The Software Revolution Meets the Real World: a16z's Visions for 2026

From Theory to Practice: How Code Is Transforming Industry and Economy

Software has now conquered the way we think and communicate. By 2026, the real battle will be fought in the physical world. As artificial intelligence continues to spread across digital processes, the most radical change will occur when code truly begins to control factories, energy infrastructures, ports, and logistics systems. This is no longer about theoretical automation but a concrete transformation that will reshape global economic balances.

America Rebuilds Its Industrial Base with AI at the Center

The United States is not just modernizing old industrial plants. A new class of companies is emerging, assuming that simulation, automated design, and AI-driven operations are the standard, not the exception. These players are identifying huge opportunities in sectors that once seemed consolidated: advanced energy systems, heavy robotic manufacturing, next-generation mining, biological and enzymatic processes for chemical precursor production.

AI is not limited to laboratories. It can design cleaner, more efficient reactors, optimize critical mineral extraction, create better enzymes, and coordinate swarms of autonomous machines with a precision that traditional operators cannot achieve. This combination of cognitive capacity and physical control is creating an unprecedented competitive advantage.

Outside factories, the same logic applies to critical systems that were once impossible to fully monitor. Autonomous sensors, drones, and modern AI models can now continuously track ports, railways, power lines, pipelines, data centers, and military infrastructure. The real world generates complex, often unstructured data—every movement of a truck, every meter reading, every production cycle is raw material for training increasingly powerful models.

American Factories Return with a New DNA

The first major American economic cycle was based on a solid industrial force. This force was largely dismantled in recent decades, but today machines are starting to run again with a fundamental difference: they are controlled by software and AI. Companies tackling challenges in sectors like energy, extraction, construction, and manufacturing are applying a mindset that combines Henry Ford’s assembly line efficiency with contemporary AI advances.

This hybrid approach enables:

• Faster handling of complex regulatory and permitting processes
• Accelerated design cycles integrating manufacturability from the start
• Precise coordination of nationwide projects
• Implementation of autonomous systems for difficult or dangerous tasks

By 2026, we expect to see mass production of nuclear reactors, rapid construction of housing to meet national demand, quickly deployed data centers, and a renaissance of American industrial strength. The principle is simple but powerful: “The factory is the product.”

Physical Observability: When the Real World Becomes as Transparent as Code

In the last decade, software observability has transformed how we monitor digital systems—every log, metric, and trace makes the invisible visible. Now, this same revolution is about to reach the physical world. With over a billion connected cameras and sensors deployed in major American cities, understanding the true state of cities, power grids, and critical infrastructure is becoming both urgent and technically feasible.

This new level of perception will have profound implications for robotics and autonomous technologies. When machines have access to a shared map of the observable physical world as if it were code, they can coordinate and operate with a fluidity impossible today.

However, tools that can detect wildfires or prevent accidents at construction sites can also create dystopian scenarios. The true winners of this wave will be those who earn public trust by building systems that protect privacy, are interoperable, natively support AI, and maintain social transparency without compromising civil liberties.

The Electronic Industrial Stack: When Software Truly Controls Atoms

The next industrial revolution will not only happen inside factories but within the machines themselves. Electrification, innovative materials, and AI advances are merging, enabling software to control movement, production, and transformation of the physical world.

The electronic industrial stack represents the integrated technology powering electric vehicles, drones, data centers, and modern manufacturing. It links the atoms that move the world to the bits that control it: from refined minerals in components, to energy stored in batteries, to electricity managed by electronic devices, to motion driven by precision motors—all coordinated by software. It is the invisible infrastructure behind every step toward physical automation.

However, from critical material refining to advanced chip manufacturing, the ability to build this stack is eroding globally. If the US wants to lead the next industrial era, it must produce the hardware that supports it. Nations that master the electronic industrial stack will define the future of industrial and military technology for the next century.

Autonomous Labs Accelerate Scientific Discovery

With advances in multimodal models and continuous improvements in robotic capabilities, research teams are closing the loop on autonomous scientific discovery. Next-generation labs can move from hypothesis to experiment design and execution, to reasoning, result analysis, and iteration on future research directions—all without continuous human intervention.

Teams building these labs will necessarily be interdisciplinary, integrating expertise in AI, robotics, physical and life sciences, manufacturing, and operations. This approach enables continuous experiments and discoveries in environments without personnel, exponentially accelerating the cycle of innovation.

