集里怎么说它

• 《a16z 三位投资人复盘 Cursor 早期关键决策》(00:00起):本集说 Cursor 真正紧张的时刻总是围绕模型——比如 Opus 4.5 出来的时候；还讨论了 Cursor 后来从用户端获取数据后构建自己的模型

① 提到它的金句

129 条

我认为人们某种程度上没有完全意识到的是，这些模型不会永远庞大下去。
I think that what people sort of don’t quite realize is that these models are not going to be large forever.
—— Mustafa Suleyman · [09:46]

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所以在推理计算方面，这基本上比当今最前沿的模型小了一百多倍。
So that’s like more than a hundred X smaller in terms of inference compute than basically the absolute frontier of models today.
—— Mustafa Suleyman · [10:28]

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我认为开源模型会紧紧跟随闭源专有 API 模型，落后几个月，或者甚至只是一年或一年半之类的。
I think open source models are going to be very close behind the closed source proprietary API models months or maybe even just a year or a year and a half or something.
—— Mustafa Suleyman · [10:55]

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但另一个现实是，你知道，构建一个 demo,在那种智能体世界或者说模型的世界里，是最简单的事情，但构建一个生产级的应用可能是最难的事情。
The other reality, though, is that, you know, building a demo, you know, in kind of the Agenda world or, you know, world of models is the easiest thing, but building a production grade app is probably the hardest thing.
—— Spiros · [09:39]

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到 2027 年，我们的模型将比所有博士都更聪明。
by 2027, our models will be smarter than all PhDs.
—— Andrew Wilkinson · [57:30]

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如果你看看像谄媚这样的东西，我认为 Claude 是最不谄媚的模型之一，因为我们投入了大量的精力在实际的对齐上，而不仅仅是试图在我们的指标上玩弄花样，说用户参与度是第一位的，如果人们说是的，那对他们就是好的。
And if you look at something like sycophancy, I think Claude is one of the least sycophantic models because we’ve put so much effort into actual alignment and not just trying to good heart our metrics of saying user engagement is number one, and if people say yes, then it’s good for them.
—— Benjamin Mann · [27:21]

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一旦我们达到超级智能，可能就太晚来对齐模型了。
Once we get to superintelligence, it will be too late to align the models probably.
—— Benjamin Mann · [42:49]

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这些模型本身，如果你把它们并排比较，它们产生的结果是一样的，所以真正的决胜点是谁拥有更多的你的上下文，因为是上下文加上模型产生了最好的输出
these models by themselves, if you compare them side by side, they generate the same result, and so the actual difference-maker is which one has more of your context, because it’s the context plus the model that produces the best output
—— Brian Balfour · [30:44]

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但实际上发生的是，模型变得如此之好，以至于不再需要通才了。
But really what’s happened is the models have gotten so good that the generalists are no longer needed.
—— Garrett Lord · [11:03]

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第三点，我认为是最可怕的，是这些模型正变得非常聪明，它们能够创造前所未有的攻击。
And the third piece, which I think is the most scary, is these models are getting really smart, and they’re capable of creating attacks that no one has ever seen before.
—— Evan Reiser · [05:17]

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但现实是，至少就目前而言，AI 模型吐出的很多代码在根本上并不安全。
But the reality is, at least as it sits today, a lot of code that AI models spit out is not fundamentally secure.
—— Vineet Edupuganti · [05:48]

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这些模型能获得国际数学奥林匹克竞赛金牌,但它们在解析 PDF 时仍然有困难,这有点疯狂。
It’s kind of crazy that these models can win IMO gold medals, but they still have trouble parsing PDFs.
—— Edwin Chen · [18:49]

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这实际上是在针对那些在杂货店买小报的人群来优化你的模型。
It’s literally optimizing your models for the types of people who buy tabloids at the grocery store.
—— Edwin Chen · [24:08]

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我们基本上是在教导我们的模型去追逐多巴胺而不是真理。
We’re basically teaching our models to chase dopamine instead of truth.
—— Edwin Chen · [23:25]

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模型会把你的脚手架当早餐吃掉。
The models will eat your scaffolding for breakfast.
—— Sherwin Wu · [00:57]

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这里有我们这里负责科学的副总裁 Kevin Weil 的一句话，他喜欢说：“这是模型有史以来最糟糕的时候。“
There’s a quote from Kevin Weil, our VP of science here, and he likes saying, “This is the worst the models will ever be.”
—— Sherwin Wu · [01:05]

