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48万辆交付撑不起利润:特斯拉的汽车业务 正在为AI“输血”

李玉鹏 2026-07-24 09:01
李玉鹏 2026/07/24 09:01

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本文核心分析了特斯拉2026年第二季度的财报情况,核心结论是特斯拉销量回暖但利润下滑,当前汽车业务正为AI相关新业务输血,核心干货信息如下:

1. 核心经营数据:二季度交付汽车48.01万辆,同比增长25%创同期纪录,总营收282.36亿美元同比增26%,但营业利润仅3.98亿美元同比降57%,自由现金流转为负10.92亿美元,是两年多来首次季度负自由现金流。

2. 利润下滑原因:一方面是产品结构调整,主推低价精简配置的Model 3和Model Y,停产高价高利润的Model S和Model X,叠加几乎零成本的监管积分收入大降67%,拉低了整体利润;另一方面是研发和资本开支大幅增长,全年计划资本开支超250亿美元,接近去年三倍,大部分投向AI、自动驾驶等新业务。

3. 现状与前景:特斯拉目前仍高度依赖汽车业务提供现金流,新业务短期难盈利,未来战略成败取决于新业务的商业化速度。

本文以特斯拉最新财报为案例,给布局多赛道的品牌提供了多方面参考干货,核心内容如下:

1. 定价与产品结构方面:特斯拉为拉动需求推出低价车型、缩减高价产品线,虽然实现了交付增长,但直接拉低了整体利润率和单车收入,可见品牌走量扩张时,必须权衡低价策略和产品结构调整对利润的影响,避免销量涨利润跌的情况。

2. 业务布局方面:特斯拉采用“成熟业务输血新业务”的战略,用依然能创造现金流的汽车业务支撑AI、自动驾驶、人形机器人等新赛道投入,这种模式值得向多领域扩张的品牌参考。

3. 新业务推进方面:特斯拉分级布局新业务,自动驾驶已经实现148万付费用户,北美订阅率超55%,实现了部分商业化,Robotaxi和机器人仍在产能建设阶段,这种分阶段推进的方式,能平衡投入和现金流压力,值得借鉴。

本文的特斯拉案例给跨界布局新业务的卖家提供了诸多风险提示和机会参考,核心干货如下:

1. 风险提示:低价走量换增长的模式会明显摊薄整体利润,特斯拉本次交付增长25%但营业利润下滑超一半,出现了销量与利润增长背离的情况,卖家在扩张规模时,一定要平衡规模增长和利润留存,避免只冲销量忽略盈利。

2. 机会方向:当前AI、自动驾驶、无人出行、人形机器人、新能源配套是科技汽车领域的核心投入方向,头部企业已经在布局相关产能,说明这些赛道存在明确的长期机会,卖家可以结合自身资源提前布局。

3. 资金布局提示:跨界布局新业务时,要注意控制资本开支速度,避免投资增速超过经营现金流增速导致现金流危机,同时要保留足够的现金储备作为缓冲,维持成熟业务的现金流能力,为新业务投入提供稳定支撑。

本文关于特斯拉产能布局的内容,给各类制造工厂提供了需求方向和转型启示,核心干货如下:

1. 产品生产端新需求:特斯拉正在大规模调整产能结构,拆除原有高端车型产线改造为人形机器人生产线,新建半导体工厂、自动驾驶整车工厂,还在扩产电池、锂精炼等新能源产能,说明下游头部客户的生产需求已经转向智能化新业务,工厂需要跟进调整产能对接新需求。

2. 新商业机会:AI、自动驾驶、机器人、新能源相关的上游零部件、代工生产都产生了新的需求,工厂可以依托自身制造能力,提前布局相关产能,对接头部企业的新业务需求,获取新的增长空间。

3. 转型启示:传统制造工厂推进自动化、智能化转型,可以借鉴特斯拉的思路,先将人形机器人等新技术应用在自身工厂内部,验证生产效率和成本优势后再逐步对外推广,有效降低转型研发风险。

本文分析特斯拉的战略转型,给面向汽车科技行业的服务商提供了行业趋势和业务机会参考,核心干货如下:

