Hotcoin Research | The Second Battlefield of the AI Super Cycle: A 24/7 On-Chain Pricing Experiment

Introduction

On July 10, 2026, the American Depositary Receipts of major global HBM supplier SK hynix landed on NASDAQ. Over the past two years, market attention has primarily focused on Nvidia and GPU shortages; entering 2026, capital has begun to reprice HBM, advanced packaging, networking equipment, power, and data center bottlenecks. The AI competition has thus shifted from model capabilities to capital expenditures, supply chain construction, and global asset pricing.

Meanwhile, the AI industry chain is forming a "second battlefield" in the cryptocurrency market. According to CoinGecko data, the monthly trading volume of perpetual contracts on major cryptocurrency platforms has surged from $831 million in July 2025 to $34 billion in May 2026, nearly a 40-fold increase. AI stocks such as Nvidia, Micron, and Microsoft are being repackaged as price exposures settled in stablecoins, operating 24/7 and supporting leveraged trading. This is not a simple replication of the traditional stock market, nor is it a true on-chain ownership of stocks, but rather a new pricing experiment centered around price, liquidity, and trading time. The traditional market is responsible for forming benchmark prices, while the cryptocurrency market continues to trade industry expectations after hours. This article will discuss how this "second battlefield" is formed from the perspectives of AI capital expenditure cycles, industry chain status, trading product structures, and risk mechanisms, and whether it could further evolve into a second pricing layer for global tech assets.

I. AI Enters a Super Cycle: From Technological Narratives to Capital Expenditure Phase

Determining whether AI has entered a super cycle cannot rely solely on model parameters, user numbers, or tech stock prices. What truly distinguishes short-term narratives from structural cycles are capital expenditures, capacity construction, supply chain orders, power demand, and balance sheet changes. When leading companies are willing to invest hundreds of billions of dollars over several years to build data centers, purchase chips, and secure energy supplies, AI transcends being merely a product innovation in the software industry and begins to represent an infrastructure cycle encompassing semiconductors, manufacturing, communications, energy, and financial markets. However, a "super cycle" does not imply that the industry will rise in a straight line, nor does it mean that every company labeled as AI will yield returns. It primarily describes the scale of investment, construction cycles, and the length of the industry chain, rather than a definitive judgment on stock prices.

1.1 From Model Competition to Balance Sheet Competition

The AI competition from 2023 to 2024 is primarily characterized by a race in model capabilities: parameter scale, training data, inference performance, and user growth determine market attention. As we enter 2025 to 2026, the focus of competition is gradually shifting. Simply possessing a model is insufficient to establish a long-term advantage; companies also need stable access to GPUs, custom ASICs, HBM, advanced packaging, network bandwidth, data center land, and power access.

According to TrendForce's forecast released in May 2026, the combined capital expenditures of nine major cloud service providers—Google, AWS, Meta, Microsoft, Oracle, ByteDance, Tencent, Alibaba, and Baidu—could reach approximately $830 billion in 2026, a 79% year-on-year increase. S&P Global Ratings, using different samples and statistical methods, estimates that the capital expenditures of five major cloud service providers—Alphabet, Amazon, Meta, Microsoft, and Oracle—will be about $750 billion in 2026, equivalent to 38% of their combined revenues. Both sets of figures point to the same trend: the AI competition has become one of the largest capital allocation cycles in the history of global tech companies.

Source: https://www.trendforce.com/presscenter/news/20260506-13033.html

On a deeper level, the AI competition has shifted from "who can train better models" to "who can continuously provide lower-cost, larger-scale, and more stable inference capabilities for models." Model capabilities remain important, but the factors determining competitive ceilings increasingly stem from balance sheets: financing capabilities, cash flow, procurement scale, supply chain control, and energy acquisition capabilities.

1.2 Semiconductor Growth is Spreading from GPUs to Entire Computing Systems

The demand for AI infrastructure initially concentrated on GPUs, but GPUs are not standalone products. A system capable of running large model training and inference requires the collaborative operation of computing chips, HBM, advanced packaging, high-speed networks, storage, servers, power supplies, and cooling systems. Any shortage in one link may limit the delivery of the entire system.

