商业的未来：Moderna创始人谈突破性创新

我是艾莉森·比德，欢迎收听HBR IdeaCast。

I'm Alison Beard, and this is the HBR IdeaCast.

今天，我们带来的是我们在最近举办的‘商业未来’活动期间，对几家大公司CEO系列访谈的第三期。

Today, we're bringing you the third in our series of interviews with CEOs of major companies that we recorded during our recent future of business event.

诺巴尔·阿菲扬是Flagship Pioneering的创始人兼CEO，也是mRNA疫苗公司Moderna的联合创始人兼董事长。

Noubar Afeyan is the founder and CEO of Flagship Pioneering and cofounder and chair of Moderna, the mRNA vaccine company.

他一直在深入思考人类与机器如何协作，来应对科学、商业乃至社会中最重大的挑战。

He thinks deeply about how man and machine can work together to solve some of the world's largest problems, whether they're in the world of science, business, or society.

我们早在2021年就请他来过 IdeaCast，专门聊过Moderna新冠疫苗研发过程中学到的领导力和创新经验。

Now we've spoken to him on IdeaCast before back in 2021 to talk specifically about leadership and innovation lessons learned in the development of Moderna's COVID vaccine.

但现在的商业环境变化太快了，他的想法也跟着不断更新。

But things move fast in today's business world, and his thinking has evolved accordingly.

他受过生化工程训练，是创业者，也是高管，亲手推动了一百多家生命科学和科技初创公司诞生。

He's a trained biochemical engineer, entrepreneur, and executive who has helped spawn more than a 100 life science and technology startups.

所以他对什么是真正有效的创新，有着独一无二的洞察。

So he has a unique vantage point to what really works when it comes to effective innovation.

我先想问一下，你是怎么定义突破性创新的？不只是在科学领域，而是涵盖各个行业？

I wanna ask first, how do you define breakthrough innovation, not just in science, but across industries?

我觉得大多数创新都聚焦在相邻领域，属于连续性创新，也就是说，你做的只是对之前工作的迭代。

I think that most innovation focuses on adjacencies and is viewed to be continuous innovation, which means that what you're working on is an iteration over what's been worked on before.

但偶尔也会出现非连续性创新，那种情况很难把新想法和过去的做法联系起来。

And once in a while you have discontinuous innovation where it's not easy to connect what's being proposed with what was done before.

这种创新更难预测，也更不可控，但往往被称作突破，因为人们既没预料到它，一开始也看不清它的价值。

And that's much harder or much less predictable, but often is categorized as a breakthrough because people neither expected it nor could they in the beginning assess its value.

我觉得这个概念其实适用于任何行业。

So I think this is a concept that applies to really any industry.

关键问题是：你能预测突破性创新吗？或者有没有一套方法能主动催生它们？

The question is, can you predict breakthrough innovations or can you follow a process to make them happen?

现在商业环境变化这么快，你认为所有公司都必须追求突破吗？还是说，对很多公司来说，渐进式创新就够了？

And given how fast the business environment is changing nowadays, do you think that all companies need to be in search of breakthroughs, or is incremental innovation okay for many of them?

我觉得两者都得考虑，而且要看到它们是怎么相互补充的。

I think you need to think about both and how they complement each other.

我觉得突破性创新得反过来想，从未来倒推到现在；而那种连续性的、贴近现有领域的创新，则应该从当下向前推进。

I actually think about breakthrough innovation as something that you really wanna think from the future back to the present, whereas the more continuous innovation, adjacency oriented innovation should be thought of as from the present going forward.

我觉得两边都得抓，既要关注眼下能看得见的需求，想想怎么提升效率、增强影响力，给客户带来更大价值。

And I think you want to attack the space from both directions, both from the needs that you're aware of today and ways you could become more efficient, more impactful, and deliver better value to your customers.

但同时，你也得去想象一些未来可能的状态，哪怕现在还不清楚怎么从这儿走到那儿；一旦发现有潜力的方向，就倒回来问：要实现它，哪些前提必须成立？

But at the same time, you wanna envision future states without necessarily knowing how we get there from here, and if you find something compelling, come backwards and say, what would have to be true?

那我现在能做点什么，让那个未来早点到来？

And what can I do today to make that future possible sooner?

你问为什么非得这么干，是不是每个人都该这么投入？

And the reason you wanna do that, and you asked, is this something people really should be focused on?

因为光盯着周边机会的创新，很快就会变成红海，谁都能做。

Is that I think that the disadvantage of adjacency oriented innovation only is that it can quickly become commoditized.

大家都砸钱找近处的机会，好处是风险和回报比较容易评估。

Everybody spends money looking for opportunities in close by places, and it has the advantage of being reasonably easy to assess risk reward.

你还能找专家来验证，他们能告诉你哪些事儿更有可能成。

You can get validation from experts who can tell you which ones are more likely than not to work.

这些确实都是做周边事情的理由。

All of those are reasons to be doing things close by.

但问题是，别人都在做同样的事。

The problem is everybody else is doing the same thing.

所以在这些靠近现有领域的地方，怎么建立可持续的优势，真很难想象。

And so how you can create a sustainable advantage in those close by places is hard to fathom.

