As AI commoditizes software, the most defensible businesses are no longer asset-light SaaS models. Instead, companies with physical world operations, regulatory moats, and liability are safer investments. Their operational complexity, once a weakness, now serves as a formidable barrier against pure AI-driven disruption.
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In the AI arms race, competitive advantage isn't just about models or talent; it's about the physical execution of building data centers. The complexity of construction, supply chain management, and navigating delays creates a real-world moat. Companies that excel at building physical infrastructure will outpace competitors.
Instead of selling software to traditional industries, a more defensible approach is to build vertically integrated companies. This involves acquiring or starting a business in a non-sexy industry (e.g., a law firm, hospital) and rebuilding its entire operational stack with AI at its core, something a pure software vendor cannot do.
As AI commoditizes user interfaces, enduring value will reside in the backend systems that are the authoritative source of data (e.g., payroll, financial records). These 'systems of record' are sticky due to regulation, business process integration, and high switching costs.
CEOs of platforms like ZocDoc and TaskRabbit are not worried about AI agent disruption. They believe the immense complexity of managing their real-world networks—like integrating with chaotic healthcare systems or vetting thousands of workers—is a defensible moat that pure software agents cannot easily replicate, giving them leverage over AI companies.
The long-held belief that a complex codebase provides a durable competitive advantage is becoming obsolete due to AI. As software becomes easier to replicate, defensibility shifts away from the technology itself and back toward classic business moats like network effects, brand reputation, and deep industry integration.
Established SaaS companies can defend against AI disruption by leaning into their role as secure, compliant systems of record. While AI can replicate features, it cannot easily replace the years of trust, security protocols, and enterprise-grade support that large companies pay for. Their value shifts from UI to being a trusted database.
AI doesn't kill all software; it bifurcates the market. Companies with strong moats like distribution, proprietary data, and enterprise lock-in will thrive by integrating AI. However, companies whose only advantage was their software code will be wiped out as AI makes the code itself a commodity. The moat is no longer the software.
Oren Zeev argues against the narrative that AI will kill all incumbents. He believes businesses with operational complexity, deep data moats, and strong distribution are not easily disrupted. These companies are more likely to leverage AI to their advantage, while simpler software companies are at greater risk.
The 50-year supremacy of asset-light software may be an anomaly. If AI makes software creation nearly free, economic value will shift back to the historical mean: tangible assets like infrastructure, energy, and regulated, liability-bearing businesses that touch the physical world.
As AI's bottleneck shifts from compute to data, the key advantage becomes low-cost data collection. Industrial incumbents have a built-in moat by sourcing messy, multimodal data from existing operations—a feat startups cannot replicate without paying a steep marginal cost for each data point.
