# How to Deslopify Your AI — Transcript (2026-11-02)

https://aidailybrief.ai/e/2026-11-02 · Listen: https://pod.link/1680633614

---

[00:00:00] The 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-slopifying 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 sponsors, KPMG, [00:01:00] Robots and Pencils, Cdata, and Blitzy. to get an ad-free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts And if you wanna learn more about sponsoring the show, send usa note at sponsors@aidailybrief.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?

I'm good. I'm excited about this one. I'm very passionate about this topic

Yeah, this is even, even within the framework of 

operators cuts, which are sort of the 

more 

[00:02:00] 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, 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 techniques, ideas, thoughts, et cetera, a lot of which they will probably have already been doing intuitively, 'cause 

my guess is that 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.

All right, so before we talk about uh, 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, a rhythm that you can hear in your own head by now. Like short sentence, then list of [00:03:00] 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 AI, 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 Merriam-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, it meant scrapes you feed [00:04:00] 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, 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 one to three percent 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 the managers and one product leader that I, I really, respect a lot, and th-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 the team turned into a slop factory. That's their [00:05:00] 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 partially or altogether, 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 de- 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, the 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 BetterUp, they did a work slop study and they said that about [00:06:00] forty percent of US desk workers had received in the past month something to be considered sloppy, and it cost close to two hours on the receiving side. In the Glean survey, I think you quoted it in one of the episode, six thousand workers this year said that almost seventy percent 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, com-complaining and so on, I wanna help you. And the way I wanna help this episode is in three parts. First, explain why we got here why slop happens in the machine and in, 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 wanna make one thing very clear, this is not an anti-AI episode. I use AI to help me with everything that I do including, of course, this session. I just wanna make sure that [00:07:00] you do it in a way that actually produces original work. So I wanna 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 trained 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 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 create because length looks like effort, and in many cases, that's how people reward it. And if you think [00:08:00] 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 wanna 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 a-a-an experiment. And what I did, I opened a ChatGPT Sol, Claude 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 [00:09:00] here, and I think this structure by now will be very identifiable by all of you, but even uh, more so, the content was very similar. And then when I re-ran the same task now with my point of view, what I believe about AI mark- 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.

I, But at least I had a much better starting point for editing. So that's the machine side. The question is why we as humans tolerate the slop. Um, and the answer is that psy-psychologists have called our brain a cognitive miser since the I guess early '80s because we are prone to save thinking and energy whe-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 workers, research found that the more people trusted the AI, [00:10:00] the less critical thinking they applied to the output, and the models make it very easy to feel very confident. So if you think about how slop gets made, the- 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 gonna 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 'cause 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 um, 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 wanna give you some ideas on how to prevent slop, I want to also give you [00:11:00] like a set of questions to detect slop in yours or in other people work. Um, 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 wanna 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 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 want 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 gonna be relevant or high-quality work. Is there a, a more effective way to convey this information? Or does this one still do the job the best it can? Namely, does it [00:12:00] 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 Um, there are a, 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 [00:13:00] I wouldn't give them too much weight as I don't think that they detect n- 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's problem was always the lack of human unique point of view, not the specific uh, 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, the thinking. And speaking of skipping the, the thinking I want to have a fine line between outsourcing everything to AI and still getting the efficiency boost where human's judgment uh, matters.

So some places, not only AI can help us, but it can also create many more opportunities that without AI we cannot do. While in other places, I am [00:14:00] wholeheartedly under the belief that those should remain uh, human. And you have a partial list here, but I am very curious 'cause I know that you are very opinionated about it, Natanial.

So how do you decide what to keep in your hands and what to delegate? 

Yeah. I, 

so I think first of all, what you said about understanding that 

you're gonna 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.

That 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, [00:15:00] 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 forty-five 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 fifteen-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 this is a show that would be impossible 

for me to do as I do it without AI, [00:16:00] 

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 the-- 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 

into 

the translator even,

even without being deep in,in that space.

So 

the way that I think about de-slopifying 

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 own research, so that has to be AI 

But two, I am unwilling to allow 

[00:17:00] 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 report.

