Digital innovations in Oil & Gas Podcast : Improving Capital Allocation For Shale Plays

Our Founder & President, Jon Ludwig, recently sat down with the Digital Innovations in Oil and Gas podcast to share his thoughts on entrepreneurship and the importance of securing energy independence in every country. He also spoke about the founding of Novi Labs and how Novi’s analytics platform addresses the current challenges in the oil & gas space.

Podcast Transcript

00:00:47:16 – 00:01:37:16 – Geoffrey Cann

In this episode, I’m in conversation with John Ludwig, who is the president and co-founder of Novi Labs, a data platform for the upstream oil and gas industry. John is a serial entrepreneur with a background in finance and oil and gas who was struck by the challenge facing onshore oil and gas producers when they allocate capital to well programs.

Legacy ways of forecasting while performance and optimal well delivery based on one or two wells at a time simply don’t work. When operators are aiming for 500 well-manufacturing programs using new tools and methods to aggregate data and machine learning algorithms to crunch the numbers. Novi Labs helps improve capital allocation and economic performance of the industry. Here’s John.

John, welcome to the podcast.

 

00:01:37:16 – 00:01:38:09  – Jon Ludwig

Thank you, Geoffrey.

 

00:01:38:23 – 00:01:49:10  – Geoffrey Cann

Now, I always like to ask guests on the podcast to share a little bit about their personal background, just as a way to get to know them. So tell me about John Ludwig, really interested.

 

00:01:51:04 – 00:02:07:21  – Jon Ludwig

Yeah. You know, I grew up in the Northeast. Got to Texas as fast as I could. It took a few detours along the way. One in Boulder, Colorado, where I started my first tech company. And then I went to college at University of Florida.

 

00:02:08:00 – 00:02:10:04  – Geoffrey Cann

So you’re kind of like a serial entrepreneur then?

 

00:02:11:00 – 00:02:20:16  – Jon Ludwig

Yes. This is, you know, depending on what happens with Novi, this will be my third, third one. No bankruptcy so far, so I have a good record in that sense.

 

00:02:21:09 – 00:02:31:18  – Geoffrey Cann

That’s really important. The investor community loves to see a track record. So Northeast. What part? Maine. New Hampshire?

 

00:02:32:04 – 00:02:40:17  – Jon Ludwig

No, not that far up. New Jersey. About 45 minutes west of New York City. That’s where I grew up and went to high school, and then college in Florida.

 

00:02:41:01 – 00:02:46:01  – Geoffrey Cann

Oh, 45 minutes. Uh, yeah. Philadelphia’s about an hour away, so.

 

00:02:46:01 – 00:02:55:06  – Jon Ludwig

Yeah, Morristown is the closest town, but I grew up in a small town called Mendham, which is near Morristown. Morristown is usually more…

 

00:02:55:08 – 00:02:56:06  – Geoffrey Cann

Very famous.

 

00:02:56:08 – 00:03:00:15  – Jon Ludwig

Part of it. Mendham is a pretty small town, so

 

00:03:01:00 – 00:03:42:06  – Geoffrey Can

Yeah, Well I share the perspective about escaping to warmer places. I grew up in New Brunswick, which was east of Maine, for those of you listening who don’t have a crisp grasp of North American geography and saw yet another time zone further east and several degrees cooler, I like to add. So here you are, now you’re in Austin and on your third technical company. And so, how did you end up being an entrepreneur? What motivated you to get into trying to solve business problems as an entrepreneur rather than someone working inside a big company?

 

00:03:43:04 – 00:04:39:06  – Jon Ludwig

Yeah, I think I suffer from the same thing that any person that does what I do suffers from, which is I’m only interested in doing things that I don’t know how to do, so when that interest connects with a business problem that I think is important, I’m drawn to it and I have to do it.

