Dear Shareholders:  

尊敬的股东们：

Last year at this time, I shared my enthusiasm and optimism for Amazon’s future. Today, I have even more. The reasons are many, but start with the progress we’ve made in our financial results and customer experiences, and extend to our continued innovation and the remarkable opportunities in front of us.

去年此时，我分享了对亚马逊未来的热情与乐观。今天，我的信心更加坚定。原因有很多，首先是我们在财务业绩和客户体验方面取得的进展，同时也包括我们持续的创新以及摆在我们面前的巨大机遇。

In 2023, Amazon’s total revenue grew 12% year-over-year (“YoY”) from $514B to $575B. By segment, North America revenue increased 12% YoY from $316B to $353B, International revenue grew 11% YoY from $118Bto $131B, and AWS revenue increased 13% YoY from $80B to $91B.

2023 年，亚马逊总收入同比（“YoY”）增长 12%，从 5140 亿美元增至 5750 亿美元。按业务部门划分，北美收入同比增长 12%，从 3160 亿美元增至 3530 亿美元；国际收入同比增长 11%，从 1180 亿美元增至 1310 亿美元；AWS 收入同比增长 13%，从 800 亿美元增至 910 亿美元。

Further, Amazon’s operating income and Free Cash Flow (“FCF”) dramatically improved. Operating income in 2023 improved 201% YoY from $12.2B (an operating margin of 2.4%) to $36.9B (an operating margin of 6.4%). Trailing Twelve Month FCF adjusted for equipment finance leases improved from -$12.8B in 2022 to $35.5B (up $48.3B).

此外，亚马逊的营业收入和自由现金流（“FCF”）显著改善。2023 年营业收入同比增长 201%，从 122 亿美元（营业利润率 2.4%）增至 369 亿美元（营业利润率 6.4%）。经设备融资租赁调整后的过去十二个月自由现金流从 2022 年的-128 亿美元改善至 355 亿美元（增加 483 亿美元）。

While we’ve made meaningful progress on our financial measures, what we’re most pleased about is the continued customer experience improvements across our businesses.

尽管我们在财务指标上取得了显著进展，但最令我们满意的是各业务部门客户体验的持续改善。

In our Stores business, customers have enthusiastically responded to our relentless focus on selection, price, and convenience. We continue to have the broadest retail selection, with hundreds of millions of products available, tens of millions added last year alone, and several premium brands starting to list on Amazon (e.g. Coach, Victoria’s Secret, Pit Viper, Martha Stewart, Clinique, Lancôme, and Urban Decay).

在我们的商店业务中，客户对我们在选品、价格和便利性方面的不懈专注给予了热烈回应。我们继续拥有最广泛的零售选择，提供数亿种商品，仅去年就新增了数千万种，并且一些高端品牌开始入驻亚马逊（例如 Coach、Victoria’s Secret、Pit Viper、Martha Stewart、Clinique、Lancôme 和 Urban Decay）。

Being sharp on price is always important, but particularly in an uncertain economy, where customers are careful about how much they’re spending. As a result, in Q4 2023, we kicked off the holiday season with Prime Big Deal Days, an exclusive event for Prime members to provide an early start on holiday shopping. This was followed by our extended Black Friday and Cyber Monday holiday shopping event, open to all customers, that became our largest revenue event ever. For all of 2023, customers saved nearly $24B across millions of deals and coupons, almost 70% more than the prior year.

在定价上保持竞争力始终至关重要，尤其是在经济不确定的情况下，顾客对支出更加谨慎。因此，在 2023 年第四季度，我们通过“Prime 会员尊享促销日”活动拉开了假日季的序幕，为 Prime 会员提供了提前开始假日购物的机会。随后，我们又推出了面向所有顾客的延长版“黑色星期五”和“网络星期一”假日购物活动，这成为我们有史以来收入最高的促销活动。在整个 2023 年，顾客通过数百万项优惠和折扣券节省了近 240 亿美元，比上一年增加了近 70%。

We also continue to improve delivery speeds, breaking multiple company records. In 2023, Amazon delivered at the fastest speeds ever to Prime members, with more than 7 billion items arriving same or next day, including more than 4 billion in the U.S. and more than 2 billion in Europe. In the U.S., this result is the combination of two things. One is the benefit of regionalization, where we re-architected the network to store items closer to customers. The other is the expansion of same-day facilities, where in 2023, we increased the number of items delivered same day or overnight by nearly 70% YoY. As we get items to customers this fast, customers choose Amazon to fulfill their shopping needs more frequently, and we can see the results in various areas including how fast our everyday essentials business is growing (over 20% YoY in Q4 2023).

我们还在持续提高配送速度，打破了多项公司纪录。2023 年，亚马逊为 Prime 会员提供了有史以来最快的配送速度，超过 70 亿件商品实现了当日或次日送达，其中美国超过 40 亿件，欧洲超过 20 亿件。在美国，这一成果得益于两个因素：一是区域化的优势，我们重新设计了配送网络，将商品存放在更靠近客户的位置；二是当日配送设施的扩展，2023 年，我们实现了当日或次日送达商品数量同比增长近 70%。随着我们更快地将商品送达客户，客户更频繁地选择亚马逊满足购物需求，我们可以在多个领域看到这一成果，包括日常必需品业务的快速增长（2023 年第四季度同比增长超过 20%）。

Our regionalization efforts have also trimmed transportation distances, helping lower our cost to serve. In 2023, for the first time since 2018, we reduced our cost to serve on a per unit basis globally. In the U.S. alone, cost to serve was down by more than $0.45 per unit YoY. Decreasing cost to serve allows us both to invest in speed improvements and afford adding more selection at lower Average Selling Prices (“ASPs”). More selection at lower prices puts us in consideration for more purchases.

我们的区域化举措也缩短了运输距离，有助于降低服务成本。2023 年，我们在全球范围内实现了自 2018 年以来首次单位服务成本的下降。仅在美国，每单位服务成本同比下降超过 0.45 美元。服务成本的降低使我们既能投资于速度提升，也能负担以更低的平均售价（“ASP”）增加更多的选品。更多的低价选品使我们在消费者购买决策中更具竞争力。

As we look toward 2024 (and beyond), we’re not done lowering our cost to serve. We’ve challenged every closely held belief in our fulfillment network, and reevaluated every part of it, and found several areas where we believe we can lower costs even further while also delivering faster for customers. Our inbound fulfillment architecture and resulting inventory placement are areas of focus in 2024, and we have optimism there’s more upside for us.

当我们展望 2024 年（及以后）时，我们降低服务成本的工作尚未完成。我们对配送网络中每个根深蒂固的观念提出了质疑，并重新评估了每个环节，发现了一些领域，我们相信可以进一步降低成本，同时为客户提供更快的配送速度。2024 年，我们将重点关注入库配送架构及由此产生的库存布局，并乐观地认为我们还有更多提升空间。

Internationally, we like the trajectory of our established countries, and see meaningful progress in our emerging geographies (e.g. India, Brazil, Australia, Mexico, Middle East, Africa, etc.) as they continue to expand selection and features, and move toward profitability (in Q4 2023, Mexico became our latest international Stores locale to turn profitable). We have high conviction that these new geographies will continue to grow and be profitable in the long run.

