DeepCube Ltd (NNDM) Earnings Call Transcript & Summary

Good day, ladies and gentlemen. Welcome to today's conference call to discuss the recently announced acquisition of DeepCube by Nano Dimension. My name is Ian, and I'm your operator for today's call. Yoav Stern, CEO of Nano Dimension, will discuss the acquisition and the strategies, followed by a question-and-answer session. This call is being recorded. Before we begin, may I remind our listeners that certain information provided on this call may contain forward-looking statements, and the safe harbor statement outlined in the press release that was published regarding this acquisition also pertains to this call. If you have not yet received a copy of the press release, please view it in the Investor Relations section of the company's website. I would now like to turn the call over to Nano Dimension's CEO, Yoav Stern. Yoav, please proceed.

Thank you very much, Ian, and good day to everybody. Thank you for taking the time and listening. We're going to discuss the acquisition of DeepCube and then of course, we'll answer questions that you may have. So to -- reminder, we're looking at 3 types of acquisitions over the last half a year, even more, actually starting in July or so last year. And this is the acquisition of the second type, B. This is an acquisition of a company that has a technology that within many technology companies we're looking at that we believe, after due diligence that it's important to have this technology in-house, and it will be much more time, much more effort, if at all, to try to develop it ourselves. Now we've been working on this specific company for the last, probably, 4 months, which included due diligence -- technical due diligence, technology due diligence, very, very intensive. Plus looking at other companies with a comparison of other prices that other companies like that have been traded or sold, not a lot of them exist that were sold. And we decided finally to move on negotiations, et cetera, and we just closed it. Now DeepCube, out of technology companies, by itself is a unique one for us. And in general, we're not buying here technology that we are involved with in materials, we're not buying technology that we're involved with in printing, or in specific hardware and specific technologies that involved with building machines. It's actually much more important. DeepCube is the technology that's going to be used as the brain of our both machines, 3D printing for electronics machines, LED manufacturing. And with the DeepCube brain artificial intelligence, which I'll explain in a second, we intend to fulfill the vision of building much more than just very smart machine, but rather building a network, what we call, smart fabrication network of machine, which brain and control will be up in the cloud or in the private cloud, and the machines will be nodes in a network of many machines printing electronics in different places in the world, owned by either a network of a customer or a network of few customers or connection to our own network, and inventory is going to become digital rather than holding inventory on location and be sent just as need -- just in time to be printed in the different locations. So it's a very big vision that can be and will be fulfilled over the next couple of years with the DeepCube brain machine. Now what is it the brain machine? DeepCube developed a deep learning technology. And I will give you 2 words just to -- people to realize what's the difference between general artificial intelligence and artificial intelligence that is based on deep learning and specifically machine learning. Our machines are in the process of developing -- being developed into being production machines. In production machines, you need to have a machine that is correcting itself. We can't afford machines that are printing, and the results are not very high yields. So by definition, a physical machine that's printing is printing with certain errors. And we need the brain that can read the errors as they happen in real-time and correct the errors as they happen in real time. And in that way, makes the machine an intelligent machine, so much so for a network of machine. Deep learning, and again, different from regular artificial intelligence, is a brain that is learning without having to teach it. The big problem with artificial intelligence is you have to teach it what is right and what is wrong. And deep learning technology was developed in the academy about 20 years ago, but it was never practical for industry because you needed great computers, huge computers to enable sophisticated programs and algorithm like this, to look at the picture of a cat and a dog and then make a decision, what is a cat and what is a dog. The old artificial intelligence and other artificial intelligence systems will start to describe a cat and describe a dog and it's 2 ears and 2 eyes and the mouth and teeth. And the computer still would not separate between them because it's the same description. So you need very, very sophisticated description of what is a cat, what is a dog, which takes a lot of manpower to teach the computer in a regular AI system. The deep learning is different. You show them 50,000 pictures of dogs, 50,000 pictures of cats. And after seeing that, by itself, it knows the difference between a dog and a cat, and it will start to identify dog and a cat at an accuracy of above 95%. That is what deep learning is. No teaching. Inference is reached by the system teaching itself. And that's what we need because if we were trying to teach regular artificial intelligence systems of what is right PCB and what is wrong PCB, good luck for us. This would cost much more than the worth to use artificial intelligence. People -- a group like DeepCube exist in about 4, 5 companies around the world as much as the level of DeepCube people. There's many other people trying to do it because the ability of what DeepCube reached is to have the deep learning/machine learning system, not on a great main computer, but on a small edge device or machine edge device. This is the breakthrough. It speeds the order of magnitude higher than anybody else in the world. There's a group like this working in Tesla. There's a group that is working in Apple. There's a group of like this working in Google. There's a group like this working in Facebook. I checked -- try to see other groups that reached -- or getting close to them, including this group, and this group is working here with scientists that got together after years of research in this field. So we feel that we're very lucky to be able to acquire them. As I mentioned in the news release, there are contracts over the last 1.5 years between DeepCube and the most major semiconductors manufacturer around the world. The reason is very obvious. They want to mount the software straight into their CPUs and into their chips. We are not going to stop the discussion on that even though it's not directly within our focus. But obviously, it's a source of potential revenue, will continue. So what we're going to focus with DeepCube is applying this engine, call it brain engine, into our machine now, starting tomorrow, actually started yesterday, and developing the next generations of our machine with the smart capability to identify automatically all problems in fabrication, increase the yield by learning by themselves of what is wrong and what is right based on dozens of variables, including thermal pictures, which in quantities of thousands of pictures through a process, including of regular pictures, including of temperature, humidity, all the process variables. Everything is going to be swallowed by this engine, processed in real-time and cause the machine to be smart as it is printing and increase the yield and obviously getting into production successfully. Next step, of course, is going to be -- the machine is going to start to be connected through this brain on the cloud between themselves. Update each other which machine -- which batch is working or not working, which machine is free and clear, which machine is identifying errors that other machines has to know. And it becomes like a neural networks of printing machines. This is all coming around to fruition -- will come in around -- come into fruition in the additive manufacturing electronics industry. However, one thing we realized as we, in the last 4 months, did due diligence on DeepCube is that the same engine that we are now going to develop into vertically to electronics really fits. If you think about what I said until now, fits any other 3-dimensional additive manufacturing printers. So if you have our engine -- intelligent engine working, we're already considering licensing it out to people who are not competitors in the other areas of additive manufacturing business, not in electronics, which can apply the same intelligent solution for production machines that are in additive manufacturing metals or plastics or anything else. So we're very, very excited not only about the ability to take our machines forward into a much better performance and intelligent results, but also expanding our business model that will enable us to be the suppliers of intelligent solutions for fabrication, even outside the electronic industry eventually. So that in so much is the DeepCube acquisition. And if there will be questions afterwards, I'll be happy to discuss it. A couple of more issues that I want to discuss with you in this opportunity that we are together -- kind of virtually together. Share price. I'm sure you care about the fact that share price of Nano is going -- was going down over the last -- a little while. Well, I care as well. First of all, I'm an investor. Secondly, all the upside of all 12 -- actually 13 management members of Nano Dimension, including the acquisition now and future acquisitions, is tied to the price of the share, which I insisted on. There's no other upside, of course, getting paid salaries, but very minimal bonuses and all the upside is from share going up. So we are on the same side of the fence, ladies and gentlemen. But the difference maybe in between what the interest of us and you lie, which is equal place. Maybe there is an illusion that I intend to do something about it -- about the share price that is artificial or superficial. That's not my business. I'm not in the business of trading wise, making the share price up or down. I'm not in the business of fighting the short players. Short players can play with any share. This is the way the market works. This is the way the cookie crumbles. Guys, whoever can't take the hit, should not get into the kitchen. The way I will make sure the share price is going up is by running the business the best way I can and reaching results as fast as possible, not for the price of spending too much money foolishly, but spending it wisely because it's your money. And we do have enough money to reach the vision I discussed before. So I'm very comfortable. I'm not going to announce events too early before they actually happen. I'm not going to push the M&A faster just because some shorts or nonshorts causing the share to go down or up. I'm going to run the business for you, for us, the way we envision it, and it will increase the value in a substantial manner because we're going to have evolution now, you know, not only in the electronic industry, but once we prove the case in the electronic industry, we're going to move on to 3-dimensional printing in general. We do not pay bonuses to people who work for you just because they are coming to the office. They will make money together with you or will not. And since we don't intend to raise more money now, we have enough to fulfill the vision I've told you on all fronts, both internal development and M&A, not pressured under many other circumstances of other companies to raise money in a down round, low prices or whatever. We're just going to run the business the best way possible. And as much as it relates to that, I want to remind you, we are not this pack. We're not running to buy or running to announce because the price of the share or because other reason that the life span of this pack is finishing in 18 or 24 months. We are an operating company, well financed, thank you, and thanks to you, with very sincere thanks, but we are going to deliver for you a value that will be multiples of the value today. And we're going to do it by the way I've described it today and then a few times in phone calls before. So thank you very much for being supportive. We do care. We are attentive, but we are going to do it the right way, which is practically the only way. So I'll stop here, and I would like to be attentive now for your questions and try to answer that.

