2026年大构想：新基建原创

incumbents 难以向初创公司销售，因为他们受制于损益规则。

Incumbents struggle to sell to startups because they're bound by the rules of P and L.

我认为，无论你是想将产品提供给加密原生受众还是更传统的受众，采取更原生的加密方法实际上更好。

I think taking a more crypto native approach is actually better regardless of whether you wanna offer the product to a crypto native audience or to a more traditional audience.

自主实验室和自主科学最先被采用的地方，可能更多取决于其运作的市场。

Where you see autonomous labs and autonomous science being adopted first is probably more of a function of the market that it's operating in.

如果你在 Formation 吸引了所有新公司，并与它们共同成长，当你的客户成长为自己的大公司时，你也会如此。

If you attract all of the new companies at Formation and then grow with them, as your customers become big companies in their own right, so will you.

随着这一年接近尾声，我们的投资团队开始展望未来。

As the year winds down, our investment team looks ahead.

我们 2026 年的五大理念突出了建设者们接下来将应对的问题、机遇和趋势。

Our twenty twenty six big ideas highlight the problems, opportunities, and shifts we expect builders take on next.

本期节目聚焦三个关于新基础设施的大理念。

This episode is about three big ideas about new rails.

不是现有系统的渐进式改进，而是能够催生全新市场和工作流程的基础性原语。

Not incremental improvements to existing systems, but foundational primitives that make entirely new markets and workflows possible.

你会听到可编程货币如何超越基础稳定币，自治性如何通过实验室进入科学研究，以及当初创公司在Formation阶段相互销售时，分销本身如何成为一种策略。

You'll hear how programmable money evolves beyond basic stablecoins, how autonomy starts entering scientific research through labs, and how distribution itself becomes a strategy when startups sell to other startups at formation.

我们将从已经可见的基础设施开始：稳定币走向主流。

We'll start with the rail that's already visible: stablecoins going mainstream.

盖伊·威尔特认为，稳定币只是开始。

Guy Willett argues that stablecoins are only the beginning.

下一阶段是链上信贷发放和新的合成产品，这些产品比简单地将传统资产复制到区块链上更容易扩展。

The next phase is on chain credit origination and new synthetic products that are easier to scale than simply copying traditional assets onto a blockchain.

以下是盖伊。

Here's Guy.

我叫盖伊·威尔特，我是十六C加密货币团队的普通合伙人。

My name is Guy Willett, and I'm a general partner on the crypto team here at a sixteen c.

今年，我的两大想法是信贷发放和紫色化。

This year, my big ideas are origination and purplification.

2025年，我们看到稳定币随着发行量和支付量的增加而走向主流。

In 2025, we've seen stablecoins go mainstream with increasing outstanding issuance and payment volume.

我一直在思考的是，巨额稳定币发行所带来的最重要的二级效应会是什么。

And one of the things I've been thinking about is what the most important second order effects of outstanding stablecoin issuance will be.

我认为，目前许多现有的稳定币实际上就像窄银行，它们将用户的存款以法币或国债的形式持有。

I think a lot of the existing stablecoins look effectively like narrow banks today, where they hold user deposits in fiat or perhaps in treasury bills.

从长远来看，我认为仅通过窄银行来扩展链上金融是极不可能的。

I think it's very unlikely in the long term that we scale on chain finance exclusively through narrow banks.

因此，我更多地在思考如何促进链上的信贷和资本形成。

And so I've been thinking much more about how we can facilitate credit and capital formation on chain.

将会出现一种非常重要的新角色或实体，类似于私人信贷基金，帮助在链上促成贷款。

There will be a sort of new role or entity that's very important, which is something akin to a private credit fund helping to facilitate loans on chain.

如果你观察加密货币链上事物的成熟过程，你会发现，这与传统金融世界正在发生的情况非常相似。

If you look at the way things are maturing on chain for crypto, you see, I think, a very similar market structure to what's happening in the traditional financial world.

