OpenAI Pays $280,000 For This Job. You Don't Have To Be An Engineer.

Every lab that promises autonomous Every lab that promises autonomous intelligence is hiring humans as fast as intelligence is hiring humans as fast as intelligence is hiring humans as fast as it possibly can go to sit inside it possibly can go to sit inside it possibly can go to sit inside companies like yours and make that companies like yours and make that companies like yours and make that intelligence work. That is a confession, intelligence work. That is a confession, intelligence work. That is a confession, right? It's a confession from the labs right? It's a confession from the labs right? It's a confession from the labs about how much they need people. about how much they need people. about how much they need people. Anthropic said it would train tens of Anthropic said it would train tens of Anthropic said it would train tens of thousands of engineers to go install AI thousands of engineers to go install AI thousands of engineers to go install AI inside banks and airlines and insurers. inside banks and airlines and insurers. inside banks and airlines and insurers. Do you know how many actually trained? Do you know how many actually trained? Do you know how many actually trained? 86. 86. And that gap is the entire story 86. 86. And that gap is the entire story 86. 86. And that gap is the entire story of the hottest job in AI. And it's why of the hottest job in AI. And it's why of the hottest job in AI. And it's why this job pays what it pays. Open AI is this job pays what it pays. Open AI is this job pays what it pays. Open AI is hiring forward deployed engineers at up hiring forward deployed engineers at up hiring forward deployed engineers at up to $280,000 in base pay plus equity. And to $280,000 in base pay plus equity. And to $280,000 in base pay plus equity. And they're not the only ones. Handshake has they're not the only ones. Handshake has they're not the only ones. Handshake has posted the same title at $300,000. Those posted the same title at $300,000. Those posted the same title at $300,000. Those numbers have a lot of us trying to numbers have a lot of us trying to numbers have a lot of us trying to figure out what a forward deployed figure out what a forward deployed figure out what a forward deployed engineer actually does and whether they engineer actually does and whether they engineer actually does and whether they have any realistic route into the work. have any realistic route into the work. have any realistic route into the work. So, this is what I made this video to So, this is what I made this video to So, this is what I made this video to help you answer. By the end of this help you answer. By the end of this help you answer. By the end of this video, you should be able to look at the video, you should be able to look at the video, you should be able to look at the job you have now and see which part of job you have now and see which part of job you have now and see which part of the FTE skill set you may already have, the FTE skill set you may already have, the FTE skill set you may already have, whether you're an engineer or not, which whether you're an engineer or not, which whether you're an engineer or not, which part you're missing and what you could part you're missing and what you could part you're missing and what you could build in the next month or so to make build in the next month or so to make build in the next month or so to make the case for an interview. AI is a the case for an interview. AI is a the case for an interview. AI is a general purpose capability and the last general purpose capability and the last general purpose capability and the last mile in AI is really hard. FTEEs bridge mile in AI is really hard. FTEEs bridge mile in AI is really hard. FTEEs bridge that gap. That's why FTEEs matter. Let's that gap. That's why FTEEs matter. Let's that gap. That's why FTEEs matter. Let's say you run a claims operations at a say you run a claims operations at a say you run a claims operations at a regional insurance company. Yes, we're regional insurance company. Yes, we're regional insurance company. Yes, we're getting specific because specifics helps getting specific because specifics helps getting specific because specifics helps us generalize principles we can all us generalize principles we can all us generalize principles we can all learn from. We're going to call you Maya learn from. We're going to call you Maya learn from. We're going to call you Maya in this story. Your CEO has seen enough

in this story. Your CEO has seen enough in this story. Your CEO has seen enough AI presentations to know the technology AI presentations to know the technology AI presentations to know the technology can read documents and now he wants can read documents and now he wants can read documents and now he wants claims processed twice as fast. By the claims processed twice as fast. By the claims processed twice as fast. By the way, if you're wondering where I got way, if you're wondering where I got way, if you're wondering where I got this story, I have talked to these CEOs this story, I have talked to these CEOs this story, I have talked to these CEOs before and I've talked to Mayers before, before and I've talked to Mayers before, before and I've talked to Mayers before, too. Now, this sounds like a clear goal too. Now, this sounds like a clear goal too. Now, this sounds like a clear goal from the CEO, but it's not a buildable from the CEO, but it's not a buildable from the CEO, but it's not a buildable job. as anyone in engineering or product job. as anyone in engineering or product job. as anyone in engineering or product will tell you because a claim might will tell you because a claim might will tell you because a claim might include a policy and photographs and include a policy and photographs and include a policy and photographs and repair estimates and medical information repair estimates and medical information repair estimates and medical information and police reports and fraud review and and police reports and fraud review and and police reports and fraud review and customer calls and several different customer calls and several different customer calls and several different approvals. If if our friend Maya gives approvals. If if our friend Maya gives approvals. If if our friend Maya gives all of that to the technical team and all of that to the technical team and all of that to the technical team and all Mia says is speed up claims with AI, all Mia says is speed up claims with AI, all Mia says is speed up claims with AI, Maya hasn't done a great job and the Maya hasn't done a great job and the Maya hasn't done a great job and the team has to make most of the product team has to make most of the product team has to make most of the product decisions for her. Maya is a great PM. decisions for her. Maya is a great PM. decisions for her. Maya is a great PM. So Maya instead pulls up actual files. So Maya instead pulls up actual files. So Maya instead pulls up actual files. She takes one claim that moved through She takes one claim that moved through She takes one claim that moved through intake in a day and another that sat intake in a day and another that sat intake in a day and another that sat there for almost a week as there for almost a week as there for almost a week as counterexamples. Right? In the slow counterexamples. Right? In the slow counterexamples. Right? In the slow file, the repair estimate arrived on say file, the repair estimate arrived on say file, the repair estimate arrived on say a Tuesday, but the signature page was a Tuesday, but the signature page was a Tuesday, but the signature page was missing and nobody noticed until Friday.

