NVIDIA controls roughly 80 per cent of the AI accelerator market — a concentration that is now prompting the world’s largest technology companies to build their own silicon. The shift from monolithic GPU clusters to modular chiplet architectures is not a distant forecast: it is already reshaping enterprise procurement, investment strategy, and the competitive dynamics of AI at scale.

AI sovereignty has moved from the margins of technology policy into the boardroom agenda of every serious multinational. With 93 per cent of global executives calling it mission-critical and at least 34 countries enacting data localisation rules, the question is no longer whether to build a sovereign AI strategy — but how fast.

Global semiconductor revenue is set to exceed $1.3 trillion in 2026 — the highest growth rate in two decades — as DRAM prices surge 125% and NAND flash climbs 234% year-on-year. For business leaders outside the technology sector, the chip super-cycle is no longer a supply chain footnote: it is a balance-sheet emergency hiding in plain sight.

Global capital is quietly migrating away from US-dominated markets. Dollar uncertainty, geopolitical fragmentation, and emerging market maturation are driving the largest reallocation in a generation.

Inflation is proving stickier than the Fed expected, and higher-for-longer rates are forcing a fundamental rethink of corporate capital allocation. Here’s the new framework CFOs need to adopt.

Bitcoin ETFs have accumulated $128 billion since their US approval — the fastest ETF launch in history. Institutional money has arrived in crypto. What happens next is more complex than the headline.

Real-world asset tokenisation has crossed $19 billion on public blockchains. BlackRock, JPMorgan, and Franklin Templeton are already live. Wall Street’s move to the blockchain is no longer a pilot.

Jerome Powell’s exit as Fed Chair is triggering the most consequential monetary policy transition in a generation. Here’s what the leadership vacuum means for markets, rates, and corporate strategy.

Gartner projects that more than 40 per cent of agentic AI enterprise deployments will be cancelled by the end of 2027 — not because the technology does not work, but because the organisations deploying it are not ready. The gap between deployment speed and governance maturity is becoming the defining risk of the AI era.