Top Dog Studios

New York, NY

Institutional market-making for prediction markets

01

The firm

Prediction markets are becoming real financial infrastructure, where regulated exchanges price measurable, discrete outcomes. The addressable market is a function of value proposition. A market must create value for market participants. Commodity futures paved the way, and now the only limitations are imagination and practicality.

Top Dog Studios provides the service every market needs: continuous, two-sided liquidity driven by truth-seeking algorithms, operating with live, intelligently processed empirical information.

We trade solely the firm's capital. We have no outside investors, and no third-party capital. Our customer is the market. We offer reliable two-sided market making subject to predetermined, market-specific performance benchmarks.

As prediction markets scale to become the backbone of how civilization shares risk, Top Dog's mission is to enrich that backbone with rigor and reliability.

02

Scott Coulter

Scott Coulter

Scott M. Coulter is the founder and managing member of Top Dog Studios LLC. He is also the founder of Cowbird Capital LP, a New York based long/short equity fund.

He received a Bachelor's degree in Applied Mathematics from Harvard College, then a Master's degree in Applied Mathematics from the Harvard Graduate School of Arts and Sciences. Scott joined Blackstone in 2006 where he worked in New York as an analyst in the private equity group. He left Blackstone in 2009 to join Lone Pine Capital where he became a managing director investing across technology, healthcare, and business services. In 2017, Scott was the third Lone Pine partner to receive Lone Pine sponsorship to spin-out and launch Cowbird Capital.

After returning outside capital to Cowbird's investors in 2024, Scott served as a Senior Advisor to the Administrator of NASA in 2025. He was later appointed by the White House to be CIO of the Social Security Administration. Scott returned to New York in 2026 to found Top Dog.

03

Venues

We provide institutional market making to the industry's leading platforms.

Kalshi

The first CFTC-regulated exchange dedicated to event contracts: elections, economics, weather, and beyond. The U.S. exchange path.

Polymarket

The world's largest prediction market, now open to U.S. institutions.

ForecastEx

The event-contract exchange founded by Interactive Brokers, sitting next to the rest of an institutional book.

04

The market

Every enterprise is in the business of risk management, one way or another. Businesses pay dearly for certainty, which pads the margins of a bloated risk industrial complex: insurance and middle men.

Reliable counterparties are essential to enable responsible fiduciaries to access the benefits of market-based risk pricing (i.e., prediction markets). Practically every industry stands to gain from market-priced risk: shipping, commodities, consumer retail, healthcare; even frontier AI companies with their volatile infrastructure demands.

Transforming unbounded uncertainty into a measurable probability distribution with upper and lower bounds is the holy grail of capital allocation. With an institutionally credible, bounded feasible set, CFOs will be able to unlock more investable cash than ever for their business.

Our goal is to facilitate this outcome, making prediction markets a standard institutional hedge.

0 1 PROBABILITY OUTCOME −∞ +∞ UNBOUNDED UNCERTAINTY 0 1 PROBABILITY UPPER BOUND LOWER BOUND BOUNDED FEASIBLE SET
Outcome against probability, before and after. Unhedged, the tails of the outcome curve run to infinity in both directions. Priced risk chops those corners off: the same business, confined to a known floor and ceiling a CFO can plan against.
05

Research

Two public studies sit behind the work. The math is the same instinct as the desk: look at the shape of a system, not the story people tell about it.

Topology · Language

Topology of Societal Decoherence

Data suggests the “left” and the “right” no longer speak the same language.

As social feeds pull left and right toward different words, the two sides slowly stop sharing a language. This is a visualization — our linguistic feasible space, our respective linguistic universes, are represented as topological manifolds (tori: donuts) — using data from a consistent platform: the floor of Congress (1981 through 2025). Here you can see that the right and left have separated like a failed emulsion. The mathematical description is a fiber bundle on a torus, decohering.

The shared linguistic feasible space, 1981–2025 — measured, not modeled: nearly 870 million words of the Congressional Record (Stanford’s hein-daily parse 1981–93, govinfo CREC from 1994), collapsing from 100% to 5%. Each event surfaces beneath the tori as it happens, then files into the ledger at top left — amber for wars, gray for structural turns. Drag the timeline dial to scrub back and forth; release to resume.