missing and nobody noticed until Friday. missing and nobody noticed until Friday. 3 days disappeared before an adjuster 3 days disappeared before an adjuster 3 days disappeared before an adjuster made any kind of insurance decision at made any kind of insurance decision at made any kind of insurance decision at all. And she finds much harder problems all. And she finds much harder problems all. And she finds much harder problems in the files as well. Right? Injury in the files as well. Right? Injury in the files as well. Right? Injury claims can take weeks. Fraud review is claims can take weeks. Fraud review is claims can take weeks. Fraud review is very expensive. Deciding whether to pay very expensive. Deciding whether to pay very expensive. Deciding whether to pay a complicated claim is clearly worth a a complicated claim is clearly worth a a complicated claim is clearly worth a lot of money, but those cases really lot of money, but those cases really lot of money, but those cases really vary. They need a lot of experienced vary. They need a lot of experienced vary. They need a lot of experienced judgment and a wrong decision can send judgment and a wrong decision can send judgment and a wrong decision can send money out the door or create a lot of money out the door or create a lot of money out the door or create a lot of legal exposure for an AI initiative. The legal exposure for an AI initiative. The legal exposure for an AI initiative. The missing document problem has a very missing document problem has a very missing document problem has a very different shape. And this is where we different shape. And this is where we different shape. And this is where we get to the generalizable principle and get to the generalizable principle and get to the generalizable principle and why I'm telling you this story. Because why I'm telling you this story. Because why I'm telling you this story. Because you see, the missing document problem you see, the missing document problem you see, the missing document problem happens all the time. It sits at the happens all the time. It sits at the happens all the time. It sits at the front of the process and everything front of the process and everything front of the process and everything behind it waits on getting that right. behind it waits on getting that right. behind it waits on getting that right. If this company receives say a few If this company receives say a few If this company receives say a few thousand claims a month and several thousand claims a month and several thousand claims a month and several hundred say 600 700 arrive incomplete hundred say 600 700 arrive incomplete hundred say 600 700 arrive incomplete and each one of those loses multiple and each one of those loses multiple and each one of those loses multiple days before anybody even notices and days before anybody even notices and days before anybody even notices and reconts the customer. That's say 1,800 reconts the customer. That's say 1,800 reconts the customer. That's say 1,800 19 2,000 days of claims sitting still 19 2,000 days of claims sitting still 19 2,000 days of claims sitting still every month. And here's what kills me on every month. And here's what kills me on every month. And here's what kills me on that. the input to make a good decision that. the input to make a good decision that. the input to make a good decision and contact the customer and say, "Hey, and contact the customer and say, "Hey, and contact the customer and say, "Hey, we need more docs." That already exists we need more docs." That already exists we need more docs." That already exists in the claim packet. Any adjuster can in the claim packet. Any adjuster can in the claim packet. Any adjuster can check and get that answer really fast check and get that answer really fast check and get that answer really fast and a flag can be sent out before any and a flag can be sent out before any and a flag can be sent out before any kind of delay happens. That rationale is kind of delay happens. That rationale is kind of delay happens. That rationale is exactly why Maya picks intake to work on exactly why Maya picks intake to work on exactly why Maya picks intake to work on in our story. And it's a it's a in our story. And it's a it's a in our story. And it's a it's a rationale I've seen play out rationale I've seen play out rationale I've seen play out specifically in the insurance industry

specifically in the insurance industry specifically in the insurance industry before, but it's also a little flag hint before, but it's also a little flag hint before, but it's also a little flag hint of how FTEES grab these skills and apply of how FTEES grab these skills and apply of how FTEES grab these skills and apply them other places. So if you're looking them other places. So if you're looking them other places. So if you're looking for a larger rule of thumb here, think for a larger rule of thumb here, think for a larger rule of thumb here, think about it as Maya finds a point of about it as Maya finds a point of about it as Maya finds a point of leverage for the AI work that she wants leverage for the AI work that she wants leverage for the AI work that she wants to do. And finding leverage is a to do. And finding leverage is a to do. And finding leverage is a generalizable FTE skill. In this case, generalizable FTE skill. In this case, generalizable FTE skill. In this case, Maya is looking for the point where a Maya is looking for the point where a Maya is looking for the point where a relatively small build moves the largest relatively small build moves the largest relatively small build moves the largest amount of work without giving the model amount of work without giving the model amount of work without giving the model a dangerous amount of authority. This is a dangerous amount of authority. This is a dangerous amount of authority. This is leverage. It's leverage applied inside a leverage. It's leverage applied inside a leverage. It's leverage applied inside a workflow. The service only has to notice workflow. The service only has to notice workflow. The service only has to notice that there's an incomplete file when it that there's an incomplete file when it that there's an incomplete file when it arrives. It just points to the missing arrives. It just points to the missing arrives. It just points to the missing item and it prepares communication item and it prepares communication item and it prepares communication appropriately. That's it. Everything appropriately. That's it. Everything appropriately. That's it. Everything else that's more complicated, injury else that's more complicated, injury else that's more complicated, injury decisions, suspected fraud, all of that decisions, suspected fraud, all of that decisions, suspected fraud, all of that remains with real people in our story. remains with real people in our story. remains with real people in our story. Now, choosing that point in the Now, choosing that point in the Now, choosing that point in the workflow, regardless of whether you work workflow, regardless of whether you work workflow, regardless of whether you work in insurance or you work in any other in insurance or you work in any other in insurance or you work in any other industry, that is the FTE skill that industry, that is the FTE skill that industry, that is the FTE skill that people are desperate for. The same month people are desperate for. The same month people are desperate for. The same month of engineering can remove 1,800 days of of engineering can remove 1,800 days of of engineering can remove 1,800 days of waiting or make a rare edge case waiting or make a rare edge case waiting or make a rare edge case slightly better. Like, you're applying slightly better. Like, you're applying slightly better. Like, you're applying the engineering either way. A company the engineering either way. A company the engineering either way. A company may have 20, 30, you know, hundreds of may have 20, 30, you know, hundreds of may have 20, 30, you know, hundreds of plausible places to add AI. The FDE plausible places to add AI. The FDE plausible places to add AI. The FDE needs to work out which delay occurs needs to work out which delay occurs needs to work out which delay occurs often enough to matter, which pain point often enough to matter, which pain point often enough to matter, which pain point is painful enough, and which fix is painful enough, and which fix is painful enough, and which fix releases a bunch of work farther down

releases a bunch of work farther down releases a bunch of work farther down the process, how you unbottleneck stuff. the process, how you unbottleneck stuff. the process, how you unbottleneck stuff. I used to do this very manually when I I used to do this very manually when I I used to do this very manually when I did Kaizen black belt process mapping, did Kaizen black belt process mapping, did Kaizen black belt process mapping, and you map out a process and find and you map out a process and find and you map out a process and find bottlenecks. These days, you're taking bottlenecks. These days, you're taking bottlenecks. These days, you're taking similar skills out of a formal Kaizen similar skills out of a formal Kaizen similar skills out of a formal Kaizen context and you're putting them into the context and you're putting them into the context and you're putting them into the real world where you're trying to figure real world where you're trying to figure real world where you're trying to figure out how you can apply AI to debottleneck out how you can apply AI to debottleneck out how you can apply AI to debottleneck real work. And the advantage of picking real work. And the advantage of picking real work. And the advantage of picking work like this is that now Maya has work like this is that now Maya has work like this is that now Maya has something she can measure. She can something she can measure. She can something she can measure. She can really quickly track how long it takes really quickly track how long it takes really quickly track how long it takes to spot missing material, how often the to spot missing material, how often the to spot missing material, how often the AI service she's built raises a false AI service she's built raises a false AI service she's built raises a false alarm or misses a document, and how much alarm or misses a document, and how much alarm or misses a document, and how much time the adjuster then spends checking time the adjuster then spends checking time the adjuster then spends checking it. And the project can then be measured it. And the project can then be measured it. And the project can then be measured appropriately, graded as a success, and appropriately, graded as a success, and appropriately, graded as a success, and she can move on to the next AI project. she can move on to the next AI project. she can move on to the next AI project. And so when we think of Maya's story, And so when we think of Maya's story, And so when we think of Maya's story, that's the kind of work I want you to that's the kind of work I want you to that's the kind of work I want you to picture when you hear forward deployed picture when you hear forward deployed picture when you hear forward deployed engineer. And and by the way, if you engineer. And and by the way, if you engineer. And and by the way, if you hear that and say that sounds really hear that and say that sounds really hear that and say that sounds really producty, that's kind of the point. One producty, that's kind of the point. One producty, that's kind of the point. One of the interesting things about forward of the interesting things about forward of the interesting things about forward deployed engineers is it mixes a lot of deployed engineers is it mixes a lot of deployed engineers is it mixes a lot of product in with a bunch of engineering product in with a bunch of engineering product in with a bunch of engineering technical skills. The title can move technical skills. The title can move technical skills. The title can move around. At one company, it can mean a around. At one company, it can mean a around. At one company, it can mean a very strong software engineer who works very strong software engineer who works very strong software engineer who works directly with customers. Somewhere else, directly with customers. Somewhere else, directly with customers. Somewhere else, it may more look like applied AI or it may more look like applied AI or it may more look like applied AI or implementation or a technical product implementation or a technical product implementation or a technical product role. But the common part is that you role. But the common part is that you role. But the common part is that you stay with the problem from choosing stay with the problem from choosing stay with the problem from choosing where to start all the way through the where to start all the way through the where to start all the way through the working system because you're committed

