Shipium’s Jason Murray on Delivery Promises, Multi-Carrier Playbooks, and Agentic AI

Episode Transcript
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Jason Murray (00:05):
You add all those paper cuts up, and a lot of what
people are doing in thesecompanies are, they're just
paper cutting themselves todeath, right? It's like, you go
look at an analyst screen.
They've got 47 Excelspreadsheets open, right? And
they're they're moving thingsaround. They're calling people
to try to do things. And so ifyou take that, if they've got
100 steps they're doing per day,and you're able to automate 10
of them, right? I mean that thatopens up like this, this massive

(00:29):
time, this area for them tostart thinking more
strategically about where theyneed to go.

Blythe Brumleve (00:38):
Welcome into another episode of everything is
logistics, a podcast for thethinkers in rate, I am your
host, Blythe Milligan, and weare proudly presented by SPI
logistics, and we've got anotherfantastic episode for y'all
today, because we are talking toJason Murray, CEO and co founder
of Shipium, and a 19 year Amazonveteran. We're going to be

(01:00):
breaking down the deliverypromises that convert smarter
multi carrier playbooks andwhere AI actually moves the
needle. So Jason,

welcome to the show. Well, thanks for

having me here.
I'm excited absolutely now, Iwas listening to a bunch of
different podcasts that you wereon. Friend of the show,
Harshita, with the E commlogistics podcast. She's
fantastic episode that you didwith her. It was really great at
providing some some background,as well as your website, which
is website is really well done.
We're going to bring that up alittle bit later with some of

(01:35):
the content that's available onthere. But I'm curious as to you
know, with your 19 year Amazonhistory, is that level of, I
guess, the today, chaos that wefind ourselves in or manage,
chaos of E commerce, logistics.
Did that exist back then, or isit just kind of on steroids now?

I think it's, I think it's a good question. I
think I think the story with mytime at Amazon is probably more
the arc and how everything justkind of continues to head in the
same DIRECT direction, you know,and it's like every incremental
step, and then there'sobviously, like these periods of
time where it accelerates,right? But I think the note, the

(02:20):
notion, or the high level metanarrative, is just that
everything kind of has gottenmore complicated, right? So
there's just, you know, you goback in time to 1990 1990s and
you think about, like, what wassupply chain, and what was
retail? And it was like, Okay,so the pinnacle was probably,
like a super Walmart, right?
Which is two or 300,000 SKUs.

(02:42):
You know, it's all kind ofmanaged through distribution,
etc. And now you think aboutlike just as as Amazon followed
this arc my career. Follow thisarc is just more SKUs, more
channels, more carriers, moremore kind of ways that people
are discovering things, moreexpectations that are, they're
different variable, and it'slike, all of those variables

(03:04):
push everything, and then youend up on the back end with just
like they multiply against eachother. So complexity just gets
worse and worse and worse. Andso, you know, I think, I think
the thing that's interesting nowis, like, you hear people like,
kind of wanting this old timewhere everything was stable, and
they reminisce about, likethese, you know, we just have

(03:27):
these containers come in andthen we sell them in the store
and how easy that was. And it'sjust like, that isn't the
reality. And so you've got nowall of this complexity that
people have to kind of digest,and it's combined with, like
exogenous events and and, likeaccelerating technology. You
know, you take the last three orfour years and you've got a
combination of the pandemicfollowed by, kind of this

(03:50):
onslaught of AI, followed bykind of, like, geopolitical
stuff that's causing things tohappen. And I just think that's
the new norm. I don't see it. Idon't see it going away. I can't
predict what the next thing'sgonna be, but there's gonna be
next thing.

And that is really a catalyst of your, not
only career trajectory, but whenyou started shipping, I believe
it was 2019 and then, I mean,everybody knows what happened in
2020 and then the years thatfollowed. And so it really feels
like you did. You hit the marketat like the the perfect time
with, with launching shippium,that, you know, there were all

(04:24):
of these issues, these greaterissues, that people needed to
solve for that level ofcomplexity. And now, you know,
you have a solution for thatcomplexity. Did you how did you
plan for something like that?

I can't say that it feels like I hit the market
at the perfect time, you know,like, often on the day to day
life. But, yeah, I think, Ithink the need, you know, we're,
we're obviously, like any otherbusiness where we're skating to,
you know, what the customerwants, right? Like, we're moving
towards that. And I, I think thewhole point of, like, why did we

(04:57):
build this, this technologystack in Amazon, this. To solve
these problems, right? Why do wedo it? And it was really like,
okay, you can use data datascience, machine learning,
coordination, all of this stuffto basically solve these
flexibility problems. And Ithink the need for that just
continues to grow, and we'refeeding off of that as a
company. And so I think in a lotof ways it was, it was good in

(05:19):
the sense that the things hadkind of reached this point that
the demand is a lot clearer,like I you know, if you, if you
go back 10 years, it would bemuch, much harder to sell what
we're trying to sell toretailers or or manufacturers,
or whoever else, because the thepain was not as acute in that

(05:40):
sense, you know. So I think thenyou kind of go back three or
four years, and when we'rereally started kind of going to
market and, and a lot of thesefactors had kind of driven home
the need for technology as a asa way to solve these problems,
which I think we discovered atour Amazon a little bit earlier
because of the scale that Amazonwas running, and kind of how far
ahead they were. In in more ofthese diversified, complex

(06:04):
supply chains that Amazon's kindof known for. So so the answer
is yes, and I just said it inlike 1000 words, which

is perfect for a podcast interview. You gotta
be able to allow, you know,shoot them up and you knock it
down. So I am curious around thesort of light bulb moment that
you know you work so long atAmazon, what was that moment
like to think of? Okay, I wantto do this, but I want to do

(06:35):
this on my own, and I want tostart a company and solve these
problems. What did that momentin that journey look like,

well, some of it is, is kind of personal, right?
Like, I'd love to say that thenarrative was, was like I had a,
you know, I woke up in themiddle of the night and had this
idea. And it that that maybeplays well in some sense. But,
you know, I mean, the thing wasabout my personal career was
just that, when I got out ofschool in the 90s, I I wanted to

(07:03):
be at a startup, and I went to astartup, and then soon after,
went to Amazon, because I was,like, really liked what they
were doing. Like, I really likedthe idea I used Amazon. I was, I
was, I was an early adopter ofAmazon, and I was buying, like,
technical books, and it was theonly place I could get them. And
I just the concept of it comingto my door was just such a it
was such a magical experiencefor me. And so I had this, like,

