Well, we are just five days out from Google I
.O., and honestly, the metaphorical dam hasn't
just broken today. Oh, it is totally washed away.
I mean, nobody saw this massive wave coming.
Yeah, it really came out of nowhere. Over the
last 48 hours, leaks just completely exploded.
We are seeing sudden Gemini 3 .2 rumors everywhere.
GPT 5 .6 internal tests are suddenly popping
up. Mythos version 2 just successfully completed
complex network breaches, and Codex Mobile screenshots
are randomly surfacing online. It is wild. Welcome
to the Deep Dive. I'm very glad you are joining
us today. Today is Thursday, May 14th, 2026.
Right. And Google I .O. kicks off in exactly
five days. Exactly. So we are taking your sources
to examine all this today. We have four massive
leaks completely shaking the space. And we really
need to look at what this acceleration means.
Because this directly impacts your daily workflow
and security setups. Okay, let's unpack this.
The AI race just got incredibly hot again. It
seemed to happen entirely overnight. Google hasn't
officially announced Gemini 3 .2 at all. No blog
posts, no documentation. Right, zero API changelogs.
But the developer community is completely losing
its mind anyway. Because developers keep sharing
these leaked test results. So let us start with
Google's unannounced Pro models. The earliest
leaked outputs focus really heavily on SVG generation.
Yeah, they do. It is a very specific technical
capability to highlight. And for those wondering,
an SVG is an image made of math, not pixels,
staying sharp at any size. That is exactly why
this specific detail matters so much. An AI generating
pixel art is basically painting blindly. Right.
But an AI generating an SVG is actively writing
code. It has to perfectly plot mathematical coordinates
and curves. The first test showed a PS5 controller
design. The result looked... Well, okay, but
nothing truly mind -blowing. Yeah, the buttons
did not line up properly at all. The overall
shape just felt a bit off ergonomically. It understood
the basic geometry, but not human hands. Exactly.
But the SVG quality improved significantly in
later tests. Another test showed a pelican riding
a bicycle. And that specific result looked much
more coherent visually. The bird had a very clear,
realistic body shape. It featured far better
physical proportions. It really understood the
abstract relationship between bird and machine.
The bicycle itself was mostly complete this time
too. No broken parts or completely disconnected
wheels. But the really interesting part was the
interactive interface. The model generated a
full UI right alongside the graphic. Oh, wow.
Yeah, users could actively customize the graphic
and inspect curve. They could export files and
edit specific geometric parts. This suggests
a huge shift toward interactive vector workflows.
It is not just about basic static image generation
anymore. The SPG engine inside Gemini looks incredibly
robust now. The coordinate shakes are cleaner
and the layouts feel stable. But the UI generation
still looks very weak, right? Unfortunately,
yeah. The interfaces feel extremely flat and
quite generic. They lack the deep polish of a
flagship model. One example, use a Web Audio
API specifically. It also incorporated Tailwind
CSS into the visual design. Yet the final UI
still looked very obviously AI generated. Having
a great SVG engine but weak UI generation is
like putting a world -class engine in a car with
no steering wheel. Exactly, or a massive engine
bolted to a skateboard. The raw computing power
is definitely under the hood. But if the interactive
interface remains that clunky... Everyday developers
cannot steer it into their actual workflows.
Well, part of this issue could just stem from
the prompts. If testers focused purely on the
vector math quality, the interface results would
naturally look much weaker. That makes sense.
Based on the leaks, Google clearly prioritized
mathematical code quality. User experience definitely
looks like the weak spot right now. But the Gemini
Pro model surprised people in other ways, too.
Another leak showed its true creative generation
power. Right. From one simple prompt, it created
four distinct robots. And all four robot designs
look surprisingly good. They were not just lazy,
simple copies of each other. They had completely
different colors and structural shapes. Yeah.
Unique, small design details were built right
in. This proves it can generate multiple creative
variations accurately. It does not just retune
one repetitive, hallucinated output. This is
incredibly useful for vector asset design workflows.
Icon designers and mascot creators are going
to absolutely love this. But the flash variants
are where things truly heat up. Let's talk about
that. There are multiple flash variants in active
internal testing. The leaks mention code names
Fanta, Sprite, and Cola. These are internal names
for different experimental model configurations.
Each variant targets completely different performance
optimization goals. One might be tuned for pure
processing speed. Another might focus purely
on high factual coding accuracy. Right, this
is standard operating practice at major AI labs.
They train several versions and basically pick
the best performer. And the Fanta variant specifically
showed very competitive benchmark results. It
is testing incredibly well against the Pro model.
