For me the issue is the lack of human explanation for mistakes. With a person, low quality comes from a source. Sometimes the source is lack of knowledge, sometimes time pressure, sometimes selfish goals.

Most importantly, those sources of errors tend to be consistent. I can trust a certain intern to be careful but ignorant, or my senior colleague with a newborn daughter to be a well of knowledge who sometimes misses obvious things due to lack of sleep.

With AI it's anyone's guess. They implement a paper in code flawlessly and make freshman level mistakes in the same run. so you have to engage in the non intuitive task of reviewing assuming total incompetence, for a machine that shows extreme competence. Sometimes.

It's not that pre-LLM era was a "golden age of quality", far form it. It's that LLMs have removed yet another tell-tale of rushed bullshit jobs.

It was and still is a negative filter, not a positive one. Meaning it is easy to reject work because there typos and basic factual errors, absence of them is not a good measure of quality. Typically such checks is the first pass not the only criteria.

It is valuable to have this, because it the work passes the first check then it easier to identify the actual problems. Same reason we have code quality, lint style fixed before reasoning with the actual logic being written.

> And AI signatures are now easy for people to recognize. In fact, these turns of phrase aren't just recognizable—they're unmistakable. <-- See what I did there?

You might spot these very obvious constructs and still miss 99% of AI generated text because it has no tells. Yet you don’t know that 99% was generated, and since you spot 100% of the pattern you outlined you think no AI generated text makes it past you.

> I don't know if I agree with either assertion…

Yes, I don't think this matters. Much of "knowledge work" was always a proxy for something else.

High quality in terms of typos and errors is mainly a signal of respect in a similar way to wearing ironed white shirts with neck-ties. "Walls of text" that no one is expected to read in depth. Basically a symbolic demonstration of sacrifice and subservience (or something). LLMs remove this mode of signalling.

If quality of content wasn't examined before, it was probably never particularly important.

I’m also not sure I agree with the assertion that LLMs will produce a high quality (looking) report with correct time frames, lack of typos, and good looking figures. I’m just as willing to disregard human or LLM reports with obvious tells. An LLM or a person can produce work that’s shoddy or error filled. It may be getting harder to differentiate between a good or bad report, but that helps to shift the burden more onto the evaluator.

This is especially true if we start to see more of a split in usage between LLMs based on cost. High quality frontier models might produce better work at a higher cost, but there is also economic cost pressure from the bottom. And just like with human consultants or employees, you’ll pay more for higher quality work.

I’m not quite sure what I’m trying to argue here. But the idea that an LLM won’t produce a low quality report just seemed silly to me.

Yes. I think the main warning here is that it is an added risk. A little glitch here and there until something breaks.

> I don't know if I agree with either assertion… I've seen plenty of human-generated knowledge work that was factually correct, well-formatted, and extremely low quality on a conceptual level.

Putting a high level of polish on bad ideas is basically the grifter playbook. Throughout the business world you will find workers and entire businesses who get their success by dressing up poor ideas and bad products with all of the polish and trimmings associated with high quality work.

The goal of automation is to automate consistently perfect competence, not human failures.

You wouldn't use a calculator that is as good as a human and makes mistakes as often.

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