Prime Venture Partners Podcast

AI x SaaS: How PrimeVP Thinks About the Next Wave of Enterprise Innovation

• Prime Venture Partners: Early Stage VC Fund

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AI is no longer experimental—it's foundational.

In this explosive episode, PrimeVP Founder & Partner Shripati Acharya and Principal Gaurav Ranjan unpack how AI is reshaping B2B software—and what that means for evaluating and investing in early-stage startups.

What you’ll learn:
⚙️ The rise of Enterprise AI and its real-world applications
📈 Prime’s investment thesis in AI-first SaaS startups
🧠 What separates hype from defensible value in AI
🚀 Advice for founders building in vertical SaaS, AI agents, and more

Timestamps:

00:00 – Introduction


02:00 – AI’s impact on startup scaling and team size


03:20 – Faster sales cycles with enterprise AI


07:26 – Will AI unlock India’s SaaS market?


13:36 – Has AI neutralised India’s cost advantage?


20:19 – Models vs APIs: Where’s the real business?


25:52 – Why UI still wins in enterprise software


27:27 – Building moats through workflow depth


34:38 – How to price AI products right


39:43 – Prime’s investment themes in enterprise AI


44:58 – Final thoughts

💡 Key Takeaways:

Where AI is driving real enterprise value today
Prime's active investment thesis in AI + SaaS
The rise of function-specific AI agents across verticals
Shifts in software buying behaviour—from IT to HR budgets

This is your tactical guide to the next generation of enterprise innovation.

📌 Follow us:
LinkedIn: https://www.linkedin.com/company/primevp
Twitter: https://twitter.com/primevp_in
Website: https://primevp.in

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Speaker 1:

Zero to a couple of million dollars of ARR, even tens of millions of dollars of ARR in a couple of months. In some cases, maybe a year or so. What do you think has changed? I think that puts Indian companies at some disadvantage. We can build the same quality of applications whether you're sitting in Bangalore or Bhopal or somewhere in Europe or US. So in this world then, how do you differentiate your product?

Speaker 3:

The nuances required to build a successful enterprise software are often underappreciated. Hello everyone, welcome to this episode of Prime in Japan. I'm excited about are. Hello everyone, Welcome to this episode of Prime Venture Partners podcast.

Speaker 1:

In today's episode, shripati and I will be discussing the implications of AI in this world of B2B software. I mean, every day we are seeing new things happening with AI new models being dropped, new business models being cracked, new companies being formed and scaled from literally zero million to like a couple of million dollars of ARR in a few months. So we'll cover a lot of that and also how this impacts the way we think about evaluating and investing in early stage startups. Welcome, shripati, to the show Absolutely.

Speaker 3:

Thank you, Karan.

Speaker 1:

So the first thing that we'd like to know from you, shripati, is that traditionally, we've seen SaaS companies scaling from inception to a million ARR in a couple of years, and then, from there on, they like triple and triple and double year on year. In this new world, we are hearing about a lot of companies going from literally zero to a couple of million dollars of ARR, even tens of millions of dollars of ARR, in a couple of months, in some cases, maybe a year or so. What do you think has changed? I mean, my assumption is the buyer. My understanding is the buyer persona remains same, the buying behavior more or less remains same. So what has changed? Because of which companies are able to scale so fast?

Speaker 3:

only a couple of comments. One thing is that remember that the we only hear about the companies which are going through these breathtaking, uh growth rates. I'd hear of cursor going from zero to 100 in like a year right, stuff like that or two years, whatever the number is and, you know, competing with some of the other companies which have gone since the product launch. So Glean I think the number was three years, and this is faster than Glean is what I recall, 200 million ARR. So, yes, we hear about the fastest of the fast-growing companies, but we should not conclude from that that that is the norm for every company, right? I'll just make that comment.

Speaker 3:

That said, some things are changing and I think one of the most striking features of the new generation of B2B SaaS companies is that the size of the company is smaller. That's like number one, and the answer for that is like a more simpler answer, which is that you require a smaller team to generate the same quality of code, thanks to, you know, co-pilots for coding and so forth, and also the use of AI in all the other aspects of a product. Right, you have sales, you have marketing, you have insights, sdrs all of these aspects are made efficient and faster and better with the use of AI, right, so that's why the companies are smaller and that's actually, I think, a systemic trend. The other thing which I feel which is different about this and where the sales cycles are going to be faster in many perhaps not all, but many enterprise businesses is that the time to wow, which is the time it takes for a customer to look at a product and say that, hey, this is going to actually really be driving meaningful value to me as a business, as an increase in top line or a significant cost saving is lower, so that gap is just lower.

