2026年人工智能状况：观察与建议

OBSERVATIONS AND RECOMMENDATIONSDANIEL W. RASMUSFOUNDER AND PRINCIPAL ANALYST SERIOUS INSIGHTS LLC The State of AI 2026: Observations and Recommendations Executive Summary Artificial intelligence has become a foundationalinfrastructure for business, government, andsociety. In 2026, AI is no longer an experiment or adisruptive novelty. It is embedded in supply chains,energy systems, research, customer operations,and public services. The strategic landscape isshaped less by technical breakthroughs and moreby constraints: compute, talent, data, regulatorystability, and energy. These factors drivefragmentation into distinct U.S., EU, and Chineseecosystems, with new hubsemerging in India, theGulf, and parts of Africa and Asia. Sovereign AIinitiatives and geopolitical competition are pushingorganizations toward multi-stack architectures andregional deployments, challenging the assumptionof a single global platform. robots, vehicles, and edge systems. Liabilityregimes are tightening, requiring robustprovenance, logging, and clear allocation ofresponsibility across OEMs, integrators, andoperators. The main labor risk is a skills gap, notsimple job loss, as demandshifts toward hybridtechnical roles. Energy and compute economics now directly shapeAI strategy. Data-center demand is set to morethan double this decade, with AI-heavy campusesdriving local grid stress and political scrutiny.Model and hardware design are pivoting toward“smaller is smarter,” multi-model routing, and edgeinference, but load growth may still outpaceefficiency. Compute supply chains remain fragileand concentrated, under pressure from exportcontrols and resource politics. Agentic AI is transforming work from linear tasks togoal-driven systems. Enterprises are deployingsemi-autonomous agents that plan, coordinate,and act across tools and APIs, especially indomains with rich data and predictable workflows.This shift demands new operational models—Agent Ops—for monitoring, guardrails, andversioned behavior policies. Hybrid human–agentworkflows are becoming the norm, with agentshandling monitoring, summarization, and rules-heavy decisions, while humans focus on judgment,negotiation, ethics, and strategy. Metrics forsuccess now emphasize joint performance, errorrates, escalation quality, resilience, and decisiontraceability. Technically, frontier multimodal models, tool use,and agent frameworks expand AI’s capabilities butdo not resolve core limits around reliability, long-term memory, security, and evaluation. Syntheticmedia is now the default production layer formarketing, training, and communications, raisingchallenges around deepfakes, provenance, IP, anddisclosure norms. Invisible AI is spreading as autility layer, reducing friction but risking skillatrophy and over-reliance if not carefully governed The market shows bubble characteristics—inflatedvaluations, me-too tools, and pilot sprawl—butdurable value is emerging where AI is tied tooperations design, proprietary data, and riskmanagement. Governance, ethics, and readinessare the differentiators: continuous inventories andrisk scoring, practical ethics embedded in designand review, strong knowledge management, andarchitectures built for portability and observability. Human–AI interaction is now a design andgovernance challenge. Prompting has evolved intointeraction architecture that must be auditable andtestable, with interfaces diversifying across text,voice, visual context, and embedded triggers.Organizations must balance seamless automationwith meaningful oversight, making deliberatechoices about where to introduce friction foraccountability. Leaders are encouraged to use scenario-basedthinking around energy, platform structure,regulation, labor, and data to avoid single-pathbets. AI is now a structural dependency alongsidefinance, cybersecurity, and supply chains; strategymust treat it with the same discipline. AI literacy and culture are central to success.Workers must learn to frame goals, interpretunexpected behavior, and understandboundaries—especially as physical AI extends into Table of Contents 1. The Strategic Context for AI in 2026.................................................................................4 1.1 From Tools to Infrastructures............................................................................................................41.2 Power, Geography, and Regulation....................................................................................................51.3 The Emerging AI Bubble(s)................................................................................................................6 2. Agentic AI and Hybrid Workflows....................................................................................7 2.1 Agentic AI at Enterprise Scale...........................................................................................................72.2 Agent Ops and Operating Models..............................................