Gartner predicts that global semiconductor revenue will reach $1.3202 trillion in 2026, a 64% year-on-year increase, marking the highest growth rate in the past two decades. Among them, revenue from memory chips is expected to grow from $216.3 billion in 2025 to $633.3 billion in 2026; AI semiconductors are projected to account for about 30% of global semiconductor revenue. Gartner also forecasts that the annual prices of DRAM and NAND Flash may rise by 125% and 234%, respectively, with meaningful price relief likely not appearing until the second half of 2027.

Source: https://www.gartner.com/en/newsroom/press-releases/2026-04-08-gartner-forecasts-worldwide-semiconductor-revenue-to-exceed-us-dollars-one-point-3-trillion-in-2026

This data explains why Micron, SK hynix, and Samsung are gaining increasing market attention. As the scale of large models expands, the limitations on computing efficiency are not only the number of GPUs but also whether processors can continuously and quickly access data. HBM, with its higher bandwidth and lower unit energy consumption, alleviates the "memory wall" and has become an irreplaceable component of AI accelerators.

TrendForce predicts that global AI server shipments will grow by over 28% year-on-year in 2026, with GPU servers still accounting for about 69.7%, while ASIC-based AI servers may rise to 27.8%. This indicates that the AI chip market is not simply a "Nvidia single growth story." Google, Meta, Amazon, and Microsoft are accelerating the development of custom chips, which will continue to drive demand to wafer foundries, HBM, packaging, and network segments, shifting the supply chain from a single GPU route to coexistence of GPUs and custom ASICs.

1.3 Industry Bottlenecks are Shifting from Chips to Power and Construction Cycles

As the supply of GPUs, HBM, and servers gradually increases, new bottlenecks are beginning to appear in the physical world. Data centers need to secure land, grid connection permits, transformers, backup power supplies, cooling systems, and long-term power contracts. Chips can increase production within several quarters, but the construction cycles for large power infrastructure and data center parks are often longer.

The International Energy Agency predicts that global data center electricity consumption may grow from approximately 485 terawatt-hours in 2025 to about 950 terawatt-hours in 2030, with AI data centers showing a significantly faster growth rate than overall data centers. Gartner forecasts that global data center electricity consumption will increase from 447 terawatt-hours in 2025 to 565 terawatt-hours in 2026, a 26% year-on-year increase; AI-optimized servers will account for 31% of data center electricity consumption in 2026, and by 2027, their consumption may exceed that of traditional servers.

Source: https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary

This means that the boundaries of the AI industry chain are changing. In the past, the market viewed Nvidia as the primary proxy asset for AI computing power; in the future, it may need to include power generation, transmission, transformers, cooling, data center REITs, and energy management companies within the AI infrastructure framework. AI has not liberated the digital economy from physical constraints; rather, it has re-centered power, land, and engineering construction in the growth of technology.

1.4 The Greatest Suspense of the Super Cycle is Whether Commercialization Can Keep Up with Investment

The growth of AI capital expenditures has been validated by data, but capital returns have yet to be fully verified. For supply chain companies like Nvidia, Micron, and TSMC, increased capital expenditures from cloud service providers mean orders and revenue; for Microsoft, Amazon, Alphabet, Meta, and Oracle, the same capital expenditure implies cash flow outlays, future depreciation, and operating costs.

S&P Global Ratings believes that most leading cloud service providers currently have sufficient balance sheet space to absorb investments, but massive capital expenditures are suppressing free cash flow. More notably, a cycle of investment, long-term procurement commitments, financing guarantees, and interdependencies between model companies and cloud vendors has emerged in the AI ecosystem: cloud vendors invest in model companies, model companies use funds to purchase cloud services, and cloud vendors then procure chips and build data centers with the revenue. This structure can reinforce growth but may also obscure the true independence of final demand.

Alphabet disclosed that its cloud business order backlog reached approximately $462 billion by the end of the first quarter of 2026, nearly doubling quarter-on-quarter, providing a demand basis for capital investment. Nvidia reported that its revenue for fiscal year 2026 reached $215.9 billion, a 65% year-on-year increase, with data center network revenue growing by 142%, indicating that AI demand has spread from computing chips to connectivity and system levels.

