Grok 4 Wows, The Bitter Lesson, Elon’s Third Party, AI Browsers, SCOTUS backs POTUS on RIFs
发布时间 2025-07-11 22:13:10 来源
I have a very funny story to tell you Jason. Where have you been? I've been trying to text you. You've been off long. What's going on? Where have you been? I've been working feverishly, but yesterday I had to go to prepare for some meetings that I have on Sunday, which I can't tell you about. But can I tell you that and I went to Pasalakwa, which is in Lake Koma, which is an, I mean, it's stunning. The grounds are stunning. The hotel is stunning. If you have a chance to go to Lake Koma, anyways, this is us at Pasalakwa. Who's the beautiful woman there? Is that the woman that's that? That's the one that's that. But the best part is we had such a good time. You know how they have like a registry book to leave a message? Sure. So I left a message. Here we go. What? Truly magnificent place. Above beyond any expectation we had. Go below, go below that stop for me. Thanks for you. We took everything. We took everything. The framework. Great. Awesome. Jason, the hangers.
我有一个非常有趣的故事要讲给你听，Jason。你去哪儿了？我一直在给你发消息。你离线很久了，发生了什么事？我一直在忙着工作，不过昨天我不得不去准备一些周日的会议，我不能告诉你详情。但我可以告诉你，我去了位于科莫湖的Pasalakwa，这个地方真的太美了。不论是这片土地，还是酒店本身都令人惊叹。如果有机会去科莫湖，一定要去看看。这里是我们在Pasalakwa的合影。照片中漂亮的女人是谁？就是那位，那就是她。不过最好的部分是我们真的玩得很开心。你知道酒店有一个留言簿可以给出感想吧？当然。我就留了一条信息。看这里。什么？真是一个壮观的地方，超过我们所有的期望。再往下看一下，停在这里，谢谢你。我们把所有东西都带走了。包括架子。太棒了。
Okay. Everything. The laundry bags. You get to base the roads. The slip roads. Absolutely fantastic. Everything. They're going to have to send a bill to the freebergs. Absolutely. All right. Listen, we've got a great panel this week. It's the summer. Things are slow. Some people are busy. I think our friends of panic attacks, our dear Sultan of science is he's at the beach. Sax is busy. Couldn't make it this week in his place. Another brilliant paypal alumni and dare I say a GOP supporter. Heath Roboi, how are you, sir? Pleasure to be with you again. Nice to see you. And I'm assuming you're in gorgeous Florida or somewhere in Italy. Yeah. I'm actually in New York. Oh, hometown. Is it safe? Is it okay? Mom, Donnie chasing you down the street? Not yet, but it's safe. It's safe. It's safe right now. We'll see on November 4th. You know, as you probably heard until life. Fourth was the first time in recorded history that there were no shootings or no murders in New York on that day. So right now things are in pretty good shape, but we may be we may be leaving New York quickly.
好的，一切都准备就绪。洗衣袋，那些上路的匝道，每一处。绝对精彩。所有这些。他们将不得不给菲伯格家寄账单。好的，听着，我们这周有一个很棒的小组。现在是夏天，事情进展缓慢，一些人很忙。我想我们的朋友有恐慌症的，我们亲爱的科学苏丹，他在海滩上。萨克斯太忙了，这周无法参加，代替他的是另一位才华横溢的Paypal前员工，我敢说是一位共和党支持者，希斯·罗布伊，你好吗，先生？很高兴再次见到你。很高兴见到你。我猜你在美丽的佛罗里达或意大利的某个地方。其实我在纽约。哦，家乡。安全吗？一切正常吗？没有妈妈，唐尼在街上追你？还没有，但很安全。现在很安全。我们会看看11月4日。你大概听说过，直到今年4号是纽约有记录以来第一次在那一天没有枪击或谋杀事件。所以现在情况还不错，但我们可能会很快离开纽约。
Yeah, you're going to probably want to sell that place if you got one there because Mamdami is going to season and turn it into a drug store for you. Yes, it's going to be a dummy drug store. Travis Kalinick is back with us. How you doing, Bestie? Pretty good. Pretty good. Yeah. Second appearance here on the round table. And third time on the show. Of course, you spoke at the summit. You've been busy with cloud kitchens. Yeah. Lots of exciting things going on. Lots of stuff. Lots of stuff. The robots. The robots are taken over. We're rolling out. We're rolling out robots. Yeah. TK. Can you tell us what you're doing with this pony? I or not, not speculation.
是啊，如果你在那里有房产的话，你可能会想卖掉，因为Mamdami会把那里改造成一家药店。是的，这会是一个虚假的药店。Travis Kalinick回到了我们这里。怎么样，好朋友？挺好，挺好。是的，这是你第二次参加圆桌会议，也是第三次参加我们的节目录制。当然，你还在峰会上发过言，你一直忙于云厨房项目。是的，有很多令人兴奋的事情在发生。很多事情，很多事情。机器人呢，正在占领市场。我们正在推出机器人。TK，你能告诉我们你在做什么吗？等等，不要猜测。
Look, you know, obviously is autonomy as we, you know, in the US, we have, of course, you want to just frame for people that don't that may not be up to speed. What was announced? Or at least why don't you frame it? So pony AI is an autonomous company doing self-driving. It's one of the few players that actually have cars on the road. They're based in China. They've got a lot of operations in the Middle East. They've got a deal with a delivery company called Uber, which you might be familiar with. Okay. So, look, well, the deal was basically that you partner with Uber, license in the pony technology and essentially start a competitor, I guess, to Waymo and Tesla. Let me work on this one.
好的，我来为你翻译成中文，并尽量简化：
听着，你知道的，在美国，我们显然都知道自主驾驶。但为了那些可能不太了解的人来框定一下情况。Pony AI 是一家从事自动驾驶的公司，是为数不多的在路上实际运行车辆的公司之一。它们总部在中国，并在中东有很多业务。它们和一个你可能熟悉的名为 Uber 的配送公司达成了合作。
所以，这个协议实际上就是 Pony AI 和 Uber 合作，授权使用 Pony 的技术，并开始一个与 Waymo 和 Tesla 竞争的项目。让我来仔细处理一下这个信息。
Okay. So, in the US, we have Waymo. We see the Waymo's in San Francisco, Los Angeles, Austin, coming soon to Miami, coming soon to Atlanta, coming soon to DC. They're even talking about New York. Tesla is sort of like the, you know, they're doing it the hard way, you know, classic Elon style. Like, let's, let's do this sort of in a fundamental foliage. Let's go all the way kind of approach. And it's unclear when it gets over the line. Of course, he launched sort of a semi-pilot of sorts in Austin recently. But there's no other alternatives. So, what happens is, is some of the folks who are interested in making sure their alternatives have reached out. They've reached out to me and there are different discussions they get going, because they're like Travis, you did autonomy way back in the day.
好的。在美国，我们有Waymo。我们在旧金山、洛杉矶、奥斯汀可以看到Waymo，它即将进入迈阿密、亚特兰大和华盛顿特区。他们甚至在讨论进军纽约。特斯拉则采取了一种相对困难的方式，典型的埃隆·马斯克风格，他们想从基础开始，全面推进。然而，何时能达成目标仍不明确。当然，他最近在奥斯汀进行了某种半试点计划。但目前还没有其他选择。因此，那些希望确保有替代方案的人就找到了我。他们找我进行不同的讨论，因为他们知道特拉维斯曾在很早之前就从事过自动驾驶。
Got the Uber autonomous stuff going in 2014. Maybe there's something to do here to create optionality. Maybe like, I'm, of course, very interested on the food side. I talk about autonomous burritos being a big deal, because if you can automate the kitchen, the production of food, and then you can automate the sort of logistics around food, you take huge amount of costs out of the food, out of what's going on in food. And that's of course near and dear to my heart. There are folks that of course I want to see autonomy and mobility. That's a real thing. It may be that, or I would say, if you get the autonomy problem right, you can use it to apply to both problems. So, there's a lot of folks interested in moving things, moving food, moving people. And if there is some kind of autonomous technology that maybe I get involved in, it might apply to a bunch of different things. And so, I've got some inbound. Let's just put it that way.
2014 年，我开始涉足 Uber 的自动驾驶技术领域。也许在这个领域可以做一些事情来创造更多的选择空间。我对食品方面特别感兴趣。我常常谈论“自动化墨西哥卷饼”是个大事，因为如果你能自动化厨房、食品生产，然后还可以自动化食品物流，那么你就能大幅降低食品领域的成本。这对我来说是非常重要的。当然，也有人希望在交通和出行方面实现自动化，这是个实实在在的需求。我认为，如果你能解决自动化的问题，这个技术可以用在解决多种问题上。 有很多人对运输事物、运输食物、运输人感兴趣。如果有某种自动化技术我参与其中，它可能会应用于各种不同的事物上。总之，我已经有了一些相关的机会。
There's no real deal right now, but there is definitely some inbound. I think there are some news about some of that inbound that may or may not be occurring. That's probably the best way to put it as long as it will try to tighten that up next time. I think it's great to get the overview here first. Paulin, thank you for sharing it with us. And everybody knows you have been doing a bull builder lab 37. I think it's called turn up on the screen. Not sure what the status of it is. And then I'll let you go to him off with your follow question. But I think there's a pretty interesting concept here of the bull getting built and then put into a self driving car. Now that machine looks huge, but it's actually 60 square feet. That picture makes it look monstrous. It's a 60 square foot machine like a imagined running like a sweet green like brand or a Chipotle like brand of making so it comes to life for people who who you know are like, hey, what is this thing?
目前没有真正的交易，但确实有一些潜在的事情在进行。我想关于这些潜在情况的新闻可能会发生，也可能不会。这就是目前最好的说法，下次我们会尝试更具体一些。我觉得先对整体情况有个了解是很好的。Paulin，谢谢你和我们分享。大家都知道你一直在做一个名为"荔梦实验室37"的项目。我想这个项目的名字在屏幕上显示为"Turn Up"。不太清楚它目前的状态。接着，我把时间留给你的后续问题。但我认为这里有一个相当有趣的概念，就是制造"bull"（可能指某种产品），然后把它放进一辆自动驾驶汽车中。那台机器看起来很庞大，但实际上只有60平方英尺。那张照片让它看起来非常巨大。它是一台60平方英尺的机器，比如可以想象它运作像一个"Sweetgreen"或"Chipotle"这样的品牌，让大家明白这个东西是什么。
Imagine you just sort of online exactly the kind of bull you want. And actually this machine could run like many brands at the same time and then does. You build the bull you want, whatever ingredients, it's sort of, if you look at that bottom, you see those little white bricks at the bottom? That's what carries the bull underneath dispensers it fills up. The machine puts it, sauces the bull, then it puts a lid on it. It takes the bull, puts it in a bag, puts utensils in the bag, seals the bag, and the bag goes down a conveyor belt where then another machine and so that what we would call an AGB takes the bull to the front of house, the bull gets put into a locker. The career via door dash, you breathes career will wave their app in front of the camera and it will open up the locker that has the food that they're supposed to pick up.
想象一下，你在网上定制了你想要的食材碗。实际上，这台机器可以同时运行许多品牌，然后开始工作。你可以根据喜好组合各种食材，看看底部那些小白砖？那是用来承载碗的，它会移到食材分配器下面进行填充。机器会为碗加上酱料，然后盖上盖子。之后，它会将碗放入袋中，放入餐具，封好袋子。袋子沿着传送带移动，然后另一台机器，我们称之为AGB，把碗运到前面的柜台，碗被放入一个储物柜中。外卖员或快递员，可以通过门上的摄像头扫描他们手机上的应用程序，锁柜就会打开，他们就能取走属于他们的订单。
So it just, it takes out a lot of what we would call the cost of assembly, which is, it produces mistakes, right? We know exactly how many grams of every ingredient are put in. That's exactly what you're supposed to get. And so you get a higher quality product, it takes a lot of the cost out. You imagine ultimately that's going to be, they're going to be couriers with that as well. I like to say autonomous burritos like is a way Mo gonna carry a burrito or is Tesla gonna have a machine that carries food or is there another, another company that ends up doing sort of the things, the autonomous delivery of things. And the point is, well where we are right now is we've got customers. And so those customers are starting to deploy this quarter and it's pretty interesting. I mean, you can see it, the in our delivery kitchens, the cost of labor is about 30% of revenue.
这段话的大意是：我们通过减少组装成本，大大降低了生产过程中的错误。这是因为我们精确地知道每种成分的使用量，这样可以确保产品的高品质，同时也降低了成本。想象一下，未来这些产品可以通过快递员配送。我常说无人驾驶技术不仅可以用来运送人，还可以送外卖，比如无人驾驶的比萨饼或快递机器。可能是Waymo或Tesla这样的公司，也可能是其他公司，最终会实现这一愿景。目前，我们已经有了一些客户，他们将在这个季度开始使用这些服务。我们的配送厨房中，劳动力成本占收入的30%左右。
That's what the successful guy, let's say 30% at 35% of revenue in a brick and mortar, in a brick and mortar restaurants, it's even higher. Okay, when they're running our machine, it's between 7% and 10% of revenue. Right. Amazing. The issue got the cost of the delivery. Now it's becoming, everybody can have a private check, which was your original vision for Uber. People don't know the original tag line, but everybody has a private driver. Everyone's private driver was the original for Uber. Basically, the infrastructure is already there. And I said this on one of your recent, I think it was at the all-en-summit Jason, but like in the mobility cars, you know, I'm transport space, the roads were already there. The cars were already built. People weren't using their cars 98% of the day.
成功人士在实体餐饮行业，大约在30%到35%的收入中有所表现，而在实体餐馆中这个比例甚至更高。然而，当他们使用我们的设备时，这个比例降至收入的7%到10%左右。真是令人惊叹。不过，现在的问题在于送餐的成本。每个人都可以拥有私人交通工具，这正是Uber的初衷。虽然很多人不知道最初的标语，但Uber的最初愿景是"每个人的私人司机"。基础设施已经就绪，我在你最近的活动中提到过，大概是在All-In Summit上，关于交通领域的事，实际上，道路已经存在，汽车也已经制造好，只是人们98%的时间都没有在使用他们的汽车。
So the infrastructure is already there to get people around. To do this as a service and do it very efficiently and conveniently, with food, the infrastructure is not there. Like yes, restaurants access capacity. That's what Uber Eats utilizes. But to go and say like, let's make 30% of all meals in a city, sort of prepared and delivered by a service, the infrastructure is not there. So you have to build it. So our company, the mission is infrastructure for better food. So that's real estate, that software and robotics for the production and delivery of food in a super efficient way.
基础设施已经具备，可以用来输送人们。然而，要将这套系统应用于餐饮服务，并做到高效便捷，现有的基础设施却不完善。虽然餐馆具备一定的产能，这也是Uber Eats所利用的，但如果想让一个城市中30%的餐点由服务提供商准备和配送，现有的基础设施是无法满足的。因此，我们需要建立新的基础设施。我们公司的使命就是为更好的食物创建基础设施。这包括用于食品生产和配送的房地产、软件和机器人技术，以实现超级高效的运作。
All right. Keith, where are you thoughts? Any questions for? Well, he's not here, but isn't this what David Friedberg tried to do a few years ago? Yeah, this came up on the last all in. Yeah, there's the last one I was at. Yeah. Yeah. It's a, it's a problem was I told Friedberg, people don't want to eat quinoa. You got a little steak in there. Maybe he's saddening, but he was kind of really, I think eventually he relented and let people have a little bit of protein. But yeah, it's what's such a great vision. Wait, he died as a vegan martyr. I think the business died as he was a lot of people have died on that hill.
