The Trump administration has used tariffs as a core diplomatic and economic weapon to drive manufacturing back to the United States. For companies, this will compress profit margins and erode cash flow step by step, an operational problem that is reflected in financial statements every day.

Under the impact of this geopolitical situation, many companies have begun to actively prepare for the battle and carefully consider what can be done when tariffs become a normal cost.

An overlooked fact: tariffs are not “irreversible” costs.

It is worth noting that through conversations with many companies, I have found an interesting phenomenon. Many people discuss tariffs with the assumption that they are a “pay it and forget it” cost. But the fact is that tariffs have never been irreversible under the US system.

The United States actually has a system that has existed for more than two centuries, called the Duty Drawback, which was originally designed to encourage exports and processing trade. In short, if a company paid tariffs at the time of import, and the goods are ultimately not sold in the US market but instead exported or transshipped to other countries after further processing, the company may be eligible to apply for a refund of the tariffs paid under the law.

What is often overlooked is that the scope of this system extends far beyond “processing for re-export.” In practice, merchandise that is returned and not resold, or legally destroyed in the United States due to inventory adjustments or quality issues, may be eligible for a tariff refund as long as it meets the relevant requirements.

The system has been revised and expanded over its 200-year history, and as the supply chain has become more complex and the regulations and circumstances have become more granular, tariff refunds have become an area that “only the experts understand.”

Why is it so difficult to carry out “tariff refunds”?

In fact, if you look deeper, you will find that the information required to apply for tariff refunds is often scattered in a variety of different systems and formats, from ERP and Excel to PDF invoices, customs declarations, and logistics records. Such information was not designed for tariff refund purposes, and organizing and comparing it is extremely time consuming.

As a result, it often takes more than half a year or even a year for a case to go from delivery to actual receipt of the tariff refund, and the relevant services are usually provided only to companies with sufficiently large tax refunds. In recent years, I have begun to observe a new shift. AI is beginning to be applied to highly regularized, data-intensive institutional problems. Tariff refunds are a clear example of this. Once dispersed, unstructured data can be converted into computable formats, and algorithms are then used to match rules with real-world contexts; processes that once required extensive human labor can be automated.

From Pax to Flexport, a trend toward AI-enabled tariff refunds is taking shape.

In the United States, several companies have begun to emerge that are trying to use technology to solve a problem that was previously considered “too complex.” Take Pax, a company specializing in customs refunds for businesses, for example. It designs its own algorithms and works with experienced domain experts to use AI to automatically process raw documents provided by companies. These include customs declarations, invoices, logistics records, and internal system outputs, all of which are converted into computable, structured data. Based on this foundation, algorithms are used to evaluate different tariff refund scenarios and identify the most advantageous combinations, after which professionals complete the compliance review and filing.

Under this model, processes that once took half a year or even a year can now be completed in just a few weeks. More importantly, because the calculations no longer rely solely on manual experience, many businesses are discovering for the first time that they are actually eligible for more tariff refunds than they had previously thought, and are often able to get back more than they had originally expected.

If Pax represents an innovative entry point for “specialized problem solving,” then Flexport proves the feasibility of this direction from another angle.

Flexport is a global digital freight forwarding giant that has grown over the past decade since its establishment. Flexport integrates sea, air, and land transportation through cloud-based software to make the complicated cross-border logistics transparent and digital, helping customers realize real-time cargo tracking, customs clearance, warehousing, and supply chain optimization on a single platform.

Over the past two years, Flexport has rolled out a series of AI-focused tools and systematically introduced AI into the customs refund process, aiming to overcome the fundamental computing limitations of traditional approaches.

For example, in the past, most tariff refund software relied on simple amount-based matching. In real-world import and export data, however, the number of possible matching combinations grows exponentially. In a public statement, Flexport noted that the volume of global import and export data is immense. Simply matching imports and exports can generate more possible combinations than grains of sand on Earth, a scale far beyond what human labor or traditional systems can handle.

To achieve this, Flexport has developed an algorithm that evaluates different import/export linkages and refund scenarios simultaneously in all possible combinations and automatically finds the combination that yields the highest refund, instead of being limited to a single, intuitive manual judgment.

Practice results show that with this type of AI-driven analysis, companies can actually obtain tariff refunds that are about 400% higher than by using the traditional method, and for the first time, processes that were previously considered too complex have the potential to operate at scale.

