I did not plan any of this. I don’t have a computer science degree or an economics PhD. I came into web3 and its governance community sideways, learned by shipping, and ended up with a strange and specific kind of expertise: what ecosystem programs and governance mechanisms do after the design doc is finished -- when real people, real money, and real incentives show up. That’s the part I’ll be writing about here.

Why “Quorum & Consequence”

Most writing about governance and ecosystem building stops at the design stage: here’s the voting mechanism, here’s the incentive structure, here’s the framework. Clean diagrams, elegant logic, and very little about what happened once it ran.

But the interesting part is the consequence. The round where your carefully designed reputation system produced an outcome everyone hated. The audit program that changed incentives on both sides of the market in ways you didn’t predict. The moment you realize your sybil resistance is working but your best contributors are quietly disengaging.

I’ve spent years accumulating those consequences, mostly in public, and mostly without writing them down anywhere outside of internal docs, closed conversations and conference hallways. This blog is where I finally write them down.

What I’ll write about

Few overlapping threads:

Governance mechanisms in production. I co-designed Neural Quorum Governance with BlockScience, a reputation-weighted voting and delegation system that has governed every SCF funding decision for nearly three years, across more than twenty rounds. I’m finishing a full retrospective on it now, and the first thing I’ll publish here is the honest version: what we designed, what the data showed, what surprised us, and what I’d do differently.

Capital allocation as a craft. How communities decide who gets money shapes everything downstream, and there’s surprisingly little written about how it actually works in practice. I’ll write about what 500+ funded projects taught me: what strong teams look like, how evaluation breaks at scale, why impact measurement is harder than it sounds, and what changes when you subsidize things like security audits.

Innovation ecosystem building. Grants are only one tool. Moving an ecosystem takes market strategy, founder and developer programs, events, partnerships, and a lot of operational work that never makes it into conference talks. I’ll write about that too, including how I approach it as a non-developer with an increasingly elaborate AI stack.

And more recently: autonomous organizations (AOs). I’m co-building Stanford AO (program of Stanford Open Lab), a structured ecosystem and incubator for organizations run partly or wholly by AI agents. We just ran its first research summit and preparing for its first cohort. There’s no shortage of new tooling and resources of agentic coordination, but in-depth research, data, and operational notes on how they actually run are still rare.

Who this is for

I write primarily for operators: people running grants programs, treasuries, accelerators, foundation ecosystems, and public goods funding -- in web3 and beyond. You have the problems I’ve spent years inside of, and almost nobody writes for you from the operator’s seat.

If you’re a researcher, you’ll get real-world data points for mechanisms you study. If you’re a founder, you’ll get an unusually candid look at how funders think. And if you’re just curious about how communities govern themselves and allocate resources, you’re welcome here, too -- I’m a practical thinker, and like to keep jargon to a minimum.

The rules I’m writing under

Views here are my own. I write retrospectives on public work and aggregated patterns, not forward-looking funding decisions. When I discuss failures, I anonymize where fairness requires it. If a post ever reads like it’s holding something back, it probably is on purpose -- but you’re always welcome to reach out to me directly.

What I can promise in exchange: specifics. Actual numbers, operational implementations, the decisions I made and the mistakes that came with them. I want to avoid the “5 lessons in ecosystem building” genre. What I’m aiming for is closer to: we built this, in round X it did something we hated, and this is what we did next.

What’s coming

The first proper posts, roughly in order:

1. Neural Quorum Governance: a Retrospective -- nearly three years of a neural network helping decide who gets funded.

2. What I’d tell 2023 me about building a new innovation ecosystem -- the biggest lessons from scaling a grants program 6x.

3. A one-time audit isn’t enough -- what running a security audit program shows about the state of web3 security, and why ecosystem-level security matters more as AI models and systems get more powerful.

4. Trust and context graphs -- what they are, and the role they play in digital and agentic coordination

5. Cohort 1 was agents -- early operational learnings from the Stanford AO Program.

If I learned anything it’s that I’m not going to tie myself to a publishing cadence -- sometimes I’ll post more, sometimes I won’t post at all.

If any of this sounds like your world -- subscribe, and better yet, reply or reach out -- I’d love to support / collaborate. The best things in my career have started as conversations with people who read something and said “we have this exact problem.”

More soon.

-- Anke