Okay, so we got a lot to unpack today.
You've sent over a really interesting mix of articles.
Everything from Amazon's AI strategy to clear reactors.
Yeah, clear reactors.
I mean, it's pretty wild.
Who saw that coming?
It is an unexpected turn.
Yeah. Right.
But I think it highlights how AI is impacting not just the tech world,
but like energy production and, you know, all of these things are.
It's all connected.
Inacted. Totally.
So today, our mission is to kind of untangle these connections
to figure out who the major players are.
And most importantly, separate the AI hype from reality.
Yeah.
And I think a good place to start is with Amazon.
Okay.
One article you shared suggests that AWS, their cloud computing arm,
might have fumbled a bit in the generative AI race.
Yeah, that's right.
While companies like OpenAI were making headlines with chat GPT, AWS
seemed to be a little slow off the mark.
And, you know, this wasn't just about missing a trend.
It sounds like they may have underestimated the demand for these tools.
And then to make matters worse, they lost a key player, Matt Wood,
a top AI executive who recently left AWS.
Okay. So not just missing the boat,
but maybe some internal turmoil too.
Right.
Not a good look for them.
So how is AWS responding to this?
Well, their new CEO, Matt Garmin,
seems to be aware of the need to catch up.
Okay.
They're aggressively acquiring AI startups like Anthropic,
which is known for its work on the safer AI development.
And they've been investing heavily in their own large language models,
even claiming to have a multi-billion dollar generative AI business already.
Wow.
They're also promising cheaper and faster AI tools for developers
trying to position themselves as the more cost effective option.
So it's like they're trying to make up for a lost time with a full on AI blitz.
The big question is, is it going to be enough?
Can they really catch up after such a late start?
Yeah.
Especially when the technology is evolving so rapidly.
It's a classic case of better late than never,
but whether it's enough to reclaim the top spot remains to be seen.
Especially since their solution to powering all this AI
involves a pretty controversial move, nuclear power.
Well, nuclear power, that seems a little extreme,
even for a company like Amazon.
Right. But here's the thing.
AI, particularly the kind of large scale AI
that companies like Amazon and Google are building,
requires a massive amount of energy.
Period.
And they're looking for ways to power these systems
in a way that's both sustainable and reliable.
OK, I get the need for energy, but why nuclear?
I feel like there are other.
Let's say controversial options out there.
Why not just go all in on solar or wind power?
Those are certainly part of the mix,
but the reality is that renewable energy sources like solar and wind
can be intermittent.
Right.
The sun doesn't always shine, the wind doesn't always blow.
And for something as data intensive as AI,
a consistent and reliable power source is essential.
Yeah.
That's where these companies see nuclear power fitting in.
So they're saying it's about reliability,
but are there specific types of nuclear power
they're looking at?
Because when most people hear nuclear, they think Chernobyl.
Yeah.
And that's not exactly reassuring.
You're right.
And both Amazon and Google have been investing
in what they're calling a new breed of nuclear reactors
called small modular reactors.
Or SMRs.
Oh, OK.
The idea is that these reactors are smaller, more efficient,
and potentially safer than traditional nuclear plants.
They argue that this technology can provide
a consistent carbon-free energy source
to meet the growing demands of AI and other data-heavy technologies.
OK, so smaller, more efficient, potentially safer.
That sounds good on paper.
But I can't imagine it's that simple.
Yeah.
I mean, are these SMRs even a proven technology?
Right.
You hit on some of the big questions there.
SMRs are a relatively new technology.
And while there are pilot projects underway,
they haven't been deployed on a large scale yet.
OK.
So there's definitely a degree of uncertainty
about their feasibility and their actual safety record.
And of course, there's the whole issue of public perception.
Right.
Nuclear power has a lot of baggage.
And it's hard to imagine that just shrinking the reactors down
is going to magically erase those concerns.
Absolutely.
Gaining public trust and navigating regulatory hurdles
will be major challenges for Amazon and Google
as they pursue this path.
Yeah.
It's a gamble, for sure.
But it highlights the lengths they're
willing to go to secure their position in the AI race.
But speaking of the AI landscape,
while the big players are making these massive bets
on nuclear-powered data centers, there's
another trend emerging that's worth exploring.
Small language models.
Or SLMs.
Right.
Those were mentioned in one of the articles, too.
Can you remind me what those are all about?
Yeah, so think of them as specialized AI tools.
OK.
Instead of trying to do everything like chatGBT or Google's
barred SLMs, our laser focused on specific tasks
like translating languages.
