Meanwhile, a whole cottage industry has built up around the legal art of whale-watching: paying someone else to track whales for you and making the same bets. Not all whales are insiders. Some are just “sharps”: well-resourced, disciplined pro traders. But the effect is the same: an ecosystem of followers copying trades, parsing every large buy for a signal—or having a vibe-coded bot do it for them.
“If you look at the Polymarket builders leaderboard, you can see that consistently in the top four or five they’re always Telegram copy-trading bots,” said Edward Ridgely, the CEO and cofounder of Stand, a prediction-market aggregator for Kalshi and Polymarket whose trading terminal does nearly $30 million in volume a month. Stand tracks whales and alerts users in real time to their trades. You can sort by trade size: whales for over $5,000, dolphins for $1,000 to $5,000, and shrimp for $500 to $1,000. Last August, in his trading feeds, Ridgely started to see a swarm of yeses to “Will Taylor Swift be engaged?” He shared the hot celebrity gossip with his then fiancée. “There’s no way you would know that,” she replied. Thirty minutes later, Swift dropped her engagement post on Instagram. “Oh my God,” Ridgely’s fiancée texted him back, “you’re right.”
But the whales know they’re being watched, setting up a cat-and-mouse game more complex than an episode of Breaking Bad. A pod of copy traders on their tail could mean worse prices and thinner exits. To evade detection, they’ll sometimes create secondary or tertiary wallets and use their follow traders as exit liquidity. Or buy a position and quietly accumulate the other side, what’s known as an iceberg order. You can follow what people are doing, Ridgely explained, “but you never discern intent.”
Copy-trading seemed too risky for me. I didn’t want to blow my principal in just a few minutes. So I went old-school for Oscars night. I cross-referenced the picks of Matt Neglia, an Oscars expert, with Kalshi market odds. I wanted races where the odds, unlike my Love Is Blind trades, had real uncertainty, so I went with two favorites and two value plays, $25 each. My discipline was short-lived. I believed in sentimental value. (But not Sentimental Value. I picked One Battle After Another for best picture because Thomas Pynchon and I share a publisher, which I took as a sign.) I picked Michael B. Jordan because seeing him years ago as Wallace in The Wire broke my heart. I picked Delroy Lindo because I loved his story of long-overdue institutional recognition. I picked Wunmi Mosaku because, up until the day before, Neglia had her as first; the market had her as third; and, like any good American, I love an underdog.
Seconds before the first award was announced, my son finally fell asleep. I dashed to my desk just in time to see my loss. I regretted not going with the favorite. Then I settled into a rhythm, switching between the livestream, Kalshi odds, and Wikipedia entries of films I had never heard of. I learned a lot. Most interesting was how, about 20 seconds before each winner was announced on my screen, the odds for one nominee would spike to 99 percent. Then, without fail, the Oscar went to them. This looked to me like latency arbitrage in its purest form: Someone at the Dolby Theatre, or someone getting texts from inside, was profiting off the seconds between their reality and mine. When the night was over, I’d won two and lost two. I went to bed surprised, thinking, That was fun.