Shift AI Podcast: Seattle's place at the center of the AI universe, with Madrona's Matt McIlwain

What you want to have is you want to have access to the ideas, the people, the experiments that are working and not working in the aggregate learning, you know, more robustly, more, you know, kind of with high fidelity and earlier than anybody else. And I think that's what makes the greater Seattle area not only the cloud capital of the world, but I think it will be, if not the, you know, one of the very best, I think it should be viewed as one of the very best in the world centers of excellence for AI.

Welcome to GeekWire. I'm GeekWire co-founder Todd Bishop. This week we're featuring highlights from an episode of the podcast Shift AI hosted by Boaz Ashkenazi, CEO of AI solutions provider Simply Augmented, in conversation on this episode with Matt McIlwain, managing director at Seattle-based venture capital firm Madrona Venture Group. You can subscribe to Shift AI at shiftaipodcast.com. Let's jump in.

Hi, I'm Boaz, founder and CEO of Simply Augmented, and I'm excited that you're tuning in to Shift AI, a show that explores what it takes to thrive and adapt to the changing workplace in the digital age of remote work and AI. In today's episode, I am thrilled to have Matt McIlwain as our guest. Matt is general partner at Madrona Venture Group and has been a key technology leader in the Seattle community for over 20 years. During our conversation with Matt, we explore his background and experience building companies and advising founders to focus on the things that drive business growth and innovation. If you're someone interested in how AI is going to factor into the startup and venture ecosystem, you won't want to miss this episode. It's great to have Matt on our podcast and we can't wait to learn more from him. Let's get to it.

All right, Matt, welcome to the Shift Podcast. Thanks for joining us today.

My pleasure. Thanks for having me.

Everything that I've been thinking about lately has been about AI, and I know that you've been talking a lot about it and it's been on your mind, and I'm sure you're talking to your partners about it. So it's just perfect timing. I'm just really excited to talk to you today.

There is a lot going on in AI and generative AI, and it's now kind of the popular topic these days. So look forward to unpacking it.

Well, before I want to do that, I, I like to always start the show where I tell you your background and you correct me if I get anything wrong. So the show, you know, primarily is about the future of work. And so a lot of the audience that listens to the show is excited about what emerging technology is going to look like in the future, how it fits into their work. And so I always like to talk a little bit about people's backgrounds and about their, their work environment. So let's, let's just dive in. So you studied at Dartmouth College, you got your MBA at Harvard, and then you came out of school and worked for McKinsey as an engagement manager. Did that for 4 years and then moved over to be vice president of business process at Genuine Parts Company, which I'm really interested in hearing more about. So, but after that, you Is it?

Yeah.

Well, after that, you've spent the last 23 years more, almost 24 years as the managing director at Madrona. And you've seen a lot during that time. And then also, I'm sure that you're involved in lots and lots of companies, but you're a board member at Fred Hutch as well. Am I missing anything about your past?

Oh, I think those are some really good highlights. I did a couple of years of investment banking between Dartmouth and grad school. Actually did a government degree as well in grad school, which was fun at the Kennedy School. And yeah, there's an interesting story arc there of investment banking, management consulting, and real-world operating experience and what's been most helpful as a venture investor. But we could get into that if we wanted to.

First question is, what was your first job where you actually got paid?

Yeah, so I grew up in Miami, Florida, and went to a big public high school. And worked as a front-end service personnelman at Publix. Publix is a grocery store, and another name for that is bag boy. But they called it front-end service personnel because we were there to be customer service for our guests, our customers who were shopping at Publix. And Publix has done incredibly well as a as a grocery store business, mostly in the Southeast over the last many decades. But that was my first job when I was 16 years old.

Well, you know, it's interesting that when I hear you speak, I hear you counsel founders to really think about customer first, but you were right there in front of the customer during that job.

Absolutely. And the expectation in the hot, sweltering Miami summers was, is that you were to take an offer to take the groceries out for every single customer. And so, you'd go between the freezing air conditioning of the grocery store out into the sun and back over and over and over again. And so you got a chance to be customer-friendly, provide good service.

What was your favorite job over the years? Is it Madrona?

