Another week, another antitrust review in semiconductors gone awry.
Last week in “Securities,” I noted that regulators are poised to cancel Nvidia’s blockbuster acquisition of chip design company Arm Holdings. Part of the calculus is certainly market power — combining Nvidia's heft with Arm's all-but-monopolistic power in mobile chip design would offer the combined company an extraordinary dominance that could quickly harm competition.
The other calculus, and one that we are increasingly seeing around so-called “critical technologies,” is the fear that U.S.-based Nvidia buying U.K.-headquartered and Japanese-owned Arm would differentially harm the national securities of countries throughout the world. The European Union has to approve the merger, and offering more power to the U.S. chip industry is not something that eurocrats are wont to do. Then there’s China, which also has to approve the deal, and it’s near impossible to envision how its competition authorities clear the deal without strict controls that benefit Beijing (and consequently, would trigger deep concerns in the U.S. and the U.K.).
We are seeing this pattern again with the overnight news that Berlin has failed to approve Taiwan’s GlobalWafers’s acquisition of Siltronic, a German chip supplier. The Financial Times noted that “The German government admitted it failed to reach a decision on its review of the deal by the January 31 deadline,” and without approval, the deal is automatically extinguished.
Siltronic is a smaller competitor to GlobalWafer, and much like the American telco merger of Sprint and T-Mobile, it was designed to bring two smaller companies together to more effectively compete in the marketplace. Much of antitrust analysis involves precisely this sort of balancing between market concentration and market benefit — three strong competitors are almost certainly better than two strong competitors and two weak competitors.
Yet, it’s not market concentration or antitrust dynamics that determined this result at all, but rather national security considerations. Berlin was worried that it was losing a crown jewel of its manufacturing base to a foreign territory. It’s a feeling that countries throughout the West have experienced repeatedly over the last few decades with deindustrialization, and has increasingly led to this “sovereignty” approach to acquisitions, particularly in critical technologies.
Siltronic is not a market winner or even much of a market leader. According to the company’s own analysis of the silicon wafer market from 2020, it holds roughly a 13% share of the market, compared with 17% for GlobalWafers, 25% for Sumco and 33% for Shin Etsu, the market leader. An acquisition would combine its 13% share with GlobalWafers’s 17% share, presumably aggregating to about 30% to compete with the market leader’s 33%. That sounds like a much more competitive company and a better market.
Instead, with Berlin’s lack of approval, Siltronic … remains at 13%, competing neck and neck with SK Siltron, which holds a 12% market share.
This is the self-defeat cycle of more and more of these national security interventions in antitrust. Every country wants a core industrial base (or, perhaps more accurately, is figuring out that they need one and working to build or protect it). Consequently, there is greater sensitivity than ever over any agreement where a government might find itself losing leverage over a company.
By blocking these transactions though, governments are ultimately weakening these companies and all but guaranteeing their decline in the market.
The top five wafer producers are Japanese, Japanese, Taiwanese, German, and Korean. Berlin’s decision implies that there are no possible combinations for the company in the future, since it is hard to believe that it would approve a Japanese or Korean takeover any more than a Taiwanese one. With fewer strategic options for growth or exit, investor interest will wane in the company, and so capital will become more expensive to procure. Thus begins a sick cycle of decay — once capital is expensive in a capital-intensive industry, the future destiny of the company is already determined.
It’s the unfortunate complexity of modern industrial policy. Saving a company may mean selling it for parts, or selling it overseas to the one global company that might be able to make it a thoroughbred. Sometimes it is about losing the least, rather than gaining the most. Such are the tough choices in a competitive market where other companies have taken the lead.
Berlin has options of course. Germany and the European Union more broadly can inject large amounts of capital into its industrial base and hope to catchup with Asian peers. In fact, it’s considering precisely this strategy, announcing last week a potential rival to the American CHIPS act that would invest in European chip champions. Whether such an investment is ultimately made remains very unclear.
