How Axios uses AI to help deliver high-impact local journali…

Axios 是一家以最高效、最具启发性且易于传播的方式，提供关键且可信新闻与分析的媒体公司。它用独到的视角和冷静精炼的笔触，结合原创与条理清晰的报道，覆盖媒体趋势、科技、商业与政治领域。

我们采访了 Axios 的首席运营官 Allison Murphy ，讨论 AI 如何助力高影响力的地方新闻，以及如何更好地服务社区。

在地方记者产出更有影响力的新闻方面， AI 帮了哪些忙？

对 Axios Local 来说， AI 已经成为运作的核心。我们的目标是证明：可以打造出可持续、盈利的地方新闻模式，把高质量的报道送到美国每一个社区。这就要求实现规模化和效率，而这正是 AI 的强项。换言之， OpenAI 所构建的技术与我们在 Axios Local 的实践天然契合。

我们在整个工作流中都使用 AI——从选题写稿到编辑再到分发。但真正让人感受到成效的，是它能让记者更快完成关键工作。读者来找 Axios 是为了“简明有料”，所以我们为此定制了一个 GPT 工具，叫做 Axiomizer 。记者把稿子放进去，它会建议更利落的标题、更清晰的“为何重要”“下一步是什么”和“潜台词”等，帮助优秀报道更好地触达读者。

这并不是替代记者，而是把强劲的专业报道打磨得更简明、更清晰、更有用。我们还把编辑风格校验功能嵌入工具里，让校对人员能够把精力放在真正需要人工判断的地方，而不是基础修订或格式问题上。

结果是，记者和编辑都能把更多时间用在高影响力的新闻上，而让 AI 处理繁琐的背景工作。

在 AI 的帮助下，哪些以前做不到的地方性报道或社区服务变得可行了？

可以从报道覆盖面和工作方式两个角度来回答。我们的目标是让记者把时间花在只有人能做的事上——访谈线人、挖掘数据、讲好故事。每当我们在制作、格式化或琐碎事务上为他们节省一分钟，都是一个胜利。

这种效率让我们能覆盖更多社区。如果我们只靠一名出色记者就能启动一个新城的业务、而不需要额外成倍的制作与支援团队，就能去以前无法涉足的地方。这正是我们在 Boulder 和 Huntsville（阿拉巴马）等地所做的：这些是我们首批只配备一名记者的城市。

借助 AI 驱动的工作流，一名记者就能产出高质量的本地新闻产品。这意味着更多地方会有报道，且质量标准保持一致。

在新闻业承压的大环境下， AI 对缓解财政压力有多重要？

地方新闻危机本质上是个经济问题。优秀的地方新闻必须深度贴合各自社区，使得很难复用成本效率——不能把一个新闻室的产出简单复制粘贴到另一个地方。

AI 改变了这种“算术”。它能放大资深记者和编辑的产出，剔除那些对读者并无实质价值的成本。通过改善经济模型，我们得以在更多地方做高质量报道。

AI 也打开了新的信息来源。存在大量公共资料——市议会记录、学区会议录音、政府文字记录——但这些内容往往被“锁住”，因为没人有时间去逐条观看或阅读。借助 AI，记者可以快速得到可靠的摘要，找出真正重要的线索。不必再耗时坐完一个三小时的会议，也能判断新闻进展并知道该联系谁采访。

这让优秀记者能覆盖更广的范围、发现更多故事，并把那些技术上是公开但实际上难以获取的信息变成社区真正能用的内容。

在使用大量标准化工具时，如何保持社区的独特声音？

人类记者始终是 Axios 的核心，这是不可让步的。记者塑造读者的信任感，使 Axios 成为“口袋里的邻居”——知道社区、告诉你真正重要的事。失去那种人声，就失去了整个产品。

我们所标准化的是围绕记者的一切。用技术让风格一致，处理格式、数据和分析工作，这样记者就不用亲自操心这些。读者关心房价、学校表现以及社区间的比较，但把原始数据转成清晰、可信、可用的洞见需要真正的技术工作。

通过给记者提供这些工具——干净的图表、经核验的计算、透明的对比——我们让每位记者都能使用到过去不均衡或难以扩展的能力。这样每个社区都能获得同样高标准的数据驱动报道，而具体的采访和报道仍然保持本地性、人性化并根植于当地。