It’s a compulsion almost. And I’ve worked a lot in oil and gas over the years, energy in general. My background is in economics and finance. So, you know, you combine massive capital outlay that is required in oil and gas with the varying results whenever you drill a well and then combine that with the sort of technical difficulties of the problem, which are huge, and my overall interest in data, that’s what kind of led me to where I am today with Novi 

 

00:04:39:06 – 00:05:02:24  – Geoffrey Cann

Interesting, so I’m visualizing a Venn diagram with all of these various interests and there’s this sort of central point where you find yourself focused. So let’s talk a little bit about this problem that you see in oil and gas that is sufficiently intriguing that you would devote your life to trying to solve. Like, what is the problem here that you see?

 

00:05:03:12 – 00:07:08:17  – Jon Ludwig

You know the core problem is securing energy independence. I mean, we see lots of evidence of what happens when you become non-independent. Europe is experiencing this now with the business with Russia and Ukraine, and the US has essentially become a swing producer, not a rounding error. So all of those things, you know, combined together, you sort of get this collision of geopolitical incidents along with massive capital investment that in many cases takes place years before the actual results show up, and capital outlays are huge. So I was drawn to the industry because I felt like helping nations, you know, countries, etcetera. Secure energy independence is really important. It’s fundamental. Everyone likes to know that when they flip the light switch, the lights are going on, but for some of us that live in Texas, there is a period where the lights didn’t go on for a whole week in 2021. You know this is not a third-world country, and for a week, we had no lights, no power, no water, and suddenly people knew what it was like to not have energy independence, or not have a reliable infrastructure to deliver energy. 

So to me, I feel like as the population of the world continues to grow, naturally people are going to have to find ways to secure access to energy. There’s no perfectly easy solution, everything’s got drawbacks. You know, batteries, you need lithium, there’s only a number of places to get lithium in the world. Oil and gas have their own dangers. But some way, somehow we have to use technology to figure out how to do this in a way where shareholders can make profits and countries in the world can secure the energy they need to make a real functioning country.

 

00:07:09:05 – 00:07:41:17  – Geoffrey Cann

And what is the specific application that you’re speaking of? Because you’re just using the winter storm in Texas from two years ago. There was a multitude of factors there that are worth solving, you know, a grid design, which is very insular and doesn’t have enough out-of-state linkage, a reliance on infrastructure and design that didn’t anticipate prolonged cold weather. So there’s a bunch of problems here we’re solving. What’s the problem that Novi Labs is trying to fix?

 

00:07:42:14 – 00:09:18:20  – Jon Ludwig

So we focus specifically on onshore production. So if you think of the world of energy independence, the country that I live in is probably the top priority to start. And what we became interested in and we decided to focus on was helping companies or operators that drill for oil and gas, helping the investors that fund them, you know, banks and financial institutions and traders and so on have reliable access to high-quality data that was the first problem we had to solve, when you do any kind of analysis, you’ve got to have great data to start with. And then I felt the industry needed more advanced methods of determining outcomes before millions of dollars were spent. So most of our customers today, operate on a scale of drilling 20 wells in a single decision or 10 wells, and in each well, 6 to 7 million dollars. I mean, decisions you make on a Monday meeting are 60 to 70-million-dollar decisions. So I felt like the tools that were available in the early days of shale, there wasn’t much data, and the tools used to analyze the data were not very advanced. So I thought of a company that organizes the data really well, that presents it in a way where it is the best possible quality, and also builds tools on top of it that allows oil and gas operators or energy investors to more deterministically make their investment decisions using quantitative methods. That’s the problem that Novi was built to solve.

 

00:09:19:14 – 00:09:48:06  – Geoffrey Cann

Now, some would say that the industry has successfully discovered drilled-produced hydrocarbons for a century before at this point, what is particular about tight hydrocarbons, shales, tight sands, etc., that tells you that the tools and the data set required to effectively analyze these needed to be overhauled?

