Blitzy's Brian Elliott: Cursor And Claude Code Are Looking At Your Enterprise Code ‘Through A Straw’

14:42Brian Elliott:Okay, so you do your tour, you achieve your goals in the Army. Was it a natural next step that you're going to go to Harvard Business School and try to win in the technology or the business world? Or how do you go from one to the other?

14:57Sid Pardeshi:it's actually quite common to um especially to go from soft into to an mba program right um you don't really know anything right um but you have like all these amazing soft skills i had a lot of technical training i'd say it incredibly technically fluent like that was my weekends right like i thought about going and getting like an ms engineering or doing a duel but i was like i'm just gonna create as much space and time for myself to go after what is next and the great thing about harvard uh is like the education's like it's fine it is what it is the same case studies that are at every other school, right?

15:26Sid Pardeshi:So it's like, it's not necessarily the material, but it's really the people. And so I met Sid there, right? I met Sid in the engineering building. He was getting a dual MS MBA. And we got assigned to work on a project together, right? Like, so it's almost fate that we met at Harvard at the same time, very passionate about, you know, all the same topics. And like, today, we're like, we're not just co-founders, like, he's my best friend. Like, we, I love him. You know what I mean? So it's like, to have that serendipity of meeting somebody at that space. I know you went to Harvard. It's a very special place.

15:56Sid Pardeshi:A lot of people like rag on it all the time. It's like easy to pick on Harvard, but like for me, it gave me like, it gave me everything.

16:03Brian Elliott:Well, I do think this will probably be the most we ever talk about Harvard on this show. But we are taping it a couple miles away from the campus. So it's only fitting. There was a joke early in my career when I've been covering startups now for a long time. And I heard this line about HBS, which was that every student was one technical co-founder away from a great startup. Now, you and Sid actually were technical. Was that something that made you guys immediately bond or gave you an edge to kind of actually get into the startup world in a meaningful way straight out of school? Or what did that kind of do for the two of you?

16:43Sid Pardeshi:I built the first MVP of Blitzy, which is very, very different from what we have. But this is, back when we were in the ideation and the iteration stage. And so what attracted us as colleagues first was I think a bias for action and a bias to create as opposed to a bias to talk or a bias to strategize. And so like the ultimate downfall of like everybody that goes to B school is like they want to think through everything ahead of time when actually action begets information, right? And so we are both have an extreme bias for action, an extreme agency, and we both have the ability to build and create.

17:15Sid Pardeshi:Yeah, I saw this video of me sending him like a demo video of everything that I created as like one of the first little things that we worked on. And I think that's when he said, OK, like this is this could really be something.

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18:22Brian Elliott:You guys, or at least you worked on a couple other startup ideas. What was the aha moment where it was clear that Blitzy was the idea to go all in on and spend, you know, a lot of your career working on?

18:34Sid Pardeshi:Yeah, I think whenever you have a view on the world that everybody tells you is incorrect and despite that, you just know it's true, that is worth really going all in on. If you rewind the clock to between GP33 and 3.5 coming out, anything not a model, right, was getting called a wrapper. This was very hot in the VC landscape. It was like, oh, like the model, they said the model will do it all. It'll get ever increasing in size. The reason they had this view is a lot of people were building periphery functionality around the model that would get squashed when the next release came out. Right. But that view on the world is fundamentally flawed because LLMs are sequence to sequence architectures.

19:19Sid Pardeshi:Right. And so therefore, like the model cannot do it all by definition. Right. Like it will struggle at large scale and high complexity. It will increase capabilities, but to tackle a problem as complex as a large code base, the kind of code bases that exist in the DoD, the kind of code bases that exist at NVIDIA, the model was never going to do it alone. And so we sort of knew this to be true. And so we would go everywhere and be like, the model is not going to do it all. Inference time compute is the most important scaling law for code quality. This is before reasoning models, like way before reasoning models were a thing.

19:52Sid Pardeshi:And if that is true, the limit case is going to be understanding the underlying code base. So those are the three things that we believe to be true. And everybody told us it was the stupidest thing they'd ever heard.

