World Economic Forum Davos 2026 Marks the Era of AI Infrastructure

At this year’s World Economic Forum in Davos, artificial intelligence was no longer named as an emerging technology. It was considered infrastructure. In all the panels, private dinners and side discussions, the debate had clearly changed: the question is not whether AI will transform the economy and institutions, but who can use it at a low level under strengthening national and social barriers.

Polished speaking spaces and commercial communication were expected. Instead, the dominant tone was unusually open and interactive. Leaders across the industry, government and investment circles participated in open discussions about what it takes to build, deploy and manage AI systems in the real world.

From success to infrastructure

In years past, AI at Davos was often positioned as a horizon technology or a promising experiment. This year, leaders talked about it the way they talk about power grids or the Internet: as a basic skill that should be embedded in all jobs. In closed sessions and business-focused discussions, including the Emerging Tech breakfast hosted by BCG, AI was consistently framed as something organizations should build into their core operating model, not explore at the edges.

Business leaders stressed that AI can no longer reside in pilots or innovation labs. It becomes the foundational layer of operations, restructuring workflows, management structures and executive accountability. One panelist put it plainly: in the future, there may not be any Chief AI Officers, because all Chief Operating Officers will be responsible for AI The real work is now reorganizing roles, incentives and processes around systems that are always present and deeply embedded, rather than treating AI as a bolt-on feature.

The rise of agent systems

Another notable change was the focus on agent AI systems. Rather than tools that simply facilitate a person’s work, these systems are designed to plan, decide and operate throughout the workflow. In practical terms, that means AI that does more than answer questions: it can decide next steps, call other tools or services and close the loop on tasks.

This trend is forcing a rethinking of software-as-a-service models. Many founders and executives have talked about reinventing products as AI-native platforms that actively drive processes, rather than software that automatically supports human operators. As these systems assume greater autonomy, questions of liability, oversight and human intervention move from the fringes of product design to the center of both business design and control.

Workforce stress and the bending of entry-level work

Concerns about layoffs were less than theoretical compared to previous years. Management has openly talked about hiring freezes and the quiet erosion of regular entry roles. General analysis, reporting and integration work—tasks that used to stop the jobs of juniors—are where AI systems are rapidly advancing.

In response, retraining moves from one speaking area to another. Instead of taking AI power for granted, organizations are creating systematic ways to retrain existing workers for AI-augmented roles. A related trend is intrapreneurship: with testing costs reduced by AI, companies encourage employees to raise pilots and launch internal businesses, transferring business power internally instead of losing it in the beginning.

Controlling speed, not blocking

Despite the urgency of AI deployment, some of the most focused discussions at Davos focused on governance. These were not ethical debates, but rather practical discussions about how to move quickly without creating unacceptable legal, reputational or public risk.

The emerging consensus has occurred at what many describe as “controlled velocity”: rapid iteration combined with mechanisms that make systems visible and adjustable in real time. Leaders described embedding governance directly into workflows through auditing, data governance, red collaboration, human-centered checkpoints and clear ownership of AI results.

In policy-focused sessions, including gatherings of world leaders, similar themes emerged about embedding accountability in large-scale AI deployments, rather than trying to slow progress outward.

AI as a geopolitical asset and the rise of autonomous AI

One of the clearest lines was the connection between AI and geopolitical power. At the TCP House panel, Ray Dalio captured a widely shared view: whoever wins the technology race will win the geopolitical race. Throughout Davos, speakers framed the power of AI as a determinant of national influence, economic stability and security.

This framing is driving the wave of autonomous AI systems. Governments are investing in domestic data centers, local model training and tight control over critical infrastructure to reduce strategic dependencies. The goal is not so much isolation as stability, a balance between domestic power and chosen global relationships. In Semafor CEO Signal Exchange, for example, Google’s Ruth Porat warned of the risk of an AI power outage emerging if the United States fails to move quickly enough, creating space for competitors to set goals for the next era.

For businesses, these changes translate into tangible decisions about data residency, model dependency and vendor focus in a multi-vendor world.

Regional strategies are different

Regional differences in AI strategy were hard to miss. The European approach to regulation is shaping global trends, but many stakeholders have expressed concern that it could jeopardize commercial leadership. Europe is becoming a reference point for risk mitigation and rights protection, as questions continue about whether it can serve as a key engine for AI-driven growth.

In contrast, the United States and parts of the Middle East are developing strongly with a coordinated policy, large investments and the construction of large infrastructures. Discussions about semiconductors, satellites and cybersecurity have confirmed that AI deployment is now intertwined with national resilience and defense considerations. Regions that move quickly in infrastructure and distribution are likely to impose technical, regulatory and commercial inefficiencies that others will eventually be forced to adopt.

Domain-specific AI, with biohealth at the fore

While general-purpose models remain central, much of the energy at Davos focused on domain-specific AI healthcare, biotechnology, energy and agriculture stand out as sectors where AI promises great value and increasing risk. Biohealth, in particular, was central to discussions of drug discovery, diagnostics and clinical decision support.

In all of these domains, participants emphasized that success depends on close collaboration between developers, domain experts, and administrators. Transparency, authentication and accountability were repeatedly described as requirements for AI systems that affect public safety, critical infrastructure or public trust. In one session focused on AgriTech, for example, speakers emphasized that the role of AI in food security depends heavily on governance and data integrity and development.

A sign of a person in the midst of rapid change

Beyond the technical themes, the tone of Davos 2026 was striking in its human-centric nature. Panel after panel emphasized the deployment of AI in the service of humanity, not just efficiency or profit. Many speakers retreated from a deterministic or crisis-driven narrative, highlighting that humans are still writing the models, setting the rules and deciding what the AI ​​will ultimately work for.

An Oxford-style debate hosted by Cognizant and Constellation Research took this spirit. Participants were divided into a “Humanity Team” and an “AI Team,” and the format was intentionally collaborative, not about winning an argument, but about changing mindsets about human purpose in the AI ​​era. That focus on agency and commitment continued in both formal sessions and late-night conversations.

Davos does not predict the future of technology. It shows what people with power and money are already preparing. This year, the sign was clear: AI has entered its infrastructure phase. Competitive advantage will come from how organizations manage it, integrate it with work, retrain their people and manage sovereignty and dependency risks, not who can show off the flashiest model.

In the midst of the rush, what stood out the most was the human element of thoughtful, collaborative people trying to build something better. In a moment defined by rapid change, that may be the most important signal of all.