1031: Tokenomics: Why Your Agentic AI Bill Is Exploding (and How to Fix It), with Tyler Cox and Ish Shah
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How are enterprises using agentic AI, and what makes an AI system agentic?
Over a single weekend, one of today's guests burned through 2 billion tokens building a video game for his wife. He and his colleague are here to explain why agentic AI bills are exploding and how a box under your desk can cut them by up to 93%. Welcome to another episode of the Super Data Science Podcast. I'm your host, John Krohn. Today, I've got two returning guests, but they are on the show together for the first time. Those guys are Ish Shah and Tyler Cox. Both are distinguished engineers in the office of the CTO for the client group at Dell Technologies. where they work out how to run powerful AI models on the machines closest to you. In this episode, they dig into tokenomics, why agents and their sub-agents devour so many more tokens than chatbots ever did, how to pick the right model for the job, and how moving agentic workloads off pay-per-token cloud APIs and onto your own hardware can pay for itself in as little as two months.
Enjoy. This episode of Super Data Science is made possible by Dell Technologies, NVIDIA, Anthropic, Origin, Palo Alto Networks, and the Open Data Science Conference. Ish and Tyler, welcome both of you back to the Super Data Science podcast. You've been on separately with different guests, never together. I think this is a dangerous combination. and I think everyone knows why. Let's start with Ish on why this is so dangerous to have both of you together.
Good to be back, John. Thank you for having us back. I think that Tyler and I have spent the last year out of our CTO office at Dell working on a lot of the things that we talked about the last time I was on with you, John, with our friend Sharish, and Tyler was also on with Sharish, and Our job is to think about the client device. And I remember the first time we talked to you about client devices, you're like, hmm, laptops? I'm like, yes, yes, all end user compute. And I think the role of those devices has changed a lot over the last, call it 12 months, since we last talked to you. So
just as
a refresher, Tyler and I are both distinguished engineers in the office of the CTO for the client group at Dell Technologies. We've got a lot to talk about. So I hope you're ready. I hope the audience is ready, John. I think we've got a lot to cover.
Tyler can confirm this, or maybe Ish can confirm this about Tyler, but we should let Tyler speak, which is that Tyler is full of detailed facts about things. And then Ish provides great color commentary.
That's what they're paying me the big bucks for. And by big bucks, I mean not
that. Yeah, we did a little bit of play-by-play. I think last time we were talking about linear tension and the rise of hybrid and states-based models, and we've only gone from
there.
Oh,
yeah. That's right. That was fun. That was great. We'll have links in the show notes to previous episodes that Ish and Tyler have been on separately, and they are both exceptional episodes. This one, multiplying them together, as I said, dangerous combination. It might be too much for one podcast, but we're going to try to make it happen. We're going to do our best to contain the danger.
What was it used last time? The ishiness of it all?
Yeah, it's a tongue twister. All right, let's get into the technical content here. So this episode is about agentic AI, tokenomics, solutions to get you better results faster, cheaper. But For those of our listeners who aren't totally sure, or maybe it doesn't even hurt to get this definition back to you every once in a while, what makes an AI system agentic versus a traditional model or a standard chatbot?
I think it's the ability to use tools. And we actually have this conversation a lot. I mean, even within a company like Dell, there's a very broad range of folks and how deep they've gone on this technology. And I would actually say, as a company, we're pretty far ahead of a lot of others in terms of the baseline AI literacy. That phrase, agent, still confuses the heck out of people. Like if I'm using ChatGPT on my app, is that an agent? If I'm doing something like what Tyler does day in and day out, which looks like the matrix flying across his screen, like, is that an agent?
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Chapters
8 chapters
1
How are enterprises using agentic AI, and what makes an AI system agentic?
0:00–11:52
2
How do you choose the right AI model using benchmarks and Pareto curves?
11:52–19:28
3
What are AI tokens, and how do token costs add up?
19:28–25:46
4
Why do AI agents and sub-agents consume so many tokens?
25:46–32:11
5
How much can local AI hardware save compared with cloud token pricing?
32:11–39:28
6
Why can open-weight models handle tasks that do not need frontier AI?
39:28–48:13
7
What hardware and software are included in Dell DeskSide Agentic AI?
48:13–57:04
8
How can local agentic AI support software development, research, and other workloads?
57:04–1:14:04
Speakers
1 identifiedMore from Super Data Science: ML & AI Podcast with Jon Krohn
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