Two People, Ninety Days

A lot of businesses can really scale to tens of millions of dollars today profitably with AI.

今天很多生意靠 AI 真的能做到几千万美元的规模，而且是盈利的。

This is Alex, founder of Higgsfield, an AI company that hit a $200 million annual recurring revenue in just 9 months, faster than Slack or Zoom.

这位是 Alex,Higgsfield 的创始人，这家 AI 公司仅用 9 个月就做到了 $200 million 的年度经常性收入(ARR)，比 Slack 或 Zoom 还快。

And what he told me about starting a business today completely blew my mind, and you can copy his strategy, too.

他跟我讲的关于当下如何创业的那些东西彻底震撼了我，而且你也能照搬他的打法。

Focus on bringing like the first dollar by day 30 of product development, and maybe 1 million ARR by day 90.

专注于在产品开发的第 30 天赚到第一块钱，争取第 90 天做到 1 million ARR。

That's a lot.

这可不少。

I just think it's the next industrial revolution.

我就是觉得，这是下一场工业革命。

It's probably more powerful than the internet.

它可能比互联网还要强大。

For many, many young people, AI becomes social elevator.

对非常非常多的年轻人来说，AI 成了一部 social elevator(社会电梯)。

For someone who still has fear, like this is moving so fast, I don't know how I can start.

有些人还心怀恐惧：这一切变化太快了，我都不知道该怎么开始。

Can you give them one piece of advice?

你能给他们一条建议吗？

First, I would start from I have an amazing guest today.

首先，我想先说，我今天请到了一位非常棒的嘉宾。

I am so excited to learn from you.

我特别期待向你学习。

Let's get very practical right away.

我们直接进入实操吧。

You built Higgsfield and achieved $200 million in revenue in 9 months.

你打造了 Higgsfield，并在 9 个月内做到了 $200 million 的收入。

Let's imagine every I don't want this to happen, but let's imagine a scenario when you have to start from scratch tomorrow, and you have 90 days to launch a business idea.

我们设想一下——我并不希望这真的发生——但假设有这么个场景：你明天必须从零开始，只有 90 天去启动一个创业点子。

From what you've learned with your experience at Higgsfield, what would you do?

结合你在 Higgsfield 积累的经验，你会怎么做？

I think it all starts with maybe a team of two, someone who is builder, who can go within 24 hours from idea to a And now it's all becomes possible.

我觉得这一切也许都始于一个两人小团队，一个是工程/技术型创始人(builder)，能在 24 小时内把一个点子变成产品——如今这一切都成为可能了。

There are so many databases, there are so many payment systems, and so on, which simplify creation of MVP.

现在有那么多现成的数据库、那么多支付系统等等，大大简化了 MVP 的搭建。

And then someone, as I call it, go-to-market person, who has this natural empathy maybe or understanding of the sort of target distribution, whom they're selling to, and who can come up with interesting kind of new content formats, which can resonate with the target audience on social media.

另一个人，我把他叫作负责 go-to-market 的人，他天生有那种共情力，或者说对目标渠道、对卖给谁很有感觉，还能想出有意思的新内容形式，在社交媒体上打动目标受众。

And I think this is a very different skill set from the like marketing roles of the previous decades.

我觉得，这套能力跟过去几十年那种市场营销岗位所需要的完全不一样。

And how many times should they be ready to iterate?

那他们应该做好迭代多少次的准备？

How many ideas?

要试多少个点子？

For example, for us last year, we were iterating every day.

比如说，我们去年是每天都在迭代。

So, it was six um days a week and um every day we were putting new product release.

当时是一周六天，每天都在发布新的产品版本。

As we were trying to find workflows and use cases which have high frequency and which matter for our target audience.

当时我们努力去找那些高频、并且对我们目标用户真正重要的 workflow 和使用场景。

And then, once the technology gets there, it's important to develop sort of the workflow, which is easy enough but gives enough configuration as well.

然后，一旦技术到位，关键就是设计出一套 workflow，既要足够简单，又要给到足够的配置空间。

This dilemma of the perfect interface is still not solved, frankly.

坦白说，完美交互界面的这个难题，到今天都还没解决。

So, uh this is another reason why we embrace daily iteration.

所以这也是我们拥抱每日迭代的另一个原因。

And on top of all of that, every month the whole industry resets.

而在这一切之上，每个月整个行业都会重置一次。

They completely push the boundaries in terms of the capabilities.

它们在能力上不断突破边界。

There are probably, let's say, five leading research labs.

大概，可以说，有五家领先的研究实验室。

And each lab is pushing massive updates every quarter.

每家实验室每个季度都在推大量更新。

So, at some months we have even two major updates.

所以有些月份我们甚至会遇到两次重大更新。

And it typically requires to substantially rebuild the whole product around those models.

而这通常意味着，要围绕这些新模型把整个产品大幅重建一遍。

So, it's exciting time today cuz product builders, like ourselves at Higgsfield, we we just try to evolve the product so that it highlights the best possibilities of these models to our customers.

所以今天是个激动人心的时代，因为像我们 Higgsfield 这样的产品打造者，一直在努力让产品进化，把这些模型最好的可能性呈现给我们的客户。

But, that's a constant embrace.

但这需要我们持续不断地去拥抱变化。

Yeah, it sounds like a very challenging race.

是啊，这听起来是一场非常有挑战的竞赛。

And I think you mentioned that last year was one of the hardest for you when a lot of things were not working.

我记得你提到过，去年是你最艰难的一年之一，当时很多东西都跑不通。

Can you talk to me about that one thing that actually worked?

能跟我聊聊那件真正跑通了的事吗？

In we really started from the mobile apps.

我们其实是从移动应用起步的。

And things were not working well cuz retention for mobile apps is relatively low.

当时情况不太好，因为移动应用的 retention 相对较低。

Things drastically changed for Higgsfield when we started to constantly iterate with creatives.

当我们开始持续和创意人一起迭代时，Higgsfield 发生了翻天覆地的变化。

And we just asked them very simple question like, "Did you see this video?" This was a cool AI-generated video and they said, "Oh, no.

我们就问他们一个很简单的问题，比如"你看过这个视频吗？"——那是一支很酷的 AI 生成视频，他们说"哦，没看过。

How is this possible?

这怎么可能做到？

What's the cost?" And we say, "It actually cost maybe less than $500 to make this video." And then we ask, "Did you actually try these models?

