This piece does not outline an actionable “high-conviction” idea, but instead explores what is happening in the optics sector and the key trends to watch.

We partnered with Research on this piece to bring an engineering perspective that complements our investment research.

He earned his PhD in South Korea and currently works as a semiconductor engineer in Silicon Valley, with experience across chip design, testing, advanced packaging, and yield. His engineering background brings a perspective you won’t typically find in traditional sell-side research, particularly as we look at semiconductors, AI infrastructure, and optics.

Table of Contents

1. Damnang’s Take on Optics: The Road Ahead

2. Fabrinet: Optical Manufacturing
   2.1 The Business

   2.2 In Conversation with Management
   2.3 Our Take on Fabrinet

   2.4 Damnang’s Take

3. Ciena: Optical Networking

   3.1 The Business
   3.2 In Conversation with Management
   3.3 Our Take on Ciena

   3.4 Damnang’s Take

4. Our Final Thoughts

1. Damnang’s Take on Optics: The Road Ahead

When analyzing AI infrastructure, attention tends to go to GPU performance and to memory capacity and bandwidth. Adding compute alone does not make a larger system, though.

The resources have to exchange data, and as the system grows, the network that connects them has to grow with it. I focus on optics because AI is changing not only how fast connections must be but also how far they must reach.

This change appears both inside and outside the data center. Inside, larger AI clusters need high-speed optical products to tie them together, and outside, infrastructure spread across multiple facilities needs bandwidth to connect it.

As AI models grow, the compute they need has become hard to fit within a single data center’s power, space, and cooling, so scale-across links that connect multiple data centers as one system are increasing.

The reason this article analyzes Fabrinet and Ciena is to see the growth of the same industry through different business models. Companies across the optical supply chain all belong to the same ecosystem, but they earn money in different ways. Fabrinet manufactures, on contract, the optical technologies its customers choose, so I check whether its work on new technologies turns into new program wins, higher production volumes, a wider customer base, and better margins.

Ciena is an optical networking systems company for which cloud and AI data center connections have become major sources of demand, so I check whether its technical strengths turn into market share and pricing power and whether its supply chain is ready to turn growing orders into revenue. The two companies also do business with each other, yet from the same demand a manufacturer and a systems company can earn different profits. That is why this piece analyzes them together without grouping them into a single “optical beneficiary” category.

From an engineer’s perspective, I check whether technology turns into customer adoption and reliable volume production, and this piece adds Aurelion’s company analysis and conversations with management to examine the conditions under which optical demand turns into business results.

Summary: We came away from our work on both Fabrinet (FN) and Ciena (CIEN) with a positive view of the businesses and the opportunities ahead. That said, we are not adding either company to the Aurelion Index at this time. We explain our thinking throughout the piece and why both companies are on our shortlist.

2. Fabrinet: Optical Manufacturing

The first company we look at is Fabrinet, which operates on the manufacturing side of optics, producing complex optical components used in data centers and communications equipment. This is a very different business from Ciena, which sits on the networking side. We’ll get into Ciena later in the report.

2.1 The Business

Fabrinet is a contract manufacturer that specializes in optical and electronic products. It makes things like optical components, lasers, and sensors that are used in data centers and communications networks. The company focuses on products that are difficult to manufacture and require specialized expertise.

This has helped Fabrinet build long-term relationships with major technology companies, while maintaining strong margins and a solid net cash position.

Its customers include Lumentum, Infinera, Acacia, Cisco, Intel, and NeoPhotonics. NVIDIA has also become a growing customer.

Fabrinet is headquartered in Thailand and has manufacturing facilities across several locations, including the United States, China, Malaysia, and Thailand.

Fabrinet: Geographic Footprint

Investment Thesis

Fabrinet benefits from the growing demand for optical technology as AI and data centers expand. The company makes the hardware that helps move large amounts of data between GPUs, servers, and data centers.

One of the bigger opportunities is NVIDIA. Fabrinet makes optical products for NVIDIA, including high-speed transceivers. As AI systems grow larger and more connected, we expect demand for these products to increase.

At the same time, Fabrinet has been adding other large customers, making its business more diversified. This allows NVIDIA to grow as a customer without becoming an even bigger concentration risk for Fabrinet.

FN is also building direct relationships with large technology companies, including Amazon, Cisco, and Ciena. This gives the company more opportunities to grow while also reducing its reliance on a few customers.

Its communications business is also starting to recover after several weak years, creating another potential source of growth. Changes in U.S. trade policy could provide another benefit. At the same time, Fabrinet is expanding its production capacity and preparing for newer technologies such as co-packaged optics.

Ciena could be the next major growth driver.

Fabrinet’s expanded relationship with Ciena started ramping in early 2026, just as Ciena’s optical business began to accelerate. We expect this relationship to become increasingly important and believe Ciena could account for more than 10% of its revenue over the next few quarters.

AWS and Trainium

Fabrinet is also expanding into compute. Amazon became an 11% customer in FY2026, alongside Cisco at 20%, NVIDIA at 16%, and Nokia at 11%. This shows how much more diversified Fabrinet’s customer base has become.

We think much of this growth is tied to Amazon’s Trainium chips. Fabrinet has already added production lines to support the business, and we see further upside as Trainium expands and AWS develops more of its own optical hardware.

Growth Is Accelerating

Fabrinet has grown at 15% annually over the past decade, with growth accelerating to 36% in 2026 as AI and hyperscaler spending picks up.

The key question is whether this growth can continue beyond 2026.

For us, the main things to watch are the ramp of Ciena and AWS, continued growth with NVIDIA, and whether Fabrinet can keep winning new programs with large technology companies. If these relationships continue to scale, the current growth rate could prove more durable than the market expects.

Another concern is that gross and operating margins have stayed around 11–13% since 2022. Despite the strong AI-driven growth, Fabrinet has yet to see much operating leverage. This raises a question about how much of the revenue growth will actually flow through to earnings. If margins remain stuck in the same range, the upside from faster growth could be more limited than it first appears.

Methodology: Aurelion Research independently reaches out to company management teams and does not receive compensation from any companies covered. Interview responses are based on our notes and have been edited for clarity, so they may not reflect the speakers’ exact words. We selected the sections we found most relevant.

All information discussed is believed to be publicly available. This content is for informational purposes only and does not constitute investment advice or a recommendation to buy or sell any security.

2.2 In Conversation with Management

We recently had a conversation with Garo Toomajanian, Fabrinet’s VP of Investor Relations. Garo is an electrical engineer and previously covered semiconductors in equity research at a major bank, so he has a strong understanding of both the industry and the technology.

We started by asking why Fabrinet’s share price has come down so much recently. He pointed to FN’s historical exposure to communications, while noting that the company is expanding into other areas where its manufacturing expertise can be valuable. In his view, the opportunity is to focus on a few complex areas where Fabrinet can build an edge rather than trying to do everything.

He also highlighted the strength of Fabrinet’s customer relationships, which tend to be long-term and difficult to replace. From his perspective, the recent pressure is more related to costs and pricing than a fundamental change in the business.

So they clearly want to avoid staying too focused on an “old business segment” like communications, but they will need to prove it before the market trusts them and sees a clear path to more growth and less risk.

Customer Diversification

NVIDIA has been Fabrinet’s largest customer in recent years, but like we mentioned earlier, the mix is changing as the company builds more direct relationships with hyperscalers and other large technology companies.

Management was clear that it wants to avoid becoming too dependent on any single customer, while still seeing significant room to grow with NVIDIA.

FN is now getting new big customers like Amazon, Cisco, and Lumentum, but it is still reliant on a few large customers. This gives it a relatively sticky business as a manufacturer for the “big guys.” Management also pointed out that FN has the advantage of waiting for customers to decide what they want produced, without having to spend heavily on products that may never get produced.

At the same time, we think this means FN is still dependent on its customers placing orders, which gives the company less control over its own growth.

Another thing that stood out in the conversation was how early Fabrinet has been in some of the newer optical technologies. The company invested in silicon photonics roughly a decade ago and is now working on co-packaged optics.

We also asked him where he thinks we are in the broader optical cycle.

He did not want to say too much here. While the tone was optimistic, he did not seem as confident as we expected. That stood out to us, especially given how strong the AI and data-center demand story has been. This somewhat confirms what we thought: there is still uncertainty around how strong and long-lasting the optical cycle will be, which may be one reason investors have stayed away.

2.3 Our Take on Fabrinet

There are plenty of things that could happen with Fabrinet over the next few years. But when we step back, the story seems fairly simple: it looks like an undervalued company in a growing optical market.

At 21.9x NTM P/E, the valuation is reasonable, but we want to see better margins and more evidence of sustained earnings growth before getting more confident. For now, we think it makes sense to wait and see how these opportunities develop.

We also know Fabrinet is unlikely to get the same excitement or valuation as memory stocks like SK Hynix or SanDisk. Optics are becoming more important, but Fabrinet is still a manufacturing business, which also plays a role in how the market values the company.

For us, the key question is how much of today’s interest will turn into new programs, larger customers, and higher production volumes. We still do not have a clear answer, even after speaking with two of the largest optics companies in the world and doing our own work on the industry. We will have to wait for the next few quarters to get a better sense as these opportunities start to ramp.

2.4 Damnang’s Take

Fabrinet’s investment case examined above comes down to a wider customer base, rising optical demand, and readiness for new technology.

The technical question is how these opportunities connect to the company’s manufacturing capability. We should look separately at its ability to produce existing products reliably, maintain profitability, and secure a manufacturing role in the next generation of products.

Why Fabrinet’s Customer Relationships Last

The optical modules Fabrinet builds are finished only after fiber and optical components are aligned to within a fraction of a micron, and the assembled module’s optical performance is verified. A coherent pluggable for DCI, which links data centers, also has to fit a tunable laser, a modulator and receiver, and a DSP that processes the signal into one module, which makes the manufacturing process far more complex.

For products like these, the production process itself goes through customer qualification, so switching manufacturers means re-checking performance and reliability on the new process and stabilizing yield again.

That makes optical manufacturing a business in which customers cannot easily change manufacturers, and I think this gives Fabrinet, whose processes have already passed qualification with many customers, a technical moat.

Manufacturing Strength and Pricing Power Are Different

Making hard-to-make products well does not bring pricing power by itself, however. Bargaining power instead tends to arise at the supplier of a key component when that component is scarce. The laser, which I have emphasized many times in my articles, is exactly that kind of component.

So when looking at Fabrinet, a larger share of high-speed products should not be read as a structure in which margins automatically rise; what matters is how much margin it can capture from the extra alignment, assembly, and test work in new programs and from fewer defects as production stabilizes.

Fabrinet’s Role in NPO and CPO Manufacturing

Optics is also moving toward the chip in two forms:

(1) Near-packaged optics (NPO), which puts the optical engine as a separate module on the board right next to the chip, and (2) co-packaged optics (CPO), which puts it inside the same package as the switch chip.

Rather than going straight to CPO, which is harder to test and produce in volume, the industry is trying to use NPO as much as possible, and I think the current direction gives Fabrinet a bigger opportunity. That is because an NPO engine is built and tested on its own before it goes onto the board, which is closer to the module factory model Fabrinet does well.

In particular, as engines scale to 6.4T and 12.8T, I expect the ability to handle the rising manufacturing complexity and secure yield to become a technical advantage for FN. In short, I think the longer the NPO period lasts, the better the structure is for Fabrinet.

In CPO, by contrast, the optical engine is packaged together with the switch chip on a semiconductor packaging line, and the lasers move out to separate external laser source (ELS) modules, so the assembly, alignment, and testing a module factory did in one place are split among several companies.

NVIDIA listed Foxconn and Fabrinet in a 2025 technical blog as assembly and test partners for its CPO switches, but Fabrinet was not in the manufacturing chain for the 2026 production switch shown below.

The figure shows one NVIDIA design, and Fabrinet says it is already building CPO devices for a handful of customers, although not yet at full-scale volumes.

So I do not read one list as Fabrinet being pushed out of CPO for now, but it is clear that Fabrinet also needs to prepare for the CPO era. In CPO, the steps closest to the module factory work Fabrinet has done are ELS module assembly, engine testing before packaging, and fiber attach.

What Fabrinet needs now is likely wafer-level packaging, the process needed to make the optical engine itself, which builds wiring and connection points onto chips before they are cut from the wafer. This is a semiconductor process different from what module factories have done. To fill this gap, Fabrinet bought about a 14% stake in Raytek Semiconductor, a Taiwanese wafer-level packaging company, for about $32M in April 2026, and has said it plans to put this process in place in Thailand in its own lines or together with Raytek.

Fabrinet’s Manufacturing Role in Scale-Across

Scale-across routes have started to see the first orders for multi-rail systems, which lay many fiber pairs along one route. A multi-rail system packages and manages those fiber pairs as one optical system, so each deployment needs more assembly steps. Fabrinet names the dense fiber routing, fusion splicing, which joins fiber ends by melting them together, and connectorization these systems need as its strengths, and is working with several customers on multi-rail programs. This is the new work scale-across creates for Fabrinet.

In the end, I think Fabrinet’s next step depends on turning new manufacturing roles in NPO, CPO, and scale-across into real volume. It will be necessary to confirm which steps Fabrinet handles in NPO, CPO, and ELS production programs, whether multi-rail programs show up in revenue, and whether that work turns into profit.

