u/Carlene_Trammel

Every junior has a story about their land package. Few have a CEO who's spent years actually running exploration programs for other miners. That's a different kind of experience - and it's why I'm watching NovaRed.

When people think about junior mining companies, the conversation usually starts with the property.

How big is the land package?

What metals are they chasing?

How close is the nearest producing mine?

Those questions matter, but they're only the beginning.

Exploration succeeds or fails based on what happens after the claims are staked. Every project has to move through mapping, sampling, geophysical surveys, data interpretation, target ranking, and eventually drilling. Each step builds on the one before it, and mistakes early in the process can waste an entire exploration season.

That's why I pay attention to operational experience as much as geology.

Rangefront Geological, now operating as Rangefront Mining Services, works on exactly that part of the business. The company provides geological consulting, field crews, sampling, geophysical support, mapping, 3D modeling, technical reporting, and exploration management. Those services are what move a project from an idea on a map to a drill-ready target.

Brian Goss brings that background to NovaRed Mining (OTC: NREDF).

He founded Rangefront Geological, leads Rangefront Mining Services, and serves as President and CEO of NovaRed. That gives him experience with the practical side of exploration, where budgets, logistics, and technical decisions all have to come together before drilling begins.

For me, that is an important piece of the picture.

NovaRed still has an early-stage project. Wilmac has no defined resource, no production, and every exploration company eventually has to prove its case through drilling and assay results.

None of that changes.

What stands out is that the company is being led by someone whose career has been built around running exploration programs, not just talking about them.

When I look at junior explorers, I try to separate the story from the process.

Projects can look impressive in presentations. The companies that make steady progress usually have people who know how to organize fieldwork, manage technical programs, and keep exploration moving from one milestone to the next.

In a business where most discoveries begin with disciplined execution, that experience is something I think deserves a closer look.

Discovery starts with geology. But progress starts with execution. NovaRed's leadership has built a career on the execution side - and that's exactly what early-stage explorers need most.

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Copper used to trade on grade and tonnage. Now it trades on location and access. The policy shift is real - and it's changing the watchlist.

One thing I keep seeing in copper discussions is a shift in focus.

It is not just about grade or tonnage anymore. It is about where the copper sits.

Recent White House recommendations on copper imports included a 15% tariff on refined copper starting in 2027, rising to 30% in 2028. They also touched on domestic sourcing rules and tighter controls on copper scrap exports.

Even if the details change before anything is finalized, the direction is clear.

Copper is being pulled into industrial policy and supply chain security planning.

That changes how I look at early-stage exploration names.

Instead of only asking who controls copper assets, I also look at where those assets are located and how they fit into regional supply chains.

A lot of my watchlist sits in Canada for that reason, including KDKCF, CAMNF, BADEF, and NREDF. These are all tied to jurisdictions with existing mining infrastructure and established permitting systems.

NovaRed Mining (OTC: NREDF) is still early-stage. There is no defined resource at Wilmac and no production. It is a large land package in British Columbia’s Quesnel belt, and most of the value case still depends on future exploration work.

The company still has to prove the geology through drilling and field programs.

But the jurisdiction piece is already in place. It sits in a region with active copper production nearby and established access to infrastructure.

That is where the shift is showing up.

Copper exposure is no longer being judged only by what is in the ground. Location and supply chain alignment are becoming part of how the opportunity is framed before anything is built.

The tariff talk isn't just noise - it's a signal that copper is being recategorized. And that recategorization favors assets in the right zip codes.

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Infrastructure buildouts don't wait for mining timelines. That tension is what makes early-stage copper exposure worth following - even if most juniors never make it. What's your approach to the physical side of AI?

AI usually gets framed as software.

Models, chips, cloud platforms, data centers. That is the visible layer.

Underneath that is a physical buildout that looks closer to heavy industry than tech. New data centers need power delivery systems, substations, transformers, cooling infrastructure, and expanded transmission capacity. Most of that depends heavily on copper.

That is why copper keeps showing up in long-range demand estimates.

S&P Global projections discussed in Reuters coverage point to copper demand rising strongly into 2040, with potential structural shortfalls if mine supply and recycling do not scale fast enough. The gap is driven less by consumption spikes and more by how slow new mining supply is to develop.

A copper project can take more than a decade from early exploration to meaningful production. That delay is what creates the forward-looking squeeze in supply projections.

