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Adcv_ argues Tom Dunleavy's 12.55% DeFi lending yield overstates risk through double-counting independent risk premia that are already captured in expected loss, and using the wrong risk-free anchor. Using SOFR at 3.6% instead of the 10Y Treasury, the correct decomposition yields 3.95% for prime DeFi (Steakhouse USDC benchmark) and 7.1% for high-yield DeFi, implying Dunleavy's figure prices in a 7% expected loss rather than accurately reflecting current DeFi risk.

Tom argues the $292M KelpDAO exploit and subsequent $13B TVL drain exposed severe DeFi mispricing: deposits earning 5% on major protocols like Aave accept BB-rated pricing for technically worse-than-CCC risk. Using TradFi credit frameworks, DeFi's 1.5-2.0% forward probability of default with 90% loss given default requires a fair yield floor of 12.55-13%, not 5.5%, because exploits cascade in minutes rather than quarters and composability failures create unauditable contagion that deposits absorb without protocol failure.

DCo examines how vertical integrations across Hyperliquid, USDAI, MetaMask, Maple, and Centrifuge create competitive moats through compounding utility. These capital aggregators strengthen their positions by layering services across trading, liquidity, and wallet infrastructure, making it harder for competitors to replicate their full-stack offerings.

Kevin examined AI agent investment opportunities and identified where moats actually exist. The sector's real defensibility lies not in engineering patterns—which open source reimplements in weeks—but in proprietary trajectory data from execution, integration depth with customer systems, and evaluation infrastructure. Companies like Harvey ($190M ARR), Sierra ($150M+ ARR), and Cursor ($2B ARR) compound advantages through data flywheels, while Meta's $2 billion Manus acquisition signaled that 147 trillion tokens of execution data across 80 million VM sessions justifies premium valuations where framework elegance and generic tooling offer no moat.

Lorenzo argues the market prices durability over near-term fees when valuing blockchains, rewarding ecosystems like Ethereum and Solana that demonstrate deeper capital, stronger moats, and broader on-chain economies rather than those with higher immediate revenue.

Felipe sees capital markets shifting in ways that favor practitioners focused on capital allocation as both art and practice, creating new opportunities for disciplined investors.

Felipe believes a Token Transparency Framework developed with Blockworks and L1D addresses adverse selection problems in token markets by establishing credible signals for investors evaluating projects.

Felipe argues that identifying lasting competitive advantages, or moats, is essential for token investing. He applies frameworks like Helmer's 7 Powers and Porter's analysis to evaluate whether projects like $UNI and $AERO have defensible positions against competitors.

Felipe breaks down Helmer's 7 Powers framework as a tool for identifying durable competitive advantages in token investing, establishing foundational concepts that he'll extend with Porter's framework in his ongoing three-part series on moats.