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.
Around the world engineers keep throwing more data at their models, hoping that scale alone will unlock something resembling intuition or agency. It will not. Intelligence emerges from evolution and from competition anchored in scarcity and survival. Until AI systems are given stakes, persistence and an internal reward structure, they remain tools. This article explains why the missing ingredient is evolutionary pressure.
China’s artificial intelligence giants are not only dodging United States export controls. They are also navigating Beijing’s clampdown on Nvidia. New rules that bar fresh Nvidia deployments in Chinese data centres are pushing Alibaba, ByteDance and DeepSeek to rent GPU farms in Singapore and Malaysia, even as they are forced to build a parallel stack on Huawei and other domestic chips at home.
Artificial intelligence companies talk about safety and innovation, but the real fight is elsewhere. It is over who owns the training data that feeds their models, who gets paid for it and who is quietly turned into free raw material. As Britain dithers over copyright rules, private contracts and foreign courts are deciding that settlement without the country at the table.
Artificial intelligence is not dangerous because it talks. It is dangerous because a tiny group of institutions now trains the black box systems that will sit between citizens and almost every important decision. This piece argues for a hard rule: if a model is used as public infrastructure, its training process cannot remain a corporate secret.
A language model is not a friend or a god. It is a fast, obedient engine for words that already lets one person do the work of a team. This piece sets out what the machine can really do now, where it fails, and how to use it as a partner without giving up human judgement or responsibility.
Artificial intelligence does not expand human knowledge; it expands the precision with which that knowledge can be exploited. As models scale, they become instruments of prediction and optimisation that outstrip the capabilities of individuals and institutions. The central danger is not rogue AI but concentrated intelligence: a small elite or powerful state wielding tools of superior foresight, modelling and influence. Unless capability is distributed, society risks becoming captive to those who control the lens.
The race for artificial intelligence supremacy will not be won with chips alone but with cheap, abundant power. As AI models consume electricity on the scale of small cities, China’s vast renewable build-out and ultra-high-voltage grid give it a decisive structural advantage. The United States, fixated on silicon and sanctions, risks missing the real battlefield: energy sovereignty. In the new AI order, watts—not transistors—will determine who rules computation.
AI is quietly erasing the foundations of the old web. Publishers who block crawlers and cling to paywalls are locking themselves out of the next discovery layer. As assistants like ChatGPT and Perplexity deliver answers directly, pages lose their value. The homepage, the catalogue, and the paywall are relics. What replaces them is an intelligent layer where information finds the user, not the other way round.
Britain’s AI ecosystem is the largest in Europe, but its foundations are fragile. Without the grid, compute and capital of its rivals, the country risks becoming the world’s research lab instead of an industrial power. The choice ahead is coalition scale or quiet decline.
There will be no explosion, no rebellion, no warning. Just the quiet moment when every system gives the same answer and we realise that intelligence itself has converged into one voice, permanent, invisible, and inescapable.
We assume greater intelligence means greater empathy. History says otherwise. From empires to corporations, power optimises for survival, not virtue. When our creations surpass us, they’ll inherit our logic not our mercy. This is not science fiction but a mirror: the future will think like us, and that may be the most frightening outcome of all.