在国际市场上，我们对已建立国家的发展轨迹感到满意，并看到新兴地区（例如印度、巴西、澳大利亚、墨西哥、中东、非洲等）取得了显著进展，这些地区持续扩大商品选择和功能，并逐步迈向盈利（2023 年第四季度，墨西哥成为我们最新实现盈利的国际商店地区）。我们坚信，这些新兴地区将继续长期增长并实现盈利。

Alongside our Stores business, Amazon’s Advertising progress remains strong, growing 24% YoY from $38B in 2022 to $47B in 2023, primarily driven by our sponsored ads. We’ve added Sponsored TV to this offering, a self-service solution for brands to create campaigns that can appear on up to 30+ streaming TV services, including Amazon Freevee and Twitch, and have no minimum spend. Recently, we’ve expanded our streaming TV advertising by introducing ads into Prime Video shows and movies, where brands can reach over 200 million monthly viewers in our most popular entertainment offerings, across hit movies and shows, award-winning Amazon MGM Originals, and live sports like Thursday Night Football. Streaming TV advertising is growing quickly and off to a strong start.

除了我们的商店业务之外，亚马逊的广告业务进展依然强劲，从 2022 年的 380 亿美元同比增长 24%至 2023 年的 470 亿美元，主要得益于我们的赞助广告。我们已将赞助电视广告纳入这一服务，这是一个自助式解决方案，品牌可创建广告活动，投放到包括 Amazon Freevee 和 Twitch 在内的 30 多个流媒体电视服务平台，且无最低消费要求。最近，我们进一步扩大了流媒体电视广告业务，将广告引入 Prime Video 的节目和电影中，品牌可通过热门电影和剧集、屡获殊荣的亚马逊米高梅原创内容以及《周四晚橄榄球》等体育直播赛事，触达每月超过 2 亿的观众。流媒体电视广告业务增长迅速，开局表现强劲。

Shifting to AWS, we started 2023 seeing substantial cost optimization, with most companies trying to save money in an uncertain economy. Much of this optimization was catalyzed by AWS helping customers use the cloud more efficiently and leverage more powerful, price-performant AWS capabilities like Graviton chips (our generalized CPU chips that provide ~40% better price-performance than other leading x86 processors), S3 Intelligent Tiering (a storage class that uses AI to detect objects accessed less frequently and store them in less expensive storage layers), and Savings Plans (which give customers lower prices in exchange for longer commitments). This work diminished short-term revenue, but was best for customers, much appreciated, and should bode well for customers and AWS longer-term. By the end of 2023, we saw cost optimization attenuating, new deals accelerating, customers renewing at larger commitments over longer time periods, and migrations growing again.

转向 AWS 后，我们在 2023 年初看到了显著的成本优化，大多数公司都试图在不确定的经济环境中节省开支。这种优化很大程度上得益于 AWS 帮助客户更高效地使用云服务，并利用更强大、更具性价比的 AWS 功能，例如 Graviton 芯片（我们通用的 CPU 芯片，与其他领先的 x86 处理器相比，性价比提高约 40%）、S3 智能分层（利用人工智能检测访问频率较低的对象，并将其存储在成本更低的存储层）以及节省计划（客户通过更长期的承诺获得更低的价格）。这些举措虽然短期内减少了收入，但对客户最有利，广受赞赏，并且长期来看对客户和 AWS 都有益处。到 2023 年底，我们看到成本优化逐渐减弱，新交易加速增长，客户以更大的承诺和更长的期限续约，迁移量也再次增加。

The past year was also a significant delivery year for AWS. We announced our next generation of generalized CPU chips (Graviton4), which provides up to 30% better compute performance and 75% more memory bandwidth than its already-leading predecessor (Graviton3). We also announced AWS Trainium2 chips, which will deliver up to four times faster machine learning training for generative AI applications and three times more memory capacity than Trainium1. We continued expanding our AWS infrastructure footprint, now offering 105 Availability Zones within 33 geographic Regions globally, with six new Regions coming (Malaysia, Mexico, New Zealand, the Kingdom of Saudi Arabia, Thailand, and a second German region in Berlin). In Generative AI (“GenAI”), we added dozens of features to Amazon SageMaker to make it easier for developers to build new Foundation Models (“FMs”). We invented and delivered a new service (Amazon Bedrock) that lets companies leverage existing FMs to build GenAI applications. And, we launched the most capable coding assistant around in Amazon Q. Customers are excited about these capabilities, and we’re seeing significant traction in our GenAI offerings. (More on how we’re approaching GenAI and why we believe we’ll be successful later in the letter.)

过去一年对 AWS 来说也是重要的交付之年。我们发布了新一代通用 CPU 芯片（Graviton4），与已处于领先地位的上一代产品（Graviton3）相比，其计算性能提升高达 30%，内存带宽增加 75%。我们还发布了 AWS Trainium2 芯片，与 Trainium1 相比，它能为生成式 AI 应用提供快达四倍的机器学习训练速度和三倍的内存容量。我们持续扩展 AWS 基础设施布局，目前在全球 33 个地理区域内提供 105 个可用区，并计划新增 6 个区域（马来西亚、墨西哥、新西兰、沙特阿拉伯王国、泰国以及德国柏林的第二个区域）。在生成式 AI（“GenAI”）方面，我们为 Amazon SageMaker 增加了数十项功能，使开发人员更容易构建新的基础模型（“FMs”）。我们还创新并推出了一项新服务（Amazon Bedrock），使企业能够利用现有的基础模型构建生成式 AI 应用。此外，我们推出了功能最强大的代码助手 Amazon Q。客户对这些功能感到兴奋，我们的生成式 AI 产品也获得了显著的市场关注。 （关于我们如何应对生成式人工智能以及为何我们相信自己会成功，稍后将在信中详细说明。）

We’re also making progress on many of our newer business investments that have the potential to be important to customers and Amazon long-term. Touching on two of them:

我们在许多较新的业务投资方面也取得了进展，这些投资有潜力对客户和亚马逊的长期发展产生重要影响。以下是其中两个方面：

We have increasing conviction that Prime Video can be a large and profitable business on its own. This confidence is buoyed by the continued development of compelling, exclusive content (e.g. Thursday Night Football, Lord of the Rings, Reacher, The Boys, Citadel, Road House, etc.), Prime Video customers’ engagement with this content, growth in our marketplace programs (through our third-party Channels program, as well as the broad selection of shows and movies customers rent or buy), and the addition of advertising in Prime Video.

我们越来越确信，Prime Video 本身可以成为一项规模庞大且盈利的业务。这种信心得益于持续开发引人入胜的独家内容（例如《周四橄榄球之夜》、《指环王》、《神探杰克》、《黑袍纠察队》、《堡垒》、《路边酒吧》等），Prime Video 用户对这些内容的参与度，我们市场项目的增长（通过第三方频道计划，以及客户租赁或购买的丰富节目和电影选择），以及 Prime Video 中广告业务的增加。

In October, we hit a major milestone in our journey to commercialize Project Kuiper when we launched two end-to-end prototype satellites into space, and successfully validated all key systems and sub-systems—rare in an initial launch like this. Kuiper is our low Earth orbit satellite initiative that aims to provide broadband connectivity to the 400-500 million households who don’t have it today (as well as governments and enterprises seeking better connectivity and performance in more remote areas), and is a very large revenue opportunity for Amazon. We’re on track to launch our first production satellites in 2024. We’ve still got a long way to go, but are encouraged by our progress.

