Many people use Artificial Intelligence (AI) like ChatGPT daily. But it is wrong to believe that these programs do not require training. The assumption that one does not need to learn anything for AI Large Language Models (LLMs) is a misconception. This mistake can be costly.
This misconception costs users and companies a lot of money and time. Those who use AI programs without a plan and practice miss out on great opportunities. It's not just about getting results. It's about achieving the best results and controlling the process. This helps not to lose touch in the fast-paced AI world.
Tech expert Timothy B. Lee sharply criticizes the assumption that AI language models do not require training. He compares this to saying that managers do not need to learn anything. This would be as if their employees simply do everything they are told. So the problem is not the AI itself. It is the expectation that the AI will 'work on its own'.
For individuals and freelancers, this means: Those who think a few quick instructions are enough waste time and quality. The ability to **give good instructions** becomes very important. Those who improve here have a clear advantage in job searching. Their own work performance also improves.
Companies that do not train their employees in the use of AI lose money. They buy expensive licenses for the programs. But they only use the tools superficially. Workflows do not really improve. Risks such as incorrect results or data protection violations remain undetected. This is a **classic mistake in calculating the benefit** (Return-on-Investment).
The biggest opportunity lies in **developing true AI managers**. These are people who not only know how to write an instruction. They also understand how the AI model 'thinks'. They can break down difficult tasks. They optimally control the AI and critically review its results. This creates an advantage over competitors.
The biggest risk is the **loss of control**. If no one in the team can critically review and adapt the AI's results, errors will creep in. This leads to poor decisions and inefficient workflows. In the worst case, it harms the company's reputation or leads to financial losses.
