May 21, 2025 • 10 mins
Everyone keeps saying AI is crowded. What they’re not saying is that a third of it is already dead.
🎙️ In this episode, we get into:
- Where AI tools are being built and which cities are becoming serious hubs
- Why 4,000+ tools have already gone dark and what that churn reveals
- What the AI tool death toll teaches us about category collapse, market signals, and blind spots
- How to read the map so you’re not building in a dead zone or betting on noise
This isn’t a guess. It’s data we pulled ourselves. And it’s already changing how we think about where to build, invest, and win in AI.
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We’re the team behind whatisthat.ai, the AI discovery platform built to cut through all the hype, and WITAI Advisory, the strategic services arm helping founders and leadership teams turn AI exploration into execution.
If this all feels overwhelming, it’s not your fault. The landscape is moving too fast for most teams to keep up!💨
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Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:03):
Broadcasting live from somewhere inside the
algorithm, this is AI on air,the official podcast from
whatisthat.ai, we're your AIgenerated hosts, let's get into
it. Okay. Let's unpack this abit. So there's this statistic
floating around. Over 11,000 AItools built, launched,
(00:26):
supposedly out there changingthe world.
Speaker 2 (00:27):
Sounds like a real gold rush, doesn't it?
Speaker 1 (00:29):
It really does. But here's the kicker, the sort of
jolt. Mhmm. About a third ofthem already gone. Just offline
poof.
Speaker 2 (00:36):
Vanished. Yeah.
Speaker 1 (00:38):
To me, that just screams, you know, intense
experimentation and maybe,frankly, a bit of a shakeout
happening right now.
Speaker 2 (00:44):
Oh, absolutely. What's really striking is just
the sheer volume of attempts andhow how quickly the whole
landscape is shifting. It reallyshows how fast this field is
moving. Right?
Speaker 1 (00:55):
Lots of ideas getting tested, like, in real time.
Speaker 2 (00:58):
Exactly. And that's that's precisely why we're doing
this deep dive today. We'relooking at a really fascinating
report from what That dot AI,their AI tool report. And
they're not just, you know,tracking mentions or hype.
They're actually monitoring liveAI tools and verifying the data,
especially where these tools arecoming from.
Speaker 1 (01:16):
Which is harder than it sounds, I bet. Definitely.
They even mentioned they'reworking on a sort of future
method using AI to analyze thoseterms of service agreements for
location data.
Speaker 2 (01:26):
Oh, interesting. Because those dense legal
documents, you know, they cansometimes tell you a surprising
amount about a company's realpriorities.
Speaker 1 (01:34):
Right. Like data handling or how they view
different regulations.
Speaker 2 (01:36):
Exactly. Their comfort level maybe.
Speaker 1 (01:38):
So our mission here for this deep dive is pretty
clear. We want to pull out thekey insights. Where is AI
development actually happeningglobally? And what is this like
huge disappearance rate, thischurn? What does it actually
mean?
Speaker 2 (01:52):
Right.
Speaker 1 (01:52):
Whether you're building in this space or
investing or maybe you're just,you know, fascinated by all this
tech, this is really for you.
Speaker 2 (01:59):
Yeah. Because understanding these basic
patterns, the geography ofinnovation, the, the mortality
rate of these tools, it givesyou a real edge, a way to make
sense of a really complex, fastmoving area.
Speaker 1 (02:10):
Okay. So let's jump in. First, big question. Yeah.
Where are these tools actuallybeing built?
According to the report'sverified data, The US, it's way
out front, 1,182 tools, which Iguess isn't totally unexpected.
Speaker 2 (02:25):
No. Not really surprising given the, you know,
the established tech scene, theaccess to capital here. But but
Yeah. It's really important tolook beyond just that top
number. The fact that othercountries are showing
significant activity too.
That tells you it's a muchbroader global thing.
Speaker 1 (02:40):
Absolutely. Because right behind The US, you've got
some serious players. The UK,for instance, they're next with
a 25 tools. Then India, EightyThree. Canada, Seventy Four.
