Foundations of Science in Invasive Technologies

Qeios, CC-BY 4.0 · Article, February 29, 2024 Open Peer Review on Qeios Foundations of Science in Invasive Technologies Mario Coccia1 1 Italian National Research Council Funding: No specific funding was received for this work. Potential competing interests: No potential competing interests to declare. Abstract This study proposes that one of the drivers of technological change is due to technological invasion of path-breaking technologies and innovations. Invasion is anything that breaks into a place, occupying it or spreading in large quantities. This aspect is present in botany with invasive plants, in biology with invasive organism or in medicine with invasive cancer cells. The extension of this concept in the field of technologies can clarify main dynamics of technological change. In a perspective of generalized Darwinism, the theory here suggests the invasive behavior of technologies that expands the space of adjacent possible by introducing novelties and radical innovations with a dynamic interaction between the actual and possible. The prediction of the theory, given by acceleration rates of invasive technologies that conquer space of alternative technologies, is tested in emerging path-breaking technology of transformer (a type of deep learning architecture used in natural language processing-NPL- and in generative Artificial Intelligence). Transformer technology, introduced in 2017, and from 2018 is developing radical innovations in pretrained language models (Generative Pretraining Transformers, GPTs), such as OpenAI's GPT series, Google's Bidirectional Encoder Representations from Transformers (BERT) model with main products of ChatGPT introduced in November 2022 and Microsoft Copilot started on February 2023. Transformer technology and related radical innovations are spreading rapidly, invading and destroying other established technologies, changing the space of possibilities in human society. One significant way to understand the invasive behaviour of technologies is to estimate and analyze rates of spread. Statistical evidence here, based on patent analyses, reveals that the growth rate of transformer technology is 55.82% (over 2016-2023) more than double compared to 23.02% of all other technologies. The last three years (2021-2023) show that the growth rate is 25.81% for transformer models with an invasive and disruptive force of other technologies, having mere 0.76% of growth. Results are confirmed with a model of technological evolution that reveals a growth rate of invasive technology of transformers of 0.30 versus 0.13 for other technologies. Comparative analysis with a previous technology in neural network, Convolutional Neural Network (CCN), suggests that transformer architecture has a higher disruptive force that spreads rapidly invading the space of other technologies (technological invasion) with radical innovations, generating a drastic technological shift and change in the space of possibilities. This accelerated dynamics of transformer technology driving generative AI that mimics human ability is due to leading firms (such as OpenAI with ChatGPT, Microsoft with Copilot, Google with BERT, Apple with forthcoming his GPT, etc.) that are creating the innovation ecosystem based on new platforms and products directed to applications and diffusion in markets for a main technological and industrial change. Overall, the invasive behaviour of transformer technologies and related innovations Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 1/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 is fueling continuous innovations in the space of adjacent possible in generative AI and explains, in current world of knowledge-based competition, the 'creative destruction' that has revolutionized the field of NLP with new products that invade current and new markets. Implications for management of technology and innovation policy are suggested to support invasive technologies. Mario Coccia Research Director CNR -- National Research Council of Italy Department of Social Sciences and Humanities IRCRES-CNR, Turin Research Area, Strada delle Cacce, 73-10135 - Turin (Italy) E-mail: mario.coccia@cnr.it Keywords: Transformer; Deep learning Architecture; Generative pretraining transformers; GPTs; Large language models; Generative AI; Natural language processing; Emerging technology; Radical Innovation; Invasive technologies; Invasiveness; Adjacent possible; Space of possibilities; Technological change; Technological paradigm; Transformers; Attention mechanism. JEL Codes: O10; O31; O32; O33; O36; O39. 1. Introduction and Goal of Scientific Investigation One of the fundamental problems in technological studies is how a drastic technology emerges, spreads and sustains radical innovations for technological and social change (Dosi, 1988; Rogers, 1962; Sahal, 1981; Utterback et al., 2019; Utterback, 1994). This study proposes that one of the drivers of technological change is due to technological invasion of path-breaking technologies and innovations. Invasion is anything that breaks into a place, occupying it or spreading in large quantities. This aspect is present in botany with invasive plants that invade human habitats (Walker and Smith, 1997; Gholizadeh et al., 2024), in biology with invasive organism when is not indigenous to a particular area and can cause great economic and environmental harm to the new area (Pelicice et al., 2023) or in medicine with invasive cancer cell that have broken out of the lobule where they began and have the potential to spread to the lymph nodes and other areas of the body (de Visser and Joyce, 2023; Krakhmal et al., 2015). This study extends this scientific concept considering the diffusion of technologies. This suggested interpretation of a basic determinant of technological change is tested by analyzing new technology of transformer model (a neural network) to explain the invasive behaviour underlying the scientific and technological change in generative Artificial Intelligence (AI). The invasive behaviour of new technology is especially relevant in a world of knowledge-based competition to clarify the 'creative destruction' in existing competences and products with pervasive diffusion in science, markets and society having rapid changes (Teece et al., Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 2/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 1997; Tripsas, 1997). The proposed theoretical framework of invasive technologies can clarify main characteristics of ongoing technological change for supporting R&D management and innovation policy in emerging technologies having a high potential impact in almost every sphere of human activity in the current information and digital era (Hicks and Isett, 2020). The understanding of technological invasion of radical technologies is basic for understanding evolution of new technologies, emergence of major innovations and design of best practices for allocating resources to support emerging technological trajectories with unparalleled potential of growth to influence industrial and economic change (Roco and Bainbridge, 2002). The background of this study is the increasing availability of documents, patents and recorded knowledge that offers main opportunities for theoretical