Hey everyone and welcome back for another deep dive.
Today we're looking at this story that has everyone talking in the AI world a Chinese
startup called DeepSeek. They've seemingly done the impossible matching AI giants like OpenAI.
But they're doing it without the billion dollar budgets.
So how did they manage to pull this off? What does it mean for the future of AI?
Yeah, it's really interesting to find out.
The truly remarkable thing about DeepSeek is they haven't actually come up with some brand new
revolutionary technology. What they've done is they've taken existing methods and resources
and through some really clever optimization they've achieved results that actually rival the industry
giants. Okay, so let's break down what they've built. DeepSeek has released two major AI models,
Janus Pro and R1. Now Janus Pro is this incredible multimodal model, meaning it can work with different
types of data images and text. Exactly. It can generate images, analyze images,
and even handle your typical text-based AI tasks. It can do it all.
It's like a Swiss army knife for AI. Yes, very versatile.
It can tackle all sorts of jobs. And then there's R1, which is specifically designed to understand
and generate human-like text. Okay. It's going head-to-head with models like GPT-4 in terms of
performance. Wow. But here's the thing, they developed R1 for only five to six million dollars.
Wait, really? Yeah, think about that for a second. Okay.
Major AI labs are pouring billions into similar projects.
So you're saying DeepSeek is producing similar results with a fraction of the resources.
Exactly. That's got to have some people worried, right?
Oh, absolutely. Especially companies like NVIDIA who make those super expensive top-of-the-line
chips that everyone thought were essential for AI development.
Very right. If DeepSeek can get these results without those, what happens to them?
Well, when news of DeepSeek's efficiency hit the market, NVIDIA's stock took a huge hit,
like a $600 billion drop in a single day. Oh my God. Investors started to worry.
If DeepSeek can do this without the most expensive hardware, what does that mean for the demand for
these chips going forward? That's crazy. Imagine being in those boardrooms when that news came out.
I know, right? Talk about a reality check. Big time.
But the big question is, is this just a fluke? Right. Or is this a sign of a fundamental shift
in how we think about AI development? That is the million-dollar question, isn't it?
Yeah. Some experts believe DeepSeek is leading the way for a new era, an era where smaller,
more agile teams can actually compete with those established companies.
And they're showing that you don't need billions of dollars to make these big strides in AI.
So they're really challenging the foundation of the AI industry. They really are.
How are the big players responding to this? Are they just watching this happen?
Well, OpenAI, the company behind GPT-4, they've acknowledged DeepSeek's accomplishments,
but they are maintaining their commitment to a resource-intensive approach. Their CEO, Sam Altman,
argues that their huge investments are needed to keep pushing the limits of AI.
Interesting. So they're not backing down from that resource race just yet.
So it's almost like we have two different philosophies clashing here.
Yeah. DeepSeek is saying we can do more with less.
Exactly. And OpenAI is saying go big or go home.
Right. It's going to be really interesting to see how this plays out.
I agree. But let's go back to Janice Pro for a moment.
Sure. You said it's multimodal.
Yes. So it can work with both images and text.
That's right. That's pretty unique, right?
It is. Most AI models seem to specialize in one or the other.
Yeah. Janice Pro's versatility makes it stand out.
Okay. It can do all sorts of image analysis tasks.
Like what?
Well, it can accurately describe objects and their relationships in an image.
It can also struggle with more abstract concepts, like metaphors, but then models like GPT-4 vision.
They're better at grasping those deeper meanings in images.
So it sounds like Janice Pro is still a work in progress, but it has a ton of potential.
Yeah. For sure.
And here's where things get even more interesting.
Okay.
Deepseek decided to make Janice Pro open source.
Wow.
So they've made the code available for anyone to access.
That's a big deal.
Modify and even build on.
It is.
That's a pretty bold move, especially when you consider that companies like OpenAI
keep their code under lock and key.
Yeah. That open source approach is one of the things that has people so excited about Janice Pro.
I can see why.
Imagine a community of developers all over the world coming together,
tinkering with the code, sharing their ideas, and then collectively pushing the limits of what this
model can do.
That's amazing.
It's completely different from that closed-door development you see at a lot of tech companies.
So Deepseek is basically saying, hey, here are the tools.
Let's see what we can build together.
