Invest Like the Best: Brad Stulberg


About Brad: Performance coach and author of Peak Performance


  • Studying brains we find that people can summon extreme abilities if they have core beliefs which override the fear responses in their brain (is lifting a car off of a person trapped underneath). The importance lies in having core beliefs or purpose.
  • The Growth Equation: Stress + rest = growth
    • Need the right amount of stress/stimuli. Too much stress is overwhelming, too little leads to no adapting.
    • Just manageable challenges are those which are just outside your comfort zone. It’s self-defeating to onboard too many of these at once.
  • Mechanics of creativity
    1. Immersion: this work is stress
    2. Incubation: stepping away (this is the rest)
    3. Creative insight…this tends to happen after a period of rest…end of a vacation, in the shower, taking a walk.
  • Studies show deep work cycles are most effective in 45 to 90 min blocks followed by 15-20 min breaks.

Practical Tips

  • Whether you perform better in the am or pm is largely biologically determined. Manage your energy not time. If you are better at focused work in the am, then reserve that time for that.
  • Time in nature or outside is shown to improve stress, physical, cognitive markers.
  • Study of air force cadet squadrons showed that group performance was more influenced by the lowest common denominator as opposed to the leading performer. The group has more to gain from eliminating bad attitudes than from enhancing leadership.
  • Fatigue happens in the brain, not the body. Your central nervous system slows you down. How to overcome this? When interviewing peak performers you find that they are thinking about a larger purpose than what they are doing. So marathoner thinking of his family can override his brain’s fatigue signals.

Invest Like the Best: Jason Karp


About Jason: Founder and CIO of Tourbillon Capital Partners

Growth of private markets

  • Smaller supply of scalable opportunities and increased competition
    • Less companies going public and staying private for longer. 50% of listed companies compared to 20 years ago (mostly M&A and lack of IPOs as opposed to bankruptcy)
    • Unicorns able to get unprecedented amount of private funding
  • Increasingly competitive public markets for short term performance.
    • Data from prime brokers shows that over 90% of flows driven by non-discretionary accounts (CTA, systematic, quant, passive).
      • He argues that this has caused large dislocation opportunities in past 5 years in valuations when historically values typically take no longer than 3-5 years to converge to fair valuations.
    • Short term pressures on advisors (quarterly performance, fee compression) incentivize move away from mark-to-market and costs of wooing performance-chasing allocators.
    • Information edges are gone or not scalable
      • Data points include the sheer number of funds and books referencing ‘value’ investing.
      • Growth of quant platforms like Quantopian.
      • The growth of data sets favors short term trading which is the domain of quants.
      • Ratio of sell side analysts to public stocks is at an all-time high
      • Reg FD neutralized many active managers’ edge b/c large commission paying funds could no longer get privileged info

How to compete in a competitive, expensive market

  • Look for real business growth. If a company is growing faster than it’s implied multiple there is a margin of safety that can ensure an investment in the event of multiple contraction
  • The deep value game is difficult since it’s a basket of adverse selection. A small minority will turnaround their distressed situation. It’s easier to look for ‘value’ names that are growing than it is to pick ‘deep’ value names to revert
  • Align investors with long term horizon as a structural edge. The only way to profit from longer-term dislocations. The current environment has a historic high correlation of growth to momentum which is a trend that continues to pay off, in turn, reinforcing the exit of value flows and increase in quant/momentum flows setting up a historic relative value opportunity.
  • Understand cyclicality and stickiness- He calibrates the riskiness of a business with the nature of its sales. Stickier businesses are less risky (ie high consumer daily engagement)
    • Fashion is unpredictable
    • Cyclical’s are dependent on GDP
    • Staples are more dependable

