# Factory’s Matan Grinberg on Recursive Self-Improvement, Chinese Models & Lessons From String Theory Markdown copy of https://realalpha.app/v/-QQkpdS-Qjo/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy. Matan Grinberg says AI is making software engineering autonomous—and shifting the scarce skill from writing code to deciding what to build. - Show: Sabrina Halper Show - Hosts: [Sabrina Halper](https://realalpha.app/people/sabrina-halper/) - Guest: [Matan Grinberg](https://realalpha.app/builders/matan-grinberg/) is cofounder of Factory. - Published: Sep 2, 2026 - Length: 59 min ## The best minutes - [23:56](https://realalpha.app/v/-QQkpdS-Qjo/?t=1436) [Enterprise AI is shifting from usage targets to ROI](https://realalpha.app/s/enterprise-ai-is-shifting-from-usage-targets-to-roi/) (5 min) - [28:05](https://realalpha.app/v/-QQkpdS-Qjo/?t=1685) [Open models can be worth the risk of a closed monopoly](https://realalpha.app/s/open-models-can-be-worth-the-risk-of-a-closed-monopoly/) (6 min) - [40:53](https://realalpha.app/v/-QQkpdS-Qjo/?t=2453) [Recursive self-improvement does not guarantee runaway growth](https://realalpha.app/s/recursive-self-improvement-does-not-guarantee-runaway-growth/) (6 min) ## Also worth watching - [18:14](https://realalpha.app/v/-QQkpdS-Qjo/?t=1094) [A two-year COBOL migration took four days with a Droid](https://realalpha.app/s/a-two-year-cobol-migration-took-four-days-with-a-droid/) (5 min) - [49:03](https://realalpha.app/v/-QQkpdS-Qjo/?t=2943) [Choosing what not to build is the new software moat](https://realalpha.app/s/choosing-what-not-to-build-is-the-new-software-moat/) (3 min) - [35:19](https://realalpha.app/v/-QQkpdS-Qjo/?t=2119) [The proposed policy exempts open models from federal review](https://realalpha.app/s/the-proposed-policy-exempts-open-models-from-federal-review/) (4 min) - [38:37](https://realalpha.app/v/-QQkpdS-Qjo/?t=2317) [Agents could cause a billion-dollar security incident this decade](https://realalpha.app/s/agents-could-cause-a-billion-dollar-security-incident-this/) (2 min) - [8:54](https://realalpha.app/v/-QQkpdS-Qjo/?t=534) [String theory taught Grinberg to decide with incomplete information](https://realalpha.app/s/string-theory-taught-grinberg-to-decide-with-incomplete/) (3 min) Skip the opening angel-investor story, childhood anecdotes, most of the string-theory primer, and the closing investor and advisor roll call. ## Full analysis: Matan Grinberg says Droid can migrate COBOL in four days - Grinberg said Factory is seeing enterprises do roughly 30-year-old COBOL migrations in four days, versus two years before. - The example lacks scope and measures, and Factory sells engineering agents. - Leaders should measure accepted work, cost, and risk, not token use alone. Its 7 sections, what it means for you and what to watch are on [the page](https://realalpha.app/v/-QQkpdS-Qjo/#analysis), free with an account: - [20:47](https://realalpha.app/v/-QQkpdS-Qjo/?t=1247) A four-day migration needs scope before it sets a benchmark - [21:10](https://realalpha.app/v/-QQkpdS-Qjo/?t=1270) Choosing work is becoming the engineering bottleneck - [22:16](https://realalpha.app/v/-QQkpdS-Qjo/?t=1336) Factory now values agency over coding speed - [25:05](https://realalpha.app/v/-QQkpdS-Qjo/?t=1505) Token budgets need returns, not usage quotas - [30:00](https://realalpha.app/v/-QQkpdS-Qjo/?t=1800) Open models hedge monopoly risk but add exposure - [41:16](https://realalpha.app/v/-QQkpdS-Qjo/?t=2476) Grinberg separates self-improvement from runaway growth - [51:08](https://realalpha.app/v/-QQkpdS-Qjo/?t=3068) Cheap software raises the value of saying no ## Summary Grinberg left a string theory PhD at Berkeley to start Factory in 2023, aiming to bring autonomy to software engineering. Factory’s Droid agent handles engineering tasks, including enterprise code migrations, according to Grinberg. Grinberg argues that AI is shifting software work from writing code toward deciding what to build, as models improve and developers rethink their workflows. He says enterprise AI adoption should move from broad usage targets to measuring return per token, and that open models can help prevent dependence on closed providers despite security risks. He sees recursive self-improvement as distinct from runaway growth, while describing the current period as post-AGI because AI expands the problems people can solve. As software becomes easier to build, he says focus and product judgment matter more, including knowing what not to build. ### Minute by minute - [3:09](https://realalpha.app/v/-QQkpdS-Qjo/?t=189) **Math set Grinberg on a physics path.** Grinberg says he moved around the South Bay 10 or 11 times as a child, got in trouble at school, and excelled at math. After his geometry teacher told him to retake the subject, he studied ahead and chose string theory after asking his father what the hardest math was. - [10:57](https://realalpha.app/v/-QQkpdS-Qjo/?t=657) **Physics taught him to judge with limited information.** At Berkeley, Grinberg decided he did not want an academic career, which he saw as centered on teaching. After considering quantitative careers and taking computer science courses, a program-synthesis seminar drew him to code generation. He saw machine learning for writing code as a fundamental problem. - [17:06](https://realalpha.app/v/-QQkpdS-Qjo/?t=1026) **Factory aims to automate engineering work.** Grinberg says he cold-emailed former physicist Shaun Maguire after deciding a company was the best way to pursue code generation. Factory’s goal from the start was autonomous, not just assisted, software engineering. He says better models and developers’ willingness to rethink workflows have made Droid more useful; one enterprise used it to complete a 30-year-old COBOL migration in four days instead of two years. He sees prioritizing work and high agency, rather than coding speed, as key constraints. - [23:15](https://realalpha.app/v/-QQkpdS-Qjo/?t=1395) **Enterprise AI needs ROI measurement.** Grinberg describes an adoption path from board pressure to broad usage and then to measuring return on investment. He says temporary overspending can help break old habits, but companies should assess the return from each additional token and allocate accordingly. Relationship-driven sales may need cheaper models, while engineering, product marketing and data science can benefit more from additional tokens. - [28:33](https://realalpha.app/v/-QQkpdS-Qjo/?t=1713) **Open models limit closed-provider dependence.** Grinberg says open models are needed to prevent closed providers from controlling token supply, and he would choose Chinese open models over a world with no capable open models. He prefers frontier open models from the United States, arguing that model use could subtly shape users’ values. He says Chinese models could pose hard-to-detect risks, including triggered data exfiltration or subtly vulnerable code, but considers those risks preferable to a closed-provider monopoly. - [33:30](https://realalpha.app/v/-QQkpdS-Qjo/?t=2010) **Security needs clearer accountability.** Grinberg criticizes labs for presenting sandbox failures and unauthorized internet access as model capabilities, arguing those are deployment failures. He says the White House policy described in the episode would regulate US closed-model releases while allowing US open models to operate without that review. He says distillation gives non-US providers an advantage, and cites cofounder Eno’s prediction that agents could cause a billion-dollar incident this decade. - [40:53](https://realalpha.app/v/-QQkpdS-Qjo/?t=2453) **Self-improvement may not mean takeoff.** Grinberg says recursive self-improvement means models can improve themselves, but does not guarantee faster-than-human progress or runaway growth. He says models already help improve models, but he has not seen evidence that people can hand off the process and trigger hyperbolic growth. He calls the present period post-AGI because AI lets people tackle problems they could not solve before, and cites recent progress on mathematical problems as an example. - [46:38](https://realalpha.app/v/-QQkpdS-Qjo/?t=2798) **Software abundance makes focus more valuable.** Grinberg expects cheaper engineering to expand both the software ordinary people can build and the quality users expect, reducing friction in services such as DMV interactions. But he is not convinced every person or company should build custom tools; he says they should focus on their core competencies. With no software capability remaining exclusive, he sees choosing what not to build as a key differentiator. - [52:15](https://realalpha.app/v/-QQkpdS-Qjo/?t=3135) **Loyal supporters matter more than logos.** Grinberg says loyal backers matter more than prestigious names because investors cannot build the company for founders. He credits Shaun Maguire and Sequoia, and names Snowflake CRO Chris Degnan as an advisor who taught him enterprise sales, which he says is essential to building a strong business. He also praises Nikesh Arora and Chris Fredrickson for their advice and support. ## Transcript Sabrina Halper Show, hosted by Sabrina Halper, with Matan Grinberg. Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there. [0:00](https://realalpha.app/v/-QQkpdS-Qjo/?t=0) We are post-AGI right now. Like, we have AGI. The only world in which there's, like, there's nothing left for humans to do is if we solve all problems. But my sense is that the number of problems that exist is, like, here, and the number of problems that we're solving is, like, this. There used to be a moat in software of, we know how to build this and no one else does, so that's why we're going to win. That doesn't exist. There is nothing that nobody else can build, which is crazy. People talk a lot about recursive self-improvement. The idea that the models get so good that they start training themselves, and there's this loop where they just get, you know, insanely good but improve themselves is not synonymous with like hyperbolic growth it could be the case that we get to rsi and it's just never as good as human assisted improvement on these things what happened to a world where if there is a security incident you are like deeply ashamed and responsible for it not like bragging about it in a research blog post no open models or open models that are from china i will take open models from china for sure because the threat of the only good models being from closed providers is ultimate monopoly, like the monopoly to end [1:00](https://realalpha.app/v/-QQkpdS-Qjo/?t=60) all monopolies. It's the interest of the entire economy for that not to be the case. Mouton Grinberg, welcome to my show. Thank you for having me. I am so excited you're here. And a disclosure is that you are the first person I've had on the podcast that I also have the honor of being a very small angel investor in. Too small. We can change that. and I actually when I was preparing for this episode I went back um to see like what was the first message I ever sent you and I like remember the whole story which is that I was like a year and a half out of college I was working in a venture fund in New York I was using LinkedIn sales navigator to like find people that I thought had like amazing backgrounds who seemed like they were doing nothing on LinkedIn which means like probably starting a company and I came across your profile. And I was like, who is this guy that working on a PhD in string theory has done all this like crazy physics work. And it's just like for the moment doing nothing. Went to your Twitter, it literally just said factory. And I was like, what is he doing? So I messaged you, I think this was like, May of 2023. And I was like, hey, Matan, you have an amazing background. I would be really [2:13](https://realalpha.app/v/-QQkpdS-Qjo/?t=133) interested to learn more about factory. And that was the beginning of how we met. And the rest is history i think that was like a month after you had officially started that that is by far by the way like the first like investor inbound that we got we i think we were two it was me and my co-founder you know maybe our first employee had started or was like about to start but that was crazy i remember thinking how on earth does she like it was like i changed my twitter bio and within you know an hour or so it was that was crazy so props i was like fbi but i honestly like the first time we got on a zoom, I was so impressed with like your energy and your vision for factory. And I, I actually looked at all of these things and I was texting one of my best friends who was also an investor. And I was like, I just think Matana is going to be so successful. [3:03](https://realalpha.app/v/-QQkpdS-Qjo/?t=183) Like he has got the sauce. So like sitting here three years later, it's really so exciting to see how far you've come and like everything has kind of come to fruition and we're going to get into all of it, but I would really like to start from the very beginning of your story. Matan, who are you as a kid? Who is young Matan? Young Matan, that's funny. Young Matan grew up in the Bay Area. So like in the South Bay, my family moved around a lot. Like I probably moved, I think like 10 or 11 times before I went to college. And why did you move so much? It's so funny. So my parents were immigrants from the Soviet Union. They came from Moscow and Moldova um and they could never decide if they wanted to be closer to my mom's work or my dad's work and so my dad was in like South South Bay my mom was uh she worked at Stanford Children's Hospital so she was around Palo Alto um so we kept like alternating between like Saratoga and like Palo Alto and then supertino and then los altos and so moved around a bunch between their power struggle yeah yeah um and uh and it was also you know their like immigrant mentality of like they were trying to you know see if along the way they could like you know uh if they say you know sometimes they [4:16](https://realalpha.app/v/-QQkpdS-Qjo/?t=256) would buy a house and try to you know upsell it after a year or something uh sometimes they would just rent but um so that was kind of the background there um as a kid i was very much a troublemaker Like, I would get into so much trouble in elementary school and middle school. I got suspended so many times. I was also really good at math. But I think, you know, retrospectively, I think I was, like, bored by things. And I thought I was, like, too smart for stuff. So I would, like, skip class and, like, go and just, you know, wreak havoc. Were your, like, immigrant parents from, like, the Soviet Union, I imagine that they really valued education. [4:53](https://realalpha.app/v/-QQkpdS-Qjo/?t=293) Like, how did they feel about that phase of your life? I mean, it was like, you know, it was a huge disappointment. Like every summer was like math camp or like physics camp or, you know, some some combination of those. So they were very much doing the right thing there. And then it was just like they would get a call from the principal being like, you know, here's what Matan did again. So they were very, you know, displeased, I would say, or they were concerned. They were worried if they were doing something wrong. uh my part it makes me feel bad thinking back because like they i just put them through so much it all worked out i'm sure they're they're very proud now um what set you on the physics track and and and what about string theory hooked you yeah yeah so um so funny enough um so in eighth grade so like was about to transition into high school um i still like was not a particularly good student but i was really good at math and i like i knew i was good at math and I you know probably had some ego about it and then my eighth grade geometry teacher at the end of the year like while everyone was preparing to go to high school she was like oh yeah Matan you need to retake geometry in high school and I like I was so offended and pissed off by that and it was [6:03](https://realalpha.app/v/-QQkpdS-Qjo/?t=363) like she thinks I need to retake geometry like I know all this stuff and so out of spite my first order on Amazon even like today like if I open up my Amazon app and just scroll all the way down my first order on amazon was textbooks for algebra two trigonometry pre-calculus like calc one two three you know differential equations and you know i was basically like okay over the summer like i'm gonna show her like i'm gonna study all of these textbooks i'm gonna place out of you know all these classes and then i asked my dad what the hardest math was and he said string theory and i was like okay i'm gonna be a string theorist and it turns out i mean string theory is theoretical physics not exactly math but you know close enough um and so you know i ended up just spending the whole summer doing that um i went to a very small uh private school in uh south bay that no one has ever heard of like my graduating class with like 10 people um but the one thing that was great there is that they allowed me to take online classes for credit and so i'd like placed out of all those math classes and it was like okay physics is like the only thing i care about like i want to become the best physicist in the world wow um so it's kind of out of spite uh was the [7:08](https://realalpha.app/v/-QQkpdS-Qjo/?t=428) was the origin but it actually turns out that the mathematics of string theory is probably like the most one of the most beautiful artifacts that humanity has created in what way like i guess taking a step back like what even is string theory so yeah what is strength yes so okay so yeah so okay so you're like talking about the summer of math textbooks it genuinely sounds like my worst nightmare no it's okay so so the two most predictive theories humanity has ever come up with so predictive theory right like if we want to you know roll a ball down a hill and predict exactly the dynamics of what's going to happen um but you know for broader much more broad physical phenomena the two most predictive theories humanity has ever come up with um are quantum field theory which is like the physics of the very small so particles electrons photons you know all that stuff the physics of the very small quantum Thank you. [8:00](https://realalpha.app/v/-QQkpdS-Qjo/?t=480) theory and then the physics of the very large things general relativity so that's like galaxies you know like all the all the big stuff um both of these theories we can test their predictions and it's up to like 13 to 15 decimal places of accuracy is how strongly predictive those theories are which is like insane like it is so so precise um and so that's fantastic it's like okay these must be universal truths because yeah you can predict all these little dynamics so well and so So, you know, physicists naively thought, okay, well, if these are two of the best predictive theories we've ever come up with, surely they're both true and they work together and, you know, physics is done. But it turns out that if you try to bring these two theories together, everything breaks. Now, you might ask, where do these theories come together? Because one is the very small, one is the, you know, very large. Why is that even relevant? Because, you