In addition to getting an ad-free version of the show, subscribers also get bonus segments for the interviews. In this segment, Christopher Penn and Mignon talk about climate concerns related to AI and how you can get a small LLM running on your own computer, their worries about AI's effect on the future of jobs and what writers and editors can do to protect themselves, and why Christopher isn't worried about copyright problems after using AI to write his latest book.
In addition to getting an ad-free version of the show, subscribers also get bonus segments for the interviews. In this segment, Christopher Penn and Mignon talk about climate concerns related to AI and how you can get a small LLM running on your own computer, their worries about AI's effect on the future of jobs and what writers and editors can do to protect themselves, and why Christopher isn't worried about copyright problems after using AI to write his latest book.
Find out more about Christopher at and his books at trustinsights.ai and ChristopherSPenn.com.
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COMPUTER-GENERATED TRANSCRIPT
Mignon: Thanks so much. Okay. So now we're here. We're in our, you know, our smaller group conversation for our wonderful supporters. Thank you .
Christopher: All the new people are gone. Ha ha.
Mignon: You know, it's funny because I hear, when I hear people talk about the risks of AI, you know, often they are talking about sort of the Skynet thing you were talking about, like, oh, it's going to decide humans are irrelevant and kill us all.
You know, you hear that conversation a lot that I do not worry about that. I worry about the effects on the economy and job loss. I know editors who've lost work. I know writers who feel like they're losing work. Um, you know, what do you worry about when you think about AI?
Christopher: I worry about structural unemployment. So structural unemployment means that the overall system changes in such a way where those jobs are never coming back. And I'll give you a really good example. A human being can pick a bushel of corn in about 10 hours. It takes a long time. It's painful work.
It's not particularly fun. If that single human being is driving a John Deere, uh, X9 1100, that same human being can pick 23,000 bushels of corn. In the same 10 hours, because the machine is incredibly efficient. And as a result, that one human being's output is 23,000 times greater. What does that mean?
You don't need a thousand people picking fields in the field anymore. You need one dude. It's cause it's almost always a dude who's driving around, listening to podcasts as he drives this house-sized combine through a field. That means you get more food. You get presumably at least somewhat better food.
It's more standardized, but it means that the 999 other people who would have been working in that field are no longer employed there because the one guy is just, you know, you need some people to maintain the machine and things like that. But for the most part, that job in agriculture is radically different. AI is to knowledge work what machinery is to agriculture. Where, yes, there are going to be job losses, and not small ones, significant ones. And the more straightforward a task is, the more likely it is that a machine will be doing it. So proofreading, for example. Not developmental editing, but just proofreading.
Like, hey, there's some grammar issues here. A machine can do. A machine, it's been able to for a while. But machines can really easily do that. Now, writing first drafts, writing outlines, ideation, brainstorming, all the machines are very capable of that. In the end, the marketing world, it's estimated that, you know, websites that publish content will lose 20 to 40 percent of their traffic because generative AI will simply … consumer, if you can ask Perplexity for an answer and you don't have to click anything.
Well, why would you because you know, you don't need as a consumer to give traffic to a marketer, but that has
Mignon: Definitely seeing that. We're seeing that,
Christopher: Yeah. And so the things that individuals have to be thinking about is what is the human value that you provide that is above and beyond just the skill itself? Because the machines are skill levelers.
I'll give you an example. There's a school called Suno Songwriting, song composition software. It's good. It's not great. Not amazing. Ain't gonna win a Grammy. But it's better than me, who is completely incompetent at all forms of music, right? I can barely sing, and you don't want to hear it. Can play no instruments, can read no music, and yet I can give directions to a machine, and I'll produce an okay song. Means if I was going to make that song, I would not hire a composer, I would not hire a band, I would not hire a recording studio and stuff like that. I would just have the machine do it. Now, would that song have been made? No, because I would not have hired them to make a silly song of some kind.
But, it's a skill leveler. I don't need musical skill to make music. I don't need writing skill to make writing. I don't need editing skill to edit when I have machines that can help do those tasks, as long as I have the proficiency to operate the machine. And so for people who have those skills, what is the human value on top of your skills that separates you, that differentiates you, that is a unique point of view, that is a value add that a machine can't do.
Mignon: Right. Because people don't always make the most rational decisions when they're hiring people or spending money. You know, we don't look for the most efficient writer I can possibly hire. I mean, a lot of times people are writing, you know, I know this writer, I like them, and they have, you know, do good work for me.
