To my students

Overall reaction to the essay

• Many readers find the message inspiring, humane, and “the best honest advice” they’ve seen, especially the emphasis on ethics, love over fear, and caring about craft.
• Others see it as naive or nihilistic: too pessimistic about industry, too absolutist on AI, and not practical for students who need jobs and have debt.
• There’s debate over whether publishing this required “courage”; some say there are real career risks in academia, others see little downside for a tenured professor.

Ethics, responsibility, and education

• Multiple commenters stress the importance of explicit ethics training, citing engineering disasters and software safety case studies.
• Others are cynical: mandatory ethics courses often become box-ticking or “communications” classes and don’t meaningfully shift behavior.
• Some describe leaving tech for more overtly ethical fields (e.g., nursing), framing software as structurally misaligned with public benefit.

Generative AI and “LLM vegetarianism”

• The essay’s categorical refusal to use LLMs is highly polarizing.
• Supporters see LLMs as built on labor exploitation, unlicensed data use, and high resource consumption, and as tools that outsource thinking and erode agency.
• Critics call this hyperbolic, inconsistent with using modern hardware, and suspect shifting goalposts even if energy costs drop or training data is “clean.”
• A few hope for “ethically trained” small models that such people could study without compromising principles; others argue there is no obligation to engage with LLMs at all.

Craft, refactoring, and “going slowly” vs industry reality

• One camp embraces the advice: deep thinking, refactoring, and documentation are seen as key to maintainability, profitability, and genuine engineering. “Slow is smooth and smooth is fast.”
• Another camp argues this is misaligned with commercial incentives: entry-level engineers who “go slowly” and polish endlessly risk getting fired or never hired.
• Several note that industry often values shipping and “product as the artifact” over code as craft; automatic coding tools and agents intensify this trend.
• A recurring theme: high-level system designers who can’t code are already ineffective; relying on LLMs without technical depth will be worse.

Deep work, distraction, and life habits

• Many resonate with the call to carve out distraction-free time; some say they only grasped how pervasive distraction is once they tried to fight it.
• Exercise and reading are cited as surprisingly powerful enablers of deep work and better time use.

Academia vs industry and preparing students

• Some criticize academics with little or no industry experience for giving career advice; they see the essay as detached from “messy” commercial constraints.
• Others reply that education isn’t solely job training, and that students should be encouraged to define success beyond “succeeding in this market.”
• There’s disagreement on whether ignoring current trends (especially LLMs) is responsible preparation or principled self-marginalization.

Luddism, inevitability, and tech pessimism

• Several label the stance “Luddite”; defenders counter that Luddites had coherent, labor-focused critiques and that opposing harmful tech is rational.
• Some warn against accepting narratives of inevitability (“we’re never going back to manual coding”); others argue that economically powerful automation will not be reversed.
• Broader worries surface about “move fast and break things,” enshittification, surveillance, and software’s role in social harm.

Careers, money, and meaning

• One axis of disagreement: study CS for beauty, curiosity, and social good vs. study it primarily as an income-producing skill.
• Some argue non-instrumental motivations are a luxury if you’re not rich; others share anecdotes of leaving software for lower-paid but more meaningful work and not regretting it.
• The essay’s core challenge—setting ethical boundaries early and caring more about people than profit—is seen by some as necessary moral grounding, and by others as incompatible with current hiring and productivity expectations.