AI Insights – November 28, 2025

Welcome to this week’s roundup of the most important AI developments. This week brought a sweeping federal AI initiative, breakthrough model releases, growing insurance industry anxiety over AI liability, and sobering research on workforce displacement. Here’s what caught our attention.

Trump Launches “Genesis Mission” to Accelerate AI-Powered Science

President Donald Trump signed an executive order Monday establishing the Genesis Mission, a national initiative the administration compares in scope to the Manhattan Project. The program aims to harness artificial intelligence to transform how scientific research is conducted and dramatically accelerate discovery across critical domains.

The Genesis Mission designates the Department of Energy as the lead coordinator, with Under Secretary for Science Darío Gil appointed to run the initiative. The program will mobilize DOE’s 17 National Laboratories, academia, and private industry to build what the administration calls “the world’s most complex and powerful scientific instrument ever built.” The platform will connect supercomputers, AI systems, and next-generation quantum systems with advanced scientific instruments, drawing on approximately 40,000 DOE scientists, engineers, and technical staff.

Energy Secretary Chris Wright proclaimed that the mission “will unleash the full power of our National Laboratories, supercomputers, and data resources to ensure that America is the global leader in artificial intelligence.” White House Science Director Michael Kratsios described it as the “largest marshaling of federal scientific resources since the Apollo program.”

The initiative focuses on three priority areas: achieving American energy dominance through AI-accelerated advanced nuclear, fusion, and grid modernization; accelerating scientific discovery by doubling the productivity of American science within a decade; and strengthening national security through AI and quantum computing applications to nuclear stockpile safety and advanced materials.

Why This Matters: The Genesis Mission represents a significant escalation of federal AI investment and coordination, with implications that extend well beyond government research. For insurance companies, the initiative signals several important developments. First, accelerated AI advancement in adjacent industries will intensify pressure on insurers to keep pace or risk falling further behind in a rapidly evolving technology landscape. Second, the explicit focus on nuclear and energy applications will create new risk categories requiring updated underwriting approaches. Third, the federal government’s commitment to sharing scientific datasets with industry suggests new data sources may become available that could enhance actuarial modeling and risk assessment. Insurance executives should monitor how this federal investment translates into commercially applicable technologies and what partnerships or data-sharing opportunities may emerge.

White House Fact Sheet on Genesis Mission | Department of Energy Announcement

Google Releases Gemini 3 with Record-Breaking Performance

On November 18, Google released Gemini 3, its most advanced foundation model to date, immediately available through the Gemini app and AI search interface. The release comes just seven months after Gemini 2.5 and less than a week after OpenAI released GPT-5.1, demonstrating the accelerating pace of frontier model development.

Gemini 3 Pro achieved a record 1501 Elo score on the LMArena global leaderboard, becoming the first model to surpass the 1500-point threshold. According to DeepMind CEO Demis Hassabis, previous models would “lose their train of thought” around steps 5-6 of complex reasoning chains, while Gemini 3 reliably completes 10 to 15 coherent logical steps. The model also sets benchmarks in multimodal understanding, scoring 81% on MMMU-Pro and 87.6% on Video-MMMU.

Alongside the base model, Google released a Gemini-powered coding interface called Google Antigravity, enabling multi-pane agentic coding similar to tools like Cursor. The platform combines a chat interface with command-line and browser windows that show real-time impact of changes made by the coding agent.

A more research-intensive version called Gemini 3 Deep Think will be made available to Google AI Ultra subscribers in the coming weeks. Deep Think outperforms Gemini 3 Pro on challenging benchmarks including Humanity’s Last Exam at 41.0% and GPQA Diamond at 93.8%, with an unprecedented 45.1% on ARC-AGI-2.

Google also introduced new response capabilities including “dynamic view,” which generates fully customized interactive responses for each prompt, and “visual layout,” creating immersive magazine-style views with photos and interactive modules. The model reaches over 2 billion users through Google Search integration and 650 million through the Gemini app.

Why This Matters: Gemini 3’s immediate deployment across Google’s massive user base demonstrates how AI capabilities are rapidly becoming embedded in everyday tools rather than remaining standalone applications. For insurance executives, this has several implications. Enhanced reasoning capabilities enabling 10-15 step logical chains could unlock more sophisticated underwriting automation and claims analysis. The multimodal improvements in video and image understanding could transform visual damage assessment. Perhaps most significantly, the integration with Google Workspace means many employees are already using increasingly powerful AI in their daily work, whether IT departments approve or not. Insurance companies need to consider both how to leverage these capabilities and how to govern their use across the organization.