I think nurturing the relationships you have with your clients, if you're a freelancer, for example, is going to be critically important for those people to stay employed as writers and editors.
Christopher: Absolutely. And to the extent that you can, and this is going to rub a lot of people the wrong way, and I'm sorry for it. You should be figuring out how to automate as much of your skill as possible to get the machines to do what you do, so that you can scale what you can do, so that what makes Mignon Fogarty a great writer can be bottled and reproduced.
Inside the machines so that Mignon Fogarty can write five business books a year, or 15 business books a year. My next book I've just finished doing the human editing pass on it. It took me, it's 000 word business book called "The Intelligence Revolution." It took me two years to create the raw materials because it's composed of all my podcasts and all my newsletters which are all human led, and approximately six hours to write the first draft because I said to Google Gemini, "We're gonna write a book. And here's the outline for the book, which is basically my keynote talk. And here's two years of material. Your job is to steal from me as much as possible. Because you can't really steal from yourself. And assemble a business book that sounds exactly like me composed of all my material."
And in the same way we were talking earlier about, you know, how to write a fiction story, I followed the exact same process for a nonfiction book.
And yeah, the first draft came up to 110,000 words, and it was decent. It really sounded very much like me. I had to do a decent amount of editing because there's some quirks to AI that if you, you get to see them after a while, and you can just sort of edit them out. So the editing process took about a week and now I'm about ready to go to press on it.
That was trained on me with my stuff. But I was able to create a book out of it. And I could probably create three or four more books from it that is uniquely my style. And this is what writers and editors and everyone has to think about is how can we take who you are as a person, bottle it, and have a machine do as much of it as possible.
Mignon: Okay. I have like three different thoughts. I'm frantically scribbling notes here. So, first, like what, what do you, there, I mean, there's already a deluge of content. So I hear people talking about how Amazon has already been flooded with low-quality AI books. And if you can write seven books a year, and I can write seven books a year, like who's going to read all these books?
Like, like how do you conceptualize the tsunami of content that's going to be coming at us?
Christopher: Well, the thing that matters is build it. Brand, your personal brand, your network, your audience who is loyal to you. Those are the people who buy from you because even before AI there, I get these emails saying like, "Hey, you can buy for $149 a perpetual license to download 500,000 eBooks on any topic. And you are legally permitted to rebrand them, rename them, stick your name on them as an author and upload them to Amazon." You know, it's like, you know, The Money Machine Miracle or whatever. Uh, it's always, you know, crappy self help books and things and keto cookbooks, you name it. And think about that.
If you wanted to just be a published author, you could spend $149, scratch off the name on the cover, upload it to Amazon and boom, you're an author. And you, and you've just added one more piece of text. That's probably identical to like, you know, the hundreds of other people who buy that exact same package and assign the exact same books.
And at that point, it's kind of like vodka, right? Every vodka theoretically should be the same: two things in the bottle, which is 40 percent ethanol and water. And that's it. And the only thing that makes vodka sell is marketing what, you know, how the bottle looks and how good your marketing program is.
So what's in the bottle doesn't actually matter. With books and content, It's kind of the same thing. Yes, there is objectively better quality content and lower quality content, but we've all had that experience of reading a book going "How is this author so popular? This is terrible. This is, I would, why did I pay for this? Can I get a refund?"
And you realize because the author is a better marketer, right? But they are a writer, and that's what every author has, and every writer, and every content producer has to confront is can you build a community, can you build your group, your conclave of people who are loyal to you and will buy what you produce, even when there's a market full of alternatives.
Mignon: Okay. Now I have like eight more thoughts. So first of all, what about copyright? Right. So what I was going to say is people are going to, certainly there will be people who build their brand on the fact that I don't use AI, that this is a human written piece of work and you're getting my authentic words.
Right. And, you know, I mean, in one of my newsletters at the bottom, I put "written by a human" because I want people to know it's written by a human, but also I do it for copyright reasons, which is something you taught me. So, you know, the book that you wrote with AI, how does that, what are the copyright issues around that?
Christopher: So this is interesting, and I will preface this by saying, I am not a lawyer, I cannot give legal advice. Please contact a qualified attorney in your jurisdiction for advice specific to your situation. You can tell I say that a lot. There's a difference between a generative and a derivative work. A generative work is one in which you say, "Hey, write me a blog post about blah," right?
Okay. Whatever the thing is. There is no original work to point to. If you can say, here's the human work that this originated in, and you can very clearly see that what the AI summarized, is derivative of that, then like any other derivative work in court, the derivative work inherits the original copyright.
