… whose the most resilient of them all? I am down in Tampa Bay in the post-Tropical Storm Debby wetlands and so no column this week.
Podcast: Why engineers are using chaos to make computers more resilient
Design by Chris Gates.
The CrowdStrike meltdown on July 19th shut down the world with one faulty patch — proving once again the interconnected fragility of global IT systems. On Tuesday this week, the company released its Root Cause Analysis as both an explanation and a mea culpa, but the wider question remains: with so much of our lives dependent on silicon and electrons, how can engineers design resilience into their code from the bottoms up? And more importantly, how can we effectively test how resilient our systems actually are?
Kolton Andrus is one of the experts on this subject. For years at Amazon and Netflix, he worked on designing fault-tolerant systems, building upon the nascent ideas of the field of chaos engineering, an approach that iteratively and stochastically challenges systems to test for resilience. Now, as CTO and founder of Gremlin, he’s democratizing access to chaos engineering and reliability testing for everyone.
Kolton joins me and Lux’s scientist-in-residence and complexity specialist Sam Arbesman. Together, we talk about why resilience must start at the beginning of product design, how resilience is aligning with security as a core value of developer culture, how computer engineering is maturing as a field, and finally, why we need more technological humility about the interconnections of our global compute infrastructure.
The Orthogonal Bet: The Art of Cultivating Curiosity
Design by Chris Gates.
In this episode, Sam speaks with our multi-time returning guest Eliot Peper. Eliot is a science-fiction novelist and all-around brilliant thinker, whose thrillers set in the near-future explore many delightful areas of the world at the frontiers of science and technology. In Eliot’s most recent novel, Foundry, he takes the reader on a journey through the intricacies of semiconductors, from their geopolitical implications to their profoundly weird manufacturing processes.
Sam talks with Eliot not only about the plotting of Foundry, but also how Eliot uncovers these key topics while building wondrous worlds that are incorporated into his books. After writing almost a dozen novels, they talk about how to continually cultivate the muscles of creativity and curiosity, keeping our minds nimble to the rapidly changing world around us.
I supremely enjoyed reading Rory Stewart’s memoir, Politics on the Edge (published as How Not to Be a Politician in the United States). Stewart was a candidate for British Prime Minister against Boris Johnson, eventually losing to the former London mayor and several other candidates in a bruising fight for the Conservative Party’s nomination. But the book’s thesis is that the UK’s governance model — centered on an unwritten constitution, Parliament, a centralized London bureaucracy and rapidly rotating political leaders attached to entrenched civil servants — is no longer capable of serving the British public’s needs. Frustrated and at times a touch histrionic, Stewart narrates the inanity of governance in a once-proud country.
Our head of platform Tracie Rotter is a cheery colleague who loved this menacing story in Wired by Sandra Upson on “How Soon Might the Atlantic Ocean Break? Two Sibling Scientists Found an Answer—and Shook the World.” “Tipping points are absolutely everywhere. Throw water on a fire, and the flames will shrink but recover. Dump enough water on and you’ll cross a threshold and snuff it out. Tip a chair and it’ll wobble before settling back onto its four feet. Push harder, and it topples. Birth is a tipping point. So is death. Once you’ve pushed a system to its tipping point, you’ve removed all brakes. No exit. As one 500-page report recently put it, climate tipping points ‘pose some of the gravest threats faced by humanity.’ Crossing one, the report goes on, ‘will severely damage our planet’s life-support systems and threaten the stability of our societies.’”
Zach Dorfman (who will be joining us on the Riskgaming podcast shortly) wrote a spy thriller from Silicon Valley for Politico Magazine in “Moscow’s Spies Were Stealing US Tech — Until the FBI Started a Sabotage Campaign.” “During the Cold War, FBI spy hunters like Rick Smith were thinking hard about the issue, too. ‘At the time, there was a lot of interest in technology transfer,’ recalled Smith. So his chance run-in, at that local watering hole, with a tech entrepreneur who had sprawling business connections in Europe — well, that presented some tantalizing possibilities. Smith says the Austrian didn’t take much convincing. That night, over drinks, the two began hatching a plan, one refined over many meetings in the months that followed. Working under the FBI’s direction, the Austrian agreed to pose as a crook, a man willing to sell prohibited technology to the communist Eastern Bloc.”
I talk about China and U.S. export controls more frequently than the typical newsletter, and the New York Fed last week had a major report on early statistical evidence of America’s trade actions against China the last few years. The conclusion? American export controls accelerated Chinese industry to indigenize, while undermining many of America’s most important companies. “On the extensive margin, we observe that the Chinese targets offset the reduction in relations with U.S. suppliers by forming new ones with alternative Chinese suppliers—an indication of reshoring on the Chinese side.”
Finally, Lunch with the FT last week was with Helen Toner, a notable AI safety researcher at Georgetown’s Center for Security and Emerging Technology and former board member of OpenAI, where she was part of the movement against Sam Altman in the coup that electrified the tech industry — and the world — late last year. Toner: “For the board, there was this trajectory of going from ‘everything’s very low stakes, you want to be pretty hands-off’ to ‘actually, we’re playing this critical governance function in an incredibly high-stakes — not just for the company, but for the world — situation’.”
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.”
