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Speaker 1: Hey, it's been here at the top, letting you know.

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Speaker 1: Peter and I talk a bit about the share market

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Speaker 1: in this episode, but neither of us are financial advisors,

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Speaker 1: so please don't take what we say and run with it.

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Speaker 1: Go and get proper financial advice before you make any

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Speaker 1: investment decisions. Now onto the episode.

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Speaker 2: This week on the Business of Tech, powered by Two

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Speaker 2: Degrees Business, we catch up with one of the countries

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Speaker 2: leading science communicators on the state of STEM education, the

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Speaker 2: personal toll that speaking out on controversial topics can have,

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Speaker 2: and the need to demystify AI.

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Speaker 1: If you haven't taken your kids to a Nano Girl

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Speaker 1: show or seeing doctor Michelle Dickinson hosting international stars like

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Speaker 1: Neil deGrasse Tyson on stage, you've probably seen her on

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Speaker 1: TV talking about emerging technology, stem education, or a few

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Speaker 1: years ago aspects of the big science related issue of

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Speaker 1: the moment, the COVID pandemic.

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Speaker 2: Yeah, Michelle has had incredible cut through with thousands of

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Speaker 2: kids in newscas and engaging them with science, tech, engineering

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Speaker 2: and maths related topics and nurturing the next generation of technologists.

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Speaker 2: But as Nanogirl points out in this week's featured guest,

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Speaker 2: interview Steam Education and our TAIROA is in crisis.

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Speaker 3: Sadly during this government psycho, we have cut existing programs

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Speaker 3: that we're doing great things. It's like the whole system's

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Speaker 3: been shut off. And the consequences, I mean, it's easy

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Speaker 3: to see what the consequences up for that nobody's going

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Speaker 3: to come to New Zealand for our high talent pipeline,

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Speaker 3: and nobody's going to be trained here for the jobs

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Speaker 3: that need to.

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Speaker 1: Be trained here. Much more on that and Nano Girl's

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Speaker 1: latest AI related project coming up shortly, but first a

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Speaker 1: brief dive into the big tech stories of the week.

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Speaker 1: And we're starting this week with the turmoil and the

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Speaker 1: US markets and what it says about the valuations of

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Speaker 1: the big tech companies that dominate those markets.

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Speaker 2: Yeah, as we record this on Tuesday, being we've seen

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Speaker 2: relatively big drops in the magnificent seven, those big tech stocks,

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Speaker 2: which now include Nvidia, Tesla's in there, and then the

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Speaker 2: other usual suspects like Alphabet, Microsoft, Meta, and they have

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Speaker 2: taken by a large five to ten percent cuts in

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Speaker 2: their market capitalization, which is significant. But you know, these

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Speaker 2: companies have been on a tear, you know, on a

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Speaker 2: real bull run since twenty twenty or so, So really

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Speaker 2: is it that much of a problem. People in the US,

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Speaker 2: in particular are worried because some of the signals out

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Speaker 2: of the US economy are looking a little bit worrying.

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Speaker 2: And that's the job data and also last week the

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Speaker 2: manufacturing data, and we had some of these big tech

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Speaker 2: companies report their earnings and their revenue in late July,

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Speaker 2: and that sort of has spooked some analysts and Americans

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Speaker 2: in general who are looking at it going wow, you know,

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Speaker 2: are these running out of momentum the great run of

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Speaker 2: growth that they've had. Then we had Warren Buffett sell

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Speaker 2: down a massive stake in Apple, seventy odd billion dollars

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Speaker 2: worth of Apple stock. So I guess that's not a

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Speaker 2: great sign for a trendsetter like a Warren Buffett in

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Speaker 2: his mid nineties now sort of saying I want out

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Speaker 2: of Apple it's running out of growth. Not that he's

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Speaker 2: actually said that, but there's clearly a reason why he

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Speaker 2: is liquidating a big chunk of Apple stock still has

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Speaker 2: a lot but once out. At this point, he is

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Speaker 2: a guy who is notorious for picking the market very accurately.

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Speaker 2: A lot of people are just saying something's not right here,

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Speaker 2: what is coming?

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Speaker 1: I think you would be remiss if you didn't consider

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Speaker 1: the fact that every time a sector of stocks goes

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Speaker 1: up and up, eventually it has to go down as well.

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Speaker 1: What goes up must come down, right, That's the old

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Speaker 1: adagets is true I think in the share market, and

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Speaker 1: it may not always go down by as much as

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Speaker 1: it's gone up, but the market correction is something that

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Speaker 1: is spoken about all the time, and we've just seen,

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Speaker 1: like with in Nvidia, for example, that stock went out

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Speaker 1: of control. It it went way higher than I think

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Speaker 1: it probably should have, and we saw that it returned.

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Speaker 1: I think an interesting one is probably Intel. That we

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Speaker 1: saw a massive drop in the share price of Intel

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Speaker 1: after they announced a bunch of layoffs and some tightening,

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Speaker 1: and they're clearly really struggling in this world of arm

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Speaker 1: processors and GPUs to try and keep up with the

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Speaker 1: new technology. So I think that you can look at

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Speaker 1: as an example of where there has been a real

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Speaker 1: downturn for a stock. But I think overall, I probably

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Speaker 1: wouldn't say this is, you know, the end of tech

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Speaker 1: stocks as we know it. It's just that they are

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Speaker 1: reaching their new normal and mediating out a little bit, maybe.

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Speaker 2: Year fifteen thousand jobs going globally. At Intel, which has

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Speaker 2: sort of been in crisis mode really since the rise

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Speaker 2: of Invidia and its dominant in AI related chips. It

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Speaker 2: supplies eighty nine twenty percent of the market for the

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Speaker 2: chips that go into data centers to process workloads, large

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Speaker 2: language model training and the like. Now and Nvidia is

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Speaker 2: actually facing US Department of Justice action and investigation into

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Speaker 2: complaints from its competitors that it may have abused its

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Speaker 2: market dominance and selling those chips that power artificial intelligence.

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Speaker 2: So we're seeing this on a number of fronts. When

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Speaker 2: we're about to talk about the big one this week,

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Speaker 2: which is Google after four years of Department of Justice litigation,

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Speaker 2: the Department of Justice basically saying, Yep, you're a monopolist

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Speaker 2: in the search game and we have to do something

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Speaker 2: about it. So it seems between the unease about whether

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Speaker 2: the US, which it's sort of done well in the

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Speaker 2: last couple of years, is starting to face the same

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Speaker 2: sort of hard landing that we did coming out out

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Speaker 2: of COVID and we're paying the price of that now.

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Speaker 2: Has the US just been pushing that down the road

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Speaker 2: through injecting money into the economy through the Inflation Reduction Act,

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Speaker 2: all that money that has gone into things like semiconductors

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Speaker 2: and manufacturing, pumping out all this money for infrastructure that

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Speaker 2: Biden's been doing. Is it just a delayed reaction now?

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Speaker 2: And are we going to see a few hard years

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Speaker 2: for the US? So? I guess there's that, But also

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Speaker 2: people looking at the Stella run in tech and going

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Speaker 2: are we ready for that sort of correction we saw

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Speaker 2: in the late nineties and two thousand with the bubble bursting.

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Speaker 2: I don't think it's going to be that, but clearly

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Speaker 2: between regulation and the hollowness of AI at the moment

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Speaker 2: where the revenue isn't coming, but the expenses there from

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Speaker 2: the likes of Microsoft and NVIDI and all that, a

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Speaker 2: lot of people are going, we knew this already there

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Speaker 2: overinflated in priced, but we're going to take some money

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Speaker 2: off the table now.

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Speaker 1: Yeah, the shine is wearing off. I saw our Business

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Speaker 1: Insider piece early last week and talking about a pharmaceutical

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Speaker 1: company that had five hundred seats I think it was,

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Speaker 1: and upgraded them all to co pilot for another one

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Speaker 1: hundred and eighty thousand dollars a year and then just

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Speaker 1: said it just wasn't worth it for them.

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Speaker 2: We'll linked to that Business Insider piece. It was really

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Speaker 2: good because it was quite refreshing to see your company

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Speaker 2: sort of say, yeah, look we in good faith, we

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Speaker 2: implemented it across the business, and we're pulling the plug

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Speaker 2: because we're just not seeing the returns. So yeah, it's

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Speaker 2: good to see that. You talk to Microsoft and it's

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Speaker 2: going gangbusters. Everyone's seeing mental productivity gains and frankly some

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Speaker 2: of the analysts are sort of saying that as well.

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Speaker 2: But actually getting that tangible, independent feedback is great.

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Speaker 1: It was just the evidence that it's about specificity, not

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Speaker 1: on mass application, isn't it. And I think that's what's

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Speaker 1: maybe causing a little bit of downward turn in these

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Speaker 1: tech stocks as well, that there was a lot of

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Speaker 1: excitement and that was reflected in those prices for a while.

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Speaker 2: Yeah, let's have a look at this Google case that's

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Speaker 2: been running for over four years now with the Department

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Speaker 2: of Justice. So they just ruled this week turner it.

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Speaker 2: In seventy seven page ruling, Judge Ammett Metta basically said

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Speaker 2: that Google has made these massive payments to Apple and

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Speaker 2: Mozilla to make the Google Search Engine the default on

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Speaker 2: new devices, and including that they did this with Samsung

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Speaker 2: and others as well, paid billions of dollars to other

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Speaker 2: companies to ensure that the first thing you see when

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Speaker 2: you boot up a new device is the Google Search Engine.

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Speaker 2: So they basically said that after having carefully considered and

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Speaker 2: weighed the witness, testimony and evidence, the court reaches the

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Speaker 2: following conclusion. Google is a monopolist and it has acted

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Speaker 2: as one to maintain its monopoly. The consequences of that

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Speaker 2: is that it's charged advertisers higher prices than they would

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Speaker 2: have in a more competitive market. And that's why we

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Speaker 2: see Google with ninety percent of the search market and

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Speaker 2: being and others scrambling for the crumbs off the table.

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Speaker 2: That has a tangible impact on how much a business

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Speaker 2: pays to advertise on the Google search engine.

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Speaker 1: Yeah, by keeping the Google Search engine as the main

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Speaker 1: search engine that ninety eight plus percent of people are using,

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Speaker 1: that means that they can continue to sell ads on

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Speaker 1: that search engine. They know everybody's going to go there,

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Speaker 1: and they can put the prices up on those ads.

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Speaker 1: And I don't know if you've ever spoken to a

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Speaker 1: small business person in New Zealand. Talk to them about

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Speaker 1: advertising on Google. They always have that same thing. There's

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Speaker 1: just nowhere else I can go. It doesn't matter. I

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Speaker 1: have to pay if I want to get those leads

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Speaker 1: through Google. And you know we're not talking small change,

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Speaker 1: it says in the article Apple got an estimated twenty

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Speaker 1: billion dollars from Google in twenty twenty two. It's in

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Speaker 1: one year just to make the search engine the default

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Speaker 1: on Apple devices. Why would Apple need a twenty billion

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Speaker 1: dollar incentive to have that? If everyone want to Google anyway,

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Speaker 1: That's a really big and interesting question, and I think

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Speaker 1: why the courts have decided the way that they have.

