Photo by Ankita Uppal via Flickr / Creative Commons
2022Q1 Lux LP Letter Excerpts: Entropic Apex
First up before the main story, Josh Wolfe has published some excerpts from our latest LP quarterly letter on Twitter (start of thread attached below). It was lengthy given the chaos of the markets and the sheer amount of macro changes transpiring right now, but it’s a cohesive story centered around a theme he dubbed “entropic apex” — “a moment of maximum instability and imbalance where things are easily torn apart or fall to pieces.”
In past letters, we have emphasized to founders the importance of raising capital and then immediately husbanding it, in preparation for an inevitable down cycle. Well, the down cycle has arrived, and so our advice for startup founders and fellow GPs has adapted as well. Our line today is to be thoughtfully defensive but aggressively opportunistic — we expect to see a wealth of opportunities arrive for founders to expand products and to grow revenue through M&A rollups, to buy intellectual property at uniquely low prices, and to grow companies creatively that take advantage of the eddies in that great ocean of capital.
Due to confidentiality, we can’t release the whole letter in its entirety (or at least, not yet as it goes through review), but the public excerpts are a good overview of how we see the market and what we believe will happen next.
Multiple Valuations Syndrome
Anyone who has followed the markets the past few months has had to come to terms with a simple notion: the markets have no idea what anything is worth right now.
Just this past week, Walmart and Target had their worst one-day stock performances in more than three decades as weaker than expected consumer purchasing led to ominous warnings from both companies on future revenue growth and profitability. Given their focus on consumer staples, those warnings scared investors, with markets experiencing one of their worst single-day performances since the early stages of the Covid-19 pandemic.
What’s strange though is that all of this should have been foreseen. Credit card data is widely monitored by hedge funds and other investors for signals about consumer spending. The results from major retailers should come as a surprise to no one, and yet, we see a sudden drop in share prices on news that should be well-known.
Meanwhile, we have seen wild swings of valuations in tech stocks as well. Coinbase seems to double only to halve a few days later, a level of volatility that belies the fact that its revenues are public to any sophisticated investor who bothers to aggregate transaction data from Bitcoin, Ethereum and a few other critical chains. We see this volatility across tech stocks from new IPOs to Big Tech itself.
As much as the macro environment is complicated, the news each day is typically not all that surprising. Why such chaos? Why such massive swings?
One answer is that valuations have become mercurial. Or put another way, what something is valued is dependent on who is asking and even when they are asking, in what might be dubbed Multiple Valuations Syndrome.
Take two standard assets: a house and the equity of a startup. These assets theoretically have a single “correct” valuation, a price that the asset is worth given the demand from the market. These valuations can be calculated by comparisons to similar assets (comps) and also by just testing the market and seeing what price it will bear. You can put the house on the market, see who throws in an offer, and choose not to sell. Similarly, a founder can fundraise, see what term sheets arrive, and get an accurate sense of the market price for their company.
Yet, even at this elementary stage, these assets already have plural valuations. The value of a house from a market perspective will be higher than the value reported to the property tax authorities. A startup’s value from an investment perspective will be higher than the value reported through a 409A. Valuations change depending on who is asking and the incentives for what number to communicate.
What’s happening now is that those valuations are shattering into more and more numbers in order to accommodate ever more diverse parties.
Take a hypothetical startup that recently raised a high-priced growth round that is now staring into the abyss that is public comps. Some VCs mark to market, and have already reduced the value of the company. Other VCs, perhaps dependent on raising a new fund soon, hesitate and keep the valuation at the last round price, or even tweak it higher. They may even do an inside extension round in order to prove that the company is still valued at that last round price. Meanwhile, debt providers do their own revenue and operations underwriting, and determine a different valuation from all of the above. And then there is the value negotiated in the 409A process, and also the valuation that is offered to employees to keep morale high.
What was once a relatively coherent valuation — one communicated in press releases and TechCrunch articles — is now opaque. Founders now demure from talking valuations, not just because of the shame of mentioning a decline, but also because they just don’t know how to answer the question anymore.
As time goes on unfortunately, that dissonance actually gets worse instead of better. Valuation “stickiness” — the phenomenon of an asset price that doesn’t adjust to new market conditions — becomes a huge driver of new complexities.
