May 28, 2025 • 43 mins
My first ever guest on the podcast is Anthony Bay — a veteran product and technology executive with decades of experience shaping some of the world’s most impactful tech platforms.
After starting his career in early startups, he spent eight years at Apple across the U.S. and Europe leading product marketing efforts in networking, communications, and media. He then moved to Microsoft, where he launched the original MSN, and led major product groups focused on e-commerce and digital media through the 1990s.
Anthony later took on a global leadership role at Amazon Prime Video in its earliest phase and went on to become CEO of Rdio, a digital music streaming company acquired by Pandora.
Today, he’s CEO and founding partner at Techquity, an advisory firm made up of senior product and engineering leaders from companies like Amazon, Google, and Microsoft. Techquity helps CEOs and investors navigate complex tech and AI decisions by embedding experienced operators directly into the process — from hiring and team-building to product strategy and infrastructure modernization.
In this episode, Anthony and I talk about what AI means for modern execs, how non-technical leaders can make smart bets, and how seasoned operators are guiding the next wave of transformation.
Takeaways
- AI is transforming how businesses operate and make decisions.
- Techquity helps non-technical leaders navigate complex tech challenges.
- Data governance is crucial for leveraging AI effectively.
- Companies must focus on building a tech culture to innovate.
- The pace of AI development is unprecedented and offers new opportunities.
- Understanding data sources is key to creating value with AI.
- Experimentation with AI tools is essential for staying competitive.
- Organizations need to prioritize what to build before how to build it.
- Family and personal values are important for work-life balance.
- Techquity aims to raise awareness of its unique advisory services.
Sound Bites
- "AI is transforming how businesses operate."
- "Experimentation with AI tools is essential."
- "The pace of AI development is unprecedented."
Chapters
00:00 - Introduction to AI and Tech Leadership
06:20 - Anthony Bay's Career Journey
10:19 - Techquity: Bridging the Tech Gap
13:05 - Navigating AI in Business
14:41 - Enhancing Business with AI
20:59 - Data Governance and Quality
26:48 - Assessing AI Tools in Organizations
32:56 - Understanding Organeering
36:42 - Vision for Techquity in 2025
39:39 - Personal Reflections and Legacy
Connect with us
Where to find Anthony:
LinkedIn: https://linkedin.com/in/anthonybay/
Website: https://techquity.ai/
Where to find Sani:
LinkedIn: https://linkedin.com/in/sani-djaya/
Get in touch: sani@gridgoals.com
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Actually been really incredible how how many people
(00:02):
were worried about the cost ofLLL models. And then as we
started building, every fewmonths, it'd be like, it's
cheaper and better. And thenit's like, it's cheaper and
better I get. And I thinksomebody mentioned that it's
like the fastest depreciatingtechnology that we've ever seen.
It's been at an incredible pace.
You were around during theadoption of personal computers
(00:22):
and also the internet. Do youbelieve that this current wave
in technology is quicker thaneither of those?
Speaker 2 (00:30):
Yes. I think there's no doubt. The pace of change and
the speed of reduction in cost,they're I don't know whose law
it's gonna get named It's
Speaker 1 (00:42):
more law anymore. Oh, no. Altman's law is
Speaker 2 (00:45):
what it is. It's orders of magnitude. I mean,
it's crazy, but it's crazy good.I think the great news if you're
a user is the pace of innovationis in sync.
Speaker 1 (00:58):
Oh, yes.
Speaker 2 (00:59):
Yeah. I'm old enough to have, you know, been through
a few of these, starting backwith, you know, PCs and Internet
and then mobile and then cloudcomputing.
Speaker 3 (01:10):
Hey, everyone. Welcome to Beyond the Prompt.
I'm your host, Sunny. This isthe show where we go deeper than
the hype, where we go beyondjust the prompts, and that's
where the name of the podcastcomes from. I'm talking with
product, engineering, and go tomarket leaders who are building
AI native products and using AIto supercharge how their teams
operate.
If you're looking to scale yourbusiness with AI or want to
(01:32):
learn from those doing it at thefrontier, you're in the right
place. And if you're interestedin coming onto the podcast or
just want to chat with me on thecool things you're doing with
AI, then click on the link inthe description to get in touch.
Now this is the first episode ofthe podcast, and it was before I
had set up this mic. So bearwith me on
Speaker 1 (01:48):
the audio. It's not the best, and
Speaker 3 (01:50):
I promise the audio in the next episodes will be
better.
Speaker 1 (01:53):
The audio on Anthony's side also changed, so
just bear in mind, about ten,fifteen minutes into the
podcast, the audio for Anthonyactually gets a lot better. But
the first ten, fifteen minutes,it's not as good.
Speaker 3 (02:05):
But the podcast still has incredible content, so
Speaker 1 (02:08):
I'm looking forward for you to enjoy it for sure.
Speaker 3 (02:11):
Regardless, I was super, super excited about my
first ever guest on the podcast.My first ever guest on the
podcast is Anthony Bay, aveteran product and marketing
executive with decades ofexperience shaping some of the
world's most impactful techplatforms. After starting his
career in early startups, hespent eight years at Apple
across The US and Europe,leading product marketing
(02:33):
efforts and networking,communications, and media. He
then moved to Microsoft, wherehe launched the original MSN.
Yes, the original MSN.
And led major product groupsfocused on e commerce and
digital media through the 1990s.Later, he took on global
leadership role at Amazon PrimeVideo in its earliest phase and
(02:53):
went on to become CEO of Ardeo,a digital music streaming
company acquired by Pandora.Today, he's CEO and founding
partner at Techquity, anadvisory firm of senior product
and engineering leaders fromcompanies like Amazon, Google,
and Microsoft. Techquity todayhelps CEOs and investors
navigate complex tech and AIdecisions by embedding
(03:14):
experienced operators directlyinto the process, from hiring
and team building to productstrategy and even infrastructure
modernization. In this episode,Anthony and I talk about what AI
means for modern execs, how nontechnical leaders can make smart
bets, and how seasoned operatorsare guiding the next wave of
transformation.
Let's get into it.
Speaker 1 (03:35):
Awesome. Welcome to today's episode. We have a very
exciting guest today. So,Anthony, thank you for for
joining us. I'd love for you totell me a little bit about your
career background and tell thelisteners about your career
background.
Speaker 2 (03:50):
Sure, well first of all, thank you. Thanks for
having me. I look forward to thediscussion. So my background is,
I spent many years in variousroles in, if you will, the big
tech companies you mightrecognize. I did a couple of
startups first and then had twosmall children and decided I
(04:13):
need a little more stability, soI had just to date myself.
My first computer was an AppleII. Bought a Macs the day they
came out and went to work forApple. So I was with Apple for
eight years in The U. S. And inEurope and ran various product
marketing oriented initiativeson networking, communications,
(04:37):
and media.
Went to Microsoft, and spentmost of the nineties, at
Microsoft running variousproduct groups. I ran I shipped,
for those who may remember, theoriginal MSN. I shipped the
original MSN, and then I wasresponsible for all the e
commerce platforms and thenmedia, digital media at
(04:58):
Microsoft. Left, used some angelinvesting and advising, went on
some went on boards, publicboards, private boards, So I've
been on a of both. And realizedthat I actually kind of missed
an operating role, so I wentback, actually joined Amazon in
the very early days of PrimeVideo.
(05:20):
I ran Amazon Prime Videoglobally for a while and then
went and, ran a company calledArdio, digital music company,
which we sold to Pandora andthen kind of shifted mode to, a
bit of what I'm doing now, whichis advisory, more of an
advisory, work. And a few yearsago with some other, other
(05:41):
former senior not former, seniortech execs from product and
engineering founded this companycalled Techquity. And so what
Techquity does, and, you know, Iwon't spend too long because I'm
sure we'll get into it, but thebasic premise of Techquity is
that the vast majority ofcompanies, vast majority of CEOs
and investors, frankly bothprivate equity and venture, are
(06:05):
not tech themselves, are notsuper strong technically.
They're good, you know, greatCEOs, good business people, it
comes to investors, very goodinvestors. But when it comes to
navigating, you know, criticaland complex, you know, tech
decisions, teams, techprioritization, you know, they,
you know, they, they're tryingto do that without the
(06:27):
expertise.
And so Techquity is a collectionof senior tech executives,
builders and operatorsourselves, and we work with, you
know, our clients, our CEOs andinvestors, we get involved as
our copilot, helping themnavigate complex, you know, tech
decisions, hiring key people,you know, understanding where
(06:48):
they are, which will lead to,you know, AI at the moment.
Speaker 1 (06:52):
Yeah. Can you talk a little bit about some of the
folks that you partner with? Ithink I saw Niantic as one of
your partners, and then I'vealso some some authors.