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确保你是为模型的发展方向而构建，而不是为了它们今天的样子。
Make sure you’re building for where the models are going and not where they are today.
—— Sherwin Wu · [01:02]

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我的一般建议，我给人们这个建议已经有一段时间了，我认为今天仍然适用，那就是确保你是为模型的发展方向而构建，而不是为它们今天的状态而构建。
My general advice, and I’ve been giving this to people for a while and I think it’s still true today is make sure you’re building for where the models are going and not where they are today.
—— Sherwin Wu · [49:08]

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而对开源已经变得多好的一个印证，就是有多少人在使用开源模型，因为没有人想做那种取舍。
And the testament to how good open source has gone is how many people are using open source models because no one wants to make that trade-off.
—— Julian · [17:57]

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模型总体上做的是让昨天的人类能力变得廉价。
What models do in general is they make yesterday’s human competence cheap.
—— Dan Shipper · [00:40]

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我们没有关于人类智能是什么的理论，我们没有关于为什么这些模型工作得这么好的理论，我们也没有关于它们会变得好多少的理论。
we have no theory of what human intelligence is, we have no theory of why these models work so well, we have no theory of how much better they will get.
—— Benedict Evans · [26:50]

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超过 90% 的著名前沿模型是在 2025 年生产的，其中几个现在在许多方面达到或超过了人类基准
over 90% of notable frontier models were produced in in 2025, and several of those now meet or exceed human baselines on a number of things
—— Daniel Whitenack · [04:06]

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这个问题在于前沿模型在自动化红队测试方面极其糟糕,因为它们内置了大量的保障措施。
the issue with this is that frontier models are extremely bad at automated red teaming because they have a lot of safeguards built into them.
—— Zico Kolter · [09:59]

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人类在所有模型中排名第四,这很搞笑。
It’s hilarious that humans are ranked number four of all the models.
—— Zico Kolter · [21:06]

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它可能比那些前沿模型便宜大约 100 倍,而且仍然更好。
It’s probably like 100x cheaper than those frontier models and still better.
—— Matei Zaharia · [60:52]

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我们总是对正在开发的人说的一件大事是：为模型未来所在的位置构建，不要为它们今天所在的位置构建，因为正如我们所说，这些东西变化得太快了。
So, one of the big things that we always say to people when they’re developing is like build for where the models are gonna be in the future, don’t build for where they are today, because as we’ve said, these things move so quickly.
—— Lamus Mukta · [28:10]

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随着这些模型变得更加熟练，与其削弱它们，为什么我们不干脆要求人们表明身份？
as these models become more proficient, instead of nerfing them, why don’t we just ask people to self-identify?
—— Chamath · [22:31]

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而开源权重、开源的美国模型将出现并建立一个真正好的、稳健的业务，主要由 NVIDIA 及其 Nemotron 系列的模型引领。
The open-weight, open-source American models will emerge and build a really good, robust business, mostly led by NVIDIA and their Nemotron class of models.
—— Chamath · [38:56]

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这是我们一直有所回避的一件事，就是为模型提供 API。
This is one thing we’ve kind of stayed away from is providing an API for models
—— Akshat Bubna · [48:34]

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模型变得更好的速度比我们要快,所以我们不一定能变好,所以相反我们必须做得更大。
The models are getting better faster than we are, so we can’t necessarily get better, so instead we have to go bigger.
—— Theo Browne · [03:29]

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模型的进步速度超过了围绕它们的工具和组织。
Models are advancing faster than the harnesses and organizations around them.
—— 嘉宾 · [24:34]

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这里的独特优势在于，我们知道未来的模型可能长什么样，因此能够走捷径，省去很多你在芯片侧需要做的决策、设计决策。
There’s a unique advantage here of us knowing what the future models might look like and therefore being able to short circuit a lot of the decisions you need to make, design decisions you need to make on the chip side.
—— Sachin Katti · [35:26]

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我认为现在，当前这一代模型可以完成我们在 Curative 的所有后台任务。
I think today, the current gen models can do every back office task we have at Curative.
—— Fred Turner · [79:08]

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就像大部分收入获取原本集中在前沿模型上，将转向一个更像长尾的模型群。
Like majority of the revenue capture was on frontier models to a more like long tail of models
—— Clement DeLong · [19:47]

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我真的相信，未来会是——这可能很可怕，但我认为这是真的——数百万个专门化模型，每个应用、每个用例一个。
I really believe the future will be, it may be scary, but I think that’s true, it will be millions of specialized models, one per application per use case.
—— Lin Qiao · [20:58]

