破解人工智能试点失败之谜

欢迎收听《实用人工智能》播客，在这里我们将解析人工智能在现实世界中的应用，以及它如何重塑我们的生活、工作和创作方式。我们的目标是让AI技术变得实用、高效且人人可及。无论你是开发者、企业领袖，还是单纯对科技热点背后的技术感到好奇，这里都适合你。请务必在领英、X或Blue Sky上关注我们，以获取最新节目动态、幕后内容和AI洞见。更多信息请访问practicalai.fm。

Welcome to the Practical AI podcast, where we break down the real world applications of artificial intelligence and how it's shaping the way we live, work, and create. Our goal is to help make AI technology practical, productive, and accessible to everyone. Whether you're a developer, business leader, or just curious about the tech behind the buzz, you're in the right place. Be sure to connect with us on LinkedIn, X, or Blue Sky to stay up to date with episode drops, behind the scenes content, and AI insights. You can learn more at practicalai.fm.

现在，节目正式开始。

Now, onto the show.

欢迎来到《实用人工智能》播客的新一期节目。这次是纯对谈集，只有我和克里斯，没有嘉宾。我们将深入探讨AI新闻和趋势中发布和讨论的一些内容，希望能花些时间帮助大家提升机器学习和AI技能。我是丹尼尔·维特纳克，Prediction Guard公司的CEO，和往常一样，我的搭档主持人是克里斯·本森，他是洛克希德·马丁公司的首席AI研究工程师。

Welcome to another episode of the Practical AI Podcast. In this fully connected episode. It's just Chris and I, no guests, and we'll spend some time digging into some of the things that have been released and talked about in AI news and and trends and hopefully spend some time helping you level up your machine learning and AI game. I'm Daniel Witenack. I am CEO at Prediction Guard, and I'm joined as always by my cohost, Chris Benson, who is a principal AI research engineer at Lockheed Martin.

最近怎么样，克里斯？今天很棒，丹尼尔。AI世界真是风云变幻啊。天哪。确实。

How you doing, Chris? Doing great today, Daniel. So much happening in the world of AI. Holy cow. Yes.

是的。有很多内容需要跟进。新闻中出现了许多有趣的进展，可能大家有所耳闻，或许值得提炼和综合一下，看看它们意味着什么，传递了什么信号，人们该如何看待某些领域的进步。所以，我很期待这次讨论。克里斯，有没有哪些特别突出的消息让你印象深刻？

Yes. Lots lots to catch up on. There have been a number of interesting developments in the news that maybe people have heard about and yeah, might be good to just kind of distill down and synthesize a little bit in terms of what you know, what they mean, what they signal, how people can think about how certain things are advancing. So, yeah, looking forward to looking forward to this this discussion. Any things that have been particularly standing out to you that that you've been hearing about, Chris?

当然。我认为最近几周最引人注目的现象是，无论是AI圈内人士还是受其影响的圈外人士——实际上每个人都受影响——都在讨论就业问题。我们曾在节目中多次探讨AI对就业的影响，但现在人们真切地感受到了压力。就业市场相当紧张。我接触过许多正在求职的人，无论他们目前在职还是刚毕业。

Sure. I think the thing that has really been noticeable in recent weeks has been so many people that are both in the AI world and outside of it, but impacted like everybody is, are talking about jobs. And we've talked about the impact of AI on jobs many, many times on the show over time, but people are really, really feeling it at this point. The job market is pretty tight. I've talked to lots of people out there looking, whether they're currently employed or whether they're out of school.

特别是许多从大学毕业的技术专业学生，目前处境非常艰难。我记得麻省理工学院最近有份报告重点提到了这个问题。

And particularly, there's a lot of people in technology coming out of university that are really struggling right now. And I believe there was a report recently from MIT that highlighted that.

是的。有趣的是，我们在这个播客上花了不少年头，当然偶尔会谈及这项技术在某些公司或行业内的广泛影响。而现在这几乎成了关乎全人类、所有领域的事情。你会看到某些领域受到重创，特别是在销售营销或初级开发人员这类岗位上。如果我没记错的话，克里斯，你提到的MIT报告——我们可以附上相关新闻报道的链接。

Yeah. It's interesting that we spent a good number of years on this podcast, of course, talking about, you know, occasionally talking about some of the wider impacts of this technology, you know, within a certain company or industry. Now this is kind of like a global across all people thing, you know, all sectors. You see things being hit hard, especially in, you know, maybe it's sales and marketing or kind of junior developer type of cases. If I remember right, Chris, the MIT report that you're talking about, which we can link to some of the news articles about the MIT report.

我还没亲眼看过那份MIT报告，所以听众们请注意这点。但我在多个场合听到过——比如上周参加的一个企业AI活动，许多公司高管都在讨论这个。报告指出95%的AI试点项目会失败。这个数字显然让商界领袖、投资者等各行各业人士感到恐慌。你对这个数据怎么看？

I don't think I've actually seen the actual MIT report yet, so I guess our listeners can keep that in mind. But one of the things that I've actually heard on multiple calls, And I was at, last week, at an AI corporate type event where there were a bunch of corporate leaders, they were certainly talking about this. One of the things talked about in the MIT report was that ninety five percent of AI pilots fail. And that I think has generally spooked a lot of business leaders, investors, lots of different people across industry, just the level at which these kind of 95% of AI pilots fail. What's your thought on that?

我觉得当前处于一个矛盾的境地：一方面确实有大量生成式AI项目失败，但另一方面企业又停止招聘应届初级开发者。这就形成了诡异的局面——人们狂热尝试AI编程却收效甚微，难以落地应用；同时他们又通过停止引进传统初级人才来押注未来。如果考虑到历史上初级开发者最终都会成长为资深开发者，未来几个月可能会面临有趣的'假设'情境。

I think it's a weird juxtaposition right now that we're in, in that that's accurate, that you're having a tremendous number of Gen AI, in particular, efforts fail. But at the same time, are holding back on hiring junior devs out of school. And so you have this weird mesh of people going hardcore on trying vibe coding and things like that, but with very limited success, struggling to get it adopted. And at the same time, they're they're making bets on the future by not going ahead and bringing in the the junior level developers that that they always have, which kind of leads to an interesting kind of what if situation in the months ahead. If you're, you know, junior developers eventually, historically have turned into senior developers.

现在企业正指望这些具备新AI能力的资深开发者来弥补缺口以节省成本。但如果95%的项目都失败，局面就会变得非常微妙。

And right now, companies are betting on those senior developers with these new AI capabilities over the last couple of years to make up for that deficit, hoping to save money. But if you're failing 95% of the time, it puts things into an interesting place.