成本多少？"我们回答"其实做这支视频的成本可能还不到 $500。"然后我们问"你自己真的试过这些模型吗？

What's your experience with AI?" And and obviously everyone tried AI by then.

你用 AI 的体验如何？"显然到那时候每个人都试过 AI 了。

Even by maybe February last year, everyone tried AI, but everyone had some issues with that.

甚至可能到去年二月，每个人都试过 AI，但每个人都遇到过一些问题。

Back then, we realized that the core limitation was around camera control.

那时候我们意识到，核心的局限在于镜头控制。

Like a lot of creative directors, they really want to control all the camera effects, camera angle, and so on.

很多创意总监，他们非常想控制所有的镜头效果、镜头角度等等。

Back then, there was no system to achieve that.

当时并没有一套系统能实现这一点。

So, that's the initial traction of Higgsfield came from these camera controls, which we implemented on engineering side based on the feedback from creatives.

所以 Higgsfield 最初的 traction 就来自这些镜头控制，是我们工程团队根据创意人的反馈做出来的。

So, how many interviews did you have to conduct to come up with this feature?

那你们做了多少次访谈才想出这个功能？

This is a very good question, but it kind of puts me on a weak spot, frankly, cuz we interviewed eight people.

这是个很好的问题，但坦白说有点戳到我的软肋，因为我们只访谈了八个人。

Eight out of eight said the same thing.

八个人里八个都说了同样的话。

And we talked from Hollywood level movie to like regional producers of commercials.

我们聊的对象从好莱坞级别的电影制作，到地方性的广告片制作人都有。

Everyone had the same And how did you select those people?

每个人都有同样的——那你们是怎么挑选这些人的？

Were they customers already or you just wanted to talk to people in the industry?

他们已经是你们的客户了，还是你只是想找行业里的人聊聊？

Actually, this was probably a challenge as we wanted to talk to people we don't have a very close relationship to just get an unfiltered opinion.

其实这可能是个挑战，因为我们想找一些跟我们关系不那么近的人聊，好拿到没有滤镜的真实意见。

But, the feedback was very consistent.

但反馈非常一致。

Everyone was missing these camera controls.

每个人都觉得缺了这些镜头控制。

So, that's what we delivered March last year.

所以这就是我们去年三月交付的东西。

Then in April, we delivered a library of visual effects.

然后在四月，我们交付了一个视觉特效库。

And then, I think in June, industry completely changed.

接着，我想是在六月，行业彻底变了。

We saw the emergence of AI native marketing agencies.

我们看到了 AI 原生营销代理商的兴起。

So, essentially those agencies, they completely go end-to-end with AI.

本质上这些 agency，他们完全用 AI 做端到端的全流程。

And very often, they try to bypass incumbent tooling like for example Adobe or something else and go end-to-end with AI.

而且他们经常试图绕过 Adobe 这类现有工具，完全用 AI 走端到端。

On the one side they are very AI capabilities back in June were a little limited.

一方面，六月那会儿 AI 的能力其实还有点有限。

On the other hand, they drastically improve their margin profile and they kind of show their clients that they can build ads within days.

另一方面，他们大幅改善了自己的利润结构，还向客户证明，他们能在几天内做出广告。

A lot of brands actually want to have constant content flow on their socials and they want to embrace AI.

很多品牌其实希望在自己的社交媒体上保持持续的内容产出，他们想拥抱 AI。

And then from June to December last year, this um this new industry of AI native agencies completely exploded.

然后从去年六月到十二月，AI 原生 agency 这个新行业彻底爆发了。

But basically you said the the start of your growth was the multi-angled uh camera view and it came from talking to people in the industry.

不过基本上你是说，你们增长的起点是那个多角度的镜头视角，而它来自于和行业里的人交谈。

I love that.

我太喜欢这一点了。

And also the number eight is actually very consistent from what I'm getting uh talking to other founders.

还有，八这个数字，其实跟我和其他创始人聊下来得到的结论非常一致。

It's normally like 12 to 20, but it's not too many interviews cuz I feel like a lot of people think they need to talk to thousands of people to figure out the problem, but it's actually like around 10 interviews.

通常大概是 12 到 20 个，但也不算太多访谈，因为我感觉很多人以为要跟成千上万的人聊才能搞清楚问题所在，但其实大概 10 次访谈就够了。

Yeah, absolutely.

对，完全正确。

So eight people who actually helped us to shape the product and then I think we hired four of them.

所以这八个人真正帮我们塑造了产品，后来我想我们雇了其中四个。

Oh, you also Yes.

哦，你还——是的。

So now we have this feedback loop within the team.

所以现在我们在团队内部就有了这个反馈闭环。

That's awesome.

那太棒了。

And that's how we realized that probably the best products in creative AI is going to be built in symbios uh like in collaboration between engineers and between cre- creators.

我们也正是这样意识到，创意 AI 领域最好的产品，很可能会诞生于工程师和创作者之间的共生协作。

So today roughly half of our maybe 40% of the team are um engineers, maybe 40% are creators.

所以今天我们团队里大概一半，或者说可能 40% 是工程师，可能 40% 是创作者。

Like you said, two founders, right?

就像你说的，两个创始人，对吧？

One is technical, one knows the consumer.

一个懂技术，一个懂消费者。

It's basically reflected in your team.

这基本上反映在了你们的团队构成上。

Okay, let's let's get back to that tough year because I feel like for a lot of people that's what they're scared of.

好，我们回到那个艰难的年头，因为我觉得对很多人来说，那正是他们害怕的东西。

So when you were building this and nothing was really working and then Google releases the new video Did you ever think about giving up on that particular market and starting something else because this was getting so crowded?

所以当你在做这件事、什么都跑不通，然后 Google 又发布了新的视频——你有没有想过干脆放弃这个特定市场、去做点别的，因为这个赛道变得太拥挤了？

I think we were committed to figure this There are two reasons why we had a First, is that prior to that I was at Snapchat, I was running Genie I there, and I saw the uprise of TikTok and CapCut.

我觉得我们当时是铁了心要把这件事搞明白，我们之所以有这份笃定，有两个原因：第一，在那之前我在 Snapchat，负责那里的 Genie 项目，我亲眼看到了 TikTok 和 CapCut 的崛起。

CapCut became top five apps in the world.

CapCut 成了全球前五的应用。

And it's unprecedented says it's not a messenger, it's not a social media.

这是史无前例的——它既不是通讯工具，也不是社交媒体。

And this was a strong signal for me that the needs of social media creators are simply unmet in the markets.