Because the AI industry chain has become one of the most closely watched asset classes in the global capital market, its price fluctuations and trading demands have begun to break through the time and geographical limitations of traditional stock markets. An increasing number of cryptocurrency platforms are attempting to transform publicly traded companies within the AI industry chain into price exposures that can be traded around the clock through stablecoin settlements, perpetual contracts, and 24/7 trading. Thus, the AI super cycle is no longer confined to traditional capital markets but is also entering the cryptocurrency market, forming a "second battlefield."

II. Analysis of the AI Industry Chain: The Underlying Assets of the Second Battlefield Trading

The second battlefield is not trading in abstract "AI concepts," but rather the price exposures of different companies within the AI industry chain. The AI industry chain is not a static list of tech stocks, but a value transmission path driven by capital expenditure, with supply bottlenecks constantly shifting. Whenever a new bottleneck arises, funds will reprice the listed companies in the corresponding segments, which is the fundamental reason for the continuous evolution of trading hotspots in the second battlefield.

The companies listed in the table below are representative publicly traded enterprises within the AI industry chain, illustrating the correspondence between potential trading targets in the second battlefield and industry segments. This does not imply that all companies are listed on crypto platforms, nor does it constitute any investment advice.

2.1 Cloud Service Providers as Both Supercycle Engines and Cost Bearers

Microsoft, Amazon, Alphabet, Meta, and Oracle are the most important sources of capital expenditure in the AI industry chain. They determine GPU procurement volumes, the pace of data center construction, and investments in custom chips, while also distributing computing power to model companies and enterprise users through cloud platforms. However, simply categorizing these companies as "AI beneficiaries" obscures their true position. Cloud service providers are primarily payers of capital expenditure. Growth in capital expenditure can drive cloud revenue, but it can also reduce free cash flow and lead to depreciation in the future. Only when the newly added computing power is efficiently utilized and converted into sustainable cloud service and software revenue can the investment form a positive cycle.

Different cloud service providers also exhibit significant differences in their business models. Amazon, Microsoft, and Alphabet have mature cloud businesses that can directly sell computing power to external customers; Meta primarily realizes AI value through advertising, recommendation algorithms, and consumer applications, lacking equivalent external cloud revenue; Oracle relies on databases, cloud infrastructure, and large AI training contracts for expansion, but its balance sheet may also be under greater pressure. Therefore, evaluating cloud service providers cannot solely focus on the absolute value of capital expenditure; it is also essential to consider the ratio of capital expenditure to revenue, operating cash flow, and order reserves. A larger investment does not necessarily mean better outcomes; what truly matters is how much sustainable revenue each dollar of capital expenditure can generate.

2.2 Chips, Manufacturing, and Equipment Form the Strongest Order Fulfillment Layer

Nvidia, AMD, and Broadcom are positioned in the AI chip design layer. Nvidia's advantage comes from the platform capabilities formed by GPUs, CUDA, networks, and complete systems; AMD attempts to expand its share through accelerators, CPUs, and software ecosystems; Broadcom benefits from custom ASICs, network chips, and high-speed interconnect demand.

Following chip design are TSMC, ASML, and semiconductor equipment companies. Whether for GPUs or custom ASICs, advanced processes and packaging are required. TSMC stated in its Q1 2026 conference call that to meet AI demand, the company is increasing investment in N3 capacity, with capital expenditure expected to approach the high end of the guidance range of $56 billion in 2026; management expects that revenue related to AI accelerators may achieve a compound annual growth rate of over 50% by 2029. At the same time, advanced packaging capacity remains tight.

Therefore, chip, wafer, and equipment companies are typically the first layer to realize revenue from capital expenditure and are also the assets that are first repriced by the market during the AI supercycle. When trading in the second battlefield revolves around the AI industry chain, this layer often receives the most attention from funds.

2.3 HBM, Networking, and Cooling Become New Bottleneck Assets

The rapid growth of Micron's trading volume on crypto platforms is not a coincidental single-stock event. It reflects that the focus of the AI market is shifting from GPUs to HBM and storage. GPUs are responsible for computation, but model training and inference require continuous reading of parameters and data. When computing power grows faster than data transfer speeds, memory bandwidth becomes a performance bottleneck. HBM improves bandwidth and energy efficiency by vertically stacking multiple layers of DRAM close to the processor. Thus, SK hynix, Micron, and Samsung are no longer just traditional storage cycle companies but have also become core suppliers of AI computing systems.

