a16z Predicts by 2026, AI Will Restructure Industries, Applications, and Orgs (Part 2)

Original Article Title: Big Ideas 2026: Part 2
Original Article Author: a16z New Media
Translation: Peggy, BlockBeats

Editor's Note: If the breakthrough of AI in the past year has redefined the "model's capability," today's trends are reshaping "application logic" and "industry boundaries." In 2026, AI is no longer just a passive tool but actively integrated into every workflow, becoming an invisible operating system driving comprehensive upgrades in industry, finance, consumer, and enterprise collaboration.

In the annual "Big Ideas 2026" series, in Part 2, a16z's American Dynamism and Apps team believe that the keyword for 2026 is "reconstruction": reconstructing infrastructure, reconstructing distribution logic, and reconstructing the boundary of human-machine collaboration. Those who can seize these trends first will define the next decade.

The following is the original article:

Yesterday, we published the first piece of the "Big Ideas" series, covering what our infrastructure, growth, life sciences and health, and Speedrun teams believe startups will address in 2026.

Related Reading: "a16z Predicts Four Trends Leading in 2026 (Part 1)"

Today, we bring you the second part of this series, including insights from the American Dynamism and Apps teams. Stay tuned, as tomorrow we will share creativity from the crypto team.

American Dynamism Team

David Ulevitch: Building an AI-Native Industrial Foundation

The U.S. is rebuilding the economic sectors that truly constitute national strength. Energy, manufacturing, logistics, and infrastructure are once again in focus, and the most significant transformation is the emergence of a truly AI-native, software-centric industrial foundation. These companies start from 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-duty robotic manufacturing, next-generation mining, biological and enzyme catalysis (producing key chemical precursors required by various industries), etc. AI can design cleaner reactors, optimize resource extraction, engineer more efficient enzymes, and coordinate autonomous machine swarms with insight unmatched by traditional operators.

The same transformation is also happening outside of factories. Autonomous sensors, drones, and modern AI models can now continuously monitor key systems such as ports, railways, power lines, pipelines, military bases, data centers, which were once difficult to manage comprehensively.

The real world demands new software. Entrepreneurs who can build it will shape a century of American prosperity. If you are such a person, let's talk.

Erin Price-Wright: The Renaissance of American Factories

America's first great century was built on industrial might, but we lost much of that strength—partly due to offshoring, partly due to overall societal failure to sustainably build. But now, the rusted 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 tackling challenges in energy, mining, construction, and manufacturing with a "factory mindset." This means: modular deployment of AI and autonomous technology, collaboration with skilled workers to make complex, customized processes run like assembly lines. For example: rapidly and iteratively addressing complex regulations and approvals; accelerating design cycles while considering manufacturability from the outset; better management of large-scale project coordination; deploying autonomous technology to expedite tasks that are difficult or dangerous for humans.

By applying Henry Ford's century-old idea of planning for scalability and repeatability from day one and layering the latest AI technology, we will soon achieve mass production of nuclear reactors, meet housing demands, rapidly build data centers, and usher in a new industrial golden age. To quote Elon Musk: "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. The same revolution is about to occur in the physical world.

American cities have deployed over a billion IoT cameras and sensors, and physical observability—real-time understanding of the operation of cities, the power grid, and other infrastructure—is becoming urgent and feasible. This new perceptual layer will also drive the next frontier of robotics and autonomous technology, enabling machines to rely on a universal network that presents the physical world as observable as code.

Of course, this transformation brings real risks: tools that can detect wildfires or prevent construction site accidents could also give rise to dystopian nightmares. The next wave's winners will be those who earn public trust by building privacy-preserving, interoperable, AI-native systems that make society more transparent rather than less free. Whoever can build this trusted network will define the observability of the next decade.

Ryan McEntush: The Electrotechnical Industry Stack Will Drive the World Forward

The next industrial revolution is not only happening in the factory but inside the machines that power the factory.

Software has changed our way of thinking, designing, and communicating. 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 gaining the ability to perceive, learn, and act autonomously.

This is the rise of the electrotechnical industry stack—a comprehensive technology driving electric vehicles, drones, data centers, and modern manufacturing. It connects the atoms propelling the world with the bits commanding them: minerals refined into components, energy stored in batteries, power controlled by power electronics, motion transmitted by precision motors, all coordinated by software. This is the invisible foundation behind the breakthrough of physical automation; it determines that software can not only summon a car but also drive it itself.

