This week, we are launching a beta version of “Securities” — a weekly newsletter that will investigate and analyze science, technology, finance and the human condition.
Over the last several years, Sam Arbesman has carefully built Lux Recommends into a regular source of interesting science and complexity news. We’re going to continue that mission and expand upon it, bringing all the previous great highlights with even more analysis and insight. Thanks to Sam for his years of hard work. Thankfully, he’s going to continue writing, including a piece below today.
“Securities” for the 21st Century
We’ve branded our little corner of the web “Securities” — why?
When I look around the world today, it isn’t hard to get cynical. Right now in Kazakhstan, authoritarian president Kassym-Jomart Tokayev has ordered his security forces to “shoot to kill” protesters fighting rising fuel prices and government corruption. The Omicron variant is laying waste to health care systems throughout the United States and across the planet.
Thousands of motorists in Virginia were stranded for more than a day this week on I-95 due to extreme weather, and climate change has wrecked global havoc affecting billions of people. Supply chains remain mired in backlogs, authoritarianism is on the rise, and if tech stocks are any indication the past few weeks, the global financial system seems poised for a crisis of confidence.
The bedrock of innovation — the fundamental kernel that allows all of what we do in startups and entrepreneurship to function — is security.
Health security ensures that founders and innovators have the physical and mental capacity to do their best work. Capitalism and markets function when workers and investors alike have the economic security to take risks and be rewarded on the upside and cushioned on the downside. Defensive technologies ensure that liberal democracies don’t fall flat against rising militaristic totalitarian regimes.
The story of progress we have witnessed the past two centuries thanks to the Enlightenment, capitalism, science and democracy has offered the world an incredible bounty. Yet, that progress feels like it has entered a period of malaise, perhaps even decline.
At Lux, we see the forefront of science and technology every day, and we know the future can continue to witness the progress we have seen over the past decades. New scientific instruments, new therapeutics for disease, better access to space, automation to improve industrial output, complete transformations of agricultural crops and nuclear power plants, instant and accessible mobility — the future has so much wonder in store for us that founders are building as I write.
Yet, the story of progress is entwined with the story of finance — the other half of “securities” and the source of our little double entendre. VCs are too often afraid to go head first into the deepest depths of technology, choosing companies with better sales motions over companies delivering fundamental tech that changes the quality of life of present and future generations.
With “Securities,” my hope is to make the deepest reaches of science and technology accessible and popular to the widest possible audience, everyone from other VCs and founders to policymakers and citizens. It’s about bringing “lux” to bear against the darkness, overcoming the cynicism that is increasingly creeping into our collective meditations.
Sam Arbesman: We need to create and foster new types of scientific organizations
The National Science Foundation headquarters in Alexandria, VA. Image Credits: NSF / U.S. Government
There are many activities that are valuable for science. However, only a small subset of these are actually valued by scientific academia; in other words, there are only certain activities that will get you tenure (certain kinds of research, certain types of scientific publication).
As a result of this, we need to create and foster new types of scientific organizations, ones that make space for a broader set of research activities that are valuable for science, whether it’s allowing for more interdisciplinary science, undertaking longer-term research projects, or building software tools to spur further discovery.
While it’s certainly a good thing that research can be conducted within universities, corporate industry labs, or even within deep tech startups, we must recognize that these are just a few points within a high-dimensional space of potential research institutions. We need to begin exploring this high-dimensional space more deliberately and find new models to allow for the full spectrum of science to occur.
Beginning over a year ago, I began to compile a list of these kinds of new research institutions in the Overedge Catalog (there are also educational institutions included, as well as other “misfit” organizations). And since then, I’ve been delighted to see that there has been an acceleration in the construction of new organizations, from Arc Institute and New Science to Arcadia and Convergent Research .
The institutional models of all of these organizations compiled in the Overedge Catalog, both the new ones as well as the more established ones, are quite varied. Some of these organizations are more traditional in structure or funding style and some are weirder (in the best sense!), some are for-profit and some are non-profit, some are distributed and some have physical spaces, some fund projects and some fund people. But all of these are important attempts that merit following closely.
I liken this work to an evolutionary process, with a Cambrian Explosion currently happening in scientific institutional innovation, with many new attempts being tried and new forms being created.
Which ones will succeed? I have guesses for what might work, but in the end I am reasonably agnostic, and am more invested in this evolutionary process overall. Some will succeed and some will fail, and that’s okay, because we need to begin to explore this high-dimensional space as thoroughly as possible. Only then can we discover new models for how science can be done, new models for institutions to allow the full spectrum of scientifically valuable activities to be accomplished.
Lux Recommends
“An Eikon of instrumental progress” by Danny Crichton— one of the big pieces of news out of the Lux portfolio this week was that Eikon received about $518 million in Series B funding to build its live cell imaging technology. The creation of new scientific instruments has often ushered in the largest transitions in science, and this piece looks at their history, the James Webb Space Telescope and more to contextualize why these investments are so important.
“Finally, patent troll forum shopping may finally be on its way out” by Danny Crichton — one of the major overlooked announcements this week came from U.S. Supreme Court Chief Justice John Roberts, who called for reforms to patent litigation that could increase the rate of startup innovation by lowering the costs of handling patent trolls.
“Constructing Signposts in the Memescape” by Sam Arbesman. Sam looks at how ideas spread and the power of a pungent phrase to induce additional distribution of smart concepts.
“Best Articles I Wrote (2021 Edition)” by Danny Crichton — in the department of pure self promotion, before I joined Lux Capital at the end of last year, I was the managing editor at TechCrunch. If you’re curious about what I wrote about in 2021, including U.S.-China trade relations, disastertech, climate change, and more, be sure to read some of my favorites.
That’s it, folks. Have questions, comments, or ideas? This newsletter is sent from my email, so you can just click reply and castigate me.
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.”