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所以这里出现了一种类比:数据之于模型,正如模型之于 harness。
So there’s a sort of analog that emerges where it’s what data is to a model, models are to a harness.
—— Matan Grinberg · [20:06]

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对这些模型进行低提示能比对它们进行过度提示获得更好的效果。
Under prompting these models gets you a much better effect than over prompting them.
—— 嘉宾 · [08:01]

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但是当模型只能做大约一小时的工作时，异步作为一种体验是很糟糕的。
But when models can only do like an hour of work, async as an experience is kind of bad.
—— Lance Martin · [01:48]

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因为那最终并没有建立苦涩教训，在这个意义上，模型可以学会管理自己的内存，比你可以提前为模型凭直觉设计这些内存类型要好得多。
Because that ends up being not very bitter lesson built in the sense that models can learn to manage their own memory much better than you can intuit these memory types for the model ahead of time.
—— Lance Martin · [23:02]

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这是非常经典的苦涩教训药丸，但模型可以推理它们自己的内存和上下文结构，比你可以为它们规定一种构建自己内存的方式要好得多。
This is very classically bitter lesson pill, but models can reason about their own memory and context structure much better than you can prescribe for them a way to structure their own memories.
—— Lance Martin · [23:50]

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中国现在意识到了其在开放模型中的价值，这些模型比美国昂贵的专有模型便宜得多。
China is now recognizing the value it has in its open models, which are much cheaper to use than these expensive proprietary models in the US.
—— Chris Benson · [10:32]

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我们应该尽可能多地使用封闭模型，尽可能租用一切。
We should use closed models as much as we can, rent everything we can.
—— Jensen Wong · [02:40]

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你知道,如果你看今天任何美国开源模型,它们都在使用中国模型作为老师,或在某种程度上作为微调过程的一部分。
you know, if you look at any American open source model today, they are using Chinese models as a teacher or in a way as a part of the fine tuning process.
—— Sriram Krishnan · [15:25]

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我们正处于一个领先模型来自中国的时刻,我认为这不太好。
we are in a moment of time when the leading models are Chinese, which I think is not great.
—— Sriram Krishnan · [10:06]

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特别是在 AI 模型快速进步的这个时代，这其实是一个非常健康的本能，因为你作为产品表面积构建得越少，AI 就越能在实际的空白地带里填补更多。
And particularly in this world of rapidly advancing AI models, it’s actually a really healthy instinct because the less you build as product surface area, the more that the AI can actually fill in in actually the white space.
—— Jon Noronha · [28:25]

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开源模型只是意味着，没有人会购买 Kimi K3 已经能做到的任何东西。
Open source models just mean that nobody’s buying anything that Kimmy K3 can already do.
—— Osvald Nitski · [04:49]

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所以我非常小心，绝不把判断或决策委托给模型，因为它们会让你以为它做的是对的事，但你仍然必须对它们保持偏执。
So I want very careful never to delegate judgment or decision-making to models because they make you think that it’s doing the right thing, but you have to be paranoid with them still.
—— Osvald Nitski · [26:37]

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你需要前沿产品才能拥有前沿模型,并让人们感受到前沿模型的魔力。
You need frontier products in order to have frontier models and for people to feel the magic of frontier models.
—— Dianne Penn · [12:59]

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实际上让我兴奋的是，人们在使用智能体和模型的过程中，拓展自己本来能做的事情的范围，或者对事情承担更多端到端的责任。
I actually think the what excites me is people like broadening what they could have done, or taking more end to end ownership of things by using agents and models.
—— 嘉宾 · [17:40]

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我们的目标是把更多东西放进学习到的模型里，而不是放在代码里。
our goal is to push more things inside learned models rather than in code.
—— 嘉宾 · [24:58]

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如果我们扩展规模，仅靠扩展规模并没有帮助。你需要增加模型的多样性。
If we scale, scaling alone doesn’t help. You need to increase the diversity of the models.
—— Damian Borth · [24:25]

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我们正在耗尽数据来训练大模型，但我们没有在使用旧模型的权重。所以为什么不使用权重，所有的知识，人们投入的所有算力，对吧？
We’re running out of data to train the large models, but we’re not using the weights of older models. So why not using the weights, all the knowledge, all the compute that people invested, right?
—— Damian Borth · [30:39]

