July 15, 2025 • 22 mins
What if your next coworker isn’t human? Welcome to the frontlines of the Robot Revolution—where AI isn’t just a buzzword, it’s your new colleague, your competition, and maybe even your boss.
In this electrifying episode of The Robot Revolution is Here: Meet Your New AI Coworkers, we pull back the curtain on the explosive rise of artificial intelligence in the workplace. From jaw-dropping productivity boosts to the controversial ethics of AI and automation, we dive deep into how AI is transforming jobs, businesses, and the global economy—for better and for worse.
Are you worried about job displacement or curious about the new opportunities AI and robotics are creating? We tackle the tough questions: Will your job survive the AI revolution? How can companies balance AI productivity with ethical AI implementation? What about data privacy, algorithmic bias, and the psychological impact on real people?
Get ready for a wild ride through the world of AI coworker adaptation, where we share relatable stories, expert insights, and actionable strategies for thriving in the age of automation. Whether you’re a business leader, employee, or just fascinated by the future of work, this episode is your must-listen guide to AI’s workplace impact.
Don’t just watch the future—shape it. Hit play, share with your team, and join the conversation on how we can build a smarter, more human-centric workplace together. Subscribe now and be part of the revolution!
Become a supporter of this podcast: https://www.spreaker.com/podcast/tech-threads-sci-tech-future-tech-ai--5976276/support.
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In this electrifying episode of The Robot Revolution is Here: Meet Your New AI Coworkers, we pull back the curtain on the explosive rise of artificial intelligence in the workplace. From jaw-dropping productivity boosts to the controversial ethics of AI and automation, we dive deep into how AI is transforming jobs, businesses, and the global economy—for better and for worse.
Are you worried about job displacement or curious about the new opportunities AI and robotics are creating? We tackle the tough questions: Will your job survive the AI revolution? How can companies balance AI productivity with ethical AI implementation? What about data privacy, algorithmic bias, and the psychological impact on real people?
Get ready for a wild ride through the world of AI coworker adaptation, where we share relatable stories, expert insights, and actionable strategies for thriving in the age of automation. Whether you’re a business leader, employee, or just fascinated by the future of work, this episode is your must-listen guide to AI’s workplace impact.
Don’t just watch the future—shape it. Hit play, share with your team, and join the conversation on how we can build a smarter, more human-centric workplace together. Subscribe now and be part of the revolution!
Become a supporter of this podcast: https://www.spreaker.com/podcast/tech-threads-sci-tech-future-tech-ai--5976276/support.
You May also Like:
🎁The Perfect Gift App
Find the Perfect Gift in Seconds
⭐Sky Near Me
Live map of stars & planets visible near you
✔Debt Planner App
Your Path to Debt-Free Living
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Imagine waking up one morning and your new cubicle neighbor
isn't quite human. Yeah, or maybe your latest health diagnosis
came from an algorithm and your groceries re zipped right
to your door by a self driving bot. That future, well,
it isn't some distance sci fi fantasy, is it. It's
unfolding right now.
Speaker 2 (00:19):
Absolutely, the robot revolution is truly here, and these intelligent machines,
they're rapidly becoming just an integral, everyday part of our
global workforce.
Speaker 1 (00:28):
And that's exactly what we're diving into today.
Speaker 2 (00:30):
Yeah, this deep drive is all about that increasing integration
AI and robotics right into the workforce.
Speaker 1 (00:36):
We'll explore the latest advancements, unpack how they're transforming industries
think manufacturing, healthcare, you name it.
Speaker 2 (00:44):
And grapple with the really profound societal and economic ripples.
You know, a world where humans genuinely work alongside intelligent machines.
Speaker 1 (00:53):
So our mission today is to pull out the most
important insights from our stack of sources. We've got everything
from comprehensive economic reports to ethical guidelines.
Speaker 2 (01:02):
And some fascinating real world case studies.
Speaker 1 (01:04):
Too, right to help you understand what this robot revolution
really means for you, for your job and well the
very fabric of future work and our insights.
Speaker 2 (01:14):
They're coming from a great collection of recent analyses, reports
from Citigroup, PwC, McKinsey Global Institute.
Speaker 1 (01:20):
Plus articles from places like Forbes, the European Parliament, some
deep dives into AI guidelines.
Speaker 2 (01:26):
And even a pretty lively Reddit discussion we found about
jobs safe from AI.
Speaker 1 (01:31):
Okay, so let's get into it. This robot revolution. It's
important to understand we're not just talking about AI and isolation, right.
Speaker 2 (01:38):
No, exactly. AI's been around since sweat the nineteen fifties.
Speaker 1 (01:41):
And industrial robots since the sixties.
Speaker 2 (01:43):
Right. The real game changer now for the twenty first
century is putting those two together. Sophisticated AI, like those
large language models.
Speaker 1 (01:50):
Those ones they can understand and talk like us basically.
Speaker 2 (01:53):
Yeah, combining that with robots that actually move, that's synergy.
That's what's creating the future we're seeing unfold right now.
Speaker 1 (01:59):
And the scale, I mean, the forecasts are genuinely staggering.
City Group analysts are predicting we could see one point
three billion AI robots by twenty thirty five, uh huh.
Speaker 2 (02:09):
And surging to an almost unbelievable four billion by twenty fifty.
Speaker 1 (02:14):
Just think about that. For a second four billion, that's
like half the current global population potentially augmented or even
replaced by machines in many tasks.
Speaker 2 (02:24):
It really makes you pause, doesn't it.
Speaker 1 (02:25):
This isn't just about you know, small efficiency games. It
feels like a complete redefinition of human labor, maybe even
society on a scale we haven't seen before.
Speaker 2 (02:34):
And the adoption rates right now certainly reflect that speed.
As of this year twenty twenty five, seventy eight percent
of global companies are already using.
Speaker 1 (02:43):
AI seventy eight percent wow.
Speaker 2 (02:45):
And seventy one percent are using generative AI that's the
kind that creates new stuff text, images, code in at
least one part of their business.
Speaker 1 (02:53):
So that means over ninety percent of the world's companies,
something like three hundred million of them are.
Speaker 2 (02:57):
Either actively using or at least exploring AI. It's definitely
not a fringe technology anymore, no way, and.
Speaker 1 (03:02):
That's a huge commitment from businesses, and it looks like
it's not slowing down either.
Speaker 2 (03:06):
Not at all. A striking ninety two percent of companies
plan to increase their AI investment in the next three years.
Speaker 1 (03:12):
So this isn't just a passing fad. It's a massive
strategic shift.
Speaker 2 (03:15):
We've seen AI usage really ramp up just from twenty
twenty two to twenty twenty five, especially among the bigger companies,
the enterprise level ones, They're about twice as likely to
deploy these technologies.
Speaker 1 (03:27):
So the big players are really leading the charge here definitely. Okay,
so what's making this all possible now? Why the sudden acceleration.
Speaker 2 (03:35):
Well, it's really a powerful convergence coming together of several
AI breakthroughs.
Speaker 1 (03:40):
More than just those language models.
Speaker 2 (03:41):
Oh yeah. Beyond the LM's translating commands into robot actions,
we're seeing vastly improved robotic skill dexterity.