这对我来说是个强烈的信号：社交媒体创作者的需求在市场上根本没被满足。

And the second reason why we had conviction, we constantly heard that creators feel a from sort of feeling pressure to record multiple videos a day for socials with their own face.

我们有信心的第二个原因是，我们不断听到创作者感到压力，因为要每天用自己的脸录好几条视频发社交平台。

Mr.

Mr.

Beast, he spoke very openly about that.

Beast 就非常公开地谈过这件事。

But that's I think that that has been a primary challenge for the whole industry over last four or five years.

但我觉得这在过去四五年里一直是整个行业的核心难题。

And then what was surprising to me is that advertisers have the same problem.

然后让我意外的是，广告主也有同样的问题。

Most of the advertisers talk about like maybe mid-market brands, they can be spending hundreds of millions of dollars on marketing and they don't have any production team.

大多数广告主，比如那些中端市场品牌，可能在营销上花掉数亿美元，却根本没有自己的制作团队。

So you chose the right part of the market.

所以你选对了市场里的那部分人。

This is what I'm hearing.

我听到的就是这个意思。

So I feel like for every entrepreneur who's watching, uh when you see a big opportunity in the market, you also have to spot who's paying the most.

所以我觉得，对每一位正在看的创业者来说，当你在市场上看到一个大机会时，你还得看清谁付的钱最多。

Not just go after each user, right?

而不是一味去追每一个用户，对吧？

Absolutely.

完全正确。

We live in a very interesting um era where all the powerful companies try to give AI to everyone.

我们生活在一个非常有意思的时代，所有强大的公司都想把 AI 给到每一个人。

Literally, Meta, XAI, Microsoft, OpenAI, Anthropic, ByteDance, and many, many more.

真的，Meta、xAI、Microsoft、OpenAI、Anthropic、ByteDance，还有很多很多公司。

Think each of those companies want to give a yacht to all its customers really.

可以说这些公司每一家都想给它所有的客户送一艘游艇。

That's why startups have to have more nuanced view of the world and have to have more nuanced set of customers.

正因如此，创业公司必须对世界有更细腻的判断，必须锁定更细分的一批客户。

And I think um startups today are sort of incentivized to stay cash flow positive and build real business from day zero.

而且我觉得，如今的创业公司多少是被驱动着去保持正现金流、从第零天起就做真正的生意。

This is what I see across Higgsfield and other top application AI companies.

这是我在 Higgsfield 以及其他顶尖应用层 AI 公司身上看到的。

So, I think this also creates a very interesting dynamic that the target audience should be maybe tens of millions of users who are willing to spend hopefully couple thousand dollars a month.

所以我觉得这也带来一个很有意思的格局：目标受众也许应该是数千万级、且愿意每月花上几千美元的用户。

So, this is not for everyone, but the delivered value should be so so the product should be so good so that it kind of sells itself.

所以它不是给所有人的，但交付的价值必须非常高、产品必须好到能自己把自己卖出去。

I love that and I love that you have a concrete number, $2,000.

我很喜欢这一点，也很喜欢你给出了一个具体的数字，$2,000。

Yeah, $2,000.

对，$2,000。

I'm not sure like with Higgsfield, for example, this is the core metric which we are tracking.

拿 Higgsfield 来说，这就是我们在追踪的核心指标。

Like how much of the value we believe we provide, which is more like through the user interviews, but also how much we charge annually.

也就是我们相信自己提供了多少价值——这更多是通过用户访谈得来的——以及我们每年收多少费。

So, this is one key metric and we are not chasing just monthly active users, for example.

所以这是一个关键指标，而我们并不是单纯去追月活用户（monthly active users）。

Cuz the monthly active users number can be inflated through some viral effect.

因为月活用户这个数字可能被某种病毒式传播效应吹大。

Monthly active user doesn't really speak to the frequency of the usage and the value So, that's why for us really daily active users and average ACV, average contract value, those two metrics are the most important ones.

月活用户其实说明不了使用频率和价值，所以对我们来说，真正重要的是日活用户（daily active users）和平均 ACV（average contract value，平均合同价值）这两个指标。

One of the reasons I was so excited to sit down with Alex is that our team has actually been using Higgsfield for a while now.

我之所以这么兴奋能和 Alex 坐下来聊，原因之一是我们团队其实已经用 Higgsfield 有一阵子了。

My producers and editors absolutely love that all the top AI models are in one place.

我的制片人和剪辑师特别喜欢所有顶尖的 AI 模型都集中在一个地方。

Nano Banana, Seedance, Kling, Veo, Sora.

Nano Banana、Seedance、Kling、Veo、Sora。

And I love that they don't need 10 different subscriptions.

我也很喜欢他们不需要开 10 个不同的订阅。

And it's kind of incredible.

这挺不可思议的。

Alex just told us that last year they were shipping a new product release 6 days a week.

Alex 刚刚告诉我们，去年他们一周有 6 天都在发布新产品。

And now everyone knows them, but they haven't slowed down.

如今大家都知道他们了，但他们并没有慢下来。

They keep launching new features.

他们一直在推出新功能。

And one of the latest ones is SOUL 2.0.

其中最新的一个就是 SOUL 2.0。

SOUL 2.0 is Higgsfield's own image model, and it's not like anything else out there.

SOUL 2.0 是 Higgsfield 自家的图像模型，它和市面上任何其他产品都不一样。

Most AI image generations, you type a prompt, you get something that looks fine.

大多数 AI 图像生成，你输入一个 prompt，得到的东西看着还行。

SOUL was built specifically for creative work, fashion, editorial, content.

SOUL 是专门为创意工作打造的——时尚、大片、内容。

It actually understands aesthetics.

它是真的懂美学。

You give it a reference photo, and it doesn't just copy it.

你给它一张参考照片，它不会只是照抄。

It reads the lighting, the grain, the mood, the era, like a creative director would.

它会读懂光线、颗粒感、氛围、年代感，就像一位创意总监那样。

There are three models.

一共有三个模型。

SOUL, that's the core model.

SOUL，这是核心模型。

You prompt, you get a beautiful image with real aesthetic awareness.

你写 prompt，就能得到一张有真正美学意识的漂亮图片。

SOUL reference, you upload a reference and it generates new images that match the vibe, not just the composition.

SOUL reference，你上传一张参考图，它会生成新的图片，匹配的是那种感觉，而不只是构图。

Same visual DNA, different shots.

相同的视觉 DNA，不同的画面。

And SOUL ID, you upload 10 or more photos of yourself, it learns your face structure, skin tone, expressions, then it generates you in any style.

