New York doesn’t want its workers to invent the future
New York Governor Kathy Hochul delivered a lump of coal for Christmas, vetoing a bill that would have universally banned non-compete clauses in the Empire State. The bill had passed the legislature last summer, but after months of intense lobbying from Wall Street and other industry trade groups, Hochul outright rejected the legislation in a surprise end-of-year decision.
The single most important ingredient to a region’s economic success is the talent of the people who choose to reside there. As emphasized by researchers like Ed Glaeser in Survival of the City and Richard Florida in The New Urban Crisis, quality of life is the basis for attracting and retaining the world’s best talent and the economic spillovers of their presence. That’s why fears of an urban doom loop in prime cities like San Francisco and Washington DC get so much attention from commentators. The dense networks of people that weave into a region’s community can strengthen wealth for all — or fray, leaving blocks of blight behind.
People don’t serve cities; cities serve people. The human right to freedom of movement (more and more exclusive to the world’s top talent) allows people to migrate their economic and social lives to wherever they see the most promise. That peripatetic market invigorates cities, placing them in constant competition with each other to provide the best quality public services, the highest quality of life, and the most vibrant culture at the most affordable price, namely taxes, rents and general inflation.
New York City is a competitive city, but one that has stagnated, with a population that has barely budged in a decade. Despite the Big Apple’s allure for millions across America and around the world, a lack of housing growth, an anemic expansion of the transportation system and extraordinarily uneven public schools have combined to limit the city’s dynamism.
Those are long-term problems requiring long-term solutions, but sometimes the wounds are much more acute and self-inflicted, such as what happened two weeks ago with Hochul’s veto.
Non-compete clauses are employment provisions that ban a worker (which can include both full-time employees as well as freelancers) from working for a competitor or even in the same broad industry for a period of time. That prohibition can extend up to a year or longer, which means that any person with a specialized talent (i.e. most of us in the knowledge economy) will forego the income from their most valuable skills. While it’s customary in the finance industry to pay for this non-compete time in what is traditionally dubbed “garden leave,” many non-compete clauses do not offer any pay at all. You are just not allowed to work – good luck with that mortgage!
The use of non-compete provisions has increased. Once exclusive to the contracts of senior corporate executives, these provisions have spread all over the American economy. The Minneapolis Fed’s survey data from mid-2023 found that roughly one in ten workers are now covered by a non-compete, with the highest percentage found among mid-career professionals in their twenties and thirties and declining with each additional decade of experience (a pattern that’s opposite the logic of senior employees jumping ship for a competitor).
The prevalence of non-competes shouldn’t be surprising, as companies view their employees as “assets” to be traded and protected. As I noted in “Brainwash Departures” last year, governments are similarly viewing their own citizens as assets to be economically controlled, often by abridging freedoms for people deemed important to national competitiveness (I gave the example of South Korea preventing chip engineers from traveling without an exit visa — a requirement not placed on any other type of worker).
For companies (and countries), the strategic question is how to protect themselves from unfair competition (and, let’s be frank, fair competition as well). In the industrial era, the barrier to entry was the factory itself, which required a large capital investment and significant time for construction. But in the knowledge era, the “asset” can walk out the door in a matter of seconds.
In New York, the finance industry is the biggest proponent of non-competes, worrying that, for instance, a trader at a quantitative hedge fund could copy a lucrative proprietary trading strategy and steal the alpha. That mirrors a concern from the biotech industry during a similar legislative fight over non-competes in Massachusetts in 2018. Biotech firms feared that employees could walk out with valuable research results in their head the moment a new entity has been identified as a potential cure, rushing the treatment to market and capturing the upside. In the end, Massachusetts governor Charlie Baker ultimately reached an agreement that would limit the scope of non-competes while still allowing them.
There are admittedly moments in certain industries and in certain positions where an employee has an extremely lopsided incentive structure for cheating. Copying a trading strategy could net an individual tens of millions of dollars when their normal compensation is a mere fraction of that sum. Copying a clinical result could capture expansive future revenue, without the millions of dollars in venture capital investment that were required to reach that result in the first place. It is worth protecting America’s innovation system from such cheating.
Nonetheless, such moments of employee leverage are plentiful once we start to look for them. Every employee will learn some proprietary knowledge that will benefit them personally if they were to use it effectively at a competitor or simply for themselves. That’s the whole point of the knowledge economy and the value of dense professional networks offered by cities: the constant and rapid diffusion of the best knowledge to as wide a number of people as possible. We don’t want cheaters, but all of us learn skills and knowledge on the job.
