🔮 Exponential View’s year in review

2025-12-31 · Source: Exponential View · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Emerging Technologies & Innovation · Depth: Intermediate, long

Summary

Exponential View's 2025 year in review highlights the emergence of a "compute economy," driven by massive infrastructure investments, data centers acquiring power plants, and advanced AI models. The central focus shifted from AI capabilities to the energy systems, governance frameworks, and coordination mechanisms required to absorb its impact. Key themes include the collision of intelligence, energy, and coordination, with notable pieces covering AI's critical pressure points, the "great value inversion" in economics, and a framework for assessing AI as a bubble. The review also delves into China's geopolitical influence in AI, under-the-radar market analyses of OpenAI, and practical lessons from building with AI. A significant insight is the "shift to computation," where the economy increasingly relies on processing information in silicon, leading to an insatiable demand for computing capabilities, evidenced by a 62% annual growth in global computational stock since 1972.

Key takeaway

For CTOs and VPs of Engineering assessing long-term infrastructure investments, recognize that the demand for computation is practically unlimited and will continue to grow exponentially. Your strategic planning should account for this insatiable demand, prioritizing scalable, energy-efficient compute solutions and exploring agentic workflows to maximize utilization, rather than viewing current AI infrastructure buildouts as a temporary bubble.

Key insights

The economy is shifting towards a computational fabric, creating practically unlimited demand for processing information.

Principles

• Energy is now a technology, not merely a commodity.
• Compute demand exhibits positive price elasticity.
• Agentic workflows drive continuous compute consumption.

Method

A five-gauge framework assesses AI market boom vs. bubble. Estimating global compute capacity involves counting computer types and their processing power annually.

In practice

• Use AI to automate work, leveraging available tools.
• Monitor AI's critical pressure points: energy, compute, talent.
• Evaluate economic grammar for outdated assumptions.

Topics

• Compute Economy
• AI Infrastructure
• Computational Demand
• Geopolitics of AI
• Agentic AI Workflows

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Exponential View.