and empirical explorations about new aspects in the emergence and evolution of radical technologies (Arthur, 2009; Fortunato et al., 2018; Iacopini et al., 2018; Nelson, 2008) that pave the way for development of many inter-related technologies by ‘‘expanding the adjacent possible’’ (Coccia, 2018; Coccia and Watts, 2020; Kauffman, 2000, 2016, 2019; Kauffman and Clayton, 2006; Kauffman and Gare, 2015; Lehman and Kauffman, 2021; Wagner and Rosen, 2014). Proposed interpretation of invading technologies can explain important aspects in technological evolution and economic change of modern societies more and more based on high speed of knowledge and information turnover. One significant way to understand the invasive behaviour of technologies is to estimate the rates of spread in technological space having different and alternative technologies. Overall, then, proposed theoretical framework of invasive technologies, here can clarify main aspects of disruptive technologies to explain modern scientific and technological change for a better theory to support effective science and technology policy implications for societal benefits. 2. Critique and Limitations of Current Theories in the Evolution of New Technology Some studies suggest the notion of technological paradigms and trajectories to explain determinants and directions of technical change based on solution of specific technological problems that also generate a normal problem solving activity on the domain of technological paradigm (Dosi, 1988; Foster, 1986; Freeman, 1974; Nelson, 2008; Rosenberg, 1976). Other studies in technological dynamics have attributed the emergence and evolution of technologies to new discoveries (Basalla, 1988; Coccia, 2022; Tria et al., 2018). Different approaches focus on university-industry-government relations (Etzkowitz and Leydesdorff, 1998), converging systems of preexisting technologies (Barton, 2014; Roco and Bainbridge, 2002; Farrel, 1993) and problem-driven approaches in the emergence of radical technologies (Coccia, 2016, 2017, 2017a). The birth and evolution of radical technologies are also explained by social interactions among scientists and scientific communities (Crane, 1972; Sun et al., 2013; Wagner, 2008) and the leading role of some firms in markets (Coccia, 2018; Denning, 2018; Den Hartigh et al., 2016). Although the vast literature, quantitative works on invasive behaviour of path-breaking technologies in domains with manifold alternative technologies are lacking to date, owing in part to the difficulty of formally analyzing overall aspects of the pervasive diffusion and evolution of new technologies, based also on intangible aspects (e.g., software, algorithms), that generate technological shifts, industrial and technological impact in current knowledge and information economy. Regardless the sources of radical technologies, technology analysis of the specific dynamics of pervasive diffusion that generate radical changes in a short period of time, are critical to scientific and technological development, and progress of human society (Bettencourt et al., 2009). The goal Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 3/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 of this study is to propose in science the invasive behaviour of technologies to analyze the dynamics of path-breaking technologies that destroy established technologies, occupy their space and become dominant technology supporting technological and social change. Proposed theory is tested with a patent analysis of emerging path-breaking transformer technology (a type of deep learning model used in natural language processing-NPL- and in generative Artificial Intelligence). Transformer architecture is introduced in 2017 and from 2018 is developing pretrained language models (Generative Pretraining Transformers, GPTs), such as OpenAI's GPT series and Google's Bidirectional Encoder Representations from Transformers (BERT) model with radical innovations of ChatGPT introduced in 2022 and Microsoft Copilot started on February 2023. Proposed theoretical framework of invasive technologies and analysis of findings can suggest main characteristics of moder technological change and support theoretical and policy implications for management of new technologies directed to support economic and social change. 3. Theoretical Framework and Research Setting Path-breaking technologies have the characteristic of a destructive behaviour that generates radical innovations, based on new products and/or processes, which have high technical and/or economic performance directed to reduce market share or destroy the usage value of established technologies/products/processes previously used in markets (Christensen, 1997; Christensen et al., 2015; Tria et al., 2014). Calvano (2007) maintains that "Destructive Creation" is the deliberate introduction of new and improved generations of products that destroy, directly or indirectly, current products inducing consumers to change their habits with consequential economic and social change. The dynamics of disruptive technologies generates technological, industrial, economic and social change (Coccia, 2020). Adner (2002, pp. 668-669) claims that: “Disruptive technologies . . . introduce a different performance package from mainstream technologies” (cf., Adner and Zemsky, 2005; Calvano, 2007; Coccia, 2019). Abernathy and Clark (1985, pp. 4ff and pp. 12-13, original emphasis) claim that: An innovation is . . . . derived from advances in science, and its introduction makes existing knowledge in that application obsolete. It creates new markets, supports freshly articulated user needs in the new functions it offers, and in practice demands new channels of distribution and aftermarket support. In its wake it leaves obsolete firms, practices, and factors of production, while creating a new industry .… innovation that disrupts and renders established technical and production competence obsolete, yet is applied to existing markets and customers, is … labelled ‘Revolutionary’. It thus seems clear that the power of an innovation to unleash Schumpeter's ‘creative destruction’ must be gauged by the extent to which it alters the parameters of competition, as well as by the shifts it causes in required technical competence. An innovation of the most unique and unduplicative sort will only have great significance for competition and the evolution of industry when effectively linked to market needs. Christensen (1997) argues that disruptive technology has specific characteristics: a) higher technological performance; b) provide products/processes that satisfy needs that are demanded by mainstream market. Christensen et al. (2015) claim that disruptive technologies can be generated by small firms with fewer resources that successfully challenge established Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 4/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 incumbent businesses (e.g., the case of OpenAI for ChatGPT, funded in 2015). Innovative firms, generating disruptive technologies and innovations, grow more rapidly than other ones (Abernathy and Clark, 1985; Tushman and Anderson, 1986, p. 439). Christensen’s (1997) approach also shows that disruptive technologies or innovations (these terms are used here interchangeably) generate