AI Is Eating Software. Industrial Software Eats With It.

Why industrial platforms are absorbing AI—and getting stronger because of it

Every few years, technology narratives collapse into something overly simple.

Right now, the loudest version sounds like this: AI is killing software.

There’s a kernel of truth there—but it misses what’s actually happening. AI isn’t eliminating software; it’s reordering where software value lives, which business models work, and who ultimately captures the upside.

From where we sit at Energize—focused on energy, infrastructure, and industrial systems—the picture looks very different. In our view, industrial software is becoming one of the primary beneficiaries of the AI shift, not a casualty.

The Contradictions Are Real—and They’re Converging

Across technology, energy, and infrastructure markets, several forces are moving at once:

• Power and energy demand is accelerating, shifting the conversation from pure “energy transition” to large-scale energy addition

• Climate and renewables remain politically contested, even as deployment continues

• AI is disrupting traditional software economics and user interfaces

• Industrial digitization is driving some of the strongest M&A activity in the software market

These dynamics may appear contradictory. In practice, they reinforce one another.

AI is driving unprecedented demand for data centers, power generation, grid reliability, and physical infrastructure. That infrastructure must be planned, permitted, built, connected, monitored, and optimized. All of that work depends on software that understands the physical world.

The question isn’t whether software survives AI. The question is which software does.

Why Industrial Software Looks Different

Much of the “software is dead” narrative is rooted in horizontal SaaS—tools designed for broad adoption, shallow integration, and workflow replacement. Those companies face real pressure from AI-native interfaces and foundation models.

Industrial software operates under very different conditions.

Companies serving energy, infrastructure, and industrial customers are deeply embedded in mission-critical workflows. Many act as systems of record, managing long-lived assets, regulatory complexity, and operational data that customers cannot easily move or replicate.

That position creates a powerful advantage in an AI-driven world.

Take Sitetracker, which supports the deployment of critical infrastructure at global scale. Years of trusted customer data and workflow ownership allow AI to be layered directly into planning, search, and execution—improving outcomes without displacing the underlying platform.

Or ZEDEDA, which sits at the edge of industrial systems, managing distributed compute across real-world assets. As AI moves closer to the edge, that control layer becomes more—not less—valuable.

If a company has:

1. A trusted data relationship with industrial customers, and

2. The right to operate within existing contracts and workflows

then AI becomes an accelerant, not a replacement.

AI Changes Services Economics—It Doesn’t Eliminate Them

Another misconception is that AI removes services from the equation. In industrial markets, services have always been part of the value creation—implementation, configuration, diligence, and operational support are unavoidable.

What AI changes is the economics.

We’re seeing companies use AI to compress timelines, automate repeatable work, and turn bespoke services into scalable, productized offerings. That shift improves margins and deepens customer relationships at the same time.

For example:

• Greenlite applies AI to permitting and regulatory workflows—an area that historically slowed infrastructure projects due to manual processes and fragmented data.

• GridStrong leverages AI to help utilities better understand, plan, and operate the grid, turning complex system data into actionable intelligence.

In each case, AI doesn’t remove human involvement—it amplifies it, making expertise more scalable and outcomes more predictable.

The Rise of AI-Native Industrial Companies

Alongside established platforms, a new generation of AI-first industrial companies is emerging.

These businesses are “born” into this era. Early on, the line between software and services is often blurry—but that’s how next-generation industrial stacks get built. What matters is speed to value, domain understanding, and the ability to integrate directly into real-world systems.

What stands out is how quickly these teams are scaling with relatively lean organizations, solving narrow but critical problems across energy, infrastructure, and climate markets. Over time, many will become foundational layers in how physical systems are designed and operated.

Examples I have recently seen include:

• Nixtla: AI-native forecasting infrastructure that enables industrial and energy companies to generate more accurate, real-time predictions across complex, time-series–driven systems.

• Halcyon: An AI-assisted energy intelligence platform that helps professionals navigate regulatory filings, market signals, and complex energy data with natural-language search and agentic alerts — accelerating better decisions in rapidly evolving energy markets.

The Second-Order Effect Most People Miss

One of the most underappreciated consequences of the AI boom is its impact on physical infrastructure.

Data centers, power generation, transmission, and grid modernization are all seeing renewed urgency. While headlines focus on megawatts and hardware, a meaningful share of that capital flows to software and services.

Roughly 15% of large infrastructure CapEx budgets are allocated to digital systems and services. These are enormous and expanding markets—supported by new budgets, tighter timelines, and technologies purpose-built for today’s constraints.

This is the intersection Energize has focused on for over a decade. Companies in our portfolio like DroneDeploy, Amperon, Nira, PVcase, Banyan Infra, and Aurora Solar are all serving this market and benefiting from t he scale of recent deployments and operations.

The Takeaway

AI is reshaping software.

But industrial software won’t be displaced by AI—it will absorb it.

The winners will be companies that understand physical systems, hold trusted data relationships, and know how to integrate AI into workflows that actually matter. Identifying those opportunities requires specialization and deep domain expertise—not trend-chasing.

That’s where we believe the next generation of enduring technology companies will be built.