Welcome to artificially intelligent marketing,
a weekly podcast where we stay on top of the
latest trends, tips, and tools in the world of
marketing AI, helping you get the best results
from your marketing efforts. Now let's join our
hosts, Paul Avery and Martin Broadhurst. Hello
and welcome to Artificially Intelligent Marketing.
You remember us from 1991 or whether it was that
we last published an episode, but I'm back today
with my fantastic colleague, Martin Broadhurst.
Martin, how are you, sir? Sorry, who are you?
Is this a spam call? I don't even know who I
am anymore. Hello. Yes. Nice to be back, isn't
it? It is. What have you been up to? How long
have we been offline? Maybe not offline. Come
on, we're all attached to our phones 24 -7. Off
air. How long have we been off air? It's over
a year. Oh, I think our not particularly large
subscriber base have probably fled. They're probably
somewhere else. But hopefully you've still got
this. on your podcast app and this lands in and
you're like oh good i'm glad the gent's back
i really want to know what they feel about what's
going on um i have got a pretty good excuse i
think for not being around martin um those of
you who listen to the podcast that know me will
probably already know this but um i was co -founder
of an agency and we were acquired last year and
we were going through the acquisition process
and it takes a huge amount of time and effort
And then that gobbled up all my time. And yeah,
I couldn't really invest too much time in anything
else, to be honest. Hence having to take a hiatus
that probably should have been two months, but
then turned into like 18 months. So sorry about
that, Martin. Martin's been messaging me. He's
been like, dude, we've got to do the podcast.
We've got to do the podcast. I'm like, yeah,
no, I will. I'll get back to it. But well, how
have you been filling your time, Martin? Because
obviously I know this is all you live for. Hanging
out with me on here. So what have you been filling
your time with? I've been sat here. Sat here
waiting. Just clicking, going, he'll be back
soon. I believe in Paul. And now you're back.
What have I, in all seriousness, what have I
been up to? Well, there's been a huge amount
of delivering workshopping and training and implementing
AI, unsurprisingly, alongside the HubSpot delivery
as well. uh really wanting to see their systems
speak to one another and i think that's going
to be a theme of the episode today to some extent
but uh yeah it's the adoption and the application
of ai is is really becoming front and center
of lots of leadership teams conversations now
hey man well that feels like quite a nice little
segue into what we're going to cover today because
we have been around for a while We were thinking
that we'll go through all the things that have
happened in the last 18 months, not in detail,
because that would be extremely boring and very
lengthy. But we're going to touch on some of
the evolutions that have happened in the world
of AI, especially for marketers, with a focus
on where are we now? What can we do that we couldn't
do before? What is working well for marketers?
What is still over -promising and under -delivering
on the AI front? just to bring you up to speed
if you haven't been paying attention to the world
of AI and marketing, which I'm sure, honestly,
you have, because since we started the podcast
two and a half years ago, probably more, you
can't really get away from AI in the world of
anything these days, can you, Martin? So I'm
sure most people are paying attention even if
they don't want to. yeah certainly uh copilot
is plastered across every microsoft surface you
can possibly find gemini's creeped its way into
every google application you care to use and
even ones that you don't yeah and then we've
got tv ads for chat gpt and google gemini and
smart ai driven features on your mobile phone
to improve taking your pictures and yeah it's
everywhere we're covered in it um rather than
being covered in it let's cover it in some detail
on the podcast today so um one of the first things
we should probably touch on is i don't think
ai reasoning was a thing when we last did our
episode was it mine it wasn't the nearest we
had to it at that time was the prompting technique
chain of thought prompting where you would ask
the ai to show it's working and think step by
step before delivering the answer which would
improve the the outputs but no that has evolved
into a new model form yeah i mean i think you're
right it kind of took that chain of thought approach
and turned it up to 11 and it just vastly improves
the outputs especially for more strategic questions
or challenging questions where the ai needs to
think and plan a bit it also helps massively
reduce hallucinations and other factors. Don't
get rid of them, unfortunately. No, sadly not.
But I think it does a better job of it. So yeah,
if you haven't been using reasoning models, you
should probably be giving them a try. You should
probably get better outputs. It's kind of hard
to escape them now, given that Google Gemini
sort of uses its reasoning capabilities automatically
when it feels it needs them, as does ChatGPT.
But we'll talk about that a little bit later,
because I know you've got a bee in your bonnet
about... auto routing of uh of models yeah i
mean there is a downside to the to the reasoning
in terms of speed of response so the latency
obviously downgrades because before you get your
response the model thinks through interestingly
it does give you a description of what it's doing
which i always enjoy joy reading through but
yeah i think this is this is a marked improvement
on the the capabilities the difference in in
the quality of the output as you say is is vast
and all of the model providers are now providing
a whether it's called with extended thinking
or gemini has got uh gemini 2 .5 pro which is
reasoning by default as opposed to 2 .5 flat
which is the kind of quick response model um
and just to kind of articulate this for the audience
that might not be fully familiar with kind of
how or why this this works the example i often
give is If I ask you to work out 86 times by
32, you're probably not going to give me an answer
straight away off the top of your head. And you
could approximate and give me a ballpark figure,
but likely most people aren't going to just spit
out the right answer first time. Whereas pen
and paper, given a bit of time to write it down
and plan all the steps and figure it out. you
can arrive at the right answer and it's the same
thing with these models the models that are non
-reasoning non -thinking models they give a response
kind of quickly they just say the first thing
that comes to their mind so to speak whereas
the reasoning it takes the time to figure it
out yes interesting there's going to be a lot
of tangents in today's podcast folks so hopefully
they'll all be valuable but i was listening to
a podcast yesterday with the ceo of grok this
is not elon musk's ai model grok this is grok
the chip technology that's been built specifically
for inference which is the process that happens
when you ask the model a question there's ai
training which has a particular sort of infrastructure
and sort of the way it works and then there's
um inference which is you know works a bit differently
um So he's got skin in the game on this one.
He's been building chips with his team to be
really fast at giving you answers. But he's also
quite credible. He did build the TPUs at Google,
so he's got a track record. He was saying yesterday,
when I was listening to this, that speed of answer
is critical. And at Google, they... I don't know
if they showed it with data or whether it was
sort of like a rule of thumb they used, but for
every hundred milliseconds faster, they could
get a person an answer translated to 8 % increase
in market share. Wow. So he's massively betting
on the fact that as consumers, we won't be willing
to wait to get our answer. And whilst it's fantastic
to have access to these reasoning models, like
if you do a deep research. which might not have
existed either. I can't remember. So deep research
for listeners is when you have to specifically
activate this in ChatGPT, but you can basically
give it a research project and it will go read
like 100 sources on the internet and come back
and give you a 5 ,000 word, 10 ,000 word report
on a topic. But you set that running, then you
have to go make yourself a cup of tea because
that takes like 25, 30 minutes. And I guess what
he's thinking is we want things quickly. for
the model companies that can crack speed actually
and you can use reasoning you can use deep research
and still get an answer in near real time we're
going to switch to those models or at least to
those sort of inference providers which I think
is going to be quite interesting to see how that
plays out because I don't know about you Martin
but as it stands I'm quite tolerant with letting
the tool work for 20 minutes on a project that
would have taken me three hours but if a tool
came out that would deliver it in 30 seconds
and the results were nearly or as good I would
probably switch yeah and it's the biggest frustration
with turning on thinking in chat gpt 5 you want
it to do a bit of thinking but then you're waiting
for two minutes and actually if you could compress
that down to 10 seconds that would be fantastic
i must say just on the grok thing if anyone is
kind of vibe coding and looking for an llm integration
my word grok is fantastic i created a little
application yesterday that was giving real -time
responses to a little game that i created so
it was like running commentary on the outputs
of the game and because it can deliver 500 tokens
in a second which is like 450 400 words in a
second it's amazing like the real time back and
forth um it's kind of unparalleled Yeah, I think
real time's critical. As you know, Martin, I
hate type these days. Listeners don't know, I
broke my collarbone about a year ago and I couldn't
type. And then that was when I went all in on
dictating everything. And I've got a piece of
software on my computer that uses local models.