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Speaker 2: Yeah, the question is what happens now. And a lot

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Speaker 2: of the commentary on this is that it will literally

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Speaker 2: take five years to sort this out, minimum because Kent Walker,

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Speaker 2: the company's president of global affairs, has come out today

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Speaker 2: and said, we are going to appeal this. It's great

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Speaker 2: that you think our search engine is so fantastic, thanks guys,

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Speaker 2: but we don't like the monopolist aspect of it as

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Speaker 2: well that you're accusing us of being. So we are

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Speaker 2: going to appeal this. So it we'll go through court.

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Speaker 2: It will move into a new phase where they will continue.

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Speaker 2: They will start hearing arguments now in court about what

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Speaker 2: remedy should be implemented to address this monopoly that the

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Speaker 2: court has decided is monopoly. So nothing's going to happen

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Speaker 2: in the immediate future. But some of the things that

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Speaker 2: could potentially happen is what has happened in Europe where

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Speaker 2: they've the EU Court action near decided a similar thing,

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Speaker 2: which Google has monopolistic properties there, and sort of what

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Speaker 2: they've done there is this what they call a ballot screen.

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Speaker 2: So when you boot up a new device there, you

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Speaker 2: get to choose what search engine you want, say a

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Speaker 2: new Android phone, it will give you a list of them,

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Speaker 2: including the Google search engine. But apparently it's had negligible

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Speaker 2: effect on the company's market share in Europe, so people

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Speaker 2: are still in that mindset of Google is the best,

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Speaker 2: Google is the default. So what will actually make a difference.

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Speaker 2: Will it have to go to structural separation where they

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Speaker 2: sort of break up Google, And how do you even

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Speaker 2: do that without killing the effectiveness of its search engine.

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Speaker 2: It is renowned for being very good, so how they

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Speaker 2: actually fix this problem will create another sort of headache.

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Speaker 2: They're going to have to work through.

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Speaker 1: Yeah, I mean, there is a lot of rumbling, slow movement.

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Speaker 1: I would say in some certain places online about complaints

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Speaker 1: around the Google search engine. And I've read articles saying

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Speaker 1: that the Google search engines saying there are one hundred

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Speaker 1: thousand results is a lie and you can't actually get

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Speaker 1: if you keep clicking through it's not those are not

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Speaker 1: actually there, and that it will only ever display those

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Speaker 1: has a very strong recency bias, and all of these

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Speaker 1: kinds of criticisms of the Google search engine that are

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Speaker 1: slowly growing. So maybe that movement, in conjunction with the

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Speaker 1: ability to select, with new search engines potentially coming out

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Speaker 1: with new technologies underpinning them, we don't know, you know,

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Speaker 1: there could be a step change, a shift in consumer

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Speaker 1: mindset around the corner when it comes to search. It's

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Speaker 1: hard to see because Google is so all consuming it

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Speaker 1: is a verb at this point, but perhaps we will

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Speaker 1: start to see a shift, if you know. I mean,

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Speaker 1: all empires fall at the end of the day, don't they.

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Speaker 1: I don't think digital ones are any different.

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Speaker 2: Yeah, they did well twenty five years and it would

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Speaker 2: be great to see three or four players with twenty

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Speaker 2: percent market share between them, so they're still profitable. They

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Speaker 2: still have a lot of money to invest in search,

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Speaker 2: and as you said, you know these new technologies like

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Speaker 2: AI generated search results and conversational AI could change the

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Speaker 2: game and give new entrants more of an opportunity. So

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Speaker 2: we'll see how this play out next week. We do

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Speaker 2: have an expert on search advertising, paid advertising and that

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Speaker 2: and how some of these changes are affecting what businesses

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Speaker 2: will need to do when it comes to trying to

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Speaker 2: find an audience. That's Ryan McMillan from Atlas Digital. So

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Speaker 2: that's on next week's episode. But just finally on news

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Speaker 2: this week, being some interesting commentary about Spotify, and I'm

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Speaker 2: no longer a Spotify user. I bailed out of Spotify

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Speaker 2: a long time ago just because it was a better

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Speaker 2: deal to do YouTube music, because I get bundled in

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Speaker 2: the ad free experience on YouTube, which as a super

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Speaker 2: user of YouTube, I really appreciate. And there are real

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Speaker 2: fanatical loyalists to Spotify, which I remember being a bit

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Speaker 2: sad to leave it because it was a great discovery

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Speaker 2: machine for new music. But increasingly we're seeing commentary a

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Speaker 2: big piece in the New Yorker that's got a lot

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Speaker 2: of attention. Another one in the Guardian, people going what

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Speaker 2: is up with Spotify? It's not showing the music I

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Speaker 2: want to discover anymore. It seems to be recycling all

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Speaker 2: the same big name artists. To me, are they paying

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Speaker 2: behind the scenes to have this stuff featured? And it

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Speaker 2: turns out it's not as simple as that. But what

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Speaker 2: is going on is this new discovery mode, which effectively

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Speaker 2: allows an artist to forego some royalties to be featured

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Speaker 2: prominently in parts of the Spotify app, which effectively gives

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Speaker 2: them more exposure to people, encourages people to follow them

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Speaker 2: and start listening to their music. So the argument is

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Speaker 2: that by manipulating that discovery mode, they're favoring some artists

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Speaker 2: and it's why people like you've been are seeing more

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Speaker 2: of them.

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Speaker 1: Yeah, yeah, I still am a Spotify user, and I

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Speaker 1: think it's mainly I did try to bail out into

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Speaker 1: Apple Music not long ago, but I just couldn't figure

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Speaker 1: out how to get my playlists over, which is just

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Speaker 1: a huge thing, like I don't want to lose all

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Speaker 1: of the work that I've put into following those particular

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Speaker 1: songs that you know that I have over the years.

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Speaker 1: That also is an interesting area to look at for

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Speaker 1: monopoly and consumer data right kind of approach there in

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Speaker 1: terms of who owns the data around my preferences. But yeah,

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Speaker 1: I definitely have noticed over the years that Spotify stops

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Speaker 1: serving up unexpected everything I hear. Now it kind of

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Speaker 1: fits within these kind of expected realm of music that

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Speaker 1: it keeps serving to me. And I have fairly broad tastes,

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Speaker 1: so it's nice to just hear some new random stuff

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Speaker 1: every now and again. That could be completely terrible, but

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Speaker 1: when you're actively looking for new music, it's okay to

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Speaker 1: have terrible stuff, well, stuff that you think is terrible

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Speaker 1: put in front of you, because it's kind of part

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Speaker 1: of the fun to.

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Speaker 2: Go, oh, what's this crap? Turn that off?

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Speaker 1: You know, oh this is quite interesting. It's all part

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Speaker 1: of the joy of it, rather than just aiming for

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Speaker 1: homogeny and just having bland. But where you have an algorithm,

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Speaker 1: you will have gaming of algorithms at the end of

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Speaker 1: the day. I think that's the reality of it. And

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Speaker 1: now Spotify, with all of its podcasts and all of

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Speaker 1: its kind of pretty terrible approach to audio books, to

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Speaker 1: be honest, there's a whole new level of kind of

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Speaker 1: trying to get through the user experience, and the New

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Speaker 1: York A piece talks about how hard it is to

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Speaker 1: find stuff that you're looking for, and it's absolutely right,

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Speaker 1: like trying to find a playlist, and yeah, so it

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Speaker 1: feels like it should be split into a few different

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Speaker 1: apps Spotify podcasts, Spotify audiobooks, Spotify Music, and then you

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Speaker 1: can kind of go where you want to. But by

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Speaker 1: trying to shove what they want in your face, it

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Speaker 1: speaks to that kind of inification trope. And if you

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Speaker 1: have successfully moved from Spotify to Apple Music, let me

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Speaker 1: know how you did it. Until all your playlists with you,

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Speaker 1: that would be great. Thank you.

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Speaker 2: Now. Our guest this week is an engineer, entrepreneur, and

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Speaker 2: former academic, Michelle Dickinson aka Nanogirl. Michelle grew up in

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Speaker 2: Hong Kong, the USA, the United Kingdom, did a PhD

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Speaker 2: in Biomedical materials engineering at Rutgers University in New Jersey,

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Speaker 2: worked in the tech industry, and eventually found a way

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Speaker 2: to Auckland, New Zealand, where she set up the country's

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Speaker 2: first nanotechnology testing lab.

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Speaker 1: And so that's where she gets the nano and nanogirl

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Speaker 1: and Michelle is fascinated with the science of small stuff,

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Speaker 1: manipulating individual atoms and molecules to come up with cool

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Speaker 1: new materials and technologies.

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Speaker 2: But she's also got her teeth into some big issues

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Speaker 2: over the last decade or so, including the skill shortage

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Speaker 2: in the so called STEM subjects as science, tech, engineering,

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Speaker 2: and maths. She co founded OMG Tech to address that issue,

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Speaker 2: and later Nanogirl Labs, which is her vehicle for bringing

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Speaker 2: STEM to kids here and around the world.

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Speaker 1: She really is a science communication dynamo, which we really

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Speaker 1: need as a country. But it hasn't been smooth sailing

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Speaker 1: in recent years for Michelle or other high profile science

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Speaker 1: communicators like Susie Wiles.

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Speaker 2: Yeah, COVID wasn't just bad for business for Nanogirl Labs,

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Speaker 2: at least for a while. It unleashed a lot of

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Speaker 2: toxic sentiment towards scientists, which Michelle had to deal with.

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Speaker 2: So here's Nanogirl on all of that and her take

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Speaker 2: on artificial intelligence and what it means to the next

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Speaker 2: generation of STEM graduates and the workforce in general. Michelle,

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Speaker 2: thanks so much for being on the business of tech.

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Speaker 2: So good to see you again. It's been quite a while,

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Speaker 2: haven't caught up with you recently, but I think our

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Speaker 2: relationship goes back over a decade. Related to the Science

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Speaker 2: Media Center. We were working together on the Cydeblogs platform.

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Speaker 2: You were a big contributor at the time to the

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Speaker 2: commetary that we were putting out to the media. At

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Speaker 2: the Science Media Center, you went through our Science Media

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Speaker 2: Savvy course, and it's just been incredible watching your science

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Speaker 2: communication journey, going from being an academic that didn't do

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Speaker 2: a lot of communication to being on stage with Neil

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Speaker 2: de grasse Tyson doing a Q and A with him

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Speaker 2: at spark Areen in front of tens of thousands of people.

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Speaker 2: What's that been like? What was the key really to you?

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Speaker 2: Sort of coming out of your shell and really building

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Speaker 2: communication into your career?

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Speaker 3: You were the key, Peter. I always say people go,

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Speaker 3: how did you get air? And I was like, there's

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Speaker 3: a man called Peter Griffin that I met, Not the

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Speaker 3: family guy, the family guy you, I mean, you really

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Speaker 3: are in the Science Media Center that you were running

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Speaker 3: at the time. Is the reason why I'm here. It

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Speaker 3: opened my eyes to something that I didn't know exists

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Speaker 3: did I was a nerdy, socially awkward academic doing nanotech

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Speaker 3: in my lab and frustrated that a lot of the

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Speaker 3: false and the misinformation I was seeing at the time

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Speaker 3: around the field I was in wasn't being corrected. And

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Speaker 3: then saw a course and managed to get the scholarship

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Speaker 3: to go on the course, and then was like, hold on,

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Speaker 3: I could be the person who talks about this and

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Speaker 3: I had no skills in it, right, I was painfully

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Speaker 3: shy and it was awful. But the Science Media Center

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Speaker 3: and the media Savvy course I went on that you

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Speaker 3: led showed me that there are different ways to communicate,

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Speaker 3: and there are different platforms, and actually it's really important

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Speaker 3: for scientists to have a voice when it's about things

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Speaker 3: that you know, the public just don't know where to

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Speaker 3: get good, solid, evidence based information on. Then I've also

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Speaker 3: seen a need for it as I've got deeper into

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Speaker 3: science communication around you know, different pockets of the community

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Speaker 3: that really don't get this. So yeah, you you, Peter,

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Speaker 3: you where I am here?

