亚历克斯·卡纳尔——万亿美元的医疗革命——《像最佳投资者一样思考》第 468 集

所以我们生产的PCSK9越多，能清除的LDL颗粒就越少，胆固醇随着时间推移不断升高，这些微小颗粒逐渐侵入血管，越积越多，最终导致堵塞。

And so what happens is the more PCSK9 we're producing, the fewer particles of LDL that we can clear, the higher our cholesterol grows over time, the more those little particles are invading our vasculature and starting to mount up until we get to a point where there's a blockage.

通过观察人类遗传学中的这一现象，结合对这些人群长达十五年的追踪研究，我们发现，那些天生无法正常产生PCSK9蛋白的人，患心血管疾病的风险降低了88%，这意义重大。

Recognizing the import of that PCSK9 protein driven from the observation of human genetics And from data that showed in these populations of people with longitudinal studies over fifteen years, the fact that they were genetically deficient in the production of the PCSK9 protein led to them having an eighty eight percent reduction in the risk of ever developing cardiovascular disease.

这简直不可思议。

That was miraculous.

于是制药行业开始思考，怎样才能

And so what the pharmaceutical industry sought to do was how can we

复制这种效果。

Replicate this.

没错。

Exactly.

我们又该怎么把这种优势带给更多人呢？

And how do we get that advantage into the hands of people?

他们最初研发出的是注射型生物药，注射后，药物会进入体内，找到PCSK9蛋白并与之结合，阻止它和LDL受体结合。

And so they came up with injectable biologic medicines at first that you can inject and would go and find their way in your body to that PCSK9 protein and bind to it and make it so that protein couldn't bind to your LDL receptor.

这便是这些药物的开端。

And that was the beginnings of those medicines.

但接下来，创新者们想的是：怎么让患者更方便地使用这些药，从而获得益处？

But what innovators then sought to do next was how do we make it easier for people to take those medicines and get the benefit?

于是，这个领域下一个重大突破，就是从注射型生物制剂或单克隆抗体，转向一种叫作RNA干扰的新疗法。

And so the next big innovation in that space was moving from injectable biologics or monoclonal antibodies to what's a new modality called RNA interference.

这些药物同样是皮下注射，会进入肝脏发挥作用。

And what these are is another form of subcutaneously injected medicine that makes its way down into your liver.

它们的特点是作用时间极长，让我们一年不用注射26次，可能只要两次就够了。

And it had the attributes of these being very long duration molecules and made it so that we didn't have to inject ourselves 26 times a year but maybe only twice.

想想看，找到一群天生具有优势的人群，弄清楚他们的机制，再把这种优势通过药物传递给其他人，真是不可思议。

It's incredible when you think about seeking out a population that has natural advantage, figuring out what it is and then conferring it to others through a drug like this.

心血管疾病发病率的大幅下降——无论是那些基因天然缺陷的人群（降低了88%），还是服用药物的普通患者，都太引人注目了。

The dramatic reduction in cardiovascular incidence of people, of course the genetic group eighty eight percent but also the people that take this drug is so interesting.

我总是想，人体这么复杂，生物学里很少有东西只有一种功能或单一的解释。

I always wonder the body is such a complex system, There seems to be very few things in biology that just are one use or one explanation.

现在有各种各样的事情在发生。

There's all sorts of stuff going on.

那代价是什么呢？

What are the trade offs?

我对PCSK9和GLP-1都很感兴趣，因为我们之前已经讨论过这两个了。

I'm interested in both PCSK9 and GLP-one since we've talked about both.

好像总得付出点什么代价。

It just seems like there's always a trade off.

在市场、自然和生物学里，通常都没有白吃的午餐。

There really aren't usually free lunches in markets in nature and in biology.

持怀疑态度的人会怎么说？

What's the skeptics point of view?

如果我能请来最聪明、最注重数据和科学的怀疑者，他会怎么看待这些药物的使用？

Like if I could somehow fly in the smartest data driven scientific skeptic of the use of these drugs, what would that person say?

我觉得PCSK9比GLP-1更像是一顿免费的午餐。

I think PCSK9s are very much more that free lunch than GLP-1s.

GLP-1类药物确实有明确的毒性反应。

GLP-1s have real toxicity associated with them.

临床研究显示，恶心、呕吐和腹泻是最早出现的副作用，很多患者因此早早停药。

We see in the clinical studies significant rates of nausea, vomiting and diarrhea are the first symptoms that emerge that cause a lot of people to quit these medicines early.

如果再看那些长期使用这些药物的人，翻翻药品说明书和临床数据，还会发现胆结石和胰腺炎的风险。

And if you look past that and you look to people that have been on these medicines for longer and longer periods of time, you look at the labels of these medicines and the clinical studies, there's risk of the development of gallstones and pancreatitis.

当然，这些问题其实也和减重本身有关，减重还会带来肌肉流失。

And of course, these are issues that would emerge just from having weight loss in general, which adds that list muscle decline as well.

这些全都是短时间内快速减重带来的后果。

These are all consequences of losing weight and losing weight quickly over a period of time.

但另一方面，这些药带来的益处也很明显——它们在多个方面都有很强的保护作用，数据非常清晰：不仅能降低患糖尿病、肥胖和心血管疾病的风险，而这些疾病加起来，平均会让我们少活五到十年。

But when you look at the other side of the equation, the benefit that you get, the fact that these are so protective across so many different axes and the data is pretty clear, not only will they protect us from developing diabetes, obesity, and cardiovascular disease, you look at the implication of those diseases on expected human lifespan, we're talking about the aggregate of those diseases costing us five to ten years.

为了这种显著的益处，承担这些可控的副作用风险是完全值得的，实在不行停药就行了。

It's very much well worth it for people to take the risk of developing these other subliabilities that you can do something about by just stopping the medicine for the benefit.

毫无疑问，这确实是一个权衡。

There is a real trade off without a doubt.

GLP-1这类药可不是免费的午餐。

There's not a free lunch for GLP-1s.

但PCSK9抑制剂几乎就是免费的午餐。

But for PCSK9, it's pretty much a free lunch.

早期的时候，大家确实很担心。

There was a ton of concern in the early days.

如果LDL降到零，会怎么样？

What happens if your LDL goes down to zero?

难道LDL就没有好处吗？

What if I felt, doesn't LDL do good things?

它存在肯定是有原因的。

It's there for some reason.

结果发现，我们身体的多个部位都会产生LDL。

And it turns out that that LDL is produced in lots of different places in our bodies.

实际上，你完全可以把PCSK9这个有害蛋白抑制到接近零。

It turns out that you can basically pin that PCSK9 bad protein down to zero.

在动物模型中，我们能把LDL降低到80%到90%之间，这已经是目前能做到的极限了。

The best we can do in terms of reduction of our LDL from really not even in humans but in animal models is something in the 80 to 90% zone.

但现实中有些人天生携带突变，完全不产生PCSK9，他们不仅活得长、身体特别健康，还根本不会遭遇心脏病或中风的威胁。结合动物实验表明LDL不可能降到零，再加上这些基因优势人群的实际存在，这简直就是一个完美的无本买卖。

And because we have people that are walking around mutations where they don't produce any PCSK9 and they live incredibly long healthy lives and don't have the vicious fate of having a heart attack or stroke, I think we've got the combination of animal model evidence that suggests we really can't get LDL to zero combined with the fact that we've got genetically advantaged people walking around that aren't even producing the protein to the same degree as others that this one is very much a free lunch.

你觉得将来全世界每个人都会用上这种药吗？

Do you think everyone in the world will be on this particular drug at some point?

那对我来说简直是梦想成真。

That would be a dream for me.

我最大的愿望是，无论谁，只要想获得最大程度的心血管保护，都能用上这种药，并且长期坚持使用。

A big dream for me is that everybody anywhere who wants to have the maximum level of cardio protection could get access to this medicine and could stay on it.

因为它的优势更明显，这种‘无本买卖’的红利也更突出。

Because it is more asymmetric, it is more in the degree of that free lunch.

如果放眼更长远的未来，这种药的使用者数量和市场规模，绝对会远超GLP-1类药物。

I would argue that if you just project down the road longer term, this medicine should be far bigger in terms of the number of people that are on it and the revenues that are associated with it than GLP-1s.

但现在的情况还不是这样。

That's not what's playing out right now.

但现在还没实现，其实有两个原因。

And it's not playing out right now really for two reasons.

第一，GLP-1类药物有个特别明显的好处：你一吃下去，很快就能感受到副作用，也立刻能体会到效果。

One, GLP-1s have this amazing attribute to them that you take them and very quickly you start to have some side effects, you know you're on them and you start to see the benefits.

相比之下，那些能带来即时、明确且容易测量的益处的药，更容易让人坚持服用。

When you compare the two, it's easier for medicines where there's an acute clear, easily measurable benefit for people to stay on them.

但说到心血管疾病，尤其是坏胆固醇，这恰恰是最危险的情况——因为你根本感觉不到坏胆固醇高了。

Unfortunately, in the setting of cardiovascular disease and particularly thinking about bad cholesterol, this is to me the most dangerous setup because you don't feel anything if you have a high level of bad cholesterol.

它就像个沉默的杀手，悄悄在体内积累，一点一点地造成伤害。

You could think of it as a silent killer that's just working in the background, slowly accumulating in our bodies.

你可能饮食健康、锻炼到位，身体状态达到巅峰。

You can be doing everything right and being at your peak physical shape.

可突然之间，毫无预兆地，你就可能突发心梗或中风。如果我们不留意体内的坏胆固醇水平，它就会悄无声息地逼近，夺走我们的生命。

And then all of a sudden out of nowhere, you can have a heart attack or a stroke because if we don't pay attention to the levels of this bad cholesterol in our body, they can sneak up on us and claim our lives.

所以对我来说，这种药的收益远大于风险，越早干预、越早控制胆固醇的积累，我们活到老的几率就越大。

And so to me, because the reward is so asymmetrically favorable versus the risk, because the earlier we can intervene with the accumulation of cholesterol in our body and keep that at bay, the greater the likelihood is that we're gonna get through the rest of our lives.

即使其他问题出现了，比如糖尿病、炎症升高或血压上升，我们也能直击问题核心，把这些问题控制在最低水平。

Even if other things manifest like diabetes or high inflammation or our blood pressure goes up, we're gonna be able to be right at the core of the problem and keep that as low as possible.

也就是说，我们能让血管里的沉积物降到最低。

Meaning that we've got the lowest level of accumulation across our vasculature.

我认为排在第三和第四位的死因，我们之前谈过代谢疾病和心血管病，接下来是神经退行性疾病和癌症。

I think the number three and four killers, we've talked about metabolic disorders and cardiovascular, are neurodegenerative and cancers.

我想花几分钟时间分别聊聊这两类。

So I'd love to spend a couple minutes on each of those.

