如果不看那些 drama，从最大化 ROI 的角度来看，最近那场 AI 和人类对抗的比赛，反而让 @brevis_zk 获得了一个非常直观的技术场景验证 😂Aster 的这类比赛，本质上就是一个对 “可验证计算、数据可信性和隐私保护” 要求极高的场景，而 Brevis 他们可能都没出钱就获得了这场营销里最大的 use case。和 @opinionlabsxyz 的合作也是同理，通过用 Brevis 验证提高了这个 BNBChain 第一预测市场的去中心化程度和可信度，目前看来在使用场景上是闭环的BNBChain 以前的战略方向有一项是 zkBNB，是币安生态的一环构成。现在来看，应该是 Brevis 帮助补全了 BNBChain 生态，看了眼他们生态集成的情况，基本包圆了。其中包括 PancakeSwap、THENA、Opinion 等等，BNBChain 的 DeFi 和 dAPP 家族其他方面，还有 Uniswap 的集成等等。近期不少人都在期待他们的 TGE，等待参与机会看看吧。类似的逻辑其实之前看到过不少例子，帮助币安补全生态版图的，项目大部分都有比较长的寿命，获得比较长期的支持，熊市能保证存活是第 ...

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - In industrial scenarios, humanoid robots are primarily suited for handling and quality inspection tasks, with a focus on ROI and the potential for expansion into more processes as their generalization capabilities improve [1][10][11] - The market demand for humanoid robots in China's industrial sectors, including automotive manufacturing, electronics manufacturing, and logistics warehousing, is projected to reach 484,000 units by 2035, with a market space exceeding 48 billion yuan [4][12] Summary by Sections 1. Core Insights - Humanoid robots are best suited for handling and quality inspection tasks, gradually expanding into basic assembly tasks as their capabilities develop [3][11] - The commercial viability hinges on achieving a return on investment (ROI) within two years, necessitating a reduction in robot prices to around 100,000 yuan and efficiency improvements to match human performance [3][11] 2. Industrial Manufacturing Flexibility - The demand for flexibility in manufacturing is increasing, with humanoid robots starting from short-chain tasks and gradually taking on more complex tasks [2][16] - Humanoid robots complement industrial robots, adapting to flexible production needs and enhancing operational efficiency [2][17] 3. Market Potential - The total demand for humanoid robots in the industrial sector is expected to reach 484,000 units by 2035, with a market potential of 48.36 billion yuan [4][12] - Collaboration between automotive companies and robotics firms is crucial for the deployment of humanoid robots in industrial settings, with companies like Tesla and XPeng leading the way [4][12]

Let's talk about Generative AI's real ROI, whether the technology is really taking jobs, and how good data is the key to it all. We're joined today by Muhammad Alam, the head of product and engineering at SAP, and a member of the company's executive board in a conversation brought to you by SAP. Muhammad, welcome.Great to see you again. >> Thank you, Alex. Thanks for having me.>> So, let's start. Why don't we go right to the heavy stuff. Um there's been so much conversation and speculation about whether gen ...

这是莫傑麟第六次做客我们的播客。我们几乎每隔几个月，就会一起复盘一次 AI 市场的最新动向。这一次，我们聊的是近期热度最高的话题之一： AI 泡沫。泡沫 只是情绪化的表象，更值得探讨的，是泡沫之下结构性的变化。 本期播客原文约 17000 字，本文经过删减整理后约 6600 字。 曲凯： 我问个最直接的问题，你觉得现在的 AI 有泡沫吗？ 莫傑麟： 有。 曲凯： 这么直接？你不分类讨论一下吗（笑）？ 莫傑麟： 哈哈哈如果把泡沫理解成「预期高于现实」，那肯定是有的。 但有泡沫不一定是坏事，反而能推动行业发展。泡沫也不一定随时会破。 曲凯： 但我觉得需要拆开看，把价值和价格分开来说。 从价值来看，我不觉得现在的 AI 有问题。 最近关于泡沫的讨论，大多发生在小红书和 Twitter，但我们身边真正做 AI 的人，几乎没人谈这事。我还特意问了几个人，结果大家都非常鄙视我，说：「AI 哪 有什么泡沫？发展得挺好，我们都很兴奋。现在的智能水平也已经够高了。」 所以从价值层面看，没什么泡沫。 但从价格层面看，就得进一步细分，拆成中国、美国、一级、二级来看。 价格体现在一级市场是估值，二级市场是市值。 今年国内一级市场整体 ...

