Digital Disruption

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Hey everyone!

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I'm super excited to be sitting down

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with Marie Imafidon, chair

of the Institute for the Future of Work.

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What's so cool about Anne-Marie

is that she does it all and sees it all.

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Not only is she leading the charge

on the future of work, but she's a tech

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CEO, computer scientist,

and former child prodigy in her own right.

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I want to ask her how she sees the nature

of work changing, what role AI plays

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and what each of us can do

so that we're not left behind.

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Let's find out.

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I'm here with Anne Marie Imafidon.

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And Marie,

thanks so much for joining today.

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Maybe just to get things started,

I wanted to talk a little bit

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about some of the work you're doing

with the Institute for the Future of Work.

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I know you're

you're a trustee with that organization.

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Maybe just to kick things off

from your perspective.

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what are you seeing

in terms of the future of work?

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What's your outlook, what's what's

changing? And what do we need to know?

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All lot.

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Lots of things that we're seeing, lots

of things that we're still figuring out.

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I think we could say as an institute,

we're working through the details.

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As different things

come onto the horizon, as we start

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to explore different uses of what we're

calling the kind of fourth industrial,

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revolution technologies coming

on, on, on stream.

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Not just AI, but of course, big data,

quantum, lots of other bits and pieces.

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So in terms of what we're seeing,

I think there's, there's

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a, there's a great kind of figuring out of

how we're making all of these

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things, work for the workforce.

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But I think the, the kind

of two big things that I like to explore

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with folks kind of based off our work.

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One is the levels

at which these impacts are being felt.

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So a lot of the times

when we talk about the future of work,

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we end up focusing on the worker

and on the employee.

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Where actually where we tend to

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kind of it's one of the lenses

that we have over the work that we do.

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This is not just about individuals,

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but this is about companies,

and this is about society.

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And so that's one of the

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one of the kind of big things

that we end up trying to work

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through with folks is it's not just about

what happens to an individual,

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what's happening to a group,

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what's happening to a company,

or what's happening to a team,

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what's happening to an industry

and therefore what's happening.

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Society's at three levels that we look at.

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But then the other big one, that's maybe

a little bit further off that I don't know

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if you have got to explore

with folks on the podcast previously

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is also this idea of identity and work,

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and we've had this

since right at the beginning.

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So I'm the chair now at the Institute

for Future of Work.

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I also run an organization called Stem.

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It's and we work with young people, young,

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young folks,

young them binary, young women.

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Kind of people

that have historically marginalized

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from the tech industry in the Stem

y the Stem and steam industries.

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And a couple of years ago,

we ended up working in a part of the UK

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that historically had a lot of minors.

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And this is like mines, like coal mines,

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which in the UK is an industry

that shot decades ago.

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And we were working teenagers.

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And it kind of came back as this feedback

that, yeah, these are minors kids.

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That's why they're having

this kind of response to the events.

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And this is kind of early

this is kind of 20 tens.

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And I remember

saying to a couple of people there,

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what do you mean they're minors?

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Kids like the mines shot the decades ago.

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These are teenage like it physically.

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How is that a

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and of course, no, it was that they were

they were kind of second generation.

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Their grandparents had been minors

as their entire job for life.

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That was the entire density

that they had of these people.

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And so even that their children

had never seen a mine and never been in

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life, was anyone in their lineage

just working in mine?

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That notion of work

for life was so embedded

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that not only was it the identity

of their grandparents that were miners,

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but now there's grandchildren at them.

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And so it's something

that when I graduated, we'd said

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that I think something

like I'd have six roles in my career,

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and now our relationship with work is one

that folks are kind of maybe

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even doing multiple jobs

at the same time versus that one for life.

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And so actually,

if we start to think of it that way,

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what's the new identity

that people might have?

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If work is not for life and it's not,

you know, and all these technologies

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are enabling us

to have different relationships with work,

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with, you know, income generation,

with what we spend our time doing.

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And so that's the other

big overarching piece that I like

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to kind of explore that runs away

a little bit from the technology.

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But so much of this is about,

you know, the impact of the technology

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rather than purely

about how it gets deployed and gets used.

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The in and it's a super it's a super,

super interesting piece.

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And I had had laugh I had sort of sardonic

thought, which is that I don't think,

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I don't think my grandchildren are going

to say they're like podcasters, children,

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you know, that said, either of ours are

and I, I hope they're I hope they're not,

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whether the miner's

children are good or bad,

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but but you know, what are Anne-Marie,

what are some of the implications of that?

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Like, what are we not seeing here

and what does it mean as a society

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for us when we move from jobs for life?

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Or, you know, my work is my identity to

I mean, it feels like the economy.

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And, you know, I tell me

if you're seeing the same thing in the UK,

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but it's not even that.

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It's that it's shorter

stints, that organization.

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It feels like it's more part time.

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It's more piecemeal work.

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Like, what are you seeing

and how is that impacting people?

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So what we're what we're therefore

seeing what you're saying is there's maybe

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less, slightly

less of a sense of identity in work,

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or there's a mismatch

where folks have been looking for that.

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I've assumed that they have had that,

and it's no longer

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being supported by the way

that the industry is being transformed.

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So, you know, the, the

I don't know, as an example,

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we can say if we look

at the legal practice, for example,

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the way that folks kind of move

through the ranks,

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a lot of those kind of entry

level roles are transforming and morphing.

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And so therefore, the way

that we're evaluating folks to then

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be future leaders, you know,

the legal industries having to rethink

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elements of how that's done as we bring in

the new, forms of technology.

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I think the other thing

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that we are seeing, and we're big

proponent is kind of where we started,

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actually, as an institute

was our good work charter.

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But definitely looking at how do we ensure

that folks, are intentional

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in the way that they're

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deploying technology in the workplaces

to ensure that people still have good work

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that does promote, dignity, that does

promote learning, that allows them to have

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all these things that I think we might

have had as part and parcel of jobs.

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And the way that work was built

in previous generations.

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But yes, if we remove

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the sense of identity, removed

the way that things are working now,

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then actually we

you've got to be mindful of

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I'm deploying this technology in a way

that allows folks to continue to learn.

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And so

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then that

becomes the way that we set work up

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and that we instill that we're still going

to have good work for folks

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to be doing, rather than them

maybe being at the mercy of

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technical and kind of robotic overlords.

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Right.

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Well, and it seems like it seems like

we're kind of backing into something

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you discussed earlier, which is that

there's the worker lens on this,

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but there's also the company lens

and the societal lens.

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And you described it

as sort of a sorting out period.

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But it feels,

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at least from where I'm sitting,

it feels like we're increasingly at odds

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with some of these mandates

as we sort that out and that,

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what's best for the company doesn't

necessarily feel like it's best

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for the worker or best for society.

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And there's this, you know,

kind of this triplicate tension there.

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What what does that look like

from your lens?

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And, and, you know,

can you tell me a little bit more

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about what the institute and,

and you are exploring.

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Yeah,

I think I think it's it's a funny one.

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You say it's at odds.

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I think it's something that

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because we've never had to explicitly say,

that's how we're developing work.

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Well,

that's what we're creating work to do.

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I think, folks, it ends up feeling like it

jars a little bit to say,

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what do you mean?

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Good work

means that this person is learning,

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or that we're ensuring

that there's dignity, right?

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Or that they've got access to kind of

what it is that they need to do.

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You know, a good work

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was just that that person got paid

well and that was good enough.

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And so I think we're definitely

seeing elements of that job.

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But I think what we're seeing more though,

is that a lot of companies,

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a lot of industries

are trying to make sense

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of how they make the most of this fourth

industrial revolution.

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What is the transformative everyone saying

it's going to be disruptive?

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You know, we're here

talking about digital disruption, right?

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You know,

what is that actually going to look like.

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And what we're seeing is that

this is allowing folks to have

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a better way

to approach this transformation

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in a way that they can feel at peace,

maybe with the idea of legacy.

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I talked to a lot of audiences, actually,

about this notion of legacy

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that the decisions you're making today

aren't just about the next quarter

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or the next annual round, or the next

strategic, from the 3 to 5 years.

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But actually, these

a lot of these decisions we're making now

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have this kind of 50 years down the line.

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Think if the

if the person sat in your seats

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and the decisions you're making today,

the norms that you're codifying

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the intentions that you have for

the industry, for the job, for the role,

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for the nature of the products

and services that you're developing.

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If when you think about it

kind of from a long term perspective,

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then actually it helps for folks to say,

yeah,

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what part of this allows people

to continue to learn?

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What part of this allows

folks to have dignity?

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And so we're finding that organizations

are thankful actually to have a framework

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to which to think about this, that isn't

purely about profits and more money.

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I think even more so there's so much

rhetoric that we have in this hype

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cycle that ends up being quite dystopian

and a bit of Black Mirror

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or wooly or, you know, in some other

kind of dystopian end tier of

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things are going to be terrible

because we're just going to follow

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along with the technology

and we're all going to end up

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killing ourselves by accident

or whatever else it might be.

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And I think some of these things end up

being checks and balances

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that folks can

then feel a little bit better as,

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yes, I've used this audit,

I've used this assessment,

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I've used this tool from the Institute,

I've read this this paper, I've understood

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some of the ideas that are floated here

and the warning signs that we have.

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And so actually,

any step that I make in the write down,

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any set that I'm making, this direction

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is going to be in the right direction

and is going to, you know,

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prove a good one in the long term

so that that's what we're beginning to see

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with people

genuinely wanting to work up these details

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and whether that's unions,

whether that's regulators,

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whether that's academics,

whether that's, you know, we work

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with so many different,

organizations and types of stakeholders

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that we're able to have

this kind of holistic view

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of what good looks like

in lots of different places and spaces.

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want to clarify a little bit because

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we're talking about this notion

of the fourth industrial revolution.

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We're talking about all this change

and the need to do. Right.

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And by the way I love the,

I love the framework around legacy for,

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for a lot of reasons

that I want to get into in a minute.

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But just before we go there, what

when we talk about this fourth industrial

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revolution, like, is this 90% AI?

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Is it 10%

I like, aside from the IP, like what?

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What are some of the other big drivers

in your mind of what are shifting for

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for work?

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It shifts, it shifts, and it differs.

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So I it's not an 80%.

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Not 90%. I'd say it's probably more 60%.

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I think quantum is definitely on

the horizon.

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We've had big data for quite a while.

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We do

a lot of kind of physical manufacturing,

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kind of psych pieces, pieces as well.

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So for us, it's rather than focusing

specifically on, on the technology across

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the kind of wider set of research

that we're doing, we've gone quite broad,

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and I think we've had to do that

because of the longitudinal nature of what

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we're trying to establish or set up.

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I think if we got caught up in AI,

we'd end up restricting, really, you know,

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the, the impact we're able to have, but

also, the quality of what we're doing.

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So I think with AI, actually,

there's quite a lot of things.

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I mean, when we started,

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you know, there was also a notion of

what is AI and what's not I.

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Right?

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A lot of people were kind

of taking statistical analysis.

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And calling it wrap up packaging is AI

and kind of getting more funding

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and getting more eyeballs on it

based on that.

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So I think we kind of wanted to rise

a little bit above the hype,

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and not be kind of AI specific.

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Quantum is one that has is something

I was reading yesterday.

00;12;32;02 - 00;12;34;16

I'm still kind of surprised. Maybe not.

00;12;34;16 - 00;12;36;29

I mean, I'm

I'm more surprised than I'm not.

00;12;36;29 - 00;12;39;29

That quantum isn't featuring

so much in conversations now,

00;12;39;29 - 00;12;43;18

as much as AI is, though,

this research is going on.

