Original Title: Big Ideas 2026: Part 2
Original Author: a16z New Media
Translated by: Peggy, BlockBeats
Editor’s Note: If the breakthroughs in AI over the past year have redefined "model capabilities," today's trends are reshaping "application logic" and "industry boundaries." By 2026, AI will no longer be just a passive tool but will actively integrate into every workflow, becoming an invisible operating system that drives comprehensive upgrades in industry, finance, consumer sectors, and enterprise collaboration.
In the second part of the annual "Big Ideas 2026," a16z's American Dynamism and Apps teams believe that the keyword for 2026 is "reconstruction": reconstructing infrastructure, reconstructing distribution logic, and reconstructing the boundaries of human-machine collaboration. Those who can seize these trends first will define the next decade.
Here is the original text:
Yesterday, we released the first piece of content in the "Big Ideas" series, covering our infrastructure, growth, life sciences and health, as well as the issues the Speedrun team believes startups will address in 2026.
Related Reading: "a16z Predicts 2026: Four Major Trends Unveiled (Part 1)"
Today, we bring you the second part of the series, featuring insights from the American Dynamism and Apps teams. Stay tuned as we will share ideas from the crypto team tomorrow.
American Dynamism Team
David Ulevitch: Building an AI-Native Industrial Foundation
America is rebuilding the economic sectors that truly constitute national strength. Energy, manufacturing, logistics, and infrastructure are back in focus, with the most significant shift being the rise of a truly AI-native, software-centric industrial foundation. These companies start with simulation, automated design, and AI-driven operations; they are not transforming the past but building the future.
This brings enormous opportunities: advanced energy systems, heavy robotics manufacturing, next-generation mining, and bio- and enzymatic processes (producing key chemical precursors needed across industries). AI can design cleaner reactors, optimize resource extraction, engineer more efficient enzymes, and coordinate autonomous machine fleets with insights that traditional operators cannot match.
The same transformation is happening beyond factories. Autonomous sensors, drones, and modern AI models can now continuously monitor critical systems that were once difficult to manage comprehensively, such as ports, railways, power lines, pipelines, military bases, and data centers.
The real world needs new software. Entrepreneurs who can build it will shape America's prosperity for the next century. If you are one of those people, let’s talk.
Erin Price-Wright: The Revival of American Factories
America's first great century was built on industrial strength, but we have lost a significant amount of that power—partly due to offshoring and partly due to society's overall failure to continue building. But now, the rusty gears are turning again, and we are witnessing the rebirth of American factories centered around software and AI.
By 2026, I believe we will see companies responding to challenges in energy, mining, construction, and manufacturing with a "factory mindset." This means modular deployment of AI and autonomous technologies, collaborating with skilled workers, and making complex, customized processes operate like assembly lines. For example: quickly and repeatedly responding to complex regulations and approvals; accelerating design cycles with manufacturability in mind from the start; better managing large project coordination; deploying autonomous technologies to expedite tasks that are difficult or dangerous for humans.
By applying Henry Ford's ideas from a century ago—planning for scalability and repeatability from day one, while layering in the latest AI technologies—we will soon achieve mass production of nuclear reactors, meet housing demands, and rapidly build data centers, ushering in a new industrial golden age. As Elon Musk said, "The factory itself is the product."
Zabie Elmgren: The Next Wave of Observability Will Be in the Physical World, Not the Digital World
Over the past decade, software observability has changed how we monitor digital systems, making codebases and servers transparent through logs, metrics, and tracing. A similar revolution is about to happen in the physical world.
American cities have deployed over a billion connected cameras and sensors, and physical observability—real-time understanding of how cities, power grids, and other infrastructures operate—is becoming urgent and feasible. This new layer of perception will also drive the next frontier of robotics and autonomous technologies, enabling machines to rely on a universal network that presents the physical world as observably as code.
Of course, this shift brings real risks: tools that can detect wildfires or prevent construction site accidents could also give rise to dystopian nightmares. The next wave of winners will be those who earn public trust, building privacy-protecting, interoperable, AI-native systems that make society more transparent rather than less free. Those who can create this trusted network will define observability for the next decade.
Ryan McEntush: The Electrical Industrial Stack Will Propel the World Forward
The next industrial revolution will not only occur in factories but also within the machines that drive them.
Software has changed how we think, design, and communicate. Now, it is changing how we move, build, and produce. Advances in electrification, materials, and AI are converging to bring true software control to the physical world. Machines are beginning to possess the ability to perceive, learn, and act autonomously.
This is the rise of the electrical industrial stack—a comprehensive technology driving electric vehicles, drones, data centers, and modern manufacturing. It connects the atoms that power the world with the bits that command them: minerals refined into components, energy stored in batteries, electricity controlled by power electronics, and motion transmitted through precision motors, all coordinated by software. This is the invisible foundation behind breakthroughs in physical automation; it determines that software can not only hail a ride but also drive it.
However, the capability to build this stack—from refining key materials to manufacturing advanced chips—is waning. If America wants to lead the next industrial era, it must master the hardware that underpins it. Countries that master the electrical industrial stack will define the future of industrial and military technology.
