0:20

Back in 1990, Robin Allenson was studying

0:20

AI at university and he fell hard for

0:27

the vast potential of this technology.

0:29

So Robin went on to build several

0:29

AI startups, including similar AI's

0:34

current project, an intelligent SEO

0:34

platform for corporate websites.

0:38

Robin's been in the AI game for over

0:38

two decades and this past year he's

0:43

companies that are

0:43

actively seeking AI solutions rather

0:48

than dismissing them is too risky. There was a joke in AI , in the

0:50

nineties that all of the working

0:53

AI models were called software. because you do some research, you get

0:55

something to work, and then people

0:58

would just assume it was software. Cause software was actually

0:59

the thing that worked but now we have inbound from people

1:02

who are searching for ai for seo, ai

1:07

for internal linking, ai, for new

1:07

page creation and topic research.

1:10

so it feels like the whole

1:10

world has changed around us.

1:15

Join us to get an insider perspective on

1:15

the latest AI developments, and discover

1:20

how Robin found success by defocusing

1:20

on tech and understanding his customer's

1:25

pressing problems and struggling moments.

1:33

Thank you Robin for being here.

1:35

I met Robin years ago when we worked

1:35

together, on an earlier version of his

1:42

startup, which he's gonna catch us up on.

1:44

Robin is one of the smartest

1:44

and most inspiring and,

1:49

forward thinking people I know. He's been working in AI

1:51

for a really long time.

1:54

And I think he's gonna help us

1:54

understand what's going on right now.

1:58

That's very kind of you, Amy, Joe. I don't know, if all of that is entirely

1:59

true, but, I'll try and live up to that.

2:03

, it's like the blind men

2:03

in the elephant, right?

2:05

Like we all can only speak

2:05

to our own, experience.

2:09

So, start by please, Robin,

2:09

introduce yourself and tell us

2:14

about the startup you're working on

2:14

now, who it serves, what it does.

2:19

And then we're gonna drill

2:19

into some of your background.

2:22

Cool. Thanks. so Hello, I'm Robin. so I'm the CEO and the co-founder

2:24

at Similar AI and Similar

2:27

ai, we're automating SEO for

2:27

agencies and for in-house teams.

2:32

So search engine optimization is a very

2:32

manual job typically, and there's a lot of

2:37

folk who are, working perhaps in a large

2:37

enterprise site or perhaps in an agency.

2:41

And, mostly they're taking some of the

2:41

existing SEO tools out there using their

2:46

user interface and spitting out some

2:46

data which they throw into an Excel

2:49

sheet, or a Google sheet, and spend

2:49

a lot of time manually updating that

2:53

and trying to match that up to their

2:53

pages to take some kind of action.

2:56

and what we're enabling is basically

2:56

teams to, to use our no-code, tool, and

3:02

toolbox to automate that, to speed it up.

3:05

So , we are kind of, taking a those manual

3:05

things and sticking all the data together,

3:10

and we call that data ingredients. And on top of that, we allow our, users

3:12

to run, recipes we call them, which are

3:16

no-code programs, which can publish and

3:16

update , their websites to make them more

3:21

user-centric and grow organic revenue. so that's, kind of in a nutshell.

3:25

I could talk for days about it,

3:25

but I'll try and keep it succinct.

3:28

So who's your core target? Who needs this the most?

3:32

so, for the longest time we've

3:32

been, focusing on, in-house SEO.

3:37

And typically there's one SEO working for

3:37

one, search engine optimization, product

3:41

manager working in a big enterprise.

3:44

the site has perhaps hundreds of thousands

3:44

or millions or tens of millions of pages,

3:49

and they, on their own sum have to update

3:49

the site to match it up to how people

3:54

are actually searching and make it more

3:54

intuitive , for search changing users

3:57

or as we call them nowadays, people,

3:57

and try to make the whole site work.

4:02

and so that's just a monumental task. SEO is normally something you

4:04

do by hand, and so it's just

4:06

incredibly hard to do that. Normally the way you solve that is

4:08

maybe you get an agency involved.

4:11

And you get a lot more hands, on deck. or you use some in-house, tools and you're

4:14

using, you're building that yourself.

4:18

But when you use in-house tools,

4:18

mostly you have in-house data.

4:21

And so that becomes a struggle

4:21

to actually match up to how

4:23

users search on the internet. so that, that struggle means that a

4:26

lot of those sites end up with too

4:30

many pages, and those pages aren't

4:30

interesting for search engine users.

4:34

so that's the struggle that we're

4:34

trying to help and we're trying

4:36

to turn those users, effectively.

4:40

So one of our customers, they said

4:40

that we give our, we give our users SEO

4:45

superpowers, which is, I think it's a

4:45

great way of thinking about it, using ai.

4:49

Cool. So we're gonna get to how you came up

4:50

with this, more about how it works.