The Data Journey: The Next Frontier in Critical Sectors

In 2025, the limits of computational resources and data center construction defined the AI debate. By 2026, the focus will shift to a different challenge: the limits of data resources and how our critical sectors become inexhaustible sources of information.

Traditional industrial sectors remain a treasure trove of potential and unstructured data. Every truck trip, every meter reading, every maintenance intervention, every production cycle, every assembly, every test is raw material for training sophisticated models. Yet, terms like data collection, labeling, and model training are not yet part of the standard industrial lexicon.

The demand for this data is already infinite. Specialized companies and AI research labs pay high prices to access process data from the “sweat factories.” Existing physical infrastructure gives industrial companies a natural competitive advantage: they can capture massive amounts of data at near-zero marginal cost and use it to train proprietary models or license it to third parties. Soon, startups dedicated to providing the coordination stack—software tools for data collection, labeling, and licensing; sensor hardware and SDKs; reinforcement learning environments and training pipelines—will emerge.

AI Strengthens Business Models, Not Just Cost Reduction

The most sophisticated AI startups are not just automating repetitive tasks. They amplify the economic value customers can extract from their business. In profit-sharing legal models, for example, law firms only earn if they win. Innovative companies use proprietary data on process outcomes to predict success probabilities, helping firms select the best cases, serve more clients, and increase win rates.

AI does not simply cut operational costs—it strengthens business models by generating more revenue. By 2026, this logic will extend across all vertical sectors, as AI systems align more deeply with customers’ economic incentives, creating compounded advantages that traditional software cannot achieve.

ChatGPT Becomes the App Store of AI: A New Era of Distribution

Consumer success cycles require three elements: new technology, new consumer behaviors, and new distribution channels. Until recently, the AI wave satisfied the first two but lacked a native distribution channel. Most products grew through existing networks like social media or word of mouth.

With the release of the OpenAI Apps SDK, support from Apple for mini-apps, and ChatGPT’s group chat feature, the landscape has changed dramatically. Consumer developers can now directly access ChatGPT’s 900 million user base and leverage new mini-app networks to grow. This final link in the consumer product lifecycle promises to inaugurate a decade-long tech gold rush in 2026. Ignoring this paradigm shift risks significant setbacks.

Voice Agents Conquer the Business Space

In the last 18 months, the idea that AI agents manage real interactions for companies has moved from science fiction to daily operational reality. Thousands of companies—from SMEs to large enterprises—use voice AI to schedule appointments, complete bookings, conduct surveys, and gather customer insights. These agents not only save costs and generate additional revenue but also free employees for more valuable and engaging tasks.

Since the sector is still in early stages, many companies remain in the “voice as entry point” phase, offering one or few interaction types as a single solution. By 2026, we expect voice assistants to expand to handle entire workflows, potentially multimodal, and even manage the full customer relationship cycle.

With ongoing improvements in underlying models—modern agents can already call tools and operate across different systems—every company should implement voice-driven AI products to optimize key processes.

Proactive Applications Replace Prompts

In 2026, mainstream users will say goodbye to text boxes for prompts. The next generation of AI applications will not show search interfaces at all—they will observe your actions and offer proactive suggestions without you asking.

Your IDE will suggest code refactoring before you ask. Your CRM will automatically generate follow-up emails after a call. Your design tool will produce alternative options as you work. The chat interface will simply become a marginal support tool. AI will be the invisible scaffolding of every workflow, activated by user intent rather than explicit commands.

Banks and Insurers Finally Modernize

Many financial institutions have integrated AI functions like document import and voice agents into their legacy systems, but this is not true transformation. Only by rebuilding the underlying infrastructure can AI truly transform financial services.

By 2026, the competitive risk of not modernizing will outweigh the risk of failure in the attempt. Major financial institutions will abandon contracts with traditional vendors to implement newer, native AI solutions. These companies will surpass the limits of old classifications, becoming platforms capable of centralizing, normalizing, and enriching underlying data.

The results will be significant:

• Workflows will be greatly simplified. No more switching between systems. Imagine managing hundreds of pending activities in a mortgage system while agents complete the boring parts.
• Known categories will merge into larger ones. KYC, account opening, and transaction monitoring can be unified into a single risk platform.
• Winners in these new categories will be ten times larger than traditional companies: scale is greater, and the software market is devouring the workforce.

The future of financial services is not applying AI to old systems but building a new native AI operating system.

AI Reaches 99% of Companies Through Forward-Looking Strategies

AI is the most exciting technological breakthrough of our lifetime, but so far most startup benefits have gone to the 1% of Silicon Valley companies—either physically in the Bay Area or part of its vast influence network.

