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Results for “media mention”
Which 2027 Candidate Will Talk Digital Best? The Forecasting Machine’s First Campaign Forecasts on POL/N
On Delphine Sabattier’s POL/N podcast, Émile Servan-Schreiber presented The Forecasting Machine’s first forecasts on digital issues in France’s 2027 presidential race, from AI’s likely effect on the vote to Europe’s best chance of a real technology race, and debated them with Bernard Benhamou of the Institut de la souveraineté numérique.
Polls, Prediction Markets or AI? Servan-Schreiber and Verian’s Laure Salvaing on Reading France’s 2027 Election
In an Atlantico cross-interview, Émile Servan-Schreiber and pollster Laure Salvaing disagreed on what prediction markets measure and whether AI-simulated voters can replace real ones. A B SMART interview the same month added his view that, for forecasting at scale, AI has already won.
“Performance = Expertise + Diversity”: Servan-Schreiber on the If This Then Dev Podcast
In an hour-long episode of the French developer podcast If This Then Dev, published on 12 August 2026, Émile Servan-Schreiber explained why groups need rules to be smart, how many novices it takes to match an expert, and why he sees large language models as collective intelligence.
L’Express Interviews Émile Servan-Schreiber on Polymarket and Money-Free Forecasting
Days after France blocked Polymarket, L’Express interviewed Émile Servan-Schreiber, who argues that cash wagering is not what makes a crowd forecast accurate—and who described The Forecasting Machine’s all-AI system to the paper.
On B SMART, Servan-Schreiber Explains Why a Panel of AI Models Can Forecast Like a Panel of Experts
On B SMART’s Smart Tech on 2 March 2026, Émile Servan-Schreiber told Delphine Sabattier how The Forecasting Machine combines several language models into one sourced forecast, and why he sees those models as collective intelligence in their own right.
