2026 AI's Top 10 Trends: From Competing on Models to Competing on Real-World Deployment
As the marginal returns of model-size scaling keep falling, and as AI moves from "generating answers" to "completing complex tasks," the AI industry is standing at the threshold of a deep transformation. The latest Tencent Research Institute report argues that the center of gravity of 2026 AI competition is undergoing a fundamental shift — away from the race for parameter counts, and toward a comprehensive contest on external model-engineering capability, commercial deployment efficiency, and adaptation to the surrounding social organization.
Jiwei's VIP channel recently released Co-evolution: 2026 AI Top 10 Trends Report, jointly produced by Tencent Research Institute, Tencent Cloud Intelligence, and Tencent Youtu. The report distills AI industry change into three chapters — Evolution, Landing, and Restructuring — and decomposes them into ten core trends: online evolution, multimodal cognition, scientific intelligence, harness engineering, citizen development, trusted execution, intelligence-as-a-service, the agentic web, liquid organizations, and role reconfiguration. Drawing on industry practice and progress from multiple vendors, and combining benchmark tests, enterprise case studies, and industry statistics, the report systematically analyzes the direction of change in the technical, engineering, commercial, organizational, and professional dimensions of the AI industry in 2026.
Part I — Evolution: Learning, perception, and discovery
This chapter focuses on the evolution of the model's own capabilities, pointing out that the growth model of simply piling up parameters, compute, and data is hitting diminishing returns. Model evolution is branching out into three new paths: post-deployment learning, deep multimodal understanding, and AI for Science.
Trend 1 — Online Evolution: Evolution doesn't stop at training; the model keeps learning after deployment
In the past, model capability gains mostly happened during the pretraining stage. In 2026, model evolution extends into the deployment-and-use stage. Reinforcement learning has shifted from RLHF (human feedback) to RLVR (programmatically verifiable feedback); this pattern is breaking through first in code and mathematics, and is now spreading into biology, materials science, and other research domains. The industry is overturning the assumption that "strongest equals largest" — CL-bench experiments show that even frontier models have clear shortcomings in their ability to read new information from context. Model iteration paths now show a four-stage progression: 2024 Scaling — piling up size; 2025 Reasoning — emergent inference; 2026 Context Learning — learning from context; and looking further out, Memory Consolidation.
Trend 2 — Multimodal Cognition: From renderer to creator; a deeper understanding of the world
The focus of multimodal AI is shifting from pursuing photorealistic rendering to building an understanding of the objective world. The evolution proceeds through four layers: controllable generation improves generation usability; Agent-ization implements intent understanding and step planning; native multimodal architectures let image and text encode and interact in the same latent space; and world models simulate physical operating rules, serving embodied scenarios like robotics and autonomous driving. The report's judgment: the standard for evaluating multimodal models has changed — it's no longer just about generation quality, but whether the model understands the physics, emotion, and ethics behind the picture.
Trend 3 — Scientific Intelligence: A trinity; the assault on AI for Science
In 2026 AI for Science enters its toughest phase, taking shape as a trinity of "foundation research model + research agent + self-driving lab (SDL)." Foundation models are gradually evolving into a research operating system; research agents participate in hypothesis generation and scenario reasoning; self-driving labs hand the experimental design decision-making over to AI, forming a complete "experiment planning — orchestration and scheduling — robot execution — online characterization — data and knowledge layer" closed loop. Application landing is concentrated in four major tracks — biomedicine, materials science, meteorology, and mathematics — with hundreds of AI-assisted drugs already in clinical trials. At the same time, the industry exposes significant risks: the number of AI-generated fake references is growing fast, and scientific literature pollution is worsening.
Part II — Landing: From available to usable infrastructure
Beyond the model's own capability gains, this chapter points out that the model's external engineering system has become the key bottleneck in releasing AI capability — covering the Agent engineering buildout, democratized AI construction, and trusted secure execution as a complete infrastructure.
Trend 4 — Harness Engineering: Put reins on the wild horse; the front line of competition moves outside the model
The report proposes the concept of "Harness Engineering" — the runtime container outside the model, responsible for managing memory, tool calls, task planning, error recovery, and multi-agent coordination. The technical evolution has gone from prompt engineering to context engineering and now, in 2026, to harness engineering: the focus has shifted from the quality of a single response to whether the whole system can stably complete complex long tasks. People's work patterns change accordingly — no longer step-by-step directing the AI to execute, but defining goals and acceptance criteria and designing the environment and boundaries the AI runs in.
