The ESG Mirage

Capitalism has taken its fair share of hits over the past few decades, castigated for all that’s wrong in the world from wealth inequality to climate change. Those criticisms have provoked a range of responses, from revolutionary demands to the more mundane incrementalist approaches of “stakeholder capitalism” and what has transfused into the strange alchemy of the ESG movement.

ESG — or ”Environmental, Social, and Governance” — is … well, it’s complicated. It’s a combination movement, investment thesis, framework, and slogan that’s meant to direct risk capital to opportunities that are … better. Better for the environment, or social integration, or globalization, or some other opportunity with positive externalities or at least not negative externalities. Sovereign wealth funds and pensions love it, and the global growth of the industry has been staggering. Bloomberg Intelligence earlier this year predicted that “Global ESG assets may surpass $41 trillion by 2022 and $50 trillion by 2025, one-third of the projected total assets under management globally.”

That’s a lot of moolah.

Given the trillions flowing into the thesis, one would expect precision on what investments are encompassed with the ESG label. Yet, what allows an investment to be labeled ESG has been nearly impossible to pin down. The realistic complexity of the world ensures that there is always some observable downside to a company. Building a new neighborhood filled with sustainable green architecture? Well that’s sprawl, or potentially gentrification if it’s infill. What about a data center service using sustainable energy? Well, what if that data center handles Chinese surveillance infrastructure? Can an oil company be ESG if it slows down investment in exploration or drilling compared to its original plans? Do weapons manufacturers who sell their arms to Ukraine to fight off Vladimir Putin’s murderous war count?

Thousands of professionals have made their bureaucratic livelihoods trying to answer such questions — answers that were always vague, mercurial and riven with caveats. Now, a dizzying array of scandals is making it clear what was always obvious from its first formulation: ESG is filled with decent, good intentions, but with a depth of corrupt incentives that will never allow the concept to be properly realized.

First was Deutsche Bank-owned DWS, which was raided by German police over allegations that it lied about the scale of its ESG portfolio. As quoted in the Financial Times, “‘After examination, sufficient factual evidence has emerged that, contrary to the statements made in the sales prospectuses of DWS funds, ESG factors … were not taken into account at all in a large number of investments,’ added the prosecutors’ office, calling the potential wrongdoing ‘prospectus fraud’.”

DWS had said that half of its $900 billion in assets were ESG, a number that now looks strikingly exaggerated.

That raid was on the heels of the SEC’s settlement with BNY Mellon Investment Adviser that it made misstatements and omissions about the extent of its ESG reviews of its investment assets. From the agency’s statement, “The SEC’s order finds that, from July 2018 to September 2021, BNY Mellon Investment Adviser represented or implied in various statements that all investments in the funds had undergone an ESG quality review, even though that was not always the case. The order finds that numerous investments held by certain funds did not have an ESG quality review score as of the time of investment.”

What’s interesting about this collection of cases is that they aren’t even predicated on an in-depth misunderstanding of the definition of ESG. These managers and advisors apparently willy-nilly slapped the label on investment products to make them more enticing.

Unsurprisingly, the SEC is currently promulgating new disclosure standards for ESG, including stronger “naming rules” of funds (if you name a fund “green”, it better be the tree variety) and clearer guidelines of what exactly ESG means. Those proposals have set off a tempest of commentary on their worthwhileness, with ESG advocates mostly lauding the agency’s attempt to clarify the boundaries of the asset class and protect consumers while the investment management industry generally argues that the attempt is burdensome and expensive.

Yet, attempts to rein in the excesses of exuberance of ESG’s loud backers are misguided, since the very premise of ESG is impossible to operationalize.

This connects into a field that’s gotten ever more interesting to me in academia, the sociology of quantification (a great recent overview and reading list is available from the nonprofit research organization Data & Society). It’s a field that investigates how metrics, definitions and categories are designed: What gets included and what’s left out? It also looks at how humans and organizations responds to metrics, as well as how those incentives can intensify measurement error as well.

