Good afternoon, everyone. Welcome to BizLink's 4Q 2025 earnings call.

My name is Derrick Yang, the coverage analyst for BizLink here at Morgan Stanley. It's our honor today to have BizLink's top management with us today to discuss the 4Q '25 financial numbers, as well as the outlook and industry dynamics.

Without further ado, let me pass it to BizLink's IR Manager, Mike Wang, for a brief rundown of this call.

Thank you, Derrick, and also thank you to Morgan Stanley for hosting today's earnings call. Good afternoon, everyone, and welcome to our fourth quarter '25 earnings conference call.

My name is Mike Wang, Senior Investor Relations Manager. Joining me today are Roger Liang, our Chairman; Felix Chien, our CEO; Florian Hettich, our COO; and Charles Tsai, our CFO.

Our earnings results were released earlier today and are available on our Investor Relations website, where you can download the latest earnings materials and access them through MOPS. Today's call will begin with Charles, who will review our financial highlights, followed by Florian for operational highlights.

Felix will then conclude with corporate updates before we move to the Q&A session. You may submit your questions at any time through the public or private chat function, and we will address as many as time permits.

Before we begin, please note that today's discussion may contain qualitative forward-looking statements based on our current expectations, and they are subject, of course, to risks and uncertainties. Actual results may differ materially.

Please refer to the safe harbor notice in our earnings materials for further details. This call is being recorded and will be available on our Investor Relations website within 24 hours.

With that, I would like to turn the call over to Charles.

Thank you, Mike. Before reviewing the quarterly numbers, I would like to briefly step back and frame how we think about BizLink financially as BizLink continues to scale and evolve.

Over the past several years, we have been transforming BizLink from a collection of strong operating businesses into an integrated platform with formal capital governance designed to allocate capital prudently, manage risk, and convert growth into durable financial outcomes. As our mix shifts toward more complex, system-level engagement, particularly across high-performance computing, infrastructure, and capital equipment, we believe it is increasingly important to anchor the financial discussion in earning quality, cash conversion, and capital discipline, rather than short-term volume dynamics.

Our objective is not simply growth, but growth that is self-funded, resilient across cycles, and aligned with long-term shareholder value creation. Our global teams executed strongly, achieving our eighth consecutive quarter-on-quarter increase in sales since bottoming 2 years ago.

Quarter-on-quarter sales growth was primarily driven by HPC, rising 16%. Capital equipment sales remained flat quarter-on-quarter and stayed at the level we saw in second quarter 2025.

Electric vehicle and factory automation sales continue to gradually recover after bottoming out near the start of the last year. Healthcare sales remained at third quarter 2025 highs.

The quarter reflected the early ramp phase of several new high-complexity programs where production maturity, procurement optimization, and yield learning curve have not yet fully normalized. As these programs scale over the coming quarters, we expect meaningful cost absorption and margin improvement consistent with historical ramp dynamics.

This resulted in our gross margin falling from the third quarter 2025 levels. OpEx was roughly stable quarter-on-quarter and stayed at year-to-date levels, showing that our cost efficiency efforts are flowing through into our P&L.

During the second quarter last year, we completed a review of certain co-invested production assets and recognized a non-operating, non-cash impairment to align carrying value with updated long-term cash flow assumptions. In the fourth quarter, we continued to apply the same conservative framework to reassess asset recoverability as market conditions evolved.

The resulting adjustments were program-specific, non-cash, and accounting-driven, and do not reflect a deterioration in the underlying competitiveness or strategic relevance of the business. Our effective tax rate this quarter reflects a year-end adjustment that captured full-year impact, and therefore should not be interpreted as indicative of our ongoing tax rate.

Operating leverage further boosted earnings with our EPS continuing to grow quarter-on-quarter. On full-year basis, our total sales was stronger than expected.

Sales was driven by HPC, which more than doubled year-on-year, and by capital equipment, which was up more than 50% year-on-year and was more than double 2023 levels. Industrial sales recorded its second annual increase.

