Marc Andreessen's 2026 Outlook: AI Timelines, US vs. China, and The Price of AI

this new wave of AI companies is growing revenue like just like actual customer revenue, actual demand translated through to dollars showing up in bank accounts at like an absolutely unprecedented takeoff rate. We're seeing companies grow much faster. I'm very skeptical that the form and shape of the products that people are using today is what they're going to be using in 5 or 10 years. I think things are going to get much more sophisticated from here. And so I think we probably have a long way to go. These are trillion dollar questions, not answers. But once somebody proves that it's capable, it seems to not be that hard for other people to be able to catch up, even people with far less resources. When a company is confronted with fundamentally open strategic or economic questions, it's often a big problem. Companies like need to answer these questions and if they get the answers wrong, they're really in trouble. Venture, we can bet on multiple strategies at the same time. We are aggressively investing behind every strategy that we've identified that we think has a plausible chance of working. If you want to understand people, there's basically two ways to understand what people are doing and thinking. One is to ask them and then the other is to watch them. And what you often see in many areas of human activity, including politics and many different aspects of society, the answers that you get when you ask people are very different than the answers that you get when you watch them. If you run a survey or a poll of what, for example, American voters think about AI, it's just like they're all in a total panic. It's like, "Oh my god, this is terrible. This is awful. It's going to kill all the jobs. It's going to ruin everything." If you watch the revealed preferences, they're all using AI.

A lot of folks have sent questions ahead of time and what I've done is kind of curated into a few different sections in an AMA this morning with Mark. So, what we thought we'd do is cover four big topics. So, AI and what's happening in the markets, policy and regulation, all things 816Z, and then we've got a fun catchall which we're calling sandbox of things if we get to it. So, starting first maybe with the biggest question. We're sitting in the middle of the AI revolution. Mark, what inning do you think we're in and what are you most excited about?

First of all, I would say this is the biggest tech technological revolution of my life. And you know, hopefully I'll see more like this in the next whatever 30 years, but I mean this is the big one. And just in terms of order of magnitude, like this is clearly bigger than the internet. Like the comps on this are things like the microprocessor and the steam engine and electricity. So that this is a really this is a really big one. The wheel. The reason this is so big, I mean maybe obvious to folks at this point, but I'll just go through it quickly.

So if you kind of trace all the way back to the 1930s, there's a great book called Rise of the Machines that kind of goes through this. If you trace all the way back to the 1930s, there was actually a debate among the people who actually invented the computer. And it was this this sort of debate between whether computer they kind of understood the theory of computation before they before they they actually built the things. And they had this big debate over whether the computer should be basically built in the image of what at the time were called adding machines or calculating machines where you know think of sort of essentially cash registers. IBM is actually the successor company to the national cash register company of America. And so like and and and that was of course the path that the industry took which was building these kind of hyper literal you know mathematical machines you know that could execute mathematical operations billions of times per second but of course had no ability to kind of deal with human beings the way humans like to be dealt with and so you know couldn't understand you know human speech human language and so forth and and that's the computer industry that got built over the last 80 years and that's the computer industry that's built all the wealth of uh and financial returns of the computer industry over the last 80 years you know across all the generations of computers from mainframes through to smartphones.

But they knew at the time they knew in the 30s actually they understood the basic structure of the human brain. They understood they had a theory of sort of human cognition and and and actually they had the theory of neural networks. And so they they had this theory that there's actually the first neural network academic paper was published in 1943 you know which was over 80 years ago which is extremely amazing. There's an interview you can read an interview or you can watch an interview on YouTube with these two authors Makulla and Pitts and you can watch an interview I think with Makulla on YouTube from like I don't know 1946 or something. He was like on TV you know in the in the ancient past and it's literally like it's amazing interview because it's like him in his beach house and for some reason he's not wearing a shirt and he's like you know talking about like this future in which computers are going to be you know built on on the model of a human brain through through neural networks.

And that was the path not taken. And basically what happened was right the computer industry got built in the in the image of of like the adding machine. But and the neural network basically didn't happen but the neural network as an idea continued to be explored in academia and sort of advanced research by sort of a rump you know movement that was originally called cybernetics and then became known as as artificial intelligence basically for the last 80 years and and and essentially it didn't work like essentially it was basically decade after decade after decade of excessive optimism followed by disappointment. When I was in college in the 80s, there had been a famous kind of AI boom bust cycle in the 80s in venture and in Silicon Valley. I mean it was tiny by modern standards, but it at the time was a big deal. And um you know by the time I got to college in '89 in computer science departments, AI was kind of a backwater field and everybody kind of assumed that it was never going to happen.

