Beyond the ETA: The Context-Dependent Robotaxi CX

Autonomy is table stakes, and supply-side orchestration, which Uber is already winning, is becoming table stakes too. The next differentiator in robotaxi customer experience is demand-side. Namely, a system that anticipates each traveler’s intent (route risk, speed-versus-safety trade-offs, the right mode for the moment) and orchestrates the trip to satisfy it. I call this Context-Dependent Ride Management. It is the layer where the premium, monetizable, flagship customer experience will be built.

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The AI Delegation Trap and the Productivity J-Curve

The Productivity J-Curve best explains AI’s productivity paradox. The investments across different categories beyond technology initially depress measurable output. Rather…

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Architecting the Corporate Industrial Policy to Survive the AI Stakeholder Squeeze

Incumbent corporations are investing in and incorporating AI, yet most fail to fundamentally alter their operating models or achieve strong ROI as a result. The root of this failure is not technological. It is macroeconomic and organizational. US enterprises are attempting to execute a paradigm-shifting technological transition within a market environment that offers them no structural shock absorber, in the way a formal industrial policy can. These enterprises must navigate the AI transition while addressing a rapidly changing market environment and the AI Stakeholder Squeeze.

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Adaptive and Humanoid Robots

Embodied AI is the next AI frontier. Adaptive and humanoid robots will play a key role in many industries. Beyond the technology challenges that remain before conquering this frontier, how should we view embodied AI? Will it be the cause of massive job losses in various industries, or an opportunity to improve productivity by forming collaborative teams that combine humans with intelligent machines? We define four dimensions to answer these questions.

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Three AI Factory Types

As 2025 drew to a close, the narrative surrounding enterprise AI began to shift. We moved from 2025 being the “Year of Experimentation” to 2026 shaping as the “Year of Deployment.” Under such a mandate, enterprises must develop and deploy their AI applications scalably, efficiently, and economically. For the large enterprise, starting with the Fortune 500, achieving these goals will require the adoption of a factory-like approach, leading to the development of AI Factories.

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