Finally, we closed our XFS communication acquisition on January 5th, 2026, and began consolidating its financials on the same day. From a portfolio perspective, our industrial segment continue to provide a stable earnings base, supported by high-mix, project-driven demand across automation and equipment applications.

Over the recent quarters, customer procurement behavior has begun transitioning from broad inventory correction toward normalized ordering patterns, with visibility increasingly tied to new program activity rather than restocking. Our consumer-linked segments are being managed conservatively.

While automotive and electrical appliances are not expected to be primary near-term growth drivers, they continue to play an important stabilizing role within the portfolio, where prior adjustment and disciplined planning help support operational resilience and limit volatility. In parallel, demand related to AI infrastructure remains structurally strong.

While engagement level continue to increase, we're managing this business with appropriate discipline given its step-function deployment profile. This diversified mix allows us to maintain financial stability while retaining exposure to long-term infrastructure investment cycles.

What is changing? Growth across the portfolio is increasingly driven by higher system content, deeper engineering engagement, and earlier qualification cycles rather than pricing actions.

As customer programs increasingly follow platform-based deployment cycles, a portion of our capability investment occurred early in the development phase to establish engineering and manufacturing readiness. Importantly, this investment are reusable across multiple customer programs and generations, allowing revenue and content to scale once platform enter volume deployment without requiring proportional increases in incremental capital deployment.

Across multiple segments, customers are also engaging earlier in platform design and qualification cycles, modestly extending planning visibility while increasing execution requirements. As a result, growth increasingly reflect platform scaling, rather than incremental capacity expansion, supporting more efficient capital deployment over time.

At the same time, earning quality continue to improve, with cash conversion and return on equity trending upward as operating leverage improves and working capital intensity declines. Growth is being absorbed efficiently rather than funded through balance sheet expansion.

As systems scale, progress is rarely linear. Platform transition and deployment schedule do not move in straight line, even when long-term demand is clear.

Our focus is therefore on execution reliability, rather than maximizing short-term throughput. This discipline is reflected in our capital allocation approach.

We maintain tight working capital management, steady CapEx intensity, and selective M&A focus on capability enhancement, rather than growth for its own sake. During the first quarter of 2026, we completed the acquisition of XFS communications, adding optical interconnect capability consistent with our disciplined, capability-enhancing M&A approach.

Consistent with our framework, integration is being executed methodically within our existing financial discipline. What does this mean going forward?

NPI activity has become an increasingly important forward indicator, with many programs aligned to customer platform refresh cycles extending well into 2026 and even into 2027. During the quarter, BizLink achieved several milestones reflecting increased institutional maturity, including index inclusion and joining RE100.

This step reinforced governance, transparency, and balance sheet discipline as the company scale. Taken together, we believe BizLink is positioned to continue scaling with financial resilience as customer architecture evolve.

Florian will now provide update on our latest quarterly operational takeaways.

Thank you, Charles. Yes, from an operational point of view, the customer engagement is shifting from component execution toward more system-level collaboration as architectures become more complex.

Power delivery, also data connectivity, and mechanical integration increasingly interact across customer platforms. Higher performance requirements and tighter deployment schedules raise the cost of execution error and increase the value of early engineering engagement.

This increasingly includes both electrical and optical connectivity domains, where integration and manufacturability requirements are converging as system architectures evolve. What we are seeing is not a collection of isolated programs, but rather a repeatable operating pattern across multiple customers and industries.

From an operational perspective, we frame our business not around individual products, but around how systems function as a whole. As customer architectures evolve, power delivery, data connectivity, mechanical engineering, and manufacturability increasingly interact with one another.

Customers are no longer optimizing isolated components. Instead, they are designing platforms that must operate reliably under higher density, tighter tolerances, and faster deployment timelines.

In this environment, execution reliability and integration discipline become as important as technical performance. This shift changes how customers engage suppliers.

Rather than sourcing individual parts late in development cycles, customers increasingly seek partners capable of contributing earlier in design and supporting global execution once platforms scale. Our operating model is built around supporting this transition, enabling us to participate where system-level constraints are emerging rather than where specifications are already fixed.