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Speaker 2: Well, that was a stepping off point, but then you

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Speaker 2: did so much more to take that to great heights.

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Speaker 2: What process did you go through to get to the

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Speaker 2: point where that's sick and nature to you?

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Speaker 3: I would never say that I don't have nerves even today.

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Speaker 3: I think all good people who should be nervous. It's

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Speaker 3: a good feeling to have. It means you're doing something serious.

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Speaker 3: And I don't think people realize how much preparation goes

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Speaker 3: into even a two minute interview live on TV. I've

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Speaker 3: met extroverted communicators who can really, nearly on the fly,

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Speaker 3: just come up with something that's not me. So the

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Speaker 3: amount of work that I will do behind the scenes,

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Speaker 3: the research, the memorizing soundbites, the things to make myself

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Speaker 3: confident is massive, and maybe people don't appreciate how much

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Speaker 3: work happens behind there.

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Speaker 2: Yeah, and at short notice when a major issue is

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Speaker 2: going down, you know that takes dedication and carving out

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Speaker 2: the time to do that preparation.

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Speaker 3: And yeah, all night usually if there's a breaking news.

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Speaker 3: And usually you'll get the call from the media the

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Speaker 3: night before at eight pm and they'll be like, can

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Speaker 3: you be on at six thirty eight. I won't sleep

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Speaker 3: that night. I'll have read around the subject. I'll make

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Speaker 3: sure that I've got my soundbites. I write them on

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Speaker 3: post it notes, I'll start memorizing them. Often you'll see

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Speaker 3: I've written on my hand before I've gone into the

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Speaker 3: studio right before we're live, I've just chucked my hand.

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Speaker 3: So it's in my brain like there's lots of little

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Speaker 3: things that I do at least to make sure I'm prepped.

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Speaker 2: And it's great. We saw this cohort that really around

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Speaker 2: the same time as you were building your science communication skills,

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Speaker 2: others were doing it as well. Richard Easter, Sean Hendy,

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Speaker 2: Michael Plank, all these people, and a lot of them

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Speaker 2: became very prominent during the COVID pandemic, most notably I

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Speaker 2: think Susie Wiles, who went through hell really with her

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Speaker 2: own institution and recently won that employment court case against

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Speaker 2: the University of Auckland, was a great communicator for the

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Speaker 2: university and in her own right didn't get to support

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Speaker 2: that she needed. For you. You were at the University

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Speaker 2: of Auckland. I think you said you've sort of had

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Speaker 2: to put up with that as well then going out

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Speaker 2: on your own. I guess the freedom that gave you,

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Speaker 2: but also no safety in it, no institutional support at all.

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Speaker 3: Look, academia is a really interesting place. It was a

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Speaker 3: place that I learned, you know, early on, but I

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Speaker 3: didn't quit it early enough that I didn't really fit

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Speaker 3: for that. There's lots of people there who are a

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Speaker 3: type personalities, who believe, and they often are the best

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Speaker 3: in their field at what they do academically. And so

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Speaker 3: when you've got what I call lots of egos in

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Speaker 3: a room, you can feel like there are lots of

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Speaker 3: fifdoms everywhere. And so when communicators like myself and like

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Speaker 3: Susie come along, where our craft is not just our

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Speaker 3: academic knowledge, but our ability to get information out to

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Speaker 3: the layperson, to the public quickly. It's a really a skill,

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Speaker 3: and I think people think they could do it but

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Speaker 3: don't understand that actually it's a skill that has to

435
00:23:46,880 --> 00:23:49,840
Speaker 3: have work. So yeah, there was a few more, probably

436
00:23:49,840 --> 00:23:51,840
Speaker 3: more times that I want to admit that I was

437
00:23:51,880 --> 00:23:54,679
Speaker 3: told that my communication was a waste of time. I

438
00:23:54,720 --> 00:23:58,399
Speaker 3: remember winning the Prime Minister's Science Communication Prize and I

439
00:23:58,480 --> 00:24:00,720
Speaker 3: literally got off the plane and I went straight to work,

440
00:24:01,119 --> 00:24:04,240
Speaker 3: went to my office, put the it was this massive trophy,

441
00:24:04,280 --> 00:24:06,399
Speaker 3: it was amazing, it was so heavy, put it on

442
00:24:06,440 --> 00:24:08,280
Speaker 3: my shelf and I was so proud. And within three

443
00:24:08,320 --> 00:24:11,840
Speaker 3: minutes an academic in my department walked into it, picked

444
00:24:11,840 --> 00:24:13,720
Speaker 3: it up, looked at me and said, well, it's not

445
00:24:13,720 --> 00:24:15,639
Speaker 3: like it's a real reward, is it. And then walked

446
00:24:15,640 --> 00:24:18,080
Speaker 3: out and I just went, why would you take that

447
00:24:18,320 --> 00:24:22,600
Speaker 3: from me to elevate yourself Like it's crazy. But that's academia, right.

448
00:24:22,640 --> 00:24:25,160
Speaker 3: It's just filled with lots of quirky people. And that's

449
00:24:25,200 --> 00:24:27,560
Speaker 3: the amazing thing about academia. And that's when I realized

450
00:24:27,560 --> 00:24:30,639
Speaker 3: that great, that it's not a space that I probably

451
00:24:30,720 --> 00:24:33,160
Speaker 3: want to stay in. But I've come from other places,

452
00:24:33,160 --> 00:24:35,000
Speaker 3: so I sort of had to think to compare it

453
00:24:35,040 --> 00:24:38,240
Speaker 3: to But during that time became great friends with Richard

454
00:24:38,280 --> 00:24:40,720
Speaker 3: Easter and shown Hendy and Susie and sort of became

455
00:24:40,760 --> 00:24:43,760
Speaker 3: this amazing communication cohort where we could bounce ideas off

456
00:24:43,800 --> 00:24:46,879
Speaker 3: each other and talk about what we were doing. And

457
00:24:46,920 --> 00:24:51,040
Speaker 3: then COVID and so I had left the university by

458
00:24:51,040 --> 00:24:52,760
Speaker 3: that point, and I had my own business, and so

459
00:24:53,200 --> 00:24:56,040
Speaker 3: I was also asked by lots of people, including the government,

460
00:24:56,040 --> 00:24:58,760
Speaker 3: to help communicate things around COVID, and I think the

461
00:24:58,840 --> 00:25:01,480
Speaker 3: difference is when you run your own business and you

462
00:25:01,520 --> 00:25:06,320
Speaker 3: don't have academic freedom to protect you. I made choices

463
00:25:06,359 --> 00:25:08,959
Speaker 3: about what I went into or one I didn't, and

464
00:25:09,000 --> 00:25:11,200
Speaker 3: what I said and what I didn't. So as a communicator.

465
00:25:11,640 --> 00:25:14,159
Speaker 3: I turned down lots of lots of media when they

466
00:25:14,160 --> 00:25:15,480
Speaker 3: said can you do this? And I was like, do

467
00:25:15,480 --> 00:25:18,000
Speaker 3: you know what, No, I feel like it's too high risk,

468
00:25:18,119 --> 00:25:20,600
Speaker 3: especially things that are aligned with telling people what they

469
00:25:20,640 --> 00:25:22,560
Speaker 3: should do. I never once said you should do this.

470
00:25:22,640 --> 00:25:24,720
Speaker 3: I said, hey, here's the evidence that we know right now.

471
00:25:25,480 --> 00:25:27,760
Speaker 3: This might change tomorrow because it's coming in thick and first,

472
00:25:27,840 --> 00:25:29,919
Speaker 3: I'm not going to tell you what to do, but

473
00:25:30,000 --> 00:25:32,600
Speaker 3: this is what the evidence says. And I never aligned

474
00:25:32,640 --> 00:25:36,760
Speaker 3: myself with any sort of political party. Labor was obviously

475
00:25:36,800 --> 00:25:39,199
Speaker 3: in at the time, so I went, hey, you know,

476
00:25:39,240 --> 00:25:41,600
Speaker 3: these are the people who are in. This is what

477
00:25:41,640 --> 00:25:43,680
Speaker 3: they're saying, this is what the evidence says. But I

478
00:25:44,040 --> 00:25:47,440
Speaker 3: had to be really careful because I have stuff, and

479
00:25:47,480 --> 00:25:49,600
Speaker 3: if it all goes horribly wrong, it doesn't just go

480
00:25:49,640 --> 00:25:52,280
Speaker 3: horribly wrong for me. It's the livelihood of all of

481
00:25:52,280 --> 00:25:54,399
Speaker 3: the stuff that they have, who have mortgages, you know,

482
00:25:54,440 --> 00:25:57,639
Speaker 3: that depend on me bringing an income in to the organization.

483
00:25:57,800 --> 00:26:00,080
Speaker 3: So I had to think about those people. And I

484
00:26:00,119 --> 00:26:02,119
Speaker 3: think if I was an academic, I maybe would have

485
00:26:02,800 --> 00:26:05,560
Speaker 3: taken on more things, hoping that academic freedom would have

486
00:26:05,600 --> 00:26:08,560
Speaker 3: protected me. And I saw that, you know, Susie was

487
00:26:08,600 --> 00:26:10,560
Speaker 3: able to do that. Sean Hendy was able to take

488
00:26:10,560 --> 00:26:14,120
Speaker 3: on more things, or at least they thought they could. Yeah,

489
00:26:14,280 --> 00:26:17,080
Speaker 3: and obviously it went pretty horribly wrong for them. And

490
00:26:17,119 --> 00:26:19,640
Speaker 3: I'm not saying that I didn't have issues too. So

491
00:26:19,960 --> 00:26:23,760
Speaker 3: my home address, photos of my home were taken, pictures

492
00:26:24,000 --> 00:26:26,080
Speaker 3: places that you could shoot me from the easiest as

493
00:26:26,080 --> 00:26:28,640
Speaker 3: a gas station across my old house. I moved, actually,

494
00:26:28,880 --> 00:26:30,360
Speaker 3: but it did the same with Susie, you know, taking

495
00:26:30,400 --> 00:26:33,440
Speaker 3: picture of a cat outside a house, posting her address, saying, well,

496
00:26:33,440 --> 00:26:35,159
Speaker 3: do what you want, this is where she lives. And

497
00:26:35,520 --> 00:26:38,359
Speaker 3: it was a really awful, frightening time. And then she

498
00:26:38,440 --> 00:26:41,360
Speaker 3: had a public workplace right that people could and they

499
00:26:41,400 --> 00:26:44,760
Speaker 3: did go to and confront and I think it was

500
00:26:44,800 --> 00:26:47,000
Speaker 3: a really scary time because people were just a little

