We don’t have an accurate model for weather, so we can’t predict it well.

I don’t see a reason to assume our model for climate is accurate, either.

Weather may or may not be random. It could be entirely deterministic for all we know. However, we lack the ability to fully model all the factors that contribute to weather and therefore our predictions are inaccurate.

Now let’s consider long term climate predications. Do you think these predictions are more like coin flips, where we have an extremely accurate model of the process, or more like weather, where unknown unknowns have outsized impact on accuracy?

That’s not to say climate change isn’t real, but your analogy doesn’t make sense.

No, I’m asserting that an LLM can help formulate ideas in a coherent, understandable way. You can give it a brain dump with a rough outline for an argument and it fill in the details. If the argument isn’t to your liking, you can try again. But the end result is basically equivalent to the human-written equivalent.

You seem to be implying Salesforce’s business is successful because they have their own query optimizer. But the causality is reversed. Salesforce has their own query optimizer because they’ve built a successful business.

However, the hypothesis in the SaaS market is that LLMs have made software have zero value and therefore the SaaS companies will be less profitable. That’s like if wood was suddenly free, expecting home builders to go out of business. If anything, home builders are going to do better, because they can apply their expertise while deploying capital elsewhere. We should expect software companies to be more profitable, not less.

Of course, there are exceptions. Sometimes AI replaces the product itself, e.g. image generation models vs. contractors on fiverr.

SaaS stocks are currently the buying opportunity of a lifetime.