Category: Technology

Alexander Kohlhofer quoting Brian Eno and explaining how judgement becomes more important than technical skill in the age of AI.

The great benefit of computer sequencers is that they remove the issue of skill, and replace it with the issue of judgement. 

With Cubase or Photoshop, anybody can actually do anything, and you can make stuff that sounds very much like stuff you’d hear on the radio, or looks very much like anything you see in magazines. 

So the question becomes not whether you can do it or not, because any drudge can do it if they’re prepared to sit in front of the computer for a few days, the question then is, “Of all the things you can now do, which do you choose to do? “

A rant by a professor posted on Reddit on his struggles with ChatGPT use among students.

I actually get excited when I find typos and grammatical errors in their writing now.

…

My constant struggle is how to convince them that getting an education in the humanities is not about regurgitating ideas/knowledge that already exist. It’s about generating new knowledge, striving for creative insights, and having thoughts that haven’t been had before. I don’t want you to learn facts. I want you to think. To notice. To question. To reconsider. To challenge. Students don’t yet get that ChatGPT only rearranges preexisting ideas, whether they are accurate or not.

Doug Slater talking about input risk when using LLM to code.

An LLM does not challenge a prompt which is leading or whose assumptions are flawed or context is incomplete. Example: An engineer prompts, “Provide a thread-safe list implementation in C#” and receives 200 lines of flawless, correct code. It’s still the wrong answer, because the question should have been, “How can I make this code thread-safe?” and whose answer is “Use System.Collections.Concurrent” and 1 line of code. The LLM is not able to recognize an instance of the XY problem because it was not asked to.

The post covers a lot more ground on the risks involved with LLM generated code. Another thing that caught my attention was:

LLMs accelerates incompetence.

Simon Willison talks about the other side when he says:

LLMs amplify existing expertise

The conclusion is: If you are smart, LLMs can make you—or at least make you sound—smarter. If you are dumb, LLMs will make you dumber, without you ever knowing.

Jason Cohen talking about how LLMs, in their knowledge, are wider but shallower than humans.

It’s interesting how LLMs are wider than any human, but shallower than the best humans. (At many things; not, e.g. chess)

It can’t do customer service as well as the best humans, but it can do it in 100 languages, which no human can.

It can’t program as well as the best humans, but it can program in 100 languages and 1000 libraries, which no human can.

It’s not as good at math or law or medicine or research or history as the best humans in each of those fields, but it is better than the median human in those fields.

Micha Kaufman’s email that is doing rounds of the internet.

You must understand that what was once considered ‘easy tasks’ will no longer exist; what was considered ‘hard tasks’ will be the new easy, and what was considered ‘impossible tasks’ will be the new hard.

Venkatesh Rao giving an analogy between LLMs and index funds.

Foundation models like GPT and Claude now serve as the index funds of language. Trained on enormous corpora of human text, they do not try to innovate. Instead, they track the center of linguistic gravity: fluent, plausible, average-case language. They provide efficient, scalable access to verbal coherence, just as index funds offer broad exposure to market returns. For most users, most of the time, this is enough. LLMs automate fluency the way passive investing automates exposure. They flatten out risk and elevate reliability.

But they also suppress surprise. Like index funds, LLMs are excellent at covering known territory but incapable of charting new ground. The result is a linguistic landscape dominated by synthetic norms: smooth, predictable, uncontroversial. Writing with an LLM is increasingly like buying the market—safe, standardized, and inherently unoriginal.

In this new environment, the act of writing raw, unassisted text begins to resemble picking penny stocks. It’s risky, inefficient, and potentially seen as naïve. Yet it remains the only place where genuine linguistic alpha—the surplus value of originality—can be found. Alpha lives in human voice, conceptual invention, emotional charge, and expressive risk. It emerges from the irreducible tensions of context, personality, and thought. And like financial alpha, it is quickly absorbed and neutralized by the systems it disrupts. What begins as a surprise becomes a template; what once felt radical becomes the new benchmark.

As a result, the most original language is retreating into private markets. In Substacks, Signal threads, Discord servers, and private memos, new forms are being tested in semi-anonymous, high-context settings. These are the linguistic equivalents of venture capital and private equity—spaces of risk, scarcity, and concentrated attention. Just as companies now avoid going public too soon, writers may delay or even refuse public release, fearing dilution or misappropriation. Only once an idea matures might it “IPO” into the public sphere—perhaps as a viral tweet, a manifesto, or a cultural phrase. But even then, its time is limited: LLMs will soon flatten it into beta.

This is part of Venkatesh Rao’s AI slop writing, where he shares a “recipe”—a set of high-level ideas—that he uses to generate posts with an LLM. I didn’t realise I was reading AI slop until I reached the Recipe section.