有哪些最具体的方式，让 AI 帮助地方记者更快工作并更好服务社区？

我们特别注重识别读者真正喜欢的新闻产品部分，然后想办法让它们更易生产。

一个典型例子是我们的新闻汇总。这些并非简单的链接清单，而是由本地记者精心策划：他们知道哪些社区博客、区域媒体或小众渠道在当地重要。这样的策划很费时间。

所以我们把记者的判断过程整理出来——他们读什么、如何决定值得分享、信任哪些来源——把这些规则写进 AI 的提示里。现在，记者每天不必从零开始，而是能拿到一份短而经过筛选的链接清单，已经反映了他们的判断，只需挑选就好。过去要花数小时的工作现在只需几分钟，每座城市都能得到既高质量又有本地感的汇总。

我们在整个时事通讯的工作流上采取了类似做法：把整份稿件拆成多个组件，而不是试图一次性自动化所有环节。任务越具体，效果越好。这样我们既能保持控制和一致性，也能显著提升质量。

另一个例子是如何倾听读者。我们对所有城市每季度做一次问卷，但公司只有一个受众洞察负责人。以前把这些数据变成记者可用的见解需要好几周，现在借助 AI，我们能在不到一天的时间内为每个城市生成清晰的一页总结。记者几乎能即时拿到读者反馈，调整报道的内容与方式。

这些工作看起来不显眼，但非常有力。它让我们与读者保持紧密联系，帮助每位记者交付更好的本地产品。

展望未来五到十年，随着 AI 更深入地进入新闻室，你对新闻业的走向有何设想？

真正原创、具专业深度的新闻价值只会愈发提高。没有任何 AI 能建立采访来源的信任或独家突破；人类信任不可替代，这始终是卓越报道的基石。

但 AI 可以让这些报道发挥更大作用。其一，它能解锁那些已公开但难以获取的信息——会议记录、文字档、数据——让记者能提出更好的问题、更快找到更多故事。其二，它改变了新闻触达受众的方式。一篇有分量的报道现在可以被转化为时事通讯、视频、播客或社交短片，而不再需要完整的制作团队支持。

因此，一个重要的独家报道不再只存在于一个渠道，它可以以多种形式、更广的受众群体、更小的摩擦被传播。媒体行业当然会经历震荡——它一直如此。但潜在的好处很大：更多问题被回答、更多社区得到服务、更多高质量新闻送到真正需要的人手中。

对我们而言，这正是让本地使命成为可能的原因。我们还处在早期，路上会有颠簸，但只要我们持续把信任与质量放在首位，技术就能成为推动本地新闻扩展的强大工具。

补充： Axios 使用 ChatGPT 支持内部研究、分析与沟通稿件的起草；同时 OpenAI 与 Axios 合作，资助 Axios Local 向包括 Pittsburgh 、 Kansas City 、 Boulder 和 Huntsville 在内的城市扩张。

Axios is a media company delivering vital, trustworthy news and analysis in the most efficient, illuminating and shareable ways possible. It offers a mix of original and smartly narrated coverage of media trends, tech, business and politics with expertise, voice and smart brevity. 

We spoke with Allison Murphy, Chief Operating Officer at Axios, about AI supporting high-impact local journalism and serving communities better.

AI is already a huge part of how Axios Local works. At the core, what we’re trying to do is prove that you can run a sustainable, profitable local news model that delivers high-quality journalism to every community in America. That means solving for scale and efficiency—and that’s exactly what AI is good at. So there’s a really natural fit between what OpenAI is building and what we’re building at Axios Local.

We use AI across the whole workflow—from story creation to editing to distribution—but where it’s really made a difference is helping reporters do important work faster. Readers come to Axios for smart brevity, so we built a custom GPT called the Axiomizer. Reporters drop in their drafts and it suggests sharper headlines, clearer “Why it matters,” “What’s next,” and “Between the lines”—basically helping great reporting land even better with readers.

It’s not replacing journalists. It’s taking strong, expert reporting and making it crisper, clearer, and more useful. We’re also adding editing and style checks into the tool so copy editors can focus on what really needs human judgment, instead of spending time on basic fixes or formatting.

The result is that everyone—reporters and editors alike—gets more time to focus on high-impact journalism, while AI handles the busywork in the background.

“[AI] has already become central in how we do the work of Axios Local.”

There are a lot of ways to think about this, but it really comes down to both coverage and how we work. Our goal is to let reporters spend their time doing what only humans can do—talking to sources, digging into data, and telling great stories. Every minute we save them on production, formatting, or busywork is a win.