[Operator Instructions] And our first question comes from David Mathisen of Taylor Frigon.

This is Dave, and congratulations on the acquisition. Yoav, I was wondering if you sketched out a vision, you could explain what -- if I'm a customer -- an additive manufacturing customer making PCBs, is the idea that this deep learning network is going across customers or is it just looking at the data from my manufacturing? I'm just thinking about the dynamics of this.

Yes, it's a very, very good question, Dave. The plans once we get to establishing the network, the neural network for additive manufacturing is multiple. And your question is right on. First of all, a large customer, if they have multiple locations or even in their own location, they will have their own network and all the machines netting within yourselves. Obviously, certain, for instance, defense customers would not want to share this, maybe not even the public cloud. They'll have their own personal cloud. We will have our own network, which will connect to all the customers that allow us to connect, and we'll have to make sure we supply them with the cybersecurity that they request. So they don't feel that certain information is running out without control. And we will use this network, connecting to our customers, to help them in maintenance, to help them in updating the machines in real time about errors happening on the other side of the world or maybe batches of ink that are not proper or certain things that takes afterwards weeks or months, otherwise, to conclude by reading reports by manpower, this artificial intelligence and deep learning/machine learning can, in real time, reach results. And if it's not "secretive results," it will and can publish it to customers to improve their performance on the machines. So that will be our part of the network. So it's multilevel.

And our next question comes from Sam Rebotsky of SER Asset Management.

The -- could you discuss the DeepCube's sales and backlog and the $40 million that we're paying? Plus is the $30 million depository receipts for Israeli stock? And how does that compare to domestic U.S.?

ADR are securities that are traded on NASDAQ. It's completely comparable to any other share you buy on NASDAQ. The company is not traded in Israel. Everything you trade, everything you buy is American depositories against one each depository note, call it, there is a share behind it, which is the share of the company. So as much as you're concerned, you're buying American security, selling American security is no difference. That's point number one. It's just a mechanical tool that enable easily to have the Americans invest in American paper, which is backed up with an Israeli company's share -- common share. Point number one. Point number two, regarding your question, if can I describe the backlog and customers of DeepCube? The answer is, no.

In other words, the backlog, has DeepCube been profitable?

No.

No. We're not going to talk about that now. Is that what you're saying?

No, because it's confidential, and I don't -- do not want to describe this publicly, but the answer is, they were not profitable. And we're not going to discuss other numbers regarding DeepCube.

Okay. Do they compete with Sigma Labs?

With who, Sigma Labs?

Yes.

I don't recognize the name, I'm sorry. I did my -- specifically, I don't recognize the name Sigma Labs.

Okay. Well, good luck. Hopefully, the DeepCube becomes profitable, and everybody gets to know it.

No, I want to correct you. DeepCube -- the plan is not to get DeepCube profitable at all, actually. The plan is to integrate DeepCube technology into Nano's machines and network -- fabrication network and make Nano Dimension profitable. We don't -- we're not going to build DeepCube as a subsidiary and run it and make it profitable. Absolutely not. We are going to integrate the technology, which will enable us to be profitable on Nano level and grow Nano sales faster.

[Operator Instructions] Our next question is going to come from Brian Herman of ViewTrade Securities.

My question is, can you summarize the terms of the acquisition and talk about if there's any performance bonuses on the DeepCube side and just a general summary? I read a couple of news...

