductors, materials, and passive components saw the largest jump in growth among all tiers of the supply chain.
This tier still lags the growth levels seen in mid- and downstream.
New capacity is coming online as strong growth among module and final-assembly manufacturers continues to drive demand.

Mid-stream:

Revenue data for companies in the middle of the supply chain shows the strongest growth among all tiers.
This growth rate fell in March, as module makers, mechanical suppliers in racks, draws, & fittings, and cooling systems manufacturers saw their downstream clients absorb inventory while facing shortages from upstream.
March was the 5th month of moderating growth after a brief peak in September and October 2025.

Downstream:

Companies focused on system integration and final assembly of AI servers saw continued strong growth in March, although this growth rate has been flat over the past three months after a solid 4Q 2025.
New supply coming from upstream has helped this growth rate stay high, but tightness in both supply for, and orders of, new products puts this tier in wait-and-see mode.

Capacity:

The business of supplying equipment and constructing facilities remains strong.
Revenue here is more a reflection of contract or phase completion, than actual new sales or orders, so caution is warranted when viewing this tier.
That said, recent data shows this tier has accelerated from a more neutral period experienced in the last half of 2025, indicating that more production capacity is coming online soon.

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CASCI is a weighted index tracking revenue of Taiwanese companies in the AI compute supply chain. Taiwanese companies make up the majority of upstream, mid-stream, and downstream of AI server manufacturing.

Notes for Editors & Analysts:

CASCI is published monthly by Culpium as a service to readers.
Media and analysts are welcome to cite CASCI or the Culpium AI Supply Chain index, with attribution and link. For example, “AI server manufacturing remains strong, according to the latest CASCI report from Culpium.”
Data graphics may be reproduced with credit.
Entire contents of Culpium or CASCI reports may not be reproduced unless a publishing fee is paid.

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Last month I outlined the depth of the AI supply chain, and Taiwan’s unique role at the center of the action. I followed that piece by opening my playbook for tracking the manufacture and sale of AI compute infrastructure, and the components which go into them.

Numerous readers responded with enthusiasm. Many asked why I am not charging for Culpium (because I am dumb!?), and why I provided such a valuable inside-look for free. I appreciate that enthusiasm and support.

Next week, I will go one step further.

On Monday 13th April — at 20:00 Taipei, if you want to set your alarm — I’ll be publishing the first edition of the monthly Culpium AI Supply Chain Index — CASCI.

It’s not well known that my background is in economics and econometrics. My undergrad major was in economics and finance, and I even did an internship at Australia’s Department of Foreign Affairs and Trade where I analyzed Australia’s trade with Europe. I have never formally worked as an economist or statistician because journalism was always going to be my career path. But these analytical tools have been fundamental to the way I dig up ideas and chase down news, and it’s a key feature of Culpium.

CASCI (pr. Kass-key) is the product of many months of data compilation, analysis, and testing. The index has existed in rudimentary form for more than a decade — not for AI specifically, but for my own use to more broadly track the technology supply chain.

Now it’s time to share it with you.

CASCI is built on monthly revenue data released by more than 1,000 Taiwanese publicly listed companies. Many of these businesses are entirely unrelated to AI — bicycles, restaurants, textiles. But a crazy number do have their fingers in the AI pie. I spent an inordinate amount of time going through earnings statements, annual reports, IR calls, client announcements, and media reports to sort the key cast from the background extras.

A core goal of this research was to find those companies which get a significant amount of their revenue from AI and are themselves major players in their own slice of the AI market. The result is a basket of three-dozen companies across the supply chain — upstream, mid-stream, downstream, and capacity.

Almost the entire value chain has been captured. But there are some gaps. Taiwanese companies are not players in the DRAM space, nor are they major suppliers of semiconductor-manufacturing equipment. But thankfully, there are ways to fill these holes.

The contract-assembly business model includes components such as memory at the top line. Hon Hai’s revenue, for example, incorporates the cost of buying DRAM from SK Hynix. So, CASCI does not capture SK Hynix revenue but does include Hon Hai. There are also companies directly correlated to memory, such as those which make memory-controllers.

Capacity capex is similar. A collection of local firms, for example, help TSMC build its fabs and kit them out with utilities like HVAC and wiring. Another subset offer unique production and testing tools that are used in the fabrication and assembly process. They’re not ASML or Tokyo Electron, but they follow the same trends.

So, while there are important companies that are not captured by CASCI, I am confident that I have enough solid proxies to track the AI supply chain without them.

Then there’s the size and scale issue. Hon Hai and TSMC are such huge companies that a revenue or market-cap weighted index would be dominated just by them, meaning that important signals would be lost in the shadows of the giants. But equal-weighting a tiny supplier of circuit-boards alongside a behemoth that spends $50 billion a year wouldn’t make sense either.

So, I dove deep into peer-reviewed research to find the best, most-trusted, and most robust methodologies. This was a nergasmic experience, and it reminded me how much I enjoy the study of economics and its cousin discipline of econometrics. CASCI employs smoothing and weighting techniques already used in major statistical series compiled by the OECD, the US Federal Reserve, and other economic institutions.

The result is an index which aims to capture signals while minimizing noise — which, frankly, is the goal of any statistical measure.

CASCI is different to any other publicly available data release for a few reasons.

• CASCI is real.
  Closely-followed measures such as a Purchasing Managers’ Index (PMI), export orders, and manufacturing activity are based on sentiment surveys of people or samples of data from companies and factories. They’re generally quite robust and consistent, but they’re not built on a full set of numbers. CASCI is based on revenue, a real, complete and actual measure of what companies are doing (and selling).

• CASCI is fast.
  By the time a company reports its quarterly revenue, two-thirds of the signal (the 1st two months of the quarter) is 30-60 days old, and the entire set is around 90 days old. Thanks to Taiwan’s monthly reporting requirements, including the mandate to file by the 10th of each month, CASCI provides the fastest signal possible. A sudden cut in orders or rush of shipments will show up long before quarterly earnings. Furthermore, you can pinpoint the timing of that action to within a few weeks, not months.

• CASCI is global.
  Most robust measures of activity like PMI, exports, and even GDP are country-specific. Because Taiwanese companies must report all of their revenue, including that which comes from overseas factories, their data captures global signals. This is particularly crucial as supply chains fracture and move away from China. Servers built in Houston are captured by CASCI, chips fabbed in Arizona are captured by CASCI, motherboards assembled in Thailand are captured by CASCI.

Going forward, you can expect a monthly CASCI update. It’ll be a single number showing the strength and growth of the AI compute industry — bigger is better — along with a cycle-phase indicator approximating the current place within the broader timeline of the industry’s ebb and flow. I’ll then give you some analysis on the data, what it means, and the current state of play in the AI compute build out.

Companies are required to file by midnight on the 10th of each month — some report just a little later. Therefore, CASCI will come out at 20:00 Taipei the following day, except when these deadlines fall on a weekend or public holiday.

Oh, and it’s free (yeah, I’m dumb).

I hope you find CASCI interesting, insightful, and useful. Look out for the first edition next week.

Thanks for reading

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Apple is in talks with suppliers to handle a massive dilemma posed by sales of its MacBook Neo that have surpassed expectations, I am told. The question they must answer, and soon, is whether to boost production of the hit laptop, or let their inventory of parts run out, my sources tell me.

MacBook Neo comes in four colors and two configurations, but each model runs on the same processor: the A18 Pro. That’s the chip used in the previous generation’s iPhone 16 Pro, whereas the latest device uses the A19 Pro chip.

But as Ben Thompson at Stratechery astutely observed, the MacBook Neo doesn’t use fresh batches of A18 Pro chips, they’re leftovers from the original production run.

MacBook Neo was designed around useable but leftover chips which would otherwise have been scrapped — remember, Apple are the masters at recycling! But with MacBook Neo being insanely popular, the stock of those binned chips will run out before demand gets satisfied.

Prior to the dilemma posed by this runaway success, Apple was only planning to have suppliers build a new Neo next year, powered by the current generation of binned A19 Pro chips, I am told.

Final assembly of the MacBook Neo is evenly divided between Quanta and Foxconn at factories in Vietnam and China, and the initial plan was to build around 5 to 6 million units before calling it a day, I am told. Right now, suppliers are unsure whether to expect production to continue beyond the original plan.

Leaving all that demand on the table is a painful prospect for Apple executives, but going back for another round would risk killing the sweet profit margins it enjoyed on making a device with “effectively free” chips.

MacBook Neo’s A18 Pro was — past tense — built on TSMC’s 3nm process (N3E). That node is now hugely popular and effectively sold out. Apple could, in theory, beg TSMC CEO CC Wei for a few hot lots — paying a premium to jump the queue — but that would almost certainly kill profits on the low-cost laptop. Alternatively, it could crib from its own wafer allocation originally planned for other devices, but the cost would still be higher than what it paid for the first batch of A18 Pro wafers (and infinitely higher than “free”).

Then there’s the matter of buying more aluminum — whose cost is rising — not to mention DRAM and NAND storage. Apple has a certain amount of power to get aluminum and memory at less-crazy prices than the rest of the industry, but one place their leverage is waning is at TSMC.

Neo Dilemma

Whatever happens — and to be clear, this next part is my analysis not what my sources tell me — Apple’s decision comes with compromises. If they do proceed with a fresh order, including asking TSMC for 10,000 to 30,000 wafers of A18 Pro yielding around 2.3 million to 7 million dies,1 then it will most likely need to raise prices of the MacBook Neo. And it would need to turn off the extra GPU core (of top-bin dies) so all Neos are created equal (theoretically, it could sell a 6-core GPU Neo — call it Neo Ultra — but I don’t see that happening).

One way to do deal with the higher cost would be to kill the cheaper 256GB model ($599) and only sell the one with 512GB of storage ($699). This wouldn’t entirely solve the margin shrink, but may mitigate it. Another approach would be to offer some new colors — I think (Product) RED would work well here — throw in some generous iCloud storage for free, raise the price, and thus preserve the margin.

Right now, the standard iCloud bundle is 5GB free. Giving away 200GB for one year, currently priced at $2.99/month, would follow Apple’s strategy of offering Apple TV+ for those who bought a new device. This is both an incentive to buy the product, and also a way to hook customers in the hope they’ll pay once the free period expires.

But there’s a bigger strategy Apple ought to consider here, and probably are: Getting more consumers hooked on Mac. To date, Mac has largely been an elite product for a niche group of customers by virtue of its high price tag, keeping market share at around 10%. This group is dominated by two categories of user: designers, and coders.

Unfortunately for both, AI threatens to decimate their industries. Furthermore, AI is doing this design & code work in the cloud. High-powered personal computers just aren’t needed when you can do video editing, compile code, or crunch data on a lower-spec laptop with an internet connection.

Coincidentally or deliberately, MacBook Neo is just that: a lower-spec laptop with an internet connection. Concerns that Neo could cannibalize sales of premium devices like MacBook Air and Pro are fully justified. But only in the short-term. In the long run, Neo will likely steal customers from Windows PCs, broadening the Mac ecosystem. As more enterprise software gets run from the cloud, Mac will in turn become a more viable option for corporations otherwise stuck with legacy software tied to Windows.

I also think the Neo will have a shorter replacement cycle, akin to the two-to-three-year interval between iPhone purchases, than the longer five-to-seven-year cycle for items like MacBook Air.

The Stocking Strategy

Twenty years ago, the most common default gift for the festive and back-to-school seasons was the iPod. It was a risk-free item that anyone would love to get, but not so expensive that either the giver or receiver would feel uncomfortable. The iPhone doesn’t quite fit that bill, partially because phones are usually tied to a telecom subscription. The low price of Neo makes it a viable stocking stuffer.

Furthermore, the high price, low supply, and production problems — h/t Lauly Li & Cheng Ting-fang at Nikkei — for the forthcoming iPhone Fold make Apple’s most epic product launch in over a decade unlikely to be an easy win. The Neo, especially given its array of exciting colors, has a much better chance of bringing new buyers into the MacOS ecosystem. But only if Apple ponies up for more components.

Thanks for reading.

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TSMC has swapped metal cutlery for disposable plastic in its dining rooms, shut a swimming pool, and curtailed access to gymnasium showers in response to Taiwan’s driest winter on record, my sources tell me.

The restrictions were announced and rolled out over the past week after Taiwan’s Water Resources Agency pointed to low water levels at key reservoirs in the nation’s north, I am told. Production is unaffected and the move is a precautionary measure as Taiwan waits out an especially water-starved dry season in the hope that plum rains, usually in May, fill the country’s water stores.

Rainfall in the northern region which includes Hsinchu, location of TSMC’s HQ and eight fabs, has been around 25% below the historic average over the past five months, spurring the WRA to raise its water alert for the region to yellow on March 12, Taiwan’s Central News Agency reported last month. Taichung, in central Taiwan where TSMC also has facilities, had its alert raised to green last week.

Taiwan uses a four-light alert system ranging from green (warning) through yellow (reduced water pressure), orange (reduced water supply), and red (water rationing). Almost 80% of Taiwan’s annual rainfall comes in the months of May through October, with December and January the driest, according to the WRA. As a result, the country starts bracing for shortages by March if the previous wet season was below average and the subsequent dry season failed to make up for the shortfall.

TSMC’s water management protocol, including temporary restrictions for non-manufacturing usage, tends to follow the government’s alert system. While the situation at Taiwan’s northern basins remains healthy, the water level at the Second Baoshan reservoir, a key source of water for the Hsinchu Science Park, is currently below 22%. Low reserves in the center and south are more acute, data shows.

“In response to the light stage of water pressure reduction, TSMC’s facilities within Hsinchu Science Park have put conservation measures in place including: decrease water discharge from the system, reduce facility water consumption, increase wastewater recycling of facilities, improve water production rate of the system, and other relevant water management actions,” TSMC told Culpium in a statement. “We do not expect water conditions to have a substantial impact on operations.”

In reality, TSMC’s measures are unlikely to have much impact on the company’s water usage, which was around 130 million metric tons — 52,000 Olympic-sized swimming pools — in 2024. Instead, the move can be seen as part of the company’s broader conservation strategy and efforts to be a good corporate citizen.

If significant rainfall doesn’t come soon, though, water restrictions could quickly escalate with direct and indirect consequences for production across the country.

More broadly, the current drought highlights the many challenges Taiwanese industry must overcome to keep production humming along.

The US attack on Iran, and subsequent retaliation against Qatar and closing of the Strait of Hormuz caused fears that supply of helium might dry up. Helium, the second-smallest and lightest of chemical elements, is used in chip production as a carrier gas and to cool equipment. Potential shortages have yet to materialize, with Taiwan’s government last week announcing that alternative supply from the US is available.

A bigger issue, though, is Taiwan’s vulnerable electricity supply. As TSMC expands, and Taiwanese manufacturers bring more capacity back from China, energy needs will continue to escalate. That demand is colliding with the government’s shutdown of nuclear power stations and bigger reliance on gas-powered generation.

Last month, however, President Lai Ching-te backed away from his party’s anti-nuclear stance by suggesting the government may be open to restarting Taiwan’s nuclear reactors. Lai’s Democratic Progressive Party has long held a ‘no-nukes’ stance in favor of renewables such as offshore wind, but higher demand from manufacturers plus a rollout of AI data centers has forced his administration to rethink that policy.

Thanks for reading.

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Cheng Li-wun may be one of the world’s most pivotal politicians. Not forever. Perhaps not for long. But at least for now.

I’ve long held the belief that people don’t care about other people’s domestic politics. Except for the US; everyone cares about US politics. Taiwan’s domestic machinations used to fit that mold of being unimportant to anyone but locals.

Not anymore.

The US, and the entire world, will be impacted by decisions that Taiwanese people, and their leaders, make over the next decade. That’s because the country sits uniquely at the nexus of supply chains, great-power rivalry, shifting trade routes, and energy demand.

What may look like mere domestic affairs will impact Taiwan’s ability to shape its own future, and as a result the future of global manufacturing. And that’s before considering outward-facing policies such as industrial development, foreign relations, and military procurement.

If things go awry in Taiwan, it won’t just be the world’s supply of leading-edge chips that go up in smoke. The flow of AI servers will grind to a halt, and the management & logistics of industries spanning shipping, shoes, golf clubs, plastics, bicycles, yoga pants, and military hardware will get stuck. It’ll be like the Ever Given blocking the river of global trade, with no tugboat in sight.

That’s why Cheng’s role is more important than perhaps she herself realises. Her power comes not from who she is as a person, but whom she represents: Taiwan’s Kuomintang.

