Welcome to the Deep Dive. We're really glad you're
joining us today, ready to take a closer look
at some significant developments happening right
now in the fast -moving world of AI. Yep, definitely
fast -moving. We've got this stack of sources
here. It's a mix of recent news excerpts, a couple
of market reports, and some quick highlights
pulled from various places. That's right. And
our mission for this Deep Dive is basically to
unpack these pieces of news, get past just the
headlines. and figure out what's the most important
stuff buried within them. What does it really
mean? Exactly. What does it really mean for the
evolving AI landscape that you're navigating?
Right. So we're going to dig into the details
that matter. We'll be talking about OpenAI's
new model that's quite the brainiac, apparently.
Some pretty interesting moves Google is making,
not just in video, but also... kind of surprisingly,
in government efficiency. And yeah, honestly,
a bunch of other fascinating tidbits that really
show us, you know, where things are heading and
the sheer breadth of AI's impact right now. It's
pretty wild. Okay, let's unpack this stack. First
up, the big news coming out of OpenAI. They have
just unveiled a new model. It's called O3 Pro.
Right, O3 Pro. And what's fascinating here, and
the sources make this super clear, is its core
identity. This model isn't positioned as another
lightning -fast chatbot built for casual conversation.
It's specifically described as a reasoning -focused
AI model. It's an evolution from their previous
O3, but it's fundamentally engineered with thinking
and logic as its primary function. reasoning
first yeah i kind of love that framing built
for thinking and the source material really highlights
where this focus pays off we're not talking about
like writing a quick email here no definitely
not the areas it shines in uh are quite demanding
coding math science complex academic writing
and detailed business analysis these are places
where you know you need robust step -by -step
logic not just speed right and it's not just
some sort of uh experimental model being tested
in a lab somewhere it's actually already rolled
out Oh, really? Yeah, as of Juneteenth, it replaced
O1 Pro in their ChatGPT Pro and Team plans. So
if you're using one of those, you now have access
to this model. It's also available via their
API, and the sources give specific pricing. Let's
see, $20 million input tokens and $80 per million
output tokens. Okay, so the reasoning first approach.
How does that actually translate into how the
model works? What's the, like, technical difference
the sources hint at? Well, this raises an important
point about model architecture and design philosophy,
right? Instead of simply predicting the next
most statistically probable word or phrase in
a rapid sequence, which is how many models achieve
speed O3 pro, is designed to process information
more deliberately. It's built to simulate a step
by step problem solving process. It kind of thinks
things through methodically. That's the fundamental
difference driving its capabilities in those
complex domains we just mentioned. And the sources
give us some pretty compelling proof points that
this isn't just marketing fluff. They show benchmarks
where it, well, frankly, outsmarts some of the
top competitors on specific difficult tests.
Yes, the benchmark results provided are quite
telling and really give weight to that reasoning
-focused claim. For instance, it beat Google's
Gemini 2 .5 Pro on the AIME 2024 math test. Amy,
and that's like serious math, right? Yeah. Amy
is the American Invitational Mathematics Examination.
It's a very challenging competition math test,
way beyond simple arithmetic. Beating a top competitor
there is significant. Oh, wow. So not just high
school math, but like competitive math. Exactly.
And it also beat Claude For Opus on the GPQA
diamond test. GPQA. What's that? GPQA stands
for Graduate Level General Knowledge Question
Answering. The Diamond subset is specifically
curated for extremely difficult questions that
often require deep, nuanced understanding across
various scientific fields. It's essentially testing
PhD -level science knowledge and reasoning. PhD
-level science. OK. Yeah. And expert reviewers
who tested O3 Pro alongside O1 Pro and O3 also
consistently ranked it higher across various
tested areas, confirming the perceived improvement
in logical processing. Beating top models on
both challenging math and complex multidisciplinary
science, that really does back up the idea that
it's designed for deeper thinking. It seems so.
And it still has all the modern AI capabilities,
right? Like browsing the web, using Python code
interpreters, analyzing documents, processing
visual inputs, even using memory for personalized
interaction. Correct. It's fully featured in
terms of tool access and multimodal capabilities.
You get that enhanced reasoning, plus the ability
to interact with various data types and tools.
Okay, so you get all that power. But the sources
also point out a significant trade -off, right?
