Do AI agents make companies more generic? Been chewing on this and want to see where it breaks. Take two companies. Similar tech stack, same AI models underneath, agents doing more and more of the actual work. One sells CRM software. The other sells machine tools. Now assume neither company has clearly encoded the reasoning that makes it the company it is. The judgment calls. The “we don’t do it that way here.” The why behind the rules. The risk tolerance. The lessons people picked up over years but never wrote down in a form an AI agent can actually use. Give it a year. My worry is that the agents drift toward whatever the underlying model defaults to, because that is the only logic consistently available to them. The CRM company and the machine tools company may still have different products, customers, and logos. But operationally, their agents may start making the same kinds of calls in the same generic way. Whatever made them different was never really in the model or the infrastructure. It was in the business reasoning nobody encoded. So the questions I keep landing on: Do AI agents make companies more generic if the company’s own reasoning is not encoded? Is the real moat becoming how much of your operating logic you can make usable by AI, not just how much data or scale you have? Is “governance” even the right word for this? Because this does not feel like just access control, rate limits, or safety filters. It feels more like keeping a business’s specific reasoning attached to what AI agents do while they are doing it. Where is the hole here? Is this just abstraction, or are people seeing versions of this already? submitted by /u/rohynal
Originally posted by u/rohynal on r/ArtificialInteligence
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