Transcript: Matt McIlwain – MD Madrona Venture Group on all Things Venture Capital

Host: Hello everyone. Welcome to another episode of Startup Project where we talk about the business of technology startups and venture capital. In today's episode we have Matt from Madrona Ventures.

He's a managing director at Madrona Venture Group, which is an iconic venture firm here in Seattle. They've funded companies like Amazon, Apptio, Redfin, Snowflake and UIPath.

Before joining Madrona, Matt worked at McKinsey and was the VP of business process at Genuine Parts Company. Uh he's a he's a graduate from Dartmouth and has an MBA from Harvard.

Um, in this episode we discuss, you know, all about venture capital, investing in great companies and what Matt is seeing in what's happening in venture capitals broadly and specifically in Seattle area. Um, I hope you enjoyed this conversation.

Host: Hey Matt, uh it's great to have you with us. Welcome to the show.

Guest: Thank you very much.

Host: Um so to just uh set context for our audience, uh you know, uh obviously you work at now uh at Madrona Venture Group, but can you talk a little bit about, you know, your early career before Madrona and what you've done before?

Guest: Well, sure. I I did a mix of things prior to Madrona. Uh I was in investment banking right out of uh college and then went back to graduate school. And in graduate school I had the opportunity to uh go to work for McKinsey and Company.

And so I spent uh four and a half years at McKinsey doing strategy and management consulting, particularly in technology driven industries. Um, that led me to an opportunity in an in a holding company which had a bunch of different businesses.

And those businesses in the mid 90s were getting disrupted by this thing called the internet.

And so I had this opportunity to work both inside of our businesses and this was just things like Napa auto parts and industrial products, like really traditional businesses and figure out how to embrace all the technology trends and capabilities as well as some of the business model implications, including working with a whole bunch of companies that were venture backed uh startups.

And that's how I got to know even more deeply the venture backed startup world, which eventually took me to joining Madrona back in 2000 and as you as you know, you know, Madrona had been the first investors uh in Amazon and so one of the ways I got to know the Madrona team was exploring a joint venture that we ended up not doing between Madrona and Napa Auto Parts uh and Amazon.

Host: So you've been at Madrona if I'm not wrong, 20 plus years.

Guest: Yeah, it's approaching 24.

Host: So obviously you've seen multiple venture cycles, right? I mean we've seen that uh 2000, 2008 crash and you could probably say the 2015 correction, right? you've been around to see the ups and downs of this uh, you know, venture and in general the markets in general. Um, where do you see the venture landscape today?

Guest: Yeah, well I do think it's helpful to have had that perspective of these different market cycles.

You know, I joined right at the peak at the time of the Nasdaq uh index in, you know, kind of early 2000, you know, kind of April 2000 and uh actually joined full-time in May and then right away the the market started to have a big uh downturn.

And so you see then in those kinds of environments how important it is to react uh smartly to the market conditions around you. You know, we always think at Madrona about the things you can't control and the things you can't control.

Uh and the things you can't control, you need to deeply understand because they should influence at some level, you know, your operating plan, um and your uh focus on particular use cases and how many different experiments you can be running at once.

And so to get to your specific question, I think we're in an environment right now which is a bit of a you know, kind of a a tale of uh two cities as it were or or two barbells depending on how you want to think about it.

Um, you know, on the one hand there is a lot of businesses that are challenged, uh they're later stage businesses in many cases and they have not fully found a scalable product market fit.

They may have found product market fit, but they haven't found scalable capital efficient product market fit. And it's a particularly tough time in the later stage uh world of both in investing in companies as well as, you know, M&A transactions.

But on the other hand, there are a number of companies that had been, you know, either more disciplined on the cash side or got started a little bit later, a little bit outside of the sort of the euphoria of um, you know, maybe that, you know, 2019 to 2021 time period and those companies are doing quite well, particularly those that have embraced and are really kind of Gen AI native, you know, style of companies.

And so they're having uh you know, a little trouble raising capital, they're having good success at um uh working with um different kinds of partners and building products and uh you know, getting growing their businesses.

So it's a little bit of a a bifurcated time.

Host: So does this sort of like disconnect in terms of like how public market is viewing uh private companies versus how the private market is viewing and how do you see sort of this difference in between like how the public is judging, you know, startups when they go public versus we're still seeing some high valuations um right even at late stage.

Like how do you see that disconnect or if there is one?