4:55

and before I get into your background to

4:55

figure out, you know, like sort of how you

4:59

got here, I can't resist asking about the

4:59

relationship between SEO and Chat GPT and

5:07

the explosion of chatbots, because a lot

5:07

of people are saying, wow, who needs SEO?

5:13

Websites are dead. Why are websites even gonna matter in a

5:15

few years if we're all using something

5:21

like Chat GPT instead of a search engine?

5:25

I'm sure you've heard

5:25

the same argument, right?

5:27

What's gonna happen to

5:27

websites with all this stuff?

5:30

Is it, you know, what's your perspective on this? You're right smack in the middle of this.

5:35

So I think there's a few things,

5:36

which are kind of, interrelated,

5:39

and where this is impacted. So people have been saying SEO is dead for

5:41

many years, and it, it hasn't died yet.

5:46

and, but there are a few impacts

5:46

where, that the ChatGPT or where large

5:51

language models, could, fundamentally

5:51

change, what the SEO industry is doing.

5:56

one of those is search results. So Google has this Ste so this

5:58

experimental approach to integrating,

6:02

large language models in search. and so, the initial feedback

6:04

on that is it, is that those

6:09

results haven't been superb. large language models have a number

6:10

of, weaknesses, such as their ability

6:15

to, yeah, I call it confabulate.

6:18

so, confabulate is a, a term kind

6:18

of, meaning they bring up false

6:22

memories, is what happens in psychology

6:22

commonly called hallucination.

6:25

So these things have a tendency to lie,

6:25

egregiously, and in search results,

6:30

I think that's a very different

6:30

kind of intent than when you are,

6:33

when you're chatting to ChatGPT. that can be a very bad problem,

6:35

for certain types of results.

6:38

I also think that right now,

6:38

ChatGPT is a toy that's being used

6:42

in a lot of interesting cases. And there's kind of an adage in product

6:44

management is that a lot of the best

6:47

products, start from toys, right? and so, maybe it will grow into

6:48

that if we can solve a lot of

6:51

these, hallucination problems. but then, So that's the first piece is

6:53

it's already a disruption in that we see

6:57

that Google is changing the front page

6:57

of search in order include AI results.

7:02

and actually messing with not

7:02

just seo, but also, the revenue,

7:06

that it gets from an ads, right? So it's willing to sacrifice that, to

7:08

contend with this potential, competitor

7:13

in the form of OpenAI and Bing. That's now, I think there are

7:15

still a lot of unknowns, like

7:19

will that actually work out? our hallucinations a solvable

7:20

problem, for web search?

7:23

Don't know. we don't have a clear answer on that. So the assumption is kind of, yes, it's

7:25

all gonna work out, but it doesn't, there

7:29

are very large categories where they

7:29

just can't use this kind of approach.

7:33

You already see in a lot of, like

7:33

your money or your life categories.

7:36

So those are things where you might

7:36

give medical or financial, information.

7:39

they're not using AI there, cause you

7:39

can imagine that the risks are too great.

7:44

so it could be that actually the

7:44

number of places where you use AI

7:47

shrinks over time, where they're more

7:47

focused on which kind of answers they

7:51

can, reasonably and rationally give. so the second piece of seo, disruption is

7:53

it's a lot easier to, to produce content.

7:59

and so the internet kind of

7:59

disrupted the ability to publish.

8:03

content that's been generated, large

8:03

language models and, AI is now generative.

8:07

AI is now disrupting the ability to come

8:07

up with the content in the first place.

8:12

and, and so that's much in the

8:12

way that the internet democratized

8:16

access to publishing information. so, large language models

8:18

and AI democratizing access.

8:21

So it's no longer just say,

8:21

copywriters who can write copy,

8:25

but everybody can write, copy. and so that, means that there's

8:27

just an awful lot more competition.

8:30

and so that's gonna make

8:30

finding great answers harder.

8:34

So a aka web search. so that's another form of disruption.

8:37

And then the third piece is there

8:37

are a lot of, folks in the SEO

8:41

industry who make most of their

8:41

money from generating content today.

8:44

and so when the price of that effectively

8:44

drops to zero, it's gonna be much

8:48

harder to make money doing that. It's always been hard in a way,

8:50

because, what you were really

8:54

paying for was amazing, content,

8:54

excellent content, fabulous

8:58

content you can get anywhere else. And so I still think that there will

8:59

be folks selling that kind of content,

9:03

and it'll be hard to write that with ai.

9:06

but, yeah, it's got harder to do, because

9:06

there are lots of incredibly cheap, almost

9:11

free alternatives out there using ai. So those are the three

9:14

kind of disruptions I think. Wow, that's really interesting.

9:18

That really helps me

9:18

understand the landscape.

9:21

Thank you. So let's wind it back and can you

9:23

explain to us how you got from

9:29

tinkering around with software as

9:29

a kid to studying and developing AI

9:35

and becoming a serial entrepreneur?