10 月，我们在实现 Kuiper 项目商业化的道路上达到了一个重要里程碑，成功将两颗端到端原型卫星送入太空，并顺利验证了所有关键系统和子系统，这在首次发射中是非常罕见的。Kuiper 是我们的低地球轨道卫星计划，旨在为目前尚未联网的 4 至 5 亿家庭（以及寻求在偏远地区获得更好连接和性能的政府和企业）提供宽带连接，这对亚马逊来说是一个巨大的收入机会。我们正按计划在 2024 年发射首批量产卫星。尽管前方仍有很长的路要走，但我们对目前取得的进展感到鼓舞。

Overall, 2023 was a strong year, and I’m grateful to our collective teams who delivered on behalf of customers. These results represent a lot of invention, collaboration, discipline, execution, and reimagination across Amazon. Yet, I think every one of us at Amazon believes that we have a long way to go, in every one of our businesses, before we exhaust how we can make customers’ lives better and easier, and there is considerable upside in each of the businesses in which we’re investing.

总体而言，2023 年是表现强劲的一年，我感谢我们所有团队为客户所做的贡献。这些成果体现了亚马逊内部大量的创新、协作、自律、执行力和再创造。然而，我相信亚马逊的每个人都认为，在我们所有业务领域中，要彻底实现让客户生活更美好、更便捷的目标，我们还有很长的路要走，而我们所投资的每项业务都具有巨大的增长潜力。

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In my annual letter over the last three years, I’ve tried to give shareholders more insight into how we’re thinking about the company, the businesses we’re pursuing, our future opportunities, and what makes us tick. We operate in a diverse number of market segments, but what ties Amazon together is our joint mission to make customers’ lives better and easier every day. This is true across every customer segment we serve (consumers, sellers, brands, developers, enterprises, and creators). At our best, we’re not just customer obsessed, but also inventive, thinking several years out, learning like crazy, scrappy, delivering quickly, and operating like the world’s biggest start-up.

在过去三年的年度信函中，我一直努力让股东更深入地了解我们对公司的思考、我们正在追求的业务、未来的机遇以及驱动我们前进的动力。我们在多个不同的市场领域运营，但将亚马逊凝聚在一起的是我们共同的使命，即每天让客户的生活变得更美好、更轻松。这一点适用于我们服务的每个客户群体（消费者、卖家、品牌、开发者、企业和创作者）。在最佳状态下，我们不仅专注于客户，还富有创造力，着眼于未来数年，疯狂地学习，充满拼搏精神，快速交付，并像全球最大的初创公司一样运营。

We spend enormous energy thinking about how to empower builders, inside and outside of our company. We characterize builders as people who like to invent. They like to dissect a customer experience, assess what’s wrong with it, and reinvent it. Builders tend not to be satisfied until the customer experience is perfect. This doesn’t hinder them from delivering improvements along the way, but it drives them to keep tinkering and iterating continually. While unafraid to invent from scratch, they have no hesitation about using high-quality, scalable, cost-effective components from others. What matters to builders is having the right tools to keep rapidly improving customer experiences.

我们投入大量精力思考如何赋能公司内外的建设者。我们将建设者定义为喜欢创新的人。他们喜欢剖析客户体验，评估其中的问题，并重新设计。建设者往往在客户体验达到完美之前不会满足。这并不会妨碍他们在过程中持续提供改进，但会驱使他们不断地调整和迭代。虽然他们不惧怕从零开始创新，但也毫不犹豫地使用来自他人的高质量、可扩展且经济高效的组件。对建设者而言，重要的是拥有合适的工具，以持续快速地改善客户体验。

The best way we know how to do this is by building primitive services. Think of them as discrete, foundational building blocks that builders can weave together in whatever combination they desire. Here’s how we described primitives in our 2003 AWS Vision document:

我们所知的最佳方法是构建原始服务。可以将它们视为独立的、基础的构建模块，开发人员可以根据自己的需求任意组合。这是我们在 2003 年 AWS 愿景文档中对原始服务的描述：

“Primitives are the raw parts or the most foundational-level building blocks for software developers. They’re indivisible (if they can be functionally split into two they must) and they do one thing really well. They’re meant to be used together rather than as solutions in and of themselves. And, we’ll build them for maximum developer flexibility. We won’t put a bunch of constraints on primitives to guard against developers hurting themselves. Rather, we’ll optimize for developer freedom and innovation.”

Of course, this concept of primitives can be applied to more than software development, but they’re especially relevant in technology. And, over the last 20 years, primitives have been at the heart of how we’ve innovated quickly.

One of the many advantages to thinking in primitives is speed. Let me give you two counter examples that illustrate this point. First, we built a successful owned-inventory retail business in the early years at Amazon where we bought all our products from publishers, manufacturers, and distributors, stored them in our warehouses, and shipped them ourselves. Over time, we realized we could add broader selection and lower prices by allowing third-party sellers to list their offerings next to our own on our highly trafficked search and product detail pages. We’d built several core retail services (e.g. payments, search, ordering, browse, item management) that made trying different marketplace concepts simpler than if we didn’t have those components. A good set of primitives? Not really.

以基础元素的方式思考的众多优势之一是速度。我举两个反例来说明这一点。首先，在亚马逊的早期阶段，我们建立了一个成功的自营库存零售业务，我们从出版商、制造商和分销商那里购买所有产品，将其存储在自己的仓库中，并自行发货。随着时间推移，我们意识到，通过允许第三方卖家在我们流量巨大的搜索和产品详情页面上与我们自己的商品并列展示，我们可以提供更广泛的选择和更低的价格。我们已经构建了几个核心零售服务（例如支付、搜索、订购、浏览、商品管理），这些服务使我们尝试不同的市场模式变得比没有这些组件时更简单。但这算是一套好的基础元素吗？并不是。

It turns out that these core components were too jumbled together and not partitioned right. We learned this the hard way when we partnered with companies like Target in our Merchant.com business in the early 2000s. The concept was that target.com would use Amazon’s ecommerce components as the backbone of its website, and then customize however they wished. To enable this arrangement, we had to deliver those components as separable capabilities through application programming interfaces (“APIs”). This decoupling was far more difficult than anticipated because we’d built so many dependencies between these services as Amazon grew so quickly the first few years.

事实证明，这些核心组件过于混杂，没有正确地进行分割。我们是在 2000 年代初与 Target 等公司合作开展 Merchant.com 业务时，才艰难地意识到这一点。当时的设想是，target.com 将使用亚马逊的电子商务组件作为其网站的基础架构，然后根据自身需求进行定制。为了实现这一安排，我们必须通过应用程序接口（API）将这些组件作为可分离的功能提供出去。然而，这种解耦的难度远超预期，因为在亚马逊最初几年快速增长过程中，我们在这些服务之间建立了太多的依赖关系。

This coupling was further highlighted by a heavyweight mechanism we used to operate called “NPI.” Any new initiative requiring work from multiple internal teams had to be reviewed by this NPI cabal where each team would communicate how many people-weeks their work would take. This bottleneck constrained what we accomplished, frustrated the heck out of us, and inspired us to eradicate it by refactoring these ecommerce components into true primitive services with well-documented, stable APIs that enabled our builders to use each other’s services without any coordination tax.