significant shifts in markets and society (cf., Henderson, 2006). In general, technological and market shifts of path-breaking technologies embody competence-destroying and competenceenhancing because these technologies can either destroy or enhance the competence of established technologies existing in industries (cf., Hill and Rothaermel, 2003; Tushman and Anderson, 1986). Moreover, disruptive innovations undermine the competences and complementary assets of existing producers, and change habits of consumers, fostering economic changes in many sectors (Christensen and Raynor, 2003; Garud et al., 2015; Markides, 2006; cf., Coccia, 2005). The diffusion and growth rate of disruptive innovation are also important drivers to create and sustain competitive advantage of firms and nations amidst rapidly changing business environments (Kessler and Chakrabarti, 1996, p. 1143; Porter, 1980). Disruptive technology also affects the behavior of other technologies, generating a process of actual substitution of a new technique for the established one and, as a consequence, convergence of manifold technologies generating new technological paradigms and trajectories for technical, industrial and corporate change (Sahal, 1981; Fisher and Pry, 1971). The proposed development of invasive technology approach is basic to explain evolution of technologies in modern economies knowledge based having rapid change. A significant goal here is to understand invasive behaviour of technologies by analyzing the rates of spreads. Next section presents the research philosophy, methodology and study design to structure the theory and test the prediction with empirical evidence. 4. Conceptual Framework 4.1. Research Philosophy Proposed theoretical framework here is developed with an evolutionary perspective of technological change guided by generalized or universal Darwinism (Dawkins, 1983; Nelson, 2006; Levit et al., 2011). Hodgson (2002, p. 260) maintains that: “Darwinism involves a general theory of all open, complex systems”. In this context, Hodgson and Knudsen (2006) suggest a generalization of the Darwinian concepts of selection, variation and retention to explain how a complex system evolves (cf., Hodgson, 2002; Stoelhorst, 2008). In the economics of technical change, and in Science of Science (Sun et al., 2013) the generalization of Darwinian principles (“Generalized Darwinism”) can assist in explaining the multidisciplinary nature of scientific and technological processes (cf., Hodgson and Knudsen, 2006; Levit et al., 2011; Nelson, 2006; Schubert, 2014; Wagner and Rosen, 2014). In fact, the heuristic principles of “Generalized Darwinism” can explain aspects of scientific and technological development considering analogies between evolution in the biological systems and similar-looking processes in science and technology (Oppenheimer, 1955; Price, 1986). Arthur (2009) argues that Darwinism can explain technology and science development as it has been done for the development of species in environment (cf., Schuster, 2016, p. 7). In general, technological and scientific evolution, as biological evolution, Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 5/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 displays radiations, stasis, extinctions, and novelty (Kauffman and Macready, 1995; Solé et al., 2013). Kauffman and Macready (1995, p. 26) state that: “Technological evolution, like biological evolution, can be considered a search across a space of possibilities on complex, multipeaked ‘fitness,’ ‘efficiency,’ or ‘cost’ landscapes”. Schuster (2016, p. 8) shows the similarity between technological and biological evolution, for instance technologies have finite lifetimes like biological organisms. In this perspective, the principle of selection can explain the successful in evolution of some research fields and technologies (e.g., their survival and diffusion in markets, Bowler and Benton, 2005). However, the invasive behaviour in the domain of science and technology is hardly investigated in social studies of technology but it can be basic to explain important characteristics of technological evolution. The general theoretical background of “Generalized Darwinism” (Hodgson and Knudsen, 2006), described here, can frame a broad analogy between science and technology and evolutionary ecology that provides a logical structure of scientific inquiry to analyze invasive behaviour of technologies in economic systems and society (Coccia, 2019; Ziman, 2000). In fact, the goal of this study is to propose the approach of technological invasion to clarify dynamics in scientific and technological evolution that guide the diffusion of technologies in scientific and technological domains having alternative or competitive technologies. In fact, technology analysis of the technological invasion can create the framework within which a synthesis of basic properties on evolutionary pathways could be worked out, extending lines of research of economics to explain technological evolution. Therefore, since the invasion dynamics is assumed to be one of the characteristics that clarifies the evolution of technology, it deserves to be investigated because the understanding of the role of invasive technologies can extend the theories of technological evolution with a new conceptual approach. 4.2. Theory of Invasive Technologies Invading organisms or elements play important roles in both economy and ecology (Wang and Kot, 2001). However, the role of invasive behaviour in the study of technologies and innovations is unknown but its examination is basic for uncovering new basic aspects of technological evolution. In a broad analogy with principles of biological systems, the recognition that invasive technologies is central to explain evolutionary processes for determining main properties of technological evolution. Some basic concepts, structure the proposed theoretical framework: Invasion is an organism or elements that bursts into a space, occupying it or spreading in large quantities. Invasive technologies can replace, in a specific space, other technologies in several life cycles, producing new technologies and innovations that have the potential to spread in different domains and sectors leading to technological, economic and social change on invaded environment (‘impacts’) Postulates Invasive technologies are basic for technological change Invasive behaviour ⇒ technological evolution Invasive technologies, as new technologies, change environment and have profitable adaptive behaviour to changing Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 6/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 environments and at the same time eliminate the less suitable technologies, leaving the more suitable ones to survive. Predictions of the theory of invasive technologies Let me show at some testable implications of the theory of invasive technologies for technological evolution Technological change =f(invasive technologies) Rate of growth of invasive technology i in a space S > 2 × rate of growth in alternative technologies j in spaceS, j=1, …, m Invasive technology (i) is better adapted than alternative technologies (j) in S, if and only if (i) is able to spread, survive and produce new innovations in S than is (j). Interrelationships of invasive technologies