Guest: Well, I think I think that um one of the big differences between the public and private markets is the public markets, the value of companies uh, you know, is is changed and assessed every day, basically.

Um, especially with with the activeness of the the the after markets versus the kind of the, you know, kind of the the common, you know, public market trading windows that take place, you know, day in and day out.

And so, you know, those markets went way up and you know, one of the best metrics I like to use is the average revenue multiple for public software as a service companies.

And so if you look at like the last 10 years, the average uh revenue multiple, you know, forward 12-month revenue multiple is about seven times.

In other words, if I um if I had a company and they did a bill, they're expecting to do a billion dollars in revenue over the next year, then they would be valued around $7 billion on average today uh for kind of a reasonable growth rate.

And then there was an era where that average, you know, for a period of time, a short period of time in 2021 went as high as 15 times.

So that same company with that same billion of revenue and same growth rate would be worth 15 billion rather than $7 billion. And now that's come back down in the public markets to $5 billion or 5X.

So we've gone from 7X to 15X back down to 5X, all in the public markets all within two years.

The private markets are much slower to adjust to those types of things because you only really adjust the value of a company's shares when, you know, you know, formally when it raises a new financing round.

And they often raise those at higher valuations and sometimes at lower valuations. And so there's many, many companies that raise money in 2021 as private companies and are still private and they haven't yet actually raised a new round since then.

So while some people either in the secondary markets or in their own private books of how they keep track of these companies may have lowered their valuations, their official valuations and their some later mindset around what they're valued at hasn't changed all that much in many cases.

And that's that kind of highlights the difficulty and the disconnect between the public markets and the private markets.

And so I guess a related question or two related questions to that then would be, oh if I'm thinking about going public and I raised money in 2021 and at that time I was able to command a, you know, 15 times revenue multiple and maybe I was doing, you know, forward, you know, uh, you know, revenue of $500 million.

Well, you know, then that would be a company that's worth $7.5 billion back in 2021. But now at a 5X forward revenue multiple, I might only be worth two and a half billion dollars.

Do I want to go public if that's what the market, the public market's going to value me at or am I willing to stay private?

Can I stay private longer because I have enough cash and enough of a cash generating business to remain private and independent until I can quote grow into some of those, you know, 2021 levels of valuations by growing my revenue.

Host: What do you think about this uh idea of, you know, some of the funds have seen raising um just purely to invest in late stage companies. Sort of like what Tiger Global and Softbank did, but in next, I don't know, three, four years. Like do you think that that might be a better strategy or like what do you think about that strategy in general?

Guest: Well, I think it it the question is, you know, is there going to be uh a re-emerging market for later stage growth rounds um in the private markets. I think the answer is that time will come.

And I think there's a few, you know, pieces at play here.

Uh, you know, so one question you'd have to be, you know, kind of, you know, uh thinking through is, you know, will some of these public investors that came into the private market come back into the private markets.

A lot of them have gone away from the private markets.

That's probably positive from the perspective of another investor, like in your example, somebody new coming in with a new fund because it's not as crowded a market. crowded markets with lots of demand for something tend to drive up prices.

So that's one consideration. The second consideration is, you know, what you know, happens in the public market valuations and how does that sort of cascade down into private market valuations.

So could I get into later stage companies at comparatively better or lower private valuations because they've eventually caught up with the public valuations and there's enough new companies that are starting to move their way into the growth stage that I might get some quote better, you know, kind of value for my for my dollar.

The third thing is and we've certainly seen some of this in the last, you know, 12 to 18 months is, sometimes you can go invest in a company not by buying new shares they issue, which is a primary round, but you buy existing shares from some other owner of those shares in a secondary purchase.

And oftentimes you can get those secondary purchases at a at a lower price, at a significant discount to the last primary round.

Part of the reason is is because you're stretching out these periods where there's not really great liquidity in that company's stock. And so somebody's willing to sell it at a discount because they want to be liquid. They want to sell now.

And so I think that's the place that's probably the hottest at the moment in the later stage companies is secondary purchases in their in their shares, whether that's from an early investor who for them maybe they're still making good money on that in on that sale or a founder or longtime employees and there's a whole bunch of, you know, details we could dig into on that front.

But I think those are some of the ways that it might be interesting to explore and and potentially invest in later stage companies.

Host: I think the difference is in the last two, three years, even getting secondary transactions done was different a difficult because all the companies were buying buying back their own secondary shares when like they had right of first refusal.

So uh we would have seen most of those transactions at higher price and in some cases they've been blocked by the company because the company was purchasing their own shares, which is slightly different from now.

