Welcome to Antitrust Code by Concurrences.
Concurrences is the leading antitrust database with over 40,000 articles on competition law.
Concurrences is also the largest network of antitrust experts with lawyers, economists, enforcers and academics in 85 countries.
By listening to this podcast, you will learn the fundamentals of competition law and hear about the latest antitrust news.
Thanks to our guests, the best experts in the antitrust world.
Hello and welcome to a new episode of the Antitrust Code podcast.
My name is Alden Abbott of the Mercatus Center, George Mason University.
I'm joined by Professor Thibault Schrepel of the Amsterdam Law and Technology Institute, Rie Universiteit Amsterdam.
We are here today to discuss a newly published book on Artificial Intelligence and Competition Policy, which we co-edited.
According to my AI Research Engine Assistant or Search Engine Assistant, Artificial Intelligence is a set of technologies that allow computers to perform tasks that mimic human intelligence.
AI increasingly pervades competitive interactions throughout the economy.
Since all areas of commerce are affected by AI, it understandably is of great interest to competition law enforcers.
AI issues unquestionably will be raised in the full spectrum of competition law investigations and enforcement actions around the world.
This reality makes Concurrences' newly published volume, Artificial Intelligence and Competition Policy, incredibly timely and important.
It includes 20 essays covering the full gamut of AI related competition issues authored by experts in law, economics, and computer science.
These essays include practical analyses of recent enforcement initiatives, as well as theoretical analyses of future questions enforcers and scholars will raise concerning AI's competitive impact.
In short, the new volume lays the groundwork for future specialized work on the discrete AI competition topics.
In so doing, it highlights a broad range of issues that will be at the center of AI competition debates for many years to come.
Accordingly, the new volume will be of interest to policymakers, enforcers, competition law practitioners, scholars, and businesses alike.
To assist the reader, the essays are organized into three categories.
One, market dynamics, mergers, and partnerships in AI.
Two, AI challenges for competition law.
And three, policy responses to the AI boom.
In her foreword, Canny Fernandez, distinguished president of Spain's National Markets and Competition Commission, spotlights six topics covered by the volume that she states, quote, should be in a current agenda of any competition authority.
Close quote.
Professor Schrepel and I will take turns briefly discussing these topics.
The first topic is AI-driven collusion.
A particular interest is an essay by former US Federal Trade Commission Chair Maureen Ollhausen, Taylor Owings, and Cora Allen, focusing on the use by competitors of the same pricing algorithm to facilitate collusion over price.
The authors review recent case law and evaluate the illegal implications of various pricing scenarios.
One scenario involves situations when an algorithm may, quote, teach itself, close quote, to initiate collusion without human intervention.
Christian Bergquist of the University of Copenhagen and Camila Ringling of the George Washington University examine AI collusion under United States and European Union Competition Law.
Their focus is on whether the proliferation of AI-driven decision-making could enable entities to coordinate in anti-competitive ways that are beyond the scope of enforcers.
According to the authors, there are indeed enforcement gaps.
In particular, they explain that US enforcers face greater challenges than their European counterparts as American case law has developed a narrow notion of agreement or understanding under high and inconsistent evidentiary burdens.
They argue that United States court should adopt a more welcoming approach to considering plus factors in determining whether illegal anti-competitive agreements may be inferred.
Now, I turn to Thibault to discuss the second topic.
Thanks very much, Alden.
So the second topic covers algorithmic discrimination.
And we have a couple of papers in the volume about the subject, including one by Yusup Choi of the Hankuk University of Foreign Studies in Korea, in which he provides an overview of Korean competition rules on algorithmic discrimination.
The point he makes is that with personalized pricing becoming one of the most important competition issues in Korea, you see now discussions as to whether new rules should be adopted to tackle the subject.
And he argues against a new digital law in Korea on the basis that existing competition rules are sufficient to tackle any conduct involving algorithmic discrimination.
I should also mention that Jennifer Poulin of the University of St.
