Excerpts From Byrne Hobart on Hedge Funds, VC, and Finding Alpha

Eric Torenberg interviews Byrne Hobart and Daloopa CEO Thomas Li on Hedge Funds, VC, and Finding Alpha

I pulled some excerpts below I’d like to hold for future reference. I used ChatGPT to clean up the transcribed excerpts — the result is a mix of quotes and paraphrases.

On Alfred Winslow Jones first hedge fund being similar to the modern pod shop:

But other things were just generally a sensible part of the model that you charge based on performance. You try to hedge your exposure. So you’re not just betting the market goes up. But you’re trying to differentiate among different companies and try to figure out what makes each company unique. Not just understanding the business, but also understanding what makes the stock move. A bunch of other hedge funds started appearing in the 60s. Then, in the early 70s, almost all of them went under. Many realized they could borrow as stocks were going up. They borrowed a lot to buy a lot, and it worked really well for a while. Then most of them got wiped out. However, there were a few survivors. You had this golden age in the 80s where you had people like Soros doing more macro stuff, and people like Tiger doing more company-specific fundamental stuff. As computers got faster and we started having more data, people came up with systematic strategies across different asset classes. A lot happened between the 80s and today, but the current evolution has been towards some funds that run classic strategies like value-based stock picking and being mostly long, with a couple of short positions. The strategy that’s gaining a lot of market share in terms of assets and public attention is the multi-strategy, multi-manager, platform, or pod shop model.

In this model, you give a portfolio manager some capital budget/risk budget. You tell them they are picking stocks in their sector, the kinds of companies they pick, and they must have no net exposure to the market, large stocks versus small stocks, or one industry versus another, or momentum stocks versus value stocks. Once you hedge out all those factors that cause different companies to correlate, you end up with a very pure view of which stock is going up relative to its peers. This model has worked really well as a way to create uncorrelated streams of alpha. So if you have 100 different people doing that in 100 different subsets of the market, and they all stay on top of these companies better than anyone ever before, they will generally figure out when orders are slowing down or picking up, when an airline will accelerate its growth, or when a price war between steel companies will abate. If you are continuously tracking and turning over a portfolio, you end up always identifying the idiosyncratic news that’s going to drive a given stock’s movements, beyond just the random noise that drives prices. That’s a general overview of what those funds do and how they think.

Examples of HF strategies

We’ve mentioned the multi-manager, multi-strategy funds, and they encompass a large number of different strategies within them. We’ve talked about the fundamentals and different strategies, but many of those funds will have systematic strategies. These range from broad-scale strategies, like looking at all the different asset prices and what correlates with what. For example, if there’s a view that deviations from these correlations will snap back. So, if oil stocks generally move together with the price of oil and then one stock is lagging, that’s the one you buy and you short a basket of other stocks against it. You can also have much more sophisticated systematic strategies.

One category that goes through booms and busts is index inclusion strategies. This involves predicting who will get added to or removed from the S&P 500 or other indices. The first order problem is predicting who gets added or removed based on explicit index inclusion criteria and your view of the index committee’s decision-making. You’re also trying to bet on the volume of trades that is already making this bet. For instance, if the index inclusion means that the index funds have to buy 10 million shares of Company X, but traders betting on that inclusion have already bought 15 million anticipating selling them to the index, then it’s actually a bad catalyst. When the inclusion happens, they are trying to sell more stock than the index funds want to buy.

Another style that goes in and out of fashion is global macro, which can be split into two things with opposite cycles. One is doing these global relative value trades, where you look at the world and basically look at which countries seem to be converging in terms of standard of living and government norms with the United States. You buy their currencies or revive their assets, expecting that convergence to continue. The other is, you look at the state of the world, decide something is totally unsustainable, figure out what’s going to break, and find the most cost-effective way to bet that it breaks. This kind of strategy can work extremely well during a crisis or sometimes when there is a crisis in one place or some outlier event.

Every time there’s an election surprise, you wait a couple of weeks, and you’ll find out that some macro hedge fund is up significantly, like 300%, because they had a massively levered bet on something like Argentinian stocks. They were the only ones who truly believed it would happen and that the rally would be as magnificent as it was. Regarding Brexit, there was a lot of activity where hedge funds were commissioning private polling and trying to track the developments over time. They tried to predict what would happen and, if so, what the magnitude of the price impact would be.

Risk-parity and 60/40 being implicit macro bets on low inflation

If you look at a long chart of the equity and fixed income correlation, you see that the sign flips depending on the level and uncertainty of inflation. When I started working at a hedge fund in 2012, it was a given that when stocks went down, treasury bonds went up, and vice versa. This pattern essentially started in 1998, triggered by a market dip due to long-term capital management, the East Asian financial crisis, and the Russian crisis. The Fed significantly increased liquidity, boosting bond prices, and eventually, stocks snapped back while bonds came down, channeling liquidity into the stock market. This led to the highs of 1999 and part of 2000, which was enjoyable for everyone except the short sellers.

This situation was possible because inflation had been steadily declining since the early 80s. Around 1998, it could be argued that China’s labor supply was almost infinite compared to the world’s demand for physical goods. As long as people could move from the countryside to cities to produce goods, the cost of tradable physical items like TVs, toys, furniture, and apparel would either remain flat or decline. This price drop was largely due to production moving from more expensive countries to cheaper ones, with China offering a huge labor force and good ports, plus a government eager to grow its industrial base and invest in infrastructure.

For a long time, inflation wasn’t a concern. Whenever growth slowed, stocks would drop, and rates would decrease, causing bonds to rise. This made risk parity an excellent trade. However, it turns out that risk parity is essentially a macro bet that inflation will remain low, implying that the risk-adjusted return of stocks plus bonds is significantly better than that of either one alone.

Most strategies are implicitly a bet on the yield curve

If you’re a venture capitalist, you’re interested in the tail end of the yield curve being as low as possible.

In risk parity, the preference is for the yield curve to have a traditional curve shape, essentially what people envision when they think of a yield curve.

For a market neutral or factor neutral hedge fund, the ideal scenario is for the short end of the yield curve to be high, and for it to be flat or almost inverted, indicating high volatility.

…

Don’t tell the VCs, but it’s true. A flat low yield curve implies a very low growth environment where real rates are extremely low. This means that if you can invest in a company that can produce secular growth at a time when rates are low, the valuation becomes completely nonlinear. For instance, look at what companies were trading at in 2021, it was because the present value of profits in 10 years was really close to the value of those profits today. As a result, many of them were valued on a multiple of 2027’s revenue or something similar. As long as they were growing really fast, that multiple made them look quite cheap. [Kris: See Negative Interest Rates and the Perpetuity Paradox]

These things work really well when rates are extremely low. Low rates also mean there’s a lot of capital floating around. This goes back to the earlier point on what Limited Partners (LPs) want; often they seek a single digit return. If you can buy 10-year treasuries at 5%, a single-digit return is not hard to achieve with very simple assets. But if your Treasuries are earning 70 basis points, then you absolutely have to take risks. This creates an interesting feedback loop where a lot of money flows into the growth parts of the economy. Many startups sell things to other startups. So, every time another large check goes into Snowflake before its IPO, suddenly there are more Zoom and DocuSign seats being sold, more Slack seats being sold, and there’s more usage on AWS. It all feeds into the same ecosystem. If everything’s trading at a high enough price-to-sales ratio, then every dollar that goes into the ecosystem increases the market value of that ecosystem by more than that dollar.

Additionally, if companies are increasingly paying people in equity, then you don’t need much cash to keep the flywheel going for a long time. Venture capital turned out, at least in modern venture where you have an ecosystem of startups selling to other startups, to be about understanding unit economics well enough to look at companies burning cash and ask, “What are they getting when they burn that cash? How much Lifetime Value (LTV) are they getting for the Customer Acquisition Cost (CAC) they have to spend?” If that number looks good, then you could put a really high valuation on these companies.

That’s one of the things that changed in the venture ecosystem, even over the five years up to 2021. People got really good at quickly identifying companies with a product-market fit, looking at what the unit economics look like, and discounting that by looking at the Total Addressable Market (TAM) and then basically saying, someone else can also figure out these numbers, so someone else can capture this TAM. Therefore, we absolutely need to give this company massive funding. The playbook for growing a company fast by dumping a lot of money into it got very refined by that time. You could find someone who had worked at a company that scaled at that speed and who knew where the bottlenecks were. Meanwhile, some of the scaling got easier because of all of these third-party services.

You didn’t have to build out an entire internal communications infrastructure like Amazon did when they were getting started; they built their entire customer service system in Emacs Lisp. But now you would just use Front or something similar, so you don’t have to put any engineer hours into building that system, which means you can scale much faster. More of the money went more directly into the company’s core competency because everything that was non-core was somebody else’s SaaS product that you could just buy.

Why shorting overvalued or fraudulent companies is a weak hedge from a correlation point of view

I wrote a piece on shorting recently and how it’s become a worse hedge over time. The basic argument is that when people are shorting, whether it’s on an unconstrained generalist basis or within an industry, they tend to find the same companies. They tend to identify companies that are over-earning, have dishonest CEOs, or are overly promotional, and so they short them by default. Alternatively, they might do the funding short of just picking a company where nothing is going to change over the next decade. So if they have to have a short position, they could just short this and not think about it anymore.

One problem with this is that it means when there are extreme market disruptions and hedge funds are telling all their portfolio managers to cut their exposure in half as quickly as possible, they’re all selling the same stuff or, more likely, selling some of the same stuff and also buying some of the same stuff. Sometimes it’s gratifying when I’m on Twitter and I see a rumor that some pod somewhere blew up, and then I look at the stocks I’m short and see they’re all up five or 10%. It feels good to know that I’m shorting the same things the professionals are, even if I found out because that particular professional didn’t perform well and got fired.

An interesting example of this I stumbled on recently was a company called Zion Oil and Gas, which seems like a scam. They’re drilling for oil in Israel, which is one industry that Israel does not excel in. It’s one part of the Middle East where that’s not the main economic activity. But they’re raising money from American investors who think this is really cool or maybe it’s biblical somehow. The stock in Zion Oil and Gas was at $6 a share in December 2008 and then went up to $14 a share in February 2009, making it one of the better-performing US equities over that time period. This was during the depths of the crisis. I have to assume that a lot of it was that very smart people were shorting this, thinking it’s a retail promotion that’s going to run out of money and die. Then all of them were losing money on everything else they did and had to cut exposure and buy back. So the stock went up. Maybe they did a big promotion, or maybe they had some sort of financial crisis, the End of Times themed stock promotion, but a lot of the worst companies in the world all go up on bad days because everyone is covering. So it becomes harder; over longer periods, shorts do hedge a portfolio, but day-to-day, it’s more painful.

Framing the competition between retail and professional investors

Why different time horizons mean different arenas

In many ways, everyday investors will generally either have a really short timeframe and are more or less gambling, or making educated bets on minor market movements, or they’re making longer-term bets like, “I know this company, I like the company, I use the products all the time, I’m going to buy the stock and hold it for 20 years.” If you’re doing that, it doesn’t really matter if Citadel is better informed about how this quarter is shaping up. Sure, it’s unfortunate that you might have bought the stock for 10% less if you waited a week until they reported bad earnings. But if you truly believe in the company, then it’s a minor difference, especially over longer timescales. And if you’re investing continuously, saving money, and putting a little money into the market every so often, then it all averages out.

One of the nice effects that hedge funds have for you as an investor is that they price in all the incremental changes in the outlook all the time. So every time there’s a new round of data that tells you a bit about share shift within some industry, hedge funds immediately adjust to that, or they have predicted it and already adjusted. This makes you less likely to be blindsided by certain types of surprises, especially on the revenue side of consumer-facing companies. It’s broadly true that hedge funds do make the market more efficient, so you’re getting a better deal.

Hedge funds are not trying to figure out where the stock will trade in 10 years. To the extent that they are, it’s more like they’re trying to reverse engineer the process of large, long-only investors, like Fidelity and Capital Group, etc...and what incremental news flow over the next two weeks will adjust their 10-year price target in a predictable way that you can trade ahead of.

Retail advantages over pros

The single largest source of advantage in the markets, ironically, are not owned by hedge funds but by retail investors, and that’s the time horizon. Over a long enough time horizon, you can actually outperform most hedge funds if you do things with discipline. Hedge funds have some disadvantages which you can easily avoid as a retail investor. The first disadvantage is that hedge funds incur a lot of short-term capital gains tax when they make money because of trades that mostly don’t go above a year. For retail, holding a stock for over a year is not that difficult. The second key benefit is that hedge funds need to show short-term performance; monthly returns matter, quarterly returns absolutely matter. They are forced to take movements when the markets are not favorable. For instance, there’s a grossing down problem. If the markets are bad, and everybody’s losing money, that’s the time you want to be deploying capital. But what typically happens is they’re reducing their exposure to the market to figure out what is going on, and that’s when you see huge market dislocations. As a retail investor, you can sit there and say, “8% is nothing if I’m going to hold the stock for the next 10 years, I’ll just hang on to it.” And that time horizon difference is a huge source of alpha in a market that, for the most part, isn’t competed away, even with the biggest hedge funds, because they don’t have the ability to do that.

Hedge funds measure themselves on a risk-adjusted basis, and part of it is just how they’re structured and capitalized. They’re often levered, like six or eight to one is the usual ratio. So if you’re an individual portfolio manager at one of those funds, if you have a billion-dollar allocation, you think their target return is like 10% a year, but no, their target return is on the order of like two or 3% a year. Because they are hedging so many things out, they just aren’t taking enough risk to make massive returns. The risk comes from stacking a bunch of these portfolios together. And if you make a trade and it’s not working right away, you’re probably going to exit that trade because you don’t know why it’s not working. It means that hedge funds are in this constant effort to generate new ideas. There’s this idea of velocity, like if you have a portfolio and it has X amount of names, and you’re turning over all of the stocks in that portfolio every Y trading days, then you need at least one original long or short idea every workday to have a portfolio with the right structure. The median quality of the ideas is not necessarily good, but it is a volume game.

What is a hedge fund solving for fundamentally?

You’re in the risk removal game, trying to remove as much risk as possible, because you have access to cheap enough leverage that if you can consistently generate a 3% return, it’s world-class, it’s absolutely phenomenal. With that consistency, you can borrow 10 times the money and make a 30% return. So, to achieve a consistent 3%, the key being consistency, you are removing every type of risk possible. However, the challenge of doing that is you often end up in situations with many other funds trying to do the exact same thing. Hedge funds tend to get into crowded longs and crowded shorts, where everyone is following the same thesis. For example, everyone might be long Amazon and short a bunch of other e-commerce tech names, or long Booking and short out the rest of travel.

In these nuanced situations, if a company like Amazon reports earnings and beats them, but not by enough due to the high number of long positions, the stock may trade down. These funds that are long Amazon then have to sell because the earnings, though fundamentally good, didn’t meet the high expectations set by the market. In trying to remove risk, these funds actually take on a significant risk by not considering that everyone else is removing the same risk.

To avoid this problem, one strategy is to engage in areas others are not focusing on. This approach, however, can be challenging because it often means fewer resources, fewer people to talk to, fewer conferences to attend. You’re often on an island, which can be a more difficult psychological battle. When working for a large platform, especially those managing double-digit billions, you quickly realize you can’t deploy hundreds of billions of dollars in ideas that others aren’t looking at. The equity markets will tap out very quickly in those spaces. Thus, the risk many hedge funds end up ultimately taking, which they want to avoid, is the risk of everyone else doing the same thing.

[Kris: This section touches on a few ideas I’ve observed before:

GPs have some misalignment with LPs (and non-partner PMs)
The trading mindset is merging with investing as the focus on alpha marries and operationalizes what “trading as a business” understands with informational inputs that come from understanding what drives business fundamentals and market reaction]

The curse of hedge fund managers is that they start out because they enjoy picking stocks, building systematic models, or day trading, but as they grow, that becomes 0% of their job. Instead, 100% of their time is spent on risk management, investor relations, or recruiting. They end up building a system that automates a lot of what they’re good at and then have to find their own idiosyncratic source of returns. If a hedge fund has access to the best prime brokers, best exchange connectivity, and best algorithms for implementing trades with low slippage, they need to gain an idiosyncratic return by hiring unique people early and onboarding them effectively.

A significant part of the business becomes structuring the trade in a way that defines a person’s incentives and non-compete agreements to capture as much of the alpha as possible at an acceptable price. These funds often offer experienced portfolio managers guaranteed bonuses and agree to hire them at the beginning of a non-compete, allowing them to wait it out. The hedge fund entity’s trade is about defining the person’s incentives so that they capture as much alpha as possible.

From the LP perspective, a hedge fund is like a marvelous treasury bond, producing a stable, non-correlated, and safe return. From the GP perspective, it’s more like a venture fund, looking for the handful of superstars who will consistently generate that 3% growth every year to make the business the best it can be.

Surprisingly, the big platform funds like Point 72, Millennium, Citadel, and Balyasny, which have backgrounds in day trading and systematic fixed income, do not come from a background of deeply assessing management integrity, which was a focus of Tiger Management. Tiger Management, once one of the biggest funds, wound down but seeded funding to its best analysts and network, creating an implicit multi-manager fund. However, they didn’t have the central risk management that current multi-strategy platform funds have. Julian Robertson’s funding led to a sort of implicit multi-manager fund, but they all used very similar strategies and often crowded into the same stocks.

This paradox shows that a background in assessing portfolio managers and analysts does not necessarily translate to success in managing a multi-strategy platform fund. The people who excelled at it were those who deeply loved creating the game.

“Peak-pod thesis” and efficiency

If you look back, there was a time when hedge fund returns significantly outperformed the market. However, starting around 2000, this gap began to shrink, and by 2010, it was minimal, closely aligning with the drag from fees and taxes. Hedge funds were once consistently generating a lot of alpha, but that started to decline. Now, the quality of reported alpha is higher, with more funds truthfully reporting no net market exposure or accurately disclosing their exposure and additional returns. However, as the skill level of investors increases and they understand the model better, the quantity of alpha available inevitably shrinks.

Hedge funds have become so proficient at generating ideas and maintaining a certain hit rate that they continue to produce risk-adjusted returns. But as more capital flows into these strategies and into competing funds, it becomes harder to execute large trades. The industry might reach a peak where the role becomes more routine and systematized, potentially leading to lower compensation per person but still remaining a significant job category.

Regarding total investment returns, imagine a stock market chart resembling a zigzag line deviating from a straight linear path. The area under this zigzag line represents the total market returns, predominantly beta. Alpha is the difference between this zigzag line and the linear path. In a market where volatility is high, hedge funds tend to perform better because the deviation from beta is greater, thus increasing the total alpha available. The current question is whether we have reached the peak number of portfolio manager “pods.” This depends on the total market volatility, which has been increasing due to higher interest rates, suggesting a potential for more pods and higher alpha generation.

However, if interest rates decrease and market volatility diminishes, hedge funds may face challenges in maintaining their current levels of alpha generation. They would need to diversify into other sectors to find new sources of volatility and alpha. Theoretically, if the market were to move in a perfectly linear trajectory, there would be little need for hedge fund pods, but such a scenario is unlikely to occur.

The concept that alpha sums to zero before taxes and transaction costs is crucial. If you’re making above-average returns, it’s typically because someone else made less optimal trading decisions, either buying high when you sold or selling low when you bought. Hedge funds rely on a supply of traders who are either valuation insensitive or simply poor at trading. However, this reliance draws other traders to exploit the same opportunities.