9:37

yeah, when I was, I wanna say

9:37

seven or eight, but, I'm probably

9:41

getting the timeliness all wrong. But my dad came home, one on Christmas

9:42

time with a computer called a ZX81.

9:47

So one of the earliest computers. And, I started, as a kid

9:49

to learn, to program.

9:52

so I'm thinking ZX81, probably

9:52

in 81, so I was probably 10.

9:56

but then, I started to learn to

9:56

have a program in basic, and then

9:59

in Assembler, when I was about

9:59

11, I read this book called Gödel,

10:02

Escher, Bach by Douglas Hofstadter. and I was smitten.

10:06

I read that a couple of times. I just completely fell in love with it.

10:09

and I started writing programs as a

10:09

teenager to do things like, translate.

10:14

Between two languages. So I'd learned Esperanto as a kid,

10:15

and so I tried to build a machine

10:19

translation program failed miserably, but

10:19

it was a very interesting, experience.

10:23

and I also built a program as

10:23

a kid to, predict which

10:26

horse was gonna win the race. So you could send away in those days

10:27

for books, in the post I sent away

10:31

for a book, explaining a horse racing

10:31

system where you gave different points

10:34

depending on how the horses are traced. but then I took that system and I

10:36

turned it into a computer program

10:40

and I would enter in the, the

10:40

horses that won the races, and which

10:43

horses were coming up each week. so you could just, find

10:45

that in the newspaper. And I tagged that all in.

10:48

And, so, that program worked

10:48

and it was profitable.

10:52

there were two downsides at the time

10:52

was that, we made about, we would've

10:55

made about a 15% year betting tax

10:55

in the UK at the time was 12%.

10:58

So that wasn't, amazing. And then, yeah, I was a minor and,

11:00

my parents didn't really want to go

11:03

to the, to the bookies every day. so that project kind of failed, but

11:05

I took those, ideas and, aspirations.

11:10

And then I studied, ai, artificial

11:10

intelligence and computer science

11:13

and, uh, joint honors at University of

11:13

Edinburgh, which at the time was the only

11:16

place in the UK where we could study ai. now, AI is, everywhere.

11:20

and then, at, yeah, at the time my dad

11:20

was a research physicist, and he kind of

11:24

said, I want to do AI and linguistics,

11:24

and he said, don't do AI and linguistics.

11:28

You're never gonna find a job. there's no real future in that.

11:30

So I took his advice and did a

11:30

joint honors with computer science.

11:33

but like a couple of years after that,

11:33

Google was hoovering up every AI and

11:37

linguistics PhD , in the country. but then I didn't, study a PhD.

11:42

I went straight into, programming's

11:42

job and gradually did some different

11:45

jobs in, programming, kind of

11:45

technical architecture, and then,

11:49

kind of running, larger commercial

11:49

teams, and more and more on the kind

11:53

of, online business side of things. And then fast forward to

11:54

2009, did my first startup.

11:58

and then, we worked on lots of really

11:58

interesting machine learning problems.

12:02

that didn't really pay the rent. and then we found a really boring

12:04

software problem, that everybody wanted,

12:07

and we started to scale very quickly. We got bought out by a competitor of ours.

12:11

so I took some of that, money and then

12:11

in, 2016, a long, long, long time ago,

12:16

I sometimes say when at university I

12:16

studied AI when it was mostly philosophy.

12:21

it's funny to think that a lot of

12:21

the essentials of what we're doing

12:23

now with large language models,

12:23

the way that, AI works way, deep

12:28

learning works was still what we

12:28

studied at university in the 1990s.

12:32

So back propagation and neural networks,

12:32

still the principles are the same,

12:36

but we've had a few, fundamental

12:36

restorations in how the algorithms work.

12:40

Like reinforcement learning as an example. but also we've got, I don't know, a

12:42

trillion times more compute and data.

12:46

and so that's mostly what's actually,

12:46

I think given the power into,

12:49

into current life language models. anyway, that, that was kind of a

12:51

whirlwind back and forth and kind

12:55

of all kind of other things that, I dunno if I actually answered. It's fun though.

12:57

It's interesting that there's

12:57

a whole thread of probability

13:02

running through your story. Yeah. Messaging probability.

13:05

there's some inevitability to, to

13:05

ending up doing what I'm doing.

13:09

And, interestingly, my, 16 year old

13:09

is about to study statistics and

13:14

she's like, what's it all about? And I'm like, well, probabilities.

13:18

Yeah. It's, that's kind of it so much

13:22

probabilities. Right. And you know, when you talk

13:24

about reinforcement learning,

13:27

it's, you know, sophisticated

13:27

messing around with probabilities

13:30

if you learn to think in, in kind

13:30

of in more dimensions, a lot of this

13:34

is kind of, if you can think about

13:34

weight spaces, A lot of the stuff we

13:38

were thinking about then was how you

13:38

would think about an AI system now.

13:42

so I was gonna say, so my son is

13:42

studying AI at university, today.