Gallen addresses the topic, although indirectly.
And more specifically, what she discusses is how firms might display a uniform online price while employing targeted coupons for specific customer segments using AI, which will allow those firms to implement less detectable forms of price discrimination.
We have also another paper by Professors Terras Nerashania of the University of California, Berkeley, and Janice Citaraman of Vanderbilt University, in which they tackle the subject of price discrimination implemented by oligopoly firms controlling an essential layer of the AI stack against the downstream firms.
And lastly, we have a contribution from Quentin Schaffer of the University of Strathclyde, in which he explains how unclear the role of friend, that is fair, reasonable, and non-discriminatory, currently is regarding obligations in the AI value chain.
And I give you back the floor to you, Alden.
Well, thank you, Thibault.
The third topic is Predatory Pricing Strategies.
And Thibault already alluded to the article by Jennifer Pullen of the University of St.
Gallen examining the evolving challenge of predatory pricing in the context of artificial intelligence.
And she explores contrasting approaches to assessing recoupment, which is a big issue in the United States law, generally regarded as a second phase of predatory pricing strategies after predation under both US and EU law.
Now, the author argues that sophisticated AI tools make recoupment easier to achieve, making a potential convergence between US and EU approaches to recoupment more likely.
She concludes, however, that both jurisdictions may need to adapt their frameworks, who effectively address the nuances of predatory pricing in the era of algorithmic pricing strategies.
All right, let me now tackle the fourth topic that relates to what Kelly Fernandez calls monopolistic mega firms in AI.
Professor Dan Crane of the University of Michigan explains that the continuing shift in economic value with overlapping and mutually reinforcing general purpose technologies, such as AI of course, but also robotics, quantum computing, synthetic biology, energy expansion and nanotechnology, will likely generate an inevitable and perhaps unstoppable tendency toward monopolistic mega firms.
He explains that the big choices ahead of us boiled down to ownership and regulation specifically who owns the mega firms.
For example, do they become socialized and part of the states, or do they remain private or semi-private public utilities, and how they should be regulated.
In another contribution that I mentioned already, Professor Neerajayan of the University of California Berkeley and Professor Sitaraman of Vanderbilt University also express concerns of monopolistic tendencies in the space.
They make the case differently from Dan Crane for a anti-monopoly approach to governing AI.
That approach includes difference to play on different levers such as the law of networks, platforms and utilities, industrial policy, public options, and cooperative governance.
And last, we have a contribution from John Barnett of the University of Southern California, in which he argues differently from the two previous contributions that I had mentioned, in that he explains that the history of digital markets does not support a dominant tendency toward entrenched winner-take-all outcomes, but rather, frequent disruption.
And on that basis, John forecasts that the AI ecosystem will move toward a structure that is composed of a mix of vertically integrated AI model providers and hybrid multi-firm arrangements involving various contractual relationships between LLM providers and larger platforms.
Back to you, Alden.
Thank you.
The fifth topic is the need for a nuanced approach to antitrust interventions.
And various authors explored a theme of AI-related complexities that may require enforcers to adjust the traditional modes of antitrust analysis.
Professors Diana Coyle of Cambridge and Haiyan Daman of the University of Toronto, respectively, provide a surprising insight on the competitive effects of so-called openness.
As they explained, the term open has been presumed to be a pro-competitive attribute for generative artificial intelligence.
Yet each gen AI model's openness can vary significantly, and this variation affects firms' incentives.
The authors conclude that competition agencies should employ a graduate concept of open in gen AI markets, taking into account not only the specific open features of the model and any partnerships involved, but also the specific character of the ecosystems, involving related upstream models and downstream AI products.
Professors Fausto Genone and David Ties of University of California at Berkeley stress AI's complexities in calling for quote, moving beyond a static view of markets, as isolated pools of substitutable products, and building a framework that acknowledges the interconnected and ever evolving nature of competition in the age of AI, close quote.