I believe that tariffs have brought the world back to a new era, and in this environment, there may be an opportunity to rethink cost management and cash flow strategies; companies should make better use of the power of AI to find resources that are rightfully theirs in a complex system.

Looking back through history—from the steam engine to the computer, from the carriage to the automobile—every technological revolution has made humanity more efficient. But AI is different. For the first time, technology is directly replacing human thinking. As AI agents become widespread, even the act of “execution” will be automated. When technology shifts from being a “tool” to becoming a “competitor,” the speed and depth of this wave of impact will far exceed any previous industrial revolution.

Even more worrying is AI’s impact on the education system. For over a century, there has been a stable pathway from education to the workplace: graduating from school, taking an entry-level position, learning on the job, and gradually advancing through the ranks.

But the rise of AI is disrupting this pathway. Companies now prefer to buy a few more AI tools rather than invest time in training newcomers. The Federal Reserve Bank of New York warns that the U.S. unemployment rate for recent graduates climbed to 5.8%. Meanwhile, research by the Stanford Digital Economy Lab reports a sharp decline in employment among 22- to 25-year-olds, especially in software development, customer service, and clerical roles.

Yet what concerns me even more is this: where will those who are replaced go?

While most companies are busy using AI to cut costs and increase efficiency, another market with huge potential is emerging. McKinsey & Company predicts that by 2030, more than 400 million people worldwide will need retraining or career transitions. This means that for every person displaced by AI, another will need to return to the workforce. I believe this is not just a crisis—it could become the next trillion-dollar industry.

Amid this wave, countries around the world are taking action. Our government plans to train 200,000 AI professionals by 2028, building a workforce ready to meet industry demands. Japan has gone a step further: starting in 2024, it launched a five-year reskilling support program, investing a total of one trillion yen to help companies and workers relearn in the areas of AI application and digital transformation.

In the United States, startups are also entering this field. Inference.ai is developing an AI-driven, human-centered “employment infrastructure” designed to help displaced white-collar workers reenter the job market. The team began by focusing on high-demand positions such as machine learning—roles that have long faced talent shortages but have high entry barriers. The Inference.ai system functions like a “driving school for the AI era,” using AI to scan global job postings, break down required competencies, and build skill trees and personalized training maps.

Leveraging its proprietary GPU partitioning technology, Inference.ai enables thousands of participants to gain hands-on experience in real computing environments at low cost, guided by mentors from leading U.S. tech companies and AI-based coaching systems. Participants then use simulated question banks and AI interviewers to validate their skills and prepare for job applications.

Without any publicity, Inference.ai has already attracted more than 1,000 engineers and professionals to join its community, which continues to grow rapidly each week. This shows that “helping people become needed again” is emerging as a central theme of the new workplace.

The AI revolution is advancing quickly, and new forms of employment, education, and social order are already taking shape. To me, this is not merely a labor market crisis—it is a global experiment in how humanity can coexist with AI, a question that we must all take part in answering together.

Faced with this steep revenue decline, pharmaceutical companies have no choice but to accelerate their transformation. They are reducing costs, shortening R&D time, and even outsourcing more manufacturing to CDMOs (Contract Development and Manufacturing Organizations.) To support this transformation, “Pharma 4.0” is gradually shifting from an option to a necessity.

Pharma 4.0, inspired by Industry 4.0, emphasizes leveraging data to integrate R&D with clinical, manufacturing, and quality management, turning previously fragmented processes into a closed loop that enables real-time decision making. Artificial Intelligence (AI) is the core driver of this change.

With the global pharmaceutical industry investing more than US$100 billion in R&D each year, any improvement in efficiency means a huge return in value. International giants are already taking action: Genentech, a Roche subsidiary, has partnered with NVIDIA to accelerate drug development using AI. French pharmaceutical leader Sanofi was the first to integrate AI into R&D, collaborating with UK-based Exscientia in oncology and immunology, as well as forming alliances with OpenAI, Formation Bio, and others to speed up the clinical development of new drugs.

However, the real game-changer is not AI that merely analyzes data, but AI that can delve into R&D and manufacturing processes and “take direct action.” In recent years, similar initiatives have begun to emerge in Asia.

