BlackRock's Rieder and the Macro Case for an AI Productivity Revolution

The market's recent shift from a near-universal winner to a selective performer frames the central investment challenge of the coming era. For five years, from 2020 through 2024, the equity market operated like a casino where almost every risk paid off. In that period, more than half of S&P 500 companies delivered annualized returns above 15%, and about 90% had positive annualized returns. Simply "putting chips on the table" was a winning strategy. That phase is ending. As 2025 concludes, the odds have changed. Around 40% of the S&P is heading for a negative year. The coming year looks less like a casino and more like an investor's market, where success demands sizing positions thoughtfully and focusing on high-probability outcomes.

This transition creates the structural case for a lasting economic transformation. The central problem has shifted from inflation to labor. The good news is that the inflation storm appears to be passing, with underlying price volatility returning to the stable norms of the pre-2020 era. The bad news is that labor market dynamics are deteriorating. The share of workers who are "marginally attached" to the labor force has been drifting higher, and efficiency-driven layoffs now dominate. With healthcare doing almost all of the heavy lifting for job creation, the rest of the economy is showing signs of fatigue.

Labor costs represent the critical lever. They account for roughly 55% of business-sector expenses. In this context, the promise of artificial intelligence is not merely a tech story-it is a macroeconomic one. Policymakers are watching, but the Federal Reserve remains in a cautious "too early to tell" stance on AI's economic impact. Yet, the market is already pricing in a revolution. As Treasury Secretary Scott Bessent argued, the next Fed chair should have an "open mind," echoing the Greenspan-era view that innovation can fuel growth without inflation. BlackRock executive Rick Rieder, a top candidate to succeed Chair Powell, sees a productivity revolution and points to a softening labor market as a key reason for advocating rate cuts to 3%. The narrative is clear: the era of easy casino returns is over, and the path forward depends on whether AI can deliver the kind of sustained productivity gains that will reinvigorate growth and reshape the economic landscape.

The Productivity Engine: Data, Drivers, and Benchmark

The macro case for an AI-driven productivity revolution rests on a simple, measurable premise: can the technology demonstrably lift the economy's growth engine? The current data shows a rebound, but also a clear gap. Labor productivity growth, a key measure of economic efficiency, rebounded to 2.4 percent in second quarter 2025. That's a welcome improvement from the prior quarter's contraction, driven by a solid 3.7% output gain. Yet, this figure remains well below the 3.5%+ average of the 1990s tech boom. The economy is healing, but not yet firing on all cylinders. This sets the stage for AI's potential impact.

The catalyst is adoption. Generative AI is moving from novelty to utility at a remarkable pace. A nationally representative survey found that 44.6% of adults ages 18 to 64 used generative AI overall in August 2024, a rate that has since climbed. More telling is the early evidence of its effect. One analysis of real-world usage suggests a cumulative productivity boost of 1.89 percentage points since ChatGPT's launch. This isn't just about saving a few minutes; it's about accelerating the pace of work across knowledge-intensive tasks.

The most concrete projection comes from a task-level analysis of current AI models. By examining how much time AI can save on complex, real-world tasks, researchers estimate that today's systems could increase US labor productivity growth by 1.8% annually over the next decade. That's a significant acceleration, roughly doubling the recent run rate. The mechanism is clear: AI acts as a force multiplier on human capital, automating routine cognitive work and freeing employees for higher-value activities.

The benchmark is now set. The economy needs to grow productivity faster to offset labor market pressures and sustain wage gains. AI adoption is accelerating, and early productivity gains are visible. The coming decade will test whether these initial efficiencies can scale into a sustained structural shift, lifting the entire economy's growth trajectory.

Structural Shifts: Labor Markets, Costs, and Corporate P&L

The productivity narrative now translates into concrete shifts in the economy's fundamental cost structure. The immediate impact is a compression of unit labor costs, a direct result of productivity gains outpacing wage growth. In the second quarter of 2025, a 4.0-percent increase in hourly compensation was more than offset by a 2.4-percent increase in productivity. This dynamic pushed unit labor costs up by just 1.6%, a notable deceleration from the 6.9% surge seen a year earlier. This is the first tangible sign that AI-driven efficiency is beginning to bite, providing a real-time buffer against inflationary wage pressures.

The longer-term implication hinges on whether this initial compression can be sustained and amplified. A task-level analysis suggests that today's AI models could increase US labor productivity growth by 1.8% annually over the next decade. That's a significant acceleration, roughly doubling the recent run rate. For corporate earnings, this sets up a powerful but delayed tailwind. The benefit isn't instant; it requires time for adoption to scale, for businesses to integrate AI into workflows, and for the full efficiency gains to flow through to the bottom line. The timing and magnitude of earnings accretion will be determined by these adoption rates and the often-overlooked integration costs.

This structural shift points to a clear investment rotation. The primary beneficiaries will be capital-intensive sectors that provide the AI infrastructure. This includes the semiconductor industry, which manufactures the underlying chips, and cloud infrastructure providers, which deliver the compute power. These are the new engines of the productivity cycle. Conversely, the rotation may work against labor-intensive services, where the cost advantage of AI automation is most direct. The market is already pricing in this transition, with capital expenditure plans in tech and communications sectors showing a renewed focus on building out AI capacity. The bottom line is that the era of easy labor cost pass-throughs is ending. The new playbook rewards those who own the tools that make work faster, not those who simply employ more workers.

Catalysts, Risks, and the Forward View

The thesis for an AI productivity revolution now faces its first major data test. The critical near-term catalyst is the January 29, 2026 release of revised Q3 2025 productivity data. This report will provide the first official, comprehensive look at the trend that began to emerge in Q2. The initial Q2 figures showed a rebound to 2.4% productivity growth, but the Q3 numbers are expected to be stronger, with labor productivity up 4.9% and unit labor costs actually falling. A sustained acceleration here would validate the early signs of AI's impact and provide a concrete benchmark for the projected 1.8% annual boost over the decade. Conversely, a slowdown would challenge the narrative and likely pressure market expectations.

Key risks remain on the horizon. The first is slower-than-expected adoption. While generative AI usage has climbed to 54.6% of adults in the past year, translating that broad usage into measurable, economy-wide productivity gains requires deeper integration into business workflows. The second risk is the offsetting effect of integration costs. As businesses invest heavily in AI infrastructure and retrain workforces, these expenses could temporarily dampen earnings and delay the promised bottom-line benefits. Finally, there is the risk of regulatory or labor market pushback. The Fed is in a cautious "too early to tell" mode, but as AI-driven displacement becomes more apparent, policymakers and workers may demand safeguards that slow deployment. The recent softening labor market is a key reason for advocating rate cuts, but it also heightens the political sensitivity of automation.

For investors, the forward view requires monitoring a few specific metrics. Quarterly surveys on AI adoption rates and time savings, like those from the Real-Time Population Survey, will track the pace of real-world implementation. More importantly, they must watch Fed communications for any shift in tone on labor market data and inflation. The central bank's stance will be a leading indicator of whether policymakers see enough productivity to justify a dovish pivot. The bottom line is that the macroeconomic narrative is now in a waiting game. The data from January will be a crucial checkpoint, but the true test of the AI revolution will be its ability to deliver sustained, broad-based gains that outpace the costs and complexities of adoption.