Sovereign AI as a Growth Driver: Inside SoftBank's Homegrown LLM Strategy
SoftBank Corp. (TOKYO: 9434) released its Integrated Report 2025 on October 31, 2025. The English edition looks back at FY2024 (ended March 31, 2025) and details management's medium- to long-term bets on AI, along with financial strategy, shareholder returns, ESG, and risk management. One theme stands out for engineers and product leaders: sovereign, homegrown Generative AI as a lever for scale and control.
All About SoftBank's Management AI
SoftBank is developing in-house Large Language Models across its group, led by Hironobu Tamba, Head of Homegrown Generative AI Development and President & CEO of SB Intuitions Corp. The strategy is clear: build a high-performance "teacher" model, then distill it into production-grade "student" models that are faster, cheaper, and easier to deploy. This approach aims to drive practical AI adoption across client workloads while compounding capability for the next wave of models.
Teacher-Student Architecture That Ships
The "teacher" model holds broad knowledge and strong reasoning, but it's compute-hungry and slower for day-to-day business use. The production path is a smaller "student" model distilled from the teacher's knowledge-optimized for latency, accuracy, and cost.
SoftBank's "Sarashina mini" follows this pattern. It uses techniques such as model distillation to keep as much of the teacher's quality as possible while reducing footprint. For background on the method, see the classic paper "Distilling the Knowledge in a Neural Network" (arXiv).
From Strong "Students" to the Next "Teacher"
"Sarashina mini" is not the end state. SoftBank plans to combine multiple 70B-parameter models with different areas of expertise into a coordinated "team of specialists." By continuously training this team, they aim to build the next high-performing teacher faster, targeting a one-trillion-parameter class model on a shorter cycle.
This thinking echoes mixture-of-experts and ensemble strategies-route tasks to the right specialist, then use the collective to improve the next foundation. For context on sparse expert routing at scale, see Switch Transformers (arXiv).
What This Means for IT and Development Leaders
- Set clear latency and cost budgets. Use the large teacher offline for data generation, evaluations, and fine-tuning. Serve a distilled "student" for production inference.
- Build a distillation pipeline: synthetic data from the teacher, safety filtering, domain corpora blending, and a prompt curriculum that targets your KPIs (accuracy, refusal quality, reasoning depth).
- Specialize where it pays. Train 13B-70B experts per domain (support, legal, coding, operations) and route with a lightweight classifier or gating policy.
- Stretch smaller models with systems design: retrieval-augmented generation, tool/function calling, structured outputs, and response caching. Quantize and use speculative decoding for throughput.
- Plan for sovereignty and compliance. Keep sensitive data on controlled infrastructure, enforce audit trails, and maintain jurisdictional guarantees that match regulatory needs.
- Engineer for efficiency: GPU scheduling, batching, KV cache management, and autoscaling. Track cost per 1K tokens, tail latency, and safety incidents as first-class metrics.
- Adopt a rigorous eval harness: golden sets, regression tests, adversarial probes, and red-teaming. Gate every model release with hard thresholds tied to business outcomes.
- Watch energy and ESG impact early-optimize inference stacks and capacity planning to reduce electricity draw while meeting SLOs.
Why This Strategy Matters
The teacher-student loop shortens time-to-value and keeps operating expenses in check. The "team of specialists" creates compounding returns: expertise today becomes training signal for a stronger foundation tomorrow. For enterprises in Japan and beyond, this provides a blueprint for deploying useful AI while keeping control over data, cost, and governance.
Further Learning
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