Welcome back to the tidy design principles substack. I’m bringing it back to life to talk about what I’m thinking most about these days: AI. There are so many voices talking about AI, and I feel ambivalent about adding one more. And frankly, I have a lot more questions than answers. But writing and listening are my keys of tools for understanding a new domain, so I hope you’ll join be on this journey. (View Highlight)
Let me start with the bits of AI that I feel genuinely excited about: Programming accessibility. There are tons of people who could benefit from a programming language like R, but can’t justify the investment in learning it. AI has dramatically lowered that barrier and you can now get many of the benefits of reproducible programming with R much faster than you could before. Similarly, effective usage of git is now within reach to a much broader audience. Translation. While machine translations are still far from perfect, their quality has improved radically in the last few years. This means that much more of the programming ecosystem is now available to the majority of the world who are not fluent English speakers. Voice input. Voice input is a super exciting technology because it means that you no longer need to be a fluent touch typist in order to quickly get your thoughts into a computer. (Not to mention making a lot more technology available if you can’t read or write.) That’s a meaningful expansion of who gets to participate in technology. Wide and shallow expertise. I love Tukey’s quote that statisticians get to play in everyone’s backyard. And it’s now easier than ever thanks to AI. AI will not make you an expert but can give you shallow expertise in basically anything you’re curious about. I think that’s pretty cool.
Finally, I have found AI to be a tremendous accelerator in my own work. It’s allowed me to fix 100s of issue in core infrastructure packages like roxygen2 and testthat. This is not AI slop; this is carefully vetted code that I can now write ~2-5x faster than I could before. (View Highlight)
But you can’t use AI without also considering the harms, of which there are many. Environmental impact. At the individual level, I believe that if you want to reduce your environmental footprint, there are higher-leverage changes that you can make. But at the societal level, the picture is more concerning: the rush to create new data centers is increasing need for electricity and water, and leading companies to rollback their climate commitments. Copyright theft. LLMs are trained on vast quantities of copyrighted material, taken at an unprecedented and industrial scale, without permission or compensation. Concentration of wealth. The AI craze is pushing more and more money into the hands of fewer and fewer people. I find the concentration of wealth and power into the hands of a very small number of people to be genuinely disturbing and I think is something that we should all be concerned about. Intellectual laziness. AI supports a kind of shallow engagement where you never have to strain your brain on any task. The path of least resistance is to disengage and just let the model handle it. You no longer have to experience any mental discomfort, and thus you never really learn. Equity and access. I’ve built my career around open source software, and one of the things I love about it is that it’s available to everyone, everywhere in the world, regardless of their means. That’s not possible with AI. The best tools cost real money, usually charged in US dollars, and that makes them out of reach for a lot of people in a lot of places. (View Highlight)
How do you resolve the tension between the empowering and harmful parts of AI? I wish I could give you an answer. All I can suggest is to sit with this conflict. Acknowledge that it’s complicated and there are no easy answers. And do your best to ignore the AI boosters and AI doomers who want to make it easy.
Why am I engaging so heavily with AI? Firstly, I see my job as broadly empowering data scientists. If data scientists are now using AI, then it’s my job to look at what they’re doing and see if I can help them to do it better. Secondly, it feels crucial for the future of Posit. We’re a successful company and doing well, but if we don’t seriously engage with AI then I don’t think we can survive. And while I’m admittedly biased, I do think the failure of Posit would be a loss for the world since we invest so much into free and open source tools. (View Highlight)
