A Proof Is Only as Good as Its Spec

2026-06-06T00:00:00+00:00

I want Ethereum to have more formal verification, not less. That’s why I’m writing this.

When Risk Models Create Risk

2026-06-06T00:00:00+00:00

The last essay ended with Taleb’s objection. The tail is where the risk is, and the tail is exactly the part of the distribution the data refuses to pin down. If you cannot know the probability of ruin, stop pretending you can. Change your exposure instead.

Jón Daníelsson adds a colder institutional version of the same argument. He says something stronger than “risk models are inaccurate”: in finance, the act of measuring risk changes the risk. A model works less like a thermometer held up to the weather and more like a rule handed to people who then trade, hedge, deleverage, report, and regulate according to it. The number enters the system it tries to describe.

That is the final turn in this series. If everyone copies each other, small shocks cascade. If everyone uses the same risk model, the model itself becomes one of the things they copy.

The Limits of Knowing

2026-06-05T00:00:00+00:00

Six essays in, we have a toolkit. Bouchaud’s branching ratio, Sornette’s critical time, Gabaix and Koijen’s flow multiplier, Scheffer’s slowing-down: different instruments, all pointed at the same condition, a system loaded near its edge. This essay is about the catch that has been sitting in the room the whole time. That condition, the distance to the edge, is the single hardest thing to pin down on exactly the kind of system where it matters most. Nassim Taleb built a career on this objection, and it is the strongest case against everything that came before.

Why the Calm Is Dangerous

2026-06-04T00:00:00+00:00

So far the series has been about why crashes happen and why you cannot read them off their triggers. This one is about the rare practical payoff: the chance of seeing a break coming. A system heading for a tipping point often gives off warning signs, and the odd thing is where they hide. They hide in the calm, in a precise, measurable sense: the quietest system can be the one closest to breaking.

What Actually Moves Prices

2026-06-03T00:00:00+00:00

The last essay put a number on reflexivity. A branching ratio near one says the market spends most of its time reacting to itself, with one trade setting off the next. That is already a hard blow to the tidy picture where prices mostly digest outside news.

But there is an even cleaner number, and it comes from mainstream economics rather than econophysics. Xavier Gabaix and Ralph Koijen asked a blunt question: if one dollar flows into the aggregate stock market, how much does the market’s total value rise?

The old intuition says the answer should be close to one dollar, or maybe less. A deep market should absorb flows. Prices should move mostly because expected cash flows, discount rates, or risk premia changed. Buying pressure should be a sideshow.

Their estimate is about five dollars.

That is the most important recent bridge into the whole Edge of Chaos story. The market is an inelastic object, far from the vast ocean of the old intuition. Push it with flows and the whole level moves.

The new macro number and the older microstructure law tell the same story. At the aggregate level, flows have a multiplier. At the trade level, latent liquidity makes impact nonlinear rather than harmless.

Reflexivity by the Numbers

2026-06-02T00:00:00+00:00

Three essays in, the same idea keeps surfacing in different words: markets react to themselves. Prices move because prices moved, and the big moves arrive with no outside cause. George Soros built a whole investing philosophy on this and gave it a name, reflexivity. Markets, he said, act on their own reflection, and that reflection feeds back into the world.
It is a good story, and the trouble with a good story is that it explains everything and predicts nothing until someone turns it into a number. This essay is about the number: how much of what a market does is the market answering itself, and how much is it answering real news from outside. You can actually measure that, and the tool comes from an unlikely place, the study of earthquakes.

Faster Than Exponential: Can You See a Crash Coming?

2026-06-01T00:00:00+00:00

If you believe the last two essays, prediction is mostly a fool’s errand. Crashes come from inside, the biggest moves have no cause worth the name, and looking for the grain that set off the avalanche means looking for something that was never special. Take that seriously and the only sane move is defense: carry slack, cut leverage, expect the slide.
Didier Sornette spent a career arguing that this gives up too soon. Not for every crash, but for one particular kind, the blow-off top at the end of a bubble. His claim is that a bubble has a shape, that you can see the shape while it is still forming, and that the shape tells you something, with all the usual hedges, about when it will break. This is the optimistic case in the series, and it deserves a fair hearing, including the places it falls apart.

Sandpiles and Crashes: How Systems Tune Themselves to the Brink

2026-05-31T00:00:00+00:00

The last essay ended with a puzzle it did not solve. Markets, it argued, sit near a critical point, the knife-edge where a tiny shock can cascade into a huge move. But phase transitions are fussy. Water turns to ice at one exact temperature. A magnet loses its magnetism at one exact spot on the dial. To sit near that kind of edge, something usually has to hold the dial there with great care.
So who holds the market’s dial? Who tunes millions of independent traders to balance near the edge?
The answer, worked out in 1987 by three physicists, is that nobody does. Some systems walk to the edge on their own and stay there. Per Bak, Chao Tang, and Kurt Wiesenfeld called it self-organized criticality, and the toy they used to show it was a pile of sand.

Crashes Without a Cause: Markets as Phase Transitions

2026-05-30T00:00:00+00:00

On October 19, 1987, the S&P 500 lost more than 20 percent.
The size of the fall was only part of the strangeness. The usual story never arrived. No bank failed that morning. No war started. No earnings report, rate decision, or political announcement came close to explaining a one-day move of that size. People searched for the cause because markets are supposed to need causes. Black Monday mostly refused to provide one.
Bouchaud and his collaborators later found the same problem at a smaller scale. Look at large jumps in individual stocks and the matching headline is often missing. The move is real; the news is too small, or absent.
That leaves an uncomfortable possibility: sometimes prices move because the market has already begun to move.
The model that makes this feel less mysterious was not built for finance. It was built for magnets. Before turning it into a market, we need to build the magnet first.

Discipline Without Love Optimizes for the Wrong Variable

2026-05-21T00:00:00+00:00

The last year and a half, but particularly the last six months, were incredible but also very very tough. I went through difficult personal problems and had to expand my tolerance for pain to extremes I didn’t know existed. You don’t fight pain by toughening up. You fight it by controlling what you let near you. I left alcohol, lowered my consumption of caffeine to bare minimums, deleted my instagram and removed most of the people I didn’t care about from my life and I doubled down on living with friends family and partners I love.
My father told me multiple times that I have lived multiple lives in one. It’s very likely that he has some responsibility for this because I became obsessed with adventures thanks to Jules Verne when I was a kid. I wanted to explore. I did trips to places and met people that you see only in nightmares. After getting burnt and almost dying multiple times I created LambdaClass</a> and now Ergodic Group</a>. Those near deaths weren’t only pain. Each one was teaching me the same thing: turn this into something that outlasts you, or it was just damage. I’m trying to do the same right now with what happened to me the last few weeks.

Blockspace Forum Cannes - Fede and Justin Drake

2026-04-22T00:00:00+00:00

A Fact-Producing Compiler

2026-04-09T00:00:00+00:00

Series note: this article is part of the Concrete series and responds to Dmitri Sotnikov’s Giving LLMs a Formal Reasoning Engine for Code Analysis</a>. Related: When the Compiler Is the Oracle</a> and Why Concrete Exists</a>. </blockquote>
When an AI agent explores a codebase, it usually greps for names, reads a few matches, searches for callers, reads those, and tries to piece together a mental model of the program from text fragments. This works about as well as you would expect. The agent is asking structural questions about a program, things like “can user input reach this SQL query?” or “what changes if I touch this function?”, but the only tool it has is text search.
Yesterday I read Dmitri Sotnikov’s article</a> about giving LLMs a symbolic reasoning engine for code analysis. His tool, Chiasmus</a>, parses source code with tree-sitter (a syntax parser), turns definitions and calls into logic facts, and lets an LLM run graph queries instead of grepping through files. That is a much better interface: the agent asks a structural question and gets a structural answer.
Reading the post gave me a better phrase for part of what we are building with Concrete: a fact-producing compiler.
Concrete is the systems programming language we are building for programs that need auditability. It compiles code into an executable and into checked statements about what that executable can do.

CommitLLM: How to Verify an LLM Inference

2026-04-02T00:00:00+00:00

You send a prompt to an LLM API. The provider says it ran Llama 70B. Maybe it did. Maybe it served a smaller model to save money, changed the quantization, altered the decode settings, or patched the answer after generation. Today you usually cannot tell. You get text back, an invoice, and a promise.
For casual use, a promise is often enough. For enterprise procurement, regulated systems, benchmark evaluation, or agent workflows making consequential decisions, it is not. If the model behind the answer matters, “trust us” is not a satisfying interface.

What Concrete Makes Worse

2026-03-24T00:00:00+00:00

Series note: this is the tradeoffs entry in the Concrete series. For the foundation, start with Why Concrete Exists</a>. For the most practical demo, read When the Compiler Is the Oracle</a>. </blockquote>
The previous articles in this series argued that Concrete’s design constraints are worth it. Explicit capabilities make code auditable. Linear types prevent resource leaks at compile time. No hidden behavior means the compiler can report what your program actually does. I believe all of that. But I have been writing Concrete code for long enough to know where the constraints bite, and I have not been honest enough about that in public.
This article is about what Concrete makes worse. Not in theory, not as an abstract “it’s stricter.” Specific code that is uglier, longer, or more painful to write in Concrete than in Rust or Zig. If you are considering whether these tradeoffs are worth it for your domain, you deserve to see the cost up front.

When the Compiler Is the Oracle

2026-03-20T00:00:00+00:00

Series note: this is the most practical entry point in the Concrete series. If you want the shorter manifesto first, read Why Concrete Exists</a>. If you want the language reference behind this article, use Concrete Spec</a>. </blockquote>
I have been building Concrete</a> for a while now. Something happened this week that I did not plan for, and it may end up mattering more than the things I set out to build on purpose.
I let an AI agent improve a Concrete program using only the compiler’s reports as feedback. No profiler. No benchmarks. The agent read what the compiler knew about the program, tried refactorings, checked if the compiler’s answers improved, and kept or reverted. It worked better than I expected. The compiler had made the search space clean enough that the agent did not have to wander through benchmark fog.
That points at why Concrete is useful in the first place. A language that makes authority, allocation, trust boundaries, and proof surface explicit is easier to audit, easier to optimize, and easier to automate against. You do not have to reconstruct the truth from profiler traces, stale docs, and reviewer intuition. The compiler can tell you what is true about the program, and that changes how you build systems software. To explain why, I need to start with what Concrete is and what makes it different.

