OpenAI vs Anthropic vs Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning
The world going forward there is going to be nothing that no one can build.
未来这个世界，没有什么是造不出来的。
Everyone is trying to commoditize the other.
每个人都在试图将对方商品化。
Value acrruel is a time dependent phenomenon.
价值积累是一个时间依赖现象。
Meet Matan Grinberg, CEO and co-founder of Factory.
大家来认识一下 Matan Grinberg，Factory 联合创始人兼 CEO。
Before factory, he was a physicist.
加入 Factory 前，他是物理学家。
He spent 12 years trying to be one of the best string theorists in the world.
他花了 12 年，立志成为世界顶尖的弦理论物理学家之一。
Now he's changing the world of software development.
如今他正在改变软件开发的世界。
He works with some of the biggest enterprises in the world.
他的客户涵盖全球最大的企业。
He does look like Matt Damon from Goodwill Hunting, but he is one of the best founders I've met.
他确实长得像《心灵捕手》里的 Matt Damon，但绝对是我见过最出色的创始人之一。
So many of the tasks that we're doing, we don't need the very frontier to do it.
我们在做的很多任务，根本不需要用到最前沿的模型。
We might see a short-term contraction of usage of the very frontier models.
短期内我们可能会看到前沿模型使用量出现收缩。
I think it's pretty embarrassing that we don't have frontier open models in the United States.
美国没有前沿开源模型，说实话挺丢人的。
Name a legendary company that has a [ __ ] sales or marketing team.
你来说说，哪家传奇公司有过顶尖的销售或市场团队？
You can't.
你说不出来。
The age of the polymath is back.
通才的时代回来了。
We will see the best companies treat teams more and more like Seal Team 6 or like professional athletes.
最好的公司会越来越把团队当成海豹突击队六队或职业运动员来对待。
Ready to go.
准备好了。
Matan, it is so good to have you in the studio.
Matan，太高兴你来到节目现场了。
You've just uh insulted my continent with the suggestion that we've only come up with bottle caps while you came up with Transformers.
你刚才侮辱了我的大陆，说我们只发明了瓶盖，而你们搞出了 Transformers。
Um, not wildly untrue, but this is going to be a fun show.
嗯，也不是全错，不过这期节目肯定很好看。
So, thank you so much for joining me.
非常感谢你来参加节目。
Thank you for having me, Harry.
谢谢邀请我，Harry。
It's a pleasure to be here.
很高兴来到这里。
Now, I was just doing a show yesterday with Rory and Jason, and Rory was basically saying, you know, the fundamental question is, will we see an increase in GDP?
昨天我刚和 Rory 还有 Jason 录了一期节目，Rory 基本上说，你知道，核心问题就是，我们会看到 GDP 增长吗？
Um, coming from AI and the coding developments that we're seeing, and will it lead to GDP increasing above the 2% average for the last 200 years, do you think we will see meaningful productivity gains from the AI tooling that we're seeing, or is Uber's concerns validated?
嗯，AI 和编程领域的进展，会让 GDP 突破过去 200 年 2% 的平均增速吗？你认为我们能从当前 AI 工具中获得实质性的生产率提升，还是说 Uber 的担忧是有道理的？
So I think yes absolutely we will we will see tremendous growth from these tools.
我认为，是的，毫无疑问，这些工具会带来巨大的增长。
I think it takes time to permeate through um because you can tell like on an individual basis like almost like on a problem bypro basis.
我觉得渗透需要时间，因为在个体层面你能感受到，几乎是一个问题接一个问题地在进步。
We can solve problems faster with these tools.
有了这些工具，我们能更快解决问题。
Now companies generally or organize around solving problems.
公司通常围绕解决问题来组织运营。
Um, and if you're organized around solving problems and you have some set of personnel, you might say this is the number of problems we can solve at a given time based on how many people that we have, everyone is now going to be able to solve more problems with the same number of people or solving the same number of problems with fewer people.
如果你围绕解决问题来组织运营，且有一定数量的人员，你可能会说，基于现有人手，这是我们某个时间点能解决的问题数量。而现在，每个人都能用同样的人手解决更多问题，或者用更少的人解决同样数量的问题。
But it takes time for the resource allocation to adjust.
但资源重新配置需要时间。
A lot of businesses will have to ask do we want to solve more problems now because of the increased leverage that we get or do we want to solve the same problem but now we can do it in a more efficient manner.
很多企业都要问自己：有了更强的杠杆，我们是要解决更多问题，还是用更高效的方式解决同样的问题？
That's I think a question that a lot of businesses will be will be uh grappling with.
我觉得这是很多企业都要绞尽脑汁思考的问题。
Do you think we will have fundamentally smaller teams which ultimately suggests that number two is the option that most people take or do you think we will have actually the same size teams and we'll just go after a more expansive area?