Oh, you know, there's really two. I mean, Madrona is, you know, I just love the opportunity that we have to come alongside incredible founders and be company builders with them. I mean, they're doing so much more of the the hard work and heavy lifting. But I think our team and the experiences that we've had and being able to coach and mentor and advise and just roll up our sleeves and help is such an awesome job to be able to have. Interestingly, one of the things that informed me very early on when I was in college, I actually started a company and it was building a board game business. It was a Dartmouth game. It was kind of in that era when Trivial Pursuit was all the rage, but this was more of a, like, you know, kind of how do you graduate from college? And it had a kind of a Trivial Pursuit style board that you had to navigate around, and it was the Dartmouth green. And so that was a lot of fun for me to do that, to learn a lot, you know, make some mistakes as the founder and CEO. Ultimately was a reasonably successful business. So that was, that was fun from a learning perspective and helps me just in a small way emphasize, empathize with with founders.

Yeah, for sure. And how about your family? You know, what were your parents' jobs? And was work and business talked about at the dinner table? Was that a topic that you guys discussed? Was that part of your growing up?

You know, my parents actually met in the military. My mom never went to college. My dad was the first in his family to go to college. and they met in the US Army, actually at Fort Knox. Um, my dad had a, you know, a business, uh, you know, undergraduate degree, and he, he ended up in the technology world, uh, quite a bit, you know, back like early modem companies and things like that. I learned a little bit about that from my dad. My dad was a pretty quiet guy, probably not so much. It really wasn't a conversation around, around the dinner table, but it was something that was kind of in me. I guess genetically somewhere that I did have a fascination for entrepreneurship and innovation and always being curious. And so I was in Junior Achievement when I was in high school and different things like that, that kind of reinforced that personal interest. And I would say even more so subsequently in our family with our 3 children, that probably was much more the case of kind of having conversations around the dinner table about businesses and startups and innovation and technological trends, those sorts of things.

Yeah, super interesting. I mean, the last 23 years, you know, I want to talk more about Madrona. There's just so much has happened in Seattle. And I've been here for the last 20 years too, moved here from San Francisco in 2000. And so, you know, there's been a lot of changes in the city and also in the tech community. I really like to think about Seattle as at the forefront of so much technology at these big companies, but also in cloud and now in the startup ecosystem. It's really exciting. So, with AI coming, it seems like because of our cloud experience, the time is right for us in Seattle to really, really shine. So, tell me about the work and tell the audience about what you do as a venture capitalist and, you know, how your investment thesis relates to the future of work and emerging tech. Generally speaking about your work at Madrona?

Well, I'll talk about that, but I completely agree, Boaz, with your perspective on Seattle. I moved here in 2000 and honestly, that year, if it hadn't been for a debt financing that Amazon raised as a relatively new public company, they may not be around still. And Microsoft was kind of on top of the world then, but did go through some some challenging times. And one of the interesting things to note, even if you look at just the past 10 years of Microsoft, since it's almost on the 10-year anniversary for Satya Nadella as CEO, their market capitalization's grown from $250 billion to $2.5 trillion. And so this economic opportunity and value creation and therefore kind of capital and people and talent that can be reinforced into kind of creating a flywheel in the Seattle innovation ecosystem is super exciting for me and for us. And then that leads into what we do at, you know, Madrona. I mean, Madrona's strategy is to be really early stage. It cannot be too early for us. You know, I actually prefer being involved in kind of company formation stage. So whatever we call that these days, pre-seed, seed, you know, stage, we also lead Series A financing rounds and then Over the last 7, 8 years, we started a whole separate fund that is for more Series B and Series C rounds that will be new investments for Madrona. So we can really cover, you know, investing from a pre-seed stage to kind of a Series C stage, you know, as initial investors. But then we have a phrase, you know, being there from day one for the long run. And so that doesn't just mean with our time and our kind of ability to hopefully add a ton of value to the company. But also our capital and our network and our mindshare for you as founders. And that really does involve a lot of different elements of, as a venture team, we're trying to get to know founders and develop what we call prepared mind thinking, thematic investing before the rest of the world has kind of figured out that that is going to be one of the next big waves. And so, if you go way back, of course, consumer internet in the mid-'90s with Amazon and others, classmates and others, classmates could have been Facebook but was actually a good outcome for us but was back in that era into then turning software into software as a service. It's just taken for granted that software is delivered as a service these days in the 2000s. And then you got to the later part of the 2000s and we'd been thinking a lot and doing a lot of interesting things in virtualization and realized that this thing that came to be known as the cloud and Amazon Web Services, we did an event in 2007 with the Amazon folks to launch AWS up on Capitol Hill in Seattle. And lo and behold, the last big piece, which came really about, and we'll get into it, but it really came about about a decade ago. We were making our first investments in applied AI back in 2012 and 2013. And we can unpack why that time, you know, kind of seemed to be the sort of the first big turning point in applied AI.

