In just 30 days, the whole game for large language
models got, well, it got flipped on its head.
A major player, the one everyone thinks of as
the leader, saw its market share on the web drop
by a massive 19 percentage points. Yeah, it was
a huge alarm bell, a real signal that the rules
have changed and speed is just everything now.
So today we're doing a deep dive into why that
happened, both strategically and technically.
We're going to look at what AI agents really
need to survive this new phase. Welcome to the
Deep Dive. We've gone through some key findings
from the 2026 AI landscape and a few other reports
to give you the knowledge you need. Our goal
is pretty simple, help you navigate these incredibly
fast shifts in the market and the tech. So our
roadmap today starts with that market data, the
big reversal, and why distribution is king again.
Then we'll pivot to what's happening on the ground,
you know, why businesses are moving from simple
automation to building real AI agents. And finally,
we're going to break down a brand new way of
thinking about agent memory, sort of the brain
architecture for the AI of the future. Okay,
let's start with that market reversal. The numbers
are pretty stark. Stark is a good word for it.
I mean, the sources show the dominant player
ChatGPT fell from 87 .2 % of public web traffic
all the way down to 68 .0%. That 19 -point drop
in one month. That's why you hear people calling
it a code red. And at the exact same time, Gemini
was just surging. They jumped from 13 .7 % up
to 18 .2%. That's a huge gain in just four weeks.
And what's really clear from the analysis is
this wasn't really about one model suddenly becoming,
you know, way better than the other. The reason
Gemini gained so much so fast was just pure distribution.
Distribution wins. It's a classic lesson, but
the scale of it here is... Something else. Gemini
is just showing up where people already are.
There's no friction. Exactly. It's embedded right
inside Google search. It's on every Android phone.
It's in Chrome. The user doesn't have to consciously
think, OK, I'm going to go to the AI now. It's
just there, ambient. We should add some perspective
here, though. ChatGPT is still the giant in the
room. The denominator, I mean, the total number
of people using generative AI is exploding. So
losing market share doesn't automatically mean
they're losing users. But it's definitely some
serious pressure. Oh, for sure. And you can see
that pressure elsewhere, too. Look at Microsoft
Copilot. Its public web traffic actually dipped
a little from 1 .5 % to 1 .2%. basically flat.
Right. But the sources are pretty clear that
the web data for Microsoft is misleading. That's
the key. Most copilot use isn't on a public website.
It's native. It's running inside Word, inside
Excel, inside Teams. And that activity, you just
can't see it in public metrics. It's real successes
in the enterprise. So the path forward for ChatGPT
seems pretty clear. They have to integrate more
deeply. They have to live where their users are.
Does this really mean that raw model power is
starting to matter less than just being accessible?
Utility often beats purity. Getting the model
in front of the user is the real challenge now.
Okay, let's shift from the market landscape to
what the builders are actually doing, this pivot
to agents. Yeah, we're seeing a big change in
strategy. The sources point out that a lot of
AI automation agencies are actually struggling.
And it's because simple automation is basically
a commodity now. It's cheap. Anyone can do it.
So they're pivoting. They're moving up the value
chain. Instead of just building simple bots,
they're selling outcomes. They're doing AI audits.
They're focusing on enterprise adoption. And
that kind of pivot demands incredible speed from
the top. AI CEOs today have to iterate constantly.
They have to watch user signals like a hawk and
build systems that compound in value. It's why
the tech itself has to change. We're moving beyond
that simple if this, then that logic. We're talking
about real AI agents, digital workers that don't
just follow a script. They make decisions. And
this is where the old tools, you know, the Zapiers
and makes of the world, they start to fall apart.
They get really expensive, really fast, and they
just weren't built for complex, multi -step AI
reasoning. The sources highlight tools like NEN
as being the choice for pros who need more power.
The end goal is pretty wild. Building systems
that run while you're asleep. Automating an entire
YouTube content pipeline. Or managing customer
support across five different channels. It's
the next level of efficiency. And the big players
know it. I mean, look at Meta. They just acquired
Manus AI, a company known for building agents
that were outperforming some of OpenAI's own
research models. That tells you everything. The
race is on for smarter agents. It's a huge step.