Exactly.
It's like a giant experiment.
It is.
And the possibilities are endless.
They really are.
I read somewhere that someone actually put Janice Pro up against stable diffusion,
which is another popular image generating AI.
I know it.
And they asked both of them to create an image of a cute baby fox in an autumn scene.
That's a good one.
I know, right?
What happened?
Well, Janice pretty did OK.
OK.
It got the baby part of the prompt.
Yeah.
But stable diffusion ended up creating a much better-looking image.
So it's like we're seeing a trade-off faithfulness to the prompt versus the overall artistic quality.
That's an interesting point.
So it seems like Janice Pro needs to improve its artistic side a bit.
Yeah.
But with the open-source community helping out, who knows how quickly it could catch up.
Exactly.
That's what's so great about open-source.
It allows for quick improvements and changes.
What might take one company months or years to develop?
It could happen so much faster with a global community working together.
That's really cool.
It is.
Speaking of exciting things, DeepSeq's AI Assistant app is causing quite a stir.
It is.
This thing's shot to the top of Apple's App Store in the US.
Wow.
It even beat ChatGPC in popularity.
That's huge.
That really shows how interested people are in these AI tools.
Definitely.
People really want to see what these technologies can do.
Yeah.
And use them in their everyday lives.
It seems like AI is really going mainstream.
I think you're right.
DeepSeq getting such a huge audience so quickly says a lot about where AI is going.
Of course, with that many new users.
Yeah.
There were some issues.
Like what?
Some reports of temporary outages.
Oh yeah.
Some glitches.
That happens.
But that's almost expected in the tech world these days.
Yeah.
It's a good problem to have.
It means you've really made something people want.
Exactly.
Any publicity is good publicity.
Right.
And in this case, it's definitely true.
Yeah.
DeepSeq's app is the talk of the town.
Yeah.
And it really makes them stand out in the AI world.
But here's where things get a little mysterious.
Okay.
I like a good mystery.
Despite all this success, DeepSeq is still relatively unknown.
They were only founded last year in 2023.
Wow.
So new.
Yeah.
And they're based in Hong Kong.
Interesting.
So naturally, people are curious.
Yeah.
Who are these guys?
Yeah.
Where do they come from?
How did they do all this so fast?
Yeah.
It's like they came out of nowhere.
And just disrupted the whole AI industry.
It's true.
And that's where we'll leave you hanging for now.
Oh, no.
Don't worry.
We'll be back in part two to dig deeper into the mysteries
surrounding DeepSeq.
Okay, good.
And we'll explore what their sudden rise could mean.
Can't wait.
Stay tuned.
I will.
Welcome back.
Before the break, we were talking about DeepSeq's incredible rise
and the shockwaves they've sent through the AI world.
It's been pretty wild.
They've pulled off these amazing AI developments
for a fraction of what it normally costs.
Yeah.
And that's got people wondering,
could this be a sign of things to come?
Yeah.
Could we be seeing a new era of AI development
that's more efficient and cost effective?
There are definitely people who think so.
They believe that DeepSeq's success shows a new path forward.
Okay.
A path where smaller, more agile teams can compete with the big guys.
I like it.
By using clever techniques and open source resources.
And speaking of open source.
Yeah.
We can't forget about Janice Pro and what that could mean.
Right.
That decision to make Janice Pro open source
could have a huge impact on the development community.
It could change the game entirely.
Think about it.
A community of developers from all over the world.
Yeah.
They're not limited by the profit motives of those big corporations.
Right.
And they're all working together to push the limits of AI.
Creating these powerful tools and technologies that anyone can use.
That's a pretty amazing vision.
It is.
But some people argue that while DeepSeq's achievements are impressive.
Yeah.
They might not be easy to copy or keep up in the long run.
Yeah.
That's the other side of the coin.
The amount of data and computing power you need to train and use AI models globally is huge.
Exactly.
Companies like Meta have said that if you want to serve billions of users.
Yeah.
You need serious infrastructure and a ton of computing power.
So it's more than just making the model.
Oh yeah.
It's also making sure it works for everyone everywhere.
Exactly.
It has to be reliable and accessible on a global scale.
So DeepSeq's approach is super innovative.
Oh yeah.
Totally.
