更快科学，更优药物

我想加速科学发展。我们在ARC研究所的宏伟目标是创建虚拟细胞，并利用基础模型模拟人类生物学。既然我们连单个细胞都无法完整建模，为何还要如此担忧随时间推移对整个身体的建模呢？

I want to make science faster. Our moonshot is really to make virtual cells at ARC and simulate human biology with foundation models. Why are we so worried about modeling entire bodies over time when we can't do it for an individual cell?

如果我们能找出如何建模生物学的基本单位——细胞，那么从这一点出发，我们应该能够构建更多。

If we can figure out how to model the fundamental unit of biology, the cell, then from that, we should be able to build.

我的目标是真正探索在我们有生之年改善人类体验的方法。有几件事，如果我们能在这一生中做对，将从根本上改变世界。

My goal is to really try to figure out ways that we can improve the human experience in our lifetime. There are a few things that if we get them right in our lifetime, will fundamentally change the world.

今天，我们要讨论如何加速科学发展。我的嘉宾是ARC研究所联合创始人Patrick Xu和十六z基金的普通合伙人Jorge Conde。我们将深入探讨虚拟细胞与生物学基础模型、科学为何陷入激励困境、细胞生物学领域可能出现的AlphaFold级别变革，以及突破如何转化为实际药物和商业成果。让我们开始吧。

Today, we're talking about making science move faster. My guests are Patrick Xu, cofounder of the Arc Institute, and sixteen z general partner, Jorge Conde. We get into virtual cells and foundation models for biology, why science gets stuck in incentive knots, what an alpha fold level movement for cell biology could look like, and how breakthroughs translate into actual drugs and business outcomes. Let's get into it.

Patrick，欢迎来到播客节目。感谢你的参与。

Patrick, welcome to the podcast. Thanks for joining.

谢谢邀请我。

Thanks for having me on.

多年来我一直想邀请你上节目，终于等到了你的时间。

I've been trying to have you on for years, but finally, I could get your time.

我来了。我很兴奋要做这件事。一定会很棒。

Here I am. I'm excited to do it. It's gonna be great.

对于不太了解您及您在ARC和其他领域工作的部分观众，您如何描述您的宏大目标？您究竟在尝试实现什么？

For some of the audience who aren't familiar with you and your work at ARC and beyond, how do you describe what's your moonshot? What what is what what you're trying to do?

我想让科学发展得更快。明白吗？我们可以用加速科学进步这样高层次的哲学目标来框定，但对人们来说可能不够具体。我认为最重要的是科学发生在现实世界中。

I want to make science faster. Right? You know, we can frame this in high level philosophical goals like accelerating scientific progress. Maybe that's not so tangible for people. I think the most important thing is science happens in the real world.

如果不是像AI研究那样能在GPU上快速迭代的领域，你就必须实际移动物体。原子将液体从试管转移到试管，才能真正制造出改变生命的药物。这些都是实时发生的事情。你必须实际培养细胞、组织和动物。我认为我们今天在生物学中运用机器学习的承诺，就是能够真正加速并大规模地推动这一进程。

If it's not AI research, which moves as quickly as you can iterate on GPUs, right, you have to actually move things around. Atoms clear liquids from tube to tube to actually make life changing medicines. And these are things that take place in real time. You have to actually grow cells, tissues, and animals. And I think the promise of what we're doing today with machine learning in biology is that we could actually accelerate and massively massively paralyze this.

因此我们的宏大目标是在ARC创建虚拟细胞，用基础模型模拟人类生物学。我们希望开发出对实验主义者——那些对技术持怀疑态度的人——真正有用的东西。他们只想看到数据和结果，让这成为他们进行细胞生物学研究时的首选工具。

And so our moonshot is really to make virtual cells at ARC and simulate human biology with foundation models. And, you know, we'd like to figure out something that feels useful for experimentalists, people who are skeptical about technology. You know, they just wanna see the data and see the results that it's actually the default tool that they go to use when they want to do something with cell biology.

好吧，等等。让我们退一步说。为什么科学最初发展这么慢？这是谁的错？

Okay. Well, hold on. Let's back up. Why is science so slow in the first place? Like, whose fault is that?

这是谁的错？这个问题可说来话长。我们应该深入探讨。这真的是多因素造成的。

Whose fault is that? Now that is a that is a long one. We should get into it. We should get into it. It's really multifactorial.

好的。

Okay.

对吧？这是一个奇怪的戈尔迪之结，归根结底是激励机制的问题。对吧？人们经常讨论科研经费以及如何改进科研经费，但这也关乎培训体系的运作方式。对吧？

Right? It's this weird Gordian knot that ultimately comes down to incentives. Right? Comes down to you know, people talk a lot about science funding and how science funding can be better, but it's it's also about how, you know, the training system works. Right?

我们如何激励长期职业发展。我们如何试图区分基础科学工作与商业可行的工作，以及当前人们能够研究的问题领域。我认为事情越来越跨学科。个别研究小组或公司很难擅长两件以上的事情。对吧？

How we incentivize long term career growth. How we, you know, try to separate, you know, basic science work from, you know, commercially viable work, and generally the space of problems that people are able to work on today. I think things are increasingly multidisciplinary. It's very hard for individual research groups or individual companies to be good at more than two things. Right?

你可能能做计算生物学和基因组学，对吧？或者化学生物学和分子胶。但同时做五件事越来越难。我们建立ARC作为一个组织实验，试图看看当把神经科学、免疫学、机器学习、化学生物学和基因组学聚集在一个物理屋顶下会发生什么。对吧？

You might be able to do, you know, computational biology and genomics, Right? Or, you know, like chemical biology and molecular glues. But, you know, how do you do five things at once is is increasingly hard. And we really built ARC as an organizational experiment to try to see what happens when you bring together neuroscience and immunology and machine learning and chemical biology and genomics all under one physical roof. Right?

如果你增加这五个不同领域之间的碰撞频率，希望你能研究一个巨大的问题空间，而这是你原本无法做到的。显然，在任何大学或地理区域，所有这些独立领域在大范围内都有代表，对吧，分布在不同的校园中。但人们是分散的，而你希望所有人在一起。是的。

If you increase the collision frequency across these five distinct domains, there would hopefully be a huge space of problems that you could work on that you wouldn't be able to. Now, obviously, in any university or any kind of geographical region, you have all of these individual fields represented at large, right, across these different campuses. But, you know, people are distributed, and you want everyone together. Yeah.

好的。但好吧。如果我可以的话。我以为大学是试图将多个学科聚集在一个屋檐下的尝试。你说不是这样。

Okay. But okay. If I may. So a univer I would have thought a university was an attempt to bring in multiple disciplines under one roof. You're saying it's not.

它太分散了。

It's too diffuse.

跨越整个校园。

Across an entire campus.

好的。所以物理上的距离，字面意义上，造成了效率低下。

Okay. So the physic like, literally, the physical distance creates inefficiency.

这是一部分原因。我认为另一部分原因是人们有自己的激励机制。对吧？他们需要发表自己的论文，专注于自己的事务，做出自己的发现。

That's part of it. And I think the other part is folks have their own incentive structures. Right? They need to publish their own papers. They need to do their own thing and, you know, make their own discovery.

在当前学术体系中，很多方面确实没有真正激励大家合作。我们做的很多努力是试图让人们参与那些需要远超个人、团队或单一想法所能完成的大型旗舰项目。

And you're not really incentivized to work together, I think, in many ways in the current academic system. And a lot of what we've done is to try to have people work on bigger flagship projects that require much more than any individual person or group or idea.

是的，这很酷。所以ARC研究所最初的假设是，如果能将多个学科聚集以增加'碰撞频率'（如你所说），并且能消除传统结构中存在的一些交叉激励问题，这两者的结合将加速科学进程。

Yeah. That's cool. So, like, sort of the original hypothesis for the ARC Institute is if you can bring multiple disciplines together to increase the collision frequency, as you said, and if if one could remove some of the the cross incentives that may exist in sort of traditional structures, the combination of those two things will make science faster.

没错，这些绝对是其中的关键部分。我们有两个旗舰项目：一个寻找阿尔茨海默病的药物靶点，另一个构建这些虚拟细胞。我认为不仅是人员和基础设施，这些模型本身也有望真正加速科研——如果模型足够精确实用，你就能以神经网络前向传播的速度进行实验。

Yeah. These are these are absolutely part of it. Right? We have two flagship projects, one trying to find Alzheimer's disease drug targets, the other to make these virtual cells. And the I think it's not just the people and the infrastructure, but also the models models will hopefully literally make science faster, that you could do experiments at the speed of forward passes of a neural network if these models could become accurate and useful.

嗯。

Mhmm.

是的。因此，通过技术手段压缩发现过程所需时间，自然就能解决发现周期过长的问题，尽管这可能有过度简化的风险。

Yeah. So that that will be one thing that solves the length of discovery is you compress the time discovery takes naturally by just throwing technology at the problem, at the risk of oversimplifying.

嗯，我们这里可是技术乐观主义者，对吧？

Well, we're techno optimists here, no?

确实如此。

We are.

没错。为什么AI在图像生成和语言模型方面的发展速度远快于生物学领域？如果我们能挥动魔法棒，我们最希望加速哪些方面的进展？

Yeah. Why has AI progressed so much faster in sort of image generation and language models than than biology? And and if we could wave a wand, like, where are we excited to to speed certain things up?

老实说，这要容易得多。

To be honest, it's a lot easier.

是啊。

Yeah.

对吧？也许这是个大胆的观点。对吧？但

Right? Maybe that's a hot take. Right? But

我是说，技术比生物学更容易掌握。

I mean, technology is easier than biology.

自然语言和视频建模确实比生物学建模简单。没错吧？某种程度上，如果你了解并学会了机器学习，知道如何训练这些模型，你就已经掌握了语言能力。你也已经懂得如何解读图像。

Natural language and video modeling is easier than modeling biology. Correct. Right? And to some degree, like, if you understand and learn machine learning, right, and how to train these models, you have already learned how to speak. You already know how to look at pictures.

