Margaret Atwood says the problem with AI is ‘garbage in, garbage out'

At the Babell Literary and Cultural Festival in Porto, Portugal, Margaret Atwood didn’t soften the conversation about artificial intelligence. In an interview recapped by Deadline, the author said she tried Anthropic’s Claude chatbot once, looking for details on the British detective series Father Brown. The model offered a wrong answer, and Atwood called it a lie, noting that a large language model can’t recognize its own falsehoods.

Atwood’s reaction echoes a growing chorus of creators who see hallucinations as a core trust issue. When a system generates incorrect facts, users may not have a quick way to spot the error, especially if the output sounds confident. The problem isn’t just the model’s knowledge base; it’s also the prompts and data fed into it. If the input is vague or the training data contains errors, the output will reflect that mess.

The phrase “garbage in, garbage out” has been used for decades in computing, but it has taken on a new urgency with AI. Large language models are trained on massive text corpora that contain both reliable information and noise. Without rigorous curation, the models can amplify misinformation, making fact‑checking a mandatory step for any professional use.

From an entrepreneur’s perspective, the incident highlights why AI should be treated as an assistant rather than an authority. Verification layers, whether human editors or automated fact‑checkers, become essential safeguards. Companies that embed these checks into their workflows will avoid costly mistakes and maintain credibility.

The broader AI landscape is responding with tools that flag dubious statements, provide source citations, or ask the model to self‑evaluate its confidence. These features aim to reduce the surprise of an AI “lying” when it simply doesn’t know the answer. As the technology matures, we can expect tighter integration of retrieval‑augmented generation, which pulls verified data from external databases in real time.

What this means for you: if you rely on AI for quick research or content ideas, always follow up with a verification step. Try this prompt with your preferred AI assistant: “Summarize the plot of Father Brown, then list the sources for each claim, and highlight any statements you’re uncertain about.” This workflow forces the model to surface its confidence level and gives you a clear path to fact‑check before publishing.

By treating AI output as a draft rather than a finished product, you protect your brand and keep the creative spark alive without compromising accuracy.