What are the key variables that determine the AI bull market?

Writing: Zhao Ying

Source: Wall Street Insights

Oil prices remain above $100 per barrel, the Strait of Hormuz has not yet returned to normal operation, inflation and interest rate pressures are resurfacing, and Fed rate cut expectations have become more fragile. According to traditional macro frameworks, this is not the most comfortable environment for high-valuation tech stocks. But U.S. stocks are hitting new highs, and the AI sector continues to be chased by capital.

Guojin Securities macro analyst Song Xuetao pointed out in a research report on May 25: "The current AI rally is in a stage of rational exuberance; bubbles are present but not out of control." The key point of this statement is not "bubble," but "rational" enthusiasm: Agentic AI is shifting from an auxiliary tool to an autonomous execution tool, allowing the market to see more clearly the business cycle from "burning money" to "making money."

On the rational side, the diffusion of Agent applications has led to rapid growth in token consumption, inference computing power demand, and ARR for leading vendors; on the exuberant side, valuations have already priced in growth expectations for 2027–2028. As of May 20, the forward P/E ratio of the seven U.S. tech giants was about 35 times, and the remaining 493 companies in the S&P 500 about 25 times. This premium implies not ordinary growth stock logic, but that AI penetration must reach 5 to 8 times the speed of past technological revolutions.

But what truly determines whether the AI bull market can continue is not quarterly earnings or a single blockbuster application, but three variables: short-term liquidity shocks, especially oil prices, inflation, interest rates, and yen carry trade unwinding; medium-term industry realization—whether AI penetration can match current valuations; and long-term constraints such as energy, power grids, employment, social resistance, and hardware technological breakthroughs.

Agent shifting from "副驾" (co-pilot) to "主驾" (main driver), markets begin rewarding capital expenditure

In the previous AI trading cycle, the market's biggest concern was that giants were spending too fast: data centers, GPUs, cloud infrastructure investments are huge, but revenue recovery paths are unclear. The evolution of Agentic AI is that it is no longer just a Copilot-style auxiliary tool but evolving into an Autopilot-style autonomous execution tool.

This has two results.

First, token consumption has accelerated again. The first demand wave after GPT emerged was driven by model capability improvements; the second wave, after Agent deployment, comes from explosive inference computing power needs. Autonomous task execution involves longer contexts, more complex steps, and more frequent model calls, making inference no longer just a training afterthought but the main battlefield for continuous computing power consumption.

Second, revenue expectations are being revised upward. After the proliferation of representative Agent applications like Openclaw and Claude Cowork, model vendors' annual recurring revenue (ARR) is growing rapidly. Mid-year estimates cited in the material show that Anthropic's full-year ARR has been revised from $9 billion at the start of the year to $44 billion, doubling roughly every six weeks. If this trend continues, next year's ARR could exceed $300 billion.

This explains why the market is no longer simply punishing Capex. As long as revenue growth is fast enough, capital expenditure shifts from a burden to a moat. Nvidia, Broadcom, and hardware chains like optical modules and storage are thus gaining renewed support.

Why can AI assets still rise when oil prices are above $100?

This round of AI asset rally against rising oil prices is not because macro risks have disappeared, but because several forces are temporarily overriding the risks.

First is the diffusion of industry chain demand. Inference stage not only requires GPUs; CPUs, optical modules, and storage are also pulled into high prosperity logic. 800G/1.6T optical modules are in tight supply, and high-end storage demand is rising. Light Counting predicts that by 2026, 800G transceivers will see shipments double or more, and 1.6T port shipments will grow from a small base in 2025 to tens of millions, with 1.6T chipsets exceeding $2 billion in sales in 2026, maintaining high growth over the next three years.

Second is the strong performance of tech giants. In Q1, the S&P 500 EPS growth was about 27.1%, a new high since Q4 2021, with Meta, Alphabet, and Amazon contributing 70% of the index's profit increase. As long as these heavyweight companies continue to profit, oil price shocks will be delayed in impacting the index.

Third is the increased dependence of U.S. growth on AI infrastructure. Over the past few quarters, AI infrastructure investment contributed to over half of U.S. GDP growth. Non-farm and retail data are still positive, and although employment structure has become more segmented, as long as the overall economy does not weaken significantly, the market will find it hard to immediately shift to stagflation trading.

Another more direct factor: large tech companies are less sensitive to oil prices than industries like airlines, express delivery, railways, chemicals, automotive, and tourism. They are more concerned about electricity prices than oil. When traditional real economy sectors are squeezed by oil prices, capital tends to flock into AI assets, blending "hedge" trades with growth trades.

Valuations have already priced in the good days of 2027–2028

The danger of the AI rally is not the lack of industry support but the market pricing it too quickly.