As might be obvious from the term “non-compete,” these provisions wreck the most important quality of an entrepreneurial, dynamic economy, namely the fierce competition between people, firms and cities that goads the best talent to congregate around the most meritorious ideas. An employee typically leaves a company for a better job, one that offers some combination of improved pay, better advancement and more seniority. A non-compete clause is like an anchor tied to a slower ship, forcing the employee to stay marooned from the propulsive vitality available to them.
California is often regarded as the most dynamic economy in the world, and one of the bedrocks of that success is the state’s broad prohibition on non-competes that dates back decades. By forgoing non-competes, Silicon Valley firms face keener competition to perform — if a startup falls behind a competitor, market incentives induce employees to quickly join the winner or leave for better opportunities. No quality of an innovative ecosystem is more important than bringing talent together around the best ideas — and ensuring that bad ideas are throttled to nothing. Ultimately, such a system delivers innovations faster, benefiting us all.
New York had an opportunity this year to match the West Coast’s economic dynamism, and outright declined. New York’s bill would have gone much further than the Massachusetts reform from 2018 by practically eradicating all non-competes without any form of reasonability standard. A last-minute compromise effort between the bill’s sponsor and Governor Hochul would have banned non-competes only for workers paid less than $250,000, which ultimately wasn’t enough to get a final signature from the governor.
The outcome is a shame, since there has been a nationwide effort to eliminate non-competes. In 2021, President Joe Biden directed the Federal Trade Commission to "to curtail the unfair use of non-compete clauses and other clauses or agreements that may unfairly limit worker mobility” as part of a wider executive order focused on antitrust and promoting competition. Last year in 2023, my home state of Minnesota passed a comprehensive ban on non-compete clauses, one of the first new statewide bans in a generation. That’s not surprising given Minnesota’s ambitious desire to be the top startup hub in the American Midwest.
Everyone and every institution wants to avoid competition, particularly if there are legal remedies that can reduce competition cheaply and easily. Who doesn’t want to work and invest less? But eliminating competition also takes the pressure off the impetus for growth and innovation, and represents a paean to incumbents rather than a bold boost to the next generation of entrepreneurs. New York’s decision to continue non-competes is opposed to the vigorous spirit of innovation that made the state the preeminent economic hub of America for centuries. New York’s mistake will, unfortunately, be another state’s gain.
Lux Recommends
Tess Van Stekelenburg recommends Alex Telford’s deep dive into the history of drug development and why it’s gotten harder to bring drugs to market. “The most significant factor preventing us from taking better molecules into clinical trials is not our ability to design arbitrary molecules, but our (lack of) understanding of underlying disease biology. In other words, we often know how to make something, but not what to make. Hence, the most important advancements in drug candidate selection are likely to come from improvements in how we collect, analyse, and use biological data.”
The Wall Street Journal has had a great series on “The New Era of Great Power Conflict” and one of my favorite entries so far is “American Spies Confront a New, Formidable China” by Warren P. Strobel. “Recruiting, or even meeting, Chinese agents is more perilous than ever. Beijing’s pervasive surveillance system uses big data analytics to mine feeds from millions of cameras in major cities, combined with armies of human watchers. ‘We’ve got to find a way to be able to enhance our ability, with the deployment of new technology by China and other nations, to be able to operate within their countries to gain intelligence,’ said [Rep. Mike Turner], the House Intelligence Committee chairman.”
Derek Thompson always has great ideas on the progress of science at The Atlantic, and over the holidays, compiled his nine favorite breakthroughs of 2023. “The theme of my second-annual Breakthroughs of the Year is the long road of progress. … CRISPR is this year’s top breakthrough not only because of heroic work done in the past 12 months, but also because of a long thread of heroes whose work spans decades.”
Our scientist-in-residence Sam Arbesman recommends Dave Karpf’s analysis on "An AI Future Is Much Shakier Than You Think.” “The world we inhabit today barely resembles the one that seemed inevitable during the Napster years. Copyright law did not bend to accommodate new technologies. The industries built upon those new technologies bent to accommodate copyright law instead. The new status quo hasn’t been great for musicians or artists. Their interests weren’t represented at the bargaining table, and it shows. Musicians in the ‘00s got pennies on the dollar from iTunes sales—more than the zero they received when music was pirated, but hardly enough to live on. Today’s struggling artists make a fraction of a cent from Spotify streams.”
Finally, Viola Zhou and Nilesh Christopher have a fantastic piece in Rest of World, “Inside Foxconn’s struggle to make iPhones in India.” "They detailed the successes, struggles, and cultural clashes that, over the past year or so, have played out on one of the world’s most consequential factory floors. In China, Foxconn demands long days, high targets, and minimal delays and mistakes — all of which proved difficult, if not impossible, to replicate in India. The stress clearly took a toll on the company’s local workforce.”
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.”
