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==1 Title, abstract and keywords<!-- Your document should start with a concise and informative title. Titles are often used in information-retrieval systems. Avoid abbreviations and formulae where possible. Capitalize the first word of the title.
 
  
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The advent of generative artificial intelligence (AI) marks a pivotal moment in the reconfiguration of economic and social structures. As we observe the early stages of this technological revolution, the potential for generative AI to reshape the dynamics of competitive markets is becoming increasingly apparent. This study proposes a comprehensive exploration of how generative AI is influencing social patterns and transforming the competitive business terrain through its dual capabilities: augmentation and automation.
  
An abstract is required for every document; it should succinctly summarize the reason for the work, the main findings, and the conclusions of the study. Abstract is often presented separately from the article, so it must be able to stand alone. For this reason, references and hyperlinks should be avoided. If references are essential, then cite the author(s) and year(s). Also, non-standard or uncommon abbreviations should be avoided, but if essential they must be defined at their first mention in the abstract itself. -->==
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In March 2023, Geoffrey Hinton, one of the godfathers of Generative AI, when asked what careers younger people should be planning for, gave a one-word answer: plumbing. Hinton's remark hints at the underlying tectonic shifts in labor markets that generative AI could precipitate. This paper endeavors to scrutinize the breadth of generative AI's societal repercussions, particularly in the realms of innovation, job market, and market dynamics.
  
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This paper aims to delve into this impact through an analysis of the dual and mix effects of generative A.I. on tasks: automation, and augmentation, on both output and the job market, considering market elasticities and the creation of novel tasks and expertise.
  
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So far augmentation is the most remarkable effect of generative AI. In fact, we can observe a multiplicative effect resulting on 30%-75% increase in productivity in areas such as programming, together with the creation of new jobs and expertise, such as prompt engineer. There are however many trials directed to automation that promise higher multipliers and transformative effects.
  
  
==2 The main text<!-- You can enter and format the text of this document by selecting the ‘Edit’ option in the menu at the top of this frame or next to the title of every section of the document. This will give access to the visual editor. Alternatively, you can edit the source of this document (Wiki markup format) by selecting the ‘Edit source’ option.
 
  
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The job market's response to generative AI remains a complex phenomenon. In sectors like software development, where AI tools are widely adopted, there are emergent trends that may offer predictive insights for broader occupational categories. Our research aims to extrapolate these trends to forecast future economic contours.
  
  
2.1 Subsections
 
  
Divide your article into clearly defined and numbered sections. Subsections should be numbered 1.1, 1.2, etc. and then 1.1.1, 1.1.2, ... Use this numbering also for internal cross-referencing: do not just refer to 'the text'. Any subsection may be given a brief heading. Capitalize the first word of the headings.
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We anticipate a discourse on the competitive landscapes emerging from the integration of generative AI, emphasizing the potential emergence of power-law distributions within markets. This metamorphosis, driven by AI adoption, poses critical questions about market consolidation and the balance of economic power.
  
  
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By presenting this analytical framework, our work aspires to shed light on what could be the most profound technological disruption in recent history. It is an exploration of how generative AI is not merely a tool for economic advancement but a catalyst for a comprehensive reimagining of our social and competitive fabric. Through this lens, we aim to contribute to a nuanced understanding of generative AI's role in shaping the future of work, competitiveness, and societal evolution.
 
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Please insert tables as editable text and not as images. Tables should be placed next to the relevant text in the article. Number tables consecutively in accordance with their appearance in the text and place any table notes below the table body. Be sparing in the use of tables and ensure that the data presented in them do not duplicate results described elsewhere in the article.
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Number the figures according to their sequence in the text. Ensure that each illustration has a caption. A caption should comprise a brief title. Keep text in the illustrations themselves to a minimum but explain all symbols and abbreviations used. Try to keep the resolution of the figures to a minimum of 300 dpi. If a finer resolution is required, the figure can be inserted as supplementary material
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For tabular summations that do not deserve to be presented as a table, lists are often used. Lists may be either numbered or bulleted. Below you see examples of both.
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1. The first entry in this list
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Supplementary material can be inserted to support and enhance your article. This includes video material, animation sequences, background datasets, computational models, sound clips and more. In order to ensure that your material is directly usable, please provide the files with a preferred maximum size of 50 MB. Please supply a concise and descriptive caption for each file. -->==
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==3 Bibliography<!--
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Citations in text will follow a citation-sequence system (i.e. sources are numbered by order of reference so that the first reference cited in the document is [1], the second [2], and so on) with the number of the reference in square brackets. Once a source has been cited, the same number is used in all subsequent references. If the numbers are not in a continuous sequence, use commas (with no spaces) between numbers. If you have more than two numbers in a continuous sequence, use the first and last number of the sequence joined by a hyphen
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Revision as of 12:32, 23 April 2024

The advent of generative artificial intelligence (AI) marks a pivotal moment in the reconfiguration of economic and social structures. As we observe the early stages of this technological revolution, the potential for generative AI to reshape the dynamics of competitive markets is becoming increasingly apparent. This study proposes a comprehensive exploration of how generative AI is influencing social patterns and transforming the competitive business terrain through its dual capabilities: augmentation and automation.

In March 2023, Geoffrey Hinton, one of the godfathers of Generative AI, when asked what careers younger people should be planning for, gave a one-word answer: plumbing. Hinton's remark hints at the underlying tectonic shifts in labor markets that generative AI could precipitate. This paper endeavors to scrutinize the breadth of generative AI's societal repercussions, particularly in the realms of innovation, job market, and market dynamics.

This paper aims to delve into this impact through an analysis of the dual and mix effects of generative A.I. on tasks: automation, and augmentation, on both output and the job market, considering market elasticities and the creation of novel tasks and expertise.

So far augmentation is the most remarkable effect of generative AI. In fact, we can observe a multiplicative effect resulting on 30%-75% increase in productivity in areas such as programming, together with the creation of new jobs and expertise, such as prompt engineer. There are however many trials directed to automation that promise higher multipliers and transformative effects.


The job market's response to generative AI remains a complex phenomenon. In sectors like software development, where AI tools are widely adopted, there are emergent trends that may offer predictive insights for broader occupational categories. Our research aims to extrapolate these trends to forecast future economic contours.


We anticipate a discourse on the competitive landscapes emerging from the integration of generative AI, emphasizing the potential emergence of power-law distributions within markets. This metamorphosis, driven by AI adoption, poses critical questions about market consolidation and the balance of economic power.


By presenting this analytical framework, our work aspires to shed light on what could be the most profound technological disruption in recent history. It is an exploration of how generative AI is not merely a tool for economic advancement but a catalyst for a comprehensive reimagining of our social and competitive fabric. Through this lens, we aim to contribute to a nuanced understanding of generative AI's role in shaping the future of work, competitiveness, and societal evolution.

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Published on 31/05/24
Submitted on 21/04/24

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