Mark Robins

We chose a good subject for our march Autotech Council meeting, and after this year's CES, it seems even more obvious. Our meeting on the AI Defined Vehicle will show how it's a natural next step after the Software Defined Vehicle phase that we've been in Tesla launched the Model S in 2012. Now, most carmakers have SDVs. It's time to layer on the AI in user-facing roles, and in invisible functions managing the vehicle.

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Project Vend reads less like a capability story and more like a governability story. Phase two improved because institutional scaffolding (tools, procedures, role separation) reduced predictable judgment failures—persuasion, compliance blind spots, boundary confusion, values drift. The board-level question isn’t “can an agent run a business?” but “what governance primitives must exist before we delegate authority.” Until incentives, escalation rights, and refusal boundaries are structurally enforced—not merely prompted—agents remain high-performing managers, not autonomous executives.

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I spoke with Amplify Partners about Kyutai and Gradium, and more broadly about why voice is the modality where small, disciplined teams routinely outcompete larger organizations despite having far less compute and staff. The post goes into how we approached audio end-to-end, why we insisted early on full-duplex, real-time interaction, and how much of that came from tight iteration loops rather than scale. It also reflects on our culture, competition as a forcing function, and why having fewer people can actually make technical progress faster, not harder. No grand vision statements. Just how things were built, why certain bets were made early, and what it feels like to work at the edge of audio and interaction. If you’re interested in audio, voice, or small teams doing real systems work, this captures it well: https://lnkd.in/e8X_zmiP

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What a great example of #airporttech! I like the "...friendly nudge by an officer." Excellent use case of curb management. Thank you Andrew K Kirk and Automotus for your partnership. INRIX #airports #govtech

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Dashboards don’t create accountability. They create meetings. Charts get shared. Everyone nods. Action items get “noted”. Same problems. Next quarter. Accountability doesn’t come from visibility. It comes from systems — where ownership is clear and decisions can’t be ignored. I wrote a short note on why dashboards feel productive but rarely change behaviour — and what actually does. If your org has great dashboards but the same issues every review… this will sound familiar. Dashboards Don’t Create Accountability — Systems Do: https://lnkd.in/gCGpm-VU

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Thank Dani Cherkassky Great piece on the architecture shift voice AI needs. The core thesis: cloud-centric voice systems fail in real-world conditions because they are too slow, too expensive to keep always-on, and deaf to spatial context. The solution is a hybrid architecture—fast, always-on edge processing (Spatial Hearing AI and Cognition AI) handling 80% of interactions locally, with cloud LLMs reserved for complex reasoning. But the Kahneman thesis runs deeper than the article suggests. System 1 and System 2 are not just useful metaphors—they reflect an actual biological sequence. Fast, intuitive processing (System 1) evolved first; slow, deliberate reasoning (System 2) was layered on top. The brain's architecture reflects this: spatial-acoustic processing happens in milliseconds at the brainstem and primary auditory cortex, long before language centers engage. Kardome is not just borrowing Kahneman as marketing. Their architecture is neurologically correct—it replicates the hierarchical, parallel, spatially-aware processing that makes human auditory cognition so robust. Here is the deeper point: hearing preceded language in human evolution, and spatial hearing preceded both. The mammalian auditory system evolved to detect predators and locate prey—survival functions that required sub-second latency and continuous environmental awareness. Language processing was layered on top of this infrastructure. Current voice AI inverts this evolutionary logic: it starts with language models and treats spatial audio as an afterthought. Kardome restores the natural hierarchy. 🙌 Danny Shapiro, Rory Sutherland

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To truly grasp China's AI success, it's essential to understand its dynamic, layered ecosystem: > Infrastructure: The foundational layer of chips, robust systems, and advanced networking that powers everything. > Model Layer: Where we're seeing rapid breakthroughs from "excellent technologies" like DeepSeek, Alibaba Qwen, and Moonshot AI, pushing the boundaries of what AI can do. > Application Layer: This is where the magic truly happens, with technology being integrated and deployed at an even faster pace into real-world solutions across various industries. This integrated and fiercely competitive structure is what makes the ecosystem "quite dynamic." From an innovation perspective, which of these layers do you believe holds the most potential for disruptive change in the coming years, and why? #AIEcosystem #TechStack #AIApplications #GlobalTech #FutureofAI #DisruptiveTech

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