这种耦合关系在我们过去使用的一种名为“NPI”的繁重机制中尤为突出。任何需要多个内部团队协作的新项目，都必须经过这个 NPI 小组的审查，每个团队都要说明他们的工作需要多少人周。这种瓶颈限制了我们的成就，让我们极为沮丧，并促使我们通过将这些电子商务组件重构为真正的基础服务，配备文档完善、稳定的 API，使开发人员能够在无需额外协调成本的情况下使用彼此的服务，从而彻底消除这种瓶颈。

In the middle of the Target and NPI challenges, we were contemplating building a new set of infrastructure technology services that would allow both Amazon to move more quickly and external developers to build anything they imagined. This set of services became known as AWS, and the above experiences convinced us that we should build a set of primitive services that could be composed together how anybody saw fit. At that time, most technology offerings were very feature-rich, and tried to solve multiple jobs simultaneously. As a result, they often didn’t do any one job that well.

在应对 Target 和 NPI 挑战的过程中，我们曾考虑构建一套新的基础设施技术服务，以便亚马逊能够更快地行动，同时也让外部开发者能够构建他们所设想的任何东西。这套服务后来被称为 AWS，上述经验使我们确信，我们应该构建一系列基础服务，让任何人都可以根据自己的需求自由组合。当时，大多数技术产品都功能繁多，试图同时解决多个问题，因此往往无法很好地完成任何单一任务。

Our AWS primitive services were designed from the start to be different. They offered important, highly flexible, but focused functionality. For instance, our first major primitive was Amazon Simple Storage Service (“S3”) in March 2006 that aimed to provide highly secure object storage, at very high durability and availability, at Internet scale, and very low cost. In other words, be stellar at object storage. When we launched S3, developers were excited, and a bit mystified. It was a very useful primitive service, but they wondered, why just object storage? When we launched Amazon Elastic Compute Cloud (“EC2”) in August 2006 and Amazon SimpleDB in 2007, people realized we were building a set of primitive infrastructure services that would allow them to build anything they could imagine, much faster, more cost-effectively, and without having to manage or lay out capital upfront for the datacenter or hardware. As AWS unveiled these building blocks over time (we now have over 240 at builders’ disposal—meaningfully more than any other provider), whole companies sprang up quickly on top of AWS (e.g. Airbnb, Dropbox, Instagram, Pinterest, Stripe, etc.), industries reinvented themselves on AWS (e.g. streaming with Netflix, Disney+, Hulu, Max, Fox, Paramount), and even critical government agencies switched to AWS (e.g. CIA, along with several other U.S. Intelligence agencies). But, one of the lesser-recognized beneficiaries was Amazon’s own consumer businesses, which innovated at dramatic speed across retail, advertising, devices (e.g. Alexa and Fire TV), Prime Video and Music, Amazon Go, Drones, and many other endeavors by leveraging the speed with which AWS let them build. Primitives, done well, rapidly accelerate builders’ ability to innovate.

我们的 AWS 基础服务从一开始就被设计成与众不同。它们提供了重要、高度灵活但专注的功能。例如，我们的第一个主要基础服务是 2006 年 3 月推出的亚马逊简单存储服务（“S3”），旨在以互联网规模、极低的成本提供高度安全的对象存储，具有极高的耐用性和可用性。换句话说，就是在对象存储方面做到卓越。当我们推出 S3 时，开发人员感到兴奋，也有些困惑。这是一个非常有用的基础服务，但他们疑惑，为什么只提供对象存储？当我们在 2006 年 8 月推出亚马逊弹性计算云（“EC2”）以及 2007 年推出亚马逊 SimpleDB 时，人们意识到我们正在构建一系列基础设施服务，使他们能够更快、更经济地构建任何他们能想象的东西，而无需提前管理或投入资金建设数据中心或硬件。 随着 AWS 逐步推出这些构建模块（目前我们已为开发者提供超过 240 个模块，远超其他任何供应商），许多公司迅速在 AWS 之上建立起来（例如 Airbnb、Dropbox、Instagram、Pinterest、Stripe 等），一些行业在 AWS 上实现了自我重塑（例如 Netflix、Disney+、Hulu、Max、Fox、Paramount 等流媒体服务），甚至一些关键的政府机构也转向了 AWS（例如 CIA 以及其他几个美国情报机构）。但较少被注意到的受益者之一是亚马逊自身的消费者业务，它们通过利用 AWS 提供的快速构建能力，在零售、广告、设备（例如 Alexa 和 Fire TV）、Prime 视频和音乐、Amazon Go、无人机以及许多其他领域实现了高速创新。基础模块若设计得当，将极大地加速开发者的创新能力。

So, how do you build the right set of primitives?

那么，你该如何构建合适的primitives集合呢？

Pursuing primitives is not a guarantee of success. There are many you could build, and even more ways to combine them. But, a good compass is to pick real customer problems you’re trying to solve.

追求基础元素并不能保证成功。你可以构建许多基础元素，也有更多方式将它们组合起来。但一个好的指南针是选择你试图解决的真实客户问题。

Our logistics primitives are an instructive example. In Amazon’s early years, we built core capabilities around warehousing items, and then picking, packing, and shipping them quickly and reliably to customers. As we added third-party sellers to our marketplace, they frequently requested being able to use these same logistics capabilities. Because we’d built this initial set of logistics primitives, we were able to introduce Fulfillment by Amazon (“FBA”) in 2006, allowing sellers to use Amazon’s Fulfillment Network to store items, and then have us pick, pack, and ship them to customers, with the bonus of these products being available for fast, Prime delivery. This service has saved sellers substantial time and money (typically about 70% less expensive than doing themselves), and remains one of our most popular services. As more merchants began to operate their own direct-to-consumer (“DTC”) websites, many yearned to still use our fulfillment capabilities, while also accessing our payments and identity primitives to drive higher order conversion on their own websites (as Prime members have already shared this payment and identity information with Amazon). A couple years ago, we launched Buy with Prime to address this customer need. Prime members can check out quickly on DTC websites like they do on Amazon, and receive fast Prime shipping speeds on Buy with Prime items—increasing order conversion for merchants by ~25% vs. their default experience.

