AI is rapidly advancing, and AI-assisted coding is becoming integral to software development. As an ML engineer, I’m fascinated by AI’s role in coding, and I wanted the Python community’s take on two questions:
- How long have they been using Python, and what percentage of their code is AI-generated now?
- What are their top sources for staying up to date on AI/ML news?
PyCon JP is the largest Python conference in Japan. PyCon JP 2025 was held in Hiroshima from September 26-28, and Mercari was a gold sponsor. I, Prashant, along with Yasuhiro Shiwaku and Tomoko Suzuki from the Engineering Office, led Mercari’s sponsorship effort. With help from my teammates (@ayato, @bosco, @kanta, @wakuchan), we ran Mercari’s sponsor booth and talked to attendees about these two questions.
Let’s get to what people said!
AI Generated Code Percentage
Over the last couple of years, I’ve experimented a lot with generating working code using AI. From copying and pasting code between the ChatGPT/Claude web interfaces and my code editor to using agentic coding tools like Claude Code, Cursor, Cline, Codex, and GitHub Copilot, I have tried them all. In the last 6 months, I estimate Claude Code has written about 80% of my code. This is a drastic change in the way I write code now compared to when I first started writing code.
PyCon draws people with a wide range of Python experience, from people who started last year to people who have been using Python for years and years. Given my experience with AI-generated code, I wanted to see how AI has affected the workflows of people with different levels of Python experience.
40 attendees shared their years of Python experience and the percentage of their code that is AI-generated. I have visualized their responses in the bubble chart below.
Responses spanned 1 to 15 years of Python experience, and the median developer reported 50% AI-generated code. Using Python and pandas, I analyzed the data and found a few more insights:
- AI adoption does not correlate with years of experience. The Spearman correlation was near zero (-0.037).
- Adoption of AI is high across the sample. A majority of developers (62.5%) generate at least half their code with AI, and 27.5% of developers (11 out of 40) generate 80%+ of their code with AI.
- After bucketing experience into 1-3, 4-7, and 8+ years, each group had similar average and median AI-generated code percentages, both near 50%. For the 4-7 years group, the median was slightly higher at 60%.
AI/ML News Sources
News about major updates to open-weight and proprietary LLM performance, benchmarks, agentic coding tools, research advancements, and image generation has become common. Almost every week, we see one or more major updates in these areas, and a myriad of minor ones. There is also a whole lot of knowledge shared by the community about best practices for AI tools and how people get these tools to work best for their use cases.
With this rapid pace of development and a constant stream of updates, months feel like decades, and what we learn becomes obsolete in a couple of months. Personally, I find staying up to date quite challenging. Now, you might say keeping up with every new thing isn’t necessary, and that there are always shiny new things in tech to chase. But I’d argue staying on the sidelines is also not an option. Whether we like it or not, software development has fundamentally changed in the last couple of years and will continue to do so. I don’t want to end up being the old man yelling at the cloud. With more and more companies around the world already mandating the use of AI in development and incorporating it into performance reviews, it’s in our best interests to understand the tools we need to use. So I asked the Python community which sources they use to keep up with this rapidly advancing field.
44 people shared how they stay up to date with AI/ML news. 31 listed a single source, while 13 listed two or more. I have compiled the sources in the bar chart below.
X/Twitter was the clear winner, mentioned by 45.5% of respondents (20 out of 44). Learning from co-workers and Zenn were a distant second, each mentioned by 18.2% of respondents (8 out of 44). I didn’t expect "talking to coworkers" to rank highly, but it makes sense. YouTube was third with 15.9% (7 out of 44). Also, I was surprised to see so few mentions of Hacker News.
Conclusion
Mercari Sponsor Booth at PyCon JP 2025
Survey Responses by PyCon JP 2025 Attendees
Our informal survey at PyCon JP 2025 offers a snapshot of AI’s impact on the Python community. The data suggests AI-assisted coding is now a standard practice, with a median of 50% AI-generated code reported across all experience levels. The fact that both newcomers and veterans report similar adoption rates suggests we’re witnessing a fundamental shift in how code gets written, not just a trend among early adopters. This widespread adoption is supported by a fast-moving information loop, where developers rely on X/Twitter and direct collaboration with coworkers to keep pace.
While the sample size for the survey is small, it indicates a significant shift in developer workflows. Thanks to everyone who shared their experiences; I’m keen to see how these trends evolve over the coming years.