That efficiency lets us reach more communities. If we can launch a new city with just one amazing reporter—without needing a whole extra layer of production and support—we can go to places we never could before. That’s exactly what we’ve done in places like Boulder and Huntsville, Alabama, which are our first one-reporter cities.

With AI-powered workflows behind the scenes, a single reporter can produce a great local news product. It means more local coverage, in more places, with the same high bar for quality.

At its core, the local news crisis is really an economic one. Great local journalism has to be deeply tailored to each community, which makes it hard to get the cost efficiencies that other industries rely on. You can’t just copy-and-paste a newsroom.

What AI does is change that math. It lets us get more out of our expert reporters and editors, and it strips out costs that don’t actually add value for readers. By improving the economics, we make it possible to do high-quality journalism in more places.

AI is also opening up whole new sources of information. There’s already a huge amount of public data out there—city council meetings, school board recordings, government transcripts—but it’s basically locked away because no one has time to watch or read all of it. With AI, reporters can get quick, reliable summaries and spot what actually matters. Instead of sitting through a three-hour meeting, they can see where the story is moving and know who to call.

That means great reporters can cover more ground, uncover more stories, and serve their communities better—by turning information that was technically public, but practically inaccessible, into something people can actually use.

“We want to make it so that a reporter can spend all of their time doing the unique work that only an expert human reporter can do.”

Human reporters are always going to be at the center of Axios. That’s non-negotiable. They’re what create trust with readers. They’re what make Axios feel like a neighbor in your pocket—someone who knows your community and tells you what really matters. If you lose that human voice, you lose the whole product.

What we standardize is everything around them. We use technology to make the style consistent, and to handle things like formatting, data, and analytics so reporters don’t have to. Readers care deeply about things like housing prices, school performance, and how their community compares to the next one over—but turning raw data into clear, trustworthy, useful insight takes real technical work.

By building tools that handle that for them—clean charts, vetted math, transparent comparisons—we give every reporter access to capabilities that used to be uneven or hard to scale. That way, every community gets the same high-quality data-driven journalism, while the reporting itself stays local, human, and deeply rooted in the place.

One of the things we’ve really focused on is identifying the parts of our newsletters that readers love—and then figuring out how to make those easier to produce.

A great example is our news roundups. These aren’t just lists of links; they’re deeply curated by local reporters who know which neighborhood blogs, regional outlets, and niche sources actually matter in their community. That kind of curation takes a lot of time.

So we worked with our reporters to capture their process—what they read, how they decide what’s worth sharing, which sources they trust—and built that into our AI prompts. Now, instead of starting from scratch every day, reporters get a short, vetted list of links that already reflects their judgment. They just pick what works. What used to take hours now takes minutes, and every city gets a high-quality roundup that still feels local and human.

We’ve taken a similar approach across the newsletter—breaking it into components rather than trying to automate the whole thing at once. The more specific the task, the better the results. That gives us control, consistency, and much higher quality.

Another great example is how we listen to readers. We run quarterly surveys across all our cities, but we only have one audience insights lead. Before, turning that data into something reporters could actually use took weeks. Now, with AI, we can analyze the responses and generate clear one-page summaries for every city in less than a day. That means reporters get real reader feedback almost immediately, and they can adjust what they cover and how they cover it.

It’s not flashy, but it’s powerful. It keeps us tightly connected to our readers—and it helps every reporter deliver a better local product.

“It's absolutely critical that we have AI in the hands of the journalists [...]”

The value of truly original, expert journalism is only going to keep rising. No AI can build a source relationship or break a scoop. That human trust is irreplaceable, and it’s what great reporting will always be built on.

What AI can do is make that reporting go further. First, it unlocks information that’s already public but hard to access—meeting transcripts, records, data—so reporters can ask better questions and find more stories faster. Second, it transforms how journalism reaches people. A single reported story can now become a newsletter, a video, a podcast, or a social clip without needing a whole production team behind it.

That means a great scoop doesn’t just live in one place anymore—it can reach more audiences, in more formats, with far less friction. There will be disruption, of course. Media always has been. But the upside is huge: more questions answered, more communities served, and more high-quality journalism getting to the people who need it.

And from our perspective, that’s exactly what makes our local mission possible. We’re still early, and there will be bumps along the way—but as long as we stay focused on trust and quality, technology gives us a powerful way to keep expanding what local journalism can be.

Axios uses ChatGPT to support research, analysis, and drafts of internal communication updates. OpenAI has partnered with Axios to fund the expansion of Axios Local to cities including Pittsburgh, Kansas City, Boulder, and Huntsville. 

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