Building the Real-World Infrastructure for AI, with Google, Cisco & a16z

AI isn’t just changing software, it’s causing the biggest buildout of physical infrastructure in modern history. In this episode, live from Runtime, a16z's Raghu Raghuram speaks with Amin Vahdat, VP and GM of AI and Infrastructure at Google, and Jeetu Patel, President and Chief Product Officer at Cisco, about the unprecedented scale of what’s being built, from chips to power grids to global data centers. They discuss the new “AI industrial revolution,” where power, compute, and network are the new scarce resources; how geopolitical competition is shaping chip design and data center placement; and why the next generation of AI infrastructure will demand co-design across hardware, software, and networking. The conversation also covers how enterprises will adapt, why we’re still in the earliest phase of this CapEx supercycle, and how AI inference, reinforcement learning, and multi-site computing will transform how systems are built and run. 00:00 Intro 01:16 The Scale of the AI Buildout 03:00 CapEx, Demand Signals, and the Power Bottleneck 05:56 Data Centers, Scarcity, and Global Power Constraints 08:18 Rethinking Systems and Networking 10:08 Scale-Out vs. Mainframe Architectures 12:18 The Next Wave in Processor Innovation 14:36 Specialized Chips, Power Efficiency, and Geopolitics 16:14 Networking Evolution and Scale Challenges 18:52 Building Networks for AI: Power, Bursts, and Bottlenecks 21:00 Inference Architecture and Cost Reduction 24:00 AI Inside the Enterprise: Code Migration and Productivity 27:30 Rewiring Culture Around Rapid AI Adoption 29:40 Startups, Models, and Intelligent Routing Layers 31:55 The Future of AI Models, Agents, and Media 33:10 Closing Thoughts Resources Full Transcript: https://a16z.substack.com/p/surviving-the-ai-sprint-up-close Follow Raghu on X: https://x.com/RaghuRaghuram Follow Jeetu on X: https://x.com/jpatel41 Follow Amin on LinkedIn: https://www.linkedin.com/in/vahdat/ Find a16z on X: https://x.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  The good news is infrastructure sexy again, so that's kind of cool. This is like the combination of the buildout of the internet, the space race, and the Manhattan Project, all put into one. Where there's a geopolitical implication of it, there's an economic implication, there's national security implication, and then there's just a speed implication that's pretty profound.

• 0:19Speaker 01

  I mean, I think it's easy to say. I've seen nothing like this. I'm fairly certain no one's seen anything like this. The internet in the late 90s, early 2000s was big, and we felt like, oh my gosh, can't believe the buildout, the rate. This makes it, I mean, 10x is an understatement. It's 100x what the internet was.

• 0:37Erik Torenberg?

  The AI boom isn't just changing software. It's transforming the physical infrastructure that runs it. Today, you'll hear a conversation with Amin Vahdat from Google, G2 Patel from Cisco, and Raghu Raghuram from A16Z on what it takes to build the real-world systems behind large-scale AI, from chips and power to data centers and networking. They discuss the scale of the current buildout, the new constraints on compute power and interconnect, and how specialization in hardware and architecture is reshaping both the industry and global geopolitics. It's a grounded look at how infrastructure itself is being reinvented for the AI era and what comes next. Let's get into it.

• 1:18Speaker 03

  What better time and place to talk infrastructure? All right. So we were back in the green room, and just as the first question was getting answered, I got cut off, so this could be an entire repeat for all I know. So, but anyway, let's go, right? The first question is similar. So, both of you, firstly, welcome and thank you for being here. And I hope you'll have a great day and a half as well. Both of you have been in the industry for a while, and both of you have lived through many infrastructure cycles, right? So have you seen anything like this cycle from your vantage point? Not from an investor vantage point, but from your internal vantage point, where you are responsible for building things

• 2:02Speaker 03

  and planning for things and so on? Any one of you, where do you want to start? You want to start, Amin?

• 2:07Speaker 00

  Go ahead, Amin.

• 2:08Speaker 01

  I mean, I think it's easy to say. I've seen nothing like this. I'm fairly certain no one's seen anything like this. The Internet in the late 90s, early 2000s was big and we felt like, oh my gosh, can't believe the build-out, the rate. This makes it... I mean, 10x is an understatement. It's 100x what the Internet was. I think the upside is, as big as the Internet was, same thing, 10x and 100x. Yeah, nothing like it.