00;12;43;26 - 00;12;47;19

There's supposedly, updates and progress

that's being made on that front,

00;12;47;19 - 00;12;50;19

and it is going to be incredibly

transformative,

00;12;51;01 - 00;12;54;07

once this goes live

and once we really do have the kind of the

00;12;54;10 - 00;12;56;06

we've worked out

all the elements of what needs to

00;12;56;06 - 00;12;59;08

to happen for it

to be kind of stable and, and at large.

00;13;00;02 - 00;13;03;11

So I think for us, yeah, as a, as

an institute, we're wanting to be broader

00;13;03;16 - 00;13;07;09

and look at kind of, all manner of different technologies in the here and now.

00;13;08;17 - 00;13;09;10

Sure.

00;13;09;10 - 00;13;13;23

And you know, you mentioned AI hype,

which I think for anyone

00;13;13;23 - 00;13;16;27

even tangentially tied to this space,

it's just impossible

00;13;16;27 - 00;13;18;23

to get away from these days.

00;13;18;23 - 00;13;21;01

And so I'm curious,

from your perspective, Anne-Marie,

00;13;21;01 - 00;13;23;14

what are the things you're hearing

in this sort of hype sphere

00;13;23;14 - 00;13;26;04

that from your perspective

and what you've seen on the ground,

00;13;26;04 - 00;13;30;04

like what's BSW versus

what are the pieces of it and the,

00;13;30;09 - 00;13;30;27

you know,

00;13;30;27 - 00;13;33;22

implications and implementations of it

that you really think

00;13;33;22 - 00;13;36;05

are going to be transformative

and disruptive.

00;13;36;05 - 00;13;39;05

What I think is really funny about

this is the.

00;13;39;08 - 00;13;41;12

So much of the piece is on the potential.

00;13;41;12 - 00;13;42;11

Right.

00;13;42;11 - 00;13;45;17

And I get to work at the institute

and in other work I do,

00;13;45;18 - 00;13;47;13

I get to work with all manner of AI.

00;13;47;13 - 00;13;50;12

That's what we governments, right?

I get to work with NGOs.

00;13;50;12 - 00;13;54;03

I get to work with industries

kind of various different ones.

00;13;54;12 - 00;13;58;10

And what I find the funny,

frustrating is that the

00;13;58;18 - 00;14;01;20

the use cases, the proven use cases

00;14;02;16 - 00;14;04;15

where we've said this is something

that folks are able to say,

00;14;04;15 - 00;14;06;24

we're going to set the AI to do it

and we're going to look away.

00;14;06;24 - 00;14;09;24

We're not still trying to figure out

we're not still trying to make it happen.

00;14;10;01 - 00;14;14;19

We're not still trying to, realize

have a kind of a really have benefit.

00;14;14;19 - 00;14;19;11

Realize

ROI realized is kind of fraud cases.

00;14;19;11 - 00;14;23;07

It's customer service

and then it's not much else.

00;14;25;12 - 00;14;28;10

So I think a lot of folks

are trying to use it for creative ends.

00;14;28;10 - 00;14;31;17

A lot of folks are trying to use it for,

00;14;31;17 - 00;14;34;17

tagging kind of en masse, but

maybe that's another one that I've seen.

00;14;34;17 - 00;14;38;03

But again, the tagging as a use case

kind of doesn't stand on its own.

00;14;38;03 - 00;14;41;22

It's kind of been part of a wider process,

maybe fraud,

00;14;41;29 - 00;14;43;28

but fraud is one customer services.

00;14;43;28 - 00;14;46;28

The other,

and then folks tend to go quiet.

00;14;47;09 - 00;14;51;28

I've seen at all levels and elsewhere

really getting the AI to do.

00;14;51;29 - 00;14;55;23

And so a lot of it, it's

not that it's completely BS, it's just

00;14;56;02 - 00;15;00;26

this is so it's still so theoretical

or it's still so early on that

00;15;00;26 - 00;15;04;22

no one's been able to genuinely realize

that benefit drove it to the ground

00;15;04;22 - 00;15;07;13

and say from that,

because we've done it faster,

00;15;07;13 - 00;15;10;23

because we've done it with less resource

or fewer resources,

00;15;10;26 - 00;15;16;05

we no longer pay for this or we, you know,

the speed at which we're able to serve

00;15;16;05 - 00;15;19;20

our customers is now this

and that's been realized on their end.

00;15;20;01 - 00;15;22;27

I think for me, it's quite frustrating

actually not getting not

00;15;22;27 - 00;15;26;07

that folks are struggling,

I guess, to identify that and stick to it

00;15;26;19 - 00;15;30;23

and have it repeat again, unless it's

customer service or fraud detection.

00;15;31;18 - 00;15;32;08

really interesting.

00;15;32;08 - 00;15;35;10

And I'm, you know, I've seen something

similar with a lot of the organizations

00;15;35;10 - 00;15;36;10

we work with over here.

00;15;36;10 - 00;15;40;07

But I'm curious when you see that

and when you see that it's, you know, 1

00;15;40;07 - 00;15;42;03

to 2 things that

00;15;42;03 - 00;15;45;17

companies or organizations, NGOs,

governments are getting value out of.

00;15;45;17 - 00;15;47;05

what's typically your advice for them?

00;15;47;05 - 00;15;49;10

Is it, you know,

you're not looking hard enough.

00;15;49;10 - 00;15;51;03

Go back out there. Is it.

00;15;51;03 - 00;15;52;20

You know, go all in on AI.

00;15;52;20 - 00;15;54;17

Is it okay? This makes sense for now.

00;15;54;17 - 00;15;58;20

Let's wait until we find more promising

use of the technology.

00;15;58;23 - 00;16;00;22

Well, what do you typically tell them?

00;16;00;22 - 00;16;04;05

So my my advice is,

I mean, not it's not as negative

00;16;04;05 - 00;16;05;26

if you're not looking hard hard enough.

00;16;05;26 - 00;16;09;05

I think my advice is

there are a lot of exciting, sexy

00;16;09;05 - 00;16;13;02

things that folks,

the big, big tech thinks are the problems.

00;16;13;18 - 00;16;15;10

And so go look for the, exciting things.

00;16;15;10 - 00;16;18;10

One of my favorite ones

recently has been potholes.

00;16;18;17 - 00;16;19;29

The eye of potholes.

00;16;21;18 - 00;16;22;16

Who's thinking about that?

00;16;22;16 - 00;16;24;17

Talking about I know the big folks.

00;16;24;17 - 00;16;27;18

There's not a huge amount of money

supposedly in potholes,

00;16;27;26 - 00;16;30;26

but actually, if you were able to sit down

and think about it

00;16;30;27 - 00;16;34;05

and the folks working in that space

really could get focus on

00;16;34;14 - 00;16;37;17

what is the data that we have,

what's the logic that we have around this?

00;16;37;17 - 00;16;39;12

What what are the learnings that we have?

00;16;39;12 - 00;16;40;27

And we can look at the air of potholes.

00;16;40;27 - 00;16;43;12

It's almost like that

kind of what's the unsexy stuff?

00;16;43;12 - 00;16;45;11

And then

how do we start to experiment with that?

00;16;45;11 - 00;16;48;23

And you have to do those experiments

and you have to make those mistakes

00;16;50;02 - 00;16;53;05

in order for you to then

have that kind of first mover advantage.

00;16;53;13 - 00;16;56;24

And so that's what I end up talking to

folks about, is there will be things

00;16;56;24 - 00;17;02;06

internally that are almost hidden problems

that you have to then go and work

00;17;02;06 - 00;17;05;21

and experiment on, and you want to make

quality mistakes, is the other notion.

00;17;05;21 - 00;17;10;27

I'm talking to audiences a lot about,

I give the example of there was a time

00;17;10;27 - 00;17;14;02

before cloud when we'd have to save files

as we went along.

00;17;14;27 - 00;17;15;06

Right?

00;17;15;06 - 00;17;19;07

And most folks who are old, most folks

you know of a certain age will remember

00;17;19;07 - 00;17;22;07

you'd have to control s or command s

as you went,

00;17;22;08 - 00;17;25;21

and God forbid you forgot to do that

or your file was corrupted,

00;17;25;21 - 00;17;29;14

then you'd have to rewrite your document

entirely from scratch, right?

00;17;29;14 - 00;17;32;02

And you'd kind of do that

and you'd call it final,

00;17;32;02 - 00;17;35;12

and then I'd know you'd use the loo,

the toilet, you'd come back, you'd realize

00;17;35;12 - 00;17;37;23

there was like a heading

at the end that you changed.

00;17;37;23 - 00;17;40;16

So you changed that

and you'd call it final. Final.

00;17;40;16 - 00;17;43;04

And then maybe your mum would call you

and you're on the phone to her.

00;17;43;04 - 00;17;45;10

How much?

Something she says triggers your mind.

00;17;45;10 - 00;17;47;18

And then you call this

final, final, final.

00;17;47;18 - 00;17;49;03

And most of us

would then be like, brilliant.

00;17;49;03 - 00;17;50;12

Would you send final?

00;17;50;12 - 00;17;52;10

Final off and then realized

00;17;52;10 - 00;17;55;10

and then a final, final,

final is what you should have sent, right?

00;17;56;04 - 00;17;59;00

And I know

we're reaching back into our memory banks.

00;17;59;00 - 00;18;02;02

Then the next time you sit down to write

a document, not only you control thing

00;18;02;05 - 00;18;06;10

compulsively the whole way through,

but now you've called it final.

00;18;06;10 - 00;18;09;10

I don't know, 5th of January 2026,

00;18;09;14 - 00;18;12;11

maybe that's when you're listening

to this podcast, right?

00;18;12;11 - 00;18;13;27

And then you're like, okay, cool.

00;18;13;27 - 00;18;15;14

So now I know which final it was.

00;18;15;14 - 00;18;16;19

I'm going to take you off the date.

00;18;16;19 - 00;18;20;05

Or if you're particularly sophisticated,

you start versioning

00;18;20;09 - 00;18;23;28

and you have version

and you don't even use the word final.

00;18;24;08 - 00;18;26;29

And I talked to folks in this example

that there's a journey you've been on,

00;18;26;29 - 00;18;28;04

which means that now

00;18;29;07 - 00;18;30;16

you don't use the word final.

00;18;30;16 - 00;18;32;22

You've got a fairly sophisticated

versioning,

00;18;32;22 - 00;18;35;24

system that you're following along with,

00;18;36;15 - 00;18;37;20

but actually,

you wouldn't have gotten there

00;18;37;20 - 00;18;40;02

if the file wasn't

corrupted the first time.

00;18;40;02 - 00;18;43;14

And I talk about now, when we're using AI

in the same kind of way, there's

00;18;43;14 - 00;18;46;00

going to be so many different elements

that you want to try and use it on.

00;18;46;00 - 00;18;46;18

And then you remember.

00;18;46;18 - 00;18;49;01

Now, actually,

I doesn't understand humans.

00;18;49;01 - 00;18;51;04

So what's the non-human

element of what we're doing that

00;18;51;04 - 00;18;54;22

I can train this on for me to then

get the results that I want to have, or,

00;18;54;26 - 00;18;55;25

you know, all those kinds of things.

00;18;55;25 - 00;18;58;25

And so I think there's a high quality

mistakes that folks need to be making.

00;18;59;07 - 00;19;02;11

But as you do that, you're going to make

that you can have that quality

00;19;02;11 - 00;19;03;13

of learning that others don't have.

00;19;03;13 - 00;19;07;20

And that's going to be your competitive

advantage in making the best use of AI.

00;19;08;05 - 00;19;11;08

And so that that's where

I want to end up talking to folks about.

00;19;13;03 - 00;19;14;02

I love that approach.

