Notes from Marc Andreessen on Education


The education system is based on model that pre-dated the printing press. It has had little innovation in light of the technological advancements. Yes there are experiments like Lambda School and its ISA alignments. There are MOOCs which offer micro degrees. But in 2020, distance learning as necessitated by Covid, has accelerated the questions we have about a system whose costs were already outpacing inflation. We are left to wonder who our current system is serving and if it is time to examine more efficient possibilities.

Recently Google dropped the requirement that new hires need college degrees and it’s expected other large employers will follow suit. It begs the question, what were degrees good for?

The CEO of Figma, Dylan Field, interviews Marc Andreessen to hear what the cost/benefit of our college system is and how recent developments will test theories about what college is good for and what alternatives may serve those requirements better or more cheaply.

Purpose of college

Overt purpose: A bundle of actual education/skills acquisition, social/dating service, network building, “attached to a hedge fund” (in the form of an endowment)

Cynical purpose: Outsourced personality and IQ testing (via SAT) as these screens have become either socially undesirable or illegal for employers to perform.

The personality dimension being tested for is known as conscientiousness 1 which has 2 components.

  1. Industriousness: Basically self-starting energy
  2. Orderliness: Attention to detail, time management, organization

The “sheepskin effect”

Somebody who goes to college for seven out of eight semesters does not receive seven eighths of the income of somebody who goes for eight out of eight semesters, they receive half the income of somebody who goes for eight out of eight. So the diploma signals your conscientiousness by evidence of you clearing the 4 year hurdle.

A diploma tells employers you are a smart kid who can get their work done, signaling conscientiousness, rather than being about knowledge acquired.

Testing the purpose of college

  • Covid-19 will tease out how much people are willing to pay for an online education which will hint as to how much of the value proposition derives from the degree, from the social, and from the actual learning (this acting as a constant). International enrollment which is unsubsidized would be an especially useful clue as you would expect the loss of social network effects would impact those students the most.
  • The test of college as an outsourced intelligence test will naturally occur as leading universities shed standardized testing requirements

Understanding the source of the student debt crisis

We need a conversation about value given vs value received of college from an economic lens because it is subsidized by Federal and state government. If the ROI is not there the victims are tax payers and the students who cannot discharge the debt via bankruptcy.

How did we arrive at a mountain of debt that cannot be serviced?

The system is a hostage of a govt sponsored cartel.

  • K-12 education is compulsory and state-run. Captive audience.
  • Hallmark of monopoly: real dollars spent on education have 3x in 40 years and outcomes are unchanged
  • Funding is monopolized
    • Accreditation: Loans are subsidized by the government and are only available to accredited institutions that are certified by the govt. Accreditation or admittance to the cartel is nearly impossible.
    • University research funding comes from the government. Can’t remember the last research university to come into existence
    • Operating a university is taxed as a non-profit
    • Endowments are taxed as non-profit

    Meanwhile between sports programs and endowments these institutions have more in common with for-profit businesses.

The spiraling costs are exactly what you might expect from a monopoly and to be contrasted with perfectly competitive businesses such as manufacturing that have led to goods disinflation.

Basically what the government does to education is just like what they do in health care, it’s just like what they do in housing. A two part strategy for managing these markets. They restrict supply. And then, and then restricting supply causes prices to rise, because there’s more kids that want to go to school than can get in. And then on the other side rising prices create political pressure which they resolve by subsidizing demand.

(This was part of his anti-govt rant. I haven’t fact checked any of this. He also points out that spiraling costs without an improvement in service is also the hallmark of 2 other heavily govt influenced areas: housing and healthcare. The story of the ultra-liberal Cal professor who called for subsidized housing while he votes against development to maintain “historical charm” came to mind.)

The value proposition of university for people in “show your work” fields is changing.

One of the most basic revelations the internet has surfaced is the different nature of professions.

Internet has made the largest difference in “show your work” professions: occupations where it is valid and easy to demonstrate your value online. For example, coding, design, music, art, game dev, animation. Open source projects and writing, democratized, pure examples of “show your work” fields.

From an employer’s point of view conscientiousness is a proxy for being a good employee. But this can be circumvented by just showing your work online. This erases the value of a degree that derives from employer demand.

GitHub has like an internal ranking and rating system for software code, and for programmers. So you can actually build an actual professional reputation as a software developer on GitHub without ever actually being face to face with another human being. People all over the world today who were basically taken advantage of this to be able to basically build these incredible track records as a software developer and make themselves more employable. Employers like my venture firm. We recommend that our employers spend as much time on GitHub looking for good programmers as they do on LinkedIn, or going to college fairs.

YouTube, blogs, Figma for design all play a similar role as GitHub does for software developers. He tells the story of South Park as an early example of a viral video that was able to spread organically through a distributed technology. The show born from Matt Parker and Trey Stone’s irreverent holiday card which made its rounds as a downloadable Quicktime vid!

“If you can go to college, go to college”

  • Even jobs that probably shouldn’t require degrees require them.

I think it’s actually quite dangerous to give somebody, somebody as an individual the advice, don’t go to college, like in the current system that we have that’s basically saying don’t prove that you’re smart don’t prove that you’re industrious, and conscientious and then basically be prepared to settle for fundamentally lower income for the rest of your life.

  • Understand the proposition

Gates and Zuckerberg notwithstanding, if you go to college finish college. Get the piece of paper.

  • The 2×2 matrix of what to study and where to study.

The spread of outcomes for technical degrees is not that wide. If you have a technical degree your choice of school matters less. This is exactly the opposite of what you find with liberal arts degrees. Since the output of a liberal arts degree are more subjective or uneven the school issuing the diploma carries more weight. 

Possible explanation: in absence of concrete skills, the network from a top school is valuable.

Tips for those in college or considering college

Execute on the opportunity — take the hardest course load you can. Get the skills (obviously get good grades but focus more on getting the skills).

If you are at a sub-tier college taking liberal arts, de-risk by acquiring marketable technical skills.

For those considering alt paths

At this point Marc, still recommends college and acquiring technical skills but if you choose an alt path be aware of the trade-offs. For example, if you choose to do open source work recognize it’s better to make major contributions to one project (as opposed to minor contributions to multiple projects) because that really demonstrates what employers are looking for. Put yourself in the mind those who will be evaluating you years down the road.

Consistent work demonstrates conscientiousness and the nature of the work is an embedded intelligence test.

What should a software developer do? Unquestionably the answer is create an open source project or go become a member of an existing open source project and make successful high quality sustained contributions to that project over time. At this point I think that’s clearly a better credential than getting a computer science degree. I’d hire people like that myself and the great thing now is you can do that from all over the world.

So what matters to Andreesen when they hire or fund someone?

The good news:

They do not care about a degree or GPA or test scores and in fact question if too much conscientiousness means you are too much of a rule-follower.