Guest: Well, the right of first refusal is is a mechanism. Um, and some companies have chosen to do a bit of that.

I don't think that's been all that pervasive, partly because I think, you know, fairly quickly after things turned around in late 2021, a lot of these companies were focused on, you know, kind of careful cash management.

And so if if they were going to buy those shares back, they they were going to be forced to buy them back at the price that somebody was able to get in the, you know, other secondary markets and that may not be a the best use of their cash.

Um, in but the other mechanism that companies could do is in some different ways, they could either have explicit restrictions or create kind of ground rules around, we're only going to allow you to sell so many of your shares.

Um, and so that creates some complexity for employees. There are some work arounds to that. Again, there's lots of second and third order details here you could get into.

Um, but uh, yes, there's been some roadblocks to doing some of those secondary transactions, but there have been people that have found ways, um, and willing sellers to actually get positions in some of those companies.

Host: Well, I feel like sort of venture came into the retail um landscape a little bit in terms of like it became more popular in last uh three, four years and you know with IPOs and spacks because startups were so inaccessible when stayed so private.

Um, and overall felt like there was a lot more attention to like VCs and, you know, what investments are being made into startups etc, right? Did that attention change in terms of like LP perspective?

Like are LPs allocating same amounts to venture capital? Like are new funds able to raise uh in a similar fashion? Like did that change?

Guest: Well, that's a that's a very interesting question um uh on on on the limited partners, the folks that invest into venture capital firms and how their mindset has shifted.

You know, one of the positive developments that you know, kind of came about starting about five years ago, kind of 2018 time frame through 2021, early 2022 is a lot of companies were going public, a lot of companies were being acquired and so the funds were returning significant amounts of capital to their LPs.

And so the LPs were getting liquidity, they were getting returns. And so I think that actually led to a season of those LPs being more open and willing to invest in the next fund, you know, I've been successful with this venture firm.

I'm going to invest in their next fund. And I think the pace of that got too quick and the size of many of those funds got too big candidly.

And so, uh then you know, as a result of that, you also saw the venture funds investing much more aggressively.

And so to quantify that, and I'm going to use round numbers here, you know, up until 20 uh 21, the biggest year ever in history of venture capital dollars in the US going in was about $150 billion. Um, 2021 was this huge jump up to $350 billion.

And then last year in 2022, it dropped to 250. So now let's just pause on that a second. So the 250 in 2022 was actually the second largest by far year in the history of investing in companies.

You know, this is again the the venture firms investing in companies.

Um, and even though this year it's going to drop again by another hundredish billion back down to about 150 billion, it's still going to be roughly tied for third in terms of the all time biggest year in venture dollars going in.

So while we've had this massive correction relative to 2021, uh in venture money being put to work, uh we're still at, you know, kind of at and approaching historic all-time highs on an annual basis.

Now, when you step back and say, well what's happening with the limited partners, the university endowments and foundations and pension funds and what are called sovereign wealth funds or the the the the funds um the endowments of of big, you know, country nation states and small country nation states for that matter, there's sovereign funds.

Um, I think there's been some you know, introspection there and stepping back and analyzing, well what did we not like? We didn't like the pace of new fund raises. We now don't like all these write downs that are happening in companies.

We also don't like the fact that there hasn't been material returns back to us. There hasn't been good liquidity and I think they are slowing their pace and doing really two things just to summarize it. One is to make um less new fund commitments.

So it's harder to get some new endowment to invest in me if they hadn't previously been an investment.

And then secondly, they're even pairing back either actually not backing some of the firms they backed in the past or putting less money into them or maybe the same money as they did in the last in the last fund versus expanding it.

So I think that's some of the dynamic. You're really going to see that into 2024. 2023, I think has been just a tough year in general for venture fundraising.

You know, speaking of Madrona, we raised our new fund in late 2022 and that's about a 700 million fund. It's actually two different funds of seed and series A fund and then a kind of a series B, series C fund which we call our acceleration fund.

And so we're fortunate to be in a very strong position in terms of the capital, you know, that we can invest in, you know, and making a number of investments in new companies.

Host: I'm glad you brought that up because uh, the size of the fund sort of drastically changed in last five years, right? We have seen funds with billion dollars, um, you know, multi billion dollar sized funds in crypto.

Um, and you've consistently kept that fund size, I would say, you know, in in looking at the industry sort of conservative. Um, like I think benchmark also in its recent fund was around 600 million if I'm not wrong.

