AI Hustle: News on Open AI, ChatGPT, Midjourney, NVIDIA, Anthropic, Open Source LLMs: Why AI Spells Doom for Big Corporations: Insights from Conor Jensen at Dataiku

Welcome to the OpenAI podcast, the podcast that opens up the world of AI in a quick and

concise manner.

Tune in daily to hear the latest news and breakthroughs in the rapidly evolving world

of artificial intelligence.

If you've been following the podcast for a while, you'll know that over the last six

months I've been working on a stealth AI startup.

Of the hundreds of projects I've covered, this is the one that I believe has the greatest

potential.

So today I'm excited to announce AIBOX.

AIBOX is a no-code AI app building platform paired with the App Store for AI that lets

you monetize your AI tools.

The platform lets you build apps by linking together AI models like chatGPT, mid-journey

and 11Labs, eventually will integrate with software like Gmail, Trello and Salesforce

so you can use AI to automate every function in your organization.

To get notified when we launch and be one of the first to build on the platform, you

can join the wait list at AIBOX.AI, the link is in the show notes.

We are currently raising a seed round of funding.

If you're an investor that is focused on disruptive tech, I'd love to tell you more

about the platform.

You can reach out to me at jaden at AIBOX.AI, I'll leave that email in the show notes.

Welcome to the AIChat podcast, I'm your host, Jayden Schaefer.

Today on the podcast, we have the pleasure of being joined by Connor Jensen, who is

a technology AI and strategy executive who currently leaves the field CDO team at Daydai

Koo, a platform designed to build, deploy and manage data and AI projects.

With a really diverse background that spans from being a barista and archery instructor

to an Air Force weather forecaster and insurance actuary, Connor brings a really unique perspective

to the world of AI.

So when he's not helping companies navigate their AI journeys, he enjoys spending time

with his wife, seven children and a small flock of ducks and a very fancy goose on the

outskirts of Chicago.

Welcome to the show, Connor.

Did I sum that up right?

Am I correct in your intro there?

It's a weird intro when I listen to it, but you hit the eye points.

Thank you, Jayden.

I'm super excited to be here.

Awesome.

Well, super happy to have you on the show.

For those that don't know, I met Connor back at the AI for conference earlier this year,

was impressed by some of the things he was working on.

Really solid guy and visionary in the AI space.

So excited to pick your brain, get some of your insights.

What I would be curious on from your perspective, kind of to kick this off, tell us a little

bit about like your background and your journey, right?

So obviously we got archery instructor, we got Air Force, we got all these different

areas.

What brought you, I guess, what was your interest there, but like what really brought you to

technology?

What brought you to AI?

Sure.

So, you know, to, to not go too, too deep into it, but I sort of been, you know, I like

to switch fields.

I like to learn and, and sort of continually be challenged, right?

Sort of like meandering after my background sort of clearly shows that, but I've always

been a big fan of sort of math and analytics and applied side of things.

So other than sort of my post college dropout dirtbag years, which is, you know, when I

did a lot of the, you know, weirder or esoteric stuff, you know, I joined the Air Force and

I started as a meteorologist there very much and applied science side of thing, right?

Studying models, using data, making forecasts and, you know, how to defo sort of a sciencey

and math bit that, you know, that, that really sort of like should me, right?

Like we were the ones building the models, it's not like we were guys working up like,

you know, the, the research stations, things like that, but that sort of has shaped a lot

of what I've done since then, you know, I've been back to schools, actually got a degree

in mathematics, ended up in insurance after, you know, spending some more time in the retail

and so I'm like, so is I kind of walking around with always with that idea of like, how we're

going to use data, how we're going to make better decisions and was lucky to be at an

insurance company now, almost 12 years ago, that was, I think, early on in saying, Hey,

how are we going to use data science and sort of this emerging field coming out of the tech

industries to change how we do business and insurance is really interesting space to have

done that because insurance has, I mean, a long, long history of using data to make decisions,

right? Actuarial science, sort of like precursors of statistics, right? So totally, you know,

in one regard, you could almost look at them as like, one of the like, OG data science fields,

but yep, I'd slowly see that, but it's also been doing things the same way for a long time,

but it doesn't because they work, but it also then has, you know, some sort of,

some renaissance to move into a new and different things. And so it was really cool to be in a

place that had both a long history of using data, but then also enormous amounts of opportunity

to be changing how they can do that. And so I was obviously coming at it from working in the

business, working as an actuary, you know, more the analytic side, and really was lucky, didn't

always feel that way at the time, because we were building an on a friend to do cluster to do all

this stuff and realize how hard the tech was, especially at a non tech company, right? You're

dealing with it outsourced to contractors to do the work and everything. And like the tech side

ended up being sort of like the biggest challenge. And so I kind of got put on that.

