Ep 45: Unlocking the Practical Power of AI to Drive Enterprise Innovation with Grainger CTO Jonny LeRoy

‍Evan: Hi there, and welcome to Enterprise Software Innovators, a show where top tech executives share how they innovate at scale. In each episode, enterprise CIOs share how they've applied exciting new technologies, and what they've learned along the way. I'm Evan Reiser, the CEO and founder of Abnormal Security.

‍Saam: I'm Saam Motamedi, a general partner at Greylock Partners.

 Evan: Today on the show, we’re bringing you a conversation with Jonny LeRoy, Chief Technology Officer at Grainger.

Grainger is a Fortune 500 industrial supply company, ensuring seamless operations for a broad range of customers, from hospitals to manufacturing plants and everything in between. With over $16 billion of annual revenue and 26,000 employees, the company provides over 30 million products to support their 4 and a half million customers.

In this conversation, Jonny shares his thoughts on how AI transforms operations at Grainger, quick wins for AI applications in the enterprise, and realistic expectations around today’s AI capabilities.

Well, Johnny, first of all, thank you so much for taking time to join us today. I know Saam and I are really looking forward to this conversation. Um, maybe to kick us off, do you mind sharing a little bit about kind of your career and how you got to where you are today? 

Jonny: Yeah, sure. Great to meet you and great to be here.

I've been at Grainger, this industrial supply company for the last, just over four years. And so working backwards before that, I spent around 15 years at a tech consulting company called Thoughtworks. I actually joined in London, but they took me out to San Francisco pretty soon after that. And I spent most of my time there bridging between some bigger enterprise companies, sort of retail, financial services, healthcare, some of the big well known tech companies and some interesting startups.

And a large part of my role there was really trying to bridge between those. And it would help startups as they were hitting architectural and organizational scaling points at the same time. And I used to joke that some of my work was really helping startups grow up and helping enterprises, kind of loosen up.

So how they can sort of what's the sweet spot between them. In many ways that's some of what I've been trying to do at Grainger is get some of that technology startup culture into a large 97 year old enterprise. And before that, I had a bit of a background in the London startup scene, was CTO of a startup, late 90s, early 2000s.

And then had a weird background. I was self taught because, um, I'd made the strange choice to study Latin and Greek literature, language, and philosophy at, uh, at university and detoured via the law, but I finally found my way into computers, while teaching myself, while working in a pub in a good English tradition.

Saam: Grainger is one of these important and foundational companies to the way we all live and work. Yet I'm sure much of our audience may not be familiar with Grainger, which I think is why this episode is set up to be a really interesting one. 

Maybe just starting very high level, can you share a little bit about what Grainger is and the impact that it has?

Jonny: So we are a 97 year old company. We're a distributor of largely of industrial supply products, and we sell mainly into maintenance, repair, and operations organizations. And I'll really sort of simplify that by saying, largely we sell into the basements of big buildings. Whether it be an airport, a hotel, manufacturing plants, hospital, we're selling into the folks who keep that building and those operations running. In fact, the folks whose job it is, is to not be seen because if they're seen running around, something's going wrong. So we're trying to make sure that they're successful.

Whether it's making sure they've got the right safety supplies, the gloves, the glasses, goggles you need, whether it's the hand tools, material handling, or in some more complex high end tools in sort of in pharma and so on. But our job is to make sure they have all of those products that are needed to keep their companies working. And so that led us up to our overall mission is we keep the world working. That's what we do as an organization. About six, just over 16 billion of revenue, three quarters of that comes through digital channels that I'm largely responsible for.

Evan: Wow. That's actually quite, quite impressive. I didn't realize that ratio. I imagine that there's probably some things that the outsider would naturally assume you guys are really good at to put a lot of technology, right? I'm sure like supply chain is extremely complex, but do you mind sharing some of the areas where you guys are using technology to, either run the business or kind of deliver a better product service to your customers and clients, maybe in some ways that, you know, people might not be super familiar or might not be obvious to others.

Jonny: Yeah, I can lay out a few of sort of historically what we've done and weirdly, although we're 97 years old, we were founded around new technologies. Those technologies were electric motors and pumps. Um, Mr. Grainger was helping people have the information they needed to work out how to repair and fix them and keep them running.

Then through newer technologies, we were early into e-commerce, had a whole set of e-commerce sites, early into ERP, so we ended up with a lot of SAP for better or worse. We were early in some of the distribution center automation with goods to person, mechanics. So there's lots of big robots and some newer robots coming into our distribution centers and supply chain.