Um, how do you see the fund size approach? Like is there a point uh where if you raise beyond certain size, does it become much more harder to return that fund?

Guest: Well, I think that, you know, it is harder to return a fund if the fund size gets bigger. Um, and we could talk a little bit about the math around that.

Um, and you know, we've always had a practice of, you know, aspiring to have, you know, every dollar that our investors entrust us with that we can somehow over time turn that into $5, you know, so 5X return.

Um, and you know, we also make a big point of being aligned with them by putting a substantial portion of our own capital into every one of our funds. So we've always done that and we always will.

Um, and so, you know, those two things are a bit of uh sort of governors on as you really think through, okay, you know, what fund sizes do I want to have?

What fund sizes are aligned with my longstanding investor partners in terms of our collective desire to kind of generate those, you know, industry leading, you know, styles of return.

You know, in reality, if you can generate a 3X cash on cash return, you know, 700 million dollars to use round numbers in our case into 2.1 billion dollars over the course of time, you're going to be a top quartile if not top decile performer in every fund cycle, every vintage year as they're called.

Um, but then you say, well let's say if you had, you know, 25 companies that you invested in out of a $700 million dollar fund, you know, and I'm using just round numbers here, but you know, you would need to have, you know, a few of those companies on their own return the entire fund in order to have really any shot at getting to the two, $2 billion dollars plus, you know, in in in returns.

And that's hard to do because, you know, especially if I'm now at a $700 million dollar fund in aggregate, we technically have these two funds, uh, but I'm I'm using these simple numbers, you know, that means that, you know, some company, let's say you own 20% of it, has to be worth $3.5 billion when it gets sold or it goes public in order for your 20% to be worth $700 billion.

And there's not too many companies that end up worth, you know, $3.5 billion and not just worth it on paper, but worth it in an actual, you know, monetization event, whether that's an M&A transaction or sustainably post IPO.

And so it is really hard and again, you probably need two of those in a fund and then you need some other pretty good results on top of that to get to 3X or better. So a lot of work be put into um and it only gets harder when you raise bigger funds.

Host: Um so talking about, you know, you mentioned investing in sort of late companies with the fund, but you you also mentioned like you have the seat and series A and your growth stage fund, right?

Like how are you thinking about, I think we've seen this phenomenon as well where venture funds going early in general, like there used to be much more clear distinction of a venture fund or firm being, you know, we are a series A firm or we are a series B firm.

Well we've seen this new approach where firms are also doing C plus series A or, you know, multiple stages. Like how how do you at Madrona think about that?

Guest: Well, our core strategy for a long time, really since the beginning of the firm has been being there at an early stage with companies. And so we love investing in preseed rounds, seed rounds, series A rounds.

And then we have this expression of being there at day one and being there for the long run.

Uh, so this idea that not just with our capital because even if we invest $5 million or whatever millions and small or millions of dollars in the first investment, you know, we could end up putting 30, $40 million dollars into an individual company over the life of the company.

And so there's this capital element of being there and reserving capital in a fund to support a company all the way through the journey.

But more importantly is rolling up our sleeves as the the partner who's on the board, the team at Madrona that's the main team team and as well as the broader Madrona team and family that are doing everything they can to help you as a founder in your team win over the long term.

So that's our bread and butter. That's what we love and I think back to companies like ICellon systems that I helped, you know, you know, from day one in the in the series A round back in 2001.

That was back in the day when a series A round was the first round in many, many cases.

And then you had Smart Sheet and and Apptio in 2007 or I could think about Place or Turiry or I mean these are all different examples of companies that we were there, you know, at at literally day one and helped them build those companies over the long term.

In fact, you know, 15 plus years later I'm still on the board of Smart Sheet, which is a public company now.

So then you've also got to say, well, well about 10 years ago we realized, especially since a lot of our investments, not all of them, but many of them are are based in the Pacific Northwest, we said, gosh, once a company's found product market fit and they're in some of these sectors that because of our focus in Seattle, we deeply understand.

Sectors like software as a service, sectors like cloud, sectors like applied AI that in those areas there might be some great companies that are outside of the region that we could add a lot of value to.

And we also have some uniquely deep and special relationships with Microsoft and Amazon and they're based in Seattle too. So why don't we go try to help a handful of those companies?

And that's what led to this other vehicle, which we call the acceleration fund, where it's a new investment for us, usually series B, maybe series C stage, broader geographic look and we were we're going to roll up our sleeves and help those companies too, just getting in a little bit later.