好的，Keith，你有什么想法吗？有问题要问吗？嗯，他不在这里，但这不正是David Friedberg几年前尝试做的事情吗？对，这在上次的"all in"中提到过。是的，那是我最后一次参加的。问题是，我告诉Friedberg，人们不想吃藜麦。如果加入一些牛排，也许会更吸引人，但他当时有点坚持自己的想法。我觉得最终他还是让步了，让人们可以摄取一点蛋白质。不过，他的愿景非常伟大。等一下，他以素食主义者烈士的身份去世了吗？我想他的生意失败了，因为在这方面很多人都失败了。
But the bottom line is if you're going to get an automation, you have to, it has to be end to end automation. What I mean by that is like there are pizza, there are pizza companies that have come and gone, automated pizza companies, where it's like, we have a pizza machine and everybody's like, yeah, this is amazing. And you have a guy, you have a million dollar pizza machine. And then on the left, you have a guy feeding ingredients into the pizza machine. And on the right, you have a guy taking the pizza out and then putting it in a box and doing all this. So instead of one guy making pizzas, I have a million dollar machine and two guys making pizza.
重点是，如果你要实施自动化，就必须是端到端的自动化。我意思是，有些披萨公司曾经尝试自动化，比如有个披萨机的时候，大家都觉得很厉害。但实际上，你会看到一边有人往机器里放原料，另一边有人把做好的披萨取出放进盒子里。这意味着，本来一个人做披萨的工作，现在不仅要用一个价值百万的机器，还要两个人来操作。这并没有实现真正的自动化。
And so when you look at these, a, a, a, a like a robotic food production machines or food assembly machines, you have to look at the full stack and say, does it work with the ecosystem that exists in a restaurant? And does it go full stack from, you know, like, like we had this thing that machine we saw earlier, the staff preps the food, they put the food in the machine and then they leave. They're gone. This restaurant runs itself for many hours without anybody there. But this could be McDonald's Burger King and Taco Bell. Nobody would know.
当你看到这些像是机器人的食物生产机器或食物组装机器时，你必须全面地去看，看看它们是否能与餐厅现有的生态系统兼容。你还要考虑它们是否能够全面处理整个流程，比如我们之前看到的那台机器，员工准备好食物后，将食物放入机器中，然后离开。这个餐厅可以在没有人的情况下自动运行好几个小时。而这可能发生在麦当劳、汉堡王或塔可钟，顾客可能根本察觉不到。
That right there, that machine is a, it's an assembly machine, right? The food is prepped by humans and then assembled by this machine for a Chipotle or a sweet green. This is like a, a majority of their labor, right? You go up to a Chipotle, there's like 10 guys at lunch and you're still in line. That machine right there does 300 bowls an hour, right? And so you go, okay, that's the, this is what's called, like the assembly line. It's just that front line where you basically assemble things. I think sometimes I will call it the make line. What will happen over time, you'll have perpendicular lines going into it where you're producing food, right?
那台机器就在那里，它是一个装配设备，对吧？食材由人工准备，然后由这台机器为Chipotle或Sweetgreen这样的餐厅组装。这几乎是他们大部分的工作流程，对吧？当你去Chipotle的时候，午餐时间可能有十个人在忙，而你依然在排队。那台机器每小时能制作300个碗，是不是很厉害？这就像一个所谓的装配线，只是它专注于前端装配。我有时候会称其为制作线。随着时间的推移，会有其他垂直生产线与其连接，用于生产食物。
So you'll have production or make line going into an assembly line here and then you go, oh wow, so you have it, something that dispenses burgers on buns. That's the dispenser. That's the assembly. But it's like, it's like, it's like, it's like, how do you cook that burger? That's what I call, that's what we call state change. So state change is the cooking of the food. Assembly is like, how do I put it together and plate it? Doesn't this collapse? Like, for example, if you have a yield of 300 per hour, you said about one machine. Yes.
所以，你会有生产或者制造线进入装配线，然后你会发现，哇哦，这里有个设备，可以把汉堡放在面包上。那是一个分发器，那是装配过程。但就像，就像，你会想，怎么把那个汉堡煎熟呢？这就是我所说的状态变化。状态变化就是食物的烹饪过程。而装配则是如何把它组合好并放在盘子上。这样不会出问题吗？比如，如果你说一台机器每小时能产出300个。是的。
Very quickly, you can impute the value of having a smaller footprint store with five of these things in a faceless warehouse with drone delivery or cars. You don't need the physical infrastructure. So then don't you create a wasteland of real estate or how do you repurpose all the real estate? Well, the way to think about is like 90 percent, well, let's say 85 percent of all meals in the US are at home. They just are and a vast majority of those meals are cooked at home. So like Uber Eats and DoorDash, they represent like 1.8 percent or 2 percent of all meals right now. It's very tiny.
很快，你就可以理解，将商店规模缩小并置于无面孔的仓库中，再配备五种设备，通过无人机或汽车进行配送的价值。这样一来，你不再需要实体建筑。那是不是就在房地产领域造成一种荒地，或者如何重新利用这些房地产呢？可以这样来理解，美国大约有90%，或者说85%左右的餐食是在家里进行的，其中绝大多数都是在家里烹饪的。而像Uber Eats和DoorDash这样的外卖服务，目前只占所有餐食的1.8%到2%，比例非常小。
So what you're doing is you're using real estate to an infrastructure to prepare and deliver meals to people at their homes. And so it's not the restaurant still exists. We're still going to want to go to restaurants. We're still going to want to go outside. We learn that during COVID. We do it before. We definitely know it after. And so I don't it's not really like a decimating real estate situation. It's taking a thing we used to do for ourselves and creating a service that doesn't hire quality. Sort of I like to say you don't have to be wealthy to be healthy and just infrastructure you get that cost down.
所以你的意思是，你正在利用房地产作为基础设施，为人们在家中准备和配送餐食。这并不意味着餐馆会消失，我们仍然会想去餐馆，仍然会想外出。在新冠疫情期间我们学到了这一点，无论是之前还是之后，我们都清楚这一点。因此，这并不是一种摧毁房地产的情况，而是将我们以前自己做的事情变成了一种服务，并且不影响品质。我想说的是，你不必富有才能健康，通过这样的基础设施，成本就能降下来。
And so you're doing something as a service that needs to do at home. I think in the super long run you're like what where's the story on grocery stores? If you go to like in 20 years I think everybody agrees you will have machines making very high quality, very personalized meals for everybody. This would be good for Keith because he measures stuff down to like 5 calories based on his histogram. What's your what's your body fat like Oh god yeah. It's like just open his Instagram. He pops before. He's like so disgusted with himself at 10%. It's like bad it's 10. But I actually think the vision of this actually the natural implication and maybe the home run version of this is everybody has a private chef in their house. Robot in their house actually does this personalized because people do want to cook at home but they don't have it's fine.
你正在提供一种在家里需要做的服务。我认为从长远来看，我们会质疑传统杂货店的存在。再过20年，大家可能都会同意，每个人都可以通过机器制作出高质量且个性化的餐食。对于像Keith这样注重饮食的人来说，这样的机器会非常有帮助，因为他根据自己的饮食记录控制热量精确到5卡路里。你问起他的体脂率，他可能会说：“哦天哪，去看看他的Instagram就知道了，他一旦有所懈怠就会很不满，比如达到10%就觉得糟糕。” 实际上，这种愿景的自然延伸，或者说是最理想的状态，就是每个家庭都有一个私人厨师。事实上，是一个可以在家烹饪个性化餐食的机器人。因为人们确实想在家做饭，但却缺乏时间、精力或技术去实现。
Yeah. Of course space and infrastructure but man these delivery services are charging. Rich people do this all the time right they do these crazy meal delivery services for 200 bucks a day. And this is just going to abstract it down to everybody. And man people get creative when there's empty space to your point Shemaaf about what happens to all this space. When I lived in New York in the 80s and 90s it was common to in Tribeca in West Chelsea where I lived to take storefronts put your little architect's office in the front and live in the back. And many people were hacking real estate. It's only 5, 10 million homes in this country and they're already doing this with malls.
当然，空间和基础设施是一个问题，但这些配送服务的收费真是高。富人经常这样做，他们使用那些每天要花200美元的高昂的餐饮配送服务。这会使得这种服务逐渐普及到普通大众手中。而且，当空间闲置时，人们会变得非常有创意。Shemaaf，你提到这些空间会发生什么。我在80年代和90年代住在纽约时，这种情况很常见。在我住的TriBeCa和西切尔西，人们常常把店面改造成建筑师的小办公室，前面办公后面居住。许多人都在变相利用房地产。全国只有500万到1000万套房子，但人们已经开始这样对待商场了。
I keep seeing malls being turned into colleges and creative spaces. One of them in Boston they turned like the second and third floor into studio apartments for artists. So you know where there's a world there's a way we could use the space I might know where this goes with Shemaaf saying where the real estate goes is we call it the internet food court where you know you're on Amazon right it's the everything store now imagine that for food. And then imagine you have an 8,000 square foot facility where basically anything can be made. Anything can be made because if you have that machine you saw has 18 sort of dispensers for food 10 different sauces you get the idea.
我看到商场越来越多地被改造成学院和创意空间。在波士顿，有一个商场将二楼和三楼改造成了艺术家的公寓。所以你知道，哪里有想法，哪里就有办法利用这些空间。我可能知道这会走向何方，当Shemaaf提到房地产的时候，我们称其为互联网美食广场。想象一下亚马逊，它是一个无所不包的商店。现在想象这样的概念应用于食品。再想象一下，你有一个8000平方英尺的设施，基本上任何东西都可以在那里制作。因为如果你有那种机器，它有18种食材分配器和10种不同的酱料，你应该能明白其潜力。
Now what what about when it's 50 or 100 dispensers for food what if you have multiple machines with 100 dispensers for food. That's crazy. You can the combinatorial math in terms of what's possible what can be made. Sort of you know goes exponential. And so the internet food court is sort of the vision for where this all goes. Another example of the bitter lesson. The bitter yeah we're going to get to that I guess today in a very full docket before we get to that just a little bit of housekeeping here. September 7th 8th 9th in Los Angeles be all in some it again. Well in comm slash yada yada yada line up is stacked.
那么，当有50个或100个食物分配机器时会怎么样？如果你有多个这样的机器，每个都有100个食物分配器，那真是太疯狂了。组合数学显示可以制作的东西种类会呈指数级增长。因此，互联网美食广场就是这项发展的愿景。另一个艰难学习经验的例子。艰难学习经验的意义我们今天会详细讨论，但在那之前，我们先处理一些事务。9月7日至9日，我们将在洛杉矶举办一次峰会，安排非常丰富。在wellin.com上可以查看详细信息。
And we're going to start announcing the speakers people have been begging us to announce the speakers. I don't know. I don't know. Maybe you've got to hold some back careful. Hold a couple back but we got some really nice speakers lined up. It is going to be. It is the it is the best one yet. I mean well done. Every year we have this. Yeah yeah every year we have this little bit of panic like you know we're going to get great speakers and man they started flowing in this week it's going to be extraordinary almost as extraordinary as this delicious to kill a behind my head here. Get the all and kill it to kill it dot olin.com. If the deliveries begin late summer.
我们将开始公布大家一直期盼的演讲嘉宾。真的，我不知道，我们可能需要保留一些，但我们已经安排了一些非常出色的演讲者。这将是有史以来最好的一次。每年我们都会有些紧张，比如我们会不会请到优秀的演讲者，但这周他们纷纷应邀而来，这将是非凡的活动。就像我身后这瓶美味的龙舌兰一样非凡。访问 get the all 和 kill it to kill it 的网站 olin.com。如果交付将于夏末开始。
Moving to the side you can't even tell us to do that. That's our cure. Oh yeah. Yeah. All right listen. Oh wow. Lots to discuss this week. Obviously AI is continuing to be the big story in our industry and for good reason our bestie Elon released GROC 4 Wednesday night. Two versions base model and a heavy model 30 bucks a month for the base $300 a month for this heavy model which has a very unique feature. You can have a multi-agent feature. I got to see this actually when I visited XAI a couple of weeks ago where multiple agents work on the same problems and they do that simultaneously obviously and then compare each other's work and it gives you kind of like a study group the best answer by consensus really interesting.
移动到一边，你甚至不能让我们这样做。这才是我们的解决之道。哦，是的。好吧，听我说。哦，哇。本周有很多事情要讨论。显然，人工智能依然是我们行业中的大热点，而且理由充分。我们的好朋友埃隆在周三晚上发布了GROC 4。它有两个版本，基础版和高级版。基础版每月30美元，高级版每月300美元。高级版有一个非常独特的功能，你可以使用多代理功能。我其实在几周前访问XAI的时候看到过这个功能，多名代理可以同时处理相同的问题，然后互相比较各自的工作，给你一种类似学习小组的感觉，是由共识得出的最佳答案，真的很有趣。
According to artificial analysis benchmark you can pull that up Nick GROC's 4 base model has surpassed opening eyes o3 pro google jamin ice 2.5 pro as the most intelligent model this includes like seven different industry standard evaluation tests you can look it up but reasoning math coding all that kind of stuff this is you know book smarts not necessarily street smart so it doesn't mean that these things can reason and obviously there was a little there was a little kerfluffle on X formerly known as twitter where Xai got a little frisky and was saying all kinds of crazy stuff and needed to maybe be read teamed a little bit more decisively many of you know GROC 4 was trained on colossus that's that giant data center that Elon's been building and we showed.
根据人工分析基准，Nick GROC的四代基础模型已经超越了Opening Eyes O3 Pro、Google Jamin Ice 2.5 Pro，成为最智能的模型。这包括大约七个不同的行业标准评估测试。你可以查一下，其中包括推理、数学、编码等方面的能力。这里讲的是“书本聪明”，而不一定是“街头聪明”。所以这并不意味着这些模型可以真正理解或推理。最近在曾被称为Twitter的X平台上，XAI有点冒失，说了些疯狂的话，可能需要更彻底地进行“红队”测试。许多人都知道，GROC 4是在Elon正在建设的Colossus这个大型数据中心中训练的。
The chart here at schemoff you sent us a link to the bitter lesson by rich Sutton in the group chat that's the 2019 blog post we'll pull it up here for people to take a look at and put it in the show notes maybe just generally yeah a reaction to both how quickly Elon has a net chart showed it how quickly Elon has caught up and I don't think people expected to take the lead but here we are before we start Nick can you please show Elon's tweet about how they did on the aji benchmark it's absolutely incredible two things one is how quickly starting in march of 2023 so we're talking about less than two and a half years what this team is accomplished and how far ahead they are of everybody else that's demonstrated by this but the second is a fundamental architectural decision that Elon made which I think we didn't fully appreciate until now and it maps to an architectural decision he made a Tesla as well and for all we know will figure out that he made an equivalent decision at SpaceX and that decision is really well encapsulated by this essay the bitter lesson by rich Sutton and Nick if you can just throw this up here but just to summarize.
这段文字主要讨论了关于Elon Musk在人工智能发展上的进展，以及他做出的一个关键架构决策。这里提到了一篇2019年的博文《The Bitter Lesson》由Rich Sutton撰写，它会在聊天中分享给大家。
以下是要点：
1. Elon Musk的团队在人工智能领域的快速进展令人惊讶，尤其是从2023年3月开始，在不到两年半的时间里，他们在竞争中遥遥领先。
2. 这种快速进展的背后是Elon做出的一个关键架构决策，这与他在特斯拉所做的决策类似。
3. 该决策可能与SpaceX也有类似之处，而这个决策的本质与Rich Sutton的《The Bitter Lesson》中所阐述的观点一致。
最后，讨论中提到要展示Elon关于他们在AGI（通用人工智能）基准测试上表现的推文，说明这项成就是多么不可思议。
What this says it basically says in a nutshell that you're always better off when you're trying to solve an AI problem taking a general learning approach that can scale with computation because it ultimately proves to be the most effective and the alternative would be something that's much more human-labor and human-involved that requires human knowledge and so the first method what it essentially allows you to do is view any problem as an endless scalable search or learning task and as it's turned out whether it's chess or go or speech recognition or computer vision whenever there is two competing approaches one that used general computation and one that used human knowledge the general computation problem always one and so it creates this bitter lesson for humans that want to think that we are at the center of all of this critical learning and all of these leaps in more AI specific language.
