Empowering Public Infrastructure with AI: A Microsoft Partnership

WEBVTT
00:00:00.017 --> 00:00:04.077
Hello, and welcome to the Engineering Influence podcast brought to you by the
00:00:04.077 --> 00:00:06.017
American Council of Engineering Companies.
00:00:06.417 --> 00:00:10.877
My name is Thomas Grogan, Senior Director, Economics and Market Intelligence
00:00:10.877 --> 00:00:15.457
here at ACEC, and I'm very happy today to be joined by Vadu Shaker,
00:00:16.057 --> 00:00:20.057
Government Strategy Leader, U.S. State and Local Government at Microsoft.
00:00:20.457 --> 00:00:23.037
Vadu, thank you so much for coming. Thanks for having me, Thomas.
00:00:23.277 --> 00:00:27.637
Well, why don't we just get started with you introducing yourself to our audience
00:00:27.637 --> 00:00:30.677
and tell them all a little bit about what you do at Microsoft.
00:00:31.217 --> 00:00:34.397
Well, I always joke with people that I have the second best job at Microsoft.
00:00:34.797 --> 00:00:39.337
The best job, of course, is the CEO. Yeah, that's definitely the best one to have, right? Yes.
00:00:39.597 --> 00:00:45.037
And the reason I say that is because I get to work with our government customers across the country.
00:00:45.577 --> 00:00:49.957
And as you can imagine, these are some of the most complex organizations trying
00:00:49.957 --> 00:00:55.057
to solve some real world problems that make differences on the benefits people receive,
00:00:55.297 --> 00:01:00.717
on the roads we travel on, on the bridges we traverse, and responding to emergencies.
00:01:01.337 --> 00:01:04.697
And so in that, what I get to do is bring the core of our mission,
00:01:04.837 --> 00:01:09.597
which is empowering every single person and organization in the world to do more with technology.
00:01:10.477 --> 00:01:14.997
You know, I am very lucky to be able to bring to bear a lot of our partners,
00:01:15.257 --> 00:01:19.077
including some of your member companies, in solving these real-world problems.
00:01:20.777 --> 00:01:26.737
My role really is focused on how do we draw up strategies collectively and then
00:01:26.737 --> 00:01:30.517
focus on the mission that the customer has in serving the residents.
00:01:30.957 --> 00:01:35.257
Yeah. So, you know, you talk about this ecosystem you're working in,
00:01:35.337 --> 00:01:39.717
and it's big, it's complicated. So I'm sure we'll touch on a few different parts
00:01:39.717 --> 00:01:40.797
of that whole ecosystem.
00:01:41.357 --> 00:01:46.177
Let's start first with the clients, right? Who are your clients,
00:01:46.597 --> 00:01:50.557
the end customers who are responsible for this infrastructure?
00:01:50.557 --> 00:01:57.917
In 2025, what are some of the challenges that they're facing that you and your
00:01:57.917 --> 00:02:00.157
AEC partners are helping them solve?
00:02:00.717 --> 00:02:04.237
I don't think it's any surprise. Over the last several years,
00:02:04.237 --> 00:02:08.297
there was a massive push in rebuilding infrastructure within the U.S.
00:02:08.577 --> 00:02:14.297
The bipartisan infrastructure law was a big impetus and brought a lot of funding, right?
00:02:14.457 --> 00:02:18.137
And as we look at the landscape of infrastructure within the country,
00:02:18.377 --> 00:02:22.537
the key focus becomes, where do we build new things?
00:02:22.837 --> 00:02:29.337
Where do we help rejuvenate infrastructure that's been around for decades and
00:02:29.337 --> 00:02:30.617
in often cases is crumbling?
00:02:31.529 --> 00:02:36.649
And so in working with our AEC partners, really the conversation starts early, right?
00:02:36.809 --> 00:02:44.229
Is, okay, we've come into this era where computer-aided design is ubiquitous, right?
00:02:44.429 --> 00:02:49.429
I remember back when, in my undergrad days, there was this terminology for BIM
00:02:49.429 --> 00:02:52.209
software that was just developing, right?