Speaker 2 (07:01):
Yeah. So Niantic has been a client and one of our one
of our partners. So the if youif you go to techwitty.ai, you
see the set of people. So mypartners include a guy named Al
Lindsay, who was firstengineering leader in Alexa,
(07:22):
number three person in Alexa,built the entire Alexa
engineering and science teamfrom, you know, zero to however
many thousand it was, you know,and he built and led a big part
of Amazon Prime on the technicalside before that. Another one
another ex Amazon person whobuilt out what became Amazon's
third party market, and then theyou know, all their content
(07:43):
platform ran Kindle, andMicrosoft did, I mean, Amazon's
digital content, and then, led,Amazon's, AI shopping effort.
So what Rufus, if you're you'refamiliar with Rufus. Another one
was, the CISO of Azure Security.So we got people from, you know,
from a variety. And the one atNiantic, one of our partners as
(08:07):
a former, senior Google productleader and got involved with
Niantic to, to lean in there andhelp them with what became, you
know, the more recent pivot fromthe gaming to more of
essentially a data platform, anAPI based data platform for a
very different way to domapping, you know, based on, you
(08:30):
know, less on street. So Nianticis one.
That's not a classic client forus. The majority of our clients
are non tech clients, but thebench of people who are
Techquity partners are allpeople of that level.
Speaker 1 (08:44):
Yeah. And then you didn't mention it, but for folks
who don't know, Niantic is thecompany that created Pokemon GO,
which is worldwide sensation.
Speaker 3 (08:52):
And I think Google Maps,
Speaker 1 (08:54):
I think, put an April Fools thing, like, way back
before Pokemon GO, and then itbecame a reality, which is,
like, an incredible story. And Ithink Niantic was also a spin
off off of Google, if I'm
Speaker 2 (09:05):
Niantic was. The founders, the founder and CEO,
they started a company calledKeyhole in their early 2000s,
which Google bought and becameGoogle Maps. And that person
who, and then that bent becameGoogle Earth. And then they
merged Google Earth inside ofGoogle with Maps and that whole
(09:27):
geographic thing. And then theyspun out Niantic in I think like
2015.
Yeah. And so super strongtechnical organization, as you
said. You know, most of themapping that goes on, you know,
are things that are basicallybased on cars driving around for
the most part or, you know, orgeospatial and satellites. You
know, their innovation waseverybody walking around with,
(09:48):
you know, their phone, whichled, you know, Pokemon GO. So
the, you know, the derivative ofwhat of what allowed Pokemon GO
was a tremendous amount ofmapping data.
And then so, you know, they'vethey've now changed the company
around. Think it's they renamedNiantic Spatial and sold off the
gaming business.
Speaker 1 (10:05):
Yeah. That's right. Because they're moving more to,
like, the data and API platform.
Speaker 2 (10:08):
Yep. They have a remarkable you know, in a world
of AI, data becomes the dataunderlying the models is
ultimately where a lot of theinteresting differentiation and
value creation comes from. Theya great data, a great table set.
Speaker 1 (10:27):
Yeah, that's awesome. That's awesome. So then what
does your kind of like day today at Techwoody look like now,
now that you're over helping abunch of different clients?
Speaker 2 (10:38):
So our main time is spent helping clients, and
clients include both investors,if you will, private equity
firms and venture firms. And sothe life cycle starts really
with some form of an assessment.In some cases, it's due
(11:02):
diligence before they make aninvestment or make an
acquisition. And so we'll do adeep dive technical due
diligence. I've been surprisedreally at the depth of technical
diligence compared to financialand all the other parts of
diligence, because obviouslyinvestors have to get a bunch of
very critical information inorder to make decisions.
(11:25):
A lot of times the diligenceisn't as strong. Think an
example in the last couple ofweeks was that JPMorgan acquired
that company. I'm trying toremember the name, but the CEO
was just convicted of fraud.JPMorgan spent $175,000,000 to
buy a company that was helpingessentially student loan
(11:47):
borrowers and claimed they had4,000,000 customers when in fact
they had a fraction of that andit didn't get discovered in
diligence. So there's a lot, youknow, there's a lot of times
that happens.
And in, you know, in the case ofa company itself, the company
realizes they're not, thingsaren't being shipped on time,
(12:08):
they're not sure if they havethe right leaders, they're
wondering, are they making theright tech decisions or in many
cases, the company scales to thepoint where the people who are
great at one stage may not begreat at another. And so a lot
of what we do is in a sense sortof like a health journey except
tech, to come in and say, okay,where are the issues? Where are
(12:29):
the challenges? And before youbuy, it's one, after is that,
and then working with the CEO onwhatever the issues are. So in
some cases, it's helping themhire a strong leadership team,
helping them build that team, inother it's helping them make
architectural decisions, helpingfigure out spend, it's a big
(12:49):
issue right now, is how muchmoney is being spent and on
vendors as well.
You know, AI, the spike in costis huge. And then more recently,
you know, helping companies makeinformed decisions about AI. A
substack called Techquity Takes,and we've written a few pieces
(13:09):
really, you know, around this.And our our our main focus again
is on, I would say, notcompanies building building AI
tech per se, but on companiestrying to understand, which is
really the vast majority of usall, you know, what do I do?
Yeah.
And how how do I think properly?And so one of our first pieces
(13:30):
was really around the idea of,you know, how do you get
started? You know, and this thisidea of how do you use AI to
enhance what you do and thenultimately figure out where you
can do products or services thatare AI first. So one of the
discussions was really that. Andthen another one on really
(13:50):
starting to understand buildingyour own, digging your data
wells, figuring out the data.
Most companies do not have theirdata well structured, well
organized, and so for mostcompanies one of your core
assets, your core IP is the datayou have. The data you have that
(14:12):
over many years that's developedin some cases around the
projects and the customers andthe business and the
deliverables and so figuring outhow to organize that, they're
certainly using the classicmodels and the tools that are
out there which are helpful in alot of ways, so they're gonna
augment and there's so many waysyou could do that, But when it
(14:34):
comes to companies buildingtheir own products and services,
really for the most part needsto start with what are your core
assets. So we help people startto think about that.
Speaker 1 (14:44):
Awesome. So much to dig into. I'm curious to hear
more about what do you think orwhat have you seen as successful
in terms of using AI to enhancewhat people are currently doing
in existing organizations?
Speaker 2 (15:00):
Look I see.
Speaker 1 (15:01):
There tools as well that like you've seen had been
successfully adopted?
Speaker 2 (15:04):
Well you know I think for most, if you sit on X'd or
LinkedIn, there's a bunch ofpeople posting here's all the
really cool things and here's alot of things you can do and
those are all true. I think theinteresting question becomes
(15:24):
kind of on a more substantivesense for most companies, all
right that's interesting how doI apply those things?
Speaker 1 (15:33):
Totally totally. And the
Speaker 2 (15:36):
main thing, and I don't think it's a surprise, is
you've got to get on thelearning curve. You have to
start using these tools and andstart learning. You know,
whether it's programming, youknow, there's all sorts of
there. You know, there's allsorts of Yeah. Streams about
whether people use a cursor orCopilot or, you know, there's
there's dozens.
And the I think the great newsif you're a user, if you're a if
(16:01):
you're a customer of thesethings is the pace of innovation
is in sync.
Speaker 1 (16:08):
It really is. Yeah.
Speaker 2 (16:09):
I'm old enough to have been through a few of
these, and where theserevolutions, if you will, drive
sea change. Starting back withsort of PCs, and internet and
then mobile and then probablycloud computing if you look at
(16:32):
these, and AI is a version ofthat. Others haven't had that
kind of impact, but this is oneof those ones where the the the
the fundamental sea changes areones that I think are, you know,
are less obvious in the, youknow, in the beginning. It's
like what turned out to matterin the Internet was less
(16:54):
pets.com, you know, and morecompanies, you know, figuring
out how do they reorient theirbusiness around using those
technologies. You know, in incloud computing,
Speaker 1 (17:06):
it changed the fundamental set of
infrastructure you needed tohave.
Speaker 2 (17:10):
You know, I ran a music company. We had to have
our own data centers. You don'teven think about that anymore.
And, I mean, for the most part,people don't. And so I think the
thing with AI is the good newsis these insane amounts of
Speaker 1 (17:24):
money are being spent on on both on compute and then
on the evolution of the modelsand the tools on top of that.
Speaker 2 (17:32):
And and so experimenting with those, I
would say kinda two things.Number one is experimenting
Speaker 1 (17:40):
with the tools that are there and looking at your
own business.
