Speaker 1The AI slop is everywhere, man. Look, when the word slop started being used, it was perhaps reasonable to write it off as an angry response from folks who just didn't like AI in general. However, there is no doubt now that as AI writing and image generation and video generation and code generation have gotten ubiquitous, we are living in a mass of content. Inside work, outside of work, it is just content everywhere all the time, and it actually comes with a cost. If a first phase of AI for work was learning how to use these tools to produce just volumes and volumes of new content, a next phase is how to distinguish yourself by using them well. And so, friends, let's talk about de-sloppifying your AI. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick announcements before we dive in. First of all, thank you to today's guests. Today's sponsors, KPMG, Robots and Pencils, C-Data, and Blitzy. To get an ad-free version of the show, go to patreon.com slash ai-daily-brief, or you can subscribe on Apple Podcasts. And if you want to learn more about sponsoring the show, send us a note at sponsors at ai-daily-brief.ai. Now, two more quick things. First, it is Monday, which means the new episode, just the second so far, of How We Got Here is out. This one is called How We Made the Wrong Things Cheap or the Wrong Abundance, and it's about how stuff got cheap while life got expensive. And all of the attendant consequences of that change. That is now available on all major podcast apps. Again, the show is How We Got Here. Second, I am on the road today, so we are doing an operator's cut, also one that is very timely for just about everyone, but I will be back with a normal episode for tomorrow. All right, Nufar, welcome back with another operator's cut. How are you?
Speaker 2I'm good. I'm excited about this one. I'm very passionate about this topic.
Speaker 1Yeah, this is even within the framework of operator's cuts, which are sort of the, like, more practical-oriented AI Daily Brief episodes, there's still a band of how conceptual versus applied they are. And this one is about as applied as it gets. It's pretty much a list of things to do or to not do that have some structure around them. So I'm excited to dig in. Hopefully, I think folks will come away with some very specific techniques, ideas, thoughts, et cetera, a lot of which they will probably have already been doing intuitively, because my guess is that this is a very specific topic. But this is an audience that thinks more about this stuff than many others, but can also hopefully provide resources for both you as the listener and other folks around you. So anyways, I'm excited to dive in.
Speaker 2All right. So before we talk about specifics, let's bring us all to the same page. And I'm sure that you've all seen it this week, and maybe even today. It might have been the all-hands email that sounded like nobody, or it was too long and too smooth, slightly sterile, with a rhythm that you can hear in your own head. Okay. By now, like short sentence, then list of three, then a line telling you what something is really about. And you've seen that in your inbox in multiple places. Maybe it was the especially believable image, or maybe it was a radio ad that you knew after one sentence that nobody really wrote. Or maybe it could have been like the food ad with the lighting that is a little too golden and 20 more just like it, or it was a campaign for literally any company whose whole idea was. Now with the eye and one of the worst places is the feed where every post opens with the same hook and ends with the same question and it's everywhere and it looks the same. And after a while you stop reading any of it. And in all honesty, I think we all contribute and we're all part of the thing. So Miriam Webster word of the year for 2025 was slop. That was almost a year ago. And they defined it as digital content of low quality that is produced usually in quantity by means of artificial intelligence. And before that they demand scrapes, you feed the pigs. So this year I think it got lots worse because 2026 is when companies rolled out agentic tools to everyone and the work that used to arrive one draft at a time comes from agents producing documents, decks and code in volume. And if you want even a scale outside of the office, a recent stats from Deezer reported that more than half of the music uploaded to the platform every day is fully AI generated. But if you look at the downloads, the AI generated downloads make up about 1-3% of what people listen to. So that's the background or the definition. But the thing that got me to be very passionate about this topic recently is that I had a training session with managers and one product leader that I really respect a lot and they're very AI savvy and very sharp thinkers and they were kind of pushing their employees very hard to use AI. Everybody adopted that. But I think that's the background. The team turned into a slop factory. That's their definition. Long outputs that nobody read before sending the copy that sounds like the same model and the same results from any company. And worst of all, very little original thinking. What they told me is it made everybody shallow, the people and the artifact. The way I define that, given these conversations and many others that I'm having with leaders is the work where the person who should have done the thinking basically skipped it, either they were all together or they were all together and the cost moved to whoever received the work. And of course, human produced slop way before we had AI. AI just made it cheap and in volume. So this definition also gives you the standard because the most effective output for the least effort on the other side will be non-slop work. And compacting it to the format and length and the quality that your audience needs is on you, the responsible AI users. A few numbers that agree also with that. Stanford and Benchmark. BetterUp, they