We'll be right back with more.

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Welcome back to GeekWire. This week featuring highlights from an episode of the podcast Shift AI. Let's return now to host Boaz Ashkenazi in conversation with Madrona Venture Group's Matt McIlwain.

You're in a unique position because you're seeing deals all the time. People are pitching you all the time. I imagine that you're seeing AI in pitch decks all over the place these days. And, but you've been thinking about this for a while on the automation side. And so Yeah, let's talk a little bit about your thesis around AI, around automation, around machine learning. A lot of people this year have gotten really excited about AI and it's some of the, they're thinking about it for the first time, but this has not, this has been around for a really long time. So yeah, let's talk about your sense of that and how does that relate to the frenzy that we're feeling right now?

Well, if you go in the way, way back machine, artificial intelligence as a term is broadly viewed to be coined back in the late '50s at a summer gathering of professors and academics at Dartmouth College. It was a group that was well beyond Dartmouth, my alma mater, but that there was this gathering. And so it's definitely been one of those technological areas that has been around for decades and it's had its ups and downs. And then I think it's also helpful to recognize that data has always been a critical part of how we kind of can improve the human condition. And, you know, we first had, you know, data stores, databases that were largely helpful, even in the simplest sense of them, you know, to be able to be systems of record. And systems of record are important because we could keep track of things that previously maybe we had to use, you know, much more manual processes and, you know, books and booklets and ledgers and things like that. And then we moved on to, you know, in the last, you know, 30, 40 years, what happened is those systems of record were being, you know, queried a lot, you know, for things that were more analytically oriented. And so there, there was a whole world that evolved around systems of analysis and largely, you know, speaking, we think of those things as data warehouses. And even the more, you know, we, we've been investors in Snowflake for many years and, you know, Snowflake was, you know, kind of the innovation there was being a cloud-native data warehouse. But it still was, at least in its early years, much more focused around being a system of analysis on top of all the data that was gathered in these systems of record and sometimes across different systems of record, hence the data warehouse construct. But now what's happened is that as more and more in the past, really couple of decades, more data has been generated and often more data that can be generated than how humans could sort of kind of typically process and more data types have been generated in digital including interesting types of data like biological types of data or even audio types of data that are increasingly captured from a digital form versus an analog form, is we could apply some of these algorithms that sat on the shelf in fancy research papers and apply them first in an embedded way into interesting applications. And that's really where data and data warehouses in these different kinds of data stores and now data models have become more systems of intelligence. And so I think that's that evolution in a broad sense and puts a little bit of context around what's happening more in the here and now. So getting into the systems of intelligence a little bit, then you say, well, wait a minute, hasn't that been around for a while? And at some level it has, but again, mostly embedded. And we experience those systems of intelligence when we do a query in Google, we do a search on Google and we get a result. They're using algorithmic techniques to basically predict through what started out as the PageRank system, but have built out into far more sophisticated algorithmic, kind of speedy algorithmic approaches. What's the best results for me from a search perspective? But the same thing applies to Amazon. I was talking to some folks yesterday about how many places within Amazon AI is embedded. Whether it's product recommendations, whether it's Alexa, whether it's all kinds of things and all the finished services and the underlying services in AWS, their own advertising business, which is a $40 billion business. So they're doing quite a bit themselves as well and have been embedding it. Or of course, we get the benefit of these things with recommenders and Netflix and Spotify and so on and so forth. So there's lots there. What's different now in the last set of years And by set of years, really kind of when some of these transformer models, things like BERT, now we've got this whole evolution of different kinds of frameworks and so-called large language models, foundation models have emerged, is twofold. One, we can interact with that predictive capability through natural language. We can just write or speak and sort of ask it to do something for us. Versus it being embedded within the recommendation that Netflix gives us. And secondly, those systems are appearing to and actually are generating things. So they're not just giving me recommendations, predictive systems, but they're actually generating something new. Now, they're actually under the covers using very fancy forms of predictions to predict what word to put next to the next word or what code and what sequence to generate as code within something like a copilot system from GitHub, Microsoft, or the CodeWhispers that is Amazon's version of that. And there are others, but also, of course, pixels. That's all that like these image and all, I'm not trying to trivialize it at all, but a Midjourney and a Stable Diffusion and other such services are DALL-E. Are generating images based on pixel data that's, again, been predicted in terms of what you're looking for, you know, from a generative image.