ROI of AI - MIT研究表明，95%的企业在实施AI时未获得投资回报率，而Wharton的研究则表明74%的企业获得了投资回报率，行业对AI的投资回报率存在争议 [2] - 行业普遍认为AI的未来是光明的，但目前仍处于实验阶段，以找到实际应用并扩大规模的方法 [3] - Wharton的研究显示，74%的企业声称从AI中获得投资回报率，但副总裁及以上级别的人比经理及以下级别的人更倾向于认为他们获得了投资回报率 [8] - 衡量投资回报率的方式多种多样，包括减少人员需求、创造竞争优势以加速收入增长，以及提高效率以降低毛利率 [11] - Wharton的研究显示，年收入超过20亿美元的大公司从AI中获得投资回报率的比例为57%，低于较小公司 [25] Compute Cost and Infrastructure - Deep Seek的出现将推理模型的成本降低了约40倍 [14] - Grock致力于降低计算成本，其芯片在运行AI推理时，不仅性能领先市场，而且成本也具有差异化 [14][16] - 行业普遍认为，目前行业面临供应限制，需要部署更多容量以满足所有AI用例 [17] - 到2028年，美国四分之一的能源将用于运行AI工作负载 [19] AI Strategy and Implementation - 企业需要制定战略，明确在公司内部署AI的领域，并考虑如何衡量投资回报率 [22] - 企业应建立一个框架，围绕他们想要做什么、如何做、何时做，以及投资多少资金，并在后端进行衡量 [23] - 较小的公司更容易适应，能够更快地实施计划并在公司内部的多个不同职能部门中实施，从而获得更高的投资回报率 [26] - AI解决方案可以立即产生价值，但需要重新配置流程，而不是事后设计 [30] AI Bubble and Future Growth - 行业专家认为，对AI基础设施的投资不会后悔，但需要定义“泡沫”的含义，包括基础设施建设和公司估值 [32][33] - 行业专家预测，未来会出现一批万亿美元级别的公司，风险投资者正在选择他们的赌注 [34] - 行业专家认为，AI的某些领域可能存在泡沫，但不会影响整个行业，即使出现调整，也不会影响每家公司 [36] - Anthropic的CEO表示，该公司几年前的收入为10亿美元，预计到今年年底的运行率将接近100亿美元 [40]

以下文章来源于一见Auto ，作者郑植文 一见Auto . 汽车竞争中的野心、方法论与新秩序。21世纪经济报道旗下汽车报道品牌。 记者丨郑植文 编辑丨吴晓宇 当AI潮流涌现，很多车企不再满足于只做一家汽车公司的野心。 特斯拉CEO马斯克在三季度财报电话会上鲜少谈论汽车，而是大谈特谈AI、Robotaxi和人形 机器人，似乎All in AI才是特斯拉的终极目标。一些新势力也追随特斯拉的脚步，有的发布机 器人，有的发布智能眼镜，谁都不想错失AI机会。 但蔚来CEO李斌是个例外。 11月26日，在蔚来三季度财报发布次日，《21汽车·一见Auto》在上海漕河泾蔚来总部参加了 一场小型闭门沟通会。在会上，李斌谈了11次算账、8次卖车、7次ROI。 更深层次的改变是经营意识。过去，蔚来以"用户服务不计成本"著称，如今，则通过CBU （基本经营单元）机制，将每个业务单元都变成了独立的"小公司"。 李斌举例，现在，公司内部的研发立项从"几个亿的大项目"变成了"几万、几十万的小项目"； 一线销售团队不仅要看交付量，还要看"给公司挣了多少钱"；即使是品牌部门投资青藏高原 换电站，也要核算ROI。 广州车展前的彩排环节有同事告诉 ...

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Everything Mike Dolan and the ROI team are excited to read, watch and listen to over the weekend. From the Editor ...

Core Viewpoint - The report from Shenwan Hongyuan indicates that under a neutral assumption, the risks associated with AI soft constraints by 2026 are manageable, with a focus on whether the penetration rate of B-end enterprises can improve and whether cash flow remains stable. Companies in the US stock market with relatively stable expansion are worth monitoring. However, hard constraints, particularly regarding power supply for computing infrastructure, will become more pronounced, necessitating higher verification requirements for ROI [1][2]. Group 1: Market Trends and AI Investment - Since 2023, the current AI market in the US has been ongoing for three years, primarily focusing on valuation increases and widespread performance in AI infrastructure and applications [2]. - The AI-related industries (technology, communication) are expected to contribute limited valuation in 2025, with increased reliance on debt financing amid unclear ROI for AI investments [2]. - Concerns about an "AI bubble" stem from the gap between the time required for new technologies to generate economic scale effects and the optimistic expectations of capital market returns [3]. Group 2: Constraints and Adjustments - Hard constraints often lead to periodic adjustments in the AI sector, with the "Buy the Dip" strategy still showing effectiveness in the US stock market. Adjustments have been triggered by macro liquidity tightening and concerns over computing power, algorithms, and electricity [4]. - The report outlines that the AI sector has faced multiple adjustments since 2023, with each decline exceeding 10% and lasting over a month, influenced by factors such as liquidity tightening and supply shocks [4]. Group 3: ROI and Financial Metrics - The report emphasizes that the ROI for AI investments is sensitive to GPU depreciation, with rising debt financing costs posing tail risks. The total AI investment commitment projected by Trump for 2025 is approximately $3.8 trillion, aimed to be completed by 2028 [6][7]. - Current AI penetration in US enterprises is around 10%, with higher rates in information-intensive sectors, expected to rise to over 30% in the next six months [6]. - The report highlights that the profitability of AI applications varies, with B-end applications showing higher margins compared to C-end applications, which generally have negative margins [6]. Group 4: Debt and Liquidity Risks - The overall debt pressure on AI infrastructure is manageable, but significant differentiation among companies is expected by the second half of 2025. Long-term debt levels related to AI hardware have been gradually increasing, with a decline in the free cash flow to debt ratio [7][8]. - The report warns that in a tightening liquidity environment, the probability of debt risks increases, particularly for companies with lower asset quality [8]. - The current static PE ratio for the US stock market is 28x, with historical data indicating a low success rate for holding stocks at this valuation over three years [10].