As compute density rises, the challenge is no longer meeting individual specifications in isolation. Validation becomes multi-layered, failure tolerance tightens, and execution consistency across regions become critical.

In AI infrastructure, higher rack-level integration is driving redesigns in both power delivery and data connectivity. Similar dynamics are emerging in semiconductor equipment, where subsystem complexity and engineering constraints are accelerating outsourcing toward integrated partners.

As requirements increase, the universe of suppliers capable of supporting these platforms at scale naturally narrows. Our response has been to deepen system-level engagement through expanded NPI capability and tighter integration between engineering, box build, and system integration.

Increasingly, customers engage us not only for components, but at the system level where power, data, and mechanical constraints intersect. Once architectures are validated, continuity and execution reliability become critical.

At the rack level, power delivery is becoming a primary architectural constraint as densities exceed 100 kW. Increasingly, we are being engaged earlier in rack architecture discussions, particularly around power distribution geometry and serviceability considerations.

Across capital equipment platforms, we see a similar pattern. OEMs who are facing engineering and cleanroom constraints are outsourcing more integrated subsystems, rewarding suppliers capable of multidisciplinary execution at scale.

What is that meaning going forward? As the integration increases, manufacturability and repeatability become as important as design performance.

Our focus is therefore on building execution capability tied directly to customer programs rather than expanding capacity ahead of demand. We believe this disciplined approach positions BizLink to scale alongside increasing system complexity without increasing operational risk.

Felix will now provide updates on our latest quarterly corporate takeaways.

All right. Thank you, Florian.

BizLink was founded on solving customer problems through engineering engagement and execution discipline, rather than focusing on any single product or market. That operating philosophy continues to guide the company today.

As systems scale, the constraints our customers face are changing. Power density, data movement, and integration complexity have become binding factors across clear centers: semiconductor manufacturing and emerging automation environments.

We view this not as a short-term technology cycle, but as a long-term industrial transformation. Importantly, these constraints are not emerging only within AI infrastructure.

Similar shifts toward higher system integration and earlier engineering collaborations are appearing across semiconductor equipment, industrial automation, healthcare, and advanced equipment platforms. This reinforces our view that the changes underway represent a broader industrial evolution.

BizLink has never been organized around a single product category or technology cycle. Instead, we frame our role around solving physical layer constraints that emerge as systems scale.

Across industries, periods of technical advancement eventually shift bottlenecks away from compute or core intellectual property, and toward integration, power delivery, reliability, and manufacturability. These constraints determine how efficiently systems can be developed and scaled.

Our strategy is therefore not to predict a single technological end-state, but to position BizLink at the layer where these constraints consistently appear. By focusing on execution capability, engineering engagement, and global manufacturability, we aim to remain relevant across successive generations of platform transitions rather than any single technology wave.

What is changing? Value creation is increasingly moving away from discrete components toward system-level integration and execution reliability.

Suppliers capable of engaging early, qualifying globally, and executing consistently become more critical as architectures evolve. BizLink's role remains consistent: solving physical layer constraints as systems scale.

How we are executing: NPI has become a strategic capability, enabling earlier participation in architectural decisions and positioning BizLink ahead of volume ramps across HPC and capital equipment platforms. We view AI infrastructure development similarly to the evolution of a modern city.

Early phases prioritize scale, while later phases optimize efficiency and coordination within the existing footprints. Our role is to ensure the physical foundations—power, data, and connectivity—support whatever technologies follow.

As integration increases, we are preparing for higher levels of optical adoption over time by expanding our interconnect capability across both electrical and optical domains. We view this evolution not as a binary transition, but as a convergence of electrical and optical interconnect technology addressing different system constraints as architectures scale.

We do not view this as a binary transition. Electrical and optical interconnects address different constraints and are expected to coexist for an extended period as architectures evolve.

Our focus is therefore on building manufacturing and execution capability, rather than betting on a single technological outcome. AI infrastructure represents a long-term industrial upgrade cycle rather than a single demand wave.