在金融危机之后，部分由于巴塞尔协议III，出现了大量非银行贷款，由拥有大量股权资本的大型私人信贷基金提供。

After the great financial crisis, in part because of Basel III, there's a lot more nonbank lending from entities like large private credit funds that have significant equity capital.

因此，银行正在更多地向信贷基金放贷，而这些基金再向最终存款人发放贷款。

So banks are doing much more lending to credit funds who are making loans to end depositors.

如今在加密货币领域，这与我认为稳定币最终将向内容创作者或资产管理人放贷，而这些人再向终端用户放贷的模式非常相似。

This is very similar today in crypto to how I think stablecoins will effectively end up lending to curators or to asset managers who will end up making loans to end users.

我花了很多时间思考的一个问题是，如何在链上实现信用创造。

And one of the things I'm spending a lot of time considering is how we move credit origination on chain.

与其在链下发起一系列贷款，比如信用卡应收款，然后将其代币化，可能再证券化，并作为链下资产的副本转移到链上，

So instead of originating a series of loans off chain, a credit card receivable, for example, then tokenizing that, potentially securitizing it, and moving that on chain as a copy of an off chain asset.

我们该如何在链上原生地发起信用？

How we originate credit natively on chain.

我认为这一点非常重要，因为它可以大幅降低后台成本，比如贷款服务，而在许多情况下，这些成本每年可能占信贷额度的1%到3%。

And I think this is important specifically because it can drastically reduce back office costs like loan servicing, which in many cases can take one to 3% of the outstanding credit facility itself every year.

因此，这成本极其高昂。

So it's incredibly expensive.

但我还认为，链上发起信用将使不同的DeFi项目之间实现更强的可组合性。

But I also think doing on chain origination will allow for much more composability between different DeFi projects.

同样地，当我们思考如何将传统资产迁移到链上时，许多人专注于将这些资产代币化，即创建一个存在于链上的记录或链下资产的副本。

In a very similar vein, when we think about how to move traditional assets on chain, lots of people focus on tokenizing those assets, meaning creating an on chain record or copy of the existing asset that exists off chain.

我们已经看到许多永续期货项目围绕链下价格馈送或链下资产构建了永续合约。

We've seen many more perpetual futures projects purpify or create a perp related to an off chain price feed or an off chain asset.

与其将股票等资产代币化并上链，不如为该股票创建一个永续期货。

Instead of tokenizing something like an equity and putting it on chain, you could create a perpetual future for that equity.

我认为这在美国、欧洲和成熟的资本市场中很有意思，但对于新兴市场股票，比如印度股市，尤其值得关注，因为现有的衍生品（如零日期权）的名义交易量常常超过标的现货市场。

This, I think, is interesting in The US and in Europe and in robust capital markets, but I think it's particularly interesting for emerging market equities, things like the Indian equities market, for example, where existing derivatives like zero day options often trade more notional volume than the underlying spot.

因此，我认为这些标的资产的衍生品已经具备良好的产品市场契合度，未来几年我们可能会看到大量此类衍生品上链。

So I think there's already pretty good product market fit for derivatives on these underlying assets, and we could see a lot of those move on chain in the coming years.

稳定币如今已进入主流，但我认为我们不仅需要简单地将法币或法定货币代币化。

Stablecoins have gone mainstream today, but I would argue we need more than simple tokenization of fiat dollars or fiat currencies.

我认为有几个原因支持这一点。

I think there are a couple of reasons for this.

首先，我认为我们需要一种比铸造稳定币再用其发放贷款更好的方式来实现链上信贷。

The first would be, I think we need a better way to facilitate credit on chain than minting stablecoins and then using those stablecoins for loans.

在许多情况下，稳定币看起来像狭义银行，我们需要某种方式来实现链上信贷。

I think in many cases, stablecoins look like narrow banks, and we need some way to do credit on chain.

