贝索斯致股东信(2016)

qimoe 发布于 2 个月前

致我们的股东:

“Jeff,Day2看起来是什么样的?”

这是一个我在最近的会议上被问到的问题。这几十年来我一直在告诉人们这是Day1。我在Amazon一座叫Day1的大楼里办公,当我搬到别的大楼的时候我会把这个名字带走放到新的大楼上。我花了一些时间思考这个问题。

“Day2是停滞,然后是无关,然后是煎熬,然后是死亡。这就是为什么我们永远在Day1。”

当然了,这个转变会慢慢发生,一个公司可能在Day2呆了特别长的时间,但最终的命运永远会到来。

我感兴趣这个问题:如何拒绝Day2?有什么技巧和方法?如何持续保持Day1的活力,即使在一个很大的组织。

这些问题没有一个简单的答案,会有很多元素、路径和陷阱。我不知道全部答案,但我知道一些。这些就是守住Day1的新手装备:用户至上,警惕“代理化”(proxy),饥渴的采用外在的趋势和高速决策。

用户至上

一个业务可以有很多种中心,可以是竞争对手至上,可以是产品至上,可以是技术至上,也可以商业模式至上,还有很多其他的种种。但是在我心中,用户至上是保有Day1活力最有效的。

为什么?以用户为中心有太多好处了,但是这是最大的一个好处:用户永远不满足(customers are always beautifully, wonderfully dissatisfied),即便在他们说他们很高兴你们做的真棒的时候。甚至在他们自己都不知道的情况下,他们始终需要更好的服务,而以取悦用户为己任的你会站在他们的角度替他们创新。没有任何用户高速Amazon他们需要Prime,但是有了之后他们发现这正式他们需要的,这种例子我数不胜数。

待在Day1要求你耐心尝试、接受失败、种下种子、保护小苗、然后在发现用户高兴了之后加倍。一个用户至上的文化能够提供让以上事情发生的最好土壤。

抵御代理化

随着公司变得越来越大、越来越复杂,就需要处理“代理化”的情况。它会以各种形式和大小出现,并且它非常危险、微妙,而且非常Day2。

一个非常常见的例子就是流程的“代理化”。好的流程能服务于你也能服务于用户。但是如果你不够小心,流程就成了问题。这在大型组织里可以轻易发生。流程成为你追求的结果的“代理”。你不再看真实的结果,而是仅仅关注与流程是否合规。咽口水。(译者注:我也不知道为什么这里加了这么一句Gulp)你常常会听到一个年轻领导为一个坏结果这样辩护:“Well,我遵守流程了”一个更有经验的领导会抓住机会调查并改善流程。流程不是真正的问题,我们要始终问自己,是我们控制流程还是流程控制我们?在一个Day2的公司,你可能会看到后者。

另一个例子:市场营销和用户调研很可能成为你真正接触用户的“代理”——这在你创新和设计产品时极为危险!“55%的Beta测试人员反映满意。这比第一次测试的47%有所提高”这种结果很难诠释而可能导致无意的误解。

好的创新者和设计师深入的了解用户。他们用非常多的精力来修炼自己的直觉。他们学习和理解各种奇闻异事,而不是盯着那几个调查里的数字。他们活在自己的设计里。

我不反对Beta测试或者调查研究。但是你,产品和服务的拥有者,你必须理解用户,有视野,并且热爱给予。然后,Beta测试和调查才能帮你找到盲点。一个可圈可点的用户体验流程是从心、直觉、好奇心、玩性、胆识和口味出发的。这些你不可能在任何调查里找到。

拥抱外在趋势

如果你不能接纳最新的潮流,外在的趋势就会把你推到Day2。如果你和它们抗争,那你就是在和未来抗争。拥抱他们,你就能搭上顺风车。

这些大趋势并不是那么难发现(人们到处都在讨论),但是很奇怪的是大公司旺旺很难真正拥抱它们。我们正处在显而易见的大趋势之中:机器学习和人工智能。

在过去几十年,计算机广泛的自动化了各种程序员可以用逻辑和算法表述清楚的工作。流行的机器学习技术能让我们自动化那些很难把规则讲清楚的任务。

在Amazon,我们已经连续多年进行机器学习的应用研究了。有些用到的地方显而易见:我们的Prime送货无人机,Amazon Go无人免排队商店,和Alexa基于云计算的AI助手。(我们尽力维持Echo的库存,因为我们确实遇到了一个高级问题,正在努力解决。)

但是更多的机器学习应用是在表层之下。我们用机器学习算法来预测订单量,做商品搜索排序,产品推荐,货物存放,诈骗检测,翻译等等。尽管不是那么可见,但是大部分的机器学习带来的影响都是这样——潜移默化改变了我们核心的运作方式。

