AI Brief: 找100个被资本选中的答案｜2026“最受关注”企业名册评选启动
This article is summarized from 36kr. The main point is: 出品｜36氪创投研究院 文｜罗婧、杜梦 当资本褪去浮躁、回归理性，当概念让位于落地、实效成为行业硬通货，当AI深度重构产业格局、硬科技筑牢行业发展底色， 2026年的一级市场，已然迈入真实价值主导的全新创投时代。 又一波浪潮袭来，谁在被看见，谁在被押注？ 市场逻辑已然彻底迭代：从盲目追逐风口，转向审慎布局长期；从为资本故事买单，转向为技术壁垒、商业增长、成熟商业化路径下注。行业用最直白的规则印证：能赢得市场认可、能穿越经济周期的，从来不是虚无的资本泡沫，而是扎实的核心技术、清晰的商业闭环、可验证的稳健成长。 从技术趋势看，AI早已跳出实验室范畴，深度融入产业底座，大模型、智能体、具身智能、机器人全面迈向规模化落地；国产算力、算法、数据工具形成全栈自主可控，AI与千行百业深度融合，跑出可复制、可盈利的成熟发展路径。从资本投资逻辑来看，行业彻底告别烧钱换规模的粗放模式，转向营收可验证、壁垒可量化、增长可持续的严苛评判标准，资金持续向技术领先、场景扎实、商业化闭环跑通的优质企业集中。 站在产业重构与价值重塑的关键节点，36 氪「2026 年度“最受关注”企业名册」正式. It is categorized under 技术/工具 and saved in English at digest build time.
Background and Context The launch of the 36Kr "2026 Most Watched Enterprises" roster marks a pivotal moment in the global venture capital landscape, signaling a definitive shift from speculative hype to rigorous value assessment. As the first quarter of 2026 concludes, the primary market has entered a new era dominated by tangible value rather than abstract narratives. This transition is driven by a maturation in both technological application and capital allocation strategies. Investors are no longer satisfied with mere technological demonstrations or proof-of-concept trials; they demand clear return on investment, measurable business value, and reliable service level agreements. The era of burning cash to buy scale is over, replaced by a strict evaluation framework that prioritizes verifiable revenue, quantifiable barriers to entry, and sustainable growth trajectories. Technologically, artificial intelligence has moved beyond the laboratory into the foundational layers of industry. Large language models, intelligent agents, embodied intelligence, and robotics are now undergoing large-scale implementation.
In China, domestic computing power, algorithms, and data tools have formed a fully autonomous and controllable stack, allowing AI to integrate deeply with thousands of industries. This integration has produced replicable and profitable development paths, distinguishing successful enterprises from those relying on capital bubbles. The selection criteria for the 2026 roster reflect this reality: only companies with solid core technologies, clear commercial loops, and verifiable steady growth can withstand economic cycles and win market recognition.
Deep Analysis
The "2026 Most Watched Enterprises" selection process requires a multi-dimensional analysis of technological maturity, commercial viability, and ecosystem strength. From a technological perspective, the AI stack has evolved from isolated breakthroughs to systematic engineering. Every stage, from data collection and model training to inference optimization and deployment operations, now requires specialized tools and professional teams. This complexity raises the barrier to entry, favoring companies that have built comprehensive technical infrastructures rather than those relying on single-point innovations. Commercially, the industry is shifting from technology-driven to demand-driven models. Enterprises are no longer the primary customers; end-users and industry-specific clients are driving adoption. These clients require solutions that solve specific pain points with clear ROI. Consequently, AI products are becoming more integrated into existing workflows, requiring robust security, compliance, and interoperability. The roster highlights companies that have successfully navigated this shift, demonstrating not just technical prowess but also the ability to deliver consistent business value. Furthermore, the competition has expanded from single-product battles to ecosystem wars. Success now depends on building a complete ecosystem that includes models, toolchains, developer communities, and industry-specific solutions. Companies that foster strong developer engagement and provide comprehensive support structures are better positioned to retain users and drive long-term adoption. The data from Q1 2026 supports this trend, showing that open-source models are gaining ground in enterprise adoption rates, challenging the dominance of closed-source alternatives and forcing a reevaluation of licensing and support strategies.
Industry Impact The implications of this shift extend throughout the AI supply chain, creating ripple effects across infrastructure providers, application developers, and talent markets. For upstream infrastructure providers, including GPU manufacturers and data service companies, the changing demand structure means that resource allocation priorities are being adjusted. With GPU supply remaining tight, the focus is shifting towards efficiency and specialized applications, benefiting companies that offer optimized hardware and software stacks for specific workloads. For downstream application developers and end-users, the landscape is becoming more complex. In the context of the "hundred-model war," developers must consider more than just current performance metrics. They must evaluate the long-term viability of suppliers, the health of their ecosystems, and the potential for future integration. This has led to a more cautious approach to technology selection, with enterprises prioritizing stability and support over experimental features. The result is a consolidation of market share among a few key players who can offer reliable, scalable solutions. The talent market is also reflecting these changes. Top AI researchers and engineers are becoming the most sought-after resources, with their movements often indicating the future direction of the industry. Companies are investing heavily in talent retention and development, recognizing that human capital is the primary driver of innovation.
In China, domestic models like DeepSeek, Tongyi Qianwen, and Kimi are rising rapidly, challenging global players and offering differentiated strategies based on lower costs, faster iteration speeds, and closer alignment with local market needs.
Outlook
Looking ahead, the next three to six months will be critical for assessing the immediate impact of these trends. Competitors are expected to respond quickly to major strategic shifts, accelerating product launches or adjusting differentiation strategies. Developer communities will play a key role in evaluating and adopting new technologies, with their feedback determining the actual influence of these changes. Investment markets will also see value re-evaluations, with funding activities potentially fluctuating as investors reassess the competitive positions of various companies. Over the next 12 to 18 months, several long-term trends are likely to emerge. The commoditization of AI capabilities will accelerate, as model performance gaps narrow and pure model capability becomes less of a competitive barrier. Vertical industry AI solutions will gain prominence, with companies possessing deep industry knowledge gaining a significant advantage. AI-native workflows will reshape business processes, moving beyond simple augmentation to complete redesigns centered around AI capabilities. Finally, the global AI landscape will continue to differentiate, with regions developing unique ecosystems based on their regulatory environments, talent pools, and industrial foundations. Key signals to monitor include the product release rhythms and pricing strategies of major AI companies, the speed of open-source community replication and improvement, regulatory responses, enterprise adoption rates, and talent flow directions. These indicators will provide a clearer picture of the industry's trajectory and help stakeholders navigate the evolving landscape. The 2026 roster serves as a benchmark for this new era, highlighting the companies that have successfully adapted to the demands of a mature, value-driven market.