Zoom to Salesforce: How Emergence Capital Picks Billion-Dollar Winners

Today Molly O’Shea speaks with Founding GP Gordon Ritter & new Partner Yaz El-Baba of $2.3B AUM VC fund Emergence Capital. We cover everything from the IPO window, Stargate, the macro environment, the state of AI, the app layer to infrastructure, preventing the AI plateau, to Emergence’s history & evolution. Emergence Capital is a venture capital firm known for its specialization in early-stage investments in enterprise cloud software companies. The firm's focus is on businesses that leverage technology to improve workplace productivity, business outcomes, and the customer experience. Emergence is widely regarded as a pioneer in the SaaS (Software-as-a-Service) space and has backed some of the most successful companies in the category, such as Salesforce, Veeva, Box, Yammer, Bill.com, Zoom, Blend, and Doximity. Founded in 2003, Emergence Capital operates with a philosophy of fostering long-term partnerships with entrepreneurs and helping them build world-class companies. Notable Investments: Salesforce (CRM software giant) Zoom (video conferencing leader) Veeva Systems (cloud solutions for life sciences) Box (cloud-based content management) Emergence Capital is recognized for its deep commitment to its portfolio companies and its expertise in scaling enterprise-focused startups. Stats: $320B+ Market Cap of Portfolio (current and former) $2.3B AUM Six (VI) Funds 100+ Portfolio Companies 20 Exits and Private Valuations Greater than $500 Million Molly on X: https://x.com/MollySOShea Gorfon on X: https://x.com/gordonritter Yaz on X: https://x.com/yazanelbaba Follow Sourcery for the latest updates! https://www.sourcery.vc/ TIMESTAMPS: 0:00 - Welcome Yaz & Gordon! 1:20 - Current State of VC 6:45 - Emergence's IPOs, Salesforce, Zoom, Bill.com http://bill.com/ 10:00 - Veeva $37B story 18:10 - Stargate $500B Investment In AI 21:20 - ROI of AI 29:50 - Is SAAS Dead?! 32:30 - Together AI 38:40 - Which Founders Will Win Today? 41:00 - The AI Plateau 58:00 - How Marc Benioff Helped Start Emergence 1:03:10 - Navigating Changes In The Market 1:09:30 - Resolving Partner Tensions In A Fund 1:13:40 - Investment Announcement! 1:16:00 - 1 Deal That You Regret 1:17:50 - Yaz & Gordon's 2025 Outlooks #podcast #investing #technology #venturecapital #entrepreneur #startup #siliconvalley

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Molly O'Shea?

  This is really incredible. We have Salesforce, Doximity, Zoom, Box, Bill, Blend, Viva.

• 0:08Speaker 01

  Big thing about things that matter in any given market, if you're doing what is obvious, it's not going to be a big winning.

• 0:16Speaker 02

  But I actually think this is the time where you can potentially build really meaningful and iconic businesses with not that much capital. But I don't think SaaS is in its entirety is dead.

• 0:27Molly O'Shea?

  Well, you've heard it here. SaaS is not dead. It's just evolved. What is the most recent risk that you took?

• 0:41Molly O'Shea?

  Gordon and Yaz, thank you so much for joining us.

• 0:45Speaker 01

  Great to see you, Molly. Thanks for including us.

• 0:49Speaker 02

  Hey Molly, great to see you again. So fun to be on.

• 0:51Molly O'Shea?

  Great to see you as well. I thought it would be so fun to have you both on because it's like a wonderful convergence of the generations at Emergence and everything that you've built, Gordon, from the founding GP to the newly appointed partner with Yaz. We're going to cover everything from Stargate, the macro environment, the state of AI, the app layer to infrastructure, preventing the AI plateau, the fund's history and evolution. And this will be great. So let's get started. So first things first, I think both of you probably have seen this already, but UTIMCO has released a report and Eric Newcomb, a very fun and friendly

• 1:36Molly O'Shea?

  friend of Sorcery, was able to actually report on this and brought out some numbers. And the VC environment is not looking too good. I don't think this is a surprise to anyone, given the lockup on exit markets, but I'd love to get your perspective. Has VC died?

• 1:56Speaker 01

  Well, yeah, that's the classic case. People have called VC out many times and the best investors know that when it looks the darkest is actually the time, certainly not to back out and maybe even to move more aggressively in. What is different this time is the amount of money that's in this industry right now. It is, there is so much capital that came in over the pandemic and in the 2021 timeframe that that is, you know, it's probably more than the industry needs, but we'll talk about how different firms are addressing that. Either if they're very large, they're going to, you know, bigger and bigger markets where they can put money to work effectively,

• 2:38Speaker 01

  or like us being more, we think, right-sized for true venture, having conviction and having certain swim lanes that we care deeply about.

• 2:48Molly O'Shea?

  And what do you mean by that you're right-sized?

• 2:51Speaker 01

  Meaning that the major venture is a hits business and you have to be able to have one or two really exciting companies in each portfolio and produce wonderful LP returns for our investors. And if you're either too small, where you simply can't be in those great companies, or too big, where you need basically not dozens, but certainly five to 10 of those great hits to produce the same kind of multiple that emergence can produce, we like, we like kind of where we're sitting. We're certainly bigger than we were when we

• 3:31Speaker 01

  started and we can talk about that history, but we like the size range that we're in today.

• 3:39Molly O'Shea?

  I ask the follow-up question because I do think we're evolved into an interesting category. Like, I'm not sure we can call it venture capital anymore. The asset class has expanded incredibly large and the once traditional funds have become mega funds, going into private equity. It's not only crossovers anymore, but it's like everything under the sun for investing in technology. So do we just call it? Technology investing, what do we, what do we call it?

• 4:08Speaker 01

  It's unfortunately, it's a continuum. Right now, we've got wonderfully, we have young firms starting just like Emergence did 20 plus years ago, coming into the market with unique new areas that they're, they're passionate about, that they have background in, and they're fresh and new, and that's the lifeblood of this industry. Then you have the Emergences, more established, where we think again, we're right sized to take advantage of the, the big opportunities where you need capital to get into the best companies, but we're nimble enough that we don't have to put too much to work in, as you mentioned, hospitals and other areas where it really is feeling like a buyout firm using,

• 4:52Speaker 01

  you know, benefiting from technology, but it's, it's getting, you're putting such large amounts of money to work, that it certainly isn't early-stage venture anymore. And most of the LPs that are backing this segment, the venture capital segment, know that the real returns come from early-stage. And later-stage is great, but it's a different, it's closer to their buyout category than it is to their, what they call venture capital.

• 5:21Molly O'Shea?

  Since the markets got pretty frothy and that capital was available to get into buyouts, how did you and the firm remain focused not to be so opportunistic and expand your investment theses into, let's say, more, more categories?

• 5:38Speaker 02

  I mean, it's one of the reasons why I joined Emergence. Like, it's a pretty values-driven firm, and I think one of the values that is so common throughout all the things we do is, we all collectively as a team focus to drive conviction. And that matters in the way we decide to conduct ourselves in all types of markets, whether it's a super frothy market with a lot of capital and a lot of things going on, or it's a market where people are scared and saying, oh my God, the UTIMCO report says venture is dead, everyone should run. I think like what, what I found so, so great about the way the founders of Emergence kind of set the tone, was that through all of these peaks and troughs, you stay steady. You focus on what you're convicted in.

• 6:20Speaker 02

  You decide to invest in the areas that you think are going to to yield the fund returning outcomes of the future for the firm, for your LPs and for the founders that you're backing and stay close to that. And for us, it's been B two B software at the earliest stages and backing these companies to become really iconic businesses one day. And, and obviously the theses change, right? Like if we, if we came out and all we were saying is we're cloud software investors, we're investing in the cloud, that's no longer a differentiated perspective. Maybe 20 years ago, when the firm started, not many firms were saying that. So we've continued to push the envelope into what does the future of work look like? And what are the technologies that are going to help enable that? And when we think forward, the next five, 10 years, we're really excited about the

• 7:03Speaker 02

  new innovations around AI and other things that are going to really drive, drive returns forward for the firm and otherwise.