Fanta is nearly rivaling Pro in many complex
aspects. Which raises a big question. Will the
Fanta Flash variant cannibalize the Pro model's
market if speed wins out? If Google ships a Flash
variant this strong, It changes everything. Users
prioritize speed for fast, practical, everyday
engineering work. It could completely rewrite
current pricing and tier models. So speed and
daily efficiency might actually win out over
raw, complex reasoning power. Right. And that
is exactly what we are seeing. Pro handles complex
reasoning and multi -step architectural planning
tasks. While Flash handles the fast, repetitive
practical daily coding work. Exactly. But here's
the really critical thing to consider. If Google
nails that daily efficiency with their models,
OpenAI cannot just sit around on their hands
quietly. Very true. What's fascinating here is
the sheer development speed. The leaks show OpenAI
is definitely feeling the heat. While everyone
watches Google, OpenAI is testing very quietly.
GPT 5 .6 is already in active internal testing.
Yeah, the first development checkpoints were
evaluated very recently to internal code. code
names showed up in the recent leaf. Amber alpha
and beacon alpha, right? Those are the ones.
These are likely two advanced variants being
heavily tested. They want to see which gives
better complex benchmark results. They run advanced
candidates in parallel and pick one. We might
see these internal candidates appear. very quietly
soon. They often show up on LM Arena testing
first. They just run public blind testing without
telling anyone beforehand. The gap between major
releases is shrinking drastically. I mean, GPT
5 .5 just launched a few short weeks ago. And
it was a massive structural success for the company.
Reviewers said it outperformed Anthropix Opus
easily. That momentum gives OpenAI strong reason
to push forward. especially with Google about
to show new flagship models. If OpenAI waits
too long, they lose the narrative entirely. Some
reliable sources actually suggest a release by
next month. Wow. That would be one of the shortest
release gaps ever. One major reason for this
acceleration is quite profound. AI itself now
actively helps build new AI models. Right. Models
constantly assist with complex code generation
tasks now. They handle vast data preparation
and evaluation during training. They optimize
the actual architectural training process itself
continuously. They completely remove the human
bottleneck. from data collection. The model generates
millions of complex edge case coding problems.
And then it autonomously solves those exact same
coding problems. And finally, an internal reward
model grades the final solution. You know, Ibeat,
it is a very humbling reality. I still wrestle
with prompt drift myself on a daily basis. Yet
these models are already debugging their own
training code. It really makes you realize how
quickly we become bottlenecks. Models generate
synthetic data way more efficiently than human
teams. They instantly spot critical processing
bottlenecks earlier than us. Every lab knows
slowing down means losing crucial ground. Absolutely.
So since AI is building AI right now, are we
going to see massive capability leaps or just
better integration? We might not see those massive
historical capability jumps anymore. Early years
were defined by those massive intelligence leaps.
Now, updates will focus heavily on pure processing
speed. They will focus on smoother workflows
and deep personalization. Right. Smoother workflows
and integration matter far more now than chasing
massive capability leaps. And that concept of
smoother workflows is actually very important.
It connects directly to highly complex, high
-stakes digital environments. Which brings us
to the intense cybersecurity sector today. This
specific space does not get enough mainstream
attention at all. It really doesn't. And the
cybersecurity AI crown is shifting incredibly
fast lately. We started with Claude Opus 4 .6,
leading everyone. Then the original Mythos preview
appeared on the scene. It stayed slightly behind
the Opus model initially. But then GPT 5 .5 Cyber
closed that performance gap. It was tuned specifically
for complex security network workflows. Right,
and now Mythos Preview Version 2 is officially
arrived. Some elite developers are currently
pronouncing it as Mythos. It just received a
major upgrade that is turning industry heads.
Early benchmark results show a clear capability
jump immediately. Institutions like the AI Security
Institute are actively testing it. It has confidently
jumped back into the absolute lead. This constant
back and forth really shows the development speed.
The competitive cybersecurity AI race is incredibly
intense right now. The most striking test involves
a complex network simulation. They use a 32 -step
corporate network attack simulation. And this
is definitely not a simple theoretical toy benchmark.
The model needs to identify obscure digital system
vulnerabilities. It has to actually chain multiple
obscure software exploits together. It must execute
lateral movement smoothly through the whole network.
moving from a compromised printer straight to
an admin laptop. It has to complete a full, complex
attack path. A human cybersecurity expert needs
roughly 20 hours total for this. But this new
mythos preview completed the entire simulation
rapidly. Yeah, it took only 6 to 10 digital attempts
total. This is a dramatic difference in operational
security capability. Whoa, Pete. Imagine simulating
a 32 -step network breach in just a few tries.
The magnitude of that technical achievement is
honestly staggering. A 32 -step attack chain
requires deep architectural understanding. It
must know exactly which obscure exploits actually
work. It strings everything together in the correct
logical sequence. The fact that an AI does this
reliably really matters. It is a massive structural
deal for the entire industry. But does giving
an AI the ability to chain exploits hand a dangerous
weapon straight to bad actors? That is where
things become significantly more complicated
ethically. The exact same model defending a network
can attack it. The potential risks of widespread
misuse are incredibly high. That is exactly why
software releases are carefully staged. Governments
and security institutions usually get private
access first. They have crucial time to prepare
necessary security defenses. They actively do
this before tools become widely available. Exactly.
