David Monnerat

Product + AI | Systems Thinker | Enterprise Reality

Category: product

  • Defining Success Criteria: Do You Know Where You Are Going?

    Defining Success Criteria: Do You Know Where You Are Going?

    It was the tenth call.

    I had been on the first one. A customer data project, matching records across systems to connect outcomes to the right source. It seemed straightforward enough that I handed it off and moved on. What followed was eight more calls between my team and the customer, each one ending with a tweak to the logic, each tweak fixing something and revealing something else.

    The problem wasn’t the code. It wasn’t the team. It was that nobody had defined success criteria before the work started. Nobody had asked: what does done actually look like?

    By the time my team pulled me back in, it was already swirling. The customer’s manager had joined too. I suspect that because the issue still wasn’t resolved, he felt the need to get involved. We had a piece of logic built by people who were no longer on the team, results that were almost always right, and a team that had been grinding on this for weeks.

    I asked a simple question: What do you actually want?

    That was it. Every previous conversation had been about what the code wasn’t doing right, assuming the approach was sound and just needed adjustment. Nobody had stopped to ask whether the approach itself was the right one. The answer to that simpler question was: when this happens, here is what we expect to see. And the solution that followed was far simpler than what we had been building toward. A defined set of conditions, clearly mapped to outcomes. The complexity we had been wrestling with wasn’t a feature of the problem. It was a feature of never having properly defined the problem.

    We left that meeting with a clear destination. We should have had that conversation on call one.

    The Road That Never Shortens

    The frustrating thing about this kind of problem is that it doesn’t feel like you’re lost. You can see the destination. The direction feels right. Every adjustment produces a result that is better than the last one. You can look back and see real distance covered.

    But the destination never gets closer.

    It is not a treadmill. On a treadmill, nothing changes. This is different. The scenery changes. The code changes. The outputs change. Progress is real. It just isn’t progress toward the right place, because the right place was never precisely defined.

    I think of it as a receding horizon. You walk toward it. The ground behind you is real and covered. But the horizon moves as you move, and no amount of forward motion closes the gap. The destination stays visible, just always slightly further ahead.

    What makes it so hard to catch is that the evidence of progress is genuine. In our case, the logic was better after each call than it was before. The team wasn’t spinning their wheels. They were solving real problems. The issue was that the problems they were solving were symptoms of a question nobody had fully asked.

    The Assumption Cascade

    When I look back at those ten calls, the thing that strikes me isn’t that anyone did something wrong. It’s that everyone did something reasonable, and the reasonable things stacked up into an expensive mess.

    The customer assumed the existing logic was doing what they needed, just imperfectly. My team assumed the approach was sound and needed tweaking. When a new team member joined and inherited the code, they assumed the foundation was correct. Their job was to fix the edges, not question the core. I assumed the problem was simple enough that my continued involvement wasn’t necessary. Each assumption was defensible in isolation. Together, they meant that nobody stopped to validate whether the foundation was right in the first place.

    There was another assumption underneath all of them. The code had been built by someone with more context than anyone currently on the team. That history gave it authority. When the results looked wrong, the natural conclusion was that something in the current implementation needed fixing, not that the original approach was built on the wrong question. We trusted the expert’s work even after the expert was gone. And in doing so, we inherited not just the code but the assumptions baked into it.

    When we finally looked at what the logic was actually doing versus what the business needed, the gap was significant. There were layers of code built on assumptions about the data that were no longer accurate. That had always been true. Nobody had thought to check because everyone assumed the foundation had already been validated. Each tweak revealed new issues that had always been there. It felt like discovery. It was actually just uncovering more road.

    The Same Pattern in AI

    I have watched this exact dynamic play out in AI projects more times than I can count, and it is worse there for a specific reason: AI systems always produce output.

    A piece of analytics logic can return null or error in a way that is obviously wrong. A model returns a prediction. An LLM returns a response. The output looks like an answer even when the question was never properly defined. That makes it even easier to convince yourself you are iterating toward something real when you are actually just generating variations on an undefined target.

    I have seen teams spend months adjusting prompts, tuning thresholds, and debating evaluation criteria for systems where nobody had written down what success looked like in concrete, measurable terms. Defining success criteria sounds like a step teams can skip when they’re moving fast. It isn’t. The model kept producing output. The output kept improving on the metrics the team had defined. The business kept saying it wasn’t right yet. And the team kept tweaking, because tweaking was the only tool they had.

    The problem was never the model. It was the missing destination.

    Two Questions That Define Your Success Criteria

    There is a question I now try to ask at the beginning of any project, before any code is written or any model is selected or any dashboard is designed.

    Do you know where you are going?

    Most teams will say yes. They have a goal. They have a use case. They have a sense of what they are trying to accomplish. But the follow-up question is the one that matters.

    How will you know when you get there?

    That second question is where undefined destinations get exposed. A team that can answer it will describe arrival in specific, observable terms. A team that cannot will describe a feeling. Something like: the results will just look right, or the stakeholders will be satisfied, or we’ll know it when we see it.

    Justice Potter Stewart famously said that about obscenity. The bar for a product should be higher.

    If a team cannot describe what arrival looks like in concrete terms, they do not yet have a destination. They have a direction. And direction without destination is how you end up on your tenth call, fixing logic that was never going to get you where you needed to go.

    Stop Before You Tweak

    When a project starts accumulating meetings, when each session ends with a new adjustment rather than a resolved question, when the results are always almost right but never quite there: stop. Not to debug the logic. Not to adjust the model. Stop to ask the two questions.

    Do we know where we are going? And how will we know when we get there?

    Challenge every assumption that has been made about what the system is doing and what the business actually needs. In our case, the assumptions were layered three teams deep. Nobody was hiding anything. Everyone was trying to help. But the assumptions meant that the wrong question had been quietly powering the work for weeks.

    The destination, when we finally named it, was clear. Focused. Achievable.

    It didn’t require less ambition. It required less swirl.

  • Enterprise AI Implementation: You Were Promised Everything. Here’s What It Took.

    Enterprise AI Implementation: You Were Promised Everything. Here’s What It Took.

    It was, by all appearances, a standard enterprise AI implementation.

    The summaries looked clean.

    At the top of the screen was a concise paragraph capturing a customer interaction: what was requested, what was explained, and what follow-up was required. Action items were listed neatly below. It was the kind of output you could screenshot for a slide deck. Efficient. Polished. Convincing.

    The premise was simple. If employees spent less time documenting interactions, they could spend more time serving customers. Efficiency would increase. Costs would decrease. The model worked in the demo. It summarized transcripts fluently and quickly. The business case felt straightforward.

    It moved forward.

    The strain didn’t appear in the demo. It appeared in real use.

    Transcripts did not always flow through the system in the way the workflow assumed. Attribution of who said what, acceptable in curated samples, became less reliable in the face of the variability of real conversations. When attribution shifted, the summary shifted with it. For some stakeholders, that was inconvenient. For others, it introduced risk.

    Then something more structural surfaced.

    The assumption had been that there was a single summary for each interaction. In practice, different stakeholders needed different things from the same conversation. Someone preparing for the next engagement cared about context and commitments. Someone evaluating performance cared about adherence to the process. Leadership cared about patterns across many interactions.

    One summary could not satisfy all of those needs equally well.

    The original framing of saving time on notes began to feel incomplete. Documentation was only one part of the job that documentation performed. Good records preserve continuity. They prevent repeated effort. They carry context forward to the next conversation, the next decision, the next relationship moment. If a generated summary omitted a critical detail and someone had to go back to the original interaction to find it, the downstream cost could easily outweigh the time saved up front. And unlike writing notes, which happens once, the cost of a missing detail can repeat itself across every subsequent interaction with that customer.

    Under light use, the system worked. Under sustained use, the edges became visible.

    The model had done what it was designed to do. The surrounding system had not yet fully defined its requirements.

    It’s tempting to treat generative AI as an easy button.

    Providers will say they do summarization. And they do. Models can summarize text. They can condense transcripts. They can produce coherent output from messy inputs.

    But capability in isolation is different from capability under context.

    The gap isn’t whether the model works. It’s whether the system around it is ready.

    I’ve seen this play out repeatedly. The hard questions aren’t technical. They’re the ones that should have been answered before anyone opened a laptop. What is the actual job this tool is supposed to do? Not the elevator pitch version. The operational one. Is the goal speed? Accuracy? Compliance? Relationship continuity? Performance management? Each of those implies a different design, a different metric, and a different definition of done.

    Who owns the output if it’s wrong? What happens when accuracy and speed pull in opposite directions and someone has to choose? What does good actually look like, and how will anyone know when they’ve reached it?

    These weren’t philosophical questions. They were the kind of questions that get answered eventually, either intentionally before you build or expensively after you scale.

    AI lowers the barrier to building. It does not lower the barrier to clarity.

    When the summarization tool moved from demonstration to deployment, it functioned less like a feature and more like a pressure test. Variability in data pipelines surfaced. Differences in stakeholder needs became more pronounced. Cost assumptions changed once usage expanded beyond a controlled subset. Metrics that seemed sufficient in theory proved inadequate in practice.

    The pressure did not create the weaknesses. It revealed them.

    I’ve watched the same pattern unfold in other contexts.

    In one case, a generative model was introduced to help draft customer communications. The demo was compelling. With curated prompts and examples, the system produced usable content. It hinted at real scale and the leadership team liked what they saw.