但如果你愿意跳出去，探索新的可能、新的现实，至少你最初的孤独可能会换来对这片领域的主导权。

Whereas if you're willing to leap to new possibilities, new realities, at least you'll have your initial loneliness rewarded potentially with owning the space.

对于其他组织的领导者，哪怕不是科技或生命科学领域的，你有什么建议，该怎么组织才能实现这种创新？

What advice would you give other organizational leaders, you know, even outside of tech or life sciences about how to structure for this kind of innovation that you're talking about?

是该专门成立一个部门来做吗？

Do you set up a separate division for it?

还是说，每个领域都要指定一个人专门负责思考这个问题？

Know, do you assign a key person in every area to think about?

还是说，关键是要营造一种文化，让每个人都时刻留意突破性的机会？

Is it about creating a culture where everyone's on the lookout for breakthroughs?

是的。

Yeah.

我不会让每个人都盯着突破性进展。

I wouldn't have everyone look out for breakthroughs.

第一，这不现实也做不到；第二，这会严重打乱他们日常的工作节奏。

One, it's not sustainable and possible, and two, it'll be pretty disruptive to how they do their jobs on a day to day basis.

关键在于，你如何看待持续改进——如果真能算的话——和那些颠覆性的飞跃，各自占你注意力和未来预期价值的多大比例。

The question is what ratio of your attention and future expected value do you see coming from the kind of continuous improvement, if you can call it that, versus the discontinuous disruptive leaps.

其实是有办法去思考这个问题的。

There are ways one could think about it.

我们当然靠这个吃饭，所以这问题对我们来说没那么纠结，因为我们本来就没干别的。

We of course do it for a living, so it's a little bit less of a structure issue, that's all we do.

如果我是一家在某个领域领先、又不想被意外闯入者打个措手不及的公司，我可能会组建一个跟传统研发部门动机、组成和激励方式都不同的团队。

If I was a company that was leading in a space, but also didn't wanna get surprised with unexpected entrance into a space, I would likely have a group that is somewhat differently motivated, composed, rewarded than is the traditional R and D organization.

顺便说一句，这其实挺难的，因为研发部门的人都觉得自己在做前沿的事，但真正意义上的前沿，本质上就是不可预测的，而预算里很难批给真正不可预测的项目。

That's by the way sometimes hard, because everybody in R and D wants to believe that they're doing far out things, but in reality, the definition of a far out things is something that's unpredictable, and it's very hard to get money in R and D budgets for truly unpredictable things.

所以你得意识到，这中间其实是有一定限制的。

So you do need to kind of think about it as having some constraints.

你得确保有足够多的不同路径和选项在同时推进，因为我觉得这个领域更偏向于不确定性，而不是风险。

You wanna make sure that you have enough different avenues being pursued and options being pursued, because this is a space that I would propose is more about uncertainty than risk.

不确定性是指概率未知，风险是指概率已知——不管是高是低，这里讲的完全是不确定性的问题。

Uncertainty being about unknown probabilities, risk being about known probabilities, whether high or low, this is the domain of uncertainty.

要对冲或缓解不确定性特别难，因为根本没信号可循。

And it's really hard to hedge mitigate uncertainty because there's no signal there.

你得同时试多种方法，坚持足够长的时间，看看哪些能成真；一旦成真，就要保护好自己，这样才能开辟出新的价值空间。

What you need to do is to take multiple approaches and persist long enough to see if you can make them real, and if they can, to protect yourself so you can create a new value pool.

这个想法，我们人人都能讲得头头是道，但真正能落地、能工业化，才是我们过去二十五年真正干成的事。

That notion, which we can all theorize about, can be practically set up and industrialized, and that's kind of what we have experienced doing over the last twenty five years.

AI发展这么快，你们在Flagship的创新过程中，有没有发现新的方式用它来增强人的智能？

With AI advancing so rapidly, are you finding new uses for it, you know, to augment human intelligence in your innovation process at Flagship?

我听说你们提到过一个叫‘多元智能’的东西。

I've heard you talk about something called Polyintelligence.

是的

Yeah.

我们从2018年就开始用AI最新的形态来支持我们的工作了。

I mean, we started applying AI in its most recent forms back in 2018 to what we do.

你可能会想，我们向来敢试那些还没成熟的东西，既然创新本来就是我们的基因，那用AI去生成新点子这种事，我们早就开始做了。

You would expect that since we're fairly fearless about trying things that are not yet ready and mature, etcetera, etcetera, it would make sense that we would attempt with the earliest of even notions of being able to generate, for example, novelty, given that's our, it's in our DNA, I guess we've been doing that for quite a while.

我们既摸清了它的潜力，也认清了它的局限，更对这些年来那些突如其来的重大突破感到敬畏——连我们自己都没想到会这么快。

And we've learned both the possibilities and the limitations, but also become quite humble about the unpredictability of the major leaps that have happened, even since we got deeply engaged in this.

我常把人工智能叫做‘增强型智能’，或者‘增强型想象力’，它确实有这个本事。

I've called at oftentimes artificial intelligence as augmented intelligence, I've called it augmented imagination, which it has the ability to do.

