For over 60 years, we've been promised robots that can do
almost anything. I think robotics has given
itself a reputation of being one of the biggest technology over
promises ever. But right now, something's
different. In this month, October 2025 / 6
billion dollars have been poured into robotics by some of the
biggest names in investing, and they've claimed that their
future is physical AI. By the end of today's episode,
you'll start to understand what's actually changed, why
people are putting so much money into robotics all of a sudden,
and whether we're at the beginning of an iPhone era
moment or just the start of another period of
disappointment. This is another loop with Jack
Horton. I hope you enjoy the show.
So let's start by, I guess, setting the scene a little.
I think ever since the Jetsons in I think the 1960s or 70s had
this like robot made, it's been promised by I guess
technologists and robot makers that these are the future of
household helpers. And in fact, I think experts,
you know, ever since the 1950s have been pretty confident in
predicting that, you know, by the year 2000, most American
homes will have some form of automation to make coffee or
close windows. But I don't think we've ever
reached that cool and exciting future because none of this has
happened, really, other than the ability, I guess, to use a
mobile phone app to shut your windows in certain hotels.
And also, you can't really forget the awful, like, robot
hoovers and lawn mowers that you get in some places.
So then why all of a sudden is someone pouring $6 billion into
robotics ventures? Because that's what's happened
this month. SoftBank pretty famous for
throwing away money at times, but they've made the headlines
recently because they've put $5.4 billion alone to acquire a
massive robot division with the CEO of SoftBank saying that
Softbank's next frontier is physical AI.
And Elon Musk in the last month for XAI, such as AI startup
that's inside of X and Twitter has actually started hiring top
NVIDIA researchers to develop what is described as world
models. So these are like AI language
models and systems that I guess give robots an intuitive
understanding of physical world objects and gravity and
basically how the world just works.
There's also been big humanoid robots making strides in the
world. For anyone that's been on
Instagram and is connected to technology accounts, really
you've just seen the robot called Figure.
So Figure O3 is a humanoid robot that's basically aimed at
households to do general chores. And in their videos, you can go
YouTube. This Figures robot has been, you
know, picking up clutter or taking clothes out the washer
and loading dishwashers. And obviously as a result, a lot
of the ecosystem around robotics is racing to support these
smarter robots. So NVIDIA have announced Project
Groot. So Groups is basically platform
for training robots, they call it for robot intelligence.
And CEO of NVIDIA, Jensen Huang actually called this the
foundation for making robots. One of the most exciting
problems to solve in AI today. And there are lots of new
breakthroughs coming out to allow robotics to take giant
leaps forward because of AI. So as you can imagine, Nvidia's
teamed up with basically every single robotics firm in the
world from Agility to Boston Dynamics to start-ups like
Figure and Sanctuary. So obviously there's a, there's
a renewed focus on robotics right now.
And there's a good question like why, why is this happening?
You know, why is this time different than all the others?
And I think it's a really interesting question to ask.
And in one word, it's because of AI.
Not the term AI that used to be used over the last few decades,
but the specific recent breakthroughs in large language
models because they've completely transformed the way
that vision and language is interpreted by machines.
So the big bet right now is that these same breakthroughs can
give robotics the adaptability and I guess St. smarts that
they've historically lacked. So as soon as a human child has
been birthed into the world, they can learn things by just
picking up a toy or observing people through trial and error.
But yet pre twenty 20s robots just cannot do that.
They're either explicitly coded step by step or trained with
very narrow data sets. Which means soon as it
encounters something new IT just gets confused.
Whereas new large language models, as most of us have
experienced, are incredibly adaptable and have a huge amount
of training data. So they've really turned this
old world robotics, I guess idea on its head into something that
now is potentially very plausible and exciting.
For example, Google's DeepMind robotic centre model wasn't
trained to do 1 specific task. It was trained to integrate all
of its vision and language capabilities.
So this robot is able to interpret commands and map them
to what actions they should take.
So in a demo, when the robot was asked to hand me a green
vegetable from the fridge, a Gemini robot arm was able to
understand this is a vegetable that's green, this is a fridge
and hand the person the vegetable.
And coming back to that Xai and Elon Musk's idea of a world
model. This is where models suddenly
can teach robots physics. So a world model is essentially
an internal simulation where AI can start to predict outcomes of
actions before taking them because they just intuitively
understand the world. It just adds a massive layer of
reasoning instead of just reflex or hard coded instructions.
Another massive breakthrough has been training data and
exponential data at scale. Obviously companies are now
using techniques like reinforcement learning in
virtual environments. So imagine virtual robots in a
video game that are all essentially characters trying
tasks in a simulator so they can learn from failures and share
knowledge, but without actually suffering a negative
consequence. A good example of this might be
learning and teaching them how to stack dishes.
And if they fail, they haven't broken edition real life, it's
just a video game. But all of that data that
they're capturing through that trial and error process is
maintained in its memory for future interactions.
