A breakdown of what software development teams actually cost, what they need to generate to be financially viable, and why most organizations have no visibility into either number.
How teams build – Linear
AI usage patterns in software teams: who is adopting AI, how it reshapes where teams spend their time, and how much more they ship.
A Guide to Measuring Engineering Team Performance
“You can’t manage what you can’t measure.” While software development practices constantly change, there will always be a tier of truly top engineering teams who stand above…
Pure and impure software engineering
Why do solo game developers tend to get into fights with big tech engineers? Why do high-profile external hires to large companies often fizzle out? Why is AI-assisted development amazing for some engineers and completely useless for others?
Theory and Memory: Two Forces Shaping Software Team Knowledge
How insights from cognitive science and social psychology explain why software knowledge is so hard to preserve
Why AI Makes Things Worse for Enterprise Teams, by Paul Ford
Why are so few engineering teams reaping the benefits of AI? On this week’s episode, Paul presents Rich with the findings from a recent report from CircleCI and
Laws of Software Engineering
A collection of principles and patterns that shape software systems, teams, and decisions.
AI makes weak engineers less harmful
Like other kinds of puzzle-solving, software engineering ability is strongly heavy-tailed. The strongest engineers produce way more useful output than the average, and the weakest engineers often are actively net-negative: instead of moving projects along, they create problems that their colleagues have to spend time solving. That’s why many tech companies try to build a small, ludicrously well-paid team instead of a large team of more average engineers, and why so far this seems to be a winning strategy.
Augment Code Pricing - Plans for Teams and Enterprise
Simple, transparent pricing for AI-powered development. Plans for individuals, teams, and enterprise.
What We Know We Don't Know: Empirical Software Engineering
Empirical Software Engineering is the study of what actually works in programming. Instead of trusting our instincts we collect data, run studies, and peer-review our results. This talk is all about how we empirically find the facts in software and some of the challenges we face, with a particular focus on software defects and productivity. Talk doesn’t seem to be online yet; in the meantime, you can see a recording of an older version of the talk here.
Open Source is a cost-allocation system
Because the right to use the software is not tied to payment, the license does not connect the people who benefit, the people who decide, and the people who bear the costs. Projects have to build those connections deliberately through governance.
Large tech companies don't need heroes
Large tech companies operate via systems. What that means is that the main outcomes - up to and including the overall success or failure of the company - are driven by a complex network of processes and incentives. These systems are outside the control of any particular person. Like the parts of a large codebase, they have accumulated and co-evolved over time, instead of being designed from scratch.
The Real Reason We Still Need Software Developers in the World of AI
The dream of AI churning out perfect production-ready code doesn’t hold up against the reality of modern software development.
Software Engineering Productivity Research - Home
Open Source Software and Corporate Influence — Andrew Lilley Brinker
Open source software projects are frequently enmeshed with the interests of corporations. We should update mental models of who works on open source accordingly, and build or modify power structures to be more resilient to corporate capture.
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…
Microsoft reports are exposing AI's real cost problem: Using the tech is more expensive than paying human employees | Fortune
Companies are racing to incentivize employees to use AI. But as some companies are finding, the more employees that use the technology, the heavier the bill.