3. Ciena: Optical Networking

The second one we look at is Ciena, which operates on the networking side of optics, building the systems that move massive amounts of data across fiber networks.

3.1 The Business

Ciena helps companies move large amounts of data through fiber-optic networks. Its equipment is used by telecom companies, cloud providers, and large businesses to connect data centers and cities.

Ciena is one of the major players in optical networking and competes with companies such as Nokia, Huawei, and Cisco. It is also expanding its presence in data centers as cloud and AI demand grows.

Investment Thesis:

We see AI as one of the main drivers of increasing demand for Ciena’s products as data centers become more connected. As computing continues to grow faster than many expect, more data will need to move between locations, driving demand for faster and higher-capacity networks.

Global Presence: Ciena has manufacturing and supply chain operations across North America and Asia, giving it the ability to support customers worldwide.

Ciena: Geographic Footprint

Revenue & Growth Outlook

Management expects Ciena to grow revenue at 30% annually from 2026 to 2029. That is a big target, especially after the recent acceleration.

The other part of the story is margins: Ciena is targeting a 50% adjusted gross margin and a 20% free cash flow margin by 2029. If both growth and margins move in that direction, the earnings and cash flow growth could be substantial.

Over 50% of Ciena’s revenue now comes from direct cloud customers. The rest is spread across service providers, cable companies, and government customers, giving it exposure to several different sources of networking demand.

Ciena has a strong position in optical line systems. The company has continued to gain share in this market, supported by its product roadmap and execution.

At the same time, Ciena has continued to grow revenue while gaining market share, which says a lot about the strength of its position in the market.

3.2 In Conversation with Management

We recently had a conversation with Gregg Lampf, Ciena’s VP of Investor Relations. Management expects data centers to become a larger part of Ciena’s business as AI infrastructure expands.

For us, the more interesting part is what this could do to the quality of Ciena’s revenue. Data-center customers typically require much higher-capacity equipment and are investing heavily in network upgrades. If the company is able to capture more of this spending, which management believes it will, we see this as something that could shift the business toward larger, higher-value programs rather than simply adding more volume to its existing customer base.

Our conversation also changed how we think about Ciena.

Even if we know it is no longer just a traditional telecom equipment maker, part of the market still seems to see it that way. If we, as generalists, are starting to see Ciena differently, maybe the rest of the market will too.

If Ciena is increasingly seen as an optical infrastructure company tied to the future of AI and data centers, we think this could help the growth story.

TAM & Market Share

We asked about Ciena’s TAM and market share.

No surprise here, since it is already in their presentation: management sees its addressable market nearly doubling from $27B today to $52B by 2029, with much of that expansion coming from new data-center opportunities.

What caught our attention is that Ciena has consistently gained market share in a fragmented and competitive market. Management credits its product roadmap and execution. If Ciena can keep taking share as the market expands, the upside could be much greater than simply growing with the market.

Supply Chain

Ciena recently appointed a new supply chain leader who reports directly to the CEO. They expect this to help the company handle higher demand and growing data-center volumes. We think it’s a smart and needed move, but Ciena is still relatively small within the broader optical supply chain. They will need to scale and move into larger, more demanding programs. Having the demand is one thing, but securing the components and capacity to deliver on time is another.

What About Pricing?

That’s one of our concerns, so we asked them about it.

Ciena has been raising prices since last year and continues to push for higher pricing with customers. Contract terms have also become less flexible, with no return or refund provisions. This suggests Ciena has some pricing power, which is encouraging, but we still need to see how long demand can stay this strong.

For now, demand is there, but Ciena was not very specific on how long it expects this to continue, even though we would have liked more clarity here.

3.3 Our Take on Ciena

Overall, we see Ciena as the more interesting setup of the two in the near term, with more direct exposure to the AI buildout and continued market share gains. That said, the main issue here is valuation.

At 35.7x NTM P/E, the market is already giving Ciena a lot of credit for the growth ahead. We like the direction of the business, but we would rather wait for either a better entry point or more evidence that the growth can continue.

3.4 Damnang’s Take

When I look at Ciena as an AI infrastructure company, I think what matters more than the share of revenue from cloud customers is how Ciena’s products and technology solve the technical limits customers run into as they connect more data centers. When that technology lowers a customer’s actual investment burden, the share gains and pricing power shown above will have a lasting basis.

Modems: Carrying More Data on One Fiber

Links between data centers carry data on a single fiber by splitting it across several colors of light, or wavelengths. Ciena’s core technology is WaveLogic, the coherent modem that puts data onto this light and reads it back.

The latest WaveLogic 6 Extreme carries 1.6T on a single wavelength, while the current products from competitors Cisco (Acacia) and Nokia are in the 1.2T class.

The more data the same fiber can carry, the less equipment and power customers need. Even when a hyperscaler designs its own equipment, Ciena has won a deal to supply WaveLogic 6 as a module. Competitors’ 1.6T products are due around 2027, though, so I see the modem lead as roughly one generation.

Line Systems: Letting Customers Use More Fiber

Because one fiber can only carry so much, capacity between data centers ultimately grows by using more fiber. Over long distances, weakened light has to be boosted at amplifier huts roughly every 80 km, and once fiber counts reach the hundreds, the huts run out of space and power first.

Ciena’s Hyper-Rail is a line system that raises the number of fiber pairs one rack can amplify from 4 to as many as 128. In Ciena’s example of a 20 Pb/s route, the number of huts needed at each amplifier site falls from 22 to one.

Amplifiers boost light using energy from pump lasers, so more fiber also means more pump lasers. This is the background to Ciena naming pump lasers first among its supply constraints and signing supply agreements through 2029.

Cisco and Nokia have announced similar multi-rail products, and Nokia cites 160 pairs per rack, but Ciena is the one that co-designed its system with hyperscalers and won the industry’s first order.

Extending Into Data Centers

The inside of the data center is a new market for Ciena. Ciena expects coherent technology to be used inside data centers from the 3.2T generation, and through the Nubis acquisition, it gained Nitro, a chip that boosts signals in copper cables inside the rack, and Vesta, a 6.4T CPO optical engine. Vesta has sample orders, with revenue expected from 2027. Competition in this market is only starting, so the point of judgment will come when customers actually adopt these products.

Technical Edge and Moat

In short, I think Ciena’s technical edge is clearest in modems and line systems for long-distance, high-capacity links. The modem gap narrows with each generation, and competitors are catching up on, or even exceeding, line system density. However, a route often runs on one company’s line system, and as shown above, Ciena has the largest installed base in this market.

So I see Ciena’s moat less in individual product specifications than in getting into cloud customers’ routes first as the standard. Whether hyperscalers standardize on Hyper-Rail by the end of 2026 will be the test.

4. Our Final Thoughts

There is no doubt that optics is becoming an increasingly important part of AI infrastructure as more data needs to move between chips, servers, and data centers. Fabrinet and Ciena give us two different ways to play this trend.

Fabrinet is focused on manufacturing the optical hardware, while Ciena builds the networks that move the data. Both are benefiting from growing demand and increasing investment across the data center ecosystem.

After speaking with both IR teams, we came away impressed by both businesses and think the opportunity in optics is still in its early stages. However, we still don’t have enough visibility on how quickly demand will translate into sustained growth, so we are not ready to take a position yet.

Valuation is the main reason. Ciena trades at around 35.7x NTM P/E, while Fabrinet is cheaper at 21.9x, but both still look quite expensive to us.

The other issue is timing. We like both companies, but we need more visibility on how the market will value their changing growth profiles. For now, we’ll wait for a clearer setup before adding either to the Index and continue to follow the optical cycle closely.

We’ll continue to follow these companies closely as the industry evolves.

Damnang and Aurelion Research

Disclosures & Methodology

All forecasts and estimates are based on publicly available information, management commentary, and our own modeling assumptions. This report reflects independent analysis by Aurelion Research and/or Damnang Research. All opinions and estimates expressed herein reflect the author’s judgment as of the date of publication and are subject to change without notice. This publication is for informational and educational purposes only and does not constitute investment advice, a solicitation, or a recommendation to buy or sell any security. Aurelion Research, Damnang Research, and its analysts may or may not hold positions in securities discussed. Use only where legally permitted.

We took the time to meet with Andrew Kramer, Treasurer and VP of Investor Relations. That helped us understand what the opportunity looks like today, what the large shareholders are doing, and how management thinks about growth.

While our entry point was wrong, we like the stock at current levels and would buy it today if we did not already own it. We view the valuation as very attractive relative to the quality and growth of the company.

Note that Cellebrite generates a significant amount of cash flow, converting more than 110% of EBITDA into free cash flow, driven by favorable working capital.

That means the company trades at only 12.5x the free cash flow it generates. This type of multiple is usually reserved for highly capital-intensive companies or businesses with little to no growth.

Despite the recent reset in expectations, we view Cellebrite as a high-quality company that can grow earnings by 15% or more for several years. The new guidance framework and execution over the next quarter could provide the foundation for a strong turnaround.

Table of Contents

1. Company & Thesis Recap

2. Q2 2026 Earnings

3. The Point on SaaS

4. Potential Acquisition Target

5. Our Discussion with Cellebrite

6. The Road Ahead

1. Company & Thesis Recap

Read more

The CEOs of the three largest AI companies called for a slower pace of AI development. Markets initially reacted negatively but recovered quickly.

We are not suggesting that the market has peaked. We remain optimistic and expect markets to perform well over time, although short-term downturns are becoming more likely. Earnings growth across U.S. companies is still very strong, and large investments in AI technology are likely to continue.

What has changed is the number of things that can go wrong at once. The AI trade, higher inflation, and the war in Iran are now three separate sources of risk, although inflation and the Iran conflict remain somewhat linked.

The bond market has started to price this in.

The U.S. 10-year crossed 5% today, and the odds of a September hike have increased to roughly 80%, from 30% last month.

Given that short-term risks have increased, we think this is the right time to own companies that are largely uncorrelated with them. This company has a beta of -0.01, meaning its daily movements have almost no correlation with the S&P 500.

The company we are adding today cleared our process on its own merits.

We see a high-growth business (~20% earnings growth) benefiting from the aging U.S. population, operating in a stable and profitable industry that is growing faster than usual due to a supply shortage expected to persist for years.

The company remains relatively unknown and, in our view, undervalued given the growth ahead.

We also like the timing, with earnings starting to inflect and the next quarter looking particularly strong. Management is actively pursuing very accretive acquisitions, while the balance sheet is in good shape. We like the setup and see a clear opportunity for the market to catch up as earnings continue to improve.

Table of Contents

1. Why Now?

2. Company Overview

3. Investment Thesis

4. Market Dynamics: The Widening Supply Gap

5. Valuation & Forecasts

6. Management & Governance

7. What Could Go Wrong

8. Our Final Thoughts

Read more

Our recent post-earnings discussion with NVIDIA a week ago gave us a closer look at where the company is heading and what will drive the next phase of the AI cycle.

Because NVIDIA touches so many parts of the AI ecosystem, the conversation also gave us useful insights into several areas of the industry:

• Memory. AI chips rely on a special type of high-speed memory that is in short supply. Prices have risen sharply, and we expect them to stay high into 2027–28, which is good news for the companies that make it.

• Inference. Most AI spending so far went into building models. The next wave comes from people and businesses using them, which will require more servers and power than expected.

• Robotics. Robots are starting to move from demos into real factories. The companies supplying the computing power and automation tools can benefit no matter which robot maker comes out ahead.

NVIDIA has lagged because of concerns that AI spending may not generate sufficient returns, but we do not think the stock deserves to remain a laggard.

We believe inference will drive NVIDIA’s next growth phase. Growing agentic workloads create a significant new source of compute demand.

In this piece, we will explore why NVIDIA sits at the heart of the AI ecosystem, the exponential growth opportunities ahead, and what is driving hyperscaler spending. We will also look at the memory bottleneck and the next areas of growth across inference, agentic AI, robotics, and physical AI.

Methodology: Regarding our conversation with NVIDIA’s Investor Relations team, we were not granted permission to expressly quote or identify the individuals with whom we spoke. Our analysis below was informed by this discussion, but all views and analysis expressed are those of Aurelion Research and are based on publicly disclosed information.

Nothing in this report should be construed as a direct quotation from, endorsement by, or representation of NVIDIA or its views. Aurelion Research does not receive compensation from the companies it covers.

Table of Contents

1. Why We Are Bullish on NVIDIA

   1.1 NVIDIA Is the Heart of the AI Ecosystem

   1.2 Exponential Growth Opportunities

   1.3 Hyperscaler Spending Is Durable

2. AI Computing & Infrastructure
   2.1 The Race for Compute
   2.2 How Long Can the Memory Bottleneck Last?

3. The Rise of Inference

4. The Agentic AI Opportunity

5. Physical AI: Where Are the Robots?

6. Key Risks

7. Our Final Thoughts

AI Terms Glossary

• Training: Teaching an AI model using large amounts of data.

• Inference: Using a trained AI model to produce an answer or complete a task. Every time someone asks a chatbot a question, that is inference.

• Tokens: Units of text AI processes or generates, used to measure AI usage.

• Agentic AI: AI that completes tasks and uses tools (agents).

• Physical AI: AI operating in the real world through robots, vehicles, and machines.

• Accelerated computing: Using GPUs alongside or instead of traditional CPUs to run AI faster.

• AI factories: Data centers built to train and run AI models.

1. Why We Are Bullish on NVIDIA

Our discussion with the company reinforced our view that the next phase of the AI chip race will be about making intelligence cheaper to use.

