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,
everyone. Welcome to Artificially Intelligent
Marketing. Paul Avery here, as usual, joined
by the fantabulous Martin Broadhurst. Martin,
how are you, sir? I'm great. Unlike the weather
outside, which is gloomy, miserable, very, very
December. Generally speaking, I'm good. The weather
is frightful, but the podcast is extremely delightful.
So everyone's a winner. We've got some really
interesting stuff to talk about this week. Last
couple of weeks have been crazy. So what have
we got on the docket today? A few new models
have been released. You might have slept on this,
Paul. I know you've been very busy, but Google
have released Gemini 3 Pro and the exciting Nano
Banana Pro. Anthropic responded with their own
release of Opus 4 .5. Away from model releases,
we've had... Gartner releasing their 2026 strategic
predictions, which you'll be unsurprised to hear
contains some AI mentions. I know I was shocked.
Google have started rolling out ads in AI overviews,
and there's some rumors flying around that chat
GPT might be looking at doing something similar
in their system. And Adobe has purchased. semrush
lots going on then lots going on let's get into
that first one i mean it's been it's been the
model week isn't it gemini 3 came out and we
started playing with it instantly and we were
like oh this is decent all of the evals look
very good but as we're finding mostly in our
own work you have to test your own use cases
you can't massively trust too many uvals at this
point at least in my experience um and then yeah
opus 4 .5 comes straight out and there was who
is the who is winning and then gpt 5 .1 as well
so lots going on take us through what google's
been up to so google was sitting at the top of
the leaderboards with gemini 2 .5 for a long
time and then there were some rumors flying around
that three would be coming out certainly the
uh the x .com discussions and reddit boards were
awash with speculation and then yeah they launched
a new model gemini 3 pro which we think will
be followed by gemini 3 flash the lightweight
version the kind of not reasoning model soon
uh but yeah it came out and smashed the leaderboards
it was a real leap as well i think sometimes
there's been so little between the the state
-of -the -art models the frontier models it's
been a few points here or there that when this
came out on some of the the leaderboards it was
a significant step forward um on leaderboards
that were quite saturated or on ones that are
have a long way to go they were really pushing
things forward lm arena they were smashing the
competition so yeah it's certainly been well
received my initial play with the models has
been has been very good both in the in the api
and the uh gemini chat interface i've really
enjoyed putting some challenging scenarios in
it there's been things like uh trying to design
a complex power automate flow which is the kind
of workflow that their models generally aren't
very good at working with the only time i've
had any decent responses is from chat gpt 5 pro
um gemini 2 .5 wasn't really very good at understanding
some of the the mechanics of power automate whereas
i found with gemini 3 pro it did an excellent
job of getting into the detail of how to automate
a particular workflow Yeah, I mean, if we take
a look at some of the evals, the Humanity's last
exam, Gemini 3 Pro with code execution got 45
.8 % and the previous high was 26 .5, which was
GPT 5 .1. So that's a pretty big leap. Another
one that jumped out to me was an eval called
ScreenSpot Pro, which is basically AI's ability
to understand a computer screen. The previous
high on that was Claude Sonnet at 36%. Gemini
3 does 72 % on that eval. That's important, right,
for computer use by agents. So that's kind of
interesting. The Vending Bench 2 eval, it has
the highest score so far. So for listeners that
are not familiar with this, basically the AI
is... briefed to run a vending machine business
and it has to like buy supplies, control pricing,
deal with customer complaints and stuff like
this and basically try and make money. So it
nearly, probably not doubled, but maybe 50 %
greater than the previous benchmark score. And
that one's really interesting because it's much
more closer to the world of real work, right,
rather than some of the other evals which maybe
get a bit esoteric. The criticism of all the
evals is that once you know how the evals work,
you can overfit the model in post -training.
So again, you have to take a lot of this stuff
with a pinch of salt perhaps, but it is interesting
to see them massively increase. And I was listening
to the Moonshots podcast with Peter Diamandis
-Martin and the team on the podcast were talking
about how we are drifting to a world where the
evals that are going to be the most meaningful
are really business outcome oriented. measured
more in dollars like vending bench than in particular
scores or percentages like completion percentages
so definitely better my own personal experience
has been it seems more thoughtful and knowledgeable
to interact with it's much better at self -critique
its writing quality is a slight improvement although
i'm still not absolutely convinced that the models
are getting much better at writing the We'll
talk about Opus in a minute, but that's probably
the best one I've seen so far. And then GPT -5
.1 is better than GPT -5. So there's a bunch
of nuances in there. But is there anything you've
been doing with Gemini Feed particularly that
you're getting good results on? Yeah, there was
one particular project that I'd been struggling
with, needed a front end that tapped into a few
government APIs for property data. things like
planning information, zoning, things like that.
And I tried a few different tools. They took
me 90 % of the way there, but there were some
nuances in the way that things would render on
the map. But Google Gemini one -shotted it. It
was really powerful. It used the same prompt
that I'd used elsewhere. and quite literally
it one -shotted the output so yeah i was very
impressed to see to see that improvement in the
model been very good at spinning up front ends
isn't it we've talked previously about for marketers
moving beyond content as lead magnets and thinking
about tools that you might build for your for
your target audience that you would not have
spent money on a developer on but if an ai can
spin it up so as these things mature and improve
and Gemini 3 is definitely one to try if you've
been thinking about building tools and the other
AIs have not been quite good enough or you haven't
had confidence because you thought you'd have
to get in and like wrangle the code yourself
it's definitely worth playing with Gemini 3 from
that perspective the other thing I've been testing
this weekend is I've been running quite a lot
of deep research reports um and it's been interesting
in the past I've kind of got similar things like
if I've run against like GPT 4 .0 against Gemini
2 .5 I'd get a report that was by and large talking
about the same stuff. But they started to diverge
a bit, which is interesting. I ran Gemini 3 Pro
against GPT 5 .1 Deep Thinking this weekend.
And somewhat surprisingly to me, 5 .1 was better
than Gemini Pro in terms of clarity of expression,
depth of ideas, number of sources cited. Like
it really did seem to do a much better job. So
yeah, it's... I really thought when Gemini 3
came out, it would just become my obvious go
-to. But I'm not so sure that's going to turn
out to be the case, if I'm honest. I think I'm
still going to use GPT 5 .1 and other models
for stuff. That chimes with my general experience
with the Gemini chat experience and chat GPT.
I still find, despite model improvements across
the board, my daily driver... The app that is
open on my desktop at all times is ChatGPT. They
have just got the user experience down in a way
that Google Gemini just doesn't quite have refined
yet. But interestingly, Logan Kirkpatrick from
the team has said that they are doing a big 2
.0 relaunch of the UX coming soon. Yeah, it'll
be interesting to see. what that entails i mean
one of the cool things about gemini is they're
starting to play with spinning up like dashboards
and interactive experiences as part of the answer
we've talked previously and i think it's pretty
well discussed in the industry at this point
that ux on demand is probably going to be how
AIs work. Like sometimes it will give you an
answer in text, if that's the most appropriate,
but maybe it will create images. Interesting
thing is Gemini 3 is much better. It goes out
on the web and it tries to find images that will
support its point and it intersperses them in
the answer. And for me, on a couple of occasions,
it even created images, which was super interesting.