• 2:32Speaker 00

  Yeah, I'd agree. I don't think there's any priors to this size, the speed and scale. I'd say, the good news is infrastructure's sexy again, so that's kind of cool. It was a long time where it wasn't sexy. The thing I would say that's really interesting is, this is like the combination of the build-out of the Internet, the space race, and the Manhattan Project, all put into one. Where there's a geopolitical implication of it, there's an economic implication, there's a national security implication, and then there's just a speed implication that's pretty profound. So, yeah, none of us have ever seen it at this size and scale. On the other hand, I think we're grossly underestimating... Like, there's... The most common question I ask right now is, is there a bubble? I think we're grossly underestimating the build-out.

• 3:16Speaker 00

  I think there's going to be much more needed than what we are putting the projections towards.

• 3:21Speaker 03

  So, that's the follow-on question, is where are we, do you think, in the CapEx spend cycle? But more importantly, what are the signals that you guys use internally, right? In your thinking? I mean, you have to plan data centers, whatever, four, five years in advance, you have bineuclear reactors and whatnot. So, how do you think about the demand signals, as well as your technology signals? And, Jito, the same thing for you, but from the point of view of enterprise and NeoCloud, etc. Amin?

• 3:51Speaker 01

  We're early in the cycle, is what I would say, certainly relative to the demand that we're seeing. Our internal users are... We've been building TPUs for 10 years, so we have now seven generations in production for internal and external use. Our seven and eight-year-old TPUs have 100% utilization.

• 4:08Speaker 03

  Oof.

• 4:09Speaker 01

  That just shows what the demand is. Everyone, of course, prefer to be on the latest generation, but whatever they can get. So, this tells me that the demand is tremendous, but also who we're turning away and the use cases that we're turning away. It's not like, oh yeah, that's kind of cool. It's, oh my gosh. We're actually not going to invest in this. And there's no option because that's where we are on the list. Same with many of you in the room. We're working with many of you in the room and many of you are telling me directly. And thank you. We need more earlier.

• 4:42Speaker 01

  Now, the challenge here, though, is, as you said, we're limited by power. We're limited by transforming land. We're limited by permitting. And we're limited by backup delivery of lots of things in the supply chain. So one worry I have is that the supply isn't actually going to catch up to the demand as quickly as we'd all like. I heard in the previous session, some of the discussions of the trillions of dollars that we're going to be spending, which I think is accurate. I'm not sure that we're going to be able to cash all those checks. In other words, literally, you all have some money you can't spend at all as fast as you want. I think that's going to extend for three, four, five years.

• 5:22Speaker 03

  Wow. And how do you deal with the depreciation cycles that are involved there? Is the demand curve and the depreciation cycle curves match up?

• 5:30Speaker 01

  Well, fortunately, we buy just in time. But the nice thing is just in time for the hardware. The depreciation cycle for the space power is more like somewhere between 25 and 40 years. So we have benefits there.

• 5:42Speaker 00

  I think if you think of on the networking side and you look at both enterprise and the hyperscalers as well as NeoClouds, I think the story is quite different. So the enterprise is pretty nascent. And it's built out of true infrastructure. I just don't think that the data centers, like if you assume that 100% of the data centers at some point in time will need to get re-racked. And you will need a very different level of power requirement per rack that's going to be there compared to what used to be there in the traditional data centers. I just don't think that the enterprises are far enough along. Maybe the few enterprises that are at super high scale might be there, but I don't think the enterprises are far enough along. Hyperscalers and NeoClouds is a completely different story.

• 6:27Speaker 00

  And to a means point on this notion of scarcity of power, compute and network being the three big constraints in this thing. I would say right now that because there's not enough power singularly in one location, data centers are being built where the power is available, rather than power being brought to where the data centers are. And that's why you're seeing a lot of projects that are being built out all throughout the world. The other point, though, is The lion's share of the constraints that we're going to have, I think are going to be sustainable for a long period of time. And as you have data centers that are being built farther and farther apart, one, there's going to be a huge demand for scale up networking,

• 7:10Speaker 00

  so that you can have a rack that gets more and more networking for scale up. The second is you're going to have a lot of demand for scale out, where you have multiple racks and clusters that need to get connected together. But we just launched a new piece of silicon, as well as a new chip and a system for scale across networking, where you might have two data centers that act as a logical data center that could be up to 800, 900 kilometers apart. And you will see that just because there's not going to be enough concentration of power in a single location. So you'll just have to have different architectures that get built out.