There's got to be a catch. They do. And this
is crucial. The sources emphasize that responses
from O3 Pro are notably slower than those from
its predecessor, O1 Pro, and certainly slower
than models optimized purely for speed. And this
isn't a bug. It's a direct consequence of its
design. That deeper step -by -step reasoning
process simply takes more computational time
than quick prediction. It just takes longer to
think. Okay, so what does this all mean? Why
would open AI in a market obsessed with speed
release a model that is explicitly slower? What's
the play here? This connects directly to the
bigger picture of OpenAI's strategic positioning,
according to the sources anyway. They frame O3
Pro as their response to what they see as a growing
issue with some speedy AI models in the market.
Right. Which they imply can hallucinate too much
or produce illogical outputs when under pressure
to respond instantly. This new model is explicitly
designed for reliability and trust in complex
tasks over sheer speed. Ah, gotcha. So they're
splitting their offerings. kind of specializing.
Precisely. It looks like a deliberate split lineup
strategy. Yeah. You have GPT -4 -0, which is
incredibly fast, great for real -time interactions,
handles multimodal inputs, seamlessly good for
creative tasks, quick summaries, conversations.
Yeah, the flashy one. Kind of. And now you have
O3 -FRO, which is purpose -built for deep logic,
accuracy, and trust in those highly demanding,
reasoning -heavy applications. Okay, that makes
a lot of sense. So for you, the listener, this
distinction is really important because it means
choosing the right tool for the specific job
you need done. If you need rapid creative brainstorming,
quick information retrieval, or just conversational
flow, GPC 4 .0 might be your best bet. But if
you're tackling a complex coding problem, analyzing
dense financial reports, writing a detailed academic
paper, or trying to solve a difficult scientific
query where the accuracy and soundness of the
logic are paramount. Yeah, where you really needed
to be right. Then O3 Pro is specifically designed
for that kind of deep work, even if it takes
a little longer to give you the answer. It's
about balancing speed with trustworthiness depending
on your task. Exactly. It caters to different
user needs by offering specialized strengths.
Makes sense. All right. Shifting gears a bit,
let's look at what Google has been up to. According
to these sources, they've got some pretty diverse
things happening, too. Definitely. On the creative
side, building on their existing capabilities,
they've unveiled VO3 Fast. This is an update
to their video generation tool. VO3 fast. Okay.
The key highlight here is speed vidits. The sources
say it's generating videos two times faster now.
They're also mentioning improved serving optimizations,
which implies it's also getting better at delivering
those videos efficiently. It maintains a 720p
resolution as well. Two times faster is... Pretty
significant for video generation. That can be
a bottleneck, right? Waiting around for renders.
Oh, absolutely. Cut sound waiting time. And there's
this wild user example the source has pointed
to. Someone apparently used VO3 for these Stormtrooper
-style vlogs. Yeah, the Stormtrooper -style vlogs.
Quite a mental image. The report mentioned that
one such account reportedly garnered something
like 8 million views on Instagram in a single
day using videos generated with VO3. Whoa! 8
million views in one day. That's a crazy viral.
Just for stormtrooper blogs. It really underscores
the power of combining a novel creative idea
with accessible, fast -generation tools. You
can produce content at a volume and speed previously
impossible, and apparently turning everyone into
a stormtrooper resonates with 8 million people
very quickly. Who knew? Unbelievable. OK, so
from viral stormtrooper vlogs to government bureaucracy,
the sources also mention Google partnering with
the UK government. That seems like a jump. Yes.
And in my view, this particular application is
one of the most practical and immediately impactful
deployments highlighted in the sources. Google's
Gemini extract is being leveraged to tackle a
massive real world bottleneck in the UK public
sector. The incredibly slow infrastructure planning
process. Right. Think about everything involved
in getting approval to build houses, roads or
other essential infrastructure. Just mountains
of paperwork. Government paperwork. And planning
documents specifically can be a notoriously complex,
messy thing. Absolutely. And the problem is often
the format. These aren't always neat digital
files. Extract is designed to scan and process
incredibly messy, handwritten or scanned planning
documents. OK. And the source is specific here.
It can handle things like blurry maps and even
handwritten notes scrolled in the margins. Stuff
that is usually really hard for computers. Oh,
wow. So it's not just OCR on a clean typed page.
It's dealing with like. The real messy analog
world, coffee stains and all. Exactly. It's built
to interpret and understand unstructured data
that isn't in a standard digital format. Its
job is to convert that physical, often chaotic
information, whether it's a faded stamp, a drawing
on a map, or handwritten comments into searchable,
structured digital data. Ah, searchable and structured.
