Goldman Sachs Signals Pivot to 'Phase 4' AI Trade Focused on Productivity
The investment thesis is shifting from hardware providers to enterprises that can use AI to slash labor costs and expand margins.
The AI investment landscape is undergoing a fundamental transition as the market moves away from the hardware providers that fueled the initial boom. Goldman Sachs research indicates that the trade is entering a 'Phase 4' evolution, shifting the focus from infrastructure builders to the productivity beneficiaries of the technology.
According to Goldman Sachs, the next wave of AI winners will be defined by a specific set of criteria: high labor costs, significant exposure to AI automation, and a documented history of discussing AI in the context of productivity. The goal for these companies is to demonstrate tangible margin enhancements by using AI to lower operating costs. As a concrete example of this thesis, Goldman Sachs has assigned a Buy rating to Franklin Resources (BEN), setting a target price of $29 for the firm.
The Evolution of the AI Trade
This shift follows a progression of investment phases. The initial rally was dominated by 'Phase 1,' which centered on chips and hardware—most notably NVIDIA—and 'Phase 2,' which focused on cloud and infrastructure. While these phases provided the necessary foundation for the AI era, they have also sparked growing concerns about a potential bubble. These fears are driven by massive capital expenditures across the industry and a perceived lack of widespread, empirical evidence that these investments are translating into broad productivity gains.
Why the Pivot Matters
This transition represents a critical pivot in the AI investment thesis: a move from betting on the 'sellers of shovels' to the 'users of shovels.' For years, the market has rewarded the companies providing the compute power and data centers. However, if the trade shifts toward productivity as Goldman suggests, the massive valuations of hardware companies may begin to plateau.
Conversely, traditional sectors—such as finance and consulting—that have historically been burdened by high human-capital costs could see unexpected growth. For these enterprises, AI is no longer a speculative tool but a mechanism for operational efficiency that can directly impact the bottom line.
What to Watch
Investors are now tasked with identifying which legacy companies can successfully integrate AI to realize these efficiencies. The primary metric for success will be the ability to prove that AI automation is actually reducing overhead and increasing margins rather than simply adding to the tech spend. While the infrastructure phase provided the tools, the market is now waiting to see which enterprises can actually turn those tools into profit.