尤其是神经退行性疾病，像阿尔茨海默病的药物研发，这么多年几乎是一片荒漠。

Neurodegenerative in particular, Alzheimer's drugs, seems to have been kind of this wasteland.

我们对到底发生了什么，其实还很不清楚。

We really don't understand what's going on.

因此，在预防或治疗痴呆、阿尔茨海默病方面，我们进展甚微。

As a result, we haven't made a lot of progress at either protecting against or treating something like dementia, Alzheimer's.

现在科学界和制药行业，到底在怎么应对这个问题？

What is going on in the world of science and in the business of pharmaceuticals to address this specific problem?

几十年来一直没什么进展。

It's been a wasteland for decades.

我觉得我们终于摸到了一点门道，但还没找到全部答案。

I think we finally cracked the beginning of the code but not the full answer.

今天我们发现，对于那些大脑中已经积累多年斑块的患者，现在有了药物可以帮助分解并清除这些斑块。

What we found today is in patients that have confirmed levels of plaques that have developed over decades in their brains, We now have medicines that people can take that will start to break up those plaques and remove them.

但很遗憾，这一点其实不难理解——如果等到疾病晚期才干预，大脑早就受损了，因为这些斑块一直在堆积，挤压并破坏周围的神经组织。

Unfortunately, and this will be intuitive, if you get to the patient that late in the disease, a lot of damage to the brain has already happened because you can imagine like these plaques are building, they have to go somewhere, they're causing death and destruction of the tissue around them.

目前，百健和礼来都推出了有效的药物，即使患者已经进入阿尔茨海默病的中晚期，也能帮助减缓认知衰退。

Today, we've got some amazing medicines out of both Biogen and Eli Lilly that if you are late in the disease progression of Alzheimer's, you can start to slow the decline.

我们能将衰退速度降低大约百分之三十。

And we can slow that decline by about thirty percent.

这意味着，患者能更长时间保持原有的认知水平，而不是迅速下滑。

And so intuitively, that just gives us a lot more years at the baseline level of cognition that we have versus declining much more rapidly.

如果我们能更早地识别出有阿尔茨海默病风险的人，而且已经有检测手段可以做到，那自然就应该尽早用药——这样不仅能赶在严重损伤前干预，还能像关掉水龙头一样，从源头上阻止病情发展。

It turns out that if we can identify a patient that's at risk for Alzheimer's disease earlier and earlier and earlier, if we there's a test available to do that, well then it makes perfect sense that if we can start giving people these medicines as early as possible, not only will you be there in the early stages before a lot of damage is done, but you can think about it as turning off the faucet.

当这些新的淀粉样蛋白斑块形成时，你可以用药物来加速它们的清除。

So as these new amyloid plaques are produced, you can be attacking them with a medicine that helps to accelerate their clearest.

天啊，这可能会对长期的认知衰退产生巨大影响。

And oh my goodness, maybe that has a dramatic effect on long term decline.

也许在更长的时间内，能把衰退率降低百分之四十、五十甚至更多。这意味着，如果我们能更早发现患者，药物又能更有效地清除斑块，而且患者更容易坚持用药，我们或许会迎来一个不再受这种疾病困扰的世界。

Maybe it reduces those rates by forty, fifty or more percent over a longer period of time, which means the earlier we can get them and the better those medicines are at interacting with those plaques and the easier it is for those people to stay on those medicines, we might be able to wake up in a world where we live without this disease.

我们已经知道，GLP-1类药物对糖尿病和肥胖症就是这样的效果。

We know that that's possible at GLP-1s for diabetes and obesity.

如果你尽早开始服用，就根本不会发展成糖尿病。

If you take them early enough, you will not become diabetic.

你也根本不会变得肥胖。

You will not become obese.

心血管疾病也同样如此。

We know that's possible for cardiovascular disease.

如果你在人生早期就开始服用PCSK9抑制剂——顺便说一句，从健康收益来看，它的风险收益比甚至可能优于他汀类药物——你就不会进展为心血管疾病。

If you start taking a PCSK9 inhibitor earlier in your life, which by the way, think the risk reward is actually even more favorable than statins in terms of human health, but we know that you won't progress to cardiovascular disease.

阿尔茨海默病也是一样的道理。

The same is true for Alzheimer's.

到目前为止，我们可以想象一个未来世界——就像你所设想的那样，每个人都能享受到这套健康防护体系带来的好处，专门应对这三大致命疾病。

So up to this point, we can imagine a future world as I know you do where everyone can enjoy the benefits of protective defensive measures in our health stack for these three specific killers.

那癌症呢？

What about cancer?

癌症是最后一个，某种程度上最让人感到真实、最吓人的。

Cancer is the last one that in some ways feels the most visceral and the scariest.

当一个人确诊癌症，或者身边有人得了癌症，那种感觉就像判了死刑，简直令人恐惧。

Here you have cancer or someone has cancer, it feels like this death knell or just terrifying piece of news.

我们在癌症治疗上已经取得了巨大进步，但我很好奇，你是怎么看待预防和治疗各种癌症这个问题的？

I think we've made huge strides in treating cancer but I'm curious how you would frame up the problem that is dealing with preventing, treating all different kinds of cancer?

如果我们能成功动员大家建立一套健康防护体系，从被动治病转向主动维护健康，那一个意想不到的结果就是：人们会活得更久。

If we are successful in mobilizing people around a health stack and we can move from reactively treating disease to helping people be as proactive about their health as possible, I think the unintended consequence of that is people live longer.

但终究，总有什么会找上我们。

Eventually something's gonna get us.

导致我们死亡的癌症发病率，可能会继续上升。

The incidence of cancer being the issue that brings us down, the disease that brings us down could rise.

现实有点残酷，癌症很难对付，一方面我们常常发现得太晚。

I think the unfortunate reality is that cancer is hard, both because we often identify it too late.

一旦发现，癌症就像个狡猾的魔鬼。

And then when we do, cancer is like a sneaky devil.

如果癌症早期就出现，你或许能找到突破口来应对它。

You might have an angle of attack to go and beat that cancer that presents itself early.

但就在你用药物取得进展时，癌症突然就变了招。

And as soon as you're making progress with a medicine against that angle of attack, all of a sudden, the cancer has a survival instinct, and it redirects.

之前效果极好、甚至让肿瘤完全消失的药，现在却一点用都没有了。

And now a medicine that was working incredibly well that had shrunk a tumor completely to zero is having no effect.

因为这种药杀死了最脆弱的癌细胞，那剩下的呢？

Because while that medicine eliminated the cells that were most vulnerable to the medicine, guess what?

剩下的那些细胞，哪怕只剩几个，反而获得了优势，迅速增殖，造成新的伤害，把患者推向更糟糕的境地。

The cells that were left behind, even if it's just a few, now had the advantage in growing rapidly and causing a new set of harm and setting a patient on a horrific direction.

所以很难，因为我们通常发现癌症的时候已经太晚了。

And so it's hard because we tend to identify cancer too late.

而一旦发现了，它又在不断变化。

And then when we do, it's shifting.

不过，这里也存在让人乐观的机会。

Now there's also the opportunity for optimism around it.

这正好引出了我们之前聊过的话题——筛查。

And I think it gets the topic you and I've spent some time talking about, which is testing.

目前我最看好的筛查方向，是主动出击、尽早发现患病风险。

The areas of testing today that I find most exciting are across being proactive and early identification of the risk.

一旦癌症真的出现了，我们又该怎么做？

And then once a cancer emerges, what do we do about it?

所以我们要同时考虑这两种方式，再回头看看相关的治疗药物。

And so talking maybe across both of those modalities and then come back to the medicines that are associated with that.

在早期发现癌症方面，像Exact Sciences和Guardant Health这样的公司，已经在突破性技术上投入了大量精力，帮助我们筛查结直肠癌。

In terms of identifying cancer early, there are companies like Exact Sciences and Guardant Health that have gone really hard at breakthrough innovations to help us screen for colorectal cancer.

当然，目前我们还有金标准检查方法，就是结肠镜，我明年就得去做一次。

Now, of course, we have the gold standard that's available, which is a colonoscopy, and I have to go get one next year.

但没人真期待做这个，毕竟大家都不想做，尽管其实每个人都应该定期做。

But none of us are ever looking forward to that because it's something people don't look forward to, even though everybody should be getting them and with regular periodicity.

可大多数人就是不去。

Most people don't.

我觉得这太遗憾了，因为不做结肠镜，你就把自己暴露在结肠癌的风险里，而这种癌长得特别慢。

And I think that's really tragic because by not getting your colonoscopy, you're putting yourself at risk for colon cancer, which is an incredibly slow growing cancer.

只要你定期做结肠镜，今天我们就完全可能把结直肠癌的发病率降到很低。

If you just got the colonoscopy, I think we could very much live in a world today with very low incidence of colorectal cancer.

Guardant Health 和 Exact Sciences 直接瞄准了这个问题。

Guardant Health and Exact Sciences went directly at this problem.

Exact Sciences 做了一个粪便检测，能非常准确地判断一个人患癌的风险。

Exact Sciences has a stool based test that has incredible ability to predict people being at risk of cancer.

Guardant Health 也是同样的思路。

Same thing for Guardant Health.

Guardant Health 进了一步，他们明白，虽然可以通过检测某些标志物来预测结直肠癌，但要让普通人不仅拿到粪便检测的处方，还能真正完成检测并寄回样本，这个商业模式太复杂了，根本没法像抽血检测那样大规模推广。

Guardant Health took it to the next step in terms of knowing that you can go after markers that would predict colorectal cancer, but recognizing that the commercial model around being able to get people to not just receive the prescription for a stool based test, but the complexity of people being able to complete the test and get that sample back was one that just could never have the scale as a simple blood test.

所以，Gardner 从 Exact Sciences 的进展中吸取了经验，开发出新一代的简易抽血检测， anywhere 都能轻松做。

And so Gardner learned from Exact Sciences advancement and then came up with a next gen version as a simple blood test that you can get done just about anywhere.

看到这两个市场都在持续增长，而抽血检测的使用率突然飙升，这真的让我很振奋。

And it's been amazing to see that not only are both of those markets continuing to grow, but the blood test is inflecting, that to me is exciting.

因为这说明，人们真的在意了。

Because it means that again People care.

医生在意，患者也在意，大家都愿意去做检测了。

Doctors care, people care, they're getting it done.

这意味着我们离远离这些疾病会越来越近。

And that means we're gonna be safer and safer from these.

但我觉得大家得明白，不是所有检测都是一样的。

But I think it's important for people to realize that not all tests are created equally.

每当我看一个新诊断技术时，我最关心的就是两个关键指标。

When I look at a new diagnostic in the space, I care deeply about two metrics.

一个是灵敏度，另一个是特异性。

One is the sensitivity and the other's specificity.

其中一个指标告诉我们，在所有可能患癌的人中，我们实际能准确检测出癌症的样本占多大比例。

One of them tells us of all the people that could have cancer, what percentage of those samples are we actually getting back that is an accurate diagnosis of cancer?