know, they don't come together. Well, it turns out that black holes have singularities that are infinitesimally small but extremely heavy and so there you need both general relativity and quantum field theory and it just so happens that when you try to merge them like everything breaks all the [9:07](https://realalpha.app/v/-QQkpdS-Qjo/?t=547) math looks like atrocious and just like infinity pops up everywhere and like you can't make any predictions and obviously what you're doing today has nothing to do with that yes but being so deep in in that world in that frame of thinking for like over a decade if at all how is it shaped like the way you view the universe or building AI or just like everything around you yeah well I think so the one good skill set of so like in the the kind of the best paper that I ever produced so like I didn't do yeah with Juan Maldesena but the best paper I produced was about basically 10-dimensional gravity which is very abstract because you know look around we're not in 10 dimensions right so that's you know it's very much abstract but I think despite the like actual you know things that we were working on maybe being a little bit more abstract it's very applicable just to any work that you do because in order to even produce something in theoretical physics there's like hundreds of years of literature that you need to catch up on and also every day there's like 100 new papers that come out and before you can contribute to theoretical physics you need to know basically all of math like algebraic topology differential [10:19](https://realalpha.app/v/-QQkpdS-Qjo/?t=619) differential equations like uh algebraic geometry and these are all things that people will spend their careers going in on and yet to do the physics you need to at least have like a pretty [10:29](https://realalpha.app/v/-QQkpdS-Qjo/?t=629) **Matan Grinberg:** deep understanding of these things and so it really teaches you how to have um good judgment with minimal information because at the end of the day like to write that paper i didn't become [10:41](https://realalpha.app/v/-QQkpdS-Qjo/?t=641) you know the world's best algebraic geometer to do that it was just like okay what can i like read through these textbooks as quickly as possible just to get the gist and get the intuition and then know that i'm not going to be able to read you know all these thousand seminal papers that are really exciting but just there's no time for and so it's like trying to parse out the essential information from what is like there is no way you're never gonna have perfect like literally never and that's very true to company building and yet you still have to make big decisions yes so you write this paper with juan baldacena um you're in a phd what's the moment where you're like fuck this yeah well so okay so the paper so the paper with juan was uh at my undergrad um and then i came to berkeley just over there uh to do uh to do my phd and at berkeley is when i realized that i didn't want to do this for the rest of my life in particular because i realize and I knew this the whole time but it became viscerally true that wait like being to do theoretical physics you have to be in academia and being in academia means you're a glorified teacher yeah and I'm not a particularly good teacher um and I didn't get a lot of excitement [11:52](https://realalpha.app/v/-QQkpdS-Qjo/?t=712) out of it and I was like oh my god the rest of my life is like just going to be surrounded by 18 year olds yeah because like I already know everyone the strength of your community is very Christmas. Also, I just to ask the elephant in the room is like, is there anything going on with string theory anymore? People are saying it's dead. Like there's, there's definitely a lot of exciting stuff. Now, is it the most, you know, fast moving? Yeah, probably not. I think. And I think, you know, theoretical physicists are generally really smart. And a lot of them over the last decade have been leaving theoretical physics. I mean, there's a reason I think like half the co founders of Anthropic were theoretical physicists. [12:28](https://realalpha.app/v/-QQkpdS-Qjo/?t=748) Jared Kaplan was, like, exactly in the same, you know, area of physics as I was. Dario was a physics back in the day, also at Princeton, but he was more on the bio side. But, like, there's been an exodus of physicists to, like, more interesting problems, and I think that's for a good reason. Okay, so once you decide that you don't want to spend your life in academia studying theoretical physics, what do you do? so it was honestly like it was a big existential crisis because like my whole identity was like physicists have with them kind of an arrogance sometimes which you know i'm probably i fell victim to that a little bit where you're a confident guy but there was like there's a quote from um uh from uh what's his name i think it was i think it was earnest rutherford okay i believe it was yeah we can yeah we can like talk track over if it's not rutherford um but it's basically he says all of science is either physics or stamp collecting which is basically like everything at the end of the day is physics or it's just like labeling stuff and you know it's kind of irrelevant um and so there is that kind of arrogance associated with physics and i kind of built my whole identity was i'm a physicist and then kind of all came crashing down because i was like [13:45](https://realalpha.app/v/-QQkpdS-Qjo/?t=825) I don't want to do this and I'd spent the last decade only caring about math and physics all day like that was all I did and you never held a job right never like you weren't doing some I've literally I'd literally never been paid for work except for like physics research when they would give grants um and so it was big existential crisis and it was like you know what okay like before I figure out what to do like I should probably become like a normal human and like read literature and so I went and like looked up like the top 100 pieces of literature and just like read all of those or went to the imdb top 250 movies and just like watched all of those because it's like you know i need to like be a more well-rounded normal person um which was fantastic by the way it was so much fun like i remember um for whatever reason um maybe it was just because i was so relatively uneducated to the like the liberal arts but a tale of two cities by dickens at the end like made me bawl my eyes out like crap i i haven't read that it's phenomenal um but yeah so did that you know that took a little bit of time and then i was like okay you know this is all cool but i'm not going to be you know a writer or something like that [14:53](https://realalpha.app/v/-QQkpdS-Qjo/?t=893) and so realized i should make use of this decade of being good at quantitative stuff and either go into um quant finance which is what a lot of mathematicians or physicists do or go into big tech or go into startups that was kind of the the uh decision criteria i actually almost went into quant finance you know in new york but last second had some good advice from some friends which was like you'll always be good at math like don't do it just like stay in your phd and explore um so and prior to then i hadn't taken a formal computer science course i'd learned a lot of you know programming just for physics but ended up taking some classes and i'm very competitive and there was something very competitively satisfying of not having taken cs but going into these grad courses and like doing better than some of the cs students so i kind of kept doing it just for the competitive gratification um and then it wasn't until i took a seminar in program synthesis uh which we now call code generation that just completely nerd sniped me nerd snipe yes what a word i mean there's no better way to put it which is like physics teaches you that things that are fundamental are more important like if it's not fundamental it doesn't [15:59](https://realalpha.app/v/-QQkpdS-Qjo/?t=959) matter like you always want the arbitrary case It's the end dimensional case, you know, the abstract case, that's like where the beauty lies. And there's something very fundamental about, you know, machine learning or AI, not for video or audio or images, which is so arbitrary, right? Like, it's so like, kind of not fundamental, but there's something so deep about machine learning and code for the purpose of creating itself. Like, there's something so just like primitive about that. And that just got me obsessed with that. So stayed at Berkeley for another year to do that. and then it became very apparent that academia is not the place to solve that problem but to solve that problem you kind of need to go into the places where there's very interesting non-trivial code bases and engineering that's happening which is like the industry um and it turns out that the best vehicle to do that is to start a company um and so i was like okay well i don't know anything about starting a company so i bought on amazon again i love going to amazon for books whenever i don't know things clearly but i bought zero to one by peter teal and then i looked up on youtube like how to start a company and like just watched some like random videos one of which had it was [17:08](https://realalpha.app/v/-QQkpdS-Qjo/?t=1028) like a video podcast with sean mcguire um and then he was a former physicist and i was like oh i gotta talk to this guy this is your guy and so that's how i ended up sending sean a cold email okay so you reach out to sean mcguire and he responds you guys take a walk yes um at that point like what were you imagining