As inference becomes a larger part of AI workloads, the companies that can deliver more intelligence per dollar of compute will have the biggest opportunity. We believe NVIDIA is best positioned to lead this shift.

The AI infrastructure opportunity is expanding in two important ways: there are more applications for AI, and there are more customers spending on it.

The first phase of the AI cycle was largely driven by a handful of hyperscalers building massive amounts of infrastructure, with AI-related infrastructure commitments expected to exceed $3.1 trillion. That is still happening and continues to grow, but the next phase will be much broader, with spending coming from enterprises, AI-native companies, and cloud providers.

In the past, NVIDIA was obviously an attractive company, but we felt the story was still heavily driven by its chips and a relatively concentrated group of hyperscaler customers. Today, we see a much wider opportunity.

Demand is still being driven by hyperscalers, but our recent discussion with NVIDIA’s Investor Relations team reinforced our view that the customer base is expanding well beyond the largest cloud providers. We see this broader demand as an important source of growth for NVIDIA in the years ahead.

1.1 NVIDIA Is the Heart of the AI Ecosystem

The next AI phase will be less concentrated, with spending spreading across a much wider range of companies and applications. This should create new sources of compute demand beyond the infrastructure buildout we have seen so far.

The key with NVIDIA is that the market often seems to be questioning whether the change happening right now is sustainable. If it isn’t, the concern is that the large investments being made in AI infrastructure may not generate the returns expected, or that some of that capacity could ultimately go unused.

What stands out from our conversation with NVIDIA is how broad the platform has become. Its exposure across so many parts of the value chain means that even smaller opportunities can add meaningfully to growth.

The other side of that is execution risk. It is difficult to be the best at everything, and NVIDIA is expanding into more areas where it will face strong competition. If it cannot maintain a leading position across the different workloads and markets it is targeting, that broad exposure could become a problem.

We think this is one of the reasons NVIDIA has lagged in 2026.

It has been one of the biggest under-performers among U.S. chip stocks, while Micron, Marvell, Intel, and AMD have all outperformed. The performance gap is substantial, but the market is already pricing in many of these concerns.

As we said earlier, we think those concerns are overdone. The disconnect between NVIDIA’s fundamentals and its stock performance is where we see the opportunity. The company continues to grow at an exceptional pace, generate significant free cash flow, and expand into new areas of AI computing.

The key for us is that NVIDIA has several years of growth ahead, while the market is already debating when that growth will slow. If AI infrastructure spending continues to rise, we see a clear path to much higher earnings and cash flow from here. That is what makes the current setup so attractive to us.

1.2 Exponential Growth Opportunities

NVIDIA needs to sustain a very high rate of growth, and we think it can. Valuation is one of the biggest challenges in AI today, with some companies trading at 50x or even 100x earnings despite generating little or no free cash flow. We find those valuations difficult to justify. NVIDIA looks very different.

At 18.1x NTM P/E and 14.8x NTM EV/EBITDA, the valuation looks reasonable given its growth, profitability, and cash generation. Management will rarely say outright that its shares are cheap, but NVIDIA’s aggressive buyback activity suggests that it sees its stock as an attractive use of capital at current levels.

But where will the growth come from? That is the key debate around NVIDIA today. Some investors believe it can continue growing at the pace needed to support a higher valuation, while others think growth will slow much sooner.

In a recent earnings call, NVIDIA’s CFO said the company expects revenue to grow by 70% in FY2028, adding that this is a supply-constrained outlook.

She also highlighted the pressure from memory costs, describing the current environment as ‘‘extreme pricing conditions in memory.’’

Both comments point to the same thing: demand for NVIDIA’s products remains ahead of available supply. NVIDIA expects these supply constraints to persist through at least the end of FY2028, which should continue to support strong demand for its computing products as more capacity comes online.

What makes NVIDIA particularly interesting to us is the number of different areas that can drive growth from here. Training, inference, enterprise AI, agentic AI, physical AI, and accelerated computing are all at different stages of adoption, giving NVIDIA multiple ways to grow as the AI market develops.

Some of these areas are still in their early stages, while others are already scaling quickly. CPUs are a good example. The CPU market is often seen as mature or slowing, but NVIDIA’s Vera CPU could open up another meaningful source of growth as more data centers adopt accelerated computing.

Another opportunity comes from NVIDIA’s complete computing systems.

TrendForce expects the output value of NVIDIA NVL72-class rack systems to grow from $226B in 2026 to $711B in 2027.

As AI systems become more complex, customers are buying complete computing systems instead of individual GPUs. This will give NVIDIA more opportunities to grow revenue within its existing computing business and capture more value from each deployment.

1.3 Hyperscaler Spending Is Durable

One of the biggest concerns around NVIDIA is what happens if hyperscalers slow down their spending. If capex growth fades, it would clearly be a material headwind for NVIDIA given how much of its business is tied to AI infrastructure.

Could that happen? Yes. Do we expect it to? Not really. The hyperscalers have been clear that continued investment in AI infrastructure is necessary to support their growth. Amazon, Microsoft, Google, Meta, and Oracle are all committing significant amounts of capital, and their earnings calls and industry conferences continue to reinforce that message. If these companies were to suddenly cut capex, it would be negative for both their own growth and NVIDIA.

For context, projected hyperscaler capex in 2026 is equivalent to around 87% of the annual U.S. defense discretionary budget.

The spending estimates also point in the same direction, with plenty of room for hyperscaler capex to continue growing from current levels. And even if we are partly wrong about the pace of spending, NVIDIA still has significant room to grow. It’s generating massive amounts of cash, expanding its business, and benefiting from a market where computing demand continues to increase.

One thing that also gives us confidence that hyperscalers can continue spending heavily is that their EBITDA and EBIT margins are still rising.

They are generating strong profits and cash flow, giving them plenty of capacity to keep investing in AI infrastructure while continuing to grow their businesses.

2. AI Computing & Infrastructure

NVIDIA is far more than a GPU company. The company has shown this clearly through its recent events and product launches, but we think the market still underestimates how broad its business has become.

The company is present across edge computing, data centers, hyperscalers, AI clouds, industrials, and enterprises, while its platform spans GPUs, CPUs, networking, memory, software, and complete computing systems. Very few companies are positioned across as many parts of the AI ecosystem.

2.1 The Race for Compute

The $1 trillion installed base of general-purpose CPU data center infrastructure is being upgraded to accelerated computing. NVIDIA sees this as a much broader shift in computing, with GPUs, networking, software, and complete systems all becoming part of the new AI infrastructure stack.

This is also expanding the size of the data center opportunity.

NVIDIA estimates that AI factories could push the data center market to $2 trillion and beyond in the coming years, as companies across industries begin building infrastructure to train and run AI models.

AI Clouds, Industrial, and Enterprise customers now account for roughly 50% of NVIDIA’s Data Center revenue, and the company expects this group to outgrow hyperscalers over the long term. It also has visibility into more than $1 trillion of cumulative Blackwell & Rubin revenue from 2025 through 2027, before factoring in additional opportunities across CPUs, networking, and other products.

The customer base is expanding as well, with companies such as Anthropic, Meta, and OpenAI, as well as models such as Gemini and multiple open-source models, driving additional demand. This is why NVIDIA should be seen as more than a semiconductor company. That said, the broader platform also creates risks.

Bears could argue that NVIDIA is expanding into too many areas, increasing execution risk and giving competitors more opportunities to challenge its position.

The company’s advantage comes from bringing together the hardware, software, and developer ecosystem required to build AI systems. As customers become more deeply integrated into its ecosystem, moving to an alternative becomes more difficult, helping it defend its position as AI adoption expands.

2.2 How Long Can the Memory Bottleneck Last?

One of the most important comments from NVIDIA’s earnings call was around memory. CFO Colette Kress said the company is experiencing “extreme pricing conditions in memory,” with price increases exceeding previous expectations and likely to move even higher into next year.

This is putting pressure on NVIDIA’s margins, with Q4 gross margins now expected at around 71% to 72%, compared with the previous expectation of roughly 74%. We view this as a headwind that could become more significant over time, but for now, it remains manageable given NVIDIA’s overall profitability.

But how long will these memory pricing conditions last?

NVIDIA believes the current memory shortage is being driven largely by the AI buildout, as AI systems require large amounts of high-bandwidth memory (HBM). In other words, higher memory costs are partly a consequence of the same infrastructure spending that is driving demand for NVIDIA’s products.

The HBM market is expected to keep expanding rapidly through 2028. Total HBM bit demand is forecast to grow at a 37% CAGR, while HBM’s share of total DRAM demand is expected to rise from around 7% in 2025 to 10% by 2028.

NVIDIA’s share of HBM demand is expected to fall from roughly 58% this year to 41% next year as competing AI accelerators gain ground. While the decline in share is worth monitoring, we do not view it as a material concern on its own.

The key is the size of the overall market: NVIDIA can lose share while still increasing its HBM consumption as total demand continues to grow.

3. The Rise of Inference

An interesting takeaway from our conversation is that, for now, inference gets much less attention than training, but this is about to change. Training gets the headlines because building capable models requires enormous amounts of compute. But once a model is trained, it has to run every time someone uses it.

As AI moves from occasional use to everyday software, business workflows, and increasingly autonomous agents, a shift we are already seeing, inference demand should grow rapidly.

We think inference will become one of the biggest drivers of NVIDIA’s next phase of growth. NVIDIA said on its latest earnings call that inference is now larger than training, after the two were roughly balanced only 18 months ago.

It also said its revenue could grow by more than 100% YoY on an unconstrained basis, but ultimately guided to nearly 70% growth because supply is limiting how much it can deliver. To us, that is an important distinction: the constraint is increasingly how much NVIDIA can supply, rather than a lack of demand.

The economics of inference are also changing quickly.

SemiAnalysis () estimates that inference infrastructure can generate extremely high revenue per megawatt, with some API workloads potentially generating around $100 billion per GW annually at more than 90% gross margins.

In that environment, paying more for compute or accepting lower efficiency in exchange for faster deployment can make economic sense. The value of getting inference capacity online quickly is becoming extremely high.

The bigger opportunity, in our view, is how quickly AI usage is growing.

Dell estimated late last year that AI inference would reach 1 quadrillion tokens per month by the end of 2028. Its latest estimate is now 57 quadrillion tokens per month for the same period. That means Dell’s estimate increased by 57x in less than a year. Goldman Sachs has since published an estimate that is even higher, at roughly 10x Dell’s revised forecast. That is a massive change in expectations, and it shows just how quickly the amount of AI being used is growing.

If token generation continues to grow at anything close to these rates, the amount of compute required to serve AI workloads could expand far beyond what current infrastructure spending suggests. For NVIDIA, this creates an important opportunity: inference does not end when a model is trained.

For NVIDIA, the key point is that if AI usage grows anywhere close to these forecasts, the amount of compute required to serve that usage could be far higher than today’s infrastructure spending suggests.

4. The Agentic AI Opportunity

Another standout from our conversation and research is how agentic AI could become one of the most important catalyst for the next phase of AI adoption.

AI is moving beyond answering questions toward completing tasks, using tools, and interacting with existing workflows. As these systems become more capable, companies should have more incentive to deploy them at scale, creating another source of demand for AI compute.

Enterprise AI is already an area where NVIDIA has a very strong position, with more than 90% market share today and a similar share expected over the next five years. The competitive picture is different in hyperscaler and consumer internet workloads, where NVIDIA currently holds roughly 70–90% share but is expected to fall toward 50–70% as custom chips and alternative accelerators gain ground.

NVIDIA may lose share in hyperscaler and consumer internet workloads while maintaining a strong position in enterprise AI and continuing to grow. The market should keep this in perspective when assessing the competitive risks.

5. Physical AI: Where Are the Robots?

Beyond digital AI, physical AI and robotics could become another meaningful growth driver for NVIDIA over the next decade. We are bullish on the long-term potential, but we also think adoption will vary significantly across industries. Autonomous vehicles, and humanoids are moving toward broader commercial use, while many traditional industries are likely to adopt robotics more slowly.

NVIDIA is positioning itself as the computing and software platform behind the robotics industry, providing the technology needed to train, simulate, and operate these machines. CEO Jensen Huang has been vocal about the opportunity.

At CES 2026, he said: “Physical AI has arrived. Every industrial company will become a robotics company.”

While we are very bullish on robotics, we disagree with Jensen on one point, We have a hard time seeing a world where every of companies become robotics companies. Some industries do not change that quickly, and many traditional businesses will continue to operate in ways that have worked for decades.

Take an aluminum plant or an oil refinery. Both can benefit from robotics and automation, but that does not mean they will suddenly become robotics companies. The important point for the AI community is that some industries are inherently slower to change, and many of these businesses will continue to have a strong human element even as they adopt more AI and automation.

An oil refinery may use more robots, AI, and autonomous systems over time, but we doubt the people running these traditional industries will suddenly become AI evangelists. Many of these companies are run by operators who have spent decades building businesses around processes they know and trust.

They tend to value control, reliability, and proven technology, and they may have little interest in replacing people with robots simply because the technology exists.

We can already imagine a landman in the Texas Permian Basin saying: “So they really think we’re going to let robots replace us? They’ve probably never been to Texas. This is an oil field, not a robotic daycare.”