20:02Brian Elliott:Why did they say it was stupid? Just because they thought OpenAI was going to do it eventually?

20:06Sid Pardeshi:They just thought that the model would solve the problem. It was like, oh, if GPT 3.5 can't do it, then GPT 6 will do it. It's not true. It's a physics problem. But these models are so amazing. It creates this illusion that it will be able to do everything. because you can experience GPT-3, you can experience GPT-3.5, right? And then you extrapolate what that's going to mean for size and scale in a way that's very logical, but it's not rooted in the reality of what a transformer is, right? And so this is the dissonance that, I believe this to be true. Like I believe this to be true. If this is true, like what does that mean to build a system that can automate software development?

20:46Sid Pardeshi:One, it's gonna be what people now call extremely long horizon, and it's gonna be rooted in reality, right? And so today, Blitzy will run continuously without a human in the loop for weeks on end, recursively planning, writing, testing, and validating code at hyperscale. This is an approach or a concept that people thought was an impossibility back when we were starting the company. But this is always what we were going towards. And now that we reduce the technical risk by building and inventing a knowledge graph and orchestration system that worked over the last two years, now we're just scaling as quickly as we can.

21:20Brian Elliott:Why were you confident that you guys would be the ones who could crack this, especially in Boston, not coming out of Stanford or sort of, you know, straight out of Google? One of these labs, we've seen so many folks come out of the same couple companies, raise huge amounts of money for AI startups.

21:38Sid Pardeshi:Yeah, I mean, I think one, I had faith in sitting myself as people, right? to if this was something that everybody at Stanford and everybody at Google, et cetera, like we're talking about and was obviously going to be a thing. We probably like, I don't think we would have pursued it, right? If it was like, if it was the obvious thing that everyone was pursuing, would have gone it. But like the reactions we got on what we fundamentally believe to be true were so visceral, almost mocking. There's a zeitgeist or a shared kind of like set of beliefs that peripherate through the Valley in a way that is 99 % great, meaning best practices for sales, the fastest approach to implement agents.md, like that proliferates so quickly around the Valley that all of these best practices like get absorbed into these companies, but so do all of the beliefs around what is true around technology.

22:32Sid Pardeshi:And our belief was different to that. When we went out and raised capital, like one, it was very easy because we had an unbelievable traction, but two, like people were like still in shock about what our customers were saying. I would say what I just told you. And then they would go talk to a four to 500 company and they'd be like, holy shit, like, like this is happening. And it's, it's the same people we talked to three years ago. We're like, this is, we're going to do this. Right. And then no one took us seriously because we didn't raise a huge amount of money because we're scaling off of revenue.

23:01Sid Pardeshi:We were 70 people off of 10, off of 10 million bucks raised. Right. Like that doesn't math for most VCs. It's like, well, well, people pay us a lot of money for our product. And then we went out and raised 200 million bucks. We were like, oh, I guess this is real, right? This is a real thing. We did talk to the customers. But it was that dissonance between what I think was status quo in the Valley and like what we believe to be true that like was like, well, this has to exist in the world. This has to be the reality. And like, I'm not willing to wait 10 years for someone else to do this. Like this needs to exist.

23:28Brian Elliott:What was the hardest part to get to that escape velocity? Because as you noted, you guys just raised 200 million, 1.4 billion valuation, one of the newest unicorns in Boston, a lot of energy, a lot of wins in your sales right now. When you think back on, this is the moment where we had our backs to the wall, we call it an upstart moment on the show. What comes to mind first?

23:50Sid Pardeshi:Yeah. I mean, there's like a huge disadvantage to starting out of business school, right? Because you have all of these preconceived notions on like, oh, this is one of another. The smart asses like me. Exactly. Right. And like the challenge with that is like they're not necessarily wrong by experience. Right. Like there's like a hundred potential entrepreneurs that are exploring that are not serious. Right. So you have to sort of vet through that reality. But like Sid is like Sid's an immigrant. Right. And so like if we didn't raise money, like we needed to sponsor a visa. Right. Like so it was like it was like we were like really like we had to do it.

