Why People Are Paying 10x More for AI | Sid Sheth, d-Matrix

We talk about organizations being entirely run by agents, right, how far away from that, eventually, you have to run large portions of most companies. Do you see emerging companies, startups that are agent first? The New York Times had a piece recently about a guy and his brother running $1,600,000,000 sales company that basically a agent first company. Do you see that segment of the economy growing? It's really an opportunity for everyone to go into something new. I think at the d matrix, we are being very proactive about the use of AI across the whole organization for various different use cases. We have an AI team in the company that is entirely focused on building AI across the board. Right? So it is not an option. It is mandated. Tell us what you're talking about at Humanex. I mean, I I read that you've had some acquisitions since the last time we spoke. We're moving into a a larger system, not just providing chips. And I'm interested in that. You know, my interest is generally in algorithmic research. Mhmm. But I do try and follow hardware, and you guys are are a player in that space. Mhmm. I think I told you last time. I talked periodically to Andrew Feldman at Cerebras, to Rodrigo Liang at Semba Nova. I haven't spoken to Brock. If I'm not mistaken, those you and those three are the primary players in this new, internship space or maybe not. I mean, if you could give me, an overview on what you guys are doing and how you're differentiating. The inferencing space specifically, right, is, kind of bifurcating. Right? Because, I think everyone looked at inference as one, you know, kind of, you know, singular entity. Right? But I think there is lots of nuance. And when it comes to inference, there's not a one size fits all. Yep. Something we've been saying for for a long time. So, you know, depending on where you are doing the inferencing, what your target markets are, what applications you're going after, you can't really build a single chip that serves all of influencing's needs. Right? And, so you gotta really kinda break it down. And, not every company is playing in every pocket of the market. You can't. Right? GPUs, you know, just because they are so general, the ecosystem is so broad. Clearly, GPUs can go into many different applications, for inference. Right? But, they're not very, very efficient because of that. Right? I mean so, yes, they're very broad in their applicability, their general purpose. But but when it comes to certain breakaway applications where applications need, a very specific metric, fully optimized, then GPUs will not do well in that market. And I think so I think the way to look at it is, okay. Yeah. The inferencing market is gonna be the largest part of AI compute. It's expected to be over a trillion dollar market, you know, call it in …