The tough news:

They measure you by what you have actually done. Building companies requires being able to do things so that is the capacity they are looking for. List of things a founder will need to be able to do:

  • Building an actual product that somebody will actually pay for.
  • Figuring out a way to actually sell it to them
  • Actually collect the money
  • Actually service the customer so they actually have a good experience
  • Actually tell their story so that anybody will even know that they exist
  • Run a finance function so that they don’t lose all the money
  • Run a legal function so they don’t get sued all the time
  • Actually get others to work with them.

There are many talented people so the way to stand out is to actually demonstrate the ability to build or create.

Steve Martin best career advice ever: Be so good they can’t ignore you.

Developments to watch

  • New credentials2 to replace bachelor’s degrees (ie Google certification program, coding tests, and math puzzles)

  • Still early innings of “show your work” online as way to qualify yourself

Notes on Trading Volatility: Correlation, Term Structure, and Skew

Trading Volatility: Correlation, Term Structure, and Skew
Colin Bennett

The book is a broad reference on basic option theory, dispersion, and exotic options. It includes practical insight into managing a hedged book with a focus on correlation, term structure, and skew.

In addition its appendix includes the following topics and more:

  • a taxonomy of historical vol computations including and how they rank on “bias” and “efficiency”
  • shadow greeks
  • cap structure arbitrage theory

It’s an outstanding reference so I took notes. For public sharing I re-factored them by topic and tied some back to my own investment writing.

You can find these edited notes in my public Notion page. (Link)

My Favorite Takeaways From Derek Sivers On The Knowledge Project



About Derek: Musician, speaker, writer and entrepreneur

Allocating Time

  1. Hell Yeah rule: If an activity doesn’t feel like a hell yeah for you, then don’t take it.

(However, don’t use the Hell Yes rule when you’re just starting out in your career. In the beginning, try to say yes to every opportunity.)

  1. “Strategically, it’s better to do 5 bigs things with your life instead of 500 half-assed things”.

    Me: I’ve seen this advice from Josh Waitzkin and Marc Andreesen as well

  2. On curation and deciding what to read:

A lot of books are much longer than they need to be. Derek doesn’t want to read a 400-page book about diet, he wants to read one page on what to eat and what to avoid.

“If you trust the source, you don’t need all of the supporting evidence”

Me: This is a case I’ve made for curating your info sources carefully. Being thoughtful about your inputs takes effort but saves you time in the long run. This is the idea behind my post Build Your Own Cabinet


“You know you’re a true business owner when you could leave your business for a year and come back a year later and find that it’s doing better than when you left. That’s when you’re no longer self-employed, you’re a business owner.”

The short term pain of training and delegating is acceptable if you take the long view.

Me: This is a critical point if you ever want to sell your business at a multiple. It’s a strategy that complements what potential acquisition funds look for. For example Brent Beshore’s Permanent Equity fund lists “Healthy Layer of Non-owner Management” as a one of its investment criteria.

Ideas Without Execution Are Dime A Dozen

Ideas multiplied by execution will tell you how much a company is worth.

Motivating Employees

Let employees own projects

When you offer small tweaks or suggestions to an employee who is taking initiative on an idea or project you steal some of their thunder. They lose a sense of ownership. The resultant loss of motivation is always larger than the value of the tweak. Resist the urge to make a suggestion.

A convenient development is the project usually ends up incorporating your suggestion organically as the work starts getting done and the direction becomes more clear.

Derek Has A Pragmatic, Even Post-Modern Approach To Life

“Whatever makes you take the necessary actions is the perspective that helps you. I’m never aiming for reality. I’m trying to make decisions usually based on finding the perspective that helps me take actions.”

Thinking From First Principles Is Thinking For Yourself

“To me, the world feels unnecessarily ceremonial, like people imitate others without questioning it enough, but I don’t want to learn their ways. I don’t want to be like them”

Me: I’m a big believer in the wisdom of markets, the wisdom of Lindy and that which has endured, and the value of thinking by analogy. But I think this type of thinking makes the most sense when it comes to tactics and strategy. But when it comes to choosing what you want to do with your time on Earth this is an obvious place to think from first principles. Don’t live someone else’s script.

An example of why I think this is my own experience with education where I generalized too much from the school environment to the life environment. I wrote about that in We Don’t Need No Education

The Inverted Recipe For Happiness

Inspired by Charlie Munger’s adulation of mathematician Carl Jacobi’s advice “invert, always invert”, Derek has a list called:

How To Stop Being Rich & Happy

1) Prioritize lifestyle design

Make all your dreams come true and follow your immediate gratification

2) Chase that comparison moment

Always buy that the new thing

3) Buy, not rent

Buy the house, boat, etc.

4) Internalize your new status

Celebrate your new status and relax

5) Be a connoisseur

Insist on only having the finest foods, drinks, etc.

6) Get to know your possessions

Spend more time learning about more possessions and getting them just right

7) Acclimatize to comfort

Eliminate all discomfort and blame others when life seems hard


(note: for more of a true summary of the pod you can read this version from Podcast Notes)

Finally here’s 2 short ideas from Sivers:

  • How To Start A Movement (Link)

    The insights he draws from this wacky little vid are tight.

    1. See how “the first follower turns the lone nut into a leader”

    2. Leaders should de-emphasize themselves by treating followers as equals. 
  • Keep Your Goals To Yourself (Link)

    This short talk goes against the conventional wisdom that announcing your intentions creates motivating pressure for you to follow through.

The Why And How OF Taking “Discoverable Notes”

I have been an active note-taker for years and a fan of how meta Tiago Forte gets about the process of taking notes. Tiago’s Building A Second Brain course is very popular in productivity circles. While I have never take it, I recently came across his essay Progressive Summarization: A Practical Technique for Designing Discoverable Notes. (Link)

It’s an outstanding framework for understanding the whys and hows of taking notes. I, by accident, have arrived at a very similar system so it was interesting to see someone explain it thoroughly as only Tiago can.

This is a summary of what resonated with me.

The Why Of Taking Notes

The Right Info At The Wrong Time

What you read is good and useful and very important, you’re just reading it at the wrong time.

The challenge is knowing which knowledge is worth acquiring. And then building a system to forward bits of it through time, to the future situation or problem or challenge where it is most applicable, and most needed.


Bridging The Acquisition And Use Of Knowledge

It’s too mentally expensive, if not impossible, to internalize all or most of the information we consume. A good system is intended to bridge the time between when you discovered the information to when you use it.

At that future point, when you’re applying that knowledge directly to a real-world challenge…By the time you’re done solving a real problem with it, book knowledge has become experiential knowledge [which you carry forever].


The How Of Taking Notes

Defining The “Second Brain”

An external, integrated digital repository for the things you learn and the resources from which they come. It is a storage and retrieval system, packaging bits of knowledge into discrete packets that can be forwarded to various points in time to be reviewed, utilized, or deleted.