But the scientists kept working on it to their credit and they they they built up this kind of enormous reservoir of of concepts and ideas and then basically we all saw what happened with the CHIGPT moment. all of a sudden it it sort of crystallized. It's like oh my god, right? It turns out it works. And and so you know that that's the moment we're in now. And then you know really significantly that was what you that was less than three years ago, right? That was the summer of 20 it was the the Christmas of 22. So, we're sort of three year we're we're sort of three years in to, you know, basically what is effectively effectively an 80-year revolution of actually being able to deliver on all the promise that the that the people on the all the on the alternate path, the sort of human cognition model path, you know, kind of saw from the very beginning and and then, you know, the great news with this technology is it's already it's kind of ultra democratized. you know, the best AI in the world is available. Launch at GPD or Grock or Gemini or or um you know, these other you know, these other products that you can just use and you can just kind of see how they work and you know, same thing for video, you can see with Sora and VO kind of state-of-the-art uh with that you can see with music, you can see you know Suno and IDO and so forth.

And so like you know we're basically seeing that happen and now and now Silicon Valley is responding with this just like incredible rush of enthusiasm. And you know, really critically this gets to the magic of Silicon Valley, which is, you know, Silicon Valley long since has ceased to be a place where people make silicon that, you know, that's that not long ago moved out out of the out out of California and then ultimately out of the US, although we're trying to bring it back now. But but but the great kind of virtue of Silicon Valley o over the last you know over the last you know 80 years of its existence is its ability to kind of recycle talent from previous waves of technology and new waves of technology and then inspire an entire new generation of talent you know to basically come join the you know join the project. And so Silicon Valley has this recurring pattern of being able to reallocate capital and talent and build enthusiasm and build critical mass and build funding support and build you know human capital and build you know everything enthusiasm you know for each new wave of technology. So, so that's what's happening with AI.

I think probably the biggest thing I could just say is like I'm surprised I think essentially on a daily basis of what I'm seeing. And you know, we're we're in the fortunate position to kind of get to see it from from two angles. One one is we track the underlying science and and, uh, and kind of research work very carefully. And so I would say like every day I see a new AI research paper that just like completely floores me of some new capability or some new discovery or some new development that I that I would have never anticipated that I I'm just like wow I you know I can't believe this is happening. And then on the other side of course you know we see the flow of all of the new products and all the new startups. And you know I would say we're routinely um you know kind of seeing things and again kind of have my my jaw on the floor.

And so, you know, it feels like we we we've unlocked this giant vista. I do think it's going to kind of come in fits and starts. These things are messy processes. This is an industry that kind of routinely gets out over risks and overpromises. And so, you know, there, you know, there will certainly be points where it's like, wow, you know, this isn't working as well as people thought, or you know, wow, this turns out to be too expensive and the economics don't work or whatever. But, you know, against that, I would just say the capabilities are truly magical. And and by the way, I think that's the experience that consumers are having when they use it. And I think that's the experience that businesses are having for the most part when they uh you know, when when they're working on their pilots and and looking at adoption and and and then and then it translates to the underlying numbers. I mean, we're we're just seeing a this new wave of AI companies is is growing revenue like just like actual customer revenue, actual demand translated through to dollars showing up in bank accounts.

You know, at like an absolutely unprecedented takeoff rate. We're seeing companies grow much faster. the the key leading AI companies and the companies that have real breakthroughs and have real have very compelling products are growing revenues that you know kind of faster than any any way I've certainly ever seen before. And so like just just from all that it kind of feels like it has to be early. Like it it's kind of hard to imagine that we've like we we've topped out in any way. It feels like everything is still developing. I mean quite frankly it feels like the products to me it feels like the products are still super early. Like I'm I'm very skeptical that the form and shape of the products that people are using today is what they're going to be using in five or 10 years. I think I think things are going to get much more sophisticated from here. And so I think we probably have a long way to go.

Maybe on that that topic. So one of the big knocks is yes the revenue is immense but the expenses seem to also be keeping pace. So like what are people missing as a part of that discussion and topic?