501
00:26:47,000 --> 00:26:50,320
Speaker 3: bit unhinged. I think everybody was super stressed and you

502
00:26:50,600 --> 00:26:53,399
Speaker 3: just never knew. And the challenge with being in the

503
00:26:53,440 --> 00:26:56,080
Speaker 3: media spotlight is people know who you are and you

504
00:26:56,119 --> 00:26:59,960
Speaker 3: don't know who they are. And being recognized in part

505
00:27:00,400 --> 00:27:02,720
Speaker 3: is a double edged sword for sure, because if they're

506
00:27:02,720 --> 00:27:05,040
Speaker 3: a friendly, it's great, But if they're a foe, let

507
00:27:05,080 --> 00:27:07,000
Speaker 3: me tell you, they'll come up to you personally and

508
00:27:07,480 --> 00:27:11,320
Speaker 3: tell you what for. So watching what happened with Susie,

509
00:27:11,359 --> 00:27:14,679
Speaker 3: I mean, she's so brave for taking on the university

510
00:27:14,720 --> 00:27:18,639
Speaker 3: and luckily being awarded the win. And I think it's

511
00:27:19,200 --> 00:27:21,480
Speaker 3: I hope it's better for academics to be able to

512
00:27:21,480 --> 00:27:24,239
Speaker 3: come out and talk about things that might be a

513
00:27:24,240 --> 00:27:28,439
Speaker 3: bit hairy. But in the meantime, we've lost I'm an

514
00:27:28,480 --> 00:27:31,639
Speaker 3: amazing communicator. She's, you know, a shell of herself. She

515
00:27:31,640 --> 00:27:34,560
Speaker 3: doesn't come out anymore to talk about the things and

516
00:27:34,960 --> 00:27:37,679
Speaker 3: she was at the top of her game around communication,

517
00:27:37,800 --> 00:27:40,639
Speaker 3: and it's been really distressing to see that. So you

518
00:27:41,040 --> 00:27:44,040
Speaker 3: do take more risk as a communicator. I do it

519
00:27:44,080 --> 00:27:47,400
Speaker 3: because I really see I really feel the benefit of

520
00:27:47,640 --> 00:27:49,639
Speaker 3: the work that I do, because I do get people

521
00:27:49,680 --> 00:27:52,440
Speaker 3: saying thank you for that. I just didn't know. It

522
00:27:52,480 --> 00:27:55,440
Speaker 3: really helped me navigate choices in my life, and those

523
00:27:55,480 --> 00:27:58,479
Speaker 3: can be choices, big choices that people you know are

524
00:27:58,480 --> 00:28:00,960
Speaker 3: trying to make because they don't have great science literacy.

525
00:28:01,000 --> 00:28:05,400
Speaker 3: And that's the challenge. Our population has terrible science literacy

526
00:28:05,520 --> 00:28:08,000
Speaker 3: in general. And so where do you go when you're

527
00:28:08,000 --> 00:28:10,040
Speaker 3: an adult and you're trying to make a good informed

528
00:28:10,040 --> 00:28:13,400
Speaker 3: decision about something When Google says anything you wanted to say.

529
00:28:13,680 --> 00:28:17,160
Speaker 2: Yeah, and you still do a lot of that sort

530
00:28:17,200 --> 00:28:21,320
Speaker 2: of communication aimed at the population in general on often

531
00:28:21,359 --> 00:28:25,159
Speaker 2: contentious issues. But your real focus in recent news has

532
00:28:25,160 --> 00:28:29,000
Speaker 2: been nanogurl Labs. It's an online platform, but it's live events.

533
00:28:29,040 --> 00:28:32,320
Speaker 2: You go to schools, you do big shows, lots of

534
00:28:32,440 --> 00:28:35,920
Speaker 2: special effects and that to get people engaged. And it's

535
00:28:35,960 --> 00:28:39,560
Speaker 2: what's that journey been like? Leaving academia, setting up on

536
00:28:39,760 --> 00:28:42,360
Speaker 2: your own steam and having to navigate some hard years

537
00:28:42,440 --> 00:28:44,920
Speaker 2: when you couldn't do live events because of COVID.

538
00:28:46,600 --> 00:28:48,960
Speaker 3: Yeah, I mean I you know, anybody who owns a

539
00:28:49,000 --> 00:28:52,680
Speaker 3: business knows that you don't get it in it because

540
00:28:52,720 --> 00:28:54,440
Speaker 3: you want to sleep more or you want to make

541
00:28:54,480 --> 00:28:56,760
Speaker 3: more money. You do it because you see a problem

542
00:28:56,760 --> 00:28:58,760
Speaker 3: that you think you might be able to help, at

543
00:28:58,840 --> 00:29:01,200
Speaker 3: least not solve, but go in the right direction and

544
00:29:01,280 --> 00:29:03,360
Speaker 3: be part of a cohort. So those who don't know

545
00:29:03,400 --> 00:29:07,360
Speaker 3: my background, I was not great at school. I didn't

546
00:29:07,360 --> 00:29:09,160
Speaker 3: grow up with much money. I didn't grow up with

547
00:29:09,280 --> 00:29:12,280
Speaker 3: educated parents, so even going to university was never something

548
00:29:12,280 --> 00:29:15,600
Speaker 3: that was on my radar. I think, you know, having

549
00:29:16,000 --> 00:29:20,040
Speaker 3: fallen into lots of lucky situations and lots of grown

550
00:29:20,120 --> 00:29:23,240
Speaker 3: ups at the time who helped me becoming an engineer

551
00:29:23,280 --> 00:29:26,120
Speaker 3: in the end, and then having an academic career and

552
00:29:26,160 --> 00:29:29,080
Speaker 3: having a career in tech in the US. I just

553
00:29:29,160 --> 00:29:32,320
Speaker 3: I always, every day felt so grateful that I was here,

554
00:29:32,560 --> 00:29:34,320
Speaker 3: especially when I look back at the kids I went

555
00:29:34,360 --> 00:29:37,280
Speaker 3: to school with. Granville Harvey I went to school with

556
00:29:37,520 --> 00:29:40,520
Speaker 3: is in prison for attempted murder, no surprise, but he's

557
00:29:40,560 --> 00:29:42,680
Speaker 3: the guy I shared my textbooks with. You know, we

558
00:29:42,680 --> 00:29:46,360
Speaker 3: were rough kids, And I go, well, it was only

559
00:29:46,600 --> 00:29:49,520
Speaker 3: a hot for me not following in his footsteps. Right,

560
00:29:49,680 --> 00:29:51,720
Speaker 3: So how am I not in jail? How do I

561
00:29:51,760 --> 00:29:53,960
Speaker 3: have this amazing career? And how do I make sure

562
00:29:54,000 --> 00:29:56,800
Speaker 3: that kids like me, who through no fault of our own,

563
00:29:56,960 --> 00:29:59,280
Speaker 3: just not living in areas with lots of tech, not

564
00:29:59,560 --> 00:30:04,440
Speaker 3: having those ecosystems where academia is promoted, how do those

565
00:30:04,520 --> 00:30:08,080
Speaker 3: kids get to do this? And so I always had

566
00:30:08,080 --> 00:30:10,560
Speaker 3: this inner fire to go how do I get that

567
00:30:10,560 --> 00:30:13,000
Speaker 3: to happen? And when I worked at the university, and

568
00:30:13,040 --> 00:30:15,640
Speaker 3: even when I worked in sort of the tech space

569
00:30:15,640 --> 00:30:17,320
Speaker 3: in the US, it became very clear to me that

570
00:30:17,360 --> 00:30:19,120
Speaker 3: the people I was working with and the students we

571
00:30:19,120 --> 00:30:22,960
Speaker 3: were bringing in our privileged students. That's privilege through education.

572
00:30:23,160 --> 00:30:27,040
Speaker 3: That's privileged through Often parents are academic or would push

573
00:30:27,040 --> 00:30:29,120
Speaker 3: them into that system. And the kids who weren't making

574
00:30:29,160 --> 00:30:31,600
Speaker 3: it through are those kids you expect not to write,

575
00:30:31,640 --> 00:30:34,240
Speaker 3: those growing up in poverty, those growing up in rural

576
00:30:34,400 --> 00:30:37,480
Speaker 3: those growing up with parents who aren't in this sector

577
00:30:37,520 --> 00:30:39,400
Speaker 3: and don't know it exists. I said, well, how do

578
00:30:39,480 --> 00:30:44,280
Speaker 3: we because diversity is so important in tech, especially because

579
00:30:44,280 --> 00:30:46,600
Speaker 3: otherwise all we have is a bunch of Caucasian Silicon

580
00:30:46,680 --> 00:30:49,960
Speaker 3: Valley nerds inventing tech for us, and that doesn't represent

581
00:30:50,000 --> 00:30:54,000
Speaker 3: our problems. And it became very clear to me that

582
00:30:54,280 --> 00:30:57,560
Speaker 3: even our academic system shuts off so many students based

583
00:30:57,600 --> 00:31:01,320
Speaker 3: on grades, and the grades these kids getting might not

584
00:31:01,440 --> 00:31:04,160
Speaker 3: be the top because their school doesn't have a physics teacher,

585
00:31:04,200 --> 00:31:06,880
Speaker 3: because there's so many things that affect our young people

586
00:31:06,920 --> 00:31:11,000
Speaker 3: because of their postcode. And so I'm still on this

587
00:31:11,120 --> 00:31:13,480
Speaker 3: mission to go, how do we help more young people

588
00:31:13,480 --> 00:31:16,760
Speaker 3: who come from diverse backgrounds to realize that STEM is

589
00:31:16,800 --> 00:31:19,440
Speaker 3: a place that they should be, They should be welcomed,

590
00:31:19,480 --> 00:31:21,080
Speaker 3: and we have to figure out how we get them

591
00:31:21,120 --> 00:31:24,600
Speaker 3: into it when so many doors are closed because the

592
00:31:24,640 --> 00:31:27,520
Speaker 3: system's been the system for a long time, and then

593
00:31:27,600 --> 00:31:30,479
Speaker 3: how do we then keep them in the system and

594
00:31:30,600 --> 00:31:33,840
Speaker 3: help them to think through problems that nobody else in

595
00:31:33,880 --> 00:31:36,160
Speaker 3: the field is really thinking about. And we've just rewritten

596
00:31:36,160 --> 00:31:38,760
Speaker 3: the high school curriculum for four Pacific island nations, which

597
00:31:38,760 --> 00:31:42,280
Speaker 3: has been such an honor, and we've shown that those

598
00:31:42,440 --> 00:31:45,920
Speaker 3: students who now study It was a year ten program

599
00:31:46,040 --> 00:31:48,120
Speaker 3: and now fifty percent of them are now staying on

600
00:31:48,160 --> 00:31:52,880
Speaker 3: to study science at year eleven because we did localized context.