And of course, you know, Professor Ties is well known for his analysis of corporate firms capabilities, which he thinks transcends the traditional sort of neoclassical analysis of markets.
Now, Professor Daniels Folber of Northwestern University argues that investments and partnerships involving large digital platforms and AI entities are not likely to diminish innovation in AI.
Instead, he believes that such investments and partnerships can make important contributions to innovation, competition, and the market for technology.
Along somewhat similar lines, Professor Jonathan Barnett, who's already been discussed, argues against preemptive application of Antitrust in the GENAI ecosystem.
He believes that entry into the models and application segments of GENAI is robust.
Investments and alliances involving large tech platforms, venture capital, and institutional investors, and model developers, which have elicited regulatory concern, currently appear, according to him, to be efficient arrangements to aggregate the complementary assets required to produce GENAI models and applications, and face competition effectively from a variety of different business models.
Other commentators, however, have a somewhat more critical view of investments in AI enterprises.
Teodora Grossa of Ciel's Poor Law School, and Alexandra Wiersbyska of the Cleary-Gottlieb Law Firm, discussed the need to balance innovation incentives with competition concerns, using the Microsoft OpenAI partnership as a case study.
Also, Maria Teresa Marzolino of Bocconi University, and Laro Alzoboli of the University of Brescia, delve into the series of harm that could justify authorities intervening to block such transactions.
Professors Tejas Narachania of the University of California, Berkeley, and Ganesh Chittamaran of Vanderbilt, again already mentioned by Professor Schrepel, offer the most critical perspective of the status of competition in the AI sector.
They assert that AI's industrial organization rooted in AI's tech structure suffers from high market concentration across a number of layers, and argues that the non-regulated AI oligopoly has a range of undesirable consequences.
They conclude that anti-monopoly market-shaping tools, the law of networks, platforms, and utilities, industrial policy, public options, and cooperative governance can help facilitate competition.
They're talking really about a multi-pronged approach.
Laza Radic and Christian Stout of the International Center for Law and Economics highlight the problem of AI market definition.
They argued that the lack of a proper understanding of the outward and inward boundaries of AI markets is practical implications for antitrust policy and regulation, because it may lead to inaccurate assessments of market concentration, market power, resulting in possible under and over enforcement compared to the social optimum.
Economist William Blair of Massachusetts Institute of Technology, invoker, a stalker of the Weizmann Institute, assert that the emergence of generative AI signals has brought us a global waterfall moment, an inflection point that will result in profound and wide-ranging changes for all aspects of society and economy and clean competition policy.
And they believe that the digital infrastructure is, the metaphoric will shift, carrying us over the falls.
And its tierage capabilities prove essential in enabling any hope for regulating AI, including effective competition policy in the future digital economy.
And they put a particular stress on evidence-based decision-making, eBDM, enabled to a digital infrastructure platform.
And finally, again, let me turn once more to Thibault Schrepel.
Thanks.
And indeed, I will now to discuss the sixth and final topic of the edited volume.
And this one is related to merger review involving AI.
And as you can guess already from the title, we have quite a few exciting contributions.
The first one is authored by Teodora Grossa of Sciences Po Law School and Alexandra Weer-Bisca that you mentioned already, Alden, in which they explain that AI has hushed in a new era of collaboration between tech giants and AI startups.
And indeed, as you also mentioned, they are using the Microsoft OpenAI partnership as a case study to show that we need to strike a better balance when it comes to innovation incentives with competition concerns, which is done too rarely as we speak.
In another contribution, Maria Teresa Maggiorino of Bocconi University and Laura Zoboli of the University of Brescia that you also mentioned, explore a merger control regulatory framework that is tailored to scrutinize big techs generative AI investments.
They also delve into the theories of harm that could justify agencies intervening to block such transactions.
And ultimately, their chapter seeks to offer nuanced reflections on the practicality and the efficacy of deploying antitrust authorities' interventions within this very rapidly evolving landscape.
