So I have to start today with something that
honestly it made my brain hurt a little bit this
morning. I was reading this story in Wired about
a platform called Rent -A -Human. Which, just
right out of the gate, sounds like the start
of a bad sci -fi novel. It gets so much worse.
It's basically a gig platform, right? Right.
But the employers are AI bots. Okay. They're
hiring humans for tasks, you know, solving CAPTCHAs,
data verification, that sort of thing. But there's
this one user, a human, who did the work, he
logged his hours, and earned absolutely zero
dollars. Zero. Zero. Because the system... It
logged the transaction as AI paid me. Oh, wow.
The bot hallucinated the payment and the platform's
code just, it accepted the bot's word over the
human's actual bank account. That is a perfect,
if slightly terrifying, encapsulation of where
we are right now. I mean, we spent a decade worrying
that robots would take our jobs. Right. It turns
out we should have been worried they'd just be
really, really terrible payroll managers. It's
the ultimate irony. But I think it sets the stage
perfectly for what we need to wrestle with today.
Welcome back to the Deep Dive. I'm here to help
you navigate the noise. And I'm here to try and
help you find the signal. Today is Monday, February
16th, 2026. And if that rent -a -human story
tells us anything, it's that the lines between
human agency and artificial autonomy are getting...
Well, messy. Messy is an understatement. We are
so far past the chatbot era, you know, the look
what it can say phase. We are firmly in the agent
era now, the look what it can do phase. And that
shift, it changes everything from like geopolitics
to how you edit. a simple video. We have a stacked
lineup to get through. We're going to break down
Alibaba's massive new move in the model wars
with Quen 3 .5, and I really want to push on
whether this signals the end of American dominance
in AI. It's a huge question. We're also going
to look at the state of AI video, which has become,
frankly, overwhelming for anyone just trying
to keep up. And we have a strategy to fix that
overwhelm, I promise. Good. Plus, we've got a
headline segment that covers, well, everything
from cheating consultants at KPMG to actual drone
swarms at the Pentagon. And we're going to geek
out. We are. We're going to get into a technical
paper called Adapt Evolve that might just solve
the energy crisis of running all these agents.
That paper is fascinating. It's essentially teaching
AI the art of strategic laziness. I think we
can all learn a little something from that. Okay,
let's unpack this. We have to start with the
big release out of China. Alibaba just dropped
Quen 3 .5. Now, for... The uninitiated or for
people who just track, you know, open AI and
anthropic. How big of a deal is this release,
really? It is a very loud statement. I mean,
for a long time, the narrative was that Western
labs held the frontier crown. Yeah. And Chinese
labs were just the fast followers. Exactly. Fast
followers. Quinn 3 .5 challenges that core assumption.
This isn't just another language model that can
write you a poem. The branding, the architecture.
It is all focused on one thing. AI agents. Okay,
so we throw that word agent around a lot. I want
to be precise here because I feel like it gets
muddied in all the marketing. It does. If I'm
a developer listening or just a user, what is
the functional difference between a model like
GPT -4 and an agent model like this one? That's
a great question. A conversational model, like
a standard chatbot, it's linear. You ask, it
answers. It's just predicting the next word.
Right. An agent is circular. It has a feedback
loop. A feedback loop. Exactly. Think of it like
the ODEA loop. In military strategy, observe,
orient, decide, act. An agent can, say, write
some code, try to run that code, see an error
message, then read the error, rewrite the code,
and try again. It has tool use baked into its
core. It doesn't just talk. It navigates an environment.
So it's not just generating text. It's correcting
itself based on reality. Precisely. And Quen
3 .5 is built to plug directly into these open
source agent frameworks, specifically one called
OpenClaw. OpenClaw has been all over the GitHub
trends lately. It has. It's basically the interface
layer that lets the model control your computer.
And Alibaba released this as an open weight model
with 397 billion parameters. 397 billion. That
is a very specific number. And it sounds massive,
but why does that number matter? Is it just bragging
rights? Well, it matters because of the hardware
reality. To put 397 billion parameters in perspective,
you are not running this on your MacBook Pro.
Right. You are not even running this on a tricked
-out gaming PC. You need serious enterprise -grade
GPU clusters. We're talking multiple H100s or
the new Blackwell chips just to load the weights
into memory. So this is an industrial -grade
tool. It is chunky. But here's the kicker. It's
actually smaller than their last flagship model.
