Rumors of the Death of Software are Greatly Exaggerated
When people talk about the end of software, oftentimes it’s because of a too narrow definition of software.
In some cases, the issue is mostly semantic. When Marc Benioff famously declared the “End of Software” in 2000 and staged a fake protest outside of the annual Siebel user conference, it was a genius marketing move, but the end of software marked the beginning of Software-as-a-Service. Software was never going to die. The old way of building and delivering software was. Similarly, AI is not going to kill software, it is software.
However, there are at least two lines of thought that aren’t merely semantic:
The first alleged cause of death is that running a SaaS company is now like managing a fast-food franchise: high competition, limited differentiation, and low margins. The argument is that software development has become so easy that there’s no differentiation or defensibility in the product or the technology.
I don’t find this argument very compelling. If I think about the companies from our portfolio and other startups we’re talking to, product is a key factor. Always has been, always will be. Knowing what to build and forming a great product and tech team that can deliver on that roadmap is crucial. Sales and marketing are, of course, critically important too, but so are product and tech. This is why the vast majority of P9 portfolio companies have one or more technical co-founders.
It’s true that it has become much easier to build a basic SaaS application. What gave a company an edge 5–10 years ago is now table stakes, but that doesn’t mean that the best entrepreneurs don’t find new ways to differentiate and out-innovate the competition.
With AI (more on that below), this trend will continue and accelerate. Thanks to products like GitHub Copilot and our portfolio company Poolside, more and more code will be written by AI. If everyone is equipped with better tools, the bar will go up further. But it doesn’t follow that there’s no room for differentiation unless you assume that what can be built is somehow finite.
An adjacent argument relates to the fact that in the last 15 years, the SaaS business model has moved from obscure to obvious. The idea is that SaaS metrics have become so well understood that SaaS companies are perfectly priced. As a result, there’s no opportunity for outsized returns for investors and investing in SaaS companies will resemble investing in a broad index of stocks.
I would argue that this is wrong. If you look at most public SaaS companies and do a regression analysis of metrics like growth, FCF margin, NRR etc. vs ARR multiples, these metrics explain only 50–60% of the multiples. So the rest must be due to less obvious metrics, views on the market, quality of the leadership, or other subjective factors that smart people disagree on. If this is the case for large SaaS companies in the most efficient capital market, imagine how things look like for a Series A or B company, let alone a seed-stage startup, where not easily quantifiable factors play a much, much bigger role.
Similarly, a few years ago people argued that SaaS has become so predictable that it can be financed with debt. One of the things I love about SaaS is that recurring revenues make it comparably predictable, but as the last few years have shown, there’s still a huge amount of uncertainty. Look at how growth rates of SaaS companies have declined across the board — I think no one expected this.
When you hear about the end of software these days, it’s usually related to AI. There are two flavors of the argument.
One is that thanks to AI, software development will become so cheap that every market will become hyper-competitive. This is related to the first point in the franchise argument. I very much agree that software development is being revolutionized by AI, but again, my thinking is that it won’t prevent the best teams from innovating.
A related line of thought is that software development will become so easy that the market for software products will shrink, and companies will develop more custom software based on exactly what they need. This may happen in some areas, but I doubt that non-tech companies will suddenly become awesome at building great software for their teams.
The other flavor of the argument is that all of the value will accrue to a small number of foundational model providers and hyperscalers. The idea here is that AI is going to replace software as we know it. How could this look? I guess no one knows exactly, but imagine you have a highly intelligent successor of ChatGPT that has access to all of your company’s files and documents (or let’s say data and knowledge, maybe by that time we will no longer have files) and that can do actions on your behalf. If you think about what people do when they use a business application, a significant part of it is looking up some information from a database (e.g. a customer record in a CRM), doing some action (e.g. sending an email to the customer) and updating the database (e.g. updating the CRM). With a very large context window, RAG, or other techniques to make data accessible for an LLM, you can imagine a future version of ChatGPT acting like an application based on high-level instructions that you provide in natural language.
The way you’ll interact with this “application” could be a combination of natural language (you tell the AI what to do, via text chat or voice) and a UI that the AI creates on the fly based on the input it needs from the humans. **If you think this is far-fetched, ask ChatGPT to act like an address book, enter some contacts, and look up some “records” from your “database” based on different criteria. You might be surprised how well this works already today.