一个人眼里的幻觉，另一个人可能看成是灵感的飞跃。

One person's hallucination is another person's kind of leap.

所以把AI用在全新领域，特别适合去挖掘那些人类自己根本想不到的点子。

And so applying it into novel spaces is a very interesting way to prospect for things that a given human may not be able to think of.

我的意思是，我们经常一口气就能生成几百上千个新点子，这都成家常便饭了。

I mean, you can ask for, and we do quite routinely hundreds and hundreds of new ideas in a space.

那接下来问题就来了：你怎么去分析这些结果，判断哪些值得试，哪些不值得？

And then the question becomes, how do you analyze for them and see what might or might not make sense to try out?

我甚至管它叫‘外星智能’。

I've even called it sometimes alien intelligence.

这东西以前我们根本没机会接触过。

It's really something that we have never had available to us.

感觉特别怪。

It feels unusual.

它不像我们这个世界的东西，因为我们从来没法一下子冒出成千上万种想法。

It feels other than from this world because we've never had the ability to simultaneously come up with thousands.

说实话，是上百万个新点子。

In fact, I'll tell you millions of new ideas.

我给你举个例子。

I'll give you an example.

大概二十五年前，Flagship成立的第一家公司，大多数人以为我们只做生物科技，其实那家公司早就用在线的进化算法来生成新产品，再通过在线的评审小组来评估，形成一个闭环，不断催生新点子，优化出更好的方案。

Some twenty five years ago, the very first company Flagship formed, unbeknownst to most to think we're only in biotech, is actually a company that used evolutionary algorithms online to create new products, and then used panels also online to evaluate them in a way creating an evolutionary loop to create novelty and optimize for better solutions.

那是2001年、2002年的事了。

This was 2001, 2002.

再往后推，这家公司一干就是十年。

Roll the clock forward, that company went on for a decade.

后来它被尼尔森收购了。

It was eventually merged with Nielsen.

如今，我们又回头重新探索这个领域，这次手里有了生成式AI和基于智能体的系统。

Today, we've actually gone back to explore the space now armed with generative AI, armed with agent based systems.

最近，我们刚宣布启动了一个叫Extuitive的项目，本质上还是做同样的事，但能力提升了几百倍—— literally 看着十五万个代表人类的智能体跟数百万个对象互动，那场面简直太震撼了，这事儿只有AI能办到。

And just recently, we announced the formation of a project called Extuitive, which is essentially trying to do the same except with massive advances in capability, literally watching 150,000 agents representing humans interacting with millions of objects is quite a scene to behold, and you can do that with AI.

我觉得，要是不靠这些强大的增强工具搞创新、做突破，根本没法跟人竞争。

So I actually think it's almost unimaginable that you could compete without deploying these significant augmentation capabilities in doing innovation and breakthroughs.

听你这么一说，这不正好对应了你和加里·皮扎诺在《哈佛商业评论》那篇文章里说的突破性创新的两个步骤吗？

It sounds like in that description, you talked about both of the steps in breakthrough innovation that you wrote about in your HBR article with Gary Pizzano.

第一个是生成多样性，就是拓宽研究领域，根据新发现不断问‘如果……会怎样’。

The first is variance generation, you know, that is considering different areas of research, asking what if questions based on what you learn.

不过退一步说，在第一步里，你怎么决定往哪儿看呢？毕竟这些方向，连这个领域的专家都从来没想过，比如mRNA技术就是个例子。

Taking a step back though, you know, in that first step, how do you decide where to look when these are things that even experts in the field haven't really considered before as with the mRNA technology?

专家们当然非常擅长理解当前的局限，清楚现在有什么、下一步大概能有什么。

You know, experts in the field are very, very good at understanding the limitations of variation and asserting what exists today and what's likely to be possible next.

但他们特别不擅长想象‘再之后’、‘再之后’会有什么——那种未来，他们通常不是专家。

But they're particularly not advantaged at speculating on what will be available next, next, next, they will likely not be the experts in that.

真正让人成为专家的，是掌握着当下最棒的想法。

What makes somebody an expert is ownership of the best ideas today.

而这些想法，往往不是下一个冒出来的东西。

And those are not usually the next ones to come along.

真正罕见的是那些足够谦逊、愿意承认自己可能错了的专家——而这，恰恰是颠覆性创新的核心。

It's a rare expert who is humble enough to allow for the possibility that they're wrong, which is the essence of an unexpected disruptive innovation.

所以我觉得，你得把这看作一种‘跳跃’式的行动。

So I think that what you've got to do is really think about what you're doing as a combination of leaping.

所谓‘跳跃’，就是别只盯着眼前的小改动，同时也要明白：到底什么能创造价值，很难预知，但你可以去试。

Leaping meaning not limiting yourself to slight variations of where you are, and then realizing that it's gonna be hard to know what will and won't create value, but you can try.

你可以去找那些能提供真实反馈的人，不管是未来的客户、用户，还是其他任何能给你市场压力的地方，然后不断迭代。

You can essentially engage with people who can represent feedback that represents value, whether that's eventual customers, users, you name it, wherever you can get selection pressure, and then do the iteration cycles.