Software has consumed the world, and now it will propel the world.
Oliver Hsu: Autonomous Laboratories Accelerate Scientific Discovery
As model capabilities continue to advance in multimodal fields and robotic operational capabilities improve, teams will accelerate their pursuit of autonomous scientific discovery. These parallel technologies will give rise to autonomous laboratories capable of closed-loop scientific exploration—from hypothesis generation to experimental design and execution, to reasoning, result analysis, and iteration of future research directions. Teams building these laboratories will be interdisciplinary, integrating expertise from AI, robotics, physics, life sciences, manufacturing, operations, and more, achieving cross-domain continuous experimentation through "unmanned laboratories," unlocking a new era of scientific discovery.
Will Bitsky: The Data War in Key Industries
In 2025, the defining characteristics of the AI era will be computational power limitations and data center construction; by 2026, it will be defined by data limitations and the frontlines of the next data war: our key industries.
These key industries remain sources of potential, unstructured data. Every truck departure, gauge reading, maintenance operation, production run, assembly, and test firing provides material for model training. However, data collection, labeling, and model training are not common terms in the industrial sector.
The demand for this data is not lacking. Companies like Scale, Mercor, and AI research labs are tirelessly collecting process data (not just "what was done," but also "how it was done") and paying a high price for every unit of "sweat data."
Industrial companies with existing physical infrastructure and labor forces have a comparative advantage in data collection and will begin to leverage it. Their operations will generate immeasurable data that can be captured at nearly zero marginal cost for training their own models or licensing to third parties.
We can also expect startups to emerge to provide assistance. These startups will deliver coordination stacks: software tools for data collection, labeling, and licensing; sensor hardware and SDKs; reinforcement learning environments and training pipelines; and ultimately, even their own intelligent machines.
Apps Team
David Haber: AI Enhances Business Models
The best AI startups are not just automating tasks; they are amplifying the economic benefits for customers. For example, in the field of risk proxy law, law firms only make money when they win cases. Companies like Eve leverage proprietary outcome data to predict case success rates, helping law firms select better cases, serve more clients, and improve win rates.
AI enhances the business model itself. It not only reduces costs but also generates more revenue. By 2026, we will see this logic expand across various industries, with AI systems deepening alignment with customer incentives, creating compound advantages that traditional software cannot reach.
Anish Acharya: ChatGPT Becomes the AI Application Store
The consumer product cycle requires three conditions: new technology, new consumer behavior, and new distribution channels.
Until recently, the AI wave met the first two conditions but lacked new native distribution channels. Most products relied on existing networks (like X) or word-of-mouth.
With the release of the OpenAI Apps SDK, Apple's support for mini-programs, and the introduction of group messaging features in ChatGPT, consumer developers can now directly reach ChatGPT's 900 million user base and achieve growth through mini-program networks like Wabi. As the final piece of the consumer product cycle puzzle, this new distribution channel will trigger a once-in-a-decade gold rush in consumer technology in 2026. Ignoring it will be at your own risk.
Olivia Moore: Voice Agents Begin to Take Space
In the past 18 months, the concept of AI voice agents managing real interactions for businesses has transitioned from science fiction to reality. Thousands of companies, from small to large, are using voice AI to schedule appointments, complete bookings, conduct surveys, and gather information. These agents help businesses reduce costs, increase revenue, and free human employees to engage in higher-value, more enjoyable work.
However, as the field is still in its early stages, many companies remain at the "voice touchpoint" stage, offering only one or a few types of calls as solutions. I look forward to seeing voice agents expand to handle complete workflows (potentially multimodal) and even manage entire customer relationship cycles.
This may involve agents deeply integrated into business systems, granting them the freedom to handle more complex interactions. As the underlying models continue to improve—agents can now call tools and operate across systems—there is no reason why every company shouldn't run voice-first AI products to optimize key aspects of their business.
Marc Andrusko: The Arrival of Promptless, Proactive Applications
2026 will mark the end of prompt boxes for mainstream users. The next wave of AI applications will have no visible prompt input—they will observe your actions and proactively suggest actions for your review. Your IDE will suggest refactoring before you even speak; your CRM will automatically draft follow-up emails after you finish a call; your design tools will generate variations while you work. Chat interfaces will merely be auxiliary wheels; today, AI will become an invisible scaffolding, permeating every workflow, triggered by intent rather than commands.
Angela Strange: AI Will Truly Upgrade Banking and Insurance Infrastructure
Many banks and insurance companies have already layered AI capabilities on traditional systems, such as document processing and voice agents, but AI can only truly transform financial services when we rebuild its underlying infrastructure.
By 2026, the risk of not upgrading to fully leverage AI will outweigh the risk of failure, and we will see large financial institutions allow old vendor contracts to expire and begin implementing updated, AI-native alternatives. These companies will no longer be constrained by past classification boundaries but will become platforms that centralize, standardize, and enrich underlying data from traditional systems and external sources.
What will the results be?
