The Stanford campus last week. Photo by Danny Crichton.
Buffeted by crises, Stanford trundles along serenely undiminished
Stanford is an Arcadian utopia bewitched by the curses of excessive success. The maledictions just keep striking, and even seem to be accelerating. The latest crisis has been with athletics, where the breakup of the Pac-12has orphaned Stanford along with Berkeley, Oregon State and Washington State. Stanford is now in the position of pleading with a dozen East Coast schools for admission to the Atlantic Coast Conference (ACC). This sudden plight follows the controversy from July 2020 that the university would cut 11 sports, only to reinstate them one year later under extreme alumni pressure.
Now, add in the further ethical and fraudulent catastrophes of Sam Bankman-Fried of FTX, who grew up on Stanford’s campus (although attended MIT as an undergraduate) and Elizabeth Holmes of Theranos, who dropped out of campus to pursue her biotech dream and is now sitting in prison serving a nearly decade-long sentence. Then there’s the whole crisis around the death of fun on campus, which has been notably covered in an excoriating essay by Ginerva Davis in Palladium. That’s not even getting at America’s cultural civil war, which has repeatedly descended on Stanford like a biblical plague of locusts.
Among my fellow alums, the opinion of this institutional epicenter of Silicon Valley ranges from apathy to extraordinary vitriol, with the latter gaining ground and momentum over the past year. For them, Stanford is unabashedly declining, losing the very culture and entrepreneurial idealism that makes the institution so singular among the upper strata of American academia. Rudderless, leaderless, directionless — a lot of “less” and not a lot more.
But much as how reporters remain obsessed with the so-called “doom loop” up in San Francisco (see coverage in WSJ, NBC News, NY Post, CBS News, FT and quite literally dozens of other publications), Stanford’s extensive travails are similarly blown all out of proportion.
As former president, Tessier-Lavigne is the target of most of the vituperative blame for Stanford’s impending demise, but a wider look at his record as president belies the university’s absolutely dominant success. Last year, Stanford raised $1.39 billion in donations, holding the prime spot among all universities. Venture capitalist John Doerr of Kleiner Perkinsdonated $1.1 billion for the Stanford Doerr School of Sustainability, in what’s been declared the first new school at Stanford in 70 years. Stanford’s endowment rose to a record high of $38 billion in 2021, before receding to a bit more than $36 billion with the post-Covid market malaise.
Meanwhile, Stanford continues a building spree both on campus with dozens of new buildings and increasingly all across the Bay Area. Stanford’s admission rate dropped to a new record low (as it typically does) this year, now at 3.68%. That’s despite Stanford’s luxurious sticker price of $87,833 for a year of undergraduate attendance. Plus, I’d be remiss in pointing out Tessier-Lavigne’s adroit handling of the Stanford Rape Case and its fallout, which saw the perpetrator found guilty the month after his nomination to the presidency, as well as the organizational success of Stanford’s Covid-19 response.
In short: Prices are up, demand is up, buildings are going up — even as the president is going down, infamous alums and former residents are locked down in prison, and sports teams are locked out.
The story of Stanford is patterned across American institutions, of quantitative success bedeviled by qualitative failure. There’s never been so much profit, but also such a scale of fraud. Research has never been so well-funded, but laboratories remain mired in the publish-or-perish culture and the concomitant replication crisis. Students are encouraged to be CEOs, even as they lack the basic tools of leadership that would prepare them for such roles.
Leftist intellectuals would perhaps dub this “late-stage capitalism” and rightist intellectuals “decadence,” but what we have here is the infinite pursuit of wealth over everything: innovation, national service and patriotism, the moral and intellectual development of men and women. The statistics around Stanford are reported just as I wrote them, of endowment gains, drops in admissions rates, regional economic impact, jobs created, buildings constructed. One never sees a quantitative assessment though of student and faculty morality or societal engagement (and we won’t because who wants to see those numbers!)
I mention E.O. Wilson’s Consilience and C.P. Snow’s The Two Cultures every once in a while. They share the idea of the arts and sciences jumping together into a fermented fusion of ideas on the frontiers of human knowledge. The observational and creative talents of art complement the logical and empirical rigors of the sciences, serving both and pushing all to the furthest reaches.
Consilience is dead at Stanford though. Even as the student body remains relatively stable, Computer Science soared from 452 declared majors in Autumn 2013 to 776 majors last year, the most recent data available. That’s a gain of 72% in a decade. Symbolic Systems (AI, Philosophy and CS) went from 98 to 174 while Mathematical and Computational Sciences (rechristened Data Science this year) went from 69 to 134.
