AI in recruiting: Why Tech Investors Are Watching Cyclical Industries and Digital Infrastructure

How does the headline-grabbing "Stargate" buildout and the broader AI wave are reshaping investor thinking — not just around chips and cloud providers, but across real estate, energy, and industrial sectors? If you're working on talent acquisition or trying to understand how AI will change hiring, this article explains the connections and practical implications for both investors and hiring teams. Throughout the piece I'll highlight how AI in recruiting interacts with infrastructure, policy, and the entry-level workforce.

Outline: What you'll learn

• What the so-called "Stargate" investment means and how much of it is likely to materialize
• Why foreign manufacturers moving production to the U.S. matters for digital infrastructure
• How tariffs, export rules, and "pay to play" dynamics affect companies like NVIDIA and AMD
• What AI means for employment, training, and recruitment — especially entry-level roles
• Why macro policy (the Fed, inflation, rates) influences tech-capex and cyclical sectors
• Actionable takeaways for investors and hiring managers adopting AI in recruiting

The $500 billion "Stargate": hype, reality, and the strategic angle

There's a lot of attention on a multi-hundred-billion dollar buildout often referred to in shorthand as "Stargate." The key question: how much of that figure is real, and how much is aspirational? Based on what we're seeing across the industry, the project is more than a headline — it's a widespread theme — but large numbers always require proof of execution.

Part of what makes the Stargate narrative compelling is that it's not just chip fabs or server builds: it's an entire digital infrastructure portfolio — data centers, power plants, cooling systems, industrial logistics, and specialized real estate. I often describe digital infrastructure as the analog equivalent of a strategic reserve. As I put it during the interview:

"Digital infrastructure really is something that the U.S. government needs to make sure we are at the forefront. Similar to having a strategic petroleum reserve, we really need to make sure we have that digital infrastructure that is strategic..."

Viewed through that lens, investments in data centers and supporting industries become national priorities as much as commercial opportunities.

Manufacturing on U.S. soil: tariffs, incentives, and "pay to play"

Trade policy and tariffs have nudged many foreign-owned manufacturers to invest in U.S. capacity. Companies such as TSMC and Foxconn have signaled bigger footprints here, and that has knock-on effects across construction, industrial equipment, and local labor markets. The dynamic presents an interesting juxtaposition: foreign-owned plants operating in the U.S. can be seen as both a reversal of offshoring and as a means to secure critical supply chains.

At the same time, American technology giants are facing policy costs for doing business in China. There are export controls, and some policies functionally pressure companies to "pay to play." As discussed, for certain semiconductor and AI-related businesses, China remains a massive, tens-of-billions-of-dollars market — NVIDIA itself sees large addressable revenue there — so exclusion is not a simple choice.

"It's sort of just the pay to play issue that we're seeing here."

For investors, this means evaluating winners not only by technical competency but also by geopolitical positioning and supply-chain footprint. For hiring and recruiting leaders, it means talent pools and talent strategy must adapt regionally — U.S. plant expansions create demand for specialized operations staff, technicians, and site-level engineering that often live outside traditional tech hubs.

AI, productivity, and the unanswered question for the workforce

One of the most important topics I discussed is how AI will change productivity — and what that means for employment, particularly entry-level roles. At the moment, the productivity gains are visible but still early-stage. The bigger uncertainty is structural: how will democratization of knowledge via AI shape the career path for new graduates and junior hires?

When knowledge becomes more widely accessible and tools can generate analysis or draft work, employers face a new question: how do you validate and train people to use and check AI outputs? This has direct implications for recruiting and onboarding processes. Recruiters and hiring managers must now evaluate not only technical ability but also a candidate's capability to critically assess AI suggestions, validate data, and exercise domain judgment.

That is where "AI in recruiting" becomes an operational issue, not just a buzzword. Using AI tools to screen, source, and assess candidates can increase efficiency — but only if the human side of the process is adjusted. Onboarding programs must incorporate AI literacy, and interview frameworks will need to test for skills like model validation, prompt design, and bias recognition.

Practical implications for entry-level hires and recruiters

• Design interviews that include AI-augmented tasks: ask candidates to critique or validate AI-generated outputs.
• Invest in learning pathways that couple domain training with AI tool training — for example, pairing technical mentorship with guided use of models.
• Recognize that "AI in recruiting" isn't just sourcing — it includes candidate experience, bias mitigation, and measurable outcomes.
• Build partnerships with local technical schools and community colleges where operations and infrastructure talent are trained.