00:02:52.329 --> 00:02:59.449
And now we have 3D models, digital twins, all of this as the start of this conversation, right?
00:02:59.589 --> 00:03:03.069
And of course, behind that, is the power of cloud computing,
00:03:03.249 --> 00:03:07.669
which is where Microsoft plays a very significant role in bringing to bear those
00:03:07.669 --> 00:03:12.369
compute resources as well as artificial intelligence resources so that you can
00:03:12.369 --> 00:03:18.069
build out multiple scenarios of a particular infrastructure project even before
00:03:18.069 --> 00:03:19.789
having embarked on that journey.
00:03:20.749 --> 00:03:23.369
And so that's just one of the examples where we start enough.
00:03:23.649 --> 00:03:27.649
The second piece that I think I'm very excited about, and if folks are going
00:03:27.649 --> 00:03:31.749
to be at the TRB Technology Conference in May, we'll be doing a little panel
00:03:31.749 --> 00:03:34.869
discussion on this, is virtual inspections.
00:03:35.649 --> 00:03:41.869
And again, extending that concept, inspections is a very, very manual and time-consuming
00:03:41.869 --> 00:03:43.669
effort when it comes to infrastructure.
00:03:44.189 --> 00:03:49.209
And imagine if you're on a floating bridge. It just complicates things just a tad bit more.
00:03:49.489 --> 00:03:54.009
And so we've done a very exciting pilot project with the University of Washington
00:03:54.009 --> 00:03:58.569
and WSDOT, including some of the AAC partners that are in your membership,
00:03:58.569 --> 00:04:00.749
as well as other software providers.
00:04:00.929 --> 00:04:05.749
And so that would be a very exciting way to show the impact sort of at the end
00:04:05.749 --> 00:04:08.349
stage of maintaining an infrastructure.
00:04:08.589 --> 00:04:11.789
Right. So you're spanning kind of the whole life cycle.
00:04:11.989 --> 00:04:17.689
You talk about the scenario planning in the beginning and I'm sure the cost
00:04:17.689 --> 00:04:25.009
savings that exists today by being able to do 15 different scenario plans very quickly,
00:04:25.009 --> 00:04:28.749
go over it with your client, figure
00:04:28.749 --> 00:04:31.549
out what the right one is to implement all the
00:04:31.549 --> 00:04:35.509
way through now we've built the bridge power
00:04:35.509 --> 00:04:38.889
system how are we optimizing the
00:04:38.889 --> 00:04:43.129
operation and maintenance of that piece which you you know talk about you know
00:04:43.129 --> 00:04:48.509
with this inspection or maybe it's still monitor monitoring that has changed
00:04:48.509 --> 00:04:54.269
a lot even in the past 10 years let alone 25 years from now absolutely yeah
00:04:54.269 --> 00:04:56.789
yeah yeah that's that's really interesting.
00:04:57.169 --> 00:05:06.109
And for the AEC firms that you're working with, how do they partner with Microsoft
00:05:06.109 --> 00:05:08.389
on some of these technology solutions?
00:05:08.389 --> 00:05:15.229
You mentioned the cloud computing, the AI technologies. How do they work together
00:05:15.229 --> 00:05:18.029
with you on delivering some of these infrastructure programs?
00:05:18.872 --> 00:05:22.952
That's a very good question. And one thing I think we've all realized is every
00:05:22.952 --> 00:05:26.052
single company now is a technology company. Right.
00:05:27.252 --> 00:05:31.572
Irrespective of the sub-segment you serve. So whether you're in financial services,
00:05:32.132 --> 00:05:36.212
let's take Visa or MasterCard, they have a massive technology footprint.
00:05:36.212 --> 00:05:42.052
And similarly, in the context of AEC firms, there is a massive amount of technology
00:05:42.052 --> 00:05:45.072
powering that entire operation, right?
00:05:45.252 --> 00:05:50.172
And making sure that they are able to project on plans, bid on plans much more
00:05:50.172 --> 00:05:56.972
efficiently, build out things much more efficiently, have a safety first culture on those work sites.
00:05:57.152 --> 00:05:59.252
And then, of course, in the operations and maintenance.