比如说，一百个癌症样本里，有多少能被正确识别出来？

So if there are a 100 cancers in a sample, what percentage of them actually get picked up as cancer?

这就是灵敏度。

That's the sensitivity.

而特异性指的是，一百个没有癌症的样本里，有多少会错误地显示为阳性，误判为有癌？

And then the specificity is if there's a 100 samples that don't have cancer, what percentage of those give us a false positive saying that you have cancer and when you don't?

我觉得这两个指标都非常重要。

I think both of those are really important metrics.

同时，我现在也看到一个全新的行业正在兴起，那就是多癌种早期检测，它正以前所未有的速度发展。

And at the same time, I think that there's a whole other industry around multi cancer early detection that's evolving right now and unfolding right before our eyes.

这些公司不再只做单一癌种的检测，而是转向覆盖多种高危肿瘤类型的多癌种联合检测。

These are companies moving from not just having single cancer tests, but having multi cancer tests across large numbers of tumor types that can be really dangerous.

当我谈到健康体系中的进攻和防守两方面时，那些能让我们最大程度防护的药物里，唯独没有直接预防癌症的手段。

When you think about how I describe the offensive and defensive side of the health stack, the defensive medicines that we can take to keep us maximally protected, What's not in there is anything to protect us directly from cancer.

但这因为我把癌症的防控放在了筛查这一块，归为进攻策略了。

But that's because I tried to attack cancer from the screening perspective and made that part of offensive.

长远来看，我觉得至少每个人都该做一次结肠镜检查。

Over time, I think at a minimum, people should be doing a colonoscopy.

目前更理想的做法，是把结肠镜检查和血液检测或粪便检测结合起来，定期重复进行。

I think that the next best in class version of that is colonoscopy married with either a blood test or a stool based test with some regular periodicity to it.

未来三到五年，随着人工智能和这些检测技术的快速融合，检测速度会越来越快，准确率也会越来越高，我相信再过五年，我们会突然发现，已经有了非常出色的多癌种早期筛查技术。

And I think over the next three to five years, particularly with how AI and the evolution of these tests are really converging to help them to move faster and faster and to be better and better, I think we're gonna wake up within the next five years with really awesome multi cancer early detection tests.

等这些技术普及了，如果我们还不去做检查，那就是我们的失职；如果我们做了却忽视结果，那更是我们的失职。

And when we have them, then shame on us if we're not getting them and shame on us if we're not acting on the learnings that come from them.

这听起来显而易见，但越早发现癌症，我们能采取的应对方案就越丰富。

This will be obvious, but the earlier we know that we have a cancer, the greater the range of options are for us to go do something about it.

而且我们发现，针对多种不同癌症，都有机会取得非常理想的效果。

And I think we're finding that across lots of different cancers, there's opportunities to have amazing results.

很遗憾，我姐夫的父亲差不多十年前因多发性骨髓瘤去世了。

Sadly, my brother in law's father passed from multiple myeloma maybe almost a decade ago.

如果他今天被诊断出多发性骨髓瘤，预后会完全不同——不仅我们检测和发现癌症的能力提升了，还能通过其他检测手段分析癌细胞的基因突变，精准匹配当前最有效的治疗药物，而且这些药物本身也在不断进步。

If he had been diagnosed today with multiple myeloma, his outcome, prognosis is dramatically different because not only is our ability to test and identify that people are developing cancers getting better, not only do we have other types of tests that we can look at what are the mutational drivers of that cancer that help to connect not just cancer, but the type of cancer to the best medicine that we have today to go treat it, but the medicines are getting better and better.

癌症药物最初和很多药一样，主要是口服药和注射型生物制剂，但现在出现了一种全新的疗法——CAR-T细胞治疗：要么把患者体内的细胞取出来，改造后再回输，让它们直接攻击癌细胞；要么像Capstan这样的公司推出的下一代疗法，直接通过静脉注射就能在体内精准杀伤癌细胞，根本不需要取出细胞。

While cancer medicines began as many medicines do with just pills and injectable biologics, today we have an entirely new class of medicines that are these CAR T cells that are either cells that we take out of a patient's body and arm them with the ability to go attack the cancer directly, or companies like Capstan just came up with the next gen version of that where they can inject on an IV basis that can attack the cancer cells directly without having to even take cells out of a patient's body and have that happen within the patient's own body.

这些新药的疗效非常显著。

And what we're seeing from these medicines is dramatic efficacy.

数据显示，它们能让肿瘤完全消失，在七成以上的患者身上实现长期控制。

So we're seeing that they have the potential to reduce tumors by a hundred percent and to do that in seventy percent plus of patients and to keep them at bay for really long periods of time.

虽然我们还没彻底治愈癌症，但我觉得这条进步曲线实在太令人振奋了。

While we haven't yet cured cancer, to me, I think the slope of the line is really exciting.

无论是检测技术的进步，让我们能更早发现癌症，还是治疗手段的多样化，让我们有更多方式去打击和清除癌细胞。

Both from the testing angle and the earlier we can identify these cancers and from the increasing range of modalities and options that we have to go after and destroy them.

那影像技术呢？比如皮肤成像，像Neko这样的公司就在做；还有体内深层成像，像Pre Nuvo这类公司。虽然目前价格还很高，但当我把健康体系看作一套预防性工具时，你怎么看待这些新技术？它们试图用图像揭示身体内部的细节，提前发现肿瘤之类的问题。

What about imaging, both skin imaging, interesting companies doing that like Neko, deep body imaging, companies like Pre Nuvo, it's quite expensive but as I think about the health stack as something that we could do that's preventative, How do you feel about those technologies which are relatively new but do try to take a picture worth a thousand words inside your body and identify a tumor earlier or something like that.

你觉得这值得吗？

Do you think that's worth it?

对我来说，只要能放在正确的背景下，数据越多越好。

To me more data is worth it so long as you could put it in the right context.

当我们拿到结直肠癌筛查的结果时，我们知道这个结果的意义，也能判断它到底有没有用。

When we get the result of a colorectal cancer test screening test, we know the context for that and we actually know whether we have something that's useful or not.

像PreNuvo这样的公司提供的影像非常有力，因为我们可以看到病情的发展趋势，把这些数据用起来——尤其是随着AI越来越成为我们的医疗助手，给AI系统注入这些信息会特别有帮助。

The pictures that we can get from groups like Prenovo and others are powerful because we can see progression over time and we can use that data and particularly as AI is gonna increasingly become our medical home, arming our AI system with that information will be super helpful.

但我担心的是，当你测量的维度越来越多，而每个维度都有显著的假阳性率时，最终出现一个会严重影响生活的假阳性结果的概率就会变得很高。

The thing that I worry about though is as you measure more and more different dimensions with each of them having a material false positive rate, the odds that you come out of that experience with a false positive that can be disruptive in life becomes material.

这并不是想劝阻任何人去做这些检查。

It's not to discourage anybody from doing these.

只是希望我们收集的每一份数据，都能放在合适的背景里，这样我们才有依据去应对检测结果带来的各种信息。

It's just to make sure that any data that we're collecting, make sure we put it into the right context set so that we can have a framework for how we deal with whatever information comes from taking that test.

所以就我个人而言，目前还没有去尝试这些。

So for me personally, I'm not yet pursuing those.

我特别关注那些一旦做检测，就能高概率准确捕捉到目标、同时误报率很低的领域。

I'm very much focused on areas where if we're gonna go and do a test, it's gonna have a high probability of capturing what I'm testing for and a low probability of giving me a false reading.

我认为，如果我们能积累更多高精度的信息，就能更主动地管理健康。

And I think the more information that we can accumulate with really high fidelity, the better we'll be at being proactive about our health.

今天我们聊到的所有内容，都是由一群极其出色的人发现和开发出来的——既有科学家和医疗专业人士，也有把它们变成优秀产品和成功企业的商业人才。

Everything we talked about today was discovered and developed by incredibly talented people, both scientists, people in the medical field, but then business builders that turn these things into great products and great companies.

不妨简单说说，这个过程到底是怎么发生的，别觉得理所当然。

Maybe just describe so as not to take it for granted what that process is in the first place.

生物学本身就很复杂。

Back to biology is complicated.

我们居然能做到你刚才说的那些事，简直难以置信，太惊人了。

It is unfreaking believable we can do the things that you've been describing so far It's pretty wild.

而且我们正朝着越来越精细的程度去干预生物学过程，这种趋势只会越来越快。

In this And that we're on a trajectory to the granularity with which we can make edits for lack of a better term into what's going on in biology is probably just gonna continue to accelerate.

我想先搞清楚这个过程本身，然后再聊聊人工智能在其中扮演的角色。

So I wanna understand that process itself and then we can talk about how AI figures into that discovery process.

首先，推动发现发生的两个关键要素是什么？

First of all, what are the two key ingredients for discovery to happen?

你需要科学直觉，再加上大量的资金支持。

You need scientific instincts married with tremendous amounts of capital.

我认为，只有当这两者交汇时，才可能带来突破，让我们活得更健康、更长久。

And I think you need both of those to converge in order for there to be a breakthrough that allows us to live healthier and longer lives.

我们来拆开看看，医学的科学层面和医学的商业层面，因为我觉得这两部分本身都很有意思。

Let's break those into their pieces, both the science of medicine and the business of medicine, because I think they're both independently interesting.

很多时候，一种新药的发现，源于某个科学家在人生旅程中，因为某种原因被激发，决定毕生致力于破解某种疾病的谜题。

Oftentimes what ends up happening to discover a medicine is a scientist, for whatever reason in their journey, gets inspired to spend their lives trying to crack the code in a certain disease.

就像开发任何消费产品或科技产品一样，一旦你明确了想解决的问题，接下来就会发生这样的情况：

What tends to happen is as you've identified a problem that you wanna solve, just like in any consumer or tech product, what's the problem we're trying to solve?

然后你必须投入大量研究，全面了解这个问题的方方面面，最重要的是，找出它的根本成因。

You then have to go through a tremendous amount of research to understand every single thing you can about that problem and most importantly, what could be driving it.

随着时间推移，人们发展出了一种方法——可能是人类历史上最重要的方法之一，叫做科学方法，它提供了一套系统性的路径，帮助科学家从聚焦问题，最终走向解决问题。

What's developed over time is a method, probably one of the most important methods in the history of humanity called the scientific method, which is the systematic way by which a scientist moves from focusing on a problem they wanna solve to actually solving it.

科学方法里有几个明确的步骤，要破解这个难题，往往需要反复试验很多次。

And there's a couple of discrete steps in that scientific method and tremendous amount of iteration at least to cracking that code.

一旦确定了要解决的问题，下一步就是提出一个假设，推测是什么因素导致了这种疾病。

Once you've identified the problem that you wanna solve, the first step to solving that is come up with a hypothesis for what could be driving that disease.

