AI Economics for Operators
Pricing, capital, unit economics, vendor strategy, and the market structures behind AI operating decisions.
Artificial intelligence is both a technical system and an unusually capital-intensive market. This topic tracks the money underneath the models: token prices, inference costs, funding rounds, valuations, acquisitions, monetization, procurement, and the margins hidden behind headline benchmarks.
The operator’s question is always explicit: who captures the value, what does adoption actually cost, and which assumptions make a build, buy, partner, or investment decision rational? Company news appears here when it reveals a durable economic mechanism—not simply because a large AI company made another announcement.
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OpenAI Chose Ads. Anthropic Chose Users. The Score Flipped.
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74% of AI's Value. 20% of Companies. Everyone Else Lost.
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This AI Rewrote Itself 100 Times. Then They Open-Sourced It.
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$200 Billion and Negative Cash Flow. Jassy Says Trust Me.
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$35 Billion for GPUs. CoreWeave Bet the House.
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OpenAI Wrote the Rules. Now It Wants to Tax Them.
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Gemma 4 Beat Models 20x Its Size. Google Gave It Away.
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The $13 Billion Divorce Starts with Three Models
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$150 Billion in AI IPOs. Not a Dollar of Profit.
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Apple Paid Google $1 Billion for Siri. It Still Can't Ship.