AI市场的真相：繁荣与困境并存

https://twitter.com/0xSamHogan/status/1680725207898816512

本文来自Twitter 文章的翻译，说明了当下 AI 市场的一些情况，深有共鸣，与君共享。

【正文开始】

6 months ago it looked like AI / LLMs were going to bring a much needed revival to the venture startup ecosystem after a few tough years. 

6个月前，人工智能（AI）/ LLM（语言模型）似乎将为风险创业生态系统带来急需的复苏，之前经历了几个艰难的年头。

With companies like Jasper starting to slow down, it’s looking like this may not be the case. 

随着像Jasper这样的公司开始放缓，情况似乎并非如此。

Right now there are 2 clear winners, a handful of losers, and a small group of moonshots that seem promising. 

现在的状况是：有两个明显的赢家，一小部分输家，还有一小群看起来很有前途的创新项目。

Let’s start with the losers. 

让我们从输家开始。

Companies like Jasper and the VCs that back them are the biggest losers right now. Jasper raised >$100M at a 10-figure valuation for what is essentially a generic, thin wrapper around OpenAI. Their UX and brand are good, but not great, and competition from companies building differentiated products specifically for high-value niches are making it very hard to grow with such a generic product. I’m not sure how this pans out but VC’s will likely lose their money. 

像Jasper和支持他们的风险投资公司现在是最大的输家。Jasper以10位数的估值筹集了超过1亿美元，实际上只是OpenAI的一个普通、薄薄的包装。他们的用户体验和品牌都不错，但并不出色，而且那些专门为高价值细分市场构建差异化产品的公司的竞争，使得凭借这样一个普通产品实现增长变得非常困难。我不确定这将如何发展，但风险投资公司可能会损失他们的资金。

The other category of losers are the VC-backed teams building at the application layer that raised $250K-25M in Dec - March on the back of the chatbot craze with the expectation that they would be able to sell to later-stage and enterprise companies. These startups typically have products that are more focused than something very generic like Jasper, but still don't have a real technology moat; the products are easy to copy. 

另一类输家是在应用层面上筹集了250K-25M美元的风险投资支持团队，他们在聊天机器人热潮中期望能够向后期和企业公司销售。这些初创公司通常比Jasper这样的普通产品更专注，但仍然没有真正的技术壕沟；这些产品很容易被复制。

Executives at enterprise companies are excited about AI, and have been vocal about this from the beginning. This led a lot of founders and VC's to believe these companies would make good first customers. What the startups building for these companies failed to realize is just how aligned and savvy executives and the engineers they manage would be at quickly getting AI into production using open-source tools. An engineering leader would rather spin up their own @LangChainAI and @trychroma infrastructure for free and build tech themselves than buy something from a new, unproven startup (and maybe pick up a promotion along the way). 

企业公司的高管对人工智能感到兴奋，并从一开始就公开表示了这一点。这导致许多创始人和风险投资公司相信这些公司将成为良好的首批客户。这些为这些公司开发产品的初创公司没有意识到的是，高管和他们管理的工程师在迅速使用开源工具将人工智能投入生产方面是多么一致和精明。与其从一个新的、未经验证的初创公司购买产品（也许还能获得晋升），工程领导者宁愿免费启动他们自己的@LangChainAI和@trychroma基础设施，并自己构建技术。

In short, large companies are opting to write their own AI success stories rather than being a part of the growth metrics a new AI startup needs to raise their next round. 

(This is part of an ongoing shift in the way technology is adopted; I'll discuss this in a post next week.)

简而言之，大公司选择编写自己的人工智能成功故事，而不是成为新的人工智能初创公司筹集下一轮资金所需的增长指标的一部分。

（这是技术采用方式的持续转变的一部分；我将在下周的一篇文章中讨论这个问题。）

This brings us to our first group of winners — established companies and market incumbents. Most of them had little trouble adding AI into their products or hacking together some sort of "chat-your-docs" application internally for employee use. This came as a surprise to me. Most of these companies seemed to be asleep at the wheel for years. They somehow woke up and have been able to successfully navigate the LLM craze with ample dexterity. 

