Melissa Perri chimes in on how AI is changing the product role, and makes the case for measuring PMs by decisions changed and outcomes shipped, not by tickets written and docs generated:

If you are a PM, stop measuring your productivity by how many tickets you wrote, how many pages of documentation you spun up, or how fast you closed the loop on the last sprint. That work is going to keep getting easier.

Measure your productivity by how often you changed a decision that mattered, how often you saw around a corner, how often a senior leader walked out of a room thinking differently because of something you said. How often your shipped features translate into real customer outcomes is what matters.

Everything I read is saying the same thing right now: judgment, customer understanding, and the ability to change a senior leader’s mind in a room are the skills that AI can’t touch. I’m not disagreeing necessarily, but I do think that narrative is missing a big new skill that is needed. I wrote about this in What actually changed about being a PM:

I was talking to my wife the other day about what I’m doing, and she asked the obvious question: “Why are you automating your job away?” My answer: the people who automate their own jobs away are the ones who become more valuable, because the craft is now in orchestration — setting up the layers so the AI does the right thing.

I also continue to think about this quote from Org Design in the Age of AI and how the focus is shifting from “information movers” to builders:

The old PM spent most of their energy making ideas legible to other people. The new PM validates directly — prototyping, running data analyses, generating first-pass implementations. […] The managers who thrive will be the ones whose real contribution was always judgment, coaching, and navigating ambiguity — not routing information.

Carson Gross has a good essay on whether junior programmers should still learn to code given how capable AI has become. His core warning to students:

Yes, AI can generate the code for this assignment. Don’t let it. You have to write the code. I explain that, if they don’t write the code, they will not be able to effectively read the code. The ability to read code is certainly going to be valuable, maybe more valuable, in an AI-based coding future. If you can’t read the code you are going to fall into The Sorcerer’s Apprentice Trap, creating systems you don’t understand and can’t control.

And on what separates senior engineers who can use AI well from those who can’t:

Senior programmers who already have a lot of experience from the pre-AI era are in a good spot to use LLMs effectively: they know what ‘good’ code looks like, they have experience with building larger systems and know what matters and what doesn’t. The danger with senior programmers is that they stop programming entirely and start suffering from brain rot.

This maps directly onto what I’ve been writing about with AI for product work and the second brain setup I’ve built. The system works because I spent years writing and reading PRDs, strategy docs, and OKRs—enough to develop actual opinions about what good looks like. You have to do the work first, then the second brain is worth building.

Matt Shumer’s Something Big Is Happening has made the rounds over the last couple of weeks, but just in case you haven’t seen it, I think it’s very much worth reading. He’s an AI startup founder writing for the non-technical people in his life:

AI isn’t replacing one specific skill. It’s a general substitute for cognitive work. It gets better at everything simultaneously. When factories automated, a displaced worker could retrain as an office worker. When the internet disrupted retail, workers moved into logistics or services. But AI doesn’t leave a convenient gap to move into. Whatever you retrain for, it’s improving at that too.

Previous waves of automation always left somewhere to go. The uncomfortable implication here is that the escape routes are closing as fast as they open.

There are too many quotes worth commenting on, but this observation about what we tell our kids feels important:

The people most likely to thrive are the ones who are deeply curious, adaptable, and effective at using AI to do things they actually care about. Teach your kids to be builders and learners, not to optimize for a career path that might not exist by the time they graduate.

Predictions about the pace of change tend to be simultaneously too aggressive and too conservative in ways that are hard to anticipate. But the direction feels right, and the practical advice is sound: use the tools seriously, don’t assume they can’t do something just because it seems too hard, and spend your energy adapting rather than debating whether this is real.

Geoffrey Huntley wrote about what happens when people finally “get” AI:

If you’re having trouble sleeping because of all the things that you want to create, congratulations. You’ve made it through to the other side of the chasm, and you are developing skills that employers in 2026 are expecting as a bare minimum.

The only question that remains is whether you are going to be a consumer of these tools or someone who understands them deeply and automates your job function? Trust me, you want to be in the latter camp because consumption is now the baseline for employment.

Knowing how to use these tools is no longer a differentiator. The gap is between people who consume AI outputs and people who understand the systems well enough to build on top of them.

For product managers, this means that prompting ChatGPT for a first draft doesn’t count as an AI skill anymore. The question is whether you can wire together agents, automate your own workflows, and spot opportunities others miss because they’re still thinking in manual processes.

Scott H. Young has a thoughtful piece on what’s still worth learning in a world with AI. He cuts through both the panic and the hype to look at what the data actually shows. The biggest finding is probably not all that surprising: early-career workers in AI-exposed fields are getting hit hardest.

Another report from the Stanford Digital Economy Lab notes that early-career workers in AI-exposed fields (such as programming) have seen a relative decline in employment, even as employment among workers aged 30 years and older increased. This matches my intuition that AI coding agents can do a lot of junior developer tasks pretty well, but struggle to match the experience needed to tackle more serious work.

Young’s advice is to cultivate generalist skills. Not the content-free “critical thinking” kind, but genuinely transferable knowledge:

In an environment of change, it’s better to be the hardy dandelion rather than the hothouse orchid. Similarly, I expect with AI-induced change, people who have maintained diverse interests and skills will be best positioned to take advantage of the change, whereas extreme specialists will face a greater risk of extinction.

Some tough love here about what it means to have “executive presence”.

When someone tells you that you need more business sense, or that you’re not ready for more scope, or that you need to level up, this is typically what they’re trying to communicate. That you’re more concerned with how work happens than with what work should happen in the first place.

Source: The price of admission →

A great list of things to keep in mind when communicating via writing. The article is focused on “managing up” but these principles are relevant in a much broader context as well.

What questions does your manager usually ask? Answer those questions yourself. If you take anything away from this article, make it this. Every manager has their own idiosyncrasies, worldview, values, etc. That’s why the best thing to do is to pattern match. Consider what they’ve asked you in the past, when talking to you or others. Try to give context through that lens.

Source: How I give the right amount of context (in any situation) →

This is an essay I think everyone should read, front to back. It’s about all the things we are living through right now, but it’s especially about work (and AI):

I’m not sure hiring can ever be much more efficient, because neither side has reason to show themselves as they really are, warts and all. Idealistically, both would come straight; pragmatically, it is a game of chicken. Candidates polish résumés and present curated versions of their abilities, listing outcomes and impact statistics with dubious accuracy and provenance. Companies do the same, putting culture and mission front and center while hiding systematic dysfunctions and looming existential risks. When neither side is forthcoming, you’re left with proxies: a famous logo on a resume, a polished culture deck.

Source: Selling Lemons →