Davos 2026: The inflection of intelligence — how AI is changing the world

The World Economic Forum Annual Meeting 2026 marks a clear inflection point in how technology and AI in particular are understood by business leaders, investors, and economists. In Davos this year, AI is no longer framed as a future catalyst or a strategic option. It is treated as a core economic variable that already influences growth forecasts, capital markets, productivity assumptions, and competitive dynamics. AI has moved from promise to dependency.

The 10 most thought-provoking topics of discussion in Davos: 

1. AI is a general-purpose technology, not just another tool 

A fundamental shift underpinning all current AI discussions is the recognition that AI is not a single-purpose productivity tool, but a general-purpose technology comparable to the printing press, electricity or the internet. Those technologies did not merely improve existing processes; they reshaped entire economies by multiplying a core human capability. Electricity mechanized physical labor, the internet multiplied information, and AI is now multiplying cognition itself. This distinction matters because general-purpose technologies consistently account for a significant share of GDP over time and create new industries, roles, and value chains. Treating AI as “just another software tool” underestimates its structural impact and explains why AI adoption is increasingly discussed in economic, not merely technological, terms.

2. AI has become a pillar of macroeconomic stability 

One of the clearest signals emerging from Davos 2026 is how deeply AI investment is now embedded in macroeconomic reality. Global growth expectations have remained more resilient than anticipated despite ongoing trade tensions and broader uncertainty, with AI spending acting as a key stabilizing force. Large-scale investments in data centers, computer infrastructure, software, and energy systems are no longer viewed as cyclical technology bets, but as foundational economic inputs. At the same time, elevated valuations of AI-driven companies are contributing to a broader wealth effect, supporting consumption and confidence. 

This combination has shifted AI from being a sector-specific growth driver to a systemic factor influencing overall economic performance. A meaningful slowdown in AI investment would therefore have implications well beyond technology markets, affecting growth trajectories at the macroeconomic level. AI is no longer just a lever for growth; it has become a stabilizer within the global economy. 

3. AI valuations are being treated as economic infrastructure 

A significant reframing is also underway in how AI companies are valued. OpenAI, for example, is being discussed as a potential IPO candidate with valuations up to around USD 1 trillion. This is no longer framed as speculative exuberance, but as a reflection of how AI businesses are increasingly perceived: not as conventional software companies, but as economic infrastructure. 

This shift places leading AI platforms in the same conceptual category as:

• telecommunications networks
• energy grids 
• financial market infrastructure 

The core debate is therefore less about whether such valuations are high and more about when and how AI-driven productivity gains will convert into sustainable cash flows and broader macroeconomic impact. 

4. Hiring is slowing because of AI, not recession 

One of the more subtle yet consequential dynamics emerging around AI adoption is its impact on labor markets. Organizations are not engaging in large-scale layoffs, but many are quietly adjusting hiring behavior by:

• slowing hiring
• freezing entry-level positions
• allowing AI to absorb incremental workload growth 

This pattern is reinforced by concrete enterprise use cases where AI is deployed to expand capacity rather than reduce headcount. In several cases, AI agents have been used to extend service availability and operational coverage without shrinking teams. The value creation comes from increased availability, speed, and service quality, not from immediate cost cutting. 

This creates a competitive dynamic. Pressure to improve margins through AI does not stem from ideology or top-down mandates, but from market competition. Once one organization uses AI to scale output or efficiency, others are forced to respond. The implication is clear: AI-driven labor effects are unfolding gradually and competitively, reshaping hiring patterns long before they appear as outright job losses. 

5. AI is reshaping M&A logic, not just valuations 

AI is fundamentally changing how companies approach mergers and acquisitions. Rather than pursuing large, speculative bets driven by hype, leading organizations are treating AI as a core capability that must demonstrably reshape business models, productivity, and time-to-value. In M&A assessments, the focus has shifted from the algorithm alone to how deeply AI is embedded in the operating model, data foundations, and infrastructure. AI-driven targets are valued for their ability to compress timelines dramatically, reduce risk earlier in the investment cycle, and integrate development with production or delivery. This has led to a more disciplined, ecosystem-oriented M&A approach: staged investments, minority stakes, joint ventures, and partnerships are increasingly preferred over full acquisitions when uncertainty remains high. Premiums are paid only where AI enables visible, scalable value creation. In this environment, AI-driven M&A is less about buying optionality and more about securing strategic control over future productivity, integration, and competitive advantage.

