Hey, Luca here! Welcome to a new edition of the 💡 Monday Ideas 💡 — ideas and readings to start the week on the right foot.
I also publish an original essay every Wednesday. Here is the one from last week in case you missed it:
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🛝 Governance can make you faster
Many teams feel conflicted about putting real AI governance in place, because it looks like a brake at a moment where everybody expects you to accelerate.
I have a contrarian take: I believe good governance can be an accelerant.
In 2006, Peter Summerlin ran a simple study about kids in playgrounds. Teachers took kids to a playground with no fence, then to a comparable one with a defined border. Without the fence, the children stayed huddled around their teacher, afraid of leaving her sight. With the fence, they felt free to explore within the boundaries. So in playgrounds with fences, kids used more space.
People work the same way. Once they know where the fences are, they feel empowered to do more within them. But that’s only true if governance is good.
We already see this in coding. We talk about guardrails for AI coding all the time, and having strict guardrails is exactly what allows me to be more liberal with the tasks I give to my agents, and with what they can ship.
Data also seems to point the same way. A late-2025 Google Cloud / CSA study looked at agentic AI adoption by governance maturity:
Full governance → 46% adoption
Partial governance → 25% adoption
Still in development → 12% adoption
Correlation is not causation, but it makes it hard to look at governance as just a tax.
I wrote a full guide about how to get started with AI governance, including what you should govern and how rules graduate from common sense to software 👇
🚲 Put gears, not modes
In July I talked with Geoffrey Litt, design engineer at Notion, about malleable software, and he shared one of his favorite product design parables.
Imagine a bicycle whose gears are labeled not with numbers, but with modes like “gravel gear” and “uphill gear”. It sounds user-friendly, but it’s actually terrible. What do you do when you’re going uphill on gravel?
When you hide the underlying model behind labels, you take away the mental model people need to develop to handle new situations. And humans are smart! They can learn numbered gears, and then combine that knowledge creatively.
What malleable software means is having a smaller set of primitive components that can be reassembled to make anything, and doing a really good job introducing and teaching those components to everyone using the software, just like bicycle gears.
Notion is a good example of this. Every paragraph, heading, and image is a self-contained block you can drag, nest, and sync across pages. It’s a small learning cost upfront that keeps paying back over time, and now it pays back with agents too: they reuse the same primitives, so Notion agent instructions are just Notion pages.
You can find the full conversation with Geoffrey here 👇
📚 Weekly Readings
Finally, here are the best articles I have read this week:
🥇 On the Death of Amazon’s Writing Culture
7 min • by Steve Huynh
Steve spent almost twenty years at Amazon, and he argues that its famous writing culture didn’t survive AI.
Bezos used to say you can’t write a six-page memo without clear thinking, and that is simply not true anymore, so a doc is no longer proof that someone has thought about the problem. Food for thought.
🥈 Someone Behind the Software
3 min • by Karri Saarinen
Karri argues that good products carry something of the people who make them, and that many products today feel messy because adding new capabilities is much easier than rethinking the existing ones. This is a great take, that I also needed to hear for Tolaria.
🥉 How to Be More Ambitious: A Tutorial for Humans With Models
9 min • by Felix Rieseberg
Felix works on Claude, and wrote a practical guide to using AI for more than doing the same work faster. He splits ambition into altitude, depth, and range. For example he went from asking Claude to fix single crashes to asking it to keep the apps crash-free, which first meant giving it crash reports and machines to reproduce bugs on. Lots of good prompts to steal, too!
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I wish you a great week.
Sincerely 👋
Luca
The fence idea matches my experience, with one failure mode worth adding: a fence that is inert without anyone noticing. I had a block list that was empty in one environment, and an empty list looks exactly like "nothing to block". Same with a setting that did nothing for months because no test would fail if its value changed. What fixed it was resolving the list per environment and failing loudly when the reference doesn't exist, plus a test that breaks if the setting changes. A fence nobody exercises is decoration.
Agree on the coding part, with one catch. A fence the agent can't see is just a wall it keeps hitting. The guardrails that actually sped me up were written down where the agent reads them, plus a check that fails loudly when it crosses one. How do you keep the written rules and the enforced ones from drifting apart?