《數位時代》2026年發布的全球AI創新企業榜單顯示，AI正從基礎建設、企業應用、個人賦能到垂直產業，全面滲透並重塑世界。
AI的角色，已從工具轉變為基礎設施。這不只是技術升級，而是整體產業邏輯的重寫。
在這波變革中，有兩個關鍵轉變正在發生：一個發生在產業底層，一個發生在勞動市場。
一、算力競爭走向平台化
隨著AI工廠（AI Factories）興起，算力需求呈現爆發式成長。市場主要分為兩條路線：通用GPU，以及高效率客製化ASIC。過去兩者是競爭關係，但輝達透過投資Marvell並推出NVLink Fusion，開始將競爭者納入自身生態系。
關鍵轉變在於，競爭焦點正從產品競爭轉向平台競爭。這代表企業可以在同一平台內取得接近客製化晶片的效能，而不需要自行開發。
AI基礎設施的競爭，本質已從硬體性能，轉變為生態系掌控能力。
二、混合型經濟的出現
根據McKinsey研究，約57%的工作時數具備自動化潛力。這波自動化不同於過去，而是以雙軌方式推進。
AI代理（AI Agents）負責知識型與非實體工作，例如文件生成、分析與決策支援；實體機器人（Robotics）則負責製造、物流與高風險作業。未來職場將是人類、AI與機器人共同協作的混合型經濟。
- 人類從執行者轉為監督者
- 從操作轉向判斷
- 從完成任務轉向定義問題
工作價值的核心，正在從執行能力轉向決策能力。
三、AI素養成為核心能力
技能並未消失，而是轉型。超過70%的既有技能，需要重新運用。寫作正在轉向Prompt設計與內容整合，數據分析則轉向AI結果判讀與決策。
同時，AI素養需求在短時間內快速成長。所謂AI素養，不只是會操作工具，而是能把AI整合進工作流程、與AI協作，並判斷AI輸出的品質與風險。
不是會使用AI的人勝出，而是能駕馭AI的人勝出。AI素養已成為跨產業、跨職位的基本能力。
四、台灣的關鍵轉型
台灣在AI時代具備半導體產業、供應鏈整合能力與全球關鍵角色等優勢，但下一階段的競爭，不在硬體，而在人才。到2030年，人機協作可望創造高達2.9兆美元的經濟價值。
企業若要掌握這波紅利，至少需要同步推進三件事。
- 投資AI素養，讓員工具備與AI協作能力
- 重設工作流程，以人機協作為核心設計組織
- 建立信任機制，確保AI決策透明與可控
未來十年的競爭，不只是技術競爭，而是人與AI協作能力的競爭。
Digital Times reported in its 2026 global ranking of AI innovators that AI is now reshaping the world across infrastructure, enterprise applications, personal enablement, and vertical industries.
AI is no longer just a tool. It has become infrastructure. This is not merely a technical upgrade. It is a rewrite of the underlying logic of entire industries.
Within this wave of change, two critical shifts are taking place at the same time: one at the industrial foundation level, and one in the labor market.
1. Computing competition is becoming platform competition
As AI factories rise, demand for computing power is growing explosively. The market has mainly followed two paths: general-purpose GPUs and highly efficient custom ASICs. In the past, these two approaches competed directly. But Nvidia, through its investment in Marvell and the launch of NVLink Fusion, has begun pulling competitors into its own ecosystem.
The key shift is that competition is moving from product competition to platform competition. This means companies can gain near-custom-chip performance within a shared platform without having to build chips themselves.
- Lower technical barriers for enterprises
- Stronger platform stickiness
- Greater control over the ecosystem
In other words, competition in AI infrastructure is no longer fundamentally about raw hardware performance. It is about control over the ecosystem.
2. The rise of a hybrid economy
According to McKinsey, about 57 percent of working hours have the potential to be automated. Unlike previous waves of automation, this one is moving forward on two tracks at once.
AI agents are taking on knowledge work and non-physical tasks such as document generation, analysis, and decision support. Robotics is taking on manufacturing, logistics, and high-risk operations. The future workplace will therefore be a hybrid economy where humans, AI, and robots collaborate rather than compete.
- Humans shift from executors to supervisors
- From operation to judgment
- From completing tasks to defining problems
The core of work value is shifting from execution capability to decision capability.
3. AI literacy becomes a core capability
Skills are not disappearing. They are being transformed. More than 70 percent of existing skills will need to be redeployed in new ways. Writing is shifting toward prompt design and content orchestration, while data analysis is shifting toward interpreting AI outputs and making decisions from them.
At the same time, demand for AI literacy is rising rapidly. AI literacy does not simply mean knowing how to use AI tools. It means integrating AI into workflows, collaborating effectively with it, and judging the quality and risks of its outputs.
The winners will not simply be people who can use AI. They will be the ones who can direct and govern it. AI literacy has become a baseline capability across industries and roles.
4. Taiwan’s next critical transformation
Taiwan enters the AI era with strong advantages in semiconductors, supply chain integration, and its global strategic role. But the next phase of competition will not be centered on hardware alone. It will depend on talent. By 2030, human-machine collaboration could create as much as 2.9 trillion US dollars in economic value.
To capture that opportunity, businesses need to push forward at least three priorities in parallel.
- Invest in AI literacy so employees can collaborate with AI effectively
- Redesign workflows around human-machine collaboration
- Build trust mechanisms to keep AI decisions transparent and controllable
Competition over the next decade will not be only about technology. It will be about the ability of people and organizations to collaborate with AI effectively.
