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Software buying decisions used to be made once, by someone far removed from the people actually using the tool. That model is breaking down. Teams now expect software to work out of the box, without weeks of setup, integration, and configuration before anyone sees value. At the same time, a growing…
Four years into the AI boom, headlines still promise agents that will run entire departments, yet most companies can't point to the transformation they were sold. The gap isn't intelligence — today's models are remarkably capable — it's context: no model arrives knowing how your company actually ge…
Across the data and AI industry, infrastructure that once served dashboards and human analysts is being rebuilt to serve autonomous agents instead. That shift changes what "AI-ready data" actually means, pushing teams to rethink documentation, governance, and the semantic layer so agents pull consi…
Technology is now moving faster than the organizations trying to adopt it. A model can be tested in an afternoon, but the approval to test it can take half a year, and that gap is where most transformation budgets quietly disappear. The structures that made companies safe and predictable — layers o…
AI capability in mathematics jumped before most people noticed, tackling Olympiad-level problems and unsolved research questions that had resisted attack for years. But the pattern of where AI succeeds and where it stalls is uneven and worth understanding. It's much better at grinding through cases…
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