没错。我正读到一篇关于AI试点的文章，它强调从报告角度来看，问题不在于AI模型无法实现试点目标，而在于人们严重缺乏对如何设计有效AI工具/流程的理解。这在我所有对话中都极其普遍，尤其是研讨会中——人们习惯使用通用模型处理个人事务，却误以为企业流程的AI化就像用ChatGPT总结邮件那么简单。

Yeah. One of the things that I I'm just reading one of these these articles about the the AI pilots. And one of the things that it highlights is that this isn't from the report's perspective, it's not that the AI models were incapable of doing the things that people wanted to prove out in the AI pilots, but that there was a kind of major learning gap in terms of people understanding actually how an AI tool or workflow or process could be designed to operate well. So I find this very, very prevalent across all of the conversations that I have, especially in workshops and that sort of thing, is there's kind of this disconnect and people are used to using these kind of general purpose models, maybe personally. And there's this concept that the way I implement my business process with an AI system is similar to the way I prompt an AI model or a chat GPT type interface to summarize an email for me.

这必然会导致问题：首先这些AI模型未必遵循指令；其次企业流程本身就很复杂——它们依赖公司独有的数据，这些数据AI模型从未接触过（除非意外泄露）。行话术语等更是如此。

And that is always due to create some pain. Number one, because these AI models only sometimes follow instructions. But number two, your business processes are complicated, right? They're complicated. They rely on data that is only in your company and probably has never been seen by an AI model unless you accidentally leaked it.

第二点，人们试图自动化或工具化的业务流程，往往最不适合用通用聊天界面实现。比如某个场景中，你可能需要把文件拖到SharePoint文件夹，触发流程提取信息、比对数据库、生成邮件发送给相关人员——这类流程根本不需要聊天界面。

Jargon, all of those sorts of things. And number two, these business processes that people are trying to automate or create a tool around, often the best thing for that is not a general chat interface, right? It's not like you want to create a chat interface for everything you wanna do in your business. No, actually in this, in a one particular case, right, it may be that you wanna drop a document in this SharePoint folder. And when it's dropped in a SharePoint folder, it it triggers this process that takes that document and extracts this information and compares it to information in your database and then creates an email and sends out an email to the proper people or, you know, add something or These sorts of processes are not general chat interfaces.

所以人们对待它的态度就像是，哦，我知道如何通过提示让这些模型做些事情。于是他们试图以某种方式构建或提示这些模型，却不知道为什么存在这样的认知脱节——他们其实真正需要的是定制工具或自动化方案。他们需要的是数据整合、数据增强，而不仅仅是一个模型加几句提示语。我认为这正是一个我经常不幸目睹的误区。

So people are coming at it like, Oh, I know how to kind of prompt these models to do some things. And so they try to kind of build or prompt these models in a certain way without the kind of I don't know why there's such a disconnect, but without the understanding that really what they need is maybe a custom tool or automation. They need data integration, data augmentation to these models. They don't just need a model plus a prompt. And I think that that's a pitfall that I see unfortunately very often.

因此，在MIT报告中看到这类问题被重点强调，我并不感到特别意外。

So it's not super surprising for me to see this kind of highlighted in the MIT report.

是的，我同意。根据你的观点，这些聊天界面正在大多数人脑海中变成万能锤子，所有问题都开始看起来像是需要这把锤子敲打的钉子。他们忽视了工具箱里那些能为工作流提供合适软件组件的工具。我认为这某种程度上是过度期待导致的，再加上选择了错误的或不完整的软件方法来完成工作流程、业务需求，从而加剧了问题。我的感觉是，很多这类情况是自上而下驱动的，那些兴奋的高管们没有真正花时间去理解如何最优使用这些工具。

Yeah, I agree. I think to your point, these chat interfaces, it's kind of becoming the universal hammer in most people's heads and everything is starting to look like a nail for that hammer to hit and they are neglecting a toolbox full of tools that give them the right software components for getting their workflows put together the way they want. And so yeah, I think I think it's a I think it's a certain amount of of over expectation that is then exacerbated by choosing the wrong software approach, or an incomplete software approach to try to get the job done, that workflow, that business workflow done. So that's certainly my sense of it. I think a lot of these are driven top down, you know, by excited executives that haven't taken the time to really understand how to optimally use these tools.

没错。研究中另一个有趣的现象是，单纯对模型进行提示的做法普遍失败了。在如何整合数据、如何构建能成功完成概念验证的定制解决方案方面存在知识缺口。但我其实不完全同意报告的前提——这应该吓退投资者对AI领域的投入，也许某些AI公司会受影响，但尤其是那些垂直领域的AI公司。

Yeah. Yeah. And another thing that was was kind of interesting in the study is this kind of just prompting of the models generally kind of failed. There's kind of a knowledge gap on how to integrate data and how to build custom solutions in a way that could succeed in kind of a POC sort of thing. But I actually don't kind of agree with the premise of the, you know, that this should spook investors, from kind of AI, maybe some AI companies, but AI companies that especially are verticalized in general.

我本身领导着一家AI初创公司，可能有偏见，但我们不属于那种垂直应用层的类型。因此我觉得自己可以相对客观地评价：那些深耕医疗、公共部门、教育或金融等垂直领域的AI公司，他们中的许多确实在努力理解业务流程，构建稳健的AI工作流和适配特定商业场景的工具。报告中提到这类工具的试验实际上大部分时候取得了成功。

I'm part of an AI, I lead an AI startup, so I may be biased, but our AI startup isn't one of these kind of verticalized application layer things. So I feel like I can maybe speak objectively with respect to those. It's really these kind of AI companies that are in whatever it is, healthcare or the public sector or education or finance or that sort of thing. They are putting in the work, I think, at least many of them are to understand business processes and build robust AI workflows and kind of tools that are fitting certain business use cases that sort of thing. And I think one of the stats in the report were that a lot of the kind of trials of these sorts of tools did actually succeed kind of a majority of the time.

总结来说，我想表达的是：一边是认为只需要接触模型就足够的想法，另一边是针对垂直领域、理解业务流程的预制AI系统，中间存在巨大断层。因为许多企业（尤其是大型企业）必须进行定制化——最终他们不可能总是找到完全适用的现成工具。看看任何企业软件，不都是经过定制的吗？

And so there's this kind of major gap between on one I guess in summary, what I'm trying to say is there's this major gap between on one side, you have this idea that all you need is access to a model. And then on the other side, these kind of pre built, purpose built AI systems for particular verticals that understand the business processes. There's a whole kind of gap in the middle because many companies, especially in the enterprise setting, will have to customize something. I don't think in the end they will be able to always use a tool off the shelf that works completely for them. If you look at any enterprise software, it's always customized, right?

