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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.

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Speaker 3: I'm Joe Wisenthal and I'm Tracy Alloway.

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Speaker 4: Tracy.

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Speaker 2: One of the topics that we like to talk about,

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Speaker 2: but we haven't talked about it for a while, public

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Speaker 2: sector tech, public sector are hiring. We've done a number

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Speaker 2: of episodes on this. It's been a while though, since

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Speaker 2: we've revisited this question, but it raises so many fascinating things.

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Speaker 3: I remember the ones that we did with Jennifer Palca,

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Speaker 3: which were fantastic and we got a good sense of

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Speaker 3: some of the issues facing public technology, including I think

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Speaker 3: it was seven thousand requirements in a single RFP that

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Speaker 3: was at the state level. But you hear stuff like

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Speaker 3: that and it just kind of blows your mind how

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Speaker 3: complex the whole process of developing new technology actually is

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Speaker 3: in government.

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Speaker 5: Right.

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Speaker 2: So, and we've also Patrick McKenzie is the other one

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Speaker 2: we've talked about this with, like these legacy systems, they

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Speaker 2: have all of this croft or whatever, and some of

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Speaker 2: its tech, and some of it's related to hiring. Everyone

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Speaker 2: I feel like comes in to the public sector and

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Speaker 2: they're like, why can't this look more like the private sector.

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Speaker 2: But I have an observation that I thought about after

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Speaker 2: the last time we talked to Jennifer, which again they're

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Speaker 2: excellent conversations.

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Speaker 4: We'll move the ball forward. So they're all kinds of

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Speaker 4: obvious problems with the public sector.

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Speaker 2: We'll get into them. That being said, and I think

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Speaker 2: you had the same experience. You hear these descriptions of like, say,

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Speaker 2: what's broken with the hiring process, and it's like, you

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Speaker 2: know what, that sounds kind of familiar.

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Speaker 4: I've experienced that in the private sector too.

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Speaker 2: Yeah, a lot of these pathologies seem to exist in

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Speaker 2: large organizations.

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Speaker 3: I was going to say, I think it's it's endemic

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Speaker 3: to bureaucracy. Yeah, right, And I remember Jennifer specifically saying that,

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Speaker 3: like one of the main choke points is just the

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Speaker 3: operational process of getting stuff approved or getting a new

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Speaker 3: candidate through a hiring process that has been dictated by

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Speaker 3: Human resources and you have to fit into certain slots

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Speaker 3: or certain categories. So it's a big deal.

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Speaker 2: But that's the whole dealing with HR thing and their

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Speaker 2: role as gatekeepers of the hiring process. And they're all,

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Speaker 2: you say, jar I love our HR people here a Bloomberg.

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Speaker 2: I'm just saying some of it sounded a little bit familiar,

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Speaker 2: that's all I'm gonna I'm gonna leave it at that.

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Speaker 2: Some of it sounded a little bit familiar. Fair So,

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Speaker 2: of course we know that the new administration. Of course,

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Speaker 2: lots of people from the tech world have come to it,

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Speaker 2: I think probably most famously Elon Musk and the whole

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Speaker 2: Doge endeavor, which you know, we'll get into perhaps where

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Speaker 2: we stand on the verdict for that, but widely viewed

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Speaker 2: is not particularly transformational et CIEC. Nonetheless, a lot of

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Speaker 2: like buying a lot of people from the tech world

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Speaker 2: thinking about what aspect of our most dynamic industry in

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Speaker 2: America can be brought into the public sector to perhaps

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Speaker 2: make it a more dynamic place, maybe more efficient, maybe

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Speaker 2: more effective, and a maybe a more desirable place to

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Speaker 2: work for talented people.

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Speaker 3: Right, and the government, the Office of Personnel Management is

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Speaker 3: launching a new cross government program right to recruit top

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Speaker 3: technologists to modernize the federal government. And I would just

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Speaker 3: add it kind of has some new urgency because the

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Speaker 3: Trump administration has identified the AI race with China as

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Speaker 3: like this existential threat to the US. So you can

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Speaker 3: see stuff kind of coalescing totally.

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Speaker 2: Well, we literally do have the perfect guest because we

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Speaker 2: are going to be speaking with the Director of the

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Speaker 2: Office of Personnel Management in DC. We're going to be

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Speaker 2: speaking with Scott Cooper. He's a former venture capitalist who's

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Speaker 2: at Indrees and Horowitz, so he knows all about the

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Speaker 2: public world, the private world, et cetera. Literally the perfect guest. Scott,

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Speaker 2: thank you so much for coming on the Outlots podcast.

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Speaker 5: Well, thank you for having me. I'm glad to be here.

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Speaker 4: Absolutely.

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Speaker 2: What is let's start from real basics, what is the

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Speaker 2: job of the office the Director of the Office of

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Speaker 2: Personnel Management.

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Speaker 5: Yeah, it's a great question, and I didn't know the

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Speaker 5: answer to this one before I started talking to the

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Speaker 5: administration about the job either. So I think the simple

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Speaker 5: way to think about it is, Look, we're the talent

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Speaker 5: management organization for the federal government. So there's about two

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Speaker 5: point one million federal employees roughly in the government today,

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Speaker 5: and we are responsible for all policies related to people.

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Speaker 5: So how do you hire, how do you fire? Performance

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Speaker 5: management system so HRIT systems, And then we're also across

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Speaker 5: functional organization that everything we do obviously touches all the

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Speaker 5: different agencies. So we're trying to make sure that we've

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Speaker 5: got consistency of policies across the government. And the simple

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Speaker 5: goal in my mind is just how do we make

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Speaker 5: sure we have the right people in the right jobs

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Speaker 5: in the federal government. That's basically the forward articulation of

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Speaker 5: what we do.

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Speaker 3: Why did you decide to go from Andrewson Horowitz to

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Speaker 3: what seems like, you know, something of a challenging job

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Speaker 3: and possibly lower paid. You don't have to tell us

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Speaker 3: your salary.

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Speaker 2: But public somewhere, but I assume it lower than It's

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Speaker 2: definitely lower than.

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Speaker 5: A partner in a Jerisa Arty, right, right, my salary,

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Speaker 5: My salary is public. Actually, in fact, you know, you

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Speaker 5: know what you know when you've hit a hype point

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Speaker 5: in your life when your oldest daughter, who is I

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Speaker 5: won't give her age, but she is, let's say, under thirty,

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Speaker 5: is making more money than I am at this day.

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Speaker 5: So exactly, I'm very proud of her. Yeah, it's a

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Speaker 5: great question. Look, the honest answer is it was a

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Speaker 5: couple of things. One is just it was the right time.

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Speaker 5: You know, I'd been an injuriesent, as you know from

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Speaker 5: the Founding for sixteen years. Super proud of what we

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Speaker 5: did there, and Ben and Mark were incredibly gracious in

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Speaker 5: giving me this opportunity, and I really felt like this,

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Speaker 5: there is an opportunity in this administration to do something differently.

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Speaker 5: So you mentioned a bunch of technology people coming to

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Speaker 5: the administration, there's also just a bunch of you know,

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Speaker 5: business people more generally, and look, we're not going to

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Speaker 5: make a mistake of applying every private sector you know,

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Speaker 5: method and model to the federal government. But I think

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Speaker 5: what the President has asked us to do is to

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Speaker 5: think differently about things and giving us kind of the

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Speaker 5: permission and the flexibility to do that. And so to me, look,

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Speaker 5: I I've been around the venture capital business. You know,

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Speaker 5: ultimately company succeed or fail. My lesson from the venture

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Speaker 5: capital business is they succeed or fail almost entirely because

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Speaker 5: of the people in the organization and the organizational structure

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Speaker 5: and the culture and the incentives. It's pretty simple. And

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Speaker 5: so my view is, look, why can't we learn some

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Speaker 5: of those lessons and apply them as appropriate to the

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Speaker 5: federal government and think about creating a culture where we've

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Speaker 5: got great people who can do great stuff on behalf

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Speaker 5: of the American people.

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Speaker 2: So let's say, I want you to tell me about

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Speaker 2: your new initiative to bring in tech talent, and also

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Speaker 2: in the answer, maybe like situated within maybe what makes

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Speaker 2: it different from other endeavors where you know it was

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Speaker 2: the code for America Endeavor, which you know probably had

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Speaker 2: some similar themes. Obviously there is the Joje experience, which

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Speaker 2: I want to get into further. But talk to us

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Speaker 2: about this new project. What is it and then why

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Speaker 2: is it different from past efforts to infuse technology or

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Speaker 2: infuse tech thinking into the government.

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Speaker 5: Yeah, so let me take you through the program. So

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Speaker 5: the program is called US Tech Force, So anybody who's

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Speaker 5: interested can go to our x at us TESK Tech

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Speaker 5: Force and find more information about it. Basically, here's the program.

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Speaker 5: The goal is to bring a thousand engineers into the government,

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Speaker 5: so software developers, AI experts, data scientists, product managers, so

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Speaker 5: anybody who can help us basically with the job of

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Speaker 5: modernizing government infrastructure. It's a two year program and we

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Speaker 5: partnered with about twenty five of the largest private technology companies,

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Speaker 5: private sector technology companies, i should say, And we're really

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Speaker 5: trying to accomplish a couple things here. Number one is

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Speaker 5: the government has a dearth of what I would call

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Speaker 5: modern software expertise and modern AI technology expertise. And that's

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Speaker 5: not a knock at all on the organizations, but a

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Speaker 5: lot of what these big cio organizations of the government

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Speaker 5: have to do as they're maintaining these massive legacy infrastructures,

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Speaker 5: and so the ability to do bespoke development is quite

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Speaker 5: difficult in those organizations. So this will be a vehicle

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Speaker 5: to help them accelerate bespoke development across the entire government.

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Speaker 5: The second problem we're trying to solve is we have

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Speaker 5: a massive early career pipeline problem in the federal government.

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Speaker 5: So seven percent of the federal workforce is early career

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Speaker 5: let's call that less than five or seven years worth

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Speaker 5: of work experience, and that compares to about twenty two

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Speaker 5: to twenty three percent in the non federal sector. So

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Speaker 5: by a factor of three to one, the federal government

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Speaker 5: is completely failing at getting early career people into government.

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Speaker 5: And if you look at the other end of the spectrum.

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Speaker 5: By the way, so we've got about forty four percent

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Speaker 5: of our population that's over the age of fifty, and

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Speaker 5: that compares with about a third in the you know,

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Speaker 5: non federal workforce. So we've got this looming problem, which

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Speaker 5: is we're going to have a ton of people, you know,

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Speaker 5: over the next five percent years who rightly so you know,

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Speaker 5: are at retirement age and want to go do other things.

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Speaker 5: And we've done very very little to actually replenish the pipeline.

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Speaker 5: So this program is really intended to do that. And

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Speaker 5: one of the ways we're doing that is by making

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Speaker 5: it a two year program. So I don't think it's

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Speaker 5: reasonable for us to go to a twenty two to

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Speaker 5: twenty five, twenty seven year old person and say, decide today,

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Speaker 5: do you want to commit forty years of your life

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Speaker 5: to be a career public civil servant. What I'd rather

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Speaker 5: do is expose them to the problems in government, let

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Speaker 5: them actually have a great experience, and you know what,

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Speaker 5: like we should convince them over time how great this is.

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Speaker 5: But you know what, if the end of the two

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Speaker 5: year period they want to go back to the private sector,

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Speaker 5: that's great. And that's why we've lined up all these

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Speaker 5: private sector partners who are going to actually help run

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Speaker 5: a job fair for us and make sure that we

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Speaker 5: have kind of greater a connectivity between the public and private sectors.

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Speaker 3: Can you talk a little bit more about why historically

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Speaker 3: government roles have not been that desirable for tech workers.

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Speaker 3: Is I assume you diagnosed the problem before you came

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Speaker 3: up with a solution. But in your view, what is

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Speaker 3: the main choke point or market failure that is leading

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Speaker 3: to difficulty for the government to recruit the kind of

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Speaker 3: people that it wants to.

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Speaker 5: Yeah, so I think there's a couple of problems. So

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Speaker 5: one is it's I think the fundamental issue, quite frankly,

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Speaker 5: in my mind, is a messaging problem, which is I'll

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Speaker 5: give you an anecdote. Right, So, when I first came

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Speaker 5: into office, I've been in the office about six months now.

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Speaker 5: How I met with one of my managers in the

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Speaker 5: organization who said, hey, you guys are ruining things. And

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Speaker 5: I said, what do you mean? And by you guys,

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Speaker 5: she meant DOGE. And you know, I'm not part of DOGE,

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Speaker 5: but obviously, look I come from you know, I know

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Speaker 5: lots of those people. And I said, well, how are

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Speaker 5: we ruining things? She said, well, our whole value prop

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Speaker 5: to employees has been come to the federal government and

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Speaker 5: have lifetime employment. And I said, oh my gosh, you're

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Speaker 5: not going to like my answer, because number one, I'm

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Speaker 5: going to tell you like, that's a myth and a

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Speaker 5: lie that people have been telling you. There is no

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Speaker 5: such thing as lifetime employment. And two, I said, look,

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Speaker 5: that's the most that's the worst least compelling kind of

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Speaker 5: narrative that I've ever heard that I'm going to go

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Speaker 5: tell some twenty five year old like, that's the value proposition.

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Speaker 5: I'm coming to work with the government. So number one

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Speaker 5: is like, we have to change the narrative. The narrative

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Speaker 5: in my mind is come work on the toughest, biggest,

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Speaker 5: most complex problems. You know, do some public service, but

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Speaker 5: then give yourself career options and you know what, if

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Speaker 5: you want to go to the private sector after that,

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Speaker 5: you know, God bless you, that's a perfect thing to do.

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Speaker 5: So that's part of the problem. The other problem is

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Speaker 5: the nature of what happens in government. And you know,

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Speaker 5: you know Jen Polka, as you guys mentioned in your intro,

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Speaker 5: who's like brilliant on this stuff, diagnosed it per which

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Speaker 5: is so much of what government does is it's a

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Speaker 5: compliance based culture. Right, It's not just literally you know

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Speaker 5: that we follow laws, but if I do something wrong,

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Speaker 5: you know, I'm worried about getting an inspector General report,

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Speaker 5: or I'm worried about a GAO report, or I'm worried

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Speaker 5: about some congress person hauling me in front of their

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Speaker 5: committee to testify about stuff. And so the short answer

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Speaker 5: as a result is like, we have a zero risk

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Speaker 5: based culture inside of government. There's no concept of you know,

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Speaker 5: you know, you guys mentioned I came to the venture

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Speaker 5: capital world. You know, in that world, look, we take

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Speaker 5: crazy risk because all we're doing is trying to maximize

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Speaker 5: upside opportunity. Now, I'm not proposing we do that in government,

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Speaker 5: and I've been using the term measured risk as a

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Speaker 5: better way to try to describe it in government, but like,

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Speaker 5: we have to kind of change the culture and enable

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Speaker 5: people to think about the upside opportunity that comes from

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Speaker 5: taking modest amounts of risk. And unfortunately, particularly on the

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Speaker 5: technology side of things, that has not been the case.

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Speaker 5: And so again, if you're a young, you know, early

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Speaker 5: career person, I think you want to do cutting edge stuff,

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Speaker 5: you want to learn things, you want to kind of

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Speaker 5: go out a little bit on the risk curve. And

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Speaker 5: right now, at least historically, government has not been in

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Speaker 5: a minimal place for that.