The same interconnect disciplines underpin emerging applications such as robotics, autonomous systems, and intelligent manufacturing. We see this area as a long-dated extension built on top of today's infrastructure foundation, reinforcing the importance of disciplined execution across current platform transitions.

Now let me turn the call over to Mike.

Thank you, Felix, Florian, and Charles. This concludes our prepared statement section.

Now let us begin the Q&A section. Please type in your questions, and then we will answer as many of them as possible in the remaining time.

I want to remind everyone that there will be no forward-looking quantitative comments. Looking at the questions, Morgan Stanley's Derek, Daiwa's Helen, Fubon's Billy, BofA's Doris all asked about our outlook, growth trajectory, quarterly pacing.

It seems there's still a lot of questions about how sensitive our HPC business is, potential moderation in AI CapEx spending. There's been continued concerns about an AI bubble.

For a more sort of mid-to-longer-term outlook, especially in regards to AI CapEx sustainability and industry transition, I'll hand it over to Felix.

All right. We view the current discussion around AI capital intensity as a natural progression of the infrastructure build cycle, rather than a signal of weakening demand.

You can think of this as a transition similar to building out a transportation network. Early investments focus on adding more vehicles, while later investment shifts toward highways, traffic management, and efficiency so the system can operate reliably at scale.

Earlier phases of AI investment focus primarily on accelerating compute development. As clusters scale, the industry's priority increasingly shifts toward deployability, efficiency, and long-term operation of installed capacities.

This changes spending composition more than spending directions. Operationally, we are seeing greater emphasis on rack-level architecture, power delivery, thermal coordination, and reliable high-speed connectivity, areas that become more critical as power density rises.

Even if the rate of CapEx growth normalizes over time, infrastructure content per rack continues to increase because higher density systems require more sophisticated integration. As a result, our engagement is less tied to short-term procurement cycles and more aligned with multi-generation platform architecture.

From our perspective, the key driver is not simply total CapEx, but how AI infrastructure is being built, and that trend remains constructive.

Thank you, Felix, for a colorful outlook, as well as to the analysts that posted their questions. Now for the next set of questions talks about rack-level power, how that's becoming central.

This is coming from Nomura's Kenny, BofA's Doris, Morgan Stanley's Derek, Fubon's Billy. Why is rack-level power becoming more important, and how does this extend BizLink's opportunity?

Now for this one, since it's more operational, I'd like to hand it over to Florian.

All right, thanks. Yes, power is indeed one of the major topics for future development.

As AI workloads scale beyond the let's say traditional server environments, the rack power density is really significantly increasing, in many cases now exceeding several hundred kilowatts per rack. At those levels, power delivery is really becoming a system-level reliability constraint rather than, as I said earlier on, only a component decision.

At lower densities, the power design is similar if you compare it to wiring individual homes within a city. If you go at AI-scale rack densities, it becomes closer to, let's say, managing a power distribution for an entire city block or part of the city, where stability and also coordination matter more than any single device.

Inside individual servers, power paths are relatively localized and also maybe standardized. At a rack level, however, operators must manage significantly higher current, also higher thermal load.

Redundancy design is important, and also serviceability across the entire cluster segment is an important topic because a fault at this level can affect a large portion of the deployed compute simultaneously, and this is elevating validation and reliability requirements tremendously. The impact of an effect on this deployed compute is really serious.

Hyperscalers, therefore, increasingly define rack power architecture mainly independently from individual PSU vendors in order to maintain flexibility and also avoid single-vendor dependence. This shifts engineering responsibility toward integrated rack solutions, where, as I said, the electrical and thermal and operational considerations must be validated together.

As architectures get mature, qualified designs typically remain fixed for platform lifecycle. This dynamic increases the importance of early engineering engagement.

That is the key, I think, the early engineering engagement and also the execution afterwards, the consistency execution and structurally raising the content in infrastructure when we scale AI systems. Also as power density increases, the infrastructure reliability increasingly determines how much of your compute power is usable and is bringing into performance.

