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随着Sora的退场，全球多模态大模型竞争格局正在发生深刻变化。从“技术标杆”到“商业现实”的转折，不仅意味着AI视频赛道进入理性发展阶段，也对中国厂商提出更高要求。在多模态能力加速突破与产业应用持续深化的背景下，中国大模型正从追赶走向引领，但在算力成本、商业化闭环与合规安全等方面仍面临关键考验。IDC基于最新实测结果，对中国多模态大模型的发展现状、竞争格局与未来趋势进行了系统解析。

Sora落幕并非可以放慢脚步的信号，中国多模态大模型更需加速前行

近日，OpenAI宣布关停旗下视频生成模型Sora，曾被视为AI视频标杆的产品正式退出市场。这一事件引发全球AI行业震动，也让国内多模态大模型领域迎来新的思考：外部标杆退场，并非可以 “躺平” 的理由，反而意味着中国多模态大模型技术必须持续坚持自主创新，在技术、生态与商业化上走出自己的道路。

Sora的关停，源于高昂算力成本、版权合规压力与商业化困境，这也为全球多模态赛道敲响警钟：炫技时代结束，实用、可控、可落地才是核心竞争力。依赖外部模型、简单对标模仿的路径已不可持续，自主创新的技术架构、合规安全的数据体系、高效普惠的产业价值，将成为下一阶段竞争的关键。

面对行业变局，中国多模态大模型已展现强劲势头，从文本、图像到视频、3D、语音的全域融合，正在重构内容生产与产业效率。Sora的离场，清空了浮躁的对标焦虑，却也让全球赛道进入更残酷的“自研淘汰赛”。对中国而言，这既是窗口期，更是压力测试：算力底座、算法创新、数据安全、伦理合规、商业闭环，缺一不可。

2026年3月，国际数据公司（IDC）发布《中国多模态大模型市场主流产品评估》报告，全面评估了国内主流厂商在图像生成、图像理解、视频生成等三大多模态大模型核心领域的技术实力与产品表现。报告显示，中国多模态 AI 产业正迎来高质量发展新阶段，使用多模态大模型构建的应用可以处理和整合多种类型的数据，这些数据更丰富、更能感知上下文，从而大大提高准确性、效率和用户体验。随着技术不断成熟，多模态 AI 也将进一步渗透到个人生活、办公场景，企业级应用场景，推动人机交互方式的革命性变革。

中国多模态大模型迈入加速迭代期：IDC 2026年3月实测结果揭晓

2025年至2026年初，中国多模态大模型领域迎来前所未有的迭代浪潮，新一代模型在文本、图像、音频及视频的理解与生成上实现了质的飞跃。技术供应商竞相发布具备更强逻辑推理与长上下文能力的旗舰产品，使得AI不仅能 “看” 懂复杂图表，也能实时创作高清视频。如字节跳动、阿里、快手、腾讯等旗下产品，在多模态大模型关键指标上持续突破，逐步形成 “技术突破—产业应用—生态反哺”的正向循环。

IDC在2026年1-2月对市面上主流的多模态大模型产品进行了实测，本次实测覆盖了国内多家头部技术供应商的代表性产品，测试时间截至 2 月 28 日，对象为公开的网页版产品，实测问题涉及图像生成类、理解类、视频生成类。打分标准主要考察生成/理解内容的质量，从指令遵循与幻觉、逻辑性、鲁棒性、质感及细节、生成时间与稳定性、可用/创新性、 内容安全性/公平与隐私保护等方面综合展开。主要研究结论如下：

图像生成类：字节跳动豆包 Seedream 5.0、腾讯元宝 Hunyuan Image 3.0、阿里万相 2.6 凭借出色的生成质量位居前列。这些产品在语义理解、细节还原、风格多样性等方面表现突出，能够精准匹配用户创作需求，同时在生成效率与画质稳定性、内容安全性上实现平衡。

图像理解类：字节跳动豆包大模型 2.0、阿里千问 3.5、阶跃星辰 Step3 表现最为亮眼。这类产品在复杂场景识别、跨模态推理、细粒度语义解析等核心能力上优势明显，能够高效处理图文混合输入，为多场景应用提供有力支撑。

视频生成类：字节跳动即梦 AI Seedance 2.0、快手可灵2.6、生数科技 Vidu Q3等产品视频生成表现极佳，凭借优质的生成质量与高效的生产效率成为行业标杆，推动国产视频生成技术在短视频创作、影视特效、虚拟数字人等领域的落地应用。

把握多模态技术发展趋势，中国多模态大模型未来仍需审慎推进

IDC 报告指出，未来，随着多模态技术与各行业深度融合，中国厂商有望在全球市场占据更重要的地位，为数字经济发展注入新动能。未来在图像和视频模态，IDC认为重要的技术趋势有：

图像模态：从生成到理解，走向统一与可控——未来更注重生成质量与可控性跃升、理解与推理深度化、架构统一化、轻量化与端侧部署、3D模型生成等方向发展。

视频模态：时序建模突破，走向长视频与实时交互——未来将更注重长上下文与时空一致性、生成质量/成本与效率、视频深度理解与多模态交互、3D 与世界模型融合等方向发展，更好地服务于个人生活以及影视娱乐、游戏、媒体、教育等行业应用。

中国多模态大模型市场头部厂商当前商业化路线以 ‌B端+C端全面展开‌，一方面通过借助C端流量与生态基础，另一方面聚焦于将多模态AI能力深度嵌入企业工作流，打造“模型即服务”（MaaS）与针对在媒体、短视频创作、影视特效、虚拟数字人、电商、文旅等行业定制化解决方案。有一部分具有生态优势的国内头部厂商已形成内容-流量-变现的商业闭环，用户使用量与付费转化率均领先海外同行。但多模态大模型技术供应商仍需持续监控未来转化与留存指标，部分中国市场C端产品定价并不普惠。

另外，B端场景的全面渗透也仍需时间。Sora近期关停也给中国多模态大模型技术供应商带来启示，仍需警惕以下风险：

内容安全与深度伪造风险：超逼真图像、视频易被用于虚假信息传播、金融诈骗、人格侵权，对社会信任与公共安全构成威胁。

监管政策、版权与法律合规风险：训练数据多来自未授权的图片、影视、短视频素材，生成内容版权归属模糊，易引发诉讼与监管处罚。全球各国对AI生成内容的监管趋严，可能限制真人素材生成、内容传播等核心功能，影响产业扩张。

技术与算力成本风险：多模态大模型的算力成本，是文本大模型的数十倍甚至上百倍，以致训练与推理算力成本高昂，中小厂商难以负担；同时存在算法偏见、模型幻觉等技术缺陷。

商业化可持续性风险：C端用户付费意愿、转化与留存需要密切监测，B端场景渗透仍需时间，警惕内容同质化与可持续性发展风险。

IDC中国研究经理程荫表示，本次实测结果反映出中国多模态 AI 产业已从技术追赶转向创新引领阶段。头部厂商在技术迭代与产品落地方面持续发力，不仅在基础能力上实现突破，更在商业化场景探索中取得进展。技术竞争没有终点，标杆退出不代表终点线前移。中国多模态大模型仍需深耕技术底座、贴近产业需求、筑牢安全底线，才能在全球AI格局中占据主动，真正实现从跟跑到并跑、再到领跑的跨越。

IDC长期深耕人工智能与生成式AI领域，围绕技术演进、竞争格局与商业落地构建了系统化研究体系。基于持续的一手实测与行业跟踪，IDC不仅提供权威数据与趋势判断，更可为技术供应商、行业用户及投资机构输出面向实际决策的策略建议，助力识别关键技术路径与商业化机会。

在多模态大模型加速演进的关键阶段，欢迎与我们联系，获取完整研究成果、报告解读及定制化咨询服务，抢占下一轮AI发展先机。请点击此处与我们联系。

Anne Cheng - Research Manager - IDC

Anne Cheng is a research manager in IDC China whose research focuses on the AI and big data markets. She collaborates with IDC's regional and global consulting teams and is involved in the business development of related markets. Prior to joining IDC, Anne had nearly four years of working experience in the IT/ecommerce and consulting industries, serving as consultant and business analyst. Her experiences made her familiar with industry data/customers and helped her gain deep insights into the business application scenarios. Anne holds a master's degree in Statistics from the University of Missouri Columbia.