Invest Like the Best: Dan Egan


About Dan: Managing Director of Behavioral Finance and Investing at Betterment

Uses insights from behavioral economics to nudge more adaptive behavior

Design a better dashboard

  • speed bumps on mobile platform to discourage impulsive trading
  • ‘tax impact preview’; the magnitude seems to be less influential than the outright presence of this speedbump; interesting side note is that this has a larger effect in Census areas known to be Republican
  • When messaging entire user base, they would sometimes prompt action (ie selling stock) in users who would have done nothing; they learned to send messages to people who were about to commit an action. This improved efficiency of the message by eliminating ‘false positives’
  • Displaying information which aligns focus with objectives instead of just defaulting to emotionally charged performance metrics or even the red/green triggers we are used to seeing
  • Recent related WSJ article pointing out the tyranny of what is displayed to you:

Designing a system informed by your beliefs when you are rational to pre-empt decisions you might make when emotional

  • Cranky judges: parole sentences are heavily influenced by time since their last meal. For such important decisions, we need a better system. Doctors’ diagnosis subject to the same effect!
  • Building custom tailored indices aligned with people’s stated goals

Notes from Invest Like the Best: Richard Craib


About Richard: Founder and CEO of Numerai; hedge fund which crowdsources machine learning algos

What does Numerai do?

Numerai runs open contests where they supply scrubbed financial data which is unlabelled and respondents submit ‘targets’. They do not need to submit their algorithms. Contrasting with Quantopian, the system doesn’t rely on trust. You preserve your IP which encourages collaboration and also has zero interpretability.

Problem is set up in a specific way, data is highly normalized, and doesn’t seem to be too non-stationary. Respondents compete on the predictive value of their machine learning algos.

Important criteria for crowdsourcing to be effective

  • Diversity of opinion
  • Non-overlapping (ie low correlation )
  • Decentralized


  • Model Edge
    • Users are using more unique methods than just neural nets, random forests, and support vector machines.
    • There is diminishing returns to model improvements but because you can crowdsource not hire it is cost effective to seek the best signals
  • Data Edge
    • 2 Sigma claims that extremely clean data is a huge driver of edge
    • RenTec reportedly does not use machine learning, and much of their edge is surmised to be a long, unique data history. In fact the data may be >> the talent. [Me: Interesting as a moat to think of unique data sets trading firms can have: executions, unstructured data such as voice trades that are passed on vs executed]
    • They do not introduce the complexity of unstructured data
    • Data normalizing is important as well as normalizing the targets to risk-adjusted returns
  • Ensembling the models via staking
    • The info content in how users staked their entries with NMR crypto exceeded the conclusions from Numerai’s extensive research into how to ensemble the models to construct portfolios. Craib, being friends with many of leaders in the crypto space, saw that bitcoin could be used to trustlessly pay contestants. NMR evolved as a smart contract to enable the entire staking mechanism, providing Numerai with a powerful ‘skin in the game’ filter. It also served to discourage spam since there is a cost to submit an entry.
      • The Sharpes of the strategies with staking was about 2 vs 1.5 for unstaked. The ‘floor sharpe’ was 1 since the data set was already high quality
    • They pay about 5k per week in prizes. $8mm to date.

Notes from Invest Like the Best: Michael Kitces


About Michael: Leading expert on financial planning and building advisories


Financial Advisory Fee Model History

Commission Model

  • Until 1975: high commission — stock brokers making $200 in 1975 dollars for execution; fees were set by price control in aftermath of the 1920’s free for all where clients were ripped off during the bull market preceding the Great Depression1975: May 1st — ” May Day”: deregulation of stock commissions. Some brokers thought they could raise prices but Bay Area-based Charles Schwab uses computers to undercut and in the next 20 years commissions fell 90%.

Fee Model

  • 1980s to 2011: Mutual fund model rises as stockbrokers go out of business. The rise of independent broker dealers as the creation of the financial product unbundled from distribution. Financial advisors would recommend mutual funds that were not manufactured by the wirehouse they worked for creating less conflict of interest from stockbroker model. Mutual funds assets rise from 1/2 trillion to 5 trillion! Advisors lobbied for 12b-1 fees which allowed them to charge a recurring fee on assets to support their advisory business since commission dollars were now unsustainably low.

