The Promise of the AI-Native Manufacturer

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Some of the most important AI opportunities in manufacturing will come from AI-native industrial companies built around the workflow itself. In many cases, these businesses may look less like traditional software companies and more like operators: suppliers, service centers, manufacturers, and logistics businesses whose advantage comes from embedding intelligence directly into the physical system.

At my last company, we were effectively trying to build this future, but we did it with a great deal of manual labor. What feels different now is that the technology is finally catching up. We may be reaching a point where building the product itself and using AI to produce it better, faster, and more intelligently is a more durable model than simply selling software to those who do.

I sat down this week with Zane Hengsperger, founder of Nox Metals, to talk about what that future could look like. Nox is an AI-powered metals supplier and modern metal service center. The company sits between mills and machine shops, cuts metal to the sizes customers need, and is building software to optimize pricing, inventory selection, nesting, scheduling, and fulfillment. Nox sells metal at the end of the day, but it uses AI to drive prediction and optimization throughout the operation.

“Software companies are important, but vertically integrated factories are 10x more important,” Zane told me.

In manufacturing, and industrials more broadly, there’s an opportunity to build entirely new forms of capacity.

Prediction, Optimization, and Capacity

One reason manufacturing is such fertile ground for AI is that so much of the industry still runs on fragmented systems and manual workflows.

At its core, manufacturing is made up of a bunch of physical processes. Material comes in, parts go out. But each process in between requires thousands of decisions that determine whether a factory can scale profitably.

How long will this job take? Which piece of inventory should we use? How should this sheet be cut? What should we quote? Can we promise this lead time? Should we take this order or say no? Which machine should run what, in what sequence, at what margin, with what downstream impact on the rest of the schedule?

These decisions drive throughput, yield, utilization, and working capital.

“How do you get metals at the cheapest price and the fastest speed? How do you supply every factory in America in 24 hours at the best price for any of their metal needs? If American manufacturers are going to move faster, they can’t keep waiting so long for metals.” Zane said. “And they can’t keep getting overcharged.”

This is about building supply chain infrastructure, which is a set of prediction and optimization problems embedded inside a physical business.

Some of the highest-value problems are prediction problems: quote accuracy, lead time forecasting, time estimation, and demand prediction. Others are optimization problems: nesting, cut-path generation, scheduling, inventory allocation, and job sequencing.

“Nox is learning from every decision we make on the factory floor. How long something takes. How we decide to cut a piece of metal. What piece of inventory we choose.” Those learnings are key for Nox to improve its own outcomes.

A more accurate estimate means more optimal capacity planning, which means more accepted work, better utilization, lower idle time, better pricing, and faster delivery. “Afterall, capacity determines price in manufacturing,” Zane said. “If you schedule a job for four hours but it actually only takes three, you’ve effectively lost an hour of capacity. That can mean turning down other work or leaving the machine idle just because your time estimate was wrong.”

Controlling the Workflow

These prediction and optimization gains only compound if you actually control the process where they happen. The question is how. There’s been a lot written about the value of finding your wedge – finding one narrow but economically central piece of the stack to own. In manufacturing, this may matter more than almost anywhere else because the right wedge gives you control over the workflow itself. I learned this the hard way at my last company.

In Nox’s case, that wedge is metals supply and processing.

“We sit in between a mill,” Zane said, “and someone who has a CNC machine who is going to make a part.” Nox cuts metal to a specific size for the machinist. And unlike a purely software business, it also holds inventory. “We do hold it on our balance sheet,” he told me. “The inventory is really important so you can move fast.”

In many industrial settings, speed and reliability come from ownership and physical readiness. You cannot compress lead times if the material is not there. You cannot promise speed if you do not control the schedule. You cannot build a learning loop if the important decisions are still happening outside your system.

That is part of what Zane means by vertical integration. “Vertically integrated means you’re building technology internally and applying that to a process,” he said. “That allows you to learn from the data, which is so important.”

I think that is directionally right, especially in industrial markets where the best data is generated from repeated execution of a physical workflow. In these cases, the moat is the operating environment that produces the model’s feedback loop.