Speaker 1 (03:51):
Like that MIT robotic can.
Speaker 2 (03:53):
You mess exactly? Mastering what? Two thousand different objects? That's
incredible precision. And then there's these incredibly flexible businessiness models.
Think about this, Robot labor can now cost as little
as two dollars per hour.
Speaker 1 (04:05):
Two dollars an hour. That just makes them a very practical,
almost compelling reality for businesses, doesn't it.
Speaker 2 (04:10):
It really does. It changes the whole economic.
Speaker 1 (04:12):
Equation, and that low cost is just an undeniable catalyst,
and these advances aren't only for those big, heavy industrial
robots anymore.
Speaker 2 (04:20):
Right, These breakthroughs are finally making humanoid robots feasible for
the first time, not just hype but actual initial use
cases already popping up.
Speaker 1 (04:29):
Where are we seeing them first?
Speaker 2 (04:31):
Mostly industrial settings for now, manufacturing warehouses okay, but the
longer term vision is even more profound, augmenting care in hospitals,
in homes, especially helping the elderly with tasks like cleaning, tidying,
maybe even using dishwashers.
Speaker 1 (04:48):
That's interesting. You mentioned a study about Japanese nursing homes.
Speaker 2 (04:51):
Yeah, fascinating finding there. Robot adoption actually increased human employment
and worker retention. Really, it allowed the human staff to
focus more more on the high touch person to person
care tasks while the robots handled some of the routine stuff,
and it also improved overall care quality and productivity.
Speaker 1 (05:09):
That's a really interesting twist on the usual narrative of
just replacement.
Speaker 2 (05:13):
It absolutely is, and the real world examples are already everywhere.
Some might even surprise.
Speaker 1 (05:17):
You, like in warehouses. Amazon's a been one right, definitely.
Speaker 2 (05:20):
They use robotic arms like Cardinal for stacking packages and
these proteus autonomous platforms moving carts around.
Speaker 1 (05:26):
But you also noted, didn't you, that Amazon actually created
human jobs after bringing in robots.
Speaker 2 (05:31):
Yeah, that's the particularly interesting part. Since they first introduced
robots back in twenty twelve, they've simultaneously created over a
million human jobs. It's clearly not a simple one for
one replacement scenario.
Speaker 1 (05:44):
That definitely challenges the common perception, doesn't it. Okay, So
beyond warehouses.
Speaker 2 (05:48):
Well transportation, think Waymo's autonomous vehicles. They're already doing over
one hundred and fifty thousand weekly trips in cities, quietly
transforming how people get around.
Speaker 1 (05:58):
And let's not forget the Humble robot vacuum cleaner.
Speaker 2 (06:01):
Right exactly, They're already in what twenty percent of US households,
just quietly doing their thing.
Speaker 1 (06:06):
Okay, healthcare, you mentioned caregiving.
Speaker 2 (06:08):
Yeah, we're seeing robots like Moxie from Diligent Robots helping
nurses with non clinical tasks, things like fetching supplies.
Speaker 1 (06:15):
Freeing them up for more direct patient interaction.
Speaker 2 (06:18):
Precisely, and Hexel robots assisting in patient recovery, maybe with
physical therapy exercises, needing precise repetitive movements.
Speaker 1 (06:28):
And manufacturing still a bit area.
Speaker 2 (06:30):
Huge companies like Symbioidotics are helping car manufacturers forward Toyota
with really complex final assembly tasks, things that need high precision,
manual dexterity, stuff that used to be exclusively human domains.
Even farming yep, agricultures getting in on it too. Autonomous
drones for planting, monitoring crops, and these things called burrow.
Speaker 1 (06:54):
Cobots, cobots, collaborative robots exactly.
Speaker 2 (06:56):
They assist farm workers with the really labor intensive stuff.
Speaker 1 (07:00):
Flippy the kitchen cobot.
Speaker 2 (07:02):
Ah. Yes, Flippy can Flipburger's fried chicken for one hundred
thousand hours without a break.
Speaker 1 (07:06):
Makes you want about the long term implications for the
fast food industry, the service sector.
Speaker 2 (07:10):
Absolutely, and it's crucial to remember. AI isn't just about
physical robots you can point.
Speaker 1 (07:15):
To, right, it's behind the scenes too.
Speaker 2 (07:17):
Deeply embedded yeh. Smart assistance like Siri, how Uber matches
prices dynamically, how social media feeds curate content just for.
Speaker 1 (07:24):
You, all based on our past behavior.
Speaker 2 (07:26):
Exactly. Yeah, this intelligence is working away transforming pretty much
every sector.
Speaker 1 (07:30):
So given this massive adoption, these incredible advancements. The natural
next question is what's the economic upside?
Speaker 2 (07:38):
And the numbers here are well truly astonishing. Go on
PC estimates global GDP could increase by up to fourteen percent.
That's equivalent to a massive US fifteen point seven trillion
dollars by twenty thirty.
Speaker 1 (07:52):
Fifteen points seven trillion. That's a mind boggling amount of
new wealth.
Speaker 2 (07:57):
And Mackenzie Global Institute projects an additional US thirty teen
trillion dollars and economic output by twenty thirty, increasing global
GDP by about one point two percent annually.
Speaker 1 (08:05):
Okay, so multiple huge projections, and x amer takes it
even further.
Speaker 2 (08:10):
They forecast that AI could literally double annual global economic
growth rates by twenty.
Speaker 1 (08:14):
Thirty five, double the growth rate, and.
Speaker 2 (08:16):
Just looking specifically at humanoid robots, that market alone is
projected to hit an incredible seven trillion dollars by.
Speaker 1 (08:22):
Twenty fifty seven trillion for just humanoids. These aren't minor bumps,
are they? These are seismic shifts in global wealth creation.
Speaker 2 (08:27):
Absolutely.
Speaker 1 (08:28):
So this next part sort of lays out the how
how does AI actually become this engine for economic growth?
Speaker 2 (08:33):
Okay, so first it leads to strong increases in labor
productivity potentially up to forty percent.
Speaker 1 (08:40):
Forty percent productivity jump.
Speaker 2 (08:42):
How through more efficient time management and what they call
intelligent automation basically machines doing more, faster and often with
fewer errors.
Speaker 1 (08:50):
Okay, that makes sense. What else?
Speaker 2 (08:52):
Secondly, AI effectively creates this kind of new virtual workforce,
capable of problem solving, even self learn arning. That fundamentally
changes how tasks get done across industries.
Speaker 1 (09:04):
A virtual workforce.
Speaker 2 (09:05):
Oh, it's interesting. And third thirdly, AI is expected to
boost consumer demand how by creating more personalized, higher quality
products and services.
Speaker 1 (09:14):
Ah, the personalization angle exactly.
Speaker 2 (09:16):
PwC describes it as a virtuous cycle. More data leads
to better insights, which leads to better products, which generates
more data.
Speaker 1 (09:23):
And so on, and ultimately it frees up human time.
Speaker 2 (09:26):
That's the idea, automating those routine, maybe tedious tasks so
humans can focus on more stimulating, higher value activities.
