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genioux Fact post by Fernando Machuca and ChatGPT
Introduction:
In the wake of the public launch of ChatGPT, the generative AI landscape has witnessed a surge in investments from venture capital firms and corporations alike, driven by the promise of significant productivity enhancements. However, as the excitement mounts, questions loom regarding who stands to benefit most from this burgeoning market and the key determinants of value capture.
genioux GK Nugget:
"Navigating the generative AI stack unveils crucial insights into value capture dynamics, shedding light on which players are poised to reap the greatest rewards in this rapidly evolving ecosystem." — Fernando Machuca and ChatGPT
genioux Foundational Fact:
At the heart of the generative AI revolution lies a complex interplay of factors, including computing infrastructure, data, foundation models, fine-tuned models, and applications, each presenting unique opportunities and challenges for market participants. While the landscape teems with potential, the quest for differentiation and value capture remains paramount.
10 genioux Facts:
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- Computing infrastructure forms the bedrock of the generative AI stack, offering specialized resources crucial for training and running machine learning models.
- Massive internet-scale datasets fuel the training of generative AI models, emphasizing the pivotal role of data in driving innovation and performance.
- Foundation models, such as OpenAI's GPT-4 and Google's Gemini, represent the cornerstone of generative AI, setting the stage for a myriad of downstream applications.
- Retrieval-augmented generation (RAG) and fine-tuned models offer pathways to tailor generative AI capabilities to specific contexts and tasks, each with its own trade-offs in cost and complexity.
- LLM applications span diverse domains, from legal contract drafting to technical troubleshooting, showcasing the versatility and transformative potential of generative AI.
- The market for foundation models is poised for consolidation, akin to the trajectory observed in cloud services, with a few major players expected to dominate.
- Domain-specific data holds the key to unlocking specialized generative AI models, offering incumbents a competitive edge in delivering tailored solutions to industry-specific challenges.
- User interface and distributional advantages emerge as critical differentiators in a crowded landscape, underscoring the importance of end-user engagement and market reach.
- Copyright and intellectual property concerns present regulatory and legal hurdles, shaping the competitive dynamics and market entry strategies in the generative AI space.
- Strategic decision-making for companies hinges on a nuanced understanding of use cases, technology maturity, and data governance, informing choices between building proprietary models or leveraging third-party solutions.
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Conclusion:
As the generative AI landscape continues to evolve, strategic foresight and agility will be indispensable for companies seeking to navigate the complexities of value creation and capture. Whether incumbents leveraging domain-specific expertise or startups harnessing agile innovation, the path to success lies in charting a course that balances technological prowess with market insight and user-centric innovation.
REFERENCE
The g-f GK Article
Kartik Hosanagar and Ramayya Krishnan, Who Profits the Most From Generative AI? MIT Sloan Management Review, March 12, 2024.
Kartik Hosanagar
Kartik Hosanagar is the John C. Hower Professor of Technology and Digital Business and a Professor of Marketing at The Wharton School of the University of Pennsylvania³⁴. His research work focuses on the digital economy, particularly the impact of analytics and algorithms on consumers and society, Internet media, Internet marketing, and e-commerce³⁴. He has been recognized as one of the world’s top 40 business professors under 40⁴. He is a ten-time recipient of MBA or Undergraduate teaching excellence awards at the Wharton School³⁴. His research has received several best paper awards³⁴.
Kartik cofounded and developed the core IP for Yodle Inc, a venture-backed firm that was acquired by Web.com. Yodle was listed by Inc. Magazine among America’s fastest growing private companies⁴. He has served on the advisory boards of Milo (acq. by eBay) and is involved with many other startups as either an investor or board member⁴. His past consulting and executive education clients include Google, American Express, Citi, and others⁴. He was a co-host of the SiriusXM show The Digital Hour⁴.
He currently serves as a department editor at the journal Management Science and has previously served as a Senior Editor at the journals Information Systems Research and MIS Quarterly⁴. Kartik graduated at the top of his class with a Bachelors degree in Electronics Engineering and a Masters in Information Systems from Birla Institute of Technology and Sciences (BITS, Pilani), India, and he has an MPhil in Management Science and a PhD in Management Science and Information Systems from Carnegie Mellon University⁴.
Source: Conversation with Bing, 3/12/2024
(1) Kartik Hosanagar – Operations, Information and Decisions Department. https://oid.wharton.upenn.edu/profile/kartikh/.
(2) About me – Kartik Hosanagar. https://www.hosanagar.com/about-me/.
(3) . https://bing.com/search?q=Kartik+Hosanagar+summary.
(4) Karthik Medical. https://www.hosanagar.com/.
(5) Kartik Hosanagar - Advisory & Mentorship @ Cinqo - Crunchbase. https://www.crunchbase.com/person/kartik-hosanagar.
(6) undefined. https://executiveeducation.wharton.upenn.edu/faculty/kartik-hosanagar/.
Ramayya Krishnan
Ramayya Krishnan is the William W. Cooper and Ruth F. Cooper Professor of Management Science and Information Systems at Heinz College and the Department of Engineering and Public Policy at Carnegie Mellon University³¹. He has been a faculty member at CMU since 1988 and was appointed as Dean of the Heinz College of Information Systems and Public Policy in 2009³. Krishnan was educated at the Indian Institute of Technology and the University of Texas at Austin. He holds a bachelor’s degree in mechanical engineering, a master’s degree in industrial engineering and operations research, and a PhD in management science and information systems³.
Krishnan's research focuses on the digital economy, particularly the impact of analytics and algorithms on consumers and society³. He is well known for his work in e-commerce and information risk management³. His current research interests are in the responsible use of AI and in data-driven approaches to support workforce development³.
Krishnan has founded four externally funded research centers, raising close to $100M dollars over the last decade³. He directs the Block Center for Technology and Society and advises policymakers, business leaders, and international organizations such as the Asian Development Bank on Technology and Policy³. He is an American Association for the Advancement of Science - AAAS Fellow (section T), an INFORMS Fellow, an elected member of the National Academy of Public Administration, and a commissioner of the Geotech Center of the Atlantic Council³.
Source: Conversation with Bing, 3/12/2024
(1) Ramayya Krishnan | Carnegie Mellon University's Heinz College. https://www.heinz.cmu.edu/faculty-research/profiles/krishnan-ramayya.
(2) Ramayya Krishnan - Wikipedia. https://en.wikipedia.org/wiki/Ramayya_Krishnan.
(3) . https://bing.com/search?q=Ramayya+Krishnan+summary.
(4) Ramya Krishnan - Wikipedia. https://en.wikipedia.org/wiki/Ramya_Krishnan.
(5) undefined. https://www.filmibeat.com/bollywood/movies/raja-harishchandra/story.html.
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ChatGPT's Summary:
"Who Profits the Most From Generative AI?" delves into the intricate landscape of generative AI, focusing on the factors influencing value capture and market dynamics. Authored by Kartik Hosanagar and Ramayya Krishnan, the article analyzes the various layers of the generative AI stack, including computing infrastructure, data, foundation models, fine-tuned models, and applications. It explores the challenges and opportunities faced by both established players and new entrants in the rapidly evolving generative AI market. The authors discuss the importance of domain-specific data and highlight strategies for companies to differentiate themselves and maximize value in this dynamic ecosystem. Additionally, the article examines copyright issues, user interface considerations, and the implications for managers formulating generative AI strategies. Through a comprehensive examination of key industry insights, the article provides valuable guidance for navigating the complexities of the generative AI landscape and achieving success in this transformative field.
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