HTX Research Examines U.S. AI Equities: Technology Remains Early, While Capital Expenditure and Valuations Have Entered the Late Cycle

HTX Research, the dedicated research arm of HTX, has released a new report titled The Industrialization of Intelligence and the Bubble Cycle: Token Economics, Capital Expenditure, and the Repricing of Risk-Reward Across U.S. AI Equities . Its central argument is that the AI industry and AI equities are not at the same point in their respective cycles — technological diffusion remains in its early stages while capital expenditure, valuations, and investor sentiment have moved well ahead of it. The Variables Driving Equity Prices Continue Reading Markets first priced the scarcity of GPUs, high-bandwidth memory, servers, and data-center capacity, and later the capability gains delivered by frontier models and coding agents. In 2026, the variables driving equity returns are shifting away from model parameter counts and the scale of capital expenditure toward token production costs, task-completion reliability, usage intensity, enterprise-workflow penetration, and the ability of enormous AI investments to generate durable free cash flow. Behind that shift is a change in the magnitude of capital spending. J.P. Morgan Asset Management estimates that five U.S. hyperscalers will spend approximately $697 billion in 2026, with capital expenditure rising from roughly 33% of their operating cash flow in 2023 to an estimated 93%. Once capital expenditure consumes the overwhelming majority of operating cash flow, market attention necessarily moves from revenue growth to return on capital. The Bubble Sits in the Financial Architecture, Not the Industry Cloud revenue, coding-agent adoption, semiconductor sales, and enterprise demand are all growing in real terms, meaning AI technology itself is not a false narrative. Capital expenditure, external financing, data-center projects, private-model valuations, and a number of high-multiple second-tier equities, however, display increasingly speculative characteristics. Headline price-to-earnings ratios also fail to represent true valuation levels: Alphabet's multiple is distorted by investment income, and Amazon's current accounting profit does not reflect a normalized valuation. What genuinely offers value is the closest alignment among normalized valuation, competitive moats, cash flow, and AI optionality. At current prices and cycle positions, the report views Alphabet as offering the most compelling overall asymmetry, and applies the same framework across Microsoft, Meta, TSMC, NVIDIA, Amazon, Oracle, Micron, AMD, Arista, and Vertiv — distinguishing businesses with high fundamental win rates from those whose valuations already demand near-flawless execution. AI Is Reshaping How Crypto Investors Allocate Capital AI's role as a shared theme across global capital markets extends beyond U.S. equity pricing into the allocation behavior of crypto investors. As names including NVIDIA, Micron, TSMC, Broadcom, Meta, and Alphabet enter the everyday portfolios of crypto users alongside gold, crude oil, ETFs, and pre-IPO assets, a growing share of users now treat crypto and U.S. equities as different allocation directions within a single global risk-asset system. HTX has been one of the earliest crypto exchanges to systematically pursue this direction. According to data disclosed in August 2026, cumulative trading volume in the platform's TradFi perpetuals section has exceeded $2.5 billion, with support for more than 170 TradFi-related assets spanning U.S. equities, ETFs, gold, silver, crude oil, AI semiconductors, memory, aerospace, and pre-IPO themes such as OpenAI and Anthropic. What makes this model work is the platform's existing base of crypto users who have completed registration, verification, and funding. Holding stablecoins such as USDT, they can trade TradFi assets within the same account without opening a brokerage account or moving capital into a separate financial system — allocating toward gold, ETFs, or large-cap technology when risk appetite decli...