• 7:10Molly O'Shea?

  Speaking of iconic, Emergence is an iconic enterprise investment fund. So I'm going to read off these names and these are all fund returners, I would assume, because this is really incredible. We have Salesforce, Doximity, Zoom, Box, Bill, Blend, Viva. Gordon, given your career experience and success with public markets, do you think the IPO markets will open back up in 2025 as much as people are hoping they will?

• 7:43Speaker 01

  So it's a great question. And, and again, I'm a venture capitalist, not a optimist of, of the global, of the global world and what's happening in this world. But the comment from, you know, the bullpen here is there's been a huge run up in the market already, right? A lot of, of excitement about both how the, the economy's been doing and for the upcoming election and what's happened since. So it's, the, the market is actually probably less stable and has a less of an opportunity to kind of hit a new height in this year, I think, than it did in the past year. Markets love stability and I'm not sensing we're going to have tons of stability.

• 8:27Speaker 01

  Stability during this year, it doesn't mean it's not necessarily good for the long run perhaps, but stability is important. Interest rates are probably not going down. My bet is that they're either going to kind of stay where they are, which is fine for kind of absolute, you know, uh, levels. But we have some potential policies coming that could drive up interest rates and, and basically inflation, which is, uh, is a challenge. Those are some of the macro pictures. The, the micro is things like ServiceTitan, which did go out and, um, hasn't performed as well as we all had hoped. It certainly hasn't been, you know, a terrible offering, but it certainly is, has not been running up after the IPO.

• 9:10Speaker 01

  And, um, so I don't know, there's a bunch of really interesting companies, sort of a crop of companies that are exciting on the sidelines that have 2021, uh, valuations stuck to them that they can't get out of. And it's making their boards question when to go out and make sure if we do go out, that it's, uh, it's successful. So all of that is telling me I, uh, I'm not sure that the IPO markets are going to be as, as, uh, good as I had hoped coming into 2025.

• 9:41Molly O'Shea?

  What do we think about secondaries?

• 9:43Speaker 01

  In terms of secondaries for venture firms or for, um, yeah, it's certainly, you know, it, it is certainly done. It's not something we are actively looking at, but we're certainly, you know, evaluating if there are ways to, to, uh, to pull that off. We think, again, if we had a huge portfolio, we would definitely go in and carve out bits and pieces. Um, uh, so I think it's a scale business that if you're, you've really got a large portfolio, you have a chance to take advantage of secondaries. For us, it doesn't fit our, our model as well.

• 10:21Molly O'Shea?

  So there's one company that I want to dig into that, uh, I, I mentioned. Um, personally, as a finance geek and, and technology geek, I think this was like one of the most interesting outcomes ever. And maybe we've experienced similar.

• 10:38Molly O'Shea?

  Kinds of instances of this kind of success in different ways, but I'm curious. So let's talk about Viva. Viva was one of the most prolific IPOs of all time. It had only 25 employees and less than a million dollars in revenue. Now it trades with a market cap of 37 billion. Can you go deep on Viva's story? And is this possible to recreate?

• 11:08Speaker 01

  So yeah, Viva is a really interesting story. I will tell it, but the big thing about things that matter in any given market in, in 2008 and now in 2025, if you're doing what is obvious, it's not going to be a big win. It doesn't mean it won't be an okay win. It might, you know, it might help your portfolio along, but unobvious opportunities are where it happens. So I'll, I'll tell you why Viva was so unobvious and why it became this great success and continues to be. At the time, client server software, for those of ancient history now, but it was, there were many companies

• 11:50Speaker 01

  that started in the client server days and started in a single vertical. Company called Documentum, again, in the history books, but Documentum started in life science space. Did you content management for life science companies that focused there? But in the client server days, you couldn't vertically integrate. You couldn't then do other things within one industry. It was too hard to write the software to get that done. What Viva figured out as the early days of the cloud, Salesforce.com, Peter Gaster came out of Salesforce and, and PeopleSoft before that was kind of the ground floor of what Salesforce was able to do with multi-tenancy.

• 12:33Speaker 01

  And saying, is there an industry strategy that we can come up with? And he left Salesforce and he started Viva to do just that. It was originally called verticals on demand. It was going to be a set of vertical companies. Once we got into the life science space, which is where they focus, he figured out that we can then, they can then add layers of the cake, as we call it, and vertically integrate. You couldn't do that in the old software days. It was just too hard to write another half a billion lines of code to do CRM within life science. So you just simply couldn't then go from, you know, content management to CRM,

• 13:16Speaker 01

  to, to marketing cloud, all within one company. You could, in the case of Aviva. And so that's what, what they did. They used three million dollars. Thank goodness it was our three million dollars worth, but that was all they needed to get to this scale of a, you know, a two and a half billion dollar revenue plus company. And it's a really exciting story. So your question around, is there another one today? It's not going to be like Viva. You're not going to use the same model. It's going to be something new. It's some brilliant team, hopefully one that we get to work with. But the main thing we want to tell people is don't model yourself after the past.

• 14:01Speaker 01

  It's, it's your future. It's your opportunity. Create something and you call the next layering of the cake or the next. Maybe it's the horizontal market that matters, which we can touch on. Maybe more horizontal and less vertical. A lot of AI companies are now doing very tight verticals for good reason, because they have, you know, they have tighter context around what they can do with AI. And we love that, but it may not be the, the, the best strategy to reproduce those kind of results.

• 14:37Speaker 02

  And I think one of the fascinating things about the story that always sticks out to me is that that point around they only burned $3 million in their entire journey to become almost a $3 billion run rate business. You don't see that much anymore these days. And I think this new trend that people are talking about, like I forget what it was Sam Altman saying like, "Oh, there's going to be a billion dollar company with one person." I actually think there's going to be more Viva cases, not in the sense that they're going to be vertical this or they're going to follow this exact playbook, but I actually think this is the time where you can potentially build really meaningful and iconic businesses with not that much capital. Like, we're starting to see that the most nimble teams are the ones that are operating most effectively.

• 15:19Speaker 02

  I think there was a Brian Alligan quote from a few days ago saying something like, "The best teams right now are the ones with the most nimble teams and are moving the quickest, and those are the ones that are going to win within this new paradigm." And that's what gets me excited is long gone are the days of where you're going to have to raise billions of dollars before you go public 15 years down the road. You actually can follow the Viva capital efficiency story and build a really great company that can drive really strong venture outcomes.

• 15:48Molly O'Shea?

  I think that's a great point, and that's exactly also what I wanted to get into, is just how efficient that business was and how early it got to an exit or IPO market and somewhere you can have outside accountability and that sort of thing to continue to grow the business. And we've been stuck in this weird place of closed markets. You can't do any sort of M&A, it'll get blocked, IPOs are shut. And it's really weird, but I hope, and we'll get into this, but with AI, we'll have much more efficient businesses and growth stories out of that. In terms of the market, the exit markets,

• 16:31Molly O'Shea?

  what would that new ideal exit market look like? What do we think is going to be possible or the opportunity for these companies that have been cooking for some time?

• 16:44Speaker 01

  Well, so I mean, if we're talking about the a bit of the macro picture, again, just to to remind us for for anything in the recurring revenue, software space, low rates are really important, right? And just to make sure folks understand why you're looking at at growth rates that could be very, very big for these companies. So they have excellent growth rates, but to get the kind of multiples that software companies are used to, it's growth rates over a very long period of time that that are they are compared against just buying a bond and bonds are interest rates. And if your your ability to produce cash flows well in excess of interest

• 17:26Speaker 01

  rates is where those incredible multiples come from. That was happening certainly over the last decade, but rates have certainly ticked up off those near zero rates. We're going to need to create, you know, exciting companies to get to those kind of multiples again. I think we can, but that's important. And on top of that, it's, it's the you want disruptive technology that all customers need to buy, need to have within their four walls, and it can't be innovation teams, can't be dabbling, it can't be trying out, which is we are a lot of where we are right now in AI. And that's okay. As long as we punch

• 18:08Speaker 01

  through it, it's absolutely okay. We'll get to the infrastructure we're spending money on now to build to get ready for that future. You know, it's, we'll talk about later on about is that the right investment at the right time? Or is this just really piling in, you know, that build it and they will come? And we don't know now. And there's a there's a military, what I call the the kind of military advantage of this arms race we're in. And I see why we have to do it for national security. But we got to separate national security from consumer and commercial purchasing power and what they're going to really pay for this. And that's still up in the air.

