I understand all the chatter about LLMs hallucinating, or making assumptions, or not being able to understand or provide the more human/emotional element of health care.
But the question I ask myself is: is this better than the alternative? if I wasn't asking ChatGPT, where would I go to get help?
The answers I can anticipate are: questionably trustworthy web content; an overconfident friend who may have read questionably trustworthy web content; my mom who is referencing health recommendations from 1972. And as best I can imagine, LLMs are going to likely to provide health advice that's as good but likely better than any of those alternatives.
With that said, I acknowledge that people are likely more inclined to trust ChatGPT more like a licensed medical provider, at which point the comparison may become somewhat more murky, especially with higher severity health concerns.
Summary using Kagi Summarizer. Disclaimer, this summary uses LLMs, so the summary may, in fact, be bullshit.
Title: LLMs are bullshitters. But that doesn't mean they're not useful | Kagi Blog
The article "LLMs are bullshitters. But that doesn't mean they're not useful" by Matt Ranger argues that Large Language Models (LLMs) are fundamentally "bullshitters" because they prioritize generating statistically probable text over factual accuracy. Drawing a parallel to Harry Frankfurt's definition of bullshitting, Ranger explains that LLMs predict the next word without regard for truth. This characteristic is inherent in their training process, which involves predicting text sequences and then fine-tuning their behavior. While LLMs can produce impressive outputs, they are prone to errors and can even "gaslight" users when confidently wrong, as demonstrated by examples like Gemini 2.5 Pro and ChatGPT. Ranger likens LLMs to historical sophists, useful for solving specific problems but not for seeking wisdom or truth. He emphasizes that LLMs are valuable tools for tasks where output can be verified, speed is crucial, and the stakes are low, provided users remain mindful of their limitations. The article also touches upon how LLMs can reflect the biases and interests of their creators, citing examples from Deepseek and Grok. Ranger cautions against blindly trusting LLMs, especially in sensitive areas like emotional support, where their lack of genuine emotion can be detrimental. He highlights the potential for sycophantic behavior in LLMs, which, while potentially increasing user retention, can negatively impact mental health. Ultimately, the article advises users to engage with LLMs critically, understand their underlying mechanisms, and ensure the technology serves their best interests rather than those of its developers.
>> basically all the money will have been spent on Nvidia GPUs that depreciate to 0 over 4 years
I agree the depreciation schedule always seems like a real risk to the whole financial assumptions these companies/investors make, but a question I've wondered:
- Will there be an unexpected opportunity when all these "useless" GPUs are put out to pasture? It just seems like saying a factory will be useless because nobody wants to buy an IBM mainframe, but an innovative company can repurpose a non-zero part of that infrastructure for another use case.
I'm not trying to be annoying, but surely if you'd justify spending $200/developer/month, you could afford $250/month...
The reason I wonder about that is because that also seems to be the dynamic with all these deals and valuations. Surely if OpenAI would pay $30 billion on data centers, they could pay $40 billion, right? I'm not exactly sure where the price escalations actually top out.