Transcription
[Music] Welcome all to the panel on AI and mining industry. Our panelists, our esteemed panelists are going to share their views and their uh knowledge about the intersection of AI and mining. Um, on the on the right-hand side, you have links uh to our um including my link to our website. So please feel free to click on the links, learn a little bit more about each of the companies, but also post your questions in the chat box. Uh, we would love to hear from you. So, we'll move on. Uh, Lori, can you please introduce yourself as well as your company?
Good morning everyone. Uh, well, I'm out here in uh Vancouver, so uh good morning uh for me and good afternoon for uh some of the others. Um, my name is Lori Clark. I'm the CEO of Onion Corporation. I come from the capital markets. Um, I was a trader back in the day when uh women were very scarce in that uh in that profession. Um, so all things governance, compliance, I've spent my career building businesses, six to date, and three I've sold. Um, and there are regulation and financial performance. It intersects in all my businesses. Um, I've chaired boards, sat on advisory panels, and worked across sectors where reporting isn't just paperwork. Um, it impacts valuation, capital access, and executive risk. Um, I founded Onion to solve a real problem uh and I see it firsthand uh and continue to see it. Uh, it's the chaos and inefficiency around risk and compliance reporting. So we built a system that uses AI uh to bring structure, speed, and um and strategic value to something every company has to do but nobody wants to spend time or money on. Um, so a little bit about Onion. Uh, we're the leading AI-powered software for risk and compliance reporting. Um, the cloud-based platform enables companies across industries and geographies to simplify complex regulatory and operational reporting. Um, and it identifies and manages key business risks. Um, Onyion's intelligence system transforms fragmented data into real-time auditable insights that support stronger governance, faster decision-making, and most importantly, improved access to capital.
Okay. Thank you, Lori. Daniel, thank you, Nina. Pleasure to be here. I'm Daniel Dinken, founder and CEO of Six. We build spaces for investors and companies to come together. And so tomorrow, we have a really large update where we're going to allow anyone to create investor communities, and we're going to be having coverage of about 120,000 public companies. So you can go and join your favorite investor community.
Excellent. Looking forward to that feature. Uh, Gordon.
Okay. Hi. Uh, good afternoon everyone. Gordon Bachton. Um, sorry, my background. I I was a geophysicist for years um for 10 years, co-founding a geophysics company that's still around today, Quant Geoscience. And then moved into become an investment banker um for uh a number of years, 25 years at CIBC, uh Rothschild, Newcrest Capital, National Bank. And then uh co-founded uh my other, my own investment bank, Griffin Partners, and which was then acquired by Standard Chartered Bank. Retired out of investment banking for a while, but spent a number of years on boards. I've been on 14, 15 public company boards, including IAMGold and NextGen Energy. Was one of the founding chairman there, Royal Gold, Vulta Resources. And so, been very part engaged in the in the mining strategic side of the business. And um um I'm currently um uh the board chair and senior advisor for Origin Merchant Partners, another merchant bank. And I'm um chair, chair of Black Loom Group, which is my own uh investment uh business, private equity business. And um I was uh the chair, past chair of Rethink Mining, which was formerly known as Canada Investment, excuse me, Canada Mining Innovation Council, which is where I became familiar with Very Discoveries. Um, one of the uh uh the key shareholders of Very Discoveries approached me to be get involved as an advisor to the company, and that was uh three or four years ago now. And I've since been asked to be chair, chair of the company um and um where it's moving and such. So um, Very is a private business that um has generated a portfolio of um uh mineral targets and projects using AI as an engine, our proprietary AI. And um, you know, years ago, Verai recognized the founders of Very that, you know, there's an insatiable need for metals. We all recognize that most of the discoveries that have been made have. And um the most of the obvious discoveries rather have been made. And new discoveries going forward are going to be need to be found undercover or areas where we have a man without uh without um um outcropping or any kind of for to facilitate getting into prospecting. So um uh we've developed our our technologies uh for uh delineating and uh helping to project where these uh potential mineral systems could be delineated. And then we've assembled, again, a portfolio. So we own the leases or claims around some of these targets in um in Canada, in the US, and in in southern Chile. And um, you know, we're now looking for partners to help move these projects along. So we're not a a SAS business. We're not a a service business, but uh we've used AI as our engine to be able to delineate and identify targets u better, faster, cheaper than uh we could have previously using conventional techniques. So that's that's the background.
Thank you, Gordon. Uh, Gordana.