Internet Era

  • Technology platforms all funds to be distributed direct-to-consumer. Etrade: “It’s so easy a baby can do it”
  • 1998: Schwab One Source Program pioneers the no-load fund
  • Advisor model becomes the AUM model where once again the value prop to consumers improved as advisors were now constructing diversified portfolios for clients instead of jamming them into a single loaded fund. The rise of the fee-based account and RIAs. The value prop being constructing diversified portfolios tailored to the client’s goals
  • Mutual funds in decline as advisors no longer incentivized to sell them and their inferior structure to etfs. Advisors competing wanted to actually cut high fee funds from client portfolios. We are witnessing the acceleration of this process now as we are actually seeing net flows out of mutual funds in aggregate. It took 15 years to get to this point, he expects another 10-15 to finish the trend.
  • Advisors are disintermediating funds — “wholesale transfer pricing”. When there is competition up and down the value chain the owner of the relationship, in this case the advisor stands to survive.
  • Roboadvisors have competed for assets of self-directed investors much more than displacing human advisors. They were the biggest threat to Schwab and Vanguard ironically and turns out they were the first to respond with robo-advising of their own

The new model: Barbell

  1. Economies of scale and tech. From 1995 until now fees dropped yet another 90%. Commoditized funds and etfs fees going to zero. Large firms like Fidelity, Schwab, Vanguard ok with this because as fund managers they capture AUM fees on the back end.
  2. Niche advisors that add value in ways that often has nothing to do with asset management.

Understanding the New Model

  • Advisor fees average about 1%, although closer to 75bps on a weighted basis (larger accounts get discounts).
  • Robo advisors started at 25 bps which was a venture backed hypothesis price. They have been steadily raising fees and it looks to settle in around 35 bps. So the gap between a robo-advisor and a full service human advisor is about 40 bps.

Advisors recognize they need to add enough value to justify the premium.

How are they doing it?

  • Comprehensive financial planning (taxes, estate)
  • Upgrade talent. The bar is going up dramatically in terms of credentials. A basic license no longer viable qualification to compete.
  • Specialized expertise: most advisors advise about 100 clients and the top 20% are 80% of the revenue, meaning you can serve tiny niches
    • Niches:
      • Seniors
        • Social security timing (when to take it)
        • Medicare and health plan guidance
      • Narrow cohorts
        • Doctors at a certain hospital with complex hospital negotiation process
        • Competitive bass fishermen (endorsements and prize money guidance)
        • Expats from certain countries

Does the data support advisor fees declining or them fighting to add more value at constant revenue?

Instead of average fee declining, we are seeing advisor profit margins decline as they add value and costs to ‘defend the 1%’ fee. Profits are not exploding despite markets on record highs

Michael’s view on flat fee versus AUM fees

On AUM fees:

“the only business model that I’m aware of anywhere in the history of any industry or your average revenue per client automatically goes up at a real rate above inflation simply by keeping your client because your [fee] lifts with the return of the markets and return the markets is generally risk premium plus inflation so we get this natural lift in a world where every other industry that ever existed has to actually go back to their people to ask for an increase and get them to buy in”

This makes the flat fee business difficult to compete.

But why are flat fees the future?

This will be the dominant structure in 20 years because the size of the addressable market will widen.

Currently, only about 1/3 of households have over $100k in investable assets. But 1/2 of these assets are in 401k plans which cannot be advised. This leaves less than 20%.

Of that proportion,

  • 1/3 are DIYers
  • 1/3 are “validators”. They have an idea of what they want to do but need some guidance but unwilling to pay AUM fee for validation
  • 1/3 are “delegators” and thus great clients

Current AUM model only addresses about 7% of the population

So fee-for-service has an opportunity because 50% of the population need financial guidance but neither the asset level nor will to delegate to an advisor.