For example, Taiwan-based startup Therapi AI is seeking to enter the pharmaceutical industry with deployable AI agents, transforming AI from a passive adviser into an active “executor” on the production line. These agents have clearly defined roles, enabling them to quickly identify high-potential cell lines from data or assist with compliance reviews. Using a “no centralization, no retention” architecture, researchers can access data across systems simply by issuing natural-language commands, thereby reducing compliance and cybersecurity risks.

The potential of this type of technology is beginning to be proven in practice. Some cases have shown that introducing AI models can dramatically reduce the cost of cell line screening and shorten the R&D cycle by several-fold. For CDMOs, this is not just about improving efficiency—it also determines their ability to take on more orders and fully embrace Pharma 4.0.

The digital transformation of the pharmaceutical industry is no longer a matter of whether to invest, but a race to see who can achieve it fastest. From multinational pharmaceutical companies integrating AI into the heart of R&D to startups and CDMOs collaborating to build smart production lines, the Pharma 4.0 wave is rapidly taking shape worldwide. Over the next decade, this wave will redefine the rules of the pharmaceutical industry and determine who emerges as the leader in the competition.

“Sometimes they talk very politely, but I’m still not sure if it’s really an offer or a refusal.” He gave a wry smile and recalled a time when he asked a client for help in introducing a contact, and the other party politely replied: “考えさせてください” (Please let me think about it.) He thought he would be contacted again, but that was the end of the letter.

Such a tone of voice is actually a common Japanese euphemism for refusal—”It’s not that I won’t help, it’s just that it’s not convenient to say no.” For those unfamiliar with Japanese culture, this style of communication may easily lead to misunderstandings.

This just resonates with me so much. Because language is never just about translating words, but about tone, context, and mutual understanding.

I can’t help but think of the AI Large Language Models (LLM) that we now use every day. Even entrepreneurs who have lived in the country for a long time can misunderstand each other’s meaning, so how can AI models trained in the “English context” really grasp the nuances of these cultures?

AI’s Cultural Bias

In fact, this question has recently been confirmed by a study from MIT Sloan.

The team experimented with large-scale language models, such as OpenAI’s ChatGPT and Baidu’s Wenxin Yiyan, and found that the models showed different cultural biases when answering in different languages. When prompted in Chinese, the AI tends to show a “group-oriented” mindset, whereas in English, the AI tends to express itself in an “individual-oriented” way.

For example, when prompted to design an insurance advertisement slogan and the input is in Chinese, the AI model may produce something like: “Your family’s future is your promise.” In contrast, the English input might generate “Your future, your peace, our insurance.” The same question, in different languages, reflects a very different ordering of cultural values.

More critically, the study revealed that these cultural tendencies can unknowingly influence users and even permeate society through AI-curated media and educational materials. In other words, generative AI is quietly replicating a certain set of cultural norms and values. Even without directly interacting with language models, we may already find ourselves immersed in the worldview they construct—often without realizing it.

Large Language Models Have Become the Infrastructure of Culture

Language models carry more than just technology—they embody culture. Their built-in corpus and values are gradually shaping our tone standards and modes of communication: the generated response of large language models quietly redefines what tone is “reasonable” and what response is “normal.” In engaging with these models, we are adopting the underlying logic of their value systems.

This is why an increasing number of countries are beginning to incorporate language models into the framework of “sovereign governance.” In 2024, the European Union passed the Artificial Intelligence Act (AI Act), pioneering a risk-based classification system for AI applications. The act also asks that developers of foundation models disclose the sources of their training data, ensuring transparency and oversight of the cultural values embedded within these models.

Singapore, on the other hand, is developing Sea-Lion, an open source large language model for Southeast Asian cultures. Through the extensive collection of Southeast Asian languages and cultural data, Sea-Lion aims to have AI that aligns with local needs and serves as a foundation for building new applications.

Saudi Arabia has made a direct entry through its sovereign fund by establishing Humain, a company led by the crown prince, to develop supercomputing hubs and large-scale data centers, with a total investment of US$100 billion.

All these different strategies are sending the same message: language models are not merely algorithms; they are tied to cultural values, information governance, and national security and must be actively shaped, rather than passively outsourced.

Relying long-term on language models trained overseas means inheriting foreign norms around tone and interaction, and potentially introducing structural risks into our systems. As generative AI continues to permeate industries, if its training data leans toward a particular culture, its values may also seep into society, quietly reshaping how we think and express ourselves.