 

00:09:48:06 – 00:11:20:05  – Jon Ludwig

Yeah, there were a couple of things. First of all, in the early shale boom, let’s say 2012 to 2014, a lot of capital went into the industry and a lot of wells were drill. And every well that’s drilled represents an opportunity to learn about what you should do in the future. So, having tools that actually help you analyze the prior historical results before you create new historical results going forward, hence are quite valuable.

So yes, there’s been tons of drilling, but the per-well cost of say, a vertical well is millions less than a horizontal well. So hydraulic fracturing and horizontal drilling are orders of magnitude more expensive and really to make money at it consistently, you’ve got to be operating almost like a manufacturer. So you’re in the business of manufacturing wells, so all of your processes have to be set up this way, you have to be capitalized in order to do that. So even when you take the most basic element, one well, $6 million dollars, you’re never making a $6 million dollar one-well decision, you’re making a 20-well decision for $120 million dollars. And really, if you are thinking about your annual capital planning, it’s hundreds of millions to billions, each company that is operating at a significant scale.

So a 5% difference when you’re operating at that scale or a 10% difference is a huge difference.

 

00:11:20:06 – 00:11:25:23  – Geoffrey Cann

Huge money. Yeah. There is a difference between adding another well or not or two wells in the case of a 20-well package.

 

00:11:26:11 – 00:11:58:21   – Jon Ludwig

I mean that’s the rationale for Devon Energy buying multiple companies this year or Conoco doing large consolidation as well or any of these large-scale operators. Right. They’re trying to get inventory, wells that they can drill in the future. They’re also trying to get scale so they can perform at a manufacturing level versus a one-off or five-off well level. 

So the consolidation we’ve seen in the industry has actually made the stakes go up. Not the other way around.

 

00:11:58:23 – 00:12:43:14  – Geoffrey Cann

Reminds me of the situation in Australia with the expansion of the coal seam gas program there. Well, one of the CEOs at one of the oil and gas companies turned the company around because he said; “Look, we’re not drilling 10,000 wells, we’re going to drill one well and then I’m going to repeat 10,000 times.” That’s a fundamentally different way of thinking about these companies. When you start to feel like, “hey, I’m actually a well assembly business, not an individual well drilling company.” You kind of have to approach the whole concept of how you think about your company has to shift. And if you don’t have the tools, obviously you’ll struggle to kind of get there.

 

00:12:43:14 – 00:13:45:02  – Jon Ludwig

I mean at the end of the day, the ideal scenario is you could say, look, I’m going to spend a billion dollars next year in the Permian. I have 4 thousand drilling locations that I could potentially drill. So for my billion dollars, I’m probably going to drill 500, right, or whatever the number is. So what you need is some kind of tool that allows you to run all the possible ways you could develop it, but use machines and compute instead of, engineers to run all of those scenarios. And then the engineers could look at all of the outcomes available, and select the one outcome that they believe strikes the best balance between capital being allocated and return and long-term value in the asset, you know, commonly measured by NPV or PV10 or PV25 or whatever the metric is, but something that sort of correlates to the long term value of the asset.

 

00:13:45:15 – 00:14:13:01  – Geoffrey Can

What kind of data goes into this kind of analytic when you’re drilling 20 wells or 100 well or 500 well program as distinct from I’m drilling just one well? Is it more logistics that’s going into it? or is it a mixture of logistics, cost, weather, like what drives the new data that you pull in to be able to do the analytics?

 

00:14:13:23 – 00:16:15:15  – Jon Ludwig

Yeah. So for us, our products and our data acts as an advisor to that informs the design of the drilling program. Meaning, how far apart the wells underground are? or how many wells do you stack horizontally? or how do you stagger them? or how do you complete the wells? All of those things are things that drive cost, they drive your actual reserves, drilling inventory which is a major inputs into how these companies are valued by the market. 

So what our software does is it helps people figure out, for the wells that I’ve drilled, what are they going to produce next year? so that that’s you’re producing developed forecast. And then for the wells that I plan to drill next year, what is the best way to go about doing that? How do I invest the money in the best possible way to get the best return next year?, but also for the long term as well. 