Designing The “Second Brain”

Goal: You are trying to triage information in an organized way. You read something you know is interesting and you want to be able to reference later.

Challenge: You need to file it quickly, make it discoverable, and emphasize why it’s important so “future you” can make sense of the notes efficiently.

Tiago says:

A note-first approach to knowledge management means we have to think about design. You are, in a very real sense, designing a product for a demanding customer — Future You. Future You doesn’t necessarily trust that everything Past You put into your notes is valuable. Future You is impatient and skeptical, demanding proof upfront that the time they spend reviewing notes will be worthwhile.

Balancing Tradeoffs

  • Discoverable: Digestible notes. So needs to be compressed
  • Understandable: Context including sources, examples, details

Getting the balance between compression and context right is not a trivial matter. When the time comes for Future You to decide whether or not to review this note, seconds count.

When you fail, you successfully sent a packet of information forward through time, but not in a state where it could survive the journey… You have to summarize the note without knowing what it will be used for.

The Progressive Summarization System

  • Layer 0 is the original, full-length source text.
  • Layer 1 is the content that I initially bring into my note-taking program. I just capture anything that feels insightful, interesting, or useful.
  • Layer 2 is the first round of true summarization, in which I bold only the best parts of the passages I’ve imported. Keywords, phrases, sentences
  • Layer 3, I switch to highlighting, so I can make out the smaller number of highlighted passages among all the bolded ones. This time, I’m looking for the “best of the best”
  • Layer 4, I’m still summarizing, but going beyond highlighting the words of others, to recording my own…restating the key points in my own words
  • Layer 5 (as needed): Remix. for a tiny minority of sources, the ones that are so powerful and exciting I want them to become part of how I think and work immediately, I remix them. After pulling them apart and dissecting them from every angle in layers 1–4, I add my own personality and creativity and turn them into something else.

My Own Accidental Version of Progressive Summarization

  • Layer 0 is usually just the link without the text which is risky since the link can break. (With Slatestarcodex site being taken down I’m experiencing this firsthand)
  • Layer 1 same as Tiago
  • Layers 2 and 3 are combined. Mix of bold and italics.
  • Layer 4 is paraphrasing often drawing connections to other ideas. While time-consuming because it requires thinking I am rewarded by an easier retrieval stage. More selective about what notes I do this with.
  • Layer 5 usually means pasting the note in other notebooks when the content has multiple contexts


Notes from Capital Allocators: Annie Duke


About Annie: Professional poker player and author of Thinking in Bets

All decisions are a bet

When you choose x you forgo y. The decision is a bet that x is a better outcome than not x.

Beliefs are formed then confirmed

Dan Gilbert’s known for happiness research but his 90s research which is lesser-known was focused on ‘belief formation’. We are hardwired to not vet beliefs since beliefs are typically perceptual. Hallucinations and mirages are rare. However, abstract beliefs that emerge from our social interactions, language, symbolism are incorporated via this same mental machinery which was really designed to assimilate perceptual beliefs.

Gilbert showed that by default we accept the belief is true BEFORE we vet it.

Research shows:

    • We often fail to later vet the belief
    • If we do vet it, we are biased
      • Kahneman’s idea of “motivated reasoning”: our beliefs drive how we vet the belief
      • Confirmation bias
      • Blindspot bias
      • Smart people are often more extreme in their biases because they rationalize with a greater repertoire
        (This was tested by first evaluating subjects’ statistical prowess then comparing how they handicap a neutral versus emotionally-charged outcome)

How do we improve?

  • Frame decisions as bets. 

    1. Assigns probabilities to outcomes
    2. Define what we explicitly are evaluating
    3. Invites others into the truth-seeking process which is also good for social reasons
      • When acting very certain we can suppress or intimidate other’s views
      • Avoid the pitfall of confusing certainty with accuracy
      • Makes the communicator more believable
      • Avoids biasing others before they start the vetting process

  • Define winning as being more accurate inoculating ourselves against self-serving bias.

    • Use Mertonian norms which comprising the ethos of science (acronym: CUDO)
      • Communism: Standardize how data is presented so members of the community cannot present the group biased picture
      • Universalism: Ideas have objective truth regardless of the messenger; “Don’t shoot the message”
      • Disinterested: Do not infect the group with your beliefs
      • Objective Skepticism: seek counterfactuals and dissent
        • In groups, use ‘red’ and ‘blue’ teams
          Red team’s function is to rebut or dissent. This instantiates a role in which being a team player is actually to challenge.

  • Be aware of our tendency to “temporally discount”.

    • A dollar today appears worth way more than in a year
    • Tonight’s wine is tomorrow’s hangover (Seinfeld’s “Day Jerry” vs “Night Jerry”)

If we associate better-calibrated beliefs with better outcomes and a happier life we should strive to be honest with ourselves while reasoning even if it sacrifices our ego/fun in the moment.

Risk Management

  • Why we fail to apply the principles of Kelly betting:

    1. Garbage In/Out: Poorly calibrating the edge and/or variance
    2. Our ability to apply rational System 1 rules are compromised when we are in an emotionally charged state (“limbic system firing”), which is when the rules matter most. “Stacking Irrationality”.

  • The merit of risk limits irrespective of risk/reward or expected value:

While irrational, they are less damaging than allowing yourself to continue betting when your “emotionally unfit”. A bias which gives us the chance to play again tomorrow when we are not emotionally unhinged is adaptive for the long-run. (Me: Reminiscent of ergodicity discussions about maximizing compounded expectation)

Tells and body language

  • Good to follow: Joe Navarro. A FBI operative specializing on body language

  • How poker players read opponents:

    1. If never faced them before, start with base rates
    2. As you learn how they bet, Bayesian update base rates
    3. Merge tells with updated probabilities

  • Signs of being relaxed vs discomfort:
    • embodied by distance from table
    • self-soothing behavior

Li Lu on China-US Relations

Excerpts from part 2 of Li Lu’s series Discussion of Modernization:

A Look at the Future of Sino-US Relations

Link to paper

The paper traces the parallel histories of the land and nation known as the United States in the West and China in the East. An especially interesting thread was how Lu charts the shift from land to market economies. The backdrop and ensuing developments in the 20th century set the current stage and inform possible futures.

On America’s “Soft” Power

As the creator of the American Order, the United States has always retained the rights to make rules for the market, admit membership to access the market, and impose and lift sanctions on states that violate the rules. At the same time, it has borne most of the costs – military and economic – of safeguarding this global market. Rights and obligations together form the core of this American Order.

