- Okay, let's dive right in.
Welcome to Crash News.
Today is May 22nd, 2025.
You just imagine this for a second.
A pocket-sized device, totally screen-free.
- Right.
- Designed by the person, Joni Ive,
who basically helped shape the look and feel
of the iPhone, the MacBook,
aiming to completely change how we use AI.
- That's a pretty wild concept.
- Or how about AI that digs through scientific papers,
lab data all on its own,
and points researchers toward
promising new medical treatments,
like speeding up drug discovery massively.
- Yeah, that's actually happening.
- And then there's the tech job market.
It's a bit stark right now.
Hiring for new grads, apparently down over 50%
since before the pandemic.
- A huge drop.
- But experienced AI engineers, they're like gold dust.
One company is apparently poaching talent
from the big players at an incredible rate.
- The competition is fierce.
- So welcome to today's deep dive.
We've got a stack of sources, industry reports,
company news, research updates, newsletters, the works.
- All focused on AI.
- Exactly, and our mission really
is to cut through all that noise
and give you the essential briefing
on what's actually moving the needle in AI, like right now.
- Think of it as your shortcut to staying current,
because things are moving so fast
across so many different fronts.
- They really are.
It's hardware, it's software models,
it's how AI is showing up in the real world,
even who's getting hired and where.
- It's a lot to track.
- Absolutely.
So speaking of those big moves,
let's start with maybe the biggest headline grabber.
OpenAI making a massive bet on hardware,
and they're doing it with Joni Ive.
- Yeah, this is huge news.
OpenAI has fully acquired IO,
that was the company born from their joint venture.
- Acquired them, okay.
- For a reported $6.5 billion in equity.
One source said 6.4 cents, another $6.5 billion,
but either way.
- That's a serious number for hardware.
- It absolutely signals they are dead serious
about moving beyond just software.
This isn't a side project.
- And bringing in Joni Ive,
I mean the design legend from Apple, close to Steve Jobs,
that sends a message, doesn't it?
They're aiming for something big, something mainstream.
- Definitely.
And Ive, through his firm love from,
he's going to oversee design for OpenAI's hardware
and software now.
- So he's deeply involved.
- Fully integrated.
And IO, the company itself, was only founded in 2024
with other Apple veterans.
About 55 of their engineers are joining OpenAI.
- Wow.
- Okay, so it's a whole team, a whole vision being absorbed.
- Exactly, it's a full integration
of top-tier hardware design into a leading AI company.
- So what are they actually trying to build,
this screen-free device?
What's the vision?
- Well, they're talking about a pocket-sized AI companion.
Screen-free is the key part.
- Right.
- And the ambition is kind of mind-blowing.
They're talking about potentially shipping
a hundred million units.
- A hundred million.
- And they're positioning it as maybe a third core device,
like alongside the iPhone and the MacBook,
or maybe even replacing some of their functions.
- A third core device, screen-free.
That's a fundamental shift
in how we think about personal tech.
- It is.
And the idea, they say, is to tackle the quote,
"Unintended consequences of smartphones."
- Like distraction maybe, endless scrolling.
- That seems to be the implication.
They want a more natural, more intuitive way
to interact with AI,
calling it a totally new kind of thing.
Sam Altman and Joni Ive apparently even called it
potentially the coolest piece of technology.
High praise from them.
Okay, and they're aiming for a 2026 launch.
That feels incredibly fast for something so different.
- It's super aggressive, no question.
But the key takeaway here for you listening is this.
When a major AI player like OpenAI
teams up with someone like Joni Ive
and bets billions on dedicated screen-free hardware,
it signals a serious potential shift
in how we interact with AI day to day.
It's moving AI beyond the screen,
maybe into something more ambient, more integrated.
- Which could change our habits,
our whole relationship with tech.
Okay, huge move.
But let's shift gears because it's not just about hardware.
Our sources are also showing just a flood of new AI models,
new tools, new capabilities hitting the market.
It's almost hard to keep up.
- It really is.
It's incredibly dynamic.
And what's interesting is how specialized
these models are becoming.
- Specialized how?
What are some examples from the sources?
- Well, one big area is coding agents.
Mistral AI, working with All Hands AI,
just released something called Devstral.
- Devstral, okay.
- It's an open source model
trained specifically for software engineering tasks.
It's relatively small, 24 billion parameters,
so maybe usable on a laptop.
