Ces's random twalks on tech and stats.
by Cesaire Tobias
For as long as I’ve been coding, my preference has always been towards open-source tools. When I started out, more than ten years ago — long before ChatGPT, GitHub Copilot, or any AI copilots existed — open source just felt like the natural choice.
Development happens in the open, unencumbered by corporate roadmaps. That means you can access tools at the bleeding edge, while still relying on older, stable packages.
Trust is earned. A library like ggplot2 becomes a standard by being independently chosen by thousands of users. A vendor’s “best in class” badge carries no such evidence.
Open source is accessible. As a student or employee without a company credit card, I didn’t need special permission or budget approval to experiment, build, and innovate. I could just download Python or R and get to work.
Open ecosystems are unbounded. You can extend, remix, and connect your tools however you like, and the licence never gets a say in it.
Meanwhile, proprietary software like Stata, SAS, or MATLAB thrived for decades because they solved a different problem: they made advanced analysis accessible to non-programmers. Their value proposition was simple: you didn’t need to be a software engineer to get reliable, reproducible results. Universities taught them. Governments and firms standardised on them. They became safe bets with someone to blame if anything went wrong.
The selling point of proprietary languages was their “easy syntax.” For a social scientist, economist, or engineer who wasn’t trained in computer science, it was easier to type a Stata command than to write Python code.
Today, that barrier no longer exists. With AI, you can ask in plain English:
“Run a fixed effects panel regression in Python and plot the residuals.”
…and get working, reproducible, open-source code in seconds.
The selling point that once justified sticking with Stata or MATLAB — “approachability for non-programmers” — no longer holds up when AI makes Python or R just as accessible.
Proprietary vendors are not ignoring this shift.
MathWorks, for example, already offers the MATLAB AI Chat Playground and promotes a MATLAB Copilot to help users turn natural language into MATLAB-ready code. Other vendors will almost certainly follow suit.
They are embedding AI directly into their environments, doubling down on the promise of “no setup, no hassle — just results.”
Once AI has levelled the ease-of-use playing field, though, the differentiators that remain favour open source:
Maybe not tomorrow. Stata, SAS, and MATLAB still have niches where they’ll survive — in certain research departments, government workflows, and among long-time users who value stability.
But AI has taken away the thing they were bought for. Add that to the advantages open source already had, and it’s hard to see a future where proprietary analysis languages remain relevant at scale.
I preferred open source long before AI had anything to do with it. What AI adds is speed: with the ease-of-use barrier gone, the migration away from proprietary languages is accelerating.
September 11, 2025
tags: languages - ai - open-source