你看自然界就很清楚，变异、选择和循环迭代，造出了意想不到的新东西——比如生命，还有我们身边所有的生物。

I mean, it's clear in nature that variation selection and iteration creates unexpected novelty, namely life, and all the living things that we have.

在市场里，这种情况天天都在发生。

And likewise in markets, we see that all the time.

一部手机影响另一部，一双跑鞋带动十万双，汽车市场也一样，它们的演化方式跟自然界如出一辙，只不过这里驱动设计和创新的，是消费者的购买力和市场反馈。

I mean, the influence of one mobile phone onto another, one running shoe onto a 100,000 others, cars, and all of those markets have been evolving much like it happens in nature, except in this case, the purchasing kind of power and that feedback in markets is what's driving what the generators and the designers are doing.

我觉得这些道理完全可以用数字市场来体现，我们现在就在多个领域积极尝试，把生态学和自然界的思路，用到无生命的、人工设计的产品上，这本身就是一个很大的跨越。

So I think all of that can be represented in digital markets for these ideas, and we are very much leading in trying to demonstrate that in lots of different fields, taking our knowledge from ecosystems and ecology and nature into the realm of inanimate objects, designed objects, and that's an interesting leap.

你刚才问到‘多元智能’，那我就简单说说我们指的是什么。

Now, you asked a little earlier about polyintelligence, so let me just say what we mean by that.

当然，现在大家对AI特别着迷，尽管AI这个概念早在七十五年前就被提出来了，人们一直在思考人类该怎么给人工智能腾出空间。

There's a fascination today, of course, even though it's seventy five years after AI was initially described and kind of envisioned with the notion of how humans are making room and making space for artificial intelligence.

但让我觉得有意思的是，这还是头一回，逼得人类不得不承认：我们可能不是地球上唯一有智慧的存在。

But what's interesting to me is that that for the first time has forced humans to allow for the possibility that they're not the only intelligent thing on the planet.

到目前为止，我们是这么定义的，因为我们把语言限定死了，觉得智能就是人类的专利。

So far, that's how we defined it because we controlled language and we said intelligence means humans.

现在机器也能算智能了——至少有些人这么认为——我猜下一步很自然就会问：为什么大自然就不能算智能呢？

Now that machines can be intelligent, at least according to some, I would conjecture that the next simple step will be to say, why can't nature be intelligent?

为什么不能把所有活的东西——不管是你肝脏里的细胞、植物细胞、病毒，还是整个生态系统——都看作是智能体呢？

And why can't we treat all living objects, whether it's cells in your liver or plant cells or viruses or entire ecosystems as intelligence?

我说的智能，是指那些能快速适应、能预判、能完成我们如今跟神经网络挂钩的那些复杂处理的系统。

And by intelligence, I mean, systems that are able to adapt very rapidly, able to anticipate, able to do a lot of the processing we associate with neural networks or computational neural networks today.

我今年早些时候写过一篇相关的文章，现在我们的很多想法都受它驱动：如果我们开始把大自然看作是一系列智能体——如果‘智能体’真能用复数的话——那我们对生物学的理解就会彻底不一样。

And I believe, I wrote an essay on this early in this year, and it's very much driving some of our thinking now, that when we start thinking of nature as a series of intelligences, if there is such a plural word, then we will start understanding what's happening in biology very differently.

这样做的好处是，我觉得我们过去用还原论、解构主义的方式理解生物学，所得到的只是冰山一角；未来我们会更深刻地理解疾病、食物来源，以及这些系统如何被感染影响等等。

And the payoff for that is that I think what we're gonna find is what we've learned so far about biology in the reductionist, deconstructivist way is a tiny fraction of what we're gonna understand about disease, about where we get food and how these systems are affected by infections, etcetera.

在我看来，未来要走的路，就是把这一切都放在‘智能系统’这个新框架下来重新审视。

All of that rendered onto a platform of thinking of intelligence systems is what's ahead in my view.

所以有趣的是，人类现在面临的困境是：我们原本只有一个搞不懂的东西——大自然；现在变成了两个——大自然，还有那些参数超过十亿的AI模型。

So the interesting dilemma of course, will be that we will have gone as humans from having one system we don't understand, nature, to now two systems we don't understand, nature and AI models that involve a billion plus parameters.

挺有意思的是，看看人类会怎么适应这种现实。

And it'll be interesting to see how humans adapt to that reality.

我不觉得这是个大威胁，因为人类早就习惯了不懂身边的大自然，现在不过是给我们的自然又添了一块新拼图。

I don't consider that a major threat because humans have been living without understanding the nature around us for a long, long time, and we're adding a piece, frankly, to our nature.

这不过是另一个极其复杂的系统，而我们人类希望能善加利用，而不是搞砸，去应对世界上一些最棘手的问题。

It's just another system that is extremely complex, and that we as humans can hopefully harness positively, not negatively, in order to impact some of the biggest problems in the world.

对。

Yeah.

去利用它、向它学习，你知道吗？你和加里·皮萨诺那篇文章其实讲的是，实验的方法可以模仿自然选择和进化。

Harness and learn from, you know, because your piece with Gary Pisano basically argues that the approach to experimentation can mimic natural selection and evolution.