你觉得我们最终会有规模大幅缩小的团队，也就是大多数人选择第二条路，还是团队规模不变，只是去追更广阔的目标？
It's really not obvious because um there are dynamics that it's hard for me to predict.
说实话不好判断，因为有些动态我也很难预测。
But what I will say is again bringing it back to problems.
但我想说的还是回到问题本身。
All of these companies are now going to have to think okay we have all this new leverage.
所有公司现在都得想清楚：好，我们有了这么强的杠杆。
Do we want to solve the same problem?
我们是要解决同样的问题？
Do we want to increase our ambition and solve a bigger problem or do we want to solve more problems
还是提高野心，去啃更大的问题，或者干脆解决更多问题，
uh that might you know our users maybe have?
那些用户可能遇到的问题？
I was watching Andre Capathy and he was talking recently about you know the 10x engineer actually is wildly misunderstood and you won't see the 10x engineer
我最近在看 Andrej Karpathy 说的，10x 工程师这个说法其实被严重误解了，你不会看到那种 10x 工程师，
you'll actually see a smaller number of 100x engineers and kind of the rest and this bifocation of engineering talent do you think that is the right way to look at the future of engineering talent
你看到的会是少数 100x 工程师，加上其余的人，工程人才就这样出现了两极分化。你觉得这是看待工程人才未来的正确角度吗？
directionally yes because what is a 10x or 100x engineer I
大方向是对的，因为什么是 10x 或 100x 工程师，我
I don't necessarily agree with the language around it but like I think like it just implies as if like 10 10x of what is it?
不太认同这种说法本身，我觉得它隐含的意思就好像是 10，10x 是相对于什么的 10 倍？
Pure output like if if it if you when you say 10x it means like how much code they're writing.
纯粹的产出量，就是说你说 10x 的时候，意思是他写的代码多。
Yeah, now I can write a billion lines of code with these tools.
是啊，有了这些工具，我现在能写十亿行代码。
It might be [ __ ] lines of code though.
但可能是垃圾代码。
Um so maybe the way that I like to think about it is like loadbearing individuals in an org.
我更喜欢的说法是，组织里的关键承重个体。
It's kind of like if you remove this person things fall where there in some orgs there might be people where if you remove them nothing happens and they're you know not loadbearing in that case
就好比说，把这个人拿走，整件事就垮了；而有些组织里有些人，拿走了什么都没发生，他们在这里不是承重的。
and so you know the basically these people who have very high leverage are now being handed a tool that gives them even more leverage and so using the language of 10x or 100x
所以那些本来就有很高杠杆的人，现在又被交到一个能给他们更多杠杆的工具手上，用 10x 或 100x 的语言来说，
yes they're levered up they can have even more impact
对，他们杠杆更大了，能产生更大的影响力。
um but uh and I think you know with that leverage language those who know how to use leverage average will be able to have even more impact and those who don't will kind of on a comparative basis be that much less valuable to a business.
我觉得，懂得使用杠杆的人，能发挥更大的影响力；而不懂的人，相对而言对企业的价值就会低得多。
When we think about kind of the two different parts you said there hey you have the option of you can do more with the same size teams or you can reduce teams and do what you already did.
回到你说的两条路：用同样规模的团队做更多，或者缩减团队做同样的事，
If I am thinking as a leader today what would be your biggest advice to me on how I should think about resource allocation for tokens internally?
如果我是一个领导者，今天你最大的建议是什么，关于我该怎么思考内部的资源分配和 token 分配？
Yes, this is a this is a a great point.
这是个好问题。
um this resource allocation problem of token it's not just tokens it's like dollars it's tokens it's people this is I think going to be the thing that over the next 24 months every seuite is going to be thinking about and I think the right way to go about it is what is the core competency for our business what actually matters for the business that we are doing um and then how do we allocate resources accordingly in other words if you're a logistics company your core competency is probably not software development now you might have add a lot of software engineers as a means to an end to deliver on your logistics goals, let's say.
这个资源分配问题，不只是 token，是钱、是 token、是人。我认为这将是未来 24 个月里每个高管都要思考的事情。正确的思路是：我们业务的核心能力是什么？什么对我们在做的这门生意是真正重要的？然后据此分配资源。换句话说，如果你是一家物流公司，核心能力大概不是软件开发，你只是把大量工程师当作实现物流目标的手段，比如说。
Um, but that might not be your core competency.
但软件开发可能不是你的核心能力。
And so what you should be thinking about is not how do we get more engineers to make more features because that's what engineers have in the past been judged by like how many features do they ship in a quarter.