Technology adoption is very different across industries, and some of the world’s oldest businesses will remain much more resistant to change than Silicon Valley expects. But that doesn’t mean we don’t see robotics growing massively.

Our take: After inference, we see robotics as NVIDIA’s second-largest growth opportunity in the years ahead, with potential to generate substantial revenue.

The opportunity is enormous. The computing giant estimates that physical AI could eventually be 10x greater than digital AI, given that roughly 90% of the world’s actions happen in the physical world.

NVIDIA estimates that its physical AI business is already running at $10B in annual revenue, with Huang seeing a path toward $100B within the next decade.

What makes NVIDIA particularly interesting is that it can participate across several stages of the robotics ecosystem.

First comes training. Robots need to learn how to see, understand their surroundings, and perform tasks in the real world. NVIDIA’s GPUs provide the computing power needed to train the models that make this possible.

Then comes simulation.

Before putting a robot into a factory or warehouse, developers can train and test it in virtual environments. NVIDIA’s Isaac platform, Omniverse and other simulation tools allow developers to generate synthetic data and test robots in different environments without having to collect all of that data in the real world.

Finally comes deployment.

Once the robot is operating in the real world, it needs enough computing power to process information and make decisions in real time. NVIDIA’s Jetson Thor is designed for this, bringing powerful AI computing directly into the robot and enabling it to understand its surroundings and respond with very low latency.

“We have built three key computers for robotic systems: training computers, synthetic data generation and simulation computers, and robotic body embedded computers.” — Jensen Huang, GTC 2026

This is an important part of the investment case. It can benefit from the growth of robotics without having to predict which company will build the winning robot. Its technology can be used across different types of robots and applications.

NVIDIA is also building the software ecosystem around this hardware.

Its Isaac GR00T platform provides foundation models and tools for humanoid robots, while the company is working with robotics companies including Figure, Agility, Boston Dynamics, FANUC, KUKA, ABB and Yaskawa.

“NVIDIA’s full-stack platform — spanning computing, open models and software frameworks — is the foundation for the robotics industry.”
— Jensen Huang, GTC 2026

The opportunity also goes well beyond humanoids.

NVIDIA is targeting industrial automation, warehouses, logistics, autonomous vehicles and other physical AI applications. This gives the company several ways to benefit even if humanoid adoption takes longer than expected.

We are already seeing early signs of commercialization. TrendForce expects China’s humanoid robot output to increase by up to 94% in 2026, with Unitree Robotics & AgiBot together accounting for 80% of shipments.

The market is still small compared with AI infrastructure today, but the pace of development is what stands out to us. Robotics is moving from research and demonstrations toward real deployments across manufacturing, logistics and other industries. If that adoption continues, we think robotics could become a meaningful new source of AI compute demand over the next decade.

“Robotics is a once-in-a-generation opportunity for the European nations.”
— Jensen Huang, World Economic Forum, Davos 2026

The opportunity is still very early, and there are clear questions around the cost, reliability, and economics of humanoid robots. For us, the key thing to remember is that robotics could open another large market for NVIDIA’s existing technology as AI moves further into the physical world.

6. Key Risks

Many risks exist around anything related to AI, and NVIDIA is no different.

We see four main risks for the company.

1. AI Spending & Returns

NVIDIA’s growth depends heavily on continued AI infrastructure spending, particularly from the hyperscalers. The key risk is that these investments fail to generate the expected returns, eventually causing customers to slow or reconsider their spending. That said, this does not mean hyperscalers are unprofitable or unable to keep investing. Most of the largest players continue to generate strong profits and operating cash flow, giving them capacity to fund AI infrastructure. The risk is whether the returns are high enough to justify the pace of spending.

2. Custom Silicon & Competition

There is significant attention today on custom silicon and new AI accelerators. Another company may be able to build a competitive chip for a specific workload, but replicating NVIDIA’s broader platform is much harder.

This includes its software, developer ecosystem, networking, and complete systems. For us, the real competitive risk is therefore whether another company can replicate the full NVIDIA ecosystem, instead of simply matching its chips.

For example, OpenAI is developing its own custom AI chip with Broadcom, designed specifically for inference. Early testing suggests it can outperform NVIDIA and AMD solutions on certain inference workloads.

OpenAI plans to begin using the chips internally in late 2026, with broader production expected in 2027. This shows that some of NVIDIA’s largest customers are increasingly looking to develop their own alternatives.

3. Supply Chain Constraints

The AI buildout requires the entire supply chain to expand at the same time. Advanced memory, semiconductor manufacturing, packaging, power generation, and data-center capacity all need to increase alongside demand.

This creates the risk of not securing enough components or having to pay more to secure supply, something we have already seen in the past. At the same time, we would not interpret supply constraints as a sign that AI demand is weakening. The industry may simply be growing faster than the supply chain can keep up.

4. Pace of Technological Change

AI is moving extremely fast, with new architectures, accelerators, and models emerging constantly. It will need to keep innovating across its platform while maintaining strong profitability and defending its position against competitors.

7. Our Final Thoughts

After spending time with NVIDIA and looking at the different areas of the AI ecosystem, we came away more confident in the long-term opportunity.

The market is increasingly broadening beyond training and hyperscalers, with inference, agentic AI, robotics, and physical AI creating new sources of demand. This gives the company several ways to continue growing as AI adoption expands.

At the same time, we understand why the market remains cautious.

NVIDIA needs to maintain a very high rate of growth, AI infrastructure requires enormous amounts of capital, competition is increasing, and supply constraints remain a real challenge. We also cannot assume that every area of AI will develop as quickly as the most optimistic forecasts suggest. These are important risks, but they are already well priced in by the market.

Our view is that NVIDIA is one of the best-positioned companies to benefit from the next phase of AI. It has multiple growth opportunities, a strong ecosystem, and exposure to most areas of AI computing. We think the market still underestimates how much room the company has to grow from here.

View how the portfolio is positioned here: Aurelion Index Link.

Below is our latest stock addition to the portfolio.

The Aurelion Team

Questions? Reach us directly on Substack or at contact@aurelionresearch.com.

Disclosures & Methodology

All forecasts and estimates are based on publicly available information, management commentary, and our own modeling assumptions. This report reflects independent analysis by Aurelion Research. All opinions and estimates expressed herein reflect the author’s judgment as of the date of publication and are subject to change without notice. This publication is for informational and educational purposes only and does not constitute investment advice, a solicitation, or a recommendation to buy or sell any security. Aurelion Research and its analysts may or may not hold positions in securities discussed. Use only where legally permitted.

What attracted us to company is the combination of world class management, high grade gold assets, production already underway, and substantial production growth ahead, all in mining-friendly jurisdictions.

We have also heard very positive feedback about the company through our conversations with people familiar with the business. That combination is difficult to find, especially in a company where we still see meaningful upside.

We also think the timing is attractive.

Earnings could inflect higher over the next two quarters as production ramps up, providing a potential near-term catalyst for the stock.

Table of Contents

1. Why Now?

   1.1 An Attractive Entry Point

   1.2 The Gold Bull Case

2. Company Overview

3. Investment Thesis

4. The Path to Sustainable Growth

5. How We Value the Company

6. What Could Go Wrong

7. Our Final Thoughts

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Most of its operations are exactly where we live, in Montreal, Canada.

We know quite a few engineers who have recently joined the company.

In Montréal, there is a real effervescence around MDA. Talent is joining, and developments are starting to get discussed frequently in the news.

Yet, we think its bright future is not fully reflected in the current share price. We see the recent pullback as an attractive opportunity to invest in a high-growth company benefiting from the long-term expansion of the space industry.

This piece analyze the company per our usual framework but also provides differentiated research not covered anywhere else in certain areas:

• Our discussions with MDA engineers and recruiters. Top engineering talent is actively joining the company. While this may weigh on profitability in the short term, given the cost of hiring top talent, we see it as a strong positive for long-term innovation and execution.

• Our understanding of Canadian politics and the Carney government. When a company becomes a source of national pride, the government tends to support it through policy, promote it internationally, and stand behind it during periods of difficulty. MDA was recently described as a “Canadian champion” by Carney’s office.

MDA has been executing very well recently, signing several large contracts and expanding its facilities while leveraging its growing US footprint.

We expect more announcements of this kind in the months ahead.

If MDA continues to execute, we think the stock is trading at a very attractive valuation. It’s rare to find a high-growth space company with this level of defense exposure at such a low multiple.

The market either hasn’t fully recognized the growth ahead or isn’t willing to give the company credit for it yet. Some peers have more scale and deserve a higher multiple, but the difference in valuation is still significant.

We are adding MDA Space (NYSE: MDA) to our portfolio, the “Aurelion Index”. It also trades under the MDA ticker in Canada on the TSX.

Table of Contents

1. The Space Industry Is Taking Off

2. MDA Space: At a Glance

3. The Investment Thesis

4. Customers & Revenue Mix

5. The Canadian Government Advantage

6. Talent Acquisition & Hiring Trends

7. The Path to Free Cash Flow

8. Drivers of Value Creation

9. Valuation & Price Target

10. More Thoughts on the Space Industry

11. What Could Go Wrong

12. Our Final Thoughts

1. The Space Industry Is Taking Off

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We view this pair of equities as a compelling combination of exposure to prices and production of uranium. The producer is one of the few that is cash flow positive, with high visibility into long-term demand given its signed contracts.

Hyperscalers are rushing to get access to nuclear, signing large contracts in recent years. The large voices in the AI industry have been calling this out for some time now, for example Jensen Huang (NVIDIA’s CEO), who said that nuclear, such as small reactors, will be the solution to the “bottleneck”.

As we mentioned ourselves in our Uranium Primer:

“The issue is simple: we don’t yet have the capacity to produce enough energy, especially clean energy, to support what’s coming.” A lot of the supply is fixed, meaning incremental demand drives the price of uranium.

We think even if hyperscalers cut their spending forecasts, nuclear demand should still be larger than supply over the next years, directly benefiting uranium equities.

We’ve gone in depth on the uranium market in our previous Primer.

Here, we explain why we are getting another, and differentiated exposure.

Table of Contents

1. The Uranium Bull Case

   1.1 The Main Driver of Uranium Demand: AI

   1.2 Supply Can’t Keep Up With Demand

2. Our Highest-Conviction Uranium Stock

3. An In-Depth Look at the Business

   3.1 Overview of Business Segments

   3.2 Strategic Investments & Partnerships

4. Investment Thesis & Valuation

5. Our Uranium Price Forecast

6. What Could Go Wrong

7. Our Final Thoughts

1. The Uranium Bull Case

1.1 The Main Driver of Uranium Demand: AI

The main driver of uranium demand is the rapid growth in global electricity consumption. Nuclear demand was already increasing as countries looked for reliable, carbon-free power, but the rise of AI is pushing demand even higher.

The chart below shows just how much more energy-intensive AI can be.

This surge in AI usage is translating into much higher electricity demand.

Data center power consumption alone is expected to rise by around 165% by 2030. Nuclear is one of the few carbon-free sources that can provide consistent output, which is why the uranium supply-demand gap continues to widen.

1.2 Supply Can’t Keep Up With Demand

Nuclear power cannot simply be turned on and off.

Utilities typically secure their uranium supply years in advance, but a growing share of future demand remains uncontracted.

As utilities return to the market to secure additional volumes, competition for available uranium could increase, putting further upward pressure on spot prices.

The amount of uranium countries will need to purchase, especially the US, is growing faster than supply.

2. Our Highest-Conviction Uranium Stock

Read more

However, with a chart like this, we decided to speak with the Investor Relations team at BSX. We like the entry point at these levels despite the special situation.

You’ll also see that this type of position is very similar to our prior ones in the healthcare field, like Medpace (MEDP), Stevanato (STVN), and Bruker (BRKR). Simply put, buying “high-quality” healthcare companies that are facing short-term issues can provide a very attractive risk/reward investment.

We are adding Boston Scientific (NYSE: BSX) to our portfolio, the “Aurelion Index”.

The story in short:

Boston Scientific was a Wall Street darling, with industry-leading growth and a track record of successfully developing new healthcare solutions. Since 2021, the company had been growing even faster, at around 15% organically, driven by its 2 new growth engines (Farapulse and Watchman).

However, in early 2026, both of those growth drivers started performing poorly. Farapulse is facing increased competition while Watchman has recently seen weaker demand from doctors.

With those 2 growth engines losing momentum, BSX is growing more slowly this year. Both products also had some of the highest margins in the business. Investors are clearly uncertain about the path forward and have sold the stock significantly.

3 things stand out from our call with investor relations:

1) After listening to the drivers behind their 2027 guidance, we believe it assumes close to a worst-case scenario, with plenty of potential positives that could lead to an upside surprise. New products in the pipeline are exciting and the rest of the business is generally on track.

2) BSX is making significant share buybacks, the first time since 2020.

3) Restructuring is recurring, rather than something being done simply to cut costs because they lack confidence in the two underperforming segments.

Methodology: Aurelion does not receive compensation from companies covered. We believe all information discussed is public, and answers from management are summarized and interpreted by us. They do not reflect the speakers’ exact words.

Table of Contents

1. Why Now?

2. Company Overview

3. Industry Landscape

4. In Conversation with Boston Scientific

5. Financials

6. Valuation

7. Risks

8. Conclusion

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1. Why Now?

1) Recent Insider Buying

The CEO, Michael Mahoney, bought $9M worth of shares on August 5th, 2026. This was his largest direct purchase in several years and increased his total ownership position by 15%.

