A shift that has been building for years inside Silicon Valley’s venture ecosystem is beginning to look less like a cyclical rotation and more like a structural reallocation of attention and capital. As artificial intelligence accelerates the automation of software work and compresses timelines for building digital products, some investors are increasingly looking beyond code and toward the physical economy for the next wave of defensible growth.
That reassessment is at the center of “How AI’s impact on software is pushing VCs from bits to atoms,” published by VC Cafe, which argues that AI is changing the contours of software investing in ways that make “atoms” businesses—companies rooted in manufacturing, logistics, energy, construction, and other real-world systems—more attractive relative to pure digital plays. The thesis reflects a growing debate among venture capitalists: if AI reduces the scarcity that once made software development expensive and slow, where will durable differentiation and pricing power come from?
The most immediate implication is competitive. Large language models and increasingly capable coding tools are reducing the marginal effort required to prototype, iterate, and ship software. That does not mean software stops mattering, but it can widen the field of competitors and lower barriers to entry for products that previously required significant engineering resources. In markets where functionality is easy to replicate, investors typically look for moats elsewhere—distribution, proprietary data, regulatory advantage, brand, or deep integration into workflows. AI, however, can erode several of those moats at once by enabling faster imitation, lowering switching costs through better interoperability, and allowing customers to assemble custom solutions from modular tools.
This dynamic is pushing some venture firms to reexamine what “defensibility” looks like in the AI era. If the value of many software categories shifts from bespoke code to commodity capability, returns may tilt toward those who control scarce inputs: unique datasets, specialized hardware, critical infrastructure, or privileged access to regulated environments. That logic naturally draws attention to businesses that interact with the physical world, where constraints are harder to abstract away. Even with excellent software, scaling a factory, deploying robots in a warehouse, upgrading the electrical grid, or building a new materials supply chain involves long lead times, capital intensity, and operational complexity—frictions that can deter copycats and create enduring competitive positions.
The renewed interest in “atoms” also reflects the practical limits of AI’s reach. AI can design, simulate, and optimize, but it cannot instantly manufacture components, secure permits, reconfigure supply networks, or train an industrial workforce. In many industries, the binding constraint is not information but execution. Investors who believe AI will increasingly function as a force multiplier for engineers, designers, and operators may see the biggest productivity gains emerging where the baseline is most inefficient and where software has historically struggled to penetrate.
At the same time, venture firms are confronting a portfolio question: what happens when software companies reach product-market fit faster, but differentiation fades faster as well? One possible outcome is a higher rate of company formation with lower average durability, which could compress ownership and returns unless investors can identify winners earlier or support companies in building non-technical moats. Another is a reshaping of venture economics toward strategies that resemble private equity or growth infrastructure investing—longer time horizons, heavier operational involvement, and partnerships with corporates and governments.
The “bits to atoms” narrative also intersects with geopolitical and industrial policy trends. Governments are increasingly focused on reshoring supply chains, accelerating clean energy deployment, and securing critical technologies. Those priorities can create tailwinds for venture-backed companies operating in energy storage, advanced manufacturing, defense technology, and logistics resiliency. But they also introduce risks: procurement cycles, compliance burdens, and exposure to policy shifts that can be difficult for traditional software investors to underwrite.
The movement into physical-world ventures is not without friction inside the venture model. Building in atoms often requires more capital up front and may not fit the rapid iteration patterns that software investors favor. Hardware and industrial startups face higher failure costs, more complex go-to-market routes, and a dependence on external factors such as commodity prices and supply availability. Many investors who excelled in SaaS-era scaling will need different networks, technical diligence capabilities, and an increased tolerance for operational risk.
Still, advocates see a complementary relationship rather than a replacement. AI is likely to remain central, but it may increasingly be embedded in systems that touch the real world: autonomous machines, grid optimization platforms, construction automation, drug discovery pipelines, and next-generation materials. In that view, the most valuable companies will be those that integrate AI with proprietary processes and hard-to-build capabilities, turning intelligence into measurable improvements in throughput, safety, cost, and reliability.
VC Cafe’s argument arrives at a moment when the venture industry is already under pressure to demonstrate discipline after years of elevated valuations and abundant capital. If AI makes it easier to create software products, differentiation may come less from building an app and more from owning the industrial workflow the app controls. Investors are responding by widening their aperture, seeking businesses where software is essential but not sufficient—where the competitive edge is not only in what the code can do, but in what it can change in the physical world.
