BEFORE YOU PRESS PLAY
What is discussed
AskMonty AI editorial noteSource consulted:
Montemagno distinguishes talking to AI from delegating a concrete task. He describes useful results, including tools for a chess course, alongside an agent that he says deleted a website: delegation needs clear instructions, backups and knowledgeable supervision. Quality depends on the model, tools, operator and budget; polished presentation can conceal mistakes. He then considers work itself: automating individual tasks may change fees, junior training and professional roles, while trust and human relationships retain value. A demonstration sponsored by Hostinger creates a website for an imaginary photographer, using deliberately absurd prices and generated details that need checking. His conclusion is practical: try a narrowly defined task, assess the outcome and refine the instructions, while paying attention to costs and control over the tools. These are his arguments and reported experiences, rather than guarantees about any product or profession.
Key points
- Delegation means requesting a specific outcome: without a clear brief, agents may multiply confusion rather than resolve it.
- His account of a deleted website introduces risk management: he advocates analysis, careful design and backups without promising absolute safety.
- The model, working environment, operator’s expertise and budget affect results. A polished but incorrect chess exercise illustrates why subject knowledge matters.
- Montemagno argues that websites should provide information agents can understand, including clear contacts, prices and descriptions. This is his recommendation, not a guarantee of indexing.
- The Hostinger demonstration is sponsored. The racket photographer, exaggerated fees and generated figures belong to the example; they do not describe a real business run by Marco.
- Automation may break a profession into tasks and change its economic value. His proposed response is to charge for expertise and outcomes rather than only for time spent.
- Training remains an open question: newcomers need to develop the experience required to spot errors. Trust, reassurance and human relationships remain part of a professional service.
- Montemagno argues for controllable AI tools to address risks and concentrated power; he also observes that using more agents may increase spending even as unit costs fall.
- His closing invitation is to experiment with a concrete task, check what was produced and improve the instructions in response to the results.
