This sounds like something from a dystopian novel, but it’s true. People can now bet on clinical trial results and FDA regulatory decisions, just like they would bet on the Super Bowl or the Kentucky Derby.
Last week, prediction market company Kalshi announced that users will now be able to place bets on events such as whether a drug meets its primary endpoint or whether it is approved.
The company, in partnership with data firm AppliedXL, launched the markets as a pilot program, starting with a dozen contracts linked to advanced trials conducted by established pharmaceutical companies. For example, you can bet on when the FDA will approve Takeda PharmaceuticalIt is ovéporexton, Intellia Therapeutic‘ lonvo-zAnd Elie LillyIt is retatrutide And VERVE-102. You can also bet on when Intellia Therapeutics will submit a biologics license application for lonvo-z and when Compass Pathways will submit a new drug application for COMP360 psilocybin.
Kalshi describes the move as a play on transparency, saying the move could surface pharmaceutical information that is not typically made public.
But not everyone is convinced. A healthcare executive — Shashi Shankar, CEO of Newa platform that helps patients consolidate their medical records and share anonymized data with drugmakers — worries that the model invites exactly the kind of insider trading that regulators have already seen in prediction markets like Kalshi.
Just last week, reports emerged saying a teleprompter operator made six figures off Kalshi by betting on speeches he had advance copies of. And earlier this year, a Special Forces soldier earned over $400,000 on Polymarket betting on a raid he knew was coming.
“I think at best it encourages predictable bad behavior from people with privileged access, and at worst it treats a patient’s illness like a coin toss and dehumanizes what it means to live with a serious or complex illness,” Shankar said.
He noted that a phase 3 clinical trial typically involves several hundred people: biostatisticians, data and safety staff, site coordinators, sponsor staff, etc.
“If employment verification couldn’t stop a guy using a teleprompter, I don’t know how it’s going to stop someone who already knows the numbers for a massive drug development program. This isn’t about bashing people involved in drug development, it’s about ignoring this very likely outcome,” he remarked.
But Shankar’s concerns go beyond mere compliance. He is not comfortable with the impact of testing the betting markets on the meaning of the data itself.
Shankar emphasized that trial results aren’t just numbers: They indicate whether a person’s cancer has responded, how their rare disease is progressing, or whether a parent lives long enough to attend their child’s graduation or wedding.
Patients agree to share this data because they are told it will fuel medical progress for people like them, and turning it into a “yes” or “no” contract for a stranger unrelated to that patient undermines the entire principle, he argued.
Amy Bucher, chief behavior officer at patient engagement startup readraised a different concern. She said prediction markets could influence the behavior of people working on the trials.
Once a prediction becomes public, it becomes part of the environment in which researchers, patients, and sponsors operate – and of behavioral research. watch that expectations can quietly influence attention, interpretation and decision-making, often without people realizing it, Bucher explained.
“I’m not worried that scientists will suddenly act unethically because there is a prediction market. Most researchers are deeply committed to scientific integrity. But humans are susceptible to cognitive biases, social influence and incentives,” she said.
When there are publicized expectations about whether a trial will succeed or fail, those expectations could influence how people involved in the trial allocate funds and interpret ambiguous results, as well as what evidence they pay attention to and how they communicate results, Bucher said.
This raises the question of expertise, as many players in prediction markets have little scientific or clinical training.
“Their judgments may be based on incomplete information, market sentiment, media coverage or broader beliefs rather than a deep understanding of the underlying biology. If these markets begin to influence investment decisions, public perceptions or organizational priorities, we should think about whether the signal actually reflects scientific evidence or simply an aggregation of opinions,” Bucher remarked.
The Kalshi driver is small by design. It remains to be seen whether concerns raised about internal access and trial integrity will scale.
Photo: Eugène Mymrin, Getty Images