Speaker 3:

So if you just take a step back and look at how the cloud adoption, for instance, happened in the enterprise, it took a while for that to happen, because it's not like these large enterprise let's just take, you know why, wall Street Enterprise, or even like a large university or any of those things which had a deep deployment of software. They had on-prem software right, or their own captive data centers, and then, when that, when cloud came in, they had to, like, make a decision, like okay, how much benefit am I going to get? Is there a significant change in the way I'm doing things? What will I do with my existing infrastructure. And, of course, over a period of time, they realized that, you know, I can scale seamlessly and it's more OpEx versus CapEx and so on and so forth.

Speaker 3:

But in the case of AI, I feel that the decision makers are able to see that much more clearly. So, for instance, they might be able to say wait a minute, this particular piece of software is going to enable me to respond to my customer faster, develop more products faster or actually reduce my headcount in customer service, which is like our support which is, you know, in the news all over the place. So I feel that that time to value and the leap of faith which the enterprise decision maker has to make has come down significantly, which means the sales cycle are smaller, companies are smaller, the ability to create a product is faster and the sales cycle are smaller. But that doesn't mean that the average company would actually go to 10 million of year one. But I do think that the total revenue ramp for successful companies is going to be higher than the pre-AI generation of companies. What do you think I mean like? Does that make sense?

Speaker 1:

I mean you covered everything. One thing that we also see. I mean we do speak to a lot of potential AI customers the way, like 15, 20 years back, every enterprise had a digital transformation budget.

Speaker 1:

These days, everybody has an AI budget, so they're open to trying new things and, as you said, like with these AI solutions, the time to wow is like very fast, you can quickly see the value, so they have a budget for it. Once they start investing in it, they see the value very fast and then, from there on, it's very easy for them to adopt. So that's the other factor that we have seen, at least in large enterprises, where they have a dedicated budget for AI, which at least creates a room for experimentation pilots and from there on it converts to a real customer.

Speaker 3:

Yeah, and if you look at, like the history of like really the various you like, really the various stages or errors in software, like we had the PC software, then we had the client server computing and then we had the cloud and mobile and so forth, in all of these cases, if you think about the person on the other side, this was not a product which they were familiar with, right, they were not using client server computing in their day-to-day stuff.

Speaker 3:

They were not using cloud in their day-to-day uh stuff, or even if it is there, it was very abstract. But I feel also that now every decision maker, if not them, their children, are definitely using, uh, you know, chat, gpt or equivalent or gemini, uh and cloud and so forth, right, so they actually have a firsthand impression and a firsthand experience with really the magic of AI, because it's quite magical, the user experience, and that also helps because now they're able to correlate, like this is happening and this demo is actually delivering value, which I can see. And perhaps there's the other thing which is also happening that competitive dynamic here they can also see that they're competitors. There's so much noise about industries adopting it. So I feel that all of this is leading to a significant compression in the cycles. I think it's a good. It's an exciting time from that aspect because one of the biggest issues in enterprise sales as you and I know from our direct experience investing in this field is a sales cycle like.

Speaker 1:

It takes time to sell and a time to deploy yeah, the other thing we're seeing some of that impact even in the indian market. Traditionally, like india market, has been not very attractive when it comes to sas companies. Like going after india market. Now, with ai coming in, at least we're seeing some early signs where enterprises are willing to adopt AI solutions, are willing to pay for that. Do you think they'll have any change in the India software market? Like with AI coming in, do you think things will change and the market will become more?

Speaker 3:

interesting. So India software, india enterprise SaaS has always been a challenging market, in my opinion, from a market size perspective. So, honestly, the three companies we all keep talking about which have a billion dollars or more of revenue are, from an enterprise software standpoint, salesforce, which has talked about it's more than a billion dollars in India, and SAP and Oracle, which haven't disclosed the India-specific revenues, but likely quite significant. They are in the same zone. But if you think of these, these are like one very foundational pieces of software and it's taken a while, with their heft and brand the global brands to actually get to that level. So until now we have at least not seen a significant total addressable market for enterprise SaaS in India.

Speaker 3:

I feel that AI is really going to open that up. Now how big and how fast it opens remains to be seen, but I'm very optimistic about it and the way I'm thinking about it, gaurav, is that AI is coming in with a software solution in areas where there was actually no software at all. So, for instance, think about a form filling thing. You're actually filling I don't know your compliance audits. Think of just take any particular area like that which you traditionally think about. Wait a minute, it's a lot of people, a lot of form, filling, a lot of, you know, reading regulations, this, that, and filling and submitting and so forth, and a back and forth going on with the service provider, with some government website and this and that and the other. Just putting software there seems like a complete nightmare. Right Come AI. Now you can actually visualize and you're already beginning to see, at least in other markets, folks actually delivering software for that, which can look at all your existing internal documentation, which can correlate with external documentation, come up with the recommendations and so forth, and then also help you in the workflows there.