In The Laws of Trading you hear alpha doesn’t last forever, and this applies to both positive and negative alpha. For instance, negative alpha can occur in large pension funds that execute market orders for stocks every two weeks when employees contribute. Over time, traders might notice this pattern and begin buying these stocks a day earlier, selling them back to the pension fund at a higher price, thus reducing the fund’s impact and making it harder for them to systematically lose money. If it were possible to deliberately lose substantial money consistently, then inversely, one could make money by doing the opposite of their losing strategy. In public markets, it’s almost impossible to consistently lose money in absence of significant transaction costs.

How this can get quite meta

Concerning alpha capture, multi-manager funds analyze their portfolio managers’ decisions to determine their strengths and weaknesses. They can identify managers who consistently perform poorly with certain stocks or situations. This information helps build a meta portfolio that represents what the firm’s portfolio would look like if managers were perfectly self-aware of their abilities. Interestingly, someone who is consistently wrong about a particular stock, like consistently mispredicting Nvidia earnings, can be valuable. Their predictability, even in failure, can be leveraged by a quant model to generate profits by taking the opposite position.

This leads to a somewhat disconcerting situation where a financial professional might realize their value came from consistently incorrect predictions about a specific stock, contributing to their firm’s success by serving as a reliable contrary indicator. It’s this weird Marxist alienation from your labor, where if you find out that you had a really lucrative financial career, and it was entirely because you were really, really bad at Netflix earnings or something, but you were so bad that the quant model realized it would just fade you in much larger size every single time and make money like that’s gonna be a depressing realization. But someone, someone someday will probably come to that realization that they were just so reliably bad in certain situations that they actually made their employer money.

Understanding the good and bad of the job can help you determine if pro investing is for you

It’s exhilarating to feel like you’re always in the flow, that when something happens, you either anticipated it or are among the first to grasp its implications and strategically position yourself. That’s a thrilling feeling, although it’s not the norm. Usually, you feel clueless, underperforming, and stressed by random bad news. It’s like walking into the office and getting hit in the face. But occasionally, it’s extremely fun. The most gratifying things often come through ongoing stress and suffering. If you learn to enjoy that, you’re set.

Working at a hedge fund is unique because of the day-to-day variability. You’re dealing with extreme uncertainty and making decisions where being wrong 45% of the time means you’re top-notch. If you value intellectual honesty and variety, it’s a fantastic career.

However, when things go bad, they can be drastically different. The high level of trust and unpredictability can significantly impact your personal life.

There’s a trend of hedge funds starting venture practices and vice versa. It’s interesting to see if there will be more crossover, as both sectors tolerate a high rate of being wrong. One key difference in venture capital is the longer feedback loop. You won’t know if you’re a good venture investor for many years, unlike the quicker feedback in hedge fund investing.

The hedge fund industry is known for high burnout rates. Many enter in their early to mid-20s and leave by their 30s. Often, these employees haven’t experienced a full market cycle; they’re hired in good times and shocked by downturns. For instance, the downturn in Q4 2018 was mistaken by some as an apocalypse, but it was followed by a great year, giving a misleading impression of real downturns. In 2022, with an actual downturn, the industry faced a harsh reality check.

Updating is something people do a lot within a cycle on kind of minor stuff, like on Netflix, for example, it was more of a net subscriber additions story for a long time, and then became more of a revenue story. And it was also partly a margin story. However, when there’s a quarter where you correctly predict the net adds but get the revenue wrong, and the stock reacts more to revenue, you must quickly adjust your focus.

You have to very quickly tell yourself “the thing I was really good at predicting actually does not matter as much as this other thing. And so now I have to get good at predicting that.” And it’s when the really big shifts happen — like when the focus shifts from growth to profitability, or when we can’t assume infinite capital or money having zero cost doing lazy discounts. Now you actually have to think about what is the value of 50 cents in five years versus $1 in 10 years, instead of treating $1 in 10 years is worth roughly $1 today.

The ability to quickly adjust perspectives and decide what matters is crucial. Adapting your mental model rapidly during major shifts, such as a shift in focus from growth to profitability, is challenging.

Those who can adapt and last through multiple market cycles do extremely well due to their growing experience and opportunities.

[Kris: See 5 Takeaways From Todd Simkin on The AlphaMind Podcast to understand how a trading firm trains cognitive flexibility. This is especially important when you hire smart people who aren’t used to be wrong. This is echoed below.]

There’s a saying: “a smart person knows what to do, and an experienced person knows the exceptions to what to do.”

The average age in a hedge fund is relatively low compared to many other industries, including their mutual fund counterparts. You often see people working in hedge funds who have had a series of successes throughout their lives to reach their current positions. The typical profile of an analyst, for example, is someone who excelled in high school, attended a prestigious university, graduated at the top of their class in finance or economics, then went on to work at a major investment bank. After one or two years, they’re recruited from that investment bank to a top private equity shop or hedge fund. It’s a chain of success where they haven’t experienced significant career failure.

However, once in a hedge fund, the measure of success is not about the ability to study well or work hard. The skills required for success in a hedge fund are different from those correlating with educational success or early career achievements at places like Goldman Sachs or Morgan Stanley, where hard work is more directly linked to success. In a hedge fund, working harder does not necessarily equate to generating more alpha. If it did, everyone would be working 20-hour days.

If you use options to hedge or invest, check out the moontower.ai option trading analytics platform

The GOAT’s Parting Wisdom

Just popping in on my writing break to share a 1 lot…

🎙️Stripe’s John Collison Recently Interviewed The Late Charlie Munger (Invest Like The Best)

Loved the rapid fire format. RIP Charlie, a true legend. I’m hesitant to put my favorite excerpts because it’s no substitute for listening to it, but I’m going to anyway to make it easier to quote from in the future.

Excerpts

Did you learn the big ideas in the various disciplines because you were just intellectually curious about them? Or because you thought they’d be instrumentally useful in the work?

Both. I saw instantly, for instance, when I was introduced to the math of Pascal and the elementary probability, I saw immediately how important this math was. My math teacher had no idea that he’d come to a part of the math that was very important in the regular world to everybody, but I saw it immediately and I just utterly mastered it. And I used it. I’m still using it. I used it routinely all my life quite intensely.

And when I got to study in the Harvard Business School, in the early days at the Harvard Business School, they were proudest of something called decision tree theory. And they taught it at the Harvard Business School, a lot of pomp and ceremony and many examples, all these graduate students.

Decision tree theory, it’s a Harvard Business School — in those early days, what they were teaching you was that Pascalian probability math works in real life. Here’s the Harvard Business School needing to do remedial high school math to a bunch of graduate students, and they weren’t wrong. They were right in those days to teach decision tree theory because other people hadn’t mastered probability math the way it should be mastered.

My teacher in high school, if you don’t pay attention to anything else, this stuff you ought to master. And he should explain how carny operators and casinos take advantage of ordinary people. It should have been taught, and it wasn’t taught right in high school, and it wasn’t taught right in college and it wasn’t taught right. Finally, the Harvard Business School got so they taught high school math to graduate students. And you can say how could that be correct? But it’s because the earlier education was so ineffective.

In Poor Charlie’s Almanack, you advocate the multidisciplinary approach and knowing the big ideas from all the different disciplines. And one of the ones that I particularly liked and stuck with me was the one from biology of stable ecosystems and understanding how entities prosper within ecosystems. And in particular how you don’t want necessarily to be in this robber baron, monopolistic, rent extraction position. But instead, businesses that sustain and endure over the long term are ones where they are not rent extracting.

Well, some of the robber barons last a long time. And there are a lot of real estate operators that are basically sleazy. And they don’t even think their business is sound unless they’re doing something sleazy. They’re doing something sleazy, they have a safe advantage. And of course, that’s exactly the opposite to my idea.

My idea is so simple, is that if you make your living selling things to other people that are good for them, that is safer and more profitable averaged out than selling them stuff that’s bad for them like gambling, drugs, crazy religions, all kinds of things that are terrible for people. And so of course, you want to sell things that are good for them. And it’s amazing the people who don’t pay any attention to that rule.

And I think it was sleazy products and investment banking has sort of you willing to sell and the sleazy stuff that compensation consultants are perfectly willing to sell. And I just decided I wasn’t going to do any of that. I was going to sell what kind of stuff that I would buy if I were on the other side. And I also wanted to work with the kind of people that I admired. And that’s a very important thing to learn to just search out the reliable people that you can trust and be the kind of person in dealing with everybody else that they can trust.

It’s just a huge advantage if you start doing that young and keep doing it consistently through life. It isn’t very hard, stay awake in high school math and deal with the good people instead of the bad people and sell what you would buy if you were the buyer, not what you can sell by misleading people. These are very simple ideas. But it’s just absolutely amazing how well they work for people who relentlessly follow these simple ideas.

On Investing

Has investing gotten harder?

Of course, it’s gotten harder, way harder. It’s gotten so hard that most of the people who are in wealth management have an almost zero chance of outperforming an unmanaged index like the S&P.

How has it gotten harder?

There’s so much more of this wealth invested in securities. And so we’ll get a whole lot of big sums to manage. And of course, it’s a long time to buy in, a long time to sell out, costs are higher. And so it’s way harder to manage a large sum of money to make a lot of money at high returns than it is to manage a small sum of money. And then way more brains came into the business. So it’s gotten brutally competitive.

And then we have these manias that get — when things are hot and they’ll start running like the behavior gets almost crazy. It’s almost like a delusion. Of course, it’s harder. And in my lifetime, a guy who just bought the best common stocks and sat on his ass, would have made about 10% per annum before inflation. Maybe 8% after inflation. That is not the standard return that a man can expect from investment. That was a very unusual period in a very unusual place. And I do not anticipate that the average result is going to be nearly that good over the next 100 years.

Why was the results so good? Why was it 10% per annum?

Let’s call it 8% after inflation. The Great Depression so demoralized everybody, they were utterly despised and then the economic system improved a lot. And the combination of the investment climate, the economic situation together evolving, just made it unusually good. If you go back to what the rich people of England did back, say, in 1900, they bought consols (type of perpetual bond), 2.5%, no inflation. Two and a half percent return if you wanted to stay safe, you’d be satisfied with that. No rich people thought there was any safe way of getting 8% if you go back to 1880 among the rich people of England.

And so this is an unusual period. And now everybody who’s in investment management teaches everybody, you’ll get 8% after inflation by dealing with us because that’s the way it worked for the last 100 years. Just because it worked for the last 100 years does not mean it’s going to work for the next 100 years.

So it’s been a period of significant economic growth. I think there’s also maybe the U.S. stock market that has outperformed…

Yes, everything, United States, country prospered, a lot of good stuff happened at once that caused that very good result. It’s not always going to work that way.

What do you think of the SEC?

We’re a lot better with an SEC. The tendency to prosper through financial chicanery in all forms of wealth management is perfectly enormous. So of course, you need something to throttle that back and control it. So I’m glad we have an SEC. It would have been crazy not to have one. By the way, that came in as part of the Roosevelt, and I would argue that its main trouble is that it isn’t tough enough.

Tough enough on what?

Miscreancy. If I were running the SEC and had the power to do it, I wouldn’t allow people to publish a record saying, “Here’s what I did over the last 20 years, when I started with $2 and went up to $200 million.” because it misleads people. And of course, we will create mutual funds, create little ones to get a phony big record. I would forbid that kind of stuff.

I would force everybody who is a big-time money manager to report his investment record per dollar year instead of historical, and that would take the miscreancy out of it. And it would be so simple, and it would radically change the whole industry.

And how many people have you ever heard say it will be mandatory that all wealth management will report its results per dollar year, which would be easier to do mathematically? And it would totally change the way everybody is promoting their service in a way that fosters truth and excellence and a lot of the things.

What I just suggested is so goddamn simple and so obviously required in terms of honorable disclosure, that it ought to be automatic. And yet who has ever suggested — why is little Charlie Munger, 98 years old, think the SEC or the government ought to require that all investment professionals report results per dollar year instead of per historical? Nobody suggest it. But to me, it’s obvious it ought to be required.

And when you say per dollar year, you mean dollar weighted results, basically?

Yes. How much return — for every dollar year, what was your return? And of course, that’s a very different figure. I know of a case of a hedge fund where the proprietor made a lot of money, but per dollar year, the net return was zero. Because when he got a lot of money, he really made a lot of dumb mistakes.

He made a lot of money when this one didn’t matter much. And yet it looks like a wonderful record. But in fact, it was terrible. And why wouldn’t that be a fair thing to require?

The Principal-Agent Problem

It’s very interesting reading the book with the lens post the financial crisis. It’s also interesting to see you railing against derivatives in this a few years before the financial crisis.

That derivative railing was so manipulative and they marked the books like, “Two guys that make a big trade, they both recorded a big profit to their accounts, the accounts would less the profit on both sides.” It’s the same trade. One was reporting a profit, and the other’s reporting a profit. It couldn’t both be — if it gets too easy and too manipulative, and into that culture, the stock brokers, big banking, the guys who did the ordering, they take them to Las Vegas, they buy them a stack of chips, negotiable chips, and give it to them.”

“There was cocaine, they were prostitutes. It was not a pretty culture and kind of tolerated. What do you expect from a bunch of security traders? Everybody knew that his traders were behaving that way, but it was a mistake to let all that stuff to creep in. And it got pretty extreme. And then the bankers deal — that deal that Goldman Sachs did with Malaysia, that sovereign wealth fund…that guy obviously should have been avoided on moral grounds, and prudential grounds, too, but these get so intoxicated by the easy money.

It feels like a lot of the objections you have, sort of, say, professional money managers or Wall Street or whatever, can be summed up by people should be more cognizant of principal agent problems. Is that fair?

You can hardly imagine a field more full principal agents with their money than wealth management. Of course the wealth managers take care of themselves. That includes the foundation manager. A foundation manager basically wants to get $400,000 a year while a professor gets $110,000.

He’s got one way of doing it: picking money managers who get 3% off the top and in various forms of private equity. That’s the only way he justifies his big peso. It’s a principal agent problem. Of course you’re going to want to invest a lot of money with private equity. And of course, private equity is going to do all kinds of horrible things to try and get 3 points off the top. Imagine you get 3 percentage points off the top of somebody else’s money.

It’s a good business model.

You can only do that if you have some miraculous way of making money. By the way, the guys in your field, Jim Simons, Jim Simons is a world-class mathematician. Here’s what he did. He just used his damn computers to identify trading patterns that had deep human psychological background.

One of them was very simple. He took his computer data and he found that patterns in the market as a whole, there are 4 different patterns: win-win, lose-lose, win-lose, and lose-win. If it’s just random, then all 4 are going to be equal. And low and behold, he sifted the data and win-win was more common than win-lose or lose-win. And lose-lose was more common.

So all they had to do is use program at computers to make these modest moderate-sized trades, or big -by his standards were moderate compared to the market. On that basis, the business whirled and whirled, the money just poured out of it.

The tax shenanigans.

Billions poured out of the clearance system. And it was so simple and so elementary. And as a social utility of making money that way is about zero, so if I’d done that, I suppose I would be pleased that I was so clever, but I would have been a bit ashamed of not delivering anything to society in exchange for my big winnings. But luckily, I wasn’t enough of a computer science to even think about such things, and I don’t like short-term trading. And I don’t want to be hanging over some trading desk punching keys.

Why do you think Sequoia has done so well?

Sequoia got early into the game, and it’s a fanatic meritocracy. So they work very hard, all of them. And they’ve gotten big and successful way ahead of everybody else, and they kept writing it like some chip manufacturers, each generation of chips they get. And in the end, they have a file. We have an example.”

“In our apartment houses, we use some little computer program in adjusting the rents or something or other that somebody — and this one little guy, I had him check, Sequoia already had a file on this guy. So every little asshole with a little tiny computer program, they got an army of young guys out there finding every little guy and on big files and so forth.”

“So they see more — they see better opportunities sooner and more than other people. And they’ve got the reputation. So people who are usually successful, they want to go with Sequoia, not some lesser firm. And the combination is just unbeatable. But lately, were they right to go into big Robinhood, but no, they made a huge mistake for Sequoia there, and they shouldn’t have gone…

Morally or…

Morally and professionally, it’s a big mistake. Really stupid. But it got so much, we’ve got to be in every new thing that’s hot. They got to thinking like investment bankers, but it was a huge mistake for Sequoia to get involved with Robinhood and…

Is your objection to Robinhood that it encourages short-term trading and trading options?

Yes, they lie and so forth.

Do they?

Oh my God.

What do they lie about?

Anything that works. They try and sell it, hey, this is a new fraternity of freedom or it’s — the whole thing is a lie.

You don’t like the movement aspect of it.

Oh no, no. They’re trying to create mass hysteria. I don’t like luring people in and screwing them, basically. You’re successful with Sequoia and you’re identified with financing people like Apple and so on, why in the hell would you take Robinhood? It’s totally crazy. You don’t want to do all the business that’s legal for you to do. You want to exclude all kinds of things because it’s beneath you. This shows that you work at these things intelligently. It gets hard, but it doesn’t get impossible.”

But the other side of it is, if you take the — I have been very well located in life. But with minor exceptions, what do I have relative to investments in life? I’ve got Costco stock, Berkshire stock, Li Lu’s China fund and Avi’s apartments. So I have four investments, basically, after 60 years or something — by the way, I feel perfectly adequately diversified. Nobody teaches that’s adequate diversification.

And they’re dead wrong. Simple fact is that it’s easier to find four things that are above average than it is to find 40. It’s not that damned easy to find. You find something that’s almost sure to work because you figure — you’re asking to finding a gold mine in your backyard. When it works, is that easy? How many gold mines are you going to find in your backyard? You shouldn’t expect to have all that many opportunities that are clearly identifiable.

“It’s going to be very hard and you’re lucky if you get only a few in a lifetime. And then you have to be a combination of very patient and very aggressive. You have to sit patiently waiting, watching, surveying, hunting and pounce very occasionally. You get four pounces in a lifetime that really work big time, and that’s a very successful lifetime. And other people think — like that guy on TV, he’s an expert in every company every time. That’s crazy. He’s an expert in saying something that’s mildly plausible. That’s not being an expert investor.

Doesn’t it feel like the narrative on that is changing, where I think people are coming to understand the merits of concentration in positions that really work?

I had dinner with a whole crowd of Fidelity this very week, and they’ve got trillions under management, and they scrape only a modest amount off the top. And they’ve got a wonderful business, but they have the moral problem that they have no possibility at all of exceeding what an index man could do with their common stock investments.

Maybe they have an occasional analyst that’s a little better than average that works into the system. Basically, what they do is they force everybody to be a closet indexer because nobody wants to be an extreme outlier on the losing side because that can destroy your investment management business. But I would argue that the whole damn system is corrupt in investment management.

They take care of the agents way better than they take care of the principals, and they lie to themselves and they lie to others. And that’s our system. And everybody that wants a fair amount of easy money pretty fast. And that requires a plausible narrative. That’s what’s admired now. I regard modern venture capital as investment banking in disguise. Just a little different form of investment banking, same morality, same obsession with a lot of quick wealth. There’s nothing wrong with investment banking, properly done, venture investing.

Excerpts From Grant Sanderson on The Lunar Society Podcast

Dwarkesh Patel interviews Grant Sanderson (who runs the excellent 3Blue1Brown YouTube channel) about:

Whether advanced math requires AGI
What careers should mathematically talented students pursue
Why Grant plans on doing a stint as a high school teacher
Tips for self teaching
Does Godel’s incompleteness theorem actually matter
Why are good explanations so hard to find?

Watch on YouTube. Listen on Spotify, Apple Podcasts, or any other podcast platform. Full transcript here.

Kris: I snipped several excerpts for future reference. Emphasis mine. I cut up the excerpts as I want to remember them which means there are missing sections so I encourage you to listen to the whole episode or read the sections of transcript I’m pulling from if you want a closer look.