Uh, good afternoon. Uh, I'm Gordana Slapcho. I'm the CEO and president of Eco Metals. I'm a mining engineer by by training. And I have been a COO of Anaconda Mining, that's now Signal Gold, and BMSI prior to joining Lumo in 2021. So Lumo is an explorer and developer of the critical minerals. Our flagship property, Looter, is uh you know, slated as the seventh biggest graphite deposit in the world, and for sure is the biggest one in North America, undeveloped one, for sure. And then LOM has benefited from many grants, including Department of Defense Title III, $8.35 million US, and federal government $4.9 million to pilot anode production of graphite from Looter. Aside of Looter, we also have a number of early-stage development properties in graphite, with the most promising one, Ruso. So that one has four zones discovered with two fairly well-defined with the grades really 10 to over 27% carbon content, very high-grade deposit. So that's going to be our focus as well this year. And also, we have acquired a new discovery in Newfoundland. It's called Yellow Fox. It is very prospective in antimony, 11% in some of the grab samples, but also in gold and silver, 60 and 73 grams uh per ton. So um, we're working with Metal Creek on that one, and we're looking to start field program as soon as the snow's off.
Excellent. Thank you. That's really a great introduction by um by all panelists. I'll just say a little bit about myself. I'm by training an automation engineer, degree in electrical. I've been in the uh industry for 40 years now. Uh mostly mining, some chemical, petrochemical, and offshore wind um in progressive management roles from engineering all the way to advisory and oversight roles on multi-billion dollar projects in some cases. And uh with the advancement of AI, of course, being an automation engineer, that part of me kind of woke up again and said, well, waited for this area to come to this stage for the past 40 years. I'm going back into automation. So, uh last couple of years, I um have been writing about AI mining, holding webinars for mining webinars. My um consulting business is in project development and capital project development and execution, but also in AI adoption process. Um, so that's a little bit about uh OMOD. Now, going back to why we are here, we really want to talk about the solutions that AI brings to the mining industry. One of the biggest issues is the um length of time it takes from the exploration or from the um prospecting phase to the operation. Right now, it's an average of 18 years. So, Gordon, where AI is, you have a spec very specific um model which is very interesting, but in its core, the exploration part or technology behind the uh ver exploration part is something that can shorten that um period, and then you can talk a little bit about your model as well, which can further contribute to uh shortening and and expediting a mining project development, basically.
No, sure. Thanks Narina. Um, one of the advantages that we feel that we've developed here um using AI as an engine is that we can, we have increased the high, we have a high degree of confidence or high degree of probability of intersecting u delineating a mineral system, not just in uh a a a mineral occurrence or alteration. So, we have a high degree of confidence using our technologies then to uh look at data, and we're not generating new data. We're looking at existing data, geophysics data, magnetics, gravity, other sources, and um using that uh training that our models on a known deposit type or a known a known line, and um then looking to see u from pattern recognition, you know, where are there predicting a similar type of mineral system in the vicinity um undercover? You know, is it within 20, 30 uh kilometers away, 200 kilometers away, and a complete uh new area? So, and there's a lot of areas of course that um are major mining camps that um there's a lot of open ground in that vicinity within 100, 200 kilometers or even 50 kilometers there is that because they're undercover, they're open, they're not stable. But we've been able to, using our AI as an engine, go in there and identify where we believe there's a high degree of probability. So, we we've we believe that um and to do this, we can generate these targets using uh this data that is available um in two months um to deliver these targets, and and we can take a project from um in five months to actual drilling. So, we can compress that time, that prospecting time that would typically take two to three, four years to get the level of confidence that we have that we've identified in our system in in uh in in months. We can compress it to uh to to months rather than years, and significantly, we can reduce the amount of cost to do that. Rather than going through spending two to three million bucks a year over that course period of time, we can compress that down into the highest probability of intersecting this mineral system within that those first couple of holes. So, we we can shorten up that um early stage discovery to production from 18 years, lopping off some of that front end of it.
Yeah, that's very interesting. Um, are you able to also to define the ore body to some extent, the extent of the ore?
Again, it's identifying uh a mineral system. So, it's rather than coming in and you get into an area that's that's under a covered area, overburden sitting up in in northern Ontario, for example, where do you start? There's no outcrop. Where do you start? Where do you begin and to to begin that prospecting? And so, we're able to, we can with a higher degree of confidence move in with uh to to do this, and we've done this successfully, and we have the verifier, we've verified um our our u our system and its predictive power, and it it it it works very, very well. And this is a VMS target up in Ontario that we partnered with with Conquest, for example, and um it delineated um um, you know, a a a VMS mineralization system. Um, is it economic? We need, of course, any system, any to determine, make that determination requires more more drilling, but we're out of the gate that we uh were able to, working with Conquest um, we sorry, had these targets identified within, as I said, within uh several weeks, and then it developed that first drilling was several months after that.