This shifts actually the value creation towards integrated rack-level solutions where engineering execution and validation become, as I said, more important than the individual component specifications on the component level.

Thank you, Florian, for providing the feedback to those questions from those analysts. For the next set of questions, again thank you to Morgan Stanley's Derek, BofA's Doris, and Daiwa's Helen for this topic on our HPC strategy and positioning.

How should investors and, of course, analysts understand our HPC strategy within AI infrastructure? This is a very big, big, big picture sort of a view.

For this one, I'd like to hand it over to our Chairman, Roger. Oh,

well. Our HPC strategy is built around enabling the continued scaling of AI infrastructure.

As AI deployment evolved from individual servers toward increasingly large accelerator clusters, the industry primary challenge are shifting away from compute performance alone and toward the physical infrastructure required to support system-level expansion. In practical terms, the limiting factors increasingly become how power is delivered into denser racks and how data moves reliably and efficiently across the growing cluster.

Accordingly, our focus is on two infrastructure layers that directly determine how fast AI systems can scale: rack-level power delivery and high-speed data connectivity. In many ways, AI infrastructure is evolving from stand-alone computers toward something closer to an electrical grid, where power delivery and data connectivity determine how much compute can actually be utilized.

On the power side, AI workloads are driving a significant increase in rack power density. As racks move toward much higher power levels, traditional data center power approach require redesign to improve efficiency, thermal performance, and operational reliability.

This transition elevated the importance of high-current power connectivity, including power whips, busbar systems, and integrated rack distribution solutions. Our long-standing experience in high-power interconnect and system integration positions us naturally within this architectural shift, where power delivery is becoming a defining element of next-generation AI infrastructure rather than a standardized supporting component.

On the data connectivity side, the majority of current industry investment is focused on scale-out architecture, where thousands of accelerators must communicate across racks within large clusters. This deployment requires solutions that balance bandwidth, power consumption, signal integrity, and serviceability at high speed at hyperscale.

Our portfolio of direct attach and active electrical cable solutions support these requirements by extending reach and maintaining performance efficiency across real-world data base environments. As clusters continue to expand, reliable short-to-mid-reach connectivity remains essential to enabling practical deployment at scale.

We also participate in scale-up connectivity environments, including PCIe-related solutions, which broaden our exposure across different system architectures and provide diversification as compute platforms continue to evolve. While scale-out deployment currently represents the largest area of infrastructure expansion, participation across multiple connectivity layers allows us to support customers throughout the system architecture.

From a financial perspective, this positioning also supports greater durability in our operating model. Engagement increasingly occurs earlier in customer platform design cycles and extend across multi-generation architectures, rather than single-product deployment.

As system complexity increases, qualification requirements, validation cycles, and execution continuity become more important, which tend to lengthen program lifecycle and improve visibility. As a result, growth is increasingly driven by expanding system content and deeper engineering participation, rather than short-term volume fluctuation, supporting more stable cash conversion and disciplined capital deployment over time.

As interconnect requirements advance, the industry is exploring multiple technology approaches to address performance and efficiency needs across different distances and use cases. Our strategy is not centered on any single transmission medium, but rather on supporting customers' evolving connectivity architectures.

Today, our disclosed activities are primarily focused on electrical interconnect solutions, where we have deep engineering and manufacturing capabilities. Recent development has expanded our capability into adjacent areas of next-generation interconnect technology, including optical connectivity solutions that complement our established electrical expertise.

These capabilities allow us to participate more broadly in evolving system architecture as customers balance performance, efficiency, and manufacturability across both electrical and optical domains. Importantly, we view electrical and optical technology as complementary rather than mutually exclusive.

AI infrastructure increasingly applies different solutions to different portions of the system, depending on distance, power efficiency, and operational considerations. By focusing on the underlying infrastructure challenge, delivering higher power density and enabling reliable high-speed data connectivity, we believe our positioning will remain relevant across technology transitions and future architectural changes.

