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Today’s Not Boring is brought to you by… Masterworks

I have a confession to make: I can't stop investing in art. It started small. I invested in Basquiat’s Loin in 2020. And I got hooked. 

Today, there are 10 paintings in the Not Boring portfolio. I recently added the Picasso masterpiece from the striking image above. Instead of breaking my addiction, my New Year's resolution is to double down and buy as much art as possible, because I believe that art is a key part of a balanced portfolio. Here's why:

• UBS reports that two-thirds of high-net-worth collectors buy art for an expected ROI

• Real assets like art appreciate well during periods of high inflation

• Blue-chip Art prices outpaced the S&P 500 from 1995 to 2021, according to Citi

But even though I’m bullish, I didn't throw down $100 million at auction. I just used Masterworks. They're the fintech unicorn that lets you invest in multimillion dollar works at a fraction of the cost.  

And Masterworks has results: they've sold two paintings that netted their investors a 30%+ IRR in 2020 and 2021. 

Their offerings tend to sell quickly. Last week, I tried investing in their new $7.4M Banksy painting, but it sold out in less than three hours (and I missed out 🥲). No time like the present.

If you want to join me on the platform and get priority access – click this Not Boring link *

Hi friends 👋, 

Happy new year! Hope you all enjoyed your holidays, got to relax and recharge a little bit, and, if you live in New York, that your COVID wasn’t too bad. 

Last year, for the first piece of the year, I came out swinging with my only-ever bear case on Bill.com. I was hilariously wrong on the stock: it opened that morning at $137.46 and a year later, it’s up 81.25% to $249.15. It handily outperformed the NASDAQ (+22.3%) and the BVP Cloud Index (-1.8%). It grew revenue 151.9% last year, faster than any other BVP Cloud Index company by far (Snowflake is second at 109.5%). Just clobbered me. 

I learned a few lessons from that: 

1. Never underestimate strong distribution, even in the face of a shitty product.

2. Kick the year off optimistically.

And whew, there’s a lot to cover! I go away for a couple weeks and an all-out war over what web3 is and isn’t, who owns it and who doesn’t, and whether or not it’s total bullshit breaks out. 

You know where I come out on this, but words are words. Actions speak louder.

Let’s get to it.

The Laboratory for Complex Problems

2022 is the year that web3 starts making a meaningful impact on atoms-based challenges like healthcare and climate. 

Thus far, web3 has been focused on bits, on creating a user-owned internet by infusing physical properties like scarcity, uniqueness, ownership, and self-custody into digital items. That’s an important track; as more of our time, money, relationships, and work goes digital, it’s important that we have the right to own as well as rent. 

But all of the wild shit in web3 also serves another purpose. In the immortal words of Allen Iverson…  

“We talking about practice. Not a game. Not the game that I go out there and die for and play every game like it's my last. Not the game. We talking about practice, man.”

Expensive Apes? Practice. ConstitutionDAO? Practice. KrauseHouse? We talking about practice. $OHM? Practice. Memecoins? Not the game, we talking about practice, man. 

Will the world be a better, more fair and equitable place if my friend Brett succeeds and FriesDAO buys a Subway? No, probably not. But we might learn something about organizing groups of people around a shared mission on the internet that we can apply to the real game. And we might make some more internet millionaires who can fund new projects. Practice. 

Every time a new NFT project comes together and falls apart, every time people ape into a seemingly worthless meme, and every time a DAO makes a subtle innovation in an attempt to circumvent some constraint, the whole system evolves and it produces new tools and tricks that entrepreneurs and policy makers can use to attempt to solve large, thorny problems, both digital and physical.

This view of web3 is different than the idea of owning the internet. It’s less about decentralization and censorship-resistance, and more about rapid experimentation of governance and incentive models. It harnesses greed and speculation for good. 

In this view, web3 is a global, real-money economic and social simulation, a digital laboratory for complex problems. 

To date, the focus and excitement around web3 has been on the bits side: specifically around ownership of digital items and governance among large groups of internet strangers. 

Meanwhile, an added benefit is that every new NFT project, novel DAO, DeFi mechanism, and even memecoin is a digital experiment being run in real-time that can also feed back into the world of atoms to help coordinate and incentivize large groups of people to solve hairy challenges. 

1. Bits: building a new digital economy, each attempt is also an experiment.

2. Atoms: pull the best mechanisms in to solve challenging physical world problems.

Importantly, the digital pieces have value. The total crypto market cap passed $3 trillion this year. ConstitutionDAO raised $47 million. These three pictures are worth over $18 million. There’s real money on the line, which makes people behave like it’s the real thing. 

The fact that these are high-price but ultimately low-stakes (the world won’t end if the Bored Ape Yacht Club disappeared) projects is a feature, not a bug. These projects combine internet iteration speed with real-world-huge sums of money to let humanity speed-run simulations on group coordination, all while picking up specific tools to help address hard problems.