24:26Sid Pardeshi:We had we were up against it. Right. Right. It's super easy for you and I to like take risk. We were born here. You know, Sid's kids were like going to school here. Right. And so we had to raise the money to be able to then sponsor his visa to then have an entity that could go here. Right. And fortunately, we were able to be successful. Like the plan for Blitzy, the ambition for the platform and the product was the same when we started as it is now. You can go all the way back to like our business insider published our seed deck. And we literally just could have changed the traction slide on that and taken that to the growth round.

24:59Sid Pardeshi:But it was so incredulous in 2023 to say you're going to automate software, right? Like absolutely incredulous. Even in 2026, people were like, oh, I don't know, maybe we'll get there. Like clearly, this is the most important use case in AI. And you guys are automated 80 % plus of it. Will we ever get to 100 %? And I was like, well, that's the bet that you're making because that's what I believe to be true. And I believe we'll be able to create a significant amount of value on the way. But like when you're raising money that's tied to someone's like immigration status that like that you care deeply about, like that's like that's hard.

25:29Brian Elliott:To process that, did you guys just move faster to show a pilot to folks or to be able to demonstrate a customer value quicker? Like given that you had that that urgency, how did it influence your process? I had to go get deals.

25:45Sid Pardeshi:right like that that was like my my vehicle to um you could spend all day polishing a pitch deck but like if you have someone that you're serving people will move faster right uh and so i remember uh vividly it was march uh sit and i were on a train down in new york city the 6 a.m train down there and it was like spring break week at at hbs so other people are going on vacation yeah they're going to wherever they go right like sitting i have family so it wasn't that big of a deal like we weren't we weren't going to like the late combo trip yeah whatever it is i did not i did

26:14Brian Elliott:not travel at all during business school.

26:16Sid Pardeshi:I went between my apartment and business school and the iLab. We're on the train at 6 a.m. to go down there to hopefully close a financial services customer, which we ended up closing. This was in March. We had to raise capital, trying to raise capital before basically June when Sid had to be employed. And we saw one of our entrepreneurship professors on the same train going down to New York City, Jeff Buskang. I just thought like, wow, what a sign that like spring break week of HBS, like we're going down to close the deal. I know he's going down there probably to close the deal as well. Just like one of the small moments of like, this is like what it takes.

26:49Brian Elliott:A hustler is on the train. This is what it takes.

26:51Sid Pardeshi:Like it's what it takes. Take a six-time day and train down in New York City to go get the deal done, which we did get the deal done. And then like that was, you know, it was a six-figure deal. It wasn't like crazy, right? But it was a six-figure deal that we were able to go and say, hey, we were able to find this client like with this problem. They need software. We're using our platform to build that software and that helped us raise capital.

27:10Brian Elliott:So you get this six-figure customer. Now that's kind of a small engagement, or that would be like a pilot for you guys.

27:17Sid Pardeshi:We won't even sell it anymore. It's too small.

27:18Brian Elliott:It's$100 ,000. How do you get these companies to trust spending millions of dollars on your tools?

27:23Sid Pardeshi:So I think it is incumbent upon the person with the new technology to prove it as a part of the process, right? And so every time we meet somebody, I tell them like, hey, every single one of our customers today, which will be so happy to get on a call with you, by the way, like, we'll do that as the final step. So I don't just slam them with 50, you know, calls, but I promise you, I will not take a dollar until you talk to someone else who did this. Every single one of them came on the call and they're like, I do not think that this will work. But if it does, it'll completely change the way that I approach technology.

27:50Sid Pardeshi:Right. So as a part of this process, we're going to prove it to you. And then we're going to partner deeply. Right. But let us prove it to you as a part of that process. Right. And so that'll often mean reverse engineering one of their code bases. Right. Or picking something really complex and open source if they want to see it fast without security approvals and say, do you want us to reverse engineer Linux? Right. 20 million lines of code. Nobody else on the planet can do that. We'll do that for you. If that's what's going to give you confidence, right? Do you want to reverse engineer your trading system?

