This week we have a new language model announcement, and in fact,
not even just one, but two announcements, and those two
have broken the Internet and they're both from Google.
Now, we know that Google, you could argue, has been not
winning the AI race. You could probably argue that
it's not losing, but it's definitely not winning for some
time. But obviously now everything
just changed to the point where Sam Altman has come out to his
entire company and said that he is worried.
He's worried about slower growth and huge competition.
And for context, the Gemini app and the Nana Banana applications
have hit #1 on the App Store, have generated hundreds of
millions of images and have gone completely viral.
It's scored higher on almost every single benchmark than most
language models out there. And it can do things that make
image generation finally truly useful for everyday users.
Anyway, in today's episode, I'm going to go through the
different things you can now do with these Gemini models.
This is in the loop with Jack Horton, and I hope you enjoy the
show. So let's start with a little bit
of background on this because on November 18th, Google launched
Gemini 3 Pro, the main competitor model to the Open AI
Chachi BTS of the world. Two days later came Nano Banana
Pro, which is their image generation model.
Now officially it's called Gemini 3 Pro image, but
everyone's been calling it Nano Banana from the previous earlier
versions that have been teased online.
And if you've been online recently in the AI community,
you will know that this model has been hyped by the community
in a way that we've probably never seen in a model since
early Chatchi BT days. Now, if you remember the episode
I think five or six weeks ago where I talked about the new
Google Pixel phone having AI in it, it was actually overshadowed
in some ways by the fact that everybody was hoping that Nano
Banana was going to be released and they didn't release it.
So that gives you a kind of insight into just how much hype
there's been around this model release.
And I mean, they're really building Google here because
they've got about 650 monthly users on their Gemini
application. They have 13 million developers
now building on top of their models, and now both of these
models they've just released are available inside the main
applications and as we speak, being rolled out across every
single product feature you can possibly think of.
Now let's get into all the cool things you can now do with these
models. OK, so I think of the two models
I want to talk about today. So Gemini 3 Pro, which is kind
of the competitor to Open AI, ChatGPT and Cloud, and then the
Nano Banana, which is the competitor to the likes of Saura
and other big image generation models.
I think I want to start with Nano Banana because it's
probably provided some of the most exciting new capabilities
I've seen in a very long time. So the first major capability
it's developed and built on is the way it deals with text.
So it's now supports every single language and deals with
text perfectly. So every other image model
breaks when it comes to text. You know?
Ask Dali or other providers to create a product label with
English, Korean and Arabic. You often get a lot of mistakes
and AI slop mid journey. Can't handle non Latin scripts
at all. Chinese characters might come
out as random strokes, Arabic reads right to left incorrectly.
There's just a lot of errors, and it's not something that's
easy to do because there's a lot of detail there.
Well, none of Nana completely flips that on its head.
And it enables you to do really cool things.
So first, it can generate multilingual text from scratch.
So someone tested, you know, generating product cans with
text on it across different languages, and it worked
perfectly. Second, big leap is the way it
is able to now translate text that's already in images.
This is genuinely new, you know, take a photo of a friend's
restaurant menu and it'll be able to translate it to English.
But going even further than that, it can match handwriting,
or it can take information and text from one image and generate
it inside another. So one of the most amazing
examples so far I've seen is its ability to even get this example
up on my phone. Here, match your handwriting so
you can actually write something on a notepad.
Imagine writing on a notepad, a maths equation or a problem and
then asking it to replicate that handwriting but complete the
equation or write a page of notes and it will just do that
on the fly and it matches your handwriting.
So yeah, the way it deals with text is incredible.
Another one as a result is its ability to just generate entire
infographics and visual ways of representing information using
text. Again, this is something that
models just sucked up previously and anyone that's tried to do
this has probably been disappointed over and over
again. So you can now just generate
entire amazing whiteboard and infographic images instantly.
So just imagine how this impacts marketing teams, for example,
generating entire marketing campaigns for different regions
at scale. You can just imagine Facebook or
meta ads that changes ads for not just a country, but a region
or a street or so many different layers of subculture.
Now, I, I personally find that kind of dark and terrifying, but
you can see where this rolls out to and how this impacts the way
things like marketing and information sharing go.
