The 281st Investor Summit, held on September 17, brought together private investors, venture capital participants and family office leaders to examine one of the most closely watched questions in technology investing: how the rapid development of artificial intelligence is changing the way investors evaluate SaaS businesses. Rather than treating AI as a separate investment category, participants examined its relationship with established software models, enterprise adoption, valuation metrics, operating efficiency and the growing role of AI agents.

The discussion revealed that investors are approaching the AI-versus-SaaS question from several different angles. Some participants emphasized the risk of disruption for companies that fail to integrate AI, while others pointed to the continued importance of established software platforms, enterprise data and embedded workflows. Across the discussion, the emphasis repeatedly returned to measurable business performance rather than technology narratives alone.

AI as an Extension of SaaS and a Source of Disruption

One of the central themes of the summit was whether AI will ultimately replace traditional SaaS or become an increasingly important layer within it. The discussion suggested that the answer depends largely on the strength of a SaaS company's underlying value proposition. Businesses with established customer relationships, valuable enterprise data and deeply embedded workflows may retain their position, while companies whose products can be easily replicated or replaced may face greater disruption.

Several investors described AI as a tool that can make existing software more efficient, improve product capabilities and create new ways of using data. At the same time, the pressure to integrate AI is becoming more significant. Participants discussed the possibility that SaaS businesses that fail to adapt could gradually become less relevant, particularly as customers become accustomed to software that can automate more of the work traditionally performed by people.

Elizabeth Addonizio, an investor from the United States emphasized that AI can strengthen the value proposition of existing technology businesses, while investment opportunities are also emerging around the infrastructure required to support the expanding AI ecosystem. Her comments reflected a broader view expressed during the discussion: AI is not only about applications, but also about the systems and resources that enable them. She said, “I think that a lot of companies that I have invested in the past are trying to see ways in which AI can make their value proposition easier, more efficient.”

An investor from Canada, similarly focused on the importance of adaptation rather than assuming that SaaS disappears altogether. His comments highlighted the continuing role of enterprise software while pointing to AI integration as an increasingly important factor in determining how individual SaaS businesses evolve. He said, “I think the key issue is to recognize that AI will not replace SaaS ”

Looking Beyond AI Hype: The Metrics Still Matter

A significant part of the discussion focused on how investors should evaluate AI-enabled software businesses. Participants questioned whether AI companies should be assessed using entirely new valuation frameworks or whether traditional SaaS metrics remain relevant. The discussion largely returned to established measures such as annual contract value, growth, customer acquisition cost, lifetime value, CAC-to-LTV ratio, payback period and churn.

Vishal Arora, Founder and Managing Partner at PanCosmic Capital, from the United States, argued that the presence of AI does not eliminate the need to understand the underlying economics of a software business. From this perspective, investors still need to determine how much revenue a company generates, how quickly it grows, what it costs to acquire customers and whether those customers generate sufficient long-term value. The technology may be new, but the business fundamentals remain central to evaluating the opportunity.
This perspective also connected to the resilience of established enterprise software. Participants noted that major enterprise platforms continue to be used because switching decisions involve more than the availability of a new AI tool. Governance, compliance, security, data location and cloud-sharing policies can all affect whether an enterprise is willing to replace an existing system. As a result, investors need to distinguish between technological capability and the practical ability of a new solution to replace software that is deeply integrated into an organization. He said, “When I am looking at AI companies, I'm actually looking at them with the same metrics and the same philosophy that I would evaluate a SaaS business.”

Honish Zaveri, partner at Kiani Ventures, from India, added another dimension to the discussion by pointing to changes in software pricing. While subscription models remain familiar to enterprise buyers, AI can introduce usage-based or volume-based pricing depending on how the product is consumed. This creates another area for investors to examine as they compare traditional SaaS economics with newer AI-enabled business models. He said, “I think a lot of companies themselves, in fact enterprises, especially large enterprises themselves are comfortable with subscription based businesses paying for the opex part.”

From Systems of Record to Systems of Action

The discussion then moved toward one of the more significant structural changes associated with AI: the possibility that software will increasingly be operated by AI agents rather than directly by users. An investor from the United States, described a shift from the traditional software-as-a-service model toward vertical AI applications that can actually perform tasks on behalf of customers. He said, “I think for the first time in 15, 20 years you're starting to see the system of record kind of shift.”

Under this model, established SaaS platforms may continue to hold important information and workflows while AI systems interact with those platforms to perform operational work. This creates a distinction between a system of record, which stores and organizes information, and a potential system of action, where AI performs tasks using that underlying information. The change could affect how investors think about the competitive position of software companies and where value ultimately sits in the technology stack.