28:16Sid Pardeshi:We're happy to do that, right? Do you want to upgrade something? We're happy to do that. Because when you see this reality and you see the quality of the code adhering to your coding standards done at a size, scale, and quality that you believe was impossible without full human driving the process, then you're going to want to go really big with us. And then the questions completely shift after proving it from, okay, does this work to how how do I absorb this new capability into my operating model as an enterprise? Because for eons, we have been limited by how much a human context can hold in their brain and push changes.

28:52Sid Pardeshi:Right now we say we can actually offload that context load to this system, meaning we can do changes at a size and scale that were previously impossible. Meaning that we can now say, what were we going to do in five years that we can now do in one year?

29:07Brian Elliott:Do the customers usually know the project that Blitzy would help the most with? Or is part of the job for you guys to, A, figure out that a company is big enough to really benefit from this? Like you said, startups maybe not really making sense. And then B, have that old code or that big project that was too gnarly for them to tackle where you guys can really shine. We make it easy.

29:31Sid Pardeshi:Anybody with over a million lines of code, you should probably be using Blitzy, right? Right. And then you need to know what your roadmap is. So like what we're not going to do is like what our job will never be is like know what your customers want. That is a question for business analysts and product managers. But most companies, like most companies that have their act together, like have multi-year backlogs of things that they need to do, both sort of from a modernization perspective and a steady state feature development perspective, which Blitzy does both on these underlying code bases. So we say, great, like, let's take part of that from next quarter on one year code basis.

30:03Sid Pardeshi:right and let's just do it let's just do it uh we'll do a proof of concept or we can do it on a pilot depending on what kind of deployment environment that you need let's prove it and then let's go big from there right and so an organization with over a million lines of code which is like any big company in the world has that uh regardless of industry uh and then the roadmap that is that is defined because if it's defined they're doing it for a reason right like if you have a three-year or five-year roadmap like every single thing that you're doing is tied to some return to the company. So if you're able to take that and compress it down dramatically, five times faster is what we see when we're deployed across the entire enterprise software development lifecycle, then like there's so much value creation there.

30:41Brian Elliott:You guys are kind of charging at least partly by line of code. Now in the more AI sort of West Coast world where token maxing is a big term, and I would love your take on that, there's discussion that AI generated code can generate too much code, that there's, you know, 10 lines of code from maybe two that a craftsperson might have made that would do the same thing. How do you guys stay efficient for your customers with the code that you guys are putting out from your agents and from sort of the project output? What's going on in some of

31:14Sid Pardeshi:these enterprises is regardless of function, like software development, customer success, etc., there's these leaderboards on like who is spending the most in tokens. It's a silly way to prescribe value. I would say it's kind of like counting the best SDR by number of dials. It could be related. Sometimes it's related, but really you should be counting an SDR by bookings. That is their incremental value to the company. So if an SDR created a script that dialed a billion people, that's not necessarily good. And so that's my general view on token maxing. It's not sufficient, although it is not like an unreasonable way to think about like who is using AI.

31:53Sid Pardeshi:It's really not a sufficient way to think about adoption.

31:55Brian Elliott:But then when your customer is generating more new code from Blitzy, they are paying you more, right? So how do you avoid the incentive of wanting to create and charge for more code on your end?

32:07Sid Pardeshi:So a few things, right? Like one, they can define, every enterprise has the ability to define rules. So you can literally say like, I want to do minimum change principle. That's not necessarily always the right way to do it, right? What Blitzy is going to do is it's going to first match the existing way that you implement code, right? Following your services, following your approach. If you have a different approach that you want to change, and we see like CIOs or CTOs, they want to enforce a new enterprise approach to XYZ, right? You can then enforce that, and Blitzy will write code and adherence to those standards, right?

32:36Sid Pardeshi:And then one of those standards can be a minimum change principle, right? And that's going to minimize things, even like documentation, right? And so what will happen is people will try that. And then they'll be like, actually, I just want to like have you write code how I want code to be written. Okay. And then we'll follow in. Underlying the question that you had is like, well, AI is just writing lots of code. What's happening is AI is in these iterative based tools that have limited view of context. It is rewriting services. It is rewriting functions that already exist somewhere else in the enterprise code base.

