硬AI·2026-01-11 11:12Core Insights - The competition in large models has shifted from "Chat" to "Agent," focusing on executing complex tasks in real environments rather than just scoring on leaderboards. The industry anticipates 2026 as the year when commercial value will be realized, with a technological evolution towards verifiable reinforcement learning (RLVR) [2][4][5]. Group 1: Competition Landscape - The engineering challenges of the Chat era have largely been resolved, and future success will depend on the ability to complete complex, long-chain real tasks. The core value of AI is transitioning from "providing information" to "delivering productivity" [4]. - The bottleneck for Agents lies not in cognitive depth but in environmental feedback. Future training paradigms will shift from manual labeling to RLVR, enabling models to self-iterate in systems with clear right or wrong judgments [5][6]. - The industry consensus suggests that while China has a high chance of catching up in the old paradigm (engineering replication, local optimization, toC applications), its probability of leading in new paradigms (underlying architecture innovation, long-term memory) is likely below 20% due to significant differences in computational resource allocation [5][11]. Group 2: Strategic Opportunities - Opportunities for catching up lie in two variables: the global shift towards "intelligent efficiency" as scaling laws encounter diminishing returns, and the potential paradigm shift driven by academia around 2026 as computational conditions improve [5][19]. - The ultimate variable for success is not leaderboard scores but the tolerance for uncertainty. True advancement depends on the willingness to invest resources in uncertain but potentially transformative new paradigms rather than merely chasing scores in the old paradigm [5][10]. Group 3: Perspectives from Industry Leaders - Industry leaders express cautious optimism regarding China's potential to lead, with probabilities of success varying. For instance, Lin Junyang estimates a 20% chance of leading due to structural differences in computational resource allocation and usage [11][12]. - Tang Jie acknowledges the existing gap in enterprise AI lab research but bets on a paradigm shift occurring around 2026, driven by improved academic participation and the emergence of new algorithms and training paradigms [15][19]. - Yang Qiang believes that China may excel in toC applications first, drawing parallels to the internet history, while emphasizing the need for China to develop its own toB solutions to bridge existing gaps [20][24]. Group 4: Technological Innovations - The future of AI will require advancements in multi-modal capabilities, memory structures, and self-reflective abilities, which are essential for achieving higher levels of intelligence and functionality [68][70][73]. - The introduction of new optimization techniques, such as the MUON optimizer, aims to enhance token efficiency and long-context processing, which are critical for the performance of agent-based models [110][116]. - The development of linear attention mechanisms is expected to improve efficiency and performance in long-context tasks, addressing the limitations of traditional attention models [116]. Group 5: Future Directions - The industry is focused on distinguishing between scaling known paths through data and computational increases and exploring unknown paths to discover new paradigms [98][99]. - The potential for AI to participate in scientific research is anticipated to expand significantly, opening new possibilities for innovation and application [101].