Industrial stocks offer several ways to follow the AI data center buildout, from manufacturing components to wiring facilities and building utility infrastructure. Comparing those businesses takes more than finding an AI connection. Their work, demand and valuations matter, too.
The Hennessy Cornerstone Growth Fund’s selection process brings those distinctions into focus. Its criteria combine valuation, improving net income and share-price momentum, while fund manager Josh Wein favors businesses with steady demand and substantial backlogs.
Different businesses behind the infrastructure buildout
NN raised its full-year outlook, identifying AI data centers among its stronger markets. That provides a company-specific example of data center demand entering a manufacturer’s business outlook. It does not, by itself, establish the same earnings trajectory for other industrial companies.
Infrastructure contractors offer another way to examine the theme. The Hennessy fund holds Centuri Holdings, Primoris, MYR Group and Tutor Perini, whose activities span gas infrastructure, electricity distribution, electrical work and construction. Those roles belong in the comparison because they describe different kinds of work.
| Company | Business activity |
|---|---|
| Centuri Holdings | Retrofitting natural gas pipeline equipment |
| Primoris | Building gas and electricity distribution systems |
| MYR Group | Electrical grids and data center wiring |
| Tutor Perini | Construction |
MYR’s data center wiring offers a more explicit connection to facilities than the broader construction exposure described for Tutor Perini. Gas pipeline retrofits and electricity distribution work provide different infrastructure exposure again. Grouping all four under an AI label would obscure those distinctions.
How the valuation screen works
Hennessy’s process adds financial criteria to that business comparison. Its screening requirements include company size, sales-based valuation, changes in profitability and stock performance:
- A market capitalization above $1.75 billion.
- A price-to-sales ratio no higher than 1.5.
- Improving year-over-year net income.
- Strong share-price momentum over 12 months.
Together, those requirements describe a more specific selection process than simply looking for industrial companies connected to AI. A business must combine the fund’s valuation limit with improving profits and price momentum to satisfy the screen.
The distinctions matter when interpreting the results. A low price-to-sales ratio addresses one valuation measure. Improving net income addresses the direction of profitability. Twelve-month momentum addresses share-price performance. Each adds a separate condition; none makes the others redundant.
Why backlogs matter to the comparison
Wein’s preference for steady demand and substantial backlogs adds an operating consideration alongside those numerical filters. His interest centers on businesses carrying out the data center buildout through a pipeline of work.
That emphasis asks a different question from a valuation screen: what work supports the business? For a comparison of infrastructure companies, the nature of that work helps explain what an investor is actually evaluating. Manufacturing components, distributing electricity and constructing facilities should not be treated as interchangeable activities.
Comparing exposure without assuming a winner
NN’s outlook and Hennessy’s screening approach serve different purposes. One describes management’s expectations for an individual manufacturer. The other defines criteria for selecting investments across a wider set of companies.
A useful comparison keeps both the business and the valuation in view. An AI connection identifies a potential demand driver; the company’s activities, profit trend and selection criteria provide the detail needed to assess that connection. The theme alone does not establish which stock offers the better investment.