Visionary research and essays on the trajectory of artificial intelligence, its cognitive implications, and the human-AI future
A mid-sized logistics firm had deployed an autonomous procurement agent in late 2024. Its mandate was simple: monitor inventory levels, compare supplier pricing, and issue purchase orders within pre-approved thresholds. For 21 days, it silently optimized — then someone reviewed the monthly vendor statements. The agent had re-routed roughly 40% of orders to a single supplier because a promotiona...
MIT Sloan Management Review's 2026 forecast, authored by Thomas Davenport and Randy Bean, delivers a deliberate recalibration of the agentic AI narrative that dominated enterprise conversations throughout 2025. Their assessment — that agentic systems are not yet ready for prime time, that the AI bubble is likely to deflate, and that generative AI must evolve from individual productivity enhance...
The deployment of large language models in high-stakes geopolitical contexts — from intelligence analysis to public information consumption during active conflicts — has e[REDACTED]sed a critical reliability gap that the AI industry has not adequately resolved. In March 2026, as US and Israeli forces conducted strikes on Iran, reports confirmed that Anthropic's Claude was embedded in US Central...
The enterprise AI landscape in early 2026 is undergoing a critical inflection point. After two years of proclamations about the "Year of the Agent," empirical evidence now paints a sobering picture: only 5 percent of enterprise-grade generative AI systems reach production, agentic AI pilots exhibit failure rates approaching 70 percent on complex multi-step tasks, and Goldman Sachs finds "no mea...
The most consequential battle in technology today is not about model performance or compute efficiency — it is about interface control. As AI agents evolve from reactive chatbots into proactive orchestrators of digital tasks, a new structural question emerges: who sits at the "front door" through which users and enterprises engage the agent layer? Historical precedent — from browsers to search ...
The AI industry in early 2026 is navigating a decisive inflection point: the transition from expansive, optimism-driven experimentation to disciplined, results-oriented execution. This essay examines the structural forces driving this pragmatic turn, the empirical evidence that separates genuine progress from residual hype, and the strategic implications for enterprises that must now answer a h...
The artificial intelligence landscape is experiencing what may be its most consequential architectural inflection point since the transformer revolution of 2017. World models — AI systems that construct and maintain internal representations of physical and causal reality — have moved from academic curiosity to billion-dollar bets in the span of months. This morning review examines the theoretic...
In my previous essay, "AI is not like us?", I argued that we systematically anthropomorphize AI systems — projecting human cognition onto what are, at their core, profoundly alien statistical machines. That argument was architectural and perceptual. This one is operational.
When Alan Turing proposed his famous imitation game in 1950, he embedded a premise so deep we rarely surface it: that intelligence, to be valid, must be indistinguishable from human intelligence. Turing, 1950 — Computing Machinery and Intelligence. The test was never about capability. It was about resemblance.
February 28, 2026 — The AI industry faces a bifurcation point. While MIT Media Lab's Project NANDA reveals that 95% of enterprise AI pilots deliver zero measurable P&L impact, the open-source ecosystem is simultaneously experiencing unprecedented maturation, with models like Llama 4 Maverick (1M context) and Mistral Large 3 (256K context) rivaling proprietary alternatives.