However, the capability to build this stack—from refining critical materials to manufacturing advanced chips—is eroding. If the United States wants to lead the next industrial age, it must master the hardware that underpins it. Nations that dominate the electrotechnical industry stack will define the future of industrial and military technologies.

Software has devoured the world, and now it will propel the world.

Oliver Hsu: Autonomous Labs Accelerating Scientific Discovery

As modeling capabilities advance in multimodal domains and robotic operating abilities continue to improve, teams will expedite the pursuit of autonomous scientific discovery. These parallel technologies will give rise to autonomous labs capable of closed-loop scientific exploration—from hypothesis generation to experiment design and execution, to inference, result analysis, and iterative future research directions. The teams building these labs will be interdisciplinary, integrating expertise from AI, robotics, physical and life sciences, manufacturing, operations, and more, achieving cross-disciplinary continuous experimentation through "unmanned labs" to unlock a new era of scientific discovery.

Will Bitsky: Data Warfare in Key Industries

By 2025, the hallmark of the AI era is computational power constraints and data center construction; however, in 2026, it will be defined by data constraints and the forefront of the next data warfare: in our key industries.

These key industries remain sources of untapped, unstructured data. Every truck dispatch, meter reading, maintenance operation, production run, assembly, and test firing serve as material for model training. Yet, data collection, labeling, and model training are not common parlance in the industrial domain.

The demand for this data is insatiable. 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 workforce 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, used to train proprietary models, or licensed to third parties.

We can also expect startups to emerge to provide assistance. These startups will deliver a coordination stack: software tools for data collection, annotation, and licensing; sensor hardware and SDKs; reinforcement learning environments and training pipelines; and eventually, even their own intelligent machines.

Applications Team

David Haber: AI-Enhanced Business Models

The best AI startups don't just automate tasks, they amplify the economic benefit for the customer. For example, in the risk agent legal field, law firms only make money when they win cases. Companies like Eve use 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 to all industries, with AI systems deepening alignment with customer incentives, creating compound advantages that traditional software cannot reach.

Anish Acharya: ChatGPT Becoming an AI App Store

Consumer product cycles require three conditions: new technology, new consumer behaviors, 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-apps, and ChatGPT's introduction of group messaging capabilities, consumer developers can now directly reach ChatGPT's 9 billion users and drive growth through networks like Wabi. As the final piece of the consumer product cycle puzzle, this new distribution channel will trigger a once-in-a-decade consumer tech gold rush in 2026. Ignore it at your own risk.

Olivia Moore: Voice Agents Begin to Occupy Space

Over 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 SMBs to enterprises, are using voice AI to schedule appointments, make reservations, conduct surveys, gather information, and more. These agents help businesses reduce costs, increase revenue, and free up human employees to do higher-value, more enjoyable work.

However, since this field is still in its early stages, many companies are still stuck in the "voice-first touchpoint" phase, only offering one or a few types of calls as a solution. I look forward to seeing voice agents expand to handle full workflows (potentially multimodal) and even manage the entire customer relationship cycle.

This may involve deeper integration of agents into business systems and granting them the freedom to handle more complex interactions. With continuous improvement in underlying models—agents can now invoke tools and operate across systems—there is no reason why every company shouldn't run a voice-first AI product, optimizing critical aspects of their business.

Marc Andrusko: No Prompt, Proactive Applications on the Horizon

2026 will mark the mainstream farewell to tooltips. The next wave of AI applications will have no visible prompt input at all—they will observe your actions and proactively suggest actions for your review. Your IDE will propose refactoring before you speak; your CRM will automatically draft a follow-up email after your call ends; your design tool will generate variants as you work. The chat interface is merely a supporting wheel; now AI will be an invisible scaffolding throughout every workflow, triggered by intent rather than command.

Angela Strange: AI Set to Truly Upgrade Banking and Insurance Infrastructure

Many banks and insurance companies have already overlaid AI capabilities on top of legacy systems, such as document processing and voice agents, but AI will only truly transform financial services when we rebuild their 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 both legacy systems and external sources.

What will be the result?

Workflows will be significantly streamlined and achieve parallel processing, no longer requiring jumps between systems and interfaces. For example, you could view and process hundreds of tasks in parallel in a mortgage system, with agents even handling more menial tasks.

Traditional silos will merge to form larger new categories. For instance, customer KYC and onboarding and transition monitoring data can be integrated into a single risk platform.

