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.
Welcome to the Artificially Intelligent Marketing podcast.
I'm Martin Broadhurst and today I am flying solo. That's right, my regular co-host Paul Avery is not
joining us in this episode. Instead, he had to take a bit of a breather after many weeks
of intensive AI announcements, he just couldn't hack it, and quite frankly he had to just take
a bit of a lie down in the Caribbean sun. So Paul, I hope you are feeling refreshed and ready to go
next week, because if this week is anything to go by, the relentless deluge of AI news and
announcements is not going away anytime soon. In this week's episode we are going to be talking
about four big stories. First thing we're going to look at is Italy banning chat GPT. We'll also be
looking at Metta's announcement of the Segment Anything foundational model for computer vision.
We'll then be looking at the comments made by Sundar Pichai this week as he dismissed the AI
chat bot threat posed by the likes of chat GPT and Bing AI. And then we'll be looking at the AI
index report measuring trends in artificial intelligence before wrapping up this week's
episode with a fascinating long form interview. Let's kick off with the first story of this week
then. Italy has banned chat GPT. Last week's episode of the podcast was called chat GPT under fire.
Little did we know when we recorded that, that little more than 72 hours after we published
the episode a country would ban chat GPT outright. So Italy's data protection authority has criticised
OpenAI for not checking user ages and said that there's been massive personal data collection
during the training of the model. So OpenAI immediately blocked access to Italian users
in response. Now this has obviously upset quite a few Italian users. It didn't take long to find
people on Twitter saying what on earth is going on. So OpenAI has actually this week sat down and had
talks with the Italian data protection authorities and they're presenting remedial measures that
hope to mitigate and allay the concerns of the authorities. The company has sent over a document
outlining measures to address these issues and I think they are hoping that they can get up and
running very soon. Now why does this matter? Well Italy isn't the only country with concerns
regarding chat GPT. In fact Germany this week has signalled that it may also look to block access
to chat GPT. Now what I personally find slightly confusing about this story is as far as I can tell
chat GPT is the only model that has been restricted and Bing AI has not seen any limitations put on it
and as far as I can tell models such as Google's Bard whilst I realise it's only a small audience
at the moment that has not had any restrictions either. So it seems like the authorities in the
EU are really pushing to get the new technology providers in this space to basically increase
transparency. A big concern is obviously around the way that they use people's data, they scrape the
internet and all of the data that they're able to find goes into the training set, yet nobody knows
what that data contains. We do know that if you search for people's names and search for companies
it's able to access and present relevant information about people. You don't need to be
a high profile figure to come up in the outputs of chat GPT. I've tried this with relatively
small businesses and the CEOs of SMEs that I'm aware of and it can tell you who somebody is.
So we know that is using this kind of data and the lack of transparency around the large language
models training data, particularly GPT-4 which OpenAI has been very secretive with,
is a cause for concern for many. So I think this really is just the start of what we can expect
to see from European privacy regulators and no doubt before long they will start to coordinate
their actions across the EU and we can expect to see companies having to make significant
adjustments in the way that they open their books so to speak and let you interrogate their models.
We can expect to see serious changes in this regard in the near future.
What does this mean for marketers? Well watch this space really, the legislation and regulation
around AI is nascent as we discussed in great detail last week. I think we can expect to see
the EU taking a leading role in this. Their policy papers at the moment suggest that companies using
and developing AI models are going to have to be more transparent and more open with the training
data and the weights and biases that they use within their models, all of which I think will
be a good thing in the long term. But when you have a transformative technology like chat GPT that
really does capture the imagination of huge amounts of people all around the world, any restriction
on that can just seem draconian but I'm sure it's all done for the best, with the best of intentions
for the citizens of the EU. While we're talking about tech giants and their AI models that might
come in for some scrutiny, we might as well talk about Meta. This week Facebook's parent company
Meta announced Segment Anything, a new foundational model for computer vision.
