Have you ever wondered if that free AI you're
hearing about is maybe actually a hidden money
pit for businesses? Or if a machine could genuinely
outperform a doctor in training on really complex
medical stuff? Today, we're taking a deep dive
into some pretty surprising AI realities. Welcome
to the deep dive. Yeah, we're here today to unpack
a fascinating stack of sources. They really challenge
some of our core assumptions about artificial
intelligence. That's right. Our mission today
really is to give you a shortcut to understanding
AI's rapidly changing landscape. We'll start
by busting the myth of cheap open source AI.
It's not always what it seems. Definitely not.
Then we'll sort of race through a series of rapid
-fire AI, highlights everything from, believe
it or not, viral cat videos to some big corporate
strategy shifts. And finally, we'll grapple with
a truly groundbreaking study, an AI that, well,
it just outperformed human medical interns. It's
quite the journey into AI's surprising new landscape.
Okay, so let's unpack this first big idea. Many
people assume open -source AI is always the most
affordable option. It just feels intuitive, right?
Free code. It does feel that way but a recent
study tells a very different story about the
actual cost. Yeah. Tell us about that. It really
does. A new NASA research study just dropped,
and their findings are pretty eye -opening, especially
for anyone actually running AI systems. They
found that these so -called free open -weight
models can actually cost you more in the long
run than maybe using something like OpenAI's
APIs. Oh, so? Well, the research shows these
open -source models can burn through 1 .5 to
4 times more tokens than their closed counterparts.
Okay, wait. When you say tokens, what exactly
are we talking about here? For folks maybe not
deep in the weeds on this. Good question. So
tokens are like the small pieces of words or
data and AI processes. You can think of them
as tiny building blocks of information the AI
works with. So more tokens just means more work
for the AI, more processing power needed. And
ultimately more cost to you, the user. Right,
exactly. So if a model uses more tokens for the
very same task, it's essentially less efficient.
And the study highlighted this inefficiency even
further, didn't it? It got pretty extreme in
some cases. Oh, absolutely. For really simple
Q &A tasks, some of these open source models
used a shocking 10 times more tokens. Imagine
asking, you know, what's the capital of Australia?
And the AI basically writes a short novel to
give you Canberra. It's overkill. Wow. That's
significant. Yeah. Meanwhile, closed models like
OpenAI's 04 Mini, they demonstrated superior
efficiency, particularly with complex tasks like
math, where their internal reasoning seems much
more compressed. That's a huge difference in
efficiency. And you mentioned something like,
what, 51 % of companies are already running AI
in production. This kind of inefficiency quickly
translates into runaway compute costs. It really
highlights how that per token pricing we see
advertised can be, well, pretty deceiving if
the model just eats way more tokens to get the
job done. Precisely. Your seemingly cheaper open
source option could quietly, you know, devastate
your compute budget if you're not paying close
attention to its actual token consumption. So
why is this happening? What's the fundamental
difference in how these models reason that leads
to such a big gap? Well, it seems to come down
to their architecture, maybe their training philosophy.
Closed source providers are internally compressing
their reasoning pathways. They've really optimized
their models to perform complex tasks with fewer
internal thinking steps, basically. That shrinks
the token count significantly. Open source developers,
on the other hand, often extend these reasoning
chains. They might add more explicit step -by
-step thinking, maybe for accuracy or robustness,
perhaps. to cover more edge cases. Well, that
means more tokens. But that means more tokens
and inevitably more cost. It's a clear trade
-off. So it's not just about getting the right
answer anymore. It's becoming about how efficiently
the AI gets to that answer. Exactly. Token discipline.
It isn't just some technical jargon anymore.
