Big Ideas 2026: Part 2

The biggest problems builders will tackle in 2026, according to our American Dynamism and Apps partners

Yesterday, we shared the first installment of our Big Ideas package, which included the problems that our Infrastructure, Growth, Bio + Health, and Speedrun partners think startups will tackle in 2026.

Big Ideas 2026: Part 1

We’re back with part 2 of this package today, featuring contributions from our American Dynamism and Apps teams. Stay tuned for ideas from our Crypto team tomorrow.

American Dynamism

David Ulevitch: Building the AI-native industrial base

America is rebuilding the parts of the economy that create real strength. Energy, manufacturing, logistics, and infrastructure are back in focus, and the most important shift is the rise of an industrial base that is truly 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 what comes next.

This is opening major opportunities in advanced energy systems, robotics heavy manufacturing, next-generation mining, biological and enzymatic processes that produce the precursor chemicals every industry depends on, and much more. AI can design cleaner reactors, optimize extraction, engineer better enzymes, and coordinate fleets of autonomous machines with a level of insight no legacy operator can match.

The same shift is reshaping the world outside the factory. Autonomous sensors, drones, and modern AI models can now give continuous visibility into ports, rail, power lines, pipelines, military bases, datacenters, and other critical systems that were once too large to manage comprehensively.

The real world needs new software. The founders who build it will shape the next century of American prosperity. If that’s you, let’s talk.

Erin Price-Wright: The renaissance of the American factory

America’s First Great Century was built on industrial strength, but it’s no secret that we’ve lost much of that muscle—some of it due to offshoring, some of it due to an intentional, society-wide failure to build. But the rusty wheels are starting to creak into motion again, and we’re witnessing the rebirth of the American factory with software and AI at its heart.

In 2026, I think we’ll see companies approach challenges spanning energy, mining, construction, and manufacturing with a factory mindset. This looks like the modular deployment of AI and autonomy alongside skilled workers to make complex, bespoke processes operate like an assembly line. Think:

• Quickly and repeatedly navigate complex regulation and permitting

• Speed up design cycles, and design-for-manufacture from the get-go

• Better manage large-scale project coordination

• Deploy autonomy to speed up tasks that are difficult or dangerous for humans to manage

By applying techniques that Henry Ford developed a century ago, planning for scale and repeatability on day 0, and layering in the latest advances in AI, we’ll soon be mass-producing nuclear reactors, building housing that meets our nation’s demand, constructing datacenters at breakneck speed, and entering a new Golden Age of industrial strength. To quote Elon Musk, “the factory is the product.”

Zabie Elmgren: The next wave of observability will be physical, not digital

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

With more than a billion networked cameras and sensors already deployed across U.S. cities, physical observability—understanding what’s happening in cities, power grids, and other infrastructure in real time—is becoming both urgent and possible. This new layer of perception will also enable the next frontier of robotics and autonomy, where machines depend on a common fabric that renders the physical world as observable as code.

Of course, this shift carries genuine risks: the same tools that can detect wildfires or prevent jobsite accidents could also enable dystopian nightmares. The winners in this next wave will be those who also earn public trust, building privacy-preserving, interoperable, AI-native systems that make society more legible without making it less free. Whoever builds that trusted fabric will define the next decade of observability.

Ryan McEntush: The electro-industrial stack will move the world

The next industrial revolution won’t just happen in factories, but inside the machines that power them.

Software transformed how we think, design, and communicate. Now it’s transforming how we move, build, and produce. Advances in electrification, materials, and AI are converging, bringing true software control to the physical world. Machines are beginning to sense, learn, and act on their own.

This is the rise of the electro-industrial stack—the combined technologies that power electric vehicles, drones, data centers, and modern manufacturing. It connects the atoms that move the world to the bits that command it: minerals refined into components, energy stored in batteries, electricity directed by power electronics, motion delivered through precision motors, all coordinated by software. It’s the invisible foundation behind every breakthrough in physical automation; it’s the difference between software that merely summons a taxi and software that takes the wheel.

But the capacity to build this stack, from refining critical materials to fabricating advanced chips, is slipping away. If the United States wants to lead the next industrial era, it must make the hardware that underpins it. The nations that master the electro-industrial stack will define the future of industrial and military technologies.

Software ate the world. Now it will move it.

Oliver Hsu: Autonomous labs accelerate scientific discovery

As model capabilities progress across modalities and robotic manipulation capabilities continue to improve, teams will accelerate their pursuit of autonomous scientific discovery. These parallel technologies will enable autonomous labs that can close the loop on scientific discovery — from hypothesis development to experiment design and execution and through reasoning, results, and iterating on future research directions. The teams that build these labs will be interdisciplinary in nature and will unify expertise across AI, robotics, the physical and life sciences, manufacturing, operations, and more to unlock continuous experimentation via lights-out labs for discovery across fields.