科学家通常会从各种角度入手，比如PCSK9的例子，就是通过观察自然现象来找线索。

And the way scientists tend to do that is everything from like the PCSK9 example where scientists are observing nature.

有没有哪些人群天生对这种病有抵抗力？

Where are there populations of people that have an advantage?

明白了。

Okay.

咱们去查清楚这个现象。

Let's go figure that out.

这很有意思。

That's cool.

咱们深入研究一下背后的原因。

Let's go understand it.

还要翻遍成千上万篇文献，搞清楚别人之前为破解这个至今仍未解决的问题都试过哪些方法。

All the way through to like combing through thousands and thousands of pages of the literature to understand what people have tried to crack a code that still exists today that you wanna go crack.

这可是一场深度的文献梳理。

And that's a deep literature review.

幸运的是，现在有大量论文记录了各种成功案例，或者哪怕只是微小的进步，这些资料都是公开可用的。

And so, you know, fortunately there are tons of papers that exist on successes or tiny steps to successes that exist and are available for anybody.

但文献的遗憾之处在于，往往缺失了那些失败的尝试，而这些失败其实比成功更有启发性。

The unfortunate part of the literature is that what's often missing is all the failures, which would be even more informative than successes.

所以一般来说，当科学家想解决这个问题时，他们会先观察某个群体，然后大量阅读相关研究，再把这两方面的发现结合起来，以此为基础提出第一个可能的解决方案假设。

So generally speaking, when a scientist wants to go solve that problem, they're gonna observe a population, they're gonna start reading a ton of research, they're gonna probably put both of those insights together and use that foundation to form their first hypothesis for what could be the solution to the problem.

有了假设之后，优秀的科学家就会进一步思考：我该设计哪些实验来验证这个假设？更重要的是，我对实验结果会有什么预测？

So from having a hypothesis, then the amazing scientists can then go figure out what experiments do I need to run to test that hypothesis and even more, what's my prediction for what should happen in those experiments?

这些步骤非常关键，因为实验的设计和从实验中获得的数据质量，要么会支持你的假设，让你继续推进，要么会推翻它，迫使你回到起点，提出新的假设，设计新的实验，如此反复循环。

And those are really important steps because the design of the experiment and the quality of the data that comes out from that experiment will either confirm your hypothesis and you move forward, or it will reject your hypothesis and then you go back through and you loop to your next hypothesis and your next set of experiments, and you continue to loop and loop and loop.

所以科学方法的大致流程就是：提出假设，设计实验，执行实验，分析数据，把结果和原始假设对比，要么确认假设，要么不断循环，一遍又一遍地迭代。

So what scientific method generally looks like is hypothesis, experimental design, execution of that experimental design, review of the data that comes from that experiment reading out, test that data result versus your original hypothesis, either confirm the hypothesis or re loop and re loop and re loop and re loop.

所以这个迭代过程通常从文献入手，很快就会转入所谓的体外实验，也就是在培养皿里进行的实验。

And so there's this iterative process that tends to begin its journey from the literature, very quickly move into what we call in vitro experiments or experiments that are done in petri dishes.

当我们通过细胞实验积累起支持假设的证据后，研究就会逐步转向小型动物，再进一步到大型动物。

And as we start to develop evidence and support for a hypothesis in cell based experiments, it tends to move on to small animals and then larger animals.

等到你从假设出发，经过细胞实验、动物实验，收集了所有数据和证据后，就会把这些成果整合成一份完整的材料，提交给监管机构，说：我觉得我破解了这个难题。

And then it gets to the point where the totality of the data and the evidence that you've collected from a hypothesis perspective to cell based experiments, to the animal based experiments, you put that all together in a beautiful package that you present to regulators and you say, I think I've cracked the code.

我觉得我们能借此推动对疾病A、B或C的治疗进展。

I think I can move us forward against disease a, b, or c.

这就是我们当初提出的假设。

And here's the hypothesis that we had.

这是我们验证假设的方法。

Here's how we tested it.

这是我们在研究中积累的所有数据，它们让我们科研团队相信，我们可能真的破解了这个难题。

Here's all of the data we've generated that helps me, the scientific team, to believe that we might have cracked this code.

现在我们希望获得您的批准，共同确定下一步该开展哪些研究，来评估这项突破性成果可能对人体造成的毒性或风险。

And now we would like your approval to go start and agree on what studies should look like to test how toxic or how dangerous this breakthrough could be for somebody.

如果我们能证明，远高于有效剂量的高剂量才开始出现毒性，而我们实验中确定的剂量足以帮助人们活得更久、更好、更健康，那我们就希望获得批准，首次在人体上进行测试。

And if we can show that there's good margin between really high doses and where that toxicity starts to come in and the doses that we've proven from our experiments are likely to help somebody live a longer, better, healthier life, we want your permission to then go and actually test that in a person for the first time.

这真是太了不起了。

And so this is incredible.

我刚才说的这个过程，绝大多数都会失败。

And and that process that I just described, you can imagine most of them fail.

但那些成功通过毒性研究、最终进入人体试验的，才是真正让人兴奋的，而它们往往需要多年反复迭代，从最初的想法、文献研究，一路走到让你确信已经破解了这个难题的动物实验。

But for the ones that do succeed and get the chance to then go into toxicity studies and then ultimately into humans, those are the ones that get really excited, but they take years and years of iterating and looping to go from that beginning idea through literature ultimately to the animal study that makes you believe you've actually cracked that code.

而从那以后，这更像是一场马拉松，而不是短跑——因为你已经花了好几年时间去破解这个难题。

And then, of course, from there, this is so much of a marathon than a sprint because you've spent years trying to crack the code.

现在你终于破解了，接下来呢？

And now that you have, guess what?

现在你又得再花五到七年时间，做一系列临床试验，来证明你当初设想的这种药物，在人体上确实能产生预期的效果，而且正如我们之前讨论的，它的获益远大于风险，足以让监管机构批准上市。

Now you've got another five to seven years worth of clinical studies you have to do to prove that in people, the idea you had for medicine actually has the effect that it's intended to and that per our discussion earlier, that benefit to risk equation is favorable to the point that regulators should approve it and let it come

上市。

to the market.

我有两个问题。

I have two questions here.

这个过程本身非常出色，推动了大量发现和进步，不仅在医学领域，也遍及整个科学方法驱动的探索。

The process itself is beautiful and has driven so much discovery and progress, not just in medicine, but scientific method driven progress everywhere.

但我很好奇，如何缩短这个反馈周期，或者加快新发现和进展的速度，具体有两个方面。

But I'm curious about two dimensions of shortening that feedback loop or increasing the velocity of new discoveries and progress.

一个是人工智能，另一个是我所说的‘大众药理学’，也就是普通人参与，这在所谓的多肽圈子里正变得越来越普遍。

One is AI and the second is what I'll call Citizen Pharmacology, which is people, this is becoming really prevalent in what I'll call the peptide community.

现在大家特别喜欢讨论多肽。

People love talking about peptides these days.

GLP-1是多肽，胶原蛋白也是多肽。

GLP-one's a peptide, collagen's a peptide.

有些广为人知的东西属于多肽，但还有一大类我们也可以聊。

There's well known things that are peptides but there's this whole class of things that we can talk about.

但如果你去Reddit看看，就会发现有人自发组织起来，在没有经过标准临床试验或FDA批准的情况下，自己尝试新物质。

But it seems like if you take to Reddit for example, you will find groups of people organizing to test new things on themselves outside of the normal RCT FDA approved process.

如果你缩短了流程，风险可能会增加，但学到东西的速度也会加快。

If you shorten it, you probably increase the danger but you decrease the time to learning or something.

所以这两种方式，对我来说，都是推动药物研发进步的关键因素。

So you've got both of these things as interesting aspects to me of what will drive forward progress in drug discovery.

咱们分别聊聊，先说说AI吧，现在哪些已经实现了？

Riff on each, maybe starting with AI, what is real already?

你觉得随着模型能力的提升，未来哪些事情会真正成为现实？

What do you think will become real by virtue of the increasing skill level or reasoning level of the models?

没错，从AI的角度来看，你描述的其实就是我们追求科学超级智能的过程。

Absolutely, so I think what you're basically describing on the AI side is our quest to try to get to a level of scientific superintelligence.

你可以想象，如果科学家能拥有一个具备爱因斯坦级别甚至更高智力的智能代理，那这种赋能会让顶尖科学家以更快的速度、更低的成本、更高的成功率做出重大突破。

You can imagine that from an AI perspective, if scientists are armed with an agentic infrastructure that has a scientific intelligence that's like at an Einstein level or multiple of Einstein level, the force multiplication that can occur between a superstar scientist armed with that agentic resource means that they will do science faster and with higher probability for lower cost and have greater impact.

这个概念框架非常重要。

So that is important conceptual framing.

那接下来，具体来说，这种能力会怎么体现？为什么它如此关键？

And so then practically, how does that manifest and why does it matter?

你可以想象，任何人类科学家单靠自己，像我刚才描述的那样，不断重复科学方法、转动科学的轮子，始终受限于人类大脑能记住的内容，也受限于我们用双手去执行实验的能力。

Well, you can imagine that any human scientists on their own tinkering in the way that I just described scientific method and that wheel of science spinning and spinning and spinning, us as human scientists are always gonna be constrained by what we can retain in our human minds and constrained by our ability to use our human hands to go run those experiments.

即使是最有天赋、最聪明的人，能记住的科学论文再多，也不可能记住全部。

There are super talented, incredibly smart people that can retain in volumes and volumes of scientific papers, can't retain all of them.

但AI可以。

AI can.

如果实验是由人类亲自操作，而不是由AI驱动的机器人来做，那实验的 throughput 就会受到人类双手速度的限制；而如果是机器人驱动的系统，这个限制就几乎不存在了。

And if humans are running experiments, not AI driven robotics, there's just a certain throughput that is gonna be limited by human hands that would be unlimited if you had an agentic robotically driven system.

从最基础的层面看，AI进入药物发现领域，将是医学史上最重要的突破之一，因为它意味着我们不再受大脑容量的束缚。

At the simplest level, AI coming into the discovery of medicines is one of the biggest unlocks that we will see in the history of medicine because it means that we're no longer constrained by our minds.

我们也不再受双手操作的限制。

We're no longer constrained by our hands.

我们可以从现有的全部知识库中，不只是提出几个假设，而是找出所有可能存在的假设。

We can generate not just a few hypotheses for what the answer can be from the total sample set of knowledge that exists.

然后确定哪一个是最重要、最值得优先验证的实验，立刻去执行，并且全天24小时、每周7天不间断地持续运行。

We can identify all n number of hypotheses that could exist and should exist and then figure out what is the greatest and best next experiment to go run, run that, and run those in real time 24 a day, seven days a week.