Speaker 3:

So I feel that software now is entering areas where previously the substitute was humans doing paperwork, humans using essentially spreadsheets, email or a word processor like Microsoft Word or something like that to do their work Like, if you ask, even like lawyers, there are two big pieces of software which they use are Microsoft Word and email. Yes, right, so that's what you are really displacing, right, if you look at the larger legal tech companies that are coming up, especially in the US. So I feel that we have that opportunity now in India, which is that it's not like enterprises are not doing workflows, just that the workflows are entirely manual. So now software can actually go and deliver value.

Speaker 3:

There and in our earlier comment, there are time to wow for that which is value, realization is more obvious. So thing which remains to be seen is that what is the average order value or average contact acvs of these kind of things? I think that some discovery has to happen there. Obviously it won't be as large as you know some of some of the ACVs we are seeing in AI SaaS companies in the US, but I believe that you know it's going to start becoming a significant market now, precisely for that reason. Certainly, that's my belief.

Speaker 1:

Yeah, I totally agree with that. Like new use cases have come up which has not previously been addressed by software. In fact, one of our portfolio companies which is in the QC space, like computer vision based QC, yeah, quality control you mean. Yeah, quality control On assembly lines. You had people looking at stuff coming out of assembly line and looking at that and discarding the bad stuff. Now with computer vision, ai, you can do that in real time with the model sitting on the edge and things happen very fast. Yeah, so this use case was not previously possible with like a simple software. Computer vision models were there around but the models were not great, you could not deploy that on the edge.

Speaker 3:

Now, with lighter models, faster models, you can do that quite easily yeah, and in one sense, this actually opens up fairly large and you, you said, right, the AI transformation budget. Yes, ai transformation budget. So this is a conversation which is not just happening in the CEO's office, it's happening in the boardrooms of these companies, right? So if you're an investor and the first thing which we ask all our portfolio companies which we are involved with is, hey, what is your plan? And we were asking this back in 23 when GPT came out, right, and this is a conversation which is in all large enterprise boardrooms as well which is how are you going to leverage AI?

Speaker 3:

So this is top of mind for Fools, so it is not something where you have to, as a startup, go and sell that. Hey, look, you need to deploy AI. And the question is can you actually have a demonstrable return on investment, demonstrable ROI for them? Yes, right, that's the key in terms of these things. So I'm hopeful. I think that this definitely is a new era. It definitely is going to be a larger market than our existing TAM. Whether it will open multi-billion dollar TAMs within India, I don't know, but we'll see. I think it will definitely lead to more successful startups in India, targeting India enterprise.

Speaker 1:

Yeah, I agree. I mean, while we hope that the India market opens up for software, at least with a forcible feature, we do see US as a large market for companies building out of India. And so far, one advantage that we had was the cost advantage. Whether it was in terms of development, testing, sales support, all of that India had an inherent cost advantage. Whether it was in terms of development, testing, sales support, all of that india had an inherent cost advantage compared to a us native company. Now, with ai coming in something that we were talking about earlier the size of the teams have come down. Ai will do heavy lifting in terms of sales and support, with ai agents taking over human agents or like working together human agents. Do you think that puts Indian companies at some disadvantage or takes away the advantage that they had before when it comes to the cost structures and they'll have to compete at par with a US company which will have a similar cost structure as an Indian company?

Speaker 3:

So, as the, I mean that's definitely is a very significant aspect which is at play here in the sense that the coding teams are smaller and the sales force is smaller, because now the outbound calling with all the agent, API software and so forth, the support sales ops operation, for example, is smaller and customer support teams are smaller and so forth. So if the total size of the company has shrunk, then definitely the arbitrage you might have from a development team in India versus a development team in the US becomes definitely significantly smaller. I think that aspect you're right, Gaurav significantly smaller. I think that aspect you're right, Gaurav. I am also seeing, though, that there is a potentially countervailing force here, which is that a lot of a new opportunity we believe on enterprise SaaS is service as a software or outcome as a service, or however you want to call it, wherein the end customer is getting service and it's just like there might even be a human interface. Think about it as a contact center or a customer support, where you're actually talking to a customer agent and you're a medium or a large enterprise, and I feel that a lot of the non-tech companies will not want to in-house this. So, for instance, you know there's a lot of talk about clarna. Companies will not want to in-house this. So, for instance, there's a lot of talk about Klarna, which is this fintech wherein they went ahead and essentially reduced their customer support by 90% or more and have the same NPS, same customer side by going entirely into an agent tech bot which is actually doing both voice and text support. Right, but if you think about a regular enterprise, they both voice and text support right, but if you think about a regular enterprise, they're not going to go ahead and develop that software in-house first of all.