    The stated goal was efficiency. Produce more output in less time.

    But efficiency was a proxy for something nobody had fully defined. Was success higher engagement? Improved response rates? Stronger brand consistency? Faster turnaround? The system could generate text, but it couldn’t determine which message was right for which audience segment. It couldn’t encode organizational voice without deliberate structure. It couldn’t tell you whether what it produced was actually better, because nobody had agreed on what better meant.

    The complexity didn’t disappear when the tool was adopted. It surfaced.

    Measurement frameworks had to be built from scratch. Editorial standards had to be written down for the first time. Experiments had to be designed carefully enough to mean something. The promise of speed ran well ahead of the work required to turn speed into value.

    The technology functioned. The surrounding system required definition.

    There is a broader pattern here.

    AI doesn’t introduce ambiguity into organizations. It finds the ambiguity that was already there and makes it move faster. Unclear ownership becomes a bottleneck overnight. Imprecise metrics become arguments about whether anything worked. Inconsistent data becomes a reliability issue in production. The model doesn’t create these conditions. It removes the slack that had been quietly absorbing them.

    I think about stress tests in engineering. They aren’t performed to prove a system works under ideal conditions. They’re performed to understand how it behaves under load, where the weak points are, what fails first, and why.

    Generative AI acts as a similar test inside organizations.

    The demo proves possibility. Deployment applies pressure.

    Under that pressure, organizations discover whether they defined the job clearly enough, whether their measurement systems are disciplined enough, whether their governance structures can absorb additional complexity, and whether they’re willing to slow down long enough to align before they scale.

    The promise of AI was not inherently wrong. Many of the projected gains were directionally sound. But the promise assumed a level of structural readiness that most organizations had never examined, because nothing had ever required them to.

    That is what it took.

    This is not a story about bad technology or careless leadership. It’s a story about what happens when building gets easier before thinking does.

    When a working model exists, momentum builds quickly. The demo impresses the room. The business case gets approved. The roadmap shifts. And the slower work, the kind that requires sitting with hard questions before anyone writes a line of code, starts to look like unnecessary delay.

    Under acceleration, patience feels irresponsible.

    But ambiguity doesn’t disappear under pressure. It compounds.

    In both of these initiatives, the most significant challenges were not technical. They were definitional. What exactly were we trying to improve? For whom? How would we know when we got there? What tradeoffs were acceptable once we operated at scale?

    Those questions don’t disappear because a model performs well in a demo. They become more urgent.

    AI does not eliminate the need for product leadership. It intensifies it.

    So what does clarity actually look like before you build?

    It starts with the job. Not the efficiency narrative or the cost reduction story that fits neatly into a business case, but the real work the tool is supposed to do and for whom. In the summarization example, that meant asking not just whether time could be saved writing notes, but what those notes were actually for. Who reads them next? What decision do they support? What happens downstream when they’re incomplete? A summary isn’t valuable because it exists. It’s valuable because of what it carries forward.

    It extends to the people who will live with the output. Not just the ones in the demo. Different stakeholders interact with the same artifact in fundamentally different ways. Designing for one and discovering the others in production is an expensive way to learn something that a few deliberate conversations could have surfaced earlier.

    It forces agreement on what success means before the first model is trained. Not directionally, but specifically. What metric moves? By how much? Over what timeframe? What would failure look like, and how would you know? These conversations are uncomfortable because they expose tradeoffs. But they are far less expensive than months of development followed by a room full of people debating whether anything worked.

    And it requires honesty about the foundation. Clean data. Clear ownership. Defined workflows. Realistic cost assumptions at scale. These aren’t bureaucratic hurdles. They are the conditions that determine whether what gets built is worth sustaining.

    None of this is slow for its own sake. It’s the work that makes speed durable. Organizations that did it well weren’t cautious. They were precise. They moved quickly once they knew what they were building and why. The ones that skipped it moved fast too, right up until the moment they didn’t.

    Clarity before speed isn’t a philosophy. It’s the actual cost of doing this right.

    The summaries looked clean.

    Under pressure, the gaps appeared.

    The model did what it was designed to do.

    The question was whether the organization around it was ready to carry the weight.

    You were promised everything.

    What it took was clarity before speed.

  • Chasing Fool’s Gold

    Chasing Fool’s Gold

    It’s November 2025, nearly three years after ChatGPT became publicly available.1 Three years of hype, three years after the record-breaking user growth2, three years of promises that AI would transform everything, and three years of that transformation always being just around the corner. 

    I’m generally pro-LLM. At my last two companies, I ran user groups to bring people together — technical and non-technical — to educate, connect, and evangelize around the responsible use of AI. I’ve led product teams building models to improve customer experience and home security, seeing measurable impact on satisfaction and adoption.

    Often, these successes came despite headwinds: misunderstanding, fear, and leadership unfamiliarity with AI. We had to educate executives on what AI was, what it wasn’t, and where it could help. We pushed to let data scientists do the data science, rather than forcing them into traditional software development models.

    The Gold Rush Hits

    Then ChatGPT arrived, and it felt like everything we’d built — metrics, prioritization, careful problem selection — could suddenly be replaced by simply ‘throwing an LLM at it.’ Promises flew: search is dead, coding is dead, thinking is dead. AGI is just around the corner.

    Businesses rushed to stake their claims, building wrappers around LLMs. One API call to solve everything. CoPilots for every task. Flashy demos everywhere. Executives saw dollar signs from revenue gains and headcount reductions.

    Projects worldwide were paused, shelved, or converted into LLM initiatives. Funding poured in, often for initiatives that hadn’t even existed weeks earlier. The goal shifted: from solving important business problems to showcasing generative AI quickly.

    The Barons and the Tools

    The “barons” who built the models and hardware were rewarded with massive investments, copyright protection, and enormous data access. Vendors selling platforms and tools gained huge funding and an endless supply of prospectors eager to mine their land.

    And like every gold rush, there were always “better” tools on the horizon. A new API promising 10x productivity. A new model promising “real” multimodality. A new agent framework that would “finally” automate everything. The land just over the ridge was always more fertile than the land you were currently standing on. And teams spent real money and real time chasing it — sure, this time the promise would finally pay.

    The promise of “grab a shovel and get your gold” was marketing, not reality. Easy-to-get gold runs out; mining becomes technical, requiring skill and know-how. The dream of instant wealth fades. Too often, it’s fool’s gold — investments in tools and access are never recouped.

    Reality Hits

    Suddenly, hallucinations become a board-level word. Reliability matters. “Just call the LLM” is no longer enough.

    Hallucinations, integration friction, and workflow complexity appear. Legal briefs with fabricated citations, inconsistent customer support responses, and hallucinated business documents turn reliability into a top concern. A model that works in a demo may fail in production, exposing operational, financial, and reputational risks.

    The illusion of ease, the desire for speed, and the dream of instant ROI never materialized. Rapidly built demos often worked only on the surface. Quick prototypes, bolt-on integrations, and low-discipline AI-generated code created massive technical debt3 — problems no LLM could solve alone. Many early adopters found fast paths to value required extensive rework, refactoring, and governance. Projects stalled or never reached production.

    These failures weren’t a surprise — they echoed the same issues we’d faced when hype outran preparation.

    Mining Real Value

    Three years in, many companies still haven’t figured it out. They’re digging for gold, chasing demos, hoping for a lucky strike. A few got lucky and saw big value — but most only saw modest gains, if any. Articles and studies show the promised ROI often didn’t materialize. The dream of instant impact remains elusive.

    In that scramble, businesses and their customers often suffer. The barons still own the land, controlling the most valuable resources. Vendors who sold the tools have already moved on to the next rush. The cycle repeats.

    The hope is that we finally learn the lesson: generative AI doesn’t deliver value through hype, demos, or shortcuts. True success comes from patience, discipline, and relentless focus on real value — careful engineering, thoughtful product design, high-quality data, and robust workflows. These principles aren’t just for today’s LLM hype; they matter for whatever technology or “next rush” comes next. 

    Shiny demos grab attention, but only foundational work separates the companies that thrive from those still chasing fool’s gold.

    1. https://openai.com/index/chatgpt/ ↩︎
    2. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/ ↩︎
    3. https://www.techradar.com/pro/from-vibe-to-viable-the-hidden-cost-of-ai-tech-debt ↩︎

  • The Illusion of Intelligence: How We’re Still Missing the Promise of AI

    The Illusion of Intelligence: How We’re Still Missing the Promise of AI

    When I started my first role as a product manager, my portfolio included solutions built with AI. I leveraged my product experience and joined forces with a team of data scientists as we sought to tackle complex problems with data.

    It was a great time to be in the space. Everyone wanted to work with AI, and the promise of an AI-driven future was highlighted at every quarterly meeting and town hall. There were piles of data and just enough maturity in the tools and teams to start developing and deploying powerful algorithms at scale.

    But progress was much slower than most people anticipated.

    We stood in front of the piles of data without a shovel, unable to make efficient use of the resource because it was in the wrong format or inside an inaccessible system. Sometimes, we’d discover too late that our intuition about the relevance of the data in a particular pile was wrong, and the data wasn’t useful to solve a particular problem.

    To overcome those challenges, we’d start by looking for more data by checking other piles or generating additional data by adding telemetry to our systems to close the gaps in coverage.