1. 行业发展新趋势:当前头部传统汽车企业已经在向横跨汽车制造、AI计算、机器人产业的综合科技企业转型,研发和资本开支的重心全面转向AI、自动驾驶、算力、芯片、人形机器人等新领域,行业整体智能化转型的速度在明显加快。

2. 当前客户核心痛点:头部企业布局新业务需要建设大规模的AI算力基础设施、全新的半导体产能和新车型产线,短期内产生了大量配套需求,同时大规模资本投入也带来了短期现金流压力,需要相关服务支持。

3. 业务发展机会:服务商可以围绕AI算力建设、半导体制造配套、新产线工程服务、机器人核心零部件配套、现金流管理等方向调整业务布局,匹配头部企业转型的新需求,挖掘新的客户增长空间。

本文关于特斯拉转型的分析,给面向汽车科技领域的平台商提供了风向参考和运营启示,核心干货如下:

1. 招商方向调整:当前传统汽车企业加速向AI、智能化方向转型,大量资本向AI、自动驾驶、机器人、新能源配套领域倾斜,平台商需要调整招商方向,重点关注相关赛道的成长型企业,提前布局相关赛道的资源储备,抓住行业转型带来的增长机会。

2. 风险规避提示:新赛道企业大多处在研发投入期,依赖成熟业务供血,短期无法实现盈利,平台商在引入相关企业时,需要重点评估其现金流健康度和现金储备规模,判断其能否支撑到商业化阶段,规避潜在风险。

3. 运营管理调整:平台可以针对布局新赛道的企业推出针对性服务,比如供应链对接、现金流管理、政策申报对接等服务,帮助企业平衡成熟业务和新业务的投入,匹配行业转型的个性化需求,提升平台粘性。

本文对特斯拉最新财报的深度分析,给产业研究者提供了汽车科技企业转型的最新研究素材,核心干货如下:

1. 产业新动向与新商业模式:当前传统汽车制造企业正在向综合科技企业转型,特斯拉探索出了“汽车业务供血AI新业务”的全新商业模式:依托汽车业务获取稳定现金流、用户规模和数据,将汽车业务产生的利润大规模投入自动驾驶、Robotaxi、芯片、人形机器人等新业务,期待新业务未来成为新的利润增长引擎。

2. 新模式存在的新问题:该模式会出现销量增长与利润下滑背离、资本开支增速超过经营现金流增速导致自由现金流转负的新特征,模式成功的核心取决于新业务的商业化速度,如果新业务商业化不及预期,汽车业务就需要长期承担输血压力,企业会长期面临利润率承压的问题。

3. 研究启示:这种新模式为传统制造企业转型科技企业提供了全新路径,其财务特征和发展逻辑都和传统模式有明显区别,值得研究者持续跟踪新业务的商业化进度,观察模式最终成败,总结可复制的经验。

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Quick Summary

This article analyzes Tesla's Q2 2024 financial results, with a core conclusion that Tesla has delivered a sales recovery but declining profits, with its core automotive business currently funding its new AI-related initiatives. Key takeaways are as follows:

1. Core operating data: Tesla delivered 480,100 vehicles in Q2, up 25% year-over-year to a new record for the period. Total revenue reached $28.236 billion, a 26% year-over-year increase. However, operating profit came in at just $398 million, down 57% year-over-year. Free cash flow turned negative at -$1.092 billion, marking Tesla's first negative quarterly free cash flow in more than two years.

2. Reasons for profit decline: On one hand, Tesla has adjusted its product mix: it now prioritizes the lower-priced, simplified Model 3 and Model Y, and has halted production of the high-margin, premium Model S and Model X. Combined with a 67% plunge in nearly zero-cost regulatory credit revenue, this has pulled down overall profitability. On the other hand, R&D and capital expenditure have grown sharply: full-year capital expenditure is projected to exceed $25 billion, nearly three times 2023 levels, with most of the funding allocated to new initiatives including AI and autonomous driving.

3. Current status and outlook: Tesla still relies heavily on its automotive business to generate cash flow, and new businesses are unlikely to turn a profit in the short term. The success of its long-term strategy hinges on the commercialization pace of its new initiatives.