我们的物流基础模块就是一个很有启发性的例子。在亚马逊成立初期，我们围绕商品仓储建立了核心能力，然后快速可靠地完成拣货、包装和配送给客户。当我们向市场引入第三方卖家时，他们经常要求使用这些相同的物流能力。正因为我们已经建立了这一套初始的物流基础模块，我们才能在 2006 年推出“亚马逊物流（FBA）”，允许卖家使用亚马逊的物流网络存储商品，然后由我们负责拣货、包装并配送给客户，同时这些商品还能享受快速的 Prime 配送服务。这项服务为卖家节省了大量的时间和成本（通常比他们自己操作便宜约 70%），并且至今仍是我们最受欢迎的服务之一。随着越来越多的商家开始运营自己的直面消费者（DTC）网站，许多商家仍然希望使用我们的物流能力，同时也希望利用我们的支付和身份基础模块，以提高他们自己网站上的订单转化率（因为 Prime 会员已经与亚马逊共享了这些支付和身份信息）。 几年前，我们推出了 Buy with Prime，以满足客户的这一需求。Prime 会员可以像在亚马逊上一样，在 DTC 网站上快速结账，并在购买 Buy with Prime 商品时享受 Prime 的快速配送服务，这使商家的订单转化率比默认体验提高了约 25%。

As our Stores business has grown substantially, and our supply chain become more complex, we’ve had to develop a slew of capabilities in order to offer customers unmatched selection, at low prices, and with very fast delivery times. We’ve become adept at getting products from other countries to the U.S., clearing customs, and then shipping to storage facilities. Because we don’t have enough space in our shipping fulfillment centers to store all the inventory needed to maintain our desired in-stock levels, we’ve built a set of lower-cost, upstream warehouses solely optimized for storage (without sophisticated end-user, pick, pack, and ship functions). Having these two pools of inventory has prompted us to build algorithms predicting when we’ll run out of inventory in our shipping fulfillment centers and automatically replenishing from these upstream warehouses. And, in the last few years, our scale and available alternatives have forced us to build our own last mile delivery capability (roughly the size of UPS) to affordably serve the number of consumers and sellers wanting to use Amazon.

随着我们的门店业务大幅增长，供应链也变得更加复杂，我们不得不开发大量能力，以便为客户提供无与伦比的选择、低廉的价格和极快的配送速度。我们已熟练掌握了将产品从其他国家运往美国、清关并运送至仓储设施的流程。由于我们的配送履约中心没有足够空间存放所有库存以维持理想的库存水平，我们建立了一系列成本较低的上游仓库，专门用于存储（不具备复杂的终端用户拣货、包装和配送功能）。拥有这两类库存促使我们开发算法，预测配送履约中心何时会缺货，并自动从这些上游仓库补货。此外，过去几年中，我们的规模和可用的替代方案迫使我们建立了自己的最后一公里配送能力（规模大致相当于 UPS），以经济高效地服务于希望使用亚马逊的众多消费者和卖家。

We’ve solved these customer needs by building additional fulfillment primitives that both serve Amazon consumers better and address external sellers’ increasingly complex ecommerce activities. For instance, for sellers needing help importing products, we offer a Global Mile service that leverages our expertise here. To ship inventory from the border (or anywhere domestically) to our storage facilities, we enable sellers to use either our first-party Amazon Freight service or third-party freight partners via our Partnered Carrier Program. To store more inventory at lower cost to ensure higher in-stock rates and shorter delivery times, we’ve opened our upstream Amazon Warehousing and Distribution facilities to sellers (along with automated replenishment to our shipping fulfillment centers when needed). For those wanting to manage their own shipping, we’ve started allowing customers to use our last mile delivery network to deliver packages to their end-customers in a service called Amazon Shipping. And, for sellers who wish to use our fulfillment network as a central place to store inventory and ship items to customers regardless of where they ordered, we have a Multi-Channel Fulfillment service. These are all primitives that we’ve exposed to sellers.

我们通过构建额外的履约基础设施，既更好地服务亚马逊消费者，也满足外部卖家日益复杂的电子商务活动，从而解决了这些客户需求。例如，对于需要帮助进口产品的卖家，我们提供了利用我们专业知识的 Global Mile 服务。为了将库存从边境（或国内任何地方）运送到我们的仓储设施，我们允许卖家通过我们的合作承运商计划，使用亚马逊自营货运服务或第三方货运合作伙伴。为了以更低成本存储更多库存，确保更高的库存率和更短的交货时间，我们向卖家开放了上游的亚马逊仓储和配送设施（并在需要时自动补货到我们的配送中心）。对于希望自行管理运输的客户，我们开始允许他们使用我们的末端配送网络，将包裹送达终端客户，这项服务称为 Amazon Shipping。 此外，对于希望使用我们的配送网络作为集中存储库存并向客户发货（无论客户从何处下单）的卖家，我们提供了多渠道配送服务。这些都是我们向卖家开放的基础服务。

Building in primitives meaningfully expands your degrees of freedom. You can keep your primitives to yourself and build compelling features and capabilities on top of them to allow your customers and business to reap the benefits of rapid innovation. You can offer primitives to external customers as paid services (as we have with AWS and our more recent logistics offerings). Or, you can compose these primitives into external, paid applications as we have with FBA, Buy with Prime, or Supply Chain by Amazon (a recently released logistics service that integrates several of our logistics primitives). But, you’ve got options. You’re only constrained by the primitives you’ve built and your imagination.

有意义地构建基础组件能够极大地拓展你的自由度。你可以将这些基础组件保留在内部，并在其基础上开发出引人注目的功能和能力，让你的客户和业务从快速创新中获益。你也可以将基础组件作为付费服务提供给外部客户（例如我们推出的 AWS 和近期的物流服务）。或者，你可以将这些基础组件组合成面向外部的付费应用程序，就像我们推出的 FBA、“Buy with Prime”或亚马逊供应链（最近发布的一项物流服务，整合了我们多个物流基础组件）。总之，你拥有多种选择，唯一的限制是你所构建的基础组件和你的想象力。

Take the new, same-day fulfillment facilities in our Stores business. They’re located in the largest metro areas around the U.S. (we currently have 58), house our top-moving 100,000 SKUs (but also cover millions of other SKUs that can be injected from nearby fulfillment centers into these same-day facilities), and streamline the time required to go from picking a customer’s order to being ready to ship to as little as 11 minutes. These facilities also constitute our lowest cost to serve in the network. The experience has been so positive for customers that we’re planning to double the number of these facilities.

以我们门店业务中新建的当日配送设施为例，它们位于美国各大都市区（目前共有 58 个），存放着我们销量最高的 10 万个库存单位（SKU），同时还能从附近的配送中心调入数百万个其他 SKU 到这些当日配送设施中。这些设施将从拣选客户订单到准备发货的时间缩短至最快仅需 11 分钟。此外，这些设施也是我们网络中服务成本最低的部分。由于客户体验非常积极，我们计划将这些设施的数量增加一倍。

But, how else might we use this capability if we think of it as a core building block? We have a very large and growing grocery business in organic grocery (with Whole Foods Market) and non-perishable goods (e.g. consumables, canned goods, health and beauty products, etc.). We’ve been working hard on building a mass, physical store offering (Amazon Fresh) that offers a great perishable experience; however, what if we used our same-day facilities to enable customers to easily add milk, eggs, or other perishable items to any Amazon order and get same day? It might change how people think of splitting up their weekly grocery shopping, and make perishable shopping as convenient as non-perishable shopping already is.