Writing Your First Proofs in Lean

2026-03-20T00:00:00+00:00

The first article</a> in this series explained the Curry-Howard correspondence: propositions are types, proofs are programs. The second</a> built a tiny theorem prover from scratch in Python. The third</a> embedded the same ideas inside Julia’s type system.
Now we use the real tool. This article takes the exact same theorems you proved by hand in Python and shows them in Lean 4. You will see what changes and what stays the same: the syntax shifts, the logic does not.

Programming a Mini-Lean in Julia's Type System

2026-03-19T12:00:00+00:00

This article is based on Guillermo Angeris’s talk “Programming a (mini-)Lean in Julia’s type system”</a>.
A theorem prover, stripped to its engine, is a small trusted kernel, a type checker, and a boundary between the two.
Guillermo Angeris answers this by live-coding a toy theorem-proving kernel inside Julia that illustrates how Lean works architecturally. The result is a tiny kernel that makes the trust boundary visible: if you accept the kernel, then anything built on top of it has to pass through the type checker.
This is the third article in the Theorem Proving</a> series. The first article</a> covers the Curry-Howard correspondence. The second</a> implements a tiny prover explicitly in Python. This article embeds those same ideas inside a host language’s type system.

Building a Tiny Theorem Prover in Python

2026-03-19T00:00:00+00:00

The first article</a> in this series explained the Curry-Howard correspondence: propositions are types, proofs are programs. That tells you why theorem proving fits so naturally with programming languages. It does not yet tell you what the machine looks like.
A theorem prover, concretely, is smaller than most people expect. A tiny theorem prover is just:

a language for terms</li>
a language for types / propositions</li>
a checker that decides whether a term has a type</li>
a tiny trusted kernel that defines the legal moves</li> </ol>
This article builds that architecture in plain Python. We are not abusing Python’s own type system. Python is just the implementation language. The prover we build has its own terms, its own propositions, and its own checker.
That distinction matters. The goal here is not to show off host-language cleverness. I want the moving parts to be impossible to miss.

Self-Replicating Programs Emerge from Random Noise

2026-03-18T00:00:00+00:00

Most programmers think Turing completeness is the interesting threshold for a computational system. It gets all the attention. But a lower, stranger threshold matters more for the origin of complex behavior: self-replication.
A recent paper</a> by Agüera y Arcas et al. shows that self-replicating programs spontaneously emerge from soups of random code. No one designs them. No fitness function selects for them. They assemble themselves from noise, take over the soup, and keep evolving. I reproduced the core result in about 300 lines of code.

Propositions Are Types, Proofs Are Programs

2026-03-18T00:00:00+00:00

In the 1930s, Haskell Curry noticed something strange. He was working on combinatory logic, a system for manipulating abstract functions, and he realized that the rules governing his combinators looked identical to the rules of a logical system called intuitionistic propositional logic. It was as if he’d found two different maps of the same territory.
Three decades later, William Howard found the same thing in a richer setting. He showed that the simply typed lambda calculus, the foundation of functional programming, corresponds precisely to natural deduction, a standard system of logical proof. Every type corresponds to a proposition. Every program corresponds to a proof. Every function corresponds to an implication.
This is the Curry-Howard correspondence: a structural identity between proofs and programs.

Fede's Guide to a Healthier Life

2026-03-13T00:00:00+00:00

When I was young I loved science and engineering. Like most nerds, I thought thinking was the only thing that mattered. Working out seemed like a vanity project, something for people who cared about how they looked and not much else. I didn’t understand the body-mind connection at all. I was a skinny kid who spent all day reading, tinkering with computers, and hanging out with friends. The idea that physical health could affect how well I think would have sounded like nonsense to me.
It took me a long time to figure out how wrong I was.

Can I prove Concrete programs in Lean?

2026-03-12T00:00:00+00:00

Series note: this is the proof-roadmap entry in the Concrete series. For the language overview, start with Why Concrete Exists</a> and Concrete Spec</a>. For the most practical compiler-report demo, read When the Compiler Is the Oracle</a>. </blockquote>
I have been thinking about what it would take to prove Concrete</a> programs in Lean. Not the compiler itself, but actual user programs: take a function written in Concrete, connect it to its Core IR representation inside the compiler, and prove properties about it using Lean’s existing theorem library.

Designing a Programming Language for the AI Era

2026-03-11T00:00:00+00:00

Series note: this article assumes the basic Concrete frame is already in place and asks a narrower question about AI-era language adoption. For the series foundation, read Why Concrete Exists</a>. For the main Rust comparison, read The Rust Effects Debate and Concrete’s Case for a Smaller Language</a>. </blockquote>
Edgar Luque recently wrote about how AI creates a new adoption barrier for programming languages</a>. His claim is that AI coding assistants need training data, training data only exists for popular languages, and so new languages get bad AI support, which prevents adoption, which in turn prevents training data from accumulating. A self-reinforcing loop that locks in whatever is already dominant.
If you are building a new general-purpose language that competes with Python, Go, or Rust on roughly the same terms, Luque’s analysis is devastating. What makes it worse than previous adoption barriers is that you cannot community-effort your way out of it. The AI training pipelines belong to a handful of companies, and those companies will always prioritize the languages where the most data already exists.
But there is a blind spot in the argument.

The Rust Effects Debate and Concrete's Case for a Smaller Language

2026-03-09T00:00:00+00:00

Series note: this is the main Rust-comparison entry in the Concrete series. If you are new here, start with Why Concrete Exists</a>. If you want the language reference, use Concrete Spec</a>. </blockquote>
Yosh Wuyts recently wrote about his “grand vision” for Rust</a>, outlining three directions he thinks the language should pursue: effects, stronger substructural types, and refinement types. The Hacker News thread</a> that followed split predictably: one camp saw a safer, more principled systems language taking shape while the other saw echoes of Scala, C++, and a language that becomes harder to read than the software it is meant to clarify.
Both camps are seeing something real, and I think resolving the tension between them requires something other than adding more features to Rust. That is where Concrete</a> comes in.

Dissolution Without Construction

2026-03-06T00:00:00+00:00

I have been circling the same problem for a while now. Friction produces value. Legibility destroys what it measures. Formation requires lived time. The modern self is dissolving through redundancy. Previous technological shifts gave people decades to adapt, and this one might give them months.
These kept feeling like separate observations. I no longer think they are.
#</a>The Speed Mismatch</h2>
Every major coordination technology in history has dissolved the form of selfhood that preceded it. Writing destroyed oral memory, the printing press destroyed manuscript culture, and institutions dissolved kinship. None of this is new.
What is new is the asymmetry between how fast things dissolve and how fast things grow.
Writing spread slowly. A scribe copied a text, carried it to another city, taught someone to read it. The dissolution of oral memory and the construction of literate thought happened at roughly the same pace, both measured in generations. The printing press was faster, but still slow enough that cultural forms could emerge alongside the destruction. The novel, the diary, the public library, liberal education: these took centuries to stabilize, but centuries were available because print moved at the speed of physical objects.
Algorithmic systems dissolve at computational speed. A recommendation engine can reshape the taste-formation process of millions of people in months. An AI assistant can make inner deliberation feel unnecessary within a single product cycle.
But construction, the formation of new human capacities, new cultural forms, new modes of perception, still happens at biological speed. A person becomes a particular kind of person through years of encounter, revision, failure. A cultural form stabilizes through generations of practice. There is no computational shortcut because the capacity is constituted by the process, not by its output. You cannot compress becoming.
The problem is the gap between dissolution speed and construction speed.
This reframes a conversation that has been stuck for years. One side says previous transitions worked out, so this one will too. They are wrong for a structural reason: previous transitions worked out because dissolution was slow enough for construction to keep pace. That condition no longer holds. The other side says technology is destroying us. They are also wrong, or at least imprecise. Dissolution can be the beginning of construction; every previous transition dissolved something real and produced something new. The issue is speed: dissolution now moves fast enough to make construction impossible.
#</a>Friction as Governor</h2>
Desire depends on resistance. The deeper point is that resistance imposes a pace. The distance between wanting and obtaining is a temporal structure. It gives the person time to become someone who can integrate what they receive. Remove the distance and the person receives before they can absorb.
Friction performs this function at civilizational scale. When the printing press dissolved oral culture, books were expensive. Literacy spread gradually. The oral self did not vanish overnight. It weakened over decades and centuries, and during that time the constructive side, new practices of reading, new institutions, new literary forms, had room to develop. The friction inherent in physical media imposed a speed limit on dissolution, and that speed limit happened to match the speed of human formation.
Friction in taste formation gives a person time to develop the capacity to perceive, beyond a set of preferences. Friction in professional training gives the practitioner time to develop judgment that resists articulation. Friction in deliberation gives the self time to form. Remove it and dissolution outruns construction. The gap opens. Nothing grows where the old capacity was.
#</a>Legibility as Accelerant</h2>
The German forest again. Eighteenth-century foresters replaced diverse, messy woodland with uniform Norway spruce plantations. Yields surged. Then the undergrowth died, the soil degraded, and the forest collapsed. What looked like inefficiency was the system’s life support.
Legibility does something specific to the speed problem. It converts illegible processes into optimizable targets. Once a process is visible, it can be measured. Once measured, optimized. Once optimized, the friction in the original process gets removed as inefficiency. Legibility is the mechanism that identifies friction, and optimization is the mechanism that strips it out. Together they accelerate dissolution.
The self depended on opacity. Taste formed in private. You encountered things by accident, sat with discomfort no algorithm could detect, revised your sensibility through a process invisible to any external system. Professional judgment lived in knowledge that could not be articulated. Inner deliberation happened in a space that was, by definition, not observable from outside.
AI reaches into all of this. It makes taste formation legible through behavioral data. It makes deliberation legible through interaction logs. It makes professional intuition legible through performance metrics. Each act of legibility strips away a layer of friction that was functioning, without anyone noticing, as a speed governor on dissolution.
#</a>The Compounding Problem</h2>
This is the part that worries me most. Dissolution outruns construction and degrades the conditions under which construction is possible.
Formation requires complete cycles: effort, feedback, adjustment, repeated across lived time. Acceleration fragments these cycles. What spreads fastest diverges from what works best. Imitation spreads faster than learning. Every act of dissolution removes some of the friction, opacity, and time that construction requires. The more functions the self loses, the less capacity remains to develop new ones. The process compounds. Dissolution accelerates while the ground for construction erodes.
Someone who has never formed taste through friction is not going to develop whatever post-algorithmic perception might look like. Someone who has never exercised inner deliberation will not develop whatever comes after deliberation. The new capacity, if it exists, will not emerge from a vacuum. It will emerge from people who have developed enough of the old capacity to transcend it, the way literate thought emerged from people who had first mastered oral memory. If dissolution destroys the old capacity before the new one can develop from it, the sequence breaks. What looks like another transition is actually an interruption.
#</a>What Would Have to Be True</h2>
I do not know what comes after the literate self. Nobody in 1450 could have described what the printing press would produce either. But I can describe the conditions under which construction becomes possible.
It would require friction. Not arbitrary difficulty, but the specific resistance that imposes a pace compatible with human formation. It would require opacity. Domains where the slow work of becoming is not legible to optimization systems, where a person can develop without being measured. And it would require time. Actual time. Complete cycles of effort and revision that are not compressed or interrupted.
The current system removes all three. Optimization treats friction as waste. AI treats opacity as a problem to solve. Markets treat slowness as a competitive disadvantage. The incentives point uniformly toward faster dissolution.
Previous transitions produced their own constructive forms because the speed of dissolution left room for them. Print was slow enough that the novel could emerge. Institutional life was gradual enough that liberal education could develop. The printing press did not spontaneously generate the literate self. People built schools, designed curricula, established practices of reading and argument that took centuries to stabilize. That work was deliberate, institutional, and slow, but it was possible because the dissolution it responded to was also slow.
The question I cannot answer is whether anything can grow at biological speed in an environment that has been optimized for computational speed. Whether construction is possible when the conditions for construction are precisely what the system is most efficient at removing.
There is an irony I should not avoid. I build coordination infrastructure for a living. Ethereum clients, cryptographic proof libraries, distributed systems. Tools that accelerate exactly the process this essay describes. And the essay itself is not construction. It is diagnosis. I am genuinely confused by my own position. I see the dissolution clearly enough to write about it. I also build the tools that produce it. I have not resolved this. I am not sure it can be resolved. Naming the speed mismatch does not slow it down. It may even accelerate it by making the problem legible, which is what I argued legibility does: convert things into objects of optimization. I do not know whether writing about dissolution is a form of friction, something that slows the reader down, forces them to sit with discomfort, or a frictionless take on friction, consumed and forgotten at algorithmic speed. I suspect the answer depends on what you do after reading it.