因此，我认为对于那些目前在Morpho或私人信贷基金等平台上运作的策展人来说，比如Apollo这样将信贷上链的机构，存在一个有趣的机会，即更积极地参与管理现有的稳定币抵押品。

And so I think there's an interesting opportunity for curators that today operate on something like Morpho or private credit funds, someone like Apollo that has put a credit on chain, take more of an active role in helping to manage the existing stablecoin collateral.

这种有趣的形式是，存在大量所谓的合成美元——它们严格来说并非稳定币，但代表了由链下传统资产支持的美元价值，其普通代币完全由DYAT美元的链下资产抵押，而其质押版本则由更高风险、更高收益的信贷资产抵押。

An interesting form of this is there are lots of, call them synthetic dollars because they're not strictly speaking stablecoins today, but that means a dollar representation backed by off chain traditional assets where the normal token itself is fully collateralized by DYAT Dollar's off chain, but the staked representation of it then would be collateralized by higher risk and higher yield credit assets.

Athena推广了这一理念。

And Athena popularized this idea.

最初，这种合成美元由现金与现货套利交易、永续期货的基差交易支持，但现在我们开始看到这种模式扩展到其他资产类别和结构，例如货币、现金与现货套利交易，以及由实体基础设施（如太阳能电池板、电池或GPU）支持的合成美元。

Initially, a synthetic dollar that is backed by a cash and carry trade, a basis trade for perpetual futures, but we're starting to see this proliferate into other asset classes and into other structures, things like currency, cash and carry trades, synthetic dollars that are backed by physical infrastructure, like into solar panels or batteries or or GPUs.

我认为，相较于美国股票，新兴市场股票可能是更有趣、更适合上链的资产，因为它们具有根本性的产品价值主张，而不仅仅是访问权限。

I think emerging market equities are perhaps a more interesting asset to bring on chain than, let's say, US equities because they have a fundamental product value proposition as opposed to simply access.

让我最兴奋的是将美国股票上链，使全球用户能够获得传统的美国金融服务和资产。

The thing that most excites me about putting US equities on chain is allowing global access to, you know, traditional American financial services and assets.

但当我们考虑印度股票和衍生品市场这样的例子时，在许多情况下，零日期权的名义交易量超过了其底层现货资产。

But when we think about an example like the Indian equities and derivatives market, in many cases, zero day options trade higher notional volume than the underlying spot assets do.

因此，我认为在许多地方，用户和散户投资者实际上更感兴趣的是交易衍生品，而这些衍生品通过上链可以变得更加高效，并获得更广泛的访问权限。

And so I think there are many places where users and retail investors are actually more interested in trading derivatives, and those derivatives could be made much more efficient and given much greater access by moving on chain.

所以我认为‘永续化’这个概念非常有趣。

So I think the idea of PERPification is very interesting.

简单来说，这意味着将今天现有的现货资产转化为永续合约或永续期货。

And just to say what that means very literally, that means to make a PERP or a perpetual future out of what is today an existing spot asset.

永续期货是一种衍生品，允许终端用户以非常直观和简单的方式调整其杠杆水平。

So a perpetual future is a derivative that allows an end user, an investor, to tune their leverage limits in a very intuitive and simple way.

存在一个永续合约价格，它与标记价格进行交易。

And there is a perp price that trades against a mark price.

当这两个价格出现分歧时，持有资产的一方会被收取费用或资金费率。

And when the two prices diverge, there are fees or a funding rate that are charged to the holder the asset.

我认为，永续化对广泛的传统资产都很有趣，因为创建一个可在链上扩展至极高名义规模的合成表示，比试图将现有资产代币化（即在链上直接复制它们）要容易得多。

And I think purplification is interesting for a wide swath of traditional assets because it's much easier to create a synthetic representation on chain that can scale to very high notional volume than trying to tokenize existing assets, basically copying them on chain.