Yeah, it's amazing to think about the ways that people who don't know how to write code can now query this data and ask it questions using their own— using English and using their own language. And that's really powerful. We've been talking to a lot of businesses who are sitting on a lot of data And there's a lot of people in these companies that would really want to ask it the right questions. And now they have an opportunity and it kind of democratizes things, don't you think?

Well, I think that it does democratize access to unlocking the power of the data. It wasn't very many years ago, and I imagine this still happens in many companies where you have a set of, whether it's sort of analytical questions or questions where you're actually looking for kind of insights and intelligence, and you have to sort of set up the questions and then go ask the people that are in the data warehouse group or data analysis group, and then they get you— takes weeks and they get you some answers. And well, gosh, either that leads to new questions or it turns out we didn't quite ask the questions the right way. I mean, all this sort of high-friction, slow-turnaround process. So that in and of itself, I think it's dramatically improved. In a world of underlying much more flexible cloud-based data stores like a Snowflake or a Databricks, but also, you know, these new ways to use natural intuitive language to ask for something. And so, you know, your point on code is a really interesting one because in that case, you're actually helping people be more productive as code writers. And you'll notice that there's plenty of kind of AI skeptics. We could talk about that. I'm more on the AI optimist side of this equation, more Marc Andreessen than Elon Musk, I guess, if you want to frame it that way. And so there is quite a bit of case to be made. You can look at some of the work that GitHub, Microsoft's done, Amazon's done already. Of showing the productivity gains with, hey, why don't I have, you know, my, you know, foundation model and my Copilot draft a bunch of the code? Of course, I'm going to then inspect it and edit it and complete it and refine it, you know, before I ever put anything into production. But the productivity gains that you're hearing about are in the, you know, 30 to 50%, you know, time efficiency types of pickup. And that's, You know, we just, you know, you talk about the future of work, you know, and you think about coders being a logical early adopter of this kind of a technology. We're really just getting started.

We'll be right back with more. Welcome back to GeekWire. Let's return now to our special presentation of an episode of the Shift AI podcast featuring host Boaz Ashkenazi in conversation with Madrona Venture Group's Matt McIlwain.

You mentioned VR/AR before. Does this feel, does this hype cycle feel different to you? I mean, we've been through a lot of hype cycles here in the last, you know, 5 years, but does this one feel different than some of these others?