• 7:43Speaker 03

  Actually, that brings us to the next topic that I want to discuss, the future of systems and networking and so on and so forth. So Google bought the first one, at least large scale, scale out commodity servers and production for the web revolution. And now Nvidia is bringing back the mainframe in a different form. So what do you think happens next? I mean, is this the new style of coherent, cluster-wide computing that we need? And there's going to be shared memory and all sorts of things? Or do you think the pattern changes again?

• 8:15Speaker 01

  I don't think we're quite too back to mainframes in that it is still the case that people are running on scale-out architectures across these pools. In other words, whether you have GPUs or TPUs, you're not necessarily saying, hey, that's my GPU supercomputer. You're saying, I've got 16,384 GPUs.

• 8:33Speaker 03

  Yep.

• 8:33Speaker 01

  And maybe I'm going to go grab some subset. Now I've got a uniform all-hall connectivity in many cases, which is fantastic. Same with TPUs. It's not like I say, I have a 9,000 chip pod and I have to make my job fit on that. Maybe I actually only need 256. Maybe I need 100,000. So I do think that actually the software scale-out is still going to be there. I'll note two things, though. One, you're absolutely right that, say, about 25 years ago at Google and other places simultaneously. There was really a transformation of computing infrastructure. Like the notion that actually you would scale out on commodity PCs, essentially, the same ones that you could buy off the shelf, running a Linux stack. And that's what you would do for disk, that's what you would do for compute, that's what you do for networking.

• 9:17Speaker 01

  I mean, you all take it for granted. This is sort of, it was radical. There are many people who thought that this was a terrible idea. That wasn't going to work. I think the exciting thing about this moment right now is actually that we're going to be reinventing, I'm not saying Google, we are going to be reinventing computing. And five years from now, whatever the computing stack is, from the hardware to the software, it's going to be unrecognizable. And by the way, there was this co-design, because if you think about it, I'll use Google examples because I know those the best. Bigtable, Spanner, GFS, Borg, Colossus. They were hand in hand co-designed with the hardware, the cluster scale-out architecture. And you wouldn't have done the scale-out hardware if you didn't have the scale-out software.

• 10:00Speaker 03

  Yep.

• 10:01Speaker 01

  Same thing is going to happen in this moment. So I think actually the mainframe is going to look very, very different.

• 10:06Speaker 03

  Okay.

• 10:06Speaker 00

  Yeah, I do think there'll be like this extreme demand for an integrated system because right now we are very fortunate at Cisco where we do everything from the physics to the semantics, you think about the silicon to the application. And other than power, one of the constraints is how well-integrated are these systems? And do they actually work with the least amount of lossiness across the entire stack? And so that level of tight integration is going to be super important. And what that means the industry will have to evolve into is we will have to work like one company, even though we might actually be multiple companies that actually do these pieces. And so when we work with hyperscalers like Google or others, there's a deep design partnership that actually goes on for months and months together

• 10:50Speaker 00

  ahead of time before we actually even do the deal. And then once the deal is done, of course, there's a tremendous amount of pressure to make sure that they're moving pretty fast. But I think the industry's muscle of making sure that you operate in an open ecosystem and not be a walled garden is going to get important at every layer of the stack.

• 11:08Speaker 03

  Yep, really, right. And so let's talk about the, to segregate the stack a little bit.

• 11:16Speaker 03

  One of the most interesting topics is processors, right? Clearly, there is an amazing vendor producing an amazing processor that has massive market share today, right? And we see startups all the time doing all sorts of processor architectures. You've got an amazing processor inside your fortress. What do you think happens next in processor land?

• 11:40Speaker 01

  Yeah, we're huge fans of Nvidia. We sell a lot of Nvidia products and chips. Customers love them. We're also huge fans of our TPUs. I think the future is actually really exciting and actually we're, it's not that, I don't think that we've hit the point of, okay, there's TPUs, there's GPUs, there's whatever, triniums or something else. We're really seeing the golden age of specialization. And that's my observation. In other words, if you look at it, a TPU, I'll use that example again because I know it best, for certain computation is somewhere between 10 and 100 times more efficient per watt. And it's this watt that really matters than a CPU. It's hard to walk away from, right? 10 to 100x.

