“Jeff, what does Day 2 look like?”
“杰夫，Day 2是什么样子？”
That’s a question I just got at our most recent all-hands meeting. I’ve been reminding people that it’s Day 1 for a couple of decades. I work in an Amazon building named Day 1, and when I moved buildings, I took the name with me. I spend time thinking about this topic.
这是我在最近一次全员大会上刚刚被问到的问题。几十年来，我一直在提醒大家，我们永远处于Day 1。我在一栋名为“Day 1”的亚马逊大楼里上班，当我们换了大楼，就把这个名字也带了过去。我也经常思考这个话题。
“Day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death. And that is why it is always Day 1.”
“Day 2意味着停滞。接着是被边缘化。然后是让人难以忍受的、痛苦的衰落。最后是死亡。这就是为什么永远都是Day 1。”
To be sure, this kind of decline would happen in extreme slow motion. An established company might harvest Day 2 for decades, but the final result would still come.
当然，这种衰落会以极其缓慢的方式发生。一家已经成熟的公司可能会在“Day 2”状态下苟延数十年，但最终的结果依然会到来。
I’m interested in the question, how do you fend off Day 2? What are the techniques and tactics? How do you keep the vitality of Day 1, even inside a large organization?
我感兴趣的是，如何抵御“Day 2”？有哪些技术和策略？即使在大型组织内部，怎样保持“Day 1”的活力？
Such a question can’t have a simple answer. There will be many elements, multiple paths, and many traps. I don’t know the whole answer, but I may know bits of it. Here’s a starter pack of essentials for Day 1 defense: customer obsession, a skeptical view of proxies, the eager adoption of external trends, and high-velocity decision making.
这样的问题不可能有简单的答案。会有许多要素、多个路径，以及诸多陷阱。我不知道全部答案，但也许知道一些窍门。以下是“Day 2”防御的入门要素：极度关注客户，对替代指标保持怀疑，热切采纳外部趋势，以及高速决策。
True Customer Obsession
真正的客户至上
There are many ways to center a business. You can be competitor focused, you can be product focused, you can be technology focused, you can be business model focused, and there are more. But in my view, obsessive customer focus is by far the most protective of Day 1 vitality.
将企业聚焦的方式有很多。你可以以竞争对手为中心，可以以产品为中心，可以以技术为中心，可以以商业模式为中心，还有更多选择。但在我看来，对客户的痴迷式关注无疑是最能保护“Day 1”活力的方式。
Why? There are many advantages to a customer-centric approach, but here’s the big one: customers are always beautifully, wonderfully dissatisfied, even when they report being happy and business is great. Even when they don’t yet know it, customers want something better, and your desire to delight customers will drive you to invent on their behalf. No customer ever asked Amazon to create the Prime membership program, but it sure turns out they wanted it, and I could give you many such examples.
为什么？以客户为中心的方法有很多优势，但最重要的一点是：即使客户说很满意、业务蒸蒸日上，他们内心总是美好而又微妙地不满足。即便他们自己还不知道，客户渴望更好的东西，而你取悦客户的愿望将推动你为他们创造价值。没有客户曾经要求亚马逊推出 Prime 会员计划，但事实证明，他们确实需要它，我可以举出许多类似的例子。
Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight. A customer-obsessed culture best creates the conditions where all of that can happen.
要保持“Day 1”状态，你必须耐心地进行实验，接受失败，播下种子，保护幼苗，并在看到客户欣喜时加大投入。一个以客户为中心的文化最能创造所有这些发生的条件。
Resist Proxies
抵制替代指标
As companies get larger and more complex, there’s a tendency to manage to proxies. This comes in many shapes and sizes, and it’s dangerous, subtle, and very Day 2.
随着公司规模变大、结构更加复杂，往往会倾向于管理那些替代指标。这种情况有多种形式，危险而微妙，非常具有“Day 2”的特征。
A common example is process as proxy. Good process serves you so you can serve customers. But if you’re not watchful, the process can become the thing. This can happen very easily in large organizations. The process becomes the proxy for the result you want. You stop looking at outcomes and just make sure you’re doing the process right. Gulp. It’s not that rare to hear a junior leader defend a bad outcome with something like, “Well, we followed the process.” A more experienced leader will use it as an opportunity to investigate and improve the process. The process is not the thing. It’s always worth asking, do we own the process or does the process own us? In a Day 2 company, you might find it’s the second.
