In 2026, the global AI industry officially leaves behind the era of extensive expansion driven by incremental growth and enters a profitability verification cycle where computing power input is closely aligned with commercial revenue. According to the mid-2026 global technology industry research report by Barclays Capital, the total annual AI capital expenditure on computing power by the four major North American tech giants — Microsoft, Google, Meta and Amazon — is projected to approach USD 750 billion, hitting an all-time high.

Meanwhile, many enterprises face pronounced pressure on free cash flow from their AI businesses. Leading generative AI firms maintain rapid revenue growth yet continue to incur substantial losses. The intensifying computing power arms race has given rise to a global cost crunch across the entire industry. How to break away from the path dependence of "burning capital for growth" through massive computing power investment and build a sustainable revenue closed loop has become a core challenge shared by global AI vendors.

I. Core Crunch of Global AI Computing Power Costs: Three Universal Contradictions Across the Industry

1.1 Rigid Global Inflation of Computing Power, Sustained Upward Costs Throughout the Industrial Chain

Upstream along the industrial chain, costs for high-end AI chips, high-bandwidth memory and supporting infrastructure keep rising, which has partially reversed the historical cycle of year-on-year price declines across the cloud computing sector. Constrained by chip production capacity and advanced packaging bottlenecks, procurement prices for mainstream AI servers have trended upward.

Coupled with rising data center operation and power costs, public industry monitoring data shows that the average price increase of AI computing services offered by leading global cloud vendors exceeded 30% in 2026. Both North American tech giants and regional AI providers are confronted with dual cost pressures from model training and inference deployment.

Furthermore, upgraded global AI security and compliance requirements have brought extra cost overhead for computing verification and security protection, further lifting the rigid cost floor of the industry. No regional player enjoys exemption from such cost pressures.

1.2 Structural Mismatch of Computing Resources, Prevalent Low Utilization Worldwide

Beyond high costs, structural mismatch of computing resources is a common source of waste in the global AI sector. Most vendors currently suffer from imbalanced investment: heavy spending on training computing power while neglecting the scheduling of inference computing power. Large model training requires one-off massive investment in computing resources.

However, a large amount of computing capacity sits idle after training is completed, and the average utilization rate of training computing power across the industry remains low. Inference-side computing demand shows obvious peak-valley fluctuations: computing shortages are prominent during peak hours, while substantial resources remain vacant in off-peak periods.

This structural mismatch manifests differently among vendors of various tiers. Leading players hold ample computing reserves yet suffer greater idle-time losses; mid-tier and smaller vendors, limited by capital strength, struggle to meet peak computing demands. Collectively, this results in global redundancy and efficiency losses of computing resources.

1.3 Imbalanced Commercialization Conversion Efficiency, Revenue Growth Lags Behind Investment Growth

Dual pressures from rising costs and resource waste further highlight the imbalance in commercialization conversion efficiency within the AI industry, and industry divergence worldwide continues to widen. Leading North American tech firms have initially built subscription-based revenue systems, yet the growth rate of computing power investment still significantly outpaces that of AI business revenue.

For numerous small and medium-sized AI vendors globally, the dilemma is universal: no competitiveness without computing investment, yet no profits even with such investment. One-off project-based revenue cannot match the cost nature of long-term amortization of computing assets, squeezing profit margins continuously.

Data from the 2026 Global AI Industry White Paper indicates that the share of global AI inference revenue has risen to 55% that year, but the overall pace of profit realization across the industry still falls far behind the expansion of computing power investment. The focus of industry competition is gradually shifting from scaling computing capacity to improving computing operation efficiency.

II. Differentiated Breakthrough Practices by Global Vendors

From the perspective of global industrial practices, the core solution to the computing power cost crunch lies not in blindly cutting computing investment, but in restructuring the computing cost structure and revenue matching model. Optimizing business and operation models maximizes the value of computing power, rather than merely cutting costs.

2.1 Microsoft: Capitalized Operation of Computing Power to Amortize Long-term Investment Costs

Microsoft adopts an operational approach of capitalizing computing power. By building ultra-large-scale computing clusters in-house, it hedges against risks of continuous hardware price hikes, transforming computing power from a one-off cost item into reusable infrastructure assets for the long run.

Leveraging the Office and Azure ecosystems, Microsoft deeply embeds AI capabilities such as Copilot into its existing product portfolio, accumulating tens of millions of paid subscription seats. This model of "computing infrastructure + recurring subscription revenue" spreads massive computing costs across ongoing consumer and enterprise subscription income.

The model enables long-term amortization and stable recovery of computing investment, yet it carries certain limitations. Computing clusters require large fixed investment, offer relatively low flexibility to adapt to technical route iterations, and feature a lengthy cost amortization cycle.

2.2 Google: Precise Scenario Matching to Boost Value per Unit of Computing Power

Google avoids homogeneous competition in general computing power and focuses on high-end enterprise scenarios to realize precise monetization of computing resources. Through the Gemini Enterprise suite of enterprise services, Google targets high-end clients including the world’s top 100 enterprises and deeply binds itself to rigid core business scenarios.

Compared with consumer-facing general AI services, customized enterprise services deliver higher customer unit prices and resource reusability, markedly lifting the token revenue value per unit of computing power. According to Google’s public financial reports and industry research, the revenue share of its enterprise AI cloud business kept rising in 2026.

Its revenue per unit of computing power ranks among the higher levels disclosed publicly in the industry, yet this model also has drawbacks. Its audience coverage is relatively limited, highly dependent on demand from top enterprise clients, with weak penetration in the consumer market.

