以下文章来源于TOP创新区研究院 ，作者趋势研究组 TOP创新区研究院 . 创新区研究，就在TOP研究院。TOP研究院专注于全球创新区的一体化研究，从Talent(个人), Organization(组织), Place(区域)三大维度出发，通过"研究/连接/ 分享"，探索中国创新区的实践路径。 错得离谱。 这份报告（其实也是鬼故事）让无数聪明的机构投资者惊出一身冷汗， 甚至真的让科技股剧烈震荡，市场陷入恐慌 ，原因无他，是 因为它推演了一场即将到来的 "宏观经济系统重构" 。 作者 / 趋势研究组 来源 / TOP创新区研究院 （ID： TOP_Lab ） 最 近 ， 华 尔 街 的 各 大 交 易 群 和 硅 谷 的 密 室 里 ， 都 在 传 阅 同 一 份 极 具 挑 衅 性 的 做 空 檄 文 —— 来 自 Ci tri n i Re s e a r c h 的 最 新 宏 观 预 测 报 告： 《2 0 2 8全球智能危机》（2 0 2 8 Gl o b a l I n t e l l i g e n c e Cr isis） 。 过去2 0 0年，人类的"智能"始终是一种稀缺资源，而全球经济体系 ...

为何说 HALO 交易刚刚开始？20260226 摘要 大模型公司为融资和估值竞争，通过强调替代性，从美股企业软件公司 手中争夺 IT 预算，压低软件股估值以抬升自身估值弹性，对软件股情绪 与估值形成压制。OpenAI 已将 Salesforce 等列为潜在替代对象，强化 市场空间想象。 云厂商在现金流压力下仍强化 AI 投入，资本开支优先级上升，压缩回购 与分红。谷歌等倾向高举高打以震慑对手，可能采取更积极的防御，对 市场估值框架与投资范式产生不确定性，引发波动与重估。 传统设备制造商面临"AI 税"，中间品如存储涨价导致利润率下滑。财 报已显示存储价格上涨带来的利润率下滑，成本端上行对硬件链条盈利 质量的压制成为交易约束。 美股风格切换为从成长向价值，电力相关板块表现强势，并沿产业链扩 散至核电、绿电、气电、机械、铀矿、天然气、油气、电网、配电、钢 铁等，核心围绕 AI 扩张带来的电力基础设施建设需求。 政治维度上，中期选举年背景下"还电于民"诉求强化，电力可负担性 危机上升为重要政治主题。政策预计加码推动云厂自建电力与疏通电力 "梗阻"，特朗普将召集科技企业高管保证数据中心支付电费。 Q&A 为何"He ...

[Table_Page] 宏观经济|定期报告 2026 年 3 月 1 日 证券研究报告 [Table_Title] 怎么看所谓 2028 年"全球智能危机"的观点 宏观周度述评系列（2026.02.24-03.01） [报告摘要 Table_Summary：] 识别风险，发现价值 请务必阅读末页的免责声明 1注：未特别说明，报告数据来自 wind，彭博 | [Table_Author] 分析师： | 郭磊 | | --- | --- | | | SAC 执证号：S0260516070002 | | | SFC CE No. BNY419 | | | 021-38003572 | | | guolei@gf.com.cn | | 分析师： | 陈礼清 | | | SAC 执证号：S0260523080003 | | | 021-38003809 | | | chenliqing@gf.com.cn | | 分析师： | 陈嘉荔 | | | SAC 执证号：S0260523120005 | | | 021-38003674 | | | gfchenjiali@gf.com.cn | | 分析师： | 钟林楠 | ...

