ExpertsFMCreating prediction markets (and suing the CFTC) with Tarek Mansour and Luana Lopes Lara
Cheeky Pint · March 17, 2026
5 Selected Lessons:
priority · 2 case · 1 framework · 1 heuristic · 1
Four pillars for winning a derivatives exchange
Source
Cheeky Pint — Creating prediction markets (and suing the CFTC) with Tarek Mansour and Luana Lopes Lara (March 17, 2026) · at 38:37 ▸ · at 42:18 ▸
When this applies
when building a platform/marketplace business and deciding what capabilities to prioritize to dominate a category
The takeaway
Rank marketplace investment priorities as: breadth of what's tradeable, flexibility of how it's structured, efficiency of capital required to trade it, and depth of liquidity — and ensure every roadmap item maps to one of these.
Why it works
Each pillar unlocks a different axis of competitive advantage — coverage, flexibility, capital efficiency, and depth — and compounding advantage requires strength across all four simultaneously.
One level up
In platform businesses, sustainable dominance requires compounding advantage across multiple structural dimensions, not excellence in just one.
Where it stops holding
This stops working when a market is too small or too regulated to support multiple structures (e.g. binary-only niche questions may never need margining or swaps).
Detail
hierarchy:
- Breadth of topics/markets (compute, sports, elections, securities, etc.)
- Market structure variety (beyond binary yes/no: futures, swaps, options)
- Margining systems (capital efficiency vs. tying up full capital upfront)
- Liquidity depth
rationale: Building all margining systems from scratch lets Kalshi move faster than incumbents who are locked into legacy infrastructure — 'if we win, have the broadest set of markets, broadest set of market structures, great and cheap margin, and good liquidity, we're going to win on everything.'
In their words
So the way that we think about we want to be the biggest derivatives exchange in the world, right? And for that, when we think about product roadmap, there's four things that matter.
Pro-innovation AND pro-regulation stance for policy advocacy
Source
Cheeky Pint — Creating prediction markets (and suing the CFTC) with Tarek Mansour and Luana Lopes Lara (March 17, 2026) · at 1:11:07 ▸ · at 1:14:39 ▸
When this applies
when a company in a historically regulated sector must advocate for policy that balances growth with public trust
The takeaway
Advocate simultaneously for innovation leadership and stronger fairness/consumer protections, framing regulation as risk-mitigating insurance rather than an obstacle — this differs from typical 'move fast, avoid regulators' tech industry posture.
Why it works
Treating regulation as 'insurance' that protects against downside catastrophe (rather than as pure friction) allows a company to argue for both aggressive innovation and stronger consumer/fairness protections without contradiction, differentiating it from a typical anti-regulation tech posture.
One level up
A company's policy stance should reflect the actual risk profile of its industry (financial markets have always been regulated) rather than importing norms from unrelated industries (e.g. general tech).
Where it stops holding
This stops working when regulation becomes protectionist/incumbent-serving rather than risk-mitigating, at which point advocating 'more regulation' could entrench incumbents rather than ensure fairness.
Detail
hierarchy:
- America must lead/win in innovation (don't cede the category)
- But also be pro-regulation as insurance against catastrophic failure
- Specifically prioritize: banning insider trading (including by Congress), full trade-data transparency/auditability
- Prioritize customer protection standards (self-exclusion, spending limits, education) as an industry-wide norm, not just Kalshi's own practice
rationale: 'regulation is a bit like insurance. It's like it's protecting you from things going wrong and bad times.'
In their words
So we are pro innovation. Innovation needs to happen in America
Dual-sided liquidity incentive design (pay to build the book, rebate for stability)
Source
Cheeky Pint — Creating prediction markets (and suing the CFTC) with Tarek Mansour and Luana Lopes Lara (March 17, 2026) · at 20:56 ▸ · at 25:34 ▸
When this applies
when designing incentives for liquidity/supply providers across a marketplace with both thin long-tail and thick mainstream segments
The takeaway
Segment your supply-side incentive design by whether natural demand already exists: subsidize presence where demand is absent, and condition rewards on quality-of-service where demand is already strong.
Why it works
Long-tail markets lack natural demand so providers must be paid directly to show up; liquid markets already attract providers, so incentives there should target behavior (spread tightness, uptime) rather than mere presence.
