广告
加载中

联想能在AI时代实现“阶层跃迁”吗?

杨谭 2026-06-29 16:18
杨谭 2026/06/29 16:18

邦小白快读

EN
全文速览

本文核心围绕联想能否在AI时代成功转型展开,梳理了联想转型的背景、进展和现存问题,核心干货如下:

1. 联想转型进展获资本市场初步认可,今年上半年股价逆势上涨,5、6月股价翻倍,市值一度超越百度,成为行情低迷的科技市场中少数获得正回报的企业;2025/26财年联想营收利润创下历史新高,AI业务收入同比增长105%,占总营收33%,第四财季提升至38%,转型成果初步验证。

2. 联想从2017年启动All in AI转型,推出3S战略,将业务拆分为智能设备、智能基础设施、智能方案服务三大模块,分别承担终端入口、增长引擎、利润底座的功能,目前已将全球60%的研发资源投入AI模型和智能工厂领域。

3. 转型仍面临诸多核心问题:核心芯片依赖上游厂商,研发投入占比仅3%远低于同行,AI业务利润率极低,PC涨价压制终端需求,传统PC业务现金流可持续性存疑。

本文以联想AI转型案例为核心,为传统硬件品牌的战略转型提供了多维度的参考干货,核心内容如下:

1. 行业与消费趋势层面:当前全球传统PC已经进入存量竞争阶段,AI是传统硬件品牌重构增长逻辑、获得资本市场认可的核心方向,成功的AI转型预期能推动品牌估值重构,实现市值逆势增长。

2. 定价与竞争层面:上游核心元器件涨价周期中,品牌可通过终端涨价转移成本,维持短期利润,但需要警惕涨价对终端需求的压制,尤其是价格敏感的入门级市场需求下滑明显,会影响长期现金流稳定。

3. 产品研发与转型层面:传统品牌AI转型可依托现有成熟业务提供稳定现金流,拆分不同业务模块承担不同转型功能,但是必须警惕核心技术依赖上游的问题,需要匹配足够的研发投入,才能支撑转型目标,获得更高的商业价值。

本文分析联想AI转型的行业背景与现状,能给硬件类卖家提供关于趋势、机会、风险的多维度干货参考,核心内容如下:

1. 行业机会层面:AI大潮下传统硬件行业迎来估值重构与增长新机会,传统PC硬件企业通过AI转型可获得资本市场认可,实现逆势增长,卖家可抓住AI硬件的增长风口,调整产品布局。

2. 供应链应对层面:当前存储、CPU等核心元器件产能大量转向AI专用产品,消费级硬件核心元器件涨价明显,成本压力大幅提升,卖家可提前备货应对后续即将到来的第二轮涨价,同时要警惕存货积压风险。

3. 需求变化层面:PC涨价后入门级市场需求下滑明显,价格敏感用户普遍延后换机,卖家可调整产品结构,加大中高端AI相关产品的布局,规避入门级市场的下滑风险。

4. 风险提示:做AI硬件不能只停留在原有产品叠加AI功能的层面,核心技术缺失会导致同质化竞争,利润空间被持续压缩,需要提前布局差异化竞争能力。

本文对联想AI转型的全维度分析,能给硬件生产制造工厂带来产品需求、商业机会、转型方向的干货参考,核心内容如下:

1. 产品需求与商业机会层面:当前AI终端、AI服务器的市场需求快速增长,AI相关产品已经成为行业增长的核心方向,工厂可及时调整产能布局,匹配AI硬件的生产需求,获得更多来自头部品牌的合作机会。

2. 转型启示层面:联想当前将60%的研发资源投入智能工厂领域,说明智能工厂是AI时代硬件制造的核心发展方向,工厂推进数字化、智能化升级是必然趋势,提前升级可更好匹配头部品牌的转型需求,提升自身竞争力。

3. 风险提示层面:当前硬件行业核心元器件涨价波动大,终端需求因涨价出现明显下滑,工厂需要重点管控存货和应收账款风险,避免需求下滑导致的库存积压;同时组装型工厂要警惕核心技术缺失带来的利润压缩问题,可尝试提升技术能力,向价值链上游延伸,获取更高利润。