• 12:23Speaker 01

  And yet, we know that there are other computations that if you built even more specialized systems for, but not just a niche computation, computations that we run a lot of at Google, right? For example, maybe for serving, maybe for agentic workloads, that would benefit from an even more specialized architecture. So I think that actually one bottleneck is how hard is it and how long does it take to turn around a specialized architecture? Right now, it's forever.

• 12:51Speaker 03

  Yep.

• 12:51Speaker 01

  Right? For the best teams in the world, really from concept to in live in production, speed of light is two and a half years.

• 13:00Speaker 03

  Yep.

• 13:00Speaker 01

  I mean, that's if you nail everything, right? And there are a few teams that do. But how do you predict the future two and a half years out for building specialized hardware? So A, I think we have to shrink that cycle. But then B, at some point, when things slow down a little bit, and they will, I think we're going to have to build more specialized architectures because the power savings, the cost savings, the space savings are just too dramatic to ignore.

• 13:24Speaker 00

  And this will actually have a really interesting implication on geopolitical structures as well, because if you think about what's happening in China, China actually doesn't make 2 nanometer chips, they make, you know, 7 nanometer chips. And so if you think about what But they have unlimited amount of power. And they have unlimited amount of engineering resource. And so what they can do is do the optimization on the engineering side, keep the seven nanometer chips, and make sure that they give people unlimited amount of power. We might have a different architectural design, where you have to get extremely power efficient. You don't have as many engineers as you might enjoy in China. And you can actually go to two nanometer chips. But and those might be power efficient in some ways, but then might have thermal loss in other ways.

• 14:09Speaker 00

  Like there's a whole bunch of things that have to get factored in on the architecture that will get more specialized even by geo and by region. And then depending on how the regulatory frameworks evolve, you know, how that that geo then expands. Like if China expands to different regions in the world, you will have a very different architecture that plays out than if America expands to different regions in the world. So this is a very interesting kind of game theory exercise to go through on what happens in the next three years in in tech in general. And no one knows right now.

• 14:47Speaker 03

  That's the beauty of the world that we live in.

• 14:49Speaker 00

  Yeah, yeah.

• 14:49Speaker 03

  So we'll soon be measuring systems by engineers per token in addition to watts per token. All right, so let's turn to another topic which.

• 14:59Speaker 00

  Engineer per kilowatt.

• 15:00Speaker 03

  Engineer per kilowatt.

• 15:01Speaker 00

  In the US.

• 15:04Speaker 03

  Networking, right? Obviously, you alluded to it, scale up, scale out. In your case, you mentioned scale across. So it seems to me that networking is also going to get reinvented in a fairly significant way. So what are the leading signs that you're seeing that and the signals that you're seeing and the direction networking is going to take?

• 15:25Speaker 01

  Yeah, networking is going to need a transformation for certain. In other words, the amount of bandwidth that's needed at scale within a building is just astounding. I mean, and it's going up. The network is becoming a primary bottleneck, which is scary. So more bandwidth translates directly to more performance. And then, given that the network winds up actually being a small power consumer, that delivered utility you get per watt, like it's a super linear benefit. Like spend a little bit here, get way more there. So I think that that side is absolutely there. I'll put in a plug here in that in this, for these workloads,

• 16:08Speaker 01

  we actually know what the network communication patterns are a priori. So I think this is a massive opportunity. In other words, do you then need the full power of a packet switch? When actually you know what the rough circuits are going to be. And I'm not saying you need to build a circuit switch, but there is an optimization opportunity. The other aspect of this here is these workloads are just incredibly versatile.

• 16:31Speaker 03

  Yeah.

• 16:32Speaker 01

  And to the point where, and we've written about this, power utilities know this when we're doing network communication relative to computation at the scale of tens and hundreds of megawatts. Right? Like massive demand for power. Stop all of a sudden and do some network communication, and then burst back to computing. So how do you build a network that needs to go at 100% for a really short amount of time and then go idle?

• 17:00Speaker 03

  Yeah.

