Generative AI wave ignites invasive technologies with far-reaching consequences

Transformer-based models like GPT-4 are more than disruptive tools. Instead, they are “invasive technologies” - a new classification for innovations that don't just outperform rivals, but overtake and eliminate them across entire industries, according to a new study published in Technologies.

The study titled “Destructive Creation of New Invasive Technologies: Generative Artificial Intelligence Behaviour”, introduces the concept of technological invasiveness to explain paradigm shifts more intense and pervasive than traditional disruption. It draws empirical support from patent data spanning 2014 to 2024, revealing that transformer technologies exhibit the highest growth and substitution rates in the deep learning ecosystem.

What defines invasive technologies and how are they different from disruptive ones?

Invasive technologies, unlike merely disruptive ones, do not merely displace incumbents within a market; they eradicate and replace entire technological ecosystems. Their growth follows not just exponential trajectories but also generates clusters of innovations and interdependencies with other emerging technologies. This distinguishes them from disruptive technologies that typically affect single industries or sectors.

Invasive technologies, according to the study, possess six key traits: rapid short-term diffusion, broad adaptability, deep inter-tech interaction, competitive dominance, ecosystem restructuring, and significant social-economic impact. They act as “predator technologies” by occupying the space previously held by incumbent solutions. This behavior mirrors ecological invasion, wherein an aggressive species overtakes native populations.

The concept draws from a generalized Darwinian lens, arguing that technological evolution, much like biological evolution, features survival, adaptation, extinction, and symbiosis. Within this framework, transformers exhibit a more aggressive form of innovation diffusion than Long Short-Term Memory (LSTM) or Recurrent Neural Networks (RNNs) - two of the prior leading deep learning methods.

How rapidly are transformer technologies expanding?

The study’s comparative patent analysis shows a stark contrast in growth between transformers and their predecessors. Between 2020 and 2024, transformer technology patents grew at a staggering annual exponential rate of 45.91%, more than double the rates for LSTM (21.17%) and RNN (18.15%). The invasion coefficient, used to quantify how aggressively one technology supplants another, was highest for transformers at 2.2, compared to 1.39 for LSTM and 1.22 for RNN.

This growth isn’t isolated to language models alone. Transformers have catalyzed progress in fields as diverse as healthcare (e.g., neurology, epidemiology), remote sensing, image processing, and autonomous vehicles. Their architecture, characterized by self-attention and high parallelizability, enables unprecedented scale and adaptability. The patent data suggests that transformer technologies are evolving faster and with broader application than any of their neural network predecessors.

Moreover, the study uses a substitution model to show that for every 1% increase in patents in RNN or LSTM technologies, transformer-related patents grow disproportionately, by up to 1.8% in the case of RNNs. This pattern of accelerated substitution is interpreted as evidence of an ongoing technological regime shift.

What are the implications for industry, policy, and innovation management?

The emergence of invasive technologies like transformers has far-reaching consequences for industries, policymakers, and innovation managers. The study warns that such technologies can create or annihilate entire sectors in short timeframes, underscoring the urgency for strategic foresight and adaptive regulation.

For businesses, the recommendation is to adopt an ambidextrous approach to innovation management: balancing exploitation of current assets with exploration of emerging possibilities. Firms should invest in training, ethical governance, and technical security to accommodate the rapid shifts introduced by invasive technologies.

On the policy front, governments are urged to frame legal and ethical standards early in the adoption cycle. These should include public R&D funding for foundational transformer technologies, incentives for responsible innovation, and international cooperation to avoid monopolistic lock-in and ensure equitable access.

The study also highlights the need for new educational frameworks to prepare future workforces for the pervasive integration of transformer-based AI systems across sectors. This includes reskilling initiatives and inclusion of AI literacy in general education curricula.

Moreover, the study acknowledges some limitations. It focuses solely on patent data and a single technological archetype, transformers. Future research could assess invasiveness across a broader range of technologies and industries to better understand the generalizability of this concept.