And they actually worked. And I'll give you an
example. I think the advice turned out to suck,
but I was... it's time for us to buy a new mattress.
So I've been using Gemini 3 to help me find this
new mattress. So I bought a new mattress, but
the delivery time is six weeks and I'm getting
a bad back from the current mattress. So Gemini
3 gave me a bunch of recommendations about what
I should do. One of them was to put a yoga mat
under the dip in the mattress, which I think
is actually terrible advice, but there's a number
of different ways you could orientate that. And
it wasn't quite clear to me. So I was like, can
you draw an image that demonstrates this? came
up with a beautiful image that absolutely um
made it clear what its advice was and that's
a new capability as far as i'm aware but this
probably ties into probably potentially even
more exciting part of gemini release which is
the improvements to nano banana image gen model
have you um had a chance to play with that i
have so this is quite a step on from nano banana
this is again it's a anyone that isn't aware
of the differences google has imagine or imaging
which is a diffusion based image generation model
the clarity of those images is very good but
it isn't baked into isn't native within the large
language model gemini itself and that meant that
getting edits of a design is quite tricky so
every time you created an image with it it was
creating a brand new image whereas with nano
banana when they launched that within 2 .5 in
the previous release suddenly we could upload
images and get very detailed very accurate edits
created to images well now they're talking about
nano banana pro being a foundational image model
which has almost a world model baked inside it
so it understands so much more context about
the image so you can ask it to do tasks not just
like you know add a hat to this dog or something
silly like that but you can actually ask it to
isolate elements of the image or to mask certain
parts of the image to turn the image into a line
drawing you can ask it to you can even use it
within video where you can put an image of say
a a mouse stuck in a in a maze and ask it to
animate the mouse running through the maze and
it will accurately one shot it and this when
it's linked with or nano banana pro when linked
with something like vo3 opens up a whole world
of amazing possibilities with visual work it's
not just creating things but explaining solving
um editing reasoning that there's so many possibilities
that are unlocked with it i think the system
card for this is quite illustrative and it's
got some bits in it that i feels like a tipping
point so the first thing you can now provide
i think it's up to five characters as prompt
images so i could put you know pictures of you
and me and a couple other agency gang from the
HubSpot inbound days and asked Nanobananas to
start creating a bunch of unlikely scenarios
for us to be in. And rather than maintaining
character consistency for just one person, you
could do it for more, which is really interesting.
It can also do the same with up to 14 objects,
which again seems a very bizarre number. Like
15, oh, it's going to break. 13, safe. 14, on
the edge. But again, if I wanted to control that
scene even more, I could actually... add a load
of objects to the scene and then it would put
them all in the right places, which is pretty
astounding. And I think what we may see, I suspect
this won't be the model, but the next one will
be, is a genuine image creation capability where
if you're a marketer and you need to do a bunch
of product images in different settings, or you're
even trying to come up with like brief videos
for those, because obviously you can use the
image generation capabilities and then you can
ask Veo to... turn those into video for you,
is we can, as marketers, actually start to now
create product imagery, right? Give it a product
shot and then use it as you see fit. Put it on
the moon, give it wings flying in space, like,
you know, whatever you can imagine, really. And
up until now, that's been a bit hit and miss.
But this model feels like the one that's more
consistently capable. So are we drifting, therefore,
to a... post canva adobe world probably not for
specialists people who are really good at creating
images are probably still going to create things
much better than you can just do in nano banana
but yeah if you're just creating quick product
imagery i wonder you know and you could probably
semi -automate this with mcp or so there's probably
some quite interesting things you could do that
you just couldn't do with image models until
now there's definitely been a shift in people's
willingness to accept and to use publicly ai
generated images I'm seeing it in posters, in
shop windows. I'm seeing it for flyers, for events.
I was on an e -commerce website this weekend
and there was just something about the text that
made it very clear to me it was a GPT -4 or GPT
-5 generated image. It had that kind of softness
to it that was very recognizable. So we're certainly
seeing this more and more in the public, even
though in some domains... People are still very
anti -AI. I don't know if you saw this recently,
but there's some pushback against Steam in the
video game industry, Steam tag games if they
use AI -generated content. And some developers
are pushing back against that tag and that label.
Yeah, I think the big issue there, I read that
story, it was an interesting one. I think the
big issue is... Everybody in the games development
industry is probably using Gen AI in one way
or another. So is it even a meaningful tag? I
think that's probably quite a big issue. I thought
you were going to say the Coca -Cola TV ad, which
has been AI generated and has triggered a bit
of a backlash. It's better than last year's ad.
It still looks a bit weird in place as I saw
it on TV over the weekend. I saw somebody do
a breakdown of the different lorries that are
in different trucks and they... took the bodies
of them and turned them into a diagram. And apparently
there's 14 different truck bodies used in that.
Yeah, I looked at that and I thought, old tech.
Yeah, honestly, I don't know how long they've
been working on that as much as they've got AI.
I think I read somewhere that there was still
like ridiculously large amounts of people who
worked on that project, like drifting towards
100, which for me, I'm like, why are you using
Gen AI then? But the reality is, I think I could
spin up something better. in an hour with nano
banana 2 and vaio 3 .1 um so that just makes
me think it's old tech and next year's will probably
be quite a bit better than that there may even
be like rules on what models maybe they have
to use open source models you know coca -cola
is a big corporate and what have you um so yeah
i'm not i'm not so sure but um yeah i think you're
right i think attitudes are changing i mean my
wife was showing me stuff on instagram this weekend
And we were trying to guess how many of the videos
were AI generated. And I reckon four out of five
that we were looking at were AI generated. It
was cats doing stuff that there's no way you
get a cat to do. You know you've got a technology
acceleration when one of the first use cases
is always a cat. I mean, that's how the internet
began. Look at these funny pictures of cats.
Now we've got funny pictures of cats, but they're
AI generated. So that in itself is a bit worrying
because just... so much of a deluge of absolute
crap but like some of them are kind of funny
i saw one where the cats are um doing like work
around the house like trying to fix like electric
switches and fix cars and stuff like that it
was kind of funny but um yeah i think it's the
explosion we've been talking about is well underway
let's shift gears let's talk claude because gemini
3 was like basking in its glory for all of about
a week, probably less, because Claude had been
cooking Opus 4 .5. So quick reminder for the
audience, because none of these AI companies
make it easy to track what these different things
mean. So Anthropix Claude is obviously not as
popular a model as ChatGPT and Gemini, but still
popular, especially among coders. And they have
three flavors. They've got Haiku, Sonnet, and
Opus, which approximately... is also the scale
and size and capability of the model so haiku
is the least capable very cheap to run sonnet's
in the middle opus is a big gargantuan model
that's very expensive to run and the thing that
was super interesting this week is opus 4 .5
came out sonnet 4 .5 is already out so the much
it's the big brother version and it's actually
still quite cheap to run also compared to like
in general the opus models are very very much
more expensive to run than the sonnet models
But this one, not so much. And some people have
been loving it online. What have you seen as
it relates to Opus 4 .5, Martin? Yeah, so it
came out swinging on the benchmarks again on
a Gentic coding. So the SWE benchmark, it scored
80 .9 versus Gemini Pro's 76 .2. On a Gentic
terminal coding, it scored 59 .3 versus Gemini
Pro's 54 .2. They had graduate level reasoning.