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, 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, that is my own 

or 

that I'm sort of challenging it and, and building it out from whatever, point I'm starting with. 

I think a great example of why you 

literally cannot outsource your point of view in content to AI is [00:18:00] one part of that process that I often do is I will have an AI model steelman two wildly different perspectives and have it argue vociferously for each side of a particular k- issue or phenomenon.

And that means that it's, e-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, a person that has a, 

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, 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, that helps interact with it. and then a point of view. I also tend to be very strong 

for myself in architecting the narrative and the flow of the argument.

That's something that is [00:19:00] 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, e-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,

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 wanna hit. 

The same type of [00:20:00] research dossier 

that we actually produce with a human 

for the "AI Daily Brief," 

that's a combined human AI 

effort for the new show, how, 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 slopped. 

And so 

I basically had to go figure out 

how to handle this. And so [00:21:00] the two pieces to de-slopify that particular piece of content, 'cause 

I did think it was incredibly important 

because a lot of people wanna 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 hew 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 it, there is just a, a can't escape it, 

five to 10-hour rewrite process that I'm gonna 

go through with those essays.

I actually ended up building 

a, content management system into the How We Got There website, so that once [00:22:00] 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, '

cause 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 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 gonna 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, I think not outsourcing 

one's point of view ultimately, that, that key thing that [00:23:00] 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-Slopifying you're, willing to do and consider that part of the cost of doing business.

Amazing. you might have scared away some people with your five to 10 hours editing. But let's try and give a, like a more general perspective that I think agrees with everything that you're saying, but gives maybe s- a few other point of thinking for people with other positions and so on.

So a- 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 [00:24:00] notes uh, 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 N- Nathaniel just pointed out, and that everybody should kind of carve their own um, belief processes and priorities. at the very minimum, just think about the process, and that's my overall playbook on how I believe that everybody should uh, de-SLOP critical and important work. A new study from KPMG and the University of Texas at Austin found that when people work with AI, similar skills don't guarantee similar outcomes. Researchers studied more than five hundred early career professionals and found that the best performers consistently amplified the value of AI by guiding, evaluating, and refining its outputs.

These top performers, called AI amplifiers, weren't defined by what they knew alone, [00:25:00] but by how they worked with AI. Learn more about what separates AI amplifiers from everyone else at kpmg.com/us/aiamplifiers. 



The The best teams don't have a single star carrying everyone else. They know their own strengths and each other's weaknesses and play to both. That's the team Robots and Pencils has built on purpose. Nobody there is grinding through busy work to pad a headcount number.

People come for the hard problems, and they stay because everyone around them is leveling up at the same time. In a market full of companies that are just trying to hire fast, that's worth a look. check out robotsandpencils.com/careers 

This episode is brought to you by Cdata. Companies rolling out AI keep hitting the same wall. Token spend is climbing, security is holding agents back, and no one knows how AI is truly being used. The root cause, AI is cut off from the systems and context your business runs on

That's why CData built Connect AI Gateway. One control point between your AI and your systems

Built on the connectivity layer powering [00:26:00] Palantir, Google, Salesforce, and hundreds more, each agent gets exactly the data and tools it needs

It governs down to the record and lets you finally see your org's complete AI usage in one place. Accurate answers, a fraction of the token cost, and a gateway that learns your business with every interaction. See how at cdata.ai. 

Every AI coding tool on the market does the same thing first. It starts writing code. Blitzy does the opposite. Before writing a single line, Blitzy spends days reverse engineering your entire code base

Thousands of agents ingest millions of lines, mapping every dependency, every undocumented constraint, every architectural decision made over the last decade. 

the result is a dynamic knowledge graph that understands your software the way a principal engineer would after 30 years in the building

Other tools guess at context with grep searches and markdown files. Blitzy never guesses. it builds true understanding first, then delivers over 80% of entire software epics autonomously

validated end-to-end tested production grade pull requests. That's why Fortune 500 engineering teams trust Blitzy with the code bases that matter [00:27:00] most

See for yourself at blitzy.com. That's B-L-I-T-Z-Y.com



um, I wanna 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, you have a view of 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, ramble 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 wanna do. And then let the model and the tool figure out the structure. This episode, for example, for me, started with fifteen minutes of me talking to the AI tool. Very messy, but I, just told it everything I'm thinking about SLOP to, to help uh, write this down. and then the AI tool he- will [00:28:00] help you shape many of the thinking or speak back to you what it heard. And I think that in many cases, this is funnily enough, the, 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 arrive with a very strong opinion, it will probably help you justify every one of your strong opinions.