In summary, our HPC strategy is centered on supporting the physical infrastructure layers that enable AI clusters to scale. By addressing both rack-level power and high-speed data connectivity, we participate in areas that become more critical as AI deployment grow larger, denser, and more complex, while maintaining flexibility to evolve alongside future technology developments.

Thank you, Roger. That's the bigger-picture view of HPC.

Of course, thanks to the analysts that posed those questions. Now, this is going to be a little bit more financial in nature.

Again, thanks to MasterLink's Alice, Morgan Stanley's Derrick, BofA's Doris. This is about our earnings growth mechanics and generally what drives earnings expansion as AI infrastructure scales.

For this one, I'd like to hand it over to our CFO, Charles.

Thank you. That's a good question.

If we really look closely, our earning expansion is increasingly driven by structural content growth and system complexity rather than cyclical shipment recovery. As AI infrastructure scales, high power density, tighter performance requirements, and deeper system integration are increasingly increasing the amount of data interconnect, power delivery, and engineering content required per deployed [rack?]

. The earlier computing cycles were largely unit-driven, where growth depended on a number of servers deployed.

In contrast, AI architecture requires significantly higher power density, tighter signal integrity requirements, and more integrated subsystem design. This increases the amount of data interconnect, power delivery, and engineering content required per rack.

You can think of this transition as moving from simply adding more identical machines toward upgrading the electrical and structural infrastructure inside of a building. Even if the number of buildings grows steadily, each one requires substantially more internal infrastructure to operate at higher performance levels.

As a result, our growth is influenced by three structural factors. First, content per deployment increases as power and data connectivity requirements scale.

Second, product mix shifts toward higher-value system-level solutions where reliability and engineering capability become more important than commoditized components. Third, earlier engagement in platform design allows us to participate across multiple durations of a program, improving operating leverage over time.

Therefore, while deployment volume remains important, we believe our earning progression increasingly reflects the growing technical intensity of AI infrastructure, rather than pure cyclical shipment growth. I hope that answered the question.

Yeah. Thank you, Charles.

That was our first set of financially related questions. Of course, thank you to the analysts that showed those questions.

The next one is also for you. This is from CTBC's Terry, Morgan Stanley's Derrick, and of course, MasterLink's Alice.

This question is about operating leverage from AI infrastructure scaling. Seems a summary is going to be something like how much further can operating leverage improve as AI revenue grows.

This is going to be another financially related question. Charles, please, if you may.

Oh,

yeah. Thank you.

As AI infrastructure evolves, growth dynamics are shifting away from traditional volume-driven expansion toward architecture-driven scale. Increasing compute density requires substantially higher power delivery, thermal management coordination, and system integration complexity at the rack level.

This evolution increases the amount of data interconnect and power content required per system deployed. Importantly, much of the incremental content leverages existing engineering platforms, manufacturing processes, and qualification frameworks that already developed for hyperscale customers.

In practical terms, once a platform architecture is engineered and validated, incremental deployment resembles adding additional floor to an already designed structure, rather than redesigning the foundation each time. As a result, revenue growth increasingly benefits from rising content per deployment, rather than proportional increases in operating expenses.

Engineering investment made in earlier platform durations now support multiple successive architectures, allowing incremental revenue to scale more efficiently. This creates structural operating leverage, where operating expenses grow at a slower rate than the revenue as AI infrastructure adoption expands.

From a financial perspective, this leverage reflects platform maturation and architectural progression, rather than short-term pricing or cyclical utilization changes. Over time, we expect improvement in operating efficiency to be driven primarily by deeper system participation and increasing platform reuse across customer deployment.

I hope that also answered the question.

Thanks again, Charles, for taking the second one right after the first one. Also to the analysts for those questions.

Now, let me see. The next set of questions from BofA's Doris, Nomura's Kenny, and again Morgan Stanley's Derek.

Now this is going to be on HPC's value capture within the AI stack. In fact, we have received lots of questions around AI.

As AI systems scale, I guess what the analysts are trying to ask about is where's the value increasingly being created within the infrastructure stack. This is going to be another bigger-picture question, I'd like to hand over the floor to Roger.

