It's sort of the great paradox of January 2026,
isn't it? It really is. We have these incredible
models, Dash GPT 5 .2, Gemini 3, and they can
do amazing things. They can write poetry, they
can code, they can even, you know, fake empathy
pretty convincingly. Right. But then you put
them in a digital office. You give them a Slack
login, a Google Drive folder, and a basic analysis
task. You just fall apart. It's like a nervous
intern on their very first day. That's it. It
is the intern paradox. And looking at the data
we have today, it feels like the biggest reality
check we've had in, what, the last two years?
Oh, easily. Welcome back to The Deep Dive. It
is Thursday, January 22nd, 2026. We're here to
slow down, take a breath, and just try to make
sense of the noise. And I'm here to help connect
some of those dots. So today is really about
taking a hard, honest look at where we actually
are. We're going to unpack why this idea of the
autonomous office agent just isn't working yet.
We'll be looking at a new benchmark called Apex
that is, frankly, a little embarrassing for the
big labs. But we're also looking at the other
side of that coin. Because while the brains of
the AI are struggling with paperwork, the senses,
I mean, vision, audio, they've just taken this
quantum leap forward. Right. We're talking about
Google's new 4D vision. Yeah. Pretty surreal
story involving Liza Minnelli of all people.
It is such a strange week. The tech is stalling
in some areas and just hitting warp speed in
others. Before we jumped into the data, though,
I kind of have to make a vulnerable admission
here. I honestly still wrestle with prompt drift
myself. I was at my desk this morning trying
to get an agent to format a report from three
different documents. And I must have spent 45
minutes just. tweaking instructions, you know,
correcting its errors, telling it to stop making
updates. And then it hit me. What's that? I could
have just done the work myself in 10 minutes.
Oh, absolutely. That is the automation tax. You
pay it in patience just to prove the machine
can do it. Exactly. And that feeling, it's not
just me being impatient. It turns out there's
hard data to back it up now. So let's start with
segment one, the Apex failure. Yeah, this is
a really significant report. It's called the
AI gap. For, I'd say, the last 18 months, the
narrative From OpenAI, from Google, from Anthropic
has been pretty clear. AI is ready for real jobs.
Exactly. We've been told they can be analysts,
paralegals, executive assistants. Yeah, just
give them the keys. Right. But we haven't really
had a standardized way to test that claim. Not
in a messy, real -world setting. That's the key.
We had tests for write a poem or solve this math
problem. We did not have tests for be an employee.
And that's where the Apex Agents Benchmark comes
in. So this is not a multiple choice test. No,
not at all. They literally dropped the top models.
We're talking Gemini 3 Flash, GPT 5 .2, Opus
4 .5 into these simulated white collar workflows.
So things like read the Slack thread, then cross
-reference it with the PDF and Google Drive,
and draft a reply to the client explaining why
we can't do the refund. Precisely. The stuff
that's actually, you know, 90 % of knowledge
work. Yeah. It isn't just generating text. It's
context switching. It's reasoning across different
sources that might even contradict each other.
And the results were? Well, I'm looking at this
chart and shocking feels like an understatement.
They were abysmal. So the winner was Gemini 3
Flash. You want to take a guess at the accuracy
rate? I mean, I would hope for at least a passing
grade, maybe, what, 65 %? 24. 24%. That was the
gold medal. GPT 5 .2 came in at 23%. And the
others, like Opus 4 .5 and the standard Gemini
3 Pro, were all hovering around 18%. That is
just incredibly low. That means three out of
four times the employee gets the task wrong.
If I had a human intern with a 24 % success rate,
I mean, I'd have to let them go before lunch.
or they'd just quit. The report notes that in
most cases, the models just couldn't piece the
information together, they'd hallucinate a policy
that didn't exist, or they'd miss a key detail
in the Slack thread because it contradicted the
PDF. It really feels like a connective tissue
problem. They can process the individual data
points, right? They can read the PDF, they can
read the chat, but stringing them together into
a coherent chain of logic, that's where it all
breaks down. It is the perfect analogy for an
intern. You ask an intern, what's the capital
of France? They nail it. That's retrieval. Right.
But if you ask them, look at these three messy
files and tell me if we can legally fire this
vendor, they panic. That's reasoning. It really
makes you wonder about the timeline. We've all
been preparing for this wave of autonomous agents
to take over our inboxes in 2026. So looking
at these numbers, I guess I have to ask, is the
dream of the autonomous AI employee officially
dead for now? Not dead, just delayed. They lack
connective tissue reasoning. That distinction
is key. But while the current models are struggling,
the labs aren't just sitting still, are they?
No, not at all. And this brings us to the leaks
and the new tools emerging this week. The rumor
mill is really spinning. We've got a leak about
GPT -5 .3, which is codenamed Garlic. Garlic?
That's an interesting choice. I know, right?
But the leak suggests a pivot. Instead of just
bigger is better, which has kind of been the
strategy for five years, this next version is
supposedly all about cost, speed, and crucially
reasoning. Which would be a direct answer to
the Apex failure. Exactly. If they can crack
that reasoning bottleneck without making the
model 10 times more expensive to run, well...
