Dissonance reigns. Hard-fought episodes of tranquility are shattered as undercurrents of feverish discontent surges through the surface. The venture industry, slammed by plummeting valuations earlier this year, spent much of the winter and spring fortifying balance sheets and rapidly triaging portfolios. That chaos has transformed into relative calm this summer, with VCs perspicaciously analyzing the environment and voyaging on long-delayed vacations (I get all of the autoresponders to this newsletter — everyone is traveling wide and far).
Yet, the winter of our discontent has not been made a glorious summer. Quite the opposite — the eerie quiet of venture the past few weeks has been punctuated by a great morass of global macro disturbance.
Josh Wolfe wrote in our last Lux Quarterly Letter on the theme of “Entropic Apex” — that we’re reaching a period of peak chaos as the economic, social and political systems undergirding our world break and are rebuilt. That tumult doesn’t exist in a vacuum; it courses through our veins, and necessarily elicits an emotional response:
It can be easy at the Entropic Apex to want to smash and destroy. Anger is a natural reaction to the overwhelming chaos we experience. It is natural to want to smash a broken pot. There is an alternative: to create in the face of adversity, to use the challenge as the impetus for evolution and growth. The reason we love the founders with chips on their shoulders is that they’ve often endured unenviable adversity. And sometimes they need just one more ingredient before beyond merely believing in themselves––they need others to believe too.
I’m hopeful for the creative energies to come, but one can’t help but feel the vitriol of billions wanting to smash already-broken pots.
The news this week was truly dizzying, overwhelming and strangely mercurial (and probably changing even more before I can hit the send button on this newsletter). We saw:
… The Economist could move from a weekly to an hourly publication and still fill its dense pages.
(Editor’s Note: After I finalized this newsletter Thursday night, news came out Friday morning that former Japanese prime minister Shinzo Abewas assassinated by an armed assailant. It’s a shocking event in a country known for its low levels of violence.)
It’s not just that we are crescendoing to the apex of Entropic Apex, but more pressing is the increasingly visible negative feedback loops that are feeding these interconnected crises. Higher consumer prices across a range of goods — most notably energy but also food — are inflaming tensions and animosity across the world. That’s leading to more labor actions, further stressing supply chains, leading to higher prices, leading to more protests, and onwards in an infinite helix of doom.
That discontent is burbling into politics across much of the world, and particularly in Europe. Weak governments in France, Britain, Italy, and Germany are disintegrating the bedrock that holds Europe together. Meanwhile, while Russia has faced significant economic damage from its war on Ukraine, and China likewise has suffered under its increasingly authoritarian zero-Covid policy, both countries seem — at least to the naked, untrained eye — to be surprising bastions of controlled tranquility.
The basis for these different outcomes is ultimately state capacity. Eli Dourado at the Center for Growth and Opportunity argued a few weeks ago in a short piece that “State capacity eats interest rates for lunch.” He pleas for a simple model of the world:
In practice, however, interest rates matter a lot less than state capacity, the ability of a government to effectively accomplish its policy goals. When bad policies and poor governance make projects expensive to complete, then the cheaper cost of money due to low interest rates is swamped by the sheer magnitude of a project’s costs. Good governance matters more than interest rates.
What’s been surprising is not that these succession of crises are happening, but that so many of them were predictable and eminently avoidable, or at a minimum, solvable. Germany and wider Europe’s reliance on Russian gas was always going to yoke the continental project to the whims of the next tsar. The economic collapses in Sri Lanka and Ghana were years in the making, although in many cases obscured by the positive façade of economic data. U.S. supply chains were always distended and fragile — right up until they broke. Just in time is now out of time.
Men and women — citizens — should be called to take up the mantle of solving this succession of calamities. Or as we put it earlier in our LP letter, “to create in the face of adversity, to use the challenge as the impetus for evolution and growth.”
If only.
