The trajectory of transformative technologies often follows a predictable, albeit volatile, arc: a period of frenzied infrastructure building followed by a long-term era of utility and application. In the current economic climate, the artificial intelligence (AI) sector appears to be reaching a critical inflection point. While the initial years of the AI boom focused almost exclusively on the "builders"—the semiconductor giants, server manufacturers, and networking firms providing the physical foundation for large language models—historical precedence and recent market movements suggest that the next phase of wealth creation will belong to the "appliers." These are the traditional enterprises that integrate AI into their existing business models to drive efficiency, expand margins, and create new revenue streams.
This transition mirrors the evolution of previous industrial and technological revolutions. By examining the history of the commercial radio industry, the recent earnings performance of hardware leaders, and the integration of AI in traditional sectors like agriculture and pharmaceuticals, a clear pattern emerges: the most sustainable returns often accrue to those who use the technology as a tool rather than those who sell it as a commodity.
Historical Parallels: The Radio Boom of the 1920s
To understand the current AI landscape, one must look back to the 1920s, a decade defined by the birth of commercial radio. Following the first commercial broadcast in 1920, a "radio mania" gripped the American stock market. Between 1922 and 1929, more than 600 companies entered the radio manufacturing space, all vying to build the hardware necessary for this new medium.
The Radio Corporation of America (RCA) emerged as the "Nvidia of its day." As the primary builder of radio technology, RCA’s stock price soared nearly 200-fold during the 1920s. However, the hardware market quickly became oversaturated. By 1934, only 18 of those original 600 firms remained—a survival rate of just 3%. RCA itself saw its stock value plummet by 98% between 1929 and 1932. It would take the company until the 1960s to reclaim its pre-crash peak.
In contrast, the "appliers" of radio technology—companies that viewed the radio not as a product to be sold, but as a medium to enhance their core business—showed remarkable resilience. Procter & Gamble (P&G) serves as the quintessential example. Rather than building radios, P&G used the technology to reach consumers more effectively. By sponsoring serialized radio dramas to market its cleaning products, P&G effectively "invented" the soap opera. While P&G’s stock was not immune to the volatility of the Great Depression, the company’s focus on application allowed it to maintain a dividend streak that has remained unbroken since 1890. The lesson from the 1920s is clear: the technology was only as valuable as the profitability of its applications.
The Performance Paradox: When Record Earnings Meet Market Skepticism
In the contemporary AI market, a similar phenomenon is beginning to manifest. High-profile "builders" of AI infrastructure are reporting record-breaking financial results, yet their stock prices are increasingly met with selling pressure. This suggests that the market has moved into a "priced for perfection" phase, where meeting expectations is no longer sufficient to drive further gains.
Hewlett Packard Enterprise (HPE), a leader in providing the enterprise technology and physical infrastructure required for AI data centers, recently reported the strongest quarterly results in its history. The Houston-based firm posted record revenue of $12.2 billion, a 34% increase year-over-year. For the first time, the company’s earnings surpassed the $1.00 per share threshold, landing at $1.11. Despite raising its forward guidance twice during a single earnings call, HPE’s stock fell approximately 5% following the announcement.
This "Sisyphus effect"—where companies must continually push the boulder of expectations up an increasingly steep hill—is not limited to HPE. Market data from mid-2024 indicates a broader trend among AI hardware stocks, including chipmakers and memory firms. These companies continue to post massive earnings beats, yet their valuations are being compressed.
The volatility seen in the semiconductor sector serves as a cautionary tale. While certain firms saw gains exceeding 700% in the early part of the year, subsequent reports of slightly weaker-than-expected guidance led to double-digit corrections in mere days. This suggests that the "easy money" in infrastructure building has likely been made, and investors are now looking for the next phase of the AI cycle: the realization of ROI through implementation.
Case Study: John Deere and the Advent of Precision Agriculture
As the market begins to rotate away from hardware builders, traditional industrial companies are emerging as the new vanguard of AI utility. Deere & Co., the venerable manufacturer of agricultural machinery, provides a blueprint for how a legacy business can transform into an "AI Applier."
In August 2024, at the Farm Progress Show in Iowa, Deere & Co. unveiled "JD," an AI-powered assistant integrated into the John Deere Operations Center. This tool allows farmers to interact with decades of accumulated agricultural data through a natural language interface. By applying AI to their existing data sets, farmers can make real-time decisions regarding seed density, fertilizer application, and crop health.
The economic impact of this application is already tangible. Reports from the field indicate that Iowa farmers using Deere’s AI-powered precision sprayers have reduced corn-seed usage from 34,000 per acre to 29,000, while simultaneously cutting fertilizer consumption by 40%. In terms of herbicide use, the AI-driven targeting systems are reportedly saving individual operations upwards of $75,000 annually.
For Deere & Co., this pivot toward AI implementation comes at a critical time. The farm equipment sector has faced a cyclical downturn characterized by high interest rates and fluctuating commodity prices. However, by providing AI-driven value that directly improves the bottom line of its customers, Deere is insulating its business model. The company’s stock reached record highs in late 2024, signaling investor confidence in its role as a technology applier rather than just a manufacturer of heavy steel.
The Broader Landscape of AI Application
The shift toward "AI Appliers" is expected to permeate multiple sectors beyond agriculture. In the pharmaceutical industry, AI is being utilized to accelerate the drug discovery process, potentially shaving years off the development timeline for life-saving medications. By using machine learning to predict how different molecular structures will interact with biological targets, these companies are reducing the massive costs associated with laboratory trial-and-error.
In the retail and consumer goods sectors, companies are following the P&G model by using AI to refine pricing strategies, optimize supply chains, and personalize marketing at a scale previously impossible. These applications do not require the companies to build their own chips or servers; they simply require the strategic implementation of existing AI tools to gain a competitive edge.
Industry analysts suggest that the "second wave" of AI investment will focus on companies with the following characteristics:
- Proprietary Data: Companies that possess decades of industry-specific data that can be used to train or fine-tune AI models.
- Margin Expansion Potential: Businesses where AI can significantly reduce operational costs (OpEx) or cost of goods sold (COGS).
- High Barriers to Entry: Established firms that can use AI to widen their "moat" against smaller competitors.
Economic Implications and Market Outlook
The transition from AI builders to AI appliers has significant implications for the broader economy. While the infrastructure phase was characterized by massive capital expenditure (CapEx) from a few "hyperscalers" (like Microsoft, Google, and Meta), the application phase is expected to be more decentralized. As AI becomes a ubiquitous utility, the productivity gains could lead to a sustained period of non-inflationary growth.
However, this shift also brings risks. Companies that fail to adapt to the "applier" model risk becoming obsolete, much like the radio manufacturers of the 1930s that failed to see the technology as anything more than a box with vacuum tubes. For investors, the challenge lies in identifying the "quiet" winners—modest, traditional companies that are turning AI into an untapped advantage before the market fully recognizes their transformation.
As we move into the final months of 2024 and look toward 2025, the investment narrative is clearly changing. The focus is moving away from the "physical guts" of the AI boom and toward the intellectual and operational application of the technology. History suggests that while the builders create the revolution, it is the appliers who eventually capture the lion’s share of the enduring value. The "Smart Money" is no longer just buying the technology; it is buying what the most efficient companies in the world plan to do with it.
