Beyond AI: The Two Competitive Advantages That Actually Matter in Startups

Claiming to leverage AI in your business pitch no longer signals competitive strength—it merely describes a technology layer. This year's Products That Count Product Awards received nominations from 97% AI-integrated products, making it clear that AI adoption has transitioned from differentiator to baseline expectation.

The real question investors now ask is fundamentally different: What would remain defensible if a competitor with superior funding and a better model launched tomorrow? Researchers examined Crunchbase data covering 576 venture-backed AI B2B companies that completed funding rounds of $50 million or more starting in 2025, applying Hamilton Helmer's 7 Powers framework alongside insights from Products That Count's community of over 600,000 product leaders.

The findings reveal an uncomfortable truth for many founders: when development costs approach zero, only advantages that models themselves cannot replicate provide genuine protection. Among the seven traditional competitive powers, exactly two function effectively without requiring founders to outspend giants like OpenAI, while two others present significant pitfalls, and one remains largely unexploited.

Counter-positioning: The zero-cost defense

Counter-positioning emerges when a startup constructs a business architecture so fundamentally distinct that established players cannot adopt it without dismantling their own profit engines. The Netflix-versus-Blockbuster comparison remains the definitive illustration: Blockbuster possessed the capability to embrace subscription models but recognized that doing so would eliminate the late-fee revenue streams sustaining their physical locations. Strategic inaction became their undoing.

Within the AI startup landscape, this advantage remains scarce and underdeployed. Merely 5% of analyzed companies employ counter-positioning, yet investors reward this rarity with a median valuation multiple of 5.3x relative to capital raised—the strongest multiple across all competitive powers examined.

Real-world applications include vertically integrated AI insurance providers selling directly to employers, a distribution method that traditional insurance brokers cannot replicate without severing client relationships and eroding their underwriting margins. Similarly, AI-native revenue management platforms would cannibalize the high-margin consulting services that legacy software vendors depend upon, preventing those incumbents from matching the innovation.

The incumbent recognizes the competitive threat but chooses not to respond. This deliberate restraint constitutes the moat itself. Founders should ask themselves: Could a well-capitalized incumbent theoretically adopt my model? The correct answer signals opportunity when it reads: Technically possible, but the financial damage to their existing business exceeds what it would cost us to build.

Network economies: The self-reinforcing advantage

Network economies materialize when each additional user increases the product's value for all existing users. This dynamic frequently produces winner-take-most results within defined boundaries—geographic regions, professional communities, or industry sectors. LinkedIn exemplifies this pattern: more recruiters attract more job seekers, who in turn draw more professionals, which subsequently attracts additional recruiters.

Network economies characterize only 5% of companies in the dataset, yet command a 4.2x valuation multiple. This represents the most capital-efficient route to premium valuations currently available to founders.

The B2B instantiation deserves greater recognition. Rather than individual people forming network nodes, these systems connect organizations: manufacturers to brands, audiences to advertisers, partners to platforms. Each additional participant amplifies value for every existing participant.

Information generated through these interactions—manufacturing costs, production timelines, audience preferences—accumulates and strengthens in ways that become progressively more difficult for competitors to duplicate. While recruiting both sides of a two-sided marketplace simultaneously presents genuine challenges, founders who overcome the cold-start problem possess something that even a well-funded competitor with superior technology cannot simply purchase.

Competitive powers that mislead

Cornered resources—proprietary datasets, distinctive intellectual property, and exclusive partnerships—appear in 44% of the dataset, ranking second in prevalence. However, they generate the weakest valuation multiple at 2.6x. Investors have witnessed too many proprietary datasets eroded by foundation models and synthetic data generation. Unless a data advantage compounds in ways that grow progressively harder to replicate, it fails to constitute a genuine moat.

Switching costs represent the most prevalent power at 37% and superficially resemble a moat because customers genuinely remain loyal. The underlying problem lies in the expense of constructing them. Achieving deep enterprise integration—embedded systems, organizational knowledge, architectural risks—demands costly sales processes before stickiness materializes. The valuation multiple approximates network economies at 4x, yet the capital expenditure required to reach that level runs roughly 10 times higher. Founders pursuing this path can reduce costs through product-led growth strategies or by engineering collaborative features that transform switching costs into network effects over time.

Scale economies remain peripheral to most founders' strategies. When excluding OpenAI and Anthropic, the median multiple drops dramatically from 6.1x to 3.2x, with those two companies accounting for 88% of capital deployed in this category. Assuming unit economics improve with volume does not equate to building a scale advantage. The difference between these concepts measures in billions of dollars that most startups will never access.

The singular decisive question

Every company in this analysis incorporates AI into its offering. Those commanding premium multiples have constructed something beneath the AI layer that models cannot independently generate.

That something is structural in nature. It resides in business model architecture or network design rather than model sophistication, feature breadth, or data quantity. It answers the question every founder must articulate in a single sentence: What about my business would survive a competitor who starts today with more capital and a better model?

Inability to answer this question indicates you are constructing a product. Founders commanding 4x to 5x multiples are constructing a power.

SC Moatti serves as founding managing partner of Mighty Capital and board chair at Products That Count. Recognized on the Kauffman Top 30 Index and Power100, she has invested in pioneering companies including Amplitude, Netskope, and Groq. During the cloud and mobile era, she built products used by billions at Meta and Siebel Systems, earning industry recognition and nominations from The Wall Street Journal and the Emmy's Foundation. She authored an award-winning bestseller on product excellence, holds a master's degree in electrical engineering and an MBA from Stanford, and is a Kauffman Fellow and Young Presidents Organization member.