Lecture XIII - Presentations and Discussion
Programming with Python
EP 13: Demo Day
The investor is in the room
Back in Session VI she stood at a whiteboard and asked why everything here was built from scratch. Today the answer is on your screens: built by you, with the standard library, pandas, plots, git, and an agent you learned to supervise.
The rules: 7 minutes presenting, 3 minutes of questions, order drawn at random.
While you watch
- Note one thing you would have done differently, and one thing you are taking for your own next project
- Questions are part of the format: specific, kind, useful
Your Future with AI
AI and your first job
- Thousands of developers: +26% tasks done with AI, juniors gained most (Cui et al. 2026)
- Experienced developers on their own code: 19% slower, while feeling 20% faster (Becker et al. 2025)
- Learners with AI: 50% on a mastery quiz vs 67% without, worst on debugging (Shen and Tamkin 2026)
- The fix: hints instead of finished solutions erased the harm (Bastani et al. 2025). Part I was exactly that
What stays valuable
- The verification boundary: usable delegation ends where your ability to check ends. Growing that boundary outward is your career
- Comprehension debt: code that works today but nobody understands tomorrow. The interest comes due when a reviewer asks “why does this work?”
- Your edge, deliberately kept: explain every line before you accept it, and code without AI sometimes
Discussion: your future with AI
Three questions, five minutes, show of hands:
- Which task from the past weeks would you never delegate to an AI?
- What will an entry-level logistics analyst do in 2030, when AI drafts the first version of everything?
- If AI writes most junior code, where do the senior people come from?
Send-off
Keep programming
The skill fades if it sits idle. A few ways to keep it alive:
- Use Python in your thesis: data cleaning, analysis, plots you can defend
- Find a way to apply it at work: the repetitive task nobody wants to do by hand
- Advent of Code: free, ad-free programming puzzles, one a day from December 1st. A genuinely fun way to keep the muscle warm.
Thank you
- We covered the basics of Python: from a first
printto a real project on your own machine - You debugged, you verified AI, you shipped real numbers
- I hope you enjoyed it, and I hope it’s useful long after the grade
- If you have questions or feedback, please tell me
All the best for your studies and your career. Now go build the thing.
Literature
Keep reading
The literature list of this course has more books, several of them free online.
References
Bastani, Hamsa, Osbert Bastani, Alp Sungu, Haosen Ge, Özge Kabakcı, and Rei Mariman. 2025. “Generative AI Without Guardrails Can Harm Learning: Evidence from High School Mathematics.” Proceedings of the National Academy of Sciences 122 (26): e2422633122. https://doi.org/10.1073/pnas.2422633122.
Becker, Joel, Nate Rush, Elizabeth Barnes, and David Rein. 2025. Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity. arXiv. https://doi.org/10.48550/ARXIV.2507.09089.
Cui, Kevin Zheyuan, Mert Demirer, Sonia Jaffe, Leon Musolff, Sida Peng, and Tobias Salz. 2026. “The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers.” Management Science, February, mnsc.2025.00535. https://doi.org/10.1287/mnsc.2025.00535.
Downey, Allen B. 2024. Think Python: How to Think Like a Computer Scientist. Third edition. O’Reilly. https://greenteapress.com/wp/think-python-3rd-edition/.
Elter, Stephan. 2021. Schrödinger programmiert Python: das etwas andere Fachbuch. 1. Auflage. Rheinwerk Computing. Rheinwerk Verlag.
Shen, Judy Hanwen, and Alex Tamkin. 2026. How AI Impacts Skill Formation. arXiv. https://doi.org/10.48550/ARXIV.2601.20245.