The Semiconductor Earnings Boom Is Just Getting Started | Ben Pouladian on why AI is Real, Nvidia is Mispriced, and Capacitors Are Overrated

**Jack Farley** (0:00)
Got a special conversation today. I'm joined by Ben Pouladian of BEP Research. Ben is a specialist investor and analyst in the semiconductor world and the semiconductor supply chain. Of course, that is what powers AI. Ben, welcome to Monetary Matters.

**Ben Pouladian** (0:16)
Thanks for having me, Jack. Excited to be here. Love your podcast, and would love to dive in on some interesting topics and some things that your listeners care about.

**Jack Farley** (0:26)
I'm really glad you're here too. I really like your work. You're very in the weeds on the semiconductor world. I want to start by asking kind of the proposed bear argument. Ben, perhaps many people watching this, many institutional investors who are a little skeptical about AI, skeptical about semiconductors, they may think that this is the repeat of the.com bubble. And so my first question to you is Cisco was a very profitable company. The earnings and the growth experience in the.com bubble was extreme, just like it is now in AI.
Why isn't Nvidia Cisco? Why isn't this a replay of the.com bubble?

**Ben Pouladian** (1:04)
Thanks.
A lot of people would bring up Cisco and overlay the Nvidia chart. The challenge with Cisco and comparing it to Fiber, in my opinion, things like that are sort of like a commodity.
So when you talk about datacom or telecommunications, it's the idea of transmitting the data, right? So it just goes from point A to point B. With Compute and Nvidia, you have the chance of actually creating intelligence, something from nothing. Everybody wants intelligence. It's not a commodity that anyone else can make. And I think that is a discerning difference. You needed a company like Cisco to basically overbuild or global crossing to die on that hill, to bring that capacity and bandwidth, to have the compute that we have today. A little different.

**Jack Farley** (1:53)
So here's, I'll start where I disagree. I think intelligence is kind of a commodity. Like OpenAI creates geniuses and Anthropic creates digital geniuses. Sometimes some geniuses are better than others. Now the other one company pulled ahead and there are some are better at math, better at writing, okay. But that is kind of like, it is kind of a commodity. But here's where I agree with you that semiconductors are not a commodity.

**Ben Pouladian** (2:17)
The challenge is people are just still using AI to do routine work. Like, hey, help me plan my trip.
Obviously write software or how do I make this recipe? What's this bug that I took a picture of, right? The next inflection of AI, which we'll see coming in what I'll be writing about soon, is the intersection of artificial intelligence and the bigger sciences, mostly material science, biotech and other things. Once you start discovering new materials, new medicines, that impact of doing something 10 years in the lab, you're trying to figure out if you can model it with AI and the actual GPUs and computers and find drug targets, that is a big unlock. You compress 10 years of work into one year, and people need to realize the speed up that you get with intelligence.

**Jack Farley** (3:08)
So you're saying that your bull case is not only 15% of the population is using AI every day to plan their trips and do routine tasks, and that 15% is gradually going to head to 70 or 80%.
That is not your bull case. Your bull case is that mainly the intelligence is going to be advanced at the frontier that can do tasks that previously required millions, tens of millions, billions of dollars in human intelligence, and now that can be done much more cheaply and at scale. That's what you're saying.

**Ben Pouladian** (3:40)
Yeah, I mean, going back to what Jensen has been saying at GTC for a while from Nvidia, the whole point of all this is can you do your life's work in your lifetime? So when you look at R&D for medicine and people working in the labs, there's a lot of trial and error.
If you can scale that and compress the time to get to better patient outcomes with better options, that is the unlock. You couldn't do that with classical computing. You can only do that with high performance computing, larger clusters, more data. The more data points you throw into it, genomics, organs, NHS data from different places of the world. Once you have multivariable data points, you can all of a sudden triangulate into things that you weren't able to do before and find that needle in the haystack to help that one patient.

**Jack Farley** (4:31)
Yes, and that bullcase for semiconductors and AI is something that I'm seriously entertained, and I encourage my watching to entertain. I also think, Ben, that a short-term 12 to 18-month bullcase is that a tremendous amount of capital, way more than has already been spent, is going to be spent on that goal with the anthropic opening. I basically trying to build a digital god or a digital IQ person that never sleeps and has, you know, it's a billion years of a 160 IQ person in five minutes. You can do. And whether or not that is going to be achieved within a short period of time, so much capital is going to be spent, and that money is going to go to Nvidia, Lamb Research, ASML, the entire semiconductor supply chain. So I think that that's something I want to see, is that over the next 12 months, the bullcase doesn't need building the 100 IQ person in the data center to become true. It just needs the accelerated capex. That's what I would say. What's your reaction to that?