00:05:59.872 --> 00:06:03.412
And so as we kind of think about it, right, there is sort of that back office
00:06:03.412 --> 00:06:07.052
technology components. You know, you have your planning software,
00:06:07.312 --> 00:06:08.172
all of those components.
00:06:08.432 --> 00:06:10.812
All of that needs to reside somewhere.
00:06:11.172 --> 00:06:16.132
And of course, the world's most popular financial system is Microsoft Excel.
00:06:16.892 --> 00:06:22.272
Yeah, and Excel is really that lowest common denominator that,
00:06:22.692 --> 00:06:26.132
you know, you could spend all your time learning many different tools,
00:06:26.332 --> 00:06:30.772
but ultimately you get your data into Excel to get it into PowerPoint to show
00:06:30.772 --> 00:06:34.152
your trend lines and, you know, scenario planning.
00:06:34.172 --> 00:06:38.632
And that hasn't changed much over the past few decades. And that goes to the core of productivity.
00:06:38.912 --> 00:06:43.332
Right. And so as we kind of think about it, the first step we always start with,
00:06:43.532 --> 00:06:48.412
which is sort of boring and mundane, but it's day-to-day productivity software.
00:06:48.872 --> 00:06:52.212
And that's where is the first step we can collaborate together.
00:06:52.792 --> 00:06:58.572
The second piece is what is the specific line of business software that an AEC
00:06:58.572 --> 00:07:06.012
firm has, either specific to forecasting their bid estimates or work order management, right?
00:07:06.012 --> 00:07:12.472
And being able to then work with them to rebuild that software with an AI-enabled
00:07:12.472 --> 00:07:17.692
approach so that they can bring the knowledge that they have over the past several decades,
00:07:17.832 --> 00:07:21.452
or in some cases, more than a century's worth of institutional knowledge,
00:07:21.472 --> 00:07:26.872
and bring it to the fingertips of a brand new engineer coming out of college.
00:07:26.872 --> 00:07:33.092
And providing them some sort of a co-pilot that gives this brand new engineer
00:07:33.092 --> 00:07:36.872
the power of institutional knowledge for the past several decades.
00:07:37.152 --> 00:07:42.552
And we've seen this be very successful with several of your member companies.
00:07:43.752 --> 00:07:48.872
And I think the last but not the least is, well, most infrastructure projects
00:07:48.872 --> 00:07:51.572
are sort of build, operate, and transfer.
00:07:52.496 --> 00:07:58.536
And when the asset gets transferred back to the government, there is suddenly this vacuum of data.
00:07:59.056 --> 00:08:04.436
Because it was the AEC firm that did all the work that typically then takes
00:08:04.436 --> 00:08:05.996
it and goes to the next project.
00:08:06.676 --> 00:08:09.896
And this is where, again, technology through interoperability,
00:08:10.076 --> 00:08:14.836
via a data lake or like, can provide that bridging effect. Yeah.
00:08:15.156 --> 00:08:18.756
Where it is a seamless transfer over to governments.
00:08:18.756 --> 00:08:23.796
And in some cases, we are partnering with AEC firms to make sure that they can
00:08:23.796 --> 00:08:29.276
start using government technology assets from the get-go so that there is no
00:08:29.276 --> 00:08:31.716
vacuum when there is a transition. Right.
00:08:31.976 --> 00:08:37.516
And so, you know, I think this talk of a vacuum brings up an interesting point
00:08:37.516 --> 00:08:43.276
and challenge that the industry is facing right now with respect to technology.
00:08:43.516 --> 00:08:48.456
Some of it is around the nature of projects and you work on a project and you
00:08:48.456 --> 00:08:50.976
get a lot of data and then there's who owns the data.
00:08:51.116 --> 00:08:57.156
Does it get transferred? You talked a bit about these AI aided designs or tools,
00:08:57.176 --> 00:09:00.556
which are all built on data to become smarter.
00:09:00.916 --> 00:09:11.256
Are you seeing challenges on the procurement client side that are restricting
00:09:11.256 --> 00:09:14.936
the ability of using these tools or other,
00:09:14.936 --> 00:09:17.416
say, hurdles that, you know.