We still live in a globalized trade world where the best companies need to outcompete all of their foes. Germany can’t just solve the market in its favor and presume that it has saved its industry from defeat. Trade barriers can’t protect porous economic borders, a lesson we apparently have to keep relearning again and again.
Forcing China’s AI researchers to strive for chip efficiency will ultimately shave America’s lead
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Right now, pathbreaking AI foundation models follow an inverse Moore’s law (sometimes quipped “Eroom’s Law”). Each new generation is becoming more and more expensive to train as researchers exponentially increase the number of parameters used and overall model complexity. Sam Altman of OpenAI said that the cost of training GPT-4 was over $100 million, and some AI computational specialists believe that the first $1 billion model is currently or will shortly be developed.
As semiconductor chips rise in complexity, costs come down because transistors are packed more densely on silicon, cutting the cost per transistor during fabrication as well as lowering operational costs for energy and heat dissipation. That miracle of performance is the inverse with AI today. To increase the complexity (and therefore hopefully quality) of an AI model, researchers have attempted to pack in more and more parameters, each one of which demands more computation both for training and for usage. A 1 million parameter model can be trained for a few bucks and run on a $15 Raspberry Pi Zero 2 W, but Google’s PaLM with 540 billion parameters requires full-scale data centers to operate and is estimated to have cost millions of dollars to train.
Admittedly, simply having more parameters isn’t a magic recipe for better AI end performance. One recalls Steve Jobs’s marketing of the so-called “Megahertz Myth” to attempt to persuade the public that headline megahertz numbers weren't the right way to judge the performance of a personal computer. Performance in most fields is a complicated problem to judge, and just adding more inputs doesn't necessarily translate into a better output.
And indeed, there is an efficiency curve underway in AI outside of the leading-edge foundation models from OpenAI and Google. Researchers over the past two years have discovered better training techniques (as well as recipes to bundle these techniques together), developed best practices for spending on reinforcement learning from human feedback (RLHF), and curated better training data to improve model quality even while shaving parameter counts. Far from surpassing $1 billion, training new models that are equally performant might well cost only tens or hundreds of thousands of dollars.
This AI performance envelope between dollars invested and quality of model trained is a huge area of debate for the trajectory of the field (and was the most important theme to emanate from our AI Summit). And it’s absolutely vital to understand, since where the efficiency story ends up will determine the sustained market structure of the AI industry.
If foundation models cost billions of dollars to train, all the value and leverage of AI will accrue and centralize to the big tech companies like Microsoft (through OpenAI), Google and others who have the means and teams to lavish. But if the performance envelope reaches a significantly better dollar-to-quality ratio in the future, that means the whole field opens up to startups and novel experiments, while the leverage of the big tech companies would be much reduced.
The U.S. right now is parallelizing both approaches toward AI. Big tech is hurling billions of dollars on the field, while startups are exploring and developing more efficient models given their relatively meagre resources and limited access to Nvidia’s flagship chip, the H100. Talent — on balance — is heading as it typically does to big tech. Why work on efficiency when a big tech behemoth has money to burn on theoretical ideas emanating from university AI labs?
Without access to the highest-performance chips, China is limited in the work it can do on the cutting-edge frontiers of AI development. Without more chips (and in the future, the next generations of GPUs), it won’t have the competitive compute power to push the AI field to its limits like American companies. That leaves China with the only other path available, which is to follow the parallel course for improving AI through efficiency.
For those looking to prevent the decline of American economic power, this is an alarming development. Model efficiency is what will ultimately allow foundation models to be preloaded onto our devices and open up the consumer market to cheap and rapid AI interactions. Whoever builds an advantage in model efficiency will open up a range of applications that remain impractical or too expensive for the most complex AI models.
Given U.S. export controls, China is now (by assumption, and yes, it’s a big assumption) putting its entire weight behind building the AI models it can, which are focused on efficiency. Which means that its resources are arrayed for building the platforms to capture end-user applications — the exact opposite goal of American policymakers. It’s a classic result: restricting access to technology forces engineers to be more creative in building their products, the exact intensified creativity that typically leads to the next great startup or scientific breakthrough.