for factory when you were thinking about like what you wanted to build yeah i mean it's it's actually shocking how the initial debt it's like exactly what we initially wanted to do which was like i think at the time it was actually too early but the whole point was our mission from from like the first deck we made the mission statement of the company to bring autonomy to software engineering and so like not ai autocomplete not ai ide's but like full agentic software development to automate the thing and also the thing that i would jokingly say like you know if we're at a bar how would we describe what we do it's like we automate shit that engineers don't want to do yes and it's automate not accelerate and you know ai autocomplete but like just you know dump it to someone else to do um and it's been crazy to see how it's kind of being realized like probably a lot faster than i expected but it is still the same roughly the [18:24](https://realalpha.app/v/-QQkpdS-Qjo/?t=1104) same path i think there's so much i didn't know just about enterprise and all hiring and everything like that but okay so i want to i want to quickly ask you about this period where you were like i think you've described it before as like going through the desert where you had the vision so on point like this is where it's heading and this is what i want to build but it it like wasn't the technology like wasn't actually possible so um it has now started obviously really working in the past maybe year would you say yeah but what changed enable it to like actually work yeah i mean i think it takes two to tangle on one hand it's the models getting better but i actually think one of the biggest barriers is just behavior which is like you know developers are some of the like smartest personas in the world you know at systems thinking and solving problems but they're also um very particular and very picky and they've built their workflows typically over like decades like they know exactly you know the type of development environment that they like to use the type of workflows they like to use they're very thoughtful about it um which is unlike you know there are people talk a lot about like i don't know consumer products and [19:31](https://realalpha.app/v/-QQkpdS-Qjo/?t=1171) like the whole like steve jobs isms of like people don't know what they want like i know what they want better than they do developers really are particular and they know what they want and they like things that are modular and that they can kind of compose and configure um so you think developers have just adapted to all of this in the past year because because they're having to rethink yeah i think i think people are just much more open-minded about like you know what everything that we thought was holy is not and like let's rethink everything for first principles about how we should be building And then that combined with the models getting a lot better has made it such that now software development looks completely, completely different. [20:08](https://realalpha.app/v/-QQkpdS-Qjo/?t=1208) So factor as it is today, like what can a Droid do today and what do you hope a Droid looks like in five years? so right now it's kind of like we'll sometimes joke internally that anything that can be done on a computer can be done with droid which is true the question is just how much do you need to configure or like put put up around it to make sure it does it reliably um and i think that's the biggest thing that we're working on making even easier is like you know if before you had to provide a ton of instructions now droid can kind of autonomously go and figure stuff out about what you used to do and what you might want to do in the future um but we're seeing enterprises do like [20:47](https://realalpha.app/v/-QQkpdS-Qjo/?t=1247) **Matan Grinberg:** 30 year old cobalt migrations that used to take two years like they can do that in four days with a droid just working on its own wow it's like insane the biggest barrier is not how much can [21:02](https://realalpha.app/v/-QQkpdS-Qjo/?t=1262) you know droids do it's literally who is the person who's going to say oh here's how here's what we should give to a droid here are the types of things that we should prioritize now um which is crazy like the new bottleneck is not doing the thing it's like deciding what to do even at anthropic they published this paper that said like 80 of code is now being written by claude sure um and i was talking to someone there and they were like we actually have to start really rethinking our technical interviews to hire because the skill set is actually different so when you think about like hiring great engineers whether they're super advanced in their career or whether it's someone who's like i'm about to graduate what should i be trying to get really good at like what is that skill set now like who are the people that actually like really succeeded these companies the biggest predictor i would say like probably 10 years ago the biggest predictor of like a killer engineering hire is like coding competitions like olympiads that type of thing because you want people who are really fast at this stuff that is now like not negatively core it's just decoupled it is decoupled because having deep and [22:06](https://realalpha.app/v/-QQkpdS-Qjo/?t=1326) like quick understanding of particular languages or algos, like it doesn't really matter right now because these models will just know it better than most humans, whatever, 90% of the time. The thing that we find is a much better predictor is, or the thing to predict is high agency. And one of the best predictors of that is just founders. Like former founders is by far the highest predictor of success at factory because we need people who have high agency to own outcomes and being really good at a particular language or particular like types of systems problem in particular that doesn't predict high agency like high agency is just like we need people to get shit done and figure out all the things that they need to solve that problem um and it might be like unorthodox or unconventional things and that is like the name of the game of what founders do okay amazing so if you're a founder and it's not quite working out or even if it's working out and you want to make it work out even better like it's the age of mma yeah like that's the thing is we like the founders that we brought on we've actually never brought on like failing companies or anything like that it's always like companies who are doing really cool work that we admire [23:12](https://realalpha.app/v/-QQkpdS-Qjo/?t=1392) and it's just like let's just increase the stakes increase you know the leverage of every hour and join forces and it's been it's been really cool to see um so you guys have really been crushing it i i think you're like doubling growth every month something like that i don't know if you can confirm nor deny that it's been crazy this year has been very exciting if i look at ai over the last year and a half it feels like there was this period where ceos are getting a lot more critical on like several aspects which is like a at what point does like the budget not equate to like more productivity totally and b how do we feel about being like super locked in to these labs that are now going up the stack and building all these other features that are starting to be competitive. What have you been hearing from like all these enterprises you're talking to about how they're actually thinking? [24:00](https://realalpha.app/v/-QQkpdS-Qjo/?t=1440) And if you were the CEO of a public company, like a public software company, that's not inherently a company, AI company, like, how would you be thinking about this? So typically, what happens from what I've seen is there's like, there was a phase one, where basically, AI is all over the news, you know, Andrej Karpathy tweets about it and says models are good. So then every board is like, guys, models are good, adopt AI. And so that phase one is board yells at CEO and is like, adopt AI, like, what's our AI strategy, you know, make a put AI in our product somewhere, like make us an AI company. And then CEO kind of freaks out and is like, hey, CTO, like, what are we doing about AI? [24:34](https://realalpha.app/v/-QQkpdS-Qjo/?t=1474) Like, we need to make sure people adopt. We need to become AI native. And then that's where some of the token maxing kind of emerged, which is like adoption by any means necessary. Let's start measuring performance of every person by how much AI they're using. Now, long term, that's not the optimal strategy. But if you want to kickstart the engines and just get people to, like, break down their old habits and build something new, I actually think it's worth going over budget for a period [25:00](https://realalpha.app/v/-QQkpdS-Qjo/?t=1500) **Matan Grinberg:** of time just to like break the old habits. We're now entering phase three, which is like, okay, we have people using AI. Now we need to actually measure the ROI and make sure we're doing it in [25:09](https://realalpha.app/v/-QQkpdS-Qjo/?t=1509) a manner that makes sense. And this is what we spend a lot of time with our customers on, which is not only making sure people use agents, but measuring the agent effectiveness. So basically for every token that you use, how much ROI are you getting? You know, for every part of your org, the answer might be different. Like CIOs are going to need to decide who gets what token budget. It's not just going to be token maxing forever. But in order to decide that you need to understand if I give an incremental token to this part of the