Open and closed models are on different exponentials

2023-11-24 · Source: Interconnects AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Intermediate, medium

Summary

The economic divide between open and closed AI models will define the future balance of power, with each operating on different exponentials. Early 2026 is a seminal time, as coding agents have shown users will pay a substantial premium for top closed models like those from Anthropic and OpenAI. These labs will focus on integrated, efficient models, optimizing for intelligence, speed, or utility per watt, potentially forming an oligopoly valued at \$2-10 trillion in 5-10 years. Conversely, the open model economy, while having lower margins and commodity pricing, will achieve a far larger total market value by enabling diverse enterprises to build in-house solutions for niche tasks. This involves a wide stack of companies coordinating to serve non-integrated open models, with a steady rise in open model inference across hyper-scale clouds and new AI infrastructure companies.

Key takeaway

For Directors of AI/ML evaluating model adoption strategies, recognize that top-tier closed models will command significant premiums for critical knowledge work, akin to an oligopoly. You should budget for these higher costs where absolute intelligence is paramount. Conversely, leverage the open model ecosystem for broader, cost-effective enterprise-specific applications and niche tasks. Plan for a diverse AI infrastructure that integrates both model types to optimize for performance and cost across your organization.

Key insights

Open and closed AI models follow divergent economic exponentials, creating distinct market structures and value capture mechanisms.

Principles

• Users pay a premium for superior AI intelligence in critical tasks.
• Closed labs optimize for absolute intelligence and integrated systems.
• Open models drive commodity pricing and broad diffusion across diverse enterprises.

In practice

• Closed labs protect best models, rolling out later in APIs.
• Enterprises use open models for in-house niche agents.
• Customizing open models becomes easier via finetuning stacks.

Topics

• AI Model Economics
• Open-Source AI
• Closed AI Models
• Coding Agents
• Market Dynamics
• Enterprise AI Adoption

Best for: CTO, VP of Engineering/Data, AI Architect, Investor, Executive, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Interconnects AI.