If America was serious about slowing the growth of China’s still-nascent semiconductor market, it really should have taken a page from the Chinese industrial policy handbook and just dumped chips on the market, just as China has done for years from solar panel manufacturing to electronics. Cheaper chips, faster chips, chips so competitive that no domestic manufacturer — even under Beijing direction — could have effectively competed. Instead we are attempting to decouple from the second largest chips market in the world, turning a competitive field where America is the clear leader into a bountiful green field of opportunity for domestic national champions to usurp market share and profits.
There were of course other goals outside of economic growth for restricting China’s access to chips. America is deeply concerned about the country’s AI integration into its military, and it wants to slow the evolution of its autonomous weaponry and intelligence gathering. Export controls do that, but they are likely to come at an extremely exorbitant long-term cost: the loss of leadership in the most important technological development so far this decade. It’s not a trade off I would have built trade policy on.
The life and death of air conditioning
Photo by Ashkan Forouzani on Unsplash
Across six years of working at TechCrunch, no article triggered an avalanche of readership or inbox vitriol quite like Air conditioning is one of the greatest inventions of the 20th Century. It’s also killing the 21st. It was an interview with Eric Dean Wilson, the author of After Cooling, about the complex feedback loops between global climate disruption and the increasing need for air conditioning to sustain life on Earth. The article was read by millions and millions of people, and hundreds of people wrote in with hot air about the importance of their cold air.
Demand for air conditioners is surging in markets where both incomes and temperatures are rising, populous places like India, China, Indonesia and the Philippines. By one estimate, the world will add 1 billion ACs before the end of the decade. The market is projected to before 2040. That’s good for measures of public health and economic productivity; it’s unquestionably bad for the climate, and a global agreement to phase out the most harmful coolants could keep the appliances out of reach of many of the people who need them most.
This is a classic feedback loop, where the increasing temperatures of the planet, particularly in South Asia, lead to increased demand for climate resilience tools like air conditioning and climate-adapted housing, leading to further climate change ad infinitum.
Josh Wolfe speaking at Nvidia Auditorium at Stanford. Photo by Chris Gates.
Josh Wolfe gave a talk at Stanford this week as part of the school’s long-running Entrepreneurial Thought Leaders series, talking all things Lux, defense tech and scientific innovation. The .
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Our scientist-in-residence Sam Arbesman recommends Blindsight by Peter Watts, a first contact, hard science fiction novel that made quite a splash when it was published back in 2006.
Mohammed bin Rashid Al Maktoum, and just how far he has been willing to go to keep his daughter tranquilized and imprisoned. “When the yacht was located, off the Goa coast, Sheikh Mohammed spoke with the Indian Prime Minister, Narendra Modi, and agreed to extradite a Dubai-based arms dealer in exchange for his daughter’s capture. The Indian government deployed boats, helicopters, and a team of armed commandos to storm Nostromo and carry Latifa away.”
Sam recommends Ada Palmer’s article for Microsoft’s AI Anthology, “We are an information revolution species.” “If we pour a precious new elixir into a leaky cup and it leaks, we need to fix the cup, not fear the elixir.”
I love complex international security stories, and few areas are as complex or wild as the international trade in exotic animals. Tad Friend, who generally covers Silicon Valley for The New Yorker, has a great story about an NGO focused on infiltrating and exposing the networks that allow the trade to continue in “Earth League International Hunts the Hunters.” "At times, rhino horn has been worth more than gold—so South African rhinos are often killed with Czech-made rifles sold by Portuguese arms dealers to poachers from Mozambique, who send the horns by courier to Qatar or Vietnam, or have them bundled with elephant ivory in Maputo or Mombasa or Lagos or Luanda and delivered to China via Malaysia or Hong Kong.”