Workflows will be significantly simplified and parallel processing will be achieved, eliminating the need to jump between systems and interfaces. For example, you could view and process hundreds of tasks in a mortgage system in parallel, with agents even completing more mundane tasks.
Traditional categories will merge, forming larger new categories. For instance, customer KYC and onboarding and transition monitoring data can be integrated into a single risk platform.
Winners in these new categories will be ten times larger than the old giants: the categories will be larger, and the software market will be consuming the workforce. The future of financial services is not about applying AI to old systems but about building a new operating system based on AI.
Joe Schmidt: Front-Loaded Deployment Models Will Bring AI to 99% of Businesses
AI is the most exciting technological breakthrough of our generation. But so far, most of the gains for startups have been concentrated in the 1% of companies in Silicon Valley—whether in the literal Bay Area or its extended network. This is understandable: entrepreneurs want to sell to companies they are familiar with and can easily access, whether by driving to the office or through connections via VCs on the board.
By 2026, this situation will reverse. Businesses will realize that the vast majority of AI opportunities exist outside of Silicon Valley, and we will see new entrepreneurs adopting front-loaded deployment models to uncover opportunities hidden within large traditional industries. These opportunities have immense potential in traditional consulting and service industries (such as systems integration and implementation companies) and in slow-moving sectors like manufacturing.
Seema Amble: AI Creates New Orchestration Layers and Roles in the Fortune 500
By 2026, enterprises will further shift from isolated AI tools to multi-agent systems that need to operate like coordinated digital teams. As agents begin to manage complex, interdependent workflows—such as planning, analysis, and execution—organizations will need to rethink work structures and the flow of context between systems. We have already seen companies like AskLio and HappyRobot deploy agents throughout processes, not just for single tasks.
The Fortune 500 will feel this shift most acutely: they have the deepest isolated data pools, institutional knowledge, and operational complexity, much of which resides in human brains. Transforming this context into an underlying structure shared by autonomous workers will unlock faster decision-making, compressed cycles, and end-to-end processes that no longer rely on manual micromanagement.
This shift will also force leaders to rethink roles and software. New functions will emerge, such as AI workflow designers, agent supervisors, and governance leads responsible for orchestrating and auditing digital worker groups. On top of existing record systems, businesses will need coordination systems: to manage multi-agent interactions, adjudicate context, and ensure the reliability of autonomous workflows. Humans will focus on handling edge cases and the most complex scenarios. The rise of multi-agent systems is not just another step in automation but a reconstruction of how businesses operate, make decisions, and create value.
Bryan Kim: Consumer AI Shifts from "Help Me" to "See Me"
2026 will be the year consumer AI products shift from productivity to connectivity. AI will no longer just help you get work done; it will help you see yourself more clearly and build stronger relationships.
Of course, this is challenging. Many social AI products have been launched and failed. But thanks to multimodal context windows and decreasing reasoning costs, AI products can now learn from the full texture of your life, not just what you tell the chatbots. Imagine: photo albums showcasing real emotional moments, 1:1 messages and group chat modes changing based on the subject, daily habits adjusting under stress.
Once these products are launched, they will become part of our daily lives. Overall, "See Me" products will have better retention mechanisms than "Help Me" products. "Help Me" products monetize through discrete tasks with high willingness to pay and optimize subscription retention; "See Me" products monetize through continuous connected daily interactions: lower willingness to pay, but usage patterns are stickier.
People are already constantly exchanging data for value: the question is whether what they receive is worth it. And soon, this will become a reality.
Kimberly Tan: New Model Primitives Unlock Unprecedented Company Forms
By 2026, we will see some companies emerge that could not have existed in the past, now made possible by breakthroughs in reasoning, multimodality, and computer operations. So far, many industries (like legal or customer support) have only leveraged improved reasoning capabilities to enhance existing products. But we are just beginning to see companies whose core product capabilities are entirely driven by these new model primitives.
Advances in reasoning capabilities can unlock new functionalities, such as assessing complex financial claims or handling intensive academic or analytical research (like adjudicating billing disputes). Multimodal models make it possible to extract potential video data rooted in the physical world (for example, cameras on manufacturing floors). And computer operation capabilities enable automation in vast industries long locked down by desktop software, poor APIs, and fragmented workflows.
James da Costa: AI Startups Selling to Other AI Startups and Scaling
We are in an unprecedented moment of company creation driven by the current AI product cycle. But unlike before, existing giants are not "asleep"; they are actively adopting AI. So how can startups win?
One of the most powerful and underrated ways for startups to gain distribution rights is to serve companies at the formation stage: that is, greenfield companies (new businesses). If you can attract them at the time of their formation and grow with them, you will also become a large company as your customers grow. Companies like Stripe, Deel, Mercury, and Ramp have followed this strategy. In fact, when Stripe was founded, many of its customers did not yet exist.
By 2026, we will see these greenfield-focused startups scale across a range of enterprise software categories. The secret is simple: build better products and focus on new customers who are not constrained by existing giants.
Stay tuned, as we will share ideas from the crypto team tomorrow.
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