a16z Major Vision for 2026: Part Two

Author:a16z New Media,Compiled by：Block unicorn

Yesterday, we shared the first part of our "Major Visions" series, which included our infrastructure, growth, bio + health, and the challenges our Speedrun team partners believe startups will face in 2026.

Today, we continue with the second part of this series, featuring contributions from American Dynamism (an investment team established by a16z in 2021) and the application team. American Dynamism David Ulevitch: Building an AI-Native Industrial Foundation The United States is rebuilding the very components of the economy that truly empower the nation. Energy, manufacturing, logistics, and infrastructure are once again in focus, but the most significant shift is the rise of a truly AI-native, software-first industrial foundation. These companies are starting with analog, automated design, and AI-driven operations. They are not modernizing the past, they are building the future. This is creating tremendous opportunities in areas such as advanced energy systems, heavy robotics manufacturing, next-generation mining, and biological and enzymatic processes (producing precursor chemicals relied upon by various industries). Artificial intelligence can design cleaner reactors, optimize mining, design better enzymes, and coordinate swarms of autonomous machines with insights unmatched by traditional operators. The same transformation is reshaping the world beyond the factory. Autonomous sensors, drones, and modern AI models can now continuously monitor ports, railroads, power lines, pipelines, military bases, data centers, and other critical systems that were once too large and difficult to manage comprehensively. The real world needs new software. The founders who build this software will shape America's prosperity for the next century. Erin Price-Wright: The Revival of the American Factory America's first great century was built on a strong industrial base, but we have, as we know, lost much of that industrial power—partly due to offshoring and partly due to a deliberate lack of constructive engagement in society. Yet, rusty machines are starting up again, and we are witnessing a revival of the American factory, centered on software and artificial intelligence. I believe that by 2026, we will see businesses adopting a factory-like approach to challenges in sectors such as energy, mining, construction, and manufacturing. This means combining artificial intelligence and automation with skilled workers to make complex, customized processes operate as efficiently as an assembly line. Specifically, this includes: Rapidly and iteratively addressing complex regulatory and licensing processes; accelerating design cycles and designing for manufacturability from the outset; better managing large-scale project coordination; and deploying autonomous systems to accelerate tasks that are difficult or dangerous for humans. By applying technologies developed by Henry Ford a century ago, planning for scale and repeatability from the outset, and integrating the latest advancements in artificial intelligence, we will soon achieve mass production of nuclear reactors, build housing to meet national needs, construct data centers at astonishing speeds, and enter a new golden age of industrial power. As Elon Musk said, "The factory is the product." Zabie Elmgren: The Next Wave of Observability Will Be Physical, Not Digital. Over the past decade, software observability has transformed how we monitor digital systems, making codebases and servers transparent through logs, metrics, and traces. The same transformation is about to sweep the physical world. With over a billion connected cameras and sensors deployed in major U.S. cities, physical observability—the ability to understand the real-time operation of cities, power grids, and other infrastructure—is becoming both urgent and feasible. This new level of perception will also drive the next frontier in robotics and autonomous technologies, where machines will rely on a universal framework that makes the physical world as observable as code. Of course, this shift also carries real risks: tools capable of detecting wildfires or preventing construction site accidents could also trigger dystopian nightmares. The winners of the next wave will be companies that earn public trust, build privacy-preserving, interoperable, and natively AI-enabled systems, thereby increasing social transparency without compromising social freedom. Whoever builds this trustworthy framework will define the observable trajectory of the next decade. Ryan McEntush: Electronics Industrial Architecture Will Change the World. The next industrial revolution will not only happen in factories, but also inside the machines that power them. Software has already revolutionized how we think, design, and communicate. Today, it is changing how we travel, build, and produce. Advances in electrification, materials, and artificial intelligence are converging, enabling software to truly control the physical world. Machines are beginning to sense, learn, and act autonomously. This is the rise of the electronics industrial stack—the integrated technologies that power electric vehicles, drones, data centers, and modern manufacturing. It connects the atoms that drive the world to the bits that control it: from minerals refined into components, energy stored in batteries, electricity controlled by electronic devices, to motion achieved through precision motors—all coordinated by software. It is the invisible foundation behind every breakthrough in the field of physical automation; it determines whether software merely summons a taxi or truly takes control of the steering wheel. However, the ability to build this stack, from refining critical materials to manufacturing advanced chips, is dwindling. If the United States wants to lead the next industrial age, it must manufacture the hardware that underpins it. The nation that masters the electronics industrial stack will define the future of industrial and military technology. Software has devoured the world. Now, it will drive the world forward. Oliver Hsu: Autonomous Labs Accelerate Scientific Discovery. With advancements in multimodal modeling capabilities and continuous improvements in robotic manipulation, the team will accelerate autonomous scientific discovery. These parallel technologies will give rise to autonomous laboratories capable of achieving a closed loop in scientific discovery—from hypothesis formulation to experimental design and execution, to reasoning, results analysis, and iteration on future research directions. The teams building these laboratories will be interdisciplinary, integrating expertise from fields such as artificial intelligence, robotics, physics and life sciences, manufacturing, and operations, enabling continuous cross-disciplinary experimentation and discovery through unattended labs. Will Bitsky: The Data Journey in Critical Industries In 2025, the zeitgeist of artificial intelligence will be defined by the constraints of computing resources and data center construction. By 2026, it will be defined by the constraints of data resources and the next frontier of the data journey—our critical industries. Our critical industries remain a treasure trove of potential, unstructured data. Every truck trip, every meter reading, every maintenance operation, every production run, every assembly, every test run provides material for model training. However, neither data acquisition, annotation, nor model training are commonly used terms in industry. The demand for this type of data is inexhaustible. Companies like Scale, Mercer, and AI research labs are tirelessly collecting process data (not just "what was done," but "how it was done"). They pay exorbitant prices for every piece of data from these "sweatshops." Industrial companies with existing physical infrastructure and labor have a comparative advantage in data acquisition and will begin to leverage this advantage. Their operations generate massive amounts of data that can be captured at virtually zero marginal cost and used to train their own models or license them to third parties. We should also expect startups to emerge and help. Startups will provide a coordinated stack: software tools for collection, annotation, and licensing; sensor hardware and software development kits (SDKs); reinforcement learning (RL) environments and training pipelines; and ultimately, their own intelligent machines.

