Starbucks Taps AI to Reduce Reliance on Microsoft, IBM Software
THE SO WHAT
Starbucks building in-house AI tools to replace third-party software is a clear signal: large enterprises see AI as a lever to insource generic SaaS. If you sell horizontal software into big brands, assume your feature surface is a target for internal AI teams and double down on domain depth and integration moats.
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Applied AIMicrosoft’s Brad Smith: the US is regulating AI with rules nobody can read
Opaque AI rules mean your real risk surface is enforcement discretion, not just the text of any single law. Compliance leaders should map exposure across agencies now — don’t wait for a ‘final’ framework that may never fully stabilize.
Applied AI‘The gap between AI ambition and infrastructure reality is widening’ Google Cloud report finds 83% of organizations must overhaul their infrastructure in order to maximize the agentic AI opportunity
If 83% of orgs need infra overhauls for agentic AI, the constraint isn’t ideas — it’s plumbing. CIOs should freeze net-new ‘agent’ pilots until there’s a clear plan for data access, orchestration, and observability that can handle continuous, autonomous workflows.
Applied AIThe EU AI Act deadline has moved, but data lineage can’t wait
Deadline slippage on the EU AI Act doesn’t buy you time on data lineage — it just widens the gap between compliant and exposed teams. If you can’t trace training and inference data today, you’re already behind on both regulation and internal risk management.
Applied AIMeta to put its own AI chip into production in September, aiming to double computing capacity
Meta pushing its MTIA chip into production and targeting a 2x compute bump is another proof point that hyperscalers are internalizing key parts of the AI stack. If you’re building AI infra or accelerators, assume your primary customers will be everyone except the largest platforms — and design go-to-market accordingly.