601
00:31:53,760 --> 00:31:56,040
Speaker 3: Right now, they're being taught by a non science teacher

602
00:31:56,080 --> 00:31:58,440
Speaker 3: and they're being given a free New Zealand or Australian

603
00:31:58,440 --> 00:32:00,800
Speaker 3: textbook to read from right, And that's just how we've

604
00:32:00,840 --> 00:32:04,160
Speaker 3: dealt with the Pacific And so we wrote this amazing

605
00:32:04,240 --> 00:32:07,600
Speaker 3: program that was I mean, it was amazing that you know,

606
00:32:07,600 --> 00:32:09,440
Speaker 3: they could look out of the window and suddenly see

607
00:32:09,480 --> 00:32:12,000
Speaker 3: the context of what they were doing. The examples were

608
00:32:12,040 --> 00:32:15,040
Speaker 3: local to them, and it's I mean, it's not brain science,

609
00:32:15,120 --> 00:32:17,640
Speaker 3: like it's not rocket science. You just go, hey, yeah,

610
00:32:17,880 --> 00:32:21,600
Speaker 3: make kids understand why we're doing this'll stick with it.

611
00:32:22,600 --> 00:32:26,320
Speaker 3: So for me, yeah, I have this inner burning desire

612
00:32:26,400 --> 00:32:28,520
Speaker 3: to help increase diversity in STEM and that's been a

613
00:32:28,520 --> 00:32:32,040
Speaker 3: big part of what we do, and it's been a

614
00:32:32,200 --> 00:32:36,800
Speaker 3: rollercoaster because funding keeps getting cut. So covid obviously happens

615
00:32:36,800 --> 00:32:38,680
Speaker 3: our in person stuff. We were about to do a

616
00:32:38,680 --> 00:32:42,000
Speaker 3: big global tour. We had just opened our California office

617
00:32:42,040 --> 00:32:45,080
Speaker 3: in February twenty twenty. We shut it in March twenty twenty,

618
00:32:45,120 --> 00:32:48,880
Speaker 3: I think, with the shortest New Zealand company ever see

619
00:32:50,200 --> 00:32:52,040
Speaker 3: having built up all of that stuff setting up a

620
00:32:52,120 --> 00:32:53,920
Speaker 3: Delawes c or like all of the work that goes

621
00:32:53,920 --> 00:32:57,160
Speaker 3: into setting up your American office and then hiring a

622
00:32:57,240 --> 00:32:59,160
Speaker 3: GM there, only to have to let them go, like

623
00:32:59,200 --> 00:33:01,640
Speaker 3: it's a huge spence and it's a huge learning curve.

624
00:33:01,680 --> 00:33:04,480
Speaker 3: So it's been rookie. But what I've loved is being

625
00:33:04,520 --> 00:33:06,560
Speaker 3: able to build all these communicators. So we currently have

626
00:33:06,600 --> 00:33:10,240
Speaker 3: thirty five science communicators in New Zealand. They're all students,

627
00:33:10,320 --> 00:33:14,160
Speaker 3: usually graduate students doing their musters who also are Nana girls.

628
00:33:14,400 --> 00:33:16,680
Speaker 3: They all have their own superhero names and they're local

629
00:33:16,720 --> 00:33:20,200
Speaker 3: to their communities and they go out and they do schools, parties,

630
00:33:20,440 --> 00:33:25,040
Speaker 3: mall shows, public performances under the Nanogol brand. And I go, well,

631
00:33:25,040 --> 00:33:26,760
Speaker 3: this is also how we get to sort of create

632
00:33:26,840 --> 00:33:30,520
Speaker 3: the next generation of science communicators and show young people

633
00:33:30,840 --> 00:33:35,320
Speaker 3: that there's an amazing young female scientists in their neighborhood

634
00:33:35,320 --> 00:33:37,160
Speaker 3: and this is what they're doing. And we've got everything

635
00:33:37,160 --> 00:33:41,480
Speaker 3: from jumping spider experts to music physicists. I mean, it's

636
00:33:41,560 --> 00:33:42,240
Speaker 3: just incredible.

637
00:33:42,640 --> 00:33:48,400
Speaker 2: Yeah, And it's such a compelling proposition the STEM subjects,

638
00:33:48,520 --> 00:33:50,680
Speaker 2: I mean, the careers that people can have, the salaries

639
00:33:50,680 --> 00:33:53,760
Speaker 2: that people can earn from Maria and Pacifico who are

640
00:33:53,840 --> 00:34:00,200
Speaker 2: underrepresented in the STEM related industries. It's really tough for

641
00:34:00,320 --> 00:34:03,719
Speaker 2: STEM organizations at the moment. Funding has been cut. In

642
00:34:03,760 --> 00:34:06,960
Speaker 2: real terms, a lot of them are barely holding on.

643
00:34:07,040 --> 00:34:10,480
Speaker 2: It's ironic, isn't it that as any government that is

644
00:34:10,560 --> 00:34:13,080
Speaker 2: in power at the moment is all about we need

645
00:34:13,120 --> 00:34:18,160
Speaker 2: to diversify our economy using engineering, science and technology. But

646
00:34:18,239 --> 00:34:20,239
Speaker 2: we're not backing it up with that commitment at the

647
00:34:20,239 --> 00:34:21,000
Speaker 2: early stages.

648
00:34:21,800 --> 00:34:24,640
Speaker 3: I think worse in that. I think sadly during this

649
00:34:25,120 --> 00:34:27,319
Speaker 3: government cycle, not only way not backing up, we have

650
00:34:27,520 --> 00:34:31,799
Speaker 3: cut existing programs that we're doing great things. I'm going

651
00:34:31,800 --> 00:34:33,080
Speaker 3: to give you a list because I've been writing it

652
00:34:33,080 --> 00:34:35,440
Speaker 3: down because it's been depressing. So OMD Tech obviously that

653
00:34:35,480 --> 00:34:37,879
Speaker 3: I co founded with Born rosel from then back ten

654
00:34:37,960 --> 00:34:41,640
Speaker 3: years ago, they were the main national digital technologies education

655
00:34:41,800 --> 00:34:46,319
Speaker 3: provider if you wanted to learn anything about coding robotics,

656
00:34:46,960 --> 00:34:50,399
Speaker 3: and they had a massive MADI and PACIFICA Group two

657
00:34:50,760 --> 00:34:53,640
Speaker 3: making sure that they were doing it bilingually and trilingually.

658
00:34:53,680 --> 00:34:56,040
Speaker 3: It was amazing. So they have just closed down. So

659
00:34:56,080 --> 00:34:57,759
Speaker 3: that means it's going to be really hard now if

660
00:34:57,760 --> 00:35:01,319
Speaker 3: you're a teacher to do PD development, to learn or

661
00:35:01,400 --> 00:35:04,319
Speaker 3: upskill in tech. But that's just one of them. So

662
00:35:05,160 --> 00:35:08,719
Speaker 3: we lost the Curious Minds funding now that's one point

663
00:35:08,760 --> 00:35:12,480
Speaker 3: six million dollars a year. That has a significant effect

664
00:35:12,600 --> 00:35:15,680
Speaker 3: not only on cool projects that spun out of it,

665
00:35:16,120 --> 00:35:19,720
Speaker 3: but also it was where Comet, which is the big

666
00:35:19,800 --> 00:35:24,000
Speaker 3: South Auckland STEM group got all their funding from. Now

667
00:35:24,040 --> 00:35:26,600
Speaker 3: they're independent and I think it's going to be really

668
00:35:26,640 --> 00:35:29,200
Speaker 3: hard for them to do what they used to do

669
00:35:29,320 --> 00:35:31,600
Speaker 3: with that. So they are obviously downsizing and trying to

670
00:35:31,600 --> 00:35:37,240
Speaker 3: figure out who they are. Otago Museum, who do amazing outreach.

671
00:35:37,600 --> 00:35:41,120
Speaker 3: Curious Minds funded all of their communicators to go up

672
00:35:41,120 --> 00:35:43,560
Speaker 3: to schools and do those programs. They have cut all

673
00:35:43,560 --> 00:35:47,440
Speaker 3: of those programs now because they've lost that funding. The

674
00:35:47,520 --> 00:35:51,000
Speaker 3: Wonder project, which is an incredible program by Engineering New

675
00:35:51,080 --> 00:35:54,200
Speaker 3: Zealand where they would go into schools and so my

676
00:35:54,800 --> 00:35:57,000
Speaker 3: finger dab into that is I helped to build that program.

677
00:35:57,080 --> 00:35:58,799
Speaker 3: And what I wanted to build was a long term

678
00:35:58,840 --> 00:36:02,640
Speaker 3: relationship with engineers and scientists with school so they would

679
00:36:02,640 --> 00:36:05,120
Speaker 3: go in for nine to twelve weeks every week and

680
00:36:05,360 --> 00:36:07,240
Speaker 3: help kids build a rocket. And they had a project

681
00:36:07,239 --> 00:36:11,120
Speaker 3: from start to finish. They've lost a million dollars a

682
00:36:11,200 --> 00:36:14,759
Speaker 3: year of Callahan funding, which is basically their big breadwinners.

683
00:36:14,800 --> 00:36:18,399
Speaker 3: So who knows what's going to happen to them. House

684
00:36:18,440 --> 00:36:21,319
Speaker 3: of Sciences. You may have seen City and Upper Hut

685
00:36:21,320 --> 00:36:25,040
Speaker 3: Council stopped their funding. That was access for fifteen thousand students.

686
00:36:25,360 --> 00:36:28,200
Speaker 3: Now Chris luckily has managed to get enough because she's

687
00:36:28,280 --> 00:36:31,800
Speaker 3: worked hard on raising some funds till twenty twenty five.

688
00:36:31,880 --> 00:36:35,759
Speaker 3: But then what and I just go, oh onn this

689
00:36:35,840 --> 00:36:38,120
Speaker 3: is nuts. This is on top of all of the

690
00:36:38,640 --> 00:36:41,319
Speaker 3: academic jobs that have been cut and the knee were

691
00:36:41,320 --> 00:36:45,319
Speaker 3: in the CRI jobs and so the jobs have gone,

692
00:36:45,560 --> 00:36:47,880
Speaker 3: and the training and the funding has gone, and teachers

693
00:36:47,880 --> 00:36:50,320
Speaker 3: now don't have anywhere to go to learn this stuff.

694
00:36:50,840 --> 00:36:53,400
Speaker 3: It's like the whole system's been shut off and the

695
00:36:53,440 --> 00:36:55,719
Speaker 3: consequences are I mean, it's easy to see what the

696
00:36:55,719 --> 00:36:57,840
Speaker 3: consequences are for that. Nobody's going to come to New

697
00:36:57,960 --> 00:37:00,960
Speaker 3: Zealand for our high talent line and nobody's going to

698
00:37:01,000 --> 00:37:02,440
Speaker 3: be trained here for the jobs that need to be

699
00:37:02,480 --> 00:37:02,880
Speaker 3: trained in.

700
00:37:03,080 --> 00:37:06,400
Speaker 2: Yeah, we've got our Science Minister are out there championing

701
00:37:06,400 --> 00:37:09,680
Speaker 2: the space sick difference. It's our first Minister of Space,

702
00:37:09,719 --> 00:37:12,120
Speaker 2: which is all well and good, but where's the pipeline

703
00:37:12,160 --> 00:37:15,719
Speaker 2: of future space engineers that are going to feed this industry.

704
00:37:16,280 --> 00:37:19,919
Speaker 2: If we don't nurture them at those early stages, we're

705
00:37:19,920 --> 00:37:22,040
Speaker 2: going to be relying on people coming to this country

706
00:37:22,080 --> 00:37:23,120
Speaker 2: to fill those positions.

