When ackai opened its first Asian office in Tokyo in April 2024, the company highlighted Japan’s strengths: A deep commercial sector known for precision, and a government that was able to develop AI last month, anthropic followed by his office. This company, well known for its Claude models, signed a Memorandum of cooperation with Japan Pheetmite – making Japan only the second country after the US Thropic works directly with national regulators for the development of responsible AI.
Instead of treating Japan as a simple export market for western models, both acapic and anthropic now view it as a hub of strategic integration development. Their products are quickly becoming embedded in Japanese society and industry.
Major companies such as Daikin, Toyota Connected and Rakuten have adopted Chatgpt to accelerate data analysis, change workflows and build Japanese business culture. Claude, meanwhile, is now fully localized for Japanese users, with adjustments to cultural nuances, language exchange difficulties and local compliance regulations. Panasonic, NRI and Rakuten have increased their use of Claude for planning, creative vision and secure business delivery.
These organizations move in sync with the government that enforces one of the AU’s most ambitious agendas. HOLCOMA’s policy makers combine domestic law, international geographic cooperation, employment opportunities and Fealigs digital infrastructure to ensure that AI becomes a catalyst for economic recovery. An independent analysis estimates the technology could raise Japan’s GDP by 16 percent.
In May, Japan passed the AI Promotion Act – a law that makes the technology a national priority that requires planned oversight and rapid adoption. At the center of the effort is the AI strategy headquarters, a body headed by Prime Minister Sanae Takaichi. Japan has also deepened its technology with India, they agreed at the G20 summit this past weekend to expand cooperation in AI, critical technology, digital public infrastructure and cyberpositure and cyberpositure and cyberpositionructure.
Japan LLMS challenge GPT and Claude
Most of the major language models are trained primarily in English text and struggle to serve non-English markets at scale. That gap has made room for some language models designed for local precision and privacy. One of the most prominent is Tsuzumi 2, released last month by Japanese Telecom Giant NTT Inc.
“Companies before AI will never offer indestructible models that have Japanese languages as part of their Global RoadMap. Jan Wuperpman, Senior President of Service Assurance Gap, Data and AI at NTT, tells the viewer.
Tsuzumi 2 is also more efficient than its western counterparts. It works more modestly with a single GPU than with a bunch. The 30-billion-parameter model runs on a GPU-grade GPU, while the one-parameter model can run without a GPU at all, Wupperman said. It works faster and sometimes better than models many times its size, including the GPT-5 and Claude 3.5, according to the Japanese language consultation. AI is multimodal, capable of processing text, images and voice in workflows.
“We don’t intend to compete with GPT-5
Combining quantum computing with AI
The next bottleneck for Global AI growth is raw computing power. Data centers are damaging power grids, GPU wait times have stretched into months, and chip-based chips are hitting physical limits. Japan believes that the solution lies in combining computer computing with AI
In collaboration with OptQC, NTT is developing optical quantum systems that operate at room temperature, thus avoiding the large cooling systems required by traditional quantum devices. This hardware aims to include light-reducing electrons, the best to improve speed and energy efficiency.
“One of the biggest challenges in today’s Quantum Naturalscape, energy efficiency, durability and thermal slippage. Photonics give us the opportunity to heat, they enable thermal processing,” explains quantum processing at room levels, “explains WUPPERMAN. “This makes photoc quantum systems very compatible with large AI workloads.”
These programs aim to accelerate molecular unraveling, climate modeling, high efficiency and AI training tasks that are often not available in classical machines.
“If quantum computing capacity reaches maturity, AI model training will look completely different. Instead of incremental improvements, this can reduce the cycle of training a few models from months to hours,” said Wuperperman.
In the near term, he added, AI will continue to advance faster than quantum. But in the next five to ten years, that relationship is expected to end, with quantum becoming the Multipling of AI-and AI Help Accelerate Quantum Hardware Design in.