And now we've been leaning into more of the digital tooling, and really going through that transformation over the last five, six years, and that's a lot of what I've been driving, and that's beginning to segue into how we think about ML and AI. And I might just say a couple of bits on what we're focused on, because we've got this quite old landscape, we're trying to, as we're using our new technology, we're beginning to use more and more custom software, custom built software that we can tailor to our needs. And we're trying to focus that on areas where we think we can and should be differentiated. We, sort of, earned the right to have our own software there. And we really focused a lot of that around, understanding our products and understanding our customers really well. So we're building custom software in those areas.

So one thread is build software to drive advantage for us as a company. Other big thread is modernize all our older systems as we're going and try to do those two things at once, almost the, the walk and chew gum strategy. And to enable all of that, we're trying to grow our talent and ways of working, and culture to make sure that that's a long lived transformation.

Saam: I think it's hard to, uh, have a conversation in, in 2024 about technology transformation and not mention AI. You've been a technology leader through multiple technology waves. Like where do you think we are in this one and the hype cycle around generative AI? 

Jonny: There's definitely some hype, but there's actually a bunch of value and we've got some value that we're seeing in production. The sort of phrase that I've stolen from others, is I think if you look at, there's a spectrum from some of the sort of breathless stuff you'll hear on Twitter, or X, we'll be talking about these sort of God-like deity AIs, And the other end of the spectrum is you've got access to a bunch of interns and on that spectrum of deity to intern, you know, we're a couple of clicks beyond, you've got, you've got a set of interns. And so you can do a lot of really good stuff with semi unlimited interns. But you've got to be careful about where you apply that.

The structures you put in and around it. So that actually drives a fair amount of how we're using it. Interns are very energetic, very smart, but they don't always come up with the right answers so you need some ways of checking their work before you push it out the door and you need to do the same with, with AI. So, um, that's one big area I think about.

Saam: I really loved the distillation you just had, you know, deities versus interns and of all the ones I've heard on the show, that one really resonates with me. And I agree. I think many people, when they start interacting with these systems are expecting a deity and then when it doesn't act that way, they get frustrated or they say it's just all hype and no substance. And the reality is even if you can get really productive interns, that can be quite kind of transformational to a company, and to a business. So I love that framing. 

And then I also appreciate what you said around when you're working with interns, you've got to put guardrails and kind of break up processes to make them successful. You know, maybe just continue with that analogy. Can you connect that to some use cases and sort of like, what are the ways you've seen these interns, these AI interns, be successful and have a business impact? I don't know if there's one or two examples that stand out. 

Jonny: Yeah, And we're doing all the sort of standard basic stuff on the guardrails around governance and so on, and picking the right partners to make sure that we, our data is well protected and so on.

As a company, we really believe in continuous improvement. We have a lot of the sort of lean manufacturing background, and that works for our supply chain. So a lot of our processes, we're working out, how do we improve them?

We have been looking for areas where one step in the process is quite painful or low quality and we think may be addressable by AI. A couple of examples I can talk about. One, for our customer intelligence as, as new customers are coming in or they're hitting certain spend thresholds, we'll do some research on them to try to work out, are they going to be a big customer? What sort of potential do they have? And based on that, we can work out how we market to them, whether we apply sellers, whether we send them one of our awesome catalogs or how we approach them. 

And one important step is working out what industry they're in. And, it sounds surprisingly simple, but there are these industry NAICS codes, and it can take 20 or 30 minutes to manually go and research per company. We've put in a step that seems to be working quite well now, of have a large language model, go off, do the research, come back with a recommendation of a, an industry or two, with a couple of bits of proof of here's the links to go check out, if you don't trust us and really reducing that step from 20 or 30 minutes to two or three minutes.

And when you scale that out to our sort of multiple millions of customers, that's actually really quite impactful. So that's one small step that's, again, you could throw a bunch of interns at, and so it's similar, you've got guardrails bounded around the edge of it, but it has a really quite dramatic improvements.

Another similar-ish. One is, as we are taking on new customer facilities, often we'll go in and someone else will have been managing, or they'll have been self-managing their supply room or their tool crib where they're storing all of the products that, that they want us to look after. And they'll either have a spreadsheet of a list of all the products we sort of weird and wonderfully named products in there, or we'll go in and sort of walk around and look at the labels, and the process of trying to work out. What on earth is this fastener or specifically was this tool and you've got a really sort of obscure bit of text describing it. That might be really compressed the power or the size or the color or the brand of that tool. We'll tend to sort of throw that into systems to try to say, do we have a direct product match? Or do we have ones that are similar? 

