Damian Kao
The Problem

The Backbone Without the Tools

Small manufacturers are most of US manufacturing, yet they are the ones locked out of the operational AI that large firms use. Why that gap is a national problem, with the numbers.

· 9 min read

There is a fact about American manufacturing that almost everyone gets backwards, and once you see it, the way AI is arriving in the sector starts to look like a problem rather than a triumph. Here it is: manufacturing is enormous, and it is overwhelmingly small. Both things are true at once, and the tension between them is where this piece lives.

Start with the size, because it sets the stakes. US manufacturing adds on the order of two-point-nine trillion dollars of value a year, about ten percent of GDP, and employs roughly thirteen million people. Taken on its own it would rank among the largest economies in the world. And it pulls more than its own weight: every dollar of manufacturing activity generates an estimated two dollars and sixty-nine cents in total economic output, one of the highest multipliers of any sector. When people say manufacturing is load-bearing for the economy, this is what they mean. It is not nostalgia. It is arithmetic.

Now the part that gets forgotten. That enormous sector is not mostly made of giants. Of the roughly 239,000 manufacturing firms in the United States, only about 4,000 have 500 or more employees. The other ninety-nine percent are small. Around three-quarters of all manufacturers have fewer than twenty people. These are the machine shops, the cabinet makers, the metal fabricators, the print and textile and ceramics operations in every county in the country. They are the backbone in the most literal sense: take them out and there is no sector left to talk about. Small businesses broadly tell the same story, having generated fifty-five percent of net new jobs over the decade to 2023. The economy runs on small operations far more than the headlines about a handful of large firms suggest.

So here is the question this piece is about. If small manufacturers are most of the sector, and the sector is this important, what happens when the technology that increasingly decides who stays competitive arrives mostly for the large ones?

The gap is real, and it is widest at the bottom

The technology in question is operational AI: not chatbots, but the systems that price work accurately, schedule a floor, catch an estimate before it loses money, keep a catalog current, and turn production data into better decisions. This is the capability that has quietly separated efficient manufacturers from struggling ones, and AI is now the lever that widens the difference.

Adoption is sorted by size, and sharply. The Census Bureau, which has been tracking AI use across hundreds of thousands of firms, found that by early 2026 about thirty-seven percent of firms with 250 or more employees were using AI in their operations, compared with under twenty percent of the smallest firms. Earlier in the cycle the gap was even wider: in early 2024 large firms were adopting AI at roughly one and a half to two times the rate of small ones. The encouraging reading is that the overall gap has narrowed as cheap tools spread. The honest reading is that the narrowing is happening fastest among firms that already have some scale, and that the very smallest operations, the ones that are most of manufacturing, are still the furthest behind.

operational AIlarge firmsabout 1% of firmssmall manufacturersabout 99% of firms, mostly without
Operational AI, the capability that increasingly decides who competes, is concentrated in the largest firms, which are about one percent of US manufacturers. The other ninety-nine percent, the small shops that are most of the sector, are mostly still without it. The tools are reaching the smallest share of the firms.

It is worth being precise about why, because the reason is not that small manufacturers are slow or unwilling. The reason is structural, and I have written about it at length in why small manufacturers can’t just use enterprise AI. The short version: the tools that deliver this capability are built for the conditions large companies have and small ones do not. They assume clean, standardized data already exists. They assume a budget that runs to five and six figures a year. They assume an IT team to run them and a process discipline that only scale tends to produce. A thirty-person shop has none of those, so the enterprise tool does not fail loudly, it simply never fits, and the capability stays out of reach. The gap is not a preference. It is a mismatch between how the tools are built and how most of the sector actually operates.

Why this is a national problem, not a private one

It would be easy to file this under “some businesses modernize faster than others” and move on. That would be a mistake, because the consequences do not stay inside the firms that fall behind. They add up to problems the whole country has a stake in.