6. The “good bubble” hypothesis: Why this AI boom is different 

There has been a notably nuanced debate around whether current AI valuations constitute a bubble. A clear distinction is being drawn between the present cycle and the dot-com era. Today’s AI investment is largely driven by cash-rich incumbents rather than debt, and it is directed toward infrastructure that is productive and reusable. Even in the event of a valuation correction, the underlying assets (data centers, compute capacity, and software ecosystems) would remain in place.

The comparison most often invoked is the fiber-optic overbuild of the 1990s, which delivered limited returns for some early investors but ultimately laid the foundation for today’s digital economy. The emerging view is pragmatic: even if AI valuations adjust, the economic value created by the resulting infrastructure is likely to persist. 

7. Europe’s role in AI is strong on talent, but constrained on scale

Europe enters the AI era with strong fundamentals: world-class talent, leading research institutions, and emerging hubs such as Paris and Zurich. At the same time, structural constraints limit Europe’s ability to scale AI globally. Unlike the US single market, Europe remains fragmented across 27 countries with different regulations and languages, making rapid expansion significantly harder. 

Regulatory and tax frameworks further amplify this challenge. In some cases, founders are taxed on unrealized gains from illiquid shares, creating billion-euro valuation hurdles without liquidity to pay. Combined with the risk of talent migration and slower platform formation, the message is clear: Europe’s opportunity lies in focusing on selected domains where scientific depth, industrial strength, and long-term ambition can translate AI innovation into globally relevant scale. 

8. AI is supercharging cybercrime at an industrial scale 

Cybersecurity discussions have made clear how decisively AI is reshaping the threat landscape. AI enables cybercrime to scale industrially, from large-scale, culturally tailored phishing to the growing prevalence of cybercrime delivered as a service, including scams, ransomware, and AI-driven attack tooling. As a result, the barriers to entry for cybercriminal activity are rapidly collapsing. The magnitude of this shift is underscored by concrete outcomes: Interpol-led operations referenced in these discussions resulted in more than 1,200 arrests and the recovery of USD 97.4 million in a single coordinated cybercrime crackdown in Africa. 

9. AI is forcing access to operational technology, creating new risks 

A key issue emerging from AI adoption is the growing overlap between IT and operational technology. AI systems increasingly rely on data from production lines, logistics networks, and energy infrastructure, which means systems that were once isolated are now being connected to corporate IT and cloud environments. This breaks down the traditional separation between IT systems that manage information and OT systems that control physical processes, significantly increasing cybersecurity risk. Attack surfaces expand, incidents become harder to contain, and failures can affect not only data but real-world operations.

At the same time, cybersecurity investment remains heavily skewed. Approximately 95% of cybersecurity spending still protects enterprise IT, while only 5% is directed toward operational technology, where revenue generation and physical risk reside. AI adoption intensifies this imbalance: as organizations connect operational data to AI systems, long-standing security assumptions no longer hold, and governance gaps become more visible. Addressing AI risk, therefore, requires rethinking cybersecurity across both digital and physical systems, not just adding controls on the IT side.

10. AI ROI and governance are becoming board-level issues 

Another critical issue discussed is the difficulty many organizations face in defining clear AI ROI before scaling use cases. In several examples, weak governance led to AI initiatives consuming USD 5,000 per day without a transparent link to business value. 

To counter this, some organizations have started publishing internal AI usage reports that show how executives and teams actually use AI in practice. This creates visibility, accountability, and more disciplined adoption. The takeaway is clear: AI risk is not purely a technical matter. It is organizational, financial, and governance-related, and increasingly requires active oversight at the board level.

At OMMAX, we work with organizations to translate AI, data, and technology into measurable economic value by embedding them into core business and operating models. Learn more about our services.