The transaction was done at $70 million. Actually, if you look precisely, the company has about $2 million of cash. So really the net is $68 million. But let's think about rounded numbers because we paid $70 million, and we got the company with $2 million of cash. So if we speak about $70 million, so it's easier to understand. $40 million of it was paid in cash, $30 million of it is paid in shares. Most of the shares were paid to the founders, Dr. Eli David and a couple of partners that are very, very important to the continuation of the business. They joined Nano Dimension, and those shares are being subject to standstill. Some of them 12 months standstill, which means nobody can sell them in the market in 12 months. And for Dr. Eli David, the father of the technology and one of the world leaders in deep learning/machine learning, being an adviser to people like Samsung and to others that I can't name their names, but I told you about the semiconductors company around the world, him personally, his shares that he received are going to be held in a standstill or in a block to sell for 3 years and will be subject to him being with the company and performing. So that's kind of a long answer to your short. Good question.

Our next question comes from [Asad] from [One-on-One Consultant].

Thank you for explaining all the process. I just want to know that acquiring this machine learning and integrating with your existing machines, how complex this process is? And have you run any prototype on this? Or the idea of integrating the deep learning with the Nano Dimension machines, how it was ensued? And how long will it take to get it to the implementation level?

The complex -- the process is very complex, but it's not a technology barrier because the most complex part of the process is to develop a deep learning/machine learning brand. That is done. That's what we're paying the money for. So that, we probably short cut the cycle that would have taken us 3 to 5 years, I would say. So we have the engine, we have the brain. Now we are implanting the brain into our machine. So the implantation is not building a brain. It's about connecting the brain, if you want to use this metaphor, into the nerve system of our machine. So the conclusions and the results of what the brain is thinking, analyzing in real time is being sent through the nerve system into the different parts of our machine. That's a much less complex process, but it's a lengthy process of developing mostly software, by the way, it's not a lot of hardware. And it will get into the -- it's not going to being planted into the existing machines. We are already in the process of developing the next 2 generations of machines. It's going to be in the next generations. And until it will be effective, it will take no less than 4 quarters and probably more than 6.

So we can say that it will take up to 3 to 5 years to complete that integration and end-to-end operational module?

No, no, no. I said exactly the opposite. I said it would have been 3 to 5 years to develop what DeepCube have developed in deep learning and machine learning. That one we saved. We don't have to go 3 to 5 years. You heard me the opposite way of what I said. I said that what we have to do is to implant the brain into our machine, and I said how long it'll take.

This concludes our question-and-answer session. At this time, I would like to turn it back to you, Yoav Stern.

Thank you very much.

Actually. Pardon me.

Yes. If there are no questions, please let people that want to ask. We'll just be patient and wait if more questions come up.

Certainly. Our next question comes from [Bob Sagarena] of Retail.

Great acquisition. I just wanted to ask a question on the next-generation machine. In one of your interviews, you said that it was -- looked like it was coming out over the summer or maybe even prior to the summer. Are we still on schedule for this?

Yes. The next -- I want to make it clear. The next machine, which is based on the DragonFly is coming this summer. It's entering alpha stage in a couple of weeks and then beta stage. There's only no -- there's practically no risks of development. It's not now -- only in now implementation and getting into a commercial model that is -- all development is completed. So yes, coming in the summer. The next, we have 2 more generations already on the -- I would say, on the drawing boards, but it's more on the drawing boxes. It's already in development, one for 2022, one for 2023. And those are more advanced machines. One -- the second from now is totally not based on the existing machine. It's totally new technologies, and they will come and get us into the inflection point of sliding into the early production models and where the market size grows 10 to 15x because we are beyond just prototyping. So those in 2022, 2023. The summer one will come on time.

So this next-generation that you're working on now, that will handle light manufacturing, correct?

The first one will be early manufacturing, just very short runs. And the next one over is full manufacturing, yes.

Our next question comes from [Moshe Santos], a private investor.

Sir, I just have a quick question. For the next acquisition, is Nano Dimension is going to be looking to buy an existing -- and you don't have to answer like exactly, but are we looking to buy an existing 3D company or just something in the field?

What was the other alternative or something what?

Or like something that's going to add to Nano Dimension's productivity. Are we looking to buy an existing company or some -- or like just a small pieces to add to the productivity of Nano Dimension?

