In April 2026, Uber's engineering leadership discovered that a five-thousand-person team had burned through the company's entire annual AI budget in four months. Per-engineer token costs were running $500 to $2,000 a month, usage had doubled in eight weeks, and the company's COO admitted the surge in spend wasn't translating into proportionally better products (Forbes,Reuters). Uber was not an outlier. The FinOps Foundation's 2026 State of FinOps report found that 73% of enterprises exceeded their original AI cost projections this year, even as the price of a single token kept falling (TechCrunch).
That paradox, cheaper tokens and bigger bills, is exactly the terrain Meryem Arik has spent the last several years mapping. Meryem is Co-founder and CEO of Doubleword, a London-based inference platform that helps enterprises self-host open source and custom AI models (Doubleword), and she was recently interviewed by our Chief Editor, Fawn Hudgens, to talk through what she calls the "hot open weight summer": a moment when cost pressure and a jolt of geopolitical risk are forcing enterprise leaders to rethink how they buy intelligence, not just which model they buy.
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On 30 July 2026, the European Commission opened a call to build up to seven AI Gigafactories, pairing €10 billion in public funding with an ambition to mobilise at least €20 billion in private investment. It is Brussels’ largest infrastructure push yet—and a reminder of how far Europe still has to go to secure the compute, cloud capacity and strategic options that advanced AI requires. European Commission | Reuters
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In 2026, agentic AI has quietly moved from the interface to the core of the enterprise. What started as “smart assistants” on top of products is becoming infrastructure that reads, routes and acts across workflows. This blog looks at what that shift really means: how agents behave when they’re operational, what tends to go wrong, and how senior leaders can introduce them without negatively impacting their organisations.
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Agentic AI Infrastructure,
AI Infrastructure,
AI Automation,
Workflow,
Agentic AI
The next AI governance problem is not theoretical – it is already inside the business. Employees are using copilots, SaaS agents and browser-based AI tools to get real work done, often before IT, security or leadership have decided what good use actually looks like.
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World AI Week is where the global AI ecosystem stops talking about connections and actually makes them. For DutchBasecamp, it is one of the few moments each year where founders, investors, corporates, and ecosystem builders all move within walking distance of one another in Amsterdam. In their words, “for one week, the probability of running into exactly the person you need to meet becomes unusually high.”
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The strategic promise of agentic AI is not that work gets faster. It is that organisations can start to redesign how work moves and what an agentic AI operating model actually looks like in practice.
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AI is no longer just answering questions. In more and more systems, it is deciding what to do next: moving money, changing prices, rewriting workflows, dispatching people and machines. “Agentic AI” has become the shorthand, but the more important reality is this: a growing share of consequential actions in the economy is being taken by software that is allowed to act with limited human oversight.
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Geopolitics
For years, data centres sat in the background: expensive, essential, and mostly invisible. In the AI era, that has changed. Data centres have quietly become the physical backbone of digital power – and increasingly, a geopolitical asset in their own right.
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Geopolitics