OK, so let's get into this. We're looking at
three really different stories about where AI
is going right now. We have this new household
robot that's designed to be, you know, cute on
purpose. Right. Then you have these AI models
just battling it out in these secret benchmarks.
And on the other end, a group just dropped the
entire blueprint for a major language model.
True open source. And if you need proof of just
how intense all of this is getting, get this.
Meta is now asking the government for permission
to trade electricity. Yeah, just to feed their
AI, the stakes are getting incredibly high. Welcome
to the deep dive. Our mission today is to unpack
these sources for you and really figure out what
matters most. We've got three main areas we're
going to hit. First up, that dish doing robot
and the surprisingly simple cheat code they found
for training it. Then we'll get into the latest
industry headlines. Who's winning the AI race
and what does all that energy consumption really
mean? And finally, we'll look at a really radical
new definition of open source from the AI2 team
that could change everything. Let's start in
the home, in the kitchen, actually. Yeah, the
cute robot. Sunday Robotics. just came out of
stealth with their memo robot. And we've all
been hearing that promise of a Rosie the robot
for, what, decades now? Right. A real helper
around the house. It's finally starting to feel
a little more tangible, and I think it's because
they solved the data problem in a really different
way. The huge roadblock for home robots has always
been the data. Right. Getting good data on how
humans actually do things. Exactly. How do you
open that specific drawer or pick up a mug without,
you know, crushing it? It's all about dexterity.
And traditionally that meant using a super expensive
telop rig. Yeah. Where a skilled operator wears
all this motion capture gear to control the robot
remotely. It's precise, but it's also incredibly
slow and costs a fortune per hour. So they just
threw that whole idea out. Completely. They swapped
it for the $200 skill capture gloves. $200. Yeah.
Real people just put them on in their own homes
and do chores. Live their lives. Pretty much.
They're clearing tables, folding laundry, loading
dishwashers, even pulling espresso shots. It
captures pure human motor skills, but cheaply
and at a massive scale. That's brilliant. You're
just crowdsourcing the training data from the
8 billion people who already know how to do the
job. And the sources say this method generated
10 million episodes of data. 10 million. That's
just, that is a massive shortcut. It is. It just
rapidly accelerates how you teach a robot complex
home dexterity. So how good is it? Well, the
results are pretty impressive. Memo can do a
full table -to -dishwasher run. It's a sequence
of 68 different dexterous moves. And the big
question. The big question. Across more than
20 live demos, they recorded zero broken wine
glasses. Wow. Okay, that's the real test. That's
the real test. That kind of delicate handling
tells you the data quality is there. But wait,
if the gloves are that cheap, isn't filtering
all that messy crowdsourced data? Just an absolute
nightmare. I mean, couldn't cheap data just lead
to cheap Dex Purdy? That's the core challenge
for sure. But look at the team. It's a bunch
of Stanford PhDs and ex -Tesla FSD engineers.
They lived through that whole millions of miles
data collection problem. They know how to filter
noise. They explicitly said they chose to leverage
8 billion humans instead of millions of miles.
OK, that background brings up some skepticism
for me, though. Yeah. The FSD team was, let's
be honest. Famous for overpromising on timelines?
Mm -hmm. Does that affect how we should see their
late 2026 ship date? It definitely raises an
eyebrow, but I think the domain is more constrained
here than full self -driving. And their other
big choice is maybe just as important. The design.
The design. The robot is soft. It's round. It
wears a little hat. So cuteness is now a technical
feature. Exactly. Because it builds trust. If
this little non -threatening robot makes a mistake
while it's learning, you're more likely to forgive
it and let it keep trying. Instead of unplugging
it and throwing it in the closet. Right. User
empathy actually becomes part of the training
loop. So if we boil this down, leveraging crowdsourced
human experience. rapidly accelerates complex
home dexterity. That's it. It just fundamentally
changes the robotics timeline. Okay, let's shift
from the kitchen counter to the wild world of
AI rankings. Yeah, it's a high -stakes race.
And these benchmarks, they used to be pretty
stable. Not anymore. There's a new one, the Humane
Benchmark, that just sent out some shockwaves.
It's designed to test really modern stuff like
advanced reasoning, handling images and text,
and safety. And the big surprise? GPT came in
at number eight. Number eight. I mean, that is
shockingly low for a model that basically felt
like the default for so long. If GPT is at eight,
who's at the top? Top spot went to Gemini 2 .5
Pro. After that's Tight Race. You've got Deep
Seek, Mistroll, and then get this Grok 4 and
Grok 3 took spots 4 and 5. So the performance
gap is just closing incredibly fast. Faster than
ever. And these technical leaps are leading to
some pretty wild spectacle too. You mean the
Gemini 3 Pro ad on the Las Vegas sphere? Yeah,
that thing. People were genuinely arguing online
about whether it was real footage or AI generated.
Because the quality was just too good to tell.