in innovation ecosystem with related impact are in Figure 1. Figure 1. A Schematic diagram of invasive technologies 4.3. Research Setting to Test the Theoretical Prediction: Transformers Technologies The predictions of proposed theory of invasive technologies is verified empirically in some main technologies. In a context of Artificial Intelligence (AI) R&D of new products and processes, this study focuses on new technology of transformer architecture, a new type of neural network, described by Vaswani et al. (2017). Traditional Recurrent Neural Networks (RNNs) are powerful tools, but they have limitations, such as slow training, they do not retain old connections well, etc. Instead, new architecture of transformer technology is based on three powerful elements: a) self-attention; b) positional embeddings and c) multi-head attention. Unlike traditional RNN models, transformer models are designed to learn contextual relationships between words in a sentence or text sequence by the mechanism of self-attention, which allows to the model to weigh the importance of different words in a sequence based on their context (Menon, 2023). Transformer models have revolutionized some research fields, such as Natural Language Processing (NLP) for tasks of language modeling, text classification, question answering, sentiment analysis, computer vision, spatial-temporal modeling for video analysis or time series data, and others (Menon, 2023). In these domains, a critical advantage of transformer models is the ability to process input sequences in parallel, which makes them faster than RNNs for many NLP tasks (Dell, 2023). One of the main radical innovations in transformer technology is the development of large-scale, pretrained language models, referred to as Generative Pretraining Transformers (GPTs), such as OpenAI's GPT series, from GPT-1 in 2018 to Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 7/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 ChatGPT-4 in 2023 capable of generating human-like content (OpenAI, 2015, 2022), Google's Bidirectional Encoder Representations from Transformers (BERT) model (Devlin et al., 2018), Microsoft copilot (Mehdi, 2023), etc.. These pretrained models can be used for specific NLP tasks with reduced additional training data, making them highly effective for a wide range of NLP applications, such as (cf., Assael et al., 2022; Kariampuzha et al., 2023): machine translation document summarization document generation named entity recognition biological sequence analysis writing computer code based on requirements expressed in natural language. video understanding computer vision, protein folding applications, etc. Overall, then, science advances in computer sciences have generated the advent of the large language model (LLM, Bowman, 2023). In this domain, new technology of transformers is directed to model some activities of the human brain and has led to the new research field of generative AI — software that can create plausible and sophisticated text, images and computer code at a level that mimics human ability (Pinaya et al., 2023; Tojin et al., 2023). Transformer architecture has revolutionized the field of LLM with main applications in NLP and generative AI that has generated radical innovation in GPT and its continuous incremental improvements directed to shape the landscape of generative AI with a consequential technological and social change. The technological analysis of main technologies of transformer models can clarify basic characteristics of the evolution of new technologies and radical innovations, having a pervasive behaviour that supports paradigm shift in technological fields and society (Dosi, 1988). 4.4. Study Design: Logical Structure of Searches and Measures We assume that Transformer architecture is an invasive technology and to generalize the scientific concept with a backward induction, we also analyze a previous technology CNN to assess its behaviour. A comparative analysis of these main technologies in Large Language Model can support general characteristics and properties of the invasive technologies that can explain technological evolution and change. Logic structure of search In order to detect with accuracy the invasive technologies under study in the database Scopus (2024), we define the General Domain D for queries to detect scientific documents: D= ("machine learning" OR "data science" OR "artificial intelligence"). After that we refine the Domain for two technologies under study to analyze predatory research fields. Transformers, period under study 2017-2023 Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 8/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 Domain Restricted for Transformers is called DTR DTR= ("machine learning" OR "data science" OR "artificial intelligence") AND ("large language models" OR "LLM" OR "Natural Language Processing" OR "Natural Languages" OR "Sentiment Analysis" OR "Text Mining" OR "Question Answering Systems" OR "Semantic Web" OR "Chatbot" OR "Knowledge Representation" OR "Natural Language Understanding" OR "Text-mining" OR "Opinion Mining" OR "Topic Modeling" OR "Word Embedding") Or DTR= (D) AND ("large language models" OR "LLM" OR "Natural Language Processing" OR "Natural Languages" OR "Sentiment Analysis" OR "Text Mining" OR "Question Answering Systems" OR "Semantic Web" OR "Chatbot" OR "Knowledge Representation" OR "Natural Language Understanding" OR "Text-mining" OR "Opinion Mining" OR "Topic Modeling" OR "Word Embedding") In order to detect the impact of Transformers (TRF) in science that is also used with other terms, the query is given by: TRF= (DTR) AND ("bert" OR "chatgpt" OR "transformer" OR "attention mechanism"). This set TFR includes the technology with predatory behaviour. The complement of set TRF is TRFC : TRFC = (DTR) AND NOT ("bert" OR "chatgpt" OR "transformer" OR "attention mechanism"). This set included the technologies that have been predated by TRF. Of course, TRF+ TRFC =DTR Convolutional Neural networks, in short CNN, period under study before 2017, year of the emergence of Transformers The general domain is D, as defined above, but in order to detect the science dynamics of CNN, we refine the search with a restriction considering the field in which CNN operates. The keywords are stopped when the restricted set has a marginal increase of scientific documents. Domain Restricted for CNN is called DCNN DCNN= ("machine learning" OR "data science" OR "artificial intelligence") AND ("computer vision" OR "image recognition" OR "Image Processing" OR "Object Detection" OR "Image Segmentation" OR "Image Enhancement" OR "Object Recognition" OR "Image Analysis" OR "Image Classification" OR "Images Classification" OR "Face Recognition" OR "Machine Vision" OR "Image Interpretation" OR "Gesture Recognition" OR "Machine-vision" OR "Augmented Reality") Or DCNN= (D) AND ("computer vision" OR "image recognition" OR "Image Processing" OR "Object Detection" OR "Image Segmentation" OR "Image Enhancement" OR "Object Recognition" OR "Image Analysis" OR "Image Classification" OR "Images Classification" OR "Face Recognition" OR "Machine Vision" OR "Image Interpretation" OR "Gesture Recognition" OR "Machine-vision" OR "Augmented Reality") In order to detect the impact of CNN, the query is given by: