Trying it out

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.

"Young people these days don't have enough sex. It's hard to believe." In 2016, the American media outlet Bustle reported the shocking results of a study showing that the frequency of sexual intercourse among young people in their early 20s had plummeted, and declared this. Since then, this phenomenon has been called the "Sex Recession" and has become a hot topic. The image of a bee and a bird turning their backs on each other on the cover of the American magazine The Atlantic strongly expressed this generation's sexual disconnection. In the past, adults were worried about 'young people who are too promiscuous,' but today's older generation is more worried about 'young people who avoid sex.' In fact, recent statistics show that the frequency of sexual intercourse among Generation Z (born in the mid-1990s to early 2010s) is unprecedentedly low. According to a 2018 survey, about a third of men and a fifth of women aged 18 to 24 had not had sex in more than a year, and the pandemic has further exacerbated this phenomenon. In 2021, nearly 40% of Californians aged 18 to 30 said they had never had sex. A generation where everything is possible but nothing is wanted Interestingly, Generation Z is a generation that is more open and has more diverse choices about sex than previous generations. They live in an era where they can easily have short and casual encounters through dating apps with just a smartphone, and various sexual preferences are freely accepted. So why do they stay away from sex? On this issue, British journalist Louise Perry offers a somewhat conservative but interesting perspective. Her book A New Guide to Sex in the 21st Century takes sex seriously, acknowledges the biological differences between men and women, and warns of the dangers of casual sex. Perry strongly warns that “any man can kill almost any woman with his bare hands,” and argues that women should choose their sexual partners carefully. Meanwhile, Guardian journalist Carter Sherman, in his book The Second Coming, explains why Generation Z is experiencing a sexual recession as a result of being caught between political conservatism and the massive power of the internet. The internet provides an endless supply of sexual content while also encouraging the commodification of sexuality, which in turn hinders real intimacy. Generation Z is overexposed to pornography from a young age, making it harder for them to form healthy attitudes toward sex. The Real Reason for the Sexual Recession is the Recession of 'Relationships' The fundamental problem of the sexual recession is 'loneliness'. Generation Z suffers from anxiety and depression much more than previous generations, and has difficulty forming close relationships with others. It is also noteworthy that alcohol consumption has decreased. Alcohol was a medium for forming close relationships quickly with previous generations, but Generation Z is distancing itself from this and is having even more difficulty forming relationships. The influence of social media also hinders the formation of intimacy in an environment where people are constantly evaluated on their attractiveness based on numerical criteria. Ultimately, the sexual recession reflects a social phenomenon where relationships are difficult to form and true connections with others are scarce. What should we do? Both Louis Perry and Carter Sherman point out the causes of the sexual recession from their own perspectives, but what both authors ultimately overlook is the 'power of pleasure and connection' that sex has. Sex is not just about physical pleasure, but it is a precious area where humans can connect most deeply and directly with others. In intimate relationships, we learn to understand, respect, and love each other. Our society needs to discuss how to help Generation Z re-establish healthy relationships and enjoyable experiences through sex. Rather than reducing the root causes of the sexual recession to mere personal problems, it is time to start a more comprehensive and in-depth social conversation. Recently, I've been reading articles like this, and I think that what Generation Z wants is not just a 'simple meeting', but a process of understanding and getting to know the other person. I'm not sure if that's the area called 'self-satisfaction'... or something like the resume blind date that was popular in the past. I found the expression 'sex recession' interesting, so I looked it up.