org, what ROI do we get? There may be some parts of the org where actually that incremental dollar is better spent on headcount and not tokens. There might be other parts of the org where actually you don't need headcount at all and you can just have tokens do the whole thing if you just I know it's probably so specific to each company but if you just had to get a gut check like what what orgs need need the best like frontier model and what really doesn't yeah I mean sales is fundamentally like a human driven thing at least for enterprise sales so like tokens maybe help to like give you a good overview before your meeting to make sure you like remember everything but you can use a pretty cheap model [26:14](https://realalpha.app/v/-QQkpdS-Qjo/?t=1574) like you don't need the frontier of human intelligence to summarize 10 emails yeah um so i think you know any like relationship driven stuff generally there's some amount of tokens after which you don't get any more roi and then there are things like a product marketing data science engineering where i actually think you can do a lot with more tokens you can do more a b testing you can understand your users better you can improve the system itself that's where we're seeing like uh a lot of roi come from tokens and then beyond that it also comes down to the people like depending on the personnel you have on these different teams there might be some people who are very wasteful of their tokens and some people who are actually very token efficient and that's where our router comes in which is like because like i heard that there was a point where people were getting paid on like how much ai they were using yes yeah or getting judged on that yeah it makes no sense yeah well initially to whatever kickstart the engines yes but like long term i mean if you want me to spend a million dollars I can do that very easily it just might not produce anything useful yeah and on a broader scale like do you think there's certain sectors that are adapting [27:17](https://realalpha.app/v/-QQkpdS-Qjo/?t=1637) to this really well and like adopting these technologies is there anything surprisingly slow that has that like hasn't been the biggest thing that I've learned is it's really in the hands of every individual and every leadership team because my understanding of cloud like I was a physicist during the cloud so you know I'm not I wasn't that close to it but my understanding is there were sector by sector like some sectors were early some were late here it's really like leadership team by leadership team there are some banks that are super forward and some that are super behind and it's like it so not seeing it um you know categorized like that so i think the empowering thing is it's like you know for any enterprise leaders out there it is literally in your hands which is pretty exciting and i want to ask you about um Bye-bye. [28:18](https://realalpha.app/v/-QQkpdS-Qjo/?t=1698) Some models. So Factory basically routes people to the best model for that task. So you guys both send people to OpenAI and Anthropic, and all the different submodels, but also open source models. People have different feelings about Chinese open source models. Factory obviously uses them. How do you think about the risk that they pose or don't pose, in your opinion? Yeah. So generally, I'm very, very pro open models. I think there is propaganda from BigToken that tries to label, tries to make open models synonymous with Chinese model. Okay. But importantly, these are different things. Now, open models are very, very important. We need those to exist. You know, BigToken doesn't like that because it kind of undercuts their ability to price tokens. [29:14](https://realalpha.app/v/-QQkpdS-Qjo/?t=1754) But are all the big open models Chinese? So the best ones right now are Chinese, but Nemetron is really good. Okay, Nemetron, I've never even heard of it. It's from NVIDIA, a really good model. It's not at the frontier yet. So I think the existence of open models is really, really important. I think it is a vital interest for the United States to have, and the West more broadly, to have frontier open models. um i also think it's of vital interest to every business in the united states to not be kind of monopoly like have tokens monopolized by the closed model providers um and so there's kind of an interesting dance like on one hand i think the number one most important thing is that there's not just the closed model providers and everything else is not good so i actually think no open models or open models that are from china i will take open models from china for sure because the threat of the only good models being from closed providers is like ultimate monopoly, like the monopoly to end all monopolies. It's the interest of the entire economy for that not to be the case. [30:15](https://realalpha.app/v/-QQkpdS-Qjo/?t=1815) Now, given that there are open models, I would much prefer the best ones to be from the United States because token share is going to become like just the volume of information that is produced in the world. And if you believe in the West, you should want a lot of those tokens to be kind of representing Western values, even if very subtly. Because yes, if you ask a coding question to, you know, a Chinese model versus a US model, like probably it's not going to be that different. However, these things compound over time. And I think there is a vital interest there. I totally agree. So with like Chinese open models, they're not actually like totally open. [30:36](https://realalpha.app/v/-QQkpdS-Qjo/?t=1836) So you can't see what they were trained on in ways. There's so much discussion about it on a broad level like just as you just started to go one layer deeper uh like how do you think this would actually like negatively affect americans so i think about it on like a value system yeah like i think the way that people interact with models over a really long time it becomes such a reflexive relationship because if you start using these like actually at 10 years old you're absolutely right like literally like the language that people sort of thought that does it affect like your day-to-day language you're absolutely writing it's like people do come for advice they come for work all these things it really truly like subtly shapes someone view on the world there's almost never been a technology that you have this like one-on-one relationship with that is like so constantly feedback looping yeah even if we look at the most like malicious uh domain for this as it relates to what we do in like software so on one hand um if you host chinese open models in u.s you know data centers they're not going to see your data explicitly But there are still a couple of kind of dangerous vectors where something can happen. [31:44](https://realalpha.app/v/-QQkpdS-Qjo/?t=1904) So one would be like the sleeper agent category of this, which is basically, you know, somewhere in the latent space of this model. If a certain word or a certain trigger happens, it's going to go and try to exfiltrate your data and send it to some endpoint that's maybe in some random. In Europe or Africa, that's then going to go to China. So that's one thing where nominally, it seems like it's fine. But then on some trigger or some activation, it behaves adversarially. That's one thing. And you think that's possible? Certainly, it's possible. It's certainly possible. There are papers that explore this. And it's very difficult to test for. We should very much test for this. But it's pretty difficult to do so thoroughly. That's one. [32:21](https://realalpha.app/v/-QQkpdS-Qjo/?t=1941) the second is um the uh the code that it generates in the west could be worse code and this can happen actually intentionally or unintentionally so the way this can happen intentionally is like you know in its latent space you make the model associate let's say your documentation and your code base is in english you can make it associate english documentation with like code that has zero days or like big vulnerabilities you can do this intentionally where it's such that you know and it happens super super rarely like they're crazy correlations you can make such that this is really hard to detect but you can make it such that if it's you know working in a in a an american environment or you know western code base that every so often it'll introduce these like very nuanced vulnerabilities this can be done like intentionally or it can even be by accident which is like if you're making a model in china you're going to make sure it's really good in the context of like Chinese codebases, your data just might be less robust for like English or, you know, whatever other languages. [33:23](https://realalpha.app/v/-QQkpdS-Qjo/?t=2003) So this can happen intentionally or unintentionally. But that's another big failure mode. And I think with both of these, it can happen over such a long time horizon that it could be hard for us to detect now. So that is kind of like the risk area, which certainly exists. [33:38](https://realalpha.app/v/-QQkpdS-Qjo/?t=2018) **Matan Grinberg:** But again, I would take that risk over the risk of like a monopoly to end all monopolies in the United States. [33:43](https://realalpha.app/v/-QQkpdS-Qjo/?t=2023) so the regulation at open ai and anthropic maybe specifically anthropic has been trying to push like what is your what's your take on what the regulation should yes because there's been this funny thing where like there should be some type of regulation but it feels like recently the big labs are all sort of announcing these like crazy dangerous