The State of AI: Elon’s $1T Package, Apple’s $600B for Trump & How Startups Win w/ Dave, AWG & Blitzy Founders Brian Elliott & Sid Pardeshi | EP #193

A piece of news I saw yesterday that had me scratching my head, which was the trillion dollar pay package for Elon.

Elon Musk could officially become the first trillionaire. Just about a trillion dollars in stock. It's a striking number. But the benchmarks that he has to meet are also equally striking.

He's not just the leader of the company, he's the marketing voice.

If we really do expect to find ourselves in an abundant society soon, we should expect to have a lot of trillionaires in our society.

Money will start to have far less value than ever before.

If we're really on the verge of abundance, then what comes after that? What's going to remain scarce even as energy and intelligence, the cost of both of those goes to zero.

But stuff is changing so quickly. Media is changing quickly. Elon is paving a new path for what it means to be a great corporate CEO. But it's going to change again. And it's going to change again. It's going to change again.

$1 trillion here, $1 trillion there. How do you compete? What's your MO? Brian and Sid, welcome. Pleasure to have you both.

Now that's a moonshot, ladies and gentlemen.

Everybody, welcome to Moonshots, the news that really matters in your life. I'm here with my moonshot mates, Dave Blunden, Alex Wiesner. Gross. And we're going to have a conversation today about David versus Goliath. We're talking about trillion dollar commitments, trillion dollar pay packages. It's insane. But first of all, one of the most important pieces of news is ale. I'm reading the comments and what I keep on hearing is people want you ask you a question is have you talked for the entire episode? They love what you have to say. So I'm not going to do that today. But everybody, if you're new to Moonshots, Alex has been just receiving huge fan mail because of his brilliance.

It's an honor to be here, Peter. I think I got maybe two adoption offers.

I think you did, Dave.

Richly deserved.

Are you jealous, Dave?

Am I jealous of Alex? I mean, I'm surrounded by so many people that are so brilliant. I don't know, I'm used to it by now.

That's one of the most important things, is finding incredibly brilliant people to have around you in life. The old saying, you're the average of the five people you spend the most time with. And if you've got a community of individuals that are really uplifting you and challenging you, that have you do the best you can, that's critically Important. So one of the things I love about Moonshots and our WTF episodes is sort of measuring toe to toe and trying to really have this conversation in a meaningful fashion. We're missing Salim Ismail again. Saleem, we miss you, buddy.

I think he's still in India.

He's probably got a crate full of iPhone 17s coming, but we'll be talking to him soon about that. So I want to open up with a piece of news I saw yesterday that had me scratching my head, which was the trillion dollar pay package for Elon, for Tesla. It's like, that's extraordinary. Have you guys gotten an offer from your boards for a trillion dollar payment?

A trillion? Well, yeah, if you hit the metric. The metrics have to be multi trillion dollar market cap, but sure, why not? It's just a fraction of what you create.

If Elon grows Tesla to an $8 trillion company so it doubles the size of Microsoft and Nvidia, he'll earn a trillion dollars. Not like he needs help becoming the first trillionaire on planet Earth.

Yeah, but he's unique. He's really changed the definition of what it means to be a CEO. And he's not just the leader of the company, he's the marketing voice. Most car companies will spend 7% of revenue or thereabouts on marketing. Tesla spends zero because Elon's a one man force of nature driving consumers to the product. And we're going to meet a couple entrepreneurs in a minute here that have that same. Like we embrace social media, we're creating morale and momentum like you wouldn't believe. But you know that that's a 20% pay package as opposed to the normal 5. It's worth it.

I agree with you. If you're a founding CEO and if you're very, very shy, that's not going to bode well for the company. I know when I'm investing in companies, I'm looking for a CEO who's a great communicator, who's able to go out there in front of the crowd, able to convey their passion, what he or she is loving in life. And for all of the insanity Elon does with chainsaws or whatever the case might be, it grabs attention and people either love it or hate it.

One of the many, many reasons we love Alex so much is because he's not just brilliant, but he has very, very high situational awareness. And that's really rare around the brilliant community. But stuff is changing so quickly. Media is changing quickly. Elon is paving a new path for what it means to be a great corporate CEO. But it's going to again and it's going to change again. And it's going to change again. So you know, if you map your behavior to the change, but most people study backward in time and they say, well what did Jack Welch do? Or what did Genghis Khan do? You know, it's okay, but that's, that's not gonna. Anyway, we'll go on this branch, we'll go too long. If I go too far down this.

I also think, Peter, it's worth noting if, if we really do expect to find ourselves in an abundant society soon, we should expect to have a lot of trillionaires in our society.

We will. And then we'll have an expectation that money will start to have far less value than ever before. Right. I mean, do you and I have had this conversation, Alex, about a post capitalist society? Right. Thoughts on that? Do you still believe that's going to be the case?

Well, so I think that that's always the question. What does so called late stage capitalism even look like? To the extent the concept makes sense, if we're really on the verge of abundance, then what comes after that? And I think the what comes after abundance is closely tied to what remains scarce in an abundant society. In Star Trek, a common foil, energy is relatively abundant, intelligence is relatively scarce, the ability to travel between stars relatively scarce. So the question I would ask is what's going to remain scarce even as energy and intelligence, the cost of both of those goes to zero.

That is a critical question. You know my, my endpoint here, my mental experiment is if I, you know, in Drex, in Eric Drexler's parlance, if I build a number of assemblers that are able to rearrange atoms and I drop an assembler into my hand and I say, hey, make me, you know, five copies of yourself and I give each of you an assembler and the assembler is able to use energy and matter resident and build anything. And I drop an assembler into the soil here and it starts pulling the atoms together to make me an electric Ferrari. And it says I need a little bit of titanium, a little bit of lithium. You add it and all of a sudden you've got an electric Ferrari. Everything starts to become effectively zero marginal cost. And that becomes a pretty cool society where anyone can do anything, does it?

Or I mean again, not to over index on Star Trek, but in Star Trek everyone has replicators, but not everyone gets to travel between the stars. So maybe that the new post scarce ability is the ability to travel outside the solar system?

Well, we're going to find out because we're getting there really fast. On the news item of trillion dollars here, a trillion dollars there, there is a dinner with Tim Cook and, and Sam Altman and Mark Zuckerberg and Trump. And I guess during this dinner an offer was made by. By. Was it Zuck first or was it Tim first to invest $600 billion into the U.S. economy?

I think it was Tim first. I can't tell from the clips, actually, because they get cut and mingle, but.

Then the other one matches it. And all of a sudden over dinner, Trump is getting $1.2 trillion of commitment into the US economy.

He should have.

You missed the really fun punchline there. Like Tim Cook had it all scheduled, planned and budgeted. And then Mark said, I'll match that. It was like a YMC fundraiser. If you can do that. I can do that. I'm sure a CFO back in Silicon Valley going, what the hell did he just commit to?

Oh, my God. But it is. There's a third piece that comes out in this related story, which is Altman announces to his employees that he expects OpenAI to be the most capital intensive company in history. And what was the number, Alex? 119. 119 billion dollars of additional investment between now and 2029?

It was something like that. I mean, do you remember it was a whole, what, a year and a half ago or so that this number of five or six or seven trillion dollars of CapEx into AI chips was being floated and a lot of people laughed at that. And yet we're finding ourselves a year and a half later in a world where it is entirely plausible that the true amount of capital expenditure in fabs and AI chips and data centers and new energy sources completely exceeds that.

Yeah, I think, yeah, file, file that away because you're dead right. And this is the effect we see all the time. Something insanely mind blowing is predicted six months into the future. Everybody is like, impossible. Then it actually happens. And then they're like, oh, yeah, well, it's just part of life. And this trend is going to, you know, it's happening over and over and over again. But the numbers you just quoted.

Yeah.

Everybody was like, oh, Sam's just blowing smoke. There's no way that's trillion being thrown around. But that's not a real word. That's just sort of a euphemism. And then here we are just a few months later. You're going to hear some benchmarks actually Like Sweebench later in this podcast where things have just been crushed, that the timelines will blow your mind. Well, we'll get to it when we get to it.