这段话的大意是，当你试图解决一个人工智能问题时，采用一种可以随着计算能力扩展的通用学习方法，总是更有优势的，因为这种方法最终被证明是最有效的。相反的选择是需要大量人工劳动和人类知识的方法。而第一种方法实质上让你能够把任何问题视为一个可以无限扩展的搜索或学习任务。事实证明，无论是在国际象棋、围棋、语音识别还是计算机视觉中，当有两种竞争方法时，一种是使用通用计算，另一种是使用人类知识，通用计算方法总是胜出。因此，这给那些希望自己处于所有这些关键学习和人工智能进步中心的人类带来了一个痛苦的教训。
What it means is that a lot of these systems create these embeddings that are just not understandable by humans at all but it yields incredible results so why is this crazy well he made this huge bet on this 100,000 GPU cluster people thought wow that's a lot is it going to bear fruit then he said no actually I'm scaling it up to 250,000 then he said it's going to scale up to a million and what these results show is a general computational approach that doesn't require as much human labeling can actually get to the answer and better answers faster that has huge implications because if you think about all these other companies what is llama been doing they just spent 15 billion to buy 49% of scale AI that's exactly a bet on human knowledge what is Gemini doing what is open AI doing what is anthropic doing so all these things come into question and then the last thing I'll say is if you look back he made this bet once before which was tesla fsd versus weimo and tesla fsd only had cameras it didn't have lidar.
这段话的意思是，很多系统创建的嵌入是人类完全无法理解的，但却能产生极好的结果。这为什么令人震惊呢？是因为某人对一个拥有10万个GPU的集群进行了重大投资，人们觉得这已经很多了，并猜测是否会有成果。随后他说要将规模扩大到25万个，然后他说要扩大到100万个。这些结果展示了一种普通的计算方法，不需要太多的人为标记就能更快找到答案，而且答案质量更好。这具有巨大的影响，因为如果你想到其他公司，你会想，Llama在做什么？他们刚花了150亿美元买下Scale AI的49%股份，这是对人类知识的一个赌博。Gemini在做什么？OpenAI和Anthropic在做什么？所有这些都变成了问题。最后要说的是，如果回顾过去，他曾做过类似的赌注，就是特斯拉的全自动驾驶（FSD）与Waymo的比较。当时，特斯拉的FSD只用了摄像头，并没有使用激光雷达。
But the bet was I'll just collect billions and billions of driving miles before anybody else does and apply general compute and it'll get an autonomy faster than the other more laborious and very expensive approach so I just think it's an incredible moment in technology where we see so many examples travis is another one what he's just talked about you know the bitter lesson is you could believe that you know food is this immutable thing that's made meticulously by hand by these individuals or you can take this general purpose computer approach which is what he took waited for these costures to come into play and now you can scale food to every human on earth I just think it's a it's so profoundly important.
但是赌注在于，我将比任何人都更早收集数十亿的驾驶里程数据，并应用通用计算，这样会比其它更费力、更昂贵的方法更快实现自动驾驶。所以我认为这是技术上的一个非凡时刻，我们看到了许多例子，比如Travis刚才提到的。你可以相信食物是一个由个人精心手工制作、不可改变的东西，或者你可以采取他所用的这种通用计算的方法，等待这些因素的出现，现在你可以把食物扩展到世界上的每一个人。我认为这真的具有深远的重要性。
One thing I'll throw out there to moth is the tesla approach for autonomy is taking human knowledge in fact the whole idea is to approximate human human driving right this is the whole damn thing now depending on your approach and the technology you can do like what's called an end to end approach or you can look at okay perception prediction planning and control which are like these four modules that sort of you you sort of engineer if that makes sense but it's approximating human driving to do it the difference is that you know I think Elon's taken a it almost a more human approach which is like I've got two eyes why can't my car why can't my car do it like a human like I don't have any lidar spinning around on my head as a human why can't my car so it's kind of interesting he's sort of taking what you're saying to moth on the computation side because hardware five is coming out on tesla probably next year which is going to make a big difference in what fsd can do that's the compute side you're talking about but then he is approximating human.
有一点我要指出的是，特斯拉在自动驾驶方面的方式其实是利用人类的知识，整个理念其实就是模拟人类的驾驶方式。这是整个大背景。根据你选择的方法和使用的技术，你可以选择所谓的端到端方法，或者分成四个模块：感知、预测、规划和控制，这些是通过工程方式解决的，希望能解释清楚。但是基本上都是在模拟人类驾驶的方式来实现。不同的是，我认为埃隆·马斯克采取了一种更偏向人类的方式，比如他说“我有两只眼睛，为什么我的车不能像人类一样做同样的事情？我头上没有激光雷达在转动，我的车为什么需要？”这有点有趣，他在计算方面做了一些你提到的事情，因为可能明年特斯拉会推出硬件5，这将在计算能力上对完全自动驾驶（FSD）产生重大影响，他是通过这样的方式在模拟人类驾驶。
Yeah I just spent that you know other than the first versions of fsd which I think Andre talked about hundred carpet he talked about you know they're not really so reliant anymore on human labeling per se right so that's back yeah that that interference and then yeah the other crazy thing that he said subsequent versions of grog are not going to be trained on any traditional data set that exists in the wild the cumulative some of human knowledge has been exhausted in AI training that happened basically last year and so the only way to then supplement that is with synthetic data with AI creates it'll sort of write an essay or come up with with the thesis and then it will grade itself and and sort of go through this process of self-learning with synthetic data he said that he's going to have agents creating synthetic data from scratch that then drive all the training.
好的，我刚刚花了一些时间思考，除了早期版本的FSD（完全自动驾驶）之外，我认为Andre提到过一个转折点，他说他们现在已经不像以前那么依赖人工标注了。然后，还有一件疯狂的事情他说后续版本的Grog不会再用到现有的传统数据集。直到去年，人工智能的训练几乎用尽了人类知识的总和。所以，唯一能补充这些知识的方法就是用AI生成的合成数据。AI会自己写文章或者提出论点，并通过自我评分和类似的自学过程来进步。他还提到他会有专门的代理创建全新的合成数据来进行所有的训练。
Which I just think is it's crazy just explain this concept one more time with a better lesson hand coding heuristics into the computer and saying hey here are specific openings and yeah use yeah use chest right yeah chest coding specific examples of openings in their end games etc versus just saying play every possible game and here's every game we have so here's the yeah so the two approaches would be let's say like Travis and I were building competing versions of a chest solver and Travis's approach would say I'm just going to define the chess board I'm going to give the players certain boundaries in which they can move right so the bishop can only move diagonally and there's a couple of boundary conditions and I'm going to create a reward function and I'm just going to let the thing self-learn and self-play that's his version.
这段话的意思是：我认为这个想法很疯狂，请再详细解释一次。这个概念就是手动将启发式方法编码到计算机中，比如给出特定的开局，并说“这是一些具体的开局和残局的例子”这种方式，与另一种方式进行对比，即“让计算机玩所有可能的棋局，并提供所有已知的棋局”，这两种方法是不一样的。假设我和Travis正在开发互相竞争的国际象棋求解器。Travis的方法是，他会先定义棋盘，给玩家设定一些移动的规则，比如说主教只能沿对角线移动，然后设定一些边界条件，再创建一个奖励函数，让程序通过自我学习和自我对弈来掌握棋艺。这就是他的策略。
And then what happens is when you map out every single permutation when you go and play Keith who's the best chess player in the world what you're doing at that point is saying okay keep made this move so you search for what Keith's move is and you have a distribution of the best moves that you could make in response or vice versa that was the cutting edge approach the different approach which is more you know what people would think is more quote-unquote elegant and less brute force would be Jason for you and I to sit there and say okay if Keith moves here we should do this we should do this specific variation of the Sicilian defense and and it's too much human knowledge and I think what what it turned out was there was a psychological need for humans to believe we were part of the answer but what this is showing is because of Moore's law and because of general computation it's just not necessary you just have to let go give up control and that's very hard for some people.
当你逐一列出每一种可能的变化并与世界上最好的国际象棋选手Keith对弈时，你所做的就是观察Keith的每一步，然后搜索他可能的下一步，同时也分析出自己应对的最佳策略。这是最先进的方法。另一种被认为更加"优雅"而不是单纯依靠蛮力的方法，可能会是Jason和我一起坐下来讨论，假设如果Keith走这一步时，我们应该使用西西里防御的某个特定变化。但是这涉及太多的人工知识。结果表明，人们在心理上希望相信自己是解决方案的一部分，但这种情况显示，由于摩尔定律和一般计算能力的发展，这已经不是必须的了。我们需要放手，放弃控制，而这对于有些人来说非常困难。
And there's also very hard on some circumstances where a car is driving down the road and it's learning in that process which is why he needs safety driver and I think Elon made the right decision to put one in there Keith you're awesome yeah a couple of points it's not quite that binary schemoff I generally agree with your art but if you think about LLM's being the most important unlock in AI LLM's are all trained on human writing so if someone wrote every piece of data that every LLM used a human wrote at some point in history so yes it's true that they've shocked everybody including open AI's you know original team on the implications the broad implications the general applicability to almost every problem but it's not like there was some tablets floating in space that weren't drafted by humans that we've trained on as you get in non LLM based models you may be totally right but almost no one's really using non LLM based models at scale on driving specifically.
这段文字的大意是：在某些情况下，一辆汽车在行驶过程中学习，因此需要一名安全驾驶员。我认为Elon在这方面做出了正确的决定，Keith你很赞。这里有几点需要说明，情况并不是非黑即白的。虽然我通常同意你的观点，但如果考虑到大型语言模型（LLM）是人工智能的一个重要突破，这些模型都是基于人类写作训练出来的。因此，模型使用的所有数据都可以追溯到某个时点由人编写的内容。虽然LLM的广泛应用和影响超出了所有人的预期，包括OpenAI的创始团队，但这并不是基于某些漂浮在太空中的神秘文本，而是基于人类的作品。虽然在非LLM模型中可能会有其他情况，但在大规模应用中，几乎没有人使用非LLM模型，尤其是在自动驾驶领域。
Travis is totally right the humans are actually really good drivers except when they get distracted they get distracted by drugs or alcohol they get distracted by being tired they get distracted by turning the radio they get distracted by chatting with their passenger so trading against human behaviors actually turned out to be a great decision because what for whenever said are Darwinistic reasons humans are pretty ideal drivers and so you don't have to reason from first principles this is a much better path and I think again there may be a broad sort of lesson there the most important thing I think as I've you see that you said as we've been debating for years should we invest in companies like scale or work or any of these surge.
特拉维斯完全正确，人类其实是非常优秀的驾驶员，只不过当他们分心时，就不那么出色了。他们可能因为药物或酒精而分心，因为疲倦而分心，因为开关收音机而分心，或者因为与乘客聊天而分心。因此，与人类行为进行交易实际上是一个明智的决定，因为出于某种达尔文式的原因，人类已经是相当理想的驾驶员，所以你不需要从基础原则进行推理，这是一条更好的道路。我认为这里可能有一个更广泛的教训。我们多年来一直在争论的最重要问题是，是否应该投资像Scale或Work这样的公司，或者参与任何这种激增。
The truth is I think there's a very short half-life on human label data and so everybody who's investing in these companies just look at revenue traction really didn't understand that there may be a year two years three years max when anybody uses human label data for maybe anything because we hit the end of human knowledge or just the collection of it or you 99% done or you train on you train on it so well that you don't need to label anymore like the machines know how to label as good or better than a human and so like we're seeing this in the self-driving space is labeling was huge right you would have a three dimensional sort of scene that's created by video plus LiDAR let's say okay I have to label all of these essentially would become boxes like I've identified objects you're you're some of the players in the in the autonomous software space the tons of vehicle software space are no longer doing any labeling because the machines are doing it all just probably it'll just be built into the chipset that this is a stop sign like it's like we know what a stop sign is we don't need the millionth time for some like the captures like you're like find the stop sign or what's the traffic light and eventually the machines are just way better.
这段英文可以翻译为中文如下：
实际上，我觉得人类标注数据的“半衰期”很短。所以，那些投资这些公司的人员只关注收入增长，却没理解这样的数据可能只能用一到三年。因为要么人类知识的极限到了，要么这些数据已经基本收集完毕，要么机器已经被训练得如此出色，不再需要人类去标注。就比如在自动驾驶领域，标注曾经非常重要。你需标注通过视频加上LiDAR（三维激光雷达）创建的三维场景，会把物体识别为一个个框。但现在一些自动驾驶软件公司已经不再需要任何标注了，因为机器已经可以完全胜任这种工作。也许以后芯片就自带这种功能，比如能识别停车标志，我们不需要一百万次地标记停车标志或者交通灯，因为机器已经比人识别得更好了。
The humans that identify these things for you to be very practical when you see a stop sign you don't have to identify that it's a stop sign you just see that every human when they encounter a stop sign 99.9% of the time they hit a break and they never actually nobody actually knows it's a stop sign it's just that hit a break when you see something that looks like this object it's just a vibe yeah it's a vibe I would just say that that's like intuitive knowledge versus like the expressly labeled human knowledge the question for me is if everybody was so reliant on human labeling initially if you're an investor now when you see these grok for results how do you make an investment decision that's not purely levered to just computation.
当你看到一个停车标志时，你不需要去识别那是一个停车标志，你只是本能地知道要踩刹车。其实每个遇到停车标志的人，99.9%的时间都会踩刹车，他们并不是真的识别这是个停车标志，只是看到类似的东西就会踩刹车。这是一种直觉，就像是一种本能的知识，相较于明确标记的人类知识。对我来说，问题在于如果每个人最初是如此依赖于人类的标记数据，那么作为一个投资者，当你看到这些"grok"（理解）结果时，你如何做出不是单纯依赖计算的投资决策。
So if you look at these results does it mean that the you know there's 300 to a thousand basis points of lag between just letting the computers vibe itself to the answer versus interjecting ourselves if interjecting ourselves slows us down by 300 to a thousand basis points per successive iteration then over two or three iterations you've totally lost so what does it mean for everybody that's not grok when they wake up today and they have to decide how do I change my strategy or double down I think look I'm not in the investment game but if I were it would be all about scientific breakthrough so I sometimes get in this place where I'm looking I'm going down a path I'll be up at four or five in the morning my day hasn't quite started but I'm not sleeping anymore and I'll start like I'll be on Kora and see some cool quantum physics question or something else I'm looking into and I'll go down this thread with GPT or GRO and I'll start to get to the edge of what's known in quantum physics and then I'm doing the equivalent of vibe coding except its vibe physics and we're approaching what's known and I'm trying to poke and see if there's breakthroughs to be had.
如果你看这些结果，这是否意味着在由计算机独自找到答案与我们插手之间，会有300到1000个基点的延迟？如果我们的介入使每次迭代的速度降低300到1000个基点，那么经过两到三次迭代，你可能就完全落后了。那么对于那些没有完全理解这种情况的人来说，这意味着什么？今天早晨醒来后，他们需要决定如何改变策略或加倍下注。我认为，虽然我不在投资行业，但如果我在其中，我将完全专注于科学突破。
有时，我会处在这样的状态：沿着一条路径探索，凌晨四五点时，我的白天还没真正开始，但我已经不再困倦。我可能会去看Quora上的一些酷炫的量子物理问题，或者是其他我正在研究的东西。我会继续使用GPT或GRO深入探索，在量子物理学的已知领域的边缘徘徊。然后，我就像在进行“直觉编码”，但这次是“直觉物理学”。在接近已知领域时，我尝试寻找是否有可以突破的地方。
And I've gotten pretty damn close to some interesting breakthroughs just doing that right you know I pinged. I pinged you on at some point I'm just like dude if I'm if I'm doing this and I'm super amateur hour physics enthusiast like what about all those PhD students and postdocs that are super legit using this tool and this is pre GROC 4 now with GROC 4 like like there's a lot of mistakes I was seeing GROC may that then I would correct and we would talk about it GROC so it could be this place where breakthroughs are actually happening new breakthroughs so if I'm investing in this space I would be like who's got the edge on scientific breakthroughs and and the application layer on top of these foundational models that orient that direction is your perception that the LLMs are actually starting to get to the reasoning level that they'll come up with a novel concept theory and have that breakthrough or that we're kind of reading into it and it's just trying random stuffs at the margins it's uh or maybe it doesn't have no no so what I what I've seen and again I haven't used GROC 4 I tried to use it early this morning but for some reason I couldn't do it on my on my app.