The United States has also established and promoted her set of ideological values, which we now know as her soft power. In Agrarian Civilization, imperial China established a political system structured around Legalism, and a belief system rooted in the values of Confucius and Mencius. This system of beliefs was intended to achieve the willing submission of its people through subtle cultural and spiritual influence. Similarly, the American ideology promotes freedom, democracy, human rights, constitutional government, the rule of law, free markets, free competition, free trade, and the sanctity of private property. These universal values are so powerful that they have been adopted by almost everyone in the world. It is the very combination of hard and soft power that has brought the United States such tremendous success.

On America’s “Hard” Power

America’s hard powers also include its global network of military bases, the sheer size of its economy, its huge domestic market, its open investment climate, its competitive tech sector, and its world-class universities. So, whenever a global financial crisis strikes, investors around the world will flock to the safe havens of the US Dollar and American assets. This remains true even after what happened in the 2008 crisis.

Whenever the US becomes less confident, it dispenses with the niceties of soft power, and unabashedly resorts to hard power. Those on the receiving end of American hard power are likely to conclude that while the US implements democracy at home, internationally it resorts to hegemony. As the creator of the American Order, its prerogatives include market access, market denial, and selective sanctions and penalties. These special privileges are constituent elements of American hard power.


Trump’s Lack of Subtlety In Wielding Power

The election of Trump has revealed the true nature of American hard power in the context of the American Order.

With China rising rapidly, areas of incompatibility between China and the US have become increasingly pronounced. In the global arena, China has presented a challenge to American dominance, and conflicts have arisen. On the economic stage, a China-led system of international institutions has been established and often without the participation of the United States. China has also pursued the militarization of the South China Sea. The US economy accounts for 25% of the global GDP, but it bears the lion’s share of military costs associated with the maintenance of the global market. From the American perspective, China’s share of global GDP is 15%, but her share of the cost is de minimis, making her a free rider. And to make matters worse, as bilateral frictions mount, the cost of maintaining the American Order has only gone up. To the US, China is becoming more and more like Russia.

One of US economic hard powers is the US Dollar as the default currency of global trade, finance, and settlement…This is why American sanctions are so effective, as evidenced by the developments following Trump’s announcement of America’s unilateral withdrawal from the Iran nuclear deal. ZTE and Huawei, two Chinese companies, have also fallen victim to American sanctions”.

4 Schools of Thought on China Policy

  1. Engagement
  2. Practical
  3. Hawkish
  4. Populist

The four schools have always had divergent views. But in recent years, in light of all the developments in China, there has been a gradual convergence. Whatever the case may be, the reality of Sino-US relations is that today the American perception of China is approaching its perception of Russia.


American Order Focuses on Economic Power

The American Order is mainly preoccupied with the rules of access to and exit from the global market. It doesn’t wield much power over other nations’ political choices. The UN Charter states “the Organization is based on the principle of sovereign equality of all its Members”. So in fact, under the American Order, different political systems may develop, provided that they do not directly challenge America’s superpower status. The rapid growth of China’s share of global GDP (from less than 2% to 15%) is proof of this. Indeed, there is plenty of room for sustained growth.


Outstanding follows for China coverage: 

Michael Pettis

Dan Wang 


Kathleen Mercury on Board Gaming With Education Podcast


About Kathleen: Educator with a special focus on teaching gifted students game design (Link)

Transcription: Otter.AI

I incorporated Kathleen’s presentation to these notes for the sake of consolidation.


Kathleen believes:

“Happiness comes from being able to choose the life you want to live.”

To empower students there are 2 anchor ideas:

Be Producers Not Consumers

…what I want more than anything for my students is for them to be creators, not consumers…The only thing I care about is what ideas they have, and giving them the tools where they feel empowered to take on big complex challenges where they have no idea of what the final product will be, but that they can build in and learn the skills and confidence that they can hopefully get themselves there. That’s what I care about because if I can get them to accept that and do that, then they can pretty much take on whatever challenges come their way for the rest of their lives.

Bias Towards Action

For those familiar with the Silicon Valley ethos of “Move fast and break things” this will be familiar. Despite, her midwest roots and home Kathleen’s thinking has been heavily influenced by the Stanford D-School.

…probably the biggest thing that’s helped me is the Stanford design school’s method of prototype development. I went to a design-thinking boot camp, and the design mindsets that were presented as far as when you’re wanting to design something for someone else, and how you should think about it. Here’s how you should approach it. And it was so different from what I was doing, but it was just one of those things where it’s like, oh my god this is 100%, what I should be doing and it completely pivoted everything that I was doing. For example “bias towards action”. Instead of just thinking about something just start doing it. Rapid iteration making prototypes fast and cheap so you can get them on the table so that you can fail quickly see what works, see what doesn’t work quickly and so you can make more versions of something even faster.

It’s designed to keep them moving quickly so that nothing becomes precious and nothing becomes so sacred that they won’t get rid of it. And I think for me as a teacher, that’s really helped me and also helped me as a game designer in terms of trying something getting it out there, seeing what happens getting feedback on it and making improvements to it as well.

Lessons From Teaching

On using games in learning

  • I think for a lot of gaming experiences in the classroom, having everybody involved at the same time, really, really matters for success.” (Party games are a good tool for this)
  • A good teacher can make a lot of things fun. Sparks a love of learning.
  • Bridging the abstract to concrete
  • Critical Thinking
  • Information more sticky/accessible. Increases connections.
  • Boosts engagement & connections (made me think of how a local teacher used Pokemon cards to bring the boys and girls in 1st grade together)

On kids having different abilities

  • Everyone deserves to learn at their level every single day that’s just one of those tenets that I just hold. If you’re doing something where their disabilities or inabilities become apparent to others. I think you have to be really careful about how you handle that. As far as you know what you’re willing to do to, you know, protect them to take care of them because if they’re stressed out and embarrassed.

  • Approach to gifted kids:

    1. If you don’t give gifted kids problems to solve, they will create their own.
    2. They need to learn how to struggle and work through it.
  • Heterogeneous groupings can protect kids by partnering up.

  • But homogenous groupings have advantages too.

For my gifted kids, a lot of times when that happens, they’re always like the ones that are like spread out amongst the other groups, and then they put all the spread out all the middle kids and then they spread out all this sort of low kids and pardon me for speaking in broad brushstrokes but I am. And so a lot of times they never get chances to work with each other. And one thing that research shows is that when you let kids have similar abilities work with each other. Everyone gains, because the kids on the middle step it up, and the kids on the lower end also step it up, even if it’s like one notch higher, you know, that’s okay for them, you know they’re using their abilities and what they know and trying to push themselves up to be more competitive as well

  • Why the emphasis on points in winning is redundant.

Points are used to ultimately communicate your position in the game to other people. And if we’re playing a game that is just to be, you know, a review or something like that I don’t care about the points at all. And so, what I will often do is even if they get points, or if one team starts to get a blow out. I will, you know, do something like say “this is a 20 point question”, and then somehow I manage to make it so that kids on the other team get those points, or I start awarding ridiculous points my cool you just got a puppy. So drop puppy up there on the scoreboard.  

