Key takeaways:
- The narrative that large language models and native AI can replace many – if not most – SaaS companies ignores the breadth of these legacy entities’ business models and management teams’ ability to adapt.
- Far from just coding, SaaS companies add value through deep domain expertise, workflow design, integration, compliance, and security – functions that are much harder to replicate with AI.
- AI versus SaaS is not an either/or proposition; there is room for both to thrive, borrowing the other’s strengths and in some cases, likely partnering.
SaaS stocks have fallen sharply out of favor as investors grapple with the fear that “vibe coding” and native artificial intelligence (AI) could replace large segments of the enterprise software marketplace. The selloff in SaaS names is the most recent example of how disruptive innovation has the potential to upend existing business models, if not entire industries.
That said, to paint all SaaS companies as existentially threatened by native AI, in our view, is an overly simplistic narrative that ignores the likelihood that many software leaders will adapt in this new era. For that more favorable outcome to occur, SaaS companies will have to demonstrate their resilience, especially as business models migrate from traditional seat‑based pricing to usage‑based consumption. Complicating this transition is uncertainty about AI’s long‑term economics.
False promises?
The narrative that AI can instantly replace most SaaS applications dramatically oversimplifies the problem. Coding represents a fraction of enterprise software value. The remaining share is rooted in deep domain expertise, workflow design, integration, compliance, security, data governance, and deterministic (instead of probabilistic) outcomes. While this part of software is less exciting, it is also harder for large language models (LLM) to replicate. We have consistently stated that AI disruption is never linear and that infrastructure and workflow layers introduce natural governors that should slow down full replacement cycles.
To be sure, native AI software companies are encroaching on SaaS’s turf, and many investors understandably may prefer these upstart’s attractive growth profiles. Still, it is far too early to declare SaaS dead. Incumbents capable of re‑architecting their platforms around LLMs, agentic workflows, and AI‑native design patterns, in our view, have the potential to adapt just as many legacy systems as applications-software companies did when navigating transition from on-premises businesses to the cloud.
We believe several best-in-class incumbents are in the catbird seat as they are uniquely positioned to become the disruptors rather than the disrupted. We view 2026 as a sorting mechanism, revealing which SaaS vendors evolve and which could become structurally impaired.
Focusing on the right risks and opportunities
Valuation compression highlights the pessimism. Application software stocks now trade at roughly 20 times 2027 earnings – well below their historical average and below a market multiple[1]. Software has fallen roughly 18% year to date versus the NASDAQ Index, which is only modestly negative. This discount reflects real competitive concerns, but it also represents potential opportunity if some of the beaten down companies can demonstrate credible AI roadmaps. While it is tempting to focus on valuation, we believe it is less of a risk factor than is execution volatility around AI adoption cycles.
Expanding the pie?
This is not just about replacing incumbent SaaS vendors with AI native versions or agents from the LLMs (e.g., Claude, Gemini, ChatGPT, Grok); it is also about expanding the total addressable market. Long-term, AI has the potential to augment labor. Labor operating expense spent is orders of magnitude larger than the $500 billion to $1 trillion spent on software. The decision between SaaS and native AI doesn’t have to be either/or. Incumbents who successfully incorporate AI can grow sustainably at a healthy rate while LLMs and AI native software companies also do well.
Strategy, execution, results
The future is unlikely to be strictly “AI replaces SaaS” or “SaaS survives unchanged.” More realistically, AI will encroach on some SaaS categories while simultaneously empowering a subset of incumbents that successfully reorients their platforms around LLMs, agentic automation, and workflow orchestration.
As this unfolds, we expect consolidation to accelerate as vendors acquire native AI capabilities, rationalize product portfolios, and rebuild their value propositions. In this hybrid future, both displacement and reinvention should define the competitive landscape. The winners, in our view, will be those who treat AI as a foundation, not a feature. Relatedly, those who rebuild margin structure around productivity gains versus legacy pricing strategies could be competitively advantaged as they demonstrate their value proposition.
In our view, the next generation of software winners will likely be those that embrace AI not as a feature but as the foundation of their operating models.
IMPORTANT INFORMATION
Artificial intelligence (“AI”) focused companies, including those that develop or utilize AI technologies, may face rapid product obsolescence, intense competition, and increased regulatory scrutiny. These companies often rely heavily on intellectual property, invest significantly in research and development, and depend on maintaining and growing consumer demand. Their securities may be more volatile than those of companies offering more established technologies and may be affected by risks tied to the use of AI in business operations, including legal liability or reputational harm.
Technology industries can be significantly affected by obsolescence of existing technology, short product cycles, falling prices and profits, competition from new market entrants, and general economic conditions. A concentrated investment in a single industry could be more volatile than the performance of less concentrated investments and the market as a whole.
National Association of Securities Dealers Automated Quotation (NASDAQ) System is a nationwide computerized quotation system for over 5,500 over-the-counter stocks. The index is compiled of more than 4,800 stocks that are traded via this system.
1 SaaS price-to-earnings ratio is non-GAAP and is based on a peer group we use of ~60 application software companies, as of 4 February 2026.