In another contribution, Tornis Mark Neifer of the Scalia Law School and Aaron Hogg of the US Department of Justice and Antitrust Division draw on the existing literature on innovation, uncertainty and mergers, as well as their own experience as computational enforcers, to discuss the ways in which competition authorities might encounter and address uncertainty about the competitive effects of a merger involving AI.
Finally, Victoria Noskova and Oliver Budinski of the Institute of Economics at the Ilmenau University of Technology delve into computational methods in the evaluation of mergers and acquisitions.
They conclude that each step of the merger review process offers space for computational tools, which despite having considerable imperfections, can enhance merger control proceedings.
However, and this is important, they highlight the fact that some elements of the institutional framework of merger control require adaptations to specific characteristics of computational tools.
Now, with all this in mind, we trust that our short presentation of the book shows how diverse the expertise is, and how complementary the point of views are, and also how multidisciplinary the book ends up being.
Back to you, Alden.
Well, thank you, Thibault.
Indeed, I think we sort of scratched the surface of the issues that the authors have discussed, and I think it's important because some of the debates you see among these scholarly authors about the modes of analyzing markets, about the modes of analyzing competition, are undoubtedly debates that will play out before competition authorities and policy makers in the coming years when difficult decisions have to be made how to control, regulate, or apply competition law to AI systems.
So we believe all of the essays will be of great interest to the competition law and policy community, as well as to followers of global tech policy.
Indeed, Artificial Intelligence and Competition Policy is a book that should have a prominent place on your bookshelves, we believe.
Thank you, Thibault, and thank you to the audience for listening.
Remember that the book is available to buy through the Concurrence website.
Thank you very much, Alden, and to everyone out there, take care of yourself, and if you can, someone else too.
You listened to an episode of Antitrust Code by Concurrences.
If you want to read more about this topic, check the Concurrences website, where you can find all relevant articles.
Follow us on Twitter, at CompetitionLoss, and join the Concurrences group on LinkedIn to receive updates on our next podcast.
Concurrences is the leading antitrust database with over 40,000 articles on competition law.
Concurrences is also the largest network of antitrust experts with lawyers, economists, enforcers and academics in 85 countries.
By listening to this podcast, you will learn the fundamentals of competition law and hear about the latest antitrust news.
Thanks to our guests, the best experts in the antitrust world.
Hello and welcome to a new episode of the Antitrust Code podcast.
My name is Alden Abbott of the Mercatus Center, George Mason University.
I'm joined by Professor Thibault Schrepel of the Amsterdam Law and Technology Institute, Rie Universiteit Amsterdam.
We are here today to discuss a newly published book on Artificial Intelligence and Competition Policy, which we co-edited.
According to my AI Research Engine Assistant or Search Engine Assistant, Artificial Intelligence is a set of technologies that allow computers to perform tasks that mimic human intelligence.
AI increasingly pervades competitive interactions throughout the economy.
Since all areas of commerce are affected by AI, it understandably is of great interest to competition law enforcers.
AI issues unquestionably will be raised in the full spectrum of competition law investigations and enforcement actions around the world.
This reality makes Concurrences' newly published volume, Artificial Intelligence and Competition Policy, incredibly timely and important.
It includes 20 essays covering the full gamut of AI related competition issues authored by experts in law, economics, and computer science.
These essays include practical analyses of recent enforcement initiatives, as well as theoretical analyses of future questions enforcers and scholars will raise concerning AI's competitive impact.
In short, the new volume lays the groundwork for future specialized work on the discrete AI competition topics.
In so doing, it highlights a broad range of issues that will be at the center of AI competition debates for many years to come.
Accordingly, the new volume will be of interest to policymakers, enforcers, competition law practitioners, scholars, and businesses alike.
To assist the reader, the essays are organized into three categories.
One, market dynamics, mergers, and partnerships in AI.
Two, AI challenges for competition law.
And three, policy responses to the AI boom.