So if I took Fogarty's latest book. Had generative AI rewrite it in my tone of voice, it's still your book, right? No matter how many times I use AI on it, as long as it is recognizable as the original work, same structure, same major points. Yeah, I'm going to lose that lawsuit. The same is true for my book.
So all the stuff that's in my book is structured identical to my keynote. I did that on purpose. So you could say like, yeah, here's my keynote that I've been doing for two years on the topic. And the book is weirdly straight, structurally exactly the same. And because it's fueled from my newsletters that I wrote myself and from my YouTube channel and all this stuff, it's my work.
And so every piece of the book I can point to here's where this part is derived from. And therefore, if it's challenged in court, I have the receipts to say, here's why this is a derivative work and not a generative work.
If you were to not do that and just have the machine create it from whole cloth, which is a series of prompts, then the prevailing law on AI-generated work, purely AI-generated works would be in effect, which is that it has no copyright.
Back to…
Mignon: Right. Okay. So, sorry, I lost my train of thought.
Christopher: …the list.
Mignon: Back to the list. Good. Look at my list. Okay. Oh, so another thing I I've been reading about a lot is about the role of climate: AI and climate change and water use. So you know, there are huge concerns that the data centers, the rate at which they're building data centers is ramping up, problems with climate change and also the amount of water that are used to cool the data centers.
You know, is, is terrible, but you know, there are already data centers and it's already a problem. Like tech already has a problem with energy use. I feel like, you know, I've been trying really hard to get my head around this, and I feel like where I'm kind of coming down is maybe that the AI is like gas-driven cars in that, you know, we know they're bad for the environment, but they give such benefit that we do it anyway, that we decide that it's worth it.
That, because I mean, that's what I see happening. Like Google is putting this in every search, like the individual choices I make suddenly feel kind of like they don't matter because suddenly Google is doing, you know, I don't know, 20 million searches a day on AI and my one more, what does it matter?
But I'm not sure this is the right way to think about it. Like, what, how, how do you think about it? Because, you know, I use AI intermittently. I get the impression you're using AI like, all the time. So tell me, tell me how you scale this.
Christopher: It, so here's the thing is it depends on what models you are using. The big foundation models like Google Gemini Pro 1.5, ChatGPT, et cetera, the GPT 4 Omni model. Yeah. Those ones you have big data centers that, you know, Microsoft saying, "Hey, we need to build some more nuclear reactors, just our stuff."
Yeah, those things consume a lot of power. They do. There's, there's no, There's no denying that reality. However, there's more than one AI model. There's in fact close to 900,000 different models, from models that are so small, they can run on a smartphone all the way to something that needs a nuclear reactor for a lot of tasks. Depending on the consumer tool you're using, there is invisibly what's called a router that reads the query, the prompt.
You put in and then it routes it to the appropriate model. So just in the Google ecosystem, there is Gemma 29B, Gemma 227B, Gemini 1.5 flash, Gemini 1.5 Pro in that order. They get more and more energy intensive. If the router says you just asked me to summarize this transcript, I'm going to send that to Gemma 29B because that is a super lightweight, super fast, super cheap model that will accomplish what the user wants.
So it will handle that, and it will route it to the lowest cost thing because one of the things is true about energy and water consumption is it costs money. This is where capitalism kind of comes in handy to say, like, these companies are going to do things as cheaply as they possibly can. So they're not going to put a simple request into the most powerful model, which is going to consume a tremendous amount of energy.
That's a waste for everybody. It costs them money. It costs you money. It's going to send it to the cheapest possible model. And we see that, for example, with Apple Intelligence and the way that they're outlining how AI is going to work on iPhones. When WWDC was on, they said, you know, we're going to try and do as much on this device in your hand as possible and only route to the cloud when we absolutely positively have to, because this thing is powered from, you know, whatever you plug it into at night.
And so as the technologies improve and increase, we're seeing more usage across different levels of power. So they'll allow the models that I use on a day-to-day basis, run on my laptop. So they are, they don't, there's no nuclear reactor at my house, doesn't consume any water whatsoever. It just has my laptop and the fan that's on it.
But again, as these things evolve, we are seeing differentials. We are seeing, for example, people talking, Microsoft's got a data center that they're trying to build for AI training off the coast of Ireland, because it's just, you know, why, why take fresh water? We can just sink the data center under, you know, deep down in the ocean where the ocean will cool it.