Germany's got 68. France, 40three. Netherlands, 30 four. I
mean, it's a pretty strongdiverse group.
Speaker 2 (02:56):
It really is. And that international spread, it
kind of suggests AI innovationisn't just stuck in one place.
Different countries probablybring different strengths,
different focuses, maybedifferent research priorities,
you know.
Speaker 1 (03:05):
Okay. So let's zoom in a bit. From countries to
cities. The report alsopinpoints the top urban hotspots
for AI development.
Speaker 2 (03:13):
In San Francisco.
Speaker 1 (03:14):
Still number one. Yeah.
Speaker 2 (03:15):
Awesome.
Speaker 1 (03:15):
370 tools, which, you know, fits the narrative. Major
tech hub.
Speaker 2 (03:19):
It does fit the narrative. But here's what's
really revealing, I think. Those370 tools, that's only about
what? 18% of the total verifiedtool?
Speaker 1 (03:28):
Exactly. Which means, doing the math, over 80% of
these AI tools are starting upsomewhere else. Right. That
really challenges that wholeidea that AI innovation is only
a Silicon Valley story.
Speaker 2 (03:40):
It really does. And that distribution, that's a key
takeaway here. SF isinfluential, sure, but the data
clearly shows a much moredispersed landscape.
Speaker 1 (03:50):
Yeah.
Speaker 2 (03:51):
You've got cities like New York, Hundred And
Twenty Nine tools. London, ahundred and three. Those are
major centers in their ownright.
Speaker 1 (03:57):
And it doesn't stop there. You see Mountain View,
Paris, Singapore, Berlin, PaloAlto, LA, San Jose, all in the
top 10. Plus, the reportspecifically flags London,
Toronto, Bangalore, Berlin, andTel Aviv as serious emerging
hubs.
Speaker 2 (04:11):
And they mentioned reasons, right, like access to
engineer?
Speaker 1 (04:14):
Yeah, speed, cost maybe.
Speaker 2 (04:15):
Right. Those concentrations in specific
cities, probably reflectsexisting tech infrastructure,
strong universities, pumping outtalent, good investor networks,
mentors.
Speaker 1 (04:25):
And
Speaker 2 (04:26):
the growth in these other hubs suggests those
enabling factors are spreadingout globally.
Speaker 1 (04:31):
But it's not just the big guys, is it? The report also
highlights some, small playerson the rise, which I thought was
really interesting. Estonia,Switzerland, and Israel.
Speaker 2 (04:42):
Yeah. What's kind of remarkable there is that their
output, the number of AI tools,seems pretty high compared to
their population size or eventheir overall tech sector size.
Speaker 1 (04:51):
Right. Disproportionately high. They
called Estonia the Davos ofdigital. Yeah. Suggests a very,
like, forward thinking digitalenvironment.
Speaker 2 (04:59):
Mhmm. And Switzerland, described as neat,
clean, quietly AI forward.
Speaker 1 (05:05):
Which paints a picture, doesn't it? Yeah. Maybe
a more measured approach, butstill really effective.
Speaker 2 (05:11):
Quietly effective. Yeah. And Israel well, Israel's
always been strong in techinnovation. The report notes
their strength in areas reallycrucial for AI, like
cybersecurity, coreinfrastructure.
Speaker 1 (05:20):
That foundational stuff.
Speaker 2 (05:21):
Exactly. That expertise probably helps support
building all sorts of AIapplications on top.
Speaker 1 (05:26):
Okay. So this all leads to a really basic
question. Why should we evencare where these tools are being
built? It's more than just dotson a map.
Speaker 2 (05:34):
Precisely. Location gives you critical context. It
hints at the regulatory world atool lives in.
Speaker 1 (05:41):
Right.
Speaker 2 (05:41):
The availability of certain skills, investment
climate, maybe even like thebasic assumptions baked into the
tool about data privacy.