We've noticed for when some of those strings, bits of text that we're searching on fail or get poor matches. We're now trying to expand them out with a large language model to work out, actually, if we expand that out to, this was 12 inches, whatever it was, 100 watts, whatever the brand is. Push that back into the matching algorithm. We get much, much better results. But we think there are so many of those potential areas all the way across the business that if we take that continuous improvement approach, understand the process and understand the inputs and outputs. We can actually really see if we're having an impact, or not. 

Evan: You've been able to kind of find some very concrete specific problems that drive some, you know, some progress forward now.

I guess what would be your advice to, you know, some of your peers about how to identify those things and how to get value out of what we have today without, you know, well, while still being mindful of the future, but without having to wait six years to like deliver an impact. 

Jonny: I think you've got to dive in and get started. Now, I'm a big believer that in technology, the right way to approach is you take big problems and you break them down into small problems, and then you deal with small problems one by one by one. Some of the art comes in actually how you break problems down. But we're trying to do a lot of sort of incremental small release pieces.

For example, we've been doing a lot of work to support our customer service agents. And so we took a small set, we took the Text channel where customers can text with our customer service agencies, is the smallest channel for us. We took a thin slice of people who are looking for product information out of that small channel and started peeling those requests off to see if we can provide better information back to our customer service agents.

And this was important for us to start small and we can learn and grow. It was also important to have humans in the loop there, partly for the AI guardrails piece, but actually because of our business strategy, we operate this high touch service model. So we try to go the extra mile to make our customers problems go away.

We're not just shipping them products in the nice red and white boxes. Our job is actually to make sure that they can keep their operations running. So we need to be one step ahead of that. We need to make sure that they never run out of the products they need. We know what they're going to need. We get it to them ahead of time.

We make it easy for them to procure and interact with us. And so to do that, we deploy a mixture of people, humans, our team members and technology. And so really we want our customers to be interacting with people first and so we're trying to augment them with these extra powers.

And what was interesting as we were beginning to spin up this LLM Gen AI piece to start helping with product selection is first up, we thought, well, we can just give the customer an answer and the customer service agent will push it through. But we started realizing that the right thing was to slow down and work out the right next questions to ask.

So a customer says, Hey, I need a drill. The answer isn't, Hey, we've got a drill because we've got thousands of drills. It's okay, what's what industry and what uses it for? What sort of power do you need? What sort of protections do you need? And so this digital assistant was actually recommending better questions for our customer service agents. So that guided the customer to good answers. 

But also the feedback from those customer service agents was they loved it because it made them seem smarter because they got to ask smart questions about the product, about their industry. We're now looking at other problem areas we can look at and potentially going to other channels, like sort of email or voice, but they have their own challenges that we'll need to address as we get there. 

Evan: That's a great example. I think particularly for you guys, cause the catalog is so big, right? You take the world's best customer service agent in the world and you put them in your organization, like obviously, how are they going to know about a thousand or a million SKUs, right? And so 

Jonny: 2, 2, 2 million SKUs in our main Grainger brand and across the whole portfolio, something like 30 million products that we're managing.

So yeah, small challenge.

Evan: I like that example because I think, and there's some, there's some people who might be in your shoes are saying, Oh, we'll just wait a couple of years. We got the full AI thing. Right. And I like kind of the incremental approach saying, Hey, let's start with just like advising the agent, right. Give me a couple extra questions, maybe some additional information that some point you're kind of, you know, it's a AI general response. And then reviewed by human, then at some point, like a 2%, we can probably just respond automatically. And you can kind of move up from there.

But that, that seems like a very tractable approach to, you know, going on the kind of AI gerb, you're getting practical results, you know, along the way, while also going towards like that, whatever that kind of future state is. 

Jonny: Yeah, and I think that's one of the lessons learned as we've been doing more and more custom software delivery. And that's one of the major things I came in to help us bring a culture of, of how to build our own software. And what's different there from rolling out large off the shelf systems, which are largely big bang, you install, buy the thing, install the thing, integrate it, and hope it works with custom software, getting to these really, really small iterations, continuous delivery, well tested, pushing out small pieces and learning about the customer feedback and the sort of resilience and scalability of that software at the same time. That mindset, that test and learn rapid mindset that we'd really sort of internalized for software delivery is really helpful as you're pushing out some of these AI systems as well. We're finding so that that mentality and that culture was already in place. And so we're just leaning on that. 