From Squiggles to a cure for cancer, Apes to carbon reduction, Punks to saving the fishes.

That’s a big leap. How’d I get here? 

Complexity

Over the break, I asked people on Twitter for the one book or essay they’d recommend reading to get ready for the next decade. I wanted to zoom out, think about atoms more than bits for a change, and shake my brain up a little bit. 

Complexity: The Emerging Science at the End of Order and Chaos by M. Mitchell Waldrop, won. 

On the surface, it’s a funny winner of the “prepare for the next decade” contest. Waldrop wrote the book thirty years ago, in 1992. It’s not new enough to be expressly written for the next decade, and not old enough to sit among the timeless classics that work in any decade. But the crowds are wise; it was an excellent pick.

Complexity tells the early history of complex systems science through the stories of the founders of the Santa Fe Institute (SFI), “a research institute in Santa Fe that is devoted to the study of complexity in all its forms.” Thirty years later, SFI is still running and complex systems science is still relevant. In October, the 2021 Nobel Prize in Physics went to three scientists, “For groundbreaking contributions to our understanding of complex systems" used to create more accurate models of the effect of global warming on the climate.

Complex systems are everywhere, from the climate to the human brain to the power grid to the economy to the beginning of life on this planet from the primordial stew of molecules. They’re devilishly hard to capture in models and differential equations, but crucially important to understand. 

Before complex systems science, the prevailing approach to understanding these complex systems was reductionism: breaking them down into their component parts and trying to build a complete picture by adding together each piece. Challenge is, complex systems do all sorts of things that can’t be predicted by looking at each piece. They’re emergent: the whole is greater, and different, than the sum of its parts. 

(Incidentally, I think many of the anti-web3 arguments miss by being overly reductionist.  I’m actually pretty sure the midwit meme is just reductionism vs. complexity.)

Take economics. The economics that I studied in college, except for one frisky Behavioral Economics course, assumes that “economic man” is perfectly rational and seeks to fit the economy into a series of differential equations and supply-demand curves on top of those assumptions. It seeks equilibrium. It’s mathematically elegant, all fits together, and… is pretty much entirely disconnected from reality. 

The real economy, like most complex systems, doesn’t reach equilibrium. It evolves, based on the actions, rational and irrational, of its billions of individual participants. 

Grokking the status quo’s shortcomings, and armed with the heretical idea of increasing returns – a concept familiar to all of us in tech today but much less accepted in the early 1980s – Stanford economist Brian Arthur set out to create a new economic framework at Santa Fe. 

The stakes were more than academic. The world was coming out of a painful period of stagflation in the 1970s that called into question the Keynesians’ ability to stabilize the economy. A more realistic economic framework would help policymakers and financiers design interventions that would stimulate growth and employment, stabilize the market, and better achieve the goals of their fiscal and monetary programs. 

So instead of differential equations based on assumptions, Arthur proposed agent-based simulations:

I had this notion that you could have within your office in the university a little peasant economy developing under a bubble of glass. Of course, it would really be in a computer. But it would have to be all these little agents, preprogrammed to get smart and interact with each other. 

Then in this dreamlike idea, you’d go in one morning and say, ‘Hey, look at these guys! Two or three weeks ago all they were doing was bartering and now they’ve got joint stock companies.’ Then the next day you’d come in and say, ‘Oh – they’ve discovered central banking.’ Then a few days later you’d have all your colleagues clustered around you and you’re peering in: ‘Wow! They’ve got labor unions! What’ll they think of next?’ Or half of them have gone Communist. 

In practice, there was no Sims-like economic simulation running in Arthur’s office. It was just that, instead of this… 

Arthur would let the agents in his simulations run wild, resulting in graphs that look like this: 

Complexity Economics: A Different Framework for Economic Thought, Brian Arthur

Beyond math versus simulation, there were a number of major differences between Arthur’s complexity economics and neoclassical economics. In a January 2021 Nature article, Foundations of Complexity Economics, he laid those differences out, feature-by-feature. 

In Complexity, Waldrop explained Arthur’s economic agents further: “They would be no more governed by mathematical formulas than human beings are. As a practical matter, of course, they would have to be far simpler than real human beings…”

The Every Icon NFT project that I used in the cover art captures the images I have in my mind for these simulations. Set the starting conditions and a few parameters, and watch what these little dots do. 

But what if they didn’t have to be little dots or bits of code, simpler than human beings? What if they could be human beings? 

I got the vague sense reading the book, and it slapped me in the face when I saw the table in Nature: Web3 is a complexity economics simulation played out with real human agents and real money. 

To learn about the web3 simulation and the DAOs for the Future… 

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Thanks for reading and see you next week,

Packy

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