Trend 5 — Citizen Development: The programming feasibility battlefield is won; AI-native work moves toward all industries
AI programming has completed its feasibility validation: capability has moved from code completion to full engineering autonomous delivery. The role of professional developers has shifted — the focus is no longer writing code, but defining intent, decomposing tasks, and orchestrating Agents. The capability to build is being democratized to non-developers: ordinary people can use natural language to generate applications. But moving from prototype to shippable product still has a high threshold, and judgment is becoming the new core capability. Industry diffusion follows three conditions: the task can be defined in natural language, the output can be verified immediately, and the process can be standardized.
Trend 6 — Trusted Execution: Paving trust as a pipeline; from autonomous to trustworthy
As Agents gain the ability to autonomously call tools and access systems, the traditional post-hoc review security model breaks down. The report proposes building an end-to-end trusted-execution pipeline, with three core elements: giving the Agent a verifiable unique identity, a fully traceable action chain, and minimized operating permissions. The security system has five layers: the environment layer provides a zero-trust compute sandbox; the identity layer does unique-identifier two-way authentication; the data-flow layer implements source verification and semantic tracing; the standardization layer unifies compliance and interoperability baselines; and the application layer does real-time interception and continuous security testing.
Part III — Restructuring: When AI becomes a participant in society
This chapter analyzes the social restructuring that AI brings to commercialization models, internet forms, enterprise organization, and individual careers.
Trend 7 — Intelligence as a Service: Token retreats backstage; intelligence moves to the front
Traditional AI business uses Token as the unit of measure, but Token can only count compute consumption, not measure business value. AI business is moving rightward along a spectrum: pay-by-the-token → subscription → credits → pay-by-workflow → pay-by-result → digital-employee-as-a-job. The further to the right, the higher the business responsibility the service provider takes on, and the higher the gross-margin ceiling. Enterprise-side governance thinking needs to upgrade from "saving Token consumption" to "managing intelligence budget" — avoiding the trap of only watching the consumption metric while ignoring the business output.
Trend 8 — The Agentic Web: A new main entity on the internet, when Agents start going online
Internet users are no longer only human: a huge number of AI Agents become the new main entity of the network, giving birth to the "agentic web." Multi-Agent collaborative architectures become mainstream: the main Agent does task-level command and control, dispatching research, execution, audit, and creative specialist Agents to work in parallel, while humans retreat to goal-setting and final-decision positions. Product-evaluation metrics shift from DAU and time-on-site to TCR (task-completion rate); competitive logic shifts from traffic-and-ad distribution to capability orchestration. For vertical-domain Agents, the core moat is not the underlying foundation model, but industry-knowledge accumulation, business-system integration, and deep workflow understanding.
Trend 9 — Liquid Organizations: Organizations start flowing; from solid structures to liquid orchestration
AI is driving enterprise organizations to evolve from traditional pyramid-shaped solid organizations to liquid organizations. Solid organizations are characterized by fixed departments, fixed positions, and top-down hierarchical decision-making; liquid organizations are task-centric, dynamically forming and disbanding project teams, with humans and AI Agents working together as a unit. Liquid organizations have three characteristics: on-demand teaming, elastic contracts, and assessment orientation shifting from fixed KPIs to mission-based goals. But while positions can flow, task boundaries, permissions, responsibility attribution, and assignment rules must be clear — relying on rules to maintain order, or responsibility vacuums will appear.
Trend 10 — Role Reconfiguration: From supervisor to architect; the center of gravity of the profession is moving up
AI's impact on employment is not the direct elimination of complete positions, but the decomposition and reorganization of the internal tasks of a position. Mechanical and information-organizing tasks are easy to automate, while architecture-class work — defining goals, setting constraints, judging results, and taking risk — continues to appreciate in value. Individual career paths undergo a leap: ordinary executor → augmented employee → supervisor → Agent manager → architect. The bar to execution work keeps falling, and the abilities of architecture, judgment, decision, and bearing consequences become the core competitiveness of talent.
Report core summary
This report fully maps out the 2026 AI co-evolution panorama. At the technology level, the growth dividend of simply scaling model size is fading, and post-deployment online learning, deep multimodal understanding, and AI for Science become the new growth points; at the engineering level, the center of competition shifts to external-model infrastructure like harness engineering and trusted execution; at the industry-application level, the business model shifts from selling compute tokens to delivering intelligence services; at the social level, Agents spawn new forms of the agentic web, enterprise organizations evolve toward liquid orchestration, and workers' career focus migrates upward from executing tasks to architecture and decision-making.
The report also objectively points out various real-world constraints: the RLVR technology path has real boundaries, scientific intelligence faces the risk of fake-literature pollution, autonomous Agents bring new security challenges, and liquid organizations raise the bar for governance rules. AI is not simply replacing humans — it is re-decomposing tasks and reshaping the division of labor. The future value highland is concentrated in the human abilities to define goals, set constraints, judge results, and take responsibility.