This was an interesting one. So the GPQA diamond
test, which is PhD level research questions,
scored 87 versus Gemini Pro's 91 .9. So not quite
state of the art there. But generally speaking,
we are talking about a very, very capable model
across the board. My personal experience with
it is I'm just pleased that they've increased
the limits. One of the big criticisms with Anthropic's
policy for a subscriber, whether you are on pro
or the plus plan or whatever it is for $20 a
month, or you are on one of their max plans for
$100 or $200 per month, the limits on Opus were
laughably small. It had become a bit of a meme
that you would put in one question and that would
be you done for a month. But now they've opened
up to max plan users. They've given you the same
limits that you had for Sonnet 4 .5. And this
model is really, really smart and really good.
So I've been playing around with it for some
strategic development and it adds a bit more
finesse to Sonnet. For coding, I've only lightly
tested it at this moment using Claude Code for
a couple of projects. but looks really good really
very capable yeah i think it's quite cool i think
the thing that struck me here and it's there's
a little bit of trying to infer what the major
labs are doing my understanding stop me if i'm
wrong mine is when we see these like number increments
gpt5 becomes gpt 5 .1 is if we go from you know
gpt5 to gpt6 it's because there's been another
training run And a fundamental new model has
been created. And then it goes through all the
fine -tuning, reinforcement -based fine -tuning
and all that other stuff. And then we have our
new model. Whereas if it goes up in incrementation,
5 to 5 .1, it's because they've maybe further
fine -tuned the model or made some changes in
the back end that change its personality or how
it basically thinks and produces outputs. So
you look at Gemini 3, that one assumes is a new
training run, a new model. that was trained in
a different way, more data, whatever, and is
therefore significantly better than Gemini 2,
2 .5, whatever. Opus 4 .5 is not a new model.
And yet it has these capabilities that outstrip
Gemini 3 in a number of areas, which for me is
interesting because it suggests that the innovation
in the fine -tuning and the reinforcement -based
learning and whatever they're doing in post -training
to improve model outputs can actually have quite
a big impact on what the models can do. So I
find that quite interesting because in the beginning,
we were like, need a new model release, need
a new model release. But then obviously we got
the own models from ChatGPT, from OpenAI, which
could think they took some time before they gave
an answer. And that gave us a big leap. And there's
something, I think there's something special
in here. They've done something with Opus's ability
to think and produce good outputs that's not
based on just throwing the kitchen sink of data
at it. It's something else. Yeah, and that really
comes through in the token usage. So I don't
know if you saw this from the report. They gave
it some puzzles, a series of challenges, an escape
room challenge. And they... gave that challenge
to sonnet 4 .5 as well as opus 4 .5 and they
gave them the ability to use advanced tools so
opus 4 .5 is exceptionally good at function calling
or being able to use tools like web search or
code execution things like that and it was able
to solve the problems using far fewer tokens
i think this is really interesting for developers
or when we start to think about what this means
in real world applications within business. If
you've got long time horizon tasks that people
need to execute, you don't want it burning through
huge amounts of tokens costing you a great deal
of expense. Whereas this new model is doing exactly
what you'd want. It's optimizing. So the amount
of tokens that it was using was substantially
less so just to bring this to life software engineering
with effort controls opus 4 .5 was achieving
around 81 accuracy with around 12 000 tokens
being used whereas sonnet 4 .5 was achieving
76 77 accuracy using 22 000 tokens so it was
using double the tokens and it was less accurate
yeah i think it's i think it's cool i think this
is What makes Opus 4 .5 interesting? It's in
the nuances. There's another thing in the system
card. One of the benchmarks involves being a
customer service agent bot, like chatbot, that
solves a customer's problem. And in one of the
cases, Opus 4 .5, it failed the test because
it didn't give the outcome expected. But what
it actually did is found a really creative way
to deliver on the customer's requirements based
on the sort of knowledge base of information
it worked out how to give the customer what the
customer wanted by effectively exploiting a loophole
in how the particular service in this case it's
um changing a flight booking um so it failed
the test but because it was too creative and
it did something that was not specced that maybe
a human wouldn't have even known how to do and
that again that's the first time this type of
things come up and then there was one other thing
where Claude administers a sort of homework test
to new developers that apply to work at Anthropic.
And for the first time, Opus 4 .5 gets a higher
score on average than the humans who take that
test. And so if you're watching for things like
recursive improvement in the capability of agents,
well, here's a pretty strong indicator that Opus
4 .5 is probably at that capability, where it's
able to pass tests that a high -end developer
would probably be administered. So what else
can it do? And then the final thing I really
liked is there's an implication that Opus is
very good at managing other models. So its ability
to act as an orchestrator. And this is important,
right? We talked on a previous episode about
different trigger points for what the future
of marketing business and life looks like. And
one of those trigger points is agent models that
can manage other agent models. both in terms
of expanding their capabilities, but also then
you can create niche agents that are very good
at a certain thing, like writing social media
posts, and an orchestrator agent that's like
a strategist that knows when to deploy those
agents and can verify and validate that their
work is good. And it looks like Opus 4 .5 is
very good at delegating to Haiku 4 .5. Haiku,
very cheap, very fast to run, very low latency.
So again, one of those trigger points is potentially
been reached that we talked about on a previous
episode whereby you might have a campaign planning
agent that is opus 4 .5 and execution agents
that are being driven by haiku imagine what opus
5 and haiku 5 are going to be able to do if this
is what 4 .5 can do all of this is now available
across the major cloud platforms as well so this
was a big announcement that uh anthropic have
signed a deal with microsoft azure so it's available
on azure amazon web services and google cloud
platform for the first time interestingly anthropic
models are now available in microsoft co -pilot
as well so that's a big change if you're a co
-pilot 365 user and you've always been using
the baked in microsoft models or you've clicked
on the try gpt5 Well, now Claude has also been
unlocked and made available. However, if you
do use Anthropic, that does actually use Anthropic
servers. So I'm aware that there are some governance
issues. It does take the information outside
of your tenant at the moment. That's super interesting
because word on the street and revenue figures
would potentially back this up says it's enterprises
that are favouring Claude. especially when it
comes to coding use cases, which makes perfect
sense. And so finding more and more ways to put
their models in enterprise level software, obviously
getting it into Google Workspace is going to
be tricky. But Microsoft is the fair game route
to market because they don't have their own models.
