This Post Covers: The Macro Big Picture, What Part Of The System Needs To Be Smart, How Food Banks Use Markets to Feed the Poor, FIFA Uncovered

The Macro Big Picture, What Part Of The System Needs To Be Smart, How Food Banks Use Markets to Feed the Poor, FIFA Uncovered

"Many people agree with me that many things are not clear in football these days and it is not something people who watch football deserve. The situation will be the same while football is run by people who do not understand football." - Diego Maradona

"We cannot change anything until we accept it. Condemnation does not liberate, it oppresses." - Carl Jung

Macroeconomy / Finance : Let's Zoom Out

Long-term economic growth is a function of the growth in labor supply and total factor productivity.

In other words: it’s highly influenced by?how many people?actively contribute to generate economic output, and?how productive?the labor force and the use of capital are.

Until the mid-80s, the ability to generate organic growth in most Western economies was very solid: a combination of strong working-age population and good productivity trends led to high levels of potential GDP growth.

But things rapidly took a?turn for the worse in the late ‘80s.

By the early ‘90s, the post-WWII demographics boom had exhausted its positive effect.

Fertility rates decreased, longevity increased and hence the?share of working-age population dropped by several percentage points?in a few decades.

The number of people actively contributing to GDP growth wasn’t growing fast anymore - but perhaps an upward trend in productivity growth could offset this? It could, but it didn't.

Especially in the 2010s,?productivity growth was relatively stagnant: we made some progress, but the marginal productivity gains were rather small and definitely not enough to push structural GDP higher given the demographics headwinds.

The permanent scars left by the Great Financial Crisis, and the capital misallocations that partially generated from monetary policy decisions such as the Zero Interest Rate Policies (ZIRP) and QE were amongst the many factors that acted as a drag on productivity growth.

Remember: long-term economic growth is a function of the growth in labor supply and total factor productivity.

After the ‘90s, as both demographic and productivity trends materially weakened so did the ability to generate structural economic growth amongst advanced economies.

Today, advanced economies are looking at?potential real GDP growth in the 1.0-1.25%?area?(left chart) and required equilibrium real rates roughly around 0% for that to happen (right chart).

And given the demographics headwinds ahead, these numbers might well look?even lower?in the next 1-2 decades.

Low levels of GDP growth are?socially unacceptable?in capitalistic societies.

So, what’s the fix?

Debt.

Between 1990 and 2020, all major economies went ahead with an extensive use of credit in an attempt to cyclically boost economic growth way above its poor structural trend - Europe, Japan, US, UK, and even China saw their?total economy debt as % of GDP rise from 100-150% to 300-400%?in a few decades (left chart).

But if the underlying economic activity and wages don’t rapidly rise, how could these economies sustain such a massive build-up in leverage - especially private sector agents, which can’t print money to refinance or service their debt?

Easy:?real yields were pushed lower?and lower every time.

After all, if you make 100k/year you can probably afford a 400k mortgage at 4%.

At 2%, with the same 100k/year salary you can now take on 600k in debt. The fix was ‘‘straightforward’’: more and more debt, at lower and lower real interest rates.

Can this go on forever?

There are three main elements that could?disrupt?this fragile and leveraged system:

A) Excessive levels of (private) debt;

B) Higher real rates;

C) Recessions and de-leveraging episodes.

The policymakers’ reactions to the pandemic led to a sharp increase in debt levels: public debt soared due to unfunded fiscal deficits, and in certain jurisdictions there was also a bump up in private debt due to government-sponsored bank lending to corporates and households -?checkmark on A).

Gigantic injections of new real-economy money in the private sector (~$5 trillion in the US) coupled with re-openings led to a rapid surge in demand.

Bottlenecks in the global supply chain compounded the problem, and inflation skyrocketed and became broad and persistent over time. This forced Central Banks to tighten policy and rapidly raise real interest rates -?checkmark on B).

Tighter monetary/fiscal policies and higher real rates took a big toll on hyper-enthusiastic markets and the over-leveraged private sector slowly froze as borrowing costs became prohibitive for many businesses.