因此你评估这些模型生成或预测的能力是与生俱来的。对吧？但我们并不懂生物学的语言。充其量只能带着极其浓重的口音去说。

And so your ability to evaluate the generations or predictions of these models are very native. Right? But we we don't speak the language of biology. Right? At very best with an incredibly thick accent.

对吧？所以当你训练这些DNA基础模型时，我并非以DNA为母语。我只能大致理解输入模型的标记类型和实际输出结果。同样地，对于这些虚拟细胞模型，我认为主要目标是找到方法去解读模型给出的那些模糊难懂的输出。

Right? So when you're training these DNA foundation models, I don't speak DNA natively. So I only have a sense of the types of tokens that I'm feeding into the model and what's actually coming out. Right? Similarly, with these virtual cell models, I think a lot of the goal is to figure out ways that you can actually interpret the weird fuzzy outputs that the model is giving you.

我认为这正是迭代周期缓慢的原因——你必须进行这种实验室闭环验证，通过实际实验来检验模型输出的实验真实性。我觉得提高这个过程的效率和维度将会非常关键。是的。

And I think that's what slows down the iteration cycle is you have to do these lab in the loop things where you have to run actual experiments to actually test with experimental ground truth. And, you know, I think increasing the speed and dimensionality of that is gonna be really important. Yeah.

这其中有多少是因为——就像你说的——我们对生物学的表述很糟糕或带着浓重口音？又有多少是因为，比如训练图像时我们能直接看到图像，从而判断输出质量？但生物学中那些我们看不见甚至尚未知晓存在的事物呢？比如，我们怎么可能创建出虚拟细胞？

How much of this is the fact that, like, you talk about, we speak biology poorly or with a very thick accent. How much of this is like, if you're training on an image, we can see the image, and so we can see how, you know, how good the output is. Yeah. What about all the things in biology that we can't see or don't even know exist yet? Like, how how can we create a virtual cell?

或许我们应该先向普通观众解释一下什么是虚拟细胞模型。但关键在于，当我们甚至不能确定是否了解细胞内所有组分及其功能时，怎么可能创建出虚拟细胞模型？人们

And maybe we should come back to the what a virtual cell model is, by the way, for the lay audience. But, like, how can we create a virtual cell model when we're not even sure if we understand all of the components that are in a cell and how they function? People

在自然语言处理领域，我们也对此进行了大量讨论。自然语言处理有着悠久的学术传统，对吧？然而，当人们发现只需将大量非结构化数据输入Transformer模型就能直接生效时，这显得既怪异又反直觉，还引发了激烈争议。当然，我们并非声称这种方法能直接套用于生物学等其他领域，但关于'何为精准的生物模拟器'确实存在诸多争议。

talked a lot about this in NLP as well. There's this long academic tradition in natural language processing. Right? And then it was just weird and nonintuitive and intensely controversial that you could just feed all this unstructured data into a transformer, and it would just work. Now we're not saying this will just work in all the other domains, including in biology, but I think there is this, you know, controversy around what does it mean to be an accurate biological simulator?

虚拟细胞意味着什么？确实，我们无法测量所有指标。比如我们目前还无法在保持空间分辨率的同时，高通量地测量代谢物等物质。

What does it mean to be a virtual cell? It's true. We can't measure everything. Right? We can't measure, I think, things like metabolites and really high throughput with spatial resolution.

能力发展将分阶段推进：最初模拟单个细胞，然后模拟细胞对，接着模拟组织中的细胞，最终扩展到完整生理环境中的动物模型。这些不同尺度和复杂度的层级将随时间推移逐步整合优化。另一个反直觉的现象是数据和建模中呈现的规模法则，我举个实例说明。

And there are gonna be different phases of capability where initially they model individual cells, then they model pairs of cells. Then they model cells in a tissue and then in a broader physiologically intact animal environment. And those are length scales and kind of layers of complexity that will aggregate and, you know, improve upon over time. And I think the other of non intuitive thing in many ways are the scaling laws that you get in data and in modeling. I'll give you an example.

分子生物学界常有讨论认为RNA不能反映蛋白质及其功能。虽然目前蛋白质组测量技术远不如转录组测量技术那样具备可扩展性（单细胞分辨率领域尤其如此），但我们正在迎头赶上。实际上，可以在RNA信息基础上叠加某些蛋白质信息节点。

Right? There's a lot of discussion in molecular biology about how RNAs don't reflect protein and protein function. Right? And so while we don't have you know, proteomic measurement technologies that are nearly as scalable as transcriptomic measurement technologies today, like as the single cell resolution certainly, but we're getting there. And you can layer on certain nodes of protein information that you can add on top of the RNA information.

但从多角度看，RNA表征就像一面镜子——虽然对蛋白质层活动的反映分辨率较低，但蛋白质信号传导的最终变化总会体现在转录状态上。当然，对单个细胞而言，这种对应可能不够精确。

But in many ways, the RNA representation is a mirror. Right? It might be a lower resolution mirror for what's happening at the protein layer, but eventually, what is happening in protein signaling will get reflected in a transcriptional state. Right? And so for an individual cell, this may not be very accurate.

但考虑到基因组学和功能基因组学产生的海量数据规模，当积累足够多的RNA数据时，就能通过这些'镜像回声'间接解读蛋白质层面的活动。这种原理同样适用于代谢信息等领域。

But when you imagine the massive data scale that we're generating in genomics and functional genomics, right, you start to gather tremendous amounts of RNA data that will read in kind of like what's happening at their protein level at some sort of mirror echo. Right? And then that can, you know, be the case for metabolic information as well and so on. Yeah.

所以这就像一张低像素图片，但只要我们能拉远足够距离，依然能把握整体态势。

So it's a low pixel image, but if we can get sort of zoomed out far enough, we'll get a sense of what's going on.

你必须押注于当下可规模化的事物。对吧？如今我们能够规模化单细胞和转录信息。随着时间的推移，我们还能叠加蛋白质层面的信息。我们将需要空间信息、空间标记，以及时间动态数据。

You have to bet on what you can scale today. Right? We're able to scale single cell and transcriptional information today. We're able to add on protein level information over time. We'll need spatial information, spatial tokens, and we'll need temporal dynamics as well.

我把事情大致分为三个层次：发明、工程化和规模化。当今生物技术领域，有些已具备规模化条件，而另一些仍需我们去发明创造。对吧？

And we'll you know, I kind of bucket things into three tiers. There's invention, engineering, and scaling. And there are certain things today biotechnologically that are scale ready. And then there are things that we still need to invent. Right?

这也是我们为何认为需要建立研究所来解决这类问题——我们不甘于仅成为一家工程车间，只专注于单细胞扰动筛选的规模化。对吧？那样虽然有趣，但三年后就会显得过时。因此我们正在大量投入新技术研发，相信未来会结出硕果。

And that's part of why we felt like we needed a research institute to be able to tackle these types of problems, that we weren't just going to be an engineering shop that's just trying to scale single cell perturbation screens. Right? That would be interesting, but in three years would feel very dated, I think. Right? And so there's a lot of novel technology investment that we're making that we think will bear fruit over time.

是的。

Yeah.

能否详细阐述虚拟细胞概念？为何将其定为终极目标？实现路径上有哪些瓶颈？

Can we flesh out the virtual cell concept? Why that's the ambition we we've landed on and what it's gonna take to get there or what are the bottlenecks?

我认为机器学习在生物学领域最著名的成功案例是AlphaFold。它解决了蛋白质折叠问题——给定任意氨基酸序列，就能预测蛋白质结构。虽然不算完美，但准确率超过90%。

I I would say the most kind of famous success of ML in biology is AlphaFold. Right? And this solved the protein folding problem of, you know, when you take a sequence of any amino acid, what does the protein look like? Right? And, you know, it's pretty good.

它虽未模拟生物物理学和分子动力学过程，却能以90%以上的准确率呈现最终状态。这就是人们常说的AlphaFold时刻——当你需要研究某个蛋白质却缺乏实验结构时，直接使用这个算法进行折叠预测就行。

It's not perfect. It certainly doesn't simulate the biophysics and the molecular dynamics, but it gives you a sense of what the end state is with 90% plus accuracy. Right? Mhmm. And that's the alpha fold moment that people talk about, right, where anytime you want to, you know, work with a protein, if you don't have an experimentally cell structure, you're just gonna fold it with this with this algorithm.

我们也希望在虚拟细胞领域达到类似境界。ARC实验室实现这一目标的方式是通过扰动预测——即设想存在一个由细胞类型和细胞状态构成的流形空间，比如心肌细胞、血细胞、肺细胞等。我们知道细胞可以在这个流形上发生迁移。

And we kind of want to get to that point with virtual cells as well. And the way that at ARC we're operationalizing this is to do perturbation prediction, right, where the idea is you have some manifold of cell types and cell states. Right? That can be a heart cell, a blood cell, a lung cell, and so on. And you know that you can kind of move cells across this manifold.

对吧？有时它们会发炎，有时会凋亡，有时会停滞在细胞周期中，有时会进入应激状态。

Right? Sometimes they become inflamed. Sometimes they become apoptotic. Sometimes they become cell cycle arrested. They become stressed.

它们可能处于代谢饥饿状态，或以某种形式'饥饿'。如果你拥有这种通用细胞空间的表征模型，能否计算出需要施加哪些扰动才能使细胞在流形上迁移？这正是我们研发药物的本质所在。

They're metabolically starved. They're hungry in some way. And so if you have this sort of this representation of universal sort of cell space, right, can you figure out what are the perturbations that you need to move cells cells around this manifold? And this is fundamentally what we do in making drugs. Right?

无论是从煮树叶提取的天然小分子，还是向牛羊兔子注射蛋白获取的抗体，我们本质上都在寻求更精准的分子探针。过去我们通过实验方法制备这些结合物，如今则用计算手段零样本设计。但最终目标都是用这些结合物抑制某些靶点。

Whether we have small molecules, which started out as natural products from boiling leaves or antibodies when we injected proteins into cows and rabbits and sheep and took their blood to get those antibodies, where we were basically trying to get to more and more specific probes. Right? And we had experimental ways to kind of cook these up. Now we have computational ways to zero shot these binders. But ultimately, what you're trying to do with these binders is to inhibit something.

