When our muscles feel sore after a workout, it’s our body’s way of saying it’s adapting to stress, getting stronger, repairing itself to handle more next time.
When we challenge our muscles in ways they’re not used to, we create tiny micro-tears in the muscle fibers.
Our body responds with a mild inflammatory process – immune cells rush in to repair the damage, releasing chemicals that increase sensitivity and cause that familiar soreness.
Over the next few days, our muscles rebuild stronger and more resilient than before. The soreness, thus, is simply our body learning to handle more.
Growth, in the gym or in life, often feels like soreness first.
I loved this article about a Canadian entrepreneur who is producing paper from sugarcane fiber.
There are some fascinating nuggets – e.g., one large oak tree probably produces 5000 sheets of paper. Roy Minto’s company instead focuses on waste from the sugar manufacturing process – sugarcane “bagasse.”
While the paper production industry continues to decline with fewer people using paper, there’s still a lot of paper manufactured from hundreds of thousands of trees every year.
Paper made from such crop residue is already the norm in China, India, Mexico and many other countries. It makes full use of the crop while being significantly better for the environment.
I’m glad for the efforts of companies like Social Print. We need more of this.
We started watching MasterChef together as a family a year or two ago. After watching multiple seasons and recently finishing this year’s season, I’ve come to appreciate that, while we love the drama, we really love the lessons it offers about growth, performance, and mastery.
There are 3 lessons that stand out –
1. You can improve your performance by improving your attitude. In almost every season, there’s a familiar pattern. Early eliminations often fall into three categories:
Those with inflated self-images – their confidence far exceeds their skills.
Those with poor attitudes – these are often folks who have some talent but lack the humility.
And those who are unwilling to learn – these folks resist feedback from the judges, especially under pressure.
By contrast, the contestants who go far tend to stay calm under pressure. They listen, adapt, and keep improving.
2. The lesser the time available, the higher the ROI from organization. Whenever we see the judges cook, we always see them take the time to pause, think, and organize their stations before they start cooking. While the contestants inevitably rush in chaos, they move with quiet intent and keep things clean and organized along the way.
That little investment of planning time makes all the difference.
3. The beauty of mental models. Watching Gordon Ramsay cook is one of our favorites. His brilliance isn’t just in his taste or technique — it’s in his library of mental models. He knows instinctively what flavors pair well, what textures clash, and what compositions will fail.
That’s what expertise is – a deep set of mental models that let you separate signal from noise quickly and confidently.
It’s a delight to watch… and an inspiring reminder to build out my own mental models so the same clarity becomes instinctive in work and life.
After a few years, I restarted writing a newsletter focused on building technology products. I’m hoping to share a post every month.
It’s been years since I last wrote a product newsletter.
My first one – titled Notes on Product Management – was where I tried to make sense of my early years as a product manager. I wrote about roadmaps, storytelling, problem validation – all the messy lessons that come from learning the craft from the many mistakes and helpful counsel along the way.
I thought about writing again when the nature of the work expanded to managing PMs/PM leaders. But I never quite did even if I came close a couple times. Either the work was too consuming, or I just didn’t feel I had something new to say.
But that changed this year when I realized that the job has changed.
Not my title – I’m still a product manager – but the work itself feels entirely different. Especially when I compare how I spend my time now vs. 18 months ago.
Writing long-form has always been my way of thinking. I’ve loved that the word essay comes from the French essayer, meaning “to try.” Essays are how I try to figure things out. This series will be my way of figuring things out.
Also, while I realize that long-form writing feels a bit out of fashion in the era of podcasts and 30-second videos, it still feels like the right medium for reflecting on how the work is changing.
Why Write Now – A Counterpoint to AI FOMO
There’s another reason I wanted to write now. Some of the conversation around AI and product management has started to feel increasingly distorted. There’s an entire ecosystem emerging around AI FOMO and AI fear-mongering.
Depending on who you read, AI has already killed the PM role. Or saved it. Or will turn PMs into part-time engineers, designers, hackers, founders – and of course, prompt and eval savants.
Then there are the stories of the AI-native PM – the one who moves at superhuman speed, writing evaluations (“evals, evals, evals…”) before breakfast, shipping beautiful Cursor-powered prototypes and code on GitHub during the day, building incredible vibe-coded side projects in the evening, and operating on a different plane of existence. And, of course, they’re doing all of this at the hot AI-native startup that will save their career while the rest of the PM population goes extinct.
I’d like to offer a counterpoint.
This series will be decidedly anti-AI FOMO – and designed to get fewer clicks. My view is that these takes on the PM craft sound impressive, but are also often, in my opinion, performative.
That’s because they are often disconnected from the real, slow, iterative work of building products people actually rely on.
And because the work itself has changed, I want to focus on that real, iterative practice – not the performative mythology around it.
The Fundamental shift – Products Architected Around a Model
There are many takes on how products will be built in the future. I often find myself smiling whenever I see the confidence and certainty behind some of them. Because from where I sit, we’re still at the very beginning of something as fundamental as the Internet – and there’s very little that is certain.
That said, one thing does feel clear: we are moving from products that were architected around deterministic flows – predictable logic and carefully hand-crafted states – to products that are architected around a probabilistic model.
The model is no longer just a feature. Increasingly, the model is becoming the heart of the product. That is a fundamental shift to how we build, ship, and manage products.