707
00:37:23,280 --> 00:37:25,520
Speaker 3: Hundred percent. It's got to be immigration. That has to

708
00:37:25,520 --> 00:37:28,640
Speaker 3: be the solution, because we are not building that talent

709
00:37:28,760 --> 00:37:32,440
Speaker 3: pipeline right now, and that talent pipeline is a long

710
00:37:32,560 --> 00:37:35,359
Speaker 3: lead pipeline. You can't go oh, when they've picked their

711
00:37:35,440 --> 00:37:37,560
Speaker 3: unique subjects. That's when we're going to do it. Because

712
00:37:37,560 --> 00:37:40,080
Speaker 3: what we've seen around innovation, So David diet Downs and

713
00:37:40,120 --> 00:37:42,279
Speaker 3: I wrote a book called Number eight Recharged where we

714
00:37:42,719 --> 00:37:47,120
Speaker 3: studied our top big companies, including Rocket Lab and Peter Beckham.

715
00:37:47,160 --> 00:37:50,839
Speaker 3: What those journeys are of those top people, and it's

716
00:37:50,840 --> 00:37:55,640
Speaker 3: not academics. It's experience, it's trying things, it's being hanging

717
00:37:55,680 --> 00:37:57,560
Speaker 3: out with people who are diverse to you to solve

718
00:37:57,600 --> 00:38:00,640
Speaker 3: those problems. And if those people aren't getting into the

719
00:38:00,719 --> 00:38:03,520
Speaker 3: system and being able to test some things out, we're

720
00:38:03,520 --> 00:38:06,440
Speaker 3: not going to build the next whatever it is. Rocket

721
00:38:06,480 --> 00:38:09,480
Speaker 3: Lab is a great example, and we seem to go, oh, well,

722
00:38:09,520 --> 00:38:11,560
Speaker 3: once you've got your degree dot dot dot, you can

723
00:38:11,600 --> 00:38:14,400
Speaker 3: do this, but the evidence shows you that the degree

724
00:38:14,480 --> 00:38:17,840
Speaker 3: has very little to do with it. It's those types

725
00:38:17,880 --> 00:38:21,000
Speaker 3: of personalities having good systems around them where they can

726
00:38:21,040 --> 00:38:23,719
Speaker 3: access funding, where they can access information, where they can

727
00:38:23,840 --> 00:38:27,480
Speaker 3: just try something, fail and try again, and with that

728
00:38:27,520 --> 00:38:30,439
Speaker 3: comes confidence and with that comes your ability to when

729
00:38:30,480 --> 00:38:32,680
Speaker 3: you I mean, if you talk to Peter Beck, he's

730
00:38:32,719 --> 00:38:35,239
Speaker 3: a rocket scientist because when he was eleven and Invercargo,

731
00:38:35,360 --> 00:38:37,680
Speaker 3: he was building rockets in his shed with his dad,

732
00:38:37,840 --> 00:38:41,400
Speaker 3: like that sort of stuff. Having kids building things in schools,

733
00:38:41,440 --> 00:38:45,440
Speaker 3: trying robots, trying coding to build that confidence young is

734
00:38:45,560 --> 00:38:47,280
Speaker 3: how they make it through the system.

735
00:38:47,640 --> 00:38:50,359
Speaker 2: Yeah, so we do have a real problem to address here,

736
00:38:50,480 --> 00:38:52,719
Speaker 2: and it can't just be philanthropic money. We don't have

737
00:38:52,840 --> 00:38:55,320
Speaker 2: enough of it. It can't just be TICH companies because

738
00:38:55,320 --> 00:38:58,120
Speaker 2: they have their own spin on the technology stack. They

739
00:38:58,120 --> 00:39:01,520
Speaker 2: want you to invest your time, so it does need

740
00:39:01,560 --> 00:39:10,600
Speaker 2: to be government funding. Another thing I really admire you

741
00:39:10,640 --> 00:39:13,640
Speaker 2: for is how well you engage with the business community

742
00:39:14,360 --> 00:39:18,959
Speaker 2: around technology and working with companies to encourage them to

743
00:39:19,040 --> 00:39:23,200
Speaker 2: explore new technology. The technology of the moment is generative AI,

744
00:39:23,960 --> 00:39:27,920
Speaker 2: which you have been scoping out and are heavily involved

745
00:39:27,920 --> 00:39:30,320
Speaker 2: in as well. Just last week, you spoke at Gartner

746
00:39:30,840 --> 00:39:33,920
Speaker 2: and Sydney at their big annual conference. Rewiring for the

747
00:39:34,000 --> 00:39:36,600
Speaker 2: Future was the theme of your talk, So you've obviously

748
00:39:36,680 --> 00:39:39,799
Speaker 2: been looking at this technology. What's the message that you

749
00:39:39,800 --> 00:39:43,280
Speaker 2: were giving to people who went to the Gartner conference

750
00:39:43,440 --> 00:39:47,600
Speaker 2: about what this technology means for the workplace, about skills

751
00:39:47,600 --> 00:39:49,759
Speaker 2: and what they need to do to make the most

752
00:39:49,760 --> 00:39:51,799
Speaker 2: of it. Yeah.

753
00:39:51,920 --> 00:39:54,520
Speaker 3: As a science communicator, and I guess as a tech communicator,

754
00:39:54,760 --> 00:39:57,920
Speaker 3: my goal is to help demystify some of the jargon

755
00:39:58,040 --> 00:40:02,720
Speaker 3: that's involved around highly technical or fields, to help democratize tech,

756
00:40:03,239 --> 00:40:05,359
Speaker 3: to help people to make good decisions for them. That's

757
00:40:05,400 --> 00:40:07,600
Speaker 3: always what I've done as a communicator. I don't tell

758
00:40:07,600 --> 00:40:09,399
Speaker 3: you what you should do or what you shouldn't do,

759
00:40:09,760 --> 00:40:12,400
Speaker 3: But I will help you understand what is probably hype

760
00:40:13,239 --> 00:40:16,239
Speaker 3: and what is probably going to be very expensive. And

761
00:40:16,280 --> 00:40:19,680
Speaker 3: with businesses, it's all about return on investment and AI actually,

762
00:40:19,680 --> 00:40:21,480
Speaker 3: if you're going to build your own system in house,

763
00:40:21,719 --> 00:40:26,600
Speaker 3: is a lot of financial commitment, and so businesses are

764
00:40:26,640 --> 00:40:30,120
Speaker 3: feeling the pressure to throw all this money into something

765
00:40:30,160 --> 00:40:32,799
Speaker 3: that they really feel like is a black box right now.

766
00:40:33,160 --> 00:40:35,520
Speaker 3: So my goal is to help people understand, Hey, what's

767
00:40:35,560 --> 00:40:37,880
Speaker 3: already out there? What does it mean. There's all of

768
00:40:37,920 --> 00:40:42,360
Speaker 3: these terms, you know, like large language models, neural networks,

769
00:40:42,400 --> 00:40:46,399
Speaker 3: and they all sound so insanely technical, and I actually go, hey,

770
00:40:46,520 --> 00:40:48,439
Speaker 3: here's what it is like. This is what it does,

771
00:40:48,480 --> 00:40:49,880
Speaker 3: this is what it can do, this is what it

772
00:40:49,920 --> 00:40:53,880
Speaker 3: can't do. And my philosophy, whatever age you are, has

773
00:40:53,920 --> 00:40:57,520
Speaker 3: always been come play, because when you play with whatever

774
00:40:57,600 --> 00:40:59,560
Speaker 3: it is, you learn, whether I'm giving you a self

775
00:40:59,640 --> 00:41:01,480
Speaker 3: driving robot and you're learning how to code it to

776
00:41:01,520 --> 00:41:04,400
Speaker 3: go around in circles, or I'm giving you some AI

777
00:41:04,480 --> 00:41:07,760
Speaker 3: tools to play with that are fun, that are silly.

778
00:41:08,040 --> 00:41:11,680
Speaker 3: My favorite one is there's a website called bored humans

779
00:41:11,719 --> 00:41:14,160
Speaker 3: dot com and in there is a marriage simulator and

780
00:41:14,280 --> 00:41:17,400
Speaker 3: you can get your virtual AI husband or wife and

781
00:41:17,440 --> 00:41:20,840
Speaker 3: you can argue with them. And I love that because

782
00:41:20,960 --> 00:41:22,880
Speaker 3: suddenly you went you out of your work environment and

783
00:41:22,880 --> 00:41:26,279
Speaker 3: you're playing with an AI system and you're arguing with it.

784
00:41:26,360 --> 00:41:29,640
Speaker 3: You'll realize that humans are way better at insarting than AI. Right,

785
00:41:29,680 --> 00:41:32,920
Speaker 3: So it just takes that intimidation out. And I've had

786
00:41:32,960 --> 00:41:35,520
Speaker 3: lots of people go, I don't know where to get started,

787
00:41:35,640 --> 00:41:39,440
Speaker 3: and so credit a YouTube series to help people not

788
00:41:39,520 --> 00:41:42,440
Speaker 3: only demystify the jargon. So in three minutes unless you'll

789
00:41:42,520 --> 00:41:45,960
Speaker 3: learn about whatever the jargon is, hallucination or the difference

790
00:41:45,960 --> 00:41:49,640
Speaker 3: between traditional AI and generative AI, that's easy. But then

791
00:41:49,760 --> 00:41:52,239
Speaker 3: Joe Crib came to me and said, can you just

792
00:41:52,320 --> 00:41:55,239
Speaker 3: help me actually get started practically? I don't know where

793
00:41:55,239 --> 00:41:58,720
Speaker 3: to use this, and so I'm building a YouTube series

794
00:41:59,040 --> 00:42:01,760
Speaker 3: basically use AI to heck your life that she's following

795
00:42:01,800 --> 00:42:04,600
Speaker 3: and writing up and in the Sunday Star Times every Sunday,

796
00:42:04,600 --> 00:42:07,160
Speaker 3: but actually is free for everybody to go. Hey, So

797
00:42:07,200 --> 00:42:10,520
Speaker 3: the one that's this week is how do you use

798
00:42:10,640 --> 00:42:14,280
Speaker 3: AI to help you take the mental load off figuring

799
00:42:14,280 --> 00:42:17,640
Speaker 3: out what's for dinner every day? So we have a

800
00:42:17,640 --> 00:42:20,440
Speaker 3: simple one which is recipes by AI. Just look in

801
00:42:20,480 --> 00:42:22,239
Speaker 3: your fridge, type in what you've got and it'll give

802
00:42:22,239 --> 00:42:25,239
Speaker 3: you dinner for tonight. But the longer version is you

803
00:42:25,280 --> 00:42:27,640
Speaker 3: can actually take that meal planning away. So I'll give

804
00:42:27,680 --> 00:42:30,120
Speaker 3: you some prompts and jet GBT and just explaining that

805
00:42:30,160 --> 00:42:32,680
Speaker 3: prompts are instructions, and I have a way that I

806
00:42:32,719 --> 00:42:35,360
Speaker 3: write my prompts a really simple formula that gets a

807
00:42:35,400 --> 00:42:38,400
Speaker 3: good result out and then you literally go, hey, this

808
00:42:38,520 --> 00:42:40,480
Speaker 3: is what my family looks like. It's winter in New

809
00:42:40,560 --> 00:42:43,320
Speaker 3: Zealand and we're budget conscious, so only you seasonal things.