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Speaker 2: Well, you mentioned that the pitch to work in the

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Speaker 2: federal government should not be this is lifetime employment. That

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Speaker 2: that is like, that's not healthy for a lot of reasons,

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Speaker 2: and I think a lot of people can intuitively agree

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Speaker 2: with it. On the other hand, you know, you mentioned

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Speaker 2: the sort of like in the VC world, you're always

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Speaker 2: shooting for the right tail, but when you're talking about

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Speaker 2: the layoffs or lifetime employee means like you're cutting off

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Speaker 2: the left tail. Just from a money standpoint, the employee

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Speaker 2: of the federal government never has the chance to experience

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Speaker 2: a right tail outcome. The highest paid worker in the

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Speaker 2: federal government, it's a very decent salary, but you know,

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Speaker 2: in the compared to very even moderately successful people, you know,

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Speaker 2: the most senior successful person in the federal government will

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Speaker 2: make less very often than just sort of like someone

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Speaker 2: who's hung around Google for a long time. So, like,

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Speaker 2: how much of a problem actually just comes down to

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Speaker 2: that simple monetary fact.

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Speaker 5: So, look, there's no question that compensation is relevant, but

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Speaker 5: I actually think we've been overstating the nature of that problem.

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Speaker 5: So let me give you a couple of thoughts on it. So,

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Speaker 5: first of all, part of the reason why we're focused

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Speaker 5: on early career here is at least the pay gap

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Speaker 5: between private and public is a little bit more manageable,

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Speaker 5: and it just kind of it as themptotes like massively

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Speaker 5: as you go farther through your career. So yes, it's

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Speaker 5: totally unrealistic, you know, unless you're rich and you just

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Speaker 5: decide you want to do something different. It's totally unrealistic

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Speaker 5: for us to think we're going to compete against like

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Speaker 5: a VP at Google for a senior level executive person

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Speaker 5: in the government. So that's not even in my mind, like,

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Speaker 5: that's not even worth worrying about, basically, because like the

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Speaker 5: pay gap is just crazy and nobody's going to do that.

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Speaker 5: But my experience, at least, and you know, you guys

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Speaker 5: have been around a lot of organizations, is look, there

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Speaker 5: are some people who are purely kind of profit maximizing,

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Speaker 5: and that's great. It's not I'm not have no normal

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Speaker 5: objections to that, obviously, but my sense is that's actually

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Speaker 5: a relatively small minority the population of employees. I think

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Speaker 5: most employees come to work every day because they're being challenged,

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Speaker 5: they're learning, they have career development opportunities, they work for

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Speaker 5: someone who cares about their career and they feel like,

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Speaker 5: you know, they can be rewarded when they actually do

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Speaker 5: good stuff. They feel like they're held accountable Like that,

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Speaker 5: to me is a culture that we can create inside

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Speaker 5: of the federal government. And you know, even if it

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Speaker 5: means yes, in a short period of time, you are

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Speaker 5: going to have like a you know, remuneration challenge relative

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Speaker 5: to the private sector. But this is also why again

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Speaker 5: just to go back to it, This is why I

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Speaker 5: think this whole narrative of come, you know, work for

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Speaker 5: the federal government for forty years, to me is just crazy.

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Speaker 5: Like we should have an open idea that you can

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Speaker 5: go back and forth being the public and private sector.

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Speaker 5: And you know what, if you have points in your

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Speaker 5: life where you think, like profit maximization is the most

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Speaker 5: important thing, then yes, like you should go to the

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Speaker 5: private sector because the government's never going to deal with that.

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Speaker 5: But I think we just have to break this. Like,

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Speaker 5: to me, this dichotomy that there's one way to go

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Speaker 5: or the other makes no sense. So look, in the

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Speaker 5: perfect world, I'd wave a magic wand and say we

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Speaker 5: pay everybody like what they can get paid to Google.

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Speaker 5: But that's not realistic, and quite frankly, you know, there's

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Speaker 5: there's zero support in Congress for that. The final thing

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Speaker 5: we can do, though, just to Joe, to give you

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Speaker 5: one last perspective, is we can do things inside of

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Speaker 5: OPM to mitigate some of the challenges on the compensation

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Speaker 5: side of things. So let me give you a very

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Speaker 5: specific example. So, as you probably know, there's a GS

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Speaker 5: schedule which is like everybody gets paid, you know, GS

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Speaker 5: one through fifteen. These are different levels today. All those

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Speaker 5: GS schedules are driven off of tenure and they're driven

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Speaker 5: off of degree requirements. So if I don't have a

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Speaker 5: college degree and I haven't worked for ten years, no

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Speaker 5: matter how good I am, I can't get to a

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Speaker 5: GS fifteen level in government. In my mind, that's just silly,

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Speaker 5: quite frankly, right, that's the opposite of a merit based culture.

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Speaker 5: You all will remember, since we've talked does, right, You'll

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Speaker 5: remember I won't mention his name, but you know there's

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Speaker 5: a doge engineer with a very colorful name that turger

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Speaker 5: listeners have heard of. You know, when all the kind

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Speaker 5: of exciting dose stuff was happening, there was a quote

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Speaker 5: from an administration official who said this guy's terrible. We

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Speaker 5: never would have hired him because he didn't graduate from

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Speaker 5: college and he didn't have whatever five years of work

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Speaker 5: experience coming here. And in my mind, like, I literally

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Speaker 5: wanted to pull my hair out when I heard that,

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Speaker 5: because I'll say, that's the craziest thing in the world. Like,

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Speaker 5: if this guy is capable of performing at a GS

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Speaker 5: fifteen level, why should we care about what his tenure

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Speaker 5: is or whether he has a college degree. And so

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Speaker 5: one of the things we're doing inside of OPM is

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Speaker 5: we're going to eliminate all requirements and all tenure requirements

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Speaker 5: in the pay schedule and so as a result, if

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Speaker 5: someone is doing awesome, like we can hire them at

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Speaker 5: the appropriate level. So some of the pay gap problem

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Speaker 5: is not just a numerical problem, it's a leveling problem.

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Speaker 5: And I think we can solve that even in the

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Speaker 5: absence of congressional legislation.

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Speaker 3: Yeah, I remember Jennifer again said something very similar about

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Speaker 3: like the best coder in America or a young guy

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Speaker 3: who had won this huge coding competition and then couldn't

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Speaker 3: get hired by the government because you didn't take all

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Speaker 3: of the official boxes.

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Speaker 4: Kind of crazy specific role.

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Speaker 3: Yeah, since you mentioned doing awesome. How do you actually

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Speaker 3: go about evaluating the performance of a tech worker in

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Speaker 3: government and how do you figure out which people you

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Speaker 3: actually want to hire in terms of talent?

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Speaker 5: Yeah, so the short entwer is we have to do

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Speaker 5: it differently from how we do it today. But let

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Speaker 5: me give you the very specific So on the front end,

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Speaker 5: historically we have relied on self attestation of skills in

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Speaker 5: order to hire people into the federal government. So I

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Speaker 5: apply for a software engineering job and I get this

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Speaker 5: application where I check boxes and I say, you know what,

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Speaker 5: I'm an expert in Perl programmer, I'm an expert in

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Speaker 5: you know, AI technology, And I literally checked the boxes.

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Speaker 5: And that is the basis on which, like, my application

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Speaker 5: gets through or doesn't get through to the next round

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Speaker 5: of screening.

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Speaker 3: I assume this is at some point, though, right, there's

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Speaker 3: some sort of contency test.

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Speaker 5: So in many roles there's not. And the reason is,

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Speaker 5: let me give you APEC. There's a historical reason for this.

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Speaker 5: So way back when we used to have literally a

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Speaker 5: civil service exam, it was called PACE was the name.

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Speaker 5: I forget what the acronym stands for, but it's PACE

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Speaker 5: that existed until nineteen eighty and there was a lawsuit

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Speaker 5: at the very tail end of the Carter administration, very

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Speaker 5: beginning the Reagan administration, first Reagan administration, where there was

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Speaker 5: desparate It was a disparate impact claim basically, so the

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Speaker 5: kind of test results on this exam were disproportionately negative

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Speaker 5: for minority applicants versus majority applicants. And so in nineteen

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Speaker 5: eighty one, the government entered into a consent to free

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Speaker 5: where basically they banned essentially the use of these examinations.

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Speaker 5: And from nineteen eighty one until literally four months ago,

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Speaker 5: we were living under that consent decree. Nobody ever revisited it,

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Speaker 5: and as a result, every agency was terrified to use

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Speaker 5: any kind of actual merit based assessment for hiring because

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Speaker 5: they were afraid they would run a foul of this

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Speaker 5: consent degree. Luckily for us, we revisited it along with

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Speaker 5: the Department of Justice, and we got out of the

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Speaker 5: consent decree. We actually found the original plaintiff actually was

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Speaker 5: still alive, Levano was his name, and we actually, you know,

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Speaker 5: finally said okay, forty three years later, we think it's

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Speaker 5: time to get rid of this consent degree, and they

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Speaker 5: agreed to that. So for the first time ever, now

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Speaker 5: we have kind of avoided we've eliminated the risk that

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Speaker 5: the agencies worried about, which is if I use a

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Speaker 5: technical assessment to judge somebody in the employment process, I

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Speaker 5: might get sued, you know, under some type of disprit

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Speaker 5: impact theory. So we are now doing that. So number

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Speaker 5: one to answer your there was a long way to

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Speaker 5: answer your question. But so like, the way we're going

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Speaker 5: to evaluate people is they actually have to demonstrate merit.

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Speaker 5: So there's lots of private sector tests as you probably know,

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Speaker 5: code signal, all these other things, and every single private

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Speaker 5: sector company uses where they're like, hey, basically, let me

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Speaker 5: give you a coding test if you're applying for a

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Speaker 5: software development job, and then we can level you and

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Speaker 5: figure out are you a you know, GS five or

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Speaker 5: GS fifteen based on that, and then we can do

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Speaker 5: secondary stuff like obviously an in person interview or other

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Speaker 5: things to make sure that you know, you're actually a

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Speaker 5: good fit for the organization. So that's kind of thing.

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Speaker 5: The more when we have to do on the front end,

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Speaker 5: does that make sense at least?

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Speaker 3: And kind of you know, yeah, it totally does. It

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Speaker 3: is kind of wild to think that, you know, last

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Speaker 3: year or a few years ago or just four months ago.

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Speaker 3: Joe and I could literally say that we're like, I'm

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Speaker 3: hunting experts.

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Speaker 4: I'm a ten X engineer.

436
00:19:38,359 --> 00:19:41,920
Speaker 3: Yeah, that's right, and apply I know, I'm a cokeball expert.

437
00:19:42,240 --> 00:19:43,160
Speaker 3: Cobyl COBBYL.

438
00:19:43,200 --> 00:19:45,680
Speaker 5: I can never write cobol expert, would be very much

439
00:19:45,680 --> 00:19:49,560
Speaker 5: in demands. Coball, cobyl, Cobalt is Cobalt is the mineral.

440
00:19:49,600 --> 00:19:51,200
Speaker 5: There's a cobalt mineral.

441
00:19:51,920 --> 00:19:53,679
Speaker 4: Yeah, I never know if it's cobble.

442
00:19:53,960 --> 00:19:57,040
Speaker 3: I want to say cobble because it's like cobbled together

443
00:19:57,280 --> 00:19:58,639
Speaker 3: on across IT systems.

444
00:19:58,840 --> 00:20:01,399
Speaker 5: Oh, that's actually really good. Okay, I didn't think about that.

445
00:20:01,440 --> 00:20:05,240
Speaker 2: Okay, actually, but on the second part of Tracy's question, Okay,

446
00:20:05,240 --> 00:20:07,320
Speaker 2: so once they're in, once they're in the office, and

447
00:20:07,359 --> 00:20:10,560
Speaker 2: again because it's not a for profit entity, like, how

448
00:20:10,600 --> 00:20:13,359
Speaker 2: do we current how does the government currently go about

449
00:20:13,359 --> 00:20:16,359
Speaker 2: evaluating how someone did this year? And what would be

450
00:20:16,440 --> 00:20:19,520
Speaker 2: the optimal way for the government to evaluate how productive

451
00:20:19,560 --> 00:20:21,320
Speaker 2: someone there there is? And they're saying, and this is

452
00:20:21,359 --> 00:20:23,800
Speaker 2: a problem in private sector too. I'm pretty sure that

453
00:20:23,840 --> 00:20:26,600
Speaker 2: if you went into facebooks or Google, et cetera, they

454
00:20:26,600 --> 00:20:30,000
Speaker 2: would also not have a clean answer of like which

455
00:20:30,160 --> 00:20:33,240
Speaker 2: software engineer is more productive or core attributed more value

456
00:20:33,280 --> 00:20:33,880
Speaker 2: than the other one.

457
00:20:34,960 --> 00:20:36,720
Speaker 5: Yeah, no, you're upsote. Right. Look, this is not a

458
00:20:36,720 --> 00:20:38,560
Speaker 5: problem that's unique at the government. But what I think

459
00:20:38,680 --> 00:20:42,240
Speaker 5: is unique to the government is historically, basically everybody gets

460
00:20:42,280 --> 00:20:44,560
Speaker 5: an A in government. To give you a perspective, most

461
00:20:44,600 --> 00:20:46,840
Speaker 5: agencies rank people one through five at the end of

462
00:20:46,880 --> 00:20:48,960
Speaker 5: the year on their performances, one being the worst and

463
00:20:49,000 --> 00:20:51,880
Speaker 5: five being the best. And if you look historically over

464
00:20:51,960 --> 00:20:55,040
Speaker 5: long periods of time in government, about eighty plus percent

465
00:20:55,080 --> 00:20:57,359
Speaker 5: of people get ranked A four or five. Okay, so

466
00:20:57,640 --> 00:21:00,199
Speaker 5: meaning basically, you know, everybody is in you know, kind

467
00:21:00,200 --> 00:21:03,399
Speaker 5: of like Globa gone and well well above average. And

468
00:21:03,440 --> 00:21:06,240
Speaker 5: then about zero point two percent of federal workers get

469
00:21:06,320 --> 00:21:09,160
Speaker 5: ranked A one or two, meaning that you're performing below expectations.

470
00:21:09,480 --> 00:21:12,320
Speaker 5: So number one, like, we've had no system that actually

471
00:21:12,800 --> 00:21:16,399
Speaker 5: recognizes like outstanding performance and quite frankly unfortunately, no system

472
00:21:16,400 --> 00:21:19,000
Speaker 5: that actually holds accountability on the other end of the spectrum.

473
00:21:19,160 --> 00:21:21,480
Speaker 5: We're changing that. We've already put out regulations for the

474
00:21:21,520 --> 00:21:23,679
Speaker 5: most senior executives what are called the sees, which are

475
00:21:23,680 --> 00:21:27,280
Speaker 5: the most senior executives, where we effectively. For the SEES

476
00:21:27,359 --> 00:21:30,240
Speaker 5: have put out a regulation that requires a forced distribution,

477
00:21:30,320 --> 00:21:33,360
Speaker 5: so a cap of thirty percent of fours and five rankings,

478
00:21:34,000 --> 00:21:36,679
Speaker 5: which we think is appropriate to actually distinguish people, you know,

479
00:21:36,720 --> 00:21:39,639
Speaker 5: in terms of their performance. So number one is we

480
00:21:39,720 --> 00:21:42,040
Speaker 5: got to reform the system, period and we're working on

481
00:21:42,080 --> 00:21:44,000
Speaker 5: that and you'll see more regulation from us. In that

482
00:21:44,320 --> 00:21:46,480
Speaker 5: number two, you're actually right, which is look like sometimes

483
00:21:46,520 --> 00:21:48,240
Speaker 5: the metrics are hard, right, so you know, you don't

484
00:21:48,240 --> 00:21:50,280
Speaker 5: really want to measure like lines of code that somebody

485
00:21:50,320 --> 00:21:53,280
Speaker 5: produces because obviously all code is not created equal. But

486
00:21:54,119 --> 00:21:56,040
Speaker 5: you know, what I think does work in the private

487
00:21:56,080 --> 00:21:59,480
Speaker 5: sector is, look, managers are responsible for their frontline workers.