通过这种干预，就像点击拖拽那样，将细胞从毒性功能获得性致病状态拉回稳态健康状态。复杂疾病的特征很明显——没有单一病因，而是一系列复杂变化。你需要组合多种扰动才能实现状态转换，这就是人们常说的多药理学概念。

And then by doing so, kind of click and drag it from a kind of toxic gain of function disease causing state to a more homeostatic healthy one. Right? And the thing that is very clear in complex diseases, right, where you don't have a single cause of that disease is there's some complex set of changes. There's a combination of perturbations, if you will, that you would want to make to be able to move things around. Now, you know, people talk about this classically as things like polypharmacology.

但我们现在正从'偶然发现多重靶点'的阶段，迈向'有目的的组合操控'阶段。要实现从细胞状态A到B的转变，可能需要先做这三个改变，接着那两个，最后这六个，循序渐进。

Right? But, you know, I think we're moving from a, oh, this thing happens to have, you know, a whole bunch of different targets kind of by accident to we have the ability to manipulate these things combinatorially in a purposeful way. Right? That to go from cell state A to cell state B, there are these three changes I need to make first, then these two changes, and then these six changes, right, over time. Right?

我们希望模型能给出这类建议。之所以将虚拟细胞项目定位于此，是因为其实验实用性——它要成为湿实验室生物学家的智能副驾，帮助决定实验方案。我们不做那些只在机器学习指标上刷分的理论论文。

And we kind of want models to be able to suggest this. And the reason why we scoped virtual cell this way is because we felt it was just experimentally very practical. You want something that's gonna be a copilot for a wet lab biologist to decide, what am I gonna do in the lab? Right? We're not trying to do something that's like a theory paper that's really interesting to read where, you know, the numbers go up on a ML benchmark.

但是，你知道，实际上你可以决定在实验室里12种不同条件下要做的12件事，对吧，然后直接测试它们。这样我们就进入了模型预测与实验测量的循环：从模型预测到实验测量，再到改进或强化学习后的模型预测。目标是要实现计算机模拟靶点识别，基本上就是找出新的药物靶点，然后确定所需的药物组合来实现这些改变。嗯。

But, you know, you practically can decide what are the 12 things that you're gonna do in the lab in 12 different conditions, right, and actually just test them. Right? And then that's how we kind of enter the kind of the lab and the loop aspect of model predictions to experimental measurements to kind of improved or RL'd or whatever model kind of predictions again. And the goal is to be able to do in silico target ID where you can basically figure out new drug targets, figure out then the compositions, the drug compositions you would need to actually make those changes. Mhmm.

我认为如果我们能做到这一点，就能创建一家新型的、垂直整合的AI驱动制药公司，这在今天显然是个非常激动人心的想法。但我觉得在很多方面，这类公司的宣传和框架都先于基础研究能力的突破。而我们在ARC真正投入的，就是与领域内许多杰出同事一起，让这种可能性成为现实，为整个科研社区铺路。

I think if we could do that, we could make a new AI, like, vertically integrated AI enabled pharma company, right, which, you know, I think is obviously a very exciting idea today. But I think in many ways, the kind of pitch and the framing of these companies precedes the fundamental research capability breakthroughs. And that's what we're really invested in at ARC is just kind of just making that happen along with many other amazing colleagues in the field to just make this possible for, you know, the community.

所以简化来说，我们的目标是想达到类似AlphaFold那样的时刻——就像它90%的情况下能提供有用的蛋白质折叠结构那样。我们希望在虚拟细胞模型中实现这样的对比：当我要求模型将细胞从状态A转换到状态B时，90%的情况下它能给出一个扰动清单。嗯。假设其中90%的扰动确实能在实验中实现从状态A到状态B的转变。

So if the goal is I'm oversimplified for you. Like, if we wanted to get to the alpha fold moment where, you know, it kind of gives you a useful structure, folded structure 90% of the time to use your your data point. We wanted to take that comparison in the in the virtual cell model, and we said, okay, 90% of the time, if I ask the model, I wanna shift the cell from cell from cell state a to cell state b, and it's gonna give me a list of perturbations. Mhmm. And let's say that at 90% of the time, those perturbations, in fact, result in the shifting experimentally in the shifting from cell state a to cell state b.

嗯。

Mhmm.

我们距离虚拟细胞的AlphaFold时刻还有多远？

How far away are we from that alpha fold moment for virtual cells?

我发现用GPT一代、二代、三代、四代、五代的能力来类比会很有帮助。大多数人会同意我们目前介于GPT一代和二代之间对吧？最初的兴奋点在于我们至少实现了GPT一代，看到了通过某种缩放定律使能力迭代提升的路径。但就像我们在ARC与Brian He开发的EVO DNA基础模型所显示的，这些基因组生成物就像是生命的'模糊照片'。

I find it helpful to frame these in terms of, like, GPT one, two, three, four, five capabilities. Right? And I think most people would agree we're somewhere between GPT one and Right? A lot of the excitement was that we could achieve GPT one in the first place, that you could see a path with scaling laws of some kind to kind of make successive generations where capabilities would improve. But, you know, these are, you know, with, like, our EVO kind of DNA foundation models that we developed at ARC with Brian He, right, one of the things that we've seen is that, you know, these are really kind of these genome generations are like, quote, unquote, blurry pictures of life.

对吧？我们不认为合成这些新基因组会让它们具有生命。但我们也觉得这个目标并非遥不可及。我们需要持续跟进这些能力发展。我们正在采取非常系统化的方法来解决这个问题——包括整合公共数据资源。

Right? We don't think if you synthesize these novel genomes, they would be alive. But, you know, we we don't think that's actually also impossibly far away. We'll just have to kind of follow these capabilities. We're generating we're taking a very integrated approach to attack this problem, right, where you need to curate public data.

你需要生成大量内部和私有数据，建立基准测试，训练新的模型并构建新型架构，全面开展这些工作。我们将逐步攻克这个难题。

You need to generate massive amounts of internal and private data, build the benchmarks, and train the new train new models and build new sort of architectures and kind of doing these things full stack. And we'll just kind of attack this hill climb over time.

我想说的是，GPT-3时刻会是什么样子？我指的是那种能改变公众对技术能力认知的公开发布，同时激励新一代人才涌入生物学领域的突破。

What's the GPT I was gonna say GPT three moment gonna look like? And by that, I mean sort of a public release that alters the public's conception of just what's possible here from a capabilities perspective and also inspires a whole new generation of talent to, like, rush into into into into biology?

生物学的一个优势是我们掌握大量基础事实。整本教科书都在描述细胞信号传导、细胞生物学及其运作机制。所以即使没有虚拟细胞模型，如果你向CHATCHBT或Claude提问关于受体酪氨酸激酶信号传导的问题，它也能给出解释。

Well, the good thing with biology is we have a lot of ground truth. Right? There are entire textbooks, right, describe cell signaling and cell biology and how these things work. And so, you know, even without a virtual cell model at all, right, if you went into CHATCHBT or Claude and you basically, you know, you asked us some question about, you know, like receptor tyrosine kinase signaling. It would have an opinion on how that works.

我认为理想模型应该能预测那些生物学史上著名的经典扰动案例。举个例子：如果向模型输入诱导多能干细胞状态、人类胚胎干细胞状态和成纤维细胞状态，它能否预测出山中伸弥四因子可以将成纤维细胞重编程为类干细胞状态？也就是让模型重新发现这项获得2009年诺贝尔奖的成果。

Right? And so I think you would want the model to be able to predict perturbations that are kind of famous canonical examples of biological discovery. So I'll give you an example. If you load it into the model an iPSC, as a a kind of an induced pluripotent stem cell state or human embryonic stem cell state and fibroblast cell state, right, could it predict that the four Yamanaka factors would reprogram the fibroblast into a stem like state, right, and essentially rediscover from the model something that won the Nobel Prize in 2009. Right?

这将是经典案例之一。反过来也可以验证：给定干细胞，模型能否发现Neurogenin2、ASCL1、MyoD等分化因子，将其转变为神经元或肌肉细胞？这些都是发育生物学的经典案例，但同样适用于重现FDA批准药物的作用机制。

That that would be sort of one really kind of classic example. And then you could go do the inverse. If you have a stem cell, can it discover neurogenin two? ASCL one, MyoD, can it find differentiation factors that will turn that into a neuron or into a muscle cell or so on? And, you know, these are kind of classic examples in developmental biology, but you could also use this to try to discover or kind of recapitulate the mechanism of action of FDA approved drugs.

例如抑制乳腺癌细胞中的HER2，模型应该能预测出特定反应类型。或者预测哪些克隆体更具转移性或耐药性，导致微小残留病灶。相比当前模型仅评估差异表达基因的平均绝对误差等量化指标，我们可以逐步为这些模型添加更多教科书式的具体生物学评估标准。

Right? And so you could say, for example, you know, if you kind of inhibit HER two in, you know, breast cancer, cell states, right, it would be you know, you would get this type of response. Or it could predict the certain clones that will be able to kind of be more metastatic or resistant, and they'll lead to minimal residual disease. There are, I think, lots of kind of biological evals that you can kind of add onto these models over time that are really tangible textbook examples as opposed to, I think, what the kind of early generation of models do today, which is, you know, very quantitative things like mean absolute error over, like, you know, the differential expressed genes and stuff like that. You know?

那些只是机器学习基准。我们需要将评估标准提升到能让从未接触过终端的老教授也能理解的水平。

That's those are ML benchmarks. And we want to increase the sophistication into something that you could explain to an old professor who has, you know, never touched a terminal in their life.

顺便问一下，你把教科书当作真理。你觉得我们会发现很多教科书其实是错的吗？

By the way, you talk about textbooks as ground truth. Do you think we're gonna find that a lot of the textbooks are wrong?

我认为教科书是经过压缩的。比如，当你看到那些经典的细胞信号传导图示，信号从a到b，抑制c，那其实是对复杂系统的二维简化表达。

I would say textbooks are compressed. Right? So for example, when you you look at these kind of classic cell signaling diagrams of a signals to b, which inhibits c, right, that's a very kinda two dimensional representation of

我们对复杂系统的理解。对，没错。

our understanding of a complex system. Right. Right.