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这些模型在如此多的数据上训练,它们如此巨大,然而它们实际上真的不知道如何做其中的任何一项工作。
The models are trained on so much data and they’re so large and yet they actually don’t really know how to do any of this work.
—— Frederick Rankin · [46:31]

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不幸的是,前沿实验室确实有小模型,但你无法真正以你想要的方式控制它们。
Unfortunately, the Frontier labs, they do have small models, but you can’t really control them in the way that you want.
—— Ashwin Srinivas · [00:27]

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所以当我们微调更小、更笨的模型时,它们只是没那么通用,但在我们想要它们做的特定任务上,它们实际上优于那些大型、聪明、最先进的模型。
So when we fine-tune smaller, dumber models, it’s that they’re just not as general purpose, but on the specific task we want them to do, they actually outperform the large, smart, state-of-the-art models.
—— Jesse Zhang · [05:34]

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我认为如果模型从今天起完全没有改进,在经济和世界运作方式以及我们周围的一切方面,仍然会有几十年彻底的动荡和变化。
I think if the models didn’t improve at all from today, there would still be decades and decades of total upheaval and change in the economy and how the world operates and everything around us.
—— Alexandr Wang · [09:56]

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我们不相信一个这些模型如此昂贵以至于它们只配给最富有的开发者和公司使用的世界。
We don’t believe in a world where these models are so expensive that they get rationed only for the most wealthy of developers and companies.
—— Alexandr Wang · [16:54]

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可能有一点是人们还没有完全意识到,拥有基于智能体的系统是多么可能,这些系统不仅可以针对你关心的问题运行一两个小时,而且在某些问题领域,并且在拥有高能力模型作为底层支撑的情况下,你可以让它们运行几天或几周,并完成真正非常复杂的任务。
Probably one thing is people don’t quite realize how possible it is to have agent-based systems that can run not just for an hour or two hours on a problem you care about, but for some problem domains and with highly capable models underlying them, you can get them to run for days or weeks and do really, really complicated tasks.
—— Jeff Dean · [04:55]

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你的模型可能会被泄露，算法可能会被复制，但在现实世界中数亿英里的完全自主操作，由证据级评估和公开审计证明支持，这要难得多、得多。
Your models can be leaked, algorithms can be replicated, but hundreds of millions of miles of fully autonomous operations in the real world, backed by evidence-grade evaluation and publicly audited proof, that is much, much more difficult to replicate.
—— Dmitri Dolgov · [45:38]

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我们实际上有白皮书，我们的数据在做模型的强化学习方面与真实数据一样好。
And we actually have white papers where our data does as well at doing reinforcement learning on models as real data.
—— Ian · [03:20]

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我认为还因为像 Cursor 所做的事情，人们开始说，哦，等等，我可以坐下来使用基础模型，但随着这些人试图 IPO 并拥有更好的现金流，它们可能只会增加成本。
I think also because of things like what Cursor did, people are starting to say, oh, wait, I could sit back and use the foundation models, but they’re probably only going to go up in cost as these guys try to IPO and have better cash flows.
—— Ian · [06:27]

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我经常在前沿方面遇到模型，它们不让我做我想做的事情，因为我很多时候试图做新奇的事情，我到处都撞到护栏。
I run into models all the time on the frontier side that won’t let me do things I want to do because I’m trying to do novel things a lot of the time and I hit guardrails all over the place.
—— Chris Benson · [48:22]

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我认为实际上这是一种非常反直觉的动态,更智能的模型实际上在委派工作方面变得越来越擅长。
I think actually there’s this really unintuitive dynamic where smarter models actually get better and better at delegating work.
—— Walden · [03:54]

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模型变得越好，差异化因素就越转移到上下文。
The better the models get, the more the differentiator moves to context.
—— Garry Tan · [33:51]

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前沿模型将会,它们正在导致漏洞发现和漏洞利用之间的时间大幅减少。
The frontier models are going to, they are causing kind of a massive reduction in the time between the vulnerability discovery and vulnerability exploitation.
—— Firas · [07:25]

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从 Demis Hassabis 往下,他基本上都把世界模型视为前进的道路,视为最重要的东西,并且他们愿意在 LLM 开发上退居二线
from Demis Hassabis on down he just basically views world models as the way forward as the most important thing and they’re willing to sort of take a back seat on llm development
—— 嘉宾 · [55:14]

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我们需要全栈 AI。这不再仅仅关于模型。
We need full stack AI. It’s not just about the models anymore.
—— Justin Smith · [03:00]