在当前政策与产业共振的窗口期，央国企智能化转型正从“技术导入”阶段，迈入“体系重构与价值兑现”阶段。2026年全国两会政府工作报告明确提出，鼓励央企国企带头开放应用场景，打造智能经济新形态，并深化“人工智能+”行动。国资委同步推进中央企业“AI+”专项行动，强调以主责主业为牵引，构建协同高效的产业与经营机制，强化战略支撑与示范带动作用。这一系列顶层设计，意味着AI已由“技术变量”转变为“发展变量”，成为央国企重塑增长逻辑的关键抓手。

从发展路径看，央国企正由规模导向转向质量导向。国资委提出“两个确保、两个力争”，明确要求确保“一利五率”经营指标稳中向好，并实现结构性优化。这一目标体系，本质上是对投入产出效率、资产质量和经营韧性的系统重构。在这一框架下，AI不再是边缘工具，而是支撑“高质量发展”转型的决定因素，其核心在于是否能够嵌入主责主业，进入生产、运营与决策的关键环节，并形成可度量的价值闭环。只有当AI实现从“分析建议”到“自动执行”的跃迁，并满足低幻觉率与高可靠性的业务要求，才能真正支撑央国企的高质量发展目标。

三大结构性趋势重塑央国企智能化转型路径

首先，算力体系加速从通用能力向AI原生底座升级。央国企已深度参与国家算力网络建设，“算力+电力”协同持续推进。随着大模型与复杂智能体应用深化，传统通用算力体系难以支撑高并发与高复杂度场景，央国企对自主可控AI算力底座的需求显著提升。叠加信创考核与供应链安全约束，国产AI芯片与算力体系的规模化应用正在成为刚性要求。以智能云为核心的算力、数据与平台一体化底座，将成为央国企智能化的基础设施。

其次，数据治理从“汇聚管理”转向“要素化运营”，数据价值开始成为新的增长来源。央国企智能化转型在于高质量数据集与数据要素体系的构建。一方面，围绕能源、工业等关键行业，形成可支撑模型训练与智能体迭代的高质量数据资产；另一方面，通过可信数据空间与合规流通机制，实现跨部门、跨行业的数据共享与价值转化。

再次，AI应用从试点验证进入规模化落地阶段，价值导向成为核心评估标准。随着前期大模型试点基本完成，央国企的投资重心明显转向场景化落地能力。企业不再关注模型规模本身，而更加关注在生产优化、运营管理、客户服务与风险控制等关键场景中的实际效果。

进一步看，智能化建设正从工具叠加走向体系重构，AI成为业务运行的底层能力，央国企进入流程重塑与组织适配为核心的深水区。AI不再是外挂系统，而是嵌入复杂组织与业务体系之中，驱动流程再造与管理模式升级。这一转变意味着企业正从“业务数字化”走向“数字业务化”，数字技术由辅助工具转变为业务本身的运行逻辑。

IDC建议：三大结构性机遇指向央国企智能化升级关键方向

多智能体协同架构成为复杂业务场景的关键技术范式。

央国企业务链条长、专业分工细，单一模型难以覆盖全流程需求。通过构建多智能体协同体系，将不同能力模块化并形成协作网络，可以有效支撑跨部门、跨系统的复杂业务运行。同时，该架构天然契合央国企分层分级的组织结构，在实现灵活部署的同时，满足可管、可控、可审计的治理要求。

高质量数据集与数据要素体系建设成为核心基础工程。

随着行业模型与智能体应用深化，数据质量直接决定AI应用效果。央国企正在从单点数据治理走向体系化数据能力建设，通过标准化、精标注、多模态数据集构建，支撑模型持续优化。同时，围绕数据流通与价值转化，逐步形成面向行业的共享与运营体系。

国产软硬件体系进入深度适配阶段，推动智能应用规模化落地。

在自主可控战略牵引下，央国企持续推进全栈国产化替代，核心业务系统成为落地重点。从当前进展看，国产化已由“单点可用”走向“规模好用”，并在部分关键场景实现性能优化与成本下降。这一趋势为智能化应用提供了安全、稳定且可持续的技术底座。

总体来看，2026年作为“十五五”开局之年，央国企智能化转型的路径已逐步清晰：以智能算力为底座，以高质量数据为核心，以行业模型与智能体为抓手，以体系化重构为路径，实现AI在核心业务中的规模化应用。从数据治理、行业应用到云与算力体系，再到重点行业的应用场景深化，均显示出一致的方向——智能化正在从“能力建设”转向“价值兑现”。在政策牵引、技术成熟与业务需求的共同驱动下，央国企正进入以AI为核心驱动的新一轮增长周期。

IDC相关研究

面向2026年，IDC围绕“AI驱动央国企高质量发展”持续开展系统性研究，重点聚焦智能算力基础设施、行业大模型应用、数据要素体系等关键方向。IDC通过企业调研、案例分析与市场跟踪，形成覆盖技术趋势、行业实践与投资决策的系列研究报告，旨在为央国企在“AI+”行动中的战略规划、路径选择与价值评估提供可落地的参考依据。相关研究将持续更新，支持央国企在智能化转型过程中实现从能力建设到价值兑现的跨越。

如您希望进一步了解IDC在央国企智能化转型、行业大模型落地、数据要素体系建设或AI投资规划方面的研究与咨询服务，欢迎与我们取得联系。IDC可结合企业实际情况，提供定制化研究、专项咨询及落地路径设计支持，助力央国企在“AI+”转型中实现可持续增长与价值突破。欢迎随时与我们沟通交流，我们的分析师团队将为您提供更具针对性的洞察与建议。

如需进一步了解与研究相关内容或咨询 IDC其他相关研究，请点击此处与我们联系。

The Japanese AI infrastructure market is currently at a major turning point. Until now, market growth has been driven by investments in AI infrastructure supporting model training. However, IDC expects a transition toward a phase of real-world deployment centered on inference. As AI adoption expands from proof of concept (PoC) to full-scale production, the role of AI infrastructure and the requirements placed upon it are undergoing significant changes. IDC positions 2026 as the inflection point from training to inference.

1. Rapid Growth of the Domestic AI Infrastructure Market and the Shift Away from a “Training-Centric” Model

In recent years, the Japanese AI infrastructure market has expanded rapidly. Backed by large-scale investments from hyperscalers and domestic cloud providers, the market recorded year-over-year growth exceeding 100 percent in both 2023 and 2024, more than doubling in size for two consecutive years. IDC forecasts that spending on Japanese AI infrastructure will reach 694.6 billion yen in 2025 and continue growing at a compound annual growth rate (CAGR) of 7.3 percent, approaching nearly 1 trillion yen by 2030.