And happily we made investments in companies like Snowflake and Accolade and UIPath and Go One in that strategy and that's worked very, very well for us and for those companies where we've added complementary value.

So that's kind of how we think about it of there's this Seed and Series A focused fund and then there's this kind of acceleration Series BC fund, uh with a little broader geographic footprint and both of those have been quite, you know, successful for us and hopefully as we work hard we we we'll we'll keep being successful.

Host: You mentioned Pacific Northwest and obviously Madrona is, you know, located in Seattle. Uh what do you see is happening in Seattle ecosystem? We talked about broadly venture, but do you see some parts missing, you know, what's working, what's not working in Seattle?

Guest: Well, I think some of the great things about Seattle uh are that there is a just an incredible amount of talent. And talent that's in the flow of these big sector changes well before most of the world, you know, is is aware of it.

You know, you know, the Amazon cloud back in 2006, 2007, you know, relatively quickly followed by Azure over at Microsoft and then applied AI and ML.

I mean we made our first AI investment in 2013 and it was the Amazon Professor of Machine Learning at the University of Washington and his grad students, Carlos Gastron and that company Turiry ended up getting bought by Apple.

So I think we've got the talented people, they're in the flow of where innovation is coming and they've got great experiences. Um, I think that we are a bit underfunded honestly from a venture capital perspective.

You know, uh we're the largest firm in the region and we're super hungry and know we got to earn that every day and again we're really delighted to get involved at a preseed stage, but we love having great collaborators.

I think far too often a lot of our collaborators have come from outside of the region and we welcome and love working with the different firms, you know, the you know, the kind of the big names you know, the Sequoias and Kleiner and Greylocks and and and Dris and others.

We've worked with all those folks, they're terrific. Um, but having more of the kind of local capital, especially early on, I think is really, really important to collaborate and help build, you know, with founders together from an early stage.

That's probably the thing that's most missing.

I think the other thing is that, you know, there's been different sort of waves and eras of people coming out of the Microsofts and Amazons and Googles and being able to kind of adjust well to working in a a much smaller environment.

I think there's a a number of folks that have been able to do that, you know, you think of, you know, some of our recent investments like, you know, Abe who was at Microsoft for many years is the CEO at Typeface or Jordan Tagani over at Mother Duck, which is a terrific company.

Um, you know, there's people that have come out of Apple now, you know, uh and certainly Amazon as well.

So I think it's it's gotten better but I think there's still some more opportunity there to with those broader ecosystems around Microsoft, Amazon, Google and others.

Host: Um, so Madrona, you know, I think this is the second time you started doing the intelligent application summit.

Um, and can you talk and I was going through, you know, the guest list and you're really amazing guests, you know, founder of Lang Chain, Brad Gner from Alterator, uh and some amazing folks from Microsoft, Amazon, I think AWS CEO was there.

Um, so and I was going through some of the conversations and they're really fascinating. Can you talk a little bit about, you know, what what was the summit about and what are some of the key takeaways?

Guest: Well, well thanks and that yeah that that was it was a great summit and we will be putting out uh most of the uh the different, you know, panels and keynotes in in videos uh and maybe you want to link to in the show notes to to Madrona where we we'll have a bunch of those.

Um, and on the IA40 site. And it goes back a bit to the fact that we had, you know, we had started investing in this area over a decade ago and have built some great relationships over time.

In fact, if you actually go way, way back, Brad Gner and I were on the board almost 20 years ago of a company called Faircast that was founded by Oren Etzioni, who was then a professor uh and was doing some early things in kind of scalable statistical, you know, analysis and machine learning uh that predicted whether or not airfares were going to go up or down in the future.

Uh, that company ended up getting bought by Microsoft and becoming Bing travel.

Uh, so there's a long history of using data, using statistical methods, machine learning, now of course foundation model, you know, based techniques to innovate uh and build uh you know, build the building blocks and then build the actual intelligent apps themselves.

So about three years ago we invited the venture community to help us identify what are the most promising companies this in intelligent application 40.

And uh there were nominations, we worked with our friends at Pitchbook and we ultimately uh had a voting mechanism that led to the selection of 40 companies. There's some early, some mid, some late stage intelligent app companies and some enablers.

What's been interesting now in the three years that we've done uh that uh you know, process in that sort of selection of the intelligent application 40 and these are all private companies. There's only seven companies that have won all three years.