Speaker 1 (09:33):
But these huge gains, they're not necessarily going to be
evenly spread across the globe, are they.
Speaker 2 (09:38):
No, that's a key point. North America and China are
expected to gain the most from AI tech.
Speaker 1 (09:43):
Why them specifically?
Speaker 2 (09:45):
Well, the US is leveraging its early adoption being ahead
in development. China is benefiting from its massive manufacturing sector
and its ability to deploy technology very rapidly at scale.
Speaker 1 (09:56):
And what about say Europe.
Speaker 2 (09:59):
Well, the EU who currently seems to face a bit
of a structural disadvantage in AI uptake and investment compared
to the US in Asia.
Speaker 1 (10:07):
Even with its strong manufacturing base and skilled workforce.
Speaker 2 (10:10):
Right, despite those strengths, the adoption rate and investment levels
haven't quite kept paced so far.
Speaker 1 (10:15):
McKenzie also mentioned something interesting, a barbell shaped economy.
Speaker 2 (10:20):
Yeah, that's a fascinating prediction. The idea is that AI
facilitates the rise of both massively scaled organizations I think
huge global tech companies and also very small niche agile players.
Speaker 1 (10:33):
So the giants and the tiny startups thrive. What about
the ones in the middle.
Speaker 2 (10:37):
That's the potential problem. Mid sized companies could get squeezed out,
potentially losing ground because they can't compete on sheer scale
or on hyper agility.
Speaker 1 (10:45):
It really speaks to that winner takes all dynamic, doesn't
it It does.
Speaker 2 (10:49):
Early adopters gain a disproportionate advantage grabbing market share. If
you're a mid size company and you're not aggressively adopting AI, well,
you're potentially at risk of being squeezed out.
Speaker 1 (11:00):
Well, let's bring it back to the human element. What
does all this mean for us? For jobs? It seems
like a really mixed picture.
Speaker 2 (11:05):
It absolutely is. On one hand, you have reports like
the World Economic Forums from twenty eighteen. They projected a
net gain of fifty eight million jobs by twenty twenty
two due.
Speaker 1 (11:16):
To AI, A net gain. How did that work?
Speaker 2 (11:19):
They figured one hundred and thirty three million new roles
would emerge while about seventy five million would be displaced,
So a net positive at least in that forecast. It
offers a bit of hope amidst the understandable fears.
Speaker 1 (11:29):
Okay, but there are darker forecasts too.
Speaker 2 (11:32):
Yes, definitely. One Thing Tank suggested as many as fifty
four percent of jobs in the EU could be at
risk of computerization within twenty years.
Speaker 1 (11:39):
Fifty four percent. That's huge.
Speaker 2 (11:42):
And an MIT study found that on average, one industrial
robot replaced three point three workers and the economic concentive
for displacement it's clear and pretty powerful. Robot costs have
fallen fifty percent since nineteen ninety while human wages have generally.
Speaker 1 (11:58):
Risen, so that twenty thousand dollars humanoid robot we talked
about right at.
Speaker 2 (12:02):
Minimum wage, it could potentially pay for itself in just
twenty nine weeks.
Speaker 1 (12:06):
That's a very compelling argument for businesses. Just looking at
the bottom line.
Speaker 2 (12:10):
It is it's hard to ignore.
Speaker 1 (12:12):
So which jobs are seen as being at the highest risk.
Speaker 2 (12:14):
Here we're largely talking about areas like manufacturing obviously, but
also counting administrative roles basically any routine, manual or cognitive
task that can be codified.
Speaker 1 (12:25):
But on the flip side, new jobs are definitely emerging.
Speaker 2 (12:28):
Absolutely programmers, data scientists, robotics experts, AI trainers, ethicists, a
whole range.
Speaker 1 (12:34):
But the really crucial thing seems to be the growing
demand for uniquely human skills.
Speaker 2 (12:38):
Exactly that things like creativity, originality, critical thinking, persuasion, negotiation, empathy,
complex problem solving. Skilled machines find very hard to replicate authentically.
Speaker 1 (12:49):
Which brings us scarely to this massive skills gap challenge.
Speaker 2 (12:52):
Yeah, McKinzie estimates that around three hundred and seventy five
million workers globally might need to switch occupations or acquire
entirely new skills by twenty thirty.
Speaker 1 (13:01):
Three hundred and seventy five million. That's enormous.
Speaker 2 (13:04):
It necessitates huge strategies for reskilling, training people for completely
new roles.
Speaker 1 (13:09):
Like that Amazon initiative seven hundred million dollars upskilling twenty twenty.
Speaker 2 (13:14):
Five exactly aiming to reskill one hundred thousand employees and
also upskilling enhancing existing skills for the new environment think
PWC's New World New Skills or eighteen t's Workforce twenty twenty.
Speaker 1 (13:27):
But there's a challenge there, isn't there getting that training
to the right people.
Speaker 2 (13:30):
Yes. Unfortunately, the very workers who often need reskilling the
most are sometimes the least likely to receive it for
various reasons. That could create a real societal divide.
Speaker 1 (13:40):
And what about the psychological impacts of all this constant
change and uncertainty.
Speaker 2 (13:44):
Just as critical. It's a double edged sword. On one side,
AI can definitely boost efficiency, maybe make jobs more interesting
by taking away tedious tasks.
Speaker 1 (13:52):
Letting us focus on higher level stuff.
Speaker 2 (13:54):
Right, but it also creates significant stress. Job insecurity is real,
fear of replacement is real, and just the constant, relentless
need to adapt and learn.
Speaker 1 (14:04):
New things you mentioned a survey of teachers.
Speaker 2 (14:07):
Yeah, it was interesting. Most felt AI positively impacted their
job satisfaction and workload for things like grading or lesson planning,
but worries about displacement were really divided.
Speaker 1 (14:18):
In work life balance mixed.
Speaker 2 (14:19):
Results there too. It's clearly a complex emotional landscape for people.
Speaker 1 (14:24):
What seems to make the difference in how people feel
about it.
Speaker 2 (14:27):
What's really fascinating is that workers who perceive AI as
an augmentation, like a tool, an aid to help them
do their job better.
Speaker 1 (14:36):
Rather than a substitute or replacement.
Speaker 2 (14:38):
Exactly, those workers tend to be far more satisfied and engaged.
It really highlights how important it is for organizations to
frame and implement AI thoughtfully.
Speaker 1 (14:47):
So organizations need strategies for this.
Speaker 2 (14:50):
Definitely providing continuous learning opportunities, offering robust mental health support,
maybe more flexible work arrangements, and really fostering what's called participative.
Speaker 1 (14:59):
AI imployed, meaning getting employees involved.
Speaker 2 (15:02):
Yes, involving them in the process and setting the rules
that seems crucial to mitigate those negative psychological effects.
Speaker 1 (15:10):
Okay, that brings us to the question I think everyone's
debating around the dinner table. What jobs are actually AI
proof is anything truly safe?
Speaker 2 (15:20):
Yeah? That Reddit discussion you mentioned was lively on this
exact topic. A lot of people argue for physical labor
roles plumbers, electricians, other trades people.
