Original | Odaily Planet Daily (@OdailyChina)
Author | Asher (@Asher_ 0210)
As AI becomes a definitive trend, Web3 infrastructure is facing a new watershed moment: should it continue to linger within the industry's internal technical narratives, or move towards real users and actual use cases.
In the past cycle, many infrastructure projects accumulated a large number of developers but consistently struggled to produce applications that were widely used. "AI × Web3" is not short on narratives; what is truly scarce is the ability to transform these narratives into products and get a sufficient number of users to actually use them. Entering the AI era, whether an application has real use value has become even more critical. This issue is further amplified, forcing projects to re-examine the relationship between product, growth, and execution.
On January 27th, ZetaChain announced the official launch of ZetaChain 2.0, simultaneously introducing its first consumer-facing application—Anuma, a privacy-centric AI interface. The product is already in the testing phase and has opened a public waitlist.
Odaily Planet Daily took this opportunity to have an in-depth conversation with Jessie, Head of Investment and Incubation at ZetaChain. We discussed the development path of AI × Web3, the strategic choices behind ZetaChain 2.0, and how its first consumer application, Anuma, embodies its product and growth logic. Below are the highlights of the interview:
Q1 Could you briefly introduce your background? What experiences led you to choose to deeply engage in the Web3 industry?
I completed my high school and undergraduate studies in the United States. After graduating, I returned to China and worked in the VC industry for three years. What truly prompted my shift to Web3 happened in 2021. On one hand, the traditional VC industry was entering a relatively stagnant period with few new structural opportunities; on the other hand, the crypto industry was developing rapidly in 2021, but for me, the more important factor wasn't the price increase, but the industry's clear move towards the mainstream.
I saw traditional institutions, including large banks and consumer brands, beginning to engage with crypto assets, NFTs, and on-chain collaborations with Web3 companies, which was previously hard to imagine.
Although I had exposure to the crypto industry as early as 2015 and 2016, it wasn't until 2021 that I first clearly realized the industry had undergone a qualitative change. It was at that point I made the decision to formally enter the space.
Q2 As the Head of Investment and Incubation at ZetaChain, what are the core objectives of your department?
From the very beginning, ZetaChain's core metric has been the number of users, not indicators like TVL which are more focused on capital scale. Whether it was when I first joined or during discussions with the founders about the company's mission and vision, the consensus was very clear: ZetaChain aims to build truly large-scale, application-level products for C-end users. Therefore, "users" have always been the most important criterion.
The focus of market work varies at different development stages. Early on, from product launch to token listing and the period thereafter, we primarily focused on building brand awareness and laying the foundational system. During this phase, we organized a cumulative total of 150 to 200 various offline events globally and facilitated the token's availability on almost all major trading platforms to ensure users in different countries and regions could learn about ZetaChain. The core goal of this stage was to first broadly open the user "entry points" and "awareness".
Over the past two years, this阶段性 goal has been largely completed. Starting last year, with the R&D and gradual rollout of AI-related products, the market objective also changed significantly—from "getting more people to know us" to "truly retaining and serving real users".
This year, we have a very clear goal: to drive applications within the ZetaChain ecosystem to achieve at least 500,000 monthly active users. This is not easy, so the team's current focus is also more clearly divided into two parts: first, continuing to advance brand building, and second, results-oriented, user-centric marketing focused on the acquisition and activation of real users.
Q3 ZetaChain has already reached over ten million users. From a market perspective, which data indicators best reflect your judgment that "the product and ecosystem are moving in the right direction"?
In my opinion, ZetaChain 2.0 is the stage where we truly begin to exert force. The most critical point in judging whether the product and ecosystem are on the right track is not the scale of the overall on-chain data, but whether the 2.0 products are starting to be genuinely used and accepted by more Web2 users.
In the first two years, as a public chain, our ecosystem development was relatively in a state of "parallel multi-direction"—as long as someone was building, regardless of the direction, we would support it. This is a normal stage in the early life of a public chain. But entering 2.0, we actually made a clearer choice to focus our efforts on AI-related application directions.