没错。教科书就是它们的样子，代表可靠知识的总集，但大家都知道有无数例外存在。而发现的一部分就是寻找新的例外，对吧？

Right. I mean, yes, textbooks are what they are. They represent the corpus of reliable knowledge, but everyone knows that there are incredible number of exceptions. And part of what discovery is is to find new exceptions. Right?

你为什么不谈谈生物模拟与实际理解之间的区别？要真正模拟极其复杂的人体需要什么条件？

Why don't you talk about the difference between simulation of biology and and the actual understanding? And and what would it would it take to actually be able to model the extremely complex human body?

有些人不喜欢'虚拟细胞'这个说法，觉得太媒体化了不够严谨。但有趣的是，很多人却能接受'数字孪生'和'数字化身'这些更高抽象层次的生物建模概念。其实虚拟细胞的范畴和严谨性远超过数字孪生或化身的建模。

You know, some people don't like the phrase virtual cells because it sounds too media friendly. It's not rigorous enough. Right? But I I've always found it funny that, you know, both, you know, many people are okay with, like, digital twins and digital avatars, which, you know, talks about modeling biology at a way higher level of abstraction. You know, I think virtual cells, if anything, is actually way more scoped and rigorous than modeling a digital twin or avatar.

不过我觉得这些术语很有用，因为它们描述了目标和愿景。长远来看，我们其实根本不关心预测单个细胞的扰动反应——显然，我们真正想要的是预测药物毒性。

But, you know, I think these are useful words because they describe the goal and the ambition. Right? That, no, in the long run, we don't care about predicting the kind of perturbation responses of an individual cell at all, actually. Right? Obviously, we want to be able to predict drug toxicity.

我们希望能够预测衰老过程。我们想要预测为什么肝细胞在反复受到乙醇分子等刺激时会变成肝硬化。对吧？而且，这类化学或环境干扰应该是可预测的。我认为只需要逐步叠加复杂性。

We want to be able to predict aging. We want to be able to predict why a liver cell becomes cirrhotic when you repeatedly challenge it with ethanol molecules or whatever. Right? And, you know, these sort of chemical or environmental perturbations should be predictable. I think you just kind of have to layer on the complexity.

对吧？比如，在我们连单个细胞都无法建模的情况下——虽然我们普遍认为细胞是生物计算的基本单位——为什么要如此执着于随时间推移建模整个身体呢？我们应该从这里开始着手，对吧？

Right? Like, why are we so worried about modeling entire bodies over time when we can't do it for an individual cell, right, where we sort of, you know, accept or broadly believe that this is a kind of, you know, fundamental unit of biological, you know, computation, if you will. Right? And let's just kind of start there. Right?

就像你必须从数学、代码和语言建模这些更容易验证的东西开始，然后才能逐步构建超级智能。

Just like you kind of have to start with, you know, things like math and code and language modeling, right, and things that are sort of easier to check. You can build to superintelligence over time.

是的。我认为这很有道理。这是个值得称赞的宏伟目标。如果我们能找出如何为生物学基本单位——细胞——建模的方法，就能在此基础上继续发展。

Yeah. I think that makes sense. Right? That that that's that's a very sort of laudable ambitious goal. If we can figure out how to model the fundamental unit of biology, the cell, then from that, we should be able to build.

就像早期人工智能，我们最初只做语言翻译这类基础NLP任务。远早于今天我们拥有的宏大愿景。如果幸运的话，我们或许能复制类似的发展轨迹。

Like in early AI, we just started with, like, language translation. Just, you know, basic NLP tasks. Right? This is long before, you know, the the tremendous ambitious scope that we have today. And I think we we hopefully can mirror that type of trajectory if we're lucky.

生物技术和制药行业的增长率似乎在持续下降。要使这些科学创新反映在商业模式和行业增长上，需要什么条件？

Is it it seems that biotech and pharma has has been a shrinking in in terms of the rate of growth. What what's it gonna take for these innovations in in in the science to reflect themselves in in business models and in in growth for the industry?

许多生物技术初创公司最初试图向制药公司销售软件，后来才发现自己在争夺规模有限的SaaS预算。现在他们意识到必须争夺研发预算。我认为当前这批公司有种说法：我们的生物制剂将争夺研发预算，取代人力成本之类的资源。

A lot of these biotech startups would try to initially sell software to pharma companies, and then they would kind of realize, oh, wow. We're, like, competing for SaaS budgets, which aren't very large. And then, you know, now they're realizing, oh, we have to compete for R and D budgets. Right? And I think, you know, there's this narrative from the current generation of these companies that, oh, our biological agents will compete for R and D budgets and replace headcount or something like that.

对吧？就像我们在不同领域的代理中看到的那样。对吧？我认为这是否能成功，取决于这些技术是否真能让我们在制药背景下更有效地开发药物。对吧？

Right? Just like we're seeing in, you know, agents across different verticals. Right? Whether or not that will, I think, pan out, I think depends on just whether or not these things meaningfully allow us to build drugs more effectively in the pharma context. Right?

我认为这是这个行业最重要的事情。所以我们相信虚拟细胞，不仅因为它能为发现提供基础机制见解的源泉，还因为一旦成功，它在工业上将非常有用。对吧？但我们需要时间来验证。对吧？

And I think that's just sort of the most important thing in this industry. And so I think we believe in virtual cells, not just because we think it will be a fountain of fundamental mechanistic insights for discovery, but also because if in the case of success, it could be industrially really useful. Right? But we'll we'll we'll have to see over time. Right?

如果90%的药物在临床试验中失败，这意味着两件事，但你无法确定各自的比例。对吧？一是我们一开始就选错了靶点。二是药物本身的成分不起作用。

If we have ninety percent of drugs failing clinical trials, right, that kind of means two things, and you're not sure what percent of which. Right? One is we're targeting the wrong target in the first place. The second is the composition. The drug matter that we're using doesn't do the job.

对吧？对于每个具体的失败案例，我们不清楚是哪个原因，或是两者兼有，各自占多少比例，这需要时间来厘清。比如，即使虚拟细胞准确率达到90%，你可能还是会得到这样的建议：现在你需要只针对心脏中的这个GPCR，而不是其他任何组织。对吧？

Right? It's not clear for each individual failure which one it is or if it's both or what proportion of each, and, you know, we'll have to kind of sort that out over time. Like, you can imagine even in the case of success when we had 90% accurate virtual cells, you'll probably end up with suggestions like, okay. Now you need to target, you know, this GPCR only in heart, but not in literally any other tissue. Right?

目前我们还没有能实现这一点的药物。这也是为什么我们需要通过研究来发现新的化学生物学物质，以便以组织或细胞类型特异性的方式靶向多效性靶点。对吧？生物学进展缓慢的部分原因，就在于理解、干预和安全性方面存在这种俄罗斯套娃般的复杂性。而令人难以置信的是，就在我从事这行的短短时间里，进展已经如此惊人。

We don't have the drug matter that can do that today. And so that's also why, again, you probably need research to figure out novel chemical biology matter that allows you to drug pleiotropic targets in a tissue or cell type specific way. Right? And so, you know, I think part of why biology is slow is because there's just this Russian nesting doll of complexity in terms of understanding, in terms of perturbation, in terms of safety. And, you know, the the the crazy thing is the progress in just the short time that I've been doing this is insane.

对吧？我在博德研究所读博士时，正值单细胞基因组学、人类遗传学、CRISPR基因编辑等技术蓬勃发展的黄金时期。2010年代初的单细胞测序论文可能只研究20或40个细胞。对吧？而在ARC，用不了多长时间，我们就能生成十亿个经过干预的单细胞。

Right? Like, I did my PhD at the Broad Institute in the heyday of developing single cell genomics, human genetics, CRISPR gene editing, and so many other things. And I think the kind of early 2010s papers on single cell sequencing would have 20 cells or 40 cells. Right? And and at ARC in the next, you know, kind of n, like, I don't know, relatively short amount of time, we're gonna generate a billion perturbed single cells.

对吧？这简直...我是说，这算不算摩尔定律的体现？

Right? That's like, I mean, how's how's that for Amur's law?

是的。这很了不起。是的。

Yeah. That's remarkable. Yeah.

是的。豪尔赫，作为我们生物实践部门的负责人，我也想听听你对几个问题的看法，包括关于GDPR三阶段的可能情形。另外，我很好奇你认为这是延续之前的GOP方案还是会有所不同。同时，科学成果要如何在商业中体现，行业要如何发展才能实现这一点？

Yeah. Jorge, wanna hear your your answers to couple of these questions too as as the leader of of our biopractice, both on the g p d three moment, what what that could look like. And also, like, I'm curious if you think it's g o p ones or sort of building off that or if it's gonna be something different. And also, what's it gonna take for the science to kind of reflect itself in the business, for the industry to grow?

好的。我先回答第二个问题。我认为当前行业面临的一大挑战，正如帕特里克精辟指出的，药物研发本就艰难且耗时。而且正如你所说，失败模式往往表现为两种情况：90%的临床实验失败案例中，要么是我们选错了靶点，要么是我们针对正确靶点却做出了错误的药物。

Yeah. So I'll take the second one first, if I could. So I think in terms of where the industry is right now, I think one of the big challenges we have is, as Patrick describes very nicely, you know, discovery's hard and it takes time. And, you know, the fail modes are exactly as you described. Oftentimes, when drugs fail, which they do 90% of the time in clinical trials, it's because we're going after the wrong thing, or we made the wrong thing to go after the right thing.

对吧？这两种失败模式屡见不鲜。所以帕特里克提出的很多方案本质上是在提高我们的命中率——既要找准靶点，又要做出正确的对应药物。但行业的困境在于，关键瓶颈依然存在。其中最大的必要瓶颈是：我们必须证明所研发的药物既能精准作用于正确靶点，又要在人体试验前尽可能降低风险。

Right? Those are the two fail modes, and that happens all too often. And so I think a lot of the stuff that Patrick is describing is gonna basically improve our hit rate or our batting average on figuring out what to go after, and then making the right thing to go after said thing. The challenge we have, I think, the industry is that the bottlenecks still are the bottlenecks. And the biggest bottleneck we have, which is a necessary one, is we have to prove that whatever we make, that we have the right thing to go after the right thing, so to speak, and that when we have it, that it's going to be as de risked as possible before you put it into humans.