That's key. Data that planners and decision makers
can actually work with efficiently in a database
or system. OK, so it doesn't just digitize an
image. It makes the information within that image
usable and searchable. That's a big step. And
the statistic quoted in the trials for this is
pretty jaw dropping. It really is. According
to the early trials mentioned in the sources,
a process that previously took a human planner
two hours of manual work to extract key information
is cut down to just 40 seconds using Gemini extract.
40 seconds from two hours. I mean, that's a monumental
efficiency gain for that specific task. It's
a concrete. quantifiable benefit. And the goal
here is clear and directly addresses a major
public sector issue. By accelerating the data
extraction from these planning documents, they
can speed up notoriously slow decisions on infrastructure
and housing projects in the UK. Makes sense.
It removes that incredibly tedious, mind -numbing
manual data entry, freeing up trained planners
to focus on their actual expertise making informed
planning decisions. It's designed to cut through
the massive backlogs that have reportedly stalled
development for years. That feels like such a
powerful practical use case, not some, you know,
far off futuristic concept, but using AI to fix
a deeply rooted systemic problem that has real
consequences for things like housing shortages.
It absolutely is. The source specifically highlights
this, calling it one of the most practical AI
deployments yet in the public sector. High praise.
And this move doesn't just solve a UK problem.
It also significantly strengthens Google's position
in the enterprise and government AI market. By
directly unblocking these slow, data -intensive
processes, they're supporting a major national
target, like the UK's goal of building 1 .5 million
homes. It's AI solving a fundamental real world
bottleneck using existing documents. That's incredibly
insightful. OK, let's zoom out a bit and hit
some of the other quick takes from the sources,
because there are quite a few other interesting
nuggets that give us a broader picture of the
AI landscape right now. Sure. Yeah. There are
a number of points that paint a wider picture.
For instance, the recent chat GPT worldwide outage.
Oh, yeah. I remember seeing reports on that down
detector lit up with nearly 2000 reports of it
just being down globally for a bit. Right. While
it was a temporary inconvenience for many, it
really just served as a stark reminder of something
important, a rapidly growing reliance on these
AI systems. Totally. When they're integral to
so many workflows, even a relatively short outage
highlights how dependent we're becoming. Things
just stop. Yeah, it makes you think about the
infrastructure supporting all this. And then
there was this little kerfuffle the sources mentioned
about X users kind of dragging Apple. What was
that about? Ah, yes. That was a bit of digital
commentary. Apparently, Apple published some
research or analysis pointing out perceived flaws
or limitations in current AI reasoning models.
Okay. And some users on X were quick to retort,
essentially pointing out that while Apple is
critiquing reasoning models, they haven't actually
launched their own foundational large language
model yet. A bit of a glass houses situation,
maybe. Pot calling the kettle black. Slight chuckle.
Something like that. It sparked a bit of debate
online, though, about the state of LLMs and who
has the right to critique whom. It really shows
the competitive heat in the space right now.
Yeah, definitely. But OK, completely different
note. Here's one that really surprised me and
shows the unexpected places AI is going on medical
application. Doctors at Columbia University reportedly
used AI to help a couple with 19 years of infertility
finally achieve pregnancy. That story was really
quite moving, wasn't it? The source specifically
states it's the first known case of pregnancy
made possible through AI. Now, it wasn't like
the AI performed the medical procedure itself.
Right, right. But rather, it likely analyzed
vast amounts of patient data, treatment histories,
maybe genetic information, to identify factors
or potential pathways that human doctors might
have missed over those nearly two decades. Wow.
Beyond the productivity tools and the big models,
AI directly impacting lives in such a profound
human way. That's pretty incredible. Really shows
the potential breadth. And speaking of unexpected
connections, there was that intriguing business
detail, OpenAI quietly signing a cloud deal.
With Google, aren't they like direct competitors?
Yeah, this is definitely one of those behind
the scenes moves that caught attention in the
sources. OpenAI is famously funded and heavily
supported by Microsoft, one of Google's fiercest
competitors in the cloud and AI space. For OpenAI
to quietly sign a cloud deal with Google Cloud
Platform. The sources frame this as a kind of
arms dealer. Well, by Google, basically selling
their infrastructure capabilities even to rivals.
If you need compute. We've got it. And the sources
suggested it might indicate something about OpenAI's
relationship with Microsoft, like things are
shifting. Potentially. It could be interpreted
in a few ways. One, as the source mentions, it
could suggest that Microsoft's tight grip on
OpenAI might be loosening slightly, or at least
that OpenAI is asserting more independence in
its infrastructure choices, making its own decisions.
