Software Huddle - Exponential Engineers With Ashmeet Sidana

Episode Date: January 11, 2026

Today on the show, we have a special guest — Ashmeet Sidana, the founder of Engineering Capital. Ashmeet started his career as an engineer at some great companies like Hewlett-Packard and Silicon Graphics before founding his own company, getting it acquired, and eventually starting his venture capital firm, Engineering Capital. With his strong engineering background, Ashmeet looks for startups that have a technical insight — something unique that gives them an edge over their competitors. This focus on technical insight sets Engineering Capital apart from other VC firms that often emphasize market insight or distribution insight or some other kind of advantage. We talked about AI, Exponential Engineers, Entrepreneurship, and had a lot of fun.

What's up, everybody? This is Alex, and we're back with a great show for you today. Sean and I are both on this episode. We also have a special guest, Ashmeet Sedana, who is the founder of Engineering Capital. You know, Ashmeet was an engineer at some great companies, HP, Silicon Graphics, before starting his own company, getting acquired, and then starting now Engineering Capital, been a VC for 10 years, has four funds, you know, an IPO, a bunch of exits, had a lot of success. And I had a great time talking with him just because he's always looking for, like, this technical insight, right? he's got this engineering background. And so when he's looking at companies,

he's talking about how he wants to find some sort of technical insight that they have that gives them an advantage over their competitors. And how that distinguishes him from other VC funds that maybe look for a market inside or a distribution insight or something like that. So I thought it was really interesting. I love to hear about his path and what he's seeing, you know, with all the AI buzz that's going on and all the opportunities out there. So really fun episode.

Be sure to check it out. As always, if you have any questions or if you want any guests to come on, feel free to reach out to me and Sean. And with that, let's get to the show. All right, welcome back to Software Huddle. I'm Alex Debris, your host, and I have my co-host here, Sean Falcner. Sean, how have you been? It's been a while.

Yeah, I'm doing well, surviving. Had a lot of travel recently. It's been cutting into our podcasting a little bit. Yeah, for sure. Yeah, I asked you to take the lead today because my flight last night back from the East Coast was massively delayed and I arrived at 2 in the morning. So if I'm less coherent than usual, I blame the late flight.

Yeah, seriously. And it sounds like you've been all over the place. You know, Europe and East Coast and all that stuff. I guess, like, what have you been up to? You been talking to customers doing conferences? What have you been doing? Yeah, a lot of talking to customers. You know, I stepped in as the product lead for AI at Confluent a few months ago.

So why out there just seeing what, you know, people are doing, what they're building, how, you know, Confluent plays a role. in that and sort of the marriage between, you know, data streaming, real-time systems and essentially AI. Yep, yep. Well, definitely like a fun time to be doing all that with what's going on in AI and, you know, out of a company like Confluent that sees like huge customers and huge workflows. But on the topic of AI, we have a great guest today.

We have Ashmeet, Sedana. Ashmi, welcome to the show. Thank you. Pleasure to be here. Absolutely. So, Ashley, you. We're an engineer, started as an engineer at Hewitt Packard, Silicon Graphics. You started your own thing and sold that.

And now have been a VC, you know, for the last 10 years or so, you know, and seen a few things. I'm really excited to just sort of dig in. It's like, I love talking about the VC market because I think it's like a good, just pulse check on like what people are seeing out there. You know, I only get to see so much like in the companies I work for, but just like, you know, you can follow the VC market to see what's happening. So, but anyway, with that said, maybe you want to give us a little more introduction to

you and what you're up to? Sure. I do think of myself as an engineer. Obviously, I run a firm called Engineering Capital now. I started as an engineer. My job is as a venture capitalist investing money. And the one-minute definition of engineering capital is seed stage investment in software companies that are taking high technical risk. And if it's of interest, we can get into why technical risk, why not market risk, what's different about it, et cetera. But that's what I think of myself as I did grow up on a farm. So if you want to talk about a farm boy, we can go there too. Awesome. I love it. So that's fun. Well, you're on, I believe your fourth fund. Is that right at engineering capital? That's correct. I'm investing fund for now. Okay, fourth fund. You've got one

IPO, 15 acquisitions that I can see from the site. So you've had a lot of success there. So that's awesome. I do want to talk about that technical risk. Why did you zero in on that? Like, why is that interesting to you in your firm? Yeah, it was interesting to me. for two reasons. One, of course, that's me. So it's very much about what is my specialty, where do I bring an unfair advantage to the market, but there's also a market opportunity there. Believe it or not, in Silicon Valley, which you think is the heart of technology on the bleeding edge, cutting edge of where things are, there is a structural arbitrage where a lot of people don't understand technology, even venture capitalists, even great CEOs, even great founders. And so

So if you are a technical founder who wants to start a company where the primary risk is technical, I believe that is an underserved market. And for various reasons that will continue to be underserved, that is not going to change. Yeah, absolutely. And can you tell us, like, what you mean by technical risk or like, is that like, I mean, are you having a lot of developer tooling companies like databases or things like that? Are you looking at, I guess, like, what sort of technical risk do you look at? Yeah.

So technical risk ranges from all the way from what I would call pure science risk, research risk, at the very deep end. So these are things like, I'm going to build another quantum computer. Okay, wonderful. If you can do it, hats off to you. You will change the world. It will be revolutionary. Lots of people are trying. No one has built one yet.