Speaker 1 (05:50):
Right.
Speaker 2 (05:51):
Essentially, it gives you a deeper read on the
environment that's shaping thatAI.
Speaker 1 (05:55):
It's all part of what they call AI market
intelligence.
Speaker 2 (05:57):
Exactly that. Understanding these geographic
clusters, it gives you a realstrategic advantage potentially.
Yeah. It just allows for moreinformed decisions whether
you're thinking about investingsome more specific or just
trying to figure out thecompetition in a market.
Speaker 1 (06:11):
Okay. Let's shift gears then to the other really
big finding in this report. Thechurn rate.
Speaker 2 (06:17):
Yes. The churn.
Speaker 1 (06:18):
Out of those, was it 11,266 tools they tracked,
nearly 4,000 are already gone,ceased to exist.
Speaker 2 (06:26):
That is a lot of attrition.
Speaker 1 (06:27):
It really is.
Speaker 2 (06:28):
And it just underscores how incredibly
dynamic this AI space is rightnow. And, pretty competitive
too. Yeah. We're seeing justrapid fire experimentation. And,
you know, naturally, not everysingle attempt is gonna stick.
Not every idea is viable longterm.
Speaker 1 (06:46):
The report paints a pretty vivid picture.
Disappearing founders, websitesthat just die, entire categories
that seem to pop up and thenjust collapse.
Speaker 2 (06:55):
It sounds very, Darwinian, doesn't it?
Speaker 1 (06:58):
It really does. A Darwinian environment for AI
tools.
Speaker 2 (07:01):
Well, that kind of high volatility, it's pretty
typical for a rapidly emergingmarket. There's tons of
exploration, testing differentangles
Speaker 1 (07:08):
Right.
Speaker 2 (07:09):
And a big chunk just won't find a sustainable path.
It'll be really interesting whenthey dig into why these tools
failed. That analysis could besuper valuable.
Speaker 1 (07:17):
Definitely something to watch for. But okay, for you
listening right now, why doesall this matter? The location
stuff, the high failure rate.What's a practical takeaway?
Speaker 2 (07:25):
Well, about it. If you're trying to hire AI talent,
knowing where the bigdevelopment hubs are gives you a
huge leg up in your search.
Speaker 1 (07:32):
Good point.
Speaker 2 (07:32):
Similarly, if you're scouting for promising startups,
or maybe potential acquisitions,understanding these clusters
helps you focus where innovationis most likely bubbling up.
Speaker 1 (07:41):
And if you're thinking about expanding your
own AI work, knowing where thenext wave might be coming from,
that informs your strategy,right? Yeah. Market entry,
potential partners.
Speaker 2 (07:50):
It's really more than just geography. It's about
getting a grip on the underlyingmarket dynamics. The flow of
talent, where the money'sconcentrating, the regional
strengths that help create thesetools in the first place.
Speaker 1 (08:03):
The report also mentioned briefly some other
interesting data points they'retracking, not just location.
Speaker 2 (08:09):
Right. Like churn rates within specific AI
categories.
Speaker 1 (08:12):
Yeah. And the link between, like, search demand,
what people are looking for, andthe actual number of tools
available.
Speaker 2 (08:17):
Mhmm. And identifying categories that are shrinking
versus ones that still seem wideopen.
Speaker 1 (08:22):
That seems useful.
Speaker 2 (08:23):
Yeah. Looking at trends, like how often people
search for AI video generatorversus how many actual tools
exist. That can tell you a lotabout whether a niche is getting
saturated or if there are stillgaps, unmet needs.
Speaker 1 (08:36):
And they made a really smart point. High churn
isn't always bad news for you.
Speaker 2 (08:40):
How so?
Speaker 1 (08:41):
Well, seeing a lot of tools fail in one specific
category, that could be a prettystrong signal that maybe that's
not the area to pour your owntime and money into.
Speaker 2 (08:50):
Right. A signal of what not to do, learning from
others' failures.
