 Live from Orlando, Florida, it's theCUBE. Covering Microsoft Ignite, brought to you by Cohesity. Welcome back, everyone. You are watching theCUBE. We are theCUBE, the ESPN of tech, and we are here at the Orange County Convention Center for Microsoft Ignite. I'm your host, Rebecca Knight, sitting alongside of my co-host Stu Miniman. We are joined by Dave Cahill. He is the principal PM Bonsai at Microsoft. Thank you so much for coming on theCUBE. Yeah, thanks for having me. It's been a while. It has been, apparently. By your back. Yeah, that's right. So you are now, you were the COO of Bonsai. You're now part of Microsoft. There was an acquisition about a year ago. Tell us a little bit about Bonsai. It's the AI business system. Got a shout out from Satya on the main stage yesterday. Tell us a little bit about Bonsai and then about the transition about now being part of Microsoft. Yeah, sure. So the big vision for Bonsai from the founders, Mark and Keen, was how do you build a set of tools that makes AI more accessible than to just data scientists? How do you open up it up to developers and subject matter experts? And so from day one, they've been focusing on building this abstraction layer of platform set of tools that really enables more than just data scientists access to the low level mechanics of machine learning of deep reinforcement learning. Everything we've been working on, really they've been working on for four years prior to the acquisition was building out that tool chain. And from my side of the world, it was where do we figure out where to point that? Where are we seeing the strongest traction and adoption for the tools early days from a go-to-market perspective? And so while they worked on the technology, we really found a pocket of interest in these real world often industrial systems. And so inside Bonsai, that's a lot of the work we were doing was taking that platform to market as part of Bonsai and then, of course, post acquisition to doing a lot of the same things. So accessible AI, I love the concept, but what does it really mean? So this is so that someone could be a subject matter expert in an industrial company and be able to still program, can you explain a little bit? Give us an example of what Bonsai was doing. Yeah, sure. And there's a lot of low level mechanics in machine learning, the algorithms, the toolkits, et cetera, that are difficult for just anyone to pick up and start programming. And so the idea here is, how can you write an abstraction layer above that? And in this case, it takes a form of a programming language that allows a developer or subject matter expert to break down the concepts of the problem they're trying to solve in business terms, right? And so if you think about a wind turbine or a drill or a baggage optimization system, it's not the data scientist that intimately understands the behaviors of that system and how it works. It's the subject matter expert that can practically stand next to it and understand or hear that it's starting to fail. Or they know the way to turn the knobs most optimally to figure out how to program that system. Now, if you just took a bunch of data and threw it at infrastructure, eventually it would figure out the patterns and how to optimize that thing. But you have a subject matter expert inside the four walls of your organization that readily knows how to solve it like that. And so why not empower them with a programming language, really a mechanism to outline the core concepts that you want the AI to learn, because they've spent their entire career trying to figure them out, all right. So yeah, Dave, yesterday Satya Nadella talked a bit about the autonomous systems. And if I got it right, he said, you know, we're allowing those engineers to really build systems, become the teachers for what's going on there. So help frame this a little bit as to where this fits into kind of the broader AI discussion that Microsoft's having with companies today. Yeah, I think there's obviously a massive AI portfolio at Microsoft and there's lots of different applications and systems and use cases that are fit for more and more intelligence in the form of AI and machine learning. What we've seen is an opportunity in the real world and the physical domain that requires a different set of tools and techniques than maybe in the logical, you know, or data-centric domains. And oftentimes in the press, you see a lot of emphasis on supervised and unsupervised learning and very data-centric use cases for the logical world, right, for databases or CRM systems or things like that. We believe there's this massive opportunity in the physical world. And when you get into the physical world in these vast, practically infinite state spaces, you need different sets of tools and from a machine learning perspective, different sets of techniques. And so I think Microsoft looks at the entire portfolio and says you need the right tool for the job as opposed to hammer nailing everything. And that's really, the autonomous systems piece is really our effort in real-world systems. Yeah, so, David, you know what I'm listening to what you're saying there reminds me of some of the discussions we've been having the last five years or so about the industrial internet. A lot of the OT systems here, which are really outside the domain of traditional IT. Are those some of the same challenges that your team's facing? Oh, absolutely. So OT, it's interesting you bring that up. Oftentimes the teams that have time inside an organization to pick their head up from their day job to look at new emerging