Two other directors bought shares as well at the start of August.

For a healthcare company developing products that often require extensive trials and testing, this insider buying is a very positive sign for investors looking at the story. Management knows much more than the street.

2) Large Buybacks

The company made its first meaningful buyback since the pandemic, with $2B worth of shares, backing up its argument that the share price is undervalued.

3) Special situation with two product ranges that have significantly lowered the valuation.

There were three main events that caused the major slips. They are presented below in the price chart:

Farapulse treats irregular heartbeats.

A doctor threads a thin tube up to the heart and uses electricity to destroy the small area of tissue causing the problem. It is safer and about three times faster than the older method, which used heat to burn the tissue.

It went from nothing to roughly 80% of US procedures in just two years, meaning most of the market it was converting is now converted. Abbott, J&J, and Medtronic have also launched competing products. US growth has fallen to ~3% as BSX loses share. Management assumes sales stay flat through 2027 and gives no credit for winning share back before its next product generation launches.

Watchman helps prevent strokes. It is a small plug, about the size of a quarter, placed inside the heart to seal off the pocket where most dangerous blood clots form. It allows patients to stop taking blood thinners for life.

A wave of clinical studies came out in the last 9 months, some supportive and others less supportive. According to management and expert calls we have read, doctors responded by putting the decision off rather than working through the data because there was a lot and it was confusing.

Nothing changed about the device or the patients who need it. Management says physicians simply have not had time to absorb the data, assumes no improvement in 2027, and expects a new competitor to enter.

While we acknowledge the stock deserves a lower valuation given the lower growth coming from those two product ranges, which represent 25% of revenue, we think the stock has been unjustifiably punished. What we like is that there are several ways for investor sentiment to turn.

• Launches in 2027–2028: There are several new products coming onboard with billion-dollar market opportunities. These could fill the shoes of the two problem children.

• Farapulse: farawave ultra launches around mid-2027, with new heart imaging and faraflex coming in 2028. These could help growth recover in 2H27 and return to market growth in 2028.

• Watchman: Management is not counting on a recovery, but is increasing doctor education and direct-to-patient marketing. A possible label expansion and Medicare decision in 2028 could open a market where 30–40% of eligible patients are untreated.

We present a framework below of management’s expectations for Farapulse and Watchman as well as our own estimates. The key takeaway is that the impact is mostly that the company has lost these growth drivers quicker than expected, not that overall company growth is expected to turn negative.

Finally, we think that at 12x earnings versus its average of 20x earnings, we are paying very little for a high-quality business. Revenue generally grows 7–8% in the long term, plus acquisitions that they have proven to be successful with. There is also a margin expansion story, giving investors a great company to own even without those two performing well.

2. Company Overview

Boston Scientific at a Glance

Boston Scientific is a global medtech company that specializes in medical devices. It makes products that help doctors diagnose and treat serious health conditions, mainly in areas like heart disease, cancer, digestive, neurological, and urological care. BSX has a large and diverse product portfolio, which gives it exposure to several areas of healthcare.

Business Overview

Boston Scientific has two main businesses: Cardiovascular and MedSurg.

The company focuses on growing its existing businesses while also moving into new areas with growth potential. A big part of that comes from developing new products and gaining market share in procedures where demand is growing.

1. Cardiovascular Segment (67%)

The Cardiovascular segment is the larger and more diverse part of Boston Scientific’s business. It covers coronary and peripheral artery disease, blood clot removal, heart rhythm management, and electrophysiology.

One product is the AGENT, a drug-coated balloon used to treat patients whose arteries become blocked again after receiving a stent. Instead of putting in another stent, AGENT delivers the drug directly to the affected area without leaving any additional metal behind. It has been available globally since 2014 and is now the first and only coronary drug-coated balloon approved in the U.S.

BSX has products across the entire procedure, from imaging and vessel preparation to stents, thrombectomy, and other treatments.

It also has WATCHMAN, which is used to reduce stroke risk in certain patients with atrial fibrillation. BSX also has a growing presence in electrophysiology, where it has an interesting opportunity with its “FARAPULSE” platform.

FARAPULSE is one product we find particularly interesting. It’s Boston Scientific’s pulsed field ablation (PFA) system used to treat atrial fibrillation.

PFA is becoming a bigger part of AF treatment, with clinical data showing FARAPULSE can be as effective as traditional treatments while offering shorter procedure times.

As Boston Scientific’s Chief Medical Officer, Brad Sutton, put it: “We haven’t seen esophageal injury or pulmonary vein stenosis in nearly 40,000 cases.”

What also makes FARAPULSE interesting is that it covers several parts of the procedure, including access, mapping, and the actual ablation, which is the process of treating the heart tissue causing the irregular heartbeat. This gives BSX more exposure and more opportunities to sell its products.

A Look at FARAPULSE Products

As PFA continues to gain adoption, we think FARAPULSE will become an important part of Boston Scientific’s growth, especially as the company continues to expand its presence in the broader electrophysiology market.

2. MedSurg Segment (33%)

The MedSurg segment is mainly focused on Endoscopy, with products used to diagnose and treat gastrointestinal and pancreatic conditions.

The portfolio includes clips used to stop bleeding, stents used to open blocked areas, single-use scopes, and tools used in procedures such as ERCP. Boston Scientific also sells products for endoscopic surgery and weight loss, including the OverStitch suturing system and Orbera intragastric balloon.

One product we find interesting is OverStitch NXT, an endoscopic suturing system used to close tissue during certain procedures. It works with many commonly used gastroscopes, so hospitals may not need to buy new equipment to use it. It also gives doctors more control during the procedure and makes it easier to reach areas that can be difficult to access with standard scopes.

Overall, we see the MedSurg segment as another source of growth for Boston Scientific outside of cardiovascular, with a broad range of products used across different types of procedures.

China is a genuine growth market, with revenue above $1B and APAC growing 12% in Q1 2026. The main offset is VBP, which has cut device prices 50–93% and impacted parts of Vascular Therapies and Urology. Management is leaning into VBP by investing in local innovators and expanding its Chinese commercial team while competitors pull back.

3. Industry Landscape

Key named competitors:

Abbott Laboratories, Edwards Lifesciences, Medtronic plc, Stryker Corp.

Within offerings like endoscopy and urology (together 28% of 2025 sales) under the MedSurg segment, BSX is a dominant global player with stable and compounding revenues. In the Cardiovascular segment, BSX leads the market in LAAO with Watchman (though competitors are looking to enter) and they are #2 share in pulsed field ablation with Farapulse. Both of these markets (LAAO and pulsed field ablation) are becoming increasingly competitive, but BSX remains relevant or market-leading.

Outside of BSX’s dominant products where they are incumbents today, there are opportunities to expand the company’s product lineup, though not without competitive pressure.

One example: in intravascular lithotripsy (IVL), BSX launched SEISMIQ for peripheral use in January 2026 and is testing/awaiting approval for coronary use.

The key competitor in this space is Shockwave Medical (acquired by JNJ in June 2024). Shockwave’s C2 product entered the U.S. market in 2021 and has since become the 800 pound gorilla in the space, with over 600 peer reviewed publications covering 25,000+ patients. As a new entrant, that efficacy will be difficult to replace.

BSX’s offering in the space is slated to use laser and optical fibers instead of spark-gap electrodes to generate acoustic pressure waves which early data suggests produces a more uniform energy profile. In addition to Shockwave under JNJ, Abbott and Phillips Healthcare are seeking to enter the IVL market as well, with each player moving toward regulatory verification.

In coronary IVL specifically (related directly to or around the heart itself), BSX can take advantage of their broader coronary ecosystem in imaging, stents, and drug-coated balloons.

BSX essentially already offers steps 1, 3, and 4 of this procedure, whereas their coronary IVL offering would address step 2 in the chain and create a more unified ecosystem of products. Coronary IVL specifically has a $1Bn estimated TAM which would move the needle for BSX’s sales if they can penetrate the market.

There are many other segments and markets that can be entered organically or through investment and M&A. BSX has historically been an acquirer of companies with strong and complementary assets.

Examples include their Penumbra acquisition in January 2026 for $14.5B, granting assets in neurovascular and mechanical thrombectomy.

An interesting aspect of BSX’s capital allocation is their venture-capital playbook.

The Farapulse product that drove such strong growth in 2024 and 2025, BSX first invested in 2014, and in 2021 exercised an option to acquire the remaining stake in the company, bringing it fully in-house.

The acquisition-in-pieces structure allowed BSX to pay roughly $575M for 100% control over time for this asset that has now come to drive strong growth. BSX has run this playbook in other areas, with the initial VC investment approach to reduce risk, and a full acquisition once the application is proven.

Penetration vs Market Share

In medtech, penetration matters more than market share for determining growth. For example, Farapulse has reached ~80–85% of US procedures in two years, so Farapulse’s category-driven growth is largely over and further growth must come from taking share. LAAO is the opposite. BSX has >90% of the implanted device market, but penetration is still only in the mid-teens among ~5M eligible patients. The main issue is getting more patients referred.

Reimbursement is important too, since the procedure only grows when both doctors and hospitals have a good reason to do it.

To summarize, the industry is highly competitive but favors large, established players like BSX. The key is to look at each product category separately, as growth rates, market shares and competitors vary significantly. For BSX, management targets 7–8% long-term organic growth across its diversified portfolio.

4. In Conversation with Boston Scientific

We spoke with Boston Scientific’s Investor Relations team mid-August 2026. Below is our summary of what we took away in our interpretation.

1) The 2027 framework assumes a largely unchanged environment.

In the last call, management gave a framework (not “guidance”) of 2 - 4% organic growth for 2027, with formal guidance coming on the Q4 call in February.

The build behind it is simple: the other 75% of the business grows around 6%, the Watchman market declines mid-to-high single digits, and global EP sales are flat.

That’s it. There is no assumption that the medical education effort works, no assumption that referral patterns normalize, and no assumption that urology recovers from 1%.

We came away thinking the number is closer to a floor than a midpoint. It looks like they have more visibility into the problem. And it is reassuring with BSX’s history of surpassing expectations (they’ve cut guidance only 2x in 15 years).

2) The rest of the business grows at 6-7%

75% of the business has been growing at 6-7% for a number of quarters.

What we found more interesting is that the 6% of last quarter carries the urology segment growing at 1% and CRM at negative growth. There are measures in place to fix them. If either works, growth will be higher.

3) Urology looks fixable, although it has taken longer than expected.

The 2024 Axonics acquisition brought a “sacral neuromodulation” business that requires lots of human capital. Leadership and team turnover after the acquisition took reps out of the field. That hurt the selling effort and the patient funnel.

Refilling the funnel has taken longer than expected. Management expects growth to come back to the market growth rate which is 6 - 7% . This is definitely one of the most fixable of the 3 problems.

4) Watchman’s problem appears to be communication.

Nothing has changed in how the device is made or used, and it has been on the market for over a decade. What changed is that a large amount of clinical data came out at once, with some of it favorable and some less so.

According to management, physicians haven’t had time to fully absorb what it means in practice. For a busy doctor, the easiest decision is simply not to pursue it.

5) Spending on Watchman has not been cut.

The two largest cost lines are the sales force and direct-to-patient marketing and neither is being cut. Management was explicit that it does not want a short-term decision today to damage long-term growth.

We read continued investment into a shrinking franchise a proof of conviction.

6) The new products could increase growth in 2027 and 2028.

Management discloses the new products coming out in its investor relations deck. While we did not get more information, it’s safe to say these could be potential drivers. We look forward to commentary in upcoming earnings calls.

7) The restructuring is a scheduled program.

Boston Scientific runs one roughly every three years: 2019, 2023, and now. We see most of the benefit to land in 2028. The savings come from manufacturing efficiencies, supply chain optimization, and organizational structure. This would have happened regardless of how Watchman and EP performed.

8) Free cash flow guidance impacted by Watchman & Farapulse

The 2026 figure is now closer to $3.8B versus roughly $4B coming into the year, with the shortfall attributable to lower cash coming from Farapulse and Watchman. BSX targets 70–80% conversion from EBITDA. This appears to be a high target compared to historicals for us.

9) The broader demand environment is stable.

Management sees healthy procedure volumes and no evidence supporting the softer readings some analysts have published. The recurring variable is reimbursement, which splits into a physician fee and a facility fee. This year, the physician fee was relatively stable while the hospital fee was cut somewhat.

5. Financials

Because BSX makes meaningful acquisitions, we remove the acquired growth to look at the underlying business. The historical performance is consistent with management’s 7–8% CAGR target, with periods of higher growth when new solutions are coming out and lower growth when there are issues.

On the company’s margins, the trend has been clearly positive, driven by a mix of cost cuts and a better business mix.

2013–2016: BSX came out of the financial crisis with a bloated structure, a declining defibrillator business, and heavy litigation. Restructuring helped close plants, consolidate manufacturing, and reduce headcount, taking margins from 15.8% to 24.1%.

2016–2019: Management shifted toward higher-growth categories like endoscopy, urology, and interventional cardiology while moving away from slower-growth businesses. Margins reached 26.1% by 2019.

2020: COVID. Elective procedures stopped and margins fell. They recovered within two years.

2021–2025: mix and operating leverage. High-growth markets increased from 25% to 55% of revenue, while low-growth exposure declined from 30% to 20%. Farapulse and Watchman, both above corporate margin businesses, also grew rapidly.