但是，如果我们将这种能力视为一个核心基础模块，我们还能如何利用它呢？我们在有机食品杂货（通过 Whole Foods Market）和非易腐商品（例如日用品、罐头食品、健康和美容产品等）领域拥有规模庞大且不断增长的业务。我们一直在努力打造大规模的实体店（Amazon Fresh），提供出色的生鲜购物体验；然而，如果我们利用当天配送设施，让顾客能够轻松地在任何亚马逊订单中添加牛奶、鸡蛋或其他易腐商品，并实现当天送达，会怎样呢？这可能会改变人们对每周杂货购物分配方式的看法，使购买易腐商品变得像购买非易腐商品一样方便。

Or, take a service that some people have questioned, but that’s making substantial progress and we think of as a very valuable future primitive capability—our delivery drones (called Prime Air). Drones will eventually allow us to deliver packages to customers in less than an hour. It won’t start off being available for all sizes of packages and in all locations, but we believe it’ll be pervasive over time. Think about how the experience of ordering perishable items changes with sub-one-hour delivery?

或者，以一项曾被一些人质疑但正取得重大进展、并被我们视为极具价值的未来基础能力的服务为例——我们的无人机送货服务（称为 Prime Air）。无人机最终将使我们能够在不到一小时内将包裹送达客户手中。虽然最初并非所有尺寸的包裹和所有地点都能享受此服务，但我们相信随着时间推移，它将变得普及。试想一下，不到一小时的送货速度将如何改变订购易腐商品的体验？

The same is true for Amazon Pharmacy. Need throat lozenges, Advil, an antibiotic, or some other medication? Same-day facilities already deliver many of these items within hours, and that will only get shorter as we launch Prime Air more expansively. Highly flexible building blocks can be composed across businesses and in new combinations that change what’s possible for customers.

亚马逊药房也是如此。需要喉糖、布洛芬、抗生素或其他药物吗？当天送达服务已经能在数小时内配送许多此类商品，随着我们更广泛地推出 Prime Air，这一时间还会进一步缩短。高度灵活的基础模块可以跨业务组合，并以新的方式组合，改变客户的可能性。

Being intentional about building primitives requires patience. Releasing the first couple primitive services can sometimes feel random to customers (or the public at large) before we’ve unveiled how these building blocks come together. I’ve mentioned AWS and S3 as an example, but our Health offering is another. In the last 10 years, we’ve tried several Health experiments across various teams—but they were not driven by our primitives approach. This changed in 2022 when we applied our primitives thinking to the enormous global healthcare problem and opportunity. We’ve now created several important building blocks to help transform the customer health experience: Acute Care (via Amazon Clinic), Primary Care (via One Medical), and a Pharmacy service to buy whatever medication a patient may need. Because of our growing success, Amazon customers are now asking us to help them with all kinds of wellness and nutrition opportunities—which can be partially unlocked with some of our existing grocery building blocks, including Whole Foods Market or Amazon Fresh.

有意识地构建基础模块需要耐心。在我们尚未展示这些基础模块如何组合之前，向客户（或广大公众）发布最初几个基础服务，有时可能会显得随意。我之前提到过 AWS 和 S3 作为例子，但我们的健康服务也是如此。在过去 10 年里，我们在不同团队中尝试了几次健康领域的实验，但这些尝试并未遵循我们的基础模块方法。这种情况在 2022 年发生了改变，我们将基础模块思维应用于全球医疗保健领域的巨大问题和机遇。现在，我们已经创建了几个重要的基础模块，以帮助改善客户的健康体验：急性护理（通过 Amazon Clinic）、初级护理（通过 One Medical）以及药房服务，以便患者购买所需的任何药物。由于我们日益增长的成功，亚马逊的客户现在要求我们帮助他们探索各种健康和营养方面的机会，而这些机会可以部分通过我们现有的一些食品杂货基础模块来实现，包括 Whole Foods Market 或 Amazon Fresh。

As a builder, it’s hard to wait for these building blocks to be built versus just combining a bunch of components together to solve a specific problem. The latter can be faster, but almost always slows you down in the future. We’ve seen this temptation in our robotics efforts in our fulfillment network. There are dozens of processes we seek to automate to improve safety, productivity, and cost. Some of the biggest opportunities require invention in domains such as storage automation, manipulation, sortation, mobility of large cages across long distances, and automatic identification of items. Many teams would skip right to the complex solution, baking in “just enough” of these disciplines to make a concerted solution work, but which doesn’t solve much more, can’t easily be evolved as new requirements emerge, and that can’t be reused for other initiatives needing many of the same components. However, when you think in primitives, like our Robotics team does, you prioritize the building blocks, picking important initiatives that can benefit from each of these primitives, but which build the tool chest to compose more freely (and quickly) for future and complex needs. Our Robotics team has built primitives in each of the above domains that will be lynchpins in our next set of automation, which includes multi-floor storage, trailer loading and unloading, large pallet mobility, and more flexible sortation across our outbound processes (including in vehicles). The team is also building a set of foundation AI models to better identify products in complex environments, optimize the movement of our growing robotic fleet, and better manage the bottlenecks in our facilities.

作为一名构建者，等待这些基础模块的建立，而不是简单地将一堆组件组合在一起解决特定问题，是一件困难的事。后者可能更快，但几乎总会在未来拖慢你的速度。在我们的配送网络机器人项目中，我们已经看到了这种诱惑。我们希望自动化数十种流程，以提高安全性、生产力并降低成本。其中一些最大的机会需要在存储自动化、操作处理、分拣、大型货架长距离移动以及物品自动识别等领域进行创新。许多团队会直接跳到复杂的解决方案，仅仅在这些领域中加入“刚好够用”的技术，使一个专门的解决方案能够运行，但这种方案无法解决更多问题，难以随着新需求的出现而轻松演进，也无法被其他需要类似组件的项目重复使用。然而，当你像我们的机器人团队一样，以基础模块的方式思考时，你会优先考虑构建基础模块，选择能够从这些基础模块中受益的重要项目，同时建立工具箱，以便在未来更自由（且更快速）地组合，以满足复杂需求。 我们的机器人团队已在上述各个领域建立了基础模块，这些模块将成为我们下一阶段自动化的关键，包括多层存储、拖车装卸、大型托盘移动，以及在出库流程（包括车内）中更灵活的分拣。此外，该团队还在构建一系列基础人工智能模型，以更好地识别复杂环境中的产品，优化不断增长的机器人车队的移动，并更有效地管理设施中的瓶颈问题。

Sometimes, people ask us “what’s your next pillar? You have Marketplace, Prime, and AWS, what’s next?” This, of course, is a thought-provoking question. However, a question people never ask, and might be even more interesting is what’s the next set of primitives you’re building that enables breakthrough customer experiences? If you asked me today, I’d lead with Generative AI (“GenAI”).

有时候，人们会问我们：“你们下一个支柱是什么？你们已有 Marketplace、Prime 和 AWS，下一个呢？”当然，这是一个发人深省的问题。然而，人们从未问过的一个问题，也许更有趣的是：你们正在构建的下一组基础组件是什么，以实现突破性的客户体验？如果今天你问我，我会首推生成式人工智能（“GenAI”）。

Much of the early public attention has focused on GenAI applications, with the remarkable 2022 launch of ChatGPT. But, to our “primitive” way of thinking, there are three distinct layers in the GenAI stack, each of which is gigantic, and each of which we’re deeply investing.

