*Hey, Luca here! Welcome to a new edition of the *
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Last week I published my own commentary on the Q2 AI Impact report by DX, which among other things, showed how quarterly median AI spend grew 28x year-over-year!
My expectation is that this AI honeymoon will not last forever, and tech leaders may already face tough budget conversations in the back half of 2026. That’s especially because it doesn’t seem that such spend is leading to outrageous outcomes so far.
But we’ll see! You can find the full report below 👇
Especially in tech, networking often gets framed in a very transactional way. You meet people because one day you may need a referral, an intro, a customer, or a job.
I think this is exactly the wrong way to look at it.
I have found that the best relationships are created before there is any immediate need attached to them. You build rapport around common interests, you understand what the other person cares about, you are helpful when you can, and you do not keep score.
Long-term, this is both 1) the better behavior as a human being, and 2) the better strategy.
Because you never really know who will be able to help you in the future, or how. Opportunities often come through weak signals, second-order connections, and people who simply remember that interacting with you was... pleasant. Before it was any useful.
So my advice is simple: do not wait until you need something to show up.
But how?!
It can be anything, really. Share useful material (”I read this article today and thought you might like it”). Make intros. Reply thoughtfully. Follow up when someone is working on something interesting. Be generous in small ways that compound over time.
I wrote more about this in this full piece on growing your network 👇
A lot of team process work is not actually process work. It’s copy-paste disguised as process.
Raise your hand if you ever updated a project management ticket because the real status lives elsewhere. Or you asked an engineer for a delivery date because the current dashboard is stale and can’t be trusted.
In my conversation with Antonia Scheidel, Director of Engineering at Duolingo, I liked her distinction between two kinds of information:
Information that already exists somewhere****Information that only exists in people’s heads
The first kind should usually be automated. E.g.:
If an experiment was launched on the appropriate
experimentation platform, humans should not have to copy that into Jira.If a stakeholder needs to know that a step is ready for review, the system should notify them.
The second kind, instead, still needs people.
In normal teams’ workflows, there are plenty of judgment calls that cannot be reliably inferred from a tool, and that’s where you want to direct human attention. Things like delivery estimates, blockers, timeline changes, tradeoffs.
So, the shape of good automation looks like:
Connect the systems of record
Remove the glue work
Ask people for the (remaining) information that genuinely requires their judgment.
Antonia built this successfully at Duolingo, and you can find the full interview here 👇
Finally, here are the best articles I have read this week:
A detailed, real-world write-up about the impact of AI on an engineering team’s code reviews. PRs went from 200 to 800 lines on average, and reviewers inevitably started missing more things. Worth checking out.
Ben makes a sharp argument that open models may win for the same reasons open infra usually wins: distribution, portability, and permissionless innovation. I am not sure the geopolitical framing is the whole story, but I really like the business point.
Tiny post, big ideas. Agents make throwaway code extremely cheap, so entire categories of “not worth it” automation suddenly become worth trying. Simon makes the example of reverse-engineering home devices, which I have tried myself many times! If AI makes weird small projects viable, I am all for it.
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See you next week!
Luca