That changes the entire equation. But while we're
waiting for garlic to maybe save the day, there
are some tools right now that are quietly solving
this workflow problem just by changing how we
interact with the info. You mentioned Notebook
LM earlier. Yeah, Notebook LM really feels like
a sleeper hit to me. Everyone is chasing the
big agent dream. But Notebook LM has evolved
into this amazing all -in -one researcher. It's
gone from just a summarizer to a synthesizer.
It has. The new workflows are really impressive.
You can just dump in raw research papers, your
notes, transcripts, and it doesn't just chat
with you about them. It converts them. Right.
I saw that workflow where it takes all that raw
data and turns it into a slide deck outline,
generates an audio overview, which sounds creepily
human, and then it builds out comparison tables
all in minutes. And that connects to this broader
trend we're seeing, this idea of no PowerPoint.
Please tell me that means what I think it means.
Well. It means we stop fighting with PowerPoint.
The shift is towards creating consistent, full
slides using scripts. So you write the narrative
and the AI builds the visual container for you.
No more dragging text boxes or aligning fonts.
Exactly. That's the dream. We become editors
-in -chief instead of slide designers. Yeah.
But there was another tool leaked from OpenAI
that sounds a little more managerial. Salute.
Salute is fascinating. It's an internal tool
they're testing, and it's basically a project
manager AI. You upload your files, you assign
tasks to the AI, and it actually tracks the progress.
So it's not just do this one thing. It's here's
the project, now you manage the steps. It's trying
to bridge that gap we saw in Apex. If the model
can't reason across tasks on its own, maybe we
just need a dedicated manager layer of software
to force it to stay on track. Feels like a real
shift in our role, you know? With tools like
Salute and Notebook LM, are we actually working
less or are we just managing the AI more? Managing
more, but the output quality is exponentially
higher. That feels like the trade -off of 2026.
You're the conductor now, not the first violin.
But let's talk about the business side of this.
Because running these reasoning models, these
manager layers, it is not free. Far from it.
And the economics are getting pretty ugly. We
saw a report on Anthropic, the makers of Claude.
Their margins just took a really significant
hit. Dropping from 50 percent down to 40. That's
a huge slice of profit. It is. And the culprit
is exactly who you'd expect. The cloud providers.
Google and Amazon hiked their server costs by
23 percent. It's the unsexy reality of the AI
ecosystem, isn't it? You can have the smartest
model in the world, but if you have to pay a
landlord to run it, you're at their mercy. It's
the infrastructure squeeze, and it's driving
these massive capital deals. We just saw the
Saudi infrastructure fund team up with Humane
for a $1 .2 billion deal. Just to build data
centers. $1 .2 billion just for the plumbing.
For the plumbing. But, you know, while the infrastructure
is getting more expensive and the reasoning is
hitting a wall, the creative output is finding
these really interesting new business models.
This Eleven Labs story really caught my eye.
It's a landmark moment, I think. They dropped
a 13 -track AI music album. And we're not talking
about some, you know, anonymous generated lo
-fi beats. This features Liza Minnelli and Art
Garfunkel. Now, to be clear, because the ethics
here usually get messy, Liza Minnelli did not
go into a booth and record this, correct? No,
she did not. But her state and her team signed
off on it. And that's the key. Yes. Full royalties
are being paid. The labels are actually involved.
It's a licensed collaboration. They're essentially
treating her voice as an instrument, like a Stradivarius,
that can be played by the AI with the artist's
permission. That's a complete 180 from all the
lawsuits we saw back in 2024 and 2025. It suggests
a future where an artist's prime voice can become
immortal. Exactly. And Spotify is leaning into
this whole vibe culture, too. Their new prompted
playlists are getting shockingly good. You don't
search for a genre anymore. You just type in,
make me a playlist that feels like rainy Tokyo
nights. And it actually understands the semantic
texture of that request. It's moving from metadata
like this is a rock song to emotional data. This
song feels like heartbreak. That's it. So does
the Eleven Labs album prove that artists and
AI can actually coexist profitably? Yes. It turns
legacy voices into a scalable, renewable asset.
A renewable asset. That is a wild way to think
about a human voice. But speaking of sensing
the world, we have to talk about the breakthrough
of the week. This is the one that actually made
me just stop and stare at my screen. Google's
D4RT. Right. Dynamic 4D reconstruction and tracking.
Now, we've had computer vision for a while. My
car sees lane lines. My phone recognizes my face.
How is this different? This is the difference
between looking at a photograph and actually
stepping inside the room. Traditional computer
vision looks at a 2D image and draws boxes around
things. Cats, cars. Right. D4RT watches a video
and builds a world model. A world model. Yes.
It tracks every single pixel across time. That's
the fourth dimension here, time. And from that
flat video, it reconstructs a full 3D scene.
So if I showed a video of me walking around my
kitchen, it's not just seeing a man in a kitchen.
