The Next 12 Months Will Make or Break AI

**Ben Bajarin** (0:00)
If you just really understand what's capable with computing, you can actually predict the future.

**LGDU Set** (0:05)
What's up, everybody? It's LGDU Set here, and welcome to Milk Road AI, the daily AI show that refuses to give Claude my banking info, even though it already knows the name of my first pet, the city I was born in, and every other password clue it needs to steal my identity. Today is July 13th, 2026 We're recording on the seventh. The market feels like it's kind of changing. The up only vibe of Q2 is fading as semis and memory take a little break, and everyone, including us at Milk Road, whispers signs of a rotation. But according to today's guess, the real question isn't what to buy next. It's whether all of this intelligence actually gets adopted by enterprises the way everyone is expected. Ben Bajarin, CEO at Creative Strategies is with us today. And before we jump in, if you want to see exactly what our analysts are buying, selling, researching, plus get all that info in real time via text or email, Milk Road PRO is 33% off this week only. Check it out at the link below. Today's episode is brought to you by BitGet, Sox 2 with real liquidity, real dividends and Securitize, the regulated rails for tokenization. Ben, what's going on, man? Welcome to Milk Road.

**Ben Bajarin** (1:04)
Hey, thanks for having me.

**LGDU Set** (1:05)
Okay. I need to know because you're like a veteran of the tech industry, man. You have been doing this, looking at the data since way back in the.com bubble, is that correct?

**Ben Bajarin** (1:17)
Yeah, since 2000 was when I started as an industry analyst.

**LGDU Set** (1:21)
So you started, had the bubble burst the day you started or was it about to explode?

**Ben Bajarin** (1:27)
It's a fun story. So I grew up in the valley. I've been born and raised in Silicon Valley. So I did like what every college student would do in the late 90s, which is start a.com. So I actually started two of them. One failed miserably, one did pretty decent with an exit and right around that time, 100 percent all popping. So I tried it, lived it, watched it pop, lived through the outcome of that. And that was how that was how we started.

**LGDU Set** (1:56)
So what I should bet this is something that I think for people that are still relatively new to investing, I think you're trying to understand, right? Because as as AI has whispers of bubbles, right? At the super inflated stock valuations, a lot of capex, a lot of similar narratives. What didn't work? Like what was it just that people were expecting too much? Was it that there weren't enough users? Like what, in your opinion, was the.com burst? Like what caused it? Like what didn't line up in the data?

**Ben Bajarin** (2:23)
Yeah, I think there's a couple of things, right? And I think a good sort of precedent to, you know, how we study these things is a lot of what happened like in sort of the.com era, like business models, right? Things that got too inflated and burst, like eventually became viable, right? So it wasn't like these were just terrible ideas. It was just, they were super early and we hadn't really onboarded hundreds of millions of people to the internet yet. So it was sort of like you had to have massive amount of scale to justify these kind of microeconomics that people were doing for like home delivery pets, right? Or grocery.com, like some of these things that were like, there's just not enough people to do that and you don't have enough early adopters onboarded to pay those premiums at scale, right? So it really became a, you needed scale, there wasn't scale and things got overvalued, right? And so that was sort of the imbalance. It wasn't that those were bad ideas. It was just that they're, it was early, right? And as we like to say, sometimes being early is as big of a deal as being wrong. And it just didn't, it didn't really work out. But a lot of good things came from that, right? A lot of seasoned veterans, a lot of things that ended up becoming viable businesses later, lessons learned, all of this stuff. So it was relevant to the cycle, but it was just overinflated without the, call it underlying economics to justify those, what would you say, valuations, you know, business models, et cetera.

**LGDU Set** (3:50)
Right. You know, it's funny, I just signed up for my local grocery stores, home delivery service, and I was like, man, grocery gateway tried to solve this 25 years ago, and it made sense then, it just, it was too early. There were enough people that wanted it now, and like, of course I want that, you know, of course, I don't want to go there. I don't want to go over there. I want to come to my door.