Foreword:

Deloitte's recently unveiled <2026 Global Trends Forecast for the High-Tech, Media, and Telecommunications Industries> offers a clear perspective: the central focus of AI development is transitioning from a competitive pursuit of models' "generalization capabilities" to a specialized deepening for particular tasks and scenarios. This shift heralds the acceleration of a comprehensive "AI differentiation" process by 2026.

The Genesis of Differentiation: From 'Generalized Showmanship' to 'Specialized Practicality' as Industry Norm

By 2025, the quest for "general intelligence," typified by large-parameter models, had reached a tentative zenith, with the industry's attention subtly shifting. A core consensus is emerging: the true value of AI lies not in its ability to "engage in casual conversation" but in its capacity to "reliably solve problems and generate value within specific domains." This shift in value perception directly ignites the trend of differentiation.

Firstly, the investment rationale has evolved from "prioritizing scale" to "validating efficacy." Goldman Sachs highlights that 2026 will mark a pivotal validation phase for AI commercialization, with the market's focus shifting from mere infrastructure capital expenditures to the actual utility and returns at the application layer. Enterprises are no longer content with merely "having AI"; they demand measurable improvements in efficiency, cost savings, or revenue growth from their AI investments.

Secondly, the application landscape is transforming from "conversational tools" to "action agents." This represents the tangible realization of the "silicon-based workforce" concept. AI agents are evolving from mere "answer machines" to proactive "doers," capable of understanding complex objectives, planning steps, invoking tools, and executing tasks. Deloitte predicts that by the end of 2026, up to 75% of companies may invest in such agent-based AI, propelling the software industry into a new era. a16z's forecast is even bolder, suggesting that AI interaction will transition from "passive response to prompts" to "proactive observation and intervention," functioning akin to top-tier "S-level employees." Gartner anticipates that by 2028, leading enterprises will incorporate multi-agent AI into 80% of their customer business processes.

Comprehensive 'Computing Power Transformation' and the Sovereignty Game

The trend towards AI specialization and agentization imposes unprecedentedly stringent demands on underlying computing power, directly driving a comprehensive transformation of the computing power ecosystem in terms of layout, architecture, and sovereignty.

Deloitte's report identifies a crucial inflection point: by 2026, the inference process for running AI models will constitute approximately two-thirds of total AI computing requirements. This signifies a shift in computing power demand from centralized training for "model forging" to distributed, real-time inference for "model utilization." This has spurred rapid growth in the market for specialized hardware, such as inference-optimized chips. Concurrently, faced with diverse AI workloads, the trend towards "general-intelligent-ultra heterogeneous computing power fusion," characterized by "scale node deployment + hotspot region optimization + edge adaptation," has become evident. For instance, at the 2025 WAIC, Huawei's "Ascend 384 Super Node" and the debut of China's first optically interconnected GPU super node, "LightSphere X," exemplify the enhancement of ultra-large-scale cluster efficacy and elastic scalability through architectural innovation.

Supply chain vulnerabilities have given rise to a new arena of "computing power sovereignty." The cutting-edge manufacturing capabilities for AI hardware are highly concentrated in a few global regions, exposing supply chain risks. In response, "computing power sovereignty" has emerged as a strategic priority for nations. Deloitte predicts that nearly $100 billion will be invested globally in local computing capacity building by 2026, with regions such as the EU, Middle East, and Southeast Asia actively constructing localized AI capabilities to reduce dependency, safeguard data security, and control technological development trajectories. This will reshape the global AI computing power landscape from highly concentrated to multipolar distribution.

The Emergence and Proliferation of the 'Silicon-Based Workforce'

If computing power is the "soil" of the new era, then the "silicon-based workforce" nurtured on it are the "new workers" directly creating value. The concept of "silicon-based life," proposed by China Mobile Chairman Yang Jie, vividly portrays this future vision: intelligent agents with hardware as the body and various intelligences as the central nervous system will form a new "demographic dividend" across "360 industries."

From "Software Functions" to "Economic Agents." The evolutionary trajectory of the "silicon-based workforce" is clearly discernible. Initially, they were automated tools embedded in business processes (such as RPA). Now, they are evolving into AI agents capable of managing complex closed loops, assuming pivotal roles in government, finance, and industry. Looking ahead, they will progress even further. Gartner predicts that by 2030, 22% of monetary transactions will be executed by AI agents through programmed terms, granting them "economic agency" to autonomously participate in negotiations, procurement, and market discovery. This signifies their upgrade from "workforce" to "economic entities."

Accelerated Differentiation: Hierarchical Transformation Across the AI Industrial Chain

The top-tier driving forces and bottom-tier transformations will ultimately exert pressure on the entire AI industrial chain, accelerating the differentiation of enterprise fates and competitive landscapes.

The open-sourcing of general large models and cloud services has lowered the entry barrier, but the real competitive barriers are rising. Enterprises with vast amounts of high-quality industry data, deep domain knowledge (Know-How), and composable technical architectures can train more efficient domain models and agents faster, constructing formidable moats. Meanwhile, due to data privacy, compliance, and cultural disparities, Gartner predicts that by 2027, 35% of countries will utilize regional AI platforms, fragmenting the market.

Moreover, AI capabilities will profoundly widen the gap among enterprises. On one hand, it's the ability to "utilize AI." Whether AI agents can be deeply and organically integrated into core business processes to achieve "carbon-silicon mixed teams" will become a watershed for enterprise efficiency. On the other hand, it's the ability to "manage AI." As the proportion of AI decision-making increases, related ethical, security, and compliance risks surge. Deloitte and Gartner both underscore that establishing mature AI governance and risk management systems is imperative for enterprises to prevent catastrophic incidents like "AI-induced deaths," earn trust, and achieve sustainable development.

When AI becomes the primary "workforce," the focus of human work will shift. Gartner predicts that future hiring will emphasize both "AI capability certification" and "AI-free critical thinking assessment." The market urgently requires not just simple parameter tuners but human experts who can define problems, design human-machine collaboration processes, train and manage "silicon-based employees," and retain ultimate judgment and creativity.

Conclusion:

By 2026, AI will bid farewell to the "one-size-fits-all" utopia and enter a pragmatic era of "specialization by scenario, intelligence by data, and strength by fusion." In this accelerated evolution, the only certainty is that individuals, enterprises, and nations that can deeply comprehend the connotations of "computing power" and "silicon-based workforce" and take the lead in completing self-restructuring and fusion will have the best chance to define and secure their own intelligent future.

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