But can it really handle a global market?
Good question.
It's one thing to have a model that does well in a test environment.
Right.
But it's another thing entirely to make sure it's stable and reliable
when millions of people are using it at the same time.
Absolutely.
And then there's the geopolitical side of things to consider.
Oh yeah.
That's right.
President Trump has called on American tech companies to step up their game.
In response to DeepSeq.
Exactly.
He sees DeepSeq as a real wake up call.
He does.
He's saying that the US has to stay on top in AI.
That's interesting because this comes at a time when there's a lot of talk about restricting
chip exports to China.
It's like they're trying to slow down Chinese progress in AI.
Right.
But DeepSeq seems to have found a way around these restrictions.
Oh really?
How?
They're using Nvidia's H800 chips which are technically less powerful than those top tier
chips that are restricted.
So DeepSeq is getting similar results with chips that aren't even the most advanced.
They are.
That kind of undermines the idea that you need the best hardware to be competitive in AI.
It does.
DeepSeq's approach shows that being clever and innovative can sometimes be more important
than having a ton of resources.
So that brings us back to that big question.
Okay.
Can DeepSeq's way of doing things really work for the future of AI?
It's tough to say for sure.
Yeah.
DeepSeq has definitely shaken things up.
They have.
Everyone's rethinking their strategies now.
They've shown that you can get amazing results without spending a fortune.
But can it scale up?
Can it keep innovating?
We'll have to wait and see.
I think that's a fair assessment.
And there's been some skepticism about DeepSeq's claims too.
Especially about the $5.6 million development cost for R1.
Ah, yeah. Some experts say that number probably just covers the final training phase.
Oh, okay.
And that it doesn't include all the early research and data work that went into it.
So the real cost could be a lot higher.
Possibly.
But even if it was a few times higher, it could still be way less than what the big tech companies
usually spend.
So how did they do it?
What's their secret?
Well, DeepSeq says it's all about their innovative training techniques.
Okay.
They claim they found ways to make the model focus only on the most important data.
And that saves a lot of computing power.
Sort.
And they also used existing open source projects from companies like Alibaba and Meta.
Oh, wow.
They built on those foundations and adapted them for their own needs.
So they're really taking advantage of open source.
They are. It's a smart move.
It really shows how powerful the open source movement can be.
It does. Developers can build on each other's work.
And that speeds up innovation like crazy.
It's like that saying standing on the shoulders of giants.
Exactly.
But using those open source projects, especially ones from Western companies,
Yeah.
has been a little controversial.
Right. Some people say DeepSeq is basically writing on the coattails of others.
And they're questioning whether that's fair play.
It raises questions about intellectual property and competition.
It does. And it really shows how complicated open source development can be.
Yeah. You want to encourage collaboration and progress.
Right.
But you also need to make sure everyone gets credit for their work.
Exactly. And that no one is taking advantage of the system.
It's a tough balance to strike.
It is.
And then of course, there's the ethical side of all of this.
Oh, right. The big picture stuff.
Stuart Russell, a leading AI researcher at UC Berkeley.
I know him.
He's worried about an unchecked AI arms race.
Yeah. She's been saying that for a while.
He says that the race to create artificial general intelligence, or AGI, is really risky.
It could be.
Because we're basically trying to build systems that can become smarter than humans.
And potentially take control.
Exactly. And that's a scary thought.
It is. It makes you think about the future of humanity.
And our relationship with technology.
Definitely.
And if we bring this back to DeepSeq,
their rapid rise and their ability to do so much with so little money,
it could actually make this arms race even faster.
Oh, so.
It might push other companies to try to find even cheaper and faster ways to develop AI.
Oh, I see. So we could see a flood of new powerful AI systems.
That's a real possibility.
And it's something we need to think about carefully.
We do. We need to ask ourselves, what happens if AI development keeps accelerating at this pace?
Are we ready for the challenges that might bring?
Big questions.
You are.
And they touch on everything from society and the economy to ethics.
A lot to consider.
It is. And these are questions we'll keep asking is AI keeps evolving.
I think that's a given.
But let's pause here.
Okay.
And we'll come back for the third and final part of this deep dive.
Sounds good.
We'll wrap up our look at DeepSeq and talk about what their story might mean for the future of AI.
