Three Years In

2026-07-10 · Source: Tomasz Tunguz · Field: Finance & Economics — Capital Markets & Investment Management, FinTech & Digital Financial Services · Depth: Intermediate, medium

Summary

Theory Ventures reflects on its three-year journey, observing how AI has profoundly compressed time, accelerating market shifts and company maturation. New AI models are released every 41 days, enabling companies to reach \$100m in revenue in record time. This compression has reshaped venture capital, blurring traditional funding categories where seed rounds now range from \$1m to \$500m. Inference has emerged as a dominant market, segmenting into specialized infrastructure, while AI advertising acts as a crucial subsidy for inference costs, yielding 4-5x higher click-through rates. The gap between closed and open, cloud and local models has narrowed, with open-source and local solutions gaining enterprise adoption. AI also expands the attack surface in security, necessitating new agent-based defenses, and is rewriting operations for ERP and back-office systems. Theory Ventures operates as an AI-native firm, with 3 investors and a nine-person intelligence organization, analyzing twice the investment opportunities.

Key takeaway

For venture capitalists evaluating early-stage companies, you must recognize that traditional funding categories are obsolete; focus on company maturity and market opportunity rather than round labels. If you are an enterprise leader, prioritize specialized inference infrastructure and explore open-source or local models to manage costs and data governance. You should also proactively address the exploding AI attack surface with agent-based security and operations solutions.

Key insights

AI's time compression reshapes markets, from venture capital to security and operations, driving rapid innovation.

Principles

• AI accelerates product development and company maturation.
• Inference market segmentation creates new infrastructure categories.
• Local and open-source models reduce costs and improve control.

In practice

• Explore specialized inference infrastructure solutions.
• Consider open-source and local models for sensitive workloads.
• Investigate agent-based solutions for back-office automation.

Topics

• AI Market Trends
• Venture Capital
• Inference Infrastructure
• AI Security
• Agentic Systems
• Open-Source Models

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Tomasz Tunguz.