还有 SOUL ID，你上传 10 张或更多自己的照片，它会学习你的脸部结构、肤色、表情，然后以任何风格生成你的形象。

Y2K, editorial, film photography, Polaroid, 20 different presets at launch.

Y2K、大片风、胶片摄影、宝丽来，上线时就有 20 种不同的预设。

And here is what actually got me.

而真正打动我的是这一点。

You can specify the camera medium.

你可以指定拍摄用的相机介质。

Say, shot on Kodak Portra, or disposable camera, and it changes the grain, the color science, everything.

比如说，用柯达 Portra 拍摄，或者用一次性相机，它就会改变颗粒感、色彩科学，一切都变。

It doesn't slap on a filter, it actually shifts the entire feel of the image.

它不是简单加个滤镜，而是真的改变了整张图片的质感。

And the same thing works with color.

同样的道理也适用于色彩。

They just added hex colors, so you can pull the exact palette from any photo and apply it straight to your generations.

他们刚加了 hex 色值，所以你可以从任意一张照片里提取出精确的配色，直接套用到你的生成结果上。

If you want to try SOUL 2.0, I'll leave the link in the description.

如果你想试试 SOUL 2.0，我会把链接放在简介里。

And now, let's get back to Alex.

现在，我们回到 Alex。

Can you give advice to people who are watching who haven't started yet, but they haven't started because they think a large company is going to take over?

你能不能给那些正在看、但还没开始的人一些建议？他们迟迟没开始，是因为觉得大公司迟早会把这块市场吃掉。

How should they think about their defensibility?

他们该怎么去思考自己的护城河（defensibility）？

I think each company can really keep their focus on maybe two or three top priorities.

我觉得每家公司其实都可以把精力聚焦在两三个最重要的优先事项上。

Interestingly enough, people like to say about Anthropic that their major success from MCP and Claude Code actually was not like a top-down, but really bottom-up.

有意思的是，人们提起 Anthropic 时常说，它在 MCP 和 Claude Code 上的重大成功其实不是自上而下推动的，而是真正自下而上冒出来的。

And it like Claude Code success was not sort of planned.

而且 Claude Code 的成功某种程度上并不是规划出来的。

And I think it's true that like these large companies, they definitely can benefit a lot from applying top-down approach from two to three initiatives and really consolidating all the resources to make the most progress there.

我确实认为，这些大公司完全可以从自上而下的打法中受益很多——聚焦两三个方向，把所有资源集中过去，在那里取得最大进展。

I just think it's the next industrial revolution.

我只是觉得，这是下一场工业革命。

It's probably more powerful than the internet.

它大概比互联网还要强大。

So, the number of products and ideas to be built, I think just outweighs uh uh number of ideas which OpenAI or Anthropic can push internally.

所以我认为，值得被做出来的产品和点子数量，远远超过了 OpenAI 或 Anthropic 内部能推动的点子数量。

That's why I would encourage builders to build.

这就是为什么我会鼓励 builder 们去动手做。

Especially today when like quite small team of maybe 10 people can build like high-scale products.

尤其是今天，一个大概只有 10 人的小团队就能做出规模很大的产品。

Do you think there is that we have a certain gap of in time when we can build?

你觉得我们能做东西的时间窗口是有限的吗？

So, for example, I've been hearing a lot in social media that we only have 2 years to build something new because then we're going to have some companies that reach AGI and it's going to be impossible to find a gap in the market because those companies are going to be filling those gaps.

比如说，我在社交媒体上经常听到，我们只剩两年时间去做新东西，因为之后会有一些公司实现 AGI，到时候就不可能再在市场里找到空隙了，因为那些公司会把这些空隙都填满。

This is a good question.

这是个好问题。

We definitely make um tremendous progress as an industry to automating uh work in digital worlds.

作为一个行业，我们在数字世界里自动化工作这件事上，确实取得了巨大的进展。

And for sure, maybe next decade, there are going to be a lot of applications of AI in physical worlds.

而且可以肯定，也许下一个十年，AI 会在物理世界里有大量应用。

It's difficult to forecast how much progress we all are going to make there, but this decade is definitely the era of digital economy completely changing with with AI.

我们在那方面到底能取得多大进展，很难预测，但这个十年绝对是数字经济被 AI 彻底改变的时代。

That's for sure.

这一点是肯定的。

I really don't want to believe in the future when there are going to be maybe three labs who have the best models and these models controlling all the worlds.

我真的不愿意相信这样一种未来：也许只有三家实验室拥有最好的模型，而这些模型掌控着整个世界。

This could happen.

这有可能发生。

That would be very difficult future.

那会是一个非常艰难的未来。

So, I I try to stay optimistic and encourage everyone to stay optimistic and really focus on building and delivering value.

所以我努力保持乐观，也鼓励每个人保持乐观，真正把精力放在做东西、创造价值上。

Like, for example, um today I just want to give you a very concrete example.

比如说，今天我想给你举一个非常具体的例子。

So, I recently spoke to a very large, very, very large like property management company, which actually uses Higgsfield to to sort of advertise their buildings, their uh apartments, and so on.

我最近跟一家非常大、非常非常大的物业管理公司聊过，他们实际上在用 Higgsfield 来给他们的楼盘、公寓等等做广告。

Like, no one is building for this industry.

根本没人在为这个行业做产品。

Like, in this industry, there is a very specific um workflow of a customer.

在这个行业里，客户有一套非常特定的 workflow。

Like, customer needs to learn about about the property, then they need to go to the website and get all the details, then they need to call, then they need to show up, then they leave deposit, and the whole customer journey.

客户得先了解这个房产，然后去网站上获取所有细节，然后打电话，然后到场，然后交定金，整条客户旅程就是这样。

No one is actually building solution specifically for this industry to like cover this journey end-to-end with agents.

其实没有人专门为这个行业做一套用 agent 端到端覆盖整条旅程的解决方案。

And the reason why agents are going to deliver a lot of value in this specific business is that uh customers who want to, maybe, let's say, rent apartment, especially in certain price points, they want to make a decision rather quickly.

而 agent 之所以能在这个具体行业里创造大量价值，是因为那些想租房的客户，尤其是在某些价格段，他们希望相当快地做出决定。

So, every day of delay, every day of just moving from one stage to another, is just is just lost revenue.

所以每拖延一天、每从一个阶段挪到下一个阶段多花一天，都是白白流失的收入。

So, and this business owner just said to me that no one is building for their industry.

