Michael Dimock at The Hill:
First, we’re seeing more completely “synthetic surveys,” — polls that purport to measure public opinion but don’t actually survey humans. Instead, they use AI to model what people would say. Our research on synthetic surveys finds they are not reliable substitutes for public opinion. Separately, we have also seen polls being shared that are entirely fabricated without disclosure.
Second, uncertainty about voting mechanics, such as mail-in ballots, changing election laws in some states and even concerns about voter intimidation could influence turnout in hard-to-model ways. Widespread mid-cycle redistricting has scrambled voters’ awareness of who’s on their ballots. And the 2026 race has fewer incumbents on the ballot than any general election since 1992.
Third, the issues of AI and data centers have swelled into the public dialogue but haven’t yet become completely polarized by party. This makes the impact of AI on elections more dependent on local conditions, such as planned or existing data centers nearby, and what candidates are saying about the issue.
In short, predicting who will vote has always been hard in lower turnout elections. It’s become more challenging over time, and 2026 has a swirl of unknowns compounding the challenge — a perfect storm ripe for confusion. Any one of these factors could yield surprises that are impossible to predict, even for the highest quality polls.