Rich Party, Poor Party

Once the world’s richest political party, the KMT’s heft has atrophied over the past quarter century. It went from being the authoritarian leader of a single-party state to leaning on an upstart political party just to gain slim control over Taiwan’s legislature.

By the end of the current presidential term in 2028, the KMT will have held the top executive role for just eight years since the turn of the century. The incumbent Democratic Progressive Party, in office since 2016, will have been in power for the other twenty.

A graduate of the prestigious National Taiwan University Law School, Cheng Li-wun went on to postgraduate studies at Temple University in the US before earning a Master’s Degree at Cambridge University.

The most important line on her resume, though, came just a few months ago when she was elected Chair of the KMT.

Now Cheng has the task of herding the cats of the KMT within the legislature and the party at large. The direction the KMT takes matters a lot because not only does it offer the only viable alternative to the policies of the DPP, but it signals to the outside world what possible paths the country itself might take.

The KMT needs to give the Taiwanese people a good reason to put them back in power, but it cannot ignore the reality that the world is watching how it manages relations between two great powers.

A comment from Cheng to the Taiwan Foreign Correspondents’ Club this week encapsulates that tension.

“The Kuomintang ​has long maintained very good relations with the United States. This will not impact our efforts to improve relations with the mainland,” Cheng said.

On the surface, this looks like great diplomacy. We’re everybody’s friend, she seems to be saying, so we’re going to play both sides.

But it’s also either woefully naive, or a show of political cunning. No matter how much Taiwan, or anyone else, wants to try to be besties with both Beijing and Washington, the reality is far different.

On the surface, for matters that don’t actually matter, this may be possible. For substantive issues it’s entirely impossible.

When Washington puts pressure on Taiwan to boost military procurement, Beijing will put even more pressure on Taipei not to. If Beijing urges Taiwanese to invest more in Chinese industry, including chip fabs, you can be sure Washington will step in. In this neo Cold War, those who try to sit on the fence are sure to fall off.

Yet, actually sitting on the fence and merely appearing to sit on the fence are two different things. Right now Cheng is not in power, as she was at pains to point out to that crowd of foreign journalists and diplomats on Monday, so she has plenty of freedom to try and placate both sides. This tactic may not appease both Bejing and Washington, but it won’t upset them either.

More importantly, she gets to play to all sides of Taiwan’s political spectrum — those who are fearful of, and those who are friendly toward, each of China and the US. In so doing, she keeps the KMT’s chances of survival, and possible presidential revival, alive.

And she needs to. Put simply, all three nations — Taiwan, the US, and China — need the KMT to remain a viable force in Taiwan politics.

Failing to do so could lead to disaster, and very possibly World War III. That’s not to mean the KMT is required to actually be in power. In fact, many rightfully fear that the KMT will once again trade the interests of Taiwanese people for closer ties to Beijing. The party moved in that direction once and got swiftly flogged for it. The result was a lost decade for the KMT where there were few guardrails against the DPP’s agenda.

This period commenced in 2014 when the Sunflower Movement popped up in opposition to incumbent president Ma Ying-jeou’s opaque policies on trade and investment with China. It ended when Tsai’s term concluded and her VP Lai Ching-te kept the DPP in power but lost the legislature to a KMT-led coalition.

Tsai’s eight years as president were largely good for the country both domestically and internationally. Wages grew, the people got through Covid without a single lockdown, and more nations started standing up for Taiwan in the international arena. But escalating housing prices and a low birth rate became particularly notable concerns, while longer-term issues like energy and military security were not adequately addressed.

As a result, the KMT’s power has rebounded and the shine has gone off Tsai Ing-wen’s presidency. Whether the KMT or the DPP has the better policies, it’s not at all healthy for a democratically elected leader to govern against a weak and ineffectual opposition. The KMT’s survival also helps ensure the legitimacy of the DPP.

The US also needs the KMT to be viable, despite it not appearing that way right now. The party was once Washington’s bff in Asia. Secretary of State Marco Rubio’s recent comment that the US is “going to unleash Chiang on these people” — meaning Iran’s leaders — was a reference to the 1950s policy (or threat) of using Chiang Kai-shek to attack Mao’s Communists.

Recently, though, the KMT just seems obstructionist. It’s stood in the way of the DPP’s attempt to bolster Taiwan’s military capability while at the same time being a bit too eager to cozy up to Beijing.

On the flipside, Lai Ching-te has offered Washington reason to be wary since he’s eschewed the measured tone taken by his predecessor. I see his recent speeches, including one labeling China a hostile foreign power, more akin to the rhetoric of the DPP’s first president Chen Shui-bian — whom the US didn’t much like. If the KMT can slow its sprint toward Beijing to a mere jog, and embrace the notion that Taiwan probably needs to spend a lot more to boost its national security, then Washington and the KMT can rekindle their friendship.

Saving Face

Then there’s China.

I won’t brook the notion that Taiwan is provoking China to attack. The People’s Republic of China is an independent sovereign nation. Its leaders have full agency over whether or not they launch a military strike against 24 million people who pose no threat. The only reason to do so is to save face. Because allowing Taiwan to be recognized for its own independent sovereignty would be an admission that the CCP failed in its decades long quest to defeat the KMT and extinguish the Republic of China — and thus complete its takeover of “China.”

But it’s naive to believe Beijing will act reasonably. And that’s where the KMT comes in.

For as long as the Chinese Nationalist Party (the literal translation of “Kuomintang”) exists, then the CCP can keep fantasizing about peacefully bringing Taiwan under the PRC umbrella. The death of the KMT, or its lack of viability, removes that pretence. All that would be left is a DPP keen to certify Taiwan’s independence. (And the Taiwan People’s Party, which is barely hanging onto political relevance and would not get away with a swift move toward unification.)

The KMT’s Cheng, while well-educated, is probably not a viable long-term leader of the party as it advances toward the next presidential election. But she fits the role that the KMT needs of her, and which Taipei, Washington and Beijing need someone to play. That is, to moderate the DPP while ensuring Beijing feels it has a friend on the other side of the Taiwan Strait.

Without the KMT, Beijing would have no peaceful political path toward unification. That would in turn reduce the argument made by the CCP’s more moderate factions to hold off on a military solution and continue pursuing political dialogue.

Time and again Cheng makes comments which make me wonder whether she’s politically simplistic, intellectually vapid, or strategically sly.

The Trump Frenemy Playbook

She has indicated that part of her plan to secure peace would be to first visit Chinese leader Xi Jinping before heading to Washington. It feels like she’s taking a leaf from President Trump’s playbook — coddle your nation’s enemies and deprioritize your allies.

”I believe that the other side hopes to resolve cross-strait issues peacefully,” she told the TFCC on Monday. Maybe she’s right, but she’s ignoring the fact the only resolution Beijing seems willing to accept — Taiwan under the PRC’s umbrella — is one which Taiwanese abjectly reject. Meanwhile, Beijing has made clear it’s more than willing to “resolve” the issue by killing an untold number of Taiwanese, who pose no threat to the people of China.

Then there’s the KMT’s grasp of the one issue which truly matters to rest of the world: semiconductors.

At that TFCC event on Monday I asked Cheng whether she thinks the US is hollowing out Taiwan’s chip industry — a growing concern among Taiwanese — and what role she thinks the tech sector should play in securing its national security.

“Taiwan, including its semiconductor industry, must have a global footprint. Therefore, TSMC cannot have all its R&D centers and manufacturing facilities solely in Taiwan. A global presence is essential,” Cheng responded. “Everyone agrees that TSMC’s most advanced and core technologies must remain in Taiwan. This issue can be discussed at a national security level. As for other aspects, a global footprint is necessary.”

That’s a lot of words. But not a lot of substance, and not quite correct. I’ll admit that I’m biased. Technology and semiconductors are my beat, and I asked the question. Yet since technology is so important to Taiwan and the world, I expected the leader of the opposition to have a clearly enunciated policy.

But that’s kind of the role the KMT, and Cheng, is playing right now. They’re offering something for everyone, a buffet of policies, without being pinned down on anything. It’s clear to Taiwanese voters which flavors the KMT favors — less military spending, closer trade and political ties with China, and an eventual path toward Taiwanese being subjects of the PRC. That’s in the KMT’s DNA.

So naturally, those wary of the KMT’s stance on China see Cheng as kowtowing to Beijing. Yet those who believe Taiwan is too enamored with the US feel she’s a little too conciliatory toward Washington.

That sounds oxymoronic in light of my earlier observation that she’s trying to appease all sides. Not necessarily: she’s giving those who want to believe in the KMT something to cling to, betting that these “wannabe believers” outnumber the absolute naysayers who’d never vote KMT anyway.

If true, what this means for war and chips is that the KMT is needed. Perhaps as the party of power, perhaps as the perpetual opposition. But in either role, the KMT can keep admiring the Chinese Emperor’s new clothes — allowing Beijing to keep believing that unification is a possibility.

For as long as that happens, the world will keep enjoying its leading-edge chips, and AI servers, and yoga pants, and bicycles. And peace.

Thanks for reading.

More From Culpium

Last week I published a column explaining the depth of Taiwan’s AI supply chain. Judging by the reaction, that piece was a banger.

It was made possible by a lovely quirk in Taiwan’s securities law: All listed companies are required to publish their monthly sales by the 10th day of the following month. Even better, there’s a very systematic way the data is presented and uploaded. I don’t know of any other country which requires such regular and standardized disclosures.

As a result, Taiwan offers the best, most consistent, and deepest leading indicator of the state of global demand. Not just for AI compute but technology overall, plus a number of traditional industries including footwear, clothing, and even sports equipment. Following this data can offer insights many weeks and even months ahead of official quarterly earnings reports.

As a numbers nerd, this delights me. I’ve enjoyed bathing in this information for many years, and it’s always given me an edge in tracking the global electronics supply chain. There are other quaint Taiwan regulations which are also a boon for those of us seeking more data to analyze.

Today I’m going to explain how to use this data and what to look out for.

Before I start, there’s some rules of thumb worth following:

• Be cautious of month-on-month growth numbers.

• If in doubt, default to YoY growth data.

• Always group January & February together, because Lunar New Year moves annually.

• Some sectors are naturally lumpy, some are not. Learn which is which.

• Rolling 12-month data can be quite helpful.

• Sorting signal from seasonality is both an art and a science, which means it’s valuable.

• Consider converting to USD for exporters in order to remove forex noise.

Meet MOPS

Taiwan’s filings are found through its Market Observation Post System, known locally as mops. It’s in traditional Chinese, but there’s an English language version with the same data. And since both the data and the upload process are standardized there’s zero lag between the time the English and the Chinese versions are posted.

I tend to use the Chinese version for two reasons. I know the tickers of all the companies I usually keep an eye on, but if I see weird data pop up for something that’s not on my radar I’m more likely to know the Chinese name than the English name. Secondly, I’m likely to yield more if I Google the Chinese name rather than the English. However, in the day of Google Translate you’re really not at much of a disadvantage if you can’t read Chinese. But bear in mind, Taiwan follows the ROC dating system. What’s called 2026 in the rest of the world is known as 115年 in Taiwan.

I won’t spend time telling you how to find and retrieve the data, ask your favorite LLM for help. But let me just point out that even if you read zero Chinese, you’ll see the letters “CSV” inside a button at the top center of the page. Click it and see what happens! Unfortunately, the English version doesn’t yet offer an easy CSV download (sad!).

The first thing you’ll notice is that the data shows current month, prior month, same month year prior, and growth stats. This easy presentation of numbers alone is cool and super helpful. But there’s also cumulative year-to-date data. (Note my earlier caution about MoM data). So, March data will already show you first-quarter revenue well before official 1Q earnings are announced. For later quarters, you can just back out latest YTD from YTD of the prior quarter.

That’s why you’ll often see sell-side analysts and financial journalists writing about quarterly revenue well before the official announcement. That tends to kick off a round of analysts updating their earnings models. So, if you truly want to compare results versus estimates you should grab analyst forecasts before the 10th of the month — even better, before the quarter’s end.

Another helpful part of this data dump is that it’s clumped by industry group. From the first screenshot above, you can see that cement companies posted a 4.12% drop in revenue for the year through February. Industry category is based on whichever sector accounts for at least 50% of their operations. A company’s business can sometimes drift over time, and thus may appear on the cusp of a new category, but overall I think this delineation is about right.

Great Granularity

What I particularly love about Taiwan’s equities market categorization is how granular it is. Taiwan is a technology nation. Its economy and stock market reflect that fact, which means there’s delineation between different types of “tech.” There’s 33 categories overall, with eight subcategories of “electronics.” That means we can discern between semiconductors and optoelectronics, or electronic parts & components from electronic products distribution.

But it’s not perfect. Acer, which is a PC brand without factories, is categorized alongside its former manufacturing arm Wistron. As is Asustek and its spinoff Pegatron. Nevertheless, this segregation is good enough for a solid macro view of tech, and it’s pretty easy to build your own custom industry model in a spreadsheet anyway.

Supply chains in any sector are generally divided into upstream, mid-stream and downstream. That’s how I view tech, and I include equipment & capex in a fourth category. Here’s a non-exhaustive sample:

• Upstream — TSMC, UMC, Winbond, Nanya Tech, ASE, Mediatek

• Mid-Stream — Gigabyte, Cooler Master, Delta, Largan, Hon Hai, Mitac

• Downstream — Hon Hai, Wistron, Quanta, Wiwynn, Inventec

• Equipment & Capex — United Integrated Services, Favite

Upstream is where the seed of a product is planted: chips, passive components, chemicals, raw display panels. Mid-stream is where the saplings sprout, and includes modules, system trays, partially completed displays. While downstream is the stout tree, ready to be harvested. The boundaries between them is blurred, and companies often cross these lines. TSMC is most certainly only upstream but Hon Hai does end-to-end.

One peculiarity of Taiwan industry which distinguishes it from Japan and South Korea is that there are few vertically integrated conglomerates. There use to be more a few decades ago, such as a famous maker of rice cookers (iykyk), but today most integration is horizontal (meaning, at upstream, mid-stream or downstream). Foxconn is a rare example. For you and I, this means that tracking monthly sales offers great visibility into various stages of the supply chain. That’s harder to do for, say, Sony or Samsung unless you drill down into their quarterly data to work out which divisions contributed to revenue.

What To Look Out For

You know now what data is available, how to use it, and how to categorize companies by their place in the supply chain.

The final step is putting this all together, and one way to do so is to work backward from product launch.

Nvidia and AMD sell chips and servers, but they don’t make them. Hon Hai, Wistron, and Wiwynn do the servers (and TSMC produces the chips).1 But before these companies can assemble and ship a server to Nvidia, they need to buy the partially assembled parts such as compute trays, power suppliers, and cooling systems. That means the likes of Gigabyte and Cooler Master. But these companies must first get the chips and passive components from TSMC, Winbond, or even fabless companies like Mediatek and Realtek (via TSMC). (Note that Nvidia is a little unique in selling both chips and servers. Also, it doesn’t really sell raw AI chips, it actually ships most of them as modules assembled onto a printed-circuit board.)

Yet, these upstream players can’t do any of this until they’ve built their factories and installed equipment — that means UIS (as well as foreign suppliers like ASML and Applied Materials). They also need to set up ways to test what’s being produced, enter Favite.

As a rule of thumb, the lag to final product availability goes something like this:

• 36 months: Construction commencement to final product (not chip).

• 24 months: Equipment move in.

• 12-18 months: Start of chip production.

• 6-12 months: Packaged chip. Passive & other components.

• 3-6 months: Module assembly.

• 0-6 months: Final assembly.

You can and will find many exceptions to this timeline. New product introduction (NPI) can take a lot longer because everyone in the supply chain is on a learning curve. More complicated products and entirely new categories take longer, consumer electronics can be much shorter. Cars take forever if they’re European, no time at all if they’re Chinese.

Following trends across broader categories as well as subcategories is the bread and butter of supply chain analysis, but the true value is in finding and understanding outliers. The single most-important glitch in the matrix comes from inventory. A product made and shipped is not a product sold — a lot ends up in warehouses. CFOs hate inventory because it’s wasted money, but healthy inventory also means a business development team doesn’t miss out on a sale — CEOs hate opportunity cost. Unfortunately Taiwan monthly data doesn’t track inventory directly, but electronics distributors can be an imperfect proxy. As can export-import data.

That’s all you need to know to start your supply-chain-tracking journey. You can see that Taiwan monthly sales are robust, regular, and reliable. But the data still requires analysis.

Last week I put a poll at the end of my column — There’s a Whole Lot of Taiwanese AI Winners Not Called TSMC — where I ask whether you’d be interested in a regular Taiwan sales roundup. Please take the time to click over to that column and answer just one question. Culpium remains free, so your feedback is most valuable in guiding me toward delivering what you want and find interesting.