So I think that's kind of interesting. Last bit
of model news this week was DeepSeek's open source
model. but there's like a fine -tuned version.
Is it 3 .2 they're up to? I can't remember. Anyway,
this model managed to get gold on the IMO Math
Olympiad. So it was big news a couple of months
ago that Google and OpenAI were able to achieve
gold on these really super hard math problems
that most people would find too hard to do, which
is impressive. We in the market don't have access
to those models yet. I'm sure they'll come to
us soon, but especially now there's an open source
model that can get those level of results. So
A, I expect that Google and OpenAI will need
to release access to their smarter models soon.
And B, open source models, you can see the weights,
you can further fine tune them. This is a very
smart model. for open source community to be
able to get their hands on. What are your thoughts
on this one, Mike? Just goes to show that the
frontier is moving across closed and open models.
And I think the competition is hot. Does lead
me to a thought that ultimately models are going
to be commoditized in the not too distant future.
It's going to be interesting to see how that
plays out. Definitely the moats of user base
are going to be critical. I mean, Sam Altman
issued a war footing internal memo, didn't he?
At the same time as all this happening to say,
guess what? ChatGPT and OpenAI, we have some
real hardcore competitors now and we are going
to have to, you know, really push hard to make
sure that we stay ahead of the game and stay
relevant because whatever gap there was between
OpenAI and the rest of the market appears to
be dissolving. pretty darn quickly and they are
now well and truly in the race to try and stay
at the top of the leaderboards and usability
fortunately for them they have managed to accrue
a large number of users which is going to be
critical for them to remain in but you know for
me now because gem because we're a google workspace
company and having a strong gemini model is very
very handy for me like If I had to choose, honestly,
I'd rather Gemini just gets better and better
because it's just all my data is already in Google.
It just makes my life super easy. I don't really
want another platform that can access my data
like OpenAI and ChatGPT. So, yeah, I think they're
under pressure. They are. And that was further
compounded by HSBC putting out a report this
week saying that they think by 2030. OpenAI will
still be cash flow negative, leaving a $207 billion
funding shortfall. Yeah, growth required. Revenue
growth required. But I think something that's
in the definitely active conversation is, is
this a bubble, right? Is all the money flowing
into AI going to burst? And you certainly won't
find any financial and investing advice on this
podcast. But one of the key... retorts to that
would be supply and demand and this feeling that
intelligence baked into so many areas we don't
have the data centers and the compute to serve
even the amount of intelligence we want now you
talked about earlier not being able to get access
to enough clod to meet your needs um some of
that's because compute is expensive but some
of it's because it's limited and they're having
to ring fence compute for inference which is
when we all use the model that's called inference
when it gives us outputs um but they need to
keep some model back for training some sorry
some compute back for training and i think that's
probably proving a difficult balance so when
the when when we start to look like we are meeting
demand and starting to gain a surplus in compute
i don't know that that would probably be quite
an interesting sign um do you have any thoughts
on that line leads me Well, a story that we've
got later on in the pod, really, which is about
how much work can be automated. In fact, I think
we should maybe skip ahead to this story now.
McKinsey put out a report saying that AI can
now automate 57 % of US work. And in the report,
they don't talk about it being about the eradication
of jobs. They say what's required is... A reimagining
of workflows and the kind of operations that
we do and how we do them is required. And I think
that's the key thing here. These models are currently
underutilized. Most of the productivity gains
at the moment are seen by people having chats
with the chatbot. In fact, Anthropic did a report
this week about that very thing. They found that...
They tried to estimate how much productivity
was being driven through people using chatbots.
They found that they think 80 % of workflows
are being completed by the AI or the time reduction
is an 80 % time saving where people use a chatbot
in their workflow. But that's still in the chat
UI domain, right? That's still people using the
assistant to go back and forth. Whereas... When
you build workflows where data and processes
are being fully operated by an AI, we can remove
the need for that. And we haven't got to that
point in the economy just yet. So I think that
that's where we're heading and that's where the
compute demand will come from. If they're going
to see the gap in cash flow, that 207 billion,
if that's going to be bridged. they're going
to need companies to rebuild entire workflows
and replace entire work streams with AI. Yeah,
I'm still finding this quite hard to predict
and almost to marry up what a lot of these reports
say and a lot of my own personal experience.
And I think one of the dangers is actually, you
and I have talked a lot about the early productivity
gains that we saw, right? dictating everything
rather than typing doubles my speed instantly
and it doesn't need complex model thinking models
in fact the best thing that's happened to me
in the last three or four months is having access
to nvidia's transcription model which is near
real time because now i don't have to wait for
it to output and that and i can make it quicker
and blah blah so it's there's this weird thing
about humans that we absorb these changes pretty
quickly and then we're like right what next And
if I really step back from my workflow, I would
have to really still reflect on how much faster
I'm able to do so many of my work tasks like
producing content, writing emails, just generally
communicate with my team, writing strategic plans,
thinking through those plans and looking for
gaps in them that, to be honest, I'm not sure
I would have done if I didn't have a thinking
partner to use. So it's hard to say. I mean,
in that report. The Anthropic, they predicted
that it was reducing task completion time by
80%, which is pretty significant. But at the
same time, they only predicted that they could
increase productivity to 1 .8 % annually over
the next decade, which is a long time to get
up to that point, which is double the current
productivity growth, right? But I'm sure it's
meaningful at the level of... countries and gdps
yeah macro it's substantial right but it's like
you can do this task 80 faster you can do twice
as much work in a day and so i predict productivity
growth to go from 1 to 1 .8 i don't know it feels
like something's missing in that it's probably
a maths and statistics question and like i say
i can still imagine that a country level that
is significant but um yeah i'm start i'm struggling
to reconcile all of that because at the same
time you've got reports of work slop so it's
like yeah you're 80 faster the outputs were shit
and unusable oh okay well now that's meaningless
they did refer to that in the article as well
though they did say that it didn't take into
account time spent validating the outputs from
uh from the models and important people even
doing that well what was the there's another
report that came out that we're going to talk
about as well And in part of that report, let's
move on to that now quickly. Before we do, the
other thing to, I think, has to be taken with
a pinch of salt on some of these reports is McKinsey
predicting that, what was it, 57 % of US work
could be automated with current tools alone?
Question mark. I'm not so sure about that. What
are your thoughts? Are you saying that McKinsey
isn't the oracle? isn't wholly reliable no i
think the the major consultancies they're so
enterprise focused i think these that they they
get things wrong frequently enough but they'll
just put out another report in in six months
i'm saying something new and it gives people
like us something to talk about on a podcast
i agree i think the punch line to that report
is 57 of tasks can be automated or whatever the
statement was dot dot dot And if you just contact
McKinsey, we'll help you realise those games.