Now, I would say it gets to my will, but sort of against where I would like to spend my time,

which is with the business and working on the projects, but got stuck on the tech side. And

really then that was a huge opportunity, you know, like, right? Like, honestly, the hardest

problems are usually the ones where there's the most. And so spent, you know, a few years there

helping to sort of build the team and the platform went over to another company to do that, and then

moved over to tech now about sort of six, seven years ago, really to focus on working with sort of

my former peers in in the sort of like those non core tech industries, especially, but you know,

any company that's really sort of changing, trying to change what they do to make data science, AI,

whatever the sort of power describing the moment of a stronger fabric of their business, right?

Because if you don't start data as that sort of core asset from the beginning, which most companies,

you know, have been around long enough to not have done that, right? It's a big culture and

strategy change stuff like that. So that's sort of where I've now been lucky to focus and do it

across industries and companies of different maturity and stuff. And so it's been a weird

journey to get here in some respects, but you know, it's a blast. So I can't complain.

That's, that is awesome. And yeah, I mean, like so much of life is about the journey, what brings

us there and the backgrounds we bring from all of that that kind of bring us to what we're doing

today. Talk to me about how you started working. You've been there for a number of years at Data

IQ. Tell me about how you started working there. And maybe, I guess, for people that don't know,

tell them a little bit about Data IQ and what problem they're currently solving for customers.

Sure. And so to talk sort of real quick about Data IQ, Data IQ is an analytics work bench, right?

And so it is, I designed to be a single source for building data products, regardless of complexity.

So sort of that from your most basic things, you know, just doing, you know, data pipelines to

feed a BI dashboard or a report or something all the way through to leveraging the latest and greatest

technology of, you know, deep learning or generative AI or wherever, whatever we're talking

about two years from now, you know, we're sort of that framework in that platform that allows you

to bring all those different tools and how all you use the different capabilities work on it.

So there are the sort of full code capabilities for engineers and data scientists and you work

technical people, but also there are, you know, there's a GUI, there are ways for those non-technical

users who know the data and know, you know, the business and the problems to also be able to

work with it in sort of one platform. And then we sit sort of wherever you want to sit architecturally.

We come from the Hadoop space. So that's actually where I met Data IQ was now almost seven years ago,

doing a POC, looking at it when we're, okay, still managing an on-prem cluster. We obviously now

have sort of, you know, on-prem in VPCs, a fully SAS offering, right? So do we cover all those

different, you know, deployment capabilities for any industry? Again, we're sort of general purpose.

And so that's, that was a big part of the excitement for me when I had the opportunity to come over

here was, you know, I'd done this in the military and in the meteorology space and in retail, working

in Starbucks and then in insurance. And I'd sort of seen different domains walk through this journey

of trying to be more data-driven and more AI-driven. And so I really wanted to sort of have that

opportunity to go and somewhere where I could look across industries and see a similar and what's

different, right? And the thing that's really my hypothesis going in that so far as, you know,

holding sort of true is that there's a lot more similarities than there are differences. Like

the use cases may be different from an industry to an industry. Like, so what a finance company

wants to work on project-wise is going to be different than what a manufacturing company does

etc. But the structural changes, the cultural changes, the sort of challenges in moving there,

I see a lot more similarities across industries than I do see differences, which has been

the fun part of sort of sitting on this side of the fence and being able to look across

so many different companies at different stages of maturity from all different industries.

That's what brought me here and that's what's keeping me here on this tech side for now.

Very cool. Very cool. I would be curious, you know, based off of what you're currently seeing at

DataAikusa, the problems you're solving, what are some predictions about changes you see in AI and

perhaps AI in the enterprise over the next number of years? Where do you see things going,

changing? What are the big shifts you are forecasting? So the biggest one for me probably

really comes down to sort of who is doing this at enterprises, right? There's still, and for a long

time there was that sort of barrier to entry of working with, you know, large-scale data, working

with predictions of such, of having to have the programming skills to be able to work with, whether

it was, you know, going back to sort of days of using SAS and, you know, things like, you know,

like the various tools that IBM had created and things like that, where you had to go to the tool

that did it and it required some level of sort of coding or expertise through to R and Python and

whatever. And so, you know, for the last 10 years, at the time you hear somebody say, hey, I want to

get into data science, what do I want to do? What do I have to do? It is, you know, go learn R,