(07:25):
personal connection to it, andthat's why I ultimately went
there. But at the time I startedthere, it was a startup, right?
I mean, it was, it was not itobviously, like the the
timeframes in that period oftime or in that era were much
shorter, so they'd already iPodand stuff like that. But, but it
like, effectively, it was atotal mess there. When I got
there in 99 it was like totalchaos and and you had a lot of

(07:48):
room to grow. There wasinvention was happening all over
the place. The people, like themix of people, was just amazing,
like tons of talent. And so thatwhole energy system was, was,
was just this kind of perfectcombination. And then obviously,
like over the years, you you,you keep building, and this
thing kind of grows, and you endup in this. For me personally,

(08:09):
it was kind of like a frogboiling problem, right where you
the company is getting biggerand bigger. Your your day to day
is becoming more about politics.
You're you're less hands on,you're less building, you're
less connected to kind of what'shappening. And that's, that's a,
you know, it's what's requiredif you're going to, if you're
going to move up to the VP orthe executive level, you have to

(08:30):
kind of move to that state. Andso I think if I look back on my
Amazon career, I got exposurethe kind of 2008 to 2016 period
when Amazon really started toscale. It was a fascinating and
amazing journey. And I thinkeven more there we were, kind
of, you could kind of feel youwere part of something like

(08:51):
there was a there was thisnotion of that happening, and so
seeing that level of scale andthis stuff was really a great
experience. So I don't want totake anything away from that.
But once, you know, once youkind of get out of that, and
then you kind of start lookingaround like, what I want to do
next, and what, what am I, youknow, I've got only so many more
of these runs before i ieffectively am retire, or
whatever, you know, like, it'sgoing to be harder and harder to

(09:14):
do, right? What do I want to dowith this? Right? And I think
for me, this was, this was kindof what drove it was I, I wanted
to, I wanted to get back tobuilding, you know, and that's,
that's kind of how I ended up inthis situation. I will say that
when I left Amazon, I didn'thave a totally baked plan,
right? Definitely, this wassomething that I left Amazon,
and then I explored the space abit and and, you know, I kind of

(09:36):
started with, maybe I'll be aCTO or something. But
ultimately, kind of got to,like, I, you know, I really want
to take, I want to go throughthis process. I want to, I want
to go, I want to be in a threeor four person team and see what
we can do, and go through thisjourney and, and there's no
guarantee that that is thefinance was, financially the
right decision. But, like,personally, I. I just love these

(09:58):
experiences and building so muchthat, that that's going to trump
everything else that, that I'mkind of talking about. So I it,
for me, it was a very personaljourney. It was more that got me
here. But I'm so glad that Iwent through this, because I do
think, you know, it's like Ifinally to go from totally
bottom nothing to something,right? Is a zero to one, the one

(10:19):
to 10, the 10 to 100 process is,is a is a amazing thing to go
through, if you have theopportunity.

Now, did you know right away what you wanted
shippium to focus on, or was itkind of a product of iteration?
No,

we definitely went through the product market fit
phase. We we had a theory thatthe original kind of hypothesis
was, well, first of all, at thetime, like Flexport had a lot of
energy, and so the the thoughtwas, is there even, like, you
know, should we be even looking,are we looking in the right
spot, right? And then we kind ofeventually were like, well,
let's, let's just follow what wedid at Amazon. That's the like,

(10:56):
focus on the the end customerexperience and, and, but the
original theory was we really gohard on delivery promise, and
although that is still part ofour offering, and I actually
think it's starting to pick upsome steam Now, the reality was,
we went into the pandemic, andpeople were not really concerned
about conversion lift, becausethey already were selling more

(11:17):
than they could handle. So wekind of pivoted to more back end
optimization, which is, is notnecessarily outside of what the
road map looked like or theoverall vision. It all fits in
that, in that narrative, but thethe kind of order you do things,
and how you approach itdefinitely changed, right? And,
and, and also, just like, whatis the market you're you

(11:38):
ultimately have to come to aproduct that people won't buy,
and, and, and you're, you're,you're trying to understand,
like, where are the acute painpoints that you can focus on,
right? And I think we got tothis back end optimization piece
just was this kind of gapinghole, the deeper we dug and and
so getting into, like,transportation Lane
optimization, at the at theespecially, kind of really

(12:01):
focused on, on parcel anddiverse networks. There was just
a lot of opportunity based on onkind of what we were hearing
from our customers, what theywere dealing with, they what
they were struggling with, andthat's, that's kind of how the
path look. And so now I thinkwe've, we've the kind of go to
market, or how we've gonethrough this, it ended up being

(12:22):
way more focused on, let's,let's, like, solve that, and
then kind of build off of that,as opposed to, as opposed to top
down that we originally thoughtso. So to your question, it's
changed significantly, like, theway that we the way that we
thought we were going to do justwas totally different than what
actually happened in reality.
Yeah, I

think that's that's a journey that's similar.
For a lot of people who arebuilding, you have to, you have
this hypothesis, you take it tomarket, and you hope the market
responds positively, but you,more or less, you're going to
have to make some iterations toit in order to build something
that people want, and they'regoing to be a repeat buyer of
and and so with that, with thatall said, for folks who may not

(13:02):
be aware, what is sort of thehigh level overview of what
shipping provides.

So we have a shipping, transportation supply
chain optimization platform,right? And what we've been hyper
focused on is we have a kind oftransportation piece which,
which is really focused on,like, if I want to deliver to a
customer by a date, how do youdo that most possible in the
most efficient way possible? Sowe'll choose carriers, methods,

(13:29):
etc. It's all very real time. Wemodel out transportation lanes
for all the carriers that we'reworking with using machine
learning, etc. We have ainternal cost model to kind of
create a digital twin of what,what it's going to cost you,
what your you know, what hoursyour building runs, what are the
cutoff times, etc. So we'remodeling all of that, and that

(13:50):
lets us make a decision. So whenyou take that and you can roll
up to, like, what we callfulfillment engine, which is
basically this notion of ofwhere do you ship from. So this
is, this is a thing where you'relooking at making a decision on
how, how you should, where youshould ship it from within your
network, right? And then youtake that same set of data, and
you build up to our deliverypromise product, which is, what

(14:10):
are you telling the customer interms of expectation?