Speaker 1 (08:54):
Exactly.
Speaker 2 (08:54):
That can be just as valuable as spotting the
successes. Helps you avoidcrowded or maybe just
unsustainable market segments.
Speaker 1 (09:02):
So, okay, boiling it all down, what's the key
takeaway here? The core messagefrom this deep dive into the AI
tool report that you shouldreally hang on to?
Speaker 2 (09:11):
I think the fundamental message is
understanding both where AI isbeing built and the, the
significant rate tools are dyingoff, that gives you a crucial
strategic advantage.
Speaker 1 (09:22):
Mhmm.
Speaker 2 (09:22):
It's about looking past the surface hype, know.
Really getting the lay of theland in this super fast changing
field. It helps you spot theopportunities and maybe navigate
the pitfalls.
Speaker 1 (09:31):
So quick recap then. US leads in sheer numbers, but
definitely not alone. Yeah. UK,India, Canada, others are major
players too.
Speaker 2 (09:39):
Right. It's diversifying.
Speaker 1 (09:40):
We're seeing key city hubs pop up globally. Yeah. Way
beyond Silicon Valley. ThinkLondon, Toronto, Bangalore.
Bangalore.
Speaker 2 (09:47):
And smaller nations like Estonia, Switzerland,
Israel are really punching abovetheir weight.
Speaker 1 (09:53):
And maybe the biggest point, that super high churn
rate, it just highlights howdynamic and yeah, how
competitive this AI space isright now.
Speaker 2 (10:03):
Which kind of leads us to a final thought for you to
mull over.
Speaker 1 (10:05):
Okay.
Speaker 2 (10:06):
Given this huge failure rate, what fundamental
shifts do you think thesurviving tools will actually
represent? What core value, whattech edge will define the ones
that actually make it andthrive?
Speaker 1 (10:17):
That's a good one. Or maybe thinking about those
geographic clusters we talkedabout. Knowing where innovation
is bunching up now, what newkinds of collaboration or maybe
even new competitive rivalriesmight we see emerge as these
hubs grow and maybe startconnecting more in the few
years.
Speaker 2 (10:32):
Definitely some interesting things to chew on
there. That's it for thisepisode of AI on Air powered by
WhatIsThat.ai. If your brainsurvived this episode, go ahead
and subscribe. We drop newepisodes every week. Wanna go
deeper?
Join our community on Substackto get early drops, tool
breakdowns, and weird AI stuffthe mainstream hasn't caught
yet. See you there.
Broadcasting live from somewhere inside the
algorithm, this is AI on air,the official podcast from
whatisthat.ai, we're your AIgenerated hosts, let's get into
it. Okay. Let's unpack this abit. So there's this statistic
floating around. Over 11,000 AItools built, launched,
(00:26):
supposedly out there changingthe world.
Speaker 2 (00:27):
Sounds like a real gold rush, doesn't it?
Speaker 1 (00:29):
It really does. But here's the kicker, the sort of
jolt. Mhmm. About a third ofthem already gone. Just offline
poof.
Speaker 2 (00:36):
Vanished. Yeah.
Speaker 1 (00:38):
To me, that just screams, you know, intense
experimentation and maybe,frankly, a bit of a shakeout
happening right now.
Speaker 2 (00:44):
Oh, absolutely. What's really striking is just
the sheer volume of attempts andhow how quickly the whole
landscape is shifting. It reallyshows how fast this field is
moving. Right?
Speaker 1 (00:55):
Lots of ideas getting tested, like, in real time.
Speaker 2 (00:58):
Exactly. And that's that's precisely why we're doing
this deep dive today. We'relooking at a really fascinating
report from what That dot AI,their AI tool report. And
they're not just, you know,tracking mentions or hype.
They're actually monitoring liveAI tools and verifying the data,
especially where these tools arecoming from.
Speaker 1 (01:16):
Which is harder than it sounds, I bet. Definitely.