Core Insights - The Chinese large model market has seen a significant surge, with token usage reaching 41.2 trillion, surpassing the U.S. models for the first time [1][2] - Major Chinese models dominate the top five in usage, indicating a shift in the competitive landscape [1][2] - The market is bifurcating, with established players like BAT focusing on integrating models into existing services, while new entrants like Kimi and MiniMax are expanding their developer ecosystems [1][2] Token Usage and Developer Insights - The 41.2 trillion tokens are primarily driven by global developers, with U.S. developers accounting for 47.17% of usage compared to only 6.01% from China [2] - The surge in token usage reflects real demand from developers who are willing to invest in models that deliver performance and cost efficiency [2][6] - MiniMax M2.5 and Kimi K2.5 are highlighted for their competitive performance and cost advantages, with MiniMax achieving the highest usage in coding and search tasks [2][3] Cost Efficiency and Performance - Chinese models are significantly cheaper, costing only 1/10 to 1/20 of their U.S. counterparts, which is reshaping the economic calculations for developers [3][4] - The cost structure of models like MiniMax and Kimi allows for substantial savings in computational expenses, making them attractive options for developers [3][4] - The introduction of the "Mixture of Experts" (MoE) architecture has optimized engineering efficiency, contributing to lower costs [3] Demand Dynamics and Token Consumption - The emergence of agent-based scenarios has changed the token consumption logic, leading to exponential increases in token usage for complex tasks [5][6] - Over 70% of token consumption comes from large internet companies and professional developers, indicating a strong demand for these models in production environments [6] Business Model Evolution - The industry is shifting towards a "Results-as-a-Service" (RaaS) model, where payment is based on outcomes rather than token usage [8][9] - This transition requires a rethinking of pricing structures, moving from token-based to results-based billing, which aligns better with client expectations [9][10] - The challenge remains in accurately attributing results to AI contributions, complicating the implementation of this new model [18][19] Market Trends and Future Outlook - The current landscape shows a growing willingness among businesses to pay for quantifiable results, driven by changes in procurement processes [16][20] - The financial sustainability of new players is under scrutiny, as they face high computational costs that can exceed revenue [8][26] - The ability to successfully implement results-based pricing will be crucial for the survival and growth of these new entrants in the market [26][27]

Group 1: AI Narrative Shifts - The global AI narrative is experiencing significant marginal changes, with at least three layers of transformation observed[4] - The first narrative shift indicates a divergence regarding the Scaling Law, highlighting physical constraints, data bottlenecks, and diminishing marginal returns on investment in AI models[5] - The second narrative shift reflects a transition from "rewarding" CAPEX to anxiety over slow monetization, with projected AI-related capital expenditures in the U.S. exceeding $700 billion by 2026, representing over 2% of GDP[6] Group 2: Market Concerns and Impacts - The third narrative shift involves deeper concerns about AI's disruptive potential across various industries, evolving from changing search methods to transforming software applications and business processes[7] - The anticipated capital expenditures by major U.S. tech firms will consume approximately 90% of their operating cash flow in 2026, up from 65% in 2025, raising concerns about negative free cash flow[6] - The market is currently pricing in a relatively worst-case scenario due to panic-driven sentiment, despite resilient fundamentals in many affected companies[10] Group 3: Investment Strategies - Investors are advised to shift from a broad "buy a basket of AI" approach to a more refined selection of targets, focusing on which changes are likely to occur and which are not[11] - Key investment perspectives include identifying hardware segments with strong supply constraints, competitive model layers with proprietary data, and application layers that can quickly demonstrate AI's value[12] - The differences in AI development paths between China and the U.S. suggest that investment logic in China may focus more on "industrial empowerment" rather than mere labor replacement[14]

2028 年，AI 将导致经济危机？ 前几天，美国的独立金融研究机构 CitriniResearch 发布了一篇《 2028 年全球智能危机 》 报告。 核心观点简单粗暴：AI要是再这么能干下去，人类经济就要顶不住了。 报告一推出就爆火，现在阅读量已经破了千万。 影响力更是不容小觑，文章里面可汗大点兵，点到的公司比如金融领域的 Visa，送外卖的 DoorDash，做 SaaS 的 Service Now 等，股价纷纷大跳水。 虽然《 2028 年全球智能危机 》用倒叙的手法从 2028 回顾 2026，写得跟科幻小说似的，但说实话，差评君看完有点笑不出来。 不是因为它一定会发生，而是因为它的逻辑，是有点说服力的。 咱也带大家，再简单过一下这篇雄文。 文章其实很好理解，当 AI能一个顶十个，成本只有人工的零头，还全年无休、从不摸鱼、不会请假，你觉得老板会怎么选？ 然而，原本花钱的人却一个一个被优化掉，大伙儿兜里空空，渐渐谁也不敢消费了。 于是一种很魔幻的 " 幽灵 GDP" 出现了：企业营收在增加，GDP 看着挺好看，可消费在收缩，订单在变少，真实经济活动在实打实的降温。那些一开始 吃到 AI 甜头的企业发现 ...