但能不能再详细说说，你是怎么从大自然里学到这些，从而重塑了Flagship的流程？让它和其他公司那种实验方式截然不同？

But explain in more detail sort of how you have learned from nature in that way to sort of shift Flagship's process or create Flagship's process to experimentation and that it in such that it differs from other organizations, how they might approach it.

我们在运作中其实用了好多达尔文进化论的基本理念。

There's a bunch of ways in which we use the the basic ideas of Darwinian evolution in in how we operate.

我先再强调一遍：我们认为，除了眼下这些相邻领域，还有更大的价值洼地等着我们。

Let me just reiterate first that we think large value pools exist beyond adjacencies of the here and now.

我们认为，这些价值领域用现有的金融或融资方式几乎无法实现，因为你没法把它们塞进风险收益模型里——根本没法评估风险，毕竟没人知道那些前所未有的东西成功概率有多大，自然也很难判断回报会是什么样。

And we think those value pools are largely unattainable based on current financial or financing approaches, because you can't fit them onto a risk reward paradigm, because it's very hard to assess the risk because you don't know what the probability of success of something that's never been thought of or done before is, and therefore it's also hard to get meaningful feedback on what the reward would be.

但如果你能坦然面对不确定性，我觉得大自然本质上就是不确定性本身：还没进化出来的，就还没进化出来。你当然可以坐在那儿猜，在某个竞争环境中，什么条件最可能胜出，但事实证明，这事儿特别难搞。

But if you are comfortable prosecuting uncertainty, and I would argue that nature is essentially uncertainty in that whatever hasn't evolved yet, hasn't evolved yet, you could sit around and make bets on what's likely gonna be the condition that wins out in any given competitive situation in nature, but I think that's been proven a difficult thing to do.

所以我觉得，我们其实是在复刻‘变异、选择、迭代’这套机制，而且还有一个关键环节——传承：把那些胜出的方案传给下一代。在计算系统里，这可能是几轮迭代；在自然界里，就是实实在在的细胞代际，把有效的解决方案传下去。

So I would say that we are essentially replicating the notion that variation, selection, and iteration, where you also have this important component of descendancy, that is inheriting the solutions that prevail into the next generation and the next generation of what could be a set of game generations in the context of computational systems, or in nature, it's literally generations of cells that are able to pass on the positive solutions to the next.

事实上，我越来越觉得，大自然做的其实就是一点一点地学习：一个细胞、一个物种、一个生态系统，都是在从周围环境中学习。

In fact, I increasingly view all that nature's doing is learning from the surrounding of it, each cell at a time, each species at a time, each ecosystem at a time.

所以我觉得，从大自然身上能学的不只是比喻意义上的东西，而是实实在在的操作方法。

So I do think there's a lot, not just metaphorically to learn, but also operationally.

我举个例子吧。

I'll give you an example.

可能很多人没意识到，我们的身体其实一直在天然接触来自体外的RNA，比如病毒。

People may not realize that in a way our bodies experience, for example, RNA naturally coming from outside in the form of viruses.

病毒进到人体里，钻进细胞，然后复制自己——而我们的疫苗，本质上干的就是这事儿：复制这个步骤。

And so a lot of what a virus is doing in coming into the body, getting into cells, and essentially replicating itself, but for the replication step, that's what our vaccines do.

我们把它们送进身体，送到合适的细胞里，细胞吸收后，RNA就会接管蛋白质的生产，制造出特定的蛋白质。

We get them in the body, we get them into the right kind of cells, they get taken up, and then the RNA actually takes over the protein production to make a particular protein.

在我们这里，这种蛋白具有免疫原性，能引发免疫反应，但很多这种复杂的分子通路，病毒其实也在用——只不过它们是用来复制自己的。

In our case, it's a protein that's immunogenic and therefore causes an immune response, but much of that intricate molecular pathway is also used by viruses to actually now in their case, replicate themselves.

你随便往哪儿看，都能发现一大堆类似的东西。

So there's a lot of things anywhere you look.

我敢说，那些搞网络安全、保护电脑不被病毒入侵的人，从我们的免疫系统怎么应对这些事里，早就学到了不少东西。

I'm sure that a lot of people who deal with cybersecurity and how to protect computer systems from being infected by computer viruses, There's a lot to be learned and has been learned from how our immune systems do this.

不管你往哪儿看，都会发现越来越多的例子，说明大自然的智慧如何启发和指导人类或机器智能。

And everywhere you look, I think you're gonna find more and more examples of nature's intelligence and how it can inspire and inform humans or machine intelligence.

对于想要推进这种突破性创新的领导者和组织来说，你们觉得这个过程大概要花多长时间？

For leaders and organizations that want to pursue this kind of breakthrough innovation that you're talking about, how long can they expect the process to take?

而且，既然是实验，你怎么说服别人花这么多时间和成本？

And then how do you justify the time and cost when it is an experiment?

毕竟，谁也不能保证这事儿一定能出成果，或者从实验阶段顺利过渡到真正落地。

You know, there isn't guarantee that the work will even yield a breakthrough or be able to move from that sort of experimental phase to successful execution.

