Rather than “the book explains how bread is made” say “the sheets of paper which make up the book have ink in the shape of letterforms which correlate with information about how bread is made”.
I’m going to give this URL to Claude, ask it to propose uses of state charts in my codebase. Yes, it already has this in its training data, but I find giving it a URL brings it to top of mind.
There's certainly a risk that an individual will rely too much on AI, to the detriment of their ability to understand things. However, I think there are obvious counter-measures. For example, requiring that the student can explain every single intermediate step and every single figure in detail.
A two-hour thesis defense isn't enough to uncover this, but a 40-hour deep probing examination by an AI might be. And the thesis committee gets a "highlight reel" of all the places the student fell short.
The general pattern is: "Suppose we change nothing but add extensive use of AI, look how everything falls apart." When in reality, science and education are complex adaptive systems that will change as much as needed to absorb the impact of AI.
This sounds good, but I wonder if AI has changed the calculus on conflict resolution. It can not only chase down the conflicting changes, but also read those commit messages and PRs to divine intent. It might be that git is "good enough," given we have AI.
I have mixed feelings about the "Do X in N lines of code" genre. I applaud people taking the time to boil something down to its very essence, and implement just that, but I feel like the tone is always, "and the full thing is lame because it's so big," which seems off to me.
SO was built to disrupt the marriage of Google and Experts Exchange. EE was using dark patterns to sucker unsuspecting users into paying for access to a crappy Q&A service. SO wildly succeeded, but almost 20 years later the world is very different.
This is food for thought, but horses were a commodity; people are very much not interchangeable with each other. The BLS tracks ~1,000 different occupations. Each will fall to AI at a slightly different rate, and within each, there will be variations as well. But this doesn't mean it won't still subjectively happen "fast".
There's an HDR war brewing on TikTok and other social apps. A fraction of posts that use HDR are just massively brighter than the rest; the whole video shines like a flashlight. The apps are eventually going to have to detect HDR abuse.
Whether this exact approach catches on or not, it's turning the corner from "teaching AIs to develop using tools that were designed for humans" to "inventing new tools and techniques that are designed specifically for AI use". This makes sense because AIs are not human; they have different strengths and limitations.
To capture the individual transistors on a modern CPU, you'd need an image tens of terabytes in size, and it'd have to be captured by an electron microscope, not an optical image. And even that wouldn't let you see all the layers. Some of the very old CPUs, I'm not sure what resolution would be required.
"I created the Alphanum Algorithm to solve this problem. The Alphanum Algorithm sorts strings containing a mix of letters and numbers. Given strings of mixed characters and numbers, it sorts the numbers in value order, while sorting the non-numbers in ASCII order. The end result is a natural sorting order."
Essentially, Jenson's complaint is "When I ask an LLM to 'summarize' it interprets that differently from how I think of the word 'summarize' and I shouldn't have to give it more than a one-word prompt because it should infer what I'm asking for."
Yes, GPT-5's response above was not shortening because there was nothing in the OP about Plato's Cave. I agree that Plato's cave analogy was confusing here. Here's a better one from GPT-5, which is deeply ironic:
A New Yorker book review often does the opposite of mere shortening. The reviewer:
* Places the book in a broader cultural, historical, or intellectual context.
* Brings in other works—sometimes reviewing two or three books together.
* Builds a thesis that connects them, so the review becomes a commentary on a whole idea-space, not just the book’s pages.
This is exactly the kind of externalized, integrative thinking Jenson says LLMs lack. The New Yorker style uses the book as a jumping-off point for an argument; an LLM “shortening” is more like reading only the blurbs and rephrasing them. In Jenson’s framing, a human summary—like a rich, multi-book New Yorker review—operates on multiple layers: it compresses, but also expands meaning by bringing in outside information and weaving a narrative. The LLM’s output is more like a stripped-down plot synopsis—it can sound polished, but it isn’t about anything beyond what’s already in the text.
LLM's can shorten and maybe tend to if you just say "summarize this" but you can trivially ask them to do more. I asked for a summary of Jenson's post and then offer a reflection, GPT-5 said, "It's similar to the Plato’s Cave analogy: humans see shadows (the input text) and infer deeper reality (context, intent), while LLMs either just recite shadows (shorten) or imagine creatures behind them that aren’t there (hallucinate). The “hallucination” behavior is like adding “ghosts”—false constructs that feel real but aren’t grounded.
That ain't shortening because none of that was in his post.
We need to get the male’s genetic material into the female’s body. How many redundant copies should we send. 100? 1000? A voice in back of the room: 50 million.