So the Segment Anything model, also known as SAM, is a one billion mask dataset. Now the aim of this
as they say in their release paper is to democratise image segmentation. Now if you don't know a great
deal about computer vision, it's not an area that you're familiar with, it's basically the technology
or the machine learning models that allow computers to see and interpret what they are seeing. It
plays a role in things like self-driving cars, they'll have cameras that will detect objects and
classify the objects that they're looking at, things like that is a car, that is a lamppost,
that is a pedestrian, or they can be used in, recently saw a great example where AI computer
vision was being used to identify bugs in an insect farm, and it was looking at the the creatures on
the insect farm and able to identify which of those were injured or maybe had illnesses, a really
interesting innovative area. In more everyday usage you might come across computer vision when
you're unlocking your iPhone, the face unlock feature is using computer vision technology behind
that, but the new Segment Anything foundational model from Meta is designed to enable developers
to incorporate this new technology into their apps at scale, and it's what really sets it apart from
existing datasets is just the sheer volume of the segmentation masks. You can think of a segmentation
mask as being like on a colouring book page where you might just colour in only the shape of the
object that you're interested in, so if there's a cat on that colouring page you would colour in
the cat and leave everything else on the page white, and doing that, feeding that into the computer
will help the computer understand where the object is in the picture and then separate it out from
the background, so the more examples of masks that you've given to the model the better it is at
identifying objects within a scene and being able to separate them out from one another.
In the example that they give in the research announcement they actually say that it has 400
times segmentation masks compared to the next best model, so it's a huge increase in the data that
they've got to work with. Now they give some examples of what you can actually do with all of
this, in the section on the announcement page they say what lies ahead, and they've got some nice
little illustrations where for instance you could be using AR glasses, so you're using augmented
reality glasses and they could prompt the users with reminders and instructions based on what the
person is looking at, and the example that they actually give is somebody working in a kitchen
and in front of them in the kitchen is a chopping board with a cake on the side which has not been
iced, and in the top corner on the augmented reality glasses the user sees videos suggesting
cake decorating tips, because it recognizes that there is an undecorated cake and kind of pulls out
some relevant advice. There's another example where there is an empty dog bowl, a food bowl,
on the floor and the glasses see this bowl and it's empty and it pops up with a notification saying
that Rex was fed 20 minutes ago, so it's context aware. There are industrial applications for this,
many of these that you could think of, but in the examples that they give they talk about using it
for agriculture to be able to assist farmers kind of managing their herds, or you could use it
if you're a biologist, and this is something I'd love to discuss in the future with Paul,
if you were looking at cell division or any kind of life sciences application that really, you know,
you need to be able to count cells rapidly, I don't know why I'm talking about this,
is if I have any idea what I'm on about, this is so much in Paul's domain that I should just
reference the fact that I'm looking at a GIF with some cells moving on it right now, and apparently
it's going to be really useful for that. We'll talk more about that in the future with Paul when
he's back in the studio. Suffice to say this is a really interesting new technology that's going to
open up huge areas of innovation and allow people to do so much more with their existing products.
There's going to be applications in the security industry, you can well imagine how this is going
to be integrated into things like CCTV, how it's going to help things like crowd control,
TV and movie industry is obviously going to be able to make great use of this. Self-driving cars
which do need to be able to see objects, track objects and classify objects are about to get
quite an upgrade as well, so the impact of this is not to be understated and I think it's worth
saying at this point that Meta deserve some credit because this model has actually been released
with the Apache 2 license, which is a permissive open license, so this means that anyone can use
the model and developers can build with it now. The Apache 2 license allows for free use,
modification and distribution of the software so that makes it accessible for a wide range of
applications and research purposes, so hats off to Meta for that. In fact the top comment
that I saw when I read the tweet announcing this was from somebody saying,
that's all great but you should have led with the fact it's an Apache 2 license, wow.
Next up I want to look at a recent interview that Sundar Pichai gave with the Wall Street Journal.
The CEO of Alphabet has come out on a bit of a PR front after Google has faced significant
pushback or criticism for its lack of innovation and basically it's well to address the threat
that it may or may not be facing from the likes of ChatGPT and Bing AI. I mentioned there it's faced
some criticism and that people are saying that it is under threat but that's not how Sundar Pichai
sees it, if anything he actually dismisses and minimises the chatbot threat, he says that they
see opportunity in the space, he says the opportunity is if anything bigger than before.
Now he does say that Google will be enabling users to engage with large language models in a search
context and that they are currently testing new search products, specifically ones that allow
people to ask follow-up questions, so maybe we're going to see a more chat-like interface when it
comes to Google search results. When asked why Google has been able to do so, I think it's because
of why Google hadn't responded or released a chatbot earlier, why it was maybe resting on its laurels,
he said that they were iterating and preparing to ship something and that maybe the timelines
had changed given the moment in the industry that we saw back on November 30th last year when ChatGPT
came to the fore. He does say that there has been incredible user excitement and goes on to say that
this is interesting for the company, he says it has been incredible to see user excitement around
adoption of these technologies and that is a pleasant surprise as well. Now it remains to be
seen exactly how Google does deploy AI in this space, it's a topic of conversation that we've
had in recent weeks talking about what does this mean for search engine optimization, that was an
interesting discussion that I caught up with on Twitter this week as well where Chris Penn from
Trust Insights was saying that the era of unbranded search content is coming to an end,
branded search will be the primary driver of traffic and in a few years time when people are
searching for things that they might ordinarily find on your blog, if you're doing long-form
content marketing and inbound marketing type activity, soon those kind of answers will be
found on the likes of ChatGPT and via chat AIs, so trying to optimize for that will be pointless.