Speaker 1 (15:29):
The argument being it's harder to automate that kind of
nuanced physical work.
Speaker 2 (15:33):
Right. Fixing a specific leaky pipe in an awkward space
is very different from assembling a car on a predictable line. Though,
the debate about just how good robotic dexterity will get
is definitely ongoing.
Speaker 1 (15:45):
Okay, so maybe trades for now.
Speaker 2 (15:46):
What else? Well, Jobs requiring significant human interaction, empathy, and
trust are often cited. I think metical practitioners, doctors, nurses.
Speaker 1 (15:54):
Therapists because of the complexity of human conditions, the need
for emotional intelligence.
Speaker 2 (15:59):
Exactly, that nuanced judgment, the bedside manner, understanding unspoken cues.
That's something AI currently struggles to replicate authentically.
Speaker 1 (16:08):
And then maybe roles involving pure creativity or complex undefined
problem solving.
Speaker 2 (16:13):
Yeah, things like professional athletes, artists creating physical works. Maybe
certain social service roles. These rely on uniquely human qualities
that are exceptionally difficult for AI to mimic or fully embody,
at least for now.
Speaker 1 (16:28):
It's a fascinating debate. Reminds me of Amara's law.
Speaker 2 (16:31):
We tend to overestimate the effect of a technology in
the short run and underestimate the effect in the long run.
Speaker 1 (16:37):
Precisely so, maybe the immediate fears of mass job losses
are overblown.
Speaker 2 (16:41):
The long term changes could be even more profound and
far reaching than we currently imagine.
Speaker 1 (16:47):
So as we navigate this future, the ethics of AI
become absolutely critical, don't they.
Speaker 2 (16:52):
They cannot be overstated. These AI systems aren't just technical tools.
They directly influence decisions, affect people's lives, shape society.
Speaker 1 (16:59):
And the core concerns are pretty wide ranging.
Speaker 2 (17:02):
Definitely. Privacy and data protection are huge given the vast
amounts of personal data these systems often collect.
Speaker 1 (17:08):
And bias and fairness that's a big one huge.
Speaker 2 (17:11):
AI can unfortunately reflect and sometimes even amplify biases present
in its training data that can lead to discriminatory outcomes
in really important areas like hiring or credit approvals.
Speaker 1 (17:23):
Then there's transparency and explainability, the whole black box problem.
Speaker 2 (17:26):
Right, It's often difficult to understand how an AI reached
a particular decision that makes accountability and building trust really challenging.
Speaker 1 (17:33):
And of course the employment impacts we've discussed, plus the
potential for AI powered surveillance in.
Speaker 2 (17:38):
The workplace all major concerns. And it's worth noting that
public trust and tech companies globally has actually declined significantly
in recent years. Really by how much globally from sixty
one percent trusting them in twenty nineteen down to fifty
three percent in twenty twenty four, and the drop in
the US was even steeper, from fifty percent down to
thirty five percent.
Speaker 1 (17:57):
Wow. So that makes robust ethical frameworks and building trust
even more critical now absolutely essential. Is work being done
on this? Are there guidelines? Oh?
Speaker 2 (18:05):
Yes, thankfully, various organizations are already working on comprehensive guidelines.
You've got initiatives like the IE Global Initiative, the EUSE
Ethical Guidelines for Trustworthy AI.
Speaker 1 (18:15):
The OECD Principles on AI.
Speaker 2 (18:18):
Right. They all tend to emphasize core values like fairness, transparency, accountability, privacy,
and human centric design putting the human at the center, and.
Speaker 1 (18:28):
Corporate policies regulations.
Speaker 2 (18:30):
Crucial roles too. Think GDPR for data protection. The consensus
really is that ethical principles need to be embedded right
from the start in the design phase, not just tacked
on later.
Speaker 1 (18:41):
And we're already seeing real world ethical dilemmas play out,
aren't we.
Speaker 2 (18:45):
Unfortunately, Yes, concrete examples exist. An AI recruitment system found
to be biased against certain demographics unintentionally perpetuating historical inequalities right.
Speaker 1 (18:55):
Or AI monitoring employee performance.
Speaker 2 (18:57):
While maybe efficient, it raises significant price privacy concerns about
constant surveillance, and AI algorithms used for credit approval have
been found to perpetuate existing lending biases disadvantaging certain groups.
Speaker 1 (19:09):
Highlights the need for continuous auditing and vigilance.
Speaker 2 (19:13):
Definitely.
Speaker 1 (19:13):
So for organizations wanting to adopt AI ethically, what are
the key strategies?
Speaker 2 (19:18):
Okay, First, they need to clearly define the scope and purpose.
Why are we using this AI? Does it align with
our business goals and our core values?
Speaker 1 (19:27):
Start with the Y makes sense.
Speaker 2 (19:28):
Second, establish clear ethical principles. Ensure AI use is fair, transparent,
accountable and follows all the relevant.
Speaker 1 (19:37):
Laws and critically involves stakeholders right especially employees.
Speaker 2 (19:41):
Yes, absolutely engage them and others in the decision making
process for setting the rules around AI. Get diverse perspectives
in there from the outset.
Speaker 1 (19:50):
And it's not just about what the AI does, but
who's responsible exactly.
Speaker 2 (19:53):
Assign clear roles and responsibilities. Who designs it, who deploys it,
who maintains it, who is accountable if something goes wrong,
humans or AI or both?
Speaker 1 (20:02):
And training is vital too.
Speaker 2 (20:03):
Absolutely vital. Provide comprehensive training and ongoing support so staff
feel empowered, not threatened, to work effectively alongside AI.
Speaker 1 (20:12):
Fostering that culture of curiosity, a growth mindset, viewing AI
as a tool, an augmentation, not purely a replacement.
Speaker 2 (20:19):
That shift in perception is key.
Speaker 1 (20:21):
What else?
Speaker 2 (20:22):
Data security non negotiable. Implement robust measures to protect sensitive information.
Adherees strictly to data protection regulations like GDPR and feedback
put feedback loops in place. Get continuous input from employees
on how their AI interactions are going. That allows for
refinement and adaptation over time.
Speaker 1 (20:41):
Because the field changes.
Speaker 2 (20:42):
So fast, exactly, you have to review and update these
rules regularly. And underlying all of this should be a
human centered approach. Prioritize psychological safety, inclusivity, mental well being
support like that.
Speaker 1 (20:55):
Google Mindfulness program you mentioned.
Speaker 2 (20:57):
Yeah, initiatives like search inside yourself, things that support the
human side of the equation.
Speaker 1 (21:02):
Finally, there's this bigger policy debate happening globally, things like
the robot tax.
Speaker 2 (21:07):
Yeah, it's an intriguing idea. Bill Gates famously suggested it.
South Korea has actually taken some action lowering tax deductions
for business investments in automation.
Speaker 1 (21:16):
What's the core idea behind a robot tax?
Speaker 2 (21:18):
Essentially to try and rebalance the economic gains from automation,
use the revenue generated to fund crucial worker retraining initiatives
or other social safety nets to address the potential societal
disruption head on.
Speaker 1 (21:32):
It really emphasizes that this robot revolution isn't just a
tech shift, is it not at all?
