Moolala: Money Made Simple with Bruce Sellery
Sep 23, 2026 · 14 min · 10 segments
Job seekers are going back to old LinkedIn roles and rewriting them with today’s most desirable skills and buzzwords—but where is the line between a smart update and revisionist history? Stanford…
Gideon MooreGuest
Bruce SelleryHost
So we have in our data everybody's profiles, both from today and their profiles from years ago that they might have updated since then.
And even though those things didn't exist at the time, how did they get away with that?
So maybe I was doing machine learning and what we now call AI, we might've called machine learning five years ago, right? That sometimes language evolves and what we call things evolves.
Or sometimes you realize that the label you're using for something isn't necessarily how you'd label it today, right? For example, I had an early analyst job going through transcripts of Federal Reserve Open Market Committee meeting documents.
And today, I think on my LinkedIn, I have updated it to say I did Texas data analysis at the Federal Reserve.
And that's because today I understand that this is a skill I have and it's a skill the market wants.
When you layer in the dynamic of AI, how much of these changes are a function of what the AI filters are reading? So if I don't have AI in my CV somewhere, I'm not going to get picked up, versus in the old world, you might update it so it was contemporary for human eyes, but you didn't worry about the filters.
It's hard for us to say if this works or not, or what the employers are looking for.
We can tell you we think that it's what the workers think the employers are looking for.
That we can see in the data, when are you going back and making this type of change? Because it's not just that we have one slice, it's that we have slices from every month or so.
And so we can see people are going and making these types of changes precisely in the months leading up to when they change jobs.
Does this actually make the difference or is it just something that you do like a security blanket to feel like it makes a difference? We can't really say, but we're pretty sure people are doing it precisely when they're looking to change jobs or find a new job.
So we have in our data everybody's profiles, both from today and their profiles from years ago that they might have updated since then.
And even though those things didn't exist at the time, how did they get away with that?
So maybe I was doing machine learning and what we now call AI, we might've called machine learning five years ago, right? That sometimes language evolves and what we call things evolves.
Or sometimes you realize that the label you're using for something isn't necessarily how you'd label it today, right? For example, I had an early analyst job going through transcripts of Federal Reserve Open Market Committee meeting documents.
And today, I think on my LinkedIn, I have updated it to say I did Texas data analysis at the Federal Reserve.
And that's because today I understand that this is a skill I have and it's a skill the market wants.
When you layer in the dynamic of AI, how much of these changes are a function of what the AI filters are reading? So if I don't have AI in my CV somewhere, I'm not going to get picked up, versus in the old world, you might update it so it was contemporary for human eyes, but you didn't worry about the filters.
It's hard for us to say if this works or not, or what the employers are looking for.
We can tell you we think that it's what the workers think the employers are looking for.
That we can see in the data, when are you going back and making this type of change? Because it's not just that we have one slice, it's that we have slices from every month or so.
And so we can see people are going and making these types of changes precisely in the months leading up to when they change jobs.
Does this actually make the difference or is it just something that you do like a security blanket to feel like it makes a difference? We can't really say, but we're pretty sure people are doing it precisely when they're looking to change jobs or find a new job.
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