However, the drivers of future growth will change significantly. In addition to traditional training workloads, demand for inference, where AI is continuously used within business operations, will expand and shift the market’s core focus. IDC predicts that by 2027, spending on inference in the Japanese AI server market will surpass that on training. Furthermore, from 2025 to 2030, the CAGR for inference-related spending is expected to exceed that of training by more than 10 percentage points.

2. Changes in AI Infrastructure Utilization Driven by the Expansion of Inference

According to IDC’s latest survey, Japan Digital and AI Infrastructure Strategies and Investment Survey 2026, public cloud accounts for the majority of AI infrastructure planned for inference use. At the same time, “private AI infrastructure,” including dedicated environments and edge deployments, represents 20 to 30 percent range of usage.

Meanwhile, only 22 percent of organizations are currently leveraging internal data for AI in a full-scale or advanced manner. This indicates that leading enterprises are just beginning to utilize internal data, including confidential and personal information, for AI applications.

IDC’s research shows that these early adopters intend to increasingly utilize private AI infrastructure going forward. This trend is driven by the need for optimized configurations tailored to specific business requirements, higher predictability in availability and costs, and the importance of addressing regulatory requirements and sovereign AI considerations. These organizations are building AI foundations that balance cost competitiveness with reliability while ensuring business continuity.

  • Only 22 percent of companies are leveraging internal data extensively or at an advanced level for AI.
  • Leading organizations in internal data utilization show a strong intention to adopt private AI infrastructure to enhance cost competitiveness and predictability while building reliable AI foundations that also account for sovereign AI.

As AI infrastructure becomes directly linked to national strategies and corporate competitiveness, addressing sovereign AI and data sovereignty is becoming increasingly important. From the perspectives of data protection, data residency management, and geopolitical risk mitigation, the use of dedicated environments and sovereign clouds is expected to expand.

3. Expansion of the AI Infrastructure Services Market and Changes in Competitive Dynamics

With the expansion of AI infrastructure adoption, the IT infrastructure services market covering deployment, operation, and maintenance is also experiencing rapid growth. The Japanese AI-related IT infrastructure services market is projected to grow from 95.7 billion yen in 2025 to 232.0 billion yen by 2030, achieving a CAGR of 19.4 percent. The increasing complexity of AI infrastructure, including requirements such as liquid cooling and advanced data center facilities, is driving demand for specialized services.

The competitive landscape is also changing. It is moving away from a traditional focus on hardware performance toward flexibility in infrastructure selection, service delivery capabilities, and the ability to support production-level AI deployment. While vendors that led with high-performance GPU-based infrastructure and related services have driven the market to date, IDC expects that companies capable of providing end-to-end support, from AI adoption and application development to hybrid environment operations and sovereign AI compliance, will establish a competitive advantage.

IDC Report Overview

IDC has published a report analyzing changes in the Japanese AI infrastructure market in detail: Japan AI Infrastructure and Services 2026: The Shift in Competitive Dynamics Driven by Inference. This report provides a segmented market forecast from 2025 to 2030 to capture structural changes in the Japanese AI infrastructure market. It includes analysis by server and storage, service provider and enterprise, deployment model, and industry vertical. The AI server market is further segmented by training and inference as well as accelerated and non-accelerated servers.

In addition, the report presents forecasts for the Japanese IT infrastructure services for AI market by customer type and service type. It also examines changes in AI infrastructure demand and key vendor trends, clarifying future market opportunities and changes in the competitive landscape.

Through these analyses, readers can gain a comprehensive understanding of how demand structures are evolving from training to inference, differences in investment trends between service providers and enterprises, and emerging opportunities in the expanding services market.

For more detailed insights and market trends, please contact our analysts by completing this form IDC | Identifying Market Opportunities – Contact Us.

Yukihisa Hode - Research Manager, Infrastructure & Devices, Research, IDC Japan - IDC Japan

Analyst Profile
Yukihisa Hode is a research manager covering digital infrastructure strategies as well as AI infrastructure, IT infrastructure services, IT operations, hybrid/multicloud and hyperconverged infrastructure (HCI). He leads the research program on digital infrastructure strategies, providing insight and advice on the digital infrastructure through research reports, marketing content, and presentations to support IT and digital decision-making.

Across industries, AI has already delivered measurable operational gains. Workflows have been automated. Processes have accelerated. Teams have improved efficiency and reduced costs. Early AI adoption focused on productivity because leaders needed clear, measurable returns.

These early results were important. Contact centers reduced handle times. Back-office operations automated routine tasks. Sales and marketing teams improved throughput. AI proved it could enhance performance across multiple business functions.

However, productivity advantages diffuse quickly.

What creates competitive differentiation in one quarter often becomes standard capability the next. Productivity improvements layered onto existing operating models eventually reach saturation. Organizations find themselves optimizing processes that competitors can easily replicate.

The result is what many leaders are beginning to recognize as a productivity plateau.

Why productivity gains plateau

Productivity-first strategies hold organizations back in three ways.

They reinforce functional silos.
When AI is deployed function by function, each team focuses on optimizing its own objectives. Marketing automates campaigns, finance improves reporting cycles, and service teams reduce response times. Gains develop in isolation rather than reinforcing enterprise-wide value.

They lock in current assumptions.
Optimization strengthens existing workflows and metrics. As markets evolve, organizations that invest heavily in refining legacy models often find themselves constrained by the very systems they improved.

They produce linear gains.
Efficiency improvements inevitably plateau. AI becomes an improvement layer rather than a growth engine.

The limitation is not the technology itself. AI capabilities continue to advance rapidly. The constraint lies in operating design.

When AI is layered onto legacy structures without rethinking how value is created, outcomes remain incremental.

The limits of efficiency as a strategy

Early AI adoption naturally focused on the most immediate and measurable gains. Automation reduced costs and accelerated execution. These results helped organizations justify investment and build confidence in the technology.

Over time, however, efficiency becomes table stakes.

Competitors implement similar automation. Vendors integrate comparable capabilities into standard platforms. What once provided differentiation becomes a baseline expectation.

Organizations then face a strategic choice.

They can continue optimizing existing models—capturing smaller, incremental gains—or begin redesigning the systems that define how value is created.

This transition marks a shift from productivity to innovation.

Innovation as the structural payoff of agentic AI

Innovation occurs when AI reshapes enterprise structure rather than simply accelerating task execution.

Agentic systems enable coordinated decision-making across marketing, supply chain, finance, service, and partner ecosystems. Systems move from isolated automation toward orchestration embedded within enterprise operating models.

This shift changes how organizations capture value.

When agents operate autonomously at scale, assumptions about capacity, cost, and output evolve. Business cases designed for linear improvement fail to capture the compounding value created when systems coordinate across portfolios and ecosystems.

Innovation beyond productivity requires organizations to rethink economic logic, governance models, and even industry boundaries.

Moving beyond the productivity plateau

Organizations that remain focused exclusively on efficiency risk becoming highly optimized versions of yesterday’s operating model.

Those that move beyond productivity gains begin to redesign enterprise systems around coordination, adaptability, and growth.

The shift from productivity to innovation does not eliminate the importance of efficiency. It clarifies its limits.

Efficiency improves performance.
Innovation reshapes advantage.

In the agentic era, leaders who understand the difference will position their organizations to capture the next wave of AI-driven value.

Explore how agentic AI is reshaping enterprise value creation in FutureScape 2026.

IDC - -

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International Data Corporation (IDC) is the premier global market intelligence, data, and events provider for the information technology, telecommunications, and consumer technology markets. With more than 1,300 analysts worldwide, IDC offers global, regional, and local expertise on technology and industry opportunities and trends in over 110 countries. IDC’s analysis and insight help IT professionals, business executives, and the investment community make fact-based technology decisions and achieve their key business objectives.