Click to Answer One Quick Question.

Remember, if you want to understand where AI is going then earnings numbers from Nvidia, AMD and Google will eventually get you there. But it’ll be a long way behind data on Taiwan monthly sales.

Thanks for reading.

More from Culpium:

Everyone knows the name Taiwan Semiconductor Manufacturing Co. It’s the world’s most important company. TSMC’s position atop the global supply chain comes from its ability to do what no other organization in the world can do.

Revenue figures for Taiwanese companies point to another stark fact: the rest of the country is just as important to the global economy as TSMC, and equally irreplaceable.

Sales across the Taiwan Stock Exchange’s almost 1,000 listed stocks climbed 27% in US dollar terms for the first two months of this year. That’s not just a handful of blockbuster companies, but the average across the entire bourse including makers of chips, clothes, chemicals, rice cookers and cars. By comparison, sales for S&P 500 member companies is expected to climb 8.5%.

Taiwan is an export-driven economy. Its biggest companies by revenue are all tier-one exporters, meaning their business comes from selling stuff overseas. If these companies are enjoying massive growth then it’s because their foreign clients are desperately in need of the products they make. A decade ago, the world needed smartphones, a decade prior it was laptop computers. Supply chains for both were dominated by Taiwanese companies — with an assist from Chinese factories.

Right now, the world craves more computing systems to drive the irreversible rollout of artificial intelligence. Once again, it’s Taiwanese businesses that command the end-to-end supply of chips, circuit boards, server racks, cooling systems, and final assembly. Note also that very few Chinese companies are big players in the AI hardware space.

It’s easy to think this is Nvidia’s show, with TSMC and Foxconn merely playing supporting roles.

There’s another way to look at it, though. Nvidia is without a doubt the name to watch in AI compute, but it’s not doing it alone. In reality Nvidia makes nothing. Its chips and servers are the product of an entire ecosystem of companies that own the factories, manage the components, and handle the logistics. They’re mostly Taiwanese.

Heading the list by revenue growth is Wistron and Hon Hai (aka Foxconn) which sold $10.3 billion and $8.4 billion more in product respectively than they did in the first two months of last year. Remember, that’s additional revenue and it comes almost entirely from AI servers and the parts which go into them.

Wistron brought in $16 billion of business over the past two months largely because of fresh AI orders. The Taipei-based company, a spinoff from PC brand Acer, last year opened a new factory 10km from TSMC’s HQ just to make AI servers. Capacity was sold out almost immediately and almost entirely to make systems for Nvidia, the Economic Daily reported. It’s no less astounding that Foxconn, whose revenue topped $260 billion last year, can still manage 25% growth.

“It’s looking like a very good year,” Foxconn Chairman Young Liu told reporters last week. “Consumer electronics is looking very good because of smartphones, and AI is also looking very good.”

Top-line numbers for assemblers, also called contract manufacturers, is a little misunderstood. A large chunk of the $42 billion in revenue Foxconn posted for January and February was the resale of components that clients like Apple, Nvidia, Cisco and Sony ordered.

Although specifics vary from company to company, and product by product, the business model generally entails the client selecting parts and then having the contractor buy those components on its own dime. They’re then assembled into the devices the manufacturer was contracted to make. Sometimes the contractor can mark up the cost, other times it buys at the contracted rate but charges an agreed upon fee per unit of final product. Sometimes it’s a combination of both.

That’s why watching these companies is so useful. When contract manufacturers like Quanta, Hon Hai and Wistron are adding $2.5 billion to $8 billion in revenue per month, you can be sure their clients and the rest of the supply chain are also going strong. The caveat here is that escalating inventories can be a bad sign. These stockpiles are built based on expectations of future demand. They can usually be digested when the market slowly turns, but a one-off shock like the 2009 financial crisis can cause suppliers and assemblers a world of pain.

What makes the Taiwan supply chain run deep is the plethora of companies that provide esoteric and specialized products. Take Favite, a maker of automated optical inspection equipment, which posted $8.9 million in revenue over the past two months. That doesn’t sound like much, but it’s a whole heap more than the $133,000 it posted a year earlier. That sudden boost comes from its privileged position as a supplier of test equipment for TSMC’s future packaging technologies including next-gen Chip-on-Wafer-on-Substrate (CoWoS) and Chip-on-Panel-on-Substrate (CoPoS).

Another AI winner is United Integrated Services, which posted a 64% jump in sales. On paper, Taipei-based UIS is just a construction company, and not really an exporter. But its biggest client is TSMC, and it recently won contracts to manage the development of upcoming N2 wafer and CoWoS packaging fabs in Taiwan, as well as the chipmaker’s Arizona expansion.

Then there’s Winbond and Nanya Tech, among Taiwan’s few surviving memory-chip makers. Although not major suppliers of the high-bandwidth memory that’s fused with Nvidia and AMD GPUs, both companies are winning from higher DRAM prices and increased demand for faster DDR that goes into both AI servers and edge devices.

There are too many fascinating AI-connected suppliers for me to list them all, but if you want some extra reading then I suggest looking up Accton, Gigabyte and HiWin.

Next week I’ll show you how to use Taiwan monthly sales data to track trends in various industries. Make sure you’re subscribed.

Thanks for reading, and please take the time to fill out the survey.

More from Culpium:

South Korea’s benchmark equity index had a bad day. Trading in Seoul was suspended for 20 minutes late morning on Wednesday after the KOSPI index dropped to a level that was 8% below the previous day’s close, and it stayed there for at least one minute — triggering a Level 1 Circuit Breaker. Stocks kept sinking after the break, ending 12% lower for the worst daily drop since the global financial crisis of 2008.

The US-Israel war against Iran was cited as the cause: logical. Oil prices shot up after the aerial attack on the major oil producer began on 28 February: of course. South Korea imports most of its energy, thus higher oil prices are bad for its economy: probably. Meanwhile, shares of Korean defense makers like Hanhwa Aerospace have surged since the bombardment began: naturally.1

Iran’s targeting of oil tankers certainly makes the supply of energy less secure, and no electricity means no chips. But US President Donald Trump countered by offering to insure ships and protect them with the might of the US military, so that risk is surely ameliorated.

This from Investing.com’s Ambar Warrick summarize what happened:

”Wednesday’s losses saw the KOSPI wipe out as much as 18% from a record high hit last week. The KOSPI also effectively halved its year-to-date gains to around 20% on Wednesday,” Warrick wrote in a mid-morning update. “Declines were driven chiefly by profit-taking, as investors locked in recent gains and turned largely risk-averse with the outbreak of the U.S.-Iran war over the weekend.”

This one line in particular stood out: The KOSPI also effectively halved its year-to-date gains to around 20%. We’re barely two months into the year and an entire equity index, with a market cap of around $3 trillion, had already advanced 40%.

But none of the above explains why the KOSPI, which is heavily weighted toward memory-chip makers Samsung Electronics and SK Hynix, should plummet. Higher energy prices would have minimal impact on the cost of making memory chips since the biggest expense is the depreciation of their multi-billion dollar fabs. Besides, any increase could easily be passed on to customers such as Nvidia, Apple, and Dell which reportedly have teams camped out in South Korea to secure DRAM supply.

Citrini and the Global Intelligence Crisis

I think a good lens through which to look at this is the one offered by Citrini a few weeks ago in that now infamous report titled “The 2028 Global Intelligence Crisis.”

Citrini laid out a scenario of what the future might look like in the age of AI, it wasn’t a prediction or prescription. It was an idea.

Despite the clear explanation at the very top of the article, AI permabulls were enraged. The most common criticism it attracted was that it was “AI slop,” the internet’s insult du jour — a modern day version of “fake news.” If you don’t like something or it offends your senses, just call it AI slop.

The hate for that piece is also somewhat illogical. If Citrini’s article was the reason why billions of dollars of market cap were wiped off stocks, as has been alleged, then the weakness in the bull case is all the more apparent.

Yet, if the Citrini scenario was actually weak and fanciful, then it defies logic that such “AI slop” could possibly have such an impact on markets. For sure, traders and investors who manage billions of dollars in funds aren’t always perfectly logical, but they’re not stupid either. If Citrini’s apocalyptic scenario was entirely ill-conceived, then it would have just been ignored by most market players. The problem is that the article’s critics are so adamant that they’re smarter than the thousands of people who trade every day that they’re likely to miss the true signs of trouble when they appear.

This week’s sell off in Korean memory stocks offers another chance to check our assumptions about the AI rollout. And it’s a good time to re-examine Citrini’s essay. Here’s Culpium’s summary of Citrini’s “The 2028 Global Intelligence Crisis.”

• Much of modern white-collar work deals with removing friction.

• AI can remove friction more efficiently: booking flights, tax prep.

• AI also makes it easier to deploy new features, removing differentiation.

• No friction plus no differentiation means lower prices and fewer human jobs.

• Reducing the number of humans with salaries also reduces spending power.

• Economic malaise ensues.

Many of the wiser critics drilled down to specific arguments, specific scenarios, and specific case studies to prove their point. That’s exactly the way to approach it, but doesn’t necessarily deliver a knock out blow.

The best critique of the piece came from Ben Thompson at Stratechery, which probably isn’t surprising. Thompson is the father of Aggregation Theory, so naturally he homed in on the example of DoorDash. In his view, Citrini’s idea about the coming collapse of DoorDash shows that the author doesn’t really understand how these platforms work, as laid out in Aggregation Theory. Thompson might be right. He also might be wrong. I guess we’ll know if DoorDash is still around a decade from now (Citrini posited 2028, but that seems a little rushed).

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Whichever way it plays out for DoorDash, Citrini’s broader argument about the possible impact AI may have on white-collar work — especially that which is dedicated to smoothing out frictions and product differentiation — remains worthy of consideration. Unfortunately for the author, they may have chosen the wrong example and argued it in a way that didn’t pass the Ben Thompson smell test.

Bubbles and Being Broadly Right

I think it’s possible to be right more broadly, even if you’re wrong on specifics.

Bubbles are the best example I can think of to illustrate this point. The collapse of the housing bubble almost 20 years ago was obvious in hindsight, but few people predicted it back then and even fewer nailed the timing. Michael Burry, immortalized in Michael Lewis’s book “The Big Short,” predicted the subprime crisis. But the tension over timing is key to the drama, and to the perception of being right.

“I may be early, but i’m not wrong,” Burry is quoted as saying. But few remember the rebuttal he received: “It’s the same thing, Michael.” In the end, Burry’s overarching thesis was right, and his timing was close enough. The bulls got crushed.

Citrini’s broader thesis is that AI will eliminate white-collar jobs, which will in turn decimate the consumer economy upon which the US and many other nations are built.

“The velocity of money flatlined. The human-centric consumer economy, 70% of GDP at the time, withered. We probably could have figured this out sooner if we just asked how much money machines spend on discretionary goods. (Hint: it’s zero.)” — “The 2028 Global Intelligence Crisis”

That’s not a ridiculous notion. Just last week Jack Dorsey laid off 40% of his workforce at Block — 4,000 people in total — and cited AI as the reason. “We're already seeing that the intelligence tools we’re creating and using, paired with smaller and flatter teams, are enabling a new way of working,” Dorsey wrote on Twitter, the very platform he founded (and which Elon Musk renamed X). It’s entirely possible he was using AI as an excuse, not a reason, but that alone proves Citrini’s point: AI will be the cover executives needed to cut staff.

This brings me to the key issue which ties Citrini and the KOSPI together.

Three months ago I argued that the 2000 dot-com bust isn’t quite the right framework from which to view the current AI rollout. Instead, I believe that the development, production, and sale of memory-chips is a more apt example. My reasoning is thus: the cost of building the factories to make the product keeps getting more expensive, yet the product being made is barely distinguishable from rivals (it’s a commodity), and the price is constantly falling. Thus, AI tokens and memory chips are largely similar.

Right now, the price of DRAM chips is rising thanks to the current AI boom which is stealing capacity from traditional uses such as computers and smartphones. This won’t last. In fact, the usual trend of falling ASPs (per bit) will resume because clients demand it and suppliers will be able to deliver it. We’re in a short-term anomally, not long-term stasis. These companies will lose money one day, because the industry is so cyclical that booms always turn to busts.

Normalcy is Bad News

One reason why the KOSPI took a tumble this week is because that return to normal may be closer than we’d previously expected. Not so much because of an immediate cut in demand for DRAM, or even a lowering of the short- to long-term view of the AI rollout, but because what underpins all of this is the broader global economy.

Higher oil prices tend to boost inflation while also lowering economic activity. Data also shows that capital investment suffers during such periods. MSCI this week modeled a scenario where a 35% rise in oil prices resulted in a 13% drop in US equities.

Current massive capex budgets by major technology companies, from Amazon.com and Alphabet, to Meta and Microsoft, is only possible because they’re sitting on legacy cash-cow businesses. These are driven by discretionary spending and monetized through volatile businesses such as ads, software & cloud-services, and e-commerce. This cash needs to keep flowing because the gargantuan sums they’re burning on AI infrastructure is years away from turning a profit. Any threat to that money stream threatens their ability to keep buying expensive Nvidia chips, and the high-bandwidth memory produced by Samsung and SK Hynix.

Yet the Citrini scenario also applies. Capex, once spent, cannot be unspent. AI data centers are a drag on income for five to seven years, at a minimum, and up to 20 years for the building and plumbing. If AI truly does allow a company to do more with fewer staff, then there’s no reason not to do a Dorsey and fire people. Investment figures, one of the components of GDP, could continue to rise even as unemployment climbs — creating what Citrini calls “Ghost GDP.” Additionally, any uncertainty gives executives great cover to cut staff by blaming the economy — indeed, it almost compels them to do so.

On the flipside, however, economic uncertainty also compels executives to be a little more circumspect in how much they spend on future AI infrastructure. Past capex can’t be undone, but future earnings can be saved by rolling back or delaying their pending equipment purchases. I am not saying that’s what’s happening now, in fact I have seen no evidence yet that it is. But a savvy investor would be wise to price it in to their model given the uncertainty surrounding the current Middle East conflict which has already impacted flights and threatened maritime transport.

Ray Dalio and the Arenas of Conflict

There’s a third aspect that’s also worthy of attention. A few weeks ago Ray Dalio made the bold proclamation “It’s Official: The World Order Has Broken Down.” While he was being a little hyperbolic, Dalio’s comment on Twitter was actually a distillation of statements made by world leaders at the Munich Security Conference. His claim wasn’t entirely clickbait.

Dalio then followed up by generously copy-pasting Chapter 6 of his best-selling book “Principles for Dealing with the Changing World Order.” That chapter, titled “The Big Cycle of External Order and Disorder” is a walk through the history of the causes of war and strife, in which he presents his thesis on the five major kinds of fights between countries, and how each are connected to the other.

One particular passage struck me, and I’m reminded of it today as I look at Citrini’s thesis and the KOSPI’s plunge:

“The United States and China are in an economic war that could conceivably evolve into a military war,” Ray Dalio wrote, noting that technology is among the arenas in which nations fight. And he goes onto observe, and perhaps warn, that “protecting one’s wealth in times of war is difficult.”

The AI permabulls are convinced that artificial intelligence will upend the world, and they may be right. If they’re wrong, then being cautious about massive capex and continued demand for expensive GPUs and precious memory chips is warranted. If they’re correct, however, the possibility of this technology upending economies and nations is also high.

Surely, then, there’s wisdom in “protecting one’s wealth” in this time of war.

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TSMC is urging clients to apply for N2 production allocation as far out as the second quarter of 2027, with large allotments of capacity close to sold out for the next two years, my sources tell me. Bookings are still available but this lengthening lead time, which may soon extend to six quarters, is making it hard for chip designers to commit to their own roadmaps, I am told.

Equity analysts, including those at Deutsche Bank and JPMorgan, previously noted that N3 is sold out well into 2027, according to media reports from WallStreet CN, Digitimes, and the Commercial Times.

Culpium is the first to report that N2 is now close to facing the same level of capacity constraint well into next year and beyond. TSMC’s N3 node was launched in 2022 with revenue from the technology almost doubling last year to $25 billion. N2, on the other hand, hit volume production only in the past six months. Apple is the first customer and is initially using the node for chips that will go into its upcoming MacBook, I am told.

A newer variant called N2P will offer a 5% performance bump for the same power and will be in volume production in 2H 2026, as will TSMC’s A16 node focused on high-performance computing chips, CEO CC Wei told investors last month.