And so for a lot of especially with your
history at Amazon, you know,they pioneered the two day
shipping movement. And so for alot of folks, they that's kind
of the default expectation nowis getting that two day
shipping. But obviously that'sunrealistic for a majority of
retailers. Or is, is itrealistic? You know, if you

(14:34):
optimize properly,

I think you I think that what happens is it's
about it like it's about kind ofspecificity. And part of the
problem is, if you this is wherethe scenario where your
technology is limited in someway, you can't provide the level
of detail they need to provideyour customer so, so what, to

(14:58):
some degree, we're offering isalmost. Is almost like, given
your network, there arescenarios where, if it's nearby,
I can cheap if, if the you knowyou're in Florida, if the
fulfillment center is inTennessee, I can probably get it
to you in two days, verycheaply, right? And so are you
communicating to that, that toyour customer? Are you taking
credit for that? Do you have thetools to kind of measure what is

(15:22):
the resin revenue lift fromactually making that decision,
versus what it's going toactually cost you incrementally,
right? And are you offering thatto the customer in, you know, in
a systematic way, right? And soall of that is like, again, just
kind of back to this, this highlevel, you know, pattern that
you it's like we have, you haveall this data, you have customer

(15:44):
data, you have transportationnetwork data, you have
intention, you have you havebusiness goals, and trying to
mash that together in some waythat that meets the needs of
your business is really whatwe're doing. We're optimizing
all of those factors to say,make the right decision for your
business. So I like,interestingly, as we've spread
out into some of these otherindustries, like B to B, you

(16:07):
know, parks, distribution, or Bto B, pharma, or something like
that, right? The needs are oftendifferent for different reasons,
right? They might be moreconcerned about risk versus
cost, but the approach is thesame. It's like, I'm going to
take huge amounts of data fromall these different sources and
make a systematic decision onhow to actually solve that
problem, as opposed to kind of,you know, the, you know, just

(16:30):
imagine, like, I'm gonna putsome rules in a spreadsheet and
mostly hits it, and not all thetime, and, and as a customer,
you're like, Hey, I'm like,right next to the distribution I
can see the distribution centerfrom my window. Why does it
take? Why does it take? Why isit saying it's gonna take six
days, right? So it's just all ofthat, and I think that's the
technology gap that we're tryingto really bridge, right with the
with the product, right therejust is not you can't get you

(16:54):
cannot, you can't possibly, withhumans, solve this problem
systematically,

yeah, and I think for the end users, it's
not necessarily about, can youget it to me within two days? I
think it's more, when can I havean accurate delivery date as
soon as

possible? Yeah, you have the classic. Like, I
got the party on Saturday. Ineed new dress shoes, right?
Like it's Monday, so as long asit gets there by Thursday. I'm
fine, but I need to know that.

How complex is that? I mean, I'm sure it's
insanely complex in order for aretailer to be able to promise
that, is it realistic for, youknow, a lot of retail outside of
the the Walmarts and the Amazonsof the world, is it realistic to
be able to provide thoseestimated delivery dates? Or is
that all just dependent on theall of the supply chain?

No, I mean, this is all just totally doable. I
you know, it's not, I wouldn'tsay it's easy, but, but we, we
understand, you know, if you'regoing to even a smaller
retailer, let's imagine you hadtwo buildings, one in one in
Jersey and one in Reno orsomething, right? I mean, we're
going to understand this is whenyou will if, if an order
happens. Now, this is when youwill ship it, and this is the

(18:03):
the probability that it willarrive by a given date, right?
And that's all kind of modeledin the system based on
understanding of what yournetworks doing, our
understanding of what thetransportation looks like,
right? It's all a prediction,obviously. So, so you have to
kind of constantly be trackinghow well did you do against your
predictions? Does it? Is itwithin the tolerance of the
process, right? I mean, Amazonitself did not we. There was

(18:27):
never an expectation that wewere going to hit 100% of the
promises, right? It's just, it'sa, it's a any physical process
is going to have have, like,statistical variation. It's just
we, but we can tell you veryprecisely, like, what is that,
right? And, and are you willingto live with it? And can you,
are you able to, are you okaywith the kind of negative
consequences of the spin versusnot? So,

so it's really, I mean, it's, it's not simple,
because, as you're talkingabout, you know, bringing in all
of these different data sets,yeah, exactly. I almost, you
know, my heart starts having,you know, palpitations a little
bit, because it's like, oh,that's a lot of data. That's a
lot of, you know, room forerror. And so I, you know, as,
yeah, thinking about thisthrough, what does that process

(19:09):
look like, of just making surethat the customers that are
coming in have the data to beable to make those actionable
decisions.

I mean, it ends up being, you know, again, like,
oh, so we're going to talk aboutAI a little bit, but this is
kind of why I'm so excited aboutthis. I think you've got
validation and and inspectionprocesses that we currently do
to kind of solve this, but Ithink we're on the brink now of
these, and they continuallyspeed up as you as you automate

(19:39):
these steps, right? Like, how dowe read in a contract from
someone? Or, how do we, how dowe, you know, how do we then
verify that the costs arecorrect after that contract
with, you know, is, is ingested?
Or, how do we verify our speed?
Like, we're kind of back,constantly back, testing our
prediction models versus, versuswhat actually happened? Because
we also have the tracking dataof. Of the actuals right to

(20:01):
model that. So all of that is,all of that has to happen. I
think what's so exciting aboutkind of the AI movement is we
should be able to speed this up,right? And if you think about
what's AI, really good at, it's,it's, it's really good at taking
information and kind ofdistilling it down to something
meaningful. And so it fits thisproblem just like a glove, and
that's that's why we're soexcited about this as a future,

(20:24):
as a future thing, and whetherthat's more of an online process
that's bringing in newinformation that we just
previously couldn't have,couldn't have possibly fathom
getting access to, because itcan, it can, you know, dissect
and categorize that stuffautomatically, or it's just
taking existing processes thattook too long, and making them,
making them 1,000x faster,right? Both of those things are

(20:47):
are incredibly powerful in termsof increasing the automation,
increasing the flexibility,increasing the kind of ability
of your business to run at theway you want it to run. So and

So for, you know, let's definitely dive into
the AI side of things. Because Ithink when we mentioned AI, you
know, there's a kind of, there'sa couple different reactions to
it. Some people might roll theireyes, of course, but then
there's the other perception of,well, I could just, you know,
use ask chatgpt, and it shouldbe able to tell me. But there's
a an additional layer ofcomplexity, especially at the

(21:21):
enterprise level, where you'redealing with all of these
different data sets. And sowhere is, I guess, AI having the
most impact in those sectorsrelative to shipping?