Generating different kinds of creative text formats.
Or writing different kinds of code.
And because they're more focused,
they require less computing power,
making them cheaper to develop and deploy.
OK.
So instead of one giant AI brain trying to do it all,
we have these smaller, more specialized AI's.
Exactly.
That actually makes a lot of sense.
What are some examples of how these SLMs are being
used in the real world?
One fascinating example is a company called Mistral.
OK.
They've created a suite of SLMs called
Le Ministreux, that are designed to run on everyday devices
like laptops and smartphones.
Wow.
These models are tailored for tasks
like writing, marketing, copy generating code,
and specific programming languages.
Or even creating personalized music.
Wow.
The fact that they can run locally on your device
means you don't need a massive data center
to use them, making AI more accessible and affordable.
AI in your pocket.
Yeah.
That's pretty amazing.
But what does this shift towards smaller, more specialized AI
mean for the average person?
Are we talking about a future where everyone
has their own personal AI assistant?
Or is this more about businesses using AI
in new and creative ways?
It's probably a bit of both.
On the one hand, we're already seeing AI systems become
more commonplace in our smartphones and smart homes.
These SLMs could make those systems even more powerful
and personalized.
But on a broader level, this trend towards specialization
could be really transformative for businesses,
especially smaller companies or startups.
So instead of needing massive budgets
to develop their own AI companies,
we could just plug in these specialized SLMs
to automate specific tasks or improve their existing workflows.
I can see how that would be a game changer.
Exactly.
It lowers the barrier to entry for AI adoption.
And it allows for more targeted and efficient use
of this technology.
And this brings us to another crucial question.
The future of work.
Yeah.
It feels like every time we talk about AI,
that topic looms large.
Is this where we finally see widespread job displacement?
It's a valid concern.
And it's definitely something we need to be mindful of.
But I think it's not necessarily
about AI replacing jobs outright.
It's more nuanced than that.
Remember those neural symbolic AI systems
we talked about earlier, the ones designed to mimic
the reasoning of a lawyer?
Right, the ones that could potentially revolutionize
legal work.
But you're saying it's bigger than just one profession.
Exactly.
This type of AI, which combines intuitive reasoning
with large scale data processing.
Has implications for any job that
requires analysis decision making?
Problem solving.
You know, that's a lot of jobs.
Yeah.
But instead of thinking about it as a job apocalypse.
What if we considered it an opportunity
to kind of rethink how we work all together?
So instead of AI taking over jobs,
it's more about AI augmenting human capabilities.
Precisely.
Imagine a world where AI handles the tedious repetitive tasks
freeing up humans to focus on more creative strategic
or interpersonal aspects of their work.
This could lead to entirely new job categories
we haven't even imagined yet.
That's a more optimistic outlook.
But it also raises the question of control.
If AI is going to play such a significant role in our lives,
who gets to decide how it's deployed and regulated?
Right.
And this is where things get really interesting.
While companies like Amazon and Google
are making these massive investments in AI,
there's a growing movement advocating
for a more decentralized approach.
One of the articles you shared talked about a platform
called Assister, which uses blockchain technology
to create a network of these SLMs.
OK, blockchain.
I'm going to need a little help understanding how that fits
into all of this.
So imagine a system where communities, not just corporations,
can own, manage, and even profit from AI development.
That's the potential of blockchain in this space.
Assister, for example, allows developers
to contribute their SLMs to a shared network.
And then users can access those models
through a blockchain-based system.
OK.
This means that instead of a handful of tech giants
controlling the AI landscape.
We could see a more open and collaborative ecosystem
where the benefits of AI are more widely distributed.
So instead of having to rely on Amazon or Google
for AI services, you could have a decentralized network
where anyone can contribute and everyone can benefit.
Right.
That's a pretty compelling vision.
It is.
And it speaks to the larger question of what kind of future
we want to build with this technology.
Do we want a future where AI reinforces
existing power structures?
Or do we want a future where AI empowers
individuals and communities?
Right.
These are not easy questions, but they're questions
we need to be asking ourselves.
Because the future is being written in algorithms,
as they say.
Exactly.
And the more informed we all are,
the better equipped will be to shape that future.
Well said.
So to our listener out there who's been on this deep dive
with us today, here's something to think about.
As you go about your day, pay attention
to where AI is already present in your life,
where you think it might pop up next.
From the apps on your phone to the way you work,
AI is becoming increasingly integrated
into the fabric of our lives.
The choices we make today, both individually and collectively,
will determine the trajectory of this technology
and its impact on our world.