Many leading indicators are clearly pointing to a recession in 2023 -?checkmark on C) on its way.

A trifecta of disruptive forces, all at once. Are we looking at a regime change?

Tech / A.I. / Crypto

Some eye-catching stuff

• Meta has released?ImageNetX, which is a set of human annotations of factors such as pose, background, or lighting for the entire ImageNet1k validation set as well as a random subset of 12k training images.?The dataset is used to study the types of mistakes made by 2,200 current recognition models.?
• a16z have come out with another post around AI,?The Generative AI Revolution in Games. It discusses how generative AI is transforming the gaming industry by allowing for the creation of high-quality images in a fraction of the time it would take to generate them by hand. It also outlines the various segments of the generative AI market for games and identifies key companies in each.

When to go smart?

One debate that starts out technical, but devolves into an ideological one starts with this question: for a given network of computers, what is the most ideal way to distribute their power? The answer to this question has two extremes, one of which is a fairly non-hierarchical network where most participants are fully-fledged devices that compute locally, store their own data, and interact as peers. At the other end of the spectrum, thin clients aren’t much more than a monitor, peripherals, and a connection to some beefier server somewhere far away.

The tradeoff between distributed and centralized computing power can sometimes be framed in terms of limitations on bandwidth, latency, utilization rates over time, etc. But considering that tradeoff also pokes at deeper questions about the structure of computing and the economy at large.

One argument in favor of the broad category of thin clients is that there's a tradeoff between simple interfaces and powerful tools. A product like Uber or Doordash is partly a way to take a hugely complicated question—how do we optimize a logistics network to quickly and efficiently deliver people or goods on demand, and how do we tradeoff between speed and certainty—and turn it into a single button push.

That's an impressive feat! And sometimes increasing the sophistication of the underlying system means decreasing the complexity of the interface. Since modeling errors compound over time, and since traffic jams are not linear (one additional car has either roughly zero effect on average speed or leads to a traffic jam), it's much harder to promise an arrival time at a destination than it is to promise a vehicle as soon as possible. But a decent fraction of rideshares are trying to get somewhere at a specific time with as little waste as possible. A more full-featured local system with lots of options and flowcharts is one way to achieve this, and another is to handle all of that complexity on the backend and just ask the user what they want.1

Thin clients can get increasingly powerful—in terms of what they do, not in terms of how much control users have—as whatever backend they access changes. You've never controlled your Facebook or Instagram account, in the sense that it's always somebody else storing the data and deciding how to display it. But over time, those services have gotten far more sophisticated in how they display content. What you get when you type words into Google has also changed a lot over time; the system has gone from being entirely dependent on relationships between texts to being so smart that power users are the ones who are good at telling the search engine to be?dumb, by, for example, using quotes to suppress Google's aggressive search for synonyms, or using the date function to tell Google that you're looking for historical instances of something that just happened again.

The question of where complexity should reside ends up being a question about what computers really?are. A fat client approach is that a computer is something close to a chief of staff, helping a decisionmaker execute their ideas and keep track of everything they need to know. The thin client model is that a computer is a simplified interface on some elaborate how-we-manage-things-here model, with a flowchart for every situation leading to one and only one answer. It’s a question of whether the user should be?computing?or should just be inputting some simple data and getting an answer.

The thin client model is prolific because it’s scalable, in two senses:

1. Traversing a big, interconnected graph by hopping from one node to the next is a slow process, even if the hops are generally in the right direction, and this kind of decentralization creates lots of overhead, and
2. There are literal cost benefits to buying storage space by the exabyte, or to having enough memory for global peak demand rather than for any device's peak demand.

But there are downsides. The ownership question is a big one. Increasingly, companies and individuals own?access?to their data but don't own the data itself. This is fine the vast majority of the time, but turns out to be annoying when there's some opaque dependency that's utterly unfixable, like a hacked Gmail account or an AWS outage, that makes the rest of a system stop working. The critical infrastructure layers do have a strong incentive to keep themselves running, but it's not fully aligned with the incentives of their users.