Now, I, for one, find this quite a almost dark and scary concept.
I always find big advertising and using data to generate new
images to persuade us to buy things a little bit.
Yeah, as I say, dark and evil. But you can see how much of an
impact this language model is going to have in that arena.
So another major upgrade here, number 2 is, is its ability to
maintain image identity across many different images.
So within say 48 hours of launch, you suddenly saw images
of sort of Elon Musk SAT with a cigar with the Google CEO, with
Jensen Twangs, Sam Altman, other people like that and it looked
completely real. Now a lot of the time other
image providers, they might be able to take lots of single
images and combine them into a single image, but often it's
just not that good. With Nano Banana you can blend
up to 14 images at once whilst maintaining the appearance of up
to five people. So now suddenly you've got
people being generated in 3D. People uploaded image selfies
and turning themselves into figurines.
Now the people from A16Z use this for their leadership teams
infographics. So they had five exact headshots
with their actual appearances, family photos from individual
shots, and they blended it together with Nana Banana and
everyone looks perfect. But it's now in a completely
different context. That looks super professional,
but exactly like everybody now going on to #3 which really
blows my mind is how it can ground real time data with its
image generation. So previous image models might
only know what they learned during training.
So you asked Dali to create an infographic showing today's
weather. It'll invent A plausible looking
weather icon or image, but it won't make any sense because it
won't be the actual weather. Or perhaps give it some
coordinates of an event that's just happened and ask it to
generate an image of that. What that event would have
looked like, it wouldn't have be able to do that.
We'd have no clue. Well, Nana Banana now can use
Google search for injecting that real time information about
what's happening in the world that second to help it generate
that information and image. So you can say generate me an
infographic or a visualization of an event that happened today.
This search just happens automatically within Nano
banana. You don't have to use a
different model or see. I just think about the output of
creative in the world that's going to that's going to be
online as a result. I mean, anyone that's been
online recently, you can just see so many Nano Banana images
and videos out there all the time right now.
And I mean, one of the things that I found amazing here is you
can things like you can upload GPS coordinates and just ask
Nano Banana to generate images of what that event might have
looked like, both historic events and real time events
happening today. See, it's safe to say that I've
been blown away by this new model update.
It's really set the tone and expectation of things going
forward. Now moving on to Gemini 3 Pro
and it's upgrading capabilities and importantly how good it's
got at coding. So this is the fourth big
upgrade by Google. Here they claim that it's going
to have multi hour autonomous coding sessions because previous
coding assistants, you know, like Cloud Code or others might
run for 30 minutes, 40 minutes, 45 minutes without losing
context. Cursor can handle maybe an hour
of continuous work before needing to be restarted or
readjusted. Or they hit token limits, lose
track of what they're doing, or just start to make a lot of
errors without any human intervention.
Well, Google's what they call anti gravity agents apparently
can work for over 24 hours continuously.
So Google's documentation says in our internal evaluations we
observe Codex Max works on tasks for more than 24 hours.
Now apparently it uses something called compaction, which is a
technique that essentially prunes its history whilst
preserving contacts. So if you imagine a model going
out there and doing things, it's constantly updating the
information about that task. Now if you can imagine you
working for three hours with this agent, it suddenly
collected 8 hours worth of information, contacts, things
that you said you do and don't like.
And after a while it's trying to take too much in every time you
want it to do a new thing and so it's performance can drop.
This innovation essentially is auto reducing, improving and
compressing the amount of context it has so it can be more
efficient and therefore go on for a longer and longer amount
of time. So this means that Google claims
that you could refactor an entire code base that might take
weeks of time with a single prompt.
Now whether that's true or not, we'll see when we start to test
it, and I'm sure the team internally at Mindset will be
testing this very soon as well. So moving on to the fifth big
update by Google, you can now build complete games, which
although might not sound exciting, these games can have
complete physics and audio instantly.
So previous models could generate a game quite simply,
but it doesn't actually always work.
And you know, you could ask Claude or GBT to build a game
with physics. You might get code that's quite
plausible, a very simple prototype that will sometimes
will work OK, but often has a lot of bugs in it.
