A book that looks at artificial intelligence from a business model and strategy perspective.

Generative AI, AI agents, on-device AI… With the daily news pouring in, we feel as if we're riding a wave of change, but the most important questions still linger in the air.
"What does this technology mean to us?" "How can we leverage it to become a true strategy?" "The AI ​​Big Wave - Beyond Technology to Strategy" begins with these questions.
This book goes beyond simply explaining AI technology.
While understanding technology is important, it offers the insight that the 'perspective' from which you view that technology is the starting point of strategy.
Even the same technology can produce completely different results depending on how you view it. A consistent message runs through the book.


AI is not a technology, it's a strategy.
More important than simple introduction is the process through which organizations and individuals ask and answer why, where, and how to introduce it.
Beyond task automation, AI is now emerging as an opportunity for comprehensive change, from redesigning business models to redefining customer touchpoints.
What's interesting is that this book doesn't just look at 'AI technology', but at the entire ecosystem surrounding AI.
From semiconductors, cloud computing, data infrastructure, algorithms, and even startup service models? AI doesn't operate alone.
The author traces the trends of the downstream and upstream industries, calmly showing where technology originates and where it is spreading.
This goes beyond simply predicting opportunities; it gives readers the power to design opportunities.
As the book progresses, more specific application examples become more interesting.
Why companies fail to build profitable models, the structure of the ecosystem surrounding AI agents, and the new market possibilities opened up by the combination of the metaverse and generative AI? This isn't just anecdotal information; it provides a framework for thinking that connects business strategy and industry analysis.
What is most impressive is the perspective that views AI as an 'extension' rather than a 'replacement' of humans.


This book, which discusses how technology can expand human creativity and judgment rather than the efficiency it brings, awakens readers to a sense of growing alongside technology, rather than being swayed by it.
"AI Big Wave" isn't just a book that explains AI well. It teaches us how to "think" about AI.
Strategic thinking that moves ahead of technology, insight that penetrates industry trends, and even serious reflection on the role of humans? This book contains all the questions we need to ask as we prepare for the AI ​​era.

Same technology, different results: a difference in perspective.

How you view artificial intelligence (AI) determines where and how you will utilize the technology.
For example, if we think of AI as simply a tool that automatically writes reports or a technology that reduces repetitive tasks, its utility will remain very limited.
However, if we view AI as an opportunity to transform our organization's strategy and redesign the way we work, the same technology can be leveraged in completely different ways. How we view AI influences important business decisions.
Because that perspective becomes the basis for deciding why to introduce AI, where and how much to invest in, and how to utilize it in which department.
Approaching it with the mindset of "Let's slightly increase work efficiency" might only speed up the current process. However, approaching it with the mindset of "Let's create new value with AI" could fundamentally transform how we engage with customers, structure our products, and even provide services. Ultimately, AI isn't just about technology.
How we view it is a matter of our attitude and thinking.
Strategy begins the moment we consider what technology means to our work and our organization, rather than viewing it as a fad to be followed.
Even with the same technology, if our perspective changes, the future we can create will also change.
In this way, perspective is the starting point of strategy that must move ahead of technology.

Services abound, but revenue models are scarce.

While many companies and individuals are recognizing the potential of generative AI, they remain cautious when it comes to actual implementation.
While there is general agreement on the direction and potential of the technology, there are many practical limitations, such as security issues, information distortion (so-called "hallucination"), and ethical risks.
Above all, in situations where it is difficult to clearly predict the return on investment, it is difficult for companies to make hasty moves.
To actively adopt AI at the business strategy level, more specificity and proven case studies than expected are required. The market is experiencing explosive growth in generative AI-based services.
Although various platforms and startups are competing to release new products, they are having difficulty securing paying users.
While features are interesting, services that deliver the kind of value and differentiation that customers will pay for are still rare.
Because of this, many services remain at the level of a 'good idea' and fail to transition into a revenue model.
There still exists a gap between technological possibilities and market demand. The current generative AI market revolves around two axes.
One is for businesses (B2B) that want to reduce repetitive tasks and increase efficiency, and the other is for creators (B2C) that want to produce content quickly.
Meeting minutes management, automated summarization, and automated customer response are already being implemented in practice, and the use of AI in content planning and creation is steadily increasing.
While this isn't yet a change big enough to shake up the entire market, the accumulation of this kind of usage could be a significant turning point in the future.
The important thing is not to be swept up in trends, but to prepare a strategic approach that fits our organization and the way we work.
An eye for industry, the power to read trends

Whether we're doing business or introducing a new technology, it's crucial to understand the context in which those decisions are made.
Especially for rapidly evolving technologies like AI, understanding where the technology originated and where it's likely to spread is strategically significant.
The important concepts at this time are ‘backward industry’ and ‘forward industry.’
The supporting industry is the base industry that my industry depends on, that is, my 'supplier', and the forward industry is the 'consumer' of the products or services I create, that is, my 'demander'.
It may sound a bit unfamiliar, but it's actually a very intuitive concept. By properly analyzing this trend, you can more accurately predict future opportunities in your industry.
My competitiveness will naturally change depending on how quickly the downstream industry advances technologically, and I will be able to capture new business opportunities based on emerging demands in the upstream industry.
At the same time, it provides a useful analytical framework for proactively detecting and responding to risks as well as opportunities.
For example, technological changes in downstream industries can alter cost structures, and policy changes in upstream industries can shrink the market itself. Especially for technologies like AI, which can be linked to diverse industries, a three-dimensional perspective is essential to understand inter-industry trends.
If you only look at opportunities within a single industry, you'll likely narrow down your perspective and miss out on major market movements.
Seeing both the back and the front together means judging where I stand now and where I should move with a broader perspective.
While technological evolution is difficult to predict, industrial movements leave certain patterns.
Only those who can read the pattern can spot opportunities first and respond faster.
Ultimately, the ability to see through industrial trends becomes the starting point of strategic insight.

