The Neon Show
00:51:15 · Bhaskar Ghosh
[00:51:15] S1: It has to do with a lot of other extraneous factors, it has to do with macro economic forces, It has to do with that industry growing or not. I'll give you an example. There are two pieces of software that people always complain about. From the IT side and the IT ops side and the data ops side. Site ops side, SRE side. But they still pay. But they will absolutely switch if something cheaper comes. One is called Splunk, one is called Datadog. On the site observability side. People actually don't want to pay much. They they have been able to pay for it because their companies have been doing well. So I think how much capital you put into of budget, you put into IT ops is is the question. Yeah. Is your top line growing or not? So I think it it has to do with that. If you are in BFSI, you're in health care services, and your top line is growing, yes, you'll be willing to pay more. If your growth stabilizes, does not grow, these questions will come. That's one. The other part is the other part is quality. There is a quality aspect to AI. If AI, in line AI, shows much higher quality for the same or slightly more... So that's why I said numerator and denominator. Making people more accurate, more productive. There is a chance that it is not just about volume or budget, it's also about the quality of the outcome. People are willing to switch and maybe pay a little bit less, but that will be much more sticky. Coming back to it, I think your question relates much more to macroeconomic changes about the industry buying. Yeah. And if you are going into defense, you're going into government controlled areas or regulated industry like insurance, health care, banking. Government, you know, government policies have a lot to do with what sort of growth is happening there. Those are going to be effects that are going to... Those are going to be levers that are going to profoundly impact how you sell, what you sell, whether you're selling SaaS or you're selling outcome based services.
00:53:29 · Siddhartha Ahluwalia, Bhaskar Ghosh
[00:53:29] S0: And, you know, outcome based pricing... [00:53:33] S1: I say one thing about your Whatfix example? So if I remember correctly, what was plagued in the CRM space, which has traditionally been a very important space because it's a top line play. Sales is an investment in the top line of that company. And sales ops leaders are willing to spend an arm and a leg to hit their numbers and go top line. The moment you go into HR, the moment you go into marketing, which are probably cost centers more, the answer is not true. [00:54:06] S0: It leads to a very interesting point, if the industry has to change for pricing, outcome based pricing, what kind of tailwinds are already there and need to be there in the future to support a complete change?
00:54:19 · Bhaskar Ghosh
[00:54:19] S1: Migration? You should be able to migrate your software or your service very easily. Number one. That is not true yet. So when an outcome based AI services provider comes in, they can't just say I'm the best. You have to show how you're to migrate that. Number one. So that is a requirement. Second is, you have to have very measurable proof points around quality. It has to perform things at least at that quality that has to happen. Third is, and this is something that we have not thought about very deeply is, the buyers have to have the right tools to judge things. Which today, on the software side they do have. As I pointed out, remember you were asking me the network question about services? On the services side, there are no automated tools where you can decide a certain BPO or software services outcome is as good. So I think those are some of the those are the requirements. I wouldn't call them headwinds. Tailwind is proof point is happening much faster today. I mean, the innovation happening with Gen AI from the services side of how... We were just talking to a young company which is doing insurance workflow automation for business insurance of certain subset. And we saw the workflow that the workflows are automating. This is not in the guide where... This is not in the source of truth area. These are these are not in the these are in the workflows for say parsing documents and giving answers and stuff. Let's assume that. And we have seen that in our company, Azure also. I mean the speed at which they... This company has done correct workflows so quickly is insane. So I'm saying, when they are going and selling to fairly slow moving insurance industries, and the insurance industries are suddenly waking up and saying, Woah, I can do it this faster. The adoption curve is changing. So that's something for you to consider. That the buyer side is beginning to ask questions because there is secular pressure coming from their board, their investors, their CEO. They are also becoming more aware. So I think we are in a slight bit of a honeymoon phase, but from what little I saw from some of these seed level companies, the quality of the outcomes is insane. It is quite insane, right? That's one.