这是个非常关键的问题，但特别棘手，而且每个案例、每个行业都不一样。

That's a very important question, and it's very, very tricky, and it's very case specific, industry specific.

举个简单点的例子，如果你在利润率很高的行业，你就有能力做大量这类探索，因为一旦成功，回报就是个超级爆款。

For example, I'll say as a simple simplification, that if you have a very high profit margin industry, you can afford to do a lot of this because the consequence of being right is a massive blockbuster.

这就是为什么制药行业特别常见这种做法。

That's why it's so prevalent in the pharmaceutical industry.

但在一个竞争惨烈、利润率极低的行业，依赖突破性创新带来的不确定性——换个说法——几乎让人没法玩下去。

But in a cutthroat industry where you have very low margins, the added uncertainty, let me use the word differently, of relying on breakthrough innovation makes it almost impossible.

你只能认命，接受那些可能彻底摧毁你业务的外部冲击，但说实话，你也没啥好办法保护自己。

And so you have to kind of surrender to the unpredictable external attack that might completely destroy your franchise, but nevertheless, there's not much you could do to protect yourself.

这让我想起克莱·克里斯滕森说的‘创新者的窘境’：有些人明明知道自己面临威胁，却就是不行动，因为这种威胁一开始看起来完全不像后来演变成的那种威胁。

I'm reminded of the innovator's dilemma that Clay Christensen used to talk about in the sense that sometimes people who are highly cognizant of the fact that they may be under threat, nevertheless, don't actually act on it because it comes across as a very different kind of threat that it ultimately evolves into.

真的很难预测。

It's very hard to predict.

我还想说，做这事得有点机会主义，别陷入‘每个创新都得解决某个问题’的思维陷阱。

I also would say that you need to be somewhat opportunistic in doing this, in not falling into the trap of saying every innovation has to be a solution to a problem.

因为我们常常发现，如果你同时寻找问题和解决方案，就有可能在那些新价值池里搭上桥——但如果你非得从‘必须掌控解决方案’或‘必须掌控问题领域’这种前提出发，反而会错过一路上所有可能的匹配机会。

Because often what we find is that if you search for the problem and the solution at the same time, you might be able to match make where there's new value pools in a way that if you insisted on owning the solution space or the problem space to be your point of departure, you will miss out on all things that essentially would be matched in the journey.

所以，说我要去一个和现在完全不一样的市场里找价值，这其实是挺吓人的一个想法。

So it's the highest form of kind of a scary proposition to say, I'm gonna look for value in a particular market that is far from what's being done today.

我会以现有情况为起点，但也会系统性地探索周边以外的领域。

I'm gonna be guided by what exists, but also a systematic way of exploring beyond adjacencies.

我会让大量客户、专家，甚至非专家来帮我判断方向，然后不断试错、反复迭代，直到找到一些真正有效的点子。

I'm gonna let large crowds of customers or experts, non experts guide the kind of the way I evaluate the terrain, but then I'm gonna iterate, iterate, iterate until I get some hits.

一旦找到靠谱的点子，我就马上试一试。

And when I get some hits, I'm gonna try it out.

我会先做个原型出来。

I'm gonna prototype it.

我不会天真地以为，光靠逻辑推理就能直接推导出突破性创新。

I'm not gonna assume that I could deductively reason my way into a breakthrough.

那根本就是白费力气。

That's a fool's errand.

我觉得，大多数做出突破的人，都会把他们的成果说成是天才的杰作。

I think that most people who make breakthroughs describe them as though they were the result of genius.

但以我的经验来看，也许我只是不够天才，但这跟天才真没什么关系。

In my experience, and maybe I'm just particularly not that, that's got very little to genius.

这其实靠的是抓住机会、以价值为导向，还有不断迭代的劲头。

It's got to do with opportunism, value orientation, and the propensity to iterate.

有时候呢，你其实早就看到了眼前的东西，它一直都在那儿，只是你故意忽略，因为它不符合你现在的思维框架。

And sometimes by the way, recognizing the thing right in front of your nose, that's been there for a while, but you choose to ignore it because it doesn't fit the current paradigm.

真正带来成功的是这些，而不是埋头苦干、死磕逻辑，或者听专家告诉你该怎么做。

All of that is what leads to success versus this kind of hard work and reasoning and experts telling you what to do.

听起来，开放的心态也是关键因素之一。

It sounds like open mindedness is a key element there too.

就是你本来想解决一个问题，但要是研究带你去了别的方向，你也愿意去应对那个新问题。

You know, sort of knowing that you're trying to tackle one problem, but then being open to solving another if that's where your research leads you.

开放心态，还得开放结局。

Open minded and open ended.

是的

Yeah.

这确实是别人对它的另一种说法。

That's another thing that people have called it.

进化是一种完全开放式的创新模式。

Evolution is an entirely an open ended innovation paradigm.

如果你既开放又善于发现其中的价值，我觉得你就掌握了关键。

And if you're open ended about it and open minded to see value, I think you've got the right combination.

很好。

Great.

接下来我想回答一些观众的问题。

Well, I wanna get to some of our audience questions.