• 17:01Speaker 01

  And then same actually for the scale across use case, which we're absolutely seeing. You don't run large scale pre-training across all your wide area data center sites 12 months of the year. So, and then you're going to, this is a problem I think about a lot, is let's say you build the latest, greatest chips in these three data center sites. How long are you going to be there before you migrate to the latest, latest chips in three other sites? And then what do you do with the network that you left behind? People are going to run jobs on them.

• 17:29Speaker 03

  Yeah.

• 17:29Speaker 01

  But you're not going to need nearly the network capacity that you did for large scale training, pre-training, anyway. So the shift of needing massive networks for like 5% of the time. I don't know how to build a network like that. So if any of you do, please, please, please let me know.

• 17:47Speaker 03

  I mean, if you don't know how to build this, there's nobody that knows how to build this.

• 17:50Speaker 01

  We're trying to figure it out. It actually is a fascinating problem.

• 17:52Speaker 03

  Yeah.

• 17:53Speaker 01

  Yeah.

• 17:53Speaker 00

  I do think like if you think of if power is the constraint and if compute is the asset, I think network is going to be the force multiplier.

• 18:01Speaker 01

  Mm hmm.

• 18:01Speaker 00

  Because, you know, if a packet, if, if you have low latency and low performance and high energy inefficiency, then the pack, every kilowatt of power you save moving the packet is a kilowatt of power you can give to the GPU.

• 18:15Speaker 01

  Yeah.

• 18:15Speaker 00

  Um, which is, you know, super important. Um, the, the other thing is, you know, when you think about um, scale up versus scale out versus scale across, you'll also need, especially on inference versus training, there are different things that get optimized. Like you might optimize for latency much more on training runs. You might optimize much more for memory on inferencing. Um, there's, uh, there's architectural. And so I also feel like the way that networking will evolve is rather than it being um, a training infrastructure that then gets applied to inferencing, you might have inferencing native infrastructure that gets built um, over time.

• 18:58Speaker 00

  And so there's, there's good considerations to look at on like how all of the architectural components are um, are moving. But um, in my mind, like if I were to say strategically, one of the biggest things that's happening in networking from our vantage point is if you're just a wrapper around Broadcom, then you've got a monopoly that's going to be a very predatory one. Um, and so one of the big reasons where Cisco is um, super relevant is you don't just have a Broadcom world with people just wrapping Broadcom, that their systems are on Broadcom, but you will actually have a choice of silicon and that choice and diversity of silicon

• 19:41Speaker 00

  is going to be super important, uh, especially for high volume, you know, kind of consumption patterns.

• 19:48Speaker 03

  So last question on the system, since you brought that up and we'll move to use cases. Inference, both of you have mentioned, or you talked about it in the context of the processors. You just started talking about the architecture. Are you deploying today's specific architectures for inference? I mean, are, is it still shared workloads?

• 20:12Speaker 01

  We are deploying specialized architectures for inference. And I think as much software as hardware, but the hardware is also deployed in different configurations, is the way I would say it. And then the other aspect of inference that is becoming really interesting is reinforcement learning, especially on the critical path of serving, because latency just becomes absolutely critical. And I think that, so how you would build your system and how you would connect it up to one another, and of course, networking plays a key role there, becomes increasingly interesting.

• 20:45Speaker 03

  But are there singular choke points that if removed would accelerate the thousand-fold reduction in the cost of inference that we need? Or is this just a natural curve that we are riding down?

• 20:58Speaker 01

  So we're massive, I mean, two things here. One, again, maybe many of you are familiar with this, pre-fill and decode on inference look very, very different. So actually, ideally, if you, you would have different hardware, actually. The balance points are different. So that's, that's one opportunity. It comes with downsides. We could talk about that. What I would say, though, is that maybe something people don't realize is that we're actually driving massive reductions in the cost of inference. I mean, 10x's and 100x's. The problem or opportunity is the community, the user base, keeps demanding higher quality, not better efficiency. So just as soon as we deliver all the efficiency improvements we're looking for, the next-generation

• 21:41Speaker 01

  model comes out and it is, whatever, intelligence per dollar is way better.

• 21:47Speaker 01

  But you still pay more and it costs more relative to the previous generation. And then we repeat the cycle.