Well, we are, we do go through these, these hype cycles. And I think that, you know, this is a recurring challenge for the venture world, not just the investors like ourselves, but the founders. My college soccer coach, when we would run a drill poorly, in his Scottish accent would say, "Ah, lads, would you do the same thing, only different?" And what he meant by that was do it better and do it better now. Like, how can you do it better now? And so, there was grid computing in the '90s. It didn't work, but cloud computing clearly worked. AWS is a $100 billion business today, you know, and so trying to understand why the same thing, only different, is going to work now. And part of that is because it's actually not the same thing. You learn things, you do things differently, but timing really turns out to matter. And so, you know, in, in the case of AI, I think we're benefiting from, you know, once again, something that feels like an overnight success, but it's actually decades in the making. I think the thing that we and others got wrong in AR/VR, and by the way, the story, the final story's not written there. I think a lot of, you know, kind of what people call augmented reality or mixed reality is having, you know, kind of transformative things, you know, in a number of respects in different, you know, kind of companies and business models. We're investors in a company called Rec Room, for example, that is a really exciting— now they started out saying we're gonna be VR only. And one of the things they learned is that this kind of a next generation Roblox, which is what basically Rec Room is, it would work better as a community if people could access it from any device. So rather than being just a VR headset orientation, you can go from your PC or your Xbox or your mobile phone and your VR/AR systems. And so I think you've seen that in different contexts. You've seen interesting industrial contexts where it's been successful. But it certainly has been slower going and taken longer. What's the neat thing about AI is that we are at this point with these new architectures and the pace at which those new architectures are being innovated on. We've got the series of GPT models from OpenAI. We've got these much, ironically, much more truly open source models, like Stable Diffusion, like what Cohere and Anthropic are working on. Certainly what Meta has done, I mean, isn't it notable, right, that Meta comes out with Llama 1, Llama 2, there's a little bit of folklore about kind of the release of Llama 1 out in the wild. But now with Llama 2, you not only see Meta putting out this thing that I think they're trying to create a de facto standard around, but you've got Microsoft and Amazon and OctoML, which is a kind of models as a service business, all embracing quickly that model and hosting it for you as a managed, you know, kind of model as a service type of thing. So this gives choice and selection to the people that are the builders that want to take advantage of these models, and they can figure out the combination of models, their own data, and their own use cases, you know, what level they want to build for themselves. Do they want to be a do-it-yourselfer, which takes a lot of pretty specialized skills? Do they need to be kind of preferring sort of a do-it-with-me? Kind of like my example of, hey, OctoML is going to host the model, but I'm going to then fine-tune the model or adapt the model with my data to what I need. And then in a lot of cases, I think you're going to still have people that are do-it-for-me. I mean, I think underappreciated, for example, about Apple is there's a whole lot of machine learning and AI models that live inside your iPhone and you just take them for granted. They don't always work perfectly, autocorrect and kind of type completion and image, voice, Siri. I mean, I'm hoping now I'm not going to invoke it on my watch that is sitting on my wrist right now. But these are a lot of kinds of things that are already already out there. And so I think there's a lot that's been built up and there's a bit more of a kind of, I can incrementally adopt these things. And what's, I think, most interesting, just to add one more dimension to that, this is why deservedly so ChatGPT was quite a breakthrough. And OpenAI had been working on different kinds of natural intuitive interfaces for a couple of years. But when they came out with ChatGPT, and it wasn't perfect, of course, but it was quite impressive, they had something that, again, had this combination effect of really was accessible to almost anybody, back to your point about democratization. And it had this magical property of generating something, even just a write me a 4-paragraph essay about the history of Seattle. And it came back with something pretty good.

Yeah, delightful. Yeah, it's really kind of, it feels magical when that happens. Even though it's just recommending the next word, it just feels magical that that is possible. The other thing that strikes me too is how much cloud is playing into this phase of AI that we're talking about now. I recently heard you talk about the new Silicon Valley partner and presence that you guys have. In the Bay Area. And, but I know that you feel strongly about Seattle's place in this conversation. So, and I also, as someone that's lived here for a while now, kind of see the future and see Seattle being a big part of that. Can you talk about our city, Microsoft, Amazon Cloud, how that, how the audience should be thinking about that as we think about this AI revolution?