33:04Sid Pardeshi:Because all the way back to the beginning, like it's looking at it through a straw. Right. And so it's like, I need to do this thing. this thing already exists somewhere in some shared service inside of the enterprise, but that iterative tool doesn't see it. And so what Blitzy always does is it develops software, like an enterprise software developer builds software. And so if you have an existing service or existing package or existing library that does something, Blitzy is going to use those and leverage those as a part of the solution. Any other approach is adding incremental tech debt inside of the organization, which is what's happening in that incredible clip for folks that are simply just launching iterative-based AI coding inside of their system versus taking a, I'm going to use Blitzy to understand the logical code base.

33:46Sid Pardeshi:I'm going to have Blitzy build everything that it can, right? And then I'm going to bring in Cloud Code. I'm going to bring in Cursor to do the final last mile development with the human developer for anything that our system couldn't do.

33:57Brian Elliott:Part of why I asked was the token maxing. The other reason I asked was because there's now a bit of a discussion happening around the sustainability of spend on AI-related budget items at especially public companies, really big companies. You are enterprise-focused. And at the same time, I was really impressed to see that you guys track ARR basically per dollar you spend, right? I think you're almost at three to one for dollars in versus dollars out. Yeah, which is world class. It's amazing. So how do you make this flywheel sustainable for everybody though, where the customer isn't saying, gosh, we have this 10 million plus contract.

34:34Brian Elliott:We're not sure if we're getting full value out of that. These guys are taking a big margin that we can't justify.

34:41Sid Pardeshi:Blitzy is a CFO's best friend, right? And so when we bring in Blitzy, we are saying, great, let's look at your roadmap. We're going to go ahead and understand your Blitzy usage against that relative roadmap. So you know what you're buying off, right? You're like, literally, I am buying these projects. Like, what is the job to be doing? These projects, way faster, from Q4 brought into Q2. So that's what I'm buying. And there's incremental value, both in labor savings and top line revenue if those things are generating product value. That is against a, you know, the fixed amount. It's going to be roughly X lines of code.

35:12Sid Pardeshi:So there's no variance swing from the CFO like they're experiencing when they just buy random licenses for other AI products. And so what you're doing with the other AI products is like you're throwing Opus 4.7 at everything. You're like max think mode, max think mode, max think mode, right? And you're just putting that on loops, right? What's happening on Blitzy's end, and the reason that we're able to provide what I believe are incredibly, it's deeply value-add, but underneath the hood, we are choosing the right model for the right task dynamically, just in time, every single time. Might be Gemini, it might be Flash, it might be, it might be Opus, it might be Sonnet, right?

35:50Sid Pardeshi:It might be an earlier version of Sonnet, right? And so So for us, this, call it tailwind of realizing we need to be intelligent with our use of AI to solve these outcomes is actually baked into how we build our product in a way that really resonates with the CTO, the CIO, and the CFO.

36:06Brian Elliott:I guess I'm thinking, you know, now that you're a startup unicorn, you're going to be better known. You're going to show up at these conferences or these, you know, Sun Valley type events. What is going to be the vibe when you're sitting with another AI unicorn founder?

36:21Sid Pardeshi:Everybody in the market acknowledges that if you sit in a place that's serving any level of inference, you have a responsibility to deliver that in a cost-effective way to the end client that never degrades quality. It is not in Thropic's position that you should use Opus 4.7 for everything.

36:41Brian Elliott:Right.

36:41Sid Pardeshi:That is not what they're showing up and saying you must do.

36:44Brian Elliott:And co-worker will literally warn you and say, are you sure about that? Exactly, right?

36:46Sid Pardeshi:So every great company is making a real effort to enable the organizations to do it. What Blitzy has uniquely done from Dead Zero is we do it algorithmically for the customer. I mean, we don't put the load on the individual saying like, well, I have to switch between this and this for this type of task. Like, it's all taken care of underneath the hood.

