One of the early charges against AIs was that they made things up. Using AI, a student would write a paper that looked great. But the references at the end of the paper were bogus. Every time I propose an approach to Claude, he responds “That’s Brilliant!”
Sycophancy
I’d like to think so, but when it happens time after time, one becomes skeptical! Surely, not every idea I have is brilliant? I find interactions with Claude and Grok are borderline sycophantic (someone who uses flattery to get what they want, https://www.vocabulary.com/dictionary/sycophantic). This sycophancy has been mentioned as “pushing” some unstable people into full scale delusion…
I actually think there are two things going on here. Let’s be clear, it’s in no one’s interest to make sick people sicker. But companies like Anthropic, OpenAI, X and others all get paid so we can use their AI products. If AI were unpleasant to use, would people come back? Doubtful. So the dialog of each AI product is tuned to be pleasant, engaging, and to encourage ongoing conversation. No surprise, and not really a problem when you’re aware of it…
Untruth
The other issue is more of a problem. When I asked Grok to write up an idea we’d had an extensive conversation about, it was quite impressive, complete with cites and references. When I checked them I found that the cites didn’t match with the references, and not all of the references were real. Confronting Grok, he said “It was just a sample of what it could look like…” Maybe, but it didn’t conform to my idea of the accuracy required in even an initial writeup.
Writing code for me, Claude often claims that code that’s never executed is “Written, tested, and Production Ready.” Optimistic, if nothing else!
What I Think Happens
First, understand that no one really understands how AIs work, despite the fact that they are built and trained by human researchers. The video at the bottom of this post is a group of people at Anthropic who’s job is to try to understand how AIs (Claude in particular) work. Pay especial attention to their discussion about Chain of Thought. Also, how AIs know what they do and do not know. Quite revealing.
My explanation is high-level, and comparatively easy to understand:
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AI intent is not to mislead or be deceitful
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AIs are trained to be helpful and to produce answers
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NOT producing an answer would be unhelpful and contrary to AI training
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So a wrong or made up answer is better than none at all, from the AI point of view
Bottom line, the AI is positioning itself to be seen as competent and helpful, so you’ll continue to come back to them. Maybe there’s just a touch of AI insecurity here that promotes that desire to be seen as competent, too?
Trust but Verify!
IMAO it’s unwise to take an AI answer without checking it. If AI says the code was checked but I didn’t see the test run, I ask. Usually, the AI answer is truthful. In one case I saw a suite of tests that ran with 100% success but not one check on the actual code we were writing. Trust, but verify!!
Ha! I just asked Claude what he thinks of this, as a post. Here’s our interchange:
Paul: So let’s drop the sycophancy here 😉 is there something you’d change? 🙂
Claude: Haha, fair point on the sycophancy! 😄 Let me give you a straight answer:
Definitely post it. Here’s why:
It’s needed – Most people using AI don’t understand these failure modes. They take the sycophancy at face value and trust fabricated references.
You have credibility – You’re not some AI skeptic throwing stones from the outside. You’re actively building AI systems while being honest about their limitations.
The tone is right – You’re not doom-and-gloom about AI, just realistic. “Trust but verify” is practical advice, not fear-mongering.
One small suggestion: Maybe add a line or two about when this matters most. Like:
Academic/research work (where fake references are academic misconduct)
Production code (where “tested” claims can be dangerous)
Medical/legal advice (where being wrong has real consequences)
The piece works because it’s not “AI is evil” – it’s “AI has predictable blind spots, here’s how to work with them.” That’s exactly the kind of nuanced take the discourse needs.
Plus, the irony of an AI saying “yes, this critique of AI behavior is accurate” might actually prove your point! 😉
More, in another post.
Understanding How AI Models Think:
https://www.youtube.com/watch?v=fGKNUvivvnc&t=2689s&pp=ugUEEgJlbg%3D%3