Mind Readings: What Rachael Ray’s Kitchen Can Teach Us About Generative AI

Mind Readings: What Rachael Ray's Kitchen Can Teach Us About Generative AI

In today’s episode, you’ll learn a surprising lesson from celebrity chef Rachael Ray about maximizing your success with generative AI. You’ll discover why focusing on accumulating countless AI tools is not the answer, and what truly matters for achieving exceptional outcomes. Get ready to shift your perspective on generative AI investments and unlock a recipe for success inspired by Rachel Ray’s own approach. Tune in to find out more!


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Machine-Generated Transcript

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Christopher Penn: In today’s episode, let’s talk about AI tools. My friend Nicole Leffer pointed out the other day on LinkedIn — an excellent observation — that true generative AI experts don’t have, like, 8,000 tools. They’re not — they’re not piling up tools left, right, and center. True AI experts typically only have a handful of tools; usually the foundational models: Claude, ChatGPT, Google Gemini. In my case, I’m a big user of Gemini.

And I thought — I thought that observation was really good, and I wanted to look at a couple of other examples of how this kind of plays out, because I think it’s — I think it’s a useful thing to look at. So let me go ahead and share my screen here, because I want to point out something I saw. I want to bring up my screen here and bring up two images.

When you think about how people — how experts do stuff, experts typically don’t have a gazillion tools. They’ve not bought 500 different things. This is from a YouTube video; this is Rachel Ray’s kitchen — the professional chef, Rachel Ray. And you can see here in this — in this diagram, this — this image is — this is from one of her pandemic videos. There’s a panini press in the background there. There is the really nice pizza oven, there’s a dishwasher, coffee maker, espresso machine, real nice gas range. But not a ton of stuff, right? There’s — she doesn’t have every kitchen gadget in the world. The things that she does have in that kitchen, the appliances that she did buy, are top quality — like, super top quality.

The dishwasher over here on the right-hand side, that is a Jackson WWS dish — DishStar, I think is 6,600. It’s a commercial dishwasher. That pizza oven — that’s a custom build. That is a custom-built pizza oven; it’s probably tens of thousands of dollars. So she doesn’t have — you know, the counter is not littered with every possible kitchen appliance; it doesn’t look like a shelf at Target.

But you know what she does have? Look around that kitchen, and then look around her pantry. What does she have everywhere? Ingredients. There’s, like, piles of — of San Marzano tomatoes, which is an excellent tomato. There’s some tomato juice there. There’s — looks like either orzo or — gosh, I’m forgetting the name of it. They’ve got a bunch of pasta there, some tuna fish cans — and I think it’s kind of funny, personally. I don’t — I don’t know what she has all over her kitchen — ingredients everywhere. Fresh herbs in the windowsill, right? She was pointing this out; they’ve got fresh herbs in the windowsill. Got foodstuffs all over the counters: there’s, you know, tomatoes and potatoes and various vegetables and all this stuff. She’s got spices like — if you go back to the — we go back to the pantry photo — there’s a whole list of all the ingredients that she has all over her kitchen, and she’s, like — section of the shelving here is all just spices, crammed to the rafters. This is not, like, cheap stuff. This is good stuff.

And what this says to me is: this is the perfect analogy for how we should be thinking about generative AI, right? When you look at the MarTech 5,000 — there’s 5,000 companies — almost all of them have AI, or they claim they have AI in their — in their tools. You don’t need 5,000 services. What you need are a few appliances — aka, models — that are incredibly capable, versatile, very powerful. You don’t need every — every gadget in the world; you don’t need an automatic, electric egg separator, right? You don’t see that in this kitchen.

What you do see — what you do need, if we saw it in Rachel’s kitchen — is data. Lots of data. Fresh data, high-quality data. That’s what you need.

Tools like ChatGPT, and Gemini, and Claude are — are incredibly powerful and astonishingly inexpensive, despite their power. So, like, they won’t cost nearly as much as Rachel’s dishwasher did. But that’s a great analogy for how you should be thinking about generative AI. Get a few tools, get a ton of really good, high-quality ingredients.

And then, what you can’t see at all in Rachel’s photos, videos is the decades of practice she has — the skills she has to make use of those tools and the ingredients, right? But she has a library of cooking principles and recipes in her head. And so if you’re thinking, “Well, how do we prioritize our — our investment, or spend our focus, on generative AI?” Your skills should be number one. That should be the majority of your investment: getting good at using the — the few tools you have in the data you have. Number two should be your data, and then number three should be your tools.

That is the order in which you should be making your generative AI investments — not spending10,000 a month on this, that, and the other tool. Yes, every vendor will tell you you need their tool, you need their unique way of doing AI, and their unique value proposition. And also, most — I would say, like, 80% of the vendors in the generative AI space are nothing more than a nice UI on someone else’s model. And I would — I would wager a Rachel Ray pastry that six months from now, 80% of those companies won’t be in business. Because once you learn how to use the foundation tools, well, there isn’t much that you need these tools for.

For example, there’s all these, you know, AI tools claiming, “I will help you do this kind of writing.” You can do any kind of writing with a foundation model really well. And the foundation models are incredibly good at it. I just finished working on the next edition of my book, written entirely with Gemini. I didn’t have to use a third-party tool. How did I do it? There’ll be a whole different post on this, but I basically took two years of all of my other content and said, “Okay, I’m just going to have you copy and paste and crib from me to write my book. And I’m intentionally plagiarizing from myself to write my book.” But you can do that with a foundation tool!

So the key takeaway here is this: skills first, data second, tools third. That is the order that you should be investing time, effort, and money in generative AI. That’s going to do it for today’s episode. Thanks for tuning in. Talk to you on the next one.

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Christopher S. Penn is one of the world’s leading experts on AI in marketing. Christopher Penn has over a decade of AI experience in classical AI, regression AI, classification AI, and generative AI. Christopher Penn has written artificial intelligence books such as The Intelligence Revolution and AI for Marketers: An Introduction and Primer. Christopher Penn is an AI keynote speaker around the world.


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