Will Bitsky: Data crusade in our critical industries

In 2025, the AI zeitgeist was defined by compute constraints and data center buildout. In 2026, it will be defined by data constraints and the next frontier in the crusade for data: our critical industries.

Our critical industries remain wellsprings of latent, unstructured data. Each truck roll, meter read, maintenance job, production run, assembly, and test fire is fodder for model training. But neither capture, nor annotation, nor model training are part of the industrial lexicon.

There’s no lack of demand for this data. Companies like Scale, Mercor, and AI research labs are insatiably collecting process data (not just “what” is done, but “how”). And they pay a steep price to commission each unit of sweatshop data.

Industrial companies with existing physical infrastructure and labor forces have a comparative advantage in data collection and will begin to exploit it. Their operations generate immeasurable amounts of data that can be captured with near-zero marginal cost, and used to train owned models or licensed to third parties.

And we should expect startups will show up to help. Startups will deliver the coordination stack: software tools for collection, annotation, and consent; sensor hardware and SDKs; RL environments and training pipelines; and eventually, their own intelligent machines.

Apps

David Haber: AI reinforces business models

The best AI startups aren’t just automating tasks; they’re amplifying the economics of their customers. In contingency-based law, for example, firms only make money when they win. Companies like Eve use proprietary outcomes data to predict case success, helping firms pick better cases, serve more clients, and win more often.

AI strengthens the business model itself. It drives more revenue, not just lower costs. In 2026, we’ll see this logic extend across industries, as AI systems deepen alignment with their customers’ incentives and create compounding advantages legacy software can’t touch.

Anish Acharya: ChatGPT becomes the AI app store

Consumer product cycles require three things to work: new technology, new consumer behavior, and a new distribution channel.

Until recently, the AI wave had fulfilled the first two conditions but had no new native distribution channel. Most products grew off the back of existing networks like X or by word of mouth.

With the recent release of the OpenAI Apps SDK, Apple’s support for mini-apps, and ChatGPT’s roll out of group messaging, though, consumer developers can now tap ChatGPT’s 900M user audience directly and also grow with new networks of mini-apps like Wabi. As the final piece in the consumer product cycle, this new distribution channel is set to kick off a once-in-a-decade gold rush in consumer tech in 2026. Ignore at your own peril.

Olivia Moore: Voice agents take up space

In the last 18 months, the idea of AI voice agents managing real interactions for businesses has gone from science fiction to reality. Thousands of companies, from SMBs to enterprises, are using voice AI to schedule appointments, complete bookings, run surveys, do intakes, and much more. These agents save costs for businesses, generate additional revenue, and free up human employees to do higher leverage—and more enjoyable—tasks.

But because the space is so nascent, many companies are still in the “voice-as-a-wedge” phase, offering one or several types of calls as a point solution. I’m excited to see voice agents expand into handling entire workflows (which might be multi-modal) and even into managing full customer relationship cycles.

This will likely involve agents that are more deeply integrated into business systems and given the freedom to manage more complex types of interactions. As the underlying models continue to improve—and agents can now call tools and operate across systems—there’s no reason why every company shouldn’t have voice-first AI products running and optimizing critical parts of their business.

Marc Andrusko: Prompt-free and proactive applications arrive

2026 marks the death of the prompt box for mainstream users. The next wave of AI apps will have zero visible prompting—they’ll observe what you’re doing and intervene proactively with actions for you to review. Your IDE suggests the refactor before you ask. Your CRM drafts the follow-up email when you finish a call. Your design tool generates variations as you work. The chat interface was training wheels. Now AI becomes invisible scaffolding woven through every workflow, activated by intent rather than instruction.

Angela Strange: AI will finally upgrade banking and insurance infrastructure

Plenty of banks and insurance companies have integrated AI like document ingestion and AI voice agents on top of their legacy systems, but AI won’t truly transform financial services until we rebuild the infrastructure that powers it.

In 2026, the risk of not modernizing to take full advantage of AI will outweigh the risk of failure, and we’ll see large financial institutions let their legacy vendor contracts lapse and start implementing newer, AI-native alternatives. Unencumbered by the category seams of the past, these companies are platforms that centralize, normalize, and enrich underlying data from legacy systems and external sources.

The result?

• Workflows can be dramatically streamlined and parallelized. No more hopping between systems and screens. Think: you can see and parallelize all of the hundreds of tasks that need to be completed in your mortgage LOS, and agents can even complete the more mundane ones.

• Categories as we know them will merge to create much bigger categories. Customer KYC from onboarding and transition monitoring data, for instance, could now sit together in a single risk platform.

• The new winners of these categories will be 10x the size of the older incumbents: the categories are much bigger, and the software market is eating labor.