 

So the data you need to do that is production data. So you need all the historical wells and the best production data you can get and then you need design information about the wells.

So things like what, how is the well completed? how much profit would use? you know, those sorts of things. And then there’s a bunch of data you have to kind of engineer, that it doesn’t exist anywhere like, measuring how far apart the wells are underground. It’s possible to derive that data from data that exists, but we call those engineered features in our business.

 

So it’s kind of a combination of data that’s publicly reported, plus data that each operator would have from their own drilled well inventory, which is going to be a little bit more granular, but you’re not going to have it for as many wells and then data that you derive or engineer essentially from the underlying data that provides important information about how a well might produce in the future that you haven’t drilled yet.

 

00:16:16:08 – 00:16:44:01  – Geoffrey Cann

I’m imagining with all of this data, you’re not really outstrip the classic engineering approach of Excel spreadsheets and teams of people staring at whiteboards trying to figure this out. What incremental tools are being brought to bear here to be able to grind your way through this data, to be that kind of right, that advisor to the engineering teams and assuming it’s machine tools of some kind or other sophisticated analytic tools.

 

00:16:45:05 – 00:17:32:13  – Jon Ludwig

For us, we use machine learning algorithms which are most of them are available publicly, you know, open source. We have heavily modified those to work specifically for the types of data we deal with in oil and gas. And then you’ve got to combine that with large scale compute. So we essentially automatically provision cloud compute whenever customers run models in our software. This is a capability most oil and gas companies typically struggle with, so we do that for them as well. And then there’s a lighter aspect, which is the storage you have to store all the data and everything else, but that’s so ubiquitous and so cheap, it’s almost irrelevant.

 

00:17:33:01 – 00:17:41:24  – Geoffrey Cann

Yeah, no longer really the driver. It was a standpoint where that data storage was in fact the constraint, but that’s now gone.

 

00:17:42:20 – 00:18:37:03 – Jon Ludwig

Yeah, machine learning algorithms have been around for a while, they’ve been used heavily since the mid-nineties to help inform all sorts of things like, what shows up when you do a Google search versus me, may be different, because Google sort of figured out behind the scenes that you operate differently than Jon, you guys search for different things, so we’re going to line you up with different results. 

And essentially the more data that you compile and process in these types of systems, the better that they become. So that’s kind of what we’re after as a company is to get as much high-quality data as we possibly can, allow customers to use it to build these models on their own. And then we provide them with all the scalable infrastructure behind it in an automated way as well so they don’t have to turn this into a massive i.t. Infrastructure challenge.

 

00:18:37:03 – 00:19:11:24  – Geoffrey Cann

On-demand, infrastructure provision like this is a big value benefit because of the speed. What happens when you walk into an oil company office to talk about this, and you present what you’re doing and working on, you’re going to be dealing with individuals who have spent a lifetime trying to solve this problem manually, and you’re here with new tools. 

What’s the first reaction? And then once you unpack it a bit, how do people come around to this as a concept?

 

00:19:12:16 – 00:21:26:14  – Jon Ludwig

If we’re in somebody’s office having a discussion with them, generally speaking, they have more than a mild amount of interest in seeing if what we do is applicable to their workflows. As the conversation progresses, there really are three elements that are critical that we look for; Number one, you’ve got to have strong executive support because you are talking about using a different method to the most important thing in an oil company, which is allocate capital, right? And same thing for a financial institution, if you’re allocating money to trades and you’re talking about using a completely different way of doing it, the stakes just don’t get any higher for those type of companies. So strong executive support is key. 

 

There are some cultural differences between companies. Some companies are very engineering driven, others are more top-down type of cultures, and there’s been lots written about various oil companies and how they operate. We find companies that are very engineering-driven cultures, we do very well with. So we sort of look for that, as a marker like when I’m in the meeting with the vice president, am I talking to a finance guy or am I talking to an engineer? That makes a pretty big difference. 