On the future of education

[key ideas: reducing distance to students, educator’s role is not just explanation but more importantly “bring out knowledge” not put it in, the non-linear influence of a teacher on a student’s future, and the chaotic concept of “sensitivity to initial conditions”]

Dwarkesh Patel 0:44:44

Should the top 0.1% of educators exclusively be on the internet because it seems like a waste if you were just a college professor or a high school professor and you were teaching 50 kids a year or something. Given the greater scale available should more of them be trying to see if they can reach more people?

Grant Sanderson 0:45:01

I think it’s not a bad thing for more educators who are good at what they’re doing to put their stuff online for sure. I highly encourage that even if it’s as simple as getting someone to put a camera in the back of the classroom. I don’t think it would be a good idea to get those people out of the classroom.

If anything I think one of the best things that I could do for my career would be to put myself into more classrooms…

One of the most valuable things that you can have if you’re trying to explain stuff online is a sense of empathy for what possible viewers that are out there. The more distance that you put between yourself and them in terms of life circumstances. I’m not a college student so I don’t have the same empathy with college students. Certainly not a high school student, so I’ve lost that empathy. That distance just makes it more and more of an uphill battle to make the content good for them and I think keeping people in regular touch with just what people in the classroom actively need is necessary for them to remain as good and as sharp as they are…

The other thing I might disagree with is the idea that the reach is lower. Yes, it’s a smaller number of people but you’re with them for much, much more time and you actually have the chance of influencing their trajectory through a social connection in a way that you just don’t over Youtube.

You’re using the word education in a way that I would maybe sub out for the word explanation. You want explanations to be online but the word education derives from the same root as the word educe, to bring out, and I really like that as a bit of etymology because it reminds you that the job of an educator is not to take their knowledge and shove it into the heads of someone else the job is to bring it out. That’s very, very hard to do in a video and in fact, even if you can kind of get at it by asking intriguing questions for the most part the video is there to answer something once someone has a question.

The teacher’s job, or the educator’s job, should be to provide the environment such that you’re bringing out from your students as much as you can through inspiration through projects, through little bits of mentorship and encouragement along the way. That requires eye contact and being there in person and being the true figure in their life rather than just an abstract voice behind a screen.

Anytime I chat with mathematicians and try to get a sense for how they got into it and what got them started, so often they start by saying there was this one teacher and that teacher did something very small — like they pulled them aside and just said, “Hey. You’re really good at this. Have you considered studying more?” or they give them an interesting problem.

And the thing that takes at most 30 minutes of the teacher’s time, maybe even 30 seconds, has these completely monumental rippling effects for the life of the student they were talking to that then sets them on this whole different trajectory.

Two examples of this come to mind. One is this woman who was saying she had this moment when she got pulled aside by the teacher and he just said, “Hey, I think you’re really good at math. You should consider being a math major.” which had been completely outside of her purview at that time. That changed the way she thought about it. And then later she said she learned that he did that for a large number of people. He just pulled them and was like, “Hey, you’re really good at math.” So that’s a level of impact that you can have as a figure in their lives in a way that you can’t over screen.

Another one which was very funny. I was asking this guy why he went into the specific field that he did. It was a seemingly arbitrary thing in my mind but I guess all pure math seems to be. He said that in his first year of grad school he was sitting in this seminar and at the end of the seminar the professor, who was this old professor who he had never met him before, they didn’t have any kind of connection. He seeks this guy out and comes up and he says, “You. I have a problem for you. A good research problem that I think I think might be a good place for you to start in the next couple months” and this guy was like “Oh, okay” and he gets this research problem and he spends some months thinking about it and he comes back and then it later came to light that the professor mistook him for someone else that was someone he was supposed to be mentoring. He was just the stereotypical image of like a doddering old math professor who’s not very in tune with the people in his life that was the actual situation but nevertheless that moment of accidentally giving someone a problem completely shifted the research path for him, which if nothing else, shows you the sensitivity to initial conditions that takes place when you are a student and how the educator is is right on that nexus of sensitivity who can completely swing the fences one way or another for what you do.

For every one of those stories there’s going to be an unfortunate counterbalancing story about people who are demotivated from math. I think this was seventh grade. There was this math class that I was in and I was one of the people who was good at math and enjoyed it and would often help the people in the class understand it. I had enough ego built up to have a strong shell around things. For context, I also really liked music and there was this concert that had happened where I had a certain solo or something earlier in that week.

There was a substitute teacher one day who didn’t have any of the context and she gave some lesson and had us spend the second half of the class going over the homework for it. All of the other students in the class were very confused and I think I remember like they would come to me and I would try to offer to help them and the substitute was going around the class in these circles and basically marking off a little star for how far down the homework people were just to get a sense are they progressing. That was kind of her way of measuring how far they were. When she got to me I had done none of them because I was spending my whole time trying to help all of the others and after having written a little star next to the same problem like three different times she said to me like, “Sometimes music people just aren’t math people.” and then keeps walking on.

I was in the best possible circumstance to not let that hit hard because one, I had the moral high ground of “Hey, I’ve just been helping all these people. I understand it and I’ve been doing your job for you.” This was my little egotistical seventh grade brain. I knew that I knew the stuff. Even with all of the armor that was put up, I remember it was just this shock to my system, she says this thing and it just made me strangely teary-eyed or something.

I can only imagine if you’re in a position where you’re not confident in math and the thing that you know deep in your heart is actually you are kind of struggling with it, just a little throwaway comment like that could completely derail the whole system in terms of your relationship with the subject.

So it’s another example to illustrate the sensitivity to initial conditions. I was in a robust position and wasn’t as sensitive. I was gonna love math no matter what but you envision someone who’s a little bit more on that teetering edge and the comment, one way or another, either saying you’re good at this you should consider majoring in it or saying, “Sometimes music people aren’t math people” which isn’t even true. That was the other thing about it that niggled at my brain when she said it.

All of that is just so important for people’s development that when people talk about online education as being valuable or revolutionary or anything like that, there’s a part of me that sort of rolls my eyes because it just doesn’t get at the truth that online explanations have nothing to do with all of that important stuff that’s actually happening and at best it should be like in the service of helping that side of things where the rubber meets the road.

On explanations

[key ideas: not everyone responds to the same explanations so explanations that scale well are difficult to conjure. There’s room for multiple approaches and ways to communicate]

Dwarkesh Patel 1:02:22

Why are good explanations so hard to find, despite how useful they are? Obviously, other than you, there’s many other cases of good explanations. But generally, it just seems like there aren’t as many as there should be. Is it just a story of economics where it’s nobody’s incentive to spend a lot of time making good explanations? Is it just a really hard skill that isn’t correlated with being able to come up with a discovery itself? Why are good explanations scarce?

Grant Sanderson 1:02:47

I think there’s maybe two explanations.

The first less important one is going to be that there’s a difference between knowing something and then remembering what it’s like not to know it. And the characteristic of a good explanation is that you’re walking someone on a path from the feeling of not understanding up to the feeling of understanding.

Earlier, you were asking about societies that lack numeracy. That’s such a hard brain state to put yourself in, like what’s it like to not even know numbers? How would you start to explain what numbers are? Maybe you should go from a bunch of concrete examples. But like the way that you think about numbers and adding things, it’s just you have to really unpack a lot before you even start there.

And I think at higher levels of abstraction, that becomes even harder because it shapes the way that you think so much that remembering what it’s like not to understand it. You’re teaching some kid algebra and the premise of like a variable. They’re like, “What is X?” It’s not necessarily anything but it’s what we’re solving for. Like, yeah, but what is it? Trying to answer “What is X?” is a weirdly hard thing because it is the premise that you’re even starting from.

The more important explanation probably is that the best explanation depends heavily on the individual who’s learning. And the perfect explanation for you often might be very different from the perfect explanation for someone else. So there’s a lot of very good domain specific explanations. Pull up in any textbook and like chapter 12 of it is probably explaining the content in there quite well, assuming that you’ve read chapters one through 11, but if you’re coming in from a cold start, it’s a little bit hard.

So the real golden egg is like, how do you construct explanations which are as generally useful as possible and generally appealing as possible? And that because you can’t assume shared context, it becomes this challenge. And I think there’s like tips and tricks along the way, but because the people that are often making explanations have a specific enough audience, it is this classroom of 30 people. Or it’s this discipline of majors who are in their third year. All the explanations from the people who are professional explainers in some sense are so targeted that maybe it’s the economic thing you’re talking about. There’s not, or at least until recently in history, there hasn’t been the need to or the incentive to come up with something that would be motivating and approachable and clear to an extremely wide variety of different backgrounds.

Putting in work with calculations

Dwarkesh Patel 1:20:44

If you’re self teaching yourself a field that involves mathematics, let’s say it’s Physics or some other thing like that, there’s problems where you have to understand how do I put this in terms of a derivative or an integral and from there, can I solve this integral? What would you recommend to somebody who is teaching themselves quantum mechanics and they figured out how to put how to get the right mathematical equation here. Is it important for their understanding to be able to go from there to getting it to the end result or can they just say well, I can just abstract that out. I understand the broader way to set up the problem in terms of the physics itself.

Grant Sanderson 1:22:00

I think where a lot of self-learners shoot themselves in the foot is by skipping calculations by thinking that that’s incidental to the core understanding. But actually, I do think you build a lot of intuition just by putting in the reps of certain calculations. Some of them maybe turn out not to be all that important and in that case, so be it, but sometimes that’s what maybe shapes your sense of where the substance of a result really came from.

I don’t know it might be something you realize like “Oh, it’s because of the square root that you get this decay.” And if you didn’t really go through the exercise, you would just come away thinking like instead of coming away thinking like such and such decays but with other circumstances, it doesn’t decay and not really understanding what was the core part of this high level result that is the thing you actually want to come out remembering.

Putting in the work with the calculations is where you solidify all of those underlying intuitions. And without the forcing function of homework, People just don’t do it. So I think that’s one thing that I learned as a big difference post college versus during college.

Post college, it’s very easy to just accidentally skip that while learning stuff and then it doesn’t sink in as well. So I think when you’re reading something, having a notebook and pencil next to you should be considered part of the actual reading process.

And if you are relying too much on reading and looking up and thinking in your head, maybe that’s going to get you something but it’s not going to be as highly leveraged as it could be.

The “failure to disrupt”

[key ideas: learning is not bottlenecked by good explanations but by social incentives. Deeply resonant. Reading between the lines — we are aspirational and good at copying others or trying to impress them, so if we know that we should provide good models for learners to emulate]

Dwarkesh Patel 1:23:39

What would be the impact of more self teaching in terms of what kinds of personalities benefit most? There’s obviously a difference in the kind of person who benefits most. In a situation where it’s a college course and everybody has to do the homework, but maybe some people are better tuned for the kind of work that’s placed there versus all this stuff is available for you on youtube and then textbooks for exercises and so on but you have to have the conscientiousness to actually go ahead and pursue it.

How do you see the distribution of who will benefit from the more modern way in which you can get whatever you want but you have to push yourself to get it.

Grant Sanderson 1:24:17

There’s a really good book that’s actually kind of relevant to some of your early questions called Failure to Disrupt that goes over the history of educational technology. It tries to answer the question of why you have these repeated cycles of people saying such and such technology that almost always is getting more explanations to more people, promises that it’ll disrupt the existing university system or disrupt the existing school system and just kind of never does.

One of the things that it highlights is how stratifying these technologies will be in that they actually are very very good for those who are already motivated or kind of already on the top in some way and they end up struggling the most just for those who are performing more poorly.

And maybe it’s because of confounding causation where the same thing that causes someone to not do poorly in the traditional system also means that they’re not going to engage as well with the plethora of tools available.

I don’t know if this answers your question, but I would reemphasize that what’s probably most important to getting people to actually learn something is not the explanation…but instead, it’s going to be the social factors. Are the five best friends you have also interested in this stuff and do they tend to push you up or they tend to pull you down when it comes to learning more things? Or do you have a reason to? There’s a job that you want to get or a domain that you want to enter where you just have to understand something or is there a personal project that you’re doing?

The existence of compelling personal projects and encouraging friend groups probably does way way more than the average quality of explanation online ever could because once you get someone motivated, they’re just they’re going to learn it and it maybe makes it a more fluid process if there’s good explanations versus bad ones and it keeps you from having some people drop out of that process,which is important.

But if you’re not motivating them into it in the first place, it doesn’t matter if you have the most world-class explanations on every possible topic out there. It’s screaming into a void effectively.

And I don’t know the best way to get more people into things. I have had a thought and this is the kind of thing that could never be done in practice but instead it’s something you would like write some kind of novel about, where if you want the perfect school, something where you can insert some students and then you want them to get the best education that you can, what you need to do is — Let’s say it’s a high school. You insert a lot of really attractive high schooler plants as actors that you get the students to develop crushes on. And then anything that you want to learn, the plant has to express a certain interest in it. They’re like, “Oh, they’re really interested in Charles Dickens.” And they express this interest and then they suggest that they would become more interested in whoever your target student is if they also read the dickens with them.

If you socially engineer the setting in that way, the effectiveness that would have to get students to actually learn stuff is probably so many miles above anything else that we could do. Nothing like that in practice could ever actually literally work but at least viewing that as this end point of “Okay, this mode of interaction would be hyper effective at education. Is there anything that kind of gets at that?”

And the kind of things that get at that would be — being cognizant of your child’s peer group or something which is something that parents very naturally do or okay, it doesn’t have to be a romantic crush, but it could be that there’s respect for the teacher. It’s someone that they genuinely respect and look up to such that when they say there’s an edification to come from reading Dickens, that actually lands in a way.

The natural extension of this:

Encourage people to mentor or teach on the side!

Grant Sanderson

I think there are two things I would want to get out of teaching in a school setting. One of them, as I was emphasizing, I think you just lose touch with what it’s like not to know stuff or what it’s like to be a student and so maintaining that kind of connection so that I don’t become duller and duller over time feels important.

The other, I would like to live in a world where more people who are savvy with STEM spend some of their time teaching. I just think that’s one of the highest leverage ways that you can think of to actually get more people to engage with math

And so I would like to encourage people to do that and call for action. Some notion of spending, maybe not your whole career, a little bit of time. In teaching, there’s not as fluid a system for doing that as going through a tour of service in certain countries where everyone spends two years in the military

Shy of having a system like that for education, there’s all these kind of ad hoc things where charter schools might have an emergency credential system to get a science teacher in. Teach for America is something out there.

There’s enough ways that someone could spend a little bit of time that’s probably not fully saturated at this point that the world would be better if more people did that

Notes from RenTec CEO Peter Brown on the GS Podcast

Podcast: Goldman Sachs Exchanges: Great Investors

Raj Mahajan, global head Systematic Client Franchise interviews Renaissance Technologies CEO Peter Brown on July 27, 2023

I grabbed some excerpts from the transcript for future reference.

I include my own commentary here and there.

Newsflash: Money is a big factor in what people choose to do

Raj Mahajan: Seems that you were right at the vanguard of the machine learning movement in 1993. So, why did you leave an exciting career at IBM for a small financial company in Long Island that no one had ever
heard of?

Brown: …Three things happened. First, Bob had a second daughter accepted to Stanford. But he couldn’t afford to pay for her to go to Stanford on his IBM salary. So, she had to go to the agricultural school at Cornell, which offered scholarships to New York State residents. The second thing that happened is we had a daughter born. And a third thing was that Jim then offered to double my compensation. After that offer, I came home. I took one look at our newborn daughter and realized I had no choice in the matter. So, the decision to leave computational linguistics for a small hedge fund that no one had ever heard of was made purely for financial reasons.

Examples of Emotional IQ

[Kris: The EQ vs IQ thing is a false dichotomy. I suspect they are actually positively related but when we look at outliers on either dimension there is a major Berksons Paradox effect. RenTec has the reputation of being the true “smartest guys in the room” in the IQ/STEM sense of the word. And yet, multiple times in this interview I am struck at how people-savvy they have been. Which makes perfect sense to me. In a domain where the competition constantly learns and psychology plays an enormous role this is exactly what you expect. Only the naive who believe that investing is physics as opposed to biology cling to Spock-like caricatures of effective quants. Here are several excerpts demonstrating an deep understanding of human behavior]

Selling an approach to employees

Brown: At the end of 2002, Bob and I also took over the rest of the technical side of the firm, which included the trading of currencies, bonds, options, and futures. Now, our plan was to use the equities code that we and others had developed to trade these other instruments. But we recognized they would not be so great for morale to tell, say, one of the futures researchers, “You know all that code you spent the last decade of your life developing, guess what, we’re going to throw it out.” So, we had to spend quite a bit of time getting everyone to buy into our plan. To do this we used an approach that I learned from a biography I’d recently read of Abe Lincoln, which was to get them to come up with our plan themselves. Now, that took some time, but eventually it all worked out.

Jim Simons weighing the input to manage a risk crisis

See below: 2007 — “Quant Quake”

Jim Simons reading a situation shrewdly

Brown: In the fall of 2008, the whole financial system was stressed. So, we were concerned with the stability of our counterparties. So, we spent a lot of time with those counterparties and examined their CDS rates and so forth. I remember at one point, two senior executives from some firm we did business with came into our New York City office to meet with us. They assured us that the funds we had in our margin account were safe with them. And I was inclined to believe them. Why not? But after the meeting, Jim said, “Peter, they wouldn’t have come to our office. They wouldn’t have requested the meeting unless they were in real trouble. It’s time to get out.” So, we did. And Jim was right because shortly thereafter, that firm just disappeared.

Examples of automation and innovation within RenTec

Brown: When we got control of the New York office, the first thing I did was to walk around that office, find out what everyone was doing. And what I found was that many people were doing jobs that could be automated. So, we set out on a massive campaign to automate our back-office operations. We moved from checks and wires to SWIFT and ACH. We replicated counterparties margin calculations. We built a large legal database that could be accessed by computers to fill out regulatory forms. We brought in AI systems to automatically read and pay invoices. We automated the treasury department so that cash and margin needs could be managed by computers instead of humans. My point of view was that Stony Brook produces a huge list of transactions and New York City produces monthly statements, K1s, and government filings. And I just didn’t see why humans need to be involved in the process of translating trades to monthly statements. Now, 13 years later, we’re not done yet. And I’m embarrassed to admit that we still even have a few people who use Excel. But we’re getting there. In fact, I was told recently that we’ve eliminated 97 percent of the spreadsheets that had originally been used in the company.

Stories about risk management

March 2000 — Dot Com

Brown: Let me start with March of 2000 when the dotcom bubble burst. We were doing extremely well back then. And we had large positions in the internet stocks. They were traded on NASDAQ. At one point the head of risk control came to me and said he was worried about the size of our NASDAQ positions. But I told him not to worry, the computer knew what it was doing. Then we took a big loss one day. So, I worked through the night trying to understand what was going on. The next day we took another big loss. And I, again, worked through that night. So, now it’s the third day and I hadn’t slept for, I don’t know, 48 or 50 hours. And I was sitting in a meeting with Jim and a few others when the head of production knocked on the door and asked to speak with me. I walked out of the meeting, and he told me we were down again by a large amount. So, I walked back in the meeting, and I must have turned white or something because Jim took one look at me and said, “It doesn’t look good.” Now, not having slept the previous two nights, I remember thinking I’m not sure I can get through this. But I really didn’t have much choice in the matter. And so, we got back to work and eventually we did get through it. A couple days later I went into Jim’s office and told him that I’d screwed up in not appreciating the risk we were taking and said that if he wanted me to resign, I would resign. But he responded, “Peter, quite the opposite. Now that you’ve been through such a stressful losing period, you’re far more valuable to me and to the firm than you were before.” Now, that response really tells you something about Jim Simons.

