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Generative AI; Characteristics(Value Chain), comparisons between Google Gemini, Microsoft Copilot, OpenAI ChatGPT
In 2022, with the advent of OpenAI's ChatGPT, public interest in generative AI surged dramatically.
From simple stories to professional data collection, this groundbreaking program has played a role in
enhancing the efficiency of various stakeholders. ChatGPT has attracted many users in the market,
becoming a representative example of AI. However, this program is just one element within the category
of 'generative AI'. Many IT companies are developing generative AI and utilizing it within their business
processes to maximize overall efficiency and expertise. Therefore, this analysis will examine the
representative generative AI programs used by companies, their distinguishing features compared to
other generative AIs, and how they are implemented across different industries through various case
studies.
1. Overview of Generative AI and its Value Chain
To better understand generative AI, it is essential to compare AI with generative AI. According to the
American venture capital firm 'Sequoia Capital,' traditional AI is defined as "programs that analyze a
series of data based on various use cases to find certain patterns in the process," while generative AI is
defined as "programs whose primary role is to create new objects like humans do, rather than analyzing
existing ones". 1 (Sequioa Capital, 2022) The most well-known generative AIs include Google's
'Gemini,' Microsoft's 'Copilot,' and OpenAI's 'ChatGPT.' Before analyzing these three generative AIs,
understanding their value chain will provide a deeper comprehension of these AIs. McKinsey analyzed
that the value chain of generative AI consists of six processes: computer hardware, cloud platforms,
foundational models, model hubs and MLOps, applications, and services.
First, computer hardware is related to the concept that generative AI requires relevant knowledge to
generate a series of content. It uses processing devices equipped with 'accelerator' chips to handle large
volumes of data, and after processing, it utilizes computer hardware devices to customize the
information to fit the model. Therefore, computer hardware is the most fundamental condition that must
be met in the generative AI chain. The second concept is the cloud platform. As mentioned earlier,
processing large volumes of data and customizing models for AI to learn involves high costs. To solve
this problem, running AI models on the cloud is a good method. The third concept, the foundational
model, involves preparing data, setting goals, training the data through the model, and refining the
results to achieve the desired outcome. The foundational model is evaluated as the most basic element
of generative AI because it must produce the type of content intended through it. The next concept is
model hubs and MLOps. A model hub serves as a space where foundational models and derived
information are stored. In closed-source models, the model itself functions as the model hub, granting
access and use to contracted stakeholders and providing services that support machine learning and
operations. In open-source models, independent model hubs share the model with developers who
access it. The final concept, applications, can be compared to foundational models. While foundational
models perform various functions, applications are built on the model to perform specific tasks.2 Now
that we have briefly reviewed the value chain of generative AI, we will analyze and compare the
characteristics of three representative generative AIs (Gemini, Copilot, ChatGPT).
1
Generative AI: A Creative New World. (2022). https://www.sequoiacap.com/article/generative-ai-a-
creative-new-world/.
2
Exploring opportunities in the generative AI value chain (2023).
https://www.mckinsey.com/capabilities/quantumblack/our-insights/exploring-opportunities-in-the-
generative-ai-value-chain.
Copyright SungJun Oh. 2024. All right reserved.
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