Which SaaS survives? A mental model

It's a dangerous time to be investing in software. Falling knives abound but babies are being thrown out with the bathwater. After a couple weeks of reading and discussing, a few friends and I came up with a mental model for what's going on. I'm not saying this is complete -- it's a working theory and feedback is welcome -- but I think it's actionable and I wanted to share. 1) Software has commanded high multiples in the past because of its moats around switching costs, proprietary data, distribution channels, network effects, and brand. 2) Let's imagine that AI will move the cost of creating code towards zero, and AI agents will be able to do any digital task. 3) The risk to software is as follows: (i) once AI agents can perform a software migration, the switching cost moat will evaporate; (ii) LLMs don't need the same sort of training data that old ML models did, diminishing the impact of having proprietary data\*; (iii) finally, as employees turn to agents in the workplace, they become a distribution point for other software\*\*. 4) Given the above, one big change in the software industry is it will be easier for software companies to add adjacent features and migrate a customer. There will be a lot more competition and churn as the silos in software evaporate\*\*\*. 5) Agents are starting to look compelling, and one can imagine is that where we're going is that agents will be the front end to get things done in the workplace, with a lot of software capabilities relegated to being tools or data sources for the agent. Meanwhile many companies will be jockeying to be the agent for a given customer. 6) Who will win out? **The most compelling product for a given customer will be the agent that can work across the most functions for that customer (the largest "surface area").** Why buy the agent from Workday, which can do 15% of tasks, when the agent from Microsoft can do 75% of tasks? This will add pressure for each company to race to add as many capabilities as it can for its agent to have. **So that gives us the following model: a race to be the "agent of record" for customers, and losers get relegated to being backend tools or data sources for the agents. The most compelling sales pitch for an agent is that it can get a lot done. For most customers, that might be Microsoft. But that might not apply for SMB or in certain industries like healthcare.** \* For example, in the past you'd assume a lot of tax return data is needed to write a model that can do tax returns. Reasoning models are getting close to be able to doing a tax return without training on such a corpus. \*\* For example, you talk to Copilot on chat for some things, so you start asking things like "mark tomorrow as a sick day". Copilot interfaces with Workday to mark it as a sick day at first, but eventually they say "why don't you add our HCM plugin instead for some low price?" \*\*\* Most of this post is ground up thinking, but this particular point I took from Ben Thompson at Stratechery. I was originally thinking more about startups building software...