本文梳理AI时代传统科技企业转型的现状与痛点,能给各类科技服务商、AI服务商带来行业趋势、客户需求层面的干货,核心内容如下:

1. 行业发展趋势层面:当前AI已经进入落地层爆发阶段,大量传统硬件龙头企业都在推进AI转型,资本市场已经开始将转型企业从传统硬件估值切换为AI成长估值,AI落地层服务面临广阔的市场增长空间。

2. 客户核心痛点层面:传统硬件企业转型AI的普遍痛点包括:核心芯片等核心技术依赖上游,自身研发投入不足,转型后AI业务利润率极低,现金流承压,同时客户集中度高,议价能力弱,盈利持续性存疑。

3. 业务机会层面:服务商可针对传统企业转型的痛点,推出适配的技术服务、供应链服务、咨询服务等,帮助传统企业解决核心技术不足、成本管控难、利润率低的问题,在转型大潮中获取更多客户。另外,市场更认可具备全栈AI能力的玩家,服务商可往全栈能力方向布局,提升自身竞争力。

本文对联想AI转型的分析,能给科技平台、硬件交易平台等各类平台商带来招商、运营、风险管控层面的干货参考,核心内容如下:

1. 招商方向调整:AI转型带动AI终端、AI基础设施、AI服务的需求快速增长,平台可调整招商策略,加大AI相关品类商家的招商力度,匹配市场需求变化,抓住AI浪潮带来的增长机会。

2. 运营服务优化:核心元器件涨价周期下,品牌和商家普遍有提前备货的需求,平台可针对性推出供应链金融、库存管理等配套服务,帮助商家应对成本波动和备货需求,提升商家对平台的粘性。

3. 风险管控提示:平台运营中需要向商家提示相关行业风险,包括入门级PC市场需求下滑、库存和应收账款增速过快带来的积压风险、核心元器件产能波动带来的供应风险,帮助商家管控经营风险,提升平台整体稳定性。

4. 价值提升方向:AI转型背景下,具备核心AI能力的企业能获得更高的资本市场估值,平台可推出针对AI企业的专项扶持、分层运营,吸引高价值AI企业入驻,提升平台整体的商业价值。

本文围绕联想AI转型案例展开深度分析,呈现了当前传统科技企业AI转型的最新产业动向与核心问题,对产业研究者有较高的参考价值,核心内容如下:

1. 产业新动向:AI浪潮推动下,全球传统硬件龙头企业集体启动AI战略转型,资本市场对转型企业开启估值重构逻辑,从传统硬件的PE估值转向分部估值加AI成长溢价,老牌硬件企业集体走出独立上涨行情,目前多数头部企业的转型已经从概念验证进入大规模投入阶段。

2. 产业新问题:传统硬件企业AI转型普遍面临四大核心问题:一是核心技术依赖上游厂商,在价值链中处于偏低位置,AI业务利润率极低,差异化竞争能力不足;二是传统业务现金流支撑能力持续衰减,存量市场需求下滑,涨价只能短期维持利润,长期现金流可持续性弱;三是研发投入占比远低于同行,难以支撑AI转型的大规模持续投入;四是不同企业转型质量差距明显,转型更早的企业不一定获得更高认可,核心能力才是决定估值和商业价值的关键。

3. 商业模式研究启示:传统企业AI转型可采用老业务养新业务的模式,用成熟传统业务提供现金流,新业务承担增长功能,但必须掌握核心技术、匹配足够研发投入,才能构建可持续的增长模式。

返回默认

声明:快读内容全程由AI生成,请注意甄别信息。如您发现问题,请发送邮件至 run@ebrun.com 。

我是 品牌商 卖家 工厂 服务商 平台商 研究者 帮我再读一遍。

Quick Summary

This article centers on whether Lenovo can achieve a successful transformation in the AI era, sorting out the background, progress and existing challenges of Lenovo's transition. Key takeaways are as follows:

1. Lenovo's transformation progress has received initial recognition from capital markets. Its stock rallied against broader market weakness in the first half of this year, doubling between May and June, and at one point surpassed Baidu in market capitalization, making it one of the few tech companies delivering positive returns amid a market downturn. In FY2025/26, Lenovo hit record-high revenue and profit; its AI business revenue grew 105% year-over-year, accounting for 33% of total revenue, and the share rose to 38% in the fourth fiscal quarter, marking initial validation of its transformation results.