A- as you said, it will still menu. So say uh, how sure you are and how hard you want it to challenge you. For example, I'm about sixty percent 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 [00:29:00] experience that you want to for them to come out with whatever it is that you're creating. It can be the feeling, the, the action that they wanna 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 that you actually kick off with the tool. And then comes the, the actual, let's call it shepherding of the execution with AI, and that's gonna 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 of- often the most average one.

However, if you ask for six directions that differ in approach, angle, structure, visualize, whatever it is you're producing it push it further. You can ask for directions that borrow from other domains. [00:30:00] For example, how a game designer or a newsroom would handle this, a- 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 uh, like 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 wanna 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 strategic points and decide them in advance, like the [00:31:00] outline and then the first section and then maybe the, 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, uh, and which I really, really like is to what I call to convene a board, meaning that before any important work goes out you have AI review that from distinct personas.

So it can be the reader seeing it called uh, 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 o-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 [00:32:00] you use that, you leverage AI to really give you more perspectives and improve the confidence before it ships. 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 [00:33:00] 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 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 mo-model, either our best work the work by others 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 preferences much more pre-precisely than what you can. And for visuals, I really like to [00:34:00] create like a mood boards of a bunch of images and colors and the feelings that you want to get out of that. And b-that beats any adjective 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 reusable 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, 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 are 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 [00:35:00] and try to kind of go outside your mold, but still teach it what you prefer and so on. This is where we want to create a cultural different. The first thing that I want you to do is every time you get slop from someone else, n-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 send me your take in five sentences," or, "I want you to send me work that you think about um, more carefully." just hold the gate and don't allow for a slop 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 w-when we-we're producing an external work. And lastly, I don't want you to necessarily hide AI. If you remember a while back, and Natanel, you did an episode on writing, and you were debating on the Stanley Druckenmiller's AI written op-ed in the Wall Street. 

And the w- the [00:36:00] discussion was whether or not we should remove all the em 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 o-originally produced. Any-anything to add on that 'cause you were very opinionated back then? 

I was gonna use the phrase load-bearing in today's episode, and I, I literally just couldn't anymore because of how, how much it's been uh, 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, 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 gonna be lots of times where 

what you're trying to 

do with that 40-page [00:37:00] 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.

It's, 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, u-understanding that there are different [00:38:00] 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.

Yeah, 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 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 wi-with a, 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, a human gut feeling. And I do believe that disciplined high quality work is the job security for everybody who is afraid about their jobs, regardless of what it is. Because original thinkers, I'm sure, will be rewarded more and more, and [00:39:00] companies that keep the slop 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, 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 uh, more sharply, to expand the, the horizon and the knowledge and to see more di-directions and then survive the thing that perhaps a, 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 planning is exactly to teach you how to be an intentional AI user, like a, 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 [00:40:00] value that the technology can give us, but doing so in a more strategic way.

I guess maybe, maybe I'll add my, my cynical

take on this just to, to close it out. Or m- 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 wanna be in 

the top 1% of this 

new era 

that where AI is a part. And simply put, what that means has undergone a shift, and 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 [00:41:00] could use Gamma to produce better presentations that were more visually consistent more quickly, and having more comprehensive answers to things.

But all of that has been completely democratized 

as a skill 

now, 

right? 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. 

And so 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 and unfortunately it's, 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, the differentiation in the product is actually gonna matter.

But 

there are 

huge opportunities. 

In a world of 

endless seas of content, the stuff that's good will stand out even more, and [00:42:00] people will get enormously good even without trying to, at filtering out all of this noise. 

So 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 gonna 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, way more sort of practical 

and tangible even than the, the background slides that Nufar was talking over. So 

go check it out at aidailybrief.ai, 

and uh, thanks again for being here. 

​