In the context of the global AI wave driving up the demand for computing power, capital is pouring into "new infrastructure" on a large scale: on one side, media reports indicate that ByteDance has significantly increased its capital expenditure related to AI, with a projected cumulative investment in data centers and AI infrastructure reaching about $70 billion by 2026; on the other side, according to Bloomberg data, due to the surge in AI infrastructure investment in the US, spending on data center construction has risen to about $50 billion, surpassing the combined spending on airports, ports, and public transportation for the first time, reflecting a shift of funds from traditional infrastructure towards digital infrastructure that supports the training and deployment of large models. Data centers and crypto mining farms exhibit a high degree of similarity in electricity consumption, facilities, and cooling systems, making potential competition between the two more direct in resource-limited areas. By June 2026, IRGC spokesman Mohib warned the US that "a stronger response will follow if further actions are taken," and during a fundraiser in Maryland, Biden once again labeled Trump a "loser," accusing his administration of unprecedented corruption, together creating a backdrop of geopolitical tension and domestic political division in the US. In this tightly wound narrative influenced by both AI capital expenditure and political risk, an unavoidable question arises: how will these two forces collectively shape risk premiums and reflect through energy, liquidity, and sentiment channels onto the pricing structure of risk assets, including crypto assets?
$70 Billion Bet on AI: ByteDance's Computing Power Expansion
Beyond the macro noise, capital is reshaping risk premiums with more direct figures. Media reports show that ByteDance has significantly raised its AI-related capital expenditures, with the cumulative investment in data centers and AI infrastructure expected to reach about $70 billion by 2026. This figure is a forecast and represents multi-year accumulations "by 2026," rather than a single-year budget. Structurally, the core focus of this AI capital expenditure is on data center construction and the expansion of AI infrastructure: including civil engineering for server rooms, power and cooling systems, GPU and specialized AI chip procurement, large-scale storage devices, and network facilities—essentially building a long-term reusable computing power network on the balance sheet.
From the demand side, such a scale of expenditure not only reshapes the technology stack of a single company but also provides highly visible order prospects for the global computing power, chip, and cloud resource markets. Currently, several large technology companies are synchronously increasing their allocation of AI training and inference-related computing resources, forming an industry-wide wave of AI infrastructure expansion; correspondingly, according to Bloomberg data, US spending on data center construction has risen to about $50 billion, surpassing the combined spending on airports, ports, and public transportation, indicating a clear shift in capital from traditional "civil infrastructure" to "digital infrastructure" represented by data centers. In this context, the arms race for computing power among internet giants such as ByteDance essentially locks in medium- to long-term demand curves for upstream GPUs and AI chips, data center resources, and related cloud services, while in the crypto market, assets related to computing power, storage, and AI narratives gain more concrete demand anchors and longer story cycles.
$50 Billion on US Data Centers: New Infrastructure Squeezing Traditional Projects
The approximately $50 billion spending on US data center construction, as provided by Bloomberg data, is essentially a "structural slice": under the same caliber, the investment in this single asset category has already surpassed the total spending on airports, ports, and public transportation. Funding and government resources are not unlimited, and under the premise of roughly stable budgets, debts, and approval capacities, AI infrastructure like data centers being prioritized implies that capital and policy support originally meant for traditional "old infrastructure" is explicitly squeezed out. For engineering contractors, local finance, and infrastructure asset managers, the focus of the asset pool is shifting from runways, docks, and rail transit to server rooms, power access, and cooling systems, directly viewing AI computing power as a new "priority project."
Data center construction relies heavily on land, electricity, and local regulation, and these three resources also constrain other digital infrastructures, including mining farms, cloud service server rooms, and hosting facilities. Data centers and crypto mining farms have similar requirements for electricity, facilities, and cooling systems. When the capacity of local power grids, industrial land, and energy consumption metrics are limited, local governments are more likely to allocate scarce quotas to projects deemed "AI new infrastructure," raising the electricity costs, land acquisition difficulties, and approval thresholds for other projects. For the crypto industry, this round of $50 billion new infrastructure boom in the US is not just a macro narrative background but a concrete change that affects the competition variables for electricity price structures, park resource allocation, and policy preferences.
Computing Power and Energy Game: The Indirect Impact of AI Data Centers on Crypto Mining
From the supply side, AI data centers and crypto mining farms are essentially competing for the same basket of resources: high-power electricity access, suitable facility space, cooling, and network conditions. Bloomberg statistics show that US spending on data center construction has risen to about $50 billion, surpassing the combined spending on airports, ports, and public transportation, indicating that new transformer capacity, industrial park distribution, and high-reliability lines are being prioritized for AI projects. In regions with limited grid redundancy and rigid energy consumption metrics, such a bias can turn electricity and facilities into scarce commodities, squeezing expansion space for mining farms and raising marginal electricity prices. ByteDance is reported to be investing about $70 billion in data centers and AI infrastructure by 2026, further strengthening large internet companies' long-term locking of electricity and facility resources, significantly diminishing the bargaining power of mining companies competing for electricity quotas, industrial land, and cooling resources in the same area.