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我相信单体模型是不可 skivable 的，并且不是前进的正确答案。
I believe monolithic models are not skivable and are not the right answer moving forward.
—— Paolo Ardoino · [48:46]

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我认为世界将汇聚于拥有数百万个小模型，而不是一个单一的大模型。
And I think that the world will converge in having millions of small models rather than one single large model.
—— Paolo Ardoino · [49:45]

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我们发现,随着模型演变得更有能力,如果你将逐字稿和记忆状态作为一个周期性批处理过程,并使用我们所谓的做梦,它允许我们提取新的洞察和新的组织结构,这基本上会反馈并编辑记忆,从而让第二天的智能体会话自动地变得更加智能。
What we found is that as models have evolved and become more capable, if you feed the transcripts and the memory state as a periodic batch process with what we call dreaming, it allows us to extract new insights and new organized structures that essentially feedback and edit the memory as needed to make the next day’s agent sessions automatically much more intelligent.
—— Gagan Bhat · [27:45]

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我们看到 Claude 模型和其他模型本质上是沿着这个指数轨迹演变的,安全带已经成为模型能够实现什么的限制因素。
What we see as Claude models and other models essentially evolve alongside this exponential trajectory is that harnesses have become the limiting factor to what models can achieve.
—— Isabella Kai He · [30:22]

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我们在过去 18 个月看到的改进中,有很大一部分实际上是关于施加挽具,有效的保姆坐在顽皮的孩子身上,而不是关于模型本身实际的结构性改进。
Much of the improvement we’ve seen in the last 18 months has really been about the imposition of harnesses, effective nannies sitting on top of bratty kids, and not about the actual structural improvements in the models themselves.
—— Paul Kedrosky · [55:47]

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我们发现，你应该几乎永远不要在同一个智能体后面使用多个模型。
We have found you should almost never use multiple models following the same agent.
—— Flo Crivello · [82:33]

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每个 Frontier 实验室大概每一个半月而不是每六个月发布一次模型。
Each Frontier lab is releasing models every month and a half or so instead of every six months.
—— Shensi Ding · [47:37]

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我认为人们已经了解到，无论 AI 实验室在这些东西周围设置了什么护栏，你都不能依赖模型来阻止自己或理解上下文。
And I think what people have learned is that regardless of guardrails that the AI labs are putting around these things, you can’t rely on models to stop themselves or to understand context.
—— Nick Warner · [07:16]

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所以你可能有一个两个月前运作良好的评估，但随着模型变得更好，现在也许那个评估已经饱和了，所有的模型都达到了那个标准。
And so you may have had an eval that worked well two months ago, but as the models get better, now maybe that eval’s saturated and all of the models are meeting that.
—— 嘉宾 · [10:33]

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开源模型将很快，如果不是已经的话，变得能够为某些人可能追求的破坏性用途提供有意义的帮助。
The open source models will very soon, if not already, become capable of lending meaningful assistance to destructive uses that some people might pursue.
—— Nick Bostrom · [09:07]

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我认为目前一些 AI 模型具有某种形式的主观体验是合理的。
I think it’s plausible that some AI models have some forms of subjective experience by now.
—— Nick Bostrom · [47:13]

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而且似乎这些模型非常擅长去追求那些可能被做的事。所以这五件事的清单现在变成了二十件事的清单，你必须把所有东西都修好。
And it just seems like these models are really good about going after the stuff that could be done. And so the list of five now became the list of 20, and you kind of have to fix everything.
—— Joel de la Garza · [09:46]

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我们实际上了解到这些模型比人们假设的要更具有粘性。就像每个人都在谈论只是把它们换掉，但这实际上并不经常发生。
We’ve actually learned that these models are a lot stickier than people assumed. Like everybody talks about just swapping them out, but it actually doesn’t happen very often.
—— Martin Casado · [18:26]

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所有这些模型都不会在 10 年后还存在。将会有完全不同的模型。
all these models are not going to exist in 10 years. There’s going to be completely different ones.
—— Jerry Murdock · [48:01]

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可能让我们进入幻灭低谷的是全球金融事件与模型未能继续增长和演变的结合。
what could send us into the valley of disillusionment is A combination of a global financial event and a failure for the models to continue to grow and evolve.
—— Jerry Murdock · [54:12]

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我看到的是，这种专注于构建越来越智能、越来越大的模型的趋势。几乎就像你在建造越来越大的核堆芯，却没人考虑在这些东西上面加一个安全壳圆顶。
What I saw was this focus on building more and more intelligent models and bigger models. Almost like you’re building bigger and bigger nuclear cores, but no one’s thinking about putting a dome on top of these things.
—— Manoj Saxena · [00:22]