首先我们必须擅长制造它们

And we have to be good at making them in the first

没错。这个瓶颈的存在是必要且重要的，我并非主张消除它，而是思考能否降低突破临床试验瓶颈所需的时间和经济成本？

instance Yeah. Exactly. And so that bottleneck is a necessarily important one. That bottleneck should exist. I'm not suggesting we've gotta remove it, but are there ways to reduce the cost and time associated with getting through the bottleneck of human clinical trials?

有意思的是，当我们讨论药物研发的各方利益相关者时——包括企业、支持企业产品商业化的科研力量、监管机构等——所有人首要关注的始终是确保药物对人类安全有效。但要加速突破这个核心瓶颈却非易事，虽然可以通过加快临床试验入组速度来提升效率。

And it's interesting because we talk about all of the various stakeholders when you're making a drug. There are the companies, there's of course the science that supported the company that's trying to commercialize a product, and they're the regulatory agencies. And everyone is trying to ensure, again, that what's first and foremost is the ability to discover and commercialize drugs that are safe and effective for humans. That middle part of actually getting through that bottleneck is hard to speed up in a very obvious way. You can increase the rate the way you enroll clinical trials.

你可以利用更先进的技术来改变我们设计这些临床试验的方式，或许能让试验进程更快或周期更短等等。但有些试验本身就存在必须遵循的自然时间线。比如想证明某种抗癌药能延长生存期？那需要相当长时间才能验证生存获益。再比如研发长寿药物，顾名思义整个试验周期可能长达一生。

You can use better technology to change the way we design these clinical trials, so maybe they can be faster or shorter, etcetera. But some of them just have a natural timeline you have to go through. Like, if you wanna demonstrate that a cancer drug promotes survival, guess what? You're gonna have It's gonna take some time to demonstrate a survival benefit. Or if you wanna do a longevity drug, that by definition is a lifetime you know, of trial in terms of length.

因此存在许多难以突破的瓶颈。那么什么能帮助这个行业呢？我认为有几个关键因素：一是随着技术进步，资本密集度有望逐步降低——这是我们行业当前面临的主要挑战。

So there's a lot of these bottlenecks that are really hard to get through. So what helps the industry? I think there are a couple of things that help the industry. One is capital intensity will hopefully, at some point, go down over time as technology gets better. Capital intensity is something that our industry faces.

某种程度上，

In some ways,

看起来有点像

it looks a little bit

如今的人工智能领域，比如训练这些模型的成本，但资本密集度仍然极高。这方面尚未改善。所以我们必须提高成功率来降低资本密集度。第二点是时间压缩——优质模型能帮助我们缩短早期研发周期。

like AI now, right, in terms of the cost of training these models, but the capital intensity is very, very high. That has not come down. So we gotta get the success rates up to impact capital intensity to get it down. The second thing is, where can we compress time? So good models can help us compress early discovery time.

我们尚未见证——虽然我认为即将到来——人工智能或其他技术大幅压缩临床开发、试验实施和患者招募等环节所需的时间。虽然有些创新正在萌芽，但尚未显现显著成效。第三点在于：如果我们能针对更有效的靶点开发更好的药物，治疗效果会更显著，因此能更快获得明确结论。若能在这三方面取得突破——降低资本密集度、压缩时间线、在疑难病症领域实质性地提升疗效——我认为这就是行业破局之道。

We still haven't seen, and I think it's coming, but it hasn't happened yet, we haven't seen artificial intelligence, or other technologies massively compress the amount of time it takes us to do the clinical development, the clinical trials, the enrollment of patients, all of those things. We're seeing some interesting things coming. We haven't seen sort of the payoff there yet. And the third thing is, if we can make better drugs, going after better things, the effect size should be higher, so therefore the answer should be obvious sooner. If we can get those three things right, reduce capital intensity, compress timelines, and effectively increase effect size in some very tough intractable diseases, that is what I think fixes the industry.

从我们早期投资机构的视角来看，这种转变的意义在于：当资本密集度下降而价值创造上升时，早期投资这些公司会更具吸引力，因为提前布局能获得回报。当前的问题是大多数公司即便实现价值拐点，投资者也难获相应回报。早期投资者承担了资本密集的压力，但即便公司成功，估值也未能体现这种成功——我们看不到其他行业常见的阶梯式增值，这从投资角度而言极具挑战。

And from where we sit at the early stage, in terms of being early stage investors, the reason why that helps us is, if the capital intensity goes down and the value creation goes up, it becomes easier to invest in these companies in the early days, because you get rewarded for coming in early. The problem we have right now is that most companies aren't, you're not seeing rewards happening when there's value inflection. So you come in early, you bear the brunt of the capital intensity, and even if a company's successful, that success isn't reflected in the valuation. So we're not seeing the step ups that you see in other parts of the industry. And that's just really, really hard from an investment standpoint.

所以我认为我们需要看到这些不同因素得到解决，才能真正修复这个领域，用你的话说。

So I think we need to see those various factors addressed for this space to really get fixed, to use your word.

是的。说得很好。我有很多要补充的。

Yeah. That was great. I have a lot to add on to this.

请讲。尽管补充。你知道，

Please. Add away. You know,

只是，你知道，几点简单的观察。对吧？首先是礼来和诺和诺德基于GLP-1类药物开发所增加的市场估值。这超过了一万亿美元，明白吗？我是说，NOAA股票已经跌了很多。

just, you know, one a few simple observations. Right? The the first is the amount of market cap added to Lilly and Novo based on the, you know, development of GLP ones. It's like over a trillion dollars is is more you know? I mean, NOAA stock has decreased a lot.

所以，你知道，一万亿美元，可以说，比过去四十年所有生物技术公司加起来的总市值还要多。对吧？我认为其中一个有趣的推论是，当我们临床前药物在临床试验中的成功率只有百分之十时，你往往会采取保守策略来管理风险。对吧？因此你会选择那些机制明确的疾病作为靶点——如果我针对已被充分理解的生物学开发新药，它在试验中就应该如预期般有效，虽然你知道这实际上非常昂贵，成本在很多方面都远超临床前研究。

So, you know, trillion dollars, let's say, is more than the market cap of all biotech companies combined over the last forty years have been started. Right? And I think that, know, one one of the kind of interesting kind of corollaries of this is that, you know, when we have a ten percent kind of clinical trial success rate for kind of preclinical drug matter, right, you tend to circle the wagons a bit and try to manage your risk. Right? And so the way that you do this is you try to go after really well established disease mechanisms where if I developed new drugs that go after well understood biology, it should work the way that I hope it will in the trial, which is, you know, really, really expensive and costs a lot more in many ways than the the preclinical research.

对吧？这样做的问题是：你虽然选择了验证充分的疾病机制，但针对的患者群体非常小。对吧？因此实际预期价值相对较低。而GLP-1类药物给我们展示的正是：当你瞄准庞大患者群体时能创造的价值。

Right? The problem with this is you go after very well validated disease mechanisms but with really small patient populations. Right? So then the expected value of this actually is relatively low. One of the kind of things that we've seen with GLP-1s is the just the kind of value that you can create when you go after really large patient populations.

我认为这从文化层面上提升了整个行业的雄心，无论是投资者还是药物开发者。我觉得我们应该继续保持这种势头。是的。

And I think that has culturally ruling net increased the ambition of the industry, both from the investor and from the drug developer side. And I think, you know, that's something that we should keep our foot on the gas for. Yeah.

是的。而且你看，我认为这个趋势是积极的。没错，你说得完全正确。GLP-1类药物使用增加所创造的价值，以及流向礼来和诺和诺德等公司的价值转移，我认为是非常合理的，因为他们解决了一个普遍的社会问题——管理糖尿病并最终帮助控制肥胖。

Yeah. And look, think the trend on that is pos I would argue the trend on that is positive. Yeah. You're absolutely right. Like, the the demonstration of the value that has been created with the the use increasing use of GLP-1s and the value transfer that's gone to companies like Lilly and Novo, I would argue, is very merited, right, because they've cracked an endemic social problem in terms of managing diabetes and eventually helping manage obesity.

所以我认为这非常了不起，这带来了巨大价值，因为他们攻克了社会上一个超越科学范畴的极其棘手的难题。这很棒。我同意你的观点，投入必须值得回报，对吧？你说得对。很多生物技术公司都在追逐容易实现的目标，因为风险低且需要即时收益。

And so I think that's remarkable, and there's a lot of value that goes to that because they tackled, they cracked a very, very challenging problem for society beyond just science. So that's great. And I agree with you, the prize, juice needs to be worth the squeeze, right? You're right. Lot of biotech has been around, go after the low hanging fruit because it's low risk and we got to eat today, right?

于是你去获取那些成果，却不得不推迟那些雄心勃勃的大规模适应症或真正难以攻克的疾病。但我确实认为我们正越来越多地看到这类尝试。顺便说一句，我们可以谈谈某些基因疗法——它们正在攻克那些最困难的问题，那些除非编辑DNA否则根本无法解决的问题。我认为这极其了不起、值得称赞，而且说实话令人振奋。

So you go get it, and you just have to You push off the big ambitious indication, the large population, or the really tough to crack disease. But I do think we're seeing more and more of that. And by the way, we can get into some of these genetic medicines, but some of these genetic medicines are going after some of the hardest problems, the things that you quite literally couldn't address but for editing DNA. And I think that's incredibly remarkable and laudable and frankly inspiring.

是啊。

Yeah.

但这个行业的基本要素必须运转良好，资本形成需要支持这类事业。而目前由于...

But the fundamental elements of the industry have to work, so the capital formation is there to support those kinds of things. And right now it's hard, right, because of

我们之前讨论过的问题。十五年后，当我们再次聚在这个房间，勉强避免成为永久底层阶级的一员时，我们会如何评价这个GLP-1药物的历史地位？或者它可能超越现状发展成什么样子？你觉得会是什么？

the issues we talked about before. Fifteen years from now, we're back in this room. We've barely escaped being part of the permanent underclass. And we're reflecting on on the sort of, you know, the the g p two three moment or or maybe the legacy of GLP ones, sort of beyond where where they are now. What do you think it could be?