go learn Python or go, you know, learn some tool. And I think that's going to continue to sort of

fade away. And, you know, for me, yeah, there are still problems where that actually you need to

have that scale of the data or it's streaming or things like that where you need to have those

skill sets. But I think as we continue to lower the barrier to entry to working with larger sets

of data, to working with more complex sort of algorithmic and analytical approaches, that

that knowledge of the data and that knowledge of the domain and the problems to solve continues

to become more important and we make it easier for those folks that are in the business making

decisions today, or are, you know, across the, you know, across companies, and you get them into

this this world of making these decisions without having to be a programmer themselves, right? And

then I think that that's the second part that really starts to change is I think

companies will start to change how they hire and how they train across or I should say the

more successful companies will change how they hire and how they think about bringing people

into the organization. You know, one of the companies, one of the questions I always love

to ask a company when they're sort of talking about what we want to be made more AI drip,

right? Is what is that? What does that mean? How does that change, right? And, you know,

it's not just a bunch of data scientists that are going to, you know, sit off on the side making

projects that, you know, they throw over the fence and, you know, we've all sort of seen

the challenges of getting things deployed and everything. But, you know, I'll ask him, I was

like, okay, so what does that, what does that mean to you? How does that change? You know,

when you talk about being more AI driven, are you going to go out and add to your interview

process for hiring salespeople that they know how to work with data that they use data to make

their decisions and that, you know, that's going to change how you go out to market and sell?

Are you hiring HR people, you know, your people managers with a, what is their viewpoint on,

you know, leveraging data science tools, you know, things like, you know, like a

sharing prediction risk or employee, you know, flight risk, things like that, right? And if the,

you know, and the answer is more often than not, especially when you're talking to sort of those

business leaders is like, oh, no, that's weird. Why would I ask a salesperson how good they are

working with data, right? But that's right. If you want a data driven company that is everybody,

that's not just hiring a bunch of analysts who are building things in the corner. That's

an essential component of it. To be fair, you need to have those who can build it. But

you can't just create it, right? This isn't the field of dreams. It's not you build it and they

will come, right? Like you need to be hiring people in every facet of your business who are

looking to work with data, who are looking to work with analytics and AI products. That's how

you have to change it. And I think that we'll start to see, I think there's still a sort of a

fundamental gap in many companies and really understanding the need for changing, especially

people, right? I think that's actually one of the hardest parts to change. You know, we think

about the technology, the technology is hard, right? I don't want to pretend otherwise, you know,

maintaining especially the pace of change of the technology, you know, like,

you know, that sort of today, like working with, you know, one of the three big hyperscaler vendors

for your cloud platform is a given and everybody has their cloud strategy. But, you know, 10 years

ago, I spent six months trying to get, you know, a company to go on to cloud and it was a non-starter

for, you know, for a year. And five years ago, it started to become more accepted today. Now,

it's sort of almost the default. I mean, it's entirely feasible to 10 years from now.

Nobody goes to any of the three clouds that sort of we go to today. Like who the hell knows, right?

So that pace of change of technology is really challenging. I don't want to underscore that

by any means, but like you, the harder people are way harder than technology in the end, right?

And so if you look at a company that's got, you know, even the small company, right? A company

of 1,000 people. And I, you know, not to sort of like air dirty, like, you know, like I look at

sort of sometimes, and even with a data who I'm like, Hey, we preach this, you don't even always

act that way in sort of like how our higher decisions, things like that. And we're not

start not that big a company. And we're only 10 years old. So, you know, you look at it at a big,

you know, manufacturer who's got 20,000 employees or 50,000 employees around the world has been

around for 100 or 200 years, like you have to change so much. And it's at every layer of the

organization. And so that's that's changed. I think that we'll start to see more companies really

start to think about the people side of this holistically across their companies. And then

that will play into sort of those lowering those barriers to entry. And then the last thing that,

you know, I'll just say is I think that the pace of change will really continue to just,

okay, just be an ever accelerating challenge, right? You know, I think that maybe that's

where that's one thing that we're continuing to have to sort of keep up from. And I'm really

curious. I think that, you know, some of the biggest companies in the world will end up really

being sort of crushed by this, the inability to keep pace with the change of the technology,

especially when you look at, you know, and coming from, you know, a time in financial services,

financial services has, you know, long been a more dated driven organization, you know,

you go back to, right, who were the first adopters of IBM mainframes and decision making,

stopping algorithms, you know, financial services was certainly one of the biggest areas.