Legibility Kills What It Measures

2026-03-03T00:00:00+00:00

In the 18th century, German foresters invented scientific forestry. They looked at a messy, diverse forest and saw inefficiency. Old trees, young trees, deadwood, underbrush, species with no commercial value. They cleared it all and planted Norway spruce in straight rows, evenly spaced, same age, same species. The forest became legible. You could measure it, manage it, predict its yield with precision.
For one generation, it worked brilliantly. Yields surged. Then the forest began to die. The complex undergrowth had been cycling nutrients, retaining moisture, hosting the insects that pollinated the canopy and the fungi that fed the roots. The foresters had not simplified the forest; they had destroyed the system that kept it alive, preserving only the part they could see.
James Scott tells this story in Seeing Like a State to illustrate a pattern that recurs wherever central authorities impose legibility on complex systems. The pattern is simple: make the illegible legible, optimize what you can now see, and lose what you couldn’t see but depended on.

Finance Is Geometry, and It All Comes Back to Jensen’s Inequality

2026-03-01T00:00:00+00:00

Never cross a river that is on average four feet deep. If the river is eight feet deep in the middle and dry on the sides, the average tells you nothing about whether you will drown. You will drown in the middle, or you won’t. There is no averaging across parallel universes where you both survive and die.
The same asymmetry shows up wherever outcomes compound. Lose 50% of your wealth and you need a 100% gain just to break even, because the loss hits a larger base than the recovery builds from. Going from $100 to $200 and from $200 to $400 are different dollar amounts but the same proportional move: one doubling. Wealth is about ratios and scaling, not absolute differences.
This multiplicative structure has consequences that run deeper than intuition suggests. The mathematics that governs survival in compounding environments was invented 400 years ago to help astronomers multiply large numbers, partially rediscovered in the 18th century to solve a paradox about gambling, formalized again through information theory, and then largely obscured by theories that optimized across hypothetical worlds instead of along a single path through time.
That is the geometric claim in this essay: wealth evolves multiplicatively, while most ordinary intuition is additive. The logarithm is the change of coordinates that lets us move between those two descriptions.
This is the story of why geometry sits at the core of finance, why a single inequality ties the whole picture together, and why reducing variance can be more valuable than increasing returns.
None of the parts are mine. Kelly, Peters, Spitznagel, and Taleb each worked one of them out. My only claim is that they are the same picture, and this essay traces how the pieces fit.

The Tail Hedge Debate: Spitznagel Is Right, AQR Is Answering the Wrong Question

2026-02-26T00:00:00+00:00

Stock markets crash</a>. The S&P 500 price index fell about 57% from October 9, 2007 to March 9, 2009, and about 34% from February 19, 2020 to March 23, 2020.1</a>2</a> A put option is a contract that pays you when the market falls below a certain price (the “strike”). If you hold stocks and also hold puts, the puts can offset some of your losses during a crash. The question is whether the cost of buying puts is worth the protection they provide.
There are two sides. AQR Capital Management published “Chasing Your Own Tail (Risk)”</a> (Berger, Nielsen, and Villalon, 2011</a>). They argue that buying puts systematically costs more than it saves. On the other side, Mark Spitznagel at Universa Investments, where Nassim Taleb is scientific advisor, argues that a small put allocation improves long-term returns (Spitznagel, 2021</a>). Universa reported a 3,612% gain in March 2020 (via an investor letter, as reported by Bloomberg).3</a>
We tested both claims with our open-source options backtester</a> on 17 years of real SPY options data (2008 to 2025), covering three crashes: the 2008 financial crisis, COVID, and the 2022 bear market.
The version of the trade that survives the data is narrow but real: cheap convexity, sized small. AQR’s published critique tests neither half of that. They use near-ATM puts (the most expensive form of crash protection per dollar of notional) inside the allocation-reducing framing (selling SPY to fund the puts, surrendering the equity premium that funds everything else). In that configuration deep OTM puts still lose against SPY. Spitznagel’s externally funded overlay flips both choices: deep OTM puts, kept cheap, layered on top of full SPY exposure, sized small. At a 0.5%-1.0% annual premium the trade compounds. Past that budget the bleed dominates, the rate of convexity falls, and the strategy degrades — there is a sweet spot, not a monotonic dial.
All headline numbers below are gross of transaction costs, slippage, and taxes. Execution drag at the deep-OTM strikes — Israelov’s strongest surviving objection — is on top of these gross numbers; we return to it in the limitations section.

Detecting Crashes with Fat-Tail Statistics

2026-02-19T00:00:00+00:00

Financial markets don’t follow normal distributions</a>. That is a claim about frequency, not just theory: it tells you how often catastrophic events happen. Under a naive Gaussian model, a crisis on the scale of 2008 lands so deep in the tails that standard risk models treat it as effectively impossible. It happened on a Tuesday.
The problem is that we keep using tools designed for thin-tailed worlds. Value at Risk (VaR) models that assume normality. Risk metrics that treat the 2008 crash as an “outlier” rather than a regular feature of financial returns.
I built fatcrash</a>, a Rust+Python toolkit with 15 classical methods, to test whether fat-tail statistical methods can detect crashes before they happen. The performance-critical math (fitting, simulation, all rolling estimators) runs in Rust via PyO3; everything else (data, viz, CLI) is Python.

Twenty Centuries of Financial Data: What 240 Countries and 2,000 Years Reveal

2026-02-12T00:00:00+00:00

In 1252, Florence minted the gold florin. Within decades it became the dominant trade currency of medieval Europe. Merchants in Bruges, Venice, and Constantinople quoted prices against it. By the 1400s, the florin’s dominance had faded, replaced by the Venetian ducat. Then the Spanish real. Then the Dutch guilder. Then sterling. Then the dollar. Each transition involved devaluations, defaults, and crises that ruined anyone holding the wrong currency at the wrong time.
We have data on all of this. Not estimates. Actual recorded exchange rates, starting from 1106. And not just exchange rates: gold and silver prices from 1257, interest rates from 1311, commodity prices from 1260, GDP per capita from the year 1 CE, sovereign debt ratios from 1800, and crisis indices covering two centuries of banking panics, currency collapses, and sovereign defaults.
I assembled forex-centuries</a>, the most comprehensive open-source collection of long-run financial and economic data available. 27 sources, 1,100+ files, ~240 countries, spanning twenty centuries. Exchange rates, precious metals, interest rates, commodity prices, inflation, GDP, real wages, sovereign debt, regime classifications, and real effective exchange rates, all in one repository with an automated build pipeline, weekly CI updates, and reproducible analysis. No other free repository combines this breadth of asset classes across this depth of history. The only comparable product is Global Financial Data</a> (commercial, institutional pricing). The goal: provide the raw material for studying how currencies and financial systems behave over centuries, not decades.