The Vibe Coding article I recently posted received more attention than I expected. In particular, when I honestly shared specific profit stories, many people sympathized and found it interesting. Thanks to that, I was feeling good, but recently, I encountered an unexpected situation. This is what happened when an anonymous person reported something to me about an overseas corporation I run . At first, I was really surprised. I didn't do anything illegal, but I was suddenly reported, so it was absurd. Through this experience, I would like to share a story that may be useful to those who are considering overseas corporations or global payments like me. 🚨 What was the report? The content of the report is as follows. It was a complaint about whether I was properly following the various reporting and procedures required in Korea regarding the overseas corporation I established with Stripe Atlas. To conclude, fortunately, I had no problems. This is because I had been processing tax returns (comprehensive income tax) and foreign exchange transaction reports from the beginning through a professional tax accountant. It was also the season for the final income tax, so I was preparing the documents thoroughly with the tax accountant. I am always grateful to Ichon Tax Accounting Firm. What you need to know when operating an overseas corporation in Korea However, I learned something through this experience. In my case, I had already prepared, but if I hadn't prepared properly in advance, a complicated and troublesome situation could have arisen. There are obligations that must be fulfilled when establishing or operating an overseas corporation in Korea. I actually don't know the details, so I followed the advice and instructions of the tax accountant. Foreign exchange transaction report (reporting of overseas direct investment and regular reporting required through foreign exchange bank) Comprehensive income tax return (including income earned overseas in addition to income earned domestically) The above procedure is not difficult, but if you don't do it in advance or forget, it can become unnecessarily complicated. Tips that helped me personally The reason I was able to get through this without any problems this time is because of the following reasons. 1️⃣ Get help from a professional tax accountant Whether you are a sole proprietor or a corporation, you can deal with these situations right away by working with a tax accountant. (A tax accountant is especially helpful during the comprehensive income tax filing season.) 2️⃣ Use the Creator Automatic Deposit Service (Shinhan Bank) If you are a developer or creator, revenue management can be complicated, but I use Shinhan Bank's 'Creator Automatic Deposit Service', so revenue management is neatly organized. Thanks to this, it was convenient when reporting. 3️⃣ If you don’t absolutely need an overseas corporation, use a domestic solution In fact, if you don’t absolutely need an overseas corporation, there are many solutions that allow you to easily build a payment system domestically. Personally, I think services like Latpeed and Toss Payments are the most realistic and recommended. If you are a self-employed person and do not like complicated things, I recommend Lepid. If you can handle development yourself and are a corporate business, I recommend Toss. Of course, if you can establish and handle an overseas corporation, I recommend Stripe Atlas or LemonSqueeze. Is an overseas corporation really necessary? "Unless there's a really special reason for it, offshore corporations can be unnecessarily complicated."

The post I recently uploaded about Vibe Coding drew more attention than I expected. While many people focused only on the success story, there were actually a number of trials, errors, and failures during the process. Today, I want to share one of the more interesting 'struggles' I experienced with you. At the time, the service I developed was a PDF protection system called "PDF AI SHIELD." It came from the idea that, as more and more LLMs (large language models) easily read and summarize PDF documents, there should be a way to prevent this. When I'd distribute materials to students at university or carefully prepare and share official documents, people would just have AI summarize and read them—so I started wondering how to limit that. The way LLMs organize and summarize the documents and data we provide is pretty straightforward. They read the document → pick out key points → group and condense them → express them briefly → and then output the information for us. During this process, I came up with a method to interfere in the "reading" step, preventing LLMs from even chunking the data. Standard PDF security typically uses passwords, certificates, or OCR removal, but I added a unique approach on top of these methods. That trick was basically spraying "invisible paint" on the PDF. To the human eye, everything seemed normal, but for LLMs, it acted as a kind of "transparent paint" on the document that prevented them from reading it. I also encrypted tags in the form of certificates and the PDF metadata, making it difficult for LLMs to access. Technically speaking, it worked better than I expected—well-known LLMs like ChatGPT, Claude, and even local models were all blocked by this method. Seeing the results, it almost felt like I'd scored a small victory against the big AI companies. With this boost in confidence, I set up the pricing policy and started promoting in Reddit and several overseas communities. Buyers came in faster than I thought, and it really felt like the business would take off quickly. Honestly, I admit I was a little carried away at that point. Here's how I structured the business model: First use is free without logging in Log in and you can use it once per day (based on 24 hours) With a monthly subscription, you can use it up to 30 times a day Unlimited use with an annual subscription But it didn’t take long for an unexpected issue to pop up. While the protection worked well with models like GPT-4o, Claude Sonnet 3.5, and Gemini Pro, it was completely bypassed by newer models such as o3 and Sonnet 3.7. Plus, with some mini models or in certain situations, even the encrypted metadata was breached. Luckily, one of the early users kindly reported this problem, and I immediately processed refunds for all buyers. Since this was a security service, even a single breach meant I couldn't keep selling the product. Because of payment fees and such, the profit I'd made up front quickly turned into a loss, but handling it quickly saved me from bigger troubles. Better that than getting dragged into some security lawsuit, right? It's not like I rashly counted a deposit as profit. I've got a few more stories like this. If people are interested, I'll try sharing more of them in the future. As I said in my first post about Vibe Coding, when you get into it, there's way more trial, error, and revision than you'd expect—and I think this process actually increases the demand for developers. Plus, I feel like the more you 'struggle' through these processes, the better your product gets. People seem to prefer success stories and dramatic tales over failure stories, but personally, I have plenty of the latter. Even though I call these 'struggles,' I learned a lot from them. I'm not sure how posts like this will be received, but if folks like it, I'll definitely keep sharing parts two and three as well.

