The Doing Got Cheap. Now What? | Claude Fable 5 Changes Work

Fable 5 is the best model in the world, Fable 5 is the best model in the world, and I know you can't access it. I can't and I know you can't access it. I can't and I know you can't access it. I can't access it either. I am making this access it either. I am making this access it either. I am making this review available to you so that we can review available to you so that we can review available to you so that we can call the energy into the universe to call the energy into the universe to call the energy into the universe to bring it back. Kidding aside, we need to bring it back. Kidding aside, we need to bring it back. Kidding aside, we need to find a way to talk about what this model find a way to talk about what this model find a way to talk about what this model is capable of because it shapes what we is capable of because it shapes what we is capable of because it shapes what we are going to experience from a bunch of are going to experience from a bunch of are going to experience from a bunch of models in the next few months. And so, models in the next few months. And so, models in the next few months. And so, you should be expecting this kind of big you should be expecting this kind of big you should be expecting this kind of big model feeling from Chat GPT models that model feeling from Chat GPT models that model feeling from Chat GPT models that are dropping in the next month or so. are dropping in the next month or so. are dropping in the next month or so. You should be expecting this from You should be expecting this from You should be expecting this from open-source models in the next 4 5 6 open-source models in the next 4 5 6 open-source models in the next 4 5 6 months. I want to drop this Fable 5 months. I want to drop this Fable 5 months. I want to drop this Fable 5 model review now because, yes, I hope model review now because, yes, I hope model review now because, yes, I hope Fable comes back soon. But, also, we are Fable comes back soon. But, also, we are Fable comes back soon. But, also, we are all going to be needing to ask different all going to be needing to ask different all going to be needing to ask different kinds of questions of our models as we kinds of questions of our models as we kinds of questions of our models as we start to work with these 10 trillion start to work with these 10 trillion start to work with these 10 trillion parameter models. And yes, I think Fable parameter models. And yes, I think Fable parameter models. And yes, I think Fable 5 is a 10 trillion parameter model. I'm 5 is a 10 trillion parameter model. I'm 5 is a 10 trillion parameter model. I'm not the only one that thinks that. It's not the only one that thinks that. It's not the only one that thinks that. It's a massive new pre-train, and the big a massive new pre-train, and the big a massive new pre-train, and the big model feeling is all over this model. model feeling is all over this model. model feeling is all over this model. I'm very excited about it. I hope we get I'm very excited about it. I hope we get I'm very excited about it. I hope we get it back soon. In the meantime, enjoy it back soon. In the meantime, enjoy it back soon. In the meantime, enjoy this review. Claude Table 5 came out on this review. Claude Table 5 came out on this review. Claude Table 5 came out on Tuesday. And if you're tired of hearing Tuesday. And if you're tired of hearing Tuesday. And if you're tired of hearing that an AI model changes everything, that an AI model changes everything, that an AI model changes everything, stay with me for a second because you're stay with me for a second because you're stay with me for a second because you're the person this video is for. I get the person this video is for. I get the person this video is for. I get tired of the insane adjectives, too, tired of the insane adjectives, too, tired of the insane adjectives, too, right? It's terrifying. It's speechless.

right? It's terrifying. It's speechless. right? It's terrifying. It's speechless. Best model ever. We'll all lose our Best model ever. We'll all lose our Best model ever. We'll all lose our jobs. And look, I got to tell you, there jobs. And look, I got to tell you, there jobs. And look, I got to tell you, there is a real capability jump with this is a real capability jump with this is a real capability jump with this model. But, I am less interested in that model. But, I am less interested in that model. But, I am less interested in that and more interested in what you can do and more interested in what you can do and more interested in what you can do with this model. And that is what I with this model. And that is what I with this model. And that is what I think is worth a conversation because think is worth a conversation because think is worth a conversation because Fable 5 is not interesting because it's Fable 5 is not interesting because it's Fable 5 is not interesting because it's smarter. It's not interesting because it smarter. It's not interesting because it smarter. It's not interesting because it benchmark maxes. It's interesting benchmark maxes. It's interesting benchmark maxes. It's interesting because it's bigger. And I mean that in because it's bigger. And I mean that in because it's bigger. And I mean that in a very specific way. It is the first a very specific way. It is the first a very specific way. It is the first model I've used where the limit I kept model I've used where the limit I kept model I've used where the limit I kept hitting was not the model running out of hitting was not the model running out of hitting was not the model running out of ability. It was me running out of big ability. It was me running out of big ability. It was me running out of big things to ask for. Just sit with how things to ask for. Just sit with how things to ask for. Just sit with how strange that is. 3 years of these models strange that is. 3 years of these models strange that is. 3 years of these models breaking on our work. And the new breaking on our work. And the new breaking on our work. And the new constraint is our ability to imagine the constraint is our ability to imagine the constraint is our ability to imagine the ask. So today I want to walk you through ask. So today I want to walk you through ask. So today I want to walk you through five things that Fable 5 resets about five things that Fable 5 resets about five things that Fable 5 resets about this moment in the AI race. And only the this moment in the AI race. And only the this moment in the AI race. And only the first one is about the model. The rest first one is about the model. The rest first one is about the model. The rest are about your work, about why AI has are about your work, about why AI has are about your work, about why AI has probably felt smaller than advertised in probably felt smaller than advertised in probably felt smaller than advertised in your actual life. What skill changes your actual life. What skill changes your actual life. What skill changes that and what to do differently this that and what to do differently this that and what to do differently this week. And by the end you'll know whether week. And by the end you'll know whether week. And by the end you'll know whether you have a Fable sized job on your desk.