Because of that lag, some market participants look earlier in the pipeline instead of focusing only on producing assets.

That usually means junior exploration companies.

NovaRed Mining Inc. is one example that fits that early-stage category. It is still in exploration, with no defined resource and no production. The company is effectively positioned at the starting point of the copper supply chain rather than the production end.

At this stage, the value is not in output. It is in whether the ground eventually proves economic and whether the project advances through drilling and technical validation.

The broader idea behind this type of positioning is simple.

If copper demand continues to track AI infrastructure buildout, grid expansion, and defense-related manufacturing, then early-stage copper exposure becomes part of the upstream layer of that theme rather than a separate commodity trade.

That is the lens I have been using when I look at names like NovaRed.

Software scales in months. Mining scales in decades. That mismatch is the whole thesis for looking upstream. Anyone else tracking copper explorers as a backdoor into the AI infrastructure trade?

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Most mining AI hires are just buzzwords in a press release - but this one actually made me read the fine print. The name isn't the story; the resume is.

The part of NovaRed’s announcement that matters is not the phrase “AI advisor.”

It is the background of the person they hired.

Dr. Olamide Oladeji has worked across applied machine learning, robotics, geospatial systems, and decision modeling. His academic path includes Stanford and MIT, along with recognition as a Forbes 30 Under 30 honoree. His work sits in areas like computer vision, NLP, and systems that deal with messy real-world data rather than clean lab datasets.

That matters because mineral exploration does not produce clean inputs.

NovaRed Mining (CSE: NRED) is trying to apply AI through its MetalCore platform, which ranks and evaluates exploration targets using geospatial and geological data. That kind of system depends less on raw model complexity and more on how well it handles incomplete data, uncertainty, and conflicting signals from different datasets.

In exploration, teams are weighing geology, geophysics, geochemistry, historical drilling results, terrain constraints, claim boundaries, and permitting risk before deciding where to spend capital. A poor ranking system does not just miss opportunities. It burns drill budgets on the wrong ground.

That is the space Oladeji’s background actually connects to.

His experience lines up with problems like spatial prediction, autonomous systems, and decision-making under uncertainty. Those are closer to exploration targeting than generic AI branding.

None of this changes the core reality of NovaRed’s situation.

There is no defined resource at Wilmac. There is no production. Drilling still determines whether anything economic exists in the ground. AI does not replace that step.

What changes is how seriously the targeting layer can be evaluated going forward.

If MetalCore evolves with someone like this involved, it moves from a marketing angle into something closer to a structured decision tool for exploration planning.

The asset still has to prove itself in the field. The technology layer just became easier to take seriously on paper.

AI won't find copper for you - but it might tell you where not to drill. And in exploration, that's half the battle. What's your take - real edge or just another headline?

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The AI race isn't won in the cloud - it's won in the ground. While everyone's chasing the next software darling, I'm tracking the metal that makes the magic happen.

The more I read about AI infrastructure, the more I think people are overlooking one piece of the puzzle.

Every discussion seems to revolve around GPUs, cloud providers, and the latest language models. Those companies deserve the attention they're getting, but none of that hardware works without a massive amount of physical infrastructure behind it.

Every new data center needs power cables, transformers, substations, cooling systems, switchgear, and miles of electrical wiring.

Copper runs through almost all of it.

One forecast that stood out to me came from S&P Global. It projects global copper demand could rise by roughly 50% by 2040, climbing from around 28 million metric tons today to about 42 million metric tons each year. If mine supply and recycling don't keep pace, the market could be short by more than 10 million metric tons annually.

That's a pretty big gap, especially when building a new mine often takes a decade or longer.

Because of that, I've started following copper companies across the entire supply chain instead of looking only at established producers.

Large mining companies will probably attract institutional money first because they already have operating assets and steady production. The companies further upstream are much riskier, but they're also where future discoveries have to come from.

One name I've been researching is NovaRed Mining (OTC: NREDF).

It's important to be clear about where the company stands today. NovaRed isn't producing copper, doesn't have a defined mineral resource, and isn't generating revenue. It's an exploration company, so the risks are much higher than they are for an established miner.

Its main project, Wilmac, is located in British Columbia's Quesnel porphyry belt, roughly 10 kilometers west of Hudbay's Copper Mountain Mine. Being close to an active mining district doesn't guarantee another discovery, but it does provide useful geological context when evaluating exploration projects.