CNN=(DCNN) AND ("convolutional neural network" OR "CNN"). This set CNN includes the technology with predatory Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 9/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 behaviour. The complement of set CNN is CNN C is CNN C = (DCNN) AND NOT ("convolutional neural network" OR "CNN"). This set included the technologies that have been predated by CNN. Moreover, CNN+CNNC=DCNN Measures and sources of data This study uses number of patents concerning research topics and technologies under study. Data are from Scopus (2023), downloaded on 9 November 2023. Samples In particular, the study considers the following sample of data, detected using the previous logic: Set of Transformers TRF: 8,908 patents (all data available from 2016 to 2023). Complement of set TRF, TRFC : 79,268 patents (all data available from 2016 to 2023). Set of CNN: 69,599 patents (all data available from 1995 to 2023). Complement set of CNN, CNN C: 181,231 patents (all data available from 1995 to 2023). Data and information analysis procedures Let Patents (TRF) =number of patents of Transformers, having invasive behaviour Let Patents (TRFC) =number of patents in other technologies in domain of TRF Let DTRF = Patents(TRF) +Patents(TRFC), total number of patents in the domain of technologies of Large Language Models α= Patents (TFR) DTFR β= Patents (TFRC ) DTFR α+β =1 Let Patents(CNN) =number of patents of CNN, having invasive behaviour. Let Patents(CNNC) =number of patents of other technologies in domain of CNN Let DCNN = Patents(CNN) +Patents(CNNC), total number of patents in the domain of technologies of Large Language Models δ= Patents (CNN) DCNN ε= Patents (CNNC ) DDCNN δ+ε =1 These shares of the spatial growth of invasive technologies in the domain are calculated over time and visualized graphically. After that, the temporal growth of these technologies over time is analyzed with a rate of growth compound continuously: Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 10/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 r. In this case, the function of patent development is exponential: Patents t = Patents0erT Patents t Hence, Patents 0 = erTwhere e is the base of natural logarithm (2.71828…) Patents t Log Patents0 = rT Patents t Log r= Patents 0 T Where P0 is the patents to the time 0, Pt is the patents to time t. T= t−0 r= rate of exponential growth of technology from 0 to t period. Trends of invasive technology i at t are analyzed with the following model: log10yi, t = a + b time + ui, t yt is patents of invasive technologies t=time ut = error term (a = constant; b=coefficient of regression) 5. Empirical Evidence: Test of Prediction in Invasive Technologies 5.1. Pattens of temporal and morphological change Table 1. Parametric estimates of the relationships based on patents Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 11/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 Dependent variable Publications Log10 Patents Transformers Log10 Patents not Constant α Coefficient β R2 1.30*** 3.34*** transformers Log10 Patents CNN Log10 Patents not CNN -0.87*** 1.61*** 0.30*** 0.98 (0.016) (0.105) 0.13*** 0.91 (0.017) (0.107) 0.16*** 0.92 (0.010) (0.431) 0.10*** 0.98 (0.003) (0.125) F Period 339.95*** 2016-2023 57.71*** 292.05*** 1995-2023 1227.66*** Note: *** p<0.001; Explanatory variable: time; period is from starting year of the patent to 2023 (last year available); In round parentheses the Standard Error. F-test is based on ratio of the variance explained by the model to the unexplained variance. R2 is the coefficient of determination. Table 1 shows a regression analysis of estimated relationship based on patents over time, using a linear model. R2 is remarkably high in all models, showing a high goodness of fit. F-test is significant with p-value <.001. Estimated coefficient of regression suggests that transformers, as invading technology, has a growth rate of 0.30 (p-value 0.001) that is more than double than other technologies operating in the same domain (0.13, p-value 0.001). Moreover, the most interesting finding is that growth rate of invading transformers in space of science and technology compared to other radical technology of CNN is almost the double (0.16, p-value 0.001). This result suggests that the invasive power of transformers is of a high intensity, having a pervasive diffusion and more drastic impact to generate the conditions for a main radical scientific and technological change in science and technology (for visual representation see Figures 2 and 3). Figure 2. Estimated relationships for temporal evolution of Transformers compared to overall domain of Large language models (Patents), 20162023 period. Dotted line indicates the dynamics of invasive technology; Continuous line indicates the dynamics of other technologies Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 12/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 Figure 3. Estimated relationships for temporal evolution of CNN compared to overall domain of Large language models (Patents), 1995-2023 period. Dotted line indicates the dynamics of invasive technology; Continuous line indicates the dynamics of other technologies Table 2. Exponential rate of growth in Large language models of predators and preys Transformers Domain excluded Transformers Patents Rate% Rate % r TRF = Exponential growth 2016-2023 55.82 23.02 r’’ TRF = Exponential growth 2021-2023 25.81 Patents 0.76 CNN Domain excluded CNN Rate% Rate % r’ CNN = Exponential growth 1995-2023 33.84 36.11 Using the exponential equation to calculate the growth rate of technologies under study, it confirms that growth rate of invading technology of transformers is about 56% versus 23% of alternative technologies in space (more than double), and it is considerably higher than previous technology of CNN having about 34% (Table2). This result confirms invasive behaviour of transformer technologies in the space of LLM, based on rapid and strong diffusion, and considering the invasive dynamics of transformers, based on share of patents of transformers on total in the space of LLM, in about 7 years has a rapid diffusion that invades the space of other technologies in the related domain, changing the ecosystem of LLM with pervasive application in generative AI with manifold radical innovations that generates technological and social change (Figure 4). Share of patents in CNN technologies in 2023 is higher than transformer technology but the accumulation of knowledge is started in 1995, compared to Transformers that is started in 2017 (Figure 5). Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 13/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 Figure 4. Patterns of morphological change in domain of large language models generated by emerging technology of transformers (Patents). Large arrows indicate the direction of technological invasion Figure 5. Patterns of morphological change of CNN in domain of large language models generated by (Patents) 6. Discussions 6.1. Explanation of empirical evidence of invasive technologies in reference to previous literature Emergence of transformer technology is due to the interaction and convergence of competencies from mathematics and Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 14/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 model design in neural networks. Transformer architecture was introduced in the context of natural language processing (NLP), but they have shown to be versatile and powerful, finding new applications in diverse fields such as computer vision, speech recognition, etc. In the realm of neural networks, the Transformer architecture has emerged as a transformative force, initially revolutionizing natural language processing and subsequently finding applications in diverse domains. Before transformers, recurrent neural networks (RNNs) had many limitations but a main breakthrough is the introduction of self-attention mechanism which intuitively mimics cognitive attention. It calculates "soft" weights for each word, which can be computed in parallel in transformer architecture, unlike sequentially by RNN. Attention was developed to address the weaknesses of leveraging information from the hidden outputs of recurrent neural networks. In short, transformers large language models removed the recurrent neural network and relied heavily on the faster parallel attention scheme (Tyagi, 2023). A basic driver of invasive behaviour of transformers is the interaction with different research fields and technologies (Coccia, 2019, 2019a). In particular, invasive behaviour emerges and rapidly spreads by interacting in complex systems with parasite or symbiotic relationships (Coccia and Watts, 2020). The speed at which these invasive technologies expands its range is a fundamental parameter to explain technological evolution and change (cf., Schreiber and Ryan, 2011). Knowledge of the estimated speed calculated with the empirical analysis here enables to predict the ability of these technologies to be a dominant one and support technological and social change. Moreover, understanding its rate of spread is also important to show how the spread of a technology can evolve. Using temporal and spatial models of evolution, based on data of patents, statistical analyses reveal that the rate of growth of these pathbreaking technologies has an accelerated rate of patents compared to alternative technologies. This result suggests that interaction of path-breaking and emerging technologies is basic to accelerate co-evolution for laying the foundations for technological change in society (Coccia, 2019, 2020, 2020a, b, c). Scholars have showed that interaction among technologies can support technological evolution, and this result here is consistent with one of the multi-modes interaction of Utterback et al. (2019) given by symbiosis where each of the technologies enhances the other's growth rate. A multimode framework of technological interaction can provide a setting within which to better analyze and understand the dynamics of invasive technologies. In the case under study of transformers, the interaction generates high growth rates and a symbiotic-dependent evolution between technologies. The concept of symbiosis is closely related to that of mutualism (it is any type of relationships in which each technology benefits from the activity of the other one; cf., Coccia, 2019) and to commensalism, which is any type of relationships between two technologies where one benefits from the other without affecting it (Coccia and Watts, 2020). With respect to the technological systems of interest here, results suggest a symbiosis of transformers that seems versatility in interactions with other inter-related technologies. Interaction of technologies in the vast domain of artificial intelligence can generate synergistic combinations and foster major innovations in LLM, which are currently progressing at a rapid rate, such ChatGPT and similar ones. Progress of this interaction can become self-catalyzing and can give the means to deal successfully with challenges in manifold fields and industries opening completely new opportunities in markets (such as in AI, Burger et al., 2023; Krinkin et al., 2023; Roco and Bainbridge, 2002). Moreover, transformers have the invasive behaviour because they have the characteristics to be a General Purpose Technology in the vast domain of applications in Artificial intelligence (Coccia, 2020). The path-breaking technology of transformer is mainly of transformative nature and generate a destructive creation (Calvano, 2007), which makes prior Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 15/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 products/processes and knowledge obsolete (cf. Colombo et al., 2015). Lipsey et al. (1998, p.43) define a GPT as: “a technology that initially has much scope for improvement and eventually comes to be widely used, to have many users and to have many Hicksian and technological complementarities.” (cf., Lipsey et al., 2005). Invasive technologies, as GPTs, exert a pervasive impact across firms, industries and permeate the overall economy of nations. Bresnahan and Trajtenberg (1995, pp.86–87) show that GPTs have a treelike structure with new technology located at the top of the tree and all derived technologies represented at the bottom of the tree, radiating out towards every sector of the economy. In fact, transformer architecture, as GPTs, generates clusters of innovations in several industries because they are basic processes/components/technical systems for the structure of various families of products/processes that are made quite differently supporting co-evolutionary pathways. Bryan et al. (2007, p. 41) argue that: “co-evolution can lead to reduced product development costs and increased responsiveness to market changes”. The manifold applications of transformers as GPTs are driven by firms (such as Open AI, Microsoft, Google Brain, etc.) to maximize profit and/or to exploit the position of a (temporary) monopoly and/or competitive advantage in sectors and/or industries (Calvano, 2007; Coccia, 2015, 2016). In general, transformers as GPTs are characterized by: “pervasiveness, inherent potential for technical improvements and ‘innovational complementarities’, giving rise to increasing returns-to-scale, such as steam engine, electric motor and semiconductors” (Bresnahan and Trajtenberg, 1995, p.83, original emphasis). Moreover, Jovanovic and Rousseau (2005, p.1185) show that the distinguishing characteristics of a GPT are: 1. Pervasiveness: GPT should propagate to many sectors. 2. Improvement: GPT should reduce costs of its adopters. 3. Innovation spawning: GPT should produce new products and processes (cf. also, Bresnahan and Trajtenberg, 1995). Lipsey et al. (1998, p.38ff) describe other similar characteristics of GPTs, such as: the scope for improvement, wide variety and range of uses and strong complementarities with existing or potential new technologies that fit the transformer architecture to generate innovations (cf., Coccia, 2012a, 2012b, 2017a, 2017). Overall, then, transformers as GPTs are complex technologies that support product/process innovations in several sectors for a corporate, industrial, economic and social change (Coccia, 2015; cf. Coccia, 2012, 2012a, 2014, 2014a, 2016, 2017). 