Speaker 2 (17:47):
I would put them in kind of two buckets when you
talk about kind of enhanced.There's there's enhanced around
actually people creating, youknow, creating use cases and
applications. You know, the thefact that you don't need to be a
programmer anymore, really to beable to get, to get impact. I
mean, that's life changing.David Sachs was on you know,
(18:10):
said
Speaker 1 (18:10):
the other day and again whatever you
Speaker 2 (18:12):
whatever people's opinions about David Sachs
politically, he you know, whathe said was, you know, in his
entire career that the limitingfactor on innovation was the
number of good developers. Thatinnovation is going And in the
same way the ability to buildbig applications, CapEx was a
(18:34):
limitation, it's not there.Needing to think differently. In
our world, a lot of theapplications around, if you
will, enhancing are using toolslike deep research from
whichever platform. It's it'sit's remarkable.
(18:56):
I mean, the the value of thesetools and again, you have to be
careful that, you know, you youuse them as assistance and not
you know not to just produce thethe output of work because
they're not good enough yet. Butyou know in the world we see
which is a lot of white collaroriented work, you know those
tools are remarkably impactful.And Brad Smith, one of the I
(19:19):
guess the president of Microsoftsaid the other day, he said,
when people are worried aboutlosing their job to AI, it's
more losing your job to a humanusing AI. Know? The Totally.
You know, the the the imperativeis to start learning how to use
these, and that's been true inevery, you know, in every
generation of tech. And then,you know, you see people
(19:42):
reinventing, deeper use casesand deeper applications, and I
think those are emerging. Ithink it's still super early. I
think the the transformationaluse cases around AI and new
(20:02):
products were very early. It'shard to see.
What I think you're seeing isbusinesses shifting in their
priorities. You use Niantic whois saying, look, our core asset
turns out to be the data andthere's these applications that
you build. And so that I thinkwill be one of the biggest
shifts and you see mostcompanies aren't prepared for
(20:25):
that. A lot of the people wetalk with are you have to think
about data governance and datastructure. What do you actually
have the rights to?
You see a lot of these battleshappening now where it's
protecting your data as IP isimportant, you have to be very
(20:46):
careful about that, and thenknowing what you do and don't
have the rights to. So I thinkyou're gonna see more of that
emerging, which is really tryingto understand what you're gonna
build applications on in gettingthat foundation is a key. And
then, you know, classic, if youwill, Amazon, it's just working
(21:07):
backwards from where you know,who are your customers? Where's
the value that you can create?And then you work backwards to
you know iterating and testing.
So long winded answer.
Speaker 1 (21:18):
Yeah. Yeah. Yeah. That that was awesome. That was
that was incredible.
I definitely have used workingbackwards in various roles and
various jobs in my life as well.
Speaker 2 (21:28):
There's a great book, by the way, who a good friend
Bill Carr wrote called WorkingBackwards.
Speaker 1 (21:32):
Yeah. Yeah. Yeah.
Speaker 2 (21:34):
If she's not read that book, would highly
recommend it.
Speaker 1 (21:36):
I have not fully read it. I have read snippets of it,
had him on a pod or not not meinterviewing him, but, like,
listened to him on a podcast aswell and, like, got the kind of,
like, core ideas of it. Right?And I think that's been super
helpful for sure. For sure.
Speaker 2 (21:54):
Great model. You know, one of the things that
Amazon has been really, reallygood at and I appreciate it a
lot is Amazon has built a a wayof a way of doing things and an
approach that's incrediblysystematized so they can the
breadth of things that Amazondoes is remarkable, and so the
(22:16):
the approach they use, have toevery company has to use it
themselves, but it is highlyrelevant and applicable. The
thing I would just say before wego off that is the mistake I
think we see a lot is companiesdon't spend enough time on what
and jump immediately to how. Andthe process of deciding what to
(22:42):
do, where are there things thatyou can move the needle and
create real value versus jumpinginto, hey we need to have an AI
project, know, I gotta be doingsomething. But what you do, you
know, deserves a lot of time.
Speaker 1 (22:57):
Yeah totally. I definitely can resonate with
sometimes you're like, I justneed to feed the engineer some
tickets, some work, and you'relike, just go do this. And then
you get too lost into doing thatthing, and then you end up,
like, spending so much time thatyou have to maintain it as
opposed to taking some time to,like, hey. Actually, engineers,
(23:17):
maybe go tackle some tech debtwhile we go spend a little bit
more time talking to customersand try to identify the right
thing to go build. Yep.
Take that upfront time to thinkabout and talk to the customers
instead of committing to startdoing something and then you
realize it was the wrong thinglike two years later. Right?
Speaker 2 (23:34):
Yes. That and you're just creating more debt for
yourself.
Speaker 1 (23:37):
Yeah. A %. So I'm super curious. You you mentioned
about just trying out all thesedifferent tools. How have you
seen companies assess what toolsto allow them to use?
So for example, some companiesare like, you are only allowed
to use AI tools if the IT teamor legal team reviews and
(23:58):
approves it. And so somecompanies are like, oh, I can't
even use Cursor as an engineer,even though I want to use Cursor
because I've used it in mypersonal projects. Do you have
any recommendations around howcompanies should think about
assessing and evaluating androlling out those tools that
sometimes the individual peopleare like, I need this tool. This
(24:20):
tool is incredible. But thenthey need to get past the IT and
legal approvals and things likethat.
Speaker 2 (24:26):
Look, that is a theme that has existed way back when,
I'm dating myself, but peoplebought personal computers. They
bought an IBM PC or they boughtan Apple or they bought a Mac
when it wasn't allowed becauseit made their life better. So
you always have that classictension between and in some
(24:48):
cases, there are certain partsof it that are well founded. The
biggest issues are securityrelated and provenance of data.
You have to be very careful thatyou don't accidentally upload
confidential proprietary datainto something where you
basically, you're handing overthe rights to that.
(25:13):
So governance, my experience hasbeen you have to focus on rules
that are not about control butrules that are about guardrails
and some degree of safety. Butother than that you have to let
people experiment because firstof all it's important otherwise
(25:37):
you are a laggard and the otherthing is they will just do it.
They'll do it at home, they'lldo it outside, they'll find ways
to experiment with these thingsand so, and I think security is
one that is more understoodalthough it's not necessarily
well implemented, mean we'removing to a zero trust world
(25:58):
which I think is probably theappropriate model for thinking
about these, but the biggest gapis in this whole issue of data
governance and that field isgrowing very quickly for exactly
this reason. You couldaccidentally use a model for
research, This is less oncoding, the coding one has a
(26:21):
different set of risks but inboth of these cases you have to
be very careful about, I'll callit leakage of confidential
information and important stuff.So those are the two places to
focus and I think done properlyyou know you'll get people to
cooperate but you know if it'stoo strict people will just
(26:43):
bypass.
Mean that's you know how manypeople were told you know you
can't have an iPhone. Yeah. Imean and how well did that work?
Speaker 1 (26:50):
Yeah yeah yeah. You also touched on the topic of
like having really high qualitydata, right? You know I think a
lot of people say that, but I'dlove to learn from you around
like what does good dataactually look like?
Speaker 2 (27:05):
Yeah, look, it's a big question. I think the first
place to start is I'll go backto that thing I mentioned
earlier, is the deciding whatmatters. Most companies, any
company of any size isgenerating huge amounts of data
(27:26):
and a lot of it doesn't matter.The first part is trying to
really get your hands aroundwhat is it that's important and
what is it that you have that'sunique. And then it's a data
management and a data how do youorganize that?
(27:46):
How do you think about how toorganize that data using the
tools that are out there? Ithink one of the challenges that
we see happen a lot, we actuallywrote a sub stack about this, it
was called Does AI Spell the Endof SaaS? And it was, I think
more, you know, one of our guyswrote it and it was a bit
provocative, for many many manycompanies a lot of your data is
(28:11):
living in someone else's SaaSapplication and you know, you
you don't necessarily and if youlook at you look at the number
of SaaS apps that, you know,people are running, each of them
is its own little, you know,data universe. You know, your
data around customers is overhere, and your data around money
is over here and your dataaround manufacturing is over
(28:33):
there and your HR data is overhere. Really getting an
understanding of where our datais and what systems it's in and
ensuring that you can get accessto your data.
So I think one of the thingsyou're gonna see, one of the big
pressures you'll see on SaaSapps, I think this was if
somebody has the time to go readour Substack on that, is you
(28:57):
really start rethinkingdifferently about decisions you
make on SaaS applications andensuring that you have the
access. It may be that datalives within there for that
particular application purpose,but you need to be able to
consolidate that data yourselfexactly, and you think about
your own data lakes. I thinkthat's the first place to start
(29:20):
is what matters, what are thesources you have, what is it
that you have that's gonna beinteresting and unique when you
start putting it together, andthen figuring out how to
assemble that because the goodnews is the pace of evolution in
the AI tools, and the good newsalso is the cost of running
(29:40):
these models is also gonnadecline and so it's biggest
questions I think are the ones Ijust described.