While plagued by scandals stemming from the 2008 financial crisis, corporate credit ratings today are a reasonable quantification of the risk for default. With sufficient (accurate) data and the right model, Moody’s, S&P Global Ratings and other nationally recognized statistical ratings organizations (NRSROs) can offer a rating that is directionally accurate if not always perfect. The metric of credit ratings is purposely designed as a spectrum of values from most secure to junk, which helps their accuracy. Most importantly, the event they are predicting — bankruptcy and specifically default — is clearly defined in American law.

The ESG industry has attempted to provide the same service, offering a dizzying array of metrics, factors, ratings, rankings, assessments and more to create fungibility between assets. An oil company investing in the reduction of methane emissions from its pipelines and a forest management company that’s not all that sustainable are hard to compare along ESG lines, and so quantification is necessary to transform complicated case studies that would require dozens of analyst hours into clean numbers that can be dropped in a spreadsheet and crunched (think five-star ratings).

This quantification is built on good intentions, but there are serious mechanism design problems. With a plethora of ratings agencies using different definitions, companies can precisely select the ESG methodology that offers them the best result. In some cases (and with echoes of 2008’s scandals), the evaluation is purchased by the company under scrutiny, creating disincentives for accuracy. Meanwhile, asset managers want to do good, but they also need to deploy trillions of dollars of capital sloshing in. They could extensively investigate a company, or they can instead rely on a simple rating and add it to the fund, solving their problem with less work to boot. It’s little wonder then that asset managers are now facing harsh scrutiny for lax evaluation practices.

We’ve seen much of this story before in international development, where experts and organizations like the World Bank have long published developmental indicators to compare the path of countries to prosperity. But a major recent scandal at the Bank over the fabrication of China’s data led it to stop compiling its Doing Business report entirely and eschew rankings and metrics. That outcome would not come as a surprise to anyone who had read Morten Jerven’s 2013 book “Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It,” which argued that a combination of loose definitions and poor data quality ensured that development indicators were all but useless.

It’s time the ESG world learn the same lesson. The activities of an extremely complicated phenomenon like a multi-national corporation cannot be reduced to five-star ratings, particularly on a definition as squishy as “Environmental, Social, and Governance.” Rather than trying to strengthen definitions or improve quantification, the industry needs to come to terms with a more basic reality: the mission is impossible, and its failure began before it even started. With this much moolah at stake, the world deserves better.

Full text of the 2022Q1 Lux LP Letter

Josh Wolfe has been posting excerpts of our most recent LP letter, but now we have the complete copy available as a PDF file on Google Drive. If you haven’t taken a read, the letter covers our theme of “entropic apex,” what we’re seeing and predicting in the macro markets throughout 2022, as well as the advice and positioning we are offering our founders and CEOs as the markets remain turbulent.

Podcasts: American structural stupidity and speculative fiction

The "Securities" podcast. Artwork by Chris Gates

We have a great new two-part “Securities” podcast that launched this morning with notable NYU social psychologist Jonathan Haidt. Haidt, who co-wrote the book “The Coddling of the American Mind: How Good Intentions and Bad Ideas Are Setting Up a Generation for Failure” and also co-founded the Heterodox Academy, recently wrote an extended essay in The Atlantic on “Why the Past 10 Years of American Life Have Been Uniquely Stupid” (which Josh summarized in a nice tweet thread).