Speaker 2 (21:37):
It's a profound societal evolution that demands really thoughtful, proactive
consideration from all of us individuals, companies, governments.
Speaker 1 (21:46):
Okay, so we've really navigated the intricate landscape of this
AI and robot revolution today.
Speaker 2 (21:51):
We have from the surprising scale of adoption happening right
now it's multi trillion dollar economic impact to.
Speaker 1 (21:58):
The profound transformation it's bringing to jobs, skills, even our
psychological well being.
Speaker 2 (22:04):
And we've unpacked those critical ethical considerations to trust, bias, privacy,
their paramount and how organizations are trying to navigate this
new reality with human centered strategies.
Speaker 1 (22:15):
So as these intelligent machines get increasingly capable performing not
just physical tasks but also really complex cognitive ones, it
raises a pretty important question for you listening right now.
Speaker 2 (22:24):
If AI can perform nearly all the routine stuff and
even many traditionally complex human functions, what unique value will
we as humans truly bring to the future workforce?
Speaker 1 (22:35):
Yeah, what does it even mean to work when your
colleagues might be tireless algorithms and robots, And how will
society adapt to define human purpose, human fulfillment, maybe beyond
traditional employment as we know it.
Speaker 2 (22:47):
It's definitely a thought worth mulling over as we continue
this deep dive into our accelerating future.
Imagine waking up one morning and your new cubicle neighbor
isn't quite human. Yeah, or maybe your latest health diagnosis
came from an algorithm and your groceries re zipped right
to your door by a self driving bot. That future, well,
it isn't some distance sci fi fantasy, is it. It's
unfolding right now.
Speaker 2 (00:19):
Absolutely, the robot revolution is truly here, and these intelligent machines,
they're rapidly becoming just an integral, everyday part of our
global workforce.
Speaker 1 (00:28):
And that's exactly what we're diving into today.
Speaker 2 (00:30):
Yeah, this deep drive is all about that increasing integration
AI and robotics right into the workforce.
Speaker 1 (00:36):
We'll explore the latest advancements, unpack how they're transforming industries
think manufacturing, healthcare, you name it.
Speaker 2 (00:44):
And grapple with the really profound societal and economic ripples.
You know, a world where humans genuinely work alongside intelligent machines.
Speaker 1 (00:53):
So our mission today is to pull out the most
important insights from our stack of sources. We've got everything
from comprehensive economic reports to ethical guidelines.
Speaker 2 (01:02):
And some fascinating real world case studies.
Speaker 1 (01:04):
Too, right to help you understand what this robot revolution
really means for you, for your job and well the
very fabric of future work and our insights.
Speaker 2 (01:14):
They're coming from a great collection of recent analyses, reports
from Citigroup, PwC, McKinsey Global Institute.
Speaker 1 (01:20):
Plus articles from places like Forbes, the European Parliament, some
deep dives into AI guidelines.
Speaker 2 (01:26):
And even a pretty lively Reddit discussion we found about
jobs safe from AI.
Speaker 1 (01:31):
Okay, so let's get into it. This robot revolution. It's
important to understand we're not just talking about AI and isolation, right.
Speaker 2 (01:38):
No, exactly. AI's been around since sweat the nineteen fifties.
Speaker 1 (01:41):
And industrial robots since the sixties.
Speaker 2 (01:43):
Right. The real game changer now for the twenty first
century is putting those two together. Sophisticated AI, like those
large language models.
Speaker 1 (01:50):
Those ones they can understand and talk like us basically.
Speaker 2 (01:53):
Yeah, combining that with robots that actually move, that's synergy.
That's what's creating the future we're seeing unfold right now.
Speaker 1 (01:59):
And the scale, I mean, the forecasts are genuinely staggering.
City Group analysts are predicting we could see one point
three billion AI robots by twenty thirty five, uh huh.
Speaker 2 (02:09):
And surging to an almost unbelievable four billion by twenty fifty.
Speaker 1 (02:14):
Just think about that. For a second four billion, that's
like half the current global population potentially augmented or even
replaced by machines in many tasks.
Speaker 2 (02:24):
It really makes you pause, doesn't it.
Speaker 1 (02:25):
This isn't just about you know, small efficiency games. It
feels like a complete redefinition of human labor, maybe even
society on a scale we haven't seen before.
Speaker 2 (02:34):
And the adoption rates right now certainly reflect that speed.
As of this year twenty twenty five, seventy eight percent
of global companies are already using.
Speaker 1 (02:43):
AI seventy eight percent wow.
Speaker 2 (02:45):
And seventy one percent are using generative AI that's the
kind that creates new stuff text, images, code in at
least one part of their business.
Speaker 1 (02:53):
So that means over ninety percent of the world's companies,
something like three hundred million of them are.
Speaker 2 (02:57):
Either actively using or at least exploring AI. It's definitely
not a fringe technology anymore, no way, and.
Speaker 1 (03:02):
That's a huge commitment from businesses, and it looks like
it's not slowing down either.
Speaker 2 (03:06):
Not at all. A striking ninety two percent of companies
plan to increase their AI investment in the next three years.
Speaker 1 (03:12):
So this isn't just a passing fad. It's a massive
strategic shift.
Speaker 2 (03:15):
We've seen AI usage really ramp up just from twenty
twenty two to twenty twenty five, especially among the bigger companies,
the enterprise level ones, They're about twice as likely to
deploy these technologies.
Speaker 1 (03:27):
So the big players are really leading the charge here definitely. Okay,
so what's making this all possible now? Why the sudden acceleration.
Speaker 2 (03:35):
Well, it's really a powerful convergence coming together of several
AI breakthroughs.
Speaker 1 (03:40):
More than just those language models.
Speaker 2 (03:41):
Oh yeah. Beyond the LM's translating commands into robot actions,
we're seeing vastly improved robotic skill dexterity.
Speaker 1 (03:51):
Like that MIT robotic can.
Speaker 2 (03:53):
You mess exactly? Mastering what? Two thousand different objects? That's
incredible precision. And then there's these incredibly flexible businessiness models.
Think about this, Robot labor can now cost as little
as two dollars per hour.
Speaker 1 (04:05):
Two dollars an hour. That just makes them a very practical,
almost compelling reality for businesses, doesn't it.
Speaker 2 (04:10):
It really does. It changes the whole economic.
Speaker 1 (04:12):
Equation, and that low cost is just an undeniable catalyst,
and these advances aren't only for those big, heavy industrial
robots anymore.
Speaker 2 (04:20):
Right, These breakthroughs are finally making humanoid robots feasible for
the first time, not just hype but actual initial use
cases already popping up.
Speaker 1 (04:29):
Where are we seeing them first?
Speaker 2 (04:31):
Mostly industrial settings for now, manufacturing warehouses okay, but the
longer term vision is even more profound, augmenting care in hospitals,
in homes, especially helping the elderly with tasks like cleaning, tidying,
maybe even using dishwashers.
Speaker 1 (04:48):
That's interesting. You mentioned a study about Japanese nursing homes.