They even mentioned they'reworking on a sort of future
method using AI to analyze thoseterms of service agreements for
location data.
Speaker 2 (01:26):
Oh, interesting. Because those dense legal
documents, you know, they cansometimes tell you a surprising
amount about a company's realpriorities.
Speaker 1 (01:34):
Right. Like data handling or how they view
different regulations.
Speaker 2 (01:36):
Exactly. Their comfort level maybe.
Speaker 1 (01:38):
So our mission here for this deep dive is pretty
clear. We want to pull out thekey insights. Where is AI
development actually happeningglobally? And what is this like
huge disappearance rate, thischurn? What does it actually
mean?
Speaker 2 (01:52):
Right.
Speaker 1 (01:52):
Whether you're building in this space or
investing or maybe you're just,you know, fascinated by all this
tech, this is really for you.
Speaker 2 (01:59):
Yeah. Because understanding these basic
patterns, the geography ofinnovation, the, the mortality
rate of these tools, it givesyou a real edge, a way to make
sense of a really complex, fastmoving area.
Speaker 1 (02:10):
Okay. So let's jump in. First, big question. Yeah.
Where are these tools actuallybeing built?
According to the report'sverified data, The US, it's way
out front, 1,182 tools, which Iguess isn't totally unexpected.
Speaker 2 (02:25):
No. Not really surprising given the, you know,
the established tech scene, theaccess to capital here. But but
Yeah. It's really important tolook beyond just that top
number. The fact that othercountries are showing
significant activity too.
That tells you it's a muchbroader global thing.
Speaker 1 (02:40):
Absolutely. Because right behind The US, you've got
some serious players. The UK,for instance, they're next with
a 25 tools. Then India, EightyThree. Canada, Seventy Four.
Germany's got 68. France, 40three. Netherlands, 30 four. I
mean, it's a pretty strongdiverse group.
Speaker 2 (02:56):
It really is. And that international spread, it
kind of suggests AI innovationisn't just stuck in one place.
Different countries probablybring different strengths,
different focuses, maybedifferent research priorities,
you know.
Speaker 1 (03:05):
Okay. So let's zoom in a bit. From countries to
cities. The report alsopinpoints the top urban hotspots
for AI development.
Speaker 2 (03:13):
In San Francisco.
Speaker 1 (03:14):
Still number one. Yeah.
Speaker 2 (03:15):
Awesome.
Speaker 1 (03:15):
370 tools, which, you know, fits the narrative. Major
tech hub.
Speaker 2 (03:19):
It does fit the narrative. But here's what's
really revealing, I think. Those370 tools, that's only about
what? 18% of the total verifiedtool?
Speaker 1 (03:28):
Exactly. Which means, doing the math, over 80% of
these AI tools are starting upsomewhere else. Right. That
really challenges that wholeidea that AI innovation is only
a Silicon Valley story.
Speaker 2 (03:40):
It really does. And that distribution, that's a key
takeaway here. SF isinfluential, sure, but the data
clearly shows a much moredispersed landscape.
Speaker 1 (03:50):
Yeah.
Speaker 2 (03:51):
You've got cities like New York, Hundred And
Twenty Nine tools. London, ahundred and three. Those are
major centers in their ownright.
Speaker 1 (03:57):
And it doesn't stop there. You see Mountain View,
Paris, Singapore, Berlin, PaloAlto, LA, San Jose, all in the
top 10. Plus, the reportspecifically flags London,
Toronto, Bangalore, Berlin, andTel Aviv as serious emerging
hubs.
Speaker 2 (04:11):
And they mentioned reasons, right, like access to
engineer?
Speaker 1 (04:14):
Yeah, speed, cost maybe.
Speaker 2 (04:15):
Right. Those concentrations in specific
cities, probably reflectsexisting tech infrastructure,
strong universities, pumping outtalent, good investor networks,
mentors.
Speaker 1 (04:25):
And
Speaker 2 (04:26):
the growth in these other hubs suggests those
enabling factors are spreadingout globally.