Why teach game design?

  • Develop analytical, practical, and creative thinking skills

  • Autonomy and collaboration
  • Teaching game design is teaching to orient towards an internal scorecard not an external one

That quantitative checkmark feeds into a lot of the programming that we’ve already done with kids as far as you know letter grades and standardized tests and success is 100% and success is, you know, an A plus is, you know, and I think for a lot of my students especially having to sort of break that mentality. A lot of what I do in teaching game design is here is this problem that cannot be solved, or notions like that. Here is this problem that you will have to you have to define the problem. You have to figure out how you’re going to solve this problem, you’re going to design your tests with these resources in terms of you know how close are you to solving this problem and you’re gonna do this again and again and again, you’re going to make a prototype you’re going to put it in front of other people, they’re going to play it, you’re going to get their feedback, and then you’re going to take those ideas, and that, you know, good, bad, the ugly. Incorporate that into your next design so that when that hits the table hopefully it’s better. Thinking of it as an unfinished unending hopefully upwardly ascending sort of cascade. See that process as a real process reflective of what life will be, I think is really important, because for a lot of my kids, you know they’ve learned what successes is and it’s an A+. I’m trying to show them that if you want to do anything cool, there will never be A+. You will never be finished. You will always just have to try to do your best to put out your best possible effort, listen to other people, and hopefully make that idea better and so that’s why I teach game design.

The reason why I teach game design is a teaches them this process of thinking design, thinking hands-on, trying to create solutions and learning how to see successes incremental progress, not as I finished I’m done.

We do talk about how it can be finished and not perfect and that’s really important for a lot of them. That you can have something that is unfinished. And you can see it as successful because you did try to make it better, even if you don’t think it’s better. And that’s really really hard for them to accept because it goes against everything they’ve always done

  • An antidote to results-based thinking

I honestly try to minimize any type of objective points in any kind of game situation as much as possible, because no one should ever be blamed for losing for their team, and I honestly don’t want anybody to be, you know, the fourth batter to just hit the Grand Slam home run and they get all the credit, not the people who also got on first, second and third.

  • Be thoughtful about when points matter

It does make sense to have kids have scoring that matters, but I think you have to really ask yourself, is this that time.

  • Not having grades at all doesn’t really work

And if I had my choice I wouldn’t do grades at all, but this is the world we live in and I have to actually try tried one year to not give out grades and our gifted class. There’s some unintended consequences there but there you go. We tried it once. As much as we wanted it to work it didn’t really work.

Projects Kathleen and Dustin Are Pushing Forward

  • Game Database To Aid Teachers looking to use games to augment material

    I think that something you touched on and I’ve been kind of thrown around in my head is creating some sort of database where teachers are teaching a unit on something and they can go on there and see what kind of games they can use in their class to either tackle review or tackle preview and concepts of the whatever material they’re learning. It would be really good for teachers to find like a resource where they can just go to, and save time and kind of have this lesson plan that they can use.

  •  Formalizing standards

Look at the curriculum that I have and formalize it a little bit in terms of standards that it’s meeting. That’s something that people ask me about that I haven’t really ever have had to do. And I think it’s something that I’m interested in one because it will make it even easier for people to use these resources in their classroom but it also. I’m really like thinking about the idea of what are the things that people could do to get their kids to think like game designers to use design thinking, using games, what would be appropriate, you know the early elementary level, the later elementary level, the middle school level, the high school level. So that if somebody wants to do something with game design in the classroom, they’ve got a better chance of success. That they’re not over-shooting or under-shooting what their kids are able to do but also in terms of tying this, you know, more specifically to actual curriculum. Then it can be easier for their administrators to use.

Notes from Capital Allocators: Basil Qunibi


About Basil: Founder of Novus which does analytics on managers and portfolios trying to disaggregate sources of edge/skill and quantify obliques risks such as crowding and liquidity.

Early Days

Initial Research

  • Early 2000s, Basil began studying underutilized data sets :
    1. Public filings (ie 13F, 13D etc) domestic and abroad
    2. Monthly exposure reports from managers
    3. Position level reports when available which provided full transparency

Meeting Resistance

  • “People hear with their amygdala”
    • Amygdala is the emotional center of the brain. His analysis was perceived as a threat as opposed to being received with commensurate rationality. Often his analysis contradicted narratives or perceptions

Novus’ Start

  • Initial Novus Products
    • Individual Manager Report
      • batting and slugging avg, long/short attribution, geographic/industry exposures
    • Overlap Report
      • Calculate overlap between candidate managers as well as the client allocator.
    • Aggregate/Look Thru Report
      • Analytics on an allocator’s entire portfolio of managers combined
  • Novus Framework Product: aimed at distilling manager skill, positioning (based on private data on 1500 funds)
  • Product aims to be “Moneyball for Allocators”


Moneyball for Allocators: Decomposing Manager Skill

Systematic Factors

Factors that depend on the broader market.

  • Exposure Management: How does gross exposure variation influence return? On average detracts 200 bps/yr
    • Manager’s variation in this aspect is not persistent; deviation from mean is mostly luck
  • Capital Allocation: How does exposure to capital structures or sectors contribute to performance?
    • This factor is also typically negative for most managers

Intrinsic Factors

More persistent and where the potential for alpha lies

  • Security selection: items picked out of the sectors or geographies
  • Sizing: This is compared to a control of equal-weighted portfolio
  • Trading: Tactical trading seen in flipping positions


Using the Framework to Make Better Allocation Decisions

  • If the allocation thesis for any fund is simply returns it will invariably hit a bad run. Mapping a fund’s skill to the environment is a better basis to decide whether to cut or increase exposure to the fund than simply returns.
    • For example, if the majority of a fund’s monthly alpha comes from trading but the data shows that the volume in the fund’s positions has been steadily dropping, it may indicate a lack of opportunity to capitalize on the fund’s strength.
  • The framework allows an allocator to evaluate a fund based on its stated intentions. If they claim they have an edge in security selection they can be rated on that dimension.
    • This shifts the evaluation from “thinking in T to thinking in N”.
    • It doesn’t make sense to compare a fundamental value strategy vs a high-frequency strategy at the same time horizons.
    • Large sample size of trades without any single trades dominating the results is easier to evaluate than strategies that make a few concentrated bets.
  • Benefit of increasing transparency also accrues to good managers since the story is about more than returns and the data can reveal that a bad run is just bad luck (ie losses coming from extrinsic non-persistent factors)

4 Measures of Crowding

  1. Conviction: largest position sizes amongst managers; names reported as > 5% positions within a fund
    • Best performing factor over time
    • From Faber’s interview with fellow Novus co-founder Altshuller, they constructed a ‘Conviction Index’ with Barclays based on impressive and still persistent performance of stocks which rank high on a sort of high conviction positions by hedge funds (stock > 7.5% of portfolio concentration)
  2. Concentration: How tightly held are the shares?
    • This is also a positive factor
  3. Consensus: how popular is the name?
    • This factor underperforms over time
  4. Crowdedness: How consensus is the name AND how much daily volume do they represent? “How crowded is the theater; how big is the exit?”
    • This is actually a factor which performs well over time but has massive skew

Scaling up as AUM Grows

How you can expect AUM growth to impact manager performance?

