a16z "Major Concepts for 2026: Part Two"

Written by: a16z New Media

Translated by: Block unicorn

Yesterday, we shared the first part of the “Major Concepts” series, which includes our infrastructure, growth, biology + health, and insights from the Speedrun team members about the challenges startups will face in 2026.

Today, we continue to release the second part of this series, featuring contributions from American Dynamism (a16z’s dedicated investment team established in 2021) and application teams.

American Dynamism

David Ulevitch: Building AI-native Industrial Foundations

The U.S. is rebuilding those economic components that truly empower the nation’s strength. Energy, manufacturing, logistics, and infrastructure are once again in focus, and the most important shift is the rise of industrial foundations that are genuinely AI-native and software-first. These companies start with simulation, automated design, and AI-driven operations. They are not modernizing the past; they are building the future.

This presents huge opportunities in fields like advanced energy systems, heavy robotics manufacturing, next-generation mining, biological and enzymatic processes (producing precursor chemicals relied upon by various industries). AI can design cleaner reactors, optimize extraction, engineer better enzymes, and coordinate autonomous machine clusters with insights beyond traditional operators’ reach.

The same transformation is reshaping worlds outside factories. Autonomous sensors, drones, and modern AI models now enable continuous monitoring of ports, railways, power lines, pipelines, military bases, data centers, and other critical systems that were once too large or complex to manage comprehensively.

The real world needs new software. Founders building this software will shape the prosperity of the next century in America.

Erin Price-Wright: The Revival of American Manufacturing

America’s first great century was built on a strong industrial base, but as is well known, we have lost much of that industrial strength—partly due to offshoring, partly due to deliberate societal neglect. However, rusted machines are coming back online, and we are witnessing a renaissance of AI and software-driven American factories.

I believe by 2026, we will see companies adopting factory thinking to tackle challenges in energy, mining, construction, and manufacturing. This means combining AI and automation with skilled workers to make complex, customized processes operate as efficiently as assembly lines. Specifically:

Rapid and repeated responses to complex regulations and permitting processes

Accelerated design cycles from the start, including manufacturability considerations

Better management of large-scale project coordination

Deployment of autonomous systems to accelerate tasks that are difficult or dangerous for humans

By applying technologies developed by Henry Ford over a century ago—planning scale and repeatability from the outset—and integrating the latest advances in AI, we will soon achieve large-scale production of nuclear reactors, build housing to meet national demand, construct data centers at astonishing speed, and usher in a new golden age of industrial strength. As Elon Musk says, “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. A similar revolution is coming to the physical world.

With cities deploying over a billion connected cameras and sensors across the U.S., physical observability—real-time understanding of city, grid, and infrastructure operations—becomes both urgent and feasible. This new perception layer will also push the next frontier of robotics and autonomous tech, where machines rely on a universal framework that makes the physical world as observable as code.

Of course, this shift carries real risks: tools capable of detecting wildfires or preventing construction accidents could also spawn dystopian nightmares. The winners of this next wave will be companies that earn public trust by building systems that protect privacy, are interoperable, and natively support AI—enhancing societal transparency without undermining freedoms. Who can build this trustworthy framework will define the future trajectory of physical observability over the next decade.

Ryan McEntush: Electronic Industrial Stack Will Change the World

The next industrial revolution will not only happen inside factories but also within the machines powering them.

Software has profoundly changed the way we think, design, and communicate. Now, it is transforming how we move, build, and produce. Advances in electrification, materials, and AI are converging to allow software to truly control the physical world. Machines are beginning to perceive, learn, and act autonomously.

This is the rise of the electronic industrial stack—a comprehensive technology powering electric vehicles, drones, data centers, and modern manufacturing. It connects the atoms of the world with the bits that control it: from mined minerals refined into components, energy stored in batteries, electricity managed by electronics, to motion driven by precise motors—all coordinated by software. It underpins every breakthrough in physical automation; it determines whether software merely calls a ride or actually controls the steering wheel.

However, the ability to build this stack—from refining critical materials to manufacturing advanced chips—is eroding. If the U.S. wants to lead the next industrial era, it must manufacture the hardware supporting it. Countries mastering the electronic industrial stack will define the future of industrial and military technology.

Software is devouring the world. Now, it will propel the world forward.

Oliver Hsu: Autonomous Labs Accelerate Scientific Discovery

As multimodal model capabilities improve and robotic manipulation advances, teams will accelerate autonomous scientific discovery. These parallel developments will give rise to autonomous laboratories capable of closing the loop in science—hypotheses generation, experiment design and execution, inference, results analysis, and iteration on future research directions. Teams building these labs will be interdisciplinary, integrating AI, robotics, physics and life sciences, manufacturing, and operations, enabling continuous cross-disciplinary experiments and discoveries in unmanned labs.