Speaker 1 (29:52):
Yeah, it's actually been really incredible how many
people were worried about thecost of the LLL models and we
were as well. And then as westarted building, every few
months it'd be like, oh, cheaperand better. And then it's like,
it's cheaper and better I get.And I think somebody mentioned
that it's like the fastestdepreciating technology that
(30:15):
we've ever seen. And yeah, it'sbeen at an incredible pace.
And then you mentioned before,you were around during the
adoption of personal computersand also the internet. Do you
believe that this current wavein technology innovation or
revolution is quicker thaneither of those?
Speaker 2 (30:35):
Yes. I mean I think there's no doubt. The pace of
change and the speed ofreduction in cost. Mean I don't
know what law they're, I don'tknow whose law it's gonna get
named It's
Speaker 1 (30:48):
Moore's law anymore. No it's orders
Speaker 2 (30:53):
of magnitude, I mean it's crazy but it's crazy good
from the point of view of andit's far faster than anything
happened in cloud computingwhere costs come down, but
nothing like this. From a priceand capability point of view.
(31:14):
It's great, and the good news isI think given that you can
assume that at some point costis not the primary thing to
design around. You gotta besmart, but the fact that a small
number of companies are spendinghundreds and hundreds of
billions of dollars essentiallyfor our benefit is a great
(31:37):
thing. Whatever their returnwill be is unknown.
You also see this playing outwith the whole debate about open
source models versus closedversus these hybrid. The biggest
impact I think of open source inthese models is it's
accelerating this cost curve. Atsome point, to oversimplify,
(32:02):
think it becomes another servicethat use just like any of the
other services that are outthere when running in a cloud
but it's also the applicationson the edge are amazing as well
and so it's just again if you'rea tech person, it's super cool.
(32:25):
This is one of those moments,way faster pace of change than
any of the other ones.
Speaker 1 (32:32):
Got it. Good to hear that I feel like everybody that
I've talked to has echoed thatsentiment as well.
Speaker 2 (32:38):
I wanna say one thing is it is a step change in a way
those others weren't. And all ofthose other waves expanded the
capability of the kinds ofthings you could build and the
(32:59):
scale and the number of peopleyou could reach, but AI is AI is
augmenting people in a way that,you know, that, you know, it is
this this is one of thosemoments in history. So
Speaker 1 (33:15):
Yeah. A %. So before we jumped onto this podcast, I
was told that I shoulddefinitely ask you about
orgoneering and what it is andwhy it's important. So can you
tell us a little bit more aboutorgoneering and what that is?
Speaker 2 (33:32):
The biggest I'm trying to think of how best to
do this. The biggest issue formost companies that we come
across is less about which techto use, and more about how to
organize yourself and how tocreate a culture that knows how
to build things, because formost companies, they're not used
(33:55):
to building softwarecapabilities. They are users,
and of course there are lots oftech companies that build tech,
but for the most part mostcompanies are users of tech in
their own business. They applythe tech in whatever they are,
and so what's happening in AI isit's happening in a variety of
(34:20):
areas accelerated by AI is youneed to learn how it is that
tech companies design and buildsoftware products. What's the
role of product?
How do you think aboutengineering? How do you think
about those two work together?What are the processes you put
(34:41):
in place? What are the tools?What are the metrics?
What we found is, and if youdon't have this, nothing else
really matters. People get veryfocused on which AI tool I'm
gonna use and all these things,but it's how you build. Elon
(35:02):
Musk said something, he said alot of things, but this one
thing, nothing related to that,but he said Tesla's real product
is its manufacturing, and thathow we manufacture is the magic
and that, yes, they make carsand they're making batteries and
(35:22):
they're gonna make robots, butthe how is where the innovation
and so when it comes to buildingsoftware or building products
that are AI based and data thatare intended to be a product in
a sense that other people use,those same kind of things apply.
I mentioned a little bit abouthow Amazon has built its
(35:45):
organization. Every successfultech company, big or small, has
its own version of this.
They're not all the same, butthe themes are the same. So
orgoneering is about helping nontech companies primarily
understand how do you get theright tech culture and tech
leaders and processes to be ableto build stuff. So that's what
(36:09):
it means.
Speaker 1 (36:10):
Gotcha, awesome. Yeah, so getting the right
people in place, but also theright processes and like how to
think about building products aswell. That reminds me
Speaker 2 (36:21):
of I'm iterating, know, you're never done. I mean,
if you look at any successfulcompany whose product you use,
tech company, you know, and youjust described, you know, the
the AI models and iterations. Imean, these things it it's the
pace of iteration. Know? It'syou're not gonna your your your
main thing is to get out thereand then get really good at
(36:43):
learning and iterating andtesting.
And those are skills, those arejust skills that need to be
developed for most companies.
Speaker 1 (36:50):
Yeah, absolutely. So then what's on roadmap for the
vision for Techriti in 2025? Andif you could wave a magic wand
on a block, one major roadblockor challenge to get to that
vision for 2025 for TechCritty,what would it be?
Speaker 2 (37:08):
Well look, first of all thanks for the question. We
are not a product company. Soour real star and our mission is
helping companies in the waysthat we've been talking about
here, and so we found a coupleof things. What we found is that
what we do and who we are isrelatively unique. The idea that
(37:31):
you could have a person thathelped you build Alexa or Google
Maps or one of a dozen otherthings that you could have that
person kind of help you and be aguide and a mentor in a sense a
copilot is pretty rare.
You couldn't hire these peopleand you can't go take a course
to learn about this. And againthe metaphor is not the same,
(37:55):
but if you could have TroyAikman or Tom Brady or somebody
like that, you know, around as acoach, that would be very
valuable. And so, you know, ourmission is to provide a platform
for that kind of person to to beable to engage with clients and
help. We're a collective ofpeople who got to that place in
(38:18):
our careers where we want towork on different projects and
help people and help companieswho are trying to do important
things. So the biggest thing forus is awareness, is, you know,
having companies, you know, knowthat we're out there when
they're, you know, when they'restruggling with these decisions,
you know, whether it's diligenceor how do I scale, how do I
(38:38):
build this culture, do I havethe right leaders that there's
somebody who can help.
So our biggest breakthrough Iwould say is probably that
awareness and getting out thereand helping.
Speaker 1 (38:51):
Yeah, just getting your name out there, getting
your awareness out there so thatyou can help more companies. It
sounds like an exciting futurethough where you can come in and
help a lot of multiplecompanies, whether it's
primarily technology or nontechnology, but it sounds like
you love and love focusing onlike non technology companies as
(39:14):
well at TechBuddy.
Speaker 2 (39:15):
Yeah. That's where in a lot of cases you can have the
biggest impact. And again Iwould say a non technology
company today, really everycompany needs to be a tech
company in some degree, it'skind of where your core product
isn't tech, where you're usingtech you know to to deliver
value in one form or another,but your people aren't you know
(39:36):
people aren't buying you, you'renot they're not buying you as
you know they're not buying youas a software tool, you know
you're using there, and that's athat's you know we sort of say
every every company that wantsto be a great company needs to
understand how also to be agreat tech company.
Speaker 1 (39:51):
Yeah. And Absolutely.
Speaker 2 (39:53):
So that's true.
Speaker 1 (39:54):
Absolutely. Alright. I have a last few questions. One
is, what are you most proud ofand why? And it can be
professional or it can bepersonal.
Speaker 2 (40:05):
I'm most proud of my family and my kids.
Speaker 1 (40:08):
That's awesome. That's awesome.
Speaker 2 (40:10):
That's like, I I I made a decision which thankfully
with a lot of help from my wifeabout what to prioritize and
it's very easy to over index onyour career and under index on
your family, and I had some ofthose moments, but someone told
(40:36):
me once, no kid, if you asksomeone when they grow up, do
you wish your dad worked more?No kid would say, yeah I wish my
dad worked more. And so familyfiguring out, it's not work life
balance, but it's how you stayalive and energized and feeling
(41:00):
like you're making a difference.But I think your family to some
degree is your most importantlegacy.
Speaker 1 (41:06):
Yeah I think I heard somewhere that the folks that
get most impacted of yourabsence is your kids. And then
the other thing that I learnedlike a few years ago was the
longest running study in humanhappiness and health, the number
(41:29):
one determining factor is thenumber of high quality
relationships, whether it's likefriends or Yep.
Speaker 2 (41:37):
And I think that's absolutely true. And it's easy
to forget that.