我已经通过这样做接近了一些有趣的突破。你知道，我在某个时候联系过你。我想，如果我，一个超级业余的物理爱好者都能做到这些，那么那些使用这个工具的博士生和博士后会怎么样，这还是在GROC 4之前。现在有了GROC 4，GROC以前出现了很多错误，而我会纠正这些错误，并和GROC讨论，这样GROC可能就是那些新突破真正发生的地方。所以，如果我要在这个领域投资，我会想知道谁能在科学突破上取得优势，以及那些在这些基础模型之上应用层的方向。你觉得大型语言模型（LLM）是否真的开始达到推理的水平，可以提出新的概念理论并实现突破，还是我们只是在解读它，它只是在边缘尝试随机操作？或者可能没有看到这一点。但就我所见，尽管我没用过GROC 4，我今天早上尝试用了一下，但出于某些原因，无法在我的应用上使用。
But so let's say we're talking GROC 3 and existing chat GPT as it is no it cannot come up with the new idea these things are so wedded to what is known and they're so like even when I come up with a new idea I have to really it's like pulling a donkey source you just you're pulling it because it doesn't want to break conventional wisdom it's like really it's hearing to conventional wisdom you're pulling it out and then eventually goes oh shit you got something but then when it says that when it says that then you have to you have to go okay it's said that but I'm not sure like you have to double and triple check to make sure that you really got something to your point when these models are fully divorced from having to learn on the known world and instead can just learn synthetically yeah then everything gets flips upside down to what is the best hypothesis you have or what is the best question you could just give it some problem and it would just figure it out.
好的，假设我们在讨论GROC 3和现有的Chat GPT。就目前看来，它们不能提出新的想法，因为这些模型太依赖于已知的信息。即便我自己提出一个新想法，我也需要花很大力气，就像拉着一头不情愿的毛驴一样。它们很难突破传统的思维模式，需要费力才能把创新的东西挖掘出来，最终才会意识到“哦，你确实得到了什么”。不过，即便在这个时候，你也需要反复确认，以确保真正得到了有价值的内容。
一旦这些模型完全不再依赖已知信息进行学习，而是可以通过模拟的方法自行学习时，情况就会彻底颠倒过来。那时，最好的假设是什么或最好的问题是什么就会变得至关重要。你只需给出一个问题，它们就可以找出解决方式。
So where I go on this one guys is it's all about scientific method right if you get if you have an LM or foundational model of some kind that is the best in the world with the scientific method game the F over you basically you just light up more GPUs and you just got like a thousand more PhD students working for you keep your nodding your head here okay I agree with that I think that's fantastic because the scientific method also the faster it is the more you when you have a hypothesis the faster you get a response you're more likely to dive in and dive in and recursively and recursively and every lag every millisecond lag it causes you to like lose your train of thoughts sort of so speak so you get the benefits the Travis leading to plus speed and you go places you never really got this happens all the time when you run a company and you're doing like analytics and you have a tool that allows you to constantly query quickly quickly quickly double click triple click you get to answers that you never get to either there's even a second or two second or three second boy let alone standing at you human.
所以，在这个问题上，我的观点是，这一切都关乎科学方法。假如你有一个全球最优秀的语言模型或基础模型，利用科学方法，基本上就是让更多的GPU来运行，同时让更多的博士生为你工作。你点头表示同意，我觉得这非常棒，因为科学方法的优点之一就是速度。当你有一个假设时，响应越快，你就越可能深入研究，反复探索。任何延迟都会导致你走神、思路中断。因此，借助快速的方法，你不仅能获得更好的结果，还能达到前所未有的境地。这种情况在经营公司时经常发生，尤其是在做分析时。如果你有一个工具，能够让你迅速地进行查询、深入挖掘，你会得到其他方式无法获得的答案。即使是一两秒、三秒的延迟，也会造成影响，更不用说在面对真人时的延迟了。
Secondly were you actually see this today it's already happening if you look at foundational models that just apply to science there's lots of things about the human body let's say health biology that we humans don't actually understand all the connections like why do we do acts why do some people get cancer right out of the people not get cancer why does the brain work this right models trained solely on science tend to expose connections that no human has ever had before and that's because like the raw materials and we only have a conscious awareness of thought 110 percent but when you apply it to other human domains where they're training on human sort of data human produced data human produced output they're limited to that output
其次，如果你今天真的看到了这一点，你会发现它实际上已经在发生了。如果你关注仅应用于科学的基础模型，你会发现关于人体的许多事情，例如健康和生物学，其实我们人类并不完全理解所有连接。例如，为什么我们会有某些行为？为什么有些人会得癌症，而另一些人不会？为什么大脑会如此运作？只专注于科学训练的模型往往会揭示人类以前从未有过的联系。这是因为我们的思维材料有限，我们只能意识到其中约10%。然而，当你将其应用于其他人类领域时，这些模型是基于人类的数据和人类产出的结果进行训练的，因此它们也受限于这些输出。
so I think you just take the science and apply it writ large and you're going to want to find anything that no human has ever thought before and it's the thing about science though is that it's the hypothesis that you then have to test in the physical world so the you're like okay have you got this hive mind this like you know this computation engine this brain of sorts you wanted to say consciousness but you stop your stuff yeah I was like I was like how do I just guys the big C word yeah conscious of that but but you need to be able to test in the physical world
所以我认为你可以将科学广泛应用，你会想要找到任何人类从未想过的东西。不过，科学的特别之处在于，你需要有一个假设，然后在现实世界中进行验证。所以，假如你有一个集体意识，像一个计算引擎，某种程度上的大脑，你想说是意识，但你停住了。是的，我当时想避开那个“大意识”这个词，但你需要能够在物理世界中进行测试。
so you got to imagine a physical lab connected to one of these systems where then you could say okay like you have as a chemistry experiment you could do chemistry experiments or physics you get the idea what could go wrong it would be it's yeah no big deal it's going to be fine okay so but this is where it goes because if you have a scientific method machine you still have to be able to test your hypothesis you have to go through the science and the verification
你可以想象一下，有一个物理实验室与这些系统之一相连接。在这个实验室里，你可以进行化学实验或者物理实验。你明白可能出现的问题，但实际上没什么大不了的，一切都会顺利进行。然而，这就是问题的关键所在。即使你有一个科学方法机器，你仍然需要能够测试你的假设，必须完成科学实验和验证过程。
yeah exactly yeah wow it's kind of mind blowing reminds me of mind blowing remember I don't know if you guys remember dark matter and like the discovery of it and everything and as explained to me by Lisa Randall you know the the discovery was made not by knowing there was dark matter there and observing it but observing there was something you know gravitational forces around this other matter and then they said wait what's causing that and that's where they found dark matter
是的，确实是这样，哇，这有点令人震撼。这让我想起关于暗物质的事情。我不知道你们是否还记得暗物质以及它被发现的过程。根据丽莎·兰德尔的解释，暗物质的发现并不是通过直接观测到它来实现的，而是通过观察其他物质周围的引力效应引起的。他们发现了一些异常的引力作用，然后问道，是什么造成了这个现象，这才找到了暗物质。
so these ideas you know the idea that lllm could actually do that come up with something so novel is it doesn't it feels like we might be right there right like we're kind of on the customer one of the seven most difficult problems in math with the most important problems in math is proving a general solution to this thing called mavius tokes which is basically like viscous fluid dynamics and conservation in us we use it every day in the design of everything you know what it hasn't been proved isn't that the craziest thing where you're just like how is this even possible we use it to design airplanes to design everything
这些想法，让人觉得大型语言模型（LLM）也许真能做到这一点，提出一些全新的东西。我们似乎已经非常接近这个目标了，对吧？像是我们正站在解决七大数学难题之一的门槛上。其中一个最重要的问题就是证明所谓的纳维-斯托克斯方程的通用解。这个方程本质上涉及到黏性流体动力学和守恒定律。我们每天在产品设计中都要用到它，比如飞机设计等等。可至今还没有被证明，想想是不是很不可思议？我们天天用它来设计飞机和各种东西，却还没证明这个方程真的是正确的。
it hasn't been proved and so you could just point a computer at this thing and you would unlock all these incredible mysteries of the universe and we would probably find completely different propulsion systems we could probably do things that we didn't think were possible teleportation i mean who knows what's possible
还没有被证明，所以你可以把一台计算机指向这个东西，然后你可能会解开宇宙的所有神秘之处。我们可能会发现完全不同的推进系统，甚至可能做到我们认为不可能的事情，比如瞬间移动。谁知道还有什么是可能的呢？
remember remember you know how Elon talks about Brock and about AI generally is about why are we here what is the purpose meaning of the universe what is the meaning of the universe how does it work and it's sort of fierce true seeking mechanism there let me ask you a question Keith Travis Jason if you guys were running Grock for to be so much fun how do you judo flip open AI because they are marching steadfastly towards a billion mile then a billion down it's a juggernaut so how do you use the better product in a moment to judo flip the less better product
记得记得，你知道Elon谈论Brock和AI时，总是在说我们为什么在这里，宇宙的目的是什么，宇宙的意义是什么，它是如何运作的——就像一种激烈的寻找真理的机制。让我问你们一个问题，Keith、Travis、Jason，如果你们来运营Grock，会是多么有趣的事情。OpenAI正坚定地朝着一个巨大的里程碑前进，势不可挡，所以你们如何利用更好的产品，在某个时刻将不太优秀的产品“以柔克刚”呢？
look yeah i mean here's the thing right so you do that you on way so you you get a bunch of missionary like full on missionary engineers that work twice as hard and you have a culture that is ultra fierce true seeking and you don't you don't get caught up in politics bureaucracy yes and you just you go for it and I think you know that's where you know and then you go wow scientific breakthrough scientific method like you start winning on truth and that will start I believe that will start to give the product awesomeness of open AI
好吧，我的意思是，这里是关键：你这样做的时候，你会得到一群非常有使命感的工程师，他们工作起来加倍努力。你就会形成一种极致追求真理的文化，不被政治和官僚主义所困扰。是的，你只管全力以赴。我认为，那就是你知道的，你会发现科学突破和科学方法的奇迹。你开始在真理上取胜，我相信，这会开始赋予OpenAI产品惊人的优越性。
I run for its money but like the product of open AI the product department those guys are brushing they're really good they're not only ahead of the game but they feel like it just they're just leading in a lot of different ways but if you are better at truth you will eventually you'll eventually have an AI product manager
我在努力竞争，但是我很喜欢OpenAI的产品。他们的产品部门非常出色，他们不仅走在前列，而且在很多方面都处于领先地位。不过，如果你的真实性做得更好，最终你会有一个AI产品经理。
yeah and on a technical basis too people forget how good Elon is at factories and physical real world things what he did standing up colossus made like jensen was like how is this possible that you did this right so pressing that his ability to build factories and he said many times like the factory is the product of Tesla it's not the cars that come out of factory or the batteries it's the factory itself so if he can solving the energy problem with solar on one side and batteries and standing up you know colossus 2 3 4 5s he's going to have a massive advantage there on top of Travis you know the missionary individuals which by the way was what he backed before him Altman corrupted the original missionary basis of opening i made it closed AI in a you know nothing derogatory towards him but he did hoodwink and stand you on in the back is not nothing personal i mean he just screwed him over and would you say he banned boozled him bamboozled him screwed him hoodwinked him you know could pick your term here but he did it well he didn't dirty the original mission was to be like a shy open source all this content that's the other piece i think is a wild card and then I'll measure certain kids position but open sourcing some of this could have profound ramifications.
是的，从技术层面来看，人们往往忘记了埃隆在建造工厂和处理实际产品方面有多出色。他曾树立起庞然大物，让人感叹不已，甚至像简森这样的人都惊讶于他是如何做到的。所以，他在建造工厂方面的能力非常值得称道。他多次表示，特斯拉的工厂本身就是产品，而不仅仅是制造出的汽车或电池。如果他能通过太阳能和电池解决能源问题，并不断建立起大型工厂，他将在这一领域中取得巨大的优势。另一方面，特拉维斯也在召集那些具有使命感的人才，而这也是埃隆曾支持的理念。不过，可惜的是，奥特曼改变了这一初衷，将开放的AI变得封闭，我无意贬低他，但他确实利用了一些手段背叛了埃隆。你可以说奥特曼欺骗了他，背后捅刀，这种行为并不光彩。最初的使命是开放源代码，分享所有内容，而这其中可能蕴藏着巨大的潜在效果，也许会对某些重要的方面产生深远影响。
I think open sourcing the self-driving data could have a really profound impact you want to do something really disruptive like he open sources patents for you know charging if he open sourced the data set and self-driving does anybody have the ability to produce robo taxis that the scale he can do it i don't think so so many just hypothesis is true then yet everybody will well everybody will what sorry everybody will what you want if you have access to the money that buys the compute everyone could solve that problem but so far there a piece i'm talking which problem he said he said if he if he published all the fsd data could somebody build an autonomous vehicle well yes but could somebody produce a hundred million robo taxis from a factory with batteries in them okay that's a different that's a different question i'm saying and not really because it's like last time i was a guest on you know all and we talked about vertical integration uh products really require vertical integration so ultimately you have a self-driving something that is custom built for knowing it's going to be self-driving and it interacts differently the cost structure is different the controls are different the seating is different everything you build a product taking advantage of where the stock you have the most competitive advantage but then you leverage that and it reinforces and still while like Apple despite missing the AI still a pretty good company from any empirical standpoint.
我认为，如果公开自动驾驶的数据，可能会产生深远的影响。就像某个人之前公开其专利一样，带来了颠覆性的变化。如果有人公开了自动驾驶的数据集，那么有没有人能像他那样大规模生产无人驾驶出租车呢？我不认为有人能做到。如果我的假设成立，那么每个人都会...抱歉，每个人会做什么呢？每个人都会做你想做的事，如果他们有足够资金来购买计算资源的话。那么每个人都能解决这个问题，但目前，我在说其他问题。他说，如果他公开所有的自动驾驶数据，是否有人能制造出自动驾驶汽车？答案是可以的。但是否有人能从工厂生产出一亿辆带电池的无人驾驶出租车呢？这是另一个问题。我是说，现在还不太可能。因为，就像我上次做客节目时谈到的，产品需要垂直整合。最终，你需要一个专门为自动驾驶设计的车辆，与传统车辆不同，它的成本结构、控制系统、座位设计都不同。你需要利用自身的竞争优势来打造产品，这会加强你的产品竞争力。即便苹果在某些领域，比如人工智能，可能不足，但从任何一个实证角度来看，它依然是一家非常出色的公司。
I mean like the performance is absolutely miserable on the most important technology through the last 70 years but the company still alive and still worth for a dollar because it's vertically integrated open AI at pretty a point they do have a good product team and they need to stay ahead on the product level because they can't compete on the factory level the way to stay ahead of the product level is shipping a device they've got to ship the device it's got to be good it's got to be right it's got to be the right form factor it's got to do things for humans that are unexpected but then if they do that they're like apple plus AI. Jamal what's the paper you're talking about before it was a name of it then the bitter lesson that it could apply to autonomous driving is right now it's still like hey how do i drive like a human we talked about that but the leapfrog moment here could be like hey drive a car make sure it's efficient don't hit anybody and just simulate that quadrillion times and it's all good right but right now we're still trying to drive like humans because we don't have enough data and therefore can't do enough compute that's the global lesson by the way too often you're totally right conceptual you know the blog post is right but that's only true when you have enough data and depending on the use case the level of data you need may not be possible for years decades and you may need to hack your way there through human interaction.