Speaker 1 (04:31):
But it's not just the big guys, is it? The report also
highlights some, small playerson the rise, which I thought was
really interesting. Estonia,Switzerland, and Israel.
Speaker 2 (04:42):
Yeah. What's kind of remarkable there is that their
output, the number of AI tools,seems pretty high compared to
their population size or eventheir overall tech sector size.
Speaker 1 (04:51):
Right. Disproportionately high. They
called Estonia the Davos ofdigital. Yeah. Suggests a very,
like, forward thinking digitalenvironment.
Speaker 2 (04:59):
Mhmm. And Switzerland, described as neat,
clean, quietly AI forward.
Speaker 1 (05:05):
Which paints a picture, doesn't it? Yeah. Maybe
a more measured approach, butstill really effective.
Speaker 2 (05:11):
Quietly effective. Yeah. And Israel well, Israel's
always been strong in techinnovation. The report notes
their strength in areas reallycrucial for AI, like
cybersecurity, coreinfrastructure.
Speaker 1 (05:20):
That foundational stuff.
Speaker 2 (05:21):
Exactly. That expertise probably helps support
building all sorts of AIapplications on top.
Speaker 1 (05:26):
Okay. So this all leads to a really basic
question. Why should we evencare where these tools are being
built? It's more than just dotson a map.
Speaker 2 (05:34):
Precisely. Location gives you critical context. It
hints at the regulatory world atool lives in.
Speaker 1 (05:41):
Right.
Speaker 2 (05:41):
The availability of certain skills, investment
climate, maybe even like thebasic assumptions baked into the
tool about data privacy.
Speaker 1 (05:50):
Right.
Speaker 2 (05:51):
Essentially, it gives you a deeper read on the
environment that's shaping thatAI.
Speaker 1 (05:55):
It's all part of what they call AI market
intelligence.
Speaker 2 (05:57):
Exactly that. Understanding these geographic
clusters, it gives you a realstrategic advantage potentially.
Yeah. It just allows for moreinformed decisions whether
you're thinking about investingsome more specific or just
trying to figure out thecompetition in a market.
Speaker 1 (06:11):
Okay. Let's shift gears then to the other really
big finding in this report. Thechurn rate.
Speaker 2 (06:17):
Yes. The churn.
Speaker 1 (06:18):
Out of those, was it 11,266 tools they tracked,
nearly 4,000 are already gone,ceased to exist.
Speaker 2 (06:26):
That is a lot of attrition.
Speaker 1 (06:27):
It really is.
Speaker 2 (06:28):
And it just underscores how incredibly
dynamic this AI space is rightnow. And, pretty competitive
too. Yeah. We're seeing justrapid fire experimentation. And,
you know, naturally, not everysingle attempt is gonna stick.
Not every idea is viable longterm.
Speaker 1 (06:46):
The report paints a pretty vivid picture.
Disappearing founders, websitesthat just die, entire categories
that seem to pop up and thenjust collapse.
Speaker 2 (06:55):
It sounds very, Darwinian, doesn't it?
Speaker 1 (06:58):
It really does. A Darwinian environment for AI
tools.
Speaker 2 (07:01):
Well, that kind of high volatility, it's pretty
typical for a rapidly emergingmarket. There's tons of
exploration, testing differentangles
Speaker 1 (07:08):
Right.
Speaker 2 (07:09):
And a big chunk just won't find a sustainable path.
It'll be really interesting whenthey dig into why these tools
failed. That analysis could besuper valuable.
Speaker 1 (07:17):
Definitely something to watch for. But okay, for you
listening right now, why doesall this matter? The location
stuff, the high failure rate.What's a practical takeaway?
Speaker 2 (07:25):
Well, about it. If you're trying to hire AI talent,
knowing where the bigdevelopment hubs are gives you a
huge leg up in your search.
Speaker 1 (07:32):
Good point.
