Who?I’m a software engineer and entrepreneur focused on modern web technologies and AI.
Here's an ongoing autobiography, which also shares the story of my by-the-bootstraps "unschooling" education: now the subject of a chapter on grit and resilience in the bestselling book Mindshift by Barbara Oakley.
An angel investor once described my core soft skill in the role of founder or early team member as: "The ability to perceive exactly what needs to be done. And then to do it."
My experience working in difficult environments around the world means that I can be trusted to get things done, even when things go wrong.
In the past, I coined the term "Startup Cities" as co-founder of StartupCities.org and a startup spinoff, both of which focused on why startups should build cities. I now write about Startup Cities at StartupCities.com
I've won several awards for economic research and have been published or interviewed in Virgin Entrepreneur, a16z's Future.com, The Atlantic's CityLab, Foreign Policy, and in academic volumes by Routledge and Palgrave MacMillan.
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This is my personal portfolio, inspired by the question: "What would the opposite of the two-color template developer blog look like?"
Have fun exploring!
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Contact:hello @ zach.dev
Is AI an Equalizer?
Is AI like Robin Hood, robbing the knowledgeable to make the less-skilled smarter? Or is AI like compound interest, where the smarter you are the smart AI will make you?
Somewhat depressingly, this recent MIT study argues that AI is not an equalizer.
Top performers who used AI doubled their output. The bottom third saw little change. The difference? More skilled performers were better at evaluating AI outputs.
I want to believe that AI is an equalizer. It surely is in some domains. But my experience with AI programming tools so far shows a similar "rich get richer" dynamic.
My most skillful friends are doing 3-5 days of work in 1 day. But several have told me privately that AI is destroying collaboration on their teams.
Less skilled engineers are blasting buggy, incoherent-but-correct-looking code at warp speed.
Tools like Cursor (or, my favorite, Claude 3.5 Fast Edit in Zed!) offer insane productivity gains.
But they can also be weapons of mass destruction in a codebase.
A few clicks inserts hundreds of lines of good-looking but wrong code. It's like the stereotype of the bad Management Consultant that says obviously false things at 110% confidence.
Without good taste, nonsense looks correct. So what do about taste?
Taste is hard to train. It's hard to explain. It's classic "tacit knowledge" that seems to arise from repeated exposure to many patterns. (Ironically, not unlike how AI models are trained.)
Let's assume AGI doesn't replace software engineering entirely. How can teams use AI tools, support junior engineers, and ship great code? I can think of a few implications:
PR reviews will become even more important but also more laborious because problems will hide better. (At least until AI can do the reviews, too.)
Testing will grow in importance and net test coverage will increase. It's easier than ever to generate tests. And they're insurance against good-looking-but-wrong AI code. (Especially end-to-end and integration testing, where broad system context determines correctness.)
Mentorship will matter more for juniors because architecture, style, and hard-to-explain tacit "taste" for code will be a bigger constraint than the quantity of code an engineer can generate. (Everyone can generate infinite code).
Startups will hire smaller, more senior teams. In theory, this would maximize productivity gains from AI while minimizing the "collaboration cost" of using these tools. Teams will stay smaller for longer.
On the bright side, there's never been a better time for an engineer to bootstrap or build a side project. AI handles so much drudgery!