working system because you're committed working system because you're committed to enabling the capability inside a to enabling the capability inside a to enabling the capability inside a complex real world environment. Every complex real world environment. Every complex real world environment. Every time I have run enterprise technology time I have run enterprise technology time I have run enterprise technology deployments and I've run a bunch of them deployments and I've run a bunch of them deployments and I've run a bunch of them over the years, they always run into the over the years, they always run into the over the years, they always run into the same wall. Buying the product from the same wall. Buying the product from the same wall. Buying the product from the sales guy, that's the easy part. Then sales guy, that's the easy part. Then sales guy, that's the easy part. Then someone has to get access to the data someone has to get access to the data someone has to get access to the data and understand the policy nobody wrote and understand the policy nobody wrote and understand the policy nobody wrote down and fit the tool into the way down and fit the tool into the way down and fit the tool into the way people actually work and deal with the people actually work and deal with the people actually work and deal with the first pain point that comes back from a first pain point that comes back from a first pain point that comes back from a real customer. All of this is something real customer. All of this is something real customer. All of this is something we're familiar with in enterprise tool we're familiar with in enterprise tool we're familiar with in enterprise tool deployment. But AI makes that job even deployment. But AI makes that job even deployment. But AI makes that job even harder because the software is no longer harder because the software is no longer harder because the software is no longer just passively moving data around. It just passively moving data around. It just passively moving data around. It might be making intelligent decisions, might be making intelligent decisions, might be making intelligent decisions, right? It can decide what's missing in a right? It can decide what's missing in a right? It can decide what's missing in a document. It can draft a response. it document. It can draft a response. it document. It can draft a response. it can take actions and write in other can take actions and write in other can take actions and write in other systems. So small misunderstandings or systems. So small misunderstandings or systems. So small misunderstandings or small pain points can travel a lot small pain points can travel a lot small pain points can travel a lot farther in the age of AI. And that farther in the age of AI. And that farther in the age of AI. And that brings me to FTEEs. That is why FTEEs brings me to FTEEs. That is why FTEEs brings me to FTEEs. That is why FTEEs are so important in AI software because are so important in AI software because are so important in AI software because Maya has to choose where the technology Maya has to choose where the technology Maya has to choose where the technology can do the most work with the least can do the most work with the least can do the most work with the least potential for downstream harm. And so potential for downstream harm. And so potential for downstream harm. And so she has to take this big challenge she has to take this big challenge she has to take this big challenge around cutting claim time and she has to around cutting claim time and she has to around cutting claim time and she has to decide where is the right point to apply decide where is the right point to apply decide where is the right point to apply AI to get that done. And if she does her AI to get that done. And if she does her AI to get that done. And if she does her job well by the time she's finished she job well by the time she's finished she job well by the time she's finished she and the engineers side by side can build and the engineers side by side can build and the engineers side by side can build and the business gets really clean and the business gets really clean and the business gets really clean measurable results that it can check. In

measurable results that it can check. In measurable results that it can check. In other words, Maya is a translator. Maya other words, Maya is a translator. Maya other words, Maya is a translator. Maya is a translator from the vague large is a translator from the vague large is a translator from the vague large claims CEOs make, from the generalized claims CEOs make, from the generalized claims CEOs make, from the generalized capabilities AI models have, and she capabilities AI models have, and she capabilities AI models have, and she takes that larger context and applies it takes that larger context and applies it takes that larger context and applies it and translates it specifically for her and translates it specifically for her and translates it specifically for her codebase, specifically for her product codebase, specifically for her product codebase, specifically for her product context, specifically for her customers, context, specifically for her customers, context, specifically for her customers, so that value is realized. That's what so that value is realized. That's what so that value is realized. That's what an FTE does. Now, there are roughly an FTE does. Now, there are roughly an FTE does. Now, there are roughly three parts to the FTE job, and almost three parts to the FTE job, and almost three parts to the FTE job, and almost nobody starts equally strong in all nobody starts equally strong in all nobody starts equally strong in all three. So, if you feel like you're three. So, if you feel like you're three. So, if you feel like you're behind as you listen to this story, behind as you listen to this story, behind as you listen to this story, you're probably not. An FTE first has to you're probably not. An FTE first has to you're probably not. An FTE first has to understand the business well enough to understand the business well enough to understand the business well enough to find the leverage point, build and find the leverage point, build and find the leverage point, build and inspect the system, and then stay around inspect the system, and then stay around inspect the system, and then stay around after the launch long enough to learn after the launch long enough to learn after the launch long enough to learn whether people actually use it and whether people actually use it and whether people actually use it and whether the result is worth the cost. whether the result is worth the cost. whether the result is worth the cost. Software engineers may find that the Software engineers may find that the Software engineers may find that the building part is actually the part building part is actually the part building part is actually the part they're least worried about. They've got they're least worried about. They've got they're least worried about. They've got that part right. But the harder move for that part right. But the harder move for that part right. But the harder move for them is getting close enough to the them is getting close enough to the them is getting close enough to the customer to understand why, you know, a customer to understand why, you know, a customer to understand why, you know, a particular document issue matters, why a particular document issue matters, why a particular document issue matters, why a missing field matters, and then figuring missing field matters, and then figuring missing field matters, and then figuring out how to connect the build out how to connect the build out how to connect the build architecture they're working on to money architecture they're working on to money architecture they're working on to money and time and risk and capacity. So, if and time and risk and capacity. So, if and time and risk and capacity. So, if you're coming from operations, let's say you're coming from operations, let's say you're coming from operations, let's say you're not from engineering, let's say you're not from engineering, let's say you're not from engineering, let's say you're from product or or maybe from you're from product or or maybe from you're from product or or maybe from consulting, you may have the opposite consulting, you may have the opposite consulting, you may have the opposite feeling to those engineers. You may know feeling to those engineers. You may know feeling to those engineers. You may know why a clean process diagram doesn't fit