 

And then the third thing that we look for is, what’s driving their interest in this?

So, if they say something like “I was told to do this by the board”, that’s not good. Because they’re basically going to check the box and they’re going to move on, and they may spend money with us, but it’s not going to turn into a long-term valuable relationship for us or them.

But if they say, “I downloaded one of your technical papers and I read it and that’s why I reached out, because I was interested in the technical solution that you’re creating”, in those situations we tend to do really well. So I think if you made those the three legs of the stool, that’s sort of what we look for any time we walk into a new meeting, new opportunity with a new customer.

 

00:21:26:18 – 00:22:07:24  – Geoffrey Cann

So you look for their culture effectively, top-down versus engineering. Who’s the actual person in the room you’re dealing with? Are they commercial or are they technical? And third, why did they reach out? What’s their motivation? And there’s a very useful goal posts to kinda put out there, kind of filter quickly. 

Now I’m sure when people work with these solutions, they tell you after the fact, I wasn’t expecting that, but this has been a real surprise to us. What do they tell you that catches them off guard? They kind of go, Wow, all this was really unanticipated but really valuable.

 

00:22:09:03 – 00:24:05:16  – Jon Ludwig

One of the things that we worked really hard on to build into our software is what we call “explainability data”. So, what “explainability data” is a couple of things. First, for every forecast that our software makes for a well, it actually tells you in barrels or [Mcf/d] it’s gas, it’ll tell you exactly what drove the forecast well. The tighter spacing drove the forecast down or the increased proppant loading drove the forecast up, and you actually see like in a waterfall chart what drove it up or down. 

So I think when customers look at that for the first time, every engineer has a preconceived notion about what drives performance and what areas. Oftentimes what our models put out is not necessarily surprising to them, but it’s the only time they’ve ever seen like a deterministic output that shows them this kind of information at that discrete of a level.

 

So they may look at it like in an area level because they don’t have tools that allow them to get to the per well level. We actually give it to them at the per well level and that allows them to then see, “Oh, I move a mile to the west, suddenly the performance of the wells goes down and now I have an explanation as to why”. 

 

It’s important to understand that for financial institutions as well, right? If you’re thinking about funding an oil companies drilling program or you’re thinking about buying their stock, you might want to know if the acreage to the west or to the east of where they’re currently drilling is worse or better than the acreage they’re presently drilling. Because that will allow you to sort of forecast forward their results.

So the need exists for both the oil and gas operator side to understand this, but also on the financial side, the folks that are funding the plan or going long or going short on the stock need to know the same thing.

 

00:24:05:16 – 00:24:39:16  – Geoffrey Cann

And also imagine going up that you take your capital allocation meetings internally where you’re trying to allocate to various projects. Those engineers who are best able to explain; here’s why this program is the best and here’s the alternatives we considered but discarded for a variety of reasons. The whole explainability which you have to give to financial professionals or commercial professionals. Having that data has to be a big value when you think about it. Even the whole idea of a waterfall chart system, that’s such a financial concept really when you think about it.

 

00:24:40:17 – 00:25:06:05  – Jon Ludwig

Well, let’s say it all boils down to why. Tell me why does your model think that? or Why is it think this is the production of this well, or this part, or this area, and what causes it? That’s a very important question. Like in our early days as a company, we didn’t have this explainability data we just had answers. And then that’s kind of where you get this all machine learning is a black box

 

00:25:06:05 – 00:25:07:09  – Geoffrey Cann

Exactly

 

00:25:07:10 – 00:25:35:07  – Jon Ludwig

It’s true to some degree. But these ways to explain these forecasts that are made by any machine learning regression model have improved dramatically in the last five years, and we’ve adopted all of those tools and put them into our software so that we just output this as a standard data output, so that alongside the forecast, you’re looking at the explanation. That’s helped us tremendously, I think, as we’ve had more commercial success.