Networking is equally important. As AI systems expand from single cards to thousands or even tens of thousands of accelerators, system efficiency depends on data transfer between chips. Nvidia's NVLink and InfiniBand, Arista's high-speed switching devices, Broadcom and Marvell's network chips, and Coherent's optical communication products are all addressing the question of "how to make more chips work as one system."

Next are server power supplies and cooling. As the power density of AI racks increases, traditional air cooling may no longer meet demand, raising the importance of liquid cooling, power management, and backup power supplies. Companies like Vertiv and Eaton derive their AI attributes not from models, but from helping data centers operate stably.

The hotspots of trading in the second battlefield will not remain long with the same company but will shift with the new supply bottlenecks in the AI industry chain. From GPUs to HBM, and then to networking, power, and cooling systems, what is truly being traded is not a single company, but the most scarce production capacity within the industry chain.

2.4 The True Downstream is Not Models, but Commercialization

OpenAI, Anthropic, and various foundational model companies are at the model layer, but the ultimate downstream of the industry chain is not the models themselves, but whether enterprises and consumers are willing to continue paying. Companies like Palantir, ServiceNow, Salesforce, and Adobe attempt to embed AI into data analysis, workflows, customer management, and content production. Microsoft, Google, and Meta incorporate AI into office, search, advertising, and social platforms. Only when AI can increase revenue, reduce costs, or improve retention does infrastructure investment have a long-term economic basis.

The main contradiction at this layer is between inference costs and payment capabilities. Improved model capabilities may expand demand, but model compression, inference optimization, and open-source competition can also lower unit call prices. If the computing resources consumed by each AI task decrease rapidly, social usage may increase, but the revenue of individual cloud vendors or model companies may not necessarily grow in line with computing demand.

Because the AI industry chain has a clear capital transmission path, the trading hotspots in the second battlefield will also shift with the industry bottlenecks. The capital expenditure of cloud service providers determines the flow of funds, supply constraints determine market focus, and price expectations dictate the trading direction in the second battlefield. In other words, what is traded in the second battlefield is not the AI concept, nor corporate ownership, but the price expectations formed at different stages of different segments of the AI industry chain.

III. Formation of the Second Battlefield: How the AI Industry Chain Enters the Crypto Market

The entry of the AI industry chain into the crypto market is not about transferring stock ownership on-chain, but rather about price exposures entering the crypto market. The core of trading in the second battlefield is not corporate ownership, but the price fluctuations formed by industry expectations. The expansion of capital expenditure in the AI industry chain provides a set of assets that are naturally suitable for derivatives on crypto trading platforms: globally recognized, frequent events, sufficient volatility, relatively easy access to price data, and highly overlapping with the tech narratives familiar to crypto users. The expansion of perpetual contracts, stock tokens, and Pre-IPO products is bringing the AI industry chain from traditional securities accounts into stablecoin accounts.

3.1 Contracts Precede Ownership: The Second Battlefield First Trades Prices

According to a report by CoinGecko, the monthly trading volume of RWA/TradFi perpetual contracts grew from $230 million in early 2025 to $34.717 billion in May 2026. Among them, the monthly trading volume of major platform stock perpetual contracts increased from $831 million in July 2025 to $34 billion in May 2026, nearly a 40-fold increase. The growth of contracts outpaces that of spot tokens, driven by a clear product logic. Issuing stock tokens requires handling underlying stock purchases, custody, clearing, redemption, corporate actions, and securities regulation, while stock perpetual contracts only need to establish price indices, oracles, margin, and clearing systems. Platforms do not need to move the stocks themselves on-chain but can provide users with price exposures. This indicates that the first phase of TradFi entering the crypto market is not about ownership transfer, but about the migration of trading demand. Users first need tools to trade global asset prices with stablecoins, rather than a complete set of securities accounts that replicate traditional shareholder rights.

