What We’re Reading (Week Ending 14 May 2023) - 14 May 2023
Reading helps us learn about the world and it is a really important aspect of investing. The legendary Charlie Munger even goes so far as to say that “I don’t think you can get to be a really good investor over a broad range without doing a massive amount of reading.” We (the co-founders of Compounder Fund) read widely across a range of topics, including investing, business, technology, and the world in general. We want to regularly share the best articles we’ve come across recently. Here they are (for the week ending 14 May 2023):
1. Why Conscious AI Is a Bad, Bad Idea – Anil Seth
To get a handle on these challenges—and to clarify the confusing and hype-ridden debate around AI and consciousness—let’s start with some definitions. First, consciousness. Although precise definitions are hard to come by, intuitively we all know what consciousness is. It is what goes away under general anesthesia, or when we fall into a dreamless sleep, and what returns when we come round in the recovery room or wake up. And when we open our eyes, our brains don’t just process visual information; there’s another dimension entirely: Our minds are filled with light, color, shade, and shapes. Emotions, thoughts, beliefs, intentions—all feel a particular way to us.
As for intelligence, there are many available definitions, but all emphasize the ability to achieve goals in flexible ways in varied environments. Broadly speaking, intelligence is the capacity to do the right thing at the right time.
These definitions are enough to remind us that consciousness and intelligence are very different. Being intelligent—as humans think we are—may give us new ways of being conscious, and some forms of human and animal intelligence may require consciousness, but basic conscious experiences such as pleasure and pain might not require much species-level intelligence at all.
This distinction is important because many in and around the AI community assume that consciousness is just a function of intelligence: that as machines become smarter, there will come a point at which they also become aware—at which the inner lights come on for them. Last March, OpenAI’s chief scientist Ilya Sutskever tweeted, “It may be that today’s large language models are slightly conscious.” Not long after, Google Research vice president Blaise Agüera y Arcas suggested that AI was making strides toward consciousness.
These assumptions and suggestions are poorly founded. It is by no means clear that a system will become conscious simply by virtue of becoming more intelligent. Indeed, the assumption that consciousness will just come along for the ride as AI gets smarter echoes a kind of human exceptionalism that we’d do well to see the back of. We think we’re intelligent, and we know we’re conscious, so we assume the two go together.
Recognizing the weakness of this assumption might seem comforting because there would be less reason to think that conscious machines are just around the corner. Unfortunately, things are not so simple. Even if AI by itself won’t do the trick, engineers might make deliberate attempts to build conscious machines—indeed, some already are.
Here, there is a lot more uncertainty. Although the last 30 years or so have witnessed major advances in the scientific understanding of consciousness, much remains unknown. My own view is that consciousness is intimately tied to our nature as living flesh-and-blood creatures. In this picture, being conscious is not the result of some complicated algorithm running on the wetware of the brain. It is an embodied phenomenon, rooted in the fundamental biological drive within living organisms to keep on living. If I’m right, the prospect of conscious AI remains reassuringly remote.
But I may be wrong, and other theories are a lot less restrictive, with some proposing that consciousness could arise in computers that process information in particular ways or are wired up according to specific architectures. If these theories are on track, conscious AI may be uncomfortably close—or perhaps even among us already…
…There are two main reasons why creating artificial consciousness, whether deliberately or inadvertently, is a very bad idea. The first is that it may endow AI systems with new powers and capabilities that could wreak havoc if not properly designed and regulated. Ensuring that AI systems act in ways compatible with well-specified human values is hard enough as things are. With conscious AI, it gets a lot more challenging, since these systems will have their own interests rather than just the interests humans give them.
The second reason is even more disquieting: The dawn of conscious machines will introduce vast new potential for suffering in the world, suffering we might not even be able to recognize, and which might flicker into existence in innumerable server farms at the click of a mouse. As the German philosopher Thomas Metzinger has noted, this would precipitate an unprecedented moral and ethical crisis because once something is conscious, we have a responsibility toward its welfare, especially if we created it. The problem wasn’t that Frankenstein’s creature came to life; it was that it was conscious and could feel…
…Systems like this will pass the so-called Garland Test, an idea which has passed into philosophy from Alex Garland’s perspicuous and beautiful film Ex Machina. This test reframes the classic Turing Test—usually considered a test of machine intelligence—as a test of what it would take for a human to feel that a machine is conscious, even given the knowledge that it is a machine. AI systems that pass the Garland test will subject us to a kind of cognitive illusion, much like simple visual illusions in which we cannot help seeing things in a particular way, even though we know the reality is different.