Now whether that comes to pass remains to be seen, I should say apologies to Chris if I've
completely misrepresented that, I feel like I've paraphrased slightly just to get that across
quickly, but yeah going back to Google for just a second, PitchEye says that there are plans in
place for Google Brain, the AI research lab within Google, and DeepMind, the AI research lab
and the alphabet owned research company headed up by Demis Hasabis, there are plans for these two
organizations to collaborate more closely. Now this makes sense, PitchEye does say he expects
a lot more stronger collaboration because some of these efforts will be more compute intensive,
so it makes sense to do it at a certain scale. There has been a little bit of friction between
DeepMind and Google in the past with DeepMind not being entirely aligned with the Google mission,
there's said to be a bit of distrust and a fear of misuse of DeepMind's technology by Google
and or the military. Some sources also mention that there's something of a cultural conflict
between DeepMind's academic researchers and Google's business oriented engineers, so much so
in fact that DeepMind tried to break away from Google and become a separate division under
alphabet, but negotiations on that have failed apparently. So it will be interesting to see how
they bring DeepMind and Google Brain together, certainly feel that DeepMind have, you know,
if I go back looking over the past 10 years or so, DeepMind have always been one of these companies
that quite frankly are pushing the boundaries, they're the ones that are doing great things
in the life sciences space, they've done some amazing work with the likes of AlphaGo, you know,
that was a great DeepMind project. So when we start to see some of their capabilities
incorporated into the Google product suite, that does make you think that there's some exciting
times ahead for Google and you know, maybe they, maybe they should have been looking to make this
move some time before now, but there's nothing like a bit of healthy competition to spur things
along. Our final story of the week comes from the Stanford Institute for Human-Censored Artificial
Intelligence, which has released its 2023 AI Index report. The AI Index collaborates with many
different organizations to track progress in artificial intelligence. These organizations
include the Center for Security, Emerging Technology, Georgetown University, LinkedIn,
NetBase, Quidd, Lightcast and McKinsey. The 2023 report also features more self-collected data
and original analysis than ever, they tell us. So this is a big look at the broadening
space that is AI, and it's tracking everything from investment rates to risks to policy and
regulation, you name it. So I just want to pull out some of the top takeaways from the report to
keep you up to speed with where we're at globally when it comes to AI. So a few key highlights
taken from the report. First of all, industry is racing ahead of academia, and this came as no
surprise to me, and I think anyone that's been paying attention in this space for a few years
now will have seen this, just all of the big announcements these days are coming out of Meta,
Nvidia, OpenAI, Microsoft, etc. So the report says until 2014, most significant machine learning
models were released by academia, since then industry has taken over. In 2022, there were 32
significant industry-produced machine learning models compared to just three produced by academia.
It goes on to state that basically because of the huge amounts of data, the compute power,
and quite frankly, the deep pockets that are required, it makes sense that industrial actors
are going to need to really take up the reins here and drive things forward. Also, when you consider
the massive upside from the profit motive, if you can realize the benefits from AI, it's not a great
surprise to see that the incentive does exist for them to make these investments, because down the
road the returns on investment look to be pretty chunky. In what is something of a theme at the
moment in some of the reports that are coming out, particularly from academia regarding AI,
there is a suggestion that AI is harming the environment, though to be fair this article
does say that AI is both helping and harming the environment. So you know, not all bad.
New research suggests that AI systems can have serious environmental impacts according to one
report. Bloom's training run emitted 25 times more carbon than a single air traveller on a one-way
trip from New York to San Francisco. Which, if I'm honest, doesn't feel like a great deal to me,
I actually think that sounds quite small given the potential benefits of a big large language model
like Bloom. So yeah, I can understand why it needs to be highlighted, but it could be worse.
It does go on to say that new reinforcement learning models like B. Cooler show that AI
systems can be used to optimise energy usage. So yeah, it's not all bad. When it comes to
AI and industry and jobs, a topic that we discussed last week following OpenAI's report that showed
80% of jobs will be impacted by GPT technologies by at least 10%, the report says that the demand
for AI-related professional skills is increasing across virtually every American industrial sector.