So all my transcription is done on device. So
I can say what I like. It's not, you know, I'm
not sharing any sensitive materials into the
cloud. the provider of this software actually
i want to i want to give my shout out because
it's pretty cool it's called voicing it's for
mac only unfortunately but we now have access
to the nvidia transcription model which even
if i speak like two paragraphs they appear instantly
and that's being done on my device that's not
being done in the cloud insane um i do think
we'll get to a point where we can do fairly impressive
things on a consumer laptop and that you don't
actually need to be connected to the cloud which
will be important for me because when i'm on
a plane and i'm working i can't do any work now
if there's no ai and wi -fi and i can't think
for myself i refuse um so i will need that that
onboard model but um so outside of reasoning
some of the platforms have matured martin um
especially copilot that we used to like absolutely
kick the crap out of back in the day um but you
quite like it now I do. They've won me over.
Microsoft, who knew with all of their trillions
in market cap, they could crack this particularly
tricky nut. It used to be terrible. The deployment
of it across the Office suite was just terrible.
Trying to use it in Excel, you would ask it to
do something, it would just break or it would
tell you that it had done it or it would say,
oh, I can't do that. Whereas now it is really
quite functional and... quite impressive in some
areas i think what they've done is they've taken
the the base models and they understand the business
applications and what people actually want to
use them for so you mentioned um deep research
now deep research is it takes the agentic models
the reasoning models and applies this agentic
layer with tool use where deep research can go
on the web it can read documents it it compiles
all of this information and then writes the the
report well researcher when i've done business
related research as opposed to say holiday research
or something like that the researcher agent in
copilot versus deep research in gemini or deep
research in open ai is markedly better and The
thing that really differentiates it is it's attached
to your Microsoft tenant. So everything that's
in your SharePoint, your OneDrive, it can crawl
all of that information as well and bring that
back into the report. I mean, that's just one
example. And there are many more. There's the
analyst tool, which is fantastic. I think the
Copilot Studio capabilities are, they're very
functional, very easy to use, much like creating
custom GPTs. If anything, I would say the issue
that Microsoft have got at the moment is on the
kind of commercial and the marketing side. I
think it's a bit confusing for people to understand
the pricing of it. Understanding, okay, there's
Copilot Chat, which is free. Then there's Copilot
Pro, which is 20 odd quid per user per month.
What's the difference between those? I don't
think they've been brilliant at communicating
those differences. But then when you come to
use agents, some of it's pay as you go. Some
of it's free. And when I speak to businesses,
they're currently in a bit of a muddle about
the value of Copilot and just exactly how much
it's going to cost and what the ROI is and that
kind of thing. Cheeky plug for Martin there.
Then if you are using Copilot and you think,
no, it sucks, Martin, you're wrong. Maybe Martin
can help you unlock the value of it. But I've
got a question for you, Martin. I don't use Copilot
at all. What's Analyst do? Analyst is, as the
name suggests, is a data analyst agent. So you
have to have a pro license to access this. So
you can't be using this on the free version of
Copilot. But in the same way that Researcher
is using the reasoning models, with this agentic
capability to go away and do a research brief
analyst does the same thing but with data analysis
so they've given it tooling and they've trained
it specifically to be a data analyst and it does
a very good job again i actually if i'm doing
any um if i've got some documents or files that
i want to run some analysis on i will do that
in the analyst tool or analyst agent rather than
doing it in ChatGPT with extended thinking turned
on because the outputs are just better. They
seem to have fine -tuned it for that analyst
role, and that's what makes it a bit different.
So it's using data, Excel spreadsheets and stuff.
Yeah, you can provide the data source, you can
upload files to it, or you can tell it to go
and find resources on SharePoint. You can point
it to the... to the location that you want and
it will go away and and kind of plug it all together
and give you the report that you're after so
there you go but probably many listeners have
to use copilot because their organization won't
allow them to use anything else but um if you
have not used it for a while sounds like it used
to be terrible and is now quite good um so go
and give it a go um one of the other things we
still get asked a lot about martin is which model
is best For what? And that continues to be an
absolute pain in the bottom to answer that question
because of the model roundabout. Obviously, Copilot
got better. ChatGPT's improved. Anthropic Claud
has improved. There's been open source models.
Talk us through the model roundabout over the
last couple of months. So we were all waiting
for so long for GPT -5. But actually, what was
kind of king of the hill for a long time was...
flawed three and 3 .5 and then flawed four came
out and that was that was dominating and everyone's
going when's gpt5 coming out when's gpt5 coming
out then it came out in august um and that became
top of the heap and then google gemini came out
with their uh they've got their what's that top
of the range model is google gemini 2 .5 deep
think yes which is the one that won the um international
maths olympiad alongside chat gpt's latest pro
model as well um so yeah they're all much of
a muchness at this point i think it's really
hard to say that any of them is far and away
better than any other if you're a developer you're
probably more inclined to go with claude But
then I see lots of chat with people saying chat
GPT with GPT -5 extended thinking and pro. I
mean, pro is fantastic. If you haven't used GPT
-5 pro to one shot a bit of vibe coding magic,
you haven't lived, Paul, if you've not done that.
I've dabbled. I think it's really interesting
as you were saying that, because ultimately for
most marketers. staying up to date with the best
models is actually quite hard because every benchmark
and discussion about model employment improvement
feels like it's drifted towards coding applications.
Like, is it better at coding or not? Which for
your average marketer is like, that's not what
I spend my day doing. So I don't really care.
But the reality is, and we'll talk about this
a bit later, thanks to vibe coding and being
able to build things, even if you're not a developer,
actually having a tool, which is good, at coding
for you can be very beneficial to a marketer
as we'll speak about later but um yeah it's and
it's hard to keep up because gpt5 has codex now
which is basically a you know a specific tool
for coding obviously claude already had claude
code as well so yeah it's very hard to keep up
with but i'm the same as you i mean when gpt5
came out i was like hey gi a gi and i was like
no This is not AGI. And it was very interesting
that my interpretation of the angle that they
took was powerful AI for all. So rather than
like power users getting something that's going
to totally blow them away, they made it so free
users weren't stuck using crappy models that
made them feel like AI wasn't very good. I think
that is extremely important for the adoption
of AI overall, and I totally get it. I do think
it has lower hallucination rate and I do think
it follows instructions more precisely as well.