• 18:53Molly O'Shea?

  I love this point. So let's talk about Stargate. The US so far has gotten commitments of $500 billion to build out AI.

• 19:04Molly O'Shea?

  What do you expect the impact will be on early-stage investing in AI growth? And then again, to your points of national security and timing, how does this all lay out?

• 19:17Speaker 01

  Well, again, nobody knows. And this is a, first of all, one of the interesting things about the 500 billion, I listened to, you know, BG2, they did a nice overview of this, Brad did, where, remember, it's equity dollars and debt that's going into this. So 500 billion is probably 100 billion of equity, of money going in that is sort of risk money. The rest is building out the infrastructure, 25% of it goes to buildings and cooling systems and everything else. About 75% goes to, you know, buying the chips.

• 19:57Speaker 01

  And so it is a big number, no doubt about it, but it's, it's not as big and as sort of unbelievably at risk as it sounds on paper. But yeah, this is a, and it is a combination, I guess I'm getting out of national security and commercial acceptance. And the national security part, we should put in, it's one person's opinion, but we need to, to make sure we win this race against, against China in particular. And I, there are almost no dollar price is wrong in that, using that lens.

• 20:37Speaker 01

  On the commercial side, with the amount of money that people are going to be paying for these services, for Copilot, from Microsoft and others, it's still not proven that if you just look at the economics and put all that 500 billion just for that one area, if there weren't a national security imperative, would that be fundable? I, I don't think it's, it's clear yet.

• 21:01Molly O'Shea?

  It's interesting because part of the capital that is allocated for that is from SoftBank, which is a foreign entity. How do you think that's going to come together? Do you think more foreign entities will be investing into it?

• 21:16Speaker 01

  I'd rather not make any bets there. We've seen that we've more outreach now to Saudi Arabia and that area where there's a real question of whether where we're crossing the line in terms of national security. I think it's pretty clear that China won't be directly involved. Is everyone else fair game other than China? We'll have to see. And it's very, very hard to know.

• 21:46Molly O'Shea?

  It'll be a fun one to watch. It's definitely developing. And I think we are watching it being developed, which is the interesting part.

• 21:54Speaker 01

  Yeah, day by day.

• 21:57Molly O'Shea?

  Day by day.

• 22:01Molly O'Shea?

  So I want to talk about the overall arc of AI. Your team, you're experts. I know for a fact. Yaz, I've known you for quite some time. You're one of the most intellectually honest and smart and curious people I know, and you do a lot of research. Given your experience, how are you measuring the ROI on AI?

• 22:25Speaker 02

  Yeah, it's a great question and it's a tricky one to measure for a lot of businesses. And really, like, the best way to do it is to go back to simplest terms, really. And when you talk to the end buyer of AI, it's really a function of three, maybe four things that they really care about. It's costs. How expensive is it to do said task or job that I need done? Is it faster with AI or not? And you take the delta there. Pretty simple. But like, going back to basics is really important here because instead of giving an empty promise, it's actually saying, we can impact your bottom line. The next is just speed. Sometimes you're willing to pay the same price for something. You're not as keen on a cost advantage, but time to value has been the biggest bottleneck for you.

• 23:09Speaker 02

  And now you can automatically do something far quicker, 10x, 100 times faster than if you had to use traditional software or if humans were doing this, either in-house or outsource. And then say the last of like the very basic framework is just quality. Is the job done better? Like if it takes the same amount of time, it costs the same amount of money. Is it better than what the outcome would have been had I not had access to some of this new technology? The best companies are the ones that make improvements across all three of those things. Because I think when you think about all the latest things we're seeing in AI, the biggest question as investor you have to ask yourself is, is this just going to be a race to the bottom on pricing because now there's a new threshold for what people are going to be willing to pay.

• 23:54Speaker 02

  They used to, the provider was willing to, was able to justify, yeah, pay me a million dollars a year because I have these many people doing this. It takes us long. It doesn't take that long anymore and there's no longer that many people involved. At a certain point, either a competitor is going to come in and say, actually, I'll do it for a little bit cheaper. Or, or I'll do it a little bit faster. It becomes a little trickier then. It's like, what are you actually willing to pay for some of this if you're just going to be on a cost reduction basis? So, so in our eyes, the best companies are those that, that have improvements across each of those things, develop deep expertise around the, the specific job to be done, they're focused on, whether it be by vertical or functional domain, so that they can have that staying power.

• 24:39Speaker 02

  And that each N plus one job that they do or customer that they bring on actually makes their ability to drive higher quality, to drive better speed and to drive cost advantages over time, allow them to have staying power within the industry. Because if it's really quick to build, you're not building a data proprietary moat around what you're building, it's going to be really hard to sustain the pricing that you see today. And that's going to become a really, really challenging business proposition.

• 25:09Speaker 01

  And just one, I couldn't agree more. One area to add to that is, we had a thesis now, it was 2016, we call coaching networks. And it was the precursor to co-pilot and all the term that, you know, that name is probably a little better than coaching networks, but the logic of coaching networks is really descriptive, which is one of the moats you get to build when you're inside a company. Is you're learning from the employees in that company and learning from how they are doing the job. Yes, it can be, you know, models can come from general purpose models from the outside world, but you want to build IP. We'll talk about this later, but IP within your company. You don't want that to go out to a model. So within your company,

• 25:53Speaker 01

  where does IP come from? It comes from your brilliant employees and people who are experts in that area. And the loop that gets created between the vendor of the software that is AI enabled and, and, you know, AI first, but then learning from those habits. And that is something that becomes very sticky. If you are the engine that takes the brilliance of the employees, however many there are, maybe there'll be some that, that are, you know, unfortunately, we might, you know, have some jobs that are removed, but those that stay are going to be hopefully more and more valuable. That loop is a, what I

• 26:33Speaker 01

  call a kind of perpetual algorithm. When you bring humans into the loop, it will constantly get better or, you know, it'll learn because it'll learn from the worst habits of those employees, just like it learns from the best habits. And that loop was something we, we thought about now, what, eight years ago, and it's still, we believe, is critical today. So just running off a system that cranks away on a model and gives you the same value that your competitor gets is not the game that's going to matter in the future.

• 27:10Molly O'Shea?

  So I want to apply this to some of your portfolio companies. I, I love this topic and I think this is really interesting. So Yaz, I know you've spent a tremendous amount of time looking at the AI app layer and also infrastructure. You've invested into companies like Together AI, RC, and some more. So how did you gain conviction here? And how do they interplay with each other?

• 27:37Speaker 02

  Frankly, when, when we started seeing all the innovation happening around AI, the first place we went to was our portfolio companies. It's like, how are we existing software providers going to leverage a lot of this technology, empower some of the new products and features that we're going to roll out? And in talking to them, we understood some of the challenges associated with actually building these things into production. And so in the early days of this big post-Gen AI, post-Chat GPT craze, we spent a lot of time actually at the infrastructure layer because we knew that in order to actually take advantage of these things, you needed fine-tuning capabilities, the ability to train your own models. If you're not training your own model from scratch, being able to run human evaluations on the models that exist out there

• 28:20Speaker 02

  and leveraging that data that you're getting from your proprietary data set, from those that are using your product and incorporating that back into your overall training, post-training pipeline. So that was kind of the thought process behind backing companies like Together and RC, which are at the infrastructure, allowing enterprises that power both consumer and B2B apps, the B2B ones in our portfolio, and being able to take that and say, we can now push something out to our user base with control, with visibility. We know what the end model output is going to be. We know how to tune these details so that it powers an experience that we're going to be proud of and that we're not afraid that's going to be pushed out to our customer base.