2. Lenovo launched its "All in AI" transformation in 2017 with a 3S strategy, splitting its business into three core segments: Smart Devices, Intelligent Infrastructure, and Solutions & Services, which serve as the terminal entry point, growth engine, and profit foundation respectively. Currently, 60% of the company's global R&D resources are allocated to AI model development and smart factory initiatives.

3. The transformation still faces core challenges: it relies heavily on upstream vendors for core chips, its R&D expenditure accounts for only 3% of revenue (far lower than industry peers), its AI business has extremely low profit margins, PC price hikes have suppressed terminal demand, and the long-term cash flow sustainability of its legacy PC business remains uncertain.

This article uses Lenovo's AI transformation as a case study to deliver multi-dimensional insights for strategic transformation of traditional hardware brands. Key takeaways are as follows:

1. Industry and consumer trends: The global traditional PC market has entered a phase of stock competition. AI is the core direction for traditional hardware brands to restructure their growth logic and win capital market recognition. A credible AI transformation outlook can drive valuation re-rating and deliver market-cap growth against broader downturns.

2. Pricing and competition: During a period of upstream core component price hikes, brands can pass through costs to end consumers via terminal price increases to maintain short-term profits. However, they must be wary of demand suppression from price hikes, especially the notable demand decline in the price-sensitive entry-level market, which can harm long-term cash flow stability.

3. Product R&D and transformation: Traditional brands can leverage stable cash flow from their existing mature businesses to fund AI transformation, and split different business segments to fulfill distinct strategic roles. However, they must address the risk of over-reliance on upstream core technologies and allocate sufficient R&D investment to meet transformation goals and unlock higher commercial value.

This article analyzes the industry background and current status of Lenovo's AI transformation, providing multi-dimensional insights on trends, opportunities and risks for hardware sellers. Key takeaways are as follows:

1. Industry opportunities: The AI boom has brought valuation re-rating and new growth opportunities to the traditional hardware industry. Traditional PC companies can gain capital market recognition and deliver outperformance via AI transformation. Sellers can capitalize on the growth tailwind of AI hardware by adjusting their product portfolio.

2. Supply chain response: Currently, the production capacity of core components such as storage and chips is shifting massively to AI-specific products, leading to sharp price hikes for consumer hardware core components and rising cost pressure. Sellers can stock up in advance to prepare for a coming second round of price increases, while remaining vigilant about the risk of inventory overhang.

3. Shifting demand: Following PC price hikes, entry-level market demand has declined notably, and price-sensitive users are generally delaying device upgrades. Sellers can adjust their product mix, increase layout of mid-to-high-end AI-related products, and mitigate the risk of decline in the entry-level market.

4. Risk warning: Building AI hardware cannot stop at just adding AI features to existing products. A lack of core technology will lead to homogenized competition and continued profit margin compression, so sellers should build differentiated competitiveness in advance.

This article provides a full-spectrum analysis of Lenovo's AI transformation, offering insights on product demand, business opportunities and transformation direction for hardware manufacturing factories. Key takeaways are as follows:

1. Product demand and business opportunities: Market demand for AI terminals and AI servers is growing rapidly, and AI-related products have become the core driver of industry growth. Factories can adjust their capacity allocation in a timely manner to meet production demand for AI hardware, and secure more cooperation opportunities with leading brands.

2. Transformation insights: Lenovo currently allocates 60% of its R&D resources to smart factory initiatives, which demonstrates that smart factories are the core development direction for hardware manufacturing in the AI era. Digital and intelligent upgrades are an inevitable trend for factories; early upgrades will allow them to better align with the transformation needs of leading brands and improve their own competitiveness.

