Who wins, who loses, and why — the competitive dynamics reshaping industries, nations, and careers.
AI competition is not a single race with a single finish line. It is a set of overlapping contests — between startups and incumbents, between nations, between platforms, between industries at different stages of disruption, and between individuals whose skills either compound or decay as AI advances. This collection applies the frameworks of competitive strategy to each contest, names the specific forces at work, and identifies the structural conditions that determine outcomes. Thirty sessions across six modules move from economic foundations through company-level, platform-level, national-level, and industry-level competition to the individual competitive landscape — where the reader can act on what the analysis reveals.
Five foundational frameworks — cheap prediction, five forces, disruption theory, aggregation, and technology cycles — that structure every competitive analysis that follows.
The attacker-defender asymmetry — data moats, speed advantages, vertical positioning, the wrapper trap, and the conditions under which incumbents transform rather than fall.
Differentiation when features commoditize — distribution advantages, infrastructure-layer moats, foundation model economics, and where application-layer value actually accrues.
National competition beyond the rhetoric — US-China capability asymmetries, semiconductor chokepoints, regulatory strategy, talent concentration, and compute access as geopolitical leverage.
Which industries restructure first, why, and who captures the value — the information-first disruption sequence, healthcare's regulatory barriers, finance's compounding incumbency, and creative industries' curation shift.
When AI commoditizes cognitive tasks — the task framework, skill premium inversion, complementarity conditions, career moats, and the irreducibly human advantages that AI cannot replicate.
“The competitive strategy course the AI era actually needs — specific, structural, and uncomfortable where it should be.”
Competition in the AI Era does what most AI commentary avoids: it names winners and losers, traces the structural conditions that determine outcomes, and refuses to flatten different levels of competition into a single narrative. The analysis is anchored in specific companies, dollar figures, and policy actions from 2020–2026.
The companies best positioned in AI are not those with the best models — they are those whose products generate proprietary data that improves with use.
Data moats compound through usage while technology moats erode through commoditization. A 6-month model lead disappears; a dataset built from millions of user interactions does not. The competitive question is not who has the best AI — it is whose AI gets better fastest.
Industry Foundations curates structured learning experiences that map the competitive architecture of specific sectors and cross-cutting economic forces — built for professionals, analysts, and advanced learners who need the structural picture, not the headline.
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