    If we couldn’t assemble what we needed to solve that particular problem, we’d try to reshape the problem, or we’d move on to the next problem to solve. But if we happened to find what we needed, we ran into our next hurdle: armchair data scientists—people who watched a demo, skimmed an article, and came away convinced they knew how to build the model better than the experts trained to do it.

    When I said that everyone wanted to work with AI, I meant everyone. In some cases, developers would head to Coursera to learn about AI and how to create algorithms. Others went back to school to get an advanced degree in machine learning or statistics. As a proponent of continuing education, I applauded these efforts to level up their knowledge and skills, and, for the most part, these individuals became curious allies, trying to learn about the process from the inside.

    But the armchair data scientists, often in leadership or decision-making positions, were more disruptive. They would watch a video or read an article, then send it to the team with a brief note, stating only, “We should do this.” There would be no context, no knowledge of how the technology worked, or if what they found addressed a problem or challenge we were facing.

    For the most part, we could deflect these suggestions through thoughtful responses, explaining that we were already doing what they suggested, or why it wasn’t relevant to the actual problem we were solving.

    The more draining interactions were from leaders who wanted to prescribe how a model should be built, sometimes implicitly but often explicitly. They wanted the algorithm to reflect a vision of what they felt a system should do based on their intuition, even if their vision wasn’t technically possible—or even relevant—to the problem at hand. They would prescribe what data should be used, what data should be excluded, or how a model should be trained.

    They tried to influence how predictions were interpreted. They challenged results that didn’t feel intuitive, even when the outcomes were reproducible, measurable, and backed by data. This sometimes created another round of training a separate model driven by feelings, followed by a side-by-side comparison that consistently showed the data science approach performed better than one based on intuition and feelings.

    I’ve sat with some of those same executives to review the results of a model, only to be met with their disappointment, especially when they felt there should be a logical, straightforward solution to a problem that was so complex that it couldn’t be solved or even attempted without AI.

    They would question why the predictions weren’t 100% accurate. Even when I pointed out that we were predicting the future from past data, and even if the model was right 60% of the time, and the previous human-driven process was right only 10% of the time, their questions focused only on achieving the impossible 100%. They’d leave value on the table chasing perfection when they could improve a process now and hope to improve it over time. Or they’d go off on tangents and hyperfocus on edge cases for which there was no solution and often no data to even attempt to use to train an algorithm.

    If the performance was impressive, they’d attempt to move the targets by setting an arbitrarily higher bar, or they’d switch from the measurable metric to a different metric or an abstraction like “trust” without providing direction on what that meant or how to measure it. Trust in what? Accuracy? Fairness? Transparency? Nobody could say. When asked for clarification, the response was the classic Justice Potter Stewart response, “I know it when I see it.”

    In the end, it always seemed like AI was a disappointment. Unless it could solve 100% of a problem 100% of the time, no matter how complex or how poorly humans performed before, leaders would keep chasing a unicorn, while ignoring the perfectly capable, faster horse already in the stable.

    Over and over, the pattern was the same: impossible expectations, misunderstanding of the tools, and a tendency to chase magical thinking over measurable progress.

    Here We Go Again

    Fast forward a few years, and we’re back at it. Only this time, the technology looks smarter. LLMs have reignited AI’s promise with a seductive twist: they speak fluently. They write. They reason. They respond. And for many, that’s been enough to assume that LLMs also understand.

    But just like before, we’ve let the illusion get ahead of the reality.

    While LLMs make it easier than ever to demo something impressive, they haven’t made it easier to deliver something useful. Underneath the conversational surface, the same problems persist: inaccessible data, unclear problems, and unrealistic expectations. In fact, the expectations are even worse now, because the technology feels like it’s already “there.”

    I’ve seen teams leap into building generative AI “solutions” without a clear understanding of what problems they’re solving. I’ve seen leadership get swept up in generative demos and approve massive budgets to chase abstract goals like “productivity” or “creativity” without metrics, definitions, or infrastructure.

    The same pattern is playing out again. Impossible expectations, except this time they’re even higher. A misunderstanding of the tools, especially when it comes to differentiating the hype from the reality. And the same magical thinking chasing a hypothetical problem rather than focusing on a real problem with measurable outcomes.

    Two years in, we’re starting to see the same disappointment creep in again, too. The unrealized expectations, longer timelines, and lack of returns on the investments.

    What Useful AI Actually Looks Like

    Useful AI doesn’t always look like magic. In fact, the most valuable AI systems I’ve seen rarely impress anyone in a demo. They don’t write poetry, simulate conversation, or generate pitch decks with a prompt. They just quietly make things better—faster, cheaper, more consistent, more scalable.

    They are, by most standards, boring.

    A model that flags billing anomalies in a healthcare system might save millions. A classifier that routes customer service tickets to the right team might shave minutes off every support interaction. An optimization algorithm that suggests more efficient delivery routes could reduce fuel costs, improve ETAs, and shrink carbon footprints. None of these use generative AI. None of them are headline-worthy. But all of them create real value.

    And unlike a chatbot that sometimes gives the wrong answer with great confidence, these systems are narrow by design. Purpose-built. Measured. Tightly integrated into workflows and optimized over time. They don’t need to sound human. They just need to work.

    We often overlook this kind of AI because it’s not exciting to watch. It doesn’t feel like the future. But that’s exactly the point: the best AI doesn’t draw attention to itself. It dissolves into the process, making things work better than they did before.

    The Opportunity Cost of the Hype

    The hype around LLMs has sparked a renewed interest in AI, but it’s also warped our sense of what progress looks like. Instead of focusing on impact, we’ve become obsessed with spectacle.

    Executives see a demo of a chatbot that answers questions with a human-like cadence and see the realization of the vision that they’ve had for AI all along. Suddenly, every team is greenlit to build a “copilot.”

    LLMs make it easy to show something impressive. A few prompts, a fancy UI, and you’ve got a prototype that feels like innovation. But most of these tools don’t stand up to basic scrutiny. They hallucinate. They break when connected to real systems. They introduce ambiguity into workflows that once relied on clarity. They create new risks—ethical, operational, and technical—that teams are often unprepared to manage.

    We’re pouring talent, time, and money into building AI wrappers around problems we haven’t defined. Meanwhile, the infrastructure work that would actually make AI useful—cleaning data, improving feedback loops, building explainable systems—is neglected.

    This is the code of chasing the hype. Years of expensive effort with little or no realization of value, certainly to the scale that was promised. Real problems that didn’t require generative AI but could have been solved and added real value were ignored or neglected. Two years in, it turns out we’ve been sprinting on a treadmill. We’ve spent the energy, but we’re still in the same place.

    To be clear, there’s nothing wrong with experimentation. But exploration without a clear problem or success metric isn’t innovation—it’s expensive theater. It gives the illusion of progress while distracting from work that actually moves the needle.

    And we should know the difference because we’ve been here before. We let unrealistic expectations undermine the progress of last-generation AI. Now, we’re doing it faster. We’re skipping the discipline that made the old models work and replacing it with a dopamine hit from a prompt that feels smart.

    A Better Mindset: Value Over Novelty

    If the last two waves of AI taught us anything, it’s this: the technology is only as good as the problems we point it at and the people we trust to solve them.

    Too often, we let novelty set the direction. We ask, “What can we build with this?” instead of “What’s worth solving?” But even when we pick the right problems, we don’t always empower the right people to do the work.

    Instead, we’re seeing a return of the same behavior that stalled progress last time: leaders prescribing not just what to solve, but how to solve it. Dictating which data to use. Demanding specific architectures. Redefining outcomes midstream based on intuition instead of evidence. In some cases, they’re building solutions backwards from a flashy demo instead of forward from a real need.

    This isn’t strategy—it’s armchair data science all over again.

    And it’s especially risky now, because LLMs make it even easier to look smart without being right. It’s one thing to brainstorm ideas. It’s another to second-guess trained experts who understand the constraints, trade-offs, and mechanics of building something that works.

    A better mindset means more than just optimizing for usefulness. It means creating space for people with real expertise—data scientists, engineers, researchers, designers—to lead the “how.”

    It means:

    • Letting evidence drive decisions, not gut instinct or LinkedIn hype.
    • Empowering teams to solve, not just execute.
    • Recognizing that success isn’t always intuitive—and being okay with that.

    Adopting this mindset doesn’t mean ignoring new tech. It means respecting the disciplines that make that tech useful. It means pairing vision with humility, ambition with trust.

    Because there’s nothing wrong with being impressed by what’s possible.

    But if we’re serious about delivering real value with AI, we have to get out of our own way—and let the experts do their jobs.

  • The Dulling of Innovation

    The Dulling of Innovation

    For a few years, I was on a patent team. Our job was to drive innovation and empower employees to come up with new ideas and shepherd them through the process to see if we could turn those ideas into patents.

    I loved that job for many reasons. It leveraged an innovation framework I had already started with a few colleagues—work that earned us a handful of patents. It fed my curiosity, love for technology, and joy of being surrounded by smart people. Most of all, I loved watching someone light up as they became an inventor.

    I worked with an engineer who had an idea based on his deep knowledge of a specific system. Together, we expanded on that idea and turned it into an innovative solution to a broader problem. The look on his face when his idea was approved for patent filing was one of the greatest moments of my career. For years after, he would stop me in the hallway just to say hello and introduce me as the person who helped him get a patent.