00;19;14;02 - 00;19;18;20

And by the way I'm chuckling as well

I, I like very, I like you know

00;19;18;29 - 00;19;23;16

getting like PTSD about underscore

final underscore one,

00;19;24;21 - 00;19;27;26

the one that the one that really gave me

like philosophical pause was

00;19;27;26 - 00;19;31;13

I once had someone sent me underscore

final underscore

00;19;31;13 - 00;19;34;13

final underscore new

00;19;35;14 - 00;19;37;03

that naming convention

00;19;37;03 - 00;19;40;13

tells a story of That's exactly.

00;19;40;23 - 00;19;45;11

done and we've gone back to the drawing

board, but but all of this, all of this.

00;19;45;11 - 00;19;47;23

And the reason I laugh is, is, you know,

as I listen to you

00;19;47;23 - 00;19;50;23

talking about finding your own mistakes

and looking in the unsexy parts,

00;19;50;29 - 00;19;53;26

it feels like it's very cultural

within an organization.

00;19;53;26 - 00;19;57;02

The ability to do this successfully,

what does a culture look like

00;19;57;02 - 00;19;58;00

in an organization

00;19;58;00 - 00;20;01;14

that you find is going to be successful

with this versus unsuccessful?

00;20;02;02 - 00;20;04;28

AI is I mean, it's it's a big point.

00;20;04;28 - 00;20;08;16

And, and it's

maybe something that, again, isn't hasn't

00;20;08;26 - 00;20;12;08

it doesn't ever it's not an overt

kind of kind of message of kind of

00;20;12;08 - 00;20;14;03

you need the right culture

in order for these things to do.

00;20;14;03 - 00;20;18;01

Well, I think it is those cultures

that are open to those mistakes

00;20;18;01 - 00;20;21;24

and are open to that iterative approach,

which I think if we're working on the

00;20;21;24 - 00;20;22;27

agile framework

00;20;22;27 - 00;20;25;09

and you're particularly technical,

you will understand what that means.

00;20;25;09 - 00;20;28;09

If you build in that, you're going to have

a retrospective, right?

00;20;28;10 - 00;20;29;04

As you're going.

00;20;29;04 - 00;20;31;19

That means that you're saying,

we are going to have that.

00;20;31;19 - 00;20;33;25

We're going to have to take

the opportunity to reflect back on,

00;20;33;25 - 00;20;35;07

because there will be things

for us to reflect on.

00;20;35;07 - 00;20;37;21

There will be things that have gone wrong,

things for us to learn on, or things

00;20;37;21 - 00;20;41;27

for us to measure how that has gone

and then have a higher quality iteration.

00;20;42;04 - 00;20;43;29

So I think organizations that do that,

00;20;43;29 - 00;20;47;12

are the ones that do well,

and the other ones are the organizations

00;20;47;22 - 00;20;52;15

that culturally value difference

as they're going through these processes.

00;20;52;15 - 00;20;56;12

So the other example that I have,

and anytime anyone gives me a mic at tech

00;20;56;12 - 00;21;00;20

conference is my favorite example to give

is that of periods, periods, menopause,

00;21;00;20 - 00;21;02;11

pregnancy periods. Right.

00;21;02;11 - 00;21;05;03

These things that we don't talk about

often enough, it's taboo.

00;21;05;03 - 00;21;06;29

Everyone's

afraid of them and runs away from them.

00;21;06;29 - 00;21;10;15

But so many of us have to deal with these

anyway on a regular basis.

00;21;10;22 - 00;21;12;19

Every couple of years, the health

00;21;12;19 - 00;21;16;18

tech industry discovers

the period for the very first time,

00;21;18;10 - 00;21;18;18

right?

00;21;18;18 - 00;21;21;07

So five years ago, I won't name here it is

because it's been recorded.

00;21;21;07 - 00;21;24;28

If you read my book and she's in control,

I have the example in there.

00;21;25;01 - 00;21;26;01

There was a big health

00;21;26;01 - 00;21;29;28

tech company, discovered the period

for the very first time, and allowed users

00;21;29;28 - 00;21;34;01

to track ten days of that period

as a new feature on this fitness tracker,

00;21;35;11 - 00;21;38;11

which some folks listening will think,

oh my goodness, this is why

00;21;38;11 - 00;21;41;27

I, you know, switching off, switch it off

the women's talking periods again,

00;21;42;10 - 00;21;46;07

others are thinking ten days of a period,

what's the significance?

00;21;46;07 - 00;21;47;05

And then you have to ask yourself

00;21;47;05 - 00;21;50;04

the question, is it

that these health tech companies,

00;21;50;04 - 00;21;53;09

or this one in particular, that had

the ten day period that they'd never met

00;21;53;09 - 00;21;56;21

anyone in their team, who'd ever met

anyone who'd ever had a period,

00;21;57;16 - 00;21;59;08

or was it

00;21;59;08 - 00;22;01;24

they had a couple of people there

who might have known something about it,

00;22;01;24 - 00;22;03;09

but what they were contributing

00;22;03;09 - 00;22;06;06

and what they had to say

wasn't valued in those spaces.

00;22;06;06 - 00;22;09;22

And if you're literally working

on a period tracker in this company

00;22;10;02 - 00;22;12;04

and you can't hear that from them,

what else

00;22;12;04 - 00;22;15;11

have you literally working on that you've

not been able to hear from those people.

00;22;15;20 - 00;22;17;06

And so actually,

the right kind of cultures

00;22;17;06 - 00;22;21;07

allows those nuances

and those differences to come to the fore.

00;22;21;08 - 00;22;23;11

And it's not just about periods, it's

not just about gender.

00;22;23;11 - 00;22;24;13

We all manner all things.

00;22;24;13 - 00;22;26;17

I mean, another one I love and love to

00;22;26;17 - 00;22;30;21

reference is legally Blond,

the great 2001 film where, spoiler alert,

00;22;30;22 - 00;22;35;14

she does really well in a real law case

based on her in-depth knowledge of pants.

00;22;35;25 - 00;22;38;25

And again,

these things feel so tangential.

00;22;38;25 - 00;22;41;25

But actually, is your culture

allowing different people to show up

00;22;41;26 - 00;22;44;27

in the different ways and the different

experiences that they have.

00;22;45;07 - 00;22;48;05

Because, again,

a lot of these spaces, it's a very narrow

00;22;48;05 - 00;22;50;00

set of life experiences.

00;22;50;00 - 00;22;54;07

And so a very narrow set of, solutions

that we end up proposing to problems

00;22;54;08 - 00;22;57;25

or is actually something a lot broader,

allows you to make the best use of the AI

00;22;57;25 - 00;23;00;27

because you're not focusing

on the same doom, gloom,

00;23;01;04 - 00;23;04;09

and tiny set of use cases that

everyone else has already done to death.

00;23;05;09 - 00;23;07;25

I, I love the examples.

00;23;07;25 - 00;23;12;01

And, and who knows if

what if listeners are like,

00;23;12;01 - 00;23;15;01

clothes, clothes, clothes

00;23;18;06 - 00;23;20;25

really

I feel like it's a really important point,

00;23;20;25 - 00;23;26;04

because culturally, it's not just it's

not just an impact within the four walls,

00;23;26;04 - 00;23;26;21

you know, physically

00;23;26;21 - 00;23;30;10

or metaphorically of your organization,

and it impacts your customers.

00;23;30;19 - 00;23;34;23

And, you know, unless your customers

all look exactly like your,

00;23;35;00 - 00;23;38;16

you know, your CEO

or whoever your, you know, founder is,

00;23;39;00 - 00;23;41;25

it's important to have

those it's important to have those voices.

00;23;41;25 - 00;23;42;01

Right.

00;23;42;01 - 00;23;47;01

And how can you how can you build that

into the organization itself?

00;23;47;01 - 00;23;50;23

So is this something

you help organizations get better at?

00;23;50;23 - 00;23;52;20

I mean, one of the adages about culture

00;23;52;20 - 00;23;56;03

is that a culture

is notoriously difficult to change.

00;23;56;14 - 00;23;58;10

Do you work with organizations

on cultural change?

00;23;58;10 - 00;23;59;27

And and what does that look like?

Typically?

00;23;59;27 - 00;24;02;25

In some ways I do with the Institute

for Future of Work.

00;24;02;25 - 00;24;06;02

It's definitely something

that we are doing

00;24;06;07 - 00;24;09;05

with, with the research

that we do as a lever.

00;24;09;05 - 00;24;11;28

I think for me as an organization

I run Stamets.

00;24;11;28 - 00;24;14;23

So we work on the kind of attraction

of different types of folks

00;24;14;23 - 00;24;16;02

into the industry.

00;24;16;02 - 00;24;18;04

But then ad hoc,

there would be particular partners

00;24;18;04 - 00;24;19;26

in particular pieces of work

that we put out. Yeah.

00;24;19;26 - 00;24;24;00

To help folks think about what does that

what do that culture change look like?

00;24;24;00 - 00;24;24;15

And you're right.

00;24;24;15 - 00;24;27;04

You know, culture is the average

of everybody's actions.

00;24;27;04 - 00;24;31;10

And so it is about how do we empower

everybody to to evolve I guess.

00;24;31;10 - 00;24;32;06

And again, like I said,

00;24;32;06 - 00;24;36;02

use a legacy lens perhaps on actually

50 years down the line, would I be

00;24;36;02 - 00;24;39;28

proud of this decision that I've made

and the impact that it will have had?

00;24;40;08 - 00;24;41;25

And so that's something

I end up working, folks.

00;24;41;25 - 00;24;44;01

But it's a really it's a really tough one.

00;24;44;01 - 00;24;47;17

It's a tough one to do is a tough one

to transform, I think even more so

00;24;47;25 - 00;24;49;14

as a classically trained

computer scientist,

00;24;49;14 - 00;24;51;21

and it's definitely something

that wasn't there

00;24;51;21 - 00;24;55;09

and hasn't been a core part

of what we think knowledge is.

00;24;55;09 - 00;24;58;00

And kind of his value

across the technology scene.

00;24;58;00 - 00;24;59;24

So it's definitely

also something we're looking to see.

00;24;59;24 - 00;25;00;29

How do we change the systems

00;25;00;29 - 00;25;04;03

and the structures to make sure it's

part of the curriculum as part of,

00;25;04;03 - 00;25;06;11

you know, the way people learn about

can be science

00;25;06;11 - 00;25;08;21

and learn about a lot of these things

that they understand.

00;25;08;21 - 00;25;11;25

Also, the implications

that it's not just about deterministic,

00;25;11;25 - 00;25;12;18

not just about the numbers.

00;25;12;18 - 00;25;15;15

You know, life is so much more complicated

than the maths.

00;25;15;15 - 00;25;18;04

And so how do we reflect that

in the way that we build and that we do,

00;25;18;04 - 00;25;18;23

that we deploy?

00;25;20;02 - 00;25;20;25

Right.

00;25;20;25 - 00;25;24;07

And you know, you mentioned,

you know, being classically trained in

00;25;24;08 - 00;25;25;02

computer science.

00;25;25;02 - 00;25;26;09

And I know some of the work that you do

00;25;26;09 - 00;25;30;14

is, as you said, with Stem

Arts and beyond, with, women and,

00;25;30;24 - 00;25;34;05

you know, traditionally

marginalized groups in computer science

00;25;34;05 - 00;25;37;05

and in some of these organizations

trying to get more,

00;25;37;17 - 00;25;38;24

more of them into the field.

00;25;38;24 - 00;25;42;29

So, I mean, again, this is probably too

big a question to bite off in one go, but,

00;25;43;17 - 00;25;45;29

what what's your sense about, about

00;25;45;29 - 00;25;49;29

why women have been

historically underrepresented in tech?

00;25;50;05 - 00;25;53;02

And, I mean, does it matter?

