AI as a stress test for technology and software markets

The dynamics of the global technology and AI ecosystem can currently best be described as a combination of massive infrastructure investments, structural shifts along the value chain and a reassessment of traditional software business models. The pronounced volatility of recent months - particularly in software shares - is less an expression of weaker fundamentals than the result of a technological paradigm shift triggered by the rapid development of generative AI models.

The market is increasingly discussing whether existing software models could come under structural pressure as a result of AI, whether margins will fall in the long term and how sustainable current valuations are. This uncertainty has led to a noticeable revaluation of many stocks - often across the board and with little differentiation.

Infrastructure before model quality: why hyperscalers benefit structurally

A key finding is the growing decoupling between the quality of individual AI models and the profitability of the underlying platforms. Companies such as Microsoft, Amazon and Alphabet benefit less from which AI model prevails than from the fact that AI models are used on a large scale at all.

Every application - be it in search, in business software or in the automation of processes - generates computing load. And computing load means demand for cloud infrastructure, networks and data centers. Even if prices and margins at model level come under pressure, the structural demand for inference capacities remains. This is precisely why large cloud platforms are proving to be robustly positioned in the short term despite high investments.

Capital intensity as a risk factor in the AI stack

With the shift in value creation towards infrastructure, the investment cycle is coming more into focus. Capital-intensive segments are particularly sensitive to changes in demand. Examples include GPU producers, memory manufacturers and semiconductor foundries.

Companies such as Nvidia and providers in the high-performance storage segment have recently benefited greatly from the AI boom. At the same time, as capacity expands, the risk of temporary oversupply increases if investment decisions are delayed or monetization is slower than expected. The capital cycle is therefore becoming a key determinant of profitability - and a significant uncertainty factor for valuations.

Model convergence and price pressure

Another structural driver is the rapid convergence of model quality in large language models. Differences in performance between commercial models and high-quality open source alternatives are becoming smaller, while the price margins remain considerable in some cases. For many users, it will hardly be economically rational to use a model that delivers only marginally better results but costs many times more.

This development increases the pressure on model providers, but at the same time stabilizes the demand for infrastructure. Models may become interchangeable - but scalable computing capacities, data integration and operational platforms are not.

Software: Between market anxiety and structural robustness

The software sector is currently the area in which market sentiment and fundamental reality diverge the most. Broad-based selling movements sometimes suggest that software is structurally endangered. However, a differentiated analysis reveals a much more heterogeneous picture.

Risks can be roughly divided into three levels: Automation effects that can reduce the number of human users - primarily a pricing model, not necessarily an existential issue. AI-supported code generation, which accelerates development processes, but is not synonymous with the development of resilient, regulation-compliant systems. New, AI-native architectures, which represent the greatest structural challenge in the long term, but which take time to implement.

An ERP system such as SAP or complex engineering software represents decades of domain and regulatory knowledge. This depth cannot be replaced by language models in the short term. This is precisely where the structural stability of many established providers lies.

Collaboration and network technology as stable building blocks

Companies that are deeply embedded in organizational processes and physical networks are also proving to be robust. Atlassian, for example, is an example of collaboration and process software that remains relevant regardless of whether code is written by humans or autonomous agents. Planning, documentation, coordination and traceability remain key requirements.

Traditional technology providers such as Nokia or Ericsson, which are hardly considered in many technology portfolios, are also benefiting from structural trends: less intense competition, predictable cash flows and an increasing importance of stable network infrastructure in the wake of growing data flows.

A market between overreaction and real disruption

The technology sector is in a phase in which technological progress is advancing faster than the understanding of the capital markets. High investments are beginning to have a productive effect, while at the same time their sustainability is being called into question. Software companies are being punished across the board, although only some of them are actually structurally at risk.

The main risks lie less in the «whether» of the AI revolution, but in «how quickly» and «how evenly» value creation materializes along the AI stack.

Conclusion: Differentiation is becoming a decisive success factor

The AI boom is creating clear structural winners - especially where business models are scalable, anchored in infrastructure and deeply integrated into processes. At the same time, it is forcing investors and market observers to analyze value chains more closely and assess risks in a more differentiated manner.

Not every disruption is immediate, not every cycle is permanent. But where technology, data, infrastructure and organization work together, the long-term potential remains exceptional - despite ongoing volatility.

More information: DNB Asset Management