488
00:21:59,480 --> 00:22:01,720
Speaker 5: They meet with one on one, they have you know, weekly,

489
00:22:01,800 --> 00:22:03,639
Speaker 5: they have a sense of what they're doing. They provide

490
00:22:03,640 --> 00:22:06,520
Speaker 5: feedback along the way, like I think the performance management

491
00:22:06,560 --> 00:22:09,199
Speaker 5: ongoing process works better in the private sector. In the

492
00:22:09,200 --> 00:22:12,159
Speaker 5: public sector, what I found is instead of managers, we

493
00:22:12,240 --> 00:22:15,800
Speaker 5: use the term supervisor, which I think actually unfortunately does

494
00:22:15,840 --> 00:22:17,640
Speaker 5: a disservice to what the job is. Like, I don't

495
00:22:17,640 --> 00:22:20,240
Speaker 5: think the job is necessarily supervision. The job is to manage,

496
00:22:20,280 --> 00:22:22,439
Speaker 5: meaning are they working on the right stuff, are they

497
00:22:22,440 --> 00:22:24,159
Speaker 5: doing the right things. Do they need training for this

498
00:22:24,240 --> 00:22:26,640
Speaker 5: kind of stuff, And historically we haven't done a great

499
00:22:26,720 --> 00:22:28,199
Speaker 5: job on that. So one of the things that we

500
00:22:28,280 --> 00:22:29,720
Speaker 5: just did, for example, is we just rolled out a

501
00:22:29,720 --> 00:22:33,639
Speaker 5: brand new training program that's mandatory for all supervisors specific

502
00:22:33,640 --> 00:22:35,960
Speaker 5: to this issue on performance management, so to teach them

503
00:22:36,280 --> 00:22:38,879
Speaker 5: like what does a one on one look like? You know,

504
00:22:38,880 --> 00:22:40,520
Speaker 5: those of us who go up in the technology industry.

505
00:22:40,720 --> 00:22:42,520
Speaker 5: The first book that we were all assigned to read

506
00:22:42,760 --> 00:22:46,040
Speaker 5: was Andy Groves Only the Paranoids Survive, which I still

507
00:22:46,040 --> 00:22:47,920
Speaker 5: think is a fantastic book. I don't know if y'all

508
00:22:47,920 --> 00:22:50,960
Speaker 5: have read it. It's actually a really all I have read it.

509
00:22:51,359 --> 00:22:53,040
Speaker 4: Yeah, keep going, Yeah.

510
00:22:52,960 --> 00:22:54,960
Speaker 5: I mean basically, he literally talks about like what is

511
00:22:54,960 --> 00:22:57,560
Speaker 5: a one on one meeting with your you know, team member,

512
00:22:57,600 --> 00:22:59,520
Speaker 5: Like what should it look like? And it's like, you know,

513
00:22:59,640 --> 00:23:02,200
Speaker 5: it's very much like management one on one stuff. But

514
00:23:02,200 --> 00:23:03,920
Speaker 5: I think the understands or is like, most people don't

515
00:23:03,960 --> 00:23:05,919
Speaker 5: get that training, and so as a result, we have

516
00:23:06,000 --> 00:23:08,680
Speaker 5: had a performance management culture where we have inflated grades

517
00:23:08,720 --> 00:23:11,720
Speaker 5: and we have basically once or maybe maximum twice a

518
00:23:11,760 --> 00:23:14,000
Speaker 5: year people actually get feedback. So it's totally unfair to

519
00:23:14,040 --> 00:23:17,040
Speaker 5: the employee and it's also just unfair to top performers

520
00:23:17,040 --> 00:23:20,240
Speaker 5: who are not actually getting differentiated reviews and differentiated compensation.

521
00:23:20,960 --> 00:23:22,879
Speaker 3: I was just about to ask, just to be one

522
00:23:22,960 --> 00:23:26,800
Speaker 3: hundred percent clear, compensation is going to be tied to performance.

523
00:23:27,200 --> 00:23:30,320
Speaker 5: Correct, Okay, correct, yes, all right, yes, And again it's

524
00:23:30,320 --> 00:23:31,960
Speaker 5: not that it hasn't been before. But the problem is

525
00:23:32,000 --> 00:23:33,600
Speaker 5: if everybody's a four and a five and you have

526
00:23:33,640 --> 00:23:36,680
Speaker 5: a finite bonus pool, then you're peanut buttering out the

527
00:23:36,760 --> 00:23:39,240
Speaker 5: bonuses basically. So like, yeah, just you know, Tracey, just

528
00:23:39,320 --> 00:23:40,919
Speaker 5: give me a sense. Like when we gave guidance on

529
00:23:40,960 --> 00:23:43,720
Speaker 5: this sees these very senior executives, what we said is

530
00:23:43,720 --> 00:23:46,719
Speaker 5: a thirty percent cap. We also said we think sixty

531
00:23:46,760 --> 00:23:48,960
Speaker 5: percent of the bonus based compensation should go to those

532
00:23:49,000 --> 00:23:51,960
Speaker 5: that thirty percent of the individuals. And you know that's

533
00:23:52,000 --> 00:23:53,920
Speaker 5: a you know, we can argue whether it's sixty percent

534
00:23:53,960 --> 00:23:56,760
Speaker 5: or eighty percent, but the point is, like, let's let's

535
00:23:56,800 --> 00:23:59,840
Speaker 5: actually really pay per performance, and let's differentiate compensation as

536
00:23:59,840 --> 00:24:02,199
Speaker 5: a posed to giving everybody a three hundred or five

537
00:24:02,280 --> 00:24:04,479
Speaker 5: hundred dollars bonus, which in the scheme of things, has

538
00:24:04,520 --> 00:24:06,520
Speaker 5: little retentive or incentive value for employees.

539
00:24:07,080 --> 00:24:10,840
Speaker 3: So you mentioned project management just then, and I was

540
00:24:10,880 --> 00:24:13,760
Speaker 3: looking at some of the press releases and documentation that

541
00:24:13,800 --> 00:24:15,720
Speaker 3: you put out as part of this program, and there

542
00:24:15,760 --> 00:24:20,040
Speaker 3: really is an emphasis on getting project managers, and I

543
00:24:20,080 --> 00:24:23,480
Speaker 3: am very curious why that particular role seems to be

544
00:24:23,680 --> 00:24:27,160
Speaker 3: in short supply, because I know someone who moved from

545
00:24:27,200 --> 00:24:31,360
Speaker 3: a completely different industry into project management for a graphic

546
00:24:31,400 --> 00:24:34,240
Speaker 3: design company. They had never done it before, and it

547
00:24:35,000 --> 00:24:36,200
Speaker 3: didn't seem that hard.

548
00:24:38,760 --> 00:24:41,480
Speaker 5: Okay, So I'm going to distinguish between project management and

549
00:24:41,640 --> 00:24:45,520
Speaker 5: product management, which is I hope we wrote product management

550
00:24:45,560 --> 00:24:47,080
Speaker 5: in the in our in our Stone.

551
00:24:47,119 --> 00:24:48,119
Speaker 3: So did I miss read it?

552
00:24:49,240 --> 00:24:50,840
Speaker 5: Well, I don't know that you didn't matter. We may

553
00:24:51,160 --> 00:24:53,280
Speaker 5: have put it in there. But what we need is

554
00:24:53,280 --> 00:24:55,760
Speaker 5: what I would call a product manager job, which is,

555
00:24:55,800 --> 00:24:58,479
Speaker 5: you know, a product manager, in my mind, is basically

556
00:24:58,480 --> 00:25:01,119
Speaker 5: somebody who sometimes they ca from an engineering background. They

557
00:25:01,160 --> 00:25:03,200
Speaker 5: might actually have been developers themselves, but they're kind of

558
00:25:03,200 --> 00:25:05,720
Speaker 5: people who sit between the engineering organization and the end

559
00:25:05,800 --> 00:25:09,040
Speaker 5: user customer and they listen to the customer and say, great, okay,

560
00:25:09,040 --> 00:25:10,800
Speaker 5: tell me your problems. What are you trying to solve?

561
00:25:10,800 --> 00:25:13,280
Speaker 5: And their job is kind of to translate that into

562
00:25:13,359 --> 00:25:16,320
Speaker 5: ultimately in tech speak, what we'd call a PRD a

563
00:25:16,359 --> 00:25:19,399
Speaker 5: project requirements document for the engineers to develop against. So

564
00:25:19,520 --> 00:25:21,000
Speaker 5: like that's the role we need. So in a well

565
00:25:21,040 --> 00:25:25,600
Speaker 5: functioning engineering organization, you have developers, you have designers, uh,

566
00:25:25,640 --> 00:25:28,280
Speaker 5: and then you have product managers. And the product managers

567
00:25:28,280 --> 00:25:30,679
Speaker 5: probably aren't puting their hands on the keyboard actually you know,

568
00:25:30,840 --> 00:25:33,479
Speaker 5: you know typing, you know, ones and zeros, but they

569
00:25:33,520 --> 00:25:36,600
Speaker 5: are literally that go between between like the end user

570
00:25:36,640 --> 00:25:39,360
Speaker 5: customer and then the requirements that ultimately drive the development process.

571
00:25:40,080 --> 00:25:40,720
Speaker 4: It's interesting.

572
00:25:40,760 --> 00:25:43,639
Speaker 2: I feel like that's something I've really come to appreciate

573
00:25:43,800 --> 00:25:48,560
Speaker 2: in working for a large organization, how valuable translation skills are,

574
00:25:48,600 --> 00:25:50,359
Speaker 2: because you have this one part of the room that

575
00:25:50,440 --> 00:25:53,399
Speaker 2: knows one very specific thing, and then there's these generalists

576
00:25:53,400 --> 00:25:55,200
Speaker 2: on the other end that have some vague thing, and

577
00:25:55,240 --> 00:25:57,440
Speaker 2: that person who could sit in the middle and talk

578
00:25:57,520 --> 00:26:02,320
Speaker 2: to both sides is clearly a very is a valuable person.

579
00:26:02,200 --> 00:26:02,399
Speaker 5: You know.

580
00:26:02,760 --> 00:26:06,879
Speaker 2: So you mentioned, like the one thing that comes up

581
00:26:06,920 --> 00:26:10,280
Speaker 2: a lot in a common theme on our podcast is

582
00:26:10,280 --> 00:26:14,560
Speaker 2: like the importance of this sort of like tacit organizational knowledge.

583
00:26:14,680 --> 00:26:17,800
Speaker 2: Things that are like cannot simply be written down. They

584
00:26:17,800 --> 00:26:21,119
Speaker 2: do not exist necessarily in one person's head. They're just

585
00:26:21,160 --> 00:26:23,960
Speaker 2: things that are sort of collectively known process knowledge and

586
00:26:24,040 --> 00:26:27,800
Speaker 2: so forth. How do you think about that in light

587
00:26:27,840 --> 00:26:31,680
Speaker 2: of the fact that these are two year roles and

588
00:26:31,920 --> 00:26:35,520
Speaker 2: you know, again maybe in some cases someone will do

589
00:26:35,600 --> 00:26:38,120
Speaker 2: two years and then continue their career in the federal government,

590
00:26:38,400 --> 00:26:41,080
Speaker 2: but that but you know, especially given the pay gap

591
00:26:41,119 --> 00:26:45,560
Speaker 2: that emerges specifically for technologists, like that's probably you're taking

592
00:26:45,560 --> 00:26:47,600
Speaker 2: on the one area where the pay gap probably gets

593
00:26:47,600 --> 00:26:51,640
Speaker 2: the widest over time. How do you think about fostering,

594
00:26:53,080 --> 00:26:57,919
Speaker 2: fostering and maintaining that kind of knowledge, especially given what

595
00:26:57,960 --> 00:26:59,720
Speaker 2: you laid out at the very beginning, which is this,

596
00:27:00,240 --> 00:27:01,840
Speaker 2: I don't know if crisis is the right word, but

597
00:27:01,880 --> 00:27:05,240
Speaker 2: these looming retirement of all these people that do have

598
00:27:05,359 --> 00:27:07,760
Speaker 2: years of accumulated knowledge about how things really work.

599
00:27:09,119 --> 00:27:11,560
Speaker 5: Yeah, so you're absolutely right, which is, look, we have

600
00:27:11,600 --> 00:27:13,560
Speaker 5: to balance kind of the two year time period against

601
00:27:13,640 --> 00:27:16,640
Speaker 5: the need for actually the organization being able to maintain

602
00:27:17,240 --> 00:27:19,280
Speaker 5: these systems once people leave. So let me just give it.

603
00:27:19,280 --> 00:27:21,040
Speaker 5: I'll give you a real world example, Joe, just so

604
00:27:21,040 --> 00:27:23,119
Speaker 5: you have it because it might be helpful. So in

605
00:27:23,160 --> 00:27:25,919
Speaker 5: my organization o PM, we own the retirement process and

606
00:27:25,960 --> 00:27:29,119
Speaker 5: retirement for you know, several million and new attempts. Basically

607
00:27:29,119 --> 00:27:31,640
Speaker 5: who have you know, kind of completed their professional careers

608
00:27:32,080 --> 00:27:34,000
Speaker 5: and we are completely it's a very I won't go

609
00:27:34,040 --> 00:27:35,920
Speaker 5: into the details I want for you, but there's literally

610
00:27:36,000 --> 00:27:38,840
Speaker 5: it's a like a ridiculously manual paper based process where

611
00:27:38,880 --> 00:27:41,680
Speaker 5: for years we have had people literally filling out paper

612
00:27:41,720 --> 00:27:45,360
Speaker 5: applications as recently as the beginning of this year and

613
00:27:45,480 --> 00:27:47,720
Speaker 5: sending paper by postal mail and all kinds of stuff.

614
00:27:47,720 --> 00:27:51,040
Speaker 5: And we literally have a mine in Bowyers, Pennsylvania, which

615
00:27:51,080 --> 00:27:52,879
Speaker 5: is very fun if you ever want to come see it,

616
00:27:53,119 --> 00:27:55,560
Speaker 5: where we have hundreds of millions of file cabinets of

617
00:27:55,600 --> 00:27:56,720
Speaker 5: people's retirement records.

618
00:27:57,160 --> 00:27:58,720
Speaker 4: So we'd love to I would love to.

619
00:27:58,800 --> 00:28:01,040
Speaker 2: I've always wanted to go like those those like huge

620
00:28:01,080 --> 00:28:03,320
Speaker 2: like under mountain like big.

621
00:28:03,520 --> 00:28:07,480
Speaker 4: Yes, that's literally very lo it is. I I'd love to.

622
00:28:07,480 --> 00:28:10,240
Speaker 3: Check warehouse for government.

623
00:28:10,840 --> 00:28:13,520
Speaker 2: I've always wanted to check out on these places. We'll

624
00:28:13,600 --> 00:28:16,280
Speaker 2: arrange that, all.

625
00:28:15,280 --> 00:28:17,879
Speaker 5: Right, we're gonna we're gonna work on that one together. Anyways.