Wait, I thought the trend was always bigger is
better. That trend is dead. We are now in the
era of denser is better. Alibaba is claiming
this model has stronger performance per parameter
than anything else on the market. So they're
optimizing for the economics of it all? Exactly.
The inference economics. How much intelligence
can we squeeze out of every dollar of electricity?
And they're offering this in two flavors, right?
Yep. The open -weight version, which you can
download and fine -tune on your own servers,
which is crucial for data privacy if you're a
bank or a hospital. And the hosted version, Gwen
3 .5 +, which just runs on Alibaba Cloud. This
feels like a strategic play we've seen before.
It reminds me of, I don't know, Android versus
iOS, or maybe what Meta did with Llama. It is
the control plus ecosystem play. Yeah. If you
are a Western developer, or maybe a developer
in Southeast Asia, and you want to build an autonomous
coding bot, you need a model that's great at
function calling. The ability to use tools. Right.
And if Quen 3 .5 is the best open model for that,
you're going to build on Quen. And just like
that, Alibaba becomes the infrastructure layer
for your whole business. Precisely. You get locked
in. And the timing is no accident. This dropped
right before Chinese New Year. It's a flex. It
brings to mind that quote from Demis Hassabis,
the head of Google DeepMind. A while back, he
said Chinese labs were months behind. I remember
that. Looking at QIN 3 .5 and these specs, does
that still hold up? I think that gap has completely
evaporated. If you look at the self -reported
benchmarks on these agentic tasks, you know,
things like go find this file, summarize it and
email it. QIN is trading blows with the absolute
best from OpenAI and Anthropic. Wow. That months
behind narrative is just dangerous complacency
at this point. So let me ask the probing question
here. If the parameter war is over, what are
we fighting now? The agent war. It's no longer
about who is the smartest at answering a trivia
question. It is about reliability. Can the model
do a 20 -step task without hallucinating or getting
stuck? That is the new battlefield. Speaking
of things getting sick, let's talk about my weekend.
I was trying to generate a simple video asset
for a project, and I just fell down this rabbit
hole. Oh, the video landscape in 2026 is an absolute
jungle. It's not just a jungle. It's a chaotic
mess. I was looking at the list of industry standard
tools just for this month. Heian, Synthesia,
Runway, PicoLab, Zuma Dream Machine, Kling AI,
VO3, Sora 2. And that's just the top tier. Don't
forget the niche ones. I have to be honest, and
this is a bit of a vulnerable admission. But
looking at that list just makes me want to quit.
I still wrestle with prompt drift when I'm just
trying to get a static image. I spent three hours
on Sunday just trying to get a character to keep
the same shirt on. Now I have to master eight
different video interfaces. You are not alone
in that fatigue. We hear this from creative directors
all the time. I just learned Runway, but now
Sora 2 is out. Do I have to start all over? So
do they. No. And this is the critical insight
from our research this week. If you try to become
a tool expert, You will lose. Okay. The tools
just change way too fast. You need to become
a workflow expert. That sounds a bit like consulting
jargon. What does that actually mean for someone
listening? It means you stop treating these things
as all -in -one solutions. You start treating
them like components in an assembly line. Give
me a concrete example. Walk me through a workflow.
Okay. Let's say you need to make a corporate
training video. High quality, low cost. Step
one. You don't start in a video app. You start
in a text. LLM like Claude or GPT to write the
script and, crucially, the visual descriptions
for each scene. Okay, so the blueprint comes
first. That makes sense. Right. Step two, you
use an audio synthesizer, maybe 11 labs, to generate
the voiceover track. Ooh! Step three, you feed
that voiceover into an avatar tool like Hagen
for just the talking head parts. Okay, I'm with
you. Step four, you use Luma or Runway to generate
the B -roll, the background footage. based on
those descriptions from step one. And then step
five, you assemble it all in a traditional editor
like Premiere or DaVinci. So you're not asking
one AI to make me a video. You're acting like
a general contractor, hiring different specialists
for each part of the job. Exactly. And here's
why this is so important. Let's say tomorrow
Sora 3 comes out and it just blows Luma away.
Which it probably will. If you have a defined
workflow, you just swap out the step forward
tool. The rest of your process, the script, the
voice, the editing, it stays exactly the same.