So overall, it's pretty good. It completely broke
a bunch of my custom GPTs because they started
following the instructions properly. And I'm
like, oh, crumbs, this is the output I would
have got if GPT -4 .0 wasn't like basically just
deciding which instructions it was going to follow
and which it wasn't. So it's been a hell of a
lot of fun rewriting the prompts for all of mine.
custom GPTs, but there you go. So what's your
preferred model at the moment then? It depends
entirely on the use case. At the moment, ChatGPT
is kind of my daily driver for the kind of work
productivity stuff. So the things that lots of
people might use it for, summarization of documents,
redrafting emails, or kind of refining copy editing,
that kind of thing. ChatGPT tends to be my go
-to. this past week or two i've really started
using claude code for a lot of projects just
building little automations and small scripts
that are just going to make my life easier day
to day and helping with configuration of hubspot
portals and crm config i'll use claude code for
that but then google gemini i've built some gems
which are the custom gpt version of google gemini
where you can build your own kind of flavor as
it were um are you and i think they're really
good um i've found a lot of value for these are
more kind of personal things that i wanted to
develop and but i've got some great gems that
i'm using for power lifting for nutrition for
all sorts of things now and uh yeah i'm i'm I'm
spread a bit thin across all of them, if I'm
honest. You always loved a little test and a
dabble. And I have to admit, the scale of work
in my new role means that we've got ChachiPT
Enterprise and we're on Google. So we've got
Gemini. So I usually run the same prompt or request
through both and then choose which I like more.
I'm a bit annoyed. chat gpt5 doesn't really like
to write sentences it likes to like write three
words then add a plus symbol then add two words
and then do an arrow and like so much of that
stuff is like that um and you know i'm asking
i'm i'm still mostly honestly overcoming the
blank page problem but then i'm also very conscious
that i'm pasting the output into a document it's
still not what i want it to be it's still not
quite good enough if i'm honest um plus i feel
the need to edit it all because otherwise my
colleagues are going to be like what human uses
all of these pluses and arrows and like it's
so obviously not they're the new em dash of the
world um thanks to gpt5's obsession with them
so annoying it's almost like it's trying to communicate
as much valuable information to me and as fewer
output tokens as possible um yeah But that is
not great if you're doing blog writing or creating
e -books or scripts or something. Do you know
what I mean? So, yeah, GPT -5 got a bit of a
bash when it came out. Yeah. I think there are
other problems with the user experience as well.
So they've made it a routing model. And if you
go into chat GPT and go into the model selector
at the top, it will probably be on auto. If you
run the free account, it's definitely on. I don't
even know that you've got the option, to be honest,
on the free account. But if you're in the paid
for account, you'll have the dropdown and it
will typically be on auto. And what it's doing
there is it's running it through its own classification
engine first to look at your query and then say,
hmm, do I need to think about this one or can
I just give an answer? And I'm increasingly finding
people say, this is people with paid for accounts
that are... quite adept at using it but they're
saying GPT -5 is worse and I'm asking them about
kind of what's going on and what their use cases
are and basically they've not turned on thinking
mode so it's in instant mode and I think that
there's a in the auto model I wouldn't be surprised
if what they're doing is basically running it
through a GPT -5 mini or GPT -5 nano for cost
reduction because most of the time it's kind
of good enough you know if it's someone writes
this document nano or mini can do that great
if it's redrafted email it can do that at a level
that's kind of fine for most people but i think
it it doesn't apply its intelligence often enough
or frequently enough so lots of people are going
well it seems like a bit of a step backwards
i asked it for something the quality output is
not quite there i asked people to then turn on
thinking mode and say give it a go run the same
prompt And they're like, oh, wow, that's much
better. There's a cost to that in terms of time.
It has to think for a couple of minutes. But
the quality of the output is so greatly improved.
And I think unless people know that and it's
well communicated to them, they feel somewhat
frustrated with the experience. That said, there's
great benefits to open AI in serving lower cost
models that don't use so much compute. Yeah,
the drive for cost reduction is real. mostly
i think because of the lack of compute in the
world like we can't produce chips data centers
energy energy production facilities fast enough
and i think that serving all these people these
models i think that is genuinely a fairly big
driver of it but um i don't find thinking makes
my written outputs any better if i'm honest i
think it makes its analysis like if i'm like
here's a load of context let's create a content
strategy together or So let's brainstorm out
future scenario planning. If this happens, what
should we do? If this happens, what should we
do? I think his thinking is awesome for that
type of stuff. But yeah, I need to play with
Claude this week, really, the new Sonnet 4 .5,
10 .5, whatever we're up to. Because in general,
I always found Claude was better at writing,
but not... so good as in as instruction following
so 4 .0 was my go -to model for writing not claude
because claude thought it knew better like it
would definitely defer to its own internal style
guide no matter how hard i tried to push it whereas
4 .0 was more malleable gpt5 follows the instructions
better but just doesn't seem to be as good at
writing like we'll talk about it a little bit
later around the concept of of work slop and
where we are with technologies for marketing
applications and where we're at right now but
i haven't got a good writing model if i'm honest
at the moment um and i'm hoping that claude's
sonnet the new model is going to be it for a
while on the the model development generally
just to kind of conclude on that i think we have
definitely reached a point that we talked about
over a year ago which is we've kind of saturated
the intelligence requirements of the average
user They are plenty smart enough for the vast
majority of people. And we are now in a world
where we need to extend their capabilities beyond
just being smart and being able to think things
through into a new paradigm. I completely agree.
And if you look at the chat that's coming out
of all the major AI labs and companies, they're
looking at. solving impossible math problems
and contributing to scientific research. And
that pushing the intelligence frontier is not
to help marketers improve their click -through
rate, right? It's to cure cancer and what have
you. And what that does tell me is I think it's
another good example of what you just said, which
is the frontier is intelligence levels that your
average user doesn't need. Which is why I guess
it's still somewhat frustrating that I have a
bunch of marketing use cases that just don't
work very well. This is another tangent, but,
and I'm going to probably not do it justice.
So if you want the details, you should Google
this. Claude is basically building, Claude Anthropic
are building a team that hires human experts
to train and provide reinforcement learning to.
ai models for specific work tasks so they want
computer use and they want to record what people
do when they're using the crm when they're sending
an email when they're analyzing um a look a studio
data report to figure out you know which channel
which marketing channels working the best the
so that they can actually take humans that are
really good at this and try and apply some of
that knowledge and train their models that way
i think there's two parts to that the first is
having the computer use agent models which we'll
talk about a bit later get better at using your
standard crm software and what have you effectively
without clicking on the wrong things and basically
just breaking and not being able to do anything
but also just trying to get them to better understand
what the very best humans do to solve real world
business problems and work problems, which is
very different from trying to like meta -analyse
a hundred papers about this receptor related
to cancer to try and find some unexpected connection
and a new pathway that no one had imagined before.
That's a completely different problem, right?