一个常见的例子是把流程当作替代指标。良好的流程是为了帮助你更好地服务客户。但如果你不加以警惕，流程本身就会变成目标。在大型组织中，这种情况很容易发生。流程被当作你想要的结果的替代指标。你不再关注真正的成果，只关注流程是否执行到位。哎呀。你不难听到年资较浅的领导为糟糕的结果辩护，说“我们不是遵循了流程吗”。而更有经验的领导则会借此机会去调查并改进流程。流程并不是目的。值得时刻自问：是我们在掌握流程，还是流程在掌握我们？在“Day 2”的公司里，你会发现往往是后者。
Another example: market research and customer surveys can become proxies for customers – something that’s especially dangerous when you’re inventing and designing products. “Fifty-five percent of beta testers report being satisfied with this feature. That is up from 47% in the first survey.” That’s hard to interpret and could unintentionally mislead.
另一个例子是：市场调研和客户调查可能会成为客户的替代指标——当你在进行产品创新和设计时，这尤其危险。“55%的内部测试用户表示对该功能满意，比第一轮调查的47%有所提升。”这类数据难以解读，且可能无意中产生误导。
Good inventors and designers deeply understand their customer. They spend tremendous energy developing that intuition. They study and understand many anecdotes rather than only the averages you’ll find on surveys. They live with the design.
优秀的创新者和设计师对客户有深刻的理解。他们投入大量精力培养这种直觉。他们研究并理解大量客户故事，而不仅仅依赖调查中的平均数。他们与设计紧密共生。
I’m not against beta testing or surveys. But you, the product or service owner, must understand the customer, have a vision, and love the offering. Then, beta testing and research can help you find your blind spots. A remarkable customer experience starts with heart, intuition, curiosity, play, guts, taste. You won’t find any of it in a survey.
我并不反对内部测试或调查。但你——作为产品或服务的拥有者——必须理解客户、拥有愿景，并热爱你的产品。然后，内部测试和调研才能帮你发现盲点。一流的客户体验源于真诚、直觉、好奇、探索、勇气和品味。这些都无法从调查问卷中得来。
Embrace External Trends
拥抱外部趋势
The outside world can push you into Day 2 if you won’t or can’t embrace powerful trends quickly. If you fight them, you’re probably fighting the future. Embrace them and you have a tailwind.
如果你无法或不愿迅速拥抱那些强劲的趋势，外部世界会将你推向“Day 2”。与趋势抗争，等同于对未来抗争；拥抱趋势，则等于赢得顺风助力。
These big trends are not that hard to spot (they get talked and written about a lot), but they can be strangely hard for large organizations to embrace. We’re in the middle of an obvious one right now: machine learning and artificial intelligence.
这些重大趋势并不难识别（它们被广泛讨论并频繁登上报端），但大型组织却往往难以真正拥抱。我们当前正身处一个显而易见的趋势之中：机器学习与人工智能。
Over the past decades computers have broadly automated tasks that programmers could describe with clear rules and algorithms. Modern machine learning techniques now allow us to do the same for tasks where describing the precise rules is much harder.
过去数十年中，计算机已广泛自动化了程序员能够用明确规则和算法描述的任务。而现代机器学习技术则使我们得以在那些难以准确描述规则的任务中实现同样的自动化。
At Amazon, we’ve been engaged in the practical application of machine learning for many years now. Some of this work is highly visible: our autonomous Prime Air delivery drones; the Amazon Go convenience store that uses machine vision to eliminate checkout lines; and Alexa,1 our cloud-based AI assistant. (We still struggle to keep Echo in stock, despite our best efforts. A high-quality problem, but a problem. We’re working on it.)
在亚马逊，我们多年来一直在实践应用机器学习。其中一些成果颇受瞩目：我们的无人机 Prime Air；利用机器视觉消除排队结账的 Amazon Go 便利店；以及我们的云端 AI 助手 Alexa¹。（尽管我们全力以赴，Echo 依然供不应求——这是“优质难题”，但仍需解决，我们正在努力。）
But much of what we do with machine learning happens beneath the surface. Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type – quietly but meaningfully improving core operations.