无论是制造软件、ERP系统还是CRM，某种程度上都需要定制。这份报告可能正是揭示了企业尚未理解'拥有模型'与'获得适合自身的垂直解决方案'之间的差距。这确实需要深刻理解如何架构和构建这些系统，但遗憾的是，目前掌握这类知识的人才存在技能缺口和学习断层。

At some level, whether it's manufacturing software, ERP software, CRM, whatever, it's always customized. I think that's where maybe this is highlighting that gap of companies not understanding the gap between having a model and a verticalized solution for their company. And that actually does require significant understanding of how to architect and build these systems, which unfortunately there's a skills gap and a learning gap in terms of people that actually have that knowledge.

我认为你提出了一个非常关键的观点。我们在之前的节目中也讨论过，模型是更庞大软件架构中的一个组成部分。正如你刚才指出的，将那些业务工作流的专业知识整合到垂直软件栈中，旨在解决问题而不仅仅是一个聊天框，对于找到适合企业需求的解决方案至关重要。我想这正是当前许多企业在面临的一个挑战——他们往往忽略了这一核心原则，直接跳转到让模型从此接管业务的阶段，却缺乏相应的支持基础设施。或许未来有些公司将不得不为此付出代价，但希望他们能从中吸取教训。

I think you're drawing out a great point there. And I know we've talked a little bit about this in previous shows where the model constitutes a component in a larger software architecture. And we know, as you just pointed out, that the expertise of those business workflows being integrated into vertical software stacks, where it is designed to solve the problems and not just a chat box, is really important to getting to a good solution that works for your business. I think that there is I think this is where one of those challenges that we're seeing in in a lot of folks out there in the business world is kind of forgetting that that core tenant and leaping straight for the model will run my business from this point forward without that without that supporting infrastructure. So maybe there are some hard lessons to be learned in the days ahead for some companies, but hopefully hopefully that will that will happen.

朋友们，当你们大规模构建和交付AI产品时，有个永恒的主题——复杂性。没错。你们需要驾驭模型、数据管道、部署基础设施，然后有人说：让我们把这变成生意吧。混乱就此开始。这时Shopify就能派上用场，无论你是为AI应用搭建店面，还是围绕开发工具创立品牌。

Well, friends, when you're building and shipping AI products at scale, there's one constant, complexity. Yes. You're wrangling models, data pipelines, deployment infrastructure, and then someone says, let's turn this into business. Cue the chaos. That's where Shopify steps in whether you're spinning up a storefront for your AI powered app or launching a brand around the tools you built.

Shopify是数百万企业信赖的商业平台，支撑着全美10%的电商交易，客户从美泰、Gymshark到像你这样的创业者。平台提供数百个现成模板、强大的内置营销工具，还有能自动撰写产品描述、标题甚至优化产品图的AI功能。Shopify不仅让你轻松销售，更让你光彩照人。我们Changelog团队就是它的忠实用户。

Shopify is the commerce platform trusted by millions of businesses and 10% of all US ecommerce from names like Mattel, Gymshark, to founders just like you. With literally hundreds of ready to use templates, powerful built in marketing tools, and AI that writes product descriptions for you, headlines, even polishes your product photography. Shopify doesn't just get you selling, it makes you look good doing it. And we love it. We use it here at Changelog.

欢迎访问我们的店铺merch.changelog.com——这个店面同样由Shopify支撑起所有繁重工作：支付、库存、退货、物流甚至全球配送，就像把运营团队直接嵌入你的技术栈。所以如果你准备开卖，你就已经准备好使用Shopify了。

Check us out merch.changelog.com. That's our store front, and it handles the heavy lifting too. Payments, inventory, returns, shipping, even global logistics. It's like having an ops team built into your stack to help you sell. So if you're ready to sell, you are ready for Shopify.

现在注册即可享受首月1美元试用，立即开始在shopify.com/practicalai销售。重复一遍，地址是shopify.com/practicalai。

Sign up now for your $1 per month trial and start selling today at shopify.com/practicalai. Again, that is shopify.com/practicalai.

Chris，关于模型构建者，我观察到几点情况，这其实也与MIT报告相关。因为企业在进行AI转型时，常见的思维误区是首先纠结'该选用哪个模型'——我认为这完全问错了方向。首先，如果企业初次接触AI转型，想要构建知识助手、自动化流程或垂直解决方案，所选模型本身就会随时间不断演变。目前市场上没有所谓终极模型，虽然供应商众多且不乏优秀模型。

Well, Chris, there's a couple things that, I've been following with respect to the the model builders, but it does actually connect maybe to this MIT report as well, because one of the other kind of common things that I see that companies or a way of thinking that companies have when they approach kind of AI transformation is they come at the problem of AI adoption kind of with the question of what model are we going to use? Which I think is the completely wrong question to be asking for a number of reasons. First of all, if you're coming to AI for the first time with your company and you want to transform your company with AI and build knowledge assistance and automations and adopt other tools and build verticalized solutions, the model actually will shift over time a lot. So that's, I think number one, there's no one kind of on the market that at least right now is there's certainly many providers of models. There's a lot of good models.

没人能预知未来哪家会在模型领域领先。实际上我们看到模型正在趋于同质化，你可以从任何渠道获取模型。其次，如果目标是构建企业内部的AI解决方案（就像我之前提到的SharePoint案例——处理文档并提取内容发送邮件），纠结模型选择更是本末倒置。

No one knows who will have kind of the edge on models in the future. And I think what we're actually seeing is that the model side is fairly commoditized. You can get a model from anywhere. The second reason I think that that's the wrong question to be asking is that if you're trying to build an AI solution within your company, again, think about that SharePoint thing that I talked about. I'm gonna process this document from SharePoint and extract these things and send it to an email.

实际上你需要的不是一个模型，而是一组模型，可能还包括围绕这些模型的其他周边组件。比如你可能需要一个文档结构处理模型，比如Dockling；需要一个语言模型来处理部分内容；可能需要嵌入模型来向量化文本或进行检索。

You actually don't need a model. You need a set of models and potentially, you know, other things in the periphery around those models. So you likely need a document, you know, structure processing model, like a Dockling. You need a language model to process pieces of that. You maybe need embedding models to embed some of that text or do retrieval.