Speaker 3:

So the transition we see in those cases is those enterprises telling their service providers that hey look, we want you to use AI on your thing. We should translate into better and cheaper service for us as an enterprise. So, which means that there will be pressure on the service providers who are currently there to actually go and provide that kind of service, which is essentially powered significantly by AI, which actually opens up opportunities for, in two fronts one, new companies who can now come in and do that better than existing incumbents, which might be in India or worldwide, anywhere else in the world. It also opens up places where services were not, you know, quite as accepted as and not being offered as a service, right. So I feel that that particular aspect can actually create opportunity.

Speaker 3:

For instance you could think about I'm just kind of like, you know, doing some blue sky thinking here we have legal tech companies like Harvey, right, which are providing software solutions for law firms to use within their firms. Right, they use it for all kinds of cases, right, and workflows within the firm. But you could think of a firm which is, say, an Indian company which is targeting US companies, offering legal services which are entirely outsourced, but now these legal services are powered predominantly by AI, but they also have a legal team here which is providing that. So now a whole bunch of companies which previously did not have access to this level of high quality legal work can now think about doing those kind of things.

Speaker 3:

So I feel that more areas will open up where services can be powered by AI and where there will be new opportunities there. So that, I will say, is like another thing which kind of favors the Indian companies, because we understand tech, we understand services very well in terms of just the level of experience which is there in the workforce here. So there could be new opportunities there. So I think that the nature of what a successful global SaaS coming in company coming out of India looks like will definitely change. It will morph, but the real size of the opportunity you know who knows might actually increase.

Speaker 1:

Yeah, I agree with that. The other thing which also plays to the advantage is now, with access to tools, technologies and AI models, even Indian developers can build a world-class software, which was always the question. That is now, with access to tools, technologies and AI models, even Indian developers can build a world-class software, which was always the question that the quality of software of a US company is better than the Indian software. So maybe there also you'd have a level playing field where the Indian software is at par with in terms of quality and experience, at par with what a US company would build or a European company would build.

Speaker 3:

I think it's a fair point that we have a fairly deep bench of talent on SaaS right Thanks to the early success stories in India from both Zoho and Freshworks and so forth. So we have actually folks who have worked there, have started companies since then, are in the second or third startups, or folks have just worked there, have started companies since then, are in the second or third startups, or folks have just worked there and have deep level of experience. So I think that the talent in India definitely can compete with the global talent. So long as the overall opportunity side is increasing, I think India will get its fair share, just that the cost the traditional levers of competitive advantage might shift here.

Speaker 1:

The other thing, which was, I mean, where a lot of thinking and opinion was that Indian companies will typically or Indian opportunity lies in the application layer, with DeepSeek coming up with a model and showing that you don't need billions and billions of dollars to build a world-class model. Do you think we should look at opportunities or indian founders should look at building, uh, vertical specific or sector specific models? Is there value in building models?

Speaker 3:

so I feel that even if you look at the companies which have models like, uh, like open, ai or a clod, they are actually not so.

Speaker 3:

Yes, there there is a bunch of revenue which comes from tokens right, per million tokens or whatever. By the way, the price per million tokens have fallen by 100x in two years. Right, it's gone from about 60 dollars per million tokens to like 60 cents per million tokens right now. So it has fallen quite rapidly. But if you think about it, the way their business models are, their business models are actually APIs. Right, you build applications on top of my thing and of course, they are going ahead and will build a few of those themselves, but primarily it is that right, so, providing a platform for others to build on. And in the case of Google and Amazon, they have models, but their monetization is really the cloud services. So it's GCP and AWS and Azure which are the monetization models. So I feel that pure model building is going to, in the limiting case, just become equivalent to the cost of the hardware which is going to run on it, because there are so many competing models which are going to run and, to your point, if you can actually make a fairly sophisticated model at a lower price point, which really means that more open sources is going to come as well, because now open now think about a university researcher. One of their big complaints have been that hey look, we actually don't have the money to do this kind of like big bang research and create new models. That open ai does now. Something like deep seek, just definitely opens the door, although you know, curiously, deep seek actually used open as model to train its models, but nevertheless, a lot of innovation which has happened in the process of developing deep seek, which is obviously going to be taken forward in many of these places, which means that you're going to see a lot more open source models. So point I was getting to is that what DeepSeek itself means is that we will have more open source models coming up, which are going to be very good they will never be as good as the latest open AI model, but they'll probably just a shade behind that and which means that it gets commoditized in terms of how much you can charge, and the big boys like Google and Amazon are also going to keep their cost point low in order to get market share on their side.