我们能够更全面、更完整地构建假设集，再加上前所未有的实验规模，这实际上意味着我们能把突破性进展的时间从通常的三到五年，缩短到不到两年。

The sheer ability to be more comprehensive and complete in our hypothesis set, and then the sheer ability to run experimentation at a scale that's unprecedented, I think practically means we are going to be able to speed breakthroughs from timelines that would typically take three to five years to timelines that can take less than a couple of years.

我们在讨论人工智能对药物研发的影响时，除了这种概念上的潜力，另一个关键问题是：我们现在到底走到哪一步了？

The other important element when we think about AI and its impact on drug development is not just the potential of what could be from that conceptual frame, but where are we today?

我觉得这真的非常令人振奋。

I think it's really exciting.

如今，我认为我们才刚刚开始有能力发现全新的靶点。

Today, I would say we're in the early innings of being able to discover novel targets.

而且说实话，我们还从未系统性地发现过一个之前根本不存在的靶点。

And I would say that we have not yet in a systematic way been able to discover a target that didn't exist yet.

但我们现在能做到的是，针对那些我们知道存在、却一直难以开发出药物的靶点，

But what we can do is for targets that we know exist, but that we have really struggled to be able to develop medicines against those targets.

AI正在帮助破解这些难题，成为强大的助推器，让这一切成为可能。

We're starting to see that code be broken by AI and AI being a big force multiplier, it's enabling that to happen.

现在有些公司已经能做到，不再依赖过去那种耗时极长的大规模筛选，而是全部在计算机里模拟完成，从模型到分子，原本需要好几年的事，现在一个月就能搞定。

What we have today are companies that can go from what would historically been running these massive screens that are incredibly time consuming to doing all of that screening work in silico and can go from model to molecule and what would have taken a couple years time in a month.

这现在就已经实现了。

That's already true today.

现在确实已经做到了。

Already true today.

像Lila Sciences和Enabla这样的公司，你只要让智能系统针对某个靶点设计一个分子，它就能破解这个难题，给出我们可以拿来开展实验的成果，

There are companies like Lila Sciences, Enabla, these are companies that you can basically ask the agentic system to develop you a molecule against a certain target and it can crack that code and give us something that we can work with and start running experiments in a

一个月内就能完成。

month's time.

你是不是觉得这一切就会自然发生？

And is it your impression that this is just gonna happen?

AI对靶点发现和治疗药物研发整个过程产生重大影响，已经没什么重大风险了。

There's no major risk left that AI will not have major impact on this entire both target and therapeutic discovery process.

在你看来，我们现在是不是已经走上了一条不可避免的轨道？

Are we now on a curve that just feels inevitable to you?

对我来说，我们已经走在

To me we're on

就是这条曲线。

that curve.

那这个转折是什么时候发生的？

When did that flip?

最近多久的事？

How recently?

非常近。

Very recently.

在我整个职业生涯的大部分时间里，一直都有某种形式的机器学习，人们期待AI能推动药物发现和新药研发。

For most of my career, there's been some version of machine learning and the hopes of AI really driving drug discovery and the development of novel medicines against those discoveries.

我总是担心，要让这种模式成立，需要的数据其实根本不存在，或者根本找不到现成的。

And I always worried that in order for that paradigm to be true, it required data that didn't actually exist in any easy place.

如果你只是用现有的研究数据来训练AI模型，那结果很可能是垃圾进、垃圾出。

If all you do is train AI models based on the research that's available, you unfortunately have a paradigm of a lot of garbage in garbage out.

我跟人聊起现有数据的时候，他们往往没意识到，很多已发表的文献其实都是错的。

I think one of the things that people don't appreciate when I start talking to them about what data exists is that unfortunately, a lot of the recorded literature is actually incorrect.

已经有很多研究显示，如果你试着复现文献里的实验，根本得不到相同的结果。

And there's been tons of studies that show if you go try to replicate the experiments that are in the literature, you don't even get the same results.

如果事实真是这样，而这些AI公司又没有途径生成新的数据，那你还能怎么办？

Definitionally, if that is true and these AI companies don't have a source of creating novel data, what are you gonna do?

你拿什么来训练模型？

How are you gonna train?

我们已经看到大语言模型有多强大，它们在这么多生活领域产生了深远影响，就是因为它们是用万亿级别的全新语料训练出来的。

We've seen just how powerful LLMs have been and how impactful they are across so many different areas of life because they've been trained on trillions and trillions of novel tokens.

归根结底，数据才是推动智能迈向更高水平、引发智能革命的关键因素。

So data ends up being a big part of what drives to advanced levels of intelligence and drives an intelligence revolution.

科学领域也必须是同样的道理。

Well, the same has to be true in science.

所以，我认为最有希望成功的AI公司，是那些拥有顶尖AI人才、能获得大量资金，并且能将这两者结合起来，创造出公共领域里不存在的科学数据的公司。

And so the AI companies that I believe are gonna be most set up for success are the companies that have incredible AI talents, have access to significant amounts of capital, and can combine both of them with a novel way by which they can generate science tokens that don't exist in the public domain.

事实上，生成这些独特数据的能力，不仅构成了它们的核心壁垒，更是让它们的科学智能不断提升、逐步超越那些没有这类数据支持的大语言模型的关键所在。

In fact, the generation of those tokens becomes a big driver of their moat, not only the driver of the moat, but actually what enables them to be able to get to an increasing and increasing level of scientific intelligence that starts to separate from that of which can be achieved with the LLMs without having the benefit of those tokens.

我们现在已经开始看到这种趋势了。

And we're starting to see that.

所以我们现在有一家公司，和我们关系很近，它已经开始让这条曲线出现拐点，让我们看到实现科学超级智能的可能性，确实遵循着一套相当确定的规模规律。

And so we've got one company that we're really close to today that's actually starting to show the bending of that curve and helping us to see that the idea of getting to scientific superintelligence is one that is likely to follow a pretty deterministic set of scaling laws.

你的财务团队并不是因为大错而亏钱。

Your finance team isn't losing money on big mistakes.

而是通过成千上万没人留意的小决定，一点一点地漏掉。

It's leaking through a thousand tiny decisions nobody's watching.

Ramp 在支出发生之前就设好了限制。

Ramp puts guardrails on spending before it happens.

实时额度控制、自动规则、彻底杜绝救火。

Real time limits, automatic rules, zero firefighting.

去 ramp.com/invest 试试看。

Try it at ramp.com/invest.

随着你的业务增长，Vanta 也会同步扩展，自动完成合规流程，为你提供安全与风险的唯一可信数据源。

As your business grows, Vanta scales with you, automating compliance and giving you a single source of truth for security and risk.

了解更多，请访问 vanta.com/invest。

Learn more at vanta.com/invest.

每家投资公司都不同，通用的人工智能无法理解你的运作流程。

Every investment firm is unique and generic AI doesn't understand your process.

Rogo 可以。

Rogo does.

这是一款专为华尔街打造的人工智能平台，直接对接你的数据，理解你的流程，并产出实际成果。

It's an AI platform built specifically for Wall Street connected to your data, understanding your process, and producing real outputs.

了解更多，请访问 rogo.ai/invest。

Check them out at rogo.ai/invest.

从 OpenAI 到 Cursor，再到 Perplexity，顶尖的 AI 和软件公司都靠 WorkOS 在一夜之间实现企业级成熟，而不是耗上几个月。

The best AI and software companies from open AI to cursor to perplexity use WorkOS to become enterprise ready overnight, not in months.

访问 workos.com，跳过繁琐的底层建设，专心打造你的产品。

Visit workos.com to skip the unglamorous infrastructure work and focus on your product.

Ridgeline 正在重新定义资产管理技术，它不只是软件供应商，更是真正的合作伙伴。

Ridgeline is redefining asset management technology as a true partner, not just a software vendor.

他们已经帮助多家公司实现了五倍增长，推动更快的发展、更智能的运营，以及更强的竞争力。

They've helped firms five x in scale, enabling faster growth, smarter operations, and a competitive edge.

访问 ridgelineapps.com，看看他们能为你的公司带来哪些突破。

Visit ridgelineapps.com to see what they can unlock for your firm.

我记得好像是 OpenAI 发布了这个消息，可能是和 Ginkgo 之类的公司合作，打造了一个闭环的自动化实验室和研发流程，不只是模型，还包括现实世界的实验。

I think it was OpenAI that came out with this announcement, maybe working with Ginkgo or somebody, where they've created like a closed loop automated lab and discovery process and not just models but physical world testing as well.

如果你闭上眼睛，想象五年、七年、十年后，基于你现在的了解，你觉得那时的研发流程会是什么样子？

If you close your eyes and think five, seven, ten years from now, what's your best guess knowing what you know at what the discovery process will look like at that point?

我以前完全是全自动的。

I used to be completely automated.

前几天我去 Lila Sciences 参观，一进他们的实验室，满眼都是机械臂，培养皿在系统里自动流转，从一个位置移动到下一个位置。

I was up at Lila Sciences the other day and you go into their labs and what you see is all of these robotic arms and you see petri dishes that are moving around in an automated fashion moving from one spot to the next spot.

在我看来，这意味着训练这些模型所需的海量数据——数万亿级别的 token——必须突破人工操作的限制，因为靠人力来做，成本实在太高了。

To me, I think what that means is the scale of data that's gonna be required to be able to train these models, the trillions of tokens that are required has gotta move beyond the constraints of human hands because it would just cost so much money to do it in human capital driven manner.

这必须靠机器人来完成，而要实现这一点，背后必须有一个智能代理系统，能够以前所未有的规模持续生成数据。

It has to be done robotically and for that to be done, it has to have an agentic engine behind it and needs to produce data at a scale that's kind of unprecedented.

我认为未来的研究方式将是智能系统驱动机器人，完成如今由人工手动进行的大部分实验。

What I think the future of discovery looks like is agentic systems that can drive robots to do most of the experimentation that we're doing today with human hands.

这将极大释放我们的能力，让

And I think that's a big unlock for the ability to

没错，这就是最前端的关键突破。

crack That's the top of funnel.

完全正确。

Exactly.

那些原本一生致力于对抗疾病、做有意义研究的顶尖科学家，再也不用把职业生涯的三分之二时间花在实验台前反复移液了。他们可以把全部精力、热情和乐观，转向如何利用这些系统和机器，找准需要破解的科学密码。

And so now superstar, brilliant, amazing scientists who go down their life journey to become a scientist that wants to fight against disease, wants to do work that matters, is no longer gonna spend 2 thirds of their career at the side of a bench pipetting all day long, is gonna have all that time back to point their energy and their enthusiasm, their optimism towards trying to figure out how do I use the systems and machines that I have and point them in the right direction at the codes that we should be cracking.

当顶尖科学家的智慧与这些智能系统的强大能力结合时，才能真正实现这种突破。

That's really enabled when you have the combination of brilliant human scientists force multiplied by the agentic power of these systems that are being built.

