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🌀🗞 The FLUX Review, Ep. 104
June 15th, 2023
Episode 104 — June 15th, 2023 — Available at read.fluxcollective.org/p/104
Contributors to this issue: Neel Mehta, Boris Smus, Erika Rice Scherpelz, Dimitri Glazkov, a.r. Routh, Ade Oshineye
Additional insights from: Gordon Brander, Stefano Mazzocchi, Ben Mathes, Justin Quimby, Alex Komoroske, Robinson Eaton, Spencer Pitman, Julka Almquist, Scott Schaffter, Lisie Lillianfeld, Samuel Arbesman, Dart Lindsley, Jon Lebensold
We’re a ragtag band of systems thinkers who have been dedicating our early mornings to finding new lenses to help you make sense of the complex world we live in. This newsletter is a collection of patterns we’ve noticed in recent weeks.
“You can tell whether a man is clever by his answers. You can tell whether a man is wise by his questions.”
— Naguib Mahfouz
🧪🎲 An experiment by any other name
Minimum viable product? Minimum lovable product? Whatever philosophy you fall into when it comes to the right approach to finding product-market fit, it’s commonly accepted knowledge that those working to create a product must first create a minimal product. The Lean Startup and its successors taught us that experimentation is key to success.
This isn’t always true, though. Sometimes innovation requires finding the core of a powerful idea and slowly growing it over time. However, experiments are rightly valued. If we iterate and have the discipline to utilize the lessons we learn, then we can get better over time. Iteration and learning: these are the keys to successful experimentation. However, not all situations are amenable to true experimentation.
We can iterate quickly when the stakes are low. When the stakes are high, we cannot afford to fail. When the stakes are low — when the experiment itself doesn’t require too many resources and the consequences of an experiment gone wrong are safe — then we can hypothesize, test, fail fast, learn, and test again.
We can learn when there is ambiguity. If a situation is unambiguous, if there isn’t something we don’t know, then we are not going to learn. Ambiguity is defined by the proportion of stable constraints within the environment. Less ambiguous environments are those where most constraints are stable and known. In more ambiguous environments, constraints are ephemeral. Existing ones shift or disappear. New ones emerge. This is where opportunities arise.
We may put the label of “experiment” on anything that we want to be seen as innovative. Yet if we look at the preconditions for true experimentation — low stakes and high ambiguity, which together enable iterative learning — then we can start to see that some things we might call “experimentation” are actually something else. We can explore these via a 2x2.
Let’s start with the top-right quadrant, where stakes are low and ambiguity is high. As we’ve seen, this is the quadrant of true experimentation, where we have room to fail and legibly learn from our mistakes.
As the stakes rise, we move counterclockwise to the top-left quadrant. Here, what we call “experiments” are actually bets. The ambiguity is high, but so are the stakes. Each failure comes at high cost, and the rewards of success can be equally large. While this quadrant can feel exciting, very little learning will happen here — other than learning to avoid experimenting in high-stakes environments! That isn’t to say this quadrant is bad; this is where most startups sit. However, people in this quadrant will be well-served by remembering that they are gambling and can only afford a small number of bets before they must leave the casino.
Continuing counterclockwise to the bottom-left quadrant, the stakes are high and the ambiguity is low. This might be where we find ourselves if we have a successful existing business, a cash cow that supports everything else we do. In these situations, teams often find themselves running what they call “experiments”: which call to action is more effective? Do we put the button here or there? These are actually quality assurance tests. Because we mostly know the constraints, there is no real experimentation going on. This is just part of our pipeline for maximizing value.
Moving finally to the bottom-right quadrant, we find ourselves in a place where there’s low ambiguity and low stakes. This is the realm of play — because the constraints are known, there’s very little new insight to uncover. We’re just chilling and having a good time. That’s not to say that this is bad — lots of discoveries come from the play quadrant. However, these discoveries won’t be a direct outcome of structured experimentation. Rather, they will be the side effect of a human mind relaxing and loosening up due to the knowledge that they aren’t risking ruin.
The desire to seem innovative can encourage us to hold experimentation on a pedestal. However, if we are more precise in what we mean by “experimentation” and more conscious of what quadrant we’re in, then we can take a more intentional approach and avoid being disappointed when our “experiments” don’t have the outcome we desire. If we approach experimentation as an exploratory, low-stakes affair, we can get into the healthy mindset that our experiments will either succeed or contribute to our stockpile of lessons learned.
The final danger we must face is when we look back on our history of “experiments” and don’t have anything to show other than empty pockets. This can be a signal to ask ourselves if we’ve actually been living in one of the other quadrants.
Clues that point to where our changing world might lead us.
🚏🚕 Congestion pricing is (finally) coming to New York City
Cities like London and Singapore charge drivers for entering downtown during peak hours; this “congestion pricing” is designed to reduce traffic and pollution in the central city. New York has long been planning to implement a similar program, charging drivers between $9 and $23 to drive south of Manhattan’s 60th Street during peak hours. The plan had been delayed for years, but it just passed environmental review, and it should go into effect early next year — making it the first congestion pricing program in the United States.
