Automated transcript · Written by software from the replay's audio. It can mishear names and figures; the replay is the record.
27:56 Are you aware of that? Not really, but There are 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. The premise is to actually start with greenfield exploration and get to that 1st resource or the project or the ore body delineation sooner. So that's the biggest, you know, worry for explorers out there. So Lomiko did try some of these systems before. And usually what we found, the limitations there are really existing data or how these models are trained.
28:48 So perhaps if you are having on your property, some areas that are not covered with the previous data or no information there, you may have a holes in your recommendation. So what we've seen with these models that they read what's there, and I haven't used Gordon's company or his software to say I will put this claim in there, is just a 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 the bigger sets, more regional sets, then it can provide that information that's missing, let's say, per se on specific land claim or on specific project.
29:40 And also, depending on where is the company in its development, So would be different, of course, AI needs as your junior exploration, you want to move, of course, 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 ounce or per ton, depending on what you're exploring for. So that's really your biggest concern there. And then as a junior Explorer is 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 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 developers.
30:44 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 promised. You're going to do that here. The big focus there would be really following your operation, your parameters, reporting on this, and understanding where you're meeting them and not meeting. And of course, there is so many softwares out there that do production monitoring, comparing all of that. But what I found really, working in the mining sector, the biggest challenge is for people to actually use the data that they have and really to make this decision. So I think sometimes even having these models to learn that and really apply before even human applies, you know, help out in the plant or really in the field, anywhere that you're actually monitoring for the compliance.
31:45 Because sometimes by the time human is there, notice something is not okay, we're not there, it might be too late. Right? So it's really training these models and automation has been in the plant now for decades to say at least. And we have seen that benefit for sure. And the big thing we've been talking about, and Laurie mentioned as well, and Danielle, 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 the 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 that there is, you know, thousand and thousand companies on different exchanges, different minerals and metals.
32:46 And if somebody is coming as a retail investor in this space, how do they actually find the value for the money? So some of these really softers out there might be able to actually spotlight the company, you know, that present your advantage compared to other groups or other companies, you know, how do you really work there? And, you know, there are some of the systems out there that actually do some of the compliance. They monitor your press releases. They try to distribute that over the number of the emails and everything else. The question is how do you really distinguish yourself? I hope to learn a little bit more from Daniel on that one, but basically it's a really tough business. Mining is a really tough business and many, many of the sectors, sub-sectors in the mining, including operation, production, development, they have their own challenges.
33:50 And, you know, really using the technology would help out with many of these challenges, 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 right one. Actually, funny enough, Daniel and I met briefly over at PIDAC, and that's exactly what we talked about, a possible use case, determining which of the juniors has the highest potential of success based on all kinds of criteria. Basically, and Laurie to your point, and Gordon's point as well, we are here dealing with unstructured data and trying to combine or deduce the decision from that pile of unstructured data. That's hopefully AI is going to get there, it's getting better by day.
34:54 Usually when you listen to AI, people are deeply into AI, they said, today is the day when AI is at its worst stage. Tomorrow it's going to be better. So this is the change. Gordon, do you want to add something to that? Yeah, I think it's just an important point that we're not trying to use a whole massive data way many of our, I would say, are the competitors of the Cobalt, there's other companies out there that are using AI models 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, again, I'll use the example up with Conquest up in northern Ontario, near the Sudbury area.
35:58 And we used publicly available data. It's from the Canadian Geological Survey. It's airborne magnetics that's available. We trained our model on an existing mine, the old Murray Mine, which is 70 kilometres away from the conquest ground. Looking at the data, looking within that data, and this is the things that 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 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 within that data and recognizing that, is there a 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?
37:01 And we did, 70km away, undercover in this area, 100km2, undercover, no outcrop. Under cover in this area, 100 square kilometers, under cover, no outcrop. And we delineated some targets, which we put in 4 holes, 3 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 were using. So we're in partnership with Conquest on this. We're not here as a SaaS business. We're not using our system to go out there and you're not hiring, 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.
37:53 We're not trying to be a mining company the way Cobalt is. We're building a portfolio of mining investments, small JV pieces, royalties, et cetera, using AI as that engine. But we've developed over 10 years. This is not something that, these models are extremely sophisticated. We've predecessor companies started down in Chile, and we've moved that out with 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 database as well.