3. Risk warning: Core component prices are highly volatile in the current hardware industry, and terminal demand has declined noticeably due to price hikes. Factories should prioritize managing inventory and accounts receivable risks to avoid inventory overhang caused by demand decline. Meanwhile, assembly-focused factories should be alert to profit compression caused by a lack of core technology, and can work to improve technical capabilities, move up the value chain, and capture higher margins.

This article sorts out the current status and pain points of transformation for traditional technology companies in the AI era, delivering insights on industry trends and customer demand for technology and AI service providers. Key takeaways are as follows:

1. Industry development trends: AI has now entered a phase of large-scale practical deployment, and a large number of leading traditional hardware companies are advancing AI transformation. Capital markets have already started to revalue transforming companies from traditional hardware multiples to AI growth valuations, creating broad market growth space for AI implementation services.

2. Core customer pain points: Common pain points for traditional hardware companies undergoing AI transformation include: reliance on upstream suppliers for core technologies such as core chips, insufficient internal R&D investment, extremely low profit margins for the AI business post-transformation, cash flow pressure, high customer concentration, weak bargaining power, and uncertain earnings sustainability.

3. Business opportunities: Service providers can develop tailored technology services, supply chain services, and consulting services to address the pain points of traditional enterprises' transformation, helping them solve problems such as insufficient core technology, difficult cost control, and low profit margins, and acquire more clients amid the transformation wave. In addition, the market favors players with full-stack AI capabilities, so service providers can build out full-stack capabilities to improve their competitiveness.

This article's analysis of Lenovo's AI transformation offers insights on recruitment, operations and risk management for all types of platforms including technology platforms and hardware trading platforms. Key takeaways are as follows:

1. Adjusting merchant recruitment direction: AI transformation has driven rapid growth in demand for AI terminals, AI infrastructure, and AI services. Platforms can adjust their recruitment strategies to increase outreach to merchants in AI-related categories, align with shifting market demand, and capture growth opportunities brought by the AI boom.

2. Optimizing operational services: Amid the core component price hike cycle, brands and merchants generally have demand for advance stocking. Platforms can launch supporting services such as supply chain finance and inventory management to help merchants cope with cost volatility and meet stocking needs, and improve merchant retention on the platform.

3. Risk management guidance: In platform operations, merchants should be notified of relevant industry risks, including slowing demand in the entry-level PC market, overhang risks from excessive inventory and accounts receivable growth, and supply risks from core component capacity volatility, to help merchants manage operational risks and improve overall platform stability.

4. Value enhancement direction: Against the backdrop of AI transformation, companies with core AI capabilities command higher capital market valuations. Platforms can launch targeted support and tiered operations for AI companies to attract high-value AI enterprises to join, and elevate the overall commercial value of the platform.

This article conducts an in-depth analysis of Lenovo's AI transformation case, presenting the latest industry trends and core issues of AI transformation for traditional technology companies, offering high reference value for industry researchers. Key takeaways are as follows:

1. New industry trends: Driven by the AI wave, leading traditional hardware companies around the world have collectively launched AI strategic transformation. Capital markets have adopted a new valuation framework for transforming companies, shifting from traditional hardware PE multiples to sum-of-the-parts valuation plus AI growth premia. Established hardware companies have collectively outperformed the broader market, and the transformation of most leading players has moved from concept validation to the large-scale investment phase.

2. New industry challenges: Traditional hardware companies generally face four core challenges in AI transformation: First, they rely on upstream vendors for core technologies, occupy lower positions in the value chain, have extremely low AI business profit margins, and lack differentiated competitiveness. Second, the cash flow support capacity of legacy businesses continues to decline, with slowing demand in the stock market; price hikes can only maintain profits in the short term, and long-term cash flow sustainability is weak. Third, R&D expenditure as a share of revenue is far lower than industry peers, making it difficult to support the sustained large-scale investment required for AI transformation. Fourth, transformation quality varies widely across companies; earlier transformation does not guarantee greater market recognition, and core capabilities are the key determinant of valuation and commercial value.