但我们对机器学习的大量应用都在幕后：需求预测、商品搜索排名、商品与优惠推荐、商品展示位置、欺诈检测、翻译等算法，均由机器学习驱动。虽然不那么显眼，但这类应用将持续并深入地提升核心运营效率。
Inside AWS, we’re excited to lower the costs and barriers to machine learning and AI so organizations of all sizes can take advantage of these advanced techniques.
在 AWS 内部，我们致力于降低机器学习与 AI 的成本与门槛，让各种规模的组织都能利用这些先进技术。
Using our pre-packaged versions of popular deep learning frameworks running on P2 compute instances (optimized for this workload), customers are already developing powerful systems ranging everywhere from early disease detection to increasing crop yields. And we’ve also made Amazon’s higher level services available in a convenient form. Amazon Lex (what’s inside Alexa), Amazon Polly, and Amazon Rekognition remove the heavy lifting from natural language understanding, speech generation, and image analysis. They can be accessed with simple API calls – no machine learning expertise required. Watch this space. Much more to come.
客户已借助我们预装在 P2 计算实例（针对该工作负载优化）上的深度学习框架，开发出从早期疾病检测到提高作物产量等多种强大系统。此外，我们还以便捷形式提供了更高级的服务：Amazon Lex（Alexa 的核心）、Amazon Polly 和 Amazon Rekognition，让自然语言理解、语音生成与图像分析变得轻而易举。只需简单的 API 调用，无需机器学习专业知识。敬请期待，更多精彩即将到来。
High-Velocity Decision Making
高速决策
Day 2 companies make high-quality decisions, but they make high-quality decisions slowly. To keep the energy and dynamism of Day 1, you have to somehow make high-quality, high-velocity decisions. Easy for start-ups and very challenging for large organizations. The senior team at Amazon is determined to keep our decision-making velocity high. Speed matters in business – plus a high-velocity decision making environment is more fun too. We don’t know all the answers, but here are some thoughts.
“Day 2”公司会做出高质量的决策，但它们做决策的速度很慢。要保持“Day 1”的活力与动力，就必须以某种方式做出高质量且高速的决策。这对初创企业来说很容易，但对大型组织却是巨大挑战。亚马逊的高层团队决心保持决策的高速度。速度在商业中至关重要——而且高速决策的环境也更有趣。我们并不知道所有答案，但以下是一些思考。
First, never use a one-size-fits-all decision-making process. Many decisions are reversible, two-way doors. Those decisions can use a light-weight process. For those, so what if you’re wrong? I wrote about this in more detail in last year’s letter.
首先，绝不要使用一刀切的决策流程。许多决策是可逆的、双向的。这些决策可以采用轻量级流程。就算错了又如何？我在去年的信中对此有更详细的论述。
Second, most decisions should probably be made with somewhere around 70% of the information you wish you had. If you wait for 90%, in most cases, you’re probably being slow. Plus, either way, you need to be good at quickly recognizing and correcting bad decisions. If you’re good at course correcting, being wrong may be less costly than you think, whereas being slow is going to be expensive for sure.
第二，大多数决策可能只需在具有约70%理想信息的情况下作出。如果等到拥有90%的信息，大多数情况下你可能会变得迟缓。此外，无论如何，你都需要善于迅速识别并纠正错误决策。如果你擅长纠偏，犯错的代价可能比你想象的要小，而拖延则肯定会付出高昂代价。
Third, use the phrase “disagree and commit.” This phrase will save a lot of time. If you have conviction on a particular direction even though there’s no consensus, it’s helpful to say, “Look, I know we disagree on this but will you gamble with me on it? Disagree and commit?” By the time you’re at this point, no one can know the answer for sure, and you’ll probably get a quick yes.
第三，使用“有异议但支持”（disagree and commit）这句话。这句话能节省大量时间。如果你对某个方向深信不疑，即使尚未达成共识，也可以说：“听着，我知道我们在这件事上意见不一，但你愿意和我一起赌一把吗？有异议但支持？”到了这个阶段，没有人能确定答案，你很可能会马上得到肯定回应。
This isn’t one way. If you’re the boss, you should do this too. I disagree and commit all the time. We recently greenlit a particular Amazon Studios original. I told the team my view: debatable whether it would be interesting enough, complicated to produce, the business terms aren’t that good, and we have lots of other opportunities. They had a completely different opinion and wanted to go ahead. I wrote back right away with “I disagree and commit and hope it becomes the most watched thing we’ve ever made.” Consider how much slower this decision cycle would have been if the team had actually had to convince me rather than simply get my commitment.
