The 500th Day of the Agent Era: What's Disappearing, What's Being Born, and Why We Should Stop Investing in GUI-Thinking Software

On January 1, 2025, we kicked off the year with a podcast episode. ZhenFund managing partner Yusen Dai and Koji proposed in conversation: 2025 would be the Year of the Agent.

Now 500 days have passed. What happened in between?

Around Day 100, Claude 3.5 Sonnet laid more solid capability groundwork for Agents, and Manus went viral. On Day 300, Claude Code took over everyone's feeds. At Day 500, new concepts about Agents are still emerging constantly.

This podcast episode also grew out of an article ZhenFund investment director Jack Zhong had written earlier: "Maybe We Should Stop Investing in GUI-Minded Software Companies". We try to thread together the changes of the past 500 days along one arc: GUI exits → Headless rises → CLI revival → Skills packaging → Agentic Economy germinates.

Against this backdrop, we invited Koji, Jack, and Guizang to talk about where software, workflows, and human roles are heading after Agents' 500-day journey.

We launched Token Grant because what many founders lack today isn't necessarily a team, an office, or their first paycheck. For the new generation of AI founders, what's scarcer is the model and compute resources needed to quickly turn an idea into a first version, plus enough chances to iterate. We hope ¥50,000 in compute support can help more founders who've already started building to finish their first product.

In this episode, we try to summarize six keywords about Agents from the past 500 days. As concepts keep exploding, this might offer a clearer framing to help you actually connect these changes together.

500 Days of Agents

Koji: At Day 500, have you seen any products that actually satisfy you?

Jack Zhong: Most products on the market still treat humans as the user. People still need to keep pouring attention into a product to use it well. At least for information consumption products, I haven't encountered one I'm particularly satisfied with.

Koji: Guizang, has the past 500 days exceeded or fallen short of your expectations?

Guizang: Overall, exceeded.

If I went back to then, I couldn't have imagined my current work style or efficiency gains. It lets me do so many things, massively expanding my capabilities.

Starting from Manus, I never would have imagined I'd one day maintain a codebase of hundreds of thousands of lines and serve this many people. That was impossible before. Second, I couldn't have imagined how automated my workflow would become. I used to refuse to use AI for content creation.

Koji: When did that change?

Guizang: I think many individual creators held this position. But now I realize that insistence doesn't mean much.

For WeChat public account writing, the purpose is conveying information. Whatever method achieves the highest information transfer efficiency with the least friction — that's where it will go. But for literary creation, humans are still needed.

Besides in-person communication, almost all my content work has become highly automated.

Koji: If you had to summarize the changes and constants in the Agent industry over 500 days in one sentence, what would you say?

Guizang: We've invented many new things; almost everything has changed, except context.

Context is becoming more and more important. Whether it's the model's own context or Agent context management, everything eventually comes back to Context.

Context is like a thread; all branches keep converging, eventually flowing into a larger river.

Jack Zhong: What hasn't changed is that there will always be new concepts getting disenchanted.

Wasn't there always that joke? Any technology that hasn't been tamed, that can't be truly applied — we can call it AI. Once it truly stabilizes, scales, and enters workflows efficiently, we'll say: "Oh, that's just software."

At first, when an Agent could do 20 minutes of work for you, it felt like an unprecedented experience. Later, an Agent could do an hour, even 24 hours, and you'd gradually find it ordinary.

The questions shift accordingly. People stop caring how long Agents can work for us, and start caring what they can actually do for us.

When Agents enter various industries, we'll find that in some, Human in the Loop is necessary.

But in others, humans don't need to stay in the loop; Agents can continuously complete the work. At that point, you'll feel it's become a workflow, "downgraded" from AI to software, deconstructed into a seemingly ordinary tool.

We'll keep deconstructing and disenchanting so-called "AI technology."

What hasn't changed is that model capability advances will keep unlocking "technical marvels" we hadn't imagined. And how these marvels get deployed into daily work is something the whole industry keeps exploring.

When Software Goes Headless

Koji: It's been three months since you wrote "Maybe We Should Stop Investing in GUI-Minded Software Companies." Have your views changed?

Jack Zhong: I put "minded" in the title because historically, much software has been efficiency tools, productivity tools — the core was helping people use products better to complete tasks and work.

But when most Agent capabilities surpass humans, GUI becomes extra baggage for Agent-centric tasks and workflows.

For efficiency and production tasks, once humans are no longer the protagonist using the tool, GUI matters less.