technologies aren't in operations. They're not in the business because they're running the business. And so you have to be able to bridge the gap between central technology, central innovation teams and those that are actually running the business. And I view OT as kind of the kind of mortar between those two bricks oftentimes, as the one that has to accept this technology and figure out how to deploy it. And that's just not technically that it works, but also kind of commercially and from a safety risk trust perspective. So OT really has a big role in this and understanding not that it just solves the problem technically, but it actually can be deployed in ways that fit within corporate security requirements, data privacy requirements, trust, et cetera. It's not, there's a lot of gaps to be bridged there. So I saw statistics. Autonomous systems have been projected to grow to more than 800 million in operation by 2025. That's a big number. So what are you doing within Microsoft to prepare for that? Yeah, so I think I view autonomous systems, it's not a product, it's an endpoint, right? This is like 2000 when VMware came out and said, you're on the journey to the virtual data center, right? And their customers were in physical data centers trying to go virtual. The journey towards autonomous systems is kind of that we're on that same path. And really it's about providing customers the tools to extibute them along that journey from where they are today to kind of full autonomy, full autonomous systems. And it's a maturity, right? You start out just managing that system, you're maintaining it. Then maybe you're optimizing it and then you're controlling it a little bit better, but there's always a human in the loop and then you're at full autonomy. And I think along that path, there's lots of different pieces or tools and technologies that we can bring to bear to help them on that journey, technically, commercially, and then also from a safety and trust perspective. And so a lot of the work we're trying to do is build out that tool chain. And we think Bonsai's a core piece of that actually at the center of what we're trying to do. So how, when you're talking about the human in the loop, and I'm imagining a subject matter expert who is working in concert with you, developing whatever tool it is that is going to automate something that they are the subject matter expert and as you said, can fix it like this, calibrate the buttons and know when a system is about to fail. So how trusting are they in terms of, oh, so this is no longer something I'm going to be doing here? How do you work with them and helping them understand? No, really, you can trust this. Yeah, so I think it's really about augmenting and scaling the work of the experts. And oftentimes in every customer engagement we have, the subject matter experts are excited because they're literally codifying their expertise and then figuring out how to scale it. Those experts are frustrated because they are the subject matter expert. By definition, they're the problem solver for that problem for everybody in the organization. And so the ability for them to take that expertise and scale it means more time for them to do what they really want to do, which probably isn't solving problems tactically for everyone that's not at the expertise level they are. At the executive level, it's about scaling that quality of work so that your expert, your best expert for tuning this turbine can then be scaled across the organization and you're reducing training costs and other things because you can scale that expertise more effectively. Yeah, so Dave, what are some of the big challenges that customers are having? Is it the availability of the expertise and hiring the right people? We've looked at the big data wave, half of those deployments failed for so many different reasons there. Why will this be different? Yeah, I mean, it's certainly not without challenges. I mean, I think one of the things where we run into data readiness, like I naively thought because we use simulations, we got over the cold start problem that we don't have data, we'll just use a simulation instead. I think starting to get around the idea that simulations, there's the idea of a simulation, which is where we train our environment in and I can kind of go into that in detail, but that's very different than a machine learning ready simulation. And in having a simulation that runs, it can be parallelized, it can run on Azure, that works fast enough to train, these are all impediments to just getting to train these models before you even get to the actual model working in the real world. And so I think the pipeline for training these models is as intense in some cases as data-centric training environments. Once you get that model trained, it's then about deployment and you have a whole different set of challenges and that's where OT comes into play, is starting to figure out, okay, how do we operationalize this model? Is a human in the loop? Is there a mechanism to stop the AI and defer to the human, right? And we see a maturity model there as well where customers are starting with decision support, which means the AI is not controlling the end system. It is making a recommendation and then a business analyst would then implement that in real time. But walking through what those procedures look like is something that most customers haven't done yet until they're like right at that last step ready to deploy, saying, wait, who's going to watch this? What is our safety procedure for deploying a drill, an autonomous drill? It usually