或者，豪尔赫，我很好奇你的看法——你认为我们会回顾哪个技术突破并说'这才是真正的转折点'？还是你认为这将是多种因素的综合作用？

Or or or, Jorge, I'm curious to get your take on what do you think is gonna be the technological breakthrough that we're gonna point back to and say, oh, this is really what what said it all? That or do you think it's gonna be sort of, you know, multifactor combination?

是的。看吧。我认为这要回到我们开始讨论组合疗法的地方。抱歉。GLP-1类药物其实已经研发了大约四十年。

Yeah. Look. I think it's it's going to go back to sort of where we started this combination conversation. Excuse me. GLP-1s as a drug are, know, what, four decades in the making or something like that.

要知道，这些都不是一蹴而就的成功。但我们确实会看到更多突破——我们的希望是，当我们结合以下事实：我们越来越擅长确定靶点，越来越擅长设计药物来命中这些靶点（而且是通过各种新颖的创造性方式）。正如你之前说的，我们有从小分子到天然产物（比如煮树叶得到的成分）——现在我们设计小分子的水平突飞猛进，能让它们实现前所未有的新功能。在AlphaFold等工具的帮助下，我们设计生物制剂或蛋白质的能力也大幅提升。未来我们在基因疗法、基因编辑等复杂疗法上的设计能力还会更强。

You know, these are these are not overnight successes. But I do think what we are going to see more of, and and our hope is that when you combine the fact that we're getting better at understanding what to target, getting better at designing medicines to hit those targets, by the way, in a whole array of new creative ways. So we have small molecules, the natural products that we got from boiling leaves, as you said earlier, like, those have gotten you know, we're getting really good at designing smarter and better smaller molecules that do new things, that function in ways that they didn't before. We've gotten quite good at designing biologics or proteins with a lot of help from things like AlphaFold that helps understand how proteins fold. We're gonna get a lot better at designing some of the more complex modalities, like the gene therapies of the world or the gene editors of the world.

当这种能力与我们运用虚拟细胞模型精准定位靶点的能力相结合时，我们将迎来新药大爆发。我希望并预期行业会持续推出针对疑难病症的高效药物，造福广大患者。若真如此，那些困扰全社会的顽疾将有望被逐个攻克——肥胖症、代谢紊乱、心血管代谢疾病，甚至神经退行性疾病领域也已显现曙光。

And when you can do that and combine that with our ability to hopefully use things like virtual cell models to really understand what to go after, like, we're gonna have drugs. We I would hope and I would expect that the industry will continue to bring forward drugs that have very large effect size for very difficult diseases that hopefully affect a lot of patients. If that's true, then we'll start to see some of these really, really difficult diseases that affect all of society get tackled, hopefully, one by one by one by one. And so we have obesity, we have metabolic disorder, we're dealing with cardiometabolic disease. We're starting to see interesting promising things happening in neurodegenerative diseases.

如果我们能攻克癌症——至少让某些癌症从绝症转变为慢性病，随着这类成果积累，医药行业对社会的价值将日益凸显。这个行业理应获得社会和市场的极高评价，当然前提是我们必须兑现承诺。

If we can tackle cancer or at least several cancers that now have begun to be treated more like a chronic condition than a death sentence that they were in the past, the more we see of that, I think that value to society will accrete over time. And I think this should be an industry that is extraordinarily valued by society and candidly by the markets we have to deliver.

即便AI模型真能成功，即便我们能虚拟设计出上万亿种完美配体，这些分子仍需要实体合成、动物试验、模型验证，最终人体试验——这个流程在很多方面都会越来越成为瓶颈。

If we play this out, right, and let's say these AI models work, right, and you can make a trillion binders in silico that will, you know, be exquisite drug matter. Right? We still need to make these things physically and test them in animals and hopefully predictive models and then actually in people. Right? And think, you know, that will increasingly be the the the the bottleneck in many ways.

对了，我朋友王丹最近出了本叫《快车道》的书，讲中美两国在市场哲学和应对方式上的差异。

Right? And, you know, my my friend, Dan Wang, recently released a book called Breakneck, which talks about, you know, kind of like The US and China and the difference between the two countries and their philosophy, the way they approach markets.

我们到底是律师治国还是工程师治国的国家。

We're this country of lawyers or country of engineers.

没错。正是如此。中国是一个工程师治国国家，对吧？

Exactly. That's right. Right. China is an engineering state. Right?

政治局成员大多是工程背景出身，他们需要建造桥梁、道路和建筑。这是我们解决问题的方式。而美国前13任总统中有10位是律师出身，1980至2020年间所有民主党总统候选人——包括副总统和总统——都毕业于法学院。

It's kind of Politburo is, you know, folks who have engineering degrees. You know, you need to build bridges and roads and buildings. And these are the ways that we solve our problems. Whereas I think from, you know, the first 13 American presidents, 10 of them practiced law. From 1980 to 2020, all Democratic presidential candidates, both v VP and president, to law school.

对吧？因此你能在FDA和监管体系中看到这种影响，以及人们常说的美国药物研发瓶颈问题。越来越多人开始考虑如何在海外进行一期临床试验。嗯哼。

Right? And so you kind of see the echoes of that in the FDA and the regulatory regime and, you know, all the kind of the the bottlenecks that people talk about developing drugs stateside. And increasingly, you see folks thinking about how we can run phase ones overseas. Right? Mhmm.

建立数据包以便我们能在国内开展二期疗效试验。这个方向很有意思，但还不够。对吧？我认为我们需要解决制造和测试这两个瓶颈，即便我们能攻克设计环节

Build data packages that we can, you know, bring back domestically for phase two efficacy trials. I think that's interesting directionally, but it's not enough. Right? And, you know, I think we need to kind of figure out these two bottlenecks, the making and the testing, even if we can solve the designing

部分。

part.

噢，我同意。

Oh, I agree.

是的。没错。

Yeah. Yeah.

这就是瓶颈所在。是的，我们常开玩笑说，你得找到一个分子，先在老鼠身上测试，然后是狗，接着猴子，最后人类。这个过程耗时漫长，而且很难压缩时间。

That that's the bottleneck. Yeah. You know, we we joke about it. And you have to do is you have to get a molecule that can go, you know, first in mice and then in mutts and then in monkeys, and then in man. Like, there's you know, that takes takes a long time, and it's so hard to compress that.

所以当你这么做时，你应该让这段旅程物有所值，明白吗？是的。如果在另一端失败了，那显然很糟糕。嗯。

And so when you do, you you should make the journey worth, you know, make the journey worth it. Right? Yeah. So when you fail on the other end of that, like, that's obviously horrible. Mhmm.

因此，找到方法确保当你踏上这条路时，尽可能多地取得成功，是这个行业迫切需要的。嗯。嗯。

And so finding ways to make sure that when you when you walk that path, that it'll be a successful journey as often as possible is what this industry desperately needs. Mhmm. Mhmm.

AlphaFold解决了蛋白质折叠问题，但为什么没能解决药物发现？更广泛地说，要实现药物研发还需要什么？至少在技术层面，瓶颈究竟是什么？

AlphaFold solved protein folding problem, but why didn't it solve judge judge discovery? Or more broadly, what would it take to to get iodrigics? What what is sort of the the the bottleneck on the on the on the tech side at least?

在技术层面？

On the tech side?

是的。或许换个问法——因为我总是问创始人这个问题的变体，那些搞AI的人。当然。他们会说‘我们要用AI做生命科学药物发现’。所以我总爱问创始人：给我些你认为AI被过度炒热的例子。

Yeah. Maybe another way to ask the question is that we because I always ask the founders a version of this question, like the the AI ones. Sure. That are like, oh, we're gonna do AI for life for drug discovery. So my my question that I always like to ask founders is, give me examples of where you think AI is hyped Yeah.

可能被过度吹捧的领域，以及真正有希望的领域——我们对什么该抱有期待

Potentially overly hyped, where there's real hope, like the sort of what do we expect

嗯。

Mhmm.

接下来是什么，以及我们已经在哪些领域看到了实质性的进展。是的。比如，如果我问你，在AI领域，你知道，他们的炒作点在哪里？他们的希望在哪里？我们今天在哪些方面看到了实质性的进展？

What's next, and where we already see real heft. Yeah. So, like, if I asked you, like, in AI, you know, where is their hype? Where is their hope? And where are we seeing heft today?

我会说毒性预测模型是当前的炒作点。

I would say there's hype in toxicity prediction models.

这个想法是说，我会展示一个分子给你，模型会告诉我它是否有毒。

That's the idea that we will say, I'm gonna show you a molecule and you're gonna tell me the model's gonna tell me if it's gonna be toxic or not.

这没问题，对吧？任何与蛋白质相关的研究都有实质性进展。显然，蛋白质结合是一个方面，但蛋白质设计也越来越重要。

That's Okay. Right? There's heft in anything to do with proteins. Right? Obviously, protein binding, but increasingly in protein design.

对吧？我认为那里确实有实质性的进展。而那些被炒作的是多模态生物模型，不管那具体意味着什么。你可以选择你感兴趣的层面。

Right? And I think there's real heft there. And then where there's hype is in multimodal biological models, whatever that means. Right? And I think, you know, pick your favorite layers.

可能是分子层面，也可能是空间层面。实际上，我认为病理AI预测模型也有实质性进展，比如自动化病理学家和放射科医生的工作。

It could be, you know, molecular layers. It could be spatial layers. It could be you know? I I mean, actually, I would say there's also heft in the pathology AI prediction models, you know, like, you know, automating the work of pathologists and radiologists. That's that's

是的，我想那是

Yeah. Think that's

一个强有力的应用场景。确实。

a powerful use case. Yeah.

毫无疑问。

For sure.

没错。而且有很多领域你不需要训练那些奇怪的生物学基础模型，就能撰写监管申报文件、报告之类的东西。这既有效又重要。是的。

Yeah. And there's a lot of stuff where you don't have to train, you know, weird biology foundation models, and you can write, you know, regulatory filings and reports and things like that. That's impactful and important. Yeah.