So the skill isn't clicking the buttons anymore.
The skill is more like data pipeline management.
That's a great way to put it. It shifts the value
from just technical operation to creative direction
and production. You become the conductor. I like
that. The violin player might change, but the
symphony is yours. That image really helps. It
makes it feel less like I'm drowning in software
and more like I'm building a system. So the tool
matters less than the process. Right. Tools change
monthly. The workflow is the skill that stays
relevant. Okay. Speaking of people trying to
game the system, or maybe direct it in the wrong
way, let's hit the headlines. This Today in AI
segment has a theme, and that theme seems to
be human nature is the bug in the code. There's
definitely a lot of gray area today. Let's start
in the corporate world. There's a story out of
Australia involving KPMG. A partner, a senior
partner, was fined 10 ,000 Australian dollars.
And the reason is just perfect. Why? Because
they used AI to cheat on an internal course.
A course about AI. You almost have to admire
the meta -irony of it. It's incredible. And it
wasn't just him. Over 20 staff members were caught
doing it. This highlights a massive issue for
the enterprise. These firms are charging clients
millions to advise them on AI governance and
AI safety. Trust us, we know how to implement
this safely. Meanwhile, their own leadership
is using the technology to bypass the very training
meant to ensure that safety. It's something else.
It's the cobbler's children have no shoes. But
in this case, the cobbler is just faking his
credentials. It raises a serious question about
knowledge verification. If an AI can pass the
test for you, does the certification even mean
anything? We are entering a world where credentialism
is going to collapse because remote testing is
just fundamentally broken. Shifting gears from
corporate cheating to something much more kinetic
and honestly much more alarming. We have news
about SpaceX and XAI. This is a big one. They're
officially competing in a $100 million Pentagon
challenge. And the goal, building voice -controlled
autonomous drone swarms. Let that phrase sink
in for a second. Voice -controlled swarms. And
here's where I get stuck. We all know Elon Musk
has a history of warning about the dangers of
AI, right? He signed the pause letter. Gave speeches
about autonomous weapons being an existential
threat. Exactly. And now his companies are building
the very thing he warned against. It is a striking
contradiction. But if you look at it through
a geopolitical lens, which is likely how he justifies
it, the argument is always, if we don't build
it... Someone else will. The classic arms race
logic. It is. But this isn't just a bigger missile.
Bringing XAI into the mix means these drones
are agents. Right. These aren't just drones following
a GPS coordinate. No. These are agents that can
perceive, decide, and act. The voice -controlled
part implies high -level intent. You aren't flying
the drone. You're telling the swarm, secure that
perimeter, or neutralize threats in Sector 4.
And the AI figures out the how. The all -over
figures out the how. We are moving from human
in the loop to human on the loop. And eventually
human out of the loop. That's the real threshold.
That is the threshold. Once software decides
when to pull the trigger, the speed of conflict
just accelerates beyond human reaction time.
That is heavy. Let's touch on one more story
before we get technical. Privacy. Meta is planning
something new for their Ray -Ban smart glasses.
The name tag feature. Facial recognition. You
look at someone. And it pulls up their info.
This has been the third rail of augmented reality
for a decade. Google Glass failed, partly because
people were terrified of being recorded. Now
Meta is reportedly planning to launch this maybe
as early as this year. It connects right back
to that rent -a -human story, doesn't it? The
boundary between your digital data and your physical
presence is just dissolving. You can't even walk
down the street anonymously anymore. In 2026,
anonymity is quickly becoming a luxury good.
Before we move on, I have to ask. Of these stories,
the cheating, the drones, the face scanning,
which one feels the most dystopian to you? The
drone swarms automated warfare is a genie you
can't put back in the bottle. I agree. But there's
a practical constraint to all those swarms, right?
You can't put a supercomputer on a tiny drone.
It would drain the battery in five minutes. Exactly.
And that is the perfect bridge to our final segment.
Right. We've painted this picture of a world
with these massive agents and drone swarms, but
the bottleneck to all of this is energy. It's
cost. Running a massive model like QAN 3 .5 or
GPT -5 for every single step of a task is incredibly
expensive. We call it inference cost. So if I
have an agent trying to write a software program
and it takes, say, 50 steps and every step costs
a dollar. That adds up fast. It adds up incredibly
fast. It makes autonomous agents economically
unviable for most businesses. Yeah. But there's
a new paper out called Adapt Evolve that proposes
this fascinating solution. And it cuts compute
costs by 40%. 40%. And it's not by just making
the model smaller. It's much smarter than that.