When I quit my job and started my own business, the biggest thing I felt was a thirst for good stimulation . Thankfully, thanks to the help of many people and the flow of the times, I didn't have to worry about starving, and the company got on a stable track faster than I thought. However, my thirst for constant communication with good colleagues was not easily quenched. As a one-man business owner, I worked with several freelancers in a remote work format, so I naturally missed the relationship where we could talk comfortably and stimulate each other like coworkers . It was burdensome to hire valuable personnel just for this reason, and although I occasionally worked once a week at other companies, it was a bit different from the stimulation or inspiration I had hoped for. I tried hosting or participating in certain reading groups, but as expected, something was lacking. When I was young, I liked gathering with many people and making noise, but now I feel tired from such places. Then I thought about coaching . I had learned the skills and programs from my previous agile coaching training, so I decided to do free coaching based on them. At first, I started out without specifying the target audience, mainly targeting low-level workers and job seekers. As time went by, surprisingly, people in their 50s and 60s came to me. They were people who were preparing for the second act of their lives or were already running vigorously. One time, I met and talked with a person who builds small ships. He was running a shipyard in Geoje that specialized in small ships under 20 tons. He made hybrid ships using aluminum and fiber-reinforced plastic, which is not even known, and the way he customized the superstructure of the ship according to the ship owner’s detailed requirements was very impressive. I had seen countless ships while traveling around Busan, Incheon, and Gangneung, but I had never thought about who made the ships and how they were made. It was like getting a glimpse into a new world when I learned that these small shipyards were flexible in customizing their ships in a different way than the big ones. It was also very interesting to learn how they charge commissions and roughly how much each cost. One day, while talking to current nurses, I learned about an app called 'MyDuty'. MyDuty was a tool that helped nurses efficiently manage their complicated shift schedules and easily share them with colleagues. The biggest advantage of this app was that it allowed me to see the work schedules of colleagues in the same ward at a glance. It was a service I had never heard of even while working in the IT industry, and this app, which was created by accurately identifying the needs of a specific job group, was a great inspiration to me. It has expanded globally and is now an app that many people can't live without. Through this person who works at Mando, I learned how complex and precise the collaborative structure is needed to make a single car. Mando is a company that develops and produces core parts that are directly related to driver safety, such as braking, steering, and suspension systems. The fact that even a single car we drive every day is so closely intertwined with so many parts and companies was a very refreshing stimulus for me in the IT field, who usually works by making a single product as a whole. The manufacturing method, where individual parts are specialized and each company organically collaborates to complete the final product, showed me a completely different charm from the forms of collaboration I had experienced. Of course, I knew in my head that it was made that way, but it was even more unique when I heard the story directly from someone who actually does the work and works in the industry. As I met people from various fields through this skewer coaching, I felt that the world I see has become wider and deeper . It wasn’t just meeting people, but I felt that my perspective on the world had broadened through various lives and experiences. Sometimes, people I met would connect with each other and lead to employment, or business collaborations would begin. When I received unexpected inspiration from unexpected places, it was a great joy for me. Talking to many people eventually made me realize one thing. The world I thought I knew was so narrow, and only when we share our stories can we see a wide and colorful world. In the future, I want to meet more people through skewer coaching, weave more stories, and connect our lives more meaningfully. As I met each person, I ended up meeting them for over 100 minutes individually, not cumulatively, and this became a huge asset. It cost a lot of money for food and coffee, but I think it was worth it. I think it would be good to meet more systematically in the future, and I will end here.