All right. As AI systems scale, value creation within the infrastructure stack increasingly shifts toward components that enable reliable operation of high-density compute environments.

While advances in semiconductors drive computational capability, practical deployment of this system depends on power delivery, signal integrity, and system integration layer that ensure stable performance at scale. Higher compute density increases electrical load, thermal sensitivity, and data connectivity complexity, which elevate the importance of infrastructure solutions that manage power distribution and data interconnection reliability across entire rack architectures.

In many ways, this is similar to large transportation networks where long-term value ultimately concentrates in the infrastructure that keep the system operating reliably, such as highway traffic coordination and energy distribution, rather than in any individual vehicle moving through the network. BizLink participates in this portion of the value chain through solutions that support both power and data connectivity at the system level.

As architectures evolve toward higher density and greater integration, the relative importance of these enabling layers increase alongside compute performance. Rather than depending solely on unit shipment growth, value expansion increasingly comes from high content intensity per system and deeper integration into platform architecture.

This positions the company to benefit from continuous scaling of AI infrastructure, even as system design evolves across multiple technology generations. In this context, our role increasingly aligns with enabling infrastructure scalability, rather than supplying discrete components.

Thank you, Roger. Another bigger-picture question from the analysts and for the audience today.

For the next one, lots of questions around copper and optics lately. Then, of course, thank you to E.SUN's Allen, Nomura's Kenny, JPMorgan's Bill, SinoPac's Vicky, and CTBC's Terry.

Putting a lot of these questions together and talking about copper and optics architecture expansion, how should everyone think about copper versus optical interconnects over the longer term? We touched on this in the remarks during Florian's section, I'd like to hand it over to Florian.

Yes, thanks. This is an ongoing discussion since several years, and this is also in the community very controversially discussed also in the last couple of weeks and months.

We do not actually view that as a binary, let's call it binary transition. It is less in our view like replacing one technology with another.

It's like you can imagine a transportation system. You have streets, you have railways, you have airplanes, and you need all of them because you have different distances which you need to bridge.

Each of those transportation systems is optimized for different distances, efficiency requirements, and so on. The same is as AI infrastructure scales.

Optical adoption is increasingly driven by system architecture requirements rather than isolated component substitution, which expands overall interconnect complexity rather than reducing the electrical connectivity. Electrical and optical interconnects address different engineering constraints within this AI system architecture, particularly when it comes to distance and power efficiency, but also latency and serviceability.

Copper-based active electrical solutions remain highly efficient, especially for shorter reach, high-density environments where reliability and integration and also power efficiency are the more critical factors. On the other side, optical technologies become increasingly important as bandwidth requirements and also transmission distances expand and go up.

What we are observing is not so much a substitution, but an architectural expansion. This architectural expansion also introduces additional connectivity and distribution layers within the rack environment to support, what I said, the serviceability, modular replacement, or operational reliability as systems scale increases.

As systems scale, overall the complexity of interconnect systems increases because designers optimize different parts of the system for different performance requirements. Having said that, in addition, as integration moves closer to high-power ASICs, manufacturability and also thermal coordination and yield management become even more critical challenges.

Our focus is therefore on building manufacturing and engineering readiness that allows us to support evolving architectures rather than relying on single technology outcomes. That's also what we heard earlier on.

Hope that answers the questions, and back to you, Mike.

Florian, thank you for that. Again, the whole debate on copper and optics is something that we believe is to be a binary transition.

For the next set of questions, again we did touch on this, high-voltage DC. Quite a few questions.

Again, thanks to Derek, Doris, Kenny, Billy, and I think I see some questions also from the audience there. This will capture some of that as well.

Why is the industry moving toward HVDC, and what does that mean for copper interconnects? This is going to be another operational topic.

Florian, if you don't mind.

Yes, thanks. One is the optics versus copper discussion, and the other one is the HVDC discussion.

Yes, the transition towards higher-voltage power architecture is primarily driven by physics, not so much by the technology preference. It's just, well, it's physics.

