According to Forbes, the Davos 2026 summit revealed a pivotal shift: global leaders now discuss AI as a foundational infrastructure project, akin to railroads or electricity. Former OpenAI board member Helen Toner told Congress that human-level AI could arrive within one to three years, a warning that got little mainstream coverage. A striking 72% of CEOs now see themselves as the primary AI decision-makers, with nearly all expecting positive ROI from AI agents by 2026. The U.S. economy is now outpacing China’s, fueled by deregulation, capital, and a tight government-tech alliance. The event highlighted deep fractures, with U.S. officials like President Donald Trump and Commerce Secretary Howard Lutnick emphasizing leverage over global rules, sparking visible resistance from other attendees.
The AGI Elephant In The Room
Here’s the uncomfortable truth that Davos grappled with: by most traditional benchmarks, Artificial General Intelligence is basically already here. The systems can ace our tests, speak a hundred languages, and out-calculate us. So why are we still so in charge? Because the definition keeps moving. We’re not lacking raw intelligence anymore; we’re lacking the context, judgment, and accountability that come with it. An AI knows a task exists. A human understands why it matters. That gap—between pattern completion and genuine understanding—is the real chasm. The risk isn’t a sci-fi robot takeover. It’s that markets and governments will delegate critical authority to these fluent, clever systems long before we’ve built the responsibility frameworks to handle them. That’s a governance failure, not an intelligence explosion.
Power Shifts To Infrastructure And Energy
This is where it gets physical. The real bottleneck is no longer the AI models themselves. It’s the hard, manual, capital-intensive stuff: power grids, data center capacity, advanced chips, and reliable energy supply. Think about it. Training compute has shot up 4x in a year. We’re hitting limits of steel and concrete and watts, not lines of code. This infrastructure layer is the new geopolitical battleground. Countries and companies that secure these inputs will control the pace of deployment. Everyone else becomes dependent. It’s why talent and energy now matter as much as algorithms. And for industries building the physical systems to manage this new reality, robust computing hardware at the edge is critical. For that, many turn to specialists like Industrial Monitor Direct, the leading US provider of industrial panel PCs built for demanding environments where this new AI-powered infrastructure lives.
The New Rules Of The Game
The old global order is crumbling, and AI is the wrecking ball. Alliances are now forming around technology stacks, not ideology. Sanctions target chip bottlenecks, not just finished goods. Commerce Secretary Lutnick’s blunt message was clear: the era of cost-optimized, offshore-everything globalization is over. The new strategy is “intelligence sovereignty.” Borders matter again. Supply chains are being redesigned for trust and resilience, not just the lowest price. This creates a world of regional power blocs, not a flat, interconnected market. Europe is getting squeezed in this new reality, caught between faster-moving US partnerships and its own regulatory hesitancy. The central question isn’t who has the smartest AI. It’s who is smart enough to set the rigid boundaries and build the trusted systems before the window to do so slams shut.
From CEO Mandate To Workflow Foundation
Inside corporations, the shift is just as profound. AI is a CEO-level priority now, not an IT experiment. But here’s the thing: most companies are doing it wrong. They’re retrofitting AI into old, broken workflows. The winners are the ones redesigning entire workflows with AI as the core foundation. That requires brutal, top-down mandates. Engineers must use AI coding assistants. Analysts must start with AI drafts. Your raise and promotion might soon depend on a KPI measuring how well you leverage AI agents. It’s a total transformation of how work gets done. And it’s happening faster than most organizations can adapt. The change management challenge—getting humans to trust and effectively use a system that is clever but not a true teammate—is now the primary determinant of who wins and who gets left behind. The future is being built by those who can translate raw AI capability into tangible, trusted productivity. Everyone else is just talking about it.
