ai rating summary
🧠 Analysis Trace
🏷️ [科技/技术] | 信息密度：9 | 🆕 新颖度：8
判断: 全文以“2026年技术演进路线图”为锚点，密集输出7个垂直领域（Infrastructure、Growth、Bio+Health、Speedrun）共14项具象技术预判，全部基于当前技术断层（如数据熵、agent-native并发瓶颈、视频时序一致性缺失）提出可验证的工程级突破路径，无空泛概念堆砌；新颖度略扣1分因部分方向（如世界模型、垂直AI multiplayer）已在学术/产业界初现端倪，但本文首次系统性锚定2026年成熟临界点并定义落地形态。
🎯 核心信号 (The Signal)
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2026年将爆发一场由“AI原生基础设施”驱动的范式迁移：技术重心从模型能力转向多模态数据治理、agent-native系统架构、视频物理一致性建模、人机协作协议重构四大底层能力，企业竞争力将取决于能否在数据熵、并发风暴、跨模态语义对齐和机器可读性等新维度建立护城河。
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关键要点或时间证据链条线：
- 数据熵成为新瓶颈：80%企业知识沉没于非结构化多模态数据（PDF/视频/日志），RAG幻觉与代理崩溃根源是数据新鲜度、结构化、真实性三重衰减——催生“数据清洗-结构化-验证-治理”全栈平台机会。
- Agent-native基础设施强制升级：单次代理目标触发5,000+并发子任务将使现有数据库/限流器误判为DDoS；赢家需重构控制平面，将“雷鸣 herd 模式”设为默认态，解决毫秒级协调、状态管理与策略执行。
- 视频从被动媒介进化为可进入空间：2026视频模型将实现时间连续性、状态记忆、交互响应与物理一致性，支撑机器人训练、游戏演化、设计原型等“感知-行动闭环”场景。
- 系统记录（SoR）战略地位坍塌：AI使CRM/ITSM从静态数据库升维为自主工作流引擎，智能执行层取代SoR成为核心价值界面，后者退化为商品化存储层。
- 健康科技出现“Healthy MAUs”新客群：AI压降预防性健康服务成本+新型保险产品+订阅付费习惯，催生面向非病患但高频健康监测人群的持续服务模式。
⏳ 关键时间线 (Timeline)
- 2025年：垂直AI完成“信息检索→推理”跃迁（如Hebbia分析财报、EliseAI诊断设备故障）。
- 2026年：垂直AI进入“multiplayer模式”，跨角色代理协同（买家/卖家/医生/患者）形成闭环协作层，网络效应显现。
- 2026年：首个AI-native大学诞生，课程/评估/研究协作实时自适应，教授转型为“学习架构师”。
⚖️ 立场与倾向 (Stance & Bias)
- 作者意图：深度探讨
- 潜在偏见：作为顶级VC，隐含“技术乐观主义”立场——假设所有技术断层（如视频时序建模、agent并发调度）均能在2026年前被工程化攻克，未充分讨论算力约束、监管滞后或跨组织协作壁垒等现实阻力；对“Healthy MAUs”的商业化路径也回避了医保支付改革这一关键前提。
🌲 关键实体与作用 (Entities & Roles)
- a16z : 本文发布主体与思想策源地，以风险投资机构身份将技术预判转化为可投赛道地图 ⇒ 中立
- Kling O1 / Runway Aleph : 多模态创作工具的早期实践代表，作为“2026 AI视频革命”的现存佐证而非终局方案 ⇒ 正面
- World Labs (Marble) / DeepMind (Genie 3) : 世界模型技术标杆，证明文本生成3D环境已可行，为“生成式Minecraft”提供技术基底 ⇒ 正面
- ASU（亚利桑那州立大学）/ SUNY（纽约州立大学） : AI-native大学的先行实验体，提供从AI助教到AI素养必修课的渐进式落地案例 ⇒ 正面
💡 启发性思考 (Heuristic Questions)
- 如果“数据熵”是2026最大瓶颈，那么当企业开始为PDF/视频/日志的“结构化新鲜度”付费时，传统数据治理厂商（如Informatica、Talend）与新兴AI原生平台（如Unstructured.io、LlamaIndex）的价值捕获边界将如何重划？
original content
The biggest problems builders will tackle in 2026, according to our partners
Our job as investors is to immerse ourselves in the ins-and-outs of every corner of the tech industry in order to understand where things are moving next. So every December, we ask our investing teams to share one big idea they think tech builders will tackle in the year to come.
Today, we’re sharing ideas from our Infrastructure, Growth, Bio + Health, and Speedrun teams. Stay tuned for takes from our other teams tomorrow.
