Transcription
We need to move from a jobs format to a skills format for our roles and our career growth. And no one's ready to talk about it. That's what this video is all about.
How do you think about your job differently and think about it in terms of skills you can train and improve, preferably with the help of AI? One of my inspirations for this post was a 2019 blog post by Tyler Cohen where he talked about this idea that athletes train and musicians train, performers train, but knowledge workers really don't train. We don't train. I don't I don't shoot free throws. There's no knowledge work equivalent. And so I started to ask myself, what does it take to do something like a pianist practicing scales, but for knowledge work? And is there a way to start to address this in the AI age that helps us think about skills differently than we traditionally have?
Because I got to be honest with you, traditionally our assumptions about skills have been so laded into jobs that it's literally baked into our software. Right? If you've ever been a hiring manager and you've ever used a software tool for hiring for compensation estimates, for promotions, do you know what it starts with? It starts with the assumption that you need to layer specific skills into a job post. It's as if we can't imagine a world where skills might exist independently of a role. And yet, that is exactly the world we're headed toward. We're headed toward a world where skills are something that we acquire because we can use them with AI to get meaningful work done. And we should be measured on our outcomes. We should be measured on our ability to drive with those skills, not necessarily compensated just because we have job title A or job title B, product manager or engineer.
So in that skills world, what does practicing really look like? I know that we talk about this physically and I think that metaphor is helpful, but I want to get it into the knowledge work space because we just haven't talked about that enough. So in the physical world, you think of skills as being fractal. They're they're they're tiered, right? So, if you are trying to practice your fluency with the piano and you're moving your fingers and you're playing the scales up and down, part of that is the subskll of finger movement in a pattern and part of it is the subskll of how much pressure you place on the keys and part of it is the subskll of the speed of movement. And each of those can be practiced and repeated and you can get feedback and you can progress.
For knowledge workers, we need to find a way to get to narrow situations with repeated specific feedback that's designed to strengthen a particular pattern of recognition and response in our brain so that we get better at our skills because otherwise we do our whole careers as live performance and that's an extremely inefficient way to learn. So what does that look like? Well, the good news is I think we have never had a better chance to do that than we do now in the age of AI because AI gives us the chance to have custom feedback on practice that we just never would have been able to scale otherwise. It's just most of us aren't doing.
It's tempting to say at this point that knowledge workers are lazy, but it's structural. I don't believe that we are lazy. I believe our environment fights against this approach to practicing our skills in three different ways. Number one, we live in a world with fuzzy outcomes. In basketball, the ball goes in or it doesn't. You shoot the free throw and you miss or you make it. It's a clear signal. In product or strategy or leadership or engineering, good can mix in so many different dimensions. It can be confusing. Like speed, like quality, like politics, like relationships, like risk. There's no single bit that flips from 0ero to one.
The other reason this is difficult is that we get really delayed and noisy feedback, right? You might make a big decision in Q1 and you might learn in Q3 at best maybe whether it really paid off. Meanwhile, the market may have shifted, maybe a competitor launched something, a key higher left. You almost never get the clean comparison. If I had written the spec differently, we would have avoided X or Y event. The third issue is low repetition. A serious musician is going to play scales hundreds of times a week, but how many truly consequential decision docs do you have? How many product specs? How many strategy docs? How many technical architecture memos do you write in a quarter? If each one of these is entangled with real money and real people, there are no low stake sandboxes in traditional career pathing. And so the default is that most of us spend like 95 or more percent of our quote unquote reps on live games. We're practicing in front of the crowd. We're practicing literally for our careers. I guess that's better than nothing, but it's not the same.
So the next question I wanted to ask is I wasn't satisfied with just a a general challenge. I wanted to ask myself, what are some skills that are repeatable, practicable that we could talk about in the age of AI? I would argue that there are five that keep showing up. I think number one is judgment. How you frame decisions. How you define your options. How you choose when conditions are uncertain. Number two is orchestration. How do you turn fuzzy goals into concrete workflows that humans and AI can execute together? Can you bring clarity out of the ambiguity? Number three is coordination. How do you move groups of humans through ambiguity without creating more chaos? Right? You are still going to need the skills to coordinate. And as agents get better, you may need to learn the skill to coordinate agents and humans. Number four is taste. Do you have a meaningful quality bar for your product, for writing, for design, for strategy? Do you have a sense of what is good? And can you talk about it and improve it like a skill? And number five is updating. How do you change your mind as evidence and context shift without getting whipped around by the noise? What is your huristic? What is your rubric? How do you think about updating your priors? How do you think about changing your mind in meaningful ways?
Now, none of these really live in a LinkedIn tagline. They live in what you write and leave behind. We could call that artifacts, right? Judgment can show up in your decision documents. Judgment can show up in experiment designs. It can show up in prioritization writeups. Orchestration can show up in handoff documents. It can show off in specs. It can show off in the way you plan a project and what that looks like. Coordination can show off in emails. It can show up in meeting notes. It can show up in stakeholder maps. Taste will show up in how your UX looks, right? Which examples, which metaphors you're going to pick. And your ability to update will show up in how you evolve your plans over time in the written reference to rationale and what that looks like.