This will land society into dangerous new territory. By wrongly attributing humanlike consciousness to artificial systems, we’ll make unjustified assumptions about how they might behave. Our minds have not evolved to deal with situations like this. If we feel that a machine consciously cares about us, we might put more trust in it than we should. If we feel a machine truly believes what it says, we might be more inclined to take its views more seriously. If we expect an AI system to behave as a conscious human would—according to its apparent goals, desires, and beliefs—we may catastrophically fail to predict what it might do.
2. Breach of Trust: Decoding the Banking Crisis – Aswath Damodaran
Banks with sticky deposits, on which they pay low interest rates (because a high percentage are non-interest bearing) and big buffers on equity and Tier 1 capital, which also earn “fair interest rates”, given default risk, on the loans and investments they make, add more value and are usually safer than banks with depositor bases that are sensitive to risk perceptions and interest rates paid, while earning less than they should on loans and investments, given their default risk…
… It is worth noting that all of the pain that was coming from writing down investment security holdings at banks, from the surge in interest rates, was clearly visible at the start of 2023, but there was no talk of a banking crisis. The implicit belief was that banks would be able to gradually realize or at least recognize these losses on the books, and use the time to fix the resulting drop in their equity and regulatory capital. That presumption that time was an ally was challenged by the implosion of Silicon Valley Bank in March 2023, where over the course of a week, a large bank effectively was wiped out of existence. To see why Silicon Valley Bank (SVB) was particularly exposed, let us go back and look at it through the lens of good/bad banks from the last section:
- An Extraordinary Sensitive Deposit Base: SVB was a bank designed for Silicon Valley (founders, VCs, employees) and it succeeded in that mission, with deposits almost doubling in 2021. That success created a deposit base that was anything but sticky, sensitive to rumors of trouble, with virally connected depositors drawn from a common pool and big depositors who were well positioned to move money quickly to other institutions.
- Equity and Tier 1 capital that was overstated: While SVB’s equity and Tier 1 capital looked robust at the start of 2023, that look was deceptive, since it did not reflect the write-down in investment securities that was looming. While it shared this problem with other banks, SVB’s exposure was greater than most (see below for why) and explains its attempt to raise fresh equity to cover the impending shortfall.
- Loans: A large chunk of SVB’s loan portfolio was composed of venture debt, i.e., lending to pre-revenue and money-losing firms, and backed up by expectations of cash inflows from future rounds of VC capital. Since the expected VC rounds are conditional on these young companies being repriced at higher and higher prices over time, venture debt is extraordinarily sensitive to the pricing of young companies. In 2022, risk capital pulled back from markets and as venture capital investments dried up, and down rounds proliferated, venture debt suffered.
- Investment Securities: All banks put some of their money in investment securities, but SVB was an outlier in terms of how much of its assets (55-60%) were invested in treasury bonds and mortgage-backed securities. Part of the reason was the surge in deposits in 2021, as venture capitalists pulled back from investing and parked their money in SVB, and with little demand for venture debt, SVB had no choice but to invest in securities. That said, the choice to invest in long term securities was one that was made consciously by SVB, and driven by the interest rate environment in 2021 and early 2022, where short term rates were close to zero and long term rates were low (1.5-2%), but still higher than what SVB was paying its depositors. If there is an original sin in this story, it is in this duration mismatch, and it is this mismatch that caused SVB’s fall.
In the aftermath of SVB’s failure, Signature Bank was shut down in the weeks after and First Republic has followed, and the question of what these banks shared in common is one that has to be answered, not just for intellectual curiosity, because that answer will tell us whether other banks will follow. It should be noted that neither of these banks were as exposed as SVB to the macro shocks of 2022, but the nature of banking crises is that as banks fall, each subsequent failure will be at a stronger bank than the one that failed before.
- With Signature Bank, the trigger for failure was a run on deposits, since more than 90% of deposits at the bank were uninsured, making those depositors far more sensitive to rumors about risk. The FDIC, in shuttering the bank, also pointed to “poor management” and failure to heed regulatory concerns, which clearly indicate that the bank had been on the FDIC’s watchlist for troubled banks.
- With First Republic bank, a bank that has a large and lucrative wealth management arm, it was a dependence on those wealthy clients that increased their exposure. Wealthy depositors not only are more likely to have deposits that exceed $250,000, technically the cap on deposit insurance, but also have access to information on alternatives and the tools to move money quickly. Thus, in the first quarter of 2023, the bank reported a 41% drop in deposits, triggering forced sale of investment securities, and the realization of losses on those sales.