🚏🧯 California’s wildfires are burning 5x more land than they did 50 years ago
According to a new paper, California’s summer wildfires burned five times as much land in 2021 as they did in 1971. Climate change is a major factor: according to one analysis, “burned area grew 172 per cent more than it would have without climate change.” What’s more, scientists estimate that the amount of area burned by wildfires may increase an additional 50% by 2050.
🚏🇳🇴 30 ultra-rich Norwegians left the country after it added a 1.1% wealth tax
Last year, the Norwegian government introduced a wealth tax made up of a 0.7% tax on assets in excess of 1.7 million kroner (about $150,000) for individuals, plus a 0.4% tax on assets above 20 million kroner ($1.8 million); the wealth tax thus tops out at 1.1%. But the new tax coincided with a flood of Norwegian multimillionaires and billionaires moving out of the country, largely decamping for low-tax countries like Switzerland. More super-rich Norwegians left in 2022 than had left in the last 13 years combined — more than 30 people overall.
🚏🔋 North America is the fastest-growing market for EV battery manufacturing
Economic incentives are leading many electric vehicle battery manufacturers to set up shop in the US and Canada, including a $3.5 billion Ford battery factory in Michigan, a $700 million BMW battery factory in South Carolina, and a $75 million EV battery plant in New York State. In fact, according to a new report, North America is now the fastest-growing market for battery factories, outpacing both China (the current leader) and Europe.
🚏🪶 Robinhood is delisting 3 major cryptocurrencies after the SEC called them securities
The stock-trading app Robinhood also lets customers buy and sell cryptocurrencies, but the company will no longer be offering Solana (the 10th-largest cryptocurrency by market cap), Cardano (#7), and Polygon (#11). This is because the US’s Securities and Exchange Commission described the coins as “unregistered securities” in its recent lawsuits against Binance and Coinbase. This makes Robinhood the first exchange serving US customers to take down coins that the SEC has accused of being securities.
📖⏳ Worth your time
Some especially insightful pieces we’ve read, watched, and listened to recently.
Not Even Wrong: Predicting Tech (Benedict Evans) — Argues that the quip that many successful technologies “started out looking like toys” doesn’t have much predictive power: some things that look like toys are indeed useless toys. The reason some “toys” eventually succeed is that they have a path to becoming useful, although that trajectory often stalls out until a new technology unlocks the rest of the roadmap.
Can You Trust ChatGPT’s Package Recommendations? (Vulcan) — Introduces a new attack vector on autopilot-assisted coding: when an LLM hallucinates and recommends a package that doesn’t exist, an attacker can publish a malicious package under that name and trick developers into installing it.
Associative Thinking and Creativity (Roger Beaty) — Breaks down a new paper that looks at how people navigate through a semantic space, such as by jumping from one term to a related one (“toaster” => “bread” => “butter”). This is associative thinking, and the paper finds that “highly creative people 1) travel further in semantic space, 2) switch between more semantic subcategories, and 3) make larger leaps between associations.”
Kevin Kelly Interview (Noahpinion) — Suggests a novel distinction between Type 1 growth, which is in size, expanse, and “moreness,” and Type 2 growth, which is in betterment, improvement, and mindset shifts. Perhaps we ought to sharpen our terminology: de-growthers want less Type 1 growth while presumably continuing Type 2 growth.
Everything You Always Wanted To Know About Mathematics (Brendan W. Sullivan) — A surprisingly approachable 700-page book that teaches you how to think mathematically, building up the foundations of proofs, induction, and mathematical logic. Also introduces key math tools like combinatorics, set theory, relations, and functions.
🔍🐢 Lens of the week
Introducing new ways to see the world and new tools to add to your mental arsenal.
This week’s lens: Zeno’s project.
Some projects seem to never get done. There are many reasons this can happen, but one of the most paradoxical is the project that never gets done despite the fact that it’s going well.
Some projects we work on are important but not urgent. In software development, this might be the infrastructure migration that has to get done eventually. Or perhaps it’s a set of fit and finish features that will make our product better but are unlikely to drive metrics on their own. In our personal lives, this might be clearing out the invasive plants before they take over your yard.
These types of projects can often linger on forever. Because they are not urgent, we are inclined to pull resources away from them. We ask the infra expert to work on an urgent fire. We ask the designer to spend time on the exciting new feature. We tidy up for the guests who are about to arrive.
These are not necessarily the wrong decisions. However, when we repeatedly pull resources from projects that are slowly marching along toward completion, we can end up with Zeno’s project, the project that is always and legitimately almost done… but never seems to make it to completion.
Whether prioritizing projects for a team or in our personal life, part of what we need to do to avoid Zeno’s project is make sure that we’re prioritizing based on importance, not just urgency. However, there’s another trick we can apply. We can take a page from Kanban’s work-in-progress limits and realize that unfinished work has a cost. If something is still worth doing and close enough to being done, consider just finishing it — even if something legitimately more important comes up.
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