I Don't Like LLMs
What happened
The article is a candid critique of large language models (LLMs), arguing that they’re often over‑hyped, opaque, and prone to producing plausible‑but‑incorrect output. It points out practical problems such as hidden biases, high compute costs, and difficulty integrating them reliably into software systems. Fowler suggests focusing on more transparent, task‑specific AI tools and better engineering practices instead of relying on generic LLMs.
Why it matters
If the industry takes these concerns seriously, we may see a shift toward specialized, explainable AI components and stricter evaluation standards, slowing the rush to embed generic LLMs everywhere.
Is this a big deal?
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