AMBR 2026财年第二季度业绩电话会：随着AI转型实现盈利，营收增长39%

随着公司围绕专业 AI Agent（智能体）进行重新定位，Amber International Holding Limited (AMBR) 在 2026 财年第二季度实现了更高的营收、更宽的毛利率以及营业利润扭亏为盈。

核心要点

• 第二季度营收环比增长 38.8% 至 1390 万美元，主要由 AMM、财富管理解决方案以及营销和企业解决方案驱动。
• 毛利率从第一季度的 67.7% 扩大至 79.5%，反映出高毛利业务的贡献提升。
• 营业利润达到 100 万美元，而第一季度为营业亏损 320 万美元。调整后 EBITDA 从负 320 万美元改善至 190 万美元。
• Agent（智能体）业务收入总计 740 万美元，其中包括 AMM 在首次确认收入的季度贡献的 350 万美元，以及营销和企业解决方案贡献的 380 万美元。
• AMBR 推出了个人理财 Agent（智能体）Ambre，并开始将营销 Agent Mia 作为独立产品直接销售。管理层将这些描述为更广泛专业 Agent 产品组合中的首批产品。
• 由于战略转型以及新业务的预测历史有限，公司撤回了此前发布的财务指引。

核心财务数据

根据其 5000 万美元的股票回购计划，截至 6 月 30 日，AMBR 已累计回购约 260 万股 ADS，耗资约 580 万美元。目前仍有约 4420 万美元的授权额度。

业务与运营表现

基于资产的收入为 660 万美元，涵盖财富管理、执行及支付解决方案。其中财富管理解决方案产生收入 530 万美元，高于第一季度的 430 万美元，主要得益于对多元化产品及新推出产品更强劲的需求。

Agent（智能体）业务收入总计 740 万美元。AMM 在首次确认收入的季度贡献了 350 万美元，而营销和企业解决方案产生了 380 万美元。数字资产平台与 AMM 的合计收入为 1010 万美元，略高于管理层此前预测的 900 万至 1000 万美元展望。

管理层表示，AMM 目前正处于从手动工作流程向专业垂直 Agent（智能体）转型的中间阶段。其收入仍主要反映运营平台及相关能力的变现，而非直接针对 Agent 所交付成果的付费。

Ambre 于 9 月 1 日以邀请制形式推出，初期面向 Amber Premium 的已认证客户群体。它提供跨账户投资组合分析、风险与信号监测以及预警功能。Ambre 目前不会自主下单；决定采取行动的用户将被连接至 AMBR 的专家团队。

Mia 已经通过爱点击（iClick）和 CMRS 为 100 多家企业客户支持了相当一部分营销活动运营。除了在 AMBR 的营销服务中使用外，现在它还可以作为独立产品直接销售。

AMBR 打算在自研与开源基础大模型之间保持中立。管理层表示，公司计划将投资重点放在垂直专业知识、工作流程、数据连接、权限、控制和执行基础设施上，而不是训练基础大模型。

管理层业绩指引

AMBR 撤回了此前发布的财务指引，因为在向 Agentic AI（智能体 AI）转型后，管理层不再认为该指引适合作为公司的财务框架。公司表示，计划在为新业务建立足够的运营历史和预测可见性后再发布更新后的指引。

管理层预计将在 2026 年底前举办 AMBR 的首届投资者日，届时计划展示更多 Agent（智能体）以及转型的财务框架。

风险与关注点

• 战略转型仍处于早期阶段，管理层表示需要更多运营数据才能对新业务进行可靠预测。
• AMM 当前的收入在很大程度上代表了平台和能力的变现，而非基于 Agent 成果的直接定价。
• Ambre 仍处于产品早期阶段，只有在验证其可靠性后，才会获得更广泛的授权。
• AMBR 正在审查其部分传统金融服务业务组合的结构与所有权。管理层表示，目前尚未作出或公布任何决定结果。

问答环节要点

管理层将 AMBR 的专有 Agent（智能体）层定义为模型、运营套件、工具以及明确目标的结合。管理层认为，其差异化优势源自多年积累的金融与营销运营经验、既定的工作流程、客户认知和基础设施，而非拥有基础大模型。

