With slight homage to Douglas Adams, we’re here this week with a briefer newsletter and off next week entirely for the Thanksgiving holiday.
It’s a tough time out there in a world riven with wars, conflicts, divides and binaries. So whether you believe in the reality, the myth or perhaps the nihilism of Thanksgiving, I encourage every “Securities” reader to seek out what’s positive and what’s worth affirming in their families, friends, communities and world. Fire is destructive, but it is also the fuel for the wick that lights our dinner tables and illuminates humanity’s best path forward. Cherish the present and aspire toward the future — and eat way more than you ever thought possible (the golden formula of progress = Thanksgiving + Ozempic).
Podcast: Thanks for nothing, AI regulators
Photo by Chris Gates via DALL-E
I wrote two weeks ago in “Reckless Regulators” that politicians and government bureaucrats seem to have unified around limiting artificial intelligence technology before it even gets started. As I wrote about the situation:
For such a nascent and unproven technology, it behooves us to tread much slower on regulation. We need to encourage widespread experimentation and openness in the development of the bleeding-edge of AI performance and capabilities. We should be encouraging the distribution of open-source AI models to as many scientists and institutions and users as possible. Everyone should have access to the best AI models humanity has ever crafted.
This week’s podcast picks up the theme with a handful of experts on the AI landscape and how the regulatory regime interacts with it. Joining me is Techmeme Ride Home podcast host Brian McCullough, Supervised newsletter founder and writer Matthew Lynley, and Lux’s own general partner Shahin Farshchi.
We talk about the latest regulatory announcements from governments all around the world, and then get into the meat of the debate: is it open or proprietary models of AI that will win in the market? It’s the hot topic du jour, and a perfect complement to an extra helping of stuffing during the holidays.
Our scientist-in-residence Sam Arbesman has a good column on his personal Substack around "The Kitchen Sink Conundrum and Simulation's Balancing Act.” “This tradeoff between complexity and accuracy is a humble realization. Do not add complexity in the hopes of greater verisimilitude, as not only can there be a diminishing return to this effort, but it could even be entirely counter-productive.”
Thomas Johnson at the New Civil Engineerdiscusses why high-speed rail in the United Kingdom just can’t be built. The answer? Overengineering. “[Former Rail magazine editor Nigel Harris] gave the example of the electrification of the Great Western line. He said that, when the East Coast Main Line was electrified in 1989-90, the masts only went 2.5m into the ground and they have never been known to fall over. However, when it came to electrifying the Great Western in recent years, the masts were overengineered to go 10m into the ground to ensure stability to an even greater - perhaps unnecessary - degree.”
Sam forwards a fun clock made of marbles in Hackaday. “Here’s how it works: black and white marbles feed into a big elevator. This elevator lifts marbles to the top of the curved runs that make up the biggest part of the device. The horizontal area at the bottom is where the time is shown, with white and black marbles making up the numerical display. But how to make sure the white marbles and black marbles go in the right order?”
Our venture associate Alex Marley highlights Microsoft’s announcement this week of its first custom silicon for AI processing applications. “Microsoft is the last of the Big Three cloud vendors to offer custom silicon for cloud and AI. Google pioneered the race to custom silicon with its Tensor Processing Unit, or TPU, in 2016. Amazon followed suit with a slew of chips including Graviton, Trainium, and Inferentia.”
Shaq Vayda highlighted a milestone in pharma: the first approval in the UK of a CRISPR-Cas9 therapeutic. Jointly developed by Vertex and CRISPR Therapeutics and targeting sickle cell anemia, Time Magazine said that “Patients first receive a course of chemotherapy, before doctors take stem cells from the patient's bone marrow and use genetic editing techniques in a laboratory to fix the gene. The cells are then infused back into the patient for a permanent treatment. Patients must be hospitalized at least twice — once for the collection of the stem cells and then to receive the altered cells.”
Sam and I both read the exciting new research and model out of Google’s DeepMind called GraphCast which can predict weather with more accuracy and forewarning than any other current system. The research was written up in Science: “GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclones tracking, atmospheric rivers, and extreme temperatures. GraphCast is a key advance in accurate and efficient weather forecasting, and helps realize the promise of machine learning for modeling complex dynamical systems.”
Finally, Sam enjoyed James Somers’s thoughtful essay in The New Yorker on “A Coder Considers the Waning Days of the Craft” (a headline which doesn’t do the piece real justice). “Computing is not yet overcome. GPT-4 is impressive, but a layperson can’t wield it the way a programmer can. I still feel secure in my profession. In fact, I feel somewhat more secure than before. As software gets easier to make, it’ll proliferate; programmers will be tasked with its design, its configuration, and its maintenance.”
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.”