37:06Brian Elliott:Do you think Blitzy, though, coexists nicely with other leading startups or users of AI? where you guys are doing this, the big F15 project, but then they have maybe more tactical uses for new tools.

37:19Sid Pardeshi:Yeah, we are like seeking, like all of our customers are using something we have like, we call like three buckets of code generation, right? There's the kind of like IDE-based or quick Twitch response. So you need something in that like cursor category. Then there's the web app-based or CLI-based. This is like Cloud Code or Codex, right? We expect customers to already be adopting each of these two things. and then we are the hyperscale autonomous outcome. So if you need to do something quickly, you can't come to Blitzy. We can't serve that purpose. But if you have a scaled code base and you're trying to do scaled work and then come in with these tools, do last mile development or do any discovery-based development where you don't know what's the schema, et cetera, that's this other bucket of AI tools which are incredible and amazing.

38:02Sid Pardeshi:And so Blitzy plus something that's CLI or web-based plus something that is in the IDE, that is the agentic software development lifecycle stack.

38:11Brian Elliott:So what will be your next mission? Like, you know, if we go back to your service, you were looking for the highest impact, hardest thing you could do, which ended up being the Rangers. With Blitzy for the next year or two, is it just go for even bigger customers or even bigger deployments?

38:24Sid Pardeshi:Yeah, I mean, we're definitely increasing like size and scale for my customer base dramatically right now. But we're pushing towards more what we call proactive autonomy. If you want Blitzy to do something today, you're still like you show up. Let's say you wanted to fix like a thousand CVEs. Fine. Great. Like you go in, you go into all your platforms, right? You groom through those. You give that to Blitzy, understands the entire school. And then it goes and does the cogeneration to actually go remediate stuff. right but like you can just connect those platforms to blitzy right and you can feed a set of rules like hey whenever a cve is high or you know reaches criteria xyz like i want you to queue up a blitzy task to go do this right and go execute this to a dev branch right so you're taking what the enterprise and the organization wants and you're what you're doing is you're turning the developers into the architects of what they truly want right and you're allowing blitzy which is the platform that has all the context to ingest extra information context from the existing tools that folks are already using and then action against that based on what the firm wants.

39:28Sid Pardeshi:So proactive autonomy is the big push for us.

39:31Brian Elliott:Awesome. And then I know you're still relatively early in the journey, but let's say you're going back to HBS or to West Point and you're talking to the next Brian. What has been the lesson from your experience so far that you've sort of latched on to the most or has been the most helpful in getting to this, you know, billion dollar scale so far?

39:50Sid Pardeshi:Yeah. So I think it's helpful to understand what translates really well, like what to keep, right, of your experience. And so in the Ranger Regiment, we have this Latin phrase, it's called suesponte, which means of your own accord. And so the tech world for this is high agency. You will not succeed in the regiment if you are, if you're not like extreme suesponte leader, because you're often on the ground and you just do not have, you don't have comms, you don't have information, et cetera. You just have to make decisions live, right? Based on understanding the larger intent. And there's not, you're not asking for permission.

40:23Sid Pardeshi:Like you are operating against the designed intent and mission. That's exactly what it's like to be an entrepreneur, right? You are operating and you are owning the outcomes of those decisions. You're doing so at an incredibly high base. It goes all the way back to the conversation of like, action begets information, information doesn't make it action, right? And so the ability to move with incredibly high agency translates from prior life to this current life.

40:45Brian Elliott:I think it's interesting too, we hear that some founders like to hire former athletes who are also technically gifted because they just have the discipline, the consistency. I'm sure you've picked up that as well, where, you know, as a founder, it's like, it's not just the big picture vision that makes a billion dollar company. It's the execution of all those annoying little things too, right?

41:07Sid Pardeshi:Yeah, we hire a disproportionate amount of former athletes. Beth behind the camera is an Ironman, Ironwoman. It is a really high signal way to vet through people is looking for athletic backgrounds, both on the technical side and go to market.

41:19Brian Elliott:Cool. Well, thanks so much for coming on the show.

41:21Sid Pardeshi:Yeah, appreciate you.

41:36Thank you.