Monetary policy, inflation, and why the Fed matters to tech investors

The macro backdrop plays a major role in timing and financing large infrastructure builds. Central bank policy affects the cost of capital for cyclical investments — things like fabs, data center campuses, and industrial facilities — which are capital intensive and often financed over long horizons.

We're watching the Federal Reserve closely. There's a balance between potential rate cuts priced in by markets and recent inflation signals that could delay easing. Producer Price Index readings and other inflation metrics can change the trajectory, and that directly influences refinancing prospects for companies with heavy capex.

"I believe that... in normal times, I think we're looking at a Fed funds rate closer to three to three and a half percent... having current rates above four is still a little restrictive."

For investors, higher-for-longer rates weigh on cyclical sectors today. For hiring managers, constrained capex can slow expansions that would otherwise create hiring waves. Conversely, if rates ease and refinancing becomes cheaper, we could see a notable acceleration in both infrastructure projects and associated hiring needs.

Why real estate, energy, and industrials are now part of the AI thesis

AI's demand for raw compute has broadened the investment map. It's no longer enough to own a chip company or cloud provider; the whole ecosystem matters. That means:

• Data center real estate and REITs that specialize in colocation or hyperscale campuses
• Infrastructure providers that deliver power, backup generation, and cooling at scale
• Industrial equipment makers that produce specialty cooling systems, transformers, and switchgear
• Logistics and construction firms that can execute big campus builds quickly and efficiently

Investors are increasingly assessing exposure across these sectors as a single AI theme. Hiring teams within these industries must adapt: recruiting for HVAC, electrical, and civil engineering talent becomes as strategic as hiring machine-learning engineers.

How "AI in recruiting" ties all of this together

Let's bring the focus back to the core keyword and the practical side. AI in recruiting isn't just an isolated HR tool — it sits at the intersection of infrastructure demand, talent supply, and macro financing.

Consider these linkages:

1. Infrastructure expansion drives demand for a broad set of roles, which multiplies recruiting complexity. AI in recruiting can help source and screen at scale.
2. As firms scale, standardized validation of candidate competency becomes critical; AI tools can pre-assess technical tasks, but humans must vet final outputs.
3. Geographic shifts — such as foreign-owned plants operating in the U.S. — expand the talent market beyond coastal tech hubs, requiring recruiting systems that operate across regions.
4. Policy and financing cycles determine hiring tempo. Recruiters must plan for waves of hiring tied to capex cycles and potential rate moves.

When designing people strategies, remember: AI in recruiting should augment judgment, not replace it. The most resilient hiring organizations will pair AI-enabled scale with rigorous human oversight and structured entry-level development programs.

Actionable takeaways for investors, executives, and recruiters

• Investors: Broaden your AI thesis beyond chips and cloud providers. Evaluate real estate REITs, power providers, and industrial firms that enable data center growth.
• Executives: Treat digital infrastructure as strategic. Plan site selection, energy contracts, and workforce development concurrently rather than sequentially.
• Recruiters: Integrate AI tools to improve sourcing but redesign interviews to assess a candidate's ability to validate and work alongside AI.
• HR leaders: Build onboarding that couples domain knowledge with AI literacy; create apprenticeship-style pathways for entry-level hires.
• Policy watchers: Monitor export controls and tariff dynamics because they impact where companies build and hire.

Conclusion

The AI era is reshaping capital allocation, corporate strategy, and hiring practices. While the Stargate-scale numbers are still being proven in execution, the theme is clear: AI is not just a software story — it's an infrastructure story, a policy story, and a workforce story. That means investors and hiring professionals need to think broadly and practically about how they position capital and talent for the next decade.

If you're focusing on AI in recruiting, remember that success will depend on combining automated sourcing and evaluation tools with robust human frameworks for validation, learning, and career development. The companies — and cities — that build dependable digital infrastructure will not only host AI compute, they'll also become hubs for the new kinds of jobs that power this transition.

For more in-depth discussion and examples, you can find the original segment on Bloomberg Technology where these themes were explored in conversation. My goal here is to give you a practical, actionable map: where the money might flow, which sectors to watch, and how to adapt your hiring strategy to a rapidly changing reality.