We watched Meta start the year as the open source
darling, sharing their llama models everywhere,
inviting the community in. Right. Now they are
secretly building something called Avocado and
they are betting the entire company's future
on keeping it locked down. That pivot from, you
know, communal sharing and transparency to suddenly
locking down their biggest asset. is more than
just corporate strategy. It's an identity crisis
for Meta, signaling a massive high stakes reversal
that affects the entire AI landscape. They feel
they have to win at the very top tier, whatever
the cost. Welcome to the Deep Dive. Our mission
today is to unpack these fundamental shifts.
Using the recent source material you shared with
us, we have a lot of tension to work through.
First, we're going to dig deep into Meta's dramatic
internal culture shock and the mysterious delay
of that closed source avocado model. Yeah, there's
a lot there. Then we'll pivot to AI in the wild,
looking at practical chat GPT tips, the rapid
commoditization happening in the voice market,
and some of the unexpected ways AI is showing
up. In ads and even late -night chats. Exactly.
And finally, we'll analyze a truly significant
scientific breakthrough, Microsoft's open -source
Gigatime model, which is dramatically reducing
the cost of cancer research. Down to about $10
per analysis. It's incredible. It is. So we aren't
just reading headlines today. We are looking
for the why. Behind the biggest moves connecting
the docks between, you know, Silicon Valley drama
and breakthroughs that fundamentally change science.
So let's start with Meta. Just months ago, they
were the hero of the open source community hyping
Lama. But the sources show Lama is barely mentioned
now. It's almost as if it's been quietly abandoned.
What's fascinating here is how immediate and
radical this shift to secrecy has been. And it's
all about this new model, Avocado. The secretive
next gen model is Avocado. It was expected to
launch by the end of 2025, but that timeline
is already slipping. Push back. Push back to
Q1 of 2026. And the biggest fear among researchers
is that this will be a fully closed source model.
That's a complete dramatic reversal for them.
It is. I mean, they built their modern AI reputation
on being the most generous sharer of foundation
models. So why the sudden loss of faith in their
own strategy? Well, the core hypothesis outlined
in your sources is simple, if a little brutal.
Okay. Meta concluded that open source simply
didn't pay off. They poured billions in, but
they didn't get the revenue or the market lead
they wanted. So they feel they need to own the
next GPT level model to stay relevant. They feel
they absolutely have to. That urgency is translating
into some pretty brutal internal changes. We're
hearing the culture has really fractured. It
has. The new mantra inside key AI teams is demo,
don't memo. No more lengthy workplace updates
then. Exactly. They're forcing teams to operate
like these intense, scrappy startups, pushing
for immediate results. And that pressure cooker
environment is leading to, what was it, 70 hours?
Workweeks. Reported 70 -hour workweeks. Yeah.
Coupled with targeted layoffs that are specifically
hitting the FAIR division. And FAIR is their
fundamental AI research group. That's where all
the academic, open -minded research used to happen.
That was the heart of it. And that culture is
being dismantled. It seems less about sustainability
and more about desperation. Deliver or exit.
And we're seeing that at the top, too. The sources
highlight the replacement of longtime meta executives.
Right. And the departure of chief scientist Jan
LeCun back in October when a pioneer like LeCun,
who basically is the champion of open research,
leaves. Or is pushed out. Or is pushed out. Yeah.
It signals a fundamental housecleaning. It's
all about securing results, period. And that
pressure is also being intensified by some hard
infrastructure challenges, right? Precisely.
Meta's massive, what, $27 billion data center
still isn't fully ready. So they have to rely
on third -party clouds just to train their own
models. Which drives up costs and adds latency.
They're playing catch -up, and they're doing
it by changing their entire identity. Okay, so
let's unpack this. The main reason for Meta's
dramatic cultural overhaul. They believe open
source failed to pay off, forcing this high -stakes
closed model push. So that's the heavy corporate
drama. Let's pivot now. What ties all this together
is how fast the technology itself is moving outside
of these big labs. Yeah, let's look at what that
means for you, the user. Okay. We can start with
some practical nuggets. OpenAI recently dropped
six official chat GPT tips for getting better
output. I saw that. The concepts aren't entirely
new, but it's a useful summary. It is. Things
like starting with a clear role for the model.
And, you know, I'll admit I still wrestle with
Trump drift myself. Oh, me too. It's that frustrating
moment when the model just starts losing the
original context of your conversation. Exactly.
So having these codified tips is actually pretty
helpful. And speaking of utility, the Adobe integration
sounds like a real game changer. Oh, it is. Photoshop
and Express are finally accessible just using
natural language prompts. You can literally edit
images just by talking. Which radically lowers
the barrier to entry for creative work. No more
navigating complex menus. Not every deployment
goes that smoothly. We saw that with McDonald's.