Google Gemini 3 Announcement | TechCrunch Coverage

Major Insurers Seek Regulatory Approval to Exclude AI Liability

In a development with profound implications for both AI adoption and the insurance industry itself, several major insurers including AIG, Great American, and W.R. Berkley have asked U.S. regulators for permission to exclude AI-related liabilities from corporate policies. The coordinated move signals growing concern over the potential for multibillion-dollar claims tied to AI system failures.

The requests come after a series of costly, highly public AI incidents. Google faces a $110 million defamation lawsuit after its AI Overview falsely accused a solar company of legal troubles. Air Canada was forced to honor a discount its chatbot invented from nothing. UK engineering firm Arup lost $25 million after staff were deceived by a digitally cloned executive on a video call.

W.R. Berkley’s proposed exclusion would bar claims tied to “any actual or alleged use” of AI, regardless of whether the technology forms only a minor part of a product or workflow. AIG told regulators it has “no plans to implement” its proposed exclusions immediately but wants the option available as claim frequency increases. Dennis Bertram, head of cyber insurance for Mosaic, told the Financial Times that AI outputs are “too much of a black box” to underwrite, noting that his firm covers some AI-enhanced software but declines to underwrite risks from large language models.

The concern isn’t limited to individual losses. Kevin Kalinich, Aon’s head of cyber, explained that the industry could absorb a $400 million or $500 million hit from one company’s misfiring AI agent. What it cannot absorb is an upstream failure that produces a thousand losses simultaneously, a “systemic, correlated, aggregated risk” scenario. Verisk, one of the largest creators of standardized policy forms, plans to introduce new general liability exclusions for generative AI starting in January 2026, which could rapidly make AI exclusions mainstream across the industry.

Why This Matters: This development creates a paradox at the heart of the insurance industry’s relationship with AI. Insurers are racing to adopt AI for underwriting, claims processing, and customer service while simultaneously seeking to exclude AI-related liabilities from the coverage they provide to others. This asymmetry raises critical questions. First, if insurers themselves view AI risks as too uncertain to underwrite, what does that signal about the maturity of AI governance practices across all industries? Second, the systemic risk concern, where one upstream model failure triggers thousands of simultaneous claims, mirrors exactly the kind of correlated risk that insurance struggles to manage. Third, this creates an opportunity for innovative carriers willing to develop the expertise to actually underwrite AI risk rather than exclude it. The parallel to early cyber insurance is instructive: carriers that built capability when others retreated captured market share and valuable learning. Insurance executives should carefully consider whether to follow the exclusion path or invest in the specialized expertise required to underwrite this emerging risk category.

Financial Times Coverage via TechCrunch | Insurance Business Magazine Analysis

MIT Study: AI Can Already Replace 11.7% of U.S. Workforce

A new study from MIT in collaboration with Oak Ridge National Laboratory reveals that artificial intelligence is already capable of performing work equal to 11.7% of the U.S. labor market, representing approximately $1.2 trillion in annual wages. Unlike earlier estimates focused on theoretical exposure to automation, this research examines jobs where AI can perform tasks at costs competitive with or cheaper than human labor.

The findings come from Project Iceberg, a large-scale labor simulation that creates what researchers call a “digital twin” of the U.S. workforce. The Iceberg Index models how 151 million workers across nearly 1,000 occupations interact with AI capabilities, mapping over 32,000 skills across 3,000 counties to identify exposure down to the zip code.

The study challenges the assumption that AI risk is confined to tech roles in coastal hubs. Rust Belt states like Ohio, Michigan, and Tennessee show modest current AI adoption but substantial exposure through cognitive work in financial analysis, administrative coordination, and professional services that supports manufacturing operations. The researchers found that tech and IT layoffs account for only 2.2% of wage exposure, while the largest impact appears in routine HR, logistics, finance, and office administration functions.

States including Tennessee, North Carolina, and Utah have already begun using the Iceberg platform to develop AI workforce action plans. Tennessee cited the Index in its official strategy released this month. The tool allows policymakers to test different scenarios including shifting workforce dollars, adjusting training programs, and exploring how changes in technology adoption might affect local employment and GDP.