In her foreword, Canny Fernandez, distinguished president of Spain's National Markets and Competition Commission, spotlights six topics covered by the volume that she states, quote, should be in a current agenda of any competition authority.
Close quote.
Professor Schrepel and I will take turns briefly discussing these topics.
The first topic is AI-driven collusion.
A particular interest is an essay by former US Federal Trade Commission Chair Maureen Ollhausen, Taylor Owings, and Cora Allen, focusing on the use by competitors of the same pricing algorithm to facilitate collusion over price.
The authors review recent case law and evaluate the illegal implications of various pricing scenarios.
One scenario involves situations when an algorithm may, quote, teach itself, close quote, to initiate collusion without human intervention.
Christian Bergquist of the University of Copenhagen and Camila Ringling of the George Washington University examine AI collusion under United States and European Union Competition Law.
Their focus is on whether the proliferation of AI-driven decision-making could enable entities to coordinate in anti-competitive ways that are beyond the scope of enforcers.
According to the authors, there are indeed enforcement gaps.
In particular, they explain that US enforcers face greater challenges than their European counterparts as American case law has developed a narrow notion of agreement or understanding under high and inconsistent evidentiary burdens.
They argue that United States court should adopt a more welcoming approach to considering plus factors in determining whether illegal anti-competitive agreements may be inferred.
Now, I turn to Thibault to discuss the second topic.
Thanks very much, Alden.
So the second topic covers algorithmic discrimination.
And we have a couple of papers in the volume about the subject, including one by Yusup Choi of the Hankuk University of Foreign Studies in Korea, in which he provides an overview of Korean competition rules on algorithmic discrimination.
The point he makes is that with personalized pricing becoming one of the most important competition issues in Korea, you see now discussions as to whether new rules should be adopted to tackle the subject.
And he argues against a new digital law in Korea on the basis that existing competition rules are sufficient to tackle any conduct involving algorithmic discrimination.
I should also mention that Jennifer Poulin of the University of St.
Gallen addresses the topic, although indirectly.
And more specifically, what she discusses is how firms might display a uniform online price while employing targeted coupons for specific customer segments using AI, which will allow those firms to implement less detectable forms of price discrimination.
We have also another paper by Professors Terras Nerashania of the University of California, Berkeley, and Janice Citaraman of Vanderbilt University, in which they tackle the subject of price discrimination implemented by oligopoly firms controlling an essential layer of the AI stack against the downstream firms.
And lastly, we have a contribution from Quentin Schaffer of the University of Strathclyde, in which he explains how unclear the role of friend, that is fair, reasonable, and non-discriminatory, currently is regarding obligations in the AI value chain.
And I give you back the floor to you, Alden.
Well, thank you, Thibault.
The third topic is Predatory Pricing Strategies.
And Thibault already alluded to the article by Jennifer Pullen of the University of St.
Gallen examining the evolving challenge of predatory pricing in the context of artificial intelligence.
And she explores contrasting approaches to assessing recoupment, which is a big issue in the United States law, generally regarded as a second phase of predatory pricing strategies after predation under both US and EU law.
Now, the author argues that sophisticated AI tools make recoupment easier to achieve, making a potential convergence between US and EU approaches to recoupment more likely.
She concludes, however, that both jurisdictions may need to adapt their frameworks, who effectively address the nuances of predatory pricing in the era of algorithmic pricing strategies.
All right, let me now tackle the fourth topic that relates to what Kelly Fernandez calls monopolistic mega firms in AI.
Professor Dan Crane of the University of Michigan explains that the continuing shift in economic value with overlapping and mutually reinforcing general purpose technologies, such as AI of course, but also robotics, quantum computing, synthetic biology, energy expansion and nanotechnology, will likely generate an inevitable and perhaps unstoppable tendency toward monopolistic mega firms.
He explains that the big choices ahead of us boiled down to ownership and regulation specifically who owns the mega firms.