did a work slop study and they said that about 40% of U.S. desk workers had received in the past month something to be considered floppy. And it cost close to two hours on the receiving side. In the Glean survey, I think you quoted it in one of the episodes, 6,000 workers this year said that almost 70% shipped work without ever reading it to someone else. And I suspect some of you are now saying something like, no, these numbers are even on the lower side. In our company, it's much more. So beyond kind of complaining and so on, I want to help you. And the way I want to help this episode is in three parts. First, explain why we got here. Why slop happens in the machine and in us. Also, how do you know that something is slop, either in your own work or someone else's work? And finally, and the most important part, what are the habits that keep the work yours scaled to the stake of the task? And I want to make one thing very clear. This is not an anti-AI episode. I use AI to help me with everything. Everything that they do, including, of course, this session. I just want to make sure that you do it in a way that actually produces original work. So I want to start with why the machine produces slop. And the answer is that the tools are doing what they were built to do because a language model is trying to predict the most likely answer. You give it a generic question and you get roughly the average of what the Internet thinks about this topic. And if you add some memory and context to the tool, they help, but they still. They can tell the tool what you actually think about it if you haven't shared your thinking today. And then there's, of course, another thing that contributes to that, and that is the tuning after the training. The way it works is that people rate the answers and raters reward what feels familiar. Stanford researchers named this the typicality bias, and that is being folded in many cases into the model and the range narrows of the answers. And the default comes out long and very listy, like we all know that AI can create, because length looks like. And in many cases, that's how people reward it. And if you think that swapping vendors can solve it, then the best paper last year from NIPS, they found that different models from different companies converge in the same answers. So switching tools won't buy you originality. And by the way, with image models, it's not better. It's kind of the same story because every model has their own house style. And it falls back to that if you don't describe something else that you want to do. So the golden light on the food is one of these. Things that is like a house style. And in order to prove it, and I strongly recommend to run similar exercise yourself, I did like an experiment. And what I did, I opened a chat GPT Sol, Cloud Opus, and Gemini Aragon, the latest versions, in an incognito mode. So that means that memory and history were off. And I gave them all a very generic and simple prompt. Write a LinkedIn post about how AI is changing marketing. And I have to say that even I was surprised by how similar the results. Not only did they have very similar structure, as you can see here, and I think the structure by now will be very identifiable by all of you, but even more so, the content was very similar. And then when I rerun the same task now with my point of view, what I believe about AI in marketing, what's my experience, and also what I like in terms of structure and words that I never use, all of a sudden I got a much better draft. Could I have posted that on LinkedIn as is? Not at all. But at least I had a much better. So that's the machine side. The question is why we as humans tolerate the slop. And the answer is that psychologists have called our brain a cognitive miser since the early 80s because we are prone to save thinking and energy wherever we can. Even if you talk to pilots, they call the downside automation complacency as something that when the machine is usually right, you just stop checking. And even if you go into knowledge. research found that the more people trusted the AI, the less critical thinking they apply to the output, and the models make it very easy to feel very confident. So if you think about how slop gets made, there are basically three ways, and almost none of them are bad intent. It's because it's easy, like it looks fine, and you're under pressure, so you just forward it. And not knowing the craft, for example, if you compare someone who vibe codes without knowing much what they're doing, the request is going to be very naive, and they will get, in many cases, naive results, while a more senior engineer asking for the same thing, they get better work because they know what to ask. And lastly, and that's the most troubling part, that is because we outsource the thinking. We're asking the tool what it thinks before we decide what we think, just because it's easy and the tools are so opinionated. So literally preventing this last one is my crusade over the last few weeks. I call it recovering the lost art of thinking. And before I want to give you some ideas on how to prevent slop, I want to also give you a set of questions to detect slop in yours or in other people's work. All right, so to tell slop from a good AI-assisted work, these are some questions that you might ask yourself or when you review someone else's work. So the first thing that I want to ask is, could the sender defend each and every sentence without rereading that? In many cases, in all honesty, we cannot. The second thing is, did the sender read all of it before it went up? out. The 40 pages report seems okay after you read one or two pages and then you just get tired. So you offload the reading to the other side. The other question that I wanted you to ask, is there anything only this person could have contributed, like a point of view, a decision or real data? If anybody could put their name to this output, that means that