doesn't exist in an organization today. Yeah, it sounds, it's a little bit different as opposed to just your regular IT operations and you kind of say, here's the five step model. Oh wait, I've always done this. You're attacking some new challenges here so are they a little bit more likely to move a little bit further and let the autonomy take over? Is that the case? I mean, I think so and it's certainly lines of business, right? This is not, IT is there to kind of manage the transition as needed and kind of watch over for security and privacy concerns. I don't see the hesitation around the autonomous nature of it from the business users. It's people around the periphery whether that's security or compliance or safety that is most concerned about that and organizations I think are still trying to get all of those people in the same room and develop policies around that. And oftentimes for better or worse where they're forcing function to get them all in the same room and say, okay, what is this going to look like? But I see the businesses as really driving for the smarter and smarter and increasingly autonomous systems and excited about those pieces because the efficiencies to be gained from that are so significant in a lot of these use cases. I want to ask you about innovation. So this is, you were part of Bonsai and now you are part of Microsoft which as big tech companies go is a rather mature company. We've had some guests on this week who've said that Microsoft actually feels like a lot like a startup. I'm interested to hear the approach to innovation, the mindset that your new colleagues have and how you are keeping that more startup agile approach and inclination in this big company. Yeah, so I can certainly speak to our experience with Bonsai and it's been pretty neat I think as having been acquired a few different times by different companies, the way that Microsoft has landed this technology has actually been quite interesting and we sit within a team within Microsoft Research called Business AI and Business AI's entire charter is to incubate either acquired or organically developed technologies to the point that they're ready to graduate and scale across the organization. Up until that point in time, they're trying to figure out almost product market fit but inside a larger organization, leveraging the tools at their disposal that is the broader Microsoft whether that's the field or the marketing engine or things like that and you're seeing Bonsai be able to take advantage of things like that, the keynote with Satya and our access and collaboration with the Microsoft field but we're still in that incubation mode trying to figure out exactly how the technology goes to market, continuing to build out and mature the technology and figure out the right home for it, the right partner for it, if it's a business unit or whatever that may be and I think in that scenario we're a bit standalone in that regard while we figure this process out I think oftentimes you see innovation get stymied when you force a premature integration of technologies like this and you almost kind of determine their destiny before even knowing really where they're trying to go and just letting us breathe a little bit for a period of time, I think allows a better outcome than if you tried to guess ahead of time because at this early stage you don't know the answer, right? You're still trying to figure out what is the ideal application? What is the ideal target audience? What is the ideal part of the portfolio where they should sit, right? I think guessing those up front, even a year ago when the acquisition closed would have been impossible. So that kind of, I don't know, that gestation period is I think a key. Dave, take us inside some of the conversations you're having at the show. Key takeaways you want people to have of your group out of Microsoft Ignite. Yeah, so I think a lot of the conversations are, there's this big vision that is autonomous systems and that really is an endpoint. And what you really have to do is to still down where to get started. And that's not, the glamour is kind of use cases or the ones that you see in the press are drones, they're autonomous vehicles, right? It's things that likely fly or we saw in the Jetsons. But the reality is that where customers are seeing the strongest business opportunity is drills. It's turbines, it's air conditioners. It's an extrusion process for some food that you've probably consumed while you've been here at the conference. That's, and so really kind of, I think dialing customers into surface level use cases that are a fit for deep reinforcement learning is refreshing because a lot of people come at it saying, well, I don't have an autonomous vehicle and I don't have a drone, so I must not be for you. And that couldn't be further from the truth. All you need is a control system, right? If you have any sort of system run by a PID controller or a model predictive control, you can likely optimize that system further with deeper enforcement learning and bonds as a mechanism for making that significantly more accessible to your teams. So I think bringing it way back to like, hey, I saw this big vision on stage, where do I start? It's just really a bit of a search inside their organization for the types of applications that are good fits. AI, it's not just for the Jetsons anymore. That's right. Great. I'll take it. Dave Cahill, a pleasure having you on. Thank you so much. Yeah, thank you both. It's good to be back. I'm Rebecca Knight for Stu Miniman. Stay tuned for more of theCUBE's live coverage.