    Much of the success I saw on that team came from people who deeply understood a problem, were curious to ask why, and believed there had to be a better way. That success was amplified when more than one inventor was involved, when overlapping experiences and diverse perspectives combined into something truly original.

    When I moved into product management, the same patterns held true. The most successful ideas still came from a clear understanding of the problem, deep knowledge of the system, and the willingness to explore different perspectives.

    Innovation used to be a web. It was messy, organic, and interconnected. The spark came from deep context and unexpected collisions.

    But that process is starting to change.

    Same High, Lower Ceiling

    In this new age of large language models (LLMs), companies are looking for shortcuts for growth and innovation and see LLMs as the cheat code.

    Teams are tasked with mining customer comments to synthesize feedback and generate feature ideas and roadmaps. If the ideas seem reasonable, they are executed without further analysis. Speed is the goal. Output is the metric.

    Regardless of size or maturity, every company can access the tools and capabilities once reserved for tech giants. Generative AI lowers the barrier to entry. It also levels the playing field, democratizing innovation.

    But what if it also levels the results?

    When everyone uses the same models, is trained on the same data, and is prompted in similar ways, the ideas start to converge. It’s innovation by template. You might move faster, but so is everyone else, and in the same direction.

    Even when applied to your unique domain, the outputs often look the same. Which means the ideas are starting to look the same, too.

    AI lifts companies that lacked innovation muscle, but in doing so, it risks pulling down those that had built it. The average improves, but the outliers vanish. The floor rises, but the ceiling falls.

    We’re still getting the high. But it doesn’t feel like it used to.

    The Dopamine of Speed

    The danger is that we’re not going to see it happening. Worse, we’re blindly moving forward without considering the long-term implications. We’re so fixated on speed that it’s easy to convince ourselves that we’re moving fast and innovating.

    We confuse motion for momentum, and output for originality. The teams and companies that move the fastest will be rewarded. Natural selection will leave the slower ones behind. Speed will be the new sign of innovation. But just because something ships fast doesn’t mean it moves us forward.

    The dopamine hit that comes from release after release is addictive, and we’ll need more and more to feel the same level of speed and growth. We’ll rely increasingly on these tools to get our fix until it stops working altogether. Meanwhile, the incremental reliance on these tools dulls effectiveness and erodes impact, and our ability to be creative and innovate will atrophy.

    By the time we realize the quality of our ideas has flattened, we’ll be too dependent on the process to do anything differently.

    The Dealers Own the Supply

    And those algorithms? They’re owned by a handful of companies. These companies decide how the models behave, what data they’re trained on, and what comes out of them.

    They also own the data. And it’s only a matter of time before they start mining it for intellectual property—filing patents faster than anyone else can, or arguing that anything derived from their models is theirs by default.

    Beyond intellectual property and market control, this concentration of power raises more profound ethical and societal questions. When innovation is funneled through a few gatekeepers, it risks reinforcing existing inequalities and biases embedded in the training data and business models. The diversity of ideas and creators narrows, and communities without direct access to these technologies may be left behind, exacerbating the digital divide and limiting who benefits from AI-driven innovation.

    The more we rely on these models, the more we feed them. Every prompt, interaction, and insight becomes part of a flywheel that strengthens the model and the company behind it, making it more powerful. It’s a feedback loop: we give them our best thinking, and they return a usable version to everyone else.

    LLMs don’t think from first principles—they remix from secondhand insight. And when we stop thinking from scratch, we start building from scraps.

    Because the answers sound confident, they feel finished. That confidence masks conformity, and we mistake it for consensus.

    Innovation becomes a productized service. Creative edge gets compressed into a monthly subscription. What once gave your company a competitive advantage is now available to anyone who can write a halfway decent prompt.

    Make no mistake, these aren’t neutral platforms. They shape how we think, guide what we explore, and, as they become more embedded in our workflows, influence decisions, strategies, and even what we consider possible.

    We used to control the process. Now we’re just users. The same companies selling us the shortcut are quietly collecting the toll.

    When the supply is centralized, so is the power. And if we keep chasing the high, we’ll find ourselves dependent on a dealer who decides what we get and when we get it.

    Rewiring for Real Innovation

    This isn’t a call to reject the tools. Generative AI isn’t going away, and used well, it can make us faster, better, and more creative. But the key is how we use it—and what we choose to preserve along the way.

    Here’s where we start:

    1. Protect the Messy Middle

    Innovation doesn’t happen at the point of output. It happens in the friction. The spark lives in debate, dead ends, and rabbit holes. We must protect the messy, nonlinear process that makes true insight possible.

    Use AI to accelerate parts of the journey, not to skip it entirely.

    2. Think from First Principles

    Don’t just prompt. Reframe. Instead of asking, “What’s the answer?” ask, “What’s the real question?” LLMs are great at synthesis, but breakthroughs come from original framing.

    Start with what you know. Ask “why” more than “how.” And resist the urge to outsource the thinking.

    3. Don’t Confuse Confidence for Quality

    A confident response isn’t necessarily a correct one. Learn to interrogate the output. Ask where it came from, what it’s assuming, and what it might be missing.

    Treat every generated answer like a draft, not a destination.

    4. Diversify Your Inputs

    The model’s perspective is based on what it’s been trained on, which is mostly what’s already popular, published, and safe. If you want a fresh idea, don’t ask the same question everyone else is asking in the same way.

    Talk to people. Explore unlikely connections. Bring in perspectives that aren’t in the data.

    5. Make Thinking Visible

    The danger of speed is that it hides process. Write out your assumptions. Diagram your logic. Invite others into the middle of your thinking instead of just sharing polished outputs.

    We need to normalize visible, imperfect thought again. That’s where the new stuff lives.

    6. Incentivize Depth

    If we reward speed, we get speed. If we reward outputs, we get more of them. But if we want real innovation, we need to measure the stuff that doesn’t show up in dashboards: insight, originality, and depth of understanding.

    Push your teams to spend time with the problem, not just the solution.

    Staying Sharp

    We didn’t set out to flatten innovation. We set out to go faster, to do more, to meet the moment. But in chasing speed and scale, we risk trading depth for derivatives, and originality for automation.

    Large language models can be incredible tools. They can accelerate discovery, surface connections, and amplify creative potential. But only if we treat them as collaborators, not crutches.

    The danger isn’t in using these models. The danger is in forgetting how to think without them.

    We have to resist the pull toward sameness. We have to do the slower, messier work of understanding real problems, cultivating creative tension, and building teams that collide in productive ways. We have to reward originality over velocity, and insight over output.

    Otherwise, the future of innovation won’t be bold or brilliant.

    It’ll just be fast.

    And dull.

  • AI First, Second Thoughts

    AI First, Second Thoughts

    Over the past few weeks, several companies have made headlines by declaring an “AI First” strategy.

    Shopify CEO Tobi Lütke told employees that before asking for additional headcount or resources, they must prove the work can’t be done by AI.

    Duolingo’s CEO, Luis von Ahn, laid out a similar vision, phasing out contractors for tasks AI can handle and using AI to rapidly accelerate content creation.

    Both companies also stated that AI proficiency will now play a role in hiring decisions and performance reviews.

    On the surface, this all sounds reasonable. If generative AI can truly replicate—or even amplify—human effort, then why wouldn’t companies want to lean in? Compared to the cost of hiring, onboarding, and supporting a new employee, AI looks like a faster, cheaper alternative that’s available now.

    But is it really that simple?

    First, there was AI Last

    Before we talk about “AI First,” it’s worth rewinding to what came before.

    I’ve long been an advocate of what I’d call an “AI Last” approach, so the “AI First” mindset is a shift for me.

    Historically, I’ve found that teams often jump too quickly to AI as the sole solution, due to significant pressure from the top to “do more AI.” It showed a lack of understanding of what AI is, how it works, its limitations, and its cost. The mindset of sprinkling magical AI pixie dust over a problem and having it solved is naive and dangerous, often distracting teams from a much more practical solution.

    Here’s why I always pushed for exhausting the basics before reaching for AI:

    Cost

    • High development and maintenance costs: AI solutions aren’t cheap. They require time, talent, and significant financial investment.
    • Data preparation overhead: Training useful models requires large volumes of clean, labeled data—something most teams don’t have readily available.
    • Infrastructure needs: Maintaining reliable AI systems often means investing in robust MLOps infrastructure and tooling.

    Complexity

    • Simple solutions often work: Business logic, heuristics, or even minor process changes can solve the problem faster and more predictably.
    • Harder to maintain and debug: AI models are opaque by nature—unlike rule-based systems, it’s hard to explain why they behave the way they do.
    • Performance is uncertain: AI models can fail in edge cases, degrade over time, or simply underperform outside of their training environment.
    • Latency and scalability issues: Large models—especially when accessed through APIs—can introduce unacceptable delays or infrastructure costs.

    Risk

    • Low explainability: In regulated or mission-critical settings, black-box AI systems are a liability.
    • Ethical and legal exposure: AI can introduce or amplify bias, violate user privacy, or produce harmful or offensive outputs.
    • Chasing hype over value: Too often, teams build AI solutions to satisfy leadership or investor expectations, not because it’s the best tool for the job.

    What Changed?

    So why the shift from AI Last to AI First?