Competitiveness. Small US manufacturers do not only compete with each other. They compete with large domestic firms and, more pointedly, with overseas producers who are adopting automation aggressively. A small manufacturer that cannot price accurately, cannot keep its catalog current, and cannot learn from its own production data is not on a level field. Multiply that disadvantage across the ninety-nine percent of the sector that is small, and you are looking at a slow erosion of the country’s industrial base, one shop at a time, in a way that no single closure makes dramatic but the sum of which is exactly the kind of decline that is hard to reverse once it sets in.

The workforce cliff. Manufacturing is staring at a labor shortage that is already arriving. Deloitte and the Manufacturing Institute estimate that as many as 2.1 million manufacturing jobs could go unfilled by 2030, at a potential cost of one trillion dollars in that year alone, with most manufacturers already reporting ongoing difficulty attracting and keeping workers. This is precisely the problem that accessible automation addresses: not by replacing people, but by letting a small shop do more with the people it can actually hire, automating the repetitive estimating, cataloging, and back-office work that currently eats skilled time. The firms that get that capability will absorb the shortage. The firms that do not will feel it as a ceiling on everything they try to do.

Supply-chain resilience. The last few years taught the country, expensively, that concentrated and distant supply chains are fragile, and that bringing production closer to home reduces risk. Small and medium manufacturers are the natural backbone of a more resilient, more domestic supply chain, which is why supporting them is a long-standing federal priority rather than a private concern. The Commerce Department runs the Manufacturing Extension Partnership specifically to strengthen smaller manufacturers, through a network of roughly 1,400 advisors at more than 450 locations, with supply-chain resilience and reshoring as explicit goals. The public interest in small manufacturers being capable and competitive is already on the record. What is missing is the operational tooling reaching them at the same level the policy attention does.

This is also where the scope of the problem resolves cleanly under the way national importance actually works. The impact does not have to be uniform across the whole country to count. A single small manufacturer is a local story: a few dozen jobs, a regional supplier, one town. But the pattern repeats in every region, and the patterns add up. Help one shop reach operational capability and you have helped a town. Make the method by which any shop can reach it, and you have touched the part of the economy that most of the country actually works in. Local impact, repeated at the scale of ninety-nine percent of a sector, is national impact.

What closing the gap actually requires

If the problem were just price, the market would have solved it already, because cheap AI tools are everywhere now. The reason the gap persists is that the missing piece is not a cheaper tool. It is a method.

The capability large manufacturers buy as a stack of expensive systems is not, at its core, about the software. It is about a sequence: decompose the product into honest data, build one engine that turns that data into pricing and quoting and scheduling, put guardrails around the automation so it can be trusted, and wire feedback loops so the system corrects itself over time. A small manufacturer cannot buy that sequence. But they can follow it, with the modest, self-hostable tools they can actually afford, if someone has mapped the path and shown that it holds up in a real operation rather than a slide deck.

That is the entire reason I do the rest of the work on this site the way I do it. The framework is an attempt to make that path explicit and teachable, industry-agnostic on purpose, because the underlying move, turning a decomposed product into the foundation everything else runs on, is the same whether you make cabinets or circuit boards. And the path is not theoretical. Two concrete results from running it: a product catalog that took the better part of a month to maintain by hand now regenerates from live data in about a day, and a cutting-layout optimizer that lifted material utilization from roughly 31% to around 79%, which is the kind of efficiency gain that decides whether a small shop’s price is competitive. Those are not enterprise budgets at work. They are the method at work.

The point

So the situation, stated plainly, is this. The largest part of one of the country’s most important sectors is being left on the wrong side of the biggest operational shift in a generation, not because those manufacturers are unwilling, but because the tools were never built for them. The cost of leaving that gap open is not abstract: it is competitiveness lost to overseas producers, a workforce shortage met with a ceiling instead of leverage, and a supply chain that stays more fragile than it needs to be.

The cost of closing it is mostly a matter of making the method reachable, which is a problem of documentation and proof more than of invention. That is the part I think is worth working on, and it is what this whole site is ultimately for. The backbone of American manufacturing should not be the part of it that cannot afford to keep up. The tools exist. The method exists. What is missing is the bridge between them and the ninety-nine percent, and that bridge can be built.

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