Competition for TSMC production has long been a part of working with the world’s largest chipmaker. This struggle came to light during the Covid pandemic when car makers were caught short. The current situation, however, is more acute than ever, I am told. A confluence of factors is exacerbating the challenge, including the larger footprint taken up by AI GPUs, their higher price tag making clients less likely to baulk at the escalating rates being charged by TSMC, and the AI arms race being waged by startups and hyperscalers.

Culpium last month reported exclusive details of the changing leaderboard among TSMC’s largest clients — Apple and Nvidia — with sources telling me that the Green Machine likely pipped the Cupertino Cult as the foundry’s largest customer for at least a few quarters of 2025.

That’s indeed what happened — across the entire year. Data recently published by TSMC shows that Nvidia’s procurement more than doubled in 2025 to $23.3 billion, ahead of Apple’s at $20.7 billion, according to Culpium analysis.

TSMC typically asks clients to notify them how many wafers will be needed each quarter, the company then plans its capacity allocation and lets customers know what they’ll get. Buyers are then asked to pay for that capacity, locking in production schedules.

This latter deadline is now around six months out from the start of manufacturing, with fabrication and packaging then taking around four to six months. That means chip designers must decide on their needs as much as 12 months before they’d take delivery of the chips.

There are exceptions to this timeline, including last-minute requests called hot lots (aka hot runs) which attract large premiums and aren’t guaranteed. But these capacity constraints and lengthening lead times are causing growing uncertainty among downstream clients in computing, networking, and consumer electronics.

Nvidia this week gave hints about its own appetite — past, present and future — after reporting a 65% rise in full-year revenue to $216 billion.

“We believe we have inventory and supply commitments in place to address future demand, including shipments extending into calendar 2027,” CFO Colette Kress told investors during Nvidia’s investor call on Wednesday. “While we don’t use the term ‘sold out’ for 2027 yet, our visibility is better than it has ever been. Our customers are giving us multi-year roadmaps because they know that if they don’t secure the capacity now, they will be left behind in the agentic AI race.”

To ameliorate the problem, TSMC announced last month it will include N3 at its facility in Kumamoto, Japan. Last year, it added a third phase at Arizona which will make wafers at N2 and A16. That new fab is expected to hit volume production at the end of the decade. More factories are planned for the US site, as well as new facilities being added in Taiwan.

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Something happened last week which I think is worth sharing. A key indicator of pricing trends across a basket of AI stocks flipped to the downside.

Three of the Magnificent Seven closed below their 200-day moving average last Friday. Those companies are Microsoft, Amazon, and Meta. The other four members — Nvidia, Apple, Alphabet and Tesla — remain above their 200-day MAs.

I have been tracking this indicator since November 2025 after Joe Weisenthal and Tracy Alloway interviewed Dirk Willer, the Global Head of Macro Strategy at Citigroup, for an episode of their Odd Lots podcast. Willer uses what he calls the “Generals’ Framework,” meaning to look just at the leaders to assess whether or not the AI bubble has burst.

We look “just at the top seven leaders because people are excited about the mag seven,” Willer told Joe and Tracy in the Nov. 15 episode. “If three of the top seven breakdown, meaning they fall below the 200 moving average, that's a really dangerous sign.”

By the close of trade Friday Feb. 6th, Microsoft was 18% below its 200-day MA, Amazon was 6.4% lower, and Meta 4.3% below.

Willer isn’t the first to use the 200-day MA as a guide to bearishness, and thus a signal to take your money and run. Famous hedge fund manager Paul Tudor Jones used this as his benchmark, according to comments he gave Tony Robbins for the book “Money: Master The Game,” earning it the nickname the Granddaddy of signals.

“My metric for everything I look at is the 200-day moving average of closing prices. I've seen too many things go to zero, stocks and commodities,” he was cited as saying. “If you use the 200-day moving average rule, then you get out. You play defense, and you get out.”

Willer noted in his chat with Joe and Tracy that the Magnificent Seven is somewhat arbitrary. It could be the top-10 stocks in a category, he said, but the point being that you look at the leaders. The Mag Seven, being both big and indicative of the AI category, are as good a benchmark as any. In truth though, Tesla could be replaced by Oracle if we’re tracking AI stocks. In that case, Oracle’s recent slide puts it 35% below its 200-day MA.

Amazon’s sell off came after it reported earnings and then forecast massive expenditure to meet what executives believe will be growing and continued demand for AI services.

“We expect to invest about $200 billion in capital expenditures across Amazon in 2026, and anticipate strong long-term return on invested capital,” CEO Andy Jassy said in a Feb. 5 statement. It spent $132 billion last year and analysts had expected $146 billion in capex for 2026. Just a day earlier, Alphabet signalled it may double its own capex to as much as $185 billion. A week earlier, Meta said it’s AI-related spending could be as much as $135 billion.

Overall, four of the biggest US tech companies are set to spend around $650 billion this year, according to Bloomberg News. Investors were displeased. Amazon ended last week 13% lower than its Monday close. Both Meta and Alphabet lost around 6.3% each. Unsurprisingly, TSMC rallied on the news.

This reaction seems quite dichotomous. Tech companies are raking in profits and AI is surely a “thing,” yet investors are worried whether they can keep making money. But two things can be true at once.

“The sense in the marketplace is that even though your company is doing great and making a lot of money … if you’re overdoing it, we want to back away from your company,” economist Cary Leahey of Columbia University told BBC’s World Business Report last week. “That’s a big difference from the bubble in 2000 — in the internet and dot-com bubble — because those companies weren’t making money.”

Some people cling to the notion that companies who are raising capex, have the money to do so, and are investing in something that truly is world-changing ought to be insulated from market sell offs, or even bankruptcy. This sentiment in turn drives people to blame the likes of TSMC for not investing enough to make the infrastructure upon which this brave new world will be built.

Nobel Prize winner and MIT economist Daron Acemoglu summarized the challenge of connecting massive capex to higher revenue during that same BBC broadcast.

“The major problem is that while AI remains a very promising, very versatile technology, the huge amount of investment that we are seeing is not translating into revenue,“ he said. “And it’s not clear how it will translate into revenue because the types of applications that would make businesses adopt AI with large workforces are not there yet.”

There are quite a few differences between the current bubble and the dot-com era, some of which I outlined last December in “This AI Bubble is More Memory Than Dot-Com.”

The one I want to highlight, though, is that this time around AI expenditure is largely occurring either within the bosom of massive cash-rich public companies, like Microsoft, Amazon and Alphabet, or inside black-box startups such as Anthropic and OpenAI who are funded by FOMO-driven VCs, or vendors eager to keep inflating the bubble. In each scenario, the losses can either be hidden or plugged such that the world needn’t see nor worry about them.

On the flipside, there are only a few companies who stand truly naked to the winds of the AI boom. TSMC is one, as are Nvidia and AMD. Only one of those three, however, has a huge annual capex bill.

For public-market investors, the protection afforded by the mothership is more robust than what they enjoyed 25 years ago while riding the dot-com bubble while at the same time trying to avoid its collapse.

But now the granddaddy signal poses a new conundrum. Akin to the Prisoners’ Dilemma, we have the Investor’s Dilemma. In the Prisoners’ Dilemma, each suspect has the choice to confess or stay silent. Each choice determines the prison sentence, but you cannot know what your fellow inmate will do.

The 200-day MA is a well-known and closely tracked trend. If we pit retail investors against “the market,” then the problem is whether to sell now and take profit, or hold on in the hope of gaining more profit (and avoid losing out on further upside). The answer depends on what the other will do, but we cannot know their choice.

John Nash has an answer. The Nobel Laureate in economics was a game theorist who came up with a concept now known as the Nash Equilibrium. Applied to the Prisoners’ Dilemma it suggests that it would be most rational for any individual suspect to confess, even if that’s not the strictly optimal decision. Apply it to the Investor’s Dilemma, and we may have a possible solution. Let me point out, though, that I am neither an analyst or investment advisor. Make your own decisions.

If the 200-day MA really is the signal that investors are following, then some may feel it’s time to make a decision. If it’s not the data point they care about, or the Magnificent Seven isn’t the basket of stocks they track, then perhaps there’s other factors to consider.

The Investor’s Dilemma can also apply to companies spending on — or investing in — the equipment to build and run AI. If any individual company holds off, then maybe they’ll cap their returns but also limit possible losses. If they invest, they may make greater returns if no one else invests but lose a whole lot more if they all do.

What’s important to keep in mind, though, is that not every company invested in the future of AI has the same investment cost nor will get the same investment return. At least with the 200-day MA, there’s another data point to consider — and worry about.

I thought this was interesting, so I thought I’d share.

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TSMC CEO CC Wei delivered a fabulous pre-election gift to Japanese Prime Minister Sanae Takaichi on Thursday by announcing the addition of 3-nanometer technology to the company’s Japan venture.1

The formation of Japan Advanced Semiconductor Manufacturing Inc. was first announced in November 2021 as a joint venture in which Sony Semiconductor Solutions Corp. would take a stake of up to 20%. Plans at the time were modest: a $7 billion investment with 22/28nm technology, and monthly capacity of 45,000 wafers per month “to address strong global market demand for specialty technologies.”

Fab 23 in Kumamoto has since grown in size and scope. More nodes, more capacity, more investors and a second phase have since been added. The project’s importance has grown so much that Taiwan’s China Airlines and Starlux Airlines commenced direct Taipei-Kumamoto flights in 2023.

The addition of vehicle-components maker Denso to the shareholder table saw more talk of automotive semiconductors, which became a hot topic during the Covid-era chip shortage. Jon at Asianometry produced a timely video in the midst of this saga which pointed out that older semiconductors have been a bigger issue in the supply-demand imbalance than leading-edge capacity. JASM’s plans — centered around 40nm through 12nm nodes — were part of a long-term strategy to address this issue.

The term HPC (high-performance computing) started sneaking into discussions of Kumamoto only in the last two years as this second phase was added. But Japan is not a player in HPC more broadly, nor in AI chips specifically — which are a subset of HPC. The 6/7nm technology has been in the mix since Phase 2 was announced, but there was never any sense that this node — the first to use EUV — was a major driver of the project.

Booming demand for AI GPUs has led to progressively louder allegations that TSMC failed to expand quickly enough, and is thus holding back America’s AI dream. Jon and Stratechery’s Ben Thompson have been duking it out over whether or not it’s fair to blame TSMC, with successive videos and newsletters outlining their positions. I see Ben’s point, from a US-centric perspective, but come down on the side of viewing these complaints as a little unfair and out of touch with the realities of semiconductor capacity investments.

I doubt TSMC Chairman and CEO CC Wei is listening to Ben, Jon or myself, but he is listening to Jensen Huang and Lisa Su, while also maintaining a solid relationship with Tim Cook. As Culpium outlined in exclusive coverage of those discussions, Wei delivered a price rise to Cook back in August just as Apple finds itself in the unfamiliar position of having to scrounge for TSMC capacity amid Nvidia’s rise to the top of the client table.

Wei’s gift to Takaichi on Thursday February 5 — three days before Japan’s snap parliamentary elections — are a reflection of the Taiwanese executive’s savvy pragmatism.

TSMC has known about its plans to add 3nm to Kumamoto for at least a month or two, according to my sources. A conspiracy theorist might conclude that he chose the week prior to Takaichi’s bid for re-election to announce this news— directly to her face — as a political move. A more nuanced take would posit that he was too busy closing out 4Q and announcing results, while managing the world’s sixth-largest company, to worry about giving face to a foreign leader.

There’s probably elements of both. Here’s what TSMC said:

Note the reference to meeting “strong demand driven by AI.”

This move — which adds the potential for 50,000 wafers of monthly 3nm capacity to TSMC’s global footprint — achieves two more important goals than pandering to foreign politicians.

The first is to soak up Kumamoto capacity at a time when, according to my sources, demand at the fab from the automotive and specialty-chip sectors appears to be weaker than previously expected. JASM is not particularly big by capacity or revenue standards, but is symbolic of TSMC’s overseas expansion. The facility was built and ramped far more quickly than its Arizona site, a function of a more-similar industrial and work culture between Japan and Taiwan than the US and Taiwan. For all intents and purposes, JASM was looking like a great success. Having utilization fall too low would challenge this narrative, even if only internally.

Second, TSMC’s ability to add new leading-edge capacity is constrained by multiple factors including land availability, and construction & equipment-install times. Each node tends to build on the prior one, in terms of both process and tooling. Since EUV was already slated for Fab 23 Phase 2 as a function of 6/7nm being on the roadmap, it’s not a huge stretch to add 3nm and thus quickly add more volume for making AI chips.

I doubt many of these wafers will be going to Japanese GPU designers — if you know any, please email me. In fact, they’ll likely be popped on a Starlux or China Airlines jet and get deposited back in Taiwan for CoWoS packaging, at which point the names Nvidia, AMD, or Apple will be stamped upon them.2

Everybody wins. TSMC gets to soak up some Kumamoto capacity and the GPU players get more volume more quickly for AI chips, while on Sunday we’ll find out whether Takaichi is also a winner. And who knows, maybe the naysayers will start bitching just a little less about TSMC’s “failure” to expand leading-edge capacity quickly enough.

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Apple and its suppliers are currently tallying up the cost of severely miscalculating consumer enthusiasm for last year’s iPhone Air. Weighing 12 grams less but with a slightly larger screen than the iPhone 17, Apple made the bet that customers would be willing to pay $200 more for, well, “air.”

They were wrong, with Nikkei’s Lauly Li and Chen Ting-fang reporting as early as October that suppliers had been told to reduce output of the Air in favour of other models. Kuo Ming-chi, the Apple Whisperer, followed up a day later by noting that iPhone Air sales had fallen short of expectations.

Exactly how short has been hard to quantify, until now.

Apple and its suppliers are now stuck with components for up to 1.5 million units of iPhone Air, my sources tell me, even after the order came down in October to cut back production. What’s worse, some of that cannot be repurposed and instead may need to be scrapped, I am told. To be clear, that doesn’t mean 1.5 million iPhones will be scrapped, merely some of the components specific to the iPhone Air.

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Apple will release earnings this week, but it’s unlikely Tim Cook and his team will discuss the issue. Apple’s CEO dodged the question of Air’s poor sales last quarter, and instead chose to talk up the iPhone 17 Pro and iPhone 17 — a tacit admission that Air was a dud.

My own analysis, based on what cannot be re-used, puts the write-off into the low hundreds of millions of dollars. Frankly, that’s not a big deal: iPhone sales were $49 billion in the three months to Sept. 27, and are expected to be around $80 billion for the December quarter.

The bigger issue is how Apple failed to understand its own customers, and how this mess will ripple through the supply chain. I am told that while some vendors will be stuck with a bill, Apple itself will soak up most of the cost.

BOM Analysis

There are three main categories of components that cannot be repurposed for other models, and they just so happen to be among the most expensive items on the bill of materials (BOM).

The titanium frame is specific to the iPhone Air in both size and materials, with the other two models using aluminium. Excess inventory here cannot be repurposed but can be recycled. In fact, 80% of this inventory is already recycled, so Apple and its suppliers will recoup some of the cost.

The OLED “Super Retina XDR” screen is basically the same across all models, but the Air’s 6.5-inch size is mid-way between the 6.9-inch and 6.3-inch versions. I am told that displays which have already been cut, framed and put onto modules will need to be scrapped, though some of that will also be crushed, separated, and recycled.

Possibly the biggest hurt could be with the chips. Apple uses the same A19 Pro CPU in the Air as it does with the iPhone 17 Pro. But the Air has only 5 GPU cores — as does the base iPhone 17 — while the iPhone 17 Pro has 6 GPU cores. (To be blunt, this is merely chip binning, not a new chip).

As a result, the unused Air chips cannot be put in the the lower-end base iPhone 17 nor in the higher-end iPhone 17 Pro. They cannot be repurposed. Even worse, the Air has 12GB of DRAM while the baseline iPhone 17 has just 8GB, according to TrendForce. So, any processor modules which have already had their DRAM fused onto the CPU would also result in wasted DRAM — unless Apple and TSMC find some magical way to “unfuse” the memory from the base die.

Such an error is particularly painful when you consider that at least some of the TSMC capacity dedicated to these wasted chips could have been put to something more fruitful, not to mention the current shortage and high prices of DRAM.

Apple didn’t understand its own market, but it was also stuck in a bind. The bill of materials on the Air was quite high owing to the unique specs, such as the custom titanium casing. It attempted to ameliorate this cost by de-speccing the Air even with respect to the cheaper version — notably with one less camera. But that wasn’t enough, and in the end the Air had a worse gross margin than most of Apple’s lineup despite being $200 more expensive than the cheapest iPhone.