Speaker 3:

So, yes, building models and trying to sell them is not going to be the business model, but those who build models will probably have other business models.

Speaker 3:

A business model, but those who build models will probably have other business models, and that means that they have to either build their own vertical application stack on top of the models they have, or they will have to have a developer ecosystem which leverages their models and they can charge on a per API basis, or what have you charge on a per API basis, or what have you In the terms of applications that we are likely to see, you know, as venture?

Speaker 3:

In most cases, I would think that startups would be developing vertical apps and have an abstraction layer which enables them to, you know, choose the right models for right things, and it's not going to be unusual for the architecture to have several models for specific workflows. They might use one model for something, but if it's marrying vision, which might be a diffusion model along with some LLM for text and data analysis and so forth. So you might actually be combining that and I feel that open source will end up playing a big part, but I don't think that just model as a product will probably be there. It's already not there.

Speaker 1:

Yeah, makes sense. Now the point is, I mean, with open source models coming in, with cost of launching new models getting down and what you alluded to earlier, the token pricing has come down right, so everybody has access to the same sort of models and infra to build applications. Yeah, so in theory, everybody can build the same quality of applications, uh, whether you're sitting in bangalore or bhopal or somewhere in europe or us. Uh, right, uh, so in this world, then, how do you differentiate your product? Right, and you'll see more and more competition, because instead of like a 20 developer team, you can do this with a two developer team, so you'll have more and more products coming out trying to solve the same set of problems. So it's going to be hyper competitive. At least you are seeing that. For example, the customer support space, ai based customer support we see like hundreds, like thousands of companies in that. How do you build differentiation in such a crowded market? Like thousands of companies in that, right, how do you build differentiation?

Speaker 3:

uh, in such a crowded market. I think that, uh, this will require people. In one sense, I feel that the importance or, like the nuances required to build a successful enterprise software are often underappreciated, because I feel that if you, we are like so plugged into what the latest news coming out of it and what's going on in the Valley, and so forth, if you look at a regular enterprise company which might be in oil and gas or even in financial services or whatever it is, or in a regulated industry like healthcare, they are not like hopping from one AI news to another, to another, to another right their care abouts are going to look something like this, which is OK. What is the security of this thing? Are you going to take my data and do something and put me in violation of compliance and regulation? That's a big problem. No, that's an existential risk to the business. What is the security? Where is this data actually going? How are you actually crunching it, and so on and so forth. Third would be okay how much change does it require to my existing workflows? Because I have all these people who are doing real work. So, if you're an insurance company, you're looking at claims and so forth. Yes, you want AI to make that process better and more accurate and reduce the overhead and errors. Better and more accurate and reduce the overhead and errors, but you don't want to actually change that workflow itself right Now. If you have to actually go ahead and retrain your entire workforce, it's not going to work right.

Speaker 3:

So to be successful in enterprise, one has to realize that AI is a tool. It's a very powerful tool, but it needs to address a very specific and nuanced business problem for the enterprise, which means you need to integrate with their software and their workflows. It does not mean that you can go and dump a chat box as your interface. I mean that might be great for consumers because now you're able to use natural language to do it instead of being constrained by that one single search box of Google. Right, but in enterprise, when you have dropdown and logins and this and that and filters, those might actually be quite fast. Imagine trying to do that with a prompt and then having three other prompts after. It gives you the incomplete answer. Instead, I'm fine actually doing some dropdowns and clicking a few things.

Speaker 3:

So we have to understand in this particular case, the solution providers startups have to understand what is the problem they are trying to solve and what makes it easiest for the enterprise to adapt this solution, all right. So I feel that all of those aspects will be very important in order, and that will differentiate the solutions. So AI is part of it, but AI is not going to be all of it. The overall will be this overall solution, wherein all of these, the user interface matters, the integration with the various software matters, the right workflow understanding matters, and so in all of these cases, it is the same old things which will be required to build a business, which is you need to have deep understanding of the customer behavior. How are they using software, what is the problem they have and how can we demonstrate undeniable ROI to them in the shortest period of time? So I feel that just taking AI and like plonking it on top of the customer's heads is not going to work and that's certainly not going to be a successful enterprise startup.

Speaker 1:

I totally agree. I mean, the fundamentals of building a business does not change with AI, which is who is the customer, what are the problem and how am I going to solve that in a 10x better way with AI? So that still remains right. Of course, we'll use AI to do it in a much better, faster way. The other thing that I totally agree is that in all of these enterprises they'll use a bunch of tools, right. So AI will solve part of the problem, but then you'll have to build the integrations and the workflow that give you like an initial differentiation or a mode, and, with time, as you get access to proprietary data, maybe that is what will help you build the long-term mode for a specific industry or for a specific vertical.