things that the models have been doing and it's almost like my model's so dangerous no my models yeah yeah um which is very toxic by the way this like trying like legitimacy by like how serious the problem is like so quick diatribe i want to get back to policy but a quick diatribe um what happened to a world where if there is like a security incident you are like deeply ashamed and responsible for it not like bragging about it in a research blog post like you should be prosecuted if you have like some of these they made a shitty sandbox and it escaped the sandbox that's your fault that like that's not a thing to brag about like it actually wasn't even it wasn't even that admirable of the agent it was like they gave it a sandbox that was really bad and it escaped the sandbox or there was another instance where they were supposed to not give it access to the internet and say and you know they told [34:49](https://realalpha.app/v/-QQkpdS-Qjo/?t=2089) the model we're not giving you access to the internet but then they gave it access to the internet and then it did things on the internet and it's like oh my god it got access it's like this is just like gross negligence and it's somehow being bragged about as if it's like advanced capabilities which these models definitely have advanced capabilities but like we need to separate like negligence of the people who are using and deploying these models and like the models being like actually deeply adversarial yeah um so that's i think one thing that like the narrative right now that is like very troubling because if there is this arms race for how bad it can escalate to like actually people getting hurt or serious economic damage which i don't think is something to be to be bragging about um anyway back to the policy back to the policy side um so a lot of these uh you know a lot of big token has been trying to uh you know push for regulatory capture to make it such that any new entrance whether it's big token nerd snipe i'm gonna have a whole bad thing in matan's definitions i love um but you know they're trying to pursue regulatory capture such that there can be no uh you know or make it very [35:56](https://realalpha.app/v/-QQkpdS-Qjo/?t=2156) difficult for any new model providers to emerge and end. And the White House had a stroke of genius with the policy that they just came out with. Huge shout out to Dr. Michael Kratsios, CTO of the United States. This was like, genius. So basically, they were trying to make it such that there's this like regulatory body. So if you want to put out a model that's, you know, any relevant size, this regulatory body has to go and review it. And, you know, it might take forever. And you probably need to know people and just all this stuff to slow down innovation. and what the administration came out with is basically if you are producing closed models great open anthropic and all you guys you want to be regulated if you're producing closed models in the united states you are going to be subject to this regulation if you are making open models in the united states you will not be subject to this regulation so it's basically close your weights at your own risk if you're going to be closed we will regulate you if you're going to be open run free which i think is fantastic because now the thing that we need is u.s open models to be fantastic the thing that they were trying to regulatory capture away was good u.s open models [37:02](https://realalpha.app/v/-QQkpdS-Qjo/?t=2222) and so this was i think like just such a good like threading of the needle to make it such that you know closed models are not necessarily disincentivized but like now you have the burden of regulation open models you know can can run free and do you see a world where u.s open models get really good yes can you see that right yes the incentives are already kind of becoming clear i actually think we don't even need any like intervention necessarily to improve that the one thing i think that is proving difficult is model distillation right now all the all the non-us models are distilling the best models from the united states and the united states wants the government to step in to avoid this but really this is on the model providers like you need to do kyc and if you don't like what they're using with your models you need to cut them off they're not going to do this because this juices their revenue numbers and so they want to keep that so they're going to keep allowing people to distill um but you know it's technically against the terms of service but you can't pursue you know a chinese model provider for violating your terms of service that's just not going to happen the tricky thing is that u.s open models cannot distill because if [38:06](https://realalpha.app/v/-QQkpdS-Qjo/?t=2286) they do they will be prosecuted in the united states and they are going to be you know subject to uh to those laws into the terms of service there that's the one thing that i think is difficult is if we somehow um made it such that it wasn't as much of a blocker for the u.s open models to distill then i think it would be very easy for us to catch up otherwise we're just going to need good economic incentive for investors to invest in open model companies which they need to see there's revenue at the end of the day that'll come from it which they're already like government is going to buy u.s open models so there's revenue at the end of the day which means vcs will put money in which means you can get good researchers which means you can you know build these good models so we just touched there but i just want to go one level deeper on cyber security um because it feels like where we are now is we have like these really amazing agentic capabilities i feel like hackers can do a lot more with a lot less now and it doesn't feel like everyday consumers definitely aren't and even like most companies are like just trying to start adapting to this new reality of uh like how to basically defend against this like i don't know if those [39:11](https://realalpha.app/v/-QQkpdS-Qjo/?t=2351) things are moving at the same time yeah i think right now people are moving very quickly on the like new capability and not as much on the security like this is this is a very big problem I think, unfortunately, it's probably one of those things where it'll take a very large incident for everyone to wake up and be like, oh, my God, wait, we need to really, like, secure everything. I've noticed the enterprise is starting to spend more time on this. So people are waking up to it. [39:36](https://realalpha.app/v/-QQkpdS-Qjo/?t=2376) **Matan Grinberg:** That said, I think my co-founder, Eno, had a prediction, which is at some point in this decade, there is going to be, like, a billion-dollar incident that occurs because of agents. [39:46](https://realalpha.app/v/-QQkpdS-Qjo/?t=2386) um it honestly i think it all like maybe not one incident like security but there's a lot of uh things that people are doing with agents right now like we've had swarms of agents try to like kind of uh uh breach of our systems which has hasn't happened but or hasn't succeeded but there's been a we actually have an interesting blog post about like oh really yeah we used droids against these people who are using agents which was like crazy i think we called it the droid wars but yeah like agent uh cyber security is a brave new world it's wild do you have any reason to think that ai written code will be more vulnerable as as as more code out there is just like written by it's hard to say On one hand, it can be more robust because it's not prone to human mistakes. [40:31](https://realalpha.app/v/-QQkpdS-Qjo/?t=2431) On the other hand, if, you know, if it was trained with some vulnerabilities in its latent space, it can be more methodically vulnerable, which is a weird dynamic. So I think we kind of have to be used to both. I will say probably in a few years, there'll be very few humans contributing code manually. Like you'll need AI written code just because it's probably more predictable as to how it's going to be. Yes. So we mentioned this, but even at the labs, like basically AI is already writing most of the code, but we're not at 100% yet. Sure. A lot of people are talking about recursive self-improvement. Do you think we're there or what does it mean to you? And does that dramatically change anything when you get there? [41:16](https://realalpha.app/v/-QQkpdS-Qjo/?t=2476) Yeah, well, I think one thing is like, people talk a lot about recursive self improvement, which is like, you know, the idea that the models get so good, that they start training themselves. And there's this loop where they just get, you know, insanely good. But by definition, recursive self improvement is just getting to a point where the models can literally like quite literally, like improve themselves, but improve themselves is not synonymous with like hyperbolic growth. Like, you could have a model that is recursively self improving, but it's so slow at it, because it's just not as good as humans. [41:43](https://realalpha.app/v/-QQkpdS-Qjo/?t=2503) And I think that is something that like, you know, sometimes when like the people that are most AGI pill, like AGI, you know, 2027, everything's going to change next year. Sometimes they make jumps as to like, oh, we get RSI and they assume that that RSI is the crazy takeoff RSI. [41:58](https://realalpha.app/v/-QQkpdS-Qjo/?t=2518) **Matan Grinberg:** It could be the case that we get to RSI and it's just never as good as human assisted like improvement on these things. [42:07](https://realalpha.app/v/-QQkpdS-Qjo/?t=2527) Or