Last weekend, I spent a pleasant time at the home of a couple who both served as judges and experienced various cases. Both have now put down their judicial gavels and are living as a lawyer and enjoying rural life. As the night deepened and we gathered around a bonfire, our conversation expanded from my curious and somewhat impertinent question about "whether judges are truly impartial" to how they train for and affirm this quality. Since then, I've been organizing these thoughts bit by bit, though I'm far from being a legal professional and only possess minimal legal knowledge as a citizen. The only formal legal education I ever had was a high school course called "Law and Society" in my first year. So please be generous as you read my thoughts, which are based on news articles and my own reflections. Judicial Delays Occurring Worldwide "Justice delayed is justice denied." This age-old adage is becoming reality across the world. While researching the judiciary, I noticed cases from the UK in particular. From Europe to Korea, judicial systems are suffering from case backlogs and trial delays. Especially in the UK and Europe, serious delays are occurring one after another, threatening to paralyze the entire judicial system. According to the UK Ministry of Justice, court case backlogs that were only about 48,000 in 2016 have increased to over 70,000 as of 2024. In London, a case involving a defendant who threatened someone with a knife has been scheduled for 2028, despite requiring only a three-day trial, causing significant social repercussions. Across Europe, investment in judicial systems is chronically insufficient, with the Council of Europe reporting that judicial budgets across European countries have effectively decreased over the past decade, standing at just 0.31% of GDP. In Lisbon, Portugal, frequent strikes by court employees regularly paralyze judicial proceedings. France and Spain are struggling as well... and the US has also been facing various challenges recently. Korea's judiciary is also in crisis. According to a survey by the Legal Times, Korean judges handle about five times as many cases annually as German judges and three times as many as Japanese judges. In a situation where 12 Supreme Court Justices process approximately 40,000 appeals each year, frequent rotations of judges significantly undermine case continuity and fairness. Recently, case backlogs have worsened as political issues draw attention at both the Constitutional Court and Supreme Court, causing tangible harm to citizens' rights to trial and legal remedies. Well, even without such textbook statements... Replace the Judiciary with Artificial Intelligence? Although the National Assembly passed a bill last year to increase the number of judges by 370, many point out that simply increasing personnel will not solve the fundamental problems. Instead, there are growing calls for structural and efficient reform of the judicial system itself. In this context, some are now claiming that replacing judges with artificial intelligence could enable fair judgments. But this is a significant misconception. AI is more likely to amplify existing biases, and recent deep learning-based large language models operate like black boxes, making it difficult to even trace the sources of bias. Ultimately, the issue with judgment-making entities isn't their imperfection, but rather the consistency of their judgments. The most fundamental reason the judiciary exists is because we have entrusted them with authority, trusting they will deliver consistent judgments under clear principles and philosophy. However, we're increasingly seeing cases where the basic logic and philosophy of judgments are being shaken in the name of public opinion or majority views. This could be the beginning of the gradual collapse of the rule of law. Judicial Confusion Caused by Excessive Legislation One of Korea's problems is excessive legislation. Our country produces an overwhelmingly higher number of new laws annually compared to other nations. When there are too many laws, the room for interpretation narrows and various social and economic activities become constrained. Conflicts between laws occur frequently, creating the paradoxical situation where it becomes difficult for businesses and citizens to comply with the law itself. To solve this problem, what we need is rather the digitalization of the legislature. Instead of AI replacing or assisting judges, we need a system that thoroughly reviews potential conflicts between laws and their social impacts using data during the legislative stage. The current practice of supporting bills at the request of party colleagues must be improved. We might consider initially evaluating bill content objectively through blind assessment, followed by roll-call voting to enhance accountability. Ultimately, justice is not a luxury but an essential value we must preserve. Overcoming our current crisis requires fundamental and systematic reforms to maintain the consistency and independence of the judiciary, along with digitalization of the legislature to build a more transparent and efficient legal system. Nation Average number of bills introduced per member per year Total Annual Bills Proposed Proportion of Legislator-Proposed Bills (%) Proportion of Government-Proposed Bills (%) Overall Bill Pass Rate

