Haebom

Hello, this is Haebom. It's been a long time since I wrote a notice. Thank you so much for always subscribing to my blog and sending me warm comments and interest. Your subscriptions and comments are a great source of strength and encouragement to me. Today, I have a special project I would like to introduce to you. This book is a book that even the publishers themselves tried to make and wrote diligently during the time when e-books were all the rage, but even I forgot about it for a while. At the time, I had a hard time sympathizing with selling e-books for hundreds of thousands of won or writing books about things I had not personally experienced. So I made a series of five books based on things I had personally experienced or understood indirectly. At the time, I received publishing offers from various publishers, but I declined them because I was curious about the e-book market. Now that I think about it, I think I might have been too profit-oriented when I would have kept the physical copies of the books. In fact, each book is about 100 to 200 pages, so if I were to make it into a physical book, I think it would have been about 200 to 300 pages with graphics and typesetting. That was a long digression. •Volume 1: What to Build 101 – Blocking the Creative Process (Difficulty in Completing) •Volume 2: GTM 101 – Uncertainty of Market Reactions (The Difficulty of Selling) •Volume 3: Scale 101 – The Complexity of Growth (The Difficulty of Systematizing) •Volume 4: Iterate 101 – The Chaos of Improvement (The Difficulty of Optimization) •Volume 5: Impact 101 – The Conflict between Purpose and Profit (The Difficulty of Finding Meaning) Thanks to a wonderful person who recently purchased this book, I was able to revise it again, and this time I was able to organize everything from the cover to the ebook more neatly. ✨ In fact, I think I had completely forgotten that I had written it. I can't tell you how much strength I get when I see people contacting me after seeing things I made in the past. Whatever the reason, I sincerely thank you for finding and reading this book. Each and every one of you is a special first reader of this book. I would be even more grateful if you could recommend it to people around you. In fact, I am secretly greedy. Hahaha. And there is an open KakaoTalk room. You are always welcome. (Entry code 1024) Thank you for your continued blog comments and subscriptions. I cherish the time we spend growing together. 🤝 Thank you for always visiting my blog.

Will we ever speak like AI? ChatGPT has achieved the fastest technology adoption in history, reaching 100 million users in just two months since its launch in late 2022. Many people are already using AI to help them write, study, work, and create. But could it be that we are learning AI’s language habits without realizing it? Interesting question. In my recent personal research, I analyzed how AI is influencing the language habits of real people. The results were surprising, so I decided to blog about it. How did you conduct your research? This study consisted of two major stages. The first step was to identify ChatGPT’s signature vocabulary. We edited millions of pages of emails, essays, papers, and articles with ChatGPT, using common prompts like “clean up the text” and “clarify.” We defined the words that ChatGPT repeatedly added as “GPT words.” Examples include “delve,” “realm,” and “meticulous.” The second step was to analyze real language usage patterns. To do this, we analyzed over 360,000 YouTube videos and 770,000 podcast episodes. In particular, we compared them before and after the launch of ChatGPT to measure the degree of change. The surge of GPT words Our research found that since the introduction of ChatGPT, the use of GPT words like “delve,” “boast,” “swift,” “inquiry,” and “meticulous” has increased significantly. Surprisingly, this change occurred not only in structured scripts but also in ordinary, everyday conversations. Cultural feedback loop between humans and AI 1. Humans train AI with text 2. AI generates reconstructed text 3. Humans unconsciously imitate AI patterns 4. Go back to number 1… What’s more interesting is that this phenomenon forms a kind of cultural feedback loop. People train AI to generate text, and AI provides statistically reconstructed text based on this. Eventually, humans unconsciously imitate the language habits of this AI. The study described this as “the phenomenon of patterns stored in AI technology being backpropagated to the human mind.” This study shows that AI like ChatGPT is not just an efficient tool, but is fundamentally changing the language we use and the way we think. This unconscious feedback loop between AI and humans will continue to influence the linguistic culture of our society in the future. What is there in Korean? As I mentioned in my last post, it seems like there are some words that are obvious in Korean too. These are my personal feelings. In fact, these are expressions that are not often used in written or spoken language, but I feel like I am seeing them more and more these days. Is it just my feeling? Personally, when I see these expressions too often, I start to suspect that this person did an LLM assignment. Expression of change/development Develop Improve Improve Promote