Will Bitsky: Data Journey in Critical Industries

By 2025, the spirit of AI will be defined by compute resources and data center construction. By 2026, it will be defined by data resources and the next frontier of data journey—our critical industries.

Our critical industries remain treasure troves of potential, unstructured data. Every truck dispatch, meter reading, maintenance task, production run, assembly, and test provides material for model training. Yet, terms like data collection, annotation, or training are rarely used in industry.

Demand for such data is relentless. Companies like Scale, Mercor, and AI research labs tirelessly collect process data—not just “what was done,” but “how it was done.” They pay high premiums for data from “blood, sweat, and tears.”

Industrial firms with existing physical infrastructure and labor advantage will leverage this. Their operations generate vast amounts of data, almost free at the margin, used to train their models or license to third parties.

We should also expect startups to emerge and assist. Startups will provide coordinating stacks: software tools for collection, annotation, licensing; sensor hardware and SDKs; reinforcement learning environments and training pipelines; and ultimately, their own intelligent machines.

Application (Apps) Teams

David Haber: AI-Enhanced Business Models

Top AI startups are not just automating tasks—they are amplifying their customers’ economic benefits. For example, in legal, where success fees are contingent on winning, firms like Eve use proprietary outcome data to predict case success, helping law firms select better cases, serve more clients, and increase win rates.

AI itself can strengthen business models. It not only cuts costs but also generates additional revenue. By 2026, this logic will extend across industries, as AI systems align more deeply with customer incentives and create compound advantages beyond traditional software.

Anish Acharya: ChatGPT Will Become the App Store for AI

The consumer product cycle requires three elements for success: new technology, new consumer behaviors, and new distribution channels.

Until recently, the AI wave satisfied the first two but lacked a native new distribution channel. Most products grew via existing networks like X or word-of-mouth.

However, with the release of OpenAI Apps SDK, Apple’s support for mini-programs, and the ChatGPT group chat feature, consumer developers can now directly reach ChatGPT’s 900 million users and grow via new mini-program networks like Wabi. As the final part of the consumer product lifecycle, this new distribution channel promises to ignite a once-in-a-decade AI-driven consumer tech boom in 2026. Ignoring it will be at your own peril.

Olivia Moore: Voice Agents Are Starting to Take a Place

Over the past 18 months, the idea of AI agents handling real interactions for businesses has shifted from science fiction to reality. Thousands of companies—from SMBs to large enterprises—are using voice AI for scheduling, reservations, surveys, customer info collection, and more. These agents save costs, generate additional revenue, and free up employees for more valuable—and more interesting—work.

But since this field is still early-stage, many companies are at the “voice as an entry point” phase, offering just one or a few call types as a single solution. I’m excited to see voice assistants evolve to handle entire workflows (possibly multimodal), even managing complete customer lifecycle processes.

This likely means agents will be more deeply integrated into business systems and empowered to handle more complex interactions. As foundational models improve—today’s agents can invoke tools and operate across different systems—each company should deploy voice-first AI products to optimize key business functions.

Marc Andrusko: Proactive Applications Without Prompts Are Coming

By 2026, mainstream users will say goodbye to prompts. The next generation of AI apps will not show prompts—they will observe your actions and proactively suggest next steps. Your (IDE) will recommend refactoring before you ask. Your (CRM) will automatically generate follow-up emails after calls. Your design tools will generate options as you work. Chat interfaces will become auxiliary tools. Today, AI will be an invisible scaffolding across workflows, activated by user intent rather than commands.

Angela Strange: AI Will Ultimately Upgrade Banking and Insurance Infrastructure

Many banks and insurers have integrated AI features like document ingestion and voice agents into their legacy systems, but only by rebuilding the underlying infrastructure that supports AI can the industry truly transform.

By 2026, the risk of failing to modernize and fully leverage AI will outweigh the risk of failure. We will see large financial institutions abandon traditional vendor contracts to adopt newer, more AI-native platforms. These companies will shed past silos to become platforms that centralize, standardize, and enrich underlying data from legacy systems and external sources.

What will be the result?

Workflows will be significantly simplified and parallelized. No more toggling between different systems and screens. Imagine: you can see and handle hundreds of pending tasks simultaneously within your mortgage origination system (LOS), with agents even completing some of the more tedious parts.

The familiar classifications will merge into larger categories. For example, customer KYC, account opening, and transaction monitoring data can now be unified on a single risk platform.