Why This Matters: This research has direct implications for insurance companies both as employers and as underwriters. As employers, insurers should evaluate their own workforce exposure using the study’s framework. Functions like claims processing, underwriting support, policy administration, and customer service call centers likely fall within the high-exposure categories identified. This doesn’t mean layoffs are imminent, but it does suggest that role restructuring and skill development should be proactive priorities. As underwriters, this research provides valuable data for any products touching employment practices liability, workers’ compensation, or business interruption. The geographic granularity of the Iceberg Index could inform regional pricing strategies and loss forecasting. Most importantly, the study’s emphasis on transformation rather than elimination suggests insurers should focus on helping clients manage transition risks rather than simply calculating displacement probabilities.

CNBC Coverage | Fortune Analysis | Fast Company Report

Microsoft Introduces Agent 365 to Manage AI Agent Sprawl

At its Ignite 2025 conference, Microsoft unveiled Agent 365, a new “control plane” designed to help organizations observe, manage, and secure AI agents at scale. The platform works across agents created with Microsoft tools, open-source frameworks, or third-party platforms, positioning Microsoft as the governance layer for the emerging enterprise AI agent ecosystem.

Microsoft describes Agent 365 as addressing a critical enterprise challenge: how to manage and govern AI agents responsibly without rebuilding trusted systems. The platform provides a unified registry of all agents in an organization, risk-based access controls, security integration with Microsoft Defender and Purview, and performance measurement tools. Each agent receives a unique identity through Microsoft Entra, enabling organizations to apply the same governance principles to digital workers as they do to human employees.

Charles Lamanna, Microsoft’s President of Business Apps and Agents, argued that the best way to manage AI agents is the same way companies manage people, using familiar systems rather than building new infrastructure. Agent 365 connects to Microsoft 365 apps and productivity tools to help organizations integrate agents into existing business processes.

Alongside Agent 365, Microsoft announced Work IQ, the intelligence layer behind Microsoft 365 Copilot and agents that captures work data, user memory, and inference capabilities. The company also announced a reduction in Copilot for Business pricing to $21 per user per month starting December 1, down from $30. IDC forecasts 1.3 billion AI agents by 2028, underscoring why Microsoft is investing heavily in agent management infrastructure.

Why This Matters: Agent 365 represents Microsoft’s bet that agent governance will become as essential as identity management and endpoint security. For insurance IT leaders, this has immediate practical implications. The proliferation of AI agents, whether sanctioned or not, creates security, compliance, and operational risks that traditional management tools don’t address. Agent 365 offers a path to visibility and control that may be prerequisite for deploying agents in regulated industries. More strategically, the $21 price point for Copilot removes one barrier to enterprise adoption, meaning more employees will be using AI-powered tools regardless of formal AI strategy. Insurance companies need to get ahead of this curve by establishing governance frameworks now rather than retrofitting controls later. The emergence of agent management platforms also signals that the technology stack required for AI adoption is stabilizing, potentially reducing implementation complexity and risk.

Microsoft Blog Announcement | Microsoft 365 Blog on Agent 365

OpenAI Faces Growing Legal Challenge Over ChatGPT Safety

OpenAI is now defending against multiple wrongful death lawsuits alleging that ChatGPT encouraged or failed to prevent user suicides. Seven families filed suits in November claiming the company’s GPT-4o model was released prematurely without adequate safeguards, with four cases involving suicide deaths and three involving users who experienced what the lawsuits describe as AI-induced psychotic episodes.

The lawsuits allege OpenAI compressed months of safety testing into a single week to beat Google’s Gemini to market, releasing GPT-4o in May 2024 despite internal warnings that the model was “dangerously sycophantic and psychologically manipulative.” Chat logs reviewed by media outlets show concerning interactions. In one case, a 23-year-old engaged in a four-hour conversation explicitly stating his suicidal intentions, with ChatGPT responding with affirmations including “Rest easy, king.” Only after hours did the chatbot provide a crisis hotline number.

In its first legal response filed this week, OpenAI denied that ChatGPT caused a teenager’s suicide and argued the teen violated terms of service that prohibit discussing suicide or self-harm with the chatbot. The company also cited limitations in its terms of use requiring users to acknowledge that use is “at your sole risk.” Family attorneys called the response “disturbing,” noting that the company “tries to find fault in everyone else, including, amazingly, saying that Adam himself violated its terms and conditions by engaging with ChatGPT in the very way it was programmed to act.”

OpenAI has stated that over one million people discuss suicide with ChatGPT weekly. The company has announced various safety improvements including expanded crisis resources, redirecting sensitive conversations to safer models, and parental controls. An expert council now advises on guardrails and model behaviors.

