Meta's argument for open source AI is that powerful models should be available for developers and organizations to inspect, adapt, and deploy without depending entirely on closed platforms. The position also serves Meta's strategy: a broad open ecosystem can make its models harder to ignore.
The debate is bigger than one company. Open models can lower costs, support local deployment, and make research easier to audit. They can also spread capability faster, including to people who may use it badly. That tension is why open source AI keeps returning as a policy and education issue.
For learners: ask what is actually open. Model weights, training data, licenses, safety evaluations, and deployment restrictions are different pieces of the puzzle.