So the key is that these skills, they're not adjectives. We name them as adjectives. We associate them with roles as adjectives, but really when you come right down to it, they're not. They're patterns in the things that you produce that I produce. And once you accept that, you stop arguing about who's strategic in the abstract, and you start looking at how people actually write, how they behave, and how they decide. This has always been the gold standard in behavioral interviewing, but we've really struggled to get to this level of clarity, especially post AAI.
So, what does AI actually change? AI is not a magic brain. I say that all the time. AI is a tool that can read text. It's following instructions and it can apply a rubric consistently. This is beautiful because it gives us a wall to practice against. So, your first step has nothing to do with your models if you're serious about practicing, right? You just want to pick one artifact that matters for your team, like a decision doc for a product manager, and you want to sit down with the people whose judgment you trust. And you just want to ask them a really simple question. When you say that a decision dock is good, can you tell me what you mean specifically and and just push ask gently, ask clearly, ask persistently, and push on the people in your life that you trust until you have a small concrete list, right? Maybe it's is the decision stated in a sentence. Are there at least two real options? Are the stakes and metrics explicit? Is there a clear recommendation? Are risks and trade-offs surfaced? I could go on, but it's not just for that one thing, right? That's an example for one artifact. You need to look at it for all of your artifacts, the ones that are relevant to your discipline. Whether that's architecture docs for engineering, whether that is call summaries for CSMS, whether that is uh pipeline expectations for sales, there's all kinds of ways to do this. But the key is asking someone in your life what's good.
And then you turn that into a a grade, right? A rubric. You make it clear like what good looks like. And you set that out one to five. And then you pull three to five real examples. And you mark them up, right? Like you get a red pen out, right? And you say, "This one is really good at clarity. This one is good at risks, but it has these weaknesses. This is the rationale." You notice how none of this is with the AI yet? I promise we'll get there. But I want you to recognize that human skills are human skills and I'm asking you to take some human responsibility for developing your skills. Only then after you've red penned a few things do you bring it to an LLM. You give it the rubric and you give it literally your annotated examples. We are at a point where you could actually use a red pen, scribble all over the doc and it would still work because usually handwriting recognition is good enough now to pick it up. And then you say in effect, "When I send you a new doc, please score it like this. Quote the parts you're reacting to. Explain briefly why you gave each a score. And please suggest edits that would move one of these dimensions up by a point or two points or whatever."
Suddenly, look what that changes. Look what your effort to define good for your role shifts. Instead of a manager skimming through and thinking, "Ah, that feels fuzzy. I don't I have 15 minutes. I'm going to turn it over." No, you get a structured critique that can be applied to every single doc of that type. This one has a two on options, right? That one is a four on clarity, but a one on how I structured risk. This is what I need to do to change it. And so they give you something like a rough consistent view of how the skill is showing up across your real work. We've been missing that. That is our signal. That is the basketball going into the basket. And yeah, you can actually log this. You can say over a quarter, what are the patterns I'm starting to see in my own behavior? How are my scores changing? And yes, you can really score this out of five. So even though I air quote it, you can get actual scores. And with that, we now have something the preAI world just couldn't have. When Tyler wrote this, this wasn't possible. We can do effectively film review like athletes on our thinking and our writing at scale without having to hire an army of coaches. just with some good prompts which I'm putting together.
The next move once you've got that is to turn the film review into repeatable drills that train on the patterns that you care about. So take judgment as an example, right? In an artifact form, judgment often looks like, can I write a decision document that lets a reasonable person say yes or no without a 2-hour meeting? With your rubric in place, you can create a practice scale, right? a practice exercise that looks like this. Once a week, take a real messy situation, a slack thread, a super vague request from your manager, a fuzzy idea you had in the shower. Write a one-page decision doc that hits at the pattern that you've identified as good, clear decision, options presented, stakes, recommendations, etc. Now, run it through the same AI rubric you use on real docs. Compare your version to a stronger version that the model generates. Notice what you miss. That is your practice. You compare it to what's good. You get focused on a subs skill and you practice and practice and practice every single week. You can do this for orchestration where you define what a good spec looks like in your environment, explicit goal, inputs, outputs, constraints, etc. And you can create drills where people practice turning fuzzy objectives into timebound specs and timebound uh organizational decisions for coordination. You can define a pattern for your executive updates. The important thing is to see the chain of behavior you need to adopt to level up. You have a skill. You identify your recurring behavior. You figure out how that maps to a recognizable pattern in the artifacts that you leave behind. Then you establish a grade and then you start to practice. That's what it takes to go from being like, "Oh yeah, Tyler wrote a good thought. I'm not really changing my behavior." to, "Wow, I have AI. I have a personal coach. I just need to configure it right." And now you start to get better.