I'd like to share that my paper was recently accepted for publication in the Korean Artificial Intelligence Association journal (https://kjai.jams.or.kr/). This paper was previously uploaded to Arxiv in pre-print form, and I ambitiously named it HEMA. In fact, the name is derived from the hippocampus, a part of the brain. The hippocampus is a structure that plays an important role in memory and learning in the brain. It is particularly crucial for remembering new facts and navigating spaces, and also participates in forming emotional memories through interaction with the amygdala. The core of this paper is research on preventing quality degradation in LLMs during long-context conversations. To explain simply, just as humans don't remember every detail of events or situations, the research stores generalized summaries as "Compact Memory" in text form. Following how humans primarily remember symbols/signals, the research preserves previous conversations in Vector DB form as "Vector Memory." In this case, it's possible to maintain context longer during extended conversations and provide a more consistent experience. This can be well-utilized in operating fiction bots, companion chatbots, etc. The experiments were conducted using a fully reproducible method with repeated testing on sLMs. Although the research is based on English, the fact that it performs well even at the 6b parameter level suggests it would work even better in already commercialized services like Claude or ChatGPT. Typically, when using Claude, conversations can become too long and need to be moved to different chats, or with ChatGPT, performance can degrade as conversations lengthen. Recently, we've seen attempts to solve this with features like "Project" or "Memory." This is similar to the previous approach of using prompts like "summarize our conversation so far" to reset before starting a new discussion. I've been continuously submitting papers to KCI and SCI, but I feel the hurdles have gotten higher. The acceptance rate isn't what it used to be. The most troublesome aspect is that while I could conduct research abundantly with company resources when I was employed, now I have to do it frugally with personal funds. I'm preparing a follow-up study on maintaining writing style while producing non-repetitive and consistent output, and I'll share those results as well when they become available.

A few years ago, when my younger brother told me he was leaving LG Electronics' robotics division to join MakinaRocks, that was probably the first time I took a close look at the robotics industry. Until then, I had used robot vacuums and robot arms, but I had never been curious about who makes them and how they work. And that interest didn't last long. As someone who had only made simple robots using Arduino for repetitive tasks or Raspberry Pi to recognize and avoid obstacles when I was young, it was honestly a bit difficult, and I was about to join Kakao Brain so I had a lot to study and couldn't think deeply about it. Then, around 2023, I saw a paper that included a demonstration of using multimodal approaches to train a robotic arm to perform specific household tasks. There have been countless such papers since, but it was very impressive at the time. Later, seeing models like Tesla's Optimus or Figure 01 made me slowly start to think that humanoids might not be completely far-fetched. In fact, if you visit manufacturing plants, robotic arms are made much more precisely than you might expect. I personally saw this at H company's shipyard in 2023, so I imagine they've developed even further by now. And there are many domestic companies doing well in this field. Humanoids are somewhat different. Robotic arms are ultimately plugged in, so they receive steady power supply and can perform complex computational calculations relatively quickly and easily. However, with humanoids, because they move completely independently, there's a lot to consider from batteries to computing power. That's why I thought it was quite a distant reality. At least until I saw Nvidia's Physical AI session in 2025. I actually started compiling this in March, but kept postponing it, so