YC Root AccessThe Startup Powering Billions In Trades Every Day
CHAPTERS
OneChronos explained: atomic multi-stock trades on a new exchange
Jared introduces OneChronos as a new stock exchange already handling a meaningful slice of U.S. equities volume. The core promise is letting institutions execute complex, multi-leg strategies as a single “all-or-nothing” trade to reduce risk and slippage.
- •OneChronos functions like a new kind of exchange (Nasdaq-like) with rapid growth post-launch
- •Handles ~0.3% of U.S. equities traded daily—billions in notional value
- •Key capability: bundle multiple legs (e.g., long Tesla + short GM/Ford) into one atomic trade
- •Value proposition is strongest for large institutional bets where execution risk is costly
Co-founders’ early bond: tinkering, hacking, and long-term trust
Kelly Littlepage and Steven Johnson describe meeting in middle school and staying close through high school and beyond. Their shared curiosity about computers (and occasional trouble) built the trust that later made co-founding easier.
- •Met in middle school; close friendship reduced co-founder uncertainty
- •Early computer tinkering and “white hat” pranks shaped technical confidence
- •Went to schools on opposite coasts but maintained the relationship
- •Shared mindset carried into later careers in different industries
Auction theory roots: engineering better economic outcomes
Kelly traces a key intellectual seed to studying economics under auction theorist Preston McAfee at Caltech. That experience reframed markets as computational design problems where mechanisms can be engineered for better outcomes.
- •Undergrad exposure to auction theory and mechanism design
- •Insight: market outcomes can be “engineered,” not just observed
- •Computational complexity and optimization are central to modern market design
- •Early foundation for later combinatorial-auction approach
From hedge funds & cybersecurity to a market-structure insight
Their professional paths—Kelly in trading/hedge funds and Steven in cybersecurity—converged on a shared diagnosis: modern electronic markets resemble distributed databases with latency-driven anomalies. This led to the early goal of designing an exchange resistant to “latency race” conditions.
- •Kelly saw an “impedance mismatch” between how PMs think (portfolios) and how markets trade (single names)
- •Steven recognized market microstructure as distributed-systems behavior (stale reads / arbitrage)
- •Initial thought experiment: build an exchange immune to latency races
- •Optimization and auction-theory tools later became the matching engine’s foundation
The long road to commitment: 2011 ideas → 2016 full-time → 2022 launch
The founders outline a timeline that looks like an overnight success but wasn’t: early conceptual work began around 2011, they went full-time in 2016, and launched in mid-2022. They also discuss an early YC rejection that forced sharper MVP thinking while still aiming for a “do it right” launch.
- •Earliest conceptual timestamp around 2011
- •Went all-in and joined YC in 2016 after quitting jobs
- •YC feedback pushed them to find MVP paths even if the full build was longer
- •Ultimately raised ~($10M) and pursued a “one-shot” high-quality launch strategy
Two deep-tech problems: fast optimization + making combinatorial auctions usable
What started as a single hard engineering challenge became two: scaling optimization to real-time market constraints, and translating a foreign mechanism (combinatorial auctions) into something traders could adopt. The second problem—productizing and explaining the model—was as critical as the math.
- •Deep tech #1: run complex optimizations at market speed and scale
- •Deep tech #2: make combinatorial auctions accessible to market participants
- •Need to bridge conceptual complexity without forcing users to overhaul workflows
- •Progressively growing stakeholder conviction reinforced the approach
How trading works today vs. OneChronos: beyond price-time priority
Steven contrasts the standard limit order book model (price-time priority) with OneChronos’ approach, which emphasizes price and volume while deprioritizing time except for auction admission. This difference required extensive regulator education and a rethinking of “fairness” and matching logic.
- •Most exchanges use price-time priority (best price, then earliest time)
- •OneChronos does not prioritize time in the same way; focuses on price/volume outcomes
- •Regulators had to be educated through repeated deep-dive meetings
- •A key regulator remark: it may be time to explore alternatives to the limit order book
Regulatory gauntlet and operational reality: mission-critical details
Building a regulated exchange meant navigating a large set of non-obvious operational constraints alongside the core technology. The founders describe everything from formal approvals to mundane requirements (like proving they could print in-office), plus cost-saving automation choices.