00;25;53;02 - 00;25;54;28

And what do we do about it?

00;25;54;28 - 00;25;58;06

Oh, I mean, there's yeah, we definitely

I don't know how much we have,

00;25;58;06 - 00;25;59;18

how much time we have on this, I think.

00;25;59;18 - 00;26;02;19

So, I mean, have been this

historically marginalized

00;26;02;21 - 00;26;05;27

from the tech scene,

lots of different elements

00;26;05;27 - 00;26;09;14

of our kind of heritage in the tech scene

mean that we've taken some of this

00;26;09;28 - 00;26;12;21

unhelpful elitism, maybe from from maths,

you know,

00;26;12;21 - 00;26;15;21

it became as a field

out of mathematics, academic mathematics.

00;26;15;21 - 00;26;18;16

And you can kind of take a look

at what's going on over in that scene.

00;26;18;16 - 00;26;21;29

I think there's also this kind of myth

of the kind of the lone

00;26;21;29 - 00;26;25;00

genius and people

that are born with this knowledge.

00;26;25;00 - 00;26;26;22

And, you know,

if you're born with this knowledge

00;26;26;22 - 00;26;28;12

and born with this, these things,

00;26;28;12 - 00;26;31;12

then you look a certain way

because that's, you know, part of the

00;26;31;15 - 00;26;34;26

the kind of the, the adjacency we have to

you being

00;26;35;01 - 00;26;39;00

what I sometimes call dead and white

and male with a beard and, you know,

00;26;39;00 - 00;26;40;07

you must be all of those

00;26;40;07 - 00;26;44;02

there's four things dead included

for you to be someone that can be revered.

00;26;44;02 - 00;26;44;08

Right.

00;26;44;08 - 00;26;47;08

And so we're all aiming to be that,

that dead genius who,

00;26;47;20 - 00;26;50;28

you know, created something that's now

kind of transformed the whole world.

00;26;51;08 - 00;26;54;28

Which I think is a high bar to to say

you must be dead in order for you

00;26;54;28 - 00;26;55;24

to kind of contribute to.

00;26;55;24 - 00;27;00;04

Technically, I think that's really I mean,

we don't grapple with that often enough.

00;27;00;24 - 00;27;04;16

But I think there's lots of geopolitical,

notions.

00;27;04;16 - 00;27;07;04

Mark. Hexes, right. Written a great book.

00;27;07;04 - 00;27;11;09

Programed inequality is kind of

there's a lot lots.

00;27;11;09 - 00;27;13;20

I think the face

and kind of read to delve into why

00;27;13;20 - 00;27;15;16

this is something that happens again

and again.

00;27;15;16 - 00;27;19;01

And I think if I'm to be

completely honest, up until this point,

00;27;19;01 - 00;27;23;25

it almost okay, because so much of what

we were doing technically was very niche.

00;27;24;18 - 00;27;28;17

And for a certain set of people,

you know, in a certain space.

00;27;28;17 - 00;27;30;02

And we kind of just made it

because it would be cool.

00;27;30;02 - 00;27;33;14

Wouldn't it be cool to have a website

that rated whether people were or not?

00;27;33;21 - 00;27;34;22

We kind of just built that.

00;27;34;22 - 00;27;36;08

And and I don't think any of us

00;27;36;08 - 00;27;39;01

really grappled

with the fact that you build that website.

00;27;39;01 - 00;27;41;20

Then all of a sudden

you're moving elections

00;27;41;20 - 00;27;43;19

and you're transforming democracy

as we know it.

00;27;43;19 - 00;27;48;00

And so I think it matters because

we're building things for the world.

00;27;48;00 - 00;27;49;25

These women are part of that world.

00;27;49;25 - 00;27;52;03

And so why wouldn't

we have their experiences

00;27;52;03 - 00;27;56;04

reflected there, in what we're doing

so we can sell more if we really want to.

00;27;56;04 - 00;27;59;15

But I also, I feel that we end up

creating more problems than we're solving

00;27;59;28 - 00;28;02;24

in the way that we deploy technology

at the moment, because we don't have

00;28;02;24 - 00;28;07;11

different types of voices,

taken into account in what we're creating.

00;28;07;11 - 00;28;11;25

And ultimately,

you know, I, I, I'm a technologist

00;28;11;25 - 00;28;14;28

because of the altruistic strain

I have in me.

00;28;15;13 - 00;28;17;20

You know, if I can create this thing,

if I understand how it works

00;28;17;20 - 00;28;18;08

and logically,

00;28;18;08 - 00;28;20;05

then I can recreate it,

and then I can solve that problem

00;28;20;05 - 00;28;22;18

again and again

without me needing to be there.

00;28;22;18 - 00;28;26;03

And so I think for me, it's only fair

to the technology, right,

00;28;26;07 - 00;28;27;24

that we actually end up building things.

00;28;27;24 - 00;28;31;04

We make the right kinds of decisions

what we're prioritizing

00;28;31;04 - 00;28;33;14

and how we're building it

and what we're creating

00;28;33;14 - 00;28;35;11

and what life experiences

that we're reflecting it.

00;28;35;11 - 00;28;37;23

And so that's why it's important

to have women,

00;28;37;23 - 00;28;41;06

be a part of the, of the puzzle,

but also other groups.

00;28;41;06 - 00;28;41;19

Right.

00;28;41;19 - 00;28;44;18

You've got gender, you've got races,

all manner of different folks.

00;28;44;18 - 00;28;45;13

That should be part of it.

00;28;45;13 - 00;28;49;00

Given this is now global tech

that we're building at any given point.

00;28;49;00 - 00;28;51;23

And there are so many problems

to be solved in the world

00;28;51;23 - 00;28;53;05

without us creating new ones.

00;28;55;10 - 00;28;55;21

So when it,

00;28;55;21 - 00;28;59;27

when we talk about solving this problem

and and how right, like how we create,

00;29;00;09 - 00;29;04;02

a more equitable or democratized

environment where everybody can contribute

00;29;04;02 - 00;29;06;10

through these technologies

and with this technologies.

00;29;06;10 - 00;29;11;24

And you don't have to, look a certain way

or see yourself compared to a certain,

00;29;12;08 - 00;29;16;10

you know, reverential figure,

how how do we get there?

00;29;16;10 - 00;29;19;01

And what sort of work are you doing to

to get folks there?

00;29;19;01 - 00;29;22;09

I mean, I love the idea of,

you know, destroying

00;29;22;09 - 00;29;25;17

this narrative of, you know, the dead

white guy with the beard.

00;29;25;17 - 00;29;29;18

And, you know, if you don't look like

Albert Einstein or whoever else, like,

00;29;29;18 - 00;29;33;18

you can't possibly be successful in

this is it as simple as, you know,

00;29;33;18 - 00;29;37;16

just having more certainly role models

that look like,

00;29;37;17 - 00;29;40;17

you know,

you know, look more diverse is important.

00;29;40;24 - 00;29;44;22

What else is are kind of the key factors

in your mind in terms of

00;29;45;22 - 00;29;48;08

making this

a viable path for for more young people

00;29;48;08 - 00;29;51;04

who wouldn't necessarily

have seen themselves in these roles.

00;29;51;04 - 00;29;54;26

So I it's fun that you start

with role models kind of in your question,

00;29;54;26 - 00;29;57;03

I think it's, it's ensuring

that we have the right environment

00;29;57;03 - 00;29;58;27

for these role models to exist.

00;29;58;27 - 00;30;00;26

And so I think yes, yes,

we need the stories

00;30;00;26 - 00;30;02;28

to be told of the,

of the different types of people

00;30;02;28 - 00;30;05;15

that are thriving in the industry,

that are creating,

00;30;05;15 - 00;30;09;01

that have already made things,

you know, whether it's GG, right,

00;30;09;01 - 00;30;10;23

whether it's, you know, insert,

00;30;10;23 - 00;30;13;23

insert name of any of the things actually,

the women have created Kevlar,

00;30;13;23 - 00;30;17;04

like there's kind of a long list,

actually, of women innovators

00;30;17;04 - 00;30;20;22

who have done things already who

we haven't eulogized in Hollywood movies

00;30;21;01 - 00;30;24;20

and or whose stories we haven't told again

and again who have died off.

00;30;24;20 - 00;30;26;24

Right. And actually,

we've not given them their flowers before.

00;30;26;24 - 00;30;28;06

They've before they've gone.

00;30;28;06 - 00;30;30;00

And so there's a whole life stories and,

00;30;30;00 - 00;30;32;00

you know, I implore Hollywood

or anyone that's listening

00;30;32;00 - 00;30;34;07

that has that ability

to tell their stories,

00;30;34;07 - 00;30;37;12

to go and look up their stories

and try and tell lots of them.

00;30;37;12 - 00;30;38;28

You know, there's quite a long list

that we have

00;30;38;28 - 00;30;41;06

for this Stephanie Shirley,

whether it's Gladys West,

00;30;41;06 - 00;30;42;18

Katherine Johnson,

we've now actually seen.

00;30;42;18 - 00;30;43;26

But there's there's always more.

00;30;43;26 - 00;30;46;18

We've had so many Feinstein

and so many of the others

00;30;46;18 - 00;30;48;19

that actually let's let's kind of fill

those in.

00;30;48;19 - 00;30;50;04

So I think one is to tell the story.

00;30;50;04 - 00;30;51;25

But I think the other thing

is to genuinely work on

00;30;51;25 - 00;30;55;04

ensuring that different types of people

can thrive in our spaces,

00;30;55;04 - 00;30;58;13

and it doesn't need to be, again,

that lone role model.

00;30;58;13 - 00;31;01;11

Although that kind of list of people

that it can be just something that, hey,

00;31;01;11 - 00;31;03;19

name a female journalist,

people can can do that

00;31;03;19 - 00;31;05;24

the same way they can name

email journalist. Right.

00;31;05;24 - 00;31;08;28

And so I think it

we need to have the same sorts of spaces.

00;31;08;28 - 00;31;11;28

Then we also need to be able to hold folks

accountable for bad behavior,

00;31;12;02 - 00;31;14;09

which, you know,

when we see the use of NDAs

00;31;14;09 - 00;31;18;04

across our industry, when we see

the kind of the norms we have as to,

00;31;18;13 - 00;31;21;20

you know, how people get equity

in particular companies, how people are

00;31;21;26 - 00;31;23;09

held accountable for, for, for,

00;31;23;09 - 00;31;27;00

like I said, bad behavior

kind of corporately versus others.

00;31;27;00 - 00;31;28;27

I think there's

a lot of kind of bad habits

00;31;28;27 - 00;31;32;23

and a certain type of person

that gets protected in these spaces,

00;31;32;23 - 00;31;36;25

and that's academically as well

as, in industry entrepreneurs as well,

00;31;36;25 - 00;31;41;06

who's allowed to fail fast and often,

who's not allowed to fail fast and often.

00;31;41;15 - 00;31;43;03

And so I think there are quite

a lot of tweaks, actually,

00;31;43;03 - 00;31;46;03

that we do need to make to ensure

that different types of folks can survive.

00;31;46;04 - 00;31;49;04

Another one would be, you know,

and you have this

00;31;49;06 - 00;31;53;07

to a lesser degree in the US,

but it it still does show up in the UK.

00;31;53;07 - 00;31;56;05

We have it that you make decisions

on particular subjects.

00;31;56;05 - 00;32;00;06

You want to study at a high level at 13,

and those decisions follow

00;32;00;06 - 00;32;03;16

you whole of your life,

which I mean, I don't know.

00;32;03;16 - 00;32;05;11

I don't know

if you're even eating the same food

00;32;05;11 - 00;32;09;17

that you ate as a 13 year old,

let alone your entire career.

00;32;09;17 - 00;32;12;22

And, you know, God forbid

you made the wrong decision at 13.