你需要重排序模型，因为检索后必须对结果重新排序；需要安全防护模型来负责任地检查模型输入输出。光是处理SharePoint文档这样简单的用例，把这些组件累加起来——如果你参与我们讨论的这种概念验证项目，思考'该用什么模型'并决定使用GPT或LAMA等单一模型时，其实已经注定失败了。

You need a re rank model because you've gotta re rank your results after doing retrieval. You need safeguard models because you want to be responsible and check your inputs and your outputs of your model. So once you start adding these things up, even for that simple use case of processing this document through SharePoint and out the other end, If you're coming to one of these proof of concept projects like we've been talking about, and you're thinking, what is the model I'm going to use for this? And you decide, okay, the model I'm gonna use for this is a GPT model or a LAMA model or whatever. Well, you're already setting yourself up for failure.

因为你真正需要的不是单个模型，而是一组模型构成的AI平台，一个能提供多种功能类型的AI系统。我认为这种认知偏差正是导致这类POC项目失败的原因。

Because what you don't need is a model. What you need is a set of models. You sort of need an AI platform. You need an AI system that gives you access to multiple kind of different types of functionality, right? And so I think that that kind of perspective plays into this kind of POC's failing thing.

关于模型构建者我还有些补充想法——你觉得我的观点有偏差吗？你会如何修正？

And I have more thoughts about that related to the model builders, but do you think I'm off in that? Oh, How would you correct me?

不，我完全认同。我们节目多次探讨过：在商业架构中需要多种模型应对不同需求。随着生成式AI和物理AI的发展，这趋势愈发明显——不同领域显然需要截然不同的模型组合。公众却总纠结于'选哪个模型'，正如你指出的，这完全是本末倒置。

No, I have no correction. We've talked about this as well a bunch of times, and I keep waiting for I think we've had a lot of really in-depth conversations with people on the show in the past about the need for multiple models to tackle different concerns within your larger business focus and the software architecture that supports that. And I think as you look forward, and you know, we're seeing a genetic AI, we're seeing physical AI developing more and more. And all of those require a number of different models to do that, you know, it kind of obviously, you know, very different distinct things that you can see in those spaces. And so there seems to be this hang up in the public about the model, which model and then do I pick the model and as you pointed out, that's completely the wrong thing.

关键应该是：模型架构如何匹配业务流程和实际需求？听你描述工作流中的潜在模型时，我想到这简直像软件架构——只是每个组件现在注入了若干模型。完整业务流程仍由多个组件构成，对我们常谈这个话题的人来说这再明显不过。

It's what does your model architecture look like as related to your business workflows and and the the, you know, the job that you need doing and how do you do that. And as you were describing some of those potential models that one might have in a work flow earlier. As I was listening to your examples, I was thinking, wow, sounds a lot like software architecture, you know, just it's just each each component is invested with with one or more models now in that component. But there are still many components that make up a full business workflow. And so I guess maybe because we talk about it fairly regularly on the show here, it seems quite obvious to me that that's the case.

但现实显然并非如此。看看那些商业决策吧——当前阶段显然需要资深开发人员（无论头衔）运用软件知识来架构解决方案。如果不培养初级开发者，就是在赌未来不需要这些能力。而那份报告显示，现阶段这类工具的失败率高达95%。

But clearly, it's not. If you look at the business decisions that are being made out there, some of which is there is clearly a need at this point. You may have a senior developer type by whatever title you're applying, working and kind of sort of knowing software dev at some level and sort of vibe coding and putting their knowledge of architecture together for solution. But if you're not going to bring in junior devs, then you're making a gamble that you're not going to need that at some point. And yet what we're seeing is a is, you know, per that report, ninety five percent failure rate on using these tools at the current point in time we're at now on that.

我们最近有一期关于风险管理的节目。从风险管理的角度来看，我认为高管们正在做出许多非常冒险的决策，很大程度上是因为他们不了解模型和软件架构如何协同工作，正如你所说。所以我完全同意你刚才的观点。

We had a recent episode on risk management. And from a risk management perspective, I think that there are a lot of very risky decisions being made by executives largely in ignorance, I think, of kind of understanding how models and software architecture fit together to your point. So no, I'm in violent agreement with what you were saying a moment together.

是的。这种引入模型的想法也会在模型采用方面产生一些问题，特别是在私密安全环境中。我有亲身经历，你会想，我该用哪个模型？然后意识到模型大致分为几类：闭源模型如GPT或Anthropic等，开源模型如LAMA、DeepSeek或Quinn等。

Yeah. This idea also that you kind of bring in a model also produces a little bit of problematic behavior around adoption of models, particularly in private kind of secure environments. And I know this one from experience where you kind of think, well, which model am I going to use? And then you think, well, there's a couple categories of models, right? There's closed models, there's open models, like closed models being the GPTs or Anthropic or etcetera.

公司里总有懂行的人，比如基础设施部的Frank会说：'我能在我们的系统里部署LAMA模型'。现在有无数种实现方式，可以用VLLM或OLAMA之类的工具。

The open models being the LAMA or DeepSeek or Quinn or whatever. And you have smart people in your company and whoever, Frank over in infrastructure is like, Yeah, I can spin up a LAMA model in our infrastructure. And there's innumerable ways to do that at this point. You can use VLLM or you can use OLAMA or whatever it is. Right?

我甚至能在笔记本上跑一个模型。于是你们就用Frank内部部署的模型做概念验证。但问题不在于Frank部署得不好或工具不行（比如VLM其实很强大），而是这种对比本身就不公平——你部署的只是单一模型，而非构建丰富AI应用所需的全套功能。私有模型只能做它特定的事，无法提供完整的AI功能体系。

I can spin up one on my laptop. And so you spin up the model and then you're like, all right, well, let's build our POC against Frank's model that he's deployed internally because we now know how to do that. But again, it's not so much that Frank did a poor job and the deployment is bad or the tools are bad, like VLM or something is very powerful, but it's not a proper comparison because what you've deployed is a single model, not a set of AI functionalities to build rich AI applications, you now have a private model, which again, only does what a private model does, that one particular model. It doesn't give you that rich set of AI functionalities. And so it's not really a knock against open models.

这说明或许不该自建AI平台。这涉及很多因素，解决方法也多样。但选择模型的困惑确实影响了人们对开源模型的认知——多数情况下，部署开源模型只是获得单个模型端点，而非产品化的AI平台。

What it is an indication of is that you maybe shouldn't roll your own AI platform. So there's a lot of things that go into that and there's various ways to approach it. But I think that misunderstanding of what model do I need also impacts the perception of these open source models because most of the time when you deploy that open source model, you're only getting a single model endpoint versus kind of a productized AI platform.