Well, I think it's, it's super early days,
obviously. And I think, I thinkif you go through like, kind of
the diffusion lifecycle, whichis common all these, like, new
technology things we I thinkwhen chatgpt kind of first came
out, I think it was like late 22or early 23 something something
in that time period, 2022 Ithink, yeah, so you started to

(21:57):
see so there's obviously, like,Everyone's like, this is this is
totally magical. We got to dosomething. So all the boards
call the CEOs. The CEOs calltheir CTOs. The CTOs tell their
team to just put something inplace. And, you know, it most of
it doesn't work. I think there'sbeen several studies that have
come out. It's not surprising.
Like, this is what it's justlike, let's throw some spaghetti

(22:18):
against the wall and see whathappens. And I think from the
standpoint of someone like me,or someone like a COO, or
somebody who's evaluating thestuff like you also have seen
the like, Blockchain push in themiddle, in the 2010s right where
you're like, This is going toensure that no containers ever
get lost and they never wentanywhere. Or metaverses are

(22:39):
going to run, and are, I mean,the the goggles people are gonna
have goggles on fulfillmentcenters, productivity is gonna
triple, right? So, you know,false promises from kind of
envision technology versus whatactually occurred. And so I
think, I think, though, whathappens, though, is, you get
these personally, at a very youknow, as we're going through the

(23:01):
cycle, you start to get theseproof points of how it's going
to work, right? And for me, Ithink the thing that that starts
to bring it home right is if youlook at how the software
developers are using it, right?
And this becomes, as these proofpoints start building, it
becomes easier and easiervision, like, where is this
going in terms of our industry,right? And, I mean, I programmed

(23:25):
in the 90s, right? And it wasthis gnarly process with lots of
like testing and manual this.
And, you know, everything tookforever. And it was, it was, you
like, you know, to some degree,we were all kind of like these
artist wizards who, who weretrying to figure all this out,
right? And you now, I watch mydevelopers, right? And they're
they've got one agent runningbuilding tests as as they're

(23:46):
writing code. Across on theother side, there's two agents
writing code, and they'recompeting through has better
results, right? And the move,the the production of that is,
is, is like, the only thing Icompare it to, is like when we
switched from assembly languageto assembly language to C or,
you know what, like a really lowlevel computer language to
actually having a language atall. And this is that's this

(24:08):
kind of jump, right? So thereason I'm going in depth on
this topic is because you'restarting to actually see how
this is going to work, right,and and I so then you kind of
like become more as thismatures, as the cycle matures,
and the use cases start kind offilling in, it becomes very
clear of what's what's kind ofhappening, and where this is

(24:30):
going. And I am, like, 100%convinced that this is going to
fundamentally change theindustry. And I mean, you know,
if you kind of like play thisforward and say, like 2028 you
imagine like a COO or a cseo hasnow a team of analysts Right?
Like they have a bunch of peoplein the structure, but there's,
there's analysts doing thingsand making sure that operations

(24:50):
run right. They're checkingbuildings. They're talking to
GMs. They're they're looking forevents, extraneous events.
They're looking, they're lookingat all these things. And then
they're, they're adjusting knobsor adjusting levers or changing
the way things are moving. Andif you think about 2028, like
the way to think back from thatis there's going to be, we're

(25:10):
going to have, now, a fleet ofautonomous agents that are
running and checking all ofthis. Right? This is, this is
all of these tasks that werelike relegated to humans to deal
with all this craziness, becausethey were the only ones that
could process at the end of theday, this weird form of
information, like all thesedifferent forms of information,
it's just such a perfect usecase for this is eventually

(25:31):
going to be replaced with AI,because it's gonna be able to do
it faster, cheaper, etc, etc,etc, right? So you know that
that's what I'm working backfrom. And now we're in the, now
we're in the phase of actuallykind of building, what does this
next, next round of, kind ofmore AI focused tools that fit
this particular narrative,

right? Yeah, because I keep telling people
that it's, it's, you know, theMIT study, quote, unquote, study
that just came out recently,which was very flawed study.
That's, but it's, obviously,it's, it's getting headlines
across social media, you know, Idon't, won't go into the it's

not, it's not surprising, like it, you know,
it's like I said, I think you,when you do first, this first,
kind of, like, you just, thiswas kind of, like the first
phase of that, the the people'srollouts of the stuff. And now
we're getting to, like, thisnext level. Next level. But
anyway, keep going. Well,

it's a new information era, and that's what
I keep trying to tell people, isthat it's we are in a new
information era, and then we cantake action based on where the
AI is going to fit the most incertain specific sectors of our
life, where it's, it's, it's,it's machine learning, but with
an additional intelligencecomponent on on top of it. And

(26:48):
so for for a lot of those folks,you know, they probably just
didn't realize how bad theirdata set was, and that's why
they can't really make it tooactionable. But I do think that
there's been some some error insome of the messaging,
especially around agents, wherethey're just going to, you know,
come in and solve that problem.
You know, as a podcaster, I, youknow, I'll be able to get an
agent, and they'll be able totake care of all of the post

(27:09):
production and editing, andmaybe that will happen in by
2028 hopefully, you know, someof those things will be able to
happen. But it's more, you know,using it as your calculator, and
using it where it makes the mostsense. So I'm curious with your
your early deployments with AIand, you know, especially around
the agent side of things, wherehave you seen those early

(27:30):
winners of where it makes themost sense for retailers and
enterprise shippers

to use? Yeah. I mean, I think it is kind of, it
does end up being this, likeoperator and multiplier, that's,
that's at least the currentflavor of what, what's working
now. And so I think you have,like these mic micro
productivity things that thatkind of add up to time savings
in aggregate. And then you have,then you have, like, larger
scale kind of, you know,automation, automation type

(28:01):
efforts that help, right? And I,I think, to your machine
learning point, the the, the wekind of think of it as, that's
something that it's probably notgoing to actually go it's not,
it's, it's more or less buildingon top of it, because, you know,
these, these models that we'vespent all these years building
and of kind of understandingthis data. It's more about now

(28:22):
you have this set of tools ontop of that that can leverage
them. So, you know, so, forexample, we have a simulation
product that's kind of takingadvantage of our models to
actually run, run through like,here's a hypothetical scenario.
And so to for our and for ourkind of folks at our company to
actually run the simulationsthey have to set up, each
individually, build out theexperiment. And so you can

(28:44):
imagine, this becomes,effectively the the the AI is
taking in what you're trying toaccomplish, and then it's firing
off lots of simulations, right?
And I think if you even look atlike, what if you ever you know,
if you're kind of a curiousperson, and you're using
chatgpt, for example, ask it toshow its work, right? What
you'll see is that it's doingstuff like, if you say, like,
Okay, I'm gonna upload an image,can you pull the text out of

(29:06):
this image? Right? What you'llsee is that it's actually doing
stuff like firing off Pythonscripts. That's of stuff that
does OCR. So it's not, you know,it doesn't solve it down to the
granular level. What it allowsyou to do is come orchestrate
lots of things. But I thinkspecific examples on the micro
level, you know, we have thisinevitably, if people are

