Have a fantastic Labor Day holiday for those in the States — we’re all looking forward to the deluge of autumn startup fundraising beginning in T-2 days.
Meanwhile, one of Lux’s portfolio companies got national attention this week as our themes of science, technology and geopolitics collided in one of the most challenging strategic environments in the world.
Saildrone manufacturers autonomous ocean surface vehicles that are designed to collect sensor data for extended periods of time. Its drones have been popular for tracking hurricanes, where they can follow storms across the Atlantic, and they are increasingly being used on ISTAR missions, defense-speak for intelligence, surveillance, target acquisition, and reconnaissance.
This week, a ship operated by Iran’s Islamic Revolutionary Guard Corps stopped a U.S. naval Saildrone that was operating in the Persian Gulf.
Tweet of a picture of the Iranians targeting a Saildrone autonomous ship.
No shots were fired by either side, the defense officials added, and the Americans didn’t try to prevent the Iranian ship, which was operated by Iran’s Islamic Revolutionary Guard Corps, from leaving. Vice Adm. Brad Cooper, the commander of the U.S. naval forces in the region, said the Iranian actions, which took place in international waters, were “flagrant, unwarranted and inconsistent with the behavior of a professional maritime force.”
By next summer, the Navy said, it expects to have 100 small surveillance drones—contributed by various countries—operating from the Suez Canal in Egypt to waters off the Iranian coast and feeding information to a command center in Bahrain, headquarters of the U.S. Fifth Fleet. “I think we are truly on the cusp of an unmanned technological revolution,” said Capt. Michael Brasseur, who heads the U.S. Navy task force working to build the drone fleet in the Middle East. The drone initiative, now in its sixth month, is part of a burgeoning cooperative relationship among the U.S., Israel and Gulf nations following the Abraham Accords. It mirrors another U.S.-led effort to unite Israel and its Gulf neighbors to create a regional air-defense network.
We’ve previously covered the Navy’s penchant for massive expenditures on programs like its next generation of Gerald R. Ford-class aircraft carriers in “Defense Fordism.” As I wrote then:
The multi-decade commitments that advanced warfighting platforms like the Gerald R. Ford require are slamming straight into the Moore’s Law of autonomous machines. The Ford’s construction was underway prior to the introduction of the iPhone, and it’s still not in service because experimental Chinese weapons like supersonic missiles will likely prove effective in neutering it (an exercise that Beijing has already made a clear priority).
Thankfully, there is increasingly a new logic across the American defense world that these massive platforms need to be complemented by autonomous technologies that are both cheaper to buy and cheaper to operate. That optionality gives mission commanders strategic flexibility in how they approach all aspects of their operations. It’s a lot of labor to make such a massive technological shift, but it’s necessary labor.
Hard Sciences x Tech in LA
Few cities are as brimming with entrepreneurial potential as LA. The City of Angels’ focus on the stars has shifted from Hollywood to space, as a new generation of defense and precision manufacturing startups rebuild the American industrial base and bring innovative new technologies to domains that were once the exclusive province of governments. Lux has quite the base in the region including companies like Anduril, Varda Space, Impulse Space, Epsilon3 among a litany of others.
So it was delightful to bring the whole community together two weeks ago for LA Tech Week in an event we dubbed La-La-Launch. Dozens of founders across the hard sciences came together, and we got to hear from Tim Ellis, co-founder and CEO of Relativity Space, as well as Tom Mueller, co-founder and CEO of Impulse Space and a founding employee of SpaceX.
Lux Recommends
While we are on the subject of drones, I recommend this deep dive by Laura Pitel and Raya Jalabi on Turkey’s Bayraktar TB2 drone, which has become a hero in fighting against Russia in its war on Ukraine. “In addition to heralding Turkey’s ascendancy in global defence, the TB2 embodies a new phase in the era of drone warfare in which lower-cost technology becomes increasingly accessible to regimes that cannot buy from the world’s more-established arms producers.”
Our scientist-in-residence Sam Arbesman recommends Kai McNamee’s story in NPR about the search for alien technology on Earth. “Eight years ago, a meteor believed to have been 2 feet long entered Earth’s atmosphere at more than 100,000 miles an hour before exploding into tiny, hot fragments and falling into the South Pacific Ocean. Some scientists believe it came from another star system, which would make it the first known interstellar object of its size to impact Earth.”
Jason Collins wrote a compelling essay in Works in Progress arguing that “We don’t have a hundred biases, we have the wrong model”. Behavioral psychology and economics have gotten a lot of popular traction in recent years through concepts like loss aversion and prospect theory. But rather than continuing to find biases in human decision-making, Collins argues it’s time to move on to a whole new paradigm for understanding human rationality.
Finally, if you are looking for a nice beach read this weekend, I recommend the final report of the UN Human Rights Office on Xinjiang (XUAR), which finds that “Serious human rights violations have been committed in XUAR in the context of the Government’s application of counter-terrorism and counter-‘extremism’ strategies. The implementation of these strategies, and associated policies in XUAR has led to interlocking patterns of severe and undue restrictions on a wide range of human rights. These patterns of restrictions are characterized by a discriminatory component, as the underlying acts often directly or indirectly affect Uyghur and other predominantly Muslim communities.”
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.”