The company's plans for 2026 include additional soil sampling, IP and AMT geophysical surveys, and a drill program if permits are in place on schedule. Those milestones should provide a better picture of what the project actually contains.

For me, the bigger picture is what keeps this on my watchlist.

The AI buildout doesn't stop at semiconductor factories. It reaches into electrical infrastructure, mining, transmission, and the raw materials needed to support all of it. If copper demand keeps climbing over the next decade, investors may spend more time looking at exploration companies that control prospective ground in established mining regions.

NovaRed still has a lot to prove, and exploration success is never guaranteed. Even so, it's one of several early-stage copper names I'm following as the infrastructure side of the AI story continues to develop.

Anyone else spending more time researching copper instead of focusing only on AI software and chip companies?

Exploration is a lottery - but I'd rather buy a ticket in a proven district than chase hype in a vacuum. NovaRed's still early, but the macro backdrop is hard to ignore. What copper names are you watching for the physical AI buildout?

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Most mining press releases blur together. Then you see: Stanford PhD, dual MIT master's, Forbes 30 Under 30 - at a junior copper explorer. That's worth a second look.

Sometimes I scroll through mining news and barely register it.

This one stopped me for a second.

NovaRed appointed Dr. Olamide Oladeji as Strategic Advisor for Robotics and AI.

His background is unusually heavy on the technical side:

PhD in Applied AI from Stanford
Stanford Knight-Hennessy Scholar
Dual master’s degrees from MIT
Forbes 30 Under 30
MIT Clean Energy Prize winner
Founder of a tech platform used by over 200,000 businesses across 40+ countries

That’s the kind of profile you normally associate with AI startups or well-funded tech companies, not an early-stage copper-gold explorer.

NovaRed is still a junior mining company working on exploration in British Columbia. The core value still sits in geology, drilling results, and whether the Wilmac project actually delivers.

But there’s a second layer forming around MetalCore, their internal platform for organizing exploration data and helping rank targets.

That’s where this hire fits.

Mining companies are starting to compete for a different type of talent: AI researchers, data engineers, robotics specialists, and geospatial analysts. Not just geologists and field operators.

If that shift continues, the advantage may not only come from owning good ground.

It may come from how well a company can process information before a single drill hole is drilled.

One thing I keep coming back to:

When someone with this level of technical background joins a small, early-stage company, it usually means they see optionality in the direction of the technology, not just the industry it sits in.

If that shift continues, the advantage may not only come from owning good ground - it may come from how well a company can process information before a single drill hole is drilled.

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Geologists, drill rigs, and rock samples - that's the usual mining picture. NovaRed just added an AI advisor with a Stanford PhD and dual MIT master's. That's a different kind of exploration toolkit.

Mining isn't exactly the first industry that comes to mind when you think about cutting-edge AI.

It's usually geologists, drill rigs, rock samples, and long days in the field.

Then NovaRed (CSE: NRED) announced Dr. Olamide Oladeji as its Strategic Advisor for Robotics and AI.

His background is impressive: a Stanford PhD in Applied AI as a Knight-Hennessy Scholar, dual MIT master's degrees in AI and Technology Policy, Forbes 30 Under 30 recognition, and the MIT Clean Energy Prize. His work covers robotics, autonomous systems, computer vision, geospatial analytics, and machine learning.

That's a very different résumé from what you normally see at an early-stage exploration company.

The comparison to Tony Stark is obviously just for fun, but the broader trend is real. Mining companies are starting to rely on larger datasets, better computing, and more advanced analytics to decide where exploration dollars should go.

NovaRed has been building MetalCore alongside its Wilmac copper-gold project. The platform is intended to help organize exploration data and improve how potential targets are ranked before drilling begins.

None of that changes the fundamentals. Wilmac still has to be advanced through mapping, sampling, geophysics, drilling, and assay results. That's where the project will ultimately succeed or fail.

What this appointment does suggest is that NovaRed wants AI to play a larger role in the exploration process rather than treating it as a talking point.

Whether that translates into better exploration results is something investors will be able to judge over time. For now, it's an interesting direction to watch as more technology starts making its way into an industry that has traditionally moved at a much slower pace.

