How AI will Change in 2026
An analysis of the biggest disruptions across the AI value chain.
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Last year we went 4/4 on our major predictions. Machine Learning for Quantum Computing. Adversarial Perturbations going mainstream. Inference-Time Compute becoming the dominant paradigm. Small Language Models eating the world. All of it landed. Not because we're prophets—because we read the technical literature, talked to the people actually building, and followed the logic where it led instead of where it was comfortable. You can verify our predictions for yourself over here.
This year, we took this one step beyond. I hired a team to help me compile research, invested in a ton of interviews and business case studies, and consulted with various experts in AI-adjacent spaces (cloud deployment, cyber-security, hardware, etc) to compile together the biggest changes in every major layer of the AI ecosystem. What we have below is not just a discussion of algorithms coming next. Instead, we will discuss the most high value, disruptive changes across the ecosystem—
Layer 1: Hardware — The economics and physics that are forcing a fundamental re-architecture of AI compute. Who wins, who loses, and what it means for everyone building on top.
Layer 2: Model Architectures — We break down why the dominant paradigm is showing cracks, what’s rising to challenge it, and the technical shifts that make 2026 the inflection point.
Layer 3: Reasoning Infrastructure — The current approach to building reasoning models doesn’t work. We’ll show you why, what replaces it, and the new AI asset class emerging from the wreckage.
Layer 4: Applications — The $5.8 trillion opportunity hiding in plain sight. Which industries cross the viability threshold in 2026, who captures the value, and the math that makes it inevitable.
Layer 5: Distribution — Why current distribution strategies are getting destroyed by extreme competition and what’s rising to take it’s place.
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