An overview of what’s changed in engineering during the last six months, how various tech companies are changing how they work, and why slowing down could be a sensible strategy
Speed: How It Explains the World - Vaclav Smil
In a world that feels like it’s moving faster than ever, Smil examines how our relentless pursuit of speed – in areas such as production, travel and communication – shapes not only our technological landscape but also our social and environmental realities. And through engaging anecdotes and striking statistics, Smil challenges the assumption that faster is always better.
100 years of whatever this will be
What if all these weird tech trends actually add up to something? Last time, we explored why various bits of trendy technology are, in my o...
Scaling Engineering Teams: Lessons from Google, Facebook, and Netflix
After spending over a decade in engineering leadership roles at some of the world’s most chaotic innovation factories—Google, Facebook, and Netflix—I’ve learned one universal truth: scaling e…
Wrap to 2025, Proem to 2026
Technological Paradigm Shifts are fundamental changes in technology that reshape industries and society.
Software Acceleration and Desynchronization
A look at the ever-present drive to make software delivery faster and how it might break down various activity loops in organizations.
There's no reason for software to be slow anymore
The other day, I saw a viral tweet saying that people talking about how LLMs are causing slow, bloated, code are going to eat crow once they re-write everything in super-optimized assembly. We're not quite at the point where we want to write everything in assembly, but some variant of what Nolan Lawson said about testing, you can choose how many bugs you want now, which I less eloquently noted here, is becoming more true for performance.
Deep research requires a slower pace than tech industry work
Anyone working in an industry for a while will become accustomed to that culture—its processes, its norms, its values, its tacit knowledge. Much of this is incredibly valuable, of course, but these ideas can also represent constraints. There are some important impedances here between tech industry culture and research culture. In particular, tech culture is calibrated to a much faster pace. This can lead to impatience or early abandonment when confronting problems which require a researcher’s pace.
Deep research requires a slower pace than tech industry work
Anyone working in an industry for a while will become accustomed to that culture—its processes, its norms, its values, its tacit knowledge. Much of this is incredibly valuable, of course, but these ideas can also represent constraints. There are some important impedances here between tech industry culture and research culture. In particular, tech culture is calibrated to a much faster pace. This can lead to impatience or early abandonment when confronting problems which require a researcher’s pace.
Engineering managers have a new job description
Why engineering managers are expected to be hands-on again and how AI tools are making it possible to stay technically sharp while still leading.
Resetting on engineering expectations in the age of AI
I’m accepting new sponsorships for The Modern Leader for 2026. If you’re interested (or you think your company may be interested), you can learn more here.
The Engineering Leadership Report 2026
Our survey of 600 engineering leaders exploring how the role is evolving, where the challenges lie, and what the future holds for the job.
Engineering Career Paths at Big Tech and High-Growth Startups
Levels at big tech, the most common career paths, and what comes after making it to Staff
🐌 Slow Software for a Burning World 🔥
In a world of “move fast and break things,” we’ve chosen a different tempo — one rooted in care, deep listening, and collective stewardship. Slow software means building for long-term resilience and meaningful participation, rather than chasing novelty, speed, or scale.
The AI Engineering Report 2026: The AI Acceleration Whiplash - Ten Takeaways
What two years of telemetry data from 22,000 developers reveals about AI's real impact on developer productivity, code quality, and business risk in 2026.