you have a Fable sized job on your desk. you have a Fable sized job on your desk. I bet you do. I think you have several I bet you do. I think you have several I bet you do. I think you have several and I think that you've stopped seeing and I think that you've stopped seeing and I think that you've stopped seeing them because we've never been taught to them because we've never been taught to them because we've never been taught to see them. So see them. So see them. So what does a bigger model feel like? what does a bigger model feel like? what does a bigger model feel like? Models tend to fail this in predictable Models tend to fail this in predictable Models tend to fail this in predictable ways, right? They promote garbage into ways, right? They promote garbage into ways, right? They promote garbage into clean data, they smooth over conflicts, clean data, they smooth over conflicts, clean data, they smooth over conflicts, they fix things and leave you wondering they fix things and leave you wondering they fix things and leave you wondering what else they fixed that you didn't ask what else they fixed that you didn't ask what else they fixed that you didn't ask for. Fable did something I'd never seen for. Fable did something I'd never seen for. Fable did something I'd never seen before. It quarantined the garbage in before. It quarantined the garbage in before. It quarantined the garbage in the data instead of fixing it. It found the data instead of fixing it. It found the data instead of fixing it. It found the fake credentials and inventory them the fake credentials and inventory them the fake credentials and inventory them without leaking them and then, and this without leaking them and then, and this without leaking them and then, and this is the part that got me, it built me a is the part that got me, it built me a is the part that got me, it built me a review queue. Every call it wasn't sure review queue. Every call it wasn't sure review queue. Every call it wasn't sure about it surfaced to a human to check. I about it surfaced to a human to check. I about it surfaced to a human to check. I didn't ask it to do that. It behaved didn't ask it to do that. It behaved didn't ask it to do that. It behaved like it expected to be checked. like it expected to be checked. like it expected to be checked. Somewhere in the middle of this journey Somewhere in the middle of this journey Somewhere in the middle of this journey with Fable I stopped hovering. I handed with Fable I stopped hovering. I handed with Fable I stopped hovering. I handed work over and then I really did go and work over and then I really did go and work over and then I really did go and do something else. do something else. do something else. And I've always had to kind of keep an And I've always had to kind of keep an And I've always had to kind of keep an eye on these models. It's never been my eye on these models. It's never been my eye on these models. It's never been my relationship with these models because I relationship with these models because I relationship with these models because I care about quality to just not, you care about quality to just not, you care about quality to just not, you know, walk away and not pay attention. know, walk away and not pay attention. know, walk away and not pay attention. With Fable, if I give it the task, I With Fable, if I give it the task, I With Fable, if I give it the task, I really do feel like I can walk away. And really do feel like I can walk away. And really do feel like I can walk away. And that has not been my relationship with that has not been my relationship with that has not been my relationship with these models in the past. And I'm not these models in the past. And I'm not these models in the past. And I'm not the only one. The people with early the only one. The people with early the only one. The people with early access keep reaching for similar words.

access keep reaching for similar words. access keep reaching for similar words. And it's not necessarily smarter. They And it's not necessarily smarter. They And it's not necessarily smarter. They talk about whole projects handed off. talk about whole projects handed off. talk about whole projects handed off. Stripe says it compressed months of Stripe says it compressed months of Stripe says it compressed months of engineering work into days. And after engineering work into days. And after engineering work into days. And after touching and playing with this model for touching and playing with this model for touching and playing with this model for the last couple of days, three days, the last couple of days, three days, the last couple of days, three days, four days, it feels true. Now, before four days, it feels true. Now, before four days, it feels true. Now, before this turns into yet another hype video, this turns into yet another hype video, this turns into yet another hype video, there are real misses with this model. there are real misses with this model. there are real misses with this model. One, it's expensive. 50 bucks per One, it's expensive. 50 bucks per One, it's expensive. 50 bucks per million output tokens is not cheap. The million output tokens is not cheap. The million output tokens is not cheap. The visual taste is not where it needs to visual taste is not where it needs to visual taste is not where it needs to be, right? I asked it to do visual be, right? I asked it to do visual be, right? I asked it to do visual designs. It did not one-shot them at the designs. It did not one-shot them at the designs. It did not one-shot them at the quality bar that I would expect. For quality bar that I would expect. For quality bar that I would expect. For example, it produced clipped headings in example, it produced clipped headings in example, it produced clipped headings in PowerPoints and charts a designer would PowerPoints and charts a designer would PowerPoints and charts a designer would wince at at times. It missed information wince at at times. It missed information wince at at times. It missed information that only existed in handwritten images that only existed in handwritten images that only existed in handwritten images until I explicitly forced it to look until I explicitly forced it to look until I explicitly forced it to look there, and every single run still ended there, and every single run still ended there, and every single run still ended with a review work that had to land on with a review work that had to land on with a review work that had to land on my desk to check. So, bigger doesn't my desk to check. So, bigger doesn't my desk to check. So, bigger doesn't mean that it's finished and work is mean that it's finished and work is mean that it's finished and work is done. There are people who are out there done. There are people who are out there done. There are people who are out there saying this means engineering work is saying this means engineering work is saying this means engineering work is over, as usual, right? Like we've seen over, as usual, right? Like we've seen over, as usual, right? Like we've seen that with every model. So, bigger that with every model. So, bigger that with every model. So, bigger doesn't mean this model is perfect. doesn't mean this model is perfect. doesn't mean this model is perfect. Bigger means it can pick up and carry Bigger means it can pick up and carry Bigger means it can pick up and carry the job, and I can trust it to do that, the job, and I can trust it to do that, the job, and I can trust it to do that, and I just have to have a look at it and I just have to have a look at it and I just have to have a look at it when it's done. And uh really, the task when it's done. And uh really, the task when it's done. And uh really, the task then is to imagine something large then is to imagine something large then is to imagine something large enough, right? And that's why I talk enough, right? And that's why I talk enough, right? And that's why I talk about a whole consulting engagement, cuz about a whole consulting engagement, cuz about a whole consulting engagement, cuz that's the kind of scale that you want that's the kind of scale that you want that's the kind of scale that you want to give this model. Think back. In 2023 to give this model. Think back. In 2023 to give this model. Think back. In 2023 and 2024, asking big got you burned, and 2024, asking big got you burned, and 2024, asking big got you burned, right? You handed a model something right? You handed a model something right? You handed a model something real, and it lost the thread by step real, and it lost the thread by step real, and it lost the thread by step six, and it invented a source, and it six, and it invented a source, and it six, and it invented a source, and it gave you a confident wrong number, a gave you a confident wrong number, a gave you a confident wrong number, a hallucination. So, you did the rational hallucination. So, you did the rational hallucination. So, you did the rational thing. You found the safe size for what