Oh, that's that's definitely one of the most critical phases in the um mine development. So, uh moving on to um regulation, which is also, well, it follows really a life of the mine development. So, Lori, from your side, uh can you please introduce your um Onion um program, package, solution, and walk us through um features and capabilities and some of the potential um stories if you want to share examples.
Sure. Um, thank you very much, Darina. Um, you know um, let's I want to distill something down to a very, very basic element, and that is that um, you know, I'm known on the street, I'm known on the street, and that's, you know, on Bay Street, very much because of the fact that one of my companies uh was DataFile Software, which was bought out by ADP and and now known as Broadridge. Um, so a lot of the investors uh around the world have used um our system. Um, and so when we um, when we think about AI, which is really um, you know, when we want to talk about AI, it really is um, you know, math, or or rather, it's applied math at scale. Okay, that's really what it is. And so when you're thinking about any kind of uh system, and you're taking in huge data sets, and we're used to that. We're used to taking a lot of very enormous amounts of data. Um, and so what you're really trying to do is apply algorithms and programs to this massive amount of data. Uh, and trying to um, come out with a result that is pragmatic and practical for any kind of industry. Now, yes, we have a system that when we developed it, Onion, um, myself, you know, and my team, um, and a lot of us came from uh a lot of the Onion team is from uh capital and came from uh those days at DataFile and and Broadridge. Um, so we all have a lot of very financial backgrounds. Um, but what we did is we took a very focused approach. Um, and and we apply it to not only the mining industry but other industries as well. Um, and you know, a lot of industries are facing enormous pressure on timelines and capital. You know, especially right now, a lot of pressure out there. Um, and so we solve for one of the most overlooked constraints. Um, and it's the burden of risk and compliance reporting, and it stands between, and this is where it might sound very unglamorous, which it is. Um, however, it stands between companies and investor trust. Okay. So that's what we're trying to bridge. So our system uses AI to automate, to streamline, and digitize. Uh, and today we're talking about min mining companies, how mining companies report their risk, their compliance, their governance to the SEC, and it's across multiple sites, jurisdictions, disclosure frameworks. Um, and it's really communicating that information so that it provides a single, auditable, real-time platform that companies use to communicate risk clarity to boards, regulators, and investors. Um, so what you know, now what does that really mean in practice, instead of SP? So, what our client or customers, I don't like using the word clients because it makes me sound like a lawyer, which I'm not. Um, so our customers are actually spending less time chasing data and more time preparing for capital. Um, in fact, we've had several customers uh because they were on the Onion system, and I can give you, you know, many use cases, um, that because they were on the Onion system, their investors were able to um glean information from their projects or from their new ventures or from their project, you know, whatever they were doing or products. They were able to glean um the real story behind the metrics on the system, and so they were able to, you know, actually unlock financing and reduce their cost of capital. Uh, and this has happened over and over uh by being on the system. And it is one way for me, as a past trader, to really um see the result of AI in action, because when you're putting uh, you know, customers um onto the system that have been waiting, you know, maybe they're trying to do a financing round, and they've been at it for two, three years, and all of a sudden they, you know, the investor um goes onto the system and reviews all the data, and all of a sudden they've, they're given a hundred million. That's real proof right then and there that it works. Um, and so, um, you know, it it, and because our system is AI-powered, it's not just about automation, because that's that's the least of it. What it does is it learns patterns. It flags gaps, and so it makes complex reporting repeatable and defensible. So that's a really interesting point right there. Okay. Makes it defensible. Um, and even over a 20-year like, you know, development horizon. Okay. Um, because what it's doing is, you know, the the part of AI is machine learning and understanding the customer in its own language, in its own tone, in their own ways of performing their projects. So, it's very powerful indeed. Um, so, in in short, what we've done is we're um we're giving mining companies a system that lets them focus on building their assets while we derisk how they communicate value and accountability to the market. Um, and that's a very um, you know, and and yes, for example, if if I always, I have another company which is called um CanCheck Corporation, and they do anti-money laundering and terrorist finance checking, you know, and it sounds a lot more glamorous again than it really is. Um, what we do there, um, you know, we have over, goodness, uh on that system, we have over 350 financial companies on the system and all sorts of other companies as well. Um, but what happens is that um uh we we have um, you know, if if a financial company could get away without doing AML, they would. They really would. Okay, because it's one of those really, you know, very time-consuming, effort-driven, very costly processes that doesn't really return much on on that investment. Um, and yet, it is really important that it be done. And the same with, you know, a lot of the compliance and and risk reporting that we do, it is it's paramount to running a successful business. Uh, but if if companies could get away without doing it, they would. And so what we do is we make it easy for them. We make it easy and much more cost-effective by implementing AI hooks, as we call them, into the system and uh ensuring that um, you know, they get the real-time insights on risks and controls. They get automated scoring, so the regulators and the board and the investor uh can really understand what they're doing with their projects. They have audit-ready outputs for any disclosure requirement. Um, all of that is done immediately for them.
