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
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 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 know, name
what they don't, explain where the
system is fragile and say where the risk
is and what they would take as a 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
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.
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
sensemaking. What do we question? What
do we keep? What do we reject? What do
we understand and decide? It's easy to
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 at the 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 do it before and I'm sticking with
it. 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 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
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 refuse to
handwave 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 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 faster? What work stops? What
decision stops being relitigated? 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 judgment 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 want when we're showing
our judgment. And that's why I started
with the whiteboard example because it's
a real example and we need to find a way
to bring that up. 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
is judgment 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 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
real work instead of just credentials.
Because 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 once
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
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 overweing. Here's
the technical constraint I don't yet
understand. Here's the risk I want to
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
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,
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 codecs or clawed code to help you
to 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 and work. Cheers.