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What the proposed asset class means
In Love and Larson’s framing, an American manufacturing asset class would comprise investments in the companies and capabilities that turn designs into repeatable production: tier-two and tier-three manufacturers, process specialists, equipment, skilled labor, qualification capacity, and the systems that connect engineering to factory output. It is not an established financial asset category with standardized membership or performance measures.
The distinction at the heart of the thesis is between proving a prototype and producing a system reliably at volume. A supplier may need machines, workers, tooling, inventory, and approval to produce for a particular program before it has firm evidence that large orders will follow. That timing mismatch makes capacity difficult to finance: the supplier faces real upfront costs while demand remains uncertain.
Why the supplier base is central
The a16z article describes a fragmented manufacturing base, with many small firms. It reports that the 2022 Economic Census counted 16,876 machine shops; among those operating year-round, 83% had fewer than 20 employees and 95% had fewer than 50. The article also reports 240,644 manufacturing employers, roughly three-quarters of which had fewer than 20 employees. These are figures as reported by the article, not independently verified here.
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The authors further report that 61% of tier-two-and-below defense manufacturers named tooling, automation, or production-line limits among their top three barriers to expansion. The article does not specify the survey year or methodology in its reproduced text, so the figure should be read as the article’s account rather than a general, independently established measure.
Small scale alone does not prove that a supplier is inefficient or a sound investment. The thesis is that existing suppliers can hold valuable customer relationships, skilled workers, qualification histories, and accumulated process knowledge. Improving those capabilities may be more productive than treating every production shortfall as a reason to build a new factory or vertically integrate into a systems company.
How the proposed model is supposed to work
Make demand credible enough to finance capacity
A reliable government order or production commitment can serve as an anchor demand signal. It may give a systems company, its suppliers, and investors a stronger basis for committing to equipment and production capacity before full-rate manufacturing begins. That is the proposed mechanism, not proof that any particular commitment will produce a successful ramp.
Demand risk remains substantial. A budget request is not the same as an enacted appropriation, a signed contract, or an order delivered at the expected volume and schedule. For example, the article says $1.1 billion was needed in the FY27 procurement request for Anduril’s FQ-44 Fury production to begin. That is a dated procurement claim, not evidence here of an appropriation or purchase; current official status would need to be checked before treating it as funded demand.
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Invest below the prime contractor
The target is often the supplier network beneath the prime contractor: firms producing components or specializing in processes that are difficult to scale quickly. Capital could upgrade engineering, workflows, equipment, and qualification capacity at an existing supplier; a new factory may make sense where existing capacity cannot meet performance, cost, volume, or timing needs.
This is not an argument that systems companies should outsource everything. The authors allow for bringing capabilities in-house when the market cannot supply them adequately. Their proposed whitespace is a network of capable suppliers that can serve multiple programs while preserving enough independent sources to limit the consequences of a single supplier failure.
Bring suppliers into engineering earlier
In a build-to-print arrangement, a vendor may receive a drawing and be expected to manufacture it without taking part in redesigning the component for higher-volume production. The authors favor co-engineering: involving suppliers early enough to use their process knowledge to improve manufacturability while retaining required system performance.
That can mean considering geometry, materials, tolerances, interfaces, testing, or manufacturing methods together rather than passing a fixed drawing down the chain. It does not require handing a supplier responsibility for the entire system architecture. A systems company can retain architecture and integration while giving a supplier bounded responsibility for a component and the process that produces it.
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Use data and software to find the actual constraint
Software can connect requirements, design, test, inspection, and production information, helping teams see where work is delayed or quality problems originate. It cannot replace the physical inputs of manufacturing: facilities, machines, tooling, labor, inventory, and qualification still need resources.
The article uses a “two-second transfer test” as an operational heuristic. If a robot is added to move a part between machines but the transfer already takes only a couple of seconds, that automation may not increase output. The constraint could instead be machine cycle time, fixture changes, an inspection queue, or another step. The example illustrates why investment should target a measured bottleneck; it is not a universal rule against automation.
Match financing to the maturity of production
The authors describe a progression in which early venture capital bears greater uncertainty, followed by growth equity, private equity, strategic investment, or credit as production becomes more proven. The underlying point is that physical capacity requires capital even when software improves how a factory is planned or run. This is a proposed financing path, not a prescribed sequence for every supplier.