In short, it is the stickiness of deposits that seems to be the biggest indicator of banks getting into trouble, rather than the composition of their loan portfolios or even the nature of their investment securities, though having a higher percentage invested in long term securities leaves you more exposed, given the interest rate environment. That does make this a much more challenging problem for banking regulators, since deposit stickiness is not part of the regulatory overlay, at least at the moment. One of the outcomes of this crisis may be that regulators monitor information on deposits that let them make this judgment, including:
- Depositor Characteristics: As we noted earlier, depositor age and wealth can be factors that determine stickiness, with younger and wealthier depositors being less sticky that older and poorer depositors. At the risk of opening a Pandora’s box, depositors with more social media presence (Twitter, Facebook, LinkedIn) will be more prone to move their deposits in response to news and rumors than depositors without that presence.
- Deposit age: As in other businesses, a bank customer who has been a customer for longer is less likely to move his or her deposit, in response to fear, than one who became a customer recently. Perhaps, banks should follow subscriber/user based companies in creating deposit cohort tables, breaking deposits down based upon how long that customer has been with the bank, and the stickiness rate in each group.
- Deposit growth: In the SVB discussion, I noted that one reason that the bank was entrapped was because deposits almost doubled in 2021. Not only do very few banks have the capacity to double their loans, with due diligence on default risk, in a year, but these deposits, being recent and large, are also the least sticky deposits at the bank. In short, banks with faster growth in their deposit bases also are likely to have less sticky depositors.
- Deposit concentration: To the extent that the deposits of a bank are concentrated in a geographic region, it is more exposed to deposit runs than one that has a more geographically diverse deposit base. That would make regional bank deposits more sensitive that national bank deposits, and sector-focused banks (no matter what the sector) more exposed to deposit runs than banks that lend across businesses.
Some of this information is already collected at the bank level, but it may be time for bank regulators to work on measures of deposit stickiness that will then become part of the panel that they use to judge exposure to risk at banks…
… The conventional wisdom seems to be that big banks have gained at the expense of smaller banks, but the data is more ambiguous. I looked at the 641 publicly traded US banks, broken down by market capitalization at the start of 2023 into ten deciles and looked at the change in aggregate market cap within each decile.
As you can see the biggest percentage declines in market cap are bunched more towards the bigger banks, with the biggest drops occurring in the eighth and ninth deciles of banks, not the smallest banks. After all, the highest profile failures so far in 2023 have been SVB, Signature Bank and First Republic Bank, all banks of significant size.
If my hypothesis about deposit stickiness is right, it is banks with the least stick deposits that should have seen the biggest declines in market capitalization. My proxies for deposit stickiness are limited, given the data that I have access to, but I used deposit growth over the last five years (2017-2022) as my measure of stickiness (with higher deposit growth translating into less stickiness):
The results are surprisingly decisive, with the biggest market capitalization losses, in percentage terms, in banks that have seen the most growth in deposits in the last five years. To the extent that this is correlated with bank size (smaller banks should be more likely to see deposit growth), it is by no means conclusive evidence, but it is consistent with the argument that the stickiness of deposits is the key to unlocking this crisis.
3. Inside the Delirious Rise of ‘Superfake’ Handbags – Amy X. Wang
My plunge into the world of fantastically realistic counterfeit purses — known as “superfakes” to vexed fashion houses and I.P. lawyers, or “unclockable reps” to their enthusiastic buyers — began a couple of years earlier, in what I might characterize as a spontaneous fit of lunacy. It was early 2021 when, thrown into sensory overload by grisly pandemic headlines, I found my gaze drifting guiltily to an advertisement in the right margin of a news site, where the model Kaia Gerber arched her arms lovingly around a Celine Triomphe — a plain, itty-bitty rectangular prism that in no universe could possibly be worth, as further research informed me, $2,200.
I shut the tab, horrified. Having grown up a first-generation immigrant whose family’s idea of splurging was a monthly dinner at Pizza Hut, I refused to be the type of person who lusted over luxury handbags. I had always understood that these artifacts were not for me, in the way debutante balls or chartered Gulfstreams were not for me. But, days later and still mired in the quicksand of quarantine, I found myself cracking my laptop and Googling “buy Celine Triomphe cheap.” This led me to a Reddit community of replica enthusiasts, who traded details about “trusted sellers” capable of delivering a Chanel 2.55 or Loewe Puzzle or Hermès Birkin that promised to be indistinguishable from the original, and priced at a mere 5 percent or so of the M.S.R.P…
…Untangling the problem of duplication in the fashion industry is like trying to rewrap skeins of yarn. Designer houses spend billions fighting dupes, but even real Prada Cleos and Dior Book Totes are made with machines and templates — raising the question of what, exactly, is unique to an authentic bag. Is it simply a question of who gets to pocket the money? (Hermès recently mounted, and won, a trademark war against “MetaBirkin” NFTs.)…
…I spoke with Kelly, one such person, seeking to peek under the hood of the shadowy business. (“Kelly” is not her real name; I’m referring to her here by the English moniker that she uses on WhatsApp. I contacted more than 30 different superfake-bag-sellers before one agreed to an interview.) Five years ago, Kelly worked in real estate in Shanghai, but she got fed up with trekking to an office every day. Now she works from home in Guangzhou, often hammering out a deal for a Gucci Dionysus or Fendi Baguette on her phone with one hand, wrangling lunch for her 8-year-old daughter with the other. Kelly finds the whole business of luxury bags — the sumptuous leather, razor-straight heat stamps, hand stitches, precocious metal mazes of prancing sangles and clochettes and boucles and fermoirs — “way too fussy,” she tells me in Chinese. But the work-life balance is great. As a sales rep for replicas, Kelly makes up to 30,000 yuan, or about $4,300, a month, though she has heard of A-listers who net up to 200,000 yuan a month — which would work out to roughly $350,000 a year.
