Economic collapse and political fragility are often treated as symptoms of the same disease — the assumption being that when an economy fails, political violence follows inevitably. But the World Stability Intelligence (WSI) dataset, covering 87 countries across six regions, reveals a more nuanced picture. Some countries maintain remarkable political stability despite severe economic distress (...
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...
Two distinct analytical traditions have long operated in parallel without converging: conflict prediction — the binary question of whether armed violence will occur in a given country — and geopolitical risk assessment — the continuous measurement of how politically and economically unstable an environment is. Political scientists model the former; risk analysts calculate the latter. Yet both a...
Predicting political risk is fundamentally different from economic forecasting — and the difference matters enormously for both policymakers and investors. Economic variables like GDP growth or inflation exhibit mean-reverting behaviour around structural trends; central banks provide forward guidance; quarterly revisions are orderly. Political risk, by contrast, is punctuated by discontinuities...
Enterprise procurement of large language models (LLMs) continues to rely on academic benchmarks — MMLU, HumanEval, HellaSwag — that were designed for research comparisons rather than business decision-making. This article demonstrates why these metrics systematically mislead enterprise buyers and proposes the Business-Oriented Model Evaluation (BOME) framework, which centres on four operational...
Multi-cloud strategy has evolved from a risk-mitigation posture into a primary economic lever for enterprise AI operations. As generative AI workloads consume an increasing share of cloud budgets — projected at 10–15% of total cloud spend by 2030 according to Goldman Sachs research — the economic calculus of distributing workloads across AWS, Azure, and GCP has become significantly more complex...
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.
The fundamental economic question facing enterprises in 2026 is not whether autonomous systems can replace human labor, but when the compute-labor cost crossover makes replacement economically rational. This article examines the economics of autonomous system deployment across warehouse robotics, transportation, and knowledge work domains. Analysis of real-world implementations reveals that lab...
The AI infrastructure investment cycle has reached unprecedented scale, with hyperscalers projected to spend over $600 billion in 2026—a 36% increase over 2025. This paper analyzes the economic fundamentals underlying this capital expenditure war, revealing a stark ROI crisis: AI data centers commissioned in 2025 face $40 billion in annual depreciation costs while generating only $15-20 billion...