why a clean process diagram doesn't fit why a clean process diagram doesn't fit the real world. You may know which the real world. You may know which the real world. You may know which exceptions matter to customers, but your exceptions matter to customers, but your exceptions matter to customers, but your work is now getting technically deep work is now getting technically deep work is now getting technically deep enough to actually build the solution, enough to actually build the solution, enough to actually build the solution, inspect what your AI is working on, and inspect what your AI is working on, and inspect what your AI is working on, and understand where that solution can fail. understand where that solution can fail. understand where that solution can fail. If you're coming from solutions If you're coming from solutions If you're coming from solutions engineering or implementation or even engineering or implementation or even engineering or implementation or even sales engineering, you're often sales engineering, you're often sales engineering, you're often somewhere in the middle. You already somewhere in the middle. You already somewhere in the middle. You already have to translate between customers and have to translate between customers and have to translate between customers and systems. And so the question for you is systems. And so the question for you is systems. And so the question for you is whether you can remain responsible after whether you can remain responsible after whether you can remain responsible after the traditional handoff point for those the traditional handoff point for those the traditional handoff point for those roles after configuration after handoff roles after configuration after handoff roles after configuration after handoff when you have to actually own the result when you have to actually own the result when you have to actually own the result and the bad cases start coming in and and the bad cases start coming in and and the bad cases start coming in and you have to change your design if you're you have to change your design if you're you have to change your design if you're trying to practice demonstrating trying to practice demonstrating trying to practice demonstrating workflows and business judgment. I would workflows and business judgment. I would workflows and business judgment. I would not begin by digging into an entire job. not begin by digging into an entire job. not begin by digging into an entire job. And this is something I often see as a And this is something I often see as a And this is something I often see as a failure mode with PMs as well. Get failure mode with PMs as well. Get failure mode with PMs as well. Get specific. I would figure out how you specific. I would figure out how you specific. I would figure out how you pull the last 10 instances of the pull the last 10 instances of the pull the last 10 instances of the problem you're working on, how you get problem you're working on, how you get problem you're working on, how you get some recent specific data. And then I some recent specific data. And then I some recent specific data. And then I would start to dig into those issues and would start to dig into those issues and would start to dig into those issues and understand really specifically what's understand really specifically what's understand really specifically what's going on and why, what corrections going on and why, what corrections going on and why, what corrections already exist, where there are pain already exist, where there are pain already exist, where there are pain points in those specific common issues points in those specific common issues points in those specific common issues that you can go after. And as soon as that you can go after. And as soon as that you can go after. And as soon as you start to find recent issues, you're you start to find recent issues, you're you start to find recent issues, you're going to start to classify them and then going to start to classify them and then going to start to classify them and then do some rough math, right? differences do some rough math, right? differences do some rough math, right? differences you find across a sample of 10 or 20.

you find across a sample of 10 or 20. you find across a sample of 10 or 20. Count how many cases are hitting the Count how many cases are hitting the Count how many cases are hitting the problem and you start to be able to problem and you start to be able to problem and you start to be able to estimate rough notes how much problem estimate rough notes how much problem estimate rough notes how much problem you're solving if you solve some of you're solving if you solve some of you're solving if you solve some of those specific pain points. You don't those specific pain points. You don't those specific pain points. You don't need a perfect financial model to be an need a perfect financial model to be an need a perfect financial model to be an FTE person. You just need enough FTE person. You just need enough FTE person. You just need enough evidence and you need to know how to get evidence and you need to know how to get evidence and you need to know how to get it so you can talk specifically to the it so you can talk specifically to the it so you can talk specifically to the painoint. Right? Because in in our story painoint. Right? Because in in our story painoint. Right? Because in in our story with Maya, Maya can now compare with Maya, Maya can now compare with Maya, Maya can now compare incomplete intake with fraud review incomplete intake with fraud review incomplete intake with fraud review because Maya did rough math what each of because Maya did rough math what each of because Maya did rough math what each of those specifics are. And if you're those specifics are. And if you're those specifics are. And if you're wondering if you can get that kind of wondering if you can get that kind of wondering if you can get that kind of data in interviews, typically when data in interviews, typically when data in interviews, typically when you're given cases to work on, they you're given cases to work on, they you're given cases to work on, they specifically provide that type of data specifically provide that type of data specifically provide that type of data so they can see if you go after it. The so they can see if you go after it. The so they can see if you go after it. The next step, if you can, is to sit next to next step, if you can, is to sit next to next step, if you can, is to sit next to the person doing the work and watch the the person doing the work and watch the the person doing the work and watch the process happen. Right? One of the things process happen. Right? One of the things process happen. Right? One of the things that I find most valuable that I've that I find most valuable that I've that I find most valuable that I've found valuable in product and it's a found valuable in product and it's a found valuable in product and it's a very producty thing to do. Just sit next very producty thing to do. Just sit next very producty thing to do. Just sit next to the customer, sit next to the client, to the customer, sit next to the client, to the customer, sit next to the client, see how they do the work. A lot of what see how they do the work. A lot of what see how they do the work. A lot of what they do may not appear on the official they do may not appear on the official they do may not appear on the official process and you're going to have to process and you're going to have to process and you're going to have to learn it to effectively map the pain learn it to effectively map the pain learn it to effectively map the pain points. And so you may begin by points. And so you may begin by points. And so you may begin by classifying your 10 top pain points and classifying your 10 top pain points and classifying your 10 top pain points and you have rough math and you may come you have rough math and you may come you have rough math and you may come back after you sit with a real client or back after you sit with a real client or back after you sit with a real client or real customer and say, "Well, five of real customer and say, "Well, five of real customer and say, "Well, five of those are fake or five of those are those are fake or five of those are those are fake or five of those are really a bad description of what's going really a bad description of what's going really a bad description of what's going on and here's the real thing." Let that on and here's the real thing." Let that on and here's the real thing." Let that reality change how you assess the reality change how you assess the reality change how you assess the problem. One thing to note as you're problem. One thing to note as you're problem. One thing to note as you're going through this process, industry going through this process, industry going through this process, industry knowledge has a very practical role with knowledge has a very practical role with knowledge has a very practical role with a lot of FTEEs. And so another thing a lot of FTEEs. And so another thing a lot of FTEEs. And so another thing that can encourage you if you're