In 2026, this will change radically. Startups will realize that the vast majority of AI opportunities lie outside Silicon Valley. We will see new companies leveraging forward-looking strategies to discover hidden opportunities in large traditional vertical sectors. In slower-moving sectors like consulting, (system integrator, implementation) firms, and manufacturing, AI still offers enormous untapped potential.

Stripe, Deel, Mercury, Ramp have followed this strategy of serving greenfield companies—completely new businesses—from the start. Many Stripe customers didn’t even exist when the company was founded. In 2026, we will see zero-to-one startups rapidly scaling across many enterprise software sectors, simply by building better products and focusing on new, unconstrained customers.

Multi-Agent Systems Transform Fortune 500 Structures

By 2026, companies will shift from isolated AI tools to multi-agent systems functioning as coordinated digital teams. As agents begin managing complex, interdependent workflows—planning, analysis, and joint execution—companies will need to radically rethink work structures and how context flows between systems.

Fortune 500 companies will feel this transformation more deeply: they hold the largest reserves of isolated data, institutional knowledge, and operational complexity. Transforming this knowledge—much of which resides in employees’ minds—into a shared base for autonomous workers will enable faster decisions, shorter cycles, and end-to-end processes that do not rely on constant human micro-management.

This transformation will force leaders to rethink roles and software. New functions will emerge, such as AI workflow designers, agent supervisors, and governance managers for coordinating collaborative digital workers. Beyond existing record systems, companies will need coordination layers—new levels to manage multi-agent interactions, judge context, and ensure the reliability of autonomous workflows.

Humans will focus on managing edge cases and more complex situations. The rise of multi-agent systems is not just another step in automation; it represents a reconstruction of how companies operate, make decisions, and ultimately create value.

Consumer AI Evolves: From “Help Me” to “Know Me”

2026 will mark the transition of mainstream consumer AI features from productivity enhancement to strengthening human connections. AI will no longer just help you perform tasks but will help you better understand yourself and build stronger relationships with others.

This shift is not simple. Many social AI products have already launched and failed. However, thanks to multimodal context windows and decreasing inference costs, modern AI products can learn from every aspect of your life—not just what you tell the chatbot, but also your photos, one-on-one and group conversations, daily habits, and reactions to stress.

“Know Me” products have better user retention than “Help Me” products. “Help Me” monetizes through a high willingness to pay for specific tasks and seeks to increase retention. “Know Me” monetizes through ongoing daily interactions: willingness to pay is lower, but retention is significantly higher. Once these products are truly launched, they will become part of our daily lives.

New Primitive Models Enable Unprecedented Companies

By 2026, we will see companies emerge that could not have existed before advances in reasoning models, multimodality, and advanced computing applications. Until now, many sectors—legal, customer service—used improved reasoning to strengthen existing products. Now, we are beginning to see companies whose main product fundamentally depends on these new primitive models.

Progress in reasoning generates new capabilities, such as evaluating complex financial requests, acting on dense academic research, or automatically resolving billing disputes. Multimodal models enable extraction of latent video data from the physical world—cameras at production sites reveal hidden insights. Computing applications allow automation of large sectors previously constrained by desktop software, poor APIs, and fragmented workflows.

AI Startups Grow Rapidly Serving Other AI Startups

We are in an unprecedented phase of company creation, primarily driven by the current AI product cycle. Unlike previous cycles, existing companies are not just watching—they are actively adopting AI. How can startups win?

One of the most effective and underrated ways to surpass existing companies in distribution channels is to serve greenfield companies from the start—completely new businesses not yet bound by legacy vendors. If you can attract all new companies and grow with them, when your clients grow large, so will you.

In 2026, we will see zero-to-one startups rapidly scaling across many enterprise software sectors. They only need to build better products and focus on new, unconstrained customers. The strategy is simple but powerful: the future belongs to those who grow with new actors, not those trying to conquer the old.

Conclusion: Software Has Devoured the World, Now It Drives It Forward

Trends for 2026 do not point to a single innovation but to a systematic transformation. Software is no longer just a tool that optimizes existing processes—it has become the very substrate upon which entire economic models, industrial infrastructures, and human relationships are built.

From autonomous labs accelerating scientific discovery to multi-agent systems rethinking Fortune 500 operations, from the renaissance of America’s industrial base to the evolution of financial services, the recurring theme is clear: software will continue to devour the world, but in 2026 it will do so more deeply, more physically, and more integrated than ever before.