在AWS里,我们通过机器学习和AI大幅降低了成本,所以大大小小各种公司都能受益于我们的服务。

使用我们预先打包好的具有深度学习框架在P2上进行计算,用户可以做各种各样厉害的事情,从疾病检测到提升玉米产量。我们还使得Amazon更上层的服务变得更方便使用。Amazon Lex(Alexa的核心),Amazon Polly和Amazon Rekognition抬走了压在自然语言处理、语言生成和图片分析上的大山。他们可以使用API轻松调用,不需要任何机器学习的背景知识。好好看着这个领域,还会有更多的事情发生。

高速决策

Day2的公司会做出高质量的决策,但是他们做出这样的决策非常慢。为了保持Day1的能量和活力,你必须做出高质量又高速的决策。这对初创公司很容易,但对大型组织很困难。Amazon的资深管理人员在尽量确保我们做出快速的决策。速度在商业世界很重要,而且快速的决策环境也更有意思。我们不知道所有的答案,但是有几个想法如下。

首先,绝对不要用一刀切的决策流程。很多决策都是有回头路的,这些决策的过程可以更轻量化。对于那些会问如果我们错了怎么办的人,请参考我去年的信。

其次,大多数决策在你差不多掌握了70%的信息之后就应该做出了。如果你想等到收集到90%的信息之后再做决定,就太慢了。除此之外,不管怎样你必须能够快速发现并纠正错误。如果你善于调整道路,做错了比你想象付出的代价要小,可是做的慢一定会让你付出高昂的代价。

第三,多使用“不同意,但执行(Disagree and commit)”。这句话会省下很多时间。如果你对一个方向有信心但是团队并不能够达成共识,说这句话就很有用:“好吧,我知道我们在这上面想法不一致,可是你们愿不愿意跟我赌一把?不同意,但执行?”在这个时候没有人能够知道确切的答案,你很有可能得到一个干脆的yes。

这不是单向的,如果你是老板,你也要这样做。我就总是不同意但执行。我们最近开拍了一个Amazon Studios原创剧集。我告诉了团队我的想法:至于这部剧到底是不是有意思,是不是过于难拍摄,从商业角度来看是不乐观的,而且我们还有很多其他机会。他们却有完全不同的想法,想要继续干。我立刻回复他们:“我保留意见但是同意你们干,并且希望它能成为我们做过最受欢迎的剧。”想象一下如果他们必须来说服我而不是我直接放行的话, 我们会慢多久。

要注意这个例子里我不是对自己说:“这些家伙完全搞不清状况,我也没空追着他们说。”这是一个单纯的观点上的分歧,一个我自己想法的真实表述,给团队一个机会去考虑一下我的意见,以及一个快速的真挚的放行证,让他们决定最后怎么做。鉴于这个团队已经抱回家11个艾美奖,6个金球奖和3个奥斯卡,他们能让我进他们的屋子我就已经很高兴啦!

第四,迅速辨不协调的地方然后升级处理。有的时候不同的团队有不同的目标和观点。他们不协调。无论多少讨论多少会议都没办法解决。没有升级处理,默认的冲突解决机制只会让两方精力殆尽。最后谁挺得过谁,谁就赢。

在这些年我看过很多真挚的意见不统一。当我们决定引入第三方商家的时候,很多抱有好意的员工都和领导层意见不一致。这个大决定包含几百个小决定,很多都需要上升到高层管理团队来协调。

“我真是耗不过你”是一个可怕的决策过程,这很漫长而且浪费精力。快速上报问题,这样更好。

所以,你现在还是只关心决策质量吗?还是也会注意决策速度?世界的趋势有没有让你也搭便车呢?你有没有代理人化一些重要的东西呢?还有最最重要的,你有么有取悦用户?我们可以拥有大公司的能力和小公司的精神,但这必须要我们主动做出选择。

一如既往,我附了信,依旧Day1!


英文原文

“Jeff, what does Day 2 look like?”

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.

“Day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death. And that is why it is always 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.

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?

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.

True Customer Obsession

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.

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.

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.

Resist Proxies

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.

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.

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.

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.

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.

Embrace External Trends

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.

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.

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.

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.)

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.

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.

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.

High-Velocity Decision Making

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.

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.

1 For something amusing, try asking, “Alexa, what is sixty factorial?”

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.

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.

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.

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!

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.

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.

“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.

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.

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.

As always, I attach a copy of our original 1997 letter. It remains Day 1.

Sincerely,

Jeff

Jeffrey P. Bezos

Founder and Chief Executive Officer

Amazon.com, Inc.