因此，用更简洁的话说，我认为在链上创建传统资产的合成表示，比今天在链上创建这些资产的精确副本更具可扩展性。

And so to say it in briefer terms, I think the idea of creating synthetic representations of traditional assets on chain is more easily scalable than creating literal copies of those assets on chain today.

具体来说，当我们考虑信贷市场时，如今很多人对代币化感兴趣，但我不确定代币化为信贷资产带来的好处是否如人们想象的那么多。

So specifically, when we think about the credit markets, lots of people are interested in tokenization today, but I don't know that tokenization brings as many benefits for credit assets as people would imagine.

我更感兴趣的是原生链上贷款发起，因为我认为这可以显著降低传统上与创建资产支持证券或其他形式信贷资产相关的后台成本。

I'm much more interested in native on chain loan origination because I think it can significantly reduce the back office costs that are traditionally associated with creating basically asset backed securities or other forms of credit assets.

我认为，无论你是想向加密原生受众还是传统受众提供产品，采用更加密原生的方法实际上都更好，因为在许多情况下，你会获得更高的后台效率。

Think taking a more crypto native approach is actually better regardless of whether you wanna offer the product to a crypto native audience or to a more traditional audience because you're gonna have much greater back office efficiency in many cases.

许多这些细节仍在摸索中，但我认为这里有很大的潜力。

And a lot of these details are still being figured out, but I think there is great potential there.

当我们谈论链上的合成美元时，指的是某种代币，其价值与美元计价的资产挂钩，但由一组底层金融资产或在许多情况下某种特定结构化产品作为抵押，我认为这为将货币现金套利交易或传统基础设施融资（如GPU、太阳能板或电池）映射到链上提供了大量机会。

When we talk about synthetic dollars on chain, meaning a token, a blockchain that is pegged to a dollar denominated representation but is collateralized by some underlying set of financial assets or, in many cases, a specific structured product, I think there are a lot of opportunities to take things like currency cash and carry trades or traditional infrastructure financing for things like GPUs or solar panels or batteries and reflect those on chain.

从加密原生的角度来看，这与传统市场中将交易策略转化为ETF的方式非常相似，为用户提供了更大的可及性和透明度。

And really, this looks very similar in a crypto native sense to how traditional trading strategies have been turned into ETFs in existing markets, providing greater access and legibility to users.

我认为这种趋势将在链上更广泛地发生。

I think that will happen much more on chain.

我认为建设者们有机会打造一个所谓的‘合成美元工厂’，将这些现有的有趣交易或投资产品与美元挂钩，提供给现有投资者，并用这些合成美元为稳定币提供抵押。

I think there's a real opportunity for builders to create, let's call it a synthetic dollar factory, to take these existing these interesting trades or investing products and peg them to a dollar, offer them to existing investors, and help collateralize stablecoins with those synthetic dollars.

盖伊的观点是，一旦新的基础设施建立起来，你就能找到更原生、更可组合且运营成本更低的金融产品。

Guy's point is that once the new rails exist, you can find financial products that are more native, more composable, and cheaper to operate.

现在我们转向另一个领域，在这里新的基础设施可以改变进步的速度：科学。

Now we shift to another domain where new rails can change the speed of progress: science.

徐 Oliver 解释了 AI 推理能力和机器人学习的进步如何推动我们迈向自主实验室。

Oliver Xu explains how advances in AI reasoning and robot learning move us toward autonomous labs.

还不是完全自动驾驶的科学，但科学家、AI 系统和实验室自动化之间正在实现真正的协作，因为研究需要理解‘为什么’，而不仅仅是‘是什么’。

Not fully self driving science yet, but real collaboration between scientists, AI systems, and lab automation, where interpretability and traceability matter because research requires understanding why, not just what.

以下是 Oliver。

Here's Oliver.

我的名字是徐 Oliver。

My name is Oliver Xu.