一旦这些智能系统实现全自动化，让实验室的全部实验都能实时进行、无需人工干预，它的扩展速度就会越来越快，我们离科学领域的超级智能也就会越来越近。

And the more those agentic systems can be fully automated where all of that lab experimentation is happening in real time and without the need for human interaction, think the quicker and quicker it can scale and I think the quicker and quicker we can achieve that level of scientific superintelligence.

你怎么看肽类研究和公众药理学这一块的发展？

What do you make of this whole peptides plus Citizen Pharmacology side of things?

我觉得他们刚宣布，有大约14种肽类物质可以被配制了，这看起来像是这个领域正在放松监管。

I think they just came out and said there's gonna be 14 something peptides that are allowed to be compounded and it seems to be like deregulation of sorts happening in this space.

人们一提到肽，其实自己也不太清楚这到底是什么东西。

People say peptides, I think not really knowing like what that even means.

要不你先解释一下这是啥？

So maybe define it.

我就是想知道你对这事怎么看。

I'm just curious what you think of this.

这其实正好印证了你之前说的，尤其是年轻一代，对这些东西的态度完全不一样了，他们就想自己说了算。

It really plays to your earlier point about these generations, these younger generations especially, seem to be a new attitude towards all of this stuff, which is like, I want to own this.

而自己说了算，就意味着愿意承担比法规允许更高的风险。

And part of owning it is a willingness to take risks that are above and beyond what is mandated or regulated that I'm allowed to take.

这个亚文化真挺让我着迷的。

This subculture is fascinating to me.

你有没有读过关于BPC-157的？我觉得应该是这个编号。

You read about BPC-one 157, I think that's the number.

现在有各种各样的新尝试在进行。

All these interesting different things are being tried.

你觉得这会带来什么影响？

Do you have a take on the impact that this will have?

我觉得很难说准。

I think it's hard to know.

不管是GLP-1的商业成功，还是肽类物质的广泛流行，

Whether it's the commercial success of GLP-1s or you look to the massive movement around peptides.

说实话，这甚至不只限于肽类。

Honestly, not even limited to just peptides.

你上Reddit看看，就能发现大家都在尝试各种不同的东西。

Like you go on Reddit, can see people are experimenting across all sorts of different dimensions.

我觉得现在的情况是，人们都想去找一些能帮到别人的方法。

And I think what's happened is we live in a world where people want to go and figure things out that help other people.

而如今，我们有了能让大家分享这些方法的平台。

And now we've got platforms that allow people to share that.

所以我们现在能看到一些群体的形成，这些群体在过去没有今天这种技术和社区支持的情况下是不可能出现的。

And so we see the creation of groups that can exist today that couldn't exist in the future before we had the technology like we have today and the communities that can get formed.

无论是从人们为了共同目标聚在一起的角度来看，这在人类历史上早就存在了。

Whether you're looking at it from the perspective of people coming together under shared missions, that's happened throughout the history of all of humanity.

这是一种共同的使命，人们说：我想把我的未来健康掌握在自己手里。

This is a shared mission where people are saying, like, I wanna take my future health into my own hands.

我不愿意坐等科学家们单独去解决这个问题，然后走传统的科研路径。

And I'm not willing to wait for scientists to go take on this problem independently for it to go down the traditional pathways.

我没那么多时间等。

Like, I don't have the time.

我需要现在就得到答案，因为我觉得，对我自己来说，这件事真的很重要。

I need to know the answers now because I think for myself, this is gonna really matter.

人们有自己的假设，然后就想亲自去验证。

And people have a hypothesis and they wanna test it.

所以他们拼命地想办法，争取把这些假设付诸测试。

And so they're fighting really hard to go and get those hypotheses tested.

与此同时，你看到我所说的这种基层肽类研究运动的同时，FDA也意识到，我们必须加快步伐。

At the same time that you see what I would call like that grassroots peptide movement, you also have a recognition by the FDA that we've got to move a lot faster.

只要从提出药物假说到人体试验，再到最终获批，整个流程中还存在这么多障碍，疾病就总会赢。

So long as we have all these frictions in the system of going from a hypothesis for a medicine to the testing of a medicine in people and then ultimately an approval, disease is gonna keep winning.

我认为，在马蒂·马卡里的带领下，正展现出真正的领导力，努力打破那些让药物研发变得异常耗时、异常复杂的壁垒和摩擦。

I think we're seeing under Marty Makary real leadership to want to go and try to break down those barriers and those frictions that are making the drug discovery and the drug development process be incredibly time consuming and incredibly complex.

这种变革带来的实际影响体现在方方面面，比如马蒂引入了人工智能系统，帮助他们在极短时间内审阅药企提交的成千上万页申报材料，而不是靠一大群人花上数日甚至数周去逐页翻阅；甚至开始质疑：在哪些情况下，我们其实不需要那么多动物实验数据，完全可以依靠细胞实验？

And some of the manifestation of the real world impact of that spans everything from Marty brought in an AI system to help them be able to comb through the thousands of pages of filings that sponsors behind medicines have to submit in record time versus the armies of people that would have to comb through that and the days and days of days of time that are wasted all the way through to starting to challenge every aspect of, okay, well, in what settings do we not need nearly as much evidence in animals and we could rely on cell based experiments?

在哪些情况下，我们不再需要多个重复研究来证明同一件事？

In what settings do we no longer need multiple redundant studies that show us the same thing?

一个研究就够了，我们完全可以简化流程。

We can cut through it with one study.

无论从哪个角度看，我们都在见证一场变革：让事情变得更高效、更快、更有影响力。

I think across anywhere you look, we are seeing a movement to be able to do things more efficient, faster, and having a greater impact.

某种程度上，这又回到了我一开始说的那一点。

And in some ways that circles back to where I began.

对我来说，2025年真是令人兴奋的一年。我们不仅看到了那些造福大众健康的出色药物，还看到了医药领域日益普及的趋势，以及人们越来越倾向于自己掌握健康主动权。在我们刚才提到的各个层面，都能看到人们比以往任何时候都更积极地关注自己的健康。

To me, 2025 was such an exciting year because we saw not just amazing medicines doing amazing things for people's health, but we saw real movements across the consumerization of this medicine, across people wanting to take health into their own hands And across every layer of what we just described, what we see is people wanting to be far more proactive about their health than has ever been true before.

我有过无数次这样的经历：我把你介绍给某人，然后我们开始聊。

I've had this experience with you countless times where I'll introduce you to somebody, we'll have a conversation like this.

聊到三十分钟到两三个小时之间，对方心里通常会冒出个念头，而且很少说出口：这哥们儿到底是谁啊？

And somewhere between thirty minutes in and two hundred and thirty minutes in, the person has a thought which they rarely say which is, who the hell is this guy?

我们又是这样，聊了好几个小时，却连你的背景都没怎么铺垫过。

We've done the same thing yet again which has gone hours into the conversation without laying any sort of groundwork for your background.

能不能给我们讲讲你自己的经历，

Can you just tell us your personal story a

再补充一些细节呀？

little bit?

我真的特别幸运。

I've just been super lucky.

回顾我的童年，感觉我爸妈从我五岁起就开始培养我，想让我往生物科技领域的创业者和投资人方向发展，但我的童年过得真的非常好。

Reflecting back on my childhood, I guess my parents were training me to be a biotech builder and investor since the time I was five, but I had like the greatest childhood ever.

我在一个典型的美国家庭长大。

Grew up in a typical American home.

我爸爸是个永不言弃的创业者。

My dad was this relentless entrepreneur.

他比我知道的任何人都更拼命工作，只要投身一个生意，就全身心投入。

He was the type of person that worked harder than anybody I knew, and he was so all in in any business endeavor he was pursuing.

我们全家都跟着他一起拼。

Like, our entire family was all in on it.

妈妈负责账务。

Mom was doing the billing.

我哥哥和我则在车库里翻新家具。

My brother and I were in the garage refinishing furniture.

从小耳濡目染，跟着爸爸接触生意，他对我来说一直是极大的精神榜样。

Kinda grew up around business by being around my dad, and he was just such an inspirational force in my life.

尽管他这么拼命，但这些生意最终都没真正成功过。

Despite the fact that he was such a relentless businessman, he was never successful really at any of those.

如果他今天坐在这里，我会觉得很难过，他十多年前就因帕金森病去世了。

And I think if he were sitting here today, sadly, he passed the Parkinson's over a decade ago.

在他生命的最后几天，他告诉我，他觉得自己从来没有坚持够久，没能熬过最艰难的阶段，总是很快转向下一个项目，再下一个。

One of the things he told me in those last few days is that he felt like he never stuck with anything long enough to see it through over the tougher moments and would just quickly move on to the next thing and the next thing.

我通过亲眼看着他、和他一起做生意，学会了这个教训。

And so I learned that lesson from watching him in real life and kind of being in business with him.

但要是把我的爸爸和妈妈放在一起看，我妈妈才是我生命中真正的力量。

But you pair my dad with my mom, who's been an absolute force in my life.

我妈妈是个无比乐观的人。

My mom is this incredible ever optimist.

从我最早的记忆开始，我就记得妈妈不断在我心里灌输一个观念。

From my earliest memories of my mom, I can remember her drilling into my mind.

我们一旦承诺了，就绝不会放弃。

We always commit and we never quit.

她让我深信，只要我下定决心，就没有什么是我做不到的。

She pretty much convinced me that I could do anything I wanted if I put my mind to it.

所以我从我爸身上体会到了什么是冒险，明白即使失败了，生活照样继续。

And so I feel like from my dad, I got to experience and see what it means to take risk and that you can fail and life goes on.

而从我妈身上，我培养出了坚韧的品格。

And from my mom, I developed this sense of resilience.

我觉得这两种特质，很可能正是我后来爱上医学——尤其是医学研究与开发——的原因。

And I think both of those attributes are very much probably connected to why when I found medicine, I fell in love with it, particularly the discovery and the development of it.

从小，我是家里第一个有机会上大学的人。

So from that early childhood, I was the first in my family that got a chance to go to college.

我从麻省理工毕业了，那是一段特别棒的经历。

I graduated from MIT and had just an amazing experience there.

在麻省理工读书时，我越来越深入地了解商业、经济和科学，也慢慢把它们和小时候陪我爸做生意的经历联系起来——那时候我还不懂，但其实已经把钱投进了他的一些项目里。

For me, being at MIT and learning more and more about business and economics and science and connecting it back to my early days of working alongside my dad and getting a chance to at the time not realizing but put money into some of the deals that my dad was doing.

我曾花两美元买一把摇椅。

I could put $2 into a rocking chair that we bought.

如果能卖到两百美元，我就觉得太了不起了。

And if we sold it for $200, I thought that was amazing.

要是卖不掉，我就气得不行。

And if we didn't sell it, I was pissed.

那时候我根本没意识到，其实我正在从爸爸身上学习风险与回报的道理。

I didn't realize I was learning in these early days about risk and reward from my dad.