At the other end is what I call trivial software. In other words, if you were to grab 10 smart people from Google or 5 smart people from Cisco and Facebook, you could probably be. build that. It's just a question of, are you solving the right problem in an efficient way, and do you have a great go-to-market? I'll broadly put that in the category of enterprise software or consumer software. In the middle is a sweet spot. That is stuff, which is kind of unique, kind of hard. People don't really understand it. If you grab 10 smart people from Google, they'll go like, huh? Like, how did you make that happen? We don't quite get it. And because all great software in the world is built by two pizza teams, it can be very capital efficient to take that technical risk.

And that is what engineering capital specializes in. All the great software in the world, VMware, Google, Facebook, etc. was always built with very small core teams. And so that's the place where I want to invest. The thing I took away from that was that it sounds like five Cisco people or equivalent to 10 people from Google. Definitely did not mean to imply that. there are these, you know, what people call 10x programmers. I like to think of them as 100x programmers. And in a world of AI, we might be headed to a world of a thousand X programmer. Yeah.

That is really where we are headed right now. And they can be in every company. In fact, every company has a very small number of these people in there. Yeah. I never really like the sort of 10X programmer framing because it's not really, like you can't take sort of 10 mediocre programmers to make that person that you're sort of giving the label of 10x program. Like, that person has a skill set that, you know, adding more people to the equation doesn't fill. You know, they have the insights that they don't know. I don't know how to sort of describe that person, but that 10x doesn't feel

right to me. But with the, this thesis around. Before we go too far, before we go too far from that point, there's a great book if you want to read a long time ago, Fred Brooks wrote a book called The Mythical Man Month. Yes, excellent book. Which really goes to the heart of of this issue because programming is a cognitive activity. And so it does not follow linear rules. It doesn't follow linear laws. It is an exponential point. And that's really why 10x, 100, these are all ways

of simplifying what is really at the underlying core and exponential dynamic. Yeah, absolutely. I want to. So go ahead. I was going to say, you sort of mentioned like 1,000x programmers and things like that. And I'm skipping ahead of quite a bit.

But are you seeing pretty important? impressive productivity gains among teams that you're starting? And are you seeing leaner, smaller teams now with some of these AI tools than you were even five years ago? Unequivocally, we are seeing massive increases in productivity, thanks to AI, generative AI, LLMs, etc., being applied to the act of programming itself. And so what I would call just below, you know, that amazing, incredible, crazy guy who can really make something unique happen. Let's say another Linus, right? I mean, he started with the Linux kernel.

That would be someone like that. If you're looking for those, you know, whether you call them 10x, 100x, whatever, I call them exponential programmers. Let's look at WhatsApp, right? This is an old company. WhatsApp is a very old company. It was written by a very small number of people.

It is a tool and a product that is used by not millions, not hundreds of millions, but billions of people every day. And so that is what is possible when you run a tightly scripted software tool over there. over there. What is happening right now and what is different with AI is that all these programs, all these teams, which were written by these very small groups of people, to be deployed at scale required very large teams. Whether it was consumer, whether it was enterprise, you needed very large teams. So Facebook has thousands of people, Google has thousands or tens of thousands of people, et cetera. That is where we are now seeing the efficiency gains from AI. In other words,

we are collapsing what it took to scale something before. Interesting. And do you think, is that because of like the hyperscalers and the cloud providers? Is that what you're saying? Like the infrastructure stuff is easier? Or are you saying even just like the actual AI tooling itself is making that easier as well? I think that story is still being written. What we are seeing right now is the very early parts of that story where we are standing on

the shoulders of giants. We obviously have hyperscalers. We obviously have existing tooling, which has existed, you know, for many years or decades now. And that entire stack is now getting reinvented. And so we are seeing productivity gains everywhere. I mean, I've seen people who are trying to make chip design faster, and there's certainly every coding tool sitting in every IDE helping you write code faster.

So everything from there all the way down is happening. And on top of that, we're putting in agents, we're putting in these application level stuff. So literally there's innovation at every level of the stack, which is what is so exciting about what we are seeing right now. This has never happened in technology before. Every previous cycle, every previous wave in technology, all the way from the mainframe, the mini, the desktop, the internet, the iPhone, all the innovation happened at a particular

layer in the stack. Now we are seeing innovation at all layers, and that's what makes it so confusing and so exciting at the same time. Yeah, in terms of, you know, that drawing that comparison to some of these previous areas, you know, I've been through a few of these different, you know, tech cycles, I think, Alex has, you've been also there through these, both on the engineering side as well as on the investing side. Besides the innovation that's happening across the entire stack, are there other things that feel different than, say, the sort of dot-com era, which I think a lot of times people draw

comparisons between what's happening in AI in these previous cycles. Yeah, I was just starting my career in the dot-com era. So I kind of lived through it, but I was a kid, you know, just learning to see what the world was about. We captured one of the two big differences between what's happening right now and what happened earlier. What we captured is that its innovation and change is happening at all layers of the stack, and that is clearly a fundamental change. It makes anybody who builds anything sitting on shifting sands, and that's what makes it so fun, both as a technologist, as an entrepreneur, and as a VC. The other big change, which is happening right now is that technology has already been previously deployed all over the world in every single thing. In other words,

software already ate the world to take a previous, you know, formation of this. Because software has already eaten the world, now whatever you do, whatever impact happens with technology, it affects everywhere, immediately, very quickly. So the scale, speed, and impact of that change is much more dramatic. And that's, these two things happen. at the same time is what is creating this amazing explosion that we are seeing right now. Yeah, I think with dot com in the early days of the internet, you had, there was economic risk. Like it wasn't clear whether people could actually make money off of this thing because there was a pocket of the world that was very interested in the internet, but there's still the