Apple’s Innovation Dilemma

Yet Apple did not make a mistake. This strategy was entirely correct. Apple tried to innovate by developing a product it hoped at least some consumers would appreciate, and then attempted to price it at a level they’d accept while allowing the company to make a decent profit. If Apple only played it safe and developed boring products which it already knew consumers wanted, then it might as well just produce Marvel movies.

It was also not wrong to overbuild. When gross margins are high and consumer patience wears thin, it can be more costly to lose out on a sale than to scrap some of the materials that go into the device. Apple, like any physical products company, likely did a best and worst case analysis, calculated the BOM cost and weighed it against the opportunity cost of each scenario and set production targets accordingly. This is economically rational, but only where the cost is largely variable — such as materials — rather than fast-depreciating fixed assets, likely semiconductor equipment. Any non-manufacturing physical-products company is right to err on the side of inventory over empty shelves.

Everyone’s Problem

The bigger lesson here is Apple’s structural challenge, and one which the entire industry is learning. There was a time when all iPhones came from one company in one location — Foxconn in Shenzhen. This made logistics and inventory management pretty easy. The world has changed. The US-China trade war, rising labor costs in China, and growing concern over putting too much economic power in Beijing’s hands have spurred both clients and suppliers to manufacture elsewhere. After dragging its feet, Apple is finally catching up.

The latest iPhone was assembled by three companies — Foxconn, Luxshare-ICT and Tata — in two countries (China and India). I previously wrote about this phenomonen with regards to Foxconn (aka Hon Hai), specifically that Centralized Production is Over. Regionalism is the Future.

To ensure supply and logistics run smoothly, vendors are now forced to store components in multiple locations. Admittedly, each locale may have lower output, but taking into account transport times and the need to keep the cogs moving, we’re likely to see structurally higher component inventories coupled with less flexibility in ramp up or cut back. Yet, as I have also touched on before, this regionalised manufacturing will probably lead to localised products. This was the case during the feature phone era when Nokia and Motorola operated factories across multiple countries and sold devices that were specific to individual markets and regions.1

The iPhone Air was a failure, there’s no denying that. And the cost will be high, though tolerable. For Apple though, the lessons in product development, consumer preferences, and supply chain management maybe end up being more valuable than a dozen truckloads of scrapped components.

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Tim Cook and his team took the news on the chin. Wei had been telegraphing hikes in earnings calls over the past few quarters, and the Taiwanese chip maker’s rising gross margins were testament to its increasing pricing power.

That wasn’t the worst news, my sources tell me.

Apple, which once held a dominant position on TSMC’s customer list, now needs to fight for production capacity. With the continuing AI boom, and each GPU from clients like Nvidia and AMD taking up a larger footprint per wafer, the iPhone maker’s chip designs are no longer guaranteed a place among TSMC’s almost two dozen fabs.

What Wei probably didn’t tell Cook is that Apple may no longer be his largest client.

According to Culpium analysis and discussions with sources in the supply chain, Nvidia likely took top spot in at least one or two quarters of last year. “We don’t discuss that,” Chief Financial Officer Wendell Huang told Culpium Thursday when asked about the change in client rankings.

Final data will be unveiled in a few months when TSMC releases its annual report — which includes revenue from its top clients — but there’s every chance that Apple’s lead for the full year narrowed significantly and may have even fallen below Nvidia’s. If it didn’t happen in 2025, then it’s almost certain to do so in 2026, my sources tell me.1

Public data helps tells the story.

TSMC’s revenue climbed 36% last year to $122 billion, it reported Thursday. Nvidia’s sales for the fiscal year through January 2026 is set to climb 62% while Apple’s product revenue — which excludes services — is on track to grow just 3.6% for the 12-months to December 2025, according to Culpium estimates based on earnings reports and company guidance.

Apple’s role as the primary driver of TSMC revenue growth ended five years ago. In 2018 TSMC sales would have even fallen if not for incremental purchases by Apple that year. Now, the Cupertino company is posting low single-digit revenue growth while Nvidia is skyrocketing.

The reason for this change is two-fold, and pretty obvious: AI is driving massive demand for high-powered chips, while the smartphone boom has plateaued.

TSMC’s sales from high-performance computing, which includes AI chips, climbed 48% last year on top of 58% growth the year before. Smartphone revenue climbed just 11%, slower than 23% in the prior year.2 That trend will continue this year, and for the foreseeable future.

Revenue in 2026 will rise close to 30%, yet capital expenditure will climb around 32% to a record of somewhere between $52 billion and $56 billion, TSMC said Thursday. Longer term, growth will average 25% in the five years through 2029 yet the AI segment will climb an average of 55% or more over the same period, the company said. That’s higher than a prior forecast for a mid-40 percent figure.

The ultimate flex for TSMC came Thursday when it showed off not only record revenue and net income, but a gross margin approaching that of software makers and fabless chip designers. In the December quarter, that figure was an astounding 62.3%, 280 basis points higher than the prior period. If not for its overseas fabs (Arizona and Japan) gross margin would have been even higher.

There are two caveats that are important. First, while smartphone processors are the largest portion of chips bought by Apple, they’re not the only type. Processors for Macs come under HPC, while it also has a strong lineup of custom chips used in accessories which fall under digital consumer electronics. Second, Nvidia isn’t the only HPC client. AMD is a major buyer of capacity for its own GPUs while Amazon and Google are on the growing list of customers developing in-house AI chips.

Put another way, Apple’s chip catalog is broader and more varied, while Nvidia’s lineup is more concentrated around a huge number of wafers at, or near, leading-edge. It’s for these reasons that Apple will remain important for at least another decade.

In the near-term, however, TSMC’s technology roadmap coupled with broader industry trends favor Nvidia, AMD and their ilk, meaning Apple may need to keep fighting for capacity over the next year or two.

TSMC is already producing chips in volume at 2 nanometer (called N2), currently its most advanced node, with Apple a major buyer. But in the second half of this year it’s set to ramp up both a new variant called N2P as well as a new node called A16.

The company’s business model is a little quirky. Instead of repurposing an existing factory for new technology, TSMC just builds a new one. This ensures no interruption to output and allows it to squeeze the most out of old tools and processes. In general, this means any new capacity that TSMC builds is for a new node. As a result, it has numerous fabs still churning out chips on technology that’s a decade older or more.3

In TSMC CEO CC Wei’s words A16, with Super Power Rail, is “best for HPC with complex signal routes.” SPR is TSMC’s version of backside power, a newer approach designed to separate a chip’s signal from its power supply. Intel is also developing this technology, and many believe it’ll be the key to the US company’s prospects at stealing foundry share from its Taiwan rival.

After that, TSMC has A14 which it expects to bring into volume production around 2028. Some call this the next full node after N2, labeling A16 as not a “full node.” In truth, all of these names are as much marketing terms as they are technology designators. Nevertheless, as SemiAnalysis recently wrote in a fabulous report on the TSMC-Apple relationship, the balance will shift back to Apple because A14 is designed “for both mobile and HPC from the start.”

More importantly, what Apple offers is stability. Nvidia has been a client for a lot longer than Apple, but broadly speaking it’s a bit niche. Right now that “niche” is the hottest product on the planet, but niche it is. Apple, on the other hand, has products being made in no fewer than a dozen TSMC fabs. Even if Nvidia did overtake Apple by purchases, the breadth of its manufacturing footprint at TSMC is nowhere near as large.

This distinction may not matter now, but it probably will at some point. The AI boom won’t last forever. The bubble may burst, or it may slowly deflate, but the growth trajectory will surely flatten and that means demand for leading-edge AI chips will fall.

Wei knows this, which is why he’s expanding both quickly yet cautiously. “I am also very nervous,” he said at the company’s investor conference on Thursday in Taipei. “If we didn’t do it carefully, it would be a big disaster for TSMC for sure.”

The chip giant has recently come under fire, including from noted analyst Benedict Evans, for being “unwilling/unable to expand capacity fast enough to meet Nvidia’s book.” I think this is wrong, and unfair.

“The risk of under-investing is significantly greater than the risk of over-investing,” Evans cited Google CEO Sundar Pichai as saying back in 2Q 2024, as if to make the point. TSMC and Alphabet, Google’s parent, have approximately the same gross margin. But their business models couldn’t be more different. Nvidia’s financials are also unlike TSMC’s. Their respective capex strategies need to reflect this risk.

Alphabet’s capital intensity, calculated as acquisitions of property, plant & equipment divided by revenue, was just 15% for full-year 2024. TSMC’s is more than double that at over 33%. More importantly, depreciation — which is where the cost of capex is reflected in earnings — was just 10% of Alphabet’s cost of revenue. For TSMC, this figure is more than four times higher at 45%.

At Nvidia, which is a tier-one buyer of TSMC’s output, the data is more stark. Capital intensity was just 2.5% for 2024, while depreciation was only 5.7% of the cost of revenue. As a fabless chipmaker, it can enjoy gross margins of over 70%. Its only real risk is holding excess inventory. Even then, it could have written off its entire inventory at the end of October and still maintain a gross margin approaching that of its chief supplier. What’s more, neither of these clients have anywhere near the customer-concentration risk of TSMC.

The complaint that TSMC could and should build faster ignores the fact that it’s the one left holding the baby if a downturn comes and demand falls. It takes two to three years to build a new fab, Wei explained, so the company must skate where the puck is going without thinking too much about where it’s been. “Even if we spend 52 to 56 billion this year, the contribution this year is none,“ Wei said Thursday. Its major cost, buying equipment, remains on the books no matter what revenue it brings in for the quarter.

For the best part of a decade, Apple was the one driving TSMC’s need to keep spending on new facilities. Today it’s Nvidia, and Jensen Huang is starting to wield more power than Tim Cook. But neither has to bother with the expensive business of actually manufacturing semiconductors, merely the hassle of begging CC Wei for wafers.

For such clients, the foundry’s capacity is a fixed cost that they needn’t worry about. Which is precisely why eight of the world’s ten largest companies turn to TSMC to make their chips,4 and in return the Taiwanese giant gets to reap the rewards during boom times like this.

Thanks for reading.

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每年全球有超過兩千萬人需要啟齒這幾個字。

我是其中之一。

我在二零二四年的聖誕節前兩天第一次將這四個字說出口，那時，我的癌症進入第三期，它已經轉移。

罹癌這件事難以平淡地接收也無法輕鬆地分享，被確診後的生活永遠無法與往日相提並論。

這一陣子的抗癌經驗讓我深深體會到：縱然全球百分之二十的人口可能罹癌1，每一位癌友的心路歷程都是獨一無二的、而且無比孤寂。

癌症是一種特殊的疾病，這個疾病的字眼讓我聯想到蜘蛛，當我們帶著一點距離看到蜘蛛的時候，或許感到好奇、或許感到不舒服，但是當蜘蛛垂降到我們身上時，只剩下無限的驚慌！癌症病情的所有細小改變，都需要被持續關注，但是它有別於其他慢性病如糖尿病、狼瘡或心臟病，無法只是維持與共存。癌症必須被消滅。

我罹癌的時間點，改上了AI熱潮，絕大多數病人不使用Google反而選擇諮詢ChatGPT、Claude或Perplexity；商討內容小至感冒護理大至癌症療程等各種疑難雜症，我也不例外。

AI乍看完美，直到它不再美好的那一天。

研究顯示，臨床上使用AI，在最好的演算法的前提下，會出現百分之十五的失誤，但最差情況接近百分之四十。值得擔憂的是即便出錯率極低，但非零出錯率會造成偏差：醫療人員相信AI更勝自己的專業知識。

另一份研究點出了Limitations of using large-language models (LLMs) in healthcare。作者提出了十四個議題：包含無法從經驗中學習，不會擷取話中含意也缺少情商。他寫道：「ChatGTP無法體會到使用者的情緒也無法同理他們的感受。」「不可能了解病患尷尬的一撇。」

人類的專職

最後提出的這個議題—情商（EQ）—在AI逐漸在醫藥界佔有一席之地的過程中被遺忘了。但這是身為醫生、護理師和醫療勤務人員最重要的素養之一。醫護業比其他職業更需要人性，醫療人員和病人緊密連結，一起歷經生病、內心恐懼、甚至死亡。

我的全科醫生是第一個向我宣布癌症評估的人。她告知我的時候，神情肅穆，話語中一邊提供醫療建議一邊不斷夾雜抱歉。她在檢查其他問題時偶然間發現了警訊，請我做進一步的掃描。任何影像辨識模型都無法辨識經驗豐富的醫生親手觸摸的感受，也無法有如同高情商臨床醫生的同理心來向患者傳達這個消息。我想，即便是長得最像人類的機器人告訴我我可能會死，我也是無法接受的。

告知家人壞消息和提早結束聖誕假期後，我開始無止盡上網搜尋：療程、存活率、副作用，高度有自信的聊天機器人一派輕鬆地分享對應策略，好似一位經驗老道的醫生在視窗的另一端和我說話。然而，對話後我感到更糟糕更孤單也更脆弱。

我的醫療團隊有一位血液腫瘤科醫生、兩位腫瘤科醫生和一位外科醫生。即便和真人醫療團隊討論後，我依然無法停止在AI世界鑽研。我這麼做的目的不是為了比較挑選醫生，團隊裡的每個專家在療程中扮演不同且重要的角色。我覺得自己像一個很認真的學生，有一次我花了一整天時間找出和我這類型癌症相關的醫療研究，把印好的四頁報告帶去和腫瘤科醫生討論，宛如完成了作業和老師交代的加分題。

我的醫生沒有因此感到厭煩或是不予理會，他曾經做過很多臨床實驗，他很開心我主動投入心力研究病情，我很老實和他說我熱愛研讀PubMed裡面的文獻資料，我的醫生對我的耐心如小學老師，他很有禮貌的轉移我對四頁報告的注意力，仔細地闡述我的病情。他進一步花時間寫下數個文獻標題，建議我多花點時間研讀和我的病況更相關的研究。他不視AI為敵人，如同Google之於上一輩的醫生，但是這種工具有其限制。

經歷幾次化療後，我深刻的體會到：知道我的癌症種類或是存活率，對我的醫療照護沒有甚麼幫助，這都只是統計數字而已；那台注入精密計算過含有鉑元素的化學混合物和安眠藥的機器，也不過是執行工程指令罷了。

真正的醫療照護，是在點滴室長達六個小時的療程，護理師堅決有耐心的第三次尋找入針的靜脈，撫慰我遇上點滴針頭的焦慮與恐慌。提供醫療照護亦非昂貴的放療儀器，是溫柔堅定的腫瘤放射科醫生，她為了把對健康組織的傷害降到最低，不斷為我安排患部立體掃描，確保連續六個星期天天精準正對腫瘤放療。

人工智慧系統或許可以協助診斷，計畫安排療程或歸納紀錄，但是它們永遠不可能有能力取代人類去安撫引導另一個人類渡過凡人脆弱和臨死的創傷。

我的腫瘤放射科醫生醫治過數百位病患。我最感謝她的時刻，是她小心翼翼把五十公分的橡膠管從鼻腔放到我的胃，讓我得以獲取需要的養分。世上只有寥寥幾件事比鼻胃管進食兩個月更不人道，機器人是不可能完成這項需要細緻手感的任務，更沒有她的細心和同理心。

我的醫療團隊也有一位有著外科醫生印象中常見的那種神氣活現的外科醫生。他時不時把「別擔心」掛在嘴邊。一開始聽到他這麼說感覺他事不關己，我罹患癌症當然會擔心。但他對患者心理狀態的理解，是LLM所無法企及的。因為：「擔憂是人類的天性，縱然毫無益處」。過了數個月兩週一次的會談，其中包含療程的不順遂，我漸漸被他「別擔心」的真言感化，我還是擔心，但相較之前少了很多。聽一位有自信的真人說話比一台有自信的聊天機器人更具有說服力，也許是他聲音的溫度或是他真誠地期許我確實沒什麼好擔心的希望。

我的病況已穩定，我有信心我會活下來。但這不是終點，未來有漫長的復健等著我，也還要繼續和副作用作戰一輩子。即便如此，我是幸運者群體的一員。

我的好運決大部分來自於我有負擔得起，且容易取得的世界級醫療資源。提供我醫生會診和療程的醫療中心在第一世界國家，由億萬富翁、富士康創辦人郭台銘先生所捐贈，他的前妻和弟弟都是被癌症帶走的。我的放射治療設備是瓦里安先進的直子放射治療系統，亦是郭先生所捐贈的，我無數癌友們也得以享受一樣的特權，這確實是一種特權。我親眼看到其中數人和我一樣換上病袍靜靜等待被輔具綁住後接受放療；然而，有些癌友卻看不到明天，幸存者的愧疚感壓得我喘不過氣。