Industrial moats do not always look like software moats. In traditional SaaS, investors often look for product differentiation, switching costs, or proprietary data. But in industrials, the moat can be something more physical and cumulative: inventory position, throughput density, process knowledge, and the ability to learn from every job run through the system. This is one reason I am generally skeptical of industrial AI companies that position themselves as thin intelligence layers above the workflow. Some will work. But in many categories, the value actually accrues to whoever controls enough of the underlying system to continuously improve it.

Not every industrial AI company should build a vertically integrated operation. There will be excellent software-only businesses, and companies that sit somewhere in the middle: software plus services, software plus orchestration, software plus selective ownership of the workflow. But I do think the next generation of industrial companies will usually be some sort of hybrid. They will use AI to capture work and reshape it. They will sit close enough to the process to build genuine compounding loops. And they will pick narrow enough wedges that their learning actually transfers and deepens over time.

One thing I have come to appreciate from my own time building in manufacturing is that breadth can dilute learning if a company expands across too many different processes too early. The key is to start with a specific bottleneck where the data and workflows reinforce each other. As Zane put it, “What is the thing that you’re going to get really, really good at before you go and do something else?”

The Limits of Retrofitting

Even if you know the right approach, most existing operators aren’t in a position to execute it. Retrofitting existing factories can be brutally difficult. The data is fragmented. The workflows live partly in software, partly in spreadsheets, partly in whiteboards, partly in the heads of a few experienced operators. The machinery is old. The adoption hurdle is high. And culturally, many factories are not eager to remake themselves around new software, much less AI.

“I don’t think the people that own these factories are going to want to do it,” Zane said, referring to making legacy shops truly AI-native. “You just can’t convince someone of AI. They’re kind of stuck in their ways.”

Technology adoption is thus constrained not just by technical feasibility but by organizational willingness. And that is one reason I suspect a disproportionate amount of value will accrue to new entrants.

“I think these new firms that are like startup manufacturers will just steamroll everyone,” Zane said.

I would probably say it a bit more cautiously, but I think the core idea is right. The future may not be that every legacy factory gradually becomes AI-enabled. It may be that a relatively small number of AI-native operators become so much more responsive, efficient, and reliable that they take disproportionate share.

When I asked Zane what American manufacturing might look like in five to ten years, he said, “You’ll see like 2% of firms doing 30%-50% of the work.”

Once a system becomes meaningfully better at making decisions, it compounds. Better quoting drives more volume. More volume drives more data. More data drives better scheduling, pricing, and fulfillment. Better service drives more customer concentration, which drives better density, which improves economics and response times again.

The Layers of Reindustrialization

This is also, I think, how reindustrialization actually gets built – not top-down, but through operators like this getting faster and denser at every layer of the supply chain. There is a lot of dialogue around rebuilding American industrial capacity. And the conversation often centers on the visible tip of the pyramid: rockets, defense systems, chips, cars. Those categories matter, but industrial capacity depends on a much broader web of upstream systems: raw materials, mills, service centers, processing layers, component suppliers, logistics infrastructure, etc.

When I asked Zane about the biggest bottlenecks to US self-sufficiency, he started with missing or fragile layers of supply. “More companies in the early part of the supply chain,” he said. “Mining companies. Materials. Wire harnesses. We need better capacity.”

Better capacity means more responsive, more automated, more intelligent, and more resilient.

One example he gave was around stainless steel. “One customer we work with had to delay a launch just because they can’t get this one piece of stainless steel. You can get that in China in two weeks. America takes six months.”

Rebuilding American industrialization means solving for this supply chain fragility, rethinking and rebuilding the connective tissue itself.

The Next Generation of Factories

“For every software task startup in manufacturing, there should be 50 factory-first startups,” Zane told me towards the end of our conversation.

Some of those startups will be service centers. Some will be manufacturers. Some will be upstream supply chain businesses. Some will be logistics layers. Some will sit in categories that tech has mostly ignored because they were too operationally difficult or too asset-heavy.

Regardless, the moat in many cases will be the capacity the model helps create - that is the promise of the AI-native manufacturer.

Author’s note: An LLM was used for light copy editing only (spelling, grammar, and clarity). Content, meaning, tone, and structure remain unchanged.