626
00:28:17,920 --> 00:28:20,159
Speaker 5: I bring that up to say, Okay, so look, we've luckily,

627
00:28:20,359 --> 00:28:24,000
Speaker 5: uh we've had a small team of developers Joe Jebbia,

628
00:28:24,080 --> 00:28:25,840
Speaker 5: who you guys may recognize the name. He's a co

629
00:28:25,920 --> 00:28:28,840
Speaker 5: founder for Airbnb. He joined the administration even actually before

630
00:28:28,880 --> 00:28:30,800
Speaker 5: I got there, but he's been he's been kind of

631
00:28:30,800 --> 00:28:33,240
Speaker 5: housed in o PM, and he has like a team

632
00:28:33,280 --> 00:28:36,639
Speaker 5: of like three developers and you know, a design person,

633
00:28:37,040 --> 00:28:39,920
Speaker 5: and they've been completely redesigning this retirement application. So we

634
00:28:40,000 --> 00:28:42,640
Speaker 5: now have what we call ORA, the Online Retirement Application,

635
00:28:42,720 --> 00:28:45,400
Speaker 5: where instead of paper, people actually can go, you know,

636
00:28:45,480 --> 00:28:47,760
Speaker 5: in the year twenty twenty five onto a you know

637
00:28:47,800 --> 00:28:50,680
Speaker 5: computer and and you know, type in their Apple their information,

638
00:28:50,760 --> 00:28:52,880
Speaker 5: upload their documents, it gets routed to the HR and

639
00:28:52,920 --> 00:28:54,960
Speaker 5: it's you know, it's you know, vastly better than what

640
00:28:54,960 --> 00:28:56,880
Speaker 5: we've had. So I bring this up to say, look,

641
00:28:56,920 --> 00:28:58,880
Speaker 5: I think this is me is a good example, which

642
00:28:58,880 --> 00:29:00,320
Speaker 5: is all those people are going to disappear at some

643
00:29:00,320 --> 00:29:03,040
Speaker 5: point in time, and then the responsibility for that application

644
00:29:03,120 --> 00:29:05,960
Speaker 5: is going to shift to the existing CIO organization within

645
00:29:06,000 --> 00:29:09,040
Speaker 5: OPM to maintain it, to integrate it with other systems.

646
00:29:09,400 --> 00:29:11,840
Speaker 5: And so a requirement of being able to do that

647
00:29:11,880 --> 00:29:13,720
Speaker 5: development process for Joe and his team is, Look, they

648
00:29:13,720 --> 00:29:15,760
Speaker 5: have to do documentation, they have to do training of

649
00:29:15,760 --> 00:29:18,200
Speaker 5: the team. Look, I think it's not an insurmountable problem.

650
00:29:18,240 --> 00:29:20,080
Speaker 5: I just think it means, as part of your process,

651
00:29:20,160 --> 00:29:22,360
Speaker 5: you just have to anticipate that there is going to

652
00:29:22,360 --> 00:29:24,120
Speaker 5: be a time where people are going to disappear. And

653
00:29:24,200 --> 00:29:26,160
Speaker 5: quite frankly, it's a smart thing to do anyways, because

654
00:29:26,160 --> 00:29:27,880
Speaker 5: we just know and look, this happens in private companies

655
00:29:27,880 --> 00:29:30,640
Speaker 5: all the time. Look people's average tenure in private companies.

656
00:29:30,640 --> 00:29:32,360
Speaker 5: I can tell you in Silicon Valley it's way less

657
00:29:32,360 --> 00:29:33,920
Speaker 5: than four years, is what the average tenure of an

658
00:29:33,960 --> 00:29:36,680
Speaker 5: engineer is. So these are not new things. They are

659
00:29:36,720 --> 00:29:38,760
Speaker 5: new things to the government, which is this idea of

660
00:29:38,840 --> 00:29:41,400
Speaker 5: kind of knowledge capture and knowledge management, you know, kind

661
00:29:41,400 --> 00:29:42,800
Speaker 5: of in light of the fact that people might not

662
00:29:42,840 --> 00:29:45,000
Speaker 5: be there for their entire career. But I think it

663
00:29:45,080 --> 00:29:47,240
Speaker 5: just means, as we've designed this program that we have

664
00:29:47,280 --> 00:29:49,680
Speaker 5: to have the appropriate level of documentation, of integration of

665
00:29:49,760 --> 00:29:51,520
Speaker 5: source code notes and all those things that are you know,

666
00:29:51,560 --> 00:29:54,120
Speaker 5: you would do in a normal circumstance to recognize that.

667
00:29:54,240 --> 00:29:56,000
Speaker 5: Now look at Joe in the perfect world. I hope

668
00:29:56,040 --> 00:29:57,600
Speaker 5: people love it and they're like, wow, this is this

669
00:29:57,640 --> 00:29:59,000
Speaker 5: is exactly where I want to spend the rest of

670
00:29:59,040 --> 00:30:00,640
Speaker 5: my life, and that's awesome. Want to do that like

671
00:30:00,680 --> 00:30:02,640
Speaker 5: we want to keep them in government, but you know,

672
00:30:02,680 --> 00:30:04,400
Speaker 5: I think we'll also have to deal with the practical

673
00:30:04,400 --> 00:30:07,880
Speaker 5: realities that you know, the current generation of you know,

674
00:30:07,960 --> 00:30:10,760
Speaker 5: people are not thinking about you know, three, five, ten

675
00:30:10,800 --> 00:30:13,720
Speaker 5: year you know, employments basically think things are much more

676
00:30:13,720 --> 00:30:14,160
Speaker 5: clearer than that.

677
00:30:30,520 --> 00:30:34,120
Speaker 3: What are you doing to streamline the operational side of

678
00:30:34,160 --> 00:30:38,760
Speaker 3: the process, because I imagine you mentioned culture before. If

679
00:30:38,760 --> 00:30:42,960
Speaker 3: the pitches come work for government, it's important, but you're

680
00:30:43,000 --> 00:30:45,160
Speaker 3: going to be lower paid and you're going to have

681
00:30:45,200 --> 00:30:49,000
Speaker 3: to constantly deal with paperwork and put together you know

682
00:30:49,200 --> 00:30:52,600
Speaker 3: RFPs that have seven thousand requirements and things like that.

683
00:30:52,600 --> 00:30:55,560
Speaker 3: That that's a tough sell to someone who's used to

684
00:30:55,640 --> 00:30:58,360
Speaker 3: the whole move fast and break things culture.

685
00:30:59,800 --> 00:31:02,920
Speaker 5: Yeah, So anyways, let me just be totally clear, which

686
00:31:03,000 --> 00:31:04,920
Speaker 5: is we are not going to move fast and break

687
00:31:04,960 --> 00:31:08,120
Speaker 5: things because we can't quite do that. In government. We

688
00:31:08,160 --> 00:31:10,800
Speaker 5: can move faster than we currently do, and we can

689
00:31:10,840 --> 00:31:14,200
Speaker 5: afford to take some risk, but we generally don't want

690
00:31:14,200 --> 00:31:16,200
Speaker 5: to break things because you know, people do need to

691
00:31:16,200 --> 00:31:18,080
Speaker 5: get their Social Security checks and you know, the IRS

692
00:31:18,160 --> 00:31:19,920
Speaker 5: needs to actually make sure you get your tax refunds.

693
00:31:19,920 --> 00:31:22,719
Speaker 5: So but it's a great it's a great question so look,

694
00:31:22,760 --> 00:31:24,719
Speaker 5: there's a couple of things that we're doing both inside

695
00:31:24,760 --> 00:31:27,120
Speaker 5: and outside this program. So outside the program more generally,

696
00:31:27,520 --> 00:31:29,440
Speaker 5: one are the big things that OPM is driving along

697
00:31:29,480 --> 00:31:31,840
Speaker 5: with OMB, you know, kind of the offseome management budget

698
00:31:31,880 --> 00:31:34,960
Speaker 5: is we're working with all the agencies to basically what

699
00:31:34,960 --> 00:31:38,120
Speaker 5: I would call do headcount planning and reshape the priorities

700
00:31:38,120 --> 00:31:40,880
Speaker 5: for the organization. So there's a huge amount of government

701
00:31:40,920 --> 00:31:42,560
Speaker 5: is very good about adding things to the plate, and

702
00:31:42,600 --> 00:31:45,360
Speaker 5: they're very bad about actually ever taking anything off. So

703
00:31:45,440 --> 00:31:48,400
Speaker 5: every time something new comes down from Congress, we add

704
00:31:48,440 --> 00:31:50,280
Speaker 5: you know, thousands of more people, We add stuff to

705
00:31:50,320 --> 00:31:52,000
Speaker 5: the plate. And then we have this list now of

706
00:31:52,040 --> 00:31:54,600
Speaker 5: twenty things, probably five of which we've been doing for

707
00:31:54,600 --> 00:31:56,720
Speaker 5: twenty years, and quite frankly, nobody even remembers while we're

708
00:31:56,720 --> 00:31:59,080
Speaker 5: doing them anymore. They've just now become like, you know,

709
00:31:59,240 --> 00:32:02,320
Speaker 5: embedded into our culture. So part of the exercise in

710
00:32:02,440 --> 00:32:04,280
Speaker 5: order to kind of make the throughput faster and to

711
00:32:04,280 --> 00:32:06,280
Speaker 5: make sure that an engineer isn't like sitting on their

712
00:32:06,320 --> 00:32:10,239
Speaker 5: hands dealing with you know, crazy bureaucracy, is we have

713
00:32:10,280 --> 00:32:12,480
Speaker 5: to go back and look at prioritization and actually remove

714
00:32:12,520 --> 00:32:15,000
Speaker 5: services that are no longer statutory, and they're no longer

715
00:32:15,080 --> 00:32:18,080
Speaker 5: valuable to the American people. So that's the exercise we've started.

716
00:32:18,080 --> 00:32:20,560
Speaker 5: That's the exercise that we're kind of is ongoing right now.

717
00:32:20,880 --> 00:32:23,080
Speaker 5: But the purpose of that will be not only to

718
00:32:23,080 --> 00:32:24,640
Speaker 5: make sure that we're hopefully spending our money in the

719
00:32:24,720 --> 00:32:27,400
Speaker 5: right places, but will also be to remove layers that

720
00:32:27,520 --> 00:32:30,840
Speaker 5: ultimately slow down the actual work product of government. The

721
00:32:30,880 --> 00:32:34,080
Speaker 5: second thing we can do is, as I mentioned, we're

722
00:32:34,160 --> 00:32:36,720
Speaker 5: really trying to kind of change the performance management culture

723
00:32:36,720 --> 00:32:39,640
Speaker 5: in government and eliminate barriers to people being able to

724
00:32:39,680 --> 00:32:42,000
Speaker 5: move through the organization and change the idea that you

725
00:32:42,040 --> 00:32:43,880
Speaker 5: can't take any risk. I mean, obviously that's a big

726
00:32:43,920 --> 00:32:46,600
Speaker 5: cultural challenge that we have in government. So everything from

727
00:32:46,680 --> 00:32:48,840
Speaker 5: down to like what your performance plan look like. Your

728
00:32:48,840 --> 00:32:52,160
Speaker 5: performance plan should have, you know, efficiency, it should have

729
00:32:52,280 --> 00:32:54,200
Speaker 5: you know, some measured amount of risk taking in there.

730
00:32:54,240 --> 00:32:56,480
Speaker 5: So things that again will speed the pace of deployment

731
00:32:56,560 --> 00:33:00,200
Speaker 5: and makes sense. The third thing we're doing is and

732
00:33:00,240 --> 00:33:01,600
Speaker 5: this is again you guys asked me, you know, how

733
00:33:01,720 --> 00:33:04,280
Speaker 5: is this program different from other programs. As we hire

734
00:33:04,280 --> 00:33:07,960
Speaker 5: these thousand engineers, ultimately they will live in various agencies. Right, So,

735
00:33:08,800 --> 00:33:10,960
Speaker 5: doctor oz At CMS says, you know, it's like, look,

736
00:33:10,960 --> 00:33:13,000
Speaker 5: I need seventy five engineers to do you know, kind

737
00:33:13,040 --> 00:33:16,240
Speaker 5: of important projects there or obviously Department of War you know,

738
00:33:16,480 --> 00:33:18,360
Speaker 5: have lots of stuff, so they're going to live in

739
00:33:18,360 --> 00:33:20,000
Speaker 5: those agencies. But what we're not going to do, which

740
00:33:20,080 --> 00:33:22,120
Speaker 5: is historically where I think these programs has failed, is

741
00:33:22,160 --> 00:33:25,200
Speaker 5: if you drop these teams just into the blob that

742
00:33:25,400 --> 00:33:28,040
Speaker 5: is the federal government, they're going to have a terrible

743
00:33:28,080 --> 00:33:30,320
Speaker 5: experience and they're going to get lost, and they'll their

744
00:33:30,320 --> 00:33:32,360
Speaker 5: two years will literally go by without them actually having

745
00:33:32,400 --> 00:33:35,400
Speaker 5: accomplished anything. So our whole goal with this is keep

746
00:33:35,440 --> 00:33:37,640
Speaker 5: these teams together. So when you know what I'm what

747
00:33:37,680 --> 00:33:40,080
Speaker 5: I'm hoping from doctor Oz is there's going to be

748
00:33:40,080 --> 00:33:41,920
Speaker 5: a team of seventy five engineers who are going to

749
00:33:41,920 --> 00:33:43,560
Speaker 5: be a team, and they're going to work on the

750
00:33:43,560 --> 00:33:46,040
Speaker 5: projects that he thinks are the most important projects. And

751
00:33:46,120 --> 00:33:48,760
Speaker 5: of course they have to integrate with the existing CIO

752
00:33:48,880 --> 00:33:51,120
Speaker 5: organization because so much of what we do has integration

753
00:33:51,200 --> 00:33:53,880
Speaker 5: back ends and all that kind of stuff. But we've

754
00:33:53,880 --> 00:33:56,680
Speaker 5: got to make sure that that happens because otherwise, you know,

755
00:33:56,800 --> 00:33:59,200
Speaker 5: you hire a random you know, intern to and you

756
00:33:59,240 --> 00:34:02,000
Speaker 5: PLoP them into ananization and they get swallowed by the beast. Basically,

757
00:34:02,000 --> 00:34:05,080
Speaker 5: it's just it's understandable why that happens. So we've got

758
00:34:05,080 --> 00:34:07,520
Speaker 5: to think about the organizational implications of bringing people in

759
00:34:07,560 --> 00:34:09,719
Speaker 5: and make sure they're set up for success. And you know,

760
00:34:09,880 --> 00:34:12,239
Speaker 5: my expectations are and certainly the conversations that I've had

761
00:34:12,239 --> 00:34:14,160
Speaker 5: with all the agency heads are that they understand that,

762
00:34:14,480 --> 00:34:16,360
Speaker 5: and they understand that for them to achieve what they

763
00:34:16,440 --> 00:34:19,120
Speaker 5: want to do, they have to create the organizational structure

764
00:34:19,200 --> 00:34:21,360
Speaker 5: that enables these guys to actually do what they're there for.

765
00:34:22,120 --> 00:34:23,799
Speaker 2: Yeah, I think that this is another thing that we

766
00:34:23,800 --> 00:34:28,080
Speaker 2: could probably say from firsthand experience, is you come to

767
00:34:28,120 --> 00:34:31,120
Speaker 2: a big organization, You've spent a long time, maybe the

768
00:34:31,200 --> 00:34:34,040
Speaker 2: first year of just getting to know the lay of

769
00:34:34,080 --> 00:34:36,080
Speaker 2: the land, who are the people that you should talk to,

770
00:34:36,160 --> 00:34:39,160
Speaker 2: et cetera. It's a real challenge. It's a real challenge

771
00:34:39,160 --> 00:34:42,000
Speaker 2: being effective of actually accomplishing anything.