Core Argument - The article discusses the implications of the post-AGI (Artificial General Intelligence) economy, arguing that while AI may outperform humans in efficiency and cost, the demand for human-created goods and services will persist due to the cultural creation of scarcity and status-driven consumption [1][7]. Historical Context - Wealth was historically defined by social hierarchy rather than monetary assets, with the absence of a middle class in many societies leading to a clear distinction between rulers and the ruled [2][3]. - In pre-modern economies, money was not a core element, as social structures dictated wealth measurement based on the number of subordinates one could command [2][3]. Modern Economic Dynamics - The modern economy operates as a network of exchanges, where specialization and complexity necessitate the invention of money and finance to facilitate transactions [4]. - Current societal wealth surpasses that of the richest individuals in ancient times, yet human dissatisfaction persists, largely driven by status competition [4][6]. Labor and Automation - The Baumol effect suggests that as long as there is any demand for human labor in a rapidly growing AI economy, wages will remain high, but this could change as automation becomes more prevalent [5]. - The phenomenon known as the Jevons Paradox indicates that increased efficiency in robotic labor could lead to a higher overall demand for labor [5]. Status and Scarcity - The article highlights the importance of status competition in affluent societies, noting that as wealth increases, the competition for status intensifies, leading to a decline in birth rates [6][9]. - Veblen goods, such as luxury items, derive their value from their scarcity and the social status they confer, suggesting that the creation of artificial scarcity will remain crucial in a future dominated by AI [8]. Future of Human Labor - Despite the rise of AI and robotics, there is a belief that human-provided goods and services will still be desired, as humans have shown remarkable creativity in generating scarcity for status purposes [9]. - The article posits that as human labor becomes scarcer, the value of human-created products may increase, potentially leading to a resurgence in birth rates as the dynamics of wealth and status evolve [9].

Core Insights - The AI industry is experiencing a significant moment with multiple major model releases concentrated in a short timeframe, creating a strong sense of urgency and competition among companies [1][2]. Group 1: Model Releases and Performance - In less than two weeks, several high-profile AI models have been released, including Claude Opus 4.6, GPT-5.3-Codex, Seedance 2.0, and GLM-5, indicating a competitive landscape with rapid advancements [2][4]. - GLM-5's price increase signifies strong demand and capability, with its queue exceeding initial expectations [4]. - Chinese models are not only dominating in quantity but are also achieving quality parity and even leading in some areas, with significant contributions from domestic companies [5][18]. Group 2: Market Dynamics and Trends - The emergence of GLM-5 and other models represents a shift in the AI landscape, where companies are beginning to compete on both product and model quality, particularly in the B2B sector [13][17]. - The competition is expected to intensify as more companies release models that challenge established players like Anthropic, potentially reshaping the market dynamics [12][13]. - The AI industry is anticipated to reach a critical turning point in 2026, with expectations of significant advancements and market changes [14]. Group 3: Financial Implications - Anthropic's annual recurring revenue (ARR) is projected to surpass OpenAI's for the first time in Q1, indicating a shift in financial performance within the industry [10]. - The ability of companies to monetize their models effectively is becoming increasingly important, with a focus on the economic value generated from their applications [12][20]. - The competitive landscape is likely to lead to a re-evaluation of value distribution within the industry, as companies adapt to new market realities [12][17].