And they're still now beholden to some of those tech decisions that they made 50,

60 years ago in some cases. And so like, first of all, your advantage in some cases is actually

like trapped companies with, you know, there's all the jokes around that, you know, the entire

world global financial system was around mainframes and Excel. And like,

it's a joke that's like kind of scarily true, having lived that side of the fence.

And so, you know, I, I think that that can continues to get kicked by a lot of companies

as, you know, say, Oh, well, we want to move off of this, whatever this system that we have,

you know, processing customer transactions or so, whatever that we bought 50 years ago,

we've got all this crazy stuff tied into this, you know, mainframe and every time you go,

Hey, we should change that. And you know, you go, Oh, well, it'll be a hundred million dollars,

maybe like the price tag of these is yeah, I'll guess. And everybody else, well, what do we get

for a hundred million dollars? And the answer is usually, well, the same thing you have today,

just not on 50 year old technology. And so then everybody says like, well, okay, well,

then why are we going to go spend this like 10s or sometimes, you know, 100 million or more

dollars to make that change? Well, you know, the Southwest Airlines example earlier this year,

where they sort of had their computer system go down and it was precisely that it was just

an old system and shit breaks. And it broke and it cost them 10s of millions of dollars in,

you know, a span of hours because of that. Yeah. And I think we're going to see a couple

more examples like that, where big companies, old companies who've got this sort of technology

really have big data system failures. And it's going to force the hand of some of the,

you know, these companies that have just an, and a, hey, we all understand market forces,

we all understand the making quarterly returns and stuff. And it's hard to go to the market and

say, Hey, you know, we're going to eat hundreds of millions of dollars, you know, in costs over

the next three to five years for almost nothing, right? Essentially risk mitigation. Yeah. Nobody

wants to do that. And no CEO wants to go to the board and say like, Hey, we're taking this

giant chunk of change out of our profits or out of our returns to shareholders or whatever for

the next few years, but like, it's going to happen. And it's going to have to happen soon. And so

that's more than I really, like sort of like spectating, I'm like, Oh, like, who, who is this

going to happen to? Because it's like, I couldn't tell you there because most companies have this

risk out there, right? So that is, it's, it's, yeah, that is such a, that's such an interesting

thought. But yeah, it's totally true. Like there's so much archaic tech out there, there's people

tied into it. And like you said, right now, we're just seeing like this rapid change, this rapid

advancement of AI, everyone's got to get like on the newest thing as fast as possible. And maybe

by kicking the can down the road for the last 20 years, these people have put themselves in a really

bad spot. Like maybe, you know, like you mentioned, of course, Southwest Airlines, the whole system

goes down. Of course, they're going to feel the pain from that. But maybe like, that's not the

only pain people are feeling is a system failure. Maybe the other pain is like, they don't adapt

as fast. And like, that's the system failure, right? It's like, their, their lack of adapting

quick enough, they're going to get beat by a competitor and, and like eat market share, lose

market share very quickly. It's such a tricky question because you, you know, you can be the

first mover like, Hey, there's something new. And then, you know, spend five years chasing a

technology that the dots like map are, right? Like, you can build on things that then end up

not working out in the log run. And so there's this sort of like weird sweet side of space. Okay.

We're not the first movers, but we're not the laggard. Like, how do you sort of keep pace with

that, you know, change of technology? It's a real struggle. It really, really is. And, you know, I

think that, that I'm actually one, one potential change, which I've started to see in some companies

that I actually am curious to see if this continues to tell you is sort of IT as an organization,

moving sort of a little back to like its roots a little bit, you know, it really sort of over the

last couple of decades, at least in probably even longer, you know, we've seen IT become much more

of a like, an organization that just manages software that it buys from other places. And

most of that through outsourcing, right? Where companies don't have internal engineering talent

anymore, in most cases, these bigger companies. And, you know, and maybe I'm biased by the data

space where so much of it's built on open source and so much of it, you know, sort of requires that

ability to go, you know, to go that direction is companies are really starting to bring some more

of that, that engineering and talent expertise back in house. I think sometimes they do so from

a slightly misguided perspective, sometimes they build things and it really should go by.

But yeah, that thought that you can't just buy all of your software and be successful as a company,

I'm curious to see if that shift continues, because we've seen some more insourcing and some more

companies beefing out their internal tech a little bit. And I imagine that that trend will continue.

