Tech • AI • Robotics • Game
Are We All Going to Lose Our Jobs Because of AI?
5/10TL;DR
Rapid advances in AI are reshaping creative and technical work, reducing the value of routine production while increasing demand for business expertise, tool implementation, and personalized support.
KEY POINTS
Routine production faces pressure
Cheap, widely available AI tools are now producing work that recently required paid specialists, especially in areas such as motion design and basic coding. Tasks that once cost a few hundred or a few thousand euros can now be done through low-cost subscriptions, sharply lowering the market price of technical execution. The shift is especially severe for roles centered on production alone rather than strategy or domain knowledge.
Technical skill alone is losing value
The strongest warning concerns workers whose value is limited to pure execution, such as developers focused only on writing code or designers focused only on making assets. The emerging view is that technical output is increasingly commoditized, while knowledge of a client’s business, constraints, and goals is becoming the real differentiator. Professionals who understand why a tool is being built, not just how to build it, are better positioned to remain useful.
New jobs are emerging around adoption
Even as some tasks are automated, AI is creating demand for new forms of work, including automation setup, AI consulting, workflow design, and internal training. Many organizations still have not seriously explored these tools, leaving a large gap between available technology and practical deployment. That gap creates room for specialists who can help companies choose tools, integrate them into daily operations, and train staff to use them effectively.
AI is becoming a training tool itself
One notable change is that people can now use systems such as Claude, ChatGPT, or Codex to teach themselves. These tools can generate structured lessons, exercises, quizzes, and step-by-step guidance tailored to a user’s level and goals. That personalization challenges the value of generic online courses, especially those sold at scale without hands-on support.
Mass-market courses face skepticism
Broad, lightly supervised training programs are portrayed as increasingly outdated because conversational AI can already deliver explanations, examples, and feedback on demand. Group courses with a Discord server and occasional live sessions are seen as weak substitutes for continuous, individualized coaching. As a result, low-personalization training may struggle unless it offers something distinctly human that current tools cannot match.
One-on-one guidance may retain value
Personalized mentoring still appears more defensible, particularly when it involves direct help setting up tools, building projects, and solving specific operational problems. Higher-cost support can remain relevant because it addresses context, accountability, and real-world implementation rather than abstract instruction. In practice, the premium is shifting from access to information toward guided execution.
Small business problems are a practical entry point
A concrete path into the market is to identify repetitive tasks that consume time for people nearby, such as craftsmen, teachers, or other local professionals. Building a small tool for free to solve one of those problems can serve as a prototype and proof of value. If it saves time and works reliably, the same solution can then be offered to others in the same trade, creating a route from experimentation to business development.
CONCLUSION
The main divide is no longer between people who can use digital tools and those who cannot, but between those who pair AI with business insight and those who rely on technical production alone. As automation expands, the strongest opportunities are likely to go to workers who can identify real operational problems and deploy practical solutions.
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