The inherent weaknesses of large language models are reason enough to explore other technologies, such as reinforcement learning or recurrent neural networks.
Apache Airflow is a great data pipeline as code, but having most of its contributors work for Astronomer is another example of a problem with open source.
Yes, a tiny number of companies have relicensed their open source code. Let’s worry about actual problems, like security and megacompanies that contribute almost nothing.
Without skilled developers supervising AI coding assistants, they are likely to break your code rather than write it. Right now, only people can fine-tune and evaluate AI.
Overspending on AI infrastructure by cloud providers has some forecasting an AI bust, but there are signs that enterprises are starting to put AI to work.
Fueled by vibes and with stars in their eyes, enterprises are not taking the time to understand genAI’s limitations and to create their own rules-based approach.