Welcome to Royfactory

Introduction TL;DR: Gartner는 2026년 전 세계 AI 지출을 **2.52조 달러(+44% YoY)**로 전망했으며, 인프라가 절반 이상을 차지합니다. 이 글은 Gartner의 “AI 지출” 정의, 인프라 비중이 큰 이유, 예산·거버넌스·아키텍처에서의 실무 대응을 정리합니다. Why it matters: AI를 작은 “프로젝트 예산”으로 보면, 실제 지출이 몰리는 인프라·서비스·내장형 소프트웨어 비용을 놓치기 쉽습니다. What Gartner means by “AI spending” Gartner 보도자료는 AI 지출을 8개 시장(서비스, 사이버보안, 소프트웨어, 모델, DS/ML 플랫폼, 앱 개발 플랫폼, 데이터, 인프라)의 합으로 정의합니다. ...

Introduction TL;DR: OpenClaw is an open-source AI agent designed to run on a personal machine and execute real actions via chat-based commands. Reported incidents around Moltbook key exposure, malicious “skills,” and a published advisory show that agentic utility and agentic risk scale together. Use sandboxing, least privilege, secret hygiene, and tight input/tool controls before you grant it real accounts. Why it matters: “Agents” aren’t just chat—they’re delegated execution. Delegated execution without governance is a security incident waiting to happen. ...

Introduction TL;DR: Reuters reported on 2026-01-30 that DeepSeek received conditional approval from Chinese regulators to purchase Nvidia’s H200 chips, with conditions still being finalized. This does not automatically mean shipments are imminent. The same Reuters reporting highlights that approvals can be restrictive enough that buyers don’t convert them into actual purchase orders. The practical takeaway: treat DeepSeek H200 as a “two-gate” problem—US export licensing and China import/use approvals are separate gates that can each block execution. Why it matters: In regulated supply chains, headlines move sentiment, but conditions and documentation move shipments. ...

Introduction TL;DR: This AI training data governance checklist turns opt-out, purpose limitation, and retention into enforceable controls across raw data, derived assets, and training snapshots. It focuses on audit-ready evidence: logs, lineage, and automated enforcement (TTL/deletion jobs). Why it matters: Governance that cannot be evidenced (logs + automation) typically fails during audits and incident response. Definition and scope One-sentence definition An AI training data governance checklist is a structured set of controls to ensure training data is used only for explicit purposes, retained only as long as necessary, and subject rights (including opt-out) are operationally enforceable and auditable. ...

Introduction LLM data lineage is the practice of proving which exact dataset snapshot (and transformations) produced a specific model artifact, with run metadata that makes the training reproducible. PROV provides a standard conceptual model for provenance (entities, activities, and agents). Why it matters: When incidents happen, you need evidence—not guesses—about what data and code produced the deployed model. Core building blocks Dataset manifest (the “snapshot contract”) A manifest should lock: ...

Introduction TL;DR The 2025-09-22 announcement was a 10GW compute LOI with an “up to $100B” progressive investment framing. On 2026-01-31, reporting said the megadeal is “on ice,” while Jensen Huang publicly said Nvidia will still invest “a huge” amount—but “nothing like” $100B. The real question is deal structure (compute deployment + leasing + equity) and execution milestones (when 1GW actually goes live). Nvidia OpenAI investment is being reinterpreted in real time: a 10GW infrastructure LOI announced on 2025-09-22, followed by 2026-01-31 reports of a stalled “$100B” plan and Huang’s pushback that a “huge” investment is still planned. ...

Introduction TL;DR Google’s Project Genie is a research prototype that lets users generate and explore short interactive 3D worlds from text or image prompts. It’s limited (including 60-second generations), but it signaled a step-change in “world model” automation—enough to spook markets and ignite workflow debates. Context Project Genie is not a traditional engine; it’s a world-model-driven approach that generates the path ahead as you move, in real time. Why it matters: If you evaluate it like an engine, you’ll misread both the product and the competitive impact. ...

Introduction TL;DR: Reports say SpaceX–xAI and SpaceX–Tesla combinations are being discussed, but key deal terms remain unconfirmed. Context: The phrase “SpaceX Tesla xAI merger” is a headline magnet; the practical work is separating verified facts from unverified claims and mapping governance + privacy risks first. Why it matters: Consolidation stories move teams fast. Without a fact sheet and a risk checklist, you’ll execute on assumptions. Fact sheet: verified vs unverified Verified (reported): a SpaceX–xAI combination discussed with a stock-swap structure; two Nevada entities reportedly created (2026-01-21); no final agreement and timing/structure described as fluid. Also reported: SpaceX may consider a Tesla combination as an alternative scenario (re-citing Bloomberg). Verified (official doc): Starlink’s privacy policy includes language about AI model training by third-party collaborators unless users opt out. Why it matters: “Deal talk” is noisy; policy text and governance mechanics are concrete. ...

Introduction TL;DR C3.ai Automation Anywhere 합병 논의는 보도 기반의 미확인 상태이며, 양사 공식 발표는 없습니다. Reuters(2026-01-28)가 The Information 보도를 인용했으며, Reuters는 독자적으로 확인하지 못했습니다. 핵심은 루머 자체가 아니라, 거버넌스/보안/감사 가능성을 갖춘 AI 의사결정 + 자동화 실행 체계를 어떻게 강화할 것인가입니다. Why it matters: M&A 헤드라인은 왔다가 사라집니다. 운영 준비 상태(데이터, 아이덴티티, 로그, 통제)가 가동 시간과 컴플라이언스를 보호합니다. What’s verified as of 2026-01-29 Reuters는 2026-01-28에 The Information을 인용해 보도했으며, 양사 모두 코멘트를 거부했습니다. 보도 내용은 Automation Anywhere가 C3.ai를 인수하여 상장 경로(역합병/RTO)를 확보할 수 있다는 시나리오입니다. Automation Anywhere는 2019년 Series B($290M) 이후 $6.8B post-money valuation을 공시했습니다(과거 기준, 현재 가치는 별개). Why it matters: 이를 “미확인 입력"으로 취급하고, 아키텍처와 통제를 통해 기업 영향을 평가하십시오. ...

Introduction TL;DR: Pinterest disclosed a board-approved global restructuring plan via an SEC 8-K. The plan includes a reduction in force affecting less than 15% of the workforce and office space reductions. The phrase “Pinterest AI layoffs” often gets summarized as “around 15%,” but the filing’s exact wording is “less than 15%.” Why it matters: For fast-moving news, the 8-K is the canonical source. If your analysis doesn’t start there, the rest becomes guesswork. ...