• 29:03Speaker 02

  So that's where we spent a lot of energy in the early days. And what's interesting in this next phase, now that there are a number of these platforms that are end-to-end, like a Together and like an RC, is you can now start to power the really meaningful application layer experiences. Now, the models have gotten to a point where they are strong, both on the open source and closed source side. How can we stay ahead of that N plus one innovation that's going to come out? Every day, there's a new model, or every day, there's a new computer use model that just came out. Now, there's DeepSeek, and now who knows when Llama 4 is going to come out? And what that's going to look like, everyone has their debates on if it's going to be as good or not. And having someone on your team as an application layer software company

• 29:47Speaker 02

  that is staying ears to the ground on the latest developments on the model side has become an imperative for a lot of app layer businesses. And so when I think about 2025, like the DNA of an app layer company is very different than what a traditional SaaS business used to look like 10 years ago, five years ago, even two years ago. You're seeing more and more job openings for applied research roles, applied scientist roles in a lot of these companies because they're marrying the infrastructure and capturing data to the end user experience. And by people using more and more of the software like Gordon was alluding to with the the Delta metric concept, you're now harvesting your own proprietary data set from your existing customer

• 30:29Speaker 02

  user base, empowering a lot of the new improvements to the model experience, whether using an open source model or a closed source model.

• 30:36Molly O'Shea?

  Would you say SaaS as we know it, traditional SaaS is dead?

• 30:40Speaker 02

  I would say SaaS is a bit, SaaS, like what does SaaS mean? SaaS means you have a recurring perceived software business model. I don't think those are going away entirely. There are going to be new business models for software. There's going to be the AI enabled services. We can talk about one in our portfolio that we back that looks more at the outcome based pricing. So SaaS as we know it, is it dead? Probably not, but every SaaS business is going to have AI at its core, whether they price proceed or they price on outcome is really hyper dependent on the end market they're going after. But, but I don't think SaaS is in its entirety is dead. I'm curious to get your take.

• 31:17Speaker 01

  No. So, no, here's why. It's, SaaS is the latest incarnation of workflow, right? If you say that workflow is dead, that's really what the other way to say it is workflow software dead? No. And that is because the only way, until we are either neural linked, which maybe we will be, but even then, there's going to be some prompt that goes into our neural link that says, I should think of, ah, I should think about that. Hmm. Okay. And then I'll respond and then I'll, that'll get into the, the overall next version of software that doesn't involve typing. But the bottom line is the only

• 32:01Speaker 01

  way to, to both understand information for humans is to, is to have something presented to us. Maybe it's a chatbot, maybe it's form fields, maybe it's Neuralink, but something presented to And then we react to it and we use what is still a very powerful neural network, which is our own lived experience in our brains to figure out how do we then reply to that query? That is workflow. Where that goes away, when that goes away and everything is simply bots and we're on the beach or, you know, within the matrix, that's another story.

• 32:41Speaker 01

  But as long as humans are critical, and we are, to the advancement of these technologies and the advancement of the IP within our companies, that workflows, some form of workflow is going to continue. And that's what, call it SaaS, call it workflow software, it is going to continue because that's where you get those unique habits back, both to and from the users.

• 33:10Molly O'Shea?

  Well, you've heard it here. SaaS is not dead. It's just evolved. That's great. Okay, so I want to talk about Together AI. Together AI has raised a tremendous amount of money and when this first came out, it was a bit of a party round. So from the insider perspective, how has this, how has this come to be? Is it still, I don't know, I know the answer, but tell us, Yaz, how is Together AI doing? And how do you feel about overall AI multiples?

• 33:41Speaker 02

  Yeah, I mean, Together is one of the most foundational companies when it comes to building AI. Right? Like when you think about the ways in which AI is being used today, there needs to be a core infrastructure layer that's end to end. And in order to build models, work with models, whatever it may be, wherever you are in your model development lifecycle, some organizations want to own the end to end full experience. Others will like to take a Lama 3, a Lama, a Lama 4, or whatever Lama is next, or some of these other open source models and customize them, run them in their own premise or run them on Together's cloud, secure cloud API endpoints. They need someone that abstracts away all of the complexity with working with new models.

• 34:23Speaker 02

  And so what Together has been able to do is power that experience completely end to end from what used to be and continues to be a hair on fire problem, which is even sourcing the compute to begin with, to then pre-training, fine tuning the model, to then running inference on a model such that you have the best and most optimal speeds when you push something into production. What other approaches have been in this space is to take one thin sliver of this full problem and try to operate as a standalone business. Together's ambitions were a lot greater from the outset, which is why you saw a larger than typical Series A financing and why you saw other people come in ourselves and some

• 35:06Speaker 02

  strategics and other financial investors come in and put money into the business because there was this high upfront cost associated with getting the compute necessary to power things like training and inference. And over time, what you're able to do is layer on all of the expertise of a world-class research organization that is literally the world's best at this. If you look at the types of people, Tree Dao, the chief scientist, Soet Zhang, the CTO, obviously a lot of the folks from Chris Ray's lab, given his involvement as a founder of the company. So many of these people from the technical organization on the research and product side are at the world's cutting edge and they're taking everything that's so complex

• 35:50Speaker 02

  associated with working with a model and abstracting that complexity away through one API point. And that's the magic behind something like Together. They'll find you wherever you are in the journey of using these models and powering your out-of-consumer or SaaS application and making it as seamless and as easy and up to date as the next new thing comes out. So that's what gets us really excited about Together. Their growth has been astronomical. I mean, there was a staff, people went on, I'm forgetting which podcast it is now, and he shared like the zero to 100 in 10 months is probably one of the fastest growing infrastructure companies in history. And they've continued to grow at just breakneck speed because they're solving really hard problems for some of the world's most important organizations.

• 36:33Molly O'Shea?

  So Yoss, you mentioned something in your description of Together AI as you were laying out how it fits in the foundational model development cycle. So I want to go back to a piece that you wrote, I think it was like over a year ago now, but you wrote about the foundation model development cycle.

• 36:54Speaker 02

  Yeah.

• 36:54Molly O'Shea?

  Looking back, what are your biggest takeaways since writing that and what has changed?

• 37:00Speaker 02

  Yeah, I think the biggest thing that's different from what we expected when we wrote that piece about a year ago is how fast we get to some of the things that we concluded. Like, I think the major takeaway from that piece is that the best organizations are going to use multiple models in concert with one another to power their end experiences. It would no longer be a world in which you just plug into OpenAI's latest model and they would run really effectively. And instead, what we thought was that you would leverage a series of different models, whether it be from Anthropic, from Mistral, from Meta, from any of these different model providers, some of your own models that you've developed.

• 37:44Speaker 02

  In some cases, you'd actually choose to use small language models as opposed to large language models for some of the simpler use cases. So the way in which we thought about where the market is going is developing a course system that allows you to interchange between different models with the right tooling in place to move between them really seamlessly. And now, when we talk to application layer providers, we learn that so many of them are tinkering with multiple models. Actually, no, we learned that Claude is better for this use case than GPT. We actually learned that we can fine tune our own thing here that can reduce our cost basis by 90%. So for these types of tasks, we'll use this.

• 38:25Speaker 02

  That, to me, was the thing we were pretty shocked about how quickly we get to a world where multiple models are actually starting to be proven to be best for each different use case. Now, the challenge is, what's the best way to route between these really, really effectively? And who's going to be the source of truth? Who's going to tell you when it's best to use this model versus another for this specific use case versus the next? That's where we're really excited to see 2020, 25 heading, which is being able to build everything together in like a really cohesive way where the end user's experience doesn't change depending on which model you're using and it feels on the fly, instantaneous,

• 39:05Speaker 02

  where the best thing is used at the best price with the highest performance instantaneously.

• 39:11Molly O'Shea?