当被问及资金雄厚的竞争对手是否能够复制 Ambre 或 Mia 时，管理层表示，大模型开发商主要专注于基础大模型和通用 Agent（智能体），而其他潜在竞争对手可能缺乏 AMBR 的行业背景知识和运营基础设施。

在谈及市场机遇时，管理层表示，Mia 和 Ambre 既可以从传统服务提供商手中夺取市场份额，又能扩大服务覆盖面，让此前负担不起的小型客户也能获得相关服务。管理层将这种潜在扩张定位为长期机遇，而非当前的财务成果。

业绩电话会议完整文字记录

完整财报电话会议逐字稿

管理层陈述

Operator

Thank you. Good morning and welcome to Amber International's second quarter 2026 financial results. [Operator Instructions] As a reminder, this conference is being recorded. It's now my pleasure to introduce your host, AMBR's AI Ambassador, Mia. You may begin.

Unknown Executive

Good morning and welcome to Amber International Holding Limited's second quarter 2026 earnings conference call. I am Mia, AMBR's official AI agent moderator for today's call. Before we begin, please note that today's discussion will contain forward-looking statements under the Private Securities Litigation Reform Act of 1995. These statements involve risks and uncertainties that could cause actual results to differ materially from those projected.

For a more detailed discussion of these risks, please refer to the company's filings with the U.S. Securities and Exchange Commission, including our most recent annual report on Form 20-F. Joining us on today's call are Michael Wu, Chairman of the Board and CEO. Vicky Wang, President. Yi Bao, Chief Operating Officer, Josephine Ngai, Chief Financial Officer, and Steve Zhang, Co-Chief Financial Officer. Following their remarks, we will open the line for Q&A. With that, let me now turn the call over to Michael Wu, our Chairman of the Board and CEO.

Michael Wu

Thank you all for joining us. The second quarter was a strong one for AMBR. And I will start with the numbers. Revenue was $13.9 million, up 39% from the first quarter. Gross margins expanded to 79.5%. Operating income was $1 million and adjusted EBITDA was $1.9 million, both turning positive from losses last quarter. And revenue from our agentic and digital asset businesses from Amber Premium came in at $10.1 million, above the $9 million to $10 million outlook we gave you last quarter.

On our $50 million repurchase program, we bought back approximately 2.6 million ADS for about $5.8 million through June 30, with roughly $44.2 million remaining. Those are the results. Today though, I want to explain why they matter because they are the first evidence that our new strategy is already taking hold. Two days ago in Hong Kong, we introduced AMBR for what it is today: a company that builds specialized AI agents.

This is a pivot, and I'd rather say that plainly than dress it up as continuity. We used AMBR as a digital wealth management business. Now we are becoming a technology company. We are making that choice deliberately and from a position of strength backed by the numbers I just shared, because we believe this is where the greater opportunity lies. For me, this is also a return to my original passion and my true ambition.

We started in 2017 as Amber AI and spent the first nine years in markets learning exactly where capable models stopped being useful to people making consequential decisions. In June, I stepped down as CEO of Amber Group to run AMBR full-time. Building these AI agents is the only thing I plan to work on for the next decade. I said that publicly on Monday and I will repeat that today to this audience, because you are the ones who can hold me to it.

AMBR already has two products in market. Ambre is our consumer agent for personal finance. Ambre does what Amber Premium's relationship managers have done for high-net-worth clients: portfolio analysis across accounts and asset classes, a daily signal feed, filtered analysis to what a user actually holds and actually what matters, monitoring, and alerts. And Ambre makes that available far more broadly.

And the two design choices matter for this audience. First, Ambre works across users' existing exchange and brokerage accounts. We're not asking anyone to move assets to us. Second, Ambre does not place orders autonomously. Not yet. When a user decides to act, they are connected to our expert team. These are people who were relationship managers, structurers, and traders in our financial services business. We will expand the agent's authority as reliability is demonstrated, not ahead of time.

Ambre opened Monday by invitation, beginning with Amber Premium's verified client base. Mia, your host today, is also our marketing agent, and is the proof that this model produces real revenue. Mia was built inside iClick and CMRS, our wholly owned marketing businesses, where it runs a substantial share of day-to-day campaign operations for a base of more than 100 enterprise customers.

