Radhika Dutt is the author of Radical Product Thinking, a product leadership movement and book that has been translated into multiple languages, including Chinese and Japanese. Rising to prominence in the 2010s and 2020s, she became known for codifying a vision-driven alternative to iteration-led product development used by teams across industries from fintech to government. She currently serves as Advisor on Product Thinking to the Monetary Authority of Singapore (MAS), where she helps steer digital transformation and user-centric product delivery at one of Asia’s most influential financial regulators.
Previously, as Author and Speaker at Radical Product Thinking starting in 2017, Dutt built a global practice around a five-part methodology spanning vision, strategy, prioritization, execution and measurement, and culture. Her work equips organizations to diagnose and cure “product diseases” such as feature bloat and metric-driven drift, enabling leaders to align teams around a clear, shared change they seek to bring about in the world. Through keynotes at conferences like Productized and client work with startups and large enterprises, she has trained thousands of product practitioners and executives on how to translate vision into a repeatable operating system for innovation.
Her career highlights include founding two companies that were successfully acquired, contributing to a total of five acquisitions across broadcast, media and entertainment, telecom, advertising technology, and robotics over more than 20 years in product. As an MIT-trained engineer with an S.B. and M.Eng. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (1995–2000), she has applied product thinking to domains as varied as consumer apps, government services, and even wine, demonstrating the portability of her framework across sectors measured in billions of dollars of market value. She is widely regarded as an influential figure in the product management community for shifting organizations away from purely metric- and OKR-driven roadmaps toward what she calls “vision-driven transformation.”
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“It’s bullshit statements, right, that are slim on the details.”
Radhika Dutt doesn’t hedge when describing most product visions. Twenty-five years after founding her first startup at MIT with a vision to “revolutionize wireless,” she can admit what most product leaders won’t: she has no idea what that meant. The company had five co-founders, dorm room origins, and all the trappings of a Silicon Valley success story. What it didn’t have was clarity about the problem they were solving.
“I don’t even know what we meant by that,” she says, and something shifts in her tone. The polished product consultant gives way to someone examining an old wound. “But it was this idea of just being big, scaling. Now, you know, even today when you look at so many Silicon Valley startups, that’s sort of the mistake you often see, right?”
She calls these mistakes product diseases. Not problems or challenges—diseases. The language is deliberate. Diseases are things you catch without realizing it, things that spread through organizations, things that require diagnosis and systematic treatment rather than quick fixes.
The disease at that first startup was hero syndrome: the obsession with scale and growth without understanding what problem needs solving. But Radhika discovered something worse during her subsequent career across five acquisitions. Most product teams suffer from multiple diseases simultaneously, creating what she now recognizes as an epidemic of confused priorities and wasted effort.
“And I call them product diseases because it’s just so ubiquitous and we need to talk openly about these product diseases. ‘Cause you know, it’s just so easy to catch.”
The solution she developed—radical product thinking—starts with a fill-in-the-blanks approach to vision setting that forces teams to confront what they’re actually trying to accomplish. Not the aspirational version, not the pitch deck version, but the detailed, actionable version that can guide daily decisions.
“So today, when amateur wine drinkers want to find wines that they’re likely to like and to learn about wine along the way, they have to find attractive looking wine labels or find wines that are on sale. This is unacceptable because it leads to so many disappointments and it’s really hard to learn about wine in this way. We are bringing about a world where finding wines you like is as easy as finding movies you like on Netflix. We are bringing about this world through a recommendations algorithm that matches wines to your taste and an operational setup that delivers these wines to your door.”
She pauses after reciting this vision for her wine startup, which she founded in 2011 and sold in 2014. “Now this is a radical vision because I hadn’t told you anything about my startup, and yet hopefully when I shared this vision, you knew exactly what we were doing and why we were doing it.”
The contrast with “revolutionize wireless” is stark. One vision contains a specific customer segment, their current painful experience, why that experience is unacceptable, the desired future state, and the concrete mechanism for achieving it. The other contains marketing language that could apply to any telecommunications company.
But even teams that develop clear visions struggle with what Radhika calls the second product disease: hyperemia. The obsession with moving numbers up and to the right, regardless of whether those numbers drive long-term value.
“You know, the moment I say this, people are usually like, oh yeah, I get it. We have it. Hyperemia is this obsession with moving numbers up and to the right. Having all sorts of wonderful dashboards that all look green. But those are not even necessarily the right metrics. And sometimes they may even be the right metrics, but they drive you in the wrong direction.”
The dating app industry provides her favorite example of hyperemia in action. When Tinder launched swipe left/swipe right in 2013, user engagement metrics exploded. Every other dating app copied the mechanic because the numbers looked incredible. User engagement up, time on app up, all the key performance indicators trending toward success.
“So, you know, everyone was thrilled with these metrics, but what was happening if you looked at the longer term effect? The more they gamified intimacy, it was creating a toxic dating environment, the more it was dehumanizing interactions. And so what it created in the long term was user fatigue.”