We are reaching now power densities that traditional low-voltage data centers were not designed for, and were also never intended to support. As now the GPU clusters scale, rack power requirements are moving from former tens of kilowatts historically now towards hundreds of kilowatts per rack, and this is really a tremendous jump.

At these levels, electrical efficiency and also, as I already said, the thermal management become the primary constraint on scaling, rather than the compute performance or the networking bandwidth. Delivering the power at low voltage is something similar as trying to move large volumes of water through many small pipes.

Flow becomes inefficient, and heat losses increase, and so on. Higher voltage allows the same energy to move more efficiently through smaller currents, improving scalability.

Physics says that power losses increase with the square of current, meaning that delivering large amounts of power at low voltage requires extremely high current. High current introduces excessive heat generation, larger conductors, reduced efficiency, and also increased reliability risks.

When we now look at the scale of the AI systems, the industry is therefore moving toward or needs to move toward higher-voltage power distribution architectures. In practice, some cloud service providers are adopting a plus-minus 400V HVDC, high-voltage direct current designs, while others are moving toward 800V HVDC.

That's reflecting just different platform architectures. Both approaches significantly reduce the current levels, they are then getting to the goal that they improve efficiency, they lower the thermal stress, and also enable a safer and more scalable power delivery across the whole dense AI deployment.

In this sense, higher-voltage HVDC distribution is not simply an upgrade. It is becoming a physical requirement more for continued scaling of AI infrastructure.

A common market assumption, by the way, is that higher voltage automatically accelerates optical adoption and reduces copper usage. In practice, these are solving different engineering problems, and HVDC addresses power delivery efficiency, while interconnect technology choices are determined primarily by distance, latency, as I said, and power consumption, and serviceability requirements.

In fact, improved power architecture can enhance even operating conditions for electrical interconnects. Higher-voltage distribution improves, for example, grounding stability, reduces electrical noise, and also lowers thermal stress within the rack environment.

These are all positive effects to improve the signal integrity, allowing short-reach electrical connections to remain efficient and reliable even when data rate increases. AI system design is increasingly concentrating on compute into tightly integrated rack environments where connection distances are extremely short.

Within these ultra-short reaches, electrical interconnects will still remain highly attractive because they provide, as I said already, lower latency, lower power consumption, simpler serviceability compared with optical solutions. As a result, HVDC trends to push optical solutions outward toward longer-distance connectivity, while preserving and, in some cases, even reinforcing the copper's role inside racks and near-rack domains.

Structurally, this reflects a broader industry transition from in the past more compute-limited environment toward now an energy-limited environment. Constraint is energy, and as power delivery improves, system density increases, which drives more localized connectivity and greater overall interconnect complexity.

The likely outcome is not replacement, but a hybrid architecture. That's how we see it, in which both electrical and optical technologies coexist, each optimized, as I said, for different distances and efficiency requirements.

In that environment, electrical interconnects continue to play a critical role in enabling scalable and also energy-efficient AI deployment. I hope this answers the questions.

Back to you, Mike.

Thank you, Florian. We still have a lot of questions, but I think we'll try to address something on AEC.

From the audience, "Your largest AEC customer guided to 50% year-on-year growth for its fiscal 2027. Do we have enough capacity to meet the majority of this increase in demand?"

I'd like to hand this over to Felix. Again, this is the last question that we will have time for today.

Yeah. Actually, we are actually seeing quite a lot of pulling and also increasing demands from our customers.

For the past year or two, we're actually preparing to meet our customers' requirements. This is not a surprise to us.

Actually, there are a couple of locations we are increasing our capacity already in progress. Yes, we do have enough capacity to meet the increasing demands, and it's not just in AEC, but in multiple sectors.

Thank you, Felix, for that last question, also to the investor that posted that question. Again, thank you, Roger, Felix, Florian, and Charles.

This concludes our Q&A section. A replay of the conference call today will be available on our IR website within 24 hours from now.

If you have any further questions, please feel free to reach out to BizLink Investor Relations team. That would be, of course, me as well as Jimmy.

We thank you very much for joining today's call. You may now disconnect.