The Model Is Not The Product

InnovationThe Model Is Not The ProductByJochen Deister,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)May 26, 2026, 11:45am EDT​Jochen Deister is the founder and VP of innovation and strategy at Privacy Solutions. gettyIf you have used an AI tool, you have probably had this experience. You query AI about something in your domain, and it gives you an answer that is well-structured, clearly reasoned and properly sourced. It sounds right. But you have a nagging sense that you should verify it, and then you realize that verifying it would take almost as long as doing the work without AI. So you don’t. You move on and hope the answer was correct. It sounded plausible after all, didn’t it?​That moment, the one where you choose to trust the AI’s output because checking it would cut into your productivity gains, is worth examining. Because there is a gap between the answer you get and the model that produces it. And if you don’t reckon with this discrepancy, it can put your business at risk. How LLMs Actually LearnAI training pipelines are robust. They filter aggressively for text quality, removing spam, duplicates and poorly written content. Some use classifier models to identify educational or high-value material. By the time data reaches the model, it has passed through multiple rounds of curation.​But I believe this is the critical distinction: These filters assess linguistic quality, not domain-specific accuracy, because how could they “know” what’s right and what’s not? LLMs are notorious for hallucinating and producing believable but incorrect or misleading text. ​You may be willing to accept a reasonable amount of risk in certain AI applications, such as in marketing or customer service, but not in others, such as in legal or healthcare. Yet I am seeing companies indiscriminately incorporating AI into high-risk applicat...