13:46

So my son is, 20. and I was kind of surprised that a lot

13:47

of what he was studying, I recognized

13:51

what was going on in the textbooks. and it wasn't something fundamentally

13:53

different than the AI that I was studying.

13:58

what is it like, 20,

13:58

30 something years ago?

14:00

and so the fundamentals have

14:00

really stayed very similar.

14:03

It's just do an awful lot more with

14:03

the compute and the data we have now.

14:07

and there are some, yeah, there have

14:07

been some big leaps forward, but

14:10

those big leaps forward are still,

14:10

still based on, , on the same ideas.

14:14

It's pretty fascinating. So we'll see where this

14:16

whole craze goes, right?

14:19

Like in a year what's

14:19

gonna be happening with

14:21

. Ai. You nailed it. There's so many unknowns,

14:25

I mean, like you also said it,

14:25

there's a continuity here as well.

14:28

It's not like large language models

14:28

have popped out of nowhere and

14:30

a suddenly this amazing thing. We've had, oh no, we've had

14:32

kind of big data being a thing.

14:35

We've had AI being a thing. We've had deep learning being a thing.

14:38

We've had reinforcement learning being a thing. We've had gans, all kinds of other

14:40

kind of things where people were

14:43

going, wow, this is the moment.

14:45

Now we have large language models. I don't know if, I don't think the

14:47

large language models are the moment.

14:51

But it is pretty clear that there

14:51

is a, there's a line going through

14:54

all of that, where things are

14:54

getting more and more capable.

14:58

and the downsides to, to these kind of

14:58

connectionist systems, still remain,

15:03

but many of them are getting improved. yeah, so I dunno if this is the

15:05

moment when AI kind of, let's say

15:09

takes off, but then, it does appear

15:09

to be taking off in a way that it

15:12

hasn't in the last, six months. but I expect there'll be another

15:14

evolution of AI in, in a few years.

15:18

and it will just keep on going. So

15:20

as someone who's been inside of this

15:20

and wrestled with the difference

15:26

between the promise and the reality

15:26

of ai, what is your take on.

15:34

The doomerism and the, oh my God,

15:34

I'm gonna quit my job and go around

15:40

the country telling everybody

15:40

how dangerous this moment is.

15:44

there's obvious things

15:44

around, misinformation.

15:47

Right. what is your take?

15:50

How do you feel both as a technologist

15:50

and just kind of as a human and a parent?

15:55

So, so I think, so there are a

15:55

few different, parts of my answer.

15:59

right. So one of, there are folks in the

15:59

industry who've been studying the

16:03

risks of AI and how to make AI

16:03

fairer, and work better for, everyone.

16:09

and mostly what I see, they've been

16:09

kind of, I dunno if it's literally

16:14

fired or just ousted from, the big tech

16:14

companies that we're employing them.

16:18

not a great sign really. and then, but, and they are

16:20

really looking at say, how.

16:23

generative AI is using deep fakes

16:23

for misinformation, uh, campaigns

16:27

in actual stuff that's going on

16:27

now that can incite violence.

16:30

That's scary worrying, stuff that

16:30

I think we could be, actively

16:35

doing something about now. But I see people not doing stuff about

16:36

that and talking about how, AI is

16:41

gonna bring about an extinction event. So I think that seems a bit strange

16:43

to me Also, , I would prefer to

16:46

focus on the present day dangers. and how, LMS can make yeah, for

16:49

instance, misinformation, but also bias.

16:54

So these models concentrate the bias

16:54

that's naturally occurring in data, which

16:59

is data that's trolled from the internet. and, that concentration, I

17:00

think, can be very risky.

17:03

and we see models hallucinating in ways

17:03

that could be, could be bad for people.

17:08

that doesn't mean that, that we're gonna

17:08

have an AI that's gonna be Skynet and

17:12

take over the planet and kill everybody. it's just more like, this is a very

17:13

powerful tool, that can be misused

17:17

and it is currently being misused. so I, I think those things are risky.

17:20

A lot of the guardrails that have

17:20

been placed onis, and large language

17:25

models, yeah, like there are pretty

17:25

easy ways of getting around them.

17:28

and, it's not clear how that,

17:28

could work or should work.

17:32

I think that, I think that, say OpenAI

17:32

calling for some kind of centralized

17:37

agency, to look at AI risks, is

17:37

also a little bit self-serving,

17:41

cause I think that's gonna push

17:41

towards, more centralization.

17:45

only the big tech companies will

17:45

actually be able to afford to, conform,

17:49

to whatever regulation is put in place.

17:51

It's incredibly early in large

17:51

language models to be thinking

17:54

about that kind of regulation. and I think there's a burgeoning open,

17:55

software scene for large language

18:00

models, which will be effectively

18:00

killed off by that kind of regulation,

18:04

which seems pretty handy for big tech. Right.

18:07

who are mostly interested

18:07

in selling those, models.