that can encourage you if you're that can encourage you if you're wondering how do I get this is look at wondering how do I get this is look at wondering how do I get this is look at the industry knowledge you already have, the industry knowledge you already have, the industry knowledge you already have, right? Like a claims adjuster can just right? Like a claims adjuster can just right? Like a claims adjuster can just intuitively know when a repair estimate intuitively know when a repair estimate intuitively know when a repair estimate is correct and when it's not. A finance is correct and when it's not. A finance is correct and when it's not. A finance operator can just know when two reports operator can just know when two reports operator can just know when two reports use the same name but have different use the same name but have different use the same name but have different numbers inside them. A support lead can numbers inside them. A support lead can numbers inside them. A support lead can just know that one ordinarylooking just know that one ordinarylooking just know that one ordinarylooking sentence usually means the customer sentence usually means the customer sentence usually means the customer needs a person right away. You can't needs a person right away. You can't needs a person right away. You can't substitute for that kind of knowledge. substitute for that kind of knowledge. substitute for that kind of knowledge. The researchers looked at roughly The researchers looked at roughly The researchers looked at roughly 400,000 claud code sessions and people 400,000 claud code sessions and people 400,000 claud code sessions and people rated as experts in the task reached rated as experts in the task reached rated as experts in the task reached verified success more than twice as verified success more than twice as verified success more than twice as often as noviceses. That domain often as noviceses. That domain often as noviceses. That domain expertise, it matters. The second part expertise, it matters. The second part expertise, it matters. The second part of FTE work is technical delivery and of FTE work is technical delivery and of FTE work is technical delivery and this is where I want people to read this is where I want people to read this is where I want people to read their job descriptions carefully because their job descriptions carefully because their job descriptions carefully because it can be scary. Open AI is asking its it can be scary. Open AI is asking its it can be scary. Open AI is asking its FTEEs to write code across both front FTEEs to write code across both front FTEEs to write code across both front end and back end. Plantier describes end and back end. Plantier describes end and back end. Plantier describes people building applications, working people building applications, working people building applications, working with data, making architecture with data, making architecture with data, making architecture decisions, and owning the work through decisions, and owning the work through decisions, and owning the work through deployment. That's a different job deployment. That's a different job deployment. That's a different job description. Uh now look, if the job description. Uh now look, if the job description. Uh now look, if the job says production software engineering, says production software engineering, says production software engineering, industry knowledge in general absolutely industry knowledge in general absolutely industry knowledge in general absolutely doesn't get you past that requirement, doesn't get you past that requirement, doesn't get you past that requirement, and I don't want to pretend it does. Uh, and I don't want to pretend it does. Uh, and I don't want to pretend it does. Uh, and so whether you are Maya and you're and so whether you are Maya and you're and so whether you are Maya and you're just doing a small technical version in just doing a small technical version in just doing a small technical version in our story, which is very much an FTE our story, which is very much an FTE our story, which is very much an FTE thing, or whether you're trying to build thing, or whether you're trying to build thing, or whether you're trying to build full front-end and backend applications, full front-end and backend applications, full front-end and backend applications, your mileage will vary. You will have to your mileage will vary. You will have to your mileage will vary. You will have to look at the descriptions because this look at the descriptions because this look at the descriptions because this particular job role has a really wide particular job role has a really wide particular job role has a really wide range of technical expectations. Now,

range of technical expectations. Now, range of technical expectations. Now, the good news is if you need to learn the good news is if you need to learn the good news is if you need to learn technical skills, you absolutely can do technical skills, you absolutely can do technical skills, you absolutely can do that. There are boot camps for technical that. There are boot camps for technical that. There are boot camps for technical skills, but I will also say you can skills, but I will also say you can skills, but I will also say you can actually work with AI to learn front-end actually work with AI to learn front-end actually work with AI to learn front-end and back-end engineering now. And you and back-end engineering now. And you and back-end engineering now. And you can do so by simply practicing building can do so by simply practicing building can do so by simply practicing building small projects. It's something that I small projects. It's something that I small projects. It's something that I see people in my Substack community do see people in my Substack community do see people in my Substack community do all the time. I've seen people scale up all the time. I've seen people scale up all the time. I've seen people scale up to the point where they are technical to the point where they are technical to the point where they are technical founders in 12 months doing just simple founders in 12 months doing just simple founders in 12 months doing just simple projects where they're calling a model, projects where they're calling a model, projects where they're calling a model, they're asking for a structured output, they're asking for a structured output, they're asking for a structured output, they're displaying the results, they're they're displaying the results, they're they're displaying the results, they're learning to authenticate users, they're learning to authenticate users, they're learning to authenticate users, they're just going through all the steps that just going through all the steps that just going through all the steps that you do with software and they're just you do with software and they're just you do with software and they're just building and building and building till building and building and building till building and building and building till they really understand what they're they really understand what they're they really understand what they're doing working with AI. Why does that doing working with AI. Why does that doing working with AI. Why does that work? Because these days AI engineering work? Because these days AI engineering work? Because these days AI engineering is not about individually writing lines is not about individually writing lines is not about individually writing lines of code. You don't have to individually of code. You don't have to individually of code. You don't have to individually write the code. You have to understand write the code. You have to understand write the code. You have to understand how the system works and know the how the system works and know the how the system works and know the pitfalls and know how to get AI to go pitfalls and know how to get AI to go pitfalls and know how to get AI to go where you want to go. And that's what where you want to go. And that's what where you want to go. And that's what matters. So you don't have to become an matters. So you don't have to become an matters. So you don't have to become an expert in every technical piece before expert in every technical piece before expert in every technical piece before you start. You just have to understand you start. You just have to understand you start. You just have to understand the system in a way that gives you the system in a way that gives you the system in a way that gives you confidence that you can ask the model to confidence that you can ask the model to confidence that you can ask the model to build responsibly. as an example of build responsibly. as an example of build responsibly. as an example of responsible decision-making. In our responsible decision-making. In our responsible decision-making. In our story, Maya's service needs the intake story, Maya's service needs the intake story, Maya's service needs the intake documents in a small part of the claim documents in a small part of the claim documents in a small part of the claim record, but of course, you'd want to record, but of course, you'd want to record, but of course, you'd want to lock off payment and medical history lock off payment and medical history lock off payment and medical history because the model doesn't need to know because the model doesn't need to know because the model doesn't need to know that stuff, right? You need to think that stuff, right? You need to think that stuff, right? You need to think about those kinds of things when you're about those kinds of things when you're about those kinds of things when you're designing systems as an FTE. The other

designing systems as an FTE. The other designing systems as an FTE. The other thing that's really important for FTEES thing that's really important for FTEES thing that's really important for FTEES that is not a traditional engineering that is not a traditional engineering that is not a traditional engineering skill but is becoming an engineering skill but is becoming an engineering skill but is becoming an engineering skill and is one that all of us can skill and is one that all of us can skill and is one that all of us can learn from is testing and eval because learn from is testing and eval because learn from is testing and eval because you are telling models to build things. you are telling models to build things. you are telling models to build things. You have to tell them what criteria to You have to tell them what criteria to You have to tell them what criteria to build to. In our example, Maya may build to. In our example, Maya may build to. In our example, Maya may absolutely go with 50 different absolutely go with 50 different absolutely go with 50 different correctly adjudicated examples that say correctly adjudicated examples that say correctly adjudicated examples that say this is missing documents, this is not this is missing documents, this is not this is missing documents, this is not missing documents as part of her test missing documents as part of her test missing documents as part of her test set. And so she can then find out if the set. And so she can then find out if the set. And so she can then find out if the model is able to write software that model is able to write software that model is able to write software that passes the test, etc., etc. Evals are a passes the test, etc., etc. Evals are a passes the test, etc., etc. Evals are a key part of building agentic workflows. key part of building agentic workflows. key part of building agentic workflows. And constructing evals is a really And constructing evals is a really And constructing evals is a really important part of the engineering job important part of the engineering job important part of the engineering job these days. And it's not it's got these days. And it's not it's got these days. And it's not it's got nothing to do with code. It's it's key nothing to do with code. It's it's key nothing to do with code. It's it's key to FTE skill sets. It's usually to FTE skill sets. It's usually to FTE skill sets. It's usually considered technical, but it doesn't considered technical, but it doesn't considered technical, but it doesn't have anything to do with code. You can have anything to do with code. You can have anything to do with code. You can absolutely learn it. And part of why I'm absolutely learn it. And part of why I'm absolutely learn it. And part of why I'm making this video is I don't want people making this video is I don't want people making this video is I don't want people that have a lot of the translation that have a lot of the translation that have a lot of the translation skills that I've described to run away skills that I've described to run away skills that I've described to run away from the FGE role simply because they're from the FGE role simply because they're from the FGE role simply because they're scared of the word code. Don't be scared scared of the word code. Don't be scared scared of the word code. Don't be scared of that. In fact, in the same study I of that. In fact, in the same study I of that. In fact, in the same study I mentioned with claude code, the mentioned with claude code, the mentioned with claude code, the non-technical users who started using non-technical users who started using non-technical users who started using claude code to code got within a few claude code to code got within a few claude code to code got within a few points of the technical users and the points of the technical users and the points of the technical users and the engineers on the code they were able to engineers on the code they were able to engineers on the code they were able to produce. The larger point here is that produce. The larger point here is that produce. The larger point here is that you can get AI to write good code if you you can get AI to write good code if you you can get AI to write good code if you are willing to take the time to are willing to take the time to are willing to take the time to understand systems and willing to take understand systems and willing to take understand systems and willing to take the time to build good evals. The third

