Opportunity Radar

構造の変化を、事業仮説になる前から見る。

YORISOUは、思いつきの数を増やすのではなく、制約が変わった場所と、需要がすでに確かめられた場所を継続的に観察します。公開できる証拠だけを使い、仮説・検証・棄却を同じ研究面に残します。

How we form ventures

毎回ゼロから、会社づくりを始めない。

Opportunity sensing → Evidence → Constraint / market analysis → Venture thesis → Validation の前半と、Build → Prove → Govern → Promote → Reuse → Improve の能力蓄積を一つの形成システムとして扱います。Capability Compoundingは設計と運用ロジックが存在し、現在検証中です。ポートフォリオ経済性の実証を意味しません。

01Opportunity sensing
02Evidence
03Constraint / market analysis
04Venture thesis
05Validation
06Build
07Prove
08Govern
09Promote
10Reuse
11Improve

Improve → Opportunity sensing

Constraint Shift Radar

Dominant Design Challenge

過去に合理的だった支配的設計や運用前提を、その前提を成立させた制約まで遡って見ます。制約が変わったなら、いま何が可能になるかを公開証拠から検討します。

Market Proof Radar

Validated demand, differentiated execution

顧客、売上、導入、資金調達、実運用などの公開証拠から、確認済みの需要と未確認の製品仮説を分けます。成功企業をコピーするのではなく、異なる実行経路と未充足の入口を探します。

観察中Physical AI / Robotics

Generalist robot foundation models / 汎用ロボット基盤モデル

Open robot foundation models, synthetic-data pipelines and simulation frameworks are becoming integrated development infrastructure.

公開証拠: NVIDIA's Isaac GR00T platform combines open data pipelines, robot foundation models, simulation, middleware and deployment runtime; GR00T N1 was released as an open customizable foundation model for generalized humanoid reasoning and skills.
以前の制約: Robot learning stacks were more task-specific and access to diverse training data and simulation infrastructure was a major bottleneck.
変わった制約: Foundation models, synthetic data and simulation are increasingly packaged as reusable robotics infrastructure.
構造的な機会: Differentiation may shift from base-model creation toward domain data, deployment evidence, safety, workflow integration and post-training for specific environments.
YORISOUの仮説: Observe where application-specific evidence and operating integration become the scarce layer as generalist robotics infrastructure commoditizes. No venture claim is made here.

最終確認: 2026-09-15出典: 2

観察中AI for Science / Laboratory automation

Autonomous laboratories / 自律型ラボ

AI-guided robotic laboratories are being used to close the loop between material prediction, synthesis and experimental learning.

公開証拠: Berkeley Lab's A-Lab combines AI and robotics for materials synthesis and reports substantially higher sample throughput than manual operation.
以前の制約: Experimental synthesis and iteration were limited by slow manual laboratory throughput.
変わった制約: Robotics, machine learning and instrument automation can increasingly operate as a closed experimental loop.
構造的な機会: As laboratory automation matures, opportunities may emerge in workflow orchestration, evidence traceability, specialized operating layers and deployment into narrower scientific domains.
YORISOUの仮説: Continue observing which scientific workflows have repeatable economics and sufficiently standardized instrumentation before forming a venture thesis.

最終確認: 2026-09-15出典: 1

Berkeley Lab - A-Lab

調査中Physical AI / Regional industry

Japan SME robot adoption / 日本の中小企業におけるロボット導入

METI identifies structural labor shortages and has launched the RING Project to accelerate robot adoption across Japan.

公開証拠: Japan's 2025 SME White Paper describes persistent structural labor shortages. METI says robot adoption is constrained by specialist knowledge and by SME workplaces that are not yet ready for robots.
以前の制約: Robot deployment often required specialist integration capability that many smaller operating sites did not have.
変わった制約: National and regional support infrastructure is being organized around adoption, while robot platforms are becoming more accessible.
構造的な機会: The gap may be less about inventing another robot and more about making deployment, validation and operating integration repeatable for real SME environments.
YORISOUの仮説: Research whether a governed deployment-and-evidence layer can reduce the integration burden for Japanese regional operators. This is a thesis under research, not a validated venture.

最終確認: 2026-09-15出典: 2

最終確認: 2026-09-15

公開するもの / 公開しないもの

公開するのは、一般化した方法論、公開証拠、選択した仮説と研究状態です。内部の重み・閾値、非公開の事業戦略、取引・顧客情報、Asterionの実行詳細、権利条件、プロンプト、認証情報、内部権限ルールは公開しません。Asterionは別に統治される実行基盤であり、YORISOUの事業として扱いません。

Open research

構造の課題を、持ち込んでください。

大学、企業、研究者、自治体、運営者、創業者から、研究・課題提示・公開可能なデータ/証拠・パイロット・技術・専門知識について相談を受けます。提携や採用を事前に約束するものではありません。

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