What does this look like if you're a team lead? This is mostly just conceptual because I'll be honest with you, very few team leads do this, but let's play out the operations, right? Suppose you run a team at a midsize company. You decide that for the next quarter you're going to focus on a particular artifact you want to level up. So, you and your team define a rubric together. It's not just an individual. You guys together pull example docs that are good. You see how this is often the same set of activities, but now we're doing it at a team level. This is so much more powerful, right? You then can wire up a team LLM so that whenever someone marks a doc as ready for review, it will run the rubric pass. Basically, what this does is it's like the engineers who have codecs automatically review their PRs. Well, now you're having like Claude or Chat GPT automatically review your docs. Same thing, leaves comments. You can ask any of your teammates to do two things. Let the AI critique hit the dock before a human review, and that's a management decision. And then once or twice a week, as a team, set a 10-minute timer and practice on something that the AI keeps flagging as individually to you, a growth area, and report on it. Talk about it. Humans do better with goals when we articulate them. And so this is a case where the team gets stronger and we individuals progress faster because we're in a team environment. The goal is not to demand perfection. The goal isn't even to tie this to performance ratings. It's to ask that we use small steady habits to actually build and scale useful skills that we will need in the age of AI.
I am such a fan of these practical solutions because I think so often we stop at the generic. We stop at the vague. We don't need to do that. Right? By the end of your quarter, you should be able to have a conversation with your team where you say, "Have we improved on our on our rubric for this artifact? Did the scores get higher? Are docs getting approved with fewer iterations? Are key decisions happening faster and with less what are we deciding confusion?" If these are moving in the right direction, what you're learning is that a practice loop changes how your team thinks and writes. That's the core, right? Like that's what you're betting on.
And what's interesting is that you can use the same skill set in interviews, right? In hiring. So most companies are hiring for skills in a way that is comically indirect. So we might ask, tell me about a time you influenced a stakeholder. We will listen to the story. we will kind of squint and try to infer whether they can do the work that we need done in the next couple of quarters. If you've already done the work to define a pattern for a particular artifact, there's really a much more grounded way to to evaluate people. Give them the same game that you play as a team and see how they'll do on the job. So, instead of a traditional PM interview, maybe the PM gets a short take-home where they write or repair a decision document based on a really realistic prompt and then there's a live session where you work through that doc and you and you change a constraint like legal is going to block this or the timeline shrinks and you see how they think through it and adjust and then there's a critique exercise where you show them a deliberately mediocre AI generated doc and ask them what's wrong with it. So the beauty of this is that you can use the same rubric you develop internally and even the same AI model as a first pass scorer for consistency. The point is not to let AI decide who to hire. It's to have a shared concrete lens on what good looks like on the work you're actually doing. And the nice side effect is that hiring and development they now point at the same thing, right? The skills you test for in candidates are the skills you help them practice once they're inside the door. It's not, you know, we hired them for their strategic thinking and they're bad at Jira tickets. These are the skills we tested for and these are the skills we work on as a team.
And I want to call something out here. None of this presumes that you cannot use AI to get better. You are going to be using AI. One of the things that came out is that Anthropic has called out that I think 64 something like twothirds of AI usage is shadow AI usage. People not reporting it. People aren't incentivized to report it right now. This doesn't make you hide your AI. You can be open about using AI and still get better at these skills because the goal is the outcome. And so if your interviewee is using AI, you're going to find out real quick whether they have a healthy relationship with AI when they turn something in and then you give them a constraint live and they fumble and they can't handle it, right? Like you're going to see where the edges of those skill sets are. So, the practice loops I'm describing are designed to reinforce the kinds of skills we humans need in the age of AI. They're going to push people to clarify decisions, to surface risk, to articulate trade-offs. If someone uses AI for a pass at that, that's great, but you're going to catch them if they haven't done the heavy thinking. What's freeing about this is that you're enabling a real evaluation through live conversation where people talk through their choices, talk through how they respond, and really the interview and the development conversations feel very similar. And it's not about trying to catch people cheating with AI in either case. All you're trying to do is you're trying to see if they have a stable pattern of thought that remains visible even when their ability to do like tab tab tab as we say in cursor is gone. Right? If they're having a conversation and you change some dynamics and we talk about quality and they just stumble because they're not in front of a screen, you're going to know. Whereas, if you set it up and they have the conversation and yeah, maybe AI helps them get there faster, but they can articulate the trade-offs and they're able to start to point those skills in the right direction and practice them. That's fantastic. Now, you can measure it.
Now, I don't want to overromanticize this. There there are going to be real limits. Rubric scores will be noisy. I would not treat them as precise numerical representations. I would not treat them as a basis for promotions. I don't want people to feel like there's a surveillance risk where every single document is scored. The goal is to get better. The goal is to become useful. And I don't want program fatigue to eat this. So instead of like trying to start really big, I would strongly recommend that you start small. Pick one little thing, a short change in habit, start to practice and just start to feel into it because really the goal is to get in the habit of being athletes about our knowledge work. How do we intentionally name a skill, measure a skill, see what good looks like and use the power of AI to train and get better? If we go after that, if we have that sort of focus and goal, whether as an individual or a team manager, we are going to be in good shape and we are going to be in a position where we can actually answer Tyler's question because I think part of why Tyler wrote the question he did back in 2019 is we didn't have AI. AI couldn't be there to coach us. It was too expensive for most people to get coached. Well, not anymore. Now we have AI. AI can help each of us individually and help our teams to actually grow in our skill sets. And that's really exciting to me.