the time to build good evals. The third the time to build good evals. The third big part of the role here is deployment big part of the role here is deployment big part of the role here is deployment ownership. Maya can have a system that ownership. Maya can have a system that ownership. Maya can have a system that scores really beautifully on the files scores really beautifully on the files scores really beautifully on the files and it still might actually not work in and it still might actually not work in and it still might actually not work in production. And so she needs to be able production. And so she needs to be able production. And so she needs to be able to put live work down to notice what to put live work down to notice what to put live work down to notice what actually happens and to make corrections actually happens and to make corrections actually happens and to make corrections so that what gets built is actually so that what gets built is actually so that what gets built is actually useful. That cycle of measuring and useful. That cycle of measuring and useful. That cycle of measuring and learning from reality and coming back learning from reality and coming back learning from reality and coming back and building the capability is and building the capability is and building the capability is especially important for FTEES and AI especially important for FTEES and AI especially important for FTEES and AI because what I find is that when you because what I find is that when you because what I find is that when you have an AI capability that's general and have an AI capability that's general and have an AI capability that's general and you're bringing it into contact with you're bringing it into contact with you're bringing it into contact with specific code and specific workflows, it specific code and specific workflows, it specific code and specific workflows, it is an iterative process. It's a process is an iterative process. It's a process is an iterative process. It's a process where you have to turn the flywheel a where you have to turn the flywheel a where you have to turn the flywheel a few times to make sure that you're few times to make sure that you're few times to make sure that you're zeroing in on where the value is zeroing in on where the value is zeroing in on where the value is actually applied. And that is a process actually applied. And that is a process actually applied. And that is a process that has a ton of nonlinear value. In that has a ton of nonlinear value. In that has a ton of nonlinear value. In other words, getting AI to really other words, getting AI to really other words, getting AI to really deliver extraordinary value to save deliver extraordinary value to save deliver extraordinary value to save those 2,000 hours a month or whatever it those 2,000 hours a month or whatever it those 2,000 hours a month or whatever it is in your case, that's that's is in your case, that's that's is in your case, that's that's absolutely the impact you need to demand absolutely the impact you need to demand absolutely the impact you need to demand and expect. And if you're at a point and expect. And if you're at a point and expect. And if you're at a point where it's saving 20 hours, for example, where it's saving 20 hours, for example, where it's saving 20 hours, for example, or 200 hours, you're not where you need or 200 hours, you're not where you need or 200 hours, you're not where you need to be. And you need to look at the to be. And you need to look at the to be. And you need to look at the causes and figure out how to get the causes and figure out how to get the causes and figure out how to get the larger value that you know that you can larger value that you know that you can larger value that you know that you can unlock with AI. And that is not a skill unlock with AI. And that is not a skill unlock with AI. And that is not a skill that comes intuitively except I find to that comes intuitively except I find to that comes intuitively except I find to PMs and engineers who index highly on PMs and engineers who index highly on PMs and engineers who index highly on ownership. Those two job families have ownership. Those two job families have ownership. Those two job families have been trained for a long time. You have been trained for a long time. You have been trained for a long time. You have to own the code you put in production.

to own the code you put in production. to own the code you put in production. You have to own the impact to the You have to own the impact to the You have to own the impact to the customer. It's the same mindset with an customer. It's the same mindset with an customer. It's the same mindset with an FTE. Now I'm going to give you some FTE. Now I'm going to give you some FTE. Now I'm going to give you some clues on the job description next clues on the job description next clues on the job description next because like I said there's a whole because like I said there's a whole because like I said there's a whole range of them and I want to give you a range of them and I want to give you a range of them and I want to give you a sense of what works and what doesn't for sense of what works and what doesn't for sense of what works and what doesn't for different skill sets based on my different skill sets based on my different skill sets based on my analysis. So if it's forward deployed analysis. So if it's forward deployed analysis. So if it's forward deployed engineer, forward deployed software engineer, forward deployed software engineer, forward deployed software engineer, those usually have the highest engineer, those usually have the highest engineer, those usually have the highest coding expectations. Applied AI coding expectations. Applied AI coding expectations. Applied AI engineer, customer engineer, solutions engineer, customer engineer, solutions engineer, customer engineer, solutions engineer, implementation engineer, engineer, implementation engineer, engineer, implementation engineer, technical deployment lead, AI operations technical deployment lead, AI operations technical deployment lead, AI operations and AI product jobs may contain a lot of and AI product jobs may contain a lot of and AI product jobs may contain a lot of the same work but a different technical the same work but a different technical the same work but a different technical balance. And so you can look at those if balance. And so you can look at those if balance. And so you can look at those if you really want to get to FTE as uh you really want to get to FTE as uh you really want to get to FTE as uh skipping stones along the way. like you skipping stones along the way. like you skipping stones along the way. like you can hop along and get to FTE by taking can hop along and get to FTE by taking can hop along and get to FTE by taking some of those adjacent titles. Now, some of those adjacent titles. Now, some of those adjacent titles. Now, let's turn this into something that you let's turn this into something that you let's turn this into something that you can use specifically. If I were trying can use specifically. If I were trying can use specifically. If I were trying to prove skills around FTE over the next to prove skills around FTE over the next to prove skills around FTE over the next month or so, the first thing I would do month or so, the first thing I would do month or so, the first thing I would do is I would pick a recurring process that is I would pick a recurring process that is I would pick a recurring process that I can actually observe in detail. And I I can actually observe in detail. And I I can actually observe in detail. And I would need access to a few people who do would need access to a few people who do would need access to a few people who do the work. and I would need at least 10 the work. and I would need at least 10 the work. and I would need at least 10 or 20 completed instances that I can go or 20 completed instances that I can go or 20 completed instances that I can go through and see how the work ran. During through and see how the work ran. During through and see how the work ran. During the first week, I would actually pull as the first week, I would actually pull as the first week, I would actually pull as much real work as I can and I would much real work as I can and I would much real work as I can and I would reconstruct what happened. I would try reconstruct what happened. I would try reconstruct what happened. I would try to understand differences. I would try to understand differences. I would try to understand differences. I would try to classify issues. I would try to start to classify issues. I would try to start to classify issues. I would try to start to find the leverage points, right? Then to find the leverage points, right? Then to find the leverage points, right? Then I would talk to the person who's doing I would talk to the person who's doing I would talk to the person who's doing the work. I would sit there. I would sit the work. I would sit there. I would sit the work. I would sit there. I would sit next to them, which is what I described.