This determines that the first form of the AI industry chain entering the crypto market is not the migration of stock ownership, but the migration of price exposures. The second battlefield first addresses the questions of "can it be traded" and "can it be traded 24/7," while whether users truly own stocks, can redeem underlying assets, or receive dividends and voting rights is left to more complex stock tokens and securities infrastructure.

3.2 From Nvidia to Micron: Trading Hotspots Shift Along Industry Bottlenecks

During the rapid growth of stock perpetual contracts, AI and tech-related assets have become one of the most active trading lines. CoinGecko data shows that stocks like Nvidia, Tesla, Micron, and Circle rank among the top trading targets. Among them, the trading volume of Micron-related contracts increased from $736 million in April 2026 to $13.16 billion in May.

Source: https://www.coingecko.com/research/publications/tradfi-on-crypto-exchanges-report-2026

The significance of Micron's rising trading volume is not just that a single stock has suddenly become popular. It may indicate that the crypto market's understanding of the AI industry chain is beginning to shift from GPUs to HBM, storage, and system bottlenecks. In the past, Nvidia was almost the sole proxy asset for AI trading; in the future, trading volume may rotate among chip design, wafer foundry, HBM, networking, servers, and power segments.

3.3 CEX Competes for Trading Entry, DEX Competes for Market Issuance Rights

At the platform level, the development paths of CEX and DEX are not the same. Binance, Hotcoin, and Hyperliquid have become major participants in RWA/TradFi perpetual contracts.

Binance and Hotcoin, among other CEXs, lower trading barriers through unified accounts, USDT settlement, shared margin, and internal market-making systems. Stock perpetual contracts can be traded 24/7, settled in USDT, with leverage typically ranging from 10 to 25 times, and funding rates settled every eight hours. The platforms have covered technology and semiconductor targets such as Nvidia, Microsoft, Meta, Amazon, TSMC, and Broadcom. To address the issue of traditional stock market closures, CEXs adopt a multi-mode pricing strategy: during normal trading hours, they use third-party data sources to construct indices; during extended or low liquidity periods, they smooth prices using index-weighted methods; on weekends and holidays, the price index remains at the last known price, while the mark price gradually changes based on contract transactions within certain limits. The pricing indices, funding rate cycles, and traditional market closure handling methods of different platforms are not entirely consistent, and specific rules should be based on the corresponding contract pages.

Perp DEX representative platform Hyperliquid's HIP-3 opens part of the market issuance capability to external deployers. Deployers can decide on contract specifications, oracles, leverage limits, and market settlements, using the HyperCore order book and margin system. Its advantage lies in the speed of new listings and market innovation no longer being solely determined by a single exchange; the risk is that the responsibility for oracles and market operations is dispersed among different deployers. Hyperliquid requires HIP-3 deployers to stake a certain amount of HYPE and may face penalties due to market operations or oracle issues.

Therefore, the core of CEX competition lies in accounts, liquidity, and distribution capabilities, while DEX competition is gradually extending to "who has the right to create a market." The number of targets in the AI industry chain is vast, and the hotspots shift rapidly. This open market issuance mechanism may more easily cover long-tail assets but also relies more on the quality of deployers and external price data.

3.4 The Second Battlefield Presents a Liquidity Map, Not an Industry Value Map

According to CoinGecko data, US-listed AI stocks like Nvidia and Micron are actively traded, while non-US targets show significantly lower trading volumes. This indicates that the AI stock landscape on crypto platforms is not a complete industry map but a liquidity map. Whether a target can become a popular contract depends on:

• First, whether global users are familiar with it;
• Second, whether it can obtain continuous and reliable USD quotes;
• Third, whether the underlying market liquidity is sufficient;
• Fourth, whether price volatility is enough to generate trading demand;
• Fifth, whether the platform can easily handle corporate actions and regulatory restrictions.

Thus, the second battlefield in the crypto market is not a complete mirror of the first battlefield. It actively filters out assets with lower recognition, complex price data, insufficient volatility, or difficult market-making, concentrating liquidity on a few stocks that are most likely to form global consensus trading.

Traditional indices are usually constructed based on market capitalization, circulating shares, and industry classification; crypto platforms tend to choose targets that can continuously provide events, volatility, and funding rates. The importance of the industry determines whether a company is worth studying, while liquidity and volatility determine whether it can become a popular contract, and the two do not always align.