我是 A16z 美国动力团队的合伙人，我的核心观点是：AI 推理能力与机器人学习的进步，将通过推动我们更接近自主实验室来加速科学进展。

I'm a partner on the American Dynamism team here at A16z, And my big idea is that advances in AI reasoning capabilities and in robot learning will help accelerate scientific progress by moving us closer towards autonomous labs.

实验室自动化早已存在。

Laboratory automation is something that's existed for a long time.

这并不是什么新事物。

Like, that is not new.

这种想法是让机器人预先编程，以协助实验室中的一些操作。

This idea of having robots that you can preprogram to assist in some of the motions involved in a lab.

现在新兴的是推理能力、实验规划与实验室自动化物理元素的结合。

What is new and what is emerging right now is the combination of reasoning capabilities and experiment planning and the physical element of lab automation.

因此，短期内的场景可能是科学家与一个包含AI应用和机器人的系统进行协作，这种协作过程将在各种实验室和不同的科学领域中变得更加紧密，无论是生命科学、化工行业，还是材料科学研究等领域。

So what that might look like in the near term is collaboration between a scientist and a system that involves both an AI application and a robot and having that be a much more collaborative process in the near term in many different kinds of labs and many different kinds of scientific processes, whether that's in the life sciences, in the chemicals industry, in the material science research sphere, and so on and so forth.

不过，我认为短期内很重要的一点是可解释性。

One of the things that I think is important in the near term though is around interpretability.

如果你把AI系统看作是非确定性的计算机，那么对科研而言，真正重要的是要理解系统为何如此行动，为何以某种方式规划实验迭代，为何要执行这一特定操作。

So, you know, if you think about AI systems as non deterministic computers, one of the things that really matters for research is you want to really understand why the system is doing what it's doing, why it's planning on iterating on an experiment in a given way, why it's planning on doing this particular thing.

我认为，专为科学研究设计的系统很可能会重点关注可解释性，记录在与人类科学家协作过程中每个步骤的确切发生情况。

And I think, you know, systems that are purpose built for scientific research are probably going to focus a lot on that, on the interpretability, on recording what exactly is happening throughout each step of the process as it collaborates with a human scientist.

我认为，完全自主的科学——即一个闭环系统，AI自我迭代、执行实验，然后在无人干预的情况下持续迭代——这还为时过早。

I think this concept of fully self driving science, right, like a closed loop where you have AI that iterates on itself and then carries out an experiment then continues to iterate without human intervention, I think this is further out.

这正是我所认为的自主科学理念的最终目标。

This is what I would consider the destination for this idea of autonomous science.

我认为我们现在所处的阶段是，正在为自主科学奠定基础。

I think where we are right now is that there's a lot of work being done to form the foundations of autonomous science.

如果将科学 broadly 理解为理论、计算和实验的结合，那么在AI生态系统中，人们正在数学推理、物理推理、模拟与世界模型、以及机器人学习等领域开展大量工作。

And if you consider science as, you know, broadly speaking, some combination of theory, of computation, and of experimentation, there's work being done in the AI ecosystem across areas like mathematical reasoning, physical reasoning, simulation and world models, and robot learning.

随着这些能力的提升，所有这些技术最终都将被用于闭合这一循环。

And all of these things eventually, as these capabilities improve, can be applied to closing this loop.

但这些领域的发展进度显然不均衡，你必须等待各项能力达到足以应用于这一闭环的水平。

But progress across all these fields is, of course, uneven, and you kinda have to wait for the capabilities to get to the point where they're ready to be applied to this closed loop.

我认为这就是最终目标。

And I think that's the destination.

在近期，我们将逐步推进实验室自动化和这一系统中的推理能力。

And in the near term, incrementally, we'll make progress on, you know, lab automation, on the reasoning pieces of this.

但最终，我认为终极目标将是实现自动驾驶实验室或自主科学的理念。

But ultimately, the final destination, I think, would be around this idea of a self driving lab or of autonomous science.

这其中一部分将由科研所处的市场动态所驱动。

Part of this is gonna be driven by the market dynamic in which the research is conducted.