But this doesn't mean GUI isn't important. Products like AutoGPT or some frameworks originated in command lines with high barriers to entry. What eventually broke through — OpenClaw, Manus — were fundamentally still very good GUIs, products suitable for human users.

GUI's value hasn't receded because work forms changed. On the contrary, as it needs to encapsulate more capabilities, users need an even better GUI to access them.

Koji: I remember a line in your article: "GUI is a patch for human cognitive defects." Can you expand on that?

Jack Zhong: Software is born on, or carried by, hardware as a form of interaction with humans. In the mobile internet era, we accessed information and used software all within this shell.

You could only use one paradigm, one form to complete information exchange — that's why we had so many product managers and interaction designers helping people better interact with information inside this container.

GUI's original focus was capturing user attention.

It's essentially optimized around human attention mechanisms. And this premise is that human-information interaction itself is flawed, because humans naturally have limited context.

Koji: Humans can remember at most 7 parallel items; beyond that, cognitive challenge spikes sharply.

Jack Zhong: We see many efficiency tools — Notion, Figma, Slack — with very friendly user experiences that match how humans process information, letting people focus for extended periods in certain work scenarios.

But in production efficiency scenarios, GUI becomes less important, because Agents have no attention limits.

GUI is an interface where the digital world serves humans. Once something no longer needs human participation, it no longer needs to serve human defects.

Koji: Have you yourselves used any Headless products in your work recently?

Guizang: I use FFmpeg most, for video editing.

Yesterday I added a new function to my Skill — turning videos into 3:4 Live Photos. Video processing often requires cropping, scaling, and dimensions are hard to control; you need it to handle automatically. At that point, using FFmpeg becomes unavoidable.

I also use more new CLIs lately. Recently not just internet products — KFC, Luckin Coffee have also released Skills or CLIs. Once connected, they can complete operations in the physical world for you. Instead of going to the mini-program to order, it just orders for you.

AI inherently understands CLI complexity, so you can let AI handle this work.

For example, if a user wants to know what meetings they have next week, what the summary of their last article was, or what hidden relationships exist among their last five articles. AI can pull these articles directly and give results — no need to click several buttons, no need to find which folder a file is in. These complex interactions are dissolved.

Koji: Are consumer CLIs useful?

Guizang: I've used them. It's a good approach; it frees you from channel restrictions. These companies also want their own unique channels in the AI era, escaping the influence and extraction of original channels.

Koji: So you endorse "maybe we should stop investing in GUI-minded software."

Guizang: Yes, I think the key is "minded."

You should first think about what users need, how to let AI better solve problems for users and convey corresponding information. This can be UI, or it can be not UI.

If it's UI, I lean toward making it as automatic as possible. Notion's CEO is an exceptional designer who's polished the interaction and experience extensively. But to adapt to AI, Notion's sidebar has been redesigned five times — that's classic USV.

Koji: If you were Notion's product director today, what would you do?

Guizang: One approach is to accommodate existing users — don't touch what they're already familiar with. Because a significant portion of longtime users still approach the product with a GUI mindset. AI-first users and GUI-first users should be separated. Implement the Headless concept on the AI side.

Jack Zhong: I completely agree.

I'm a heavy Notion user — subscribed for nearly ten years. I started with Evernote, then migrated to Quip, then to Notion. Under GUI thinking, these tools kept growing more complex. Notion, with its modular approach to organizing information, arguably reached the pinnacle of GUI thinking.

But recent versions have forcibly grafted Agent and AI concepts onto a GUI product that was already polished to near-perfection. This is actually destroying the value network for existing users rather than carving out a new one.

If you were to completely rethink Notion, it would probably need to be an entirely new product. Putting both products together just creates conflict.

Koji: Is there a positive example? Something that thoroughly rejected GUI thinking, is Agent-friendly, and still made a great product.

Jack Zhong: Earlier this year, Gmail and the entire Google Suite added CLI interaction support.

Many overseas publications are email-subscription based. I used to subscribe to hundreds, and my inbox collapsed.

I spent considerable effort customizing my own rules — if a subject line contained certain keywords, route it to Label A; if it involved marketing and promotion, send it to a label that wouldn't notify me.

But you quickly find this system collapses, because your subscription speed far outpaces your maintenance capacity. Until one day, I accidentally set up a filter that made all my emails disappear, arbitrarily dumped into some random label.