Well, I guess the point I'm making here is there's a huge amount of capital flowing here. I mean we never, I mean go back to when all of us were starting our careers in the 90s and in the dot com era. The idea that the trillion dollar movements of capital in any particular company or any particular industry was just mind boggling. And here it is routine.

But this is in some sense this is sort of a wonderful opportunity. With trillions potentially of capex being invested, there's going to be an expectation, I would assume by capital markets that there's going to be enormous revenue generation that pops out of those trillions in CapEx. And the question you have to ask yourself is what form does that take? At some point with trillions invested, I think there's probably a reasonable expectation that entire classes of labor of services are going to be automated and the cost of what we currently construe as labor is going to be driven down to zero. And then perhaps at some point immediately after that you start to need transformative science, inventions, discoveries that will really justify the trillions of capex. So it's sort of a blessing in disguise. I would argue trillions of capex is going to motivate the demand and the supply of utterly transformative discoveries and inventions soon. Otherwise why invest trillions in this?

Yeah, the concern of course a lot of folks have is around inflation and are these dollars really inflated dollars? We're going to find out. But it's interesting, Dave, in particular as a venture capitalist, seeing the valuations of companies going at this level for the average public. How do you get into any of these companies when they're coming out at multi hundred billion dollar and trillion dollar valuations here? Being able to get in early is one of, I think the areas that you've been focusing on. One of the other companies that came out of Link Ventures, your early check in here was Mercor and I just saw Mercor has gotten a offer at a $10 billion valuation. Must be pretty happy about that.

It's a $10 billion valuation, but it's also a half a billion dollar revenue run rate after two years which is completely unprecedented.

So go back. When did you invest in Mercury?

Two years ago. First funding, what you really want to look for is undervalued, underappreciated talent and not so much concepts, but they had the Concept. Right. Already it's rare, but 18 years old, I mean, not a lot of people invest in the 18 year old gang.

So a couple 18 year olds come forward with this idea. Do you remember what the opening valuation.

Was when you invested 30 million plus or minus 5?

Okay. And so 30 million to 10 billion in two years time.

Yeah, yeah. It's got to shatter all kinds of records. But again, I don't want people to feel like that's a bubble because the revenue growth also shattered all kinds of records. And so from a cold start, I don't think anything like that's ever been done before. But you're going to see a lot more of them now too. They just are setting the trend for many, many other companies. I think what's different about them is they're inspiring an age of people that normally would have been uninvestable five years ago, 10 years ago, and now it's kind of, wow, mainstream.

We've made that point that the average age of a VC backed unicorn a decade ago was sort of mid-30s. Right. In terms of the average age of the founders. And today I think, Dave, what you found out of the investments we're doing, especially out of MIT and Harvard, it's age 20 to 23. And these guys were 18 when they started.

Yeah, 18. They got through one year of college and then they got frustrated with the pace like everybody does, which goes. But they met in high school, which.

Goes to the point, Alex, you made last time on the last WTF episode, which was, listen, if you really believe we're sort of post AGI on the verge of ASI advanced superintelligence, going to college during those years and trying to get credits versus building something, it's not the right trade.

It's going to distort all sorts of societal cues and societal expectations. The best I would say fiction treatment that I've seen of this is a novella by Vernor Vinge, Fast Times in Fairmont High, where you see this start to completely distort the way secondary education is run in this country. And you start to see high school students and middle school students suddenly spending all of their time doing startups. And I think it's entirely plausible we find ourselves in a near future that looks a lot like.

I agree. And one of the points here, I think that we need to realize, people need to realize is the Mac, you know, sort of peak creativity, if you measure it by when a Nobel laureate does their Nobel Prize winning work, not when they get their prize, but when they actually did Their work is typically in the first half of your 20s. Alex, do you have the data there at all off the top of your tongue?

Not at my fingertips. And I've seen those statistics too, and I've seen how they vary from field to field, purportedly math versus physics versus other fields. I also tend to discount this notion because I expect that in the very near future most of the innovation is actually going to come either from pure AIs or some sort of human AI hybrid. So I view those statistics, maybe self servingly, as more of a retrospective. This is how things used to be at best versus how they're going to be in the future.

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God, I cannot wait. So we have Brian Elliott, Sid Pardeshi, the founders of Blitzy. My son Jack interned with them this summer and I tell you, it drove my wife a little nuts. She started thinking, wow, we're going to play a lot of tennis this summer and have a great time. Jack got so wrapped into the culture of Blitzy so quickly. It's the most high energy place I think I've ever seen. Morale is off the charts. And so he pulled in his best friend from high school, Yash Balasheti. They pulled in a couple of other young computer science majors at Northwestern and a couple other places. The whole gang worked all summer on Swabench and crushing numbers. But I tell you, the morale of this group is like nothing I've ever seen. The mission is incredibly cool and fun, so can't wait to tell you all about it. So Brian, he as a West Point alum. My one experience in life with West Point was Rick Dalzell, who was my biggest and most important customer I ever had, actually. He ran all things complicated at Walmart. Massive logistics, half a million people moving around, and then he got poached by Jeff Bezos to work at Amazon. So he was the number two guy at Amazon right when they were in total hyper growth. And he had a West Point background, really understood morale, people, logistics. And then Brian went to Harvard Business School. After that, Sid went to bits, which is the MIT of India. It's actually statistically much harder to get into than mit, if you can believe that, Peter.

I can. So talking about MIT let me in. So there's got to be some flaws there.

So humble. So Sid after that spent a long time at Nvidia and saw it go from tiny to monstrous. So that's got to be inspiring. And I actually don't know their story before that, but they met at Harvard Business School and they're, I think, inspiring to a different class of people, you know, that were already in a career path and then AI hits the world. But they're nimble, you know, they're not going to watch it happen. And this is way too rare, you know, people remapping their entire life to take advantage of what's happening right now. So I hope a lot of the listeners today get a lot out of their backstory and their transition to building this incredible company. Blitzy.

Yeah.

And I really want to frame the story here as David versus Goliath. We've heard about a trillion dollars here, a trillion dollars there. How do you compete in that world? If you're a young entrepreneur, you're building a company and you're wondering are you going to get literally decimated in the wake of Google or OpenAI or XAI just happening to release a particular feature, how do you compete? What's your moat? So, Brian and Sid, welcome. Pleasure to have you both. Where are you guys this morning?

I am in 1 Kendall Square here at the Lynx Studio offices with MIT right behind me. So we just walk over the talent from the MIT AI lab right over to here to work, which works well for us.

Beautiful Nearby in Cambridge.

Fantastic.

The first thing we had to overcome was convince Dave that we could be successful when we're not 19 years old. So he totally flipped his paradigm on funding these young people, Said Dave. When I was like, when I was young, these kids ages, I was, you know, I was out, you know, across the ocean fighting a war. And I think I gained enough experience to hopefully have a second career here in technology. But Peter, I love the question that you're asking, right, which is how on earth do you compete with these frontier AI labs? With Google. Right. With OpenAI. And there's really two reactions you can have when there's trillion dollar investment, right. There's the reaction where you've built a company that you say, oh no, Right. They're going to steamroll over me.

Yeah.

But then there's the reaction where when every single model gets better in the combination of those models makes your product much better. You're jumping for joy. So we got a trillion dollars of R and D for Blitzy and we're.

Rising a rising tide. And you can float on top of all of that. That's perfect. But it's critical to find that product market that is able to benefit from the rise of these technologies.

This is the second time we're seeing this happening. I was at Nvidia back in 2016 and I heard all about the story of Cuda and I saw Gensen believe in that. That was pre Genai. The term Gen AI didn't exist and I've been working on generative AI models since the attention is all you need. Paper came out. So Jensen was asked to stop investing in CUDA. He started this back in 2006. Right. And it was negative to the, to the company, so. But he still invested in it. He still believed in AI. He worked with researchers and built the technology to solve problems that he foresaw. And that is very relatable to what we're doing. So blitzy. If you go into this data, for sure, but it's very unique in terms of how the product is built. It is specific to the enterprise and it is based on the opportunity that we've seen over many years working at.