It's actually becoming crucial for managing your
budget and, frankly, making AI deployment sustainable
long -term. So for businesses out there trying
to save money with AI, what's the real key takeaway
here, the core message? The key isn't just the
per token price. It's the total token efficiency
that matters. Okay, let's shift gears now. Let's
talk about some of the most talked about AI happenings
right now. Kind of a wild mix, really showcasing
how AI is just popping up everywhere. It really
is. We recently saw an AI builder spark this
online challenge, right? Yeah. Inviting all AI
creators to share their coolest AI -made art,
videos, tools. Yeah, that was neat. And the result
was this incredible sort of crowdsourced stream
of creativity. It really shows how AI is maybe...
democratizing artistic creation in a way. And
speaking of access, if you're looking to get
into that, Harvard University is offering 12
free online AI courses in 2025. That definitely
widens access to some pretty critical knowledge.
Okay, now here's where it gets truly interesting.
Yeah. Maybe a little weird. AI CAD videos have
gone super viral. Oh, I saw some of these. And
they're surprisingly bizarre. We're talking like...
buff cats on revenge missions or uh billy eilish
meows dubbed into 30 seconds soap operas millions
are genuinely addicted it's like a whole new
genre of internet culture just spawned beat whoa
the sophistication in these little narratives
is kind of wild makes you realize how far ai
generation has come even if You know, many of
us still wrestle with prompt drift, just trying
to get simpler outputs. Yeah, it's a truly unique
corner of the Internet, that's for sure. On a
more practical note, Genspark AI just launched
something called AI Developer, which is like
a vibe coding tool. Apparently, one user built
a working Mario game in just five prompts. Five
prompts. Wow. Talk about rapid prototyping. That
really lowers the barrier to entry for coding,
doesn't it? Absolutely. We also saw some more
controversial news recently, though. Ignite Tech
CEO. He controversially cut 80 percent of staff
who resisted AI adoption. His quote was something
like belief was harder than skills. Oof. They
even reportedly instituted AI only Mondays. It
kind of highlights the intense pressure some
companies feel to integrate AI really fast, sometimes
at the cost of human jobs. But then on the flip
side, Duolingo's CEO clarified that their AI
first memo was widely misunderstood. He stated
no full -time staff lost jobs due to AI, and
their AI Fridays are now just a weekly internal
thing for exploration. Okay, so maybe more of
a PR misstep there than a policy shift. Sounds
like it. An important clarification about their
approach, though. And finally, on the investment
front, the U .S. government, alongside NVIDIA,
is investing a pretty significant amount, $152
million, into building open -source AI models
specifically for science. That's good to see.
Yeah, the idea is to help universities catch
up, especially as the costs for private, cutting
-edge AI models continue to soar. So thinking
about all these incredibly diverse headlines,
what's the biggest takeaway here? What connects
them? I think it's that AI isn't just a tech
trend anymore. It's fundamentally reshaping culture,
business, and education. OK, now let's zip through
some quick, intriguing insights from the AI world.
These really reveal how we're starting to interact
with it, maybe without even noticing sometimes
day to day. All right. Rapid fire. PwC University
apparently offers five distinct strategies to
help avoid what they're calling AI paralysis.
Essentially, how not to get. killed by AI, in
their words. Character AI is betting big on these
persona -based AIs. You know, the idea is to
give you a personalized bestie for conversation,
companionship, maybe more. Interesting. On a
more scientific note, AI is now actually designing
bizarre new physics experiments that... get this,
actually work. Really? That's wild. Yeah. Google's
former AI lead offered an interesting perspective,
too, saying it's basically too late now to get
a PhD specifically for the AI boom timing. Right.
Yeah. And finally, Gemini Canvas now lets anyone
tweak app designs using just simple descriptive
words like make the button blue. So putting these
snapshots together, what do they reveal about
how AI is kind of weaving itself into our daily
lives and workflows? I'd say AI is fast becoming
a creative partner and just a seamless day. tool
for many people. All right. This next deep dive
is, well, it's truly remarkable. Maybe bordering
on astounding, frankly. GPT -5 has demonstrably
outperformed human medical interns in specific
diagnostic tasks. This isn't just like a small
step forward. It feels like a significant leap.