Uh, another company, I think it's Blue Origin, is saying we're going to build a model training center, which is an incredibly intensive electricity and heat intensive process, we're gonna put low earth orbit because nothing handles heat like space. Right. You know that that's a perfect use because you can power it with the sun without worrying about clouds.
And you can just let the heat radiate off and radiate off into space and there's no consequences for it. So the industry has looked,
Mignon: cost of getting it up there, I mean,
Christopher: Yes, exactly. So the industry as a whole is looking to reduce costs. And obviously, when you were talking about the environment, anything that consumes energy, water and resources is cost.
So there is a built-in incentive for all of us to do things as efficiently as possible. And as the technology matures, that will get more and more of a focus because again, companies want to cut costs and make money.
Mignon: Okay, this is great. So this troubles me a lot. I mean, because I, you know, I mean, I drive an electric car. I power it from solar on my roof. So you're telling me that, like, I can run a model that's really good now on my laptop that isn't using any electricity other than like the solar for my roof.
Christopher: Exactly. I use, there's a model called Mistral that I run. It's the 12 billion parameter model. It's a phenomenal fiction writer. Like it's really good, even though that's not what it's designed to do. It's actually designed to write code, but it's for whatever reason, surprisingly fluent at writing decent fiction.
[Hey, I'm just jumping in here and say that after the interview, I did get a smaller Llama model, 3.2, working on my 2022 Macbook, and I'm using it for all the simple things I do, like writing alt text for images, without worrying about electricity anymore, so I'll put some links in the show notes for those of you who want to try that too.]
Mignon: That's great. Okay, cool. So to follow, to finish up, you had this amazing blog post recently about how to make AI sound like you. And I think this is something I see people misunderstand a lot is, you know, they play with AI, and they get out writing that they think sounds like AI and, but that really is just prompting. Like, a couple of weeks ago, I took a quiz about like, "can you recognize a review of a hotel? Was this written by AI or a human?" And I did terribly. I mean, I got 9 out of 15. Basically, I was barely better than chance at identifying AI-written versus-human written reviews. So, you know, how, if you could just talk about this post you did, which was just so great, about how to adjust the tone of AI so it writes like you.
Christopher: The issue with writing is that this is something that I think the average writer would struggle with. What is your writing style? That wasn't rhetorical. What is your writing?
Mignon: Oh, um, fun and friendly, objective, crisp maybe. I hope, I hope it's all those things.
Christopher: So the reason why that's a bit of a trick question, so I apologize. Writing style doesn't exist. Writing style is an umbrella term for things like clarity, conciseness, audience awareness, purpose, voice, tone, diction, formality, specificity, imagery, you know, paragraph structure transitions. There's so many aspects to writing style that we don't think about when we write.
We just kind of do it and assume that, you know, it all just comes out in the wash. Machines don't know that. Machines cannot understand that. But what you can get a machine to do is say, here's a whole bunch of my writing, like a couple of hundred thousand words. Here's my writing. Here are the 20 to 25 components of writing style.
Create a detailed analysis of my writing style, right? And then once the machine does that, it can say, like, "Hey, you write like this" and use diction, the variety of words that you use and stuff. And then you can create again, very long prompts that say, you're going, here's the task, here's my writing style that you analyze as four pages of analysis.
Am I writing style? Here's three pages of example, because a writing style analysis does it. Ever replicate the writing style fully and you would say now with these examples of how I write with the guidelines about my writing style with all the stuff now start to write like me, and you will get a much, much better simulacrum of who you are from that set of techniques, and you know, like we were showing earlier with the sensitivity reader, you want to have it even build a scorecard to score your specific writing style so that you can say, "Yeah, yeah, you didn't do it right."
You rescore and try again and have models try that. So that's how you get AI to write like you is to say, first, we're going to define what writing is. Again, remember smartest intern in the world, still an intern. And then we're going to analyze my writing, and then we're going to build a scorecard so that you know whether you're doing my writing well, then I'll give you some examples of my writing and now we can write like me.
Mignon: Awesome. This has gotten to be a really long show, but thank you so much for staying with us, for giving us this much time. It's just such an interesting discussion and you're one of the most knowledgeable people I know about this topic. So I hope this was as interesting and useful to all the listeners as it was to me.
Christopher Penn, people can find you at Trust Insights and you know, I highly recommend your newsletter, the Always Timely, no, Almost
Christopher: Timely.
Mignon: Almost Timely newsletter. Thank you so much, Chris.
Christopher: Thank you for having me.