Mike Belsito

Sidney Crosby letting this Canes fan hear it after going on the power play 😆

Sidney Crosby letting this Canes fan hear it after going on the power play 😆

Everyone's saying how new grads face the toughest job market yet. Maybe so, but if they're AI pilled I think they're way more employable than experienced people who are used to doing things the old way. And if the job market is truly bad, then they should just start a company and go pursue their dream instead.

Question: Given that all PMs will eventually have access to the same AI tools, how do I differentiate myself as a product manager? I get this question a lot. And I don't love the shallow answer going around that "all that matters is taste." Taste is definitely important, but here's my far more concrete playbook for differentiating as a PM in the age of AI: 1. Stay at the frontier of AI fluency - I think too many people are dismissing this one saying that "everyone is going to have access to the same tools." But I'm a year and a half into this and I can tell you the gap is only widening on folks who can wield AI well in their job vs those that can't. And I don't see that changing anytime soon. So the people best positioned are the ones that know how to use AI effectively to produce great output, which is no easy task. 2. Taste / high standards / judgment - This is the one everyone talks about and I agree it's important. For example, I recently showed off 13 AI PM skills I built in Claude Code. What I didn't show was the 16 others that I tried to build but ultimately threw away because the output didn't meet my bar. I'm seeing lots of other people ship these skills and just accept the low quality output coming out of them. This is a mistake. The first battle is knowing what great product work looks like. The second battle is continuing to hold yourself to that standard. Don't ship slop. 3. Domain expertise - As the functional aspects of the role become more commoditized, I do think domain expertise in a given field becomes even more important. I don't think it's a fluke that a cardiologist beat experienced software developers in Anthropic's recent vibe coding contest. It's because his deep knowledge in the domain allowed him to come up with such a compelling solution to the post-visit patient problem that he deeply understood. Only a domain expert could do that. 4. Product strategy - AI is terrible at product strategy. I've tried every which way and it never comes up with a compelling, differentiated product strategy that has any chance of winning the market. I think that's going to be the case for awhile. So it's a great area to continue to build your muscle. 5. Design - The advancements coming out of Gemini, etc is impressive, but I still can't get AI to match the world-class designers I've worked with in my career. Especially on interaction design, not just visual design. Learning these skills is still valuable.

in a world of agents, the product role is going to split into two jobs: - one that organizes humans (stakeholders, design, eng) - one that organizes agents (prompts, evals, workflows, etc) Both will be in pursuit of offering the right products to customers, but how you get there will dramatically change. What happens to the typical product rituals? Instead of PRDs, OKRs, standups, product reviews, we'll need the equivalent for agents. Couple wild ideas here... instead of standups: the equivalent is that agents will report back to us based on run logs and anomaly flags. no one needs to say what they did yesterday, the system already did thousands of things. the question is where it broke, where it surprised you, and where it got better. Show us the patterns, the trends, the edge cases - particularly the ones the agents didn't fix automatically. the daily ritual becomes reviewing deltas, scanning failures, and deciding which ones matter. less reporting, more triage instead of OKRs: we’ll need adversarial agents that continuously monitor/grade the system and detect patterns, scoring outcomes on an hourly or daily basis. Rather than setting a quarterly goal of "increase X by 5%" and revisiting slowly -- instead, management will be able to monitor success in real-time and detect trends/patterns towards overall goals instead of PRDs: we won't need waterfall. Prototyping will rule the day, and we’ll need a living agentic loop that mediates customer feedback/ratings and what's being prioritized and built. you don’t hand it to eng, you deploy it into the agent loop. if it’s wrong, it fails visibly and you can revert. if it’s right, it produces the right output instead of product reviews: we'll need simulation systems to examine agent behavior in different scenarios. In an agentic world where UI shifts from buttons/menus to agents automatically doing things, you'll want to examine their behavior before you deploy. You rewind decisions, fork alternate paths, and see how different prompts or constraints would have changed outcomes. the review becomes interactive. less storytelling, more counterfactuals. The PM sits in the middle of this split. On the human side, still aligning taste, risk tolerance, and strategy across people. On the agent side, shaping the actual behavior of the system through prompts, evals, and feedback loops. one side is persuasion. The other is instrumentation. the best ones will collapse the gap, translating intent directly into systems that act on it. the fascinating part is that the agentic loop will run 10000x faster than the human one, and of course, you can "hire" them faster. Thus the “organizing humans” half starts to feel slow and lower impact unless it directly improves the agent loop. Eventually the PM will shift towards agents and maybe ignore the human coordination altogether...

CEOs/CTOs: one of the most important lens through which you should pass your decisions right now is “which of the options is more agent-friendly”. Whether it’s a programming language choice, system architecture path, or product design decision, the more agent-friendly path will win in the long term.

CANCEL your weekend plans. You NEED to: • Learn Claude Code • Learn Cowork (build 1-2 practical workflows) • Set up Perplexity Computer/Perplexity Finance • Optimise Cowork (plug-ins + skills) • Set up OpenClaw • Test Google AI products (Nano Banana 2, NotebookLM & more) • Experiment with basic agentic solutions (Manus) • Use AI to create a business plan/strategy/context files • Build an AI second-brain database (Notion) • Experiment with Notion Agents' *brand new* • Learn basic automation tools (MCPs, Zapier, n8n) • Learn prompt engineering - the better you can communicate with AI, the better your Outputs • Read AI articles • Dive into robotics • Research AI stocks/ETFs/investment arbitrages You have way too much to do...