00;32;13;13 - 00;32;16;19

Hair color what you're eating

clothing wise or whatever.

00;32;16;22 - 00;32;19;09

But still that we have an entire industry

that no, no, no, no,

00;32;19;09 - 00;32;22;16

you cannot possibly be an engineer here

because you did not make that decision

00;32;22;16 - 00;32;23;20

at that time.

00;32;23;20 - 00;32;26;11

And so there's there's nothing of value

that you have to contribute here

00;32;26;11 - 00;32;29;03

because you didn't enter onto that

pathway.

00;32;29;03 - 00;32;33;06

And again, it speaks to this idea of

you have to have been born that genius,

00;32;33;06 - 00;32;36;22

because then at 13

you would have locked in those ideas.

00;32;37;08 - 00;32;37;26

Was actually there.

00;32;37;26 - 00;32;38;12

A lot of folks

00;32;38;12 - 00;32;41;28

who have a lot of things to contribute

that we're doing other things at 13,

00;32;41;28 - 00;32;46;09

and we should value that actually

in what we're creating, what we're doing.

00;32;46;09 - 00;32;49;09

So I think it's it's updating the culture

so that we can have more

00;32;49;09 - 00;32;50;11

of those role models,

00;32;50;11 - 00;32;53;11

as well as telling the story of the role

models who have somehow made it through,

00;32;53;22 - 00;32;55;23

despite how,

00;32;57;01 - 00;33;00;03

unfriendly the industry can be.

00;33;00;12 - 00;33;03;00

So, I mean, I'll tell you off the bat

00;33;03;00 - 00;33;06;14

that 13 year olds are not typically

the target demographic of this podcast,

00;33;08;07 - 00;33;10;20

I think a lot of,

I think a lot of listeners

00;33;10;20 - 00;33;13;04

do potentially have their own

13 year old or,

00;33;13;04 - 00;33;16;14

you know, own teenager in their house,

which is what do you tell,

00;33;17;01 - 00;33;19;23

you know, young people these days

about what the future looks like,

00;33;19;23 - 00;33;20;10

what they should

00;33;20;10 - 00;33;21;16

do, you know,

00;33;21;16 - 00;33;24;16

how they should be thinking about

their own future and their own legacy.

00;33;24;25 - 00;33;28;25

So we estimates we ended up working

this notion of Stem, steam.

00;33;28;25 - 00;33;31;11

So rather than Stem

so including art and design in there.

00;33;31;11 - 00;33;35;11

And I think what you do up telling

the 13 year old is a lot of these things,

00;33;35;11 - 00;33;36;16

we haven't figured them out,

00;33;36;16 - 00;33;39;00

you know, as we as

we started at the top of this conversation

00;33;39;00 - 00;33;41;22

where the beginning of this fourth

industrial revolution,

00;33;41;22 - 00;33;44;06

a lot of the answers

we don't have the adults.

00;33;44;06 - 00;33;46;14

There's a lot of things

the adults do not know.

00;33;46;14 - 00;33;51;01

And so we end up talking to them about,

okay, you know, what would be your ideal?

00;33;51;01 - 00;33;53;27

What would you create?

What would you prioritize?

00;33;53;27 - 00;33;55;05

How would you see this?

00;33;55;05 - 00;33;58;14

And we end up relating it as well often

to what their would be interested in.

00;33;58;14 - 00;34;01;18

So you know, not AI for the sake of AI,

but how does that impact

00;34;01;18 - 00;34;05;03

the football team that they support,

or how does that impact on the,

00;34;05;05 - 00;34;08;21

clothing, brand

that they, that they, love to wear?

00;34;08;21 - 00;34;11;02

Or how does that impact on the food

that they like to choose?

00;34;11;02 - 00;34;14;22

So we start with what they're interested

in, that kind of relate the technology,

00;34;14;22 - 00;34;15;06

to that.

00;34;15;06 - 00;34;18;25

But in, in quite a big way,

I think this, this generation

00;34;19;06 - 00;34;23;26

have grown up, quite different

to previous generations

00;34;23;26 - 00;34;28;15

in that they've got to see the problems

of the world up close in real time.

00;34;29;15 - 00;34;30;11

And so I think

00;34;30;11 - 00;34;34;01

the notion of what success

looks like is quite different to them.

00;34;34;25 - 00;34;38;09

And so we end up talking to them about

how does technology, how much technology

00;34;38;09 - 00;34;40;08

and power that version of success,

how might technology

00;34;40;08 - 00;34;42;08

be something that helps a climate change

technology

00;34;42;08 - 00;34;45;16

be something that helps with fairness

and equity and social justice?

00;34;45;26 - 00;34;49;29

And so that's what we end up talking to

13 year olds about and leave the door

00;34;49;29 - 00;34;54;05

open to say, hey, like study history,

study languages, whatever it might be.

00;34;54;09 - 00;34;58;07

But understand that technology is a tool

that will have a role to play in

00;34;58;07 - 00;34;59;18

whatever you end up doing.

00;34;59;18 - 00;35;02;03

And so you can choose to study technology

that's completely fine,

00;35;02;03 - 00;35;05;24

or build your tech literacy

so that no matter what you do,

00;35;05;24 - 00;35;07;12

you're able to apply the technology

in a way

00;35;07;12 - 00;35;10;01

that makes sense to you

and solves problems that you have.

00;35;10;01 - 00;35;12;17

And so those are the kinds

of conversations, controversially or not,

00;35;12;17 - 00;35;13;12

that we're having with them.

00;35;13;12 - 00;35;15;22

It's not everybody learn to code,

00;35;15;22 - 00;35;18;25

but it is definitely learned

to have that critical thinking.

00;35;18;25 - 00;35;20;29

Learn to collaborate, learn to create,

00;35;20;29 - 00;35;22;14

because these are things

that you'll still need to do

00;35;22;14 - 00;35;24;29

no matter how many of our

jobs the robots take away.

00;35;26;15 - 00;35;28;11

I'm I love that answer.

00;35;28;11 - 00;35;31;22

And it it preempted the next thing

I wanted to talk about, which is you

00;35;31;22 - 00;35;35;06

use you said, you know, critical thinking,

you know, collaborate create

00;35;35;20 - 00;35;39;12

are those the question I wanted to ask you

is for those coming back

00;35;39;12 - 00;35;43;20

to the notion of the workers

who are facing,

00;35;44;00 - 00;35;48;20

you know, the, the,

the reality of, you know, coexisting

00;35;48;20 - 00;35;53;00

with more and more technology that can do

things that could never do before.

00;35;53;00 - 00;35;58;04

What are the the critical skills for,

you know, the roles of tomorrow

00;35;58;04 - 00;36;02;01

and for the to be kind

of a successful worker or a leader

00;36;02;01 - 00;36;05;01

in tomorrow is is it those same three

or how would you frame that out?

00;36;06;00 - 00;36;07;12

Yeah. So it's it's definitely something.

00;36;07;12 - 00;36;10;23

So we at the institute, recently completed

something called a PC release review.

00;36;10;23 - 00;36;14;13

So we did this with the Nobel

laureate, Christmas release

00;36;14;27 - 00;36;17;20

looking at under the transformation

of work and jobs

00;36;17;20 - 00;36;19;28

and how they were all being transformed

at this time,

00;36;19;28 - 00;36;23;08

one of the things we did

was a huge analysis of, job,

00;36;23;27 - 00;36;28;02

roles, that kind of job openings

across a big engine

00;36;28;12 - 00;36;31;24

and looking at over time

how the skills would be

00;36;31;24 - 00;36;34;24

transforming and changing across

this kind of big data set.

00;36;35;12 - 00;36;36;10

And what we saw that. Yeah.

00;36;36;10 - 00;36;38;09

Where we've got AI and maybe data skills

00;36;38;09 - 00;36;40;26

that are more prevalent

across the different job descriptions.

00;36;40;26 - 00;36;44;24

So going out this idea of communicating,

of collaborating, critical thinking,

00;36;45;03 - 00;36;49;29

of creativity, these sorts of skills

which some folks have called soft skills,

00;36;49;29 - 00;36;53;15

I didn't like that core skills, I think is

is really what we're talking about.

00;36;53;23 - 00;36;55;09

If you want future proof skills,

00;36;55;09 - 00;36;58;02

like those notions of synthesizing

the information

00;36;58;02 - 00;37;01;16

and connecting with it, and understanding

as well as communicating,

00;37;01;22 - 00;37;02;27

think those end up being skills

00;37;02;27 - 00;37;06;28

that folks will need, you know,

no matter what the technology is doing.

00;37;06;28 - 00;37;08;02

And you can have them

00;37;08;02 - 00;37;11;02

augmented by the technology,

but those things can't be replaced

00;37;11;06 - 00;37;12;08

by the technology.

00;37;12;08 - 00;37;14;18

I mean, we've all seen what generative

AI is spewing out.

00;37;14;18 - 00;37;16;13

Now we're calling it slopped, right?

00;37;16;13 - 00;37;18;23

It's the kind of the overarching term.

00;37;18;23 - 00;37;20;00

And so as a human being, yeah,

00;37;20;00 - 00;37;21;18

the quality of slop

that you will have put out

00;37;21;18 - 00;37;24;07

manually is probably slightly different

to what they are generated.

00;37;24;07 - 00;37;26;22

Slop, is doing is creating.

00;37;26;22 - 00;37;29;24

But critical thinking

I think is probably the biggest one.

00;37;29;24 - 00;37;30;04

Right.

00;37;30;04 - 00;37;32;21

And and it's possible

that some of this is missing and that's

00;37;32;21 - 00;37;36;16

why a lot of those, for example,

I use cases just aren't hitting

00;37;36;25 - 00;37;39;22

because folks aren't able

to critically think and say,

00;37;39;22 - 00;37;43;13

if this technology is automating

or if it's being able to do better, better

00;37;43;13 - 00;37;47;22

data exploration, or if it's generate

like critically thinking,

00;37;48;01 - 00;37;51;17

which part of the processes that I'm doing

at the moment or what part of the value

00;37;51;17 - 00;37;55;04

that I'm providing to my clients

or my customers at the moment, require

00;37;55;27 - 00;37;58;02

repetitive things

that then can be automated,

00;37;58;02 - 00;38;01;04

or it has a lot of data

that needs to be explored,

00;38;01;05 - 00;38;03;07

or is going to support me in making,

you know,

00;38;03;07 - 00;38;05;08

high quality decisions or whatever

it might be.

00;38;05;08 - 00;38;07;08

So I think critical thinking and saying,

okay,

00;38;07;08 - 00;38;09;03

just because I said

this, should I believe it,

00;38;10;28 - 00;38;13;02

critical thinking to say

when I give it this prompt, when I give it

00;38;13;02 - 00;38;16;17

that it gave me this back to actually

what what really is going on here.

00;38;17;00 - 00;38;20;06

I think there's a lot of that that ends up

going missing that will stand folks

00;38;20;06 - 00;38;24;00

in good stead and allow them to explore

and make better use of the technology.

00;38;24;01 - 00;38;27;19

Because as much as we have AI as this hype

machine as this hype cycle, sorry.

00;38;27;20 - 00;38;28;17

And as much as you know,

00;38;28;17 - 00;38;31;23

we can talk down to that, actually the

AI is good at particular things,

00;38;32;26 - 00;38;33;03

right?

00;38;33;03 - 00;38;36;03

We just have to spend the time to say,

where does that overlap with what

00;38;36;03 - 00;38;39;13

my specific use cases and work is?

00;38;40;16 - 00;38;44;00

When you talk to leaders

then who are looking at the technology,

00;38;44;00 - 00;38;45;23

they're ideally thinking critically.