6.2. Deduction form invasive behaviour of technologies Empirical research to test the prediction of invasive technologies suggests some main deduction of invasive technologies for possible generalization: Changes over time. Current invasions of new technologies are the result of the interplay of past events and processes (Kueffer, 2010b; Pyšek et al., 2010; Essl et al., 2011). The extension of knowledge in these fields is basic for resolving fundamental questions in invasion ecology of technologies and is associated with the accumulation of manifold and detailed information on invasions of particular technologies in different industries and over time. A basic invasion mechanism of transformers technology can be the hypothesis of unused resources. Large datasets gained from different technologies can pave the way for addressing interactions and testing the application of proposed framework here more effectively. Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 16/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 Invasions of new technologies can generate a change in behaviour of a technology following its introduction in designing new products and processes and spreading them. 7. Concluding Remarks Socioeconomic forces of knowledge economy are changing the foundations of economies and societies and accelerating the rate at which new technologies are being introduced with invasive behaviour and pervasive diffusion over space. This study proposes, for the first time, the invasive behaviour of technologies. The successful technology invaders can have devastating impacts on human society and structure of modern economies. To manage these impacts, it is essential to understand the rate of range expansion— the invasion speed—of these technology invaders. Here, we estimate and analyze invasion speeds to provide a general framework of invasive technologies in a variable environment having rapid changes. This study tests the theories of invading technologies focusing on transformer technologies in AI that has an unparalleled growth at expense of other technologies creating basic conditions to generate a drastic scientific change in LLM and consequential radical technological change with main effects on economic and social systems in a not-to-distantfuture. This specific behaviour of invasive technologies fosters a rapid diffusion, destroy other technologies and capture their space. This aspect in literature leads to a technological substitution. Norton and Bass (1987) consider that a new product or process generation can replace a prior one. This process between different technologies is based on a competition of performance and effectiveness. Fisher and Pry (1971) modeled the diffusion of a new product becoming a substitute for a prior one (cf., Utterback and Brown,1972). Other scholars have explained this competition as a predator and prays, the new product is a predator of current products (pray; Utterback et al., 2019). Invasive technologies have the power to disrupt, destroy and make obsolete established competences (Christensen et al., 2015, 1997; Coccia, 2020). What this study adds is that the invasive behaviour of new technology is more drastic of a competitor or predator technology as verified with transformer architecture. What is the cause that drives Transformer architecture to be an invasive technology? One of the explanations is a specific current interest in community of scholars (Sun et al., 2013). This aspect may be explained with the emergence of social groups of scientists in these topics as the driving force behind the evolution of this emerging and disruptive technology in a network of inter-related technologies and research fields (Crane, 1972; Coccia et al., 2024; Guimera et al., 2005; Wagner, 2008). In this context, the rapid evolution of invasive technology paves the way for development of other technologies in spatial-temporal areas by ‘‘expanding the adjacent possible’’ (Kaufmann, 1996). 7.1. Theoretical implications The predictions of our theoretical framework of invasive technologies are borne out in the phenomena investigated, paving the way to a better understanding and control of innovation processes in a knowledge economy. Properties of invasive technologies Let InvTi = Invasive technology i Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 17/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 Let Prj = other technologies in the inter-related domain D;j=1, 2, …, m (ITi, Prj )⊆D t = year of emergence of ITi σi = growth rate of InvTi τj = growth rate of Prj Invasive technology ITi in the domain D is when from t tot+n: ITi has a very rapid growth ITi disrupt the context of other technologies. ITi invades and captures the space of other technologies Moreover, other characteristics of invasive technologies are: Rapid growth Adaptation of a wide range of market applications and environmental conditions Prolific diffusion in manifold technical systems Associations with manifold activities of humans These results can be the basis for an emerging science of invasive technologies that can explain technological, economic and social change in three scientific main directions: 1. invasiveness of technologies; 2. invasibility of innovation ecosystems and 3. recurrent (patterns of the technologies) × (ecosystem interactions) that may support a technological invasion syndrome based on a set of concurrent aspects that usually form an identifiable pattern. A science of invasive technologies can encompass ‘typical recurrent associations of technologies and invasion dynamics with particular invasion contexts such as an invasion phases, invaded environment and socioeconomic context’ (cf., Kueffer et al., 2013). We expect that a resulting theory of technological invasions will need to be conceived as a somewhat heterogeneous conglomerate of elements of varying generality and predictive power: laws that apply to well-specified domains, general concepts and theoretical frameworks that can guide thinking in research and management, and indepth knowledge about the drivers of particular invasions of technologies in specific industries or across sectors. 7.2. Managerial and policy implications In all these systems of invasive technologies surprisingly similar patterns emerge based on two contrasting forces that can have managerial implications: the tendency of retracing already explored avenues (exploit) and the inclination to explore new possibilities. Policymakers and R&D managers can use the findings here for making efficient decisions regarding the Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 18/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 sponsoring of specific trajectories having a high rate of growth to foster technology transfer with fruitful effects for boosting up next economic and industrial change. These managerial approaches can be explained in the framework of the expansion of the adjacent possible, in which the restructuring of the space of possibilities conditional to the occurrence of radical innovations. Proposed theory and empirical findings can guide an ambidexterity strategy of innovation management based on exploration activities when rate of growth, and uncertainty in research fields and technology is higher, whereas an exploitation approach to innovation strategy when rate of growth, is lower with consequential more stable technological trajectories. Organizations can apply an ambidexterity strategy of innovation management by balancing exploration and exploitation approaches, which allow the organization to be adaptable to turbulent environments and achieve and sustain competitive advantage (Duncan, 1976; March, 1991; Raisch and Birkinshaw, 2008). In particular, the finding here suggests that in the presence of a higher rate of growth and a higher indeterminacy, organization can apply an innovation strategy of exploration based on search, risk taking, experimentation, selection, discoveries and flexibility between different technological pathways. However, organizations that focus only on exploration face the risk of wasting resources on research topics and emerging technologies that may fail and never be developed, so a stage to gate model can reduce failure risk and foster the development of new technology (Coccia, 2023). Instead, technologies having a lower rate of growth suggest an innovation strategy of exploitation based on refinement, efficiency, implementation, execution and production in more stable technological trajectories and directions. 