On a good day, Kelly can sell more than 30 gleaming Chloés and Yves Saint Laurents, to a client base of mostly American women. “If a bag can be recognized as fake,” she told me, “it’s not a worthwhile purchase for the customer, so I only sell bags that are high-quality but also enticingly affordable — $200 or $300 is the sweet spot.” Kelly keeps about 45 percent of each sale, out of which she pays for shipping, losses and other costs. The rest is wired to a network of manufacturers who divvy up proceeds to pay for overhead, materials and salaries. When a client agrees to order a bag from Kelly, she contacts a manufacturer, which arranges for a Birkin bag to roll out of the warehouse into an unmarked shipping box in a week or so.
In Guangzhou, where a vast majority of the world’s superfakes are thought to originate, experts have identified two main reasons behind the illicit goods’ lightning-fast new speeds: sophistication in bag-making technology and in the bag-makers themselves.
One such innovation in the latter is a disjointed, flat-string, hard-to-track supply chain. When the intellectual-property lawyer Harley Lewin was the subject of a New Yorker profile in 2007, he could often be found busting through hidden cellars on raids around the world. But increasingly, Lewin told me, “I’m sort of the guy in the spy novel who’s called ‘Control’ and sits in a room,” trying to sniff out “the bad guys” from screenshots of texts and D.M.s. Counterfeiting operations are no longer pyramid-shaped hierarchies with ever-higher bosses to roll: “Nowadays it’s a series of blocks, the financier and the designers and the manufacturers, and none of the blocks relate to each other,” Lewin explains. “So if you bust one block, odds are they can replace it in 10 minutes. The person you bust has very little information about who organizes what and where it goes.” Indeed, Kelly, even though she has sold every color variation of Louis Vuitton Neverfull under the sun, only handles bags in person on rare occasions to inspect quality. Sellers don’t stock inventory. They function as the consumer-facing marketing block, holding scant knowledge of how other blocks operate. Kelly just gets daily texts from a liaison at each outlet, letting her know of their output: “The factories won’t even tell us where they are.”
As for how the superfakes are achieving their unprecedented verisimilitude, Lewin, who has observed their factories from the inside, says it’s simply a combination of skillful artisanship and high-quality raw materials. Some superfake manufacturers travel to Italy to source from the same leather markets that the brands do; others buy the real bags to examine every stitch. Chinese authorities have little to no incentive to shut down these operations, given their contributions to local economies, the potential embarrassment to local ministers and the steady fraying of China’s political ties with the Western nations where savvy online buyers clamor for the goods. “They avoid taxes,” Lewin says. “The working conditions are terrible. But all of that goes to turning out a very high-quality fake at very low cost.”…
…Those whose business it is to verify luxury bags insist, at least publicly, that there’s always a “tell” to a superfake. At the RealReal, where designer handbags go through rounds of scrutiny, including X-rays and measuring fonts down to the millimeter, Thompson told me that “sometimes, an item can be too perfect, too exacting, so you’ll look at it and know something is up.” And, he added, touch and smell can be giveaways. Rachel Vaisman, the company’s vice president of merchandising operations, said the company will contact law-enforcement officials if it suspects a consignor is sending in items with the intent to defraud.
But one authenticator I spoke with confesses that it’s not always so clear-cut. The fakes “are getting so good, to the point that it comes down to inside etchings, or nine stitches instead of eight,” he told me. “Sometimes you really have no idea, and it becomes a time-consuming egg hunt, comparing photos on other websites and saying, ‘Does this hardware look like this one?’” (He asked to remain anonymous because he is not permitted to speak on behalf of his company.) He and his colleagues have their theories as to how the superfakes that come across their desks are so jaw-droppingly good: “We suspect it’s someone who maybe works at Chanel or Hermès who takes home real leathers. I think the really, really good ones have to be from people who work for the companies.” And every time a brand switches up its designs, as today’s fast-paced luxury houses are wont to do, authenticators find themselves in the dark again…
…A strange, complicated cloud of emotions engulfed me wherever I carried the bag. I contacted more sellers and bought more replicas, hoping to shake it loose. I toted a (rather fetching) $100 Gucci 1955 Horsebit rep through a vacation across Europe; I’ve worn the Triomphe to celebrity-flooded parties in Manhattan, finding myself preening under the approving, welcome-into-our-fold smiles of wealthy strangers. There is a smug superiority that comes with luxury bags — that’s sort of the point — but to my surprise, I found that this was even more the case with superfakes. Paradoxically, while there’s nothing more quotidian than a fake bag that comes out of a makeshift factory of nameless laborers studying how to replicate someone else’s idea, in another sense, there’s nothing more original.