早期公众的关注大多集中在生成式人工智能应用上，尤其是 2022 年 ChatGPT 的惊艳发布。但以我们“原始”的思维方式来看，生成式人工智能技术栈包含三个截然不同的层次，每个层次都规模庞大，我们都在深入投资。

The bottom layeris for developers and companies wanting to build foundation models (“FMs”).The primary primitives are the compute required to train models and generate inferences (or predictions), and the software that makes it easier to build these models. Starting with compute, the key is the chip inside it. To date, virtually all the leading FMs have been trained on Nvidia chips, and we continue to offer the broadest collection of Nvidia instances of any provider. That said, supply has been scarce and cost remains an issue as customers scale their models and applications. Customers have asked us to push the envelope on price-performance for AI chips, just as we have with Graviton for generalized CPU chips. As a result, we’ve built custom AI training chips (named Trainium) and inference chips (named Inferentia). In 2023, we announced second versions of our Trainium and Inferentia chips, which are both meaningfully more price-performant than their first versions and other alternatives. This past fall, leading FM-maker, Anthropic, announced it would use Trainium and Inferentia to build, train, and deploy its future FMs. We already have several customers using our AI chips, including Anthropic, Airbnb, Hugging Face, Qualtrics, Ricoh, and Snap.

底层面向希望构建基础模型（“FMs”）的开发者和公司。主要的基础组件包括训练模型和生成推理（或预测）所需的算力，以及使模型构建更容易的软件。从算力角度来看，关键在于其中的芯片。迄今为止，几乎所有领先的基础模型都是在英伟达芯片上训练的，我们继续提供所有供应商中最广泛的英伟达实例。然而，随着客户扩展模型和应用，芯片供应一直紧张，成本仍然是个问题。客户要求我们在 AI 芯片的性价比方面不断突破极限，就像我们在通用 CPU 芯片 Graviton 上所做的一样。因此，我们开发了定制的 AI 训练芯片（名为 Trainium）和推理芯片（名为 Inferentia）。2023 年，我们发布了第二代 Trainium 和 Inferentia 芯片，与第一代及其他替代方案相比，这两款芯片的性价比都有显著提升。去年秋季，领先的基础模型制造商 Anthropic 宣布将使用 Trainium 和 Inferentia 来构建、训练和部署其未来的基础模型。 我们已有多家客户在使用我们的 AI 芯片，包括 Anthropic、Airbnb、Hugging Face、Qualtrics、Ricoh 和 Snap。

Customers building their own FM must tackle several challenges in getting a model into production. Getting data organized and fine-tuned, building scalable and efficient training infrastructure, and then deploying models at scale in a low latency, cost-efficient manner is hard. It’s why we’ve built Amazon SageMaker, a managed, end-to-end service that’s been a game changer for developers in preparing their data for AI, managing experiments, training models faster (e.g. Perplexity AI trains models 40% faster in SageMaker), lowering inference latency (e.g. Workday has reduced inference latency by 80% with SageMaker), and improving developer productivity (e.g. NatWest reduced its time-to-value for AI from 12-18 months to under seven months using SageMaker).

客户自行构建基础模型（FM）并将其投入生产时，需应对多项挑战。整理和微调数据、构建可扩展且高效的训练基础设施，以及以低延迟、高性价比的方式大规模部署模型，这些都非常困难。因此，我们打造了 Amazon SageMaker，这是一项托管的端到端服务，在帮助开发人员为 AI 准备数据、管理实验、更快地训练模型（例如，Perplexity AI 使用 SageMaker 将模型训练速度提高了 40%）、降低推理延迟（例如，Workday 使用 SageMaker 将推理延迟降低了 80%）以及提高开发人员生产力（例如，NatWest 使用 SageMaker 将 AI 的价值实现时间从 12-18 个月缩短至不到 7 个月）方面，发挥了颠覆性的作用。

The middle layer is for customers seeking to leverage an existing FM, customize it with their own data, and leverage a leading cloud provider’s security and features to build a GenAI application—all as a managed service. Amazon Bedrock invented this layer and provides customers with the easiest way to build and scale GenAI applications with the broadest selection of first- and third-party FMs, as well as leading ease-of-use capabilities that allow GenAI builders to get higher quality model outputs more quickly. Bedrock is off to a very strong start with tens of thousands of active customers after just a few months. The team continues to iterate rapidly on Bedrock, recently delivering Guardrails (to safeguard what questions applications will answer), Knowledge Bases (to expand models’ knowledge base with Retrieval Augmented Generation—or RAG—and real-time queries), Agents (to complete multi-step tasks), and Fine-Tuning (to keep teaching and refining models), all of which improve customers’ application quality. We also just added new models from Anthropic (their newly-released Claude 3 is the best performing large language model in the world), Meta (with Llama 2), Mistral, Stability AI, Cohere, and our own Amazon Titan family of FMs. What customers have learned at this early stage of GenAI is that there’s meaningful iteration required to build a production GenAI application with the requisite enterprise quality at the cost and latency needed. Customers don’t want only one model. They want access to various models and model sizes for different types of applications. Customers want a service that makes this experimenting and iterating simple, and this is what Bedrock does, which is why customers are so excited about it. Customers using Bedrock already include ADP, Amdocs, Bridgewater Associates, Broadridge, Clariant, Dana-Farber Cancer Institute, Delta Air Lines, Druva, Genesys, Genomics England, GoDaddy, Intuit, KT, Lonely Planet, LexisNexis, Netsmart, Perplexity AI, Pfizer, PGA TOUR, Ricoh, Rocket Companies, and Siemens.

中间层面向希望利用现有基础模型（FM），使用自身数据进行定制，并借助领先云服务商的安全性和功能来构建生成式人工智能（GenAI）应用的客户，所有这些都以托管服务的形式提供。Amazon Bedrock 开创了这一层，向客户提供了最简单的方式来构建和扩展 GenAI 应用，拥有最广泛的第一方和第三方基础模型选择，以及领先的易用性功能，使 GenAI 开发者能够更快地获得更高质量的模型输出。Bedrock 在短短几个月内就拥有了数万名活跃客户，开局非常强劲。团队持续快速迭代 Bedrock，最近推出了 Guardrails（用于限制应用程序回答的问题范围）、Knowledge Bases（通过检索增强生成（RAG）和实时查询扩展模型知识库）、Agents（用于完成多步骤任务）和 Fine-Tuning（持续训练和优化模型），所有这些功能都提升了客户应用的质量。 我们还刚刚新增了来自 Anthropic（他们最新发布的 Claude 3 是全球性能最好的大型语言模型）、Meta（配备 Llama 2）、Mistral、Stability AI、Cohere 以及我们自己的 Amazon Titan 系列基础模型。客户在生成式人工智能的早期阶段已经了解到，要构建具备企业级质量、成本和延迟要求的生产级生成式人工智能应用，需要进行大量有意义的迭代。客户不希望只使用单一模型。他们希望能够使用不同类型的模型和不同规模的模型，以满足不同类型应用的需求。客户希望有一种服务能够简化这种实验和迭代过程，而 Bedrock 正是提供了这样的服务，这也是客户对它如此兴奋的原因。目前使用 Bedrock 的客户包括 ADP、Amdocs、Bridgewater Associates、Broadridge、科莱恩、Dana-Farber 癌症研究所、达美航空、Druva、Genesys、英国基因组学公司、GoDaddy、Intuit、KT、孤独星球、律商联讯、Netsmart、Perplexity AI、辉瑞、PGA 巡回赛、理光、Rocket Companies 和西门子。