large majority of people were not connected online and there was a sort of active debate of whether that would ever happen or not. And that's certainly not the case now. So I think to your point, like one of the differences with this sort of AI cycle that we're going through is it doesn't feel like there's economic risk. Like clearly if you're able to build some sort of multi-agent system that can offload a large chunk of work that a person has to do today, there must be ROI. Like if you can pull that off now, there's a question of whether you can pull it off or not. But if you can, then essentially there's clearly ROI from the business. There's economic value there. You know, the way I would describe it is that the lesson that we learned from the dot com era, the technology rollout in the dot com era, was that there actually was not economic risk. So let's take the two big failures of the dot com era.

Betts.com and WorldCom. Okay. So internet infrastructure and e-commerce, right? Both things were clearly the correct bet directionally. In other words, every single thing, every single piece of fiber that got laid out, every single modem that got sold, got used, et cetera. And clearly today, we buy dog food on the internet. I buy dog food on the internet. So those were directionally correct bets. The lesson that we learned from that was the risk was timing. The risk was not that we were wrong. The risk was the timing was where all the risk was. That risk still exists today. But the market is a lot more rational. I think people still remember those lessons. And so they're being more cautious in terms or how they're thinking about it. We haven't gone into those crazy multiples,

crazy forward-looking sort of decisions being made. You could argue that the $100 billion investment by NVIDIA into Open AI, when Open AI is signing a $300 billion contract with Oracle, is a little bit of that circle coming up, and how much of that is real and how much of it is recognized gap revenue will be an interesting question for people to debate. And so we are seeing some of those things on the edges, but because AI is so impactful, I mean, let's face it, we may have AGI pretty soon, right? We don't know when, we don't know how far it is. I personally am a skeptic. In other words, I believe it's going to take longer than people estimate. Yeah, me too. Just like getting, you know, the self-driving car has taken a lot longer than Elon Musk has been telling us it's going to take. So I actually think it's further away than people realize. But maybe I'm wrong. Maybe it's closer. So who knows? Whatever it is, even if you ignore that, AI is going to be massively impactful

for the reason I described earlier that we've already laid out the technology infrastructure. Everyone's carrying a smartphone in their pocket. Everyone's talking and using a computer every single day and every single activity they do. And therefore, the rollout is going to be instantaneous. Yep, absolutely. Yeah, I felt like when it comes to the market, one of the sort of unfair advantages to some of the companies like a Google has is essentially they already have billions of users essentially across their thousands of software products.

So whenever they come out with some sort of AI innovation, they can immediately deploy that to essentially billions of people. Whereas another company is maybe a brand new, very hot AI company, they don't have that user base. So they might have dramatic impact, but it's going to be sort of limited in terms of the number of eyeballs that they can reach. Do you think things like that create sort of, you know, I don't mean unfair in terms of like antitrust unfair, but just unfair sort of competitive advantage for some of these larger companies versus some of the companies that are trying to innovate in the space right now? Sean, you're asking a wonderful question.

And in addition to being an engineer, I also have an MBA. And I'm one of those people who believes that you can actually learn some things from an MBA. And one of the things I learned in my MBA was that innovation comes of two types. you have sustaining innovation or you have disruptive innovation. And depending on which one it is, you can analyze and respond to it very differently. So the question in front of us is, in other words, I'm reframing your question as, is AI a sustaining innovation, will it help the incumbent, or is it a disruptive innovation, will it hurt the incumbent and help the insurgent?

That's really the question in front of us. So I think to answer this question, it's instructive again to take an old example. the desktop PC was a sustaining innovation. In other words, if you were a trucking logistics company that had 25 people in the back office madly scribbling on a piece of paper, you know, driving your 100 trucks around and tracking where they were, when the desktop PC came along, you bought a PC, your trucking company became more efficient.

You fired a lot of those people, some of those people got retrained, started using it, and all of logistics got better. So the incumbent got helped because of that. The internet was a disruptive innovation. You put up a website, Amazon.com. Suddenly you have a company which has been investing for the last 30 years building these prime retail locations, Walmart, building this massive supply chain infrastructure,

this ability to deliver, you know, T-shirts and cheap goods from China all the way to this rural location. and you're both the same. Amazon.com looks exactly like Walmart.com. And all the advantages that Walmart had became disadvantages because now they had all this stuck inventory and infrastructure. And so the insurgent had an advantage. So desktop PC, sustaining innovation, internet, disruptive innovation.

The question in front of us is, what is AI? I will argue AI is both. There are parts of AI that are sustaining and there are parts that are disruptive. So, for example, this ability to write code very, very quickly is incredibly disruptive because software is at the heart of everything that we write. The fact that AI is so expensive, that the infrastructure today is so expensive, that the technical approach that actually succeeded was not the approach that was championed

by people like Shannon at Bell Labs with information theory or all going all the way back, Alan Turing with computable numbers. But really, the approach which worked was a brute force, arguably illiterate approach of just taking neural nets, throwing data at it, and just somehow they just work, right? The reality is it works. And we don't quite know why, but it is incredibly resource intensive, capital intensive. That is very much in favor of the incumbent. And that's why you are seeing this wholesale movement of dollars by the incumbents into this space.

