# David Crowder

David Crowder builds AI systems for manufacturing. Crowder describes their work as “software manufacturing” and focuses on making AI useful in real shops, including smaller operations with limited staff and imperfect production drawings.

- Profile: https://b2binfluencer.marketing/creators/david-crowder/
- LinkedIn: https://www.linkedin.com/in/davidcrowder
- LinkedIn followers: 3,114
- Topics: artificial-intelligence
- Based on 10 dated posts, latest 2026-10-09

## Summary

David Crowder is most useful for explaining why manufacturing AI fails on real shop drawings and small-team conditions.

- Best for: A manufacturing AI vendor seeking a co-authored piece on real shop drawing failures.
- Not for: A national reshoring advocacy campaign aimed at legislators, media, or large plant executives.
- Check first: Confirm whether their LinkedIn audience is shop owners and decision-makers, not vendors or observers.

### Takeaways

- A drawing looked normal but had duplicated file text, so AI read 0.5 as "0.50.5".
- Another drawing had notes printed at an angle, which the system did not find.
- Crowder says products assuming dedicated IT or compliance staff do not fit small shops.

## Main ideas

- **Judge manufacturing AI on real shop drawings, not clean demos**: Crowder’s central claim about AI in manufacturing is that test conditions matter more than the pitch. Vendors demonstrate on crisp PDFs that have never left a desktop — clean lines, clean text, everything where it should be — while shops receive drawings that have been scanned, faxed, folded in half, written on in pencil and passed through several hands. A system that passes the demo meets its first genuine customer drawing when a real job depends on it. The practical question for buyers is simple: what did the system get tested on, and did any of it come from a real job?
- **Software that assumes a plant ten times the size of its customer**: Crowder argues that almost everything sold as AI for manufacturing is built for the wrong buyer — a facility with an IT department, a compliance team and someone whose job includes integration. A polished demo can read a drawing, run a compliance check, flag an issue and route it to the right person; it doesn’t answer who sets it up, maintains it or handles it when it breaks. In a shop with a dozen people, the owner is already quoting parts, running the floor, handling customers and doing the books. There’s no spare person to become the AI person. Crowder’s priority is operability over capability: whether a tool survives contact with a small team, not whether it performs on a screen.
- **The small shops are the industry, and the base of any manufacturing comeback**: Crowder treats small manufacturers not as a niche market but as the capacity itself: "The 74 percent are the industry." Widening the lens turns the same structure into a resilience argument. In World War II, the whole country converted: factories that made consumer goods switched to batteries, tires and tanks, and that is how the war was won. Crowder says that couldn’t happen now because those facilities are gone, and notes China’s roughly four times the population and somewhere around 70 percent more manufacturing capacity. The position holds two things at once: the United States is still the second-largest manufacturer on earth, and Crowder wouldn’t bet on it in a long manufacturing war.
- **"Software manufacturing": a builder's vantage point, not a machinist's**: Crowder states the limits of their own authority before making any claim about shop work: not a machinist, and never pretended to be one. Their expertise is as a maker and builder of AI systems, and the parallel is deliberate — Crowder calls the work "software manufacturing" because both are constrained, both demand precision, and both break when someone skips a step; the only real difference is a physical constraint versus a virtual one. The stated background runs from Thomas Register, through years at MFG, to founding an additive manufacturing factory recognised by the World Economic Forum, described as 35 years around people who make things — standing next to the work rather than running machines, quoting parts or managing the floor.

## Work to read

- [Why AI demos pass on drawings real shops never receive](https://www.linkedin.com/posts/davidcrowder_the-way-ai-gets-sold-right-now-to-machine-activity-7511394607222534144-8qma)
- [The 74 percent: AI built for plants ten times the size of its customers](https://www.linkedin.com/posts/davidcrowder_74-percent-of-us-manufacturers-have-fewer-activity-7513568993455271937-C0ZR)
- [96,000 bombers, 86,000 tanks, and a supply base that no longer exists](https://www.linkedin.com/posts/davidcrowder_during-world-war-ii-the-united-states-built-activity-7511757038696443905-OSAJ)
- [Not a machinist: how Crowder describes their own vantage point](https://www.linkedin.com/posts/davidcrowder_i-am-not-a-machinist-and-i-have-never-pretended-activity-7514296352932163584-QECG)

## Brand relationships

- **Thomas Register** (employment)
- **MFG** (employment)

## Partnerships

David Crowder’s four long-form LinkedIn posts cover manufacturing capacity, small-shop constraints and how AI tools fail on real production drawings. They offer no sponsorship package, rate card, media kit or partner format, and don’t pitch sponsor placements or explain how brands can book a collaboration. They do, however, show subject-matter depth: firsthand accounts of drawings that break document-reading systems, the staffing gap at shops with fewer than 20 employees, and a stated position as someone who builds AI for manufacturing rather than runs machines. For a manufacturing AI vendor, the practical route is an editorial or co-authored contribution built around that expertise, negotiated directly rather than booked through a published format. The posts don’t say whether Crowder accepts sponsorship. Ask for an official inquiry route and current partnership terms.

## Alternatives

- Sercan Esen: Choose Esen when the piece needs to reach EHS, operations and IT leaders at larger manufacturing sites, or when the subject is safety and how people and machines work together. Esen has published directly on that ground: a five-point account of what changes on real shop floors when vision systems, robotics and autonomous equipment arrive, including the argument that automation should remove exposure rather than people; and a separate write-up of three months and 159 customer onsites across 11 states and 12 industries, reporting five patterns heard from 47 EHS leaders, 32 operations leaders, 10 IT leaders and about five executives. A vendor whose product is bought by a committee rather than a single shop owner gets a closer audience match, and a co-authored piece can present those patterns as findings rather than opinion.
- Chris Matan: Choose Matan when the campaign wants a first-person owner-operator account of using AI in a manufacturing business rather than a technical test of a system. Matan writes as an owner in manufacturing and commercial real estate, has described using AI to save time and become more efficient, and asks peers how they are using it and where they think it is headed. The posts also show a working format: an episode of a named show, a downloadable guide and a comment prompt. That suits a vendor seeking a podcast-length or conversational placement rather than a written argument about product behaviour.
- Kris Hutchinson: Choose Hutchinson when the article should end on measurement discipline: whether a tool’s results can be trusted, what counts as a conversion and why two systems report different numbers for the same event. Hutchinson has published two worked examples. One follows a company told its conversion rate was 80 to 85 percent when its own delivered numbers were 35 to 40 percent, tracing the problem to two honest definitions with different denominators. The other covers twenty years of B2B reporting stacks and argues that companies that grow treat marketing numbers as they treat their books, with a named owner and a reconciliation schedule. A vendor that wants to pair the drawing test with a buyer-side test of its own claims—or reach sales and operations leaders alongside shop owners—gets a stronger match on that half of the argument.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/