For example, do they become socialized and part of the states, or do they remain private or semi-private public utilities, and how they should be regulated.
In another contribution that I mentioned already, Professor Neerajayan of the University of California Berkeley and Professor Sitaraman of Vanderbilt University also express concerns of monopolistic tendencies in the space.
They make the case differently from Dan Crane for a anti-monopoly approach to governing AI.
That approach includes difference to play on different levers such as the law of networks, platforms and utilities, industrial policy, public options, and cooperative governance.
And last, we have a contribution from John Barnett of the University of Southern California, in which he argues differently from the two previous contributions that I had mentioned, in that he explains that the history of digital markets does not support a dominant tendency toward entrenched winner-take-all outcomes, but rather, frequent disruption.
And on that basis, John forecasts that the AI ecosystem will move toward a structure that is composed of a mix of vertically integrated AI model providers and hybrid multi-firm arrangements involving various contractual relationships between LLM providers and larger platforms.
Back to you, Alden.
Thank you.
The fifth topic is the need for a nuanced approach to antitrust interventions.
And various authors explored a theme of AI-related complexities that may require enforcers to adjust the traditional modes of antitrust analysis.
Professors Diana Coyle of Cambridge and Haiyan Daman of the University of Toronto, respectively, provide a surprising insight on the competitive effects of so-called openness.
As they explained, the term open has been presumed to be a pro-competitive attribute for generative artificial intelligence.
Yet each gen AI model's openness can vary significantly, and this variation affects firms' incentives.
The authors conclude that competition agencies should employ a graduate concept of open in gen AI markets, taking into account not only the specific open features of the model and any partnerships involved, but also the specific character of the ecosystems, involving related upstream models and downstream AI products.
Professors Fausto Genone and David Ties of University of California at Berkeley stress AI's complexities in calling for quote, moving beyond a static view of markets, as isolated pools of substitutable products, and building a framework that acknowledges the interconnected and ever evolving nature of competition in the age of AI, close quote.
And of course, you know, Professor Ties is well known for his analysis of corporate firms capabilities, which he thinks transcends the traditional sort of neoclassical analysis of markets.
Now, Professor Daniels Folber of Northwestern University argues that investments and partnerships involving large digital platforms and AI entities are not likely to diminish innovation in AI.
Instead, he believes that such investments and partnerships can make important contributions to innovation, competition, and the market for technology.
Along somewhat similar lines, Professor Jonathan Barnett, who's already been discussed, argues against preemptive application of Antitrust in the GENAI ecosystem.
He believes that entry into the models and application segments of GENAI is robust.
Investments and alliances involving large tech platforms, venture capital, and institutional investors, and model developers, which have elicited regulatory concern, currently appear, according to him, to be efficient arrangements to aggregate the complementary assets required to produce GENAI models and applications, and face competition effectively from a variety of different business models.
Other commentators, however, have a somewhat more critical view of investments in AI enterprises.
Teodora Grossa of Ciel's Poor Law School, and Alexandra Wiersbyska of the Cleary-Gottlieb Law Firm, discussed the need to balance innovation incentives with competition concerns, using the Microsoft OpenAI partnership as a case study.
Also, Maria Teresa Marzolino of Bocconi University, and Laro Alzoboli of the University of Brescia, delve into the series of harm that could justify authorities intervening to block such transactions.
Professors Tejas Narachania of the University of California, Berkeley, and Ganesh Chittamaran of Vanderbilt, again already mentioned by Professor Schrepel, offer the most critical perspective of the status of competition in the AI sector.
They assert that AI's industrial organization rooted in AI's tech structure suffers from high market concentration across a number of layers, and argues that the non-regulated AI oligopoly has a range of undesirable consequences.
They conclude that anti-monopoly market-shaping tools, the law of networks, platforms, and utilities, industrial policy, public options, and cooperative governance can help facilitate competition.
They're talking really about a multi-pronged approach.
Laza Radic and Christian Stout of the International Center for Law and Economics highlight the problem of AI market definition.