So no great surprise there. And it goes on to say across every sector in the United States for which
there is data, with the exception of agriculture, forestry, fisheries and hunting, the number of AI
related job posts has increased on average from 1.7% in 2021 to 1.9% in 2022. So employers in the
United States are increasingly looking for workers with AI-related skills, unless of course you're in
the world of hunting. And to be honest, I'd prefer that we just keep artificial intelligence
out of hunting. That just sounds like a bad, I've seen Terminator, that just doesn't seem like a
wise move at this stage. Or if we are going to do it, just keep it a GPT-3. The report also goes on
to say that policymakers' interest in AI is on the rise, and this was a big topic of discussion
on last week's show after the UK government announced their white paper looking at AI policy
and regulation. So the report says that an AI index analysis of the legislative records of 127
countries shows that the number of bills containing artificial intelligence that were passed into law
grew from just 1 in 2016 to 37 in 2022. An analysis of the parliamentary records on AI
in 81 countries likewise shows that mentions of AI in global legislative proceedings have increased
nearly 6.5 times since 2016. So this is no great surprise at all, and you know that was the lead
story on today's pod, Italy banning GPT, and we're going to see more of this in the future no doubt.
As the EU brings its own AI legislation to parliament and puts it on the books, and America
does the same and China is putting it through the law books as we speak, we can just expect more and
more of this interest in regulating AI to come to the fore. So watch this space, it will be interesting
to see where we land on that one. And one final takeaway, there is more on this by the way, so
if you want to read the full report it's about 380 pages long and I'm not going to pretend I've read
all of it in the few days since it came out, but there are a few more highlights, but the last one
I want to talk about is something about the sentiment that we feel towards artificial
intelligence. How do the general public feel about AI? Well according to the report Chinese
citizens are among those who feel the most positively about AI products and services,
whereas Americans not so much. In a 2022 Ipsos survey 78% of Chinese respondents, the highest
proportion surveyed, agreed with the statement that products and services using AI have more
benefits than drawbacks. After Chinese respondents Saudi Arabia 76% and India 71% felt the most
positive about AI, so you know you're really 78% of Chinese, 76% Saudi, 71% India, that's a lot of
positivity floating around there about what AI can do for people, whereas Americans are a little bit
more skeptical, only 35% of those sampled agreed that products or services using AI had more
benefits than drawbacks. What that means for adoption of new technologies or how companies
need to think about rolling out AI infused products and services remains to be seen, but
it does suggest that there's a degree of scepticism around the power and potential of AI in the US and
maybe the wider Western societies as well, that means that we're going to have to act cautiously
and not be so techno-optimistic, and I definitely fall into the side of being a techno-optimist,
just something to be aware of if you're evangelizing the benefits of AI, maybe you'll
get a more positive response or a positive reception if you go and do that in China.
Well that's it for the main stories this week, note all of the week this week because as you're
going to hear in the interview that Paul conducted, you're going to hear quite a lot about the power
of GPT-4, so without further ado I'm going to hand off to Paul who conducted this interview looking
at how chat GPT-4 can be used to boost the productivity of an e-commerce company, over to you
Paul. So a bit of a special edition artificially intelligent marketing podcast today, because we
are lucky enough to be able to speak with a good friend of mine, Emil Lamprecht. Emil is CEO of
Growth Mechanics, he is a co-founder of multiple businesses within that portfolio, he's a startup
expert having built a number of startup incubators over the years and mentored over 800 startups,
he's a marketing expert himself having previously been CMO at Athletic Greens which is now a hundred
million dollar company as well as a number of other marketing gigs and bits and pieces he's done
over the years and he's just an overall business expert and good guy. I guess the most important
thing is he is a fellow AI nerd, so today we're going to talk about some cool stuff. Emil thank
you so much for joining me, I appreciate it. Lovely to be here Paul, thanks for inviting me and it's
going to be very hard not to call you by your shorthand, I just realized I almost called you
AVO right off the bat there. I'll have to get used to calling you Paul on this conversation.
You call me whatever you like as long as it's nice, okay. That's probably going to be the hardest bit.