所以这位企业主就跟我说，没有人在为他们这个行业做产品。

And And I'm confident there are many more examples like that.

而且我有信心，像这样的例子还有很多很多。

Yeah, so riches are in the niches, as as they say.

是啊，所以正如人们说的，riches are in the niches(财富藏在细分里)。

From your experience, when you pitched VCs with another AI idea, what makes a pitch stand out these days?

从你的经验看，当你带着又一个 AI 点子去向 VC 做 pitch 时，如今什么样的 pitch 才能脱颖而出？

I think today, um there is definitely fear that OpenAI, Anthropic, and other labs are going to just uh be uh very acquisitive and uh just trying to expand their product offering to multiple different verticals.

我觉得今天，确实存在一种担忧：OpenAI、Anthropic 以及其他实验室会非常热衷于收购，并且试图把自己的产品线扩张到多个不同的垂直领域。

Pretty much every week there is cloud for X launched and stocks go down.

几乎每周都有一个『X 领域的 cloud』发布，然后股价就跌。

That definitely happens and that creates a lot of fear.

这种事确实经常发生，也制造了大量恐慌。

And I would just say that the core inside for me personally was that most of these hot and the hyped AI companies they are actually cash flow positive.

我想说，对我个人来说最核心的洞察是：这些当红、被热炒的 AI 公司，其实大多是正现金流的。

Like maybe it is a wrong mindset to go and raise venture capital today.

也就是说，今天还想着去融 VC，可能本身就是个错误的心态。

I think there is plenty of pre-seed capital available today.

我觉得如今 pre-seed 阶段的资金是很充裕的。

But I'm not sure every everyone needs like to raise series A, B, C, D and so How much revenue did you have when you raised your first round?

但我不确定是不是每个人都需要去融 series A、B、C、D 之类的——那你融第一轮的时候，营收是多少？

For me with Kickstart AI it was probably easier cuz I had previous exits and we basically raised $16 million without having any revenue just with maybe having like million users for our mobile app.

对我来说，做 Kickstart AI 那次可能更容易些，因为我之前有过退出，我们基本上在没有任何营收的情况下就融了 $16 million，靠的可能就是我们那款 mobile app 有大概一百万用户。

But this is not exactly the way how I would recommend to build today.

但这并不是我会推荐今天大家去创业的方式。

I would recommend to focus on bringing like the first dollar by day 30 of product development and maybe 1 million ARR by day 90 and then decide if someone needs VC funding or doesn't.

我会建议：在产品开发的第 30 天带来第一块钱，第 90 天做到 1 million ARR，然后再决定到底需不需要 VC 融资。

I mean a lot of businesses can really scale to tens of millions of dollars today profitably with AI.

我是说，今天很多生意真的可以靠 AI 盈利地做到几千万美元的规模。

And for such businesses there is no need to attract VC funding.

而对这类生意来说，根本没必要去引入 VC 融资。

I can give you very simple example.

我可以给你举个非常简单的例子。

So there are so many websites which just allowed to make professional photo shoot like for basically for for passport.

现在有很多网站，就是让你做那种专业的证件照拍摄，比如护照照片。

Many of them make tens of millions of dollars.

其中很多都能赚到几千万美元。

None of them are going to be Yeah, none of them are going to be venture capital backed business.

它们没有一个会是——对，没有一个会是 VC 投资支持的生意。

Well, and you said something $1 million by day 90.

对，你刚提到一个说法，第 90 天做到 $1 million。

ARR meaning like 80K a month.

ARR，意思就是一个月 80K。

80 80K a month in 3 months that's a lot.

三个月内做到一个月 80K，那可不少。

And so the the playbook to achieve that is basically generate ads and launch them and test whether whether they're landing with your target audience.

那实现这个目标的打法，基本上就是生成广告、投放，然后测试它们能不能打动你的目标受众。

Paid ads are very difficult today, I think.

我觉得如今付费广告非常难做。

A lot of distribution come through organic social media and creator And just make sure that by day uh there is monetization in place, and then there is a way to constantly grow to revenue to, let's say, 1 uh million ARR by day 90.

大量的分发是通过自然流量的社交媒体和创作者来的——你只要确保商业化在某个节点能到位，然后有一条路径能持续把营收做上去，比方说第 90 天做到 1 million ARR。

Many successful uh companies scale very quickly today.

如今很多成功的公司都增长得非常快。

Just different verticals have different capacity.

只不过不同赛道的容量不一样。

Some In some verticals, the ceiling could be just 50 million.

在有些赛道里，天花板可能就只有 50 million。

In other vertical, 1 billion.

在另一些赛道里，是 1 billion。

In other verticals, like 100 Any tips on landing first customers in the first 30 days?

在还有一些赛道里，大概是 100——对于前 30 天拿下第一批客户，有什么建议吗？

Initially, especially last year, Twitter has been the social media where the distribution starts from.

最初，尤其是去年，分发都是从 Twitter 这个社交媒体开始的。

It starts from like small communities, then it goes to AI news pages on X, then from AI news pages on X, it goes to Instagram news pages, then from Instagram news pages to creators, then it goes to Telegram and like other social media.

它从一些小社群开始，然后传到 X 上的 AI 新闻账号，再从 X 上的 AI 新闻账号传到 Instagram 的新闻账号，然后从 Instagram 的新闻账号传到创作者，接着再传到 Telegram 以及其他社交媒体。

That's what we have seen with Higgsfield and with many other products as well, that they went through the same sort of journey of popularity and news through various social media.

我们在 Higgsfield 身上以及很多其他产品身上都看到了这一点：它们都经历了同样的、在各个社交媒体之间获得人气和传播的过程。

But it all originated on X.

但这一切都起源于 X。

I think now it's being kind of changed.

我觉得现在这个情况有点在变了。

Today, a lot of hype is like a lot of companies, they sort of try to use X to boost their product.

如今大量的炒作——其实是很多公司，都在试图用 X 来给自己的产品造势。

Uh sort of signal-to-noise ratio just drops.

于是信噪比就下降了。

Twitter becomes less relevant, but still it's it's the main place for new AI products launch.

Twitter 的重要性在下降，但它仍然是新 AI 产品发布的主阵地。

That's awesome.

那太棒了。

I'm still trying to crack the the X strategy.

我到现在还在琢磨怎么攻克 X 的玩法。

I feel like if you add a word breaking or just in to whatever you're posting in the beginning, it should be in caps.