• 21:54Speaker 00

  And it's almost like the longer the reasoning that you have, the more impatient the market gets, right? So for example, if you have a 20-minute reasoning cycle, like for example, with deep research, you could have autonomous execution for about 20 minutes. That was interesting. Now you have, you know, most of the coding tools that can go up to seven hours to 30 hours of, you know, duration of autonomous execution. When that happens, there's actually a greater demand for saying, compress that time down. And so you like, it's, it's kind of a self fulfilling prophecy where you need to have more performance because of the fact that you've been able to go out and do things for a longer autonomous amount of time.

• 22:37Speaker 00

  And so it's almost a never ending loop where you'll, you'll need to have more performance for instance.

• 22:42Speaker 01

  Yeah.

• 22:42Speaker 00

  In perpetuity.

• 22:44Speaker 03

  Yeah, though intelligence per dollar is a business model metrics. Metrics, so it is not just the processor capability.

• 22:52Speaker 00

  No, it's end to end, absolutely.

• 22:53Speaker 03

  Yeah, so, okay. So let's change topics and talk about actual usage, right? So both of you have massive organizations. Where are the key wins that you're getting today with, with applying all the AI that's available to you? And then we'll talk about what your customers are doing, but I'm actually curious about what you're doing internally.

• 23:18Speaker 01

  Within the teams?

• 23:19Speaker 03

  Yeah.

• 23:20Speaker 01

  So, so I mean, coding is the obvious one. And that's actually picking up increasing traction and increasing capability. We just actually in the last couple of days, published a paper that showed how we applied AI techniques to do instruction set migration. So in other words, we actually had a fairly massive migration from x86 to ARM, making our entire code base. And at Google, it's a very, very large code base, sort of instruction set agnostic. And including to, you know, future RISC-V or whatever else might come along. Tens and thousands, hundreds of thousands of individual.

• 23:53Speaker 03

  Your entire code base, you're going to make it agnostic?

• 23:55Speaker 01

  Entire code base because we want, want to need all of our code base to be.

• 23:59Speaker 03

  Man, that's a crazy ass project.

• 24:01Speaker 01

  Yeah, so it was. And the motivation, though, for this actually was a few years ago. We had this amazing legacy system called Big Table, and then a new amazing system called Spanner. And we decided to tell the company, "Hey, everyone needs to move from Big Table to Spanner." And by the way, Big Table was amazing for its time, but Spanner was better. The estimate for doing that migration for Google was seven staff millennia.

• 24:28Speaker 00

  How much?

• 24:30Speaker 01

  Seven staff millennia. We had a new unit that we had to actually... To see what... And it wasn't like made up people being lazy. It's like, this is what it was.

• 24:41Speaker 00

  It's endearing that they came up with that, though.

• 24:43Speaker 01

  And you know what we decided? Long live Big Table. I decided, what, it just wasn't worth it, honestly. Like, the opportunity cost was too high. So, and we have these sorts of migrations. Tensorflow to JAX. We actually, I mean, again, somewhat private, but not too secret. We've affected this internally with AI Assist. Went integer factors faster. Now, there are other tasks which the tools probably aren't quite yet up to the, whatever standard for, but the area under the curve is getting bigger and bigger and bigger.

• 25:18Speaker 00

  So we're seeing probably like three or four really good use cases, and then we're seeing some use cases which are not working yet. And so, what is working? Code migrations are working relatively well so far. We use largely a combination of Codex, Claude, and Cursor, some WinSurf. And so, code migrations tends to work pretty well.

• 25:46Speaker 00

  Debugging, oddly enough, has actually been very, very productive with these tools, and especially with CLIs.

• 25:58Speaker 00

  Where we've not done as good a job and then front end zero to one projects tend to do extremely well like the engineers are super productive. When you go to code that's older and especially further down in the infrastructure stack. Much harder to go out and get that to happen, but the challenge that we have to orient our engineers on, this is actually much more of a cultural reset problem. Than it is a just a technical problem, which is if someone uses something and says this isn't working right. You can't put it back on the shelf saying this doesn't work for another six or nine months. You have to come back to it within four weeks and see if it works again, because the speed at which these tools are kind of advancing is so fast.

• 26:42Speaker 00

  That you almost have to kind of get like I was with a hundred and fifty of our distinguished engineers today. And what I had to urge them to do is. Assume that these tools are going to get infinitely better within six months.

• 26:55Speaker 01

  Yep.