这段话的意思是：我觉得公司的表现让人失望，尤其他们在过去70年中掌握了一项重要的技术，但公司仍然存在，并且价值一美元。这个公司有一个优秀的产品团队，他们需要在产品层面上领先，因为在工厂层面上他们没法竞争。保持产品领先的方法是推出一款设备，这款设备必须好，并且规格必须正确，能够以意想不到的方式帮助人类。这样一来，他们就像“苹果+人工智能”一样。
Jamal，你之前提到过的一篇文章叫什么名字？这篇文章的观点是，“痛苦的教训”可以应用在自动驾驶上。目前，我们在研究如何像人类一样驾驶，但真正的突破会是“如何高效驾驶，不出事故”，并模拟几十亿次这样的场景。现在，我们依然尝试像人一样驾驶，因为没有足够的数据，因此无法进行足够的计算。这是一个全球教训——很多时候，这种观点是正确的，但前提是你有足够的数据。根据具体应用场景，可能需要几十年才能获得足够的数据，并需要通过人类互动来实现突破。
Yeah physical world AI is lacking in data and so you just try to approximate humans. I don't know if you guys have seen this in related news open AI and perplexity are going after the browser perplexity launch comment for their $200 a month tier I actually downloaded I'll show you in a second but this is a really interesting category something developers can do already and they do it all the time you know but having your browser connected to agents let you do really interesting things I'll show you an example here that I just fired off while we're talking so I just asked it hey give me the best flights from United Airlines and business class from New York City from San Francisco to New York City it does some searches but what you see here is it's popped up a browser window and it's actually doing that work and you can see the steps it's using and then I can actually open that browser window and watch it do that this is just a screenshot of it and it will open multiple of these so you could I was doing a search other day saying like hey tell me all the auto biographies I haven't bought on Amazon put them into my you know shopping cart and summarize each of them because I like biographies I like doing it here and when it did this last time it put my flight into like and I was logged in under my account and it basically put it into my account in the checkout.
物理世界的人工智能数据不足，因此你只能尽量逼近人类的表现。我不知道你们是否看到了相关的新闻，OpenAI和Perplexity正在针对浏览器展开竞争。Perplexity推出了一个月收费200美元的高级功能，我已经下载了，待会儿可以给你们看。这是一个非常有趣的领域，开发者们已经能够并经常这样做。将浏览器连接到AI代理后，你可以做很多有趣的事情。我来给你举个例子，我在讨论中让它帮我查询从纽约市到旧金山的联合航空的商务舱最佳航班票价，它会进行搜索，你会看到它弹出一个浏览器窗口并执行这项工作，你也可以看到它使用的步骤，我可以打开那个浏览器窗口，看它具体的操作。这是它的一个截图，它还会同时打开多个窗口。比如前几天，我在搜索时问它告诉我亚马逊上我还没买的所有自传，把它们加入我的购物车，并总结每本书的内容，因为我很喜欢传记。上次这么做时，它帮我把航班信息添加到了我的账户中，直接进入了结账页面。
So again this isn't like if you're a developer you do this all day long but this really seems to be a new product category I'm curious if you guys have played with it yet and then what your thoughts are on having an agentech browser like this available to you to be doing these tasks in real time you can also connect obviously your Gmail your calendar to it so I did a a search tell me every restaurant I've been to and then put it by city and then I was going to open my open table and then pull that data as well what's interesting about this Keith and I know you're a product guy and you've done a lot of product work I'm curious your thoughts on it is you don't have to do this in the cloud you're authenticated already into a lot of your accounts nor do you have to worry about being blocked by these services because it doesn't look like a scraper or a bot it just that your browser doing the work your thoughts on this and we play with it at all.
好的，这并不像开发人员整天要做的事情，但这似乎确实是一种新的产品类别。我很好奇你们是否已经试用过它，以及对这种能实时执行任务的智能浏览器有何看法。你还可以很方便地连接Gmail和日历。我曾做过一个搜索，询问我去过的每家餐厅，并按城市分类，然后我想打开OpenTable并提取相关数据。基思，我知道你是个对产品很有研究的人，也做过很多产品方面的工作。我想听听你的看法。有意思的是，你不需要在云端执行这些操作，因为你已经验证过很多账户，也不用担心被这些服务屏蔽，因为它不像数据抓取工具或机器人，只是你的浏览器在工作。你对此有何想法？我们试过了吗？
Yeah I think it's a great Hail Mary attempt by Proplexity I think obscen something like this Proplexity is toast like for the stat about chat you be going to a billion users like it's becoming the verb you know the way you describe using AI for a normal consumer there's nothing left in Proplexity if they can't pull this off so it's a great idea because like the history of like consumer technology companies is whoever's up has uphill ground like in a military sense whoever's first has a lot of control this is actually what Google should be doing truthfully like I think Google's also Google search quast search is toast and since they have Chrome and they theoretically have a quality team in Gemini they should be putting these two things together and hoping to compete with Chad GPT they are going to lose the search game like the assets they're best at Google right now have nothing to do with search and to every other product is the only thing that's going to save that company if they can put figure out how to use them.
我认为这是Proplexity最后一搏的好尝试。如果Proplexity不能做到这一点，基本上就没有什么竞争力了。就像关于聊天软件快速增长到十亿用户的统计一样，AI正在成为人们日常生活中的一个动词。对于一个普通消费者来说，使用AI已经变得非常普遍。如果不能成功，Proplexity将难以为继。这个策略是个好主意，因为在消费科技公司历史上，谁先走一步谁就有更多的控制权。谷歌实际上应该走这条路，我认为谷歌搜索也面临困境。考虑到他们有Chrome浏览器和Gemini团队，他们应该把这两者结合起来，与ChatGPT竞争。否则，他们将在搜索市场中处于不利地位。目前，谷歌的优势资产与搜索无关，只有正确运用其其他产品，才能拯救公司。
Travis your thoughts on this category anything come to mind for you in terms of you know feature sets that would be extraordinary here I know you like to think about products in the consumer experience well it's really interesting so you know I've been spending yes you guys know I've been spending my time on real estate and construction and robotics and so I've been out of this kind of consumer software game for a long time but super interesting over the last six months there have been a number of consumer software CEOs like when I hang out them or whatever they're like you know how are we gonna how are we gonna keep doing what we do when the agents take over yeah the paradigm shift is so profound that the idea that you would visit a web page goes away and you're just in a chat don't you have an agent it's just taking care of your flights for you.
Travis，你对这个类别有什么想法吗？你有没有想到一些非常出色的功能集？我知道你喜欢思考消费者体验中的产品。这个话题真的很有趣。你们也知道，我最近一直专注于房地产、建筑和机器人领域，因此很长时间没有涉足消费者软件领域。最近六个月，我遇到了一些消费者软件公司的CEO。他们总是问我，“在智能助手逐渐接管的情况下，我们该如何继续做我们正在做的事情呢？”这种范式的转变非常深刻，以至于访问网页的概念可能会消失，你只是通过聊天来处理所有事情，像是有一个助手帮你安排航班等。
So I I kind of I think there's a leapfrog of over that I think it's just like you tell something yo I want to go to New York can you you know I'm sort of looking at this time range can you just go find something I'm probably gonna like and give me a couple options yeah and it's just a whole you have an interface and then you know is perplex is this thing that you just show them perplexly is that the interface or do I just have an agent that just goes and does everything for me and is this the start of that I yeah I just haven't spent enough time I I do know that every consumer software CEO that has an app in the app store is tripping they're tripping right now and I mean big boys I mean guys with real stuff and sometimes I I'm doing like almost like therapy sessions. with them and like it's gonna be fine you actually you actually have stuff you have a note you have real stuff that's a value they can't replace it with an agent and they're lying to them you're doing hospice care and you're telling them everything's gonna be okay but the patient stops you have options on a lot of good ones like yeah tell me more tell me more yeah guys there's certain things that are protected and there's certain not things that aren't that's all well let's talk about that because the you and I are old enough to remember general magic this vision was out there a long time ago with personal digital assistance and you would just talk to an agent it would go do this for you this feels like a step to that where it does all the work for you presents you the final moment and says approve so like a car you're a sure a butler yeah I think what you're describing is what we want but I think more specifically for today Keith and Travis totally nail it look I think building a browser is an absolutely stupid capital allocation decision just totally stupid and unjustifiable in 2025 specifically for perplexity.
翻译成中文：
所以，我有点觉得这中间有一个飞跃。就像，你告诉某个东西：“嘿，我想去纽约，可以帮我看看这个时间段有什么合适的选项吗？给我几个我可能会喜欢的选择。” 然后整个过程中你有一个界面。你知道，Perplex就是这样一个东西，你只是简单地展示给他们。那是界面，还是说我会有一个代理人来帮我做所有的事情？这是那个的开始吗？
我确实还没有花足够的时间去思考这一点，但我知道每个在应用商店里有应用的消费类软件CEO现在都在紧张，真的非常焦虑。我是指那些大公司和有实际产品的人。有时候，我就像是在给他们做心理辅导一样，对他们说：“一切都会好的。” 你实际上有东西，有价值的东西，他们不能用一个代理人来替代它。这就像临终关怀，你告诉他们一切都会好，但病人停下来了。你有很多好的选择，比如“告诉我更多，告诉我更多。” 伙计，有些东西是有保护的，而有些不是。
我们谈论这个时，要知道你我都已经足够年长至能记得General Magic。这个愿景很早之前就存在：有个人数字助理，你可以跟它说话，它会帮你去做事情。这感觉就像是朝那个方向迈出了一步，它为你做好所有的工作，然后在最后时刻呈现给你，问你是否批准。就像一个车管家或管家。
我认为你描述的是我们想要的，但对于今天来说，Keith 和 Travis 完全领会了我的意思。我觉得建一个浏览器是一个非常愚蠢的资本分配决策，尤其是在2025年，对于Perplexity来说，这完全是愚蠢且无法证明合理的。
I think their path to building a legacy business is to replace Bloomberg everything that they've done in financial information and financial data in going beyond the model is been excellent as somebody who's paid $25,000 to Bloomberg for many years the terminal is atrocious it's terrible it's not very good it's very limited and anybody that could build a better product would take over a $100 billion enterprise because I think it's there for the taking I wish that perplexity would double and triple down on that and so when you see this kind of let's do it let's do it you're off let's just go do it when you do the random sprawl I think it doesn't work I just want to say like a browser is like the dumbest thing to build in 2025 because in a world of agents what is a browser it's a glorified markup reader it's like handling HTML it's handling CSS and JavaScript it's doing some networking it's doing some securities it's doing some rendering but it's like this is all under the water type stuff I get it that we had to deal with all that nonsense in 1998 to try like us or google for the first time.
我认为，他们打造一家具有持久影响力的企业的途径就是取代彭博在金融信息和数据领域所做的一切。他们超越传统模式的努力非常出色。作为一个多年花费25,000美元订阅彭博终端的人，我觉得这个终端使用感很差，功能有限。如果有人能开发出一款更好的产品，就能接管一个千亿美元的市场，因为这个机会是存在的。我希望Perplexity能在这方面加倍努力。所以，当你看到这种"让我们去做吧"的决心时，我认为这才是正道。乱搞一通是行不通的。顺便说一下，在2025年去开发一个浏览器是最愚蠢的事情之一。因为在一个智能代理的时代，浏览器无非就是一个高级的标记语言阅读器，它处理HTML、CSS和JavaScript，进行一些网络、安保和渲染。但这些都是非常基础的东西。我理解在1998年时我们不得不处理这些琐事，为了能有像雅虎或谷歌这样的体验。
But in 2025 there's something that you just speak to and eventually there's probably something that's in your brain which you just think and it just does it you're thinking I need a flight to JFK or at the maximum today in a very elegant beautiful search bar you type in get me a flight and it already knows what to do key then some ways this is a step towards that ultimate vision so you'd think it's worth it to you know sort of forplexed to make this waypoint perhaps if you look at as a waypoint between the ultimate vision which is a command line an earpiece a honey get distribution Jason for the 19th web browser in 2025 well yeah that is a challenge and I think most people are speculating apple which has a lot of users might buy complexity or do a deal with perplexity and give them that distribution because of the justice department case against google so there's been a lot of speculation about that but Keith what do you think.
到2025年，会有一种技术，你只需要说出指令，甚至可能有设备直接在你脑中通过思考来实现，比如你想着需要一张飞往JFK的机票，或者在一个非常精美的搜索栏中输入“帮我订一张机票”，它就能知道该怎么做。在某种程度上，这是朝着最终愿景迈进的一步，所以你可能会认为值得为了实现这个中途目标而努力。如果把这看作是通往最终愿景的一个阶段性目标，最终愿景是通过耳机和命令行操作，如同为2025年的网页浏览器分配的Jason一样，这确实是一个挑战。我认为大多数人都在猜测苹果可能会购买Perplexity或者与其达成合作协议，利用其庞大的用户群进行推广，这是因为谷歌正面临司法部的诉讼。因此，这方面有很多讨论。Keith，你怎么看呢？
I don't think they'd buy anything worth it like what is apple again and we continue this failed strategy of apple right apple has missed every possible window on a uh and continues to miss it and it has cultural I think the CEO has challenges I think culturally they have challenges and they have infrastructure challenges so it's not easy fix but by complexity not going to help like to master edge he's actually pretty coherent one for complexity quad perplexity uh so I think that's not a vertical and only strategy not a bad idea um especially because you need unique data sources some of those data sources may or may not license their data to open AI so you can do some clever things there but um I don't think there's any residual value that apple would get out of perplexity except there's some product taste but what are you going to spend like a billion dollars for product taste.
我认为他们不会买任何有价值的东西，比如苹果又在干什么，我们继续采用这种失败的苹果策略，对吧？苹果错过了所有可能的时机，并继续错过。我认为这涉及到文化方面的问题，苹果的CEO面临挑战，我认为他们在文化和基础设施方面也有困难，所以这不是一个容易解决的问题。但是通过复杂性来解决问题并没有帮助。要掌握前沿技术，他在处理复杂性方面相当有见地。所以，我认为这不是一个垂直或唯一的策略，也不是个坏主意，尤其是因为你需要独特的数据来源。有些数据来源可能会或可能不会授权OpenAI使用，因此你可以在这方面采取一些巧妙的措施。但是，我不认为苹果会从复杂性中获得任何剩余价值，除了某种产品品味之外，但你会花十亿美元去购买产品品味吗？
I mean mark spending hundreds of millions of dollars or hundreds of billions of dollars or whatever he's spending these days and you know grog of anything. rock four shows that mark really it doesn't need to spend money to build a whole new team because everything they've done in AI is also missed the vote well I mean Keith the way you phrased it there almost makes it worth it for apple to throw a hell marry have a team with some taste because that's how they tend to do things is something that is elegant and why not just throw your search to it throw 10 billion at what's elegant it's a through be a bunch of what's elegant would be if there's a bunch of agents and just a chat box seeing a bunch of visual diarrhea is not elegant it's lazy right on our on our little blue bird clone I'll give you naming rights so you can call it like you like it poly hapatilla.
我意思是，马克花了数亿甚至数千亿美元，或者他现在不管花多少钱，明白吗？罗格四认为，马克确实不需要花钱去建立一个全新的团队，因为他们在人工智能方面所做的一切也都错失了良机。我是说，基斯，你这么说几乎让苹果值得冒险组建一个有品味的团队，因为他们通常以一种优雅的方式做事。为什么不投10亿美元到优雅的事物上呢？想想那些优雅的事物会是什么样的，而不仅是一些充满视觉杂乱的聊天框，因为这并不优雅，而是懒惰地仿制我们的"小蓝鸟"（社交平台）。我甚至可以给你命名权，你可以按你喜欢的方式命名，比如叫它"Poly Hapatilla"。
So hey can somebody can somebody bring up the poly hapatilla you know what's so funny it's just it's rules right off your tongue TK listen we were trying to do a screen of companies and it maxes out at five companies on a specific type of screen where like you're trying to compare stock price to EBIT and you're like okay I can only choose five I guess so which five should I choose what font was on right like two episodes ago he was like I can't pull this off it's limited to six companies dude you it's so what do people use blue and four five they use it for the messaging now like my team has traded huge position via text message on bloombrook so there is something very valuable there but the core usability and the core UI of that company has not evolved.