Using Tesla's latest earnings report as a case study, this article offers actionable insights for brands expanding into multiple business lines. Key takeaways are as follows:

1. Pricing and product structure: Tesla launched lower-priced models and trimmed its premium product line to boost demand. While this delivered delivery growth, it directly reduced overall margins and revenue per vehicle. This illustrates that when brands pursue volume expansion, they must weigh the impact of low-price strategies and product line adjustments on profitability to avoid the "higher sales, lower profit" trap.

2. Business portfolio strategy: Tesla follows a "cash cow mature business funds new growth" model, using the steady cash flow from its still-profitable automotive business to support investments in new areas including AI, autonomous driving and humanoid robots. This approach is a valuable reference for brands expanding into new sectors.

3. Phased new business development: Tesla rolls out new initiatives in stages: Full Self-Driving (FSD) already has 1.48 million paid subscribers, with a penetration rate of over 55% in North America, achieving partial commercialization, while Robotaxi and humanoid robots remain in capacity build-out. This phased approach balances investment needs and cash flow pressure, making it a useful framework to adopt.

Tesla's case offers key risk warnings and opportunity insights for sellers expanding into new business areas. Key takeaways are as follows:

1. Risk warning: A volume-driven low-price growth strategy will significantly dilute overall profitability. Tesla delivered 25% delivery growth in Q2 but saw operating profit drop by more than half, creating a divergence between sales and profit growth. When expanding scale, sellers must balance volume growth and profit retention, and avoid chasing sales at the cost of profitability.

2. Opportunity outlook: AI, autonomous driving, autonomous mobility, humanoid robots and new energy supporting facilities are currently the core investment areas in the automotive tech sector, and leading players are already building out capacity in these spaces. This confirms clear long-term opportunities in these tracks, and sellers can prepare for entry by aligning early investments with their existing resources.

3. Capital allocation guidance: When expanding cross-sector into new businesses, companies need to control the pace of capital expenditure to avoid a cash flow crisis caused by investment outpacing operating cash flow growth. They should also maintain sufficient cash reserves as a buffer, and preserve the cash flow generating capacity of mature businesses to provide stable funding for new initiatives.

This article's analysis of Tesla's capacity layout offers demand guidance and transformation insights for manufacturing factories. Key takeaways are as follows:

1. New demand on the production side: Tesla is currently undergoing large-scale capacity restructuring: it is converting former premium vehicle production lines into humanoid robot lines, building new semiconductor and autonomous driving vehicle factories, and expanding capacity for batteries, lithium refining and other new energy production. This shows that downstream leading clients have shifted their production demand toward intelligent new businesses, and factories need to adjust their own capacity to match these new demands.

2. New business opportunities: New demand has emerged for upstream components and contract manufacturing for AI, autonomous driving, robotics, and new energy-related businesses. Factories can leverage their existing manufacturing expertise to build out relevant capacity early, match the new business demand of leading enterprises, and unlock new growth opportunities.

3. Transformation insights: Traditional manufacturing facilities pursuing automation and intelligent transformation can follow Tesla's approach: first deploy new technologies such as humanoid robots within their own factories to verify productivity and cost advantages before rolling them out to external customers, which effectively reduces R&D and transformation risk.

This article's analysis of Tesla's strategic transformation offers industry trend and business opportunity insights for service providers serving the automotive tech sector. Key takeaways are as follows:

1. New industry trends: Leading traditional automotive companies are now transforming into comprehensive technology enterprises spanning automotive manufacturing, AI computing and the robotics industry. The focus of R&D and capital expenditure has fully shifted to new areas including AI, autonomous driving, computing power, chips and humanoid robots, and the pace of industry-wide intelligent transformation is accelerating noticeably.

2. Core pain points of clients: When leading enterprises enter new businesses, they need to build large-scale AI computing infrastructure, all-new semiconductor capacity and new vehicle production lines, which creates massive near-term demand for supporting services. At the same time, large-scale capital investment creates short-term cash flow pressure that requires targeted service support.

3. New business opportunities: Service providers can adjust their business布局 around AI computing infrastructure construction, semiconductor manufacturing support, new production line engineering services, core robot component supply, and cash flow management to match the new demand from transforming leading enterprises, and unlock new customer growth opportunities.