The result is 1,220bps of margin expansion in twelve years, with roughly 80bps of annual expansion in 2023–25. For balance, 2026 is guided flat at 28.0%, as the mix tailwind that drove much of the recent expansion reversed.

BSX has made numerous acquisitions in the past, most of which have been financed with cash and debt. The company has been very successful at making these acquisitions. Share issuance has been low, which is something we like to see.

In total, combining organic growth, margin expansion, and a virtually flat share count, BSX has been able to grow at a CAGR of 12.2% over the last 14 years.

6. Valuation

Revenue Growth

We assume growth to be all organic but include announced acquisitions in our revenue numbers. We are slightly over the Street in the 2027 period because we think it will be easy to beat the 2 - 4% discussed by management. For the following periods, we assume the long-term range for BSX’s growth (7 - 8%).

Margins

Because new products coming in the 2027–2028 period should boast higher margins than the rest of the business, we give a bit of margin expansion to BSX.

Moreover, the restructuring they are doing should help lower costs for the 2027–2028 period, though we expect margins to be slightly impacted in 2027 with Farapulse and Watchman.

Because BSX has proven over time to be able to grow their margins in the last decade, we think this is the base case. Our EBITDA margin increases from 30.2% to 31.5% in 5 years.

Cash Flow Generation

Management told us 70 - 80% conversion from EBITDA as a target, though we view this as high compared to what they have historically done. Our model therefore implies more in the 62% conversion range.

Capital Allocation

We assume a +0.7% growth in diluted shares outstanding coming from normal stock compensation and the rest of cash to go towards buybacks.

Because BSX is now buying back a significant amount of shares and has proven that their acquisitions enhance the value of their share price over time, we think putting all the cash to work in buybacks is a fair projection for our long-term price targets.

Technical note: A few sell-side analysts double-count amortization in their adj. EBITDA calculation because amortization is already excluded from adj. operating income, so the result overstates true EBITDA.

We use adjusted operating income + depreciation only.

We do not argue for a comeback to the all-time high multiple, nor the average, but rather to the bottom of its historical average. 10 - 12x EBITDA is a multiple for a scenario where Farapulse and Watchman become significant detractors to growth, which has not been proven yet.

Our valuation is as follows, driven by an 8% earnings CAGR, meaningful buybacks, and a slightly higher multiple than the current one.

• Our price target for year-end 2026 is $67.

We do not think BSX is worth its previous $100 PT or that it will get there quickly, given the uncertainty and slower growth around Farapulse & Watchman.

However, even under what we consider a base case, where both Farapulse & Watchman perform poorly, our valuation still points to compelling upside.

7. Risks

1) Farapulse could lose more share than we expect. Management assumes global EP sales stay flat through 2027, which we view as conservative. However, one Abbott territory manager expects Farapulse’s US share to fall from ~70% today to 35–40% by 2027 as Affera and Volt gain traction. We would not take that estimate at face value, but if share loss is even directionally close, EP revenue could decline rather than stay flat. The main issue is that BSX’s mapping remains weaker until its new imaging product launches in 2027.

2) Watchman’s issue may be more than communication. Management believes doctors need more time to digest the recent clinical data. There may also be an economic issue, as hospitals and doctors can have different incentives around the procedure. With a new competitor coming in 2027 and no major label or coverage change expected until 2028, Watchman could remain a headwind for longer than expected.

3) Penumbra could weigh on the numbers. The $14.5B acquisition closes in 2H26 and is excluded from current guidance. Penumbra has lower margins than BSX, while integration costs and amortization will also create pressure. It also reduces the balance sheet flexibility that has supported BSX’s M&A strategy.

Overall, we think the risks are real, but importantly, we do not need Farapulse or Watchman to fully recover for the investment to work.

That is what makes the current valuation interesting to us.

8. Conclusion

Boston Scientific is going through a temporary slowdown.

Farapulse and Watchman have clearly become headwinds, and we do not expect either business to quickly return to its previous growth rate. But the rest of the company continues to grow, new products are coming, margins have a long track record of expansion, and management is still allocating capital aggressively through buybacks and M&A.

At 12x earnings versus a historical average of ~20x, we believe the market is pricing in a much worse outcome than our base case. Even assuming Farapulse and Watchman remain significant headwinds, we see attractive upside from the underlying business, new product launches, margin expansion, and buybacks.

The key for us is that we do not need everything to go right. We are buying a high-quality compounder while two of its growth engines are temporarily struggling. If either one stabilizes faster than expected, or the new product pipeline delivers, the upside could be meaningful. We believe this creates an attractive risk/reward and are adding Boston Scientific to the portfolio.

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View how the portfolio is positioned here: Aurelion Index link.

Below is our most recent report covering our previous stock addition.

The Aurelion Team

Questions? Reach us directly on Substack or at contact@aurelionresearch.com.

Disclosures & Methodology

All forecasts and estimates are based on publicly available information, management commentary, and our own modeling assumptions. This report reflects independent analysis by Aurelion Research. All opinions and estimates expressed herein reflect the author’s judgment as of the date of publication and are subject to change without notice. This publication is for informational and educational purposes only and does not constitute investment advice, a solicitation, or a recommendation to buy or sell any security. Aurelion Research and its analysts may or may not hold positions in securities discussed. Use only where legally permitted.

However, we believe it is also a strong long-term compounder with solid fundamentals, and our goal is to hold the company for 5+ years.

The company is already generating revenue with surgical robots, but it is still a small percentage, and we believe the stock can do very well without surgical robotics. Given the low valuation (11x earnings, below peers and below historical valuation), we think this is an optionality, making the situation “asymmetric.”

Outside of surgical robots, the medical technology company is an established leader in spinal implants with best-in-class margins.

Further, healthcare sentiment has been shifting. Since the pandemic, performance of healthcare equities has been poor, with almost no gain since 2019.

However, in recent months we have seen a growing interest in healthcare, given that growth is proving to be strong and investors are looking to move away from AI uncertainty.

Whether AI booms or busts, you still need surgical tools.

Earnings are inflecting, margins are expanding, and the stock still trades cheap. We also provide expert calls with executives from the company.

Table of Contents

1. Why Now?

2. Company Overview

3. Industry Landscape

4. Expert Calls

5. Financials

6. The Acquisition Strategy

7. Surgical Robots Theme

8. Valuation

9. Risks

10. Conclusion

Read more

The new stock we are adding today to the Aurelion Index is a mining company that focuses mainly on copper, but also produces gold, silver and zinc.

And importantly, we go through our thinking on why we like copper-related equities now.

What we like about this one is that it offers a mix of existing copper production, mine-life extensions, and a growing pipeline of new projects. So this isn’t a mining story where you produce for a couple of years, make some money, and then need to get out. There is a long-term growth story here.

The company is Canadian, like many mining companies, but trades on both the NYSE in the U.S. and the TSX in Canada. It also owns and operates mining assets in friendly jurisdictions.

With a market cap of over $10B and strong trading volume, it is more accessible to institutional investors and has greater potential to attract momentum as the story gains attention.

Table of Contents

1. Why Now?

   1.1 An Attractive Entry Point

   1.2 The Copper Bull Case

2. Company Overview

3. Why the Valuation Is Attractive

4. The Path to Sustainable Growth

5. How We Value the Company

6. What Could Go Wrong

7. Our Final Thoughts

Read more

Listed in the U.S. on the NYSE, today we are adding a new company to our portfolio, the “Aurelion Index”.

It is currently our highest-conviction name in the Aurelion LATAM Basket.

We have followed this business closely for some time, but have been waiting for the right moment. After a period of underperformance, the stock has recently regained momentum, creating what we argue is an attractive entry point.

We believe this is a company with significant growth potential, exposure to key trends across Latin America, and an attractive valuation.

It has proven its business model, being one of the largest banks in LatAm. In our view, the market is overlooking the quality & long-term potential of the business.

Table of Contents

1. Why Now?

   1.1 Long-Term Growth Runway

   1.2 Macro Environment & Market Sentiment

   1.3 Our Belief That the Business Is Undervalued

   1.4 An Attractive Entry Point

2. Investment Thesis

3. Company Overview

4. Financials

   4.1 Why We Believe the Valuation Is Attractive

   4.2 The Path to Sustainable Growth

   4.3 Outperforming Traditional Banks

   4.4 Fee Growth: Another Competitive Advantage

5. Asset Quality: Addressing the Bear Case

6. How We Value the Company

7. What Could Go Wrong

8. Our Final Thoughts

Read more

The C-suite is bullish on the stock (that’s usual), but is backing it by buying a significant number of shares, representing a large number relative to their net worth.

With 67% revenue growth this year and 38% EBITDA margins, the company trades at 12x EBITDA. What stands out to us is that their (extreme & non-AI) growth is durable for the coming years. In our view, its profitability is misunderstood due to the current high capital expenditure requirements.

We recommend watching this video to understand what they do in 1 min:

Read more

After quite a success with Marex Group, we are pivoting to another capital markets company. This company has been on our radar for a while, and following our meeting with management on July 13, we decided the timing was right to add this compounder.

“If we did not have futures contracts today, we would need to invent them in order to move forward and progress.” — Milton Friedman, speaking to Terry Duffy (CME Group CEO), 2002

The global exchange ecosystem is instrumental to capitalism as we know it. By facilitating the trading of stocks, derivatives and enabling the transfer of risk, exchanges drive global commerce in a massive way.

As a result of their durable and nearly indefinite earnings power (exchanges are among the longest-lived businesses in existence), exchanges have historically traded at rich valuation multiples. However, so far in 2026, the love affair with exchange multiples has cooled off.

Since the recent launch of perpetual futures by crypto trading platform Hyperliquid, dominant U.S. exchanges have faced a rare level of scrutiny from investors. Three of the big four (ICE, CME, NDAQ, and CBOE) are down meaningfully this year as concerns grow around competition from new trading products.

Some investors worry that new trading products like perpetual futures and tokenization could take market share away from traditional exchanges.

Our pick today is smaller and less discussed than the large exchanges, but it has been caught in the same crossfire.

However, the company is not an exchange and we believe its position remains highly secure regardless of perpetual futures adoption or potential tokenization.

The company operates primarily in highly attractive asset classes with very durable long-term growth tailwinds in which it enjoys dominant market share.

Like many trading businesses, incremental margins are very high, but unlike most, capital requirements are extremely low. The business operates with negative working capital and generated a 30%+ ROE in 2025 when adjusting for non-operating intangible assets on the balance sheet. FCF conversion is also very strong, with margins above 45%. Low capex requirements (~5% of revenue) and a largely fixed cost base drive operating leverage, allowing excess cash flow to be returned through strategic M&A, dividends, and buybacks.

Revenue has grown at a double-digit pace for decades, with no down year since inception. Since 2022, revenue has grown at a 20% CAGR while free cash flow margins expanded, and we believe long-term growth can remain in the low-to-mid teens.

Today the business trades at 22x free cash flow, the lowest valuation it has seen since October 2022. Management is using the decline as an opportunity to repurchase shares at accelerated rate compared with repurchases in prior years:

We think the stock has dislocated from fundamental intrinsic value. Quarter-to-date, we’ve purchased nearly $100M of stock. We have over $400M left on the authorization.” — Company CFO at a conference in June

Table of Contents

1. A Digital Market Infrastructure Platform

2. Dominating the Rates Market

3. Building the Future of Credit Trading

4. The Economics of the Business

5. Strategic M&A as a Growth Driver

6. Valuation: A Compelling Entry Point

7. What Could Go Wrong

8. Our Final Take on the Company

Read more

In this case, this was one of the most interesting discussions we have had in a while, with this $50B semiconductor behemoth. Cerebras, a Silicon Valley-based AI computing company, IPO'd in May and is at the forefront of the industry.

In early July, we discussed with Sean J. Dorsey, Head of Investor Relations and Senior Director of Strategic Finance at Cerebras. Sean has been at Cerebras for five years and works directly with the CFO. Before joining Cerebras, he was a VP in equity research and investment banking, covering semiconductors at major banks. An ex-Wall Street guy, as he described himself.

Off topic from Cerebras; he gave us his thoughts on independent research and how much emphasis they put on leading Substacks. It’s simple, Substack is becoming the place to be for independent investment research:

Sean: “Major companies’ IR teams and management are increasingly approaching independent research platforms like Substack and X in a similar way to how they approach traditional sell-side analysts.

The reason is simple: independent research has become increasingly important, with the ability to influence investor perception and market sentiment. Management teams are listening and reading the research that is published on Substack.” (adapted and interpreted for context).

He brought up (SemiAnalysis) and (publications we highly respect) as examples of why they want to engage more with leading Substacks because, he said, some forecasts were very different from what the company expected.

We begin with the company’s story, dive into our conversation with management, explore what it means for the broader AI infrastructure race, break down the bull and bear cases, and finish with our view on valuation.

We usually aim to give an actionable investment idea on a theme or a market. This is not the case for this report, but you’ll get some interesting insights on the AI trade, Cerebras, and independent research.

Methodology: we independently reaches out to company management teams. Aurelion Research does not receive compensation from any company covered. The answers from interviews are based on our notes and have been adapted and presented for clarity. They may not reflect the speakers’ exact words. We have selected the sections below that we believe are the most relevant parts of our discussion. All information discussed is believed to be publicly available. This content is for informational purposes only and is not investment advice or a recommendation to buy or sell any security.

