a16z Predicts by 2026, AI Will Refactor 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 our understanding of "model capabilities," 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 second part of the annual "Big Ideas 2026," a16z's American Dynamism and Apps team believe that the keyword for 2026 is "refactoring": refactoring infrastructure, refactoring distribution logic, refactoring 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 released 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 Major Trends Leading the Way 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 insights from the Crypto team.

American Dynamism Team

David Ulevitch: Building an AI-Native Industrial Foundation

The United States is rebuilding the economic sectors that truly constitute national strength. Energy, manufacturing, logistics, and infrastructure are back in focus, with the most significant transformation being the emergence of a truly AI-native, software-centric industrial foundation. Companies in this category start with simulation, automated design, and AI-driven operations. They are not transforming the past but building the future.

This presents significant opportunities: advanced energy systems, heavy-duty robotic manufacturing, next-generation mining, biological and enzyme-driven processes (producing key chemical precursors needed across industries), and more. AI can design cleaner reactors, optimize resource extraction, engineer more efficient enzymes, and coordinate autonomous machine fleets with insights traditional operators cannot match.

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, etc., which were once difficult to manage comprehensively.

The real world demands 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 chat.

Erin Price-Wright: The Renaissance of American Factories

America's first great century was built on industrial strength, but we lost a lot of that power—partly due to offshoring and partly because the overall society failed to continue building. 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 enterprises tackling challenges in energy, mining, construction, and manufacturing with a "factory mindset." This means: deploying AI and autonomous technology modularly, collaborating with skilled workers to make complex, custom processes run like assembly lines. For example: rapidly and iteratively responding to complex regulations and approvals; accelerating the design cycle with manufacturability in mind from the outset; better managing large-scale project coordination; deploying autonomous technology to speed up 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 overlaying 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. In Elon Musk's words: "The factory itself is the product."

Zabie Elmgren: The next wave of observability will be in the physical world, not the digital one

Over the past decade, software observability has changed how we monitor digital systems, making codebases and servers transparent through logs, metrics, and traces. The same revolution is about to occur in the physical world.

American cities have deployed over a billion IoT cameras and sensors. Physical observability—real-time understanding of how cities, the power grid, and other infrastructure operate—is becoming urgent and feasible. This new layer of perception 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 observably 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 winners of the next wave will be those who earn public trust, building privacy-preserving, interoperable, AI-native systems that make society more transparent rather than less free. Those who can create 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 factories but will also take place inside the machines powering those factories.

Software has changed our thinking, design, and communication methods. Now, it is transforming 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 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 through precision motors, all coordinated by software. This is the intangible foundation behind physical automation breakthroughs; it determines that software can not only call a ride but also drive on its own.

However, the capability to build this stack—from refining key materials to manufacturing cutting-edge chips—is eroding. If the United States wants to lead the next industrial age, it must master the hardware that underpins it. Nations that master 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 operation skills continue to improve, teams will accelerate 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, then to inference, result analysis, and iterative future research directions. The teams building these labs will be interdisciplinary, combining expertise from AI, robotics, physical and life sciences, manufacturing, operations, and more, realizing cross-disciplinary continuous experiments through "unmanned labs" to unlock a new era of scientific discovery.

Will Bitsky: Data Warfare in Key Industries

By 2025, AI's defining characteristics are computational power constraints and data center construction; however, in 2026, it will be defined by data limitations and the forefront of the next data warfare: within our key industries.

These key industries remain sources of untapped, unstructured data. Every truck dispatch, meter reading, maintenance operation, production run, assembly, and test-fire is material for model training. However, data collection, labeling, and model training are not common terms 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"), paying a hefty 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 vast amounts of 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.

Application Teams (Apps)

David Haber: AI-Enhanced Business Models

The best AI startups don't just automate tasks; they amplify their customers' economic benefits. For example, in the risk-sharing legal field, law firms only make money when they win. Companies like Eve use proprietary outcome data to predict case success rates, helping law firms select better cases, serve more clients, and increase win rates.

AI enhances the business model itself. It not only reduces costs but also brings in more revenue. By 2026, we will see this logic expand to all industries, where AI systems deepen alignment with customer incentives, creating compound advantages that traditional software cannot touch.

Anish Acharya: ChatGPT Becomes the AI App Store

Consumer product cycles require three conditions: new technology, new consumer behavior, and new distribution channels.

Until recently, the AI wave has fulfilled the first two conditions but lacked new native distribution channels. Most products rely 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 introducing group messaging features, consumer developers can now tap directly into ChatGPT's 9 billion user base and drive growth through mini-app networks like Wabi. As the final piece of the consumer product cycle, this new distribution channel will spark a once-in-a-decade consumer tech gold rush in 2026. Ignoring it would be at one's own peril.

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 shifted from science fiction to reality. Thousands of companies, from small and medium enterprises to large corporations, are using voice AI to schedule appointments, make bookings, conduct surveys, gather information, and more. These agents help companies cut costs, increase revenue, and free up human employees to do higher-value, more enjoyable work.