Very, very interesting. One thing that I've heard you mention in the past, you've talked a little bit

about how AI is instead of, you know, it's more of being evolutionary rather than kind of a sudden

overhaul. We're talking about the path to AI for companies. I wonder if you could talk a little

bit about that. And at the same time, talk about some of the common points of failure in AI projects

and how those can be avoided. Yeah, so the evolutionary idea, you know, it's something that I

sort of have been mulling over for a while, sort of like watching, like, what makes the company

successful or not successful and the changes, right? And, you know, you hear people sort of talk

big pronouncements about, like, you know, oh, we're going to overhaul everything and be more

AI driven. And, you know, or, you know, we're an interest company and stuff. And I think it's

usually just words for the street. But it's interesting to see what does work and what is

successful and what does push things out. And it is a change that takes time. You know,

every organization is going to have sort of their different pace at which they can move that. But

you can't just turn it over, you know, and again, even as a company at our scale with,

you know, 1200, 1300 people, like, it takes us time to move and change things along big companies

with decades of history and, you know, tens of thousands of employees. And so making these changes,

you have to make really, like, be really committed to the iterative process that it takes to go and

and I think that that's one of the things that I've sort of seen where companies,

from a strategic perspective, you know,

bail the most is they try something, it doesn't work out. And a year later, they're,

you know, carve off a whole team or sort of start from scratch again. And I think that we're too

quick in most cases to sort of just give up on a path and say that didn't work. Or

the opposite of just put our heads in the sand and keep going. Because we said this is the way

that we're going to go. And we're just going to keep going. And I remember talking with, you know,

someone, you know, a former colleague, where we're having a conversation about sort of like

the hiring path, right? You know, and I worked there and hired a bunch of people and worked

with this person and sort of said, you know, oh, here's how I think about hiring. Look at these

different profiles and sort of like, here's how I've sort of adjusted my thinking, like,

how are you guys doing things differently now? You know, I've been like five years since we've

worked together. They're just like, oh, I hire the exact same way. And I was sort of like,

a little sort of like taking back like, okay, that made sense five years ago, six years ago,

when we tried it. And it, you know, it didn't work. Right. Like, that team is not where you

wanted it to be. Part of the reason that I didn't work there anymore. And you sort of say like,

okay, like, why are you still doing something the same way? I get why we tried it. And it made

sense. Right. So like, it's weird that you sort of end up with these two either like companies

are like, Oh, we gave it six months and it doesn't work. Throw it away. Or, you know, five years

later, you're still trying the same thing. And it's like, you have to be willing to try and give

something enough time. And I think that that like, coming up with, you know, the what is your exit

strategy, like, when do you say no, but like, you have to give it enough time to try it out. But

then you have to have a plan for them saying, here's, here's what we're going to say. Nope,

this isn't the path. And we need to write to tweak and to turn into that. And so that like

evolutionary idea, like, you have to be trying things, you have to be iterating. But it's a,

it's a slow process. It's a process of years. It's not a process of months. And so watching that

sort of play out in different companies, I think has been really interesting to see some of those

things that work and some of those that don't. But that like embracing that sometimes this will

work. And sometimes it's going to fail. And like, how are we going to decide if it's working? Or if

it's failing? And then if it's not working, what's the decision process for iterating on that? I

think far too often, we just sort of make a plan that's, you know, good for three or five years.

And either we stick to it all the way through, or if it doesn't work, we just say that it did

work. And we just sort of go away completely, rather than having a process for like iteratively

working on and changing and updating how we're approaching it. Yeah, I think that's such an

important part of creating like, you know, it's the fine line between having innovation, but also

you move too early. And, you know, you have those kind of issues. So so many different

interesting things. Connor, thank you so much for sharing your insights with us today. It's been

amazing having you on the podcast. If people want to get in contact with you, or find out about

some of the things you guys are working on at DataIQ, what's the best way for them to, you know,

contact you or learn more? So IDC Define on LinkedIn is probably the easiest way to just

get ahold of me. You know, Connor Jensen, DataIQ on LinkedIn. You can go to our website,

www.DataIQ, D-A-T-A-I-K-U, dot com. There's about a million different pronunciations out there,

but you know, that's the place to find us. I've been hoping I did it right, but okay,

there's good, there's more. As a global company, there's a lot of variations to do it. So there's

a lot of right answers, I guess. So yeah, so the website, you know, hit me up on LinkedIn,

if you'd like to dig in more, you know, at the website, you've got a lot of our sort of like,

where we've focused in different industries or, you know, different technologies, looking at like

generative AI or pieces like that. So, you know, please feel free to reach out, you know, always

happy to go deeper on these topics. And thanks for having me because I really enjoyed the conversation,

Jane. Thanks so much, Connor. And to the listener, thanks so much for tuning in to the AI Chat podcast.

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Machine-generated transcript that may contain inaccuracies.