这并非单向行为。如果你是老板，也应如此做。我时刻在实践“有异议但支持”。我们最近批准了一部 Amazon Studios 的原创作品。我告诉团队我的看法：该项目是否足够有趣可议，制作复杂，商业条款也不是很理想，而我们还有很多其他机会。他们则完全相反，想要推进。我立刻回复道：“我虽有异议，但支持，并希望它能成为我们制作过观看量最高的作品。”想想如果团队必须说服我，而不仅仅是获得我的承诺，这个决策周期会慢多少。
Note what this example is not: it’s not me thinking to myself “well, these guys are wrong and missing the point, but this isn’t worth me chasing.” It’s a genuine disagreement of opinion, a candid expression of my view, a chance for the team to weigh my view, and a quick, sincere commitment to go their way. And given that this team has already brought home 11 Emmys, 6 Golden Globes, and 3 Oscars, I’m just glad they let me in the room at all!
请注意，此例并非我心里想“他们错了，抓不住重点，但这事不值得我过多纠结”。而是真正的观点分歧，坦率表达我的看法，让团队评估我的意见，并迅速真诚地支持他们。考虑到这个团队已经赢得了11座艾美奖、6座金球奖和3座奥斯卡奖，我很庆幸他们还让我在场参与！
Fourth, recognize true misalignment issues early and escalate them immediately. Sometimes teams have different objectives and fundamentally different views. They are not aligned. No amount of discussion, no number of meetings will resolve that deep misalignment. Without escalation, the default dispute resolution mechanism for this scenario is exhaustion. Whoever has more stamina carries the decision.
第四，及早识别真正的不一致问题并立即升级。有时候团队的目标不同、观点截然相反，他们并未达成一致。再多的讨论、再多的会议也无法解决这种深层次的不一致。如果不升级处理，默认的争议解决机制就是耗尽精力。谁体力更持久，谁就掌控决策。
I’ve seen many examples of sincere misalignment at Amazon over the years. When we decided to invite third party sellers to compete directly against us on our own product detail pages – that was a big one. Many smart, well-intentioned Amazonians were simply not at all aligned with the direction. The big decision set up hundreds of smaller decisions, many of which needed to be escalated to the senior team.
多年来，我在亚马逊见过许多真诚却严重不一致的例子。比如当我们决定邀请第三方卖家直接在我们的商品详情页上与我们竞争时——这是一次重大决策。许多聪明且善意的亚马逊人完全无法认同这一方向。这一大决策引发了数百个小决策，其中许多都需要升级到高层团队进行处理。
“You’ve worn me down” is an awful decision-making process. It’s slow and de-energizing. Go for quick escalation instead – it’s better.
“你们把我磨垮了”是一种糟糕的决策流程。它既缓慢又耗尽活力。相反，应迅速升级处理——那才是更好的选择。
So, have you settled only for decision quality, or are you mindful of decision velocity too? Are the world’s trends tailwinds for you? Are you falling prey to proxies, or do they serve you? And most important of all, are you delighting customers? We can have the scope and capabilities of a large company and the spirit and heart of a small one. But we have to choose it.
那么，你是否仅仅满足于决策质量，还是兼顾了决策速度？世界潮流是推动你前行的顺风，还是阻碍你的逆风？你是成为了替代指标的牺牲品，还是利用它们为己所用？最重要的是，你是否在取悦客户？我们可以拥有大公司的规模和能力，也保有小公司的精神与初心，但前提是，我们必须做出选择。
A huge thank you to each and every customer for allowing us to serve you, to our shareowners for your support, and to Amazonians everywhere for your hard work, your ingenuity, and your passion.
衷心感谢每一位客户让我们有机会为您服务，感谢我们的股东给予支持，也感谢全球的亚马逊人，感谢你们的辛勤付出、创新精神和热情投入。
As always, I attach a copy of our original 1997 letter. It remains Day 1.
一如既往，我在此附上1997年原版致股东信。它仍旧是“第一天”。
Sincerely,
Jeff
Jeffrey P. Bezos
Founder and Chief Executive Officer
Amazon.com, Inc.
【1】For something amusing, try asking, “Alexa, what is sixty factorial?”
想找点乐子的话，不妨问一句：“Alexa，什么是六十的阶乘？”