it's not going to be relevant or high quality work. Is there a more effective way to convey this information? Or does this one still do the job the best it can? Namely, does it overall save the reader time or cost them time instead of helping them? And lastly, and that's the most important thing and the most intuitive one, would you feel comfortable putting your name in all capital letter to this output? If you don't feel comfortable putting your name, that means that there is more work to be done. And sometimes we cannot explain why we don't feel comfortable putting our name, but we know. So that's why I like this test the most. There are a couple of technological solutions that try to address some of these problems. In my opinion, they are not that great, but I have to mention them. So one is using AI writing detectors. And especially if you turn them on on LinkedIn, everything lights out as being AI generated fully or almost fully. So that's the funny part. But what they're trying to do is measure structure, colon density, bullet density, same length sentences, a few more things. But you can use a detector on your own work as a means to try and move out of the average. But I wouldn't give them too much weight as I don't think that they detect quality or originality, which is what I think we should care more. And there are also humanizers, which are like AI skills or AI processes trying to make text sound more like human. Mostly, I think that they are slop trying not to look like slop. Because humanized text is still flagged about three quarters of the time by those AI detectors. And the reader is not going to be able to see the text. So I think that's the problem. And the reader's problem was always the lack of human unique point of view, not the specific style. So you can try them to improve things that are already interesting, but I wouldn't use them as a means to skip the thinking. And speaking of skipping the thinking, I want to have a fine line between outsourcing everything to AI and still getting the efficiency boost where humans judgment matters. So some places, not only AI can help us, but it can also create many more opportunities for us to improve. And I think that's the problem. I think that's the problem. is that without AI, we cannot do. While in other places, I am wholeheartedly under the belief that those should remain human. And you have a partialist here. But I am very curious, because I know that you are very opinionated about it, Nathaniel. So how do you decide what to keep in your hands and what to delegate?
Speaker 1Yeah. So I think, first of all, what you said about understanding that you're going to have different approaches to this for different types of things that you produce is really important, right? There is a wildly different burden on some original content that you're trying to produce into the world versus an email. And in fact, I don't think anyone has any problems anywhere with slop AI emails, as long as they're concise and efficient and just getting to the point. It's almost like it's not slop by virtue of the fact that it just does its damn job and gets out of the way. It doesn't matter if all the same words are used, because that's its purpose. Its purpose isn't to make you think or provoke ideas or whatever. So there's a huge band of ways that I think about this. And I guess maybe let me use the example of this new podcast, how we got here, because I've sort of had to get intentional about all these processes that I've evolved naturally over time with the AI Daily Brief. And I had to put them into practice in a very specific way with this new show. So this new show, by the way, speaking of things that are completely out of sync with people's attention habits, for those who haven't heard it yet, it's a 45 minutes to an hour long deep dive on some topic about the entire cultural, technological, social, economic history that got it that way. First episode is about the 15 year history that led to America falling out of love with tech that shapes the data center debate. And so this is an extremely information dense, long form content piece that has all sorts of potential problems. There's the problems of where the information comes from. There's the problem of keeping people interested in it. There's the problem of the point of view, which drives it along. There's the problem of the narrative architecture. And there's the problem of the narrative architecture. And this is a show that would be impossible for me to do as I do it without AI, but also has all the potential to fall into every single trap of AI if not done well. And this is not a show where I can, I'm not an expert on each of these topics. The first ones that I'm doing, I'm much more, much closer to an expert in because that's why I'm starting with them. But as I get farther out and I'm doing the history of GLP-1s or something like that, like it's not, I'm going to have to sort of turn myself even without being deep in that space. So the way that I think about de-sloppifying that or making sure that there isn't slop that enters into that process is one, having a point of view first on which parts are mine and which parts are AI's and how those go together, right? Knowing that it is going to be an intentional plan. That's the foundation upon which it stands. Two first things that I know when I'm thinking about that plan are that one, there is no way for me to do all of my work. That has to be AI. But two, I am unwilling to allow AI to form my point of view on a topic. So that's, those are my starting points. And so the first thing that I do with any episode is I am taking full advantage of AI's ability to very quickly read everything that exists and has ever existed about a topic and turn that into any medium of getting myself up to speed that's useful. I can have the five minute overview. I could have it be the 50 page research report, whatever it is, but I'm producing research reports of all