貧窮的負擔

根據 The Lancet 的研究統計，二零二三年有一千八百五十萬個已知的癌症病例，其中至少一千零四十萬無法倖存，佔確診人數的百分之五十六。除了每種癌症的致死率不同，存活率亦取決於診斷時已達第幾期、年紀、健康狀況、人種以及有無其他並發症。然而統計數據顯示決定存活率最大關鍵是有無醫療資源的門路。

澳洲、紐西蘭、瑞典和美國的罹癌率世界最高，但從致死率角度來看，這四個國家綁上名次不高。 根據世界衛生組織統計資料顯示蒙古和辛巴威的罹癌致死率特別突出。

儘管醫療研究持續進步，本世紀下一季度罹癌率預計攀升百分之六十來到每年三千零五十萬例，死亡率預計上升至百分之七十五來到每年一千八百六十萬例，「首次確診和三分之二的死亡數會發生在中低收入國家。」Institute for Health Metrics and Evaluation 如此撰寫, The Lancet 節錄。

最後兩個事實令人震驚「一半的新案例，卻佔據三分之二的死亡數。」

對於窮人來說，癌症治療是無法承受的重擔，他們最無力負擔昂貴的化療、放療、開刀和無數的術前術後復健，因為這龐大的費用加上有限的醫療資源，貧窮的病患死亡率會不對等的更高。

全球易取得的人工智慧工具包含大語言模型，也許能夠改善這不對等關係。值得一提的是當我問身邊的醫生們，沒有一位反對運用AI輔助醫療，其中一位醫生提出他訂閱數份LLM，但也對AI的幻覺以及錯誤資訊保持謹慎之心。他回憶一起病例，病人不同的並發症讓醫生難以下診斷，ChatGPT協助集思廣益輔助醫生找到問題根源和解決方針。

然而我們不應誤解AI判診的能力。微軟六月發佈的文章標題「 The Path to Medical Superintelligence」大肆宣揚美國軟體公司「model-agnostic orchestrator」，一組電腦模擬醫生挑戰真人臨床醫生診斷病例。乍看之下電腦模擬醫生能力驚人，但進一步查驗後發現這個活動的架構有其缺陷 。挑戰題目僅限於新英格蘭醫學期刊發表過的病例，無庸置疑這些論文是這組模擬醫生的訓練來源。另一方面人類不被允許使用網路搜尋也不准和其他真人醫生協商，這兩種情況在真實世界都不太可能發生，換句話說微軟提供的是一個加強版搜尋引擎。這些診斷最初來自於人類醫生在現實生活中的臨床經驗。

另一個更令人印象深刻的是哈佛醫學院研究人員所開發的 Compass model，這篇論文「Generalizable AI predicts immunotherapy outcomes across cancers and treatments.」概述了該模型。研究團隊將上千個關於腫瘤、免疫療法及其結果輸入資料庫，讓醫生甚至病患皆可上傳腫瘤RNA以預測對免疫療法產生反應的可能性。

除了常見打頭陣的化療以外，免疫療法和基因治療更仔細地針對腫瘤，但後兩者不見得適用於所有情況。目前只有少數類型對免疫療法有反應，例如有表現PD-L1蛋白質的腫瘤。然而由於免疫療法利用人體自身的免疫系統對抗癌症，這個做法可能帶來顯著的負面影響，而且療程非常昂貴。能夠預測哪些腫瘤可能產生反應至關重要，醫生和病患得已做出更適合的決定，也省荷包。

基因治療面臨類似的挑戰，尤其是在許多癌症類型中缺乏可作為藥物靶點的標的。隨著AI被用來折疊蛋白質，發現新藥物和測試功效，開發新療法的費用和時間有機會大幅降低。這也意味著更多藥物可供選擇，包括迄今未受到製藥公司關切的罕見癌症類型。

AI的進步，由更強大的運算能力來處理大量資料所推動。這能夠協助第一世界和第三世界的醫護人員及患者，如同網路縮小了數位落差，AI有潛力彌合醫療保健的落差。

然而， AI是無法取代實際提供醫療保健的人員。

人們最常用的AI介面LLM永遠不應被視為真人醫生的代理人，我們不應寄望AI提供個人醫療建議或療程，血液檢查、X光和磁核共振也是如此，AI是幫我們發現問題的工具，而不是解決問題的手段。

人類疾病

癌症，作為人類最陰險且最令人困惑的疾病之一，將成為我們如何既擁抱又防範人工智慧進步的試金石。

我歷經過AI能做到的，不能做到的，以及其淺在的陷阱。

舉例而言，AI可以告訴你很多癌症患者選擇放棄治療以換取加速抵達生命的終點。它無法解釋原因，但醫生可以，他們親眼見到，親耳聽聞，也用自己的心感受過。他們本能地能夠明白黑暗的終點比緩慢又令人疲憊的治療過程更誘人，而且對於某些病患而言，康復甚至是永遠無法企及的目標。病患不是為了避免疼痛，現今有許多合法鴉片類藥物能控制緩解不適，他們想要擺脫失去控制和缺乏自主權的感受，AI模型在這方面毫無幫助。

真要說此刻能做到什麼，LLM回覆的冰冷科學真相反而讓痛苦加劇。大部分病患將多年遭受治療副作用的影響，聊天機器人會告訴你「許多症狀會永遠與你同在」，但它不會告訴你的是這些症狀不僅不斷提醒你那瀕臨死亡的經歷，而且持續警告你細胞中有一顆不定時炸彈隨時再次滴答作響，沒有人願意活在這種陰霾之下，不論緩解帶來多多的生活樂趣。

只要按下鍵盤，就能獲得關於療程後副作用如此「有用的訊息」，即使最堅強的倖存者也有理由相信他們的生活頂峰已經過了，不會有好的未來。最聰明的生成型預訓練變換模型任然缺乏說服患者的EQ。

縱然時常有幻覺，GPT從設計層面來說是理性的，而人類則是感性的設計。我的醫生從來不曾對我說謊，就算是善意的謊言也不說。傳遞壞消息時也總是夾雜同理心和有助益的建議。對於患者來說，在這充滿不確定的時刻裡，眼神的交流或對話中的語氣是如此重要，這些微小的人性動作意味著全世界。

遇到挫折時，真人醫生能給予人性的建議。放療造成我有口乾的問題，放射腫瘤科醫生建議喝一大口橄欖油試試，這是AI從來沒有提到過的。

在我的治療和康復過程中，最艱辛的是對抗芬太尼上癮。從卡莫德開始，透過皮膚貼片給藥的芬太尼是我在鴉片類藥物所使用的最高劑量層。在療程的初期，我下定決心避免使用鴉片類藥物，那時的我，天真地以為布洛芬就夠了。然而，我屈服了。當同步放化療的影響消退，拔掉鼻胃管，臉上和脖子上的皮膚不再灼傷，我成了吸毒上癮者。疼痛已離我遠去，但是沒有芬太尼的日子使我坐立難安、直冒冷汗而且無法入眠。

當我帶著AI開立的戒毒處方籤去看腫瘤科醫生時，他指出這不比我已經上癮的藥物更好。每個AI聊天機器人都拍胸捕保證這個組合是對付鴉片類藥物成癮的標準療法。我的醫生開了一張更好更人性的處方：睡覺。由於當時我沒有在工作，可以藉由贊安諾讓我在睡眠中戒毒。這是我身為人類需要的解決方法，而且只有人類提得出來。

在黑暗的時刻，我無法進食、無法說話、幾乎無法呼吸，人類是我唯一的救贖。我本身是個科技通，學歷資工碩士，擔任科技記者二十餘載，電子產品和程式語言原本是我的舒適圈。現在，除非我的身體和心理感到不適，我不想再在網路上搜尋數據和答案，AI的虛情假意和機械式回覆讓我感到厭惡，他們是對是錯不再重要，我只能感受到他們的冰冷。我發現，追劇《無照律師》更能帶給我溫暖的慰藉，即便是虛構的人類抓馬，仍然勝過數位同情心。

我的病例合計有十多位醫生、二十多位護理師、眾多醫療技術人員、勤務人員、志工和行政人員經手。我列出了參與我療程的每一位，冷靜地分析哪部分可以被某種程度的AI、自動化設備或機器人取代，我的結論是不到五個，大部分是實驗室技術員或行政人員。

即使我有顆水晶球讓我能預見二十五年後人工智慧會帶我們走向何方，我確信AI無法取代任何一位伴我走過癌症疼痛、創傷和死亡幽谷的一線醫療人員。

但我也有些話想對那些熱情勤奮的醫生和護理師說：

請大膽放入您的人性，不要成為AI演算法下的犧牲品。人類不是統計數據，某種療法是百分之七十五或是百分之二十五有效並不重要。對於任何一位個別病患而言，這是死去或活下來的二元論。

就我的病例來說，療程開始初期我的勝算很高，但是當計劃失敗必須改用新的藥物治療時賠率進而對我不利，我心中的不確定感揮之不去。對於醫生來說每一位眼前的病患都可能是邊緣案例，統計數據裡的異常值，不論好壞。但是對於那位病患和其親友而言，每一位都是獨一無二，需要被視為N=1的病例。

我們所有人，不論是醫護人員、病患、技術人員或是政策制定者也需要牢記這最重要的事實：AI和醫療專業人員都可以提供資訊也都會犯錯。但是提供照護的，是人。

感謝您的閱讀。

中文翻譯:張咪咪，中文編輯:林筱茵

我計畫休息幾個月，情況不如前一次一般嚇人，但是我需要一些時間休息和復原。謝謝您過去一年的支持，祝您有個健康快樂的節日。

Culpium.com

Those are the words that almost 20 million people across the globe utter to friends and family each year.

I am one of them.

That phrase first stumbled from my lips two days before Christmas 2024. The cancer was at Stage Three. It had already spread.

There’s no normal way to receive the news, no normal way to share it, and no normal way to deal with it. Life after a cancer diagnosis will never be normal.

One of the many things I learnt from my experience is that although 20% of people across the globe are likely to get cancer during their lifetime,1 each cancer patient’s journey is unique. And very, very lonely.

Cancer is like no other illness. The word itself is like a spider: a curiosity or annoyance at a distance, but utterly frightening when it lands on you. It requires ongoing attention but cannot merely be managed as a chronic condition like diabetes, lupus, or heart disease. It cannot be ignored. It needs to be defeated.

My diagnosis came as the artificial intelligence hype was ramping up. Instead of asking Google, patients are now turning to ChatGPT, Claude and Perplexity to answer queries ranging from cold remedies to cancer treatments. I quickly jumped aboard.

AI was great. Until it wasn’t.

Research shows that the best models risk clinical harm in around 15% of cases, while that rate is close to 40% for the worst models. Perhaps more worrying is that even with low, but non-zero, error rates there’s a risk of introducing bias: medical professionals start trusting AI over their own knowledge.

Another study outlined the limitations of using large-language models (LLMs) in healthcare. Among the fourteen issues identified by the authors were an inability to learn from experience, being unable to grasp context, and lacking emotional intelligence. “ChatGPT is unable to understand users’ emotions and cannot empathize with them,” they wrote. “It is not possible to understand the awkward glance by a patient.”

The Human Profession

This last point — emotional intelligence — is lost amid the hype over AI’s growing role in medicine. But it’s the single most important aspect of being a doctor, a nurse or even an orderly. Of all professions, ranging from law to landscaping, transportation to teaching, healthcare is the most human. No other occupation is more intimately connected to the very human experiences of getting sick, feeling fear, and dying.

My cancer assessment was first delivered by a forlorn-looking GP who kept uttering “sorry” between bouts of medical advice. She happened across the signs during a checkup for other issues and sent me off for scans. No image-recognition model could ever have spotted what an experienced doctor felt with her own hands, nor delivered the news with the compassion of an emotionally intelligent clinician. Besides, I couldn’t have coped with even the most human-like robot telling me I might die.

After sharing the news with family and cutting short Christmas celebrations, I went down the chatbot rabbit hole. Treatments, survival rates, side-effects and coping strategies were delivered with ease and confidence, as if a calm experienced doctor was on the other end of the chat. It didn’t help. It made me feel worse, more alone. More vulnerable.

But I couldn’t stop. Even after consulting human doctors — a hematologist, two oncologists, a surgeon — I kept on my AI crusade. I wasn’t doctor-shopping: each of these specialists had a role to play in my diagnosis and treatment, and there’d be more. I spent an entire day with Perplexity, digging out medical papers related to my specific brand of cancer, and printed out a four-page report to take to my medical oncologist. I felt like a diligent student who did all his homework plus the extra-credit assignment.

My doctor’s reaction was neither annoyance nor dismissal. He’d helped run clinical trials before and seemed genuinely happy that I was taking such an active interest in the research. I am a PubMed nerd, I told him truthfully. But with the patience of an elementary school teacher, he politely guided me away from the confident and hallucinatory directive I held in my hand and outlined my case for me. He even took the extra step of writing down journal articles more applicable to my situation, suggesting I spend time with them instead. AI wasn’t his enemy any more than Google was the enemy of doctors a generation ago, but such tools had limitations.

As chemotherapy got under way I quickly found out that the real medical care didn’t come from knowing my cancer type or the probability of survival - that’s just statistics. Nor was it from using a machine to inject precise doses of platinum-based chemical cocktails and sleep-inducing antihistamines. That’s just engineering.

It came from six-hour long sessions in the infusion ward, where my anxiety and panic collided with IV needles and the determination of a nurse patiently making her third attempt to find a vein. My medical care wasn’t delivered by an expensive high-powered proton therapy system. The care came from a kind and diligent radio oncologist who sent me back for successive 3D scans and precise tumor mapping, ensuring that six weeks of daily radiotherapy would kill the cancer but deliver minimal damage to healthy tissue.

Artificial intelligence systems may help aid diagnosis, plan treatment, or manage records, but they will never match a human’s ability to comfort and guide another human through the trauma and triumphs of being a frail, mortal human.

My radio oncologist has treated hundreds of patients, yet a moment when I most appreciated her experience came when she delicately guided two feet of rubber tube through my nose and into my stomach so that I could get the nutrition I needed. There are few things more inhumane than having to feed through a nasal-gastric tube for two straight months. A robot would never have completed that task, let alone done so with the care and empathy that she showed.

I had a surgeon, too, with that customary surgeon’s swagger. His constant missives of “don’t worry” at first seemed tone deaf. I had cancer, of course I was going to worry. But he knew something that no LLM could truly understand: to worry was a natural human instinct, yet utterly unhelpful. After months of fortnightly visits, including very worrying setbacks in my treatment, I started to imbibe his “don’t worry” mantra. I still worried. But less. There was something comforting about listening to a confident human instead of a confident chatbot. Perhaps it’s the warmth of the delivery or his genuine wish that I did truly have nothing to worry about.

My situation has stabilized. I’m confident I’ll survive. But it’s not over. I have months, even years, of post-treatment therapy to combat what could be life-long side effects. Still, I am one of the lucky ones.

A large portion of my luck comes from having affordable access to world-class healthcare. My consultation and treatments were undertaken in a first-world nation at a medical center paid for by a billionaire — Foxconn founder Terry Gou, who lost both a wife and brother to cancer. My radiotherapy was provided by Varian’s cutting-edge Proton Therapy systems which Gou also paid for. Dozens of my fellow cancer victims enjoyed the same privilege, and it is a privilege. I saw many of them with my own eyes, as we waited robed and silent for our turn to be strapped down and zapped with beams of radiation. I know that many of them won’t have made it. Survivor’s guilt is real.

Burden of Poverty

Around 18.5 million cancer cases were registered globally in 2023 and at least 10.4 million died. That’s 56% of the number diagnosed, according to data published by The Lancet.2 Of course, each type of cancer has its own survival rates. These in turn depend on factors beyond the cancer type, such as the stage of diagnosis, age, general health, race, and the presence of other diseases (comorbidities). Yet data shows that the biggest factor is access to medical care.

Australia, New Zealand, Denmark, and the US have the highest rates of cancer per capita in the world, but they’re nowhere near the top of the list when it comes to actually succumbing to the disease, according to WHO data. Mongolia and Zimbabwe have that distinction.

Despite advances in medical research, cancer rates are expected to climb by more than 60% over the next quarter century to 30.5 million annually. Deaths could rise 75% to 18.6 million, “with over half of new cases and two-thirds of deaths occurring in low- and middle-income countries,” the Institute for Health Metrics and Evaluation wrote, citing The Lancet paper.

Those last two facts are astounding: Half of new cases, but two-thirds of deaths.

