1260 summarised stories about AI Adoption, each linking back to the original source. Browse all topics →
TechCrunch 1 month ago 3 ● 2 sources
Harvard Business School’s Foundry bootcamp uses AI avatars of instructors from startup HeyGen to provide feedback during practice pitches and mock board meetings. The eight-week program costs $699. HBS shifted the AI from an initial chatbot-style concept to a more guided avatar-based experience after student feedback.
Fortune 51 ● 8 sources
The AI economy in 2026 is described as a three-body system where frontier labs, open-weight models, and application companies each influence the others but none can control the final outcome. Spending on AI is estimated at between 0.5 and 1 percent of all US white-collar salaries, driving scrutiny of ROI and pressure for cheaper alternatives as competition intensifies. Open-weight models and applications gain momentum as prices face downward pressure and the market shifts further toward multi-model choices.
Fortune 28
Asian economies are reporting stronger growth and exports tied to AI hardware demand, alongside big stock jumps for related chip and robotics firms. Exports from South Korea rose by more than 60% in July. Economists say Southeast Asia’s gains may not last because the region is mostly supplying back-end semiconductors and data-center inputs and faces constraints like skills, energy, and geopolitics.
TechCrunch 1 month ago 45
Michael Polansky’s startup Outer Biosciences feeds an AI prediction loop with experiments run on human skin kept alive for up to a month. The company has raised roughly $23 million so far. That setup speeds discovery by generating a new candidate about every six weeks based on results from living-tissue testing fed back into the model.
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20 October 2026
AI infrastructure is getting a very practical second act today: making agents faster to start, cheaper to run, and easier to govern once they’re running. Daytona, which sells an “agentic cloud” built around VMs for agent “computers,” raised $60 million in a Series B and landed at a $1 billion valuation, targeting 60-millisecond sandbox provisioning—an attempt to knock down the latency tax that makes agents feel sluggish between steps. IBM, Red Hat, and others also pushed llm-d framework 1.0, adding operational controls for in-house distributed inference, including multi-tenant QoS, zero-downtime upgrades, and router behavior measured on an agentic coding test that reportedly captured 97% cache reuse.
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