The winners in these new classifications will be ten times larger than legacy firms: broader scope and the software market consuming labor.

The future of financial services isn’t just applying AI to old systems—it’s building an entirely new operating system based on AI.

Joe Schmidt: Forward-Looking Strategies Bring AI to 99% of Companies

AI is the most exciting technological breakthrough of our lifetime. Yet, so far, most of the gains from new startups have gone to the 1% of Silicon Valley—either truly located in the Bay Area or part of its vast network. Understandably: entrepreneurs want to sell products to familiar, accessible companies—either by visiting their offices or through venture capital contacts on boards.

By 2026, this will change dramatically. Companies will realize that most AI opportunities lie outside Silicon Valley, and new startups will use forward-looking strategies to unearth opportunities hidden within large traditional verticals. Sectors like consulting, system integration, implementation, and slow-moving manufacturing will hold enormous AI potential.

Seema Amble: AI Creates New Coordination Layers and Roles in Fortune 500 Companies

By 2026, companies will move further from isolated AI tools toward multi-agent systems that operate like coordinated digital teams. As agents begin managing complex, interdependent workflows—such as joint planning, analysis, and execution—organizations will need to rethink how work is structured and how context flows between systems. Companies like AskLio and HappyRobot are already experiencing this shift, deploying agents across entire workflows rather than single tasks.

Fortune 500 firms will feel this change most acutely: they hold the largest siloed data, institutional knowledge, and operational complexity—most of which resides in employees’ minds. Transforming this information into a shared base for autonomous workers will unlock faster decisions, shorter cycles, and end-to-end processes that no longer rely on constant micro-management.

This shift will also force leaders to reconceptualize roles and software. New functions will emerge—AI workflow designers, agent managers, and governance leaders responsible for coordinating and auditing collaborative digital workers. Beyond existing record systems, enterprises will need coordinating systems: new layers to manage multi-agent interactions, assess context, and ensure the reliability of autonomous workflows. Humans will focus on edge cases and the most complex scenarios. The rise of multi-agent systems is not just another automation step; it’s a rearchitecture of enterprise operations, decision-making, and value creation.

Bryan Kim: Consumer AI Will Shift from “Help Me” to “Understand Me”

2026 marks the transition of mainstream consumer AI products from productivity helpers to tools that deepen human connection. AI will no longer just assist with tasks; it will help you better understand yourself and strengthen your relationships.

Make no mistake: this is no easy feat. Many social AI products have launched but failed in the end. Yet, 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 your chatbots. Imagine your photo albums capturing real emotional moments, one-on-one and group chat modes adapting to your conversations, and daily habits changing under stress.

Once these products truly arrive, they will be part of our everyday lives. Generally, “understand me” products will have better user retention than “help me” products. “Help me” products profit from users’ high willingness to pay for specific tasks, focusing on retention. “Understand me” products generate revenue through ongoing daily interactions: users are less willing to pay but more likely to stay engaged.

People have always exchanged data for value—whether the returns are worth it remains to be seen, but the answer is coming soon.

Kimberly Tan: New Model Primitives Will Enable Unprecedented Companies

By 2026, we will witness the rise of companies that were previously impossible before breakthroughs in reasoning, multimodality, and computer applications. So far, many industries (like legal or customer service) have used improved reasoning techniques to enhance existing products. But now, we are just beginning to see companies whose core features fundamentally rely on these new model primitives.

Advances in reasoning ability can create new capabilities—such as evaluating complex financial claims or acting on dense academic or analyst research (e.g., resolving billing disputes). Multimodal models enable extraction of latent video data from the physical world (like manufacturing site cameras). Computer applications make automation in large industries possible, industries that were once constrained by desktop software, poor APIs, and fragmented workflows.

James da Costa: AI Startups Scale by Selling to Other AI Startups

We are in an unprecedented wave of new company creation, driven mainly by the current AI product cycle. But unlike past product cycles, existing companies are not sitting on the sidelines; they are actively adopting AI. So, how can startups succeed?

One of the most effective and underestimated ways for startups to surpass existing companies in distribution channels is to serve newly founded companies right from the start—serving greenfield companies. If you attract all the new startups and grow alongside them, you’ll become a large company as your customers grow. Companies like Stripe, Deel, Mercury, Ramp have all followed this strategy. In fact, many of Stripe’s early customers didn’t even exist when Stripe was founded.

By 2026, we will see startups founded from scratch scale across many enterprise software sectors. They only need to create better products and vigorously develop new customers not yet constrained by existing vendors.