In part one, we walk through Haidt’s Tower of Babel metaphor and why Americans can’t talk to one another; the travails of free speech; and why social media is creating “structural stupidity” in America. Then in part two, we explore what the range of potential solutions are, including how social media products can reduce trollish amplification as well as how founders and CEOs can change their corporate cultures to enhance the growth of their Gen Z employees. Take a listen:

And if reality is too much to bear, last week before Memorial Day, we hosted noted speculative fiction novelist Eliot Peper on the podcast. Peper has now written 10 novels, and his most recent Reap3r was just published. In this episode, we discuss the art and craft of speculative fiction, how predicting and designing the future requires learning about the present, how Peper markets and launches his independently published books, and the power of wanderlust on opening our minds to new ideas. Take a listen:

🔊 Speculative fiction is a prism to understand people

Lux Recommends

Bilal Zuberi has been reading former PrimerAI EVP Ben Van Roo’s work on the Defense Department’s SBIR grants. In part one, Van Roo declares the program “not a useful mechanism for innovation” and in part two, he asks, “Are a few dozen SBIR mills sucking the air out of small business innovation?” Provocative, but compelling, and a challenge for Defense’s push for better and more innovative battlefield tech.
Alex Nguyen recommends new research out of Yale that shows that collective gatherings (think Burning Man) have lasting psychological effects on participants. You can also read the original paper in Nature Communications.
Our scientist-in-residence Sam Arbesman has been enjoying Randall E. Stross’s classic book from 1993, “Steve Jobs & The NeXT Big Thing.” It chronicles both the founding of Apple as well as Steve Jobs in the years following his firing from Apple, and given the timing of its publication, is a fascinating look at a person who had not yet become the legend we now know him as.
Shaq Vayda recommends this tweet comparing the relative sizes of genomes.
The use of LIDAR helped archaeologists discover historical settlements in Bolivia, which could radically update our understanding of civilization in the Americas. From Aylin Woodward in the Wall Street Journal, “For the research, carried out in 2019, scientists from Germany and the U.K. used a helicopter equipped with light detection and ranging (lidar) equipment to scan six regions, totaling 77 square miles, in Bolivia’s department of Beni, an area northeast of the national capital of La Paz. In all, the effort revealed 26 settlements in unprecedented detail, including 11 that were previously unknown.”
A group of scientists publishing in Nature have demonstrated that they can pass quantum information between two non-adjacent nodes, using a third node in the middle to entanglement swap. The experiment is a step forward in our ability to control and use quantum networks.
Shaq recommends this new article in Communications Engineering that explores how robotic stimulation of lab-grown tissues could be the best approach to delivering this resource to doctors. “Humanoid robots offer the prospect of providing physiologically-relevant mechanical stimulation to grafts and implants which may expedite their clinical deployment.”
Finally, Sam recommends this experiment and story on Astral Codex Ten about using DALL-E to create stained-glass windows.

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?

Turn to China now, and the story is in a very different place. The Biden administration has placed a series of export controls on advanced computation and semiconductor fabrication to China, including blocking the export of Nvidia’s H100 and A100 chips. Nvidia responded in March by offering tweaked versions that comply with U.S. export controls (with what it dubs the H800 chip), but the general policy remains in force: prevent China from acquiring the best chips that Earth produces.

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

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.

A few weeks ago, Bloomberg had a great piece about the rising toll of air conditioning under the similarly scary headline, “A Billion New Air Conditioners Will Save Lives But Cook the Planet.” The numbers, as always, are staggering:

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.

And there doesn’t appear to be a way out. Scientists have determined that there is a zone of comfort for humans where we can function at peak performance. Much of the industrialized world happens to sit in these temperate zones, which is one argument for their long-term economic success compared to the tropics. But as climate change intensifies, the world’s temperate zones are expected to narrow dramatically, and some countries like India may have next to no temperate zones left to protect its people. Air conditioning is a necessity (with some even calling it a human right), but greener coolant alternatives are still in their infancy. In other words, cooling innovation needs to heat up in a big way.

Lux on the Farm

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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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.”
In the happier news department of science, Ina Deljkic recommends “’Quite science fiction’: Brain-spine interface system helps man with paralysis walk again.”
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.”
Finally, Sam recommends Brick Technology’s video of “Making A Billion-Year Lego Clock.”

That’s it, folks. Have questions, comments, or ideas? This newsletter is sent from my email, so you can just click reply.

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May 28, 2022

In Memoriam

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June 11, 2022

Marginal Stupidity

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