3. Insights for business model research: Traditional companies can adopt an "old business nurtures new business" model for AI transformation, using cash flow from mature legacy businesses to fund new growth-oriented AI businesses. However, building a sustainable growth model requires mastery of core technologies and sufficient R&D investment.

Disclaimer: The "Quick Summary" content is entirely generated by AI. Please exercise discretion when interpreting the information. For issues or corrections, please email run@ebrun.com .

I am a Brand Seller Factory Service Provider Marketplace Seller Researcher Read it again.

联想就像一个本分的庄稼汉,一直坚守在产业链分工给它规定的市场定位上,用勤勤恳恳的劳作赚着属于自己的辛苦钱。遇到好年景,也会有丰收年,但也不敢奢望总有这样的好时光。

文丨杨谭

【亿邦原创】今年上半年,资本市场上中概股及恒生科技指数整体承压,多数中概股表现低迷。而联想却走出了一条独立行情,市值在6月初一度超越百度,成为少数实现正回报的大型科技企业。

市场表现是出于对联想“All in AI”战略的初步评估。自2017年起,联想便押注身家性命谋求AI转型,目标是完成从“PC集成商”到“AI服务商”的根本性重构。

联想,曾作为中国科技企业变革的标杆之一,被视为华为模式的另一面,在商业教父柳传志的带领下,历经艰辛成为全球最大的PC制造商。

而如今,这颗曾经耀眼的巨星再一次走到了变革的路口,面对的问题是:传统PC业务见顶、核心技术过度依赖上游、AI基础服务被各大巨头挤压。联想该何去何从?

20年前曾经有一句非常流行的广告语“人类失去联想,世界将会怎样?”,世事变迁,豪言壮语逐渐被人淡忘。今天,我们还有兴趣要问一句:联想还是原来的联想吗?

股价上涨背后,是价值重构

今年来,受地缘政治与全球资本流动影响,中国科技企业整体表现低迷。腾讯、小米、网易、百度等企业一度回撤超40%。

而半年来,联想股价却逆势上涨,仅5、6两个月就已经翻番,从11港元涨到25港元。特别是2026年6月9日当天,联想市值盘中超越百度,这一现象受到了整个资本市场的强烈关注。

这与英特尔、AMD、戴尔在资本市场上的表现较为相似。英特尔在年初以来翻了近3倍,AMD与戴尔也均有两倍以上的增长。这波“老登们”的上涨,是这一波AI狂潮的“鱼尾行情”,还是它们重新找回了二次曲线的增长逻辑?

中国企业资本联盟副理事长、中国区首席经济学家柏文喜对亿邦动力表示:联想逆势上涨的核心逻辑在于估值重构,市场正将其从"PC硬件公司"重新定价为"AI落地层公司"。

市场不再将联想单纯视为“PC周期股”,而是重新定价为“AI基础设施+AI终端+AI服务”的复合型科技平台。这种从传统硬件的PE估值逻辑向分部估值(SOTP)+AI成长溢价的逻辑转变,是市值上升的根本原因。

图片

这样的转变在财报上也有所体现。

据悉,2025/26财年,联想营收831亿美元(约5899亿元人民币),同比增长了20%,这是联想首次突破800亿美元大关;经调整后净利润20亿美元,同比增长42%,增速接近营收的两倍。营收与利润双双创下公司成立以来历史最高纪录。但利润率2.6%,赚的显然是靠规模和运营效率致胜的辛苦钱,对管控和节奏要求非常高。

被寄予厚望的AI业务收入也较为亮眼,同比增长105%,占总收入33%,第四财季更是进一步提升至38%,接近4成。

该财报发布当日,联想股价收涨19.77%,创上市以来新高,并且连续两个交易日总涨幅达38.33%。这被视为联想历史最优财报,也是联想AI战略转型的初步验证。

此前,上海交大建校130周年之际,杨元庆以个人名义捐赠了2亿元人民币,用于徐汇校区教学楼旧楼改造,改造后支持人工智能学科科研创新和人才培养。联想还承诺未来5年内再投入3亿元用于校企合作。杨元庆指掌联想帅印近30年,从30多岁的青年到60多岁的中年,作为一个职业CEO,实属不易。