Cancer’s burden will disproportionately fall on the poor: those who can least afford the prohibitive cost of chemotherapy, radiotherapy, surgery and the myriad pre- and post-therapy treatments that come with it. It’s because of this cost, and lack of access to general healthcare, that poor patients will die at a disproportionately higher rate.

Global access to artificial intelligence tools, including large-language models, may be able to ameliorate this inequality. It’s worth noting that when I asked, none of my doctors expressed objection to AI’s role in medical care. One oncologist noted that they subscribe to multiple LLMs while remaining wary of hallucinations and false information. They recalled one instance where a patient faced complications that couldn’t be easily pinpointed. A “conversation” with ChatGPT helped brainstorm ideas, helping the doctor diagnose the root cause and find a solution.

Yet we mustn’t misunderstand the power of AI in diagnosis. A paper published by Microsoft in June titled “The Path to Medical Superintelligence” trumpeted the US software company’s “model-agnostic orchestrator” which simulated a panel of physicians in a diagnostics competition against human clinicians. At first blush the results sound impressive. But upon closer inspection, the exercise was flawed. The model was testing only against known cases published in the New England Journal of Medicine, yet it undoubtedly was trained on those very papers. The humans, on the other hand, weren’t allowed to search the internet nor consult with colleagues — both unlikely scenarios. Microsoft’s offering, in other words, was a souped-up search engine. The original diagnoses came from human doctors in real-life clinical scenarios.

A more impressive AI system is the Compass model developed by researchers at Harvard Medical School and outlined in a paper titled “Generalizable AI predicts immunotherapy outcomes across cancers and treatments.” In this case, the team fed thousands of data points on tumors, immunotherapies, and outcomes into a computer. Their model then allows doctors, or even patients, to upload data from a tumor’s RNA to predict the likelihood of response to immunotherapy.

In addition to chemotherapy, which is a common frontline treatment for many cancers, the fields of immunotherapy and gene therapy have popped up to more carefully target tumors. But neither immunotherapy nor gene therapy are applicable in all circumstances. At present only a small cohort of tumors, such as some which show expression of a protein called PD-L1, are likely to respond to immunotherapy. Yet, because immunotherapy recruits the body’s own immune system to fight cancer there can be significant downsides in its use. It’s also very expensive. Being able to predict which tumors might respond can be crucial for doctors and patients to make better choices, and save money.

Gene therapy faces similar challenges, notably in the lack of druggable targets in many cancer types. With AI being used to fold proteins, surface new drug candidates, and test efficacy, there’s every chance that the cost and time to develop new therapies can be reduced. That also means offering up a wider array of drugs, including for rare cancer types that have so far escaped attention by pharmaceutical companies.

AI advances, driven by greater computing power crunching reams of data, can help healthcare workers and their patients in both first- and third-world nations. As the internet did with the digital divide, AI has the potential to help bridge the healthcare divide.

But AI cannot replace the people actually providing the healthcare. To believe otherwise is misguided, if not dangerous.

LLMs, the most common interface people currently have with AI systems, should never be viewed as a proxy for human doctors. Personal medical advice and treatment is not where we should place our hopes on AI for healthcare advancement. As with blood tests, X-rays, and MRIs, they should be seen as tools to help us identify problems, not solve them.

The Human Disease

Cancer, as one of humankind’s most insidious and confounding diseases, shall serve as the test case for how we both embrace and guard against the advances of artificial intelligence.

I’ve been there. I’ve seen what AI can do, what it can’t, and where the pitfalls lie.

An AI model, for example, can tell you that many cancer patients choose to forego treatment in favor of a quicker death. But it cannot explain why. A doctor can. They’ve seen it with their own eyes, heard it with their own ears. Felt it with their own heart. They instinctually understand that an achievable end to the darkness is far more enticing than a slow, exhausting recovery that for most will never be fully over. For some, it will never even come. A patient is not trying to avoid the pain — which is well-mitigated by a vast menu of legal opiates — they’re seeking to rid themselves of the loss of control and lack of agency. An AI model is no help here.

If anything, the cold scientific truths delivered by an LLM compound the pain. Most patients will suffer from the effects of their treatment for years, “many of those symptoms will remain forever,” a chatbot will tell you. What it won’t say is that these symptoms serve not only as a constant reminder of the near-death experience, but as an incessant warning that at the cellular level there’s a time bomb that may start ticking again at any time. No one wants to live under that cloud, no matter how much Joie de vivre remission inspires.

And with such “helpful information” about post-treatment side effects just a keystroke away, even the hardiest survivor could be justified in believing that their life has peaked with little good ahead. The most intelligent generative pre-trained transformer still lacks the EQ to convince a patient otherwise.

Although often hallucinatory, GPTs are logical by design whereas humans are primarily emotional, by design. I’ve not known any of my doctors to tell a lie, even a white lie, but bad news has always been delivered with compassion and helpful advice. Maybe it’s just a look in the eye or the tone of voice, but in moments of uncertainty these little human inflections mean the world.

A human doctor can also give very human responses to very human frustrations. It was my radio oncologist, for example, who suggested a swig of olive oil to offset radiation-induced dry mouth. AI hadn’t ever thought of it.

One of the toughest phases of my treatment and recovery was battling Fentanyl addiction. Administered through skin patches, the drug was the highest rung I climbed on an opioid ladder that began with Tramadol. I commenced my therapy determined to avoid opioids in the naive belief that Ibuprofen would be enough. I caved. But after the effects of concurrent chemo-radio therapy subsided, the feeding tube removed, and the skin on my face and neck stopped melting off, I was left an addict. The pain had gone, but a day without Fentanyl left me jittery, sweaty, and sleepless.

When I went to my medical oncologist with an AI-delivered prescription for kicking my Fentanyl habit he noted that these were no better than the drug I was already hooked on. Each of my AI chatbots had assured me this combo was standard treatment for opioid addiction. My doctor had a much better, more human prescription: sleep through it. Since I wasn’t working, I could afford to doze my way through withdrawal with the help of Xanax. This was the solution that I, a human, needed. And one which only a human had offered.

In moments of darkness — when I couldn’t eat, couldn’t speak, and could barely breathe — humans were my only salvation. I am very tech savvy: I’ve been a tech journalist for two decades, and have a Masters’ Degree in computer science. Gadgets and code are my comfort zone. Except when I was physically and emotionally uncomfortable. I didn’t want to search online for data and answers any more, I became repulsed by the robotic and false warmth of AI apps. It no longer mattered whether they were right or wrong, I could only feel their coldness. I found warm comfort bingeing episodes of Suits, because although fictional it seemed that human drama was still preferable to digital compassion.

In aggregate, my case was dealt with by more than ten doctors and over two-dozen nurses, in addition to medical technicians, orderlies, hospital volunteers and admin staff. I listed each person involved in my treatment and made a dispassionate analysis of which could be even partially replaced by some kind of AI, automation, or robot. Less than five, I concluded: mostly lab technicians or administrators.

Even if I peer into a crystal ball at where developments in artificial intelligence will take us in the next 25 years, I am certain that AI will not adequately replace a single one of the front-line medical workers who nursed me through the pain, the trauma, and the mystery of cancer.

But I also have a word of caution for the doctors and nurses who practice their craft with diligence and passion:

Lean into your humanity. Don’t fall prey to the mathematical algorithms which we now call AI. Humans are not statistics. To any individual patient it doesn’t matter whether a certain treatment for a given condition is 75% effective or 25% effective. For that one human, it’s binary: they’ll live through it, or they’ll die.

In my case, the odds were well in my favor when therapy commenced but started turning against me when early treatment failed and new drugs needed to be recruited. I still feel unsure. Doctors should always keep in mind that the one patient in front of them right now might be the edge case, the statistical outlier — for better or worse. But to that person, and to their family and friends, they are wholly unique. Each patient needs to be treated as an N=1 case.

All of us. Healthcare workers, patients, technologists, and policymakers also need to remember the most important fact of all. AI and medical professionals can both offer information, and both can make mistakes. But to care is human.

Thanks for reading

I plan to take a break for a couple of months. Things aren’t as dire as my last hiatus, but I need some time for recovery and rest. Thanks for your support this past year. May you have a healthy and happy festive season.

Culpium.com

President Trump announced an about face on US restrictions on the export of select high-end Nvidia chips to China. The outcry from national security circles was swift and warranted. I believe it’s a mistake for Washington to allow such powerful digital weapons to be made available to its largest adversary.

Nevertheless, as an intellectual exercise I decided I’d take the opposite side of the debate. Ben Thompson constructed a steel-man argument in August by explaining why the US should take a stake in Intel. I disagree with that transaction but found his discussion at Stratechery to be quite enlightening. So, yes, I am copying Ben’s idea for this one.

Jensen is starting to look like the new Elon, and clearly his bending of the president’s ear has had an impact. I was in the room when the Nvidia founder and CEO explained, in response to a media question, why restricting sales to China was a mistake. Since then, he’s used various permutations of the same argument across numerous platforms from a forum held by the influential Center for Strategic & International Studies to a 2.5-hour chat with the even more influential Joe Rogan.

I’ll lean heavily on Jensen Huang’s words to construct my steel man position, but I am not quoting him directly nor will my thesis be an exact replica of his argument. I am taking what I see as the best of his logic and then adding to it.

Culpium’s Steel-man Argument for Selling High-end GPUs to China

If China doesn’t get access to the best technology, the argument goes, then it’ll be incentivized to build its own. To get to the essence of this issue, we need to get into the mechanics of how Chinese innovation works. To outsiders, it’s easy to think of the entire nation, its industry, and its economy merely marching to the beat of President Xi Jinping’s drum. Xi says “make chips” and 1.4 billion people respond in unison “how fast?”

The nation doesn’t quite work like that. Xi and the Chinese Communist Party (CCP) set the overall tone and direction, and through various bureaucracies does get involved in the nitty gritty from time to time — like banning after-school tutoring. The government also allocates funds and otherwise deploys various carrots and sticks to make the country move in the “right” direction.

But, despite the name, China is not Communist. It has private ownership, a stock market, millions of entrepreneurs doing their own thing, and countless private companies following the ebb and flow of market forces. ByteDance, Alibaba, and Tencent are among them.1 Each is toiling away building and selling products, and that now includes various AI-related projects. To compete in AI, they need the best tools possible and the best AI hardware tools are built on Nvidia GPUs.

If Nvidia chips aren’t available, goes the “sell-to-China” argument then local suppliers like Huawei, Cambricon Technologies, MetaX, and Semiconductor Manufacturing International Corp. (SMIC) will fill the gap. Sure, they’re not as good as Nvidia, but they’re the best available and it’s a level playing field anyway.

More importantly for these second-choice suppliers, getting orders from domestic clients gives them revenue. This can then be ploughed back into R&D and capex which in turn allows these also-rans to get better, and bigger, and thus slowly close the gap on Nvidia.

If, however, Nvidia can sell its chips to China, then ByteDance, Alibaba and Tencent will just buy from the US giant and steal sales from the local alternatives. Nvidia, goes the argument, can suck up all the financial oxygen from Chinese chip rivals, stunting their growth.

The second plank of the Nvidia-addiction argument is CUDA. The Compute Unified Device Architecture (CUDA) is considered by many to be Nvidia’s true moat. AMD has some pretty solid GPUs that can compete with Nvidia’s offerings, while hyperscalers like Google and Amazon are designing and deploying their own custom-designed silicon. However, entire AI systems are built around not just Nvidia chips but its CUDA platform and swapping over is not easy. It can be done, but a lot of the power and efficiency advantages are lost.

Getting China’s vast and growing AI sector to build its systems on CUDA would mean Nvidia, and by extension Washington, has leverage over Beijing. Should Chinese leaders upset the US administration, then the White House, State Department, or Commerce could just pull the CUDA rug from under them. Just the threat of cutting off access might be enough to get Beijing feeling a little cooperative.

The final argument is that if Chinese companies are to spend billions of dollars per year on semiconductors, wouldn’t it be best that such money flows into American pockets? Doing so would certainly help rebalance the large trade deficit, boost the US economy, and help American taxpayers.

So that’s the steel-man case for why the US should allow Nvidia to sell a selection of high-end GPUs to China. I’ve tried to be intellectually honest in constructing this argument. If you feel I haven’t, then please let me know by email or comments.

Now, let me tell you why I still think it’s a mistake.

In my 25 years covering this space I have never seen any foreign company establish an unassailable position in the China market. From cars to pharmaceuticals, and most certainly in tech, Chinese companies have worked hard to replace foreign firms. And Beijing has helped give outsiders the boot as soon as locals could fill the gap.

Adjunct to this observation is that Nvidia has proudly outlined how its chips can help design chips. It even has a platform called cuLitho — an amalgam of the words CUDA and lithography.

With better chips at their disposal, Chinese tech firms can and will use Nvidia’s technology to design and manufacture chips to replace Nvidia itself.

Another reason why I think the reliance-and-addiction thesis is flawed is that lack of access to foreign chips was never a spur for Chinese semiconductor development. This argument was floated often during the Huawei saga, when the US cut the Chinese telecommunications giant off from American technology. But it’s simply not true. China has spent hundreds of billions of dollars over at least the past quarter century trying to advance its semiconductor prowess. Cutting China off from better technology in no way helps this effort, it can only stifle it.

Technology independence has been a stated goal of the Chinese government for decades, with a heightened sense of urgency under Xi Jinping. Not being able to buy Nvidia chips may raise the temperature a little, but the course was set long before China even knew who Nvidia was.

I’d be remiss if I didn’t make mention of the bilateral political economics of President Trump’s decision. He clearly wants to make a deal with Beijing, and this seems to be some kind of conciliatory move. But there’s been enough column-inches devoted to that angle already. You wouldn’t believe me if I told you that I know what Trump is thinking, so I won’t bother trying.

Thanks for reading.

More from Culpium:

Yes. We’re in an AI bubble. That’s not necessarily a bad thing. As Ben Thompson recently outlined at Stratechery, there are benefits to bubbles. The upside of the dot-com boom and bust was that it brought a whole generation of people online. Its related fiber-telco bubble gave us a global network of high-speed internet cables.

But this one is different in some fundamental ways. I think the remnants of a burst AI bubble will leave the world very different to what many currently imagine.

Here’s how I define a bubble:

1. Company valuations that are not supported by the revenue and profits they create,

2. Potential for a massive devaluation,

3. Impact from that devaluation.

Each is both objective and subjective since you can make up your own objective benchmark based on your subjective view of what the metrics ought to look like.

Yet an important trait of a bubble-and-bust is how the whole saga was funded. Andrew Ross Sorkin, who just published “1929: The Inside Story of the Greatest Crash in Wall Street History,” argues that debt — aka leverage — is what underpins impact. He outlined his thesis in a recent episode of the Odd Lots podcast with Tracy Alloway and Joe Weisenthal.

“You actually can have speculators and all the bad actors you want doing all the bad things you can imagine on stage, but it’s the leverage that tips it over,” Andrew Ross Sorkin.

This hadn’t occurred to me, but seems so obvious once he said it out loud.

I spent a lot of time examining the dot-com crash of the early 2000s. This involved not only reading through the narratives of that time, but analyzing the financials of some marquee names of this era including Yahoo, Lycos, Webvan, and Infospace. The one company which represented the boom and bust of the era was Pets.com which died in November 2000.

In examining Pets.com’s financials for the period just prior to its bankruptcy, it’s hard not to think of Open AI founder Sam Altman’s recent reply to a very reasonable question. “How can a company with 13 billion in revenues make 1.4 trillion of spend commitments?” Brad Gerstner asked in his B2g podcast in early November.

“First of all, we’re doing well more revenue than that. Second of all, Brad, if you wanna sell your shares, I’ll find you a buyer … Just, enough!,” Altman replied.

Altman seemed annoyed by the question. But his OpenAI today is far less sturdy than Pets.com was back then. Dot-com companies, the story goes, were forced to spend a lot on capital expenditure as the bubble inflated, and then died because of this capex burden when the bubble burst. The data doesn’t back this up.

Capex in the year immediately prior to the March 2000 collapse averaged just 15% of operating expenses across a collection of representative stocks I collated. Pets.com’s capex was equal to just 22% of opex. To blame massive capital expenditure for their downfall is just wrong.

Instead, we need only look at how much they spent on marketing to get an understanding of what went awry. The median of sales, general and marketing expenses across this basket of dot-coms was a crazy 80% of sales, and that’s before accounting for R&D. Notably, Pets.com spent seven-times more on marketing than it received in revenue.