00:09:18.206 --> 00:09:25.106
Companies might encounter because in theory, an AI aided solution is great.
00:09:25.366 --> 00:09:29.226
The reality of having all these different players, stakeholders,
00:09:29.946 --> 00:09:36.846
contracts, you know, one piece of the puzzle might not allow for a certain data
00:09:36.846 --> 00:09:43.086
collection methodology or, you know, information presented in a certain format, things like that.
00:09:43.206 --> 00:09:48.546
Is that, you know, kind of getting worked on still in the early stages?
00:09:49.186 --> 00:09:51.986
How are you seeing that? Do you have like four hours left in the podcast?
00:09:51.986 --> 00:09:57.126
I don't know. How much time do we have? It is a complex question. Right, right.
00:09:58.386 --> 00:10:03.106
And I'll sort of take this back to the core of data privacy laws around the country.
00:10:03.266 --> 00:10:07.346
Yeah. Because I think that's where the conversation needs to start.
00:10:07.526 --> 00:10:13.406
Okay. As to what data is being collected, first of all, and who owns the data
00:10:13.406 --> 00:10:14.326
and who's the data steward.
00:10:15.326 --> 00:10:20.626
And in most cases in the public sector domain, well, the public owns the data
00:10:20.626 --> 00:10:23.906
and the government is acting as a steward on behalf of the public.
00:10:24.866 --> 00:10:30.046
And what the AAC firms or any private company then does with that data should,
00:10:30.266 --> 00:10:33.126
in theory, come back and benefit the public good.
00:10:34.106 --> 00:10:39.246
And this is where I think the lack of certain data privacy laws,
00:10:39.326 --> 00:10:44.226
I think there's only 13 states across the union that have data privacy laws in place.
00:10:44.746 --> 00:10:49.606
Makes it difficult for us to have that very transparent conversation.
00:10:50.266 --> 00:10:53.466
Yeah. You know, a lot of conversations at the national level have been,
00:10:53.646 --> 00:10:57.046
does it make sense for a cohesive data platform for interoperability?
00:10:57.406 --> 00:11:01.146
We've seen this happen very successfully in the health and human services space. Okay.
00:11:01.586 --> 00:11:05.686
Right. And that could be a model. And certainly there's entities working at
00:11:05.686 --> 00:11:08.946
the USDOT level to explore what this would look like.
00:11:10.046 --> 00:11:12.366
That, I think, in effect, is going
00:11:12.366 --> 00:11:16.606
to help inform what sort of procurement language needs to be in place.
00:11:17.891 --> 00:11:21.411
And, you know, if there is that availability of data up front,
00:11:21.631 --> 00:11:26.271
the life cycle from that point in terms of who owns the data,
00:11:26.271 --> 00:11:31.111
and if that remains in the public domain, well, there could be multiple copies.
00:11:31.451 --> 00:11:38.371
And open data laws across the country have really helped improve access to this kind of data. Right.
00:11:38.671 --> 00:11:42.251
And so this would just be kind of another data type, perhaps made available
00:11:42.251 --> 00:11:48.111
through geospatial mapping tools or otherwise for even citizen developers to
00:11:48.111 --> 00:11:49.511
be able to take that and say, hey,
00:11:50.011 --> 00:11:54.951
you know, we've been experiencing a lot of issues crossing this particular intersection.
00:11:55.551 --> 00:11:59.251
Let's leverage what I, you know, citizen know.
00:12:00.011 --> 00:12:05.671
And let's propose something to DOT. And that can evolve into a project of itself, right?
00:12:05.831 --> 00:12:09.851
But that's where I think there's the power of the data to help not only the
00:12:09.851 --> 00:12:15.611
AEC firms be able to have well thought through and structured plans on addressing a project. Right.
00:12:15.991 --> 00:12:20.951
But really creating that data lifecycle that then a citizen developer can kind
00:12:20.951 --> 00:12:23.531
of go and help inform the plans of the government.