The forward P/E ratios of the seven U.S. giants at 35 times, and 25 times for the remaining 493 companies in the S&P 500, imply a very smooth future: that over the next 3 to 5 years, AI infrastructure will continue expanding, with high prosperity in computing power, cloud, data centers, and semiconductors; AI will keep penetrating advertising, search, cloud services, office software, code generation, financial risk control, customer service, research, and content creation; and revenue contribution and efficiency gains will be realized simultaneously.

But technological revolutions are rarely this smooth. Power took about 40 years from invention to large-scale application, computers about 25 years. Now, the speed at which AI is being priced into the market is equivalent to demanding it to be 5 to 8 times faster than these general-purpose technologies.

This is not impossible, but the tolerance window is very narrow. As long as AI application commercialization lags behind capital expenditure, inference demand cannot keep up with training demand, or depreciation and electricity costs start eroding profit margins, valuations will react first. Having the right industry direction does not mean stock prices can be infinitely advanced.

The biggest short-term risk: interest rates rising faster than ARR

The immediate pressure comes from liquidity.

If the Strait of Hormuz remains closed long-term, keeping oil above $100 or pushing it higher, inflation will spread from energy prices to services, transportation, and raw materials. In April, US PPI year-over-year rose to 9.8%, a new high since October 2022. Once inflation becomes entrenched, the Fed's policy path will have to be rewritten.

The swap market has already priced in about 0.8 rate hikes for the Fed this year, and over 2 hikes for the ECB and Bank of England. Meanwhile, doubts about the Fed's policy independence due to leadership changes and increasing internal disagreements are weakening market confidence in future easing.

Japan is also a gray rhino. Long a source of leverage trading financing globally, the yen's depreciation and inflation pressures have prompted the Bank of Japan to signal tightening. The 30-year Japanese government bond yield has risen above 4%. If Japan's financing costs continue to rise, triggering unwinding of global carry trades, high-valuation AI assets will find it hard to remain unaffected.

A prelude occurred on May 15: 10-year US Treasury yields broke above 4.5%, 30-year yields above 5%, leading to cooling of crowded momentum trades, with the Philadelphia Semiconductor Index dropping about 4% in a day and the Nasdaq down about 1.5%. This is not a trend reversal but shows that crowded trades are extremely sensitive to interest rates.

The most critical short-term comparison is simple: can the upward revision speed of ARR outpace the rise in interest rates? If not, capital may shift first into more certain hardware segments; if liquidity continues to deteriorate and AI revenue expectations cannot be revised upward, valuation pressures will intensify.

The more difficult medium- to long-term issues: organization, electricity, employment, and hardware pathways

The mid-term test is industry realization. General technological revolutions usually do not follow a straight line but "accelerate, decelerate, then accelerate again." First comes capital waves, then organizational adjustments, and finally productivity improvements. The early internet also experienced investment booms, capital expenditure expansion, and asset bubbles, with real productivity gains only gradually emerging over years.

Currently, the difficulty in AI pricing lies in its near-requirement for rapid organizational restructuring, worker retraining, quick commercialization of business models, and societal acceptance—speed that is uncommon in human history.

Long-term constraints are even more formidable.

First is energy and infrastructure. AI data centers require massive electricity and cooling water; grid expansion, transformers, and energy storage are not just PPT variables but real bottlenecks. If AI infrastructure continues to drive up societal electricity costs, regulatory and social backlash will intensify.

Second is employment and consumption. In the short term, AI can boost enterprise efficiency and reduce demand for engineers, customer service, and other roles; but if technological unemployment outpaces new job creation, household consumption capacity will weaken. Revenue from B2B efficiency gains ultimately depends on B2C purchasing power; if non-AI sectors fall into recession, AI will also find it hard to outperform long-term.

Third is social acceptance. Earlier this year, there was a nationwide enthusiasm for adopting Openclaw, but in the U.S., resistance to data centers raising electricity prices and technological unemployment is rising. This could slow AI penetration.

Fourth is hardware technological breakthroughs. If breakthroughs similar to "DeepSeek moments" occur—significant improvements in computing, storage, and transmission efficiency—the most scarce hardware segments today could suddenly become oversupplied. The high prosperity logic of the hardware chain is not invulnerable to disruption.

The long-term outlook for the AI industry remains optimistic. If we set aside social conflicts from technological unemployment and restructuring, AI indeed has the potential to boost total factor productivity and help the economy escape stagflation. Even if financial markets undergo deleveraging, existing data centers, low-cost technologies, and proven applications could form the foundation for the next wave of industry expansion.

But stock pricing is not the same as industry vision itself. The most critical validation for this AI bull market is whether the current market's bets on ARR, ROI, and technological penetration can continue to materialize amid rising oil prices, inflation, interest rates, and social constraints. The right direction explains why there is a bull market; the speed of realization determines whether the bubble will spiral out of control.