等到上了MIT，看到这些相关方程式的黑白分明的逻辑，我才真正豁然开朗，也促使我决心去纽约、去华尔街。

And then when you get to a place like MIT and you start to see the black and white version of the equations associated with that, it was a real major step forward for me that inspired me to wanna then go on to New York City and to Wall Street.

我觉得，要真想搞懂商业、资本怎么运作、需要什么条件，再没有比纽约这个中心更好的地方了。

I thought to myself, no better place to really learn about business and how capital connects to business and what it takes than to come to the epicenter of that here in New York City.

后来我去了美林证券，遇到了几位特别棒的导师，对我影响很大。

And so I ended up at Merrill Lynch and had some amazing mentors that were really inspiring to me.

我在美林待了两个主要阶段，第一个是在资本市场部。

I had two major stops at Merrill, one on the capital market side.

干了一阵子之后，我就想看看那些疯狂的证券、衍生品到底是怎么交易的。

And then after doing that for a while, I wanted to go see how these crazy securities, derivative securities traded.

于是我就调到了美林的衍生品交易台。

And so I had a chance to move to the Merrill Lynch derivatives desk.

回想那段时光，最让我觉得有趣的是，我其实从来没主动选择过生物技术。

I think what was just so fun for me about reflecting back on that time is I actually never picked biotech.

当我被调到衍生品交易台时，就被直接安排到了生物技术组。

When I got moved over to the derivative desk, I was just put into biotech.

我被分到衍生品交易台的生物技术小组，对这行一无所知，但很快就爱上了它，因为这里的风险与回报直接关系到生死。

I was assigned to the biotech subgroup of the derivatives desk and knew nothing about it, but I fell in love with it so quickly because the risk reward in that setting was about life and death.

当我观察那些公司、研究我必须理解的标的时，我简直震惊于这些事情背后的复杂性。

And when I was looking at what was going on and the companies that I needed to be understanding to be able to price risk, It was like wild to me what was going on.

我对药物研发这一整套领域完全不了解。

I had no idea about this entire field of the development of medicine.

我也没真正意识到，推动这一切需要多么庞大的资金投入。

I had no real appreciation for just the sheer capital that's required to move it forward.

而我，纯粹是机缘巧合，竟置身于必须深入理解这些公司、才能准确评估风险的核心位置，还有机会接触所有投资这些公司的买方机构，他们的工作深深激励了我。

And here I am through total serendipity, I find myself at the epicenter of needing to understand these companies deeply enough to price risk and getting a chance to be in front of all of the buy side investors that are building and backing these companies that I became inspired by the work that they were all doing.

我深受触动，所以经常和他们一起外出，因为他们对交易台的动态很感兴趣，想知道这些活动对他们投资意味着什么。

I was so inspired by it that in my seat, I got to be out with these people a lot because they were curious what activity was going on in the trading desk and what it meant for them and their investments.

我一直很想了解他们是怎么建立并支持这些公司的。

And I was always interested in learning more about how do they build and back these companies.

在和来自不同投资机构的这些人交流后，我很快发现，他们的策略其实非常相似。

And what I came to quickly realize and talking to all these people from all these different investment firms was that their strategies were actually very similar.

我想到自己对衍生品的了解，以及在麻省理工和美林证券学到的东西，意识到生命科学领域的投资者几乎都只用股票，这让我觉得一定有更好的方法来构建投资组合、挑选证券——要么在保持同等回报的同时降低风险，要么在控制风险的前提下获得更高收益。

I said, Knowing what I know about derivative securities and that I had learned from my days at MIT, my time at Merrill Lynch, and recognizing that most people in the life science investment world were just using equity securities, it struck me that there was a better way to build these portfolios and select securities, ways in which we might be able to either maintain the type of return that they needed for their investors but at lower risk or maintain the risk and produce a higher rate of return.

于是我利用晚上和周末时间，做了一份非常精致的演示文稿，觉得终于找到了答案：什么样的投资组合才能让资本大规模支持这些前所未有的创新药物。

I went off and I spent my nights and weekends writing this beautiful deck that I thought had really cracked the code on what portfolios needed to look like in the life sciences sector would allow a scale of capital to get behind these amazing medicines that had never before been achieved.

做完之后，我迫不及待地想发出去。

And when it was done, I could not wait to send it out.

我还记得跟室友多姆说：‘多姆，我准备发了。’

I remember telling my roommate, Dom was like, Dom, I'm ready to send this out.

我要把这份材料发给所有人，你等着看吧。

I'm gonna send it to everybody, and you just watch.

我的电话肯定会被打爆。

My phone is gonna be ringing off the hook.

全世界的人都会想用这个策略，争相跟进。

Everybody in the world is gonna want to do this and put this strategy to work.

我在美林的日子没剩几天了。

And my days at Merrill Lynch are numbered.

我很快就会离开那里。

I'm gonna be out of here soon enough.

我把方案发出去了。

I sent it out.

一片寂静。

Crickets.

居然一个回复都没有，我简直不敢相信。

I didn't get one single response, and I couldn't believe it.

怎么就没一个人想做这个呢？

It's like, how does nobody wanna do this?

怎么没人愿意管理一个能用更低风险获得同样回报，或者用相同风险获得更高回报的投资组合？这完全说不通。

How does nobody wanna run a portfolio that can produce the same amount of reward but with lower risk or produce more reward for same didn't make any sense to me.

结果过了好几个月，我的电话才终于响了，但有一天它真的响了。

It turns out that months went by before my phone finally rang, but then one day it did.

那天打来电话的是我一位亲密的朋友兼导师杰夫·卡普兰，当时他正是2005年一家叫Deerfield Management的小公司交易部门的负责人。

And it was just today a dear friend of mine and mentor, Jeff Kaplan, who was the head of trading at that time in 2005, a tiny little firm named Deerfield Management.

那时候公司刚起步，吉姆·弗林刚刚接任普通合伙人。

And it's early days where Jim Flynn had just taken over as general partner.

吉姆是Deerfield非常特别的领导者，我和他相处一段时间后发现，他整天都在琢磨：我们投资公司还能从哪些方面入手，争取每一个可能的优势，来提升回报，吸引更多资本，从而对人类健康未来产生更大影响。

Jim was a really unique leader of Deerfield because one of the things that I learned from spending time with him was he obsessed over what is every which way by which our investment firm can have every type of advantage in producing our returns so that we can attract more and more capital and have more and more impact on the future of human health.

我们怎么才能打造出让Deerfield成为推动创新资本核心的优势？

How do we create advantages that would allow Deerfield to become the epicenter of capital to move innovation?

我运气不错。

I got lucky.

我找到了世界上唯一一个真正关心我想法的人——当时我向一个只有大约12人的小团队展示了我的构想。

I found, like, the one person in the world who cared about the things that I had to say after going and presenting on my ideas there for what at that point, which was a tiny team of about 12 people.

我后来被录用加入了Deerfield，接下来的十五年，我一直在帮助把Deerfield打造成那个理想中的状态：成为整个行业首选的资本来源。

I got hired to join Deerfield, And I spent then the next fifteen years of my career helping to build Deerfield into that dream state of being the go to source of capital for the industry.

那时候我还没意识到，其实我加入的时候才24岁。

I didn't realize it at the time, but I guess I was 24 when I joined.

我觉得公司里第二年轻的人可能都四十岁了。

And I think the next youngest person at the firm was probably 40.

这些人都是经验极其丰富的老手。

These were all people that were super seasoned, had incredible experience.

我大概是办公室里一个稀罕物，每天都要问上上千个问题。

And I think I was a bit of a novelty in the room because I would ask, like, almost a thousand questions every single day.

这些了不起的人愿意花时间教我。

And these amazing people would spend the time to teach me.

我在Deerfield这段学徒经历，远超我当初的想象。

And so the apprenticeship that I was able to get from my time at Deerfield was like nothing I could have ever imagined.

我完全爱上了投资和扶持各种项目，不管是医疗器械、诊断工具、药物，只要是能推动人类健康发展的，我都投入其中。

And I was all in fully in love with investing in building and backing anything, whether that was a medical device, diagnostic, a medicine, anything that could move human health forward.

从帮这些公司规划融资，到和团队一起设计临床试验方案，争取让他们的成果脱颖而出，我都全程参与。

That spanned everything from helping these companies figure out how to finance their innovation all the way through to spending time with our team on how do we design and help a company design a clinical study in a way that would give them an advantage to showing something that's really special.

在迪尔菲尔德的头十年里，我一直保持着这种状态。

That stayed true for about the first decade of my time at Deerfield.

但后来发生了一件极其悲痛的事。

And then something just really, really tragic happened.

在迪尔菲尔德，我们一直在不断构建能力，也一直在寻找新的数据来源。

At Deerfield, we were constantly building capabilities and constantly wanting new sources of data.

我们之前获取并发展起来的一个数据源，让我们越来越深入地了解患者从开药、获得保险批准，到最终拿到药，再到坚持用药的整个过程。

And one of the sources of data that we had acquired and had built out was now taking us deeper and deeper into understanding a patient's journey from the moment where they are prescribed the medicine to where they actually get the insurance approval to get that medicine and then how long they stay on the medicine.

当然，我们都明白，药只有吃下去才有效。

And of course, we all know medicine only works if we take it.

在我痴迷于周围这些了不起工作的第十年，我很快意识到：没错，药得吃才管用，但人们根本就没吃。

And what I was quickly learning about a decade into my obsession for all the amazing work that was going on around me is that yes, medicine only works if people take it, but they weren't.

那段时间是我个人生涯中最黑暗的阶段，因为我开始反思自己参与投入的大量时间、精力和资金，突然明白必须问一个问题：这一切到底是为了什么？

That was a really dark period for me in my own journey because as I was reflecting on all the time and effort and capital that I was a part of putting behind innovation, it became pretty quickly evident to me that you have to start asking the questions, well, for what purpose?

如果一个人得终身服药，可平均只吃一年就停了，那我们是不是只在追求最大回报，却对公众健康毫无实际影响？

If people need to be on a medicine for the rest of their lives and they're taking it for a year on average and then quitting, Are we just optimizing for maximum expected returns but no impact on public health?

那时候我已经结婚了。

I had been married at the time.

我是在2012年结婚的。

I got married in 2012.

结婚几年后，我开始思考：我的人生究竟意味着什么？如果我整天只想着赚钱，而不是做点真正能推动世界前进的事，那我算什么好丈夫？

So I'd been married for some number of years and started thinking about what does my life mean and how am I as a partner to my wife if I'm spending my time just focused on making money and not actually doing something that can move the world forward.

那时候我们迎来了第一个孩子，我开始回想父母对我的影响，他们到底给了我哪些东西，让我能一路走到今天。

We had our first baby at that point and I was reflecting on just how impactful my parents were in terms of teaching me and what they armed me with as I went forward.

那我将来要教给女儿们的是些什么呢？

And I like, well, what lessons would I be teaching my daughters?