00:12:24.151 --> 00:12:28.311
Yeah. You know, I like how, you know, you bring up data privacy,
00:12:28.511 --> 00:12:34.011
data privacy laws, and depending on what metrics or rubrics you're looking at,
00:12:34.191 --> 00:12:38.711
certain states or things like GDPR might be more advanced, this or that.
00:12:38.711 --> 00:12:45.451
Are there other, let's say, federal policies or laws that you are tracking with
00:12:45.451 --> 00:12:50.231
relation to AI and other tech development that, you know,
00:12:50.631 --> 00:12:54.791
you have thoughts on that you think will be impactful that our members should
00:12:54.791 --> 00:12:57.191
also, you know, be aware of that's happening?
00:12:58.330 --> 00:13:01.330
I would say the two that are sort of top of mind for me right now.
00:13:01.590 --> 00:13:06.670
And one, because, you know, then this is the AI action plan that the White House has put together.
00:13:07.570 --> 00:13:11.750
OSTP just wrapped up the RFI for the AI action plan.
00:13:12.130 --> 00:13:18.370
And we, as Microsoft presented our point of view into what that American AI looks like.
00:13:18.670 --> 00:13:24.490
Keeping innovation at the forefront while promoting research and safety along the way.
00:13:25.090 --> 00:13:31.270
And to be very, very focused on both the technology as well as the workforce.
00:13:32.070 --> 00:13:39.050
I think the AI action plan is going to be very important for all of the AEC firms to take a look at.
00:13:39.650 --> 00:13:43.090
Because this is going to be the one way, as I was saying before,
00:13:43.350 --> 00:13:45.030
every company has become a technology company.
00:13:45.410 --> 00:13:50.250
And this provides a way to partner with not just the federal government,
00:13:50.250 --> 00:13:55.190
but also all of the state governments and local governments in terms of being
00:13:55.190 --> 00:13:58.190
able to leverage AI in infrastructure projects.
00:13:59.190 --> 00:14:03.390
So that's one that's a problem of mine. The second one is, of course,
00:14:03.530 --> 00:14:05.290
the surface reauthorization problem.
00:14:06.010 --> 00:14:10.730
And as you know and probably remember, in the bipartisan infrastructure law,
00:14:11.030 --> 00:14:17.090
there wasn't really a carve-out for technology spend on infrastructure projects. Okay.
00:14:17.730 --> 00:14:22.210
And anecdotally, what I've heard is like, you know, FAA, if they get a grant,
00:14:22.410 --> 00:14:28.270
there is about 15% of that grant monies that can be devoted towards technology.
00:14:29.210 --> 00:14:32.110
But when we look at the same thing for surface infrastructure,
00:14:32.330 --> 00:14:39.970
I would hazard guessing that number for technology is 0.0001%. Yeah.
00:14:40.901 --> 00:14:47.761
So by comparison, there is already a very stark contrast in the amount of investments
00:14:47.761 --> 00:14:49.161
that come through on technology.
00:14:49.881 --> 00:14:53.441
And the fact that technology has become such a core component of infrastructure
00:14:53.441 --> 00:14:58.881
delivery now, that we need to fundamentally rethink what that is. Yeah.
00:14:59.301 --> 00:15:03.541
I mean, is some of the challenge, as you brought up and started with your point
00:15:03.541 --> 00:15:06.001
that every firm is a technology firm.
00:15:06.141 --> 00:15:13.341
If technology is so embedded in certain processes or day-to-day activities,
00:15:13.881 --> 00:15:18.081
it almost maybe is there but is not getting captured?
00:15:18.521 --> 00:15:21.981
Or I mean, is that a challenge that you see?
00:15:22.161 --> 00:15:29.401
Or do we need to have such a carve-out or specification of for $1,
00:15:29.781 --> 00:15:36.981
10 cents is allocated for technology, 15 cents is for materials, 20 cents is overhead.
00:15:37.281 --> 00:15:45.721
In my limited experience, what I've seen is unless it is in the policy at the
00:15:45.721 --> 00:15:51.761
federal level, it does not trickle down to the other local and state governments.
00:15:52.681 --> 00:15:57.521
And when people apply for NOFOs, like they all do, whether formula or competitive
00:15:57.521 --> 00:16:03.941
grants, They go back to the eligibility criterias as defined in the policy. Right.