The underlying thesis of these internet companies was correct. Pets.com, for example, was right to believe that online commerce was the future. The problem was that dot-com CEOs were playing a game of chicken with their marketing budgets. The fear was that if they stopped spending on ads, discounts, and incentives then they’d fail to onboard enough customers and thus miss out on the future revenues which would justify all that spending. That’s just Startups 101, op. cit. Uber and Airbnb.

We are now seeing the same with AI companies. If any of them blinks, they risk falling behind and losing market share. Except they’re not spending billions of dollars on Superbowl ads, that money is going toward Nvidia-based servers and the massive data centers required to power, cool and connect them. You can cut your ad spend overnight, but capex is a much bigger commitment and lingers for years. Even if companies cancel their huge server orders at the first sign of trouble, the depreciation expenses will weigh on income for a while yet.

Also analogous to the AI bubble is the network-fiber boom which happened at a similar time yet had different characteristics. Builders and owners of intercity and subsea cables like Global Crossing, Williams Communications, and WorldCom spent billions to connect the world. Unlike dot-coms, these were capex-heavy businesses. Global Crossing’s capex was 1.3-times opex while Williams’ was at 70%. The multiples were even worse when compared to revenue. In the end, they each had massive infrastructure but lacked the operating cashflow to service their debts and went bust around 2002, two years after the dot-com bubble burst. This is the leverage Sorkin speaks of.

The dot-coms were low-capex, high-marketing consumer companies. The fiber telcos were high-capex B2B infrastructure companies.

Today’s AI companies have traits of both categories, but fit in neither.

Startups like OpenAI and Anthropic spend massive amounts of money on infrastructure, while established players like Microsoft, Amazon, Meta, Alphabet and Oracle are doing the same.

Hamilton Helmer’s framework on the 7 Powers of business strategy is a common lens through which to look at a company’s strength. Ben Gilbert & David Rosenthal of the Acquired podcast often use it to evaluate the quality of a deal.

Hamilton Helmer’s 7 Powers:

1. Scale Economies: cheaper as you get bigger, keeping others out.

2. Network Economics: increased user value as more customers use it.

3. Counter Positioning: a different approach which incumbents won’t try.

4. Switching Costs: too hard to move to a competitor.

5. Branding: name alone adds value.

6. Cornered Resource: exclusive or preferential access, could include patents.

7. Process Power: secret sauce which makes it cheaper or better.

I doubt that OpenAI, Anthropic, Meta, xAI, or Google have many of these. While they benefit from scale economies and cornered resources, they all benefit from economies of scale. Any cornered resource is likely to be temporary (a few years, at best). More importantly, none have true brand value and switching cost is non-existent. Like the incentives offered in the heyday of online commerce, users will swap seamlessly as soon as they find better value elsewhere.

As a counter example, we cannot deny the brand value of Apple and Samsung, or even iOS v Android. In addition, while it’s annoying to switch from Windows to MacOS — and a little easier to shift between Android and iOS — there’s no doubt that the lack of compatibility keeps consumers within one ecosystem or the other. This friction doesn’t exist in current AI models because early adopters can, and do, switch as quickly as they change browser tab. And I’ve seen nothing to suggest that any AI company currently has process power or gain any benefit from network economics.

Fiber telcos did have some of these powers, because not just anyone could lay down a cable at any time, and it wasn’t particularly easy to switch between network providers. As with the railway boom, the fiber companies built a lot of transport infrastructure which they kindly left behind even after going bankrupt.

But, fiber telcos also enjoyed very long depreciation schedules of up to 25 years. That means a $2.5 billion outlay appears as a $100 million annual cost on the income statement. AI companies have just trimmed their depreciation schedules to five years, from seven, so that same upfront payment appears as a $500 million annual charge. More importantly, thanks to Moore’s Law and a need to technologically “keep up with the Joneses,” AI servers near obsolescence in just a few years.

As a result, AI companies need to keep spending billions of dollars on fast-depreciating equipment to make a commodity product: tokens. These are the individual units of data used to process and analyze a request.

The label “AI factory” popped up in late 2024, and perfectly describes the server farms used to churn out tokens. I first heard the term when Foxconn Chairman Liu Young-wei explained it to reporters after announcing he was teaming up with Jensen Huang’s Nvidia to build the world’s largest AI factory in southern Taiwan. And if Foxconn’s getting involved you know commoditization is underway.

I’d argue that the ChatGPT interface, as it stands now, is rudimentary. It offers little more than a ticker of tokens, churning out responses that are just elaborate search results wrapped in prose. Google’s integration of Gemini into Colab, for example, makes it more app-like and functional. Search, to be sure, is lucrative as Google can attest, but ChatGPT is hardly counter-positioning. Right now, OpenAI is largely in the business of making AI tokens.

AI company founders may cling to the claim that they’re offering something unique, special and non-replicable. But as AI models develop, the gap between them is likely to narrow and users will find themselves seamlessly switching at the token level. The true differentiation is how they are applied — the application layer. But as we saw with DeepSeek — which “borrowed” tokens from OpenAI — this doesn’t require massive investment in token-making. Tokens will always be needed, and we’ll always need more of them, but the tokens are like the oil fueling AI applications.

This business of token-making involves massive capex, a commodity product with declining prices, minimal switching costs, fast-depreciating assets, and rapid pace of both manufacturing improvement and technological development. That’s not at all like oil, in fact it’s more like memory chips — both DRAM and NAND. You have both inside your computer (and cellphone), but I bet you don’t know the name of the supplier. And on a desktop (and many laptops) you can easily swap out or upgrade.

The only way for a memory-chip maker to survive is to keep investing in new equipment in order to both expand capacity — thus enjoy economies of scale — and improve technology, in order to stay competitive and keep customers upgrading. In memory, the tech specs which define competitiveness are capacity and speed. Both require the newest chipmaking equipment.

If you want to manufacturer the best chips, you need ASML’s EUV machines, and if you want the best AI servers you need Nvidia’s chips. Which makes Nvidia the ASML of AI. Conversely, if you do not pay top dollar for the best equipment, you’re unlikely to have the best product or be cost-competitive.

Since the AI sector at the model and token-making level is just like memory, the industry shakeout is likely to play out in the same way. DRAM used to be a big deal. For a while it was Intel’s bread and butter. These chips are fundamental to any computing system because they temporary hold data — often for milliseconds — which a processor uses to make calculations. In the early PC era, DRAM supply was the bottleneck for computer sales.

By the early 2000s, there were around a dozen memory-chip makers across South Korea, Japan, Taiwan and the US. Most were backed by industrial conglomerates. Hynix, for example, was spun off from the Hyundai group after the chaebol’s boss realized electronics were an increasingly important part of cars. Nanya Technology was born out of Taiwan’s massive Formosa Plastics Group. But heavy competition, unstable earnings, and an unwillingness by corporate parents and banks to keep funding them lead to a wave of consolidations and shutdowns by the early 2010s. Today, the DRAM sector is dominated by just three companies: Samsung, SK Hynix — both from South Korea — and Boise, Idaho-based Micron.1

What kept the leaders atop the market was both an unrelenting pace of capacity expansion, and continued technological development using the latest equipment. Both are crucial to maintaining price competitiveness on a per-bit basis. Assuming the product is largely reliable, price is the factor which truly differentiates.

Today’s AI leaders can be sorted into two categories: stand alone, and conglomerate-backed. OpenAI and Anthropic are startups that stand alone. A recent series of circular deals muddies the water a little: Nvidia is taking a stake in Open AI2, while the startup has a warrant for shares in Nvidia rival AMD. And Microsoft’s relationship with OpenAI makes the software giant as much a sugar daddy as a conglomerate parent: it’ll last as long as Microsoft’s AI friend provides benefits.3 These companies are kind of like Micron. Although it found a wealthy industrialist to back it, Micron never benefited from having a conglomerate parent.

Microsoft’s relationship with OpenAI makes the software giant as much a sugar daddy as a conglomerate parent: it’ll last as long as Microsoft’s AI friend provides benefits.

On the other side we have Meta, Microsoft, Alphabet, Amazon, Oracle and a few more. Some of these are building their own foundational models, some are distilling other people’s models, and others yet are just riding the AI wave by providing compute and network capacity. They are like Samsung, which comes out of a chaebol of the same name, and SK Hynix — itself the merger of two chaebol children. Oracle is the most leveraged to AI to a degree that is unsettling. It also has the least-varied revenue stream compared to the advertising, SaaS, and cloud offerings of its rivals.

Once the current bubble bursts, and the funding stops, AI companies will be faced with a tough predicament. As for the memory industry, those with big corporate backers have the best chance of surviving. They can slow investment and preserve cash, but risk falling behind both in capacity and technology. Or they can continue to play capex chicken, hoping a rival will flinch first. The stand-alone players will have fewer options. If the funding gravy train has already slowed, then absent continued cash from banks and corporate FWBs, they’ll need to either pivot or get acquired at a steep discount to their heady valuations.

AI is here to stay, just as railways and the internet still endure. That doesn’t mean we’re not in a bubble. It also doesn’t mean all of today’s AI leaders will survive this boom and bust cycle. Application developers— those who make use of AI models to boost productivity, reduce costs, or create entirely new product categories — will define the future. But many model makers will be wiped out.

What will be left is a handful of AI factory operators churning out cheap tokens to feed the long-march toward AGI.

Thanks for reading.

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Foxconn is a connector company. It’s right there in the name.

Chairman Liu Young-way took pains to remind guests of that fact at this year’s Hon Hai Technology Day. Although the Taiwanese company is famous for assembling final devices — such as iPhones, PlayStations and Kindles — end-product manufacturing is a relatively new business: commencing in the mid-1990s more than 20 years after Terry Gou founded Hon Hai Plastics.

This distinction matters. It matters for Foxconn. And it matters for America. Final-assembly of smart consumer devices like cellphones and games consoles is labor intensive, low-priced, low-margin, and high volume. That’s perfectly suited to a low-wage country with a large labor pool like China, where Foxconn has most of its factories. Not so much the US, which wants more factories.

High-value, high-margin, or highly automated manufacturing is a much better fit for America. So when Liu, who took over from Gou in 2019, reminded people of Foxconn’s roots at its annual confab last week it felt like he was talking directly to Americans. Hon Hai is the name Gou gave to the plastics-manufacturing company he founded with seed capital from his mother in 1974, but Foxconn is the Westernized moniker he bestowed upon the business when he started chasing more clients in the US. The conn comes from connectors.

Incidentally, Gou made a surprise return to HHTD this year. His mother passed away a few weeks ago. I don’t know if the two are related, but Liu made a point of offering his condolences on stage.

Showing off an array of components, cables, and connectors, Liu suggested people remember the term “Foxconn Inside.” Just 50 meters away, at its product showcase, Foxconn staff were on hand to introduce exactly the items he was talking about. Cooling pipes and heatsinks, power junctions, printed-circuit boards, and wafers of semiconductors — yes, Foxconn makes chips.

Last year I criticized Liu’s presentation for focusing too much on its production of finished cars, when instead I felt he could have spent more time explaining the heart of Foxconn’s business model, which is components.

“I think the company is at grave risk of screwing up if it keeps rolling out completed cars, as though it expects some incumbent or startup to just rebadge a Foxconn model with their own name. Its mixed messaging is painful to watch,” — Hon Hai Tech Day & the Future of the Global Economy.

I feel somewhat vindicated that he went back to the company’s origin story, and it turned out to be Liu’s best HHTD presentation to date. Talking about components, which may not be as sexy as iPhones, helps Foxconn explain itself to foreign leaders who seem obsessed with getting iPhone manufacturing to their shores.

During his first term, President Trump urged Apple to make its iPhones in the US. He continued that campaign in the lead up to his second term, culminating in CEO Tim Cook announcing $500 billion of investments into the US soon after Trump took office for his encore performance.

But Apple isn’t doing most of that work, Taiwanese companies like Foxconn and TSMC are. Foxconn’s disastrous adventure in Wisconsin rightly soured many Americans on the Taipei-based company, just a few years after it was pilloried for working conditions at its China factories.

Behind the scenes though, Foxconn has been expanding its operations across numerous US states, including a series of massive factories in northern Houston. They’re not making iPhones in the US. They likely never will. And that’s okay.

For the first time in more than a decade, smartphones are no longer the primary revenue driver at Foxconn. The Taiwanese company has been the key supplier for Apple’s flagship device since its launch in 2007. That created an unhealthy co-dependency for both companies.

Foxconn, however, has weaned itself off Cupertino much more than Apple has been able to wean itself off the Taiwanese company. Cloud & Networking products accounted for 39% of Taipei-based Hon Hai’s revenue over the first nine months of this year, compared to 37% for Smart Consumer Electronics. AI servers, largely those developed around Nvidia’s GPU ecosystem, are the major component of growth in the cloud & networking segment. Its consumer electronics segment includes smartphones, games consoles and tablets. Laptop and desktop PCs fall into the Computing category, while Components refers to those sold separately not including the vast number which are produced and sold in-house.

Foxconn’s new pitch to clients, and foreign leaders, is a redux of its original business model: we make all the ingredients that go inside your product. What’s different today compared to 20 years ago, is that the final products showing the most growth potential are much bigger, more expensive, and with far more components inside. I’m talking about AI servers, electric vehicles, mobile-phone base stations, and satellites.

A single Nvidia AI server sells for up to $5 million. You’d need to build and sell 4,000 to 5,000 iPhones to reach that level of revenue. More importantly, an AI server has more parts, and because of its size and design it is a better candidate for robotic assembly. This potential for automation is what allows Foxconn to be confident in its ability to scale up AI-server manufacturing in America, which it’s doing through its Ingrasys unit in Houston.

And, although many components, like sockets, plugs and cables, sell for just a few dollars — sometimes even pennies — they can also be made with almost no human intervention. I recall a visit to a Foxconn factory in China where PC-cable sockets were fed from a roll like film through a projector. Hon Hai affiliate Foxconn Industrial Internet is another unit with a plant in the Houston area.

Liu has also made a big bet on EVs. He has many skeptics. And they’re not wrong. But we ought to remember that Liu’s been in the industry long enough to know how product-manufacturing changes with the times. There was an era, Liu recalled, when PC makers were proud of making their own computers. Today, they all lean on outsourcing. As prices fall, EVs will do the same, Liu argues. And even if Foxconn doesn’t actually assemble the car — which it can and wants to do — it’ll still be there to supply the thousands of components required. This goes right down to micro-controller chips that are designed by its unit SiliconAuto unit and fabbed at TSMC.

Satellites are another area of massive potential. The rise of private operators like SpaceX and Blue Origin has slashed the cost of launch. Apple has already built satellite connectivity into its iPhones, and it’s only a matter of time before more devices like smart watches also connect to the stars.

Development and production of satellites needs to keep up, which will increase the need to cut prices. Foxconn has already built and launched two satellites. A senior executive in charge of satellite development told me that Foxconn’s big push into this arena will come once the 6G mobile telephony specs are finalized, which is likely to include provisions for satellite communications.

As with PCs, smartphones, and EVs, low prices are Foxconn’s specialty. Not because of low-cost labor, as many imagine, but because of its ability to develop and produce most of the components that go inside, giving it unique vertically-integrated economies of scale.

One thing that US business and political leaders ought to note is that none of this need include China. Foxconn isn’t exiting that country, which remains its largest manufacturing base, but it sure is leaning on the world’s second-largest economy a lot less. None of the assemblers of Nvidia AI servers — including Foxconn, Wistron, and Quanta — are making them in China. Liu himself said recently that its US operations will serve the US market, its Taiwan factories will serve the rest of the world, and a new facility in Japan will add to global capacity.

This reconfiguring of global technology manufacturing presents a great opportunity for America. Smartphones aren’t going away, but buying cycles are slowing. Growth instead will come from new areas like AI servers, EVs, and satellites — products which don’t need massive labor-intensive factories. They do, however need companies with the skillset to both develop and scale-up manufacturing of the crucial components inside — where the real value lies — as well as the ability to run automated production lines which can put it all together.

What will come of this reindustrialization isn’t tens of thousands of low-skill American jobs, as is often discussed on the campaign trail. Instead, what’s on offer is the chance to create plenty of opportunities for technically adept workers. The US currently lacks adequate supply of staff for areas like molding, machining, and precision engineering. That’s not a problem because Taiwan has the fix.

Companies like Foxconn, Quanta, Wistron, SPIL, GlobalWafers and TSMC — all Taiwanese — aren’t coming to the US for the cheap labor. While political pressure was the catalyst, they’re really expanding onshore to tap into the potential for a workforce of trainable technicians that can learn new high-value skills. Over time, America can regain these lost arts, capabilities exported to China over the past three decades.

America’s prospects for reindustrialization are bright. It’ll need Taiwan to build that future, with Foxconn as one of the chief architects.

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