State capacity has reached a nadir for much of the West and the industrialized world. As I wrote back in February in “Consensus functions”:
Society, meanwhile, doesn’t have all those layers of consensus to build upon for new decisions. There’s no algorithmic blockchain ensuring that the basic facts of reality are cross-validated, or the scientific method to ensure that evidence is considered with appropriate context. Consensus is recursive, and without better consensus functions around values and tradeoffs, it’s impossible for a nation to make decisions. America and much of the West escaped that lack of deep consensus simply through abundance. Multiple values and tradeoffs could be supported by building institutions that allowed them all to coexist. Venture funds can’t make every investment and scientists can’t accept every contradictory result, but wealthy nations can support a wide range of consensuses, each underwritten in parallel.
Well, the abundance is gone and now the dissensus is obvious.
State capacity demands basic truth from us. It requires consensus on what reality is. It requires consensus around what the strategy of how to handle that reality and how to transform a nation’s potential into stability and power. And then it requires consensus on the means for enabling that action. That’s the “what”, “why” and “how” of basic governance.
What we have seen so far in this destructive phase is a completely limp and listless response to challenges that are hardly the toughest we’ve ever confronted. We’re talking supply chain disruptions, a phenomenon that’s modelable, controllable, fixable. If America can build the arsenal of democracy and fight fascist dictatorships in two theaters during World War II, we can deliver infant formula and put a consistent number of planes in the sky. We can get ships to port, unload said ships, and propel those vessels back to where they came from. We know how to build factories, and we know how to build them fast. That’s as true in America as it is England as it is in France.
The chaos we are seeing is a choice. Jean-Paul Sartre, in his lecture “Existentialism is a Humanism,” said that:
Quietism is the attitude of people who say: ‘Others can do what I cannot do.’ The doctrine that I am presenting to you is precisely the opposite of quietism, since it declares that reality exists only in action. It ventures even further than that, since it adds: ‘Man is nothing other than his own project. He exists only to the extent that he realizes himself, therefore he is nothing more than the sum of his actions, nothing more than his life.’ … In life, a man commits himself and draws his own portrait, outside of which is nothing.
We’re committing to drawing no portrait, just thrashing and yelping in anguished horror. It’s time to redirect that venomous energy, that vituperative spirit, and guide it to the creative solutions we need heading forward. The Entropic Apex can be a moment of incredible creativity, for adversity and its constraints are the first step to invention. But we need leadership and coordination — and that remains perilously absent in a tough world.
There’s never been a greater opportunity to make a difference, and less interest in doing so. That’s the dissonance in our discontent.
Lux Recommends
I recommend Kit Wilson’s analysis in The New Atlantis on “Reading Ourselves to Death.” From the piece, “… when we put the two accounts of abstraction together, we begin to see a problem: Every time we read, we inevitably conceptualize the world, in perhaps an ever-increasingly abstract way. And it’s conceivable that we may reach a point where those abstracting effects go too far.”
Shaq Vayda recommends Brian Naughton’s look at virtual biotech and how democratized the field is becoming in “Computational tools for drug development.” He writes that "Given the right disease, target and computational approach, there is a lot of potential,” and it's a good reminder of what we can do with companies like Strateos.
Did history actually happen? That’s the question behind Jonn Elledge’s analysis of the “phantom time hypothesis” that whole centuries of European history are entirely fabricated. Our scientist-in-residence Sam Arbesman recommends the piece, which is a fun romp: “There is another school of thought, however, that has a rival explanation for the lack of written records from the centuries after Rome fell. It’s this: They never took place.”
I’m a huge fan of Five Books, a site that asks experts to walk through five core texts in their field and how they relate to each other. In a new installment, Kevin G. Bethunerecommends his top reads in the field of design, which has gained prominence and increasing acceptance across business and even in governance.
A few weeks late because I am behind, but Lucca De Paoli and Jeremy Hill’s story for Businessweek on the enduring operations of Lehman Brothers was fascinating. Not only is the bank that died in the global financial crisis not dead, but dozens of employees are still clearing out the books 14 years on and counting. “‘When I lie on my deathbed, I definitely will think about Lehman,’ Ehrmann says.”
Finally, Marcela Valdes in The New York Times Magazine has a fascinating feature “Inside the Push to Diversify the Book Business.” There are many intertwined narratives, but perhaps the most relevant is one obvious to this newsletter's audience: follow your customers.
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.”