What the article’s examples illustrate—and what they do not
The authors use Hadrian as an example of building digitally enabled factories and Amca as an example of applying engineering software and existing factory capacity. They describe Nominal as connecting test and production data. Anduril and Castelion illustrate, in the article’s account, that defense systems companies still depend on lower-tier supplier networks even when they selectively integrate manufacturing or choose commercial components.
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The article reports several company- and program-specific figures: suppliers reportedly added capacity for Anduril’s Ghost-X program in about six months; Anduril’s Barracuda-500M was described as using 70% commodity components; and the article characterizes a 2022 Hadrian comparison as 10 times faster and more than 40% more efficient than the legacy supply chain. It also reports 98% on-time delivery for Hadrian-made Javelin and TOW components on RTX programs.
For Amca, the article claims more than 50,000 components per month across six factories and a 67% reduction in development-to-production timelines using its RAPID platform. It reports $1.37 billion in Hadrian equity and a $360 million revolving credit facility, described as supporting manufacturing infrastructure, machinery, and hardware. These are claims and figures reported by the article; they should not be generalized to other firms or treated as independently confirmed performance data.
The article also invokes SpaceX’s more than 600 Falcon 9 flights and roughly 80% in-house Starship manufacturing to illustrate vertical integration. Those examples show a different strategic choice from building a broad external supplier base; they do not establish that vertical integration or supplier investment is universally superior.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate a supplier-capacity investment
The article offers a way to frame diligence, not standardized metrics for scoring every company. A useful assessment asks:
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- Demand quality: What orders or commitments exist, at what volume and timing, and how exposed are they to government procurement uncertainty?
- Qualification readiness: Which facilities and processes are approved, and what must be validated before added capacity can produce acceptable parts?
- The real bottleneck: Is the limiting factor tooling, machine time, engineering throughput, labor, inspection, inventory, or coordination among suppliers?
- Customer concentration: Can the supplier serve multiple customers and programs? Could using one efficient supplier across many programs create a systemic single point of failure?
- Engineering role: Does the supplier merely build to print, or does it contribute process knowledge and design-for-manufacture work within clear responsibility boundaries?
- Capital and balance-sheet fit: Is the funding appropriate to the maturity and risk of production, and will debt or acquisition terms leave the business able to invest in operations?
- Durable capability: After the investment, are engineering, equipment, skills, qualified output, and alternative supply sources stronger?
Risks and limits of the thesis
Orders may not arrive as planned
Suppliers can invest ahead of demand and be left with underused capacity if requirements change, procurement is delayed, or expected volumes do not materialize. A request for funding should not be described as a contract or a delivered order.
Capacity is not always interchangeable
Adding machines does not necessarily create usable capacity for a defense program. Qualification may depend on a specific facility and validated process, so moving work to another shop—or adding a different production line—can require more than buying equipment.
Supplier efficiency can create system-wide concentration
A supplier serving several programs may diversify its own revenue but become a shared point of failure for the broader industrial base. The authors’ case for stronger suppliers therefore sits alongside a need for sufficient independent sources where the consequences of disruption are serious.
Acquisitions can build or weaken capability
Investment can add engineering, equipment, workers, and output, or it can drain resources and leave a supplier overburdened with debt. The article argues for evaluating whether an acquisition leaves productive capacity stronger; it does not quantify how often acquisitions achieve that outcome.
Company examples are not proof of typical results
The performance numbers cited in the article are company claims or claims repeated by its authors. They illustrate the thesis but do not establish that comparable speed, efficiency, delivery, or production gains are typical across the supplier base.
This is not an investment recommendation
The publisher says its posts are not investment advice and that investments in its managed vehicles can result in the loss of the entire amount invested. The manufacturing thesis should not be treated as a recommendation to buy any company or security.
The authors’ case in one sentence
Love and Larson summarize their position this way: “Manufacturing is capital-in, capability-out.” Their argument is that investment should be judged not just by the money committed or the factory built, but by whether it produces durable, qualified capacity that can manufacture systems repeatedly and reliably.
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