Photo by Phil Whitehouse via Flickr / Creative Commons
Everyone wants to be on the cutting edge — who wouldn’t want to take a glimpse at the future when the future is already here?
The internet, app stores, social networks and more have accelerated the classic “S-curve” adoption of new technologies, where early adopters arrive in advance of the broad mass of consumers before finally yielding to late adopters who aren’t paying all that much attention. Acceleration is, at least superficially, a fundamental good: if a technology is worthwhile, getting it in the hands of more people is beneficial for general welfare.
Perhaps even more importantly for startups developing their products, that curve allows for iterated refinement. Early adopters who are more willing to experiment with an unfinished product can provide crucial early feedback, helping a product evolve as it begins to connect with consumers who have higher standards and less patience.
As the adoption curve has severely compressed though, that gestation period offered to startups and whole industries has largely disappeared. In fact, the public (and even some VCs!) can see the future sometimes a little too early.
Take VR, for instance. Early VR devices like the Oculus Rift were exciting — they brought the future dream of immersive reality into our homes for the first time. But these early entrants clearly needed more product development, and the consumer experience was anything but magical.
Over the holidays, I talked with a number of friends who wrote off the entire category as a fad simply because they had tried demos of devices years ago before they were ready. Even among VCs, the early excitement and disappointment around VR led to a boom and then a slow down in investment that has only recently come back to full force thanks to excitement over the metaverse.
Today’s VR devices are revolutionary compared to what existed just a few years ago, in line with the arc of improvement seen in smartphones and video game consoles. Yet, for users who tried VR five or more years ago, their impressions of the tech have in many cases already been engrained, and will take serious effort to loosen.
A more complicated example is crypto, both the intellectual activity of creating trustless, decentralized computing as well as the thousands of individual crypto projects out there. There has never been a technology in history as under-developed and yet as massively distributed as crypto. Tens of millions of people are estimated to have a crypto wallet (which is just one type of user of course), even while there are raging debates of what crypto technology is even for or whether it will even last or what should even be built.
That massive early adoption has been a boon for investment in the space, and capital is a critical ingredient to the industry’s emerging success. Yet, those tens of millions of users now have a huge stake in the existing paradigms and platforms of crypto, and change will not be easy as has been seen with the long-delayed upgrades to Ethereum and many other attempts to reform L1 technologies.
Crypto upends a lot of notions about computing — not just what products it can offer, but how developers should write code for smart contracts, how teams should manage and govern products, and even the very cognitive model of “storage” and “compute.” There is incredible work that is going to be done this decade in upgrading all of these facets of crypto, but it’s going to be done under the scorching scrutiny of tens of millions of people who aren’t just users, but investors. That’s an unnerving and untenable setup for quality innovation.
The upshot, particularly for crypto, is that with so much adoption and focus on a handful of prime projects, there is much more space for experimentation and development for other startups. They might not have the limelight, but they have the flexibility to learn and adapt in a way that larger projects will increasingly find challenging.
While different stories, both VR and crypto indicate the challenge of balancing rapid acceleration of adoption with sparing time to develop the underlying technology. Mashing early adoption and mass adoption could actually delay maximum adoption by preventing technologies from reaching the right level of maturity before first impressions are inscribed in too many users.
The key is to never hype deep tech products before they’re ready, even sometimes to tech-savvy early adopters. Stretch those early innings so that the right feedback can be executed on in development.
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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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.”