thing. You found the safe size for what thing. You found the safe size for what this AI stuff could do, right? Ask it this AI stuff could do, right? Ask it this AI stuff could do, right? Ask it for one draft. Verify everything. Keep for one draft. Verify everything. Keep for one draft. Verify everything. Keep things short and structured. things short and structured. things short and structured. But, we didn't just learn to ask small But, we didn't just learn to ask small But, we didn't just learn to ask small in that world. We got really good at in that world. We got really good at in that world. We got really good at asking small, and our whole mental model asking small, and our whole mental model asking small, and our whole mental model for AI became about the size of the for AI became about the size of the for AI became about the size of the model that we were working with. We model that we were working with. We model that we were working with. We built our routines around it. Prompt built our routines around it. Prompt built our routines around it. Prompt engineering became a skill, and then a engineering became a skill, and then a engineering became a skill, and then a job title because of the size of the job title because of the size of the job title because of the size of the model. Every AI productivity guide, model. Every AI productivity guide, model. Every AI productivity guide, including plenty of mine, has been including plenty of mine, has been including plenty of mine, has been assuming a certain model scale. But, the assuming a certain model scale. But, the assuming a certain model scale. But, the models kept growing, and our asks and models kept growing, and our asks and models kept growing, and our asks and our imagination did not. And that's our imagination did not. And that's our imagination did not. And that's really the gap. That's the whole gap really the gap. That's the whole gap really the gap. That's the whole gap between the headlines and how your day between the headlines and how your day between the headlines and how your day feels. If your asks are prompt sized, feels. If your asks are prompt sized, feels. If your asks are prompt sized, every frontier model, including this every frontier model, including this every frontier model, including this one, including Fable, is going to feel one, including Fable, is going to feel one, including Fable, is going to feel basically the same. And at the size basically the same. And at the size basically the same. And at the size you've been asking for in terms of work, you've been asking for in terms of work, you've been asking for in terms of work, none of these models are going to make a none of these models are going to make a none of these models are going to make a difference. Fable 5 has made that difference. Fable 5 has made that difference. Fable 5 has made that problem impossible to ignore. Partly problem impossible to ignore. Partly problem impossible to ignore. Partly because of what it can do, and partly because of what it can do, and partly because of what it can do, and partly bluntly because of what it costs. At bluntly because of what it costs. At bluntly because of what it costs. At these prices and speeds, small asks are these prices and speeds, small asks are these prices and speeds, small asks are not worth the money, right? It's a waste not worth the money, right? It's a waste not worth the money, right? It's a waste of the model. This is not a model I of the model. This is not a model I of the model. This is not a model I would use to write a slightly better would use to write a slightly better would use to write a slightly better email. This is not a daily driver model.