Friction as Luxury: What We Lose When AI Gives Us What We Want

2026-02-05T00:00:00+00:00

#</a>The Last Scarcity</h2>
Most discussions of AGI focus on distribution: who gets access, who profits, who loses their job, who controls the infrastructure. Those are real problems, but they’re not the deepest one.
The deeper problem is what happens to desire. I do not mean ambition in the generic sense. I mean the capacity to want something at a distance, to stay oriented toward something you do not yet have, and to find meaning in the space between reaching and arriving. That capacity is more fragile than we usually admit, and it depends more on friction than most people notice.
#</a>I.</h2>
Economists have a clean model of desire. People have preferences, goods satisfy preferences, welfare rises as more preferences are satisfied. In that framework, a technology that can satisfy almost any preference at negligible cost looks like an obvious good. The only remaining question is who gets access.
That model leaves out something important. Desire has a shape, and that shape depends on certain conditions holding.
When you want something over time, you imagine having it. You plan for it, you make sacrifices toward it. The object accumulates meaning from this process. It gets layered with your effort, your anticipation, your history of reaching. When you finally arrive, you don’t just get the object. You get the object plus everything you invested in wanting it. Those two things can’t be separated.
That is why anticipation is often richer than arrival, why the best albums sometimes need months rather than minutes, and why relationships built through difficulty have a texture that convenient ones often do not. Resistance helps produce the value.
#</a>II.</h2>
When this structure breaks down, the clinical term is anhedonia. But there is a milder and more socially acceptable version of the same pattern. People in this condition can be entertained constantly but rarely feel deeply absorbed. They consume without much appetite. They move from one stimulating thing to the next not because anything is satisfying, but because sitting with incompleteness starts to feel unbearable.
You can already see the shape of it in declining attention spans, in the difficulty of sustaining interest in anything that doesn’t deliver immediate feedback, in people who feel simultaneously overstimulated and bored. They haven’t been deprived, they’ve been saturated.
This doesn’t distribute evenly in society. In environments where discomfort is quickly solved, by money, by services, by endless entertainment, the mind gets less practice holding lack. You can grow up surrounded by abundance and still become poor in one specific way: poor in patience for distance.
Structurally, it starts to resemble addiction: craving breaks away from fulfillment. Neuroscience draws the same line. Kent Berridge’s work separates wanting from liking, and it is the dopaminergic wanting, not the pleasure, that addictive drugs and variable-reward machines exploit. Slot machines and infinite feeds are built around exactly this, unpredictable payoffs on a schedule that trains the nervous system to treat discomfort as a cue for relief and relief as a cue for repetition. A frictionless AI environment could reproduce some of that pattern without chemicals. Boredom, loneliness, uncertainty, and effort all become prompts for instant stimulation. Over time the threshold rises, what once felt absorbing becomes merely adequate, and the rest of life starts to feel slow and underpowered by comparison.
Consumer capitalism produced a weakened version of this. Desire progressively hollowed out by eliminating friction, but with enough friction remaining that the structure didn’t fully collapse. The streaming service still requires you to choose. The algorithm still occasionally surprises you. The simulation of connection is imperfect enough that you sometimes notice it’s a simulation.
#</a>III.</h2>
Imagine a system that can generate, on demand, a novel calibrated to your tastes: the style you find most pleasurable, the level of complexity you find most engaging, the length that matches your current patience. Or music that sounds like what you loved most at nineteen, except new, immediate, and endless. Or a conversation partner who is always interested in what interests you, always available, never distracted, never carrying needs of their own into the exchange.
The output might be genuinely good. The novel could be technically accomplished. The music could actually move you. The conversation could be substantive. The problem is what happens to wanting once the gap collapses to zero.
The capacity to stay oriented toward a distant goal, to defer, to invest, to tolerate incompleteness</a>, atrophies when it is never exercised. Not through a dramatic break, but through disuse. The ability to want things that require time does not vanish all at once. It gets weaker, and the weakening may not even feel like a loss because something pleasant keeps arriving on schedule.
#</a>IV.</h2>
None of this is new. Epictetus drilled his students to want only what was already in their control and to rehearse discomfort on purpose, and Seneca set aside days to live as if poor so that hardship could never be used against him. Fasting in Ramadan, the privations of Lent, and the Rule of Benedict all build scheduled scarcity into a life on the same theory: that meaning runs through resistance rather than around it.
What’s new is the scale. Previous technologies eliminated specific friction but left other friction intact. Every digital environment until now required you to bring something it couldn’t supply: attention, skill, patience.
A genuinely general AI dissolves this last requirement. It can supply the taste, the context, the judgment. You no longer need to bring anything except the desire to receive. And if that desire is itself shaped by the AI, tuned to whatever maintains engagement, then even the wanting has been outsourced.
#</a>V.</h2>
Here is the inversion. Where material abundance is the rule, the things that keep their value are often the ones that resist its logic. Their value is tied to the conditions that make them difficult, not to artificial scarcity.
A handmade object carries the trace of the hands that made it. A wine vintage can’t be accelerated. The waiting isn’t incidental to what the wine is. A community built around a shared difficult practice, painting, rock climbing, chess, building and fielding armies of miniatures, generates bonds that digitally mediated interaction doesn’t replicate, because those bonds are forged in shared difficulty.
These things become valuable not despite being harder than consuming AI output, but partly because of that hardness. When satisfaction is frictionless, friction itself becomes the luxury.
#</a>VI.</h2>
The safety, alignment, and job-displacement debates are all real. But they share a common assumption: that the humans on the other side will still be capable of deciding what to do with what they’ve been given, and that political agency and collective imagination will survive intact.
That assumption is doing a lot of work.
The atrophying of desire is already visible in what weaker technologies have done to culture. What AGI does to human psychology comes before what it does to human politics. A population that has lost the capacity to want things at a distance, to stay oriented toward a difficult future, and to find meaning in effort and incompleteness has lost something essential to self-government.
The scarcity that matters most in a post AGI world won’t be compute or energy. It will be the capacity to want something deeply enough, and for long enough, that the wanting shapes who you are.
If desire is the last scarcity, then slowness, difficulty, and incompleteness are not obstacles to overcome. They are the conditions of a life worth living.

China is trying to commoditize the complement

2026-01-22T00:00:00+00:00

China is trying to win by commoditizing the complement. The strategy is working, though not without friction. This is a structural challenge the West should take seriously instead of dismissing.
For the last two decades, the West exported cognition because it owned the platforms, the cloud, the software distribution, and the talent concentration. If the cognitive engine becomes cheap, portable, and good enough, that asymmetry weakens. A small country can buy or download the same cognitive machinery, then apply it to its own bureaucracy, its own companies, its own language, its own domain problems.
The West has dominated the thinking and services world. Software, finance, media, research, management layers, and the export of expertise. The US is the clearest example. In 2024, US services exports were about 1.1 trillion dollars, the highest on record. The US and the West sell thinking at scale. AI threatens to flatten that advantage because AI turns thinking into infrastructure.
China dominates the atoms world. Industrial capacity, manufacturing throughput, physical supply chains, cost curves. In 2023 China produced about 28 percent of global manufacturing value added.
If you can make the layer next to you cheap and abundant, you drain its pricing power and force value to move somewhere else. In AI, the complement is model access. For a lot of Western companies, the business is still basically gated intelligence sold as an API. China has every incentive to make that layer feel like electricity: available everywhere, cheap, hard to monopolize.
Open weight releases are part of that play: DeepSeek, Qwen, Kimi, and MiniMax are only a few of the Chinese open-source models. Once strong models are common, model access stops being a moat. It becomes a commodity input.
A huge fraction of what we call services is legible work: reading, writing, coding, summarizing, translating, drafting, answering, generating variations, searching a space of options. That layer is now replicable and it is getting local. Apple is publishing technical reports about on-device foundation models, including aggressive quantization aimed at making serious inference run on consumer hardware. When strong models run on a laptop, countries stop importing thinking as a service. They import weights, or they distill, fine-tune, and deploy inside their own borders.
The commodity play is working, but it is not frictionless. China faces real constraints, and they shape how far the strategy can go.
Capital controls limit how freely Chinese companies can operate globally. The state can redirect investment at a scale nobody else can match, but centralized allocation tends to overshoot. Solar panel overcapacity, steel oversupply, and the EV price war all follow the same pattern: massive subsidized buildout that ends up compressing margins for everyone, including the Chinese firms themselves.
Top talent still flows toward open research environments. By MacroPolo’s Global AI Talent Tracker, China is the single largest source of the world’s top-tier AI researchers, yet a large share of them end up doing that work in the United States. Tightening political control over universities and private firms can speed up execution on defined goals, but it makes the open-ended, high-risk research that produces real breakthroughs harder to sustain.
Predictability matters for long-term innovation, and the last few years dented it. The abrupt suspension of Ant Group’s record $34 billion IPO in November 2020, the 2021 crackdowns that erased the for-profit tutoring sector overnight and froze new game approvals for months, and Didi’s forced retreat from US markets after its 2021 listing all sent the same signal: any company can become a target without warning. Foreign firms recalibrated their exposure and some domestic founders turned cautious. Centralized coordination buys speed, but it also shrinks the appetite for bets that do not match current state priorities.
The West still has one advantage that is hard to replicate: it is where most of the world’s ambitious talent wants to live, work, and build. It is a compound effect of open institutions, freedom of movement, and decades of accumulated trust. As long as that holds, the West keeps attracting the talent and the capital that turn ideas into new industries.
None of these constraints cancel out the commodity play. They set its ceiling. China can drive the price of model access toward zero faster than anyone, but the open-ended research and the institutional trust that turn a cheap commodity into new industries are much harder to subsidize into existence.
China stays strong in atoms because it already has the scale advantage. The West still leads in areas that require deep institutions and long accumulated competence, frontier research and high trust services in particular. But AI compresses the services premium by making a large portion of cognition cheap and replicable. That is why open models matter. They attack the margin structure of the thinking economy.
If you sell intelligence, this is bad news. If you own distribution, hardware, data, or a workflow people cannot easily leave, you survive. If you own atoms and you get thinking for free, you get a scary combination, because the services premium that sustained Western economic leadership for decades can be undercut by a player with industrial dominance and access to the same cognitive tools.