Speaker 1 (41:43):
Yeah absolutely. All right lastly, where can people
find you online where they canlearn about you, Entekwity, and
then how can listeners be usefulto you?
Speaker 2 (41:54):
Sure, well that's a great question, thanks. So the
simplest thing is on LinkedIn,know Anthony Bay, I don't think
there's a lot of Anthony Baysbut you know Anthony Bay
Techwity, would probably be theeasiest way. And then our
company is called Techquity, andthe URL is .ai, so if you want
(42:17):
to learn more about Techquity,what know what we do, who we
are, there's 18 or 19 of us now.It's a collective of pretty
interesting people who worktogether. So those would be the
best ways to find me and
Speaker 1 (42:32):
us. Awesome. Awesome. Well, thanks for coming on,
Anthony. Thank you so much.
Speaker 2 (42:37):
Thank you so much.
Speaker 3 (42:39):
Thank you for tuning into the very first episode of
Beyond The Prompt. If youenjoyed this discussion, please
subscribe to the podcast so youdon't miss future episodes.
Also, if you could take a momentto review and rate the podcast,
it would help me tremendously inreaching more listeners and
bringing you more great content.Until next time, keep going
beyond the prompt.
Actually been really incredible how how many people
(00:02):
were worried about the cost ofLLL models. And then as we
started building, every fewmonths, it'd be like, it's
cheaper and better. And thenit's like, it's cheaper and
better I get. And I thinksomebody mentioned that it's
like the fastest depreciatingtechnology that we've ever seen.
It's been at an incredible pace.
You were around during theadoption of personal computers
(00:22):
and also the internet. Do youbelieve that this current wave
in technology is quicker thaneither of those?
Speaker 2 (00:30):
Yes. I think there's no doubt. The pace of change and
the speed of reduction in cost,they're I don't know whose law
it's gonna get named It's
Speaker 1 (00:42):
more law anymore. Oh, no. Altman's law is
Speaker 2 (00:45):
what it is. It's orders of magnitude. I mean,
it's crazy, but it's crazy good.I think the great news if you're
a user is the pace of innovationis in sync.
Speaker 1 (00:58):
Oh, yes.
Speaker 2 (00:59):
Yeah. I'm old enough to have, you know, been through
a few of these, starting backwith, you know, PCs and Internet
and then mobile and then cloudcomputing.
Speaker 3 (01:10):
Hey, everyone. Welcome to Beyond the Prompt.
I'm your host, Sunny. This isthe show where we go deeper than
the hype, where we go beyondjust the prompts, and that's
where the name of the podcastcomes from. I'm talking with
product, engineering, and go tomarket leaders who are building
AI native products and using AIto supercharge how their teams
operate.
If you're looking to scale yourbusiness with AI or want to
(01:32):
learn from those doing it at thefrontier, you're in the right
place. And if you're interestedin coming onto the podcast or
just want to chat with me on thecool things you're doing with
AI, then click on the link inthe description to get in touch.
Now this is the first episode ofthe podcast, and it was before I
had set up this mic. So bearwith me on
Speaker 1 (01:48):
the audio. It's not the best, and
Speaker 3 (01:50):
I promise the audio in the next episodes will be
better.
Speaker 1 (01:53):
The audio on Anthony's side also changed, so
just bear in mind, about ten,fifteen minutes into the
podcast, the audio for Anthonyactually gets a lot better. But
the first ten, fifteen minutes,it's not as good.
Speaker 3 (02:05):
But the podcast still has incredible content, so
Speaker 1 (02:08):
I'm looking forward for you to enjoy it for sure.
Speaker 3 (02:11):
Regardless, I was super, super excited about my
first ever guest on the podcast.My first ever guest on the
podcast is Anthony Bay, aveteran product and marketing
executive with decades ofexperience shaping some of the
world's most impactful techplatforms. After starting his
career in early startups, hespent eight years at Apple
across The US and Europe,leading product marketing
(02:33):
efforts and networking,communications, and media. He
then moved to Microsoft, wherehe launched the original MSN.
Yes, the original MSN.
And led major product groupsfocused on e commerce and
digital media through the 1990s.Later, he took on global
leadership role at Amazon PrimeVideo in its earliest phase and
(02:53):
went on to become CEO of Ardeo,a digital music streaming
company acquired by Pandora.Today, he's CEO and founding
partner at Techquity, anadvisory firm of senior product
and engineering leaders fromcompanies like Amazon, Google,
and Microsoft. Techquity todayhelps CEOs and investors
navigate complex tech and AIdecisions by embedding
(03:14):
experienced operators directlyinto the process, from hiring
and team building to productstrategy and even infrastructure
modernization. In this episode,Anthony and I talk about what AI
means for modern execs, how nontechnical leaders can make smart
bets, and how seasoned operatorsare guiding the next wave of
transformation.
Let's get into it.
Speaker 1 (03:35):
Awesome. Welcome to today's episode. We have a very
exciting guest today. So,Anthony, thank you for for
joining us. I'd love for you totell me a little bit about your
career background and tell thelisteners about your career
background.
Speaker 2 (03:50):
Sure, well first of all, thank you. Thanks for
having me. I look forward to thediscussion. So my background is,
I spent many years in variousroles in, if you will, the big
tech companies you mightrecognize. I did a couple of
startups first and then had twosmall children and decided I
(04:13):
need a little more stability, soI had just to date myself.
My first computer was an AppleII. Bought a Macs the day they
came out and went to work forApple. So I was with Apple for
eight years in The U. S. And inEurope and ran various product
marketing oriented initiativeson networking, communications,
(04:37):
and media.
Went to Microsoft, and spentmost of the nineties, at
Microsoft running variousproduct groups. I ran I shipped,
for those who may remember, theoriginal MSN. I shipped the
original MSN, and then I wasresponsible for all the e
commerce platforms and thenmedia, digital media at
(04:58):
Microsoft. Left, used some angelinvesting and advising, went on
some went on boards, publicboards, private boards, So I've
been on a of both. And realizedthat I actually kind of missed
an operating role, so I wentback, actually joined Amazon in
the very early days of PrimeVideo.
(05:20):
I ran Amazon Prime Videoglobally for a while and then
went and, ran a company calledArdio, digital music company,
which we sold to Pandora andthen kind of shifted mode to, a
bit of what I'm doing now, whichis advisory, more of an
advisory, work. And a few yearsago with some other, other
(05:41):
former senior not former, seniortech execs from product and
engineering founded this companycalled Techquity. And so what
Techquity does, and, you know, Iwon't spend too long because I'm
sure we'll get into it, but thebasic premise of Techquity is
that the vast majority ofcompanies, vast majority of CEOs
and investors, frankly bothprivate equity and venture, are
(06:05):
not tech themselves, are notsuper strong technically.
They're good, you know, greatCEOs, good business people, it
comes to investors, very goodinvestors. But when it comes to
navigating, you know, criticaland complex, you know, tech
decisions, teams, techprioritization, you know, they,
you know, they, they're tryingto do that without the
(06:27):
expertise.
And so Techquity is a collectionof senior tech executives,
builders and operatorsourselves, and we work with, you
know, our clients, our CEOs andinvestors, we get involved as
our copilot, helping themnavigate complex, you know, tech
decisions, hiring key people,you know, understanding where
(06:48):
they are, which will lead to,you know, AI at the moment.
Speaker 1 (06:52):
Yeah. Can you talk a little bit about some of the
folks that you partner with? Ithink I saw Niantic as one of
your partners, and then I'vealso some some authors.
Speaker 2 (07:01):
Yeah. So Niantic has been a client and one of our one
of our partners. So the if youif you go to techwitty.ai, you
see the set of people. So mypartners include a guy named Al
Lindsay, who was firstengineering leader in Alexa,
(07:22):
number three person in Alexa,built the entire Alexa
engineering and science teamfrom, you know, zero to however
many thousand it was, you know,and he built and led a big part
of Amazon Prime on the technicalside before that. Another one
another ex Amazon person whobuilt out what became Amazon's
third party market, and then theyou know, all their content
(07:43):
platform ran Kindle, andMicrosoft did, I mean, Amazon's
digital content, and then, led,Amazon's, AI shopping effort.
So what Rufus, if you're you'refamiliar with Rufus. Another one
was, the CISO of Azure Security.So we got people from, you know,
from a variety. And the one atNiantic, one of our partners as
(08:07):
a former, senior Google productleader and got involved with
Niantic to, to lean in there andhelp them with what became, you
know, the more recent pivot fromthe gaming to more of
essentially a data platform, anAPI based data platform for a
very different way to domapping, you know, based on, you
(08:30):
know, less on street. So Nianticis one.