And this is really exciting because, you know, consider hand
eye coordination and just general object recognition tasks
like distinguishing a open door from an open window used to be
something to be quite difficult for a robot.
But modern vision models and all this innovations in training
data makes this type of recognition something that's
very plausible. And a good example of this is
that Figures CEO Brett Adcock actually claimed that their
internal AI robot learnt how to fold a towel from just 80 hours
of training data, which is something that was unimaginable
10 years ago. And we also have things like
integrations and connectivity being 10 times better than say
1015 years ago. We have cloud, Internet things
and just frameworks that didn't exist before.
A good example is a robot could offload really heavy
computations to the cloud and it could get over the air updates
like software updates. So when robots learn something
in the world, they could then force a software update to all
of them, then learn something. So yeah, there's been a lot of
interesting breakthroughs and and partially explains the rise
in excitement and investment levels into the field.
However, and it is a big however, it's just never that
simple. Because I think before we get
carried away with all these exciting demos, it's important
to note some of the real big challenges because even today's
most advanced robots are still struggling with basic tasks in
very controlled environments. A Time reporter note that during
figures, that robot figure that I mentioned earlier during their
towel folding test, that robots were caught frequently dropping
things, being unable to pick the towers up, and having to have an
engineer come sort things out. I'm not just like criticizing
them for the sake of it or trying to find gotcha moments.
These are just real challenges that they really need to
overcome if this is something that's going to generate revenue
and deliver value in a house. I think this really speaks to
hype cycles because historically there's been lots of AI and
robotics hype cycles. Hype builds big expectations
form and then obviously reality triggers a big crash to Earth
and a collapse in funding. We saw this in the late 1880s
and then again in the 2000s when consumer robots with those, you
know, rubbish Hoover robots popped up everywhere and
everyone thinking that was going to be the next big thing.
But after 2015, when lots of high profile robotics companies
were shut down, then investors grew quite rare, wary and and
just refused to put money into the sector.
Even some of the really exciting companies that you've probably
seen all over YouTube, Boston Dynamics, you know, that robot
that's kind of painted yellow, the dog that runs around, the
robot that does backflips that you can't push over.
I mean, their videos have been super viral, but they've still
not made any money. Google bought it in 2013, but
finding no near term product market fit, they sold it to
SoftBank in 2017. And SoftBank then sold it to
Hyundai in 2020 for about $1.1 billion.
And the real issue is that Boston Dynamics never found any
mass market applications. They started selling Spot the
Robot Dog in 2019, but it cost $75,000 each and really it was
just doing inspections around building sites.
Atlas, the humanoid robot was the exact same.
It was a very expensive prototype with no rear clear
path to being usable by the mass market.
It's just too early. And so yeah, I think we can sum
up robotics challenges into 3 core buckets.
First is real world performance is just completely unproven.
You know, robotics demos are notorious for being very
controlled with mock kitchens, engineers, even controllers.
And a task like unloading a dishwasher that might take us 2
to 5 minutes might take a robot 20 minutes or even longer to
train to do it. Assume it doesn't get stuck or
break all the glass. Second robot is just very
expensive. You know Agility Robotics has a
humanoid robot and it costs about 150 K.
Tesla's Optimus has tried to target A20K price tag, but
that's going to be years away and it's current prices.
The return on investment for this general purpose robot is
completely unproven. We could be decades away from
this being a reality. And 3rd, the struggle with real
edge cases and generalizations. A robot might do a really good
job of folding some towels, but when that towel is wet or if
it's got lots of tassels on it, then often a robot will get
quite confused. So it's got a lot of training,
data and innovation still to take place before a robot can
really adapt to real world scenarios.
So yeah, to conclude, if I ask the question of do I think AI
powered robots are here to stay and ready to empty the
dishwasher? No.
After digging through all the evidence, the answer is
definitely no. Or maybe I'm being too harsh.
Maybe it's not quite yet, but what I would say is that this
moment starts to feel a little bit different because there are
real big technology breakthroughs which overcome
some of the major challenges robotics has faced over the last
50-60 years. There's also huge investment
going into the sector and many companies that were badly burned
before are starting to come back to the table because I think
most people all know, acknowledge, recognise that it
might take five years, it might take 50.
But robotics is going to be a major leap and one of the most
influential piece of technology in human society.
And really, if you think about how technology is typically
spread, I expect industrial adoption first, then gradual
home integration over the next 6 to 15 years.
And I know that's a big window, but it's so hard to tell.
History then gives us a lot of clues.
Smartphones went from about 0 to 50% global adoption in about 10
years. Personal computers took 15 to 20
years. Even robot vacuums launched in
2002 took 20 years to reach 14% of US households by 2023.
So yeah, robots and the technology surrounding them are
getting bigger and more well funded, with a lot of clever
people being attracted to the sector.
But it's still far off. And in many ways, the real
questions we should start to ask ourselves throughout this next
period is, are we as a society ready for them?
I think that's an even bigger and more important question.
One for another day. But anyway, that's it for today.
I hope you enjoyed the discussion and I'll see you next
week.