这使我们来到我们的第一组赢家——已经建立起来的公司和市场领导者。他们中的大多数在将人工智能应用到他们的产品中或在内部为员工使用时，都没有遇到太大的困难。这让我感到惊讶。这些公司中的大多数似乎多年来都在沉睡。他们不知何故醒来，并成功地应对了LLM热潮。

There are two causes for this:

这有两个原因：

1. Getting AI right is a life or death proposition for many of these companies and their executives; failure here would mean a slow death over the next several years. They can't risk putting their future in the hands of a new startup that could fail and would rather lead projects internally to make absolutely sure things go as intended.

对于这些公司及其高管来说，正确使用人工智能是生死攸关的事情；在未来几年里，失败将意味着缓慢的死亡。他们不能冒险将他们的未来交给可能失败的新创公司，他们宁愿在内部领导项目，以确保事情按计划进行。

2. There is a certain amount of kick-ass wafting through halls of the C-Suite right now. Ambitious projects are being green-lit and supported in ways they weren't a few years ago. I think we owe this in part to @elonmusk reminding us of what is possible when a small group of smart people are highly motivated to get things done. Reduce red-tape, increase personal responsibility, and watch the magic happen.

目前，C级高管办公室中充满了一定程度的活力。雄心勃勃的项目得到批准并得到支持，这是几年前所没有的。我认为这在一定程度上要归功于@elonmusk，他提醒我们，当一小群聪明人高度积极地完成任务时，会发生什么。减少繁文缛节，增加个人责任，然后看魔法发生。

Our second group of winners live on the opposite side of this spectrum; indie devs and solopreneurs. These small, often one-man outfits do not raise outside capital or build big teams. They're advantage is their small size and ability to move very quickly with low overhead. They build niche products for niche markets, which they often dominate. The goal is build a saas product (or multiple) that generates ~$10k/mo in relatively passive income. This is sometimes called "mirco-saas."

我们的第二组赢家位于这个光谱的另一端；独立开发者和个体创业者。这些小型、通常是单人的团队不筹集外部资本，也不建立庞大的团队。他们的优势在于规模小、能够以低成本迅速行动。他们为细分市场构建细分产品，通常占据主导地位。目标是构建一个每月产生约1万美元相对被动收入的SaaS产品（或多个）。这有时被称为“微型SaaS”。

These are the @levelsio's and @dannypostmaa's of the world. They are part software devs, part content marketers, and full-time modern internet businessmen. They answer to no one except the markets and their own intuition.

他们是@levelsio和@dannypostmaa这样的人。他们既是软件开发人员，又是内容营销人员，也是全职的现代互联网商人。他们只对市场和自己的直觉负责。

This is the biggest group of winners right now. Unconstrained by the need for a $1B+ exit or the goal of $100MM ARR, they build and launch products in rapid-fire fashion, iterating until PMF and cashflow, and moving on to the next. They ruthlessly shutdown products that are not performing. 

现在，这是最大的赢家群体。他们不受需要10亿美元以上的退出或100MM ARR目标的限制，他们以快速发布细分市场的AI驱动产品的方式建立和推出产品，不断迭代直到找到市场适合度和现金流，然后转向下一个产品。他们会毫不留情地关闭表现不佳的产品。

LLMs and text-to-image models a la Stable Diffusion have been a boon for these entrepreneurs, and I personally know of dozens of successful (keeping in mind their definition of successful) apps that were started less than 6 months ago. The lifestyle and freedom these endeavors afford to those that perform well is also quite enticing. 

LLM和文本到图像模型（如Stable Diffusion）对这些企业家来说是一大助力，我个人知道有几十个成功（根据他们的定义）的应用程序是在不到6个月前开始的。这些努力所带来的生活方式和自由也非常诱人。

I think we will continue to see the number of successful micro-saas AI apps grow in the next 12 months. This could possibly become one of the biggest cohorts creating real value with this technology. 