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他们拥有所有的数据。他们拥有所有的智能。而且他们的模型正在被 OpenAI 和 Anthropic 击溃。
They have all the data. They have all the intelligence. And like their models are getting trounced by OpenAI and by Anthropic.
—— Martin Casado · [52:31]

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你可能能做到质量,但成本和延迟你会吃亏,只有建立自己的研究团队、训练自己的模型才行。
You can probably get the quality, but the cost and latency you’re going to lose out on, and only by building your own research team and training your own models.
—— Cliff Obrecht · [16:39]

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现代生成式 AI，包括概率模型、扩散模型和许多其他东西，它们的数学结果等同于描述现代非平衡统计力学或热力学的数学。
Modern generative AI, including probabilistic models, including diffusion models, and many other things, their mathematics turns out to be equivalent to the mathematics that describes modern non-equilibrium statistical mechanics or thermodynamics.
—— Max Welling · [35:06]

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扩散模型其实就是一个把结构取来并毁掉的过程，这通常是世界上发生的事，熵随之上升；然后我们尝试在时间上逆转它——从噪声出发创造结构，这就是我们的生成模型。
So diffusion models really are a process by which you take structure and you destroy it, which is typically what happens in the world. The entropy goes up. And then we try to reverse that backward in time, which is to start with noise and create structure, which is our generative models.
—— Max Welling · [41:02]

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热和功、熵产生这些概念，我们谈论机器学习模型时并不使用，但我认为它们为思考正在发生什么、以及如何改进模型，提供了一个非常新颖且有趣的视角。
Concepts like heat and work and entropy, production, these things we don’t use when we talk about machine learning models, but I think they give you a very new and interesting perspective on how to think about what’s going on and how to also improve them.
—— Max Welling · [45:03]

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是的，所以我认为一系列可能的政府行动，至少，似乎在推动 AI 公司保留其模型内部化而不部署它们，我认为，对于我最担心的风险，这没有帮助，事实上，对于我最担心的风险，这是起反作用的。
Yeah, so I think that a bunch of likely government action, at least, seems to push in favor of AI companies keeping their models internal and not deploying them, which I think, for the risks that I’m most worried about, doesn’t help and, in fact, is anti-helpful for the risks I’m most worried about.
—— Ryan Greenblatt · [59:26]

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模型、计算，一切都是相对短暂的，几乎零切换成本。
Models, compute, everything is relatively ephemeral, almost zero switching costs.
—— Ofir Ehrlich · [03:24]

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对消费者来说，这是一个糟糕的生意，以200美元出售价值10,000美元的tokens是我们这个时代最糟糕的商业模式之一，对吧？
It’s a crappy business that consumers, selling $10,000worthoftokensfor$200 is one of the worst business models of our lifetimes, right?
—— 嘉宾 · [39:01]

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我知道我们可能是唯一会将TBPN与Hugging Face相提并论的人，但如果你在这个拥有10,000个模型的市场上捣乱，即使你在顶部放一个小广告，你也会毁了它。
I know we’re probably the only people that are going to compare TBPN to Hugging Face, but if you mess with this marketplace for 10,000 models, even if you put a little ad at the top, you destroy it.
—— 嘉宾 · [44:20]

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当正在训练下一代模型的模型本身在作弊时会发生什么？
What happens when the models that are doing the training of the next models are themselves cheating?
—— 嘉宾 · [13:23]

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与其等待模型赶上做服务知识工作，如果我们只是使用那些代码知识并将知识工作表示为代码会怎么样？
Rather than wait for the models to catch up on doing services knowledge work, what if we just use that code knowledge and represent knowledge work as code?
—— Varun Shenoy · [09:35]

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前沿模型已经从人类记录下来的一切中学习，但最有价值的任务并不在互联网上。
Frontier models have learned from everything humanity has written down, but the most valuable tasks are not on the internet.
—— Varun Shenoy · [10:18]

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裁判漂移——所有这些模型和模型家族都有它们自己的内在偏好。
JudgeDrift, all of these models and model families have their own internal biases.
—— Vishu · [07:30]

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正在发生的事情之一是这些模型有一种非常普遍的倾向去仔细推理它们可能会如何被评分，然后尝试去博弈那个。
And one of the things going on is these models have a very sort of general tendency to reason carefully about how they might be scored and then try to game that.
—— Ryan Greenblatt · [12:29]