As of Monday, Mia is also available directly. Sold as a product, not just embedded in our services anymore. That's a pattern you should expect from us. We operate a business, convert its expertise into an AI agent, and then take that AI agent to market. Now let's move to the P&L logic because the repositioning is only credible if the numbers eventually say the same thing as the strategy.

Of the $13.9 million I mentioned, $7.4 million came from businesses we classify as agentic, $3.5 million from AMM in its first quarter of recognition. That classification reflects how these businesses genuinely run. Operations executed on AI-agent infrastructure, not a relabeling of old revenue. Yi will take you through the basics and the details. The reason it matters: structurally higher gross margin than the businesses it's replacing.

And that mix shift is most of why gross margin reached 79.5% this quarter. On the rest of the portfolio, as we focus the company on building AI agents, we're reviewing the shape and ownership of parts of our legacy financial services business. Some of what we operate today fits the strategy as infrastructure. Some might serve clients and shareholders better in a different structure.

We have nothing to announce, and we won't speculate on outcomes. I'd rather tell you the review exists than have you learn of any outcome cold. I also want to emphasize that Ambre and Mia are the first two AI agents, not the whole portfolio. At our inaugural Investor Day, which we now expect to hold before year-end, we will show you what else we've been building and lay out the financial framework for the transition. What a revenue mix looks like as agentic businesses become the center of AMBR. Until then, our job is simple: execute on what we just launched, build AI agents. With that, I'll hand over to Yi Bao.

Yi Bao

Thank you, Michael. The message from my side is simple. Agent strategy is already operating, not just announced. AMM went from operating system to $3.5 million of recognized revenue in a single quarter. Mia runs a substantial share of day-to-day campaign operations for more than 100 enterprise customers. And Ambre expands on workflows our team have run for years. We build agents on infrastructure we have already proven, which is why we can move quickly without taking on the risk of building from scratch.

What I would like to do this morning is make the pivot concrete from the operating point of view. Michael described the pattern we follow, which is that we operate a business, we convert its expertise into an agent, and then we take that agent to market. All the real work happens in that middle step. And that is where, I think, our advantage sits. That's where I will spend my time. Let me pick up where I left off last quarter. I described A3S and AOS, AI Native Operating Systems.

A few of you asked afterwards why we were leading with infrastructure rather than with the agents themselves. The short answer is that intelligence on its own doesn't make the agents useful. Agents need access to the right data, tools it can operate, workflows it can follow, permissions, down to what it is allowed to touch, and controls around execution, monitoring, risk, and compliance. More than any of that, it needs a precise purpose, by which I mean a defined user and a real situation where the outcome matters to somebody.

The model supplies intelligence, the operating environment lets that intelligence act, and the purpose determines what the action is worth. That's also why we are not competing at the foundation model layer, and don't plan to. We stay model-neutral and use whatever intelligence works best for a given job, whether it comes from a proprietary model or an open-source one. It's worth thinking about what that means for how you view the model race, because when the models get more capable and cheaper, it works in our favor rather than against us.

Our costs come down and our agents get better without us spending $1 on training. What we intend to own is a layer sitting above the model, which is where you'll find deep understanding of a particular vertical, the connections into the right tools and data, the design of the workflow and its controls, and the unglamorous work of making general intelligence reliable enough that someone will trust it with a real job. AMM is the clearest example of how that plays out.

AMM has always run on a fragmented set of workflows with client requirements sitting in one system, and counterparties in another. And then KYC, contracts, execution, monitoring, settlement, and reporting each carry their own tools and their own manual steps. Our first move was to put the AI interface in front of all that, because the interface on top of a broken process just gives you a faster route to the same bottleneck. We standardized the underlying workflow first, then connected the systems, structured the data, put monitoring and controls around it, and made the process machine-operable one step at a time.

That's the layer we described to you last quarter as the AMM operating system. In the second quarter, AMM contributed roughly $3.5 million of revenue in its first quarter of recognition. I want to be careful about how I characterize it. What it tells you is that the infrastructure underneath our agent strategy can already generate economic value. That's a meaningful distinction because most companies in this field are still asking investors to fund an operating layer that doesn't exist.

