new arbiter of truth op-ed: What happens when we start asking AI instead of each other?

Asking chatbots instead of people shifts trust to opaque AI. We need to audit what each model ‘knows’ and make that invisible layer visible before machines become the uncontested arbiters of truth.

Nobody “googles it” anymore, not really. Increasingly, they open ChatGPT, Claude or Gemini and ask outright: “Which bank is best for me?”, “Is this supplement a scam?”, or “How bad is load shedding going to get this winter?” Search engines would hand you 10 blue links in response to your questions and leave it up to you to synthesise the final answer. Chatbots skip that step. They give you an answer, in a confident, conversational voice, with no rival results to compare it against, and no cognitive work required on your part.

That shift, from browsing to being told, is disintermediating almost every institution that used to sit between a question and its answer: newspapers, review sites, search engines, even friends. And a new industry has grown up around it almost overnight. Marketers now chase “AI visibility”, or “generative engine optimisation” (GEO), the chatbot-era answer to “search engine optimisation” (SEO, ensuring that your website appeared at the top of search rankings). Where SEO was about ranking in a search engine list, GEO is about being mentioned first by an AI when there is no list at all, just a paragraph.

It also turns out that the AI doing the answering matters more than people assume. LLMEKNOW, a South African-built tool that tracks how AI models talk about brands, organisations and issues for clients around the world, has been running the same questions across models to see how they differ. In one study of South African banking queries, Claude drew on live web sources in every single response; Grok and GPT-5 searched the web in roughly nine out of 10 responses; Gemini searched in fewer than two out of 10; DeepSeek never searched at all, answering entirely from its frozen training data. In other words, ask a live question, and which model you happen to open decides which version of the internet answers it.

These differences become important for modern marketers; whether a brand or an organisation. What AI models tell their users increasingly impacts what we think and believe. For example, LLMEKNOW put the same question about Nando’s to seven AI models (ChatGPT, Claude, Gemini, Google Search Summaries, xAI, Kimi and Deepseek) 364 times, pretending to be from five different countries. The sentiment with which AI models responded to the same question about the peri-peri chicken chain swung wildly by market: an average AI-sentiment score of 50.8/100 in Australia against 70.5/100 in Malaysia. It was the same brand, same product, and same question, suggesting that the brand’s reputation varies depending on which national conversation the AI model thinks it's catering to. It also demonstrates that just showing up in the responses is not the same as being liked, and a brand can be omnipresent in AI responses while being talked down in some markets and up in others.

Closer to home, LLMEKNOW’s study of South African banks asked five AI models what the best bank is 3,763 times.