38:43 But 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, you know, using the conventional methodologies that I was using myself as a geophysicist and interpreting. So- It's interesting, Gordon, that you, sorry for interjecting, but I want before, because I'm getting older and I can't remember things as well as I used to be able to. So, you mentioned a very important point and that is that you're distilling relevant data and you're focusing on relevant data. And that is a really important point to make because, just like us, because of the fact that we are focusing on the intersections of finance and mining and compliance and regulators, exchanges, risk, all of that.
39:48 We're taking in a heck of a lot of data, huge data sets, but we're still mining, because we're mining data, we're still mining the relevant data, the data that's going to speak to the investor at the end of the day. And that is extremely critical to understand. Systems that are going out, like the Googles of the world, and they have huge amounts of data sets. And they can understand with their AI power behind those engines a lot about the individuals that are on those sites.
40:35 And they've had that information for years and years and years and years. AI didn't... I mean, Alan Turing in 1950 was talking about the intelligence of the computer. And that wasn't, and that was no small thing. That was back in the 1950s. So AI has been around for a long time. And so Now we're seeing the nascent application of AI on our lives. I think that as days and weeks go by, you're going to see tremendous, tremendous outputs and the ability to gather data and really find the intelligence behind it is what, and that's ultimately our goal.
41:25 Yeah, yeah, I just would like to remind everyone that Toronto is a big central, not only for mining, but for AI technology. And event back in 2012, actually brought AI to the modern era, that's the beginning. So it happened in Toronto. So we have all reasons to continue to bring these 2 industries together because Toronto is a big hub for both industries. So one of the big reasons we are talking about it today 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 everybody's good.
42:17 Going to Daniel now, adoption of AI, and I noticed in the last couple of months actually that things did speed up again, tends to go, it was slow, nobody was, well I shouldn't say nobody, people were still kind of cold to that idea or didn't think it's time to adopt AI. But now everybody's on that path one way or the other. There's no question about that anymore. So from your perspective, and you just mentioned a couple of projects that you are going to launch very shortly, what 6IX can bring platform to this adoption process?
43:03 How can you improve that process? That's right, so there's a number of different aspects. I think that what's really interesting and illustrative of Gordon's approach 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, and I think in particular for anyone that's investing actively, especially in the metals and mining sector, we're on the TSX and D alone, we have about 1300 companies, is how do you identify the companies that match your investment theses? 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's constantly looking for a mine, means looking in different places, changing the company's focus, adapting to market circumstances.
44:04 And so a company that might be called a gold company, ABC, in 3 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 insofar as 6, 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.
45:00 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 2 or 3 days that you might be in a city and letting all 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 MDMAs, all the various press releases and financial information so that you as an investor can conduct due diligence much more rapidly.
45:57 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 3 particular examples of where 6 is putting its research dollars and increasingly rolling out products to that effect. Now that's very good because we see lots of money being spread within the junior mining sector and seeing the return of the investment is a challenge, it's a place to say, and it takes time. So that brings me to the next point.
46:47 Will investors be looking into companies that are actually applying new technologies, not only AI, but AI being the case we talk about today? Is this gonna be one of the criteria when they assess the company? What's your opinion, Daniel or Gordon? The viewpoint that I have is it's primarily going to be in the PML. I think one of the most notable things about this market since I began working in the mining space about 8 years ago is the price of gold then was something like 1200 an ounce US. And that was at a time when it was almost a parody with the Canadian dollars. Today, we're looking at gold north of 3000. But despite this remarkable rise in gold price, Up until very recently, most mining companies were operating at a loss.
47:36 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 ultimately the all-in sustaining cost 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 a benefit for much greater safety because surveillance practices, which can already be monitored, can actually be analysed at scale rather than just sampling maybe 1% of what's happening to identify where issues occur. And all of these factors should in effect put down the input cost of actually running one of these operations.
48:31 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, Nermina, that's going to result in really healthy balance sheets really healthy pnl it's gonna make major companies very very successful I think what's on the 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.
