“The next industrial revolution won’t just happen in factories, but inside the machines that power them. This is the rise of the electro-industrial stack—combined tech that powers electric vehicles, drones, data centers, & modern manufacturing.” This assertion, made by a16z Investing Partner Ryan McEntush, sets the stage for the dramatic technological shifts predicted by Andreessen Horowitz partners in their “Big Ideas for 2026.” McEntush, alongside General Partners Angela Strange and Sarah Wang, outlined three industries—American industrial production, financial services, and enterprise software—that are not just adopting AI, but are poised for fundamental re-architecture within the next three years. Their collective commentary serves as a critical strategic brief for founders, investors, and incumbents navigating the immediate future of deep technology and business automation.
The first major idea centers on the necessity of establishing the “Electro-Industrial Stack” in the United States, a foundation of integrated hardware and software essential for national competitiveness. McEntush argues that while America retains the intellectual capability to develop core technologies—citing examples like rare earth processing—the real strategic challenge lies in building the industrial ecosystem at scale. Unlike China, which possesses mature, vertically integrated supply chains spanning multiple tiers of suppliers, components, and raw materials, American companies often face the necessity of vertical integration not by strategy, but by necessity. This lack of a robust, domestic ecosystem creates bottlenecks that stifle rapid innovation and scaling.
Solving this challenge requires a deliberate blend of traditional industrial expertise and modern software talent. Companies like SpaceX and Anduril, which have successfully pulled propulsion and engineering talent from legacy contractors and combined it with agile software development, offer a blueprint. McEntush emphasized that true success hinges on co-locating engineering and manufacturing, enabling concepts like design for manufacturing to flourish. If the US fails to own these core supply chains—which include everything from batteries and power electronics to motors and compute—it risks ceding not only economic advantage but military and geopolitical power in the 21st century.
The second area facing imminent disruption is financial services and insurance, where decades-old legacy systems are finally proving too costly to maintain. General Partner Angela Strange predicts a dramatic turning point driven by AI-native alternatives. She argues that the institutional risk calculus has fundamentally changed: “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.”
Incumbents are increasingly finding their mainframe-era systems unable to cope with modern scale and complexity, leaving significant revenue on the table. New AI-first platforms are solving this by unifying disparate data silos—from legacy cores and external sources to unstructured documents—into a single system of record. This unification enables two critical shifts: workflows become parallel rather than sequential, and previously distinct categories, such as fraud, risk, and compliance, merge into a single, AI-first risk platform.
The implications for market structure are profound. Category winners will become 10x bigger, not just due to better software, but because the software consumes labor that humans didn’t want to do anyway, or that companies couldn’t hire for fast enough. This shift allows financial institutions to transform low-margin business areas into high-margin operations almost overnight, creating immense competitive pressure on slower adopters.
Finally, in enterprise software, General Partner Sarah Wang forecasts that the “System of Record” (SoR)—the traditional, centralized database controlling core business processes—is set to lose its primacy. For decades, the data gravity of ERPs and other SoRs provided an insurmountable moat for incumbents, frustrating waves of SaaS 2.0 startups that tried to compete solely on better user interfaces.
The advent of the dynamic agent layer changes this dynamic entirely. This layer, powered by large language models (LLMs), sits closer to the user and the data, gaining the ability not just to respond, but to anticipate, coordinate, and execute end-to-end processes. Wang notes that “The real disruption in enterprise software is that the system of record will finally start to lose primacy. These systems gain the ability not just to respond, but to anticipate, coordinate, and execute end-to-end processes.” This collapses the distance between intent and execution.
For instance, in IT Service Management (ITSM), requests that once required a lengthy, manual workflow can now be fulfilled nearly instantaneously by an agent that extracts intent, classifies the request, and maps it to the necessary workflow. This emergent agent layer accrues value by providing accurate and reliable solutions directly to the user, bypassing the traditional bottlenecks of the SoR. New AI-native companies, built by founders who deeply understand industry processes and have entirely re-architected their platforms for speed and flexibility, are now positioned to disrupt established players who rely on legacy data structures. This is a massive opportunity for new players to come in and win.