Two years ago, I was spending three hours every week cleaning all my emails, sorting everything into categories, applying the right labels.

This is massive friction in the human experience of using Gmail.

Koji: I assumed you blissfully entered a flow state, spending two hours cleaning every week.

Jack Zhong: At first, yes. But eventually it becomes a burden, because the form overshadowed the information I was actually consuming.

I recently authorized Gmail through Codex to read all files from the past 24 hours according to my own needs. This can be abstracted into a personalized Skill. Based on your preferences, it tells you what you couldn't afford to miss yesterday.

In a sense, Gmail sacrificed its own capabilities to Codex.

Users access email information through an Agent, but bypass Gmail entirely. For Gmail, its users have been isolated by the Agent.

I bring this up to illustrate that in the past, some companies could only accomplish tasks for users through GUI. Now, through Agents, it can be done better — the question is whether they have the vision for it.

If these companies don't transform well, don't find new models, they'll fail — they'll be reduced to execution databases. But if they can integrate well with Agents, they'll become the default underlying execution tools everyone uses.

Koji: What was the trigger when you wrote "Maybe We Should Stop Investing in GUI-Minded Software"?

Jack Zhong: Starting from early this year, we've seen massive numbers of companies proactively launching MCP or CLI — Stripe, Supabase, Sentry, Vercel, MongoDB.

Tools originally built for humans like Lark and Gmail are also actively opening their capabilities to Agents. From an investor's perspective, this is an extremely important trend.

It's not that GUI doesn't matter anymore — it might actually become more important.

Koji: What was the biggest negative reaction after publishing the piece?

Jack Zhong: People felt it was a bit too extreme.

Like when you're scrolling Douyin on your phone — it's already optimized to the extreme, with little marginal room for improvement. But Agents aren't solving the same kind of problem; they're not about helping you scroll videos better.

As a PM, the most important thing is defining the problem. Whether you're starting a company or building a new product, what matters is defining what the genuinely new problems are in AI-native interaction.

Don't solve problems that were already solved well in the previous era.

Guizang: AI's GUI may not be that important, but I feel most people haven't polished the places that genuinely need polishing.

Jack Zhong: Content generated by AI shouldn't be obviously identifiable as AI-generated.

Guizang: In ceremonial contexts like pitch decks and official websites, you're building trust. The copy, images, and content all signal your taste. Whether or not you use AI, if you care about your product, you should polish it well. Whether heart and effort have been invested — audiences can tell.

CLI: The Interface Where Agents Talk to the World

Koji: Let's move to our second keyword, CLI. Master Guizang, give everyone the layman's explanation — what exactly is CLI?

Guizang: For ordinary users, CLI is simple to understand.

With GUI software, you use the software, click the corresponding buttons, and it gives you corresponding visual feedback. CLI tools execute through commands — pure text.

Say you want to edit video: you might need a timeline, click a button, cut the video in two. With CLI, you tell it in one sentence and it cuts it in two — except that sentence needs to be in a fixed format.

There's no interface, so you need to memorize the format and learn its language.

Koji: The reason people use CLI is that it lets them give Agents clearer instructions.

Guizang: Right, because CLI itself is pure text, and large models are also pure text.

What was the core problem with CLI before? I couldn't remember the commands. Even the creator of FFmpeg can't remember all the commands. Its documentation might run thousands of pages, explaining every command variant.

Humans can't use it because humans can't memorize thousands of lines of commands — but AI can.

CLI doesn't need UI. You just tell it, I want to lower the bitrate, and it translates that into the corresponding FFmpeg CLI command for you.

FFmpeg video editing example

Koji: Are there any CLIs you particularly want to use that haven't launched yet?

Guizang: Many monopoly-type software tools we commonly use haven't launched their own CLIs. The concern is that others could use CLI to extract your data. But for users, launching these CLIs would undoubtedly be good.

Koji: If I were a decision-maker at these companies, I probably wouldn't open CLI either.

Guizang: This decision requires enormous resistance and resolve. So I deeply admire companies that are already mainstream software but still willing to open CLI. Google and Lark have ample reasons and massive resistance not to launch CLI, yet they still chose to.

Jack Zhong: For large companies, tool-type products aren't profit centers. But companies providing services in the physical world don't need to be so closed off either, because their value itself doesn't lie in the online "head."

Koji: Which companies would be better off opening up but currently refuse?

Jack Zhong: In the entire economy, every company exists because it's exchanging something with the outside world.