The future of financial services isn’t about applying AI to old systems; it’s about building a new operating system where AI is the foundation.

Joe Schmidt: Forward-deployed motions take AI to the 99%

AI is the most exciting technology breakthrough of our lifetimes. So far, though, most of the benefits from new startups have accrued to the 1% of companies that are in Silicon Valley—either literally in the Bay Area or part of that extended network. This makes sense, too: startup founders want to sell to companies they recognize and can easily get to, whether that means driving to their offices or getting a connection from the VC on their board.

In 2026, this will flip. Companies will realize that the vast majority of the AI opportunity lives outside of Silicon Valley, and we’re going to see new founders use forward-deployed motions to discover more opportunities that are hiding inside big, legacy verticals. The opportunity stands to be massive in traditional consulting and services industries, like system integrators and implementation firms, and in slower-moving industries like manufacturing.

Seema Amble: AI creates a new orchestration layer—and new roles—in the Fortune 500

In 2026, enterprises will shift further from isolated AI tools to multi-agent systems that will need to behave like coordinated digital teams. As agents start to manage complex, interdependent workflows—like planning, analyzing, and executing together—organizations will need to rethink how work is structured and how context flows across systems. We’re already seeing this happen with companies like AskLio and HappyRobot, which deploy agents across entire processes instead of single tasks.

The Fortune 500 will feel this shift most acutely: they sit on the deepest reservoirs of siloed data, institutional knowledge, and operational complexity, much of which sits in people’s brains. Turning that context into a shared substrate for autonomous workers will unlock faster decisions, compressed cycles, and end-to-end processes that no longer rely on constant human micromanagement.

This transition will also force leaders to reimagine roles and software. New functions will emerge, like AI workflow designers, agent supervisors, and governance leads responsible for orchestrating and auditing coordinated fleets of digital workers. And on top of today’s systems of record, enterprises will need systems of coordination: new layers to manage multi-agent interactions, adjudicate context, and ensure reliability across autonomous workflows. Humans will be focused on handling the edge and most complex cases. The rise of multi-agent systems isn’t just another step in automation; it represents a restructuring of how enterprises operate, how decisions are made, and ultimately where value is created.

Bryan Kim: Consumer AI shifts from “help me” to “see me”

2026 marks the year major consumer AI products shift from productivity to connectivity. Instead of helping you do work, AI will allow you to see yourself more clearly and help you build stronger relationships.

To be clear: this is hard. Many social AI products have launched and failed. But thanks to multimodal context windows and falling inference costs, AI products can now learn from the full texture of your life, not just what you’ve told a chatbot. Think camera rolls that show real emotional moments, 1:1 messaging and group chat patterns that change depending on who you’re talking to, and routines that shift under stress.

Once these products do land, they’ll become part of our everyday lives. Generally, “see me” products have better inherent retention mechanics than “help me” products. “Help me” products monetize through high willingness-to-pay on discrete jobs and optimize for subscriber retention. “See me” products monetize through daily engagement on ongoing connection: lower willingness-to-pay but more retentive usage patterns.

People already trade data for value constantly: the question is whether what they get back is worth it. And it soon will be.

Kimberly Tan: New model primitives unlock previously impossible companies

In 2026, we’ll see the emergence of companies that simply could not have existed before recent model breakthroughs in reasoning, multimodality, and computer use. Until now, many sectors (such as legal or customer support) have used improved reasoning to enhance existing products. But we’re only now beginning to see companies whose core product capabilities are fundamentally enabled by these new model primitives.

Advances in reasoning could unlock new capabilities to evaluate complex financial claims or act upon dense academic or analyst research (e.g., adjudicating billing disputes). Multimodal models make it possible to extract latent video data for industries rooted in the physical world (e.g., from cameras at manufacturing sites). And computer use enables automation in massive industries where value was historically trapped behind desktop software, poor APIs, and fragmented workflows.

James da Costa: AI startups selling to other AI startups reach scale

We’re in an unprecedented moment of company creation driven by the current AI product cycle. But unlike previous product cycles, incumbents aren’t asleep at the wheel; they’re adopting AI too. So how does the startup win?

One of the most powerful, and underrated, ways for startups to win distribution over incumbents is to serve companies at their formation: greenfield companies (i.e., brand new businesses). If you attract all of the new companies at formation and grow with them, you will become a big company as your customers become big companies. Stripe, Deel, Mercury, Ramp, and others have all followed this playbook. Indeed, many of Stripe’s customers didn’t exist when Stripe was founded.

2026 will be the year that we see the startups going greenfield reach scale across a host of enterprise software categories. Just build a better product and manically focus on new customers who aren’t captive to incumbents.

Stay tuned for more ideas from our Crypto team tomorrow.

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