Dwarkesh Podcast

Manifold Markets Founder - Predictions Markets & Revolutionizing Governance

Stephen Grugett is a cofounder of Manifold Markets, where anyone can create a prediction market. We discuss how prediction markets can change how countries and companies make important decisions. Manifold Markets: https://manifold.markets/ Podcast website + Transcript: https://www.dwarkeshpatel.com/p/stephen-grugett Apple Podcasts: https://apple.co/3cDjwBF Spotify: https://spoti.fi/3RkZD12 Follow me on Twitter to be notified of future content: https://twitter.com/dwarkesh_sp TIMESTAMPS: Introduction 0:00:00 Predicting the future 0:02:29 Getting Accurate Information 0:05:16 Potentials 0:06:20 Not using internal prediction markets 0:09:29 Doing the painful thing 0:11:04 Decision Making Process 0:13:31 Grugett’s opinion about insider trading 0:14:52 The Role of prediction markets 0:16:23 Dealing with the Speculators 0:18:17 Criticism of Prediction Markets 0:20:33 The world when people cared about 0:22:24 Grugett’s Profile Background/Experience 0:26:10 User Result Market 0:28:49 The most important mechanism 0:30:17 The 100 manifold dollars 0:32:59 Efficient financial markets 0:40:30 Manifold Markets Job/Career Openings 0:46:28 Objectives of Manifold Markets 0:48:02

Dwarkesh PatelhostStephen Grugettguest

May 5, 202250mWatch on YouTube ↗

WHAT IT’S REALLY ABOUT

Manifold Markets: Play-Money Prediction Markets To Reshape Information And Governance

The conversation explores Manifold Markets, a play‑money platform for user-created prediction markets designed to aggregate information and surface highly calibrated forecasters. Founder Stephen Griggott explains why status, competition, and reputation can motivate serious participation even without real-money trading, and how user-resolved markets make the system scalable despite some risk of fraud. They discuss why internal corporate prediction markets rarely survive, how prediction markets might inform journalism, governance, and AI risk debates, and why usability and simplicity have blocked earlier widespread adoption. The episode closes with Manifold’s internal use of markets for company decisions, emergent user behaviors, and the team’s hiring and long‑term vision for prediction markets embedded across media and institutions.

IDEAS WORTH REMEMBERING

5 ideas

Play-money plus reputation can drive serious forecasting effort.

Manifold grants users an in-platform currency and relies on status, leaderboards, and social competition, arguing that people will invest substantial effort to build an objective public track record even without direct financial payouts.

User-resolved markets unlock scale at the cost of some fraud risk.

Allowing market creators to both define and resolve questions removes the bottleneck of centralized oracles and lets anyone spin up markets, with the platform relying on user choice and reputation to avoid low-trust creators.

Managers often resist internal prediction markets because they don’t want bad news.

Even though firms like Google and GM have tried internal markets, they tend to be abandoned since managers see accurate but potentially pessimistic forecasts as undermining their vision, seeding doubt, or “stepping on their toes.”

Prediction markets add most value when used as inputs, not sole decision-makers.

For complex real-world choices where success is hard to operationalize, markets work best as information tools on multiple proxy variables, with humans still using judgment to integrate the signals into final decisions.

Long-horizon, existential questions are structurally hard for markets to price.

Issues like catastrophic AI by 2030 suffer from discounting (people care less about distant outcomes) and the impossibility of “collecting” winnings in doom scenarios, so breaking them into nearer-term, measurable proxies is more informative.

WORDS WORTH SAVING

5 quotes

People are driven more by status and competitiveness than greed.

— Stephen Griggott

User-resolved markets actually can work. There is a small amount of fraud, but it’s quite small and manageable.

— Stephen Griggott

The main reason [internal prediction markets] fail is that people literally don’t want to know the answers to a lot of questions.

— Stephen Griggott

The most important thing for creating a usable platform is making sure that the key mechanism is simple and easy to understand more so than handling every possible edge case perfectly.

— Stephen Griggott

I can imagine a world where every news article has an embedded prediction market on whichever topic they’re discussing.

— Stephen Griggott

Manifold Markets’ core model: play-money, user-created, user-resolved prediction marketsMotivations for participation: status, reputation, competition vs. financial gainTalent discovery and overlap with finance and forecasting communitiesWhy corporate/internal prediction markets struggle and managerial incentivesLimits of prediction markets for long-term, low-base-rate events (e.g., AI risk)Design tradeoffs: crypto vs. Web2, usability, fees, and market mechanismsFuture vision: prediction markets embedded in media, research, and governance

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