Video still from "Will America Make It? Space Economy”
Happy Independence Day (and very long weekend)! No column from me this week, but we do have a brand new video and a whole pile of Lux Recommends below.
When it comes to reaching for new heights, America has been a striver since its very founding. Now, as the space economy reaches a new apogee, a critical question remains: Will America Make It? Can it lead in all aspects of the new space race just as it bested the Soviets in the 1960s?
In our new “Securities” video produced by Chris Gates, we explore the potential of the private sector-led space economy, chatting with Paul Seebacher, VP of Engineering at Impulse Space, and our own Shahin Farshchi.
Many scientists and engineers are unassuming, and yet their inventions have transformed the world. This week, we lost one of the greats with the passing of John B. Goodenough, the inventor of the lithium-ion battery. The New York Times has a great obituary, and Chemistry World points out that “Then in 1986, with Goodenough’s retirement from Oxford anticipated, he accepted an offer to become chair of engineering at the University of Texas at Austin. In the US, retirement before age 67 was not required, so he kept working for another 32 years as a materials science and engineering professor.”
Our scientist-in-residence Sam Arbesman enjoyed this Nature news article reporting on the conclusion of a 25-year philosophical wager (but not Pascal’s) between David Chalmers and Christof Koch on whether we will understand the neural basis of consciousness. We don’t yet, and so the bet was settled in favor of Chalmers.
Lan Jiang recommends William J. Broad’s look at the three-body problem as applied to nuclear warfare between the U.S., China and Russia. “Experts say the tripolar age could put human survival at risk. But they also cite a number of three-body lessons from nature — starting with Newton’s — that illuminate the issue and suggest possible ways forward.” And for those excited for the 3 Body Problem sci-fi TV series (an adaptation of Liu Cixin’s book), Netflix has posted its official trailer.
Our summer associate Koko Xu selected three articles from Every, which has been producing some great work. The first is from Evan Armstrong in Napkin Math exploring how generative AI can be applied to algorithmic finance. His conclusion? Interns and entry-level jobs in finance are about to change radically. Koko’s second article is from our “Securities” friend Anna-Sofia Lesiv of Contrary Capital, who writes on “The Dawn of Spatial Computing.” Finally, Jamie Wong offers a detailed deep dive into the CO2 removal market, and all the innovation happening there as the world confronts climate disruption.
Our “Securities” summer associate Ken Bui recommends Nicholas Dockery’s analysis for the Modern War Institute at West Point on what has gone wrong for the Pentagon’s procurement of AI technologies. “DoD must remain cognizant of the lessons gleaned from the post-9/11 wars and the inefficacious efforts of the Joint IED Defeat Organization (JIEDDO).” It’s a big issue: Politico this week reports on the latest Congressional proposal trying to improve AI procurement. “Pentagon leaders have acknowledged that the procurement rules that worked for acquiring traditional weapons like fighter jets do not translate well to buying new AI-enabled software technologies.”
Our summer associate Reha Mathur points to a brand-new Nature paper (co-written by our own Feng Zhang at Aera Therapeutics) that finds that eukaryotes (the cell family for humans and most complex lifeforms on Earth) have RNA-guided DNA endonucleases. The discovery means that our cells also have a programmable DNA mechanism, indicating that genetic engineering might be possible with our cells along the lines of CRISPR-Cas9 for prokaryotes.
On the same theme as our "Securities" video this week, Ken recommends Brian Balkus’s overview in Palladium on “The Golden Age of Aerospace.” "Aerospace is one of the deepest branches of humanity’s technological tree. It is a telling fact that more countries have produced a nuclear bomb than have mass-produced a jet engine.”
Koko recommends Eric Jang’s analysis in “Takeaways from DeepMind’s RoboCat Paper.” "While none of the ideas are really new, sometimes a lack of empirical surprise paired with a lot of rigor is what the community needs.”
Finally, Sam recommends Paul Ford’s retrospective in Wired on “My Father’s Death in 7 Gigabytes.” “The sum of Frank took two days and nights to upload to the Internet Archive, at a rate of a few files per minute. I wonder what the universe will make of this bundle of information. Who will care?”
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.”