2007 — “Quant Quake”

Brown: When that happened, I was on vacation, and I was on a very long flight back to Newark Airport. And the moment the plane landed, my phone went nuts with all kinds of texts and missed phone calls. So, I called into work when it was going on and I got Kim, Jim’s assistant. And she said, “Jim wants you to get back here as soon as you’re physically able.” So, I raced out. I found a taxi, leaving my family to fend for themselves at Newark Airport. And pushed the driver to drive as fast as he could from Newark to Long Island. I ran into my office, and I found Jim, Bob, Paul Broder, who was head of risk control, all holed up. And the office was full of cigarette smoke. I could barely breathe. And then there was this, I remember seeing this, 16 oz cup full of Jim’s cigarette butts. And I’m thinking, like, why do they have to do this in my office? And they were all staring through the haze at the computer screens trying to figure out what was going on. And Jim was interpreting every little wiggle and various graphs. He was really scared. And he wanted to cut back and hard. Paul also wanted to cut back. Raj, I’m sure you know, the head of risk control always wants to cut back. Because he doesn’t get paid to make money. He gets paid to make sure you don’t lose money.

And Bob, you know, Bob’s always very calm. But he wasn’t against cutting back. But I looked at the data and saw that the model had these enormous predictions, the likes of which I had never seen before. It was clear to me what was going on. People were dumping positions that were correlated with their own positions. And they were driving prices to ridiculous levels. I felt they had to come back. I argued that we should not cut back. That this was going to be the greatest moneymaking opportunity we’d ever seen. And if anything, we should increase our positions. But it was three against one. And so, we continued cutting back. But I succeeded somewhat because we cut back at a slower pace. And then at one point, miraculously, the whole thing came roaring back. And indeed, it was an incredible money-making opportunity. Now, what we learned from that was to always make sure we have enough on reserve to just hang on. Later, when Jim was about to retire, I reminded him of this period and asked if he was concerned that I was going to be so aggressive that I was going to blow the place up. But Jim responded that the only reason I was so aggressive was because I knew he was determined to reduce risk, another example of Jim’s insight into human nature.

What RenTec does differently

Brown: I guess there are some firms that make it their business to learn how others make money and try to learn their secrets. That’s not our style. We just hire mathematicians, physicists, computer scientists with no background in finance and no connections with Wall Street.

A few principles we follow:

ScienceThe company was founded by scientists. It’s owned by scientists. It’s run by scientists. We employ scientists. Guess what, we take a scientific approach to investing and treat the entire problem as a giant problem in mathematics.
[Kris: In chatting with a friend who has proximity to RenTec, I learned of this a few years ago. I was intrigued by how they felt quite comfortable incubating highly promising individuals by offering a well-paying collegiate atmosphere that offered an alternative to traditional academia. It feels like just another instance of what I call risk absorption. RenTec is a highly efficient “bidder” for the risk of a scientist’s effort panning out. They can build a portfolio of talent in the form of a skunkworks knowing that they can scale important discoveries across their trading. Not unlike how a military R&D department might think of investments in scientists.
CollaborationScience is best done through collaboration. If you go to a physics department, it would be absurd to imagine that the scientist in one office doesn’t speak to the scientist in the office next door about what he or she is working on. So, we strongly encourage collaboration between our scientists. For example, we encourage people to work in teams. We constantly change those teams up so that people get to know others within the firm. We pay everyone from the same pot instead of paying different groups in accordance with how much money they’ve made for us and so forth.
InfrastructureWe want our scientists to be as productive as possible. And that means providing them with the best infrastructure money can buy. I remember when I was at IBM, there was this attitude that programmers were like plumbers. If you need a big project done, just get more programmers. But I knew that some programmers were, like, ten times or more productive than others. I kept pushing IBM management to recognize this fact. But it did not. I remember being in an IBM managers meeting and some guy from corporate headquarters was explaining how they created something called their headlights program. The goal of which was to identify the best programmers in the company and pay them 20 percent more than the other programmers. Now, I figured this guy from corporate was making, like, $300,000 a year. So, I raised my hand and suggested they increase the pay of their best programmers to $400,000 a year. And he was stunned. He said, “What? More than me? You’ve got to be kidding me. Well, if the guy’s Bill Gates.” I said, “No, Bill Gates was making, like, 400 million per year. Not 400,000.” Anyway, they just didn’t get it. We don’t make that mistake. We pay our programmers a ton in accordance with the value we place on the infrastructure they produce.
No interferenceWe don’t impose our own judgment on how the markets behave. Now, there’s a danger that comes along with success. To avoid this, we try to remember that we know how to build large mathematical models and that’s all we know. We don’t know any economics. We don’t have any insights in the markets. We just don’t interfere with our trading systems. Yes, of course there are a few occasions where something’s going on in the world and so we’ll cut back because we think the model doesn’t appropriately appreciate the risk of what’s going on. But those occasions are pretty rare.
Time
We’ve been doing this for a very long time. For me, this is my 30th year with the firm. And Jim and others were doing it for a decade before I arrived. This is really important because the markets are complicated and there are a lot of details one has to get straight in order to trade profitably. If you don’t get those details straight, the transaction costs will just eat you alive. So, time and experience really matters.

A word on politics

[Kris: Peter Brown is liberal and co-CEO Bob Mercer is famously conservative. I can say that coming from the trading world, the liberal perspectives are in the minority amongst the traders but less so amongst the academics.]

Raj Mahajan: Is it true that while Bob Mercer and you have different politics, you worked closely for nearly 40 years at IBM and Renaissance?

Peter Brown: Yes. It’s true. Bob and I began working together at IBM 40 years ago. And for most of the time, we’ve had offices right next to one another. So, we’ve done a lot together. And we’re still really close. In general, I find no better way of building friendships than through the collective creative process of building something together. And I see no reason why politics should interfere with friendship.

Man vs machine stories

1) My understanding is that you had nothing to do with finance until age 38 and, instead, began your career working on automatic speech recognition. How did that happen?

Brown: So, at one point during high school I learned about the Fast Fourier transform. And I thought this was about the coolest thing I had ever seen. Probably because I went to an all-boys’ school and had nothing better to contemplate. Anyway, for some reason I got into my head that with the Fast Fourier transform it should be possible to recognize speech. You just take the speech data, transform it into the frequency domain. Match it up against patterns for words. And presto, magic, HAL would be born. And this idea always stuck around in the back of my mind.

Then when I went to college I majored in math and physics. But in my senior year I had to fulfill a distribution requirement. So, I took a course in linguistics. And one day in the back of that course I heard a couple students talking about some guy whose name was Steve Mosher who started a company called Dialogue Systems that was doing speech recognition. And I thought, wow, great, I remembered this idea from back in high school. After class I raced over to the physics library. That’s because this was before the internet, so you had to go to the library. And I looked this guy up. And I found a paper he’d written. And I tracked him down. Applied for a job. And he hired me. And when I was there, I just fell in love with the idea that through mathematics it might be possible to build machines that do what humans do. I just loved the idea of exposing human intelligence to be nothing more than robotic computation.

2) I recently heard that in a talk you give at Harvard Business School you mentioned that you had a role in starting up the Deep Blue project at IBM. Can you tell us about that?

Brown: Wow. Okay. I had been at IBM for a year or two. And I was standing in the men’s room one day when the vice president of computer science, a man named Abe Peled walked up next to me. I thought to myself, now’s my chance. I turned to him and said, “Dr. Peled, do you realize that for a million dollars we could build a chess machine that would defeat the world champion? Think of the advertising value to IBM.” He turned to me, looking kind of annoyed, and said, “What’s your name?” So, I told him. And then he said, “Could you please let me finish up here?” And so, I thought, wow, I had made a big mistake. So, I apologized, and I high tailed it out of there as fast as I could hoping he’d forget my name even faster.

But a half hour later, he called me in my office and told me that if I wanted to build a chess machine, he’d put up the million dollars. I told him that I was occupied with speech recognition. I have three friends from graduate school who could build it. He said, “Okay, hire them.” So, we did. They built the machine. I named it Deep Blue. In the first match, the IBM machine was a very weak machine. Weak physically. You know, I think only one special purpose chip in it. And we lost. The final match, however, was a different story. IBM had a much, much stronger machine with hundreds of special purpose chess chips. IBM won that match and IBM’s stock jumped $2 billion afterwards. Of course, it fell back down later.

Now, a few years ago I was asked to speak at the Harvard Business School. And when I arrived, outside the auditorium, I could see all these protesters. And I thought, oh no, why are they protesting me? What have we done? Is there something I’m not aware of? I really didn’t want to do that. But as I got closer, I could see they were all holding signs about investing in Puerto Rico. And I thought, what is this all about? I was totally confused because I didn’t think we had anything to do with Puerto Rico. Then it turned out that the speaker before me was some guy named Seth Klarman from some firm named Baupost. Evidently, that firm had some investments in Puerto Rico and the protesters were protesting him. So, I went in to see Klarman’s talk, or at least the end of Klarman’s talk, to find out what all the hullabaloo was about.

At the end of his talk, someone asked him his thoughts on quantitative investing. I suppose it was a set up for my talk. I don’t know. And I carefully noted his answer which was, “To do what I do takes a certain amount of creativity and finesse that a computer will never have.” And all those Harvard Business School MBAs seemed to really like that response. So, when it was my time to speak, right after him, I began by pointing out that after defeating Deep Blue in the first match, Kasparov was elated and gave a press conference at which he said, “To play chess at my level takes a certain amount of creativity and finesse that a computer will never have.”I then went on to point out that two years later we crushed him. Now, I’m not sure that’s how things will evolve. But whether it’s speech recognition, machine translation, or building large language models, or chess, or making investment decisions, I continue to love the process of showing that human intelligence, intuition, creativity, and finesse are nothing more than computation.

[Kris: In defense of Klarman, like the pod shops, I don’t think RenTec is investing so much as trading. Marc Rubinstein writes:

Dmitry Balyasny, founder of Balyasny Asset Management, attributes the model to a trading view of markets as distinct from an investing view.

“[Its] origins go back to my origins as a trader and thinking about how to build out business around trading… It makes sense to have lots of different types of risk-takers, because you have less correlation, you could attack different areas, the markets, and have specialists in different areas.”

I’ve beat that drum in Trading vs Investing and with great humility in How I Misapplied My Trader Mindest To Investing.

Addressing Brown’s obsession with “exposing human intelligence to be nothing more than robotic computation.”

In The Introspection of Illusions, author David McRaney parses opacity of the intelligence and preferences buried in our subconscious:

Psychologically speaking, users found it easy to access the feelings that prompted them to give those films one star or five. Explaining why they made one feel that way would require the kind of guided metacognition that the Netflix interface simply couldn’t offer. Even when you stepped away from the code and the spreadsheets and asked people in person, they might not be able to tell you. They could make a guess. They could attempt to explain, justify, and rationalize their feelings, reactions, and star ratings, but without a conversational tool, a back and forth to get past all that to something honest and perhaps previously unexplored, you ran the risk of precipitating a psychological phenomenon known as the introspection illusion which would likely result in yet another phenomenon known as confabulation. There’s an entire literature of books and papers and lectures and courses devoted to this side of psychology. To put it very simply, we are unaware of how unaware we are, which makes us unreliable narrators in the stories of ourselves. You are, however, amazing at constructing stories as if you did know the antecedents of those things when explaining yourself to yourself and/or others.

There are parts of us we can’t access, sources of our emotional states we can’t divine, and I find some strange poetry in the fact that, like us, the algorithms can’t always articulate the why of what we do and do not like. Yet, through millions of A/B tests slowly zeroing in on more and more successful correlations, the Netflix Recommendation Engine can produce a glimpse of something a bit like the sort of profound, soul-exposing knowledge earned via an intense introspection that we could never achieve. Something a few fathoms deeper than “I don’t know, it just wasn’t for me.”

Speed Round

1) Is it true that at one point you went to IBM to suggest that the statistical methods you were using in speech recognition could be applied to finance, and asked to be given an opportunity to manage some fraction of IBM’s corporate cash?

Brown: Yes. I think that was in 1993. But IBM corporate had absolutely no interest. So, instead we went to Renaissance where we did the same thing we had in mind for IBM, but instead with money Jim Simons had raised.

2) Is it true that since you first joined Renaissance you have spent nearly 2,000 nights sleeping in your office?

Brown: Yes. My wife works in Washington DC. And my experience has been that when a husband and a wife work in two different towns, the husband commutes. Psychologically, if I’m going to be away from my family, I have to work. I sleep in my office when I’m in Long Island.

For me, productivity-wise it’s really fantastic being able to spend nearly 80 straight hours each week with no interruptions except sleep thinking about work before spending three more normal days at home. Of course, I really miss my family. But the freedom to concentrate nonstop on work while surrounded by my colleagues is hugely valuable. And the job is so demanding, I really don’t see how I could do it otherwise.

[Adds this] I’m just one of those types who can’t sleep. Not by choice. I just can’t sleep. So, I often am on the computer by around 2 am. And it’s true, I tend to send a lot of emails out in the middle of the night.

3) Is it true that you almost exclusively hire people with zero background and finance?

Brown: Yes. We find it much easier to teach mathematicians about the markets than it is to teach mathematics and programming to people who know about the markets. Also, everything we do we figure out for ourselves. And I really like it that way. So, unlike some of our competitors, we try to avoid hiring people who have been at other financial firms.

[Kris: The prop trading firms think similarly. My friend Joel talks about how Brown’s claim that it “is is easier to teach markets to mathematicians than it is to teach math to market experts, may seem dismissive to market-centric people but in reality is more of a statement about what “math” is at Renaissance.” He goes on to distinguish about levels of math but I latched on to this a more general observation:

Markets person isn’t a thing. Markets thinking is systems thinking and anyone from any discipline can learn that. From there go on Investopedia and learn how a zero coupon bond or share of stock works. start with a good, teachable mind then label the variables.

Math/STEM skills are legible markers of computational/rigorous thinking. Someone trained in the nitty gritty of assumptions, what follows, and so on. Making abstractions concrete.

If I’m generous it took a month of professional training for non-finance STEM grads at SIG to know everything finance grads would have brought to the table. But you can’t teach math and computer science in a month.

Ultimately this is only part of the story of getting a great start in finance. There’s a Berksons Paradox once you are in the pool of high level finance employment where the math skills don’t correlate as much with talent. You get older and realize the dichotomy of being a math person vs a verbal person that you carried as an identity when you were young is bullshit. Skills in either are likely highly correlated. But maybe the right door or guidance wasn’t there to help you see that.]

4) What do you actually look for in applicants?

Brown: Math ability. Programming ability. A love for data. A work ethic. And most importantly, the ability and desire to work will in a collegial environment.

5) How do you actually assess those qualities?

Brown: I think probably the same way other firms do. First, we get resumes. Those that look promising we give them phone interviews and we ask them for references. If those pan out, then we invite the promising applicants to give research talks. Talks like if you’re applying for a job at a university or something like that. And then we put them through a grueling day of solving problems in math, physics, statistics, computer science, and so forth at a blackboard.

6) Is it also true that your staff had to install mirrors in the corners of the office to prevent you from flying into people as you rode a unicycle around the office?

Brown: Where did you get all these questions from? Yes, it’s true. Although, I don’t ride a unicycle anymore because at one point I crashed and the unicycle broke.

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David McRaney on EconTalk

Episode description:

To the Founding Fathers it was free libraries. To the 19th century rationalist philosophers it was a system of public schools. Today it’s access to the internet. Since its beginnings, Americans have believed that if facts and information were available to all, a democratic utopia would prevail. But missing from these well-intentioned efforts, says author and journalist David McRaney, is the awareness that people’s opinions are unrelated to their knowledge and intelligence. In fact, he explains, the better educated we become, the better we are at rationalizing what we already believe. Listen as the author of How Minds Change speaks with EconTalk host Russ Roberts about why it’s so hard to change someone’s mind, the best way to make it happen (if you absolutely must), and why teens are hard-wired not to take good advice from older people even if they are actually wiser.

Link: https://www.econtalk.org/david-mcraney-on-how-minds-change/

I think the best teaser for the interview occurred during the interview when McRaney says:

The incepting point of this book was someone in a lecture came up to me and asked about their father who had slipped into a conspiracy theory and they said, ‘What can I do about that?’

And, I told them, ‘Nothing.’ They said, ‘How do I change his mind?’ I said, ‘You can’t.’ And, I really felt, the second I said it, that: I don’t know enough about this to say something like that. I don’t even know if I believe what I just said, but I know one thing I don’t like this attitude I have about this issue. I should at least learn more about it.

And, if I was in that same situation today, I would actually be able to say, ‘Oh, here’s what you should do. Here’s what you should say.’ I no longer believe anyone is unreachable. I no longer believe anyone is unpersuadable.

In conversations that don’t work out the way we think, we blame the other side. We say, ‘They’re dumb. They’re mean. They’re evil. They’re ignorant. They are unreachable, unchangeable, stuck in their ways.’ These are all things that we are using to forgive ourselves for failing.

My selected excerpts:

Motivated reasoning for social acceptance

Reasoning, psychologically speaking is just coming up with reasons for what you think, feel, and believe. And, those reasons are motivated by a desire to–a drive–to be considered trustworthy to your peers.

So, not only are you driven to come up with reasons for what you think you’re going to believe, you want them to be plausible. And, plausible, in this sense is: ‘What would your most trusted peers, your social network think?’ ‘Oh, yeah. That’s a reasonable way to see that.’

[Kris: For example, a quant might not respect reasoning that comes from intuition which they might see as excessively prone to bias whereas someone with a lot of experience might night trust the numbers for some unconscious pattern-matching reasons. The irony is both people recognize the intuitive approach is just pattern-matching but the quant thinks this is untrustworthy and the discretionary trader thinks it is.

I’d add another observation — the criteria for sense-making might be entirely context-dependent. Maybe the quant gets acupuncture when they go home. Our standards for epistemology vary depending on our expertise. The religious doctor doesn’t just pray for their patient.]

The process of radicalization

You feel something happens in the world that gives you this a negative emotion. Some anxiety starts to come up. It could be for really good reasons, but it could also be because you have some sort of prejudice or some sort of political bias.

So, then you do that thing. You go, ‘Hmm. Let me search for evidence that justifies the anxiety that I’m feeling.’

And, when you do that, online, you absolutely will find something that suggests your anxiety was justified.

And, you also might find people talking about that. And, you might end up wanting to talk with them about that. You might end up spending a lot of time talking with them.

And slowly you can radicalize yourself. You can cultivate yourself–cultivize yourself–and by, you start snipping your connections away from people who don’t share the attitudes being expressed in that community and you start strengthening the connections you do have with those.

And, now all of a sudden, you’re in a group. You’re in a community. And, the great sociologist Brooke Harrington told me that, if there was an E=mc² [energy equals mass times the square of the speed of light, Einstein’s Equation] of Social Science, it would be: the fear of social death is greater than the fear of physical death. And, if your reputation is on the line, if the ship is going down, you’ll put your reputation in the lifeboat and you’ll let your body go to the bottom of the ocean.

We saw that with a lot of reactions to COVID [coronavirus disease]. As soon as the issue became politicized, as soon as it became a signal–a badge of loyalty or a mark of shame to wear a mask or to get vaccinated–as soon as it became an issue of ‘Will my trusted peers think poorly if I do this thing or think this thing or express this feeling or attitude or belief,’ people were willing to go to their deathbed over something that was previously just neutral.

Reactance

[Kris: calling all reflexive contrarians!]

This is a concept that has been studied extensively in the context of clinical therapy.

They would come to the therapist and the therapist would say, ‘Well, you know what’s your real problem is. You should be doing this.’ Or, ‘I don’t know if you’ve noticed, but you don’t do this very much. You should do this.’..All that feels pretty good. They now call that in psychology the “writing reflex”. And, we’ve all felt that whereas someone is saying something and you’re like, ‘Oh, I have the advice for them. I know what to tell that person.’