49:17 Yeah, that's very, I'm going to say, expected. But it's interesting if you're already seeing signs of that happening, and apparently you are. Gordon, Gordana, Laurie, 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 performs 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 maybe build that path into the mining industry, really showcasing these results and the companies that use this, call it a software, you know, at the end of the day, it is type of the 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 gonna disappear from the business. Unfortunately, the mining business is a finance business. It's not
50:49 something that you find deposits that last forever. Unfortunately, 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 many, many years. I believe I read some articles that the highest productivity in the mining business was in 90s. So we're on 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 predict and see the models a little bit further down as Gordon's 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 that, your production, your operation in the real time. And I think, you know, and people may be a little bit 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 a control over the process, and that includes mining and processing plants, the better it is.
52:16 Because all these decisions could be made faster and with less lost time, to be honest, and for sure much safer. Nowadays, we have all these software that do face recognition, they have proximity detections, all of these things, ensuring that no people are close to equipment, or, you know, if you're blasting, nobody is not accounted for. You know, where are the people in the mine at that point? So we are coming close to the end. I just wanna, there is one question for Gordon. Is your software based on the commercial system? No guys, again, just to emphasize, we're not selling our system, we're not for hire.
53:09 We have used this engine to go and delineate the high probability targets that we're looking to partner with. We'd be delighted to, if you're as a junior out there and you've loved to talk to you, if you've been through project development, if you've moved a project forward, We've got over 60 mineral targets in Ontario, down in Arizona, in Nevada, down in Chile, that have a high probability of being a mineral system. And it's now we need partners to move them to the next stage. And so we're not out there using our system. You got an IRS where certainly be helpful to our resource extension.
53:56 If you've got an existing operation, that's coming towards the end of its mind life or you're moving from oxide to sulfide, and I'll say, gee, what else is out there? And to keep your plant filled, you've made hundreds of millions of dollars of investment. You want to keep that plant filled. We're delighted to work with that type of company to find that resource extension deeper or undercover, 20, 30 kilometers away. 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 and it works quite well. I think exploration is the thin edge of the wedge that this industry needs to survive. Of course it does and as 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.
54:53 We've got to continually replace that. We haven't been sweet. 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. Sherpa's place to find a new mine is next to an old mine. Grassroots exploration, greenfields exploration has fallen tremendously over the past 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 our return on our exploration investment. For the last 20 years, we haven't generated a lot.
55:40 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, grade is forgiven. We can't 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 one more question. I think we have just enough time. Laurie, from your perspective, what is the major reason companies are not walking into the adoption of AI?
56:40 Or the other way around, what is their motivation or reasons? What's the balance at the moment? What do you see in the industry happening? Okay. So 2 things. Number 1, what I see going forward in my world of technology is when and what we're building on right now is predictive risk detection, okay? And so that's where we're getting to, to the ability where, you know, we flag emerging risks before they ever appear on a compliance report or any kind of report. That's what we're headed towards.
57:27 I figure we could do that in about a year. However, why mining companies in particular? And I'll say the extractives community in general. And I might even add some chemical companies in the mix. A, risk averse, they're very risk averse. And B, frankly, they're not, and 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 they are not technologically savvy. They are not. And therefore, you will have some reticence to adopt things which they do not understand as fully as they should.
58:25 And therefore, it becomes a little more trying. Also, they tend to be, you know, especially explorers. Exploration companies is very human resource-based. They know the geologists, they're gonna go and they're gonna drill and they know where their land is and where the assets are and what the geological mapping has been telling them for years. That mindset is still very prevalent in the industry. To get them to say, okay, 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 gonna have some advantage to be able to detect what's on our land and what our assets are telling us.
59:25 That takes a different type of understanding Because now you have to learn first the tool and then apply it. Again, it's really applied math at scale. It's a totally different concept at that point. You have to go out and you have to hire different kinds of people to do that for you. I think the investor community actually is going to force this. The investor community is going to say, look, we have to de-risk some of these projects. We have to have some return on investment. 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 much more pragmatic systems that will return our investment.
1:00:21 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. I'm glad we had the chance to talk about this and we need to talk more about this and bring solutions to the forefront so it 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 opportunity. Thank you all. Join us again. Thank you All the best. Thank you very much. Have a wonderful day.
