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The gap between social acceptance and expert acceptance of delegating a given task to AI is a point of negotiation that will occur more often, in more domains over the coming years. Watch these points of friction to better understand the distribution of AI. First as a sign that AI is performing a task sufficiently against expert standards. And second, as a sign that either regulation will arrive or cultural innovations are needed to enable the technical ones.

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AI doesn't damage our brains, but unthinking use can damage our thinking. What's at stake isn't our neurons but our habits of mind. There is plenty of work worth automating or replacing with AI (we rarely mourn the math we do with calculators), but also a lot of work where our thinking is important. For these problems, the research gives us a clear answer. If you want to keep the human part of your work: think first, write first, meet first.

AI doesn't damage our brains, but unthinking use can damage our thinking. What's at stake isn't our neurons but our habits of mind. There is plenty of work worth automating or replacing with AI (we rarely mourn the math we do with calculators), but also a lot of work where our thinking is important. For these problems, the research gives us a clear answer. If you want to keep the human part of your work: think first, write first, meet first.

A lot of developers are feeling scared and hopeless about the changes being wrought by all this. Yes, AI is being used as an excuse by executives to lay people off and pad their margins. Yes, how foundation models were trained was unethical and probably also illegal. Yes, hustle bros are running around making bullshit claims. Yes, almost every party involved has a reason to make exaggerated claims about AI. All of that can be true, and it still doesn't matter. Your job as you knew it is gone.

Get serious about learning and using these new tools. You will, like me, recoil at first. You will find, if you haven't already, that all these fancy AI tools are really bad at replacing you. That they fuck up constantly. Your new job starts by figuring out how to harness their capabilities anyway. You will gradually learn how to extract something that approximates how you would have done it yourself. Once you get over that hump, the job becomes figuring out how to scale it up. Three weeks ago I was a Cursor skeptic. Today, I'm utterly exhausted working with Claude Code, because I can't write new requirements fast enough to keep up with parallel workers across multiple worktrees.

But, with these benefits comes a potential offsetting cost to all of us. For example, there are millions of people across the globe that comprise the staff of call centers and customer support. What becomes of them when an AI can answer nearly all questions with zero dollars in salary for human employees? Is the world prepared for this level of potential unemployment? Where are governmental and enterprise level programs for upskilling and retraining to keep these individuals from being left behind?

There is so much in this one article that is amazing that I am struggling to pick anyone thing so I'll just quote the first part. Read the whole article you won't be disappointed.

Why this matters – scalable authoritarianism: One of the things that makes authoritarianism expensive is the overhead that comes from building out and running a large-scale police state. One of the things AI does is make it much, much cheaper to do large-scale surveillance. Datasets like CrowdTrack are a symptom of the way AI is making it cheaper and easier to do surveillance that the dictators of the 20th century would have fantasized about but always been unable to fully fund. “Our dataset can be used for tasks like visual grounding, captioning, and appearance feature extraction,” the researchers write.

The Bottom Line

Making AppleScript work in CLI tools requires:

1. An embedded Info.plist for identity
2. Proper entitlements for permissions
3. Correct code signing
4. Optional: responsibility_spawnattrs_setdisclaim for cleaner permission dialogs

Going back to that fictional conversation in The Diamond Age, I think that the answer—the thing that Finkle-McGraw acquired during his upbringing, that he failed to confer on his children, and that he wants to give his granddaughter—isn’t simply a body of knowledge to be memorized or a set of skills to be mastered. It’s a stance. A stance from which to address the world and all its challenges. A stance built on self-confidence and resilience: the conviction that one has a fighting chance to overcome or circumvent whatever obstacles the world throws in one’s path. The way you acquire it is by trying, and sometimes failing, to do difficult things. It can be discouraging, but if you have good mentors, and if you’re collaborating with friends who are in the same boat, you can find ways to succeed, and develop a knack for it. That’s true self-reliance.

Let’s face it, prompt engineering is still new territory for most teams, but some problems feel very familiar. Just like messy code can drag down your entire product, messy prompts will do the same for any LLM-powered app. The good news is that you already know what to do about it. You don’t need a totally new mindset, just a willingness to pay attention to the quality of your prompts the same way you already care about code.