Key Takeaways

  • Public.com deliberately ignores sports and celebrity betting to focus strictly on market-moving economic events, like interest rate decisions and corporate earnings.
  • Real-time probability data from prediction markets acts as an automated trigger for equity trades, risk management rules, and alerts across multi-asset portfolios.
  • A former UBS client moved $50 million to Public, dismissed his advisory team, and deployed custom AI agents to automate covered call strategies for income generation.
  • High-net-worth investors represent the earliest adopters of portfolio AI agents because they bring pre-existing, rule-based execution strategies that traditional wealth managers overcharge to run.

Turning Event Contracts Into Real-Time Triggers

Prediction markets usually get treated as betting parlors for political junkies or sports fans. Leif Abraham sees them as live, probabilistic data feeds. Public.com built its prediction market infrastructure around a strict constraint: no sports and no pop culture entertainment.

“Our take on that is no sports no entertainment but essentially events that could impact your portfolio right events that moves the market,” Abraham explained. When event contracts focus exclusively on macroeconomic shifts, the probability numbers stop being gambling lines and start functioning as actionable financial signals.

Most traders look at an event contract and ask whether they should bet yes or no. Abraham shifts the entire interaction: “You can suddenly you know not just trade the events as you can do you know in many places but you can also use the kind of real-time probability you know data as a signal to do other things in your portfolio that could be trades it could be alerts risk management whatever you might want to do.”

Instead of watching the Federal Reserve manually, an investor creates a deterministic rule. “Like the one example I right now always make all day long here is um you know if you see that the market for the where the probability of a rate hike goes above 70% sell my treasuries for example,” Abraham said. The prediction market handles price discovery, while an agent handles trade execution.

The $50 Million Wealth Management Replacement

When consumer platforms launch automated features, mass-market retail usually arrives first with micro-accounts. Public saw the exact opposite pattern. Wealthy investors with complex portfolios became the first power users.

“It's interesting to see how kind of that first cohort that actually sees the most use in it in the agents and public have been very sophisticated wealthy people that have these like complexities and have this idea for these like specific strategies that they want to execute on,” Abraham noted.

These investors already understand their rules. They know when to write options, when to hedge downside exposure, and when to harvest losses. Traditional wealth managers charge 100 basis points annually to run those exact checklists on a calendar. An automated agent does it continuously without taking a management fee.

Abraham shared a direct example of this migration: “Like we had a guy come in who was with UBS wealth management, fired his wealth management team, moved $50 million into a public account and now has AI agents managing his portfolio mostly basically doing like covered calls and you know generating income.”

High-fee advisory desks depend on the friction of manual portfolio maintenance. Once an investor can map logic directly to multi-asset execution, paying an advisor to sell covered calls becomes indefensible.

What to Do With This

Audit your personal portfolio rules and write down any recurring trade you make manually, like trimming tech stock gains when valuations stretch or buying short-term Treasuries after yield spikes. Convert one manual hedge into a strict conditional rule with a defined numeric trigger, such as target price or probability, so you can test automating your execution instead of watching market updates during work hours.