He says the model may shift to “1% of assets to 1% of income”

Notes from Capital Allocators: Andy Redleaf


About Andy: Founding Partner – Whitebox; multi-strat fund; been trading for over 40 years

  • Some historical dates of interest he pointed out

1971: Bretton Woods: Nixon dissolves monetary regime of fixed exchange rates
1972: Listing of currency futures
1973: CBOE and listing of options
1975: De-regulation of trading commission (led to 10x reduction in commission fees)
1982: Stock index futures listed

All of these events bore the ‘fingerprints of Milton Friedman’ and the Chicago Business School who professed the wisdom of crowds and transparency of free decentralized markets in efficiently allocating risk/capital.

The idea of the ‘lone wolf’ or individual having low-cost access to markets and entrepreneurs in a garage being enabled has always been part of the American pioneering ideals. Andy makes interesting comment that ‘its place in the cultural zeitgeist ebbs and flows’.


  • From managing their own money to money management

His move from independent market making in options (he remarks that options were primarily retail driven) to money management with Deephaven occurred as option-like instruments such as ‘perks’ and convertible bonds started to gain traction with institutional investors and they could leverage their understanding of options to these markets.


  • Markets are ecologies, not fixed systems

Best to be opportunistic/agnostic to find profitable niches remaining adaptive as niches will become crowded as they attract capital.


  • Sources of edge


    • “Markets that don’t talk to each other that well”

Market segmentation that can occur for regulatory reasons or differences in customer bases, preferences, horizons. This leads to relative value opportunities as a niche can be carved by taking the steps to intermediate the transition of regimes or straddling regulatory frameworks.

These can be relatively short term (ie several months) trades and can require that there is a mechanism or salesforce who can recycle the risk to a different customer base. Try to avoid the risk of tying up capital owning orphaned securities.

      • The classic example being ‘cap structure arbitrage’. Ie investment grade bonds which become stressed or the relationship of distressed bonds to equity. “Finance is about governance” — the senior debt investors, subordinated debt investors, equity investors all have different conditions under which they are willing to finance business but in the end, it is all the same business. Relating all the pieces is a niche.


    • Why did he buy a bank?

As a hedge fund he is able to borrow at the amazingly low rates but since its overnight money and market to market, the great rates are attached to poor terms. A manufacturing business, on the other hand, borrows at inferior rates but at locked up terms.

A bank gets the best of both worlds especially at the short end of the curve. The downside is regulatory scrutiny and restrictions. An additional advantage of a bank charter: access to deposits with what he considers to be underpriced deposit insurance and a degree of ‘forebearance’ in the event of bankruptcy. He says these advantages post 2008 under undervalued albeit subtle.

Intends to use the bank to make loans to ‘bankable’ entities that they can identify as being better credits than conventional banks. Better credit analysis of mortgages for example. He says many banks claim they can do this but the idea is fundamentally opposed to their incentive as public entities to grow and consequently make more loans. They either need to lend more cheaply or loosen standards to grow.

He mentions that the best financing rates on the long end of the funding curve is being an insurance company. He comments that Warren Buffet is an above average investor, but the best borrower of all time, using the float from Berkshire’s insurance business to fund the asset purchases. He’s good on the asset side, “phenomenal” on the liability side. This is not the typical narrative or what most people focus on.

  • The GFC

Unlike many others, he attributes the 2008 crisis, not to poor incentives (‘originate to distribute’) citing the fact that the banks had plenty of skin in the game and had savings wiped out. He discusses how it was too much leverage exactly as money went from being “information insensitive securities to information sensitive” as people understood banks would fail but didn’t know which ones, forcing them to pull money from all of them. The analogy in commercial banking being run on the bank boosting reserve requirement, forcing deleveraging and a massive contraction in the money supply.