(29:29):
shipping with us, they're askingquestions all the time about,
like, why did this happen?
Right? And so the some of thatcan be handled by reporting,
but, but like in general,there's gnarly ones that come
in, and the way that processwould work, traditionally is
they submit a ticket, anengineer gets to it takes a look

(29:49):
like tries to figure out what'sgoing on, reports back, they go
back and forth over the ticket,and then some resolution is
agreed upon, right? And so we'relaunching, one of our first
things we're launching is justa. Like what happened to the
shipment, and that's a that's acase where this is something
that someone is potentiallyspending time on this throughout
the day, because inspection ispart of any healthy business,

(30:10):
but now they're able to just dothis in a chat interface to
speed up their time, right? Andthat that naturally works into
the workflow. Another example,though, might be we have a
product I mentioned at thebeginning. We have a product
that decides where to ship from.
Well, Amazon, you had this, likeyou have, we had this thing
called little red button and bigred button where you would
basically hit these buttons ifit wasn't really a button, but

(30:33):
we, we, you know, basically youwould push the little red button
if a fulfillment center washaving issues, right? And it
would basically then rerouteorders around that. But the
presumption, the assumptionthere, is that someone is
sitting there watching for, whatare the signals that indicate
that this fulfillment center ishaving a problem, right? It
might be the GM is selling youthis, or might be that there's

(30:56):
weather events starting tohappen. There might be like
people not logging in. And sothis is, this is an example of
you have an agent that canbasically be watching for these
scenarios all the time, and thentelling other agent, you know,
telling the planning agent, Ineed to then go and you need to,
you need to reroute from thisbuilding, because we're having,

(31:16):
we're struggling to keep up,right? So these are, these are
something that we're kind of inthe pilot exploratory phase,
these larger things. So it hasto be a combo of both. But I
will say one more thing, justthere is this problem. When I go
back to the dev example, you goback to the developer example I
talked about a second ago. Yougo back to like how people are
thinking about this. You eventhink about like the internal

(31:39):
DNA of a company, the workflowsdo change, and that's where some
of the resistance comes from,right? People are not wanting to
change because it inherentlychanges the workflow Right? Like
we built in softwaredevelopment, the we had these,
these stage workflows, becausethis is realistically what
humans can handle. You know,like, this is how you did it,
when you have these, this kindof AI notion, and you can write

(32:01):
tests live as this thing isgoing out. All of the plot, the
processes you had traditionallydon't work anymore. So we've,
we've entirely, we're startingto rebuild our entire dev
process right to match what thisis more going to look like in
this AI native agenda. And Ithink that's going to be the
scenario with with supply chainsalso, and logistics, right? And

(32:24):
so we're going to have to kindof fundamentally rethink how you
run the teams, what metrics youlook at, like, how do you think
about who's doing, what, how youorganize? And that's all like
coming right? It's going to bepart of this transition that
that I think the main thing isyou have to be in this kind of
experimental or trial mindset.

(32:45):
And so if I'm giving peoplefeedback, it's usually more
about culture that needs toshift within these companies,
less about, less about like,like a specific thing to do,
because it's it's going to keepchanging in three month
increments, right? Like, whatworked, what didn't work now is
going to work in three months.
So you've got to be in thismindset of, kind of, constantly
absorbing and changing andmoving, yeah,

because I think you hit the nail on the head.
Because so many people have, arethat are, you know, sort of, I'm
a tech optimist, but there areso many tech pessimists, and
they, you know, they try, AIwants, and it's like, oh, it
sucks. It failed. It'sworthless. It's overhyped. You
know, and that creates thisresistance to change, that
resistance to being adaptable.
And there's, you know, lots ofcompanies out there that, you

(33:28):
know, Coinbase, I think theirCEO recently just said, like, if
you're not going to use AI, youwon't have a job here. And I
think it's more of a philosophystandpoint that you have to, you
have to be able to be able touse these tools. And it sounds
like this is, you know,something that you've
implemented within your company,and then now can be able to help
other companies, you know, adaptto those same, similar

(33:50):
philosophies.

That's exactly right. You have to culturally
shift. And we, we have internalshipping them. And I think
there's, there's going to befolks who are kind of not on
board with that, or theydisagree with my assessment of
where the future is going. And,you know, I think great, like,
you know, I don't have anywe're, you know, that isn't it.

(34:13):
We'll, we'll ultimately find outwhether I'm wrong or
overshooting or whatever. Butyou have to kind of have
everyone marching towards thesame drum as we go into this and
and it does require kind of awine culture. So I think, I
think that totally makes sense.
I mean, culture, culture isreally important in the sense
of, like, who is going to besuccessful there or not there at
your organization, right? It's,it's true of any organization.

(34:33):
And so I think you, you, youknow we want our we want our
folks to be extremely curious.
We want them to be tryingthings. I'm implementing a
process internal to my directs,for example, where I want them
to install a new tool everymonth, right? And just to
actually feel what it feels liketo implement this thing, because

(34:55):
there are pains, right? And youdon't, you know, it doesn't
really do anyone any good. Tosay, Let's AI stuff, right? You
have to, you have to kind of getto that next level. My my
general concern, my feeling is,though, when you start trying
things, you find things prettyquickly, like, it's not it you,
maybe you will have a differentexperience. But the more I use
AI, the more bullish I become onthis. And this is kind of where

(35:17):
my 220 2028 stance comes from. Idon't, I don't use it, and go
like, Ah, it's kind of dyingoff, like going back to say, the
blockchain example from from acouple minutes ago, right? It
you never, you never gotmomentum off of that example,
because you couldn't find usecases that were fundamentally
changed, right? And this one,though, the more you use it, the
more you will because it bleedsinto it, like we're now starting

(35:40):
to use it more and more in oursales process, or SDRs, or our
10x what they could do, 10xmaybe 100x what they could do
three years ago, right? And soit you, every aspect of the
business just continues to asyou, as you establish that, that
usage pattern and DNA, it's verypowerful.

And so as you're you're building out, you
know, your own internalprocesses and reworking them.
And it's probably a process ofripping things out of what used
to work and putting things in,and probably doing it several
times over, and reiterating, asyou go with the with the
customers, that you'reonboarding. So I would love to
get into some of what theonboarding process looks like

(36:21):
whenever a new customer decidesto do business with you, what do
those early days look like? Andwhat does maybe some of the
early AI agents that they couldtake advantage of in their own
data sets?