Yeah. So just start with just like core business models, right? And so you're right. There's basically this industry basically has two two core business models. consumer business model and the quote unquote enterprise or infrastructure business model. You know look on the on the consumer side we we just live in a very interesting world now where where the internet exists and is fully deployed right. And so I'll give you an example. Sometimes people ask us like, "Is AI like the internet revolution?" It's like, well, a little bit, but like the thing with the internet was we had to build the internet. Like we we like we had we had to actually build the network and we actually had to, you know, and ultimately it involved enormous amount of fiber in the ground and it involved enormous numbers of like mobile cell towers and, you know, enormous number of, you know, shipments of of of smartphones and tablets and and and laptops in order to get people on the internet. Like there was this like just like incredible physical lift um, you know, to do that. And and by the way, people forget how long that took. Right, the the the you know, the internet itself is a invention of the 1960s, 1970s.

The consumer internet, you know, was a new phenomenon in the early '90s. But, you know, we didn't really get broadband to the home until the 2000s. You know, that really didn't start rolling out actually until after the com crash, which is fairly amazing. And then we didn't get mobile broadband until like 2010. And and people actually forget the original iPhone dropped in 2007. It didn't have broadband. it was on a it was on a narrowband 2G network. It did not have high speed like it did not have anything resembling high-speed data. And so it wasn't really until you know really about 15 years ago that we even had mobile broadband. So so the internet was this massive lift but but the internet got built right and smartphones proliferated. And so the point is now you have 5 billion people on planet earth that are on some version of you know mobile broadband internet right and you know smartphones all over the world are selling for you know as little as like 10 bucks.

And you know you have these you know amazing projects like geo and India that are bringing you know you know the sort of the remaining you know kind of the remaining population of of planet earth that hasn't been online until now is coming online. So, you know, so we're talking five billion, six billion, you know, people and and then the consumer, the reason I go through that is the consumer AI products could basically deploy to all of those people basically as quickly as they want to adopt, right? And so sort of the internet's the carrier wave for AI to be able to proliferate at kind of light speed uh uh into the broad base of the global population. And and that's a let's just say that's a potential rate of proliferation of a new technology that's just far faster than has ever been possible before. Like what you know, like you couldn't download electricity, right? you you couldn't download, you know, you couldn't download indoor plumbing. Um, you know, you couldn't download television, but you can download AI. And and this is what we're seeing, which is the AI consumer, you know, the AI consumer killer applications are growing at at at an incredible rate.

And then and then they're monetizing really well. And again, you know, we I mentioned this already, but like generally speaking, the monetization is is very good. By the way, including at higher price points. One of the things I like about the um, you know, about watching the AI wave is the AI companies I think are are more creative on pricing than the SAS companies and the consumer internet companies were. And so it's it's for example now becoming routine to have $200 or $300 t per month tiers for consumer AI which I which I think is very positive because I I think the I think a lot of companies cap their kind of opportunity by by capping their pricing kind of too low and I think the AI companies are more willing to push that which I think is good. So anyway, so that you know I think that's reason for like I would say you know considerable rational optimism for the scope of of consumer revenues that we're going to be talking about here.

And then on the enterprise side, you know, there the question is basically just, you know, what is intelligence worth, right? And you know, if you have the ability to like inject more intelligence into your business and you have the ability to do, you know, even the most prosaic things like raise your customer service scores, increase upsells, uh, you know, or reduce churn or if you have the ability to, um, you know, run marketing campaigns more effectively, you know, all of which AI is directly relevant to, like, you know, these are like direct business payoffs, um, you know, that people are seeing already. And then if you have the opportunity to infuse AI into new products and all of a sudden, you know, all of a sudden your car talks to you, and everything in the world kind of lights up and starts to get really smart. You know, you know, what's that worth? And and again there you just you you kind of observe it and you're like, wow, the the leading AI infrastructure companies are growing revenues incredibly quickly.

You know, the pull is really tremendous. And so, you know, again there it's just it feels like this just like incredible product market fit. And and and the core business model, right, is is is actually quite quite interesting. The core business model is is is basically is basically tokens by the drink, right? And so it's it's sort of tokens of intelligence you know, per dollar. And oh, and then by the way, this is the other fun thing is if you look at what's happening with the price of AI, the price of AI is falling much faster than Moore's law. And when I could go through that in great detail, but basically like all of the inputs into AI on a perunit basis, the costs are collapsing. And and and and then as a consequence there's kind of this hyperdelation of per unit cost and then that is like driving you know just like you know a more than corresponding level of demand growth you know with with with elasticity. And so you know even there we're like it feels like we're just at the very beginning of kind of you know figuring out exactly how you know expensive or cheap this stuff is getting.