Speaker 3:

It's a great point. I think that models are only as good as the data they are trained on, right, they cannot come up with information which is outside that universe of information that they're trained on. They cannot come up with information which is outside that universe of information that they're trained on. And the more relevant that information it is, and less extraneous information that is that it is trained on, the faster and cheaper and more accurate that answer is going to be, and I feel that a lot of the data, or enterprise data, is actually not in the public domain. Obviously, the information about my customers and what they are buying and their behavior and where they're logging in from and their demographic etc. Is particular to me, the enterprise. The more the software solution actually relies on non-public information to actually go and deliver that value to the customers actually, the more differentiated and sticky it actually gets.

Speaker 1:

Do you think in that case, the incumbents will have advantage over new players? Let me just think of Salesforce. Right, there are tons of data about companies, customers, sitting in the CRM. Now, if I have to build a sales enablement tool using AI or AI sales enablement platform, Salesforce has access to the proprietary data for the respective customer or the vertical. You think it'd be easier for them to build against, say, a new company trying to do that?

Speaker 3:

So I feel that, at least initially I felt is obviously yes, but now I feel that maybe not.

Speaker 3:

And this is the reason, which is that they clearly have a distribution of advantage which is compelling. So the enterprise customer has the Salesforce sitting there and they'll say like, why do I actually want to take it out and put something else in its place? And besides, I have all this data which Salesforce has. So obviously those models are actually going to be better. So I feel there are a couple of things. One is that there is this uh advent of synthetic data, so models can take some set of real data and then simulate a whole lot of synthetic data which just looks like that data. It's similar in in many ways, which can make the model itself. So one model can create the synthetic data and make your main model actually a lot smarter and in that sense, from a model performance perspective, it can be quite close to what something which is trained on all volumes of data is. So that's one point to be understood. So I feel that the data advantage is probably not there. So then it comes to the product side of things, and I just feel that this is the innovator's dilemma thing, right, which is a hungry young startup, will just be much faster in their iterations than someone like Salesforce usually thinking, hey look, if I actually go and do this, I have to think about it. You know, how is it going to impact my current stuff. I cannot go ahead and offer something at a dramatically lower price. It's going to cannibalize all my existing sales. The sales guys will yell at me, my stock price will drop because it's going to impact my revenues, and so forth. A new startup is like, completely unfettered by any of these things. They'll just go and address that particular thing. So that's one thing, one aspect.

Speaker 3:

The second thing I also think about is that you know the quality of talent which is going to be available to startups versus what is available to Salesforce. So, in one sense, this is all new, right? Ai, you know, if you look at chat, gpt, gpt-3 as the start of this era, right, so that is what November 22, or something, right, so November 22. So, essentially, anybody who is actually, you know, using AI or so so forth, has a veteran, has a grand total of two years of experience, right, two and a half years of experience. And, of course, there are people who are working on ai since 2017 18 and know about they're probably the original researchers in open air and so forth, but from an application standpoint, right. So it's not like the pool of talent that sales has is actually that much larger and, if you think about it, the most ambitious and cutting edge talent would probably want to do the startup. So I think that they'll have a talent advantage. So there is no data disadvantage, there is a talent advantage and there's a product advantage, but they have a distribution massive disadvantage. So I feel that this is going to be an interesting thing out there.

Speaker 3:

But if I were a startup, I would not be looking at something which goes directly at the heart of something like salesforce. Right, because a whole host is a system of record, the whole host of processes you'd like. You should probably try and do things which cannot be done by sales and the Salesforce does not have an existing product and you might want to at that point, talking to our integration, things actually integrate with Salesforce. Salesforce has APIs, it has got a marketplace of applications and so forth, so you actually leverage that to start providing services and I think that will be an attractive enough market and then go from there. So I feel that it will be competitive with Salesforce. It doesn't mean the demise of Salesforce, by any stretch of imagination. They're also a fairly fast-moving company. But I just feel it opens up a lot of opportunities for startups.

Speaker 1:

Yeah, makes sense. And just for audience, I mean Salesforce we have used here just as a placeholder incumbent, it could be, Microsoft, it could be Oracle, it could be SAP.

