John Camardo

VP of Product

12 years across crypto infrastructure, AI, and financial services. I ship, therefore I measure.

VP of Product at Horizen Labs, on the founding product team of zkVerify. I design tokenomics, ship protocols, and measure whether they worked. Most recently: 45M+ proofs verified on zkVerify since mainnet, 9 supported proof systems, and the 70% fee burn mechanism designed to flip the network deflationary. Earlier, my team served as contractors to Yuga Labs on the launch of ApeCoin (DAO, tokenomics, airdrop, ERC-20). Before crypto, seven years at Capital One running the commercial credit risk-rating platform under model risk management governance (SR 11-7) on a $65B portfolio. I build and ship with AI as a working partner — every shipped artifact is something I can defend end to end.

▸ New York, New York
Horizen Labs2021 – Present
New York, NY
Vice President, Product Management · 2025 – Present
  • Lead product for zkVerify, a modular blockchain for zero-knowledge proof verification, from genesis through mainnet launch in September 2025 — 5 mainnet chains (Arbitrum One, Base, OP Mainnet, Apechain, Horizen), 9 supported proof systems, ecosystem of 50+ projects including Galxe, Space and Time, Midnight, zkPass. 45M+ proofs verified and ~90% cost reduction versus L1 verification, where a single ZK proof on Ethereum consumes 200,000–300,000 gas.
  • Designed the VFY tokenomics with Delphi Digital as consulting partner — supply, emissions, staking, and the proof-aggregation fee model.
  • Prioritized native STARK verification (Risc0, SP1) over the market-default Groth16 — betting on post-quantum resistance and elimination of trusted setup — which also removes the groth16-wrapping step that adds latency for most zkVMs.
  • Shipped UltraHonk support via runtime upgrade. Honk verification runs ~2.4M gas on EVM (6–7x Groth16), so zkVerify is where it becomes economically viable to verify at scale.
  • Lead product for Vela, a confidential-compute layer on Horizen, and drove the open-source release at github.com/horizenofficial/vela (Go + Solidity, 1,281 commits) to commercialize it for AI verification — where TEEs are currently winning against ZK on inference cost.
  • Repositioned Agent Registry from a ZK-only AI-agent registry to managed AI-agent infrastructure (ZK as an option, not a requirement). Manage a team of up to 7 product managers and a 15-person engineering org.
  • Built 10 working zero-knowledge demos in 2 weeks using AI-assisted development (Gemini + Circom, later Risc0 zkVM), identifying a 30-second proof-verification latency that made real-time gameplay unusable, then pivoted to optimistic verification and cut it to milliseconds — published as a 10-post engineering series on zkverify.io and a bylined crypto.news op-ed.
Director of Product Management · 2023 – 2024
  • Led the strategic transition away from Horizen EON toward a dedicated ZK verification product: ran the framework selection across proof systems, runtime choices, and consensus models, and drove the prototyping process that produced zkVerify's architecture.
  • Built the early testnet adoption playbook: designed the incentivized testnet program, recruited the first cohort of external verifiers, and established the engagement metrics (proofs submitted, unique verifiers, time-to-first-verify) that later carried into the mainnet product.
  • Managed a small product team through the EON wind-down and token migration: defined the deprecation timeline, set the developer-communication plan, and preserved the relationships that mattered for the transition to the new product.
Senior Product Manager · 2022 – 2023
  • Led product for Horizen EON, an EVM-compatible sidechain productized from the company's sidechain SDK — including ERC-20 token tooling and developer SDKs. Co-led EON's transition to Base in 2025.
  • Co-originated zkVerify on the hypothesis that zk-rollups were paying substantial, volatile costs to verify proofs on Ethereum L1 (200,000–300,000 gas per proof, up to 100x a standard transfer). Made the case to the CEO to redirect the company from a crowded EVM market toward dedicated ZK verification. That redirection became zkVerify.
Product Manager · 2021 – 2022
  • Launched ApeCoin (APE) with Yuga Labs as contracting partner — co-designed the DAO, tokenomics, airdrop, and ERC-20 contract.
  • Drove product for the Horizen sidechain SDK, the developer toolkit that later became the foundation for Horizen EON.
Capital One2014 – 2021
New York, NY
Product Manager, Commercial Credit Solutions · 2019 – 2021
  • Owned Capital One's internal commercial credit risk-rating platform under SR 11-7 model risk management governance, assigning probability of default and loss given default ratings to commercial borrowers across every industry the bank served — REITs, oil & gas E&P, and commercial real estate — supporting clients managing a $65B portfolio.
  • Proposed and won approval to re-platform the system from Java to Python, eliminating a manual model-translation layer in which quant-built Python models had to be hand-rewritten in Java for production. Cut duplicated engineering effort and materially simplified internal audit review by making production code and model code the same language.
Product Manager · 2018 – 2019
  • Partnered with a cross-functional team of data scientists, designers, and software engineers to ship data products on big-data infrastructure.
  • Drove analytics for Retail Commercial Real Estate, Treasury Management cross-sell, and Treasury Management customer analytics.
Business Manager, Commercial Data & Digital Innovation · 2017 – 2018
  • Led commercial data and digital innovation across Healthcare, Energy, Multifamily, and Retail real estate banking verticals.
Senior Business Analyst · 2015 – 2016
  • Contributed to due diligence on Capital One's acquisition of the GE Healthcare Financial Services portfolio, evaluating the target's commercial credit exposures alongside the deal team.
Business Analyst · 2014 – 2015
  • Analyzed expansion of Capital One's commercial real estate business with third-party property managers.
Cornell University
B.S., Operations Research and Information Engineering
2010 — 2014
Product Leadership
Product strategyProduct visionMulti-year roadmap0-to-1 launchesGo-to-market (GTM)Pricing & packagingHiring & developing product managersSuccess metrics & OKRsStakeholder managementCross-functional leadership
Platform & Developer
APIsSDKsDeveloper experience (DX)Self-serve onboardingDocumentationOpen sourceDistributed systemsPLG
Crypto & ZK
ZK proof systemsSTARK / Groth16 / UltraHonkzkVM (Risc0, SP1)EVML1 / L2 / rollupsTokenomicsStakingGovernanceSmart contractsSubstrate / PolkadotWeb3DeFi
AI & Data
LLM applicationsAI agentsInferencePythonBayesian modelingMCMCSQLClaude Code

