For decades, biology had this this dirty little
secret. Researchers would look at the human genome,
three billion letters of code, and they basically
shrug at most of it. Yeah. They told us that
98 percent of our DNA was just junk. Just biological
noise, leftovers from evolution that, you know,
didn't really do anything. It's honestly wild
when you think about it. It's like reading a
book and assuming 98 percent of the pages are
just typos because you don't understand the language.
Exactly. But then all of a sudden, the lights
turned on. We just found the instruction manual
for that 98%. And at the exact same moment, we're
decoding the biological noise inside us. We are
drowning in digital noise outside of us. Andres
Karpathy just dropped this huge warning about
a slopocalypse coming later this year. Two frontiers
colliding at once. One unlocking the code inside,
the other just drowning us in code outside. It
is going to be a wild Wednesday. Welcome back
to The Deep Dive. It is Wednesday, January 28th,
2026. I hope you're having a thoughtful week.
I'm your host. And I'm ready to break this all
down. We have a fascinating stack of reports
today, and it really feels like we are living
in a moment of incredibly high contrast. Oh,
totally. We're seeing the microscopic and the
macroeconomic shifting at the exact same time.
It's like the interior architecture of, you know,
of... Humans and the exterior architecture of
the Internet are both being rewritten simultaneously.
Exactly. So for you listening in, here is the
roadmap for today's deep dive. We are going to
start with DeepMind's Alpha Genome. They have
essentially turned the lights on in a very dark
room of human biology. We need to understand
why that 98 % matters so much. Then we're pivoting
to... A classic David and Goliath story. A 30
-person startup called RCAI just took on Meta
in China for $20 million. Yeah. Spoiler alert.
They might have actually won. We will also talk
about the agentic future. Yahoo, Chrome, Amazon,
everyone is pivoting to agents and it is costing
jobs. I mean, 16 ,000 people laid off at Amazon
alone. Right. We have to talk about the economics
of that. And finally, we'll hit some rapid fire
tools, including something people are calling
the Siri killer and the tools you might need
to survive this slope apocalypse. Okay, let's
do it. Let's start with the biology. This one
really caught my attention because junk DNA is
a term, you know, most of us learned in high
school. It was just an accepted fact. It was
dogma. It was written in the textbooks. For the
longest time, science focused only on that 2
% of DNA that codes for proteins. The genes that
make your eyes blue or your hair curly, that
kind of stuff. Right. The rest, the non -coding
DNA was treated like packing peanuts, just there
to fill space. Which in hindsight, calling it
junk was pretty arrogant of us. We just assumed
if it didn't make proteins, the building blocks,
it didn't matter. But nature is rarely that wasteful.
No, nature doesn't carry around 98 % dead weight
for millions of years. But now Google DeepMind
has released Alpha Genome and this, this feels
like a watershed moment. This is the missing
piece. Alpha Genome is an AI system that predicts
how mutations in that mysterious 98 % affect
gene expression. Okay, let's pause on gene expression.
I want to make sure everyone really gets that
concept. When we say it affects expression, what
does that practically mean? Okay, think of your
DNA like a massive house with thousands of light
bulbs. We've known where the light bulbs were
for a long time. Those are the protein -coding
genes. But a house isn't just the bulbs. It's
the wiring behind the walls. It's the dimmer
switches. It's the whole fuse box. So the junk
was actually the wiring. Exactly. The non -coding
DNA tells the genes when to turn on, where to
turn on, and how brightly to shine. Alpha Genome
is essentially mapping that wiring for the first
time. It tells us if you cut this wire here in
the non -coding region, the light in the kitchen
goes out. That is a huge shift in perspective.
The source material used a really helpful analogy
here about stacking blocks of discovery. I found
it really helped me contextualize where Alpha
Genome sits. Yeah, I love this visual. It shows
how AI has just systematically conquered biology.