I mean look there's just no question tokens by the drink are going to get a lot cheaper from here. That's just going to drive I think enormous demand. And then everything in the cost structure is going to get optimized right? And so you know when when people talk about like you know the chips or you know whatever you know kind of the unit input costs for building AI you know you now have these like m the losses of blind demand are are going to are going to kick in right what's the you know in any market that has sort of commodity like characteristics you know the number one cause of a of a of of a glut is a shortage and the number one cause of a shortage is the glut right and so you have you know to the extent you have like shortage of GPUs or shortage of whatever infest chips or shortage of you know whatever data center case, you know, if you look at just the history of humanity building things in response to demand, you know, if there's a shortage of something that can be physically replicated, it it does get replicated.

And so there's going to be like just enormous build out of all I mean there is there's just hundreds of billions or at this point trillions of dollars maybe going into the ground in all these things. And so the the per unit cost of the AI companies are going to drop like a rock you know over the course of the next decade. And so like I yeah I mean the economic questions of course are very real and of course there's you know microeconomic questions around around all these businesses but the the sort of macro forces have been at least here I think are very strong and and yeah I I just given the underlying value of the of of this technology to both the consumers the enterprise users. And given the just like incredibly aggressive discovery that's happening of of all the ways that people can use this in their lives and in their businesses, like it's just it's really hard for me to see how it both doesn't grow a lot and generate just enormous revenue.

Yeah. And actually, I think it was like two or three weeks ago where AWS was saying like the the GPUs that they've been using, they've been able to extend back to even like seven plus years. So like the shelf life also of the GPUs that they're using is now extending in ways of which they can optimize better than maybe perhaps the last couple of of cycles. as well. Is that the right way to think about it as well?

Yeah, that's right. And then and then that's one that's that's one really important question and observation and and then by the way that also gets to this other kind of question where there's different theories on it. Which is basically big models versus small models. And so a a lot of the data a lot of the data center build is oriented around hosting training and and and and serving the the big the big models, you know, for for all the obvious reasons. But there's also the small the small model revolution is happening at the same time and and and and if you just kind of track you know you can get get the various research firms have these charts you can get but if you just kind of track the if you track the capability of the leading edge models over time what you find is after 6 or 12 months there's a small model that's just as capable and so there there there's this kind of chase function that's happening which is the capabilities of the big models are basically being shrunk shrunk down and provided at at at at smaller size and then therefore smaller cost you know quite quickly.

So, I I'll just give you the the most recent example that just got hit over the last two weeks. And again, this is a thing that's just kind of shocking. Is there's this Chinese company that has a well, I forget the name of the company, but it's it's uh the company that produces the model called Kimmy, which is spelled Kim Mi, which is one of the leading open source models out of China. And uh the new version of Kimmy is a reasoning model that is at least according to the benchmark so far is basically a replication of the reasoning capabilities of GPT5, right? and and and these new models of GPT5 were a big advance over GPT4 and of course GPT5 costs a tremendous amount of money to to develop and to serve and all of a sudden you know here we are whatever 6 months later and you have an open source model called Kimmy and I think I don't know if they had it's either shrunk down to be able to run on either it's like one MacBook or two MacBooks right and so you can all of a sudden if you have like an applica you if you're a business and you want to have a reasoning model that's GPT5 capable but you you know you're whatever you're not going to pay the whatever GPT5 cost or you're not going to want to have it be hosted and you want to run it locally, you can do that.

And and again, that's just like another just it's just like another, you know, it's another breakthrough. Like it's just it's another another Tuesday, another huge advance. It's like, oh my god. And then of course, it's like, all right, well, what is OpenAI going to do? Well, obviously they're going to go to GPT6, right? Uh, and you know, right? And so there there's this kind of lattering that's happening where the entire industry is moving forward. The big models are getting more capable. The small models are kind of chasing them. And then and then the small models provide you know completely different way to deploy you know at at at at very low price points. And so yeah I think and and you know we'll we'll see what happens. I mean there there are some very smart people in the industry who think that ultimately everything only runs in the big models because obviously the big models are always going to be the smartest and so therefore you're always you know you're always going to want the most intelligent thing because why would you ever want something that's not the most intelligent thing for any application.

