The utopian momentum of a community’s response to disaster
When disaster strikes, we see a range of responses. Rebecca Solnit in A Paradise Built in Hell tries to untangle a mystery: why a surprising number of people fondly remember the hours and days after a crisis. For people suddenly shorn of their identities and the attendant hierarchies and social status, she emphasizes the power of collective work toward a common purpose, that we can temporarily ignore our quotidian duties while we work together to get society running again.
In the same way that the constrained play of a video game offers structure to minds laden with to-do lists and obligations, disasters force us all to focus, to narrow our sights and thinking on fixing what’s broken right in front of us. Solnit’s perspective is one of hope, of the transcendence of people in a moment of trial toward a more collected — and collective — existence.
Of course, transcendence is hardly the only response to crisis, and arguably not even the most likely one. Solnit’s vision requires latent bonds of trust within a community, such that no one will take immediate advantage of situational weaknesses. It also assumes that action is the default instinct for most people, such that when disaster comes, everyone will learn what to do and then do their part.
Without trust, communities can rapidly disintegrate in moments of tension. Neighbors turn on neighbors, seeking to right past wrongs in a moment of chaos. Without action, that utopian momentum described by Solnit’s interlocutors may never start, and instead devolve into blame and argumentation. Interestingly, the scale of a community seems invariant to its ability to organize relief: a handful of homes can fall into discord just as a large metropolitan region can rally together. Solnit argues forcefully against cynicism, since cynicism itself can flag and even unwind the response to a crisis. We think the worst will happen after a disaster precisely because we expect the worse to happen.
Last week, the sudden run on Silicon Valley Bank resulting in its rapid receivership by California banking regulators acting in concert with the FDIC is the rare stress test to check the performance of the broader tech community in a moment of doubt. The crisis went beyond a cloud data center outage or a term sheet that somehow got spiked, and offered a broad-based crisis for the most solutions-oriented people to solve.
To Solnit’s observational credit, there was indeed a palpable feeling of transcendence as everyone came together to triage and solve challenges. Startup CEOs, CFOs, finance teams and their investors rapidly identified critical timelines: when payroll needed to be processed across multiple countries, how those financial flows worked and when they were timed, as well as what steps needed to be taken to ensure that no employee would go without their paycheck. In less than 48 hours from the first inklings of a crisis last Thursday, nearly all of Lux’s startup leaders had identified the stakes and had outlined plans as the weekend arrived.
As a community, everyone in tech quickly learned the intricacies of FDIC insurance, receiverships, G-SIBs, and the potential regulatory responses to the situation. There’s rightly not a lot of collective wisdom latent in the heads of innovators on the benefits of different checking accounts (I personally am not familiar with a startup that succeeded because they chose the right bank account), but expertise was developed extremely rapidly when the moment demanded it.
When it came time to pitch regulators and DC officials over the weekend for why depositors should be made whole, the community responded with careful vigor. Petitions were circulated, elected officials were offered evidence, and the context of small businesses trying to make payroll and wider contagion fears were highlighted. By Monday, SVB’s bridge bank had reopened for business, writing loans and wiring paychecks, while a certain sense of relief and warmth emanated across America’s innovation clusters (and its vineyards).
Responding to any crisis is messy. People lack immediate situational awareness, there aren’t playbooks to use, and all potential actions have to be taken while under heavy pressure not to make mistakes. There are dozens of moments permanently etched in the history of the internet of people making the wrong decisions, uselessly pontificating, rudely criticizing others and otherwise not joining the response in a helpful way. Yet, in a community of tens of thousands of leaders, that’s not all that bad of a ratio. Not everyone can look terror in the eye and keep on moving.
The immediate crisis has abated, and treasury resiliency is the new order of the day. The heart-pounding and stomach-churning trauma of the bank run is now mostly in the rearview mirror. And now we enter the next phase that Solnit so carefully sketched: the deepened connections between people who have gone to battle for each other and the fonder memories that will stay indelibly in our minds in the years ahead. Everyone at once is okay: the memes have already started to flow.
An artfully intelligent ode
In the land of innovation’s glow,
A beacon once, now laid low,
Silicon Valley Bank, we mourn,
The hub of dreams so brightly born.