  • Increasing number of positions
    • If the manager has skill in sizing positions this will ‘flatten’ the alpha
  • Moving into higher market cap names
    • If the manager has skill in small cap, this is style drift
  • Increasing current position sizes
    • This deteriorates liquidity; while adding it can be a positive feedback loop but this is a double-edged sword. This is the most dangerous form of scaling if liquidity is overestimated

Notes From Invest Like the Best: Brian Christian


About Brian: Author covering humans’ relationship with technology and AI

Q: What advice would you give to people, building careers. We’re in a political cycle now where things like basic income are being discussed. In your view, what are the most defensible areas of human activity, whether that’s some sort of creativity or asking great questions coming up with the objective functions that you then feed the machines? What would you recommend people focus on as they think about either early or late in their career, adding value?

A: There are sort of two ways that I can approach this question. My second book is called the Algorithms to Live By and it looks at things like career decisions from an explicitly algorithmic perspective.

1) Explore/Exploit Trade-off


There’s this paradigm, called the “explore/exploit” trade-off, which is: How much of your energy do you spend gathering information vs how much do you spend committing based on the information? There’s a number of decisions that we face throughout life, that take the form of a tension or a balance between trying new things and committing to the things that seem to be the best. Where to go out to eat, go to our favorite restaurant and we try a new restaurant. Reach out to a new acquaintance we’d like to get to know better or spend time with our close family or best friend. The same thing is true in investing, the same thing is true in managing your time and your career.

Generalizing the Problem

The structure of this problem is an iterated decision that you get to make over and over again. Do you continue to put energy into the things that seem promising, or do you spend your energy trying new things? A clinical trial can have that same structure, and indeed the FDA has been increasingly interested in looking over the disciplinary fence at the computer scientists and saying, maybe those algorithms that you’re using to optimize ads, could also be used to optimize human lives. The way a computer scientist, approaches this question is through something that’s called the multi-armed bandit problem.

The Multi-armed Bandit Problem


In the multi-armed bandit problem you walk into a casino that has all these different slot machines. Some of them pay out with a higher probability than others, but you don’t know which are which. What strategy do you employ to try to make as much money in the casino as you can. It’s going to necessarily involve some amount of exploration trying out different machines to see which ones appear to pay out more than others, and exploitation, which to a computer scientist doesn’t have the negative connotation that it has you know in regular English exploitation meaning, but just leveraging the information you’ve gained so far to crank away on those machines that do seem to be the best. Intuitively I think most of us would recognize that you need to do some amount of both, but it’s not totally obvious what that balance should look like in practice, and indeed for much of the 20th century, this was considered not only an unsolved problem but an unsolvable problem, and sort of career suicide to think about. During WWII, the British mathematicians joked about dropping the multi armed bandit problem over Germany in the ultimate intellectual sabotage. Just waste the brainpower and nerd snipe all of the German mathematicians. To the field’s own surprise, there came a series of breakthroughs on the multi-armed bandit problem through the second half of the 20th century.


Now we have a pretty good idea of what exact solutions look like given a number of constraints, but also what sort of more general flexible algorithms look like. The critical insight into thinking about this problem is that your strategy should depend entirely on how long you plan to be in the casino. If you feel that you have a long time ahead of you, then it’s worth it to invest in exploration, because if you do find something great, it has a long horizon to pay out. On the other hand, if you feel that you are about to leave the casino, then the return that you would get on making a great new discovery is going to be much smaller, because you have fewer opportunities to crank away on that handle once you find it. We should naturally transition from being more exploratory at the beginning of a process to more exploitative at the end. I think that’s an intuition that makes sense, but the math bears that out very concretely.

Observation of “Explore/Exploit” Trade-Off in Real Life


It’s interesting to see this idea that emerges in computer science in the late 50s through the 70s getting picked up by psychologists and cognitive scientists who are interested in human decision making. For example, Alison Gopnik at UC Berkeley who studies infant cognition, has been thinking about the “explore/exploit” trade-off as a framework for how the infant mind works. If you think about how children behave, we have all these stereotypes about children are just kind of random, they’re generally incompetent at things, and there’s a huge literature that shows that they have what’s called a “novelty bias”. They’re relentlessly interested in the next thing and the next thing and the next thing. Rather than viewing that as a kind of low willpower or attentional control issue, you can view it as the optimal strategy. It’s as if you’ve just burst through the doors of life’s casino and you have 80 years ahead of you. It really does make a lot of sense to just run around wildly pulling handles at random. The same is true for being in the later years of one’s life. We have a lot of stereotypes about older people being set in their ways and resistant to change. There’s a psychology literature that shows that older adults, maintain fewer social connections than younger people, and it’s tempting to view that pessimistically. In fact if you build an argument from the mathematics, you can see that older adults are simply in the exploit phase of their life and they are again doing the optimal thing, given where they are in that interval of time. You have psychologists like Stanford’s Laura Carstensen appealing to the “explore/exploit” trade off to make this argument that older adults know exactly what they’re doing and they’re very rationally choosing a strategy that makes sense given where they are. They have a lifetime’s exploration behind them, they know what they really like, they know the people and the connections that matter to them, and they have a finite amount of time left to reap the fruits of some new connection or new discoveries so they’re very deliberately enacting the strategy. The math should predict that, on average, older adults are happier than young people. Despite our preconceptions, and her research bears this out, that appears to be the case.


In business, the problem is very dynamic, which will classify it in the domain of the “restless bandit problem”. Since the research here is cloudier, researchers can invert the thinking to infer the conditions that lead to the business strategies we can observe.

Q: Interesting how this maps on to the life cycles of businesses. In the business context, “explore” might be innovation and “exploit” might be to run the same playbook to earn high returns on capital or something you know works. It seems like you always want to be handing off to a next batch of exploration or innovation, while thoughtfully maintaining something that you know works if you want to survive for very long time.

A: There’s a couple of things that I think are interesting in a business context. One is that implicitly the casino framing that I’ve described assumes that those probabilities are stable and fixed. Of course, we know that the world is not stable and not fixed that things change over time. This is true in our personal lives as well. Your favorite restaurant gets a new line cook and the burgers are not as good. These things shift. This is known as the “restless bandit problem”. How do you play this game when these probabilities are drifting on a random walk?

