저자: 미상 · 외국인
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AI가 데이터를 무한 자원인 제4 생산요소로 만들어 생산성을 끌어올리며 '포효하는 2020년대'를 이끈다는 Ed Yardeni 분석 소개. 2025년 3분기 산출 5.4%, 생산성 4.9%, 노동시간 0.5%, 단위노동비용 -1.9%를 근거로 제시. 빅테크에서 전통 기업으로 랠리 교체, S&P500 2029년 10,000 목표, 대만·연준 정치화 등 리스크도 언급.
다듬은 글
Welcome to this new insight. Today we explore an economic thesis that, trust us, is literally shaking the foundations of global financial markets. We're talking about the advent of the Roaring 2020s, a fascinating idea theorized by strategist Ed Yardeni. In essence, we are witnessing a colossal paradigm shift in real time, an era in which the use of artificial intelligence for productivity isn't just a high-tech gadget or an exciting step forward, but is acting as an engine—an invisible and relentless engine—for an economic boom that is truly unprecedented in modern history.
Okay, let's dive into this aspect, starting right from the central mystery we 've been observing in recent months. In short, let us ask ourselves: how can an economy experience such massive expansion without requiring, at the same time, an increase in the hours of human labor? Traditional economics has always taught us that growth is based on the use and optimization of resources that are, by their nature, limited and scarce.
But what's happening today, well, it directly challenges these historical postulates. The rules of the game are changing. Let's move on to chapter 1, the fourth factor of production, that is, where data comes into play. Think about it for a moment, for decades the production of goods and services depended on three primary factors: land, labor, and capital.
All inherently scarce resources, right? If you increase one, you often have an opportunity cost on the other. In the post-AI world, however, a fourth production factor emerges forcefully: data. And here lies the magic, because, unlike classic factors, data is a potentially infinite resource. If before AI it was considered simply raw information and expensive to process, today, processed by advanced algorithmic models, it evolves into a true engine of autonomous productivity that continuously feeds itself.
So this is the crux of the matter . Understanding the practical implications of this unlimited abundance. And Ardeni defines this phenomenon as the Buzz Lightyear Theory. That's right, to infinity and beyond, literally. Because these massive amounts of AI-generated data are not discarded, but stored and reused indefinitely, this creates an infinite, structural, and ongoing demand for immense computing power and storage capacity .
We are not talking about the usual passing technological fad, but the installation of a continuous economic driver. Chapter 2, the productivity boom, what we might call the economic fairy dust . And this brilliantly illustrates the theory by putting it to the test. Looking at the third quarter of 2025 metrics for American businesses, the gap is, well, startling.
Total output rose by 5.4%, driven by a dramatic 4.9% increase in productivity, but hours worked only posted a very weak increase of 0.5%. This tells us one thing unequivocally: companies are aggressively expanding their revenues, without expanding their physical workforce. And the direct result of this trend was contained in a single, very powerful figure: -1.9%, the unit cost of labor recorded exactly this decrease.
This is why productivity, when boosted by AI in this way, is called the fairy dust of the economy. Because it allows for tremendous GDP growth, protects corporate margins, and does so without triggering dangerous wage dynamics that would lead directly to unsustainable inflation. But be careful, this incredible efficiency is based on a metamorphosis of the labor market itself.
In the pre-AI world, as we know well, expertise was based on learning by doing, on the manual and often repetitive execution of tasks. Today we are moving, and very quickly, to a learning- by-supervising paradigm. The worker is no longer the physical executor of every single, tedious step; he or she becomes a supervisor who directs and validates highly scalable, machine-generated output.
This means that a single person today can multiply their productive capacity exponentially, a real turning point. And here we are at chapter 3, the BRAIN architecture. The pillars that support this efficiency. To understand how this is scaling globally, we need to look at the so-called BRAIN architecture.
It's a perfect acronym that encompasses biotechnology, robotics, artificial intelligence, and nanotechnology. Think about it, biotechnology today is leveraging AI to reduce drug discovery from years to a few months. Robotics is moving from clumsy fixed mechanical arms to ultra-intelligent mobile systems.
AI acts as the connective tissue between these worlds, and ultimately nanotechnology miniaturizes the chips needed to run it all. Moreover, it is crucial to note how US tax policies, such as the OBBA act, are pushing very hard with immense incentives to finance and repatriate the production of these technologies.