That's not a classic client forus. The majority of our clients
are non tech clients, but thebench of people who are
Techquity partners are allpeople of that level.
Speaker 1 (08:44):
Yeah. And then you didn't mention it, but for folks
who don't know, Niantic is thecompany that created Pokemon GO,
which is worldwide sensation.
Speaker 3 (08:52):
And I think Google Maps,
Speaker 1 (08:54):
I think, put an April Fools thing, like, way back
before Pokemon GO, and then itbecame a reality, which is,
like, an incredible story. And Ithink Niantic was also a spin
off off of Google, if I'm
Speaker 2 (09:05):
Niantic was. The founders, the founder and CEO,
they started a company calledKeyhole in their early 2000s,
which Google bought and becameGoogle Maps. And that person
who, and then that bent becameGoogle Earth. And then they
merged Google Earth inside ofGoogle with Maps and that whole
(09:27):
geographic thing. And then theyspun out Niantic in I think like
2015.
Yeah. And so super strongtechnical organization, as you
said. You know, most of themapping that goes on, you know,
are things that are basicallybased on cars driving around for
the most part or, you know, orgeospatial and satellites. You
know, their innovation waseverybody walking around with,
(09:48):
you know, their phone, whichled, you know, Pokemon GO. So
the, you know, the derivative ofwhat of what allowed Pokemon GO
was a tremendous amount ofmapping data.
And then so, you know, they'vethey've now changed the company
around. Think it's they renamedNiantic Spatial and sold off the
gaming business.
Speaker 1 (10:05):
Yeah. That's right. Because they're moving more to,
like, the data and API platform.
Speaker 2 (10:08):
Yep. They have a remarkable you know, in a world
of AI, data becomes the dataunderlying the models is
ultimately where a lot of theinteresting differentiation and
value creation comes from. Theya great data, a great table set.
Speaker 1 (10:27):
Yeah, that's awesome. That's awesome. So then what
does your kind of like day today at Techwoody look like now,
now that you're over helping abunch of different clients?
Speaker 2 (10:38):
So our main time is spent helping clients, and
clients include both investors,if you will, private equity
firms and venture firms. And sothe life cycle starts really
with some form of an assessment.In some cases, it's due
(11:02):
diligence before they make aninvestment or make an
acquisition. And so we'll do adeep dive technical due
diligence. I've been surprisedreally at the depth of technical
diligence compared to financialand all the other parts of
diligence, because obviouslyinvestors have to get a bunch of
very critical information inorder to make decisions.
(11:25):
A lot of times the diligenceisn't as strong. Think an
example in the last couple ofweeks was that JPMorgan acquired
that company. I'm trying toremember the name, but the CEO
was just convicted of fraud.JPMorgan spent $175,000,000 to
buy a company that was helpingessentially student loan
(11:47):
borrowers and claimed they had4,000,000 customers when in fact
they had a fraction of that andit didn't get discovered in
diligence. So there's a lot, youknow, there's a lot of times
that happens.
And in, you know, in the case ofa company itself, the company
realizes they're not, thingsaren't being shipped on time,
(12:08):
they're not sure if they havethe right leaders, they're
wondering, are they making theright tech decisions or in many
cases, the company scales to thepoint where the people who are
great at one stage may not begreat at another. And so a lot
of what we do is in a sense sortof like a health journey except
tech, to come in and say, okay,where are the issues? Where are
(12:29):
the challenges? And before youbuy, it's one, after is that,
and then working with the CEO onwhatever the issues are. So in
some cases, it's helping themhire a strong leadership team,
helping them build that team, inother it's helping them make
architectural decisions, helpingfigure out spend, it's a big
(12:49):
issue right now, is how muchmoney is being spent and on
vendors as well.
You know, AI, the spike in costis huge. And then more recently,
you know, helping companies makeinformed decisions about AI. A
substack called Techquity Takes,and we've written a few pieces
(13:09):
really, you know, around this.And our our our main focus again
is on, I would say, notcompanies building building AI
tech per se, but on companiestrying to understand, which is
really the vast majority of usall, you know, what do I do?
Yeah.
And how how do I think properly?And so one of our first pieces
(13:30):
was really around the idea of,you know, how do you get
started? You know, and this thisidea of how do you use AI to
enhance what you do and thenultimately figure out where you
can do products or services thatare AI first. So one of the
discussions was really that. Andthen another one on really
(13:50):
starting to understand buildingyour own, digging your data
wells, figuring out the data.
Most companies do not have theirdata well structured, well
organized, and so for mostcompanies one of your core
assets, your core IP is the datayou have. The data you have that
(14:12):
over many years that's developedin some cases around the
projects and the customers andthe business and the
deliverables and so figuring outhow to organize that, they're
certainly using the classicmodels and the tools that are
out there which are helpful in alot of ways, so they're gonna
augment and there's so many waysyou could do that, But when it
(14:34):
comes to companies buildingtheir own products and services,
really for the most part needsto start with what are your core
assets. So we help people startto think about that.
Speaker 1 (14:44):
Awesome. So much to dig into. I'm curious to hear
more about what do you think orwhat have you seen as successful
in terms of using AI to enhancewhat people are currently doing
in existing organizations?
Speaker 2 (15:00):
Look I see.
Speaker 1 (15:01):
There tools as well that like you've seen had been
successfully adopted?
Speaker 2 (15:04):
Well you know I think for most, if you sit on X'd or
LinkedIn, there's a bunch ofpeople posting here's all the
really cool things and here's alot of things you can do and
those are all true. I think theinteresting question becomes
(15:24):
kind of on a more substantivesense for most companies, all
right that's interesting how doI apply those things?
Speaker 1 (15:33):
Totally totally. And the
Speaker 2 (15:36):
main thing, and I don't think it's a surprise, is
you've got to get on thelearning curve. You have to
start using these tools and andstart learning. You know,
whether it's programming, youknow, there's all sorts of
there. You know, there's allsorts of Yeah. Streams about
whether people use a cursor orCopilot or, you know, there's
there's dozens.
And the I think the great newsif you're a user, if you're a if
(16:01):
you're a customer of thesethings is the pace of innovation
is in sync.
Speaker 1 (16:08):
It really is. Yeah.
Speaker 2 (16:09):
I'm old enough to have been through a few of
these, and where theserevolutions, if you will, drive
sea change. Starting back withsort of PCs, and internet and
then mobile and then probablycloud computing if you look at
(16:32):
these, and AI is a version ofthat. Others haven't had that
kind of impact, but this is oneof those ones where the the the
the fundamental sea changes areones that I think are, you know,
are less obvious in the, youknow, in the beginning. It's
like what turned out to matterin the Internet was less
(16:54):
pets.com, you know, and morecompanies, you know, figuring
out how do they reorient theirbusiness around using those
technologies. You know, in incloud computing,
Speaker 1 (17:06):
it changed the fundamental set of
infrastructure you needed tohave.
Speaker 2 (17:10):
You know, I ran a music company. We had to have
our own data centers. You don'teven think about that anymore.
And, I mean, for the most part,people don't. And so I think the
thing with AI is the good newsis these insane amounts of
Speaker 1 (17:24):
money are being spent on on both on compute and then
on the evolution of the modelsand the tools on top of that.
Speaker 2 (17:32):
And and so experimenting with those, I
would say kinda two things.Number one is experimenting
Speaker 1 (17:40):
with the tools that are there and looking at your
own business.
Speaker 2 (17:47):
I would put them in kind of two buckets when you
talk about kind of enhanced.There's there's enhanced around
actually people creating, youknow, creating use cases and
applications. You know, the thefact that you don't need to be a
programmer anymore, really to beable to get, to get impact. I
mean, that's life changing.David Sachs was on you know,
(18:10):
said
Speaker 1 (18:10):
the other day and again whatever you
Speaker 2 (18:12):
whatever people's opinions about David Sachs
politically, he you know, whathe said was, you know, in his
entire career that the limitingfactor on innovation was the
number of good developers. Thatinnovation is going And in the
same way the ability to buildbig applications, CapEx was a
(18:34):
limitation, it's not there.Needing to think differently. In
our world, a lot of theapplications around, if you
will, enhancing are using toolslike deep research from
whichever platform. It's it'sit's remarkable.
(18:56):
I mean, the the value of thesetools and again, you have to be
careful that, you know, you youuse them as assistance and not
you know not to just produce thethe output of work because
they're not good enough yet. Butyou know in the world we see
which is a lot of white collaroriented work, you know those
tools are remarkably impactful.And Brad Smith, one of the I
(19:19):
guess the president of Microsoftsaid the other day, he said,
when people are worried aboutlosing their job to AI, it's
more losing your job to a humanusing AI. Know? The Totally.