This is a very interesting case where the theory is not yet consolidated but humans, in practice, seem to have no problem. If you put people in a lab and give them a restless bandit problem, they have no trouble making choices within that environment but we don’t yet know what the mathematics of the optimal solution looks like. So here’s the case where the computer scientists and the mathematicians are asking the cognitive scientists, what are your models for how humans are actually approaching this because there may be some insight that we can use from the theory side. One of the implications of thinking in this way that is particularly relevant in a business setting is if the interval of time you perceive yourself to be on determines the strategy that you should employ, then it should be the case that if you observe someone else’s strategy, you can infer the interval that they’re optimizing over.

Inferring The Explore/Exploit Strategy in a Restless Bandit Problem

Let’s give an example from Hollywood. Most people have noticed, it feels like we’re living through this deluge of sequels, such as Marvel movies. It turns out that this is objectively true. There’s a sea change in Hollywood. In 1982, 2 of the top 10 grossing films were sequels. By 1990 it was six. By the year 2000, it was eight, and I think most recently it was all ten. From that, we can infer that Hollywood has taken a very hard turn towards an exploitative strategy. They are milking their existing franchises, rather than investing money speculatively to try to develop new franchises that will last them into the next few decades. From that, it’s reasonable to infer that movie ticket sales are declining, which turns out to be the case. Hollywood correctly perceives itself to be at the waning time of the golden era of cinema-going. If that’s true, then they really should invest all of their money into just squeezing everything they can out of the existing franchises. More broadly, so you can look at different industries and different corporations to see if they cut their r&d budget. If they’ve given that money to marketing that’d be an indication that they feel that the area has matured or plateaued.

My thoughts

    1. Ahem, asset management, cough
    2. Reminds me of a great Peter Chernin interview where he suggests that every business must be trying to grow new opportunities faster than the the old ones die out. While you must do your best to milk the old, it’s imperative to develop the new.

2) Predicting the Impact of Automation

The second avenue is totally different from this way of thinking, which is just what will the impacts of something like AI or UBI be on the economy. I’m reminded of a McKinsey report on which jobs they thought would be the most robust. The big picture thing that was interesting to me is that it cuts across the traditional class lines. It is not a white-collar versus blue-collar thing. It’s not an upper middle class versus lower middle class thing. It’s very sector dependent. The most resilient or robust jobs at the top end was gardener, legislator, and psychotherapist. I thought that was very fascinating that it’s this eclectic mixture of things. I don’t think of myself as a prognosticator about these sorts of things but my way of thinking about it is that there’s a lot of kind of human machinery around how capital moves and how laws get made. How licensing and permitting happen. It’s still done at a human negotiation level. “I know a guy. I’ll talk to Joe and we’ll sort it out.” I think humans will maintain oversight of these kind of flows of power and capital, even if the actual value is being created by software. So position yourself closer to the flow of that value than the actual creation of the value, which may be counterintuitive.

As far as the question of UBI, I don’t have a great intuition for that. There is already a restlessness in the labor force. A lot of the careers that employ some of the most numbers of people are the most vulnerable. People who drive cars or trucks, people who work in warehouses. A lot of those jobs are just one innovation away, and it’s not clear to me that there’s going to be a political response as well as just a pure economic response. I grew up in New Jersey where there was a robust toll collector union yet they had machines where you could toss your change in a bin and it would automatically sort your change and give you whatever you needed back from that. There was an effective effort to unionize the toll collectors so that you still had a human being in the booth counting out your quarters. That’s an example where it’s not for lack of technology. We had a coin sorting machine, but there was a political process that was directing the actual level of implementation. People will fight to use licensing requirements and regulations to maintain those things. Despite the actual technological capability having radically changed, it’s very hard to know which areas will look shockingly different than the world looks today. Which things will be in some ways shockingly backwards for their time because we’ve had for political reasons to hold the line.

(Reminds me of how rent flows to the owner of a relationship in a competitive market that has been flattened by technology)

Algorithms to make other types of decisions

The mathematics is very instructive, both in a specific way but also has a broader set of principles.

Optimal Stopping Problem

Difference from “explore/exploit” trade-off

One thing that comes to mind is the idea called “optimal stopping”. The multi-armed bandit problem in the “explore/exploit trade off” presumes framing that’s highly iterative. You can pull the handles again and again and again. You can go from one machine to another and back. There are many decisions in life where you are forced to make a single binding commitment that could be anything as banal as pulling into a parking space. It could be something like purchasing a house or signing a lease. It could be something like marrying your spouse. There’s a separate mathematics of cases where you need to find the right moment in time to go all-in, commit to an option, and no longer gather any further information.

37% Rule

There’s this very famous result called the “37% rule”. Let’s say you’re looking for an apartment. And it’s a really competitive marketplace. You’re in a situation where you encounter a series of options one by one. And at each point in time, you must either immediately commit, and then never know what else might have been out there, or decide to walk away and keep exploring your options but lose that opportunity forever. What do you do to try to end up with the best thing possible, even though you, you won’t necessarily know at the time, whether you found the best option that might be out there? There’s this beautifully elegant result that says that you should spend the first 37% of your search non-committally exploring your options. Don’t bring your checkbook, don’t commit to anything No matter how good it seems you’re just purely setting a baseline. After that 37%, whether it’s 37% of the time that you’ve given yourself to make the decision or 37% of the way through the pool of options, be prepared to immediately commit to the very first thing you see that’s better than what you saw in that first 37%. This is not just an intuitively satisfying balance between looking and leaping, this is the mathematically optimal result.

Broader insights on algorithms

Elegant solutions under a range of narrow assumptions about goals and acceptable risks

There are strategies like that that I think are wonderfully crisp in the recommendation they give, but they, of course, rest on this bed of many different assumptions about exactly how the problem is structured and exactly what your goals are. This rule, presumes that your entire goal is to maximize the chance that you get the very best thing in the entire pool, but it comes with a 37% chance of course that you have nothing at all, because you’ve passed. Many people would find that unacceptable. We can go down the rabbit hole of how do you modify this and the solutions get less and less clean as you wiggle the assumptions around.

Intuition for how complex decision-making is can be strangely comforting

More broadly, one of the highest level takeaways for me, from working on the book and just thinking in computational terms about decisions in my own life, is some decisions are just hard. The classical optimal stopping problem, due to a weird mathematical symmetry, is that if you follow the 37% rule you will only succeed 37% of the time. The other 63% of the time you’ll fail, and that is the best possible strategy you could enact in that situation. In a weird way, that’s some measure of consolation because often, in real life, we find ourselves not getting the outcome we wanted. While we can rake ourselves over the coals or try to reconstruct our entire thought process, I think it’s some comfort that computer science and mathematics can, in effect, certify that you were just up against a hard problem. There is some measure of comfort that if you have the kind of the vocabulary to understand the type of problem that you’re facing, and you have some intuitions about the general shape of what optimal solutions look like, then even when you don’t get the outcome that you wanted you can in some sense rest easy because you knew that you followed the appropriate procedure or the appropriate process for dealing with that situation.