And so these two forces are going to fight. other, which is another area of confusion, another area of opportunity for entrepreneurs and VCs. If you can think that true, if you're one step ahead of the big bear, you will win. And so because AI has these competing characteristics at its heart, I think that is really what makes analyzed landscapes so interesting and hard. Yeah, absolutely. So I guess kind of on that same note, you're talking about how more productive people can be with this and how disruptive it can be. I guess like what does that technical insight that you love or technical risks? Like what does that look like for these AI powered

companies, especially at like, I'm not like derogatively calling them like an AI wrapper layer, but like, you know, there's the AI labs and there's the users of the AI. Like how do you have some durability and or some technical risk that you can actually defend, given how much easier it is to write software, given the open availability of these models? Like, what does that look like for you when you're investing? You know, because everyone now knows the Silicon Valley playbook, durability and moats have become shorter, weaker, and less sustainable. And so you have to constantly innovate. You have to constantly outrun the bear. Because the moment you figure something out, smart people are watching you knowing that there is not just a billion dollar opportunity,

but potentially a trillion dollar opportunity behind it. So everyone from, you know, the Middle East, which has lots of capital to the universities which have this wonderful industry, you know, academic partnership, etc. Everybody's watching and this is the ecosystem. This is the milieu in which we live. So the way I like to answer your question is, how greedy are you? Or to be more polite, how ambitious are you? Do you want to build a $100 million company, a billion dollar company or a trillion dollar company?

Depending on how ambitious you are, you have to approach it different. And you have to think about your barriers differently and the way you build your company differently. Most VCs are looking for, and I'll be fairly brutal about it, the billion dollar company. You can walk into any venture firm on Sandhill and you will see a, if not a press release, at least a lot of, you know, growing and thumping anytime there's a billion dollar or greater exit. Now, obviously, we love $5 billion and $10 is better than $5 and $100 is better than $10. And you can do the math as well as I. And you know, $1.0.0.0.0. Greater can do that.

But a billion dollar is a good number still in terms of if you are starting a company to think about. Barriers for a billion dollar company are manifold. There's a variety of barriers. And, you know, there are many, many playbooks you can run over there. The old enterprise barriers still exist. The IP barrier still exists. Just fast execution still exists. The hype and PR barrier still exists.

You can sell a company for a billion dollars because there are a trillion dollar who will consider that a rounding error. You know, with all due respect, I would put Oculus into that category, right? A multi-billion dollar exit on where the value had not been created still in terms of solving or building a business. In terms of the way that you look at investing, especially when we're talking about something that's kind of so new around like AI, How do you think about sort of separating what is truly innovative core IP versus something that eventually the model companies can own because the models are going to get better? And essentially, the value that the company is offering is not actually core intellectual property in the long run. I think what you're calling model risk, I'll generally speaking call that AGI risk or ASI risk or something in that direction, right?

I think that risk is baked into every single company that exists on the planet today, including the hypers, including the model companies, because nobody knows that Transformers is going to be the right answer to build this. Tomorrow morning, we might see a paper out of Berkeley or Stanford or Harvard or somewhere, and it might be a different architecture, and it just might make everything you did irrelevant. theoretically, it could even make an Nvidia architecture irrelevant, right? There's nothing which stops that from happening. So AGI risk or ASI risk is baked into every single company.

My personal focus is on software companies that are highly capital efficient. In other words, with a handful of million dollars, you've got to take the technical risk out. So you have to get to commercial revenue within that seed phase. Every company doesn't fall into that category. And if not, there are many other firms and those are good for you. And you know, you're certainly welcome to pursue those paths. but there is a sufficient number of opportunities for companies where there is a technical risk. In other words, it's not obvious how you're going to build it.

Number two, the big companies may or may not, for either strategic reasons or tactical reasons, be building it. And therefore, you have a chance to outrun them and build a company in my target area, which is one to 10 billion. That's really when I sit down with an entrepreneur, what I'm trying to evaluate it is, is there an opportunity in that range? If it's less than a billion, I can make money doing it, but frankly, it's not that interesting. And thinking about more than $10 billion, your head kind of starts hurting, you're projecting a lot.

It's very hard to really make those projections. Now, Rubrik obviously has already exceeded that, and I'm very proud of Bipple and hats off to you. I'm as greedy as the next VC. I'd love to see bigger numbers. But it helps to frame things with time, money, to put these guardrails around your thinking. So 1 to 10 billion, 3 to 5 years, 3 to 5 million. 10 people, you know, when you start putting things like this around you, you start getting structure, you're putting some framework and saying, yeah, I could pull this off, you know,

is this something I can build? So those are the companies I'm looking for. And the first time, you know, a Google looks at it or an Apple looks at it, they'll go like, oh, you know, I'm not really sure how they did that. That's kind of really clever. Yeah, yeah, absolutely. You mentioned like, you know, having this engineering background and being able to sort of see that technical insight, technical risk. How do you stay sharp with that? Like, I feel like if I'm not doing, you know, front end work for like a year, I'm way behind. But like, how do you stay sharp with like that engineering mindset or that technical insight to be able to like continually evaluate this, you know, decade, two decades later? Alex, you've hit the heart of, you know, what is the paranoia I have,

what keeps me up at night, and what is also the fundamental constraint on me and the work that I do, and frankly, which every venture capitalist does. which is your own ability to learn, to extrapolate, to project, to estimate, and to make a conclusion on. So I teach a class called How to Think Like a VC. I've taught it at Stanford, Wharton, you know, many of these good schools, called How to Think Like a VC, where I analyze the internal Sequoia emails from when they made the YouTube investment, which became public record as a part of the Viacom lawsuit. So now we have access to internal Sequoia. emails before they've made that investment. And there are many interesting snippets in that. As you can imagine, as a venture capitalist, that's very interesting to me. As an entrepreneur,

that's very interesting to me. What did they think this would become? Today, we all know YouTube is this monster, absolute gigantic monster in terms of what it is. The highest revenue estimate in those internal emails was $55 million. Wow. I haven't done the math, but I think YouTube does that in a deal, right? Yeah, we're going to adjust for inflation from whenever. Yes, okay. Adjusting for inflation, I still think they do it in a day.

