Consumers are using artificial Intelligence (AI) in obvious places: diet recommendations, symptom checkers, product comparisons, and “best food” lists. These tools can organize information quickly and summarize options in a way that feels helpful and authoritative.
None of this is inherently negative. None of this is inherently negative. These tools are fast and can make research quick and easy.
But they also reinforce the idea that nutrition can be precisely modeled, optimized, and controlled based on available data.
Where Does AI Get Its Information?
AI does not generate knowledge independently. It draws from a wide range of sources, including independent websites, blogs, and books. However, it tends to favor information that is most widely available, commonly repeated, and internally consistent. In pet nutrition, that often includes a substantial amount of industry-produced promotional content. This does not make the information incorrect. But it does mean it isn’t neutral.
AI systems are very good at identifying patterns and summarizing commonly repeated ideas. They are not designed to evaluate bias, question underlying assumptions, or weigh competing interpretations of the same data.
As a result, widely published viewpoints can become reinforced simply because they are common, not because they are complete or accurate.
When you ask a question, AI doesn’t look things up like a search engine. It generates an answer by predicting the most likely sequence of words based on patterns it learned during training and the context of your question. If you use AI frequently, it can adapt to your language patterns and preferences and shape its answers accordingly.
That’s why it can sound knowledgeable and confident. It’s very good at producing language that fits the patterns of “a good answer.” But it does not:
- verify truth in real time
- distinguish between unbiased and biased sources
Pet Food Industry Use of AI
AI is also being used inside the pet food industry itself. Companies are increasingly using AI in formulation, analyzing large datasets to design diets that meet nutrient targets while optimizing for cost, ingredient availability, and manufacturing constraints. It is also used to improve production efficiency, manage supply chains, and predict consumer preferences. I recently read about AI predicting a food’s palatability long before it is tested with live cats.
This creates a self-reinforcing loop.
Industry produces products and content based on existing assumptions. That content becomes part of the information ecosystem. AI systems learn from and reinforce those patterns. Consumers then receive answers that reflect the same assumptions, often presented with increasing confidence.
Over time, this creates a sense of certainty in areas where the science is still evolving or where important variables are not fully accounted for.
The deeper issue is false precision. AI systems assume that nutritional data is accurate and stable, that ingredients are consistent, and that biological responses are predictable.
But the truth is that nutrient databases are incomplete, ingredients vary by source and processing, and individual animals respond differently. The result is a system that can be highly optimized on paper, while still being based on imperfect assumptions.
No current AI system reliably distinguishes between independent and industry-shaped information on its own. It reflects what is most available and most widely published, which in pet nutrition often includes a substantial amount of industry-produced content.
How to Use AI Effectively
None of this makes AI useless. It can be very helpful for organizing information, summarizing options, and helping people ask better questions. I use it in my research all the time.
Where caution is needed is in how those answers are used. Over-reliance on generalized advice, assuming confident answers are correct, and replacing observation with algorithmic output can all lead to poor decisions.
If you use AI tools, the quality of the answer depends heavily on the question. A few simple changes can make a big difference.
- Instead of asking for the best diet, ask where there is disagreement or uncertainty in feline nutrition.
- Instead of asking if a food is good, ask what sources the recommendation is based on and whether they are independent or industry-affiliated.
- Ask about limitations. What might be missing from the recommendation? What factors are not being considered?
- Ask for several alternatives rather than a single “best” answer. Compare a few reasonable approaches and their pros and cons.
- Bring the question back to the biology of the individual. What factors matter for this specific cat? Include the cat’s age and medical issues in the question.
- And occasionally, ask the system to challenge itself. “What assumptions are built into your answer, and how might they be wrong?”
AI works best when it is used to explore uncertainty, not just to deliver a pat answer.
In the end, AI is a tool. A powerful one. But it does not replace experience, observation, or biological reality.