@AYi_AInotes: Whoa, 6 months → 15 days! Semir used AI to rip the pants off the entire apparel industry. Seriously guys, after seeing Semir’s AI deployment, I’m stunned. Just in 2025, AI brought Semir hundreds of millions in confirmed collections, saved tens of millions on visuals, marketing, sample R&D, and crushed the new product cycle from 6 months all the way down to…
Summary
This article reports how Semir Group deeply integrated AI into over 400 business scenarios including supply chain, marketing, and visual design, successfully reducing new product development cycles from 6 months to 15 days, achieving tens of millions in cost savings and hundreds of millions in collections, demonstrating AI's real value in traditional manufacturing.
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@FinanceYF5: Over the past 12 months, the GenAI economy has generated $110 billion in sales. It is growing rapidly. On an annualized basis, its revenue scale has exceeded $175 billion. These numbers were built by Azeem's team over several months. This is the first bottom-up, deduplicated measure of full-stack consumer and enterprise AI spending…
Over the past 12 months, the generative AI economy has generated $110 billion in sales, with annualized revenue exceeding $175 billion. This is the first bottom-up, deduplicated metric built by Azeem's team to measure full-stack consumer and enterprise AI spending.
@xiaochuan8688: ByteDance Quietly Shut Down 30% of Its AI Projects — Everything Outside Doubao Is Being Cut Back. Industry insider info: At ByteDance's internal AI strategy review meeting in April, the company axed 30% of its AI application projects, including "Maobox," "Xinghui," and parts of the overseas AI video tool Dreamina's product lines. On the surface…
At an internal AI strategy review meeting in April, ByteDance cut 30% of its AI application projects — including Maobox, Xinghui, and parts of Dreamina — as no product outside of Doubao met its target DAU goals. The company will now focus on Doubao, make a hardware bet, and scale back investment in standalone AI apps.
@lifesinger: 听闻字节全面收缩在 AI 应用层的投入，应用层聚焦到豆包，硬件层押注 PICO+ AI 硬件。 原因是：烧不起，以 2025 年的投入去烧 AI 应用，字节现金流撑不过 2027 年。 巧合的是，最近还听闻有几家 ARR 过亿美元的 AI…
听闻字节全面收缩在 AI 应用层的投入，应用层聚焦到豆包，硬件层押注 PICO+ AI 硬件。 原因是：烧不起，以 2025 年的投入去烧 AI 应用，字节现金流撑不过 2027 年。 巧合的是，最近还听闻有几家 ARR 过亿美元的 AI 应用公司，开始在默默裁员，公司现金流压力非常大，很难持续。 还有一个偶然看到的新闻是，百万粉丝博主 Dan Koe 的创业产品 Eden，因烧钱太快，决定大幅裁员，产品停止迭代。 这一切都在揭示一个规律： 用互联网的思维去做 AI 产品创业，死路一条。因为 AI 产品没有规模效应，追求 DAU 等互联网时代的规模指标，会是有钱的 AI 产品存活不下去的主因。 没钱的 AI 产品创业公司，因为没钱可烧，无法追求规模，反而有机会看见真相。然后逐步赚取到真正的利润，有机会一步一步长大。 尊重经营，尊重时间。每个 AI 产品的创业者，可能都得重新审视这八个字。
@Shenmeili1213: https://x.com/Shenmeili1213/status/2065051166984351877
This article uses the case of Wang's company using AI to produce short videos and successfully monetize them. It details the AI workflow in script generation, editing, private traffic acquisition, and customer maintenance. It points out that AI can greatly reduce content production costs, and ordinary people can enter the market through three paths: niche accounts, serving offline business owners, or collaborating with partners.
@ba_niu80557: Let's talk some hardcore practical knowledge while I have time this morning. What actually happens between signing a contract for an AI project and it finally running in production? I'll lay out the entire playbook. People in this field can copy it directly, and those not in it can still understand why 95% of enterprise AI pilots end up dead. First, let me say something counterintuitive to the point you might not believe...
This article discusses common reasons for the failure of enterprise AI projects from proof-of-concept to production deployment, highlighting key practices such as MLOps, early inspection of real data, and clear human-machine boundaries. It argues that project failures are often not due to model issues but due to neglect of the engineering implementation phase.