我感觉，只要你在发的东西开头加上『breaking』或者『just in』这种词，而且得全大写。

Like then then it will to then it's some something should be like Claude, cooked.

然后就会——然后就得来点像『Claude, cooked』那种。

Yeah, yeah, yeah.

对对对。

It's RIP, like something like that.

就是『RIP』，类似那样的。

Yeah, just wiped this out of the market.

对，就说这个把谁谁彻底挤出市场了。

Yeah, it has to be very sensational next.

对，标题必须非常耸动才行。

But you're so right.

但你说得太对了。

I've heard so many and I know a lot of creators who built their whole like email newsletter, 1 million subscribers just off viral X uh posts.

我听说过好多，我也认识很多创作者，他们的整个 email newsletter、一百万订阅者，全都是靠爆火的 X 帖子做起来的。

Yeah.

对。

That's amazing.

那太惊人了。

I love this life hack.

我太喜欢这个 life hack 了。

Thank you so much.

太感谢你了。

Although this has been the primary life hack of the 25 and I do believe that's the media evolves itself as well.

不过这一直是 25 年最主要的 life hack，而我确实相信，媒介本身也在不断演变。

So 26 could be Well, I would see LinkedIn on the rise, Maybe LinkedIn.

所以 26 年可能会是——嗯，我会看好 LinkedIn 崛起，也许是 LinkedIn。

Maybe LinkedIn.

也许是 LinkedIn。

You had a previous company.

你之前有过一家公司。

You sold it for 166 Were there any key learnings from that business or mistakes that you made that you will never repeat in this one?

你以 166 的价格把它卖了——从那门生意里有没有什么关键的经验教训，或者你犯过的、在这家公司里绝不会再重蹈的错误？

It it's it's like never say never, but one of the key learnings for me and the key takeaways was to embrace meritocracy, sort of.

话不能说得太绝对，不过对我来说，一个关键的经验、一个关键的收获，就是要拥抱『唯才是举』那一套。

I'm 30 and I feel sometimes that I am quite old for this new era of AI.

我今年 30 岁，有时候我觉得，对 AI 这个新时代来说，自己已经算挺老的了。

Like a lot of new ideas at in Higgsfield today come from these kind of fresh grads, maybe 23, 25, who sort of maybe never worked in a large company, who are doing like freelancing with some web coding tools, who are doing web coding before the term web coding, basically.

就拿现在来说，Higgsfield 很多新点子都来自那些刚毕业的年轻人，可能才 23、25 岁，他们大概从没在大公司待过，一直用一些 web coding 工具做自由职业，早在 web coding 这个词出现之前就已经在做 web coding 了。

And uh they they just think differently and I think that's sort of maybe the right mindset.

而且他们的思考方式就是不一样，我觉得这或许才是对的心态。

So traditionally in any like corporation, those people would be just simply no one would just simply listen to them.

在过去，在任何一家大公司里，这些人根本没人会去听他们的。

So and the same applies to the creative role.

创意这个岗位也是一样的道理。

So there is definitely some resistance from people who especially build like, let's say, like large Hollywood projects, that's all AI is dangerous, it's not authentic.

所以肯定会有一些人抵触，尤其是那些做大型好莱坞项目的人，他们会说 AI 很危险、不真实。

And it's in the same time it's really exciting that for many, many people, young people AI becomes social elevator.

但与此同时，对很多很多人、对年轻人来说，AI 正在成为一部 social elevator，这真的非常令人兴奋。

And I sort of strongly relate to that personally cuz for me I had to do a lot of competitive programming, you know, like who solves more problem within like 5 hours, you know, like and everyone in the world competes.

我个人对这一点深有共鸣，因为我以前要做大量竞技编程，就是看谁能在 5 小时内解出更多题，全世界的人都在同台竞争。

So, this was social elevator in 2010 maybe.

这在 2010 年左右就是一部 social elevator。

And this applies both to uh creative and to software engineering as well.

这一点对创意和软件工程都同样适用。

I love how you said that AI is a social elevator cuz I feel like social media was the social elevator for me and now is the era of AI.

我很喜欢你把 AI 说成一部 social elevator，因为我觉得对我来说，social media 曾经就是那部 social elevator，而现在轮到了 AI 的时代。

I think you mentioned that video models could be a path to AGI and that you're also building a world model.

我记得你提到过 video model 可能是通往 AGI 的一条路径，而且你们也在做一个 world model。

Can you talk to me about that?

能跟我聊聊这个吗？

And for everyone who's watching, just wanted to explain I was just in Davos and everyone was talking about LLMs having a ceiling because basically just describing our world with words is something that we're used to and there's a lot of information on the internet, but understanding the physics is the next level.

另外，想跟所有正在看的人解释一下，我刚参加完 Davos，那里每个人都在说 LLM 有一个天花板，因为用文字描述我们的世界是我们早就习惯的事，互联网上也有海量这样的信息，但真正理解背后的物理规律才是下一个层级。

And once we understand the physics, then we're going to have robots walking around our house and doing chores if I'm explaining this correctly.

一旦我们理解了物理规律，如果我没理解错的话，接下来就会有机器人在我们家里走来走去、帮着干家务。

Absolutely.

完全正确。

I think Demis from Google and Elon from xAI, they started this narrative.

我觉得是 Google 的 Demis 和 xAI 的 Elon 最先带起了这个叙事。

And definitely they are top influencers in the space, that's why now the narrative goes to message to everyone.

而他们绝对是这个领域最顶级的意见领袖，所以这套叙事现在才会传到每个人耳朵里。

It's still unclear if that's sort of the path to AGI, although that's definitely a path to advanced robotic systems.

这是不是通往 AGI 的路径，目前还不好说，但它绝对是通往先进机器人系统的一条路。

Like I think Elon proved to the whole world that self-driving cars can work really well through just cameras.

比如我觉得 Elon 向全世界证明了，仅靠摄像头，自动驾驶就能跑得非常好。

And I think the same logic is going to apply to more advanced robots as well.

我觉得同样的逻辑也会适用于更先进的机器人。

That's why developing perception and visual understanding is critical for next wave of robotics.

这就是为什么发展感知和视觉理解，对下一波机器人浪潮至关重要。

And it's a top priority for a lot of research labs as it's the next frontier.

这也是很多研究实验室的头号优先事项，因为它就是下一个前沿。

And there is no other way to improve video generation without visual understanding.

没有视觉理解，就没有别的办法能把视频生成做得更好。

Roughly it takes around I think people like to say that in 1 minute we can read 200 words.

