Japan’s Bold Bid to Lead the Next Era of A.I.

When OpenAI opened its first Asian office in Tokyo in April 2024, the company highlighted Japan’s strengths: a deep engineering talent pool, a corporate sector known for precision, and a government eager to advance A.I. Last month, Anthropic followed suit, launching its own Tokyo office. The company, best known for its Claude models, also signed a Memorandum of Cooperation with the Japan AI Safety Institute—making Japan only the second country after the U.S. where Anthropic works directly with national regulators on responsible A.I. development.

Rather than treating Japan as a simple export market for Western models, both OpenAI and Anthropic now view it as a strategic co-development hub. Their products are quickly embedding into Japanese society and industry.

Major companies such as Daikin, Toyota Connected and Rakuten have adopted ChatGPT to speed up data analysis, automate workflows and build custom assistants tailored to Japan’s business culture. Claude, meanwhile, is now fully localized for Japanese users, with adjustments for cultural nuance, linguistic complexity and local compliance rules. Panasonic, NRI and Rakuten have expanded their use of Claude for strategy, creative ideation and secure enterprise deployments.

These corporate moves align with a government pushing one of the world’s most ambitious A.I. agendas. Policymakers are knitting together domestic regulation, international partnerships, workforce initiatives and sovereign digital infrastructure to ensure A.I. becomes a catalyst for economic revival. Independent analyses estimate the technology could raise Japan’s GDP by as much as 16 percent.

In May, Japan passed the A.I. Promotion Act—a law that frames the technology as a national priority requiring structured oversight and rapid adoption. At the center of the effort is the A.I. Strategic Headquarters, a body led by Prime Minister Sanae Takaichi. Japan has also deepened its technology ties with India, agreeing at the G20 Summit this past weekend to broaden cooperation on A.I., critical tech, digital public infrastructure, semiconductors and cybersecurity.

Japanese LLMs challenge GPT and Claude

Most of today’s large language models are trained primarily on English text and struggle to serve non-English markets at scale. That gap has opened space for language-specific models designed for local accuracy and privacy. One of the most prominent is Tsuzumi 2, released last month by Japanese telecom giant NTT Inc.

“Frontier A.I. companies will never provide deeply localized, private Japanese-language models as part of their global roadmap. Tsuzumi 2 fills that gap,” Jan Wupperman, senior vice president of service assurance, data and A.I. at NTT, told Observer.

Tsuzumi 2 is also far more efficient than its Western counterparts. It runs inference on a single GPU rather than dozens. The 30-billion-parameter version operates on a laptop-grade GPU, while the 7-billion-parameter model can run with no GPU at all, Wupperman said. It performs on par with—and sometimes better than—models several times its size, including GPT-5 and Claude 3.5, for Japanese-language reasoning. The A.I. is multimodal, able to process text, images and voice in one workflow.

“We don’t aim to compete with GPT-5. Our philosophy with Tsuzumi 2 is to create small, task-optimized models trained across generic knowledge, industry knowledge and client-specific knowledge,” Wupperman said.

Merging quantum computing with A.I.

The next bottleneck for global A.I. growth is raw computing power. Data centers are straining electrical grids, GPU wait times have stretched to months, and silicon-based chips are hitting physical limits. Japan believes the solution lies in merging quantum computing with A.I.

Together with OptQC, NTT is developing optical quantum systems that operate at room temperature, thereby avoiding the massive cooling systems required by traditional quantum machines. This hardware aims to replace electrons with light, dramatically improving speed and energy efficiency.

“One of the greatest challenges in today’s quantum landscape is energy intensity—cooling, stability and thermal dissipation. Photonics gives us an architectural advantage: light generates almost no heat, enabling quantum processing at room temperature,” Wupperman explained. “This makes photonic quantum systems dramatically more compatible with large-scale A.I. workloads.”

These systems aim to accelerate molecular simulation, climate modeling, high-dimensional optimization and A.I. training tasks that remain out of reach for classical machines.

“Once quantum computing capacity reaches maturity, training an A.I. model will look entirely different. Instead of incremental improvements, this could shrink the training cycle of complex models from months to hours,” said Wupperman.

In the near term, he added, A.I. will continue to advance faster than quantum. But over the next five to ten years, that relationship is expected to flip, with quantum becoming a force multiplier for A.I.—and A.I. helping accelerate quantum hardware design in return.