 

00:25:35:16 – 00:25:53:10  – Geoffrey Cann

Yeah. I mean, it’s just a transparency question, isn’t it? 

If you’re an engineer, you’re putting up a bridge or wiring through a building, your career is built on being able to explain why I’m doing it this way, not that way.

 

00:25:54:03 – 00:26:58:08  – Jon Ludwig

I worked in the oil and gas industry prior to starting Novi, and that was the one thing I would see in the meetings that frustrated me endlessly, was a scatterplot of oil production versus you name it, proppant or fluid or spacing or whatever, and then a hidden drawn line, like, “look at this very clear relationship” and I’m like, to me, it looks like somebody barf dots on the screen like it doesn’t look like a clear relationship at all.

 

So it’s very difficult without the right tools to sort of pick apart these complex relationships that do exist in the data, you just have to have the right tools to expose it and I can tell you this copying and pasting of an Excel scatterplot and then hand drawing a line in PowerPoint, like this is the trend line… That’s not it. That led to the results of 2016, which is that most of the industry would want to forget.

 

00:26:58:10 – 00:27:36:15  – Geoffrey Cann

Yeah. Spectacularly unconvincing if you’re in the finance world now with its reliance on these tools. Now, I don’t know if you’re in a position to do this, are you able to forecast or at least perhaps expand on what the amount of waste in the industry? or the misallocation of capital could be corrected with this improved data?

Are any of your clients saying, “hey, had we done it the old way, this is what we would have expected, now we’re getting this. So now we can actually quantify the impact of using these modern tools.”

 

00:27:37:04 – 00:27:44:22  – Jon Ludwig

Yeah. It’s always hard to explain with any model what would’ve happened if I had done something different.

 

00:27:44:22 – 00:27:49:04  – Geoffrey Cann

Of course. Yeah.

Because you didn’t do it.

 

00:27:49:11 – 00:30:00:02  – Jon Ludwig

You can’t point to the empirical outcome but, one of the things that we’ve allowed people to do with our software is specifically locked certain pads or wells that they have drilled. 

There’s a concept in machine learning called the test sets, think about it like, all the wells, let’s say a thousand, typical split would be 800 wells that are used to train a machine learning model and then 200 wells, 20% would be held out for in the test set.

 

So we allow customers to manipulate the test set in situations where they want to do exactly what you’re talking about, which is; I want to make sure that these three pads are in the test set, therefore the model has not seen them. And I’d like to see how well the model would’ve predicted those with the data that was available prior to those wells being drilled. And then you can compare that against the engineers forecasts that were made at that exact same time, same data available to the engineers, and that’s a very useful way, we call it a time machine model, but that’s a very useful way of sort of testing how good the model would’ve been had you had it in production prior to making whatever decision that was important.

 

You can probably imagine, when these companies, the public ones at least, when they report their results, their stock price will go up or down, and in many cases, it could be based on single pat. You know, like we had a huge miss, we’re way under the forecast for this pad and then their stock price could get nailed by 30% or more.

 

So if you’re a long-short hedge fund that’s investing in that company, you probably like to know as much about those pads that they haven’t drilled yet as possible, so that’s a simulation that could be done in our software. Then if you’re the operator, you’d want to try to learn as much as you could from the data available at that time to make sure that you don’t have a huge mess like that, given the ramifications to your shareholders are pretty big.

 

00:30:00:12 – 00:30:35:07  – Geoffrey Cann

What do you see as the upside here, the potential for the industry if it can sharpen this up? 

If you go back to the original goal, which was energy security and energy independence, What does this mean for, say, a country like the United States, which is already, frankly, quite rich in energy resources as it is? There’s now a whole export industry. 

I remember when the U.S. would not export hydrocarbons, and now it’s one of the world’s biggest exporters. What is the potential here for the U.S., given its resource mix, do you think?