2.3 Overseas Mid-tier Vendors: Lightweight Deployment to Avoid Heavy-Asset Computing Power Traps

For overseas mid-tier AI vendors without full-stack in-house computing R&D capabilities, lightweight computing deployment is a development path better suited to their resource endowments. Such players generally abandon the heavy-asset model of self-built computing infrastructure and instead conduct rental-based computing scheduling relying on global public computing resources.

Meanwhile, they concentrate resources on in-depth exploration of vertical niche industry scenarios and improve scenario monetization efficiency by developing industry-specific AI solutions. This model of "light computing investment + deep scenario implementation" effectively circumvents the cost trap of the computing arms race.

Overall, while this model delivers limited revenue scale, it supports sustainable operation at a marginal profit. The heavy-asset computing model adopted by leading vendors boasts long-term cost advantages yet comes with extremely high entry barriers; the lightweight model for small and medium players offers strong flexibility but a clear ceiling on scale. The two models suit market participants of different sizes respectively.

III. Universal Global Pathways for Sustainable Conversion of Computing Power into Revenue

3.1 Technology-driven Cost Reduction: Refined Scheduling to Revitalize Existing Computing Resources

At the technical and operational level, refined computing scheduling serves as a fundamental approach to cost reduction. Vendors may adopt staggered scheduling mechanisms for training and inference computing power: idle capacity is used for model iterative training in off-peak hours, while inference business demands are prioritized during peak periods.

This scheduling approach smooths peak-valley fluctuations in computing demand and raises overall resource utilization. Meanwhile, iterative lightweight technologies such as model quantization and distillation continuously cut token-level computing consumption, reducing ineffective computing investment at the technical source.

For vendors with multi-region deployments, dynamic scheduling across global computing networks can further compress operating costs by leveraging electricity price and time zone differences across regions, maximizing the utilization of existing computing resources.

3.2 Model Upgrade: Align with Computing Asset Attributes to Build Long-term Cash Flow

At the business model level, revenue models need to be deeply aligned with the cost attributes of computing power. Computing assets feature long-term amortization, and corresponding revenue models should evolve from one-off project-based revenue to annual recurring revenue (ARR) subscriptions, enterprise annual fees, on-demand computing billing and other formats.

Building stable recurring cash flow matches the long-term pace of computing investment and lays a fundamental foundation for profitability. For enterprise clients, bundled packages of annual computing services paired with customized value-added services can be launched.

For long-tail clients, flexible on-demand billing can be adopted to cover multi-tier payment needs, forming a diversified revenue structure that better accommodates the long-term amortization nature of computing costs.

3.3 Value Stratification: Dual-track Monetization to Balance Scale and Profit

Computing monetization requires a stratified value system to avoid limitations of a single model. Vendors may split computing services into two tracks: general computing and customized computing. General computing services follow a scalable, inclusive route, covering a broad customer base through standardized products.

General computing leverages economies of scale to amortize costs and form the revenue base. Industry-specific customized computing solutions focus on high-value vertical scenarios, delivering deeply adapted computing and model services along a premium high-end track to generate core profits.

This model resembles a system of "public transit + private bespoke vehicles". General computing acts as affordable public transport to guarantee basic capacity and user scale, while customized computing works like exclusive private vehicles to capture premium value from high-end clients. The combination creates a dual revenue system of "scale as the bottom line + high margin for incremental income".

IV. Summary and Outlook of Industrial Trends

Looking at the development trajectory of the global AI industry, the core competitive logic has evolved: from a mere arms race over computing scale to an integrated efficiency competition covering computing cost control, resource utilization efficiency and commercial monetization capability.

AI enterprises capable of sound long-term development in the future will not necessarily be those with the largest computing investment, but players that strike a precise balance among computing costs, resource efficiency and revenue monetization. In terms of industrial evolution, the weight of operational efficiency is expected to rise gradually.

Operational efficiency, alongside computing scale, will constitute the core dimensions of industry competition. Over the next 3 to 5 years, computing power will gradually evolve from a pure cost burden into reusable infrastructure assets.

Commercial monetization capability stands as one of the core factors supporting the long-term growth of AI enterprises. As the global AI industry shifts from incremental expansion to in-depth development of existing markets, the capability to efficiently convert computing power into value will become the core competitiveness for enterprises to weather market cycles and build long-term moats.

Disclaimer

All graphics, research articles, market interpretations, industry analyses, compliance guides, business explanations and related information published on this official website are for general industry information and macro market research reference only, intended for knowledge sharing and industry exchange. They do not constitute investment advice, wealth management recommendations, trading guidance, legal compliance opinions, tax planning schemes or financing and investment decision-making bases for any individuals, enterprises or institutions.

ARBORFIRE CAPITAL conducts business in strict compliance with regulatory rules of the SFC of Hong Kong China, the US SEC and relevant domestic and overseas regulators. All content on the official website is compiled and analyzed based on public and compliant market policies and industry materials. It does not make price trend forecasts, return commitments, risk underwriting or investment promotion statements for any individual stocks, funds or specific projects. All historical market data and industry trend analyses are objective reviews and do not represent future market performance.

Cross-border capital operations, overseas listings, asset allocation, offshore structures, family wealth management and other businesses carry market risks, compliance risks and policy change risks. All users shall make independent and prudent decisions in light of their own asset status and risk tolerance, and consult licensed legal, tax and financial professionals when necessary. The Company shall not bear any legal liability for decisions made by any user based on content herein or any consequences arising therefrom.

Without official authorization from ARBORFIRE CAPITAL, no entity may reproduce, alter or commercially use the content of this official website.