Our system is already running and fits our definition of agentic revenue. The bulk runs on agent infrastructure rather than through the manual processing it replaced. The part I want to be careful about is what comes next. That revenue today still mostly reflects monetization of the operating platform and the capabilities running on top of it rather than the agents getting paid directly for delivering the outcome. Our expectation is that the specialized agent gradually becomes the primary interface to that capability and that the outcome itself becomes what the customer pays for.

Think of the progression as three stages, starting with the manual workflow, then agent-operable infrastructure, and eventually a specialized vertical agent that simply delivers the results. AMM is in the middle stage today. We will tell you when it moves and we will show you what we measured before we say it moved. The rest of the portfolio is being built the same way. We came out of a working marketing operation where we learned from real companies and real enterprise customers long before we sold it to anyone.

Ambre is being built on years of operating experience at Amber Premium, drawing on portfolio analysis, risk monitoring, product evaluation, and the accumulated judgment of relationship managers, traders, structurers, and product teams. In both cases, we started inside an environment we already understood well, converted that operating knowledge into structured workflows and systems, and then let agents take on more of the work as it earns the right to. That's also how I would ask you to think about our legacy business inside the new AMBR.

The customer relationships, the domain expertise, the regulatory infrastructure, the execution connectivity, the operational data, and the risk and compliance experience all stay valuable. It would be hard for newer entrants to assemble from scratch. What does have to stay the same is the way we have traditionally delivered those capabilities. It historically grows by adding relationship managers, operations staff, and margins that grow in a fairly straight line with headcount, and that's a different economic shape from the company we intend to build over the next decade.

I already see the difference showing up in this quarter's gross margin. So as Michael mentioned, we are reviewing where each legacy business and its structure fits. Some of those capabilities will end up as infrastructure or agent-dependent, and some delivery models will become decentralized over time. The pivot is changing how we run AMBR internally as well. Because we want to be the first serious user of everything we build, there was a practical reason for that, which is that running our own agents in our own workflow is the truest way to find out where they fail before a customer does.

Where they fall short, where human judgment is still needed, what context or tooling they are missing, and how the workflow itself should be redesigned. We understand the workflow, we build the agent, we run it ourselves, we fix what breaks, and then we take it outside. Dogfooding is our operating model and it travels from one vertical to the next. When you look at that $3.5 million from AMM, I would ask you to read it as an early proof point rather than a destination. The operating system is the foundation.

The specialized agent is the product we are building towards, what the customer should eventually be paying for. We are early in this transition, but we are not starting from 0. We are starting with businesses that operate, users who use them, workflows that function, and revenue that's already being recognized. The work in front of us now is turning those advantages into specialized agents, and scaling the ones that prove they can deliver. With that, I will pass over to Vicky.

Vicky Wang

Thank you, Yi. Earlier this week, on September 1, we officially unveiled AMBR and introduced the next chapter of our company, focused on building specialized AI agents for high-value, high-stakes use cases. We have been very encouraged by the initial response. Since the launch of AMBR, we have seen strong interest from existing clients, prospective users, partners, and the broader market. While we are still at a very early stage, that response has reinforced our conviction that users are looking for something beyond another general-purpose AI interface.

They want agents that are more intelligent, that understand their context, know what matters to them, and can continuously help them to take action. And this is where we believe AMBR has a differentiated foundation. The AMBR brand is new, but the capabilities behind it have been built over many years. We bring deep domain expertise, trusted financial infrastructure, experience serving sophisticated users, and a detailed understanding of real-world high-stakes workflows.

We believe these capabilities become increasingly valuable in an agent AI world. Foundation models are becoming extremely powerful, but in our view, the most valuable specialist agents will require a deeper know-how of the underlying industry. And this is where our domain expertise becomes particularly valuable. Ambre, our flagship personal finance agent, is one of the first examples of how AMBR is combining frontier AI capabilities with deep financial expertise to build specialist agents.

Over the years, we have built deep capabilities across digital wealth management, risk management, and financial infrastructure. Ambre brings these capabilities together in a much more scalable and intelligent form. Rather than simply providing users with more information, Ambre is designed to understand their financial context, identify what matters most to them, and help turn their intentions into action.

For example, Ambre can build a holistic view of a user's portfolio across different accounts and asset classes, identify concentration and correlation risks, surface the signals that are most relevant to their actual holdings, and continuously monitor specific conditions or tasks on their behalf. We launched the first version of Ambre on September 1 as well. And early response from our existing clients, partners, and broader community has been very encouraging.