克里斯·布雷门说，有时候他或她觉得，企业内部在推动突破性创新和商业化之间存在明显的脱节。

Chris Bremen says that sometimes he or she feels like there's a significant disconnect in organizations between driving breakthrough innovation and the commercialization of that innovation.

克里斯在公司里负责多个内部创业项目，就是为了弥合这个鸿沟。

Chris has multiple entrepreneurship programs in the company that's intrapreneurship, and they're trying to bridge that gap.

但这是推动你所说的那种创新、实现商业化、触达终端消费者的最佳方式吗？有没有更好的组织方式来确保成功？

But is that really the best way to drive the kind of innovation that you're talking about to get to that commercialization of it, to get to the end consumer, or are there better ways to organize for success?

说实话，我过去三十八年都在做传统意义上所谓的创业，但最近二十五年，我创办了一家公司，它的全部使命就是孵化和创建新公司。

I mean, So I spent the last thirty eight years being involved in what traditionally would be called entrepreneurship, but the last twenty five of it, I've created a company whose whole life is to conceive and create companies.

我越来越觉得这其实是一种内部创业，因为在Flagship，我们每年都会推出六到八家新公司。

And I've increasingly viewed it as intrapreneurship, because within Flagship, we launched six to eight companies a year.

和那些行业巨头里的内部创业不同——那种情况通常只是公司一小块业务，你只是想推出新产品、开拓新市场。

And unlike a dominant player in a space where intrapreneurship usually means a small little piece of the business, where you're trying to create new products and new markets.

对我们来说，这整个公司就是内部创业，只不过我们不是靠个人或小团队自己去融资、写商业计划书。

For us, it's the whole business, but it's still intra in the sense that we're not individual, people, small teams, raising money, writing business plans.

这些我们全都不做。

We don't do any of that.

我们认为，这种企业内部的创业模式才是创新的正确路径：把团队的回报和他们实际产出直接挂钩。这跟传统研发机构完全不一样，那里总是保护员工，不让他们碰太疯狂的项目，而不是逼他们去证明自己做的事为什么合理。在我看来，这种‘合理性过滤’太无趣了，因为它逼着大家只说那些肯定能成功的事，结果反而不可能有颠覆性。

We think that kind of corporate venture creation as a way to innovate, where you align the practitioners rewards with the kind of output they generate, which is typically not the case in a traditional R and D organization, where you protect people from working on unreasonable things, instead of insisting that they describe why what they're doing is reasonable, and therefore, in my view, quite uninteresting often because the reasonableness filter forces people to say things that are likely gonna work and therefore aren't gonna be disruptive.

所以，确实有办法激发正确的动力，但在我看来，创业精神本身就能做到这一点，前提是你别硬把它塞进研发机构那套统一的框架里。我觉得，探索不同的价值池，本来就应该用不同的方式，也理应得到完全不同的回报。

So there are ways to create the right motivation, but entrepreneurship in my view allows for that, provided you don't kind of force it to be entirely commensurate with everything else in the R and D organization, which in my view is prospecting for different value pools in different ways, and therefore should be rewarded in a very different way.

我以前经营过公司，那时候负责销售的员工赚的钱经常比我这个CEO还多，但我一点都没觉得有问题。

I ran companies in the past where the sales folks who worked in my companies often made a lot more money than I did, and as a CEO, and I had no problem with that.

有些人觉得薪资得有个高低等级才行。

Other people think that there should be some hierarchy of pay.

我觉得报酬应该灵活一点，根据每个人承担的风险和创造的价值来定。

I think compensation should be variable depending on what kind of risk people are taking or what kind of value they're building.

我最后想问个问题，关于当前的政治环境，尤其是美国。

Let me just end with a question about the current political environment, particularly in The United States.

我们现在看到反科学的言论越来越多，科研经费也在被削减。

You know, we're seeing extreme anti science rhetoric and cutbacks in research funding.

你也是移民到美国的，而我们现在的政策环境里，反移民的倾向也越来越明显。

You are also an immigrant to The United States, and we've seen anti immigrant stances coming out of our policy current policy environment.

所以我很想听听你的看法：这两股趋势——反科学和反移民——对美国的科技发展到底造成了什么影响？

So I'd love to hear just your thoughts on how that's affecting scientific and technological progress in the country, sort of those two twin trends that we're seeing.

这事儿超出了我的职权范围，但作为个人，我觉得科学和移民本质上都在挑战现有的秩序。

Well, that's above my pay grade, but I will venture to say it as a personal level that both science and immigration are ways that clearly threaten the status quo.

这关乎变革。

It's about change.

这关乎进步。

It's about progress.

我觉得美国之所以吸引我，是因为它本来就是个移民国家，我觉得我没说错。

I think that The United States, I immigrated here because I consider it a country of immigrants, and I think I'm right.

凡是在美国听这段话的人，祖上都是移民。

There is nobody who's listening to this from The United States who isn't the descendant of an immigrant.

无非就是你把时间线定在哪儿罢了。

Just a question of when you cut it off.

所以我觉得美国最大的优势，在于它的自我更新能力。

And so I think the major advantage of this country is its regenerative nature.