772
00:34:42,080 --> 00:34:44,719
Speaker 4: The amount of time. I mean, I've been a bloomberg and.

773
00:34:44,680 --> 00:34:45,600
Speaker 3: There's all experience.

774
00:34:45,719 --> 00:34:47,480
Speaker 2: Yeah, they're still I've been here ten years. That there

775
00:34:47,560 --> 00:34:49,040
Speaker 2: is like who do I call it? It's just like

776
00:34:49,080 --> 00:34:52,440
Speaker 2: the nature of large organizations. I want to talk about

777
00:34:52,520 --> 00:34:54,680
Speaker 2: you know, people who you say, Okay, like you, we

778
00:34:54,719 --> 00:34:56,279
Speaker 2: need to do something about the fact that all these

779
00:34:56,280 --> 00:34:59,040
Speaker 2: people are going to retire, and there is this pipeline

780
00:34:59,080 --> 00:35:03,000
Speaker 2: et cetera. Set it aside, optimal levels of how big

781
00:35:03,200 --> 00:35:07,400
Speaker 2: the federal workforce should be, et cetera. One thing that

782
00:35:07,440 --> 00:35:10,680
Speaker 2: I could see as a bit of a problem about

783
00:35:10,840 --> 00:35:14,279
Speaker 2: maintaining careers if federal workers do not feel that they

784
00:35:14,320 --> 00:35:16,960
Speaker 2: are respected in their jobs. And there is a rhetoric

785
00:35:17,400 --> 00:35:20,160
Speaker 2: that is that you hear it's like the federal governments

786
00:35:20,200 --> 00:35:22,720
Speaker 2: they you know again some people it's like, oh, they're leeches,

787
00:35:23,000 --> 00:35:25,480
Speaker 2: they're part of the deep state. They're all look and

788
00:35:25,600 --> 00:35:28,200
Speaker 2: they spend money on this, and that's ways. But even

789
00:35:28,239 --> 00:35:31,279
Speaker 2: that spending looks like what I observe is spending in

790
00:35:31,280 --> 00:35:34,480
Speaker 2: the private sector, et cetera. And then you know, there's

791
00:35:34,719 --> 00:35:38,920
Speaker 2: at least from this administration, there's been these sort of

792
00:35:39,760 --> 00:35:43,680
Speaker 2: seemingly from the outside and probably from the inside, capricious

793
00:35:43,719 --> 00:35:47,400
Speaker 2: layoffs or arbitrary layoffs. And so if you respect this workforce,

794
00:35:47,960 --> 00:35:51,239
Speaker 2: then how how could you possibly have just like cut

795
00:35:51,320 --> 00:35:53,759
Speaker 2: so arbitrarily. I mean, these are this is what it

796
00:35:54,000 --> 00:35:59,440
Speaker 2: seems like when you think about building a talent pipeline.

797
00:35:59,600 --> 00:36:03,040
Speaker 2: Do you think think about this dimension and do agree

798
00:36:03,120 --> 00:36:05,160
Speaker 2: with the sort of premise of the question that it's

799
00:36:05,160 --> 00:36:07,000
Speaker 2: a challenge that no one is going to work at

800
00:36:07,000 --> 00:36:09,839
Speaker 2: a place if their boss is to some extent sort

801
00:36:09,880 --> 00:36:12,919
Speaker 2: of think they're you know, do not respect them.

802
00:36:13,680 --> 00:36:17,120
Speaker 5: Yeah, So the short answer is yes, like absolutely, this

803
00:36:17,200 --> 00:36:19,520
Speaker 5: is a critical part of making sure that the federal

804
00:36:19,600 --> 00:36:21,960
Speaker 5: government is an attractive place for the people who are

805
00:36:21,960 --> 00:36:23,919
Speaker 5: there and also the people who you know, we're trying

806
00:36:23,920 --> 00:36:26,360
Speaker 5: to recruit in. So I got a million thoughts. Let

807
00:36:26,400 --> 00:36:28,160
Speaker 5: me try to see if I can keep them in

808
00:36:28,200 --> 00:36:31,000
Speaker 5: the time we have remaining. So I'll give tell you

809
00:36:31,040 --> 00:36:32,520
Speaker 5: one thing. So just to back up, as you know,

810
00:36:32,560 --> 00:36:34,440
Speaker 5: I've worked at entries in Horowitz and Ben Horowitz, in

811
00:36:34,480 --> 00:36:37,000
Speaker 5: my mind, is one of the best CEO's, best managers

812
00:36:37,040 --> 00:36:38,920
Speaker 5: that I've ever learned from. And I had worked for

813
00:36:39,040 --> 00:36:41,000
Speaker 5: him for literally ten years prior, so for twenty six

814
00:36:41,080 --> 00:36:42,839
Speaker 5: years of my life I've had the benefit of learning

815
00:36:42,840 --> 00:36:45,120
Speaker 5: from Ben. The most important thing I learned from him

816
00:36:45,280 --> 00:36:47,759
Speaker 5: is exactly what you describe, which is when you have

817
00:36:47,880 --> 00:36:52,080
Speaker 5: restructurings in companies, basically you have broken trust with your employees. Okay, so,

818
00:36:52,640 --> 00:36:54,719
Speaker 5: particularly in the startup context, right, Like, you know, when

819
00:36:54,760 --> 00:36:57,399
Speaker 5: we were in startups, we personally recruited every single person

820
00:36:57,440 --> 00:36:59,520
Speaker 5: into that company, and we told them how we were

821
00:36:59,520 --> 00:37:01,279
Speaker 5: going to conquer the world and how awesome we were

822
00:37:01,280 --> 00:37:02,840
Speaker 5: going to be, and we believed that we weren't like

823
00:37:02,920 --> 00:37:05,520
Speaker 5: lying to them. But it turns out we were wrong.

824
00:37:05,640 --> 00:37:07,440
Speaker 5: Like the market change, we didn't have to no money

825
00:37:07,480 --> 00:37:08,960
Speaker 5: and and you know, we had to live through this

826
00:37:09,040 --> 00:37:11,279
Speaker 5: in our loud cloud opsware days we laid we had

827
00:37:11,320 --> 00:37:13,319
Speaker 5: to lay a bunch of people off, and so you've

828
00:37:13,320 --> 00:37:15,600
Speaker 5: immediately broken trust with those employees. So you are from

829
00:37:15,640 --> 00:37:18,319
Speaker 5: that day forward basically in a situation where you have

830
00:37:18,400 --> 00:37:20,920
Speaker 5: to rebuild that trust. Right, so you have to demonstrate

831
00:37:20,960 --> 00:37:23,640
Speaker 5: to them that you know it wasn't capricious and arbitrary,

832
00:37:23,719 --> 00:37:25,799
Speaker 5: and that you're not like lying to them in a jerk,

833
00:37:25,840 --> 00:37:27,880
Speaker 5: but that you actually do care about them. And it

834
00:37:27,920 --> 00:37:30,000
Speaker 5: turns out, through no fault of their own, their next

835
00:37:30,040 --> 00:37:31,480
Speaker 5: door neighbor who sat in the cube next to them

836
00:37:31,520 --> 00:37:33,960
Speaker 5: unfortunately lost their job because like we couldn't afford to

837
00:37:33,960 --> 00:37:37,319
Speaker 5: pay them anymore. So I say that only to say, look,

838
00:37:37,360 --> 00:37:40,080
Speaker 5: I recognize that very clearly. I understand that. And if

839
00:37:40,120 --> 00:37:42,080
Speaker 5: you literally go back to like the testimony I gave

840
00:37:42,120 --> 00:37:45,200
Speaker 5: when I went through my hearing, I said that very splicit.

841
00:37:45,280 --> 00:37:47,239
Speaker 5: I think that's important. All that being said, I think,

842
00:37:47,280 --> 00:37:49,359
Speaker 5: look the way to think about. Let me just give

843
00:37:49,360 --> 00:37:51,840
Speaker 5: you the context of the headcount reductions that happened in

844
00:37:51,880 --> 00:37:54,120
Speaker 5: the government, because I think there is in my mind

845
00:37:54,120 --> 00:37:55,640
Speaker 5: at least some misinformation out there that I think is

846
00:37:55,680 --> 00:37:57,400
Speaker 5: worth talking about. So it'll give you some numbers. You know,

847
00:37:57,400 --> 00:37:59,719
Speaker 5: you've got a very sophisticated audience. As at the end

848
00:37:59,760 --> 00:38:02,120
Speaker 5: of the year, roughly about three hundred and seventeen thousand

849
00:38:02,120 --> 00:38:05,320
Speaker 5: people will have exited the federal government. Okay, we started

850
00:38:05,320 --> 00:38:07,840
Speaker 5: the year at about two point four million ish roughly

851
00:38:07,960 --> 00:38:10,160
Speaker 5: in terms of civilian These are civilian employees, by the way,

852
00:38:10,200 --> 00:38:12,880
Speaker 5: so forget about the armed services, because my organization doesn't

853
00:38:12,920 --> 00:38:15,759
Speaker 5: touch that. About two point four million civilian employees will

854
00:38:15,800 --> 00:38:18,480
Speaker 5: be roughly two point one million civilian employees as of

855
00:38:18,520 --> 00:38:21,279
Speaker 5: December thirty first of this year. At the same time,

856
00:38:21,320 --> 00:38:23,560
Speaker 5: we hired about sixty six thousand employees. So on a

857
00:38:23,600 --> 00:38:26,160
Speaker 5: net basis, call it two hundred and seventy you know,

858
00:38:26,160 --> 00:38:28,280
Speaker 5: two hundred and sixty two hundred and seventy thousand roughly

859
00:38:28,400 --> 00:38:31,120
Speaker 5: kind of net departures from the government. If you go

860
00:38:31,200 --> 00:38:33,560
Speaker 5: back to that three hundred and seventeen thousand, number, ninety

861
00:38:33,640 --> 00:38:35,880
Speaker 5: two and a half percent of those people took some

862
00:38:36,080 --> 00:38:38,359
Speaker 5: form of voluntary retirement. So we had this thing called

863
00:38:38,400 --> 00:38:41,400
Speaker 5: DRP Deferred Resignation Program that was the back that was

864
00:38:41,440 --> 00:38:43,280
Speaker 5: literally half of it. One hundred and fifty four thousand

865
00:38:43,280 --> 00:38:45,520
Speaker 5: people took that, which was an eight month seference that

866
00:38:45,560 --> 00:38:48,520
Speaker 5: we gave to people to voluntarily leave government. And then

867
00:38:48,520 --> 00:38:50,480
Speaker 5: the rest of it was where there are some other programs.

868
00:38:50,480 --> 00:38:53,359
Speaker 5: The acronyms aren't relevant for your for your listeners, they're

869
00:38:53,360 --> 00:38:55,239
Speaker 5: called Viera and VISA if people want to look them up,

870
00:38:55,440 --> 00:38:58,440
Speaker 5: but those are other voluntary kind of you know, retirement programs.

871
00:38:58,640 --> 00:39:01,040
Speaker 5: And then only seven and a half percent of people

872
00:39:01,080 --> 00:39:05,200
Speaker 5: were removed through either rifts or what we call probationary employees,

873
00:39:05,200 --> 00:39:07,400
Speaker 5: which is in the government, if you've been if you've

874
00:39:07,440 --> 00:39:10,319
Speaker 5: been there for one or two years, it depending upon

875
00:39:10,440 --> 00:39:12,520
Speaker 5: the job. You're kind of the closest thing to an

876
00:39:12,520 --> 00:39:14,320
Speaker 5: app will employee that we have in the federal government.

877
00:39:14,360 --> 00:39:16,840
Speaker 5: And so that we're about seven thousand of those probitionary

878
00:39:16,880 --> 00:39:19,719
Speaker 5: employers who were removed in about seventeen thousand people who

879
00:39:19,760 --> 00:39:22,239
Speaker 5: were ultimately riffed, you know, reductions and forced as a

880
00:39:22,239 --> 00:39:24,799
Speaker 5: result of restructings. So I say that number one, not

881
00:39:24,840 --> 00:39:26,440
Speaker 5: to I want to be totally clear with your listeners,

882
00:39:26,600 --> 00:39:28,200
Speaker 5: not to make light of any of this. Obviously those

883
00:39:28,200 --> 00:39:30,720
Speaker 5: are real people with real jobs and real families and stuff.

884
00:39:30,760 --> 00:39:33,799
Speaker 5: But the numbers, again, you know, kind of I think

885
00:39:33,840 --> 00:39:35,680
Speaker 5: bear out what we tried to do in the government,

886
00:39:35,719 --> 00:39:38,520
Speaker 5: which is tell people that things were changing, tell people

887
00:39:38,600 --> 00:39:40,200
Speaker 5: that you know, we do have a different view as

888
00:39:40,239 --> 00:39:42,000
Speaker 5: to what, you know, we think the federal government should

889
00:39:42,040 --> 00:39:46,040
Speaker 5: be doing, and give people an opportunity to voluntarily say, okay,

890
00:39:46,040 --> 00:39:47,480
Speaker 5: this is not what I signed up for, which is

891
00:39:47,480 --> 00:39:49,120
Speaker 5: a very noble thing to do, and say, you know,

892
00:39:49,120 --> 00:39:50,719
Speaker 5: it's time for me to go do something different. So

893
00:39:51,200 --> 00:39:52,560
Speaker 5: ninety two and a half percent of the people who

894
00:39:52,640 --> 00:39:55,160
Speaker 5: left the government did so because they decided, you know,

895
00:39:55,239 --> 00:39:57,000
Speaker 5: this wasn't an environment they wanted to be in. So

896
00:39:57,719 --> 00:39:59,680
Speaker 5: that being said, now, look I totally agree with you,

897
00:39:59,719 --> 00:40:01,880
Speaker 5: which is we have to demonstrate to the people that

898
00:40:01,920 --> 00:40:03,399
Speaker 5: are there and to the people who are coming in

899
00:40:03,600 --> 00:40:05,960
Speaker 5: that you know, we obviously care about them and that

900
00:40:06,000 --> 00:40:08,680
Speaker 5: we think they do valuable work. And I think that's

901
00:40:08,719 --> 00:40:10,640
Speaker 5: the case for the vast fast majority of people there

902
00:40:10,680 --> 00:40:12,719
Speaker 5: do great work, like any organization. Yes, there are some

903
00:40:12,760 --> 00:40:15,040
Speaker 5: people who are not doing great work, and we have

904
00:40:15,080 --> 00:40:17,080
Speaker 5: a system that makes it very hard to hold them accountable.

905
00:40:17,080 --> 00:40:18,719
Speaker 5: We're going to try to fix that. But look, that's

906
00:40:18,719 --> 00:40:21,200
Speaker 5: true of any organization. So I don't think it doesn't

907
00:40:21,200 --> 00:40:23,040
Speaker 5: make sense that kind of demean the broad federal government

908
00:40:23,040 --> 00:40:24,880
Speaker 5: based upon the work of a few. But we have

909
00:40:24,920 --> 00:40:26,680
Speaker 5: to do that, and we also have to be honest

910
00:40:26,719 --> 00:40:29,360
Speaker 5: and transparent with our people, which I believe very strongly,

911
00:40:29,400 --> 00:40:31,600
Speaker 5: and is Look, we can't be all things to all people,

912
00:40:31,640 --> 00:40:34,719
Speaker 5: Like we're running two trillion dollars deficits. That's not sustainable.