One level up
Incentive structures should be matched to the specific scarcity being solved — scarcity of supply vs scarcity of quality — rather than applied uniformly.
Where it stops holding
This stops working when you can't measure quality metrics (uptime, spread, size) cheaply, or when market makers have no credible way to hedge/profit even with incentives.
Detail
procedure:
- Classify markets into long-tail (hard to price, low natural demand) vs liquid/classic (proven demand, easy to price).
- For long-tail: directly pay/incentivize market makers to participate.
- For liquid markets: don't pay for presence, rebate fees conditioned on meeting uptime, spread, and top-of-book size requirements.
- Treat this as 'incentivizing stability of the book' rather than incentivizing mere existence of a quote.
rationale: Brokers and institutional partners expect NYSE-like tight, stable spreads at all times; achieving this requires deliberate incentive engineering, unlike traditional exchanges where market making arises naturally.
In their words
So, the market making incentives on this side is actually we don't pay them for it, we just rebate fees, but they have very hard conditions to meet.
Monetize transaction fees, not customer losses, to align platform incentives with fairness
Source
Cheeky Pint — Creating prediction markets (and suing the CFTC) with Tarek Mansour and Luana Lopes Lara (March 17, 2026) · at 33:30 ▸ · at 53:10 ▸
When this applies
when choosing a business model for any platform that intermediates trades or bets between parties
The takeaway
Choose a revenue model where your profit scales with activity/volume rather than with one side's losses, to avoid structurally adversarial incentives toward your own users.
Why it works
If revenue comes from customer losses, the platform is incentivized to suppress skilled winners and retain losers (e.g. bonuses to chase losses); if revenue comes from flat transaction fees, the platform's incentive aligns with volume and fairness, not outcome manipulation.
One level up
Examine whether your business model creates incentives that are pro-social (aligned with user welfare) or anti-social (profiting from user harm) before scaling it.
Where it stops holding
This stops working when transaction-fee volume alone can't sustain the business economically, pushing operators back toward house-edge models despite worse incentive alignment.
Detail
rule: Build revenue around transaction fees across all participants rather than the house's edge against losers.
threshold: Cited rake comparison: ~1% for prediction markets vs ~10% for sports betting.
rationale: Sports bookies' incentive is 'they want the losers' and give loss-chasing bonuses; Kalshi's is 'we don't limit any winners' because its revenue doesn't depend on anyone losing.
In their words
The best outcome for us is people are like, this is fair, they have good prices, they have stable liquidity, I'm gonna go there.
Suing your own regulator after exhausting the normal path
Source
Cheeky Pint — Creating prediction markets (and suing the CFTC) with Tarek Mansour and Luana Lopes Lara (March 17, 2026) · at 6:03 ▸ · at 10:38 ▸
When this applies
when a gatekeeper with discretionary power indefinitely stalls your core offering through informal delay rather than a clear legal denial
The takeaway
When an authority stalls indefinitely instead of ruling, escalate to force a decision rather than continuing to wait, if the item is existential to your strategy.
Why it works
A pocket veto avoids an appealable decision, so the only way to force a ruling is to create a legal record the gatekeeper must respond to in court.
One level up
Boards and teams should allow 'anti-pattern' moves when the company's unique circumstance genuinely departs from normal playbooks.
Where it stops holding
This stops working when you have other viable paths to the same outcome, or when losing the suit could trigger retaliatory shutdown of an already-licensed business — so it should be reserved for genuine no-other-option situations.
Detail
situation: CFTC delayed election contract approval past the 2022 election (a 'pocket veto'), repeated the delay through 2023, causing layoffs and investor doubt.
decision: Luana pushed to sue the CFTC despite board and Tarek's hesitation ('it's an anti-pattern, it's a bad idea, but a lot of great companies are built by an anti-pattern').
outcome: Kalshi won the lawsuit, which limited the CFTC's authority to only block contracts falling into specific prohibited categories, and the business took off afterward.
lesson: A no-other-option, bet-the-company legal fight can be the right call when the core strategic pillar is being killed by indefinite stalling — but it is 'still painful' and rarely advisable.
In their words
the response was basically, it's an anti pattern, it's a bad idea, but a lot of great companies are built by an anti pattern. There's something off that is weird that happens, and maybe this is yours.