We’re off this weekend — stay safe and eat some amazing food. And make sure to consume all the good stuff before the Burning Man folks return.
Podcast: Lt. Gen. Jack Shanahan on rebuilding trust between Silicon Valley and the Pentagon
Design by Chris Gates
As the birthplace of semiconductors and computers, Silicon Valley has historically been a major center of the defense industry. That changed with the Vietnam War, when antiwar protesters burned down computing centers at multiple universities to oppose the effort in Southeast Asia, as well as the rise of countercultural entrepreneurs who largely determined the direction of the internet age.
Today, there are once again growing ties between tech companies and the Pentagon as the need for more sophisticated AI tools for defense becomes paramount. But as controversies like Google’s launch of Project Maven attest, there remains a wide chasm of distrust between many software engineers and the Pentagon’s goals for a robust defense of the American homeland.
In this episode of “Securities”, Josh Wolfe and I sit down with retired lieutenant general Jack Shanahan to talk about rebuilding the trust needed between these two sides. Before retirement, Shanahan was the inaugural director of the Pentagon’s Joint Artificial Intelligence Center, a hub for connecting frontier AI tech into all aspects of the Defense Department’s operations. We talk about the case of Project Maven and its longer-term implications, the ethical issues that lie at the heart of AI technologies in war and defense, as well as some of the lessons learned from Russia’s invasion of Ukraine the past year.
If you want a long but incredible holiday read, Patrick Radden Keefe at The New Yorker profiles art dealer Larry Gagosian in "How Larry Gagosian Reshaped the Art World.” The intricacies of the dealer business, coupled with an acute social and business intelligence, has allowed Gagosian to carve a new and so-far-unmatched path to the stratosphere: “Unlike many luxury items, art works tend to be unique objects—'one of one,' in the parlance of the trade. The designer Marc Jacobs told me, ‘Larry sells things that aren’t for sale.’ Typically, the most coveted items become available only when the previous owner dies, or gets divorced, or goes bankrupt. An élite dealer like Gagosian, however, can sometimes wrest away a treasure by offering the owner—ideally someone he knows—a whopping premium. If you want the right kind of Jasper Johns to round out your collection, you enlist Gagosian to help you find one hanging on somebody else’s wall, then make the owner an offer he can’t refuse. If he does refuse, double the offer. Then, if necessary, double it again. It is the super-rich equivalent of ordering off-menu.”
Our summer associate Ken Bui highlights Lingling Wei and Stella Yifan Xie’s analysis of China’s economy in the WSJ, which emphasizes conforming to the new political environment even at the cost of economic performance. “But top leader Xi Jinping has deep-rooted philosophical objections to Western-style consumption-driven growth, people familiar with decision-making in Beijing say. Xi sees such growth as wasteful and at odds with his goal of making China a world-leading industrial and technological powerhouse, they say.”
Grace Isford recommends a new Arxiv paper from a group of researchers on "Reinforced Self-Training (ReST) for Language Modeling.” “While ReST is a general approach applicable to all generative learning settings, we focus on its application to machine translation. Our results show that ReST can substantially improve translation quality, as measured by automated metrics and human evaluation on machine translation benchmarks in a compute and sample-efficient manner.”
With greater heat globally, Niall Ferguson argues that it’s time to reconsider the summer vacation in “The Death of Summer.” But the heat is practically omnipresent, and as David S. Jones emphasizes in the Boston Review, "Killer Heat Waves Are Coming.” “The trouble is not ignorance: we know that heat can kill. Humans have recognized the threat for millennia, and over the last two centuries they have scrutinized heat wave mortality to understand who is most at risk and to develop strategies to prevent those deaths. Still people die.”
Finally, the replication crisis has undermined the foundations of science (just this week, Stanford’s now-former president Marc Tessier-Lavigneretracted two of his papers on the last day of his presidency). Two popular articles, one on behavioral psychology by Adam Mastroianni and the other on history by Anton Howes, show that the damage has reached all fields of human knowledge. “If the lack of replication or reproducibility is a problem in science, in history nobody even thinks about it in such terms. I don’t think I’ve ever heard of anyone systematically looking at the same sources as another historian and seeing if they’d reach the same conclusions.”
That’s it, folks. Have questions, comments, or ideas? This newsletter is sent from my email, so you can just click reply.
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 .
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.”