913
00:40:35,040 --> 00:40:36,600
Speaker 5: And so look, we've got to be smarter. We have

914
00:40:36,640 --> 00:40:38,960
Speaker 5: to use technology to find ways to improve efficiency. We

915
00:40:39,040 --> 00:40:41,160
Speaker 5: have to think about if we're doing twenty things, are

916
00:40:41,200 --> 00:40:43,400
Speaker 5: all twenty of them still required? Do they still add value?

917
00:40:43,680 --> 00:40:46,120
Speaker 5: Because ultimately we're stewards of the taxpayer dollars, Like this

918
00:40:46,160 --> 00:40:48,200
Speaker 5: is not our money we're spending, and so I think

919
00:40:48,200 --> 00:40:50,279
Speaker 5: it's incumbent upon everybody. And this is the message that

920
00:40:50,280 --> 00:40:52,279
Speaker 5: I'm trying to get across to my team and to

921
00:40:52,280 --> 00:40:54,799
Speaker 5: the rest of the government. Is efficiency doesn't mean just

922
00:40:54,840 --> 00:40:57,400
Speaker 5: cutting cost. Efficiency means are you thinking about delivering a

923
00:40:57,400 --> 00:41:00,000
Speaker 5: better service but doing so you know, at or below.

924
00:41:00,000 --> 00:41:02,399
Speaker 5: So like what you do today, because you're thinking about

925
00:41:02,480 --> 00:41:05,520
Speaker 5: organizational changes, you're thinking about technology. Like to me, that

926
00:41:05,680 --> 00:41:08,560
Speaker 5: is just like quite frankly required of anybody in any

927
00:41:08,640 --> 00:41:12,000
Speaker 5: organization to do. And so that to me is like

928
00:41:12,080 --> 00:41:14,400
Speaker 5: what I'm trying to convey to people. And yes, that

929
00:41:14,440 --> 00:41:16,320
Speaker 5: means that there may be some jobs that are impacted

930
00:41:16,360 --> 00:41:19,040
Speaker 5: because of technology, but those things are going to happen,

931
00:41:19,160 --> 00:41:21,600
Speaker 5: whether you know kind of you know, we want them

932
00:41:21,640 --> 00:41:23,600
Speaker 5: to happen or not. The question is do we actually

933
00:41:23,600 --> 00:41:25,560
Speaker 5: set up the workforce and do training and do all

934
00:41:25,600 --> 00:41:26,759
Speaker 5: the kinds of things we need to do to make

935
00:41:26,760 --> 00:41:29,319
Speaker 5: sure that people have opportunities in light of what is

936
00:41:29,360 --> 00:41:31,920
Speaker 5: a very very rapidly changing, you know, technological market.

937
00:41:32,160 --> 00:41:34,200
Speaker 3: So I get that part of the problem that you're

938
00:41:34,200 --> 00:41:37,319
Speaker 3: trying to solve is an operational one. This idea of

939
00:41:37,440 --> 00:41:41,239
Speaker 3: like a COBOYL programmer who's been there for decades and

940
00:41:41,280 --> 00:41:44,399
Speaker 3: he retires and walks out the door, and no one

941
00:41:44,480 --> 00:41:46,400
Speaker 3: knows how to do what he was doing, or no

942
00:41:46,440 --> 00:41:49,839
Speaker 3: one understands the system that he's basically had control over

943
00:41:50,320 --> 00:41:53,640
Speaker 3: for ages. But at the same time, you know, we're

944
00:41:53,680 --> 00:41:56,840
Speaker 3: talking about a decent amount of turnover a lot of

945
00:41:56,920 --> 00:42:01,920
Speaker 3: new employees. If this goes the right way, how are you,

946
00:42:02,360 --> 00:42:06,680
Speaker 3: I guess, guiding for safety. Are there any sort of

947
00:42:06,719 --> 00:42:10,520
Speaker 3: like institutional guardrails that you're putting in so that the

948
00:42:10,600 --> 00:42:13,960
Speaker 3: government doesn't actually end up breaking things?

949
00:42:15,120 --> 00:42:20,200
Speaker 5: Yeah, I mean, look, there are you will hopefully appreciate

950
00:42:20,239 --> 00:42:22,440
Speaker 5: this bill. Look, there are a ton of institutional guardrails.

951
00:42:22,560 --> 00:42:24,400
Speaker 5: You know. In my mind, like the problem is we

952
00:42:24,440 --> 00:42:27,319
Speaker 5: have too many institutional guardrails. And again, you know, rational

953
00:42:27,360 --> 00:42:29,799
Speaker 5: people may disagree with me, but you know, I can

954
00:42:29,880 --> 00:42:32,799
Speaker 5: just tell you, like the entire culture of everything we do.

955
00:42:33,080 --> 00:42:35,799
Speaker 5: Any everybody I've talked to in government and luckily we've

956
00:42:35,800 --> 00:42:37,880
Speaker 5: had lots of kind of impact on making change ear on,

957
00:42:38,320 --> 00:42:41,239
Speaker 5: everybody is like, we can't do that because of X, right,

958
00:42:41,280 --> 00:42:44,640
Speaker 5: And the answer is either because like it's statutory and

959
00:42:44,680 --> 00:42:46,520
Speaker 5: the honest answers when you dig and dig and dig

960
00:42:46,520 --> 00:42:48,880
Speaker 5: the realities it's not statutory, Like we've built up this

961
00:42:48,960 --> 00:42:51,840
Speaker 5: like urban myth over time that something is statutory, or

962
00:42:51,880 --> 00:42:55,080
Speaker 5: as I mentioned, because like I'm terrified that I'm gonna

963
00:42:55,080 --> 00:42:57,520
Speaker 5: get a GAO report, an inspector General report, or a

964
00:42:57,560 --> 00:43:00,840
Speaker 5: congressional inquiry. And like, we we have built such a

965
00:43:00,840 --> 00:43:03,880
Speaker 5: compliance culture that we are just literally like unable to

966
00:43:03,920 --> 00:43:07,360
Speaker 5: do anything that actually is kind of modern and you know,

967
00:43:07,560 --> 00:43:10,200
Speaker 5: forward thinking in terms of government. And so this is

968
00:43:10,200 --> 00:43:11,480
Speaker 5: what I'm this is what I'm trying to do. And

969
00:43:11,680 --> 00:43:13,520
Speaker 5: as I told you guys from the beginning, like, yes,

970
00:43:13,560 --> 00:43:15,120
Speaker 5: this is not the private sector. We're not going to

971
00:43:15,160 --> 00:43:16,960
Speaker 5: take crazy risks. We're not going to like go like

972
00:43:17,000 --> 00:43:19,759
Speaker 5: put everything on black, you know, in the casino. But

973
00:43:20,320 --> 00:43:22,600
Speaker 5: we can have a rational conversation, which is okay, like

974
00:43:22,640 --> 00:43:24,520
Speaker 5: what are the risks we're taking and what's the upside.

975
00:43:24,560 --> 00:43:26,200
Speaker 5: So if you'll give me a minute, let me give

976
00:43:26,200 --> 00:43:28,920
Speaker 5: you a very very tangible example for your listeners. So

977
00:43:29,040 --> 00:43:32,000
Speaker 5: I mentioned to you that we handle retirement services and

978
00:43:32,040 --> 00:43:34,839
Speaker 5: it's a very manual process, and so like it takes

979
00:43:34,880 --> 00:43:37,680
Speaker 5: us a really long time to get people formally retired.

980
00:43:37,719 --> 00:43:39,360
Speaker 5: So you quit from you know, you retire from the

981
00:43:39,400 --> 00:43:42,960
Speaker 5: government on September thirtieth. In the current process, you know,

982
00:43:43,080 --> 00:43:45,719
Speaker 5: it takes sixty ninety one hundred and twenty days sometimes

983
00:43:46,000 --> 00:43:48,719
Speaker 5: for you to actually get a check because we have

984
00:43:48,800 --> 00:43:51,759
Speaker 5: this ridiculous, manually paper process that takes forever to for

985
00:43:51,880 --> 00:43:53,920
Speaker 5: us to calculate what are you actually entitled to and

986
00:43:53,960 --> 00:43:55,360
Speaker 5: to make sure you're getting the right money and all

987
00:43:55,400 --> 00:43:58,080
Speaker 5: that stuff. So that's a huge burden on retire reach. Right,

988
00:43:58,080 --> 00:44:00,040
Speaker 5: You've been getting a paycheck every two weeks for the

989
00:44:00,120 --> 00:44:02,200
Speaker 5: last forty years, and now I'm going to tell you,

990
00:44:02,280 --> 00:44:04,520
Speaker 5: like sorry, like go manage your cash flow some other

991
00:44:04,560 --> 00:44:07,000
Speaker 5: way while I'm doing this, Like, that's terrible in my mind,

992
00:44:07,160 --> 00:44:09,680
Speaker 5: and it's a complete disservice and completely disrespectful to people

993
00:44:09,680 --> 00:44:12,200
Speaker 5: who are retired with the government. So we have a

994
00:44:12,239 --> 00:44:15,080
Speaker 5: process in the Retirement Services thing called interim pay right,

995
00:44:15,120 --> 00:44:17,200
Speaker 5: which is as the name sounds, right, The idea is, okay,

996
00:44:17,360 --> 00:44:19,440
Speaker 5: can we get you some money on an interim basis

997
00:44:19,440 --> 00:44:21,400
Speaker 5: that may not be one hundred percent accurate, but at

998
00:44:21,480 --> 00:44:24,680
Speaker 5: least helps bridge this gap between you when you retire

999
00:44:24,680 --> 00:44:27,440
Speaker 5: and when you get your actual annuity check. Okay. So

1000
00:44:28,160 --> 00:44:30,520
Speaker 5: going into and then the couple this with we've got

1001
00:44:30,560 --> 00:44:32,319
Speaker 5: this massive You know, we have had a lot of

1002
00:44:32,360 --> 00:44:34,560
Speaker 5: exits in government. So the three hundred and seventeen thousand

1003
00:44:34,560 --> 00:44:36,960
Speaker 5: I mentioned, not all of those are retirees, but like

1004
00:44:37,040 --> 00:44:39,400
Speaker 5: on average people, we have about one hundred to one

1005
00:44:39,480 --> 00:44:41,680
Speaker 5: hundred and fifty thousand exits a year, so we're you know,

1006
00:44:41,719 --> 00:44:43,479
Speaker 5: more than double the number. So and as a result,

1007
00:44:43,520 --> 00:44:45,760
Speaker 5: there's more than double the number of retirees that are happening.

1008
00:44:45,800 --> 00:44:49,480
Speaker 5: So you've got this terrible papal process like massive volumes,

1009
00:44:49,960 --> 00:44:51,880
Speaker 5: and we're just going to make life miserable for all

1010
00:44:51,920 --> 00:44:54,879
Speaker 5: these people who can't get money. So I asked the team.

1011
00:44:54,920 --> 00:44:57,280
Speaker 5: I said, okay, how many people go through interim pay today?

1012
00:44:57,360 --> 00:44:59,640
Speaker 5: This is kind of pre are change, and it's about

1013
00:44:59,640 --> 00:45:01,640
Speaker 5: like percent of people. And I was like, well, what's

1014
00:45:01,640 --> 00:45:04,400
Speaker 5: the problem. Layer like, well, that's how we've always done it,

1015
00:45:04,480 --> 00:45:06,440
Speaker 5: so you know, again, part of the answer was statutory,

1016
00:45:06,440 --> 00:45:10,040
Speaker 5: which it's not statutory. And they're like, well, there's huge

1017
00:45:10,120 --> 00:45:13,040
Speaker 5: risk to the system because if we pay somebody more

1018
00:45:13,719 --> 00:45:16,359
Speaker 5: and then they die between the time we paid them

1019
00:45:16,400 --> 00:45:19,960
Speaker 5: their interim payment and we get their actual annuity, there

1020
00:45:20,000 --> 00:45:21,920
Speaker 5: may not be any assets left into the estate, and

1021
00:45:21,960 --> 00:45:24,760
Speaker 5: so we might compromise the fidelity of the trust fund. Okay,

1022
00:45:25,120 --> 00:45:26,680
Speaker 5: Now keep in mind, this is a one point two

1023
00:45:26,680 --> 00:45:29,480
Speaker 5: trillion dollar trust fund that is actually cashful, a positive.

1024
00:45:29,520 --> 00:45:32,279
Speaker 5: So among trust funds for retirement. It's actually fairy rare.

1025
00:45:32,760 --> 00:45:34,760
Speaker 5: But we earned two and a half percent on government

1026
00:45:34,760 --> 00:45:37,120
Speaker 5: treasuries a year, which is horrendously bad because we're only

1027
00:45:37,120 --> 00:45:40,319
Speaker 5: allowed to invest in government treasuries but not with Spain.

1028
00:45:40,400 --> 00:45:42,479
Speaker 5: That it's actually still pet cashul the positive and fully

1029
00:45:42,480 --> 00:45:45,840
Speaker 5: funded like almost in any any circumstance. So we finally

1030
00:45:45,880 --> 00:45:47,279
Speaker 5: went through the math, and I said, let's go through

1031
00:45:47,280 --> 00:45:49,759
Speaker 5: the math, and let's assume, you know, we get it

1032
00:45:49,760 --> 00:45:51,960
Speaker 5: wrong x amount of time and all this sudden stuff.

1033
00:45:52,160 --> 00:45:53,839
Speaker 5: And we did a bunch of math, and I said, look,

1034
00:45:53,880 --> 00:45:56,520
Speaker 5: if we're wrong one hundred percent of the time, it

1035
00:45:56,560 --> 00:45:59,799
Speaker 5: could cost us a couple million dollars over payments at

1036
00:46:00,080 --> 00:46:02,480
Speaker 5: one point two trillion dollars trust fund. But we can

1037
00:46:02,520 --> 00:46:04,799
Speaker 5: shrink from ninety to one hundred and twenty days the

1038
00:46:04,840 --> 00:46:08,320
Speaker 5: time somebody gets a payment to either zero to seven

1039
00:46:08,400 --> 00:46:11,239
Speaker 5: days if we put more people through this interim pay process.

1040
00:46:11,320 --> 00:46:13,000
Speaker 5: So in my mind, like this is just the most

1041
00:46:13,000 --> 00:46:15,200
Speaker 5: obvious thing to do, and we are doing it by

1042
00:46:15,200 --> 00:46:17,000
Speaker 5: the way we decided to do it. But like it's

1043
00:46:17,040 --> 00:46:19,320
Speaker 5: a great example, at least in my mind, of where

1044
00:46:20,160 --> 00:46:22,799
Speaker 5: we have so ingrained in the culture that risk, any

1045
00:46:22,880 --> 00:46:25,919
Speaker 5: kind of risk is bad without actually people being willing

1046
00:46:25,920 --> 00:46:27,759
Speaker 5: to have the conversation to say, Okay, like what is

1047
00:46:27,800 --> 00:46:30,360
Speaker 5: the cost of that risk relative to the upside opportunity

1048
00:46:30,360 --> 00:46:33,560
Speaker 5: that that taking that risk entails. And I'm perfectly happy,

1049
00:46:33,719 --> 00:46:35,000
Speaker 5: you know, I told them, I said, look, you don't

1050
00:46:35,000 --> 00:46:37,000
Speaker 5: need to protect me if it turns out some congress

1051
00:46:37,000 --> 00:46:39,040
Speaker 5: person wants to yell at me because like, I took

1052
00:46:39,040 --> 00:46:41,359
Speaker 5: a modicum of risk in order to like make you know,

1053
00:46:41,480 --> 00:46:43,799
Speaker 5: hundreds of thousands of retirees lives better and not have

1054
00:46:43,840 --> 00:46:46,080
Speaker 5: to like take loans out after they've given their career

1055
00:46:46,160 --> 00:46:48,640
Speaker 5: service to the federal government. Like, I'm totally fine defending that.