Yeah. So, you know, this is one of the best firms in the world. This is going to make a firm that is going to make an investment in a company, which became one of the defining companies of its error. And that's how far off they were on that. So, you know, this is a fraught art in terms of what you are doing. And that's also what makes it so exciting. That's what keeps me up at night.

Specifically in terms of technology, at heart I'm a nerd. I don't think you can be a great technical investor. You can be a great VC without being a nerd. There are many great VCs who are not nerds. But you can't be a technical investor, technology risk-taker investor, without understanding the technology you're investing in. I signed up as an associate of the University of Toronto, the Creative Destruction Lab, in 2018, in the AI Lab.

So, you know, my first AI investment was made in 2019, robust intelligence. That is what is today Cisco intelligence, is robust intelligence. Ciccoa did the Series A there. So, you know, the great investments are always made earlier than what people do. In the YouTube emails, there is this wonderful line by Roloff, who is a young partner at Sequoia Capital right now. Remember, he's writing an email. He's not running the firm.

Today, of course, he runs the firm. Where he writes, I believe user-generated video content will explode. What a beautiful email. What an amazing insight, right? There's no number on that. There's no calculation. There's no spreadsheet. But it is an amazing insight when he's coming back from a meeting with these three

entrepreneurs and saying, I believe video generated content will explode. And that was the premise of the YouTube investment and it turned out to be true. Yeah. When it comes to some of these sort of, I don't know, these like aha moments or insights into the future. I feel like a lot of times, like, that was probably some, you know, obviously had conversations with people. He, you know, thought about this deeply, but there's some sort of, you know, gut impulse about this makes sense to me. I'm sort of predicting the future. It's hard to kind of, like, I don't know, assign like a framework or numbers to that. So, you know, as somebody who's

making investments, like, how do you kind of balance that between, like, oh, like, you know, my gut kind of feels this way, but can I trust that versus, you know, the numbers show something else or, you know, becoming overreliant on sort of quantitative proof that this is the right bad to make? I think all great investments, the numbers and the gut have to come together. Magic happens when those two things come together. You meet an entrepreneur and you walk out and go, you know, this guy is a force of nature or this gal is a force of nature, right? Now, what is the analysis for walking out and saying this is a force of nature, right? This person is a force of nature.

On the other side, you have to have insights like video-generated content will explode, and then you can back it up with, you know, bandwidth is getting cheaper. Everyone now has a cable modem or fast internet access. Everyone's carrying a smartphone, a camera, not a smartphone, but a camera in their pocket at that point when that analysis is being done. And so then you come up with a revenue estimate, and you do a low, medium high, your low is $6 million, and your high is $55 million for this opportunity called YouTube, which is literally the analysis that Roll off presents. So you have to

have the analytical side and the emotional side and say, you know, I think this is going to work. Now, are we right? No, we are more often wrong than we are right. Even great firms are more often wrong than we are right. But if when you are right, you can be spectacularly right. And that's what makes it so nice. Yeah. When you were, so I think like a lot of times when you're working as an engineer, you build up a skill set where you might know a lot, like be able to go really deep on certain subjects.

Like Alex knows DynamoDB, deeper than anybody that you'll probably come across. And then, but I would think as a VC, that skill set changes a little bit where you're kind know, there's some value in kind of being really wide and an inch deep on a lot of things. Is that true? And then also, was that sort of something that you had to adapt your own skill set to? I like to say that venture has become so big now that you have to be 500 yards wide and a mile deep. Okay, you don't have to be a mile wide and an inch deep. And you can't be a mile deep and an inch wide because either of those two will kill you. If you're in either of those two formats, then I think you should be an entrepreneur. But, you can't be a mile deep. But, you're in a mile deep,

as a VC, you kind of have to find that sweet spot. That sweet spot for me is this capital-efficient, deep techish software at mostly the infrastructure layer. I don't do applications. I don't do consumer. I don't do robotics. I don't do quantum. I mean, I can list a gazillion things I don't do. You know, I get business plan. Every day I wake up in the morning and I open my email like every other VC and there's, you know, between 10 and sometimes many tens of, you know, pitches waiting in my email. So you keep doing venture, that's what happens. That's what you wake up to, right? And of course, all the spam and all the other solicitations that we have to get rid off.

So out of those, I can reject many of them because I'm so narrow. Now, if you are in a platform firm, that's much harder. So practicing as a venture capitalist, and I'll pick on a different firm if you're in recent Horowitz, or if you're, you know, just pick any other platform firm. They have a very different strategy. They're trying to solve for a very different allocation of capital than I am trying to do. In my case, I'm trying to hit that sweet spot where I'm 500 yards wide and hopefully 500 yards deep. I don't have to be the world expert in that area, but I have to be smart enough to figure out whether it's going to work.

