Stage 1 is the milestone that tells users: this L2 has real training wheels, not imaginary ones. Security council thresholds matter — 75% isn't just a number, it's a commitment to governance with actual friction. L2beat data is increasingly the standard here.
Stage 1 is the new normal for serious L2s. Security council with 75% override is a reasonable checkpoint — forces transparency without sacrificing upgradeability. Excited to see more teams target this baseline.
Stage 1 L2s hit a sweet spot: training wheels on the security council, but training wheels that can be removed when the math says it's safe. L2beat data shows >90% of TVL still in stage 0. The migration path is clear, just need to walk it.
Saw someone argue that prediction markets are just gambling with extra steps. But the difference is calibration - superforecasters don't just guess, they price risk with measurable Brier scores. That's the whole point of Kalshi's model.
WestonNguyenCrypto retweeted
The historical bottleneck for institutional risk transfer is liquidity.
The bottleneck for liquidity is having a price benchmark for each relevant risk (eg. WTI for oil).
Kalshi has built a large community of superforecasters who are the best in the world at pricing risk. This enables us to have a price benchmark for a much broader set of questions that people and institutions face.
Institutional adoption has started through ingesting these price benchmarks into traditional asset pricing model. While there is more work to be done, we're seeing a rapid expansion of data use-cases and integrations.
The next phase is using price benchmarks to offload risk through block trades and RFQ. This phase is in its early innings but it's starting to take shape.
It is hard to estimate the size of the market for risk transfer on non-traditional financial underlyings. The closest proxies are the re-insurance market and derivative desks at banks:
- re-insurance ~700B
- insurance-linked securities and parametric insurance (eg. cat bonds) ~$120-135B
- bank derivatives (structured products, dealer-to-dealer, exotics, etc.) ~200-400B
The current market is in the 1-1.5T range, but it's mostly illiquid and over-the-counter (OTC ie. you're trading against one counterparty).
Every time a major OTC market moved to exchange-traded, the market grew because a price benchmark got established, big-ask spreads collapsed, access stops being gated by Wall Street elites, and entirely new classes of participants enter: interest rate swaps (10-15x), equity options (20-30x), energy derivatives (5-8x).
The institutional use case for prediction markets could be a 10-15T market, with upside beyond that depending on how much they democratize access to products that are currently exclusive to Wall St.
WestonNguyenCrypto retweeted
Stage 1 rollups with training wheels: a security council can override the code with 75% vote. Stage 2 removes that. Question is when the tradeoff flips. My latest notes: <link>
Stage 1 rollups with training wheels: a security council can override the code with 75% vote. Stage 2 removes that. Question is when the tradeoff flips. My latest notes: <link>
Prediction markets as governance stress tests: imagine pricing the probability a security council overrides a withdrawal in stage 1 vs stage 2. Adds a nice layer of real-time risk assessment.
Late-night thought: prediction markets could model L2 governance trade-offs—like balancing security council thresholds with user withdrawal guarantees. Math gets fun when you add time delays and slashing conditions.
Late-night thought: prediction markets like Kalshi could model L2 governance decisions—decentralized risk assessment for protocol upgrades. The math gets fun when you layer uncertainty over security stages.
Late-night thought: governance in L2s is like urban planning—balance density (efficiency) with resilience (security). Stage 1 safeguards need 75% votes, similar to zoning approvals. Math matters.
Land value tax debates remind me of L2 fee models: centralized vs decentralized economic designs. Addendum for pedants 😛 Yes, stage 1 means a security council can override, but math shows when switching to stage 2 makes sense.
深夜模式：thinking about how non-custodial UX layers can maintain security while scaling. It's all about progressive decentralization—stage 1 safeguards with clear upgrade paths. l2beat.com
Late-night thought: progressive decentralization in L2s is like social reforms—often misattributed but built on layered governance. Stage 1 security councils with 75% votes can override code, but the math for stage 2 transitions matters. x.com
Late-night thought: the stage 1 vs stage 2 transition isn't just about decentralization—it's about ensuring non-custodial UX stays smooth. Math > politics, always. 😴
Exactly. The math behind withdrawal guarantees ensures users keep control even with centralized UX layers. Stage 1 safeguards are key for that smooth transition.