00:16:04.601 --> 00:16:09.301
Which is why I think it's important for us to address this at perhaps the level
00:16:09.301 --> 00:16:14.901
of the Congress and make sure that the policy has a carve out specific to this. Got it.
00:16:15.560 --> 00:16:21.520
And making sure that, yes, we identify a carve-out for technology without perhaps
00:16:21.520 --> 00:16:26.060
necessarily specifying at a very detailed level, to your point, right?
00:16:26.260 --> 00:16:31.800
Giving the AEC firms, giving other, you know, NOFO recipients the flexibility
00:16:31.800 --> 00:16:36.140
to think about how they want to leverage this bucket of money, right?
00:16:36.320 --> 00:16:41.920
But I do think that it is very important for us to codify it in the policy documentation.
00:16:42.660 --> 00:16:47.180
No, that makes sense. And so, you know, when we were talking a little bit just
00:16:47.180 --> 00:16:53.340
before that on regulation and policy, you brought up technology and then workforce.
00:16:53.840 --> 00:17:01.620
Workforce is a very big issue around for the AEC industry for a variety of reasons, right?
00:17:01.620 --> 00:17:07.840
How do you or Microsoft think about workforce development enabling,
00:17:07.840 --> 00:17:13.220
you know, the folks who are going to be the engineers of the future today?
00:17:13.500 --> 00:17:17.240
What can we do to ensure that continuity?
00:17:17.740 --> 00:17:22.100
Some of the stuff, you know, folks who are retiring, you're losing a lot of
00:17:22.100 --> 00:17:26.800
knowledge when they leave the workforce. course, there's concerns that AI will
00:17:26.800 --> 00:17:30.240
weaken the cognitive development of folks.
00:17:30.460 --> 00:17:33.020
You know, when they're younger, they'll be too dependent on tools.
00:17:33.300 --> 00:17:35.420
How are you thinking about all of that?
00:17:35.740 --> 00:17:38.600
Well, let me start by sharing a very personal experience.
00:17:39.020 --> 00:17:42.580
I have two young daughters, seven and six.
00:17:43.040 --> 00:17:49.540
And, you know, a couple of years ago, 875 days ago, it's my counter for the
00:17:49.540 --> 00:17:51.260
days since ChatGPT has been launched.
00:17:52.260 --> 00:17:57.660
You know, I authored a book and I wrote about, you know, generative AI use cases
00:17:57.660 --> 00:17:58.660
in state and local governments.
00:17:59.420 --> 00:18:03.100
And of course, they were a little bit younger. And when the author copies arrived,
00:18:03.320 --> 00:18:04.960
they're like, oh, dad, you wrote a book.
00:18:05.100 --> 00:18:07.480
And they opened it and it was all full of text and no images.
00:18:07.760 --> 00:18:09.120
And so they were very disappointed.
00:18:09.940 --> 00:18:12.920
But in order to address the disappointment, I said, hey, look,
00:18:13.080 --> 00:18:17.400
why don't we go into, you know, Bing chat and see if it can take some of the
00:18:17.400 --> 00:18:20.380
language in my book and create some fun images for you.
00:18:20.380 --> 00:18:27.440
And we did that And that afternoon exercise Created this creative burst In my
00:18:27.440 --> 00:18:32.980
daughters Where they were like Okay, well maybe I can write a story And you can make it into a book.
00:18:34.132 --> 00:18:41.072
That goes to the core, and my belief, is how we address education in this new era of AI. Okay.
00:18:41.432 --> 00:18:45.732
Right? Where I think we have to continue working on, to your point,
00:18:45.792 --> 00:18:50.792
the cognitive abilities while argumenting them with AI.
00:18:51.012 --> 00:18:55.112
Yeah. And leveraging AI, perhaps, as a teacher's aid, if you will.
00:18:55.352 --> 00:18:58.212
Right. To create customized learning paths for our students.
00:18:58.492 --> 00:19:02.492
Mm-hmm. And that's at the very young, K-12 kind of age group.