嘿，有人能提一下这件事吗？你知道，这件事有趣的地方在于，说起来特别顺口。TK，我们在试图筛选一些公司时，发现这类筛选功能最多只能选五家公司，比如说你想对比股票价格和EBIT，但最后只能选五家。我们就想，那到底该选哪五家呢？就像两集之前，他说他无法进行比较，因为限制在六家公司。是的，人们现在用这个的用途在于信息传递。比如我的团队曾通过Bloomberg的短信功能进行大量的交易，所以这其中有非常有价值的东西。但那家公司核心的可用性和用户界面却没有什么进步。
I have my contribution and complexity is very good at that by the way it they they do a very good job I got a new domain in Travis let's just one just sink in here this is my way to weasel my way into the deal begin that you know I do I'm just a little I snipe some good ones once from I got begin dot com and I got annotated dot com those are my two little little things you're like you're like one of these old people that show up at the oh my god show and then they all said he got a big show and you're like oh I have this thing that I bought 1845 guys Jason Jason is Jason is the daddy and go daddy okay daddy you're daddy.
我在这方面有我的贡献，我在处理复杂问题方面非常擅长，他们做得非常好。我在Travis上获得了一个新的域名，让我来沉淀一下。这是我巧妙地参与交易的一种方式。要知道，我曾经抓住过一些好的机会，比如我得到了begin.com和annotated.com。这是我两个小小的成就。你就像那些在“哦，我的天”节目中出现的老派人物，突然之间他们就有一场大秀，而你就想说，“哦，我有个1845年买的东西”。Jason，Jason就是老爹中的老爹，好吧，老爹。
Hey speaking of daddy let's go on oh is now the right time for their party you lot seems to think so last week he announced that axi would be creating a new political party I'll let you decide who daddy is in this one uh he said quote when it comes to bankrupting our country with waste and graph we live in a one-party system not a democracy he's not yet outlined a platform for the American party we talked about it here last week I listed four core values which seem to get a good reaction on x fiscal responsibility slash does sustainable energy and dominance in that manufacturing in the US which Elon has done a single handily here pronatalism which I think is a passion project for him and Shabbat you punched it out with the fifth technological excellence according to polymarket 55% chance that you on registers the American party by the end of the year.
嘿，谈到“老爹”，我们来继续聊聊哦，现在是他们举办派对的合适时机吗？你们好像上周觉得是合适的。他宣布将成立一个新的政党。谁是这个“老爹”，我就让你自己决定吧。他说：“当涉及到通过浪费和贪污让我们的国家破产时，我们生活在一个一党制而不是民主制度中。” 他还没有为这个“美国党”制定纲领。我们上周在这里讨论过，我列出了四个核心价值观，这似乎在X上引起了不错的反响：财政责任、可持续能源、美国制造业的主导地位（埃隆在这方面独当一面），以及我认为是他的一个热情项目——出生主义。还有你提到的第五个：技术卓越。据Polymarket预测，有55%的可能性到年底之前，“老爹”会注册“美国党”。
And you know one thing I was trying to figure out is just how unpopular all these candidates and these political parties this is a very interesting chart that I think we can have a great conversation around it turns out we used to love our presidents if you look here from Kennedy at 83% as high as approval rating his lowest was 56% that was his lowest approval rating so he operated in a very high band look at bush two during after 9-11 92% with his peak his lowest was 19 right with wartime president but then you get to trump one Biden and trump two historically low high approval their high water 49 for trump one 63 for Biden one of one and then 47 for trump two and there a lowest 29 31 40 so maybe it is time for a third party candidate let's discuss it boys I have no idea how to read this graph it's the worst unlike what is happening here this is the worst form at a chart this is a confusing chart but well the reason I'm putting it up is for debate so thank you for debating that it's trading great debating why did you put it up
你知道我一直在思考的一个问题是，这些候选人和政党到底有多不受欢迎。这是一个非常有趣的图表，我认为我们可以围绕这个展开讨论。事实证明，我们曾经非常喜爱我们的总统。比如，肯尼迪的最高支持率达到83%，即使最低也有56%，他一直维持在一个很高的范围内。再看看小布什，在9-11事件后，他的支持率高峰达到92%，但最低时仅为19%，不过那是在战时总统的背景下。但是到了特朗普和拜登，他们的最高支持率都远低于历史水平，特朗普的最高为49%，拜登为63%，而最近的特朗普任期则为47%。他们的最低支持率分别是29%、31%和40%。也许现在是第三党派候选人登场的时机了。我们来讨论一下吧，伙计们。我完全看不懂这个图表，这图表格式太糟糕了，而且令人困惑。不过，我展示这个图表是为了引发辩论，所以感谢你们的讨论。为什么我把它放出来呢？因为这是一个引人深思的辩论话题。
Here's another one Gallup pole Americans desire for a viable third party 63% in 2023 so it's it's bumping along in all time hi okay I'm really concentrating on this one okay anyway I'm gonna stop there what's the gray I'm gonna let you okay got it I got it different sense I got time period and how popular party let's stop here this is a good there's a good place to stop I just blew it yeah look a couple points yes the idea of Elon Musk's third party is for any other human being like absolutely absurd ridiculous Elon has obviously done incredible things so dismissing anything he's touching is a bad idea however I think the best metaphor I've seen is it's a little bit like Michael Jordan tried to play baseball okay he came a replacement level baseball player which actually really hard to do by the way Elon is probably a replacement level politician he's Michael Jordan for entrepreneurial stuff but the third party stuff is not going to work.
这是另一条来自盖洛普的调查——2023年，63%的美国人希望有一个可行的第三政党，这个数字一直处于历史高位。好的，我要专心于这个问题。不管怎样，我先停在这里。感觉有些混乱，我让你来想想，好，我知道了。我了解了时间段和政党的受欢迎程度。我们在这里先停一下，这是个不错的停顿点。是的，我搞砸了。来看几个要点吧。
关于Elon Musk的第三政党的想法，对于其他人来说简直荒谬可笑。Elon显然取得了惊人的成就，所以轻易否定他所涉足的领域是个不明智的选择。然而，我见过的最贴切的比喻是，这有点像迈克尔·乔丹尝试打棒球。他达到了替补球员的水平，这本身其实很难做到。Elon在创业方面是迈克尔·乔丹，但在搞第三政党这件事上恐怕行不通。
First of all their that chart is misleading it's a flaw of average it's badly designed and it's a flaw of average it's quite there's incredibly popular among Republicans he actually has the highest approval rate of any Republican ever measured in recorded history it's 95% Reagan was peaked out 93% it's just Democrats don't like them which is perfectly fine being polarizing is is an ingredient to being successful including with people on the show like the point of accomplishing things in the world is you don't really care what half the world thinks you need to make sure that there's a lot of people who like you and really approve and earth is the aspect about what you do and Trump is about as popular with his party as anybody's ever been ever pureed no exceptions.
首先，他们的那张图表具有误导性，是平均值的缺陷，设计得很差。实际上，在共和党中，他非常受欢迎，他的支持率是有记录以来任何共和党人中最高的，达到95%。雷根的支持率曾达到93%。只是民主党人不喜欢他，但这是完全正常的。引起争议是取得成功的一种成分，包括节目中的人也是如此。实现目标的关键在于不必在意世界上一半人的看法，你需要确保有很多人喜欢你并且真正认可你，而特朗普在他的政党中受欢迎程度几乎是史无前例的，没有例外。
Secondly there's maga has kind of already uh changed the Republican party Trump is sort of like a third party takeover of the Republican party and so it's kind of already happened and maybe you can do this every 20 years or 30 years I don't think you can have like this kind of transformation on one party within a two compressed period of time for a lot of reasons third is um the smart parties absorb the less than a political science on forced-sized study political science that wasted kind of my college years and instead of saying see us and you know maybe then I'd be coding stuff and doing physics like Travis but one thing I did learn is smart parties absorb the best ideas of third parties so the oxygen is usually not there because there's a Darwinistic evolution if you get traction on the idea it's really easy to conscript some of those ideas and take away the momentum no third party candidate that's a true like third party has won a senate seat since 1970 and that's actually Bill Buckley's brother and he had to name my name.
第二，MAGA运动实际上已经在某种程度上改变了共和党。特朗普就像是第三方接管了共和党，这种变化已经发生了。而且，这样的转变可能每隔20到30年才会发生一次；我认为在较短的时间内，一个政党无法经历如此剧烈的转型，原因有很多。第三，聪明的政党会吸收小党的优秀理念。我在大学时学习政治科学这一领域，但感觉有点浪费那些时光，本该去学习编程或物理。但我确实学到了一件事，那就是聪明的政党会吸收第三方的最佳理念。所以，通常情况下，小党缺乏生存空间，因为这像是达尔文式的进化：一旦某个理念获得关注，大党很容易将这些理念纳入自身，从而夺走小党发展的势头。自1970年以来，真正的第三方候选人没有赢得过参议员席位，即便是威廉·巴克利的兄弟也不例外，他在参选时不得不使用更加知名的名字。
The other thing Elon I think is missing and the proponents of what he's doing is people vote not just for ideas they vote for people it's a combination the product is what do you what do you believe and who are you and you can't divorce the two Trump is a person and that generates a lot of enthusiasm and it's one of the reasons why he has challenges in midterms because he's not on the ballot he's ideas maybe on the ballot but he is not specifically on the ballot so unless because Elon can't be the figurehead of the party he literally can't constitutionally there you need a face that's a person Obama a Clinton like there's reasons why people resonating with that out that personality specific ideas just are not going to galvanize the American people.
埃隆（Elon）和支持他的人可能忽略的一点是，人们不仅仅为理念投票，他们也为人投票。这是一种结合，产品是你相信什么以及你是谁，而这两者是不可分的。特朗普（Trump）是一个人，这就产生了很多热情，这也是为什么他在中期选举中面临挑战，因为他的名字不在选票上，虽然他的理念可能在选票上，但他本人不在。因此，由于埃隆不能成为党派的领袖，从宪法上来说，他实际上不具备这个资格，党派需要一个像奥巴马（Obama）或克林顿（Clinton）这样的人物作为代表。这就是为什么人们会被这些具有独特个性的人所吸引，仅仅靠具体的理念是无法激励美国人民的。
Okay so the counter to that and what people believe he's going to try to do is win a couple of seats in the house Travis when maybe one or two senate seats if you were to do that those things are pretty affordable to back a couple of million dollars for a house race senate maybe 25 million if Elon puts I don't know 250 million to work every two years which he may put 280 million to work on the last one he could kind of create the Joe Manchin moment and he could build a caucus a platform Grover Norquist kind of pledge along these lines so what do you think of that if he's not going to create a viable third party presidential candidate could he Travis pick off a couple of senate seats pick off a couple of congressional seats okay so first I have this axiom that I'm making up right now okay okay it's called you on is almost always right okay right and I was right about everything seriously let's just be real and like honestly the things he's upset about and that he's riled up about especially when you look at the deficit like man I am right on board that train part one part two we've never had somebody with this kind of capital that can be a quote unquote party boss outside of the system right and there's a lot of people that agree with the types of things he's saying and he knows how to draw you know he he on his own right kind of has a populist vibe like he does his thing and he's turned the x into what it is and he's he's a big part of x and so I think it's the I think it's great and honestly there's there's the moves you can make on senate and house and just having a few folks and then being you being levers then to get the things you want done.
好的，所以对此的一种回应以及人们相信他可能会尝试做的是在众议院赢得几个席位，Travis也许在参议院赢得一两个席位。如果他这样做，那些事情其实是可以负担得起的，在众议院竞选中投资几百万美元，而参议院可能需要2500万。如果Elon每两年投入2.5亿美元，在上一次他可能投入了2.8亿，他就可以创造“乔·曼钦时刻”，并建立一个核心小组，这就像Grover Norquist那样的承诺。你怎么看待这件事？如果他不打算创建一个有竞争力的第三党总统候选人，Travis是否可以在参议院和众议院上争取几个席位？
首先，我有一个我现在编出的定理，叫做“Elon几乎总是对的”。我确实觉得他对所有事情都是对的，特别是在关于赤字问题上，我完全赞同他的想法。
其次，以前从未有过这样拥有巨大资本的人能在系统外成为所谓的党派头目。很多人同意他所说的那些事情，他知道如何吸引人。他本身就有一种民粹主义的氛围，他把平台（X）发展成现在的样子，他在X中扮演了重要角色。所以，我认为这很棒。事实上，在参议院和众议院有一些可以开展动作的地方，只要有几个立法者，你就能成为在关键时刻的杠杆，推动实现自己想要的事情。
That's part one and then part two of that is the threat of that happening can make good things happen separately even if it doesn't go all the way I just love it. I'm on the train. I'm in love with this role for Elon more than picking a party because he's picking a very specific platform that I think resonates with folks which is just balanced the budget don't put us in so much debt and let's have some sustainable energy you know job done great job. Yeah the problem with that is like he's actually wrong about the reason why we have a deficit or a debt because not because we're undertaxed it's we're massively overspending if we just I think he believes we're overspending they should have been supporting the last you know beautiful bill because if you just held federal spending to 2019 levels to 2019's not like you know decades ago literally with our current tax revenues we would be in a surplus 500 billion yeah so they're all we need to do is cut spending now I admit that that happened with the big beautiful bill so this is where details do matter I think there is a willingness in a you know discipline problem on both parties and I think maybe he can help fix that.
这是第一部分，然后第二部分是，即使事情没有完全实现，仅仅是可能发生这种情况的威胁也能带来好的结果，我对此非常喜欢。我支持这个过程，我更爱马斯克在这个方面的角色，而不是选择一个政党，因为他选择了一个我认为能引起共鸣的平台，那就是平衡预算，不要让我们背负太多债务，并推动可持续能源的发展，这都是一些非常好的目标。问题在于，他对我们为什么会有赤字或债务的原因理解错了。问题不在于税收不足，而是我们支出过多。我认为他相信确实是支出过多。如果我们能够将联邦支出维持在2019年的水平，凭借我们现有的税收收入，我们将能有5000亿美元的盈余。所以我们需要做的就是削减支出。我承认这种情况在之前的重大法案中已经发生了，所以细节确实很重要。我认为在两党之间都存在纪律问题，也许他可以帮助解决这个问题。
The second thing is that we have these arcane rules particularly in a senate that you need 60 votes in many ways to cut things except through very hockey methods and that's a reality so the best thing truthfully it can do is help get a Republican party to 60 votes and then in then in theory he could be absolutely furious if you didn't cut back to 2019 levels but it's very tricky or you can just overrule like this the filibuster is an artifact of history and at some point some majority leaders just going to say we're done with the filibuster and just steamroll through all the cuts at 50 or 51 votes which you can do there's no constitutional right to a filibuster it is an artifact of centuries of American history and at some point it's going to go away so maybe the time is now maybe we should just fix everything now I think you're exactly right I think that the filibuster it's just a matter of time I think it's on borrow time and I think in a world where it is on borrow time Jason I think your path is probably the one that gives the American party if it does come into existence the most leverage which is if you control three to five independent candidates you gain substantial leverage.
