Short answer: AI burned me more than once — and almost always in the same ways: I trusted a confident output that was wrong, I built on data that was dirtier than I admitted, or I believed the hype and over-engineered a simple problem. None of these were algorithm failures. They were trust failures. Here are the mistakes, so you can skip them. This is the post I most want brewers to read.
Mistake 1: trusting confident, wrong answers
Modern AI — especially generative tools — produces output that sounds authoritative. Early on, I treated a clean, specific number as if “specific” meant “correct.” It doesn’t. Generative models hallucinate: they’ll hand you a precise recipe figure, a regulatory threshold, or a tasting note with total confidence and zero basis. I’ve watched one invent a citation that didn’t exist.
The fix is unglamorous and absolute: verify every number against real records. I learned to treat AI like a brilliant intern who writes beautifully and cannot be trusted with arithmetic. (More on this in the honest limits of AI in brewing and the very real danger of letting it near TTB/compliance numbers.)
Mistake 2: building on bad data
I once got excited about a model’s results before checking the data underneath. The data was inconsistent — gaps, mislabeled batches, two systems disagreeing. The model dutifully learned the mess and gave me confident garbage. “Garbage in, garbage out” isn’t a cliché; it’s the single most common way brewery AI projects fail, and I fell for it.
Mistake 3: chasing hype and over-engineering
I spent time on sophisticated techniques for problems that a control chart or a seasonal average would have solved. The fancy approach felt like progress; it was mostly cost. The inefficiency wasn’t slow code — it was spending effort and money on sophistication the problem never required.
Mistake 4: believing AI would do more than it can
The marketing told me AI would “transform” everything. The reality: it’s a sharp tool for a narrow band of well-defined, data-rich problems — and useless or harmful outside it. Mistaking the marketing for the map cost me time I won’t get back.
What the burns taught me
Every one of these lessons points the same direction: AI is an assistant with no judgment of its own. Which raises the question the next post tackles head-on — what, exactly, can’t it do?
From Brewer to AI — Part 6 of 8. Full series · Next: AI won’t replace brewers →
Frequently asked questions
What are common AI mistakes in brewing? Trusting confident but wrong outputs (including AI hallucinations), building models on inconsistent data, over-engineering simple problems, and believing vendor hype. Most failures trace back to bad data or misplaced trust, not bad algorithms.
Does AI really hallucinate in brewing work? Yes. Generative AI will produce precise, authoritative, and completely wrong figures — recipe numbers, regulatory rules, tasting descriptions. If you don’t verify every output, it will eventually burn you.
How do you avoid getting burned by AI? Verify every number against real records, prefer the simplest tool that works, be ruthless about data quality, and never let a confident output replace your own judgment.