    The shift happened not just because of what generative AI made possible, but how effortless it made everything look.

    Generative AI feels easy.

    Unlike traditional AI, which required data pipelines, modeling, and MLOps, generative AI tools like ChatGPT or GitHub Copilot give you answers in seconds with nothing more than a prompt. The barrier to entry feels low, and the results look surprisingly good (at first).

    This surface-level ease masks the hidden costs, risks, and technical debt that still lurk underneath. But the illusion of simplicity is powerful.

    Generalization expands possibilities.

    LLMs can generalize across many domains, which lowers the barrier to trying AI in new areas. That’s a significant shift from traditional AI, which typically had narrow, custom-built models.

    AI for everyone.

    Anyone—from marketers to developers—can now interact directly with AI. This democratization of AI access represents a significant shift, accelerating adoption, even in cases where the use case is unclear.

    Speed became the new selling point.

    Prototyping with LLMs is fast. Really fast. You can build a working demo in hours, not weeks. For many teams, that 80% solution is “good enough” to ship, validate, or at least justify further investment.

    That speed creates pressure to bypass traditional diligence, especially in high-urgency or low-margin environments.

    The ROI pressure is real.

    Companies have made massive investments in AI, whether in cloud compute, partnerships, talent, or infrastructure. Boards and executives want to see returns. “AI First” becomes less of a strategy and more of a mandate to justify spend.

    It’s worth mentioning that this pressure sometimes focuses on using AI, not using it well.

    People are expensive. AI is not (on the surface).

    Hiring is slow, expensive, and full of risk. In contrast, AI appears to offer infinite scale, zero ramp-up time, and no HR overhead. For budget-conscious leaders, the math seems obvious.

    The hype machine keeps humming.

    Executives don’t want to be left behind. Generative AI is being sold as the answer to nearly every business challenge, often without nuance or grounding in reality. Just like with traditional AI, teams are once again being told to “add AI” without understanding if it’s needed, feasible, or valuable.

    It feels like a shortcut.

    There’s another reason “AI First” is so appealing: it feels like a shortcut.

    It promises to bypass the friction, delay, and uncertainty of hiring. Teams can ship faster, cut costs, and show progress—at least on the surface. In high-pressure environments, that shortcut is incredibly tempting.

    But like most shortcuts, this one comes with consequences.

    Over-reliance on AI can erode institutional knowledge, create brittle systems, and introduce long-term costs that aren’t immediately obvious. Models drift. Prompts break. Outputs change. Context disappears. Without careful oversight, today’s efficiency gains can become tomorrow’s tech debt.

    Moving fast is easy. Moving well is harder. “AI First” can be a strategy—but only when it’s paired with rigor, intent, and a willingness to say no.

    What’s a Better Way?

    “AI First” isn’t inherently wrong, but without guardrails, it becomes a race to the bottom. A better approach doesn’t reject AI. It reframes the question.

    Yes, start with AI. But don’t stop there. Ask:

    • Is AI the right tool for the problem?
    • Is this solution resilient, or just fast?
    • Are we building something sustainable—or something that looks good in a demo?

    A better way is one that’s AI-aware, not AI-blind. That means being clear-eyed about what AI is good at, where it breaks down, and what it costs over time.

    Here are five principles I’ve seen work in practice:

    Start With the Problem, Not the Technology

    Don’t start by asking “how can we use AI?” Start by asking, “What’s the problem we’re trying to solve?”

    • What does success look like?
    • What are the constraints?
    • What’s already working—or broken?

    AI might still be the right answer. But if you haven’t clearly defined the problem, everything else is just expensive guesswork.

    Weigh the Tradeoffs, Not Just the Speed

    Yes, AI gets you something fast. But is it the right thing?

    • What happens when the model changes?
    • What’s the fallback if the prompt fails?
    • Who’s accountable when it goes off the rails?

    “AI First” works when speed is balanced by responsibility. If you’re not measuring long-term cost, you’re not doing ROI—you’re doing wishful thinking.

    Build for Resilience, Not Just Velocity

    Shortcuts save time today and create chaos tomorrow.

    • Document assumptions.
    • Build fallback paths.
    • Monitor for drift.
    • Don’t “set it and forget it.”

    Treat every AI-powered system like it’s going to break, because eventually, it will. The teams that succeed are the ones who planned for it.

    Design Human-AI Collaboration, Not Substitution

    Over-automating can backfire. When people feel like they’re just babysitting machines—or worse, being replaced by them—you lose the very thing AI was supposed to support: human creativity, intuition, and care.

    The best systems aren’t human-only or AI-only. They’re collaborative.

    • AI drafts, people refine.
    • AI scales, humans supervise.
    • AI suggests, humans decide.

    This isn’t about replacing judgment, it’s about amplifying it. “AI First” should make your people better, not make them optional.

    Measure What Actually Matters

    A lot of AI initiatives look productive because we’re measuring the wrong things.

    More output ≠ better outcomes.

    And if everyone is using the same AI tools in the same way, we risk a monoculture of solutions—outputs that look the same, sound the same, and think the same.

    Real creativity and insight don’t come from the center. They come from the edges, from the teams that challenge assumptions and break patterns. Over-reliance on AI can mute those voices, replacing originality with uniformity.

    Human memory is inefficient and unreliable in comparison to machine memory. But it’s this very unpredictability that’s the source of our creativity. It makes connections we’d never consciously think of making, smashing together atoms that our conscious minds keep separate. Digital databases cannot yet replicate the kind of serendipity that enables the unconscious human mind to make novel patterns and see powerful new analogies of the kind that lead to our most creative breakthroughs. The more we outsource our memories to Google, the less we are nourishing the wonderfully accidental creativity of our consciousness.

    Ian Leslie, Curious: The Desire to Know and Why Your Future Depends on It

    If we let AI dictate the shape of our work, we may all end up building the same thing—just faster.

    More speed ≠ more value.

    Instead of counting tasks, measure trust. Instead of tracking volume, track quality. Focus on the things your customers and teams actually feel.

    The Real “AI First” Advantage

    The companies that win with AI won’t be the ones who move the fastest.

    They’ll be the ones who move the smartest. They’ll be the ones who know when to use AI, when to skip it, and when to slow down.

    Because in the long run, discipline beats urgency. Clarity beats novelty. And thoughtfulness scales better than any model.

    The real power of AI isn’t in what it can do.

    It’s in what we choose to do with it.

  • Product Management is Dead

    Product Management is Dead

    My social media feeds have been inundated lately with bold assertions and proclamations about the future of product management.

    • Do we still need product managers?
    • Is AI going to replace product teams?
    • Has product…died?

    The claims tend to follow a predictable pattern:

    • AI writes user stories and PRDs.
    • AI generates user personas.
    • AI summarizes feedback and explores pain points.
    • AI prioritizes roadmaps.

    It makes for a compelling headline, often pushed by companies or consultants selling tools or services that claim to automate these tasks. These headlines grab attention, spark debate, and tap into the anxiety many product managers feel as AI reshapes their role.

    But this isn’t a funeral. It’s a reckoning. The old, process-heavy, adaptability-light version of product won’t survive. But that’s not the end of product. It’s the beginning of something better. Beneath the clickbait is a valid call to evolve: product management isn’t dying, it’s transforming.

    What Product Really Is

    Before we talk about what’s changing, let’s be clear about what product is.

    Product management isn’t a set of tasks. It’s a discipline of focus, alignment, and judgment.

    It’s about understanding problems deeply, prioritizing effectively, and creating the conditions for great teams to build the right things.

    AI can assist with this work, but it can’t own it. And if you think product is just a list of tasks?

    You’re already doing it wrong.

    Why People Want Product Dead

    Product is often seen as a bottleneck. It’s seen as the layer that slows down builders with meetings, documents, and decisions that feel like bureaucracy. In fast-moving, engineering-led organizations, product often looks like something that should be automated rather than a discipline rooted in insight, prioritization, and alignment.

    AI has only amplified that impulse. With tools that can instantly generate specs, synthesize feedback, and mock up features, product starts to look like a collection of tasks rather than a strategic function. And if it’s just tasks, why not let the machines do it?

    That thinking is tempting, especially to companies chasing speed and efficiency. But it’s also shortsighted. Still, the “product is dead” narrative keeps getting airtime because companies want it to be true, even if it misses the bigger picture.

    Speed Over Strategy: Engineering-Led Cultures Prefer Shipping

    In many engineering-led cultures, especially in AI, there’s a deep bias toward building, shipping fast, testing fast, and iterating fast. AI has collapsed the cost of experimentation. And with today’s AI tools, it’s never been easier to vibe code (i.e., rapidly stitch together working demos using AI and low-code tools) your way to a working prototype. You can spin up UIs, connect APIs, and generate sample data in hours instead of weeks. It looks and feels like progress.

    But without intention, you’re not building products, you’re building distractions. You’re producing, not progressing. You’re generating output, not outcomes.

    And that’s the trap: it feels like you’re moving faster, but without a clear understanding of the customer, the problem, and the strategy, you’re either moving in circles or heading in the wrong direction entirely.

    Task-Based Thinking: Why Product Looks Replaceable

    The appeal is obvious: automate the “middle layer,” and suddenly, your team is leaner, faster, and cheaper. Product work is reframed as a series of repeatable tasks: write a story, generate a persona, summarize feedback, and stack rank a backlog. It’s presented as something mechanical, like configuring an assembly line, rather than requiring focus, intention, and insight.