  • Current investing challenges
    • Orphaned securities because of a decline in ‘active liquidity’. This, in turn, has led to fewer arbitrageurs willing or able to arbitrage relative values leaving valuation spreads to persist. Corporate America via M&A is left to fulfill this role.
    • Reg FD: shut companies up and served a blow to active managers as information became more level.
    • The growth of passive vs active. Supply of active liquidity has declined, amidst increased demand for passive exacerbating valuation spreads that.
    • Expansion of central bank balance sheets has met little resistance reflecting a global tacit approval for institutional and government ownership (Fannie, Freddie purely public, Maiden Lane owning stocks). These policies seem to be in keeping with the current collective mood.
    • Whitebox funding costs increased by $30mm pa meanwhile they cut their fees by $30mm pa. This has been a direct transfer of wealth from money managers to ‘too big to fail’ or quasi-government businesses. Andy believes ‘the world liked that’. In other words, the zeitgeist that dominated the 1970s and 1980s which championed the entrepreneur has been traveling cyclically lower. Interesting story arc he draws as animal spirits get carried away culminating in GFC and now pendulum swinging the other way.

Notes from Invest Like the Best: Josh Wolfe


About Josh: Founder of VC firm Lux Capital

  • Core beliefs
    • Confident that by being curious and following leads and being relentless will lead you to the next idea.
    • Confident you won’t know when or how you happen upon the idea.
    • Confident that the idea lies in the edges of companies that are doing innovative things, often from first principles or science, and very few people are looking there.

=> These principles propagate from a commitment to benefitting from optionality and positive convexity of non- linear relationships. When analyzing how they found deals it only made linear, narrative sense after the fact

    • Examples
      • Studying meta materials with negative index of refraction led him to antennas without moveable parts which can lead to internet everywhere which led him to Bill Gates who was backing the guy will all the parents, which led him to Orbital Insight in SF and Planet which were mapping the globe with a huge network of inexpensive satellites.
      • Looking at vehicle automation led him to a company modeling software on GPUs 5 years ago which led him to learn about NVDA and then they backed Nirvana which was later acquired by Intel.
  • Moonshots
    • Exploring modalities to allow pets to communicate. Follows the trend of zeitgeist that animals are more than our possessions. This is based on observations, not predictions. The tech side leverages our expanding ability to sensor and detect signals.
    • X-men: with 7b people in the world there are extremely rare genetic conditions. Hypertrophy, height, memory etc. Hacking these genetic anomalies to discover enhancements for the general population. Kind of like prosthetics led to military exoskeletons.
  • Business Tips
    • Learning rabbit holes: talking to people, getting leads following up. Quickly see who’s full of it and what’s valid. He uses a mind map to link the people.
    • “Funnel wide, filter high”
    • Drunkenmiller was early investor. Wolfe attracted to outsiders with a chip on their shoulder. Grit and outside view.
    • Pay attention to the frauds and bad operators. Don’t short promotors and religions. They are good at fundraising!
    • Amazing salespeople, amazing operators. A pitch only demonstrates the first.
    • Best performing companies in the portfolio were the companies that were heavily disagreed upon but 1 person enthusiastically saw the value. Not surprising, outsize returns never follow consensus.
    • Jim Watson (of double helix fame) says “Avoid boring people”. It has a double meaning.
    • Keep track of people who make bad choices as opposed to people who have had success.
    • Respect for Musk, Bezos, John Malone. Although says Tesla is a bad business. Much respect for SpaceX
  • On business advantage
    • Moat because of competitive advantage, low cost of production etc. That will lead to dominant unit economics.
    • Focus on TAM is silly. It’s not predictive of return. How much capital goes into a sector is inversely correlated with future returns. Need to look where this few competitors and high barriers to entry.
    • Ironically, a bad competitor can ruin a market if they are the first customers interact with.
  • Is there more innovation or less depending on liquidity in markets?
    • Slime mold is spore which expands in every direction when resource rich. Similarly, when financial conditions are loose, many businesses are started and speculated on. The detritus of the fallout ( Global Crossing was a major disaster for investors but a boon for the Third World which got connectivity for free) will provide the nutrients for the next seeds of growth.
    • When capital is scarce, only the best projects get funded so an opportunity exists but it’s probabilistically rare to earn a return from in all parts of the business cycle.
  • Random allusions
    • Tattoo asymmetry: 60 bucks to get one and fast, but later regret and massive expense to undo
    • Possessions as anchors
    • Happiness comes in doses. Dissatisfaction is important.