Japan’s Experiment: Shisa.ai — A Language Model Rooted in Culture

In the global race for language understanding, Japan has introduced a notably distinctive local initiative: Shisa.ai. This three-person team, with limited resources, successfully trained a 405 billion-parameter Japanese large language model. According to real-world test results, the model performed well in various Japanese tasks, such as prompt comprehension, translation, and semantic reasoning, and is now on par with OpenAI’s GPT-4 and China’s DeepSeek-V3. For a small startup, this marks not only a technical breakthrough but also a profound exercise in cultural agency.

The three founders of Shisa.ai are all immigrants who chose to settle in Japan to start their business. They believe that AI sovereignty must begin with local languages and cultures. Building indigenous models is not only for preserving diversity but also for ensuring data privacy, geopolitical resilience, and national digital autonomy.

Chief executive officer Jia Shen and chief technology officer Leonard Lin are the cofounders of Shisa.ai, which was developed under Leonard’s leadership and is his defining work. One of the AI researchers on the team,Adam Lensenmayer, comes from a distinctly different background. He is a well-known subtitle translator among Japanese animation fans, having worked on titles such as Attack on Titan, Mobile Suit Gundam, Detective Conan (theatrical releases), Galaxy Express 999, Chibi Maruko-chan, Space Battleship Yamato, and even Space Brothers, which can be found in convenience stores across Japan. His meticulous attention to linguistic nuance and tonal precision has made him a key figure in the training of the model. This has allowed Shisa.ai to be more attuned to the subtle contexts and cultural details of the Japanese-language landscape.

From the beginning, Shisa.ai chose to train its models locally in Japan, deliberately focusing on tonal details and social subtext with unique Japanese linguistic features. Founder Jia Shen pointed out that the Internet corpus of the past 30 years has almost been absorbed by large language models. As AI training data approaches its ceiling, future breakthroughs won’t come from “more data,” but “data closer to the context,” such as voices and emotions that are not automatically captured online. From the conversations of the elders and local dialects to the tonal shifts in Gen Z’s dating exchanges—whoever captures the authentic, culturally intertwined corpus of language will hold the key to training the next generation AI models.

This idea is also supported by the Japanese government. In 2024, the Ministry of Economy, Trade, and Industry (METI) launched the Generative AI Accelerator Challenge (GENIAC), which provides funding, mentors, and large-scale computing power to help startups develop their own foundation models. Shisa.ai, one of the selected teams, has successfully accelerated the model-training process and revealed the strength of Japanese language modeling.

What’s more, Shisa.ai has not stopped at technical development. They are actually bringing speech understanding technology to industries, for example helping Japanese restaurants communicate with international tourists, supporting retail outlets in handling returns and exchanges, and even installing multilingual AI kiosks at train stations to assist travelers with directions and services. These applications are not simply a display of technology but a pragmatic response to cross-cultural communication.

Whose Tone of Voice Will Become AI’s Voice?

Shisa.ai may be just the starting point, but it reminds us that generative AI is rapidly integrating into everyday life and across industries. The tone it conveys will profoundly shape how we understand one another and express ourselves.

Language is not just a tool, it’s a carrier of culture. The way we speak will ultimately determine how AI understands the world.

Services such as these, in which an AI agent “does the work for you,” are rapidly gaining popularity. It’s not just Amazon, but more and more services are letting AI help you with everyday chores: checking airfares, comparing prices, booking restaurants, paying bills, and scheduling trips. AI will even know if you don’t eat cilantro, so it can automatically select the right restaurant for you.

Instead of just giving advice, AI is actually starting to “make decisions and execute actions” for you.

But How Can a Platform Tell It’s Not Being Hacked?

The shift has also redefined the “trust relationship between platforms and users.”

For platforms, new rules are needed to determine: who placed this order? Who authorized this payment? Did a human intentionally initiate the action? Was it carried out autonomously by AI? Or was it the result of a security breach?

After all, to many websites, AI agents often resemble web crawlers, and their activity may even be indistinguishable from malicious attacks. According to a survey conducted by Oasis Security, the number of nonhuman identities created by AI agents is growing at a rapid rate, far outpacing the growth of human accounts, and driving a parallel rise in security risk.

Two New Challenges in Identity Authentication

As more and more operations are performed by AI, the notion of “identity” in the digital world is being redefined. In the future, the need for identity verification will not just be about verifying whether a user is logged in or not, but will require a two-stage determination process.