Largest companies like Nvidia, I think we should begin. For those who don't know Blitzy explaining what it is want to get into. Where were you guys when you said, aha, we're going to build this? I love that story. And then I'll unleash Alex Wiesner gross on you to ask the most intelligent, important questions.

Well, let me tell you what it does today and then I'll tell you the humble beginnings of all of this. Peter. So Blitz is an enterprise grade autonomous software development platform. So we ingest and understand up to 100 million plus lines of code. Where most single LLM tools are stuck with this finite ability to understand context, we've developed some really unique context engineering systems to understand enterprise scale code basis. From there, an enterprise will express their work from a development perspective. Whether that's a Cobalt to Java upgrade very common in these old financial service institutions that use us, or steady state development work. Bloodsie will send off the most compute intensive workload in the entire AI code generation space. We've done a 12 hour run, we've done multi week runs for massive scale code bases, ultimately delivering high quality, pre validated, pre compiled, pre tested code. Right. In our view, our thesis is we want to increase the quality of code at any cost. Because the other side of a pull request that comes from AI code gen is human labor which is exponentially more expensive. And so that's really the view that we have enterprise scale, high quality code.

All right, Brian, I want you to, want you to slow that down for a second. Which is to say a lot of companies out there, a lot of traditional companies in particular in the finance world, you're saying insurance world, have large code bases, they have software that they have in, you know, have inherited for how, how old are some of the software systems that you're playing in?

I mean we're talking about PL1.

Oh my God.

These are like, these are, these are old school financial service institutions that quite frankly for a long time have been afraid to touch the code. Just the cost to get something modern. This wasn't worth it.

Okay, so you've got a company, you've got a company out there running cobalt from what, 20, 30 years ago?

Yeah, that's right.

And their system is operating, it's working, it's not doing anything significantly useful given that it's 20 or 30 years old. And do they have people that can still patch that code? I mean, are they all engineers still around?

They make you and Dave look like spring chickens.

We are spring chickens.

Oh yeah, I know you're a longevity guy, Peter. You look great.

So you've got this problem where you're too scared to touch it because you'd have to do a wholesale replacement. And so they call in the Blitzy guys and you come in and you're able to do what for this 30 year old chunk of code?

For starters? We give them visibility. So we'll ingest, index and understand the state of their underlying source code, of which times they rarely have somebody that understands the entirety of tens of millions of lines of code. It's actually an impractical problem to know that. And then we allow them to execute large scale transformations. So whether that's getting onto a modern technology stack or that's adding required functionality, these businesses, these enterprises are stuck with the inability to layer artificial intelligence on top of their existing code base because it is so old, so antiquated, and has so little visibility of what they're doing.

Massive value, massive value creation.

It's a mind blowing experience too. If you take 10 million lines of unintelligible undocumented code and you run it through blitzy and then you say, tell me what it does in plain English, explain to me where there are bugs. It's just you're talking to the code. It's mind blowing.

Like one of my favorite uses of Genai is to give it some patent that is unintelligible and say, what does this do and how could I use it in my company? Right. The ability to take something that's complex and make it understandable to grok it fully, to use that term. Thank you, Robert Heinlein.

Well, Sid got 27 patents, so I had to use that to understand what the heck he did at Nvidia.

So Sid, you're at Nvidia for five years?

Eight years.

Eight years. I heard Jensen recently say that most of the executives there are now are now billionaires. Did you make it to that status?

Well, I held on to my stock. I rode the wave from like double digit billion to trillion dollars. But then I got two degrees.

You can do the math, Peter.

And then what happened?

I got two degrees from Harvard. So they took all the money.

You. Oh, no way. Wait a minute.

Oh my God. Let's not get started on the expense and value of degrees from Harvard or MIT or any. And you have to leak schools. So this is a David versus Goliath story. And I want to understand you've got incredible success. But before we get to that story, you guys are both at Harvard Business School. When did this idea germinate? What was the causative agent that said, okay, let's build that. What was that founding story like?

Yeah, if you can rewind the clock back to the GPT 3 to 3.5 ERA, right, where these things could code, but it wasn't what we're experiencing today. Right at that time, Sid and I were doing a pro bono project for our favorite Local bakery here in Boston as a part of our time at Harvard. And they mentioned they're about to spend 300,000, $400,000 on a new mobile app, which got our attention. Some young enterprising entrepreneurs. And so Sid and I went home and we did what is now two and a half years later called Vivecoti. And we built them the application overnight. Right.

But literally over the weekend, over the night.

Yeah, yeah. Which now is like no big deal.

But did they pay you $200,000 for that?

We should have acted like it took longer. I think that was our first mistake, but was so clear during that time is that Sid and I were actually the bottleneck for development. And so we were, you know, you get an error and you feed it back to the system and then you give it to a different model. Right. And then through that practice, you're able to get much higher quality code. And so we said if we could just invent a system where all the commoditized development work could be removed. Right. And we could have multiple models going back and forth iterately refining and getting to code that compiles and tests, that is going to be what the future looks like. Right. And we learned that by doing, by being hands on and then having an idea of what the enterprise needs from Sid experience and building towards that.

Were you guys already friends or did the all nighter make you friends?

Yeah, we've only gotten. I mean, Sid's actually the godfather to my youngest son, believe it or not. So investing in best friends is pretty good.

I mean, that's another part of the story, Dave, that we've talked about is some of the most successful companies are when best friends get together and just build 24 7. I say this to the entrepreneurs that I coach. Being an entrepreneur and having co founders, you're going to spend more time with your co founders in the trenches than you do with your husband or wife or kids. It's an intense period of time and you better pick somebody or some buddies that you love spending time with.

Yeah. I always tell people, imagine you're on a long international flight, one of those 14 hour flights, and you're sitting right next to somebody. Do you walk off the plane feeling good and having fun or do you walk off the plane not waiting to get away from this person? Your startup's going to feel just like that every day. It can be work or it can be fun. It just depends on the personalities and the match.

So, Dave, what do you find most exciting about Blitzi? Just from as Investor. And so they came through Link, through Link Studios and Link Investment and yeah, tell that a little bit of that story if you would.

Well, they're definitely much more experienced than a lot of the entrepreneurs around the studio. So they had the plan and the idea fully baked on arrival. We were still first money in and still gave them space and support and have made a bunch of introductions, but they already had it more than figured out. So that's, you know, that's not all the companies fit that profile. They're also very different. A lot of the companies coming right out of dorm rooms will do image generation. They'll do. They don't understand the word COBOL or PL1 to save their lives. When I look across the range of business plans that are right in front of us with AI, more of them fit into the. You need to understand the domain space than you can just think of it in a dorm room space. There's maybe 2/3, 1/3 rep numbers. What I'm hoping with Brian and Sid is that they inspire a ton more people to go after these. You know, these are still multi trillion dollar markets, but they're not, you know, AI girlfriend, they're not, you know, apartment search. Nothing to think of while you're in the. Yeah, another photo sharing app. They're, they're, you know, and they get really deep. You know, you've got all these, you know, manufacturing, you know, semiconductor manufacturing, automation, you know, that's, that's really deep. You get insurance, actuarial risk adjustment. That's very deep. This one is actually nice in that it's very. Code generation is very broad. So it's a huge market. But it's also deep in that refactoring 10 million line code bases is a pretty deep knowledge set. The other thing that's really cool about Blitzy to me is that we have all these code generation products. So I use cursor, we've got Windsurf, Replit. Lovable companies are all worth billions now. But I write a line of code or I tell it to write a line of code, it creates a button for me. I say, I don't like that button. Make this other widget and you're doing it in real time. But you can't build something really big. When you put cursor in full agent mode, it's right in no man's land. It sits there and grinds for like five or seven minutes, which is too long to wait but too short to build something substantial. So they're getting stuck in no man's Land Blitzy just said, no, we're going all the way to the other end, where it's going to run all night long or all week long, like Brian was just saying, and come back with something really big. And that's just fundamentally a much different engineering problem than what lovable, replit, cursor, windsurf are doing. It's a different kind of company. I don't know of any other company that's there.

Amazing. Well, we're here today to announce a particular piece of news as well, some groundbreaking news. Is it Brian or Sid? Which one do you want to talk about?

What? You guys? Sid, please. You are the inventor of the technology here.