There is a strange atmosphere flowing through college these days. 'Overly slick essays' are increasing on exam papers, and assignments that make you think, 'This student couldn't have written such sentences' are being submitted one after another. A new leisure culture has emerged on campus. It is a kind of detective game where you try to guess, "Isn't this written by AI?" But the problem is not simply one of ‘misconduct’. Rather, we are at a point where we need to ask a much more fundamental question: How is AI affecting human creativity and expression? And how will we respond to it? Essays, papers, and cover letters all look like they were written by AI In fact, I decided to test the AI’s capabilities myself. When I asked it to write a Montaigne essay, it mixed one real quote with two fake ones, and when I asked it to write a poem in the style of Elizabeth Bishop, the format was different, but the content was similar, but somehow awkward. The important thing was that it was able to imitate these literary areas. After that, I tried to see if it could be applied to emails, papers, etc. Of course, it was still awkward. This is in 2024. Another surprising thing was that AI was a great helper for 'annoying' tasks such as general assignments, emails, plans, editing notes, and scheduling . Recently, I have been getting dizzy and headaches frequently due to the medication I am taking, and AI formatted my documents for me, polished my speech, and sometimes even comforted me by saying, "I know how complicated and difficult your life is right now." AI replaces the 'unconfident me'. Too gently At this point, it is undeniable that AI is becoming a 'mental labor partner' for humans. AI is tireless, never complains, compliments you, offers another version at any time, and replicates your language. The problem is… the replication is too natural, too smooth. For example, I gave the AI a few of my blog posts and asked it to analyze their style, and it said this: “Your writing style is tense and intellectually precise, revealing a sense of restrained emotion and philosophical thought.” It was so accurate that it was scary. It was like holding up a mirror, but in that mirror I looked more complete. As I repeat this experience, this thought suddenly crosses my mind. After ChatGPT's memory function was added, I felt this way even more. Now, it seems like they know almost all of my secrets? The paradox of eliminating 'writing' from writing classes The same goes for students. At first, they start out by simply summarizing, but before long, they start to entrust AI with structuring, development, and drafting. They rationalize, “Isn’t this basically the same as writing it myself?” And at some point, in a “writing class,” students get an A without even “writing.” A recent study from the MIT Media Lab backs up these concerns with data. After having 54 participants write essays, their EEGs were measured, and the group that relied on AI showed lower memory, initiative, and brain connectivity for their own writing. The researchers called this “cognitive debt,” and feared long-term learning decline. Personally, I made sure to include this part in the book I am publishing this time. The problem is not technology, but the 'outsourcing of thinking' In fact, the technology itself is neutral. The question is 'how' to use it. ChatGPT can help you prepare for class. It can help you organize your schedule and set learning objectives. However, if we indiscriminately accept that help in the name of ‘convenience,’ the muscles of our thinking will gradually become weaker. Ultimately, we are at a crossroads. Will we embrace the temptation of speed and efficiency and hand over creative control to AI? Or will you endure the pain of thinking for yourself and forming sentences in the midst of discomfort and inefficiency ? Things we might really lose

The list of Meta's newly formed team has been leaked, and it's amazing. If you're looking to independently create an AI foundation model in Korea, take a look at the team composition. According to leaked information from within Meta, the personnel composition of Meta's elite AI organization, the 'Superintelligence Team', shows the future direction. Actually, it seems like people are attracted to the aggro because of their annual salary or nationality, but the essence is to analyze what they want to do now. 🧠 Technology Focus Areas LLM Optimization Multimodal learning Eliminate knowledge bias Reinforcement learning-based fine-tuning 1. Strategic talent recruitment Recruiting 15 people (31%) from OpenAI alone is a very aggressive talent acquisition strategy. DeepMind, Google, and other major AI companies are recruiting key talents It seems like they want to solve the implicit part in the process of building and fine-tuning the foundation model. 2. Expertise by field OpenAI alumni are concentrated in the Foundation Models team Many DeepMind alumni are assigned to the Computer Vision team Secure the best experts in each field 3. Proportion of Chinese researchers 47% of the total are Chinese, especially in key research areas Most of them have PhDs from top universities in the United States. Some see this as a security or political issue, but if you look at the papers they have written before, they are just people with great ability or symbolic achievements. Leadership hierarchy Name

