I was scrolling through Reddit at 1:47 a.m. when a DraftKings alert cut through the feed. You’ve seen those push messages — a bright promise, a tiny nudge. I felt, in that moment, the company’s reach more than ever.
I write about these things because you should know what happens after you tap “Claim.” I want you to see how a string of code and a marketing calendar can change the math of someone’s losses and the company’s gains. I’ll walk you through what the New York Times reported, who said what, and why the soft glow of a promo can hide a strategy.
My phone buzzed with a “bonus” — The New York Times says DraftKings trained AI to hunt for the most profitable losers
The Times interviewed more than 40 former employees and reviewed internal memos. You may already have the app and the notifications; the reporting says DraftKings built a machine-learning model to score users by a trait engineers called elasticity. In plain terms, the model ranked how likely a person was to respond to promotions and free bets. Those who reacted most strongly were treated differently.
One former employee, Jayden Butts, told the Times his job was simple and blunt: ask whether a user would “give us more than we’re giving them.” If the answer was yes, promotions flowed. The company’s own memo, according to the article, found slots players scored high on elasticity.
That’s where the ethical red flags appear. You’re seeing offers targeted at moments when you’re most likely to play. It’s not framed as predatory in the memo language: it looks like growth engineering. But when those offers land on someone who’s losing, the result can be corrosive.
How did DraftKings use AI to target certain users?
The short version: models ingested behaviors — game type, session length, response to past promotions — and produced a score. Players with high elasticity were funneled into more aggressive marketing. Promotions weren’t blind; they were personalized signals calibrated by machine learning.
The Reddit threads complained about spam — Why the push-notification pattern matters
On r/DraftKingsDiscussion, users call the alerts intrusive and hard to disable. That’s the consumer view; the people inside were tracking a different signal. The Times says employees saw clear revenue responses when elastic users received offers.
The company responded that promotions target “sustained, engaged” customers, not people based on losses. Lori Kalani, DraftKings’s chief responsible gaming officer, told the Times that the business needs customers who bet within their means and that the firm monitors risky behavior. But several employees said models designed to predict crisis points — the moments someone needed an intervention — were shelved.
Can a company’s marketing target players who are likely to keep losing?
Yes, that’s precisely the worry. When a model scores someone as responsive, it can treat them like a high-yield customer. I’ve seen that playbook in other digital industries: targeted incentives aim at the people who will click and convert the most. In gambling, conversion can mean someone keeps playing after a loss.
A demo was canceled on the day it was set to run — The abandoned crisis model and what employees said
Jake Shanin, another ex-employee, described a model built to flag users headed toward a crisis. It looked promising, he told the Times. The team prepared to show it to company leaders, including Kalani. The meeting was canceled. Attempts to build similar algorithms were later shelved, according to multiple former staffers.
Those same insiders say an elasticity model drove the marketing decisions that remained. You can imagine how that feels when a safety system is ready but never flipped on. It’s like fitting a car with airbags and then leaving them disconnected — the protection exists but never activates.
The Times also reported early data suggesting highly elastic slots players “blew more money” than less elastic players. A 2016 U.K. study of online gamblers found problem gambling was most common among slots players after live poker, which connects a factual thread from academic research to DraftKings’s internal findings.
Employees called it a choice — The company’s public stance and the regulatory angle
DraftKings told the Times it rejects any implication of unfair marketing. The company emphasizes entertainment and says it monitors for risky behaviors. Still, several employees described being asked to open the “floodgates” on promotions when elasticity scores justified it.
You should know the platforms people use to discuss these issues: Reddit hosts user complaints; journalists at the New York Times brought internal memos into public view; researchers publish on PubMed and other archives. This is how narratives coalesce into scrutiny.
For you as a user, two things matter: how easy it is to opt out of push messages and whether the app offers meaningful safeguards when patterns point to harm. For regulators, the question is whether marketing that targets responsiveness rather than need should face limits.
I’ve used two metaphors carefully: the targeting looked like a fisherman baiting a net, and the elasticity dial felt like a dimmer switch on someone’s impulses. Both images point to an engineered process that converts behavioral data into revenue.
So where does accountability sit? Is it on companies to finish building crisis tools and deploy them, on regulators to set boundaries around promotional targeting, or on consumers to police their own use? Which is the right lever to pull when machine learning meets money and human vulnerability?