  Because so much has changed in the last year and things are settling, what exactly do you see in terms of the kinds of profiles of founders that you that you would expect to win in this environment?

• 39:25Speaker 02

  Yeah, this is, this is one I've been thinking a lot about, actually. And, and really what gets me excited are teams that are just willing to work their tail end off and seeing what is going to change in this next, next iteration. It's let me stay as close to the market as possible. And the teams that are honestly doing that, and I know this has been a little bit more of a provocative one, it's like the younger teams are the ones that are growing the fastest rate. They're the ones that are willing to just break things, try new stuff, and sometimes they'll, they'll move away from some of like the, the commandments that people learned when, when building great software companies. They're doing things that don't necessarily scale in the early days.

• 40:08Speaker 02

  They're, they're putting things into contract like opt out periods just to get their foot in the door because they're such a goldmine land grab opportunity to leveraging software. Some of these younger teams are kind of going, doing away with a little less discipline on those fronts and saying, let's just go to market really quickly. And you push the risk away from pre-sales and being able to get your foot in the door, actually pushing the risk to post-sales. So the most important thing that, that we're thinking about for 2025, especially when we work with this type of profile of founders, how can we move into a world where you're making sure your post-sales experience is the best? Because so many times you'll land within these organizations and maybe you're not yet

• 40:51Speaker 02

  at the full expand opportunity within the account, but you have your foot in the door. And that customer might also be trying three of your competitors simultaneously. And so now the burden of proving value has really been pushed to the contract's been signed. We're no longer going through 12 month, 18 month enterprise sales cycle. Sales cycle was a month. You're in the door, but we're going to evaluate you for 12 months in a POC or POV. And I'm excited about the people that'll get there, that'll get into the POC and the ones that'll iterate across those 12 months to make sure that they're in customer's line to get the value that they want to out of the product. And it really is going to fall on having an amazing post-sales, solutions engineering,

• 41:35Speaker 02

  forward deployed engineering, whatever you want to call it, team that's going to make sure these experiences are great.

• 41:40Molly O'Shea?

  As much fun as it is to always talk about exponential growth, Gordon, we need to talk about the plateau. We need to talk about preventing the AI plateau. This is a piece that you recently wrote with one of your principals, Wendy Liu. But could you just explain this out? Like, what were the core principles of this piece? And how do you see preventing a plateau shape out?

• 42:06Speaker 01

  So first of all, and I've been around this industry for a while and fortunately seen most of these firsthand, but there have been that most of us can remember these plateaus, these moments where there's a series of small factors going on in the world, either open source ideas in the tech space, or just unique concepts that people are working on even in the labs. And then something brings them all together. So TCP IP as a protocol, I was around, well, I was around when it started, but certainly around when it really became ingrained within the internet broadly.

• 42:49Speaker 01

  And it was a series of underlying activities that came together in a protocol.

• 42:56Speaker 01

  recall that opened up the world and that TCP IP has been compared to crypto in terms of if you could just have made the money that you could make in crypto like TCP IP, it would be far bigger than anything that we've seen. But it was an open source platform that that created a series of incredible markets after it. The next example is obviously the browser. The browser, I do recall when that was that came into the world and you know FTP for file transfers and other technologies like TCP IP all came together to make a human readable

• 43:35Speaker 01

  version of all of these underlying technologies and it opened up a whole new, you know, avenue of both business acumen and value for for consumers and businesses. And then the most the most recent before Chat GPT was the App Store. The idea when mobile devices, even old flip phones, used to have simple little mobile apps on it. And all this technology, people were trying lots of things, but until Apple created the App Store and a phone that was ready for apps to to roll out. All of those ideas then it looks like they happened overnight, but they didn't. It was years in the making.

• 44:20Speaker 01

  Then Chat GPT comes along November 22, opens our eyes to what we, you know, scientists knew this technology has been around a while, Berts and others. So it's just just and very importantly, they took it all together and brought it to the masses of people to understand what's possible. So what the article is about is all right, are we plateauing after that first wave of AI? And the concept is and the numbers show it that the rapid growth where we're like, oh my goodness, this is this is mind boggling that Chat GPT can answer these questions or can, you know, write a sonnet for us.

• 45:04Speaker 01

  That's all sort of calm down. Because it those things haven't dramatically changed since it started. The models have gotten more specialized, or the chat GPT, for example, has gotten more specialized in things like,

• 45:19Speaker 01

  you know, math and physics and legal. They've gone deeper into certain general areas. But fundamentally, the harvesting of publicly available data has, for the most part, run its course. And Ilya Sutskever said as much. I love, you know, it's an interesting quote by saying, AI, data is fossil fuel for AI, and we've run out of it. And I generally agree. It doesn't mean there aren't going to be new evolutions, but the next evolution is going to come from sort of two areas. It's going to come for from commercial data. But remember, commercial data sits behind firewalls.

• 46:03Speaker 01

  It has things called patents. It calls called things called, you know, intellectual property. The reason all that applies to commercial entities, not to the three of us, is because consumers hand over their IP all day long. We do it on Facebook, or on Instagram, on TikTok. And we're not looking for protecting it. We're looking for just giving it away. That isn't going to happen, at least near term, and maybe never in the commercial realm. Businesses have to differentiate. They have to have something. It doesn't mean everything, but they have to have something that is absolutely core to their future. And so the next wave, we think, is going to come from companies like Together and RC,

• 46:49Speaker 01

  bringing sort of private

• 46:53Speaker 01

  model opportunities, whether it's one model or dozens of models, to the fore. And and we think that is a whole new opportunity. And then what I commented earlier on is also then understanding how you can take advantage of the employees within your business. They're inside your IP rights, your protected area. How do you learn from them and develop new IP and new value for your ultimate customers? And not give that away to the open models? Because if you're handing everything out to open models, you're going to be leaking your IP out the door, and that cannot happen.

• 47:38Speaker 02

  I was just gonna say, a great example that we spent a lot of time, we haven't made an investment out of yet, is the financial research space. When you, when you, when you talk to a lot of really savvy machine learning and AI experts, and you tell them, okay, if you were to go into a domain, what do you want to build? There was no surprise that there were two dozen, if not more, financial research companies. Oh, we're gonna help you build a model, or we're gonna help you mine SEC filings. It's because you have this large corpus of data that's readily available, it's public, and people can train against it. Millions and millions and millions of artifacts worth of data that's publicly available on the internet, and that's been existed in unstructured and structured document formats.

• 48:20Speaker 02

  What we're seeing now with these startups is they're, they're running into great value, and then they reach a hurdle, which is, we've now mined all SEC filings data. We've run out of things to look at. Now we're gonna go crawl social media to see like, are there Twitter feeds, things, podcasts, transcripts that we can glean some alpha off of? Then there's like that next hurdle. And so businesses that have been in this space for longer, like an AlphaSense, has what I'd call like semi-proprietary data. They moved past the firewall a little bit, and they're now talking to these aftermarket research, the AMR research firms, like the bulge bracket banks. And they now have like exclusive relationships with the likes of like the big bank, the JP Morgans of the world, have you. So they have a new repository of data that they can now work with.

• 49:04Speaker 02

  And so for a startup to compete there, it's become more difficult, because there's this rich data asset that they can no longer glean insight from, that's powering the AI. Then they go and say like, okay, well, actually, you have your own data behind your own firewall. We can actually run our AI in a secure server within your premise that we can now pull data from. You have startups that are focused exclusively there, like Hebea and some of these others. And so when you look at that, you're seeing like this move towards, oh my God, we're starving for data. How can we find it? Either through partnerships, or through, through actually getting access to someone's own commercial data that's valuable. The next phase, and what gets me really excited, is there's richness in the data of the users

• 49:48Speaker 02

  that is being created as the user product. The more someone puts in an input that's unique as to how they should look at a certain certain asset class, or a certain type of stock, or whatever it is, now you start to gather even more rich data through all the analysts that are using the product. And so over time, you're building this proprietary data set. You start with some of the stuff that's readily available, and move your way up to more and more proprietary data sets over time. It's a rat race, really, to see who can get the most fuel to power the best end experience. And I think financial research is a great example of what we're seeing.

