A version of this post first appeared in City AM.

You’d have to be paying no attention not to have seen the recent repricing in publicly listed software companies. Investors have dumped a trillion dollars in value across multiple software names and sectors, and many have begun to reckon with a more uncomfortable idea: the terminal value of classic SaaS might look radically different in a world where generative AI can increasingly do the work, rather than merely help humans do it. As a result, software is trading at an average multiple of ~6x EV/LTM Revenue, vs~20x in 2021, a 70% compression. Horizontal software has been hardest hit, compressing to ~3x multiples.

We’ve been investing in enterprise software since 2007 and today have a portfolio of almost 100 software companies. For us, software, and SaaS in particular, has been a fantastic asset class. It grew at roughly ~20% YoY for the last two decades, and it did so with economics that were unusually attractive: low churn, high gross margins, and the ability to grow with customers over time. However, while SaaS has historically grown at ~20%, it is now growing at ~12% – an 8 percentage point decline. It’s also important to note that a lot of that slowdown began before any recent AI catalyst became an easy headline.

For years, the ROI case for SaaS was clear. Instead of buying physical servers and spending billions in capex and operational costs to run their own software, companies/enterprises could rent services and focus their businesses on what mattered most. Companies could amortise costs and deploy modern tools far faster than most in-house IT teams could build. The friction to adopt better software fell dramatically, and even if switching was never as easy as we all liked to imagine, the direction of travel was obvious.

On top of that, software products were just better. Having a group of people focused on solving a single enterprise problem meant they built better products. Multi-tenancy meant improvements for one customer could be rolled out across all customers, and products could get better and better. Dashboards looked better. Information was democratised across organisations. Collaboration was made possible by software.

In theory, this meant companies were more productive and could focus their efforts on growing the most strategic components of their businesses, primarily revenue. With such significant ROI, buying SaaS was a no brainer. An army of SaaS companies was born, growing to spur around $1 trillion of enterprise software spend last year alone.

But SaaS had a dirty secret: part of its historic magical growth was contractual.
Long-term contracts let SaaS companies bake in growth every year for the lifetime of a contract, often in the range of 3-5% via price uplifts and escalators. The best companies were able to raise prices by as much as 10% annually! This meant that to deliver the historical growth rates the market came to expect, companies did not need to find all of their growth from scratch each year. There was a base layer of contracted expansion, and then there was the work of finding the rest through broader deployment inside the customer, selling additional capability into the base, and adding new customers.

For many categories, the incremental engine was seat-based. But seat expansion is hard to come by once you have reached peak internal penetration, and it is especially hard in a world where headcount is not expanding quickly – Fortune 500 companies have had <1% growth in number of employees over the last few years. Against this backdrop, SaaS companies have been improving their go-to-market, optimising processes, finding accelerator partners and tightening conversion. Doing this deepens your penetration, but it does not magically grow the market you serve. At the same time, shifts in interest rates have meant that growth at all costs was no longer rewarded, and software budgets became a line item that CFOs were suddenly willing to interrogate.

The most elite SaaS companies found ways around these limits. They did not rely on seats and pricing escalators forever. They consistently chased market expansion. They expanded their product lines, moved into adjacencies, found new buyers, created new categories, either organically or inorganically. In our portfolio, Collibra expanded what it could do and who it could sell to through a steady cadence of new products and platform capabilities. Dataiku did the same, broadening its footprint and increasing the set of problems it could credibly solve for the enterprise.

Generative AI has now challenged the top line of even the best SaaS companies – not because SaaS is dead, but because the mechanisms of compounding are being questioned.

We are all marvelling at the growth of AI-native companies across the stack, including AI hardware winners like Nvidia. The reality, though, is that enterprise budgets have yet to grow meaningfully. As we wrote last year, we remain early on enterprise ROI for generative AI.

Even public market leaders like Datadog and ServiceNow, who are ruthlessly rolling out new products, have not seen significant topline expansion yet. In the case of Datadog, some of the growth has come from AI-native customers themselves. Datadog cites 12% of their revenue as AI native (and growing 17% QoQ and up from 6% 18 months ago), and ServiceNow’s AI product – Now Assist – now has $600m of ARR. However, AI revenues remain small versus broader revenues at many incumbents, so it is hard to see that growth shine through immediately in the top line.

In practice, enterprise budgets have not grown enough to fund everything at once, so the pressure shows up first in the marginal decisions: expansion seats get questioned, nice-to-have tools get consolidated, and new multi-year commitments get delayed while buyers wait to see what AI makes possible.

The key question is how long this lasts, and what exactly is changing. There is no doubt in our minds that generative AI will be pervasive in the world of business. The unknown is the pace of transformation and, more importantly, what happens to the unit of value that software monetises. SaaS was priced on the assumption that software scales with headcount; AI introduces a credible world where value scales without headcount.

None of this guarantees the end of elite software businesses. Our view is that part of the current slowdown is cyclical: enterprise buyers are waiting to see what AI can bring. It makes complete sense that enterprise leaders are wary of committing to huge amounts of new SaaS spend in the short term when there is even a small risk of it being considered legacy technology in a few years. However, this understandable caution is building technical debt. Deferred upgrades, delayed migrations, and ageing integrations do not go away, they compound. And, given that software is only 1-2%, of revenue for Fortune 500 companies, the immediate instinct is unlikely to be ripping out highly performant software that is difficult to replace. This gives the best SaaS companies some breathing room.

History suggests enterprise transitions take time. The move from mainframe to SaaS took 25 years, and it is still happening: IBM mainframes still generate billions in revenue and are growing, a reminder of just how slowly enterprise transitions are made. So, elite software companies likely have time to catch up, but not the luxury of moving slowly. Early data from Datadog and ServiceNow suggests incumbents who move fast can absorb AI rather than be disrupted by it. They need to aggressively hunt every AI dollar in their use case and their customer base. They need to quickly understand whether the problem they are solving will exist in a completely agentic world. And they need to land AI native winners as customers, because the Fortune 500 could look different five years from now.

Our view is that where AI will genuinely disrupt, rather than enhance, is in categories where good SaaS alternatives never fully emerged. Software development is the clearest example: AI coding assistants are already reshaping how code gets written and reviewed. Customer service is another, where AI agents can handle interactions that previously required both software and headcount. In these greenfield categories, AI is not competing with entrenched SaaS, rather it is filling gaps that SaaS never closed. We believe the winners in this next era will be the companies that do the hard things their customers cannot do themselves, and tie that capability to measurable value. SaaS is not dead. But the game has changed, and the companies that recognise this fastest will be the ones who define the next decade of enterprise software.

Huge thank you to Norman Fiore who helped support this piece! We’re backing teams shaping what the next generation of software will look like, so please reach out to us at shamillah@dawncapital.com and norman@dawncapital.com.