当通用人工智能（genAI）在企业中扩展时，许多领导者正在研究如何将他们的genAI原型发展成生产就绪的工具。Pinecone的CEO埃多·利伯蒂（Edo Liberty）和LangChain的CEO哈里森·蔡斯（Harrison Chase）与a16z Growth的普通合伙人莎拉·王（Sarah Wang）讨论了在堆栈中哪些部分应该构建或购买，如何通过帮助客户选择和摄取正确的数据来改进开箱即用的模型，并选择正确的合作伙伴来扩大genAI应用。
1. "We noticed a key change when we surveyed about 70 enterprise leaders this year. and were very surprised to hear that folks were tripling their budgets on AI spend."
我们今年调查了大约70位企业领导者，发现了一个关键的变化，非常惊讶地听说人们的AI支出预算增加了两倍。
2. "They were shifting away from just OpenAI into open source models, and I think in a major way are moving from just experimental workloads to production workloads."
他们正在从仅仅使用OpenAI转向开源模型，我认为在很大程度上，他们正在从实验性工作负载转向生产性工作负载。
3. "The main one that naturally comes to mind is that it’s easy to build with genAI, and in some sense it is."
自然而然想到的主要问题是，用通用人工智能构建东西很容易，在某种意义上确实如此。
4. "But I think it’s much harder to actually turn that into something that’s production ready."
但我认为实际上要将其转变为可以投入生产的东西要困难得多
5. "I think of all the hype around AutoGPT and agents when it came out in March of last year."
我想到了去年3月AutoGPT和代理出现时围绕它们的所有炒作。
6. "We’re starting to see the tide turn, as you mentioned. Starting as of this new year, I think we’re starting to see more of these agentic and interesting applications come into production in enterprises."
我们开始看到潮流的转变，正如你提到的。从新的一年开始，我认为我们开始看到更多的这些代理和有趣的应用在企业中投入生产。
7. "I agree 100%. I tell people all the time that with AI impossible things are possible, but easy things are still hard."
我100%同意。我总是告诉人们，有了人工智能，不可能的事情变得可能，但简单的事情仍然很难。
8. "The gap from prototype to production is one that we think about a lot."
从原型到生产的差距是我们经常考虑的问题。
9. "There has to be commitment to bridge that gap."
必须有承诺来弥合这一差距。
10. "It’s really easy to get a prototype up and running, and that’s what we see."
让原型运行起来真的很容易，这就是我们看到的。
11. "The general solution that you can get in five lines of code from a snippet of LangChain or wherever generally isn’t enough for high-quality production apps."
从LangChain或任何地方的代码片段中得到的通用解决方案通常不足以满足高质量的生产应用程序。
12. "There’s still a good amount of engineering that goes into it."
仍然需要大量的工程工作投入其中。
13. "It’s not quite magic yet."
这还没有完全变成魔法。
14. "There’s still work that needs to be put in."
还有很多工作需要投入。
15. "If they understand that this is an undertaking, this is a new stack, this is a new set of technologies, or new sets of services, infrastructures, nouns and objects and behaviors that they need to get used to, they need to hire their talent, they need to wait time, they need to fail a few times… if they are signed up for that, they’re going to be successful."
如果他们明白这将是一项任务，这是一套新的堆栈，这是一套新的技术，或者是一套新的服务，基础设施，名词和对象和行为，他们需要习惯，他们需要聘请人才，他们需要等待时间，他们需要失败几次……如果他们准备好了，他们就会成功。
16. "We really focus on what we call knowledgeable AI."
我们真正关注的是我们所说的知识型人工智能。
17. "At the end of the day, whatever language model you’re using, it doesn’t know anything about your company, your contracts, your customers, your code base, whatever it is that you’re trying to be intelligent about."
归根结底，无论你使用哪种语言模型，它对你的公司、你的合同、你的客户、你的代码库、你试图变得智能的任何事情都一无所知。
18. "So now the question is if you’re trying to build a knowledgeable agent, chat, assistant, whatever, you’re going to need to figure out what data… what it needs to know to be useful."
所以现在的问题是，如果你试图构建一个知识渊博的代理、聊天、助手，等等，你将需要弄清楚什么数据……它需要知道什么才能有用。
19. "We’re starting to see more and more applications like this and more and more success."
我们开始看到越来越多这样的应用，越来越成功。
20. "We’re extremely excited about this and doubling down on our investments there."
我们对此感到非常兴奋，并在这些领域的投资上加倍下注。
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