Speaker 2 (04:51):
Yeah, fascinating finding there. Robot adoption actually increased human employment
and worker retention. Really, it allowed the human staff to
focus more more on the high touch person to person
care tasks while the robots handled some of the routine stuff,
and it also improved overall care quality and productivity.
Speaker 1 (05:09):
That's a really interesting twist on the usual narrative of
just replacement.
Speaker 2 (05:13):
It absolutely is, and the real world examples are already everywhere.
Some might even surprise.
Speaker 1 (05:17):
You, like in warehouses. Amazon's a been one right, definitely.
Speaker 2 (05:20):
They use robotic arms like Cardinal for stacking packages and
these proteus autonomous platforms moving carts around.
Speaker 1 (05:26):
But you also noted, didn't you, that Amazon actually created
human jobs after bringing in robots.
Speaker 2 (05:31):
Yeah, that's the particularly interesting part. Since they first introduced
robots back in twenty twelve, they've simultaneously created over a
million human jobs. It's clearly not a simple one for
one replacement scenario.
Speaker 1 (05:44):
That definitely challenges the common perception, doesn't it. Okay, So
beyond warehouses.
Speaker 2 (05:48):
Well transportation, think Waymo's autonomous vehicles. They're already doing over
one hundred and fifty thousand weekly trips in cities, quietly
transforming how people get around.
Speaker 1 (05:58):
And let's not forget the Humble robot vacuum cleaner.
Speaker 2 (06:01):
Right exactly, They're already in what twenty percent of US households,
just quietly doing their thing.
Speaker 1 (06:06):
Okay, healthcare, you mentioned caregiving.
Speaker 2 (06:08):
Yeah, we're seeing robots like Moxie from Diligent Robots helping
nurses with non clinical tasks, things like fetching supplies.
Speaker 1 (06:15):
Freeing them up for more direct patient interaction.
Speaker 2 (06:18):
Precisely, and Hexel robots assisting in patient recovery, maybe with
physical therapy exercises, needing precise repetitive movements.
Speaker 1 (06:28):
And manufacturing still a bit area.
Speaker 2 (06:30):
Huge companies like Symbioidotics are helping car manufacturers forward Toyota
with really complex final assembly tasks, things that need high precision,
manual dexterity, stuff that used to be exclusively human domains.
Even farming yep, agricultures getting in on it too. Autonomous
drones for planting, monitoring crops, and these things called burrow.
Speaker 1 (06:54):
Cobots, cobots, collaborative robots exactly.
Speaker 2 (06:56):
They assist farm workers with the really labor intensive stuff.
Speaker 1 (07:00):
Flippy the kitchen cobot.
Speaker 2 (07:02):
Ah. Yes, Flippy can Flipburger's fried chicken for one hundred
thousand hours without a break.
Speaker 1 (07:06):
Makes you want about the long term implications for the
fast food industry, the service sector.
Speaker 2 (07:10):
Absolutely, and it's crucial to remember. AI isn't just about
physical robots you can point.
Speaker 1 (07:15):
To, right, it's behind the scenes too.
Speaker 2 (07:17):
Deeply embedded yeh. Smart assistance like Siri, how Uber matches
prices dynamically, how social media feeds curate content just for.
Speaker 1 (07:24):
You, all based on our past behavior.
Speaker 2 (07:26):
Exactly. Yeah, this intelligence is working away transforming pretty much
every sector.
Speaker 1 (07:30):
So given this massive adoption, these incredible advancements. The natural
next question is what's the economic upside?
Speaker 2 (07:38):
And the numbers here are well truly astonishing. Go on
PC estimates global GDP could increase by up to fourteen percent.
That's equivalent to a massive US fifteen point seven trillion
dollars by twenty thirty.
Speaker 1 (07:52):
Fifteen points seven trillion. That's a mind boggling amount of
new wealth.
Speaker 2 (07:57):
And Mackenzie Global Institute projects an additional US thirty teen
trillion dollars and economic output by twenty thirty, increasing global
GDP by about one point two percent annually.
Speaker 1 (08:05):
Okay, so multiple huge projections, and x amer takes it
even further.
Speaker 2 (08:10):
They forecast that AI could literally double annual global economic
growth rates by twenty.
Speaker 1 (08:14):
Thirty five, double the growth rate, and.
Speaker 2 (08:16):
Just looking specifically at humanoid robots, that market alone is
projected to hit an incredible seven trillion dollars by.
Speaker 1 (08:22):
Twenty fifty seven trillion for just humanoids. These aren't minor bumps,
are they? These are seismic shifts in global wealth creation.
Speaker 2 (08:27):
Absolutely.
Speaker 1 (08:28):
So this next part sort of lays out the how
how does AI actually become this engine for economic growth?
Speaker 2 (08:33):
Okay, so first it leads to strong increases in labor
productivity potentially up to forty percent.
Speaker 1 (08:40):
Forty percent productivity jump.
Speaker 2 (08:42):
How through more efficient time management and what they call
intelligent automation basically machines doing more, faster and often with
fewer errors.
Speaker 1 (08:50):
Okay, that makes sense. What else?
Speaker 2 (08:52):
Secondly, AI effectively creates this kind of new virtual workforce,
capable of problem solving, even self learn arning. That fundamentally
changes how tasks get done across industries.
Speaker 1 (09:04):
A virtual workforce.
Speaker 2 (09:05):
Oh, it's interesting. And third thirdly, AI is expected to
boost consumer demand how by creating more personalized, higher quality
products and services.
Speaker 1 (09:14):
Ah, the personalization angle exactly.
Speaker 2 (09:16):
PwC describes it as a virtuous cycle. More data leads
to better insights, which leads to better products, which generates
more data.
Speaker 1 (09:23):
And so on, and ultimately it frees up human time.
Speaker 2 (09:26):
That's the idea, automating those routine, maybe tedious tasks so
humans can focus on more stimulating, higher value activities.
Speaker 1 (09:33):
But these huge gains, they're not necessarily going to be
evenly spread across the globe, are they.
Speaker 2 (09:38):
No, that's a key point. North America and China are
expected to gain the most from AI tech.
Speaker 1 (09:43):
Why them specifically?
Speaker 2 (09:45):
Well, the US is leveraging its early adoption being ahead
in development. China is benefiting from its massive manufacturing sector
and its ability to deploy technology very rapidly at scale.
Speaker 1 (09:56):
And what about say Europe.
Speaker 2 (09:59):
Well, the EU who currently seems to face a bit
of a structural disadvantage in AI uptake and investment compared
to the US in Asia.
Speaker 1 (10:07):
Even with its strong manufacturing base and skilled workforce.
Speaker 2 (10:10):
Right, despite those strengths, the adoption rate and investment levels
haven't quite kept paced so far.
Speaker 1 (10:15):
McKenzie also mentioned something interesting, a barbell shaped economy.
Speaker 2 (10:20):
Yeah, that's a fascinating prediction. The idea is that AI
facilitates the rise of both massively scaled organizations I think
huge global tech companies and also very small niche agile players.
Speaker 1 (10:33):
So the giants and the tiny startups thrive. What about
the ones in the middle.
Speaker 2 (10:37):
That's the potential problem. Mid sized companies could get squeezed out,
potentially losing ground because they can't compete on sheer scale
or on hyper agility.