The first is: Are you a real human? (Proof of Humanity).

In the age of AI-generated accounts and widespread fake identities, verifying that a real human is behind an account has become increasingly important. One example is Tools for Humanity, a biometrics startup funded by OpenAI cofounder Sam Altman, which uses an iris-scanning device called Orb to recognize a real person.

The second is: Has this AI been authorized by a real person? (Agent Verification).

When AI starts to help people make decisions and handle tasks, we also need to verify: who authorized this AI? What permissions does it have?

To address this challenge, a new approach to authentication is emerging: allowing AI agents to have digital credentials resembling a “passport and visa,” enabling platforms to verify both identity and authorization in real time.

We can think of this as the process of entering a country: when you arrive in a foreign country, immigration agents do not need to know you personally; they only need to confirm that your passport and visa are authentic and valid, and then they will let you pass through customs.

One notable startup in this space is Terminal 3, which was founded to enhance security in online identity authentication and data authorization. They designed the “Agent Auth” mechanism to provide AI agents with a verifiable and traceable digital identity with task authorization attached, so that platforms can immediately determine whether the AI’s current actions have been legitimately authorized by the user. Currently, platforms and domains that place a high priority on privacy and authorization—such as education, finance, and government—have begun to adopt Terminal 3 technology to address identity trust issues.

Going back to the beginning, in the future digital world, not every transaction will be carried out directly by a human, but more often than not tasks will be executed on their behalf by AI agents.

To make this future possible, we need a new set of infrastructure: one that issues digital identities and task-specific visas to AI agents, and provides standardized verification mechanisms. This is perhaps the most critical, but currently underestimated, area of innovation in the coming decade.

These examples prompt us to rethink an important question—if AI can not only instantly access global knowledge but produce content and even simulate human thought processes, what capabilities matter the most in the AI era?

I believe that in the future, what will truly set someone apart won’t depend on how much they learn, but rather on their ability to learn deeply, flexibly adapt their knowledge, and proactively pursue learning—in other words, those who excel at Deep Learning and Self-Learning.

Deep learning refers to the ability to organize and understand the information one receives and apply it flexibly in different contexts, rather than simply memorizing or copying it. Self-learning is about the ability to actively explore a problem of interest and identify solutions when encountering an obstacle.

With AI tools, people who possess these two abilities can accomplish tasks that used to take a lot of time with greater speed and accuracy.

Taking entrepreneurship as an example, many entrepreneurs don’t lack good ideas but get stuck at the stage where they wonder whether the market really needs their product. In the past, this meant they had to spend a lot of time doing market research. Now, as long as they know how to ask the right questions, AI can help inventory competitors, organize demand, and simulate scenarios. It’s essentially like having an experienced product manager on hand, giving their ideas a better chance of coming to fruition.

Or consider how, in the past, creating a product was a very difficult task for one person, who needed to learn design, front-end and back-end development, UI/UX, and other skills entirely on their own. Just getting from 0 to 1 was a long and arduous process. But with the help of AI tools, both the barriers to self-learning and the time cost have been significantly reduced. In many cases, it’s no longer necessary to become an expert in a specific field; instead, you can learn and improve as you go along.

A few of the AI tools that have been getting a lot of attention in recent months perfectly illustrate this trend. For instance, Replit, mentioned earlier, allows users to automatically generate and deploy code simply by inputting a single sentence (e.g., “I’m going to develop a chatbot”). This has triggered a wave of “vibe coding” in the tech world, where users simply describe their goals and requirements in natural language, and AI automatically writes the code.

Such tools have not only changed the speed of our creation but also transformed how we learn—instead of needing to acquire knowledge first, we can now understand through “hands-on” practice, making adjustments as we go. That is, rather than needing to spend time learning before creating, learning and creation happen simultaneously.

The same principle can be applied to entrepreneurship. The process of starting a business can generally be broken down into three stages: having an idea, creating a product, and eventually bringing it to market. Previously, each of these three stages required a division of labor across different areas of expertise—but now, AI tools are bringing them closer together.

Returning to our earlier point, AI helps us deal with a lot of knowledge and skills that used to take time to accumulate. As a result, being able to remember the most information or memorize it the fastest is no longer as important. What matters now is the ability to digest and understand what we’ve learned, allowing us to identify and define the right problems, propose solutions, and effectively use tools to turn our ideas into reality.

