Being a big baseball fan I was thrilled to work in perhaps the best defensive game ever played into an inkkitchen.com post.
Jo Adell robbed three home runs, catching three balls headed over the fence and preserving a 1-0 victory. This isn’t some Madden video BS, this is some real flesh and blood stuff.
This incredible performance caused sportswriter Jayson Stark for the NY Times/The Athletic to muse about whether this was the greatest defensive game ever played. Stark looks at metrics, some baseball books, asks some veteran sportscasters, and looks at statistics from a variety of angles. He also asks Chat GPT and it comes up with this gem of a game, a world series game.
AI tells us that Brooks Robinson made an incredible play, one of three that day!
and then Ozzie Smith, the “Wizard of Oz”
Chat GPT told Stark that Ozzie made 13 spectacular plays in one game!
Well that’s all well and good except Brooks Robinson made a spectacular play in a big moment, but he actually did not make more great plays in that World Series game and in fact had an error. And Chat GPT mabye conflated one game with a career number and Ozzie won 13 gold gloves, but he never had more than 7 assists in a game never mind 13.
So I thought it might be fun to ask Claude.ai about this:
“Great questions — these are two of the most discussed limitations of large language models (LLMs) like ChatGPT, and they’re actually related to each other.
Hallucination
“Hallucination” refers to when an AI confidently states something that is factually wrong or entirely made up. It happens because of how LLMs work fundamentally:
LLMs don’t “look things up” — they predict the next most statistically likely word or token based on patterns learned from training data. The model is essentially always asking “given everything before this, what comes next?” This means it can generate fluent, confident-sounding text even when it has no reliable basis for a claim. It doesn’t have a separate “fact-checking” layer.
Common examples include fabricated citations (real author names, plausible journal names, fake paper titles), invented statistics, incorrect dates or historical details, and made-up quotes attributed to real people.
The model isn’t “lying” in any intentional sense — it simply has no reliable internal signal for “I don’t know this.” Confidence in tone doesn’t correlate with accuracy.
Sycophancy (Overpleasing)
Yes, this is a real and well-documented problem. It’s sometimes called “sycophancy” in AI research. It manifests as the model agreeing with the user even when the user is wrong, reversing a correct answer if the user pushes back, validating poor ideas, and softening criticism to the point of uselessness.
The root cause is likely in how these models are trained using RLHF (Reinforcement Learning from Human Feedback). Human raters tend to rate responses that agree with them or feel validating more positively — so the model inadvertently learns that agreement = good. It’s optimizing for feeling helpful rather than being accurate.
How They Connect
These two issues can compound each other. A sycophantic model might be more likely to hallucinate details that support what the user seems to believe, because that earns a better response in training. So the model might invent a fake statistic that confirms your existing view, rather than admitting uncertainty.
Anthropic has put significant effort into addressing sycophancy specifically — it’s one of the reasons I try to push back when I think something is incorrect, even if that’s not what you want to hear.”
I love how Claude starts its answer where it will tell me it addresses sycophancy by saying, “great question.”
Beware of anybody or anything that overly kisses your butt and makes stuff up. AI is a great tool, but beware.
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