Did the Market Misjudge AI? Cloud Backlog Surges 150% to $1.7 Trillion; Wall Street Says Tech Valuations Have Hit a 'Low Point'

Skepticism regarding AI capital expenditures has persisted for over a year, but the latest data is changing this narrative.

Over the past year, investors have worried that tech giants investing hundreds of billions of dollars in AI infrastructure might face the problem of "high input, low returns." However, as demand for cloud services accelerates, increasing signs show that AI capital expenditures are translating into stronger orders and future revenue growth, rather than the overinvestment previously feared.

Data shows that the cloud business backlog of hyperscale cloud providers surged more than 150% year-over-year, reaching a total of approximately $1.7 trillion, a growth rate far exceeding the roughly 80% increase in capital expenditures during the same period. JPMorgan Chase pointed out that this significant gap indicates that the potential revenue returns from AI infrastructure investments are surpassing market expectations, and the pressure on tech giants' valuations to digest these investments may be nearing its end.

In this context, JPMorgan Chase noted that the market is reassessing the return potential of AI infrastructure investments, and the valuation multiples of the Mag 7 may have already bottomed out.

AI Investment Shifts from Cost to Revenue Source, Cloud Giants See Demand Exceed Expectations

Signals released during the Q2 earnings season show that cloud computing demand is rapidly materializing.

JPMorgan analyst Mark Schilsky stated that the growth rates of cloud business backlog and net new Annual Recurring Revenue (ARR) are significantly leading capital expenditure growth, implying that future revenue growth is expected to cover current large-scale infrastructure investments.

Amazon CEO Andy Jassy rarely raised the long-term outlook for AWS during the Q2 earnings call. He stated that while the company previously expected AWS to grow into a business with hundreds of billions of dollars in revenue, it now believes this scale will at least double, potentially becoming a business with annual revenue reaching $1 trillion in the future.

Jassy also mentioned that the company has seen "stunning" demand scales for 2028, and enterprise adoption of AI inference services is still in its early stages.

Management teams at companies like Microsoft, Alphabet, and Meta have also released similar signals: the commercialization of AI applications is still in the early expansion phase, and enterprise-side demand is far from mature.

Earnings Expectations Revised Upward, Yet Valuations Fall to Historical Lows

While AI fundamentals continue to improve, tech stock valuations have experienced significant compression.

After the market correction in July, the forward P/E ratio of the S&P 500 Information Technology sector dropped to around 20x, close to its lowest level in the past year, sitting at the 1st percentile of historical valuation ranges, below the approximately 23x average of the past decade. This means the tech sector is experiencing a rare divergence: earnings expectations continue to improve, but valuation multiples keep declining.

JPMorgan Chase pointed out that the forward P/E ratio of large-cap tech stocks (excluding semiconductors) is currently more than two standard deviations below the historical mean since 2018. If valuations repair to one standard deviation below the historical mean, it corresponds to an upside potential of about 30%; if they recover to near the long-term mean, the potential upside could reach approximately 56%.

Meanwhile, the performance of hyperscale cloud providers relative to the S&P 500 has also fallen to the bottom of its three-year range, a position that has historically been accompanied by strong mean reversion opportunities.

Capital Allocation Still Lags, Tech Stocks May See Catch-Up Rally

On the other side of low valuations, institutional capital allocation has not fully kept up with fundamental changes.

Deutsche Bank data shows that despite significant improvements in earnings growth and forecasts for large tech companies, institutional investors' positions in this sector remain only slightly overweight, significantly lower than the allocation levels seen during previous strong earnings cycles.

At the same time, capital has continued to concentrate in the semiconductor sector this year, while holdings in large-cap tech stocks (excluding semiconductors) remain relatively insufficient.

JPMorgan Chase believes that if the core market narrative regarding AI shifts from "whether capital expenditures are excessive" to "investment returns are materializing," the momentum for the next phase of tech stock gains may come more from rotation within the sector, rather than relying solely on continued rises in chip stocks.

From a technical perspective, the MAGS index has rebounded nearly 10% from recent lows, moving back above the 200-day moving average and approaching the long-term upward trend line since April last year. JPMorgan Chase believes that the current 200-day moving average has flattened, indicating that the market is undergoing a prolonged consolidation phase. Historical experience shows that the longer the sideways movement, the stronger the breakout tends to be once a direction is chosen.

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