18:09

That's also maybe the reason why the

18:09

folks who are researching this kind

18:14

of, the potential misuses and what

18:14

could go wrong, when they actually

18:17

wanted to publish the results of those. Yeah.

18:19

Big, big tech broadly. So I think in that case, Google was

18:21

not very interested in them doing that.

18:24

and so that was the conflict. so I think it just feels very, it

18:26

feels a little bit one-sided right now.

18:30

and so I'd prefer to listen to the people

18:30

who are actually, have been studying

18:33

this for many years rather than, so there

18:33

are a lot of kind of, one of my first,

18:38

my thesis at university was looking at

18:38

some of the work that Jeff Hinton does.

18:42

he's an amazing researcher. I don't know, just because he's an amazing

18:44

researcher in AI means we should listen

18:48

to, his ideas, on like what AI will bring.

18:52

I don't know if being a great

18:52

science researcher means that you

18:56

are good at thinking about the

18:56

conflict, with the science and AI

18:59

and technology and, and society. there are other people who have

19:01

been studying those things. I think we be giving more,

19:02

credence to what they say.

19:06

yeah. So again, long rambling answer.

19:09

I dunno if that helps you. Well, I like it because it helps me think about

19:10

the space, which is what we're all

19:16

trying to do is have a position, right?

19:18

Because so much info coming

19:18

at you all day, every day.

19:23

It's overwhelming. So many certainties, right?

19:26

I found it all. Like, I don't know.

19:29

And so I think, from what I read,

19:29

there was a questionnaire going around

19:33

asking Ai lumin and, researchers.

19:36

The probability of some

19:36

kind of doom scenario.

19:39

And the vast majority, I don't

19:39

remember the figure, 90 something

19:42

percent just abstain from answering

19:42

that cause it seems unanswerable.

19:46

and the, you know, 1, 2, 3 something

19:46

percent that did answer it, then they

19:50

summarize those answers and then suddenly,

19:50

that's what people are talking about.

19:54

I think it'll be more useful to talk

19:54

about like, that these are unknowns,

19:59

rather than these are quantifiable

19:59

in some way cause they're not.

20:02

and I think , forcing people to

20:02

quantify it seems, seems strange cause

20:06

then obviously you take some tiny

20:06

percentage and multiply it by infinity.

20:10

and I think there was some

20:10

kind of, pascals paradox,

20:13

something like that, right? so you end up with, oh, we have to

20:14

dedicate a lot of resources to that.

20:17

I think. We should be dedicating resources and

20:18

most of the people who are, campaigning

20:22

for that are the folks who are actually

20:22

founding these, these large language

20:25

model, companies in the first base. they're also people who are using all

20:27

of the crawl data without thinking

20:30

about, say, copyright issues, or

20:30

thinking about, you know, how they're,

20:35

displacing some of the people, who

20:35

first came up with the content.

20:38

and just publishing that as quickly

20:38

as possible, publishing those

20:41

models as quickly as possible. and then saying, Hey, we should

20:42

have some kind of regulation. it, yeah, it feels very one-sided to

20:45

me, like, as if we already know what

20:50

the problem is and how to solve it. I think we should, look at the

20:51

people who are, like you say,

20:54

talking about the space, and talking

20:54

about the space of, of questions.

20:59

yeah, I think their questions are really interesting. I don't purport to have any of the

21:01

answers, but I think the questions

21:03

are a lot more interesting. Yeah, I think it's something

21:05

we're all trying to understand.

21:07

We had, Douglas Hofstadter, the

21:07

author of Gödel, Escher, Bach, which

21:11

you mentioned earlier, as a guest

21:11

a few weeks back, and he's pretty

21:16

upset and depressed about it all.

21:19

Yeah. I couldn't get a positive angle

21:19

out of him even though I tried.

21:23

But you know, everyone's

21:23

got their own point of view.

21:26

And again, just cause you wrote

21:26

a bestselling book and worked as

21:29

a researcher and as a theorist,

21:29

doesn't necessarily mean that

21:33

you have insight into everything. But I like that you're in

21:36

there working positive.

21:39

The reality is the book's not written.

21:42

The final book on AI is not written. We're writing it.

21:44

We're all writing it. Indeed. So, I'm glad to be able to

21:46

hear your point of view and,

21:51

you know, give it some oxygen. So

21:53

I mean, take it with a

21:53

pinch of salt, everything.

21:55

Like my curse as a founder is

21:55

to be perpetually optimistic.

21:59

and so I always think the world

21:59

is slanted towards, good things

22:02

and a happy, hopeful future. so in my head there's always those

22:04

kind of good things going on.

22:08

So I tend not to focus on

22:08

the, cataclysmic, potentials.

22:11

I also just think we have plenty of those. Like, I heard a quote, uh, this,

22:13

um, Zi Maza wrote a bit about this,

22:17

and, he quoted in his newsletter,

22:17

somebody else, saying, climate change

22:21

should have, um, AI's, PR team.

