AI in recruiting: Valuations, Infrastructure and Where Investors Should Look Next

In a recent conversation on Bloomberg Technology, Janus Henderson Global Technology and Innovation Team portfolio manager Denny Fish discussed the launch of a new AI-focused fund and the broader investment landscape around artificial intelligence. The exchange examined long-term opportunity, short-term volatility, valuation distinctions among leading names, and the often-overlooked infrastructure and sectoral implications of AI adoption. This article synthesizes those insights and expands on practical implications—illustrating how applications such as AI in recruiting fit into the investment thesis and real-world adoption curve.

Outline

• Why AI represents a multi-decade shift
• Valuation landscape: speculative pockets vs. durable winners
• Infrastructure constraints: power, cooling, and real estate
• Sector penetration: financials, healthcare, consumer, industrial
• Pilot failures, learning curves, and historical parallels
• Capital flows and strategic moves by major players
• Practical investor approach and use cases like AI in recruiting
• Conclusion: positioning for the long haul

Why AI represents a multi-decade shift

The launch of an AI-dedicated fund reflects a conviction that artificial intelligence will be among the most profound technological transformations in a lifetime. Historical analogies are instructive: the cloud, social media, and mobile revolutions generated decades of value creation. AI is now at a similar inflection point, but its reach is broader—affecting not just technology companies but virtually every industry. Adoption timelines will vary, but the long-term direction of travel is expected to be strongly positive over the next twenty years.

One implication of this structural trend is that investment strategies should balance near-term noise with long-term secular opportunity. Short-term pressure on specific companies or sectors does not invalidate the broader thesis that AI will rewire workflows, product functionality, and competitive dynamics across the economy.

Valuation landscape: speculative pockets vs. durable winners

Not all AI-related equities should be viewed the same way. Valuations differ dramatically depending on fundamentals, business models, and earnings trajectories. Some names trade at very elevated multiples driven by speculative narratives; others retain more reasonable multiples relative to growth expectations.

Examples cited help crystallize this distinction. Certain firms that have seen dramatic valuation run-ups may still be far from sustainable earnings power, producing elevated price-to-earnings ratios that demand caution. By contrast, companies with strong earnings momentum and dominant market positions—especially those enabling AI workloads—tend to offer more comfort for long-term investors. The difference often comes down to the visibility of earnings and the secular tailwinds supporting revenue growth.

Portfolio construction in an AI-focused strategy therefore tends to be heavy in technology while also allocating to financial services, healthcare, consumer, and industrial firms where AI can enable productivity gains. This diversified approach seeks to capture both direct beneficiaries (AI chipmakers, cloud operators) and indirect beneficiaries (equipment, power, services) across the value chain.

Infrastructure constraints: power, cooling, and real estate

Scaling AI is not just a software challenge. The physical constraints of data centers—power availability, thermal management, and suitable real estate—are front and center. Power is arguably the single biggest bottleneck for deploying compute at hyperscale. Once sufficient power is secured, cooling becomes the next major hurdle given the density of modern AI hardware. These constraints create investment opportunities beyond pure semiconductor makers: companies that provide electrical distribution, power management, thermal solutions, and data center real estate will play a meaningful role.

Firms that specialize in these areas—industrial equipment manufacturers, facility operators, and specialized real estate investors—can benefit from the rapid build-out of AI infrastructure. Including names from these sectors in an AI portfolio is a way to participate in the deployment story without relying solely on software or chip valuations.

Sector penetration: financials, healthcare, consumer, industrial

AI’s economic impact will be broad. In finance, AI systems can accelerate research, automate analysis, and enable personalized services at scale. In healthcare, AI promises faster diagnostics, more accurate imaging interpretation, and optimized patient workflows. Consumer companies will use AI to enhance personalization, recommendation engines, and customer interaction, while industrial sectors will deploy AI for predictive maintenance, process optimization, and supply chain improvements.

One concrete application that highlights cross-sector impact is AI in recruiting. Talent acquisition is fundamentally an information problem: candidate sourcing, screening, matching, and interview scheduling are workflows ripe for automation and enhancement. AI in recruiting can accelerate the identification of fit, reduce time-to-hire, surface candidates that might otherwise be overlooked, and improve candidate experience through intelligent scheduling and communications. These improvements translate into measurable productivity gains for HR teams and hiring managers.

