Asianometry cuts through the noise of global AI hype to deliver a sobering reality check: Japan's artificial intelligence revolution is being held back not by a lack of talent, but by a profound lack of ambition. While the United States chases moonshots and China mobilizes state power, the author finds a nation content with "small AI" and defensive sovereignty. This is not a story of failure, but of a strategic trap where excellent engineering meets cautious execution, a dynamic that could cost Japan its place in the next industrial era.
The Deployment Gap
The author begins by distinguishing between the flashy marketing of AI and its actual utility in Japanese daily life. While consumers see "AI buzzword marketing" leaking into everything from taxi ads to rabbit puppets, the corporate reality is starkly different. Asianometry notes that for many Japanese businesses, "AI still often means this sort of small AI, basic classifiers, and image recognition models." The author visited a major HR outsourcing firm that had only recently automated a tax form process—a task that should have been solved decades ago. When asked about generative AI, the firm's leadership admitted it was "not even close to being on their radar," insisting that "the human should always be in charge."
This hesitation reveals a cultural and structural inertia. The author argues that bringing generative AI to the average Japanese corporation "might take a generation." While forward-thinking firms are pushing top-down adoption, the broader ecosystem remains stuck in a cycle of incrementalism. The reliance on foreign tools like ChatGPT and Claude is palpable, raising a critical question: where are the domestic models that could drive local innovation?
The extent to which these tools are being used, however, I need to spend more time on in a future visit. Anecdotally, my impression is that Japanese programmers are more limited in how much they can use.
Critics might argue that the caution regarding generative AI is a rational response to the high costs of implementation and the risk of hallucinations in critical business processes. However, the author's observation that competitors are automating internal processes while others rely on manual labor suggests that the cost of inaction is already being paid.
The System Integrator Trap
The most damning critique in the piece targets Japan's software development model. The author draws a parallel between current AI trends and the software industry's past failures, warning that Japanese firms are repeating the mistakes that led to "Galapagos syndrome." Instead of building general-purpose large language models, the market is flooded with system integrators creating "extremely custom software solutions" for niche problems. Asianometry describes a case where a power company trained a model merely to "replicate the button presses of a human operator." While efficient for that specific task, the author worries that "big general solutions" will eventually render these niche models obsolete.
The financial mindset further compounds this issue. Citing Richard Katz, the author points out that Japanese executives view information technology primarily as a cost-cutting mechanism rather than a revenue generator. "Japanese companies tend to look at information technologies as more a way to cut costs and improve productivity," Asianometry writes, contrasting this with American firms that see tech as a way to "grow the pie." This fundamental difference in strategy limits the potential for breakthrough products that could compete globally.
My biggest worry is that the big LLMs or the agent layers wielding them eventually enable competitors to vastly outperform these little AI models.
Hardware Strengths and Sovereign Dreams
Despite the software struggles, Japan retains formidable strengths in hardware and materials science. The author highlights startups like Preferred Networks, which is developing its own 3D stacked processors to run large language models, and others like Matlantis that are applying AI to drug discovery and material science. These sectors align with Japan's traditional industrial prowess. The author also notes the potential of reviving nuclear power to solve the energy constraints of data centers, a move that could unlock significant compute capacity.
However, the government's push for "sovereign AI"—training models on Japanese data within Japanese data centers—is viewed with skepticism. The author argues that this focus on sovereignty is a "variant, not the end goal," and warns that it may lead to inferior products. "I doubt there is enough good Japanese data out there," Asianometry writes, suggesting that a world-class model in math or science would require global data, including from mainland China. The author uses the decline of the Japanese semiconductor industry in the 1980s as a cautionary tale, noting that companies failed because they "stuck with Japanese tools even as the latter declined." The lesson, the author suggests, is that Japan must "go out and find the best technology you can get your hands on," just as the new semiconductor champion Rapidus did by purchasing ASML lithography machines.
Once the data is collected and data centers built, you train by essentially running a script. What new competencies or leading edge knowledge are we building here?
A counterargument worth considering is that for sensitive government and defense applications, the security and control offered by sovereign AI outweigh the performance benefits of global models. The author acknowledges this for specific use cases, such as translating administrative documents, but maintains that for global competitiveness, the best technology must be used regardless of origin.
Bottom Line
Asianometry's central thesis—that Japan lacks the ambition to compete in the AI era despite having the raw talent and hardware capability—is a compelling and necessary critique. The strongest part of the argument is the historical parallel drawn between the current AI strategy and the software industry's past isolationism, offering a clear warning against repeating the same errors. The piece's biggest vulnerability lies in its somewhat deterministic view of Japanese corporate culture; while the inertia is real, the rapid emergence of startups like Sakana AI and the government's recent fiscal expansion plans suggest the landscape may be shifting faster than the "generation" timeline predicts. For investors and observers, the key takeaway is to watch whether Japan can pivot from building niche, cost-cutting tools to creating the general-purpose engines that will define the next decade of technology.