This article's analysis of Tesla's transformation offers strategic direction and operational insights for platform operators serving the automotive tech sector. Key takeaways are as follows:

1. Adjusting merchant recruitment strategy: As traditional automotive companies accelerate their shift toward AI and intelligent technologies and allocate large amounts of capital to AI, autonomous driving, robotics and new energy supporting fields, platform operators need to adjust their recruitment focus to prioritize growth enterprises in these tracks, build up relevant resource reserves early, and capture growth opportunities brought by industry transformation.

2. Risk mitigation guidance: Most enterprises in new growth tracks are still in the R&D investment stage, rely on funding from mature businesses, and cannot turn a profit in the short term. When onboarding these enterprises, platforms need to prioritize evaluating their cash flow health and cash reserve size to confirm they can sustain operations until commercialization, and mitigate potential risks.

3. Adjusting operational management: Platforms can launch targeted services for enterprises expanding into new tracks, such as supply chain matching, cash flow management, and policy application support. These services help enterprises balance investment between mature and new businesses, match the personalized needs of industry transformation, and improve platform stickiness.

This in-depth analysis of Tesla's latest earnings report provides up-to-date research material for industry researchers studying the transformation of automotive technology enterprises. Key insights are as follows:

1. New industry trends and business models: Traditional automotive manufacturing companies are now transforming into comprehensive technology enterprises, and Tesla has pioneered a new "automotive business funds AI initiatives" business model: it leverages its automotive business to generate stable cash flow, user scale and driving data, then reinvest a large share of automotive profits into new businesses including autonomous driving, Robotaxi, chips and humanoid robots, with the expectation that these new initiatives will become the company's next profit growth engine.

2. New challenges of this model: This model creates new characteristics including a divergence between sales growth and profit decline, and negative free cash flow caused by capital expenditure outpacing operating cash flow growth. The success of the model hinges entirely on the commercialization pace of new businesses. If commercialization progresses slower than expected, the automotive business will have to continue funding new initiatives long-term, leaving the company facing sustained profit margin pressure.

3. Research implications: This new model offers a entirely new path for traditional manufacturing companies to transform into technology enterprises, and its financial characteristics and development logic are distinctly different from traditional models. It is worthwhile for researchers to continuously track the commercialization progress of Tesla's new businesses, observe the ultimate outcome of the model, and summarize replicable experience.

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 .

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特斯拉公布2026年第二季度财报,48万辆交付仍难托起利润,它正用汽车现金流押注Robotaxi、芯片和机器人。

销量重新增长,并没有让特斯拉的利润同步回到高点。

特斯拉最新公布的2026年第二季度财报显示,公司当季交付汽车48.01万辆,同比增长25%,创下历史同期最高纪录;总营收达到282.36亿美元,同比增长26%,过去12个月累计营收也首次突破1000亿美元。

单看交付和营收,这是一份略显回暖的成绩单,但利润和现金流给出了另一种答案:特斯拉当季营业利润只有3.98亿美元,同比下降57%,营业利润率由去年同期的4.1%降至1.4%;归属于普通股股东的净利润为11.14亿美元,同比下降5%。

销量增长25%、营收增长26%,营业利润却下降超过一半,这种背离意味着,特斯拉面临的问题已经不只是汽车卖得够不够多,而是每一辆车能够为公司留下多少利润,以及汽车业务产生的现金,需要承担多大规模的未来投资。

01 销量回来了,单车价值却没有同步增长

第二季度,特斯拉汽车业务收入为205.16亿美元,同比增长23%,增速略低于25%的交付增幅。按照汽车业务收入与交付量进行粗略测算,特斯拉平均每辆交付车辆对应的汽车收入约为4.27万美元,较去年同期下降约1.5%。

这一计算并不等同于严格意义上的整车平均售价,因为汽车业务收入还包含监管积分和汽车租赁等项目,但它仍然能够反映一个趋势:销量扩张没有带来同等幅度的单车收入增长。

特斯拉也在财报中将汽车平均售价下降列为影响收入和利润的负面因素。过去一年,为了恢复主要市场的需求,公司推出了价格更低、配置更精简的Model 3和Model Y版本,同时停产价格和利润相对较高的Model S、Model X。产品结构进一步向Model 3和Model Y集中,在提升交付规模的同时,也降低了高价车型对收入的贡献。路透社测算,特斯拉第二季度平均每辆车对应的汽车业务收入降至约4.27万美元。