差不多吧，我觉得人们爱说，一分钟我们能读大约 200 个词。

1 minute of the video e can be described with maybe 10 60,000 words.

而一分钟的视频，可能要用 10,000 到 60,000 个词才能描述清楚。

There is just so much going on.

里面的信息量实在太大了。

If you and I were having coffee after this episode, I'd certainly pull out my phone and say, "Look what I tried this week.

如果这期节目录完后你和我一起喝咖啡，我肯定会掏出手机说：“看看我这周试了什么。

It changed how my tea works." This is what I do all the time.

它改变了我的工作方式。”这就是我一直在做的事。

To share these kinds of things that don't fit into the podcast, I started my own newsletter.

为了分享这些放不进播客里的东西，我开了自己的 newsletter。

Every week I write about AI tools, strategies, and experiments I'm running in my own business with real numbers, real results, templates that you can use, and also honest mistakes.

每周我都会写我在自己生意里正在用的 AI 工具、策略和实验，附上真实的数字、真实的结果、你能直接拿去用的模板，还有我踩过的坑，毫不遮掩。

If you want to be in the loop, the link is waiting for you in the description.

如果你想及时跟上这些，链接就在简介里等着你。

So, where do you see yourself in 2 years?

那么，你觉得两年后的自己会在做什么？

Are you still working on videos or because I cuz I saw Menlo Ventures announce their investment in you and they they said that Higgsfield is building the next world model.

你还会继续做视频吗，因为我看到 Menlo Ventures 宣布了对你的投资，他们说 Higgsfield 正在打造下一代 world model。

Do you feel like your focus is going to shift to that or you still going to stick to the marketing with video?

你觉得你的重心会转到那上面去，还是仍然会坚守用视频做营销？

I think that what differentiates us from many other is we are from day zero we are focused specifically on short-form content.

我觉得让我们区别于很多同行的一点是，从第零天起，我们就专门聚焦在短视频内容上。

And I do think that world model is going to change the way how social media content is made.

而且我确实认为，world model 会改变社交媒体内容的制作方式。

So, for example, next decade is going to be the era of interactive media.

比如说，下一个十年将是交互式媒体的时代。

Uh basically when games and videos are sort of blurred in like one experience where like it's kind of choose your own adventure.

基本上就是游戏和视频某种程度上融合成一种体验，有点像“自己选剧情”的那种玩法。

So, a lot of marketing is going to go this way, especially kind of premium marketing and customer loyalty programs.

所以很多营销都会往这个方向走，尤其是高端营销和客户忠诚度计划。

I think this decade is all about supercharging creators and marketing with those models.

我觉得这个十年的主题，就是用这些模型给创作者和营销全面加码。

You said you're seeing customers with marketing budget over a hundred million dollars, 90% of their ads are AI generated.

你说过，你看到有些客户的营销预算超过 $100 million，他们 90% 的广告都是 AI 生成的。

As a creator whose 90% of my revenue is from large corporations through from their marketing budgets, should I be afraid?

作为一个 90% 收入来自大公司营销预算的创作者，我该害怕吗？

This is a good question and um this is definitely where we should make sure that video AI can help personalities.

这是个好问题，这也正是我们要确保 video AI 能帮到那些有个人 IP 的人的地方。

Like what's I would love to see is that creators like you, Mr.

我特别想看到的是，像你、像 Mr.

Beast and so on, who are sort of AI native, start to make like more channels and just expand their media presence and be like the whole media empire.

Beast 这样本身就是 AI native 的创作者，开始做更多的频道，不断扩张自己的媒体版图，变成一个完整的媒体帝国。

So like the next media empire is going to be built maybe with like 300 people, 500 people and be worth like $10 billion.

所以下一个媒体帝国可能就靠 300 人、500 人搭起来，估值却能到 $10 billion。

So that's on the one side.

这是一方面。

On the other side, what I see personally is that essentially high-scale AI content creation maybe platforms like TikTok marketplace, So in the past those brands they could just hire sort of even programmatically thousands of creators through TikTok marketplace just to do kind of these videos.

另一方面，我个人看到的是，大规模的 AI 内容生产，比如像 TikTok marketplace 这样的平台——过去那些品牌甚至可以程序化地通过 TikTok marketplace 雇上千个创作者，就为了拍这类视频。

Like I use this product, it's so good, go buy that.

就是那种"我用了这个产品，特别好用，快去买"的视频。

So like this entry-level marketing definitely gets democratized.

所以这种入门级的营销肯定会被彻底民主化。

Although in my opinion, as we are seeing a lot of AI slop on social media and so the genuine connection and and understanding of audience now matter more than ever.

不过在我看来，正因为我们在社交媒体上看到大量 AI slop，真诚的连接、对受众的理解，现在比以往任何时候都更重要。

So there are going to be just a lot of average and above average contents on the socials.

所以社交媒体上会出现大量中等和中等偏上水平的内容。

And I think this is happening one way or and just deep understanding of the audience and authentic approach matter more than ever.

而且我觉得这早晚都会发生，对受众的深度理解和真诚的表达方式比以往任何时候都更重要。

Okay, so you think social media a ladder, social ladder is not dead.

好，所以你认为社交媒体这个阶梯、这个 social ladder 没有死。

Definitely not.

肯定没死。

I just think that authenticity is going to matter a And and the way how I think about that is if let's say top creators now can make their own shows, their own movies with AI.

我只是觉得真实性会变得非常重要，我是这么想的：比如说，现在顶级创作者可以用 AI 做自己的节目、自己的电影。

And creators can drive a lot of traffic.

而创作者能带来大量流量。

That's going to affect like streaming business a lot, I think.

我觉得这会对流媒体行业产生很大冲击。

So, I think the revolution is going to come at every level.

所以我认为这场革命会在每一个层面上发生。

Although, I think clearly people who already understand their audience, who have authentic approach, those people are definitely going to be the winners.

不过我觉得很明显，那些已经懂自己受众、有真诚表达方式的人，一定会是赢家。

Okay.

好。

My last question.

我最后一个问题。

For someone who's watching this still has fear.

对于那些看到这里、心里还是有恐惧的人来说。

Like this is moving so fast.

比如这一切变化太快了。

I don't know how I can start.

我不知道该怎么开始。

I started something today, it's outdated by tomorrow.

我今天做的东西，到明天就过时了。

Can you give them one piece of advice so they can start?