你们公司确实在弥合这种差距。但更广泛来看，市场上有服务商能提供相关服务。虽然我们一贯鼓励使用开源模型（讨论过很多次），但正如你指出的，企业界存在认知鸿沟：拥有这些技术能力后，还需要整合所有资源（广义上的）来实现预期的商业运作模式。

Yeah, I guess, I know that in your own business that you do help bridge some of that gap there and some of the things that you guys do. But in general, if you're talking in the broader market, do people they have service providers out there that can give them some of those services. But as they are going and deploying, and in a sense, we've always encouraged open source and open weight models out there. We've talked about that a lot, and we like to see that. And yet there is this skill gap or understanding gap that you've just defined in the business community of, yes, you have these capabilities, but you've got to connect all of your resources, brought used in the term in a very generic way, all of your resources together to give you the capabilities you need for your business to operate the way you envision.

显然目前对这个过渡阶段的理解存在不足。有哪些可选方案？人们如何跨越这个鸿沟？

And, you know, that's definite falling down in understanding within that gap space. What are some of the different options, how people can get through that gap?

我认为可以从软件和基础设施架构的角度来处理这类问题，正如你之前提到的。我们讨论的很多内容实际上都属于架构设计的范畴。因此从一开始，问题的关键就不在于我们要使用哪种模型以及由谁来内部部署它，对吧？而是应该基于‘我们将使用模型’这一前提来思考。

Well, I think one of the things that can be done is to approach this sort of problem from a software and infrastructure architecture standpoint to what you were saying before. A lot of what we're talking about really kind of falls in that architecting side of things. And so I think from the beginning, the question is not, again, if you come to it with the question not being, what model are we gonna use and who's gonna deploy it internally? Right? But you come to it from the standpoint of we will be using models.

未来会有许多模型，它们将连接到各种不同的软件应用。这就在管理和长期稳健性方面改变了游戏规则。许多成熟的工程团队都懂得如何扩展服务集群、维持其运行、设置运行时间监控、告警系统、集中式日志记录等。但如果你一开始就限定‘我们只会把模型放在某个地方运行’，那就永远无法展开这些层面的讨论。

There will be many models. They will be connecting to many different software applications. Okay, well, changes the game a little bit in terms of managing that and making it robust over time. And there's very many capable engineering organizations that know how to scale bunches of services and keep them up and set up uptime monitoring around them and alerting and centralized logging and all of those sorts of things. But you never get to have those conversations if you kind of cut it out before you get there by just saying, we will have a model living somewhere.

因此必须从分布式系统的角度来应对这个问题。当你开始这么做时，就会与团队中的专家展开对话。根据公司选择的路径，有大量工具可供使用——从自主管理部署内容并使用编排工具（比如Rancher这类通用非AI专用工具），到采用AI专用解决方案。关键在于视角的转换。

And so you really need to approach it from this distributed systems standpoint. And once you start doing that, you start talking to the experts that are on your team. And there are very many tools, depending on the standpoint that the company wants to approach this from, everything from the company still managing a lot of what they want to deploy and using orchestration tools, whether that be like a rancher or something like that, that's generic and not AI related, right? But they're used to using it. Maybe they're already using it in their organization and they can orchestrate deployments of various AI things.

一旦进入‘我们需要这种软件架构，需要这种SRE和DevOps方法论’的思维领域，就必须直面一些棘手问题：我们能否凭感觉编码解决？实际支持规模化运作需要哪些软件知识？人们终将发现，至少仍需要软件工程和基础设施专业知识来实现良好扩展，或至少指导那些‘感觉式编码’行为。

And then there's AI specific approaches to this as well. So I think it is really a perspective thing. And as soon as you kind of get into that zone of, well, we do need this software architecture, we need this kind of SRE and DevOps kind of approach to things, then you really have to ask some of the hard questions like, can we vibe code our way through this? What kind of software knowledge do we need to actually support this at scale? And I think what people will find is you do at the minimum, I think still need that software engineering and infrastructure expertise to do it at scale well, or at least to kind of guide some of the Vibe coding type of things that happen.

对吧？所以需要有个经验丰富的领航员来引导这些工作，确保航向正确。

Right? So there needs to be an informed pilot to help guide some of these things and make sure the ship is going in the right direction.

这个观点非常精辟。结合当前就业市场的招聘趋势和软件开发行业底层的崩塌现象来看，职业生涯中积累的专业知识仍然至关重要。虽然所有离散知识点可能都被各种模型掌握，但如何在正确上下文和顺序中提取所需内容仍不可或缺。在当今生成式AI主导的世界里，必须有人能提供这种架构视野，懂得如何构建上下文来从‘氛围编码’中获取所需。

I think that's very, very well put. And I think, you know, kind of going back to the fact, you know, kind of combining this with the hiring decisions that we're seeing out there in the job market, and kind of the collapse of the bottom end of the software dev industry, there is a lot of developed expertise over the course of a career. And while all of the discrete points of knowledge may be captured in various models out there, there's still the necessity of extracting what you need from those models in the right context and the right order. And at least for the time being in this kind of Gen I dominated world, you have to have somebody who can provide that kind of architectural view, know how to provide the context to get the things you need from your five coding. I think people are finding small successes under that where they say I want an app that does this thing and they describe the app in great detail and the models that they're using will turn out kind of an app.

人们正在这方面取得小规模成功——比如详细描述想要的应用功能后，所用模型能生成出类似应用。但这种架构可能不具备可持续性，最终可能只是个优秀原型。如果不培养未来将成为资深开发者的初级程序员，就相当于赌今天的产出能持续有效，并指望模型能无师自通地理解所有组件细节——这或许会发生，但对当前企业而言风险巨大。相比之下，继续培养初级开发者显得稳妥得多。也许未来情况会变，但现阶段采取这种策略的企业，其管理层往往意识不到正在承担的重大商业风险。

It may or may not be architected the way for sustainability and you know, there's a whole bunch of issues that might make a very good prototype. But if you're not going to bring in junior level coders that in the future will be your senior level coders that have this knowledge, then you're kind of betting on today's talent producing something and you're hoping that your model gets the nuance of all of those components and is able to generate its own context without your expertise, which may happen. But it's a big gamble if you're a company right now, it seems to me a lot less risky to go ahead and continue to bring in some junior level developers for the purpose of growing them over time and being able to have that. Maybe at some point that does change in the future. But I think the companies that are doing that today are taking some some fairly significant business risks that are largely invisible to their executives.

