Berlin, Paris, London: how Europe's AI hubs are diverging
Europe's AI scene used to get lumped together as one story. In 2026, Berlin, Paris, and London are running three different plays, and the gap between them is widening fast...
New York's AI scene: The 25 companies you need to know
New York's AI companies are embedding AI into industries the city already runs: trading floors, hospital records, compliance desks. This list of 25 names, from Hugging Face to Dataminr, maps what that looks like in practice, and why the city's AI economy no longer needs Silicon Valley's permission.
I've been spending a lot of time thinking about this, and more importantly, living it while building agentic systems at 2Q AI. What follows are real incidents, real architectural breakdowns, and a practical framework for keeping your AI agents from going off the rails...
The age of AI evangelism is over. Welcome to the evaluation era.
Transparency scores are falling, hallucination rates on user-framed statements hit as high as 94%, and benchmark performance still fails to predict real-world results. The gap between what AI can do and what organizations can actually verify is now the problem worth solving...
The US AI rulebook is being rewritten. Your compliance team can't wait
America's AI regulatory landscape just had a month that made legal counsel everywhere reach for stronger coffee. Colorado's landmark AI Act, once celebrated as the country's first comprehensive state AI law, was gutted and replaced before it ever took effect.
Boards want AI roadmaps. Competitors are shipping AI features. And 74% of companies still can't make it pay. This piece breaks down the eight-point framework that separates disciplined AI adoption from expensive noise.
AI agents keep breaking in production. Here's why nobody's fixed it yet.
The gap between what agentic AI promises and what it actually delivers in live environments is now one of the most consequential engineering problems in the industry. It is also, frustratingly, one that the field has been slow to name precisely, let alone fix...
The 3 reasons your AI never makes it to production
Most companies don't have an AI problem. They have a throughput problem. And I think that distinction matters a lot when you start talking about how to actually get AI working in production.
AI agents keep breaking in production. Here's why nobody's fixed it yet
78% of enterprises have an AI agent pilot running. Only 14% have successfully scaled one. The gap isn't a model problem. It's an engineering one (and it's hiding in plain sight....)
Your data engineers may be more influential than you think
The data engineer has gone from a largely behind-the-scenes role to one of the most strategically important positions in a modern technology organization. The leaders who understand why are making significantly better infrastructure decisions than the ones who do not.
The future AI team: What enterprise AI organizations may look like by 2030
Ask most enterprises what their AI team looks like in 2030 and you will get a blank stare followed by a reference to their current headcount.
That is understandable. It is also a problem. Because the AI team of 2030 is going to look very little like the AI team of today...
AI safety shifts from the model to the system level. As AI becomes agentic and tool-driven, risk emerges from complex interactions, widening the gap between evaluation and real-world behavior.
AI is splitting in two directions. One path is controlled, restricted, and security-first. The other is open, autonomous, and scaling fast. The real question isn’t which is better, it’s what this means for trust.
We call it machine learning. But do machines actually learn?
Today's AI systems train, optimize, and scale, but real learning is something else entirely. The distinction matters more than the industry wants to admit.