Yeah, this comes from a new study by Emory University's
radiation oncology team. And they really put
GPT -5 through its paces. They pitted it against
earlier AI models like GPT -4 -0 and actual human
medical interns. And crucially, GPT -5 wasn't
fed exam answers or pre -digested information.
It was a pure test of its analytical and reasoning
capabilities in a real diagnostic context or
simulated anyway. And how exactly did they test
its reasoning? What kind of prompts? were involved.
That seems key. They use something called zero
-shot chain of thought. Which is a bit technical,
but basically means the AI thinks step by step
to reach an answer without needing specific examples
or training on those exact problems first. It's
kind of like it figures out the logical path
on its own, showing its internal thinking process.
Okay, so it's reasoning from first principles,
essentially. And they gave it some incredibly
complex scenarios too, right? Demanding more
than just crunching text. Oh, absolutely. They
used multimodal prompts. Now that means they
combined patient history, so the text, with actual
medical images. Things like... like CT scans,
MRIs, or x -rays. GPT -5 had to understand both
the visual data and the textual context, then
connect the dots to make a diagnosis. That's
a very human -like, high -level task. And the
results were, well, quite definitive. Probably
surprising for many in the medical field, I'd
imagine. Yeah, GPT -5 crushed it. On these multimodal
reasoning tasks, it showed a nearly 30 % gain
in logic and over 36 % gain in understanding
compared to GPT -4. That's a huge jump between
versions. And where GPT -4 actually lagged behind
the interns by about 5 % to 15 % on these specific
tasks, GPT -5 surged ahead by over 24%. It's
a significant quantifiable leap in its diagnostic
capabilities in this controlled setting. But
there's always a catch, isn't there? A but. But
this was all in ideal lab settings. Real hospitals
are just incredibly messy. Very different environment.
Yeah. You've got incomplete records, the complexities
of human emotion, huge ethical concerns, legal
constraints everywhere. And AI might ace an exam
like this, but building bedside trust with patients,
that's a completely different challenge. Two
sec silence. Honestly, I still wrestle with the
concept of fully trusting AI in critical life
and death scenarios myself. It's a huge leap
from the lab to the actual clinic floor. Right.
And if GPT -4 was maybe like a helpful but still
learning med student in these tests, GPT -5 is
certainly performing at the level of an attending
physician, maybe even beyond, on these specific
reasoning tasks. The capabilities showcased here
are truly astounding. They really push the boundaries
of what we thought AI could do autonomously in
diagnostics. Yeah, this isn't just about technical
accuracy. It brings up huge questions about regulations,
liability, how existing medical workflows would
even adapt. So the implication seems clear, at
least from this study. In specific, complex tasks
involving both text and image data from patients,
GPT -5 is demonstrably beyond doctor level, outperforming
humans in multimodal reasoning within that test
environment. It's pretty staggering. So what
do you think is the biggest hurdle for AI moving
from these amazing lab results to actually being
used widely in real world hospitals? Oh, it's
got to be trust, dealing with human complexity,
and just adapting to all that real world chaos.
So wrapping this all up, what does this all mean
for us? We've seen that AI's promises, and maybe
its pitfalls, are often far more complex than
they might initially appear. From those hidden
costs that can really impact a company's bottom
line to these truly groundbreaking capabilities
emerging in fields like medicine. Yeah, it seems
like efficiency, ethical application, and just
having a nuanced understanding of AI's true strengths
and weaknesses, those are becoming absolutely
paramount for navigating this whole evolving
landscape. This deep dive really just scratched
the surface of our sources today. It really did.
We definitely encourage you to keep exploring
these fascinating shifts. You know, ask yourself,
how might AI's efficiency gains affect your work?
And where will we see AI's beyond human abilities
maybe emerge next outside of the lab? And here's
something to maybe ponder. If AI can now demonstrably
out -diagnose human interns in specific, complex
tasks. What truly fundamental human skills will
remain irreplaceable in an increasingly AI -driven
world? That's the big question, isn't it? Thank
you for joining us on this deep dive.