朋友们，其实你不需要成为AI专家也能用它做出伟大的东西。现实是AI已经无处不在。对许多团队来说，这带来了不确定性。我们的合作伙伴Miro最近调查了8000多名知识工作者，虽然76%的人认为AI能提升他们的工作角色，但超过半数仍不确定何时使用它。这正是Miro要填补的空白。

Well, friends, you don't have to be an AI expert to build something great with it. The reality is AI is here. And for a lot of teams, that brings uncertainty. And our friends at Miro recently surveyed over 8,000 knowledge workers and while 76% believe AI can improve their role, most, more than half, still aren't sure when to use it. That is the exact gap that Miro is filling.

从构思节目创意到构建全新理论框架，我一直在使用Miro。它已成为我打造创意引擎的核心工具之一。现在内置Miro AI后，效率更高了。我们在同一块画布上，将头脑风暴转化为结构化计划，截图转化为线框图，便利贴的混乱转化为清晰思路。现在你既不需要精通提示词，也不必在技术栈里额外添加AI工具。

And I've been using Miro from mapping out episode ideas to building out an entire new thesis. It's become one of the things I use to build out a creative engine. And now with Miro AI built in, it's even faster. We've turned brainstorms into structured plans, screenshots into wireframes, and sticky notes chaos into clarity all on the same can canvas. Now, you don't have to master prompts or add one more AI tool to your stack.

你正在做的工作本身就是最好的提示。通过Miro，你可以帮助团队高效完成出色成果。访问miro.com了解详情。重复一遍：miro.com，miro.com。

The work you're already doing is the prompt. You can help your teams get great done with Miro. Check out miro.com and find out how. That is miro.com, mir0.com.

Chris，我认为除了MIT报告之外，其他消息源的一些持续性报道也印证了我们讨论的观点。这些单独的数据点本身也很有意思。特别是关于OpenAI的——如果我们回顾过去几周OpenAI的动态，确实发生了不少耐人寻味的事。这些信号与我们受MIT报告启发所讨论的内容高度吻合。我先列举几个重点，之后我们可以逐个深入分析。

Well, Chris, I I do think that there's some consistent news stories from other sources outside of the MIT report that kind of reinforce some of what we've been talking about. And I also, I think are just generally interesting as individual data points. And I've seen a number of those as related to OpenAI specifically in terms of if we just look at what kind of has happened with OpenAI in the previous number of weeks, some interesting things have happened. And I think that they signal some things that are, like I say, very consistent with what we've been talking about as prompted by this MIT report. Just to highlight a few of those, and then we can dig into individual ones of them.

其中一个是OpenAI发布了GPT-5——虽然我们节目里还没详细讨论过——但公众反响普遍不佳，用'遇冷'来形容可能比较贴切。这是第一点。与此同时，OpenAI在时隔多年后再次开源了一些模型。

But one of the things that happened was OpenAI released GPT-five, which we haven't talked about a ton on the show yet, but they released GPT five. Generally, the reception in the wider public has been that people don't like it. Sort of it's fallen flat a bit, guess would be a way to put it. So that's kind of thing one. At the same time, OpenAI open sourced some models again for the first time in a very long time.

他们开源了几个推理型大语言模型（具体日期记不清了，听众或许可以在评论区补充）。差不多同期，他们还开辟了咨询业务板块，提供高价定制服务——我记得与OpenAI的咨询服务协议起价大概是1000万美元左右。所以现在的情况是：他们原本的护城河（封闭模型）表现乏力，部分模型技术公开开源，同时开拓企业服务业务。

Five Open sourcing a couple of reasoning models, LLMs that do this type of reasoning, and they open source those. And also near the same time, I forget the exact dates, someone listening can maybe provide those in a comment or something. But the other thing that happened was that they opened kind of a consulting arm of their business and are entering into these services consulting type of engagements, which are not cheap. I think the minimum price for a consulting services arrangement with OpenAI was like $10,000,000 or something like that. So you've kind of got this thing that's happening, which is a model that's kind of in this area of what has been their moat, kind of these closed models, kind of falling flat, them giving out some of the model side you know, publicly, openly, and then opening the services business side of things.

对于这些动向传递的信号，我已有自己的解读。Chris，对于这些陆续曝光的消息，你有什么初步的反应或想法吗？

Now, I've drawn my own conclusions in terms of what some of those things signal and mean, but any initial reactions or thoughts that you've had as things have come out like that, Chris?

我认为萨姆·阿尔特曼是OpenAI的首席执行官。他是个充满好奇心的人，他在一月份曾指出，OpenAI可能在开源技术方面，引用原话'站在了历史的错误一边'。但正是萨姆做出了这些决定。我认为他现在看到的是市场正在演变、逐渐成熟，正如你所预期的那样，你知道，早期阶段专注于这些基础模型，那些推动过去几年热潮的前沿基础模型，可能正在产生递减的回报。尽管GPT-5模型更强大，这也是我大部分工作所使用的，但也许在某些细微之处，比如界面本身的操作方式、模型的工作原理，人们实际上更喜欢4.0版本，你知道，公众对那个模型进行了相当程度的拟人化。

I think Sam Altman is the CEO of OpenAI. He's a curious individual with, he noted in January that maybe OpenAI had been, and I quote, on the wrong side of history, close quote, when it comes to open sourcing technologies. But it was Sam who made those decisions. And I think what he's seeing now is the market is evolving, it's maturing, as as you would expect, you know, kind of the the early phase of focusing on these foundation, you know, these kind of frontier foundation models that were driving the hype for the last few years and might be producing diminishing returns. Even though the GPT-five model is more capable and that's what I use for most of my stuff, Maybe some of the nuances, for instance, the interface itself, way it works, the way the models work, know, people were preferring the four o model, you know, there was quite a quite a bit of personification of that model, I think going on with the public.

我认为OpenAI意识到了这些担忧，此外他们还把服务市场让给了其他公司。所以，我的看法是，他们现在开始开源部分模型，采用开放权重的方式，目的是为了在服务市场的高端、昂贵领域站稳脚跟。我认为这就是他们的动机所在——确保在竞争对手都拥有开源模型的情况下，他们也能在这个领域参与竞争。

And that I think think OpenAI realized that, you know, there are these concerns, in addition to the fact that they had kind of left the services market to others. And so, you know, I, my, my belief is that the is that they are starting to open source some of these models with their, you know, open weights, for the purpose of supporting a solid footstep. And at the, you know, kind of the premier end, the expensive end of the services market. And I think, I mean, I think that's the motivator right there is is is doing that. I think that's making sure that with their competitors all having open source models that they can play in the space as well.