Infrastructure
Jennifer Li: Startups tame the chaos of multimodal data
Unstructured, multimodal data has been enterprises’ biggest bottleneck and their biggest untapped treasure. Every company is drowning in PDFs, screenshots, videos, logs, emails, and semi-structured sludge. Models keep getting smarter but the inputs keep getting messier, which causes RAG systems to hallucinate, agents to break in subtle, expensive ways, and critical workflows to still heavily rely on human QA. The limiting factor for AI companies is now data entropy: the steady decay of freshness, structured, and truth inside the unstructured universe where 80% of corporate knowledge now lives.
That’s why untangling unstructured data becomes a generational opportunity. Enterprises need a continuous way to clean, structure, validate and govern their multimodal data so downstream AI workloads actually work. The use cases are everywhere: contract analysis, onboarding flows, claims handling, compliance, support, procurement, engineering search, sales enablement, analytics pipelines, and every agent workflow that depends on reliable context. Startups that build the platform that extracts structure from documents, images, and videos; reconciles conflicts; repairs pipelines; or keeps data fresh and retrievable hold the key to the kingdom of enterprise knowledge and process.
Joel de la Garza: AI revives cybersecurity hiring
For the better part of the last decade, the biggest challenge CISOs faced was hiring. From 2013 to 2021, the number of unfilled cybersecurity jobs grew from under 1M to 3M. That’s because security teams hire highly skilled technicians to spend their days doing soul-crushing level 1 security work like reviewing logs, and no one wants to do this work. The problem is that cybersecurity teams create that drudgery by buying products that detect everything, which means that their teams need to review everything—which, in turn, creates false labor scarcity. It’s a vicious cycle.
In 2026, AI will break this cycle and close this hiring gap by automating much of this repetitive and redundant work for cybersecurity teams. Anyone who has ever spent time working on a large security team knows that half the work is easily solved with automation, but it’s impossible to figure out what to automate when you’re drowning in work. AI-native tools that figure this out for security teams will finally free them up to do what they want to do: chase down bad guys, build new systems, and fix vulnerabilities.
Malika Aubakirova: Agent-native infrastructure becomes table stakes
In 2026, the biggest infrastructure shock won’t come from outside companies, but from within. We’re shifting from human-speed traffic that’s predictable and low concurrency to “agent-speed” workloads that’re recursive, bursty, and massive.
The enterprise backend of today was built for a 1:1 ratio of human action-to-system response. It’s not architected for a single agentic “goal” to trigger a recursive fan-out of 5,000 sub-tasks, database queries, and internal API calls in under milliseconds. When an agent attempts to refactor a codebase or remediate a security log, it doesn’t look like a user. To a legacy database or rate-limiter, it looks like a DDoS attack.
Building for agents in 2026 means re-architecting the control plane. We’ll see the rise of “agent-native” infrastructure. The next generation must treat “thundering herd” patterns as the default state. Cold starts must shrink, latency variance must collapse, and concurrency limits must jump by orders of magnitude. The bottleneck becomes coordination: routing, locking, state management, and policy enforcement across massive parallel execution. The winning platforms will be the only ones capable of surviving the deluge of tool execution that follows.
Justine Moore: Creative tools go multimodal
We now have the building blocks to tell stories with AI: generative voices, music, images, and video. But for anything beyond a one-off clip, it’s often time-consuming and frustrating—if not impossible—to get the outputs you want, especially if you want anywhere near the level of control that a traditional director would have.
Why can’t we feed a model a 30 second video and ask it to continue the scene with a new character created from a reference image and voice? Or reshoot a clip so we can see a scene from a different angle, or make the motion match a reference video?
2026 is the year AI goes multimodal. Give a model whatever form of reference content you have and work with it to make something new or edit an existing scene. We’ve started to see some early products here, like Kling O1 and Runway Aleph. But there’s a lot more to be done—and we need innovation at both the model and application layers.
Content creation is one of the killer use cases of AI, and I expect we’ll see multiple successful products across use cases and types of customers from meme makers to Hollywood directors.
Jason Cui: The AI-native data stack continues to evolve
We’ve seen a lot of consolidation in the “modern data stack” in the past year as data companies have moved from specialization across ingestion, transformation, and compute towards bundling and unified platforms. See: the Fivetran/dbt merger and continued rise of unified platforms like Databricks.
While the ecosystem feels notably more mature, we’re still in the early days of a truly AI-native data architecture. We’re excited by ways AI can continue to transform multiple parts of the data stack, and we’re beginning to see how data and AI infrastructure are becoming inextricably linked.