Notes from Invest Like the Best: Will Thorndike


About Will: Author of The Outsiders: Eight Unconventional CEOs and Their Radically Rational Blueprint for Success

  • Case study of 8 CEOs he viewed as masterful capital allocators. They had very diverse backgrounds despite operating in similar fashions

Warren Buffet (Berkshire Hathaway)

Henry Singleton (Teledyne)

Bill Anders (General Dynamics)

John Malone (TCI)

Tom Murphy (Capital Cities Broadcasting)

Katharine Graham (Washington Post)

Dick Cinema (General Cinema)

Bill Stiritz (Ralston Purina)

    • Malone and Singleton were mathematicians. 4 had engineering degrees, only 2 had MBAs.
    • 1/2 of them were CEOs under age 40, they were all first time CEOs
    • They were not visionaries or charismatic. Minimized time on investor relations or simply ignored it
    • Tax savvy and efficient
    • They were emotionally cool, pragmatic, highly analytical and opportunistic as opposed to having an opinion about the way things should be.
    • Strong operational acumen often with a key deputy or partner
    • 5 Ways a CEO can allocate capital and observations from the CEOs profiled. (Good capital allocation looks a lot like value investing)
  • How they approached their capital allocation decision set
    1. Dividends
    • Tax inefficient (double taxation). These CEOs cut against the grain and didn’t pay them or paid little
    • The exception was special one time dividends which usually coincided with pending tax law changes
    • From 2000 to 2012 dividend rates were lowest in history despite enjoying a tax advantage. Since 2012, dividend rates have increased despite the law actually making them far more expensive. The companies which paid special dividends before the Jan 2013 law change self-selected for being especially tax savvy.
    • Since 2009, a high dividend yield no longer correlates strongly with other measures of cheapness (P/B, PE, etc).
    1. Acquisitions
    • Other than Malone, acquisitions were sporadic, rare and very large. The logic for them would generally be rooted in the ability to scale costs effectively.
    1. Cap-ex
    • Very analytical and disciplined. ROIC guided their decisions. Often having quantitative filters for screening projects early (the idea of ‘funneling high’)
    • To combat managers from ‘teaching to the test’ as far as showing projects which met the predefined hurdle rates, there was a decentralized budgeting process which would audit and evaluate investments to impose accountability retroactively.
    • They were not strictly wed to long term plans and far more flexible and reactive to the ‘cards dealt’
    • Lean operations, choosing to focus on expenditures on their end product whether it was goods or media content

4. Pay down debt

    • They had a strong sense of what band their leverage ratios (debt to EBITDA, ie 3x or 4x) should be to balance returns through variations of the business cycle

5. Buy back stock

    • Singleton was pioneer in buying back large chunks of the float at opportune times, capitalizing on cases where the market had underpriced his company. This contrasts with the current trend of systematic buybacks. He would use tender offers.
    • When the stock got frothy he who would issue shares
    • All Outsiders except Buffet utilized this strategy

Notes from Capital Allocators: Andrew Tsai


About Andrew: Chalkstream CIO (Pete Muller’s family office + FOF)

  • Background


Ex-Susquehanna, ex-Lehman fixed income arb, founder of quant hedge fund

Pivot to founder

Urban Fetch: was a pivot from a failed hedge fund. Used the PHds to solve the logistics problem of deliveries. Knew this was the real business, to outsource those algorithms while the actual delivery service was money losing. Company was valued in hundreds of millions pre-dot com burst but VC didn’t want to sell yet since they need to be hogs on the few winners in their portfolio. Since Andrew came from the GS/AQR school of ‘long value, short growth’ quant world he was uncomfortable being ‘long growth’ but failed to “pound on the table” to sell. The window closed for them to sell with the crash.