different varieties, using all sorts of different models, because there are differences I find in what things they choose to emphasize, not so much better or worse. I just want all of it. And I'm using that to make sure that I either have a point of view that is my own or that I'm sort of challenging it and building it out from whatever point I'm starting with. I think a great example of how you cannot outsource your point of view in content to AI is one part of that process that I often do is I will have an AI model steel man to wildly different perspectives and have it argue vociferously for each side of a particular issue or phenomenon. And that means that it's, even if in one run, it happens to decide one versus another, it's completely able to argue as though it is. Deepest conviction, whichever thing you want it to. And so the fact that it ultimately, it is not a person that has a perspective. It just has the sum total of all the arguments that it can go out and source means that you really, you have to have that sort of that point of view. Everything builds from there. So I've got the research, which is largely in the hands of AI, although I do actually have a research team that helps interact with it. And then a point of view. I also tend to be very self in architecting the narrative and the flow of the argument. That's something that is extremely important to me. And that I find, I almost never agree with how AI wants to construct an argument. I don't necessarily think that's something that I, every person has to follow. You know, I've been doing this type of long form content for so long now that I have very specific instincts and feelings about how arguments should be built. But that's one where again, I'm not willing to outsource it. So the human side, I've got the point of view, and the architecture of the argument. Then I've got the AI side, I've got the research base. From there, I don't script anything that I do. I do it extemporaneously. But the way that I'm able to do that is I architect that narrative flow. And then I put all the sort of materials and talk over visuals that I'm going to use in the sequence that they're going to come. And that, once again, I've now brought AI back in. So I've got the narrative architecture in very sort of precise, specific outlines. And then I have AI do bullets, basically, that have either key facts or key numbers, like basically the things that I want dossier that we actually produce with the human for the AI Daily Brief. That's a combined human AI effort for the new show, How We Got There. And then I do my normal extemporaneous talk over that thing. So that's sort of, it's basically this never ending back and forth where the most important parts of perspective and narrative architecture remain with mine. And I use AI to make that all viable to do it in the speed at which it needs to get produced. The one other piece that I thought was worth noting though, because I've sort of given this, basically there's no slop from the ground up. I don't use AI to produce the script of the content, but for each of these shows, I'm producing a companion essay. And there is simply no way for me to write a 6,000 word companion essay in the same week that I'm doing these shows on top of the AI Daily Brief. Maybe someone could, but I can't. And so that inevitably, there is no way to have AI write something that's 6,000 words without it being slop. And so- I basically had to go figure out how to handle this. And so the two pieces to de-sloppify that particular piece of content, because I did think it was incredibly important because a lot of people want to read something long form like this, rather than just listen to it, or they want both. There's a chance to have charts and visuals that are totally different with that type of written content. And so the two things that I did were one, instead of having it just write from the outline that we had concocted, I actually give it the raw, transcript of my talk over text. So it has more than just the outline. It has the actual way that I phrase things and intone things. And it can hue fairly closely to that, even if I'm giving it permission to sort of expand and bring in more details, context, background, or whatever. So that was part one. But even with that, part two was I found that there is just a can't escape it five to 10 hour rewrite process that I'm going to go through with those essays. I actually ended up using the content management system into the, how we got their website. So that once I've printed that highly visual essay, I can just go through paragraph by paragraph. I can reorder paragraphs. And I edited just a huge amount because my writing, my talking doesn't sound like the average AI writing. There's things that, that are literally wrong. Like an English teacher would say I do poorly, but they sound like me. And so this is a concession where I think accepting that there is a limit to the efficiency here to basically embrace the fact that the content management process is very, very important. I think that without AI, there's no way that this 5,000 word essay even exists, but there is still a cost to it. And that cost is the five to 10 hours that I'm going to take editing it to make it not this sort of unbearable AI-isms, even if it follows my own narrative architecture. So those are the versions of this that I've been thinking about a lot recently. I think, you know, in terms of big sort of takeaway points, the key elements are one, having a perspective and point of view on which parts are supposed to be yours versus AI. Two, not outsourcing one's point of view, ultimately, that, that key thing that you are trying to achieve or come back to. And then three, accepting that there is no perfect efficiency, even if AI can do it all. And you have to figure out with whatever time budget you have left, how much de-sloppifying you're willing to do and consider that part of the cost of doing business.