That's the hardest bit yeah. We're going to talk about two main things for the mill today, the
first one we're going to talk about is a Ninja marketing application of AI that he's developed,
which I think you're all going to find really interesting. So we're going to talk about that
and hopefully it will inspire you as marketers to think about other ways you might use AI outside of
the general, oh I'm going to put some stuff into chat GPT, which of course many of us have played
with. The other thing we're going to talk about is we're going to tap into Emil's role as a business
owner and founder and just get his thoughts on how is the emergence of AI really going to drive
how businesses need to operate and market themselves over the next 18 months, 24 months and beyond
because obviously things are moving really quickly. So Emil, let's start with this Ninja
application that you were telling me about because it sounded really cool to take us through what you
were doing and why and how you benefited. Yeah, so I mean, interestingly enough, or I think for me,
what's most interesting about it is we did this before the chat GPT hype, obviously aspects of
open AI and use of their APIs have been around for a while and one of the cool things is that
there have been some tools that implemented some form of availability to that early on. So like
we use a tool for a lot of things called coda.io, which if you're familiar with Airtable or Notion
is comparable to sort of both of those things or a combination of those things. And part of our
portfolio is e-commerce and in one case of business we've recently acquired is e-commerce with a very
large portfolio. We're talking thousands of products across multiple languages, across four
languages. So English, Spanish, Portuguese and French, which okay, sure, we're, you know, a small
agile team and it's a nice idea to grow that to have 100 translators to constantly monitor and
update every detail of product schema for every language all the time in the highest version of
quality, but there are limits to that being pragmatic or realistic. And so last autumn,
sort of August, September, we were looking at, okay, how could we, to a certain level of quality
and efficiency leverage existing technology to try and manage and possibly even write or rewrite and
republish product schema, whether that be titles, metadata, material information, warnings and
recommendations, descriptions, et cetera, et cetera. And we said, well, oh, okay, we play with
coda and there's currently a little bit of availability built by one of the coda team members
of using the OpenAI API, which at that point was GPT 3.0. I think, I don't think it was at 3.5
at that point. And let's see what we can do. And, you know, the advantage of being able to build
and manage a database and an easy to use UI like these tools now allow you to, we sort of figured
out a way to import, rewrite or translate as needed entire sets of product schema, concatenate
it into, you know, end results, including recommendation links to associated product types
and things like this, and then push that all the way back into the front end of the store and sort
of dynamically manage our product portfolio and all of that product metadata off site, but to a
certain extent automatically, which was quite a win. I mean, I calculated that at one point that
by using this method of translation, we were probably losing, you know, maybe like a 20, 25%
quality amounts, but we were gaining what would normally take in human hours, about 1200 hours
to execute in any one go. And, you know, we're able, we still have to manage it in small,
it's a lot of data, right? So you can't just do a portfolio of 10,000 products in one go. It takes a
progressive amount of import, export, processing, et cetera. But, you know, let's say we do
a hundred products a week or a thousand products a week over the course of a year, we're saving
thousands of human working hours from that. So let me just play that back to make sure I've
understood. You've got this e-commerce website where you're selling 5,000 plus products across
a bunch of different languages. And then what you've done is you've used access to GPT's API
to find a way of effectively automating updates to the product descriptions and stuff across
all those products in all those languages with GPT taking care effectively of the writing of those
and the translation of those so that humans didn't have to. Is that a fair summary of what you did?
Yeah, yeah, exactly. And there's actually, you know, of course, since the sort of hype of AI began
in November, December, there's of course an increased availability of similar things on
most e-commerce things natively now, right? So there's WordPress plugins, there's Shopify apps,
there's big commerce apps, there's, you know, even press to shop modules that exist for this
type of stuff within certain limitations. I think even that the fact that those exist is, you know,
a way to do some of the work either automated or, you know, push button generation. I think the
difference between that and what we built was that we can manage entire sections of the portfolio
also because we control the prompt structure. So I'm having, you know, we're writing the prompt
into the kind of database command that processes each product so that for each language, for each
product type is producing a prompt specific in style and cadence and interest to that particular
product in that context, which is not something you can really do with just sort of the plug and
play apps that you find for any e-commerce system at the moment. So I don't know if those apps will
find a way to mature to that. To be honest, it seems unlikely because, you know, marketers always
want the laziest version of things. So I'm not even sure it will occur to most marketers to try and
be that specific with generated plugins on e-commerce. But for us, it was really important
to kind of have some stylistic control. And, you know, even with the revolution of, you know,
the thousands of AI tools that have appeared in the last two months, nothing comes close that I've
seen so far to being able to produce the result we're doing with a very simple API call through
a Coda doc. So how long did it take you? I realize there's an optimization part to this where you
make sure it works as you want, but how long did it take you to basically build that?