The result: dating app backlash, mass user deletions, and in 2025, Bumble laying off 30% of its staff. The entire industry fell into a slump because short-term metric optimization destroyed the long-term value proposition. The numbers looked great right up until they didn’t.
“So my point is, hyperemia is one of these diseases where you can do fantastic and making numbers look great. And genuinely they may be the right numbers, but that’s not necessarily good for your product or good for your business in the long term.”
This is where most conversations about metrics and OKRs devolve into tactical debates about choosing better numbers or preventing gaming. Radhika thinks those discussions miss the fundamental issue: goals and targets create perverse incentives regardless of how carefully they’re designed.
“Even when someone doesn’t have malicious intent and they’re not trying to game metrics, the subconscious incentive you have is to show you’re a high performer and therefore focus on the numbers that look good, that show OKRs to be green, as opposed to focus on numbers that, you know, OKRs aren’t even measuring, but that are indicating a problem and that say, hey, there’s something off here.”
She illustrates with her experience at Avid, the company behind video editing software used for every Oscar-winning film in Hollywood. The numbers looked fantastic—sales targets consistently hit or exceeded. But underneath the green dashboards, a different story was unfolding.
“If you just looked under the hood, you would see a different scenario. The way we were hitting our sales targets was by moving further and further into the high end because our low end was being eroded by Apple and Adobe.”
The company was achieving its goals by retreating upmarket as competitors commoditized the lower tiers. The sales numbers stayed strong, but the strategic position was deteriorating. Instead of asking why the low end was being eroded or how Apple and Adobe’s business models differed, leadership focused on maintaining the metrics that made them look successful.
“The incentive is I wanna show that I’ve hit those goals and targets things are working. I wanna prove that our, that things are going well.”
This dynamic—prioritizing the appearance of success over understanding reality—is what legendary Intel CEO Andy Grove meant when he said leaders are the last to know. When you set goals and targets, everyone wants to tell you the good news. Bad news gets buried because it threatens the narrative of progress.
The alternative Radhika proposes isn’t better goal-setting. It’s puzzle-setting. Instead of declaring what numbers teams should hit, leaders should define what problems need solving and create frameworks for teams to investigate those problems systematically.
“So what I am working on in this next book. And what I advocate for is a mindset shift instead of goals and targets. It’s a mindset of puzzle setting and puzzle solving. And then the way you measure people is how well are they solving this puzzle? Are we making progress towards solving this puzzle?”
Her framework for puzzle-setting uses three O’s: Observation, Open Questions, and Objective. The observation captures what’s actually happening, not just what the metrics show. The open questions identify what the team doesn’t understand about the observation. The objective summarizes the puzzle that needs solving.
For Avid, the observation would have been: “Our low end is getting eroded by Apple and Adobe in the mid-tier. This is what’s happening. The market is getting eroded. The way we’re making the numbers is by going further into the high end.”
The open questions would probe deeper: “What is happening on the low end? Adobe and Apple are successful there. What is their business model? Can we fight this business model in a different way? Is there something we can offer that can be a complete workflow for the low end where even if Apple and Adobe are giving away the editor, people will want it and want to pay for it?”
The objective becomes: “Figure out what do we do in our video editing business. Do we invest in it, do we not, or how do we invest in it, so that we can continue to either be successful in the video editing business, or we choose to move on and adapt our business?”
This is puzzle-setting. It creates space for teams to investigate reality rather than optimize metrics. But puzzle-setting only works if teams have the skills and safety to solve puzzles effectively.
That’s where puzzle-solving comes in: three questions that teams answer as they work on the puzzle. How well did it work? What did we learn? What will we try next?
“Notice how this question, it’s not binary, did you or didn’t you hit this target? It’s not just putting you on the spot, making you feel like I have to prove something. It’s genuinely inviting the good and the bad. This is how as a leader, you’re not the last to know you’re inviting the good and the bad.”
The second question—what did we learn—requires narrative synthesis, not just dashboard reporting. Teams have to look at all their data and tell the story of what’s really happening with users, markets, and competitors.
The third question—what will we try next—forces strategic thinking based on actual learning rather than predetermined roadmaps.
“I can really tell based on working with a team who is thinking deeply and how well they’re solving the puzzle based on their answers to what have we learned and what will we try next? That’s how you can evaluate people, not just based on ta-da, I’ve hit my numbers.”
The transformation this creates in team dynamics is profound. Instead of competing to show green dashboards, team members compete to solve interesting problems. Instead of hiding bad news, they compete to surface the most important insights. Instead of gaming metrics, they compete to design better experiments.
But this approach requires a level of psychological safety that’s rare in most organizations. Teams have to be willing to admit what’s not working, leaders have to be willing to hear it, and everyone has to be willing to change direction based on what they learn.
“Did you know that he didn’t keep a corner office? He used to have a cubicle, same size cubicle as everyone else because he wanted everyone to challenge his ideas and to feel like they could speak up. Very few leaders want people to speak up and tell them this is not working.”
The Andy Grove reference isn’t accidental. Grove understood that organizational hierarchy creates information distortion. The further you are from the work, the more filtered your information becomes. Physical proximity—sharing the same kind of workspace as everyone else—was one way to counteract that distortion.
