00:16:30 · Dimitris Samouris
[00:16:30] S1: Very much so. Yeah. I don't think there is a clear answer on how you get there. Yeah. And we're still... If I took take a look across the entire industry, we are still at a stage where most of the large firms are trying to figure out how do I solve for the individual productivity use case. I... How do I make SEB 50% faster when it comes to cutting a market model or synthesizing expert call notes or making a slide? That's not the real question. Right? That is that is a step along the way of how I get to output based pricing. At some point, you will be able to shave enough hours off of the individual work stream and get to a point where I can drop in a scope of work, press a button and get an 80% complete piece of work that it is the job of the human to review now instead of create a first draft of. When you when you get to that point where it's I push a button and I get an 80% draft, that's where I think you'll be able to start charging based on on output based on the diligence or assurance or I think for other types of consulting and by the way, this is a model that's already existed for a while value creation work for example, where they will get paid on the outcome after the fact, I. E. If I help private equity fund achieve three four percentage point increase in gross margin, we get an outsized share of the rewards, not just a project fee. Think you're gonna start seeing a lot of that as well, which is great for the industry. It can like fully aligns incentives be between adviser and and principal.
00:18:14 · Sebastian Johnson, Dimitris Samouris
[00:18:14] S0: Where do you think AI will never play a role in consulting or advisory work more generally? Yeah. If if there's any part of it that it won't touch. [00:18:24] S1: Okay. I'm gonna betray some of my misgivings about this tech when I'm gonna give you this answer. I get personally very pet peeved at this use of, like, reasoning or reasoning models or thinking machines. Perhaps this is just my ignorance as someone who's non technical and come... Does not come from the machine learning world, but I like... I do not think that there is true deductive reasoning behind these next token predictors. They're like extremely powerful. One way I like to think about it is a steerable printing press that produces like cogent analysis. But anyone who has used language models for their work knows that they're off a lot of the times and you have to spend quite a bit of time refining the output to what you want. So anything that requires higher order thinking, lateral thinking, judgment, prioritization, I think that is going to remain the preserve of humans for a very very long time just because of its very nature as a next next token predictor. The more senior you go up in the consulting pyramid, the more likely that your work is... Which is sitting down with a client, being the trusted adviser in the room, helping coach them through what is often very emotional and stressful process. Like people come to consulting firms with big problems that are existential for their enterprise. That's not going away anytime soon.
00:19:59 · Sebastian Johnson
[00:19:59] S0: Do you get the impression that the that the clients of the firms, are they starting to view it differently? You know, are they starting to have different expectations around the outputs of the consulting firms or the prices? Are you getting to see any any view into that?
00:20:13 · Dimitris Samouris
[00:20:13] S1: Yeah. So I I I think a lot of ex... A lot of people expected there to be pricing pressure on the consulting industry. I ask every single partner whenever I speak to them, do you see scope pressure? Do you see pricing pressure? And the answer is almost universally no. So what has changed is where the starting point is. Your clients are smarter because they have access to these models as well. That's interesting. Yeah. And so when a PE fund goes to a consulting firm, like I'll give you an example. When I first joined the industry as a grad in 2013, the first week of a market, module was literally me opening up like 300 tabs on Chrome and reading every news article, every government research report, past market research reports that that had been produced by various vendors in the industry. That's gone. Like, no one is paying you $60.70 k a week to do a bunch of desktop reading and, it was a big moment for me like a... When deep research queries came out a year and a year and a half ago, now two years ago, where this thing produced a better piece of output in fifteen minutes than it would have taken me in a week. The previous... I was I was a very average analyst, but, like, directionally, that is, like, the order of magnitude of of difference in in time. So, a lot of that easy, like, low hanging fruit work is now being done, like internally in customers, but they're still doing three weeks of diligence and they're asking customers to do a lot more primary research. So this is speaking specifically about the private equity industry because that's a little bit more around where we we rotate.
00:21:56 · Sebastian Johnson, Dimitris Samouris
[00:21:56] S0: Mhmm. [00:21:57] S1: What is your edge when everyone has access to the same underlying models and the same secondary, like, data or research sources? Your edge is in the primary research. So people are doing more expert calls, more surveys in order to get to a differentiated view. So the the volume of work has stayed the same. The starting point has really moved up. Yeah. That's really interesting that they they are now looking for new, yeah, edges or source of information to help them form their viewpoint or their strategy. Correct. [00:22:30] S0: Yeah. You you obviously found a real market in voice. Right? It's like voice being the first use case of Junior Yes. And that being a lot of where you kind of captured value. Is is the plan to stay on voice so you've got a vision to do a lot more across different kind of verticals or technologies?
00:22:48 · Dimitris Samouris
[00:22:48] S1: Yeah. So the way we think about this, Seb, is these particular firms, these professional services firms, they all run on conversations. Mhmm. So let's take your typical engagement from the moment a partner picks up a phone call from a client saying, hey, like, what do you know about this? We're considering commissioning some work on it. Like, can you come pitch to us? All the way to the final like readout or deliverable when you when you like when you go and present your findings to the client. There's probably in the range of a 100 conversations that happen in between beginning and end that move the dial on the investment thesis or the answer. So you have the BD call or the pitch call. You have a team kickoff internal team sessions partner reviewing work, check ins, checkouts with individual, like, work streams. You've got your interim readout with the client. You... Maybe you're meeting the management team of the target you're, you're investigating. Then you have all your expert calls, which is where we started on the workflow side. All of those calls individually represent the next step in the answer of going from blank page to final deliverable. And so we think it's critical to capture that information and help professional services firms clean it, structure it, and then build automation off the back of it. I think a lot of our our peers in the market, and there's a lot... Many ways to skin a cat, they're very focused on the workflow layer, which we agree is like a necessary component of the big picture at the end of the day. We kind of feel that that's gonna fade away. Like, if you really believe in this thesis that software becomes headless, agents will do everything, The workflows will get eaten up and it doesn't really matter what interface they run on whether it's an internal platform that your customers built one of the major model vendors or an applied AI vendor that sits on top of it. What matters is the data input and being the voice intelligence layer, we think is like really really critical to that journey.