我认为，在某些科学领域，研究产出的需求市场已经更加成熟。

So I think there are certain categories of science where there is just a much more mature demand side market for the outputs of research.

例子包括生命科学和制药业、化工行业以及材料科学行业的某些方面。

And examples include, of course, life sciences and pharma, the chemicals industry, facets of the material science industry.

我认为这些领域对这类研究的产出有着现成且愿意购买的客户。

I think these are areas where there is a ready and willing buyer for a lot of the outputs of this research.

速度和能力的提升，以及任何可能带来的成本优势，对于那些研究产出已有稳定买家的市场来说，将变得更加重要。

And the increase in speed and capability as well as any cost advantage that might accrue, all of these things are gonna matter more for markets where there is a well established buyer of research output.

因此，我认为自主实验室和自主科学最先被采用的地方，很可能取决于其所处的市场环境。

And so I think where you see autonomous labs and autonomous science being adopted first is probably more of a function of the market that it's operating in.

我认为Periodic Labs是团队致力于自主科学的一个绝佳例子。

I think Periodic Labs is a great example of a team taking a swing at autonomous science.

我认为，当你观察早期初创企业格局时，像Medra这样的公司专注于生命科学和制药市场。

I think when you look at the early stage startup landscape, there's companies like Medra that are focused on the life sciences and pharma market.

还有像Chemify和Yoneda Labs这样的公司，专注于化学行业。

There's companies like Chemify and Yoneda Labs that are focused on the chemistry industry.

再放宽视野来看，政府与产业之间存在着专注于人工智能与科学交汇点的合作。

And then zooming out a bit, there's collaborations between government and industry that are really focused on this intersection of AI and science.

例如，能源部主导的Genesis计划，将学术界、政府、国家实验室以及领先的人工智能公司聚集在一起，推动人工智能驱动的科学研究。

You know, there is the Genesis mission led by the Department of Energy that brings together, you know, academia, government, and the national labs, as well as leading AI companies to pursue AI driven science.

我认为就在今天，DeepMind宣布与英国政府合作，共同推进科学发现领域的工作。

I think just today, DeepMind announced a partnership with the UK government to collaborate on areas of scientific discovery.

所以我认为，有一些初创公司正在致力于实验室自动化。

So I think, you know, there's startups that are working on lab automation.

还有一些初创公司正在致力于打造人工智能科学家。

There's startups that are working on building an AI scientist.

而这一切都发生在公共部门、私营部门与学术界广泛合作的背景下，旨在加速人工智能驱动的科学发现。

And that work is happening against the backdrop of a broader collaboration between both the public sector and the private sector in academia to really accelerate AI driven scientific discovery.

奥利弗展示了当工具演变为系统时会发生什么。

Oliver shows what happens when tools become systems.

当规划、执行和迭代开始形成闭环时，研究速度就会加快。

Research gets faster when planning, execution, and iteration begin to close the loop.

最后，我们将关注另一种基本要素：分发。

To close, we focus on a different kind of primitive: distribution.

詹姆斯·德科斯塔介绍了绿地战略：AI原生初创企业早期向其他AI原生初创企业销售产品，当时利益相关者很少，没有转换成本，并且有机会与客户共同成长，助其发展为大型企业。

James DeCosta introduces the Greenfield strategy, AI native startups selling to other AI native startups early when there are a few stakeholders, no switching costs, and the chance to grow alongside customers as they scale into major companies.

下面是詹姆斯。

Here's James.

我叫詹姆斯·德科斯塔。

My name is James Decosta.

我是应用投资团队的合伙人。

I'm a partner on the Apps investing team.

我对2026年的核心观点是绿地战略。

My big idea for 2026 is the greenfield strategy.

我们将看到AI原生初创企业向其他AI原生初创企业销售产品并实现规模化。

We're going to see AI native startups selling to other AI native startups reach scale.