Here’s what you’re watching: each cloud is one party’s footprint in language-space — shared vocabulary on one axis, emotional register on another, word meaning on the third. Through the 1980s the two clouds interleave on a single surface: one language, weathering the Gulf War and Somalia without a tear. Scrub forward and they separate like oil from water — the Gingrich snap, then slowly through the 2000s, faster after 2010 — until what remains is two distinct tori, barely touching, at 5%.

The tori genuinely re-cohere slightly in the late 90s, shudder through 2009–12, tear violently in 2020–21, and end at “Shared Linguistic Feasible Space: 5%” — not the parametric version’s clean 0%, because the data says the maximum tear was the Jan-6-era Congress with a slight retreat since. I’d argue 5% is a more powerful ending than 0%: measured, not scripted, and quietly hopeful.

Then again, perhaps a torus isn’t the right shape for language at all. Speech isn’t inherently circular — it never owes us a return to where it started. A helix is the more natural form: it lets the data breathe and evolve instead of repeating itself — arguably a truer picture of a time series. Here it is:

The natural progression — the measured data on an honest topology, extended to 1981–2025 (Stanford’s hein-daily parse of the Record for 1981–93, govinfo CREC from 1994). A torus implies discourse loops back on itself; the Record doesn’t. Here the braid’s winding rate is the measured shared space: it winds tightly through the Reagan years while a common language holds, re-tightens briefly after 9/11, and unravels as the space collapses — fibers snapping and drifting through the Tea Party ramp to the frayed divergence of the Jan-6-era Congress. This dial scrubs too.

Notice how long the braid simply holds — through Reagan, the Gulf War, Somalia, a classifier can barely tell the parties’ vocabularies apart. The fraying is already underway, though: by 1994 the shared space has quietly drifted to about 72%. Then the snap — the Gingrich Congress, when the Contract-with-America message machine handed each party its own vocabulary almost overnight and the braid jolts loose. It pulls briefly back together at 9/11 — the last genuine coherence in the record — then unwinds for twenty straight years, fraying through the Tea Party ramp until the strands of the Jan-6-era Congress barely touch. None of this is scripted: the winding is the measured data, recovering the history on its own.

Three line charts, 1994 to 2025: lexical partisanship, affective tone, and semantic distance measured from the Congressional Record, annotated with the Gingrich Congress, 9/11, the Tea Party era, and the 117th Congress
The three measured axes — lexical partisanship, affective tone, semantic distance — from 1.06 million floor speeches, single source (govinfo CREC), one Congress per point. The party-split detail covers the CREC era (1994–2025); the helix’s 1981–93 wing comes from the Stanford parse of the earlier Record.

Linguistic decoherence: because the Z-axis plots 1 − cosine similarity, it is an exact measure of divergence. When the Z-axis spikes (as seen clearly around the 2010 Tea Party era and the post-2020 117th Congress), it reveals that the two sides aren’t just using different vocabularies (which is what the X-axis measures). Instead, it shows that even after aligning their baseline languages, they are using the exact same words to mean entirely different things. The topological distance between their shared concepts is widening, indicating that the speakers are effectively talking past each other. For an intuitive explanation of the Z-axis — disconnected manifolds, the Procrustes rotation, and the angle between meanings — read the full breakdown →

Six wireframe torus-pair panels, 1981 to 2025, laid side by side: one fused donut in 1981 progressively separating into two distinct donuts by 2025, shared linguistic feasible space declining from 100 to 5 percent
The six measured stages, laid plainly: 1981–2025, 100% to 5% shared.
Markets · Topological Data Analysis

Gold's Rally: A Topological Contextualization

Is gold's price spike a bubble or the real thing? A math tool that measures the shape of how markets move together. The shape looks completely normal, even though the price move was historic. Translation: a genuine, lasting repricing of gold against paper money, not a panic. For the technical reader: persistent homology of cross-asset correlations, 43rd-percentile normal. January 2026, 12 pages.

Read the study
06

Internships

We are looking for strong builders. People who can write, measure, and ship. Internships and a fast track for the ones who can do the work.

If interested in an internship, submit your resume here: scott@topdogstudios.com.