And the key thing is it can understand context
across different files in a project.
- Okay, you used the term agentic model there.
We hear agent a lot.
What does that actually mean for coding?
It's more than just suggesting the next line of code, right?
- Exactly, that's the crucial difference.
A regular code completion tool.
you suggestions for the immediate line or function.
Right, like autocomplete.
Kind of. But an agentic model, like DevStraw is aiming to be,
is designed to act more autonomously. It tries to understand the bigger picture of your code,
navigate files, grasp the context, and potentially handle more complex tasks on its own.
Like what kind of tasks?
Things like finding bugs that span multiple parts of the code may be suggesting larger refactors,
or even generating whole chunks of code across several files based on a description.
DevStraw is showing good benchmark results, apparently,
and being open source is a big deal for developers, even though it's still a research preview.
So like having an AI assistant engineer.
Interesting. What about the creative side? The demos there are often pretty stunning.
Oh, absolutely. ByteDance has Bagel, a multimodal model for image generation,
putting it in the same league as things like GPT 4.0.
Okay. But Google's new video generator,
VO3, sounds really impressive. It can apparently create videos complete with sound effects and
even dialogue just from a text prompt. Yeah, I saw that example in the sources,
the pharma commercial creator. That really stood out.
Didn't it? This person apparently used to spend like half a million dollars on a commercial shoe.
$500,000. Yeah.
And they were able to generate one using VO3 in less than a day for about $500 in credits.
That's disruptive potential right there, reducing cost and time drastically in creative work.
It's potentially revolutionary for those fields. And there are other tools popping up too.
Google Flow for filmmaking, Luma Labs for camera motion effects,
Geometry Crafter, Triplane Turbo. Lots happening in creative AI generation.
And for the developers who want to build on top of these models,
what's happening with API Access? Access is definitely improving.
Anthropic, for instance, announced their upcoming Claude 4 Sonnet and Opus models will be available.
Okay. But Lambda's offering caught my eye.
They've launched an inference API with what they call unrestricted access to models like DeepSeek
V3, Llama 4 Maverick, even the big DeepSeek R1 671B.
Unrestricted. Yeah.
Their pitch is no rate limits, no tiers, no throttling, plus competitive pricing.
For developers needing reliable, scalable AI access without hitting surprise limits,
that could be really appealing. Got it.
So the models are getting better, more specialized, easier to access.
But how do people actually get good results? The source.
to seem to emphasize prompting a lot.
- Yeah, prompt optimization basically,
how you ask the AI for things is becoming super important.
It's a real skill.
The sources mentioned a tutorial covering five prompt types.
Things like summarize, reframe for a specific audience,
brainstorm ideas, compare things, and role play scenarios.
- Why is the way you ask so critical?
Can't you just type what you want?
- You can, but the quality you get back
really depends on how clear
and well-structured your prompt is.
It's like giving instructions to a person,
vague instructions lead to vague results.
Good prompting lets you specify the format,
the tone, any constraints,
even the personality you want the AI to adopt.
The sources basically say product teams
really need to focus on this
because good prompting makes the AI vastly more useful.
It's like learning a new way to communicate effectively.
- A new kind of literacy, makes sense.
Were there other little tools mentioned
that help people work with AI?
- Yeah, a few interesting ones briefly mentioned.
Tools like snap deck AI to make Figma slides from text,
something called unGPT
to make AI writing sound less robotic.
- Oh, that could be useful.
- Right, also idocs.ai-redact
for automatically blacking out sensitive info in documents,
respond.io for managing different communication channels,
studypal for learning,
and liner for finding citations for AI claims.
It shows a whole ecosystem building up around AI workflows.
- Okay, so tons of models, tons of tools,
but how do we even know which models are actually good?
How do we compare them?
- Ah, yes, benchmarking and evaluation.
That's a huge and actually quite tricky area right now.
- Tricky how?
- Well, the sources mentioned LM Arena
raising a hundred million dollars,
which shows there's big money
going into figuring out evaluation.
But there are ongoing debates
about whether the leaderboards are fair
or if they truly reflect how well these models work
in the real world.
- Right, like teaching to the test.
- Exactly, and models are evolving so fast,
gaining new abilities like understanding images and video,
that the tests themselves struggle to keep up.
It's a moving target.
- So even how we measure progress
is still kind of being figured out.