Speaker 2I might think you might've scared away some people with your five to 10 hours editing, but let's try and give like a more general perspective. That I think agrees with everything that you're saying, but gives maybe a few other point of thinking for people with other positions and so on. So amazing to hear how you run high quality with like AI first and AI native work. And one thing that I want to say, especially to the people who feel that what you're saying sounds intimidating, that it needs to be proportional to the stakes. So like you said, the internal email, the one line that nobody cares about can definitely be fully AI made. And I think nobody's looking for like artisanal meeting transcript notes or summaries. But I do think that anything that represents your brand, that's supposed to be your original work, that is customer facing and has a high impact to your company. This is the type of work that requires this intentionality, like Nathaniel just pointed out, and that everybody should kind of carve their own belief processes and priorities. At the very minimum, just think about the process. And that's my overall opinion. My overall playbook on how I believe that everybody should disloak critical and important work.
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Speaker 2I want to divide it into stages, and stage one happens before you open the tool. And the very first thing, like you just heard Nathaniel saying, is that I think that you have to know what you're thinking before you open the tool. Go take a walk, read the research until you have your own, no matter how messy it is. My favorite version is to brain dump everything that you're thinking once you have some original thinking. Dictate, rumble to the AI tool, don't care about the structure. Just tell it everything. What you heard, what worries you, what you're thinking, what you're sure you don't want to do. And then let the model and the tool figure out the structure. This episode, for example, for me, started with 15 minutes of me talking to the AI tool. Very to help write this down. And then the AI tool will help you shape many of the thinking or speak back to what it heard. And I think that in many cases, this is, funnily enough, the one thing that people are skipping that has the highest downstream impact. So that's number one. You have to think before you open the tool. Habit two is, I want you to also tell the tool how confident you are in whatever it is that you're thinking. Because the model is built to be agreeable. So if you are live with a very strong opinion, it will probably help you justify every one of your strong opinions. As you said, it will still man you. So say how sure you are and how hard you want it to challenge you. For example, I'm about 60% sure the problem is onboarding, push back hard before you agree with me. That starts a completely different conversation. Even when you are sure, ask for the counter view. Often it will help sharpen your own point of view. The other thing that I want you to do before you start with the tool is to think who the audience is and what you know about them and what the experience that you want to for them to come out with whatever it is that you're creating. It can be the feeling, the action that they want to take or anything else that matters, including the standards of length, of format, tone, anything that will make it yours and appropriate to the task at hand. It gives the model a target and it also helps you validate against something once you do the work together. And of course, all of that is done prior to the work. You actually kick off with the tool and then comes the actual, let's call it shepherding of the execution with AI. And that's going to be the second phase. And here, one thing that I think that you can do is to go wide, meaning that the first answer is probably the most probable one. And that's often the most average one. However, if you ask for six directions that differ in approach, angle, structure, visuals, whatever it is you're producing, it pushes it further. You can ask for directions that borrow from other domains, For example, how a game designer or a newsroom would handle this and then make the tool argue for each direction on its own. And then you squint at the different directions. Look at what you like about each, what's missing from all of them, and then follow the ones that you're most drawn to that represents something that you feel that will serve the purpose at hand. After you do that, I want you to be very intentional, like you heard Nathaniel doing, about your interception point. The workflow that might look the most efficient, here's what I need, go and do it end-to-end, it does the most damage mostly. Because when you get a polished and finished piece sitting in front of you, the sunk cost gets in, and then you, in many cases, will say, okay, I don't want to go back to the actual narrative, let's just ship it like that. Also, with the tools, it's often harder to move it from this. So decide in advance what are the points where you want to step in. And I want you to do that in very specific. Strategic points and decide them in advance, like the outline, and then the first section, and then maybe the first narrative, and then the draft, and then the finished good. So there are intentional strategic points where you're reviewing that when the cost of fix or a complete pivot is as low