第二件事是，我们在参议院有一些复杂的规则，根据这些规则，通常需要60票才能削减开支，除非通过非常特殊的方法。这是一个现实。所以，实际上最好的办法是帮助共和党取得60票，然后理论上，他可以在未能将开支降至2019年水平时大发雷霆，但这非常棘手。或者你可以像这样推翻：冗长辩论权是历史的遗留物，总有一天某个多数党领袖会说我们不再用冗长辩论权，然后以50或51票强行通过所有削减。冗长辩论权不是宪法赋予的权利，它是美国几个世纪历史的遗留物，总有一天会消失，所以也许现在就是时候，也许我们应该现在就解决所有问题。我认为你完全正确，我认为冗长辩论权只是时间问题，我觉得它的时间不多了。如果它行将消亡，Jason，我认为你的路径可能会为美国党带来最大的影响力，也就是说，如果你控制了三到五名独立候选人，你就会获得实质性的影响力。
I just want to take a step back and just note something I don't know if you guys know this but the only reason we're even having this conversation or this is even possible is because in 2023 the FEC federal elections commission they actually released guidance and they changed a bunch of rules and the big change that they made then was it allowed super PACs to do a lot more than just run ads up until that point all you could do if you were a super PAC is just basically run advertising television and radio I guess online as well but what they were allowed to do starting in 23 was they were allowed to fund ground operations they were allowed to do things like door knocking phone banking you know get out the vote so in other words what happened was a super PAC became more like a full campaign and Trump showed the blueprint of using a super PAC specifically his to win the presidential election.
我想退一步，指出一件事情。我不知道你们是否知道，我们现在之所以能进行这次讨论，或者说这件事之所以成为可能，是因为美国联邦选举委员会（FEC）在2023年发布了新指导，并修改了许多规则。他们做出的一个重大改变就是允许超级政治行动委员会（Super PAC）进行更多活动。在此之前，超级PAC基本上只能投放广告，包括电视、广播和网络广告。但从2023年开始，他们被允许资助地面活动，可以进行敲门拜访、电话拉票和动员选民投票。换句话说，超级PAC变得更像一个完整的竞选团队。而特朗普则通过利用他的超级PAC赢得总统大选，展示了这一策略的成功范例。
So he was able to fund this massive ground game he built infrastructure across the swing states he was obviously incredibly effective and now that playbook can actually be used by other folks and so to the extent that Elon decides to use those changed FEC rules Jason I think what you said is the only path but I just I thought I just wanted to double click on Keith's point because it's so important. I do think the filibuster is going to go away and it is because the arcaneness of these rules having to do a reconciliation bill then you know needing a super majority veto proof super majority and at the other case it just means that nothing gets done and I think somebody will eventually get impatient and just steamroll this thing.
所以，他能够为这个庞大的地面游戏提供资金，并在摇摆州建立了基础设施，他显然非常有效率。现在，这套策略可以被其他人使用。因此，如果埃隆决定利用这些改变后的FEC规则，杰森，我认为你所说的是唯一的途径。但我只是想强调一下基思的观点，因为这非常重要。我确实认为阻挠议事的策略将会消失，因为这些涉及合并法案的规则太复杂了，然后又需要一个能够避免否决的超级多数票，这意味着什么事情都做不成。我认为最终会有人不耐烦并强行推动事情进展。
We've never had so many people say they feel politically homeless as we did the last two cycles and that includes many people on this podcast people in our friend circle and I think just the idea that Elon could create a platform that people could opt into and support just the existence of that would make the other two parties get their act together by the way the other thing they need is a little bit of a stick there and a carrot yeah hey if you don't control spending there's this third option and if Travis and I are in it and Keith I know you'll never leave the Republican Party but you're you know you're probably set where you're where you want to be right now but I can tell you Jason our top 10 20 friend list out of those 50% will join you once party.
我们从来没有像过去这两次选举周期那样，听到这么多人说他们在政治上感到无所适从。这其中包括我们播客上的许多人，以及我们的朋友圈。我觉得，仅仅是埃隆（Elon Musk）能创建一个人们可以选择加入和支持的平台这个想法，就能促使其他两个传统政党改进。此外，他们还需要一点激励和警示。例如，"嘿，如果你们不控制开支，就有第三种选择"。即使Travis和我加入其中，而Keith，我知道你永远不会离开共和党，但你知道你现在大概就想待在你所在的位置。不过，我可以告诉你，在我们的前10至20位好友名单中，有50%的人会加入你的新政党。
Well the other the other thing Jason that the Keith said which I think is is really important is if he were to run people I think they have to transcend politics and policy and I think they need to be straight up bosses people that have enormous name recognition so that effectively what you're voting is a name and not an agenda equivalent to I think what happened to Schwarzenegger when he ran he ran on an enormous amount of name recognition in the great Davis recall he didn't run on the platform which is JD Vans JD Vans had this great book capture people's imagination he's an incredible speaker he pisses off a third or two there to the country depending on where you are in the country but you can't ignore him.
好的，杰森，基思提到的另一件我认为非常重要的事情是，如果他们要竞选，人们需要超越政治和政策。他们应该是那些拥有巨大知名度的领袖人物，这样你投票时实际上是投给了一个名字，而不是一个具体的议程。这有点类似于施瓦辛格参加加州州长罢免选举时的情况，他依靠巨大的知名度参选，而不是某个具体的政纲。同样，JD·万斯写了一本引人注目的书，是个极具影响力的演讲者，他的言论可能激怒了三分之一或三分之二的国家，取决于你在国家的哪个地方，但他是无法被忽视的。
I think Elon can find 10 JD Vans type characters and back them fairly easily he is a magnet for talent people will line up I have been contacted by high profile people I was actually thinking of running can you put me in touch with Elon I was thinking more like actors and sports stars meaning where they just come with their own inbuilt distribute like I think you almost have to to rank x followers and Instagram followers and do a join and say okay these it do you know what I mean like I think it's like totally different access it's painful like let's not get more celebrities as politicians like let's get like people who've led large large efforts large initiatives complex things you know ideally but they still have to communicate.
我认为埃隆·马斯克能够相当轻松地找到10个类似于JD Vans这样的人物，并给予他们支持。他是吸引人才的磁铁，人们会争相前来。我实际上已经收到一些知名人士的联系，他们希望参与其中，并且问我能否帮助他们与埃隆建立联系。我想到的是类似演员和体育明星这种有自己粉丝基础的人，他们自带传播影响力。我认为应该通过比较社交媒体上的粉丝数量来选择合适的人选。我的意思是，不要让更多的名人成为政治人物，我们需要那些曾经领导过大型项目、负责过复杂事务的人参与，但他们仍然需要具备良好的沟通能力。
Right he if they have to be able to get on a podcast that's the new platform if they can't spend two hours three hours chopping it up on a podcast of course we're sure we're you know that's Kamala's the reason she couldn't even contend was because she couldn't hang for two hours in an intellectual discussion you can hang you're out yeah it's pretty cool arena that's interesting to see if he can tune his algorithm for talent which is epic to tune for politics because it's a slightly different audience but if you can tune the algorithm and quality that might work I think you can win a few house races I think that's doable I don't think you can win a center race.
如果一个人想要在新的平台上崭露头角，那么能够参加播客节目就非常重要。如果他们不能在播客上持续进行两三个小时的讨论，那么很可能就会掉队。例如，卡玛拉（Kamala）无法参与竞争的原因之一就是她无法在长时间的智力讨论中保持出色的表现。在这种情况下，想要有所成就就很难。这个领域真的很有趣，如果他能够调整他的算法来识别人才，这将是一件了不起的事情。虽然政治领域的听众有所不同，但如果他能够调节算法并保证质量，这或许会奏效。我认为他有可能在几场众议院选举中获胜，但在参议院选举中赢得胜利就不太可能了。
Well there it is Elon Keith doesn't think you can win a center race but he thinks you win a couple congressional ones thanks for giving him the motivation Keith I appreciate I'm sure he's gonna win too people on the Republican party right now are going oh no don't poke the tiger listen that's so Trump got into politics so I don't want to be Obama here just Obama Elon right yeah congratulations all right listen SCOTUS made a big decision here this is a really important decision they've sided with Trump for plans for federal workforce rifts reductions in workforce for those of you don't know as you know Elon Trump they wanted to we are downsized the three million people who are federal employees this is just federal employees we're talking about we're not talking about military and we're not talking about state and city that's tens of millions of additional people.
好的，这就是情况所在。埃隆，基思觉得你无法赢得中央竞选，但他认为你能赢得几个国会席位。感谢基思给了他动力。我也很欣赏这一点，我相信他也会赢。现在共和党的人在想，“哦不，别惹老虎。” 听着，特朗普就是这样进入政坛的，所以我不想扮演奥巴马的角色，只要像奥巴马一样对待埃隆，对吧？恭喜你。好，听我说，最高法院做出了一个重要决定，这对我们来说非常重要。他们支持了特朗普关于联邦劳动力裁员计划的意见。对于那些不知道的人来说，埃隆和特朗普想要将联邦雇员人数缩减至三百万。我们讨论的仅仅是联邦雇员，并不包括军人、州和城市的雇员，那可是数千万的额外人员。
If you remember Trump issued this executive order back in February we got in office implementing the president's doge work for us optimization initiative and he asked all the federal agencies hey just prepare a riff for their departments consistent with applicable laws was part of this eo okay in April the American Federation of Government Employees a f g e sued the Trump administration saying the president must consult congress on launch gale workforce changes this is a key debate because the congress as you know has power of the purse they set up the money but the president the executive branch they have to execute on that and that's what the key is here.
如果你还记得，特朗普在他上任后的今年二月发布了一项行政命令，推行总统的政府效能优化计划。他要求所有联邦机构准备对其部门进行重组调整，以符合适用法律的规定。这是这项行政命令的一部分。到了四月，美国政府雇员联合会（AFGE）起诉特朗普政府，称总统在进行大规模人事变动之前必须与国会进行协商。这是一个关键的辩论点，因为正如你所知，国会掌握财政权，他们负责制定预算，而总统和行政部门则负责执行预算。这正是问题的关键所在。
So the accused Trump of violating the separation of powers under the constitution act a f g e has 820,000 members in may a San Francisco-based federal judge sided with the unions blocking the executive order the judge who was appointed by Clinton set any reduction in the federal workforce must be authorized by congress this is a key issue and the white house submitted an emergency appeal yada yada eight of nine supreme court justices sided with the white house in overturning this block and so the reasoning it's very likely the white house will win the argument of the executive order they have the right to prepare a riff the question is can they actually execute on that riff and who has that power chama does the power reside with the president to make large gale or you know riffs or do they have to consult congress first your thoughts on this issue.
被指控的特朗普违反了宪法中的权力分立原则。美国联邦政府雇员工会（AFGE）在5月份时有82万名会员，一名驻旧金山的联邦法官支持工会，阻止了这一行政命令。该法官由克林顿总统任命，他表示，任何联邦员工的减少都必须得到国会的授权。这是一个关键问题，白宫提交了紧急上诉。大法官中有八位支持白宫，推翻了这一禁令。因此，白宫很可能会赢得关于总统行政命令的论战，他们有权准备大规模减员行动。问题在于他们是否能够真正执行这一计划，以及究竟谁拥有这种权力。是总统有权进行大规模的减员行动，还是必须先咨询国会？请分享您对这一问题的看法。
It's an incredibly important ruling incredibly right I think president trump should have absolute leeway to decide how the people that report to him act and do their job if you take us that back Jason there are more than two thousand federal agencies employees plus contractors I think number almost three million people if you put three million people into two thousand agencies and then you give them very poor and outdated technology which unfortunately most of the government operates on what are you going to get you're going to get incredibly slow processes you're going to get a lot of checking and double checking and you're going to ultimately just get a lot of regulations because they're trying to do what they think is the right job.
这是一项极其重要且正确的裁决。我认为特朗普总统应该有绝对的自由来决定他下属如何行动和完成工作。如果你回头看看，联邦机构的员工和承包商加起来有两千多个，估计将近三百万人。如果将三百万人安排进两千个机构，再提供给他们非常落后且陈旧的技术（不幸的是，大多数政府部门就是在这样的条件下运作），你会得到什么呢？最终，你会得到极其缓慢的工作流程、大量的检查又重复检查，以及大量的法规，因为他们只是在努力做他们认为正确的事情。
So since 1993 what have we seen regulations have gotten out of control it's like a hundred thousand new rules per some number of months like it's just crazy so eventually we all succumb to an infinite number of rules that we all end up violating and not even know it so if the CEO of the united states president trump isn't allowed to fire people then all of that stuff just compounds so I think that this is a really important thing that just happened it allows us to now level set how big should the government be but more importantly the number of people in the government are also the ones that then direct downstream spend that make net new rules and if you can slow the growth of that down you're actually doing a lot in many ways.
自1993年以来，我们看到法规发展得失控了。每隔几个月就会新增成千上万条规定，真是让人觉得不可思议。最终，我们都会在无意中违反无数规则。如果美国总统特朗普这样的高管都不能解雇人，那么所有这些问题只会更复杂。因此，我认为最近发生的这件事非常重要，它让我们重新审视政府的规模应该有多大。但更重要的是，政府的人员数量也影响着下游的支出和新规则的制定。如果能够减缓这种扩张，实际上你已经在许多方面做出了很大的贡献。
I wish you on had come in and created doge now like could you imagine if doge was created the day after this supreme court ruling it would have been a totally different outcome I think because with that supreme court ruling in hand these guys probably would have been like a hot knife through butter Travis so I think it's a big deal except that ruling doesn't happen without doge that doge caused that ruling to occur true well the eo did you could have passed right but you're not always all doge style though you know what I'm saying if there was in firing people yeah they probably wouldn't felt the need to your point Travis to actually file this but Travis if you were living in the age of a i efficiency right now operations of companies is changing dramatically can you imagine telling somebody you you can be CEO but you can't change personnel that's the job you get to be CEO but you just can't change the players on the team.
我希望你之前能进来创建狗币。试想一下，如果狗币是在最高法院裁决后一天创建的，结果可能会完全不同。因为有了这项最高法院的裁决，这些人可能会有如热刀切黄油般顺利。尽管这项裁决的确很重要，但它的出现却是因为狗币，狗币促成了这项裁决。确实，执行命令可能会被通过，但你知道，有时候事情并不是一味地按狗币的风格来的。我是说，如果不是因为裁员，他们可能不会觉得有必要去提交申请。但是，特拉维斯，如果你现在正在生活在一个由人工智能提升效率的时代，公司的运营正发生巨大变化。你能想象对某人说你可以成为CEO，但你不能更换人员吗？你可以当CEO，但就是不能更换团队成员。
You can buy the nicks but you can't change the coach you know you can grow a player you just can shrink it yeah it's like running a unionized company which actually does exist our large. nice companies where you can't do any of these things right do they still exist or they all gone I think they go quickly yeah probably i think this just gets back to what what is actually congress authorizing when a bill occurs and there's certain things that are specific and certain things that aren't and i don't i'm not sure that in a lot of these bills it's not very specific about exactly how many people must be hired and so if it's i'm just doing the common man sort of approach to this which is like if if the law says you have to hire x number of people then that is what it is if the law says you he hears some money to spend here the ways in which to spend it but it's not specific about how many people you hire then that's different yeah it should be outcome base hey here's the goal here's the the key objectives right for the province it travels is totally right they are there's a variety of different laws some with the incredible specificities some with very broad managed the constitution clearly says that all executive power resides in the president of the United States period there's no exceptions there however congress does appropriate money and post watergate many people think congress has the power to force the president to spend the money and you can debate that you can debate it on a per statute basis and that will be more nuanced and that's going to get litigated whether the president can refuse to spend money that congress explicitly instructed him to spend sometimes called empowerment that's a very interesting intellectual debate.
