The Always-On Question
Apple glasses, Meta's facial recognition, OpenAI's screenless device -- the hardware is arriving faster than the norms. What happens to trust, consent, and exploratory thinking when the default shifts to capture?
Analysis of AI strategy, technology leadership, and what actually works versus what sounds good in a deck. LinkedIn posts, articles, conference talks, and media appearances.
Apple glasses, Meta's facial recognition, OpenAI's screenless device -- the hardware is arriving faster than the norms. What happens to trust, consent, and exploratory thinking when the default shifts to capture?
I watched someone automate their entire car negotiation with an AI agent—it spent three days playing dealerships against each other while they went about their life. Dealerships had no idea they were negotiating with a machine, which tells you everything about where AI agents are headed next.
57% of companies now run AI agents in production, yet quality metrics are sobering. The path to coherent software at scale runs through orchestration and human oversight.
10x cheaper inference changes the math on AI use cases. Teams can stop rationing tokens and start designing for longer context and multi-step workflows.
88% of companies now use AI, workers save 5.4% of hours weekly, and AI professionals command a 56% wage premium. The real shift: AI as superpowers that expand what becomes worth attempting.
AI uncertainty keeps users actively thinking. The most useful AI assistant might be one that's good enough to help, but imperfect enough to keep me honest.
Many organizations create AI plans in controlled environments that fail when confronted with operational complexities. Building flexible, adaptive approaches matters more than rigid strategies.
Discussion on AI strategy and technology leadership
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