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SaaS Isn't Dying: It's Going Logarithmic

It has been a tough stretch for software companies, and investors at all stages are rightly asking what to do with the sector. As in most periods of relative out/underperformance for a given sort of technology company, hyperbolic narratives often emerge to extrapolate the current trend out ad infinitum. For the last decade, SaaS was “eating the world,” and I remember well the heady feeling of being an investor in the sector that seemed to be bringing the fire of innovation to industry after industry and functional area after functional area. 

Now, with the sector struggling, it is more important than ever to be clear-eyed about what we’re seeing and what it tells us. My view is that the software sector's current struggles result from its being on the flat part of the S-curve while being addicted to and architected for exponential growth. We can speculate (and I will at the bottom of this piece), but it is likely too early to tell whether AI is a help or a hindrance to this dynamic.

An S-curve is a simple and effective mathematical model that is as useful in biology as in tech investing- when an organism capable of exponential growth (say, algae in a lake) is introduced to a new environment, it grows exponentially until environmental resistance kicks in, at which point growth slows into a logarithmic curve approaching an asymptote which reflects the environmental carrying capacity. 

Cloud-native software was on an exponential curve from its birth in the late-1990s until perhaps 2020, when the pandemic represented an infusion of fertilizer which further accelerated the growth past the steepest point where the shape of the curve changes. This shows up clearly in the numbers: of the ~50 public SaaS companies I keep models for, net new ARR was down in 2023 relative to 2022 and will recover only modestly this year (but is still below 2022 levels). Environmental resistance has kicked in, in the form of budgetary reviews, platform consolidation and “spend rationalization.” It is impossible to argue that we’re in the heady exponential days of SaaS adoption- at the sector level, each dollar of incremental ARR will be harder to generate than each dollar before. 

The industry simply has not adapted to this dynamic. Companies routinely cite a “tough macroeconomic environment” on their earnings calls despite unemployment near all-time lows and a stock market near all-time highs. After a pause in CY23, SaaS hiring is back in full force, with more headcount than ever. The typical scaled SaaS company continues to run a net loss on a GAAP basis and makes almost no effort to justify the massive level of investment implicit in running a business so far below its terminal margins. Management practices that were helpful shorthands in an age of exponent are wasteful and value-destructive in a logarithmic era. 

As bad as all of that sounds, there is nothing to fret about here per se over the long run. It isn’t surprising that an adjustment away from behaviors learned over two decades. It is actually an altogether more benign explanation for the current malaise than some of the others circulating. In this scenario, SaaS still has a healthy decade of well-above-GDP growth ahead of it (without considering AI’s impact). There are still plenty of exciting startups which for one reason or another are less mature than the sector overall and merit investment levels that harken back to headier days. We're experiencing a normal adjustment period of a disruptive, exponentially growing technology- not an existential crisis.

AI hasn’t featured much here because I genuinely believe that, other than perhaps distracting enterprise buyers, AI has nothing to do with the current challenges in software. So far, the historic advantages of SaaS software are maintained. They can easily and efficiently push new features and functionality to their users, they have strong distribution advantages, they are often embedded in expensive-to-change workflows and, at scale, they benefit from herd familiarity and other network effects that make displacing them difficult. 

Will AI reduce the cost of producing software to the point where those advantages are obsolete? We’ll see- but that could just as easily boost the margin profile and defensibility of the incumbents by trivializing their tech debt. It is already dramatically cheaper to build software than it was when, say, Salesforce was founded, yet here it stands. 

What I’m watching far more closely as a harbinger of an extinction-level event is the potential obsolescence of the graphical user interface. It is certainly possible to imagine a world where interfacing becomes so intuitive and seamless that we see a new breed of tools emerge that look and feel so fundamentally different that the phrase “legacy SaaS application” takes off. That would make for interesting times indeed, but for now we’re just watching one of the great technological disruptions of our time progress steadily toward an inevitable but perfectly healthy asymptote.

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