Tell us.

So every time a new model comes out, they benchmark on this leaderboard, which is called SW Bench, verified. The leaderboard itself was built by OpenAI. It is a subset of SW Bench. It contains 500 problems that were vetted by the researchers at OpenAI. And they confirmed that these are solvable problems. And these are worth testing models on. And it's been ubiquitous. So every time a new one comes out, you always see results. The current top of the leaderboard on the sweepbench website as of filming is 75.2%. So we hired a bunch of extremely talented interns. So, Dave, going back to Jack, if we could hire him today, we would. That's how good some of these interns are. And every single intern who worked for us, they were amazing. We really credit this to their effort and to Neeraj, who led the efforts on our end. But we ran blitzy on Sweep. And as it turns out, these are 12 repositories, but they have 500 branches. And that equates to 400 million lines of code if you ingest them on Blitzy. Right. We've ingested anywhere from 1 to 2 billion lines of code overall on Blitzy, depending on how you count it, because you count updates and the whole raw thing as well. So it's 2 billion lines of code if you count all of the updates. Right? Including Sweep. So we ingested all of that. We ran blitzy on solving the problems, and our final result, accounting for everything that we've tested and verified using SB CLI, was 86.8%. That is a significant jump over the current leaderboard on the website. And the last time this was done was when Devin had a 13% jump from 1% to roughly 14 point, some percentage. So we've come a really long way with the system. And the primary reason that we were able to achieve this, echoing some of the points that were made earlier is we're very different from the existing tools are structured. One thing is you can reproduce these results in production using plicy. We've not added any custom scaffolding just for cbench. We've not tampered with any of the features to achieve this. We've seen reports from some of the other labs that claim that even though, for example, let's say a latest frontier model claims 80% on sweep bench, if you actually run it and reproduce it, you get 60% and we wanted to not have that problem. We care deeply about reproducibility and the practical real world applicability which three Bench verified has been vetted to be good at. So you can reproduce these results and it's live as of today.

Amazing. Hey Alex, help us understand how big and important this particular hallmark is for the company and for the world. Give us some background here.

Sure. Well, first to Brian and Sid. Congratulations on your announcement. I would expect there's going to be an enormous amount of interest from the community once they hear these results in in trying blitzi and in reproducing those results. So congratulations in advance on the onslaught of interest that I expect you will receive. I think to answer Peter's question, I think software engineering is arguably the first major vertical of human labor that is very high economic value, very high productivity, that is perhaps succumbing to automation. So any sort of step function improvement in software engineering is arguably super transformative to the global economy. And maybe just pivoting on that thought, one of the first things that I was wondering when I heard that you would be announcing these results, maybe jumping back eight months. We all remember when Deepseek aka High Flyer launched R1 January of this year and there was sort of an aha moment all around the world. They didn't just announce a new reasoning model, they announced and maybe this got a lot less attention. They announced a bunch of new open source libraries at the systems level, like a new file system. So one of the first things that I was wondering when I learned that you'd be making this announcement is the whole world is sitting on this sort of palimpsest of legacy libraries and operating system code. Billions and billions of lines of code. Linux, Python, gnu, all of these libraries. Is there something that you and Blitzy and this new remarkable capability that you're announcing can do to speak to what can we do to improve the performance of this entire tech stack that the whole world runs on at this point?

That's a fantastic point of question. We've been running some of these experiments, we've been taking some of these open source libraries that for example, was written in Matlab for one of our customers that they were using and we converted it to Python. MATLAB was written 20 years ago. It was specific to Windows and we made it OS agnostic. We've run these POCs all the time where we go from OS agnostic to OS specific to agnostic, from traditional to modern. But we've also been running other kinds of POCs where, for example, we picked an Nvidia repo, we identified an issue that was marked as open and we just put blitzy edit and we saw it, we created a pull request that solved the issue.

Right?

So if you think about that problem, you can identify bugs, issues, feature requests in any of the modern frameworks and systems. And the system is not limited by how much code, how big the repo is. Right. So you can send blitzy at it, it'll come back with a solution. You don't like it, you can iterate over it. You can create five projects, get five different pull requests, see how that works, and deploy it all within a matter of days. I think that's a fundamental shift that really is going to change the way people work with open source and also closed source technology.

Amazing. What is the largest repository of code that you've actually tackled?

Brian, you want to say?

Yeah, go ahead.

So we frequently see 20 million lines, but I think the absolute largest we've seen is about 60 million lines. If you want more, it's successfully crazy.

Just for comparison, for fun, if you had to guess how long would it take in terms of human labor hours to do that?

It's insane.

Brian, I think we frequently scope these right. As part of the PoC process and I think you probably speak to that.

So what we do. So there's a question of like, how long would it take to Grok 60 million lines of code? And the reality is it's just too big for a human to understand. So you might pay Accenture $100 million in three years and they might come back with some diagrams over the 60 million lines of code.

And by the way, by the time, by the time they did that, what they came back with would be out of date.

Exactly. Which is why you can see that, you know, essentially the industry has been stuck. This is why your airlines are always misrouted and they can't get their software updated. Is this kind of fundamental problem. So we, every time we run blitzy, we actually estimate for the clients in production, all Our enterprise clients, how many hours were automated. And the CIOs love this because it's like the KPI that they could give the board on how many hours they've automated away by their intelligent vendor selection. But grokking is sort of an impossible problem. But the real value is in the code generation, right? Being able to accurately affect and develop code and accelerate that life cycle for the development team on that large underscoring corpus. But importantly, Alex, I know you want all developers to go away and we're going to live in a society of abundance, but I think it's going to go in the opposite direction where there's almost an infinite demand for code and for software development. And so Blitzi doesn't do everything. Blitzi does about 80% of the quantum of work on average for these large scale problems, but it knows exactly what it doesn't do, which is really the power. And it kind of hands off that batch of work to the human developers to finish things out. So we get a really clean full pull request plus human labor at the end to accelerate the development, but not sort of remove the need for, you know, the developers altogether.

So I think just, if I may, just to pull in the thread, Brian, I mean I would argue we're about to enter sort of an age not necessarily of just abundance, but of great projects when it's possible to send. Basically let lots of automation loose on the world and fix all the problems, solve everything, as it were. In the case of Blitzy, this is letting AI agents loose on an enormous sprawling legacy code base and just fix everything.

I think it's a good name for the episode solve everything.

Or a book or Fill in the blank. But are you familiar maybe you're tracking this project. I love this idea. The Great Refactor.

Yeah, I love this.

What is that, Alex? What's the Great refactor? Refractor.

So the Great Refactor. I love this. This is like a classic solve everything concept. This is we've built our whole civilization on a bunch of software libraries that could be better maintained, that are filled in many cases with legacy memory vulnerabilities. There are statistics out there that most of the insecurity of present day software is due to the way the software is written that exposes them to certain type of cybersecurity vulnerability, memory vulnerabilities. And if we can only rewrite all of these libraries, that sort of, if you know the meme that goes around of the entire stack being built on, civilization being built on just like one block, it hangs by a thread. If we could rewrite all of these libraries and dependencies and software supply chain upstreams that our whole civilization depends on in Rust or some other memory secure language, suddenly that would fix almost all that would solve everything in terms of so many vulnerabilities.

So I had like 200 customers send me that project. Like they just like immediately saw that it became hot news of the day and they all sent it to me like, oh, are you guys going to do this? And I said, well, are you going to pay for it? I think it's worth it.

Here's the most critical thing, right? If you think about this idea where you can give these projects refactors or whatnot to AI and have it come back with the code, you can do that with any chatbot, you can give it to any AI, have it write code. Getting code from AI is a commodity. But if you add constraints to that problem where the code needs to replicate the existing functionality, it needs to compile all the unit tests, needs to pass, it should not have newly added security vulnerabilities and all the other items that are crucial to the enterprise or the problem itself that make it valuable, that's when the challenges begin. That is not something you can achieve.

So Sid, my question is, I've got 3.2 billion lines of code which is my genome. Can you recompile that for me? Can you go and identify more, fix the broken parts?