But, you also have also experienced this other thing that happens, and this seems to be something that’s universal to human beings across all cultures. It’s just something that the brain that we’re issued at birth, it’s something that’s a feature of human thinking, rationality, psychology. Human brains do this. It’s called reactance. In the psychological parlance, they’ll say something along the lines of, you feel motivationally aroused to remove the influence of the attitude object, which just means: ‘You made me feel a feeling I don’t like and I want it to go away. So, I’m going to push you away,’ or ‘I’m going to disengage.’

What is the feeling that’s causing the motivational arousal? It’s the sense that your agency is under threat–your autonomy is under threat. It’s the ‘Unhand me, you fools,’ feeling. You’ve all felt this. If you’ve ever been a teenager or you’ve ever spoken to a teenager, you know what I’m talking about…”You shouldn’t do this. You should study more.”‘ This is good advice that the person when they’re 35 will go, ‘Man, my parents were right about that.’

But, in that moment it’s just the fact that you’re saying, ‘I have a thing in my head that should be in your head and I want it to be in your head.’

And, oddly enough, it’s the want that creates the reactance. The person’s feeling that you have approached them in some way and said, ‘I want you to think, feel, or believe, or act in a certain way that you’re not doing right now,’ and it feels coercive. It feels like they’d come at you and they’re threatening you. They’ve got a knife in their hand, and they’re saying, ‘Walk this way.’ That’s what it feels like.

We just, at a visceral level, will react by saying ‘no thanks’ to that, and we’ll push against it.

Basically, what you’re saying is ‘I have a goal and I’m not even concerned with what your goal is. This is the goal that I want you to go toward.’ Then they say, ‘Oh yeah, well, no. How about I don’t do that? I want you to stop talking to me that way.’ Well, now you feel reactance, because you’re like, ‘Oh, you’re telling me how to talk to you? How about I double down?’

And, then you enter into a horrible feedback loop.

This happened so often in therapeutic frameworks that they’re like, ‘We should really develop a way to stop doing that.’ Because what started happening was people would come in wanting to extinguish a behavior and then they would leave therapy more likely to engage in the behavior than if they had never seen a therapist because something along the lines of: they had these arguments for and arguments against. So, they were at a state of ambivalence when they arrived, but they wanted a little bit more in energy on the side of ‘Let’s not do the thing anymore.’ But, because of that, they counter-argued with the therapist. They generated counter-arguments inside of them that put more weight on the side of continuing to do the thing. So, they walked away with more arguments for than against than when they walked in.

This is also what happens when we have a conversation with someone where we disagree on an issue. Very often, if we create that feedback loop, they will walk away with more arguments in their mind than they had coming in to continue believing or feeling in the way they had before we had the conversation.

What I want to emphasize here is you can be very much correct. The facts can be on your side. You can be really trying to reduce actual harm in this world. You can have the moral high ground, and you can be dealing with a person whose intent, they’re, like, their action and behavior, their political stance harms you. They may even hate you.

So, what I’m saying is you can be on the right side of all of this, however you want to define the word right–you can be on the correct side of all those things. And yet, if you generate reactance from the other person to what we’re talking about, you will not be able to change their mind. You lose out.

And, it’s a very difficult thing to offer a person the space and give them the respect that would avoid reactance when you are dealing with a person that you feel like doesn’t deserve that treatment from you.

Shaming will cause the same defenses to kick in

If you say something that is interpreted–you may not mean to come across this way, but if it can be interpreted as, ‘You should be ashamed for what you believe. You should be ashamed for what you feel. You should be ashamed for that value or that intent to behave,’ even if I’m putting my hand at the side of my mouth. Even if they should be ashamed. If you communicate it that way, then you’re going to activate the person’s fear of ostracism. And there’s nothing more–like we said, there’s nothing more fearful for a social crime made than the suggestion that they may be ostracized. So, if you tell them they ought to be ashamed for feeling that way, it’s going to cause them to feel very viscerally upset and angry, and they’re going to push away from the conversation.

Unblocking the discussion

All you have to do is get out of the debate frame with the other person. Don’t make this feel like, ‘I need to win and you need to lose. I am and you are wrong.’ Just get out of that frame.

And, the easiest way to get out of that frame is to, first of all, say something along the lines of–instead of saying, ‘I want to show you what you ought to think, feel, and believe,’ you say, ‘Hmmm. You seem to know a lot about this issue and you seem to care about it a lot. You seem to see that these problems are problems. I’m wondering, given what you know, I wonder how it is that–because I look at a lot of this stuff, too. I wonder why we disagree on this issue? It’s really curious to me. I would love to talk to you a little bit more about that. I wonder if we could look at this issue and see what is it we disagree on here?’

What you want to do in that frame is give the other person a chance to feel like, instead of being face to face, you’re going to go shoulder to shoulder, and we’re going to–instead of looking at each other as obstacles, we’re going to turn and face in the same direction and look at the problem at hand, the goal at hand, the issue at hand. And, we’re going to collaborate now. We’re going to work together and say: Well, you’ve got your side of things, and your views, and your experiences, I’ve got mine. I bet if we joined forces, we could get to an even deeper truth on this or higher truth or a solution that works well for both of us.

You don’t even have to put it in those words. That’s another thing we have an innate inclination for which is, ‘Oh wow, we get to snap together and work together on a problem.’ You can frame things that way with just a slight change in approach and language and you will escape the debate frame that leads to reactance; and it’s much more fruitful.

Specific approaches that work

The thing that was most surprising in all that was discovering that there were all these different organizations that had said, ‘Okay. Well, what do we do about this?’ And, they started A/B testing conversation techniques. I found deep canvasing, and street epistemology, and smart politics, and then all the therapeutic models that I mentioned–motivational interviewing and cognitive behavioral therapy. And, on and on and on. There’s so many.

And, the thing that was most surprising was: Most of them had never heard of each other, never seen each other’s work. Many of them, the majority of them weren’t aware of the–if they weren’t in therapeutic domains–they weren’t aware of the science that would support what they were up to. Yet, independently, they all came up with pretty much the exact same technique. And, if you put it in a step-by-step order, it’s almost in the exact same order every time, too.

That seems to me like something almost in the world of physics or chemistry, and that if you were to build an airplane–the first person to build an airplane, it was always going to look like an airplane. It doesn’t matter where they built it. It doesn’t matter what culture they were from. It doesn’t matter how old they were, what they looked like, what they knew about anything. Airplanes have to look like airplanes because physics works like physics on the planet Earth.

Conversational techniques that actually shift attitudes and open people up to different perspectives, that get past resistance, all pretty much work the same way because brains resist for universal reasons and brains work in a very particular way.

Diving into street epistemology

Origin

Street epistemology came out of the world of the angry atheists and the militant agnostics who were having their own reaction to getting online and meeting each other. And, they’ve gone through several phases of growth and evolution themselves where they have schismed off. And, there’s some who are still very angry and there are some who are much more humanistic and empathetic.

And, within all of that, there was this movement that came about where they wanted to know, like, ‘How do we talk to people in a way that could avoid the angry pushback that we so often get when we speak with people who are not in our subculture or do not see or have our same theistic or atheistic views?’

And, they did the same thing that people did in deep canvasing. They went out. They had conversations with people. They recorded those conversations. They shared them with other people in the group. And, when something seemed to work well or get them closer to having a good conversation, they kept it. Anything that made it go the other way, they threw it away. And, through thousands of A/B-tested conversations, they started to zero in on something that worked.

And, now they’ve expanded it to: this can be applied to anything. You don’t have to be in their sub-community or have their theistic views to use it.

In the book, I talk about how there are techniques that work well on politics, techniques that work well on attitudes and values. And, then this one specifically works best with fact-based claims, things like, ‘Is the earth flat?’

How it works

It’s a stepwise method for having the conversation that we all should be having on any issue. Without going through an hour of trying to go through all the steps, I’ll give you sort of the quick version of that, which is: You open with a lot of the stuff we’ve talked about before–you open by establishing rapport. That’s that assuring the other person you’re not out to shame them. Assuring the other person, you’re not even there to change their mind. What you are there is to explore their reasoning. You ask them, ‘I would love to have a conversation with you in which we explore your reasoning on a topic and see what your views are and understand it better.’ Maybe: ‘You might shift, but you will have a deeper understanding of what we’re talking about.’ However, you want to frame it. Use your own language. You’re telling a person you’re going listen. And, most people will take you up on that offer.

I’m doing it right now. You asked if I would talk about something; you said you would listen; and I’m doing that right now. The podcast world depends on the fact that we’re all very willing to tell people what we think and feel about things.

So, give people that opportunity. You open the space for it. In this method, you ask for a claim. You ask for a very specific claim. It could be: Is the earth round or flat? And, then the person tells you–and then you repeat back the claim in the other person’s words. You make sure that you’re always using the other person’s words, because the big lesson in all of these techniques is that you are in their head, not yours.

You stay on their side. And, your job is to hold space for the other person to non-judgmentally listen and give them a chance to have a safety net, to metacognate and introspect.

And so you repeat the claim back to them. If they have definitions for terms, you ask for them; and you use their definitions, not yours. Like, if they say ‘the government,’ don’t assume that they’re talking about something from a civics textbook the way you look at it. They might be thinking of a group of reptiles in a round room talking about how they’re going to divide the country up to play golf. They have a different view of it. Let them–use their definitions.

And, then this is the big moment–and this is true across all of the conversation techniques. They all open in a pretty similar way with this space-creating moment. And, then they move to this thing that is magic.

It is asking the other person on a scale from zero to 10, or one to 100. The scale is a great way to get out of the debate frame and to assure the other person that this is not going to be a binary, right/wrong, black-and-white view of things. And, it even will work with the movie example you gave earlier, which is like, ‘Hey, Top Gun Maverick, what did you think of it?’ A person will say, ‘I loved it.’ That’s a very, like, black, white binary abstract. ‘Oh yeah? What would you give it on a scale from zero to 10?’

There is a moment when you ask a person a question like that, where they’ll go, ‘Oh, well,–that moment is, when they pop into that metacognating frame; and it could be like, ‘What did you think of this talk?’ ‘Loved it.’ ‘What would you give it on a scale of one to 10?’ ‘Oh, well–.’ That moment is what you’re looking for on any conversation topic.

[Kris: this is similar to asking someone to bet on their claim or handicap an outcome — the thinking switches from emotional to deliberate]

And you ask them, ‘What would you put it on a scale from one to 10, or zero to 10, or one to 100?’ Whatever they tell you, ask, ‘Why does that number feel right to you?’

This will encourage the other person to engage in reasoning–motivated reasoning most often. And, you let them do it. Let them do it the way they could do it. They’re going to come up with reasons that seem plausible for that position. But, what’s likely is that they’ve never done this. Not in this sort of like, ‘Please, present your reasoning to me’ kind of way.

It’s marvelous to witness a person saying–well, if they’re talking about Top Gun Maverick, they’ll have to start thinking, ‘Why do I have this emotion? Why was that so quick–why was it just like–it popped right in my head. What caused that to happen?’ And, they start coming up with reasons why that could be. Most of these are exploratory and they’re definitely going to be justifications and rationalizations.

Then, if you are actively hoping to get the person to see things closer to your perspective, if you’ve already done this for yourself and you know where you’re at on the number scale, ask the person how come they’re not in the other direction that you–appropriate to the issue. So, if I feel like–if a person says–if I say, ‘Is the earth flat?’ And, they say, ‘Absolutely.’ And, I say, ‘How certain are you of that from a scale from zero to 10?’ They say, ‘I’m probably a seven.’

Well, what you would ask is–first, you’d ask, ‘Why a seven?’ The next thing you’d ask–and this comes from motivational interviewing–is, ‘How come you didn’t say eight? How come you didn’t say nine?’ Because you’re asking how come they didn’t go all the way to 100% confidence. And, they must, on their own at that moment, generate their own counter-arguments against their position. But, you didn’t do that. No reactance. You’re not telling them what to think, feel. You’re not giving them your counter-arguments. It’s not your reasoning. They have to generate reasoning that counter-argues their position that will be new, that will be fresh, and that’ll be added to the collection of counterarguments in their mind. And, it will affect how they see things going forward.

With street epistemology, it’s more about just getting the person to examine: are they using a good epistemology to vet what they think and feel? So, after you have done all of these things with the number scales, you’d ask them what method that they used to judge the quality of those reasons that they presented. And, then you just stay in that space for the rest of the conversation as long as they’re willing to do it, and continue to listen and summarize and repeat and wish them well. And, try to make it so that you can have more than one conversation.

People do experience 180s in these moments sometimes. But, usually what happens is it’s by degrees, by increments. And, at the end of the day, the street epistemology people, they’ll tell you, ‘We’re not interested in changing people’s minds. We want people to just be critical thinkers. We want them to have more robust epistemologies.’ Which is sort of an even deeper way of changing a person’s mind. Getting a person to change their epistemological approach to the world is even more powerful than getting them to change just one belief, or attitude, or value.

Cognitive Empathy

This is a huge complex idea, but I think it all kind of plays into what we’ve been talking about previously, which is that sense of naive realism, where you just think that: ‘All people have to do is see the things that I’ve seen and they’ll naturally agree with the things that I think,’ if you don’t believe.

And it just takes–what it shows is a complete lack of cognitive empathy that other people come from completely different priors and experiences and social influences that affect the way they see–the way they form their beliefs–but also the way they interpret evidence.

An example of empathy failure:

I’ve seen this recently with a lot of these political ads that I’m seeing come across social media for places that I don’t live where they keep making these–I saw one today where someone was, like–they were in, like, the Midwest and they had these two people trying to survive in the desert. And, one of them is doing everything right because they’re a cowboy and they understand how to survive in the wilderness. And, the other one is a Senator who has no idea how to survive in the wilderness. But, the cowboy dies on Day 2 from a heart attack, because he doesn’t have good healthcare; and the Senator lives, because he’s got great healthcare, the Senator.

And the whole idea of the ad is: See. Senators have the healthcare that you don’t get to have. And, even though you’re a good, rugged individual who lives out there in the wilderness, who can survive in the wilderness, they’ll out-survive you because they’re taking away the healthcare you need.

That seems like a great political ad because it focuses on the identity of the individual that you’re approaching. But, that is an awful political ad based off of everything that I’ve learned in this domain, because it only feels like a great political ad to people on the Left–to liberals. It feels like a great ad for people who already have the values to which that makes you angry about that. It’s the inability to see that you can’t make an argument from your moral framework to a person who is in a different moral framework and expect it to land. You have to actually couch the argument in that person’s moral framework and their values. [ie “Moral Reframing”]

Lessons from Game Designer Raph Koster

I am going to be reading game designer Raph Koster’s book Theory of Fun pretty soon. For a preview, I listened to an old interview on the Think Like A Game Designer podcast with Justin Gary.

Link: https://www.thinklikeagamedesigner.com/podcast/2018/10/25/think-like-a-game-designer-5-raph-koster

Raph’s 25-year-old blog is a monument to design knowledge — it includes his writing, talks, and links to projects.

Raph is a creative force of nature. And this interview gets behind the madness. As always with my recaps, this is just what I wanted to write down for my own future reference but so much more is covered (there’s an especially great section about the use of simulations)

Ideating from scratch

When designing a game Raph will have a starting point. On one end of the spectrum might be a particular loop or mechanic the game hinges on. At the other end of the spectrum, he might start with the type of experience he’s looking to design. His approach to tabletop games tends to start with the mechanics and for video games from the experience. I excerpted the following because he decomposes the act of swimming into game mechanics off the top of his head in the interview. It was a neat example of how native this thinking clearly has become for him:

In my board game work, I find myself biased towards the mechanical. It’s unusual for me to start from the other end of the board games. But in video games, I often start from the experiential end. My goal is to establish what I know at one end and then use it to jump to the other end to draw conclusions. For example, if I start from the experiential end and I want to make a game about swimming, I think about the experience of swimming for me. There are different strokes. There’s the fear of drowning when you start to learn. Rhythm is crucial to swimming, as is breath management. The concept of a breath might be a resource. It could be something consumed periodically, but there might also be an exhaustion meter that decreases over time, limiting your breath. Different strokes might have different breath expenditures. If I decide to create a tabletop game, I think mechanically. I could set up a board with a race structure appropriate for swimming, perhaps with themes like sharks chasing or diving challenges. I’d play a game of resource management to get the necessary strokes, maybe using cards or tokens. If I were designing a digital game, I’d focus on rhythm, possibly incorporating a timing aspect and still manage resources of breadth and endurance. Different strokes would offer varied trade-offs. My aim is to establish two foundational ideas and move inward, paying attention to both. Ideally, they meet in the middle. If I have an abstract idea, like a deck of cards that “moves” me, I might not end up with a swimming game. It could fit another context but remain mathematically sound. It could be rules for moving cavalry in a supply chain. It’s crucial to consider both ends because it helps generate ideas that lead to a cohesive design.

Having a wide array of influences and skills

I’m always fascinated by and strive to understand the universal principles that apply to the creative process. It doesn’t matter whether you’re making games, poetry, art, or a movie, I believe there are common threads in how you approach creative work. You have such a polymath background, maybe you can speak to that.

My education and background is eclectic, with a consistent focus on the arts. I took studio art classes beyond the college level and I’m a musician. I play multiple instruments and studied music theory and composition in college. Interestingly, the one thing I do but never formally studied is programming. I have a Master of Fine Arts in Creative Writing and draw on all of these disciplines regularly. It’s challenging for me to imagine not being a jack of all trades or how I’d approach games if I weren’t integrating all these skills. I also frequently use Excel. A primary reason I enjoy game design is because it allows me to utilize various skills in one project.

Getting better — what does it mean to practice?

What’s the equivalent of “practicing scales” for other creative work?

I consider the practice of all those things I do as being very similar. I use the same habits for all of them. I made a list of them once in a blog post, which I think was called “practicing the creativity habit“. First was, whatever the activity is, do it regularly and make it a habit. So part of that is having the tools near you at all times. In the room I work out of, there are about 20 musical instruments within five feet, a complete art studio, a recording studio, and a game design reference shelf. Not actual games, but books about games, economics, interface design, and other topics related to games. For board games, I have a prototype kit with hundreds of dice, wooden bits of different shapes, and about 30 or 40 different decks of cards. The first thing is to make it a habit. Second, have the tools close at hand always.Third, give yourself constraints. I try to do that regularly. If it’s guitar, I might find five jazz chords and learn them, then write a song using those chords until I understand them. I picked up this habit from studying art and poetry. There are traditional poetic forms like sonnets, Villanelles, and haikus. In a writing workshop, we set ourselves the challenge of writing a poem using every single traditional form. In game design, it would be trying different game types. I haven’t succeeded at it for games, but that’s not the point. It’s about understanding design patterns. This approach applies to everything I work with, be it music composition, writing, or drawing. It’s a common underlying principle. It’s like working out — you need to rotate through the different muscle groups.

Intuition is pattern-matching against experience subconsciously

This is illustrated by an example from one of the cognitive science books on my shelf: the firefighter intuitively knows a structure is going to collapse. If you ask them why, they often have trouble explaining. I believe the process of conducting formal analysis of numerous games or seeking NP hard problem categories or compiling a pattern library and trying to internalize it helps strengthen our intuition. The exercise of building games around patterns serves as practice for honing this intuition. I may not always explain why I opt out of a conflict early, but I just intuitively sense it won’t work. The key is recognizing this earlier. I still believe 90% of ideas are shit but now, I discard them even before jotting them down, often when they’re just scribbles.