Although, I'll admit, even with these new frameworks
and tools, I still find myself wrestling with
prompt design. You know, just getting an agent
to maintain focus on a long task without getting
sidetracked. It's a real challenge. That's a
really important point. If we're struggling with
it, what does that mean for the average user?
Is that why we need a better way to think about
memory? It suggests the basic structure for deep
thinking just isn't there in the old tools. We
need a better brain. Before we get into that
new brain, let's just quickly touch on a few
other market signals that show how intense this
is. Sure. One thing that jumped out was a job
posting from Sam Altman, a role with a $555 ,000
salary. And the job was just to plan for advanced
AI. It shows the scale of thinking required.
Wow. That's not a developer role. That's a strategist.
Whoa. Yeah. Imagine trying to scale a development
team to handle a billion new complex AI queries
every single day. The job is about infrastructure.
It's about ethics. It's about risk. And on the
creative side, we saw that seven -minute movie
made entirely by one person with AI. It just
went viral. It's blurring the lines completely
between what a human can create versus a machine.
Then you have the regulatory side, which is sending
these mixed signals. China put out draft rules
that would force AI apps to intervene if a user
seems addicted. It's a huge signal that regulators
are worried about agents becoming, well, too
human. Yeah. That psychological pull is already
on their radar. That sets the stage perfectly
for the technical breakthrough we saw in the
sources, the agent's brain. Right. And this is
where it gets a little more academic. But it's
so important for anyone building in this space.
For years, we've thought about memory in simple
terms, short term, long term, stuffing things
into a context window or using RAG. Let's define
AG quickly. It stands for Retrieval Augmented
Generation. It's basically just a lookup system.
The A .I. gets a question. It finds the relevant
info in a database and uses that to form an answer.
It keeps it grounded in facts. But the consensus
now is that our rag, while it's useful, is just
not enough. It's too passive for a true agent
that needs to make decisions. And so this new
paper lays out a full taxonomy, a kind of. a
builder's checklist for agent memory it treats
memory as its own complex system exactly they
break it down into three lenses lens one is about
the forms of memory what memory actually is okay
so you have token level memory which is just
that temporary space for the current chat then
you have parametric memory that's the knowledge
that's actually baked into the model's weights
and the third form is what they call latent memory
these are sort of hidden objects like embeddings
that are created on the fly to help the agent
keep track of things then you have lens two which
is about functions, what the memory actually
does. This starts with factual memory, ground
truths, things that don't change. But the most
important one here is experiential memory. This
is where the agent actually learns. It's a log
of everything it's done, its successes, its failures.
It's how it gets better over time. And rounding
it out is working memory, which is just a temporary
scratch pad. It's how the agent keeps track of
what it's doing right now in a long task. Now,
the third lens is the big one, the real mental
leap. Lens three is dynamics. This is about how
memory changes and grows, and the paper calls
it a control problem. And that's a really important
phrase. It's not just about finding data. It's
about actively managing memory, deciding what
to keep, what to forget, how to learn. It's a
strategy. It changes everything. It means we
have to build memory systems like we're stacking
intricate Lego blocks of data, not just pouring
it all into one big bucket. So why is calling
memory dynamics a control problem such a big
cognitive leap here? What makes it so different?
It forces builders to actively manage how agents
learn and adapt. We're moving way beyond simple
retrieval. So let's bring this all together.
The big ideas from what we've looked at today.
That market share shift proves one thing above
all else. Distribution is everything. If you're
not where the user is, you're going to lose.
And that pressure is what's driving the need
for better agents. Simple automation tools are
out. The future demands agents with these sophisticated,
structured memory systems built using that three
lens taxonomy we just talked about. To stay relevant
or to build the kind of services that command
those half a million dollar salaries, AI needs
a much better way to remember and learn from
experience. And this all comes back around to
the human side of things. get better and better
as they feel more human, we see regulators starting
to step in, like with that China report. Which
leaves a final provocative thought for you to
think about. If AI agents are rapidly evolving
their memory to mimic our own complexity, especially
that experiential memory, what happens when that
becomes the global standard? What does it mean
for you when you interact with an AI that remembers
every conversation, learns from it, and adapts
its behavior just for you? We really appreciate
you sharing your sources for this deep dive.
Until next time.