As long as LLMs can write, as long as it's in the language that LLMs are trained on understanding, we can do it. The scale is the problem that we've solved for. And the other problem we solve for is making sure the requirements match so that when we put you back together or edit you, you actually look like you, right? It's validated that it is you. We didn't change or break something that we shouldn't have.

Alex, what do you think about.

Yeah, so maybe narrowly on the bio, there are many other projects that speak the language of the genome and the proteome that I think, Peter, for rewriting your genome, you'll have the opportunity over the next few years to use one of these biological sequence based foundation models to do some variant of that. I do want for Brian and Sid though, really pull on the economics of this. So, so I really want to press you guys when, when we talk about the great refactor or some of these great projects to basically rewrite the source code basis for much of our civilization today. And, and you think about the economics of that and it there is a school of thought that, that says we're seeing generative AI hyper deflate by 10x per year or so. An order of magnitude cost reduction every year. At what point in your minds do you think, using Blitzy or maybe competitive tools, does it become reasonably economical to basically rewrite all of the legacy code out there that civilization depends on?

I would argue from a value perspective, it's there today because the value that this would be providing to society is just dramatic. Now this is a, this is a question of like who's the payer. A line of code from blitzy is 100x more cost basis than a line of code from sort of any other provider, which is maybe 100x less than it would be from a human developer. Right? So we're talking about huge orders of magnitude difference. And so would it be worth it from a society value to rewrite all the software today with, let's see. Like absolutely. But am I going to continue to serve the financial service institutions and insurance companies first that are readily paying me today? Like yes. And so I think if you, if you grab the, do you grab the capital funding for us to, to break even on this awg, we'll start rewriting all the. We'll rewrite Linux if you just.

I want to insert another topic in here. You guys shot a really cool podcast. It's on your LinkedIn where you're just bantering between the two of you about the fact that the definition of truth within large scale software has always been the functional code. Here it is, this is the final thing. It runs the PL1 code that does all the nav accounting for the mutual funds over at State Street. It's like millions of lines of legacy PL1. But that debugged code is the core asset. And then the documentation is just something around the edges. Post Blitzy, the truth moves to the documentation because you can regenerate the code overnight anyway. And so your actual core asset has moved from code to a document, but it's going to move again. And this is where it was really cool to hear you guys bantering around like, well, what is then the foundational truth of this piece of. Because like Alex is saying, the entire infrastructure of society is about to move and also expand 100 or 1,000 or a million X, you know, because code is so cheap to create, all of a sudden we have much, much more of it. So you got a much bigger world. But the, the ground truth is some other format than just, you know, PL1 or COBOL or Python code. It's this human readable today, spec becomes the central asset. That's a big shift of sorts.

Yeah, the spec is still an abstraction layer, right. And so for the. That's easy for the human to look at. The real source of truth or understanding is actually we create a customer specific hybrid graph vector database that understands exactly what is going on from a functionality perspective. And you could change that functionality from one language to another. But we are capturing the core essence of what is required there. And we can display that as a spec, which is 200 pages but of 20 million lines of code. That's a intermediate representation. And really we want to get back to the core DB level. Understanding that's the core asset for these folks and blitzy makes it. It's the property of the enterprise, everybody.

There's not a week that goes by when I don't get the strangest of compliments. Someone will stop me and say, Peter, you've got such nice skin. Honestly, I never thought, especially at age 64, I'd be hearing anyone say that I have great skin. And honestly, I can't take any credit. I use an amazing product called One Skin OS O1 twice a day, every day. The company was built by four brilliant PhD women who have identified a 10amino acid peptide that effectively reverses the age of your skin. I love it. And like I say, I use it every day, twice a day. There you have it. That's my secret. You go to Oneskin Co and write Peter at checkout for a discount on the same product I use. Okay, now back to the episode. Brian, given your background, you know, in the military, I mean probably the one institution that's got the largest repository of ancient code has got to be the US government. So can you attack all of that? I mean, unlock massive productivity? There's a lot of fear that the US is a falling empire. Its inability to understand and legislate efficiently. I mean, couldn't you have like just a single massive impact on the U.S. government?

You know, Peter, I live in Back Bay here in Boston and so does this lead investor at In Q Tel, or so he tells me. Because he keeps running into me when I'm walking to work and just bumping in and seeing how things are going, which has me skeptical. But the short answer is, short answer is yes. Like the US government is sort of a fantastic end customer to ultimately modernize, to get your flights there on time to make it in IDs easier. Right? And so this is like an absolutely part of critical infrastructure that is a target customer that we have certainly. Do we have them today? No. 12 months will be serving them like I think. Yes.

Yeah. I mean that could catapult you into a, you know, decade, billion dollar company easily just landing that kind of a customer. Because once you've modernized, you know, for one of the agencies, they're all going to want it.

Yeah. I think we had the most top secret security clearance to patent ratios of any of any company out there. So.

Fascinating.

I'd like to maybe pull on the theme that we've talked about on the pod in the past, the elephant in the room, which is recursive self improvement. So how much of Blitzy is written by Blitzi?

A lot.

So it's interesting, right? This is actually a question of where's the core value. And I would say every single Sprint that we do from a software development perspective is driven by Blitzy. And so I would say a significant amount of the corpus of the code is driven by us. Now there are algorithms that are not about writing code. Right. They're about core invention. So I think most of the company's core IP is not going to be the software that it trades, but it's going to be some core invention that this is around. And think about Google with PageRank, right? And that's Larry Page, not actual Pages. But PageRank is really the core source of their original IP. And Sid's invented a number of algorithms at the core of Blitzy that allow us to for instance, always compile code to never have circular dependencies. So you could actually rewrite sort of a corpus of Blitzy with Blitzy pretty readily, pretty quickly. And it would look like it, but would it do what we do? The answer is sort of, sort of no. And it gets the question like what is the source of IP for companies? And it's got to be a breakthrough reinvention.

If you think about what we're doing right, for these large companies, we're telling them, for many of them, we're telling them how to use AI, we're coaching them and consulting with them how to use Plixie. You cannot do that unless you've actually used it yourself and perfected the process. Because they're not just making the engineering velocity changes or the tool user changes, they're also making the process changes. And that's why this is critical.

Alex, I'd love you to take a second and dive into the swabench metric here again if you could, for those who are not familiar, a little bit of the origin you said from Princeton and ratified by OpenAI, but what is it measuring and who were the top of the leaderboards before? And then I want to get into the conversation about how do you compete against the Mag 7 or against the frontier models in this regard. So let's contextualize it first. Understanding what the Swede bench metric really is.

Sure. So for context, why don't we start.

With the title of the white paper? Because that's going to drop same day that this podcast does, so people will going to need to find the paper.

We should put a link to the white paper in the show notes here as well. Alex, please.

Sure. So maybe let me just speak to Sweebench. So Sweebench is a benchmark that measures the ability for AI systems to solve typical software engineering SWE for SWE tasks in the specific form of responding and solving issues on GitHub, very popular source code management system. So Swedbench as a whole, not Sweebench Verified, consists of a couple thousand instances of tasks in which the central challenge that's posed to an AI is to respond to an issue in a code base. And what would happen in a normal software engineering context is what kind of issue, Alex? So a wide range of issues could be bugs that need to be fixed, other performance issues, and the usual workflow in a software engineering context is an issue will be identified and pull request will be submitted. So identifying an issue, responding to an issue, submitting a pull request that responds to an issue, satisfying unit tests. These are all standard parts of what would be archetypically considered software engineering. And swedbench, I think, is sort of an excellent industry standard at the moment that attempts to capture the lifecycle of high value add labor that a typical software engineer would perform. Now I'll let the guys respond to their white paper.

Sorry. Yeah. And so I'll respond by also answering your core question, Peter, which is how does one compete with the Mag 7 in this utterly important labor task? And the reality is there's a significant amount of the Mag 7 at the core of what Blitzi does. So we use Gemini's models, we use Anthropic's models, we use OpenAI's models. But what's unique about this technology moment in time is when you use these models against one another, the quality moves up quite dramatically. And when you use them against one another hundreds of times, right. In hundreds of different combinations, which hundreds of different tool sets and prompts, the combination goes up even more exponentially. Right. And so really it's the art of orchestration through what we call Extended inference time validation to move up the quality of code, ensuring at every moment in time the system has the right context to operate despite the large scale underlying code base. So we say we're excited when Gemini or Open Air release a new model like our product gets just dramatically better every single time.