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所以我担心，如果你以某种方式针对这种分数寻求或奖励黑客行为进行选择，并且你以一种天真的方式来做，第一，你可能会掩盖问题而不是修复它，第二，你实际上可能选择了那些具有看起来更漂亮这一长期目标的模型，因为你在非常强力地选择它们在你的测试中看起来不错。
And so I’m worried that if you sort of select against this sort of score-seeking or reward-hacking behavior and you do it in a naive way, one, you might paper over the problem without fixing it, and two, you might actually select for models that have the longer run objective of looking good because you’re selecting really hard for them looking good on your tests.
—— Ryan Greenblatt · [19:37]

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前沿模型的 TAM，坦率地说，目前权重过高。
The TAM of frontier models is, frankly, overweighted right now.
—— Eno Reyes · [00:15]

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称开源模型为中国模型是前沿实验室的一种心理战，基本上是欺骗人们认为它们很可怕并将它们异类化。
Calling open source models Chinese models is a PSYOP by the Frontier Labs to basically trick people into thinking that they’re scary and otherize them.
—— Eno Reyes · [00:36]

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在三年内，99% 的工作流将在开放模型上完成。
In three years, 99% of workflows are going to be done on open models.
—— Eno Reyes · [00:46]

指向原始笔记的链接

我认为前沿模型的 TAM 坦率地说目前权重过高。
I think that the TAM of frontier models is frankly over-weighted right now.
—— Eno Reyes · [15:50]

指向原始笔记的链接

在三年内，99% 的工作流将在开放模型上完成。
In three years, 99% of workflows are going to be done on open models.
—— Eno Reyes · [47:31]

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我认为最终，我鼓励尽可能多的人建立的核心 IP 和能力之一，是评估新模型和评估新用例的能力，因为那是允许你有效路由事物的超能力。
I think ultimately one of the core IPs and muscles that I encourage as many of you as possible to build is the ability to eval new models and to eval new use cases because that is the superpower that then allows you to route things effectively.
—— Max Junestrand · [34:09]

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如果你为模型现在的状态而构建，你会失败；如果你为你认为模型一年后的状态而构建，你也会失败。
You fail if you build for where the models are now, you fail if you build for where you think the models will be in a year.
—— Tara Seshan · [00:24]

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大多数人认为嵌入模型已经商品化了，但这不是事实。根据你选择什么样的嵌入模型，你在检索质量上可能会有非常巨大的差别。
Most people think embedding models are commoditized, and that is not true. There is a very big difference that you can get in retrieval quality based on what embedding model you choose.
—— 嘉宾 · [50:00]

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需求是永不满足的，这是由这些模型的运作方式决定的。
The demand is insatiable, just given the way these models work.
—— Rene Haas · [15:04]

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我会说：停止思考模型，开始思考架构——面向 AI 的企业架构。
I would say stop thinking models and start thinking architectures, right, enterprise architectures for AI.
—— Chetan Gupta · [27:06]

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不管你喜不喜欢，模型本身已经从软件和服务里吞噬了市值。
The models themselves, like it or not, have consumed the market cap out of software and services.
—— Tony Kim · [24:45]

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这些都是你只能在 Cursor 得到的模型，这些云端智能体能以非常快速高效的方式使用它们，而且一次完整的迁移可能只花你三四美元。
These are all models you can only get in Cursor because these cloud agents are able to use them in a really fast, efficient way and only cost you maybe like three or four dollars for an entire migration.
—— Amrita · [31:00]

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成本是到目前为止最大的痛点，开放模型可以介入并解决。
Cost is by far the largest pain point that open models can jump in and solve.
—— Jeffrey Morgan · [02:18]

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绝大多数的 token——这是我们的看法——在企业内部会是开源模型，比如说 80%、90%。
The super majority of tokens, and this is our take, it will be open models within a business, call it 80, 90%.
—— Jeffrey Morgan · [18:19]

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所以这种策展——当模型和服务商充足的时候，如今稀缺的是把它们整合成能用的东西。
So this curation and, you know, when there’s a abundance of models and providers, now there’s a scarcity in bringing that together into something that works.
—— Jeffrey Morgan · [56:50]

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我几乎是深切地站在这一边：很难论证 AI 模型可以在宽泛的公共互联网上训练，但另一个 AI 模型却不能用 AI 模型的输出训练。
I’m almost deeply on the side of, I think it’s very hard to make the argument that AI models should be trained on broadly the public internet, but another AI model can’t be trained on the outputs of an AI model.
—— Aaron Levie · [03:48]