next to them, which is what I described. next to them, which is what I described. And I would figure out from that lived And I would figure out from that lived And I would figure out from that lived experience where the intervention points experience where the intervention points experience where the intervention points that I've mapped out are likely to be that I've mapped out are likely to be that I've mapped out are likely to be most impactful. Where is the leverage most impactful. Where is the leverage most impactful. Where is the leverage but in real life sitting next to a but in real life sitting next to a but in real life sitting next to a person? And I would use that person? And I would use that person? And I would use that understanding to start to do back of the understanding to start to do back of the understanding to start to do back of the napkin math so that I can figure out the napkin math so that I can figure out the napkin math so that I can figure out the impact because FTEES have to have that impact because FTEES have to have that impact because FTEES have to have that business impact. And by the end of that business impact. And by the end of that business impact. And by the end of that second week, by the end of sitting there second week, by the end of sitting there second week, by the end of sitting there observing the person, I should be able observing the person, I should be able observing the person, I should be able to say very very clearly, this is how to say very very clearly, this is how to say very very clearly, this is how much work I'm going to save. This is much work I'm going to save. This is much work I'm going to save. This is what I'm doing and this is why it what I'm doing and this is why it what I'm doing and this is why it matters. And if you're like, wow, 2 matters. And if you're like, wow, 2 matters. And if you're like, wow, 2 weeks is a long time. I have seen people weeks is a long time. I have seen people weeks is a long time. I have seen people speedrun this in 2 days. People can speedrun this in 2 days. People can speedrun this in 2 days. People can absolutely, if they're experienced, dive absolutely, if they're experienced, dive absolutely, if they're experienced, dive into the data and sit with someone for a into the data and sit with someone for a into the data and sit with someone for a day and they're done. It may be your day and they're done. It may be your day and they're done. It may be your first time, so give yourself a minute. first time, so give yourself a minute. first time, so give yourself a minute. And whatever the answer is, the scale And whatever the answer is, the scale And whatever the answer is, the scale and the safety of the intervention and the safety of the intervention and the safety of the intervention should be visible before you build. You should be visible before you build. You should be visible before you build. You should be able to say, "This is why it's should be able to say, "This is why it's should be able to say, "This is why it's going to have an impact, and this is how going to have an impact, and this is how going to have an impact, and this is how I'm securing the guard rails, right? The I'm securing the guard rails, right? The I'm securing the guard rails, right? The point is to keep you from asking AI to point is to keep you from asking AI to point is to keep you from asking AI to invent a product before you understand invent a product before you understand invent a product before you understand the real person. AI is really good at the real person. AI is really good at the real person. AI is really good at inventing stuff. You got to understand inventing stuff. You got to understand inventing stuff. You got to understand the actual pain point." Now, get started the actual pain point." Now, get started the actual pain point." Now, get started building from there. You want to start building from there. You want to start building from there. You want to start to build the the simplest possible to build the the simplest possible to build the the simplest possible solution. And I do mean build. Actually solution. And I do mean build. Actually solution. And I do mean build. Actually get to code. Work with your AI agent and get to code. Work with your AI agent and get to code. Work with your AI agent and figure out how to build a solution that figure out how to build a solution that figure out how to build a solution that solves for the leverage point you've solves for the leverage point you've solves for the leverage point you've identified. I have written a ton on

identified. I have written a ton on identified. I have written a ton on Substack about enterprise Substack about enterprise Substack about enterprise implementations and guardrails. I would implementations and guardrails. I would implementations and guardrails. I would recommend going there if you don't know recommend going there if you don't know recommend going there if you don't know where to start. But you can absolutely where to start. But you can absolutely where to start. But you can absolutely dig in. Understand typical expectations dig in. Understand typical expectations dig in. Understand typical expectations around permissions, typical expectations around permissions, typical expectations around permissions, typical expectations around data, typical expectations around around data, typical expectations around around data, typical expectations around authentication, authorization, take authentication, authorization, take authentication, authorization, take those seriously even if this is just an those seriously even if this is just an those seriously even if this is just an exercise because FTE deployments are exercise because FTE deployments are exercise because FTE deployments are almost always in enterprise contexts and almost always in enterprise contexts and almost always in enterprise contexts and you have to understand how to work with you have to understand how to work with you have to understand how to work with enterprise workflows, enterprise login, enterprise workflows, enterprise login, enterprise workflows, enterprise login, enterprise decisioning, enterprise IT enterprise decisioning, enterprise IT enterprise decisioning, enterprise IT teams. Those are decisions you will need teams. Those are decisions you will need teams. Those are decisions you will need to explain in interviews. Now, once to explain in interviews. Now, once to explain in interviews. Now, once you've built the system, you need to you've built the system, you need to you've built the system, you need to start trying that system against those start trying that system against those start trying that system against those old cases. You should be doing that by old cases. You should be doing that by old cases. You should be doing that by the end of the third week. You should be the end of the third week. You should be the end of the third week. You should be able to like try clean cases, try ugly able to like try clean cases, try ugly able to like try clean cases, try ugly cases, whatever it is that's actual cases, whatever it is that's actual cases, whatever it is that's actual work. Run it through your loop. Run it work. Run it through your loop. Run it work. Run it through your loop. Run it through the thing you've built. Look at through the thing you've built. Look at through the thing you've built. Look at the failures, rerun them after every the failures, rerun them after every the failures, rerun them after every meaningful change. Sit there and make it meaningful change. Sit there and make it meaningful change. Sit there and make it work so it works locally on all of your work so it works locally on all of your work so it works locally on all of your test cases. This is where that eval test cases. This is where that eval test cases. This is where that eval piece comes in. Finally, in week four, piece comes in. Finally, in week four, piece comes in. Finally, in week four, let two or three people use it while you let two or three people use it while you let two or three people use it while you watch. Learn from them and fix the loop.