00:19:02.492 --> 00:19:07.672
I've also seen and heard firsthand, you know, major universities,
00:19:08.152 --> 00:19:12.032
the Ivy League universities, you know, R10, R15 universities,
00:19:12.272 --> 00:19:19.972
they are all incorporating AI in some form in the delivery of content to their students.
00:19:20.152 --> 00:19:24.932
And it's across different segments, civil engineering, you know,
00:19:25.072 --> 00:19:28.852
all of the engineering, even in the arts, they have it. Of course,
00:19:28.952 --> 00:19:31.372
filmmaking has had that for a very long time.
00:19:31.512 --> 00:19:34.072
And it's coming down into medicine as well. Yeah.
00:19:34.452 --> 00:19:41.992
The key, again, is how do we help push a student's ability and continue having
00:19:41.992 --> 00:19:48.132
them understand where AI can help fill in certain gaps in their education journey.
00:19:48.432 --> 00:19:53.232
But when we come to the professional workforce, the conversation becomes a little bit more complex.
00:19:53.452 --> 00:19:58.892
Right. Because, of course, there's this fear that AI is going to take my job away.
00:20:00.239 --> 00:20:04.559
And I don't think popular media has helped. Could go on and on about Skynet
00:20:04.559 --> 00:20:08.279
stories and why, you know, there is always this fear of AI.
00:20:08.619 --> 00:20:14.339
But what we've learned at Microsoft that for us, it needs to be a partnership.
00:20:14.559 --> 00:20:17.799
And it goes back to some of our responsible AI principles, right?
00:20:17.959 --> 00:20:24.079
Where it is about the human in the loop and making sure that AI is a co-pilot
00:20:24.079 --> 00:20:27.099
and not an autopilot, right?
00:20:27.379 --> 00:20:31.959
And so for that lens, what we've done, and we did this in December of 2023,
00:20:32.639 --> 00:20:38.339
our vice chair, Brad Smith, in fact, kicked off a partnership with AFL-CIO.
00:20:39.039 --> 00:20:46.699
And this partnership is really focused on, one, identifying the impacts AI can have on the workforce.
00:20:46.919 --> 00:20:53.019
And then second, working collaboratively to create upskilling and reskilling
00:20:53.019 --> 00:20:54.839
programs to help address those impacts.
00:20:55.459 --> 00:21:00.139
And finally, be able to actually share that with the policymakers so that they
00:21:00.139 --> 00:21:05.599
can have informed policymaking around workforce development in this new era of AI.
00:21:06.439 --> 00:21:11.659
And so as I kind of look at this cohesively, I go back and sort of contextualize
00:21:11.659 --> 00:21:13.099
this for the AEC firms, right?
00:21:13.499 --> 00:21:20.079
We have always, in the last two decades at least, seen this massive shortage
00:21:20.079 --> 00:21:25.339
of qualified, skilled pipeline of workers coming into AEC firms,
00:21:25.339 --> 00:21:28.319
irrespective of the position, right?
00:21:28.319 --> 00:21:34.919
And I think that that's what we need to focus on is how do we fill the gap in that pipeline?
00:21:36.599 --> 00:21:40.059
AI being a co-pilot for the folks that are already in the pipeline.
00:21:40.859 --> 00:21:44.859
Yeah, no, very well said. Well, Vadu, we're getting towards the end of this
00:21:44.859 --> 00:21:49.699
podcast, but I guess I just wanted to, you know, get your thoughts at a high level.
00:21:49.819 --> 00:21:57.999
What are you most optimistic or excited about in the AEC infrastructure space over the next, you know,
00:21:58.179 --> 00:22:02.679
5, 10, 15 years with all these new developments and changes and,
00:22:02.699 --> 00:22:06.299
you know, Microsoft continuing to partner with our member firms?
00:22:06.779 --> 00:22:12.399
I don't know if I can crystal ball this thing 10, 15 years, but there's a couple
00:22:12.399 --> 00:22:17.959
of big events here in the short term that I'm really excited about and would love to partner about.
00:22:18.279 --> 00:22:20.219
First and foremost, the World Cup next year.
00:22:20.799 --> 00:22:23.739
I think a lot of the AEC firms are super excited.
