[00:00] Microsoft says that 86% of us are treating AI output as just the beginning and not the final answer. Good job, guys. That's what we want to be doing. That number changes how we should think about proving whether we're good at work or not because that gets at the idea of what quality means. Microsoft also says 58% of AI users are producing work they could not have produced a year earlier and among advanced AI users the number rises to over 80%. That's certainly true for me. The obvious story here is that AI makes people more productive. That's
[00:31] true, but it's not the problem I want to talk about today. The deeper problem is that AI makes more people look productive and the old evidence does not carry the same signal. So, a memo can be polished, a prototype can run, a resume can sound really sharp when you read it, and a project plan can look organized on the surface. But, none of those things on their own tell you whether the person understood the situation well enough to make a great decision. And that's not a resume problem. It's an evidence problem. As AI makes us look more
[01:02] productive, we need better ways to see human judgment at work. We need to see what someone noticed, what they believed, what they rejected, what risk they saw, what changed because they were involved, and how their thinking held up when another serious person pushed them on it. And that is why I believe that the AI age is the age of whiteboards. If I want to know whether someone really understands a problem, I want to see them at a whiteboard with someone strong enough to push them. The problem should be real, the room should be serious, the person should have to draw what they
[01:33] know, name what they don't, explain where the system is fragile, and say where the risk is and what they would take as as a choice. And then the other person should push back, and that's where our understanding as humans really shows up now. A whiteboard conversation is valuable in the age of AI because it turns private judgment into really visible human work before the work gets cleaned up. The person has to think in the room. They have to hold the situation in their head and respond to pressure and update when they learn
[02:03] something and show where their confidence actually ends and they don't know the answer. That live reasoning is the kind of evidence we desperately need for our work in the age of AI. Because even though valuable work has always been difficult to see before AI, at least that output that you showed in the conversation in the interview still carried some signal, right? If someone shipped the road map or wrote the strategy doc or delivered the analysis, uh the artifact did suggest something about us, right? The people writing it.
[02:33] Production was hard enough that the finished work told a big piece of the story around our expertise. AI really breaks that link down and it exposes a new set of questions that we need to ask ourselves as we go through this job search process, as we grow in our careers. Because increasingly, the value we need to demonstrate is the value in processing, in compiling all of this in sense making. What do we question? What do we keep? What do we reject? What do we understand and decide? It's easy to
[03:04] look productive now, but it's really, really hard to see past the shiny portfolio into something that shows good judgment. Part of the challenge here is that the standard advice is kind of incorrect now. The standard advice is to build a portfolio. That's true as far as it goes, but it's incomplete because it points part that AI already makes easier, which is producing things. So yes, show you can ship. I love that part. I'm not saying don't do that. I've said to it before and I'm sticking with it.
[03:35] But, you need to find a way to show the decisions you made, what you rejected, the risks you identified, what changed because you were involved in the project, what difference you made, right? And the work sample, if it's just a work sample, isn't enough because publishing a portfolio work sample is downstream of all that thinking. What people need to see is the whiteboarding session, right? Getting into a discussion around how you can think through a problem that's difficult
[04:06] with someone who can wrestle with you on it. You have to show the problem as you understand it, and it has to survive contact with another serious human mind. And you want to get to a point where that kind of conversation can showcase four different key things that I think we're all looking for in roles. Situation, decision, risk, and change. First, write down the situation. What's happening? Who's involved? What's the system? What constraints matter? What facts do we have? What facts are missing? Where's the pressure coming
[04:36] from? Why is it this hard? Context is where judgment begins. So, show that context. Second, write down the decision. What are the plausible paths here? Which one would you take? Which one would you reject? Where does the decision sit? Good work involves rejecting really plausible options in favor of what really matters. The rejected options matter because they show what you understood and refused to hand-wave away. Third, write down the risk. What could go wrong? What risk are you willing to take? What risk are you trying to remove? What risk are you consciously
[05:07] accepting because the alternative is worse. Risk is one of the clearest ways to make that invisible work visible because a lot of good judgment, if it's done right, looks like nothing happened because you handled that risk. So, expose the risk, right? Expose the fact that the bad launch didn't happen, that the customer didn't churn, that the model output didn't go into production without review. Name that risk because prevented losses count. Fourth, write down the change. If we make this decision, what's different? What gets clearer? What gets safer? What gets
