Software engineer; co-founder and former CTO of Tailscale, previously on the Go team at Google.
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9 of David Crawshaw's claims fit 9 mental models. Most often: Activation energy, Cheaper means more, not less, Everything decays without maintenance.
Starting is the hardest part, and the bigger the change, the more energy it takes to begin.
Their wordsMost projects fail before they start. They fail because the activation energy is too high.
Look at what people are rewarded for; it explains more behaviour than what they say, your own included.
Their wordsThere is no incentive for the human driving the agent to actually read the code or spend time thinking about what the reviewer says.
Most growth slows as it matures; the question is whether the curve is flattening or something new is restocking it.
Their wordsAt some point frontier models will face diminishing returns, local models will catch up, and we will be done being beholden to frontier models.
Much of what people do is a message about themselves, and a signal is believed only when it costs something to send.
Their wordsthe reviewer is the principal, the contributor is the agent, and code review only worked because the reviewer could cheaply infer effort from reading the code. Agents collapse that signal.
Every choice gives something up, so name the cost; and check, because some trade-offs everyone assumes are not real.
Their wordsProgramming is the art of tradeoffs. You have to decide what matters and what does not.
Make something cheaper to use and people use so much more of it that the total goes up (Jevons paradox).
Their wordsAgents, by making it easiest to write code, means there will be a lot more software.
Institutions, code, cities and knowledge run down by default; keeping something alive is work that has to be paid for.
Their wordsVery few programs ever reach the point that they are heavily used and long-lived. Almost everything has few users, or is short-lived, or both.
A shortcut that hides the details works until it doesn't; when it breaks, someone has to know what is underneath.
Their wordsYou cannot solve the fundamental problems with cloud abstractions by building new abstractions on top.
Trust the judgment of people who pay for being wrong over people who only comment.
Their wordsthere is no principal-agent problem, because the human driving the machine takes on the responsibility for its actions by owning the deployment.