Archives for May 2026

May 12, 2026 by David Such Leave a Comment

Spot the Robot Can Reason, but it can’t hold a can of soda

Boston Dynamics and Google DeepMind announced last month that Spot is now running Gemini Robotics-ER 1.6, a high-level embodied reasoning model. The headline is that Spot has been taught to reason. The commercial pitch is industrial inspection: wandering around a facility, reading gauges, spotting spills, deciding what to do when something looks wrong. So far, so good.

Buried in the demo video is a small failure that says more about the architecture than the press release does. Asked to recycle cans in the living room, Spot picks one up sideways. If there is any liquid left in the can, it ends up on the carpet. The problem is using a language model to solve something handled by other more primitive layers in the organic world.

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May 7, 2026 by David Such Leave a Comment

Why 95% of AI Deployments Fail

MIT’s August 2025 study of 300 enterprise generative AI deployments found that 95% produced no measurable P&L impact. Gartner forecasts that more than 40% of agentic AI projects will be cancelled by 2027. McKinsey’s State of AI 2025 identifies workflow redesign as the single strongest correlate with EBIT impact, yet only 21% of organisations have redesigned any workflows. The data converges on a structural conclusion: enterprise AI is failing because the operational substrate is inadequate, not because the models are. This episode examines the process-readiness gap, the misallocation pattern that concentrates investment in low-ROI front-office applications, and what the 5% of high performers do differently. It is an architectural argument, not a change-management one: AI is a linear amplifier acting on a pre-existing process, and the sign of the output depends on the sign of the input.

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May 4, 2026 by David Such Leave a Comment

Why ARC’s $2M Bet Against Priors Is the Wrong Bet for Embedded AI

ARC says intelligence emerges from learning when priors are minimised. Vertebrates disagree and have spent 500 million years proving the opposite.

The ARC Prize is a public competition with $2 million on the table, run by François Chollet (creator of Keras) and Mike Knoop (co-founder of Zapier). The premise is that current AI benchmarks measure what a model already knows, not how well it learns. Their fix is ARC-AGI, a series of reasoning tasks that are easy for humans and, so far, brutal for machines. Frontier models score below 4% on ARC-AGI-2.

The newest version, ARC-AGI-3, swaps static puzzles for interactive game environments where the agent has to figure out the rules by playing, with no instructions and no pre-loaded domain knowledge. Humans score 100%. AI scores under 1%. The gap demonstrates the point. Behind the gap is a philosophy about what intelligence is and where it comes from, and that philosophy is where the Primal Layers framework and ARC differ.

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