Partner, Investing & Research
ParadigmCheck size: Paradigm backs companies from first check through public markets, but neither the firm nor Ricardo publishes his personal check range or decision authority. Portfolio round totals and a historical firm-wide range are not treated as personal allocation evidence.
Ricardo uses large-scale onchain data to distinguish real crypto adoption from activity manufactured by incentives or sybils. He focuses on new ecosystems, user acquisition and retention, token distribution, applied zero knowledge, and the infrastructure required to analyze petabyte-scale blockchain data. His core investment question is not simply whether usage grew, but who the real users are, what caused their behavior, and whether engagement persists after rewards decline.
Teams using rigorous data to understand users, permissionless products with measurable utility, incentive programs designed around long-term behavior, founders who expose their assumptions to falsifiable metrics, new ecosystems with authentic activity, applied ZK that improves allocation or privacy, and technically ambitious frontier work.
He publishes no personal pass list. His stated focus implies skepticism toward vanity metrics, volume that cannot be separated from bots or sybils, token incentives that end in churn, opaque attribution, airdrops without a durable distribution thesis, and ecosystem growth unsupported by reliable onchain evidence. These are inferred filters.
Bring cohort-level evidence, not headline wallet or transaction counts. Separate human users, sybils, bots, and incentive-driven capital; explain attribution methodology and what engagement survives when rewards stop. Show how token distribution connects to retention, governance, or network effects, why the new ecosystem matters, and where applied ZK or better data infrastructure creates a durable advantage.
Decompose observed growth into real users, sybils, bots, mercenary capital, and durable participants before using wallet, volume, or transaction metrics to support an investment claim.
Track acquisition cohorts through declining rewards and measure repeat use, economic contribution, and network participation to learn whether incentives created behavior or merely rented it.
Define the target participant, prove eligibility with resistant data or applied ZK, model adversarial farming, allocate rewards, and measure whether distribution improves ownership and sustained engagement.
For every industry or protocol metric, document source data, entity resolution, exclusions, transformations, and reproducibility before treating the result as evidence.
A small, intensive fellowship can accelerate unusually strong early-career crypto builders by connecting them directly with peers, researchers, and frontier projects.
Onchain data should inform investment and mechanism design by separating organic use from manufactured activity and measuring whether incentives create sustained engagement.
Paradigm uses a selective builder fellowship to find and support the next generation of technically formidable crypto talent.
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Ricardo began using crypto in 2017 as a 14-year-old sneaker reseller in Brazil who could not open a bank account, giving him firsthand exposure to permissionless payments. He later worked as a quantitative developer at macro hedge funds and as a data scientist at Allium on petabyte-scale blockchain infrastructure and fraud detection. He says his work has included more accurate industry metrics and helping protocols distribute over $5B in airdrops. He studied computer engineering and economics before leaving university.