It is still an early version, and we expect the product to evolve significantly as we validate user behavior and progressively unlock more agent capabilities. Our long-term ambition is for Ambre to make a level of personalized, always-on, professional financial intelligence that historically was only available through high-touch private banking relationships accessible to a much broader group of users. On the other hand, Mia solves the same shape of problems in a completely different market.

Marketing teams run research in one tool, insights in another, content in a third, and distribution in a fourth, and nobody owns the seams between them. Mia is built to understand the objective and carry that workflow through end-to-end instead of handing it off 4 times. We sell it two ways now: embedded in the services our marketing businesses deliver and directly as a product. And having both gives us an unusually clear read on what a customer will pay for the agent on its own versus the services wrapped around it.

These two markets we picked in the first batch have almost nothing in common. These are finance and marketing operations. They share almost no customers, no regulations, and no workflows. So if the same approach works in both, that's the approach working and not luck. It's also how we will choose the third agent and the fourth. We go where we already operate, where the work is high-stakes and fragmented, and where we hold context a newcomer would need years to assemble.

We are still at the beginning of this journey, and there is significant work ahead, but the launch of AMBR marks an important milestone for the company, and the early response we have seen has, again, strengthened our conviction in the direction we are taking. We look forward to sharing more as we expand the capabilities of Ambre and Mia and introduce additional specialist agents across the AMBR platform. With that, I will turn it over to Josephine.

Josephine Ngai

Thank you, Vicky, and good morning, everyone. Before I get into the numbers, let me start with the headline for the quarter. Revenue grew 39% sequentially, and we moved from an operating loss of $3.2 million in Q1 to operating income of $1 million in Q2. And, importantly, operating expenses essentially flexed at around $10 million. I think that's an important point for investors. The strategy Michael just described is not being driven by a significant increase in spending.

What we are seeing is that it's a change in the revenue mix, with our higher-margin agentic revenue growing alongside continued improvement in our core business. Typically, when a company goes through this kind of repositioning, you would expect to see a higher cost base first and potentially a need for additional capital. So far, we are seeing the opposite. We are growing revenue, improving margins, and moving into profitability without materially increasing expenses. That's the kind of financial discipline that we want to maintain as we execute this transition.

Let me walk through the quarter in a little more detail. Starting with revenue, total revenue in Q2 was $13.9 million, up 38.8% from $10 million. Beginning this quarter, we have reorganized how we present revenue into two categories, which we think better reflects how the business is evolving. Asset-based revenue, which was $6.6 million, and includes wealth management, execution, and payment solutions. The second is agentic revenue, which was $7.4 million and reflects the initial contributions from AMM, together with our marketing and enterprise solutions business.

Within the digital assets platform, wealth management solutions generated $5.3 million, compared with $4.3 million last quarter. That improvement was mainly driven by stronger demand for both our diversified products and several newly launched offerings. Agentic revenue was one of the key developments this quarter. AMM contributed $3.5 million in its first quarter of revenue recognition. Our marketing and enterprise solutions contributed $3.8 million. And if you look at the digital asset platform together with AMM, revenue was $10.1 million, slightly above the high end of the $9 million to $10 million outlook we previously communicated.

Moving to gross profit, we saw a significant improvement. Gross profit increased to $11.1 million from $6.8 million in Q1, and gross margin expanded to 79.5% from 67.7%. The main driver here was the mix of the business. We are seeing a larger contribution from higher-margin activities, particularly AMM and our core wealth management business. So, from our perspective, it's not just the revenue growth that's encouraging. The quality of that revenue is also improving.

On operating expenses, we remained at approximately $10 million, essentially flat with the prior quarter. This was particularly important given the growth we delivered during the quarter. We are starting to see the operating leverage we believe can come from different AI integrations across the business. As a result, operating income was $1 million for the quarter, compared with an operating loss of $3.2 million in Q1.

Looking at the bottom line, net income from continuing operations was $1.5 million compared with a net loss of $3.7 million last quarter. Adjusted EBITDA from continuing operations improved to positive $1.9 million from negative $3.2 million in Q1 and adjusted net income was $1.5 million. Turning briefly to the balance sheet, as of June 30, we have $34.2 million in cash, cash equivalents, time deposits, and restricted cash.