你可以购买球队的球员，但你无法更换教练。你知道你可以培养一个球员，但你无法随意压缩它。这就像经营一家工会化的大公司一样，你不能随心所欲。那么这样的公司是否还存在，还是都消失了？我觉得它们消失得很快。实际上，这涉及到国会在通过法案时授予了什么权限。有些事情是具体的，有些则不是。我不太确定很多法案中是否明确规定了到底要雇佣多少人。如果法律规定你必须雇用一定数量的人，那就是硬性规定。如果法律只是给你一些资金支配建议而没有具体说明需要雇佣多少人，那就是另一回事了。
法律应该以结果为导向。我们需要知道目标和核心任务是什么。有各种各样的法律，有些非常具体，有些则比较宽泛。宪法明确规定所有的行政权力都属于美国总统，这没有例外。然而，尽管国会可以拨款，很多人认为在水门事件后国会有权强制总统使用这些资金。这是一个颇具争议的问题，可以根据具体法律来展开更细致的讨论，这也可能会导致总统是否可以拒绝花费国会明确指令他花费的资金的争议问题，这有时被称为“收回权”。这是一个非常有趣的智力辩论。
This one's a little bit easier it'll get more complicated again like this eo is only approved to allow for the planning i think the vote might be closer i think there's still a majority on the screen court for the actual implementation but it may not be eight one when there's a specific plan that constantly navigates way through the courts again yeah it's super fascinating yeah i wonder if they're going to get to the point where they're going to say in every bill you need to hire this number of people to hit this goal i don't know if they can like that's where it gets borderline unconstitutional like where you actually prescribe the president in the exercise if his constitutional duties has to hire certain number of people that feels pretty precarious well i i i'm not sure keep that's just like they prescribe a whole bunch of other things right no but you must you must appropriate money for to this specific institution to do the specific work i mean it's not an executive function like if you said like the secretary of state has to have that's number of employees doing something the secretary of state is your personal representative to conduct foreign affairs on behalf of the president in the United States it gets a little bit more messy as you translate it to people um that the president should i mean yes congress does set you know which people are subject to consent a confirmation what their salaries and compensation bans are so it's it's never going to be fully binary where the president can do whatever he wants and it's never going to i don't think it'll be constitutional congress commandeate and put all kinds of handcuffs on the president.
这部分内容稍微简单一点，但后面又会变得复杂。这个行政命令目前只被批准用于规划阶段，我认为投票结果可能会非常接近。我觉得在最高法院中，仍然多数人支持实际实施它，但当有具体计划在法院系统中推进时，这可能不会再是压倒性的八比一票数。这真的非常有趣。我在想他们是否会在每个法案中规定雇佣一定数量的人来实现特定目标。我不确定他们能不能这么做，因为这有点接近于违宪，像是要求总统在履行宪法职责时必须雇佣一定数量的人，这种情况看起来相当不稳定。我不太确定，因为他们已经规定了很多其他的东西。不是这样的，你必须拨款给特定机构去做特定的工作，这不是行政职能。如果你说国务卿必须有一定数量的员工做某事，国务卿是总统在处理美国对外事务时的私人代表。当这个问题涉及到人员安排时就变得更加复杂。是的，国会确实制定了需要获得确认的职位、他们的薪水和补偿范围，因此不会完全是二元对立，总统可以随心所欲行动的情况。我认为也不会出现国会全面控制并对总统施加限制的违宪情况。
Well then you you also have performance that comes in here what if you look at the department of education say squarice have gone down we've spent this money we're not getting the result therefore these people are incompetent therefore i'm firing them for cause and i'm going to hire new people how are you going to stop the executive from doing that there's been a bunch of litigation you know in parallel to this litigation about the president's ability to fire people and for the most part the Supreme Court's basically with maybe the exception of the federal reserve chair said that the president can fire pretty much anybody who wants i mean that's the way to go is like i mean i hate to be cut out about the financial if the results aren't there i think if they're presidential yeah if they're a presidential appointee the president should be able to fire you out will just like if you were a VP at one of our companies the CEO should be able to fire you at will but what about key that's the whole department sucks hey you guys were responsible for early education you had to put together a plan the plan failed everybody's fired we're starting over like you should be allowed to do that.
好的，那么这里也涉及到表现的问题。如果你看看教育部门，可能成绩有所下降，我们花了这笔钱却没得到应有的结果。因此，有人可能认为这些人不称职，所以要以此为由解雇他们并雇佣新人。你要怎么阻止高管这么做呢？其实，关于总统解雇人的权力也有很多诉讼，大多数情况下，最高法院基本上同意总统可以几乎解雇任何他想要解雇的人，也许除了联邦储备主席是个例外。我的意思是，尽管这样说可能有些直接，但如果结果不尽如人意，我认为总统应该能够解雇总统任命的人员。就像在我们的公司里，如果你是副总裁，首席执行官应该可以随时解雇你。但如果整个部门表现糟糕，该怎么办呢？比如说你们负责早期教育，得制定一个计划，但计划失败了，那么所有人都被解雇，重新开始。这样做应该是被允许的。
How many have the fishing government some of these departments were created by congressional statute like the department of education in 1979 and you're right every single educational stat has got worse in the United States since the department was created but there is a law on the books that says there shall be a department of education so you may have to repeal that all right.
有多少部门是由政府通过法律设立的，比如1979年国会立法创建的教育部。你说得对，自从教育部成立以来，美国的各项教育统计指标都变得更糟。但是法律上规定必须要有一个教育部，所以你可能需要废除这个法律才行。
listen rat an hour and a half gentlemen do you want to do the phyco story or should we just wrap chima and we got plenty of show here it's a great episode anything i sure we have much to say on the phyco story i thought these other topics were really good though we did great today this is a great panel i'm so excited you guys are here let me just ask you guys any off duty stuff that you can share with us with the audience any recommendations restaurants hotels trips movies you watch book charade.
听着，先生们，一个半小时了。你们想谈谈那个精神病患者的故事，还是我们就此结束节目？我们今天的内容已经很丰富了，这集真的很精彩。我相信对于精神病患者的故事我们还有很多可说的，不过我觉得其他的话题也很不错。我们今天表现得很棒，这是一个很棒的小组讨论。我非常高兴你们能在这里。
让我问问你们，有什么可以跟观众分享的业余活动吗？有没有推荐的餐馆、酒店、旅行地、电影或者你们最近读的书？
Keith i know that you are an active guy what what's on your agenda this summer anything interesting you can share with the audience that you're consuming conspicuous or otherwise well i don't want to share any good restaurants or hotels because you're keeping you're keeping come on man give us your favorite baby is that you got a babysitter yes can i get your nanny's now.
Keith，我知道你是一个喜欢活动的人。你这个夏天有什么计划吗？有什么有趣的事情可以和我们分享一下吗？无论是显眼的还是其他的。嗯，我不太想分享好餐厅或酒店的信息，因为你总是保密。拜托，兄弟，告诉我们你最喜欢的东西。你是不是找了个保姆？可以告诉我你保姆的联系方式吗？
there are there are things that are what do you call no marginal cost consumption like Netflix so for example um you know this documentary on some of it lot and it's phenomenal like i don't know if you've seen it i have to and you know i'm a student of this stuff and i thought you know i knew the whole story and etc watch episode one just started episode one and it just blew me away with new information new footage just absolutely incredible stuff so highly highly recommend it.
有一些东西，比如Netflix这样的，没有边际成本消费。举个例子，有一个关于本·拉登的纪录片，非常精彩。我不知道你有没有看过，我自己是这方面的学生，我以为我已经知道整个故事了。我看了第一集，然后开始第二集，新信息和新画面让我大吃一惊，真是超乎想象的精彩。所以我非常非常推荐这个纪录片。
what uh what was the big takeaway for you so far i don't know there's any like specific takeaway but just like so many parts of the story are misunderstood and not really understood and how various conflances of somewhat random things lead to a very catastrophic result but it's it's as dramatic as the best movie but it's a full documentary and you will learn things and absorb things i just i've had friends while i've been recommending it to friends and for a story you think you know it's incredible incredibly revealing.
到目前为止，你有什么主要收获吗？我觉得没有特别明确的收获，只是这个故事的很多部分被误解了，实际上没有被真正理解。很多看似随机的事情汇集在一起，导致了非常灾难性的结果。它就像最精彩的电影一样戏剧化，但这却是一部完整的纪录片，你会从中学到东西，吸收很多信息。我已经向朋友推荐它了，对于一个你以为了解的故事，它揭示了令人难以置信的细节。
okay Travis anything you got on your plate there that you're enjoying the restaurant a dish i mean look you know i mean jacene i go to austin a lot yes like basically from march till october i do about 15 weekends in austin i have a late house jacens hung out a couple times so i i love water skiing that's my whole thing that's my life that's just i just love it it's just my thing since i've been very sad very sad it's lake it's i called lake life so that's a thing.
好的，Travis，有没有什么你在餐厅里很喜欢的菜？我去奥斯汀很多，从三月到十月之间，我大概有15个周末都在奥斯汀。我在湖边有个房子，Jacene来过几次。我非常喜欢滑水，这就是我的全部，我的生活，我就是热爱它。自从我非常伤心以来，这就是所谓的“湖泊生活”。
and then i recently this little bit of like a side quest i recently purchased the preeminent backammon engine xg xg that's right which uh acronym is it's extreme gammon and so the preeminent engine so all the pros rate themselves based on this it was done it was built by this amazing entrepreneur this guy's avia who is just a full on sort of ultra ultra let me just what's the word i'm looking for it's not a savant like a savant essentially but hasn't worked on it for many years so i'm getting back into it and i love it.
最近我有点像在进行一个小支线任务，我购买了一个顶尖的西洋双陆棋引擎，叫做XG。没错，这个XG是“极限双陆棋”的缩写。这个引擎被认为是最优秀的，所有专业玩家都根据它来评价自己的水平。它是由一个非常出色的企业家打造的，这个人名叫Avia，他堪称是个天才。虽然他已经多年没有在这个项目上工作了，但我最近重新开始投入其中，并且非常喜欢这个过程。
and making it like taking modern machine learning sort of deep learning techniques and like big compute and saying can we push the game of backammon forward so super exciting and ultra training apps to get people up to speed quickly i played in my first backam internment and cached so that was pretty cool no wait yeah okay yeah all the respect you know thunder uber your high profile you go to this backam it's just like held at the motel eight it's amazing.
将这段话翻译成中文并尽量易读：
"结合现代机器学习，特别是深度学习技术和大规模计算，试图推动西洋双陆棋的发展，这真是令人兴奋。而且有一些非常棒的训练应用程序，可以让人们快速上手。我参加了我的第一次西洋双陆棋锦标赛，还拿到了名次，那真的很酷。哦对，好吧，对，这真是佩服。想想看，你是个大人物，然后去参加这个在汽车旅馆8号举办的西洋双陆棋比赛，简直太不可思议了。"
it's amazing it's amazing it's amazing it's that they was at that is like a month ago or so there's like a big tournament and it was uh so that the united states back in the federation had this big tournament i guess it was uh at the los angeles lax at the lax hilton and it was in yes it was in the basement of the hilton great and it was like next to the dungeons and dragons convention it it had those kinds of legit vibes.
太神奇了，太神奇了，太神奇了！大约一个月前左右，有一个非常大型的比赛。美国在联邦时期组织了这个大型比赛，我猜是在洛杉矶的LAX希尔顿酒店举办的。是的，比赛就在希尔顿的地下室，就在龙与地下城（Dungeons and Dragons）大会旁边，整个氛围相当酷炫。
i love it and like people so so i went in super low pro just did my thing but eventually was recognized but i was not recognized as the founder of uber i was recognized as the boner of xg ooh the owner of xg and then there was like a full on melee that basically occurred the like all the owner xg travis is here trimoth i feel like we've got a window here to do the all-in backam in high-end tournament we got to lock this down now we got to lock down the all-in backam and set i get the co-branding rights on this.
我喜欢它，也喜欢人与人之间的互动，所以我以非常低调的方式参与其中，只是做我自己的事情，但最终还是被认出来了。不过，我不是被认出来是Uber的创始人，而是被认作XG的拥有者。然后出现了一片混乱，人们都说“XG的老板Travis在这里”。我觉得我们现在有机会搞一个高端的“全押”反击锦标赛。我们必须立即把这件事敲定，并确定合作品牌的权利。
okay absolutely xg well no the all-in xg you know like i said love a great backam instead if we could make like a ten thousand dollar one trimoth we could kill turtles or white rhinos all the animals that you know um and freeberg's trying to protect we could murder them and then make that would be so great yes like maybe the white. could be you know rhinos and then they could take something else elephant skin something you know just really tragic and then eat the meat and make the the backam set for you i love backam and i'm honestly like if i wasn't attempting to be like expert poker player that is the game i mean if you're talking about a pendora's box where once you open it oh my god you can go to the rabbit shemao let's go let's do that again and let's back in as a beautiful beautiful beautiful beautiful game i love the vibes of sitting with travis and i sat i got some cigars out you know we pour a little of the all-in tequila tequila dot owing dot com uh we get that going a couple of uh the all-in cigars and then we have the all-in it's a wonderful hang.
好的，绝对的，像我之前说的，我很喜欢一个非常棒的“backam”（中文没有直接对应的词，可以认为是某种产品或活动）。如果我们能每三个月赚一万美元，我们就可以捕杀海龟或白犀牛，所有这些动物，你知道，自由伯格正试图保护的那些。我们可以杀掉它们，然后制作，那绝对会很棒。是的，比如说，我们可以用白犀牛的皮，或者象皮什么的，就真的很悲剧，然后吃掉肉，做成“backam”套装。我爱“backam”，而且说实话，如果我不是试图成为一个扑克高手，那就是我的游戏，就像潘多拉魔盒，一旦开启，天哪，你就能进入探索。让我们再来一次，让我们沉醉于这美妙的游戏。我喜欢和特拉维斯坐在一起的氛围，我拿出一些雪茄，我们倒一点“all-in”龙舌兰酒，准备好一些“all-in”雪茄，然后我们拥有这美好的一切，真是个美妙的聚会。
yeah Keith would you consider giving us some of your money playing back again we got it we got to get some of that i think much more money on the table because you don't play poker with us i don't like poker but backam and yeah that sounds great you know i'll bring better tequila i'll bring better tequila well like we're gonna offer you do a little taste off yeah you show you've insulted now you on with the senate seats in fact with his uh might to get as much better trust me.
好的，Keith，你能不能考虑再和我们一起玩一局，用你的一些钱来玩？我们需要多加点筹码，因为你不和我们玩扑克。我不喜欢扑克，但是更喜欢玩西洋双陆棋，那听起来不错。你知道吗，我会带更好的龙舌兰酒来，我们可以来个小小的品鉴会。是的，你得到了某些特权，现在你在参议院的位置上，信不信由你。
okay who is left in the paypal mafia you'd like to insult before the episode is over for Peter how do you think about Peter? reek of jarn elons party he's collecting a bunch of misfits so he might as well take re too all right listen listen to been another amazing episode of the number one podcast in the world the all-in podcast for your sultan of science who couldn't make it today he's at the big conference so we don't mention and uh david sacks who is out uh making america safe in a i and crypto jimuth pao hapatia world's great to be a moderator pravis Keith thank you for coming thanks for appreciating you guys were great today what a see y'all next time bye bye.
好的，在这集节目结束前，你还有想要侮辱的 "PayPal 黑帮" 成员吗？你对彼得怎么看？他的派对有点奇怪，他收集了一堆不合群的人，所以他可能也顺便把稀奇古怪的人都带上。好了好了，感谢大家收听全球第一播客节目——"ALL-IN 播客" 的又一精彩集。本集我们的科学专家因参加一个重要会议未能到场，我们不会提及，还有大卫·萨克斯，他在努力让美国在人工智能和加密领域保持安全；以及全球最出色的主持人查马斯·帕利哈皮蒂亚。普拉维斯·基思，谢谢你的到来。感谢你们的支持，今天大家表现得很棒。下次再见，拜拜！
oh we should all just get a room and just have one big hug your cheek is there all this it's like this like sexual tension that we just need to release that out what your bb what your bb your bb what we need to get merges aren't that you i'm doing.
哦，我们都应该找个地方，然后来个大大的拥抱。我们之间有种说不清的紧张感，需要释放出来。你的bb是啥？你的bb是什么？我们需要融合在一起，不是吗？