22:24

I think right now being so sure

22:24

that AI is gonna bring this terrible

22:28

future when we have science talking

22:28

about climate change, like with this.

22:34

Enormous impact. I feel like there should be some

22:35

enormous evidence, that, that this

22:39

is really coming for climate change.

22:41

I think we have a lot of that

22:41

enormous evidence and we should

22:44

maybe be focusing on that rather

22:44

than this very speculative, stuff.

22:48

yeah, I would prefer to focus on the stuff

22:48

we, we really know about and we think we

22:52

can actually change, rather than this,

22:52

it feels way too theoretical for me.

22:57

Spoken like a true entrepreneur

22:57

who likes to get his hands dirty.

23:02

So, I wanna follow up on

23:02

your entrepreneurial mindset

23:05

that we're talking about. You're a serial entrepreneur and

23:07

having now worked with a number of

23:11

serial entrepreneurs, there's something

23:11

qualitatively different doing it

23:14

multiple times versus the first time. So from your own perspective, What

23:17

are some mistakes maybe that you

23:23

made that you, perhaps you see other

23:23

entrepreneurs making common mistakes,

23:29

especially for first time entrepreneurs,

23:29

for folks that are interested in maybe

23:33

having an awareness of those, what are

23:33

some of the things to watch out for?

23:37

Both for AI entrepreneurs, but

23:37

also entrepreneurs in general?

23:41

I was gonna say, daughters

23:41

entering living rooms when you

23:44

are, trying to, have a conversation

23:44

is one that keeps on coming up.

23:47

But, um, so, I think one of the things

23:47

that looks like, initially it's gonna

23:51

be unique to AI entrepreneurs, but is

23:51

actually, very common across everybody.

23:55

And it's also for me is, falling

23:55

in love with your secret source.

23:58

so that's kind of focusing on the solution

23:58

space rather than the, problem space.

24:03

And I think, there've been a lot of,

24:03

recent AI companies which have exploded.

24:08

Which are something like,

24:08

a thin layer on top of t.

24:12

And, kind of the answer has

24:12

been AI is gonna solve that.

24:16

I remember when I first started,

24:16

similar ai, we were starting to use,

24:20

deep learning and we had angle on how

24:20

to create a lot of training data, and

24:24

come up with, more training data means

24:24

basically better models at scale.

24:28

and so we had an angle on that, but

24:28

we had a couple of, advisors to the

24:31

company and they said, but Robin,

24:31

deep learning, you don't need training

24:34

data cause it's deep learning. and so they said it in, in a way that

24:37

made me think that deep learning was

24:39

actually in italics when they said it. so I hear something very similar about

24:41

large language models now where people

24:44

are like, what does your company do? We do large language models.

24:47

yeah, but what, I mean,

24:47

what exactly do you do?

24:49

And so I think some of those

24:49

companies are being disrupted

24:52

effectively by ChatGPT now. and also.

24:56

a lot of folks are finding it very

24:56

easy to integrate, large language

24:59

models into the existing incumbents.

25:02

and that's actually, it's kind of harder

25:02

to just do a, I dunno, an AI startup.

25:07

so we fell into that. Initially, we were building

25:08

a lot of trading data.

25:11

We'll come out with a whole bunch

25:11

of, ideas about how to, come up with

25:14

large, well, basically multimodal,

25:14

models is what we're building.

25:17

So combining, images and texts to

25:17

understand product pages and turn

25:21

them into the language that people would use to search for them. and so that was the core thing

25:23

we were doing, but we actually

25:25

found it really hard to sell that,

25:25

because we found that a lot of other

25:28

people were claiming they used ai. and so we over time just

25:29

stopped talking about ai.

25:34

We didn't bring up in the conversation, it was in our names. It was a bit, kind of hard to

25:36

avoid, but we just didn't bring

25:39

it up in the conversation. And we started really focusing on

25:40

the problems that our customers were

25:43

struggling with, but our users were

25:43

struggling with and kind of explained

25:46

how it was different, and how we, help

25:46

them make progress, in, in that struggle.

25:51

And that was a night and day shift. and so what's been, miraculous to me

25:53

in the last six months or the last

25:56

nine months is actually, there has

25:56

been a shift , in how people are

26:00

approaching us, because now, like.

26:03

I know three years ago, five years ago,

26:03

if we actually, and certainly one of

26:06

the first a studying ai, people would

26:06

say things like, why do you do that?

26:09

Because it doesn't work. And so there was a joke in AI in

26:11

the nineties that all of the working

26:14

AI models were called software. because you do some research, you get

26:16

something to work, and then people

26:19

would just assume it was software. Cause software was actually the

26:20

thing that worked a few years ago.

26:23

If we talked about using ai, which

26:23

was in the solution space, then

26:27

folks would just be, yeah, we're

26:27

not looking for anything innovative.

26:30

We're just trying to solve this problem. Right? And so for them, AI was

26:32

something that the, no, the

26:35

business innovation manager did. and not something that

26:37

the e-commerce team, used.