And the thin-versus-fat client?phenomenon is much,?much?broader than the question of whether your social media profile should have a local backup and fully portable friend graph by default. It also applies to economic actors who can treat some layers that they interact with as fully trusted and completely capable of handling complex operational details:

• There has been a long trend towards US retail acting as a frontend for China's manufacturers. This was mutually beneficial, at first because of China's extremely low wages, and over time because earnings from exports were reinvested in infrastructure. Eventually, Covid seemed to do what we thought trade war concerns were going to, and led more companies to diversify supply chains. This?made companies that access that part of the supply chain implicitly closer to a full-stack model than to a thin client: they still didn’t own their manufacturing, but instead of plugging into one existing system, they were sourcing from many places at once and carefully designing a resilient supply chain.
• Merchants on Amazon are increasingly encouraged to avoid thinking about demand generation, shipping, and storage. Every one of those can be solved by giving Amazon money, albeit not necessarily on the most favorable possible terms.
• The chip industry, by necessity, needs to abstract some complexity away. The supply chains are too unwieldy to be owned by a single company: too many highly-specialized devices whose components are also extremely specialized. But that's creates multiple chokepoints.
• Russia's long record of uninterrupted natural gas exports to Europe, even when Cold War tensions were high, made cheap natural gas an unquestioned input into the European economic model.
• Interacting with and relying on the dollar system gave countries more access to trade and finance, but also meant that they were more vulnerable to US sanctions.

Decentralized computing, deglobalization, and full-stack companies are really three instances of the same general trend. More centralized systems are powerful, but they're also brittle. Decentralized systems are expensive, but they're resilient; a world with a more diversified and local energy system, more spread-out manufacturing, and more peer-to-peer networks is less dependent on a few systemically important actors.

Controlling one’s own destiny is complicated, expensive, and risky. When companies describe their strategic decisions in these terms, they’re usually justifying a big and unpopular investment push. And many of these pushes don’t work out. Division of labor is a powerful concept, and the structure of supply chains as well as the structure of computer networks reflects the fact that redundancy has a cost and some decisions work best when they’re repeated. But over time, this model reduces agency, and discourages careful analysis of why the network or supply chain is structured the way that it is. Eventually, someone who relies on distant abstractions can find that their economic niche has disappeared for reasons that are completely inscrutable to them.

The shift doesn't have to be binary, though. One way it can happen is for some participants in the stack to accept that they're truly neutral service providers, and that moving up or down the supply chain represents a costly strategic sacrifice even if it's temporarily profitable. For companies, the right approach will be to decide early on if their customers should view them as a utility, or as an eventual competitor.

What I read

• Canice Prendergast:?How Food Banks Use Markets to Feed the Poor. This paper is a case study in which a group of food banks changed the way they allocate across the US, by switching from a queuing/rationing-based system to a market-based system with an internal currency. One problem markets are great at solving is allocating heterogeneous goods to parties with heterogeneous preferences. And this can be useful even when not everyone involved is a dedicated profit-maximizer.
• Josh Eidelson at?Bloomberg?has a piece on the evolution of the Apple Store from a great retail job to a merely okay one. Markets are brutally efficient. Apple retail jobs were better jobs when the company wasn't as profitable, and faced the real risk of extinction. Now they're safer and more attainable roles, in part because brand affinity and employee morale are potential energy that can be converted into free cash flow. It's a lot more satisfying to work at a company when it's still mostly investing in these intangibles, and that period can go on for a long time. But eventually, it will probably be run by someone who is trying to get a measurable financial return on that investment.

What I watched - FIFA UNCOVERED

If you weren't aware, the World Cup just started. This Netflix documentary was eye opening but take it with a pinch of salt - agendas are rife. But, check it out and enjoy!

"The first lesson of economics is scarcity: there is never enough of anything to fully satisfy all those who want it. The first lesson of politics is to disregard the first lesson of economics." - Thomas Sowell

(If you read this far, kudos to you!)

And, as always, please give me feedback. Which idea above is your favourite? What do you want more or less of? Other suggestions? Please let me know.?

Have a wonderful week, all.

Much love to you and yours, mon amis.