But in business... I'm sure that could be applied,
but we just don't care as much about that. So
I am quite excited for the next year from that
perspective. There were a bunch of anthropic
people on another podcast I was listening to,
in essence, where they were saying, I think it
might have been the Dwarkesh podcast, that computers
can reinforcement learn off of humans doing knowledge
work because it's all digital and they will get
better at it than humans. Right. So it's going
to be interesting to see how that how that plays
out over time as well. But should we talk a little
bit about open versus closed models? We used
to talk about it quite a lot. I think it's less
of a thing now. What are your thoughts on where
we are in the open versus closed debate, especially
over while we've been off air? Yeah. So I had
a bit of a moment at the start of the year around
Christmas, in fact, wasn't it? It was Christmas
and New Year. deep seek from china came out as
a reasoning model that was cheap to run it was
cheap to train and this announcement and model
debut effectively wiped off billions from the
us stock market but it all recovered they're
doing okay no no need to panic nvidia will be
fine um but the the model itself was interesting
and people started to learn a lot from it because
the paper that was written alongside it introduced
some new techniques for model optimization and
hence why they were able to to do it so so cheaply
but then things kind of stalled and then the
world as far as open source models turned towards
meta who were the the kind of shining light for
research and development of open source models
and the llama 4 model was what released and the
reception was oh is that it was something of
a damp squib and subsequent to that meta have
kind of shifted their approach and they're talking
more about closed proprietary models and going
after the agi model development that they're
hiring at a very aggressive rate taking um that
some of the best talent from across all the ai
labs but it signaled to me at least that open
source at the the very frontier is not where
things are going to happen right that's not where
we should be looking there's going to be lots
of interesting development in the open source
world i'm sure but if you're looking for the
biggest baddest most exciting intelligent models
that's not where the action's going to be yeah
it's going to be quite interesting quite a lot
of people who speak about this a lot in our space
are really of the belief that models are going
to be a commodity like the amount of money that
you'll make with any particular model they'll
all like there'll be there might be one model
to rule them all there might be like a handful
of models there might be Loads of model niche
specialists, like there's a GPT Finance for all
the finance people to use. Like all of these
things are still possible. But as the amount
of compute build out goes and the amount of need
for AI intelligence on demand, it's just this
idea that the underlying model won't matter as
much as whether or not you have brand affinity.
You're a ChatGPT user. You're a Gemini user.
You know, this is why I still think there's a
massive chance that Google wins. They have great
models. And they have distribution and brand
equity. I don't think Meta is going to win in
anything because no one trusts them. Like no
one wants to give them data. We were talking,
not on a podcast recording, about the new Meta
glasses, which are really cool and seem really
interesting. But do I want to go around recording
my life? No, I know that Meta is, there must
be somewhere in the T's and C's that they're
able to use the real world data from my glasses
to figure out what. human paul does and where
he goes and what he looks like and how he opens
doors and all these other things that could probably
be used to i don't know train robots to do stuff
i just don't trust a mine yeah that is a real
reputational risk for them and if you follow
any of the development team on threads it seems
like they're aware of that well they would be
aware of that because lots of people tell them
directly in the comments all the time that we
don't trust you So it is something of an uphill
battle for them. I have a pair of the Meta Ray
-Bans and they're great. I really like them.
They can do some cool things. But interestingly,
the reception, even if you as a wearer do trust
them, there is definitely mistrust amongst people
that you're with when you're wearing them. Not
everyone, but some people will be like, is that
a camera on your face? Why, yes, yes, it is.
I don't like that. Do you know, I'm going to
make a dystopian prediction here. I don't think
any of us like that. I don't think we want the
fact that someone else is walking around with
a camera recording what we say and do. Like when
you have an argument with someone, you're like,
I never said that. And they're like, well, I'm
just going to refer you to Exhibit A here. This
recording of you that you may or may not have
known that I was taking. But I think, unfortunately...
we're going to allow it and the reason we're
going to allow it is because the new version
of the meta ray bands with the wrist um controllers
where basically you can just move your hands
and some of those videos of people being able
to like scroll their instagram feed in the little
um screen that's in the glasses that no one else
can see and these are just normal looking glasses
with your hands in your pocket scrolling through
the feed so no one can even necessarily see I
think we're so addicted to consuming short form
dopamine charged crap on our phones that the
fact that we could do that on a train or even
at dinner with a bunch of other people and no
one know that we're actually watching a TikTok
video and we're not really paying attention to
them. I think that the allure of that is going
to be too great and that we're all going to accept.
The fact that all these glasses are in the world
because of the benefits that we personally get,
even though it probably creates a less private,
always on recorded state for everybody. Yeah,
I can't disagree. I think people will lean into
it. And it's that world view of it is somewhat
tragic, but probably where we're heading. Yeah,
as we talked about, I have a use case. I do want
to be able to more easily respond to emails and
Slack messages. walking the dog. And if I don't
have to have my phone in one hand and the dog
in the other, it is going to increase my life
expectancy. So I'm all for that use case. But
yeah, it's the fact they'll be on our faces all
day. Meta's entire industry is the attention
economy. So what's their incentive? It isn't
to make you more productive. It's to show you
more content and monetize that as aggressively
as possible. Yeah, like, you you need you you
put your phone down during the day for various
reasons you never need to put your phone down
again it's on your face just imagine constant
notifications in your eyeline oh my god it's
going to be super interesting to see if it's
really annoying or if it actually is good but
it's here and it's coming and i don't think it's
going to stop for the reason you just said um
right let's go on to the next section i think
this is my favorite part of the podcast today
let's talk about agentic progress because i know
this is something that you play with a lot perhaps
just there surely can't be a listener in the
world who hasn't heard agentic and the term agent
now but why don't you just outline what the emergence
of agents is and then start to talk us through
what that means for people at work people who
work in marketing etc and maybe even talk about
some of the things you've been playing with so
agentic ai this is ai models that have some form
of agency the ability to take actions or make
decisions plan and then interact with the world
now in some instances this might just be a very
simple customized version of chat gpt where you've
given it some access to documents and you ask
it a question and it can go away research those
documents and come back and present you the answer
that's a very simple ai agentic assistant you
might connect it to some tools so it can go and
plug into your crm and it can crawl your crm
and bring back some data from there it's a kind
of extended ai assistant agent then we've got
kind of workflow agents and workflow agents are
the more trigger based interactions these are
very similar to the sorts of workflows that we've
talked about for a long time in this podcast
things that you might build with make .com or
zapier or na10 which has become a big workflow
tool of choice now these systems that they're
using triggers so somebody fills out a form on
your website and then you build out a sequence
of steps at which point you might use ai to do
one of those steps so it might be a prompt that
formats some data or It might be AI -generated
video gets created to respond to somebody who's
come on the website and filled out a form and
they get this HeyGen video. And they are agentic
workflows. They are created by humans and built
by humans. And what we're doing is effectively
telling the AI how to respond within certain
steps. And these are being posited by or positioned,
should I say, as agents. quite a lot and and
i can see the argument for that and i just think