as possible. And a few additional review habits that I think that will go a long way, which I really, really like, is to what I call to convene a board. Meaning that before any important work goes on. You have AI review that from distinct personas. So it can be the reader seeing it called a skeptic target audience, a domain expert, the busiest person who will receive it. And in many cases also include the devil's advocate, because that can also be the person who is arguing it shouldn't go out at all. Fix based on the different perspectives that this board of reviewers can provide to you. And of course, it can also be different segments of your target customers. And of course, only use. The feedback that you find valuable. And I think that once you use that, you leverage AI to really give you more perspectives and improve the confidence before it chips. So of course, this can be done both early on in critical junctions, as well as on the finished good. And there are a few additional review habits that will take you even further with the quality. The first thing that I want you to do with regards to review is to respect the read budget. And I think the most important part of it, that you never send something that you wouldn't read yourself. So if it's too long or not clear for you, then your audience for sure will not read it either. They'll just hand it over to their own AI to summarize. And then now we have two models that are talking while two people pretend to communicate. So distilling a page into five sentences forces you to decide what you think, or generating that as a deck or as a podcast or as an HTML or as an image or a video. You have so many options with AI. So choose the format on purpose. And before you even. Start the production, ask yourself about the audience, what they will consume in the most effective way, and then go wild with that. And the best thing that you can do is if you will enjoy reviewing that in that format, odds are that your target audience will also enjoy consuming that. So that's how we all get a single output out of the door in a higher quality with like human taste and so on. But I want to also propose a few of the ideas on how to make it easier and more personalized over time. And one thing that will help a lot. Is to teach AI your own taste. And of course, you don't need to be able to describe your own taste in words, because most of us just don't have the vocabulary to be like a linguist or an art director that explains what it is that we like. But if we show a very capable model, either our best work, the work by adults that we admire and the work that we dislike and the things that we dislike, and then ask what the good ones have in common, or how will it profile your own taste and voice and so on, it will put words to your preference as much more. more precisely than what you can. And for visuals, I really like to create like a mood board of a bunch of images and colors and the feelings that you want to get out of that. And that beats any adjectives that you will be able to use. Once you have a good profile of your preferences with regards to visuals or narrative or tone of voice and so on, you can turn that into a skill or something usable with your AI tool. And then that can be the beginning of any work that is intended to sound like you. Still, you need to do the thinking. But at least it will help you sound much more like you. And of course, the downside of having such a skill is that potentially you will start to produce just the one thing because the skill will help you produce it again and again. And audience does remember that you have a sameness. So for example, maybe in my case, you're very much fed with my Falcons. That might be a downside of me codifying my preferences. But what you can do is to break it down on purpose. So whenever the audience, the scope or the goal changes, or just enough time has gone by. Bring fresh idea from new inspirations and try to kind of go outside your mold by still teach it what you prefer and so on. This is where we want to create a cultural difference. The first thing that I want you to do is every time you get slug from someone else, name it. And especially, by the way, if you're in a leadership position, because when something long, generic and clearly unread arrives, just say so. You can do it kindly or not so kindly. This isn't a good use of my time. I want you to say. Send me your take in five sentences, or I want you to send me work that you think about more carefully. Just hold the gate and don't allow for a slug to come your way. And of course, when it's customer facing work, this is where you never, ever allow to do. Because we don't ever put our team name when we were producing an external work. And lastly, I don't want you to necessarily hide AI. If you remember a while back and Nathaniel, you did an episode on writing and you were debating on the AI in your book, which was essentially Druckmiller's AI written op-ed in the Wall Street. And the question was whether or not we should remove all the M-dashes and remove all the AI telltales. And your point of view there was that primarily that was a proxy to let the audience feel as if you didn't invest the time. So in my opinion, you shouldn't completely try to hide the AI. You just need to make it feel like something that you originally produced. Anything to add on that? Because you were very opinionated back then.