1056
00:46:48,640 --> 00:46:51,120
Speaker 5: I think that's a perfectly rational answer. So that, to

1057
00:46:51,160 --> 00:46:51,960
Speaker 5: me is the issue.

1058
00:46:52,320 --> 00:46:54,560
Speaker 2: I have one least question, and maybe I'll sort of

1059
00:46:54,560 --> 00:46:56,640
Speaker 2: ask you a little bit to put your VC hat on,

1060
00:46:56,680 --> 00:46:59,160
Speaker 2: maybe because maybe you will go back to VC when

1061
00:46:59,200 --> 00:47:01,719
Speaker 2: this level is over. But who knows someone who knows

1062
00:47:01,760 --> 00:47:04,319
Speaker 2: a lot a lot about technology. So we had an

1063
00:47:04,360 --> 00:47:07,160
Speaker 2: episode recently with Tyler Cowhen we talk about one of

1064
00:47:07,160 --> 00:47:10,480
Speaker 2: the weird things, Yeah, is that you know, the AI.

1065
00:47:10,520 --> 00:47:12,279
Speaker 2: We haven't even really talked about AI. So this's my

1066
00:47:12,320 --> 00:47:15,000
Speaker 2: AI question. AI the tech make you know, makes your

1067
00:47:15,080 --> 00:47:16,960
Speaker 2: jaws drop when you see it. On the other hand,

1068
00:47:17,000 --> 00:47:19,359
Speaker 2: the impact so far on the economy or the real

1069
00:47:19,360 --> 00:47:21,840
Speaker 2: world is sort of muted. It maybe is like you

1070
00:47:21,920 --> 00:47:24,640
Speaker 2: might have, given the capabilities, you might have expected more

1071
00:47:25,360 --> 00:47:28,600
Speaker 2: more revolutionary change. And his thesis that he put forth

1072
00:47:28,760 --> 00:47:30,879
Speaker 2: was like, we're really not going to see that sort

1073
00:47:30,920 --> 00:47:33,880
Speaker 2: of meaningful, impactful diffusion of the tech until we have

1074
00:47:33,960 --> 00:47:37,560
Speaker 2: organizations that to some extent are built ground up from

1075
00:47:37,719 --> 00:47:40,080
Speaker 2: AI that you know, yes, you can maybe get some

1076
00:47:40,120 --> 00:47:43,239
Speaker 2: productivity gains here and there by putting AI into some

1077
00:47:43,320 --> 00:47:46,160
Speaker 2: existing workflow, but that the real breakthroughs will come from

1078
00:47:46,160 --> 00:47:49,280
Speaker 2: whether it's companies or so forth, that are truly AI

1079
00:47:49,440 --> 00:47:52,560
Speaker 2: native from day one. I'm curious, you know, when you

1080
00:47:52,600 --> 00:47:55,440
Speaker 2: think about you know, again going back to the VC world, A,

1081
00:47:56,160 --> 00:48:00,680
Speaker 2: does this feel intuitive to you that the real deployers

1082
00:48:00,719 --> 00:48:03,160
Speaker 2: will be AI native And then does that have any

1083
00:48:03,239 --> 00:48:06,120
Speaker 2: implications for like, we're not going to have a revolution,

1084
00:48:06,560 --> 00:48:09,440
Speaker 2: I guess hopefully at the at the federal level, are

1085
00:48:09,480 --> 00:48:11,839
Speaker 2: not going to have a complete destruction and starting over.

1086
00:48:12,200 --> 00:48:15,520
Speaker 2: Does that have any implications then for thinking about how

1087
00:48:15,560 --> 00:48:18,879
Speaker 2: AI will be diffused within federal processes?

1088
00:48:20,080 --> 00:48:22,560
Speaker 5: Yeah? So obviously, Yeah, Tyler is a brilliant guy, so

1089
00:48:23,000 --> 00:48:25,320
Speaker 5: far from me to disagree with him. But let me

1090
00:48:25,360 --> 00:48:26,799
Speaker 5: give you just a couple thoughts on what he said.

1091
00:48:26,840 --> 00:48:29,440
Speaker 5: So look, I guess I generally agree with most of

1092
00:48:29,440 --> 00:48:31,920
Speaker 5: what he says, which is, and this is true by

1093
00:48:31,920 --> 00:48:34,239
Speaker 5: the way I think of adoption of most new technologies,

1094
00:48:34,239 --> 00:48:36,600
Speaker 5: which is, we go to the common use cases and

1095
00:48:36,600 --> 00:48:39,200
Speaker 5: we think about incrementality as the first thing to do it.

1096
00:48:39,239 --> 00:48:42,480
Speaker 5: So if you go way back when Mark Andresen told

1097
00:48:42,480 --> 00:48:43,960
Speaker 5: me this, so if it's if it's made up, I'll

1098
00:48:43,960 --> 00:48:46,759
Speaker 5: blame him. You know, when when we when we first

1099
00:48:46,800 --> 00:48:48,840
Speaker 5: when we first had movies, you know, actual like you know,

1100
00:48:49,239 --> 00:48:52,680
Speaker 5: moving pictures, they were just plays basically that people filmed, right,

1101
00:48:52,719 --> 00:48:55,280
Speaker 5: so like they weren't like producing them, they weren't new content.

1102
00:48:55,320 --> 00:48:57,319
Speaker 5: It was literally just we said, Wow, we have this

1103
00:48:57,320 --> 00:48:59,560
Speaker 5: cool thing called a camera. Let's film a play and

1104
00:48:59,560 --> 00:49:03,000
Speaker 5: then go in a thing. Now it's obviously that's not

1105
00:49:03,040 --> 00:49:05,359
Speaker 5: that relevant to this conversation, but my point is just

1106
00:49:05,440 --> 00:49:08,239
Speaker 5: like we tend to go to these very common incremental

1107
00:49:08,640 --> 00:49:11,560
Speaker 5: use cases for things. And so look, I believe that

1108
00:49:11,760 --> 00:49:14,080
Speaker 5: if you look at any major fortune five hundred company,

1109
00:49:14,120 --> 00:49:16,600
Speaker 5: I guarantee you that every single board conversation is what

1110
00:49:16,600 --> 00:49:18,680
Speaker 5: the heck are you doing about AI? And how do

1111
00:49:18,760 --> 00:49:21,520
Speaker 5: I get three to five to seven percent greater EPs

1112
00:49:21,560 --> 00:49:24,759
Speaker 5: growth relative to last year because you can fix my

1113
00:49:24,800 --> 00:49:27,000
Speaker 5: customer support problem or stuff like that. So I think

1114
00:49:27,040 --> 00:49:29,399
Speaker 5: that's what's happening right now. I do agree that, Yes,

1115
00:49:29,480 --> 00:49:31,920
Speaker 5: what often happens is then there will be a new

1116
00:49:31,960 --> 00:49:33,680
Speaker 5: generation of companies And we see this in the venture

1117
00:49:33,680 --> 00:49:36,359
Speaker 5: capital world too already. Which is the most obvious use

1118
00:49:36,360 --> 00:49:38,160
Speaker 5: cases that people did is there's a ton of companies

1119
00:49:38,160 --> 00:49:40,799
Speaker 5: who have customer support applications for AI, and there's a

1120
00:49:40,800 --> 00:49:42,600
Speaker 5: ton of companies who have you know, how do we

1121
00:49:42,719 --> 00:49:45,640
Speaker 5: help lawyers read documents better using AI? And that's great

1122
00:49:45,680 --> 00:49:48,880
Speaker 5: and those companies are fantastic, but the real breakthrough is

1123
00:49:48,880 --> 00:49:51,279
Speaker 5: going to be yes, like when someone says, Okay, we're

1124
00:49:51,280 --> 00:49:53,520
Speaker 5: not just gonna make salesforce dot com more efficient, but like,

1125
00:49:53,560 --> 00:49:56,480
Speaker 5: we're just going to rethink customer relationship management with an

1126
00:49:56,520 --> 00:49:59,359
Speaker 5: AI first mentality, and like, the AI should know who

1127
00:49:59,360 --> 00:50:01,400
Speaker 5: my customers are. It should anticipate what I want to do,

1128
00:50:01,440 --> 00:50:03,480
Speaker 5: and it should send me emails and texts and say, like,

1129
00:50:03,520 --> 00:50:05,279
Speaker 5: you haven't talked to this person in five weeks and

1130
00:50:05,719 --> 00:50:07,600
Speaker 5: I just saw that they you know, spoke at this

1131
00:50:07,640 --> 00:50:09,480
Speaker 5: conference and they talked about this, and you know you're

1132
00:50:09,480 --> 00:50:11,279
Speaker 5: not following up on it. So that's where we will

1133
00:50:11,320 --> 00:50:13,520
Speaker 5: get and I have no doubt we will get there,

1134
00:50:13,600 --> 00:50:15,799
Speaker 5: quite frankly. And look, I'm an optimist by nature. You

1135
00:50:15,800 --> 00:50:17,400
Speaker 5: have to be to be in the venture capital business.

1136
00:50:17,480 --> 00:50:19,080
Speaker 5: But yes, I would agree with him generally. I think

1137
00:50:19,120 --> 00:50:20,640
Speaker 5: that's right. What does it mean for the federal government?

1138
00:50:21,280 --> 00:50:24,840
Speaker 5: Very simple, which is that if the private sector is incremental,

1139
00:50:24,960 --> 00:50:27,360
Speaker 5: the federal government's going to be like, you know, a

1140
00:50:27,480 --> 00:50:30,600
Speaker 5: hundred x incremental relative to a hundred x less incremental.

1141
00:50:30,600 --> 00:50:33,040
Speaker 5: I guess I would describe it, but that's in my mind.

1142
00:50:33,040 --> 00:50:35,720
Speaker 5: That's okay. Like, so again I've told everybody in my team,

1143
00:50:36,200 --> 00:50:37,640
Speaker 5: I'm not asking you. I don't want you to go

1144
00:50:37,680 --> 00:50:40,160
Speaker 5: build like the twenty forty AI plan because we have

1145
00:50:40,239 --> 00:50:41,800
Speaker 5: no idea what the heck that's going to look like.

1146
00:50:41,880 --> 00:50:43,799
Speaker 5: That's a total waste of our time. What I want

1147
00:50:43,800 --> 00:50:45,560
Speaker 5: you to do is look at a process you do

1148
00:50:45,640 --> 00:50:48,640
Speaker 5: today and figure out can you use an AI tool

1149
00:50:48,680 --> 00:50:51,440
Speaker 5: to help you get three, five, ten percent greater performance

1150
00:50:51,440 --> 00:50:53,680
Speaker 5: out of your organization? Right, So we do a ton

1151
00:50:53,719 --> 00:50:55,000
Speaker 5: of I'll give you very simple say, we do a

1152
00:50:55,040 --> 00:50:57,360
Speaker 5: ton of regulation writing obviously, because you know that's what

1153
00:50:57,400 --> 00:50:59,960
Speaker 5: we're ultimately in the business of government is producing regulation,

1154
00:51:00,520 --> 00:51:02,560
Speaker 5: and we're about to put out a regulation. We got

1155
00:51:02,600 --> 00:51:06,240
Speaker 5: forty thousand public comments on this regulation. We literally assigned

1156
00:51:06,280 --> 00:51:08,440
Speaker 5: each one of those forty thousand comments to a person

1157
00:51:08,520 --> 00:51:12,160
Speaker 5: to read and to draft a response to. Now, to me,

1158
00:51:12,280 --> 00:51:15,200
Speaker 5: that's just insane, right, And I'm not saying that like

1159
00:51:15,239 --> 00:51:17,080
Speaker 5: the AI, we should just like the AI should do it.

1160
00:51:17,120 --> 00:51:20,680
Speaker 5: But like, clearly AI is very good at you know, summarization,

1161
00:51:21,120 --> 00:51:24,359
Speaker 5: at you know, helping people think about stuff like that's like,

1162
00:51:24,440 --> 00:51:26,840
Speaker 5: that's all. If we did that, like, we would unleash

1163
00:51:26,920 --> 00:51:29,520
Speaker 5: so much creativity and opportunity to government if we just said,

1164
00:51:29,880 --> 00:51:32,839
Speaker 5: take the very basic stuff we're doing today and use

1165
00:51:32,920 --> 00:51:35,839
Speaker 5: your you know, companion, chat, GPT or XAI, whatever your

1166
00:51:35,880 --> 00:51:38,680
Speaker 5: favorite tool is, and just you know, eke out a

1167
00:51:38,719 --> 00:51:40,799
Speaker 5: little bit of productivity. So anyway, that's how I think

1168
00:51:40,840 --> 00:51:42,680
Speaker 5: about it. So, yes, the government is not gonna we're

1169
00:51:42,719 --> 00:51:45,560
Speaker 5: not gonna transform government overnight. But what we're trying to do,

1170
00:51:45,600 --> 00:51:47,120
Speaker 5: and what the US Tech Force is trying to do,

1171
00:51:47,239 --> 00:51:49,120
Speaker 5: is can we get the right people in here who

1172
00:51:49,239 --> 00:51:51,880
Speaker 5: understand those things that we start to drive ourselves forward

1173
00:51:52,200 --> 00:51:53,759
Speaker 5: so we don't wake up five years from now and

1174
00:51:53,760 --> 00:51:56,400
Speaker 5: find that we're the last dinosaur basically, and you know

1175
00:51:56,440 --> 00:51:57,480
Speaker 5: the world has passed us by.

1176
00:51:57,880 --> 00:52:00,520
Speaker 3: So you sort of mentioned it just then, But can

1177
00:52:00,560 --> 00:52:02,799
Speaker 3: you talk at all about what kind of platforms you're

1178
00:52:02,920 --> 00:52:06,359
Speaker 3: using currently or what kind of models, because I you know,

1179
00:52:06,800 --> 00:52:09,000
Speaker 3: I'm sure some people would find it very ironic if

1180
00:52:09,040 --> 00:52:11,959
Speaker 3: the federal government was using deep seek or something like that.

1181
00:52:12,920 --> 00:52:15,440
Speaker 3: And I'm curious whether you lean towards like an open

1182
00:52:15,480 --> 00:52:18,840
Speaker 3: source thing or a closed source thing because of privacy concerns.

1183
00:52:19,440 --> 00:52:21,920
Speaker 5: Right now, the honest answer is we're at the very

1184
00:52:22,000 --> 00:52:24,560
Speaker 5: very early stages, which is most of what people are doing.

1185
00:52:24,640 --> 00:52:24,680
Speaker 3: It.

1186
00:52:24,800 --> 00:52:27,640
Speaker 5: Literally, this may make your head spin because it made mine.