So basically, your company has a product that uh covers the full cycle of the of the mining business from the exploration to to close out and and and you're solving the problem of transparency, but also providing the with the through patterning capabilities, you're focusing um problems, you're focusing the companies to their real problems rather than they might be chasing or solving some other problems. And so there are multiple benefits of this um product.
Yeah. So, what we've done is there's a couple of products that we just launched, couple of extra products that we just launched recently. And one of them is um, you know, I mean, it's a simple thing, a benchmarking report, okay? But what it does is it actually um, it it, we have um peer disclosures and risk statements and filings, um, and we autogenerate it, generate comparative analytics like, where do we stand versus our peers? Um, and we reinforce that learning so that we refine the context that matters the most, right? So sector, asset, geography, that type of information. And it's extremely detailed, very, very fine in detail. And we launched that recently. And then another one that we're working on right now, which is really interesting um, is that we're um taking, because we have our own proprietary um AI engine. Okay. Uh, and and we do that because, you know, a lot of uh, let's face it, a lot of um, a lot of companies are are worried about um, you know, security, you know, cybersecurity, and and and it's well-founded. However, what we've done is we, with our, you know, closed model, we we're taking in all sorts of regulatory filings. We we map it to the issuer tone and the sector language. We suggest disclosure text that aligns with those the tone and the materiality and the jurisdictional rules. And what it does is it learns over time and it tailors the recommendations to the board or the IR preferences or to the investor community. Um, and so that's what we're working on at the present time. It's it's these are powerful um, you know, programs uh that give mining companies and other other companies in other jurisdictions, you know, in other industries, a lot of control, a lot of um, a lot of power at their fingertips now.
Excellent. Thank you, Lori. Uh, we have a couple of questions, but I suggest we leave them for later on. Um, just to give a chance to Daniel and Gordana to talk a little bit about their views or their um opportunities and concerns and constraints, I would say, when it comes to the adoption of AI. So, um, Gordana, coming from the junior mining side, exploration company, raising capital is the first and biggest issue, I'm I'm assuming still is. But adopting AI is not free. It's it's a process that requires resources, requires funding. I know there are some grants, Canadian level, federal level grants available for the exploration um phase, right? Um, but I don't know whether they are actually available now for the new technologies. Is there anything that government is providing to exploration and prospecting companies in terms of grants for new technologies? Are you aware of that?
Not really. But there are uh some grants available for applying new technologies, not specifically AI. And then really talking about early-stage projects and exploration. I really want to kind of bounce back on points that Gordon made before is, you know, the premise is to actually um start with green greenfield exploration and get to that first resource or the project or the ore body delineation sooner. So that's the biggest uh, you know, worry for explorers out there. So, Lumica did try some of these systems before, and usually what we found the the limitations there are really um existing data or how these models are trained. So perhaps, um, if you are having on your property some areas that are not covered with the previous data or no information there, you may have holes in your recommendation. So what we've seen with these models that they read what's there, and and I haven't used Gordon's uh, uh company or his, you know, his software to say I want to put a disclaimer there. It's just, recommendation would be made with the information that you have. So that's what I found that's the limit of the AI. It can use only what it has. Unless these training models are maybe built on bigger sets, more regional sets, then it can provide that information that's missing, let's say per se, on a specific land claim or on a specific project. And also depending on where is the company in its development. So, it would be different, of course, AI needs as your junior exploration. You want to move, of course, uh faster along that development line. You want to delineate that deposit faster. You want to drill less and have more confidence in your drilling. So, decrease your cost per per ounce or per ton, depending on what you're drilling for. So that's really your your biggest concern there. And then as a junior explorer, it's really collecting the data, understanding the project, the background, the community, the concerns, that kind of stuff. And as you move towards the development, the biggest thing is really understanding, you know, what are your costs, how you can track that cost. As we know, not many projects that are in the development can say that they're on time and budget. So those are some of the biggest concerns for for developers. And then once you're in operation, it's really making sure that you keep up with your Opex and that you can deliver really on what you promise you're going to do that year. And the big uh focus there would be really um following your operation, your parameters, reporting on this, and understanding where you're meeting them and not meeting. And of course, there are so many software out there that do uh production monitoring and, you know, comparing all of that, but what I found really u working in the mining sector, the biggest challenge is for people to actually use the data that they have and really to make these decisions. So I think sometimes even having these models learn that and really apply before even a human applies, you know, help out in the plant or really in in the field, anywhere that you actually monitoring um for the compliance, because sometimes by the time a human is there to notice something is not okay, we're not there, might be too late, right? So it's really training these models and automation's been in the plants now for decades to say, at least, and we've have seen that benefit for sure. And the big thing we've been talking about, and Lori mentioned as well, um, and Daniel, I believe, will talk a lot, is about that, you know, digital footprint of the company and being able to raise the funds and move all these projects. And this also applies for really early-stage companies, developers, and operators, and really having that visibility. And what is actually the best way to reach out to investors, you know, I feel like people are sometimes very much saturated. There is a, you know, a thousand and thousand companies on different exchanges, there are different minerals and metals. And if somebody's coming um as a retail investor in this space, how do they actually find the value for their money? So some of these software out there might be able to actually spotlight the company, you know, that presents your advantage compared to other groups or other companies. You know, how do you really work there? And, you know, uh, there are some of the systems out there that actually do some of the compliance. They monitor your press releases, they try to to distribute that over a number of emails and everything else. It's just a the question is, how do you really distinguish yourself? And I hope to learn a little bit more from Daniel on that one. But basically, it's really a tough business. Mining is really tough business, and many, many of the sectors, sub-sectors in the mining, including operation, production, development, they have their own challenges. And, you know, hopefully using technology would help out with many of these challenges, is tracking, reporting, and really having information for the management to make right decisions.