Nuance about the role of games in education

The changes over the years have involved the “chocolate covered broccoli” concept, where something fun is wrapped around an academic task. It’s clear this approach wasn’t effective. We’ve come to understand that games teach in specific ways that are well-suited for certain subjects but not for others. Games motivate players best through intrinsic motivation. Players choose to learn and take on tasks because they want to, with the game guiding their objective. For instance, instead of making a game to directly teach math, you create a game where players have a goal they wish to achieve. This might lead them to discover that understanding a certain type of math is the solution. They then learn it out of their own motivation. This is a realization that educational game design has recognized over time. As for games with broader themes, they can reflect social structures, human interactions, economics, politics, and other vast topics. While there’s an abundance of narrative-driven or viewpoint-based games out there, game systems can be informative as well. For instance, Sim City faced criticism for presenting an overly optimistic view of public transit and its associated challenges.

You can bias your game systems to convey specific lessons. It’s important to recognize that your game systems inherently teach lessons, intended or not.

Game-playing trains your “systems thinking”

Finding real world systems and abstracting them or boiling them down to their essence isn’t actually a very common skill. Games can teach people how to do this. The idea involves setting constraints, modeling real systems, and allowing people to experience them within a game context to understand them deeply. It provides an opportunity for individuals to experiment with these systems, unlike in real life where, for example, you only get one shot at lifetime earnings. Playing a game that emulates this system offers lessons. This is applicable to various scenarios, such as political engagement. There should be games that allow players to experiment with political engagement methods, helping them discern more effective strategies. This principle holds true in many areas.

Kris here…yea, I’ve made this point repeatedly over the years.

In Let Your Kids Play Boardgames I said this of the game Quacks of Quedlinburg:

Quacks is a bit like a deck builder. It’s known as a bag builder but with a don’t-bust-press-your-luck mechanic. To most of you, that means nothing but for the remaining, you should know this an outstanding game. It’s fun, and while seasoned gamers won’t like this necessarily, it has enough luck to allow a first grader to compete with an adult. I found myself thinking quite a bit about the value of the “options” (they’re actually chips representing ingredients in a potion recipe) in the game and their respective costs. The concepts of theta, volatility, and vega would be visible to someone with a finance background if they looked past the game skin. An engineer would see this game as a very pure simulation (most likely AI) based problem especially since the game has no trading interactions.

In Practice Second Gear Thinking I write:

We must identify second-order effects. In the options world, the “greeks” are sensitivities. Delta is the option’s sensitivity to the underlying. Gamma is a second-order sensitivity that describes how an option’s delta changes with respect to the underlying.

But this topic is everywhere. If a company sells more widgets it makes more profit. But second-order effects mean attracting more competition or saturating a market. Every satisfied customer is one less customer that needs satisfying. So if I build a model of profitability based on units sold, when does the function inflect? When does opportunity fade into unsold inventory?

A fun way to think about second-order sensitivities is playing “engine builder” boardgames like Dominion or Wingspan where synergies between your cards lower the marginal costs of later actions². In essence, the cards have gamma based on how you stack them. Every time I use a card it might increase my odds of winning by X. That’s the delta or “benefit per use”. But the delta itself increases with synergy, so as the game progresses, you get more delta or benefit/use ratio, from the same card

In Greeks Are Everywhere I write:

One of the reasons I like boardgames is they are filled with greeks. There are underlying economic or mathematical sensitivities that are obscured by a theme. Chess has a thin veneer of a war theme stretched over its abstraction. Other games like Settlers of Catan or Bohnanza (a trading game hiding under a bean farming theme) have more pronounced stories but as with any game, when you sit down you are trying to reduce the game to its hidden abstractions and mechanics.

The objective is to use the least resources (whether those are turns/actions, physical resources, money, etc) to maximize the value of your decisions. Mapping those values to a strategy to satisfy the win conditions is similar to investing or building a successful business as an entrepreneur. You allocate constrained resources to generate the highest return, best-risk adjusted return, smallest loss…whatever your objective is.

Games have mine a variety of mechanics (awesome list here) just as there are many types of business models. Both game mechanics and business models ebb and flow in popularity. With games, it’s often just chasing the fashion of a recent hit that has captivated the nerds. With businesses, the popularity of models will oscillate (or be born) in the context of new technology or legal environments.

In both business and games, you are constructing mental accounting frameworks to understand how a dollar or point flows through the system. On the surface, Monopoly is about real estate, but un-skinned it’s a dice game with expected values that derive from probabilities of landing on certain spaces times the payoffs associated with the spaces. The highest value properties in this accounting system are the orange properties (ie Tennessee Ave) and red properties (ie Kentucky). Why? Because the jail space is a sink in an “attractor landscape” while the rents are high enough to kneecap opponents. Throw in cards like “advance to nearest utility”, “advance to St. Charles Place”, and “Illinois Ave” and the chance to land on those spaces over the course of a game more than offsets the Boardwalk haymaker even with the Boardwalk card in the deck.

In deck-building games like Dominion, you are reducing the problem to “create a high-velocity deck of synergistic combos”. Until you recognize this, the opponent who burns their single coin cards looks like a kamikaze pilot. But as the game progresses, the compounding effects of the short, efficient deck creates runaway value. You will give up before the game is over, eager to start again with X-ray vision to see through the theme and into the underlying greeks.

[If the link between games and business raises an antenna, you have to listen to Reid Hoffman explain it to Tyler Cowen!]

Advice for aspiring game designers [Kris: I think much of this applies to anyone whose job is to communicate — which is basically everyone]

The first piece of advice is to make games. I understand many are familiar with this advice, but it’s valid: make a multitude of games and practice consistently. The second piece of advice, especially for aspiring game designers, is to become intellectually curious. I haven’t met any outstanding game designers who aren’t. Be a voracious reader and be open to exploring different fields. Be genuinely curious. These two traits alone can take you a long way in the game design world.

Well, ok. But, given Raph’s background, it seems incomplete for me to not quote uber-successful game designer Sadie Green’s character in the novel Tomorrow, Tomorrow, and Tomorrow responding to a rando who asks her “How did you get into making video games?”

Sadie hated answering this question, especially after a person had told her that he hadn’t heard of Ichigo. “Well, I learned to program computers in middle school. I got an eight hundred on my math SAT, won a Westinghouse and a Leipzig. And then I went to MIT, which by the way is highly competitive, even for a lowly female like myself, and studied computer science. At MIT, I learned four or five more programming languages and studied psychology, with an emphasis on Judic techniques and persuasive designs, and English, including narrative structures, the classics, and the history of interactive storytelling. Got myself a great mentor. Regrettably made him my boyfriend. Suffice it to say, I was young. And then I dropped out of school for a time to make a game because my best frenemy wanted me to. That game became the game you never heard of but yeah, it sold around two and a half million copies, just in the US, soooo…”. Instead, she said, “I like to play games a lot, so I thought I’d see if I could make them”.

Specific content recommendations

Game Design Workshop by Tracy Fullerton: A highly recommended book for those eager to dive into game system design.
Art of Game Design by Jesse Schell: Provides an expansive overview of game design, though not structured as a course.
Characteristics of Games by Garfield
Advanced Game Mechanics by Ernest Adams: Great resources for those intrigued by game systems.
Game Feel by Steve Swink: For those in the video game realm, this book focuses on the importance of haptics and controls.
Character Development and Storytelling for Games by Lee Sheldon: Stands out as a guide for game narratives.
Designing Virtual Worlds by Richard Bartle: Essential for those venturing into online communities and virtual worlds.
Understanding Comics by Scott McCloud: A recommended read outside the gaming sphere.
100 Principles of Game Design by Wendy Despain: Covers core design principles across various disciplines.
The Visual Display of Quantitative Information by Edward Tufte: Despite being pricey, it’s invaluable, especially “The Visual Display of Quantitative Information.”
Disney Training Manuals: Includes titles like “The Illusion of Life” and “Drawn to Life.”
Art from Studio Ghibli and art history collections: Resources that provide visual and historical context to design.
Design of Everyday Things by Don Norman: A foundational book on design, emphasizing that game design is a subset of the broader design discipline.
Diverse Topics: It’s beneficial to explore books on economics, history, and mythology.

Jerry Seinfeld Chats With Tim Ferris

This Seinfeld/Ferris interview is great:

Jerry Seinfeld — A Comedy Legend’s Systems, Routines, and Methods for Success (#485)

There are good notes here and I’ll list my favorite lessons despite the feeling that you should really take the whole thing in bc the delivery is as good as the lessons.

He writes every day like a job. Jack White (another artist I’m a big fan of has a similar view). Inspiration comes from perspiration kinda thing. It’s not “eureka” moments. Writing sessions should not be open-ended, that’s torture. You should have an end time to reward yourself.
The key to writing is learning to switch between these two states: When you are writing, treat yourself as a baby in need of care and love. Acknowledge that creating/writing is the most difficult thing in the world. The day after, you become a ruthless critic It’s 95% re-write.
Writing sessions feel very hard, but he knew he had to do it anyway (and realized this at a young age). Jerry attributes his success to his ability to stick with it. Today he’s so used to the frustration he doesn’t even notice it.
For Jerry, there can be no failure in going up on stage. Even if for him it’s a 4 out of 10 show, he still counts it as a win. This is part of nurturing and rewarding yourself as a creative.
“Never talk to anyone about what you wrote that day. You have to wait 24 hours to ever say anything, because you never want to take away that wonderful, happy feeling that you did that very difficult thing that you tried to do, that you sat down and wrote…Have you ever heard you never tell people the name that you’re going to give the baby — until it’s born? Bc they’re going to react, and the reaction is going to have a color. And if you’ve decided that’s going to be the baby’s name, you don’t want to know what they think”
Mind and body are intertwined deeply. Exercise is the closest thing we have to a panacea. The stress from weight training makes your nervous system more resilient. We need to be properly exerted. “a tired dog is a happy dog”
Weight-training builds your constitution. “You’re deteriorating. You’re just trying to bend that curve a little bit. I’m 66. I shouldn’t be performing at this level at 66. I should be over. So you have to cheat the biology.”
Repetition & systems “It’s like you’ve got to treat your brain like a dog you just got. The mind is infinite in wisdom. The brain is a stupid, little dog that is easily trained. Do not confuse the mind with the brain. The brain is easy to master. You just have to confine it.” Systemization is needed to harness talent:
He measures and gamifies to direct his progress. But not on the creation process:
Jerry is confrontational so he doesn’t let things fester. “I feel like if you break the human struggle down to one word, it’s confront. And so, I kind of approach everything that way.”
Survival is success. And if he could have a billboard it was say: Just Work
If Jerry could pass along something to his kids it would be ethics and boldness

A Few Excerpts from Dan Carlin on Lex Friedman

I recently listened to my first episode of Dan Carlin’s Hardcore History — a tour of the transatlantic slave trade entitled: Human Resources. Dan’s story-telling and research shine in this nearly 6-hour episode. He deserves all the accolades he gets. The style, quality and nuance of his work is well-advertised, I’m just late to the party. He immediately jumped into my favorite creators.

As a fan of the Founder’s podcast I found an interview with its host David Senra to be as compelling if not more than the books he highlights (a high bar), so I decided to hunt down interviews with Carlin. The first one I clicked on with Lex Friedman did not disappoint. I include 4 excerpts that stood out to me but most of the interview was enlightening and wide-ranging so I wouldn’t stop at these excerpts.

3+ hour interview (Lex Friedman on YouTube)

I used GPT-4 to clean up sections of this transcript: https://www.happyscribe.com/public/lex-fridman-podcast-artificial-intelligence-ai/136-dan-carlin-hardcore-history

An example of how propaganda can scramble your beliefs in a way that creates collective distortions that are hard to see

[Carlin is a war historian and while he admits to his bias towards individualistic ideals “I’m famously one of those people who buys in to the ideas of traditional Americanism”, his characteristic nuance is well-displayed in his deep skepticm of the “military industrial complex” and how its inclination towards self-preservation as an institution often exerts undue influence in when America looks at its menu of choices]

“Many people living today seem to think that patriotism requires a belief in a strong military and all the features we have in the present. However, this is a departure from traditional Americanism, which viewed such elements with suspicion during the first hundred years of the republic. They saw them as foes to the very values that Americans celebrated. The question arises, how could freedom, liberty, and individualistic expression thrive with an overarching military always engaged in warfare?

The founders of this country examined examples such as Europe and concluded that standing militaries or armies were the enemy of liberty. Today, we have a standing army deeply woven into our society. If one could go back in time and converse with John Quincy Adams, an early president of the United States, and reveal our current situation, he would likely find it terrible and dreadful.

Somewhere in our history, Americans seemed to have strayed from their path and forgotten their founding principles. We have successfully combined the modern military-industrial complex with the traditional benefits of the American system and ideology, so much so that they have become entangled in our thought process. Just one hundred and fifty years ago, they were seen as polar opposites and a threat to each other. When discussions arise about the love of the nation, I harbor suspicion towards such sentiments.

I am wary of government and strive hard not to fall prey to manipulation. I perceive a substantial part of what they do as manipulation and propaganda. Therefore, I believe a healthy skepticism of the nation-state aligns perfectly with traditional Americanism.”

The problem with dictators or strongmen even if they are wise and benevolent

“The challenge in a system such as a strongman system is the question of succession. When you have someone who can control and navigate the ship during a violent storm, if you’re not establishing a system that can function without you, then the severe instability and the dreadful future that you justify the strongman for is only postponed. Unless he’s actively building the system that will survive him and allow successors to carry on his work, you’ve merely created temporary stability. It’s the same problem that occurs in a monarchy.

In a monarchy, you have a king who might be good or perceived as good, but he will eventually hand over his duties to someone else. However, the system doesn’t guarantee a smooth transition because no one has really worked on it. For instance, you would need to inform me if Putin is establishing a system that can survive him and that will maintain the stability that the Russian people appreciate him for, even after he’s no longer in power.

If the oligarchs simply assume control afterward, one could argue that there were 20 good years of stability. However, if we consider the metaphor of a ship of state, the person steering the ship, from the Russian perspective, may have done an excellent job, but the challenges still exist and he won’t be in command indefinitely.

Therefore, it seems logical to assume that his responsibility is to ensure there’s a successor who can continue to steer the ship for the Russian people after he’s no longer there.”

Lex asks how we will “destroy ourselves”. Carlin gives a framework for handicapping what calamity will undo us.

Lex: If you were to wager on the method in which human civilization collapses, rendering the result unrecognizable as progress, what would be your prediction? Nuclear weapons? A societal breakdown through traditional war? Engineered pandemics, nanotechnology, artificial intelligence, or something we haven’t anticipated? Do you perceive a way humans might self-destruct or might we endure indefinitely?

My perspective is primarily influenced by our ability to unite and focus collectively. This informs my estimates of the likelihood of one outcome versus another.

Consider the ’62 Cuban missile crisis. We faced the potential of nuclear war head-on. That, in my view, is a hopeful moment. It was one of the few instances in our history where nuclear war seemed almost certain. Now, I’m no ardent Kennedy admirer, despite growing up during a time when he was almost revered, especially among Democrats. However, I believe John F. Kennedy, acting alone, likely made decisions that spared the lives of over a hundred million people, countering those around him who preferred the path leading to disaster.

Reviewing that now, a betting person would have predicted otherwise. This rarity underpins our discussions about the world’s end. The power to prevent catastrophe was in the hands of a single individual, rather than a collective.

I trust people at an individual level, but when we unite, we often resemble a herd, degrading to the lowest common denominator. This situation allowed the high ethical principles of one human to dictate the course of events.

When we must act collectively, I become more pessimistic. Consider our treatment of the planet. Our discussions predominantly center around climate change, which I believe is too narrow a focus. I become frustrated when we debate whether it’s occurring and if humans are responsible. Just consider the trash. Disregard climate for a moment; we’re harming the planet simply through neglect. Making the necessary changes to rectify this would necessitate collective sacrifice, requiring a significant consensus. If we need around eighty-five percent agreement worldwide, the task becomes daunting. It’s no longer about one person like John F. Kennedy making a single decisive move. Therefore, from a betting perspective, this seems the most likely scenario for our downfall as it demands a massive collective action.

Current systems may not even be in place to manage this. We would need the cooperation of intergovernmental bodies, now largely discredited, and the national interests of individual countries would need to be overridden. The myriad elements that need to align in a short span of time, where we don’t have centuries to devise solutions, make this scenario the most probable simply because the measures we would need to undertake to avoid it appear the least likely.”

[a later thread that rounds out his thinking on this]

“Returning to our primitive instincts, we are conditioned to address immediate and overwhelming threats. I hold a considerable amount of faith in humanity’s response to imminent danger. If we were facing a cataclysmic event such as a planet-threatening explosion, I believe humanity could muster the necessary strength, empower the right individuals, and make the required sacrifices. However, it’s environmental pollution and climate change that pose a different challenge.

What makes these threats particularly insidious is their slow development. They defy our innate fight or flight mechanisms and contradict our ability to confront immediate dangers. Addressing these problems requires a level of foresight. While some individuals can handle this, the majority are more concerned, understandably so, about immediate threats rather than those looming for the next generation.

Could we engage in a nuclear war? Absolutely. However, there’s sufficient inertia against this due to people’s instinctive understanding. If I, as India, decide to launch an attack against China, it’s clear that we will have 50 million casualties tomorrow. If we suggest that the entire planet’s population could be extinguished in three generations if we don’t act now, the evolutionary trajectory of our species might hinder our response.”

Will the US tear itself apart in a second civil war?

Lex: What’s the way out of this, is there some hope to avoid something, and I hate to use the terminology, but something that looks like a civil war? Not necessarily a war of force, but a division to a level where doesn’t any longer feel like a United States of America with the emphasis on “United”. Is there a way out?

I read a book a while back. George Friedman, the Stratfor guy, wrote it. It was called “The Next Hundred Years”. I remember thinking I didn’t agree with any of it. One of the things he might’ve said in the book was that the United States was going to break up. Something stuck in my memory about that.

Some of the arguments were connected to the differences we had and the fact that those differences are being exploited. We talked about media earlier and the lack of truth. We have a media climate that is incentivized to take the wedges in our society and make them wider. And there’s no countervailing force to help.

There was a memo from a group called Project for a New American Century, and they took it down. But the Wayback Machine online still has it. It happened before 9/11 and spawned many conspiracy theories because it suggested that the United States needs another Pearl Harbor type event. Such events galvanize a country that, without them, is naturally geared towards pulling itself apart. These events act as the countervailing force that otherwise is not there. If that’s true, then we are inclined towards pulling ourselves apart.

The media environment profits from widening those divisions. I was in talk radio, and there were those who used to be upset with me for not playing into this. We would have intense discussions after broadcasts, with program directors emphasizing heat. They wanted heat not for political reasons, but to attract listeners and engagement. Because of the format’s constraints, you don’t have a lot of time. They once told me the audience needs to know your stance on every issue within five minutes of turning on your show. This system is designed to pull us apart for profit.

That’s one example of many in our society that function in that manner. The Project for a New American Century document suggests we’re naturally inclined towards disunity. I think that’s what George Friedman’s book was suggesting, which I disagreed with at the time.

In answer to your question about civil wars, it’s different now. We don’t have a clear geographical division like before. Now, we’re divided within communities, families, and voting districts. So you can’t disengage. We’re stuck with each other.

If there’s a civil war now, it might resemble the late 1960s and early 1970s with bombings and domestic terrorism. You don’t even need a large chunk of the country pulling apart 10 percent of people who think it’s it’s the end times can do the damage, just like we talked about terrorism before and a can of gas and a big lighter.

Terrorism doesn’t need a particular origin or agenda. It could be someone upset about election results. If we’re looking at probabilities, everyone has to behave for society to work. Only a few need to act out for things to go sideways. For every action, there is a reaction, all they have to do is start the retribution cycle. And there’s an escalation. It creates a momentum of its own, which leads fundamentally, if you follow the chain of events down there to some form of dictatorial government as the only way to create stability. You want to destroy the republic and have a dictator? That’s how you do it. And there are parallels to Nazi Germany, the burning of the Reichstag, etc.