Speaker 2 (07:32):
Similarly, if you're scouting for promising startups,
or maybe potential acquisitions,understanding these clusters
helps you focus where innovationis most likely bubbling up.
Speaker 1 (07:41):
And if you're thinking about expanding your
own AI work, knowing where thenext wave might be coming from,
that informs your strategy,right? Yeah. Market entry,
potential partners.
Speaker 2 (07:50):
It's really more than just geography. It's about
getting a grip on the underlyingmarket dynamics. The flow of
talent, where the money'sconcentrating, the regional
strengths that help create thesetools in the first place.
Speaker 1 (08:03):
The report also mentioned briefly some other
interesting data points they'retracking, not just location.
Speaker 2 (08:09):
Right. Like churn rates within specific AI
categories.
Speaker 1 (08:12):
Yeah. And the link between, like, search demand,
what people are looking for, andthe actual number of tools
available.
Speaker 2 (08:17):
Mhmm. And identifying categories that are shrinking
versus ones that still seem wideopen.
Speaker 1 (08:22):
That seems useful.
Speaker 2 (08:23):
Yeah. Looking at trends, like how often people
search for AI video generatorversus how many actual tools
exist. That can tell you a lotabout whether a niche is getting
saturated or if there are stillgaps, unmet needs.
Speaker 1 (08:36):
And they made a really smart point. High churn
isn't always bad news for you.
Speaker 2 (08:40):
How so?
Speaker 1 (08:41):
Well, seeing a lot of tools fail in one specific
category, that could be a prettystrong signal that maybe that's
not the area to pour your owntime and money into.
Speaker 2 (08:50):
Right. A signal of what not to do, learning from
others' failures.
Speaker 1 (08:54):
Exactly.
Speaker 2 (08:54):
That can be just as valuable as spotting the
successes. Helps you avoidcrowded or maybe just
unsustainable market segments.
Speaker 1 (09:02):
So, okay, boiling it all down, what's the key
takeaway here? The core messagefrom this deep dive into the AI
tool report that you shouldreally hang on to?
Speaker 2 (09:11):
I think the fundamental message is
understanding both where AI isbeing built and the, the
significant rate tools are dyingoff, that gives you a crucial
strategic advantage.
Speaker 1 (09:22):
Mhmm.
Speaker 2 (09:22):
It's about looking past the surface hype, know.
Really getting the lay of theland in this super fast changing
field. It helps you spot theopportunities and maybe navigate
the pitfalls.
Speaker 1 (09:31):
So quick recap then. US leads in sheer numbers, but
definitely not alone. Yeah. UK,India, Canada, others are major
players too.
Speaker 2 (09:39):
Right. It's diversifying.
Speaker 1 (09:40):
We're seeing key city hubs pop up globally. Yeah. Way
beyond Silicon Valley. ThinkLondon, Toronto, Bangalore.
Bangalore.
Speaker 2 (09:47):
And smaller nations like Estonia, Switzerland,
Israel are really punching abovetheir weight.
Speaker 1 (09:53):
And maybe the biggest point, that super high churn
rate, it just highlights howdynamic and yeah, how
competitive this AI space isright now.
Speaker 2 (10:03):
Which kind of leads us to a final thought for you to
mull over.
Speaker 1 (10:05):
Okay.
Speaker 2 (10:06):
Given this huge failure rate, what fundamental
shifts do you think thesurviving tools will actually
represent? What core value, whattech edge will define the ones
that actually make it andthrive?
Speaker 1 (10:17):
That's a good one. Or maybe thinking about those
geographic clusters we talkedabout. Knowing where innovation
is bunching up now, what newkinds of collaboration or maybe
even new competitive rivalriesmight we see emerge as these
hubs grow and maybe startconnecting more in the few
years.
Speaker 2 (10:32):
Definitely some interesting things to chew on
there. That's it for thisepisode of AI on Air powered by
WhatIsThat.ai. If your brainsurvived this episode, go ahead
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