Table of Contents

1. Company Intro: A Different Approach to AI Computing

2. In Conversation with Sean Dorsey

3. Implications for the AI Trade

4. The Debate: The Promise and the Challenges of Cerebras

   4.1 The Bull Case: A New Path for AI Computing

   4.2 The Bear Case: The Challenges Behind the Ambition

5. Valuation: An Analyst’s Nightmare

6. Our Final Thoughts on Cerebras

1. A Different Approach to AI Computing

Cerebras is a semiconductor company that builds chips designed for AI processing. It went public in May 2026 and trades under the ticker CBRS.

Its main product is the Wafer-Scale Engine 3 (WSE-3), a massive AI chip built as one large piece instead of many smaller chips working together.

Traditional AI systems use many separate chips that need to constantly share information, which can slow things down. Cerebras takes a different approach by putting everything together in one place, allowing information to move faster and helping customers run large AI models more efficiently. The WSE-3 is designed to handle AI workloads that would normally require many traditional chips.

How Cerebras Is Riding the AI Compute Wave

The Silicon Valley company has two main revenue streams:

1. Direct Hardware Sales (57%)

The company sells its AI chips and computing systems directly to customers looking to build and operate their own AI infrastructure for large-scale workloads.

2. Cloud & Services (43%)

Cerebras Systems operates its own global network of AI data centers.

It offers cloud services, allowing customers to access its computing power without owning the hardware. Customers can either reserve dedicated computing capacity or pay based on the amount of computing power they use.

An important point to mention: just one year ago, the revenue mix was quite different. Around 70% of revenues came from hardware sales, while 30% came from its cloud offering. So, what happened?

The change came from rapid growth in its cloud business, accelerated by a massive multi-year inference deal with OpenAI that began ramping in early 2026, combined with an Amazon cloud partnership. Instead of only selling the infrastructure, it is increasingly monetizing access to that infrastructure.

Customers can now use Cerebras’ computing power through the cloud without needing to purchase, install, and manage expensive AI hardware themselves. This should create a more recurring, service-based revenue stream while allowing Cerebras to benefit directly from the growing demand for AI compute.

The timing is also important because the AI industry is entering a new phase. Early AI spending was largely focused on training increasingly larger models, but we are now moving into an entirely new wave that is expected to be driven by inference as these models transition into real-world applications.

Here’s an interesting quote from AWS VP Compute & ML Services, David Brown.

“Inference is where AI delivers real value to customers, but speed remains a critical bottleneck for demanding workloads like real-time coding assistance and interactive applications. What we’re building with Cerebras solves that by splitting the inference workload across Trainium and CS-3, connected through Amazon’s Elastic Fabric Adapter. The result will be inference that is an order of magnitude faster and higher performance than what’s available today.”

Amazon and Cerebras are combining two different AI chips, with each one doing the job it is best at. By working together, they can generate AI responses much faster than traditional systems, making applications like coding assistants more responsive for users. This creates a win-win situation for both companies.

2. In Conversation with Sean Dorsey

Aurelion Research (AR): ‘‘Could you walk us through your role at Cerebras, your background, and how you got to where you are today?’’

Sean Dorsey

“I’ve been at Cerebras for five years, so I was here well before the company became public. I work closely with the CFO on financial matters and have also taken on investor relations responsibilities, which gives me a unique perspective on both the business and how investors view the company.”

Thoughts

Before joining Cerebras, Sean spent time on Wall Street, working in Equity Research and Investment Banking and covering the semiconductor industry at several major banks. In other words, he went from analyzing chip companies from the outside to helping build one from the inside, giving him a unique perspective on the industry.

AR: ‘‘How does Cerebras think about its customer strategy, and what drives customers to adopt its technology and build long-term partnerships?’’

Sean Dorsey

‘‘Our customers today are massive companies, like Amazon and OpenAI. These are not your typical customers. They are so large that, in some ways, you could almost think of them as resellers or distributors because of the scale at which they operate. We have a small number of very large customers, and that makes our go-to-market strategy quite unique. We do not need a massive sales force because we are focused on a handful of strategic accounts.

The advantage is that we know these customers extremely well, understand their needs, and can work closely with them. Because of that, we can operate with a relatively lean sales and marketing organization and allocate more resources toward engineering and product development.

We believe this is a competitive advantage for Cerebras. We also believe we have built a strong market engine. Even though we are a much smaller company, we have managed to position ourselves in the same conversations as some of the largest AI infrastructure companies in the world, including Nvidia.”

Thoughts

Cerebras’ customer strategy is interesting because the company is prioritizing depth over volume. Instead of trying to sell to thousands of customers, it is focusing on a small number of very large ones. This allows the company to stay lean and focus more resources on improving its products. The key question will be whether this strategy can continue to work as Cerebras grows.

AR: “There has been discussion around whether Cerebras’ approach to handling memory could face limitations as AI models continue to grow. How does the company think about this potential challenge, and how would it adapt if the industry shifts toward using more external memory solutions?”

Sean Dorsey

“I think some of the discussion around our architecture comes from people looking at Cerebras from the outside without having spent significant time using our compute or understanding all the nuances of the technology. They are doing their best to understand the product and the value proposition, but there are limits when you are not directly working with the system.

A recent example is the assumption that Cerebras would not be able to support very large models. We were a day-zero launch partner with OpenAI for its latest multi-trillion-parameter model, delivering it at up to 750 tokens per second, which is significantly faster than GPU-based alternatives. So clearly, there are still some misconceptions around what our architecture can and cannot do.

Building a wafer-scale chip was something that many people believed was not possible for decades. We proved that it was technically viable, and now we are proving that it can also be commercially viable. We have real customers making significant commitments to our technology, and our focus is to continue proving the doubters wrong over time.”

AR: ‘‘How does Cerebras think about attracting and retaining top talent, especially when competing against much larger semiconductor companies?’’

Sean Dorsey

“I think we’ve had a lot of success hiring people because we’re still the small guy that’s growing quickly, with a lot of momentum and opportunity ahead of us. Generally speaking, that is a very attractive position to be in compared to a much larger company that is trying to innovate in new markets.

We’re able to attract talent from some of the biggest technology companies in the world. We have people here from AWS, Meta, Apple, Google, Nvidia, and AMD. There are obviously many benefits to being a large company, but talent retention can also be a challenge when you’re operating at that scale.

I think the current environment has also created opportunities for us. While AI and semiconductor companies continue to invest and grow, other areas of technology have seen more pressure, which has created a strong talent pool for companies like Cerebras. Today, we have roughly doubled our headcount over the past 18 months and are approaching 1,000 full-time employees.

What’s also interesting is the composition of the team: around 80% of our employees are engineers, and 80% of those engineers are software engineers.

So despite competing against much larger companies, we’ve been able to attract strong technical talent because people want the opportunity to have a bigger impact, take on more responsibility, and help build something at an earlier stage.”

Thoughts

Talent is often overlooked when analyzing semiconductor companies, but we believe it can be a major competitive advantage. In AI infrastructure, where technology evolves quickly, the ability to innovate and execute depends heavily on the strength of the team behind the technology.

Cerebras remains much smaller than industry giants, but its ability to attract experienced engineers from leading technology companies shows that it has created an environment where people see an opportunity to make an impact. The key question is whether this advantage can continue as the company grows.

Then came the most interesting part: the new wave of research.

We mentioned this earlier in the article’s introduction. We learned that management teams at large companies like Cerebras are increasingly paying attention to research published on Substack and by influential accounts on X.

Why? Because they agree that research produced outside of the traditional sell-side ecosystem has become increasingly important, which is something we rarely hear from companies. This means that the work being done on these platforms is being read by major companies and market participants.

AR: ‘‘Do you think independent research platforms like Substack and X have become increasingly influential in shaping market sentiment?’’

Sean Dorsey

“I think it’s something that we’re all trying to rethink in terms of where we allocate time. We should probably be spending more time with the Substack community because whether or not we like it, whether they’re right or wrong, they have the ability to influence the market. We’ve spent so much time with some of the largest institutional investors in the world because they have the ability to move stocks as well. But so do people on X.

They have a microphone, and we should probably think about them in the same way that we think about top institutional investors like BlackRock.

They really understand our technology and our story, but just because some researchers don’t fully understand it today, that doesn’t mean we shouldn’t engage. We need to continue having thoughtful conversations, educating the market, and creating opportunities to explain why they may have missed something. Having that healthy debate in the market is usually a very good thing.”

AR: ‘‘How does Cerebras approach working with independent researchers when there are disagreements about the company or its technology?’’

Sean Dorsey

“It’s a fine line. You don’t want to insert your words on top of theirs or take ownership of their work. But if we can educate them on certain aspects, we want to do that. When SemiAnalysis was preparing its report ahead of our IPO, they sent us a 50-page document and gave us an opportunity to provide feedback.

We left a lot of comments around areas where we thought things were not quite right, lacked context, or were simply incorrect. The challenge is figuring out the right way to engage. If a Morgan Stanley analyst sent us a report, we wouldn’t edit their report. We would try to help improve their understanding of the business and technology.

That’s the same approach we want to take with Substack authors and writers on X. We can’t give preferential treatment just because someone is on Twitter. The same standards should apply to everyone.”

Thoughts

We found this particularly interesting given SemiAnalysis’ strong reputation within the semiconductor industry. Cerebras is also a well-known company in AI infrastructure, making this debate more about differing views on the technology rather than a lack of information. Hearing this perspective directly from someone who has been with CBRS for years provided valuable context and helped us better understand the other side of the discussion, the one we do not usually hear about.

AR: ‘‘What are some of the biggest misconceptions investors still have about Cerebras?’’

Sean Dorsey

“For 10 years, people have questioned whether what we were trying to do technically was possible. People said you wouldn’t be able to build the chip, you wouldn’t be able to cool it, power it, run large models on it, or scale the architecture. None of that is new. We will never convince every doubter, but we will continue to prove them wrong over time.

We have a fundamentally different architecture, and you can’t apply the same assumptions used for traditional chip design to what we are doing. I think we can do a better job educating people about that as we continue to grow.”

AR: ‘‘When well-known researchers misunderstand Cerebras’ technology, how do you think about addressing those misconceptions?’’

Sean Dorsey

“I think everyone’s got a point of view based on their own experience and how they’ve arrived at it. Dylan recently told us that his mental model of Cerebras and our architecture was wrong after speaking with our CTO and our CEO.

I’m a glass-half-full kind of guy, so I see that as an opportunity. If we can continue educating researchers like SemiAnalysis and other independent writers with large audiences, it’s an opportunity not only to improve public understanding of our architecture, but ultimately how the market values the company.”

Of course, we want to make clear that Dylan has been right far more times than wrong and is an exceptional voice for semiconductor analysis.

3. Implications for the AI Trade

Here are simple implications and inferences from the conversation.

1) Inference Is the New Race

For readers new to the AI market, it helps to understand the two phases of AI computing. Training is the process of building an AI model. A company feeds enormous amounts of data through thousands of chips over weeks or months, and the model learns from it. It is a one-time, upfront cost, similar to building a factory.

Inference is what happens after: every time you ask ChatGPT a question, generate an image, or use an AI coding assistant, the finished model is running a calculation to produce your answer. That is inference. It happens billions of times a day, and every single request consumes computing power.

The first wave of AI spending went into training ever-larger models. But a model only creates value when people actually use it, and usage is exploding.

Inference is expected to represent roughly two-thirds of all AI compute in 2026, up from one third in 2023. Unlike training, inference is a recurring cost that grows with adoption, which is exactly why speed and cost per request have become the new battleground. This is the wave Cerebras is positioned to ride: its chips are built to run models faster than traditional GPUs, and its deals with OpenAI and Amazon are inference deals, not training deals.

2) Nvidia’s Monopoly Is Starting to Weaken

For years, buying AI chips meant buying Nvidia. There was no real alternative, and that scarcity let Nvidia charge premium prices and earn exceptional margins.

That is starting to change. Amazon, one of the largest chip buyers in the world, now runs AI workloads on a combination of its own in-house chip (Trainium) and Cerebras hardware, using each one for what it does best. When the biggest customers start mixing and matching suppliers instead of depending on a single vendor, that tells you something.

Nvidia still dominates the market and its software ecosystem remains a huge advantage. But the “there is no alternative” premium that supported its valuation gets harder to justify when credible alternatives exist.

The winners from this shift are the challengers: specialized chipmakers like Cerebras and Groq, and the custom chips that Amazon, Google, and OpenAI are designing themselves. The AI chip market is moving from a monopoly toward an oligopoly, and that creates opportunities further down the food chain.

There was simply no way one company could make AI chips for the entire planet.

3) Customer Concentration Is a Feature of the AI Trade and a Systemic Risk

Sean described customers so large they’re “almost resellers or distributors.” The entire AI supply chain now hangs on a handful of buyers (OpenAI, Amazon, Meta, Google). If one slows spending, the shock propagates through every supplier simultaneously. Correlation across AI names is so high, probably higher than the headline suggests.

4) The Talent Migration Is Immense

Cerebras doubled headcount in 18 months, pulling engineers from AWS, Meta, Nvidia, and AMD, while “other areas of technology have seen more pressure.” Human capital is concentrating in AI infrastructure the way it concentrated in internet infrastructure in 1999. That’s bullish for near-term execution.

Yes, this is a classic late-cycle signal worth monitoring when comparing it to the dot-com bubble. However, at that time, cash flows were basically zero.