以上种种,似乎预示着联想AI转型已经正式步入正轨,取得了阶段性成果。然而,这一切还只是开始,亮眼数据背后隐藏的远远不止这些。

从“卖电脑”到“卖AI”

事实上,联想的AI转型也是迫不得已,这条路走得并不顺利。

众所周知,联想起步于PC硬件组装,在PC领域深耕了40多年,长期以来,联想作为华为模式的另一种形式,在中国科技企业发展史上有着举足轻重的地位。

1984年,柳传志、王树和、张祖祥等11名已届中年的科技工作者聚在一起,在中科院计算所成立了一家新技术发展公司,这便是联想的前身。

1990年,联想自研首台“联想286”微机通过技术鉴定和国家“火炬计划”验收,打破了国外品牌垄断中国PC行业的局面,并于1994年成功登陆港交所。

就在联想上市同一年,国家调整进口关税,并取消部分限制批文。随后国际巨头IBM、康柏、戴尔等大举涌入中国市场。尤其是戴尔,凭借直销模式所向披靡,致使国内品牌遭受巨大冲击,长城0520等品牌烟消云散,联想也迎来史上最大危机。

关键时刻,柳传志将当年年仅29岁的杨元庆推到台前。在这位职业经理人的带领下,联想开始改制,同时成立微机事业部。策略上也选择两线作战:一边应对戴尔等品牌的正面冲击,一边秘密与国际巨头IBM进行并购谈判。这场内部变革极为艰难,到2003年底,联想才压过戴尔。此后,联想便突飞猛进,收购IBM PC改写全球格局,一跃成为全球销量最高的PC厂商之一。

2014年,联想开启第四个十年的战略布局,直接收购摩托罗拉移动和IBMX86服务器业务。致使联想以“中国PC之王”的身份,站上了世界舞台。尽管杨元庆正式接任联想CEO是2000年前后,但其实际主持时间似乎更早,这个有着近30年职业CEO身份的经理人,在中国也是极其罕见的。

然而,到2017年,全球PC市场进入残酷的存量竞争期,价格战不断压缩利润空间。联想虽然稳坐全球PC市场第一的宝座,但按台计价的商业模式,其天花板已经清晰可见。

迫于各方面压力,杨元庆在当年提出了“All in AI”的口号,表示赌上身家性命押注AI。于是,联想在中国区总裁刘军的带领下,正式启动了3S转型。

所谓3S,即智能设备(Smart Device)、智能基础设施(Smart Infrastructure)和智能方案服务(Smart Service)。这三大方向后来分别对应了联想的IDG、ISG和SSG三大业务集团。

在当时,这一战略转型并未得到资本市场的认可,能否转型成功也备受争议。因为彼时PC业务仍然是联想的绝对主业,AI还只是一个模糊的远景。

但事实上,拆解来看,其战略图景其实极其清晰:IDG定位于AI终端入口,ISG作为增长引擎,SSG满足未来利润底座,而传统PC业务在转型期内提供稳定现金流。

此后数年,联想的AI战略经历了多次迭代:从搭建云平台,到提出全栈AI战略布局,再到确立“混合式人工智能”战略。直到2025年,联想正式宣布进入“第五次再创业”周期,将全球60%的研发资源精准投入到AI模型构建与智能工厂两大领域。

图片

图:联想中国官方微博

如今来看,联想的这一选择尤为关键,这条不确定性的道路,为日后近十年的战略演进埋下了伏笔。

柏文喜对亿邦动力表示:“对于联想来说,这一转型具有战略必要性。如今,AI基础设施需求爆发,全球主要云厂商2026年资本支出合计约7250亿美元,较上年增长77%。”

PC业务涨价,AI战略现金流能否持续?

