The Army Ranger Attacking Millions Of Lines Of Old Code, With Blitzy's Brian Elliott
Blitzy CEO and Ranger veteran Brian Elliott talks building Boston's newest startup unicorn, Claude's enterprise limits, West Point and how to pay for huge code projects on The Upstarts Podcast.
As I sit down to meet Brian Elliott, co-founder and CEO of Boston’s new startup unicorn Blitzy, I’m excited to ask him about his experience as a U.S. Army Ranger.
Not what it was like to go through Ranger School, the famously grueling program that has been the subject of TV shows. I want to know about something more hardcore: Elliott’s LinkedIn profile.
There, more prominent than any word about his combat deployment in Afghanistan, or his Bronze Star, Elliott shares that he “managed $30M in assets” as chief of staff of a 140-person team with, he adds, 10 direct reports.
There’s something so perfectly LinkedIn about it: the framing of elite, brave service to America as valuable within the corporate ladder.
When I get to ask Elliott about it, he tells me he’s been fascinated by systems – how things work – since he was a kid. Even a ‘direct action raid’ involving ground and air movements is, to hear him tell it, a “very elegant system.”
“Synchronizing all of that is IRL orchestration, and I loved it,” Elliott says. “Because precision, and operating under stress with precision, mattered more than anything”
It’s an answer that any ‘LinkedIn Lunatic,’ would approve of, but it’s helpful in understanding how Elliott, who co-founded Blitzy with friend Sid Pardeshi in 2023, has been able to build it into a promising business valued at $1.4 billion so fast.
Blitzy’s agents can understand 100 million-plus lines of code, helping corporations to automate and update systems that would otherwise take millions of dollars, paid out to consulting firms over months, to barely crack.
“For eons, we have been limited by how much a human context can hold in their brain,” Elliott argues. Now Blitzy can offload that context to its AI systems: “We can do changes at a size and scale that were previously impossible.”
On this July 4 edition of The Upstarts Podcast, Elliott shares how he built Boston’s newest tech unicorn by becoming a CFO’s friend; why Cursor and Claude Code only see enterprise code through a straw; and what West Point and the Army Rangers taught him about high-stakes performance.
Plus, he shares his Upstart Moment: catching the 6am train to New York to try to close a customer, with his co-founder’s visa at stake.
Our three biggest takeaways for busy builders can be found below.
Follow the ‘dissonance’
A CFO’s ‘best friend’
A Ranger’s lesson
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Follow the ‘dissonance’
The best time to build a startup, Elliott argues, is when “you have a view on the world that everybody tells you is incorrect, and despite that, you know it’s true.”
For Blitzy, that was the conviction that large language models, for all their capabilities, would not grow into efficient tools for updating large-scale code bases like the one he’d seen at what’s now called the Department of War.
“We would go everywhere and be like, ‘The model's not gonna do it all. Inference-time compute is the most important scaling law for code quality.’
This is before reasoning models, way before reasoning models were a thing, right? And if that was true, the limit case was gonna be understanding the underlying code base.”
This was not, Elliott says now, a popular position to have in 2023. “Everybody told us it was the stupidest thing they’d ever heard.”
“They just thought that the model would solve the problem, right? 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, right? 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?”
This view informed how Blitzy set up its software: to run continuously, without a human in the loop, for weeks. The big unlock was what Elliott calls a knowledge graph, or orchestration system, that could handle millions of lines of code — not the thousands of an LLM’s context window.
“What we're doing is we are removing the pressure or the load of having something... As a human, it would be like, ‘What do you kind of remember top of mind?’
So we're removing that load off of the agent, and we're only putting in what they need, just in time, every time, dynamically, done hundreds of thousands of times. And so at any single moment in time, an agent can only do one thing. We parallelize work, and then we sequence work based on how it can be sequenced.”
A CFO’s ‘best friend’
If you listened to our episode with May Habib last week, this one might sound familiar.
But Blitzy also considers itself a CFO’s ‘best friend,’ and Elliott is also bearish about ‘token maxing,’ which Blitzy’s CEO compares to evaluating a sales rep ‘SDR’ by the number of phone calls they make.
“It could be related. Sometimes it's related. But really you should be counting an SDR by bookings, right? 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.
That's my general view on token maxing: It's not sufficient, although it is not an unreasonable way to think about who is using AI. It's really not a sufficient way to think about adoption.”
Blitzy keeps its work usage-based, but aligned with a company’s existing roadmap, Elliott says.
“You're like, ‘Literally, I am buying these projects.’ What are the jobs to be done? These projects, way faster, from Q4 brought into Q2. 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 the fixed amount. It's gonna be roughly X lines of code, right? So there's no variance swing from the CFO, like they're experiencing when they just buy random licenses for other AI products. What you're doing with the other AI products is you're throwing Opus 4.7 at everything, you're on 'max think' mode. And you're just putting that on loops, right?”
Blitzy’s value-add rests in part with the fact that it selects the best model for each task in real-time, Elliott says.
“Might be Gemini, it might be Flash, it probably won’t be Flash, but it might be Opus, it might be Sonnet, right? It might be an earlier version of Sonnet, right?
It’s baked into how we build our product in a way that really resonates with the CTO, the CIO, and the CFO.”
A Ranger’s lesson
When I ask Elliott what his top lesson across West Point, his active service and Harvard would be, he invokes a slogan from the Rangers: sua sponte.
"It means ‘of your own accord,’ and for the tech world, this is high agency,” Elliott says.
“You will not succeed in the regiment if you're not an extreme sua sponte leader, because you're often on the ground, and you don't have comms, you don't have information, et cetera. You just have to make decisions live, based on understanding the larger intent. And you're not asking for permission. You are operating against a 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, and you're doing so at an incredibly high pace.
It goes all the way back to the conversation of: ‘Action begets information.’ Information doesn’t beget action, right? And so the ability to move with incredibly high agency translates from a prior life to this current life.”
When Elliott says this, it reminds me of startup founders who’ve said they hire especially from the ranks of elite collegiate athletes, who can combine initiative with discipline. The CEO agrees.
“We hire a disproportionate amount of former athletes. Beth behind the camera [Beth York, Blitzy’s head of media and creative, and our volunteer camera person for the taping in Boston] is an Iron Woman. A really high signal way to vet through people is looking for athletic backgrounds, both on the technical side and go-to-market.”