就像我们一直说的自然规律一样——不断进化、从一次次循环中学习，美国也一样，靠着源源不断涌入的新思想、新冒险精神、还有那些拼了命想在这里闯出一片天的人，成了一个超级进化、超级灵活的社会，我觉得正因如此，它才成了一个了不起的国家。

Much like nature that we've been talking about all along, which is progressing and learning from the iteration loops that it goes through, this country has been a hyper evolutionary, hyper adaptive place because of the constant influx of new thoughts, new risk taking appetite, new levels of desperation of people coming here and trying to make it work, which I think has made for a great society.

我觉得这种优势还会持续下去，因为别的自然资源根本没法替代它。

And I think it will continue that way because that advantage is hard to replace by any other natural resource.

在我看来，有些国家的自然资源比我们还丰富。

In my opinion, there's other countries that have more natural resources than us.

也有不少国家人口比我们多。

There's other countries that have more people than us.

但我觉得，我们这个国家特别的地方在于，我们欢迎科学和知识中的不确定性，拥抱它，承认任何时候我们都可能错判了自以为确定的事——但长远来看，我们终究会找到真相。

But I think this special place we found ourselves where we welcome uncertainty in science and knowledge, and we embrace it, and we recognize that at any given time we may be wrong about what we thought we were certain about, but ultimately over time, we'll get it right.

如今搞科研之所以棘手，是因为这些争议其实早就有过几百年的辩论：什么才算真理？什么才算事实？科学方法又该如何负责任地揭开未知？

The thing about going after science today, which is troublesome, is that these are things that were fought over in various centuries old debates around what constitutes truth, what constitutes fact, and what the scientific method can do to essentially excavate the unknown in a responsible way.

但如果你把科学事实替换成观点，或者把观点包装成科学事实，那界限就彻底模糊了。

But if you can essentially replace scientific fact with opinion, or offer opinion as though it was scientific fact, that really blurs the lines.

我觉得社会得清醒一点，这种混淆的后果会很严重——药效变差、决策风险变大。

And I think that we need to, as a society, kind of recognize that the consequences of that are worse medicines, more dangers that we face in the decisions we're making.

你想想，要是造飞机都靠不住已有的科学，这事根本停不下来。

You know, if you're gonna design flying objects and you can't really rely on established science, I don't see this stopping.

我早就明说了，这股风潮不会停在mRNA或者疫苗上。

You know, I've told people pretty openly, I don't see this stopping with mRNA or vaccines.

这会影响所有药物，还有任何新出现的、让人质疑科学是否真实的东西。

It'll affect all the medicine and all of anything new that raises a question about, well, how sure are we that the science is real?

答案就是：我们有庞大的科学体系，长期坚持用假说证伪来判定当下什么是真理，这份确信就是我们的底气。

And the answer is as sure as this large scientific establishment using a long standing process of hypothesis falsification as the arbiter of truth at any given moment.

我相信，几百年积累下来的这套方法，再加上这个国家几百年来不断涌入的移民——他们让这片土地成了一个充满能力与创造力的大熔炉——这两样东西，绝对值得我们去捍卫。

And I'm pretty sure that ultimately the benefits of the centuries of experience we have in doing that, coupled with the centuries this country's had of immigrants flowing into this soup, if you will, of this melting pot of of of capability and creativity, I think those two things are worth fighting for.

努巴尔，非常感谢你。

Noubar, thank you so much.

今天特别感谢你抽出时间来聊这些。

I really appreciate you taking the time today.

谢谢你们邀请我。

Thank you for having me.

这是努巴尔·阿费扬，Flagship Pioneering的创始人兼CEO，也是Moderna的联合创始人兼董事长。

That's Noubar Afeyan, founder and CEO of Flagship Pioneering and cofounder and chair of Moderna.

下周四再见，继续聊聊商业的未来。

Check-in next Thursday for another future of business episode.

如果你觉得这期节目有帮助，别忘了分享给同事，记得在苹果播客、Spotify 或你听播客的平台订阅并给 IdeaCast 评分。

If you found this episode helpful, share it with a colleague, and be sure to subscribe and rate IdeaCast in Apple Podcast, Spotify, or wherever you listen.

如果你想支持领导者推动世界前进，不妨订阅《哈佛商业评论》。

If you wanna help leaders move the world forward, please consider subscribing to Harvard Business Review.

订阅后你能使用 HBR 手机 App，每周收到独家内参简报，还能无限访问 HBR 网站内容。

You'll get access to the HBR mobile app, the weekly exclusive insider newsletter, and unlimited access to HBR online.

赶紧去 hbr.org/subscribe 订阅吧。

Just head to hbr.org slash subscribe.

感谢我们的团队：高级制作人玛丽·杜、音频产品主管伊恩·福克斯，还有高级制作专员罗布·艾克哈特。

Thanks to our team, senior producer Mary Du, audio product manager Ian Fox, and senior production specialist Rob Eckhart.

也感谢你收听《HBR IdeaCast》。

And thanks to you for listening to the HBR IdeaCast.

我们下周二再见，带来全新一期节目。

We'll be back with a new episode on Tuesday.

我是艾莉森·比尔德。

I'm Alison Beard.