Anthropic messed up

2026-07-19 · Source: Matthew Berman · Field: Business & Management — Corporate Strategy & Leadership, Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Fundamental Awareness, quick

Summary

Anthropic, despite possessing "the smartest model on the planet" (Fable) and significant revenue, is hampered by a strategic misstep made two years ago: an overly conservative investment in compute capacity. This decision means the company cannot adequately serve its large, high-performing model today. In contrast, OpenAI maintains a competitive edge by offering "most generous quotas" and frequent resets for its paid subscribers, while Anthropic indicates Fable might not be included in its subscriptions. OpenAI's GPT 5.6 Soul, described as "almost as good as Fable" but "much more efficient" and "much less expensive," benefits from these generous quotas, allowing greater user access. This disparity is highlighted by Sam Altman's public criticism of Anthropic's customer treatment. However, Anthropic's potential for "recursive self-improvement" with Fable could still allow it to extend its lead.

Key takeaway

For AI Product Managers evaluating subscriptions or Directors of AI/ML planning infrastructure, raw model quality alone is insufficient for market dominance. Your investment in scalable compute capacity and generous user access directly impacts adoption and competitive standing. Prioritize infrastructure and customer experience to avoid being outmaneuvered by more accessible, albeit slightly less powerful, alternatives.

Key insights

Anthropic's past compute underinvestment limits its current market competitiveness despite having a superior model.

Principles

• Compute capacity is a strategic asset.
• Customer experience drives adoption.

In practice

• Invest in compute ahead of demand.
• Optimize models for cost and efficiency.

Topics

• Anthropic
• OpenAI
• Compute Capacity
• AI Models
• Competitive Strategy
• Customer Experience

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Matthew Berman.