Is SaaS Dead? | The Brainstorm EP 119

SAS, AI, and the Future of Software: A Deep Dive

Key Concepts:

• SAS (Software as a Service): A software distribution model where applications are hosted by a provider and made available to customers over the internet.
• Bessmer Cloud Index: A benchmark tracking the performance of publicly traded SaaS companies (excluding mega-cap tech).
• Payback Period: The time required for an investment to generate enough cash flow to cover its initial cost.
• AI Native SAS: Software companies built from the ground up leveraging Artificial Intelligence.
• Digital Intelligence/Tokens: Raw data processed and contextualized for use in AI applications, often charged on a usage basis.
• Innovator’s Dilemma: The challenge established companies face when disruptive technologies emerge, often failing to adopt them due to focusing on existing customer needs and profit margins.
• DATs (Digital Asset Treasury Companies): Companies holding crypto assets, offering investors exposure to the crypto market.
• NAV (Net Asset Value): The value of an asset minus its liabilities.

I. The Current State of SAS: Valuation and Uncertainty

The discussion begins with questioning the viability of the SAS model, given recent growth concerns, collapsing multiples, and layoffs. Frank, from Arc, asserts that the situation is “both” – a technology problem and a business model reset are occurring simultaneously. A key driver of this uncertainty is the rapid advancement and decreasing cost of AI models capable of building software. While AI won’t eliminate the need for software entirely (particularly for enhancing worker productivity), the competitive landscape will drastically change, leading to consolidation. The barriers to entry for software creation are now significantly lower, rewarding nimble teams over those relying on traditional renewal-based growth.

Data from the Bessmer Cloud Index illustrates this shift. Currently, the median SAS company can be purchased at a forward revenue multiple of 3.6x – the lowest since 2014. The payback period for investment (time to recoup initial cash flow) has dramatically increased, from 24 years in 2020 to 12 years currently, peaking at 37 years during the peak of the COVID boom. Despite this compression, the question remains whether even a 12-year payback period is acceptable given the ongoing uncertainty.

II. Growth Disparities: Application vs. Platform Software & the Rise of AI-Native Companies

A critical distinction is drawn between SAS applications (software for end-users) and platform/infrastructure software. Platforms and cloud services have been growing faster than SAS applications, a trend expected to accelerate with AI. While AI-native SAS companies (like ChatGPT and Claude for enterprise) are experiencing rapid growth, traditional SAS companies are facing deceleration.

The key to success in this new environment lies in effectively leveraging “digital intelligence” – contextualized data in the form of tokens – and aligning business models around its delivery. Palantir is cited as an example, growing over 100% year-over-year by charging based on access to and utilization of these intelligence tokens, rather than traditional seat-based pricing.

III. Pricing Power and the Competitive Threat from AI

The core concern revolves around pricing power. With potentially 15-20 competing versions of any given software solution, maintaining premium pricing will be challenging. The discussion highlights the difficulty of translating AI features into meaningful revenue growth for established players. Microsoft’s Copilot subscriptions, for example, represent a small fraction of overall Office 365 subscriptions.

This leads to a comparison with the newspaper industry, suggesting that traditional SAS companies may face a similar fate. However, the analogy is nuanced, as the scale and network effects of some SAS companies (like Salesforce) could provide resilience.

IV. The Innovator’s Dilemma Revisited

The conversation pivots to the Innovator’s Dilemma, questioning whether it remains a significant obstacle. The argument is made that the rise of readily available compute power (AI) lowers the cost of experimentation and internal disruption. Companies can now more easily deploy resources to develop competing solutions without needing to reallocate valuable human capital.

However, Frank counters that the core issue isn’t resource constraints, but rather the tendency of successful companies to prioritize the needs of their existing, profitable customer base, even if those needs don’t align with disruptive technologies. He points to the example of automakers resisting electric vehicles, and the potential for AI to be similarly overlooked. The sheer size of the potential AI market ($7 trillion by 2030) may force companies to overcome this inertia.

V. Crypto and the DAT Landscape

The discussion briefly touches on the recent volatility in the crypto market and the role of Digital Asset Treasury Companies (DATs). The current situation suggests a shift from retail-driven speculation to institutional adoption, hindered by regulatory uncertainty (specifically the need for a market structure bill in Congress). The trend appears to be retail investors selling to institutions, potentially anticipating further institutional investment. DATs that raised capital during the boom are now facing pressure to liquidate assets as they trade below net asset value.

VI. Key Quotes & Observations

• Frank: “The barriers to entry to create software are so low. You can copy features very quickly. Um and I think the teams that are nimble and shipping very fast will do better than the teams that are relying on oh well my customer always renews and expands a little bit and that's enough to fund my sales and marketing and R&D for next year.”
• Nick: “If you know the reason not to go after your disruptor is because you had to have historically taken capital in the form of labor, so like your best employees that are working on your core product and move them over to a new essentially internal upstart to try and disrupt your own business. Doesn't it then make sense that the dilemma in that gets wildly easier because you don't have to deal with labor internally?”
• Frank: “The companies that are going to stand out and do well and grow in this environment are the companies that are taking this new raw material which is digital intelligence in the form of tokens and applying it and delivering it into an enterprise in a unique and valuable way.”

Conclusion:

The future of SAS is uncertain, but not necessarily doomed. The industry is undergoing a significant transformation driven by the rise of AI. Success will depend on adaptability, a willingness to embrace new business models (moving away from seat-based pricing), and the ability to effectively leverage digital intelligence. While established players have advantages, nimble AI-native companies pose a significant threat. The Innovator’s Dilemma remains a relevant challenge, but the lower cost of experimentation with AI may make internal disruption more feasible. The crypto market, while volatile, is evolving towards greater institutional adoption, contingent on regulatory clarity. Ultimately, the companies that can deliver tangible value through AI and adapt to the changing landscape will be the ones that thrive.