You know, the the the imperativeis to start learning how to use
these, and that's been true inevery, you know, in every
generation of tech. And then,you know, you see people
(19:42):
reinventing, deeper use casesand deeper applications, and I
think those are emerging. Ithink it's still super early. I
think the the transformationaluse cases around AI and new
(20:02):
products were very early. It'shard to see.
What I think you're seeing isbusinesses shifting in their
priorities. You use Niantic whois saying, look, our core asset
turns out to be the data andthere's these applications that
you build. And so that I thinkwill be one of the biggest
shifts and you see mostcompanies aren't prepared for
(20:25):
that. A lot of the people wetalk with are you have to think
about data governance and datastructure. What do you actually
have the rights to?
You see a lot of these battleshappening now where it's
protecting your data as IP isimportant, you have to be very
(20:46):
careful about that, and thenknowing what you do and don't
have the rights to. So I thinkyou're gonna see more of that
emerging, which is really tryingto understand what you're gonna
build applications on in gettingthat foundation is a key. And
then, you know, classic, if youwill, Amazon, it's just working
(21:07):
backwards from where you know,who are your customers? Where's
the value that you can create?And then you work backwards to
you know iterating and testing.
So long winded answer.
Speaker 1 (21:18):
Yeah. Yeah. Yeah. That that was awesome. That was
that was incredible.
I definitely have used workingbackwards in various roles and
various jobs in my life as well.
Speaker 2 (21:28):
There's a great book, by the way, who a good friend
Bill Carr wrote called WorkingBackwards.
Speaker 1 (21:32):
Yeah. Yeah. Yeah.
Speaker 2 (21:34):
If she's not read that book, would highly
recommend it.
Speaker 1 (21:36):
I have not fully read it. I have read snippets of it,
had him on a pod or not not meinterviewing him, but, like,
listened to him on a podcast aswell and, like, got the kind of,
like, core ideas of it. Right?And I think that's been super
helpful for sure. For sure.
Speaker 2 (21:54):
Great model. You know, one of the things that
Amazon has been really, reallygood at and I appreciate it a
lot is Amazon has built a a wayof a way of doing things and an
approach that's incrediblysystematized so they can the
breadth of things that Amazondoes is remarkable, and so the
(22:16):
the approach they use, have toevery company has to use it
themselves, but it is highlyrelevant and applicable. The
thing I would just say before wego off that is the mistake I
think we see a lot is companiesdon't spend enough time on what
and jump immediately to how. Andthe process of deciding what to
(22:42):
do, where are there things thatyou can move the needle and
create real value versus jumpinginto, hey we need to have an AI
project, know, I gotta be doingsomething. But what you do, you
know, deserves a lot of time.
Speaker 1 (22:57):
Yeah totally. I definitely can resonate with
sometimes you're like, I justneed to feed the engineer some
tickets, some work, and you'relike, just go do this. And then
you get too lost into doing thatthing, and then you end up,
like, spending so much time thatyou have to maintain it as
opposed to taking some time to,like, hey. Actually, engineers,
(23:17):
maybe go tackle some tech debtwhile we go spend a little bit
more time talking to customersand try to identify the right
thing to go build. Yep.
Take that upfront time to thinkabout and talk to the customers
instead of committing to startdoing something and then you
realize it was the wrong thinglike two years later. Right?
Speaker 2 (23:34):
Yes. That and you're just creating more debt for
yourself.
Speaker 1 (23:37):
Yeah. A %. So I'm super curious. You you mentioned
about just trying out all thesedifferent tools. How have you
seen companies assess what toolsto allow them to use?
So for example, some companiesare like, you are only allowed
to use AI tools if the IT teamor legal team reviews and
(23:58):
approves it. And so somecompanies are like, oh, I can't
even use Cursor as an engineer,even though I want to use Cursor
because I've used it in mypersonal projects. Do you have
any recommendations around howcompanies should think about
assessing and evaluating androlling out those tools that
sometimes the individual peopleare like, I need this tool. This
(24:20):
tool is incredible. But thenthey need to get past the IT and
legal approvals and things likethat.
Speaker 2 (24:26):
Look, that is a theme that has existed way back when,
I'm dating myself, but peoplebought personal computers. They
bought an IBM PC or they boughtan Apple or they bought a Mac
when it wasn't allowed becauseit made their life better. So
you always have that classictension between and in some
(24:48):
cases, there are certain partsof it that are well founded. The
biggest issues are securityrelated and provenance of data.
You have to be very careful thatyou don't accidentally upload
confidential proprietary datainto something where you
basically, you're handing overthe rights to that.
(25:13):
So governance, my experience hasbeen you have to focus on rules
that are not about control butrules that are about guardrails
and some degree of safety. Butother than that you have to let
people experiment because firstof all it's important otherwise
(25:37):
you are a laggard and the otherthing is they will just do it.
They'll do it at home, they'lldo it outside, they'll find ways
to experiment with these thingsand so, and I think security is
one that is more understoodalthough it's not necessarily
well implemented, mean we'removing to a zero trust world
(25:58):
which I think is probably theappropriate model for thinking
about these, but the biggest gapis in this whole issue of data
governance and that field isgrowing very quickly for exactly
this reason. You couldaccidentally use a model for
research, This is less oncoding, the coding one has a
(26:21):
different set of risks but inboth of these cases you have to
be very careful about, I'll callit leakage of confidential
information and important stuff.So those are the two places to
focus and I think done properlyyou know you'll get people to
cooperate but you know if it'stoo strict people will just
(26:43):
bypass.
Mean that's you know how manypeople were told you know you
can't have an iPhone. Yeah. Imean and how well did that work?
Speaker 1 (26:50):
Yeah yeah yeah. You also touched on the topic of
like having really high qualitydata, right? You know I think a
lot of people say that, but I'dlove to learn from you around
like what does good dataactually look like?
Speaker 2 (27:05):
Yeah, look, it's a big question. I think the first
place to start is I'll go backto that thing I mentioned
earlier, is the deciding whatmatters. Most companies, any
company of any size isgenerating huge amounts of data
(27:26):
and a lot of it doesn't matter.The first part is trying to
really get your hands aroundwhat is it that's important and
what is it that you have that'sunique. And then it's a data
management and a data how do youorganize that?
(27:46):
How do you think about how toorganize that data using the
tools that are out there? Ithink one of the challenges that
we see happen a lot, we actuallywrote a sub stack about this, it
was called Does AI Spell the Endof SaaS? And it was, I think
more, you know, one of our guyswrote it and it was a bit
provocative, for many many manycompanies a lot of your data is
(28:11):
living in someone else's SaaSapplication and you know, you
you don't necessarily and if youlook at you look at the number
of SaaS apps that, you know,people are running, each of them
is its own little, you know,data universe. You know, your
data around customers is overhere, and your data around money
is over here and your dataaround manufacturing is over
(28:33):
there and your HR data is overhere. Really getting an
understanding of where our datais and what systems it's in and
ensuring that you can get accessto your data.
So I think one of the thingsyou're gonna see, one of the big
pressures you'll see on SaaSapps, I think this was if
somebody has the time to go readour Substack on that, is you
(28:57):
really start rethinkingdifferently about decisions you
make on SaaS applications andensuring that you have the
access. It may be that datalives within there for that
particular application purpose,but you need to be able to
consolidate that data yourselfexactly, and you think about
your own data lakes. I thinkthat's the first place to start
(29:20):
is what matters, what are thesources you have, what is it
that you have that's gonna beinteresting and unique when you
start putting it together, andthen figuring out how to
assemble that because the goodnews is the pace of evolution in
the AI tools, and the good newsalso is the cost of running
(29:40):
these models is also gonnadecline and so it's biggest
questions I think are the ones Ijust described.
Speaker 1 (29:52):
Yeah, it's actually been really incredible how many
people were worried about thecost of the LLL models and we
were as well. And then as westarted building, every few
months it'd be like, oh, cheaperand better. And then it's like,
it's cheaper and better I get.And I think somebody mentioned
that it's like the fastestdepreciating technology that
(30:15):
we've ever seen. And yeah, it'sbeen at an incredible pace.
And then you mentioned before,you were around during the
adoption of personal computersand also the internet. Do you
believe that this current wavein technology innovation or
revolution is quicker thaneither of those?
Speaker 2 (30:35):
Yes. I mean I think there's no doubt. The pace of
change and the speed ofreduction in cost. Mean I don't
know what law they're, I don'tknow whose law it's gonna get
named It's
Speaker 1 (30:48):
Moore's law anymore. No it's orders
Speaker 2 (30:53):
of magnitude, I mean it's crazy but it's crazy good
from the point of view of andit's far faster than anything
happened in cloud computingwhere costs come down, but
nothing like this. From a priceand capability point of view.
