Latest AI industry news and updates
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In this article: * What changed in 2026: the releases, standards and rules that made this a live topic * What the market and practitioners say: deals, analysts, adoption, surveys, incidents and the security of the tools * Debugging trace or evidence record: why the tracing built into tools is not audit * The
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Take a hypothetical bank. Its customer service runs on Agentforce, IT tickets on ServiceNow, and a small team has built a credit-limit agent with LangGraph on AWS. Last quarter the bank bought an agent management platform, and the platform's registry lists all three agents. On Monday, internal
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Take a hypothetical case. Your finance team deploys an AI agent that pays supplier invoices. One Friday night it pays a supplier twice, because a corrected invoice arrived with a new number. On Monday four people ask questions: * The auditor: which rule allowed the second payment? * The risk officer: who
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A hypothetical board meeting. Four people quote four reports, all of them correctly: * A director: 95% of AI pilots fail. That is MIT's count of firms with zero P&L impact from custom GenAI tools, based on 52 interviews and 153 conference respondents in early 2025. * The
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Take a hypothetical case. You run a software startup. Your product puts an AI agent to work on customers' business data: pricing models, product catalogues, approval rules. In development it runs on a closed frontier API, and it works. Then the pipeline fills with the customers you actually want:
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In this article: * One head, no handoffs — Product, architecture, execution: one person, no coordination layer * Built for expensive execution — Twenty years of infrastructure built for a specific problem * When execution gets cheap — What the layer does when the problem shrinks * Role compression is not enough — Microsoft's answer, and
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In this article: * The reframe — Why the management-problem framing is the right move * The problem is real and measurable — MAST, IBM, Berkeley, and what the failure data actually says * What Paperclip actually is — The mechanics, the quickstart, what it runs on * The skeleton and the living system — What Paperclip
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In this article: * The accident: a missing line in .npmignore A 59.8 MB source map shipped to npm. For the second time. 1,900 files of unobfuscated TypeScript became public in hours. * The agent loop: from one prompt to a chain reaction The while-loop is trivial. The engineering
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In this article: * The paradox: punished for being right Microsoft's best quarter ever. The stock fell 10%. Here is why the record results were the problem. * The contagion: agents don't buy seats The repricing spread across every company built on seat-based pricing. The replacement model
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Every project starts at zero. Getting from 0 to 0.3 (the structure, the wiring, the boilerplate) shouldn't take hours. It should take minutes. That's true whether the project is a marketing campaign, a financial model, a contract review workflow, a SaaS app, or a DevOps
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Running AI well on a project comes down to two things: the right structure and setup for the domain or tech, and the right configuration of the AI project context. This is not just about coding. Tools like Claude Code, Codex, Gemini CLI, Cursor, and Windsurf work on any digital
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At GTC 2026, Jensen Huang did not talk about GPUs. He talked about a new computing model. AI agents that run overnight, spawn sub-agents, execute workflows without human hand-holding, and make decisions on your behalf while you sleep. The headline chip — Vera Rubin — got the press coverage. But