26:40

But now we have inbound from people

26:40

who are searching for ai, for seo,

26:45

ai, for internal linking AI for,

26:45

de-duplication, ai, for for new

26:49

page creation and topic research. Those are the kind of things we do.

26:52

We also do AI for content. but you know, who doesn't?

26:55

so, it feels like the whole

26:55

world has changed around us.

27:00

so now people are coming out to us

27:00

and saying, Hey, we found out that

27:03

actually, you guys do AI for seo.

27:05

Well, you guys do AI for internal linking.

27:08

We're like, yeah, we do. That's amazing. Can you explain it to us?

27:11

Whilst nine months ago, if we

27:11

had said that they would've ba

27:13

basically gone, why do you wanna

27:13

use AI what's the point of that?

27:16

Now, their expectations about what

27:16

AI does have shifted, and with those

27:21

expectations, yeah, it suddenly means

27:21

we have a new and much larger market.

27:25

Again, I feel I answered

27:25

two of your questions

27:27

in that is, I wanna reflect back a little

27:27

bit what you said and really highlight it.

27:33

Cause it's such good advice. One, don't fall in love

27:35

with your secret sauce.

27:38

That's so hard. That's really hard.

27:42

But what great advice,

27:42

because customers don't care.

27:46

They don't care. They just want you to solve their problem.

27:50

Exactly. Yeah. So in my first startup, I had

27:52

a startup before this one.

27:55

We worked on lots of interesting

27:55

machine learning problems.

27:57

We were so interested in, in,

27:57

those machine learning problems.

28:00

They weren't customer problems. there were machine learning

28:02

problems that we could apply and we

28:04

thought there were great solutions. And then, when we found, and so, those

28:06

are building kind of sales reporting.

28:10

and then, one of the reports talked

28:10

about a problem we found we could build

28:14

some software to solve that problem. And it, it wasn't easy, but it was

28:15

pretty easy to get off the ground.

28:19

Once we got that going, suddenly

28:19

we found, a lot of those customers

28:22

were super interested in that

28:22

problem, in solving it more simply.

28:25

we were off to the moon, right? in the second startup.

28:28

So in similar ai, I basically

28:28

fell into the same trap despite

28:32

advising other startups. So despite saying you really should be

28:33

asking, you know, what problems your

28:37

customers are struggling to solve, and

28:37

have you thought about these kind of

28:40

questions, and, kind of, teaching them

28:40

about how to think about that and then

28:44

in our own business, I've found it

28:44

very hard to take that advice myself,

28:48

basically cause I don't think this

28:48

is something you can simply learn.

28:51

I think this is something that is

28:51

often, you can think of it more

28:55

like a cognitive bias as a founder.

28:58

Even if you know intellectually

28:58

what you're supposed to do when

29:00

you get up in the morning, you're

29:00

in love with the solution again.

29:03

and so you need to actually correct

29:03

course correct each time, to go back.

29:08

but when you do, it's enormously valuable.

29:11

So, I've got one more question actually, I've

29:12

got a bunch more, but I'm gonna only ask

29:16

one more So, um, you're talking about

29:16

understanding what problems your customers

29:22

have and that you struggle with that as

29:22

a founder, even though intellectually

29:27

you know how important it is, by the way. Me too.

29:30

I struggle with it too. So, what are some of your go-to methods?

29:35

Robin, you mentioned that you

29:35

like the magic wand question from

29:39

the game thinking methodology. Can you just share a little bit about

29:41

how you've used elements of game

29:45

thinking or maybe that particular one

29:45

to unlock customer value for yourself?

29:50

Yeah, sure. so, so we actually, we did a big piece,

29:51

but it was a few years ago before we're

29:56

actually working on, what we're doing now,

29:56

to talk to fashion designers at the time.

30:01

and we were planning on building a

30:01

model using gans, using generative

30:05

adversarial neural networks. But they turned out to be

30:07

kind of too bleeding edge. but what was amazing to me at the

30:09

time was, I was able to reach out

30:14

to, to, some fashion designers and

30:14

we got, a lot of responses, to an

30:19

initial survey within a day or two. and well, initially we didn't, and

30:21

then we tried again a couple of days

30:25

later and we got a lot of responses

30:25

with some screener questions.

30:28

and actually, some very quick five

30:28

minute, kind of 10 minute interviews.

30:32

We started like hitting gold, very

30:32

quickly when we started asking, you know,

30:37

what are the things you struggle with? and they started telling us, and from

30:38

10 conversations, I don't know, eight

30:42

of the things they were struggling

30:42

with were the exact same things.

30:45

And we were like, what on

30:45

earth is going on here?

30:48

And so we kind of fed in

30:48

love with those problems.

30:51

and we got deeper and deeper into the

30:51

conversations and we started talking more.

