The Neon Show
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.
01:02:03 · Bhaskar Ghosh
I think data lakes will see more and more semi and unstructured data, because you can squeeze more enterprise value out of them, which has not been true. So infrastructure around semi and unstructured data lakes, one of our companies, Aaron is working in that domain, I think that's going to be a massive area. The ingestion tier for that data is going to be a massive area. Because now you're not just bringing in site data and sales data, you're bringing in all kinds of other data. Handwritten document data, PDFs, Google Docs, you can do a lot more with them. So all things data observability, around data infrastructure, around storage in the area below LLMs is going to be important. So I can give another example, our company Acryl, which is based on the LinkedIn open source project called Data Hub, has now rebranded itself as Data Hub, is seeing secular tailwinds in AI metadata coming into the So metadata I feel there is a lot of opportunity in all things data, all things extraction. Now coming to LLMs per se, and something you asked me in the prep questions, is observability monitoring feedback loops, compliance, governance around deploying agents and co pilots in the enterprise is going to be a very important area. So there are at least two parts to it. One is companies which are doing orchestration of these tiers itself. In old school, now old school companies like LangChain, LangFuse, LangGraph, next gen companies of that sort which helps you orchestrate agents. Companies like interchange gateways which are called MCP. I think of it as an interoperability play. How will MCP scale, grow, be compliant, be fault tolerant? So these are all backbones of agentic deployments inside the enterprise. And all things monitoring, governance, feedback loop and observability. I feel these are all going to be significant time areas within the enterprise. And the second category that I just pointed out is partly forward facing. And I would say the extraction layer, the observability layer that I pointed out for side part are partly backward facing but will get revolutionized. So very exciting times. Two comments. One is a repeat comment which is the type of founding teams you need for each of these areas are special. Should be a combination of AI infra, of applied AI, of product specific expertise, as well as databases and systems. That's kind of exciting.
01:04:51 · Siddhartha Ahluwalia
[01:04:51] S0: But how do these founders without a GTM founder or a business founder land their first enterprise contract?
01:05:00 · Bhaskar Ghosh
[01:05:00] S1: That brings me to the second point. The type of founders you need, and that's something I have learnt in my life as a VC for now seven and a half years and at 8VC is, I think go to market will remain a very important area for these founding teams. What I have done personally is invested in a lot of open source companies. I still believe in open source in some of these areas, because many of these areas are developer focused. The actual adopters of the new software will be developers themselves. So I think an open source, partially open source mentality and knowing how to build community. I mean, you look at a company like Rocketlane or Atomicwork, something they have done without being open source is that they have built community around their software. Right? And I think community building around open source is a very critical tech... Is a critical power, superpower you need in the founding team. And then mentality of being community first. That's one part. But I think increasingly, given the speed of adoption of the software in enterprises, top down motions are all... Also going to be really important. So knowing much... Thinking much more about, say, conversion from open source to paid, but even clean top down motions, had... There has to be much more expertise early on in founding teams. And one interesting comment there. Remember that the act of doing sales and marketing itself is changing because of the advent of Gen AI. So founding teams in these areas also have to be forward facing and adopting sales software, marketing software, which is Gen AI infused, buying from other start ups. I think combination of open source growth hacking and combination of using new tools is going to be kind of something that the DNA of the founding team has to bring to the table. Those are some some things to think about.
01:06:56 · Siddhartha Ahluwalia, Bhaskar Ghosh
[01:06:56] S0: And the last thing, think this is another topic for another podcast [01:06:59] S1: A whole podcast. Because [01:07:02] S0: we can't give justice to it in such a short span of time and how the rate which market is evolving, right? What is the how the founder type changing on the founder required on the GTM side is of keen interest to me. But I'll leave it open ended for here. [01:07:22] S1: Meaning, the founders, the way they think about go to market itself. Yeah, [01:07:28] S0: founders, they think about go to market itself, right? How do they land their first 10 enterprise contract, right? Is the old way of SaaS bank still applicable? So should you get a co founder who has sold SaaS? Or should you like possibly try to find a different kind of a founder? I don't have an answer because the market is changing.
01:07:28 · Siddhartha Ahluwalia, Bhaskar Ghosh
[01:07:28] S0: founders, they think about go to market itself, right? How do they land their first 10 enterprise contract, right? Is the old way of SaaS bank still applicable? So should you get a co founder who has sold SaaS? Or should you like possibly try to find a different kind of a founder? I don't have an answer because the market is changing. [01:07:46] S1: Look, Siddhartha, the only thing I'll say is, even now, product building is hard. If you build software products, let's not ignore that. Building software products is really hard. It is just so that software engineering is so profoundly affected by Gen AI, you can do that iteration fast. But the taste of a product, the design aspect of a product, increasingly, AI centric design, where you are building an AI infused product, how do you land it? These are non trivial things, which I don't think can be automated away. So I think that part is hard and it requires founders to have superpowers. The second thing is very mundane. I still think the first five design partnerships are really important. I think design partnerships, when you land the right design partnership, maybe the speed of it will be much faster because people want AI infused software. I still think those mundane things will remain important, but a topic for another day. [01:08:40] S0: Yeah. Maybe this this requires another podcast as well on how to find the right design partner in in AI first era. [01:08:47] S1: Profound question. [01:08:50] S0: So last thing is, what are you doing beyond work in the India US corridor? What are your passions?