The situation with mass layoffs at OpenAI demonstrates a fundamental paradox in the artificial intelligence industry. The company, whose business model is built on automating cognitive tasks, strictly prohibits the use of algorithms in the process of creating "intelligent" systems. This is not merely a bureaucratic incident, but an indicator of a deep crisis in the RLHF (Reinforcement Learning from Human Feedback) methodology.
The essence of the conflict lies in the requirement for absolute purity of training data. If algorithms are trained on answers generated by other algorithms, there is a risk of creating a closed loop that amplifies hallucinations and reduces the creativity of systems. OpenAI likely encountered that automating the evaluation of answers led to the degradation of metadata quality. Human intelligence here serves not as a resource, but as a standard of truth that cannot be replaced by a surrogate.
However, this step also points to the vulnerability of business processes. The company's position undermines its own narrative about AI being a tool for ubiquitous efficiency improvement. For the professional community, this is a signal: automation has limits, especially where critical thinking and ethical assessment are required. The firing of "neuro-gastarbeiter" for using tools they themselves evaluate highlights the gap between the declared mission and real requirements for data quality. In the long term, this may lead to the need to revise approaches to content verification in the era of generative systems.