Apps Team

David Haber: Artificial Intelligence Enhances Business Models

The best AI startups are not just automating tasks; they are amplifying the economic benefits for their clients. For example, in profit-sharing law, law firms only receive revenue when they win. Companies like Eve use proprietary outcome data to predict case success rates, helping law firms select more suitable cases, serve more clients, and improve their success rates.

AI itself can enhance business models. It not only reduces costs but also generates more revenue. By 2026, we will see this logic extend to various industries as AI systems become more deeply aligned with client incentives and create compound advantages that traditional software cannot achieve.

Anish Acharya: ChatGPT Will Become the AI ​​App Store

A successful consumer product lifecycle requires three elements: new technology, new consumer behavior, and new distribution channels. Until recently, the AI ​​wave fulfilled the first two conditions but lacked new native distribution channels. Most products grew by relying on existing networks like X or word-of-mouth. However, with the release of the OpenAI Apps SDK, Apple's support for mini-programs, and ChatGPT's launch of group chat functionality, consumer developers can now directly leverage ChatGPT's 900 million user base and achieve growth through new mini-program networks like Wabi. As the final link in the consumer product lifecycle, this new distribution channel is expected to ignite a once-in-a-decade consumer tech gold rush in 2026. Ignore it, or face the consequences. Olivia Moore: Voice Agents Are Starting to Take a Place Over the past 18 months, the vision of AI agents handling real-world interactions for businesses has moved from science fiction to reality. Thousands of companies, from SMEs to large enterprises, are using voice AI to schedule appointments, complete bookings, conduct surveys, collect customer information, and more. These agents not only save businesses costs and generate additional revenue but also free up employees' time for more valuable—and more interesting—work. However, because this field is still in its early stages, many companies are still at the "voice as an entry point" stage, offering only one or a few types of calls as a single solution. I am excited to see voice assistants expanding to handle entire workflows (potentially multimodal) and even manage the entire customer relationship lifecycle. This likely means that agents will be more deeply integrated into business systems and given the freedom to handle more complex types of interactions. As the underlying model continues to improve—agents can now invoke tools and operate across different systems—every company should deploy voice-led AI products and leverage them to optimize critical aspects of their business. Marc Andrusko: Proactive, Prompt-Free Applications Are Coming. By 2026, mainstream users will say goodbye to prompts. The next generation of AI applications will be completely prompt-free—they will observe your actions and proactively offer suggestions. Your IDE will suggest refactoring before you even ask a question. Your CRM will automatically generate follow-up emails after you end a call. Your design tools will generate various solutions as you work. Chat interfaces will be merely auxiliary tools. Today, AI will become an invisible scaffolding throughout every workflow, activated by user intent rather than commands. Angela Strange: Artificial Intelligence Will Ultimately Upgrade Banking and Insurance Infrastructure. Many banks and insurance companies have already integrated AI features such as document import and AI voice agents into their traditional systems, but AI can only truly transform the financial services industry by rebuilding the infrastructure that underpins it. By 2026, the risk of failing to modernize and fully leverage AI will outweigh the risk of failure, and we will see large financial institutions abandon contracts with traditional vendors in favor of newer, more AI-native alternatives. These companies will break free from past categorizations and become platforms capable of centralizing, standardizing, and enriching underlying data from traditional systems and external sources. What will the result be? Workflows will be significantly simplified and parallelized. There will be no more switching back and forth between different systems and screens. Imagine this: you can see and process hundreds of pending tasks simultaneously in a Loan Origin System (LOS), with agents even handling the more tedious parts. Familiar categories will merge into larger ones. For example, customer KYC, account opening, and transaction monitoring data can now be unified in a single risk platform. The winners of these new categories will be 10 times larger than established companies: the scope of categories is much broader, and the software market is devouring labor. The future of financial services is not about applying artificial intelligence to old systems, but about building a completely new operating system based on artificial intelligence. Joe Schmidt: Forward-thinking strategies will bring AI to 99% of businesses. Artificial intelligence is one of the most exciting technological breakthroughs of our lifetime. However, to date, the majority of revenue from new startups has flowed to the top 1% of companies in Silicon Valley—either truly located in the Bay Area or part of its vast network. This is understandable: entrepreneurs want to sell their products to