Where Traditional SaaS DD Falls Short when Evaluating AI Investments

The AI Gold Rush: High Stakes and Higher Valuations

The AI sector is experiencing a gold rush, with startups commanding astronomical valuations and raising enormous funding rounds. This frenzy makes it more crucial than ever for investors to conduct structured, in-depth due diligence. But why can't we simply apply traditional software and SaaS investment strategies to AI deals?

Why AI Due Diligence Differs from Traditional SaaS

1. The Unique AI Development Cycle

AI development follows a fundamentally different path compared to traditional software:

Stochastic vs. Deterministic: AI models often produces different results on each run, even with the same input data. This variability necessitates statistical approaches to evaluation and performance guarantees which is very different than traditional deterministic software and SaaS systems.

Iterative vs. Linear: The process involves continuous cycles of model training, evaluation, and refinement. This iterative nature can make project timelines and resource allocation more challenging to predict compared to traditional software development.

Quality Testing Challenges: Evaluating AI performance requires sophisticated metrics and methodologies. AI models may perform well on test data but fail in real-world scenarios due to distribution shifts or edge cases. Comprehensive testing often requires sophisticated simulation environments and diverse, large-scale datasets to ensure robust performance across a wide range of conditions.

Redefining SLAs: Service Level Agreements take on new meanings in the context of AI-driven services.Traditional SLAs often focus on uptime and response times, but AI services require additional considerations. These may include guarantees on prediction accuracy, fairness across user groups, or the frequency of model updates to maintain performance over time.

2. Operational Complexities and Risk Profiles

The operational aspects of AI startups introduce unique risk factors that investors must consider:

Scaling Challenges: Many AI problems only become apparent at scale and in production environments. This can lead to unexpected performance issues or resource requirements that weren't evident during development or small-scale testing.

Talent Wars: Competition from Big Tech leaves startups struggling to hire experienced AI engineers. This talent shortage can significantly impact a startup's ability to innovate, meet development timelines, or maintain complex AI systems. While this competition is not new for SaaS based companies, high-salaries for AI engineers and limited talent for ML expertise in the market make the competitive dynamics for talent even more stifle.

Rapid Evolution: The fast-paced research environment and innovations pushed out daily by BigTech makes it difficult for AI startups to stay current. This rapid evolution can lead to the quick obsolescence of AI models or approaches, requiring constant vigilance and adaptation to remain competitive.

3. The Maturity Gap

The relative immaturity of AI development in the startup ecosystem creates additional unknowns:

Limited Production Experience: Many startups lack experience deploying AI at scale. This inexperience can lead to unforeseen challenges in areas such as model serving, data pipeline management, and handling real-time inference in production environments.‍

Emerging Enterprise Requirements: Startups struggle to keep pace with rapidly evolving enterprise AI needs. This gap between startup offerings and enterprise expectations can result in misaligned product-market fit or difficulties in closing deals with large, demanding clients.

Unknown Unknowns: Many factors remain uncertain, even for founders, due to the nascent nature of the field. These unknowns can include unexpected ethical implications, unforeseen regulatory changes, or novel technical challenges that only emerge as AI systems are deployed in diverse real-world scenarios.

4. Other Considerations

Regulatory and Privacy Risks: AI's dependence on data introduces new regulatory and privacy concerns that investors must navigate.

Evolving Moats and Use Cases: The competitive advantages and applications of AI are still in flux, making long-term value proposition assessment challenging.

Valuation Conundrums: AI startups often command high valuations without the ARR to support them, requiring new approaches to valuation.

The Investor's Dilemma: FOMO vs. Fatigue

AI startups often command high valuations without the ARR to support them, creating a unique challenge for investors. This phenomenon stems from the perceived potential of AI technologies and the scarcity of true AI innovation, leading to intense competition among investors. These unique challenges of AI investments create a complex landscape for investors, characterized by a paradoxical tension between Fear of Missing Out (FOMO) and investment fatigue. This dynamic underscores the critical importance of early and frequent risk mitigation strategies for AI investments. Traditional software and SaaS due diligence methods prove inadequate in this context, leaving investors exposed to heightened risks in their AI portfolios. Without specialized evaluation frameworks, investors face the dual perils of either missing out on transformative opportunities or making misinformed decisions based on incomplete understanding. This predicament emphasizes the need for a more nuanced, AI-specific approach to due diligence that can effectively navigate the intricacies of the AI startup ecosystem and provide investors with the insights necessary to make well-informed, strategic investment choices.

At Jazz Computing, we specialize in helping investors navigate this complex terrain, identifying risks and developing mitigation strategies to de-risk investments in this exciting field.