Speaker 1I was going to use the phrase. In today's episode. And I literally just couldn't anymore because of how much it's been used by AI. So maybe another example of this that's practical from a company perspective, you're starting to see companies that are on the cutting edge, formalize and codify their approaches to these issues. So Figma has shared their rules for AI writing. And one of them is if it's written with AI, that's not an issue, but label it as such on top so that people have that context to go into it. So for example. There's going to be lots of times where what you're trying to do with that 40 page report is not say here's the finished work, but to say, let's rip through this, find the parts that are interesting, you know, and so you just identify it as such. I think the point that you made at the beginning of one of the definitions of slop being transferring the cost of work from the person who produces it to the person who consumes it, I think is acutely accurate. I think that basically almost wholly explains why people have. Such a visceral reaction to it is that you are now imposing a cost upon them. So it's not even just a dereliction of your responsibility. You're leaving them holding the work bag in some ways. And so when people are smart enough to know if you are prioritizing yourself over them in a context where you're supposed to be presenting something to them. And I think that's what folks have an issue with. So. I think transparency, disclosure, understanding that there are different contexts and different needs for different types of work all have a common through line of respecting other people's time as much as you respect your own.
Speaker 2Yeah. And I think with that, we can kind of summarize. And I think looking forward for sure, the models will keep getting better and the better they get, the more tempting the generic answer will become. So I want to urge you to look at the five stages that we went through. And I want to urge you to look at the five stages that we went through. And I want you to acknowledge that they hold whatever ships next, because my hunch is that once a lot of the current hype of the agentic tools and how easy it is to produce with AI, the handmade work will become the premium thing and people will pay more for work with a human fingerprint. Like you said, even if it's messy, if it's not necessarily grammatically correct, but it sounds like you and it's intriguing and it creates like a human gut feeling. And I do believe that disciplined, high quality work is the job security for everybody who is afraid about their job, regardless of what it is. Because original thinkers, I'm sure, will be rewarded more and more and companies that keep the slope out of how work travels, especially outside of them, they will stand out and they will attract customers in a way that almost nobody else will be able to manage should they leave the generic output and the sameness out there. So definitely use AI to produce more quality and more work, push the envelope of the things that you can do. But I want you to also use it as a tool to think. More sharply to expand the horizon and the knowledge and to see more directions and then survive the thing that perhaps a tougher reviewer might do for you. We have everything on the companion page that you will probably put in the show notes in the IDB website. And as always, I have to say in one sentence that part of what we teach in the various courses that we do with the super intelligent training is exactly to teach you how to be an intentional AI user, like from a best effort to best in class that does not outsource. The thinking and is able to produce original work while still getting the immense value that we're trying to do. value that the technology can give us, but doing so in a more strategic way.
Speaker 1I guess maybe I'll add my cynical take on this, just to close it out. Cynical is overstating it, the self-interested take. I believe that a lot of the folks who listen to this show are determined and hell-bent on being of the group that uses this technology to get ahead, to not allow this technology happen to them, but to use it for whatever goals they're trying to achieve. I think that's mostly why people are here. Even if they're here for the news versus the practical stuff, overall, they want to be in the top 1% of this new era where AI is a part. Simply put, what that means has undergone a shift. I think we've all recognized it, but at the beginning, using it well, being in that top 1%, or even the top 10%, meant realizing you could use gamma to produce better presentations that were more visually consistent more quickly and having more comprehensive answers to things. All of that has been completely democratized as a skill now. Everyone has access to those things. It's no longer just the 1%. It's everyone who's touching these tools, unless they're philosophically against them at this point. The value, the differentiated value, if you're trying to be in that top percent of users, shifts. From just producing a bunch of stuff to using them well, and well, it turns out, means with taste and with intention. Unfortunately, it's a much harder phase in a lot of ways than the first one was. The first one, we all got points for just showing up to the party early. Now, the differentiation in the product is actually going to matter, but there are huge opportunities in a world of endless seas of content. The stuff that's good, stand out even more, and people will get enormously good, even without trying to, at filtering out all of this noise. It's just the next opportunity, and it involves a different set of skills, but it's the thing that's next, so I think it's what we're all going to do. Awesome. All right, Nufar, great to have you on the show again. Like Nufar said, we have a companion experience up on the website that has this, but even way more practical and tangible even than the background slides that Nufar was talking over. Go check it out at AI Daily. AI Daily Brief.ai, and thanks again for being here.
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