00;38;45;23 - 00;38;49;24

They're they're looking at,

the, the organization of the future

00;38;49;24 - 00;38;51;16

or the workforce of the future.

00;38;51;16 - 00;38;52;23

what's your best advice for them?

00;38;52;23 - 00;38;54;23

And are you finding that

they're typically,

00;38;54;23 - 00;38;58;06

you know, thinking too small

or thinking too abstractly?

00;38;58;15 - 00;39;01;17

What are some of the biggest mistakes

you're seeing?

00;39;01;25 - 00;39;03;21

I think that that thinking to short term,

00;39;04;24 - 00;39;05;14

I think, is the

00;39;05;14 - 00;39;08;14

big mistake that I see

from businesses at the moment.

00;39;08;17 - 00;39;11;17

There's a lot of

this is the problem I have here and now,

00;39;12;11 - 00;39;14;18

and you're

kind of dealing with the symptom.

00;39;14;18 - 00;39;17;22

You're not doing the root cause, you're

not dealing with the wider set of things.

00;39;18;00 - 00;39;21;22

The kind of the example

I almost always give folks is Tobias

00;39;21;22 - 00;39;23;20

comes up

and folks always end up asking me,

00;39;23;20 - 00;39;26;21

oh, you know, what can we do to mitigate

the bias in the AI that we're building?

00;39;26;21 - 00;39;27;21

And I'm like, okay, cool.

00;39;27;21 - 00;39;29;26

Why don't we rewind?

00;39;29;26 - 00;39;32;26

We can build an AI

00;39;33;06 - 00;39;37;10

that helps predict, analyze, whatever

the best sandwiches for us to create

00;39;37;19 - 00;39;41;19

and to have on the menu at our downtown

Toronto lunch space place.

00;39;41;19 - 00;39;42;13

Right.

00;39;42;13 - 00;39;46;10

And we can say we can build and fine tune

the AI to the nth degree,

00;39;47;14 - 00;39;47;25

right.

00;39;47;25 - 00;39;48;23

And say, this is the best

00;39;48;23 - 00;39;52;28

AI that's ever been known for anyone

to build to choose sandwich flavors.

00;39;53;06 - 00;39;56;06

But there are a lot of people in Toronto

who don't eat sandwiches for lunch.

00;39;58;17 - 00;40;01;07

I said, and so you have to think

a little bit, but do you know, like you've

00;40;01;07 - 00;40;04;07

prescribed this, you've set this problem,

you set this scope, you don't.

00;40;04;07 - 00;40;06;10

You're not thinking wider.

You're not thinking ten.

00;40;06;10 - 00;40;07;28

Then this is why I say 50

is down the line.

00;40;07;28 - 00;40;11;02

When you do, that allows folks to zoom out

a little bit and actually not focus

00;40;11;02 - 00;40;14;06

on the current pain point that they have,

or even the next pain point.

00;40;14;14 - 00;40;17;15

But to say, let's

go back to first principles

00;40;17;24 - 00;40;20;24

and really think about how we can do this

in a transformative way.

00;40;20;29 - 00;40;22;29

And I find that a lot of business

00;40;22;29 - 00;40;26;09

leaders are held by the neck,

by shareholders, stakeholders, you know,

00;40;26;10 - 00;40;30;26

whatever the shorter loop cycles

we are that we're operating in.

00;40;31;08 - 00;40;34;03

And so actually the folks that do the best

are the ones that kind of can pull back

00;40;34;03 - 00;40;39;24

and say, we're going to take a long term

view on our very niche industry.

00;40;39;24 - 00;40;42;21

And again, this is not about tech.

This is about other places.

00;40;42;21 - 00;40;44;20

This okay. In education.

It's like a long term view.

00;40;44;20 - 00;40;46;07

What is the point of education.

00;40;47;20 - 00;40;48;21

Therefore what are we going to use

00;40;48;21 - 00;40;53;13

AI to help teachers in the here and now

deal with admin in the current system?

00;40;53;13 - 00;40;56;22

Or are we going to use

AI to completely transform the picture

00;40;56;22 - 00;41;00;10

that we have of skills and knowledge

that our learners have?

00;41;00;28 - 00;41;03;28

And if you start to think about that,

then you work on a whole nother set of

00;41;04;19 - 00;41;05;02

problems.

00;41;05;02 - 00;41;07;04

You're working at a whole nother level,

00;41;07;04 - 00;41;09;07

and then you're able

to kind of fundamentally transform

00;41;09;07 - 00;41;12;07

the way we're looking at just the admin

that they're having to do today,

00;41;12;15 - 00;41;13;15

which is a tiny use case.

00;41;13;15 - 00;41;14;19

And, you know, it's ended up

00;41;14;19 - 00;41;18;16

not actually necessarily giving the right

kind of, payoff for folks.

00;41;18;16 - 00;41;20;24

But when you start to think about

assessment, we start to, you know,

00;41;20;24 - 00;41;23;24

and so I think is that strategic thinking

long term

00;41;24;16 - 00;41;27;01

and then working your way back

then allows folks

00;41;27;01 - 00;41;30;01

to have the vision

to have a little bit more motivation,

00;41;30;02 - 00;41;33;08

allows them to maybe have a bit more room

for the kind of accountability

00;41;33;08 - 00;41;35;28

on learning from mistakes,

as we mentioned already.

00;41;35;28 - 00;41;38;19

And that's what we're seeing is working

for, for folks, when they're able

00;41;38;19 - 00;41;41;27

to look

beyond the problems of the here and now.

00;41;43;24 - 00;41;45;03

I love that approach.

00;41;45;03 - 00;41;47;20

And it makes

I mean, to me, it makes complete sense.

00;41;47;20 - 00;41;51;24

And I can, you know, you say, and

it's like, yes, like I'm 100% bought in.

00;41;52;02 - 00;41;55;18

one of the things I've seen is

a lot of leaders

00;41;55;18 - 00;41;59;06

seemed and a lot of people,

frankly, seem to have a convenient belief.

00;41;59;06 - 00;42;02;13

Right now

that we're in such a period of disruption

00;42;02;13 - 00;42;04;16

that the long term

is completely unknowable.

00;42;04;16 - 00;42;06;16

It's just like, who knows?

00;42;06;16 - 00;42;07;25

We can't possibly know it.

00;42;07;25 - 00;42;09;20

Therefore we can't plan for it.

00;42;09;20 - 00;42;13;15

Therefore, I don't have to look beyond

the next three months

00;42;13;15 - 00;42;15;07

because that's all that matters.

00;42;15;07 - 00;42;19;18

when you look out over the horizon,

do you give much credence to that,

00;42;19;18 - 00;42;24;27

or do you think that's just an excuse for,

you know, not having to do the

00;42;24;27 - 00;42;28;20

the thoughtful work

required to, to to plan?

00;42;29;10 - 00;42;30;18

I think is fair.

00;42;30;18 - 00;42;32;04

I mean, we're in a human right.

00;42;32;04 - 00;42;35;09

And so I think

if things you can't control, you fear.

00;42;35;09 - 00;42;38;01

I think my biggest response to that,

though, is that, you know, there's less

00;42;38;01 - 00;42;38;25

there's less fear

00;42;38;25 - 00;42;41;24

if you are in the driving seat

and if you make some decisions,

00;42;41;24 - 00;42;43;29

and if you look at some of these things

that are now

00;42;43;29 - 00;42;46;23

and you do have the agency to do it,

because at the time that we're at

00;42;46;23 - 00;42;49;22

in this cycle, there will come a time

when maybe it is too late

00;42;49;22 - 00;42;51;24

and too many of these decisions

have been locked in,

00;42;51;24 - 00;42;53;15

and too many of these norms have been set.

00;42;53;15 - 00;42;55;08

But that time isn't now.

00;42;55;08 - 00;42;57;17

And so I do get folks excited.

00;42;57;17 - 00;43;00;06

You know, I do like to, you know,

there's so much you see in science

00;43;00;06 - 00;43;02;17

fiction, right,

that's ended up being real.

00;43;02;17 - 00;43;07;06

I mean, kids, Knight

Rider from the 1980s.

00;43;07;06 - 00;43;09;01

I don't remember much of the 1980s. Right.

00;43;09;01 - 00;43;11;07

But David Hasselhoff played

this guy called Michael.

00;43;11;07 - 00;43;13;02

He took to his car, called Kitt,

00;43;13;02 - 00;43;16;10

and it was such a far off thing

in the 1980s for him to talk to this car,

00;43;16;10 - 00;43;19;24

in the car, to hear him, understand him,

then to successfully fight crime together

00;43;19;24 - 00;43;22;24

and always get folks

to think that we talk to our cars now

00;43;22;29 - 00;43;26;12

in 2025, soon to be 2026, whatever,

you're listening to this, there are people

00;43;26;19 - 00;43;27;28

for real talking to their cars.

00;43;27;28 - 00;43;29;19

There's no science fiction thing.

00;43;29;19 - 00;43;30;29

They might not be fighting crime.

00;43;30;29 - 00;43;33;29

Maybe that, you know, turning up

the temperature or changing the track.

00;43;34;07 - 00;43;36;09

Right. But the car is understanding them.

00;43;36;09 - 00;43;40;02

And so I get folks to think like,

this doesn't have to be a big, major,

00;43;40;08 - 00;43;43;27

huge shift that you invent the internet

in your organization.

00;43;44;05 - 00;43;47;00

But there will be small tweaks

based off desires, dreams,

00;43;47;00 - 00;43;49;26

imaginations that you have

and so you better imagine.

00;43;49;26 - 00;43;51;22

I mean, we're human beings.

All of us have desires.

00;43;51;22 - 00;43;54;08

Us, you know, things that we wish

we could have instead.

00;43;54;08 - 00;43;59;00

And maybe not all of them are the most

worthy of desires that we want to have.

00;43;59;06 - 00;44;02;03

But actually, across an organization,

you'll have a lot of people,

00;44;02;03 - 00;44;03;24

they'll have a lot of ideas

on what could be next,

00;44;03;24 - 00;44;05;07

and you could definitely make that.

00;44;05;07 - 00;44;06;28

I mean, if we if we don't,

00;44;06;28 - 00;44;09;22

if we don't, you know, seem

that something we've got within object

00;44;09;22 - 00;44;10;19

and why do we bother

00;44;12;03 - 00;44;15;03

getting up

to work and getting out to create anyway.

00;44;15;03 - 00;44;18;03

It's, it's a thing to be human, to dream

and to hope and to explore.

00;44;18;05 - 00;44;21;15

So yeah, I mean, that's

another thing I get focused, you know?

00;44;21;15 - 00;44;23;14

Right.

If you're not a sci fi person. Right?

00;44;23;14 - 00;44;25;04

Your version is sci fi.

00;44;25;04 - 00;44;30;22

So 200 years down the line, what would be

well, you in 200 and, you know, to 2225

00;44;30;22 - 00;44;35;07

or 26 whenever you're listening to this,

what would they be doing just right?

00;44;35;07 - 00;44;37;01

That what what do you imagine?

00;44;37;01 - 00;44;39;24

Because none of us know completely

what's going on, but all of us can dream

00;44;39;24 - 00;44;42;24

and can explore and can hope.

00;44;44;13 - 00;44;46;04

But I understand that it's very human

to be.

00;44;46;04 - 00;44;49;02

To be afraid of the unknown.

00;44;49;02 - 00;44;50;21

We've talked to them.

00;44;50;21 - 00;44;52;10

It may it makes sense.

00;44;52;10 - 00;44;56;06

And it's something that I think has,

you know, is being profoundly felt

00;44;56;06 - 00;44;57;22

by a lot of people right now.

00;44;57;22 - 00;44;59;20

We've talked at length

00;44;59;20 - 00;45;02;21

and read about kind of the worker piece

about the organizational piece.

