I'd argue the opposite.

There is plenty of AI extensions, but the experience matters. The depth of integration matters. When you execute queries against production warehouses and you make decisions based on the results of AI-generated code, accuracy matters. We had our first demo of an AI agent running in 2 days, it took us another 2 years to build the infrastructure to test it, monitor it, and integrate it into the existing data source.

You'd be surprised how many people collaborate together. Software engineering is solitary, collaboration happens in GitHub. But data analysis is collaborative. We frequently have 300+ people looking at the same notebook at the same time.

.py never worked for data exploration. You need to mix code, text, charts, interactive elements. And then you need to add metadata: comments, references to integrations, auth secrets. There are notebooks that are several pages long with 0 code. We are building a computational medium of the future and that goes beyond a plaintext file, no matter how much we love the simplicity of a plaintext file.