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No final de fevereiro e início de março de 2026, uma série de desenvolvimentos rápidos no Oriente Médio lançou o mundo em um momento profundamente incerto. No centro desta crise em desenvolvimento estava uma alegação dramática do ex-presidente dos EUA, Donald Trump, de que o Líder Supremo do Irã, Aiatolá Ali Khamenei, havia sido morto durante um ataque coordenado por forças israelenses e americanas. Quase todos os principais veículos internacionais relataram o incidente, mas, nas horas imediatas, as informações eram contraditórias e confusas. Somente após várias horas é que uma confirmação mais clara começou a surgir.

In boardrooms, investment desks, and trading floors around the world, artificial intelligence is no longer an abstract future idea — it is priced into markets today. Stocks tied to AI frameworks, chips designed for model training, and software platforms promising exponential breakthroughs have all driven valuations higher. At the same time, however, a growing chorus of executives and economists are asking a simple but uncomfortable question: Are we seeing real productivity gains yet, or is the market speculating on a future that is still far from guaranteed?

One of the more striking voices in this debate — coming not from an academic journal but from within the technology and crypto ecosystem — is Changpeng Zhao, known as CZ, the founder and former CEO of Binance. CZ’s criticism isn’t aimed at AI itself. Rather, he is challenging the financial and narrative market that has grown so attached to it.

Unlike most executives, CZ’s perspective is shaped by both technology deployment and market dynamics. In public comments throughout 2025 and into 2026, he has repeatedly argued that many AI ventures — particularly those paired with new crypto tokens — are driven more by the promise of growth than by proven utility. His stance is that developers and investors too often chase capital through token launches or story-driven narratives before building products people need. In his view, utility should precede valuation — not the other way around.

This concern resonates with a broader market tension: markets are valuing AI like it has already transformed productivity, while actual businesses are still figuring out how to extract those gains in measurable ways.

The Market’s AI Optimism: A Leap of Faith

Over the past year, financial markets have priced in aggressive assumptions about AI-led productivity. Equities tied to semiconductors and cloud computing have surged; traders in bond markets have pushed yields lower on the belief that AI will catalyze long-term economic growth and thus justify looser monetary policy.

But this optimism is not uniform among economists and corporate leaders. Some analysts have explicitly questioned whether recent market movements reflect real economic improvement or simply a leap of faith — where the belief in future gains is being wagered as if they have already materialized.

The key issue at stake is productivity. Traditional economic growth requires that businesses produce more output with the same or fewer inputs: more goods, more services, more value — without proportionally increasing labor or capital costs. AI, in theory, should enable exactly that. However, the evidence so far has been mixed.

What CEOs Are Actually Reporting

A substantial survey of nearly 6,000 executives in the United States, the United Kingdom, Germany, and Australia revealed a striking dynamic:

Roughly 70% of firms reported using AI in at least some capacity.

Over 80% of those same firms said AI has not yet had a measurable impact on employment or productivity in the past three years.

Among executives who use AI regularly, the average weekly engagement with these systems was only about 1.5 hours.

That contrast — widespread adoption but limited tangible results — illustrates a gap between expectation and experience. Businesses are experimenting with AI, but many are still in the early phases of learning, integration, and scale. The productivity uplift that investors are counting on has not yet fully appeared in the financial statements or operating metrics of most firms.

At the same time, executives surveyed do expect gains in the future — modest increases in productivity and output, and small reductions in employment. This reflects cautious optimism: AI may be transformative, but its benefits are likely to unfold gradually rather than instantaneously.

Corporate Capital Spending: Betting on a Future Still Unproven

The disconnect between market pricing and productivity reality becomes even clearer when looking at corporate capital expenditure plans.

Major technology companies — including those at the forefront of AI research and deployment — have announced ambitious investment programs. Some have outlined capital budgets in the hundreds of billions of dollars, often tied to data center expansion, specialized AI hardware, and automation systems.

Yet investors have shown mixed reactions. In some cases, strong AI-related spending teased by company executives has led not to share price rallies, but to selloffs. This suggests that markets are becoming wary not just of AI’s potential, but of the costs and risks associated with pursuing that potential.

The deeper concern isn’t that AI won’t create value. Rather, it’s whether the timing and scale of current investments align with when and how that value will actually be realized.

Economies, Debt, and the Broader Macro Picture

At a macroeconomic level, international organizations and central banks have also sounded cautionary notes. While some policymakers acknowledge AI’s long-term productivity promise, they emphasize that it alone cannot resolve structural challenges like high public debt or stagnant growth without broader policy support and real wage improvements.

This is not a rejection of AI. Instead, it is a recognition that economic transformation takes time and does not follow a straight line.

The Realities Behind the Headlines

Importantly, there are genuine success stories. Companies that solve real problems with AI — for example, by speeding up legal research, improving customer support workflows, or optimizing logistics — are seeing meaningful adoption and measurable returns. In those cases, growth is tied to actual user value and incremental efficiency improvements.

What many critics, including CZ, want the broader market to understand is that not all AI projects are created equal. Some innovations are foundational and will reshape industries. Others are incremental, experimental, or still searching for product–market fit. Treating both with the same level of financial exuberance risks distorting capital allocation and inflating valuations beyond what future earnings can sustain.

A Balanced Perspective: Reality Over Narrative

In the end, CZ’s commentary serves as a useful corrective to the dominant narrative around AI: that its transformative impact is immediate, ubiquitous, and fully priced into financial assets.

The evidence suggests a more nuanced reality:

AI adoption is widespread,

measurable productivity gains are still emerging,

markets may be pricing future hope as present certainty, and

capital expenditure patterns reflect long-term belief, not short-term proof.

For investors, business leaders, and policymakers alike, the lesson is not to dismiss AI’s potential. Rather, it is to distinguish between real economic value and narrative-driven valuation. Markets that reward utility and demonstrated performance are more sustainable than those driven by promises that have yet to materialize.

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