The practical result of these investments is disruptive. Look at the proportions: a full 55% of nominal capital expenditure today is dedicated entirely to high technology, more than half. This marks the definitive transition from an economy based on heavy industry to a highly cognitively dense economy , where investment in data and infrastructure dominates everything else.
Chapter 4, the great rotation. How AI is physically taking over the real economy. Now, the really interesting thing about this slide is the passing of the baton we are witnessing in the market. At first, the technological euphoria was ridden by the Magnificent Seven, the giants that produce hardware and basic models.
However, they now find themselves facing fierce competition and the need for colossal investments. The real protagonists now are becoming what Ardenis calls the Impressive Four and Ninety-Three— that is, traditional companies, those that concretely use AI to automate processes, cut costs, and explode their operating margins.
And this widespread adoption is literally reshaping entire sectors of the real economy. Banks and manufacturers are optimizing supply chains in ways that previously seemed like science fiction, but there's more, an unexpected twist. The enormous, ongoing energy demands of AI data centers are turning boring old utility stocks into truly explosive growth stocks.
This market rotation is absolutely necessary, among other things, to give the indices some breathing space, given that at the end of 2025, the technology and communications sectors alone accounted for a monstrous 46.7% of the entire market. All this expansion produces financial effects that cannot be ignored.
Thanks to AI-induced efficiency , analysts are forced to constantly revise their earnings estimates for the S&P 500 index upwards. We're talking about projections that see $310 per share in 2026, only to make an absurd leap to $350 in 2027. Impressive numbers that confirm in black and white how this productivity boom is translating directly into real value for shareholders.
And here we are at the final chapter, number five, resilience and systemic risks. On course for 10,000 meters. To understand whether we will be able to withstand the impact of this transition, let's start with consumption: 88.5 trillion dollars. That's a mind-boggling number, isn't it? It represents the estimated net worth of baby boomers at the end of 2025.
In effect, we live in a Gen-shaped economy. With approximately $30 trillion invested directly in stocks, this demographic’s propensity to spend acts like a giant economic parachute. As long as markets hold, their spending will ensure rock-solid aggregate demand, protecting the economy just as AI revolutionizes work.
Of course, all that glitters is not gold . The analysis also objectively examines institutional risks, particularly for the Federal Reserve. According to the theory, excessive politicization of the Central Bank, perhaps through hasty rate cuts, could cause a meltdown, an artificial rally fueled by valuation multiples, reminiscent of the 1999 bubble.com.
And historically, we know that these speculative excesses almost always end in a meltdown, a disastrous collapse. Market integrity, in short, requires decisions based strictly on data, not political pressure. And on the external front, the situation is equally delicate. The analysis reports Taiwan as the single most vital global bottleneck for advanced semiconductors.
International relations are fragile, and a potential escalation between political leaders like Donald Trump and Xi Jinping, specifically over Taiwan, could objectively bring the entire technological cycle to a sudden halt. To make matters worse, the relentless appetite for AI infrastructure capital will most likely keep real interest rates at structurally higher levels than we have become accustomed to.
Let's see how all this is built over time and translated into market goals. Even considering all these risks, Yardeni's projections are extremely bold. Based on a valuation multiple of 22, the target for the S&P 500 is seen between 6,600 and 7,000 points by the end of 2025. From there, the rally should expand to 7,700 in 2026, aiming straight for the crazy 10,000 point mark by 2029.
In such a scenario, driven by AI efficiency, any corrections of 10 or 15% are not seen as catastrophes, but simply as windows of opportunity to enter the market. In short, the journey towards an economy fueled by data as an unlimited resource has definitely begun. But despite all the enthusiasm for the extraordinary capabilities of this invisible engine, I leave you with a reflection.
Will our institutions and fragile global relationships be able to maintain the balance necessary to weather the inevitable geopolitical storms unscathed? The adoption of artificial intelligence undoubtedly promises to redefine human potential, taking productivity to uncharted heights, but the triumph of the roaring 2020s will depend entirely on our ability to navigate the enormous pitfalls of this transformation.
A topic that, I assure you, we will continue to analyze very closely. Thanks for following us.
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