they're workflows with an ai step but we don't
need to get into the semantics of that um but
then we've got the kind of uh i don't really
know the word for it but the kind of true agents
which are goal oriented and you give them a task
and they go off and execute it they do the planning
they can overcome obstacles so if they hit a
problem they can see what the problem is and
then try to find a way around that problem and
they can just run in the background come back
and deliver you the final output clawed code
is a fantastic example of this from anthropic
this is primarily for developers but it runs
in terminal or in your ide of choice it can work
in vs code and others and you can just ask it
to go and create something and there's a plan
mode where it writes out its plan for your brief
and then you tell it to go and do it and watching
it work is fascinating because you can see it
type out the the things that it's doing and then
when it hits a an error code it looks at the
error code and it says hmm it looks like the
server isn't responding i'm now going to try
and update this and running through all of the
steps in the background and You know, at the
moment, these kind of models can run for limited
periods of time. But I think the exciting thing
is when we start getting into a world where he's
on 24 hours a day, seven days a week, you're
always on agentic co -workers. Yeah, I completely
agree. The sort of Zapier make style. agents
are not really agents the humans planning all
of the logic there and leveraging ai to for its
language abilities really to write something
or to analyze some text um i still think there
is the opportunity to build in routing options
where if the llm says so let's say you get customer
reviews and it processes customer reviews and
analyzes the customer reviews for sentiment and
then gives a report back on whether it's positive
or negative. And then there's separate routing
in the agent that then will, the email back to
that person will have certain text in it based
on that classification. But the human's coming
up with the plan. And these things look like
crazy spider webs, right, of like nodes, like
line, decision point, action, line, decision
point, action. etc etc um and the impressive
ones are incredible we build a lot of them at
supreme group and some of ours look like the
zoomed out view is like um chaos theory in front
like it's like oh if i zoom out far enough it
starts to turn into this beautiful swirl um and
it's really cool and i think it's the closest
that we've got to building in some deterministic
nature into these agents right where you like
i don't want to trust the ai here to not hallucinate
i'm going to make it make a decision based on
the framework that i've built but um being able
to use agents that browse the web click on websites
fill in forms gather information put them in
documents i mean you use manus a lot which is
a like a computer using agent what sort of stuff
have you been doing that marketers might find
interesting in manus so minus is really interesting
it started out as it was a like you say computer
use it could browse the web but they keep adding
new features so if you're a marketer and you're
thinking well i've got to put that slide deck
together this will go away and research it will
do external research on the web or you can upload
a transcript you can upload your own files whatever
it may be and ask it to put a slide deck together
it will just do that on its own or you can tell
it quite specifically these are the slides that
i want and outline that and it will build it
for you and make that fully downloadable and
you can view it on the web it does videos it
does image generation it creates little micro
apps and micro sites and will deploy them on
the live web so i've started using this for proposal
development when i've drafted the the proposal
and i've got the document together i'm not a
great designer and everything i create looks
a bit boring in google docs So I've just started
taking that Google Doc and that's the main document.
That's the thing where all the detail is. But
the first impression that I want the client to
have is something that's a bit more user friendly.
It looks a bit smart, a bit polished, maybe some
interactive elements. So I give the proposal
to Manus and I say, turn this into an interactive
web proposal. It's got all of my brand guidelines.
It's got. links to where it can source relevant
images that I've told it about. And it builds
me a little microsite that I then send as the
proposal. So when I email the client, I'll give
them a link to that. And then I attach the PDF,
which has all of the proposal details outlined
in full. And it just does it. It just works.
It's like using ChatGPT. It's a chat based interface
and it has lots of connectors. so you can connect
it with your you can connect it with hubspot
you can connect it with zapier you can connect
it with lots of different tools right these sas
products that you're using every day you can
integrate it with those and interestingly you
can do scheduled tasks this is a big one every
morning at 9 00 am i want you to go online and
find me the top news stories in my industry and
then deliver that to me in a google doc it will
do that quite happily um they're incredibly useful
and i think when people start to think of them
as having like an office junior that you can
just go away and i need these things doing regularly
you go away and do it but then adding in all
of those connectors and tool use um gives them
so much more value yeah i i haven't played with
manas as much as i really want to i mean chachi
bt has its own agent but and in fact they released
an updated version recently um so i do need to
play with that because the old agent mode was
basically only on pro so you had to be spending
200 odd dollars a month to be able to access
it but um it's quite interesting there is a is
it the is it the metr benchmark for agents like
how long they can work autonomously and still
get a high level of success and it's on this
really interesting exponential curve where it's
doubling something like every seven months so
it's like they could operate for 15 minutes autonomously
now that's 30 minutes now it's an hour now it's
two hours and currently i think we're sort of
around is it eight hours that we're predicted
to be at i think at about this time and i think
if you follow the trend out the ai agents should
be able to work for a week by mid to end of 2026
so you know there's a huge amount of improvement
here in a ai agents being able to conduct multi
-step processes and produce a high quality outcome
at the end so i definitely want to spend a bit
more time playing with manas because i think
this is like the one of the first forays that
we'll get into this and it'll probably be baked
in to Gemini and ChatGPT and as these things
mature you know in two years ago we'll be like
would you remember that tool Manus unfortunately
for the folks at Manus because of course if you've
got a Google account you're just going to want
to rely on the one tool you've got yeah yeah
the interesting thing on the subscriptions I
remember when ChatGPT came out with you know
they've got Plus and Teams and then Pro at $200
a month And there was talk, do you remember that
report came out and it said they're internally
talking about subscriptions that cost $2 ,000
a month or even up to $20 ,000 a month. And,
you know, a year ago when you'd hear that, you'd
think, wow, that's ambitious. But you suddenly
start to see the value. When you have a chat
GPT agent that's goal -driven, it can plan, it
can overcome obstacles. give you final outputs
if it works around the clock and it's two thousand
dollars a month a lot of companies are going
to go for that it is interesting that we were
told about that over a year ago and we haven't
seen any of those types of subscriptions emerge
which does tell me that they haven't been able
to get the agents to a point where they can reliably
deliver economically viable work like high quality
work like you say like delegating to a junior
consistently enough that somebody would pay two
thousand dollars a month and go yeah i'm getting
massive roi on this um probably again because
we've seen so much progress in coding for them
as software developers you know creating your
own recursive improvement engine for your ai
infrastructure and your models and your products
is probably much more important than, you know,
a reliable tool that can write and send emails
out to segments of your database. But obviously
they will come for that work eventually. But
the fact that, yeah, we don't have a $2 ,000
a month subscription tells me it's because they
can't do it yet. Or maybe it's a compute thing
because there's not enough compute as well for
a lot of this stuff. So maybe when these massive
data centers come online, they'll unleash all
these agents because they'll be like, oh yeah,
they always work really well. But the amount
of compute they gobbled up was just made them
completely, you know, they're just not feasible
to use. So, yeah, interesting. I want to talk
a little bit as part of this section about MCP,
Model Context Protocol, because I know this is
something else that you've been playing with
as part of some of your experiments with Claude.