• 50:27Molly O'Shea?

  It's really interesting that you say readily available, because the first, I'd say swath of data was just scraped data. And so we started to see some pushback on that, and licensing. But how do you see regulatory, and access, and permission, and licensing play into this next evolution of data aggregation?

• 50:53Speaker 01

  So, this is a big area that we're focused on, because when you think about the closed models, the big models out there, their goal is to slowly work their way from what is kind of publicly available. And even then, they've obviously crawled things where folks are saying that you didn't necessarily follow our agreements. But bottom line, publicly available is one thing, but their goal is to really try to get more and more of the actual inner workings of a company. And there are, you know, certain strategies they're using,

• 51:34Speaker 01

  and it's, again, totally fair game that they can work on this. But if I'm a corporation, the last thing I want to have is any of my employees, let's say, talking to an expert network on a phone call, and giving information away about the company, right? So there's, some of them say, "We don't want you talking to anybody." Others say, "You can talk, but you can't talk about any of these areas that could be at all, you know, used for nefarious purposes."

• 52:07Speaker 01

  That is going to be more and more of an issue going forward because these models, one person who has a chance to share an information about how something is done, that is a piece of either business process or IP, one person, and that is now not just out to, you know, somebody who happens to come across it, it's embedded in a model. And that can then be accessed by pretty much anyone in the world. So what we are counseling larger customers to focus on is remember how critical it is not to let any of your business processes go out the door. And right now, the legal ramifications, the legal issues and regulatory issues are not

• 52:51Speaker 01

  written for how that's going to be done. Metadata within a model is okay, but not your data data. What does that mean? Is it HIPAA? You know, HIPAA says that if, as long as your data about, you know, something, some drug I'm taking can't be, you know, kind of delivered back to me as a human, as long as it could be someone in this room, that's okay. That's not okay when you're a corporation trying to keep your IP secure and your business process secure. So it's a lot of work in this area ahead.

• 53:27Molly O'Shea?

  Super dense. Is that more of a cybersecurity concern or is that a contractual concern within?

• 53:34Speaker 01

  I think it's a contractual. I think that's a really excellent point. I, cybersecurity also has risk, right? Because if someone finds their way into this model that you've protected using, you know, Together or RC and found their way in, even if they can't get outside your business, you've just, you basically have the keys to everything that company does. It wasn't the case before. Remember when it was just data? You can get access to our data? Well, data's nothing. It's the, it's the way your employees are using that data to, to make value out of it. Guess what models do? And good models are going to do it even better. And we want to create them.

• 54:17Speaker 01

  We just want to make, and make sure our companies create them. We want to make sure they're secure inside the four walls of their customers.

• 54:26Molly O'Shea?

  That just might have made me more bullish on AI lawyers. But maybe it's the big law firms. Going back to the AI plateau, I just, I want to bring up the four key opportunities that you mentioned within the article because I think they're really good points that we should, we should address. So you mentioned four different areas. One, engage experts, which we've talked about a bit, a little bit. Two, leverage latent data. Three, capture in context. And four, secure the secret sauce.

• 54:58Speaker 01

  Got it. And I think maybe, maybe you can, I'll touch on secure. Why don't, maybe you can talk about both capture in context and latent data. But like on secure, I think I, I already touched on that is these models are, are your company. They are your business. You are, you're kind of nothing. If you're really bringing AI into your company, you, you, everything is held by those models, especially as time goes on and we engage experts within your business. They are the absolute secret sauce and, and without it, you will, you really will lose a lot of your IP from there.

• 55:41Molly O'Shea?

  So how does AI affect pricing models and product features?

• 55:47Speaker 02

  Yeah, there's a number of different emerging pricing models that, that companies are, are tinkering with and, and I'd say we're still in the early innings of what pricing model is going to become the ubiquitous model that people use in the age of AI. What we are seeing is that it depends on the way in which the AI is delivered to the end customer. So for example, we, we just invested in a company called Bolt or previously the, the parent organization called Stackblitz, they launched a product called Bolt new. They're a unique business that, that's really exciting in that they've democratized the ability to create web applications. Now through, through a very slick interface, you in very natural language can build your own website, a web application, or, or anything that you can really think of.

• 56:32Speaker 02

  And so it's really democratized creativity on behalf of all people to build, not just coders. This is probably one of the fastest growing software businesses we've ever seen. They've gone zero to 20 million in run rate in two months. And we were lucky to, to lead their series, be there. What, what's, what's unique about them as an, as an AI enabled software business is that they're, it's traditional per seat pricing. And now there's some variability and usage gates on top of that. But the base layer doesn't look too dissimilar from what your typical PLG self-serve software product look like, like a Notion or an Airtable. You pay per seat. Once you get to certain usage gates, just from a margins perspective for the software

• 57:17Speaker 02

  provider, then if you start burning out new credits, there's credit limits that you, once you hit them, there's now a usage component on top. But that's a good example of how it's still workflow software. It's powered by AI and this would have never been possible before without some of the new things around AI. But it looks and feels like traditional pricing models. But on the other end of the spectrum, there are businesses that are going purely off of volume and outcomes, and there is no platform fee or SaaS-based per-seat pricing as we've come to know them. Think about companies that are in the customer support space and it's on per-ticket resolution. Or companies that are truly AI-enabled services.

• 57:58Speaker 02

  There's one in our portfolio, Mechanical Orchard, that's working on legacy-to-modernization efforts, moving things from on-premise mainframes to the cloud. And that is very much on a project basis. It's, once you take one application and move it from an on-prem mainframe, push it to the cloud with the technology platform that they've built for code writing, from COBOL to Java to equivalence testing and a lot of other things they've built around the periphery. Their pricing based off of the outcome of being able to successfully move that application from one place to the next. And so, so people are now building different models around what the core end-use case is. Some of it will look like traditional SaaS, others will look like outcomes-based or volumes-based

• 58:42Speaker 02

  pricing, and a lot looks like a hybrid in between.

• 58:46Molly O'Shea?

  We've really worked our way up to present day, and this has just evolved tremendously, even in the last year. Gordon, I want to bring it back to the very beginning of Emergence's founding story. Could you tell us more about how you founded Emergence?

• 59:02Speaker 01

  Happy to. I'll try to fast forward, but you know, the people you meet change your life. And Marc Benioff, my second company that I built, I sold it to IBM and Marc Benioff was looking for a launch partnership with IBM to get his, you know, then fledgling company Salesforce.com, sort of its first real launch, you know, logo and customer. And we ended up backing them both from a marketing perspective early on and from a, you know, financial perspective, really helping, helping to fund some of those early marketing objectives.

• 59:42Speaker 01

  And Marc Benioff, 18 months later, he recruited me out of IBM to start a new company with him, a parallel company to Salesforce called Software as Service. And the concept was to be anyforce.com, that we would create any of the new software that's needed to be created, where Salesforce had been a single application. He wanted to, and we wanted to create, in effect, a development platform for the cloud. We worked on that for a while, and then Mark had to go back in and run Salesforce day in, day out.

• 1:00:23Speaker 01

  And he had, he had actually become chair and left it to another CEO for a period, went back in during the really tough time after the bubble burst and took over the reins. And obviously, the rest is history for Mark. The software service became the foundation of the Salesforce.com platform. And I loved what we were able to do there, but the question then was, do I want to start another, another SaaS company, since I had heard it from the creator of multi-tenancy, about how powerful it is, or a venture firm? So, two amazing partners that I had known in the venture industry, and I said, let's,

• 1:01:06Speaker 01

  let's consider starting a venture firm that can almost parallel what Salesforce was going to be doing as a single company. We can take a bet that cloud is going to matter in the long run. So, Emergence was always a thesis-driven firm. We've always said, we're going to have an area that if we're right, we look brilliant. If we're wrong... We took a risk and it was unobvious. And I remember when we started Emergence, the idea of multi-tenancy and cloud software was considered a toy by not only Larry Ellison, because he said that about 100 times about Salesforce, but also by the venture community.