30:54

That was an amazing experience,

30:54

mostly because the value we got

30:57

to, in the time we spent on it. And so, it was just a such a short

30:59

time compared to building a product,

31:03

which we'd already done and spent a

31:03

huge amount of money on and wasted.

31:06

compared to building a product,

31:06

this was remarkably easy, right?

31:10

And then later in the journey down,

31:10

now, into, when we're doing what

31:14

we're doing now, we had really

31:14

focused on these big enterprise teams.

31:18

So science that might have, I

31:18

don't know, 150 million pages say,

31:22

or hundreds of millions of pages. And, we thought, wow, this is a big

31:23

market enterprise seo, talking to

31:27

heads of seo, in these big companies. and we started doing some market

31:29

research where we tested smaller sites.

31:34

that only had I, no, a few hundred

31:34

thousand pages or only had 50,000 pages.

31:38

And we kind of asked 'em about the

31:38

problems, like at least the way that we

31:41

marketed the problems that we solved. And they were effectively blank stares.

31:46

And they didn't really get what we were

31:46

talking about, but we continued the

31:49

conversation and we started asking about

31:49

some of the problems that they struggled

31:53

with, and it all opened up again.

31:55

Right? And so when we actually really focused

31:55

more about their, about their experience,

32:00

what we found was that everybody

32:00

struggles with, with automating seo.

32:05

they don't always think about it in those terms. and so the way we were talking about

32:06

it, they just didn't get that at all.

32:10

but they would say something like,

32:10

wow, internal linking is like, we

32:14

dedicate time to that every month,

32:14

and it's painful and it's tedious.

32:18

Is time consuming and

32:18

it's incredibly valuable.

32:20

isn't there a way that you

32:20

could like automate that and

32:23

we can still get the value? and so we could talk

32:25

about what the value was. They were doing it like every month

32:27

they had people working on it.

32:29

They, like, they knew exactly

32:29

why they were doing that.

32:32

and what that was true for a site

32:32

that had a few hundred pages in the

32:35

same way that it was true for the fact

32:35

they had a few hundred million pages.

32:38

and that was kind of a light bulb moment

32:38

for us when we were like, oh wait,

32:42

these guys have the exact same problem.

32:44

They just don't, express it in quite

32:44

the same way, but there's a way in

32:48

which we can, kind of reframe that

32:48

job, where suddenly it applies to

32:51

this whole enormous market basically

32:51

to everybody trying to, everybody

32:55

trying to do, customer acquisition. and so, yeah, so that was a light

32:57

bulb moment for us and that really

32:59

just came across, from having a few

32:59

of these, these customer interviews.

33:04

Before sales. Like not actually reaching out and

33:05

saying, Hey, we're gonna do sales.

33:07

Reaching out and saying, we just

33:07

wanna learn from your experience.

33:10

Can we have some simple conversations? and people just opened up and and

33:12

told us, not what we wanted to hear,

33:17

cause we really wanted to hear. Yeah. What you're doing now is great for us.

33:19

We'd love to buy that. Please. but what we needed to hear,

33:20

and it, yeah, it transformed

33:24

the trajectory of the company. I love that.

33:26

And just to frame and put it on a wall,

33:26

this piece of advice, if you can get

33:32

the right customers in a room and not

33:32

talk about the solution, but understand

33:36

their problem, you will hear the language

33:36

that they use and it might connect, it

33:42

might not be the same language you're

33:42

using, but understanding the language

33:45

your customer uses to talk about their

33:45

problem is the gift that keeps on giving.

33:50

That's it. It's what? So Amy Jo, what you taught me is sometimes

33:52

it's better to have a, your scientist

33:56

hat on, than your salesperson hat on.

33:59

So sometimes founders find it very hard to

33:59

talk about their company without pitching.

34:04

Right. And so, like the antidote to that is,

34:04

stopping talking about your company,

34:09

asking about your customer, asking

34:09

about their problems day to day,

34:13

and then, shutting up and listening. Right.

34:16

And listening is just so, so powerful.

34:18

cause at some point, even though most

34:18

people can't get a word in edgewise

34:22

when they're talking to me at some

34:22

point they'll, you know, they'll

34:24

actually explain what they're struggling

34:24

with and it will make so much sense.

34:28

Awesome. Thank you so much, Robin, for

34:30

leveling up our thinking and giving

34:33

us some really , exciting and

34:33

challenging visions to wrap our

34:37

minds around. And fun.

34:39

I think Amy, Joe, like there is a

34:39

lot of fun, uh, ahead of us as well.

34:43

Right. So it's super cool to have the power of

34:43

computation in the hands of everybody.

34:48

Right. I think it's, um, it's a it

34:48

could be a very fun future.

34:51

It is. And I'm working with more and more AI

34:51

startups and I'll be sharing that for

34:55

all of you over the next few months, so thank you so much for joining us.

35:00

Yeah, thanks, uh, for inviting me and having me here. it was just amazing.

35:03

Thanks everyone. Let's get smarter together.

35:06

There's nothing better. Bye.