email. This is not a daily driver model. email. This is not a daily driver model. Nobody should spend Fable money on a Nobody should spend Fable money on a Nobody should spend Fable money on a summary of what a cheap model can do in summary of what a cheap model can do in summary of what a cheap model can do in a few seconds. The economics are begging a few seconds. The economics are begging a few seconds. The economics are begging you to ask bigger. you to ask bigger. you to ask bigger. And yes, we're going to get into what And yes, we're going to get into what And yes, we're going to get into what that looks like next. And that brings us that looks like next. And that brings us that looks like next. And that brings us to the third key point in this video. We to the third key point in this video. We to the third key point in this video. We need the skill of task imagination. Not need the skill of task imagination. Not need the skill of task imagination. Not ask them, give them. Do you feel the ask them, give them. Do you feel the ask them, give them. Do you feel the difference? Ask suggests a prompt. Give difference? Ask suggests a prompt. Give difference? Ask suggests a prompt. Give is going to produce a job. I'm going to is going to produce a job. I'm going to is going to produce a job. I'm going to give you a pile of source material, a a give you a pile of source material, a a give you a pile of source material, a a goal for a finished thing at the end, goal for a finished thing at the end, goal for a finished thing at the end, and I'm going to tell you to sort out and I'm going to tell you to sort out and I'm going to tell you to sort out the judgment calls along the way with a the judgment calls along the way with a the judgment calls along the way with a series of like rough guidelines. I've series of like rough guidelines. I've series of like rough guidelines. I've been calling the skill detailed task been calling the skill detailed task been calling the skill detailed task imagination, the ability to look at your imagination, the ability to look at your imagination, the ability to look at your own work and see the whole job that an own work and see the whole job that an own work and see the whole job that an AI could do if it had the right context AI could do if it had the right context AI could do if it had the right context and the right tools and a clear picture and the right tools and a clear picture and the right tools and a clear picture of what done looks like. And now, before of what done looks like. And now, before of what done looks like. And now, before anybody says in the comments, "Nate, anybody says in the comments, "Nate, anybody says in the comments, "Nate, this is the delegation talk again." It's this is the delegation talk again." It's this is the delegation talk again." It's not, and the difference matters. not, and the difference matters. not, and the difference matters. Delegation is about tasks you have. Delegation is about tasks you have. Delegation is about tasks you have. They're in your tracker, they have a They're in your tracker, they have a They're in your tracker, they have a name on it. I and I've made the case for name on it. I and I've made the case for name on it. I and I've made the case for a while that you can delegate jobs to a while that you can delegate jobs to a while that you can delegate jobs to AI.

AI. AI. This is about jobs that are bigger than This is about jobs that are bigger than This is about jobs that are bigger than that, that aren't on anybody's tracker that, that aren't on anybody's tracker that, that aren't on anybody's tracker yet because they're dirty and ambiguous. yet because they're dirty and ambiguous. yet because they're dirty and ambiguous. And yes, I'm picking those big numbers And yes, I'm picking those big numbers And yes, I'm picking those big numbers on purpose because these are numbers on purpose because these are numbers on purpose because these are numbers that you need to make sure that you that you need to make sure that you that you need to make sure that you think about when you assign this model. think about when you assign this model. think about when you assign this model. If you give Fable 5 a small task, it If you give Fable 5 a small task, it If you give Fable 5 a small task, it does get it done, it's just kind of a does get it done, it's just kind of a does get it done, it's just kind of a waste of the muscle of the model. So, waste of the muscle of the model. So, waste of the muscle of the model. So, look for those tasks that nobody has look for those tasks that nobody has look for those tasks that nobody has written down because until now, either written down because until now, either written down because until now, either nobody was going to get to them or they nobody was going to get to them or they nobody was going to get to them or they felt so big there wasn't any point in felt so big there wasn't any point in felt so big there wasn't any point in assigning the model to them, right? assigning the model to them, right? assigning the model to them, right? These tasks are what working here feels These tasks are what working here feels These tasks are what working here feels like, right? These tasks are what makes like, right? These tasks are what makes like, right? These tasks are what makes the job painful, right? And then figure the job painful, right? And then figure the job painful, right? And then figure out how you can make that task visible out how you can make that task visible out how you can make that task visible to Fable, right? How do you take the to Fable, right? How do you take the to Fable, right? How do you take the 40,000 reviews and shove them at Fable? 40,000 reviews and shove them at Fable? 40,000 reviews and shove them at Fable? That's one of our larger questions now, That's one of our larger questions now, That's one of our larger questions now, right? How do you take a full CRM export right? How do you take a full CRM export right? How do you take a full CRM export and say, "I want to merge across 2 and say, "I want to merge across 2 and say, "I want to merge across 2 million customer records, the million customer records, the million customer records, the duplicates, what's stale, the account duplicates, what's stale, the account duplicates, what's stale, the account briefs, all of it, and make sure it's briefs, all of it, and make sure it's briefs, all of it, and make sure it's reproducible." How do you take that reproducible." How do you take that reproducible." How do you take that 500-page board packet and make sure that 500-page board packet and make sure that 500-page board packet and make sure that it's actually fact-checked and aligned?