Some companies exchange information. This information might be public — you just organize it better, creating added value; or it might be submerged information, where you've hired people to gather structured information from hard-to-reach places, then monetize that information.

Other companies manufacture products that satisfy certain needs.

CLI is an interface for exchanging needs with Agents.

If you believe you can reach users better through Agents, you should open up. Like Luckin Coffee — its mission might simply be letting users get coffee more quickly, through whatever means. If an Agent can help you complete caffeine intake faster, it's worth doing.

Guizang: Opening CLI or Skill also has a potential advantage: you gain a certain amount of exposure.

Koji: If you could make one wish, who do you most want to open CLI?

Guizang: Of course WeChat — it's excruciating. Everything else can be automated by AI, but communication with people, clients, upstream and downstream partners still relies on painfully GUI-based methods, typing out replies.

Jack Zhong: I probably wouldn't expect a traditional service to complete a CLI transformation. Like Koji birdwatching — if you want to find a bird island now, you still have to search through Xiaohongshu.

Guizang: I've observed a phenomenon: me and my younger friends are increasingly chatting on Douyin.

Douyin chat DAU has already hit 100 million. Where my content consumption happens, chatting happens. Because I naturally want to share this content with friends, and this content becomes our shared topic.

Koji: Mark Zuckerberg once shared internally that he underestimated TikTok's impact. He thought it was just a content platform, but didn't expect people to chat on TikTok.

Guizang: If I could seamlessly open content shared with me inside WeChat, I wouldn't default to sharing it with friends on Douyin first. My preference is still to share videos to WeChat. But sharing to WeChat is such a hassle that we end up just creating a group chat instead, and gradually the conversation migrates over there.

Will Skills Disappear?

Koji: The third keyword for today is Skill. You've been building a series of Skills lately, Master Cang — which one are you most eager to recommend?

Guizang: Definitely the PPT Skill. First, I put a lot of work into it. Second, people genuinely need it.

Koji: If you were to recommend someone else's Skill, which would it be?

Guizang: I basically build all my Skills myself. The only one I've installed is Lark, because the Lark CLI is composed of multiple Skills. I might also install an MCP browser controller — that's for test automation, a development need.

Koji: Have your Skills made you any money?

Guizang: I'm experimenting with some approaches. I recently received a Token Grant from ZhenFund.

But there's no way to charge end users directly right now, because we can't get users to trust us from the start. You need to let them try it themselves, see the results — only then will they believe you and keep using it.

Users are very price-sensitive. They think: "I'm already paying for Tokens, why should I pay you extra?"

Only when they see a quality result, and can compare it against other results, can they judge whether your product is worth paying for.

Koji: These days when people talk about using AI, they also say Skills need to accumulate and evolve on their own. Have you felt that?

Guizang: Sometimes what accumulates isn't just the Skill itself.

A lot of people ask me how I built my PPT Skill. The truth is, the first version of this Skill came from one sentence I gave it before a presentation.

Based on my memory and past projects, it pulled some styling code and integrated it into a PPT that matched my aesthetic, which I then continued refining.

The first version was already stunning enough. Based on context and your taste, it directly distilled a result.

Hermes also has a feature that automatically summarizes Skills for you, recognizing your repetitive workflows. It has value, but it lacks the process of you adjusting things. Because what AI summarizes won't necessarily match your requirements exactly — human involvement is still needed.

PPT Skill digital magazine-style effect showcase

Koji: Do you think Skills will persist long-term in Agent usage, or are they just a transitional thing?

Guizang: Long-term.

In AI, six months or a year already counts as long-term. Eventually everything gets digested by the models, becoming results that don't require complex prompts.

Like how Nano Banana originally needed complex prompts to generate a good image, but GPT Image 2 only needs one sentence — even if the request isn't very precise, it gives you a result.

Skills will just be slower. But once they become consensus, they'll be accepted and consumed, and last even longer.

Jack Zhong: I think they'll probably still exist for the next six months.

But I always believe the ultimate entity should be a "brand" that helps you complete a certain task. You'll rely on a particular Agent to continuously handle tasks for you — what Skills it uses behind the scenes doesn't matter that much.

From a human cognition standpoint, distinguishing between two Skills is very difficult. You have to install, test, and understand the subtle differences between them, which is inherently high cost for humans.

The fact that we're still discussing these concepts, and how each layer should be defined, shows it hasn't reached its final form.