那么现在我来回答埃里克的问题。为什么AI还没能研制出药物？我想这是你的问题吧？

So now I'll go back to Eric's question. So why don't why hasn't AI churned out drugs yet? I think that was your question. Right?

要知道，AI制药是个奇怪的现象，业内每个人都试图宣称自己的药物是首个AI设计的分子。对吧？我感觉...确实。我是说，用不了几年，这就会成为技术栈的固有部分。对吧？

You know, AI for drugs is one of these weird things where everyone who works in the industry is trying to claim that their drug is like the first AI design molecule. Right? I feel like in yeah. I mean, increasingly, in just a few years, this will just be a native part of the stack. Right?

就像我们使用互联网和手机一样，AI将渗透到技术栈的每个环节。对吧？所以它终将成为我们所有工作的固有组成部分。那么，'为什么还没成功'？这就是我们今天讨论的漫长多因素过程。

Just like we use, you know, the Internet and we use phones, we're gonna have AI in all parts of the stack. Right? And so it's just going to become a native part of everything that we do. And so, you know, like, hasn't it worked yet? Is this long multifactorial process that we've been talking about today?

有设计环节，有制造环节，有测试环节，还有审批环节。我认为安全和有效性是这个行业的两大支柱，这两点我们必须做好。

There's designing. There's the making. There's the testing. There's the approvals side of it. And, you know, I think the I I do think safety and efficacy as the kind of two pillars in the industry are the two things that we need to get right.

对吧？我们需要找到更快速的方法来预测分子是否有效以及是否安全。人工智能可以在这方面发挥作用。比如设计一个小分子后，可以通过计算将其对接至蛋白质组中的每个蛋白质，观察它是否会与非目标分子结合。这种方法可用于调节结合选择性和亲和力，从而预测安全性和有效性。

Right? We need to be able to figure out faster ways that we can predict whether or not molecule will work and if it's going to be safe or not. So, I mean, there are, like, ways that AI can operationalize this. If you designed a small molecule, right, you can now computationally dock it to every protein in the proteome and see if it's likely to bind to off target molecules. You can use this to tune binding selectivity and affinity that might be ways to predict, you know, safety and efficacy.

对吧？至于效果如何？这需要通过实验室的实际测试形成反馈循环。耗时之处正在于此——测试需要实实在在的小时、天数、月份，甚至年复一年。这就是为什么ARC选择虚拟细胞模型作为我们的切入点，因为它能整合许多这类不同环节。

Right? And, you know, how well will that work? Well, that's a feedback loop that we'll have to actually test in the lab. And that's part of what's slow is the testing takes real hours, days, months, right, years. And, you know, that and that's really why we've picked at ARC the virtual cell models as our initial wedge because we think it can integrate a lot of these different pieces.

在Dario Amadeh的论文《充满爱与优雅的机器》中，他预言了包括预防多种传染病以及可能在未来十年内实现寿命翻倍等观点。你对他论文中的乐观态度及部分预测有何看法？

In Dario Amadeh's essay, Machines of Love and Grace, he predicts, among other things, the prevention of of of many infectious diseases and the doubling of lifespans perhaps in as soon as the next decade. What what's your reaction to his his essay, his bullishness, and some of his predictions?

我认为Dario的核心洞见在于：重大科学发现是独立存在的，或者说基本相互独立。如果它们在统计上是独立的，那么理论上我们可以并行推进。只要拥有足够精准有效的模型，就能同时运行数百万乃至数十亿个这样的发现代理或流程，从而压缩新发现的时间线，将其转化为计算问题。对吧？

I think the core intuition that Daru had was the idea that sign important scientific discoveries are independent, right, or they're largely independent. And if they are statistically independent, then it would stand to reason that we could multi parallelize. And so we had models that were sufficiently predictive and useful. You could have not just a 100 of them, but millions, billions of these discovery agents or processes running at a time, which should compress the timeline to new discoveries and turn it into a computation problem. Right?

这种未来主义框架描述的事物其实在今天已触手可及。如果我们能让原始细胞模型投入工作——比如开展我们讨论过的这些功能，辅助分子设计模型和对接模型——当某物质与特定细胞结合时，相较于其他脱靶蛋白，细胞是否能被准确修正？

I think that is a very futuristic framing for something that is actually very tangible today. Right? And if we can have original cell models at work, for example, that can start to do these kinds of things that we've been talking about, help us you know, we can have, you know, molecular design models. We can have docking models. We can then have you know, when you bind to this thing in this cell versus all the other off target proteins, will a cell kinda be corrected in the right way?

对吧？这类层层递进的抽象与复杂性，最终会触及药物研发中非常实际的环节。如果能可靠地按顺序完成这些步骤，就能看到如何实现时间压缩。长远来看，这完全可能实现。

Right? These kind of layers of abstraction and complexity start to get to things that feel very tangible to drug discovery. If you could actually traverse these steps reliably and in sequence, you could start to see how you can get the compression. Right? And and so I think in the long run of time, this should be possible.

构建优质虚拟细胞模型的一个核心假设是，我们正在为其提供所有相关数据。

One of the core suppositions in building a good virtual cell model is that we are feeding it all the relevant data.

正确的数据。

The right data.

是的。正确的数据。嗯。所以我们会处理，比如基因表达数据、DNA数据，或是各种因素，蛋白质与蛋白质的相互作用，所有你提到的这些。如果我们遗漏了一个核心要素怎么办？

Yeah. The right data. Mhmm. And so we'll work to you know, it's gene expression data or it's DNA data or, you know, any any number of factors, protein and protein interactions, all the things you described. What if we're missing a core element?

比如，如果我们尚未发现某种基本粒子之类的？我们只是不知道我们不知道什么，因此，我们输入模型的数据从根本上或关键上是不完整的。

Like, what if we just haven't discovered the quark or whatever? Like, we just don't know what we don't know, and therefore, what we're feeding the model is fundamentally or importantly incomplete.

我认为这几乎肯定是事实。对吧？似乎显而易见，我们并未测量生物学中许多最重要的方面。当然，你可以为任何这些测量技术找到许多重要的例外情况。

I think that's almost certainly true. Right? Like, it's seems almost obvious that we're not measuring many of the most important things in biology. Right? And you can, of course, find many important exceptions for any of these measurement technologies.

比如，在生物学中，我们最终有两种高通量研究方法：成像和测序。对吧？但还有那么多其他你关心的方面，这些方法未必能大规模覆盖。对吧？

Like, in biology, we ultimately have two ways to study it in high throughput. It's imaging and sequencing. Right? But there are so many other types of things that you would care about that those things aren't necessarily going to do at scale. Right?

这正因如此，我认为我们讨论的将RNA层作为生物学其他层面镜像的观点，是我们花费大量时间思考的。机制模型与气象模拟类模型之间存在差异。举例来说，如果你想预测天气，可以构建AI模型来预测下周二是否会下雨，它不会从物理或地质等角度解释为何及如何发生。但只要知道下周二是否会下雨，你可能就满意了。

And that's really why I think the stuff that we're talking about of the RNA layer as a mirror for other layers of biology is one that we spent a lot of time thinking about. And there's a difference between a mechanistic model and a meteorological simulation type of model. So for example, if you want to predict the weather, right, you can build AI models that will predict whether or not it will rain next Tuesday. It won't explain physically or geologically or whatever why and how that happens. But as long as it knows if it's gonna rain next Tuesday, you're probably happy.

对吧？我想说虚拟细胞模型也是如此，它可能不会直接告诉我原因。就像AlphaFold不会直接解释蛋白质为何及如何这样折叠，它只是展示了最终状态且相当准确。我认为这已经非常重要了。

Right? And I I would say similarly with a virtual cell model, it may not tell me literally why. Just like a alpha fold doesn't tell me literally why did the protein fold this way and how, but it just told me the end state and it was reasonably accurate. I think that would already be very important.

稍微转换一下话题。我们一直在讨论科学和生物技术，但除此之外，您还是一位更广泛意义上的顶尖AI投资者。所以我想聊聊您当前的投资重点，就AI更广泛的领域而言。您对哪些方向感到兴奋？您的时间花在哪里？

Shifting gears a little bit. We've been talking about science and and and biotech, but you're in addition, you're an elite AI investor more broadly. So so I wanna talk about how your I wanna talk about where your investment focus is right right now just as it relates to AI more broadly. Where are you excited? Where are you spending time?

您对未来哪些领域充满期待？

Where are you, you know, looking forward to?

哦，是的。我的目标是真正找到方法，在我们有生之年改善人类生活体验。我常想，关于我们将留给孩子们的未来，有几件事如果我们能在生前做好，将彻底改变世界，以及我们的生活方式。合成生物学显然是其中之一，对吧？

Oh, yeah. My my goal is to really try to figure out ways that we can improve the human experience in our lifetime. I kind of think of like, if I think about the future that we're gonna leave to our children, right, there are a few things that if we get them right in our lifetime, will fundamentally change the world, right, and, you know, how we live in it. I think synthetic biology is obviously one. Right?

想想GLP-1类药物、改善睡眠的技术、延长寿命的突破，这些都是令人振奋的领域。我认为脑机接口是未来几十年会有重大突破的另一领域。第三是机器人技术，包括工业和消费级机器人，它们能以有趣的方式扩展体力劳动。你能看到这三件事即便取得中等程度的成功，也将深刻改变世界。

Think GLP-1s, things that improve sleep, things that can improve longevity. These are all things that are kind of easy to get excited about. I do I I think brain computer interfaces is another area where we're gonna see really important breakthroughs over the decades to come. Then And I think the third is in robotics, both industrial and consumer robotics, right, that allow us to basically scale physical labor in interesting ways. You can kind of see how each of these three things, even in the sort of medium cases of success, really kind of change the world.

因此我非常热衷于助力这些可能性的实现。在这个技术乐观主义的世界观里，存在几种不同的稀缺性。做研究时产生重要创意很容易——

And so I'm very interested in helping make these kinds of things possible. Right? And so there's sort of you know, in the kind of techno optimist sort of vision of the world, right, there's a few different types of scarcity. Right? There's you know, it's very easy when you do research to come up with important ideas.