That's its own question. Like, is it a day? Is it a week? Is it six months of work to build something
like this? What does it look like? You know, I think to set up the initial version, just to prove
the concept, I think it probably took, you know, between me and another team member playing around
with it, it probably took us two days a day to get like, oh, yeah, this could work to get to that
point of, oh, yeah, this could work. Right. To get it really nailed down, it took a number of iterations
and those iterations are still happening. I mean, today we realized, for instance, just as an example,
a Portuguese friend of mine was going over some longer form stuff that we had produced through
the system and informed me in a very kind, polite way. Anytime it writes something in Portuguese
over a certain length, it assumes a Brazilian style of Portuguese. Interesting. Which I, not being a
native Portuguese speaker is not something I picked up on when I was reading various Portuguese
texts coming out of the system. So, you know, but because we built the system the way it is,
I can go back to where we have isolated the language prompts and instead of saying in Portuguese,
I'm going to edit that to say in Portuguese as you would find in Portugal, not in Brazil, and,
you know, customize that prompt even further to that sort of specificity. So these sorts of
micro iterations are happening all the time. You know, we've managed since sort of starting
to translate the product portfolio, we've managed to expand that to, okay, we also have to translate
quite a lot of product information into social posts in different languages for different social
accounts that we run. And so we've now started building out those layers and then we're also
then extending those integrations and connecting that to schedulers and saying, okay, well, can you
go through a generative and human review process that then pushes all the way straight through to
the schedule and then you're done. And that's all based on this one sort of centralized system we've
built. So again, very automated GPT is writing social posts, promoting the products, you want
a human reviewing them for quality, but then once they're approved, they get pushed through and
automatically built into the scheduled stream of posts that are going to go out across the social
profiles for the business. Is that what you're describing? Yeah, exactly. Yeah. And I, you know,
particularly for the social stuff, you know, for the product stuff on the mass that we have,
you know, we will mature to having a human layer on top of that generator form to sort of approve
and review before it pushes all the way back through, because I just, it does make a huge
difference. You know, at first glance, text looks the same, but once you get used to seeing AI text,
even once you get really good at stylistic prompts, you can still always tell it's AI. And so
we'll search engines and everything else, right? So it's important to have that human layer.
For now, we just have that human layer on the social side, but you know, we've gotten very good
at how the sort of generative human review publication cycle should work just in the past
couple of weeks. And so, you know, what would take normally would have taken someone probably
a couple hours to write, you know, multilingual versions of social posts about a series of
products, takes a few minutes, then some editing and quick review, and then pushes straight all the
way through. Awesome. It also comes to mind that, because you've got control over the prompts,
which some of these sort of third party plugins might not have, I guess you could end up doing
some quite interesting stuff running things like A-B tests to see if having a certain type of style
of description works better than another, but you can also run those A-B tests at quite,
quite an interesting scale across different product groups, because you don't have to
manually do all the writing of the different descriptions.
Yeah, exactly. I mean, we're definitely not at the point to do that scientifically enough yet,
but it's, if you look at the admin doc of things to do, it's there. It's definitely on the list.
We'd love to get to the point where we could do that. And part of that is a performance question,
right? Like, especially when you're processing this many requests, it's a lot of work.
And so, we're using the API one call at a time, right? So we dump in 100 products for each product,
it's making 28 separate requests and then collating those requests into a final
series of outcomes, right? So it's a lot of, a lot happens for every product still,
which takes a lot of time to process. And then the database has to actually load that visually
so that we can interact with it for the human review and so forth. So there's some parts of
the human review and so forth. So there's some performance questions and being able to really
do this at scale with all of those considerations in place. But again, even to just take your top
hundred products of a portfolio of several thousand and manage to do this process,
which we've now done, that and beyond has proven invaluable.
Amazing. So there you have it, marketers. What creative things can you think of to help scale
up the product? And then, I think, outside of some of the sort of commercial tools that become
available, Emil's told us a wonderful story there about figuring out how to massively scale product
description changes across a wide array of e-commerce products in multiple languages,
and then using AI to also write social posts to promote them. Hopefully that will inspire people
to go away and think, hmm, wonder how I could apply that type of approach to my own work.
I'm not sure if you've ever thought about that with me.
No, of course. I actually have one more point to add on to that, which comes to mind now,
which is just that at the status that this business is for us, we just wouldn't have done it.
Right.
Right. This isn't something that budget-wise, scale-wise, operationally, we could have done
it. It would have just waited until hopefully the business, through other means, had advanced
and matured and gotten to the point where it could then invest in that transition or the process.