Building a SaaS with Elixir/Phoenix and React

2026-01-15T00:00:00+00:00

Most SaaS codebases I’ve seen share the same problems. Authentication that sort of works until someone finds an edge case. Caching layers that nobody fully understands. Deployment scripts held together with hope. The team moves fast early on, then spends years paying down the debt.
We got tired of this cycle. Over several projects, we developed a stack and a set of practices that let us move fast without leaving landmines for our future selves. Elixir on the backend, React on the frontend, Nix for everything else. No Docker. No Kubernetes. Decisions that raised eyebrows at first but have proven themselves in production.
This post explains what we use and why.
#</a>The case for Elixir</h2>
Our backend runs on Elixir, which might seem like an unusual choice in a world dominated by Node, Python, and Go. The reason comes down to what happens when things go wrong.
Elixir runs on the Erlang VM, a runtime Ericsson built in the 1980s for telephone switches. These systems needed to stay up for years at a time, handling failures gracefully without human intervention. Crashes are expected in Elixir, even encouraged as an error-handling strategy. When a process crashes, it crashes in isolation. A supervisor notices and restarts it. The rest of the system keeps running. You don’t get woken up at 3am because one user’s request hit an edge case that brought down the whole server.
Phoenix is the web framework we use on top of Elixir, though we use it differently than most teams. Phoenix has become famous for LiveView, its technology for building interactive UIs with server-rendered HTML. We don’t use it. Instead, Phoenix serves only JSON through a REST API, and a completely separate React application handles everything the user sees.
This creates a hard boundary between backend and frontend. Backend developers focus entirely on data and business logic without thinking about UI concerns. Frontend developers own the user experience end-to-end without needing to understand Elixir. The two teams communicate through the API contract, and neither steps on the other’s work. When we eventually build mobile apps, they’ll consume the same API with no new backend work required.
#</a>Why we abandoned Docker for Nix</h2>
This is probably our most controversial choice. Docker has become the default for development environments and deployment. We use Nix instead.
When a new developer joins our team, the onboarding process is simple: clone the repository and run nix develop</code>. A few minutes later, they have everything they need. Elixir, Node.js, PostgreSQL, Redis, Meilisearch, all running natively on their machine. Not in containers. Actually installed. Without container overhead, everything runs at native speed. Debugging is straightforward because there’s no abstraction layer between you and the process. And there are no Docker Desktop licensing conversations.
But the real payoff comes in production, where our servers run NixOS. The entire server configuration is declarative and lives in version control alongside our code. When we push a change, every server ends up in exactly the same state. Deployments are atomic. They succeed completely or fail completely, with no partial states to debug. If something goes wrong, rolling back takes one command.
Nix has a steep learning curve. The documentation is notoriously difficult, and the language has unusual semantics. But once you’ve internalized the concepts, you get guarantees Docker can’t provide. A build that works today will produce the exact same result in five years, because every input is pinned and reproducible.
#</a>Building for offline use</h2> Most web applications assume users have constant connectivity. Ours doesn’t, and this assumption has shaped our entire frontend architecture. The frontend stores data locally using Dexie.js, a library that wraps IndexedDB with a friendlier API. When a user makes changes, those changes save to the local database first. A sync queue tracks what needs to go to the server, and when the network becomes available, the queue drains automatically. A salesperson updates CRM records on a flight with no WiFi. A technician fills out inspection forms in a basement with no signal. Someone’s home internet drops for thirty seconds while they’re submitting an important form. In all these scenarios, our app keeps working. Users might not even notice the interruption. The UI responds immediately to their actions, and synchronization happens in the background. We use TanStack Query for data fetching, but with caching completely disabled. Every API call fetches fresh data from the server. IndexedDB is our cache, and we control exactly when and how it syncs. No more stale data bugs because some cache somewhere wasn’t invalidated properly. #</a>Database decisions</h2> PostgreSQL. UUIDs as primary keys instead of auto-incrementing integers. This makes enumeration attacks much harder, because an attacker can’t simply guess that /users/124</code> follows /users/123</code>, though it does not replace proper authorization checks. UUIDs also let us generate identifiers on the client before the record exists in the database. For multi-tenancy, we use application-enforced tenant isolation. Every table that holds customer data includes an org_id</code> column, and every query filters by it. If you want the database to enforce that boundary too, PostgreSQL row-level security is the next step. The alternative is giving each tenant their own database schema. That provides stronger isolation, but migrations have to run once per tenant, connection pools multiply, and cross-tenant queries for admin purposes become complicated. The org_id</code> approach is simpler and scales well for most SaaS applications, as long as you’re disciplined about enforcing it everywhere. We also have a strict rule: no random data in tests. We don’t use Faker. Every test uses explicit, predictable inputs. When a test fails, it fails the same way every time you run it. You can debug it, reproduce it, and fix it. Random test data causes tests that fail one time in twenty for reasons nobody can reproduce. #</a>Authentication</h2> Most tutorials get authentication wrong in ways that create real security vulnerabilities. We use JWT tokens with a two-token system. The access token is short-lived, expiring after 15 minutes. It’s stateless, so the backend validates it without touching the database. The refresh token lasts 7 days and is stored in the database. When the access token expires, the frontend uses the refresh token to get a new one. Because refresh tokens live in the database, we can revoke them instantly. When a user clicks “log out of all devices,” it actually works. We delete their refresh tokens, and within 15 minutes every session everywhere is invalidated. Both tokens live in httpOnly cookies rather than localStorage. JavaScript cannot read httpOnly cookies, which means an XSS vulnerability cannot simply exfiltrate the tokens the way it can from localStorage. But cookies change the threat model rather than eliminating it: you still need SameSite</code> settings and CSRF protection or strict origin checks on sensitive endpoints. Most tutorials store JWTs in localStorage because it’s simpler, but it leaves users vulnerable to script injection. Password hashing uses Argon2, OWASP’s current recommendation over bcrypt. #</a>Libraries</h2> For JWT handling, we use Joken instead of Guardian. Guardian is popular but tries to do too much. It has opinions about plugs, permissions, token types. We found ourselves fighting these abstractions. Joken just encodes and decodes tokens. We handle the rest. Oban handles background jobs. Unlike Sidekiq or Celery, Oban uses PostgreSQL as its backend instead of Redis. One less service to run. Job state is transactional with your application data. You can insert a database record and enqueue a job in the same transaction, with the guarantee that either both happen or neither does. On the frontend: Zustand for client state, TanStack Query for API calls, React Hook Form with Zod for forms. For components, shadcn/ui built on Radix primitives. Radix handles accessibility correctly, which is hard to do from scratch. #</a>Deployment</h2> We deploy to bare metal servers running NixOS. No Docker in production. No Kubernetes. Kubernetes solves problems of scale that most SaaS applications don’t have. For a typical SaaS with a handful of services, it adds operational complexity without proportional benefits. You end up managing Kubernetes instead of building your product. Our setup is simple. systemd supervises the Phoenix processes. Caddy handles TLS and reverse proxying, automatically getting certificates from Let’s Encrypt. When we deploy, we push the new NixOS configuration to our servers using deploy-rs. The switch is atomic. If something goes wrong, we roll back in seconds. Secrets are encrypted in the git repository using agenix. Each server has its own age encryption key, and secrets are decrypted at deployment time on the target machine. #</a>Observability</h2> We set up logging, metrics, and error tracking before writing the first feature. Every team says they will add observability later. They never do. Logs are structured JSON. Every entry includes a request ID, user ID, and organization ID. These logs ship to Grafana Loki through Promtail. The request ID is generated when a request enters our system and propagates through everything: API calls, background jobs, external service calls. When a user reports a problem and we have their request ID, we can trace exactly what happened across the entire system. Metrics go to Prometheus, errors to Sentry. Dashboards and alerts exist before the first feature because retrofitting them later never happens. #</a>Build order</h2> First comes the foundation: Nix configuration, Makefile, project structure, database setup. It feels like yak shaving until the first time it saves you. Second, we build admin tools. A dashboard for internal use. User impersonation, which lets us log in as any user to see what they see. Seed data that creates realistic test scenarios. You need to demo to stakeholders before the product is done. You need to debug issues by experiencing the product as users do. Third is authentication, because almost everything else depends on knowing who the user is. Then the actual product features. Polish like error handling, loading states, and accessibility comes last but isn’t optional. #</a>The full guide</h2> The complete guide is at github.com/unbalancedparentheses/saas_guidelines</a>. Database connection pooling, rate limiting, circuit breakers, health checks, graceful shutdown, disaster recovery, and more.
Fede's Guide to Type Systems: From Generics to Dependent Types 2026-01-01T00:00:00+00:00 Every type error you’ve ever cursed at was a bug caught before production. Type systems reject nonsense at compile time so you don’t discover it at 3 AM. But they vary wildly in what they can express and what guarantees they provide. Why Concrete Exists 2025-12-26T00:00:00+00:00 This is the foundation piece for the Concrete series. If you want the most practical demonstration first, start with When the Compiler Is the Oracle</a>. If you want the living language reference, use Concrete Spec</a>. </blockquote> Systems programming has a recurring problem. We want to write code close to the machine, but we also want to make strong claims about what that code does. Does it allocate? Does it touch the network? Does it leak resources? Can it be audited without tracing twenty helper functions and three layers of library convention? Most languages answer those questions indirectly. You read the implementation. You profile. You infer from style. You trust unsafe</code> blocks, docs, and review discipline. Even in strong languages, much of what matters about a program lives outside the type system. Concrete exists because I think that is the wrong place to stop. Concrete is a systems language built around a single organizing principle: every important property the compiler can know about a program should be explicit enough for humans and machines to act on directly. The Death of the Inner Self 2025-12-23T00:00:00+00:00 The core argument is simple: many features of human life that appear stable and natural are historically produced. As society accelerates, a number of these features begin to lose their function and their permanence. I believe consciousness as we know it is one of them. #</a>Individuality as technology</h2> Life is organized around information that replicates under constraint. Computation generalizes this biological logic. It allows selection and optimization to occur faster and at larger scales by externalizing memory, comparison, and feedback. Problems that once required internal deliberation can be solved through external processes that test, filter, and iterate possibilities. Capital pushes this logic further. It reorganizes social life around continuous feedback, price signals, and competitive selection. As these forces compound, individuality starts to look less like a foundation and more like an interface that emerged to solve earlier coordination problems. Capital behaves as an impersonal intelligence oriented toward speed, abstraction, and self-optimization. As cognition, decision-making, and coordination migrate into automated systems, the inner self loses its structural