Well, Gemini 3 in their demos built entire games like
Ridiculous Fishing, the iOS game from 2013, which had working
mechanics, sound effects and music like the full game, and it
had accurate physics for catching fish.
And previous models really struggled with this types of
activity, whereas Gemini 3 can do it in just a few prompts.
Again, devil's in the detail here, and we'll be really
interested to see at what point you get stretched to its limits.
OK, moving to number six, which is the ability for Gemini to
read very complex screenshots and take action on them.
So previous models are really good at describing what might be
in a screenshot, but they can't interact with it.
So if you give it access to your web browser, it can take
screenshots of things maybe. So this is agent mode, but it's
not very good at figuring out, you know, I need to press this
button and that button to do this thing.
So it could tell you, you know, the information on screen, but
not really good at figuring out where to press.
And that's why I think agent mode and autonomous agents, they
get branded online have not been hugely successful or popular.
Well, Gemini 3 has got 72% on the screenshot protest, which is
double the previous best score of 36%.
And this test is only testing a model, whether it can look at a
screenshot, understand the UI it sees and figure out where to
click and take the right action quickly.
And really interesting ways this has been used is, for example,
giving it access to 3 1/2 hours of footage from a meeting and
getting it to produce transcripts, speaker names, time
stamps, key sections, action items, all those types of
important parts, even where people disagreed and agreed, but
doing so by watching the actual video and the actual footage as
well. So the type of innovations is my
unlock is the ability for agents to navigate software themselves.
Again, this isn't an area that I've been particularly excited
by within AI because I think AP is so just, you know,
transferring information through the back end is much quicker and
easier. But it wouldn't surprise me if
there were really some interesting use cases emerging
because of this over time. You know, the personal shopping
assistance, that type of idea #8 is the ability for Gemini to use
multiple valid approaches to figure out a solution to a
problem. So often a language model can
struggle when there's no single correct answer to a problem,
which is most problems in our world because it's a very
complex world. You know, ask GPT 5.1 for, you
know, three different approaches to reduce our customer churn.
You'd often get different variations of the same, same or
similar approach. Ultimately, the model is pattern
matching to common solutions rather than exploring very
different strategies. Now again, think about much of
these innovations not being suddenly made possible because
you can make a model do these things, but doing so within a
single prompt or just a couple of prompts.
So the speed to this raw intelligence and the ease to
access and utilize this raw intelligence in other models,
this might take a lot of, you know, twisting its arm, making
sure it's got the right information and context.
Whereas what Gemini is claiming to do is make this 10 times
easier. And a good example is Gemini
scored 45.1% on the ARC AGI two test, which is designed to have
multiple potential solutions. In this test, it shows you a few
different examples of patterns and we'll ask the model to infer
the rule, for example, with more often than not more than a
single valid interpretation. PPT scored 17.6, Gemini 2.5
scored 4.9%. And again, as I said, Gemini
just scored, the Gemini 3 model just scored 45.1%.
That's that's a massive leap. And I think Matt Schumer said
this perfectly, which is he said previous models often had a
certain spikiness. Their quality varied wildly
depending on different tasks. You could get brilliant at 1
task, but just OK results on another, whereas Gemini 3 feels
just more consistent across the board and when a model is
reliably good at most things instead of occasionally
brilliant, that's where you build entire systems around it.
So yeah, let's let's conclude for today's episode I mean, so
what's actually changed this week?
Well, Google has just released 2 models that have broken the
Internet and they've broken thresholds that people have
really struggled to break for a very long time.
Nana Banana now can merge 14 images perfectly.
It can generate text perfectly. Gemini 3 can apparently do a
single task within a couple of prompts for over 24 hours.
CSA to say Google is super competitive again.
And as I said at the beginning, Sam Altman announced to open AI
that he is worried, and I mean I would be too because Google is a
huge Goliath as incredibly talented people and is now
making huge leaps into the future and being a market leader
provider. Anyway, as always, the most
important thing I can now recommend is go try it for
yourself. Go play, go have fun, see if it
works in your day-to-day work. Anyway, that's it for today.
I hope you enjoyed the episode and I'll see you next week.