我认为在接下来的12个月里，我们将继续看到成功的微型SaaS AI应用程序的数量增长。这可能成为使用这项技术创造真正价值的最大群体之一。

The last group I want to talk about are the AI Moonshots — companies that are fundamentally re-imagining an entire industry from the ground up. Generally, these companies are VC-backed and building products that have the potential to redefine how a small group of highly-skilled humans interact with and are assisted by technology. It's too early to tell if they'll be successful or not; early prototypes have been compelling. This is certainly the most exciting segment to watch. 

我想谈谈的最后一组是AI Moonshots——这些公司从根本上重新构想了整个行业。一般来说，这些公司得到了风险投资的支持，正在构建具有重新定义小规模高技能人员与技术互动和协助潜力的产品。现在还为时过早，无法确定它们是否会成功；早期的原型是令人信服的。这无疑是最令人兴奋的领域。

A few companies I would put in this group are:

1. https://cursor.so - an AI-first code editor that could very well change how software is written.

2. https://harvey.ai - AI for legal practices

3.  https://runwayml.com - an AI-powered video editor

我认为属于这一组的一些公司有：

1. https://cursor.so - 一款以人工智能为先的代码编辑器，可能会改变软件编写的方式。

2. https://harvey.ai - 用于法律实践的人工智能

3. https://runwayml.com - 一款由人工智能驱动的视频编辑器

This is an incomplete list, but overall I think the Moonshot category needs to grow massively if we're going to see the AI-powered future we've all been hoping for.

这只是一个不完整的列表，但总的来说，我认为Moonshot类别需要大规模增长，如果我们要看到我们一直希望的人工智能驱动的未来。

If you're a founder in the $250K-25M raised category and are having a hard time finding PMF for your chatbot or LLMOps company, it may be time to consider pivoting to something more ambitious.

如果你是一个在筹集了250K-25M美元的范畴内的创始人，并且在为你的聊天机器人或LLMOps公司寻找PMF方面遇到了困难，也许是时候考虑转向更有雄心的事物了。

Lets recap:

1. VC-backed companies are having a hard time. The more money a company raised, the more pain they're feeling.

2. Incumbents and market leaders are quickly become adept at deploying cutting-edge AI using internal teams and open-source, off-the-shelf technology, cutting out what seemed to be good opportunities for VC-backed startups.

3. Indie devs are building small, cash-flowing businesses by quickly shipping niche AI-powered products in niche markets.

4. A small number of promising Moonshot companies with unproven technology hold the most potential for VC-sized returns.

让我们回顾一下：

1. 受风险投资支持的公司正在遇到困难。公司筹集的资金越多，他们感受到的痛苦就越多。

2. 市场领导者和现有企业正在迅速掌握使用内部团队和开源、现成技术部署尖端人工智能的能力，削减了风险投资支持的初创企业看似不错的机会。

3. 独立开发者正在通过快速推出利基市场上的利基人工智能产品来建立小型、现金流动的企业。

4. 少数有着未经证实技术的有前途的“月球登陆”公司具有最大的风险投资回报潜力。

It's still early. This landscape will continue to change as new foundational models are released and toolchains improve. I'm sure you can find counter examples to everything I've written about here. Put them in the comments for others to see.

现在还处于早期阶段。随着新的基础模型的发布和工具链的改进，这个领域将继续变化。我相信你可以找到反例来反驳我在这里写的一切。请在评论中分享给其他人看。

And just to be upfront about this, I fall squarely into the "raised $250K-25M without PMF" category. If you're a founder in the same boat, I'd love to talk. My DMs are open.

为了坦率起见，我自己也是“筹集了250K-25M美元但没有PMF”的创始人。如果你也是处于同样境地的创始人，我很愿意交流。我的私信是开放的。

If you enjoyed this post, don't forget to follow me, Sam Hogan. I share one long-form post per week covering AI, startups, open-source, and more. 

如果你喜欢这篇文章，请不要忘记关注我，Sam Hogan。我每周分享一篇长篇文章，涵盖人工智能、创业、开源等内容。

That's all folks! Thanks for reading. See you next week.

就这些了！感谢阅读。下周见。