While a wardrobe might reveal something of the wearer’s personality and emotion, a luxury handbag is a hollow basin, expressing nothing individualistic at all. Instead, a handbag communicates certain ineffable ideas: money, status, the ability to move around in the world. And so, if you believe that fashion is inherently all about artifice — consider wink-wink items like Maison Margiela’s Replica sneaker, or the mind-boggling profits of LVMH’s mass-produced luxury items — then there is an argument to be made that the superfake handbag, blunt and upfront to the buyer about its trickery, is the most honest, unvarnished item of all.
I asked the writer Judith Thurman, whose sartorial insights I’ve always admired, about the name-brand handbag’s decades-long hold on women. Why do we yearn for very expensive sacks in the first place? Why do some buyers submit to thousand-dollar price hikes and risk bankruptcy for them? “It’s a kind of inclusive exclusiveness,” Thurman told me. “A handbag is a little treat, and it’s the only fashion item that is not sacrificial.” Clothes, with their unforgiving size tags and rigid shapes, can instill a cruel horror or disappointment in their wearers. Bags, meanwhile, dangle no shame, only delight. “There is an intangible sense when you are wearing something precious that makes you feel more precious yourself,” she theorizes. “And we all need — in this unbelievable age of cosmic insecurity — a little boost you can stick over your shoulder that makes you feel a bit more special than if you were wearing something that cost $24.99. It’s mass delusion, but the fashion business is about mass delusion. At what point does a mass delusion become a reality?”
4. Berkshire Hathaway – The World’s Greatest Serial Acquirer of Businesses – Eugene Ng
Warren Buffett and Charlie Munger were previously known to me as one of the greatest investors of all time with Berkshire Hathaway (“Berkshire”). But what became clearly evident to me after reading all 5,300+ pages of the Buffett Partnership Letters, Berkshire Shareholder letters, and AGM transcripts, is that they were not only great business builders, but also fantastic and disciplined risk managers. Through countless acquisitions over decades, Berkshire have become the world’s greatest serial acquirer of businesses.
Serial acquirers are companies that acquire wholly owned smaller companies to grow. After reinvesting, they use the surplus cash flows produced by each acquisition to buy even more companies, repeating the process, and compounding shareholder value over a very long time. Including its own acquisition back in 1964, we reckon Berkshire acquired over at least 80 wholly-owned insurance and non-insurance businesses over the last 57 years, and spent in excess of US$120bn on acquisitions over the last 20 years. Berkshire currently has 67 subsidiaries as of Apr 2023. In 2022, the operating businesses generated US$220bn of revenues and US$27bn of operating earnings before taxes, and the insurance business generated US$164bn of float.
In addition to the surplus cash flows from the operating businesses, Berkshire also uses the float of its insurance companies to invest in partial stakes of publicly listed companies worth US$350bn. This insurance float arises because customers pay premiums upfront, and the claims are typically only paid much later. This allows Berkshire to invest much more in higher yielding common stocks than low yielding bonds than most typical insurers. Coupled with a strong disciplined underwriting process and prudent risk management and acquisitions, it provided them with an ever growing insurance float to invest long-term at much higher rates of returns versus their competitors.
Over 57 years from 1965 to 2023, Berkshire has grown to become the 7th largest company in the US by revenues at US$302bn, and the 2nd largest company in the world by total shareholder equity (including banks) at US$472bn.
Berkshire has also grown its market capitalization to US$722bn (as of 28Apr23), generating ~20% p.a. CAGR shareholder returns for over 57 years from 1965 to 2022, beating the S&P 500’s ~10% p.a. hands down, placing it firmly in the “hall of fame”…
…Below is what we think is our best interpretation of Berkshire Hathaway’s flywheel that combines the disciplined, profitable and well-run businesses of the (1) insurance business (run by Ajit Jain) and (2) non-insurance operating businesses (run by Greg Abel), combining with strong culture, and letting solid managers run the businesses well respectively with strong autonomy, in a decentralised format.