他们可以通过自己的服务组织进入市场，在服务上赚钱，并利用自己的开源模型来支持这种服务商业模式。也许我的回答有点愤世嫉俗，毕竟像你一样月复一月地观察他们多年。但没错，我绝对认为他们正在尝试顺应趋势，认识到AI业务正在向这个领域扩展和成熟。

And they can go in with their services organization and make money on services and and point to their own open source models to be able to support that services business model that they're doing. So maybe a little bit of a jaded answer for me potentially just having like you watch them over a number of years month by month. But yeah, I I would I would definitely say they're they're trying to lean in recognizing that the the business of AI is both expanding and and maturing into that area.

是的。如果我们将这与MIT报告中关于企业用例失败的知识结合起来，我们现在所知并可以归纳为多个数据点支持的趋势和洞察是：通用AI模型，仅仅获得模型访问权限并不能解决任何实际的商业用例和问题，需要的是垂直化解决方案。这是我们从MIT报告中学到的。无论你的公司使用的是ChatGPT、Copilot还是其他任何模型，这都是通用的工具。

Yeah. I I think if we combine this with the knowledge from the MIT report around kind of these use cases and enterprises failing, What we know at this point and what I would kind of distill down as very or or or trends and insights that are backed now by various data points is that generic AI so the generic AI models, just getting access to a model does not solve any kind of actual business use cases and problems and, you know, verticalized solutions that are needed. That's what we kind of learned from the MIT report. And this would be true whether your company has access to ChatGPT or Copilot or whatever models you have access to. These are generic tools.

这些都是通用模型。很难将其转化为定制化的商业解决方案，对吧？因此，从我的角度来看，第一个洞察就是仅仅拥有通用模型或工具的访问权限并不能解决你的商业问题。这也是为什么很多概念验证项目失败的部分原因。

These are generic models. It's very hard to translate that into customized business solutions. Right? And that is why the insight one from my perspective is just having access to a generic model or generic tools is not gonna solve your business solution. And that's partially why a lot of these POCs are failing.

现在，OpenAI提供这些通用模型和工具，对吧？这在消费者端非常棒，但在企业端，真正赚钱的——至少到目前为止——并不是OpenAI。他们亏损严重。赚钱的是埃森哲、德勤、麦肯锡等服务公司。因为真正通过AI改造公司、引入这些模型并实现价值的方式，是通过创建定制数据集成和定制商业解决方案。

Now, OpenAI offers those generic models and tools, right? Which is really great on the consumer side, but enterprise wise, the ones making the money, at least so far, not it hasn't been OpenAI. They've been losing a ton of money. The ones making the money is Accenture, Deloitte, McKinsey, etcetera, services organizations. Because really how you transform a company with AI and bring these models in and do something is by creating custom data integrations, creating these custom business solutions.

这仍然主要是服务相关的事务，或者至少是定制化相关的事务。涉及数据集成等。所以，他们在开源模型的同时创建服务业务，对我来说完全合理。因为从企业角度来看，模型构建者方面实际上没有护城河。至少在我看来——当然我有偏见——使用GPT、Quad、Lama、DeepSeek还是Quen都不重要，这些模型中的任何一个都能完美适用于你的商业用例解决方案。

And that is still really a services related thing, or it's at least a kind of customization related thing. There's data integrations there. So this is totally consistent for me with open sourcing models at the same time that they're creating the services side of the business, because essentially from the business or enterprise side, there really is not a moat on the model builders front. It doesn't matter from my perspective at least, and of course I'm biased, it doesn't matter if you're using GPT, it doesn't matter if you're using Quad, it doesn't matter if you're using Lama or DeepSeek or Quen, really doesn't matter. Any of those models can do perfectly great for your business use case solution.

我认为确实如此。我已多次见证这一点。关键在于如何结合您的数据、领域知识，与这些模型集成并创建定制化解决方案。无论是内部完成还是聘请服务商来实现，一方面您需要软件架构师和开发人员。

I think that's true. I've seen it time and time again. What makes the difference is your combination of data, your combination of domain knowledge, integration with those models and creating that customized solution. And either you're gonna do that internally or you're gonna hire a services organization to do that. On the one front, you need software architects and developers.

即便他们使用智能编码工具，仍需要这种专业能力。另一方面，您可以花费数百万美元聘请咨询公司或OpenAI等服务商。这确实是个难题，因为这类资源相当稀缺，对吧？正因如此，现在正是提供AI工程相关服务的良机。

And even if they are using vibe coding tools, will need that expertise. On the other side, you can pay millions of dollars to one of these consultants or to OpenAI and their services business, etcetera. And again, it's a hard thing because those resources are scarce, right? Which I think is why it is a good time if you're kind of providing that level of services around the AI engineering stuff.

没错。您一语中的。我想用个类比来重新阐述：当您要招待朋友举办豪华晚宴时，走进厨房可能备有各种优质食材——在我们的比喻中，这些就代表模型和其他软件组件。

Yeah. I think you've hit the nail on the head. And I'll offer sort of a way of restating it with an analogy. You know, when you you're going to have friends over and you want to have a magnificent dinner at your dinner party. And so you walk into the kitchen, and you may have a lot of great things to make stuff with and some of those might be big expensive things that are raw materials.

但组合这些食材需要技巧：从冰箱挑选合适材料，从储藏室选取配料，按照代表您业务目标的'食谱'进行搭配，最终烹制出与邻居或朋友做法略有不同的佳肴。这道晚餐之所以独特，正如同您的业务具有独特性。虽然未来冰箱可能会智能化，厨房科技会进步...

Things like, you know, in our analogy, those things represent models and other software components. But there's some skill in putting that meal together and going into the refrigerator and picking the right things out, and going into the pantry and picking the right things out, and putting them together according to a recipe that is your business objective, and understanding how to produce that final dinner, which is maybe a little bit different from the way your neighbor would do it and maybe a little bit different from the way another friend would do it, to produce that fine meal that you are able to enjoy at the end of the day. That meal is a bit unique because in our analogy, your business is a bit unique. But it takes the skill. And we do expect technology to develop in those refrigerators to be smart refrigerators other things to help in the kitchen.

这或许对应着我们讨论的智能编码理念。但目前尚未完全实现。如果您采购食材时想着'我不需要太高厨艺，因为新技术会代劳'——也许最终会实现，但据我们观察现在还为时过早。我们讨论的那份报告也佐证了这一点。

And that might be represented in our vibe coating thought. But we might not be all the way there yet. So if you're kind of buying your ingredients and thinking, well, I don't really need to have great skill in the kitchen, because I'm sure that some of this technology that's coming into play will take care of that for me. Maybe eventually, but I don't think we're quite there yet is what we're seeing. And I think that report that we've been talking about has kind of provided some evidence of that fact.

