Our producer Chris Gates and I have been working on a couple of special projects the past few weeks. We’ll get to reveal those in time, but unfortunately, this week’s column didn’t quite come together before publication.
That said, I want to briefly talk about Lux’s latest investment, which we announced this week. Founder and CEO Jake Seaton started Column to fix one of the last remaining durable revenue sources for local media: legally required public notices. All kinds of legal filings demand public notice, such as the formation of an LLC, and these notices are generally required to be published in prominent local newspapers. Getting the text precisely correct and actually in the paper edition can be a grueling task, one that is often entirely manual.
Enter Column. The company transforms what is often a challenging experience for both public notice issuers and publishers into a much more delightful and easy process. The goal is two-fold: to make required public notices much less of a complex burden for users, while also saving local media companies significant operational costs — money that could be used to buttress their journalism.
That’s only step one though. Long term, the company is building an API that would allow for quick searches and analysis of public notices, in the process building the first comprehensive corpus of these notices in the country. Column is improving local media economics while also creating more democratic transparency in these precarious times — at least to me, a quite exciting fusion.
Lux led a $30 million Series A round for the company, and we were joined by a panoply of prominent news executives and other investors deeply familiar with this critical line of revenue. Our partner Shahin Farshchi sits on the board. Read more on the funding from Axios’s Sara Fischerin her exclusive, and more on public notice economics from Bloomberg’s Sarah McBride, who covered Column’s approach earlier this year.
Lux Recommends
I’m always a fan of Five Books, and their latest interview on The Psychology of Killing I found absolutely fascinating. Cal Fly interviews forensic psychiatrist Gwen Adshead about perpetrators, victims, and misconceptions of killing:
The first thing to say about homicide is that it is not all the same. I think that’s one of the things I didn’t understand when I started out. Like anybody else, I thought that all killers must be really odd or mad. That if you killed once, you must be permanently in a homicidal state of mind. But once I began to spend time with people who had killed, I learned that killing is often highly contextual and arises from a specific set factors that are present at that time; which may never occur again.
One of the most important industrial policy initiatives of the Chinese government is indigenizing chip fabrication and design. After more than a decade of ample funding, however, progress has been slower than many CCP leaders would like to see. Now, as Bloomberg News reports, the consequences are coming down, with multiple senior leaders looking at lengthy jail sentences over corruption.
While we are on the subject of chips, there was a somewhat quiet announcement this week around a key Google initiative. The company, which has been pushing for more open-source approaches to chip design and fabrication, announced that it is joining with GlobalFoundries to offer an open-source Process Design Kit and will have large runs of chips available in about five years. That means more people can experiment with new chip designs at a fraction of traditional costs — empowering tinkerers, hackers, and chip design creatives to try their hand at new concepts. Liam Tung at ZDNet has a good writeup.
Cathedrals took centuries to build. Heliocentricity, evolution, and relativity took decades to earn widespread acceptance. Passing the Civil Rights Act took years and years and years of activism and political organizing in the face of state-sanctioned violence and seemingly insurmountable adversity. Important things take time.
Finally, one of those niche areas I have always found interesting is back in the spotlight. Reinsurance is a critical aspect of the global financial system, but reinsurers are facing incredible financial strain due to the global macro chaos that has enveloped the world in 2022. As Ian Smith in the Financial Times reports, reinsurers are increasing prices at their fastest rates in years — and sometimes foregoing coverage entirely. “Reinsurers reducing capacity can create what is called a ‘hard market’, where demand substantially outstrips supply and prices surge.” A portent of things to come.
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 .
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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.”