However, as this field is still in its early stages, many companies are still stuck at the "voice entry point" stage, only offering one or a few types of calls as a solution. I look forward to seeing voice agents expand to handle full workflows (possibly multimodal) and even manage the entire customer relationship lifecycle.

This may involve deeper integration of agents into business systems and giving them the freedom to handle more complex interactions. As the underlying models continue to improve—agents can now call on tools and operate across systems—there is no reason why every company shouldn't run a voice-first AI product, optimizing key aspects of the business.

Marc Andrusko: No Prompt, Proactive Applications on the Horizon

2026 will mark the mainstream user's farewell to prompts. The next wave of AI applications will have no visible prompt input at all—they will observe your actions and proactively suggest actions for you to review. Your IDE will suggest refactoring before you say a word; your CRM will automatically draft a follow-up email after your call ends; your design tool will generate variants while you work. The chat interface is just an auxiliary wheel; now AI will be an invisible scaffold, permeating every workflow, triggered by intent rather than command.

Angela Strange: AI 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 can 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 letting old vendor contracts expire to begin implementing updated, AI-native alternatives. These companies will no longer be constrained by past classification boundaries but will become platforms, centralizing, standardizing, and enriching data from legacy systems and external sources.

What's the result?

Workflows will be significantly simplified and achieve parallel processing, no longer requiring jumps between systems and interfaces. For example, you can view and process hundreds of tasks in parallel in a mortgage system, and agents can even handle more mundane tasks.

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

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

Joe Schmidt: Predeployed AI Bringing AI to 99% of Enterprises

AI is the most exciting technological breakthrough of our generation. However, so far, the majority of the benefits for startups have been concentrated in the 1% of Silicon Valley enterprises—whether literally in the Bay Area or in its extended network. This is also understandable: entrepreneurs want to sell to companies they are familiar with, easily accessible to, whether by driving to the office or by getting in touch through VC connections on the board.

By 2026, this situation will reverse. Enterprises will realize that the vast majority of AI opportunities exist outside of Silicon Valley, and we will see new entrepreneurs adopting a predeployed AI model to uncover opportunities hidden within large traditional industries. These opportunities are immense in traditional consultancy and service industries (such as system integrators and implementation companies) as well as slow-moving industries like manufacturing.

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

By 2026, enterprises will further move from isolated AI tools to multi-agent systems that operate like coordinating digital teams. As agents start managing 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 deploying agents throughout the entire process, not just for a single task.

The Fortune 500 will feel this shift most profoundly: they possess the deepest isolated pools of data, institutional knowledge, and operational complexity, much of which resides in human brains. Converting this context into an underlying structure shared by autonomous workers will unlock faster decision-making, shortened cycles, and achieve end-to-end processes no longer reliant on human micromanagement.

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 a digital workforce ensemble. Building upon existing record systems, enterprises will need to coordinate systems: managing multi-agent interactions, adjudicating context, and ensuring 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 restructuring of how enterprises operate, make decisions, and create value.

Bryan Kim: Consumer AI Shifting from "Help Me" to "See Me"

2026 will be the year when consumer AI products shift from productivity to connectivity. AI will no longer just help you get work done but will 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 the decreasing cost of reasoning, AI products can now learn from the full texture of your life, not just the content you tell the chatbot. Imagine this: photo albums displaying 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 part of our everyday life. Overall, "see me" products have a better retention mechanism compared to "help me" products. "Help me" products monetize through high willingness-to-pay discrete tasks and optimize subscription retention; "see me" products monetize through continuous connected daily interactions: lower willingness-to-pay but more sticky usage patterns.

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

Kimberly Tan: Unleashing New Model Primitives Unlock Unprecedented Company Forms

In 2026, we will see some companies emerge that could not have existed in the past and now, thanks to breakthroughs in modeling such as reasoning, multimodality, and computational operations, have become possible. So far, many industries (such as law or customer support) have merely used improved reasoning capabilities to enhance existing products. But we are only just beginning to see those companies whose core product capabilities are entirely driven by these new model primitives.

The advancement of reasoning capabilities can unlock new functionalities, such as assessing complex financial claims or handling intensive academic or analytical research (like arbitrating billing disputes). Multimodal models make it possible to extract latent video data from industries rooted in the physical world (such as cameras in manufacturing sites). And computational operation capabilities enable automation in huge industries long locked by desktop software, poor APIs, and fragmented workflows.

James da Costa: AI Startups Selling to Other AI Startups and Achieving Scale

We are currently in an unprecedented moment of company creation driven by the current AI product cycle. However, unlike in the past, existing giants are not 'sleeping,' and they are actively adopting AI as well. So, how can startups win?

One of the most potent and underestimated ways for startups to gain distribution power is to serve companies in their founding stage: the greenfield companies (brand-new businesses). If you can attract them at their founding and grow with them, as the customers scale, you will as well become a large company. Companies like Stripe, Deel, Mercury, Ramp, and others have followed this strategy. In fact, when Stripe was founded, many of its customers did not even exist yet.

In 2026, we will see these green-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 giants.

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