And there's another podcast I listen to where
they do some cool stuff with MCP. So can you
briefly explain, Martin, to people what MCP is
and how you've been able to? sort of access it
and start using bits of the sort of infrastructure
associated with mcps in the simplest form mcp
is a way of giving access to a tool such as hubspot
your crm and saying to the model here's all the
things you can do on this platform and you can
it's basically opening up the api so saying you
can read the information on this platform you
can edit the information you can delete you can
create new things and lots of different software
providers have opened up their tooling through
mcps and mcps can connect as a standard industry
-wide protocol now you can connect them to different
different large language models i use them through
the claude desktop app mostly i've also connected
claude code as well but primarily and where i
saw the biggest gains was creating an mcp that
connects my hubspot account and client hubspot
accounts to claude desktop which can then read
write and can edit according to the api rules
that i allow it to and the scopes that i define
and the first win that i had with it was when
i had claude being able to access all of my google
suite so google drive gmail everywhere that i
do my my work it can read all of that content
and then in hubspot i gave it full scopes so
it could do everything and i hadn't really been
staying on top of my you know my my sales administration
pipeline management wasn't really where it should
have been so staying on top of deals keeping
notes up to date stuff like that would have fallen
behind and i just asked chat gpt to sorry claude
to read all of my pipeline identified deals that
had kind of been sitting in pipeline stages for
a long time and then to go and read my emails
and find if there was any updates. Go and check
Google Drive, see if there was any updates. And
I had seven deals that had gone a bit stale as
far as HubSpot was concerned. And it went through,
it read all of my emails, it read the Google
Drive, came back and said, it looks like these
things need updating, and it outlined the seven
deals. And I said, great, make those updates.
And in one shot with zero errors, it updated
my whole deal pipeline, moving deals to closed
one, closed last, moving it to quoted, updating
the notes, changing the amounts. It did the whole
thing in, well, from start to finish, it took
minutes. And there were a few bits where I had
to give it a bit of extra input because it would
say I couldn't find anything about this deal
and I would just... write the notes about what
needed to be updated but it did my whole pipeline
and that now is my my kind of go -to way of managing
my sales pipeline i just use claw to kind of
manage that and stay on top of that um day to
day so you're using you are genuinely using that
not as a fun thing that you set up because you
were like oh i wonder how well this will work
but that is actually part of your workflow now
100 absolutely it's a that regular but it's the
only way that i really update my because it makes
better notes that's the thing you know how you
go into a crm and my crm is basically just me
uh that needs to access it but the amount of
times that i go to a client and i'm working on
their their pipeline and the deal structure and
we click into a deal and you see some notes and
the notes are terrible the call notes are incomplete
it's like what was discussed there's nothing
there well with claude it can read the emails
it can read the transcript because i recorded
the meeting in google meet which was linked to
my calendar and it can now go off and read the
transcript so it it captures all of that leaves
really robust notes and writes really good next
steps do you know what martin we won't do it
today because we're trying to do a whistle stop
tour but maybe in the next episode that's right
listeners this isn't a one -off we're bringing
it back properly uh in the next episode Maybe
we can do a bit of a deep dive on MCP because
I think it's one of those things that you've
got to download code and you have to like give
it access to your computer and you're like, oh
no, I'm scared of that. And how do I set up the
config files? Like it's not simple to do, but
a little bit of knowledge goes a long way. So
maybe you can just talk us through on the next
episode, how you set it up. We talk a little
bit more about how to get connectors working
because there are some fantastic things you could
do. I mean, if you're listening to this, you're
probably thinking, how easy it would be to semi
-automate elements of your customer support workflow
like this, right? You get a support ticket in,
analyze the support ticket, check in with the
human. This is the approach I'm going to take.
Send the email, make an update in the CRM as
to what message was sent. Like there's so many
interesting things you could do here. And my
sense is this is probably not wasted time. Because
as MCP becomes more prevalent and you want to
give agents access to more and more tools, what's
going to be important is the use cases that you've
defined and how you want the tools to interact
to get the results that you want. And I'm guessing
the prompts, some of this is still very prompt
driven in terms of being quite explicit about
what the goal is. how information should be used,
etc. So yeah, maybe we make that part of a future
episode. Yeah, I'll start off by saying my experience
of configuring MCP is one of constant frustration.
But when it works and when it is set up, it's
great. I was excited there, Martin. Then you're
like, oh, there's a but. There's always a but.
There is. That's a nice segue to the last part
of today's podcast, which is... We wanted to
discuss a bit the state of play for AI for marketers,
right? If you've listened to our previous episodes,
you'll know that we have been always testing
tools to try and be at the cutting edge of what
they can do to speed up work, make it better
quality for us, but also where they break and
can't be relied upon yet. We heard from Martin
there that NCPs are cool when they work, but
they're really hard to set up. And so your expectations
should be managed, I think, in a huge amount
of areas. it's still an expectations need to
be managed um scenario which is i think maybe
a a reflection on us as humans really martin
we're so like if you'd have told me two years
ago you were going to have this app on your computer
that would allow you to like engage in natural
language with all your other apps and then it
would go do a load of work for you i'd have been
like science fiction and then chat gpt came out
and everything gets normalized very quickly but
um let's start with content creation and writing
this is something that i do a lot of in my role
at supreme group and i'm testing tools constantly
and we talked about it a little bit earlier martin
how good is ai at writing stuff and i'm still
a bit disappointed if i'm honest and there was
a report out recently i think it was in the harford
business review about the concept of work slop
so People using AI to create documents, memos,
maybe blog posts, drafts, presentations, and
sending it to colleagues, but for whatever reason,
not really paying enough attention to the outputs
and improving the outputs. And then the poor
colleague has to spend hours fixing what they
call the AI work slop. And I think this is very
indicative of where the AI tools still are when
it comes to writing and content creation. They
are amazing at writing sensible sounding sentences
that mean nothing. Like genuinely. Even with
a very prescriptive brief, the style guide we
use in our custom GPTs is like 30 bullets long.
Like it does not leave much to chance. It's incredibly
prescriptive about the importance of the logical
progression of one statement to the next. how,
even how a paragraph should be structured to
build a logical argument, even from within a
paragraph and from paragraph to paragraph. And
it is very good at saying, if we look at this
thing A and this thing B, then we obviously get
C and it's like A and B are not related. And
even if they were, they would not generate C.
But the way it's written just sounds so darn
plausible that if you're not paying attention,
you just let it go. So I think. That's where
we are with writing in the moment. I have two
custom GPTs. I have a writing GPT and I have
an editor GPT, which has even more bullet points
that it has to follow, that basically massively
pulls to pieces everything the writing GPT produced
so that we can then go back and improve the output,
mostly to improve the logical flow of the arguments
that it's making and to stop it making these
fluffy, sexy sounding statements that don't mean
anything. And that kind of works, but still needs
a lot of human intervention. So that's where
we are on writing. And if you've suffered with
work slop, like either you've accidentally produced
some or you've been sent some, I think it's because
for very busy people, this stuff sounds so plausible
that it's very easy to copy and paste into a
Slack message and email a document and send it
on. And it is probably more important than ever.
to critically evaluate AI outputs, probably more
so than it even was a year ago, because AI has
got that much better at sounding plausible. But
you have to interrogate every sentence and every
paragraph, honestly. So you have to be willing
to invest the time and energy to do that. The
other thing is, I still believe you very much
have to have the expertise and experience to
know what good looks like. And I mean that from
a writing quality perspective. Like if you...
I'm lucky. I've spent the last 15 years training
marketing copywriters and I was very well trained
myself, fortunately, by the people I worked with
early in my career. So I feel like I've got a
good sense for what good looks like. But if I
didn't, how would I know that this stuff didn't
make sense, didn't build a logical argument,
wasn't a persuasive, compelling story, didn't
keep the audience at the forefront, was connecting
points together that should never be connected
because it doesn't make any sense. It's one of
those things that I still feel like it's probably
best applied in the hands of an expert who can
tell whether or not the outputs are any good.