The winners of these new categories will be ten times bigger than old giants: the category is larger, and the software market is eating away at the workforce. The future of financial services is not applying AI to old systems but building a new operating system based on AI.

Joe Schmidt: Pre-deployment Model Bringing AI to 99% of Enterprises

AI is the most exciting technological breakthrough of our generation. However, until now, most of the benefits for startups have been concentrated in the Silicon Valley's 1% enterprises—whether that's literally in the Bay Area or its extended network. This is also reasonable: entrepreneurs want to sell to companies they are familiar with, easy to reach, whether by driving to the office or through VC connections on the board.

By 2026, this situation will reverse. Enterprises will realize that the vast majority of AI opportunities exist outside Silicon Valley, and we will see new entrepreneurs adopt a pre-deployment model to uncover opportunities hidden in large traditional industries. These opportunities are enormous in traditional consulting and service industries (such as system integration and implementation companies) and slow-moving industries like manufacturing.

Seema Amble: AI Creating New Orchestration Layers and Roles in the Fortune 500

By 2026, enterprises will further move from isolated AI tools to multi-agent systems that need to operate like coordinating digital teams. As agents begin to manage complex, interdependent workflows—such as planning, analysis, and execution—organizations need to rethink work structures and the flow of context between systems. We have already seen companies like AskLio and HappyRobot deploy agents throughout the process, not just for a single task.

The Fortune 500 will feel this shift most profoundly: they possess the deepest isolated data pools, institutional knowledge, and operational complexity, much of which exists in human brains. Transforming this context into an underlying structure shared by autonomous workers will unlock quicker decisions, compressed cycles, and achieve end-to-end processes no longer reliant on human micro-management.

This shift will also compel leaders to rethink roles and software. New functions will emerge, such as AI workflow designers, agent supervisors, and governance leaders responsible for orchestrating and auditing the digital workforce. Building upon existing record systems, enterprises will need systems of coordination: to manage multi-agent interactions, adjudicate context, and ensure the reliability of autonomous workflows. Humans will focus on handling edge and the most complex cases. The rise of multi-agent systems is not just another step in automation but a reconfiguration of how enterprises operate, make decisions, and create value.

Bryan Kim: Consumer AI Moving from "Help Me" to "See Me" in 2026

2026 will be the year consumer AI products shift from productivity to connectivity. AI will no longer just help you get work done but help you see yourself more clearly and build stronger relationships.

Of course, this is difficult. Many social AI products have already been launched and failed. But thanks to multimodal context windows and decreasing inference costs, AI products can now learn from the full texture of your life, not just what you tell a chatbot. Imagine: photo albums showing real emotional moments, 1:1 messaging and group chat modes changing based on the participants, daily habits adjusting under stress.

Once these products land, they will become a part of our everyday lives. Overall, "see me" products have better retention mechanisms than "help me" products. "Help me" products monetize through high willingness to pay for discrete tasks and optimize subscription retention; "see me" products monetize through continuous engagement in daily life: lower willingness to pay, but more sticky usage patterns.

People are already continuously exchanging data for value: the question is whether what they get is worth it. And soon, this will become a reality.

Kimberly Tan: New Model Primitives Unlock Unprecedented Company Forms

In 2026, we will see some companies emerge that could not exist in the past and now, thanks to breakthroughs in modeling like inference, multimodality, and computer operation. So far, many industries (such as law or customer support) have just been using improved inference capabilities to enhance existing products. But we are only beginning to see companies whose core product capabilities are entirely driven by these new model primitives.

The advancement in inference capabilities can unlock new functionalities, such as assessing complex financial claims or handling dense academic or analytical research (e.g., adjudicating billing disputes). Multimodal models make it possible to extract potential video data from industries rooted in the physical world (e.g., cameras in manufacturing sites). And computer operation capabilities enable automation in vast industries long locked 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 being driven by the current AI product cycle. However, unlike before, existing giants are not "asleep," as they are also actively adopting AI. So, how can startups succeed?

One of the most powerful and underestimated ways startups win distribution rights is by serving companies at their inception stage: the greenfield companies (brand-new businesses). If you can attract them at the inception stage and grow with them, as customers scale, you will also become a big company. Companies like Stripe, Deel, Mercury, and Ramp have followed this strategy. In fact, when Stripe was founded, many of its customers did not exist yet.

In 2026, we will see these greenfield-focused startups scale across a range of enterprise software categories. The key is simple: build a better product and focus on new customers not beholden to existing incumbents.

Stay tuned as tomorrow we will share insights from the crypto team.

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