"You have to brush your teeth while showering, wear shoes without laces, and save every minute of your day." Have you ever thought about this? If you look at SNS or YouTube recently, you can see that so-called 'micro-efficiency' is deeply embedded in people's daily lives. It is a movement to save even a minute of time with a thoroughly calculated strategy for even the smallest actions. The philosophy behind a cup of tea Veronica Pullen, 54, from the UK, makes two cups of tea every time she drinks it. One cup is lukewarm with milk, and the other is perfect 40 minutes later. This way, she saves 20 minutes a day, or about 10 days over two years. Such small efficiency can make a big difference in the long run. Pullen’s habits don’t end there. She always has boiled eggs for breakfast, an omelet for lunch, and a set menu for dinner. She also repeats a set pattern for her clothes every day. This minimizes the stress of having to make new decisions every time. She says that this habit gives her enough energy to do what she wants. Why are people so obsessed with one minute? This phenomenon is by no means limited to the habits of unique individuals. The tag #LifeHack has been mentioned more than 11 million times on TikTok, and countless productivity influencers are emphasizing the “1% rule.” The best-selling book Atomic Habits preaches to the public how the cumulative effects of small habits can bring about big changes. In a social context, this micro-efficiency is not just a hobby or a trend, but also a phenomenon that reveals the pressures of modern society. Technological advancements have made it possible to do things faster, but the paradoxical situation of having more work to do in the remaining time is repeated. Psychologists interpret this as an early symptom of "active burnout." Good Habits vs. Obsessions People who pursue efficiency have various reasons. They range from people who are sick and want to save even a little energy to people who simply want to practice 'laziness' more comfortably. However, experts warn that if the habit of efficiency becomes too obsessive, it can lead to obsessive-compulsive disorder or depression. Being efficient does not necessarily mean being satisfied. In fact, a British survey found that on average, people only have 23 hours a week where they feel truly free. That creates pressure to save every minute. 3 Tips for Healthy Efficiency But unconditionally pursuing efficiency is not the answer. So how should we approach healthy efficiency? Check your purpose. It is a good idea to make a clear plan on how you will use the time you save. If it ends with just saving time, efficiency will eventually lose its meaning. Allow yourself some 'looseness'. Overly strict rules can actually increase fatigue. It's also a good idea to intentionally break away from efficiency habits on weekends or vacations. Review and reset constantly. As your life circumstances change, your efficiency strategy should change as well. Periodically check whether your current habits are really meaningful to you, and if they need to be changed, do not hesitate to modify them. What really matters is not the quantity of time but the quality. "What will we do with the time we save?" This is the key question that we must not miss. The habit of saving time should ultimately be a means to make our lives happier and more meaningful. Not simply to live busy, but rather to regain leisure in life. Actually, this is a continuation of the article...

Someone once said, "You do planning, development, and marketing. What on earth are you?" What is your specialty? To this question, I actually don't know. I just did everything I could. I just answered, "We're doing everything we can to make it work." Originally, GTM (Go-To-Market) refers to the entire process of launching a product or service to the market and making it successfully adopted by customers. The GTM manager in charge of this is in charge of everything from the product development stage to actually reaching the hands of customers. The Secret to Small Teams Growing Big While working at Kakao Brain and Nexon Korea, I definitely worked on great projects and had great colleagues, but I couldn't shake the feeling that something was lacking. In fact, I had a lot of concerns. Above all, while talking about the word 'growth', I couldn't really feel the actual growth of the product. Is what I'm making really providing value to my customers? Am I creating meaningful growth in my career? I don't like terms like growth/newbie/hexagon in the first place. Real Growth Experiences Learned from Notion Then, working with Notion completely changed my perspective. I learned how to build a product that has tens of millions of users globally, how to increase monthly recurring revenue (MRR), and how to create maximum efficiency with a small team. Notion wasn't just a company with a lot of people. It had clear values, a well-polished product, and users who believed in it. Through this experience, I realized the most important thing in my career. "Rather than focusing on large organizations, we need to focus on the relationship between products and users." Why I joined Gamma, and the results With this realization, I joined Gamma. And in two years, we achieved $50 million in ARR and have been profitable for 15 consecutive months. Gamma is a