Looking forward to it.
Stick with us.
I will.
We're back.
And we've been on this wild ride exploring DeepSeq's incredible rise in the AI world.
Yeah, it's been a fascinating deep dive for sure.
They've really shown how efficient AI development can be.
Right.
But along the way we found some interesting questions and concerns.
There's always more to uncover, right?
So much to think about.
What stands out to you is the most impactful thing about DeepSeq so far.
Hmm. That's a tough one.
I know, right? There's so much to choose from.
Yeah. What's the big takeaway here for you?
I think it's how DeepSeq has challenged the way we think about AI development.
For so long we just assumed that only the companies with tons of money like Google and Microsoft.
We're one of the big players.
Exactly. They were the only ones who could play in this space.
But DeepSeq showed us that sometimes being smart and innovative
is more important than just throwing money at the problem.
Yeah. I think their boldness is what really caught everyone's attention.
I agree.
This little known startup comes out of nowhere.
Yeah.
And shakes up the whole industry.
It's like they flipped the script.
Totally. And now everyone's trying to figure out what to do.
Yeah. How to adapt.
Exactly.
It's like something out of a movie.
It really is.
The underdog taking on the giants and showing that it can be done.
And it's not just the tech itself.
Right.
It's how they went about it.
Their whole approach.
Exactly. Open sourcing Janice Pro was a big move.
It was.
This shows they really believe in collaboration
and that community driven innovation can work.
It's like they're saying we're not hiding our knowledge.
Right.
We're sharing it.
Let's all work together and see what we can create.
So now the big question is what does this mean for the future?
Okay.
Is this the start of something new?
Will we see a shift to more open and collaborative AI development?
I think it's definitely possible.
Okay.
Deep-seek success could inspire a whole new wave of AI startups
and researchers who want to focus on efficiency.
Right.
Using resources wisely and working together openly.
They've shown that you don't have to have billions of dollars.
Right.
To make a real difference in this field.
But some people say that Deep-seek's approach just won't work in the long run.
Yeah. I've heard that too.
They say you still need a ton of resources to make these models work for everyone.
To make them accessible to billions of users around the world.
Exactly.
So how do we reconcile those two viewpoints?
That's the challenge, isn't it?
It is.
Scaling these models and making them work for everyone is a huge hurdle.
Yeah.
And serving a global audience requires a lot of infrastructure and resources.
It does.
But Deep-seek's approach could lead to new ways of doing things.
Okay.
More efficient ways to scale AI.
They've already shown they can achieve amazing results with limited resources.
They have.
Who's to say they can't do the same for scaling?
It's an exciting thought.
It is if they can figure that out.
We could see a future where really powerful AI tools are available to everyone.
Yeah. Not just the big companies.
Exactly.
And that could change everything.
It could impact education.
Healthcare.
Scientific research.
Even social good initiatives.
It's really exciting to think about.
But it's important to remember that we're still at the beginning.
Oh, for sure. This is just the start.
There are a lot of challenges ahead.
Absolutely.
Both technical challenges.
And the sidelines.
As AI continues to evolve, we need to have serious conversations about how to develop and use it responsibly.
I think that's crucial.
We need to think about the ethical implications.
Right.
The impact on jobs.
Yeah.
And how to prevent misuse.
These are big questions.
Huge ones.
And it's clear that DeepSeek's story is just one part of a much bigger story.
It is.
The world of AI is changing so fast.
It's hard to keep up.
And we're just starting to understand what it all means.
Yeah.
But one thing is for sure.
What's that?
DeepSeek has changed the game.
They have.
And their impact will be felt for years to come.
So as we wrap up this deep dive, here's something for you to think about.
Okay. I'm listening.
What if DeepSeek's success isn't just about doing more with less?
Yes.
What if it's a sign of a whole new wave of innovation in AI?
Coming from unexpected places.
Exactly.
What if the future of AI is less about brute force and more about being clever.
And collaborating.
Working together.
And being willing to challenge the way things have always been done.
Food for thought.
Right.
Definitely a lot to ponder.
Thanks for joining us as we explore this fascinating story.
It's been a pleasure.
We hope you found it informative and thought-provoking.
I know he did.
Until next time, keep exploring.
Keep questioning and keep learning.
I will see you next time.