Speaker 1 (10:45):
It really speaks to that winner takes all dynamic, doesn't
it It does.
Speaker 2 (10:49):
Early adopters gain a disproportionate advantage grabbing market share. If
you're a mid size company and you're not aggressively adopting AI, well,
you're potentially at risk of being squeezed out.
Speaker 1 (11:00):
Well, let's bring it back to the human element. What
does all this mean for us? For jobs? It seems
like a really mixed picture.
Speaker 2 (11:05):
It absolutely is. On one hand, you have reports like
the World Economic Forums from twenty eighteen. They projected a
net gain of fifty eight million jobs by twenty twenty
two due.
Speaker 1 (11:16):
To AI, A net gain. How did that work?
Speaker 2 (11:19):
They figured one hundred and thirty three million new roles
would emerge while about seventy five million would be displaced,
So a net positive at least in that forecast. It
offers a bit of hope amidst the understandable fears.
Speaker 1 (11:29):
Okay, but there are darker forecasts too.
Speaker 2 (11:32):
Yes, definitely. One Thing Tank suggested as many as fifty
four percent of jobs in the EU could be at
risk of computerization within twenty years.
Speaker 1 (11:39):
Fifty four percent. That's huge.
Speaker 2 (11:42):
And an MIT study found that on average, one industrial
robot replaced three point three workers and the economic concentive
for displacement it's clear and pretty powerful. Robot costs have
fallen fifty percent since nineteen ninety while human wages have generally.
Speaker 1 (11:58):
Risen, so that twenty thousand dollars humanoid robot we talked
about right at.
Speaker 2 (12:02):
Minimum wage, it could potentially pay for itself in just
twenty nine weeks.
Speaker 1 (12:06):
That's a very compelling argument for businesses. Just looking at
the bottom line.
Speaker 2 (12:10):
It is it's hard to ignore.
Speaker 1 (12:12):
So which jobs are seen as being at the highest risk.
Speaker 2 (12:14):
Here we're largely talking about areas like manufacturing obviously, but
also counting administrative roles basically any routine, manual or cognitive
task that can be codified.
Speaker 1 (12:25):
But on the flip side, new jobs are definitely emerging.
Speaker 2 (12:28):
Absolutely programmers, data scientists, robotics experts, AI trainers, ethicists, a
whole range.
Speaker 1 (12:34):
But the really crucial thing seems to be the growing
demand for uniquely human skills.
Speaker 2 (12:38):
Exactly that things like creativity, originality, critical thinking, persuasion, negotiation, empathy,
complex problem solving. Skilled machines find very hard to replicate authentically.
Speaker 1 (12:49):
Which brings us scarely to this massive skills gap challenge.
Speaker 2 (12:52):
Yeah, McKinzie estimates that around three hundred and seventy five
million workers globally might need to switch occupations or acquire
entirely new skills by twenty thirty.
Speaker 1 (13:01):
Three hundred and seventy five million. That's enormous.
Speaker 2 (13:04):
It necessitates huge strategies for reskilling, training people for completely
new roles.
Speaker 1 (13:09):
Like that Amazon initiative seven hundred million dollars upskilling twenty twenty.
Speaker 2 (13:14):
Five exactly aiming to reskill one hundred thousand employees and
also upskilling enhancing existing skills for the new environment think
PWC's New World New Skills or eighteen t's Workforce twenty twenty.
Speaker 1 (13:27):
But there's a challenge there, isn't there getting that training
to the right people.
Speaker 2 (13:30):
Yes. Unfortunately, the very workers who often need reskilling the
most are sometimes the least likely to receive it for
various reasons. That could create a real societal divide.
Speaker 1 (13:40):
And what about the psychological impacts of all this constant
change and uncertainty.
Speaker 2 (13:44):
Just as critical. It's a double edged sword. On one side,
AI can definitely boost efficiency, maybe make jobs more interesting
by taking away tedious tasks.
Speaker 1 (13:52):
Letting us focus on higher level stuff.
Speaker 2 (13:54):
Right, but it also creates significant stress. Job insecurity is real,
fear of replacement is real, and just the constant, relentless
need to adapt and learn.
Speaker 1 (14:04):
New things you mentioned a survey of teachers.
Speaker 2 (14:07):
Yeah, it was interesting. Most felt AI positively impacted their
job satisfaction and workload for things like grading or lesson planning,
but worries about displacement were really divided.
Speaker 1 (14:18):
In work life balance mixed.
Speaker 2 (14:19):
Results there too. It's clearly a complex emotional landscape for people.
Speaker 1 (14:24):
What seems to make the difference in how people feel
about it.
Speaker 2 (14:27):
What's really fascinating is that workers who perceive AI as
an augmentation, like a tool, an aid to help them
do their job better.
Speaker 1 (14:36):
Rather than a substitute or replacement.
Speaker 2 (14:38):
Exactly, those workers tend to be far more satisfied and engaged.
It really highlights how important it is for organizations to
frame and implement AI thoughtfully.
Speaker 1 (14:47):
So organizations need strategies for this.
Speaker 2 (14:50):
Definitely providing continuous learning opportunities, offering robust mental health support,
maybe more flexible work arrangements, and really fostering what's called participative.
Speaker 1 (14:59):
AI imployed, meaning getting employees involved.
Speaker 2 (15:02):
Yes, involving them in the process and setting the rules
that seems crucial to mitigate those negative psychological effects.
Speaker 1 (15:10):
Okay, that brings us to the question I think everyone's
debating around the dinner table. What jobs are actually AI
proof is anything truly safe?
Speaker 2 (15:20):
Yeah? That Reddit discussion you mentioned was lively on this
exact topic. A lot of people argue for physical labor
roles plumbers, electricians, other trades people.
Speaker 1 (15:29):
The argument being it's harder to automate that kind of
nuanced physical work.
Speaker 2 (15:33):
Right. Fixing a specific leaky pipe in an awkward space
is very different from assembling a car on a predictable line. Though,
the debate about just how good robotic dexterity will get
is definitely ongoing.
Speaker 1 (15:45):
Okay, so maybe trades for now.
Speaker 2 (15:46):
What else? Well, Jobs requiring significant human interaction, empathy, and
trust are often cited. I think metical practitioners, doctors, nurses.
Speaker 1 (15:54):
Therapists because of the complexity of human conditions, the need
for emotional intelligence.
Speaker 2 (15:59):
Exactly, that nuanced judgment, the bedside manner, understanding unspoken cues.
That's something AI currently struggles to replicate authentically.
Speaker 1 (16:08):
And then maybe roles involving pure creativity or complex undefined
problem solving.
Speaker 2 (16:13):
Yeah, things like professional athletes, artists creating physical works. Maybe
certain social service roles. These rely on uniquely human qualities
that are exceptionally difficult for AI to mimic or fully embody,
at least for now.
Speaker 1 (16:28):
It's a fascinating debate. Reminds me of Amara's law.
Speaker 2 (16:31):
We tend to overestimate the effect of a technology in
the short run and underestimate the effect in the long run.