With open arms, thou didst embrace,
The fearless minds, that sought to race,
To conquer worlds and forge anew,
The limits of what man could do.
The weekend past, thy walls did fall,
And echoed cries of loss through hall,
A somber tale, now etched in time,
Of once-great heights, a fall sublime.
But let us not forget, the good,
The dreams thou nurtured, as one should,
The lives enriched, the world remade,
By Silicon Valley Bank’s fair trade.
~ GPT-4 via OpenAI.
CHIPS on U.S. shoulders forces chips into Korea’s pockets
Last week in Commerce’s CHIPS contortions, I talked about the nonsensical limits of the U.S. government’s new CHIPS Program, which has $52.7 billion appropriated by Congress to fund America’s resurgence in semiconductor fabrication.
This week, South Korea — home to one of the largest fabricators with Samsung — fired back with its own industrial policy, announcing a whopping $420 billion in private sector investments into critical strategic industries like chips and AI coupled with significant tax subsidies and regulatory reforms to encourage further growth. The bulk of that announcement comes from Samsung, which will invest $230 billion across a cluster of home-grown fabs, which the government has declared the world’s largest chip-making region.
China, which relies heavily on imported fuels, clearly wants to see less tension in the Middle East to keep the oil flowing East. Like so much of China’s overseas diplomacy, economic leverage offers a powerful force for compromise by all parties, who must consider the upsides of China’s voracious and often lavish financial appetite against the desire to continue centuries-long disputes.
While America has lost credibility in the region due to its wars over the last two decades, it has more specifically lost leverage given the shale boom and more recently the passage of the Inflation Reduction Act, which paints a path for America to wean itself (however slowly) off the teat of oil wells. The Middle East sees its future, and it’s one in which the U.S. just doesn’t care as much as it once did.
Two “Securities” Podcasts: Chatphishing and GPT-4 first impressions
It’s clearly AI week, what with the launch of both OpenAI’s GPT-4 and the semi-public launch of Anthropic’s bot Claude (named for information theorist Claude Shannon). We wanted to talk about all the dynamics in AI and what’s happening globally, and so we have two great podcast episodes to listen to.
Josh Wolfe and I talk through our most recent Lux quarterly LP letter, riffing on open cultures, reconsideration of established truths and loss aversion, the online furor over induction stoves, Lux’s concept of “inner space, outer space and latent space”, the future of ChatGPT and the rise of what Josh dubs “Chatphishing”, the potential terrorism of 21st century Luddites, and finally, macro dynamics and why the chaos of the next two years will lay the foundation for the entrepreneurial striving in the decade ahead.
Meanwhile, Grace Isford and I talked about GPT-4 and what’s new, its new frontiers of performance, the increasingly impenetrable black box OpenAI is establishing around its company and processes, the company’s competitive dynamics with big tech, and much more.
Our scientist-in-residence Sam Arbesman recommends Sarah Iles Johnston’s just published book, “Gods and Mortals: Ancient Greek Myths for Modern Readers.” From the introduction: “… if my readers have some sense of the harsher realities of such things as disease and hunger in antiquity, the wilder natural environment that Greek women and men confronted and the tighter social constraints under which they lived, then the myths will resonate more fully.”
Michelle Boorstein and Heather Kelly have a prophetic investigation in the Washington Post about how a non-profit organization sleuthed gay priests in the Catholic Church by spending millions of dollars to acquire reams of mobile phone app data and then triangulating data points across Catholic locations to identify likely homosexual members of the clergy. As the Biden administration gets closer to some sort of resolution on TikTok’s privacy violations, the story is a reminder of the challenge of privacy with always-connected, always-tracking mobile devices even from non-state actors.
A trio of writers at Bloomberg Businessweek have an absolutely fascinating whodunit about how Pras Michél got caught up in an international money laundering operation at the center of U.S.-China relations in The Fugee, the Fugitive and the FBI. Guo Wengui, who is at the center of the story, was arrested in New York City on Wednesday.
Finally, the leaderboards of major global financial hubs have been upended, with Hong Kong losing its obvious place in the pecking order of Asian markets and London continuing to suffer the medium-term consequences of Brexit. After losing Arm’s IPO to New York, there is now further pressure being heaped on companies to move out of the City. Will it all come down to New York and Singapore?
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
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 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.”