The 100k Whys of AI
The author discusses the difficulty of distinguishing human-written from AI-generated text, arguing that while LLMs may be statistically indistinguishable, their quasi-deterministic nature produces repetitive outputs, such as the flood of AI-generated children's books on Amazon. The author suggests trusting gut instincts in informal settings.
One of the most painful arguments I keep having with fellow techies is the question of whether you can distinguish between human-written and AI-generated text.
Their skepticism is rooted in reason: at their core, LLMs are state-of-the-art statistical models of how humans talk. If so, the output from the model should be almost by definition indistinguishable from human language under any statistical test.
I don’t think this is always argued in good faith; at least some of the debates are started by folks who wish to maintain deniability for their own underhanded use of the tech. But if you sincerely hold this belief, I present you the following collage:
The image shows about 150 Amazon book covers that appear if you search the site for “100000 whys” (link). Some of these books are category bestsellers in children literature. You can view a zoomable, full-resolution version here.
There’s nothing inhuman about any of these titles or covers. At the same time, I probably don’t need to convince you that you’re staring at the purest form of AI slop that is now the scourge of many non-fiction book categories on Amazon. What we’re seeing here is the artifact of LLMs being quasi-deterministic: if you give them the same prompt — say, “generate a reference book for children” — they will produce functionally identical output perhaps 80% of the time.
The similarities in the collage go far beyond the choice of titles: for example, all the covers in the top row feature a roaring dinosaur in the top left corner of the design. There are many other clusters in the data, too. Look for a recurring red-and-white cartoon rocket, a golden retriever, a lion, and so forth.
This is precisely what makes LLM writing distinctive: it’s not that the models’ individual mannerisms are different from ours. It’s that they resort to the same, complex set of mannerisms in response to almost any normal prompt. This is a fuzzy signal, so you shouldn’t fire your intern when they say “it’s not this — it’s that”. But in more casual settings, it’s OK to trust your gut. In fact, these instincts are becoming increasingly important because traditional models of online interactions fall apart if it takes much less effort to produce content than to engage with it.
PS. If you’re using an LLM to automate blogging: yes, the tech is amazing, but chances are, your publication could be renamed to “100,000 Whys”.