And I think that speaks to one of the insights, which is this is a very dynamic world right now.

This world of applied AI, the pace at which innovation is happening, the pace at which some kind some kind companies are starting to break out but then hit some roadblocks and need to navigate through that.

Um, now specific and some some specific areas that we think that are quite interesting. One is that a point of view that we had a while back that I think has now become more fully adopted is, it's going to be a mini model world.

There's going to be many models, not a few models or one model. And those models are often going to be used and combined.

We had Ali Farhati who actually just succeeded Oren Etzioni as the head of the Artificial Intelligence Institute come and give a talk.

He talked about this, you know, technical concept of ensemble models in addition to model ensemble, just going to be model combining or I like to call model cocktails.

And those models will come together in ways that help you make and build better intelligent applications. So that was one of the big discussion topics, one of the big takeaways.

Another is that a lot of people are in fact experiment experimenting with generative AI and foundation models, but they're more at the prototype and experimentation stage than they really are in production.

So we're still early in the journey from prototype to production.

And then the third area that I would I would highlight is that there really is going to be this interesting um sort of evolution and you could think about it as a bit of a of a battle as it were from a competitive perspective of the incumbent software companies that are gen, you know, enhancing their applications with AI capabilities and the true Gen Native companies that are from first principles building a Gen Native foundation model derived application such as a Typeface, such as a RunwayML, both of those being two of the 40 winners and so in contrast to the Adobes and and even, you know, Microsofts and and sales forces.

And it looks like the incumbents are actually going to succeed quite well in the short term because they have the customer relationships, they have the data, they've been quick to embrace this technological disruption.

We believe that there's going to be some big winners that are Gen Native, but it's probably going to take more of a medium term time horizon, a three to five year time horizon for it to be fully evident which ones of those are going to be the bigger winners.

So those are just some of the topics from the summit, but happy to talk about more and any other specific questions.

Host: Uh you brought up the point of, you know, incumbents versus startups and one of the takeaways for me or opinion of me is, um, it used to be very difficult for big companies to sort of, you know, adapt to new things.

And I think that's sort of changed from what we're seeing from the AI cycle to sort of previous cycles like even mobile or, you know, uh previous uh shifts technology shifts that we have seen uh is big companies are actually also agile.

And one of the reasons we are seeing this AI adoption into big companies is because at the end of the day you're providing AI as a sort of like an API as a service and with you when you combine these two things that the big tech companies are actually agile because the individual teams inside them are agile uh was and when the technology is available as an API and that's sort of like, you know, made it much easier for large even larger companies to adopt and you know, create solutions as fast as we are seeing.

So that's sort of like my takeaway in terms of like why we are seeing um this um incumbents being so fast to this. And I'm I'm curious to get, you know, your thoughts on that.

Guest: Well, I think the API point is a good point. Uh, and I would add in a couple of other areas.

You know, one is that, you know, they do have an immense they these incumbents have an immense amount of data around which they can, you know, train models or fine tune models, uh, or in some cases bring in augmented data in the form of, you know, some kind of um, you know, uh a system using a vector database and something like retrieval augmented generation.

Uh and and they have that data, they and they also have context to what what their customers are trying to do. And so I think you've got some incumbent advantages.

Uh, now, you've also got, you know, an existing, you know, relationship where your customer is already using your software and if I can enhance that software with some of these features and capabilities, then I get more engagement, that's what we could sometimes refer to as sort of the reinforcement learning from human feedback within my now enhanced application that I pull back into my models and continually make my models better.

So I think there's a bull case for the incumbents. The bear case for the incumbents is is that, you know, historically software was run very deterministically. It was very, you know, predictable. It ran the same way basically every time.

Sure it was configured and customized a little bit, but it basically runs the same way. It's meant to be that way. And by definition, these, you know, types of generative models are running predictively. They're running non-deterministically.

And so there's going to be some amount of uh uncertainty and what results they come back with. That might be uh unsettling for the end user customer of a big company and and they might have different expectations about that.

It could create some friction, some tension for the incumbents. I think there's also some complexity around pricing and packaging here. There's a lot of early experimentation going on.

And then finally just, you know, well, I think you're right, it is somewhat different than the mobile era, you know, there were a lot of companies for instance in travel that built a mobile app eventually, but then they didn't invent Airbnb, they didn't invent Uber or Lyft.

So who are going to be the the companies that from a first principles perspective just reinvent something, you know, that solves a customer's problem better than has been solved before

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