Warren Buffett, Charlie Munger are responsible for the overall oversight and capital allocation, with Todd Combs and Ted Weschler are responsible for investing ~11% / ~US$34bn of the overall US$309bn equity investment portfolio under the insurance business.
It is this duo flywheel of Berkshire’s insurance and non-insurance businesses with the insurance float and the surplus capital from operating profits, that allows Berkshire to keep investing in (1) partial ownership stakes of good companies at fair prices, and to keep (2) acquiring durable, predictable profitable, wholly owned companies with able and honest management at the right price.
5. An Interview with Chip War Author Chris Miller – Ben Thompson and Chris Miller
To me that was one of the most — I mean there was a lot of interesting parts — but that was one of the most interesting parts of the book was your discussion about the Soviet Union and their attempts to compete in the semiconductor industry. It’s always tough because this is the part where you’ve been immersed in it sort of your entire life, so it’s always hard to summarize. But what’s the big picture history and lesson from Russia, I should say USSR, and its attempts to compete with the US in particular?
CM: The puzzle to me was the following: we knew the Soviets could produce a lot of impressive technology because they did it during the early Cold War. From atomic weapons — which granted they stole some of the designs, but nevertheless, they were the second country in the world to test an atomic bomb — to satellites, they were the first in the world to go into space largely thanks to indigenous innovation, the first person in space, Yuri Gagarin. So in the 1950s the Soviets weren’t seen as technologically backwards, they were seen as, if anything, overtaking the United States, and that made sense because if you had to ask what are some of the key ingredients to technological success of a country, you’d say, well, you probably want a pretty well-educated workforce, Soviets had that. Capital investment, Soviets had that. You want to focus on the industry, Soviets had that. And so the puzzle to me was why, given all these clear ingredients that were present in the Soviet Union, plus the pressure of Cold War competition to produce the next best defense technology, why was it that the Soviets couldn’t produce computing technology basically at all, and the entire Cold War they were copying IBM computers? That was the puzzle I initially started out wanting to answer and there’s a number of different ways you can answer the question.
I think this is super interesting, it’s super relevant. So walk me through them — what was it that was fundamentally different about, to your point, putting a man in space versus building a semiconductor?
CM: I think the common answer in the Western literature is “Well, they were an essentially planned economy, or they were dictatorship or both, and those societies can’t innovate”. I think that just doesn’t fit the historical facts. In fact, they did a whole lot of innovation in certain spheres at certain times, but there’s nothing about dictatorships that make them non-innovative, they innovate for their own reasons. But I think the problems the Soviets face were the following: First they didn’t have a consumer economy, hardly at all.
Why did that matter?
CM: That mattered because from almost the earliest days, the chip industry in the US, the computer industry in the US, grew thanks to sales to civilian markets and sales to consumers. The first chips that were produced were deployed in government systems, NASA and the Defense Department. But by the end of the 1960s, a decade or so after the first chips had been produced, it was civilian sales that were driving the industry. Today it’s 97% of chips produced that go to civilian uses, and so if you don’t have a civilian market, you can’t scale, simple as that.
I think this fits in because if you’re trying to get a man into space, you’re trying to get one man into space one time. Whereas the entire economics of chips and of the tech industry generally is 100% about scale. You have to put such massive investment upfront, and then the cost of goods sold for a chip is basically zero, and so to justify and to get a return on that investment and to provide the space for iteration, you need that massive demand to make it all worth it. If you just try to do a single shot, it’s probably not going to work out.
CM: Yeah, that’s absolutely right. The second thing that I didn’t realize is that I was under the impression when I started that nuclear bombs were hard to make, but computers were easy to make because there were a few nuclear bombs in the world and a lot of computers, and actually it’s the exact opposite. Nuclear bombs are so easy to make, even the North Koreans can do it.
(laughing) I don’t think I have any new North Korean subscribers, so no problem with that statement.
CM: I’m safe, okay. Whereas actually it’s the things that are the most widely produced, like chips, that are the hardest ones to make because you’ve got to drive down the cost, you’ve got to scale down the components on them, and that is the most complex manufacturing we undertake. I hadn’t really thought that through and I think most of us haven’t really thought through that dynamic and as a result, it has us focusing on the wrong types of complexity and the wrong types of technology and we, I think too often, overestimate the complexity in things that are done once and underestimate the complexity involved in scaling…
…Tell me about the contrast between the Japanese approach to chips versus the Soviet approach. Why was Japan so much more successful in entering this US-dominated industry relative to the Soviet Union?
CM: Well, the Japanese entered the chip industry not by trying to copy illegally, which the Soviets did, but by licensing technology. They were among the earliest licensees of the transistor after it was first produced, early licensees of the first integrated circuits, and they produced them better. The first chips began to be commercialized in the early ’60s, and just 15 years later, the late ’70s, Japanese firms by all accounts were producing at much higher levels of quality than US firms.