• 1:01:47Speaker 01

  It wasn't seen as, why have these long revenue streams that take six, seven, eight years to build any volume up for a company as opposed to licensed software where you get paid right upfront. But again, the best opportunities come from not doing what everybody is doing. And we took a bet and it paid off. And so that's the beginning. So we can touch maybe a little more on how we've worked on things since.

• 1:02:14Molly O'Shea?

  Did you raise outside institutional capital back then? How did you, how did you actually get the formation together?

• 1:02:21Speaker 01

  Good question. We did. We raised $125 million first fund right in the, you know, the dregs of the, after the big, the major dot com bubble burst. People met with us, took us 15 months to get to our first close, mostly going to the East Coast. So we were on planes away from our families for, for most weeks of the, of the month. And, and, you know, they would meet with us, LPs would meet with us just because they wanted to see, it's almost like we were a zoo animal. We want to see who is trying to raise a tech venture fund in the absolute worst period in, in, in the history of, of

• 1:03:05Speaker 01

  venture investing to go out and try that. We tried to get, you can, you can hire firms that'll help you fundraise, which now there are many of them and very easy to get access to if you need them. We tried and they turned us down. So we're like, oh, okay, that's not a good sign. But we just kind of got through it and it was a startup. It felt like a startup and, and we really are very happy and so appreciative of those early investors that believed in us and got us started.

• 1:03:36Molly O'Shea?

  I guess since you started in a dark market, that might inform some of your tenacity to not think about them too strongly and to just keep on going. But Yeah, I guess I just, I want to know, like, how do you think about navigating these huge market shifts? Or do they not faze you because of how you started?

• 1:03:59Speaker 01

  Look, they all, I, you know, I said to our LPs a couple of years ago, yeah, as you remember, he said, when everyone was asking about AI, it was brand new and, and happening. And I stood up there and said, you know, by the way, this is pit in the stomach stuff. Like this is, is this going to be the next great? And yes, it's going to be something absolutely tremendous. But our, you know, we've got to be careful not to overreach, not to kind of take our eye off what we think matters, not to change our model dramatically, because just change, by definition, means you're maybe not, you're not ready to do it properly. You can't like massively force fit an organization overnight.

• 1:04:40Speaker 01

  So we've applied the same strategies we did all the way along with, you know, from, uh, horizontal cloud was Salesforce, uh, industry cloud was Viva and the growth there. Enterprise collaboration was Yammer and Zoom. Deskless workforce was Doximity and a number of other companies in the mobile space. So we've been thesis every two or three years. We have a new primary thesis that we all focus on, along with the existing ones. And some roll off the back of the truck, new ones are added to the front of the truck. And we just keep motoring through that. AI was a, you know, a pretty major disruption in a, in a good way.

• 1:05:24Speaker 01

  And we've come up with new theses that we think are going to matter. And we put on a little bit of blinders, not, not to a fault, but if, if you're, if you're only trying to go with what is moving in the market. Some people say, you know, venture capital, you should never try to predict the future. It's just, don't predict the future, just go with what's right in front of you. That doesn't work, I think, to really create outside returns, because everyone is seeing what's right in front of them. And it's, it's not that hard to see that. What you need to do is see what's right in front of you and then say no to things. Say no, that isn't going to matter. Generally, and this is going to matter generally.

• 1:06:08Speaker 01

  And to me, that's where you create outsized returns. And you have to have a predictable way of doing that, which we do within the firm. Everyone not only has to be hunting for deals and great opportunities, but we're hunting for theses where we take what we're seeing in the market and raise them up into a thesis. And we beat on that thesis almost as much as we beat on a new investment as to whether it's the right thing you do or not, because they're almost as important as a great new investment, because it gives us that ability to focus on a few critical areas.

• 1:06:44Molly O'Shea?

  I love that you went into that deeper, because I think it's very well regarded that Emergences theses are iconic and they're very thoughtful. And they do set kind of a tone for how people are viewing the enterprise markets. And I was curious if they were more reflective or if they were predictive. And it seems like you kind of combine both of that and you just focus on continuously engaging and testing them out, but getting them pen to paper and actually expressing them and living them and then seeing it applied throughout the investments.

• 1:07:24Speaker 01

  Yeah. We have a whole process. We actually got together with all of our great seed partners at our seed summit and in this room actually walked through our kind of how we do our thesis development, how we start with hunches. Everybody, including associates, are coming up with hunches, presenting them on Mondays. The hunches then kind of get everybody talking about it. If it coalesces, then we take it to the next level and they present it as a minor theme or we take a little more time. And then they go out and talk to a bunch of companies around that theme. So it's an organized process that is, again, as important in many ways as just being out and seeing what's happening in the world.

• 1:08:09Speaker 01

  Because what's happening in the world informs theses and then theses inform focus, which finds the next opportunities that others may not see. That's the other fundamental part. I always love to look at it. If every venture firm... Could bring in a company to our boardroom or go and meet with them and we'd all say about the same thing. We'd be like, hmm, what's your CAC? What's your burn ratio? Let me understand. Oh, okay. I see where you're cut. If you're all going to do the same thing, where is the arbitrage there? Other than we're just hunting to get access. And access is important and we all work on that. But it can't be the only thing that you're doing. There has to be some

• 1:08:51Speaker 01

  perspective on the world that that informs the companies you really want to dive in with and spend eight to 10 years building together.

• 1:09:02Speaker 02

  And I think one of the key things too, really is sometimes the thesis isn't going to be fully fleshed out. And the best way to learn is to make an investment. Like one of our more recent ones was was around AI-enabled services. And I remember the first time we started talking about it two years ago was also in this room, also at a seed summit that we hosted here. And and we just, I remember it was you and I, we were sharing it with a bunch of investors. They looked at us as if we were crazy. And then we made an investment in mechanical work shirt. And and now, I think, two years later, a lot of other great investors have published content around AI-enabled services. And it's and so the best way in my mind to really learn is is to continuously improve and sharpen your thinking. And the only way

• 1:09:47Speaker 02

  to do that is to continue to meet the best founders building in that space, but also being the most important partner to the founders that are building in that space. And so, taking the leap, even if your thesis isn't 100% airtight, is the only way in which you're really truly going to learn where the new opportunities lie. And so, that to me is what makes this so fun is you can be as proactive as you want. A lot of times, you're going to have to walk it back and relearn what matters, what doesn't. And then you're going to be better for the next one.

• 1:10:15Molly O'Shea?

  Shifting into the organizational structure of the firm, from my understanding, it's an equal partnership. So how do you resolve the tension of being an equal partnership with, you know, a power law driven business and only very few deals drive? The returns, how does this work internally? How do you resolve the tension?

• 1:10:39Speaker 01

  Yeah, it's a great question. It's this is inherently an unbelievably human business. Like partnerships and the money and the difficulty and the risk, the amount of complexity around that I've been quoted as saying it's like a five-way marriage, you know, for five partners. It's five-way marriage where only one of the five has to have a challenge or something goes wrong or and it can blow up a firm. I mean, it really is that sensitive. So we take it super seriously. I won't go into all the detail here, but we are, you know, in terms of having an outside

• 1:11:21Speaker 01

  coach and facilitator, every quarter we get together as a partnership and work through both business issues, but also more, you know, issues personally as well, because it's that critical. And we've been doing it. It's all, all sort of getting ahead of things, but, but it's something that we believe has been really helpful to us really since the earliest days of the firm. So that's a bit of an aside. But the main part question you're asking is partially there's two big things. We work as a team and it's again, equal partnership, which is critical. And we develop from within. So when Jason Bryan and I started the firm, it was, you know, we all went through those

• 1:12:03Speaker 01

  15 months and longer to get to a final close and all the pain of that. So we were equal from day one. Then as we brought on other now senior partners, they have developed from day one at this firm. Yaz has, you know, is developing into an amazing, you know, future leader of emergence, but started very early and certainly in his venture career. So when you've come through the firm for many years, you're not as apt to say, ah, you know, I just landed this great deal and I'm not getting, you know, extreme Amounts of credit for this, I think I'll just take off and go somewhere else because you've been

• 1:12:47Speaker 01

  part of the firm all the way through your career and you're now a full partner within that firm. That is sticky. And stickier, you can never, you know, nobody is ultimately sticky in any firm, but we have had great success by basically people come up the stack and senior partners, my two partners have retired and rolled out with sort of no strings attached as they move on to their next endeavors. And I'll be doing the same thing at the appropriate time.