Deep learning and self-learning are two essential abilities throughout this process. It’s not just about keeping up with AI—it’s also about giving us the opportunity to become true creators of value in a rapidly changing era.

The Continuum Club, located in the heart of Manhattan, is more like a sophisticated health lab than a gym. In the Continuum Club, in addition to traditional gym equipment, there is also a full set of medical-grade precision equipment: a bone density scanner, a treadmill with cardio test function, a biometric system, and so on … Interestingly, the title “personal trainer” has been replaced by “human-performance specialists” for these professionals. Despite a monthly membership fee of up to $10,000, it has been reported that the waiting list for membership continues to grow.

This gym provides more than just “exercise,” offering a full regime of a health management program, in hopes of preventing the possibility of future disease occurrence.

The Rise of Preventive Medicine

The trend of preventive medicine is growing at an accelerated rate, as evidenced by market data. According to research by iHealthcareAnalyst, the global preventive medicine market is growing at a compound annual growth rate of 9.7% and is expected to reach $415 billion by 2031. There are three main forces driving this trend:

First, there is economic pressure. The health care system is currently facing enormous cost pressures. In the US, for instance, chronic diseases account for 90% of the $3.8 trillion spent on health care annually, and 70% of all deaths have been caused by chronic diseases. This has forced the medical industry to consider how to fundamentally change the current treatment-oriented model, and begin to put emphasis on preventive medicine.

Second, there is a shift in consumer awareness. People nowadays are no longer satisfied with the passive mode of seeking medical treatment only when they are sick; they instead pursue active health management, which has led to an increase in the demand for preventive health-care services such as regular health checkups, preventive screenings, and genetic testing. At the same time, the concept of personalized health care is gaining popularity, with people hoping to receive personally tailored health advice based on their health status, lifestyle, and risk factors.

The third driving force is a breakthrough in technology. Technological innovations are also accelerating the progress of preventive medicine; these include AI-driven diagnostic systems for early detection of potential health risks, wearable health devices for 24/7 health monitoring, and personalized medicine technologies that can provide precise preventive advice based on individuals’ genes and lifestyles.

The same phenomenon is reflected in McKinsey’s  Health Trends Report for 2024. The report states that biomonitoring, AI-personalized recommendations, and science-based health services are becoming mainstream in the industry.

Reinventing Chronic Care with Data Technology – Health2Sync

When it comes to preventive medicine practices, Taiwanese start-up Health2Sync is of particular interest.

Health2Sync is not just a mobile app, but a health management platform. Just imagine: your blood glucose machine, blood pressure monitor, weight scale, continuous glucose monitor (CGM), and more, can all be uploaded, and the resulting data integrated into easy-to-understand charts in real time, so that users can grasp their own health status at one glance and even find out the impact of diet and lifestyle habits on their health, as well as receiving recommendations accordingly.

After nearly a decade of hard work, Health2Sync has become Asia’s largest chronic disease management platform, with over 1.3 million registered users and partnerships with more than 400 health-care hospitals in Taiwan, Japan, Australia, Korea, and Singapore.

It is not difficult to imagine that with the popularity of wearable devices, the application of health data will be even more extensive. Service providers in the areas of nutrition, fitness, sleep, and more are able to provide more personalized advice. For example, by monitoring the fluctuation of stress hormones, service providers can recommend suitable health products or positive thinking exercises for users.

With the help of new technologies, the concept of preventive medicine has finally begun to accelerate. We have been observing start-up companies redefine health management in various ways, and we believe this will be the next blue ocean strategy for the health industry!

After building electric cars and rockets, Tesla CEO Elon Musk has set his sights on the gaming industry, announcing that his AI company, xAI, will set up a gaming studio — xAI’s first external industrial application. Meanwhile, Oasis, the world’s first game built entirely by AI, was also officially announced.

The trend of AI and gaming integration has not only caused discussion among gamer communities, but has also quickly spread to investors. Data shows that the percentage of venture capital funds focused on the gaming industry that will be invested in AI-related technologies in 2024 is rising every quarter, from 7% in Q1 all the way to 22% in Q3.

Naturally, the integration between AI and gaming draws much attention, and it doesn’t come from coincidence. One of the core attractions of AI is that it dramatically improves the efficiency of game development, lowers the cost and threshold, and brings more creativity, diversity, and quality to the game industry.