The complaint about dumping was never really quite right. People bought the Japanese chips because they failed much less and performed much better.
CM: That’s absolutely right. You had US CEOs at the time saying, “Well, we’ve got the real technology, we’re the most advanced in terms of this and that criteria”, but actually the technology that mattered again was the scaling. Japanese firms could scale with quality to a much greater degree. But that’s also what did them in, because they didn’t do a good job of managing their capability of scaling with market dynamics and they weren’t guided by profitability or guided by market share as their goal. So Japanese firms took over the market for DRAM chips, the type of memory chip that was the most prominent chip at the time, and never made any money. Kind of shockingly they dominated the market for a decade and hardly any of them ever posted a profit.
Well, I guess to just speak about Japan for a moment, because I think it’s interesting, first, why did South Korea and then also Micron in the US surpass Japan in memory, and second why did Japan never build any strength in logic? They peaked with memory and that was sort of it.
CM: So I think on the second question, Japanese firms did try to move into microprocessors at a time when they were still a niche good in the late ’70s and early ’80s, but they were doing so well in memory or it seemed like they were doing so well in memory that it was an Innovator’s Dilemma type situation. They had huge market share in memory, they had just defeated TI and Intel in then DRAM business, so why would you switch your business model to produce this low volume type of chip that seemed pretty niche? Whereas if you were Intel in the early ’80s you had no choice, you’d just been knocked out of your primary market.
It’s very underrated. Everyone wants to talk about that apocryphal, or maybe I guess it was real, meeting with Andy Grove and Gordon Moore where they’re like, “We need to get out of memory”. But it’s under-appreciated that this was not a brilliant flash of insight, this was accepting reality and probably accepting it a couple years too late.
CM: Yep, I think that’s right. I think the other benefit that the US ecosystem had writ large was that it was more responsive to new trends in the PC industry, and just the emergence of the PC itself is something that — could it have happened in Japan? I think you wouldn’t say it couldn’t have happened, but it seems like all the ingredients were much more prevalent in the US. A bigger software design ecosystem, Bill Gates being the critical representative, plus companies that were willing to innovate more rapidly to produce PCs. At the time there were a couple of Japanese firms that were good at productizing new ideas, Sony being the best example, but Sony was the exception, not the rule. What really struck me about the PC industry is that IBM created the first PC, but then they were quickly out-competed by all the clones that emerged, which drove down the cost and drove up the prevalence of PCs.
For someone that started out saying, “I assumed that the story was free markets just being better at innovation and that wasn’t the case”, I don’t know, that sounds like the case that you’re kind of making right here.
CM: (laughing) Yeah, in this case, I think it was. The Japanese did a very good job at scaling, but here is the counterfactual: Suppose that Japanese firms had been disciplined by a need to make a profit, they would’ve focused less exclusively on simple scaling to win market share. They would’ve at an earlier date tried to ask themselves, can we make money in DRAM? Some of them I think would’ve exited DRAM because they didn’t make any money there and tried to do something else. So actually I still go back to the structure of the Japanese corporate and financial system as to why their chip firms just for far too long focused on producing unprofitable chips…
…One thing you’ve said about TSMC and ASML is that, “The way to understand them is less about them being manufacturers and more about them being integrators.” So, what do you mean by that?
CM: If you want to turn to ASML, I think they’re the best example of this. They’re a company that on the one hand, manufactures the most complex tools humans have ever made, hands down, and we can dig into them. On the other hand, they’ll openly tell you that, “Their expertise is not in the tools themselves, but in bringing together such a complex supply chain.” At first when people from ASML tell me this, I was shocked. I thought they would be bragging about their manufacturing capabilities, but they were more focused on their systems integration and the ability to manage suppliers all over the world. I admit, I started the project not taking the people who manage supply chains all that seriously, but I came to develop a lot more respect for them, because them doing their jobs well is an extraordinarily difficult thing to do and when you’ve got a supply chain that does involve thousands of suppliers, you’ve got to do it really, really well…
…My view on what China should do geopolitically speaking if I were giving advice to Xi Jinping is — which I’m not, to be clear, I think that’s obvious — is the U.S. wants to continue to allow China to import tools and technologies as you noted, to build trailing edge chips. I think a big impetus for this is they don’t want to destroy the business of a lot of U.S. tech companies, where 30% of their sales were to China, and so it seems like the rational response for China would be to, and I think we’re seeing indicators this is happening, is to basically try to dominate that market.