    But this framing is dangerously incomplete. These aren’t just tasks; they’re judgment calls. They ensure teams solve the right problems in the right way at the right time. Discovery without direction is noise. Strategy without prioritization is chaos. Specifications without insight are just empty documentation.

    AI can assist with product work, but reducing it to a checklist makes it easier to sell a tool but harder to build anything meaningful.

    A Convenient Story: The Simplified Narrative That Sells

    It’s a narrative that promises clarity: eliminate the middle layer, remove the blockers, and let machines and makers do what they do best. This strategy plays perfectly in a world obsessed with efficiency and in organizations that already see product management as overhead.

    But the truth is messier.

    Good product managers don’t just write tickets or relay requests. They bring cohesion to chaos. They align teams around a shared understanding of the customer, the problem, and the goal. They ask the hard questions that AI can’t answer on its own.

    Should we build this? Why now? What matters most?

    AI can produce content, but not conviction. It can analyze feedback, but not frame a vision. And it can’t resolve the tensions between user needs, business goals, and technical constraints — at least not without someone to interpret, prioritize, and lead.

    The “product is dead” story works because it feels simple. But building good products was never simple. Removing the people who deal with complexity doesn’t make it go away. It just makes it your customer’s problem.

    The Companies Who Will Regret This

    Here’s my prediction:

    • The companies that cut product first will move fastest at first.
    • Their roadmaps will fill up. Their launches will accelerate. Their demos will look impressive.

    But then, slowly and quietly, things will start to break.

    • Customer engagement will slip.
    • Retention will fall.
    • New features will feel disconnected from real needs.
    • Teams will build for what’s easy, not for what’s valuable.

    The companies that sold them those shiny new tools, the ones that promised to replace product? They’ll be long gone, moving on to the next buyer or looking for the next hype cycle to exploit.

    Meanwhile, the companies that doubled down on the real craft of product, who invested in judgment, customer obsession, and asking why before what, will still be standing (and thriving) while others fade. They’ll have products that resonate and that evolve with their customers.

    Because tools don’t create strategy.

    People do.

    Old Product Might Be Dead — And That’s a Good Thing

    Now, here’s where I’ll agree with the AI evangelists: old product needed to change.

    The days of PMs acting as backlog managers, Jira ticket writers, or meeting schedulers? Yeah, that should die.

    PMs who only handed off requirements to engineering? Gone.

    PMs who never talked to customers? Dead.

    Let’s be honest: many organizations misdefined the PM role. They hired process managers and called it product. They built layers of communication, not layers of clarity. They were managing workflows, not products. They were pushing tickets, not pushing strategy. Those roles are vulnerable not because of AI, but because they weren’t doing product in the first place.

    The version of product that survives this shift and is worth fighting for is sharper, faster, and more essential than ever. It’s not about being an intermediary between engineering and design. It’s about creating clarity, focus, and vision where there was once noise and confusion.

    It looks like:

    • Problem curation over solution obsession: It’s not about finding the quickest fix or building what’s easiest. It’s about understanding what problem we’re solving, for whom, and why it matters.
    • Judgment over process: AI can help automate the steps, but it can’t tell you if you’re solving the right problem or if the timing is right. Good product management is still a series of thoughtful decisions, not just steps in a flowchart.
    • Context over control: Dictating requirements from above doesn’t work anymore. Context, shared understanding, and alignment are what drive teams to collaborate effectively, not command and control.
    • Collaboration over command: PMs are the glue that brings engineering, design, and business together. But that means being a partner and enabler, not a dictator. Collaboration is the new currency in product development.
    • Customer truth over corporate theater: Building the right product requires honest feedback, real conversations with customers, and deep empathy. It’s not about making the product look good on paper; it’s about making it work for the people who use it.

    The old way of doing product is over. But this isn’t about mourning its loss. It’s about embracing a new, more purposeful approach. The role of product management is evolving, and in many ways, that’s a huge opportunity to do better, build better, and have a bigger impact.

    The King Is Dead. Long Live the King.

    The “product is dead” narrative is loud right now because it’s easy. It’s easier to believe we can automate judgment than it is to build it. Easier to replace complexity than to wrestle with it. Easier to promise speed than to commit to substance.

    But the companies that endure — the ones that create real value, not just hype-fueled demos — will be the ones that lean into the harder, more human work.

    They’ll treat product not as a process to optimize, but as a practice to sharpen.

    They’ll embrace AI as a powerful tool — not a replacement for the thinking, intuition, and collaboration that make great products possible.

    They’ll stop treating product like a middle layer to cut, and start recognizing it as a critical function to elevate.

    Because here’s the truth: the best product teams won’t just survive this shift. They’ll lead it.

    They’ll be faster because they’re clearer. Smarter because they’re humbler. Stronger because they’re more aligned.

    Product isn’t dead. Bad product is dead. Shallow product is dead. Performative product is dead.

    The age of product isn’t over. The age of better product is just beginning.

    Long live product — not as it was, but as it needs to be.

  • Are You Not Entertained?

    Are You Not Entertained?

    “Give them bread and circuses, and they will never revolt.”
    — Juvenal, Roman satirist

    Over the past two weeks, my LinkedIn feed has looked like an AI fever dream. Every meme from the past 10 years was turned into a Studio Ghibli production. Former colleagues changed their profile pictures into a Muppet version of themselves. And somewhere, a perfectly respectable CTO shared an image of themselves as an ’80s action figure.

    Meanwhile, in boardrooms everywhere, a familiar silence falls: ‘But… where’s the ROI?‘

    The Modern Colosseum

    The Roman Empire understood something timeless about human nature: if people are distracted, they’re less likely to notice what’s happening around them. Bread and circuses. Keep them fed and entertained, and you can buy yourself time (or at least avoid a riot).

    Fast-forward a couple of thousand years, swap out the emperors and politicians for CEOs in hoodies, VCs in Patagonia vests, and gladiators for generative AI, and the strategy hasn’t changed much.

    Today’s Colosseum is our social feed. And instead of lions and swords, it’s Ghibli filters, Muppet profile pictures, and action figure avatars. Every few weeks, a new AI-powered spectacle sweeps through like a new headline act. The crowd goes wild. The algorithm delivers the dopamine. And for a moment, it feels like this is what AI was always only meant for fun, viral, harmless play.

    But here’s the thing: that spectacle serves a purpose. The companies building these tools want you in the arena.

    Every playful experiment trains their models, every viral trend props up their metrics, and every wave of AI-generated content helps justify the next round of fundraising at an even higher valuation. These modern-day emperors are profiting from the distraction.

    You get a JPEG. They get data, engagement, and another step toward platform dominance.

    Meanwhile, the harder, messier questions that actually matter get conveniently lost in the noise:

    • Where does this data come from?
    • Where does the data go?
    • Who owns it?
    • Who profits from it?
    • What happens when a handful of companies control both the models and the means of production?
    • And are these tools creating real business value — or just highly shareable distractions?

    Because while everyone’s busy turning their profile picture into a dreamy Miyazaki protagonist, the real, boring, messy, complicated work of AI is quietly stalling out as companies continue to struggle to find sustainable, repeatable ways to extract value from these tools. The promise is enormous, but the reality? It’s a little less cinematic.

    And so the cycle continues: hype on the outside, hard problems on the inside. Keep the crowd entertained long enough, and maybe nobody will ask the hardest question in the arena:

    “Is any of this actually working?”

    Spectacle Scales Faster Than Strategy

    It’s easy to look at all of this and roll your eyes. The AI selfies. The endless gimmicks. The flood of LinkedIn posts that feel more like digital dress-up than technology strategy.

    But this dynamic exists for a reason. In fact, it keeps happening because the forces behind it are perfectly aligned.

    It’s Easy

    The barrier to entry for generative AI spectacle is incredibly low.
    Write a prompt. Upload a photo. Get a result in seconds. No infrastructure. No integration. No approvals. Just instant content, ready for likes.

    Compare that to operationalizing AI inside a company where projects can stall for months over data access, privacy concerns, or alignment between teams. It’s no wonder which version of AI most people gravitate towards.

    It’s Visible

    Executives like to see signs of innovation. Shareholders like to hear about “AI initiatives.” Employees want to feel like their company isn’t falling behind.

    Generative AI content delivers that visibility without the friction of actual transformation. Everyone gets to point to something and say, “Look! We’re doing AI.”

    It’s Fun

    Novelty wins attention. Play wins engagement. Spectacle spreads faster than strategy ever will.

    People want to engage with these trends — not because they believe it will transform their business, but because it’s delightful, unexpected, and fundamentally human to want to see yourself as a cartoon.

    It’s Safe

    The real work of AI is messy. It challenges workflows. It exposes gaps in data. It forces questions about roles, skills, and even headcount.

    That’s difficult, political, and sometimes threatening. Creating a Muppet version of your team is much easier than asking, “How do we automate this process without breaking everything?”

    And that’s exactly what the model and tool providers are taking advantage of. The easier it is to generate content, the faster you train the models. The more fun it is to share, the more data you give away. The safer it feels, the less you question who controls the tools you’re using.

    The Danger of Distraction

    The Colosseum didn’t just keep the Roman crowds entertained — it kept them occupied. And that’s the real risk with today’s AI spectacle.