We are going to buy neither, or we're going to buy either company in the PCB business which is a traditional PCB at a very high end that is selling to our own customers or on future customers that we're going to use this PCB manufacturer with traditional analog methodologies and convert them with our machines, slowly populate them with their new machines and use them to get to our end customers and OEMs. Or we're going to buy when we're looking companies and when negotiating with companies, I told this to everybody for the last 2 quarters, and we are moving ahead with a few of them. The second type, Type B, is companies that have technologies that we believe our machines in the next 2 generations will meet those. And instead of developing it ourselves, it's worthwhile to pay even if a big premium to buy the companies to have these technologies. That's a second type. And then Type C, we're going to buy, and we're looking at companies that have complementary technologies that are not necessarily to be integrated in our machines, but they're going to be part of the fabrication network that we have bought, what we are building and are going to be machines that are catering into the same customers, even though doing other things, not rudimentary. Maybe they do inspections and maybe they do packaging and those machines, since they're being sold through the same distribution channel, same customer that we are, are going to expand our business and add contribution to our business and strengthen our position with our customers, supplying a total solution of digital manufacturing network. That's the 3 types of acquisitions we're looking at.

I'm a young investor, but I believe following you, and I'm looking forward to our bright future.

[Operator Instructions]

Okay. I think at this point, you -- is there any other question, operator?

Yes, correct. We actually have a question from [Jonathan Buckley], private investor.

I was just wondering, what are you guys doing to approve upon material that you guys have? Because whenever you say silver ink, is there something you guys are looking to expand upon that?

Yes.

Our next question comes from [Randy Simmons], private investor.

I'm curious as to when you're going to be generating more revenues that should support the share price?

I have no idea you're talk -- I have no idea what you're talking about supporting share price. I can tell you about what's our plans. And if you're looking for substantial revenue in the next few quarters, you are invested in the wrong company, I said it over the last year.

That answers my question. No. Well, I've been an investor for 2 or 3 years now. So I'm holding.

That's great. We count in you guys because, as I tell you, I'm very encouraged on how we proceed. Obviously, the revenue will be growing. But I'm much more excited from what opens up with the technologies and the fact that the corona, once it clears the way, I believe there will be a kind of a slingshot effect. I think we mentioned it and we spoke about it before. But I don't want to waste executives' time in running after customers that don't even come to the office because in Germany, for instance, because they're not allowed to be in the laboratories. So we are kind of treading the water very carefully to spend the money right in the right time, and we believe it's coming.

We have another question from Brian Herman of ViewTrade Securities.

So you'll have -- what milestones would you look at for an investor looking to see if the company can scale and actually start producing revenue? How far out do we go? Is it -- can you talk in general terms as far as from a distance, what we should look for if the company is scaling?

Yes. 2 years. So if your patient, and you'll see in the next 2 years -- not in 2 years, I'm speaking about within the next 2 years, you will see growth in revenue, both organically and through acquisitions. And -- but I believe if you ask, if I understand your question, when do you want to see inflection points substantially? You have to have attention span of 2 years.

Okay. And is it -- are we going to see 2 lines where you're selling machines and getting recurring revenue? And then another line where it's actually factory type environment...

Yes. Yes. You may -- based on acquisitions, potentially that I'm looking at, you may end up seeing a couple of lines, yes. But all the lines, they are obviously going to similar customers, similar verticals, to a similar distribution channel, similar go to market. Otherwise, we're becoming an investment company, and we are not. We need to get the lines sold in a way that is synergistic and leveraging the business model.

Okay. And do you see a need to access the capital markets equity or debt or any convertible securities in the foreseeable future?

Absolutely not. We have $1.42 million of cash, $1.2 -- sorry, $1.42 billion of cash. We believe we have proper funding for all the plans and the visions I have described here, including internal development and acquisitions.

Congrats on the progress so far. It's been great to watch.

Thank you so much, sir. Much appreciated.

At this time, it looks like we have no further questions. I would now like to turn it back to Yoav Stern.

Okay. Thank you very much. I think everybody now is, in New York at least, going back to work, and I congratulate you. And hopefully, you'll have a good day today and a good weekend. Thank you very much for listening to us. We are looking forward in the next short while to talk to you again because we think we have evolution and developments, and I promised to be back on this line and hopefully, shortly to speak with you. Thank you very much.

The conference has now concluded. Thank you for attending today's presentation. You may now disconnect.