Do you know what I mean? We also, Martin and
Paul also offer consulting and training to help
you realise the gains of AI. So I'm completely
on board with the concept that we can help you
automate 57 % of the work that your humans do.
I just doubt that that's going to be possible
in the near to midterm because processes are
messy. They haven't been designed for AI. Humans
being a lot of... creativity ingenuity problem
solving and experience -based understanding of
context that i just don't see in any ai models
yet that is going to have to significantly improve
before we can get anywhere near the number that
mckinsey's saying here as far as i'm concerned
but um what about this gartner report as well
got another report in the mix yeah so this one
was uh the strategic predictions for 2026 and
AI is peppered throughout, but there were just
a couple of insights that I thought were worth
bringing to the discussion table, Paul. So one
of their predictions is that we will see a surge
of lazy thinking. So the prediction is through
2026, atrophy of critical thinking skills due
to generative AI use will push 50 % of global
organizations to require AI -free skills assessments.
So basically putting people through skills assessments
where they are not allowed to use Gen AI as an
assistant or a tool. So the ability to think
independently and creatively will become both
increasingly rare and increasingly valuable.
Can we double click on that one for a moment?
Because I think this is a big deal. To stress,
this Gartner report is... a bunch of smart people
sitting in a room going if this happens then
this will happen then this will happen that's
a prediction rather than it being very like super
data based and analysis based as far as we can
tell doesn't mean that these predictions are
not interesting and valuable but it's important
context right um i think this is a thing like
i i have to work so much harder to keep my critical
thinking brain engaged now than ever before because
why not work with your thinking assistant to
help outsource some of your thinking right that's
kind of the point in many cases and yet in a
world of ai hallucinations critical thinking
has never been more important so but i see it
atrophying in myself if i'm not careful and i
think i think humans are very good at finding
a route to an outcome that requires the least
energy possible on their part i don't think it's
conscious i think it's subconscious and so i
think this is a real danger i wholeheartedly
agree and this is very real and very manifest
in one particular domain that I'm associated
with. So peer reviews of journal papers. It's
rife with people trying to prompt inject where
people have put notes in their paper submissions
to the AI reviewer to give them positive reviews.
And clearly there are people out there doing
peer reviews where they're throwing it into Claude
and saying, what do you think of this? Give feedback
on this paper. Now, I do peer reviews for the
Applied Marketing Analytics Journal, and I have
to make sure that, well, my approach to this
is I'll always read the paper first. I will formulate
my own ideas. I will write my first draft, but
I do. run it through an ai as well but i have
to make sure that i've done that after i've read
the paper and done the thinking first it's a
it's a fascinating process to go through i think
reviewing with an ai assistant um and if you
do it the other way around you're so biased to
what you've just been told like if you were to
throw it in and it says oh these are the things
that are good or bad whereas i'll bring my own
thoughts to the table and then kind of run it
through and and do a collaborative review and
going through that process enables me to go actually
no that's not a valid concern and there are a
few points that i i have i try not to to be over
reliance on it but there are definitely things
where feedback i've taken from a prompt from
a an ai and gone actually that's a that's a valid
piece of criticism or feedback that i'd overlooked
um but there's i would say 50 % of the feedback,
if it's just left to its own devices, is irrelevant
and not valid. But there must be armies of people
out there that are doing this journal reviews
and feedback that are literally just throwing
it in, becoming incredibly lazy, outsourcing
their thinking entirely. And it is a problem,
as we've discussed previously with the rise of
WorkSlop. Yeah, let's just break that down quickly
because I think it's such a critical point. And
it's almost a philosophical point, which is,
is it your job to look for gaps in the AI's thinking?
Or is it the AI's job to help you look for gaps
in your own thinking? And ultimately, the biggest
factor that I think is going to drive this is
workloads and time pressure. Now, do you know
anyone, Martin, whose workload is so easy and
their time pressure is so easy? that they're
going to take the hard route and the slow route,
or they're going to take the fast route and the
easy route. Most people are under crazy amounts
of time pressure. I mean, for me, the overwhelming
trend of the last 15 years is how much more work
can a single person do if we just keep throwing
stuff at them? It's very easy to forget what
it was like when we didn't have all of our work.
on our mobile phones constantly, and you could
be checking a Google Doc at 9pm at night and
rattling off a Slack message. That's not how
life used to be. And so I can absolutely see
people defaulting to this, probably because they'll
be like, I'm sure it will be okay. And in some
cases, it absolutely will be. But we often get
asked, especially by parents of kids that are
maybe like 18 to 25, what should they do, right?
What should their kids do? And that level of
critical thinking skill is going to be so, so,
so important. Your ability to call a model out
for getting stuff wrong or for being able to
provide interesting ideas that models wouldn't
come up with, I think, is where some of the magic
really lies. And it reminds me of my academic
career, like working as an academic scientist
is really thrilling in some ways and brutal in
others, because basically the training is. let's
all get very good at pulling each other's work
apart because we're in the pursuit of truth here
and egos can't be involved we have to destroy
everybody's work looking for the holes and of
course egos very much are involved because we're
all humans um and that's not an easy process
for most people to go through it certainly wasn't
easy for me because every meeting is a bunch
of smart people trying to find all the gaps but
that for me is probably one of the most important
parts of the pursuit of scientific progress.
It takes a lot of training and a big mindset
shift and a lot of energy to get your brain to
operate like that. But I think everyone in a
business context that uses AI is going to have
to think like that. Until we are absolutely confident
that AI tools can validate and critique their
own outputs better than any human, that is our
job. And it is honestly a super hard job, as
you've just described in that peer review process.
Yes. Well, that's not the only prediction they
made in this Gartner report. There are a few
other interesting things as well. And when thinking
about critiquing and making decisions and using
good judgment, that's something that people have
to do in B2B procurement quite often. And one
of the big predictions that they made is by 2028,
90%, 90%. Of B2B buying will be AI agent intermediated,
pushing over $15 trillion of B2B spend through
AI agent exchanges. So they say that procurement's
being reprogrammed, not by policy, but by invisible
agents. Traditional search engine optimization
and BPC will give way to agent engine optimization.
Products will need to be machine readable and
procurement will shift to efficient, autonomous.
Machine to machine transactions. Yeah, this is
a big deal if you're a marketer. We've talked
about this before, right? In terms of humans
marketing to humans, then humans marketing to
agents, and then agents marketing to agents.
And what do those shifts look like? And what
do you need to do? It's super interesting. I
renegotiated my satellite TV package this week.
