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In technical and managerial debates, a concept is emerging that summarises the transformation currently underway: the ‘AI-Ripped Effect’. The expression describes the moment when artificial intelligence not only improves operational performance, but also reorganises the competitive architecture of a sector, making legacy systems designed for a previous paradigm structurally uncompetitive.
This dynamic is not new in the history of general-purpose technologies. As demonstrated by Bresnahan and Trajtenberg (1995) and subsequently by Brynjolfsson and McAfee (2014), systemic innovations do not simply optimise what already exists: they transform complementary assets, organisational models and competitive hierarchies. What distinguishes AI is the combination of speed of evolution and systemic interdependence (Arthur, 2009; Helbing, 2013).
From process optimisation to outcome automation
In recent years, many companies have adopted AI to improve efficiency and productivity. However, according to Robert Valk, Deloitte’s Engineering CTO, “AI has ripped up the playbook… the value now lies in the outcome, not just the process.” The shift is significant: it is no longer a question of optimising individual operational phases, but of directly automating decision-making output.
Empirical research confirms that integrating AI into core decision-making processes generates greater productivity gains than previous waves of digitisation (Brynjolfsson et al., 2023; McKinsey Global Institute, 2023). When artificial intelligence enters the decision-making function and not just execution, the competitive benchmark shifts.
The result is structural discontinuity. Companies do not simply adopt a better tool; they operate on a different cognitive basis.
The critical variable: speed asymmetry
At the systemic level, the main tension is temporal asymmetry. Digital systems evolve exponentially, while infrastructure, governance and regulation evolve incrementally (Arthur, 2009; OECD, 2024).
Complexity science shows that when tightly interconnected systems accelerate beyond institutional control capacity, the likelihood of cascading instability increases (Helbing, 2013; Sornette, 2009). In this sense, many dynamics associated with AI fall into the category of predictable but underestimated risks, analogous to the ‘Grey Swans’ described by Taleb (2007).
Financial markets provide a practical example. Algorithmic trading operates at speeds faster than human cognition, contributing to episodes such as the flash crash analysed by Kirilenko et al. (2017). The problem is not unpredictability, but the speed mismatch between automated systems and human supervision.
Translated to the sectoral level, this dynamic implies that speed becomes both a competitive advantage and a systemic vulnerability.
Competitive divergence and structural obsolescence
Technological revolutions tend to widen the gap between early adopters and laggards (Perez, 2002). Evidence on the adoption of generative AI indicates measurable gains in knowledge-intensive sectors (Brynjolfsson et al., 2023; McKinsey Global Institute, 2023).
When competing companies operate under different technological paradigms, the competitive balance is destabilised (Acemoglu & Johnson, 2023). Obsolescence is not gradual. Once performance thresholds have been redefined, previous technologies are not simply less efficient: they become economically unsustainable.
This is the heart of the “AI-Ripped” effect. The rupture is not just about efficiency, but structural survival.
Legitimacy and governance, the second front of discontinuity
The impact is not limited to productivity. The adoption of generative models trained on large amounts of content has sparked debates about intellectual property, compensation, and copyright (European Parliament, 2023; U.S. Copyright Office, 2023). Recent surveys report widespread concerns among creative professionals about replacement and unpaid use of data (Authors Guild, 2023; International Labour Organisation, 2024).
According to institutional theory, when technological capacity exceeds regulatory adaptation, crises of legitimacy emerge (Suchman, 1995). In this context, AI not only alters business models, but also the balance between innovation and social acceptance.
Strategic implications for businesses and policy makers
For business leaders, adopting AI cannot be treated solely as an investment in efficiency. Every integration changes the competitive architecture of the industry. The question is not just “how much do I gain in productivity,” but “how does the minimum threshold of competitiveness change?”
For investors, the risk is not only technological, but also structural divergence. Sectors in which AI achieves architectural dominance may see rapid concentrations of value towards operators capable of integrating autonomous systems on a large scale.
For regulators, the challenge is to synchronise institutional resilience and technological acceleration. Most of the risks associated with AI – model drift, systemic interconnection, accountability gaps – are well documented (Lumenova AI, 2024; OECD, 2024). The problem is not unpredictability, but the pace of adaptation.
A systemic threshold
The “AI-Ripped” effect marks the transition from support technology to a structural determinant of competitiveness. At this stage, artificial intelligence is no longer a tool that fits into the existing architecture: it becomes the new architecture.
The central risk is not surprise. It is speed without alignment.
The stability of sectors will depend on the ability to evolve infrastructure, governance and organisational models at a speed comparable to that of intelligent systems. Where this alignment does not occur, obsolescence will not be gradual, but sudden. (photo by Google DeepMind on Unsplash)
References
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Bresnahan, T. F., & Trajtenberg, M. (1995). General-purpose technologies. Journal of Econometrics, 65(1), 83–108.
Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work (NBER Working Paper No. 31161).
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European Parliament. (2023). Intellectual property rights and generative artificial intelligence.
Helbing, D. (2013). Globally networked risks. Nature, 497, 51–59.
International Labour Organization. (2024). Generative AI and jobs.
Kirilenko, A. A., Kyle, A. S., Samadi, M., & Tuzun, T. (2017). The flash crash. Journal of Finance, 72(3), 967–998.
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Suchman, M. C. (1995). Managing legitimacy. Academy of Management Review, 20(3), 571–610.
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