因此，处理复杂性和细微差异的需求仍然存在。无论是闭源还是开源，这些通用模型都需要多个组合使用，并需要专属'食谱'才能实现预期效果。希望这些见解能帮助正在做决策的管理者和高管们。

And so, yeah, there's still there's still the need for nuance and complexity to be addressed. And the recognition that these commoditized models, whether they be closed source or open source, either way, it's going to take more than one and they're going to be there, you're going to need to have the recipe to make it all come together the way you're envisioning. So a lot of good lessons for hopefully some of the managers and executives in these companies making some of these decisions to do that might help them out going forward.

确实。这个类比非常贴切——您可以在内部培养'烹饪'专长，也可以聘请专业'厨师'上门服务。

Yeah. Yeah. I that analogy. And it fits so well because you can develop that cooking expertise internally. Or you can hire in professional chef into your house.

这会很昂贵，对吧？但你可以这么做，因为这是必要的组成部分。我很喜欢这个比喻。我想强调的是，我们总是试图为参与者在结束学习时提供一些学习机会。也许你正积极避免让AI概念验证失败，并希望了解构建这些解决方案需要什么。

It's gonna be expensive, right? But you can do that, but it is a necessary component. So I love that analogy. I do wanna highlight, we always try to highlight some learning opportunities for folks as they're coming out of this. Maybe you're motivated to not let your AI POC fail and you want to kind of understand what it takes to build these solutions.

有几点我想强调。首先，我非常兴奋的是，我们将在11月13日在印第安纳波利斯举办中西部人工智能峰会，我正在协助组织。这将是一次非常棒的体验。与其他会议不同的是，我们将设立一个AI工程师休息区，你可以直接与AI工程师同桌交流。也许你内部缺乏这方面的专家，但又不希望概念验证失败，这时你可以坐下来与AI工程师讨论，或许能获得一些指导。

There's a couple of things I wanna highlight. One is I'm really excited about, we're having this, Midwest AI Summit in, November, November 13 in, Indianapolis, and I'm helping organize that. It's gonna be a really great time. One of the unique features about this event, different from other conferences, is we're gonna have like an AI engineering lounge where you can actually sit down at a table with an AI engineer. Maybe you don't have that expertise in house, but you don't want your POC to fail, you can actually sit down with an AI engineer and talk through that and maybe get some guidance.

我在其他活动中还没见过这种形式。对我们能实现这一点感到非常兴奋。正如我在之前的节目中提到的，你随时可以访问practicalai.fmfmwebinars，那里也有一些网络研讨会，可能对你来说是很好的学习机会。

So I haven't seen that at another event. I'm pretty excited that we're doing that. And you can always, as I mentioned in previous episodes, go to practicalai.fmfmwebinars. There's some webinars there as well that might be good learning opportunities for you.

这太棒了。在讨论学习机会的尾声，我想快速分享一个小故事。我母亲现已八十多岁，曾经是佐治亚理工学院的计算机科学教授。多年前她也曾在我工作过的洛克希德·马丁公司任职。

That's awesome. And on the tail end, as we close out of learning opportunities, I just wanted to share one two second thing here. My mother, once upon a time she's in her mid 80s. And once upon a time was a computer science professor at Georgia Tech. She also happened to work for the same company I worked for, Lockheed Martin, years ago.

退休后她逐渐淡出了技术领域。但她非常清楚我在这个领域的工作，包括我们的播客等。然而就在这个周末，这位八十多岁的老人联系我说，她正在考虑重返校园学习AI，甚至可能攻读博士学位之类的项目。我们讨论了很久，决定先从简单的开始。

But she had retired and kind of moved out of the technology space. But she is very aware of what I do in the space and our podcasts and stuff. But she, in her mid-80s, reached out to me this weekend and said, I'm thinking about going back to school for AI, and maybe even into a PhD program or something like that. I don't know. And we talked about it for a while and starting small.

她现在已经开始学习Coursera上的课程。在我们思考学习和提升时，我想说——最近节目中我们多次讨论过学习的话题，包括几期关于'永远为时不晚'的内容，比如之前有位不再年轻的国会议员投身学习的励志故事。我想说的是，如果我八十多岁、离开计算机领域数十年的母亲都愿意通过Coursera课程重新投入技术学习，我鼓励所有人都重新考虑——年龄从来不是障碍。在讨论学习事项时，我想传递这个信息：去行动吧。世界变化太快，我八十多岁的母亲都不想落后，希望跟上时代。

She's into some Coursera courses now. And I just as we're thinking about learning and ramping up, I just want to, you know, we've talked about learning recently on the show, you know, we had a couple of episodes where we talked about kind of it's never too late, we've had some, we had a congressman who was not a spring chicken not too long back, diving in incredibly inspirational. And I want to say, if my mom in her mid eighties and decades out of the computer science space is willing to dive in and do technical work on Coursera courses, I would encourage all of you to reconsider, you are never too old. And I just wanted to leave that as we talked about learning items to say, go get it. The world is changing fast and my mom in her mid eighty's doesn't want to get left behind and wants to be on top of it.

我认为，我们都应该从中获得一些启发，然后付诸行动。

And I think, I think it's a good thing for all of us to take some inspiration from and go do.

太棒了。克里斯，感谢你分享这个视角。这是一次非常愉快的对话，感谢你抽空参与。绝对如此。

That's awesome. Appreciate that that perspective, Chris. It's been a been a fun conversation. Thanks for hopping on. Absolutely.

好的，这就是我们本周的节目内容。如果你还没浏览过我们的网站，请访问practicalai.fm，并务必在LinkedIn、X或Blue Sky上与我们互动。你会看到我们分享关于最新AI发展的见解，非常期待你加入讨论。感谢我们的合作伙伴Prediction Guard为节目提供运营支持。

Alright. That's our show for this week. If you haven't checked out our website, head to practicalai.fm, and be sure to connect with us on LinkedIn, X, or Blue Sky. You'll see us posting insights related to the latest AI developments, and we would love for you to join the conversation. Thanks to our partner Prediction Guard for providing operational support for the show.

欢迎访问predictionguard.com了解他们。同时感谢Breakmaster Cylinder提供的音乐，也感谢各位的收听。今天就到这里，下周我们再见。

Check them out at predictionguard.com. Also, thanks to Breakmaster Cylinder for the beats and to you for listening. That's all for now, but you'll hear from us again next week.