Right, their AI -generated Christmas ad. The
sources detail this quick, intense backlash that
caused them to pull it. People called it creepy
and god -awful. Yeah. It's a great example of
how quickly the public rejects AI when it just
feels unsettling. It's a crucial lesson for marketers,
for sure. And on the business front, Forbes released
a surprisingly honest prediction piece for 2026.
The honesty was the key part. Yes. They explicitly
admit they got their previous AI timing estimates
wrong. Now they are focusing on 10 specific bets
for automation and the future of work. That kind
of candor is so rare. And we're also seeing the
competition just heat up globally. Google launched
a massive move in India. Aggressive. They're
offering their AI Plus plan with Gemini 3 Pro.
It starts for new users at about $2 a month.
Just $2. They are clearly going after market
share where Meta's open model struggled to get
a commercial foothold. And all this usage is
revealing some fascinating things about us. Microsoft's
end of year report analyzed 37 .5 million chats.
And they found some highly unexpected patterns.
Things like 2 a .m. philosophy chats. Philosophy
chats. That's amazing. Not productivity, but
deep late night reflection. Exactly. And whoa,
imagine scaling that analysis to a billion queries
across different time zones. Right. It paints
a picture of AI, not just as a tool for drafting
email. but as this silent late night sounding
board for You know, human existence. A nonjudgmental
digital listener. I like that. And this pressure
is hitting niche companies, too. Eleven Labs,
the voice synthesis leader, they just raised
$100 million. But their CEO thinks voice AI itself
is going to be commoditized. He does. So their
new bet is on full AI agents, music generation,
and crucially, deepfake protection tools. So
what does the Eleven Labs shift, moving away
from just voice, reveal about the current AI
market? Even successful niche tech will commoditize
fast, pushing focus to f***. full AI agents.
That makes perfect sense. The value moves from
the parts to the whole system. Okay, let's shift
gears entirely now. We're moving from commercial
tension to a highly impactful piece of scientific
research. This is where that shared knowledge
model really, really changes lives. We're talking
about Microsoft's breakthrough with an open source
model called Gigatime. And what's remarkable
here is that it democratizes access to high -end
medical diagnosis. It really does. So how does
it do that? It takes a basic, low -cost, $10
tissue slide, the kind any local hospital can
produce, and transforms it into the rich, detailed
immune system analysis that previously required
specialized expensive machines. And human specialists
and days of work. Right. It essentially turns...
cheap, widely available data into high -end diagnostic
data. It maps the immune system and the tumor
environment. Like creating these high -resolution
cancer maps. Exactly. To do this, the model was
trained on a massive data set, 40 million cell
samples from Providence Health. 40 million! That's
a huge data scale. It's like stacking Lego blocks
of data, but for medicine. The depth is unparalleled.
And this wasn't just a lab exercise. No, it was
tested on real patients. Over 14 ,000 real patients
across 24 different cancer types. And the outcome.
It created a virtual tumor library of more than
300 ,000 high -resolution images. And it surfaced
over 1 ,200 entirely new immune patterns. Patterns
linked directly to cancer stage and survival
statistics. Yeah. That is profound. And the key
strategic choice here, again, is that Microsoft
open -sourced the model. That open source move
is strategic genius. It accelerates global adoption
immediately. Creating a massive real world feedback
loop. Yes. The playbook is clear. Cheaper tools
mean more hospitals use them, which generates
better local data, which builds better models
globally faster. It's a virtuous cycle for science.
So if we connect this to the bigger picture,
what is the critical advantage of using Gigatime's
open source strategy? It builds a massive feedback
ecosystem, leading to better models faster through
global use. So what does this all mean for us?
The AI industry right now is defined by these
incredibly dramatic internal shifts. The cultural
demolition inside metaphor avocado. Right. And
at the same time, this phenomenal, undeniable
real world utility like Gigatime, we're watching
two radically different philosophies collide.
And the key takeaway is this. The race isn't
simply about building the most powerful model,
period. It's about choosing the strategy closed
and proprietary, which is Meta's new bet, or
open an ecosystem building like Microsoft is
doing in medicine. Which one wins the long -term
innovation war? Exactly. And consider this as
you go about your day. Does the future of critical
innovation hinge on proprietary control, which
Meta is betting billions on with a secretive
model like Avocado? Or does the greatest human
impact come from shared open knowledge demonstrated
by life -saving accessible models like Gigatime?
Which path truly advances society faster? Thank
you for sharing the sources for this deep dive.
Keep exploring that intersection of technology
and human impact because that's where the real
story lives.