Now, the large companies in the AI trade, like Nvidia and the hyperscalers, are printing cash. If technology improves and more cash is generated over the long term, that’s not a bubble. When you have hundreds of thousands of the smartest brains in the world working on one new technology, it is simple to expect significant improvements in that technology.

5) Information Asymmetry in AI Names Is Shrinking

Management teams now treat top Substack and X researchers in the same way as institutional investors. When a company reviews a 50-page SemiAnalysis report pre-IPO and leaves comments, the edge is compressing.

Alpha in AI infrastructure will come less from information and more from interpretation, timing, and valuation discipline. Which is why we believe good research and contrarian thinking matter even more.

6) Valuation Is Narrative-Driven Until Cash Flows Arrive

That is an obvious one. With profitability not expected before 2027 and free cash flow in 2028, prices in this cohort trade on news flow and sentiment.

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4. The Debate: The Promise and the Challenges of Cerebras

4.1 The Bull Case: A New Path for AI Computing

The bull case for Cerebras is built around one main idea: as AI models become larger and more complex, the industry may need new approaches to computing. Today, most AI systems rely on GPUs, but scaling these systems requires connecting many chips together, which can create performance limitations.

Cerebras takes a different approach by combining computing and memory on a single large chip, which bulls believe could help address some of the challenges created by increasingly complex AI models. The Silicon Valley-based company is competing in a rapidly growing AI infrastructure market alongside companies such as Nvidia, Groq, and SambaNova.

While Nvidia benefits from a strong software ecosystem built over many years, bulls believe Cerebras’ hardware design could give it an opportunity in areas where speed and efficiency are especially important.

The company is also expanding beyond hardware sales through cloud services, allowing customers to access its systems without purchasing the infrastructure themselves and potentially creating a more recurring revenue stream over time.

4.2 The Bear Case: The Challenges Behind the Ambition

The bear case for Cerebras is built around three main concerns:

1. Customer concentration

2. Software Ecosystem

3. Long-term scalability

First, Cerebras currently depends on a small number of large customers. While the company is working to expand its customer base, bears argue that relying on a few major contracts could make future revenue less predictable.

Second, Nvidia’s advantage extends beyond its chips. Its CUDA software platform, which allows developers to build and run AI applications on Nvidia hardware, has been developed over many years and is widely used across the AI industry. Bears believe this could make it more difficult for Cerebras to convince customers to switch to a different ecosystem.

Finally, some investors question whether Cerebras’ technology can continue to support the increasing demands of future AI models. Dylan Patel from SemiAnalysis has raised concerns around whether the company’s current approach can scale as AI systems become more complex. These questions remain an important part of the long-term debate around Cerebras.

5. Valuation: An Analyst’s Nightmare

The company’s revenue growth is gigantic, expected to 20x over the next 4 years.

Wall Street expects this company to turn profitable in 2027 and for cash flows to be positive in 2028. On the positive side, the company has enough cash on the balance sheet to cover upcoming needs. The EBITDA forecast could make sense.

However, we are less confident about the cash flow forecast. The need for continuously investing and staying at the forefront of the AI race will persist, and we think the company will prefer to spend to continue trying to develop leading technologies rather than having cash generated and distributed to shareholders.

Is this company worth $20B, $50B, or $100B? Usually, it is quite easy to triangulate toward a valuation range when the company generates cash, but in this case, valuing the company with any confidence is tough enough that it makes the share price mainly dependent on short-term news.

6. Our Final Thoughts on Cerebras

The reason we are not currently adding Cerebras is that, while it operates in a highly attractive and growing sector, it has not yet proven that it can generate the level of free cash flow and profitability needed to justify its current valuation.

We believe Cerebras has built an impressive product with significant potential, but we would like to see the company demonstrate that it can successfully scale its business and translate its technology into sustainable financial results.

Another important factor is timing. At Aurelion Research, we are not only looking for great companies; we are also looking for the right moment to invest.

While we clearly see a compelling long-term opportunity, we believe it is still early in Cerebras’ journey as a public company. It has an interesting position in the AI infrastructure market, but the timing is not right yet.

The Aurelion Team

Questions? Reach us directly on Substack or at contact@aurelionresearch.com.

Disclosures & Methodology

All forecasts and estimates are based on publicly available information, management commentary, and our own modeling assumptions. This report reflects independent analysis by Aurelion Research. All opinions and estimates expressed herein reflect the author’s judgment as of the date of publication and are subject to change without notice. This publication is for informational and educational purposes only and does not constitute investment advice, a solicitation, or a recommendation to buy or sell any security. Aurelion Research and its analysts may or may not hold positions in securities discussed. Use only where legally permitted.

The name suggests it covers only micro caps, but in reality several small-cap companies were present. We were there mostly for the small-caps, where trading volumes and liquidity are higher, which matters to us. Most companies were U.S., with a few Canadian ones.

We attended the Toronto edition last year and found several interesting ideas there, so we expected this trip to be worth it.

It was very much so.

Out of 138 companies, we narrowed the field to 14 through initial research and conducted 1-on-1 meetings with the CEO or CFO. From there, 6 names stood out as most compelling.

This piece is for investors looking for new ideas. While you should always do your own research, this should give you useful insights gathered from a week of research and in-person meetings with management.

Importance of being critical toward management

Being able to talk directly with management at these conferences is invaluable.

After a few years of doing this, we have developed a few techniques to get them to talk more on what they actually expect from the business. Last year, after the 1-on-1 meetings at Planet MicroCap Toronto, we classified the companies we liked and the ones we dismissed. You can find them here:

For transparency, the 5 companies we identified as compelling have returned an average of 35% since the conference in October 2025, compared to -20% for the 7 we dismissed.

The most important thing is to stay away from promotional CEOs. Last year at the Toronto conference, we fortunately saw right through ZOOMD.

Their goal appears to be to increase the stock price in order to then raise capital… The company (addtech business) ended up losing two major clients without new demand to fill those, which led to a -76% return over 8 months.

We are not adding any of the companies discussed in this piece to our main portfolio, the Aurelion Index, yet.

At this smaller company stage, much of the analysis comes down to whether management is outright promotional or whether they can actually deliver. Most of them were pitching us the dream. We asked several questions designed to keep them accountable and we’re now waiting a quarter or two for proof, alongside additional research on our side, before potentially adding one or two.

For micro and small-cap companies, the risk is generally higher, but some below can honestly become multibaggers.

Some readers may want to buy their favorite names here and be earlier than us, which is a completely fair approach as well. Last year, we used that same process (conference, then further research) and this is how we ended up finding a company in our portfolio.

To the best of our knowledge, everything we discuss is public information.

Structure

We start by ranking all the companies using charts based on:

1. Our estimated risk-reward

2. Short-term versus long-term attractiveness

3. Market capitalization

4. Revenue growth versus EV/EBITDA multiple

We then present the 6 companies.

We also discuss the 8 we dismissed after taking to management. However, we think that depending on your investment style, some may still be a fit, so we have still detailed them. If you want to get the most out of this article with limited time, the best approach is to read the 6 ideas, then quickly skim the dismissed companies in case you find something interesting.

As always, feel free to let us know your thoughts on the companies.

Table of Contents

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If you’re new here and haven’t really looked into CLBT yet, no worries, we’ve included our initial report below, followed by an updated version so you are fully up to date and have a better understanding of what we are discussing in this post.

Before starting our recap of the call with the company, below is what we will cover. We kept it brief and focused on the most relevant points.

Table of Contents

1. Go-to-Market Strategy & Sales Model

2. AI Revenue Potential & Monetisation Strategy

3. Competitive Positioning & AI Disruption Risk

4. Market Perception & Operational Execution

5. Revenue Mix Evolution

6. Long-Term Risk Assessment in an AI Context

7. Our Updated Price Target

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We have seen a number of reports suggesting ways to benefit from the 2026 Football World Cup. Most of them focus on GDP growth or broader economic impact for the three host countries (Canada, the US, and Mexico, or even potential winning countries), or on more speculative ideas such as trading futures, betting markets, or sports betting apps. But we are not traders, and we are not looking at betting-related themes or anything of that sort.

Instead, we identified a company that we expect to benefit not only from the World Cup, but also from other factors that support the long-term story, rather than a one-off event, which is also what makes the opportunity compelling.

Before getting into the company itself, here are a few things that stood out to us.

We believe it is significantly misunderstood by analysts covering the stock and by the market more broadly, and that is where we strongly disagree.

We can understand the sentiment, given that it is a company that was historically highly leveraged with a large debt burden, but that profile has been improving meaningfully over time, and in our view it is now a very different story that the share price has yet to reflect. We believe the setup is compelling for two reasons.

First, the World Cup acts as a near-term catalyst.

Second, it sits alongside our view that Latin America is still in the early stages of a broader bull cycle. That combination is what we find most attractive.

Table of Contents

1. Introducing the Company

2. Investment Thesis

3. World Cup as a Key Catalyst

4. What’s Going On With the Company?

5. Where We Differ From the Market

6. How We Value the Company

   6.1 Valuation Metrics

   6.2 Price Target

7. Key Investment Risks

   7.1 Downside Risks

   7.1 Upside Risks

8. Our Final Take

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After an intense Q1 2026 earnings season and a meaningful number of companies in our model portfolio running up (and some down), we are doing an update on every holding.

Quite a few stocks have been exited and added since the start of the year, either because the thesis played out or the risk/reward became less attractive.

Even though we cover these stocks regularly in separate posts and in our Substack chats, getting a clean view in one place just makes sense.

Every stock that we hold and mention here is a company we believe is a buy right now. In our view, there are no “old stocks” that have run up too much. If there were, we would have removed them and of course announced it.

We always try to speak with the management teams of our companies, usually the CEO, CFO, or Investor Relations, and have included what they told us below. We believe this is one of the best ways to develop a long-term edge and a deeper understanding of a company compared to the market.

In addition to having a specific thesis for each company, most of our holdings are supported by broader investment themes, which we believe make them compelling investments over both the short and long term.

With 19 companies, we keep the article concise and only discuss value-add information. Links to the full in-depth report for each company are included for further reading.

Our model portfolio, that we called the Aurelion Index, consists of our current highest-conviction equities, and is available to paid subscribers here.

As always, anyone can view the real-time performance on Plutus here. This is what it looks like as of June 4 (117% since inception on June 30, 2025):

Thank you for following our work. Below are our current best ideas.

Several more are on the way!

Feel free to ask questions in the comments, we always try to answer all of them.

Current Portfolio Positions (June 4, 2026)

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We do not add positions often. When we do, it is because we have done the work, the timing is right, and we have genuine conviction. This is one of those moments.

It operates in one of the most defensible corners of the semiconductor value chain. The kind of business where customers do not switch suppliers, where expertise takes decades to build, and where the AI infrastructure wave creates demand that has nowhere to go but up. Very few companies in the world can do what this one does, and that is precisely why we want to own it.

While the world debates which AI model wins, China is building the infrastructure to make the entire debate irrelevant. And while most investors are focused on who makes the chips, we think the real opportunity sits one step earlier. In what makes the chips possible in the first place.

China is quietly winning the chip war. Morgan Stanley estimates the domestic self-sufficiency ratio has gone from 10% in 2021 to 41% in 2026, with industry projections pointing to 86% by 2030.

We know that sounds optimistic. But think about BYD. A decade ago, most people in the West had never heard of them. Today they are the largest electric vehicle company in the world, outselling Tesla in most markets. That did not happen by accident. When China commits to leading a sector, things tend to move faster than the rest of the world expects.

Semiconductors are following the same path. US export restrictions only accelerated that by removing any remaining hesitation in Beijing. Cutting off access to leading edge technology turned chip self-sufficiency from a long term goal into a national obsession backed by unlimited resources.

We want to be clear about something. We are adding this name because the fundamentals and timing align, the way they always have at Aurelion. This has nothing to do with chasing AI momentum. We have been early on oil tankers, dry bulk, uranium, and financial companies. We apply the same discipline here.

The segment we are most focused on within this theme is Wafer Fab Equipment, or WFE. Every time chip complexity increases, manufacturers need more equipment to process each wafer. That relationship is not going away.

According to Counterpoint Research, total WFE revenues grew 12% YoY in 2025 to $143.1B, driven directly by AI infrastructure buildout and surging demand for leading edge logic, High Bandwidth Memory, and advanced packaging.

The top five manufacturers alone grew revenues 14% to $114.6B. Counterpoint forecasts another 11% growth in 2026, reaching $159.1B.

The more complex the chip, the more equipment it takes to build it. That is the thesis, and it compounds with every new generation.

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His view was simple: insurance companies collect cash upfront and pay claims later, creating powerful long-term compounding when managed properly.

In his own words: "The best investment you can make is in insurance companies. They take in money before they pay it out." We share that view, which is why insurance remains a core focus of our financial coverage.

Financial companies are not always the easiest businesses to analyze. The accounting can be complex, the numbers move around for reasons that have nothing to do with the underlying health of the business, and it is easy to draw the wrong conclusions from a headline number. That is exactly why we go through the earnings reports, the calls, and the filings, so we can separate what actually happened from what the numbers appear to show.

Both companies in the space that we hold in the Aurelion Index recently reported Q1 2026 earnings.

In this report, we go through both quarters in detail, why we hold them, what management said, and our view on what it tells us about each business.

You will also find our full portfolio and current weights at the end, which you can always access here as a paid subscriber.

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