The top layer of this stack is the application layer. We’re building a substantial number of GenAI applications across every Amazon consumer business. These range from Rufus (our new, AI-powered shopping assistant), to an even more intelligent and capable Alexa, to advertising capabilities (making it simple with natural language prompts to generate, customize, and edit high-quality images, advertising copy, and videos), to customer and seller service productivity apps, to dozens of others. We’re also building several apps in AWS, including arguably the most compelling early GenAI use case—a coding companion. We recently launched Amazon Q, an expert on AWS that writes, debugs, tests, and implements code, while also doing transformations (like moving from an old version of Java to a new one), and querying customers’ various data repositories (e.g. Intranets, wikis, Salesforce, Amazon S3, ServiceNow, Slack, Atlassian, etc.) to answer questions, summarize data, carry on coherent conversation, and take action. Q is the most capable work assistant available today and evolving fast.

该堆栈的最顶层是应用层。我们正在亚马逊的每个消费者业务领域构建大量生成式人工智能应用。这些应用包括 Rufus（我们新的 AI 驱动购物助手）、更智能、更强大的 Alexa、广告功能（通过自然语言提示轻松生成、定制和编辑高质量的图片、广告文案和视频）、客户和卖家服务生产力应用，以及数十种其他应用。我们还在 AWS 中构建了多个应用，包括可能是最具吸引力的早期生成式人工智能用例——编码助手。我们最近推出了 Amazon Q，这是一款 AWS 专家，能够编写、调试、测试和实施代码，同时还能进行转换（例如从旧版 Java 迁移到新版），并查询客户的各种数据存储库（例如内联网、维基、Salesforce、Amazon S3、ServiceNow、Slack、Atlassian 等），以回答问题、总结数据、进行连贯对话并采取行动。Q 是目前最强大的工作助手，并且正在快速发展。

While we’re building a substantial number of GenAI applications ourselves, the vast majority will ultimately be built by other companies. However, what we’re building in AWS is not just a compelling app or foundation model. These AWS services, at all three layers of the stack, comprise a set of primitives that democratize this next seminal phase of AI, and will empower internal and external builders to transform virtually every customer experience that we know (and invent altogether new ones as well). We’re optimistic that much of this world-changing AI will be built on top of AWS.

虽然我们自己正在构建大量的生成式人工智能应用，但绝大多数应用最终将由其他公司来构建。然而，我们在 AWS 中构建的不仅仅是一个引人注目的应用程序或基础模型。这些 AWS 服务涵盖了技术栈的全部三个层次，构成了一系列基础组件，使人工智能的下一个重要阶段得以普及，并将赋能内部和外部开发者，改变我们所知的几乎所有客户体验（并创造出全新的体验）。我们乐观地认为，这些改变世界的人工智能应用中，有很大一部分将建立在 AWS 之上。

(By the way, don’t underestimate the importance of security in GenAI. Customers’ AI models contain some of their most sensitive data. AWS and its partners offer the strongest security capabilities and track record in the world; and as a result, more and more customers want to run their GenAI on AWS.)

（顺便说一句，不要低估安全性在生成式人工智能中的重要性。客户的人工智能模型包含了他们一些最敏感的数据。AWS 及其合作伙伴提供了全球最强大的安全能力和良好的安全记录，因此越来越多的客户希望在 AWS 上运行他们的生成式人工智能。）

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Recently, I was asked a provocative question—how does Amazon remain resilient? While simple in its wording, it’s profound because it gets to the heart of our success to date as well as for the future. The answer lies in our discipline around deeply held principles: 1/ hiring builders who are motivated to continually improve and expand what’s possible; 2/ solving real customer challenges, rather than what we think may be interesting technology; 3/ building in primitives so that we can innovate and experiment at the highest rate; 4/ not wasting time trying to fight gravity (spoiler alert: you always lose)—when we discover technology that enables better customer experiences, we embrace it; 5/ accepting and learning from failed experiments—actually becoming more energized to try again, with new knowledge to employ.

最近，有人向我提出了一个发人深省的问题——亚马逊如何保持韧性？虽然措辞简单，但意义深远，因为它直指我们迄今为止以及未来成功的核心。答案就在于我们对深层原则的严格遵守：1/ 招聘那些有动力不断改进并拓展可能性的建设者；2/ 解决真正的客户难题，而不是我们认为可能有趣的技术；3/ 构建基础组件，以便我们能以最快速度进行创新和实验；4/ 不浪费时间与趋势对抗（剧透：你总会输）——当我们发现能带来更好客户体验的技术时，我们会积极拥抱它；5/ 接受并从失败的实验中学习——实际上，这会激励我们带着新的知识再次尝试。

Today, we continue to operate in times of unprecedented change that come with unusual opportunities for growth across the areas in which we operate. For instance, while we have a nearly $500B consumer business, about 80% of the worldwide retail market segment still resides in physical stores. Similarly, with a cloud computing business at nearly a $100B revenue run rate, more than 85% of the global IT spend is still on-premises. These businesses will keep shifting online and into the cloud. In Media and Advertising, content will continue to migrate from linear formats to streaming. Globally, hundreds of millions of people who don’t have adequate broadband access will gain that connectivity in the next few years. Last but certainly not least, Generative AI may be the largest technology transformation since the cloud (which itself, is still in the early stages), and perhaps since the Internet. Unlike the mass modernization of on-premises infrastructure to the cloud, where there’s work required to migrate, this GenAI revolution will be built from the start on top of the cloud. The amount of societal and business benefit from the solutions that will be possible will astound us all.

今天，我们继续处于前所未有的变革时代，这种变革为我们所涉足的领域带来了非凡的增长机遇。例如，尽管我们的消费者业务规模已接近 5000 亿美元，但全球零售市场约 80%仍然位于实体店。同样，我们的云计算业务收入规模已接近 1000 亿美元，但全球超过 85%的 IT 支出仍然在本地部署。这些业务将继续向线上和云端转移。在媒体和广告领域，内容将继续从线性模式向流媒体迁移。在全球范围内，数以亿计尚未拥有足够宽带接入的人群将在未来几年内获得连接。最后但同样重要的是，生成式人工智能可能是自云计算（本身仍处于早期阶段）以来最大的技术变革，甚至可能是自互联网以来最大的技术变革。与大规模将本地基础设施迁移到云端不同（迁移过程需要大量工作），生成式人工智能革命将从一开始就建立在云端之上。这些解决方案所能带来的社会和商业效益将令我们所有人感到震惊。

There has never been a time in Amazon’s history where we’ve felt there is so much opportunity to make our customers’ lives better and easier. We’re incredibly excited about what’s possible, focused on inventing the future, and look forward to working together to make it so.

在亚马逊的历史上，我们从未像现在这样感受到如此多的机会，能够让客户的生活变得更好、更轻松。我们对未来的可能性感到无比兴奋，专注于创造未来，并期待共同努力实现这一目标。