Photo by Touch of Light via Wikimedia / Creative Commons
The Department of Defense published a fascinating report yesterday analyzing the defense industrial base and the notable consolidation of prime contractors — the contractors who directly interact with the government as project managers and who then outsource much of their work to other subcontractors.
The details are grim: “the number of aerospace and defense prime contractors shrank from 51 to 5: Lockheed Martin (LM), Raytheon, General Dynamics (GD), Northrop Grumman (NG), and Boeing.” A table compiling different markets for acquisition and the trends around prime contractors vividly shows this pattern:
Horizontal and vertical consolidation has concentrated market power down to a handful of prime defense contractors, and in many cases, to sole-source suppliers. That drastically weakens the resilience of the defense industrial base, and also limits the competitive pursuit of the most advanced research and development.
The report was initiated by a Biden executive order in July 2021, and the current administration seems poised to intensify antitrust enforcement. Just this week, Lockheed Martin called off its $4.4 billion acquisition of Aerojet, a maker of rocket jets, due to regulatory roadblocks.
Antitrust enforcement is clearly in vogue in DC, in Beijing and across much of Europe, but there are challenging intersections of interests between competition, scale, and national security that remain entirely open questions as governments actively rein in the size of technology businesses.
When it comes to national security concerns, scale — in general — is good. As I wrote last week in “The West’s self-defeating technological sovereignty” on “Securities,” Berlin blocked the acquisition of chip wafer producer Siltronic by Taiwan-based Global Wafers out of a desure to protect its domestic industrial base. Yet, that autarky means that Siltronic will remain the distant number four competitor in the wafer market — a market that requires its participants to constantly invest vast quantities of new capital to effectively compete. By blocking scale, Berlin managed to limit the long-term health of its own industrial leader.
We see a similar tension in antitrust debates over Big Tech companies. America wants its tech giants to be globally dominant in a world where alternatives (particularly Chinese giants) are rapidly expanding in emerging markets. Yet, while splitting up Google or Apple or Facebook may add competition to the U.S. consumer market, it will also stifle the ability of these companies to effectively and financially compete in overseas markets. Ironically, such antitrust actions also opens the U.S. market to more overseas competition as well.
It’s a problem of scale. Social networks, search engines, defense programs — all of them lend themselves to monopolistic incumbents. It’s hard to have 10 simultaneous social networks, any more than there are going to be a dozen search engines or 20 companies building jet fighters.
In fact, the scale problem is particularly acute in defense tech, where huge scale is often required just to design and build major programs in the first place. The Gerald R. Ford-class aircraft carrier — one single ship, to put in bluntly — has taken roughly 15 years and $13 billion to construct as I wrote about in “Defense Fordism.” It's an insane amount of money, but it's also a market design that's fundamentally monopolistic — there aren’t going to be multiple aircraft carrier companies. Lockheed Martin owns this market, and it is going to own it for the duration of the Ford's lifecycle.
The primes are a disaster, but it’s disastrous by design. Massive, complex programs need one originating and responsible contractor — and that’s true for the vast majority of the work that the Pentagon acquires. If we migrate to a world of cheap, flexible, and autonomous — it’s possible to imagine a robust and competitive market to fulfill these contracts. But when the Air Force has a next-generation plane and the Navy one next-generation aircraft carrier — there’s a limit to how much competition can feasibly be supported.
Indeed, that’s the balancing act of resilience across supply chains in general. Customers — whether the Pentagon or everyday consumers — are already struggling with price inflation, and that doesn’t even include the overhead cost of supporting multiple suppliers to maintain resilience. It sounds great to have more competition — but the key question is whether competition would automatically lower costs enough to compensate for the overhead of having multiple competitors. The Pentagon is a fairly zero-sum budget — everyone can’t win more without more defense appropriations from Congress.
To the Pentagon’s credit (and probably to the chagrin of some antitrust acolytes), the DoD’s report is quite frank about its limited ability to effect competition in a range of industries, from microelectronics to materials. For instance, in regards to critical materials:
Critical materials manufacturing is capital- and time-intensive. Mining and processing concerns are risk-averse while capital recovery times are long. Furthermore, pricing of mined material is inelastic while downstream manufacturers more rapidly change suppliers and product formulations to obtain the lowest cost source. Companies are disincentivized from spending money on a project without surety of a profit in the long run. Changing the structure of the supply chain for these materials is difficult without government incentives and partnerships with the private sector.
While defense industry consolidation has been triggered by private equity firms and a general desire for efficient return on capital, the reality is also that such consolidation has been a survival mechanism in a competitive world where as the Pentagon notes, “Competition in the critical materials sector is distorted by political intervention and unfair trade practices in adversary nations.”
As I noted earlier in the post on technology sovereignty:
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.
Antitrust is not the panacea it’s often depicted. Just lopping off the heads of the giants doesn’t suddenly make for a fair market. If the structure of a market forces consolidation, no intensity of antitrust can or will change that. Big isn’t definitionally bad. But it can be. The challenge is knowing the difference, and targeting the worst offenders with a surgical strike rather than blasting an entire industry. The challenge of antitrust and national security is how opposed the two demands can be.
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
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 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 .
Lux Recommends
As Henry Kissinger turns 100, Grace Isford recommends “Henry Kissinger explains how to avoid world war three.” “In his view, the fate of humanity depends on whether America and China can get along. He believes the rapid progress of AI, in particular, leaves them only five-to-ten years to find a way.”
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.”