And that restricts its usability because you
can't just, you know, hire a 21 year old, give
them chat GPT and say, right, now you can do
all my content marketing because you're just
not going to get good stuff out of it. No, it
can make experts faster and help them work more
efficiently and improve. sort of their outputs
because they can spot the errors and fix them
quicker and what have you but also if you put
it in the hands of someone that doesn't know
particularly around high technical topics because
and there is still the risk of hallucination
as well i had a deep research brief that i asked
open ai to to put together related to the sport
of power lifting recently and it came back with
a number which was a related to a competition
score that it said was relevant to a particular
age category in a particular group and at first
glance the whole document read really well and
i came away feeling quite informed but upon further
inspection these there were two numbers in it
and they were wrong it had misread the pdf everything
else in it was more or less true as far as i
could see um I didn't scrutinize it by line by
line, but I did spot these two things that were
wrong and thus made the whole thing irrelevant
or incorrect at quite a significant level. If
you were to hand this off to someone and say,
here, read this, that would completely have shifted
their perspective on what was required in competition,
which was the main thrust of the document, right?
it's right you're right in what you say i think
the it's very good for doing a lot of the legwork
but it's you cannot hand off entirely and outsource
all of your thinking all of your scrutiny and
you've still got to be on it yeah it's paul reuter
on the um i think their podcast is called the
ai the artificial intelligence show now because
i think they changed the name of it but they
talk about this massive missing verification
step like ai can help us produce stuff at scale
like massive volumes of it but the human time
and effort and expertise required to verify the
outputs is still very real yeah in my experience
and we'll maybe we talk a little bit about some
of the stuff we've been playing with with um
vibe coding and stuff later but um yeah finding
a way to verify the outputs is the new bottleneck
because I don't think the AI tools have got meaningfully
better. Like, I really don't. Even for GPT -5
being better at not hallucinating and not making
stuff up, it still strings arguments together
that are not logical. And so, yeah, we need to
do something there. And, yeah, if you're producing
three blog posts, five blog posts a week, and
you're a small team, I can almost guarantee you
that... there's crap in those because the humans
you'd max out your human capability to verify
and and edit them and make sure they're good
because that's still the current state of play
for content in my mind but um imaging's an area
where things have perhaps been a bit more interesting
and got a bit better what your thoughts on the
state of play and imaging mine well the fingers
are fixed so that's nice we now have five fingers
fairly consistently imaging has come along incredibly
i think when dali 3 came out in chat gpt text
as a as a you know written form on on images
was was more or less solved for the most part
and that was good but then there was a weird
stylistic thing um then 4o had image um kind
of native image generation and you could upload
images and get it to to change aspects of that
image and an example i've always liked in kind
of personal uses is taking a photo of a room
and then asking it to recreate that room with
different uh paint or wallpaper or you know different
sofa or whatever it may be and it was it was
very good for that so you've got this kind of
consistency across the models then you've got
model providers like mid journey whose aesthetic
styling is just i still think that's unmatched
if i'm if i'm honest i think their image quality
from midgen is still in very very good but struggles
with things like image editing and text in image
generation and then more recently we've had the
launch of nano banana from google which is just
superb i think that model is fantastic you can
upload your image and ask it natural language
just change this element or make the character
face a different direction and it keeps this
character consistency incredibly accurately some
of the the outputs that i've seen from that have
been really i mean if you haven't played with
it yet go to aistudio .google .com and just have
a play but from a use case perspective from a
marketing use case being able to create mock
-ups or even final outputs, if you're so inclined,
has never been easier. I've got one client that
they provide festive lighting for events and
decorative lighting for events. And people will
send them photos of a space. It might be a shopping
center or something like that. And they'll say,
this is the space we want. We want this with
some Christmas lighting. What could it look like?
Now, it used to be that they would send this
off to a designer who, you know, the design team
was typically stretched. They would create their
mock -ups. There might be a seven -day turnaround
in creating that mock -up to get it to send back
to the client for a quote alongside, oh, here's
what we think it could look like. They tried
it with GPT -4 .0 with the native image generation,
and it did a decent job, but they might have
to spend an hour going back and forth iterating
restarting the process because the model gets
a bit lost now with nano banana they can often
one shot it which means from writing the prompt
to hitting enter it takes about 13 seconds and
they the the difference in quality and speed
of production in service delivery for the client
well unmatched really it's incomparable yeah
i think for your average marketer the the major
improvement with nano banana it's not great at
image generation per se i think you're right
like if i was starting from scratch i'd still
go for mid journey um but or maybe check gpt
4o but um not 4o but now gpt 5 which is probably
still using the underlying 4o image model but
um it's the stealability of nano banana it's
being able to give it a picture of yourself and
and asking for you to be in different clothes
um and they're looking almost perfect um which
is unbelievable it's being able to give it a
picture of a product and then ask it to show
that product in different settings and the product
image that you gave it stays the same right this
was critical like maintaining that consistency
was nigh on impossible and without it you can't
really do anything interesting um i think these
things will only get better and ultimately you
and i are not designers martin we're we're marketers
who want to do quick things with images that
usually we'd have to outsource to a design freelancer
or user and internal design team but then there's
back and forth and maybe the design team's busy
and what have you so being able to just create
those images and have them be high quality and
not have to go through that process that might
mean cost and time is extremely valuable but
we can't speak to you know how useful these things
are in adobe photoshop for example i mean a lot
of people calling nano banana the photoshop killer
which is nonsense right like if you know how
to use photoshop well there are incredible things
that you can do with fine precision that you
just cannot do in nano banana or any other ai
model yet and at the moment they still can't
output in layers which is the critical piece
for most designers to be able to break that image
down and so that you can manipulate specific
elements in the image however you want to do
but But for your average marketer, it's great.
So much of this is about the application and
the use case of the image at the end of the day.
If you've got a social media post which has an
expected lifespan of about three hours, you don't
want to give it to a designer who's going to
spend a day putting it together. I had a client
that took an image of a warehouse. They work
in logistics industry. They wanted to do a cutaway.
now normally this would have taken ad modelers
3d designers that have had to have renders but
instead they took a drone photo of a warehouse
that they had in the library put it into nano
banana and said create an image of this with
a cutaway showing the racking inside the warehouse
with a forklift truck and change the weather
so that it's um i think it was like a gloomy
day so they wanted it a bit brighter a bit sunnier
it did it in 13 seconds and they said that was
going to be used on their next e -shot and it
displayed what they needed you know it wasn't
showing their particular product they hadn't
done it for that but it was just showing a cutaway
of a warehouse and the logistics industry and
it did a really nice example and it was good
to go in in under a minute yeah i think this
leads us on to You know, images have got better
and therefore, as you might expect, video has
as well. You know, lots of images shown, 24 images
a second. But there's been some really impressive
leaps with Google's Veo 3 model and now Sora
2 from OpenAI released this week. Kling has got
a new video model as well. a lot of these video
models now generate the sound to go with the
videos and i've started to see people with saura
2 trying to create like anime cartoons that could
be like five minutes long you only get 15 second
outputs so you're certainly going to burn some
some credits or uh you're gonna have to have
multiple accounts probably to be able to do that
in any reasonable time but um what's what's your
thought on the progress in video models fantastic
and specifically the well the launch of sora
this week was interesting because of one feature
in particular i think and that's the cameo feature
and that's the ability to insert yourself or
an object could be you it could be your dog into
a scene that it has generated and this has created
a bunch of viral videos of sam altman in various
scenarios i don't know if you saw the mock -up
of them Someone had done Sam Altman stealing
GPUs in Target and a bunch of other silly scenarios.