It's actually... Building the 3D geometry of
the fridge, the table, the coffee cup. And it
understands exactly where the camera is moving
in that space. Correct. And it handles the messy
stuff that usually breaks these models. Motion
blur, things getting blocked. What we call occlusion.
Ah. Like if you walk behind the fridge, old models
would think you just vanished. D4NT knows you
didn't disappear. It knows you're just behind
an object. Okay. And the speed. That is the whoa
moment. Previous models, and this was cutting
edge just six months ago. would take maybe 10
minutes to parse a video like that. 10 minutes,
okay. I4RT does a one -minute video in five seconds.
Two -sec silence, five seconds. Whoa. That's
basically real time. It's anywhere from 18 to
300 times faster than anything else out there.
It uses this unified query system. So instead
of having one model track objects, another one
gets the depth, another one map the room, D4RT
does it all in a single pass. I mean, imagine
the implications for... Well, for everything.
Robotics, obviously. If a robot can understand
the 3D geometry of a room in milliseconds, it
can navigate like a human. But also surveillance,
content creation. It's like the AI isn't just
watching a movie anymore. It's building the movie.
set in its head instantly. It's superhuman perception.
We're giving machines a spatial understanding
of reality that might actually be faster than
our own ability to process a scene. It's a fundamental
shift. So if machines can reconstruct reality
this fast, what happens to truth in video evidence?
We stop trusting our eyes and start trusting
digital watermarks. That is a chilling thought.
We're going to take a quick breather here. We've
covered the failures of the office intern and
the superhuman vision of Google. When we come
back, we are going to look at the Empowered Utility
Belt, the specific tools you can use right now
to make your life a little easier. Stay with
us, and we are back. Okay, let's get tactical.
We've talked about the big models, the high -level
concepts, but what about the tools that actually
save you time on a Tuesday afternoon? We called
this section the Empowered Utility Belt. Yeah,
there are some great ones this week. First up
is something called Clawed Cowork. This one is
interesting because it's not a new app. It's
more of a workflow, right? Right. It's a 13 -minute
exercise, and it's designed to turn Claude from
just a chatbot into a real thinking partner.
It's all about priming the model with your context
so you don't have to explain your job every single
time you open a new chat. I love that. It's like
onboarding your AI colleague one time instead
of every single morning. What else you got? ChartGen
AI. This one is for anyone who just drowns in
data. It takes raw data from pretty much anywhere.
Facebook ad exports, TikTok analytics, just messy
Excel sheets. And it turns them into professional
charts in seconds. So no more fighting with Excel
pivot tables. There are more pivot tables. And
then there's Locate Store. It's very specific,
but very cool. You have a Google sheet full of
addresses. It instantly turns that into an interactive
map with search filters. That's huge for logistics
or even just planning a trip. But the one that
really caught my eye for pure automation is Demonstrate.
Okay, how does that work? You record a browser
task one time. So say, log into this portal,
download the invoice, rename it, and upload it
to Dropbox. You just do it once, Demonstrate
records it, and then this is the kicker, it deploys
it as serverless code. So you don't just get
a macro, you get a piece of software that runs
in the cloud for you. Exactly. You are literally
programming by doing. And finally, we have Calum.
Right, the AI calendar assistant. But it handles
the hard stuff like multi -person meetings, shared
availability, all the real world constraints.
It's really trying to kill the email ping pong
of scheduling. Of all of these tools, if you
had to pick one, which of these actually saves
you an hour of sleep tonight? Demonstrate automating
browser tasks is the ultimate cure for boredom.
I am with you on that one. Anything that can
fill out a web form for me is a friend of mine.
Amen to that. So let's zoom out. What does all
of this really mean for you, the listener, sitting
here in January 2026? I think we're in what you
could call a gap year. A gap year. Explain that.
Well, the text -based agents, the brains, they're
struggling. GPT -5 .2, Gemini 3, they're getting
barely 24 % accuracy on real work. The brain
is still learning how to file paperwork. Okay.
But while the brain is stumbling, the senses
are just exploding. Exactly. Google's D4RT vision
is seeing the world 300 times faster than before.
Eleven Labs has solved the legal and creative
puzzle of AI music. So the synthesis here is,
the AI might not be ready to be your lawyer or
your project manager just yet, but it can see
your world and sing your songs better than ever
before. The brain is lagging, but the eyes and
ears have become superhuman. That's a really
powerful place to be. It means we have to stop
waiting for some magic general manager AI and
just start using the super sensory AI tools we
have right now. Use the vision, use the voice,
use the automation. Don't wait for the reasoning
to be perfect. And if you want to start somewhere
practical, I would highly encourage you to try
that Claude Cowork 13 -minute exercise we mentioned.
It's a small investment that really pays off
every time you open that chat window. It really
does change the dynamic. We're going to leave
you with one final thought to mull over. Yeah,
we talked about Google's D4RT, how it reconstructs
a 3D world from a flat video in just five seconds.
It's incredible. So here's the question. If an
AI can reconstruct a 3D world from a flat video
in five seconds, how long until it can reconstruct
a better version of your workflow than you can?
Something to think about. Thanks for diving in
with us. We'll see you next time.