• 1:13:19Speaker 02

  Being one of the newer partners that's been at the firm for a few years now. So much of the magic is, is being able to take a risk. At the end of the day, we're early stage investors. And so much of the stuff, like to your point, we want to invest in things that aren't obvious. And the fact that we approach each investment with a we all mentality. We're all excited about the investment. It's not Yaz's deal or Gordon's deal. We all collectively as emergence are really excited about what this company is building. It makes you as a younger partner coming up the stack, feel more confident in taking that risk. If I was over optimizing for let me go for the safe ones, then you won't ever find that moonshot. You'll go after the obvious stuff that everyone else at every other firm wants to go and do.

• 1:14:04Speaker 02

  And so when Gordon shares so much about what it takes to find those outliers, you're empowered to do that when the firm is structured in the way ours is. So that's, that's what I think is truly unique about being like a team first venture firm, which is pretty typical.

• 1:14:20Molly O'Shea?

  Well Yaz, again, congratulations on being one of the newest partners for the firm. We have to ask, what is the most recent risk that you took?

• 1:14:32Speaker 02

  Yeah. So, so we're really excited. One, I'm actually lucky to be working with Gordon on made an investment in a new area. We've been spending a lot of energy around, which is what are the next modalities of the future? And when we look at history and look at all the times automation has really powered net new, really amazing experiences for Companies and their end consumers, most people have tried to do things that are text based. It's easier, it's simpler to control. We decided to take a really big leap in voice and are lucky to be partnering with the best end to end voice agent platform, Bland AI. So we raised a series of $40 million Series B. And we're really excited about where they're heading.

• 1:15:16Speaker 02

  They're working with some of the world's best enterprises like the Hertz rental cars, the Cleveland Cavaliers, a lot of other great businesses in healthcare, support and what have you. And they're powering the most complex interactions between company and their end customer. And, and what stands out so much from the founders, Isaiah and Saban, that they continue to harp on is we want to power opinionated experiences that make our customers' customers happy. And, and so this is the big risk we're taking, going after a horizontal voice platform that wants to be the trusted modality in voice for these experiences.

• 1:15:55Speaker 02

  Over time, they'll layer on to all the other modalities, but chasing hard things is our approach and the hard thing is voice. After that, being able to add text and chat and all the other things, that's the easy stuff. Voice is what's really going to matter. It's the most innately human way in which people communicate. And we're lucky to be working with a company that is powering the next era of voice for 2025 and beyond.

• 1:16:20Molly O'Shea?

  So exciting. And I think Upfront Ventures, my alma mater, was an early investor in that.

• 1:16:27Speaker 01

  Yes, they were.

• 1:16:28Molly O'Shea?

  Shout out to Kaser.

• 1:16:33Molly O'Shea?

  Okay. So as we close up, I have two final questions and then we'll let you go. So I have to ask, with the partnership, what was the one deal that you regret doing most?

• 1:16:48Speaker 02

  One deal that you regret doing most?

• 1:16:50Speaker 01

  Uh, regret not doing.

• 1:16:53Molly O'Shea?

  We can do that. We can do that if you want.

• 1:16:56Speaker 01

  Oh, yeah.

• 1:16:58Speaker 02

  Do it.

• 1:16:59Speaker 01

  I'm gonna, I'm gonna take a pass on the first one.

• 1:17:03Molly O'Shea?

  That was a trick question. That was a trick question.

• 1:17:04Speaker 01

  It is a trick question. It tripped us. Uh, no, let me start with, with the ones that we wish we had done. I, I think that's a good one. And maybe if we think of one, we'll, we'll talk about it, but I.

• 1:17:15Molly O'Shea?

  No, you don't have to do that.

• 1:17:17Speaker 01

  That's unfair. And there certainly are more. Um, and I'll add a third maybe, but, but, uh, Figma was one that we all, back to, we work as a team. We all looked at Figma back when their revenue was low, but their engagement was incredibly high and spreading within organizations. And we just couldn't get comfortable. We were a little biased on, if we're gonna pay that much money, we've got to have some reasonable revenue traction and growth. And we just didn't see through the fact that the classic line now we say, when we call it the Figma effect is engagement is revenue. If, if people are using it heavily and love it,

• 1:17:58Speaker 01

  you might as well just be getting paid for it, even if you're not, because you'll find a way. And in that case, uh, we missed it. Second one was, uh, was Twilio. Um, Twilio developer first, you know, kind of model. And we weren't as clear as to the, the, the power, the, the kingmaker or queen maker of developers. Uh, was not as obvious to us and other firms basically had a thesis that was clearer about that buyer. So, or that leverage, uh, that developers had. And this was many years ago. So those are two examples. There are more, but I'll just stop there.

• 1:18:37Molly O'Shea?

  Fantastic. And to close out, what are you both looking forward to most this year?

• 1:18:43Speaker 02

  For me, it's when you think about all of the new use cases that are popping up, all these new applications, I think you alluded to it a little bit earlier during that conversation, which is, right now, security is going to matter, trust and privacy is going to matter, understanding who has what IP, who doesn't, is going to matter. In a world where voice feels as authentic as speaking to a human, where talking to a chatbot and they can help solve really complex user queries, there comes a lot of risk with that. So, right now, we're spending time thinking about what's that next problem that organizations are going to face when they're rolling out some of these technologies? And there is a whole suite of new early-stage opportunities that are going to detect malicious

• 1:19:27Speaker 02

  behavior on behalf of these orgs when rolling out this new scary technology. And that's something I'm really excited about. Finding the guardrails to make sure this doesn't become the complete Wild Wild West, and making sure that people can do things in a controlled environment, and that doesn't take advantage of end-users and consumers broadly.

• 1:19:51Speaker 01

  And it wouldn't surprise you that we'll dovetail on each other's theses, because for me, it's sort of the human side of that one, which is, how are we going to, what is the next generation of software going to look like, from a UI perspective or UX perspective? Where we get, we're so used to form fields, and typing everything into a form field, but how much can we, both using Bland, perhaps, to use voice to interact with these systems in a way that's much more natural? Maybe it's Vision Pro from Apple, maybe it's some other, "Apple, just talk to me."

• 1:20:31Speaker 02

  I don't know.

• 1:20:33Speaker 01

  And, you know, there's going to be new modalities, new ways of interacting, and the key will be, we'll be able to help users more if we know more about the kind of actions they're taking and what they're trying to get done. Back to that workflow comment from earlier in the day. So, I think there are going to be whole new ways of users interacting with software, that we are looking at a number of really interesting companies that are doing that. And... I think one of the things we believe is there's going to be a whole new, if the mobile device was something that opened up a whole new category of software for mobile devices, we've got devices, companies like Bland that may open it up for voice

• 1:21:18Speaker 01

  in unique new ways. They're going to probably be whole new categories that the Apples and the IBMs, the IBMs and the Microsofts are going to come up with that will be a whole new platform on which to deploy technology out to end users. And looking at those trends and trying to understand what's coming next is really exciting to us.

• 1:21:40Molly O'Shea?

  Fantastic. Well, Yaz, Gordon, I think we could have talked for about four more hours, but I will stop this now. It was a pleasure and thank you so much for taking the time.

• 1:21:52Speaker 01

  Thanks, Molly.

• 1:21:52Speaker 02

  Thanks, Molly.

• 1:21:53Speaker 01

  Really appreciate it. See ya.