But that's a really powerful thing. That's leading
to them creating their own Zora social network,
which is currently available only in the US and
Canada. But that's what they're looking at this
being a content generator. Again, going back
to the attention economy. How can you as a brand?
think about inserting your products into ai generated
content that people are consuming on a on a feed
much like instagram what are the opportunities
there i think that's going to be something that
we can explore in a future episode for sure um
but yeah the quality of the outputs are amazing
vo3 or i think that's the sound the first time
i heard or generated something with the sound
that is one of those holy where you just go,
oh, wow, this is pretty good. And again, as with
all of these things, it's the worst it's ever
going to be from this point on. So fast forward
18 months and wow, we're in a whole new world.
Put that into the hands of people that think
in video, video production people. I don't, you
know, I'm more of a text -based person. But if
that's the way that you think creatively, It's
a huge unlock and it's an amazing tool for you
to be able to express and create a new dimension,
so to speak. I think that's the big part of it,
isn't it? It's the creativity that it unleashes.
I mean, there's a bunch of aspects to this. So
the first one is OpenAI were a bit criticized
for taking the powerful models they've developed
and then launching a social media app. so that
people can create ai slop and send it to each
other and that that would be a good use of humans
ingenuity and time on this planet um which i
understand as a criticism it's a bit meta -like
in terms of like doing it but he in fairness
sam altman posted and i'm not going to quote
this directly because i don't have it in front
me but in essence he put on twitter stroke x
that he completely understands the point that
people are making because i think one of the
posts was like You promised us AGI, you promised
us an end to disease, and you gave us an AI slop
social app. And his response was basically, yeah,
because building those systems, those data centers
that can cure cancer costs lots of money. And
we have to have a mechanism of making money.
And ultimately, I think Gemini went to number
one on the app charts. on ios and it was because
everybody wanted down a banana because it was
fun to be able to like take a picture of your
friend and then put them in strange scenarios
and it would and it would produce that output
for you and basically sora 2 is the video version
of that with its cameo feature and you can see
why that is probably going to be really popular
and i doubt they'll be able to there'll be nowhere
near able to roll this out fast enough for the
masses that want to get in on the act so there's
sort of a you know social media dopamine hits
we talked about it a bit at the beginning i'm
a bit sad about all of that but it's kind of
how humans operate and our phones and these tech
companies have certainly learned how to keep
our attentions on these platforms there's a reason
that people doom scroll tiktok like clearly there's
something about this stuff that humans like we
might not like it and we may feel that we've
been hacked um our brains have been hacked to
make us do it but it works um and so people are
going to do it because it because it works but
When Veo 3 came out, and maybe this is going
to be easier in like B2C than B2B, but some people
did some really interesting things. Did you,
there were quite a lot of sort of fake, like
influencer type point of view type videos. And
there was, there's one of a, I think it's of
a gorilla and it was created by a dentist to
promote his private dentistry in the States.
And he's got this gorilla like. jumping out of
airplanes and doing all of this quite funny stuff
and it you know it's ai obviously but it but
it looks really good and the delivery is mostly
pretty good of the scripts that were that it
was fed um and you have to take your hat off
to it and say this is great creativity this is
how you go viral this is how you you know i suspect
um they've got more business than they can handle
now and so to your point as a brand as these
tools get better and better What creative, interesting
ideas can you use them for to promote your products
and brand in a way that's in line with your brand
values, right? Like not everybody can do a funny
gorilla video that you would never have bothered
to do if it was going to take three months and
cost $200 ,000. But if it takes a day and costs
$10 in Sora to API credits, assuming maybe we
get access to models like this, and you can access
VO3. feo3 via the api um what can you do now
right i think that's the question we should all
be asking ourselves because there's so much text
-based slop out there um and we are visual auditory
creatures and we're going to engage much more
with video than we are text anyway in my opinion
especially on on social if you're looking to
get attention so one of the things i really hope
that we're able to do in our team is to spend
some time thinking of what those creative ideas
are because that's the hard part I think prompting
the tools to get the output you want is probably
pretty difficult and takes a bit of trial and
error as well. But it's the creativity, it's
the ideation that's the bottleneck now. People
haven't played with any of these tools yet. Another
one to go check out is Blow. And this is from
Google. It uses their image and video generation
models, but it allows you to combine together
into a full... video clip using the kind of eight
to 15 second videos you generate into a storyboard
and combine them together it's a lot of fun to
play with and i've really enjoyed that for creative
concepting yeah i think i haven't played with
that but i'm definitely going to go and have
a go with it um another thing that's sort of
tangentially video related that we've been using
a fair bit in supreme group for some tests Not
only client work, I work in the marketing team
for Supreme Group. So all of the applications
I'm talking about are within our business, not
for clients. But it's the power of video avatars
and just how realistic they're getting. A colleague
of mine, Jake, was playing with a potential use
case that cloned a video likeness of him analyzing
a company's website. And it was basically a...
fairly realistic video of him explaining issues
with a website based on a automation built in
Zapier. And we're nearly there. I think most
people would still know that it wasn't the real
Jake, but they're now able to better clone your
mannerisms in terms of how you move and speak.
They're better at delivering the scripts in a
more natural way. And I think most people would
see it was AI, but... We're probably only one
or two more versions away, I think, before using
AI avatars at scale for things like prospecting
email outreach is very viable in a way that people
might not be able to tell the difference between
a person and the AI version of them. So that's
pretty cool. Do you want an AI version of you,
Martin? We've got several in various guises,
mostly through HeyGen, I must admit. there's
a great unlock i think from a sales perspective
but it leads to this ai slop debate in my head
i can't i can't get past that as cool as these
things are any application that just allows you
to kind of mass spread more content with very
little discernment as to each individual item
produced makes me kind of wince a little bit
you're absolutely right we've talked about this
a hundred times but that's still why in the future
of AI marketers in the world that we live in,
having a trusted brand, not just a brand in terms
of your products and services, but your content
and your knowledge and the thought leadership
that you can share. Because people are not going
to be able to scroll through and curate good
content out of all of the crap that we're all
going to be drowning in. They're going to be
like, I just need three or four sources that
I trust. because they just can't be bothered
to go fishing through through everything else
but um that we had one more section we were going
to do today but it's already a pretty long podcast
so i think we tease folks mine and we let them
know this is what we're going to go into next
week um which is we're going to talk about all
of the trends affecting marketers the most today
like the rise of generative search the release
of chat gpt shopping the impact of vibe coding
for marketers but Let's do that next week, Martin,
otherwise we'll have kept people too long. Sounds
great. I've got plenty to say on all of those
topics. So, you know, do tune in. We're back.
Well, as always, thank you for your time and
your thoughts and your energy, Martin. Really
appreciate it. I hope you've, dear listeners,
enjoyed the return for the podcast and you've
already had a little teaser of what we'll be
bringing you next week. Have a great week, Martin,
and I'll speak to you soon. Thank you for listening
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