However, the pace of adoption will vary. Many companies see early wins with copilots and focused tools, while other initiatives remain experimental. Investors should therefore distinguish between clear, revenue-generating deployments and exploratory pilots that may not scale.

Pilot failures, learning curves, and historical parallels

Short-term skepticism is natural, especially when early pilots fail or under-deliver. Reports suggesting a high failure rate for pilots—such as the widely cited statistic that many pilots do not progress to production—reflect a normal stage in technological adoption. Early commercial internet days saw similar boom-and-bust dynamics: an initial wave of experimentation followed by consolidation and the emergence of enduring winners.

Pilot setbacks are not, by themselves, a reason to abandon the AI thesis. Rather, they are part of the iteration process. Teams learn how to integrate models into workflows, reconfigure business processes, and align incentives. Over several years, successful applications tend to crystallize and scale. For example, conversational AI and copilots are already demonstrating clear value in specific domains, and broader uptake is expected as organizational proficiency increases.

AI in recruiting exemplifies this pattern. Initial trials may struggle with integration, bias concerns, or candidate experience. Over time, improved models, better data governance, and refined processes can convert pilots into reliable, scalable systems that deliver consistent ROI.

Capital flows and strategic moves by major players

Large strategic investors are repositioning for the AI cycle. Some conglomerates and investment firms have been reallocating capital from legacy holdings into AI infrastructure and related assets. These moves are often a response to expected undersupply in semiconductor capacity and the pressing need for power and facility expansion.

Public policy and government actions have surfaced as points of discussion, especially in relation to national champions and semiconductor leadership. While government incentives and policy can influence investment flows, they cannot instantaneously close technological gaps—particularly in advanced process technology where incumbents have multi-year advantages due to accumulated expertise and manufacturing scale.

Investors should therefore watch where capital is being deployed: direct investments in fabs and chip designers, acquisitions of specialized infrastructure providers, and strategic stakes in companies that occupy key nodes of the AI supply chain. These flows can signal where the market expects durable demand.

Practical investor approach and the role of applications like AI in recruiting

Given the breadth of opportunity and uneven valuation landscape, a disciplined investment approach is essential. Key principles include:

• Differentiate between speculative and fundamentals-driven investments. Evaluate earnings visibility, margin sustainability, and competitive moats.
• Diversify across the AI value chain. Include infrastructure, software, services, and end-user industries where AI delivers measurable outcomes.
• Monitor adoption signals. Real revenue growth and customer retention in AI deployments are stronger indicators than press headlines.
• Anticipate multi-year timelines. Long-term secular trends often play out over decades rather than quarters.
• Assess regulatory and ethical risks. Applications like AI in recruiting necessitate attention to bias mitigation, transparency, and candidate privacy.

Applying these principles to AI in recruiting means looking for vendors and adopters that demonstrate measurable hiring improvements—reduced time-to-fill, improved retention, or higher-quality matches—rather than marketing claims. Vendors that offer audited performance metrics, transparent model behavior, and robust data governance are better positioned to move beyond pilots and win enterprise budgets.

Conclusion: positioning for the long haul

The AI opportunity is expansive and multi-dimensional. While short-term volatility and pilot failures will persist, the structural case for investment remains strong—provided that valuation discipline and portfolio diversification are applied. Hardware and infrastructure will be crucial enablers, and capital flows reflect a recognition of supply constraints in compute and power. Applications across finance, healthcare, consumer, and industrial sectors, including practical functions such as AI in recruiting, will drive real economic value as models, processes, and organizational practices mature.

In crafting an investment approach, the balance between conviction and caution matters. Emphasize companies with sustainable earnings prospects, invest across the ecosystem to capture indirect beneficiaries, and pay close attention to which pilots are producing verifiable outcomes. Over the next decade and beyond, the companies that enable AI at scale—both through infrastructure and real-world applications—are likely to be among the most consequential contributors to value creation.

"AI will affect the entire economy." — A concise reminder that the technology's implications extend far beyond the companies that build it, making disciplined, broad-based strategies essential for investors and adopters alike.