small but mighty team, and I am now a senior GTM (Go-To-Market) manager. It was an incredible growth, reaching 50 million users and $50 million in annual revenue (ARR) with just 35 people. Behind Gamma’s rapid growth were the following key strategies. I was promoted from the previous GTM Manager to Senior GTM Strategy Manager . The method we use when we attack a market is simple: set principles and stick to them. Let me share a few. 1. User-centric pricing strategy (Van Westendorp) We never guess when it comes to pricing. Gamma used the Van Westendorp Methodology to figure out exactly what users were “ready to pay” and set their prices accordingly. This created a solid revenue structure from the beginning and allowed them to maintain steady growth without changing their prices once for the next two years. Price is a signal to the customer. Accurate pricing creates trust between the product and the user. 2. Maximum growth with minimum manpower Gamma creates AI-based presentation tools that sell and market themselves. Our go-to-market strategy was simple:

The intense ending scene of 『Squid Game Season 3』, which I watched on Netflix a while ago, is still lingering in my mind. The final game is the so-called 'high-altitude squid game' that takes place in the sky. The game is played on square, equilateral triangle, and perfect circular pillars in order. Especially in the round pillar scene in the final stage, the protagonist chooses to sacrifice himself to escape the shackles of greed that dominated him. Until now, the circle symbolized greed and obsession, but when the protagonist chooses death on this circle for his child and future, its meaning is completely reversed. This scene vividly shows that the reality we see is not absolute, and its meaning can change at any time depending on the environment or experience. For reference, I found Squid Game Season 3 very boring. Culture Changes the Way We See? Two Interesting Illusion Studies A similar story can be found in two recent studies of visual illusions. Ivan Kroupin's research team at the London School of Economics (LSE) in the UK showed a picture called the 'Coffer illusion' to Western people and rural Namibians. Interestingly, Western participants mostly saw squares, while rural Namibians mostly saw circles. The research team explains this with the 'carpentered world' hypothesis, which states that the environment we live in determines the way our brains see the world. But another study challenges this hypothesis. Dorsa Amir and Chaz Firestone have shown that the Müller-Lyer illusion occurs in humans, animals, and even blind children, regardless of their environment. These two studies may seem to conflict, but they actually send an important message: the world we see is not an objective reality, but something our brains are constantly interpreting and creating. Seeing and speaking are ultimately 'hallucinations of the brain' Neuroscientist Anil Seth describes reality as "a controlled hallucination created by the brain." In other words, our brain does not show us the objective world as it is, but rather interprets the world subjectively based on our experiences, environment, and culture. What's interesting is that this phenomenon applies equally to language as to vision. Take, for example, a study by Stanford University psychologist Lera Boroditsky, who asked German and Spanish speakers to describe the words “key” and “bridge,” and found that the genders of these words were reversed in both languages. In German, key is a masculine noun, and leg is a feminine noun. In Spanish, key is a feminine noun, and leg is a masculine noun. Surprisingly, German speakers described keys with masculine traits such as “heavy, strong,” and legs with feminine traits such as “beautiful, elegant.” Spanish speakers, on the other hand, described keys with the opposite traits. Likewise, the language we use ultimately determines how our brain creates and interprets reality. What we believe we ‘see’ and what we believe we ‘say’ are ultimately controlled illusions of the brain. 『Squid Game』, Visual Illusion, The Same Story That Language Tells The reason why the final circular pillar scene in 'Squid Game' was so powerful and why language changes our perception of reality is ultimately the same. The meaning of the world that we believed to be absolute can change at any time, and the world can be completely different depending on the language, culture, and experiences we have. The common message that optical illusions, language studies, and drama give us is clear. The world is always being reinterpreted and recreated in our minds. So if we want to change our lives to be more positive, shouldn't we first change the way we look at the world? Just as the protagonist of 'Squid Game' changed the meaning of the archetype from greed to sacrifice, we too can change the meaning of what we see and say in our lives. The difficulties, pain, and even happiness we experience can ultimately be changed by how we look at them. In fact, the reason why the work called "Squid Game" received attention was because the games that we remember from our daily lives or from our childhood have become games that adults who are obsessed with greed risk their lives to play. If we change our perspective a little, wouldn't something completely new or fun come out?