And it won't surprise you to know that I... asked
GPT 5 .1 and Gemini 3 to go and find what the
current prices were, give me a strategy for renegotiating
my contract, and also little tips and tricks
to out -negotiate the humans that I was going
to be dealing with. And I got a really good deal
on my satellite TV contract. I didn't do much
of the thinking. Oh, sorry, I outsourced it on
this occasion. But I was going through the process
thinking it would be really good if... chat gpt
could have just called because i had to spend
40 minutes on the phone so it wouldn't really
good if chat gpt could have just called the provider
and taken care of that with me which from a capabilities
perspective it has those capabilities now and
the reward function is get the lowest price possible
like it's not a hard thing to brief um so i was
like kind of frustrated that we're not quite
there yet but at the same time i was if ai followed
the playbook it gave me which is all i really
did probably would have got me a really good
deal as well. So yeah, I think that's definitely
one for us marketers to chew on, especially in
a world where, you know, we're all going to have
personal assistants to take care of this stuff
for us. Absolutely. And I think the big takeaway
from this in the near term, in terms of what
can we do with this information right now, is
I think marketers need to think about making
sure that their product documentation is machine
readable. For me, this is such an important point.
So much information is still held, particularly
in the B2B world, on PDFs. And I just think we
need to make sure that we're transitioning all
of that content away from PDFs into markdown
files or some sort of machine readable format
and making that publicly available so that the
models can easily access, understand it. You
know, data that's captured within tables is really
hard to pass out for a model. when it's stored
in a PDF. But stick that into Markdown, and all
of a sudden, it's very easy for the AI to understand.
So without getting too technical, you can quite
easily start making this transition to be in
a good position for agentic -assisted B2B procurement
by just turning your data into Markdown files.
Yeah, super interesting. There's a couple of
things while you're talking that spring to mind.
The first one is there's a continuum of possibilities
here. One of them is we create information experiences
for agents. So humans visit websites. But if
your website has identified that it's a bot from
ChatGPT, it's actually pushed off to a completely
separate information repository. that it gathers
the information from. Because websites of humans,
obviously, in terms of how they're laid out and
what have you, and PDFs are not particularly
helpful, and gated PDFs and all this other stuff
that agents aren't going to be able to access.
So that's one possibility. The other end of the
spectrum is computer use becomes so good so quickly
that if you embark on this digital change project
to reimagine how you... provide your products
and services to agents and then our crumbs nine
months later opus five can actually just browse
your website and read pdfs and you know so it's
like this is i think ethan mollock talks about
this this is one of those weird technological
evolutions that's happening so quickly there
can sometimes be a strategic advantage to being
a laggard right it's like let's just let the
technology catch up because by the time we've
put all the effort into this technology you'll
be able to just do it anyway so it was a wasted
couple of million dollars on a on a digital transformation
project that we didn't need to do i'm not saying
that's true by the way it's just that's that's
one possibility right so that i do find myself
thinking about how proactive do we need to be
i mean do you have opinion on this martin if
thinking about optimizing for computer use if
anything has taught me how difficult computer
interface design can be it's being in marketing
agency world for the past 15 years doing web
design projects and trying to make these interfaces
consistent across different browsers oh it works
in chromium and it but it doesn't work in firefox
do you know what a markdown file is a markdown
file it contains all of the information it's
very simple and i think taking a uh a surefire
approach to just Make sure there is a format
to get the essential information out there is
fail safe. Whilst, yes, the Opus might be able
to use your website in the not too distant future.
How long till some stupid JavaScript framework
comes along where it can't do what you want it
to do? I'll give you a good example, actually.
When I was using, which browser was it? It was
Perplexity. It was the Comet browser. And I was
trying to get it to do a task and it ultimately
completed the task. I was trying to get it to
organize an inbox using Outlook online. And it
was able to do nearly everything except as a
interface element, which is right click. But
the model can't right click. It can only have
a left click select. So this particular function
that needed you to be able to right click to
add a new folder. it couldn't do and it had to
find another way around which it did successfully
do but if that other route didn't exist that
interface is bust right and i think the web and
uis are full of these edge cases which will ultimately
catch computer use agents out from time to time
yeah i mean i i completely agree and i but i
think it's probably inherently quite fixable
as well um But yeah, I think it's going to be
interesting. I mean, one experiment I would love
to run, if I had a product -based business like
an e -commerce -based business, I'd probably
look at my products that get the most web traffic
or my most successful products. And I'd experiment
with trying to find a way of providing that type
of easily passable, markdown -driven information,
perhaps on a product page in a way that encouraged
an AI crawler to... use that information in a
way that a human wouldn't. Like, I don't know
if you could just hyperlink in the text for his
AI readable markdown or just something that a
human wouldn't click or if they did, like, fine,
but it won't be much use to them. I would expect
that it will become the norm within a CMS, actually,
to almost like in email marketing systems where
you have the plain text version. where you'll
create a markdown version of a web page and that
will exist as a kind of parallel page that you
never see but in the page schema like in the
metadata of the page it gives a link to the markdown
version of that page and that's how i would imagine
cms's in the future will operate they'll create
this other version in the background and the
crawler will go to the page it will see the schema
and go right that's where i need to look for
the information rather than passing through all
of this html that makes perfect sense especially
given i can't remember what it's called and this
is baked into platforms like hubspot because
it's fairly old school at this point but the
ability to show a mobile optimized version of
a page that's completely stripped down i can't
remember what it's called now yeah amp right
so hubspot will do that automatically for all
of your blog posts server an amp version if someone's
on a mobile and their speed is slow or whatever
um i think you're absolutely right i can i can
absolutely imagine that happening makes perfect
sense to do so i think the other thing it may
force us all to do and this harks back to a friend
of ours marcus sheridan um and uh his they ask
you answer methodology for inbound marketing
and for those that are not familiar with marcus
and his work In essence, I'd summarize it as
provide your buyer with the information they
need to make a decision. Don't hold too much
back. And one of his big things is providing
pricing information. Now, if you sell widgets,
you probably do provide pricing information.
But there's lots of things in the B2B marketing
world and sales world where pricing isn't available
on the Internet. So the question just becomes,
how will an AI agent handle? not being able to
gather pricing information and comparing that
information for different services. And if you
are the one website, backend, markdown files,
whatever that does provide that information,
will the agent be more likely to lean into you
as a recommendation because it can actually give
pricing recommendations to the human that asked
for the purchase report or able to, because if
you're an agent, you cannot make a purchase if
you don't know the price. And if you're an agent
that's optimized to make purchases, does that
mean that you just buy from the one website that
has pricing information or easy agent transaction
capability? All of these things are like predator
-prey relationships, that there will be a arbitrage
opportunity that lasts for X amount of time,
where you're the website that gave pricing in
Markdown, and you're the website that made it
easy for an agent to transact, and you... absolutely
cleaned up for the two months that it took for
the rest of the world to figure out that they
need to do that so that they can also be included
in those agent purchases. So there's probably
quite a lot of sort of war gaming to be done
in this area to think about how do you stay at
the forefront? And is it commercially viable?