The structure of the forward-looking industry driving the AI ​​agent ecosystem

The AI ​​agent market is growing rapidly, but the players in the upstream industries that are actually driving this trend are relatively clearly defined.
The most crucial pillars are global tech companies that develop large-scale AI models or provide them as services.
They are expanding diverse use cases based on high-performance language models and multi-functional agent technologies, and are playing a pivotal role in spreading AI technology throughout the ecosystem through their own platforms.
Beyond simply possessing technology, they are establishing themselves as leaders in creating standards and shaping market structures. Furthermore, cloud companies, which provide the infrastructure for AI technology to operate and be deployed, are emerging as important frontline industries.
They provide a structure in which AI agents can actually operate, while processing large amounts of data and stably supporting an environment in which learning and inference are possible.
In particular, Amazon Web Services, Microsoft Azure, and Google Cloud are responsible for the overall technological foundation of AI companies from initial experiments to commercialization, and even function as distribution platforms.
Ultimately, they are the infrastructure pillars responsible for the foundation of the AI ​​agent market. The role of startups launching specialized services leveraging AI agents across various verticals is also growing.
We are deeply engaged in specific industries, such as healthcare, law, marketing, and education, providing customized solutions and serving as a testing ground for implementing and validating AI technology into real-world business models.
They are rapidly identifying and nimbly responding to niche markets and specialized customer needs that large platforms often miss, thereby realizing the potential of AI agents.
Amidst the dynamic interactions of these diverse entities, the AI ​​agent market is gradually establishing itself as a realistic industrial structure.

The era of on-device AI dawns with the emergence of installed AI.

Most AI services we know operate on the cloud.
In other words, data generated from the user's device is transmitted to the server, where it undergoes calculations and the results are sent back to the user.
But recently, there has been a change in this structure.
As 'on-device AI' technology advances, which processes data directly within the user's device without sending it to a central location, AI is beginning to come closer to the user.
AI is being embedded in a variety of devices, including smartphones, wearables, automobiles, and home appliances, and is gaining the ability to respond and make decisions in real time based on user needs. The most significant characteristics of on-device AI are speed and security.
Because data does not need to be sent to a server, the response speed is very fast, and it also has strengths in terms of security as sensitive personal information is not leaked externally.
For example, performing commands using voice assistants, analyzing biosignals from healthcare wearables, and detecting real-time risks in vehicles are all areas where on-device AI can be most effectively utilized.
These technologies go beyond mere convenience, offering users the experience of "more secure and immediate access to AI." When the Internet of Things (IoT) and on-device AI combine, we will experience AI in a much more intuitive and organic form in our daily lives.
Home appliances learn users' lifestyle patterns to save energy, and in smart factories, sensors process real-time data to detect machine anomalies in advance. The era of "field-centered AI" is dawning across industries and daily life.
On-device AI isn't simply a technological evolution; it's a direction that demonstrates how AI can become a part of our daily lives. For businesses, it offers clues for exploring new product strategies and service opportunities.
From the reader's perspective, this change is not something that will happen in the distant future, but is already beginning to unfold within our own hands.

The Metaverse and Generative AI: New Connectivity Potential

When the metaverse first gained attention, many people simply perceived it as a form of social interaction in a virtual space or as an extension of games.
However, recently, the metaverse is increasingly viewed as a platform that transforms content creation, economic activity, and communication methods, rather than simply a spatial concept.
In particular, the combination with generative AI is emerging as a key factor that will accelerate the evolution of the metaverse.
We're entering an era where anyone can create characters, set backgrounds, and construct worldviews with simple commands, without having to manually code complex 3D content. Generative AI is breaking down the "difficulty of content creation," which has been the biggest barrier to entry in the metaverse.
3D graphics, animations, and character settings, which were previously difficult to create without expertise, can now be created using text-based commands, making a user-participatory metaverse environment more realistic.
This is not just a technological advancement, but a trend that is changing the paradigm of content production itself.
The ability to create your own virtual world, engage in economic activities within it, and interact with others in real time opens up new market possibilities. Of course, there are still many hurdles to overcome.
There are still technical challenges to overcome, including reliance on high-spec devices, network infrastructure, and quality of immersive experiences.
However, the metaverse is once again gaining recognition as a game changer, as generative AI can dramatically shorten this process.
The important question for readers is this:
Rather than asking, "Will the metaverse rise again?", it's important to understand, "How is generative AI transforming the metaverse?" That question will unlock new opportunities.

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