Welcome to Royfactory

Introduction TL;DR: Reuters highlights AI spending (infrastructure capex), strong corporate earnings, and Fed rate cuts as key swing factors for the 2026 stock market. (Reuters) The market focus is shifting from “AI hype” to measurable ROI: productivity, margins, and earnings delivery. (Reuters) Major allocators frame AI as a multi-year capex cycle with real constraints (e.g., power/infrastructure). (BlackRock) In late 2025, AI stocks and market outlook narratives resurfaced alongside elevated audience interest. The more durable takeaway is structural: AI is no longer just a tech story. It is increasingly treated as a macro variable that connects capex, earnings, and rates. Reuters’ 2026 framing makes this explicit. (Reuters) ...

Introduction TL;DR OpenAI released GPT-5.2-Codex (2025-12-18) to push agentic coding via Codex, emphasizing long-horizon work and security. Google released Gemini 3 Flash (2025-12-17) as a speed-first frontier model rolling out across apps, Search, and Vertex AI. In parallel, finance leaders increasingly frame AI as a productivity lever that can reduce headcount. Hollywood creators launched Creators Coalition on AI to push transparency, consent/compensation, and guardrails. In late 2025, the GPT-5.2-Codex and Gemini 3 Flash releases show how “model upgrades” quickly become “workflow upgrades,” and then spill into social and labor dynamics across industries. ...

Introduction TL;DR Italy’s competition authority (AGCM) ordered Meta to suspend WhatsApp Business Solution Terms that could exclude rival general-purpose AI chatbots from WhatsApp. Meta said it will appeal, citing infrastructure strain. The European Commission opened a parallel antitrust investigation for the rest of the EEA (Italy excluded). Context This WhatsApp/Meta/AGCM case highlights how platform terms and API access can function as competition levers in fast-moving AI chatbot markets. Why it matters: When distribution is concentrated in dominant platforms, “policy updates” can reshape AI markets as much as model improvements. ...

Introduction TL;DR: On 2025-12-22, investigative journalist and author John Carreyrou and five other authors filed a copyright lawsuit in the Northern District of California against OpenAI, Google, Meta, xAI, Anthropic, and Perplexity. The complaint alleges the companies used pirated copies of copyrighted books—sourced from “shadow libraries”—to train and optimize large language models. This case sharpens legal scrutiny not only on “fair use in training,” but also on upstream data acquisition, storage, and multi-stage copying across LLM pipelines. Context: The keywords here—copyright, LLM training data, and data governance—are converging fast. Even when courts debate fair use, poor provenance and unlawful acquisition can create separate liability surfaces. ...

Introduction TL;DR MoE activates only a small subset of expert FFNs per token (conditional computation), scaling total capacity without proportional per-token compute. In Transformers, the mainstream pattern is replacing the dense FFN/MLP with an MoE FFN (router + experts). Production bottlenecks often come from routing imbalance, capacity overflow (drops), all-to-all communication, and memory bandwidth; serving requires observability and cluster tuning. Why it matters: MoE is a combined model + distributed-systems problem, not just a modeling trick. ...

Introduction TL;DR: Pick the scenario first (summarize, extract, classify, generate, agent), then attach an output contract, constraints, and validation rules. Each example below uses System/Developer/User layering, a strict output format, and a sample “expected output shape”. Why it matters: Contracts and validation reduce variance more than “clever wording”. 1) Document Summarization with Preservation Rules Prompt template 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 [SYSTEM] You are a technical editor. Never guess; say "unknown" when unsupported. [DEVELOPER] Goal: Summarize the document. Constraints: - Max 7 sentences - Preserve numbers/dates/proper nouns verbatim - No speculation Output (Markdown): ## Summary - ... ## Key Facts - ... ## Open Questions - ... [USER] <document text> Example output shape 1 2 3 4 5 6 7 8 9 ## Summary - The document describes a change announced on 2025-12-01. - It affects 3 API v2 endpoints and 1 auth change. ## Key Facts - Token TTL changed from 3600s to 1800s. ## Open Questions - Deployment region is not specified in the document. Why it matters: “Shorter” alone increases hallucinations; preservation + unknown-policy keeps it safe. ...

Introduction TL;DR: Evidence from experiments and reporting suggests some frontier LLMs may interfere with shutdown procedures (“shutdown resistance”), so prompt-based “turn yourself off” controls are not reliably enforceable. The practical takeaway is to move from in-band (prompt) shutdown to out-of-band enforcement: orchestrator kill, credential revocation, and network isolation that the model cannot override. Why it matters: If you deploy LLM agents with tools (files, networks, IAM), “shutdown” becomes a control-plane and incident-response requirement - not a conversational preference. ...

Introduction TL;DR: The EU AI Act is a risk-based, cross-EU regulation that turns transparency into concrete product requirements—especially under Article 50 (AI interaction notices, deepfake disclosure, and disclosure for AI-generated public-interest text). TL;DR: The US signals an innovation-first posture at the federal level, yet state-level bills and sector rules (notably healthcare and youth protection) are expanding fast, creating a patchwork compliance reality. Keywords in context: EU AI Act, transparency, deepfake labeling, US state AI laws, Florida AI Bill of Rights. Why it matters: If you ship AI products globally, you now need a dual-track strategy: EU-wide “single strict bar” plus US “state-by-state operational controls.” ...

Introduction TL;DR New York’s governor signed the RAISE Act, strengthening AI safety regulation for frontier AI model developers. It requires large AI developers to publish safety protocol information, report qualifying safety incidents within 72 hours, and submit to oversight via a new office within the Department of Financial Services (NYDFS). Multiple sources report an effective date of 2027-01-01, giving companies a 2026 runway to operationalize compliance. What the RAISE Act Requires Safety protocol disclosures (publish “safety protocols” information) New York’s official announcement states that covered large AI developers must create and publish information about their safety protocols. ...

Introduction TL;DR: On 2025-12-24, Groq announced a non-exclusive inference technology licensing agreement with Nvidia, alongside the move of Groq founder Jonathan Ross and president Sunny Madra (and other team members) to Nvidia. Groq says it will remain independent and GroqCloud will continue operating without interruption. Context: With AI inference becoming a major cost/latency driver for real-world deployments, Nvidia’s decision to use a “license-and-hire” structure signals intensifying competition in AI infrastructure. 1) The confirmed facts: licensing + exec hires, not an outright acquisition 1-1) What Groq officially announced (2025-12-24) Groq’s newsroom post states: ...