Like if you're selling two things a month, probably
isn't. But if you're like shifting a load of
product, like in our industry, you're a consumable
and reagent company, you might be selling. hundreds
of thousands of vials per day. Absolutely worth
looking at that because it could add up to quite
a lot of revenue. I guess somewhat related to
this is some of the changes we're seeing in Google
AI overviews and potentially in chat GPT. So
Google's starting to place ads in their AI overviews,
Martin. Did you see this news? Yeah, I saw it
and it seems quite linked to the Gemini re -release
as well because they've also announced that as
part of search responses gemini 3 will actually
now create entire interfaces to explain concepts
to people as well which is a fascinating change
so i think that the search experience is evolving
at a pace but yeah interesting to see that we're
seeing the first sponsored links inserted into
AI generated search results of AI overviews.
So this is the first monetization strategy that
we've seen for the AI search experience. And
I think it's fair to say that we'll see more
of this inserted into AI mode, as well as AI
overviews, which are two distinct product surfaces.
within the search experience it's quite an interesting
one it's absolutely predictable how it impacts
on user experience is going to be interesting
so i'll tell you a little story we're doing house
renovation at the moment and we i needed to buy
what because we got a very old house and it's
not all served by the central heating system
so i needed to buy an electric radiator not something
that you do very often not something i know much
about But I knew what I didn't want, which was
a very modern or kind of crappy looking radiator
because our house is quite old and wouldn't be
in keeping. And I'd managed to find these cast
iron radiators that were electric and oil filled.
And they look beautiful. They cost an absolute
fortune. I was like, can't justify it. So I asked
Gemini 3 to go find me something similar that
cost a lot less. And I'd done my own Googling.
And it came back with three different makes that
I hadn't seen. that looked basically identical,
but cost half the price. And we'd been having
a conversation about the room size and what type
of size of radiator, what type of radiator would
you recommend? And it came back with these three
and it gave me the pros and cons of each. And
then I said, what would you buy? And it told
me which one it would buy. And it had been so
darn logical to that point, I bought the one
it said. So it raises the question, is it better
to have an over system? that says, here's a sponsored
link for a thing you could buy that's kind of
similar to what you've been chatting with me
about. Because I just, I kind of trust Gemini
3. And so I just bought the one it said. And
at the moment, that's because I suspect there
are no incentives for Google as to what product
it recommends. And if I thought there were, maybe
I wouldn't have been so gullible. But as it stands
right now, I just trusted Gemini and bought the
one that it said. And so that's kind of interesting,
right? Like where... How important will it be
that we actually have an understanding of how
these recommendations get surfaced? Will it influence
our buying behavior? Because I was so much more
likely to do that than click on a sponsored link
and buy the sponsored link. So I don't know how
this is all going to play out. Yeah, that is
a fascinating development and goes to show how
ads really change the experience. This was something
that ChatGPT were... quite clear on for a long
time saying that they weren't sure about putting
ads into the experience because of the way that
people use it and they want impartial answers
they want to trust the model outputs yet we've
seen this weak speculation arising that chat
gpt is about to get its own ad network and start
serving ads in there as well so yeah from a marketer's
perspective new ways to reach audiences right
from an audience perspective oh is this going
to change the relationship that you have with
your chat assistant right if you had a friend
who you who was like into tech that you i've
got one his name's martin and he always tries
to convince me to buy lifetime deals and then
the software that we buy goes it stops being
supported three months later but um if you knew
that your friend had like sorry can i just set
that That feels a bit on the nose, Paul. I feel
outed. I think it would be fair to say you had
a lifetime deal addiction that you then gave
to me. And I feel partially cured now. I haven't
been burnt a few times. But yeah, if you had
a friend who you knew was on commission for every
piece of advice they ever gave you, you probably
wouldn't be your friend anymore. Right, because
that would be a very weird relationship to have.
And people have these weird relationships with
their AIs. Do they want them selling to them?
I think you're absolutely right. I think it changes
the experience a lot. I mean, with OpenAI's release
of the shopping research feature, where does
that sit? Is it unpartial advice or is there
like incentives baked into that? It hasn't been
made clear as far as I can tell. And we kind
of probably do need to know. I think it's a shame
because I'm quite liking this moment in time
where I can ask AI for purchase advice and it
gives me great advice. Another thing it found,
this is probably too much information, but because
I've got this bad back and it's causing me to
get calf strains all the time. Weird, right?
So it recommended that I get these sliders. They're
like slippers that have this amazing cushioning
on the bottom that I would never have found myself.
It wouldn't have even occurred to me to buy these.
I bought them. They're like walking on their
hair. Like, I don't want to wear normal shoes
anymore. I would never have bought that. I would
never have thought of it. Jury's out on whether
or not it's helping with my biomechanical issues
or not, but successfully sold me on this pair
of these shoes that I absolutely adore now. That's
a nice experience. Once it gets tainted with
all of this advertising and do I trust it or
not, I'm back to having to think for myself again.
It's an age -old problem. A technology or...
Some new thing is launched into the world and
we go, this is great. And then the marketers
come along and ruin it. Oh, therein lies a truth,
I think. But yeah, I guess from a marketing perspective,
seeing as we are those marketers and many of
our listeners are also, keeping tabs on how this
evolves and how you can best reach your audience
is critical. And a little bit like SEO, and we've
talked about this a bit in the past as well,
there's going to be ways to have your brand show
up in AI search results, creating content, being
an authority, having your brand mentioned favorably
in lots of different areas like reviews and earned
media placements and what have you. Or you can
just buy your way to the top if you have a business
model whereby you can still make good margins
if you're paying to reach that audience. And
it does feel like we're entering that time of,
first mover advantage figuring out how to leverage
AI assistance to get in front of audiences where
the prices are reasonable. I suspect that that
is another short -lived arbitrage. I think once
these become available, I don't think there's
anybody who's going to be like they were with
Google search ads. No one's sleeping on this.
We've talked about this, and that's why there's
such an obsession with answer engine optimization,
because people are desperate to be at the forefront
of whatever this will be. And it'll be an arbitrage
for about two weeks, probably, but definitely
something we all need to keep an eye on. Well,
that neatly brings us on to our final story of
the week, which is about Adobe buying SEMrush
for $1 .9 billion. merging search engine optimization
and creative AI and AEO or geo or whatever you
want to call it, all into one platform. What
do you think of this? So we are, one of the platforms
we use is SEMrush. And my first thought was,
doesn't Adobe often buy things and break them?