A few ideas we’re excited by:
- How data will continue to flow into performant vector databases alongside traditional structured data
- How AI agents navigate “the context problem”: continuously accessing the right data context and semantic layers in order to build robust applications, like chatting with your data, that always have the correct business definitions across multiple systems of record
- How traditional BI tools and spreadsheets will change as data workflows become more agentic and automated
Yoko Li: The year we step inside video
In 2026, video stops behaving like something we passively watch and starts feeling like a place we can actually step into. Video models can finally understand time, remember what they’ve already shown us, react when we do something, and hold together with the kind of quiet consistency we expect from the physical world. Instead of producing a few seconds of disconnected imagery, these systems sustain characters, objects, and physics long enough for actions to matter and for consequences to unfold. This shift turns video into a medium we can build on: a space where robots can practice, games can evolve, designers can prototype, and agents can learn by doing. What emerges is less like a clip and more like a living environment, one that starts to close the gap between perception and action. For the first time, it feels like we can inhabit the videos we generate.
Growth
Sarah Wang: Systems of record lose ground
In 2026, the real disruption in enterprise software is that the system of record will finally start to lose primacy. AI is collapsing the distance between intent and execution: models can now read, write, and reason directly across operational data, turning ITSM and CRM systems from passive databases into autonomous workflow engines. And as recent advances in reasoning models and agentic workflows compound, these systems gain the ability not just to respond, but to anticipate, coordinate, and execute end-to-end processes. The interface becomes a dynamic agent layer, while the traditional system of record slips into the background as a commodity persistence tier—its strategic leverage ceded to whoever controls the intelligent execution environment employees actually use.
Alex Immerman: Vertical AI evolves from information retrieval and reasoning to multiplayer
AI has driven vertical software to unprecedented growth. Healthcare, legal, and housing companies reached $100M+ ARR within a few years; finance and accounting are close behind. The evolution was first information retrieval: finding, extracting, and summarizing the right information. 2025 brought reasoning: Hebbia analyzing financial statements and building models, Basis reconciling trial balances across systems, EliseAI diagnoses maintenance issues and dispatches the right vendors.
2026 unlocks multiplayer mode. Vertical software benefits from domain-specific interfaces, data, and integrations. But vertical work is inherently multi-party. If agents are going to represent labor, they need to collaborate. From buyers and sellers, to tenants, advisors and vendors, each party has distinct permissions, workflows and compliance requirements that only vertical software understands.
Today, each party uses AI in isolation, which creates handoffs without authority. The AI analyzing purchase agreements doesn’t talk to the CFO for its model adjustments. The maintenance AI does not know what the onsite staff promised the tenant. Multiplayer changes by coordinating across stakeholders: routing to functional specialists, maintaining context, syncing changes. Counterparty AIs negotiate within parameters and flag asymmetries for human review. The senior partner’s markup trains the system for the entire firm. Tasks performed by AI will be completed with higher success rates.
And when value increases from multi-human and multi-agent collaboration, switching costs rise. Here we’ll see the network effects that have eluded AI applications: the collaboration layer becomes the moat.
Stephenie Zhang: Creating for agents, not humans
In 2026, people will start interfacing with the web through their agents. And what mattered for human consumption won’t matter the same way for agent consumption.
For years, we’ve optimized for predictable human behavior: rank high on Google, appear among the first few items on Amazon, lead with a TL;DR. When I took a journalism class in high school, we were taught the 5Ws + H for news, and to start with a hook for features. Maybe a human would miss the deeply relevant, insightful statement buried on page five, but the agent won’t.
This shift is about software, too. Apps were designed for human eyes and clicks, and optimization meant good UI and intuitive flows. As agents take over retrieval and interpretation, visual design becomes less central to comprehension. Instead of engineers staring at Grafana dashboards, AI SREs can interpret telemetry and post insights in Slack. Instead of sales teams combing through CRMs, agents can surface patterns and summaries automatically.
We’re no longer designing for humans, but for agents. The new optimization isn’t for visual hierarchy, but for machine legibility—and that will change the way we create and the tools we use to do it.
Santiago Rodriguez: The end of the screen time KPI in AI applications
For the last 15 years, screen time has been the best indicator of value delivery in both consumer and business applications. We’ve been living in a paradigm focused on hours of Netflix streaming, mouse clicks in a healthcare EHR UX (to demonstrate meaningful use), or even time spent on chatGPT as the key performance indicator. As we move to a future based on outcome-based pricing that perfectly aligns incentives between vendors and users, we’ll first move away from screen time reporting.
We’re already seeing this in practice. When I run DeepResearch queries on ChatGPT, I capture an enormous amount of value despite almost no screen time. When Abridge magically captures the patient-provider conversation and automates downstream activities, the doctor barely looks at the screen. When Cursor develops entire applications end-to-end, the engineer is planning the next feature development cycle. And when Hebbia drafts a pitch deck from hundreds of public filings, the investment banker is getting well deserved sleep.
