Engineering

I had the pleasure of corresponding via email with Andy Nian, cofounder and head of growth at PracHub, a site that catalogs technical interview questions reported by candidates. He wrote to me in August asking whether I would cite his AI-assisted question set in a piece I had written about why interviews should measure judgment rather than output. I came back asking for his data instead, and he graciously answered a few questions on the record: figures, clear caveats about what those figures could not support, and three public examples. Almost no founder does that. One of his answers has stayed with me since, and most of what follows is an argument with it, in the good sense. ...

Anthropic published a new piece last week, The AI-Native SDLC Playbook. It is long, detailed, and worth the time. It also lines up closely with what I have been writing here for the better part of a year. The argument at its center takes one sentence: code is no longer the bottleneck. Everything in the document follows from it. Once agents write most of the implementation, the constraint moves to the stages on either side, the ones still running at human speed. Approval queues build. Controls sized for human output stop matching what arrives at them. Governance that meets monthly cannot govern work that ships hourly. ...

A June 2026 NBER working paper matched more than 100,000 GitHub developers against their AI usage telemetry. Developers using autonomous agents committed 180 percent more code. Their releases rose 30 percent. The gain is enormous where code gets written, and it decays at every step after that. Marty Cagan named the phenomenon last week: the AI productivity paradox. He also points out that while nearly everyone now agrees it exists, almost nobody agrees on why. ...

The last post left the engineering station with a one-line test: if the reasoning behind a decision would be useful to the engineer who touches this code in six months, write it down. That test tells you what deserves an entry, but it does not tell you how to recognize the moment, in the middle of a session, when you have crossed from accepting a suggestion into making a decision. Those are different skills, and the second one is a skill nobody ever had to develop before. ...

I ended the last post with a question: the next challenge is not building the Judgment Log. It is whether anyone writes in it once the deadline is two hours away. That question only has a useful answer if the artifact is light enough to actually use. So instead of arguing for it further, I want to show it. Take a fictional but familiar scenario. A checkout flow. A promotional window. A promo code validation feature built using AI-assisted development. It shipped. Three weeks later, it broke when the campaign introduced expired codes. In the post-mortem, nobody could answer the three questions that mattered: what did the PM cut and why, what did the designer choose between, and what did the engineer override. ...

In April, Meta employees burned through 73.7 trillion tokens in roughly thirty days. The company found out not because spending crossed some alarming threshold, but because an internal leaderboard, nicknamed Claudeonomics, had turned token consumption into a competition. Employees and teams were ranked by how much they used. The system did exactly what it was built to do: usage went up. What it could never show anyone was whether any of that usage produced something worth the cost. Meta is now dismantling the leaderboard in favor of a centralized monitoring platform called AI Gateway, built to track spending in real time and flag unusual spikes. ...

In 2012, I was given a project at Dalet that I did not fully understand at the time: move the entire company from waterfall to Agile. We were a mid-size product company with engineering teams spread across time zones, and waterfall was doing what waterfall always does at that scale, creating the illusion of control while real problems accumulated in the gaps between phases. The transition worked. And the productivity gains were not incremental. Engineers who had spent months waiting for approval to write code were suddenly shipping every two weeks. The business could see working software, give real feedback, and change direction without blowing up a six-month plan. It felt like the industry had discovered something fundamental. ...

In a post I wrote last December, I discussed the production triangle: the constraint that governs every production system, including software. You can optimize for two of three dimensions, time, quantity, and quality, but never all three. Push velocity while adding features, and quality absorbs the cost. Every time, without exception. This constraint is not new. Kahneman mapped the cognitive mechanism in Thinking, Fast and Slow: under time pressure, we disengage System 2, the slow, deliberate reasoning that catches the things that will hurt us later, and default to fast, intuitive pattern-matching. Kuutila et al.’s 2020 systematic review of the software engineering literature confirmed the outcome: increased throughput, decreased quality, and a root cause that almost always traces back to the pressure itself being a product of bad estimation. The triangle is not a theory. It is physics. ...

The bar for building has collapsed. Anyone with an AI assistant can produce a prototype, a draft, a working script. That is not the challenge anymore. The challenge is identifying people who can tell whether what was built is right, catch what is wrong, and make the call on what to do about it. That is a different hire than the one most managers have been trained to make. And the stakes are higher now, because your hiring decision is no longer a baseline. It is a multiplier. ...

Your team closed 47 tickets last sprint. Deployment frequency is up. Lead time is down. The sprint review looked clean. And you have no idea whether your team is getting better at what matters. Every metric on that dashboard measures motion. None of them measures whether the team made the right decision. In an era where AI generates the first draft of almost everything, code, architecture proposals, incident summaries, and product specs, that gap is no longer academic. It is the difference between a team that compounds in judgment and a team that accelerates toward the wrong destination. ...