And I was sad because I've invested time in setting
up campaigns and... you know projects and stuff
um and i don't want the tool to suddenly become
10 times more expensive or break a lot because
now it's been wrapped into some other thing that
has a bunch of capabilities that perhaps i didn't
need so if i'm honest my first thought was a
bit selfish my second thought was if they've
mostly done this for for aeo and they've spent
all that money then had to be clearly the things
that's going to be really important or it They
think their audience, their customers think it's
really important to spend so much cash. But what
did you think? Not a user of the product, but
I certainly saw the same reaction on LinkedIn
of people going, oh, they're going to make it
rubbish now. So, yeah, I think that's a valid
concern shared by many users. I do think it validates
the idea of generative engine optimization. If
they're moving into this space with a... two
billion dollar acquisition ai search clearly
isn't just a side project anymore and i think
marketers should be investing more into this
now we know that ai search is still a relatively
small part of the overall market google's own
data talks a lot about how they've got more people
using search more frequently and actually engagement
in the search experience is increasing with people
asking longer more complex questions in there
so i guess part of my thought process is yes
geo or generative engine optimization is on the
rise and something that we should be doing but
what are the generative engines that we should
be paying attention to and is it going to end
up just being Are they still going to be the
big player with whether it's Gemini, AI mode,
AI overviews or the classic SERPs window? Do
all paths ultimately end up leading to Google
anyway? Or is ChatGPT going to actually make
a significant inroad? It's such an interesting
question because why did, I'm not an expert in
this, but why did Google win the search engine
wars? And we talked about this previously, how
I could like probably completely misrepresented
a fact about how Google could correlate a 100
millisecond increase in delivering the speed
of the result to X, Y, Z of traffic or clicks
or conversions on ads and what have you. And
really simple, easy to use web page that did
one thing. You searched and it found your information
on the web. contrast that with how like yahoo
search looked in the day it was just full of
widgets everywhere and you know it's probably
like made you feel ill just to look at it if
you tried to look at it now um so i think it
will come down to user experience but where i
think that's really super interesting is user
experience in this sense is very broad let's
imagine that for whatever reason i'm a chat gpt
user and i give it access to loads of stuff about
me Like I want to use the memory feature. I give
it access to my email and my calendar. It somehow
makes its way into my messaging app, WhatsApp,
whatever. And it knows a ton about me. Maybe
it knows my Amazon purchase history, whatever.
Its ability to surface things that are genuinely
valuable to me, maybe paid for placements, but
still things I would actually buy and get value
from and want. That's the type of experience
that we're looking for now. Right. Isn't just
about surfacing information. It's about bridging
that gap between almost predictive purchase behavior
in a way that we haven't been able to, at least
to my knowledge, really been able to do through
any cookie based tracking or automation platforms
like to get the most access and benefit out of
our assistants. We're probably going to let them
know a lot about us. And I think that's probably
going to be as big a differentiator to the user
experience as how messy is the UI and all this
other stuff. So I don't know. I mean, Google's
well placed because it already has Gmail and
Calendar and it has a lot of stuff that people
already use for this type of stuff. And obviously
it has a lot of data on a lot of people already.
What are your thoughts? Yeah, I think the more
the systems know about us, the better. And actually,
it's one of the reasons that ChatGPT remains
my daily driver as a chat assistant is because...
actually the memory function works really well
and there's some features in it that just make
the experience feel more useful as a daily driver
when i need specialist tasks completing and like
if i need if i'm doing some coding or vibe coding
i'm going to go to claude um if i'll for certain
applications i find gemini 3 is is fantastic
and i go to that but the tool that i use i would
say for 80 % of my prompting is still chat GPT
because they've just got something dialed in.
I guess the question is how much of this tool
use is actually rolled out across the population
at this point. I was with someone last week who
just got a new phone and we were talking about
Gemini. They'd never used Gemini before in their
life. So I told them to open it up and we just
tried a few prompts. And all of a sudden they
were an AI user, but they'd never used it before.
And it wasn't on their radar at all. Google stands
to win quite significantly if they can get that
audience shifted onto using their platform. And
I think with the deals that they're doing with
Apple through the iPhone tie -up and obviously
the market coverage that they have through Android.
they're really really well placed to just to
clean up here and they've run this playbook before
right they pay billions of dollars a year to
apple to have chrome or google search be like
they're not chrome sorry but google search to
be this default search on you know on um apple
devices and what have you so yeah i think i think
that's key i also think just while i was listening
to you speak there that if you think about a
cookie -less world you know that didn't actually
come to pass quite as it was expected to do um
but working in a in a world of first party data
and maybe it's harder to track people you know
finding a way to have your audience like volunteer
loads of information about themselves and then
extremely happy to do it like there are people
talking with chat gpt about um i don't know dealing
with relationship issues with their friends and
partners talking about like their illnesses feeling
i don't know depressed and suicidal or there's
a person at work i really like and how can i
get them to like like the amount of like personal
information that people are sharing with these
tools is like way beyond anything that you would
ever have done you know in a browser and all
of the ai companies are launching their own browsers
as well so i really think that will be the secret
is trying to unlock the promise i mean i remember
I can't remember what the book's called now,
but one of the examples it gives for where AI
could go is Amazon's... And the praises here
is Amazon invested in drone technology because
they wondered if there would be a time when AI
could predict your purchase needs so well that
if you could make delivery cost almost close
to zero, you could have a drone turn up at someone's
house with something that they didn't even know
they needed. And they'd just take it. They'd
be like, oh. Cheers, Amazon drone for. Yeah,
it's funny to say that the toothpaste did run
out exactly today. Well played you. And if half
of that package you didn't want and the drone
took it back, if you got the cost of delivery
down to near zero, it didn't matter. But I think
one of the reasons that that hasn't come to pass
and not just because of drone technology is because
predicting what people want is actually still
very hard. But this could be one of those things
that makes it much easier to predict what people
want. And I think, again, that's where. the the
winner comes in is that predictive ability not
just the ui and all of this other stuff so i
don't know as a in b2b marketing is probably
not something we have to worry about huge amounts
but if you're in b2c you should be thinking about
this how long do you think it will be before
the ai assistance you know at the moment they
all have that um they've got that trait where
at the end they'll say would you like me to turn
this into a convenient edf handout for you or
would you like me to turn it into a table for
you or would you like me to do something else
how long is it till they start having conversations
with you and just start randomly going so how
much milk have you got left in the fridge yeah
because your fridge said that you're nearly out
but it is uh it is a brand that i don't trust
and so i because it's got poor cameras inside
it so i think it'll definitely happen i also
think there's a world where There's the buying
committee of humans. The complex B2B purchase
is a buying committee of like eight humans. And
they're like thrashing it out and you're getting
the absolute, you know, camel as a solution designed
by a committee. And then all of the assistants
of those humans are having like a meta discussion
in the cloud. Also thrashing it. Well, my human,
I've known what I know about my human. I think
my human would actually go this way. And it's
like. Who then ends up making the decisions and
how does that work? I think the humans would
probably go, I can't be bothered to be in those
meetings anymore. Can we just send all of our
assistants off to be the buying committee? We'll
get some quite weird purchases then based on
that, I would have said. Well, it's all good
though, because Claude 4 .5 is very good at being
the orchestrator and Claude Heike 4 .5 will be
the purchase executor. So, you know, in Anthropic,
we trust other models are available. Well, that
feels like a pretty good place to close out the
podcast. Anything else you wanted to cover this
week, Mike? I think that is plenty. Well, thanks
for your time as always, and I will catch you
on the next one. Thank you for listening to Artificially
Intelligent Marketing. To stay on top of the
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