Allison Bishop On The Growth From Failure Podcast

My wife Yinh’s podcast Growth From Failure is in its 5th year. Her guests are extremely wide-ranging and rarely found on “the circuit”. Many of you know her and would agree — her multi-standard deviation superpower is a genuine interest in people’s individual stories. It’s a power that is not simply recognized by people she meets — it’s felt. You feel like she’s listening to you because she listens with her whole self. Some may wish for an invisibility cloak or a crystal ball. I’d take her ability to listen (and for every keto dude working on his abs — I’d be willing to bet that this listening skill is the OP aphrodisiac — the problem is it’s rarer than a six-pack.)

She doesn’t need me promoting her pod but yea you should check it out if you want to break out of your normal pod routine especially if you get the awkward sense that you’ve slotted too many quarters into the same dudes-who-use-business-idioms-like-open-the-kimono merry-go-round.

That said, if I promoted every episode I’d dilute my recommendations. I restrain unless the interview is:

☑️ too compelling to not share. Examples: Elizabeth Shaughnessy, founder of the Berkley Chess School (one of my favorite humans) or T-Vu, engineer and rapper (maybe my favorite GFF episode).

☑️if they intersect with quant finance. Past in this category include interviews with my former colleagues Kelly Brennan, partner at CitSec, and options trader Tina Lindstrom or power trader Noha Sidhom.

Today, I’m boosting her latest episode because it checks both boxes.

🎙️ALLISON BISHOP – COMPUTER SCIENTIST, WRITER, COMEDIAN. PRESIDENT AND CO-FOUNDER, PROOF TRADING (Growth From Failure)

Yinh’s intro:

This is the story of Allison Bishop, president and co-founder of Proof Trading, an institutional equities execution platform. In this episode, we cover Alison’s journey from aspirations in creative writing, which had a rejection and led her down a path that was much more quantitatively oriented than she thought. That led her to Masters and PhDs, in math, computer science, and cryptology.

I learned so much from her like the branch of math called combinatorics, which I’ve never heard before. And also the practical applications of cryptology and computer science and for the first time, finally, understanding how those were actually used.

But more importantly, I learned about controlling how you set people’s expectations and reclaiming that power, either emotionally or physically. Allison literally changed the way she moved in the world.

Select excerpts:

On her transition into math after being rejected from the hallowed Princeton Creative Writing path, her entire reason for choosing Princeton in the first place:

I was devastated, it was the entire point that I was here. And at the time, I was also supposed to be just getting rid of my general distribution requirements. One of those was to take a math course. And I refused to take calc, which was what most people are taking. I’d taken calc AB in high school, and I find it kind of boring. And so they’re like, you could just take Calc II.

Meh, what else you got?

I ended up taking number theory because I thought it was the “physics for poets” of math. It is not. It’s a foundational course in cryptography. And it was a course math majors were taking and it set me on this very different path of learning that I liked math and that math could be creative. But it happened because of this simultaneous confluence of events that I got rejected from Creative Writing, and I refused to take calculus.

I also did take the “physics for poets” version at Princeton. I thought it was great. It was really philosophical and insightful, and I quite enjoyed it. But yeah, the math class was not at all that. It was a serious math class and I also had no conception of what Princeton math is. What the Department was or what that meant. And I’m really glad I didn’t know because I think I would have found it very intimidating. I got pretty deep into this process as a math major before people started, like telling me, you do know, this is one of the best math programs in the world. Oh, no, I didn’t know that. You know, the guy teaching linear algebra is a Fields medalist. What’s the Fields Medal? And it was just very perplexing to the people around me how I got there because there are usually about 60 to 100 people every year applying to Princeton for math and in my year, only 15 graduated with that major. I ended up being like this wild card person who did not know what was going on, but was in for the ride.

What she found creative in math

It was the first time I actually got to write proofs. This concept of using math not as a vehicle to just calculate something, but as a way of reasoning about the world. That was new to me because I feel we lose so many opportunities in the way we teach math at the elementary, middle school and high school level. Here’s the procedure and you do it. If we’re lucky — “here’s what that means”.

But there’s not “here’s how people came up with it. Here’s the history of it, or here’s the question from first principles, and have you come up with the procedure?” So the first time having these open-ended questions be asked, and then having to reason about it myself, assemble proofs out of the building blocks that we’ve seen in the class.

The one that I think fascinated me at first, which is actually a relatively simple one that you can teach at the middle school level, is a proof that there are infinitely many prime numbers. The fact that you could prove there are infinitely many of something as complicated as prime numbers. What’s fascinating to me, and the creativity that goes into this, okay, how do I reason about this? How do I create a procedure to always produce a new prime outside of the set of primes that I produced so far? That constructive building process was very appealing to me.

But when you’re writing a proof and realize how creative it is, but then you’re going through it again and it’s still the same answer does it then feel black and white, and then QED?

Not really. And I think this is something that mathematicians are at varying degrees of denial about, because there is this process of peer review that happens when we prove new theorems and people put them out. And it’s highly nontrivial to go through and check someone else’s proof and make sure everything makes sense. And in some sense, when we can reduce each little step to this logic axioms, then you can check that we mostly gain confidence. But the process of putting those things together really shapes the theorems that you’re proving. So it’s not really that there’s this a universe of facts. And then there’s these proofs that get attached to the facts. And then as long as we have a proof attached to the fact that we can check we can move on. There’s so many different things that we could reason about, or relationships between objects that we could try to prove theorems about so much more of the interesting stuff goes into the human process of what do we find worth studying? What questions do we ask?…I think mathematicians are a little bit reticent to admit how subjective the process of curating what’s important and what things we study is, because the process of checking a proof is already nontrivial, but is solvable in some sense. But the process of deciding which proofs are worth doing, and which questions are worth investigating is where most of the fun stuff happens.

From math to computer science

Math separates into two sub-fields. For me, at least the way I think about it, some of them have very complicated objects, and then you prove relatively simple theorems about them. So all the complication goes into the objects themselves. And so there’s things like topology, which is about shapes in the world, and things like that. And algebraic geometry. And combinatorics is more like the objects are pretty simple. Graphs, you’ve got dots, and you’ve got lines connecting the dots are things but then the theorems and the patterns that you study on them are complicated, which is much more aligned with computer science.

I ended up wandering from math to computer science. So as an early PhD student at Austin, I would go around to the different mathematicians in the department and try to learn about their research, I would always ask them, “What is the application of this in the world?” And I got a range of answers from there aren’t any applications of this in the world to tons of applications to other areas of math. And so I was pretty underwhelmed by the potential impact of the things that were being studied. And there’s a lot of fundamental research that happens. And we don’t know yet how it’s gonna get applied. And it eventually gets applied. And I think this stuff is really cool. But from the perspective of I have one life, and I like being very people-facing and doing things that impact people directly, it wasn’t really compelling enough to me. So I started wandering over to the computer science department.

It’s always a joke I make with mathematicians, “what are you guys doing over here?” It’s all the same stuff. But it’s got much better research grants in computer science, and it’s much more potentially impactful and relevant, pretty directly. So I ended up switching halfway through my PhD to be in the computer science department, which meant doing all of their requirements, again from scratch. So I basically did two PhDs and left with one of them. Totally the wrong way to do it. But I ended up in a good place sort of finishing my PhD with an emphasis in cryptography in the computer science department.

[Skipping over a discussion of the 2 major branches of cryptography which solve the same problems with different tools but remain silo’d for complex reasons as well as her human interest in the field]

While I’ll also skip how she started working with IEX before starting Proof Trading I found the transition story useful:

Cryptography and finance are similar fields — they both try to scare people away with enough acronyms. So I was somewhat comfortable at that point, being the person who just asked everyone questions all the time. And so my strategy on the trading floor was just sitting in the middle of all these people and whenever they their take their headphones off, I would ask them a question. “Hey, I’m wondering what this graph means. Can you come over here?” I was just shameless about asking questions. But I think also just having that prior experience of learning a jargon-heavy field, knowing that the fact that I didn’t know what the things were, didn’t mean I was stupid. And it didn’t mean it was going to be hard. It just meant that I had to ask, although I do think finance makes it unusually tough.

I’ve also been documenting some of my process. Proof has put out a market structure primer, which is basically my writing down the things that I didn’t know, and therefore asked and putting that into a form that hopefully helps other people. Because we do think that, unlike cryptography, where there are textbooks and there are public facing things and surveys that are helpful, in finance, you’re piecing this together from different people’s memories. I’m in the middle of the trading floor asking “What does this four-letter code mean on this trade flag?” There are no public references for so much of this. So I do think as a field, we need to do a better job of giving people friendlier entry points, but the people on the trading floor at IEX, around me were great at just having the patience to answer a million questions.

What problem is Proof Trading trying to solve?

The biggest surprise to me learning about the stock trading system was that the entire purpose of stock trading is to provide public information about prices. That’s the whole reason for being of the stock market. And yet, absolutely, every structure around that is completely opaque, which I just think is ludicrous. I will make the same argument with people about pay transparency, we need better insight into where these prices are coming from, for exactly the same reasons that we need public price discovery for stocks, but taking it specifically to the broker layers.

What we saw from our seat at IEX, was that we were trying to build tools that would ultimately help long-term investors on the buy side, but we didn’t have direct access, because that’s not who IEX is — its customers are brokers. And every time we’d ask a question, well, why are brokers doing this? Or why are brokers acting this way? There’s no insight available into that because brokers are this black box.

Thinking about this coming from cryptography, cryptography is all about keeping secrets, but they are much less secretive than this. Because fundamentally, whenever somebody comes to the cryptography community and says, I have a cool new way of encrypting data but I can’t tell you how it works, we laugh at that person, then we break their system immediately. Usually, “I can’t tell you how it works” means it’s not going to stand up to scrutiny. And the reason we believe in the encryption algorithms that we have is that they’ve been exposed to decades of people trying to break them and improving them and all these things. So it seemed crazy to me that the science of electronic trading algorithms of how to round certain ways or how to build or design a schedule, or how to optimize to certain goals for order performance, that that was all individualized inside these black boxes, instead of benefiting from an open science culture. It seemed completely handicapped to me as a scientific field and completely unnecessary.

There are things that need to be protected and confidential, like, what stock are you trading and when are you trading it. But if I’m claiming to you that I have a way of taking big trades and breaking them up into pieces in the market, so that they blend in with the noise, if they really blend in, then I should be able to tell you my mechanism, and it should still work. And if I’m forced to expose that mechanism, it should open up all of the surface for public science and collaboration that should help us get much better outcomes. So I’m a big believer in making science public, whenever there’s not a great reason to do otherwise, rather than secrecy being the default.

So one of the main things we wanted to solve for as a broker was to make as much of our decision-making process public as we could. So to make us more accountable, to open that up as a surface for collaboration with our clients that if we tell them what we’re doing, they can say, Oh, well, that’s not aligned with our goals in this case. And that’s a much more rich surface for communication and improvement than just saying, Here’s the box, you can use it or not. So it’s kind of our hope that bringing a scientific process into the open in this area would really jumpstart progress toward better solutions.

[Skipping a balanced discussion of the challenges they face as a remote-first company]

Can you give me an example of what Proof Trading does for a client? (I highlighted the refreshingly honest part of the answer)

Once a client has decided they want to buy a large amount of stock or sell a large amount of stock, our job is to figure out how to split it over time and space into pieces, so that the market doesn’t disproportionately react. How do we make their total activity blend in with the random activity of the market in such a way that we can get done what they want to get done without the price going up, because they’re buying or going down, because they’re selling so it’s reducing impact relative to getting the trade that you want? That is the value that we’re looking to add.

One thing that’s very tricky about this process is that the market is very noisy. So there’s a lot of noise in the data performance. And it’s very hard to compare two algorithms, apples to apples, because they get different kinds of orders and different market conditions, huge sample sizes. So it’s a very tough sales position. I can’t point to a single order. “Look, this is the amount of money we saved you.” And people seem to expect that, which seems super weird. When we go into the sales call it’s “explain to me how many basis points your algorithm is going to save me versus my competitors.” One, your competitors won’t tell me anything about their performance. So how would I know that? And two, I can’t even measure my own performance cleanly, because it’s a function of all this noise. And so I get a lot of befuddled expressions on the other side, it’s like, well, then what are you selling?

We think we’re better. We just also think this is really hard to show and I’m not going to show you some half-baked thing.

Addressing her recovery from a deeply personal trauma:

As a growth moment, it was pretty significant because I was having symptoms of depression and PTSD as a young faculty member. And what was strange to me about it was that my ability to function and do my job was the last thing to go. I was not sleeping, I was not eating well, everything was off. And yet, I was still publishing papers and teaching classes on paper, everything looked fine. And I had to convince myself that it was bad enough to heal, just give myself permission to heal.

And I think one of the things that surprised me about that process was it was less of the growth — it was more, almost shedding a limb, just completely resetting. And that was actually the time a lot of people have asked me about. I started dyeing my hair, blue and purple, and I started getting tattoos that were meaningful to me and changing my appearance. I felt like I was walking through the world looking like this person that I wasn’t able to be anymore. And having people react to her the same way they used to react to her was stopping me from healing, people would expect things in me and I would do them because that’s what I’d always done. And so I needed to find a way to change what people expected of me. And so I think one of the biggest things was reclaiming my physical presentation and space. That was also when I started learning, boxing, and just changing the way I move in the world.

So for me, I think the biggest growth moment was realizing that sometimes you need to reset, and you need to reclaim some of the things that are in the way of that progress and giving myself permission — also to quit, as a faculty member and not say it is a failure. I think a lot of academics think of leaving academia is always intrinsically a failure. It was a continuation of my journey, the willingness to be seen as a failure. And that sense, I think was really important.

And I loved this part:

Realize that we have more control over setting people’s expectations for us. And I think it takes a certain amount of separation from people’s reaction to you just realize that that lever is there to some extent. And for me, it really came out of a place of necessity. And from feeling it in myself, I had to wake up in the morning and look in the mirror and see this person that I didn’t feel existed anymore. And that was this weird, disconnecting feeling.

This was always the biggest thing I tell people thinking about public speaking or comedy, is that if you make yourself laugh, it’s contagious. If you’re getting on stage, and you want to convey an emotion, or you’re pitching your startup or whatever, if you feel it, the audience will feel it. And so it’s getting to the point where you feel good about what you’re doing, or you feel excited about what you’re doing is 90% of it. And the rest is just icing on the cake.

Check out the full episode to hear about how Allison defines success, what she learned from her blunder as an 18-year-old intern working on the Mars Rover with the JPL (Jet Propulsion Lab), her boxing and comedy outlets and how her professor Jordan Ellenberg (my notes on his book) inspired her to embrace her varied interests and her recent acceptance to a creative writing MFA program!

Allison also founded a conference called CFAIL: Failed Approaches and Insightful Losses in Cryptology.

It’s an annual event where we give people a platform to talk about research that didn’t ultimately succeed in whatever they were trying to do. So it’s whether they tried to prove something and they couldn’t, or they tried to build something, and it was broken. And that has been, I think, just one of the funnest and most energizing research conferences that I’ve been a part of people embracing talking about failure.

I’ll leave you with this skit Allison created:

“The MMWF Award” — a fictional award for Men Who Support Men Who Support Women in Finance.

If you use options to hedge or invest, check out the moontower.ai option trading analytics platform

Venkatesh Rao On Infinite Loops

Introduction by Infinite Loops host Jim O’Shaughnessy:

Venkatesh Rao is a writer, consultant, and author. He has been writing about indie consulting for years and has recently published The Art of Gig, Volumes 1 & 2, which together take an in-depth look at the gig economy.

Venkatesh joins the show to discuss tragic luck, becoming slightly nonsensical, the advantages of mediocrity, and a whole lot more!

My intro:

Venkat is one of my favorite writers. I pulled a number of ideas out of this interview for posterity. This is not a summary, just things I want to keepsake.

Episode link

[All bold is mine]

I had an equal number of people calling me a communist and a capitalist. – Venkat

I loved this line for the same reason I love this quote by Niels Bohr:

“How wonderful that we have met with a paradox. Now we have some hope of making progress.”

On Arthur C. Clarke’s distinction between a failure of imagination vs a failure of nerve: Arthur C. Clark has this wonderful essay called Failures of Prophecy, I believe, and he talks about two kinds of failure in thinking about the future, failure of imagination and failure of nerve, and he makes the very interesting claim that the failure of nerve is, by far, the more important. A lot of
people are extremely imaginative, they can take in the vast amount of confusing information in the world now and come up with very imaginative sort of interpretations and sense-making constructs. But very few people can look the confusing or mess of reality in the face and say, “This is actually the nervy, courageous thing to do,” And go against their instincts. And it’s easy to, I don’t know, spend as much time as you like on the fun imaginative stuff and never do a courageous thing in your life, whereas the nervy thing is kind of the hard thing to do. So that’s where that particular phrase came from.
“Paycheck people” The Paycheck People connection is, I guess, the industrial economy over a century, has created this gamified environment relating to work where it is easy to get through life never making a hard decision ever. So long as you’re smart and imaginative, you will always be valued, you’ll have a job, somebody will give you interesting problems to work on. If you have the right kind of imagination, maybe you’ll come up with good answers, but your courage is not routinely tested. And this, I think, was probably the case in most sectors in the, at least, developed industrialized world, and the paycheck economy in particular, until, I would say, the mid to late nineties when things started wobbling and the old certainties were really starting to unravel and it became clear that you could not go through life only being smart and imaginative and playing the game that was laid out in front of you because at some point, you had to make courageous decisions, and the paycheck world is not really set up to allow you to make courageous decisions.In fact, in the paycheck world, I would argue, courageous decisions are, generally, decisions that kind of break the gamification model of the world itself…. In the brief time I was paycheck employed for several years at Xerox, I dropped a few bombs, I made few bold decisions, but what was shocking was not that a few senior executives kind of spotted me and chose to sponsor me and back my decisions and help me take, I don’t know, risky decisions and risky projects, but the complete lack of reaction in the rest of the corporation, and this, I think, is generally true. It’s like it’s outside their frame of reference to understand risk at all. It’s like, all right, you make this narrow band of maybe very intelligent and imaginative decisions, but within an extremely narrow band of acceptable risk. And beyond that, risk-taking is for senior executives, weird people in the investing world, artists and creative types who live hand-to-mouth and are starving. Risk is not within the frame of reference for how to navigate the world. And I think that’s kind of why I relate the paycheck economy to kind of a structural failure of nerve, it sort of trains you to not have nerve, it trains you to survive without it, and I think there’s a cost to that over a long term.
The false sense of security in the paycheck world
A lot of the sense of security in the paycheck world is a completely false sense of security. That security does not actually exist. It’s as risky as being a gig economy independent, it’s just that they manifest and structure themselves differently. And if you refuse to take risks for year after year, quarter after quarter, for years on end, you’re going to end up with lots of risks. So yeah, it blows up in your face.[Kris: this feels like another one of those failures of mental accounting. We have a “first order effect” myopia where we are sedated by a steady paycheck while the wild world evolves and the sociopathic forces that govern corporate life conspire at least subconsciously against you — unless you are also all-in on the corporate Hunger Games. The person who is not playing the game ruthlessly is the one quietly accumulating all the risk when they thought they were playing it safe]
Is there a character type or archetype that tends to be more open to risk?
This is a very interesting and complex question, and I think there are layers to it that are truly worth unwrapping…
1. On privilege
  Some of the basic criticisms of risk-taking, especially from the liberal corner, it’s like, yeah, the people who look like they’re bold risk-takers, if you look at their background, you’ll often find that they come from privilege and the risk, subjectively, for them, is not actually that big. The marginal cost of a hundred thousand dollar risk is not the same if you sort of grew up in poverty and made that first hundred thousand dollars with painstaking hustle versus if that hundred thousand is just 1% of a very large trust fund you inherited.
2. Psychological conditioning that leads you to have different perspectives
  Another interesting layer, and this comes up in the famous two marshmallow test, for example, which has been kind of discredited, but it’s interesting how it’s been discredited, where the original research claims that kids who were willing to wait for the researcher to come back so they could have two marshmallows instead of having just one marshmallow right now, they did better in life and future. And poking at that, some more recent research looked at the backgrounds of children who made the two kinds of different decisions, and it turned out that the kids who picked one marshmallow now, versus two marshmallows later, tended to come from less privileged backgrounds where the short-term environment was relatively stable, but the medium and long-term environments were so unstable that it was actually rational for them to say, the future is so uncertain, I’d rather take the one marshmallow now than risk this.And if you poke deeper, there was this research done, I think by Phil Zimbardo, he’s another person who’s gotten canceled for questionable research. But one of the interesting things he did were these tests to study time perspectives, was asking children to tell autobiographical
  stories, and it’s very telling. People who grew up in deprived environments tend to tell stories that span a day. People who grew up in more privileged environments tend to tell stories that span a lifetime. So a poor kid who grew up in the slum might say something like, I got up in the morning and went to the market and found, I don’t know, a sandwich or something. It’s a day-long story. And a rich kid might tell a story about how I’m going to go to college and study law and become president and everybody will love me and I’ll be famous.
3. The subjective perception of the quality of the risk
  Let’s say you were bullied as a kid and developed an extremely strong fighting instinct. And in a particular situation where somebody might be very combative and another person might be very conciliatory, you take what looks to other people like a very risky decision to escalate a conflict and blow things up. And maybe that traces back to some, I don’t know, early childhood memories of fighting back against bullies
  or something like that. So to you, in that situation, psychologically, it could be the case that, even though there are lots of explicit risks, like saying something objectionable and potentially losing your job, the sort of utility of that outcome or disutility of that outcome is actually less than the disutility of challenging, say, very deeply repressed traumas and learned behaviors for dealing with those traumas.
  