The comparison to Tony Stark is obviously just for fun, but the broader trend is real. Mining companies are starting to rely on larger datasets, better computing, and more advanced analytics to decide where exploration dollars should go.

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Junior miners used to hire geologists and mining executives. Now they're adding people with backgrounds in AI, computer vision, and data science. That's not a replacement - it's a signal.

I've been paying more attention to who mining companies are hiring, not just what projects they're working on.

Lately, I've noticed a few companies adding people with backgrounds in AI, computer vision, and data science instead of sticking to the usual mix of geologists and mining executives. That doesn't replace exploration, but it does suggest they're investing more in how exploration decisions get made.

NovaRed Mining is one example. The company recently appointed Dr. Olamide Oladeji as Strategic Advisor for Robotics and AI. His background includes graduate work at Stanford and MIT, with experience in applied AI, autonomous systems, computer vision, and technology policy.

The appointment fits with what NovaRed has been building around MetalCore, its platform for organizing exploration data and supporting mineral targeting through predictive modeling.

Fieldwork is still the foundation of exploration. Geological mapping, geophysics, sampling, and drilling are what determine whether a project has economic potential.

Where technology can help is before those stages. Exploration teams work with large volumes of geological, geochemical, geophysical, and historical data, and every drilling decision carries a real cost. If better data analysis helps narrow the list of targets before a rig arrives, that could improve how exploration budgets are used.

It's still too early to know how much impact platforms like MetalCore will have in practice. The proof will come from future exploration programs and whether these tools lead to better target selection over time.

For anyone following mining technology, I think this is an interesting trend to watch. The projects themselves remain the main investment story, but the way companies analyze data before drilling is starting to evolve, and the people they bring onto their teams reflect that shift.

It's still too early to know how much impact platforms like MetalCore will have. The proof will come from future exploration programs and whether these tools lead to better target selection over time.

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The advisor title isn't the story. It's what it connects to inside the company that actually matters.

The part of NovaRed’s latest update that stands out isn’t the advisor title on its own. It’s what it connects to inside the company.

NovaRed appointed Dr. Olamide Oladeji as Strategic Advisor for Robotics and AI. His background is unusually dense for a junior mining advisory role: Stanford PhD in Applied AI as a Knight-Hennessy Scholar, dual MIT master’s degrees in AI and Technology Policy, Forbes 30 Under 30, and MIT Clean Energy Prize recognition. His work spans computer vision, NLP, geospatial analytics, and machine learning systems.

That matters more when you place it next to what NovaRed is building.

$NRED / $NREDF is still an exploration-stage copper-gold company. The Wilmac project in British Columbia is the core asset, and it still depends on conventional exploration work: field mapping, geophysics, sampling, and drilling.

Alongside that, the company is developing MetalCore, an internal platform focused on mineral targeting and exploration data analysis. That is where the AI angle sits.

Tools like this only matter if they change how decisions get made before drilling starts. Exploration programs generate large, uneven datasets from geology, geophysics, historical drilling, and remote sensing. The hard part is turning that into a consistent way of ranking targets and allocating capital.

That is the layer MetalCore is aimed at.

The appointment doesn’t reduce geological risk at Wilmac. It doesn’t change what comes out of the ground. But it does add technical weight to the idea that NovaRed is trying to build a structured decision system on top of its exploration work, not just a standard junior mining setup.

If MetalCore starts influencing how targets are selected and prioritized, then this kind of hire becomes more than symbolic. It becomes part of the operating model.

For now, the company is still early-stage and speculative. The difference is that the AI layer around the exploration work is starting to look more intentional.

If MetalCore starts influencing how targets are selected and prioritized, then this kind of hire becomes more than symbolic. It becomes part of the operating model.

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Predictive geological modeling. Autonomous systems. Environmental intelligence. That's not basic data organization - that's a platform designed to change how exploration decisions get made.

Most people will read the headline and stop at the obvious part:

NovaRed appoints an AI expert as strategic advisor.

The more interesting detail is buried in how the company describes the role and where it plugs into their platform.

NovaRed said Dr. Olamide Oladeji will support AI and robotics initiatives tied to MetalCore, including predictive geological modeling, autonomous systems, environmental intelligence, and decision support tools at the enterprise level.

That scope goes beyond basic data organization or property screening.

MetalCore is already positioned as a system for collecting and structuring exploration datasets. If those additional functions are actually being developed, it suggests the platform is aimed at more than just ranking land packages.