00;45;02;29 - 00;45;05;23

We haven't talked a lot yet

about kind of the society piece,

00;45;05;23 - 00;45;10;17

which you said is sort of the third leg

of this stool and in some ways is,

00;45;11;19 - 00;45;12;04

maybe the most

00;45;12;04 - 00;45;15;04

difficult to nail down,

but also the most powerful.

00;45;15;27 - 00;45;18;06

What are some of the big,

00;45;18;06 - 00;45;21;24

I guess, risk

like change, risks of society

00;45;21;24 - 00;45;25;10

as well as some of the benefits

you see kind of emerging that, you know,

00;45;25;20 - 00;45;29;04

maybe we're not taking seriously enough

or not planning for well enough.

00;45;29;14 - 00;45;31;23

So I look at this

from the social justice lens.

00;45;31;23 - 00;45;32;02

I think.

00;45;32;02 - 00;45;35;02

I think the biggest risks

that we have at societal level

00;45;35;12 - 00;45;40;28

is that we forget or we ignore or we,

00;45;42;00 - 00;45;44;09

dull to or dumb

00;45;44;09 - 00;45;48;19

to the social inequalities

that we've already had thus far.

00;45;49;20 - 00;45;52;16

Is the frustration that I have,

00;45;52;16 - 00;45;55;24

in quite a lot of the discussions

that I have with people

00;45;55;24 - 00;45;57;00

and with technologies in particular,

00;45;57;00 - 00;46;00;11

I said just earlier, you know, less

more complicated in the maths,

00;46;01;07 - 00;46;03;21

there are a lot of data sets

that we just don't have a lot of things

00;46;03;21 - 00;46;06;21

that we just don't know about people

and have never known,

00;46;06;24 - 00;46;09;24

those in power have never haven't

deemed important enough

00;46;09;28 - 00;46;13;01

for us to have,

a very UK specific example.

00;46;13;02 - 00;46;16;12

Before Covid, when folks got married,

00;46;17;01 - 00;46;19;29

they entered their names onto the marriage

registry, you know, marriage rich.

00;46;19;29 - 00;46;23;22

So we had we have a central marriage

registry in the UK before Covid.

00;46;23;27 - 00;46;25;24

They asked you for your father's

occupation.

00;46;25;24 - 00;46;27;04

Only.

00;46;27;04 - 00;46;30;01

If you've got married since Covid,

you've entered your mother's

00;46;30;01 - 00;46;33;01

and your father's occupation

onto the marriage register.

00;46;33;11 - 00;46;36;04

So if you're at the

whole of eternity pre-COVID,

00;46;37;07 - 00;46;37;18

that's just

00;46;37;18 - 00;46;40;18

information we do not have for people

that got married.

00;46;40;23 - 00;46;43;23

Whether there's something wrong with that

or not,

00;46;44;03 - 00;46;46;16

let folks unpick.

00;46;46;16 - 00;46;49;07

But actually when we then build new models

and we take in data

00;46;49;07 - 00;46;52;13

sets from lots of different places

without asking the question

00;46;52;13 - 00;46;55;25

or being blind to gaps

that we've had thus far,

00;46;56;01 - 00;46;59;21

then we're only going to repeat the issues

and the problems that we've had already,

00;46;59;25 - 00;47;02;27

but we're just now going to do it at scale

millions of times a second.

00;47;02;27 - 00;47;03;27

Whereas before,

00;47;03;27 - 00;47;06;22

I don't know, in in medieval England

that was just something

00;47;06;22 - 00;47;09;13

that when someone went back through

they didn't have that information.

00;47;09;13 - 00;47;12;24

And so I think, I think there's

so many things that we have to sort of

00;47;13;17 - 00;47;14;13

these are real people.

00;47;14;13 - 00;47;16;18

These are real

things happening in real spaces

00;47;16;18 - 00;47;18;10

that people should really be doing

something about.

00;47;18;10 - 00;47;22;20

Another example, Invisible Women, another

book that I always talk to folks about.

00;47;22;20 - 00;47;25;03

And if you haven't had Caroline

on, you should definitely have,

00;47;25;03 - 00;47;26;03

because it's fascinating.

00;47;26;03 - 00;47;27;17

Some of the things that she was able

to unpick

00;47;27;17 - 00;47;29;19

and kind of communicate in that book.

00;47;29;19 - 00;47;34;12

Another example that I would love to share

from that book is, single parents.

00;47;35;18 - 00;47;36;09

So again, this is

00;47;36;09 - 00;47;41;15

a facet of life that actually, you know,

single parents, people being widowed,

00;47;41;23 - 00;47;46;18

whatever reason it is that you end up

as a single parent is not a new notion.

00;47;46;18 - 00;47;47;22

But whether that's a concept

00;47;47;22 - 00;47;50;21

that we've had kind of reflected in

law and spaces, whatever it is.

00;47;50;25 - 00;47;51;26

No. Right.

00;47;51;26 - 00;47;55;02

Caroline gives us example of someone

who's a director of a company

00;47;55;23 - 00;47;58;02

and also a single parent,

so that people who are directors

00;47;58;02 - 00;48;01;04

at this company,

they're all invited to this dinner.

00;48;01;23 - 00;48;03;27

They have a look at where it is

they or their assistant

00;48;03;27 - 00;48;06;15

book themselves into the hotel next door.

00;48;06;15 - 00;48;11;19

Let's say it's $200 for the night for them

to attend this dinner, and they accept.

00;48;11;19 - 00;48;13;18

And that's good.

That's done. Books on the company card.

00;48;15;00 - 00;48;17;09

The person who's a single parent

doesn't need a hotel room.

00;48;17;09 - 00;48;21;11

They need childcare for that night,

let's say quite expensive,

00;48;21;11 - 00;48;24;16

but whatever, $200

or less on the childcare for that night.

00;48;24;16 - 00;48;27;16

And that person takes

accepting on the invite

00;48;27;25 - 00;48;29;08

and this will differ. I'm not sure.

00;48;29;08 - 00;48;31;01

I guess in the US,

I think it might be the same.

00;48;31;01 - 00;48;34;04

Actually in the UK,

the hotel, the $200 on the hotel

00;48;34;04 - 00;48;37;09

is an allowable

business expense in the UK.

00;48;37;21 - 00;48;41;01

The $200 or less on the childcare

is not an allowable business expense,

00;48;42;10 - 00;48;45;00

but it's the same amount of money

being spent by the same company for people

00;48;45;00 - 00;48;48;00

at the same rank to attend the same event,

why does it matter

00;48;48;07 - 00;48;49;07

and what have we not built?

00;48;49;07 - 00;48;51;29

And so I think this is the thing

is, is size is again there's so this all

00;48;51;29 - 00;48;55;28

these little things to unpick of us

doing better for different types of folks.

00;48;56;07 - 00;48;59;10

And like I said, solving problems

at a faster rate than we're creating them.

00;48;59;10 - 00;49;02;16

And all the other ideas

that people are creating and products

00;49;02;16 - 00;49;04;13

that people are kind

of pushing out to the market.

00;49;04;13 - 00;49;07;14

And so I think societally, again,

there's an opportunity.

00;49;07;14 - 00;49;09;18

And with AI in particular,

I think this is something

00;49;09;18 - 00;49;12;04

I've been really excited about

is so many of these things.

00;49;12;04 - 00;49;13;02

People have had a hunch,

00;49;14;03 - 00;49;15;11

and we're

like, yeah, that doesn't seem right.

00;49;15;11 - 00;49;17;19

That doesn't seem fair, or that's a gap,

but it's not.

00;49;17;19 - 00;49;21;07

AI is helping to show

a lot of these biases

00;49;21;07 - 00;49;25;03

that are in real life, not necessarily

really for us to mitigate getting the AI,

00;49;25;03 - 00;49;28;23

but for us to mitigate in life, mitigate

in the way that we run our societies

00;49;28;26 - 00;49;33;20

to get in the way that we are building,

our communities and our groups.

00;49;33;20 - 00;49;34;21

And so I think, you know,

00;49;34;21 - 00;49;36;12

there's it's an opportunity,

there's an opportunity

00;49;36;12 - 00;49;39;13

for all of us to do better for everybody

and for all of us to gain from it.

00;49;41;20 - 00;49;43;12

I like the examples that you use

00;49;43;12 - 00;49;46;12

precisely because there's so

00;49;46;28 - 00;49;49;05

the scenario is so obviously dumb.

00;49;49;05 - 00;49;50;19

Right. Like the example isn't dumb.

00;49;50;19 - 00;49;54;14

The scenario is dumb that

how can we not just be better at this?

00;49;54;14 - 00;49;55;28

And I think for all of us, you know,

00;49;55;28 - 00;50;00;06

in some capacity we've encountered this

and we just say like, why?

00;50;00;11 - 00;50;02;11

Why is this fair?

Why does this make sense?

00;50;02;11 - 00;50;03;29

Why am I being penalized for this?

00;50;03;29 - 00;50;06;17

Or why is that person being penalized

for this?

00;50;06;17 - 00;50;08;15

there's

certainly a lot of intractable problems.

00;50;08;15 - 00;50;10;01

But I do it to your point.

00;50;10;01 - 00;50;12;04

I think there's a lot of problems

that are not at all intractable.

00;50;12;04 - 00;50;15;02

We just have to say that

this does not make sense.

00;50;15;02 - 00;50;17;19

Let's figure it out. Yeah, I sort it out.

Yeah.

00;50;17;19 - 00;50;20;19

is that, you know, a mindset thing

because again, there's

00;50;21;00 - 00;50;24;10

yeah, I don't know, I feel like there's

so much talk about like, oh no, that's

00;50;24;10 - 00;50;29;01

like that's not my job

or you know that that should be government

00;50;29;01 - 00;50;32;06

that's not me is like a business leader

or in the government being like,

00;50;32;06 - 00;50;34;18

that's the business leader.

That's not me or government.

00;50;34;18 - 00;50;38;06

Like, is this just a matter of all of us

kind of pulling up

00;50;38;06 - 00;50;41;10

our bootstraps and saying, let's

try to make the world a better place?

00;50;41;10 - 00;50;45;14

Or what's kind of your best guidance for

actually making some of this happen in.

00;50;46;08 - 00;50;47;18

I think so.

00;50;47;18 - 00;50;49;22

Ideally, that would be it, right?

00;50;49;22 - 00;50;53;14

We'd also let's all do better

for everybody and we all work together.

00;50;53;21 - 00;50;54;29

I there's a lot of it.

00;50;54;29 - 00;50;58;11

I, I, I see just ends up being norms.

00;50;58;22 - 00;51;01;16

So we have so many unintended

consequences from

00;51;01;16 - 00;51;05;03

I don't know if our existing has meant

that the way that different folks

00;51;05;03 - 00;51;08;11

like the laws around

gig gig work have transformed

00;51;08;11 - 00;51;11;11

in so many different countries, right,

as a result of Uber existing.

00;51;11;11 - 00;51;14;22

And so I feel like there's, there's

we haven't kind of kind of scratched

00;51;14;22 - 00;51;17;29

all use that lever as much as we can of,

hey, these are norms

00;51;17;29 - 00;51;20;29

that we can build in the technology

that might just solve this problem.

00;51;21;18 - 00;51;24;14

That means that then actually,

you know, a woman bearing the title

00;51;24;14 - 00;51;27;29

doctor doesn't get locked out of her

gym locker every time she goes to the gym.

00;51;29;03 - 00;51;30;22

You know, I and there's so

00;51;30;22 - 00;51;34;23

many like there's so many small bits, but

it's almost like some of the small bits.

00;51;34;23 - 00;51;38;01

And yeah, government and business

eventually can solve the big.