So first you had Alpha Fold. That solved protein
structure. That was figuring out the actual shape
of the legal blocks. Which was huge for drug
discovery, because if you know the shape, you
know how to attach things to it. Precisely. Then
came alpha -mescence. That showed us what happens
when the blocks break or mutate, but only within
the coding regions. And now alpha -genome. It's
the final layer. It combines multiple massive
data sets on gene regulation to show us the rules
for the non -coding regions. It can predict how
a single letter change in the junk can cause
a gene to malfunction. It is staggering when
you actually think about it. We are effectively
turning the human genome into a programmable
map. We are. But I have to ask, why now? I mean,
why was this the hardest part to crack? Because
the signal -to -noise ratio is just terrible.
In coding regions, there are strict rules. Three
letters equals one amino acid. It's a clear language.
In the non -coding regions, it just looks like
random chaos. You need massive transformer models,
basically the same tech behind ChatGPT, to look
at that chaos and find the subtle patterns that
human eyes could never see. Now, we should temper
the excitement just a little bit. The report
mentions this isn't clinical grade yet, right?
You can't go to your doctor tomorrow and get
an alpha genome scan. Correct. It is strictly
a research tool right now. And it's currently
only trained on humans and mice. So don't expect
it to cure rare diseases by next Tuesday. But
that raises a question for me. If it's just a
research tool for now, why is this a headline
breakthrough? Why should the person listening
care about a mouse model tool? Because it unlocks
the 98 % of instructions that actually control
our biology. That is the key. It shifts the entire
map of discovery. It does. Speaking of shifting
maps and finding efficiency where we didn't think
it existed, let's pivot to the world of large
language models. Let's do it. From biological
code to synthetic code. There has been this narrative
for a few years now, the scaling laws. You know,
if you want to build a frontier model, something
that can reason, code, write poetry, you need
billions of dollars. You need to be open AI,
anthropic or meta. You need a warehouse full
of H -100s and the GDP of a small country. That's
the rule. Or so we thought. Enter RCAI. This
story is just... They're a scrappy 30 -person
startup. They don't have a massive campus or
Microsoft backing them. And yet they just released
a model called Trinity. A 400 billion parameter
model built from scratch in six months. For $20
million. That number is just absurdly low in
this industry. Usually $20 million is the coffee
budget for these training runs. We are used to
seeing training costs. In the hundreds of millions.
So how did they do it? Is it some sort of magic
trick or did they actually find a better way
to bake the cake? It's efficiency over brute
force. The CEO, Mark McQuaid, he's an interesting
character. He came from Hugging Face. He realized
that most of the data giants feed into these
models is actually, well, junk. Kind of like
the junk DNA we were just talking about. So they
just curated the data better. Aggressively. They
focused on high quality, dense information. They
also used really advanced model merging techniques,
taking the best parts of different training runs
and stitching them together. And his motivation
was fascinating, too. It wasn't just about saving
money. No, it was ideological. He wanted independence.
He didn't want to rely on China -sourced models.
And he was tired of Meta changing their licenses
all the time. He wanted a sovereign U .S. model.
Yeah, so he just built his own. And the performance
isn't just good for a startup. No, it's legitimately
competitive. It benchmarks against Meta's Lama
4 Maverick. It also goes toe -to -toe with China's
GLM 4 .5. And the report says it actually wins
in some key areas. In coding, math, and reasoning
tasks, you know, the hard stuff, it beats them.
It might lose on creative poetry or writing a
generic email, but for logic, it's a beast. That
is significant. It comes in three flavors, right?
Yeah. You've got base. which is the raw model,
large preview, which is instruction tuned to
be helpful, and then TrueBase. TrueBase. That
one sounds interesting. What makes that different
from just a regular base model? It's zero post
-training. It's designed for labs or companies
that want a totally clean foundation to build
on. Okay. See, most models come with safety filters
or biases baked in by the company that made them.
TrueBase is pure clay. You can mold it into anything
you want without fighting the original creator's
hidden rules. This really disrupts that whole
bigger is better narrative. It suggests efficiency
might actually matter more than raw capital.