Well, you know, I think you and I have talked about this too, you know, that Seattle really is the cloud capital of the world. And that's not just because of our winters, which have plenty of clouds. It's, it's because we have, you know, clearly the number one and number two players in cloud innovation. And, and, and, and especially at that infrastructure layer, you know, all of the, you know, the compute and the, and the storage and the networking and the data stores and all the, and the security and governance and all that enables that, you know, you know, with AWS, you know, clearly the leader, even, even still today, clearly the leader. And then Microsoft doing an absolutely amazing job to be a fast follower and increasing innovator in a lot of different areas. What's sometimes also overlooked is there's close to 10,000 employees of both Google and Meta up here. And as we were just saying, Google's done some kind of amazing things and Meta's done some really interesting things, including kind of applying some of these kind of cloud-based opportunities to AI with some of these open source models. But we should make sure that Google gets good credit here as well. Areas like their tensor processing units are kind of very customized chips to do better kinds of AI and deep learning around. They've been innovators with BigQuery, which is kind of their competitor to Snowflake, and they've been evolving that to take advantage of AI. We already talked about the search algorithms, but I'll give you one more example. Google's done some really interesting things with their group called DeepMind, which is more based in London, but it's got good ties here into Seattle as well. And one of the interesting ties is this whole area of designing proteins and understanding the structure of proteins. So here we have this area in life sciences where a team in Seattle out of the University of Washington, the Institute of Protein Design, has been kind of collaborating and competing with the DeepMind team out of out of Google to build better and better predictions of the structure of proteins. Proteins being the applications of life, basically. And so that brings us all the way back to Seattle where you've got these clear leading players in cloud, really running the kind of the whole gamut. Even Apple has a decent presence here. And even more importantly, like I was saying, 10 years ago, 10-plus years ago now, when we started investing, In, in applied AI, the insight was, is more and more of the data was digitized and could be accessible to build recommender systems, predictive systems, and to do that in the cloud, you know, with your data so that you could build these kind of models and then deploy them and manage them at scale. And again, you look at all of those companies, I mean, you know, Apple and their, they have a center of excellence here in Seattle around applied AI, partly because they They bought 3 of our companies and then have built up that center. And so I think all of these businesses have— what you want to have is you want to have access to the ideas, the people, the experiments that are working and not working in the aggregate learning more robustly, more kind of with high kind of fidelity and earlier than anybody else. And I think that's what makes the greater Seattle area, not only the cloud capital of the world, but I think it will be, if not one of the very best, I think it should be kind of viewed as one of the very best in the world centers of excellence for AI.

When you think about the next 5 years around emerging technology in the future, what are the things that you think are going to be the most impactful looking into the future?

Well, I hope we don't have it wrong on this kind of this GenAI front or more broadly kind of applied AI. But that really is the major technological driving force of the next decade. I think the modern data stack is a big contributor. And I think one of the manifestations of all that that's just early and is starting to get more kind of conversation, but it's this notion of personalized agents. Right now, we're thinking about large language models as these monoliths. I think soon we're going to be hearing more about kind of domain-specific models. We talked about proteins, for example, but what about financial services? What about creative areas in marketing? But I don't think we're too long off from having these customized personal agents that are not only relevant in our individual lives. I want a buyer's agent that helps me plan my travel better or buy a car better or whatever that might be, but also in our business lives. And I think you're seeing lots of experimentation coming right now. It's too early to know what's going to emerge. But I think that it's not just going to be the model side, but it's going to be the agent side and the agents often at the edge, on our phones, on our devices, that will become much more pervasive in the years ahead.

That's exciting. Well, I always like to finish the show asking you to describe the future of work in 2 words. You can elaborate. A little bit, but how would you describe the future of work in only two words?

Gosh, that's a great question. I would say creative collaboration. And the creative part, as we said, there's going to be more things that are more kind of the heavy-lifting basic work that we can let others do or let really the machines do. And so, we're going to have more opportunity to be creative. And I think in an incredibly networked world and even in a hybrid world, the power of kind of collaborative work and collaborative work being unlocked is greater than it ever has been. And so I think creative collaboration would be my two words.

I love it. Well, these are very exciting times, Matt. Thank you for taking the time talking to us today. It was really, really wonderful spending time with you.

Thanks so much, Boaz. Really enjoyed the conversation.

That's a wrap. Thanks for tuning in. Such a pleasure to have Matt as our guest on today's episode. Matt's knowledge and experience growing successful businesses from zero to one and his insights into how AI is transforming our lives sets the stage for how emerging technology is changing the way that we work. If you want to learn more about Matt, You can find him on LinkedIn and Twitter. If you're interested in how he thinks about venture investing, definitely connect with him and keep up with his posts and activity online. I continue to be amazed by the guests we've had on the show, and I'm excited about the ones joining us in the near future. I truly appreciate you spending your time with us. Thank you for listening to this episode, and don't forget to subscribe to the podcast for more exciting interviews and to the Simply AI Weekly Newsletter for updates on how AI can help you scale your business. The Shift Podcast is produced and sponsored by Simply Augmented, and our theme music was created by Dave Angel.

Thanks to Boaz Ashkenazi for letting us share his conversation with Madrona's Matt McIlwain from Shift AI, which is a podcast that explores how AI and machine learning are changing the way that we work in the digital age. You can hear the full conversation and find more episodes at shiftaipodcast.com. Thanks for listening. I'm GeekWire co-founder Todd Bishop. We'll be back next week with a new episode of the GeekWire Podcast.