If you work in product or tech, you have probably sat through this meeting. A vendor demo or viral post arrives, and suddenly the roadmap needs an agent, an MCP app, or a harness. Client meetings often begin the same way with "we need an agent for this." After decomposing the request from first principles, the client frequently needs something different: a custom predictive model, better use of the LLMs they already have, or no AI at all. Someone has to say "not so fast" without sounding defensive, and that is hard because the AI conversation is loud and convincing.
The article offers three moves to stay grounded. First, understand the AI value chain and where you realistically sit in it. Second, build a foundation of knowledge that lets you structure and reuse what you learn about AI. Third, consume information intentionally while keeping your own perspective. Together, these practices make you a stronger sparring partner when the next hype wave hits your team.
The value chain runs from chips at the top to enterprises at the bottom. Returns are close to banked at the top, where silicon is paid for on delivery, but grow less certain further down. Cloud providers and model labs are betting on future demand, and much of that demand circulates inside the ecosystem itself. Real end-user demand is only decided at the end of the chain.
If you are an enterprise adopter or application developer, you sit on the receiving end. AI companies promote an "accessibility illusion" that AI is cheap, easy and transformative, while vendors know the limits of their products far better than you do. Surveys underline the gap: more than 80% of companies using generative AI reported no clear impact on overall profits, according to McKinsey's 2025 survey.
Source: Towards Data Science · Summarized by HeadlinesBriefing