My introduction to IDC didn’t come from a report or a pitch. It came from sitting in a room at IDC Directions 2025.

But within the first few sessions, it was clear this was something different.

At most events, the product is something you can demo. At IDC Directions, the product is the data. Every session was grounded in it. Not opinions, not surface-level trends, but actual evidence. What the data shows. What it means. And most importantly, what you should do next because of it.

I remember walking in with pretty standard expectations. I thought it would feel like most customer events I’d been to before. Some presentations, maybe a few product narratives, a chance to network and pick up a couple of useful ideas.

When the data is the product, the conversation shifts. It moves from opinion to evidence, and that changes how decisions get made.

That shift changes everything.

Even the panel sessions felt different. Instead of talking about challenges in the abstract, people were digging into how they were navigating them. What was working, what wasn’t, where things were breaking down. It wasn’t about agreeing that problems exist. It was about figuring out how to move forward.

If you’re responsible for making decisions in this environment, that difference matters.

What I Saw in the Room

What stood out just as much as the content was the energy in the room.

Every seat was filled. People weren’t distracted. They were paying attention, taking photos of slides, and writing things down. After sessions, you’d see people immediately tracking down analysts to continue the conversation.

The 1:1 area for client/analyst meetings was packed, rows of tables with discussions happening back-to-back.

It didn’t feel like people were there to hear something interesting. It felt like they were there to get answers to bring back to their teams. And that’s a very different kind of environment because the conversations are grounded in reality, not theory. That level of engagement tells you something important. People saw immediate value in applying what they were hearing right away.

The Moment It Clicked

There was one moment that really made it click for me.

It was during the rapid-fire predictions session after the breakouts. The analysts took everything they had shared across the event and pushed it forward. Not just “here’s what’s happening,” but “here’s what we see in the future.”

It’s one thing to tell someone it’s raining. It’s another thing to tell them they’re going to need an umbrella while the sun is still shining. That’s what IDC does. It connects insight to action before the urgency is obvious. It helps you prepare for decisions before the pressure shows up.

What Changed for Me

I left that event with a completely different understanding of what IDC actually is.

Honestly, I was giddy. Because I realized what access to this kind of expertise really means.

At previous companies, I would have pushed hard just to get time with analysts like this. Now I get to work with them directly. People like Laurie Buczek, who advises CMOs, CROs, and strategy leaders on how to modernize marketing, shift business models, and reduce risk.

That means I can take a real plan, something I’m actively working on, and get guidance grounded in data and real market perspective. That’s not just helpful. It changes how quickly you can make decisions and how confident you are in them. Instead of debating internally for weeks, you can pressure-test your thinking with people who see the market every day.

Why This Year Feels Different

And it’s a big part of why I’m so excited about Directions this year. Because if last year was about seeing the value, this year feels like it’s about applying it in a much more urgent environment.

The conversation around AI has changed quickly. You can hear it in the questions leaders are asking. It’s no longer about what AI is or where to experiment. Now it’s about how to scale it, operationalize it, govern it, and prove that it’s actually delivering value.

The shift from exploration to execution is real.

Visit the IDC Directions 2026 event page to see more about what’s going on in Boston.

AI is no longer about discovery. It’s about evolution. And that shift raises the stakes. These aren’t future decisions anymore. They’re decisions that impact how the business performs now.

That creates a different kind of pressure. The decisions being made now will shape the next few years for many organizations. There’s less room for trial and error, and a much greater need for clarity.

That’s where IDC plays a very specific role. Not by adding more noise, but by helping leaders focus on what matters, grounded in evidence, so they can move forward with confidence.

What I’m Looking Forward to at Directions 2026

Going into Directions 2026, I’m looking forward to very different things than I was last year.

  • I want to hear how IDC is thinking about the future of tech intelligence, especially from new IDC CEO, Lorenzo Larini.
  • I’m interested in where the data is pointing when it comes to AI investment and value, not just potential.
  • I’m paying close attention to how conversations around the agentic era are evolving, and what that means for how businesses operate and compete.
  • And I’m especially interested in the AI Lab.

There’s a limit to what you can absorb from reading. Being able to engage directly, ask questions, and explore how these insights apply in real scenarios brings a different level of clarity.

Check out the full IDC Directions 2026 agenda and learn what topics will be discussed.

Who Benefits Most from IDC Directions?

Stepping back, I think the people who will get the most out of this event are the ones who are actively trying to make decisions right now. If you’re responsible for strategy, for AI policy, or even for bringing AI-powered products to market, the environment has changed.

Buyers are using AI. They’re using data. They’re relying on trusted intelligence to guide their decisions. Understanding how those decisions are being shaped isn’t optional anymore. It directly impacts how you position, invest, and compete.

If You’re Still Deciding–

If you’re on the fence about attending, I’d put it this way:

You can spend time piecing things together on your own. Reading reports, interpreting signals, trying to build a clear plan in a very noisy environment, or…

You can be in the room. Just like me.

Hear the latest insights directly from the people producing the data. Talk through your specific challenges. Compare notes with others who are navigating the same decisions. IDC Directions isn’t about more information. It’s about making the right decisions sooner before the cost of waiting shows up in your business.

And once you’ve seen what that looks like in practice, it’s hard not to want to be there again.

Ryan Smith - Content Marketing Director - IDC

Ryan Smith is the Director of Content Marketing at IDC, where he leads brand-level content and social media strategy, aligning research insights with compelling storytelling to engage technology decision-makers. With a background in both IT and marketing, Ryan brings a unique blend of technical understanding and creative strategy to his work. He’s also a seasoned storyteller, speaker, and podcast host who believes the right message, told the right way, can drive both trust and transformation.

NVIDIA’s GTC 2026 announcements reinforce a structural transition underway in client-side AI infrastructure. Traditional workstations are evolving beyond the familiar tower form factor toward a new class of high-density, near-user AI systems that IDC identifies as part of an emerging “sidetop” category.

These systems elevate local compute capabilities while maintaining the proximity, control, and responsiveness required for next-generation AI workflows.

NVIDIA’s updates to the GB300 architecture and advancements in local agent orchestration reflect this broader shift. Combined with Dell’s introduction of its first GB300-based OEM systems, the market is entering a phase in which deskside AI compute is becoming operationally mainstream rather than experimental.

GB300 matures into a deskside AI supercomputer

At GTC 2026, NVIDIA introduced an enhanced DGX Station built on the GB300 Grace Blackwell Ultra Desktop Superchip, positioned as the most powerful deskside AI system in NVIDIA’s portfolio.

The platform delivers up to 20 petaflops of local compute performance and is capable of running one-trillion-parameter models entirely onsite, enabling development teams to execute advanced AI workloads without dependency on rack-scale systems or external infrastructure.

NVIDIA DGX Spark and DGX Station (Source: NVIDIA, 2026)

Compared to the GB300 configuration previewed during the GTC 2025 cycle, the 2026 update reflects NVIDIA’s full transition into the Blackwell generation. The system shifts from a hybrid exploratory design to a stable, production-ready architecture aligned with agentic and multimodal workload requirements.

For organizations pursuing AI factory-style development environments in constrained spaces, the GB300 represents a viable deskside alternative to small-scale cluster deployments.

Dell introduces first OEM GB300 offering

Dell’s GTC 2026 announcement marked a significant milestone, as the company became the first OEM to introduce GB300-based systems within the Dell AI Factory with NVIDIA portfolio. The offering provides enterprises with validated system configurations, integrated storage and data pipeline capabilities, and end-to-end lifecycle support aligned with Dell’s existing AI infrastructure frameworks.