role. Over time, many assumptions we take for granted are worn down by this acceleration. Individuality and consciousness appear increasingly exposed to this process. #</a>The construction we cannot see</h2> Fish do not realize they live in water. The medium that sustains them is so constant that it disappears from perception. Some of the most important structures are overlooked for the same reason. Individuality and consciousness belong to that category. We tend to treat individuality and consciousness as self-evident facts, as if humans have always experienced themselves as bounded selves with an inner voice, a private mental space, and a continuous narrative identity. Because this experience feels natural, it is assumed to be timeless. Serious thinkers have argued it is not. Julian Jaynes pointed out that the heroes of the Iliad show almost no inner mental space, hearing the voices of gods where we would hear our own deliberation. Charles Taylor traced how the modern “buffered self,” sealed inside its own mind, was assembled over centuries out of religious and philosophical practice, displacing an older “porous self” open to forces from outside. For most of human history people did not describe themselves as individuals in the modern sense. Decisions were not understood as outcomes of inner deliberation, and agency was not located inside a private interior self. Action was organized through rituals, traditions, kinship, and prescribed roles. Meaning arrived from outside the person rather than from introspection. In many societies outside the Western trajectory, this structure remains largely intact. The idea of a you inside your head observing your own thoughts is therefore a learned construction. It depends on language, habits, metaphors, and social practices that had to be developed and stabilized over time. Lev Vygotsky argued that inner speech is not innate but internalized social speech: children first talk to others, then to themselves aloud, then silently, until the dialogue goes underground and starts to feel like the private voice of a self. Narrative memory, moral self-examination, and the sense of authorship over action emerged the same way, as cultural achievements layered on top of older biological processes. Modern societies actively reproduce this configuration. From early childhood, people are trained to understand themselves as autonomous units with opinions, preferences, goals, and an inner life that belongs only to them. The training is so pervasive that it becomes invisible. Other ways of being human recede from view, even though many have existed and some still persist. #</a>The weakening of the conditions</h2> The conditions that once made individuality functional are weakening. Earlier systems relied on human subjects to think, decide, judge, and take responsibility. Cognition and coordination were constrained by human minds. Individuality emerged as a solution: a stable self enabled long-term planning, moral accounting, and institutional continuity. Earlier societies coordinated without modern consciousness. Contemporary systems increasingly coordinate without modern selves. Decision-making proceeds without inner deliberation. Meaning is delivered through incentives, metrics, and feedback loops. At the cultural level, individuality remains constantly invoked. People are urged to be themselves, express themselves, optimize themselves. Yet the channels for expression arrive pre-shaped, quantified, and monetized. What appears as selfhood increasingly takes the form of managed performance within narrow bounds. #</a>Replacement by degrees</h2> The modern self does not collapse in a single moment. It is replaced function by function, each substitution small enough to go unnoticed. Taste was once formed through a slow, private process: encountering things by accident, sitting with discomfort, learning to love what initially resisted you. Algorithmic recommendation compresses this into a profile that updates in real time. The system knows what you will like before you do. The inner process of forming a preference, the hesitation, the revision, the gradual shaping of sensibility, loses its purpose when an external system performs it faster and with better accuracy. What remains looks like taste but functions as consumption. Judgment follows a similar path. In organizations that once depended on accumulated experience, performance metrics now determine what counts as competent work. The slow formation of professional intuition, the kind that takes years to develop and resists easy articulation, gets flattened against quarterly targets. This is Goodhart’s law turned structural: once a measure becomes the target it stops measuring what it was meant to, and here the target gradually replaces the faculty it was only ever a proxy for. When the metric becomes the institution’s memory of what the work is for, the judgment it was meant to approximate quietly disappears. People still show up. They optimize what is measured. The rest erodes. Inner deliberation faces the same pressure from a different direction. When an AI assistant can draft your emails, plan your week, summarize your reading, and suggest your next decision, the internal process of thinking through a problem starts to feel unnecessary, not wrong exactly, just slow. The assistant never tells you to stop thinking; it just makes thinking feel like friction in a system that rewards speed. Over time, the habit of sustained internal reflection weakens for the same reason any unused capacity weakens: through disuse. Each of these substitutions is individually reasonable. Each solves a real problem. Taken together, they describe a pattern where the functions that once required a self are gradually absorbed by systems that do not. #</a>What is at stake</h2> The modern self once felt inevitable because it solved concrete problems. It enabled abstraction, continuity, and responsibility at scale. Its future usefulness is far less certain. The self depends on performing certain functions, and when those functions migrate outward, the self weakens not through suppression but through redundancy. Individuality was real. It produced philosophy, law, science, art, and institutions that reshaped the world. The question is whether it will remain functional as the systems around it absorb more of what it used to do. A coordination technology that no longer coordinates does not persist on sentimentality alone. The self will not simply switch off. But the conditions that produced it are changing, and what comes next may look different enough that the word “individuality” stops pointing at anything we would recognize. Whether that transition is a loss, a transformation, or simply the next phase of the same process that produced the self in the first place is not something that can be settled in advance. But it should be named clearly, because what cannot be seen clearly cannot be preserved deliberately. Notes on permanence, time, and ergodicity 2025-12-15T00:00:00+00:00 Some systems improve the longer you stay with them. Repetition sharpens execution, experience carries forward, and judgment builds on itself. At Hermès, a leather worker trains for two years before touching a bag. One artisan makes one bag start to finish, every stitch by hand, fifteen to twenty-four hours of work per piece. This is the opposite of speed at all costs</a>. It is also one of the most successful luxury companies in the world. The constraint is part of what the customer is paying for. The broader culture moves in the other direction. Cycles shorten. Signals multiply. Decision horizons shrink. A lot of institutions keep moving while quietly losing the judgment they once had. They stay busy, but they stop getting better. Copying outruns learning. In that environment, endurance tells you something. If a system keeps working under stress for a long time, its structure probably matches reality better than its competitors’. The internet did not flatten everything. It made it easier to see who had substance and who was living off distribution. #</a>Two forms of time</h2> Time operates in human systems in two fundamentally different ways. Measured time is divisible and uniform: schedules, deadlines, accounting periods, discount rates. It can be allocated, optimized, and exchanged. Most planning systems live here. They assume value can be judged apart from history. Lived time works differently. It accumulates. Learning, memory, and judgment develop through it, and each cycle changes the next one. Anything that depends on formation happens here. Snapshots miss the point because the value is in what compounds. In 2001, Boeing moved its headquarters from Seattle to Chicago. The stated reason was to position the company closer to “Wall Street and governments.” Engineers who understood the planes were physically separated from executives who understood the spreadsheets. Over the next two decades, Boeing spent more than $40 billion on stock buybacks while cutting capital expenditure to half of Airbus’s rate. Harry Stonecipher, who took over as CEO, said he wanted Boeing “run like a business rather than a great engineering firm.” The 737 MAX, designed to avoid the cost of pilot retraining, killed 346 people. This is what happens when lived time is forced into measured time. Berkshire Hathaway made the opposite bet. Buffett has refused quarterly earnings guidance since 1996. Shareholders are told to judge the business over decades, not quarters. The result is six decades of compounding judgment, the longest sustained record in American corporate history. Same markets, different use of time. When lived time gets forced into measured time, formation breaks down. Standards do not settle. Judgment does not compound. You only find out what a system really is if you leave it alone long enough to show you. #</a>Formation under constraint</h2> Excellence comes from sustained practice under the right constraints. Errors have to be survivable. People need room to adjust without every bad iteration becoming fatal. Judgment improves when experience carries over from one attempt to the next. At Pixar, every film is terrible for years before it is good. Ed Catmull describes the process as taking movies “from suck to not-suck.” The mechanism is the Braintrust: a group of fellow directors and storytellers who meet every few months to review each film in production. The key detail is that the Braintrust has no authority. The director is not required to take a single suggestion. That keeps candor high without turning feedback into bureaucracy. Most studios kill projects after one bad screening. Pixar treats bad screenings as information, not verdicts. The difference is structure, not talent. Formation takes time, and the Braintrust protects that time by separating honest feedback from the power to cancel. #</a>Four domains</h2> We built Ergodic Group</a> around the idea that enduring organizations work across four domains: mathematics, code, culture, and craft. Most live mostly in one. The edge comes from connecting them. Mathematics sets the structure and the constraints. Code turns that structure into action and tests it against reality. Culture lets intent survive changes in personnel. Craft brings the whole thing back to materials, tolerances, and physical consequences. SpaceX shows how these domains work on each other. The math: a technique called lossless convexification lets an onboard computer solve fuel-optimal landing trajectories in real time, computing the exact moment to fire the engines so velocity hits zero at touchdown. The code: autonomous guidance software recomputes trajectories during descent, adjusting for wind and sensor readings, which makes landings on ocean platforms possible. The culture: failures are instrumented, not hidden. Between 2013 and 2016, SpaceX crashed booster after booster, and each crash produced telemetry that led to a specific fix. Hydraulic fluid ran out, so they added more. A throttle valve stuck, so they redesigned it. The craft: when carbon fiber layup produced wrinkles at roughly $200 per kilogram, SpaceX switched Starship to stainless steel at roughly $3 per kilogram. Steel gets stronger at cryogenic temperatures, handles far more heat, and opened reentry profiles that carbon fiber could not. A materials decision changed the vehicle, the software, and the math. Learning compounds when these domains stay connected. #</a>Ergodicity as a filter</h2> Ergodicity describes a situation where repetition improves the usual outcome because learning carries over from one round to the next. Claude Shannon spent fifteen years at Bell Labs before publishing “A Mathematical Theory of Communication” in 1948. He was not being graded on quarterly output. Bell Labs gave researchers something modern organizations rarely give anyone: enough uninterrupted time to get to the bottom of a problem. That setup produced the transistor, information theory, Unix, the laser, and cellular telephony. The transistor came not from a brainstorm but from people with different specialties working near each other for years. When AT&T was broken up in 1984, that model disappeared with it. No later technology company has reproduced the same output. The institution itself held the judgment, and that judgment did not survive disassembly. As acceleration intensifies, most sectors get noisier and more fragile. Coordination gets harder. Institutional