Speaker 1:

Yeah, exactly, got it. Okay, now that brings to the next question, which is around pricing. Right, and I mean pricing models are evolving. Every day we meet a startup, they come with a new pricing model. But the core of the question here is that with AI, the promise is that you can do a lot more with fewer resources, right, so I'm actually helping you reduce the headcount in some ways. In most of the use cases, the traditional software has been priced on a per user, per seat basis, at least most of it. Now, with AI coming, you'll say that, okay, earlier you were having like 20 sales reps or like 100 customer support reps. Now, with AI coming in, you can bring down a customer support rep by like 80. So how do you price it right? Will you be like undercutting the existing offerings? Are you like analyzing the pricing here? Like, how should one think about pricing here? Is the price pool, is the? Is the profit pool coming down with ai coming in, or the market size coming down? How should one think about it?

Speaker 3:

so I feel that the foundation of a good pricing is that it should be simple. It should be fair and transparent to the, to the customer right and of course that should be should make sense from an roi perspective. So these things will continue to remain the same. It has been like that before until it continues to be here now. So while there might be a temptation to come up with a newfangled pricing model, it shouldn't be so complicated that the customer is confused about what's going on. It needs to be fairly simple for them to understand. Like, okay, if I do this, this is how much I pay. If I use this much, this is how much I pay, and so on and so forth. And that's one part. If I use this much, this is how much I pay, and so on and so forth. And that's one part.

Speaker 3:

The second part I would say that enterprises are used to thinking in a certain way. So if I were providing an enterprise software and thinking about a pricing framework, I will try to go and see where the customer is today and try to move from there. Customer is today and try to move from there. So if they are actually used to a per seat pricing, then there is no issue with providing a per seat pricing yourself. Right, because now it is a less cognitive load on the customer to decide how to do this. Because one of the things which customers would hate is not knowing how much they'll end up getting charged. Right at the end of the they sign up for something, at the end of the quarter you come up with a different set of bill. It's a problem for them in a big way, right, so it needs to be predictable. So if you are changing the pricing model and you want to make it an outcome-based pricing, it has to be very clear and obvious to the customer about what their comparable alternative is today and how it is a better thing from there. So in some cases it does work.

Speaker 3:

So, for instance, if we say in customer support you're talking about earlier, instead of having a per person, I would actually do a per ticket, okay, that broadly makes sense, right? And they will say, hey look, yeah, this I used to spend so much, I have this much tickets. I used to spend so much, I have this much tickets, I used to spend so much. Now I can actually go ahead and give it to you per ticket and kind of make it seems fair, because, instead of doing it on a per person basis, you're using AI, and that's probably right, but I wouldn't try to shoehorn it in all cases. Right, you have to kind of like see what the customer is. My framework here is and try to adapt to that, but ultimately, it should be very easy for the customer to calculate a return on investment for themselves. That's what I would say is the simplest thing out there.

Speaker 3:

Now there's this question about an implicit in your question was this piece about okay, if I'm replacing people and there's a lot of chatter about okay, now my ECUs are going to be significantly higher because I'm paying somebody. Let's pick a number, $50,000 and you are able to reduce headcount by one person. You should, in theory, be able to charge one dollar less than fifty thousand dollars, right, but the fallacy there is that, um, the competitive environment will make you not not do that, right, right, it is. So I think that that's one.

Speaker 3:

The second thing, of course, that the incremental value which you're saving to the customer should also will need to. There'll be certain expectation of what percentage of that value addition you're able to charge. Right, it'll be maybe 10, 20 percent. So I think that there's a lot of price discovery which is going to happen and that's why the rest of the pieces which make the total solution are important. So the more the total solution is, you know, looking end-to-end for the customer, then they're not just looking for like one piece and just thinking, comparing you with the other software, but they're able to look at the total value at which you have provided, which might make it fairly more differentiated than the other competitors. The more differentiated it is, the more you can actually justify having that piece, and that piece remains the same.

Speaker 1:

The other interesting thing which we see could play out is so far the software budget. I mean the software expense used to come from the IT budget. We do hope that with AI replacing a lot of humans, some of that human capital budget will also go into this. Of course not like for like, but at least some part of that will go here. So if I'm able to replace the headcount by, say, 20%, I may be able to charge, like, say, 5% of that reduced headcount as my pricing.

Speaker 3:

That is one part of it, gaurav. I would say that that's the cost part of it, but in a lot of cases, AI is going to drive incremental revenues. Yes, right, and I feel that the products in general which drive more revenues will have access to a larger time, larger market. Right, and you also have a better pricing, more pricing flexibility there, and I feel that there'll be a lot of opportunity for that.

Speaker 1:

Yeah, makes sense. Finally, what are you excited about in this new world of B2B SaaS being supercharged by AI?