the post is coming out late. I've written it in the same format as my previous data center post. Looking at materials compiled by various consulting firms about humanoid robots, they typically divide the humanoid "fields" or "drive systems" into about 12 categories: Head, Shoulder, Elbow, Waist & Pelvis, Hands, Upper Arm, Forearm, Thigh, Calf, Feet, Battery Pack, and Others – these 12 fields. Of course, I'm still learning, so if you disagree, you're right. The market is projected to expand from approximately $3.28 billion in 2024 to $66 billion by 2032, representing an average annual growth rate of 45.5%. Additionally, manufacturing costs have plummeted by 40% in recent years, far exceeding previous projections (15-20% annual decrease), which has accelerated industrial applications and investment timing. I've examined all 12 core component categories, focusing on high-value areas to map investment potential. The Importance of Hands: Sophisticated Manipulation Drives the Value Chain The component I paid most attention to is "hands." This component costs about $9,500 per robot (17.2% of the total cost), by far the highest. As a result, a dedicated market of about $3.5 billion is expected to form by 2032. The technical difficulty of being as delicate yet robust as human hands is driving this market. Novanta Inc. (NASDAQ: NOVT): Provider of end-effector technology and multi-axis (force/torque) sensors FANUC Corporation (TYO: 6954): Manufacturer of 6-axis force sensing sensors Teradyne Inc. (NASDAQ: TER): Strengthens End-effector Solutions After Acquiring Universal Robots Shadow Robot Company: Dexterous Hand with 24 degrees of freedom, 20 drive motors, and over 100 sensors SCHUNK GmbH: Modular gripping systems Figure AI: February 2024, $675 million investment (corporate value $2.6 billion) I particularly noted multi-layer tactile sensors that mimic human skin and tendon drive systems. These two features realize more degrees of freedom in tight spaces, and the tactile sensor market is expected to reach $35.5 billion by 2030. I think these tactile sensors will be used in various applications besides humanoids. Lower Body Components: The Foundation of Mobility and Stability Thigh, calf, and foot components account for 38.6% of the total humanoid cost. The leg section, which determines the robot's "gait," presents significant investment opportunities. For thigh and calf, the market size is projected to grow rapidly from $433 million in 2024 to $8.71 billion by 2032, and the foot section connecting to the ground is expected to reach $800-900 million by 2025. Emerson Electric, Thomson Industries (Altra Industrial Motion): High-Duty Linear Actuators MISUMI Group: Precision Machinery Parts Bosch Rexroth: High-power electric actuators Agility Robotics: Actuators for Digit Biped Robots Figure AI: Next-generation lower body actuator

"I easily made $10,000 a month using AI!" "I made hundreds of millions in additional income with artificial intelligence." Every time I open Thread, Instagram, or KakaoTalk, I see a flood of brilliant success stories about making money with AI. As I read the posts that come up every day, I honestly feel more skeptical than envious. There are many people around me who study AI or develop models themselves, but ironically, those who teach AI seem to make faster and bigger profits than those who actually develop technology or build services. I have been making a living in IT for a long time, but the reality I experienced was not that dramatic. Then, starting last year, out of curiosity and fun, I started using various AI coding tools. At first, the tools I used were just for maintenance or writing small scripts, but at some point, they started bringing in unexpected profits. If we make this more systematic and purposeful, it is called 'Vibe Coding' by Andrej Karpathy. Over the past few months, I've used various AI coding tools including Cursor , Replit , Trae , V0 , Copilot , and more recently Windsurf and Lovable. Through hands-on experience, I discovered each tool has distinct characteristics and differences. I also realized these tools can be categorized into 'Cold Start' tools (advantageous for those without coding experience to quickly create prototypes) and 'Boosting' tools (ideal for those with some coding knowledge to dramatically increase productivity). Of course, it doesn't mean anything because I divided it up arbitrarily! Equipment Classification Key Features One line review Lovable Cold start Frontend, design, and backend integration automation Supabase (database) and email integration Good to use when you are an IT person and have clear ideas. Replit Cold start/boosting Browser-based development environment Deployment and auto-scaling, hosting Available as a mobile app