# Why AI Isn't Hype: Marc Andreessen Explains the 80-Year Foundation

*The Builder Weekly* · https://www.youtube.com/watch?v=-HMY6dFhAeI

## Summary

Marc Andreessen argues that AI is the biggest technological revolution of his life, bigger than the internet and comparable to the microprocessor, electricity, and the steam engine. He traces the field back to a 1930s debate among computer inventors between building machines like adding machines versus modeling them on human cognition, with the first neural network paper published in 1943. After decades of AI boom-bust cycles, the ChatGPT moment in late 2022 proved the neural network path works, and he sees unprecedented revenue growth in AI companies while believing today's products are still very early in their evolution.

## Key points

- Marc Andreessen calls AI the biggest technological revolution of his life, comparing it to the microprocessor, electricity, and the steam engine rather than just the internet.
- In the 1930s, computer inventors debated whether computers should be built like adding machines or modeled on human cognition and neural networks.
- The industry took the adding machine path, while neural network research continued in academia, starting with the first neural network paper by McCulloch and Pitts in 1943.
- AI went through decades of excessive optimism and disappointment, becoming a backwater field by the late 1980s before scientists' persistence paid off.
- The ChatGPT moment in Christmas of 2022 crystallized that the neural network approach actually works, roughly three years into what he frames as an 80-year revolution.
- State-of-the-art AI is already ultra democratized through products like ChatGPT, Grok, Gemini, Sora, Veo, and Suno.
- Silicon Valley's magic is its ability to recycle talent and capital from previous technology waves into each new wave.
- Leading AI companies are growing real customer revenue at an unprecedented takeoff rate, and he believes current products are still very early in form.

## Notes

### Why AI Is Not Hype
- Marc Andreessen describes AI as the biggest technological revolution of his life, bigger than the internet, comparable to the microprocessor, electricity, the steam engine, and the wheel.

### The 80-Year Foundation
- In the 1930s, the inventors of the computer debated two paths: build it like an adding machine (cash register logic, the IBM lineage) or model it on human cognition.
- They already understood the theory of computation and had a theory of neural networks.
- The first neural network academic paper was published in 1943 by McCulloch and Pitts; Andreessen mentions a YouTube interview with McCulloch from around 1946, filmed at his beach house.
- The industry took the adding machine path, building hyper-literal mathematical machines with no ability to deal with human language — this built the computer industry's wealth from mainframes to smartphones.
- The neural network idea survived in academia through a movement originally called cybernetics, later known as artificial intelligence.

### Boom, Bust, and Breakthrough
- AI saw decade after decade of excessive optimism followed by disappointment, including a famous boom-bust cycle in the 1980s.
- By the time Andreessen reached college in 1989, AI was a backwater in computer science departments, but scientists kept building a reservoir of concepts.
- The ChatGPT moment at Christmas of 2022 showed it finally works — roughly three years into an 80-year revolution.

### Democratization and Silicon Valley's Role
- The best AI is available to everyone through ChatGPT, Grock, Gemini, Sora, Veo, and Suno.
- Silicon Valley's virtue is recycling talent and capital from previous tech waves and inspiring a new generation for each new wave.

### What Comes Next
- Andreessen is surprised daily by new AI research papers and new startups.
- Progress will come in fits and starts, with moments of overpromising and economics not working.
- Leading AI companies are growing real revenue at an unprecedented takeoff rate, and he is skeptical today's product forms will resemble those used in five or ten years.

## Transcript

[00:00] First of all, I I would say this is the biggest tech technological revolution of my life. Um, and you know, hopefully I'll see more like this in the next whatever 30 years, but I I mean this is the big one. Um, and just in terms of order of magnitude, like this is clearly bigger than the internet. Um, like the the 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. Um, the wheel. Um, the reason this is so big, I mean, maybe obvious to folks at this point, but I'll just go through it quickly. So, um, if you kind of trace all the way back to the 1930s, uh, there's a great book called Rise of the Machines that kind of goes through this.

[00:36] Um, if you trace all the way back to the 1930s, there was actually a debate among the people who actually invented the computer. Um, 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. Um, and they 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. You know, think of sort of essentially cash registers. Um IBM is actually the successor company to the national cash register company uh of America. Um and so like and and that was of course the path that the industry took which was building these kind of

[01:08] 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 um 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 uh and and financial returns of the computer industry uh over the last 80 years you know across all the generations of computers through mainframes through to through to smartphones. Um but but they knew at the time they knew in the 30s actually they

[01:38] understood the basic structure of the human brain. They understood they had a theory of sort of human cognition and and and and actually they had the theory of neural networks. Um and so they they had this theory that um the there's actually the the first neural network uh paper academic paper was published in 1943 you know which was over 80 years ago which is extremely amazing. Um, there's an interview, you can read an interview or you can watch an interview on YouTube with uh 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

[02:12] because it's like him in his beach house and for some reason he's not wearing a shirt. Um, 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. Um and and 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. Um but and the neural network basically didn't happen but the neural network as an idea continued to be explored in academia um and a 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. uh basically

[02:43] 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 uh followed by disappointment when I was in college in the '8s there had been a famous kind of AI boom bust uh cycle in the '8s uh in venture in Silicon Valley um I mean it was tiny by by modern standards but it it at the time was a big deal um and um you know by the time I got to college in '89 um 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

[03:17] then basically we all saw what happened with the CHI GPT uh moment all of a sudden it it sort of crystallized like oh my god right it turns out it works um 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 3 years ago right that was the summer of 20 it was the the Christmas of 22 and so we're sort of three year we're we're sort of three years in um to you know basically what is effectively effectively an 80-year revolution um of actually being able to deliver on all the promise that the that 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

[03:52] 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 um and 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 you know, and IDO and so forth. Um, 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,

[04:26] Silicon Valley long since has ceased to be a place where people make Silicon, you know, that that not long ago moved out out of the out of California and then ultimately out of the US, although we're trying to bring it back now. Um 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 uh recycle talent from previous waves of technology and new waves of technology uh and then inspire an entire new generation of talent you know to basically come join the you know join the project. Um and so Silicon Valley has this recurring pattern of being able to reallocate capital and talent and build enthusiasm and build critical mass

[04:59] and build funding support and build you know human capital and build you know everything enthusiasm um you know for each new wave of technology. So, so that's what's happening with AI. Um, you know, I 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. Um, uh, and and you know, we we're we're in the fortunate position to kind of get to see it from from two angles. Uh, you know, 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 um of some new capability um or some new discovery uh or some new

[05:34] 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 um on the other side of course you know we see the flow of all of the new uh products uh and all the new startups. Um and you know I I would say we're routinely um you know kind of seeing things that again kind of have my my jaw on the floor. Um and so you know it feels like we we we've unlocked this giant vista. Um I do think it's going to kind of come in fits and starts. Um you know the these things are messy processes. Um you know you know this is an industry that kind of routinely gets out over riskies and overpromises. Um and so you know there you know there will certainly be points

[06:09] 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 this new wave of AI companies is is growing revenue like just like actual customer revenue, actual demand uh translated

[06:38] through to dollars showing up in bank accounts. um you know at at like an absolutely unprecedented takeoff rate, we're seeing companies grow much faster. Um uh you the the key leading AI companies and the companies that have real breakthroughs um and have real have very compelling products or growing revenues that you know kind of faster than any any way I've certainly ever seen before. Um 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 I'm very

[07:10] 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. Um and so I think we probably have a long way to go. >> [music]