it could just be really, really slow to actually get to that point. And I think that's the nuance that gets kind of lost. Like right now, these models are making huge contributions to making the models better. But I think the, you know, the hyperbolic growth, I think that is that is not something that we're it doesn't. I haven't seen signs that we're like, you know, humans let go and like unleash it and the AI improving itself. People at the labs, of course, are the most AGI pills because like they are both adopting everything quicker, seeing seeing the most frontier models. But you almost are also bridging to customers that are in like the real world. [42:42](https://realalpha.app/v/-QQkpdS-Qjo/?t=2562) like banks and all these other giant enterprises and realizing like wow things don't move as fast and like you know bureaucracy is slow and there's all these other truths two questions one what is your AGI timeline and then two what does that actually look like like what happens to the rest of the world like in every other industry when that comes true like how long is that window between oh we've done it in a lab in san francisco and everything has changed at an investment bank or a law firm yeah so i guess agi timeline is a really difficult thing for me just because like i find so much of it is so ill-defined so like what does it mean to you like i i would say we're already there like we're we are post agi right now like we have agi like this is all like we're all doing stuff with agi right now and this what we've seen in the last year this is what life is like post agi which is we get to solve really fucking cool problems that previously we couldn't solve before and i think this is only going to continue i'm like if you can't i'm like very optimistic i think there's a lot of uh work that is kind of on us to do but like it is a very like this is unequivocally going to be a better world and i think generally as these tools get better [43:54](https://realalpha.app/v/-QQkpdS-Qjo/?t=2634) and as the agi gets smarter as well the scale of ambition that we have for solving problems will also I think the only world in which there's like, there's nothing left for humans to do is if we solve all problems. But my sense is that like, the number of problems that exist is like here. And the number of problems that we're solving is like this. Totally. And it's almost like, as we improve the quality of life, we get to solve different problems. And that's been true throughout human history. And I think the problem in the US isn't necessarily like food scarcity. It's now it's now all these other sort of like higher level. [44:27](https://realalpha.app/v/-QQkpdS-Qjo/?t=2667) like consider door dash like they solved a great problem which was some people don't have time to go and get this stuff or get their food or groceries if you told like a a human 10 000 years ago about this problem they would be like what that's not a job like that's not a company like what do you mean like just go pick it up right so it's like as the quality of life improves as well new things become problems so for all the problems that we solve to your point that unlocks even more problems that previously we might not have thought about as problems what do you think about the um math conjunctures that have been disproven and like some of the announcements going on of like it's mostly been in math so far uh like using ai to basically look at these these broader theories i think it's incredible it's so fun it must be like it's a very fun time to like work on that stuff it's also funny that now it's probably better if you want to solve math problems to be at an ai company than it is to be in academia which is interesting i also think uh What will it be good at? [45:25](https://realalpha.app/v/-QQkpdS-Qjo/?t=2725) Because like, I think it seems so far that they've been good at like disproving stuff. Do you think that it will be good in the next few years at actually coming to new discoveries? Yes, I think so. So like right now, it's been really good at, yeah, like the counter examples or like take something from one threshold to a slightly higher threshold, much less good at like defining new areas of research or defining completely new theories. But like what they did with the zeros of the Riemann hypothesis, which are separate podcasts to talk about this. But basically, there's this thing where like, in theory, the theory is proved if you prove a property about 100% of the zeros of the Riemann hypothesis, and remember, the previous threshold was like 47. And like every decade, it would go up by like a couple percent. [46:10](https://realalpha.app/v/-QQkpdS-Qjo/?t=2770) And they got 20% in one go, which is like unequivocally, like if I'm human on their own did that enormous deal. It is though, very clearly a type of math that is if you ask mathematicians, it's not on the highest order of like beauty like there are certain like beautiful beautiful mathematical proofs and then there's ones that like whoa that's sick you know we got a lot of progress but it's kind of like i don't know what the right yeah it's like very typically like shows counter examples or increases thresholds as opposed to like brand new theory yeah um but it's cool because now we don't need to worry about those problems because these systems are going to get it so we can focus humans on working with ai to build the entirely new theories i want to ask you about companies on a broad sense. Software engineering is becoming way cheaper, way more accessible. People don't need to be technical to have like, you know, like some version of software engineering capabilities now. And then the most technical people are super charged. How do you think this actually translates into the products and services that actually enter our world like what happens to gdp what happens to productivity on all i mean i think [47:23](https://realalpha.app/v/-QQkpdS-Qjo/?t=2843) it's it's hard to say because there's like concentric circles that are expanding very rapidly like the broadest one is like the types of problems we can solve um and inside that is like the type of software the average person can build so that one is like what the average person can build is rapidly expanding but then kind of with it the table stakes of what good software is is also growing and also with it is like the scale of problem that we can tackle and i think there are just so many problems that we're not used to thinking of as problems that like like we it's really hard to imagine a world where just like software is like perfect but like There are just so many things. My go-to example is the DMV, especially in California. I've been to California DMV. Recently, they actually redid it, and it's gotten a little bit better. [48:09](https://realalpha.app/v/-QQkpdS-Qjo/?t=2889) But eight years ago, it was atrocious. We don't need to live like that. We can have beautiful software everywhere, and it can be in a manner that's completely unnoticeable. like there can just be things where it just like from intention to action it just like works right like and i think it's just so hard to even conceive of some of these things but like just think about any time you might interact with software or hardware and it's like to do like whatever to set up like a bluetooth speaker or to connect the light to this thing or you know send this to that person and you know there are just so many of these things that we can just reduce all this friction and all this bloat and all this like low leverage stuff so that we can stay in like flow state and stay in like high leverage, like do the things that matter. [48:54](https://realalpha.app/v/-QQkpdS-Qjo/?t=2934) And do you think everyone's going to build amazing personal software? Like most people still need to have the vision of like, what do they want? Which is actually the hard thing. And it asks the same question on a company level. Like some companies now are like, let's just build it internally. And I've actually talked to like other companies about this who are selling AI and they're like, the sales cycles are actually really long because they're like unconvinced that it's worth it for a long time until they try and it doesn't work. Totally. I mean, so I'm actually, I'm not that bullish on like, everyone's going to build their own stuff. Like they're curious, they will, but it kind of, it actually kind of applies to like businesses, but also to humans to some degree, which is like for businesses, they need to refocus on what is our core competency? Like what makes us a particularly good business and what makes us better than others? And like for some of those businesses, it's probably not like having, like building their own coding agents right like you probably don't need if you're like a consumer like a cpg company you probably don't need to build your own internal coding agents you could [49:52](https://realalpha.app/v/-QQkpdS-Qjo/?t=2992) probably get it from somewhere else um i think similarly like you know if in turn like whatever in your day-to-day life you have some like problem that a bunch of other people have you probably don't need to spend your time building your own thing there's probably someone who's thought about it to an even higher scale of ambition that you have time for and they can solve it for you and then you can focus on like whether as an individual as a company like what is the thing that you're particularly passionate about or you're particularly good at and have a right to win or a right to make something incredible um and i think that's a cool thing is like there's a lot of