So the most important thing about here is not that this is the best end result. Like I said,
quality-wise, it's probably 70 out of 180 out of 100. But if you're going to kind of look at that
from an Occam's Razor's lens, if we can do that now and get 80% of the results, that value at this
stage is so much higher than what we would eventually be able to invest in human-wise if
we ever got to that point in the business. Especially for smaller low-budget or low-operating
teams or smaller operating teams, it's about maximizing efficiency. It's about how do you look
at it through the assistant lens and say, okay, if I could get an assistant to do anything,
what would it be? And then, yes, you have to design that. And there's a lot of system
design and logic that went into that process. You do have to be good at that. But if you're
good at it and creative enough, you could get some pretty excellent outcomes.
Yeah. No, I appreciate you adding that extra detail and that concept of, if I had an assistant,
what would I ask them, I think is a muscle that a lot of us are going to have to develop
when we're looking at our business and marketing challenges. And maybe we can use that as a segue
to, I know you're a deep thinker and you've been doing a lot of thinking. We've had a number of
discussions recently about what the emergence of certainly generative AI tools, but a lot of other
AI enabled tools is going to do in terms of having an impact on how people run their businesses and
market their businesses. So what do you see happening? How do you think this is going to
play out over the next months and years as these tools take hold?
Well, first things first, we all know marketers will ruin everything because
because no one's better at it than we are. So that's number one. But the reason I start
with that is because that's actually the lesson. So like right now, what we see happening in the
AI market is people creating wonderfully intelligent or simple AI tools to more efficiently do or
maximize current standards of marketing, which very quickly means that those standards will
become more saturated and very much more quickly irrelevant. That is not the lens we should be
looking at this is it's not about how do we use AI to maximize the kind of performance or automated
pieces of marketing that work now. It's about when this shift takes place in it might be 18 months
or two years or four years doesn't really matter. What is then the marketing things that will
survive or emerge on the other end of that? And that's where I've, as a portfolio, we have
different companies that operate in different ways. And everyone's feeling a bit of the recession,
everyone's feeling the AI tech pressure on top of that, which makes it more amplified.
So everyone in our teams are going, whoa, whoa, whoa, whoa, wait, wait, what do we have to do?
So I just start printing content like mad using chat GBT. Is that what I should be doing? Like
writing 10 articles a day instead of one good one? That's literally the types of questions that
marketers and even non-marketers in our team are asking right now. And I think that's probably
what a lot of people are feeling some version of conscious or not in the space right now. And so
we've had to kind of look a little bit deeper and start to think, okay,
what works now that will probably work no matter what happens with AI? That's question number one.
Okay. So what will probably not be affected fundamentally as a marketing style or tactic
regardless of AI? And then two, once AI makes certain types of obvious marketing,
probably not work as well. What will be the new tactics that might emerge from that? And that's
a bit more sort of hedging and risk betting and like kind of trying to use your creative brain
as a marketer to think through to the next systemic move. And then the third one is,
regardless of number two, if people start to change their technical behavior,
right? So if plugins on chat GPT get really sticky, which means people move away from
dedicated solution, point and click interaction, they move away from direct, you know, two to five
word search, and they actually do eventually start to adopt a more lingual form of interacting with
multiple services. What will then the knock on effects of that be on other channels or stable
systems, stable quote unquote systems that we use, right? So we did a little bit of very rough math,
right? Please, please no one quote me on these numbers, please. But some very rough math on what
happens if an average of around two or just over 2% of the English speaking market changes from
changes from a dedicated UI based interaction and using search engines to using a lingual platform,
whether that be chat GPT and plugins or something like it, right? 2% moves from the way everyone
operates at the moment to exclusively that English market. So even that you could consider as like
early adopters, that's not a huge amount percentage wise, right? Yeah. It's like, you know,
a couple hundred million people move their everyday behavior over there. What happens? Well,
one of the things that happens is ads almost double in price for almost every single niche.
Right. Right. Because that is the 2% that is digitally active enough that they are sought out
by most advertising categories. And so if you just remove 2% of that population, that entire sort of
stable platform of that of ad performance and how that's calculated and generated starts to buckle.
