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「Short Essay」 Sparks Panic Again: AI Bubble Bursts, Google May Become First Tech Giant to Cut AI Spending

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Given the uncertain ROI, energy and regulatory constraints, and pressure on free cash flow, Alphabet's decision to either increase or slow down capital expenditure could potentially have a negative impact on its stock price and the overall US AI sector.

Original Title: AI Bubble Burst: Alphabet Could Be The First To Cut AI Capex
Original Author: Damir Tokic, Seeking Alpha
Original Translation: Suleeg

Editor's Note: Damir Tokic, a Seeking Alpha columnist, finance professor, and CTA (Commodity Trading Advisor), published a forward-looking analysis of Alphabet's earnings report earlier this week, sparking widespread discussion in the US stock investment community. In the article, it was suggested that Google's parent company Alphabet may become the first among large tech companies to reduce AI capital expenditures. It is worth noting that Alphabet will hold its Q2 2026 earnings conference call at 4:30 AM Beijing time on July 23.

Driven by this viewpoint and market sentiment, discussions on AI capital expenditures and the "AI bubble" have once again heated up, leading to a reversal from gains to losses in several AI semiconductor concept stocks today, including Micron.

Alphabet to Report Earnings First

Alphabet (GOOG) (GOOGL) will be the first to report earnings, with the market's focus on AI capital expenditure guidance. In April of this year, Alphabet raised its 2026 AI guidance, hinting at a significant increase in AI capital expenditure in 2027.

However, the market is increasingly concerned about the sustainability of AI capital expenditures on multiple fronts. Hyper-scale companies may have to slow down or even cut AI capital expenditures in the remaining time of 2026 and in 2027. With Alphabet set to report its Q2 earnings first, it may become the first mega-cap company to lower its AI capital expenditure expectations, with other companies—Meta (META), Microsoft (MSFT), Amazon (AMZN), and Oracle (ORCL)—likely to follow suit.

In fact, the recent underperformance of AI capital expenditure beneficiary stocks, such as in the semiconductor sector (SMH), reflects the market's anxiety over AI capital expenditures.

As discussed later, Alphabet's precarious situation is exacerbated by its adjusted free cash flow issues and the need for debt and equity financing for AI capital expenditures.

Even a mere hint of slowing down AI capital expenditure guidance from Alphabet could accelerate the bursting of the AI bubble—thus serving as a macro signal.

Big Picture

First, let's look at the big picture. The U.S. economy, especially the stock market, is currently heavily reliant on AI capital expenditure. It is estimated that AI capital expenditure will be around $700-800 billion by 2026, surpassing $1 trillion by 2027, with a major focus on data center infrastructure construction.

However, due to various constraints, this projected AI capital expenditure is challenging to materialize.

· Tokenomics and Return on Investment (ROI): The most significant constraint is that the ROI of AI capital expenditure is uncertain and likely much lower than the cost of capital—meaning AI capital expenditure may be a wasteful investment. Specifically, U.S. data centers are designed for expensive cutting-edge models, while AI-adopting enterprises require inexpensive open-source models. Hence, there is a mismatch. With the model substitution effect (from expensive to cheap), token prices are consistently dropping, which will squeeze the profit margin per token of massive enterprises, making the entire project financially unsustainable. The emergence of the new China open-source model Kimi K3 illustrates that powerful large language models can be built at an extremely low cost—rendering all this AI capital expenditure unnecessary.

· Energy and Water Resource Constraints: Data centers require a significant amount of energy for power and water for cooling, which are tangible physical constraints that limit further expansion of data center infrastructure.

· Regulatory and Resistance Sentiment: New York State was the first to impose a one-year ban on data center construction, and several other states are considering similar measures. In the U.S., it seems that "red states" like Texas allow data center construction, while "blue states" like New York hold opposing views. Therefore, the issue of AI infrastructure construction has taken on a political trend. Even in Texas, consumer resistance to data centers is increasing, which could pose a challenge for the Republican Party in the upcoming elections.

Therefore, AI infrastructure construction faces real financial, physical, and political constraints, making it difficult to see how current AI capital expenditure guidance can be sustained.

As mentioned earlier, even a mere sign of a slowdown in AI capital expenditure could accelerate the bursting of the AI bubble.

Alphabet's Dilemma

The market narrative around Alphabet has always been:

· "The risk of underinvestment is greater than the risk of overinvestment" — implying that Alphabet is aware of the risk of overinvestment but is still willing to continue AI capital spending due to competitive and strategic needs;

· "Demand for computing power outstrips supply" — suggesting that Alphabet has not seen oversupply in capacity yet and has clearly stated that more revenue would be generated if more capacity were built.

Therefore, there is no indication that Alphabet will slow down or reduce its AI capital expenditure guidance.

However, challenges are emerging on the financing side of AI capital spending — these are real financing constraints.

In particular, Alphabet seems to have a significant issue with free cash flow; hence, it has been forced to maintain AI capital spending through debt issuance (issued over $300 billion in bonds in the first quarter) and equity financing (announced over $800 billion in new stock issuance) — even terminating stock buybacks in the first quarter.