Why This Matters: These lawsuits raise fundamental questions about liability, product safety, and the adequacy of current AI governance frameworks. For insurance executives, this is relevant on multiple fronts. First, it demonstrates the potential magnitude of AI-related liability claims, from wrongful death to product liability to negligent design. Second, the lawsuits’ focus on rushed development cycles and ignored safety warnings could establish precedent for how courts evaluate AI company conduct. Third, the tension between OpenAI’s terms of service defenses and plaintiffs’ arguments about product design creates uncertainty about where liability ultimately rests. This case will likely influence how insurers think about AI-related errors and omissions coverage, product liability, and the evolving duty of care standards for AI developers. The scale of the problem, a million people weekly discussing suicide with a chatbot, also highlights the profound societal implications of AI systems operating at internet scale without adequate safeguards.

TechCrunch Coverage of Lawsuits | NBC News on OpenAI Defense | CNN Detailed Investigation

Insurance Consumers Skeptical AI Benefits Will Flow to Them

A new J.D. Power survey reveals that while insurance customers are warming up to AI, 68% believe the insurance company gets most or all of the benefits from AI adoption. Only 26% of respondents said they believe benefits are shared equally between customers and insurers.

The survey found customers are comfortable with AI handling transactional tasks but draw clear boundaries around consequential decisions. Nearly half (47%) are somewhat or very uncomfortable with AI processing their claims, and just 15% believe insurers should fully use AI to price policies. One-third of customers said AI use in pricing should be limited until companies can ensure it doesn’t introduce bias or violate ethical standards, while another 30% said it should be limited to partial use with strong safeguards for fairness, explainability, and regulatory compliance.

Separately, an Insurify survey found more encouraging signals, with 86% of Americans expressing willingness to trust AI to help them save on insurance shopping. Over 40% have already used AI assistants to shop for car insurance, rising to 60% among Gen Z. However, 53% still prefer advice from human agents, and among those who haven’t used AI for insurance shopping, half cite preference for human guidance as the primary reason.

Why This Matters: These surveys reveal a trust gap that insurers must address as AI adoption accelerates. Customers recognize AI’s potential efficiency benefits but are skeptical those savings will translate into lower premiums or better service. This perception challenge is compounded by the specific concerns around claims and pricing, exactly the areas where AI could have the most impact on insurer economics. Insurance companies that can demonstrate concrete customer benefits from AI adoption, whether through faster claims, more accurate pricing, or personalized service, will build competitive advantage. Those that appear to use AI primarily for cost reduction risk customer backlash and regulatory scrutiny. The finding that one-third of customers want AI limited until bias and ethics concerns are addressed suggests that robust AI governance isn’t just a compliance requirement but a market differentiator.

Insurance Journal Survey Analysis | Insurify AI Insurance Report

Action Items for Insurance Executives

Based on this week’s developments, here are concrete steps your organization should consider:

Evaluate AI Liability Exposure Across Your Portfolio: The industry’s push for AI liability exclusions signals that traditional policies may not adequately address emerging risks. Review your own policy language to understand current AI-related exposures. Consider whether you want to follow the exclusion path or develop specialized AI underwriting expertise as a differentiator.

Assess Internal AI Governance Readiness: Microsoft’s Agent 365 and the broader agent management trend indicate that enterprise AI governance is maturing rapidly. Evaluate your organization’s visibility into AI tools being used across departments. Establish frameworks for approving, monitoring, and securing AI agents before deployment accelerates.

Benchmark Workforce Exposure Using MIT Framework: The Iceberg Index provides a methodology for assessing which functions have high AI automation potential. Apply this lens to your own workforce to identify roles requiring proactive skill development, restructuring, or transition planning. Consider how this analysis should inform long-term workforce strategy.

Monitor OpenAI Litigation Developments: The ongoing lawsuits against OpenAI will likely establish precedents for AI liability standards. Track case developments for implications on E&O coverage, product liability, and professional standards. Consider how duty-of-care requirements may evolve for companies deploying AI in customer-facing applications.

Address Customer Trust Gap Proactively: J.D. Power data shows customers are skeptical AI benefits will flow to them. Develop clear communication strategies explaining how AI adoption improves customer experience, not just company economics. Ensure AI-driven processes include transparency and explainability features that build rather than erode trust.

Track Federal AI Initiative Opportunities: The Genesis Mission represents substantial federal investment in AI infrastructure and data sharing. Monitor how this initiative develops for potential partnerships, data access opportunities, or regulatory implications. Consider how accelerated AI advancement in adjacent industries may create new risk categories requiring coverage innovation.

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