companies they know and can easily reach, whether by visiting their offices in person or by establishing connections through venture capitalists on their boards. By 2026, this will change dramatically. Businesses will realize that the vast majority of AI opportunities lie outside Silicon Valley, and we will see new startups using forward-thinking strategies to uncover more opportunities hidden within large, traditional verticals. AI holds immense potential in traditional consulting and service industries (such as systems integrators and implementation companies) and slower-growing sectors like manufacturing. Seema Amble: Artificial Intelligence Creates New Coordination Layers and Roles in Fortune 500 Companies By 2026, enterprises will further shift from siloed AI tools to multi-agent systems that need to function like coordinated digital teams. As agents begin to manage complex and interdependent workflows (such as co-planning, analysis, and execution), enterprises need to rethink the structure of work and how context flows between systems. We've already seen companies like AskLio and HappyRobot undergoing this shift, deploying agents throughout processes rather than in individual tasks. Fortune 500 companies will feel this shift most acutely: they possess the largest reserves of siloed data, institutional knowledge, and operational complexity, much of which resides in the minds of their employees. Transforming this information into a shared foundation for autonomous workers will unlock faster decision-making, shorter cycle times, and end-to-end processes that no longer rely on continuous human micromanagement. This shift will also force leaders to reimagine roles and software. New functions will emerge, such as AI workflow designers, agent managers, and governance leaders responsible for coordinating and approving collaborative digital workers. Beyond existing record-keeping systems, businesses will need coordination systems: new layers to manage multi-agent interactions, determine context, and ensure the reliability of autonomous workflows. Humans will focus on handling edge cases and the most complex situations. The rise of multi-agent systems is not just another step in automation; it represents a restructuring of how businesses operate, make decisions, and ultimately create value. Bryan Kim: Consumer AI Shifts from “Helping Me” to “Understanding Me” 2026 marks a shift in the function of mainstream consumer AI products from improving productivity to enhancing interpersonal connections. AI will no longer simply help you get things done, but rather help you understand yourself better and build stronger relationships. It needs to be clear: this is no easy task. Many social AI products have been launched, but all have ultimately failed. However, thanks to multimodal context windows and decreasing inference costs, AI products can now learn from every aspect of your life, not just what you tell a chatbot. Imagine your phone's photo album revealing genuine emotional moments, one-on-one messaging and group chat modes adapting to the chatter, and your daily habits changing under stress. Once these products truly emerge, they will become an integral part of our daily lives. Generally, "understand me" products have better user retention mechanisms than "help me" products. "Help me" products monetize through users' high willingness to pay for specific tasks and focus on improving user retention. "Follow me" products monetize through continuous daily interaction: users have a lower willingness to pay, but higher user retention. People are constantly exchanging data for value: the question is whether the rewards they receive are worthwhile. The answer will soon be revealed. Kimberly Tan: New Model Primitives Foster Unprecedented Companies By 2026, we will witness the rise of companies that simply could not have existed before breakthroughs in reasoning, multimodal models, and computer applications. To date, many industries (such as legal or customer service) have leveraged improved reasoning techniques to enhance existing products. But we are only now beginning to see companies whose core product functionality fundamentally relies on these new model primitives. Advances in reasoning capabilities can foster new abilities to assess complex financial claims or act based on intensive academic or analyst research (e.g., adjudicating billing disputes). Multimodal models enable the extraction of potential video data from the physical world (e.g., cameras at a manufacturing site). The application of computers enables automation in large industries whose value has historically been hampered by desktop software, poor APIs, and fragmented workflows. James da Costa: AI Startups Achieve Scale by Selling Products to Other AI Startups

We are in the midst of an unprecedented wave of company creation, primarily driven by the current AI product cycle. But unlike previous product cycles, existing companies are not sitting idly by; they are actively adopting AI. So, how can startups win?

One ​​of the most effective and underrated ways for startups to outpace existing companies in distribution channels is to serve them from the outset: that is, to serve those greenfield companies (i.e., brand new businesses). If you can attract all the new companies and grow with them, you will become a large company as your customer base grows. Companies like Stripe, Deel, Mercury, and Ramp have followed this strategy. In fact, many of Stripe's customers didn't even exist when Stripe was founded.