I have to be honest: anyone not at least a little afraid is likely missing the architectural shift happening in the backend. The standard counter-argument is a

I have to be honest: anyone not at least a little afraid is likely missing the architectural shift happening in the backend. The standard counter-argument is always "hallucination rates." I have had this debate with medical pros who use AI for diagnostics. They laughed when I warned them about 3% error rates, noting that human error rates are often worse. But that misses the real point: relying on raw model output is already outdated. I am a Team Lead for security testing at a major crowdtesting provider. I see a massive disconnect between public perception and enterprise reality. People judge the risk based on generalized chatbots like ChatGPT or Gemini which are optimized for cheap, broad usability. They do not see the specialized agentic workflows being deployed behind closed doors. With frameworks like MAKER, we are seeing error rates drop to near-zero even with cheap APIs like GPT-5-Nano. It is just engineering: break jobs into micro-tasks, multi-agent voting, strict format validation. If a JSON output does not match the schema, it is rejected and re-rolled automatically. The "hallucination" problem is being solved by process, not just better models. In my day job, I watch clients validate plans to replace 90% of their developers. And this is happening right now. Mass layoffs have already started. Personally, I have no fear of not finding a job, but MY job being replaced? Well, to be honest: I think I am the only one who could actually implement that correctly. I am certainly not going to automate myself away on purpose, but I take the risk that it could happen absolutely seriously. The bubble might pop for investors, but the efficiency gains are too real for companies to ignore.