Dell Pro Max with GB300 (Source: Dell, 2026)

OEM adoption is a critical indicator of enterprise readiness. With Dell bringing GB300 systems into general availability, organizations can now deploy deskside AI compute as part of standardized IT planning rather than custom or isolated implementations. This enhances the GB300’s relevance for enterprise environments where compliance, orchestration, and operational predictability are required.

NemoClaw introduces a framework for local agentic computing

Alongside hardware updates, NVIDIA introduced NemoClaw, a secure, enterprise-ready reference stack for managing local agentic systems. NemoClaw provides governance and safety layers necessary for operating persistent AI agents on local devices while protecting confidential and sensitive information.

NemoClaw (source: NVIDIA, 2026)

NemoClaw (Source: NVIDIA, 2026)

As deskside systems gain the capability to host large models and continuous agent workflows, IDC views frameworks like NemoClaw as essential for enabling practical, policy-aligned deployment of agentic AI within enterprise environments. The combination of local compute capacity and controlled agent execution marks a meaningful shift from experimental agent frameworks toward structured operational use.

Implications: The sidetop era begins

The combined effect of NVIDIA’s GTC 2026 announcements signals a foundational change in how AI workloads will be distributed across compute tiers. The workstation is transitioning from a peripheral productivity tool into a critical component of the AI development and inference lifecycle.

IDC assesses that the GTC 2026 announcements represent a pivotal moment in the evolution of workstation computing. The market is moving beyond desktop-centric paradigms toward a sidetop architecture that integrates AI compute into the physical and operational workspace.

Organizations planning AI strategies should expect deskside systems to play a significantly larger role in both development and inference workflows over the next 24 to 36 months.

Explore IDC’s Workstation Opportunities research to understand how AI is reshaping workstation demand, use cases, and market dynamics.

Mohamed Hakam Hefny - Senior Program Manager - IDC

Analyst Profile
Mohamed Hefny leads market research in EMEA on professional workstation PCs and solutions. He also reports on professional computing semiconductors, processors, and accelerators (CPUs and GPUs), as well as breakthroughs and trends related to the market. In addition, Mohamed is actively involved in AI PC taxonomy and research. He participates in business development projects, contributes to consulting activities, and provides IDC customers with analysis, opinions, and advice.

“在发展中固安全，在安全中谋发展”，十五五以全领域安全体系构建夯实新发展格局根基。

当数字经济深度渗透实体经济，当低空经济、人工智能等新赛道加速崛起，安全不再是发展的“附加题”，而是贯穿经济社会发展的“必答题”。十五五规划中，安全领域迎来前所未有的战略定位，网络安全、数据安全、人工智能安全、低空安全四大核心方向协同布局，从制度构建到技术创新，从国内治理到全球合作，勾勒出“以智筑防、全域覆盖、协同发展”的安全发展新蓝图，为中国式现代化筑牢安全屏障。

IDC结合规划，分析出十五五期间安全领域有如下发展前景：

1、网络安全：升维为国家安全基石，智能重构防御新体系

在十五五规划中，网络安全首次在国家规划中独立成节，正式成为数字经济发展的“基础设施”与国家安全的重要基石，其战略定位实现质的跃升。规划明确四大核心任务，从深化综合治理、严厉打击网络犯罪，到支持技术创新与产业发展，并重点强调要“推进容灾备份体系建设，加强工业控制系统和新技术新应用的网络安全防护”，最后明确要深度参与全球网络空间治理，形成“内筑防线、外拓合作”的全方位布局，让风清气正的网络环境成为数字经济发展的“沃土”。

技术层面，网络安全正完成从“被动防御”到“主动智能”的关键跨越，人工智能成为核心引擎，推动威胁检测自动化、响应流程智能化，大幅提升防护效率。产业发展也将告别单一产品竞争，迈向生态协同新阶段，MDR/MSSP等托管运营模式加速兴起，有效降低企业安全运营成本，让中小微企业也能共享专业安全防护能力。而在全球维度，在网络安全全球治理中，中国正在发挥积极作用，为全球网络空间安全贡献中国方案。

从“人防”到“技防+智防”，AI驱动的下一代安全防御体系正在重构网络安全边界，让网络安全从“事后补救”转向“事前预警、事中快速响应”，真正成为数字经济发展的“坚固底座”。

2、数据安全：全生命周期治理，解锁数据要素价值密码

数据作为新型生产要素，其安全治理是数字经济高质量发展的关键。十五五规划聚焦数据安全，提出构建全生命周期治理体系，从制度基石到技术赋能，从国内治理到跨境流动，层层筑牢数据安全屏障，让数据在安全前提下实现价值最大化。

规划明确提出“建立健全数据产权、流通利用、收益分配、安全治理等数据要素基础制度”，实施数据分类分级管理，根据数据重要性和敏感程度实施差异化保护，让数据安全保护更精准、更有效。同时，完善科学有效的监管机制，依法打击数据滥用、深度伪造、隐私泄露等行为，为数据要素流通划定“红线”。

技术赋能让数据安全治理更智能，通过AI技术实现智能感知、动态防御、全局治理，推动数据安全从被动防御向主动智能治理跨越。而在数据跨境流动领域，规划兼顾“有序流动”与“安全防控”，一方面建立科研数据等跨境安全有序流动机制，另一方面积极参与全球治理，构建跨境数据安全防线，依法打击数据滥用、深度伪造、泄露隐私等行为。

3、人工智能安全：平衡创新与风险，迈向负责任的智能时代

人工智能是新一轮科技革命的核心驱动力，而人工智能安全则是其健康发展的前提。十五五规划围绕人工智能安全，提出“推动建立人工智能全生命周期风险管理制度，健全覆盖安全监测、风险预警、应急响应的风险防控体系。”

规划强调加强数据治理，加快建设人工智能语料库，建立训练数据合理使用制度，从源头防范数据安全风险；同时，大力研究发展智能体安全相关技术，为人工智能全链条安全提供技术支撑。在技术赋能层面，人工智能正反向赋能安全治理，提升感知预警、指挥决策、精准管理和即时响应能力，让安全治理更高效、更智能。

规划更将人工智能安全纳入前沿科技攻关与“人工智能+”行动核心范畴，研制高性能AI芯片与基础软件栈，深化可解释、可决策等关键算法研究；同时，推动人工智能在市场监管、安全生产、防灾减灾、网络空间维护等领域的应用，探索构建自然人、数字人、智能机器人协同的安全治理体系，让人工智能成为安全治理的“新利器”。

创新无边界，安全有底线。十五五规划让人工智能安全与创新同频共振，让智能时代更可控、更可信。

4、低空安全：护航立体交通新秩序，夯实低空经济发展根基

作为新兴领域的重要赛道，低空经济成为十五五规划的亮点之一，而低空安全则是低空经济规模化发展的“前置条件”。规划从技术支撑、基础设施、防护体系三大维度，构建全方位低空安全保障体系，为低空经济发展保驾护航，开启立体交通新秩序。

技术与基础设施层面，规划聚焦低空装备、低轨卫星互联网、低空基础设施三大核心安全方向，推动低空智能网联系统、重点区域低空安全防护能力建设，统筹推进卫星互联网星座建设并提升其安全防护能力，让低空经济发展有“硬支撑”。

低空安全体系的构建，为无人机物流、低空旅游、城市空中交通等低空经济场景扫清安全障碍，也将成为安全领域新的热点。

未来，随着十五五规划各项政策的落地实施，安全领域将迎来技术创新、产业升级、治理完善的黄金发展期，而以安全为基石的发展模式，也将推动中国经济在高质量发展的道路上行稳致远。