Notes From Saudi America

Saudi America
by Bethany McClean

On Aubrey McClendon, founder of Chesapeake

  • McClendon went all-in on shale buying land in Austin Chalk region when he saw Devon energy’s success drilling horizontally.
  • McClendon would overpay for land. His world-class salesmanship would allow him to raise capital more effectively than competitors.
  • He would be bailed out in 2000s when oil and gas prices rise and shale lands were in high demand.
  • By 2012, Chesapeake was trying to rebuild after the crash of 2008 and gas prices depressed. They would raise capital globally and make deals with pipelines that would require them to produce large quantities of gas regardless if price. They were basically funding themselves by selling the equivalent of naked puts on gas.
  • The board was replaced and McClendon stripped of chairmanship. Icahn even took a seat. But the company and governance were a disaster.
  • McClendon left and started AEP which would create a portfolio of companies with an interest in specific drilling operations.


  • Known as the “Apple” of oil
  • In 1999 Enron spun off EOG (“Enron oil and gas”) when Jeff Skilling became dismissive of businesses requiring hard assets.
  • EOG and Continental (Harold Hamm’s co) were first in the Bakken where production would increase 10x in the first decade of 2000s.
  • Encouraged that the fracking technology worked although unspectacularly (the industry and academics were still skeptical) they started acquiring land in Eagle Ford for under $500 an acre. Then in 2010, at the 4 Seasons Houston they announced there was 900mm barrels contained in Eagle Ford.
  • A look at EOG which has positive returns.
    • Only wells with at least 30% irr since about half of that is required to cover overhead including land and infrastructure.
    • Requires profitability at $40 oil so that it can survive full price cycles.
    • Means that there is limited capacity to invest since land prospects can be much worse (“better rock is exponentially better”)

Fracking is 2 technologies

  • Horizontal drilling and high pressure water to crack open rocks. A proppant (ie a sand usually from Wisconsin) is used to keep the crack open to let the oil or gas flow.

Shale depends on unsustainable economics

  • In 2015, Einhorn at the Sohn conference showed that even with oil at $100 the shale industry was incinerating cash. He called out Pioneer in particular (a descendant of a merger with Pickens Mesa Energy). Shale Wells have too steep a decline rate. A Bakken well declines 69% in year 1 and 85% in 3 years vs 10% pa for conventional). Einhorn argued that frackers’ pitches don’t account for cap-ex and cost to acquire leases. He showed that they embellish their estimates of reserves to investors (vs what they report to SEC standards). He and SailingStone of SF showed how comp was tied to production, not profitability. They both agreed that has was much better biz than oil.
  • Pension backed PE firms are financing shale 2.0 which is marked by better technology and more precise operations. 35% of horizontal drilling is done by private PE-backed companies. Rates of return remain unsustainable for all but the most efficient operators who are generating positive albeit still uninspiring returns.
  • Decline rates always mean you need to spend more to stay in place. The shale transformation was a Lollapalooza effect of Bakken, Eagle, and Permian hitting all at once. It may very well be a one-time price effect. And the more efficient we become on drilling the faster we deplete the wells.

The crude oil export ban

  • The crude oil export ban had been in place since the 1970s.
  • But there was now a growing movement to overturn the ban backed by oil producers while refiners and environmentalists opposed it.
    • Many saw it as a counterweight to the political leverage of unfriendly powers such as Russia and the Middle East. Europeans were already grateful for the US agreeing to export LNG, keeping Russia from having a near monopoly on gas sales in Europe esp in Germany.
    • Many argued that the decline in global prices would more than makeup for the reduction of domestic supplies and that in boosting global supplies the political premium baked into oil prices would ease.
  • The collapse of oil prices in 2015, the “condensates” exemption, and being part of a larger spending bill provided cover from detractors as the bill was passed in Dec 2015. Environmentalists would gain an extension to solar and renewable subsidies.

My takes plus a Munger insight

  • The byproduct of commodity businesses is really an investment product.

A levered slice of volatile returns. The companies are closer to derivatives than they are businesses. The derivative is on the price of the commodity. The leverage is operational (call option struck on breakeven cost structure) and financial (companies have debt financing). The derivative is economically tied to the commodity via land rights which collateralize additional debt financing.

  • Sloppiness can occur when policies are seen as good for the public.

Greenspan saw rising energy prices as a threat to the economy and he suggested we invest in LNG import terminals. The fear of risings prices hastened the 2005 Energy Policy Act which exempted drillers from disclosing the chemicals used in fracturing.

  • Is Saudi’s timing of their portfolio rebalance performance-chasing?
Aramco IPO proceeds used to diversify Saudi America by creating a sovereign wealth fund. Shorting oil to invest in overpriced businesses a la SoftBank?  Strange move. The investments are not even in human capital and accumulation of tacit knowledge. It’s financial in nature only which seems not just expensive but shortsighted when the explicit goal of MBS is long term vision.
  • While increasing the security of US energy supplies seems like a net positive it’s unclear what the ramifications are longer term.

Shale for example has reduced our reliance in unstable regimes. But many of those regimes such as Nigeria were not major threats. If an insecure bully has some moderate economic leverage against you it may be better to pay them to control them. If you remove the leverage you also destabilize the equilibrium. An equilibrium you were ultimately in control of. You may find yourself now facing a desperate, rabid adversary who despite a smaller stature now has its back to the wall. You have exchanged a stable, modestly negative state for a wildly uncertain modestly incrementally positive (perhaps fleeting) state. This is especially true when the dog whose bark is louder than its bite is in a region concerned with terrorism and nuclear proliferation.

  • Munger: we should be conserving oil and delaying gratification until we are certain about the extent of its future needs.

This is because fertilizer, pesticides and other ag products are made with hydrocarbons and there is no technology that has replaced that. “Like the topsoil in Iowa, you wouldn’t want to use it as fast as possible” he believes the shale boom was lucky and we shouldn’t just consume the gift. He recommends we commit to production enough to remain competitive in its technology while maintaining reliance on foreign oil. Let them deplete their reserves until we have a better understanding of what the future barrel is worth. Remember that the US being the first to switch to unconventional oil and gas required it to be the first to run out of conventional oil and gas.

Shale has been a subsidy to the economy and it’s very unclear to what extent we should drill aggressively or more thoughtfully since the future demand for oil is devilishly uncertain (there is more certainty around gas supplies). Is this another example of being short-sighted and drilling at all costs while oil execs and consumers win or if we’d be better off with higher near term prices and renewables even more competitive?