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For years, product teams treated engagement like an uncomplicated win. More sessions meant success. More clicks meant traction. More time in an app meant users were interested. More repeat behavior meant the product was working.
Mitchell Wakefield thinks that conversation is finally becoming more honest.
Engagement can be useful. It can show that people find value in a product. It can help teams understand what users return to and why. But it can also hide a more uncomfortable truth. Some products keep people coming back because they solve real problems. Others keep people coming back because they have learned how to press on impulse, anxiety, reward, or habit.
“Engagement is not a moral category,” Wakefield says. “You have to ask what kind of behavior you are creating, who benefits from it, and whether you would defend the mechanism if a regulator or a user asked you to explain it plainly.”
That is the kind of question Wakefield has spent years learning to answer. As a UX researcher and product advisor, he studies why people behave the way they do inside products and how teams should use that understanding. His work has moved through healthcare, gambling, AI, fintech, and regulated consumer software, where small product choices can have larger consequences.
This is also why he thinks product teams need to move beyond shallow definitions of usability. A product can be easy to use and still create the wrong behavior. A journey can be smooth and still leave the user worse off. A reward mechanic can drive retention and still deserve scrutiny.
“The interesting question is rarely whether the product works,” Wakefield says. “It is what the product is training people to do next.”
FanDuel gave him a close view of that question. Wakefield ran loyalty, rewards, and competitive research across FanDuel Casino and Sportsbook, two categories where user behavior is unusually complex. These products combine money, risk, anticipation, reward, identity, habit, and trust. They do not simply ask whether someone can complete a task. They ask how design shapes behavior over time.
That work put him near one of the most difficult lines in consumer software: the line between engagement and manipulation.
The broader industry is now being forced to look at that line more carefully. Loot boxes, infinite scroll, variable reward schedules, dark patterns, and aggressive retention loops have all faced growing cultural and regulatory scrutiny. In gambling and casino products, the stakes are even clearer because the product is directly tied to financial risk.
Wakefield does not think successful products have to depend on the darkest version of engagement. He believes operators can build commercially strong products without pretending every repeat behavior is healthy by default.
“Good product work does not mean squeezing every possible action out of a user,” he says. “It means understanding the behavior you are encouraging and being honest about whether that behavior is defensible.”
That is where regulation becomes part of the product conversation, not an external nuisance. In gambling, prediction markets, AI, and fintech, compliance tools increasingly shape the user experience itself. KYC, age verification, responsible gambling controls, safety guardrails, user limits, and account protections are no longer back-office requirements. They can define whether users trust the product at all.
Wakefield sees the strongest operators treating those controls as part of the core experience.
“Regulation is becoming a product feature,” he says. “The companies that treat it like a checkbox will build clumsy experiences. The companies that treat it as part of trust will have an advantage.”
That shift is especially visible in prediction markets. Kalshi, Polymarket, and PredictIt have moved from niche forecasting circles into a much larger public conversation. The line between an event contract and a sports bet is becoming harder for many users to see, even as the legal and regulatory arguments remain very different.
Wakefield sees a real product challenge in that blur. Prediction markets may need to feel intuitive to users who understand sportsbooks, while also defending themselves as financial markets. That creates design problems around trust, explanation, risk, and user expectation.
AI-native products create another version of the same issue. Traditional apps usually follow predictable paths. Users click, select, search, submit, or complete a flow. AI-native products often revolve around model output, which means the product may be uncertain, incomplete, overconfident, or wrong.
Teams have to decide what happens when a model answer nudges a user toward a decision. The design has to show uncertainty clearly, give users a way to challenge the output, and avoid turning machine confidence into human overconfidence.
Wakefield’s background in Human-Computer Interaction gives him a frame for those questions. His MSc from the University of York and his work across high-stakes environments taught him that trust is not created by visual polish. It is created by how the product behaves when the user is unsure, vulnerable, or making a consequential decision.
“AI-native design is not just a new interface problem,” he says. “It is a trust problem. If the system can be wrong, the product has to be honest about that without making the user feel lost.”
Data alone does not solve this. Analytics can show what users did. It can show drop-off, retention, session length, conversion, and return behavior. But numbers do not automatically explain why behavior happened or whether the product should encourage more of it.
Wakefield’s value sits in that interpretation layer. He connects behavioral research, product judgment, growth thinking, and ethics in categories where the wrong design choice can scale quickly.
Today, he works as Growth Lead, Product and Research Advisor at Golden Egg Media. There, he helps teams translate user behavior into product choices that can grow commercially without pretending every retention tactic is worth defending. His work as an a16z Scout also gives him a front-row view of founders building new behavior loops in AI, fintech, and consumer software before those patterns become mainstream.
The future he sees is not anti-growth. It is more demanding than that. Product teams will still need to grow. They will still need users to return. They will still need strong engagement. But they will also need to explain the behaviors they create.
“The best products will not be the ones that simply maximize attention,” Wakefield says. “They will be the ones that create trust, sustain growth, and respect the person on the other side of the screen.”
For more information on Mitchell Wakefield, visit his LinkedIn.