00:56:42 · Bhaskar Ghosh, Siddhartha Ahluwalia
The other thing that is a slight bit of a detail, but is interesting for you to note is, many of these new AI enabled businesses that you are building, whether it is pure software or services, has a Palantir esque flavor to it. That in delivering this service and in delivering this software, you will need this, you know, the way Palantir built it, what they call Forward Deployed Engineering. We are finding that that is a whole new tier of talent that these new services companies and these new software companies have, which is also enabling adoption much more rapidly. I'll just leave it at that. There's some fundamental new talent movements happening which is making adoption faster. [00:57:27] S0: So Bhaskar, we are coming towards conclusion of a podcast, you know, amazing discussion, lot of food for thought. I might need to listen to this conversation twice to prepare for a third one. Right? But very important. [00:57:41] S1: We should pick a vertical. Hopefully by November, I'll be thinking of some new stuff and we can add to this thing because as I said in AI enabled services, the domains matter. It's okay to construct horizontal thesis, but domains matter. But yes, back to you. [00:57:59] S0: And I would love for you to meet some of our founders who are building hardcore, they're building compliance, [00:58:09] S1: Would love to. [00:58:10] S0: For banking, for example. Enterprise infrastructure domain, right, that's your specialty. [00:58:17] S1: That's how it used to be, true.
00:58:17 · Bhaskar Ghosh, Siddhartha Ahluwalia
[00:58:17] S1: That's how it used to be, true. [00:58:20] S0: Right, so how is that shaping up right now? What are the areas left for founders still to conquer where you think are tailwinds because lot of this model company that we were discussing. I think the biggest challenge for founders today are model companies because they are eating forward revenue, are eating backward revenue. Forward revenue means they're eating application revenue and they are eating backward revenue which is cloud revenue and because these model companies are extremely hungry, I have never seen hungry companies as these right, their aim is to become $10,000,000,000,000 companies, they're not they'll not be satisfied at the pace of growth to become a trillion dollar company, at least my observation is that people might disagree with it, I don't know what your views are. [00:59:05] S1: No, I agree with you. [00:59:06] S0: Yeah, so enterprise infra, what are the areas for founders to tap in, what are you learning from your existing company, you are invested in some of the fantastic companies like Nile.
00:59:16 · Bhaskar Ghosh
[00:59:16] S1: Yep, Nile, Yugabyte, Acryl, which is now called Data Hub. So I would say in enterprise infrastructure, which is a vast stack, includes data, includes security, includes orchestration, includes site ops, SI, many departments, many personas. One particular sub area, which is a legacy area that I am super excited about to see how LLMs will impact them is in all things observability. So the area say that today, Datadog, Splunk, Elasticsearch, all of them play in, the old New Relic and all of them. Massive budget, software that you pay for to operate your business and to know if your site is working okay. Right? And then debug problems. I feel... And the underlying infrastructure is using Mongo in some cases. It's using Clickhouse in some cases. But a lot of it is based on the back end of Datadog. It's based on Elasticsearch on the Elastic Stack. I think this area requires a couple of new companies with LLMs as their backbone. Throwing a challenge out. The interesting thing about this area is, what kind of founders do you want to find there? I think this area is both hard with massive upside, is because the DNA of founders needs to... There has to be a co founder who knows applied AI really well. There has to know... Be a co founder who knows the observability and the whole thing about what we call MET, the nature of data. It's like metrics, events, and logs, and traces, and how Datadog and Splunk are used today. So you need a product expert, a domain expert in product. Third is, you need a founder with systems and data background. Traditionally, it's hard to bring founders like that together. But I feel if you find a founding team like that, in these areas, where now with say if you see open source time series foundation models like TimesFM coming out of Google, I think there is a massive opportunity. So in the observability area, off the site observability. Similarly for security observability, I think there is a massive area at SIEM. I would say these are backward facing categories, which are massive TAM, which will be impacted by LLMs and by using Chen AI. Suddenly, am interested in the forward facing categories that are opening up. I think LLMs will be data hungry. I think unstructured data now coming into blob stores, into the unstructured data warehouses, into what is below a data bricks, into what we call data lakes.