In the world of AI applications, companies are automating jobs vs. providing workflow tools for existing information workers. This has led to a set of companies (including in our portfolio) with extremely high ASPs and extraordinary growth rates. Companies are willing to pay a lot more for software that automates an entire job compared to software that makes an existing job more efficient.
The question is what happens down the road. Workers are easy to hire and fire. In fact, much easier to hire and fire than ripping out workflow software. So in theory, these AI nurses, call center agents, SDRs, bookkeepers etc will also be easy to hire and fire. Companies can simply try vendor A today and switch to vendor B tomorrow if they provide an automated agent that is cheaper, better, or faster, potentially leading to massive price compression over time. [Look what happened to the BPO space]
Models can always be improved on and the quality of a model is a tough thing to rely on for defensibility. In the early days of website personalization, many startups got formed to provide recommendation engines ("if you like product A you will also like product B"). These engines initially took off and provided massive lift to the ecommerce sites...but over time, the websites would bake-off these incumbent vendors against new entrants. If the new entrant was better, then the ecommerce site would switch. As a result, despite the value delivered, this space never became huge because there was churn and price compression.
Will this happen with this current crop of AI software. Unclear. Personally, I don't think defensibility will be based on training data or quality of the model/engine. Ultimately, the best of these newer companies will win because they are deeply integrated into their customers' workflow and because their solution is being touched or interfaced with lots of information workers on daily basis.