And so you suddenly get, you know, we're already seeing crazy price increases in most ad platforms
the past two years. This isn't necessarily new. It's just, but it's potential to be accelerated
massively by a very small transitional adoption of the population to this new type of behavior
could have massive implications. Now, if Google ads doubles in price, particularly for e-commerce,
half of e-commerce classes, it's the primary margin on it anymore. No, it's the primary channel,
like ROAS on Google ads and Google shopping ads is like the primary channel for the majority of
mid to at scale e-commerce platforms. So they either have to kind of get way ahead of that
and start changing how those tactics work now so that in two, three years, they're, you know,
it's a much less reliant channel or they're going to wait too long and it's going to start to collapse
and they're going to struggle and a lot of them are going to die. What does that do to the sort of
economic stability of business and having a startup and all of that? These are sort of the
existential crises that we're considering in this equation, right? But it's those sort of three
core points that we've been looking at. Yeah, I completely agree. I think the first one is
definitely the immediate reaction I see in a lot of places because it's practical and applicable
quickly, right? You can log into ChatGPT or some other tool. You can start to have a play with it.
You can get it generating content for you very quickly. And all of a sudden you feel like
I should be doing this no matter what, because surely it's better and more efficient. And we've
talked on the podcast previously and I know you and I have spoken about what happens when you get
a deluge of AI generated content that may not be that well curated or edited and it just is junk.
And, you know, hopefully the cream rises to the top and most of that is at best AI enabled. I
don't think it will be AI generated. So I think there's some stuff in that. In terms of looking
at the wider ramifications like you just described, I don't think most marketers are even thinking
about number one, if I'm honest. I think some are and I think that proportion is growing.
The number of people who are thinking about number two is very low and the number of people
who are looking like number three, it's probably just you, Emil. It's probably just me. Yeah,
it's probably just me having an existential crisis about something happening in four years. That's
that sounds about on point. Right. But I think, you know, when you're trying to predict the future
and manage your business and your marketing strategy, you have to keep these things in mind.
So I think that is hopefully going to give our listeners real food for thought. You know,
where are you listeners on your journey from one to two to three there? Maybe we can summarize
those in a word, Emil, right? Like number one is using tools to produce content now.
Yeah. Number two is how do things change as people start to use these tools and how people
actually search for information changes? And then three is what happens to the rest of the business
ecosystems as a knock on from that factor in terms of how the ad existing ad platforms naturally
adapt to changes in user behavior, the emergence of new ad platforms, et cetera. So is that a fair
summary? Yeah, I think the tweak in review of one and two and at this point, I've been waffling so
much. I've probably lost my own threads there a couple of times, but you know, the one thing
that's important, most important to recall is just like the tools that are being pushed upon us to
use now are those that amplify current strategies, but that doesn't insulate you against once
everyone is using those strategies and they're saturated, no longer relevant. Right? So that's
kind of where point two is leading. Yeah. And that then leads to the inevitable existential crisis of
point three, but like it's really more about like, if you're going to be a good marketer,
you have to stop being a lazy marketer and you have to think, okay, if everyone does this,
what am I going to do? That's a great question. And what are the things that are still going to
work regardless of this? And so I think an inevitable question from someone listening to this,
I hope would be, well, what are those things? You know, and we could go probably on a separate
rant about what some of those things may be. I can leave a little like nugget hanging here,
which is that if it's human, it's probably going to stay. Right? There's a level to that, that is
real. Right? And you have to think of human on many levels. Partnerships are human. Agreed.
Communities that exist within current constructs of self-identity and alignment are human. So
access to those with our partnership are human. Hint, hint, hint. Which we already knew from the
past couple of years, right? We see performance-based marketing kind of collapsing.
We're returning to sort of more of a brand human driven thing anyway. Yeah. And now AI is just
pushing that all the way. I love that. I think we're about on time now, Emil. So let's leave that
dangling in people's minds. I want to come back and maybe we'll do this again in a month or so,
so it's time to really dig into some of those other number three scenarios, because I think
they're really interesting. But I think for now, hopefully everyone that's given you something to
chew on. Emil, thank you so much for your time today. I always love hanging out with you. Good
to speak with you. And yeah, hopefully we'll do this again soon. Yeah, no, it's been a pleasure as
always. I love doing this. So thanks for hearing out my existential ramblings and thanks to your
audience and to your audience as well, like chase us down with questions, especially in the potential
that we might do a follow-up conversation. I'd love to hear what listeners are actually thinking
about or feeling when they start considering this for themselves. Great stuff. Yeah, completely agree.
I'm sure you'll find this on the socials, on the LinkedIn's and the Twitter's when we put it out
there. So we'd love to hear your questions on those channels. Thanks, Emil. I'll speak to you soon.
All right, we'll do. Thanks, Paul. Take care. Thank you for listening to Artificially Intelligent
Marketing. To stay on top of the latest trends, tips and tools in the world of marketing AI,
be sure to subscribe. We look forward to seeing you again next week.