memory thins out. Advantages that looked durable turn out to depend on a few people, a few habits, or a distribution edge that disappears. Infrastructure and culture last longer because they are environments people operate inside, not products to be sold. When learning carries forward, time starts working in your favor. #</a>Operation</h2> In 1984, GM and Toyota opened a joint factory in Fremont, California called NUMMI. Toyota sent over four hundred trainers from Japan for months of side-by-side work with American employees. Absenteeism dropped from twenty percent to two percent. Defect rates fell to the lowest in the United States. GM tried to export the lessons. A vice president told employees to “take a picture of every square inch” of NUMMI and replicate it at other plants. It failed everywhere. The visible process looked the same. The results did not. The missing piece was judgment, and judgment does not travel well as a memo</a>. Toyota had not built a checklist. It had built a way of working. The NUMMI lesson reaches past any specific practice. The value was never in the visible process; it lay in the judgment that accumulates when people have time to learn, when the links between domains stay intact, and when repetition actually improves the work. The new financial backend of the world 2025-12-09T00:00:00+00:00 By Federico Carrone and Roberto Catalan The previous article</a> argued that the internet left a gap in institutional infrastructure: it moved information but not ownership. Ethereum fills that gap by embedding ownership, transfer, and enforcement into shared software. Financial institutions today spend enormous resources on authorization, accounting, reconciliation, and compliance. Ethereum substitutes a portion of that apparatus with a programmable execution environment and cryptographic enforcement. This article looks at the specific economic mechanisms through which that substitution works. #</a>Three frictions</h2> Some economists describe transaction costs through three frictions: triangulation, transfer and trust. Triangulation concerns how economic actors identify each other and agree on terms. Transfer concerns how value moves between them. Trust concerns the enforcement of obligations. Traditional financial architecture manages these frictions through scale, proprietary systems, and coordination among intermediaries. Ethereum lowers all three, and the numbers are now hard to wave away. Take transfer: stablecoins, dollar tokens that live on the chain, settled about $27.6 trillion in 2024, more than Visa and Mastercard combined, with roughly 95 percent of that volume on Ethereum and its rollups. Most of that figure is exchange and bot flow rather than honest payments, so discount it heavily, but the payments slice is real and growing, and a dollar can move between two strangers in different countries in seconds for cents, with no chain of correspondent banks in between. Take trust: a loan on Aave never asks who you are. The collateral rules sit in a contract that liquidates the position automatically when it crosses a threshold, and anyone can read that contract before they sign. None of this removes institutions; it changes which parts of the stack they have to build. A startup offering dollar accounts in Lagos or Buenos Aires no longer builds settlement, custody, and clearing. It inherits them the way a web startup inherits TCP/IP, and spends its effort on product and distribution. That lets firms serve markets incumbents wave off as too small or too complex. Having a single global ledger also changes operational dynamics. Many institutions operate multiple databases that require frequent reconciliation and remain vulnerable to error. Ethereum maintains a continuously updated and replicated record that cannot be amended retroactively. Redundancy and recoverability become default properties rather than costly internal functions. Security follows the same pattern. Instead of defending a central database, Ethereum distributes verification among many independent actors. Altering history requires coordination at scale and becomes prohibitively expensive. Confidence arises from system design rather than institutional promises. #</a>New financial services and global reach</h2> You can see this in what people actually do with it. In Argentina, where the peso lost more than half its value against the dollar in 2023, ordinary savers hold USDT the way they once held paper dollars in a drawer, except this version moves. Remittances that cost the global average of around 6 percent through a money-transfer operator move as stablecoins for a fraction of that. And the instruments are climbing the respectability ladder: tokenized US Treasury funds grew from about $140 million in early 2024 to roughly $8 billion by late 2025, led by BlackRock’s BUIDL at around $2.8 billion, with Franklin Templeton running a government money-market fund whose shareholder records live on seven different chains. The pattern underneath is always the same. Work that used to live inside an organization, reconciling ledgers, proving balances, enforcing the terms of a deal, moves into shared software that every participant can read. The firm is left with the parts that actually differentiate it, product and distribution, and it grows by winning users rather than by rebuilding plumbing its competitors already have. The impact is most visible in markets with fragile financial systems. In economies with unstable currencies or slow payment networks, Ethereum provides immediate functional gains. In developed markets the benefits appear incremental but accumulate as more instruments and processes become programmable. #</a>Institutional transformation and long term dynamics</h2> Many financial instruments are heterogeneous. Corporate debt is a clear example. Terms differ by maturity, coupon, covenants, collateral, and risk. Trading depends on bilateral negotiation and intermediaries who maintain records and enforce obligations. Ethereum can represent these instruments digitally, track ownership, and execute terms automatically. Contracts retain their specificity, while administration becomes standardized and interoperable. The boundary between what firms must build and what software can enforce is moving. Regulation and legal systems remain central, but the institutions sitting on top of them look different when settlement, custody, and enforcement are handled by shared infrastructure instead of proprietary systems. Ethereum already functions as an alternative financial rail. Multiple independently developed clients, substantial real world usage, an active research community, and a commitment to openness and verification set it apart from other blockchain networks. #</a>Conclusion</h2> Ethereum converts core financial frictions into software functions, and that changes the economics of building and operating financial services. Institutions become lighter, focused on product and distribution rather than internal infrastructure. Technological transitions begin in niches where incumbents do not meet demand. As systems mature, costs fall and broader adoption becomes feasible. Ethereum followed this path. It began with internet native communities, expanded across emerging markets where users lacked reliable financial tools, and is now positioned to upgrade mainstream markets by making financial companies easier to create and operate. Software is becoming the organizing principle of financial infrastructure. Ethereum makes that concrete. Regulation and institutional adaptation will shape how far it goes, but the economic incentives already point toward systems that are open, verifiable, and resilient. #</a>Further reading</h2> DefiLlama. Stablecoin market cap and supply</a>.</li> The Defiant (2025). Stablecoins Process $27.6 Trillion in 2024, Surpassing Visa, With 95% Settled on Ethereum</a>.</li> Yellow.com (2025). Tokenized U.S. Treasuries Hit $7.3B in 2025</a>.</li> </ul> The missing institution of the Internet 2025-12-02T00:00:00+00:00 By Federico Carrone and Roberto Catalan Modern economic systems rest on two foundations: tools that expand productive capacity and institutions that define who controls their output. The internet transformed how information moves, but it did not reconstruct the institutional machinery that governs ownership and exchange. Digital economic life therefore expanded without a durable system of rights, enforcement, or jurisdiction. Blockchain networks, and Ethereum in particular, address this gap by embedding institutional functions in software and enforcing them through economic incentives and cryptographic verification</a>. #</a>Technology, Culture and Institutional Design</h2> Humans build two kinds of things: tools that expand what individuals can do, and institutions that coordinate what groups can do together. Fire, agriculture, medicine and computing are tools. Property rights, contracts, markets and corporate structures are institutions. The two feed each other. Tools expand capacity, institutions channel it into collective action, and the combination compounds over time. #</a>Property Rights and Markets as Social Technologies</h2> People invest when they believe they will keep what they earn. Property rights give that assurance by specifying who owns what, who can use it, and who is excluded. Markets sit on top of these rights and coordinate production through prices. None of this is natural. It is all engineered through law and political settlement. Prices, money and contracts compress information about scarcity and risk so that strangers can trade without needing to trust each other or answer to a central planner. The global expansion of trade in the twentieth century ran on this machinery: neutral jurisdictions, corporate structures, and legal containers that let participants from different regulatory environments collaborate. Whatever you think of it, this infrastructure underwrote the international economic order of the late twentieth century. #</a>The Missing Architecture of Digital Ownership</h2> The internet lowered the cost of communication and commerce across borders, but it did not establish a neutral mechanism for defining and enforcing claims on digital assets. Offline, ownership is adjudicated by courts, enforced by states and geographically bounded. Online, in the absence of a global authority, ownership defaults to either national legal systems or to the platforms that mediate activity. Corporations filled this vacuum by providing infrastructure for identity, communication and exchange. They set terms of access, mediate transactions and retain discretionary control over assets generated within their systems. Users and firms may create content, build businesses and accumulate value, but their rights are contingent on the policies of the platform operator. The experience of Zynga illustrates this dynamic. The company developed a profitable games business on Facebook and briefly achieved a valuation exceeding that of Electronic Arts. Its fortunes deteriorated when Facebook revised its policies and altered its revenue share. Zynga owned its intellectual property and its infrastructure but not the environment on which its business model depended, a common position for firms built on platform economies. In digital markets, platforms function as de facto landlords. Platform centered economies make this structural: extensive participation paired with limited control. #</a>Ethereum as an Institutional Experiment</h2> Ethereum is a response to this institutional absence. It provides a mechanism for creating, transferring and enforcing digital assets without reliance on corporate or national intermediaries. The system operates as a verifiable computing environment in which rules are encoded in software and enforced collectively by a distributed network. Traditional computing systems require users to trust the operator. Ethereum distributes computation across thousands of machines that execute identical code and verify each other in a continuous process. Outputs are accepted when consensus is reached and misbehavior is economically penalized. Under these conditions, property rights and contractual commitments can be represented as digital objects whose enforcement does not depend on courts or discretionary authority. This architecture automates functions normally performed by institutions. Auditors repeat financial records to detect manipulation. Courts resolve disputes. Regulators impose compliance standards. These systems are essential but costly and slow. Ethereum replicates aspects of verification and enforcement at the system level using software, mathematics and economic incentives. The network is open to participation without authorization and resistant to censorship because no single entity can unilaterally block or rewrite transactions. These properties arise from the structure of the system rather than ideological intent. #</a>The Emergence of a Digital Financial System</h2> The first adopters of Ethereum were technologists experimenting with new mechanisms for ownership and coordination. Most of the culture and products were created for themselves. Over time, a broader range of actors began using the system for financial services. The most consequential development has been the rise of stablecoins</a>, dollar tokens backed by reserves of cash and short-term Treasuries. Their combined market value passed $300 billion in 