Can Software Survive AI?

Does AI pose an existential risk to the software industry? Investors have been grappling with that question leading to profound sell offs in software stocks this year. But are their concerns actually merited? And how can investors sort out the winners from the losers? I'm Allison Nathan, and this is Goldman Sachs Exchanges. Today, I'm joined by Gabriela Borges, who covers the software sector with Goldman Sachs Research. We recently featured Gabriela in our latest top of mind report, which explored these very questions. Gabriela, welcome to Exchanges.

It's a pleasure. Thank you for having me.

And this is the first time,

so I'm excited to have

this conversation. It's been a very tumultuous time for the software industry, so great to have you. So, Gabrielle, let's just set the stage. Generative AI has been around for several years now, but the market narrative around AI seems to have really shifted from one of enthusiasm to one of concern and concern in particular in recent months about AI disruption risk, with the focus really on your sector, on the software sector. So walk us through why that is and what made investors rethink the prospects for software companies?

Yeah. Absolutely. It's been a really interesting three months. And if I were to pick out a couple of the milestones that really had investors level up the amount of attention that they were paying to software in particular and enterprise software even within that, I would say it started with significant improvements in coding algorithms. So that's things like Anthropics Clawd Code, which is a tool that helps you code more effectively if you're a developer. In January, Anthropic then put a wrapper around Clawd Code that made it accessible to the average everyday knowledge worker like you and me

head in the last three months. Interesting. So there have been real technological developments really ushering in this concern. But then when we talk about where the software companies sit among on this, so as you just said, increased competition. But we're hearing a lot about these moats and whether they are defensible. Talk to us about what those are and what they mean for the space.

There's a couple of examples that we've been that we think have been resonating with investors. If you think about the cybersecurity space as an example, a company like CrowdStrike, they're actually collecting data on endpoints. What is an endpoint? It's a mobile phone. It's a laptop. It's a server. And when they collect that data, they actually have human in the loop doing reinforcement learning and infusing threat intelligence, threat data, and their own tradescraft into being able to label that data for cyber attacks. CrowdStrike's been doing that for ten plus years now. So the data set the

lot of dynamism, as we know, in terms of the evolution of technology and of these enterprises. So what will it take for you to be convinced that those moats will remain defensible or that AI enabled native companies and tools will catch up?