Defense and attack grow together. Early access
for human defenders is absolutely crucial. But
this immense autonomous computing power is expanding
very rapidly elsewhere, too. It is not staying
securely locked inside corporate data centers
forever. Right. It is moving directly to your
personal mobile phone. recent leaked screenshots
started showing OpenAI's codecs running locally.
It is operating directly from a standard mobile
device. This immediately caught the attention
of many online developers. It does not look like
a highly polished app yet. It seems much closer
to a remote coding workflow. Yeah, they are using
a mobile CLI right now. Let's quickly define
that. A mobile CLI is a text -based command line
to control remote computers from your phone.
That is a perfect, concise summary of the tool.
You can manage complex coding tasks away from
your desk. You can comfortably review outputs
and interact directly with codecs. Earlier leaks
hinted at much deeper workflow integrations coming
too. Tools like NotionHQ are heavily rumored
right here. You could plan, document, and code
in one single place. There is also specific mention
of remote control features. You can trigger heavy
codecs compiling tasks directly from your phone.
Because the heavy processing still runs safely
on a remote server. Rohan Burma from the Core
Codex team was actually mentioned. He was specifically
cited as a primary source for this leak. Which
adds major industry credibility to these current
screenshots. This definitely does not look like
random internet speculation. If Codex arrives
on mobile, it completely changes developer workflows.
Right now, most professional tools need a heavy
desktop IDE. Mobile access lets you actively
review code from anywhere. You can approve massive
pull requests while on the go. It's not about
typing syntax with your thumbs. It's like carrying
a senior developer in your pocket as a remote
control. That is exactly how it will psychologically
function for people. You could be sitting on
a busy train commute. Or you could be sitting
quietly in a long meeting. You can still keep
your complex coding projects moving forward.
You open your phone and check the project documentation.
You trigger Codex to actively review the new
logic. Codex finds a bug, fixes it. and pushes
the commits. Will developers actually want to
manage heavy processing tasks away from their
desks? They'll definitely not do heavy typing
on a mobile screen, but they will urgently want
to unblock their teammates. They will want to
review AI -generated code snippets very quickly.
No, it adds flexibility to keep projects moving
anywhere without replacing the desktop entirely.
Mid -roll sponsor break. So what does this all
mean? If we connect this to the bigger picture
today... A very clear, undeniable historical
pattern is showing up. All four leak stories
point to the exact same trend. AI development
labs are releasing massive models faster than
ever. The technical time between major versions
is completely collapsing now. Internal testing
cycles overlap heavily with public release cycles.
Significant leaks happen before official corporate
announcements way more often. Two massive market
forces constantly drive this rapid acceleration.
Advanced AI tools actively make software development
much faster. An extreme competitive corporate
pressure drives the entire industry forward.
No single lab can afford to look completely behind.
We are looking at a massive volatile pressure
cooker. OpenAI aggressively pushes 5 .6 because
of Google I .O. Google rushes Gemini development
because Anthropic's Mythos is gaining ground.
Anthropic accelerates Mythos testing because
of GPT -Cyber's incredible performance. Every
strategic move by one lab creates intense market
pressure. It forces the other major competitors
to sprint even faster. That is exactly why we
see this massive flood of leaks. The broader
AI space moves way faster than people can track.
The real practical lesson here is about managing
the pacing. The technical field heavily rewards
people who stay consistently informed. You must
actively act on what actually matters most. Right.
Do not treat every single internet leak as gospel.
You should patiently wait for the official software
releases. Compare the real technical outputs
when they are finally public. Pay close, careful
attention to actual real -world software deployments.
Track the quiet, unannounced deployments on LM
Arena closely. The most meaningful software improvements
are often very quiet. They just secretly make
your daily work much faster. They smoothly integrate
into your workflow without you even noticing.
Two secs silence. Here's where it gets really
interesting for the future. Google I .O. officially
happens on May the 19th. You need to keep a very
close eye on it. We must actively see if the
pro versus flash reality actually matches the
leaks. We desperately need to see if flash truly
wins out. I want to leave you with one final
thought today. We know AI is successfully debugging
its own training code. It is autonomously accelerating
the software development loop drastically. What
happens when these models begin designing the
next generation of physical hardware and chips
optimized purely for themselves? That is a truly
staggering philosophical concept to consider.
They could autonomously design the next generation
of silicon chips. They could completely remove
the human bottleneck from the physical timeline.
It is something to seriously ponder on your own.
Stay sharp.