所以你真正应该思考的，不是怎么招更多工程师来做更多功能，因为工程师过去被评价的标准就是一个季度能交付多少功能。
Instead, it's like what are the actual output metrics that matter for our business and how do we now allocate resources whether it's dollars, whether it's tokens, whether it's headcount to uh more dramatically move the needle on that business outcome.
关键是：对我们的业务真正重要的产出指标是什么？我们现在该如何分配资源，不管是钱、token，还是人头，来更大幅度地推动这个业务结果。
And I think this is this is great for the world because I think part of the reason why so many organizations got so bloated is because we were in a period of time where everyone was focusing on intermediate metrics.
我觉得这对世界是好事，因为那么多组织变得如此臃肿，部分原因就是大家都在盯着中间过程指标，而不是最终结果。
If you're an engineering team, we wanted to ship three features this quarter.
作为工程团队，我们本季度想上线三个功能。
Did you ship three features?
你们上线了三个吗？
We shipped four.
我们上线了四个。
What a great quarter.
好棒的一个季度。
Like that doesn't necessarily matter for the business at all.
但这对业务不一定有任何意义。
And so now it's like finally coming back to what matters in the first place.
所以现在才算终于回归到真正重要的事情上了。
Like what are the business metrics that we want to, you know, move the needle on.
业务指标是什么，我们想拉动哪些数字？
Is it customer satisfaction?
是客户满意度吗？
Is it revenue?
是营收吗？
Is it market share?
是市占率吗？
Um and you can kind of tie back every individual's work to that, whether it's marketing, sales, engineering, all of it.
然后把每个人的工作都和这些指标挂钩，不管是市场、销售还是工程，所有部门。
Kirkland announced a $500 million spend.
Kirkland 宣布了 5 亿美元的支出计划。
You're you're friends with Winston from Harvey.
你跟 Harvey 的 Winston 是朋友吧。
Uh fantastic guy.
超棒的人。
Um who obviously I'm sure has I don't know if you guys have spoken about this actually, but like that's a it's a big spend.
嗯，我不确定你们有没有聊过这个，不过这笔钱花得真不小。
$500 million across 5 years to internally build their own Harvey or Lora.
5 年内花 5 亿美元，自建他们自己的 Harvey 或 Lora。
Um
嗯
how did you think about that?
你怎么看这件事？
Um I mean it's fun, you know, talking about core competencies.
说到核心竞争力，这话题挺有意思的。
Kirkland spending half a billion dollars to build their own AI tools.
Kirkland 花半十亿美元自建 AI 工具。
Uh my understanding is that building AI technology is not a core competency of that firm.
据我所知，构建 AI 技术并不是那家律所的核心能力。
I think I'm I'm I was surprised to see it.
说实话，看到这个消息我挺意外的。
Now I actually think this is good for Harvey because it's nothing like trying to do something yourself to make you realize, oh [ __ ] this is actually really difficult.
但我其实觉得这对 Harvey 是好事，因为没什么比自己亲手做一遍更能让你意识到，这玩意儿真他妈难。
This doesn't actually matter for us to have the in-house ability to build this ourselves.
这不是我们需要自己具备的内部能力。
Let's go and have someone who is an expert in this to go and build this for us.
找这方面的专家来帮我们做就行了。
That is my sense.
这是我的判断。
My favorite is also the amount of people that like, see, we told you how easy it was.
我最喜欢那种人，到最后说，你看，我早说这多简单。
And you're like, it's so easy.
然后你心想，这不是很简单吗。
They're committing half a billion dollars.
结果他们砸了半十亿美元进去。
That would suggest the opposite.
这恰恰说明了相反的情况。
Um, I had Brendan on from a call the other day and he was fundamentally saying that the next 12 months would be the most value acrewing 12 months for AI infrastructure companies.
嗯，前两天我跟 Brendan 通了个电话，他的核心观点是：接下来 12 个月，将是 AI 基础设施公司价值积累最密集的 12 个月。
We would see that the models of the products and the AI application layer companies would be most at risk.
模型、产品，以及 AI 应用层公司，风险最大。
denigrated.
会被贬值。
Would you agree with that?
你同意这个判断吗？
I would disagree.
我不同意。
I'd pretty strongly disagree.
我相当强烈地不同意。
Um, for a couple things.
嗯，有几点原因。
One, actually, sticking with the Kirkland thing.
第一，就说回 Kirkland 那个例子。
I think as an example, we're so used to a world where moat in software was I know how to do this and you don't and so you're going to pay me because I have the engineers who know how to build this and you simply cannot.
我觉得，以前软件行业的护城河逻辑是：我知道怎么做，你不知道，所以你得付钱给我，因为只有我的工程师有能力把这东西做出来，你根本做不到。
Now, the world going forward, there is going to be nothing that no one can build.
而往后的世界里，没有什么是没有人能造出来的。