The YTD-26 decline in software-specific public indices¹ vs. the relatively flat overall market likely means that the emergence of AI solutions is having a negative effect on all software-as-a-service (“SaaS”) companies. Interestingly, one interpretation of this recent reaction in the public software markets assumes that SaaS companies are capitulating on AI.  We do not believe this to be the case.  SaaS businesses, whether they have the right to exist or not, and even non-software companies, are investigating how to implement AI in an effort to generate new revenue and reduce the cost of production.

There will be winners among the new crop of native AI companies as well as among the existing SaaS players and there will, of course, be losers in both categories.  The outcome for any particular company, SaaS or not, will likely depend on management execution capabilities, flexibility within processes and systems, and an ability to attract talent, specifically to implement these AI solutions. 

Let us set the context first, however, with a few charts that show the potential impact of AI.  Figure 1 suggests that AI-related solutions could be larger than the cloud revolution and perhaps even greater than all of the prior computing innovations combined.

The underlying thesis suggests that AI is likely to affect the entire labor market and potentially displace large swaths of it.   For example, let us look at two of the most direct applications of AI: engineering / coding and customer support.   AI is often credited with being able to produce basic code within minutes vs. the days or longer it might take a human coder to develop the same code.  While an experienced coder could be more productive using AI, an inexperienced coder may be less efficient because they may not know whether, or how, to edit or incorporate the AI generated code.  Similarly, customer support is a job which often requires incessant problem-solving skills without becoming burnt out.   AI is available around the clock, not emotionally affected by negative customer interactions, and able to handle call routing and scheduling and multiple customers at the same time.  Unlike prior technologies that needed to be built from scratch or required infrastructure to deploy, whether it was electricity or the computing network, the delivery mechanism for AI already exists as any user with a phone can download AI and start using the technology.

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A SAAS <> AI FRAMEWORK

How will the immense potential of AI solutions affect the current software environment?  One way is through an understanding of the three different kinds of existing SaaS companies and how AI is likely to affect the prospects of each.  Inspired by the efforts of Alex Rampell, a general partner at Andreessen Horowitz, below is a framework of the SaaS universe:

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MARGINS

AI native and traditional SaaS companies can reflect fairly distinct margin profiles. 

• SaaS delivered what was once viewed as the best business model in history.
• AI applications presently exhibit a margin profile that is not as profitable as SaaS, and the former can generate muted margins due to high inference costs in some instances.²  In certain respects, a low gross margin posted by an AI start-up could be interpreted as a positive signal indicating that users are truly utilizing the product, which is responding to queries (i.e., the more queries that are run push up inference costs because an AI model has to ‘think’ or compute a response).
• Sitting at the top of the software stack, AI applications may be exhibiting muted profitability because they encumber the expense of inference comprised by the cost of GPU chips, electricity, rent from data centers, and the costs of the foundational players through which the queries are run.
• These inference costs are expected to decline precipitously as the underlying model technology (and energy / processing requirements) continues to improve representing a “Moore’s Law” for AI.²
• Even if inference costs do not decline rapidly, AI businesses could still generate profits through higher revenue as their products continue to expand and gain traction through the replacement of Tier 2 / 3 software companies as observed above.  However, those companies may not be valued at the levels that investors are expecting today should lower margins hold.

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VALUATIONS AND PUBLIC MARKET REACTIONS

The valuation of public markets, particularly public software companies, could be considered to be priced to perfection, as public holdings are theoretically reflective of all information available to every market participant. Announcements that growth at SaaS businesses may not surpass expectations have resulted in sometimes acute corrections.³

• The market may be throwing the baby out with the bathwater.  We believe some SaaS companies will continue to not only exist but thrive with their own AI implementations.  The challenge is to figure out which will prevail (i.e., Tier 1 companies) and which may fail (Tier 3 companies). 
• As detailed in Figure 3, some of the more established public market SaaS companies are growing between 15-20% with the fastest growing 60%.  However, AI companies are presently expanding at a faster clip than their SaaS counterparts, having notched triple-digit year-over-year revenue gains since 2023.⁴  Even though the valuations of public software companies may decline, there is a financial rationale to pricing private AI assets at higher multiples.
• Customer CTOs may continue maintaining their existing SaaS products, but could be withholding approval for new projects and saving budgets for AI-related solutions.  It would not be surprising if while SaaS-based revenue of a private company is flat, its new AI products are generating hundreds of millions of sales within a short period of time.
• However, despite seemingly robust growth in revenue exhibited by certain AI-native startups or products, investors must determine whether that revenue is sticky, and what valuation those revenues are worth.  That valuation question has fundamentally been the challenge for any new market development.  Getting in at the right price with a transformative technology can lead to substantial returns.  Paying too much, however, even with the correct thesis, may lead to mediocre results if not outright losses.  This is a crucial and difficult factor to consider in the AI market today.

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A FUTURE OF DISRUPTION

While it is not obvious how or which companies will be changed by AI, it seems inevitable that AI will have a profound impact on companies, technologies, and the humans that inhabit this planet Earth.  Will AI enhance the productivity of workers or be one of the few technologies that actually destroys labor (with potential gains accruing to the asset owners)?  Could any systemic productivity gains from AI temper inflation and warrant lower interest rates?

Perhaps most importantly for the markets, what valuations should investors ascribe to both existing SaaS and emerging AI businesses as the technological shift evolves?  Successful (and perhaps lucky) investors will select the companies that are able to capitalize upon AI’s trends while avoiding those that are wiped out by it.

FOOTNOTES AND IMPORTANT INFORMATION

¹YTD-26 as of March 9, 2026.  BVP Nasdaq Emerging Cloud Index: https://cloudindex.bvp.com/; S&P Software & Services Select Industry Index: https://www.spglobal.com/spdji/en/indices/equity/sp-software-services-select-industry-index/#overview

²A16z, State of the Markets (as of January 22, 2026): https://a16z.com/state-of-markets/

³Meritech, Time’s Up for Saas (Grow Faster of Vanish) (as of February 5, 2026): https://meritech.substack.com/p/times-up-for-saas-grow-faster-or; Financial Times, Salesforce chief dismisses ‘SaaS-pocalypse’ fears of AI overtaking business software (as of February 25, 2026): https://www.ft.com/content/b74b8227-d7cb-4976-ba95-a3a27b79cbdd

⁴A16z, State of the Markets (as of January 22, 2026): https://a16z.com/state-of-markets/

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