Speaker 1 (16:37):
Precisely so, maybe the immediate fears of mass job losses
are overblown.
Speaker 2 (16:41):
The long term changes could be even more profound and
far reaching than we currently imagine.
Speaker 1 (16:47):
So as we navigate this future, the ethics of AI
become absolutely critical, don't they.
Speaker 2 (16:52):
They cannot be overstated. These AI systems aren't just technical tools.
They directly influence decisions, affect people's lives, shape society.
Speaker 1 (16:59):
And the core concerns are pretty wide ranging.
Speaker 2 (17:02):
Definitely. Privacy and data protection are huge given the vast
amounts of personal data these systems often collect.
Speaker 1 (17:08):
And bias and fairness that's a big one huge.
Speaker 2 (17:11):
AI can unfortunately reflect and sometimes even amplify biases present
in its training data that can lead to discriminatory outcomes
in really important areas like hiring or credit approvals.
Speaker 1 (17:23):
Then there's transparency and explainability, the whole black box problem.
Speaker 2 (17:26):
Right, It's often difficult to understand how an AI reached
a particular decision that makes accountability and building trust really challenging.
Speaker 1 (17:33):
And of course the employment impacts we've discussed, plus the
potential for AI powered surveillance in.
Speaker 2 (17:38):
The workplace all major concerns. And it's worth noting that
public trust and tech companies globally has actually declined significantly
in recent years. Really by how much globally from sixty
one percent trusting them in twenty nineteen down to fifty
three percent in twenty twenty four, and the drop in
the US was even steeper, from fifty percent down to
thirty five percent.
Speaker 1 (17:57):
Wow. So that makes robust ethical frameworks and building trust
even more critical now absolutely essential. Is work being done
on this? Are there guidelines? Oh?
Speaker 2 (18:05):
Yes, thankfully, various organizations are already working on comprehensive guidelines.
You've got initiatives like the IE Global Initiative, the EUSE
Ethical Guidelines for Trustworthy AI.
Speaker 1 (18:15):
The OECD Principles on AI.
Speaker 2 (18:18):
Right. They all tend to emphasize core values like fairness, transparency, accountability, privacy,
and human centric design putting the human at the center, and.
Speaker 1 (18:28):
Corporate policies regulations.
Speaker 2 (18:30):
Crucial roles too. Think GDPR for data protection. The consensus
really is that ethical principles need to be embedded right
from the start in the design phase, not just tacked
on later.
Speaker 1 (18:41):
And we're already seeing real world ethical dilemmas play out,
aren't we.
Speaker 2 (18:45):
Unfortunately, Yes, concrete examples exist. An AI recruitment system found
to be biased against certain demographics unintentionally perpetuating historical inequalities right.
Speaker 1 (18:55):
Or AI monitoring employee performance.
Speaker 2 (18:57):
While maybe efficient, it raises significant price privacy concerns about
constant surveillance, and AI algorithms used for credit approval have
been found to perpetuate existing lending biases disadvantaging certain groups.
Speaker 1 (19:09):
Highlights the need for continuous auditing and vigilance.
Speaker 2 (19:13):
Definitely.
Speaker 1 (19:13):
So for organizations wanting to adopt AI ethically, what are
the key strategies?
Speaker 2 (19:18):
Okay, First, they need to clearly define the scope and purpose.
Why are we using this AI? Does it align with
our business goals and our core values?
Speaker 1 (19:27):
Start with the Y makes sense.
Speaker 2 (19:28):
Second, establish clear ethical principles. Ensure AI use is fair, transparent,
accountable and follows all the relevant.
Speaker 1 (19:37):
Laws and critically involves stakeholders right especially employees.
Speaker 2 (19:41):
Yes, absolutely engage them and others in the decision making
process for setting the rules around AI. Get diverse perspectives
in there from the outset.
Speaker 1 (19:50):
And it's not just about what the AI does, but
who's responsible exactly.
Speaker 2 (19:53):
Assign clear roles and responsibilities. Who designs it, who deploys it,
who maintains it, who is accountable if something goes wrong,
humans or AI or both?
Speaker 1 (20:02):
And training is vital too.
Speaker 2 (20:03):
Absolutely vital. Provide comprehensive training and ongoing support so staff
feel empowered, not threatened, to work effectively alongside AI.
Speaker 1 (20:12):
Fostering that culture of curiosity, a growth mindset, viewing AI
as a tool, an augmentation, not purely a replacement.
Speaker 2 (20:19):
That shift in perception is key.
Speaker 1 (20:21):
What else?
Speaker 2 (20:22):
Data security non negotiable. Implement robust measures to protect sensitive information.
Adherees strictly to data protection regulations like GDPR and feedback
put feedback loops in place. Get continuous input from employees
on how their AI interactions are going. That allows for
refinement and adaptation over time.
Speaker 1 (20:41):
Because the field changes.
Speaker 2 (20:42):
So fast, exactly, you have to review and update these
rules regularly. And underlying all of this should be a
human centered approach. Prioritize psychological safety, inclusivity, mental well being
support like that.
Speaker 1 (20:55):
Google Mindfulness program you mentioned.
Speaker 2 (20:57):
Yeah, initiatives like search inside yourself, things that support the
human side of the equation.
Speaker 1 (21:02):
Finally, there's this bigger policy debate happening globally, things like
the robot tax.
Speaker 2 (21:07):
Yeah, it's an intriguing idea. Bill Gates famously suggested it.
South Korea has actually taken some action lowering tax deductions
for business investments in automation.
Speaker 1 (21:16):
What's the core idea behind a robot tax?
Speaker 2 (21:18):
Essentially to try and rebalance the economic gains from automation,
use the revenue generated to fund crucial worker retraining initiatives
or other social safety nets to address the potential societal
disruption head on.
Speaker 1 (21:32):
It really emphasizes that this robot revolution isn't just a
tech shift, is it not at all?
Speaker 2 (21:37):
It's a profound societal evolution that demands really thoughtful, proactive
consideration from all of us individuals, companies, governments.
Speaker 1 (21:46):
Okay, so we've really navigated the intricate landscape of this
AI and robot revolution today.
Speaker 2 (21:51):
We have from the surprising scale of adoption happening right
now it's multi trillion dollar economic impact to.
Speaker 1 (21:58):
The profound transformation it's bringing to jobs, skills, even our
psychological well being.
Speaker 2 (22:04):
And we've unpacked those critical ethical considerations to trust, bias, privacy,
their paramount and how organizations are trying to navigate this
new reality with human centered strategies.
Speaker 1 (22:15):
So as these intelligent machines get increasingly capable performing not
just physical tasks but also really complex cognitive ones, it
raises a pretty important question for you listening right now.
Speaker 2 (22:24):
If AI can perform nearly all the routine stuff and
even many traditionally complex human functions, what unique value will
we as humans truly bring to the future workforce?
Speaker 1 (22:35):
Yeah, what does it even mean to work when your
colleagues might be tireless algorithms and robots, And how will
society adapt to define human purpose, human fulfillment, maybe beyond
traditional employment as we know it.
Speaker 2 (22:47):
It's definitely a thought worth mulling over as we continue
this deep dive into our accelerating future.
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