Two, it could simply be a pragmatic move by OpenAI
to diversify its infrastructure providers, which
is a standard practice for ensuring resilience
and redundancy, and maybe leveraging competitive
pricing. hedging their bets. Right. Don't put
all your eggs in one basket. Either way, it's
a fascinating dynamic in the competitive AI ecosystem.
Lots of maneuvering. Complex corporate stuff
going on there. Also saw some funding news that
highlights where investment is heading. Right.
Plug and Play, the global accelerator, secured
a substantial $50 million fintech and AI fund.
$50 million. Okay. The focus is specifically
on AI applications within financial services,
which is a massive industry ripe for AI disruption
and efficiency gains. Shows investor confidence
in that particular vertical. Money's flowing
there. And just a few quick hits to sort of round
things out and give a sense of the pace of development.
Saw mentions of Apple's expected AI announcements
at WWDC 2025. Hinching perhaps at things like
visual AI capabilities integrated into their
ecosystem. Yeah, always anticipation around Apple
events. And OpenAI hitting that pretty staggering
$10 billion annual recurring revenue mark. Wow,
$10 billion ARR. That's approximately $833 million
a month. It shows the commercial scale they've
reached surprisingly quickly. Also, news that
their first planned open model in years has been
delayed until later this summer. Yeah, that delay
is interesting, especially after their big closed
model announcements like O3 Pro. Also saw AI
companies prominently featured on the CNBC Disruptor
50 list, which again just underscores how much
AI is seen as reshaping industries right now.
No surprise there. And Microsoft backed Mistral
launching its own reasoning model specifically
positioned to rival OpenAI. Exactly. That Mistral
point is key because it shows that the competition
isn't just in speed or size, but also in that
specific crucial capability of logical reasoning
that OpenAI is emphasizing with O3 Pro. So everyone's
jumping into the reasoning game now. The race
is definitely happening on multiple fronts. It's
not just one dimension. And just a couple examples
of like practical tools being able, things like.
Hunter, an AI tool that can provide a resume
review in under five minutes. Or Bubble, which
lets you build no -code applications powered
by AI. So tools for everyone. Right. Those highlight
that it's not just about the foundational models,
but the proliferation of applications and tools
built on top of them that are directly impacting
workflows and creating new possibilities for
individuals and businesses. The ecosystem growing
around the big models. Okay. So wrapping up this
deep dive. What are the big picture takeaways
that surface from this collection of sources
today? What should we leave people with? I think
the key insights. Pulling it all together are
quite clear. First, we're seeing the AI frontier
push towards not just bigger and faster, but
smarter and more reliable models like OpenAI's
O3 Pro. The reasoning focus. Specifically engineered
for complex logic and trustworthiness, even if
it means accepting a tradeoff in speed. Second,
AI is clearly enabling faster, more accessible
creative workflows like Google's VO3, potentially
changing how content is generated and consumed,
you know, like those. Stormtrooper vlogs. And
perhaps most significantly, AI is moving beyond
just high tech or creative domains and is increasingly
tackling fundamental, often mundane, but absolutely
critical real world problems like that government
paperwork bottleneck with Gemini Extract or even
enabling breakthroughs in deeply human areas
like health care. Yeah, it's not just about the
raw advancement of the models themselves anymore,
is it? It's this parallel trend of specialization
speed versus logic, for instance, and this really
accelerating integration into fundamental parts
of society, business, and even our personal lives.
The integration point is vital. It's showing
up not just in our chatbots, but in the infrastructure
of government, in healthcare, in finance. It's
becoming woven into the fabric of how things
work, sometimes invisibly. Which brings us to
a final provocative thought for you to consider
based on everything we've just explored in these
sources. Given the clear tradeoffs we're seeing
in these new AI models, things like balancing
speed versus reliability and deep reasoning and
recognizing our increasing dependence on these
systems, starkly highlighted by things like that
recent widespread outage. Yeah, that dependence
is growing fast. What aspects of AI do you find
yourself prioritizing as these tools become more
powerful and integrated into your own life or
work? How do we collectively and individually
think about balancing this incredible push for
innovation and speed with the absolute critical
need for robustness, accuracy, and trust in systems
we're starting to rely on so heavily? That's
the big question, isn't it? It's something worth
mulling over as AI continues its deep dive into
our world.