Core Insights - The article discusses a significant shift in the software industry, where software is transitioning from being viewed as a long-term asset to a disposable product, driven by changes in production costs, organizational structures, and business models [1][4][22]. Group 1: Changing Nature of Software - Software is increasingly being developed for short-term use, often created for specific tasks and discarded after completion, rather than being maintained as long-term systems [1][3][4]. - Examples of this trend include applications developed for single events or temporary needs, such as a birthday party app or a family news app, which are deleted after use [2]. Group 2: Structural Changes in Software Production - Four structural changes are occurring simultaneously in the software industry: 1. Software is shifting from system-based to task-based forms, focusing on completing specific tasks rather than long-term operation [5][6]. 2. Business departments are taking the lead in system development, utilizing low-code and no-code platforms to create temporary solutions [7]. 3. AI development tools are making it more cost-effective to rewrite software rather than maintain it, leading to frequent replacements of internal systems [8]. 4. Result-based payment models are emerging, allowing businesses to pay for software based on quantifiable outcomes rather than long-term usage [9]. Group 3: Impacts on the Software Industry - The traditional criteria for evaluating software quality are becoming obsolete, with a shift towards valuing speed of delivery and quantifiable results over long-term maintainability [11][12]. - The focus of development is moving from building long-lasting systems to creating reusable components and workflows that can be quickly adapted for various tasks [14]. - Pricing models are evolving from annual subscriptions to more flexible structures based on results or task completion, reflecting the transient nature of many software applications [15]. - Customer relationships are shifting from long-term partnerships to project-based collaborations, requiring vendors to continuously demonstrate efficiency and results to secure future contracts [16]. Group 4: Boundaries of Software Consumption - Not all software should adopt a disposable model; critical systems related to core business functions, security, and compliance must maintain long-term viability due to their high stakes [17][18]. - The article warns against blindly applying the disposable model in inappropriate contexts, as it may lead to technical debt and a lack of understanding of key processes [20]. Conclusion - The trend of software consumerization is a natural outcome of increased production efficiency in the AI era, leading to a proliferation of software with shorter lifecycles [22][24]. - Companies must develop the ability to distinguish between different software types, determining which should be disposable and which require long-term investment [21][25].

Core Viewpoint - The article discusses the recent sell-off in global software stocks, termed "SaaSpocalypse," triggered by the launch of Anthropic's Claude Cowork, which poses a significant challenge to traditional SaaS business models by offering high-level results at lower costs [5][10]. Group 1: Market Reaction - On February 4, major software companies experienced significant stock declines, with Thomson Reuters dropping 15.8%, LegalZoom nearly 20%, and Salesforce and Workday also seeing notable decreases [5]. - The S&P 500 Software and Services Index fell nearly 13% over five trading days, marking a 26% drop from its October peak [5]. - The sell-off is compared to a previous market panic caused by DeepSeek, highlighting the similarities in market reactions to disruptive AI technologies [7][10]. Group 2: Comparison of Two Market Panics - The panic caused by Cowork is expected to be more prolonged than that of DeepSeek, as Cowork represents a novel AI application, while DeepSeek was a cheaper alternative to existing models [10]. - The market's response to both events shows a pattern of overreaction, with analysts suggesting that the fears may be exaggerated [9][10]. - Cowork's impact has spread beyond the U.S. to global markets, affecting stocks in London, Tokyo, and India, indicating a broader concern within the tech industry [11]. Group 3: SaaS Pricing Models and Challenges - Traditional SaaS pricing models are under pressure, with many companies shifting from fixed pricing to usage-based models due to increased efficiency and cost-cutting measures [14][15]. - The average SaaS company in the PricingSaaS 500 index has experienced 3.6 pricing changes per year, with a significant increase in companies adopting usage-based pricing [15]. - Companies like Salesforce have struggled with pricing strategies, leading to a transition from fixed pricing to more flexible models to accommodate rising operational costs [15][17]. Group 4: Emergence of AI-Native Startups - AI-native startups are gaining traction, with their revenue growth rates significantly outpacing traditional SaaS companies, highlighting a shift in enterprise spending towards these new players [18]. - For instance, companies like Harvey and Glean have achieved valuations of $5 billion and $7.25 billion, respectively, indicating strong investor interest in AI-driven solutions [18]. - The article notes that AI-native companies are expected to capture over half of enterprise AI spending, reflecting a fundamental change in the software landscape [18]. Group 5: Vibe Coding and Its Implications - The rise of Vibe Coding could lead enterprises to create their own tools rather than relying on third-party SaaS products, potentially disrupting traditional software markets [20][21]. - If Vibe Coding matures, it may enable employees to develop solutions quickly, reducing reliance on complex software development processes [21]. - The article suggests that traditional software companies may face a "three-step path to extinction" if they fail to adapt to these emerging trends [22].