I like Erik’s thoughts about AI and coding in Replacing my Right Hand with AI:

I do think that AI will lower the bar for anyone to be able to create software, just like anyone can use Excel to do their own personal accounting. This is a good thing!

And:

Human engineers won’t go away. We’ll still be needed to drive high-level prioritization, understand the overall architecture and scope of the problem, and review the AI’s work, especially as systems get bigger. But we’ll spend much more of our time thinking about what to build, and much less on the repetitive “how” of building it.

On the Product side of this argument, there is Paweł Huryn’s Will We Lose Our Jobs to AI? Cutting Through the Hype. Short answer: no! But he makes some points about how we should adapt that I agree with, especially these two:

• Educate yourself in AI: You should understand concepts like fine-tuning and AI agents, but there’s no need to obsess over them. YouTube videos are perfectly fine unless you want to tie your career more closely to AI.
• Get interested in the business side of the product: How do your organization’s Sales, Success, and Support teams work? How exactly does your company make money? How do you acquire customers? What are the key acquisition, retention, and revenue metrics? How do these metrics differ depending on the customer segment? How have they changed over time? Who are your competitors? What’s unique about your strategy?

In short, use AI for the things that it is good at, and get better at the things that it’s not good at.

I loved the story of the honking Waymos when it came out, and I’m glad it got the classic “but what does it all mean!?” treatment from The Atlantic:

Watching the Waymos circle the lot under the cover of darkness—and occasionally getting stuck in an endless loop—scratches a childish itch, akin to the fantasy of watching one’s toys come alive at night. In one video, the cars, bathed in taillight red and trying to exit, give off an aggressive vibe. In others, they seem clumsy. What do robots do when we can’t see them? Tung’s webcam answers the question. The stream makes it easy to spin up fictionalized, anthropomorphized yarns about the cars, because it feels like we’ve caught them in a private moment.

This whole story reminds me of scene from I, Robot where Will Smith’s character discovers a bunch of decommissioned robots in a junkyard just… standing around doing nothing. Well, until they don’t… But no spoilers.

Charity Majors makes some really interesting points about the consequences of “replacing” junior engineers with AI:

By not hiring and training up junior engineers, we are cannibalizing our own future. We need to stop doing that. People act like writing code is the hard part of software. It is not. It never has been, it never will be. Writing code is the easiest part of software engineering, and it’s getting easier by the day. The hard parts are what you do with that code—operating it, understanding it, extending it, and governing it over its entire lifecycle.

Also:

To state the supremely obvious: giving code review feedback to a junior engineer is not like editing generated code. Your effort is worth more when it is invested into someone else’s apprenticeship. It’s an opportunity to pass on the lessons you’ve learned in your own career. Even just the act of framing your feedback to explain and convey your message forces you to think through the problem in a more rigorous way, and has a way of helping you understand the material more deeply. And adding a junior engineer to your team will immediately change team dynamics. It creates an environment where asking questions is normalized and encouraged, where teaching as well as learning is a constant.

Lots more in the full article, it’s well worth a full read.

Yes, I’m a relentless fanboy of whatever Paul Ford writes, but this is a truly wonderful post about what makes AI so addictive and impossible to look away from. He frames AI as a technology that truly has no shame because “it possesses an absolute willingness to spout foolishness, balanced only by its carefree attitude toward plagiarism.” And so:

By aggregating the world’s knowledge, chomping it into bits with GPUs, and emitting it as multi-gigabyte software that somehow knows what to say next, we’ve made the funniest parody of humanity ever. These models have all of our qualities, bad and good. Helpful, smart, know-it-alls with tendencies to prejudice, spewing statistics and bragging like salesmen at the bar. They mirror the arrogant, repetitive ramblings of our betters, the horrific confidence that keeps driving us over the same cliffs. That arrogance will be sculpted down and smoothed over, but it will have been the most accurate representation of who we truly are to exist so far, a real mirror of our folly, and I will miss it when it goes.

This isn’t entirely surprising but it’s a sad state of affairs, and it’s worth highlighting not just how, but also where LLMs are being trained:

Hundreds of thousands of hours of work goes into providing enough feedback to turn an LLM into a useful chatbot, and that means the large AI companies outsource the work to parts of the global south, where anglophonic knowledge workers are cheap to hire.

I know it’s too dismissive to call chatbots “fancy autocomplete” like many do, but we have to remember that this isn’t magic. The words the bots use come from somewhere. And in the case of “delve”…

I said “delve” was overused by ChatGPT compared to the internet at large. But there’s one part of the internet where “delve” is a much more common word: the African web. In Nigeria, “delve” is much more frequently used in business English than it is in England or the US. So the workers training their systems provided examples of input and output that used the same language, eventually ending up with an AI system that writes slightly like an African.