Speaker 3:

So I feel that it's a very exciting time for software. I think this is one of the most seminal inventions wherein you can make. What we are saying is that, hey look, intelligence is now available to everybody, and so forth. But my feeling is that is not available to everybody and so forth. But my feeling is that you're seeing intelligence everywhere available to everybody is probably a little bit incorrect, in the sense that what ai models are is that they take a huge amount of information, they compress it and then they give you an output.

Speaker 3:

So, if you think about it, if you think you know, even if you think about, like, if you ask it, some piece of information, right, okay, so what are the? You know? Five places I should see in India, whatever it is right. Or five places to visit in Paris right, it might give you five things, but it is not like those are the five definitive things. It's really a mishmash of what is there in, of what is there in Wikipedia, blogs, twitter, youtube, all of the social media put together, and some probabilistic answer of that. Right, if you just go a level deeper, that is what the model is saying and, unfortunately, the model is saying it in a very definitive way. So you feel like, okay, this is gospel, right, but if you, as technical folks, would know that's what's really going on underneath, gospel right. But if you, you know, as technical folks, would know, that's what's really going on underneath it, right. It's a probabilistic machine which is generating these answers and the probability depends on the data which has been ingested. And the data which has been ingested is the net, yes, and it is not like and, of course, the folks at OpenAI or wherever would be clever enough to say, hey look, I should overweight wikipedia because it's a more validated information, also versus tiktok or or twitter. But ultimately, that's what's happening. So it's not saying that, hey look, you know, everybody will become intelligent, isn't in terms of, like, their deep and understanding of what's going on is not entirely true, but at any rate, I think that aside, it does make a lot of. It's a very significant inflection point.

Speaker 3:

To answer your question, areas which we at Prime are excited about I'm excited about are vertical AI, wherein we are looking at certain domains. So you're looking at manufacturing, you might be looking at retail, you might be looking at accounting, you might be looking at security, these specific domains in which you're actually going and creating an application, because it increases the surface area of adding more value to the enterprise by doing more integration, by looking and understanding deeply the workflows, by looking at what is the domain-specific UI which needs to be there or the domain-specific considerations like security and compliance. You talk about manufacturing things have to be on-prem there, for instance, and things like that. So vertical AI is one big thing. The second area which we talked about, which is as far as the horizontal pieces, are like more services software, which we talked about, wherein you can think about delivering services in an extremely cost-effective and very differentiated price point and with a very high quality. And a third area which I'm really hoping that we'll start seeing some more startups out of India we're definitely seeing that in other places is physical AI, and what I mean by that is automation.

Speaker 3:

I'm not talking about autonomous systems. I probably should differentiate between the two, because autonomous systems are you know, there's a robot which is going around in the thing serving chai and then doing this and cleaning the windows at the same time as vacuuming the floor and greeting the visitor. Okay, that's like autonomous. You know completely autonomous stuff, right, and there's a lot of things which make it both mechanically complicated and the software of it fairly complicated in terms of just the wide range of things they need to do.

Speaker 3:

But automation, I think, is a different thing and I feel that we will see a lot of innovation automation. So automation is very specific, narrow area in which you are actually functioning, and the example you gave earlier about manufacturing right that is being automated. So the robot that is just taking something and putting it there, right, but for instance, just to use that, the vision systems and the advancement in vision, which means that we can actually dramatically reduce the cost of automation, because what it takes to train a robot like that has come down. How flexible that robot can be in terms of the range of activities it can do has come down, which means that you can provide automation in industries which previously wouldn't have been possible at all because the price points were higher. In one sense, you're democratizing automation.

Speaker 3:

So that is happening and the reason that is possible is that the models the SLMs, not the LLMs, not the large language models, but the small language models are now getting more and more powerful. Actually, just as DeepSeq is a smaller version of the larger models, but the small language models are now getting more and more powerful. Actually, just as DeepSeq is a smaller version of the larger models, it's yet very powerful. Think of even smaller models which will have very domain-specific things which we are very, very good at and they are very fast at, and they are fairly cheap and can be embedded inside a robot in a small form factor. So that just opens up opportunities for automation. So I'm very excited about that and I feel that we will see, you know, just new greenfield opportunities there. So three things which I'm looking at, but I'm sure that more will probably come up.

Speaker 1:

Yeah, I'm sure somebody is building an AI VC while we're discussing software. Ai will eat software. Somebody might be building a AI VC will eat traditional VCs yeah, you're absolutely right.

Speaker 3:

Until then, we'll keep our day job.

Speaker 1:

Okay, Thank you, Duthi. Thank you everyone. I hope you'll enjoy the discussion that we had. Do share your comments and feedback.

Speaker 3:

Thanks, gaurav, pleasure talking to you.

Speaker 2:

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