Margaret Atwood Calls Out AI's Core Problem — And Business Teams Should Be Paying Attention

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Margaret Atwood Calls Out AI's Core Problem — And Business Teams Should Be Paying Attention

Margaret Atwood, the celebrated author of The Handmaid's Tale and The Blind Assassin, did not come to the Babell Literary and Cultural Festival in Porto, Portugal to pull punches about artificial intelligence. According to a recap by Deadline, covered by Terrence O'Brien at The Verge, Atwood shared that she has actually used AI tools herself — and what she found was not encouraging. Her verdict, delivered with the directness you would expect from one of the most respected literary voices alive, was simple: the problem with AI is "garbage in, garbage out."

It is a phrase that has been floating around computing circles for decades. But coming from Atwood, in the context of generative AI's current cultural moment, it lands differently — and carries real implications for every business team that has quietly started integrating AI into their workflows.

What Atwood Actually Said

The remarks came during a conversation that, as O'Brien notes, has become something of a ritual at literary and cultural festivals: the AI question. What made Atwood's comments notable was not that she dismissed AI outright, but that she engaged with it critically from a position of firsthand experience. She used it. She evaluated it. And her conclusion was that the output is only ever as good as what goes in.

The original article is available at The Verge.

Why This Critique Is More Technically Accurate Than It Sounds

Atwood's "garbage in, garbage out" framing is not just a literary flourish. It is a precise description of a structural challenge that AI researchers, enterprise software teams, and data scientists wrestle with constantly. Generative AI models are trained on vast datasets scraped from the internet — and the internet contains a staggering amount of low-quality, biased, outdated, and outright incorrect information.

When a business deploys an AI tool trained on poor data, the results are predictably poor. A customer service bot trained on incomplete product documentation will give customers wrong answers confidently. A content generation tool trained without editorial standards will produce text that sounds authoritative but contains errors. A sales intelligence tool pulling from stale or unverified sources will send your team chasing the wrong leads.

This is not a theoretical problem. It is the operational reality that many teams are running into right now, often quietly, because admitting that your AI rollout is producing unreliable outputs is not exactly something leadership wants to announce.

The Business Stakes of Data Quality

For SMBs in particular, the "garbage in, garbage out" problem is acute for a specific reason: smaller organizations rarely have the dedicated data engineering resources to audit and clean the inputs feeding their AI tools. Enterprise companies can afford data governance teams. A 20-person agency or a regional professional services firm typically cannot.

This creates a meaningful risk. A team that adopts AI tools without understanding the quality of the underlying training data — or without implementing quality controls on the content and data they are feeding into those systems — is essentially outsourcing critical business outputs to a process they do not fully control.

Atwood's critique should push every business leader to ask harder questions before and after AI adoption. What data was this tool trained on? How recent is it? What verification mechanisms exist? When your team uses AI to draft proposals, summarize customer feedback, or generate marketing copy, what editorial review process is in place before that output reaches a client or customer?

What Good AI Practice Looks Like for Business Teams

The answer to the garbage-in problem is not to abandon AI tools — it is to build better input and review practices around them. That means establishing clear guidelines for how AI-generated content is reviewed before use, being selective about which tools your team adopts based on transparency about training data, and treating AI outputs as drafts that require human judgment, not finished products.

Prompt engineering and AI workflows for teams are increasingly critical skills, not just for technical staff but for anyone in a content, operations, or client-facing role. Understanding how to give AI tools high-quality inputs — specific, accurate, well-structured prompts and data — is the practical antidote to the problem Atwood is describing.

Platforms like WRRK.ai are built around exactly this principle: helping business teams work with AI in structured, accountable ways that account for the quality control challenges Atwood and others have identified.

The Bottom Line

Margaret Atwood is not an AI researcher. She is something arguably more useful in this conversation: a sharp observer of how systems of power and technology actually behave in practice, not in press releases. Her "garbage in, garbage out" critique deserves to be taken seriously in every boardroom and team meeting where AI adoption is on the agenda.

The technology is powerful. The inputs still matter enormously. That tension is the central operational challenge of the AI era for business teams — and it is not going away.

Original reporting by Terrence O'Brien, The Verge.

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Frequently Asked Questions

What does "garbage in, garbage out" mean in the context of AI?

"Garbage in, garbage out" is a longstanding computing principle that means the quality of a system's output is directly determined by the quality of its input. In the context of AI, it refers to the fact that if an AI model is trained on low-quality, biased, or inaccurate data, its outputs will reflect those same flaws — regardless of how sophisticated the underlying technology is.

Why is data quality such a challenge for AI tools used by small businesses?

Small and mid-sized businesses typically lack the dedicated data engineering or governance resources to audit the training data behind AI tools they adopt, or to rigorously clean and structure the inputs they feed into those systems. This makes SMBs particularly vulnerable to poor AI outputs, because they may not have the internal infrastructure to catch errors before they affect clients or operations.

How can business teams improve the quality of AI-generated outputs?

Business teams can improve AI output quality by using structured prompting and workflow practices, establishing editorial review processes for AI-generated content before it is used externally, choosing AI tools that are transparent about their training data, and treating AI as a drafting and augmentation tool rather than a fully autonomous decision-maker.