In areas with tight electricity supplies or rising costs, this resource misallocation will directly reshape the cost curves of mining operations. On one hand, local governments are more willing to allocate limited energy consumption and infrastructure metrics to AI data centers deemed "new infrastructure," while mining farms either have to accept higher electricity prices and shorter electricity contract cycles or migrate to marginal areas with lower electricity prices and looser regulations. For computing power coins, rising electricity costs and supply-side constraints imply a slowdown in the overall expansion speed of computing power, an increase in marginal production costs, which may raise the bargaining space of leading mining companies and infrastructure operators with substantial assets, while increasing the pressure of price volatility on weaker miners. Eventually, the wave of AI capital expenditure acts bidirectionally: on one hand, it raises the capital value of computing power assets by increasing the scarcity of electricity and facilities, while on the other, it pushes the entire crypto mining industry towards a higher barrier and more selective competitive landscape by squeezing resources and raising costs.
Iran's Hardline Statement: Geopolitical Risk Premium on Risk Assets
While electricity and computing power resources are being repriced by AI investments, geopolitical risks in the Middle East are also raising the global risk premium. An IRGC spokesman Mohib openly warned that if the US takes further action, Iran will respond "more strongly," accusing the US of showing "contradictory nature" at this sensitive moment. Such wording itself is an escalation signal, indicating that the market cannot simply assume that conflicts will automatically de-escalate and that the tail risks of potential conflicts escalating are re-integrated into pricing.
Historical experience shows that when similar conflict risks rise, global asset portfolios go through a round of "risk appetite-hedging demand" rebalancing: on one hand, geopolitical tension expectations amplify commodity and transportation costs, increasing inflation and growth uncertainties, thereby raising overall risk premiums and discount rates; on the other hand, funds concentrate on assets perceived as safe, weakening allocations to high-beta assets. For crypto assets, this shock path typically manifests as: rising macro-level risk premiums and increasing funding costs initially suppress the willingness to allocate to high-volatility assets, then through fluctuations in commodities and financial markets amplify volatility premiums, placing crypto assets under heightened macro sensitivity amidst the dual narratives of "computing power capital expenditure + geopolitical conflict risk."
Biden Slams Trump Again: US Political Division and Market Sentiment
According to CNN, during a Democratic fundraising dinner in Maryland, Biden openly referred to Trump as a "loser" and accused his administration of unprecedented corruption, using clearly escalated language. Such strong statements in public reflect the continuous escalation of bipartisanship opposing views as the election cycle approaches: political positions are magnified through personal attacks, leaving little room for compromise, and the market, while pricing future policy paths, must contend with a more fragmented and opposed scenario set.
From an asset pricing perspective, political division means a prolonged lack of stable "centers" for fiscal, regulatory, and industrial policies, resulting in a passive rise in risk premiums. For tech stocks pushed up by the AI narrative and new infrastructure assets related to data centers, partisan battles will magnify several uncertainties: first, whether AI regulatory frameworks and subsidy directions will see severe swings; second, whether anti-monopoly and data security requirements for large tech companies will become firmer or more lenient with changes in political parties; third, whether regulatory stances towards crypto assets may differ significantly between different governments. Until these variables are locked in, both tech stocks and crypto assets need to embed a higher "policy option value" in their discount rates, manifesting as wider valuation ranges, increased volatility, and heightened sensitivity to sudden regulatory news.
AI Capital Frenzy Combined with Political Noise: The Crypto Market in Search of Anchors Amidst Volatility
In this macro context, reports indicate that ByteDance is projected to cumulatively invest about $70 billion in AI and data centers by 2026, and US data center expenditures have been estimated by Bloomberg to be around $50 billion, forming two typical samples of the capital frenzy for AI infrastructure, coupled with Iran's IRGC's hardline statements towards the US and Biden's public denouncements of Trump, which together deepen the risk premium for global risk assets, including crypto assets. For participants in the crypto market, this means that price volatility is increasingly influenced not just by a single industry logic but by the interplay of AI capital expenditure cycles, data center construction rhythms, geopolitical conflicts, and domestic political struggles in the US. While tracking on-chain capital flows, it's essential to integrate these AI and political variables into the monitoring framework of liquidity and sentiment. It is critical to emphasize that the widely cited figures of about $70 billion and $50 billion essentially come from media and data institutions' forecasts or statistics based on existing information, which may be revised in the future with corporate decisions and project adjustments. The crypto market must view these figures as dynamic hypotheses rather than fixed pivots to avoid amplifying already high volatility amidst misplaced expectations.
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