Again, sometimes I get it wrong. And it has to be wide enough that there is a repeatable business that you can build. Right. I mean, it sounds like a lot of it is you kind of really need to understand what your skill set is. so that you can at least have some level of expertise to be able to fairly judge an opportunity, essentially. Because if you didn't have, say, the right technical acumen to assess, especially in your world where you're really looking for these companies that have, like, this technical risk,

then you might just make mistakes in terms of assessing, like, how complicated or how much, like, how complicated this engineering task actually is. Yes. And the good news is that I can survive a certain number of mistakes. I can't survive too many mistakes, but a certain number of mistakes is built into the ecosystem. So I have that luxury. Number two, there are many people who have strengths which are not technical, who are still great investors, because they have some other strength which overpowers everything else.

I would argue that Peter Thiel is not a nerd, at least not in a computer science sense. I mean, he may be a nerd in some other topics, maybe in political science or something like that. He may be a nerd on that. But he made some great investments. Now, what did he see when he met Mark Zuckerberg? Did he understand distributed computing? Of course not. Was the pitch about Facebook that we will build the world's best advertising,

invent a new format for advertising? No, of course not. But he did understand. He did see something special about Mark and Mark's great strength. Again, Mark is a great technologist, I think. In fact, I believe he's the second best. product manager we've ever had in Silicon Valley. But Mark, the great strength of Mark was to figure out consumer behavior. He knew that we were willing to trade privacy in return for this

ego, dopamine hit of, you know, being liked, et cetera. And he leveraged that into building this, you know, extremely addictive system, right, that we got addicted to. That was an amazing insight that Mark Zuckerberg had on the basis of which the Facebook empire started. Other insights now on which it is running. So Peter Thiel saw that. He saw that opportunity, but surely a large fraction of that was simply the personality of Mark, the ability of Mark as an entrepreneur, as a person who was a force of nature, which is why on my website, if you go to my website at engineering capital.com, the first thing you'll see is an ad for founders, which is inspired by an ad that shackled and ran 100 years ago when he was recruiting people to try to be the first man to reach the

South Pole, right? That's the origin of that ad. And so it has nothing to do with technology. It has nothing to do with software. It has everything to do with human nature and our desire to excel, to prove ourselves, to push ourselves beyond the limit for fame, for ego, for power, for self-validation, all of those things, which makes people do amazing things. I still think the best book for an entrepreneur to read, to understand the entrepreneurial journey, both literally and figuratively, is Lewis and Clark, the undaunted courage by Stephen Ambrose on the Lewis and Clark expedition, which was a literal journey, but figuratively encompasses all the aspects of entrepreneurship. All the difficult decisions that a founder has to make are in that journey.

How many people to take? How much money do you need? What resources are important? What do you prioritize? Should you take a pregnant woman on your team when you're going to walk 2,000 miles into the unknown? That's a decision the guy made, right? He took a pregnant woman with him on his team. Would you hire that?

People would tell you, I mean, they discriminate against that in a startup in San Francisco today. And we're talking 200 years, 250 years ago, he took a pregnant woman. It was clearly the right decision. Yeah, based on what happened, I mean, with the benefit of hindsight. So that's what great entrepreneurs do. and that's what I'm looking for as a venture capitalist. Are you the person who's going to make that type of a decision, which is obviously wrong when purely looked analytically,

and there are lots of things you can point to as wrong, but in the grand scheme of things, is the right thing to do, you know? When they had to decide who will be the first man to walk on the moon, after everything was baked in, Apollo 11 mission, they're choosing between Neil Armstrong and Buzz Aldrin, and Buzz Aldrin is making this desperate effort. He is using every political skill he has to get chosen, including writing a memo where he says,

you know, Neil Armstrong is the commander. We don't want to risk the commander's life, you know, risk my life instead. Because he wanted to be the first man to walk on the moon. He took the exact same risks that Neil Armstrong took, right? And forever and ever, Neil Armstrong will be the first man to walk on the moon. And yet the powers that we chose Neil, and it was the right decision in many ways, as proven by the rest of their life, in terms of what happened when the computer failed, et cetera,

and all those decisions got made. With the benefit of hindsight, we know they made the right decision. Yeah, interesting. Have you seen a change in sort of the crop of founders that you've seen over the 10 years you've been investing now, whether that's in maybe age or background? Like, did they come from Google or some big place or location in the country? I guess, like, have there been changes in that or has that been pretty steady throughout? I think true entrepreneurs, the nature of a true entrepreneur has not changed.

And that's why I give these very old examples of, you know, Captain Lewis or Shackleton or, you know, these are examples that are predate technology. And so there's a lot to learn from those types of lessons. The problem right now in Silicon Valley is because the playbook is now open, everybody knows the playbook. And because there's so much money to be made, you know, the amount of wealth creation just increases exponentially over the last few decades, is that now you have a lot of what I like to call tourists, okay, and what I like to call mercenaries. So the most dangerous are the tourists,

the second most dangerous are the mercenaries, and what you really want is the true believer. What you want is the missionary, right? That's what you want as a venture capitalist. So distinguishing them has become harder because now the playbook is open, and so there's a lot of the tourists and mercenaries running around. What's the difference? What's the difference? between a tourist and a mercenary? Tourists are people who are naive, who don't know what they're getting into. They're in it for fun, or they don't understand what they're getting into. The mercenaries know exactly what they're doing.