 

00:30:35:07 – 00:32:52:14  – Jon Ludwig

There’s a certain amount of technical innovation just on the execution side of the wells that you can kind of rely on over time. So, techniques to complete wells, techniques to drill wells, to improve results. Those continue to get better over time, but the single best thing that could be done is to avoid mistakes.

 

So, if you’re going to drill ten pads with eight wells on each, it’d be great if all ten of them came in close to your expectations because then you can sort of rely on that money coming in to run your company. We’ve seen evidence of what happens when the industry sees underinvestment. Everyone felt that at the pump, filling your car if you’ve got a gas car, filling your car with gas, and suddenly it was reliably right around $3 a gallon, at least in Texas, and then it went to like $5,50 a gallon and we went on summer vacation or our gas bill was two or three times more than the year before.

 

So when you think about how much impact that has on the average consumers pocketbook, if you’re driving a lot to work, a lot of miles, that can be significant. So those are the types of things, if you constrain the funding available to oil companies, they constrain their investment and if they constrain their investment, the supply is this and the demand is this and this continues to get worse. And that’s not good for energy independence. 

 

So the best thing you can do to secure energy independence is to miss smaller when you’re going to miss, or be on the slightly lower side relative to your forecast. And that’s kind of where technologies like ours come into play, right? It’s all about the decision to appropriate the capital to go build the next pad. How do I make that had the best possible decision that it can be? And if you do that right, the industry attracts investment. The industry attracts investment, it increases production. If it increases production, everyone feels that when they buy a plane ticket or fill their car with gas to go on vacation or whatever the case may be.

 

00:32:52:23 – 00:33:33:11  – Geoffrey Cann

Or heat your house in the winter, if you happen to be in the northeast with fuel oil, it’s very expensive. I once convinced my parents to sell their house because fuel oil was tied to the price of oil and it was getting hideously expensive just to heat the house in the winter. 

 

So, as a serial entrepreneur, Jon, you’ve built a roster of life experiences that you share with others to try and encourage other entrepreneurs as they pursue their own ambitions, their own vision, and dreams. You know, if you had a couple of life lessons to share with others, what would those be?

 

00:33:33:22 – 00:35:18:09  – Jon Ludwig

I would approach being an entrepreneur with a great deal of humility. I don’t care how many times you’ve succeeded, you’re in line to be humbled one day or the next. So, that’s one thing, approach everything you do with a degree of humility. And then second, if you have investors in your company, take care of them.

 

Even if it doesn’t go well, take care of them, do your best. Because if you ruin those relationships, it’s a small world. And the next time you have a great idea, you’re not going to get funded. So try very hard to take care of your investors. They need to see that you’re committed and you’re willing to do whatever it takes to allow them to get their capital back and make an appropriate return given the risk.

 

And the third thing is, you know, I’ve been fortunate. I have a very supportive family. I have to work long hours sometimes, but there’s trade-offs sometimes. I also have the option to not work, because I’m, I guess, far enough along to where that can happen. So you just got to be prepared to put the work in.

There’s nothing easy about doing this, no matter how great your ideas. I would say that’s the third thing that I tell people, make sure you’ve got an infrastructure to support you, family first and then friends and so on, because there’s going to be some bad times mixed in with the good, failures with success. You need that support. It’s pretty critical.

 

00:35:18:24 – 00:35:21:20  – Geoffrey Cann

Jon, thanks so much for coming on the podcast today.

 

00:35:22:12 – 00:35:23:22  – Jon Ludwig

Yeah, great. Thank you, Geoffrey. Cheers.

 

00:35:25:08 – 00:36:52:16  – Geoffrey Cann

That was Jon Ludwig, president and co-founder of Novi Labs, a data platform for the upstream oil and gas industry. I was struck by John’s insight into identifying the right customer for these kinds of innovative solutions. First, they have an engineering first culture, there’s an engineering-oriented buyer, and that engineering buyer is looking for help. No better advice from Jon.

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