Eran Savir, Founder & Managing Partner at Savyon Ventures, from Israel, expanded on this idea by arguing that SaaS platforms may increasingly require an agentic layer through which AI systems can operate the software. For investors, this introduces a new question: not simply whether a company has added AI functionality, but whether AI fundamentally improves the efficiency and operating model of the business. He said “We are seeing a fundamental shift in software economics. Traditional SaaS metrics like Net Retention Rate or the Rule of 40 were built for high-headcount models, but in an AI-native world, metrics like ARR and EBITDA per employee will define the next generation of defensible platforms.”

Eran also identified ARR per employee as an increasingly important efficiency measure. As AI allows smaller teams to perform more work, investors may pay closer attention to how much recurring revenue a company can generate relative to its employee base. This creates a new lens through which AI-first companies and more traditional software businesses can be compared.

The discussion also touched on customer acquisition and user behavior. AI-first companies with small teams, working products and early revenue may be able to develop and scale differently from previous generations of startups. Changes in customer acquisition, online user flows and the competition for attention could therefore become part of the broader investment equation alongside conventional SaaS metrics.

Infrastructure, Regulation and the Cost of AI

Another important part of the conversation was the realization that AI investment extends beyond software applications. Participants discussed infrastructure such as semiconductors, data centers, energy, water, communications networks and other suppliers that support the growing computational demands of AI.

Anneliese Sound, Managing Director at Future Potential Management, from Germany, highlighted several risks associated with this expansion. Increasing demand for infrastructure can place pressure on resources such as water, energy and copper, while higher interest rates can affect the economics of large infrastructure investments. She also pointed to the complexity created by the structures surrounding major technology infrastructure investments, suggesting that investors need to understand where capital is actually being deployed rather than simply following the headline AI narrative. She said, “Where I see a risk at the moment is AI and investments in AI are driving the prices regarding water, energy, copper and on the other hand the increasing interest rates at the moment are also a risk for hyperscaler and their investments.”

At the same time, regulation and accountability emerged as important considerations. AI systems can operate across sensitive areas, but responsibility for their outcomes ultimately remains with people and organizations. Participants therefore discussed data quality, trust, liability and the growing number of regulatory requirements affecting AI and technology businesses. European frameworks including GDPR, the EU AI Act, the Cyber Resilience Act and machinery-related regulation were cited as examples of the increasingly complex environment companies may have to navigate.

Mazin Gadir, Director of Healthcare Life Sciences at Alvarez and Marsal, from the United Arab Emirates, brought a related perspective from digital health and AI applications, emphasizing that promising use cases still need to meet evidence-based regulatory requirements. His comments illustrated how the investment case for AI can depend not only on what a technology can technically accomplish, but also on whether it can satisfy the regulatory and institutional requirements of its target market. He said, “So I think there's a lot of great use cases but I think the challenge will always be to get the evidence based regulatory approvals.”

A More Disciplined Approach to AI Investment

The summit ultimately presented AI as a technology that is changing software economics and operating models while leaving many fundamental investment questions intact. Investors discussed disruption, but also resilience; new AI-native businesses, but also established SaaS platforms; technological potential, but also customer adoption, regulation and measurable economics.

Dale Cohen, CEO at Topaz Asset Management, from South Africa, captured the uncertainty surrounding the current environment by describing AI as part of a broader economic inflection point. Rather than assuming that the direction of the market is already clear, he described taking a more cautious approach while observing how the technology develops. This perspective complemented the more operational discussion throughout the summit: investors are seeing significant opportunities, but they are also looking for greater clarity about which applications create durable value. He said, “I think that on a global macro basis we kind of feel like we're at an economic inflection point.”

The 281st Investor Summit therefore gave participants an opportunity to move beyond the simple question of whether AI will replace SaaS. The more detailed discussion focused on how AI changes the economics of software, how enterprise customers adopt new technologies, how agents may alter the role of established platforms, and which metrics can reveal whether an AI-enabled business is genuinely becoming more efficient.

The event brought together different investment perspectives around a rapidly changing technology landscape. For investors evaluating SaaS and AI opportunities, the discussion highlighted the importance of examining the underlying business model, customer behavior, efficiency, governance, infrastructure requirements and regulatory exposure alongside the technology itself. The combination of investor perspectives and subsequent one-to-one discussions provided a practical setting for examining how these factors may shape technology investment decisions as AI continues to develop.