Top AI & Tech News (Through December 14th)

Hello AI Citizens,

2025 was the year AI jumped from demo to deployment. Frontier models got sharper, “agentic” workflows started doing real work end-to-end, and capital poured into chips and data centers as governments raced to shape the rules. We also saw AI diffuse into daily life—from copilots at work to creative co-creation—while hard questions about safety, mental health, energy, and geopolitics moved from panel talks to policy.

Looking to 2026, expect three big swings: (1) Work orchestration—fewer tool chains, more reliable mega-agents that plan, call tools, and deliver finished outputs; (2) AI-native content & products—licensed IP deals, provenance-by-default, and co-creation funnels that tie directly to commerce and streaming; (3) AI for science & industry—automated labs, domain “super-learners,” and on-device + edge models that shrink latency and cost. The leaders will pair governance with ROI: watermarking and audit trails, energy-aware architectures, and clear playbooks that turn pilot wins into P&L impact.

Here are the key headlines shaping the AI & tech landscape:

• Copilot Usage Report: Health at No. 1, Advice Up, Behavior Follows the Clock

• OpenAI: GPT-5.2 Rolls Out for Pro Work and Long-Running Agents

• Disney Bets $1B on OpenAI; Sora to Generate Shorts Using Disney IP

• TIME Names ‘Architects of AI’ Person of the Year

• DeepMind–UK Deal: Frontier AI for Science, Classrooms, Public Services, and Security

• White House Seeks to Preempt State AI Laws With DOJ Task Force, FCC/FTC Actions

Let’s recap!

Copilot Usage Report: Health at No. 1, Advice Up, Behavior Follows the Clock

Microsoft’s MAI unit analyzed 37.5 million de-identified Copilot conversations and found health queries dominate mobile use throughout the year, with advice-seeking steadily gaining share. Behavior varies by day: coding activity rises on weekdays while gaming peaks on weekends. In February, relationship questions spike around Valentine’s Day. Late-night sessions skew toward religion and philosophy, whereas travel planning clusters during daytime and commuting hours. The study relied on privacy-preserving summaries rather than raw chat transcripts, according to MAI. Source: Microsoft MAI, It’s About Time: The Copilot Usage Report 2025 

💡 View AI as a daily companion channel by designing health, wellness, and “life admin” workflows for mobile first and by surfacing coaching modes, not just search. Align content and support staffing to temporal rhythms—weekday work tasks, weekend leisure, daytime travel planning, and late-night reflective queries—and measure advice quality separately from retrieval accuracy. Build privacy-preserving analytics to learn intent patterns without storing raw chats, and create moment-based nudges (e.g., February relationship helpers) that respect consent and add real value.

OpenAI: GPT-5.2 Rolls Out for Pro Work and Long-Running Agents

OpenAI rolled out GPT-5.2 to paid ChatGPT tiers and its developer API, pitching broad gains in professional tasks, software development, long-context reasoning, tool use and vision. The company said the “Thinking,” “Pro” and “Instant” variants are available immediately. On internal and third-party tests, GPT-5.2 “beats or ties” human experts on 70.9% of knowledge-work comparisons in the GDPval benchmark, posts a 55.6% score on SWE-Bench Pro for real-world coding tasks, and shows higher factual accuracy than prior versions.

OpenAI also reported near-perfect retrieval on tough long-context MRCR variants and better performance parsing charts and software UI screenshots, with early partners citing more reliable end-to-end “agentic” workflows on multi-step jobs. API pricing starts at $1.75 per million input tokens and $14 per million output tokens for GPT-5.2, with GPT-5.2 Pro priced higher. In ChatGPT, GPT-5.1 will remain available to paid users for a transition period before being sunset. Source: OpenAI 

💡 Start controlled pilots where GPT-5.2’s strengths matter (spreadsheets/slides, code, long documents, multi-tool agents) and compare output, latency, and cost against GPT-5.1. Treat it as a higher-quality but more “targeted” tool: design jobs that exploit long context and tool-calling, and keep human review on critical outputs. Update guardrails for stronger tool use (permissions, rate limits, audit logs) and refresh evals for factuality, coding, and reasoning to verify real gains in your domain. Revisit FinOps: higher per-token price can be offset by better token efficiency—measure cost per task, not per token. Plan migration paths across tiers (Instant for speed, Thinking for depth, Pro for toughest asks) and maintain fallbacks while you benchmark.

Disney Bets $1B on OpenAI; Sora to Generate Shorts Using Disney IP

The Walt Disney Co. struck a three-year pact with OpenAI that makes Disney the first major content licensor for Sora, OpenAI’s short-form generative video platform. The deal will let fans generate brief videos and still images featuring more than 200 characters across Disney, Pixar, Marvel and Star Wars, while explicitly excluding real actors’ likenesses and voices. Select Sora-generated shorts are slated to stream on Disney+.