Evan: If you think about this, kind of, AI dream we’re on and, you know, Grainger goes through, You know, the next 10 iterations of kind of, you know, evolving and adapting.

Where do you think this like ends up in like, you know, five years from now, right? What are some of the ways you see, you know, AI and machine learning and some of these new technologies kind of transforming the business in ways that, you know, maybe one of your, your customers, the average employer, someone on the outside, you know, might not expect. 

Jonny: We're almost like a dating site. We're, we're dating customers with products that they need. And so we need to understand our customers and their industries, and assign the products and the suppliers and how to get those together. And I think AI is just one extra tool in helping us do that. 

We do see a virtuous cycle between the human efforts of researching these products, talking to the manufacturers, working out from a merchandising perspective, what's most important about these products and these assortments. That actually generates better data for us. And a lot of the customer software we're building is to empower, whether it's our merchandising agents or customer intelligence people, to do their work that drives a business, but spit out better data. That data then feeds into potential machine learning AI systems. We can get that into motion, get that in front of customers, and improve that data and feed it back.

I think if you take this sliced approach of looking at small steps inside existing processes that you understand well, and how can you improve a bunch of them where there are so many processes across the organization that could be improved in that way. So I think, I think we'll see a ton of those. 

There may well then be some of those sort of next step leaps where you realize that a whole process is maybe slightly unnecessary, you can leapfrog over that and that's where some of the sort of innovation or next level thinking, um, comes in. 

And that is actually one of the challenges or opportunities we're looking at in our technology transformation efforts is challenging ourselves and our business partners to think about how might we want to operate the business if we were unconstrained by our current technology. And that's a big, big part.

I partner with our chief product officer, Brian Walker. A lot of his job is to go drive that question repeatedly until, until he actually gets an answer he likes, because often people are so conditioned by the software they've been using for the last decade or so to think that's the only way to operate the business. And pushing them to think differently about, you know, What do we actually want to do for the customer? What's the value that the customer gets? Get a little creative and curious about how we might do that, that differently. And then backing that up by building the software or training the systems that can actually support that different way of operating. So that's, that's one portion of the transformation journey that we're trying to be on.

Evan: I think we would all agree, there's like countless examples of boundless opportunity for us to deploy these technologies. What are some of the areas like, you know, you feel a little bit more skeptical of? Or you think maybe it's like overhyped or, um, underestimated by, you know, the average kind of enterprise technology leader? 

Jonny: Yeah, there's a handful of it. I'll start with sort of some of the products we're, we're seeing. We see great products. We've been using a fair amount of the Microsoft copilots tools. Excellent in some areas, but in some areas that they're not there yet. And that's just the state of the technology. And to be honest, they're kind of priced like a product, but probably operate more like a feature. And so that's, that's one of those things to sort of wait and navigate on. But, we have a good relationship with Microsoft. So we're working through that. So, so I think those are areas where they're not as amazing as they could be. 

There are some use cases that are great. Summarizing large PowerPoints is excellent. I get so many of them and I can get a good idea of what's happening in there. Creating PowerPoints from a sketch I've scribbled out. I've struggled to make that work well. 

Similarly on the software development side, we see a lot of benefits. There's a lot that helps with coding. There's a fair amount that actually helps upstream, with information discovery. We had a team who is part of a hackathon, put together just a GenAI based search across Slack and GitHub and Veer and Confluence and various other places. And you're saying how does this API work? What does this error message mean? What was the decision of this last architectural decision record? And it's really good at providing that information really quickly. And those are some of the slow pieces of the software engineering process. So, so there are pieces there that are great.

I see good demos of generating code for very small applications, but generating it for much larger applications, I think we're still a fairly long way off.

As I mentioned, the progress of large language models, I think it's beginning to taper the benefit we get, I'm ready to be surprised by the next models that come out. And I'm also a little worried about the energy usage and the sort of running out of data to train on because neither of those are unlimited. Particularly, and what makes me think that maybe the design we have for large language models right now isn't the final or perfect design is the amount of power they need. Cause our brains can do as much or more and they operate on what, 20 Watts. Rather than the amounts of gigawatts that we're looking at investing in right now, to train AI, so I, I think we'll need another iteration on the architecture of some of these models that we're using before we really get to more interesting pieces.

Evan: Yeah, Saam, you kind of like implicitly this point earlier, but like the technology is advanced really, but the application of the technology, there's still just so much there, right?