如果说AI转型是联想股价上涨的“主动叙事”,那么产品被动涨价则是理解其短期盈利与潜在风险的另一个关键维度。

2026年,联想宣布进行两轮大规模涨价。第一轮在3月,覆盖ThinkBook、拯救者、小新等主力笔记本系列。官方全系产品平均涨幅约15%,部分型号终端零售价涨幅最高超1000元。第二轮已确定于7月启动,涨幅与第一轮基本持平,联想已通过内部渠道建议经销商提前备货。

柏文喜表示:“联想涨价反映的核心困境是上游成本失控。原因在于全球DRAM(动态随机存取存储器)与NAND闪存价格持续上行,存储占笔记本BOM成本15%-20%,部分季度涨幅超50%;同时CPU、显卡、PCB等核心部件也因产能挤压和制程成本攀升而涨价,消费级CPU交付周期从1-2周拉长至8-12周。”

据了解,年初的关税波动引发渠道恐慌性备货,存储芯片等核心元器件进入极端的上行期。三星、SK海力士、美光将超过70%的先进产能转向AI专用高带宽内存(HBM),留给消费级PC的标准DRAM和NAND产能被极限压缩。致使全球通用DRAM合约价环比上涨90%至95%,PC DRAM单季度涨幅达110%至115%。

存储成本暴涨直接压缩的是PC厂商的毛利率,杨元庆也坦言:“在这一挑战下,比的是看谁能够拿到更多的供应和更好的成本"。联想的规模优势能否转化为成本优势,是其必须面对的考验。

图片

图:联想中国官方微博

事实上,联想选择涨价是将成本转移给消费端,从而来维持短期利润,是否可持续仍然值得怀疑。因而,这也导致了另一个问题的出现:长期来看,传统PC业务还能否给AI转型提供可持续的现金流?

从近期联想交出的历史最佳成绩单来看,营收830.75亿美元,其中经营现金流从上一财年的11亿美元跃升至40.24亿美元,同比增长约74%,涨了近三倍。

40亿美元的经营性现金流,18.59亿美元的资本性支出。表面看,自由现金流充裕,足以覆盖当前的资本开支与扩建。但是,背后的问题却不容忽视:

第一,毛利率在下降。营收上涨20%的同时,毛利率却从16.1%下降到15.4%。0.7个百分点的下滑,在831亿美元的整体营收下,意味着近6亿美元的毛利流失。同时,其收入增长有相当一部分来自低毛利业务的堆量,而非高价值业务的结构性改善。

第二,应收账款和存货增速远超营收。数据显示,其应收账款从103亿美元涨到141亿美元,存货从79亿美元涨到117亿美元,增速均为营收增速的两倍。虽然这不必然意味着立刻出现危机,但也揭示了一个风险,那就是业务扩张背后,有多少在消费端进行真实消化?又有多少积压在渠道和仓库?

第三,PC涨价压制终端需求。据集邦咨询预测,2026年全球笔记本电脑出货量同比下滑13%。其中最受伤的是500美元以下的入门级市场,价格最敏感的用户可能直接延后换机、买二手,或者干脆不买。

AI之路走好了吗?

尽管联想最新财报给出了初步验证,但其AI转型之路是否真的走好,一直还是一个问题。

从财务上看,ISG虽然实现扭亏为盈,但全年经营利润率仅约0.38%。尽管第四财季经营利润率提升至3.58%,但与国际AI服务器大厂相比仍有显著差距。

最直接的,就是来自戴尔的压力。

尽管戴尔转型更晚(于2018年确立AI战略支柱),但似乎更为成功,其早已不是单纯的PC制造商,基础设施业务(含AI服务器)营收已超过PC业务,占比高达53.6%。AI服务器成为其核心增长引擎,全年出货额超250亿美元,订单积压高达430亿美元。戴尔构建了从算力硬件到企业级服务的全栈AI能力,被资本市场视为高利润的“AI算力科技股”。

反观联想,联想70%的营收仍来自消费级PC,其PC业务毛利率仅7%-9%,净利率常年不足3%,是典型的“薄利多销”模式。虽然联想早在2017年就提出AI战略,但在资本市场看来,其AI布局更多是在PC和服务器上叠加AI功能,核心芯片依赖外部采购,更像一个集成商而非核心技术玩家。因此,市场长期给予其10倍左右的低市盈率,与戴尔的23倍形成鲜明对比。