It proves you don't need a trillion dollar market
cap to compete at the frontier. It implies the
moat that Google and OpenAI built, that we have
more money than God, moat. might not be so deep
after all. So let me push you on this. Does a
$20 million model actually threaten a company
worth trillions? Or is this just a nice niche
product? Yes, because it proves efficiency and
open access can outmaneuver raw cash. It pushes
the intelligence to the edge. And that efficiency
is accelerating everything. Which brings us to
our third segment, the agentic shift. And this
is where things start to get messy. We are seeing
a massive rollout of agents. And when we say
agents, we don't just mean a chatbot you talk
to. We mean software that goes out and does things
for you. Right. Action -oriented AI. Yahoo just
launched Scout, which feels weird to say. I mean,
Yahoo is back in the innovation game. Which is
pretty cool, actually. It lets you chat with
Claude while you search the web. But the key
thing is... Real links, no hallucinations. It's
grounded in Yahoo's index. Then there's Google
Chrome. It's basically becoming an agent now.
Oh, yeah. Chrome is eating the entire OS. It's
doing everything. Auto -browse, side panels,
editing images. It's not just a browser anymore.
It's a doer. It can log into your Gmail, find
a receipt, and update a spreadsheet without you
clicking a button. Exactly. An Airtable -launched
super agent. that one is for the real power users
for sure it can run 20 task specific agents at
once to compile research reports i mean imagine
having 20 interns working in parallel for 30
seconds that's super agent and of course mark
zuckerberg is teasing meta's 2026 plan personal
super intelligence for commerce which is really
just code for agents that buy things for you
and harvest your data They want an agent that
knows you better than your spouse, so when you
need toothpaste, it's already ordered. But there
is a cost to all this speed, and this is where
the tone of the reports gets a bit darker. Andres
Karpathy, one of the godfathers of modern AI,
is warning of a slipocalypse in 2026. I love
that word, slipocalypse. It's terrifyingly descriptive.
What exactly is he worried about? It's the supply
and demand of content. Agents like Claude and
Codex have just gotten too good too fast. We
can now generate 1 ,000 blog posts, a million
comments, or 10 ,000 videos in minutes. We're
generating content faster than we can possibly
process it. Exactly. We are flooding the entire
digital ecosystem with slop. Low -quality AI
-generated noise designed to be read by other
algorithms, not humans. It's the dead internet
theory coming true. Agents writing slop for other
agents to index. And this sufficiency is hitting
the workforce hard. Amazon just laid off 16 ,000
people. That's the second major cut in three
months? It is. And the internal memo was leaked.
They explicitly said it was too streamlined for
the AI race. That is the brutal reality of this
agentic shift. Companies are trading headcount
for compute. They look at the budget and say,
why pay a middle manager 100K when 10K of GPU
credits can run an agent that does the same reporting?
It's a massive capital reallocation. And it's
not just Amazon. I mean, look at Anthropic. They
just raised $20 billion at a $350 billion valuation.
But look at their cash flow. They don't expect
to be positive until 2028. So it's a high -stakes
gamble, high revenue forecasts, $55 billion for
2026, but a massive, massive burn. It's a land
grab. And the territory is our attention and
our jobs. They are betting that whoever builds
the best agent controls the whole economy. I
have to admit, reading about all these super
agents that can run 20 tasks at once, it makes
me feel a bit inadequate. Oh, totally. There
was a piece in the source material about the
real reason you're still slow with AI. I related
to that so hard. Even you. You track this stuff
for a living. You're supposed to be the cyborg
here. Absolutely. I look at these tools. Airtable
running 20 agents. And I'm still just trying
to write a good prompt. I find myself falling
into old habits. Right. I'll default to doing
things manually just because it feels safer or
because I don't quite know how to articulate
what I want to the AI. It's actually comforting
to hear that. I think a lot of listeners probably
feel that same AI anxiety. I still wrestle with
prompt drift myself. I feel overwhelmed rather
than optimized half the time. It's the paradox
of automation, right? The tools get faster, but
we still have to direct them. And that's the
bottleneck, the human manager. If you don't know
what to ask, the speed of the answer doesn't
really matter. Exactly. Speed without direction
is just crashing faster. Which leads to a really
difficult question. If agents do everything...
The execution, the browsing, the coding. What
happens to the human workforce mentioned in those
layoffs? We move from doing the work to managing
the slop in the strategy. Managing the slop.