it's actually fact-checked and aligned? it's actually fact-checked and aligned? This model needs a lot of material to This model needs a lot of material to This model needs a lot of material to chew through. It needs a clear sense of chew through. It needs a clear sense of chew through. It needs a clear sense of what done looks like, and it needs a what done looks like, and it needs a what done looks like, and it needs a trail you can review. And yes, this is trail you can review. And yes, this is trail you can review. And yes, this is also a coding model. I know that I have also a coding model. I know that I have also a coding model. I know that I have given you several examples that are not given you several examples that are not given you several examples that are not coding. I'm doing that on purpose coding. I'm doing that on purpose coding. I'm doing that on purpose because I think we often assume these because I think we often assume these because I think we often assume these models are good at coding, and that's a models are good at coding, and that's a models are good at coding, and that's a correct assumption here. Fable 5 tests correct assumption here. Fable 5 tests correct assumption here. Fable 5 tests well and is a very, very strong coding well and is a very, very strong coding well and is a very, very strong coding model. It's thoughtful, it's thorough, model. It's thoughtful, it's thorough, model. It's thoughtful, it's thorough, it tackles big task. It's exactly what it tackles big task. It's exactly what it tackles big task. It's exactly what I've been describing for code, right? I've been describing for code, right? I've been describing for code, right? It's something where you can ask it to It's something where you can ask it to It's something where you can ask it to refactor an entire repo and it can do refactor an entire repo and it can do refactor an entire repo and it can do it. So, it. So, it. So, you need to think now in that world, you need to think now in that world, you need to think now in that world, whether you're an engineering or not an whether you're an engineering or not an whether you're an engineering or not an engineering, about what it takes to put engineering, about what it takes to put engineering, about what it takes to put that scale in front of this model. And I that scale in front of this model. And I that scale in front of this model. And I would encourage you along the way, write would encourage you along the way, write would encourage you along the way, write down what done means before you start to down what done means before you start to down what done means before you start to feed the model. Make sure it's really feed the model. Make sure it's really feed the model. Make sure it's really clear. Make sure it's a clear paragraph clear. Make sure it's a clear paragraph clear. Make sure it's a clear paragraph what you want to exist at the end of what you want to exist at the end of what you want to exist at the end of this model's task. Then hand it over, this model's task. Then hand it over, this model's task. Then hand it over, and this is the hard part, walk away.

and this is the hard part, walk away. and this is the hard part, walk away. Let it run. The itch to hover is 3 years Let it run. The itch to hover is 3 years Let it run. The itch to hover is 3 years of trained habit with AI, and that habit of trained habit with AI, and that habit of trained habit with AI, and that habit is what's out of date. It's actually not is what's out of date. It's actually not is what's out of date. It's actually not our business, judgment, our sense of our business, judgment, our sense of our business, judgment, our sense of what's good. It's actually that we have what's good. It's actually that we have what's good. It's actually that we have trained our habits around AI being too trained our habits around AI being too trained our habits around AI being too small. And so, when Fable comes back, small. And so, when Fable comes back, small. And so, when Fable comes back, review it like an owner reviewing a review it like an owner reviewing a review it like an owner reviewing a senior stakeholder's work. Check that senior stakeholder's work. Check that senior stakeholder's work. Check that the work was done correctly. Check that the work was done correctly. Check that the work was done correctly. Check that it's angled right. Check that it it's angled right. Check that it it's angled right. Check that it actually reflects the full scope of what actually reflects the full scope of what actually reflects the full scope of what you asked it to do. you asked it to do. you asked it to do. And then, if you need to, assign it work And then, if you need to, assign it work And then, if you need to, assign it work to do to fix what's what's necessary to do to fix what's what's necessary to do to fix what's what's necessary along the way, right? Assign it revision along the way, right? Assign it revision along the way, right? Assign it revision work. If every finished job work. If every finished job work. If every finished job helps you to run your business faster, helps you to run your business faster, helps you to run your business faster, if every finished job helps you as a if every finished job helps you as a if every finished job helps you as a worker to feel like it lifts a tangible worker to feel like it lifts a tangible worker to feel like it lifts a tangible load off your shoulders cuz it's load off your shoulders cuz it's load off your shoulders cuz it's tackling something that was incredibly tackling something that was incredibly tackling something that was incredibly painful for you that was too big for AI painful for you that was too big for AI painful for you that was too big for AI before, before, before, that's going to matter. That's how you that's going to matter. That's how you that's going to matter. That's how you know you're assigning Fable-sized work. know you're assigning Fable-sized work. know you're assigning Fable-sized work. And that's true whether you're in And that's true whether you're in And that's true whether you're in product management or engineering or product management or engineering or product management or engineering or sales or marketing. Think at Fable sales or marketing. Think at Fable sales or marketing. Think at Fable scale.

scale. scale. You basically have an incredibly You basically have an incredibly You basically have an incredibly powerful magician powerful magician powerful magician sitting there in your computer, and all sitting there in your computer, and all sitting there in your computer, and all you have to do to make it work is to you have to do to make it work is to you have to do to make it work is to give it raw material to work that magic give it raw material to work that magic give it raw material to work that magic on, and give it a sense of what good on, and give it a sense of what good on, and give it a sense of what good looks like. When you want to try Fable, looks like. When you want to try Fable, looks like. When you want to try Fable, don't sit down in front of your don't sit down in front of your don't sit down in front of your computer. Instead, sit down and write computer. Instead, sit down and write computer. Instead, sit down and write the weather around your work. Write the the weather around your work. Write the the weather around your work. Write the stuff that is hovering like rain clouds stuff that is hovering like rain clouds stuff that is hovering like rain clouds over your work that are nasty and gnarly over your work that are nasty and gnarly over your work that are nasty and gnarly and that nobody owns, that everyone on and that nobody owns, that everyone on and that nobody owns, that everyone on your team knows needs to be done. If it your team knows needs to be done. If it your team knows needs to be done. If it makes you sigh and and face-palm, it's makes you sigh and and face-palm, it's makes you sigh and and face-palm, it's going to qualify. Then, take the time to going to qualify. Then, take the time to going to qualify. Then, take the time to say which is the one that's most say which is the one that's most say which is the one that's most valuable to me to get done. valuable to me to get done. valuable to me to get done. Figure out where that data lives, and Figure out where that data lives, and Figure out where that data lives, and then start to build a data pack that you then start to build a data pack that you then start to build a data pack that you can hand over to Fable to do that job. can hand over to Fable to do that job. can hand over to Fable to do that job. It should take you time to assemble that It should take you time to assemble that It should take you time to assemble that data pack, by the way. You may take a data pack, by the way. You may take a data pack, by the way. You may take a couple three or four hours to get ready couple three or four hours to get ready couple three or four hours to get ready to give this model this job, but if you to give this model this job, but if you to give this model this job, but if you do that and it saves you 2 weeks of do that and it saves you 2 weeks of do that and it saves you 2 weeks of work, it's clearly worth it. That's the work, it's clearly worth it. That's the work, it's clearly worth it. That's the level of preparation we need to have to level of preparation we need to have to level of preparation we need to have to prompt this model and that's why I do prompt this model and that's why I do prompt this model and that's why I do not call it a daily driver. It's just not call it a daily driver. It's just not call it a daily driver. It's just it's too expensive and it's too overkill it's too expensive and it's too overkill it's too expensive and it's too overkill to do that. You do it for serious work.