810
00:42:44,200 --> 00:42:46,799
Speaker 3: My kids don't eat, bean, speech, treat, whatever it is.

811
00:42:47,000 --> 00:42:48,680
Speaker 3: Give me a meal plan for the week, and also

812
00:42:48,800 --> 00:42:51,399
Speaker 3: write me a grocery list with everything in order that's

813
00:42:51,440 --> 00:42:53,880
Speaker 3: in the grocery. I also, I'm not having to find everything,

814
00:42:54,360 --> 00:42:56,080
Speaker 3: and then you can just print it out and go

815
00:42:56,239 --> 00:42:59,160
Speaker 3: and you've suddenly taken away all of that mental load

816
00:42:59,360 --> 00:43:01,200
Speaker 3: of what's for dinner? What are I need to get

817
00:43:01,200 --> 00:43:03,759
Speaker 3: from the sharps, blah blah blah. And the goal is

818
00:43:03,800 --> 00:43:06,040
Speaker 3: just to give people time back in their life, in

819
00:43:06,080 --> 00:43:09,360
Speaker 3: their personal life. And what I've seen in AI is

820
00:43:09,400 --> 00:43:11,600
Speaker 3: the big hype, especially around the media, is it's going

821
00:43:11,640 --> 00:43:14,000
Speaker 3: to take all of our jobs, and everybody's petroviied, and

822
00:43:14,040 --> 00:43:16,480
Speaker 3: I just try and sort of show people how you

823
00:43:16,480 --> 00:43:18,600
Speaker 3: can really push it to its limits in ways that

824
00:43:18,640 --> 00:43:21,239
Speaker 3: you're not afraid of, like doing your grocery shopping or

825
00:43:21,239 --> 00:43:24,400
Speaker 3: picking what's for dinner, and then see what it's greater

826
00:43:24,560 --> 00:43:27,400
Speaker 3: and what it's terrible at, and learn when it lies

827
00:43:27,480 --> 00:43:30,279
Speaker 3: to your faith through hallucinations like how many hours are

828
00:43:30,280 --> 00:43:32,720
Speaker 3: in the word strawberry, and how it tries to convince

829
00:43:32,719 --> 00:43:35,280
Speaker 3: you that you're wrong as a human and that's okay

830
00:43:35,440 --> 00:43:38,319
Speaker 3: because that's just genera to AI. That's what it does.

831
00:43:38,600 --> 00:43:42,640
Speaker 2: Yeah, Gartner has its infamous hype cycle, and at the

832
00:43:42,680 --> 00:43:46,920
Speaker 2: moment appears GENAI is sort of on the trough of disillusionment.

833
00:43:47,360 --> 00:43:49,840
Speaker 2: And I think that's really that it was hyped up.

834
00:43:49,840 --> 00:43:52,040
Speaker 2: A lot. A lot of money has gone into the industry,

835
00:43:52,080 --> 00:43:55,520
Speaker 2: and the initial adopters of this, who maybe gave copilot

836
00:43:55,600 --> 00:43:57,919
Speaker 2: to everyone in the organization, are going and paying thirty

837
00:43:58,000 --> 00:44:01,359
Speaker 2: dollars extra a month. I'm not getting the the productivity

838
00:44:01,400 --> 00:44:03,239
Speaker 2: boost that I was promised to you. So we still

839
00:44:03,280 --> 00:44:05,200
Speaker 2: have a bit of work to do, don't we Until

840
00:44:05,480 --> 00:44:09,360
Speaker 2: we do see productivity efficiency, But also new product generation,

841
00:44:09,480 --> 00:44:13,320
Speaker 2: new ideas coming through in businesses that these tools are enabling.

842
00:44:14,040 --> 00:44:16,680
Speaker 3: Yeah, and there's so much happening so quickly, like every day,

843
00:44:16,800 --> 00:44:18,759
Speaker 3: you know, like now matters coming out with that, Like

844
00:44:19,000 --> 00:44:21,040
Speaker 3: everybody's trying to launch the next new and big thing.

845
00:44:21,080 --> 00:44:23,000
Speaker 3: And I think it could be really confusing as to

846
00:44:23,440 --> 00:44:25,960
Speaker 3: what should you use and what's the best and how

847
00:44:26,000 --> 00:44:28,440
Speaker 3: do you find it? And I think what happens is

848
00:44:28,640 --> 00:44:31,960
Speaker 3: businesses productize the system, so you just have to wait

849
00:44:32,000 --> 00:44:34,640
Speaker 3: a little bit and somebody will find a good solution

850
00:44:34,800 --> 00:44:37,600
Speaker 3: for you that will be great for solving your problem.

851
00:44:37,880 --> 00:44:39,920
Speaker 3: And AI has been around for a long time, obviously,

852
00:44:39,960 --> 00:44:42,719
Speaker 3: I think it's just the jargon of saying, Hey, what

853
00:44:42,760 --> 00:44:45,319
Speaker 3: people are talk about is generative AI. That's great, this

854
00:44:45,400 --> 00:44:47,320
Speaker 3: is what it can do. This is where it gets stuck.

855
00:44:47,560 --> 00:44:50,200
Speaker 3: You've been using AI on your Spotify or on Netflix

856
00:44:50,239 --> 00:44:52,400
Speaker 3: for a long time. You don't even think about it.

857
00:44:52,440 --> 00:44:55,759
Speaker 3: So let's not stress about the big scary words that

858
00:44:55,800 --> 00:44:57,839
Speaker 3: are being thrown down. If you want to understand them,

859
00:44:57,880 --> 00:45:01,239
Speaker 3: here are some videos just be a way and rewaring

860
00:45:01,360 --> 00:45:03,759
Speaker 3: the brain. The talk that I give is about, hey,

861
00:45:03,800 --> 00:45:05,920
Speaker 3: you don't have to be an expert in everything, but

862
00:45:06,080 --> 00:45:09,360
Speaker 3: just be open to. If you hear something new, just

863
00:45:09,520 --> 00:45:12,239
Speaker 3: dabble in. What is it? And do you think that's

864
00:45:12,239 --> 00:45:14,520
Speaker 3: going to be the next big thing? And the easiest

865
00:45:14,520 --> 00:45:18,280
Speaker 3: way to tell is ROI like, does it have huge

866
00:45:18,280 --> 00:45:21,880
Speaker 3: economic potential? If the answer is yes, great, it's going

867
00:45:21,920 --> 00:45:24,240
Speaker 3: to be big. But we've been through the cycle through

868
00:45:24,640 --> 00:45:27,120
Speaker 3: you know, blockchain and NFTs, like I feel like there's

869
00:45:27,160 --> 00:45:30,680
Speaker 3: always something on the cards and what succeeds is the

870
00:45:30,719 --> 00:45:34,399
Speaker 3: thing that has huge economic potential. So just go can

871
00:45:34,520 --> 00:45:36,600
Speaker 3: somebody make lots of money out of this? If the

872
00:45:36,640 --> 00:45:39,120
Speaker 3: answer is yes, keep an eye on it. If the

873
00:45:39,160 --> 00:45:41,440
Speaker 3: answer is no, keep an eye on it too. But

874
00:45:41,560 --> 00:45:45,160
Speaker 3: don't start trying to move your whole business to incorporating

875
00:45:45,239 --> 00:45:48,680
Speaker 3: something early. And we all know that early adopters are

876
00:45:48,719 --> 00:45:52,640
Speaker 3: never the ones that actually succeed. I think it's good

877
00:45:52,640 --> 00:45:54,920
Speaker 3: that New Zealand's a little bit behind sometimes because we

878
00:45:55,080 --> 00:45:57,479
Speaker 3: just get to sit and watch everybody else throw money

879
00:45:57,520 --> 00:45:59,719
Speaker 3: into something and then we go cool that one would

880
00:45:59,800 --> 00:46:00,760
Speaker 3: let's go with that one?

881
00:46:01,040 --> 00:46:03,880
Speaker 2: Yeah, And I really love that message of experimentation. And

882
00:46:03,920 --> 00:46:07,000
Speaker 2: you can do that safely without putting all your company

883
00:46:07,040 --> 00:46:10,200
Speaker 2: information into the large language model, so you know, have

884
00:46:10,280 --> 00:46:12,319
Speaker 2: some governance around that, and there's some great tools out

885
00:46:12,320 --> 00:46:14,680
Speaker 2: there now from the AI Forum and others around how

886
00:46:14,719 --> 00:46:17,919
Speaker 2: to do it safely, but experimentation just finishing up back

887
00:46:17,960 --> 00:46:21,959
Speaker 2: on in the classroom in the STEM environments. For years,

888
00:46:21,960 --> 00:46:24,080
Speaker 2: the mantra was we've got to get more kids coding

889
00:46:24,080 --> 00:46:28,520
Speaker 2: in that. What do you think the implications of AI

890
00:46:28,920 --> 00:46:34,239
Speaker 2: are for coding? We've got GitHub Copilot, we've got increasingly

891
00:46:34,360 --> 00:46:38,479
Speaker 2: automation off the coding process. Are you still recommending kids

892
00:46:38,920 --> 00:46:40,080
Speaker 2: go and learn how to code?

893
00:46:40,440 --> 00:46:43,040
Speaker 3: Yeah, one hundred percent. And there is a being is

894
00:46:43,120 --> 00:46:47,440
Speaker 3: that you still need human checks and look, I have

895
00:46:47,560 --> 00:46:50,600
Speaker 3: loved generative AI for coding, like it helps me to

896
00:46:50,640 --> 00:46:52,880
Speaker 3: be a better coder. I think if you're going to

897
00:46:52,920 --> 00:46:56,080
Speaker 3: learn anything, if you understand data and you understand Python

898
00:46:56,239 --> 00:46:59,680
Speaker 3: right now, you are probably the most sought after person

899
00:46:59,800 --> 00:47:03,280
Speaker 3: in industry because that's sort of what the AI field

900
00:47:03,360 --> 00:47:06,120
Speaker 3: is looking for. The thing about learning coding is it's

901
00:47:06,160 --> 00:47:08,719
Speaker 3: like learning any language. I don't care what language you

902
00:47:08,760 --> 00:47:11,120
Speaker 3: actually learn to code in. What you're understanding is the

903
00:47:11,160 --> 00:47:15,560
Speaker 3: process of how do these systems work? And that I

904
00:47:15,560 --> 00:47:18,000
Speaker 3: think is the most important thing. It gives you confidence

905
00:47:18,040 --> 00:47:21,399
Speaker 3: around tech. It allows you to turn something that wasn't

906
00:47:21,480 --> 00:47:24,960
Speaker 3: moving into something that does something. And I think having

907
00:47:25,160 --> 00:47:29,200
Speaker 3: digital literacy is really important. So if you're learning to

908
00:47:29,239 --> 00:47:30,960
Speaker 3: code at the age of eleven, like, it's not going

909
00:47:31,000 --> 00:47:34,200
Speaker 3: to harm you. You're learning about language, you're learning about

910
00:47:34,320 --> 00:47:38,520
Speaker 3: order and structure. And also if your AI coding system

911
00:47:38,640 --> 00:47:41,880
Speaker 3: has a bug, you can see where it is, and

912
00:47:41,920 --> 00:47:45,120
Speaker 3: that's the most important human side. AI is not going

913
00:47:45,120 --> 00:47:47,760
Speaker 3: to replace us. What you need is a good human

914
00:47:47,800 --> 00:47:50,160
Speaker 3: to overview the AI to know where the bugs might be.