It starts to move into areas like:

• integrating geological and geospatial datasets into a single system
• building models that help prioritize targets before field work
• using automation to reduce manual interpretation of exploration data
• layering environmental and operational inputs into exploration planning

Mining hasn’t traditionally changed quickly when it comes to software. Most decisions are still built on fragmented datasets, specialist interpretation, and field validation.

That gap is where companies usually try to improve efficiency first, because drilling is expensive and mistakes are hard to recover from.

The key point isn’t the advisor on its own. It’s the direction implied for the platform he is being attached to.

Whether MetalCore ends up staying a supporting tool or becomes part of actual exploration decision-making will depend on how it shows up in future programs at Wilmac.

The key point isn't the advisor on its own. It's the direction implied for the platform he is being attached to.

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When a junior explorer appoints an AI advisor, the question isn't about the rocks - it's about how decisions get made before the drill even turns.

CSE: NRED added another layer to its development plan today with the appointment of Dr. Olamide Oladeji as Strategic Advisor for Robotics and AI.

The role is focused on applied systems rather than general capital markets work. NovaRed outlined areas including artificial intelligence, robotics, computer vision, natural language processing, geospatial analytics, autonomous systems, and machine learning. The stated goal is to support how these tools are applied across mineral exploration, including predictive geological modeling, decision support systems, and resource optimization.

The company is still anchored by its main exploration asset. NovaRed controls the Wilmac copper-gold project, a 16,078-hectare property in British Columbia’s Quesnel porphyry belt, roughly 10 km west of Hudbay Minerals’s Copper Mountain Mine. Work at Wilmac still depends on standard exploration steps: field mapping, geophysics, drilling, and assay results.

Alongside that, the company is building MetalCore, a geospatial data platform designed to consolidate and analyze mineral exploration datasets. That includes integrating large historical and modern datasets to help rank exploration targets before drilling decisions are made.

The AI component fits more directly into MetalCore than into the physical exploration program itself. The role is centered on how data is processed and interpreted, not on replacing fieldwork or drilling.

Exploration remains a physical process. Drill results still define outcomes.

What is changing is the amount of data feeding into early target selection. Large-scale geospatial datasets, when structured well, can shift how companies prioritize ground work and allocate exploration budgets.

The appointment adds another signal that NovaRed is building a parallel focus around data infrastructure alongside its copper-gold exploration work. The asset risk at Wilmac has not changed, but the workflow around how targets are evaluated is getting more technical.

The asset risk at Wilmac hasn't changed. But the workflow around how targets are evaluated is getting more technical - and that's a different kind of build.

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Better inputs, fewer bad calls - that's the logic behind every exploration budget. NovaRed's AI advisor is just formalizing what good geologists already do.

Software companies spend most of their time talking about decision quality. Better inputs, better datasets, fewer bad calls.

Mining already runs on that same logic.

Before a drill ever hits the ground, exploration teams go through layers of soil sampling, rock sampling, geophysics, historical drilling logs, and mapping work. Each layer is meant to reduce uncertainty and narrow down where money actually gets spent.

NovaRed’s recent move to bring in an AI and robotics advisor fits into that workflow. It doesn’t replace fieldwork. It sits on top of it.

The company is also expanding its MetalCore database while continuing early-stage work on the Wilmac project in British Columbia. There is no resource defined and no production story yet. It is still squarely exploration.

What stands out is how the data side is being built in parallel with the field side. Millions of records from past exploration work combined with new sampling data creates a larger pool of inputs for ranking targets before drilling decisions are made.

The practical outcome is simple. If better tools help narrow down weak targets earlier, fewer drills get spent on low-probability holes.

Mining is still a physical business. Rocks don’t change because the software improves.

But the way decisions get made around those rocks is slowly shifting toward more structured, data-heavy processes each year.

The rocks don't change because the software improves. But the way exploration teams decide where to spend money is evolving.

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I've been digging into Quantum-Si (QSI), and the biggest reason I'm interested isn't the stock itself. It's the person behind it.

Jonathan Rothberg has built a career around taking expensive lab equipment that only large institutions can afford and redesigning it around semiconductor technology. He's already done it more than once.

His earlier companies, 454 Life Sciences and Ion Torrent, helped push DNA sequencing onto semiconductor chips. That change made sequencing faster, cheaper, and much easier to scale. It also helped accelerate the next-generation sequencing market.