to do that. You do it for serious work. to do that. You do it for serious work. This saves you serious time. If Fable This saves you serious time. If Fable This saves you serious time. If Fable gets you one job a week that saves you gets you one job a week that saves you gets you one job a week that saves you two weeks of time, it's easily worth it. two weeks of time, it's easily worth it. two weeks of time, it's easily worth it. It's easily worth it. And so that's It's easily worth it. And so that's It's easily worth it. And so that's that's what I want you to do. Write down that's what I want you to do. Write down that's what I want you to do. Write down what is stressing you out about work. what is stressing you out about work. what is stressing you out about work. Write down what you want Fable to tackle Write down what you want Fable to tackle Write down what you want Fable to tackle and then make sure that you think about and then make sure that you think about and then make sure that you think about the data Fable needs to do that job and the data Fable needs to do that job and the data Fable needs to do that job and give it to it. give it to it. give it to it. And then make sure you think about the And then make sure you think about the And then make sure you think about the data Fable needs to do that job and give data Fable needs to do that job and give data Fable needs to do that job and give Fable that data. So the fifth part, I Fable that data. So the fifth part, I Fable that data. So the fifth part, I want to be really honest with you. I'm want to be really honest with you. I'm want to be really honest with you. I'm going to talk about how a model that can going to talk about how a model that can going to talk about how a model that can do two weeks of work is going to change do two weeks of work is going to change do two weeks of work is going to change the jobs picture. People will look at it the jobs picture. People will look at it the jobs picture. People will look at it and I've seen takes like this from very and I've seen takes like this from very and I've seen takes like this from very prominent content creators and they will prominent content creators and they will prominent content creators and they will say, "This is going to be a job killer." say, "This is going to be a job killer." say, "This is going to be a job killer." The only jobs that this model is going The only jobs that this model is going The only jobs that this model is going to kill are are jobs that are strict to kill are are jobs that are strict to kill are are jobs that are strict execution, where there is zero judgment. execution, where there is zero judgment. execution, where there is zero judgment. And part of why I'm saying that is that And part of why I'm saying that is that And part of why I'm saying that is that this model needs a lot of care and this model needs a lot of care and this model needs a lot of care and feeding. You need model managers for feeding. You need model managers for feeding. You need model managers for this model to do well. You need people this model to do well. You need people this model to do well. You need people who will be able to say, "This is the who will be able to say, "This is the who will be able to say, "This is the scope and scale and direction and this scope and scale and direction and this scope and scale and direction and this is the data that we're feeding this is the data that we're feeding this is the data that we're feeding this model to get the work done." Now, does model to get the work done." Now, does model to get the work done." Now, does that mean that we're seeing a tremendous that mean that we're seeing a tremendous that mean that we're seeing a tremendous disruption in how we spend our days with disruption in how we spend our days with disruption in how we spend our days with AI and we should expect more along the AI and we should expect more along the AI and we should expect more along the way as these models scale? Yes. Does it way as these models scale? Yes. Does it way as these models scale? Yes. Does it mean that we need to abstract ourselves mean that we need to abstract ourselves mean that we need to abstract ourselves and operate at a different level and and operate at a different level and and operate at a different level and think of ourselves, even if we're not think of ourselves, even if we're not think of ourselves, even if we're not people managers, as model managers?