Disney will also adopt OpenAI’s APIs and deploy ChatGPT to employees as part of a broader technology tie-up. The entertainment giant plans a $1 billion equity investment in OpenAI and will receive warrants for additional shares. Both companies said the collaboration will include safeguards around user safety, creator rights and age controls, with initial consumer experiences expected in early 2026, pending definitive agreements and required approvals. Source: The Walt Disney Company

💡 Treat licensed AI content as its own channel—design pilots for UGC co-creation, fan prompts, and short-form IP activations tied to tentpoles and measure completion-to-stream conversion. Build rights and safety guardrails up front by using age gating, geo restrictions, and provenance/consent tracking, and require C2PA-style metadata for all outputs. Prepare marketing and legal workflows for IP review at prompt, generation, and publish stages so brand safety and creator rights are protected end-to-end. Stand up a “co-creation” analytics stack to track prompt themes, watch time, share rates, and lift to Disney+ or commerce. Plan partnerships and talent deals that complement, not conflict with, licensed character use and communicate clearly how user creations can be used, remixed, or monetized.

TIME Names ‘Architects of AI’ Person of the Year

TIME’s 2025 Person of the Year spotlights the “Architects of AI”—a cohort led by Nvidia’s Jensen Huang—framing AI as a global industrial build-out as much as a software revolution. The package traces Nvidia’s ascent to world’s most valuable company, the scramble to erect hyperscale data centers, and Washington’s pivot to national-scale AI programs, even as China accelerates via homegrown models and robotics.

The reporting also flags mounting externalities: power demand from AI factories, bubble-risk financing, and social impacts from chatbots—alongside leaders’ claims that AI will multiply productivity and rewire work. Net: AI is now statecraft and infrastructure, not just apps, and 2026 will test whether the boom translates into broad economic gains—or volatility. Source: TIME

💡 Treat AI like a capital program—budget for compute, power, and data logistics alongside models and apps; negotiate multi-year GPU and cloud commitments with exit ramps. Stand up a “macro risk” dashboard that tracks energy availability, regulatory shifts, and vendor concentration; model cost sensitivity to token prices and latency SLAs. Build dual-track governance: one stream for safety (misuse, mental-health and privacy safeguards), one for resilience (incident drills, vendor failure playbooks, and regional failover). Tie AI ROI to operating metrics (cycle time, defect rate, cash conversion) rather than vanity usage, and pressure vendors for verifiable benchmarks on long-horizon tasks. Finally, pilot low-power/edge workloads and green PPAs to blunt energy risk while maintaining capacity for peak training and agentic workflows.

DeepMind–UK Deal: Frontier AI for Science, Classrooms, Public Services, and Security

Google DeepMind is expanding its partnership with the UK government to deploy frontier AI across four tracks: (1) science & education, (2) public service modernization, and (3) national security & resilience. Scientists will get priority access to “AI for Science” models (AlphaEvolve, AlphaGenome, an AI co-scientist, WeatherNext), and DeepMind will open its first automated science lab in the UK in 2026 to robotically synthesize and test new materials under Gemini control. In education, a Northern Ireland pilot found ~10 hours/week saved for teachers using Gemini; an Eedi RCT showed +5.5 pp gains on novel problems with short, supervised AI tutoring.

For government delivery, the UK’s i.AI team is trialing Extract, using Gemini to convert legacy planning documents into structured data in ~40 seconds (vs. up to 2 hours). On safety, DeepMind will deepen work with the UK AI Security Institute (explainability, alignment, societal impact) and explore cyber-resilience tools like Big Sleep and CodeMender to find and auto-fix code vulnerabilities. Source: Google DeepMind

💡Treat this as a playbook. Stand up (a) an “AI for Science” lane with domain models + lab automation pilots, (b) an education lane focused on teacher time-savings and supervised micro-tutoring RCTs, (c) a public-records digitization lane targeting 100× faster turnaround on document workflows, and (d) a security lane pairing red-team evaluations with auto-remediation agents. Define metrics upfront: hours saved, cycle-time reductions, learning gains (pp), and mean time-to-patch.

White House Seeks to Preempt State AI Laws With DOJ Task Force, FCC/FTC Actions

The White House issued an executive order establishing a national AI policy designed to override conflicting state laws. It creates a DOJ AI Litigation Task Force (within 30 days) to challenge state statutes, directs Commerce (within 90 days) to identify “onerous” state AI laws, ties BEAD broadband funding eligibility to state compliance, and asks the FCC to consider a federal AI reporting/disclosure standard that could preempt state rules. The FTC must also clarify how the FTC Act applies when state laws would require altering “truthful outputs” of AI models.

If implemented, the order centralizes AI governance at the federal level while carving out areas like child safety, state procurement, and most compute infrastructure. For companies, the near-term impact is reduced regulatory fragmentation but increased federal scrutiny via forthcoming FCC/FTC processes and DOJ challenges. Watch the 30/90-day milestones and prepare for a preemption bill aimed at setting a uniform baseline.

Source: Executive Order, “Ensuring a National Policy Framework for Artificial Intelligence,” (The White House)

💡 Treat this as a move toward a single federal bar. Build a requirements matrix mapping your AI uses to (a) likely-preempted state rules and (b) carve-outs (child safety, procurement). Modularize disclosures: a core “federal-ready” model card/impact log with state add-ons you can detach if preemption lands. Update MSAs with “change-in-law” and rollback provisions. Stand up a regulatory watch and comment plan for FCC (disclosure/reporting) and FTC (deceptive practices) dockets. Align internal risk reviews to outcomes-focused controls (safety, provenance, incident reporting) rather than prescriptive output edits.

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