Therefore, the most important metric we now look at to judge the direction is the real usage by Web2 users, such as the scale of users actually using the product, their activity levels, and whether sustained usage behavior emerges. From this perspective, the current stage is still a process of "just beginning to validate" for us, and this real user data is the key to judging whether this directional choice is correct.
Q4 Behind these key metrics, what do you think is the most underestimated aspect of ZetaChain currently? Is it the user base, technological maturity, or what developers are building?
That's a great question. My answer might sound a bit "abstract," but I believe it's very crucial—the most underestimated aspect of ZetaChain is actually the mindset for long-term building and the ability to execute consistently.
In the current market environment, information is highly transparent. Both users and investors are well aware that the vast majority of projects enter a state of stagnation very quickly after their token launch. Many teams might maintain some activity before unlock, but after unlock is completed, regardless of the project's size, innovation and iteration often slow down significantly or even stop completely.
Where ZetaChain is relatively different is that we are always continuously thinking and trying: what direction can truly bring real usage, what kind of innovation can generate long-term user value. Over the past year, we don't guarantee that every attempt is successful, but we can say with certainty—we have never stopped product iteration and directional exploration.
In my view, this ability to continuously experiment, quickly adjust, and keep pushing forward in a complex and even unfavorable market environment is itself a very scarce and valuable competitive advantage. And this is precisely the part that is most easily underestimated in the current market's perception of ZetaChain.
Q5 ZetaChain initially stood out among interoperable L1s through a simpler and more universal approach, and 2.0 clearly extends this capability to AI. How did you judge that now is the right time to incorporate AI into the core strategy?
Looking at the development of the entire crypto industry, the most successful aspect of Crypto so far has been building a highly open, permissionless system for value and asset circulation. This has been fully validated and has become the industry's most important foundational capability. Next, whether it's stablecoins, cross-border payments, or more complex data and application forms, they are essentially extensions built upon this foundation.
The rapid proliferation of AI is another variable that can no longer be ignored. Over the past year, AI has entered the daily lives of ordinary users at an unprecedented speed, forming extremely high usage frequency and stickiness. This means the generation, use, and concentration of data are being dramatically amplified.
It is against this background that we believe "now" is a very critical point in time. On one hand, AI's reliance on data continues to deepen; on the other hand, the centralization of data also brings issues of privacy, security, and control. The market has begun to tangibly feel these contradictions, and this is precisely where decentralized infrastructure can deliver value.
From ZetaChain's perspective, 2.0 is not simply "chasing the AI hype", but an extension of the design philosophy. In the past, we solved the interoperability problem of the multi-chain world; today, we face the challenges of data collaboration and privacy in the multi-model era. Essentially, we are always building a cross-system coordination layer—just expanded from between chains to between models.
In our view, AI has become a definitive trend, but the underlying issues of data ownership and privacy have not been systematically resolved. As models become the new infrastructure, and data and memory become core assets, privacy is no longer an add-on feature but a structural necessity. Therefore, incorporating AI into the core strategy and building capabilities around data and privacy is a natural extension of the architectural logic, not a directional shift.
This judgment also stems from our team's DNA. ZetaChain core contributor Ankur Nandwani is also the co-creator of Brave and $BAT. Brave, with privacy as its core philosophy, provides users with a fast, secure, tracking-free browsing experience. As of last October, its monthly active users reached 101 million. The long-term commitment to privacy makes us more confident: in the multi-model era, true infrastructure must simultaneously solve interoperability and data sovereignty issues.
Q6 ZetaChain 2.0 launched its first consumer-facing application, Anuma. As a product that can operate across multiple AI models while preserving user memory, how do you prefer the outside world to view Anuma? Is it a growth product, or a window to "instantly understand ZetaChain 2.0"?
For us, Anuma is first and foremost a standalone consumer product, not just a display window existing to "explain ZetaChain 2.0".
From product and market perspectives, we made it clear from the start that Anuma's target users are Web2 users, not just Web3 users. Our market promotion, product design, and user communication are almost all done according to Web2 product logic—the goal is those users who are willing to use it long-term and genuinely need the product, not to showcase a technical concept.