In this case, use a willingness to be unprofitable as a weapon and to actually do what we accuse the Japanese of doing back in the day, of flooding the market, driving all other trailing edge capacity out, which is basically TSMC and a bit of GlobalFoundries, but there’s bits and pieces still scattered it around. Once you build a foundry, you might as well keep it and then suddenly, the actual chips that are used, to your point, in guided missiles, and are used in cars and are used in appliances were totally dependent on China. That seems like where this is going, does it not?
CM: I think I agree completely, China’s going to build out a ton of capacity. I think there’s some uncertainty as to whether we’re going to have enough demand to meet that capacity build out or not and I think there’s still uncertainty about what our demand will be for lagging edge chips. In ten years time, people who are more bullish on demand say, “Look, every year, there’s on average twenty new chips added to a car.” No one knows how long this is going to go for, but it’s gone for a long time, etc.
And the chip that controls a window going up and down never actually has to get faster.
CM: Right, exactly. So, set aside the uncertainty about the demand picture. If China built out all this capacity, will non-Chinese firms go to Chinese foundries? I think five years ago, the answer was certainly yes. Today, it’s a lot less clear. And when you have Michael Dell on the front page of the Financial Times reporting that his customers are asking him to remove Chinese-made components from PC supply chains, that’s not the political environment that I think will send non-Chinese customers racing to take advantage of cheaper funding capacity in China…
...So what’s your — as someone, again, you’re coming in from sort of a historical perspective, but having dived deeply into this — what do you think about the long-term Chinese prospects as far as basically rebuilding the leading edge capacity? This is a subject of much debate amongst people that are deep in the weeds about it, but as you’ve been able to talk to people all over the place, what’s your takeaway? How far behind are they? Can they even catch up?
CM: First off, what does catch up mean? I think that this is really a key question, because catch up doesn’t mean catching up to 2023 levels of technology in ten years time, then you’re five Moore’s Laws behind. So I think we’ve got to define catching up as reaching 2033 levels of technology in exactly ten years time, just as the rest of the world does. That seems to me like a really tall order, because the trend in the chip industry has not been catch up, it’s been fall behind. Everyone’s been falling behind the leading edge in every single node of the supply chain.
At basically every major lithography transition, another foundry falls off.
CM: Yep, exactly. So the Chinese government’s going to put a lot of money behind it, that’s going to help. There’s the necessity of it that Chinese firms face, that’s going to help. I think the Chinese government’s going to do more to wall off the domestic market, which will give some end market for Chinese firms that will help, at least in the short run, for Chinese chip makers. But at the end of the day, if Chinese firms are selling to 20% of global GDP and TSMC is selling to 80% of global GDP, I think I know who I’d bet on.
So what are the implications of this? I mean, again, as you noted, it doesn’t necessarily make a difference for conventional weapons, if we think about today. Is this where the question of AI systems and stuff comes to bear?
CM: Yeah, I think that’s right and right now, we’re seeing a shortage of GPUs, given all the generative AI boom underway. But I guess there’s a more complex long term question, which is — is compute a real point at which the US can try to constrain China’s AI capabilities? I think we’re seeing the US test out that strategy right now.
What’s your prognostication? I’m going to put you on the spot here.
CM: I think there are people who say, “Well if China can’t get access to the most advanced GPUs, aren’t they just going to build data centers that are four times as large or eight times as large or sixteen times as large with sixteen times as many chips, and therefore scale up that way?” You can’t scale down your transistors, you scale up your data centers, is basically the strategy, and then we have to figure out — what are the inefficiencies involved in scaling up your data center? I’m sure they’re pretty substantial.
Well, this is why it’s interesting, I was actually surprised — what the chip ban really focused on was memory interconnects, or interconnects, which is actually the limiting factor in pursuing that exact strategy.
CM: Yeah. I mean, I think you can’t accuse the US strategy of being incoherent, I think that they put their homework into it. Whether it’s going to work, we’ll see. I’ve got a lot of faith in the Chinese government’s willingness to brute force things when it comes to national security, so I think we should expect them to try really hard. But at some point, I go back to one of the more interesting anecdotes from the Soviet experience was an interview of a weapons designer in the Soviet Union, who was asked to explain why it was that he didn’t use the most advanced integrated circuits in his guidance computer in his missile. And his answer was, “Well, our computing industry, sometimes it works, sometimes it doesn’t. The state’s pretty bureaucratic. It’s just hard to work with, it’s not as easy.” The implication was it’s not as easy as buying from TSMC. So I do think if you get a situation where we’re throwing a lot of sand into the gears of the Chinese computing industry, the Chinese government’s going to respond with lots of cash in response and that’s kind of the race that we’re playing out right now, our sand in the gears versus Chinese government cash.
Disclaimer: None of the information or analysis presented is intended to form the basis for any offer or recommendation. We currently have a vested interest in ASML and Taiwan Semiconductor Manufacturing Company (TSMC). Holdings are subject to change at any time.
