AI enterprise deployment
The work of taking a trained model from a lab environment and making it run reliably inside an organization's real processes, data, and accountability structures.
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A model that works in a demo can still fail in production: the organization's data is messier, its workflows are more constrained, and someone has to own the output when it's wrong. Enterprise deployment is the discipline of closing that gap — integrating the model with internal systems, establishing who reviews its decisions, governing what data it sees, and building the change-management layer that gets workers to trust and use it. Most of the cost and most of the failure live in these steps, not in the model itself.
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Overall = breadth + depth + substance + ½·novelty + freshness (a small boost that halves each week)
Claude for Legal
Anthropic shipped an open-source legal stack that sits underneath Westlaw and LexisNexis rather than trying to replace them — and signed Thomson Reuters, whose stock it had crashed in February, as a launch partner.
The harness is the moat
Blitzy raised $200M at a $1.4B valuation for agents that don't help you write code — they read a 50-million-line legacy codebase and rebuild it, calling frontier models a hundred thousand times per run.
The Jevons bill comes due
AI inference keeps getting cheaper per token, yet the bills are climbing fast — because every price cut invites buyers to spend the savings on more tokens, and then some.
The last lock comes off OpenAI
When the press declared Microsoft no longer OpenAI's exclusive cloud in early 2025, one quiet lock survived: Azure was still the only place anyone could buy an OpenAI model. That lock fell in April.
Winning the bill, losing the phone
An independent tracker puts Claude's app at 56 million monthly users growing 640% a year — a rate that flatters how small it still is next to ChatGPT.
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