I Buy Robinhood Stock to Long ETH | Santiago R. Santos, Inversion

Summary of main arguments and coverage

• Ethereum’s growth didn’t translate into proportional ETH value capture. Santiago Santos argues that while Ethereum’s user activity exploded from 2020–2021 (fees, transactions, DeFi/NFT/stablecoin usage), Ethereum’s share of fees declined sharply after rollups/L2s took most of the execution and fee capture. He frames this as investors underestimating the “value-destructive” effect of rollup architecture for L1 fee ownership.

• “Can Ethereum win and ETH lose?” Santos’ stance: winning is not guaranteed to mean ETH outperformance. He maintains there isn’t a credible path for ETH to regain the fee share it had in 2021—suggesting Ethereum may end up capturing less than a tenth of peak fee capture. Even if Ethereum remains important for infrastructure and mindshare, investor returns may not align with total ecosystem activity.

• An investment substitute: owning “distribution/value capture” rather than the base settlement token.

  • His long thesis for Ethereum-related value is expressed as: buying “Robinhood stock” (with Robinhood used as an analogy for a profitable distribution platform).
  • He claims Robinhood-style distribution chains can capture most fees, citing examples such as ~90% going to the chain/app layer, Arbitrum L2 ~8–9%, and Ethereum L1 ~1%.

• Possible Ethereum upside is more about ecosystem success than current ETH earnings power. He acknowledges Ethereum’s advantages (security flywheel, governance/coordination leadership, and continued mindshare). However, he doesn’t view it as a high-conviction “buy ETH today” setup at valuations that assume earlier fee-capture levels.

• A “demand vs supply” lens: crypto may have underestimated demand growth, but value delivery moved to cheaper layers. He argues regulators and institutional rails improved demand conditions (e.g., ETFs, onchain Visa, prediction markets). But Ethereum’s rollup-centric supply-side design means more demand doesn’t automatically create more ETH revenue.

• Macro framework: crypto shifted from a “technology race” to a “distribution race.” He agrees crypto has increasingly become about who controls users/relationships, not who built the best protocol. Using internet-era parallels, he argues value flows to winners like Amazon/Google rather than the underlying infrastructure—implying crypto winners may be exchanges/payment providers, stablecoin issuers, and large integrators (e.g., Robinhood/Stripe/Visa/Tether).

• Multi-chain reality: value capture will be application- and venue-specific. He discusses Hyperliquid as an example where integrated trading venues capture most activity, even if that’s narrower than Ethereum’s general-purpose vision. He also argues future “agents” and automated users may choose chains based on liquidity, security, and use-case, not ideology.

• Stablecoins: not a silver bullet for replacing legacy businesses—especially due to incentive alignment. In his Inversion thesis on bringing crypto to existing businesses, he argues the main challenge isn’t technology but incentives and transitions. Stablecoins can improve transaction speed and interoperability, but they aren’t enough to justify acquisitions or full business reinvention unless stakeholders benefit or can be compelled to change.

• Western Union case study: distribution + trust is powerful, but “reinventing rails” is hard. He explains his prior bet on Western Union versus competing chains/L1s as “own the most trusted remittance distribution.” While stablecoins could theoretically improve remittances, he says it’s difficult to persuade parties embedded in the legacy model to accept losing revenue/interest flows—making transition risk high.

• AI agents and crypto: skeptical that only crypto rails enable agentic commerce. He argues traditional financial systems may already support agents; stablecoins/crypto may still matter, but it’s not automatic. He emphasizes liquidity and off-ramps (not just settlement speed), especially for FX and conversion bottlenecks.

• Prediction markets: generally supportive; wants integrity rules, not a blanket ban on insider value. He supports prediction markets to surface “wisdom of crowds” amid skepticism toward media narratives. His view is that some markets should restrict trading by people who can influence outcomes (analogy: players shouldn’t bet on their own games). He favors self-regulation/clear terms-of-service to protect fairness and transparency, noting that while insiders exist in real markets too, crypto’s transparency makes integrity rules especially important.

• Applications of crypto + AI + prediction markets: hedging and “insurance at the point of risk.” He highlights 24/7 markets and real-time pricing advantages (with Hyperliquid and prediction markets as examples). He expects growth in insurance-like derivatives and contracts—such as hedging farm output costs or covering weather events (e.g., hurricanes/floods)—where agents/LLMs can help consumers evaluate and automate protection.

Presenters / contributors

• Amanda Casset (host)
• Santiago R. Santos (guest; founder of Inversion)