  So I think the narrative people tell themselves of what the risk they’re taking actually means to them, is actually very different from what you might assume just watching a drama unfold from out there. So my point here is, the risk looks very different to the person taking it then it might to different spectators, and this is part of the other mind’s problem. Like, I’m in a meeting, somebody else is saying something very risky that might get them fired and I’m like, “I would never say that because if I put myself in their place, I would see the risks and utilities very differently.” But part of that is… There’s the seen part, which is explicit incentives which affect everybody like money, losing positions, jobs, and investments, but there are also internal psychological risks. Maybe I don’t have the traumas that could be badly exposed and brutalized if I did certain things that another person does.

Venkat reveals his system for dealing with the complexity:

I’ve gotten, I think, both more empathetic and more judgmental about this stuff…The way I like to phrase this is, I like to keep my psychology complex, but my moral judgment’s simple. It’s like, I can never quite put myself in your shoes and unpack the complex psychology of why you’re taking the decisions you are, but I am going to draw some hard lines in the sand and say, “All right, murder is wrong.” I don’t care how traumatized and messed up you were in your childhood or how murdering someone seemed to you, the less risky thing, as opposed to facing the consequences of some actions and drawing a line in the sand at murder is wrong, you’re infringing another person’s life. So I think that’s kind of the layers of how I think about this stuff.

So to your original question of how should we think about the distribution of risk and is there a genetic predisposition to risk-taking, I think the answer is probably yes. There are probably people who are fundamentally more, I don’t know, risk-positive, and more likely to just blow things up to see what happens. So there’s probably some genetic predisposition, but there are these layers of trauma management, behavioral conditioning, and circumstantial assets that create so much more context that, I would say, the genetic component, at least in modern conditions, is probably
almost a rounding error.

I don’t take extreme risks and I’m like somewhere in the middle. But I probably present as somebody who takes a lot more risks than I actually do, because, from my subjective perspective, the actions I take are not actually as risky as they appear. So I think that’s a big theme in a lot of my writing. I’ve written about it in a few places, but the risk that I think I’m taking is not the risk you think I’m taking.

Rationality as nihilism
There’s a philosophical notion that rationality is actually an extremely nihilistic mode of cognition. An extremely rational approach to say scientific discovery and experimentation, science is a fundamentally nihilistic process. If you let it, it’ll tear apart any sense of meaning you have in the world from any source whatsoever. And this is true of any approach to thinking and decision-making that sort of draws inspiration from more scientific and rational approaches to decision-making. Which is, you define your terms of reference, you define the variables in play, you sort of make up mental models of how those things relate to each other, hopefully in a scientific spirit. So you kind of make sense of the world and understand how the world works. Then you kind of decide what you value and say, “I value this much, I value that that much.” There are constraints. And so you end up solving an optimization problem that there’s a natural progression from thinking of the world in rationalistic modes and being sort of rational and building your models of reality, to moving to synthesis and normative considerations through optimization problems. And what that leads to is seeing sort of life itself as an optimization problem where you are like, “All right, what’s my utility function? What are the weights on the different factors? How am I going to solve this cost function? If I have constraints in the picture, are they hard or soft constraints?So you kind of almost turn yourself into an automaton that’s trying to solve an optimization problem. And there’s a long story about why this happens, but this is basically a doomed process. It will lead nihilism. By the time you’re done solving your perfectly rationally formulated and pose the problem, and you maximize your utility function and say, “Hey, this is the most utility maximizing outcome and decision I could hope for, and I’m going to do X, Y, and Z, and I’ll live the optimal life,” you will find that what you’ve solved for is a completely meaningless life.

It’s sort of this conundrum of complete rationality and optimization as an approach to decision making leads to completely valueless, but technically correct, utility maximizing outcomes. So it’s the whole economics idea of knowing the price of everything and the value of nothing. This is the manifestation of that.
How fixed point futurism allows you to break out of this nihilistic process of optimization
The answer is, you actually have to start not from rationality, but from complete arbitrariness. It’s like, “I’m going to pick something utterly arbitrary. I’m not going to attempt to justify it.” You want arbitrariness in a completely… How do I put it? Artistic sense. It’s like, “I’m just going to declare that blue is my favorite color.” So that’s the kind of thing I mean by arbitrariness. And it’s amazing how often committing to this kind of arbitrariness actually kills nihilism inherent in otherwise rational postulates. And then you can be as rational as you want about everything else so long as you hold faster that one arbitrary commitment you’ve made, which is what I mean by fixed point futurism. And an example that’s on my mind right now, my wife and I, we are shopping around to buy a house for the first time. And it’s like that’s a classic rational optimization decision-making problem. You can make up a spreadsheet about buying houses with all sorts of attributes about property values, income taxes, property taxes, blah blah, blah. And you can lose yourself in this optimization problem of a million variables, and sort of get everlastingly trapped in this hell of trying to decide what your utility function is.But on the other hand, you can pick something arbitrary that is going to break you out of that. So for example, my wife is arbitrarily attached to the idea that she must have beautiful view, where either of the ocean or mountains or something like that. And that can be an optimization variable where you can talk about, “All right, how much view am I willing to give up for lower taxes or whatever.” But it can also be arbitrary. You can say, “I want a view of Mount Rainier,” and that’s an extremely arbitrary thing that can lock you in.

It’s a little philosophical sleight-of-hand trick of turning a nihilistic process into a meaning-making process. It’s like now you’ve decided no matter what happens to the rest of the world and whether we are all doomed to die in a zombie apocalypse, you are going to make one thing true about the future.

You’re going to be wearing blue shirts, you’re going to be staring up at the sky with a telescope, even if it means zombies are coming at you and you’re like killing them with the machete, right? That’s what I mean by fixed-point futurism.
If you’re mired in the “paycheck economy”, you may be in such a routine that more existential questions about meaning are ignored or not given space. How does Venkat think about this?
Meaning I think is extremely strongly related to the first topic we were talking about, nerve versus imagination. I think meaning-making begins when you first take your first courageous decision in your life and then realize just how much agency you have. To what extent you are operating in a condition of learned helplessness in institutionalized environments. And the first time you sort of make a reach for a truly autonomous decision, despite the risks, you realize how much more opportunity there is to do so. And for me, the first time that happened was actually long before my leap into the gig economy, when I was unhappy with my first PhD advisor. And I made the decision that even if it’ll costs me my financial aid and I’m sort of adrift for a while, I’m going to break up with my PhD advisor and sort of go off in the wild for a while and find another advisor and new funding.And that is what I did. It was very sort of a tough decision. I quit that advisor, I lost my funding, I had to go off and work at a startup for a year. Then I came back, worked with another advisor I got along with better. But I think that flipped a switch in me where after that, solving for meaning became so easy and so much second nature because it’s not an intellectual problem. You don’t have to be smart to solve for meaning-making, you just have to be courageous. You have to do the tough and hard thing as opposed to the maybe intellectually complex but easy thing. So I think I’m a reasonably smart guy, but I think what unlocked meaning-making for me was that first choice to make a tough decision. And after that, it was brain-dead obvious to me. Anytime I came to a fork in the road, it’s like, “Yeah, this is obviously the more meaningful thing to do, so I’m going to do it.”
How the world obscures this
I would say that today the world is set up in a way where it’s actually hard to learn this meaning-making trick except by accident. And one of the things that I think this growing conversation around the gig economy is doing, is sort of reinforcing the intense practicality of looking for meaning. A lot of people don’t get this.If you look at conversations about meaning-making in the abstract, the way talking heads talk about them and talking about lost voice, listening to podcasters and getting radicalized, that level of conversation about the meaning crisis, it seems like a philosophical spiritual problem that should be addressed with religion and philosophy, ideas and so forth. It’s not. It’s really as simple as meaning-making is unlocked when you first learn to take courageous decisions and keep doing that, so it becomes a habit. And after that, you kind of unlock this idea of fixed point futures and all these other little tricks become sort of self-evident.

Learning to make meaning is the most intensely practical thing you can do. It’s not a matter of spiritual retreats and going on soul-searching journeys and having shamans take you on Ayahuasca retreats and things like that. It’s not about that at all. It’s the first time you come to a hard decision in your career or life, make the hard decision, see how good you are at making tough calls, and then keep doing that and meaning-making will take care of itself.

And I think that’s a lesson that the emerging conversation in the gig economy is driving home for a lot of people. And a lot of people who stay in the paycheck economy, stay in an environment that makes this way too hard, that tells them, “You have to go on spiritual retreats and read Zen philosophy and take drugs to learn meaning-making.” And it’s not that hard.
The “tragically lucky”
At age 23, it’s very tempting to conflate agency and determinism in scripts. You think you control your future, you think you can make a very specific future happen. You think “I’m a smart guy, I have these resources at hand and I can solve the problem and solve the equation of how to turn my talents and resources into the outcomes I want.” And then of course, life hits you in the face. You realize it’s much more complex.Then you ask, “What happens next?” Do you then refactor your sense of agency as in “I still have agency, it just doesn’t work the way I thought it did, so I’d better get about understanding how the world actually works and understanding how agency actually operates.” That’s one path.

The other path is of course the world sort of mashed my plans to bits and pieces and I’m going to be helpless from here on out, and that happens to people too.

So I think a naive sense of youthful agency does not survive first contact with the enemy…but for some people it does. And of course, there are also people who just get lucky in a very naive sense in the sense that they plan a particular future and actually it unfolds because they never get hit in the face with conflicting reality. So there are people I know who are these spirits who go through life everything having gone exactly as they planned.

But there’s a certain tragedy there, which is, they think they’re super agenty then things go out exactly as planned and they become president or whatever, and then they’re like 60, 70 or these old people and they come across as children. I talk to them and they’re like, there’s a sense of a lost child about them. It’s like they were never really tested by life, so they’d never really actually learned what was going on. It’s like they’re 60 or 70 and they act like children or 23-year olds maybe. And part of the reason is they were on the surface, they were super lucky that things bent as they planned, but at the deeper sort of philosophical level, they’re the most intensely tragic figures in the world because things went exactly as they planned. The most interesting thing that can happen to you in your life is things don’t go as you plan. And because that forces you to come to terms with what’s the actual nature of the world, what’s the actual nature of agency.

[Kris: I think most people’s desire or goal in life is to become tragically lucky. Ignorance is bliss and all that. Hard to think of anything more boring.]
Finding a healthy sense of agency
2 versions of the problem

a) There are people who were so battered down by early life traumas that they never make those naively optimistic 23-year old statements at all.

The first challenge is to get them to that place of naive optimism in the first place. So I think of that as a much more basic challenge of humane treatment of young people, which is if they’ve been battered, just create kind environments for them where they can develop some confidence and say, “Hey agencies, actually I think that exists”, even if it’s at a very toy level. So often when I’m thrown into position of trying to mentor younger people, which I try to avoid, I’m not a mentor type person, but when I am thrown in the situation, my tendency is to ask “how burned are you? How much are you a devastated landscape of bad parenting and bad childhood conditioning that we have to get you to the starting line of being a naive optimistic age 20?” And this requires kindness and nurturing, and I’m not very good at that, other people are better.

b) But let’s assume some people are already at that starting line of naive optimism

How do you ensure that you when throw them into reality a) they don’t get tragically lucky. Let’s hope they don’t get tragically lucky. Throw them in something that actually challenges their assumptions about the world and breaks them in some way. But then how do you ensure that if they’re broken, they’re actually not going to react with complete helplessness, but then sort of pick themselves up and say, “No, the world works differently and I’m going to rethink what agency means.”

And I think yes, that is a learnable, teachable skill, but it’s one that the industrial environment with schooling and the paycheck world is actually anti-optimized for. It’s designed to teach you exactly the opposite of that. It’s designed to take you from an naive starting point and keep you tragically lucky for the rest of your life. And if they fail at it, you’re tossed by the wayside. That’s what the industrial world is set up to do, make you tragically lucky or throw you into the garbage heap.

In the developing world, more people are thrown by the wayside, and in places the US more people enjoy the tragically, enjoy is the wrong word, but suffer the tragically lucky outcome.

We don’t want either of those outcomes. We want you to be thrown into the world, into a test environment where you’re
actually tested and then you kind of learn the skills through trial and error of acquired realistic agency

The self-defeating fear of technology
Go back as far back as you like in tech history. And you will always find that it’s the case that whenever tech sort of automates or disrupts a category of labor, it creates 10 times more new kinds of labor, right? So we feed the entire planet with maybe 4% of modern populations in agriculture. It used to be 80 plus percent a couple of centuries back, but we produced more food and feed everybody. And it’s not that all those farmers banished. Future generations just weren’t farmers anymore. There was no need for them to be farmers. So in one sense, it’s true. If you’re attached to your idea of yourself being a farmer or a creative writer who’s going to get disrupted by LLMs and you’re attached to that idea, you are going to go obsolete. So your expectation is, in fact, tragically correct. You are going to be extinct. But the question is, “Why are you choosing that path of extinction?” I think the reason is that these people like the character in Office Space who says, “I’m a people person,” they’re actually the most degenerate sort of lesser subhumans in our world, because it’s kind of weirdly dumb to live as part of a species that’s been a technological species for 6,000 years and reject this hugely important aspect of our environment that’s been growing for 6,000 years, right? Saying, “I’m a people person,” and sort of distancing yourself from the world of technology that you’re completely entangled with is like I translate it as saying, “I choose to be a 10th of what a human being actually is.” So yes, the thing is it’s not the technologies or tools or that we are sort of masters of our technology, that we use technology as tools, but that we co-evolve with them. The medium is the message, and we are inextricably entangled with our tools… what tools you sort of came of age with… I’m as much a product of my human conditioning environment, my parents and my friends and so forth, as I am a product of computers and various other technological artifacts through which my brain became what it is today, and that process kind of continues.
The key to playing infinite games: mediocrity
I would say, my least popular idea ever, and therefore, my absolute favorite idea ever, which is this call for mediocrity. I have a whole series on my blog about mediocrity and how I love mediocrity and how I solve for mediocrity and I hate excellence. And finally, it’s just me being a troll and a contrarian in this culture of excellence and trying to win and 4.0s and being excellent and Six Sigma and optimizing and all that whole world of being Paycheck People. This is why the term Paycheck People evokes a whole world of stuff. They are people who play finite games. They do all these things. But yeah, how do you get away from that? Be mediocre. I think I got onto this line of thinking starting with David Allen, the author of Getting Things Done. I got friendly with him in 2005 or 2006. I met him a couple of times. Lovely guy. In his workshops on Getting Things Done, he starts off with a very good joke, which is, “How do you get things done? I’m going to give you an absolutely perfect hack. Lower your standards.” I love that. And he doesn’t mean it flippantly. A lot of people assume he’s just making a joke, and the rest of the workshop is about, “All right, how not to lower your standards and how to actually maintain high standards and do it.” No, David means it. He means fricking lower your standards.This, I think, is an unbelievably important key to unlocking infinite game attitudes, because the conceptual connection is optimizing in a finite game and trying to win by particular standards and maximizing according to those standards means you’re operating in a closed universe of sort of contextual information and you’re ignoring everything outside of that. And the infinite game is fundamentally about recognizing the fact that there is an infinite universe out there beyond the little scope of a little game you’ve set yourself to play, and there is no such thing as ultimately winning or losing in that little fake game you’ve made up. All that happens is that you either win or you don’t. And then you sit back and say, “All right, life hit me in the face or made me tragically lucky.” So I think winning in a finite game is tragic luck, losing in a finite game is life hitting you in the face.

But then what happens next? You’ll sit back, let that boundary dissolve, expand your horizons and say, “All right, what else is out there in the universe? What else is out there beyond the scope of this game that I was just in, that I can pay attention to a,” I don’t know, “new fixed point that I can sort of latch onto and create a whole new game around?” That’s how you continue the game, right?The way to do this is to let go standards. The way to do this is to appreciate that mediocrity is the key, which is when you’re in any given finite game, you don’t go all out. You reserve a part of sort of your human potential for just paying attention to peripheral vision, the world outside the little particular game you happen to be playing. Of course you play it sincerely. And ironically, you do right by commitments you’ve made to other people. If you say you’re going to do a job and other people are depending on you to do that, you do that. So I’m not saying cut your standards in the sense of being unethical or unreliable or anything like that. I’m saying maintain a reserve of who you are, your thinking, and devote it to the infinite universe beyond the particular game you happen to be playing. That looks, from the context of the game, kind of like mediocrity. It kind of looks like you’re not willing to go 110% and hustle and go all out and stuff like that. It looks like, at some level, a part of you is philosophically checked out because it’s curious about the rest of the universe. It’s not checked out because you’re lazy or lack courage or insufficiently committed. You’re checked out because you’re bigger than the particular thing you happen to be doing right now. You’re a larger being that has a bigger faith in your life, and you want to reserve a part of your attention to that. And there’s a strong evolutionary logic to that.[Kris: the logic is basically how slack or redundancy in nature gives the randomness embedded in evolution to be expressed.]

This is why success can be so tragic for people who’ve been overcommitted to it. It’s like you’ve actually won and you’re acting devastated and destroyed, like the meaning of your life has been rugged from you. The rug has been pulled out from under your feet. That’s because you overcommitted. You had too high of a standard.
Divergentism
As people grow and age, they fundamentally diverge from each other in thought space, and that’s okay. It’s okay to not be understood, because that’s what happens when you diverge from other people. It’s like people don’t understand you as well, and you don’t understand them as well. So I would say the compact form of the thought is it’s okay to not be understood and to not understand other people.

[Kris: I’ve had a similar thought. When people are surprised by others my response is always…”there are 8b people in the world”]