Right. Thank you, Gordana. All of it is, we are drowning, all of us, I guess, in the in the information, segregating the the uh the right one. Actually, funny enough, um Daniel and I met briefly over at PAC, and that's exactly what we talked about, possible use case, determining which of the juniors has the highest potential of success based on all kinds of criteria, basically. And Lori, to your point, and Gordon's points as well, uh we are here dealing with unstructured data and trying to u combine or deduce the uh decision from that un pile of unstructured data. And that's hopefully AI is going to get there. It is getting better by day. Usually, when you listen to AI uh people who are deeply into AI, they say today is the day when AI is at its worst stage. Tomorrow, it's going to be better. So this the the change. Gordon, do you want to add something to that?
Yeah, I think it's just an important point that uh we um we're not trying to use a whole mass of data. Way many of our, I would say are the competitors, the Cobalts, the, there's other companies out there that are using um AI models for uh for mining exploration delineation. Cobalt, Verify, we're not going in and taking the whole all the data. We're trying to use the right data, and that's a big distinction between a lot of AI approaches. It's not just taking as much data and crunching it. It's going in and using the right data, looking into the data. And this is um again, I'll use the example up with Conquest up in uh in northern Ontario, near in the Segreg area. And uh we we we used uh publicly available data. It's from the the the Canadian Geological Survey. It's magnetics and uh airborne magnetics that's available. We we trained our model uh on an existing mine, the old Murray Mine, which is 70 kilometers away from the Conquest ground. And we using looking at the data, looking at the within that data. And this is things that I when I was a geophysicist 35 years ago, you know, collecting the same sort of data, we weren't able to see in that data. So now with this technology, we can see into the data itself. We're not we're not massaging this data. We're not interpreting this data. That's the big difference. This is not an interpretation. It's actually what's in that data and looking at within that data. And in uh recognizing that uh is there predicting, does this data and this model that we've trained, excuse me, this deposit that we've trained our model on. Do we see something else somewhere else? And we did 70 kilometers away undercover in this in uh this area, 100 square kilometers undercover, no outcrop. And we uh delineated some targets, which we put in uh four holes. Three of them all hit, the fourth didn't hit bedrock. So we and we intersected mineralization. It's been published and it verifies the predictability, the predictive power of the models that we've been using. So, we're in partnership with Conquest on this. We're not here as a SAS business. We're not using our system to go out there and um, you know, you're not hiring Veron, hey, can you come out and run this across? We're here to, we generated a portfolio of properties and projects using the system. So, we're a project generator effectively. So now we're looking for partners to come in to advance these. We're not trying to be a mining company the way Cobalt is. We're we're building a portfolio of of mining investments, small JV pieces, royalties, etc., using AI as that engine, but that we've developed over 10 years. This is um this is, you know, this is not something that um these models are extremely sophisticated. We've uh um predecessor companies started down down in Chile, and we've moved that up. They had a lot of money invested into them, and that's been moved forward with the data scientists out of MIT, and we have quite a crew of data scientists and geoscientists on our staff that worked in developing these models. We have over 300 different geological models also in our in our database as well that we look to. But this is not, this is using the power of this and the proprietary system to generate potentially new discoveries, better, faster, cheaper than we've been able to as an industry um, you know, using the conventional methodologies that I was using myself as a geophysicist and interpreting it. So, it's interesting.