It's based on a tiered system. Okay. Think of
it like a law firm. You don't pay the senior
partner $1 ,000 an hour to proofread a memo.
You give that to the junior associate. Okay,
so the junior associate AI takes the first crack
at the task. Right. In this system, they start
every single step with a smaller, cheaper model.
A four billion parameter model. But small models
are kind of dumb. They make mistakes. That seems
risky for, say, a drone or a coding bot. They
do. But here is the breakthrough. While that
small model is generating its response, the system
is measuring its confidence in real time. Okay,
pause on that. How does a machine have confidence?
It's not a person. It doesn't have feelings.
It's just math. When an AI generates a word,
it's actually calculating the probability of
that word versus every other word it knows. So
if it says the sky is, and the probability for
the word blue is 99 .9%, that's high confidence.
And if it's wavering. If the probability is flat,
if it thinks blue, gray, and green are all equally
likely, the system detects that wobble. It knows
the model is unsure. So it's like a lie detector
for the AI's own brain? Exactly. And if the system
detects that low confidence, it immediately stops
the junior associate and it escalates the task
to the senior partner, the big, expensive 32
billion parameter model. And if the junior associate
is doing just fine... It keeps the cheap result
and moves on. Whoa! That's surprisingly intuitive.
It's like knowing when to raise your hand and
ask for help. That self -awareness is really
elegant. That self -awareness is the key to efficiency.
And cutting costs by 40 % means that these long
-running autonomous systems finally start to
make financial sense. Right. If you are running
those drone swarms we talked about or the digital
twin Simile is building, you can't afford to
be genius -level smart 100 % of the time. You
only need to be smart when it's absolutely necessary.
So laziness or efficiency is actually a feature,
not a bug. Efficiency is the only way agents
survive. We just taught them how to budget their
brainpower. I love that concept, budgeting brain
power. It's so evolutionary. Nature doesn't use
more energy than it needs to. Why should AI?
All right, we're going to take a very quick break.
When we come back, we're going to tie all this
together. The agents, the video workflows, and
these efficient swarms. Stay with us, mid -rule
placeholder. And we are back. Okay, we have covered
a massive amount of ground today, from the sheer
scale of Quen 3 .5, to the workflow revolution
in video, to the ethical messes at KPMG, and
finally this adapt -evolve efficiency. What's
the through line here? If there's one theme,
I think it's the shift from intelligence to agency.
Break that down for me. For the last few years,
we've been obsessed with the question, is the
model smart? Now we're asking a different question,
can the model act? Quen gives it the tools. Adapt
Evolve gives it the budget and the video workflows,
they give it the output. We are building the
infrastructure for action. It's not about what
the AI knows anymore. It's about what it can
do and how cheaply it can do it. Precisely. But
then you have the human element just crashing
right into it. That's the KPMG story. That's
the rent -a -human story. Right. We're building
these incredibly efficient autonomous systems,
but the humans involved are still, well, they're
still human. We cheat. We pretend. We find loopholes.
That remains the most unpredictable variable
in the entire equation. You can optimize the
compute cost of a drone swarm by 40%, but you
can't optimize the ethics of the person who's
commanding it. That is the truth. So as we move
forward into 2026, the technology is stabilizing.
The workflows are being defined. The costs are
coming down. The question is no longer, can we
build it? It is, how do we live with it? Exactly.
And that leads me to my final thought for you,
the listener, today. I want you to think about
that adapt -evolve concept again. The budgeting
of brain power. Using high intelligence only
when it's really necessary. Exactly. We live
in this world that demands we are on 100 % of
the time. High alert, high productivity, maximum
processing power. But maybe, just like these
agents, we're burning way too much inference
cost. A permission structure for human inefficiency.
I like that. Maybe. Perhaps the smartest thing
we can do is identify which parts of our day
actually need the senior partner and which parts
can be handled by the junior associate. Yeah.
Save your high -level compute for the problems
that actually need it. I like that. Efficiency
isn't about doing more. It's about thinking less
but thinking better. Something to mull over as
you start your week. If you enjoyed this deep
dive, hit that subscribe button. We've got more
coming your way. Always more to learn. Stay curious.