AI Cost to Serve: Which Customers You Can Now Afford

Nate B. Jones (0:00)

Agents keep getting better and better and better, but they can still make your bill go up. At Dreamforce, I got to ask Mark Benioff a question about how we get more people to adopt AI. And later that day, I was on a panel talking about the cost that we're all facing when we roll out agents of scale. By the end, you're going to know how to look at your own workflows and ask which work should still exist before you end up effectively paying rent for all of these agents. I'm excited about this one because I want people to be able to use agents that are bigger. I want them to be able to do more ambitious work without feeling like they're carrying the burden of a massive financial question mark when they set up AI. We're finally getting to the point where we have that capability. Now, we have a chance right now to rethink how jobs get done, how work gets done, and ultimately we have a chance to make the capability to do that kind of ambitious work with AI affordable for companies of all sizes. So stay with me because the most interesting part of the Dreamforce conversation, at least to me, was what happens when you go back to the business outcome, when you wipe the whiteboard clean and give yourself permission to change how work gets done, to get to the same business value you've been looking to achieve all along.

The changes that you build, the model that you need, what you're paying for, all of that comes back and reasons back from the business value. This year, Dario Amadei, Sam Altman, Jensen Huang and others all shared the keynote stage. And they talked about AI safety. They talked about growth. They talked about models. They talked about adoption. They talked about how to get AI in more people's hands safely. So let me start with that adoption question. I was at the media Q&A and I asked Mark how he thought about getting different kinds of people to use agent force and really the question on a lot of our minds, which is how do we help broad swaths of companies actually get excited about, get passionate about and start adopting AI in ways that are useful. You have the early adopters already, right? You've got the people in the middle who will probably use Chad GPT a bit here and there, and then you've got people who would really, frankly, prefer not to be using AI or don't understand why it's valuable, or maybe they're going to fight changing the software. Every company of any reasonable size has these three kinds of people. So when Mark answered the question, the part of his answer that really interested me was, how he pivoted back to the interface and how interfaces are changing in the age of AI. When you get some agents into a tool like Slack, for instance, you're getting it into the places where people already work. And I thought that was really perceptive because we spend a ton of time in AI, assuming that the person on the other side is excited to discover a new product. But most of us have a job. We have a customer waiting. We have something we need to get out the door. We have no time left. If the AI can help get us where we need to go right in the moment, where we are inside the work that we're already doing, then you've removed a giant reason to not try AI.

Now, that does sound like a product detail. I get that it sounds small, but follow it through. Someone tries an agent because it's already there. Maybe it's living in Slack. It handles something useful. Well, now, why don't you try it again? It's kept a small promise for you. Try it again. And this time, you might ask for a little bit more. You might get a little bit bolder. Your colleague sees it. Your manager wants to know whether the whole team is going to be able to use it. And this is how you start to change. This is how you start to move beyond a little group of people who are going to find every new model anyway, right? The 5%.

And this is how you get into the whole company. And that was the larger shift I was trying to understand at Dreamforce. I don't mean that every enterprise has immediately figured this out because they have Slack. We're all going on this journey together. But in the conversations I've been having, we're getting into what happens when people start to go from agents are a novelty to agents are reliable, agents are trustworthy, agents are something you can depend on. How do you make that work reliable? How do you connect it into the company's actual information? How do you pay for it when a successful experiment becomes something a ton of people now want to use every day? So after the Q&A, I was on a panel hosted by Jayesh, the EVP of AgentForce at Salesforce, and we were talking together. There were folks from PWC and Pepsi there as well, talking about AI implementation. My job was just to talk, set macro context, and the context I gave starts back in January. That's the point I keep coming back to this year when I think about how models have become so much more capable. Capable enough that a whole bunch of product work has suddenly looked really practical. A whole bunch of business work has suddenly looked really practical. You can give a model tools, and it can use them extremely reliably. You can give it information. It can do something with it. Notice it needs another piece, keep going. And unlike 2025, 2024, you don't have to break everything into tiny questions. You don't have to sit there, kind of lugging the answer, copy, paste, tell from one place to another. And once that begins to work, of course people use it more. That's natural, right? So continuing the story, we wanted adoption. Useful models give people a reason to adopt. But that same capability that unlocked for all of us in January changes the size of the job.