That's a really important insight, right? Building so that the better your, your components are, the stronger you are as a whole. And that's a unique niche. When did you realize that? I mean that's sort of a fundamental for you?

I think we realized it back when we were just initially figuring out this first project together. But I'll let Sid expand on this.

I think we made a bet and we said that there's not a doubt, like we were building this when models had 5,000 tokens of context. And we made a bet that there's no doubt that context windows are going to expand and the models are going to get better at writing code. Now do we want to go and Compete with the Max 7 and build our own model or do we want to stand on the shoulders of giants and use the technology to solve the problems that they're actually meant to solve? And that's exactly what we did.

Amazing. And just for context again, who had the record before you? Who had the record last week?

I think there were some open source labs and there's also been some other unpublished reports that have claimed around 80%. But the 86.8% that we're claiming is unprecedented and the highest number we have seen.

I think bytedance was the most recent tray model to, to be at the top there. So you know, we can, we can rebrand this US versus China if you want to.

Every time I hear 80 something percent, 90 something percent, I'm thinking that these benchmarks are getting super saturated. Right. And so where does this go next? I mean, what are you going to measure when you're at 100%?

Yeah, we talk about this in the white paper. Like this is we, we certainly need new benchmarks, but the, the reason we didn't do suite bench verified for a long time is it's just not representative of the scale of problems that most people use blitzy for. So the typical pull request size is like 100 lines of code and the largest repository is a million lines within this. And so we really need a set of benchmarks that's on Linux and VS code, which is 20 million and 4 million lines respectively, with holdout sets against those trying to do larger scale work to ultimately show how far we can push the Bounds on autonomy.

Yeah. And in terms of Peter's saturation question, the paper does a really, really good job of describing the landscape of benchmarks and Sweebench in particular, and the need for a new benchmark. But one of the points it makes is that when you score 86% on this benchmark, that's effectively very close to 100% because the remaining subset of questions are just flawed. They're not harder, they're just not structured well. And so you've basically capped out this benchmark now.

So folks want to look up the paper. What's the name of the paper? Do you have it down?

Alex is urging us to retitle it. So I'll give you the subtitle, which is domain specific context engineering paired with extended inference time validation breaks the barriers of LLM driven software development. So that's really what we're talking about.

At that rolls off the tongue and onto the floor.

It is a technical favor. Sid, you can search Blitzy and Sid's name. He has a searchable name. Brian Elliott. There are thousands of Brian Elliott, it turns out. But Sid Pardeshi plus Blitzy will get you to the paper.

Okay, perfect. Alex, where do you want to take this next? What have you found important and fascinating about what Blitzi is doing? What's the implications in the long term?

Buddy, it's so interesting, so many different directions to go in. Maybe just to go back to this idea of great projects, because I think Blitzi has the potential to be sort of an embodiment of an era when we just again turn all the AI agents loose on all of the problems in a discipline. So what I heard, I think Brian, you say a few moments ago was something like 100x price difference per line of coder or profile between Blitzi with its again, congratulations state of the art performance announcement and other competing tools. When I hear you say 100x price difference, I immediately internally say, oh well, that's just two years worth of cost. Hyper deflation. So you're sort of two years more expensive than the competition is the way that I heard that. So if you project forward two years, three years, four years, do you think we will find ourselves in a world where AI really has. The great refactor has been completed and we've rewritten all of our foundational systems with Blitzi or maybe copycats of Blitzy. Do you think we find ourselves in a near future like that?

I would say pontificate first here.

I think we will see the models get significantly better at doing this. And the cost go down. The key thing that I would like to underscore is, you know, a lot of the approach at some of the labs. And then what's happening right now is the labs aren't really making money on the inference that they're running. Right. For all the models. But what we're doing is because we're using the labs and we're able to charge a premium. Right. For the work that Blitzy does and also provide the validations with it, we're not losing money on the code that we're writing. Right. So as this equation improves over a period of time, the difference that Blixi is able to create is going to also grow.

So I think somewhere in that double negative I heard the answer is that yes, as hyper deflation kicks in, call it an order of magnitude cost reduction per year, maybe more, not only does blitzi become very profitable, but also becomes very feasible to start to tackle these solve everything level grand challenges in software engineering.

Yes.

So I've been attempting to kick the tires on Blitzi myself. My first project with Blitzi was I wanted to rewrite Python, the very popular programming language. And I gather you, Brian and Sid, you've had access to your own product longer than I have, which has only been two days. Have you tried to take some large scale project? I think, Brian, you mentioned Linux a few minutes ago. Have you tried to take some large project and either say, gosh, I want to ask Blitzy to improve performance by 10% on some relatively mature code base or add some crazy transformative new feature? Have you tried that?

Yeah, we did this.

Fun project where we've done a number of these, Alex. But one of the most fun thing we did, we onboarded VS code and we said, hey, add a chat experience to VS code. At the time there was Cursor still is, right? One of the biggest tools out there. We tried and we did. We built one of a subset of the features of Cursor using Plitzy and we tried using that internally. So Anytime we consider SaaS products at this point of time, we're trying to first see if we can replicate that internally using Plitzy. And if we are, you know, a few months out or a few years out, we'll say, hey, let's just use the SaaS products as a starting point and consider rebuilding it later on. I think a lot of enterprises will do this.

Have you thought, I mean, so sort of free marketing advice before the public. Have you thought about taking all of these open source projects that are in Many cases starved of core development team members and hungry for human capital or human capital equivalents, taking these projects and just aggressively setting loose the AI agents to submit very friendly, very polished pull requests to these projects to launch improvements.

We've actually done that for MLflow, I believe, right, Brian?

Yeah, yeah, we've done this especially for one that enterprises specifically rely on. It's quite possibly the best, bdr, which is just sending pull requests to open source libraries. There's an open source one right on the homepage of our website that I think you'll find fascinating, Alex, which is. It goes all the way back we were talking at the beginning of the episode. So AWS invented this or created this repository specifically to be incredibly messy mainframe code. Right. So all different styles to represent different decades of people working on it and ultimately say, how would one use code generation to be able to move this from target COBOL to target Java?

Oh cool.

We ran that through blitzy. Because mainframe is such a big problem in all these large even government organizations, and we've moved it from COBOL to Java completely autonomously with the ability to compile right out of the box. Right. And now there's sort of like remaining development to work on, some runtime stuff. But this is a. This is a sort of like a multi year long project to move mainframe from a target messy COBOL into Java. And the results of that have been probably one of the best business development tools that we've ever created.

How fast did you do it?

How long did that take? A couple days.

Yeah. If you count everything from start to end, it was a week's worth of inference ingestion.

Yeah, amazing.

So if we project that going, maybe, if I may back to Peter's question about what the human equivalent of this is. Do you have any metrics that you can point to for either cost savings relative to humans for a given unit of code, a line of code or per file, or how much faster than humans this is in general.

We typically see from a speed perspective a 5x velocity difference when this is brought into the enterprise. And the biggest challenge is really operational deployment. You're used to starting your work the same day that you pick pull up your ide. What we're having these organizations do is start to sprint for the next development work the week prior. Instead of developers starting with tickets and tasks, they're starting with code that's mostly written and a project guide with all of the human tasks to begin. So we really focus on this 5x anytime we engage an enterprise to work with us, we say pick a real world, real world project that you have upcoming next quarter, let us prove a 5x difference and we should do the remaining development work so we have an end to end solution. And if we do that then you dot blitzy across the Org. It's incredibly successful because people don't realize the cost of coordination and development and all of the sort of requirements to actually get a piece of full software out that when you can offload a significant chunk of that to agents. The velocity gains are honestly unbelievable.

Brian that's really cool insight because a lot of the younger teams are going into enterprises and then they've never been in enterprises before and they're saying, look, the raw code generation is 1000x, 10,000x. You're like, yeah, but what's it going to do for me in my enterprise? And there are very few that are credible in saying, well we actually have done it and we know the final answer. And right now it's 5x apparently. But they don't know the overhead of the organization and the documentation and just getting all of the things that happen before you can even start code generation. And so it's really nice actually to have at least one vendor that understands how to get the real thing. We actually need this to work. In the end, it can't just be a hypothetical 1000X.