7.3. Limitations and development of future research This study shows for the first time, to our knowledge, the behaviour of an invasive technology to explain some properties of technological and scientific change in knowledge economies. One of the significant aspects to explain the invading technologies is to estimate and analyze their rates of spread and behaviour. The speed at which a technology expands its range is a fundamental parameter in explaining technological invasion and consequential evolution. Knowledge of this speed enables us to predict the ability of a technology to become a dominant technology, to generate radical and incremental innovations, in a space with manifold alternative technologies. This study analyzes the invasive behaviour of transformer technologies in AI that has an unparalleled growth at expense of other technologies creating basic conditions to generate a drastic scientific change in LLM and consequential radical technological change with main effects on economic and social systems in a not-to-distant-future. These conclusions are, of course, tentative. This study provides some interesting but preliminary results in these complex fields of emerging technologies, but some limitations to deal with future studies can be summarized as follows. Many fundamental questions in a science of invasive technologies can only be answered through integrative studies such as, a research that encompasses comprehensive studies of invasions of a particular technology at specific fields, comparative studies of invasions of the same technologies across multiple fields and industries, and integrating diverse information about a particular technology to analyze invasive behaviour with context-dependencies. In a proposed theory if invasion technology, in-depth analysis of invaded innovation ecosystem focuses mostly on scientific area dominated by a single dominant invader technology (transformer). However, studies of the multifarious and longer term dynamics of innovation ecosystems affected by multiple technology invaders are mostly lacking. Such studies are, however, important to Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 19/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 understand shifts in dominance of invading technologies, possibly leading to interactions among multiple invaders. In a context of invaded ecosystems, an emerging challenge is also to understand the role of gradual changes of technologies and environmental factors in determining invasion trajectories (e.g., Smith et al., 2009). Hence, it is interesting to compare the invasive behaviour of the same technologies across multiple industries and research fields, to assess if ‘invasiveness’ and effects on the environment of technologies may be highly variable at different sites . Such differences in invasion dynamics of technologies between industries might stem from (1) the variability of the architecture of a technology between industries– through product differentiation; (2) technologies and environment interactions. In analogy with biology, the impacts of invasive technologies are strongly co-shaped by the relation of (technologies) × environment interactions (Hulme et al., 2012; Pysek et al., 2012) which can only be understood through comparative studies across industries (cf., Kueffer et al., 2013). More studies that compare the technology in native research fields or sector and invaded ranges are needed (van Kleunen et al., 2010), because such insights form the baseline necessary for drawing conclusions about the importance of specific technologies in invasions (Parker et al., 2013). Therefore, given the variation in performance of a technology within both native research field and invaded research fields, studies that compare data only from one research field are very likely to arrive at spurious conclusions. Thus, synthetic analyses in invasion science for technologies must be constrained to appropriate subsets of invasions, rather than seeking universal explanations (Pyšek & Richardson, 2007; Jeschke et al., 2012; Kueffer, 2012). A future idea is to verify if technological superiority or flexibility applies to all invasions (e.g., Daehler, 2003; Blumenthal et al., 2009; Cavaleri & Sack, 2010; Chun et al., 2010; Jeschke et al., 2012a; Moles et al., 2012). For instance, characteristics that are most frequent among invasive technologies and general disruptive technologies in markets might not be relevant for predicting invasive technologies within a specific industry. Other limitations are that: scientific outputs and research topics can only detect certain aspects of the ongoing dynamics of technology and next study should apply complementary analysis; confounding factors (e.g., level of public and private R&D investments, international collaboration, etc.) affect the evolution of new technologies and these aspects have to be considered in future studies to improve data gathering for technological analyses. In short, there is need for much more detailed research into the investigation of the role of invasive technologies to clarify evolutionary patterns of technologies in society. Despite these limitations, the results here clearly illustrate that an invasive technologies can clarify basic characteristics of technological, economic and social change. These findings here can encourage further theoretical exploration in the terra incognita of invasive technologies within and between scientific and technological domain that have rapid change in the new digital era. These aspects are basic for improving the prediction of evolutionary pathways in emerging and disruptive technologies and supporting R&D investments towards new technologies and innovations having a high potential of growth and of impact on socioeconomic system. However, a comprehensive explanation of sources and diffusion of invasive technologies to explain technological change is a difficult topic for manifold complex and inter-related factors in the presence of changing and turbulent environment, such that Wright (1997, p. 1562) properly claims that: “In the world of technological change, bounded rationality is the rule.” Acknowledgments Qeios ID: NR1YME · https://doi.org/10.32388/NR1YME 20/35 Qeios, CC-BY 4.0 · Article, February 29, 2024 I would like to thank E. 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