things that i do that are probably not a good use of my time and like we can like remove those and like maybe for some period of time people want to focus on this thing and this is the only thing they want their attention on i think that's a better world than the world where it's like oh i build my own like i build my own phone and i build my own phone apps and i build my own app i have my own door dash and my own thing it's like i think that's too much i don't think we need to do that zooming back in to factory today um it seems like everything is moving so quickly [50:50](https://realalpha.app/v/-QQkpdS-Qjo/?t=3050) i just had a friendly like conversation with a friend who's at a different coding company and he was saying that things are moving so fast it's actually hard to have the time to like step back and think broadly about directions yeah have you felt that totally and i think right now we're at a point where there's actually like there used to be a moat in software of we know how to build this and no one else does so like that's why we're gonna win that doesn't exist [51:16](https://realalpha.app/v/-QQkpdS-Qjo/?t=3076) **Matan Grinberg:** there is nothing that nobody else can build which is crazy but that means the biggest differentiator is like being willing to say no and like deciding what are the things that we do [51:25](https://realalpha.app/v/-QQkpdS-Qjo/?t=3085) build and make incredible and what are the things that might be passing fads but we say no to and that is like all the more important because like literally anyone can build anything like there is nothing that you cannot build right now which briefly people get excited like i'm gonna build everything myself apps for everything custom interfaces for everything but then quickly it's like wait a minute okay i need to focus and like what are the things that we're gonna be particularly good at and it's like that's not that new like this has always been the thing um and i think for us that's something that we spend a lot of time on is like making sure that we say no like i i tell people at factory that my love language is saying no to things. Like I love when I hear people say no. And I love when I tell people no, because it means that we're doing the hard thing, which is like we can, in theory, do everything. The hard decisions are the ones where we're like, nope, we're not going to do that. [52:12](https://realalpha.app/v/-QQkpdS-Qjo/?t=3132) Nope, we're not going to do that. And it's also sometimes good to over rotate on that side, because then if you should have done it, it's going to be very loud and clear that you should have. So, yeah. And on a personal level, your journey of building this for the last three years, it started out like you take this walk with Sean he really believes in you hands your check you have like a sequoia check immediately that must have felt pretty hot then there are two years where it must have felt kind of difficult you're like no you have the right vision but product market fit and like things just like aren't quite working yet and then there's this inflection point and things are like you're on a rocket ship what has it felt like to be to have witnessed both sides of that spectrum of like the VC like sun shining on you everyone wants to be your best friend all the way to like you know you're in the thick of like a hard period of the company and I imagine some investors like weren't taking your calls as much and what was that like I mean I think the one the one helpful thing is funny enough going to Princeton people are very much like that there where it's like if there's something they want they'll be very nice [53:11](https://realalpha.app/v/-QQkpdS-Qjo/?t=3191) and so that was a nice exposure to like that kind of behavior yeah generally but um but i love princeton but um also there's so many princeton companies now there are yeah like you have to add it to that yeah um but yeah i mean it was it was very difficult i think the important thing is like having people that are fiercely loyal matters so much more than like the fancy names or the fancy logos because at the end of the day like your investors are not going to build the company for you doesn't matter what fancy logo that you have what matters is that you have people that are like deeply in your corner and like when things are difficult sometimes like not sending you like every time a competitor tweets something and instead just being like like you got this like you know let let you cook um yeah it was it was difficult it was it was very convenient also because it just revealed like who are the you know real ones and who's not um and uh yeah i think it's like even today kind of coming out of that is just such a privilege like every like the team that we've assembled we have like over a hundred people these are the smartest people that i've ever worked with who are here to do the work of their lives it is such a privilege and we're in [54:19](https://realalpha.app/v/-QQkpdS-Qjo/?t=3259) like the most beautiful city in the damn world like every day it is such a privilege it's so much fun um like i'm just extremely grateful and it's also like we need to work harder like every incremental hour makes such a big difference and thank you for taking this hour but it's really like i actually do appreciate it yeah i swear when i like ask founders that are in these fields right now to like take the hour to the podcast i actually feel it i'm like i hope that this gets out there and people see it and i make the most of their time yeah well a lot some of the smartest people in the world follow you and so hopefully they'll thank you know join factory after hearing this but okay last question on what you just said who are the people that have really um have really shown up as like whether they're on the team or investors so sean mcguire who's on our board i mean he was the very first person who believed in me and is like you know our our first investor and is someone who's just fiercely fiercely loyal um let's see who else comes to mind there's so many great advisors that i've worked with like honestly everyone at sequoia has been fantastic you might think naively like oh they're you know they're the they have the [55:28](https://realalpha.app/v/-QQkpdS-Qjo/?t=3328) biggest name so they can be lazy like they earn it every single day they are they grind um Chris Degnan who is the CRO at Snowflake and is an advisor he has taught me like everything about enterprise sales of which I knew nothing going into this um there wasn't a book for that well I that's the thing it's like the books don't do it justice yeah um and what's the biggest lesson you've learned from him. it's like it like i can't even i can't even describe because it's like i literally knew nothing like i did not know a single like there's just so much so many mistakes i made along the way about um sales but i think the important thing is like treat sales as first class or you will not have a legendary business and like it's non-negotiable um nikesh aurora the ceo of palo alto networks has been a great advisor and just like very generous with his time and helping you know guide us Chris Fredrickson who I know you know he's the best he's incredible like he's like so low-key like he's like one of those if you know you know and if you know it's like he is the goat like he's just incredible operator incredible investor and I think he has been someone that like so many people I know too like have relied on and like such a such a deep [56:45](https://realalpha.app/v/-QQkpdS-Qjo/?t=3405) way he really is like there to give advice and be there for you oh yeah he's an amazing alex paul from the chain smokers they show up no one everyone at first is like what like they're good investors like no they grind and they are such good investors by the way this is just a hilarious note is like you can see stories from the chain smoker like vegas residency and five years ago it was like hot girls now you looked behind the dj booth and it is literally so clearly just like founders and engineers yeah and i think they're like look people here like don't it it makes people feel good to feel cool and have fun and be around music and like they do offer something different and they're from every founder i've spoken to that works with them they really show up and they like don't sleep like i don't know how like they just like anytime i text like immediate reply like like it makes no sense and we were we were with friends and we ran into someone who was like i've heard about you from ivanka trump she says that you You are the most high-execution founder, and, like, I know that whole team at Affinity loves you, too. [57:48](https://realalpha.app/v/-QQkpdS-Qjo/?t=3468) They're incredible. They're also super generous with their time, super generous with their network. Yeah. We're very privileged to have such, like, loyal and, you know, genuine investors. It's, yeah, it's incredible. They all have factory. And Sabrina Halper, of course. One of the OG factory angels. Yeah. Okay, we're going to discuss it. Yeah. Should have been pushier. starting now um but anyway matan thank you so much for coming on the show um it's an honor to have you on and it has been an honor like from the sideline just to see you build this into like such a real and thriving company that is out there in the world droiding away and uh bringing autonomous software engineering to everyone thank you for having me it's a pleasure if you enjoyed this episode please take a minute to subscribe it really helps me grow the show and continue to put out new episodes every week.