It's three years from now. We've got digital superintelligence has landed. It's come out of, you know, I'm going to put my bets on Google but we'll see. What does Blitzy look like?

Yeah, I think Blitzy is the core system of record and system of action for software development in the enterprise environment. Right. And so the source of truth moves from documentation and code, which is sort of like this hybrid today, to organizations relying on Blitzy's hybrid graph vector database that understands a core functionality. And organizations are going to be able to move incredibly quickly from a software perspective. And the source of value is going to be sort of core IP that's not easy to replicate.

To add more to that, Peter there's always going to be some tasks where it's better to have a human in the loop and do them sequentially. Right. You're solving a problem that has never been solved before and you need quick feedback from AI that's always going to be there where you use the copilots. But there's always going to be this other category of tasks where you can automate them away. Right. Build the code, run it, deploy to production and execute maintenance. Blitzy now gives you the code. The code is the final output but we are going to go into autonomously, maintaining, deploying and keeping the applications running. So you're not going to need humans for specific sections of the entire enterprise. It's all going to be driven by AI.

Interesting.

I think Alex was about to paint kind of a two year view. He asked the question about what's the force multiplier today. But then I think we were going to next segue into okay, but there's 100x and another 100x coming. So I would love to finish that thought Alex.

Totally. So there's a lot of to Dave's point and Brian and Sid, I think you were starting to gesture in this direction as well. There's a lot of interest in the benchmark out of meter that's measuring the effective time of autonomy that the characteristic timescale over which AI systems, including AI cogen systems can basically operate without a human intervention. Sort of like a disengagement with a driverless car. How far can it drive without a human needing to take the wheel, as it were? So I'm curious, have you thought about the characteristic timescale over which Blitzi is able to do autonomous cogen or the human equivalent really of autonomous coding before which the human needs to step back into the loop and be involved? Right. Now, if I remember correctly, the current state of the art is something like one to three hours. There's a nice, very clean at least on a semi log plot expectation that, and I think we've discussed this previously, if you project it out a decade or two, we get to many, many years and perhaps hundreds of millions of years. And in a few decades where does Blitzy fall in this apparent exponential trend towards exponentially increasing times without humans needing to be in the loop?

I think this is next project if.

You think about all the pieces needed to achieve this. Right, let's take a very small example. Let's take the AWS example. There's the part where you identify the requirements, decide what you need to do, get the code. And there's a part where you get all the way to production. And if you look at those parts, each of them have already been automated in isolation. For example CI cd, how do you deploy the code to production, you have automations for that. Debugging, security analysis, monitoring in production, tracing and viewing the logs, ensuring that the system is not doing anything malicious. All of these items exist today and there are these glue layers that we are now seeing like, for example MCP A2A that allow agents to form this mesh and automate work. The only thing that's left to be done, really, in my opinion for these projects is to just connect the dots. And that's exactly what we're working on. So to answer your question, you know, how far out I would say we are months out actually from delivering projects completely autonomously as long as they meet a certain set of criteria and conditions.

So what I just heard you say, Sid, correct me if I'm wrong, is that the documentation writers, the spec writers, are the new limiting factor for the speed of software development. Is that correct?

That is correct.

Great insight.

How do you think about automating that.

Process, if at all, that is also automated. So if you go to ChatGPT and you ask it to write documentation, it will following your criteria. The reason we have document writers in the first place is to have quality. We have concerns that models lose context or a period of time and they skip and omit things or they can be gamed into adding things that you don't want. And we're really concerned for quality control, which is why we have humans. But as you can see, we've solved the context problem for large code bases and there's nothing really stopping anyone for that matter, from effectively adding in the right safeguards and layers of protection to ensure that we minimize the need for humans. I think it's a matter of us becoming comfortable with AI doing that. And I definitely see that happening over.

The coming months to try to begging, begging for a follow up white paper. Because Alex's question is infinitely recursive, right? If you said, okay, well then that's not the constraint, then what? What you know, because there's always going to be a constraint is turtles all the way down. You got to just ask, okay, speed of light, buddy.

Speed of light.

Yeah, Douglas Adams, right? That famously pointed to that the problem is far more, far harder to pose than the solution. And to the extent that the new limiting factor is the spec writer or the prompt engineer, whatever we end up calling it in the future, I really would like to press you, Sid, on this. Like when do we get our automated program manager, product manager, spec designer, documentation writer, if that really is the limiting factor for the speed of software engineering in the near future.

Automation all the way down.

I'll tell you something, Alex. You know how the blimy platform works? We have these thousands of agents and each of these agents has a Persona. There is a product manager agent, there is a software architect agent, there is a QA agent. And there is an agent that writes the prompts for the other agents. So all of the challenges that you're describing are live today, in production with the Qlik platform.

This is such an important question though, because I know it sounds very hypothetical, but look at this timeline on Sweetbench here. This is only 18 months ago that you got like 12% and now it's saturated. It's only been 18 months. So what we think of as the distant scientific future, it's all science fiction. It's only a year, a year and a half in the future.

Star Trek's coming, buddy.

Really, really hard to anticipate. Yeah, no, I love this question. Listen, I want to white paper waiting to happen.

I want to wrap this episode with a conversation amongst all of us on a particular topic. We opened up talking about trillion dollar pay packages, trillion dollar investments, numbers that are extraordinary. The sovereign funds, the venture funds, family offices are just supporting this with massive capital inflows. The question is that I put to all four of you. Think about competing in the long term with the Mag 7 who've got this incredible access to capital. How should founders consider going about that? What's your advice to others who are getting in here during this period of exponential growth in the AI economy? How do you compete? How do you think about that?

I think you want to be a large customer of those folks as well. We are major customers of all the AI Frontier Labs. And so they're quite excited that we're going to continue to push the bounds of autonomy and their market caps are going to continue to grow probably dramatically in line with that return on investment. And Blitzy is going to ride those waves as well. And so if you're happy when they're successful and they're happy when you're successful, then I think you're in a pretty good strategic position.

But there's one more thing to add on to that. I'd like to go back to what Dave said. Mercor, for example, was able to do that because it went deep. I think that's also the case for us. We've seen the enterprise, Ben and I, from the enterprise side of the challenge and the enterprise perspective and the security roadblocks and the product roadblocks and the process gaps that stop them from taking the full advantage of the product. So if you're an entrepreneur or a founder and you've seen this personally and you've struggled with this problem, and you think you have a solution that addresses the core of that and you've been able to test that with the actual enterprise and demonstrate effectiveness. I think you're holding on to something that is core.

So you're saying understand the problem deeply.

Yes, understand the problem. Because look, you have these Vac seven, they're giants, they have all the money. That's fine. You're an entrepreneur, you're nimble, you can find the right investors. We were grateful to find Dave who believed in us the moment we pitched it and we were able to get just the right amount of capital to get started and that's really all you need. If you have the right talent, the right amount of capital and you have the right problem that you're going after that you're convinced about because you've experienced and solved it, then you're going to be so nimble, gamble and make these moves and get a product out that is significantly better than anything that the Max 7 can put together. Because they're struggling with their own challenges like bureaucracy and you know, all of the hurdles that they have to go through to actually put out politics.

Struggling with politics. What to say over dinner with Donald Trump.

Exactly.

While they distracted all of that, you can build a kick ass product, get it to market, solve real world problems and you've changed the world effectively.

Alex, what's your thought? How do founding entrepreneurs compete with companies? I mean I remember famously Amazon was out there as a platform for people to sell their products. But then when Amazon saw a product that had incredibly high margin and growth, they would clone the product and compete directly. How do you keep from that happening?

Two words, solve everything.

Solve everything.

The world is filled with so many problems that a startup standing on the shoulders of the trillions of dollars of capex that are being invested in cloud, AI chips, fabs, energy, are now poised to solve so many problems, thousands of problems. I think Brian and Sid, and again congratulations on the benchmark announcement, are well poised potentially to solve the problem that we face of decades of civilizational software cruft, legacy code that's just piled up without enough human capital to invest in reinventing it. And now I think we're arguably on the verge of doing that. That's one of thousands of problems, entire domains that can be solved. Protein was solved by AlphaFold essentially overnight transforming a subset of structural biology. So many more opportunities.