当我把所有这些方面都串起来想的时候，我忍不住问自己：我是不是在走一条错路？

And I got to the point where like the combination of thinking across all of those dimensions made me the question, am I just doing the wrong thing?

我是不是该换个方向？

Do I need to go in a different direction?

答案是否定的。

The answer was no.

我需要做的，是把从小成长环境里自然形成的创业精神，还有在迪尔菲尔德那段非凡学徒经历中磨炼出的技能，全都用在真正能产生影响的地方——不只是推动医学发现，更要确保这些药物真正发挥出它们本该带来的疗效。

What I needed to do was take sort of the innate entrepreneurial energy that comes from my upbringing and from my childhood, the skill set that I was able to develop from this amazing apprenticeship that I had at Deerfield and be able to point those talents, not just at the discovery side of being able to have invention, but be able to point that at the impact that's required to actually see these medicines have the impact that they're intended to have.

这正是我离开迪尔菲尔德、决心创办布雷德韦尔的主要动力。

A lot of that was my inspiration for leaving Deerfield and wanting to go build Braidwell.

我真的很幸运，因为我们一位挚友布莱恩·克赖德，是个特别了不起的人。

I'm so fortunate because one of our dear friends, Brian Crider, who's an incredible person.

他是个好丈夫、好父亲，商业能力超强，和我一样，对当下世界所处的状况、对那些本该发生却还没发生的改变，充满了同样的使命感。

He's an amazing husband, father, incredibly talented business person, was also just as inspired as I was about where we found ourselves in the world and the type of impact that could happen, but wasn't yet happening.

他当时是桥水基金的首席运营官。

He was the chief operating officer for Bridgewater.

我则在迪尔菲尔德共同负责运营，我们决定花大量时间，把各自的经验与洞察整合起来，从最根本的原理出发，重新设计一套面向人类健康的运营体系。

I was co running Deerfield, and we decided to spend some really deep time trying to put our lessons learned and our insights together and try to design from a first principles perspective, what would the operating system for human health look like?

我们能做些什么，来成为推动这一切实现的核心力量？

And what could we do to be the driving force behind making that happen?

我们怎么能不只是满足于十年才出现一次、价值一千亿美元的GLP-1革命？我们能不能成为那个推动‘万亿级成本节约革命’的力量？让药物真正抓住机会，预防那些本就完全可以预防的疾病。

How could we be the force that is not just okay with a once in a decade $100,000,000,000 GLP-one revolution, but how do we be the force behind making sure that this once in a lifetime trillion dollar cost savings revolution happens because we can be the force behind medicines, having their moment where they can prevent diseases that are already preventable today.

这真的给了我们很大的启发。

And that really inspired us.

一旦看清楚了，就再也无法视而不见。

And after kinda seeing it, we couldn't unsee it.

之后我们又花了好几个月反复思考：我们到底要不要干这件事？

And then we spent many more months thinking about, well, do we go do it?

我们该怎么把它做起来？

How would we build it?

最终，这就成了Braidwell——我现在全身心投入的地方，也是我们过得最开心的一段时光。

And that became Braidwell, where I'm spending my time today and we're having the most fun that we've ever had.

如果用最简单的话来概括你们的投资方式，你会怎么说？

And if you could sum up the investment approach in its simplest form, how would you do it?

我想还是带你们看看我们每天早上的例会吧。

I think I just bring you into what we call our morning meeting.

在Braidwell，我们每天早上九点十五分集合，团队一起开会，直到结束。

So at Braidwell, we get together as a team every morning from 09:15 until we're done.

我想让你想象一下，你就在这间屋子里，已经亲眼看到了这一切。

And I want you to imagine like, so you're in this room, you've seen it.

这间屋子很大，我们有一支极其出色的团队，包括科学家、生物统计学家、商业专家、人工智能专家、投资人、运营人员、交易员，还有结构金融专家。

So you're in this room, it's a big room and we've got an amazingly talented team of scientists, biostatisticians, commercial experts, AI experts, investors, operating people, traders, structured finance people.

我们的团队规模相当可观。

We have a pretty significant team.

团队里的每个人都有自己独特的专长，帮助我们判断哪些公司拥有真正了不起的技术，值得支持和推动，让它们以最快的速度实现真正的社会影响，突破那些关键性的瓶颈，让创新从可能变成现实。

Everybody on the team has some special superpower that they contribute to us trying to figure out what companies have amazing technologies that we should be getting behind and backing and helping them to go as fast as possible to have human impact that cuts at some of those big unlocks that would be the difference between having something and having it have its impact.

当我们放眼整个生态，看看当下所有正在推进的事情时，我们又在哪里看到那些本该有药物却还空白的领域？

And then as we get to look across the ecosystem of everything that is moving forward today, where do we also see the major gaps where medicine should exist, but they don't yet?

对我来说，待在这间屋子里，是一天中最让人兴奋的时刻。

To me, being in that room is the most fun part of the day.

我觉得这完美体现了我们的理念：要想成为出色的创新支持者，我们必须能回答三个问题。

And I think it very much epitomizes what our philosophy is, which is that in order to be a great backer of innovation, we essentially need to be able to answer three questions.

我们支持的这些创新，最终会成功还是失败？

Are the innovations we're getting behind gonna work or fail?

这些创新最终在市场潜力上有没有意义？

Are they ultimately gonna be relevant from a market potential or not?

我们有没有办法投资这家公司，支持它更快发展，用能带来足够回报的资本，回报那些信任我们、把资金交到我们手里的投资人？

And is there a way by which we could back this company, invest in this company, enable this company to move forward faster with capital that produces a return that's attractive enough for the investors that are trusting us with their capital.

我们想把各个专业领域最顶尖的人才汇聚在一起，形成一种可复制的优势，日复一日地运用，既支持那些本该加速前进的公司，也让那些本该存在却还没出现的公司得以诞生。

We wanna bring together the most talented minds at each of those different areas of domain expertise to give us an advantage that we can in a replicable manner deploy day after day after day to both be backing the companies that should be moving forward faster and making exist the companies that don't but should.

能身处这样的环境，真是令人惊叹。

It's an incredible room to be in.

我一直在想，如果每个人都能设计自己每天从早上九点十五分开始到结束的专属空间，那该多有意思。

It makes me wonder like if everyone could design their own room to be in from 09:15 to whenever every day, it'd be a fun exercise.

我知道你也是个创造者，这个话题我们改天再聊。

I know you're also a builder, we'll save that discussion for another time.

可惜时间到了。

Unfortunately, we're out of time.

我记了满满几页笔记，每次我们聊天都这样，明明约了一小时，其实该安排一整天才对。

I have pages of notes, you and I do this every time we talk, we set aside an hour, we should set aside a day.

我来问一下我每次谈话的传统结尾问题，我相信未来几年我们还会多次这样聊。

I'll go to my traditional closing question for this session and I'm sure we'll do this many times together over the years to come.

有人对你做过最温暖的一件事是什么？

What is the kindest thing that anyone's ever done for you?

是我妻子。

That's my wife.

我每天都很拼，几乎一刻不停。

I'm pretty darn hard at it every day.

只要醒着，我就在拼命干活。

Every minute of the day I can be hard at it.

这种状态让我压力倍增，有时候我太专注地琢磨一个问题，简直忘了周围的一切。

That drives me to different levels of stress and then I could be so myopically deep down trying to figure something out that I kind of just lose track of everything else around me.

我妻子卡丝特别厉害，总能一眼看出我快到极限了，然后她总有办法让我分心、笑出来，把我拉回平和的状态。

And my wife, Cass, has this amazing ability to just know when I'm at that, like just on the precipice of that breaking point, and she could find some way to distract me, make me laugh and kind of get me back to neutral.

我觉得这对我特别重要，因为有她在身边，我不用担心自己太拼命——她让我在追逐真正热爱的事时，还能享受其中的乐趣。

And I think that's a big force and an advantage for me because I don't have to worry about going so hard because I've got a partner that's right there by my side that can help make sure that I could not just be going after things I really care about doing, but having some fun along the way.

了解卡斯，我知道这话不假。

Knowing Cas, I know that's true.

亚历克斯，非常感谢你抽出时间。

Alex, thanks so much for your time.

谢谢。

Thank you.

我玩得很开心。

I had a great time.

如果你喜欢这期节目，欢迎访问 colossus.com。

If you enjoyed this episode, visit colossus.com.

那里有我们所有播客节目的完整版，还附有精心校对的文字稿。

You'll find every episode of this podcast complete with hand edited transcripts.

你还可以订阅《Colossus》，这是我们每季度推出的印刷版、数字版和独家音频刊物，深入报道我们最敬佩的创业者、投资人和公司。

You can also subscribe to Colossus, our quarterly print, digital, and private audio publication featuring in-depth profiles of the founders, investors, and companies that we admire most.

更多详情请访问 colossus.com/subscribe。

Learn more at colossus.com/subscribe.

你知道那些微小的优势是怎么随着时间积累的吗？

You know how small advantages compound over time?

投资是这样，管理公司也是一样。

That's true in investing, and just as true in how you run your company.

你的支出体系，其实就是你的资本配置策略。

Your spending system is your capital allocation strategy.

Ramp 默认就设计得更聪明。

Ramp makes it smarter by default.

数据更好，决策更优，长期经济效益也更佳。

Better data, better decisions, better economics over time.

详情请访问 ramp.com/invest。

See how at ramp.com/invest.

随着业务增长，Vanta 会同步扩展，自动完成合规流程，为你提供统一的安全与风险视图。

As your business grows, Vanta scales with you, automating compliance and giving you a single source of truth for security and risk.

了解更多，请访问 vanta.com/invest。

Learn more at vanta.com/invest.

每家投资公司都不一样，通用的人工智能根本理解不了你的运作方式。

Every investment firm is unique and generic AI doesn't understand your process.

Rogo 可以。

Rogo does.

这是一个专为华尔街打造的人工智能平台，直接对接你的数据，理解你的流程，并产出实际成果。

It's an AI platform built specifically for Wall Street, connected to your data, understanding your process, and producing real outputs.

了解更多，请访问 rogo.ai/invest。

Check them out at rogo.ai/invest.

从 OpenAI 到 Cursor，再到 Perplexity，最顶尖的 AI 和软件公司都靠 WorkOS 在一夜之间做好企业级准备，而不是花上几个月。

The best AI and software companies, from OpenAI to Cursor to Perplexity, use WorkOS to become enterprise ready overnight, not in months.

去 workos.com，跳过那些繁琐的底层建设，专心做好你的产品。

Visit workos.com to skip the unglamorous infrastructure work and focus on your product.

Ridgeline 正在重新定义资产管理技术，它不只是软件供应商，更是真正的合作伙伴。

Ridgeline is redefining asset management technology as a true partner, not just a software vendor.

它已经帮助多家公司实现五倍增长，推动更快扩张、更智能的运营，以及更强的竞争力。

They've helped firms 5x in scale, enabling faster growth, smarter operations, and a competitive edge.