I mean, if you just froze ChatGPT 4 for 10 years, nine years from now, we figure out new ways to kind of use this, right? It's somewhat similar of like, even, you know, cloud architecture, right? Cloud architecture, you know, that's been around for like 30 years at some level. Right. But like, even today we're like, Oh, what if you did this? Right. We never thought about that approach. Right. So like, it is, it is interesting. Like, yeah. When that kind of the core, when the core technology is evolving very quickly, like you almost don't get the time or space to really think about all the ways these things can be used. 

Jonny: There's this old phrase in tech of choose boring technology. Which is great, great advice for anyone is actually sometimes the boring simple technologies is the right one. So if like, 3.5 is the boring technology, maybe just actually pick that. And you could, and if that does what you need, actually at a better price point and a better latency, then maybe that's one to use. 

And actually, this is an aside, but actually, as we've been building out our customer service digital assistant, we've been doing some arbitrage across models because while the sort of four generation models are higher quality, their latency is, is a lot worse or more variable. And so we've actually got a couple of steps in the process and we can throw, One set of things at an earlier model that's cheaper, and faster, and then for more complex piece, then sort of upgrade which model. And so that sort of model arbitrage, even sort of AI FinOps of sort of working out how to manage the finances of which models you're using, that's going to become more and more of a thing.

Saam: You know, I was catching up with, um, one of our partners was the founder of Workday and he like, you know, has been in technology for a long time. And he was just commenting to me, he feels like it's the first time in 20 years that you can really have like fundamentally new types of applications because the interaction and data model looks so different with AI, like that really resonated with me. And then I think whether it's inside an organization or outside for new companies, figuring out how to actually go do that is like the opportunity of our time. It's exciting, like, cause that opportunity hasn't existed in a while. 

Jonny: Yeah, and I would say the, the innovation in a vacuum of, Hey, I've got a, I've got a decent idea to put a startup and put a thin wrapper around ChatGPT, maybe, maybe it's a little thin or doesn't get traction. Um, we really believe in coupling innovation with a sort of tech innovation with some real understanding of the business problem. And so I think being able to go deep into a business domain is the key. And you couple the tech and say, actually, what's, what's a very specific problem we're trying to solve in supply chain or in marketing or in understanding our, um, our customers and apply it there.

We've done a bunch of that on computer vision. There's now traditional AI, um, I guess. Um, but, um, I think that's where the more interesting innovation happens is getting into understanding a business area and whether that's the, you know, the semantic layer or the ontology of that business, but I think that's, that's an area that we'll definitely see more movement on.

Evan: That also, Eric, just from the entrepreneur perspective too, like that's the area that seems like way more durable as well, because anything that's, anything that's a thin layer or even a medium layer on top of ChatGPT, it's like, well, ChatGPT 8's going to do that. Right. 

But if you go into these more niche kind of business problems, you know, the, the core thing there is not just like use a large language model, right? So, okay. Like. Okay. Bye. What about the hundred other data sets and the operational that and all the human interfaces and workflows for supervision and like, it seems like that's where like the more durable opportunity is for, you know, in a technology landscape that's like changing very quickly. 

Jonny: Yeah. And, and I think for us, we feel like we have the rest of the scaffolding of what we need for the other systems that can actually then sort of process before and after that, and the, the standard work of the people and the relationships and so on to, to slot that into.

We've been putting a fair amount of effort into our ML ops, ML platform team, and I'm a big believer in testing and test driven development, and we're having to rethink that for ML and, and AI. And in fact, some of the tests are really sort of data driven scoring and coming up with what's a good scoring mechanism to work out if we're giving good answers to a search or to a chat conversation so that as you're changing models or upgrading models, you've got some notion of, are you better or worse? Or you've, have you done something weird? And so we've almost got these sort of algorithmic, um, tests, um, that we're now putting in place. 

Evan: So the one thing we like to do at the end of the episode is do a bit of a lightning round. Just trying to get your kind of like one tweet answers. All these questions are impossibly answering one tweet to just forgive us and, you know, be able to talk to us after the show, but, uh, Saam, do you want to kick it off for us? 

Saam: Yeah, absolutely. So, Johnny, to start on the lightning round, um, how do you think companies should measure the success of a CTO?

Jonny: Oh, ask my boss. Um, no, I, I'd probably break it down into, there's probably three things that, um, I would, if, if you did ask my boss that he would probably say. So one, are we driving tangible value, measurable value for the business on the key strategic areas that we, that we think are important? That's one.

Second one is, do we, by some measure, are we getting better? Yeah. Um, in terms of effectiveness or productivity, and there's all sorts of traps of trying to measure productivity in, um, in technology, but are we getting more for what we're putting in and spending? 