Hired as CEO

After Urban Fetch, Carlyle group invited him to be a CEO of one of their portfolio companies. This gave him the first exposure to long term investing horizons that is more typical of corporate leaders in contrast to the the more transactional disposition of trading and much of finance/banking world. PE investors try to ‘move the spread in their favor’ by using their resources to influence the business whether its expertise or connections to get their hands dirty.

Meets Peter Muller

Pete Muller thought he handled the closures of Urban Fetch and prior hedge fund well, continuing to try when others might have “crawled into a ball”

  • Chalkstream Family Office

General investing strategy is value-oriented, optionality (sub-prime CDS, CDS on large Japanese companies in secular declining industries as China hard landing contagion hedge), low or no beta, small capacity constrained strategies like power trading. Concentrated bets. They have an intrinsic mistrust of correlations when considering traditional asset management since they are so dependent on capital flows and thus distortion. They prefer strategies that are natively unrelated to broader markets. Instead of filling asset allocation buckets, they search for potentially mispriced bets. They categorize bets according to themes instead (ie small-cap regional banks, Japan trade, Korea trade, power trading). They only get involved in trades that they deep dive into.

Quantitative market making in spaces that have lots of dispersion (ie power trading)

Focus on spaces where active management can add value.

They focus on “space, team, and alignment” when evaluating a manager

If you believe in your team and strategy, rule #1 is ‘stay in business’. Need long term focused capital which was a credit to how AQR survived the 1997 currency and quant meltdowns despite being a recent launch. Long value, short growth didn’t start working til post 2000 bust!

Notes from Invest Like the Best LiveStream: Peter Attia M.D.


About Peter

Maximizing lifespan and more importantly healthspan including recommendation
  • Insight about lifespan 
If you are >40, if you don’t smoke and don’t commit suicide there’s an 80% chance you will die from:
1. Heart disease and stroke
2. Cancer
3. Neuro-degenerative disease Alzheimer’s)
4. Accidental death
    • 50% of fatal car accidents happen on freeways; his hack to maintain concentration: imagine there is a random driver on the road whose sole intent is to kill you
    • 40% of fatal accidents occur at intersections with an oncoming driver approaching from the left, usually running a red light
  • Defining Healthspan 
1. Cognition
    • Executive function
    • Processing Speed
    • Short Term Memory
2. Physical
    • Maintenance of muscle mass
    • Functional movement
    • Freedom from pain
3. A sense of Purpose and Social Support
4. Distress Tolerance
  • Medicine regimes and their associated breakthroughs which increased longevity
    • Medicine 1.0
      • Up to last century; not scientifically rigorous; breakthrough’s in this era for increasing longevity was an understanding that microscopic germs caused sickness and then antibiotics and the advent of sewers/sanitation
    • Medicine 2.0
      • Randomized Control Trial which provided a means to test; this allowed us to more accurately map the proper to treatments to a diagnosis (HIV, heart disease, life support)
      • A common feature of advances in this era is that its methods were best suited to treat conditions which afflict patients quickly (heart at
      • tacks, infections, HIV)
    • Medicine 3.0 is concerned with increasing healthspan
      • While the earlier regimes made sharp jumps in improving lifespans, the challenge of studying techniques to increase healthspan is the impossibility of truly randomized, controlled, longitudinal, humane studies.
      • This regime is more of an empirically driven strategy that we must embed while tolerating a need for certainty that will not be satisfied. It relies on inference from studies and logic to make bets on the healthspan maximizing courses of action. These bets can and will be re-calibrated if the weight of evidence warrants unseating prior knowledge. The parallels to investing include being highly critical, curious, probabilistic in thought, and a servant to both a degree of randomness as well as the cumulative sum of compounded decisions. Recognizing every treatment has an opportunity cost or even an outright side-effect is health’s version of TAANSTAFL. Every habit or remedy has risk and reward. In some cases (ie antibiotic for a UTI), the trade is an easy choice. In some cases, it may be a difficult choice (extend a cancer patient’s life for X months with a brutal treatment) and in some cases, the choice may not present its pros and cons so plainly (“do these vitamins even work?”).