Most leaders won’t give up their corner offices. But they can start role-modeling the kind of reflection and transparency they want from their teams. Taking time in meetings to discuss what didn’t work in past initiatives. Sharing their own learning and uncertainty. Creating space for teams to investigate puzzles rather than just hit targets.
“You can role model for your team, the psychological safety and sharing the good and the bad of what didn’t work, what you learned from it, what you’re going to try next. You can role model this so that you can invite the team to solve puzzles like you are.”
For individual contributors stuck in goal-driven organizations, Radhika recommends starting small. Take a past feature release and work through the three puzzle-solving questions privately. Look at the data, but focus on the narrative: what really happened with users? What did the numbers mean in context? What would you try differently next time?
Once you’ve practiced this approach yourself, try it in one-on-ones with your manager or conversations with peers. Create small bubbles of psychological safety where honest reflection and learning can happen.
“Instead of just chasing OKRs, you’re working on puzzles. Puzzles are so much more fun. We are all energized by puzzles. Instead of just focusing on OKRs, think about what puzzles you’re solving for the company. That in itself will energize you for your work.”
The energy difference is real. Goals feel imposed—something you have to hit to prove your worth. Puzzles feel intrinsic—something you want to solve because the solution creates value. The shift from external validation to internal motivation changes how people approach their work.
But the business results matter too. Radhika’s recent consulting engagement provides a concrete example. A company stuck with stalled sales in 2023 doubled sales in 2024, then doubled again in 2025 after switching from goal-setting to puzzle-solving. Customer churn dropped from 26% to 4%.
“We did all of that by puzzle setting and puzzle solving instead of being driven by OKRs.”
The transformation didn’t happen overnight. It required leaders willing to let go of familiar frameworks, teams willing to embrace uncertainty, and everyone willing to prioritize learning over looking good.
The alternative—continuing with product diseases like hero syndrome and hyperemia—leads to the dating app outcome. Short-term metrics that mask long-term erosion. Features that optimize for engagement instead of value. Teams that hit their numbers while slowly destroying what they’re trying to build.
“Or are we all doomed to just constantly learning from these failures, making mistakes and having to learn the hard way?”
That was the question that drove Radhika to develop radical product thinking in the first place. After watching team after team catch the same diseases, make the same mistakes, and suffer the same consequences, she wanted to understand whether systematic approaches could prevent predictable problems.
The answer is yes, but only if teams are willing to diagnose their diseases honestly and treat them systematically. Most organizations prefer to treat symptoms—choosing better metrics, writing clearer requirements, running more experiments—rather than address root causes.
The root cause is the gap between great ideas and great products. Steve Jobs called it out in his lost interview: most people think the idea is 90% of the work when it’s actually 5%. The other 95% is the systematic translation of vision into strategy, strategy into priorities, and priorities into daily activities.
“And I think filling that gap is exactly what I talk about in terms of systematically translating a vision for change into action, into everyday activities. And that’s how we close that gap.”
Product diseases spread when teams try to shortcut that translation process. Hero syndrome emerges when teams skip from big vision to scaling without defining the problem. Hyperemia emerges when teams skip from activities to metrics without understanding the connection to long-term value.
The systematic approach isn’t glamorous. It requires detailed problem statements, clear frameworks, consistent reflection, and honest measurement. It requires admitting when things aren’t working and changing direction based on learning rather than predetermined plans.
But it’s the difference between revolutionary wireless and amateur wine drinkers who can’t find wines they like. One vision launches a company that doesn’t know what it’s doing. The other launches a company that gets acquired because it solves a real problem in a specific way.
“Now this is a radical vision because I hadn’t told you anything about my startup, and yet hopefully when I shared this vision, you knew exactly what we were doing and why we were doing it.”
That clarity—knowing exactly what you’re doing and why—is what prevents product diseases from taking hold. It’s what enables teams to choose long-term value over short-term metrics. It’s what transforms abstract strategies into concrete progress.
The vision template is just the beginning. The systematic framework for translating vision into action is what makes the vision matter. And the puzzle-solving approach is what keeps teams connected to reality as they execute against the vision.
Twenty-five years after revolutionizing wireless, Radhika has learned to revolutionize something more specific: how product teams think about the problems they’re trying to solve. Not with better tools or processes, but with better questions and frameworks for finding answers.
The questions aren’t complicated. What problem are we solving? Why does it need to be solved? How will we solve it? How well is our solution working? What are we learning? What will we try next?
The complexity comes from creating organizational conditions where teams can ask those questions honestly and act on the answers systematically. Where puzzle-solving is rewarded over performance theater. Where learning from failure is valued more than hitting arbitrary targets.
“Puzzles are so much more fun. We are all energized by puzzles.”
That energy—the intrinsic motivation to solve interesting problems—might be the strongest antidote to product diseases. When teams are genuinely curious about the puzzles they’re solving, they’re less likely to settle for bullshit statements that are slim on details. They’re more likely to demand the clarity that prevents revolutionary wireless from becoming just another failed startup story.
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