They argued that the lack of a proper understanding of the outward and inward boundaries of AI markets is practical implications for antitrust policy and regulation, because it may lead to inaccurate assessments of market concentration, market power, resulting in possible under and over enforcement compared to the social optimum.
Economist William Blair of Massachusetts Institute of Technology, invoker, a stalker of the Weizmann Institute, assert that the emergence of generative AI signals has brought us a global waterfall moment, an inflection point that will result in profound and wide-ranging changes for all aspects of society and economy and clean competition policy.
And they believe that the digital infrastructure is, the metaphoric will shift, carrying us over the falls.
And its tierage capabilities prove essential in enabling any hope for regulating AI, including effective competition policy in the future digital economy.
And they put a particular stress on evidence-based decision-making, eBDM, enabled to a digital infrastructure platform.
And finally, again, let me turn once more to Thibault Schrepel.
Thanks.
And indeed, I will now to discuss the sixth and final topic of the edited volume.
And this one is related to merger review involving AI.
And as you can guess already from the title, we have quite a few exciting contributions.
The first one is authored by Teodora Grossa of Sciences Po Law School and Alexandra Weer-Bisca that you mentioned already, Alden, in which they explain that AI has hushed in a new era of collaboration between tech giants and AI startups.
And indeed, as you also mentioned, they are using the Microsoft OpenAI partnership as a case study to show that we need to strike a better balance when it comes to innovation incentives with competition concerns, which is done too rarely as we speak.
In another contribution, Maria Teresa Maggiorino of Bocconi University and Laura Zoboli of the University of Brescia that you also mentioned, explore a merger control regulatory framework that is tailored to scrutinize big techs generative AI investments.
They also delve into the theories of harm that could justify agencies intervening to block such transactions.
And ultimately, their chapter seeks to offer nuanced reflections on the practicality and the efficacy of deploying antitrust authorities' interventions within this very rapidly evolving landscape.
In another contribution, Tornis Mark Neifer of the Scalia Law School and Aaron Hogg of the US Department of Justice and Antitrust Division draw on the existing literature on innovation, uncertainty and mergers, as well as their own experience as computational enforcers, to discuss the ways in which competition authorities might encounter and address uncertainty about the competitive effects of a merger involving AI.
Finally, Victoria Noskova and Oliver Budinski of the Institute of Economics at the Ilmenau University of Technology delve into computational methods in the evaluation of mergers and acquisitions.
They conclude that each step of the merger review process offers space for computational tools, which despite having considerable imperfections, can enhance merger control proceedings.
However, and this is important, they highlight the fact that some elements of the institutional framework of merger control require adaptations to specific characteristics of computational tools.
Now, with all this in mind, we trust that our short presentation of the book shows how diverse the expertise is, and how complementary the point of views are, and also how multidisciplinary the book ends up being.
Back to you, Alden.
Well, thank you, Thibault.
Indeed, I think we sort of scratched the surface of the issues that the authors have discussed, and I think it's important because some of the debates you see among these scholarly authors about the modes of analyzing markets, about the modes of analyzing competition, are undoubtedly debates that will play out before competition authorities and policy makers in the coming years when difficult decisions have to be made how to control, regulate, or apply competition law to AI systems.
So we believe all of the essays will be of great interest to the competition law and policy community, as well as to followers of global tech policy.
Indeed, Artificial Intelligence and Competition Policy is a book that should have a prominent place on your bookshelves, we believe.
Thank you, Thibault, and thank you to the audience for listening.
Remember that the book is available to buy through the Concurrence website.
Thank you very much, Alden, and to everyone out there, take care of yourself, and if you can, someone else too.
You listened to an episode of Antitrust Code by Concurrences.
If you want to read more about this topic, check the Concurrences website, where you can find all relevant articles.
Follow us on Twitter, at CompetitionLoss, and join the Concurrences group on LinkedIn to receive updates on our next podcast.