“戴尔对联想PC业务的冲击,其核心并非体现在销量上,而是一场发生在资本市场的降维打击上。尽管联想在PC销量上遥遥领先,但戴尔凭借在AI时代的高价值转型,在市值和商业价值层面形成了巨大反差。”有业内人士这样表示。

关于联想的AI基础设施服务业务潜在的问题,柏文喜也认为:“一是利润率极薄,3.6%的利润率在服务器行业处于低位,一旦AI需求增速放缓或竞争加剧,极易重回亏损;二是客户集中度高,CSP(云服务商)业务占比高,议价能力强,压缩利润空间;三是重组效果待验证,前三财季ISG仍分别亏损6亿元、2亿元,盈利改善主要发生在第四财季,持续性存疑。”

事实上,联想的AI转型,正处于从“概念验证”到“大规模化投入”的关键阶段。但是,其在研发投入方面却极为有限。

数据显示,联想全年研发费用同比增长9%,但占营收比重仅约3%。相比华为(约20%)、腾讯(约10%)等科技巨头,这一比例明显偏低。一家宣称要“全面转型为AI原生公司”的企业,3%的研发投入能否可以支撑背后的野心?这是市场反复追问的问题。

另外,放在全球来看,相比于英特尔和AMD,联想在AI核心技术方面更是相差甚远。联想更像是系统集成商,而英特尔与AMD是核心芯片与算力提供商,联想与后两者处于AI价值链完全不同的位置。

尽管英伟达在GPU市场上断层式领先,但CPU作为计算核心,英特尔正大力推动CPU重回AI计算中心。比如,其近期推出的至强6+处理器,采用Intel 18A制程,专为AI推理和智能体任务优化。反映在资本市场上,投资者对英特尔也有较大的认可,其股价年内也是翻了两倍。

AMD则更为智慧,其策略是提供强大的CPU和GPU组合,推出整合了CPU与GPU的Helios机架级AI平台,其服务器市场份额已达46.2%。

这是联想最根本的弱点。其AI服务器和PC的核心算力高度依赖英伟达、AMD、英特尔等外部芯片厂商。这使得联想在产品差异化和成本控制上缺乏主动权,角色更偏向“组装厂”,面临极强的同质化竞争风险。

图片

因而,我们总结下来发现,尽管联想的AI转型取得了初步成效,但这种可持续性仍然较为脆弱。

第一,PC业务涨价不是护城河。存储芯片涨价周期终将结束,届时PC价格能否维持高位?如果不能,靠涨价撑起来的现金流将随之回落。

第二,传统PC业务的“现金牛”属性正在衰减。全球PC市场已回归存量竞争,AI PC换机需求能延缓萎缩,但无法根本改变方向。集邦咨询预测2026年全球笔记本出货量下滑13%,如果这一预测成真,联想的PC收入将面临实质性压力。

第三,AI战略的投入正在加速。ISG业务虽然盈利,但利润率极低。AI研发需要持续加码,新产线建设需要大额资本开支。如果传统PC业务的现金流贡献增速放缓,而AI业务的“造血”能力尚未充分释放,联想可能面临阶段性的现金流缺口。

第四,也是最深层的问题:联想PC业务的核心价值——芯片——不在自己手里。高通、英特尔、AMD掌握着AI PC的算力命脉,联想是组装者、是渠道、是品牌,但不是AI能力的定义者。这种价值链位置,决定了其可持续空间始终受制于上游。

回到最开始的那个核心问题:联想的AI路走好了吗?答案或许是:有阶段性进展,但走得很艰难。

作为一颗中国企业史上的科技明星,联想经历过无数黑暗,回过头看,似乎都没有当下感受深刻。有野心者比比皆是,而最终登顶者寥寥无几。曾经的革命者终究站上被革命的路,这是终局还是命运使然?值得所有人反思。

亿邦持续追踪报道该情报,如想了解更多与本文相关信息,请扫码关注作者微信。

文章来源:亿邦动力

广告
微信
朋友圈

这么好看,分享一下?

朋友圈 分享

APP内打开

+1
+1
微信好友 朋友圈 新浪微博 QQ空间
关闭
收藏成功
发送
/140 0