1187
00:52:27,920 --> 00:52:30,040
Speaker 5: Until about a month ago, we did not even have

1188
00:52:30,120 --> 00:52:33,000
Speaker 5: chat GPT on our government desktops. So, like you know,

1189
00:52:33,080 --> 00:52:35,000
Speaker 5: I literally would sit there on my phone and ask

1190
00:52:35,080 --> 00:52:37,400
Speaker 5: chat GIPT questions myerson my personal phone, just to be

1191
00:52:37,480 --> 00:52:39,719
Speaker 5: totally clear on my personal phone, and then I would

1192
00:52:39,760 --> 00:52:41,880
Speaker 5: go back to my work laptop and do stuff. So

1193
00:52:42,320 --> 00:52:44,000
Speaker 5: as of about a month or two ago, we actually

1194
00:52:44,000 --> 00:52:47,319
Speaker 5: have chat GPT, we have Xai. Now we might even

1195
00:52:47,400 --> 00:52:49,200
Speaker 5: have We not even have a third one. I don't

1196
00:52:49,200 --> 00:52:51,759
Speaker 5: even know. And of course we're the government's a big

1197
00:52:51,760 --> 00:52:54,480
Speaker 5: Microsoft client. So we have my soft copilot for an

1198
00:52:54,480 --> 00:52:57,040
Speaker 5: individual user in government that's basically the state that is

1199
00:52:57,160 --> 00:53:00,320
Speaker 5: like the state of you know, kind of advanced automation basse.

1200
00:53:00,360 --> 00:53:02,680
Speaker 5: We literally have it on our laptops. Now for the

1201
00:53:02,719 --> 00:53:05,839
Speaker 5: development work that's happening. You're absolutely right, which is, Look,

1202
00:53:05,880 --> 00:53:08,040
Speaker 5: there are major security issues that we all have to

1203
00:53:08,040 --> 00:53:10,920
Speaker 5: think about. Look, I'm a big believer as you can

1204
00:53:10,960 --> 00:53:13,920
Speaker 5: imagine an open source and certainly our firm entries in Horowitz.

1205
00:53:13,920 --> 00:53:16,040
Speaker 5: You know, you've seen Mark make lots of public comments

1206
00:53:16,040 --> 00:53:18,399
Speaker 5: about this. So I think the government will use open

1207
00:53:18,400 --> 00:53:20,560
Speaker 5: source and can appropriately do so. Clearly we're not going

1208
00:53:20,600 --> 00:53:23,839
Speaker 5: to use like the deep seat commercial version, but you know,

1209
00:53:23,920 --> 00:53:25,759
Speaker 5: so much of that stuff is open source, and if

1210
00:53:25,760 --> 00:53:27,719
Speaker 5: you bring it in you know, to your environment, and

1211
00:53:27,760 --> 00:53:29,520
Speaker 5: you make sure that you get the right security standards.

1212
00:53:30,320 --> 00:53:32,439
Speaker 5: You know, there's tremendous value to develop on open source.

1213
00:53:32,440 --> 00:53:33,759
Speaker 5: But I would just just to give you a sense

1214
00:53:33,800 --> 00:53:35,279
Speaker 5: of where we are, I would say we're very very

1215
00:53:35,320 --> 00:53:36,560
Speaker 5: early in that journey in government.

1216
00:53:37,080 --> 00:53:41,040
Speaker 3: I have one last, very quick question, which is how

1217
00:53:41,160 --> 00:53:44,960
Speaker 3: much of a constraint is drug testing on new hires nowadays?

1218
00:53:45,160 --> 00:53:47,680
Speaker 5: You know, I wish I had a really smart answer

1219
00:53:47,760 --> 00:53:51,359
Speaker 5: to your question. I don't know the answer to that.

1220
00:53:51,440 --> 00:53:53,440
Speaker 5: I have not seen it. It hasn't I say, it

1221
00:53:53,440 --> 00:53:56,600
Speaker 5: hasn't shown up as some like big red flag where

1222
00:53:56,600 --> 00:53:58,960
Speaker 5: we're losing people in the recruiting pipeline because of that.

1223
00:54:00,320 --> 00:54:02,560
Speaker 5: But the very honest answers, I don't know, but I

1224
00:54:02,560 --> 00:54:03,120
Speaker 5: will find out.

1225
00:54:03,400 --> 00:54:04,520
Speaker 4: I think it's a good que we should.

1226
00:54:04,560 --> 00:54:07,360
Speaker 2: I'm actually very curious about drugs in the workforce in general.

1227
00:54:07,440 --> 00:54:08,279
Speaker 4: We should do more on that.

1228
00:54:08,400 --> 00:54:11,120
Speaker 2: Scott Cooper, thank you, no for real, for real, I

1229
00:54:11,160 --> 00:54:13,160
Speaker 2: you hear about it. My bark was talking about how

1230
00:54:13,200 --> 00:54:15,720
Speaker 2: hard it was to hire because he's like everyone is sort.

1231
00:54:15,600 --> 00:54:17,799
Speaker 5: Of if somebody I would just say, Joe, if somebody,

1232
00:54:17,800 --> 00:54:20,640
Speaker 5: if somebody clips that and puts it on Joe is

1233
00:54:20,760 --> 00:54:23,320
Speaker 5: very I'm very I'm very curious about drugs in the workforce.

1234
00:54:23,360 --> 00:54:24,359
Speaker 5: That's gonna be a great click.

1235
00:54:24,840 --> 00:54:27,520
Speaker 2: We'll get our producer, we'll clip it. Well, we'll get ahead,

1236
00:54:27,560 --> 00:54:29,719
Speaker 2: We'll get ahead of that one. Scott Cooper, thank you

1237
00:54:29,760 --> 00:54:31,680
Speaker 2: so much for coming on od Laves. That was really

1238
00:54:31,760 --> 00:54:32,719
Speaker 2: really appreciate your take.

1239
00:54:32,760 --> 00:54:33,200
Speaker 4: Your time.

1240
00:54:33,360 --> 00:54:35,319
Speaker 2: Very helpful, and I would love to stay in touch

1241
00:54:35,360 --> 00:54:38,760
Speaker 2: and hear more about how these projects evolved during your tenure.

1242
00:54:39,800 --> 00:54:41,480
Speaker 5: Right, thank you both for your time. I appreciate it.

1243
00:54:57,880 --> 00:54:58,680
Speaker 4: I thought that was great.

1244
00:54:58,719 --> 00:55:01,600
Speaker 2: I love I love these comm conversations. I mean because

1245
00:55:01,880 --> 00:55:04,919
Speaker 2: I think they're just I guess the sociology of them. Yeah,

1246
00:55:05,080 --> 00:55:07,520
Speaker 2: like that is what these conversations are really about. And

1247
00:55:07,600 --> 00:55:10,600
Speaker 2: especially you know, we talk about some of these pathologies

1248
00:55:10,680 --> 00:55:14,360
Speaker 2: that replicate from the public to a private sector, et cetera,

1249
00:55:14,440 --> 00:55:17,400
Speaker 2: that are both like, they're really interesting sociological questions.

1250
00:55:17,480 --> 00:55:19,360
Speaker 3: The Department of War needs to get a bunch of

1251
00:55:19,400 --> 00:55:22,320
Speaker 3: ping pong tables and tree snacks to recruit the most

1252
00:55:22,360 --> 00:55:24,040
Speaker 3: cutting edge tech workers, right.

1253
00:55:24,080 --> 00:55:25,879
Speaker 4: With a macha bar, that's right.

1254
00:55:26,000 --> 00:55:28,000
Speaker 2: I wonder, I wonder if they're experiment We should have

1255
00:55:28,040 --> 00:55:30,920
Speaker 2: asked that whether they have a macha on tap at

1256
00:55:30,960 --> 00:55:32,560
Speaker 2: the office and whether that could move the dial.

1257
00:55:32,880 --> 00:55:37,640
Speaker 3: But it's also interesting, again, there's that cultural sociological aspect,

1258
00:55:37,719 --> 00:55:42,040
Speaker 3: but it does apply to large bureaucracies and private organizations.

1259
00:55:42,080 --> 00:55:44,840
Speaker 3: And I think everyone at some point in their lives

1260
00:55:44,920 --> 00:55:49,799
Speaker 3: will experience this type of unnecessary paperwork, whether it's in

1261
00:55:49,840 --> 00:55:52,520
Speaker 3: their jobs or when they're applying for a driver's license

1262
00:55:52,640 --> 00:55:53,080
Speaker 3: or whatever.

1263
00:55:53,680 --> 00:55:55,759
Speaker 2: Yeah, you know what like this is like where I

1264
00:55:55,800 --> 00:56:00,000
Speaker 2: have like the sympathy for like sort of like modern life,

1265
00:56:00,160 --> 00:56:04,160
Speaker 2: large complicated things. How can the individual with our I mean,

1266
00:56:04,400 --> 00:56:06,920
Speaker 2: how could we do How could you navigate insurance? How

1267
00:56:06,920 --> 00:56:09,120
Speaker 2: can you navigate all this stuff? It like drives you

1268
00:56:09,200 --> 00:56:12,560
Speaker 2: crazy at any scale. I do think, you know, And

1269
00:56:12,600 --> 00:56:15,279
Speaker 2: this came up also in our conversations with Jennifer, and

1270
00:56:15,320 --> 00:56:18,040
Speaker 2: I believe it that there is a lot there is

1271
00:56:18,160 --> 00:56:23,000
Speaker 2: more to a career than pay, right, But I also

1272
00:56:23,040 --> 00:56:25,680
Speaker 2: think that's like a very real issue, especially when you're

1273
00:56:25,719 --> 00:56:28,600
Speaker 2: talking about the tech workforce specifically, where we know you

1274
00:56:28,600 --> 00:56:31,280
Speaker 2: could just make an absolute fortune, and in many cases

1275
00:56:31,400 --> 00:56:33,400
Speaker 2: you can make it a fortune as a very young person.

1276
00:56:33,400 --> 00:56:35,240
Speaker 2: You'll be in the right company at the right time,

1277
00:56:35,320 --> 00:56:39,400
Speaker 2: and many overnight multimillionaires in Silicon Valley. So it's not

1278
00:56:39,719 --> 00:56:42,320
Speaker 2: just the late stage, although that's where it becomes the

1279
00:56:42,320 --> 00:56:45,520
Speaker 2: most obvious. I really the pay is a very tricky situation.

1280
00:56:45,760 --> 00:56:45,920
Speaker 5: Well.

1281
00:56:45,960 --> 00:56:48,759
Speaker 3: I do think the emphasis on time constraints for the

1282
00:56:48,800 --> 00:56:52,600
Speaker 3: tech force that's really interesting, and that's kind of a

1283
00:56:52,719 --> 00:56:55,600
Speaker 3: shift again, a cultural shift from the way people have

1284
00:56:55,760 --> 00:56:58,840
Speaker 3: perceived government jobs for a long time. And I can imagine,

1285
00:56:58,920 --> 00:57:01,680
Speaker 3: you know, like if you were a young tech worker,

1286
00:57:02,040 --> 00:57:05,799
Speaker 3: maybe you're having trouble getting into the private sector. You know,

1287
00:57:06,320 --> 00:57:09,600
Speaker 3: just last year, you know, two years ago, remember there

1288
00:57:09,680 --> 00:57:14,040
Speaker 3: was the wave of like layoffs. Yeah, and now everyone

1289
00:57:14,080 --> 00:57:15,279
Speaker 3: is recruiting because of AI.

1290
00:57:15,560 --> 00:57:18,280
Speaker 2: But well, I mean no, I mean you're to your point,

1291
00:57:18,400 --> 00:57:20,560
Speaker 2: like there are even like there are many talented tech

1292
00:57:20,560 --> 00:57:23,160
Speaker 2: people if they're right there on the sweet spot, even

1293
00:57:23,160 --> 00:57:24,400
Speaker 2: though that's not what it used to be.

1294
00:57:24,560 --> 00:57:26,680
Speaker 3: So I can imagine, like you go to the government

1295
00:57:26,720 --> 00:57:30,680
Speaker 3: for two years and it's it's an educational process, and

1296
00:57:30,720 --> 00:57:33,000
Speaker 3: you can take some of that knowledge away from you

1297
00:57:33,040 --> 00:57:35,400
Speaker 3: when you then go into the private sector. Although that

1298
00:57:35,520 --> 00:57:38,760
Speaker 3: also opens up the question of rotating doors between the

1299
00:57:38,760 --> 00:57:42,680
Speaker 3: government and private companies, but maybe that's a topic for

1300
00:57:42,760 --> 00:57:43,480
Speaker 3: a different day.

1301
00:57:43,720 --> 00:57:46,680
Speaker 2: I just and I do think there is and it's

1302
00:57:46,720 --> 00:57:49,120
Speaker 2: not just this administration, but I think it's been very notable,

1303
00:57:49,240 --> 00:57:51,520
Speaker 2: at least with some parts of the administration or some

1304
00:57:51,520 --> 00:57:54,720
Speaker 2: parts of the rhetoric. There's this sort of like vilification

1305
00:57:54,760 --> 00:57:56,720
Speaker 2: of bureaucrats, which is again of sort of a long

1306
00:57:56,760 --> 00:58:00,600
Speaker 2: time thing, Like bureaucrats has sort of been a kind.

1307
00:58:00,400 --> 00:58:02,440
Speaker 4: Of a pejor, I mean a beer word.

1308
00:58:04,160 --> 00:58:06,600
Speaker 2: Anyway, I do think that's an issue. And I also think,

1309
00:58:06,760 --> 00:58:10,280
Speaker 2: you know, like maybe we should just bring back civil

1310
00:58:10,320 --> 00:58:12,800
Speaker 2: service exams everywhere and if you could know seriously and

1311
00:58:12,840 --> 00:58:15,880
Speaker 2: if you could pass this test, you can get a job.

1312
00:58:15,960 --> 00:58:18,160
Speaker 2: Like it's Lindy, you know, it's like famously, you know,

1313
00:58:18,360 --> 00:58:19,960
Speaker 2: go back thousands of years in China.

1314
00:58:19,960 --> 00:58:21,560
Speaker 3: It's like the Big I was about to say, do

1315
00:58:21,600 --> 00:58:24,040
Speaker 3: you know about the Chinese civil service exams? Those are

1316
00:58:24,040 --> 00:58:27,160
Speaker 3: so interesting and crazy? Shall we leave it there?

1317
00:58:27,240 --> 00:58:28,320
Speaker 4: Let's leave it there, all right?

1318
00:58:28,360 --> 00:58:30,720
Speaker 3: This has been another episode of the Oud Loots podcast.

1319
00:58:30,800 --> 00:58:33,560
Speaker 3: I'm Tracy Alloway. You can follow me at Tracy.

1320
00:58:33,240 --> 00:58:35,880
Speaker 2: Alloway and I'm Jill Wisenthal. You can follow me at

1321
00:58:35,880 --> 00:58:38,880
Speaker 2: the Stalwart. Follow our guest Scott Cooper, He's at s Cooper.

1322
00:58:39,160 --> 00:58:42,240
Speaker 2: Follow our producers Kerman Rodriguez at Kerman Arman, dash O

1323
00:58:42,280 --> 00:58:45,240
Speaker 2: Bennett at dashbod at kil Brooks at Keil Brooks. From

1324
00:58:45,240 --> 00:58:48,000
Speaker 2: where Oddlots content go to Bloomberg dot com Flash Odd

1325
00:58:48,080 --> 00:58:50,640
Speaker 2: Lots were the daily newsletter and all of our episodes,

1326
00:58:50,880 --> 00:58:53,080
Speaker 2: and you can chat about all these topics twenty four

1327
00:58:53,080 --> 00:58:57,120
Speaker 2: to seven in our discord Discord GG slash oddlines and.

1328
00:58:57,120 --> 00:58:59,200
Speaker 3: If you enjoy od loots, if you want us to

1329
00:58:59,240 --> 00:59:01,960
Speaker 3: go to Pennsylvany to look at a bunch of file cabinets,

1330
00:59:01,960 --> 00:59:04,800
Speaker 3: then please leave us a positive review on your favorite

1331
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Speaker 3: podcast platform. And remember, if you are a Bloomberg subscriber,

1332
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Speaker 3: on Apple Podcasts and follow the instructions there. Thanks for listening.