We often see this phenomenon in housing, where nearby sales comps drive valuations. If no one sells though, valuations can’t settle to a new market normal — the exact same pattern in startup equity these days. Insiders are using extension rounds to delay the valuation reckoning and avoid triggering ratchets and down round protections. Valuations are driven by data, and without that data, they can’t be recalculated.
The truth of course is that those valuations are already being recalculated and adjusted by different parties. And so we get the Multiple Valuations Syndrome of startup equity (and pretty much all assets right now).
As these valuations diverge, startup equity becomes more and more of a Veblen good — a good valued for what it is priced, or in other words, a luxury good. A startup is worth $5 billion because it is worth $5 billion, just as a $550 Louis Vuitton t-shirt is valued far above its utility or materials as a garment. Maintaining the image of premium becomes more important than the quality of the business itself.
This dynamic has huge negative social implications. When markets can’t cohere on a valuation, capital gets misallocated — or not allocated at all as investors hold back waiting for a clearer signal. I’m already seeing this phenomenon occasionally in our own partner meetings, where some founders stop by to present and somehow still put on a brave face upon asking for a valuation that is wildly out of sync with the market today. They have to keep that premium brand value.
We need a reset to move forward — a reset of valuations, a reset of expectations and a general recalculation of what all assets are worth given the present state of the economy. Past valuations are now irrelevant, and exist in a bubbly fugue that ended about six months ago. We need to forget that period and focus on the present, because that is the only valuation that matters. We need to unstuck these numbers.
A reset will cause acute pain immediately, but the benefits long term are legion. Yes, the collective write-offs will be massive, but then the market will be functional once again, with valuations that align with present thinking.
It’s a collective action problem though, since who will step forward first to reset their valuation? Today, it’s basically whoever absolutely must. The trickle of recapitalizations and down rounds though won’t stop, even for companies that survived the first few months of the hurdle. The reality of the markets will eventually catch up with everyone.
Multiple Valuations Syndrome is not healthy. It prevents markets from working correctly and efficiently, and ultimately, the lack of coherence is just plain wrong — all these different valuations for different people don’t actually add up. The faster we readjust and cohere prices, the faster that the tech markets can start growing again.
New “Securities” Podcasts: Tony Fadell on Product Design and Josh Wolfe on Everything Else
The "Securities" podcast. Artwork by Chris Gates
Two great new episodes this week on the “Securities” podcast.
First, Peter Hébert and I chatted with legendary product leader Tony Fadell about his new book Build: An Unorthodox Guide to Making Things Worth Making, covering such topics as how to lead inside companies, when to walk out the door, the importance of product marketers, and why he thinks the metaverse is not worth entering. Listen here:
Second, Josh Wolfe and I talked about the wild week of news from Elon Musk and Twitter to South Korea’s growing penchant for nuclear weapons. We also discussed a bit about how the Lux quarterly LP letter came together. Listen here:
It’s not every day that you hear about how to “hack coral sex.” But Benji Jones in Vox writes up a fascinating tale of how scientists accidentally discovered in the lab a way to accelerate the growth of coral reefs, just as many great reefs around the world die from ocean acidification and increasing climate temperatures.
We have a lot of comedy lovers at Lux, and few comedians have been as legendary or as long-lasting as George Carlin. Timed with the new HBO documentary called George Carlin’s American Dream, Dave Itzkoff writes in The New York Times about how the comedian has spoken to different, irreconcilable audiences and how he regularly reinvented himself to stay relevant over a multi-decade career. The documentary will be released this weekend.
Sam Arbesman recommends Erik Hoel’s argument that “AI-art isn’t art.” "Even if no one could tell them apart, one lacks all intentionality. It is a forgery, not of a specific work of art, but of the meaning behind art.”
Peter Guest writes in Rest of World about the long rise of internet shutdowns by authoritarian regimes, which now number nearly 1,000 instances across 60 countries. A harrowing and sobering look at the challenge of keeping the internet free and open for all people worldwide.
GRIDS Capital founder and long-time “Securities” reader Guy Perelmuter recommended the new Netflix film “Operation Mincemeat,” a World War II deception and detection operation in which British intelligence leaders hid the coming invasion of Sicily in 1943.
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.”