初创企业面临的最大问题之一，是能否在现有巨头实现创新之前建立起分发渠道。

One of the biggest issues for startups is whether they can reach distribution before incumbents reach innovation.

在这个软件周期中，现有企业也在加入人工智能。

And in this software cycle, incumbents are also adding AI as well.

那么初创公司能做什么呢？

So what can the startup do?

初创公司赢得分销渠道最强大却最被低估的方式之一，实际上是服务处于初创阶段或空白领域的公司。

One of the most powerful and underrated ways for startups to win distribution is actually to serve companies at formation or greenfield companies.

每个初创公司与现有企业之间的竞争，最终取决于初创公司能否在现有企业创新之前实现分销。

The battle between every startup and incumbent ultimately comes down to whether the startup can get to distribution before the incumbent innovates.

在这个软件周期中，现有企业也没有对人工智能掉以轻心。

And in this software cycle, incumbents aren't asleep on AI either.

但初创公司赢得分销竞争最好且最被低估的方式之一，实际上是向其他初创公司销售产品。

But one of the best and underrated ways startup can win the game of distribution is actually selling to other startups.

这些初创公司的利益相关者要少得多。

Those startups have far fewer stakeholders.

你只需要说服一位首席执行官和几位联合创始人。

You just need to convince a CEO and a couple of founders.

这些初创公司不需要完整的解决方案，而且它们也没有切换成本，因为它们今天甚至还没有任何解决方案。

Those startups don't need a complete solution, and those startups don't have any switching costs because they don't even have a solution in place today.

如果你能吸引所有新成立的公司，并与它们共同成长，当你的客户自身也成长为大公司时，你也会如此。

If you attract all of the new companies at Formation and then grow with them, as your customers become big companies in their own right, so will you.

这实际上是Stripe十多年前使用的策略。

This is actually the playbook that Stripe used over a decade ago.

Stripe的许多早期客户在Stripe成立时还不存在。

Many of Stripe's first customers did not exist when Stripe was founded.

但随着Stripe的客户成长为自身的大客户，Stripe利用它们作为案例，向硅谷以外的其他企业进行销售。

But as Stripe's customers grew to become big customers in their own right, Stripe used them as a case study to then sell to other enterprises outside of Silicon Valley.

incumbents 难以向初创公司销售，因为它们受制于损益规则。

Incumbents struggle to sell to startups because they're bound by the rules of p and l.

对于现有企业而言，新初创公司带来的新收入微乎其微，但为它们提供服务却需要投入大量营销、销售或开发新产品的成本。

New startups represent very little in the form of new revenue for incumbents, but they cost money to serve in terms of marketing spend or sales or building out a new product.

作为一家新创公司，你仍在摸索中，只需把初步产品送到客户手中即可。

As a new startup, you're still figuring it out, and you just need to get your initial product in customers' hands.

如果你遵循这一策略，最重要的一点是找到持续的新客户来源，像Y Combinator、Speedrun或Entrepreneur First这样的加速器正是绝佳的机会。

One of the most important things if you're following this strategy is to find a constant source of new customers and accelerators like Y Combinator or Speedrun or Entrepreneur First offer a perfect opportunity for that.

例如，Mercury与每一批Y Combinator初创公司中的一半合作，并随着时间推移与这些公司共同成长。

Mercury, for instance, works with 50% of every single YC batch and then grows with those companies over time.

每一个新的初创公司都需要一个客户关系管理系统。

Every single new startup still needs a CRM.

每一个新的初创公司都需要一个人力资源和员工管理系统。

Every single new startup still needs a HR and workforce system.

如果你能找到一个狭窄的切入点，为这些AI初创公司打造更好的解决方案，就可以以此为突破口进入市场。

If you can fundamentally find a narrow wedge and build a better solution for those AI startups, you can use that as a way to get into the market.