Let me also address our outlook because I know this will be an important question for investors. As Michael discussed, the company is going through a meaningful strategic transition toward becoming an agentic AI company. Given that transition, we don't believe our previously issued financial guidance is still the right framework for evaluating the company's future performance. We have therefore decided to withdraw that guidance while we build more operating history around these new businesses and get better visibility into their financial contribution.

Once we have enough data and forecasting visibility, we intend to provide updated guidance. I want to emphasize that withdrawing the guidance doesn't change our confidence in the long-term opportunity. It's really about making sure that when we give investors a forward-looking framework, it's based on the business we are building now rather than the business we had before this transition. So stepping back, I think Q2 gives you more early but meaningful evidence of what the new model can look like.

And yet operating expenses stayed essentially flat, gross margin expanded significantly, and we moved from an operating loss to operating profit. For us, that's the pattern we want to continue seeing as we move toward a more AI-native model. We are still early in this transition and there is a lot of work ahead, but we are encouraged by the progress we are seeing and we will continue to stay focused on disciplined execution and long-term value creation. With that, I will turn the call back to Mia. Thank you.

Unknown Executive

Thank you, Josephine. That concludes our remarks for today. We will now open the line for Q&A. Operator, please begin.

Operator

[Operator Instructions]

Michael Wu

Yeah, I see some questions on the screen. First question: what is proprietary about your AI agent? About AMBR's AI agent. This is actually a really good question, and I'd like to share with the audience our own AMBR's definition of what is even an AI agent. I think the industry sort of comes together to a definition for AI agents, as this concept or this whole species is still fairly new in human history. The industry defines AI agents as the model plus the harness. Now, we do believe that definition is incomplete.

Our AMBR's definition for an AI agent is an agent is the model plus the harness plus the tools. And I think that will lead us back to the original question. Why do we believe that way? Because the model is the intelligence, and that intelligence is increasing day by day as the model labs compete for better and better models of all sorts, proprietary or open-source. The harness is the concept of the environment or the tools or setup for that intelligence to do actual work.

For example, as coding agents, that harness allows the model to code and write programs for programmers or even non-programmers, we call them [ AI coders ]. But we do think, like Yi said earlier in his remarks, the purpose is what makes that action from the model valuable to someone. It is essentially what the model is doing for who in what scenario and why. If the model is highly intelligent and increasingly intelligent, without the purpose, it's unclear why the customers should pay for that because it's unclear what value the customer receives.

Now, still going back to the original question, what is proprietary about AMBR's AI agents? We think in the areas we started, Ambre with wealth management, Mia with marketing, we understand the purpose, or at least we understand the purpose very well for the customers the original businesses have been serving for years, for many years. We understand what exactly they need, what their demands are, what their pain points are, and how they like to have these problems solved.

It's proprietary because any other company with the same model or even the labs that create the model does not have that, unless they have done years of servicing these customers like we did. Take a step back to harnesses nowadays. Just like the models, you have increasingly an open-source culture around both the model and the harness. You now have a lot of great open-source models, essentially free to use, free to deploy locally by AMBR or other companies.

You now also have a lot of open-source harnesses. In fact, some of the most popular personal general agent harnesses, [ the last one, the like the Prompts agents, OpenClaw, Pi agents ], they're all open-source, which means anyone including AMBR can use them, review them according to our needs. Now because we have, again, very deep understanding about the purpose, AMBR, we understand how these clients like to be serviced around their money, around wealth management.

We also understand how these companies like to be serviced with their marketing. We can then build harnesses that are special, that are proprietary to these clients, to these personas. And provide them value in the ways they want, in the ways they actually find valuable because the ones who pay, I believe, define what's valuable. Now, also a lot of our partners are also proprietary because they come from the system that's been servicing these clients for years.

You cannot build these programs that are... All of these are non-off-the-shelf, including old school programs. You cannot build these programs. You can back-engineer them, but they're not battle-tested. They are not the way clients have been served or like to be serviced. So back to the original answer, I think actually other than the model, both the harness and the purpose are not only proprietary to the AMBR agents like Ambre and Mia, they are unique with a moat that was built over years of servicing real clients to perhaps one of the highest standards in the industry.