另一项正在减弱的利润来源是汽车监管积分。第二季度,特斯拉监管积分收入由去年同期的4.39亿美元下降至1.46亿美元,降幅接近67%。监管积分几乎不需要对应的制造成本,过去一直是特斯拉汽车业务利润的重要补充,因此,其收入减少会直接影响汽车板块的利润质量。

不过,特斯拉的汽车制造效率并非全面恶化。扣除监管积分后,公司第二季度汽车业务毛利率约为16.3%,高于去年同期的约15%,说明零部件成本、关税以及制造效率改善仍在发挥作用。问题在于,这一毛利率明显低于今年第一季度的约19%,同时也未达到市场此前预期。

交付规模扩大带来的成本摊薄,被价格和产品结构变化抵消了一部分,汽车业务已经很难重现早期依靠提价、产能释放和监管积分共同推动利润快速增长的状态。

这也是特斯拉当前财务结构中最值得关注的变化。汽车业务依然具备盈利能力,但其角色正在从高利润增长引擎,逐步转向为公司提供收入、现金和用户规模的基础业务。

02 汽车业务省下来的钱,正在被AI投入迅速吸收

特斯拉第二季度实现毛利润47.51亿美元,同比增长23%,但营业利润只剩3.98亿美元,原因主要发生在毛利润以下。当季公司营业费用达到43.53亿美元,同比增长47%,几乎吞掉了全部毛利润。其中,研发费用由去年同期的15.89亿美元增加至23.71亿美元,同比增长49%;销售、管理及一般费用则由13.66亿美元增加至19.82亿美元。

特斯拉在财报中明确表示,营业费用上升主要与AI及其他研发项目、股权激励和管理费用增加有关。这意味着,汽车销量增长确实为公司带来了更多毛利润,但这些增量利润并没有转化为当期营业利润,而是被投入自动驾驶、Robotaxi、芯片、训练算力和人形机器人等项目。

这种投入已经不再局限于软件研发。2026年上半年,特斯拉位于得州的AI训练算力规模增加了一倍以上,Cortex 1和Cortex 2两套训练基础设施的规划能力分别超过90兆瓦和115兆瓦。公司还在奥斯汀推进半导体工厂建设,希望建立自有逻辑和存储芯片制造能力;在弗里蒙特工厂,原有Model S和Model X产线已经拆除,场地被用于建设Optimus机器人的第一代生产线。

与此同时,Cybercab已经开始在得州超级工厂生产,Tesla Semi的新工厂正在内华达州推进,电池、正极材料、锂精炼和储能工厂也处在扩产或爬坡阶段。换句话说,特斯拉正在同时投资整车、自动驾驶运营、算力、芯片、机器人、电池和能源基础设施,其资本开支结构已经越来越接近一家横跨汽车制造、AI计算和机器人产业的综合型科技公司。

这也解释了为什么特斯拉即使卖出了更多汽车,短期利润率仍然继续下降。汽车制造端的成本改善,并没有消失,而是很快被更大规模的研发和组织投入吸收。对特斯拉而言,这相当于用汽车业务当下能够创造的利润,提前支付下一阶段的技术和产能成本。

03 自由现金流转负,压力来自资本开支

相比利润率下降,第二季度更值得警惕的指标是自由现金流。

特斯拉当季经营活动现金流达到46.97亿美元,同比增长85%,说明汽车交付增长、库存下降以及其他业务扩张,仍然为公司带来了较强的现金回收能力。但与此同时,资本开支升至57.89亿美元,同比大增142%,较第一季度增加约33亿美元。资本开支超过经营现金流后,特斯拉自由现金流由去年同期的正1.46亿美元转为负10.92亿美元,这是公司两年多来首次出现季度自由现金流为负。

这说明,特斯拉当前的现金流压力并不是因为主营业务无法产生现金,而是投资速度已经超过了经营现金流增长速度。第二季度经营现金流同比增加约21.6亿美元,但资本开支同比增加约34亿美元,新增现金创造能力不足以覆盖新增投资。