915
00:47:50,600 --> 00:47:52,520
Speaker 3: And to do that you have to be proficient in

916
00:47:52,560 --> 00:47:56,080
Speaker 3: the language that it's speaking. And so that's code, that's

917
00:47:56,160 --> 00:47:59,399
Speaker 3: understanding how things work in sequence, that's understanding brackets, that's

918
00:47:59,440 --> 00:48:01,960
Speaker 3: all of the nerdy stuff. And I think if you

919
00:48:02,000 --> 00:48:05,239
Speaker 3: do that young, you just have a confidence throughout that

920
00:48:05,280 --> 00:48:08,000
Speaker 3: you can, even if you don't know the language that's in,

921
00:48:08,080 --> 00:48:10,799
Speaker 3: you know the process of that language, and then you

922
00:48:10,840 --> 00:48:13,520
Speaker 3: can sort of debug it. So I'm still an advocate

923
00:48:13,600 --> 00:48:16,120
Speaker 3: because I don't see any harm in it in having

924
00:48:16,120 --> 00:48:18,840
Speaker 3: our kids learn how to Even if you're just coding

925
00:48:18,880 --> 00:48:21,080
Speaker 3: a self driving robot that drives from A to B,

926
00:48:21,719 --> 00:48:24,760
Speaker 3: go through that sequence and you'll never lose that skill.

927
00:48:25,239 --> 00:48:28,880
Speaker 2: Yeah, and you look at the Titan New Zealand at

928
00:48:28,880 --> 00:48:31,360
Speaker 2: the moment. But the bit's paying jobs is still paying

929
00:48:31,480 --> 00:48:35,600
Speaker 2: great money for coding all those sort of languages like

930
00:48:35,640 --> 00:48:38,680
Speaker 2: Python you were talking about, still a great career to

931
00:48:38,719 --> 00:48:38,960
Speaker 2: be in.

932
00:48:39,840 --> 00:48:42,000
Speaker 3: Yeah, and a career where you can work remotely now

933
00:48:42,360 --> 00:48:44,720
Speaker 3: and be working if there's no money in New Zealand,

934
00:48:44,760 --> 00:48:47,600
Speaker 3: working for an American or as Singaporean company and still

935
00:48:47,800 --> 00:48:50,759
Speaker 3: being a kei we I mean, it's flexibility. It's best

936
00:48:50,800 --> 00:48:53,520
Speaker 3: with an amazing paycheck. And you know, if we can

937
00:48:53,560 --> 00:48:57,400
Speaker 3: help New Zealanders to get on a road to a

938
00:48:57,560 --> 00:49:00,960
Speaker 3: job that actually gives the security and finance, security and

939
00:49:01,000 --> 00:49:03,480
Speaker 3: housing security, why wouldn't we want to build that?

940
00:49:03,560 --> 00:49:03,759
Speaker 2: Here?

941
00:49:08,440 --> 00:49:11,799
Speaker 1: Lots covered there, lots of really fascinating stuff. I think

942
00:49:11,800 --> 00:49:15,040
Speaker 1: the thing that stuck out to me most was when

943
00:49:15,040 --> 00:49:17,680
Speaker 1: she was talking about the needless their education and reaching

944
00:49:17,719 --> 00:49:22,400
Speaker 1: people who were not reaching, and she made the point

945
00:49:22,480 --> 00:49:25,400
Speaker 1: that what we need is more of these people who

946
00:49:25,640 --> 00:49:29,520
Speaker 1: are super passionate and excited and will just take something

947
00:49:29,600 --> 00:49:32,239
Speaker 1: and run with it like you're Peter Beck. That was

948
00:49:32,560 --> 00:49:34,759
Speaker 1: one of her examples, but I think Peter Jackson is

949
00:49:34,800 --> 00:49:38,960
Speaker 1: another example, Edmund Hillary and Kate Shepherd. But the thing

950
00:49:39,000 --> 00:49:41,680
Speaker 1: about those people and a lot of people is that

951
00:49:41,719 --> 00:49:47,080
Speaker 1: they are generally from families that have the privilege of

952
00:49:47,160 --> 00:49:49,680
Speaker 1: kind of giving them space to be able to do

953
00:49:49,840 --> 00:49:54,440
Speaker 1: what they want. And you know, she talks about the

954
00:49:55,560 --> 00:49:58,279
Speaker 1: creating a new curriculum for PACIFICA students and putting it

955
00:49:58,320 --> 00:50:01,960
Speaker 1: in a context that they understand or not they understand

956
00:50:01,960 --> 00:50:05,960
Speaker 1: that suits them, I should say, And it's always going

957
00:50:06,000 --> 00:50:07,400
Speaker 1: to be the case that if you have kids who

958
00:50:07,440 --> 00:50:12,040
Speaker 1: are coming to something and they are having to understand

959
00:50:12,440 --> 00:50:15,440
Speaker 1: a new context for it as well as the new content,

960
00:50:15,800 --> 00:50:19,000
Speaker 1: they're going to be doing double the mental workload. So

961
00:50:19,520 --> 00:50:22,319
Speaker 1: being able to make it more accessible is not just

962
00:50:22,400 --> 00:50:25,359
Speaker 1: about taking the same curriculum and putting it in front

963
00:50:25,400 --> 00:50:28,239
Speaker 1: of new kids. It's about finding new ways of engaging

964
00:50:28,239 --> 00:50:32,120
Speaker 1: them and then giving them wrap around support so that

965
00:50:32,200 --> 00:50:34,799
Speaker 1: they can try and fail and explore and create and

966
00:50:34,840 --> 00:50:36,920
Speaker 1: build in all of those kinds of things as well,

967
00:50:36,960 --> 00:50:41,120
Speaker 1: Like just restructuring the school curriculum in minor ways is

968
00:50:41,160 --> 00:50:43,799
Speaker 1: not actually going to make the major shift that we

969
00:50:43,920 --> 00:50:44,479
Speaker 1: might need.

970
00:50:44,960 --> 00:50:47,760
Speaker 2: No, And I was just blown away. I knew about

971
00:50:47,960 --> 00:50:52,000
Speaker 2: OMG tech winding up, but when Michelle was sort of

972
00:50:52,520 --> 00:50:57,360
Speaker 2: listing off all of those other organizations that have wound

973
00:50:57,480 --> 00:51:01,640
Speaker 2: up or have had massive budget cuts. It's really worrying

974
00:51:01,760 --> 00:51:04,920
Speaker 2: if we want to build this high tech workforce, all

975
00:51:04,920 --> 00:51:08,240
Speaker 2: of these opportunities, whether they be with Mari and Pacifica

976
00:51:08,400 --> 00:51:11,960
Speaker 2: or just in general in schools, a lot of that

977
00:51:12,000 --> 00:51:14,399
Speaker 2: stuff is not going to exist anymore. It's the same

978
00:51:14,440 --> 00:51:18,520
Speaker 2: with digital equity stuff around subsidizing broadband for poorer households.

979
00:51:18,560 --> 00:51:21,080
Speaker 2: A lot of that stuff is getting cut as well.

980
00:51:21,200 --> 00:51:25,279
Speaker 2: So I think we're at risk, and times are tough

981
00:51:25,360 --> 00:51:28,359
Speaker 2: across the economy, but we're at risk of really taking

982
00:51:28,400 --> 00:51:33,279
Speaker 2: a step backwards in terms of having those opportunities for

983
00:51:33,320 --> 00:51:36,400
Speaker 2: people to really get exposed to STEM subjects and therefore

984
00:51:36,480 --> 00:51:39,200
Speaker 2: want to pursue a career in those topics. But great

985
00:51:39,239 --> 00:51:42,879
Speaker 2: to see what Michelle's doing and nanogirl Labs, and we'll

986
00:51:42,880 --> 00:51:44,960
Speaker 2: be seeing a lot more of her, including on our

987
00:51:45,320 --> 00:51:48,840
Speaker 2: YouTube channel around artificial intelligence as well.

988
00:51:49,080 --> 00:51:51,840
Speaker 1: Yeah, it's great to have people like that. It's a

989
00:51:51,880 --> 00:51:54,840
Speaker 1: pity to see all those cuts and to see less spaces.

990
00:51:54,960 --> 00:51:58,120
Speaker 1: A central city library has this awesome maker space, but

991
00:51:58,160 --> 00:52:00,319
Speaker 1: a lot of the suburban libraries don't have those kinds

992
00:52:00,360 --> 00:52:02,279
Speaker 1: of things, or if they do, they're not really that

993
00:52:02,520 --> 00:52:05,719
Speaker 1: well known. We could be doing so much more as

994
00:52:05,800 --> 00:52:10,920
Speaker 1: a society to provide these amazing spaces in communal areas

995
00:52:10,960 --> 00:52:14,520
Speaker 1: that don't require huge expense for people to just go

996
00:52:14,640 --> 00:52:16,120
Speaker 1: and try and be well.

997
00:52:16,200 --> 00:52:18,880
Speaker 2: We could be our Central Library in Wellington's a shell

998
00:52:19,080 --> 00:52:20,880
Speaker 2: at the moment, it's going to cost hundreds of millions

999
00:52:20,920 --> 00:52:24,880
Speaker 2: of dollars, so there's no getting away from experience there unfortunately.

1000
00:52:25,400 --> 00:52:28,520
Speaker 1: Yeah, no, that's true. Well, that's it for the Business

1001
00:52:28,560 --> 00:52:30,879
Speaker 1: of Tech this week. Thanks so much to Nano Girl

1002
00:52:30,920 --> 00:52:33,840
Speaker 1: Michelle Dickinson for coming on the show. We've put links

1003
00:52:33,840 --> 00:52:37,800
Speaker 1: to her various ventures, including her Ai Life Hacks YouTube

1004
00:52:37,880 --> 00:52:39,280
Speaker 1: series in the show notes.

1005
00:52:39,440 --> 00:52:42,719
Speaker 2: The Business off Tech is on all major podcast platforms

1006
00:52:42,760 --> 00:52:45,760
Speaker 2: as well as iHeartRadio, where you can stream every episode.

1007
00:52:45,880 --> 00:52:48,080
Speaker 2: Show notes are in the Tech section on the Business

1008
00:52:48,160 --> 00:52:50,839
Speaker 2: Desk website. Leave us a review and share it with

1009
00:52:50,880 --> 00:52:51,960
Speaker 2: your friends and colleagues.

1010
00:52:52,120 --> 00:52:55,360
Speaker 1: Get in touch with your feedback, ideas, topic and guest suggestions.

1011
00:52:55,360 --> 00:52:57,719
Speaker 1: You can email me ben at business test dot com

1012
00:52:57,800 --> 00:53:00,600
Speaker 1: dot ben Z. We'll find both of us on LinkedIn

1013
00:53:00,719 --> 00:53:01,799
Speaker 1: and x and.

1014
00:53:01,800 --> 00:53:04,200
Speaker 2: You'll find the next episode of the Business of Tech

1015
00:53:04,280 --> 00:53:07,080
Speaker 2: in your podcast app bright and early next Thursday.

1016
00:53:07,239 --> 00:53:11,000
Speaker 1: Until then, have a great week.