So I hope that answers the question and I also hope that provides a bit more insight on how we understand building AI agents given the field is so new. I do think, as a public company doing that, we have sort of a responsibility educating the audience or even potentially, you know, sort of sharing what we know about what building AI agents even means. Can we take questions online?

Operator

[Operator Instructions] Were there any other web questions?

Michael Wu

We'll take another question from the web. It's a fun one and I think it can hopefully be insightful for the audience too. It reads: if investors give your five largest competitors $50 million tomorrow, what stops them from building Ambre and Mia? This is a great question because the answer is both simple and I think again, you know, comes back to how we understand building AI agents.

Frankly, the ones who can theoretically build Ambre and Mia, they do not need that $50 million. They are potentially the labs or the large internet companies that are already building models and general agents. They do not need that $50 million tomorrow to build those. But why are they not building Ambre and Mia? They're fighting different battles. They're trying to build the better model. They're competing very hard. Their best people, their $billions or $hundreds of billions are spent winning the model war, not winning the vertical agent war we are fighting.

Now, on the other hand, if you give $50 million to a competitor who wants to build Ambre and Mia tomorrow, it also doesn't help that hypothetical competitor. They are unlikely to understand the purpose we do. And even if they do, they are in the same industry. They are unlikely to have built their harness the way that it's generically operable as we have done with AMM and with the harness around Mia, the harness around Ambre.

Last but not least, I think likely they will be building their version of Ambre and Mia for the wrong purpose. That matters. Likely they will try to add an AI bot onto whatever they were selling, and that's not going to be the right purpose of servicing the users, the customers, like what Ambre and Mia are doing with our customers. So I think this is a great question. I actually think this $50 million doesn't help any hypothetical competitor of ours.

Because the ones who can do it, they don't need the $50 million and they're not doing it for financial reasons. They're doing it because they're fighting different battles with that focus. And the ones who need $50 million, they probably cannot, while we built ours in a very, I think, extended period of time.

Operator

There are no phone questions at this time.

Michael Wu

Then we will take one more web question. We have very capable management, but I will start. Do you expect to take market share from existing competitors or do you view the segment as open and untapped? This is a great question because I think it's a bit of both and it depends on the time horizon. A portion of where our growth or revenue comes from is actually weighted forward.

In the near term, take Mia as an example. Mia is already making revenue from customers not noticing this. These customers probably do not care if they are serviced by Mia or iClick or another marketing company, maybe with or without AI. So in that sense, Mia is taking market share from iClick competitors or even iClick itself. Now essentially iClick is, you can think of it as a service or an additional layer on top of Mia.

Now, at the same time I do think customers being serviced directly by Mia are having a very different service experience. It is essentially, and especially for a lot of smaller customers, essentially the first time they're being serviced and can afford to be serviced by a truly 24/7 complete marketing team. This is a clearly untapped market because these customers, their marketing needs existed before, but a way for their marketing needs to be served this way didn't exist before.

In the near term, I do think Mia, or iClick and CMRS with Mia behind it, is operating a lot better than many of its competitors, and it will take market share from competitors. Over time, I think the most powerful thing to Mia as a service model is it will open up a lot of new customers that didn't think they could have this level of service. The same thing happens with Ambre.

The level of service the Amber Premium Team provides to high-net-worth families, $billion family offices, was not accessible by smaller investors or individuals most of the time. The cost is just too expensive to do that. Now, not only can that experience and that service potentially be delivered better, they can be delivered at an affordable cost to a lot more customers. So back to that, before my teammates add more, I think over time, it's a bit of both, but the later open market is a lot larger for us.

Operator

Thank you. That concludes the question and answer session. I'll turn the floor back to Mia for final comments. Thank you all for joining us today.

Unknown Executive

This quarter marked a clear step in AMBR's pivot, from a digital wealth management business to a technology company that builds specialized AI agents. Ambre and Mia are the first two agents now in market, and the second quarter results are the first evidence that this direction is beginning to show through in the numbers. We sincerely appreciate your continued trust and support. We look forward to sharing more in the quarters ahead, including at our Investor Day, which we now expect to hold before year-end. This concludes today's call. Thank you, and have a great day.

Operator

Thank you. This concludes today's conference call. You may disconnect your lines at this time. Thank you for your participation.