• 26:55Speaker 00

  And make sure that you get your mental model to where that tool is going to be in six months and what are you going to do to be best in class in six months rather than. Assessing it for where it is today and then putting it aside for six months, assuming that that's not going to work for the next six months. I think that's a big strategic error. So we've got twenty five thousand engineers. I'm hoping that we can get at least. Do a three X productivity within a very short amount of time within the next year. And we will be able to see if that happens. The second, a couple of the big areas that we are starting to see some good responses is in sales. Preparation going into an account call really good, legal contract reviews.

• 27:39Speaker 00

  Actually, much better than what we had thought. And then the last one is not super high inference volume, but product marketing.

• 27:49Speaker 00

  I think the first chat G P T. Take on competitive is always better than what my, any product marketing person comes up by themselves. So we should never start from my slate, just start from chat G P T and then go from there.

• 28:02Speaker 03

  Okay, you could be talking about the topic for a long time, but they showed me the two minute warning. So I want to focus on one last question here. So we've got a lot of founders here, right, building amazing companies. So what is the most interesting development they should look forward to in the next calendar year? Let's call it, or the next 12 months? A, from your company and B, from the industry? If you are looking at a crystal ball.

• 28:30Speaker 01

  I mean, I think to build on the point, these, these models are getting more spectacular by the, by the month. And then there'll be from whatever companies you like, a bunch of really exciting, including ours.

• 28:41Speaker 03

  Okay, I forgot to say, you're not allowed to say models will get better.

• 28:44Speaker 01

  Yeah.

• 28:44Speaker 03

  Everybody knows.

• 28:45Speaker 01

  Models, the models are going to get, but I mean, they're getting scary good is the part that I would say.

• 28:52Speaker 01

  But I think that then the agents that get built on top of them and the frameworks for making that happen are also getting scary good. So the ability to have things go quite right for quite long over the coming 12 months is going to be transformative.

• 29:08Speaker 03

  I mean, do you want to leak any aspect of your roadmap? Next 12 months?

• 29:13Speaker 01

  Not, not, so not right now, yeah.

• 29:14Speaker 03

  Okay. Do you too?

• 29:16Speaker 00

  I'd, I'd say the, the, the big shift and what I would urge startups to do is don't build thin wrappers around models that are other people's models. I think the, the, the combination of a model working very closely with the product and the model getting better as there's feedback in the product is going to be super important. So you are going to need foundation models, but if you just have a thin wrapper, I think the durability of your business will be very, very, you know, short-lived. So that would be something that I would, I would urge you on. And I think that intelligent routing layer of some sort that says, I'm going to use my models for these things. I'm going to probably use foundation models for other things and dynamically keep optimizing will be, I think Curse does that pretty well.

• 30:01Speaker 00

  But that, that'll be a, a good way that the, the software development lifecycle will evolve. What you should expect from Cisco is, look, truth be told, for the longest times, people thought Cisco was a legacy company. Like there, there were has been, I think there's a level of momentum in the business, there's a spring in the step in the employee base. So you should expect.

• 30:22Speaker 00

  Like I said, from the physics to the semantics in every layer, from silicon to the application, a fair amount of innovation in silicon and networking and security and observability and the data platform, as well as applications from us. And we're excited to work with the startup ecosystem. And so if you ever feel like you want to work with us, make sure that you reach out to us.

• 30:46Speaker 03

  What are you going to say, Sadiq?

• 30:48Speaker 01

  I mean, one aspect that I want to highlight about the models is where we were with, let's say, text models two and a half, three years ago, they were fun. Like, hey, write me a haiku about Martine. Did a great job. Now they're amazing. I think that what's going to happen in the next 12 months is the same thing is going to be happening with input and output of images and video to these models. And to the extent that even for images, imagine them as productivity and educational tools, not just, okay, here's Martine as Superman on a, like, that's cool, too, right? But using it for productivity gains and learning, I think, is going to be really, really transformative.

• 31:28Speaker 03

  Awesome. So I'm not really allowed to end this session. Thanks for a great conversation, Amin. Thanks to you, too.

• 31:37Erik Torenberg?

  Thanks for listening to this episode of the A16Z Podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X at A16Z and subscribe to our Substack at A16Z.substack.com. Thanks again for listening, and I'll see you in the next episode. As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed

• 32:20Erik Torenberg?

  in this podcast. For more details, including a link to our investments, please see A16Z.com/disclosures.