[05:38] faster? What work stops? What decision stops being re-litigated? What does the team understand after the conversation that it didn't understand before. This keeps the exercise from becoming a diary. The point is not to record everything. The point is to connect your judgement to a change in the work and a good whiteboard conversation shows that and allows us to walk into this kind of digital recording space in a way that is light and dynamic and that's what we
[06:08] want when we're showing our judgement and that's why I started with the whiteboard example cuz it's a real example and we need to find a way to bring that on. And part of the goal here is to show how we learn. Think back to that whiteboard session. Do people get defensive when challenged at the whiteboard? Do they update too quickly to please the room? Do they hold a useful line when the argument is sound? This is what we're all trying to see as we grow and learn and process all of this AI generated content. We're not looking for perfect confidence or perfect recall. What we're looking for
[06:39] is judgement under pressure. And this ties directly into why I introduced the Nate's Talent Board project. The Talent Board idea started from the same problem because standard career advice tells people to build a portfolio but AI has made all of that building and polishing so much easier that generation is kind of solved now. And so our portfolios have somewhat less value. The scarce thing now is comprehension. And that was the point of the Talent Board frame. Comprehension over generation. Explanation as artifact and a record of
[07:10] real work instead of just credentials. Cuz a resume can say that you're qualified and a portfolio can say what you've made but the better version says here is the work and here is the evidence that I understood it, made sense of it and actually made good choices as a result. Whiteboarding is the live version of that and Talent Board is where that evidence can live afterward. In the room you're going to take the real problem, you're going to make the reasoning visible, you you should show what should change, your how your thinking gets sharper. And And
[07:40] you've done all of that, once you've understood how people push back and you wrestle and sharpen the idea, you turn that into a talent board entry, a work sample, a promotion note, a hiring packet, a record because you want to preserve that evidence of your thinking. All talent board does is it gives you a chance to put that thinking in front of hiring managers. And this is especially important when you start a new role. Most onboarding advice tells you to listen and learn the org and meet stakeholders and get a few quick wins. And that's fine, but in the age of AI, I
[08:11] think it's incomplete. If judgment is the valuable work, then starting strong means forming a point of view early and letting people see how that point of view improves over time. That doesn't mean showing up super loud. It means putting your early model of the work in front of people who know more than you do. A useful first month move is to ask for that whiteboard session with someone who understands the domain deeply. Like, talk about the customer problem. Here's what I think it is so far. Here's where I think the team is over waiting. Here's the technical constraint I don't yet understand. Here's the risk I want to
[08:42] validate. Then let that person who knows more push back on you. If they correct you, write it down. If they disagree, ask what evidence would settle the question. If they point out a missing constraint, put it on the whiteboard. This is not about proving you arrive fully formed in a role. It's about showing that you can learn in public without becoming mushy. The same discipline works when there is no physical board, right? You can use a shared doc, you can use a digital whiteboard, you could use a Loom video, or an annotated prototype. The format
[09:12] matters less than the discipline to show that thinking. You want to make the reasoning visible while it still feels alive and it feels like a dynamic decision. And that will help you organically show the situation and the decision and the risk and the change that you want to show as the elements of a good story that shows human judgment in the age of AI. Because that's really what we're doing. We're telling stories live about what AI has generated so that we can show how it works. So, if you're trying to prove you're good at work,
[09:42] don't start by making the artifact shinier. Start with a real problem. Put your reasoning in front of someone who can challenge it. Then, preserve what survived that conversation in a way that's easy for people to understand your thinking and how you wrestled with it in the choices you made. That is the evidence people need now, and that is how to show that you are now good at work. And if you want to dig deeper on this, I have a whole set of prompts that I developed for this that you can put into Codex or Claude Code to help you to
[10:15] actually get all of that juicy stuff out of your head and elicit it and put it down and structure it in a way that other people can understand your thinking because that's really important now, and I want you to be able to do that. And of course, there's talent for it as well. All right, I'll see you next time. Subscribe for more cool updates on where AI is taking us in work. Cheers.