Which majors have lost the most ground? Science, Technology & Society (STS), which combines the sciences and engineering disciplines with ethics, philosophy and sociology, declined from 210 declared majors to 79, a drop of 62%. History dropped 53% in a decade, from 89 to 42.
Again, the story of Stanford is patterned across America. Rapacity reigns even as the creative engine of our culture sputters from lack of funding. Americans are wealthier than ever before, and the GDP is expected to cross 5.8% in the third quarter, accelerating from more demure numbers posted earlier this year. Even America’s waistlines are collapsing with the final approvals of Ozempic and Wegovy. Thinner, hotter, richer — it’s an Instagram society, after all.
Yet, satisfaction remains elusive. Gallup’s extremely long-run polling on the general mood of the country remains at its nadir. Just 19% answered ‘Satisfied’ to the question, "In general, are you satisfied or dissatisfied with the way things are going in the United States at this time?” 80% said ‘Dissatisfied,’ among the lowest results since the 2008 financial crisis and roughly in line with June 1992 (early 90s economic crash) and July 1979 (Carter and the crisis of confidence).
Stanford has become the standard. Yes, it lacks vision and direction, or any sense of mission, or even proof that it is inculcating the next generation of citizens. But the endowment has never been thicker with lucre. Tessier-Lavigne’s career might have gone ka-boom, but Stanford’s future? Ka-ching.
“Securities” Podcast: Simulating Evolution: Playing God or the Next Frontier?
Design by Chris Gates
Artificial life, aka “A-life”, is an intellectually vital field simulating life within computational systems. By allowing simulations to run uninterrupted for extended periods, researchers can observe emergent behaviors, patterns, and even evolutionary trajectories. What's particularly intriguing is that these artificial systems often exhibit behaviors and patterns reminiscent of natural life, reinforcing that certain principles of life and evolution might be universal, whether in a biological context or a digital one.
In this episode of "Securities," I’m joined by our scientist-in-residence Sam Arbesman as well as special guest Olaf Witkowski, who is the director of research at Cross Labs and the current president of the International Society for Artificial Life. Among many topics, we discuss cellular automata, the origins of evolution, and the open-endedness of A-life.
Sam recommends Andy Tomaswick’s overview of a new plan by David W. Jensen to make space habitable to humans. “He released a 65-page paper that details an easy-to-understand, relatively inexpensive, and feasible plan to turn an asteroid into a space habitat. With admittedly 'back-of-the-envelope' calculations, Dr. Jensen estimates that the program would cost only $4.1 billion.”
Our summer associate Ken Bui points to the next cover story of Foreign Affairs by Eurasia Group head Ian Bremmer and DeepMind’s co-founder Mustafa Suleyman on “The AI Power Paradox.” “Whether they admit it or not, AI’s creators are themselves geopolitical actors, and their sovereignty over AI further entrenches the emerging 'technopolar' order—one in which technology companies wield the kind of power in their domains once reserved for nation-states.”
India’s prime minster Narendra Modi has faced a barrage of scrutiny from critics at home and in the West over his anti-liberal administration of the world’s greatest democracy. The right-wing analyst Christopher Caldwell writes an engaging if partially blinded contrarian take in “India’s Uprising.” “That is in power in the first place means that the old 'managed' democracy of the Congress party system has been replaced with a more freewheeling variant—a more democratic democracy, if you will, a democracy that answers not to 'values' but to the society as it actually exists.”
I recommend Matt Ellison’s interview and discussion in Palladium with Walter Kirn on “How America Lost the Plot.” "When you live perpetually slightly ahead of the present, never bonding, never settling down, never letting yourself be penetrated—even by experience let alone memories to hang onto—you are a digital bit of information to be formed into anything. You volunteered for it!”
Sam recommends Étienne Fortier-Dubois’s essay “In Defense of Tech Trees.” If you enjoyed the gratifying milestones of acquiring new technology in the Civilization games, why can’t we experience that in the real world?
Finally, Ken recommends Nyah Stewart and David Lin’s analysis on U.S. and Chinese innovation in "A Tale of Two Tech Cities,” which compares Boston to Hangzhou. “Boston and Hangzhou are both success stories — testaments to the very different approaches to place-based innovation in the United States and the People’s Republic of China (PRC).”
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.”