Recently, I met someone who is fluent in Chinese through Skewer Coaching. We talked about apps that are frequently used in China, and I told them about the ones I have used. Even those who don’t know much about China have probably heard of WeChat. There is even a joke that you can’t do almost anything in China without WeChat. Ordering food, calling a taxi, shopping, making payments, and even using government services can all be done within WeChat. Compared to KakaoTalk or Naver, which are commonly used in Korea, it offers a much wider range of functions within a single app. Is this “super app” phenomenon in China simply because Chinese people prefer convenience? Or is there another reason? When explaining the differences between Eastern and Western app design, it is often said that "Asians like apps with a lot of information and complexity, while Westerners prefer simple apps." However, this is not actually the case. Recently, apps in Asian countries such as China, Korea, and Japan have also gradually changed to a cleaner and simpler design. Nevertheless, why have super apps become so strong in China? The starting point of the 'mobile first' era that began with smartphones In the early 2010s, the Internet was not yet fully established in China. At that time, the Internet penetration rate in China was less than 35%, and there were not many people using desktop computers. In this situation, smartphones became the first personal computing devices for Chinese consumers. Since the mobile-centric Internet environment was built from the beginning, existing Internet habits were not formed. For example, in the US and Europe, desktop-based Internet usage habits such as web browsers and emails were already established, but in China, mobile apps were mainstream from the beginning. This gap was filled by Tencent's WeChat. WeChat started as a messenger, but began to provide almost all Internet services through 'Official Accounts' and 'Mini Programs'. In fact, WeChat is like a browser that acts as China's Google Chrome. Instead of moving existing web-based services to mobile, it created an app-centric environment from the beginning. Here, I always think of the words of Tencent Chairman Ma Huateng. When Tencent's QQ and games were ridiculed as copycats, Ma Huateng said, "We drew a tiger after seeing a cat." The early days of China’s digital economy were poorly developed. Banks did not provide consumer-friendly online payment systems, shopping was largely cash-based, and e-commerce was in its early stages of lack of trust. In this situation, Alibaba developed Alipay for online transactions on Taobao, creating its own payment system. Companies had to create their own services if they didn’t have what they needed. This is the real reason why super apps were born. In other words, it was an inevitable choice to fill a gap in the market and quickly take over the entire industry, not for the sake of an ideal user experience (UX). The emergence of the 'wall-building' competitive strategy The rise of China’s super apps can be explained by another reason: the extreme competition. Giants like Tencent, Alibaba, Baidu, and ByteDance have long used a so-called “walled garden” strategy, blocking links to each other’s platforms. For example, if you try to open a shopping link on Taobao in WeChat, it won’t work. So each company tries to pack as many services as possible into its own app. This has led to super apps like WeChat offering food delivery, taxi hailing, payments, shopping, and social media all in one app. Although the Chinese government only banned such link blocking in 2021, consumer habits have hardened and the super app model is deeply entrenched. Another reason can be found in economic terms. The online spending power of early Chinese consumers was lower than that of the West, so the lifetime value of each individual app was small. Accordingly, companies tried to maximize revenue per customer by providing as many services as possible to each customer. Also, because the initial cost of acquiring customers was very high, they integrated more services to avoid sending users to competitors once they had gathered users in one app. This was part of the strategy when Tencent ran the “red envelope (hongbao)” campaign during the Chinese New Year (CNY) to promote WeChat Pay. Strategic choice, not cultural preference The reason super apps succeed in China is not because of user demand, but because of a combination of unique market conditions: a mobile-first environment, lack of industry infrastructure, fierce competition, and low spending power. So should companies in other countries just copy the super app model? Not necessarily. Rather, the important lesson is to understand what drives product design decisions. Super apps aren’t always the answer. In some cases, it might be better to bundle multiple services into one app, while in other cases, it might be more effective to create a single app that provides the best user experience. In reality, WeChat is not a 'perfect super app' that can solve everything. The way it allows users to experience simple services through mini programs is essentially the 'Open Web' of the mobile environment. Users can experience the service in advance and then download a separate app if necessary. In Korea, there were some who once boasted that super apps were the future. But who actually achieved this? Personally, Toss is the only company that has truly achieved a super app. I think Toss's current app is the real beginning of this. The biggest Takeaways we can get from the case of China's super apps is to understand how business needs evolve products. After all, product growth begins not with user needs, but with strategic responses to the environment and market conditions that companies face. Personally, I think Korea is the place that most underestimates China's digital ecosystem and technological prowess. As I always say, if you go to a developed city in China or even Shanghai, you will realize that this is not the China I knew.

