The AI market is unlikely to settle into a simple winner-take-all outcome. Instead, AI will diffuse through the economy as a set of competing ecosystems, each winning different segments...

2025-11-28 · Source: Pascal’s Substack · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Advanced, long

Summary

A RAND working paper, "Multi-Ecosystem Competition in Artificial Intelligence Adoption and Diffusion" (April 2026), posits that the AI market will not consolidate into a single winner-take-all scenario. Instead, it predicts a fragmented ecosystem where frontier, low-cost, and specialized AI models coexist. This fragmentation is driven by users valuing diverse factors beyond raw capability, including cost, integration ease, trust, compliance, reliability, and suitability for specific workflows. The paper introduces "relative net value" as the primary driver of AI adoption, encompassing perceived value minus cost, and considering factors like implementation risk, maintenance, and legal certainty. This framework suggests that technically superior models may fail commercially if they are difficult to integrate or legally uncertain, leading to durable market segmentation across various sectors.

Key takeaway

For CTOs and VPs of Engineering evaluating AI solutions, you should prioritize "relative net value" over raw model benchmarks. Focus on how well an AI system integrates into existing workflows, its compliance, reliability, and total cost of ownership, rather than just its technical superiority. This approach will help you build a resilient, multi-model AI stack tailored to specific organizational needs and legal constraints, avoiding premature lock-in and maximizing long-term value.

Key insights

AI adoption hinges on "relative net value," not just raw model power, leading to a fragmented market.

Principles

• AI markets will be segmented, not winner-take-all.
• Integration often outweighs benchmark performance.
• Cost competition is not a durable long-term advantage.

Method

The paper adapts the Bass model of technology adoption to AI markets, incorporating switching dynamics and "relative net value" as the key adoption driver, to predict market fragmentation.

In practice

• Evaluate AI based on "relative net value" for specific use cases.
• Adopt a portfolio strategy for AI models.
• Invest in AI literacy and procurement discipline.

Topics

• AI Market Segmentation
• Relative Net Value
• Multi-Ecosystem Competition
• AI Adoption Drivers
• Licensed AI Content

Best for: Investor, CTO, VP of Engineering/Data, Director of AI/ML, AI Product Manager, Consultant

Related on AIssential

• AI Eats the World? A Reality Check with Benedict Evans — AI's rapid coding adoption highlights market disequilibrium and unresolved questions about long-term value capture.
• Germany and Europe's AI sector has strong research but few models, limited compute, and regulations that favor US competitors — Germany and Europe excel in AI research but struggle to convert it into models and economic value due to regulatory and infrastructure gaps.
• The AI Revolution will not be Monopolized — AI's future will be a diverse ecosystem of many developers and projects, not a monopoly by a single winner.
• The Updated Map of AI — AI industry leaders are vertically integrating across infrastructure, models, and distribution, consolidating power.
• How Do We Fix This? — Focus AI investments on use cases where returns outpace costs to achieve economic viability and avoid negative unit economics.
• anthropic vs. openai — Anthropic has surpassed OpenAI in new enterprise AI adoption, driven by targeted technical focus and broader non-technical applications.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Pascal’s Substack.