That is a grim but likely very accurate job title
for the future. It's the new middle management.
You're not writing the email. You're checking
the email the bot wrote to make sure it didn't
hallucinate a meeting with a client that doesn't
exist. Before we look at the specific tools that
might help us manage that met... The weapons
for the slipocalypse. Let's take a quick pause.
We are back. Let's talk about the toolkit for
2026. If the internet is becoming this noisy,
agent -filled jungle, what do we use to cut through
it all? The survival kit. First up, something
called Moltbot. Or Claudebot. People are calling
this the Siri killer. That is a bold claim. We've
heard Siri killer every year for about a decade.
We have. But this one is different because it's
always on. It runs your computer. It sees your
screen. It remembers your workflows. So it actually
has context. Deep context. Siri fails because
it doesn't know what you were doing five minutes
ago. Multbot knows you just opened that invoice.
So when you say pay it, it knows what it is.
That's why it's going viral. Then there is OpenAI's
Prism. This seems targeted at a very specific
demographic. This is 100%. For the scientists.
It's a workspace powered by GPT 5 .2. It allows
them to write and collaborate on research. Yeah.
And it's trying to accelerate the kind of discoveries
we talk about with Alpha Genome. It's designed
to reduce hallucinations and citations. It connects
to real academic repositories. It's an attempt
to keep science grounded in truth while using
AI for speed. We also have Kimi K2 .5. This one
is coming out of the Asian markets, right? Yes,
a new intelligent model. It excels in visual
understanding and in code. It's really just another
proof point that the model layer is becoming
a commodity. Everyone has a smart model now.
And for the marketers, the people perhaps contributing
to the Slopocalypse, there's Rumora and Autosend.
Rumor is fascinating and also kind of terrifying.
It finds viral videos in your niche and places
brand comments on them automatically. That sounds
like it absolutely contributes to the slop, doesn't
it? That's literally automated noise. It 100
% contributes to the slop. It's bots talking
to humans in the comments section. But it's effective.
And AutoSend handles transactional emails. Boring
but essential plumbing for the internet. Yeah.
It ensures the receipt gets to you without a
human ever having to hit send. When I look at
this list prism for scientists, automated viral
comments for marketers, I wonder about access.
How so? Are these tools for everyone? Or are
we creating a divide where only the 1 % you see
in the headlines get the super intelligence?
They start with the 1%, but agents like Chrome
make them accessible to everyone. That democratization
is key. It is. The barrier to entry is dropping
to zero. So the question isn't, can you access
it? It's, do you know how to use it? So let's
zoom out. We have covered a lot of ground today.
From the microscopic DNA to the macroeconomic
layoffs. A lot. The theme of 2026 so far really
seems to be the interior versus the exterior.
That's a great way to frame it. On the interior,
you have DeepMind unlocking the dark matter of
our biology with Alpha Genome. We are finally,
finally reading the code that actually runs us.
We're looking inward and finding a programmable
map. And on the exterior, we have agents. Yahoo,
Chrome, Moltbot managing our digital lives. And
potentially leading us right into a slopocalypse
of infinite noise where we have to fight to keep
our heads above water. It is this tension between
understanding our natural code and being completely
overwhelmed by our synthetic code. And the human
is stuck right in the middle. Just trying to
figure out which buttons to push. So what is
the takeaway for you listening to this? If you're
feeling that anxiety you mentioned earlier. Don't
just let the agents run on autopilot. That's
the trap. Right. Look at those habits that are
keeping you slow. Be the manager, not the bystander.
If you just watch, you're going to get buried
in the slop. You have to be the editor of your
own digital life. You have to direct the flow.
You have to maintain your own agency. Exactly.
Agency over agents. I want to leave you with
a thought today. We talked about a 30 -person
startup beating meta. We talked about an AI reading
the 98 % of DNA we ignored for 50 years. Two
things that were considered completely impossible
just a few years ago. Exactly. So if RC can beat
the giants with efficiency and Alpha Genome can
read the unreadable, what other impossible barriers
in your life or your business are just illusions
waiting to break this year? That is the question.
Thanks for diving in with us. We'll see you in
the next one.