people managers, as model managers? people managers, as model managers? Absolutely. But, this model still needs Absolutely. But, this model still needs Absolutely. But, this model still needs to be directed, it still needs to be to be directed, it still needs to be to be directed, it still needs to be aimed, it needs to be fed, it needs to aimed, it needs to be fed, it needs to aimed, it needs to be fed, it needs to be judged, it needs people to review the be judged, it needs people to review the be judged, it needs people to review the work it did. And so the only people who work it did. And so the only people who work it did. And so the only people who should be worried about AI and jobs are should be worried about AI and jobs are should be worried about AI and jobs are the people who are doing manual tasks the people who are doing manual tasks the people who are doing manual tasks that really could have been automated that really could have been automated that really could have been automated anytime in the last 10 years and maybe anytime in the last 10 years and maybe anytime in the last 10 years and maybe they were. You can watch this video and they were. You can watch this video and they were. You can watch this video and you can say and to yourself, I want to you can say and to yourself, I want to you can say and to yourself, I want to use Fable. How can I do it in my role? use Fable. How can I do it in my role? use Fable. How can I do it in my role? And pretty much no one's going to stop And pretty much no one's going to stop And pretty much no one's going to stop you. If you have the imagination and the you. If you have the imagination and the you. If you have the imagination and the courage to think differently about what courage to think differently about what courage to think differently about what you can do because you did the exercise, you can do because you did the exercise, you can do because you did the exercise, you wrote down the things that are you wrote down the things that are you wrote down the things that are stressing you out, you gave it to Fable, stressing you out, you gave it to Fable, stressing you out, you gave it to Fable, Fable did this work and now it's working Fable did this work and now it's working Fable did this work and now it's working better, no one's going to stop you from better, no one's going to stop you from better, no one's going to stop you from doing that. In fact, that's that's an doing that. In fact, that's that's an doing that. In fact, that's that's an invitation to a promotion these days. invitation to a promotion these days. invitation to a promotion these days. That's an invitation to career success. That's an invitation to career success. That's an invitation to career success. And yes, if you're a leader, it's going And yes, if you're a leader, it's going And yes, if you're a leader, it's going to require you to think differently to require you to think differently to require you to think differently about data availability. It's If Fable about data availability. It's If Fable about data availability. It's If Fable is going to require you to think is going to require you to think is going to require you to think differently about token economics.

differently about token economics. differently about token economics. There's all kinds of leader questions There's all kinds of leader questions There's all kinds of leader questions that this opens up as well. But the that this opens up as well. But the that this opens up as well. But the larger thing I want you to take away is larger thing I want you to take away is larger thing I want you to take away is that if we just reduce this to this that if we just reduce this to this that if we just reduce this to this assumption that the magician that is assumption that the magician that is assumption that the magician that is this model can magically do our jobs, we this model can magically do our jobs, we this model can magically do our jobs, we are guilty of simplistic thinking. We are guilty of simplistic thinking. We are guilty of simplistic thinking. We are guilty of over assuming what this are guilty of over assuming what this are guilty of over assuming what this model can do. A model that can do a lot model can do. A model that can do a lot model can do. A model that can do a lot of work at a pretty high quality bar is of work at a pretty high quality bar is of work at a pretty high quality bar is a very, very powerful thing and a very, very powerful thing and a very, very powerful thing and simultaneously simultaneously simultaneously not going to be a model that can just not going to be a model that can just not going to be a model that can just lift and shift out jobs at will because lift and shift out jobs at will because lift and shift out jobs at will because it needs that direction, because it it needs that direction, because it it needs that direction, because it needs someone to manage all the data, needs someone to manage all the data, needs someone to manage all the data, because it needs judgment about what because it needs judgment about what because it needs judgment about what works. These models take a ton of care works. These models take a ton of care works. These models take a ton of care and feeding. The people I know working and feeding. The people I know working and feeding. The people I know working in AI, working with AI models not at in AI, working with AI models not at in AI, working with AI models not at hyperscalers are working harder than hyperscalers are working harder than hyperscalers are working harder than they've ever worked in their lives. They they've ever worked in their lives. They they've ever worked in their lives. They are not working themselves out of a job, are not working themselves out of a job, are not working themselves out of a job, they are working themselves into new they are working themselves into new they are working themselves into new jobs because these models take that kind jobs because these models take that kind jobs because these models take that kind of care. So, if this is you and you are of care. So, if this is you and you are of care. So, if this is you and you are worried about worried about worried about AI layoffs and you look at the AI layoffs and you look at the AI layoffs and you look at the capability and my my description of this capability and my my description of this capability and my my description of this model is larger and the fact that this model is larger and the fact that this model is larger and the fact that this has incredible benchmark results like to has incredible benchmark results like to has incredible benchmark results like to be honest with you, I'm going to have to be honest with you, I'm going to have to be honest with you, I'm going to have to change my benchmarks because this model change my benchmarks because this model change my benchmarks because this model has already maxed my benchmark. If has already maxed my benchmark. If has already maxed my benchmark. If you're worried by that, I would invite you're worried by that, I would invite you're worried by that, I would invite you to instead think about asking the you to instead think about asking the you to instead think about asking the model for bigger tasks and what that model for bigger tasks and what that model for bigger tasks and what that asks of us. If we can do that asks of us. If we can do that asks of us. If we can do that collectively, we are going to be in a collectively, we are going to be in a collectively, we are going to be in a much better spot to manage these models much better spot to manage these models much better spot to manage these models and the impact that they can have on and the impact that they can have on and the impact that they can have on work. Because we'll be asking ourselves,

