AI Agents Are Not Ready to Replace Work Yet—but They Are Already Changing Prices, GDP, and the Rules of Online Stores

In the context of “AI Agents Are Not Ready to Replace Work Yet—but They Are Already Changing Prices, GDP, and,” this criterion applies: agency becomes an economic force before it becomes a reliable digital employee — it already affects prices, GDP, and platform rules, even though it does not yet replace work.

For the “How bans affect the market” scene, the decisive point is this: new rules and bans change not abstract safety but concrete access, price, and behavior — the effect shows where the rule is enforceable and it is clear who is accountable for a violation.

The “Who is responsible for AI errors: a comparison with books, courses, and offline experts” issue should be assessed with one constraint in mind: a book, a course, and an expert have an author and accountability, while a model's answer has a blurred chain; so the question is not whether AI errs but who answers for the harm and how it is verified.

The “The new Update button in ChatGPT: why it was made and how it actually works” topic becomes clearer once this point is included: the new button and Deep Research modes improve individual scenarios, but the interface logic still makes a person figure out what to click and why the system asked the wrong question.

The decision in “Deep Research, Ask, and UX pain: where the logic of large models breaks” depends on one criterion: a million enterprise customers do not remove the UX pain — the modes' value appears only when the interface stops making you guess the right step and no longer loses part of the request.

The discussion of “How AI affects US GDP growth” yields a practical test: models speed up documents, code, search, and support, and part of the productivity gain enters GDP; but the net effect is hard to compute — companies spend on licenses, infrastructure, and checks, and the saved hour sometimes goes to fixing the answer.

In the context of “Andrej Karpathy: disappointment in autonomous systems and NanoChat,” this criterion applies: an autonomous system can run a long time, but errors accumulate, tools change, and infrastructure is expensive; so trying to automate the whole process in advance is often worse than a narrow assistant that does one step and hands the result back to a person.

The discussion of “Delta Airlines and AI ticket prices: optimization or hidden discrimination” yields a practical test: agency exists even without a human-like interface — a system analyzes demand and can set different prices for different customers; for the business it is optimization, for the user a risk of hidden discrimination, and the question is not the algorithm's ‘intelligence’ but which decisions it has been allowed to make.

For the “Amazon vs Perplexity: agents pretending to be human and the future of e-commerce” scene, the decisive point is this: an agent enters a store like a person and acts on that person’s behalf, while the platform wants to know who is really making the request. The future of e-commerce depends on rules for identity, access, and commissions. AI agents are not yet ideal workers, but they are already forcing companies to rewrite economic and legal relationships.