It's interesting, Gordon, that you um, I'm sorry for interjecting, but I want before I because I'm getting older and I can't remember things as well as I used to be able to. So, um, you know, you mentioned a very important point, and that is that you're distilling, you know, relevant data, and you're you're focusing on relevant data. And that is an a really important point to make because just like us, because of the fact that we are um focusing on, you know, the the intersections of finance and mining and compliance and regulators, exchanges, risk, you know, all of that. Um, we're taking in a heck of a lot of data, huge data sets, but we're still, we're still mining, you know, because we're mining data, we're still mining, um, the relevant data, the data that's going to speak to the investor, uh, you know, at the end of the day. Um, and that is extremely critical to understand. Um, you know, systems that are going out, you know, like the Googles of the world and the, you know, and and they have huge amounts of data sets, um, and and they can understand uh with their AI power behind those engines, um, you know, a lot about the individuals that are on those sites, uh, and and they've had that information for years and years and years and years, you know, AI didn't, I mean, Alan Turing in 1950 was talking about the intelligence of the computer, um, and that wasn't um, and that was no small thing. That was and that was back in the 1950s. Um, so AI has been around for a long time. Um, and so now we're seeing the nascent application of AI on on our lives. Um, and I think that as days and weeks go by, um, you're going to see tremendous tremendous outputs um, and the ability to gather uh data and really find the intelligence behind it is what, and that's ultimately our goal.
Yeah. Uh, yeah, I just would like to remind everyone that Toronto is um a big central, not only for mining but for AI technology. And the event back in 2012 um actually brought AI to to the modern era. That's the beginning. So it happened in Toronto. So um we we have all reasons to continue to bring these two industries together because Toronto is a big hub for both industries. So, and one one of the big reasons we are talking about it today is the um is our basically tradition, especially in the case of these trade wars now, where we have to look internally, what do we know internally, how can we apply it for for everybody's good. Going to Daniel now, um adoption of AI, and I've noticed in the last couple of months actually that things did speed up again. Tends to go, it was slow. Nobody was, no, I shouldn't say nobody, but people were still kind of cold um to that idea or didn't think it's time to adopt AI, but now everybody's on the on the u on that path, one way or the other. There's no question about that anymore. So, uh from your perspective, and you just mentioned a couple of projects that you are going to launch very shortly, what Six can bring, platform, uh to this adoption process? How can you improve that process?
That's certainly so. There's a number of different aspects. I think that what's really interesting and illustrative with Gordon's approach uh to mining investment is that ultimately every investor is going to be able to program their own AI to identify and surface opportunities. One of the biggest issues in the capital markets, I think, in particular for anyone that's investing actively, especially in the metals and mining sector, on the TSX and V alone, we have about 1300 companies, is how do you identify the companies that match your investment thesis? And it's not enough to just filter it based on region or commodity or geophysics because these companies are constantly changing. In my portfolio, many of the investments that I've done the best on are companies that completely change their focus. And I think that's one of the hallmarks of the junior space, that constantly looking for a mine means looking in different places, changing the company's focus, adapting to market circumstances. And so a company that might be called uh Gold Company ABC in three years might actually be searching for copper or some or antimony or some other resource altogether. And so, as an investor, using AI to identify what your particular thesis is, matching that with the universe of ever-changing junior mining companies, and surfacing those opportunities right as they're making those movements or achieving a critical milestone, the moment it happens, when it's through a press release or through some other announcement, is critical. And so I think we're going to be seeing the adoption of AI on the investor side because it generates alpha, and we'll see that both on the geology, like what Gordon is doing, as well as on the finance and capital market side. And so, in so far as Six, we're investing heavily in building out these tools for investors so they can go and identify opportunities as soon as they match the investment thesis, the moment it happens. Similarly, for companies, as a company looking for investors, doing roadshows, going to different cities, whether it's New York or London or elsewhere, being able to identify those funds that are actively investing in companies similar to yours and being able to meet with them during the two or three days that you might be in a city, and letting a lot of them know, hey, I'm going to be here without spamming them. So doing so in a very focused way where the people that receive these communications are very happy to hear from you, and where those meetings can actually be booked. And so that's the other part that we're investing heavily in. The third piece, and this is what's going to be live as early as tomorrow, actually, is the ability for investors to chat directly with mining companies, where we are connected to all the MD MD&As, all the various press releases and financial information, so that you as an investor can conduct due diligence much more rapidly. That way, often in the context of making an investment decision where you might only have hours or weeks or a finite period of time to act, you have an advantage being able to kickstart your research process. And so, all of these things are ways in which AI, I think, is going to be transforming how investments happen. And these are three particular examples of where Six is putting its research dollars and increasingly rolling out products to that effect.