一旦你找到了切入点，并用AI将这个单一切入点做得更好，非常重要的一点是，你需要持续向客户交付有用的功能，才能真正与他们共同成长，并降低长期流失的风险。

Once you've found a wedge and made that single wedge much better with AI, one of the really important things is that you have to keep shipping useful features to your customers to actually be able to grow with them and reduce the risk of churn over time.

一旦你获得了这些新客户，就必须快速迭代、发布功能，并随着客户的发展不断扩展产品范围。

Once you've actually got these new customers, you have to rapidly iterate and ship features and build out a wider product as your customers grow.

里程碑时刻也为初创公司应用绿田战略提供了绝佳机会。

Graduation moments also offer the perfect opportunity for startups to apply the greenfield strategy.

这些时刻往往是初创公司已经超越了他们现有的简单解决方案，比如从QuickBooks迁移到NetSuite，而像Rilla这样的AI公司正在做 exactly 这件事。

This is the moments where maybe startups have outgrown a simple solution that they have in place, like moving from QuickBooks to NetSuite, and AI companies like Rilla are doing exactly this.

你也不必受限于企业软件现有的分类。

You also don't have to be constrained by the existing categories of enterprise software.

CRM、客户成功和线索挖掘曾经是不同的软件系统。

CRM, customer success, and finding pipeline all used to be different software systems.

但如今有了AI，下一代原生AI的CRM或许能够帮你发现潜在客户、追踪这些线索，并在客户入驻你的平台后确保他们的成功。

But now with AI, maybe the next new AI native CRM can find your leads, can track those leads, and actually ensure your customers are successful once they're onboarded to your platform.

这三个想法共同构成了一个统一的论点。

These three ideas fit together as a single thesis.

新的基础设施催生了新的复利效应。

New rails create new compounding.

Guy展示了可编程货币如何从稳定币演变为链上信用发放，并通过合成产品实现规模化，同时降低运营摩擦。

Guy shows programmable money evolving from stablecoins into on chain credit origination in synthetic products that scale with lower operational friction.

Oliver展示了自主性如何通过实验室协作进入科学领域，而可解释性则是使其在近期可用的关键要求。

Oliver shows how autonomy entering science through lab collaboration with interpretability as the near term requirement that makes it usable.

詹姆斯展示了一种复利型的分销策略：在初创企业形成阶段就赢得客户，并在现有巨头反应过来之前与他们共同成长。

James shows a distribution strategy that compounds: win customers at formation and grow with them before incumbents can catch on.

这正是新的基础设施原语在这里的真正含义——不是空洞的流行语，而是让全新系统得以诞生和扩展的底层轨道。

This is what new infrastructure primitives really means here, not a buzzword, but the rails that let entirely new systems emerge and scale.

感谢收听本集a16z播客。

Thanks for listening to this episode of the a16z Podcast.

如果你喜欢这一集，请务必点赞、评论、订阅、给我们打分或留下评价，并分享给你的朋友和家人。

If you like this episode, be sure to like, comment, subscribe, leave us a rating or a review, and share it with your friends and family.

如需收听更多集数，请访问YouTube、Apple Podcasts和Spotify。

For more episodes, go to YouTube, Apple Podcasts, and Spotify.

在X上关注我们@a16z，并订阅我们的Substack：a16z.substack.com。

Follow us on x at a sixteen z, and subscribe to our Substack at a sixteen z dot Substack dot com.

再次感谢收听，我们下一期再见。

Thanks again for listening, and I'll see you in the next episode.

提醒一下，此处内容仅作信息参考，不应被视为法律、商业、税务或投资建议，也不应用于评估任何投资或证券，且并非针对任何a16z基金的投资者或潜在投资者。

As a reminder, the content here is for informational purposes only, should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any a sixteen z fund.

请注意，a16z及其关联方可能也持有本播客中讨论的公司的投资。

Please note that a sixteen z and its affiliates may also maintain investments in the companies discussed in this podcast.

如需更多详情，包括我们的投资链接，请访问a16z.com/disclosures。

For more details, including a link to our investments, please see a 16z.com forward /disclosures.