I think that we are the two couple places we're
leaning into. So I would say theonboarding process itself is
going to be pretty worked out,reworked over the next six
months. So I would say, likeattrition. What we've done is
like, give us your contracts,give us your details. Let's go
to the site and it's it ends upbeing, you know, let's say more,

(36:56):
like weeks, right? But we feellike this is really has the
possibility of being days orhours, and if you can bring
information in that fast, andmaybe it's like, in the virtual
sense, maybe it's like 80%correct or 90% correct, but it's
still so. So that allows us toeffectively bring your data in
very quickly and then even provesome points for you early,

(37:17):
right? Which is, which ismagical. And that's, that's
like, you kind of have to partof the same. You know, everybody
who's in this space is gettingpitched by vendors who are going
to save them 10x 5x 3x 12xright? Whatever. The thing is
that they're getting hammered bythat. I mean, if you are a CSCO,
you are getting blasted byvendors all the time making all

(37:37):
sorts of promises. And if youadded them all up, your business
would basically be free. Butobviously that that isn't true,
and there's and I thinktherefore people are health
police are, in a healthy sense,skeptical, and so our ability to
kind of bring your data into oursystem, that's a huge factor,
right? So because then we canprove it quicker, we can move
along those axes, I think, Ithink then you have kind of

(38:00):
incremental improvements toplatform which I was talking
platform, which I was talkingabout, the question the Q and A
thing, right? This is just likethis makes their day to day
life. We already believe we madetheir life simpler by being in a
console and and kind of having amore modern approach to this.
But you throw in the you throwin the question and answer piece
that's even that like takes youup to the net stocks, right? And

(38:21):
then I think our first kind of,the thing that we feel like is
probably the most interestingearly is the simulation brought
you know, we're effectivelycreating a digital twin of your
network. And where we're gettinga lot of the interest is, how
are we able to run thesesimulations using a combination
of humans and AI to basically dosomething that would have taken,

(38:44):
you know, weeks to set up andanalyze and take that down to
days now, right? And so that'sthat's really where we're
leaning in on on some kind offirst pass type game changing
stuff,

yeah. And for folks who are just tuning in or
maybe even listening, what Ihave open right now is a
screenshot from one of youronboarding videos that's on the
website. You know, talkedearlier about how I love your
website, and I love this videobecause it just, you know, we
could be talking we're talkingabout, you know, complex things
for for a lot of folks, and it'stough to visualize it, but this
graphic is perfect atvisualizing it, talking about

(39:18):
your systems, where it's thewebsite, the OMS, the WMS, the
TMS, the ERP, the planning, andthen bringing it all together
under the shipping platform. Andso with a lot of your stuff,
with a lot of the things thatyou've been saying during this
conversation, it really centersback to what you call the
shipping operating system. Socould you kind of explain that

(39:39):
where shipping comes into play,bringing in all of this, these
different data

sets. Yeah. I mean, this is really back to
kind of even, you know, pre,like AI, just back to kind of
fundamental philosophies.
There's kind of two things thatreally separated Amazon's
approach to supply chain. AfterI got out of after I got out of
Amazon, I was able. To kind ofsee how the world ran, right?
And it, I think when we were init, we were just kind of doing
it, and we weren't reallythinking about what it meant

(40:04):
holistically, right? But if youstep back from my time at
Amazon, there was two bigthings. The first was kind of
what you're alluding to here,which is the, what I call, like
this coordination effect. And soyou put what typically will
happen in enterprises is theywill have these vertical slices,
right? So the order managementsystem is responsible for

(40:26):
everything that's an order, andso therefore it's going to make
the decision about where to shipfrom, right? So the magic at
Amazon, we had a team big,there's probably 1000 people on
that team that did ordermanagement stuff, and we had a
big team that did warehousemanagement stuff, right? Like it
was all there was these hugeteams that were focused entirely
on those problem spaces. Andreally, what I was doing at

(40:48):
Amazon was I lived in thismiddle area where we were
coordinating these decisions.
And to use, I heard you say theword calculator a bit ago. And
really, to some degree, we'reacting as this super
sophisticated calculator thatcould make these decisions
systematically. And it really,we really stayed in our lane. I
had no interest. I like, there'sa bunch of things that OMS do

(41:09):
that I have no interest indoing. I'm not, I don't, you
know, there's, it's a hardproblem. It's complicated.
There's rules with all stuffgoing on. But what we're trying
to do is very good at if youmake a decision to ship from a
given building, there'stransportation implications,
right? There's inventoryimplications, there's there's
like cost structure implicationsthat unless you are factoring in

(41:30):
all these things that we haveaccess to horizontally, you're
not going to be able to consideryou're you're going to make the
wrong decision inherently. Andyou know, sometimes you'll make
the right decision, but ifyou're making it wrong, let's
just say 10% of the time that'sthat's on the orders of millions
of dollars for even a mediumsized enterprise, right? So

(41:51):
that's the first piece, thiskind of coordination thing. And
then I think the second piecethat was just really magical
was, was I took over the supplychain tech stack around 2010,
2009, and that was right aboutthe time that kind of machine
learning was starting to takehold, right? And so we had, of
course, like traditionaloperation research scientists on

(42:12):
our team and and, you know, butthe thing that we did a really
good job of is getting them toplay together such that we
started using these moderntechniques for our forecasting,
for our predictions, for our ourmodeling, transportation lanes,
all the stuff that's kind ofrequired to make these decisions
and and so that is a that is aneffort, where these data sets

(42:33):
tend to span all of thesedifferent stages, right? And so
you take those two thingstogether, and you kind of end up
with this operating system thatthe way we're viewing it is, we
do not look again. I don't wantto be in the WMS space. I don't
be in the OMS space. What I wantto be is, is this optimization
data layer that lives, thatlives, kind of below all those
that helps you make really gooddecisions about transportation,

(42:56):
inventory, etc. So, and

for a lot of I just the the industry as a
whole, or just retailers as awhole supply chain feels like
there's, you know, they have aseat at the the executive table,
the boardroom table now, and soin your eyes, with using
shipium, they're able to thosesupply chain managers or
executives can now sit down andbe able to see if I pull this

(43:19):
lever, if I Make this, thisswitch, x, y, z, is going to
happen, and I'll be able to showup to these meetings better
informed about some of thefuture forecastings That that
we're going to be making usingthis dashboard of all of these
different levers that we couldbe pulling. Is, is that kind of
the idealistic way that someoneis going to sit at a computer,

and also a great AI use case, right? I mean,
like, like, bringing all thistogether, but it is. It's
exactly that. I mean, you have,if you're making a decision
about about what you're doing inyour network, or how you're
doing it, it has implications oncustomer experience. It has
implications on cost, which,which basically just means that
you have, you have money you'repotentially losing on the

(44:00):
conversion and sales side, orgaining, right? And you have
cost structure implications onthe back end, and there is this
kind of ideal trade off, right?
Like, it's not a it really is amath problem. And I think we're
trying to bring that whole, likeholistic thought process to
bear. We want people tounderstand these trade offs and
make the right decision fortheir business.