Yeah, that's very good because we see a lot of money being u spread within the uh junior mining sector and seeing the return of the investment uh is um a challenge, place to say so, and it takes time. So that brings me to to the next point. Uh, will investors uh be looking into companies that are actually applying new technologies, not only AI, but AI being the case we talk about today, is going to be one of the criteria when they assess the uh company? What's your opinion, Daniel?
The viewpoint that I have is it's primarily going to be in the P&L. I think one of the most notable things about this market since I began working in the mining space about eight years ago is the price of gold then was something like $1,200 an ounce US, and that was at a time when it was almost at par with the Canadian dollar. Today, we're looking at gold north of $3,000. But despite this remarkable rise in gold price, um, up until very recently, most mining companies were operating at a loss. They weren't printing cash because their input costs rose with inflation. Mining is a very human and labor-intensive business, and there are many input costs from the supply chain to equipment to other factors that can drive up possibly the all-in sustaining cost of taking of delivering that ounce out of the ground. One of the expectations I have for this business is that it's going to become a lot more lucrative. In particular, I expect there to be far fewer people involved in the mining process. The people that are involved, I think, in turn, are going to have and benefit from much greater safety because surveillance practices, which can already be monitored, can actually be analyzed at scale rather than just sampling maybe 1% of what's happening to identify where issues occur. And all of these factors should, in in effect, put down the input cost of actually running one of these operations. Meanwhile, I have no expectation that the price of gold or other metals is going to go down, particularly in the environment that we have today. And so subsequently, I expect the mining companies to be far more profitable than ever before. I think subsequently to your point, Amina, that's going to result in really healthy balance sheets, really healthy P&Ls. It's going to make major companies very, very successful. And I think what's going to stimulate a lot of their appetite to acquire juniors. And so I think investors really ought to be trying to identify those companies that will benefit from those lower input costs. And the companies that apply AI technologies in a way that drives down their cost and increases their profit are going to be the first that are going to ultimately benefit from it and then drive that process.
Yeah, that's that's very, I'm going to say expected. Uh, but it's interesting if you're already seeing uh signs of that happening, and apparently you are. Um, Gordon, Gordana, Lori, anything else to add?
Yeah, I'd like to kind of balance on that. The biggest use of that, as we mentioned and talked about before, is really driving that cost curve down. And what really comes down, you know, in the current markets, is how does the company perform against what they put out is going to be their outcome at the end of the year. And if you can impact that bottom line, that's the biggest really advantage of the AI. And as these groups are trying to kind of maybe uh build that path into the mining industry, really showcasing these results and the companies that use this um, call it a software, you know, at the end of the day, it is a type of software to better themselves, to decrease the cost and increase the revenue. Those are the companies that want to stay there. Not just because they're now more popular, but just because they have the revenue. If you're not able to generate the revenue, if you're not able to continue exploring or acquiring other goods, you're going to disappear from the business. Unfortunately, the mining business is not is is a finite business. It's not, you know, something that that you know, you find a deposit that lasts forever. Unfortunately, all all these deposits, especially nowadays, are the smaller, the grades are lower. So you need more resources first to outline these deposits and then to mine them economically. So those are some of the challenges in the mining industry, and productivity has been going down for for many, many years. Uh, I believe I read some articles that the highest productivity in the mining business was in the 90s. So we're on a 30, 35-year decline. So talking nowadays, and yeah, the reason is deposits are deeper, they're more expensive, the grades are lower. Everything that was closer to the surface was already discovered and mined out. And that's why some of these other technologies that can, you know, predict and see the models a little bit uh further down, as Gordon's are looking into, that's maybe the future to find these deposits that are not outcropping, not easy to see. And also being able to really control your production, your operation in real time. And I think, you know, and people may be a little angry on this. The more you can remove the human from the operation, the better it is. The more you can automate, the more you can have control over the process, and that includes mining and processing plants, the better it is because all these decisions could be made faster and with less lost time, to be honest, and, you know, for sure, much safer. You know, nowadays we have all these software that do face recognition, that they have proximity detections, or all of these things, you know, ensuring that no people are close to equipment or, you know, u if you're blasting, nobody's not accounted for, you know, where are the people in the mine at that point? So um, we are coming uh close to the end. I just want to There is one question for Gordon. Um, is your software based on a commercial system?