  • Our active role in medicine 3.0
There are 7 macro levers we can pull which can provide us with our own experiments and each lever has an infinite number of combinations within the lever and between the levers.
    1. Diet
    2. Exercise
    3. Sleep
    4. Modulation of stress
    5. Drugs
    6. Hormones
    7. Supplements
Without a sound abstracted strategy we will hopelessly flail between levers, so before diving into the latest fad we must take inventory about what we might know about these levers, how much confidence to have in this knowledge, and have a model for how to evaluate what new information makes sense to incorporate into our canon.
The strategy (imperfect as it must be) is to triangulate on possible answers using the 3 sources of knowledge we have which are capable of informing longevity.
    • Data on centenarians (about 1 in 250 people);
      • pro(s): they are humans
      • con(s): no experimental evidence, purely observational. No insight into causality
      • These studies suggest a large genetic component to longevity. While they smoke at 2x the rate as everyone else they are far more likely to carry genes which code for certain growth hormone receptors, APO-C3, APO-E2 etc
    • Studies on animals
      • pro(s): we can have controlled experiments
      • con(s): they are not humans so we are always making a leap by generalizing
    • Molecular biology
      • Discovery of senescent cells and studies of mTOR protein; these are cellular agents of biological aging
      • Studies of parabiosis (blood transfers); shows promise but we don’t understand the mechanism
      • Understanding of autophagy (body ‘rebalances its portfolio’)
  • Using evolution as a guide
    • Agriculture period coincides with .10% of our genetic history.
      • Ramifications
        1. We now live in an era of caloric abundance
          • Our ancestors thrived despite regular periods of fasting
          • We should at least be critical of ‘food in a box’
          • Preference for nutrient-dense organ meat or dark green veggies
        2. We do not need to exercise the way our ancestors did
          • Effect of sitting (our ancestors had no chairs)
          • Look how pre-school age children move perfectly
    • Sleep
      • Reasoning to why sleep must be important
        • Our ancestors’ purpose was to eat and procreate; in a dangerous world sleeping 8 hours per day (which is what the record shows our ancestors did in fact do) would appear to be a vulnerable activity to be selected against.
        • It is easy to imagine how adaptive it would appear to not require sleep and the advantage it would have conferred to those individuals. However, the odds of carrying the gene which lowers your need for sleep is less than 1 in 1000!
        • In modern times with electricity and reasons to be awake longer, it is conceivable that such an adaptation will be selected for but such changes occur over thousand of generations.
        • Summary of Matthew Walker’s “Why We Sleep”
    • Evolution can be an irrelevant guide
      • He did not find evolution to be a relevant lens for evaluating mindfulness (he expected that our ancestors were far more ‘present’) nor pharmacology (the intensity and depth of drugs being absent in ancient times)
      • In the last 200k years, our APO genes have expanded from a single type to three different types. The newer types have made our brains more resistant to brain parasites and less resistant to alzheimers.
      • Paleo people argue that meat was the way of our ancestors while vegans argue that our ancestors had no need to worry about heart disease or health in old age. Ancestral considerations will have more difficulty bearing on the sources of food.
  • Studying the sources of knowledge which represent the pillars of his strategy there will be many tactics within each of the 7 levers to employ. Some of the tactics he has incorporated as the longest standing habits in deference for the overwhelming evidence of their benefits:


    • Lifts weights and sprint/similar (strength and bone density as insurance against accidental/falling injuries in older age)
    • Sleep 7-9 hours per night
    • Time-restricted feeding
    • Don’t spike insulin; eat fewer carbs
    • Movement matters; be strong in the positions your ancestors would have been in
    • Meditation
    • Gamified feedback (wearing a glucose meter or aura ring) to nudge desired behavior. Note how the 7 levers do not offer immediate feedback making them difficult to learn from.