Artificial intelligence is beginning to lift productivity in parts of the US economy. In Britain, it is not. The difference is not technological capability, but institutions, incentives, and who is allowed to capture the...
AI driven data centre growth and rapid electrification are increasing electricity demand in Britain’s most concentrated corridors at the same time that critical grid components such as high voltage transformers face replacement lead times measured in years. If a major node fails under that pressure, the risk is not permanent blackout but prolonged, managed shortage, and once electricity becomes scheduled and uneven, it becomes political.
As Washington accelerates frontier AI and tightens chip controls, Beijing is building something different: a state-coordinated system that treats artificial intelligence as national infrastructure. The decisive question is no longer who builds the smartest model, but who can govern intelligence at scale without destabilising labour markets, information systems, and political legitimacy.
Science is no longer limited to campuses. As AI and automation take over experimental work, discovery shifts to the corporations that own compute, robotics, and power. Britain risks dependence if it does not build its own infrastructure.
Artificial intelligence is not simply changing jobs. It is destabilising the apprenticeship ladder that modern education was built to serve, forcing a reversal from supply-side credential pipelines to demand-side adaptability.
Big Tech’s web of AI cross-investments looks like cooperation, but it is a ceasefire forced by compute and power scarcity. As constraints tighten, this détente will give way to control, consolidation, and vertical integration.
OpenClaw and Moltbook mark the shift from AI that advises to AI that acts. As autonomous agents execute tasks without direct supervision, they create real harm without clear defendants. This article examines how OpenClaw and Moltbook expose a growing liability vacuum that law and regulators will be forced to confront
This is the second article in a series examining why artificial intelligence can raise productivity without raising living standards. While the first piece focused on how AI increases output per hour, this follow-up explains why Britain’s economic structure absorbs those gains instead of translating them into broader prosperity.
Elon Musk has consolidated his artificial intelligence venture xAI into SpaceX in a deal valued at around 1.25 trillion dollars, framing the merger as a response to a deeper constraint now shaping AI’s future. Behind the valuation story lies a harder question about power, infrastructure and limits that SpaceX alone cannot wish away.
Conversational AI is no longer just answering questions. It is shaping belief, identity, and decisions in moments of vulnerability. As people turn to chatbots for therapy, relationship advice, and emotional support, the risk is no longer theoretical. When fluent language nudges users toward despair, self harm, or even suicide, the absence of accountability stops being a technical issue and becomes a public safety failure.
China is not racing the West to build smarter artificial intelligence. It is racing to embed AI into everyday digital life, turning messaging, shopping, and payments into a single action layer. That shift may matter more than any benchmark result.
McKinsey has acknowledged that artificial intelligence agents now operate alongside its human consultants at scale. This essay examines how that shift is dismantling the traditional consulting pyramid, creating a hidden training debt, and forcing a new settlement around liability, judgment, and institutional survival.
For two decades, companies rented business software because building it was slow, costly, and risky. That assumption has collapsed. As artificial intelligence turns software creation into an industrial process, subscription platforms begin to hollow out: the thinking moves outside the product, the platform becomes a record keeping shell, and renewals become optional. The real disruption is institutional, not technical
CES 2026 did not prove that humanoid robots are ready for the world. It revealed something more consequential: an overcrowded market rushing toward the same idea at the same time. History suggests what comes next. When innovation peaks in abundance rather than differentiation, consolidation follows. Most of today’s humanoid robotics pioneers will not survive the shakeout.
The most important technological shifts rarely arrive with ceremonies or consensus. They become infrastructure first, and history later. Artificial intelligence is now undergoing that kind of transition—quietly reshaping coordination, decision-making and medicine while public debate remains fixated on milestones and definitions that lag reality.
Artificial intelligence has not solved drug discovery. It has exposed where pharmaceutical development really fails. As decision-making replaces invention as the bottleneck, Western drugmakers are quietly reorganising pipelines and partnerships pulling China into the system not by admiration, but by necessity.
As AI intelligence becomes cheap and interchangeable, power shifts to the Jarvis layer: the always-on personal assistant that mediates daily life. This analysis explains why proximity, not intelligence, is the new AI chokepoint shaping autonomy, education, and governance.
India’s economic rise was built on exporting educated, English speaking labour at scale. Artificial intelligence is now collapsing the price of intelligence itself. As cognitive work becomes cheaper than human labour, India’s outsourcing and IT services model faces a structural shock arriving far sooner than policymakers admit. This analysis examines why reskilling narratives are failing and what is now at stake.
London is not heading for mass unemployment. It is heading for class compression. As artificial intelligence reshapes white-collar work, service jobs endure, elite power concentrates, and the middle quietly erodes. The result is a city that keeps working while becoming poorer, narrower and more fragile.
The debate over artificial general intelligence is becoming a distraction. As AI capability races ahead of law and language, definition lag now poses a serious governance risk.
Artificial intelligence is exposing structural flaws in GDP by driving prices down, embedding value inside firms, and delivering rapid quality gains that official statistics struggle to capture. As AI matures, GDP risks misleading policymakers about real economic progress.
Artificial intelligence is usually framed as a jobs problem. That framing misses the deeper risk. The real shock is psychological: the rapid invalidation of skills, status, and expectations that once gave effort meaning. The danger is not unemployment alone, but the collapse of trust in work, institutions, and the future itself.
AI has not ended propaganda or exposed truth once and for all. It has ended narrative monopolies and replaced them with something quieter and more powerful: systems that decide what feels reasonable before debate even begins
Trillions in market value and hundreds of billions in infrastructure spending rest on one assumption: scarcity. China’s open model push is testing whether that assumption can survive.
Europe says it wants to become the “AI continent” and is now planning AI gigafactories and sovereign compute by 2026. But while Brussels drafts tenders, frontier labs in California and Shenzhen move at weekly cadence. The problem is not European intelligence or talent. It is metabolism: regulation, culture and capital flows that move on political time while the AI race moves on benchmark time.
Sam Altman’s “code red” over Google’s Gemini 3 is not a colourful memo. It is the visible edge of a frontier arms race in which OpenAI, Google, xAI and soon Microsoft will ship ever more capable models on a weekly cycle while asking investors for power station levels of capital. Benchmarks rise, valuations rise, and the first thing that falls out of the room is safety.
The next phase of AI will not be about clever chatbots but about systems that learn like brilliant teenagers, copy themselves at scale, and quietly become the dominant intelligence on the planet. When that happens, the only survivable response for humans will be to integrate with these systems rather than compete against them.
Artificial intelligence is sold as the triumph of digital “mind,” but the reality sits in the racks: GPUs, hyperscale data centres, energy contracts and private ownership. This article argues that Marx, not Hegel, explains the real engine of AI: material power, extractive relations, and the enclosure of society’s shared knowledge inside proprietary models. The ideas sit in the marketing; the contradictions sit in the data centre.