In the past, high costs and long development cycles have been a pain point for many teams in the field of game development. But AI tools are revolutionizing this situation.

Take Cybever, an AI gaming start-up founded in 2022, as an example. Instead of choosing the most common “generative” solutions, Cybever focuses on automating 3D scene design. This approach allows developers to seamlessly integrate AI tools with existing mainstream 3D scene development software (such as Unreal Engine and Unity), speeding up the development process and product implementation.

Imagine you’re a game developer ready to create a desolate post-apocalyptic world. In the traditional 3D design process, you may need to spend weeks manually building 3D models for rivers, hills, buildings, etc., or purchasing off-the-shelf components from 3D asset libraries, and then spend a huge amount of time placing these assets into 3D maps, making adjustments and layouts. But with Cybever’s product, all you need to do is type in a few simple prompts, such as “deserted village,” “cloudy weather,” “surrounded by rivers,” and then Cybever will automatically help you find suitable assets from the asset libraries and place them into the map. Within a few minutes, a lifelike 3D world will be completed. Not only that, it can also adjust the terrain, reverse the flow of the river, and even change the weather immediately.

For developers, this AI tool condenses scene design tasks that used to take one to two weeks to complete into a single day. Currently, a lot of game studios are using Cybever’s products to help develop their games.

Cybever’s convenient tool that automates set design is also packed with AI technology. For example, when placing assets, this AI tool can take account of historical, cultural, and logical backgrounds, such as the proportion of farmland in a medieval village, or how wide a street should be in a modern city. At the same time, it can avoid common imperfections, such as floating trees or out-of-scale buildings, to ensure that the scene is realistic.

Cybever’s technology has not only been accepted by game developers but also acknowledged by the movie and entertainment industry. Recently, Cybever teamed up with The Day After Tomorrow director Roland Emmerich, whose new sci-fi TV series Space Nation relies on AI to generate spaceships, backgrounds, and other visual effects.

And the impact of these technologies has long gone beyond gaming. For example, in the future, Tesla will also be able to apply xAI’s gaming technology to autonomous driving simulation tests, having virtual scenes simulate real traffic conditions and optimize algorithms. Earlier, Intel already used the gaming engine of the racing game Grand Theft Auto 5 for its self-driving study. “Digital twin” technologies as such have been widely used in areas such as urban planning, medical training, and aviation. As you can imagine, with the continuous improvement of AI generation capability, the more realistic the game scene will be, and the more diversified its extended application.

Gaming has always been more than just entertainment. It’s also a unique way to explore the real world. With the incorporation of AI, the line between the virtual world and reality has become more blurred, and the scope of this exploration has become broader and deeper. We believe that as AI technology becomes more mature in the future, we will witness more cross-disciplinary innovations and breakthroughs.

AI has already exhibited capabilities surpassing human expertise in various professional domains, such as excelling in complex exams, solving mathematical problems, and even taking over certain white-collar roles. Yet, tasks as simple as those performed by a four-year-old—like holding a pen or gently handling an egg—remain significant challenges for robots.

A promising new research avenue in robotics, known as Embodied AI (“AI with a body”), seeks to tackle these challenges.

Traditional robot training relies on predefined rules and instructions to complete tasks. While effective for repetitive actions, this method struggles to adapt to unfamiliar tasks or environmental changes. For instance, a traditional robot might navigate to a designated location based on programming but falters when faced with unexpected obstacles or new scenarios.

Embodied AI, by contrast, emphasizes “learning through doing,” enabling robots to modify their behavior based on environmental feedback—much like how children learn. For example, when training a robot to pick up a cup, it first identifies the cup’s position using its camera. If it fails initially, it adjusts the grip’s force and angle through its tactile sensors until successful.

This iterative approach allows Embodied AI to swiftly adapt to shifting environments, making it more flexible than traditional robots. With its potential to autonomously learn new tasks in increasingly complex scenarios, Embodied AI represents a crucial step toward creating intelligent, general-purpose robots.

Nevertheless, substantial volumes of training data remain essential for developing human-like “reasoning” and “adaptation” capabilities. For instance, ChatGPT is estimated to have been trained on approximately 400 billion characters of text, while the image-generation model Midjourney utilized around 6 billion image-text data pairs. In stark contrast, DeepMind’s open-source robotics database contains only about 2.4 million data points—insufficient to train a truly intelligent general-purpose robot.