真正的难点是在正确的时间框架内解决它们。写未来科幻题材并不难，但要在未来五到八年内真正实现，那要困难得多，对吧？

The hard thing is to tackle them in the right time frame. Right? It's like, you know, writing futuristic sci fi things is not that hard. Being able to actually execute on it in the next five years or eight years, much, much harder. Right?

那么我想说，学术发现中充斥着许多有趣且重要但时机尚未成熟的理念。从很多方面来看，技术发展的故事就是试图用新技术解决老问题。对吧？比如我们的大多数工具本质上都是为了提高生产力，无论是工业革命、计算革命还是当前的AI革命，我们都在尝试做同样的事情。

And then I would say, academic discovery is littered with plenty of ideas that are interesting and important, but kind of long before their time. And in many ways, the story of technology development is trying to use new technologies to solve old tricks. Right? Like, most of our tools are, you know, for productivity, right, in many ways, whether that's the industrial revolution or the computing revolution or the current AI revolution. We're trying to kind of do the same stuff.

我认为存在一组相对较少但极其强大的核心理念。新技术为我们提供了攻克这些理念的新机遇，而某些人和团队将具备实现这一目标的优势。他们需要在技术创新、产品直觉和商业思维这三个领域达到某种平衡——就像角色扮演游戏中的技能点分配，每个人的起点各不相同。可能有个技术天才却缺乏商业头脑，也可能有个天生的商业奇才却对产品感知薄弱，对吧？

And, you know, and so there you know, I think there's a relatively small set of very powerful ideas. New technologies give us new opportunities to attack them, and there's a set of people and teams that are gonna be positioned to be able to do that. They need to have technical innovation and then an intuition about product and business in a way that, you know, you know, you kind of in the RPG dice roll of the skills that you get in these three domains, people start at different base levels. Right? And you might have an incredibly technical founder who doesn't know how to think commercially or someone who's just natively a very commercial thinker who doesn't have very strong product sense, right, even though they could sell the crap out of Right?

因此我认为需要将这三大类能力有机结合，并在恰当时机配置资源，才能以真正差异化的方式实现这些构想。就像某些事情如果没有合适的人在正确的时间以正确的方式获得资金支持就根本不会发生。这正是激励我的核心动力——比如我投资Neuliman这样的长寿科技公司时就是如此兴奋。

And so I think these sort of these sort of three broad categories of capabilities, you need to kind of bring together in a way that you can allocate capital to in the right times in order to make these ideas possible in a really differentiated way. Like, this thing literally wouldn't happen if we didn't get these people together and fund it at the right time in the right way. Right? And that's that's really what what motivates me. And these are kinds of things that I've been excited about, you know, backing, you know, longevity companies like Neuliman.

对吧？像Nudge这样的脑机接口公司，像Bot Company这样的机器人公司，对吧？

Right? BCI companies like Nudge. Right? Robotics companies like the bot company. Right?

这些都是我认为世界上必然会发生、因此也应该发生的领域。关键在于如何找到合适的人和时机，开启这场'魔戒远征队'般的探索之旅。

You know, these are some of the examples of kind of, you know, things that I think must happen in the world and therefore should happen and, you know, how do we actually find the right people and the right time to actually kinda go on the fellowship of the ring hunt.

是的。确实。

Yeah. Yeah.

如果不太困难的话，我想请Jorge将他的三个问题适配到机器人、脑机接口和长寿科技这些新增领域：什么是过度炒作的？哪里看到了机遇或路径？以及哪些已经具备实质进展？

If not too difficult, I wanna ask Jorge's question adopted to these additional spaces, robotics, sort of BCI, and and longevity, if appropriate, in terms of and the three questions, I believe, were what's overhyped? What's where do you see opportunity or path, and what's got heft already?

我认为智能体最酷的地方在于它们能完成实际工作，对吧？相比之前的SaaS公司，智能体真正替代了生产力。当然，目前它们还存在很多错误。

I think the cool thing about agents generally is that they do real work. Right? Compared to, like, SaaS companies that came before, agents replace real productivity. Right? And I think, you know, they have a lot of errors today.

我认为计算机使用类智能体的发展可能会比编程类智能体晚一年左右。但趋势已经形成，我们将见证它们从零错误完成几分钟工作，逐步发展到处理数小时乃至数天的工作量。这个演进过程将重塑法律流程外包、医疗健康等领域的服务形态。

And I would say the computer use agents will probably trail the coding agents by maybe a year. Right? But but it's coming, and we'll follow the trajectory as these go from doing, you know, minutes of work without error to hours to days. Right? And I think, you know, you're gonna get a completely different product shape as we march through that across legal, BPO, you know, medicine, health care, whatever.

对吧？整个行业都会追随这个趋势，这确实令人振奋。真正的价值将体现在服务经济领域——毕竟国民经济主体是服务消费而非软件消费。这正是我们对此感到兴奋的原因：它能革新服务经济。

Right? And we'll kind of follow that as an industry, and that's gonna be really exciting. And I think that's where we're gonna see real heft is because most of the economy is services spent. It's not software spent. And the reason why we're all excited about this stuff is that it can attack the services economy.

要说泡沫在哪里？毫无疑问是模型能力方面存在大量过度炒作。

And I would say, where is there hype? There's tremendous amount. Right? That's no doubt. The hype is in the model capabilities.

我们目前使用的架构可以追溯到2017年。纵观深度学习发展史，大约每八年就会出现革命性突破。

Right? And we we we're working with an architecture that, you know, dates back to 2017. Right? And if you look at the history of deep learning, it's like kind of every eight years, there's something really different. Right?

感觉2025年确实该出现全新的架构了。现在有很多正在酝酿的前沿研究构想可能实现这一点，特别是2009到2015年机器学习研究黄金时期产生的那些学术理念——许多被引用不足30次的冷门论文里藏着瑰宝。

And we feel like in 2025, we're really overdue for some net new architecture. And I think there are lots of really interesting research ideas that are bubbling up that could do that thing. And in many ways, there's a set of really interesting academic ideas, especially in the golden age of machine learning research from, I don't know, like 2009 to 2015. Right? There's so many interesting ideas and little archive papers that have, like, 30 citations or less.

随着算力边际成本逐年下降，这些构想都将有机会进行规模化验证。当年在千万级参数规模下看不到的扩展规律，当模型发展到10亿、70亿参数时就会显现。这为新锐的超级智能实验室创造了巨大机遇，使他们能突破现有基础模型公司的局限——毕竟那些公司正转型为应用型AI企业，需要构建商业产品、开展企业强化学习、通过编程智能体和API接口实现盈利。

And as the marginal cost of compute goes down year on year, I think you're gonna be able to take all of these ideas and actually scale them up, right, where you don't see the scaling laws when you're training them at 100,000,000 or six fifty million parameters like back then. But if you can scale them up to one b, seven b, 35 b, 70 b, right, you start to see whether or not these ideas will pop. Right? And I think that's very exciting because there's just gonna be a lot of opportunity for new superintelligence labs to do things beyond what the kind of established foundation model companies are doing today, right, as they kind of in addition to these research teams, right, these are in many ways becoming applied AI companies. They need to build product shape and all kinds of different enterprises and do RL for businesses and make money, or build coding agents and make API revenue.

这很重要，我认为是当今生存的及时竞赛。但我对Sakana AI这类研究非常乐观，它是由《Attention Is All You Need》的作者之一Ian Jones创立的。他们在模型融合方面做着极其有趣的工作，探索如何在混合专家模型中进行类似进化选择的不同模型组合。长远来看，这里存在超越单纯强化学习环境的机遇，比如探索新的学习方式和发现奖励信号，这将非常令人兴奋。

That's important and, I think, a timely race to survive today. But I'm just very bullish on the research of, say, like a Sakana AI, right, which was founded by one of the authors of Attention Is All You Need, right, Ian Jones. And they're doing incredibly interesting stuff on model merging and how you can have kind of sort of like evolutionary selection of, you know, kind of of different kind of, you know, models in MOE. And I think the there are sort of opportunities here in the long run to move beyond just like RL gyms, for example, also to kind of figure out new ways to learn and and find, like, kind of reward signal is is gonna be really exciting.

我觉得这是个很好的收尾点。接近尾声了，ARC有什么即将到来的消息想让我们知道吗？有什么想透露的？或者大家应该了解些什么？

I think it's a great place to wrap. Gearing towards towards closing, anything upcoming for ARC that you'd like us to know of? Anything you wanna tease? Anything for people wanna learn what should they know about?

AlphaFold在很多方面源自一个名为CASP的蛋白质折叠竞赛，即蛋白质结构关键评估。我们在virtualcellchallenge.org创建了自己的虚拟细胞挑战赛，由英伟达、10x Genomics和Ultima等公司赞助，奖金10万美元。这是一个开放竞赛，任何人都可以参加，训练扰动预测模型。我们可以公开透明地评估这些模型当前及未来几年的能力，追踪它们直至取得突破性进展。对吧？

So AlphaFolders, in many ways, came out of a protein folding competition called CASP, right, critical assessment of the structure proteins. And, you know, we created our own virtual cell challenge at virtualcellchallenge.org where we have $100,000 prizes sponsored by NVIDIA and 10x Genomics and Ultima and others. And it's an open competition that anyone can enter where you can train perturbation prediction models. And we can openly and transparently assess these model capabilities both today and in subsequent years, follow them to get to that chatty bitty moment. Right?

我对此感到无比兴奋。我们希望更多人训练模型并应用，无论是生物机器学习专家还是其他领域的工程师。我希望这个世界能实现这个目标。虽然我们是推动实现的重要部分，但只要有人能做到，我就很高兴了。是的。

And so I'm extremely excited about this. We'd like more people to train models and apply both bio ML experts and engineers in any other domain. I want this thing to exist in the world. Hopefully, we're important parts of making that happen, but I'd just be happy that someone does it. Yeah.

这是个鼓舞人心的结束语。Patrick Jorge，非常感谢这次对话。

That's an inspiring note to to wrap on. Patrick Jorge, thanks so much for the conversation.

非常感谢大家。

Thanks so much, guys.

感谢邀请我参加。

Appreciate it. For having me.

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