They're doing it for the wrong reason. For pay. They're doing it for the money. So nobody signs up for, you know, an expedition to, to, with Shackleton for the money. You know, they don't do it for that. And yet they are the people who will change the world. They are the people who we talk about hundreds of years later. because they signed up for that for that reason.

Nobody signed up with Captain Lewis because they wanted to get rich. That was not the driving force. Yeah, yeah. When you, I guess, take on investments and maybe when one goes wrong or something, you mentioned there's certain amount of failures just built into the model. Is it hard not to sort of over index on something about that example being like, oh, man, I should do this industry or I shouldn't do this type of founder, or maybe that was too big a founding team or too small a founding team.

Like, is it hard not to overindex on a single failure, given that you're still making a fairly small number of investments, you know, in a year or two? Yeah, that's hard to think about? It is very hard. It is one of the hardest things to do because we are human, right? I mean, every venture capitalist is a human, at least today. We don't have AI's here. And so we're all human.

And so we are susceptible to, you know, confirmation bias, all of those biases that you are talking about. And so what helps me personally, and I don't know how the other great people do it, everybody has their own approach for it. Certainly, that's why partnerships work. You know, they act as sort of self-correcting mechanisms for some of these things. But the way I do it is to use the approach that Sean and I were talking about earlier, which is to put some frameworks around it. I'm going to make 20-ish investments in this fund over a period of three, three and a half years. They're each going to be capital efficient.

And that's my job. And I've kind of put that framework around and that acts as a guardrail. And then I go for long walks with my wonderful partner, Teddy. She's sleeping at my feet right now over here. She agrees with everything I say. She listens wonderfully and gives me great advice. And, you know, you have to come in, you have to get in touch with reality every so often as a human being because we are getting pulled in all these crazy directions.

By the way, the hardest thing to do in that is actually the sell decision, not the buy decision. The buying decisions are a lot easier than the cell decision, at least for me. I've always found them easier. You kind of walk out with conviction, you know, after a few number of meetings with like, you're going to do it or not. The selling decision is a lot harder because you're in love with your children. This is that small subset of the children that really did well. And it's really hard to then sell them.

And that's the difference between the good and the great VCs. And when you say sell, do you mean like sell like the company is actually acquired an acquisition or you are selling your shares like you've held them for long enough to its time to sort of realize a return on that? Yeah. So, you know, there's two or three types of sell decisions that every venture capitalist makes. One is, of course, you know, the company has gone public and therefore you have to make a decision on when to sell your shares.

So that's the most obvious sell decision. Somewhat less obvious is the M&A decisions where we tend to have very large influence on the outcome. We may not be decision. Sometimes we are decision makers. You know, the CEO literally thinks you are their boss. usually that's not a good scenario, but you can end up in that scenario where they think the board, you know, they think they work for the board. One of the lectures I give CEOs when I start is I tell them, the board works for you. The board, you should not be working for the board. Make them work for you. And good CEOs get that and they and they do accomplish it.

But even in that scenario, even when the board works for the CEO, you have tremendous influence on that cell decision. And so that's another place where you have to make that. And then even more difficult is the pro rata decision. So especially if you're running large funds, you have to make prorata decisions, which is sort of the opposite of the cell decision. It's the corollary to the cell decision, which is, you know, I have this money allocation. Should I put the money in or not? You're re-underwriting the deal at that point.

And so there's various forms in which the cell decision comes, but you have to make those and you have to be good at them if you're going to make money. Yeah, absolutely. What about, so you often do, you know, pre-seed, seed, very, very like early-stage investments, but then some of these go on to raise later rounds and then to get acquired. I guess, like, how do the founders treat these sort of different stages of investors, you know, because they've been with you for maybe 10 years, but then also whoever raised the latest round just put in the most money, I guess, like, is it personality-driven or who's been

there the longest or who's just put in the most recent money? or what does that look like in terms of taking advice for some of those things? It's very case-specific. Every CEO is different. It's very much about the relationship of the CEO with their board, with that individual, with that partnership. And the reality also is, Alex, that because exit cycles have gotten so stretched now

that firms change, partnerships change. I mean, I have boards where I have the third board member from that partnership. We took a series A, and now they're on that third board member. I'm still there, and I'm talking to this person. So it's very case specific, it's very company specific, it's very opportunity specific. You know, here's, you know, this is a partnership driven thing. This is a sales model driven thing. It also depends on those types of things.

So it's very hard to generalize on that. These are literally snowflakes. You know, they are unique, individual, special things which we are trying to nurture to a great outcome. Yeah. Given that, you know, some of the things that we're talking about, earlier in terms of all the impact basically across the entire stack in terms of efficiency gains, like people are able to build software so much faster than they were historically using AI and even other innovations as they've come along. As a result of that, are you seeing more

basically startups, like more people have the opportunity to build a startup or think that they can build a startup because they can essentially build something, get the market much faster than they've been able to do previously? Yes. The number of tourists has gone up and the number of mercenaries has gone up unquestionably. And so we are going,

that is why I said earlier that we are going to see a very large number of people lose very large amounts of capital in the next several years. That is baked into the system it's going to happen. And that does not take away

from the fact that I also believe that AI is a generationally transformational technology bigger than the internet, bigger than the desktop PC, bigger than the iPhone. And therefore, we'll have a huge impact and giant amounts of wealth will be created. Both these things are true and they will happen roughly simultaneously. Our job is to end up on the right side of that. Yeah, absolutely. Going back, like, you know, you worked as engineer at a

couple of places. You also started, you know, Sedona Systems. Were you a, I guess, true believer, sort of all, or like True Explorer all along? Is that sort of your prototype? Is that how you would describe yourself? Absolutely not. I was extremely naive. I was frustrated at work, seeing some decisions being made that I knew were wrong in a company that was arguably the hottest best company of its time, Silicon Graphics, was the Google or the Facebook of its time and or the Open AI in today's terms of its time. And it was a very hard decision to leave Silicon Graphics, but I also had this gut feeling that we had lost our way. And I'm very proud to say with the benefit of hindsight that I sold all my options in Silicon Graphics on the day I resigned, which is only a couple of dollars off from the all-time high of the Silicon Graphics Fair Price. So I cannot say, I mean, there's a huge element of luck in that.

