Everyone s been talking about AI Agents over the past year.

But the real leverage doesn t come from the agent itself. It comes from the context you provide:

One thing I rarely see discussed in B2B software is inspection fatigue.

Not user fatigue.

I've sat on both sides of a lot of interviews and some questions still puzzle me.

"Are you a leader or a follower?" is my pick. It's a false binary with an obvious right answer, so nobody says "follower" and the interviewer learns nothing. Good people are both depending on the situation, which is the one answer the question doesn't leave room for.

Curious what other people have run into. The ones with no right answer, the brainteasers that measured nothing, the questions that only tested how well you'd rehearsed. Or the opposite, if you've ever been asked something genuinely good that told them something real about you.

AI is everywhere right now - from copilots and chat assistants to analytics, research, and planning tools. But beyond the hype, I m curious about what s truly useful in day-to-day product work.

From a PM or founder perspective:

• Where has AI genuinely saved you time?

• What tasks do you trust AI with - and what do you never delegate?

• Has AI changed how you write specs, manage roadmaps, or talk to users?

• What AI use cases sounded great in theory but failed in practice?

Personally, I see a lot of potential, but also a lot of noise. I believe that in the future, AI should help us much more. Create good roadmaps, convert product specs into concrete tasks, prioritise them, assign people, push for realisation, and much more.

When mass layoffs started in tech, many people suggested that:

1. The layoffs were happening because, during COVID, companies hired too many people for online and remote roles.

2. That AI was attacking jobs.

And I still keep seeing statements from creators of various AI tools saying:
No, AI won t replace you. Employees will just have time for more meaningful tasks in a company.

Building and launching products, testing them in real markets and building a business are like mini-MBAs. It teaches you a lot of things about human behavior, finance, marketing, sales, entrepreneurship, management and more.

We become wiser; and wish someone had given us the right advice at the right time.

The pattern is everywhere and I'd built it without thinking about it. Before the native App Store rating sheet, show your own little screen. Enjoying the app? Happy face goes through to the review sheet. Sad face goes to a feedback form that emails you instead. Every growth writeup calls it a best practice, and it does measurably raise your average rating.
It raises it by making the negative reviews not happen. That's the entire mechanism. You're not collecting more sentiment, you're routing half of it into a private inbox where it can't reach anyone deciding whether to download. Once I'd said that out loud I couldn't keep it.
What replaced it is dumber and I like it much more. Two positive moments an actual save, an actual "this helped" bank a signal. On the second one the native sheet fires directly. No pre-ask, no fork, no branch where I get to decide who's allowed to count. iOS makes the final call on whether the sheet even appears, which I have zero visibility into and have made peace with. Sixty-day cooldown, three prompts lifetime, and if the sheet isn't available on that device the signal stays banked instead of burning a prompt.
Two things I got wrong on the way there. The first pass counted opening the app as a positive moment, which isn't a positive moment, it's a Tuesday it now only counts things the person actively chose to do. The second: the ask initially sat behind a paid surface, so the people most likely to be having a good time were the ones least able to be asked. Moving one of the two signals somewhere freely reachable mattered more than anything I did to the prompt itself.
The part I'm still unsure about: I removed my own read on sentiment and replaced it with nothing. Unhappy people used to land in a form. Now they land in the App Store, or nowhere. Has anyone kept an always-available feedback door open without it quietly turning back into the sad-face branch?

December is the month that officially closes the launch chapter of the year, so you still have a last chance to come up with something that will get attention.

Such December classics are definitely products with these narratives:

• Wrap-ups

• Advent Calendars

• Most searched topics

• Prognosis for the coming year

• Productivity and health apps (ready for New Year's resolutions)

I wrote a long essay following a talk I gave at AI DevCon in Brooklyn last month.

It starts out with an anecdote about hunting ChatGPT in December 2022 and goes on to explore what I think will be necessary to thrive as code becomes a commodity:

In December 2022, I hunted ChatGPT on Product Hunt.

It ranked #1 product of the day, then the week, and went on to be named Product of the Year.

Having co-founded a YC-backed conversational AI startup in 2018 (long before LLMs) I recognized in ChatGPT the missing ingredient that would have made that venture viable.

The future we d anticipated had arrived. I could revisit my old problem, or I could expand my area of potency by raising and deploying my own venture capital fund.

I chose the latter.

Three years later, on December 9th, I watched a 24-hour window on Product Hunt cross 500 launches roughly double what I observed throughout the preceding 825 days. Only 13 were featured; most were unremarkable.

The LLM has fundamentally shifted the economics of software development.

As someone with a dual vantage point being the #1 Product Hunter while investing in AI startups I watch the floodwaters rise in real-time.

What s become clear: SaaS is dying; VC is withering . Building software is not uniquely compelling. Code has become a commodity.

What most people miss about commoditization is that when a product or resource becomes abundant, it doesn t just get cheaper. It unlocks new and previously uneconomic uses.