work. Because we'll be asking ourselves, work. Because we'll be asking ourselves, how can we tackle really gnarly problems how can we tackle really gnarly problems how can we tackle really gnarly problems with these models that humans struggle with these models that humans struggle with these models that humans struggle with? How can we make a trade where the with? How can we make a trade where the with? How can we make a trade where the pain, the frustration of working through pain, the frustration of working through pain, the frustration of working through 500 pages looking for typos, and working 500 pages looking for typos, and working 500 pages looking for typos, and working through 500 pages looking for through 500 pages looking for through 500 pages looking for consistency issues, and working through consistency issues, and working through consistency issues, and working through 40,000 customer records, that's painful. 40,000 customer records, that's painful. 40,000 customer records, that's painful. How can we trade that pain to a model How can we trade that pain to a model How can we trade that pain to a model that's good at it and in return get time that's good at it and in return get time that's good at it and in return get time back to do more high-leverage stuff. back to do more high-leverage stuff. back to do more high-leverage stuff. That's the invitation of models like That's the invitation of models like That's the invitation of models like Fable. Use Fable to eat the pain in your Fable. Use Fable to eat the pain in your Fable. Use Fable to eat the pain in your business. If you do, you are not going business. If you do, you are not going business. If you do, you are not going to regret it. In fact, you, even as an to regret it. In fact, you, even as an to regret it. In fact, you, even as an individual contributor, are going to be individual contributor, are going to be individual contributor, are going to be on the road to an incredible career on the road to an incredible career on the road to an incredible career uplift. Because these models effectively uplift. Because these models effectively uplift. Because these models effectively will be like a personal magician in your will be like a personal magician in your will be like a personal magician in your pocket and will make you incredibly pocket and will make you incredibly pocket and will make you incredibly powerful at work. And I'm not saying powerful at work. And I'm not saying powerful at work. And I'm not saying that because I want, you know, to hype that because I want, you know, to hype that because I want, you know, to hype this and say, "Oh, it's going to be this and say, "Oh, it's going to be this and say, "Oh, it's going to be amazing." It's just a fact. Like these amazing." It's just a fact. Like these amazing." It's just a fact. Like these models have that kind of power. I have models have that kind of power. I have models have that kind of power. I have seen hundreds of examples and real seen hundreds of examples and real seen hundreds of examples and real stories in my Substack community from stories in my Substack community from stories in my Substack community from people who had done that with weaker people who had done that with weaker people who had done that with weaker models than Fable, and Fable is stronger models than Fable, and Fable is stronger models than Fable, and Fable is stronger yet. So, it's possible. You just have to yet. So, it's possible. You just have to yet. So, it's possible. You just have to imagine bigger. If this was useful to imagine bigger. If this was useful to imagine bigger. If this was useful to you, if you want to go deeper and you, if you want to go deeper and you, if you want to go deeper and understand what does What does understand what does What does understand what does What does imagination look like? What do I do imagination look like? What do I do imagination look like? What do I do differently with this model? That is differently with this model? That is differently with this model? That is what the Substack is for. I have a whole what the Substack is for. I have a whole what the Substack is for. I have a whole job spec that I give you to like go job spec that I give you to like go job spec that I give you to like go through and tackle if you want to tackle through and tackle if you want to tackle through and tackle if you want to tackle a big a big task with Fable. I have a a big a big task with Fable. I have a a big a big task with Fable. I have a entire guide on stripping out your

entire guide on stripping out your entire guide on stripping out your prompts and adjusting them for Fable cuz prompts and adjusting them for Fable cuz prompts and adjusting them for Fable cuz you're going to have to do that, too. you're going to have to do that, too. you're going to have to do that, too. I've got a a set of Fable specific I've got a a set of Fable specific I've got a a set of Fable specific skills that help when Fable is skills that help when Fable is skills that help when Fable is struggling with something for you. For struggling with something for you. For struggling with something for you. For example, writing. Like if you need to example, writing. Like if you need to example, writing. Like if you need to get Fable to read your voice, what does get Fable to read your voice, what does get Fable to read your voice, what does that look like? If you need to get Fable that look like? If you need to get Fable that look like? If you need to get Fable to understand your house style with to understand your house style with to understand your house style with PowerPoint and Excel. If you need to get PowerPoint and Excel. If you need to get PowerPoint and Excel. If you need to get Fable to understand how you structured Fable to understand how you structured Fable to understand how you structured your data. How do you communicate that your data. How do you communicate that your data. How do you communicate that when you're not using the traditional when you're not using the traditional when you're not using the traditional pro- I go into all of that detail on the pro- I go into all of that detail on the pro- I go into all of that detail on the Substack so that you can take this and Substack so that you can take this and Substack so that you can take this and actually level up your productivity with actually level up your productivity with actually level up your productivity with the biggest model in the world, the best the biggest model in the world, the best the biggest model in the world, the best coder in the world. It is the best model coder in the world. It is the best model coder in the world. It is the best model in the world. Let's just not sugarcoat in the world. Let's just not sugarcoat in the world. Let's just not sugarcoat it. But how do you use it? That's what it. But how do you use it? That's what it. But how do you use it? That's what matters. I'm going to have more very matters. I'm going to have more very matters. I'm going to have more very clear-eyed analysis coming up soon. We clear-eyed analysis coming up soon. We clear-eyed analysis coming up soon. We have some exciting stuff from OpenAI have some exciting stuff from OpenAI have some exciting stuff from OpenAI coming. And we have other models in the coming. And we have other models in the coming. And we have other models in the landscape, too. We're going to talk landscape, too. We're going to talk landscape, too. We're going to talk about a wider range of models beyond the about a wider range of models beyond the about a wider range of models beyond the traditional OpenAI and Claude models, as traditional OpenAI and Claude models, as traditional OpenAI and Claude models, as well. So, lots more coming. Subscribe, well. So, lots more coming. Subscribe, well. So, lots more coming. Subscribe, and I'll see you next time.