I’m in London this week, and so there won’t be a column. Back next week (assuming this global plane outage stops at some point)!
Podcast: My interview with Medium’s CEO Tony Stubblebine
Design by Chris Gates.
Medium is one of those media platforms that everyone knows and everyone wonders where it went. Well, it really went nowhere, to be frank — and it’s doing better than ever. Medium has been reinventing itself for years as it seeks a path through the fast-churning crises of media economics. Now, according to CEO Tony Stubblebine, the company has found a formula that he thinks will sustain the company for a decade or more. By offering free distribution to writers with expertise, no one has to spend their days trudging to build up their own audience just to be read.
Tony and I talk about the subscription economy and the challenges of building media platforms, the rise of user-generated content sites like Forbes, LinkedIn and Substack, and the future of Medium and the media industry more broadly.
This is a bit of a follow-up episode to our previous one on AI and media with Eric Newcomer and Reed Albergotti, so do listen to that one as well.
The Orthogonal Bet: The Quest to Find the Poetic Web
Design by Chris Gates.
In this episode of our Riskgaming podcast mini-series The Orthogonal Bet, our scientist-in-residence Sam Arbesman speaks with Kristoffer Tjalve. Kristoffer is hard to categorize, and in the best possible way. However, if one had to provide a description, it could be said that he is a curator and impresario of a burgeoning online community that celebrates the “quiet, odd, and poetic web.”
What does this phrase mean? It can mean a lot, but it basically refers to anything that is the opposite of the large, corporate, and bland version of the Internet most people use today. The web that Kristoffer seeks out and tries to promote is playful, small, weird, and deeply human. Even though these features might have been eclipsed by social media and the current version of online experiences, this web — which feels like a throwback to the earlier days of the Internet — is still out there, and Kristoffer works to help cultivate it through a newsletter, an award, an event and more.
Perfectly timed with yesterday’s massive global tech outage due to a bad update from CrowdStrike via Microsoft Windows, Sam has an article in The Atlantic on "What the Microsoft Outage Reveals.” “Engineers can induce only so many errors. When something happens that they didn’t anticipate, the network breaks down. So how can we expand the range of failures that systems are exposed to? As someone who studies complex systems, I have a few approaches. One is called ‘fuzzing.’ Fuzzing is sort of like that engineer at the bar, but on steroids. It involves feeding huge amounts of randomly generated input into a software program to see how the program responds. If it doesn’t fail, then we can be more confident that it will survive the real and unpredictable world. The first Apple Macintosh was bolstered by a similar approach.”
Shaq Vayda loved Elana Simon and Jake Silberg’s completely comprehensive deep-dive (overflowing with illustrations!) of how, exactly, AlphaFold3 works and why. Extensively detailed, it brings to mind Stephen Wolfram’s epic post on how the transformer architecture works for AI. “We’ll start by pointing out that goals of the model are a bit different than previous AlphaFold models: instead of just predicting the structure of individual protein sequences (AF2) or protein complexes (AF-multimeter), it predicts the structure of a protein, optionally complexed with other proteins, nucleic acids, or small molecules, all from sequence alone. So while previous AF models only had to represent sequences of standard amino acids, AF3 has to represent more complex input types, and thus there is a more complex featurization/tokenization scheme.”
Sam recommends an interesting story via David Lang at Asimov Press on “The Flower Designer Who Built a Laboratory In His Home” aka Sebastian Cocioba. “I want to see amateur biology thrive, and while a lot of regulations exist for a reason, I’m unconvinced that molecular biology requires all the crazy expensive equipment that has become associated with it. I mean, how did people do molecular biology fifty or one hundred years ago? If somebody really wants to learn biology, but they're in an environment without resources, I hope they will ask: What biology can I do?”
I’m a fan of Byrne Hobart’s work at The Diff, and he has a monstrous takedown (that I mightily disagree with) on the late David Graeber’s notion of “bullshit jobs.” “And: some of these jobs may be fake, or fake-ish. Some may be the result of corporate empire-building, or might exist to help create and sell products that customers would be better-off not buying. That's always a valid suspicion! But it's also valid to ask: when you encounter other people's behavior, and find it surprising, is it more likely that you noticed something, with only a few moments of thought, they've missed for their entire career? Or that they've figured out something you don't understand after years of work?”
Finally, Sam recommends the always-fascinating Brian Potter’s look at what it might take to rebuild Bell Labs in Construction Physics. “AT&T’s size also gave it a low bar for what constituted a valuable technical improvement. Even a tiny improvement that saved a few cents on a component or service would be large when multiplied by the enormous scale of the Bell System. This low bar made it far more likely a given research effort would be successful.”
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
In incididunt ad qui nostrud sint ullamco. Irure sint deserunt Lorem id officia dolore non. Anim dolor minim sit dolor et sint aliquip qui est. Ex in tempor laborum laboris dolor laboris ullamco quis. Enim est cupidatat consequat est culpa consequat. Fugiat officia in ea ea laborum sunt Lorem. Anim laborum labore duis ipsum mollit nisi do exercitation. Magna in pariatur anim aute.
In incididunt ad qui nostrud sint ullamco. Irure sint deserunt Lorem id officia dolore non. Anim dolor minim sit dolor et sint aliquip qui est. Ex in tempor laborum laboris dolor laboris ullamco quis. Enim est cupidatat consequat est culpa consequat. Fugiat officia in ea ea laborum sunt Lorem. Anim laborum labore duis ipsum mollit nisi do exercitation. Magna in pariatur anim aute.
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.”