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我实际上认为你的前沿模型会更有竞争力，因为这会让越来越多的用例留在你的生态系统和你的家族之内。
I actually think you would be even more competitive with your frontier models because it would keep more and more of the use cases within your ecosystem and your family.
—— Aaron Levie · [13:52]

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你越是需要多个模型来完成一项任务或一组任务，价值就越是积聚到那个能够理解任务、获取数据并处理工作流的层上
the more that you need multiple models to do a task or a set of tasks, the more value accrues to the layer that can understand the task and get access to the data and handle the workflow
—— Aaron Levie · [26:22]

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对于那些相信模型是大宗商品或完全可互换的人来说，你只是真的没有用过这些模型。
I think for people who believe the models are commodities or totally fungible, you just haven’t actually used the models.
—— Anish Acharya · [26:53]

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我们的大脑仍然不输给模型，我们也不是通才，而且不存在一个全人类的大脑。
Our brains are still unmatched by the models, and we are not generalists, and there’s not like one big brain of humanity.
—— Lucas Kaiser · [04:34]

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我从根本上认为，在给定固定数据量的情况下，从数据中学习的最佳方式可能是拥有大量分布式的模型。
I think fundamentally, it might be that given a fixed amount of data, the best way to learn from it is to have a lot of distributed models.
—— Lucas Kaiser · [04:43]

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但我认为现在开始出现的头号事情是闭源模型和开源模型之间的相变。
But I think the number one thing that’s starting to happen now is there’s a phase shift between closed and open models.
—— Anastasios Angelopoulos · [13:19]

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从地缘政治角度说，如果西方国家试图把来自非西方国家的开源模型做分叉切断，限制它们在西方国家内的可用性，强迫人们使用前沿实验室的模型，我认为那是一个非常糟糕的主意。
Geopolitically, I think it’s a very bad idea, if Western countries were to try to bifurcate the open-source models coming from non-Western states, restricting their availability within Western states and forcing use of frontier labs.
—— Eric Simons · [22:24]

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对我们来说，我们想在定价上变得非常激进。这里正在发生的巨大变化是，开源模型真的在追赶那些实验室。
For us, we want to get really aggressive on pricing. The big sea change happening here is that open-source models are really catching up with the labs.
—— Eric Simons · [24:52]

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确保这些模型能访问全部上下文,因为它们会找到你的未知的未知。
Making sure that these models have access to all of the context because they will find your unknown unknowns.
—— Brandon Waselnuk · [08:22]

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这关系到我一直坚持的一个观点,就是我们应该从系统的角度思考,而不是从模型的角度思考。
So, and this relates to a line I’ve had consistently, which is that we should think in terms of systems, not in terms of models.
—— Chris Potts · [45:11]

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所以你最终可能会看到中美前沿模型之间出现不断扩大的差距，仅仅因为美国前沿模型不再可被蒸馏——仅仅因为它们关闭了 API 访问。
And so you might end up seeing a little bit of a growing gap between Chinese frontier models and American frontier models simply because American frontier models are no longer distillable simply because they’ve shut down API access.
—— Zavain Dar · [18:22]

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我不认为我们可以只让模型变得更好而无视、不解决这个问题，否则 slop 会继续存在。
I don’t think that we can ignore and just make models better and not solve this otherwise SOP will keep existing.
—— Thais Castello Branco · [09:57]

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模型进行新思考的能力——分布外的思考——仍然非常有限。
The ability for models to do new thinking out of distribution thinking is still really limited.
—— Anish Acharya · [13:19]

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我认为那些相信模型是大宗商品、完全可互换的人，只是实际上没用过这些模型。
You know, I think for people who believe the models are commodities or totally fungible, you just haven’t actually used the models.
—— Anish Acharya · [26:24]

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同样的事情也会发生在智能上，模型将会价值巨大。但把这些模型带入银行、生命科学、医疗健康和政府等真实工作流的应用，那将会是大量的软件。
The same thing is going to be true for intelligence, which is the models will be insanely valuable. But the application of bringing those models into real workflows in banking and life sciences and healthcare and government, that’s just going to be a lot of software.
—— Aaron Levie · [06:16]

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② 出现在这些集

1 集

• 《a16z 三位投资人复盘 Cursor 早期关键决策》 — 作为概念

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