Recently, while consulting on AI planning and architecture for a company that creates financial services, I felt that this kind of discussion is accelerating in the financial industry. Although generative AI such as ChatGPT is receiving a lot of attention, the financial sector has been actively utilizing AI for a long time. From fraud detection to credit risk management, and even ultra-short-term trading strategies, AI is playing a key role in many core financial tasks. However, there are still many tasks left for AI to be actually trusted and used ethically. The most important issue among them is explainability. In the financial sector, the more complex the AI model, the more difficult it becomes to understand how it makes decisions. This is the so-called 'black box' problem. Even if an AI model makes accurate predictions, if it cannot explain the criteria and process by which the predictions were made, it can be a serious problem, especially in a field like finance where trust is essential. So today, I would like to take a deep look at what this 'explainability' is and how it can be implemented in the financial sector. Can I give you an interesting example? In 2019, Apple Card became a social issue due to the controversy over gender-discriminatory loan screening. A couple with the same income and credit rating applied, but the husband's credit limit was set much higher than the wife's. People immediately criticized this decision as 'gender-based discrimination.' However, the card issuers and financial institutions that managed the screening algorithm failed to explain exactly why this problem occurred. As a result, their image suffered serious damage. This case illustrates the potential problems that can arise when AI operates in the financial sector. AI makes decisions based on data, but if the data itself is biased or the algorithm’s judgment criteria are not clearly revealed, financial institutions can face serious ethical and legal responsibilities. In this context, the financial sector must ask the following questions when using AI: “Why did our AI model make that decision?” “Are the decisions made by AI really fair?” “Can we explain the judgment criteria of AI models?” Three Key Elements of AI Explainability Explainability is more than just showing the technical details of how a model works. To properly implement explainability in AI in finance, all three of the following elements must be present: (1) Transparency It's about making it clear to stakeholders how the AI model is structured, what data it was trained with, and what prerequisites or assumptions it operates on. For example, trust can be built by disclosing to customers and regulators the data sources for credit rating models and the reasons for selecting assessment variables. (2) Interpretability The goal is to make AI decisions easily understandable to humans. The way the model works should be explained using simple algorithms or visual tools. For example, you should be able to explain why you declined a loan application with specific data points (“Your loan was declined because of your high credit card utilization”). (3) Accountability It's about establishing clear accountability for decisions made by AI models and deciding in advance how to respond when problems arise. When a model makes a bad decision, establish clear processes and accountability to immediately correct it and remediate the damage. An integrated approach that embraces all three elements is key to properly implementing the explainability of AI in finance.

When we visit a hospital, we always expect accurate diagnosis and quick treatment. However, the reality is that it is not easy to receive treatment when you want it due to long waiting times and a lack of doctors. However, a surprising study recently released by Microsoft has presented a new possibility to the medical field. It is that a diagnosis system using artificial intelligence (AI) made a diagnosis four times more accurately than a human doctor. It is true that there are doubts such as, "Can AI really replace doctors?" AI Diagnostic Orchestrator Enters the Medical Field Microsoft 's AI Diagnostic Orchestrator (MAI-DxO) isn't just a simple AI model. It's designed like a panel of five doctors with different roles. Each AI agent develops a hypothesis, selects a test item, and consults with each other to arrive at a final diagnosis, deriving the most appropriate treatment method. What’s interesting here is that it clearly shows the process by which the AI arrives at its conclusion. Microsoft calls it the “Chain of Debate,” and it transparently discloses the logic through which the AI solves the problem. Accuracy that surpasses human doctors? So how effective is this AI in real-world medical settings? To test this, Microsoft presented the AI with 304 of the most challenging diagnostic cases published in the New England Journal of Medicine (NEJM), the top medical journal in the United States. The results were astonishing. When AI worked best (using OpenAI’s o3 model), the diagnostic accuracy was a whopping 85.5%. Experienced human doctors diagnosing the same cases had a success rate of only 20%. Despite the limitations of human doctors not having access to textbooks or colleagues, AI’s overwhelming performance came as a huge shock to the medical community. AI that saves both cost and time In addition to accuracy, the cost-saving effect was also noteworthy. Microsoft set the AI to consider cost in the diagnostic process, and as a result, the number of tests required was significantly reduced, saving hundreds of thousands of dollars in real-world cases. “This system is the most advanced AI performance we’ve ever seen, and could open new doors to healthcare accessibility,” said Microsoft’s Dr. Dominic King. AI models are now 'products', real competitiveness is 'combination power' In this experiment, Microsoft used AI models from several companies, including OpenAI, Meta, Anthropic, Google, and xAI. In particular, Mustafa Suleyman emphasized that even the best-performing OpenAI model will ultimately be “commoditized,” and the real difference lies in the “orchestrator” that integrates and combines these models. Microsoft said it plans to apply the technology to its AI chatbot Copilot and its Bing search engine, which could have huge potential on a platform that processes more than 50 million health-related questions a day. The Era of 'Medical Superintelligence' Needs Preparation Mustafa Suleiman describes this research as a first step toward “medical superintelligence.” A future where faster, more accurate, and cheaper diagnoses are possible is just around the corner. However, further verification is needed before it can be applied to clinical settings. Dr. Eric Topol, a cardiologist and AI medical authority, also evaluated this research as an important study that proved the possibility of AI’s medical efficiency, although it was not conducted in a real medical setting.