你能给他们一条建议，让他们能迈出第一步吗？

First, I think I would start from a position that large companies, Amazon, Microsoft, Google, OpenAI, Anthropic, think they're all relatively well positioned to be winners in AI era as they simply control data centers, GPUs, and so on.

首先，我会从这样一个判断出发：大公司，Amazon、Microsoft、Google、OpenAI、Anthropic，我觉得它们在 AI 时代都处在相对有利的位置、都能成为赢家，因为它们本身就掌握着数据中心、GPU 这些东西。

But I'm not sure there is much insurance policy for everyone else regardless, Buy their stocks, yeah?

但对于其他所有人，我不确定有什么保险可言——那就买它们的股票，对吧？

I don't know.

我也说不好。

Yeah.

对。

You just buy their stocks.

你就买它们的股票。

Yeah, yeah.

对对。

Buying their stocks is a good But that's I think I'm not sure we can share that.

买它们的股票是个好……不过我不确定这话能不能公开说。

advice.

……建议。

It's just personal chat.

这只是私下聊聊。

Yeah, I do think that there is a a lot of value in these companies.

对，我确实觉得这些公司里蕴含着很大的价值。

But I mean for for lots of others, right?

但我是说，对于其他很多人来说呢？

I mean we see the sell-off across SaaS.

我们看到 SaaS 领域出现了抛售。

We see the sell-off across cybersecurity.

我们看到网络安全领域也出现了抛售。

And then there is an ultimate question.

然后就有一个终极问题。

If I want like basically depend on someone else to figure out AI strategy or I want to embrace AI myself and benefit the most, right?

我到底是想依赖别人去搞清楚 AI 战略，还是想自己拥抱 AI、从中获益最多？

I think that's a personal question for everyone.

我觉得这是每个人自己的问题。

And as we all know that these technology revolutions, they're both fair and and unfair.

而且我们都知道，这些技术革命既公平又不公平。

I think in long term every technology revolution is fair and GDP per capita and quality of life goes up on the horizon of 10 years.

我认为长期来看，每一场技术革命都是公平的，在 10 年这个时间尺度上，人均 GDP 和生活质量都会提升。

On the horizon of like 3 years, I think it's extremely unfair cuz market just loves to pour money into companies which are winning.

但在 3 年这个时间尺度上，我觉得极其不公平，因为市场就是喜欢把钱砸向那些正在赢的公司。

And companies which are like not winning, they immediately go down.

而那些没在赢的公司，马上就会跌下去。

But in the short term, I think that it's going to be fair cuz people um both on the creative side, marketing side, engineering side, those who are embracing AI, they can propel their careers so quickly.

但短期来看，我觉得它又是公平的，因为无论是在创意端、营销端还是工程端，那些拥抱 AI 的人，都能让自己的职业生涯飞速提升。

In the end of the day, net it's going to be net positive, although I think um individually we all just need to think how I can personally benefit the most from AI, how I can become more efficient with the AI, how the value of my skill set can actually grow with the AI.

说到底，总体上这会是净正面的，不过我觉得就个人而言，我们每个人都得想清楚：我个人怎么才能从 AI 中获益最多，怎么才能靠 AI 变得更高效，我这套技能的价值怎么才能真正随着 AI 一起增长。

And um this always require using those um agents and various AI models several hours a day to to build this intuition.

而这总是需要每天花好几个小时去用这些 agent 和各种 AI 模型，来建立这种直觉。

Let's give them a home task.

那我们给他们布置个课后作业吧。

What should they do right after watching this video?

看完这个视频之后，他们应该马上做什么？

Which tool should they start using and how?

他们应该开始用哪个工具，怎么用？

For me personally, I'm an immigrant, so you know, it just takes quite a bit of effort to come with a logical linear storyline.

拿我自己来说，我是个移民，所以要理出一条有逻辑、线性的叙事线，是挺费劲的。

All three mini became my coach, really.

这三样东西真的都成了我的教练。

This was the first aha moment for me.

这是我的第一个 aha 时刻。

The second aha moment was around Gemini 3 Pro model.

第二个 aha 时刻，是在用 Gemini 3 Pro 模型的时候。

So, I felt that my economic productivity really depends on like how much I use Gemini 3 model.

所以我感觉，我的经济生产力真的取决于我用 Gemini 3 模型用得有多多。

These capabilities of the model which can process voice, which can make image, but which has also deep reasoning capabilities and deep research, this was mind-blowing to me.

这个模型的能力——能处理语音、能生成图像，同时还具备深度推理和 deep research 能力——这对我来说太震撼了。

So, that's why I feel that my just economic throughputs really depend on how much I use Gemini 3 today.

所以这就是为什么我觉得，我的经济产出真的取决于我今天用 Gemini 3 用得有多多。

Do you use it to make decisions about your company, like strategic operations?

你会用它来做关于公司的决策吗，比如战略运营？

I really feel that communication with becomes way more important skill set for me.

我真切地感觉到，与人沟通对我来说成了重要得多的一项技能。

Cuz a lot of other decisions, I'm sure Gemini 3 and Claude are going to be better than myself.

因为很多其他决策，我敢肯定 Gemini 3 和 Claude 都会做得比我自己好。

AI is not is not yet good in So, that's where I think I try to put a lot of my personal emphasis.

AI 目前还不擅长的地方，恰恰就是我个人试着重点投入精力的地方。

Mhm.

嗯。

Think other than that, um a lot of process can actually be built with AI.

我觉得除此之外，很多流程其实都可以用 AI 搭起来。

So, um just human-to-human communication and sort of conflicts resolution and maybe goal set goal setting.

所以，就剩下人与人之间的沟通、冲突的化解，还有可能是目标设定。

Yeah.

对。

And like number-driven goal setting really is where I put a lot of my time, my personal time.

还有以数字驱动的目标设定，这些才是我真正投入大量时间、我个人时间的地方。

For everything else, I'm trying to evolve Gemini as much as possible.

其他所有的事，我都尽可能地交给 Gemini 去推进。

But also use Claude for Excel, Claude for X, like for example, Claude for cybersecurity, as it really increases the productivity.

但也会用 Claude 来做各种事，比如 Claude for Excel、Claude for X——比如网络安全，因为这确实能大幅提升生产力。

Alex, thank you so much.

Alex，太感谢你了。

It was amazing.

这太精彩了。

I'm looking forward to reading your comments.

我很期待读到大家的评论。

What was your key takeaway and what you're going to do right after this video?

你最大的收获是什么，看完这个视频后你打算马上去做什么？

Thank you so much.

非常感谢。

Thank you.

谢谢。