ZetaChain 2.0 is more like the underlying infrastructure, solving problems of data, privacy, and collaboration; while Anuma is an intuitive, usable product form built on top of this foundation for ordinary users. The relationship between the two is that of underlying capability and upper-layer application, but in terms of execution order, we chose to perfect the product itself first.
In this sense, Anuma is not a "façade to explain 2.0", but a product polished entirely to Web2 standards. It's just that we believe, in the current environment, using blockchain to protect data and privacy is the best technical choice to achieve this goal.
Q7 From a market and growth perspective, which type of developer is ZetaChain 2.0 currently most prioritizing to attract? Web3 native Builders, independent AI developers, or traditional teams in transition?
Currently, our top priority is independent developers in the AI field, as well as AI teams that already possess certain product capabilities, rather than traditional Web3 native Builders.
Our developer strategy itself is not limited to Web3. The reason we chose blockchain as the underlying architecture is that it is currently a more suitable technical choice for data collaboration, privacy protection, and openness, not because we want to confine developers to the crypto industry.
From a practical execution standpoint, the team is currently spending significant effort on collaborations with the AI developer ecosystem, including independent developers and AI startup teams, while relatively less investment is going into pure Web3 scenarios.
We prefer ZetaChain 2.0 to be understood as a kind of underlying technical infrastructure for the AI era: developers can focus on making their products and applications well, rather than building around tokens or short-term narratives. This is also why we judge that the AI developer community is a better match for the long-term direction of ZetaChain 2.0.
Q8 In this cycle, many infrastructure projects face a problem: many developers, but very few applications that truly break out. What do you think is the most important way to avoid path dependency in the ZetaChain 2.0 stage?
I think the most important point is to avoid the path dependency of "only serving the Web3 internal loop" from the very beginning.
In the 1.0 stage, common practice in the industry was to attract developers and users through hackathons, token airdrops, etc. But looking at the results, this model tends to attract short-term, profit-seeking participants more easily, rather than teams that polish products long-term and are truly user-oriented. This is also why many infrastructures have "many developers, but applications don't break out".
Entering 2.0, we made a very clear adjustment in our developer strategy—shifting the focus to AI Builders with a Web2 background. Whether from the perspective of ecosystem size, product capability, or understanding of user needs, the developer base in Web2 and the AI field is more mature and more likely to produce products that are actually used.
At the same time, in terms of user and application growth, we have deliberately avoided the "incentive-driven" methods common in the last cycle. Since the goal is to make products for Web2 users, the growth logic must return to Web2—through genuine product strength and user growth methods, not relying on airdrops or short-term incentives.
Ultimately, what we value more is whether the developer's starting point is for short-term gains or whether they are willing to build truly user-valued applications long-term based on ZetaChain 2.0's underlying capabilities. This choice itself is the most important "de-path-dependency" move we made in the 2.0 stage.
Q9 Standing at this point in time, how do you view the various narratives in the AI × Web3 field? Compared to "which directions are overestimated or underestimated", are you more concerned with another layer of issues?
If I must use "overestimated or underestimated" to describe it, I would actually say the problem isn't with the narratives themselves, but with the determination to execute these narratives.
Over the past two years, I've actually seen many ideas related to AI × Web3. The directions themselves are very good, and many have even been validated in the Web2 world. From a technical perspective, Web3 is indeed a more suitable solution in many scenarios. When these projects first appeared, I would think, "This is a great idea."
But what I find regrettable is that many projects, after actually launching, did not continue to invest resources to truly complete the thing they initially talked about. The story was told completely, but execution noticeably slowed down or even stalled after the token issuance.
So if there's anything that's overestimated, I think it's the expectation of "long-term execution capability"; and what's underestimated is precisely the ability to continuously invest, keep experimenting, and truly get things done amidst uncertainty.
This doesn't just happen in AI × Web3, but is a common problem across the entire Web3 industry. Many teams are full of idealism in the early stages, but after the project achieves阶段性 success, continuing to take long-term risks and reinvest resources to do something even more difficult becomes increasingly rare.
From the perspective of industry development, this short-sightedness is actually very unfortunate. Because what can truly push Web3 into the mainstream has never been a particular narrative, but rather having teams willing to take a good direction and work on it long-term and steadfastly.
