Infinite Code Context: AI Coding at Enterprise Scale w/ Blitzy CEO Brian Elliott & CTO Sid Pardeshi

**Erik Torenberg** (0:00)
Hello, and welcome back to The Cognitive Revolution. Today, my guests are Brian Elliott and Sid Pardeshi, CEO and CTO of Blitzy, a company that uses AI in just about every way you can imagine to help enterprise software teams implement large-scale features and execute modernization plans with unprecedented speed. Regular listeners will know that Blitzy has recently come on as a sponsor of The Cognitive Revolution. And while this does technically make this a sponsored episode, you can rest assured that this conversation absolutely stands on its merits. In fact, I've noticed over time that my interviews with sponsors often end up being among my favorite episodes. And I think the reason is that founders who have achieved real product market fit are often unusually willing to share the nitty-gritty details of their approach. It's a uniquely effective way to convince prospective customers that they're better off buying from an AI pioneer than attempting to recreate such a sophisticated system in-house. And it also signals that their product is still rapidly improving. So over the course of the next two full hours, we will go super deep on Blitzy's approach, what they mean when they say infinite code context, and what enterprise software development looks like when more than 80% of major projects can be done autonomously in days. Highlights include the architecture they use to generate agents dynamically, just in time, with prompts written and tools selected by other agents. Why they actually run enterprise apps in a parallel environment as part of their onboarding process. How they ingest 100 million line code bases and deliver value in the form of improved documentation, which also improves coding copilot performance even before the code generation process begins. How they use detailed knowledge graphs to support sophisticated context management strategies, which minimize models' context anxiety and other strange behaviors. The critical role of taste in evaluating new models and framework changes on such large-scale projects. Which models they find strongest for which purposes, and why they always use models from different developers to check one another's work. Why they are more bullish on advances in AI memory than on fine-tuning. How they came up with their 20 cents per line of code pricing model, and why they will do anything they can to deliver more value for customers, even if it forces them to raise prices in the future. What it will ultimately take to achieve 99% project completion and even full autonomy in enterprise software development. And finally, their outlook on the software engineering labor market, which favors senior engineers in the short term, but junior engineers who can use AI effectively over time. Brian and Sid are both high-energy guys, and they were remarkably forthcoming in this conversation. I learned a ton, and I expect that any enterprise software leaders who listen will come away thinking about specific projects where they'd love to put Blitzy to the test. So, without further ado, I hope you enjoy this deep dive into the present and future of autonomous software engineering with Brian Elliott and Sid Pardeshi of Blitzy. Brian Elliott, CEO at Blitzy. Welcome to the Cognitive Revolution.

**Brian Elliott** (3:07)
Awesome. Let's get into it.

**Erik Torenberg** (3:09)
One of my favorite things to do in life is talk to AI maximalists. And I've known Blitzy by reputation for a while. As the company that has figured out a way to create infinite code context. And it doesn't get more maximalist than infinite. So I'm excited to unpack what you guys are building, how it all works, and the impact that it's having on the enterprise software industry. We're going to go through all the layers. But first question, just to orient myself and the audience to you. How AGI-pilled are you? How AGI-pilled is Blitzy? How AGI-pilled are your customers?

**Brian Elliott** (3:44)
We believe we can get AGI-type effects out of non-AGI LLMs, right? And so, as folks are thinking about the impact of artificial general intelligence, they're talking about huge swaths of work being able to be done to provide economic value autonomously across domains, right? That's one amongst many definitions that is a moving target for defining AGI.
And so, the core question is, like, how can you achieve that output with the limitations and constraints of LLMs? We might be the most, like, bearish on LLM capabilities as a pure standalone, like, single LLM asset, and the most bullish on the orchestration of those in long-running complex systems.

**Erik Torenberg** (4:35)
Yeah, that really echoes the conversation I recently had with Daniel Measler, who created this personal AI infrastructure framework. His mantra is, Harness is more important than model. Obviously, one big limitation there is the context window is finite, and even at a million tokens, relative to the size of an enterprise codebase, that's not nearly enough. Any other, you know, kind of, limitations of LLMs as standalone creatures that you think are kind of most important to have in mind?