(31:14):
It's great, and the good news isI think given that you can
assume that at some point costis not the primary thing to
design around. You gotta besmart, but the fact that a small
number of companies are spendinghundreds and hundreds of
billions of dollars essentiallyfor our benefit is a great
(31:37):
thing. Whatever their returnwill be is unknown.
You also see this playing outwith the whole debate about open
source models versus closedversus these hybrid. The biggest
impact I think of open source inthese models is it's
accelerating this cost curve. Atsome point, to oversimplify,
(32:02):
think it becomes another servicethat use just like any of the
other services that are outthere when running in a cloud
but it's also the applicationson the edge are amazing as well
and so it's just again if you'rea tech person, it's super cool.
(32:25):
This is one of those moments,way faster pace of change than
any of the other ones.
Speaker 1 (32:32):
Got it. Good to hear that I feel like everybody that
I've talked to has echoed thatsentiment as well.
Speaker 2 (32:38):
I wanna say one thing is it is a step change in a way
those others weren't. And all ofthose other waves expanded the
capability of the kinds ofthings you could build and the
(32:59):
scale and the number of peopleyou could reach, but AI is AI is
augmenting people in a way that,you know, that, you know, it is
this this is one of thosemoments in history. So
Speaker 1 (33:15):
Yeah. A %. So before we jumped onto this podcast, I
was told that I shoulddefinitely ask you about
orgoneering and what it is andwhy it's important. So can you
tell us a little bit more aboutorgoneering and what that is?
Speaker 2 (33:32):
The biggest I'm trying to think of how best to
do this. The biggest issue formost companies that we come
across is less about which techto use, and more about how to
organize yourself and how tocreate a culture that knows how
to build things, because formost companies, they're not used
(33:55):
to building softwarecapabilities. They are users,
and of course there are lots oftech companies that build tech,
but for the most part mostcompanies are users of tech in
their own business. They applythe tech in whatever they are,
and so what's happening in AI isit's happening in a variety of
(34:20):
areas accelerated by AI is youneed to learn how it is that
tech companies design and buildsoftware products. What's the
role of product?
How do you think aboutengineering? How do you think
about those two work together?What are the processes you put
(34:41):
in place? What are the tools?What are the metrics?
What we found is, and if youdon't have this, nothing else
really matters. People get veryfocused on which AI tool I'm
gonna use and all these things,but it's how you build. Elon
(35:02):
Musk said something, he said alot of things, but this one
thing, nothing related to that,but he said Tesla's real product
is its manufacturing, and thathow we manufacture is the magic
and that, yes, they make carsand they're making batteries and
(35:22):
they're gonna make robots, butthe how is where the innovation
and so when it comes to buildingsoftware or building products
that are AI based and data thatare intended to be a product in
a sense that other people use,those same kind of things apply.
I mentioned a little bit abouthow Amazon has built its
(35:45):
organization. Every successfultech company, big or small, has
its own version of this.
They're not all the same, butthe themes are the same. So
orgoneering is about helping nontech companies primarily
understand how do you get theright tech culture and tech
leaders and processes to be ableto build stuff. So that's what
(36:09):
it means.
Speaker 1 (36:10):
Gotcha, awesome. Yeah, so getting the right
people in place, but also theright processes and like how to
think about building products aswell. That reminds me
Speaker 2 (36:21):
of I'm iterating, know, you're never done. I mean,
if you look at any successfulcompany whose product you use,
tech company, you know, and youjust described, you know, the
the AI models and iterations. Imean, these things it it's the
pace of iteration. Know? It'syou're not gonna your your your
main thing is to get out thereand then get really good at
(36:43):
learning and iterating andtesting.
And those are skills, those arejust skills that need to be
developed for most companies.
Speaker 1 (36:50):
Yeah, absolutely. So then what's on roadmap for the
vision for Techriti in 2025? Andif you could wave a magic wand
on a block, one major roadblockor challenge to get to that
vision for 2025 for TechCritty,what would it be?
Speaker 2 (37:08):
Well look, first of all thanks for the question. We
are not a product company. Soour real star and our mission is
helping companies in the waysthat we've been talking about
here, and so we found a coupleof things. What we found is that
what we do and who we are isrelatively unique. The idea that
(37:31):
you could have a person thathelped you build Alexa or Google
Maps or one of a dozen otherthings that you could have that
person kind of help you and be aguide and a mentor in a sense a
copilot is pretty rare.
You couldn't hire these peopleand you can't go take a course
to learn about this. And againthe metaphor is not the same,
(37:55):
but if you could have TroyAikman or Tom Brady or somebody
like that, you know, around as acoach, that would be very
valuable. And so, you know, ourmission is to provide a platform
for that kind of person to to beable to engage with clients and
help. We're a collective ofpeople who got to that place in
(38:18):
our careers where we want towork on different projects and
help people and help companieswho are trying to do important
things. So the biggest thing forus is awareness, is, you know,
having companies, you know, knowthat we're out there when
they're, you know, when they'restruggling with these decisions,
you know, whether it's diligenceor how do I scale, how do I
(38:38):
build this culture, do I havethe right leaders that there's
somebody who can help.
So our biggest breakthrough Iwould say is probably that
awareness and getting out thereand helping.
Speaker 1 (38:51):
Yeah, just getting your name out there, getting
your awareness out there so thatyou can help more companies. It
sounds like an exciting futurethough where you can come in and
help a lot of multiplecompanies, whether it's
primarily technology or nontechnology, but it sounds like
you love and love focusing onlike non technology companies as
(39:14):
well at TechBuddy.
Speaker 2 (39:15):
Yeah. That's where in a lot of cases you can have the
biggest impact. And again Iwould say a non technology
company today, really everycompany needs to be a tech
company in some degree, it'skind of where your core product
isn't tech, where you're usingtech you know to to deliver
value in one form or another,but your people aren't you know
(39:36):
people aren't buying you, you'renot they're not buying you as
you know they're not buying youas a software tool, you know
you're using there, and that's athat's you know we sort of say
every every company that wantsto be a great company needs to
understand how also to be agreat tech company.
Speaker 1 (39:51):
Yeah. And Absolutely.
Speaker 2 (39:53):
So that's true.
Speaker 1 (39:54):
Absolutely. Alright. I have a last few questions. One
is, what are you most proud ofand why? And it can be
professional or it can bepersonal.
Speaker 2 (40:05):
I'm most proud of my family and my kids.
Speaker 1 (40:08):
That's awesome. That's awesome.
Speaker 2 (40:10):
That's like, I I I made a decision which thankfully
with a lot of help from my wifeabout what to prioritize and
it's very easy to over index onyour career and under index on
your family, and I had some ofthose moments, but someone told
(40:36):
me once, no kid, if you asksomeone when they grow up, do
you wish your dad worked more?No kid would say, yeah I wish my
dad worked more. And so familyfiguring out, it's not work life
balance, but it's how you stayalive and energized and feeling
(41:00):
like you're making a difference.But I think your family to some
degree is your most importantlegacy.
Speaker 1 (41:06):
Yeah I think I heard somewhere that the folks that
get most impacted of yourabsence is your kids. And then
the other thing that I learnedlike a few years ago was the
longest running study in humanhappiness and health, the number
(41:29):
one determining factor is thenumber of high quality
relationships, whether it's likefriends or Yep.
Speaker 2 (41:37):
And I think that's absolutely true. And it's easy
to forget that.
Speaker 1 (41:43):
Yeah absolutely. All right lastly, where can people
find you online where they canlearn about you, Entekwity, and
then how can listeners be usefulto you?
Speaker 2 (41:54):
Sure, well that's a great question, thanks. So the
simplest thing is on LinkedIn,know Anthony Bay, I don't think
there's a lot of Anthony Baysbut you know Anthony Bay
Techwity, would probably be theeasiest way. And then our
company is called Techquity, andthe URL is .ai, so if you want
(42:17):
to learn more about Techquity,what know what we do, who we
are, there's 18 or 19 of us now.It's a collective of pretty
interesting people who worktogether. So those would be the
best ways to find me and
Speaker 1 (42:32):
us. Awesome. Awesome. Well, thanks for coming on,
Anthony. Thank you so much.
Speaker 2 (42:37):
Thank you so much.
Speaker 3 (42:39):
Thank you for tuning into the very first episode of
Beyond The Prompt. If youenjoyed this discussion, please
subscribe to the podcast so youdon't miss future episodes.
Also, if you could take a momentto review and rate the podcast,
it would help me tremendously inreaching more listeners and
bringing you more great content.Until next time, keep going
beyond the prompt.
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