但在构建情境之前，它必须先 存在 。这意味着需要可靠、实时地访问开放网络——不仅仅是一次性的数据抓取或数据集， 而是能够反映 当前 情况的强大管道。 人工智能社区痴迷于越来越大的模型、十亿令牌上下文窗口和GPU 的微调运行让人感到厌倦，而人 工智能堆栈中最被忽视 的力量倍增器却静静地位于这一切之下的一层： 数据 。 各位，这就是 基础设施 。如果说计算让 NVIDIA 变得不可或缺，那么我认为下一个重大突破不是更多层，而是更多信号 而 不是噪声 。而这始于将数据采集视为生产基础设施。 "好数据"是什么样的？ 如果你正在构建一款 AI 原生产品，那么系统的智能程度将不再取决于你的提示有多巧妙，或者你能在上下文窗口中塞入多 少个标记。而是取决于你能多好地为它提供当下重要的上下文。 让我 们 明确一点：虽然扩展模型规模仍然很重要，但对于大多数现实世界的人工智能产品而言，性能提升越来越取决于数 据质量和新鲜度，而不仅仅是参数数量。将模型规模翻倍以榨取边际收益不仅成本高昂，而且在环境方面也难以为继，因为 惊人的电力和水成本根本无法扩展。 该瓶颈已从堆栈中移出。 构建 AI 原生产品的创始人和首席技术官 开始意识到，他们 ...

Core Insights - Unisys Corporation is strategically repositioning itself in the AI-driven IT services sector through its ClearPath Forward 2050 strategy, focusing on AI, automation, and data-driven intelligence across its offerings [1][11] - The company is addressing enterprise challenges such as data fragmentation and low productivity by leveraging AI technologies, exemplified by its Service Experience Accelerator [2][11] - Unisys is developing agentic-AI frameworks to enhance decision-making and service delivery, creating new revenue opportunities in endpoint management and AI-enabled infrastructure [3][11] AI Innovations - The Service Experience Accelerator utilizes generative AI and workflow automation to improve service desk operations, gaining traction among clients [2] - The intelligence accelerator connects siloed datasets, providing secure and real-time insights, which enhances service delivery [3] Industry Positioning - Unisys is recognized as a "disruptor" in the AI Services sector and is investing in post-quantum cryptography and threat detection solutions [4] - The company competes with major players like C3.ai, Palantir Technologies, and IBM, each with unique AI strategies [6] Financial Performance - Unisys shares have increased by 12% over the past three months, while the industry has grown by 20.1% [9] - The stock has risen by 22.4% in three months, with projected earnings per share (EPS) growth of over 120% for 2026 [11] - The current valuation shows a forward price-to-earnings ratio of 4.73X, indicating a discount compared to industry peers [12] Earnings Estimates - The Zacks Consensus Estimate predicts a year-over-year earnings increase of 28.9% for 2025 and 120.7% for 2026 [14] - Current estimates for EPS in 2025 and 2026 are 0.58 and 1.28, respectively, reflecting significant growth potential [16]

Core Viewpoint - IBM's stock has increased over 60% in the past year, driven by successful cloud and AI strategies, leading to consistent revenue and free cash flow growth, with expectations for continued acceleration in revenue growth [1][12]. Financial Performance - IBM's revenue grew by 3% in 2024 when adjusted for currency, with projections of at least 5% growth in 2025 despite economic uncertainties [1]. - The company anticipates generating approximately $13.5 billion in free cash flow by 2025, resulting in a price-to-free cash flow ratio of 19, which is considered reasonable given its AI success [10]. Analyst Insights - An analyst from Wedbush raised the price target for IBM stock from $300 to $325, indicating a potential 16% upside from the recent closing price, labeling IBM as an "AI winner" and expecting a continued growth renaissance [3][12]. AI Strategy - IBM's AI strategy focuses on solving real problems for enterprise customers rather than competing with other tech giants in AI infrastructure [4][9]. - The AI business comprises two main components: software, including the Watsonx platform for managing AI models, and consulting services, which are crucial for implementation and guidance [5][7]. AI Product Offerings - IBM's Watsonx platform allows enterprises to train, deploy, and manage AI models with advanced governance features, essential for companies with sensitive data [5]. - The Granite family of AI models, designed for cost efficiency and specific tasks, includes models that can run on consumer-grade hardware, making AI more accessible [6]. Market Positioning - IBM has secured over $6 billion in generative AI-related business, primarily through its consulting services, which help clients deploy AI workloads effectively [7]. - Watsonx is compatible with third-party clouds like AWS and Azure, allowing clients to utilize IBM's AI services regardless of their existing cloud commitments [8]. Future Growth Opportunities - Beyond AI, IBM is exploring long-term growth in quantum computing, with plans to develop a fault-tolerant quantum computer by 2029, which could unlock significant market opportunities [11].

Financial Performance & Growth Drivers - IBM's rally initially stemmed from its free cash flow performance, particularly after a significant beat five to six quarters prior to the report [3] - Revenue growth is expected to be in the low to mid single-digit percentages in the coming years [6] - Software growth is the primary focus for investors, followed by GenAI consulting and infrastructure [9] - IBM saved $500 million (three half billion) in run rate costs by deploying Gen AI [11] GenAI & Technology - IBM is positioning itself to enable the "Magnificent 7" through its hybrid cloud and GenAI infrastructure [4] - IBM's GenAI consulting business currently accounts for approximately 80% of its book of business [11] - IBM's Granite models are small, open-source language models focused on enterprise cost efficiency [11] - IBM has 75 quantum computers, with 13 currently running in production, and anticipates this becoming a significant part of their business by the end of the decade [16] Risks & Considerations - A shift in competitive positioning, especially concerning GenAI, could pose a risk to IBM's stock [18] - Sentiment around GenAI and hybrid cloud is driving multiple expansion [18]

Industry Overview - The AI industry is facing an uncertain macroeconomic environment, with signs indicating a potential cooling in demand for AI infrastructure [1] - Super Micro Computer has reduced its guidance due to delayed purchasing decisions from customers, and some tech giants are scaling back on data center expansions [1] Investment Focus - Investors are still interested in AI, with a recommendation to avoid AI hardware companies like Super Micro and Nvidia, as well as hyperscalers like Microsoft [2] - The focus should be on companies specializing in AI inference and efficient, specialized AI models, with IBM and Cloudflare highlighted as top picks [2] IBM's AI Strategy - IBM has secured $6 billion in generative AI-related business, with $1 billion added in the first quarter, primarily from its consulting business [3] - The Watsonx AI platform is central to IBM's AI efforts, enabling enterprises to build, test, deploy, and manage AI models, with potential cost reductions of up to 98.5% by using specialized models [4] - IBM's upcoming z17 mainframe, launching in June, can perform 450 billion AI inferencing operations daily, making it suitable for real-time tasks like credit card fraud detection [6] Cloudflare's AI Capabilities - Cloudflare focuses on speed, allowing developers to perform fast computations on user requests, covering various use cases including AI inference tasks [8] - The platform supports over 50 open-source AI models and integrates with Cloudflare Workers for programmatic access [9] - By focusing on AI inference with smaller models, Cloudflare can utilize older, less expensive AI accelerators, reducing costs while maintaining performance [10] - Cloudflare's revenue grew by 27% year over year in Q4 2024, with a record number of large customers spending over $1 million annually [11]