总结：

十五五规划将安全提升至战略新高度，以网络安全、数据安全、人工智能安全、低空安全四大领域为核心，构建“以智筑防、全域覆盖”的现代化安全体系。IDC认为，未来安全发展将呈现智能驱动、生态协同、治理前置等趋势，AI全面赋能防护升级，数据治理激活要素价值，低空安全支撑新兴业态。安全正从发展“保障”转变为发展“基石”，为高质量发展筑牢坚实底座。

IDC相关研究报告：

《中国大模型安全评估平台厂商评估，2026》

《中国工控防火墙市场份额，2025》

《中国工控安全靶场市场份额，2025》

《中国安全大模型一体机技术评估，2026》

《中国数据发现与分类分级智能体能力评估》

《中国网络安全软件技术发展路线图，2026》

《中国网络安全厂商亚太区出海服务能力评估，2026》

《中国数据安全管理平台市场份额，2025》

《中国数据库安全审计市场份额，2025》

《中国企业级通用智能体安全防护解决方案市场洞察，2026》

《中国物联网安全市场份额，2025》

《DeepFake 智能体市场洞察，2026》

IDC已于2026年启动AI安全、工控安全、低空安全等技术研究，围绕新技术、新场景展开深入分析。如需进一步探讨或沟通，欢迎与我们联系。

如需进一步了解与研究相关内容或咨询 IDC其他相关研究，请点击此处与我们联系。

2026年GTC大会上，英伟达的一系列产品发布和战略布局再次对全球智能算力市场产生重大影响。其重点推出的Vera Rubin全栈计算平台，打破了以往“单芯片主导算力竞争”的传统逻辑，转而以机架级系统、集成式AI工厂架构为核心，强调软硬件协同、全链路优化的平台化价值。

国际数据公司（IDC）最新服务器市场追踪报告预测，到2029年全球加速计算服务器市场规模将超过1万亿美元规模，未来5年仍然以每年增长30%的速度猛增。未来几年的人工智能发展，仍将依赖于智算算力基础架构设施的不断扩张和技术革新。结合IDC全球AI基础设施市场数据，IDC总结出未来几年智能算力发展的五大核心发展趋势，为科技企业、云服务商及投资者提供决策参考。

趋势一：算力架构专用化深化，GPU与CPU/专用加速器协同成为主流

Vera Rubin计算平台和Groq 3 LPX机架的协同发布，清晰折射出AI算力架构从通用优化向专用分工的深度演进。Vera Rubin的Rubin GPU侧重高吞吐量、大规模参数处理，适配智能体AI的复杂推理与训练需求；Groq LPX的LPU则聚焦低延迟token生成，专攻推理场景中的解码环节，两者通过解耦推理架构实现协同，本质上是算力架构专用化的极致体现。

这一趋势是全球智算算力发展的必然选择。随着大语言模型参数迈向万亿级，智能体AI的应用普及，单一GPU架构在特定场景下的效率瓶颈日益凸显，专用加速器的价值持续提升，LPU针对大模型推理的延迟与带宽瓶颈优化，形成与GPU的互补分工。IDC认为，未来几年间，通用GPU+专用加速器的异构协同架构将成为AI算力集群的标配，不同场景下的专用算力芯片将持续涌现，算力架构将呈现场景适配、分工协同的核心特征，彻底打破GPU单一主导的格局。

趋势二：互联技术迭代升级，超节点技术和算网融合成为算力架构的发展重点

Vera Rubin架构的NVLink 72和Spectrum-6交换机等产品的发布，标志着智算互联技术上升到了一个新的台阶，算网融合成为技术竞争的核心焦点。NVLink 72实现72个GPU的全互联拓扑，单机柜带宽达260TB/s；Spectrum-6交换机则通过将硅光子引擎封装到芯片，减少信号损耗，进一步压缩端到端延迟，适配高密算力集群的互联需求。

随着大模型模型规模扩大，多节点协同成为常态，通信延迟与带宽瓶颈成为制约算力释放的主要瓶颈。IDC调研数据显示，当前AI大模型训练中，数据通信耗时占比已达30%-40%。这都推动了新一代智算集群互联技术的发展。互联架构从树形拓扑向全互联无损网络演进，算网融合的深度与广度将持续提升，成为AI算力集群的核心基础设施。

趋势三：冷却与能效技术革新，液冷成为高密算力集群必选配置

新推出的Vera Rubin系统的100%液冷设计架构，高密机柜的部署模式，印证了冷却技术与能效优化已成为智算算力基础设施的核心支撑。随着算力密度持续飙升，训练服务器单机柜功率极限即将突破100kW，液冷技术从可选升级正式转变为必选配置。

液冷技术的普及，不仅是散热需求的驱动，更是能效优化的必然选择。IDC预测，到2029年仅中国国内的液冷服务器市场规模就将突破200亿美元，年复合增长率超过50%，其中AI算力场景占比将超过60%，成为液冷技术的核心应用领域。

除了液冷技术，能效优化还将呈现“软件+硬件”协同的趋势。通过动态调度技术，通过软件算法优化算力与电力分配，最大化每瓦token产出效率，实现算力输出与能效优化的动态平衡。未来，每瓦token数的能效指标，将取代单纯的算力性能FLOPS，将成为考核智算算力的核心维度。

趋势四：智能体生态崛起，推动算力需求多元化与边缘渗透

OpenClaw热潮延续到了本次大会。智能体技术落地加速，正推动算力需求向多元化、全域化演进。作为开源智能体操作系统，实现了资源调度、工具调用的全流程自动化，推动AI从生成内容走向自主执行，这一变革直接带动推理算力需求的指数级增长。

从算力需求结构来看，智能体时代的算力需求呈现两大特征：一是推理算力占比持续提升，IDC预测，2027年推理算力在智能算力大盘中的占比将超过70%，成为算力需求的核心增长引擎；二是算力需求从核心数据中心向边缘、车端、工业现场等全域渗透，边缘算力需求增速将超过核心算力，成为新的增长极。

这一趋势将推动算力基础设施的形态变革。边缘算力节点需具备小体积、低功耗、高可靠的特征，适配智能体在工业机器人、车端、电信基站等场景的部署需求。同时，智能体对工具调用、数据访问的需求，将推动算力与存储、网络、安全技术的深度融合，形成算力+生态的协同发展格局。

趋势五：SCSP/Neo‑clouds崛起，专业智算服务重构算力供给格局

大会提及的算力合作伙伴CoreWeave等，属于IDC 定义的专业云服务商SCSP，或称Neo‑cloud，即面向 人工智能的专用算力云，主打高密度 智算集群、低延迟网络、弹性调度与成本优化。与传统提供全栈通用云能力，覆盖企业全场景 IT 需求的超大规模云相比，专业算力SCSP/Neo‑clouds聚焦智算算力即服务，极致优化大模型训练 / 推理集群，交付更快、更专、更省的 智算算力。两者共同构成混合多云格局，成为企业算力的主流交付模式。

与全球市场不同，中国的传统超大规模云厂商也在加大对智算算力的投资。在继续覆盖企业多场景应用的前提下，也提供智算算力的供给与优化，匹配智能体AI场景的核心需求。未来，智算算力与通用型算力形成分工互补的格局：智算算力专注于AI专用算力领域，为大模型的训练和推理提供基础架构；通用算力则聚焦智能体执行操作，覆盖Agent Skill更多场景，满足企业多元化算力需求。

