A Lesson From the Cockpit

Writing is clarifying

Subbu Allamaraju’s Journal

A Lesson From the Cockpit

Friday, May 8, 2026

The tech industry has declared that AI will increase developer productivity. Task-level productivity feels obvious, though concrete evidence is elusive — for example, METR reported a widely cited result: a 19% decline in productivity with AI, but eight months later, they had to drop the survey as developers increasingly refused to participate without AI tools. “Claude is down” is now like “Github is down.” Productivity metrics may also sound hollow due to a gap between measured productivity and developers’ perceptions of it. I don’t think we understand this enough yet.

End-to-end productivity is anything but clear in brownfield situations — most money-making software companies are brownfields with real customers, real data, and code evolved over years. In brownfields, most technical discovery and decision-making is done via Slack or Zoom meetings, where people need to talk to each other to piece together facts from tribal knowledge. It is yet unclear how to deal with brownfield complexity that seems immune to AI.

Nonetheless, engineering leaders are expected to improve and demonstrate productivity while operating with fewer engineers.

On the other hand, critics have been cautioning with counter-evidence. Erosion of skills, due to cognitive surrender, is a real challenge. Experienced engineers are facing identity loss as AI can do in seconds or minutes what they used to do in days or weeks. I know of engineers who are worried about ever getting promoted, since some junior engineers can do their work faster. Some are worried about losing purpose, too. What would intrinsically motivate a competent engineer when AI can do their work?

I have written about AI contributing to increased entropy in software. Last year’s GitClear study showed a spike in duplicated code blocks, an increase in short-term code churn, and a continued decline in code reuse. There is also evidence of AI-assisted development creating persistent debt, security, and correctness issues. The tools are changing so fast that any specific finding may be outdated within months, but the patterns — deskilling, entropy, cognitive surrender — are consistent across studies and across domains. The specific numbers change, but the direction doesn’t.

As I continued my sense-making research, I found something striking: the current AI productivity argument has a precise and surprising historical parallel.

Back in the 1980s and 1990s, the aviation industry faced similar arguments and concerns when Airbus introduced fly-by-wire automation. Their philosophy was radical at that time: automation should have authority over the pilot. Airbus’ idea was to eliminate human error by constraining what a human could do. Bernard Ziegler, a senior Vice President of engineering at Airbus, said at that time that he was building an airplane that even his concierge could fly.

Boeing held the opposite design philosophy: the pilot is the final authority, and the human must always be able to override automation. That philosophy influenced the design of their fly-by-wire aircraft.

Regardless of these philosophical differences, aviation reality played out scenarios that the software industry might face.

In the 1994 Nagoya crash, the pilots and an Airbus A300 worked at cross-purposes — the pilots attempted to pitch the aircraft down while the autopilot was pitching it up. The pilots had the skills and experience, but the system’s behavior was opaque to them in the moment of crisis. This is what happens when humans operate systems they don’t fully comprehend. In the 1995 Cali crash, the pilots could not adapt to the changed circumstances and made errors in judgment, putting a Boeing 757 on a collision course with a 9,800-ft mountain. That’s cognitive surrender in a cockpit. Then, in the 2009 Air France crash, the autopilot disengaged after icing knocked out its airspeed data, and the crew failed to manually recover from a high-altitude stall they inadvertently induced. This is skill atrophy.

The current AI productivity arguments map on this exactly. One camp says trust AI, that it will continue to get better to drive extreme productivity and sky-high returns on investment. The other says cognitive surrender is real; never-skilling (not acquiring foundational reasoning skills) and deskilling (erosion of such reasoning skills) are dangerous, so trust the human. Neither camp was entirely wrong in aviation, and neither is wrong now with AI.

What software engineering hasn’t yet learned?

What Other Disciplines Learned

Fly-by-wire automation reduced aviation accident rates by an order of magnitude. However, overreliance on automation, mismatches between the pilot’s understanding of the system state and the actual automation, and the lack of transparency about what’s going on have had severe consequences in the aviation...