- •Regulation added a major parallel workstream beyond engineering
- •Many “small” requirements accumulated into hundreds of tasks
- •Examples: FINRA operational checks; proving capabilities like on-site printing
- •Found ways to automate expensive compliance/regulatory services in the cloud
Cold start: winning early liquidity when there’s no volume
Even with approvals and tech in place, the exchange had to solve the marketplace chicken-and-egg problem. Early participants connected despite no liquidity because they believed in the mechanism’s advantage and valued being first movers with clients.
- •Liquidity bootstrapping was harder than the technical problems
- •Early adopters joined to learn the model and signal innovation to clients
- •Initial day-one activity was tiny but strategically crucial
- •Founder gratitude emphasized for the first firms willing to trade early
Launch story: from ~200 shares to a real growth curve
They recount the first production trades in late June 2022, which were mostly connectivity and pipeline validation. Meaningful commercial activity began to accelerate toward late August/September, forming the early “hockey stick” adoption pattern.
- •First production trade occurred in the last week of June 2022
- •Early trading was dominated by test trades to validate reporting/settlement
- •Ramp to steady state occurred by Q3 with visible growth by Aug–Sep
- •Operational readiness (post-trade processes) mattered as much as matching
Staying power: “people said we were right, just impossible”
During six years before real traction, skepticism wasn’t about correctness but feasibility—printing a trade and bootstrapping liquidity. Their motivation came from conviction in the mechanism, repeated validation from experts, and a commitment to solving the problem rather than “starting a startup.”
- •Critics often agreed the model was correct but doubted execution feasibility
- •Long-term tailwind: frequent feedback that it was the “coolest” idea in market structure
- •Persistence driven by mission and problem-obsession rather than novelty
- •Mindset: if they needed more time, they put in more time
Combinatorial auctions 101: exposure problem and “ships passing in the night”
Kelly explains why combinatorial auctions emerged (e.g., FCC spectrum auctions) and how complements/substitutes create inefficient outcomes in sequential auctions. Applying this to finance, OneChronos lets traders express true portfolio intent, reducing exposure risk and improving trade discovery.
- •Spectrum auctions illustrate complements/substitutes and need for contiguous outcomes
- •Exposure problem: bidders shade bids due to fear of ending with an incomplete bundle
- •Combinatorial auctions allow direct expression of portfolio-level intent and constraints
- •Also solves discovery failures where offsetting interests can’t find each other
A new trading ‘API’: multi-symbol orders with constraints, built by a small team
The founders describe OneChronos as changing the fundamental interface of trading from single-symbol orders to portfolio and constraint-based instructions. They also share the company’s engineering-led culture, a small-team philosophy, and a talent mix from non-finance engineers to veteran market-structure builders.
- •Old model: algorithms juggle legs sequentially; new model: all-or-nothing atomic execution
- •“Call signature” expands from one symbol to multiple symbols + constraints
- •Team size ~40 despite significant daily market impact
- •Culture: first-principles, engineering-led, “stay small,” per-capita output focus; includes a senior ex-Goldman co-founder/advisor
Future scope: beyond equities—any market with complements, substitutes, and constraints
They argue smart/combinatorial markets generalize to many asset classes and even real-economy markets where current exchanges can’t express non-price factors. The conversation ends with GPUs/compute as a vivid example of a fragmented market that fits the same mechanism-design toolkit.
- •Roadmap includes more asset classes (e.g., FX, European equities mentioned)
- •Thesis: most transactions involve non-price factors (constraints) and correlated goods
- •Compute/GPU capacity seen as an inefficient, fragmented market ripe for better allocation mechanisms
- •Broader mission: drive marginal cost of matching toward zero via aligned, value-based infrastructure
Career decision: leaving high-paying paths, minimizing regret, and building for Wall Street
Jared prompts a discussion on the opportunity cost of leaving lucrative quant/tech roles. The founders emphasize preparing financially, choosing a problem you deeply care about, minimizing regret, and adopting a “critical systems” engineering mindset where you can’t ‘move fast and break things.’
- •High upfront pay at top trading firms creates real opportunity cost for founders
- •Their advantage: worked years first, built runway and confidence they could return to jobs
- •Decision framing: minimize regret; accept wide outcome range beyond money
- •Wall Street demands reliability and rigor—mission-critical engineering and limited tolerance for breakage