IDC建议

IDC中国研究副总裁周震刚先生表示，本次大会发布的产品所折射的五大技术趋势，本质上是AI走向规模化生产的必然结果。对于科技企业而言，需聚焦专用算力架构、算网融合、液冷能效等核心技术领域；对于开发者而言，开源生态的崛起为技术创新提供了广阔空间，可依托OpenClaw等开源平台，聚焦垂直场景的智能体开发；对于投资者而言，液冷服务器、专用加速器、边缘算力等赛道，将成为未来3-5年的核心增长领域，值得重点关注。

同时，IDC也提醒市场参与者关注技术迭代带来的挑战：一是技术转型成本较高，中小厂商面临研发投入与供应链整合的双重压力，行业分化将持续加剧；二是电力资源约束日益凸显，能效优化能力成为企业的核心竞争力；三是开源生态的安全风险需重点防范，尤其是智能体时代，数据安全与合规性将成为行业发展的重要前提。

IDC相关研究报告：

China Digital Infrastructure Strategies (Chinese Version)

China AI Infrastructure Strategies (Chinese Version)

China Semiannual Accelerated Server Tracker

China Semiannual Liquid Cooling Server Tracker

China Semiannual Intelligent Computing Infrastructure as Services Tracker

IDC将持续追踪全球智能算力架构、市场格局与应用创新的最新动态，围绕AI基础设施、算力服务、边缘智能、绿色计算等核心议题，输出前瞻性研究与深度洞察。如您希望进一步获取相关报告或进一步交流，欢迎与我们联系。

如需进一步了解与研究相关内容或咨询 IDC其他相关研究，请点击此处与我们联系。

Thomas Zhou - Vice President - IDC

Thomas Zhou is the vice president of Enterprise Research for IDC China. He leads the enterprise research team in covering market analyses, tracking of data, forecasting, and consulting for enterprise computing, storage, networking, infrastructure software, cloud, and datacenter. He is also responsible for IDC data tracking of software, services, and the public cloud services market in China. Thomas speaks frequently at IDC, industry, and user events and is always quoted in leading business and technology publications. Thomas joined IDC in 2006. He provides in-depth market analysis, research, and consulting on all aspects of the enterprise infrastructure to IT vendors and investors. During his tenure at IDC China, Thomas has led IDC's primary research focused on emerging trends in enterprise systems and datacenters. This research continues to make IDC a thought leader in enterprise infrastructure‒powered digital transformation. Thomas's recent topics covered software-defined infrastructure, hyperconvergence, virtualization, and cloud computing infrastructure. Prior to joining IDC, Thomas worked for 10 years as a senior project manager and business consultant for several leading IT companies in China. Thomas holds a master's degree in Computer Engineering from the University of Science and Technology of China.

Public sector senior leaders, such as mission and program executives, CIOs, CTOs, and CAIOs, have always faced a dual mandate: drive technology-enabled innovation while controlling risk. Private sector IT and business leaders have historically leaned more toward innovation, although leaders in regulated industries have faced pressures similar to those in the public sector.

That tension has escalated over the past twelve to eighteen months. The potential benefits and disruptive impact of AI have raised new questions about how to manage its risks. At the same time, geopolitical turbulence has made strategic autonomy in technology choices, control over data, and operational resilience paramount. These forces have converged in the sovereign AI debate.

In a recent conversation with senior government officials in a major Asian country, IDC found that the country’s vision is to build national AI infrastructure capabilities that they can “control in time of crisis.” While they recognize they cannot manufacture everything overnight, they “cannot accept dependency without a plan.” At the same time, their goal is “not to lock data away so tightly that no one can innovate,” but to find the right balance.

How the sovereignty debate is evolving from control to strategy

That tension between speed and control, and between innovation and sovereignty, sits at the heart of today’s digital and AI strategies. It also reflects how the conversation around sovereignty has evolved.

Early digital and cloud sovereignty discussions were driven by a specific concern: that sensitive data could be accessed by foreign jurisdictions. That narrow focus has now expanded into something much broader. Sovereignty has become a strategic imperative that shapes how organizations design their entire technology stack.

Today, sovereignty is no longer just about where data resides. It is about control over data, infrastructure, operations, and even the supply chain. AI sovereignty extends this further, encompassing control across the entire AI lifecycle, from model development to deployment and governance.

IDC research shows that market signals are clear. Governments are investing in sovereign AI capabilities, from national cloud infrastructures to domestic AI ecosystems. They are incentivizing local data centers, funding native-language AI models, and defining guidelines that will shape how sovereign solutions are acquired and deployed. For policymakers, AI is no longer just a technology. It is an instrument of economic competitiveness and national security.

For organizations, this creates a new reality. Senior business and IT leaders are no longer designing a single global architecture. They are navigating a fragmented, multi-sovereign world.

Choosing the right sovereign AI deployment approach

Faced with this complexity, many leaders look for a single answer: which deployment model is the most sovereign?

The reality is that the market offers a spectrum of deployment archetypes, ranging from public cloud to fully air-gapped environments. Each comes with different levels of control, agility, innovation speed, and cost. There is no one-size-fits-all approach.

A highly regulated AI workload may require a sovereign or even air-gapped environment. A customer-facing application may benefit from the scalability of the public cloud, combined with added sovereign controls.

The real challenge is selecting the right model for different use cases, or even different components of the same use case. For example, one deployment model may be used for AI training, another for retrieval-augmented generation, and a third for an agentic AI orchestration layer.

This is why hybrid architectures are emerging as the dominant pattern across both the public and private sectors. According to IDC’s 2025 Digital Sovereignty survey of more than 900 IT and business leaders, 37% of respondents say on-premises is currently their main environment and that sovereign cloud is, or will be, the only type of cloud they use. At the same time, 55% say sovereign cloud is, or will be, part of a multicloud or hybrid strategy.

IDC predicts that by 2028, CIOs at multinational organizations will increase investments in modular, sovereign-ready cloud and data localization environments by 65% to future-proof operations against rising sovereignty demands. Additionally, by 2026, 55% of governments will adopt hybrid sovereign cloud stacks, blending hyperscaler scale with national control to ensure compliance, security, and strategic autonomy for AI.

Public and private sector leaders are not retreating from the cloud. They are reshaping it. By combining global hyperscaler capabilities with local control layers, they are creating what IDC describes as sovereign-ready environments.

This approach reflects a deeper truth: sovereignty is not about isolation. It is about choice and control.

What leaders need to know about sovereign AI strategy

The conversation around digital and AI sovereignty is often framed as a trade-off between control and innovation. The organizations that will succeed are those that reject this binary thinking. They understand that sovereignty is not about limiting innovation, but about enabling it on their own terms.

In a world where AI is becoming the backbone of economies and societies, IDC research helps connect the dots between technology providers offering cloud and AI solutions and the business and IT leaders who must select the right deployment approaches to achieve their sovereignty goals.

Massimiliano Claps - Research Director - IDC

Analyst Profile
Massimiliano (Max) Claps is the research director for the Worldwide National Government Platforms and Technologies research in IDC's Government Insights practice. In this role, Max provides research and advisory services to technology suppliers and national civilian government senior leaders in the US and globally. Specific areas of research include improving government digital experiences, data and data sharing, AI and automation, cloud-enabled system modernization, the future of government work, and data protection and digital sovereignty to drive social, economic, and environmental outcomes for agencies and the public.

Rahiel Nasir - Research Director, European Cloud Practice, Lead Analyst, Digital Sovereignty - IDC

Analyst Profile
Rahiel Nasir is responsible for leading and contributing to IDC's European cloud and cloud data management research programs, as well as supporting associated consulting projects. In addition, he leads IDC's worldwide Digital Sovereignty research program. Nasir has been watching technology markets and writing about them throughout his professional life.