This presents a unique challenge: how much an application can charge per user requires a more complex method of measuring ROI. Doctor satisfaction, developer productivity, financial analyst wellbeing and consumer happiness all increase with AI applications. The companies that tell the simplest sales pitch on ROI will continue to outpace their competitors.
Bio + Health
Julie Yoo: Healthy MAUs
In 2026, a new healthcare customer segment will take center stage: the “healthy MAUs.”
The traditional healthcare system has primarily served three main user segments: (a) “sick MAUs”: people with spiky, high-cost needs; (b) “sick DAUs*”: like those in intensive, long-term care; and (c) “healthy YAUs*”: relatively healthy individuals who rarely see a doctor. Healthy YAUs are at risk of becoming Sick MAUs/DAUs, and preventive care could slow that shift. But our reaction-pilled healthcare reimbursement system rewards treatment over prevention, so access to proactive check-ins and monitoring services are not prioritized, and insurance rarely covers them anyway.
Enter Healthy MAUs: consumers who aren’t actively sick but want to monitor and understand their health on a recurring basis—and also represent the potentially largest portion of the consumer population. We expect a wave of companies—both AI-native upstarts, and repackaged versions of incumbents—to start offering recurring services to serve this user base.
With AI’s potential to reduce the cost structure of care delivery, the advent of novel health insurance products focused on prevention, and consumers more comfortable paying for subscription-based models out of pocket, “healthy MAUs” represent the next high-potential customer segment for healthtech: continuously engaged, data-informed, and prevention oriented.
Speedrun
Jon Lai: World models take the spotlight in storytelling
In 2026, AI-powered world models will revolutionize storytelling through interactive virtual worlds and digital economies. Technologies like Marble (World Labs) and Genie 3 (DeepMind) already generate full 3D environments from text prompts, allowing users to explore them as if in a game. As creators adopt these tools, entirely new storytelling formats will emerge, potentially culminating in a “generative Minecraft,” where players co-create vast, evolving universes. These worlds could blend game mechanics with natural language programming, such as commanding, “create a paintbrush that changes the color of anything I touch to pink.”
Such models will blur the boundary between player and creator, transforming users into co-authors of dynamic shared realities. This evolution could spawn interconnected generative multiverses, where diverse genres like fantasy, horror, adventure can exist side by side. Within them, digital economies will flourish as creators earn income crafting assets, guiding newcomers, or developing new interactive tools. Beyond entertainment, these generative worlds will serve as rich simulation environments for training AI agents, robots, and perhaps even AGI. The rise of world models thus signals not just a new genre of play, but an entirely new creative medium and economic frontier.
Josh Lu: “The year of me”
2026 will become “the year of me”: the moment when products stop being mass-produced and start being made for you.
We’re seeing it everywhere already.
In education, startups like Alphaschool are building AI tutors that adapt to each student’s pace and curiosity, giving every kid an education that matches their pace and preferred method of learning. This level of attention previously would not be possible without tens of thousands of tutoring dollars spent per student.
In health, AI is designing daily supplement stacks, workout plans, and meal routines tailored to your biology. No need for a trainer or a lab.
Even in media, AI lets creators remix news, shows, and stories into personalized feeds that match your exact interests and tone.
The biggest companies of the last century won by finding *the average consumer.
*The biggest companies of the next century will win by finding the individual inside the average.
2026 is the year the world stops optimizing for everyone and starts optimizing for you.
Emily Bennett: The first AI-native university
In 2026, I expect we’ll see the birth of the first AI-native university, an institution built from the ground up around intelligent systems.
Over the past several years, universities have dabbled in AI-enabled grading, tutoring, and scheduling. But what’s emerging now is deeper, an adaptive academic organism that learns and optimizes itself in real time.
Picture an institution where courses, advising, research collaboration, and even building operations continuously adapt based on data feedback loops. Schedules optimize themselves. Reading lists evolve nightly and rewrite themselves as new research appears. Learning paths shift in real time to meet each student’s pace and context.
We’re already seeing precursors. ASU’s campus-wide partnership with OpenAI produced hundreds of AI-driven projects across teaching and administration. SUNY now embeds AI literacy into its general education requirements. These are the building blocks for more foundational deployment.
In the AI-native university, professors become architects of learning, curating data, tuning models, and teaching students how to interrogate machine reasoning
Assessment shifts, too. Detection tools and plagiarism bans give way to AI-aware evaluation, grading students on how they use AI, not whether they used it. Transparency and tactful application replaces prohibition.
And as every industry struggles to hire people who can design, govern, and collaborate with AI systems, this new university becomes the training ground, producing graduates fluent in orchestration who help augment a rapidly shifting workforce.
This AI-native university will become the talent engine for a new economy.
Stay tuned for part 2 tomorrow.
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