Before I go to you Dave, I just want to remind people, you know, I define an entrepreneur as someone who finds a juicy problem and solves a juicy problem. And the more entrepreneurs in the world, the more problems that get solved, the better the world is. That's why we're going to hit on this over and over again. I think the career of the future is being an entrepreneur finding a problem, falling in love with the problem. Not the solution, not the tech. Because if you understand the problem deeply, as the tech evolves and continues, you're going to use the newest version to go and solve that problem. And again, some of my favorite lines. The best way to become a billionaire is help a billion people. And the world's biggest problems are the world's biggest business opportunities. So that's what entrepreneurship means. Dave, you see hundreds and thousands of companies. You've got how many companies right now in Lynx Studios?

28 in the building and about 50 total.

Amazing. When you're looking to invest in a young entrepreneurial team like Brian and Sid, or like the founders of Mercor, or again, some of the incredible unicorns that we've backed out of Link Exponential Ventures, what are you looking for to make sure that that company isn't going to get disrupted in the wake of a of OpenAI or Google? Slight jog to the right.

It's funny, Kevin Wheel we asked that exact question in that podcast we did two weeks ago and he answered exactly the way I had hoped he would answer, which is in a world where the foundation model companies get to AGI and can do virtually anything, are you just going to take over the world? Kevin was really clear that maybe we can do that, maybe we can't. We probably can't anyway. But even if we could, we don't want antitrust to come in here and break us up. You know, that's the last. We want a huge thriving ecosystem of partners that give us money. You know, is Blitzy one of those companies that gives us money? Yes. Therefore they're our best friend. Go conquer the world. I love it. Take over. Change the entire foundation of all legacy code base. Make a trillion dollars and give us half of it. We'll all be happy. That's what they want.

I took a two hour walk yesterday with a dear friend of mine here who runs a large venture fund. And we were talking about the notion that his bet was Google had so much more capability than they unleashed. And they said, look, it's an OpenAI. Go and do as much of this as you can because we need someone out there competing with us. Otherwise we'll get broken up for antitrust reasons. Which is a fascinating idea. You need viable competition to help you price, to help you remain on the edge, to help you not be sort of broken down by the government and.

Be a good partner. That was when Google was growing like crazy and we had all these portfolio companies. We made a ton of gains. But be a good partner to Google while they're growing like crazy. And now it's the foundation. Just be a good partner. Talk to them all the time. Make sure you know where they're going and they'll love you.

Amazing.

I have a selfish question. I don't know if we're running out of time here. Wait.

No, it's fine. We'll close with your selfish question.

Okay. Okay. Well, there's some. Always looking for traits like this has obviously been one of our best investments ever. And the sky's the limit from here. And I'm always looking for traits of success. And the morale at Blitzly is like nothing I've ever seen, you know, which is not a no brainer. When you're doing video generation for a movie studio or whatever, it's easy to keep high morale. But when you're doing, you know, 5 million lines of code, core COBOL conversion, but yet you guys have just this crazy thriving culture. And Sid mentioned, you know, we were, we're first money in. I don't remember why we loved the deal so much. I do remember we absolutely was a no brainer to invest in you guys. So two things jump out at me. One of them is bits, which is just the hardest place in the world to get into, and Nvidia, which is, you know, you've seen growth. The other one is Brian, I think.

You had Army Ranger, Bangalore Institute of Technology.

That's the Birla. Birla Institute of Technology.

Okay.

Yeah, It's a cool name though, bits. And it's like MIT B I T but it's bits.

And it was, you know, it was. By the way, MIT designed the curriculum for bits. So that statement, that was actually true.

Oh, that's cool. The other one though was, you know, Brian, I think you had not just West Point, but Ranger training, which is freakishly hard, and then first boots on the ground in Syria. So literally the first people touching a war zone. So I got to feel like there's something in those experiences that puts you a cut above in terms of building a team, managing logistics, building morale. Any clues there that other founders can pick up on and build on?

Some evidence of being incredibly mission driven and ambitious is what you would see if you were an anthropologist looking at both of our backgrounds. But if you take me for instance, if you fast forward or I guess rewind to 2017 when I was serving in the 75th Ranger Regiment, the Mandate was going to Syria. There's about 2,000 ISIS fighters that hold Raqqa. We're going to send you with 100 guys, recruit everybody else and take back the city. Right? And oh, by the way, we can't let anybody in the United States know we're here because we're there covertly, right. And, and to be able to sort of, sort of go in and solve that problem, like, that's a very ambitious undertaking where we're like, conquering cities isn't something that like most people have spent their time doing. So when you look at, for the level of ambition of the company, everybody here at the business, let's see, has that ethos, right? And the very first thing we do, we interview is we screen for ambition and the ability to invent and we have those core values. And if you talk to any single person that sits in this building right here, they will tell you, and they're right, that what we're doing is one of the most important things they will do in their lifetime. Because the economic expansion that the globe gets, the GDP expansion that you get from automating software development, or at least huge chunks of software development, there's almost no better incremental use of energy than driving towards that goal.

Wow, that's a beautiful thought. Are you guys a996 or997 shop Saturday today?

We'll be in as of tomorrow? Yeah, we're a 997 category.

Oh my God.

Just to back Dave up on his997.

That was a mistake.

I hope ByteDance was the last leader on Street Bench verified. So we can, we can, we can, we can work longer than them and beat them up leaderboards. So.

Oh my God, guys, listen, congratulations on hitting that new benchmark. But more importantly, thank you for the work that you're doing from the companies that will benefit from our government, that will benefit from the world that will benefit. You know, this is. You're upgrading the DNA of, of industries and of our planet. So, so grateful for you, Alex. Dave, any closing thoughts here?

I'm just super excited to see what you guys can bring to the future. Very few things would excite me more on the software engineering front than few years from now to learn that the entire software stack that I run, that companies that I work with run, has been 99% rewritten by Blitzi by Blitzy's agents to remove all the vulnerabilities, improve all the performance. I think it's the sort of challenge before you guys that sets us on the road to recursive self improvement and abundance and also solving everything in software.

Solving everything. That's my phrase for the day. Let's solve everything.

Good catchphrase.

It's better to Dave's 997 than 10 ping tangping the opposite of 996 lying flat in response to overwork Dave, let's.

Give you a closing thought. Definitely. Everybody read the White Paper. The title may sound very complex, but the paper itself is very, very readable. So please read it and then your takeaway will be. Wow. Okay, now we need a new benchmark. Inside Baseball Blitzi's already working with MIT to create the next generation benchmark, but catch up to what they did right here by reading the paper.

To all our subscribers, thank you for following Moonshots and WTF episodes. We're grateful for your time. We hope that you know in spending the time with us, you're able to understand how incredibly powerful this technology is for transforming our world, our lives, creating a future of abundance. I hope this counters all the dystopian news you get on the six and seven o' clock news. That stuff I don't watch. This is the stuff I focus on. I hope you do too. I'm grateful to my moonshot partners awg Dave Blunden Saleem, wherever you are, transiting the Atlantic to come back here to the us and again, Brian and Sid, congratulations on your epic wins. Excited for your future success. Every week my team and I study the top 10 technology metatrends that will transform industries over the decade ahead. I cover trends ranging from humanoid robotics, AGI and quantum computing to transport, energy, longevity, and more. There's no fluff, only the most important stuff that matters that impacts our lives, our companies and our careers. If you want me to share these meta trends with you, I write a newsletter twice a week, sending it out as a short 2 minute read via email. And if you want to discover the most important meta trends ten years before anyone else, this report's for you. Readers include founders and CEOs from the world's most disruptive companies and entrepreneurs building the world's most disruptive tech. It's it's not for you. If you don't want to be informed about what's coming, why it matters, and how you can benefit from it. To subscribe for free, go to dashmandis.com metatrends to gain access to the trends 10 years before anyone else. Alright, now back to this episode.

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