AI agents are coming to Rivian’s cars
THE SO WHAT
Automakers moving from static assistants to AI agents means the car UI becomes a software surface that can change meaningfully over the vehicle’s life. If you build in automotive or mobility, assume driver workflows, support, and even upsell paths will be mediated by an agent layer you don’t fully control—design integrations and safety nets accordingly.
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MORE FROM THE WIRE
Applied AIFriends don’t let friends feed their data to AI agents
Consumer AI assistants that ambiently capture bystanders’ data without clear consent are creating a new privacy externality—your users’ friends and coworkers become the de facto attack surface. If your product records or transcribes shared spaces, you need an explicit policy and UX for third-party consent this quarter, or regulators and app stores will eventually write it for you.
Applied AINvidia launches Nemotron 3.5 Super VL, a 120B-parameter open model for enterprise agents, and says it outperforms frontier models on specialized benchmarks
A 120B-parameter, open Nemotron tuned for enterprise agents that can beat frontier models on narrow benchmarks is a direct nudge to keep high-value workflows on Nvidia’s stack—model, infra, and evals bundled. If you’re building agents for contracts, finance, or analytics, test Nemotron 3.5 Super VL against your current model this month and price in the lock-in vs performance trade.
Applied AIJensen Huang wants Europe to dream bigger — with Nvidia’s help
If Nvidia is publicly urging Europe to “dream bigger,” it’s a signal that the company sees underutilized demand for AI infrastructure on the continent. European operators should expect more aggressive courting on sovereign cloud, local data centers, and public–private capex deals—and should use that competition to lock in better economics and policy carve-outs.
Applied AIBank of America had to hire 1,000 contractors to patch software flaws spotted by Anthropic Mythos
When an AI system forces a bank to hire 1,000 contractors and spend $100M+ to patch legacy flaws, the bottleneck isn’t detection—it’s remediation capacity. If you deploy AI code or security scanners at scale, budget for the downstream fix pipeline now or you’ll surface more risk than your org can realistically close.