My Thesis: Why LLM Recsys (Recommender Systems) Are the Core Primitive
At the center of this shift is a single primitive: a model or system that observes context, predicts intent, and recommends what should happen next.
That brings us to my thesis: Most AI-native products share the same underlying loop – and that loop is, fundamentally, a recommender system.
Once we strip away the hype, the interfaces, and the new tools, AI-native products need to: understand context, infer what the user is trying to accomplish, recommend the next step or action, and learn from every interaction.
That is the recommender-system loop. Except that, with LLMs/Large Language models, recommender systems are vastly more flexible and capable. A traditional ranking model used to just choose from a fixed index. An LLM-powered system can reason, generate, act, and orchestrate tools.
What about agents then? An agent is simply the most expressive version of a recommender system: a system that not only recommends what should happen next, but can take that action, explain it, refine it, or even ask questions to clarify intent.
The surface doesn’t matter – it could be a feed, a copilot, a customer service agent, an orchestrator that calls tools, LLM-powered search, or an inbox assistant. They all rely on the same underlying loop.
Even chat interfaces aren’t really “just generation.” Useful ones are making a choice: Should I summarize? Recommend? Rewrite? Plan? Ask a question? Trigger a tool? Take an action? That choice comes from a recommendation policy.
And so, as products shift from deterministic flows to loops that power recommender systems, product teams shift with them. Our job becomes designing the loops – the feedback, guardrails, policies, and signals – that shape how the LLM powered recommender system behaves.
A Note on Product vs. Product Management
A quick note on scope – I’m intentionally framing this series around product, not just product management.
Model-centric products blur the traditional boundaries between PM, design, engineering, data science, and AI/ML. When the core of a product is a learning loop – not a static workflow – every function shapes how the product behaves and improves:
Designers shape the interaction surfaces and conversation design
Data scientists architect the measurement and diagnosis of improvement opportunities
Engineers shape the orchestration and safety constraints
AI/ML teams ensure the model’s capabilities keep getting better
PMs ensure the policy and evaluation reflect the ideal end-to-end user experience
Everyone touches the loop. Everyone shapes how the product learns. While the disciplines remain deep and distinct, we definitely see a lot of cross-functional flex that, when managed well, result in a better product.
That’s why this series is about LLM Recommender Systems as a product primitive, not a PM technique.
What This Series Will Explore
Once you see AI-native products as learning loops rather than deterministic workflows, a number of implications follow:
Agents aren’t a new paradigm – they’re Recsys that can act
Product policy becomes as important as the PRD – a key step to designing a great human-in-the-loop system
Teams evolve around capabilities (“atoms”) and surfaces (“molecules”)
Evaluations shift from one-time tests to continuous diagnostics
Velocity depends on how well the system learns, not how fast teams ship
These are the ideas I’ll explore in upcoming editions – one concept at a time, likely once or twice a month.
This isn’t a guide or a manifesto from someone who’s figured it out. It’s a set of reflections written in motion – from someone who’s building, operating, and trying to understand this shift in real time.
There’s a lot changing, and a lot we don’t yet know. My goal is simply to ground in first principles, learn publicly, and share the process along the way.
If any of this resonates, I’d love to hear from you. The best part of writing is the dialogue that follows.
Building a large product is often like putting together a massive jigsaw puzzle.
You have people working on different ends – some on the left corner, some on the right, some deep in the middle. You begin with the end in mind, imagining what the final picture might look like, and then you start connecting the pieces.
In the middle of the journey, it’s messy. You’re just working through bottlenecks – sometimes on one edge, sometimes in the center, moving wherever the next piece demands your attention.
And then, every once in a while, there’s that magical moment when you zoom out. Suddenly, you can see the picture taking shape. The edges fit, the colors align, and the hours of agonizing over small details start to make sense.
It’s in that moment – when all the effort, doubt, and iteration come together – that you feel a quiet rush of joy.
Goosebumps, even.
It doesn’t last long. You admire the finished puzzle for a few seconds before you’re on to the next one. But if you’ve done it right – if you’ve enjoyed the process, the collaboration, the craftsmanship – those few moments make it all worth it.
One of the more important work lessons is to be someone who knows how to say no.
Every once in a while, you will see a horrible idea being proposed. When that happens, you need to be unequivocally clear that your answer is a “no way.”
In all the other cases, the goal isn’t to shut an idea down with a “no”. It’s to help others understand what it would take to get to yes, and to help them get there.
It’s an easily forgotten truth: the job isn’t about pointing out why something won’t work. It’s about using critical thinking and healthy skepticism to strengthen the eventual solution.
Saying no is easy.
Helping someone get to a better yes – that’s leadership.
It is amazing how the answer to so many conundrums in our lives and careers becomes so much clearer when we are able to answer the question – what are you solving for?
Beware spending your life seeking validation from people who don’t really care about you.
It’s one of the easiest traps to fall into – shaping your choices and maybe even your dreams around the approval of people whose opinions won’t matter in the long run.
When in doubt, focus on the validation of the person you see in the glass.
“You may fool the whole world down the pathway of years, and get pats on the back as you pass. But your final reward will be heartaches and tears If you have cheated the man in the glass.” – The Man In The Glass by Peter Dale Winbrow Sr
Someone said recently that their partner has a simple maxim – in the final analysis after this life is done, the only question you’ll be asked is – did you make the most of your gifts?
It is a beautiful question.
A reminder to do the best we can with what we have where we are.
A Learning a Day 