To address this shortfall, startups are pursuing innovative solutions. Hillbot, for example, uses 3D simulation technology to create virtual environments, enabling robots to “learn” how to handle complex scenarios virtually.

Consider training a robot to arrange chairs of various shapes. Hillbot can generate tens of thousands of chair designs using basic text commands, helping the robot master diverse arrangements. To familiarize a robot with a specific location, such as a coffee shop or warehouse, Hillbot can also convert site photos into 3D virtual models, providing realistic environments for practice.

Robots have yet to experience their “ChatGPT moment,” but as the technology matures, we will witness their gradual transition from industrial and service contexts into everyday household settings. One day, robots may seamlessly integrate into our lives—and perhaps even become our most trusted companions.

Founded in 2017, Hims is a DTC (Direct to Customer) healthcare company that provides online physician consultation 24/7 as well as drug delivery services. Starting with men’s hair loss treatments and sexual dysfunction medication, Hims has now expanded into cardiovascular health, mental health, and weight management. In 2021, Hims was listed on the New York Stock Exchange. It had been less than five years since the company’s founding.

Early this year, Eli Lilly and Novo Nordisk launched weight-loss injections that were immediately in short supply due to an exceedingly high demand. Seizing the opportunity, Hims launched GLP-1 in May, a compounding product priced at only 15% to 20% of the big brands, and the rush to buy it was rapid. According to US regulations, in order to address shortages, manufacturers are allowed to make manufactured drugs under certain conditions to supplement the market.

This brings us back to the question at the beginning: How should Hims stabilize itself in a highly competitive market and, in turn, challenge the traditional pharmaceutical companies? The company’s success can be attributed to the flexible use of three strategies at different stages: first-mover advantage, branding, and reverse positioning.

First-mover advantage: seize the opportunity to enter the market quickly

With limited resources, start-ups must quickly achieve a product-market fit and acquire their first customers at an early stage. Hims’s establishment as a company coincided with the liberalization of e-health regulations in the United States, which now allowed physicians to consult online and write certain types of prescriptions. At the same time, patents on several medications for sexual dysfunction and hair loss were about to expire, allowing other players in the industry to introduce alternatives at a lower cost.

Hims has capitalized on these opportunities and rapidly moved into areas usually ignored by the traditional healthcare industry. Sensitive issues such as sexual dysfunction and hair loss are particularly suitable for telemedicine services. This allows doctors to avoid the potential embarrassment of face-to-face consultation, and also satisfies consumers’ need for privacy. These strategies allowed Hims to attract a large number of younger customers and quickly establish a solid market base.

Branding: Consolidating market position and building brand influence

With their successful entry into the market, Hims has further strengthened its brand position. From its website design to its product packaging, Hims has adopted a pastel-colored, simple, and modern style to make cold pharmaceutical products more approachable and attractive. At the same time, Hims has successfully transformed what used to be embarrassing medical products into more consumer-friendly, everyday healthcare products by presenting them in a humorous and elegant way (e.g., using a cactus as an analogy for sexual dysfunction) in its advertisements.

Reverse Positioning: Subverting Traditions and Redefining Market Rules

During their expansion period, Hims used a reverse positioning strategy to redefine the rules of the market. “Reverse positioning” refers to a situation in which a new, emerging company offers a fundamentally different and better business model than incumbent companies, putting the incumbent businesses in a dilemma — not copying the new model may cause them to lose customers and fall behind, but copying it may hurt their existing business.

Traditional weight management services often involve cumbersome face-to-face counseling sessions, high prescription costs, and complicated insurance processes. Hims’s D2C model, which offers consultations and products over the internet, is a direct challenge to traditional healthcare providers with their face-to-face visits and insurance billing, putting strong pressure on traditional businesses. For example, in response to competition, Eli Lilly has also introduced affordable vials of weight-loss medications. However, most traditional businesses are maintaining a conservative attitude toward new models in order to protect their established models. This attitude creates further opportunities for new start-ups.

After gaining a foothold in the market, Hims acquired Nivagen Pharmaceuticals, a specialty pharmaceutical company, in September this year. By incorporating some of Nivagen’s products, Hims continues to deepen its competitive moat through different strategies. After all, it’s always the entrepreneurs who dare to break the status quo who will truly win in the market.