00;51;38;06 - 00;51;42;02

But the tiny, the small irritating pieces,

00;51;42;09 - 00;51;44;02

we can definitely just say that's

something

00;51;44;02 - 00;51;46;12

we're not going to do on this platform

or something

00;51;46;12 - 00;51;48;15

we're not going to have going forward

or something that in our company

00;51;48;15 - 00;51;51;25

we're going to set as a norm

that that means in our supply chain

00;51;51;29 - 00;51;54;08

and in our customer chain,

these are things that then

00;51;54;08 - 00;51;57;06

in our ecosystem,

our sphere of influence that we have,

00;51;57;06 - 00;52;00;07

that then becomes part of the norm

that we've upgraded and we've changed.

00;52;00;07 - 00;52;02;16

And so I think,

I think it's a combination of both.

00;52;02;16 - 00;52;06;17

But if everybody took it upon themselves

to understand this way of influence

00;52;06;17 - 00;52;08;20

and then to implement things

properly across there,

00;52;08;20 - 00;52;11;12

then actually there'd be

a lot of good things that would happen.

00;52;11;12 - 00;52;13;21

I think the other thing

would be potentially that,

00;52;13;21 - 00;52;16;27

you know, if we go back to our point,

if you have different folks valued

00;52;17;06 - 00;52;19;29

and different folks have access

to make these technical decisions,

00;52;19;29 - 00;52;23;18

then an upstream would mean that

we have better solutions for things,

00;52;24;00 - 00;52;25;13

which I think is the other thing that we

00;52;25;13 - 00;52;28;08

we end up missing out on,

and we probably see it the most in health.

00;52;28;08 - 00;52;31;21

Examples of the different things

that come up from pharmaceuticals

00;52;31;29 - 00;52;35;04

because of who had the most money,

who they thought were the biggest market

00;52;35;11 - 00;52;39;05

share, rather than the actual solving

of a problem that we've had

00;52;39;05 - 00;52;42;07

for ages, that endometriosis

is not a brand new condition.

00;52;42;21 - 00;52;45;11

And so someone should just have that

as something they're going to work out

00;52;45;11 - 00;52;49;19

and they're just going to do and solving

in the small with the use of this AI.

00;52;49;19 - 00;52;52;19

And it's why I get excited about things

like AI for potholes.

00;52;52;26 - 00;52;56;06

There are so many problems that actually,

the more that we have this accessible,

00;52;56;06 - 00;52;57;05

the more that it's democratized,

00;52;57;05 - 00;53;00;07

the more that folks understand

they can just experiment.

00;53;00;18 - 00;53;05;09

The more of new types of ideas

and solutions we might get to see.

00;53;05;14 - 00;53;07;16

I mean, a couple of years

ago, I had someone come to me

00;53;08;25 - 00;53;09;27

who had

00;53;09;27 - 00;53;12;27

conceptualized a new social media platform

00;53;13;03 - 00;53;16;03

type that centered on the idea,

not the person.

00;53;17;02 - 00;53;20;02

And I remember thinking yet, like,

if you were in those rooms

00;53;20;13 - 00;53;22;13

at the beginning

when we were doing bulletin boards

00;53;22;13 - 00;53;24;24

and all these other forums,

you wouldn't have censored it on the user,

00;53;24;24 - 00;53;26;26

or it would have been another way

that we cut the information

00;53;26;26 - 00;53;30;11

that we center on the thought,

on the nugget of the idea

00;53;30;24 - 00;53;32;06

and how many lives could have been solved.

00;53;32;06 - 00;53;34;22

I mean, you know, all these things

that could have happened if we

00;53;34;22 - 00;53;37;08

we just had something slightly different

and there's nothing to say.

00;53;37;08 - 00;53;39;20

This person wasn't there at the beginning.

00;53;39;20 - 00;53;42;09

That couldn't have been something,

you know, cyberbullying in the way

00;53;42;09 - 00;53;45;13

that we know it could have actually,

maybe just we would have just missed that

00;53;46;05 - 00;53;48;29

for just having something

slightly different there at the beginning.

00;53;48;29 - 00;53;51;27

And so it excites me, excites

me, the capacity we have to solve problems

00;53;53;03 - 00;53;53;15

by taking a

00;53;53;15 - 00;53;56;15

slightly different approach

to the way that we engage with technology.

00;53;56;15 - 00;53;58;24

Well it excites me that it excites you.

00;53;58;24 - 00;54;02;28

And that's something that, that it is,

is, you know, really is really great

00;54;02;28 - 00;54;05;19

to see because not, not everybody

I talked to is this excited by it.

00;54;05;19 - 00;54;09;16

Because I think, you know, it's so easy

to, to, you know, look out

00;54;09;16 - 00;54;14;11

over the horizon and see so much change

and so much uncertainty and, you know,

00;54;14;11 - 00;54;17;14

there's obviously no shortage of things

that aren't even going well today.

00;54;17;14 - 00;54;20;02

And there's, you know, things

that will be going better, but

00;54;20;02 - 00;54;21;09

and things that will be going worse.

00;54;21;09 - 00;54;24;12

But it seems like, you know, net net your,

00;54;24;25 - 00;54;27;27

you know, optimistic For now, yes.

00;54;28;09 - 00;54;29;21

while so so tell me more.

00;54;31;04 - 00;54;31;26

I'm optimistic.

00;54;31;26 - 00;54;36;04

For now, there will be a point, I'm

sure, of no return where we didn't act.

00;54;36;04 - 00;54;37;21

So we should have in the time.

00;54;37;21 - 00;54;41;13

And I'm, you know, we're much further

along into this next revolution.

00;54;41;26 - 00;54;45;18

And, you know, the norms have been set

and the dice have been cast.

00;54;45;18 - 00;54;47;25

And, you know,

we've we've locked ourselves out.

00;54;47;25 - 00;54;49;02

We shot our selves in the foot.

00;54;49;02 - 00;54;52;21

At which point then I will run to a bunker

or run somewhere, I don't know, East

00;54;52;21 - 00;54;53;10

coast of Kenya.

00;54;53;10 - 00;54;56;10

I just go, how do I do maths on the beach

00;54;56;10 - 00;54;57;19

forever? I'll retire.

00;54;57;19 - 00;55;00;28

So just just do solve

math problems on the beach, but on

00;55;00;28 - 00;55;03;28

so that I don't feel like

it's too late yet.

00;55;04;25 - 00;55;05;19

So so now's

00;55;05;19 - 00;55;10;14

the time to set the norms to influence,

you know, some of these, you know,

00;55;10;14 - 00;55;14;07

cultural changes and decisions and try

and try and build the legacy that we want.

00;55;14;19 - 00;55;15;06

Exactly.

00;55;15;06 - 00;55;18;19

Now's the time to try to genuinely do

better,

00;55;18;19 - 00;55;21;19

and be inclusive of folks

to help us do better.

00;55;21;26 - 00;55;24;19

I don't think it's too late for us to edit

00;55;24;19 - 00;55;27;19

things, change things,

and we see this even more.

00;55;27;21 - 00;55;30;20

I see this so much that the work

that we do at the institute,

00;55;30;20 - 00;55;32;18

that folks are still trying

to figure out these use cases,

00;55;32;18 - 00;55;34;09

they're still trying

to get these things out.

00;55;34;09 - 00;55;37;01

So it's not it's not set in stone yet.

00;55;37;01 - 00;55;40;11

There's definitely a lot more disruption

on, on, on the way,

00;55;40;23 - 00;55;42;21

a lot of these

kind of white collar industries

00;55;42;21 - 00;55;45;21

that are kind of having to change

how folks kind of enter.

00;55;45;26 - 00;55;48;26

So there's there's a lot of disruption

that's happening, I think is when,

00;55;48;26 - 00;55;52;15

when the dust settles on that,

then might be slightly closer to me

00;55;52;15 - 00;55;53;16

saying, maybe it's too late.

00;55;57;06 - 00;55;57;23

you the question.

00;55;57;23 - 00;56;00;22

I'll ask you the question

that everybody hates, which is

00;56;00;22 - 00;56;03;20

what's your kind of predicted timeline

for when the dust settles on that?

00;56;03;20 - 00;56;08;05

Is that 2027 is a 2030, I don't think

it's it's I know I don't think

00;56;08;05 - 00;56;12;15

is the time thing, I don't I it's not

a, I don't think it will be a time.

00;56;13;00 - 00;56;16;00

As we've seen with this hype cycle,

actually, it's not just about

00;56;16;01 - 00;56;19;00

and we know this kind of classic thing,

you know, this about technology

00;56;19;00 - 00;56;19;28

anyway, right? It's

00;56;19;28 - 00;56;22;28

not in the presence of the technologies

and the adoption of the technology.

00;56;24;10 - 00;56;26;24

And so and you can never really tell,

I don't know.

00;56;26;24 - 00;56;28;06

I don't know how many of us

could have predicted

00;56;28;06 - 00;56;31;07

that we'd be this deep into the AI hype

cycle.

00;56;31;19 - 00;56;32;04

Still here.

00;56;32;04 - 00;56;35;16

And now, three years ago

we would have been blockchain and bitcoin.

00;56;36;09 - 00;56;40;07

I'm guessing maybe in five years

the quantum volume would have come up

00;56;40;12 - 00;56;42;04

ever so slightly.

00;56;42;04 - 00;56;44;07

But I don't know

that it's a particular time

00;56;44;07 - 00;56;46;06

that when it's the technology,

it's relevant.

00;56;46;06 - 00;56;49;21

I think now, again, as a classically

trained computer scientist,

00;56;49;24 - 00;56;53;06

it's less about the tech, it's

more about the adoption and the

00;56;53;17 - 00;56;57;02

the kind of the socio economic scenario

in which it's being deployed

00;56;57;02 - 00;56;59;01

and it's being used.

00;56;59;01 - 00;57;00;18

I don't think it's a time thing.

00;57;02;12 - 00;57;03;03

Then there'll be

00;57;03;03 - 00;57;06;25

there would be some particular markers,

in particular norms

00;57;07;26 - 00;57;12;13

in which, which suit tech superpowers

using technology for folk.

00;57;12;13 - 00;57;14;20

To what end

00;57;14;20 - 00;57;16;07

instances might sound so

00;57;16;07 - 00;57;19;07

mysterious, doesn't it?

00;57;19;18 - 00;57;20;10

Be an internal.

00;57;20;10 - 00;57;23;10

Failing.

00;57;24;01 - 00;57;24;27

She just saw a website.

00;57;24;27 - 00;57;27;18

Where is am I? Am I on the beta? I'm not.

00;57;27;18 - 00;57;28;26

I'm not on the beach or I'm.

00;57;28;26 - 00;57;30;25

I'm in the bunker or I'm not.

00;57;30;25 - 00;57;32;29

And that'll be how we know

the time is in the day.

00;57;32;29 - 00;57;34;18

The day that I book the ticket.

00;57;34;18 - 00;57;34;29

The one way

00;57;34;29 - 00;57;38;06

ticket against the on the beach,

then the yeah, everyone can hunker down.

00;57;38;28 - 00;57;39;19

Yeah.

00;57;39;19 - 00;57;41;15

If you're on the beach,

we're in big trouble.

00;57;41;15 - 00;57;43;02

Exactly. Yeah, this is it.

00;57;43;02 - 00;57;44;21

So I'll just kind of sit on

the beach, which means that.

00;57;45;23 - 00;57;46;08

There you go.

00;57;46;08 - 00;57;49;08

It sounds, I can think of worse things.

00;57;56;26 - 00;57;57;21

Thanks for having me, Jeff.

00;57;57;21 - 00;58;00;14

Good luck everybody.

00;58;00;14 - 00;58;01;26

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