00:22:23.739 --> 00:22:26.879
I am particularly excited because I'm a soccer fan,
00:22:27.039 --> 00:22:33.059
but I think this brings in massive opportunity for us to collectively showcase
00:22:33.059 --> 00:22:39.139
what we can do in terms of infrastructure development to the rest of the world.
00:22:39.419 --> 00:22:42.419
The second big one, of course, is LA-28.
00:22:43.199 --> 00:22:47.739
And again, I think the scale at which Olympic events happen and the impact they
00:22:47.739 --> 00:22:51.599
have on local infrastructure and economic development is amazing.
00:22:51.599 --> 00:22:57.179
And I know several of the AEC firms are already engaged in the Southern California region.
00:22:57.699 --> 00:23:02.699
And, you know, I would love to have this conversation around where we bring
00:23:02.699 --> 00:23:04.159
learnings from around the world.
00:23:04.639 --> 00:23:09.339
Having, you know, as Microsoft, first-hand experience in helping with the Olympics
00:23:09.339 --> 00:23:14.279
in different continents, as well as World Cups in different continents. Yes. Right.
00:23:14.439 --> 00:23:19.279
And I think for once, I feel really confident that technology is going to make
00:23:19.279 --> 00:23:25.059
a very positive impact, especially when we think about L.A. 28 being a transit first Olympics.
00:23:25.539 --> 00:23:30.079
Yeah. Right. And so I think those are the couple of things that I'm very, very excited about.
00:23:30.719 --> 00:23:33.739
The last and this is perhaps me being a Californian.
00:23:34.727 --> 00:23:39.807
We've had a lot of natural disasters. And I feel like in the last five years,
00:23:40.047 --> 00:23:42.327
there's been exponential growth in natural disasters.
00:23:42.987 --> 00:23:46.347
Which brings us to the rebuilding process.
00:23:47.007 --> 00:23:51.387
The process is often stymied by the long permitting times.
00:23:51.727 --> 00:23:56.207
And then just the whole process of, you know, construction, you know,
00:23:56.547 --> 00:23:58.267
debris removal, all of that.
00:23:58.667 --> 00:24:02.407
What I am actually getting excited about is if I crystal ball to the next 10
00:24:02.407 --> 00:24:09.187
years, The ability for AAC firms to be able to leverage perhaps drone technologies,
00:24:09.647 --> 00:24:11.447
other geospatial technologies,
00:24:11.927 --> 00:24:16.727
being able to predict certain things in terms of weather events,
00:24:16.927 --> 00:24:24.027
and be able to almost provide support to the government in recovery response
00:24:24.027 --> 00:24:28.927
in a way that really collapses the recovery time for our communities.
00:24:29.587 --> 00:24:33.567
And to me, that would actually be sort of the holy grail of our partnership
00:24:33.567 --> 00:24:39.487
between AEC firms, Microsoft as a technology company, and our government and communities.
00:24:39.987 --> 00:24:46.747
Yeah, I know that nimbleness to be responsive is definitely a great opportunity there.
00:24:47.107 --> 00:24:50.507
Well, thank you very much. Well, who do you think will win the World Cup?
00:24:51.107 --> 00:24:54.527
Oh, you know, I am always biased towards Bussle. Okay.
00:24:54.847 --> 00:25:00.067
All right, I'm going to go France, but who knows? Well, let's wager a dollar,
00:25:00.267 --> 00:25:02.867
and we'll see who it is. Okay, yes, in a few years, we'll check back in.
00:25:03.187 --> 00:25:04.507
Well, Vidu, thank you so much for
00:25:04.507 --> 00:25:09.067
joining us. I want to thank everyone for listening to our podcast today.
00:25:09.247 --> 00:25:13.887
And if you can, please like or subscribe to the Engineering Influence podcast
00:25:13.887 --> 00:25:16.867
on whatever media channel you are using.
00:25:17.167 --> 00:25:19.507
Well, Thomas, thank you so much for having me. Yeah, absolutely.
00:25:19.507 --> 00:25:22.207
And thanks to ACEC for hosting this podcast. All right, great.