So it sounds like it's, it's very reminiscent
of maybe some of your, yourearly days, where it's like,
you, I think you mentioned youwere kind of these, like,
creative wizards, and it maybesounds like that's exactly what
you're doing on this side of thething.

Yeah, now it's, now it's coming in the well, I
mean, that's the classic thing,right? You figure out the low,
you know, you just kind of keepno I think in those days, what I
was talking about was just kindof like the mechanics of
actually programming requiredall this and and like the bot,
the bottom level programming isthese tools have gotten so good
they other things help too alongthe way cloud and. And SAS and

(45:01):
APIs and all the other stuffthat's kind of happened over the
last 30 years, but, um, but thatkind of gets more taken away.
And so then you, then you end upin this, like, dynamics of the
system. What do you do with yourdata? How do you, how do you
kind of use this new set oftools? So obviously, yeah, it's
a new puzzle at a differentlevel. But totally, totally
like, you know, it's, it's a itget, like, I just, I love these

(45:25):
puzzles. This is what I lookfor, like figuring out how to,
how to solve these, how to getvalue out of this stuff, how to
and I love that it has this kindof, like, physical world feel
and touch to it. So

now you talked about some of those early wins
that are happening internally atyour company. But what about,
you know, maybe some of yourcustomers have they had a chance
to sort of pilot some of theseagents, maybe get some early
wins. What does that processlook like for from a rollout
perspective,

I think we're kind of in the getting feedback
phase, and so the appetite isdefinitely there. I think, I
think there's a, there's a, youknow, people are, people are
looking for and I think when youdemo this stuff, they're there,
it's like, very clear to them,there's a there's a win. I think
it a lot of it ends up being alittle, you know, we've been so

(46:12):
as a company focused on kind ofthe optimization side of it. So
just like, what's the costsavings, right? And and so now
we're getting into this newterritory of like, it's more
about automation andproductivity and flexibility,
which are a little squishier,but the excitement level at
these companies, and we showthem, like, Okay, what if I was
able to, you know, here's thischat bot demo that we're
starting to roll out that isgoing to tell you the state of

(46:35):
the shipment. What would thatdo? And they're like, I mean,
this is like, you know, I can, Ican now free myself up to be
focused on these strategicissues, as opposed to as a piece
of grinding away, on, on, on,trying to get through these
small tasks, right? And that'sso, that's, that's the stuff.
That's where it's, like, veryexperimental as we figure this
stuff out, but, but incredible.
I just, I think the like, youadd all those paper cuts up. And

(46:59):
a lot of what people are doingin these companies are, they're
just paper cutting themselves todeath, right? It's like, you go
look at an analyst screen.
They've got 47 Excelspreadsheets open, right? And
they're, they're moving thingsaround. They're calling people
and trying to do things. And soif you take that, if they've got
100 steps they're doing per day,and you're able to automate 10

(47:20):
of them, right? I mean that thatopens up like this, this massive
time, this area for them tostart thinking more
strategically about where theyneed to go, which are, frankly,
just things that probably needto happen at the company,
whether it's a renegotiation ofa contract or following up on
things or driving some changewithin a build, all that stuff

(47:41):
needs to happen, and there isn'ttime for that, because there's
just all of these minusculethings around information flow
within supply chains. Is

there anything that's coming down the pipeline?
Maybe in the next you know, youmentioned 2028 in reverse
engineering, where you thinkwe're going to be about in 2028
but I'm curious as to what issort of those present day wins
that that shipping provides. Iread a couple different case
studies you know that you had onyour your website. I think it
was peak readiness withManhattan WMS, Saks off Fifth

(48:12):
Avenue, moved from singlecarrier to multi carrier fast,
and saw that faster is cheaper.
Which which of those. I guesslevers can be pulled today for
some of your customers thatthat, or maybe even prospects
that that are listening to thisepisode.

Well, I think I've been trying to kind of keep the
conversation mostly focused onkind of the brand new AI stuff,
but, yeah, certainly, certainlywith, with, within our
ecosystem, like we're selling,you know, we open up a whole new
carrier network. We are able tounilaterally, almost every
scenario save a bunch of moneyby method optimization. We've

(48:53):
got customers who are now usingour fulfillment engine to more
precisely make decisions wherewe have delivery promise
installations that basicallyhelp customers say, you know,
tell their customer this isprecisely when you will receive
it. And so all of this is, allof this is in the mix. And, you
know, I apologize for I maybewent too far on the other side,

(49:14):
but this is stuff that we thatthe company has been focused on
over the last four years, aswe've kind of gone to market
with this and built out all thisand built out all this
infrastructure, and so that,that all that all continues to
move along. I think a lot ofdiscussion today, and it's, you
know, we pride ourselves in thefact that they almost always
have some sort of financialbenefit attached to them, like

(49:36):
it's, it's always about, it'salways about. After we've
installed this, we've, you know,we, we saved a big retailer on
the order of $17 million right,like annualized, right this
year. And so by, by optimizinghow they're running their
methods and and kind ofeliminating having a very, very
precise model of what their coststructure is compared to their

(49:56):
carriers, we're able to wringout all of this money out of
their current spend. So thatthat's, that's and, you know,
from their standpoint, it's allin, almost entirely automated.
They don't have to do anythingthis money. It's just, it's just
free on the books, works, etc,right? So all of that is going
extremely well, and is, is partof the, you know, continues to

(50:17):
be what we build upon. And I,like I kind of said, I think
very early in this episode,really, we think of this new AI
stuff as accelerants for that.
So how do you get to that stufffaster, right? Like, how do you,
how do you, if you want to runscenarios against our
simulations? Like, instead ofhaving to set it up, work with
our customer success, etc, canyou just basically interact with
a chat bot that gets this thinggoing? And that's the, that's

(50:40):
kind of the, it's back to yourpoint of, like, if I'm sitting
at the table, I'm the cseo, andI want to, I want to talk to
people about about what are someof these scenarios. I want to be
able to turn those around fast,because you can't be at the
table and say, we're thinkingabout implementing this customer
experience piece, right? Youcan't be, well, okay, in four
months, I'll be able to give youthe impact of that, right? It

(51:02):
has to be very fast. It has tobe accurate, or you're going to
get grilled on the back end. Andso that is all of this, of this
notion of, like, moving faster,flexibility, autonomous, right?
And that's, that's what we'rekind of building off of. This is
new AI stuff,

yeah. I mean, it sounds like you're, freeing
up from an overall philosophy.
So you're getting all the datain to be able to make smarter
decisions and make those smarterdecisions faster, but then also
adding the agentic AI model ontop of that, to be able to get
rid of the mundane, so thosesame folks that are sitting at
the table then be able to focuson the strategy, the money side

(51:39):
of things, anytime you can getmore profit back, that is,
you're doing your part as a, youknow, as an executive. So

yeah, and I think culturally, the even the process
of bringing the data in that'sgoing to be dramatically
improved as we get better andbetter at this and that's that's
part of this kind of, likecultural transformation of how
we think about all theseproblems, like we, we solve
problems a certain way three orfour years ago, that now you
just fundamentally solve themdifferently. It's just, it's
part of the game.