That line is getting very, very blurry. And it's
not just visuals, it's speed. We just saw the
first fully driverless race cars in Abu Dhabi.
I saw that. Hitting 155 miles per hour, racing
wheel to wheel. That's all algorithmic decision
making at insane speeds. And then on the creative
side, you have things like Google's Nano Banana
Pro. Which is just doing crazy things with images.
Fixing specific text inside a photo or blending,
what, 14 different images together seamlessly.
It's just... It's hard to even keep up. It really
is. I have to say, even with all these powerful
new tools, I still wrestle with prompt drift
myself. Oh, really? Yeah. I'll find a model,
gives me perfect output for like the first week
it's out. Then a month later, the consistency
just degrades and I'm back to reengineering my
prompts all over again. Getting reliable output
is still a huge challenge. That inconsistency
makes the whole infrastructure problem even worse,
doesn't it? We're talking about massive amounts
of energy to run these things. Which brings us
back to meta. Bloomberg is reporting that because
the grid is struggling to keep up with their
AI hunger, they've asked the feds for permission
to trade electricity. They're trying to become
an energy company. Essentially, yeah. A utility
company just to service their own data centers.
They're so power hungry, they're trying to shape
national energy strategy just to keep the lights
on for their models. Wow. So if we connect the
dots here, the hidden cost of scaling these huge
models is that AI is now powerful enough to dictate
national energy infrastructure strategy. It changes
the entire planning horizon for the energy sector.
We hear the term open source. A lot in AI. And
for a while now, that's usually just meant one
thing, sharing the model weights. Right. And
we should probably define that. The weights are
just the final set of numbers that make the model
work. It's the finished product, but not the
recipe. Exactly. So if you're a researcher trying
to figure out why a model is biased, just having
the weights is not enough. It's a black box.
You can use it, but you can't truly understand
it. Which is why this new release from the AI2
team, OMO3, is such a big deal. They've... They've
radically redefined the term. They're providing
everything, the full training data, every line
of code, every training checkpoint. The checkpoints
are like saved games, right? The model at different
stages of learning. Exactly. And every decision
they made along the way. It's a full -on anti
-black box effort. It's all about traceability.
That is a level of transparency we just haven't
seen. It's like, imagine if Tesla didn't just
give you the car blueprints, but handed over
the entire assembly line. Okay. The notes from
the engineers, the faulty parts they threw out.
Every software tweak, it completely opens up
the science behind it. Let's talk about the actual
models. They released Olmo 3Think, a 32 billion
parameter model. And that one is focused specifically
on chain of thought reasoning, so it's perfect
for researchers. And you have the base models,
a 7B and a 32B. And these have a huge 65 ,000
context window. That's 16 times bigger than the
last version, so they can handle enormous documents
for coding, math, comprehension. And there's
also a smaller 7B instruct model, right? Tuned
for chat and using tools, which is great for
running locally. Yeah, but what's really revolutionary
is the cost efficiency here. Right. The sources
say the 32B model rivals the performance of something
like Quinn. But it used six times fewer training
tokens to get there. Six times. That's an incredible
reduction in resources. Which means the training
cost was only about two point two million dollars.
That's that's. astonishingly cheap for a model
this capable. It's like building a high -performance
rocket on a motorcycle budget. It just shows
what smart data curation can do. Whoa. I mean,
imagine scaling that kind of full traceability
to a billion queries, knowing exactly why you
got a certain output, not just guessing. And
it's all out there now. AI2, Playground, Hugging
Face, you can run it locally. And it's all under
the Apache 2 .0 license. That permissive license
is key. It is. But that ability to trace a response
all the way back to the original training recipe?
That's the unique part. Researchers can now dig
in and see exactly where a bias comes from or
why the model learned a certain thing. So if
you look past just the low cost, the single biggest
benefit here is that complete transparency allows
for critical investigation and much faster model
improvement. It just eliminates all the guesswork.
So we've really covered three huge shifts today.
We have, first, robotics found a way to achieve
complex dexterity, not with expensive... gear,
but with a clever, cheap, crowdsourced data trick,
the $200 gloves. Right. Then second, the AI performance
race is shifting the whole landscape and putting
this just unbelievable strain on our energy grid.
Meta trying to become an energy trader is the
perfect example of that. And finally, the very
definition of open source has been expanded in
a radical way. Yeah, showing that efficiency
and transparency can actually rival sheer scale.
These three things, dexterity, strain, and transparency,
they're all colliding right now. And that collision
is going to define the next few years in tech.
That's the big takeaway. So here's a thought
to leave you with. If these highly efficient,
fully traceable models like OMO3 become the standard
models that cost just a couple million dollars
to train and give you total insight, how quickly
do the old proprietary black box AIs become obsolete?
The ones that demand meta -sized power grids
start to look really expensive and you can't
even see inside them. something for you to consider.
That's a really important question for where
this whole industry is headed. Thank you for
joining us for this deep dive. We look forward
to diving into the next stack of sources with
you.