Okay, let's shift focus a bit.
From the tech to the people, the talent building AI
and the trust needed to use it.
The sources had some really interesting,
maybe sobering stats on the job market.
- Yeah, the SignalFire report painted a stark picture,
new grad hiring in tech generally.
down significantly over 50%.
from pre-pandemic levels.
- Wow, that's tough for people starting out.
- It is, but at the same time,
the demand for experienced engineers,
especially those with real AI skills, is incredibly high.
They're in huge demand.
- So it's a tale of two markets almost.
Is there a specific talent war heating up
between the big AI labs?
- Seems like it.
The report highlighted that companies really focused on AI,
like Anthropic, are doing remarkably well
at keeping their employees, high loyalty.
- Okay.
- And here's the really eye-popping stat.
Apparently, talent is eight times more likely
to move to Anthropic from places like open AI
or DeepMind than the other way around.
- Eight times?
- Yeah, it's-- - That's a massive differential.
What does that suggest?
Something Anthropic is doing right
or maybe something the others aren't?
- It certainly suggests Anthropic has built
a very attractive environment for top AI talent right now.
For you, our listener, perhaps someone in tech
or thinking about it, what does this mean?
- Yeah, what's the takeaway?
- It really highlights the premium being placed
on deep, practical AI experience.
While getting that first general tech job
might be harder for new grads now,
specializing in AI development, research or application
could put you in a very strong position.
And for the industry, this talent flow really shapes
who's likely to lead the next wave of innovation.
- Yeah, makes sense.
Talent follows the most interesting work, often.
Okay, beyond attracting talent,
there's the challenge of building trust in AI.
That includes security, right?
Prompt injection came up.
- It did.
Prompt injection is a major headache.
It's basically when someone tricks the AI
by hiding malicious instructions
inside a normal-looking prompt,
trying to get it to bypass its safety rules
or reveal things it shouldn't.
- Right, that have vulnerability.
- Exactly.
Google DeepMind has been working hard
on making their models more resistant to this,
using techniques like adversarial training for Gemini 2.5,
basically training the AI on tricky prompts
to make it tougher.
- And is it working?
Are the defenses getting better?
- According to their research, yes, significantly better.
They reported that combining different defense techniques
dropped the success rate of these attacks on Gemini 2.5
from almost 95%.
- That high, wow.
- Down to just over 6%.
That's a huge improvement and a really important step
for making these powerful models safer to use widely.
- Definitely need that trust.
And related to trust is knowing what's real, right?
Distinguishing AI-generated stuff.
- Yes, the authenticity issue.
As AI gets better at creating text, images,
even video that looks totally different,
really real, we need ways to tell what's human made and what's not. The sources mentioned
tools like Google's Syntheday Detector, which aims to help identify AI generated content.
That's crucial for tackling misinformation and maintaining trust online.
Absolutely. Okay, let's zoom out a bit. We've talked hardware, models, talent, trust. How
is all this AI actually showing up in the real world now? What tangible impacts are
we seeing?
As soon as some really concrete applications emerge, take Medical Discovery, a company
called Future House, used what they call an autonomous AI system.
Autonomous meaning?
Meaning the AI did parts of the research process itself, analyzing scientific literature, experimental
data. And through that process, it identified an existing drug, Rupasudol, as potentially
promising for treating age-related blindness. They're releasing their results publicly soon,
May 27th. It just shows how AI can really accelerate finding new scientific leads.
And AI finding potential drug candidates.
Yeah.
That's incredible potential. What about in the physical world? Robotics?
Tesla's optimist robot is making progress there. Recent demos showed it doing basic
household chores, sweeping, vacuuming, tearing paper towels, stirring things, opening cabinets.
Stuff around the house, yeah.
And what's interesting is how it's learning. They're training its neural network using
videos recorded from a first-person human perspective.
So it learns by watching humans do tasks?
Essentially, yes. And the plan is to expand that to learning from third-person videos
and even just general videos from the internet. It's a fascinating approach to teaching robots
physical skills.
So AI is not just thinking, it's learning to do physically. And it's getting embedded
in other daily items too.
Definitely. Google's working with Samsung on AI-powered smart glasses. They're aiming
for 2026 running something called Android XR.
Smart glasses. Again.
