For over two decades, the enterprise software industry has been characterised by relatively stable competitive advantages. Major software platforms have built solid market positions thanks to high switching costs, strong customer lock-in, network effects and deep integration into organisations’ operational workflows. In many cases, these factors have enabled companies to maintain dominant positions for long periods, supporting business models based on recurring revenue and consolidating the strategic power of digital platforms (Cusumano, Gawer, & Yoffie, 2019).
In recent years, however, the rapid development of artificial intelligence is beginning to reshape the fundamentals of competition in the software sector. As AI capabilities advance and increasingly powerful development tools become more widespread, analysts and investors are beginning to question how long software companies’ competitive advantages will actually last. In a context where technological innovation is accelerating rapidly, even the most established market positions may become less stable than they have been in the past.
A useful theoretical framework for understanding these emerging dynamics is the so-called Red Queen effect. This concept describes competitive situations in which organisations must innovate continuously simply to maintain their relative position in the market. In systems of this kind, survival does not necessarily depend on achieving a permanent advantage, but on the ability to adapt to the pace of change driven by technological competition (Barnett & Hansen, 1996).
The metaphor is taken from Lewis Carroll’s novel *Through the Looking-Glass*, in which the Red Queen explains to Alice that one must “run as fast as you can just to stay in the same place” (Carroll, 1871). The image effectively captures the logic of dynamic competitive systems: players must keep moving forward simply to avoid being overtaken by others. In technology markets, this principle translates into the need for firms to constantly improve their products, infrastructure and capacity for innovation, even when their relative position within the sector remains unchanged.
In recent years, artificial intelligence has accelerated the pace of innovation in the software sector. Advances in generative AI are boosting productivity in software development, enabling developers to generate code, automate testing and speed up iteration cycles. Empirical studies suggest that generative AI tools can significantly improve the productivity of knowledge workers engaged in programming and development activities (Noy & Zhang, 2023).
Reducing development times and testing costs could help lower certain barriers to entry in the software market. If developing new products becomes quicker and cheaper, competitive pressure tends to increase, and the technological advantage built up by established firms could become less sustainable.
The financial markets are already beginning to reflect this potential transformation. Some analysts believe that artificial intelligence could shorten the lifespan of so-called ‘economic moats’ – the competitive advantages that enable software companies to sustain superior returns over the long term. According to some industry analyses, uncertainty regarding the long-term impact of AI has led several investors to shorten the timeframe within which software companies can maintain excess returns (Romanoff, 2026).
When the perceived duration of competitive advantages decreases, financial valuation models also tend to adapt. If the period during which a company can sustain above-average profits shortens, the present value of future cash flows becomes more uncertain and market valuations may be revised (Damodaran, 2024).
This shift in expectations has contributed to a broader reassessment of the software sector in the financial markets. In recent years, investors have gradually shifted capital towards companies perceived as leaders in artificial intelligence infrastructure, whilst more traditional software providers are facing greater scrutiny regarding their ability to maintain competitive advantages in the long term (Bloomberg Intelligence, 2026).
Faced with this scenario, many companies are stepping up their investment in AI to maintain their technological relevance. Corporate strategies clearly reflect the dynamics of the Red Queen. When the technological frontier shifts rapidly, companies increase their investment in research, computing infrastructure and product development to avoid falling behind their competitors (Goldman Sachs Research, 2026).
These investments often require significant organisational changes. Companies must reallocate resources, redefine strategic priorities and restructure certain internal functions to support large-scale AI development programmes.
A recent example is Atlassian, which announced a workforce reduction of around 10 per cent, equivalent to approximately 1,600 employees, with the aim of reallocating resources towards the development of artificial intelligence technologies and strengthening its commercial strategy in the enterprise segment (TechCrunch, 2026). Similar initiatives have been observed in numerous technology companies, many of which are reorganising their operational structures to fund increasingly substantial investments in artificial intelligence (Reuters, 2026).
A key feature of the Red Queen effect is that when one competitor steps up its efforts, this tends to prompt similar responses from other market players. When a company increases its investment to gain a competitive advantage, its rivals are incentivised to do the same. The result is that the technological frontier of the entire sector is pushed upwards.
From the perspective of complex systems, this dynamic can be interpreted as a self-reinforcing feedback process. The actions of one competitor trigger increasingly intense reactions from others, progressively amplifying the level of competition within the technological ecosystem (Meadows, 2008). Over time, this process raises the minimum technological threshold required to remain competitive in the market.
In this context, the Red Queen effect provides a useful framework for understanding the transformation currently taking place in the software industry. Although for many companies, revenue generated directly from artificial intelligence still accounts for a relatively small proportion – often estimated at between 1 and 5 per cent of annual recurring revenue – investment in the sector is growing rapidly (Romanoff, 2026).
These decisions are not driven solely by immediate financial returns. Rather, they reflect a strategic necessity: to keep pace with the speed of innovation among competitors in an environment where the technological frontier is shifting ever more rapidly.
In this sense, artificial intelligence is transforming competition in the software sector not only through direct technological innovation, but also through expectations about the future. Companies invest in AI to avoid falling behind; competitors respond with similar investments; and the overall pace of innovation accelerates across the entire sector.
The result is a dynamic typical of the Red Queen: organisations must continue to innovate, invest and adapt, not necessarily to gain a definitive advantage, but simply to maintain their relative position in an increasingly fast-paced competitive environment. (photo by Tide_trasher_x on Unsplash)
References
Barnett, W. P., & Hansen, M. T. (1996). The Red Queen in organizational evolution. Strategic Management Journal, 17(S1), 139–157. https://doi.org/10.1002/smj.4250171010
Bloomberg Intelligence. (2026). AI investment reshapes technology sector valuations. Bloomberg.
Brynjolfsson, E., Li, D., & Raymond, L. (2024). Generative AI at work. National Bureau of Economic Research Working Paper Series. https://www.nber.org/papers/w31161
Cusumano, M. A., Gawer, A., & Yoffie, D. B. (2019). The business of platforms: Strategy in the age of digital competition, innovation, and power. Harper Business.
Damodaran, A. (2024). The corporate life cycle and valuation multiples. New York University Stern School of Business. https://pages.stern.nyu.edu/~adamodar
Goldman Sachs Research. (2026). AI investment cycle and implications for technology firms. Goldman Sachs.
Meadows, D. H. (2008). Thinking in systems: A primer. Chelsea Green Publishing.
Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586
Organisation for Economic Co-operation and Development (OECD). (2025). Artificial intelligence and the future of digital competition. OECD Publishing. https://www.oecd.org
Romanoff, D. (2026). Qual è l’impatto dell’IA sui vantaggi competitivi delle aziende di software. Morningstar.
https://global.morningstar.com/it/azioni/qual-limpatto-dellia-sui-vantaggi-competitivi-delle-aziende-di-software
TechCrunch. (2026). Atlassian follows Block’s footsteps and cuts staff in the name of AI.
https://techcrunch.com/2026/03/12/atlassian-follows-blocks-footsteps-and-cuts-staff-in-the-name-of-ai/
Reuters. (2026). Tech firms restructure operations to fund AI investments.
https://www.reuters.com
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