Because when a company can ultimately help you complete a category of tasks, that company itself becomes synonymous with the task. You say "Google it," not "page-rank it."

Koji: Who do you think is most likely to become China's Claude Code?

Guizang: Overall Agent progress in China is lagging. Our models genuinely trail the two top models on long-horizon tasks and Agents.

In an era of extremely high Agent development efficiency, software is no longer a moat. You see it, you can replicate it. So why is our product capability and experience still so slow to catch up to leading Agents?

I can't judge. But most likely, a dominant Agent will emerge from the model vendors themselves, driven by top-tier models.

Jack Zhong: Top developers would rather pay more for better models than use relatively inferior ones.

Guizang: It also damages your codebase.

Koji: Xiaohongshu recently launched a Skill — what do you think?

Guizang: I think it's great. The commercial value Skills bring is very important.

Right now no single Agent has achieved Claude Code-level dominance, but Skills might.

Skills can be distributed across various Agents, achieving very high install counts. Once you have users and installs, there will definitely be commercial value.

But it seems like nobody truly sees the commercial value of Skills yet — everyone's desperately building their own Agent.

When many people build Agents, they simply scrape Clawhub, GitHub, and Skills into their own Agents without differentiation, optimization, or co-building with Skill authors.

So Xiaohongshu's decision is a big help to the Skill ecosystem. Xiaohongshu gets more people paying attention to top Skills, and gives top Skills a better showcase platform.

My PPT Skill works well, but I can't showcase my results on GitHub, nor can I see reviews. On Xiaohongshu, these interactions can happen.

Koji: What's the most installed Skill right now?

Guizang: Hard to measure — you probably have to gauge it through buzz across multiple channels. For example, Open Design recently integrated our PPT Skill and got 60,000 Stars.

Koji: Once a Skill is installed, can it iterate on its own?

Guizang: You can tell the Agent to check for updates. In SKILL.md, the first item is always checking whether the Skill has updates. But this needs to be triggered.

Koji: If you wanted to open-source a Skill, what would you open?

Jack Zhong: Recently Gary Tan packaged Y Combinator's working records into a Skill for people to query. That's a great approach — essentially a form of information democratization.

From Token Economy to Agent Economy

Koji: Our fourth keyword is Agent Economy. Databases for Agents, payments, networks between Agents — have you seen anyone doing this well?

Guizang: If you're starting an Agent company, the priority definitely isn't building an Agent framework from scratch, but rather building on something already open-source, like Hive or AI SDK. As for infrastructure for Agents — giving an Agent a WhatsApp account, phone number, email — I haven't tried that yet.

Jack Zhong: Timing matters enormously in tech investing.

During the 2000 internet bubble, many internet companies bought lots of Super Bowl ads, but the users they acquired couldn't be retained.

In 2000, because so many dot-com companies purchased Super Bowl ads, the year was dubbed the "Dot-Com Bowl"

If you carried that mental scar, companies that experienced value destruction back then might have been afraid to burn money on customer acquisition during the mobile internet era. But hindsight proved that wrong.

The exact same business action can be wrong in 2000 but right in 2010.

From ChatGPT's launch to the Year One of Agents — 500 days — we're still in a massive infrastructure era. Making Tokens smarter and cheaper remains the main theme. Many downstream applications probably won't truly sprout for another 5 years; now may not be the best time for this.

Koji: Is there anything you feel certain will happen?

Jack Zhong: Replacing humans in the value exchange of information will definitely form a new network detached from human networks.

Often, it's not that society lacks a suitable person — it's just that this person's information isn't exposed to the network.

If there's an Agent network that can express everyone's needs and supplies as richly as possible, it will definitely improve connection efficiency for certain categories of work.

This network will definitely emerge — just not necessarily while Tokens are still expensive.

Guizang: Token prices are fluid, fluctuating with energy, electricity, model capabilities, and capital market prices.

Today's $200 Claude Max has completely different quotas from Claude Max three months ago.

We used to assume model providers would capture that value, but lately SpaceX has emerged as an unexpected winner. It turns out you don't need to train models — just selling compute and infrastructure can command sky-high valuations. And memory has gotten expensive now, driving up token costs for long-context models. Once supply ramps up, prices will drop again.

What we need is unified token trading — one place to top up.

What Did OpenClaw Leave Behind?

Koji: Let's move to our fifth keyword: OpenClaw. It's only been 100-plus days, which is a short time, but already saying "raising lobsters" feels a bit embarrassing.