2025, dominated by Tether’s USDT (about $176 billion) and Circle’s USDC (about $74 billion), most of it issued on Ethereum. The flows are no longer a rounding error: stablecoins settled around $27.6 trillion in 2024, edging past Visa and Mastercard combined, roughly 95 percent of it on Ethereum, though much of that volume is exchange and bot activity rather than genuine payments. Stablecoins replicate core financial functions such as store of value and transfer of funds without geographic restrictions and with continuous settlement. Their programmability enabled the construction of lending protocols that allow users to lend and borrow assets with risk parameters enforced in software rather than through institutional mediation. These systems differ from traditional financial infrastructure. Participation is global rather than jurisdictional. Switching costs are low because services are built on interoperable standards. Exit is immediate. Risk is transparent though often misunderstood. Compare that to countries like Argentina</a>, where interoperability between banks and fintech wallets, something as trivial as scanning a QR code, is still an ongoing regulatory battle. Incumbents try to use their market position to avoid being interoperable. On Ethereum, interoperability is structural. Individuals can receive payment, convert assets, provide liquidity and borrow collateralized funds within minutes from a mobile device. In legacy systems, similar transactions take days and incur high fees. Adoption reflects demand for neutral infrastructure in environments where intermediation is unreliable. #</a>Implications</h2> Several areas of financial activity are migrating to blockchain based systems, including remittances, trade finance and private credit. Others, such as corporate debt markets, remain fragmented and costly but exhibit characteristics that may make them suitable for digital reconstruction on top of Ethereum. Plenty can still go wrong. Regulatory uncertainty, operational risk and rough user experience all constrain adoption. Scaling throughput without giving up decentralization remains an open engineering problem. Software vulnerabilities and governance failures have already cost real money. In 2016 a bug in The DAO drained about $60 million of ether and split the community into Ethereum and Ethereum Classic over whether to claw it back. Cross-chain bridges have fared worse: the Ronin bridge lost about $625 million to North Korea’s Lazarus group in March 2022. More is coming. Early evidence suggests that parts of financial intermediation can run at lower cost and with more transparency than existing systems. Whether that continues depends as much on how regulators and incumbents respond as on the technology itself. #</a>Artificial Intelligence and Coordination</h2> Artificial intelligence increases productive capacity by automating tasks but does not resolve questions of ownership, governance or compliance. Output may be generated more efficiently, but disputes over entitlement, liability and compensation persist. Artificial intelligence and blockchains, but in particular Ethereum, are the two biggest innovations in the decades to come. Between them they address two basic human needs: producing more and coordinating with others. Artificial intelligence will make people more productive, but it will not eliminate the bureaucratic machinery required to verify and enforce outcomes. Ethereum introduces a technology that complements AI: a system where humans and autonomous agents can coordinate, trade, and settle disputes directly through code, without relying on institutions to prove that everyone followed the rules. #</a>Conclusion</h2> The internet lowered the cost of transmitting information but left digital ownership in the hands of whoever runs the platform. Ethereum reconstructs elements of property rights and contractual enforcement as public infrastructure encoded in software. It may end up as core infrastructure or it may remain a specialized tool. That depends on regulators, incumbents, and whether the engineering problems get solved. But it has already shown that financial coordination can run at a different cost structure, and that digital property can exist without a central administrator. The internet built an economy without institutions. Ethereum is an attempt to build them. #</a>Further reading</h2> DefiLlama. Stablecoin market cap and supply</a>.</li> The Defiant (2025). Stablecoins Process $27.6 Trillion in 2024, Surpassing Visa, With 95% Settled on Ethereum</a>.</li> CoinDesk (2022). Axie Infinity’s Ronin Network Suffers $625M Exploit</a>.</li> </ul> Next 10 Years of Ethereum 2025-11-15T00:00:00+00:00 Ethereum's Native Rollup Roadmap with Justin Drake 2025-10-01T00:00:00+00:00 Crypto doctrine 2025-09-25T00:00:00+00:00 #</a>Crypto and the accelerated and chaotic 21st Century</h1> Crypto has been most useful where trust is weakest. In practice, it has found product-market fit in two places: In countries where inflation, capital controls, or censorship are ordinary constraints, crypto gives people and companies tools they actually need.</li> In internet-native communities, crypto provides a financial layer that lets people coordinate, speculate, and build markets at a scale the web did not support before.</li> </ul> People that don’t live in a developing country or that didn’t grow up with the internet have enormous difficulties understanding crypto because they don’t have skin in its game. They believe crypto doesn’t have any “real” use case or that is not serious enough. They are right. The thing is that we are living in a world that’s becoming more absurd. Memes do not only make you laugh anymore, memes are now winning elections. These use cases will grow with time and probably new ones will be found. The world is becoming more chaotic and more divided each day. Crypto benefits from that kind of environment because it reduces the number of places where trust has to be taken on faith. One of crypto’s prime advantages is that it kills many of the middlemen and allows us to coordinate</a> even in the harshest environments. Trust assumptions fall because more of the system is enforced by incentives, compilers, distributed systems, and cryptography. That does not remove politics or disagreement; it just narrows the set of things people need to argue about. Most of us are internet natives. We grew up on IRC, 4chan, Reddit, Hacker News, Twitter, Bitcoin, and Ethereum, and we also have roots in unstable countries. We are the Fremen of crypto, raised in a harsh environment. We know what chaotic societies feel like from the inside, and we know what it takes to build inside them. At the same time, we are builders who like working at the frontier of engineering and scientific change. Open source and decentralization are not just philosophical preferences for crypto. They are practical conditions for the ecosystem to work. Building in the open, helping other people onboard, and creating systems larger than the original project are part of how crypto survives long term. This can look irrational if you assume the only goal is short-term extraction. It is more legible if you assume the goal is to help a new financial and coordination layer persist. Our main objective is to help these new internet highways get built in sustainable ways. Economic sustainability matters, but so do resilience, openness, and the ability to resist the usual drift toward centralization. Centralization is almost always easier in the short run. If pure money were the only objective, there would be simpler ways to make it. We treat money as a tool, not the final point. Whether crypto is useful is already settled. It is, in places most critics never look. The open question is whether the systems being built today will be resilient enough to matter when the next round of chaos arrives. Zero-Knowledge Proofs and the Economics of Verification 2023-04-13T00:00:00+00:00 Most progress in science and engineering is incremental: thousands of small improvements made by researchers, engineers, and companies. Every so often a new tool or idea changes the constraints and the whole field jumps, shifting what is practical rather than what is merely possible on paper. The public usually notices breakthroughs in areas like aerospace, energy, or AI. Cryptography moves more quietly, even when the consequences are just as large. During the COVID years, the field made real progress, especially in zero-knowledge proofs and other primitives that make verification cheaper and privacy easier to preserve. I think these advances will matter well beyond cryptography itself. One of the most important changes has been practical speed. Zero-knowledge proofs go back to a 1985 paper by Goldwasser, Micali, and Rackoff, but for decades they lived mostly in papers because proving was far too slow. Two things changed that. Zcash shipped zk-SNARKs in production in 2016 and made them genuinely usable with its 2018 Sapling upgrade, which cut proving memory by roughly 97 percent and proving time by about 80 percent. And a newer generation of proof systems pushed the cost down further while keeping the output tiny: a Groth16 proof is only a few hundred bytes and verifies in milliseconds, no matter how large the computation it stands in for. The financial system still depends on intermediaries</a>: auditors, regulators, accountants, banks, and payment networks. That system works when the institutions inside it are trusted and the surrounding state has enough legitimacy to enforce the rules. Bitcoin introduced a different model: a permissionless monetary network where users can move value without asking an intermediary for access. In places where inflation, capital controls, or institutional weakness are part of daily life, that difference is not theoretical. It is immediate. As trust becomes more uneven, systems that can be independently verified become more attractive. That does not mean social trust disappears. It means more parts of the stack will be built so they rely on less of it. Bitcoin was designed primarily as a monetary asset and settlement network, so expressive computation on top of it has always been limited by design. Ethereum expanded that design space by allowing more complex programs, which is why lending, exchanges, and other financial applications grew there first. But blockchains still impose severe constraints. Computation is expensive, throughput is limited, and the cost of moving meaningful value is often too high. This is where zero-knowledge proofs and related distributed-systems primitives become useful. A zero-knowledge proof lets one party show that a computation was done correctly without revealing the underlying data and without forcing everyone else to rerun the computation. The key asymmetry is that proving is expensive, but verification is much cheaper. That is what makes the technique economically meaningful rather than just mathematically elegant. At the beginning it’s difficult to grasp, even for engineers, that this technology is possible. The mathematics behind it, until recently, seemed magical, and that’s why it was called moon math. Thanks to ZKPs, transferring money in systems similar to Bitcoin becomes cheaper and much faster because there is no need for every node to re-execute each transaction. In some architectures, one node can process all the transactions and prove them using ZKPs, while the rest simply verify them, saving valuable computing resources. Among other things, ZKPs enable creating a financial system that doesn’t depend on social trust like traditional finance and that doesn’t depend as much on re-executing algorithms as Bitcoin. This is already how zk-rollups work. A single sequencer executes thousands of transactions off-chain, produces one proof, and Ethereum verifies only the proof instead of re-running the work. StarkEx has done this in production for years behind dYdX and Immutable, and in March 2023 two general-purpose versions, zkSync Era and Polygon zkEVM, launched to mainnet within days of each other. Ethereum is evolving from a slow but secure distributed mainframe, where every node re-executes every program, into a machine that mostly stores and verifies proofs generated elsewhere. Blockchains are not the only systems that benefit from these primitives. As AI-generated content begins to overshadow human-generated content on the internet, ZKPs become useful for verifying that content came from a specific model, system, or approved pipeline. Proof-of-personhood systems such as Worldcoin already use zero-knowledge proofs so that someone can prove they are a unique human without revealing which human. More broadly, some industries will face growing pressure to prove they are operating correctly rather than asking users to trust them. Online gaming, ad networks, and other opaque intermediaries are obvious candidates. Fully Homomorphic Encryption, a related primitive that enables computation on encrypted data without exposing it, will play a similar role wherever privacy constraints are binding. The shift is already visible in the numbers: proving that once took minutes now takes seconds, and a proof a few hundred bytes long can stand in for a computation that would be ruinous to re-run. The systems that benefit most are the ones where the cost of checking, not the cost of doing, has been the binding constraint, and those exist well beyond finance</a>.