Yeah. But maybe with more AI focus this time, they might have options like an in-lens display,
real-time translation, interpreting what you're seeing, embedding AI right into your view
of the world. And Volvo is putting Google's Gemini assistant into their cars. So AI is
moving into our vehicles, our eyewear, becoming part of the environment.
So it's becoming less of a destination we go to on a screen and more part of the fabric
around us.
What about the impact on businesses, the economy? Eric Schmidt's views were mentioned.
Right. The former Google CEO. He's been...
arguing quite forcefully that AI is actually under-hyped, not over-hyped.
Under-hyped.
Really.
With all the buzz.
His point is that its potential goes way beyond what we're seeing now.
He talks about AI agents being able to manage entire business processes end to end just
using natural language instructions.
Manage whole processes.
Yeah.
And he suggests that could lead to efficiency gains of maybe 90 percent in some areas.
The 90 percent.
That would fundamentally change how businesses operate.
Absolutely.
It would reshape industries.
And Schmidt connects this to bigger strategic issues, too.
He warns about a serious AI arms race between the U.S. and China for technological dominance.
An arms race.
Yeah.
And he also points out the massive increase in computing power that future AI will need
maybe a hundred to a thousand times what we have now.
These aren't just technical forecasts, they're about global competition and economic power.
So the stakes are incredibly high geopolitically and economically.
The sources also touched on this idea of an AI trough of disillusionment.
What's that about?
Uh.
That comes from the Gartner hype cycle idea.
After the initial peak of excitement, there's often a period where progress seems to slow
down, challenges become clearer, and some of the initial hype fades.
Right.
Reality sets in.
Exactly.
And the discussion is around what it takes to get through that phase and make AI work
sustainably.
Things like strong leadership, continued investment in research labs, and importantly, involving
the crowd users providing data, feedback, real world testing.
So keeping the momentum going through the hard part.
Precisely.
There was also that interesting, maybe slightly provocative mention of Sean Pury's idea that
the next Zuckerberg might use AI companions to tackle loneliness.
AI companions for loneliness.
Wow.
It suggests AI could start impacting really fundamental human social needs, for better
or worse.
So let's connect these big picture ideas back to you, the listener.
Yeah.
How does talk of an AI arms race or a trough of disillusionment or these super efficient
AI agents, how does that affect you personally?
Well, these large scale trends shape everything.
National focus on an arms race means huge government and private investment in AI R&D,
which influences technology direction and job creation.
Okay.
Navigating a trough means the focus shifts to practical, valuable applications.
where many job opportunities might actually lie in making AI useful day to day.
and AI agents automating business processes,
that could fundamentally change the nature of work.
- How so?
- It might shift jobs away from performing routine tasks
towards managing, overseeing,
and directing these AI systems.
Skills like prompt engineering,
strategic thinking about AI deployment, ethical oversight,
these could become much more crucial.
It forces all of us really to think about how we adapt
and what skills we need in a world where AI
is becoming more integrated and autonomous.
- Right, it's not just about using the tools,
but shaping how they're used.
- Yeah.
- We've got a ton today.
Open AI and Joanie Ive's big hardware play.
- A huge bet.
- The absolute explosion of new AI models
and tools for developers, for creatives, for everyone.
- The pace is relentless.
- The dynamics in the AI talent market, tough for some,
huge demand for others.
- A real shift happening there.
- Plus the critical work on trust, security,
detecting AI content.
- Essential foundations.
- And then all these real world applications popping up
from medicine to robotics to smart classes,
alongside these massive strategic questions
about the economy and global competition.
- Yeah, putting it all together from these sources,
the picture is one of really rapid fundamental change.
It's not just tech updates.
It's shifts in industry structure, jobs,
and potentially how we live and interact.
- Absolutely.
And look, if you found this deep dive valuable,
if you appreciate us trying to connect these dots for you.
- Which we hope you do.
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It lets us keep digging into these sources
and bringing you this analysis.
- So to leave you with something to chew on,
given everything we've talked about,
the speed, the talent race, the push towards new forms,
like screen-free AI, AI in your glasses, AI driving cars.
As AI shifts from being this thing on our screens
to potentially being this always-on, context-aware presence
woven into our lives,
what's the biggest unintended consequence
we should be thinking hardest about right now?
What happens when AI is truly ubiquitous?
- That's a really important question to ponder
as all this unfolds.
- Definitely something to think about.
Thanks for joining us for this deep dive.