    It’s not that the Ghibli portraits or action figure avatars are bad. It’s that they’re incredibly effective at giving the illusion of progress while the hard work of transformation stalls out behind the scenes.

    Distraction doesn’t just waste time. It creates risk. It creates vulnerability.

    Because while everyone is busy playing with the latest AI toy, the companies building these tools are playing a very different game — and they are deadly serious about it.

    They’re not just entertaining users. They’re capturing data. Shaping behavior. Building platforms. Creating dependencies. And accelerating their lead.

    Every viral trend lowers the bar for what people expect AI to do — clever content instead of meaningful change, spectacle instead of service, noise instead of impact. Meanwhile, the companies behind the curtain aren’t lowering their ambitions at all. They’re racing ahead.

    And the longer you sit in the stands clapping, the harder it gets to catch up.

    Leaders lose urgency. Teams lose focus. Customers lower their standards. And quietly, beneath all the fun and novelty, a very real gap is opening up — between the companies who are playing around with AI and the companies who are building their future on it.

    This is the real risk: not that generative AI fails but that it succeeds at the completely wrong thing. That we emerge from this wave with smarter toys, funnier memes, faster content… but no real shift in how work gets done, how customers are served, or how value is created.

    And by the time the novelty wears off and people finally look around and ask, “Wait, what did we actually build?” it might be too late to catch up to the companies who never stopped asking that question in the first place.

    Distraction delays that reckoning. But it doesn’t prevent it.

    The crowd will eventually leave the Colosseum. The show always ends. What’s left is whatever you bothered to build while the noise was loudest.

    Leaving The Arena

    If the past year has felt like sitting in the front row of the AI Colosseum, the obvious question is: do you want to stay in your seat forever?

    Because leaving the arena doesn’t mean abandoning generative AI. It means stepping away from the noise long enough to remember why you showed up in the first place. It means holding both yourself and the technology providers to a higher standard.

    It means asking harder questions about how you’re using AI and who you’re trusting to shape your future.

    • What real problems could this technology help us solve?
    • Where are we spending time or money inefficiently?
    • Who owns the value we create with these tools?
    • Where are we giving away data, control, or customer relationships without realizing it?
    • What assumptions are these LLM providers baking into our products, our workflows, our culture?
    • What happens to our business if these providers change the rules, the pricing, or the access tomorrow?
    • Are we designing for leverage or locking ourselves into dependency?
    • What happens if these companies own both the means of production and the means of distribution?

    It means shifting the focus from what AI can do to what people need. From delight to durability. From spectacle to service. From passive adoption to active accountability.

    Because the real work isn’t viral. It doesn’t trend on social media. No one’s sharing screenshots of cleaner data pipelines or more intelligent internal tools. But that’s exactly where the lasting value gets created.

    The companies (and people) who figure that out will not only survive the hype cycle but also be the ones standing long after the crowd moves on to whatever comes next.

    The arena will always be there. The show will always go on. The next shiny demo will always drop.

    But at some point, you must decide whether you’re in this to watch or are here to build something that lasts and ask the uncomfortable questions that building requires.

  • Automation’s Hidden Effort

    Automation’s Hidden Effort

    In the early 2000s, as the dot-com bubble burst, I found myself without an assignment as a software development consultant. My firm, scrambling to keep people employed, placed me in an unexpected role: a hardware testing lab at a telecommunications company.

    dm automation hidden effort test cable box telecommunications

    The lab tested cable boxes and was the last line of defense before new devices and software were released to customers. These tests consisted of following steps in a script tracked in Microsoft Excel to validate different features and functionality and then marking the row with an “x” in the “Pass” or “Fail” column.

    A few days into the job, I noticed that, after they had completed a test script, some of my colleagues would painstakingly count the “x” in each column and then populate the summary at the end of the spreadsheet.

    “You know, Excel can do that for you, right?” I offered, only to be met with blank stares.

    “Watch.”

    I showed them how to use simple formulas to tally results and then added conditional formatting to highlight failed steps automatically. These small tweaks eliminated tedious manual work, freeing testers to focus on more valuable tasks.

    That small win led to a bigger challenge. My manager handed me an unopened box of equipment—an automated testing system that no one had set up.

    “You know how to write code,” he said. “See if you can do something with that.”

    Inside were a computer, a video capture card, an IR transmitter, and an automation suite for running scripts written in C. My first script followed the “happy path,” assuming everything worked perfectly. It ran smoothly—until it didn’t. When an IR signal was missed, the entire test derailed, failing step after step.

    To fix it, I added verification steps after every command. If the expected screen didn’t appear, the script would retry or report a failure. Over weeks of experimentation, I built a system that ran core regression tests automatically, flagged exceptions, and generated reports.

    When I showed my manager the result, he was amazed as he watched the screen. As if by magic, the cable box navigated to different screens and tested various actions. At the end of the demo, he was impressed and directed me to automate more tests.

    What he didn’t see in the demo was the effort behind the scenes—the constant tweaking, exception handling, and fine-tuning to account for the messy realities of real-world systems.

    The polished demo sent a simple message:

    Automation is here. No manual effort is needed.

    But that wasn’t the whole story. Automation, while transformative, is rarely as effortless as it appears.

    Operator: Automation’s New Chapter

    The lessons I learned in that testing lab feel eerily relevant today.

    In January 2025, OpenAI released Operator. According to OpenAI1:

    Operator is a research preview of an agent that can go to the web to perform tasks for you. It can automate various tasks—like filling out forms, booking travel, or even creating memes—by remotely interacting with a web browser much as a person would, via mouse clicks, scrolling, and typing.

    When I saw OpenAI’s announcement, I had déjà vu. Over 20 years ago, I built automation scripts to mimic how customers interacted with cable boxes—sending commands, verifying responses, and handling exceptions. It seemed simple in theory but was anything but in practice.

    Now, AI tools like Operator promise to navigate the web “just like a person,” and history is repeating itself. The demo makes automation look seamless, much like mine did years ago. The implicit message is the same:

    Automation is here. No manual effort is needed.

    But if my experience in test automation taught me anything, it’s that a smooth demo hides a much messier reality.

    The Hidden Complexity of Automation

    automations hidden effort ai machine learning operator

    At a high level, Operator achieves something conceptually similar to what I built for the test lab—but with modern machine learning. Instead of writing scripts in C, it combines large language models with vision-based recognition to interpret web pages and perform actions. It’s a powerful advancement.

    However, the fundamental challenge remains: the real world is unpredictable.

    In my cable box testing days, the obstacles were largely technological. The environment was controlled, the navigation structure was fixed, and yet automation still required extensive validation steps, exception handling, and endless adjustments to account for inconsistencies.

    With Operator, the automation stack is more advanced, but the execution environment—the web—is far less predictable. Websites are inconsistent. Navigation is not standardized. Pages change layouts frequently, breaking automated workflows. Worse, many sites actively fight automation with CAPTCHAs2, anti-bot measures, and dynamic content loading. While automation tools like Operator try to solve these anti-bot techniques, their effectiveness and ethics are still debatable.3,4

    The result is another flashy demo in a controlled environment with a much more “brittle and occasionally erratic”5 behavior in the wild.

    The problem isn’t the technology itself—it’s the assumption that automation is effortless.

    A Demo Is Not Reality

    Like my manager, who saw a smooth test automation demo and assumed we could apply it to every test, many will see the Operator demo and believe AI agents are ready to replace manual effort for every use case.

    dm automation test hidden effort operator

    The question isn’t whether Operator can automate tasks—it clearly can. But the real challenge isn’t innovation—it’s the misalignment between expectations and the realities of implementation.

    Real-world implementation is messy. Moving beyond controlled conditions, you run into exceptions, edge cases, and failure modes requiring human intervention. It isn’t clear if companies understand the investment required to make automation work in the real world. Without that effort, automation promises will remain just that—promises.

    Many companies don’t fail at automation because the tools don’t work—they fail because they get distracted by the illusion of effortless automation. Without investment in infrastructure, data, and disciplined execution, agents like Operator won’t just fail to deliver results—they’ll pull focus away from the work that matters.

    1. https://help.openai.com/en/articles/10421097-operator
      ↩︎
    2. A CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a security feature used on websites to differentiate between human users and bots. It typically involves challenges like identifying distorted text, selecting specific objects in images, solving simple math problems, or checking a box (“I’m not a robot”). ↩︎
    3. https://www.verdict.co.uk/captcha-recaptcha-bot-detection-ethics/?cf-view ↩︎
    4. https://hackernoon.com/openais-operator-vs-captchas-whos-winning ↩︎
    5. https://www.nytimes.com/2025/02/01/technology/openai-operator-agent.html ↩︎

  • The White Whale

    The White Whale

    In Moby-Dick, Captain Ahab’s relentless pursuit of the white whale isn’t just a quest for revenge; it’s a cautionary tale about obsession. Ahab becomes so consumed by his singular goal that he ignores the needs of his crew, the dangers of the voyage, and the possibility that his mission might be misguided.

    This mirrors a common trap in problem-solving: becoming so fixated on a single solution—or even the idea of being the one to solve a problem—that we lose sight of the bigger picture. Instead of starting with a problem and exploring the best ways to address it, we often cling to a solution we’re attached to, even if it’s not the right fit or takes us away from solving the actual problem.