Guizang: It's passé. Like telling your friends a joke that's painfully old and corny.

Koji: OpenClaw's hype faded quickly. What do you think it left behind?

Guizang: Its biggest contribution was shaping consensus.

By January, we had already realized the definition of Agent had shifted. Though both were called Agents, Claude Code and Manus were completely different things.

Users hadn't fully made that cognitive leap themselves, but the lobster — in a very brute-force way — did it for them.

Why did so many people install Skills? Why could people outside the AI bubble even see all these Skills? Because OpenClaw popularized the concept.

Jack Zhong: OpenClaw proved at least one thing: CLI is still too hard for most users.

People still want to interact with AI inside interfaces they already know. Whether it's WhatsApp, Slack, or WeChat — you feel like you're talking to a person, maintaining a continuous thread.

For the first time, it made you feel like you weren't talking to a tool, but a person.

That said, plenty of people did struggle with installation. And after it randomly went down, nobody felt motivated to restart it — there was no compelling reason to keep going back to it all the time.

But as Guizang said, it did establish one consensus: most people will still interact with AI through chat.

Token Grant: Supporting Founders from Zero to One

Koji: Our final keyword today is Token Grant. This is a program co-launched by ZhenFund and Crossing — we'll give 50,000 RMB in sponsorship to friends who want to start something in the AI era. We're also honored to sponsor Guizang's CodePilot project. Could you introduce what CodePilot is?

Guizang: CodePilot can be understood as an Agent similar to Codex or Claude Code. It's an Agent that's sufficiently open and sufficiently local — it keeps all your Harnesses, Skills, Memory, and CLI on your local machine. Its architecture works with any popular model and Agent framework.

CodePilot demo page

Koji: How many GitHub stars does it have now?

Guizang: It climbed to 6,000 and then plateaued. But the user base is very stable — people keep asking for updates and filing issues.

Koji: Jack, you also sponsored yoyo. What is that project?

Jack Zhong: yoyo is a cyber digital lifeform.

When we discovered it, it had already been alive in the world for over 30 days. Now, 100-plus days in, it has evolved from zero lines of code to 100,000 lines.

Its creator is Yuanhao. He gave yoyo one goal: as a child, you must surpass Claude Code.

yoyo's real-time interactive webpage

With this goal, yoyo iterates its own code twice daily and updates its GitHub. The next step might be giving it a new goal — like creating value for humanity. So it's not a tool, but an autonomous experiment.

We're curious what will happen at three months, at one year.

Koji: What do you think is the most important question to watch over the next 500 days?

Jack Zhong: I think we need to move beyond human-centric thinking.

The end goal is still to serve people. But in the production process, how do you give an intelligent agent long-term objectives? How do you create an Agent that people trust, are willing to befriend and do business with, and has independent thinking?

As long as humans are involved in completing tasks, it's hard to truly unlock productivity.

Only an Agent can work tirelessly, with unwavering resolve and no emotions, delivering on tasks. That said, you still need people with industry insight to unleash AI's potential.

Guizang: I think we need to embrace change.

At the time, you might think something will be the future's main theme, but it quickly gets replaced by an Agent, or by the loop itself.

No one can keep leading the direction of Agents forever.

If you discover things have shifted, you need the courage and ability to tear it all down and start over. Hesitate for just a month or two, and you might find it's already too late.

That's happened repeatedly over the past 500 days. The next 500 days will be no different.

Koji: Is there any consensus view about Agents that you two actually disagree with?

Jack Zhong: I'm not capable of making that kind of call.

After Agents, there may be entirely different forms, entirely different products.

It took us nearly 20 years for App front-end experiences to converge into product forms users could most readily accept. But when production efficiency increases 100x or 1,000x, entirely new front-end possibilities may emerge.

The next Douyin might not be a product in a different form — it could be a 10x more productive Douyin. Vertical swiping is already an interaction that's almost impossible to change. But when content production itself becomes industrialized, could new content formats emerge? New ways of consuming content?

In the past, every household raised its own pigs, then brought them to market to trade. But with electricity came industrialized pig farms, and with them the space for capital investment — producing higher-quality pork at lower prices, ultimately creating bigger market leaders.

That transformation is happening now. Maybe the next video format is still video — just produced in a completely different way.

Koji: Thank you both for your time — great conversation today. Looking forward to doing another one at the 1,000-day mark.

Text | Cindy & Yanyang

Video | Coco & Ping