You know the counterargument is just there's a huge number of tasks that take place in the economy and in the world that don't require Einstein. you know, where, you know, where, you know, 120 IQ person is great. You don't need a, you know, 160 IQ, you know, PhD in, you know, string theory. You just like have somebody who's competent and capable and it's great. And so, you know, I I, you know, and I we've talked about this before. I tend to think the AI industry is going to be structured a lot like the computer industry ended up getting structured, which is you're going to have a small handful of basically the equivalent of supercomputers, which are these like giant, you know, kind of we call god models that are, you know, running in these giant data centers. And then and then you know I I I I'm not like convinced on this but my my kind of working assumption is what happens is then you have this cascade down of smaller models all ultimately all the way the very small models that run on embedded systems right run on run on individual chips inside every you know physical item in the world.

And that you know the smartest models will always be at the top but the volume of models will actually be the smaller models that proliferate out and right that's what happened with microchips. it's what happened with computers which became microchips and then it's what happened with operating systems and with with a lot of everything else that we built in software. So you know I tend to think that's what will happen. Just quickly on the chip side again like chips you if you look at the entire history of the chip industry shortages become gluts and you get just you know like anytime there's a giant profit pool in a in a new chip category you know somebody has a lead for a while and kind of gets you know let's say the the the profits appropriate to what we u what we call robust market share but in time what happens right is that that draws competition and of course you know that that that's happening right now.

So Nvidia's, you know, Nvidia is an absolutely fantastic company, fully deserves the position that they're in, fully deserves the profits that they're generating, but they're now so valuable, generating so many profits that it's the bat signal of all time to the rest of the chip industry to figure out how to advance the state-of-the-art AI chips. And that's, by the way, and that's already happening, right? And so you've got other major companies like AMD coming at them, and then you've got really significantly, you've got the hyperscalers building their own chips. And so, you know, a bunch of the big a bunch of those kind of big tech companies are building their own ships. And of course then the Chinese are building their own ships as well. And so it's just it's like pretty likely in 5 years that that you know AI chips will be you know cheap and plentiful at least in comparison to the situation today. Uh which again I think will you know will tend to be extremely positive for the economics of of the kinds of companies that we invest in.

Yep. And that startups are also starting to go after new chip design as well which is exciting.

Yeah. Well, that's the other thing is yeah, you have these disruptive startups and actually that just for a moment on the chips, they were not really big investors in chips because it's kind of a big it's kind of a big company thing, but it's a little bit of historical happen stance that AI is running on quote unquote GPUs you know which GPU stands for graphical processing unit. So and basically just for people who haven't tracked this there were basically two kinds of chips that made the personal computer happen. the so-called CPU central processing unit which classically was the Intel x86 x86 chip that's kind of the brain of the computer and then there was this other kind of chip called the GPU or graphical processing unit that was the sort of second chip in every PC that does all the graphics and you know and this is graphics you know 3D graphics for gaming or for CAD CAM or for you know anything else you know Photoshop or for anything that involves you know lots of visuals and so the the kind of canonical architecture for a personal computer was a CPU and a GPU by the way same thing for smartphones but by the way.

And over time, you know, these have kind of merged and so like a lot of CPUs now have GPU capability built in. Actually, a lot of GPUs now have CPU capability built in. So this, you know, this has gotten fuzzy over time, but like that that was like the classic breakdown. But the fact that that was the classic breakdown, you know, kind of meant that while Intel had a you know, monopoly for a long time on CPUs, there was this other market of GPUs which Nvidia you know basically fought the GPU wars for 30 years and and and came out the winner like what was the best company in the space. But it was like a hyper competitive market for graphics processors. it was actually not that high margin and it was actually not that big. And then basically it just it turned out that there were two other forms of computation that were incredibly valuable that happened to be massively parallel in how they operate which which happened to be very good fits for the GPU architecture. And those two basically highly lucrative additional applications were cryptocurrency starting about you know 15 years ago and then AI starting about you know whatever four years ago.

