AI is an economic story: It redirects capital to compute, reshapes market power, and reconfigures labour and productivity. G.E.N. tracks these shifts using audited corporate disclosures, official statistics, and deep mathematical models to publish policy briefs on model governance, competition, and workforce skills. Our work quantifies impacts and trade-offs, translating validated evidence into implementable decisions for policymakers and boards.
-Global Economic News
The Diffusion : measuring when installation becomes deployment - A White Paper Preview
This white paper preview sets out a measurement and modelling framework that tracks how artificial intelligence moves from infrastructure build-out to enterprise value and economy-wide outcomes. The aim is decision-grade monitoring rather than narrative. Indicators are defined precisely, linked to public data where possible, and embedded in a model that can separate transient noise from structural change.
Is AI adoption stalling? A two-speed diffusion seen through the data
AI adoption is not stalling; it is bifurcating. Capital and compute are scaling rapidly, while measurable enterprise value and productivity gains lag. Hyperscaler capex and GPU revenues confirm the installation phase is in full flight. Firm surveys show usage doubling, yet the share reporting material EBIT impact is stuck. Our employment-weighted sector DiD finds no statistically significant post-2022 productivity lift in high-exposure industries. Monetisation should inflect when three conditions hold: inference costs fall by another order of magnitude, evaluation and guardrail stacks standardise, and firms rebuild workflows so AI transforms processes rather than isolated tasks. Until then, value will stay concentrated with infrastructure providers and the few firms that have already re-engineered for AI. Patience is warranted; discipline on governance, cost curves and ROI thresholds is essential.