In fact, Alphabet's free cash flow issue may be more severe than reported. The Wall Street Journal, in an article "How Big Tech's Financial Data Masks the True Cost of AI Buildout," cited views from "Heard on the Street" columnist Jonathan and Purdue University professor Kevin Koharki, pointing out that after adjusting for economic costs like equity incentives, Alphabet's free cash flow for 2025 could be 67% lower than reported and 62% lower in the first quarter of 2026. This means that the reported $73 billion free cash flow in 2025 would drop to $24 billion, and the reported $10 billion free cash flow in the first quarter of 2026 would drop from $10 billion to around $4 billion.

The crucial implication is that Alphabet cannot sustain AI capital spending without borrowing and issuing new shares — increasing debt would raise credit issues (especially in a low ROI scenario), and new stock issuance would dilute existing shareholder equity.

Now, as we approach the second-quarter earnings report, Alphabet must update its AI capital expenditure guidance — a challenging situation for investors.

· If Alphabet raises its AI capital expenditure guidance despite facing financial constraints (tokenomics, ROI), physical constraints (energy, water, backlash sentiment), and financing constraints (free cash flow, debt, equity), investors will question management's motives and prudence — stock prices are likely to fall.

· If Alphabet cuts AI capital spending guidance, it will be interpreted as a macro signal, likely accelerating the AI bubble burst, especially dealing a heavy blow to AI capital spending beneficiaries such as SMH.

· Even if Alphabet only reaffirms its 2026 guidance and cautiously signals strength for AI capital spending in 2027, it may still be interpreted as a slowdown in AI capital spending growth, thus creating a similar macro effect to cutting capital spending.

Therefore, this is indeed a dilemma for Alphabet and the broader market. Investors are unlikely to reward an increase in capital spending, while a cut in capital spending is likely to punish the AI capital spending beneficiary sectors.

Looking at the recent performance in the semiconductor sector, investors seem to be preparing for a slowdown or cut in AI capital spending.

Alphabet Financials

Alphabet generates about 80% of its revenue from its service business (driven by ads), primarily from Google Search, and around 20% from Google Cloud.

Alphabet First Quarter Earnings Report

Alphabet CEO Sundar Pichai said after the first-quarter performance:

2026 started strong. Our AI investments and full-stack strategy are activating every aspect of the business. The Search business delivered strong results this quarter, with AI experiences driving usage growth, queries reaching an all-time high, and revenue up 19%. Google Cloud revenue grew by 63%, with backlog orders almost doubling quarter-over-quarter to over $460 billion. Fueled by the Gemini app, this was the strongest quarter ever for our Consumer AI business. Overall, paid subscription user numbers have reached 350 million, with YouTube and Google One as key drivers. Gemini Enterprise Edition has strong momentum, with a 40% increase in paid monthly active users quarter-over-quarter. Finally, I'm pleased to see Waymo surpassing 500,000 fully autonomous ride hailing trips per week. These outstanding results are built on top of our differentiated full-stack strategy. Our in-house models, like Gemini, currently process over 16 billion tokens per minute via direct customer API calls, up 60% quarter-over-quarter. Seeing our AI investments create value for users, customers, and the business is truly exciting.

Therefore, Google Search saw a growth of around 19%, with the overall profit margin increasing from 34% to 36%, and AI Overviews have not yet eroded advertising revenue.

Google Cloud witnessed a 63% growth, with a continued strong demand for computing power—this being Alphabet's primary growth driver. Additionally, Gemini's API usage increased, with a 60% growth in token usage, indicating that the model substitution effect has not yet materialized.

All of these are positive signals, with no factors in the financial data pointing to a downward revision—in fact, Alphabet is seen as an AI winner, with even Berkshire Hathaway investing in Alphabet.

However, issues have gradually emerged in the cash flow statement, even without adjustments to free cash flow. In a scenario of declining free cash flow, Alphabet cannot achieve the indicated AI capital expenditure without taking on significant credit risk or diluting existing shareholder equity.

Alphabet First Quarter Earnings Report

Impact and Insights

Alphabet reported strong first-quarter financial data, with the only cautionary signal being a decrease in free cash flow—especially when adjusted—and this could be a significant warning sign to downgrade GOOG to a "sell" rating.

If Alphabet does not raise additional debt or equity, it cannot meet the AI capital expenditure guidance, which not only increases credit risk and dilutes shareholder equity but also reduces buybacks. Against the backdrop mentioned above, this is negative news.

If Alphabet cuts back or slows down its AI capital expenditure guidance, investors will have to lower their growth expectations, leading to a contraction in valuation multiples—a similarly negative development. It is worth noting that GOOG's GAAP P/E ratio stands at 26 times, which is not particularly expensive. However, given concerns about inflated overall earnings, the actual P/E ratio may be much higher.

It is important to note that this is also a macro judgment—as the AI bubble bursts accelerate, the S&P 500 (SPY) (SP500) and the Nasdaq 100 (QQQ) may face a deep retracement, with Alphabet potentially becoming the first large-scale enterprise to cut AI capital expenditure guidance.