    A Cautionary Tale

    Call me Ishmael.1 – Herman Melville

    I once worked on a project to identify potential customer issues. The business provided the context and success metrics, and we were part of the team set out to solve the problem.

    After we started, an executive on the project who knew the domain had a specific vision for how the solution should work and directed us on exactly what approach to use and how to implement it. While their approach seemed logical to them, it disregarded key best practices and alternative solutions that could have been more effective.

    We ran experiments to test both the executive’s approach and an alternative, using data to demonstrate how a different approach produced better results and would improve business outcomes.

    But the executive was undeterred. They shifted resources and dedicated teams to their solution, intent on making it work. We continued a separate effort in parallel but without the resources or backing of the received by the other team.

    The Crew

    Like the crew of the Pequod, the teams working on the executive’s solution were initially excited about the attention and resources. They came up with branding and a concept that made for good presentations. The initial few months were spent creating an architecture and building data pipelines under the presumption that the solution would work. Each update gave a sense of progress and success as items were crossed off the checklist.

    That success, though, was based on output, not outcomes. Along the way, the business results weren’t there, and team members began to question the approach. However, even with these questions and the evidence that our approach was improving business outcomes, the hierarchical nature of the commands kept the crew from changing course.

    The Prophet

    In Moby Dick, Captain Ahab smuggles Fedallah, an almost supernatural harpooner, onto the ship as part of a hidden crew. Fedallah is a mysterious figure who serves as Ahab’s personal prophet, foretelling Ahab’s fate.

    Looking for a prophet of their own, our executive brought in a consulting firm to see if they could get the project on track. The firm’s recommendations largely mirrored those of our team. However, similar to Fedallah’s prophecies, the recommendations were misinterpreted. What we saw as clear signals to change course, the executive saw as a chance of success and doubled down on their solution.

    The Alternate Mission

    Near the end of the novel, the captain of another vessel, the Rachel, pleads with Ahab to help him find his missing son, lost at sea. Ahab refuses because he is too consumed by his revenge. Ultimately, the obsession costs Ahab his life as well as those of his crew, with the exception of Ishmael, who was, ironically, rescued by the Rachel, the whaling ship that had earlier begged Ahab for help.

    We tried to bridge the gap between the two efforts for years, but the executive’s fixation on their solution made collaboration impossible. We made a strong case using data to change the mission from making their solution work to refocusing on the business goals and outcomes. Unfortunately, after many attempts, we weren’t able to convince them or affect their bias and feelings that their solution should work. Too many claims had already been made, and too much had been invested to change course. The success of their solution was the only acceptable end of the journey, with that success always being just over the horizon.

    A Generative White Whale

    I’ve been thinking about this story lately because I see the same pattern happening with generative AI. Just as Captain Ahab chases Moby Dick, many companies chase technological solutions without fully understanding if those solutions will solve their real business problems.

    Since ChatGPT was launched to the public in 2022, there has been pressure across industries to deliver on generative AI use cases. The impressive speed at which users signed up and the ease at which ChatGPT could respond to questions gave the appearance of an easy implementation path.

    Globally, roadmaps were blown up and rebuilt with generative AI initiatives. Traditional intent classification and dialog flows were replaced with large language models in conversational AI and customer support projects. Retrieval-augmented generation changed search and summarization use cases.

    Then, the world tried to use it. Everyone quickly learned that the models didn’t work out of the box and underestimated the amount of human oversight and iteration needed to get reliable, trustworthy results.2 We learned that their data wasn’t ready to be consumed by these models and underestimated the effort required to clean, label, and structure the data for generative AI use cases. We learned about hallucinations, toxic and dangerous language in responses, and the need for guardrails.

    But the ship had sailed. The course had been set. Roadmaps represent unchangeable commitments3. The mission to hunt for generative AI success continued.

    What started with use cases with clear business outcomes inherited from the pre-generative AI days started to change. Rather than targeting problems that could significantly impact business goals, the focus shifted to finding problems that could be solved with generative AI. Companies had already invested too much time, money, and opportunity cost, and they needed to deliver something of value to justify the voyage.4,5

    It became an obsession.

    A white whale.

    Chasing the Right Whale

    I try all things, I achieve what I can.6 – Herman Melville

    That’s not to say there isn’t a place for generative AI or other technology as possible solutions. I’ve been working with AI for almost a decade and have seen how it can be truly powerful and transformative when applied to the right use case that aligns with business outcomes and solving customer or business problems.

    Experimenting with the technology can foster innovation and uncover new opportunities. However, when the organization shifts focus away from solving its most critical business problems and towards delivering a solution or leveraging a specific technology for the sake of the solution or the technology, misalignment between those two paths and choosing the wrong goal can put the entire mission at risk. The mission should always be the success of the business, not the technology.

    That’s the difference between chasing the white whale and chasing the right whale.

    Assess Your Mission

    The longer a project goes on, the more likely it will veer off course. Little choices over time make small adjustments to direction that can eventually lead to being far away from the intended destination. The same thing can happen with the overall mission. Ahab started his journey hunting whales for resources and, while he was still technically hunting a whale, his mission changed to revenge. If he took the time to reassess his position and motivation, Moby Dick would have had a less dramatic ending.

    As product and delivery teams, it’s healthy practice to occasionally look up and evaluate the current position and trajectory. While there may be an argument for intuition in the beginning, as more information becomes available, it’s important to leverage data and critical thinking rather than intuition and feelings which are more prone to bias.

    These steps can help guide that process.

    1. Reaffirm Business and Customer Priorities.

    Align leadership around the most critical problems. Start by revisiting the company’s core objectives and defining success. Then, identify the biggest challenges facing the business and customers before considering solutions.

    2. Audit and Categorize Existing Projects

    Identify low-impact or misaligned projects. List all ongoing and planned AI initiatives, categorizing them based on:

    • Business impact (Does it solve a top-priority problem?)
    • Customer impact (Does it improve user experience or outcomes?)
    • Strategic alignment (Is it aligned with company goals, or is it just chasing trends?)

    An important factor here is articulating and measuring how the initiative impacts business and customer goals rather than relates to a business or customer goal.

    For example, a common chatbot goal is to reduce support costs (business goal) by answering customer questions (customer goal) without the need to interact with a support agent. A project that uses generative AI to create more natural responses might look like it’s addressing a need, but it assumes that a more conversational style will increase adoption or improve outcomes. However, making responses more conversational doesn’t necessarily make them more helpful. If the chatbot still struggles with accurate issue resolution, customers will escalate to an agent anyway.

    3. Assess Generative AI’s Fit

    Ensure generative AI is a means to an end, not the goal itself.

    Paraphrasing one of my mantras I would use when a team approached me with an “AI problem” to solve:

    There are no (generative) AI problems. There are business and customer problems for which (generative) AI may be a possible solution.

    For each project, ask: Would this problem still be worth solving without generative AI?

    If a generative AI project has a low impact, determine if there’s a higher-priority problem where AI (or another solution) could create more value.

    4. Adjust the Roadmap with a Zero-Based Approach

    Rather than tweaking the existing roadmap, start from scratch by prioritizing projects based on impact, urgency, and feasibility.

    Reallocate resources from lower-value AI projects to initiatives that directly improve business and customer outcomes.

    5. Set Success Metrics and Kill Switches

    Define clear, measurable success criteria for every project. Establish a review cadence (e.g., every quarter) to assess whether projects deliver value. If a project fails to meet impact goals, have a predefined exit strategy to stop work and shift resources.

    This structured approach ensures that AI projects are evaluated critically, business needs drive technology decisions, and resources are focused on solving the most important problems—not just following trends.

    Conclusion

    The lesson of Moby-Dick is not just about obsession—it’s about losing sight of the true mission. Ahab’s relentless pursuit led to destruction because he refused to reassess his course, acknowledge new information, or accept that his goal was misguided. In business and technology, the same risk exists when companies prioritize solutions over problems and fixate on a specific technology rather than its actual impact.

    Generative AI holds incredible potential, but only when applied intentionally and strategically. The key is to stay grounded in business priorities, customer needs, and measurable outcomes—not just the pursuit of AI for AI’s sake. By regularly evaluating projects, questioning assumptions, and ensuring alignment with meaningful goals, teams can avoid chasing white whales and steer toward solutions that drive success.

    The difference between success and failure isn’t whether we chase a whale—it’s whether we’re chasing the right one.

    And I only am escaped alone to tell thee.7 – Herman Melville

    1. “Call me Ishmael.” This is one of the most famous opening lines in literature. It sets the tone for Ishmael’s role as the narrator and frames the novel as a personal account rather than just an epic sea tale. ↩︎
    2. https://www.cio.com/article/3608157/top-8-failings-in-delivering-value-with-generative-ai-and-how-to-overcome-them.html ↩︎
    3. Roadmaps are meant to be flexible and adjusted as priorities and opportunities change. ↩︎
    4. https://www.journalofaccountancy.com/issues/2025/feb/generative-ais-toughest-question-whats-it-worth.html ↩︎
    5. https://www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025 ↩︎
    6. This quote from Ishmael reflects a spirit of perseverance and pragmatism, emphasizing the importance of effort and adaptability in the face of challenges. ↩︎
    7. The closing line of the novel echoes the biblical story of Job, in which a lone survivor brings news of disaster, underscoring the novel’s themes of fate, obsession, and destruction. ↩︎