And so and and Nvidia like I would say very cleverly set itself up with an architecture that works very well for this, but it's also just a little bit of a twist of fate that it just turns out that if AI is the killer app, it just turns out that the GPU architecture is the best legacy architecture is devoted to it. And I go through that to say like if you were designing AI chips from scratch today, you wouldn't build a full GPU. you would build dedicated AI chips that were much more much more specifically adapted to AI and would have I I think would just be much more economically efficient and you know John to your point there there there are startups that are actually building entirely new kinds of chips oriented specifically for AI and you know we'll have to see what happens there you know it's hard to build a new chip company from scratch you know it's possible that one or more of those startups makes it on their own and some of them are you know doing very well it's also possible of course that they get bought you know by big companies that that have the ability to scale them.

And so, you know, you know, we'll see exactly how that unfolds. And of course, we'll also, by the way, see, you know, the Koreans are going to play here for sure. The Japanese are going to play. And then, you know, the Chinese in a major way, as well. And, you know, they have their own, you know, native chip ecosystem that they're that they're building up. And so there there there there are going to be many choices of AI chips in the future. And it's going to be that, you know, that'll be a giant battle that'll be a giant battle that we observe very carefully and that we make sure that our our companies basically are able to take full advantage of.

While while on the topic of of international we you mentioned Kimmy earlier. So it seems like some of the best open source models today are from China. Should this be worrisome to to folks? How are you thinking and talking about this topic with with folks in DC? I know you were just there last week. How much of this is a concern for US companies particularly just having seen the rise of China do unnatural things in solar markets, car markets? Are they kind of flooding the ecosystem so that they can eventually kind of take share and and increasingly own the the ecosystem?

Yeah. So uh you know a couple things. So one is you know you know you want to start these discussions by just kind of saying like you know look there's there's vigorous debate in in the US and around the world of look like you know how much are we in a new cold war with China you know and exactly like how hostile you know should should we view them and it you know it's very tempting by the way it's very tempting and I think it's a very good case made that we're in like a new cold war that's like that in a lot of ways is like the US versus USSR in the in the 20th century you know it is the counter argument would be it is more complicated than that because the US and the USSR were never really intertwined from a trade standpoint And and a big part of that quite frankly was the USSR never really made anything that anybody else needed I guess other than weapons.

But like you know the USSR's primary exports were literally like you know literally like wheat and and oil. Whereas of course China exports just a tremendous number of physical things right including like a huge part of like the entire supply chain of parts that basically go into everything that American manufacturers you know kind of make right and so by the time a US you know whatever by the time an American company brings a toy to market right or a uh you know or a car or anything or a computer or a smartphone or whatever like it's got a lot of componentry in it that was made in China so there so there is a much tighter in interlinkage between the the American and Chinese economies than there as the American and Soviet economies and you know may maybe you know Adam Smith or whatever might say you know that's good news for peace and that you know both countries need each other by the way the other part of that argument is that the Chinese basically the Chinese you know the Chinese governance model is based on high employment you know because you know if if you know at least all the geopolitical people say if China ended up with like 25 or 50% unemployment that would cause civil unrest which is the one thing that the CCP doesn't want and so the corresponding part of the trade pressure is China needs the American export market you know the American consumer is like a third of the global economy. A third of global consumer demand.

And so you know China needs the US export market or it has high all of a sudden a lot of its factories would go kind of instantly bankrupt and you know would cause mass unemployment and unrest in China. So so anyway like you know we there is this complicated it's a it's a complicated intertwined relationship. Having said that you know the the mood in DC basically for the last 10 years on a bipartisan basis has been that we need to take we the US need to take China more seriously as a geopolitical foe. And you know under under under that school of thought there's sort of the sort of you know there's there's the military dimension which is you know the sort of the you know the the risk of some kind of war in the South China Sea the risk of some kind of war around around Taiwan and so that you know that that has everybody in Washington on high alert you know there's also this this economic question around the kind of de-industrialization of the US potential re-industrialization and what that means about you know dependence on China and then and then there's and then there's this this this AI question and and the AI question is an economic question but It's also like a geopolitical question which is okay you know basically AI is essentially only being built in the US and in China.

You know the rest of the world either you know can't build it or doesn't want to which which we could talk about. So it's basically US versus China. And then AI is going to proliferate all over the world and is it going to be American AI that proliferates all over the world or is it going to be Chinese AI that proliferates all over the world and so and I was saying just generally across party lines in DC this you know the the things I just went through are kind of how they look at it. And and the Chinese are in the game and so the you know the