On a deeper level, the second battlefield presents a liquidity map composed of user recognition, USD quotes, market-making capabilities, and trading sentiment, rather than a complete AI industry value map. This is both the reason for its rapid expansion and the root of its limited price representativeness.

4. Structural Contradictions of the Second Battlefield: 24/7 Trading, Price Discovery, and Leverage Risks

AI stocks and the crypto market share similar trading attributes: high growth expectations, large valuation spans, high event density, and strong global attention. Earnings reports, chip releases, model upgrades, export restrictions, capital expenditure adjustments, and energy contracts can all trigger rapid price changes. These characteristics provide continuous trading demand for the second battlefield and make AI stocks easier to package as perpetual contracts than most traditional assets.

However, 24/7 trading does not automatically mean 24/7 reliable prices. Stock perpetual contracts connect two markets with completely different operating times, clearing systems, and regulatory attributes. When traditional stocks are closed and crypto contracts continue to operate, the second battlefield may absorb new information in advance but may also deviate from fundamentals due to the absence of spot arbitrage, declining liquidity, and concentrated leverage.

4.1 The First Competition is for Volatility, Not Ownership

The expansion of crypto exchanges into TradFi products superficially increases asset classes but is actually searching for new sources of volatility. When overall activity in the crypto market declines, gold, oil, stocks, and ETF contracts can bring new trading demand to the platform. AI stocks are particularly suited to this model because their event frequency is higher than most traditional industries: quarterly earnings, capital expenditure guidance, new chip releases, data center orders, regulatory restrictions, and model collaborations can all serve as price catalysts.

The AI industry chain also exhibits significant narrative rotation. When there is a GPU shortage, Nvidia is the main trading target; when HBM prices rise, Micron and SK Hynix attract attention; when networks and optical communications become bottlenecks, Arista, Broadcom, Marvell, and Coherent may gain traffic; when there is insufficient power, the market may turn to nuclear power, grid, and data center infrastructure. This makes the AI industry chain a collection of assets capable of continuously "producing new hotspots." For trading platforms, what is truly valuable is not just a single stock rising but the ability of the industry chain to continuously generate new trading objects, funding rates, and market-making demand.

However, this logic also reveals a reality: the first criterion for crypto platforms in selecting targets may not be long-term value but rather volatility. The importance of the industry determines whether a company is worth studying, while volatility and liquidity determine whether it is likely to become a popular contract. The two do not always align.

4.2 24/7 Trading Does Not Equal 24/7 Price Discovery

One of the most attractive selling points of stock perpetual contracts is that they can still be traded after traditional stocks are closed. However, this is also the most core structural contradiction of the product.

During normal trading hours in the US stock market, contracts can approach spot prices through indices, funding rates, and arbitrage activities. After hours and during night trading, underlying liquidity decreases, and price sources diminish. During weekends and holidays, the underlying exchanges are completely closed, and real stocks cannot be used for immediate arbitrage. At this time, the trading of stock perpetual contracts is no longer based on immediately executable spot prices but on the market's expectations for the next opening price. It simultaneously possesses the attributes of stock derivatives, pre-market, and prediction markets.

Assuming an AI company announces a significant chip defect, regulatory investigation, or large order over the weekend, the stock contract price may change immediately, but there are no spot stocks to help arbitrageurs lock in price differences. Prices may reflect new information or be amplified by thinner liquidity. After the market opens on Monday, the underlying stocks and contracts reconnect, and the previously accumulated price differences need to converge quickly, which can easily trigger gaps and liquidations.

Different platforms handle closing prices differently. Some freeze the index, allowing only limited changes to the mark price; some use external after-hours data; others rely on contract transactions and moving averages. What users see is all "24/7 stock prices," but the underlying price meanings may be completely different. Therefore, assessing the quality of stock contracts cannot only consider whether they can be traded all day but also look at oracle sources, closing rules, mark prices, price change limits, handling of exceptional events, and opening convergence mechanisms.

4.3 Fundamental Cycles and Leverage Cycles May Amplify Each Other

Traditional stock markets typically rely on quarterly earnings reports and annual capital expenditures as the main information cycles. Stock perpetual contracts, however, convert fundamental changes into shorter trading cycles through leverage, funding rates, and automatic liquidation.