And then the final one is a sort of team member one. Are our team members engaged and happy, and do they feel like this is a place they can do their best work? And I think if you're sort of doing well across those three dimensions, um, you're probably in a decent place. 

Evan: What's one piece of advice you wish someone told you when you first became a CTO?

Jonny: Well, I first became a CTO in the late 90s and I was a CTO of myself. And so the problems there were different. As a CTO now, pick which decisions you actually make. So I actually think a fair amount about how decisions are made and where and when and by who they're made in an organization, and actually one of my jobs now is to make as few decisions as possible, so really try to design the organization so that you can push decision making down closer to the work.

Now, the sort of flip side of that is people have to know when they can't make a decision. They've got to bring it back up. And understanding how quickly is this a rapid fire two way door decision, or is this a more complex one that's better to circle that decision? 

And then how you make decisions visible, we use these architecture decision records so we can, sort of broadcast decisions we're making and have people weigh in on them. But, the fewer decisions I have to make the better. In fact, maybe that's a key metric of how many decisions per day do I need to make, or have I set up the organization to be good at making decisions? 

Saam: What do you think most IT leaders underestimate about the opportunity with AI? 

Jonny: The non-data science work that goes around it. So having good data, getting the data into the right places, you still got to feed it into applications, got to test those applications and get them out. And you got to operate them, you got to secure them. And so there's a rush to hire data scientists and ML scientists. 

We're decomposing actually a fair amount of the roles and skills around that and looking at what are some of the non PhD dependent roles that are easier to sort of cross skill, or train people into. So, yeah, data science is the tip of the iceberg. There's a fair amount underwater and there's a whole bunch of skills and grunt work that needs to be done there. 

Some of that may be AI addressable over time. And there's some opportunities for startups to address some of that. But don't underestimate the size of the iceberg under the water. 

Evan: So just maybe switching gears to the more personal side, what's a book that you've read that's had a big impact on you? 

Jonny: I was a big fan of Zen and the Art of Motorcycle Maintenance, because for me, that was a real sort of insight into, um, while I was repairing motorbikes, I saw it very much as the similar work to debug software and some of the mentality you need behind that and some of the joy of that as well. And it had a good slice of, uh, you know, good West Coast hippiness before I moved out to the West Coast.

Saam: Yeah. I really enjoyed that book as well. Staying on the personal side, what's an upcoming new technology, and it doesn't need to be AI related, that you're personally most excited about?

Jonny: I actually love some of the robots we have in our, in our distribution centers. They're kind of cool, and some of them have got names. Our favorite one is called Tuner and he runs around, but there's these little, um, robots that we like partly because they're helping our supply chain get better. Partly because robots are really cool. But partly because their power consumption is way lower as well, and so actually the sort of energy impacts, we do a whole bunch on carbon neutrality and trying to improve our energy footprint. And these are way lower energy than some of previous conveyance systems we had. So, so there's really interesting stuff happening there. 

Evan: Okay. Final question. I think we're gonna have to, you know, um, we'll have to part ways soon. So, um, what do you think will be true about technologies future impact in the world that most people would consider science fiction, like looking for a kind of contrarian view, right? What do you think is gonna come true that most people be like, ah, that's, that sounds crazy. 

Jonny: I think that at the last possible moment, humanity will find a way to address the climate crisis through technology. Um, I used to hope that we could sort of, uh, um, all live sort of simple, um, sustainable lives, but I actually think we need some more aggressive technology, um, interventions. Um, it'll be a weird and wonderful future, but I, I think we will need to, and I think we will, and I really hope we'll be successful. 

Evan: All right. What a great way to end the episode, um, with a note of optimism, which I share as well. So, um, yeah, Jonny, really appreciate you making time to chat. Um, really enjoy learning more about Grainger and looking forward to chatting again soon.

Saam: Thanks a lot, Jonny. 

Jonny: Really appreciated the conversation.

Evan: That was Jonny LeRoy, Chief Technology Officer at Grainger.

‍Saam: Thanks for listening to the Enterprise Software Innovators podcast. I’m Saam Motamedi, a general partner at Greylock Partners.

‍Evan: And I’m Evan Reiser, the CEO and founder of Abnormal Security. Please be sure to subscribe, so you never miss an episode. You can find more great lessons from technology leaders and other enterprise software experts at enterprisesoftware.blog.

‍Saam: This show is produced by Luke Reiser and Josh Meer.

See you next time!