Yeah, I used to tell people that I would and I
would do this, that I would handtranscribe a podcast episode,
and now I don't have to thinkabout that

and that that's

incredible.
Tell you how many hours in theday that that saves,

yeah, yeah. And it is really amazing how much
better these things are at that.
I just, it's, you know, there'sobviously been, like, dictation
software for years, right? Butit's just, it's like, the the
the the level and the quality,like, you know, language
translation and all this, youknow, super minor thing. But we,
I remember early at Amazon, wewere launched when we launched a

(52:44):
new country, like the late thelocalization process. It took
probably six months right to getthrough the the kind of us to
Germany translation, right? Andit's like, you know, we, when we
launched our kind of the the ourSpanish version of our console
for a Mexico installation wedid, you know, it was like,

(53:05):
just, it just happened, right? Imean, it's just really
incredible how these things justspeed up certain parts of the
information flow that were justso difficult,

yeah, well said, because that's just, you
know, there's all thesedifferent aspects of whenever
you're implementing newtechnology and making these
iterations that you you can'tsee until you try and so not a
try once, and then, you know,throw it in the trash can and
never read it again. It's, youknow, let's think about how
creatively we can solve thesebig problems, because big

(53:38):
problems probably also have bigcosts associated with them, and
every business is looking tosave money at all levels. So
Jason, I know we've, we'vetalked about, you know, a lot of
different aspects within thisepisode, but I'm curious where
you kind of think the, you know,obviously the holiday season is
approaching, you know, where ecommerce isn't going anywhere.

(53:59):
It obviously it's a mainstayhere. Where do you kind of see,
you know, the next, I guess, sixto 12 months shaping up, and how
can retailers and and shippersbe best prepared for that?

Yeah, I mean, I think, I think you're, I love
your kind of quick summarythere. I mean, I think obviously
we have all of our peakreadiness stuff going on right
now. Everybody's been, been,been, you know, the tariff thing
that's kind of dominated the thethat part of the narrative all
year has been, has been floatingaround in the background, and I
think is mostly settled at thispoint. I think we, we saw that,

(54:33):
you know, our shippers, ourcustomers, were mostly upset
about the unpredictability ofit, less, though, than the
actual kind of what itspecifically was. But I think
you take all that, and I think,you know, I just hate to be a
broken record, but I just think2026, is going to be, kind of,

(54:54):
you're going to start to see theacceleration of of these AI
installations and and. And a lotof companies, probably a lot of
people, are kind of in similarsituation that we are, is that
we are just frantically buildingfor the next six months to try
to kind of get ready for thisonslaught, right? Because it the
the the kind of hunger for it,the the attitude towards it, the

(55:17):
whole it's very, very apparentin the market. It shows up in
even the stock market, right?
Obviously, the best top stocksare, like, I think, I think
that's, I think 2026, is goingto be largely about that story.
And if I, if I'm going to make aI think, you know, the the
interesting, like, kind ofcurveball stuff, is, how does it

(55:38):
translate to physical and whenso like, self driving trucks,
self driving cars, when doesthat stuff all start to hook up
also? But I think that's a mess.
That's that's harder to predict.

Yeah, it's technology. It's if you're, if
you're anti technology is goingto be a bad time for you. Yeah,

yeah. I think, you know, use also ask specifically
what I would do. I'm advocatingcultural changes to the way we
think about this stuff, right?
And I think you have to push inyour org for this stuff to be,
to be front and center, and kindof start leaning in towards
this, because I think otherwiseyou're kind of getting, you're
going to get caught flat

footed, yeah.
Well, well said. Now, is thereanything that you feel is
important to mention that wehaven't already talked about?
No,

I feel like, I feel like we've covered
everything we've been prettysure, honestly.

I mean, I had a whole list of questions, but it
just that the conversation wentin a great direction. And I love
diving deep into this, the AIagent, you know, sort of world.
So this is this lined upperfectly. So

my marketing guy will yell at me. He's always, he
always gives me these things I'msupposed to do, and then I end
up talking about, you know,whatever I'm doing, all this
stuff. But anyway, well,

I think we hit all the important stuff. You
know, it's AI, it's technology,it's data, and then it's, how
can folks actually use this tomake real impact within their
company? So, alright, Jason,where can folks, you know,
follow you, follow your more ofyour work, maybe get signed up
for shipping demo.

Yeah, so shipping.com, obviously you can
request a demo there, LinkedIn.
Jason Murray, shipping them.
CEO, should go find me. Youknow, always, I think LinkedIn
is probably our most accuratesocial media platform. So, so
anything on that front, we tryto keep pretty up to date on
what we're posting and doing.
But we'd love to talk to anyonewho wants to talk. So, great,

awesome. Well, this was a fascinating
conversation. So, so thank youso much for for joining us, and
we'll have to have you back onin the in the future, whenever
you know the AI agents are, youknow what taking hold, and you
know how we can kind of takeadvantage of

them. Thank you so much.

Thanks for tuning in to another episode of
everything is logistics, wherewe talk all things supply chain
for the thinkers in freight, ifyou liked this episode, there's
plenty more where that camefrom. Be sure to follow or
subscribe on your favoritepodcast app so you never miss a
conversation. The show is alsoavailable in video format over
on YouTube, just by searchingeverything is logistics, and if

(58:13):
you're working in freightlogistics or supply chain
marketing, check out my company,digital dispatch, we help you
build smarter websites andmarketing systems that actually
drive results, not just vanitymetrics. Additionally, if you're
trying to find the right freighttech tools or partners without
getting buried in buzzwords,head on over to cargorex.io
where we're building the largestdatabase of logistics services

(58:36):
and solutions. All the links youneed are in the show notes. I'll
catch you in the Next episode inGo jags.

Unknown (59:02):
You you.

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