No, guys, again, just to emphasize, we're not selling our our system. We're not for hire. Um, we have used this engine to go and delineate high probability targets that we're looking to partner. If what um uh we'd be delighted to to to if you're as a junior out there and um you've uh love to talk to you if you've been through project development, if you've moved a project forward. Um, we've got uh over 60 mineral targets in Ontario, down in Arizona, in Nevada, down in Chile that have a high probability of of being a mineral system. And uh it's now we need partners to move them to the next stage. And um so we're not out there using our system. Uh, again, AI would certainly be helpful to um a resource extension if you've got an existing operation that's, you know, coming towards the end of its of its mine life, or you're moving from oxide to sulfide, and what else is out there? And to f keep your plant filled, you've made hundreds of millions of dollars of investment, you want to keep that plant filled. Be delighted to to work with that type of company to find that resource extension deeper or undercover, or you 20, 30 kilometers away. Uh, that's what we that's what we provide. We have a high degree of confidence in our predictability power of our engine that we've developed our AI since, again, it's over 10 years. Uh, and it works quite well. I think that the thin exploration is the thin edge of the wedge that this industry needs to
survive. Of course it does. And as uh Gordana pointed out, you know, every day we deplete the value of our assets in this industry because we're producing. We've got to replace that. We got to continually replace that. We haven't been we uh exploration expenditures as we all know have declined since 2012. 2012 was peak global mining exploration. We've fallen off the pace there. The major companies have fallen off the pace. Most of their exploration dollars are spent near mine. Chirp's place to find a new op new new mine is next to an old mine. Grassroots explor exploration has fallen tremendously over the past uh decade. So where do we find these new deposits? It's where we haven't looked before. It's undercover. The obvious ones haven't been found.
Also, as an industry, guys, we've not done well on a return on our exploration investment for the last 20 years. We haven't generated a lot. We haven't found anything. We've been spending millions of dollars, hundreds of millions of dollars on the same stuff over and over and over again. A lot of juniors, and we've heard this, are lifestyle companies. So, we have to break that. We want to sustain this industry. We've got to put our money, spend our money smartly, wisely, and AI can help us do that by increasing the probabilities of getting into areas that are higher grade. Higher grade is forgiven. We don't we don't can't can't be afford to be mining dirt the way we have been. We've got to be finding higher grade. Be smart about it and the technology allows us to be smart about it.
Exactly. And and one more question. I think we have just enough time Lori from your perspective. Um what is the major um reason companies are not walking into the um adoption of AI or the other way around? What is their motivation or reasons? What's the balance at the moment? What do you see in in the industry happening?
Um okay, so two two things. Uh number one um you know what I see going forward in my world uh of technology is when you know and what we're building on right now is predictive risk detection. Okay. Um, and so that's that's where we're getting to to the ability where um you know uh you know we flag emerging risks before they ever appear on a compliance report uh or any kind of report. Uh and that's what we're headed towards and and I figure we we could do that in about a year.
Um, however, why mining companies in particular, and I'll I'll throw I'll I'll say the extractives community in general. Um, and I might even add some chemical companies in the mix. Um, a risk averse. They're very riskaverse. um you know they're and B uh frankly um they're not you know and this is going to sound um this is going to sound odd because you have a lot of engineers and you have wonderfully gifted talented people working in these industries and yet uh they are not technologically savvy. they are not and therefore you will have some reticence uh to to adopt uh things which they do not understand as fully as they they should. Um and therefore it becomes a little more trying.
Um also um they tend to be um you know especially explorers explore exploration companies is very human resource-based. you know, they want they they know the the the the geologist. They're going to go and they're going to drill and they know where their land is and their where the you know, the the assets are and where their where their their um you know, what the geological mapping has been uh telling them for years. And so that that mindset is very um is still very prevalent in the industry.
Um, and so to to get them to say, "Okay, um, wow, we're going to be able to use AI and we're going to be able to do a closed engine like Gordon did or I did where we're going to have some advantage to be able to detect what's it what's on our land and what what our assets are telling us." Um, that takes a to a different type of understanding because you're you're now you're you have to learn first the tool and then apply it, right? again um you know it's it's really um you know applied math at scale right you're taking it's a totally different concept at that point um and so you have to go out and you have to hire different kinds of people uh to do that for you and so I think um you know the investor community actually is going to force this right the investor community is going to say look we have to derisk some of these projects we have to have some return on investment But um you know, we're going to have to make money. And the way we're going to make money is you guys are going to have to start adopting um much more pragmatic u you know, systems that will that will, you know, return our investment. Um and so the investor community is actually going to force this issue and that's when you're going to see a change.
Okay. Well, that's that's excellent actually closing statement for this talk. Hour went by very quickly. um I'm glad we had a chance to talk about this and we need to talk more about this and uh bring solutions to to the forefront. So um can be recognized and can be adopted. So thank you all for participation and looking forward to continuing this discussion in some other time some other space and some other uh opportunity. Thank you all. Join us again. Thank you. All the best. Thank you very much. Have a wonderful day. [Music]