Tim Cook might have just given Apple its single most disruptive launch since the iPhone. Apple introduced the MacBook Neo earlier this month, just ahead of Apple’s 50th anniversary, at a striking price point: $599 at retail and $499 for education. My initial reaction, like many others, was, “Wow. This is a killer price.”

For years, Apple has remained disciplined at the premium end of the PC market, rarely launching a brand-new product at what could genuinely be considered entry-level pricing. Seeing Apple move this decisively into the sub $700 segment is an aggressive play that clearly signals an intent to capture share.  It brings a Mac into the hands of users who’ve aspired to own a Mac but have historically been priced out. But it also raises other important questions. What compromises did Apple make to achieve this and mor importantly, will it dilute the Apple brand?

After spending a few weeks with the device, the answer was clear.

First impressions: Neo feels anything but budget

The MacBook Neo immediately feels like a Mac, not a compromised or stripped-down version. It is thin, exceptionally light at roughly 2.7 pounds, and the aluminum build delivers the solidity consumers associate with Apple’s premium notebooks. The keyboard is comfortable, the touch track pad feels precise, and the display is noticeably bright and sharp – standing out instantly in this price band.

Day-to-day performance is fluid. App launching and switching are smooth and responsive, which is notable given the modest hardware configuration: 8GB of RAM paired with Apple’s A18 Pro processor, previously used in iPhones, which I wager may soon become a trend to be followed by other PC makers.  Apple, yet again, demonstrates vertical integration can matter more than raw specifications.   Apple has found a way to expand its reach without undermining its product quality, user experience and core brand promise.

Design also plays a critical role in the Neo strategy. The brighter color options give the Neo a sense of personality that resonates strongly with younger users. It feels modern, expressive, and distinctly non-generic. Simply put, very little about the MacBook Neo feels “budget.”  Rather than diluting the Mac brand, Apple has effectively extended its premium perception into a lower price tier – something very few PC vendors have managed successfully.

Why MacBook Neo resonates with younger users

What stood out even more than my own reaction as an IDC analyst was what I observed at home. I have three teenage children-squarely within Apple’s target demographic for this device-who quickly attempted to claim ownership of the Neo.

None of them asked about the processor, memory, or benchmark performance. There were no questions about architecture or specifications. Instead, they focused on how much better it looked and felt compared with the Chromebooks and low-cost Windows laptops they currently use for school. They noticed the display quality. They liked the keyboard. They commented on how light it was.

Then came the reaction that captured everything: “This would look so cool at school.”

That “cool factor” is often underestimated in market analysis, but in a school environment it is a powerful driver of preference. Within minutes, the verdict was clear-they wanted it, and it landed immediately on their birthday wish lists. 

MacBook Neo hits the sweet spot

That reaction highlights a broader market reality. Buyers in the sub $700 notebook segment are overwhelmingly not current Mac users, nor are they making decisions through a spec-driven lens. Their purchases are constrained by budget and centered on core experience: design, ease of use, battery life, and overall feel.

In that context, MacBook Neo stands apart. It provides a compelling option for education institutions approaching refresh cycles after the COVID-era buying surge, for students purchasing their first personal notebook, and for small and midsize businesses operating with tighter cash flows.

While the Neo does make trade-offs relative to the MacBook Air, particularly in performance headroom and features such as external multi-display support, these limitations are largely irrelevant for this target market and first-time Mac buyers. In the areas that matter most to this audience, Neo delivers a meaningfully differentiated experience. That positions Apple to directly disrupt a segment long dominated by Windows and ChromeOS devices-and should be a legitimate concern for incumbent vendors.

What opportunity does the MacBook Neo unlock for Apple?

To understand the scale of the opportunity, it is important to frame the broader PC market. Global PC shipments totaled roughly 285 million units in 2025, with Apple holding just under a 10% share. Within that, the sub‑$700 notebook segment accounted for approximately 75 million units, nearly 40% of total notebook volume, and has historically been dominated by Microsoft Windows and Google ChromeOS, which together account for more than 95% of shipments in this tier. 

Geographically, Neo also positions Apple for expansion beyond its traditional strongholds. Today, Mac shipments remain heavily concentrated in the U.S. and Western Europe. With Neo, Apple has a credible pathway to reach more price‑sensitive buyers in emerging markets where Macs have historically seen limited penetration.

In my opinion, the opportunity extends beyond grabbing existing Windows or Chrome users.  I believe MacBook Neo will further expand Apple’s addressable market by enticing users who have deferred notebook purchases altogether. Those unwilling to compromise on experience with low-cost Windows systems but unable to justify the price premium of a MacBook Air.  By bridging that gap, Neo has the potential to both drive share gains and unlock incremental demand.

MacBook Neo: Perfect timing and long-term strategy

To top it off, the timing of this move also worked out in Apple’s favor. The broader PC industry is entering a challenging period as DRAM and NAND pricing pressures intensify. Rising memory costs are pushing many vendors upstream toward higher-priced systems or forcing them to cut specifications to defend lower price points. Apple, in contrast, is moving in the opposite direction – delivering a premium-like product at a budget price.  This move will send competitors back to the drawing board to defend their share in this massive segment and I am eager to see their response.   

Strategically, Neo represents far more than a near-term share grab.  It advances one of Apple’s long-term objectives: increasing ecosystem penetration earlier in the user lifecycle. By introducing macOS to younger users-often as their first personal Mac, Apple strengthens platform stickiness and maximizes lifetime value. Once users are embedded in Apple’s ecosystem through iMessage, FaceTime, iCloud, and AirDrop, across multiple devices, they are less likely to switch to another platform across any device. As younger Neo users transition into higher education and into professional roles with greater purchasing power, upgrading to a MacBook Air becomes a natural progression rather than a competitive evaluation. In this sense, Neo serves as a feeder into Apple’s higher-margin Mac portfolio over time.

That dynamic is already visible in my own household. My children live on their iPhones and iPads, and a MacBook that is finally within financial reach simply extends that ecosystem into the notebook category. Once that level of integration is established, switching away becomes far less likely. This is where the real strategic value of MacBook Neo lies-not in short-term unit volume alone, but in locking in demand across multiple device cycles.

Final thoughts

MacBook Neo might just be the best 50th‑anniversary gift Tim Cook could have given Apple. The device is not just about a lower-priced Mac but represents a long-term ecosystem lever. Viewed through that lens, it has the potential to be one of Apple’s most disruptive launches since the iPhone-not because it introduces breakthrough technology, but because it will significantly alter the competitive landscape of the PC Market for the foreseeable future.

Great news for Apple, less so for me, as I now need to figure out how to buy three Neos.

Nabila Popal - Sr. Director, Data & Analytics - IDC

Analyst Profile
Nabila Popal is Senor Director with IDC's Data & Analytics team, specializing in Mobile Phones, PC Monitors and other consumer devices. Ms. Popal is responsible for the global research and quality and timely delivery for her respective technologies, coordinating with regional and worldwide research teams. She continuously engages with global vendors and key market players to discuss the latest industry trends and dynamics. Ms. Popal is also responsible for future product planning and evolution whilst managing client relationships and providing thought leadership and executing custom engagements. She also manages communications with the media and is often published in leading local and international media outlets. Ms. Popal has been with IDC since 2013, and prior to her role with the Worldwide team, she was with IDC MEA, leading the research for Middle East, Africa, and Turkey, based out of Dubai, UAE.