Later he founded Butterfly Network (BFLY), where the goal was similar. Traditional ultrasound machines rely on piezoelectric crystals, making them expensive and difficult to shrink. Butterfly replaced that architecture with a silicon chip and eventually launched the Butterfly iQ, a handheld ultrasound system that received FDA clearance across multiple clinical applications.

Different products, same idea.

Find a medical technology that's expensive, difficult to manufacture, and mostly limited to large hospitals or research centers. Move the core function onto silicon. Reduce cost, simplify manufacturing, and make the technology available to many more users.

Now Rothberg is trying to do something similar with proteins.

Most people know DNA because it's easier to explain, but proteins are where much of biology actually happens. While humans have roughly 20,000 genes, proteins exist in far more forms because of alternative splicing, post-translational modifications, and countless structural variations.

Today, most protein analysis depends on mass spectrometry. It's an extremely capable technology, but high-end systems often cost hundreds of thousands of dollars, require highly trained specialists, and aren't designed for widespread deployment.

That's where Quantum-Si comes in.

Its next-generation platform, Proteus, aims to perform single-molecule protein sequencing using semiconductor technology. If it succeeds, the economics could look very different from traditional proteomics equipment.

What has made me pay closer attention this year is the technical progress.

In April, Quantum-Si reported running customer samples on a Proteus prototype. Compared with its earlier Platinum Pro platform, Proteus showed improvements in amino acid coverage, read length, peptide identification, and overall sequencing output.

A few weeks later the company announced automated end-to-end sequencing on integrated Proteus instruments while increasing amino acid detection from 15 to 17.

That may sound like a small step, but I think it's one of the more important updates they've released.

Management has explained that the platform currently relies on six molecular recognizers to identify 14 amino acids. Their goal is to expand that system to eight recognizers capable of identifying all 20 standard amino acids. The optical hardware has already demonstrated it can distinguish eight fluorescent dyes, so much of the remaining challenge appears to be improving the underlying chemistry rather than redesigning the instrument itself.

That's an important distinction because hardware problems and chemistry problems usually have very different development timelines.

The roadmap from management is fairly straightforward.

Early-access systems are expected to reach customers this summer. Pricing is expected in the $300,000 to $500,000 range. Broader launch capabilities and upgrade programs are planned during 2026, with commercial release targeted toward the end of the year using 18-amino-acid coverage. Full 20-amino-acid capability is currently targeted for 2027.

I'm careful about comparing this directly with Butterfly Network because the stock performance there has been rough despite meaningful technological progress.

To me, the comparison isn't about predicting the share price.

It's about recognizing a familiar development pattern. Once semiconductor technology reaches a level where customers can actually use it, established platforms suddenly have a new competitor with very different manufacturing economics.

If Proteus performs well in real-world research labs, traditional mass spectrometry vendors could eventually face pressure in applications where cost, instrument size, and accessibility matter more than extracting every last bit of analytical performance.

Another piece that interests me is the software side.

Every run on Platinum Pro or Proteus generates sequencing data that flows into QSI Cloud. As adoption grows, the company could build a proprietary database of protein sequencing information. That kind of dataset becomes more useful as additional customers contribute data, especially if pharmaceutical researchers begin incorporating the platform into drug discovery workflows.

Financially, the company appears to have enough runway to reach the planned launch window.

At the end of the first quarter, Quantum-Si reported about $190 million in cash and projected funding into 2028. Current guidance calls for limited revenue during 2026 while keeping operating expenses and cash usage within management's planned range. That reduces the risk of needing immediate financing before commercialization begins.

Of course, there are still plenty of ways this story could disappoint.

I'll be watching whether amino acid coverage continues improving over the next several quarters, whether early-access customers publish encouraging feedback, and whether adoption moves beyond evaluation into routine use. I'll also be keeping an eye on Nasdaq listing compliance since share price remains a separate risk from the technology itself.

For now, my investment case is fairly simple.

Jonathan Rothberg has repeatedly shown an ability to move complex laboratory technologies onto semiconductor platforms. Quantum-Si is his attempt to apply the same strategy to proteomics. Whether it succeeds will depend on execution over the next couple of years, but I think it's one of the more interesting medical technology stories currently developing.

Position: Long QSI.

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