I design for the end state. I write the spec, the test, and the budget before the first line ships. I would rather ship a smaller thing in production than a larger thing on a slide.

I instrument what I build. Every system I own has a metric I check weekly. If a metric is below trend, I escalate it before the quarterly review. The public zkVerify fee dashboard I maintain is how I keep myself honest.

I use AI as a working partner. Claude Code is the primary tool in my loop — research, code review, debugging, writing — but I review every artifact and I'm the one who has to defend it. I have built applications with AI assistance that I could not have built alone in the same time, but I have not shipped anything I did not understand.

I keep score. I am most useful on teams that measure outcomes and act on what the data shows.

zkVerify Protocol Fee Dashboardzkverify-fee-dashboard-production.up.railway.app ↗

Live dashboard instrumenting the zkVerify network's fee and burn economics against a public Substrate Subsquid indexer. Tracks total fees, the 70% burn activation, net inflation rate, and progress to the deflation threshold, with methodology documented inline.

PythonSubstrateSubsquidProtocol Economics
Bayesian Forecasting Portfoliomajor-league-baseball.vercel.app ↗

Public forecasting models (baseball, basketball, soccer, MMA, tennis, World Cup, Kentucky Derby) built with hierarchical Bayesian models, MCMC, and walk-forward validation. Calibration against market-implied probabilities is documented in each app.

PythonBayesianMCMCHierarchical