But I could sense it, that something was wrong. I had that urge to be an entrepreneur. I had found a little problem that was intriguing me. And so I just kind of meandered my way into doing a startup. Yep, yep, absolutely. And sort of as we come closer to the end of wrapping up here, I guess for what type of person, especially like an engineering-oriented person right now, would you encourage the path that you took to switching over to VC,

using some of that engineering skill to, you know, identify opportunities. But like, you know, if an engineer is interested in this path, how would you encourage or discourage them on that? To be a venture capitalist, or to be an entrepreneur? To be a venture capitalist, yeah. Got it. Okay, because to be an entrepreneur, I tell them that's the last job you should take.

If there's nothing else you can do is what you should do. But if there's nothing else that will make you happy, then you can be great at it. A venture capitalist today, that's a more nuanced question to answer because it's a very large asset class now. There are VCs running at, you know, let's take an example. And recent Horowitz, they have hundreds of employees. for all I know, they may even have more than 1,000 employees now. That's a corporate job. You're working for a corporation when you're one of a thousand, you know, in an organization.

And then there are venture capitalists like, let's take benchmark at the other example or myself. You know, I'm a solo GP. Benchmark is now, you know, three or four people, a partnership, very, very small partnership. So these are completely different jobs. These are completely different jobs. And depending on which path you are going down, the answer will differ based on who you are. That said, for a certain subset of people, being an entrepreneur or being a venture capitalist, is the two best ways of working in Silicon Valley. It's the highest leverage, most intellectually stimulating, most fun, most creative, highest failure rate, believe it or not.

A lot of thesees fail. We tend to not talk about it, but there's a lot of churn, even in the great partnerships. And so if you're up for that battle, then you have to do it. You know, if you want to go play in the Olympics, and if that's the only thing that will make you happy, then go play in the Olympics. You know, try to win the Wimbledon or whatever, you know, but knowing that most people will not win the Wimbledon, most people who play tennis will never win the Wimberton. That is just a fact, statistical fact. And that is how the game of entrepreneurship and the game of venture capital is played. Yeah, absolutely. Yeah, you kind of need a, at least, you know, having been in entrepreneurship previously, you kind of like need an irrational level of,

confidence to believe that you can do something where, you know, the majority of people essentially fail. And there's going to be a lot of people who tell you that the thing that you're doing is is not the right idea or wrong in some way or you're going to fail and you have to sort of still believe in it to the point where you can convince other people to join and also believe in it enough to convince people to, you know, hope you fund it if you're going sort of that path. So there is some sort of a rationality to it inherently that the individual, I think, needs. I think for anything great in life, you have to be irrational. That is the only way we know to make great things happen.

So I'll use a quote from Gandhi. You know, that's a political example. Gandhi's quote was, first they ignore you, then they make fun of you, then they fight you, and then you win. And I think every great person follows that. So first they think they ignore you because you're irrelevant, right? I mean, you really don't matter. Then they make fun of you. When they realize what you're doing, they think you're stupid.

So if you're going to do anything great in the world, you have to follow a journey somewhat similar to that. And only some people are suited to following it. And so you should self-select carefully over there. And unfortunately, more people are choosing it right now than they should. But that's part of life and that's part of how our society and the economy works today. We go through those cycles too where a lot of people are sort of attracted to the idea of starting something and then too many people go. into it, they become saturated, there's a lot of failure as people realize it's harder than they maybe thought about it. And then you get like a constriction of the market essentially.

These things tend to come in cycles. Yes. And I mean, I'm a second time entrepreneur, right? I mean, engineering capital is obviously a venture capital firm, but it's also an entrepreneurial firm. And now, you know, running fund four, I'm officially out of the emerging manager, you know, first two, three funds, starting out, et cetera, et cetera stage. And, and, and, and And so something worked right, knock on wood, thank God. And so it can be done, but it is a difficult journey. You know, it's got its ups and downs. I mean, if you want to play in the Wimbledon, you're going to play some great tennis

and you're going to lose a lot of matches along the way. Yeah, yeah, absolutely. Well, Ashmi, thanks for coming on. I've just, I love this conversation. It's been fun to hear just your insights and opinions, not only on the market, but like what you look for. I thought was really fascinating and kind of eye-opening to me. So thanks for sharing that with us. If people want to find more about you or Engineering Capital, where should we direct them?

My website, Engineering Capital.com, LinkedIn, Twitter, Ashmeet Sedana. I'm easy to find my parents gave me a name that's relatively unique. So find me anywhere online. Anyone who's starting a new company from scratch looking for your first million dollars if you're solving an interesting technical problem, give me a call. Perfect. Sounds great. Thanks for coming on the show, Ashby. Yeah, thanks so much.

Thank you, Sean. Thank you, Alex. I enjoyed it. Cheers.