Generative AI & SaaS: Is the Future of Software Changing?

The software sector is undergoing a significant repricing, fueled by the rise of generative AI and a reassessment of long-held assumptions about growth. Investors have shed roughly $1 trillion in value across software companies in recent weeks, signaling a growing concern that the traditional Software-as-a-Service (SaaS) model may face fundamental challenges in a world where AI can increasingly perform tasks previously requiring human effort.

Currently, software trades at an average multiple of around 6x EV/LTM revenue, a dramatic compression from the 20x multiples seen in 2021 – a 70 percent decrease. Horizontal software, applicable across industries, has experienced the most significant contraction, now trading at around 3x multiples. This shift isn’t simply a market correction; it reflects a deeper questioning of the future value proposition of software.

Why SaaS Became a Dominant Force

For nearly two decades, SaaS revolutionized enterprise technology. Prior to its widespread adoption, businesses faced substantial capital expenditures (capex) and operational costs associated with purchasing and maintaining physical servers and in-house software. SaaS offered a compelling alternative: renting software as a service, allowing companies to focus resources on core business functions. This model facilitated faster deployment of modern tools and reduced the friction associated with adopting new technologies. While switching wasn’t always seamless, the overall trend was clear.

Beyond cost savings, SaaS delivered superior products. Dedicated teams focused on specific enterprise problems could develop more effective solutions. Multi-tenancy – where a single instance of software serves multiple customers – enabled rapid improvements and widespread benefits. Dashboards became more intuitive, information became more accessible, and collaboration was enhanced. The return on investment (ROI) was compelling, driving the growth of an industry that reached approximately $1 trillion in enterprise software spend last year.

The Contractual Engine of SaaS Growth

A key, often overlooked, aspect of SaaS growth was the prevalence of long-term contracts. These contracts typically included annual price increases, often in the range of 3-5 percent, and sometimes as high as 10 percent for leading companies. This built-in expansion provided a baseline for revenue growth, reducing the demand to secure entirely new business each year. This contracted expansion was supplemented by efforts to broaden software deployment within existing customers, sell additional capabilities, and acquire new clients.

Historically, seat-based licensing was a common growth driver. However, expanding seat counts becomes increasingly difficult as internal penetration reaches its peak, particularly in organizations with limited headcount growth. Fortune 500 companies, for example, have seen employee growth of less than one percent over the past few years. Companies have refined their go-to-market strategies, optimizing processes and forging partnerships to improve conversion rates. While these efforts deepen market penetration, they don’t inherently expand the overall market size. Simultaneously, rising interest rates have shifted investor focus away from prioritizing growth at all costs, leading to increased scrutiny of software budgets.

The most successful SaaS companies proactively addressed these limitations. They didn’t rely solely on seat expansion and price escalators. Instead, they consistently pursued market expansion, broadening their product lines, entering adjacent markets, and identifying new customer segments. Companies like Collibra and Dataiku, within the Dawn Capital portfolio, exemplify this approach through continuous product innovation and platform expansion.

Generative AI’s Impact on SaaS

Generative AI is now challenging the top line even for the best SaaS companies, not because SaaS is becoming obsolete, but because the mechanisms driving compounding growth are being questioned. While attention is focused on the rapid growth of AI-native companies, particularly in hardware like Nvidia, enterprise budgets haven’t expanded commensurately. As noted last year, the enterprise return on investment for generative AI is still in its early stages.

Even public market leaders like Datadog and ServiceNow, actively introducing new AI-powered products, haven’t yet experienced substantial topline expansion. Datadog reports that 12 percent of its revenue is now attributable to AI-native customers, growing at 17 percent quarter-over-quarter (up from 6 percent eighteen months ago). ServiceNow’s AI product, Now Assist, generates $600 million in annual recurring revenue. However, these AI revenues remain relatively small compared to overall revenues, making it difficult to discern their immediate impact on topline growth.

In practice, limited budget growth forces enterprises to make difficult choices. Expansion seats are questioned, non-essential tools are consolidated, and multi-year commitments are delayed as buyers assess the potential of AI.

Cyclical Shift or Structural Change?

The central question is the duration of this slowdown and the nature of the underlying changes. Generative AI is undoubtedly poised to develop into pervasive in the enterprise. The uncertainty lies in the pace of transformation and, crucially, the impact on the unit of value that software monetizes. SaaS was traditionally priced on the assumption that software usage scales with headcount. AI introduces the possibility of value scaling without a corresponding increase in headcount.

This doesn’t necessarily spell the end for successful software businesses. A portion of the current slowdown is likely cyclical. Enterprise buyers are adopting a wait-and-witness approach, evaluating the potential of AI before committing to significant new SaaS investments. However, this caution creates technical debt. Deferred upgrades, delayed migrations, and aging integrations don’t disappear; they accumulate. Software typically represents only 1-2 percent of revenue for Fortune 500 companies, reducing the immediate pressure to replace high-performing software.

Historical precedent suggests that enterprise transitions accept time. The shift from mainframe to SaaS spanned 25 years and continues to unfold (IBM mainframes still generate billions in revenue and are growing), demonstrating the slow pace of enterprise change. Elite software companies have time to adapt, but not the luxury of inaction. Early data from Datadog and ServiceNow suggest that incumbents who embrace AI can absorb it rather than be disrupted by it. They must aggressively pursue every AI-related opportunity within their existing use cases and customer base, understand how their solutions will function in a fully agentic world, and actively court AI-native companies as customers.

AI will genuinely disrupt categories where viable SaaS alternatives were lacking. Software development is a prime example, with AI coding assistants already transforming how code is written and reviewed. Customer service is another, where AI agents can handle interactions previously requiring both software and human agents. In these emerging categories, AI isn’t competing with established SaaS solutions; it’s filling gaps that SaaS never addressed. The winners in this next era will be those who deliver capabilities their customers cannot replicate and tie those capabilities to measurable value. SaaS isn’t dead, but the rules of the game have changed, and companies that recognize this fastest will shape the next decade of enterprise software.

Shamillah Bankyia is a partner at Dawn Capital