watch. Learn from them and fix the loop. watch. Learn from them and fix the loop. That's really the key. That's that last That's really the key. That's that last That's really the key. That's that last part. That's that ownership part. And part. That's that ownership part. And part. That's that ownership part. And again, I have seen people speedrun this again, I have seen people speedrun this again, I have seen people speedrun this whole process in three or four days. It whole process in three or four days. It whole process in three or four days. It can be much, much faster, but I'm giving can be much, much faster, but I'm giving can be much, much faster, but I'm giving you a chance to dig in if it's your you a chance to dig in if it's your you a chance to dig in if it's your first outing at this skill set. Finally, first outing at this skill set. Finally, first outing at this skill set. Finally, you want to actually summarize the you want to actually summarize the you want to actually summarize the impact you had. That's the last piece. impact you had. That's the last piece. impact you had. That's the last piece. However long it takes you, you should be However long it takes you, you should be However long it takes you, you should be able to say, "I came in, I sat down, I able to say, "I came in, I sat down, I able to say, "I came in, I sat down, I saw how real people worked. I mapped out saw how real people worked. I mapped out saw how real people worked. I mapped out the problems. I built this to fix it. the problems. I built this to fix it. the problems. I built this to fix it. When I built it, I was able to put it When I built it, I was able to put it When I built it, I was able to put it into production and actually get it to into production and actually get it to into production and actually get it to work at enterprise grade." That's the work at enterprise grade." That's the work at enterprise grade." That's the whole FTE skill set right there. Now, if whole FTE skill set right there. Now, if whole FTE skill set right there. Now, if you would like a place to start, I put you would like a place to start, I put you would like a place to start, I put together a companion FTE skill builder together a companion FTE skill builder together a companion FTE skill builder that has a self assessment for you. It that has a self assessment for you. It that has a self assessment for you. It has that 30-day project brief. It has a has that 30-day project brief. It has a has that 30-day project brief. It has a portfolio checklist. And I'm also going portfolio checklist. And I'm also going portfolio checklist. And I'm also going to link to my AI jobs guide and the full to link to my AI jobs guide and the full to link to my AI jobs guide and the full interview guide so that you don't have interview guide so that you don't have interview guide so that you don't have to turn this video into your own to turn this video into your own to turn this video into your own curriculum. I also think that this is a curriculum. I also think that this is a curriculum. I also think that this is a particularly good use of the Substack particularly good use of the Substack particularly good use of the Substack community. Lots of us are there building community. Lots of us are there building community. Lots of us are there building and trying interesting stuff. I would and trying interesting stuff. I would and trying interesting stuff. I would recommend posting a real workflow in the recommend posting a real workflow in the recommend posting a real workflow in the place where you're stuck. You might say, place where you're stuck. You might say, place where you're stuck. You might say, "Hey, vendor applications arrive through "Hey, vendor applications arrive through "Hey, vendor applications arrive through email. Half of them are missing one of email. Half of them are missing one of email. Half of them are missing one of four documents. Here is what I'm trying four documents. Here is what I'm trying four documents. Here is what I'm trying to solve for and here's where I'm to solve for and here's where I'm to solve for and here's where I'm getting stuck." And people will just getting stuck." And people will just getting stuck." And people will just jump in. Like I've seen folks come back jump in. Like I've seen folks come back jump in. Like I've seen folks come back and just jump in. And it's a really sort and just jump in. And it's a really sort and just jump in. And it's a really sort of rich experience that way. So where of rich experience that way. So where of rich experience that way. So where does this leave us? We've talked about does this leave us? We've talked about does this leave us? We've talked about training. We've talked about your first training. We've talked about your first training. We've talked about your first 30 days resources that I have in my 30 days resources that I have in my 30 days resources that I have in my community. Ultimately, this is a job community. Ultimately, this is a job community. Ultimately, this is a job that is growing exponentially. There are that is growing exponentially. There are that is growing exponentially. There are a few thousand FTEEs now, but there's

a few thousand FTEEs now, but there's a few thousand FTEEs now, but there's demand for tens of thousands or even demand for tens of thousands or even demand for tens of thousands or even hundreds of thousands. Anthropic hundreds of thousands. Anthropic hundreds of thousands. Anthropic recently announced that DXC will train recently announced that DXC will train recently announced that DXC will train tens of thousands of existing engineers tens of thousands of existing engineers tens of thousands of existing engineers as claude certified FTEEs for banks and as claude certified FTEEs for banks and as claude certified FTEEs for banks and airlines and insurers and manufacturers airlines and insurers and manufacturers airlines and insurers and manufacturers and government agencies. And I think and government agencies. And I think and government agencies. And I think that the starting point here is that the starting point here is that the starting point here is interesting to note, right? DXC is interesting to note, right? DXC is interesting to note, right? DXC is taking people who already work inside taking people who already work inside taking people who already work inside complicated systems and adding AI complicated systems and adding AI complicated systems and adding AI training tied to those industries. You training tied to those industries. You training tied to those industries. You can use the same approach for your own can use the same approach for your own can use the same approach for your own career. Remember I said domain knowledge career. Remember I said domain knowledge career. Remember I said domain knowledge matters because the background you bring matters because the background you bring matters because the background you bring to the table is actually really to the table is actually really to the table is actually really important as a forward deployed important as a forward deployed important as a forward deployed engineer. Your industry context matters engineer. Your industry context matters engineer. Your industry context matters a lot. If you're deep in healthcare, a lot. If you're deep in healthcare, a lot. If you're deep in healthcare, don't try to jump over, right? If you're don't try to jump over, right? If you're don't try to jump over, right? If you're deep on manufacturing, don't jump over. deep on manufacturing, don't jump over. deep on manufacturing, don't jump over. That's where you'll be an FTE that's That's where you'll be an FTE that's That's where you'll be an FTE that's most effective. And if you're in a most effective. And if you're in a most effective. And if you're in a company and trying to figure out how to company and trying to figure out how to company and trying to figure out how to sort of jump to FTE, I don't think you sort of jump to FTE, I don't think you sort of jump to FTE, I don't think you need to wait for a title. All you do is need to wait for a title. All you do is need to wait for a title. All you do is need to find that piece of work, show need to find that piece of work, show need to find that piece of work, show that you can solve the problem with AI, that you can solve the problem with AI, that you can solve the problem with AI, and you're going to start working your and you're going to start working your and you're going to start working your way there. Do you want to know how I way there. Do you want to know how I way there. Do you want to know how I know that? There are people in my know that? There are people in my know that? There are people in my community that come up and button hole community that come up and button hole community that come up and button hole me at my at my happy hours, and they're me at my at my happy hours, and they're me at my at my happy hours, and they're like, "Nate, look at where I got right.

like, "Nate, look at where I got right. like, "Nate, look at where I got right. I got to an FTE title. I got to an AI I got to an FTE title. I got to an AI I got to an FTE title. I got to an AI engineer title because I did exactly engineer title because I did exactly engineer title because I did exactly this because I jumped in and I started this because I jumped in and I started this because I jumped in and I started solving problems with AI. That is the solving problems with AI. That is the solving problems with AI. That is the way forward and that's why I talk about way forward and that's why I talk about way forward and that's why I talk about it with such confidence. I've seen it it with such confidence. I've seen it it with such confidence. I've seen it happen over and over and over and over happen over and over and over and over happen over and over and over and over again. You can do it too. Uh I hope this again. You can do it too. Uh I hope this again. You can do it too. Uh I hope this has been helpful. FTE is going to be a has been helpful. FTE is going to be a has been helpful. FTE is going to be a dependably hot job for the next few dependably hot job for the next few dependably hot job for the next few years. This is not just a fad. The years. This is not just a fad. The years. This is not just a fad. The reason why is really simple. AI is a reason why is really simple. AI is a reason why is really simple. AI is a generalpurpose capability and the last generalpurpose capability and the last generalpurpose capability and the last mile in AI is really hard. FTEES bridge mile in AI is really hard. FTEES bridge mile in AI is really hard. FTEES bridge that gap. That's why FTEEs matter. If that gap. That's why FTEEs matter. If that gap. That's why FTEEs matter. If you're an FTE, let us know in the you're an FTE, let us know in the you're an FTE, let us know in the comments and let us know how you got comments and let us know how you got comments and let us know how you got there. If you would like to be an FTE, there. If you would like to be an FTE, there. If you would like to be an FTE, drop the project you're working on in drop the project you're working on in drop the project you're working on in the comments or head over to the the comments or head over to the the comments or head over to the Substack and share what you're working Substack and share what you're working Substack and share what you're working on. We'd love to hear about it. All on. We'd love to hear about it. All on. We'd love to hear about it. All right, cheers.