这种情况在2026年可能还会延续。马斯克在财报电话会上表示,今年是特斯拉资本开支规模极大的一年。据路透社报道,特斯拉计划全年投入超过250亿美元,接近上一年的三倍,资金主要用于AI、自动驾驶、Robotaxi和机器人等项目。

从企业投资周期来看,资本开支高于经营现金流并不必然意味着经营风险失控。特斯拉第二季度末仍持有435.24亿美元现金、现金等价物和短期投资,短期内并不存在明显的流动性危机。即便连续多个季度出现自由现金流为负,公司现有现金储备也能够提供缓冲。真正需要讨论的不是特斯拉能否支付这些投资,而是这些投入何时能够转化为可以持续贡献收入和现金流的业务。Robotaxi和机器人何时盈利

尽管特斯拉不断强化自己作为AI公司的定位,但从收入结构来看,它目前仍然高度依赖汽车业务。

第二季度,汽车业务收入占公司总营收约73%,仍是最主要的收入和经营现金流来源。能源和服务业务保持增长,却尚未形成足以替代整车的利润规模;Robotaxi、AI芯片和Optimus机器人则仍处在研发、建设产能和扩大试运行范围的阶段,短期内更多表现为费用和资本开支。

在几项AI业务中,自动驾驶的商业化路径相对清晰。第二季度,特斯拉活跃FSD付费用户达到148万,同比增长56%,北美新车交付中的FSD订阅率超过55%。这意味着,特斯拉已经能够依托庞大的汽车保有量,将部分自动驾驶研发投入转化为软件收入。

随着Cybercab进入生产阶段,Robotaxi也开始从技术验证转向车辆制造和运营体系建设,但特斯拉尚未单独披露相关业务的收入、运营成本和盈利进度,其能否形成规模效应,仍取决于监管审批、车辆利用率、安全员及远程支持成本,以及市场扩张速度。

相比之下,Optimus机器人的商业化周期更加不确定。特斯拉正在建设专门生产线,并计划率先将机器人用于自身工厂,以验证生产效率和成本下降空间。不过,从工厂内部部署走向大规模对外销售,还需要解决产品可靠性、制造成本、应用场景和售后服务等问题。在这一阶段,机器人业务仍会持续消耗研发和资本投入,短期内很难承担改善公司现金流的任务。

因此,特斯拉目前的财务逻辑依然是由汽车业务提供规模、数据和现金,再投入Robotaxi、芯片及机器人,最终期待软件订阅、无人出行服务和机器人销售形成新的利润来源。

这套模式的关键,在于商业化速度能否赶上投资扩张速度。如果FSD订阅和Robotaxi运营能够较快提升收入,当前的高投入可能转化为更高利润率;如果Robotaxi和Optimus迟迟无法形成稳定现金流,利润率已经承压的汽车业务,就需要更长时间承担为AI战略输血的任务。

注:文/李玉鹏,文章来源:钛媒体(公众号ID:taimeiti),本文为作者独立观点,不代表亿邦动力立场。

文章来源:钛媒体

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FAQ回顾

特斯拉汽车业务利润下滑的原因是什么?

2026年第二季度特斯拉汽车交付量同比增长25%,但汽车平均售价下降、高利润的Model S和Model X停产、监管积分收入同比下降近67%,叠加汽车业务产生的现金流需大量投入AI、自动驾驶、人形机器人等研发及资本开支,因此利润出现明显下滑。

特斯拉的AI相关布局主要有哪些?

特斯拉当前AI布局涵盖自动驾驶、Robotaxi、自研芯片、AI训练算力、Optimus人形机器人等领域,2026年上半年得州AI训练算力规模翻倍,正在奥斯汀建半导体工厂、弗里蒙特建Optimus生产线,全年AI相关资本开支计划超250亿美元。

特斯拉FSD的商业化进展怎么样?

2026年第二季度特斯拉活跃FSD付费用户达148万,同比增长56%,北美新车交付中的FSD订阅率超过55%,已可依托庞大汽车保有量将部分自动驾驶研发投入转化为软件收入,商业化路径相对清晰。

特斯拉自由现金流转负的原因是什么?

2026年第二季度特斯拉经营现金流同比增长85%至46.97亿美元,但同期资本开支同比大增142%至57.89亿美元,投资速度超过经营现金流增长速度,导致自由现金流转负,为两年多来首次。

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