During the upward phase of AI capital expenditures, cloud service providers increase investments, chip companies receive orders, supply chains expand, and the market raises profit expectations. Rising prices attract more leveraged long positions, and increasing funding rates attract arbitrage and market-making funds, forming a positive cycle. Once cloud service providers cut capital expenditures, risks may transmit in the opposite direction: budget cuts by cloud service providers first affect GPU and server orders; chip companies reduce procurement, which in turn affects foundries, equipment manufacturers, and HBM suppliers; delays in data center projects further impact power equipment, cooling systems, and financing needs. After stock prices fall, leveraged positions in perpetual contracts are liquidated, liquidity decreases, price deviations expand, and trigger more passive selling.

Adjustments in capital expenditures in traditional markets may take several quarters to transmit, while perpetual contracts may complete expected repricing within hours. The crypto market has not eliminated industry cycles but has compressed the market response time of cycles through leverage.

4.4 "Trading AI Stocks" Does Not Equal "Owning AI Companies"

Stock perpetual contracts provide price exposure, not ownership of stocks. Users typically do not have voting rights, shareholder status, direct dividend rights, or bankruptcy claims. Even if the contract price tracks Nvidia or Microsoft, the contract position should not be understood as holding that company's stock.

The rights structure of stock token spot is more complex. Some products are issued by purchasing the underlying stocks and issuing tokens in a 1:1 structure, but users typically hold securities certificates, structured notes, or economic rights defined by the issuer, rather than ordinary shares directly registered in the company's shareholder register. Dividends, stock splits, mergers, and redemptions need to be transmitted through issuers, brokers, and custodians.

Therefore, the entry of AI stocks into the crypto market at least involves three completely different types of products:

• The first type is stock perpetual contracts, which trade prices and do not involve underlying ownership;
• The second type is 1:1 backed stock tokens, which may have underlying assets, but user rights depend on the issuance structure;
• The third type is Pre-IPO contracts or tokens, which may only represent valuation exposure, SPV economic rights, or event-based products.

All three types of products may appear as a single stock code at the front end, but the legal and economic substance differs greatly. The more a platform emphasizes a unified trading experience, the more users need to actively identify the underlying structure of the products.

5. Outlook and Conclusion: From the Second Trading Field to the Second Pricing Layer

The first battlefield of the AI super cycle is the industrial competition among cloud service providers, chip companies, data centers, and energy companies around capital expenditures, production capacity, and commercialization; the second battlefield occurs in the crypto market. Platforms transform popular assets in the AI industry chain into price exposures that are settled in stablecoins, operate 24/7, and can be traded using leverage, providing global investors with an all-weather trading entry outside the traditional stock market.

However, the second battlefield currently forms more of a liquidity map than a complete industry value map. The growth in trading volume of stock perpetual contracts only indicates that trading demand is expanding and does not prove that the market has mature price discovery capabilities. What truly needs to be verified is whether, during the closure of traditional stock markets, the crypto market can rely on reliable oracles, sufficient liquidity, and robust clearing mechanisms to continuously form prices of reference value. If these conditions gradually mature, the crypto market is expected to evolve from a supplementary trading venue for traditional stock markets into a second pricing layer for global tech assets, gathering global investor expectations more quickly during events such as earnings releases, product upgrades, and regulatory changes, and providing price references for traditional market openings; conversely, 24/7 pricing may still just be 24/7 trading without spot constraints, with prices easily influenced by insufficient liquidity, leveraged positions, and market sentiment, making it difficult to form truly independent price discovery capabilities.

The second battlefield may ultimately become a short-term speculative tool or grow into a new price discovery layer for global tech assets; the answer remains unclear. However, it is certain that as more AI industry chain assets begin trading in the crypto market 24/7, traditional securities markets will face a continuous price competitor for the first time. What truly determines whether the second battlefield can evolve into a long-term infrastructure is not whether trading volume continues to grow, but whether price, liquidity, product rights boundaries, and risk management can mature in sync. Only by completing these market mechanism constructions and conducting 24/7 on-chain pricing experiments can it have the opportunity to grow into the second pricing layer within the global asset system.

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