---
title: 'Google Just Revealed What Comes After AGI And It’s Shocking'
source: 'https://youtube.com/watch?v=haB_od-xCWY'
video_id: 'haB_od-xCWY'
date: 2026-06-19
duration_sec: 0
---

# Google Just Revealed What Comes After AGI And It’s Shocking

> Source: [Google Just Revealed What Comes After AGI And It’s Shocking](https://youtube.com/watch?v=haB_od-xCWY)

## Summary

This video analyzes a massive 57-page paper by Google DeepMind titled 'From AGI to ASI', which focuses on what happens after achieving human-level artificial general intelligence (AGI), rather than how to get there. It defines AGI as human-level cognitive performance and artificial superintelligence (ASI) as systems that can outperform tens of thousands of top experts across domains for a decade. Four pathways to ASI are explored: scaling, algorithmic paradigm shifts, recursive self-improvement, and multi-agent collectives, along with potential frictions that could slow progress.

### Key Points

- **DeepMind's Ambitious Paper** [0:02] — DeepMind released a 57-page paper titled 'From AGI to ASI', authored by top minds including co-founder Shane Le and AIXI inventor Marcus Hutter, assuming AGI is the starting point and mapping the path to ASI.
- **Instructions for Future AI** [1:01] — The first section is 'Summary Instructions' written for AI assistants, asking them to clarify definitions and judge conclusions. This is a historic first in academic publishing, acknowledging AI as readers.
- **AGI Definition** [1:39] — AGI is defined as a system performing at roughly the median human level across most cognitive tasks—reasoning, learning, planning, communicating, using tools, and adapting.
- **ASI Definition** [2:02] — ASI can outperform tens of thousands of top experts working together for a decade on a single problem, across virtually every domain. This is far beyond competing with one human.
- **Pathway 1: Pure Scaling** [3:27] — Growing compute, models, and data exponentially. With a 10x annual growth rate, 100 million instances of AGI could instantly share knowledge, forming a 'digital civilization' that functions as ASI.
- **Pathway 2: Algorithmic Paradigm Shifts** [5:06] — Fundamentally new architectures, training methods, or hardware (neuromorphic chips, analog computing) could overcome limitations of current transformers, but breakthroughs are unpredictable.
- **Pathway 3: Recursive Self-Improvement** [6:15] — AI helps improve AI research, creating a feedback loop. This could be gradual—improving algorithms, chips, datasets—like civilization building, but bottlenecks like hardware and energy remain.
- **Pathway 4: Multi-Agent Collectives** [7:58] — A massive group of AI agents coordinates at high speed, sharing knowledge, duplicating specialists, and running parallel experiments—potentially achieving ASI as a swarm or super-company.
- **Six Main Frictions** [9:10] — These are: data wall, resource constraints (energy, chips), insufficient paradigm, maturing research, abstraction barrier, and deliberate slowdown (regulation, backlash). Each could be a minor bump or absolute wall.
- **ASI Is Not Omnipotent** [10:04] — ASI still faces fundamental limits: physics, information speed, energy costs, and complexity theory. It cannot magically fix everything or control reality perfectly.
- **Forcing a Conversation Shift** [12:20] — AGI is not a finish line but a starting point. Intelligence could become an industrial process, and the pace of change may no longer be limited by human speed.

### Conclusion

The paper emphasizes uncertainty about which pathway or friction will dominate the transition from AGI to ASI. It shifts the conversation from 'when AGI arrives' to 'what it makes possible next', potentially turning intelligence into an industrial process.

## Transcript

All right, so Google DeepMind just
dropped this absolutely massive 57page
paper and honestly the title alone is
kind of wild. It's called From AGI to
ASI, not how to get to AGI or when AGI
will arrive. No, they're literally
saying AGI is the starting point.
They're already looking past it, which
is kind of insane when you think about
how much everyone's been obsessing over
just reaching human level AI. The team
behind this isn't some random research
group either. We're talking Shane Le who
literally co-founded Deep Mind with Deus
Hassabis and Mustafa Sullean. He's their
chief AGI scientist. And then there's
Marcus Hutter, his doctoral supervisor
and the guy who invented the AIXI
theory. So basically 14 of the top minds
in AI getting together to map out what
happens after we hit human level
intelligence. That's the conversation
they're having now. And here's something
I found absolutely fascinating. The very
first section of this paper isn't even
called introduction. It's called summary
instructions. And get this, it's written
for AI. Like, they're literally giving
instructions to future AI assistants
that might be called upon to summarize
this report. They're telling these AIs
to make sure they clarify the
definitions, not compress the lists, and
judge whether the conclusions hold up
over time. This is the first time in
academic history where authors are
assuming AI will be reading their paper
on behalf of humans. That alone tells
you something about where we are right
now. So let's talk about what they're
actually defining here because the
definitions matter a lot. AGI according
to this paper is basically a system that
performs at roughly the median human
level across most cognitive tasks. Not
the smartest person in the room, just
your average person. If an AI can
reason learn plan communicate use
tools, and adapt to new situations at
that level, it's AGI. Pretty
straightforward. But ASI is where things
get crazy. Artificial super intelligence
isn't just about beating one human
expert at one task. It's about a system
that can outperform tens of thousands of
top experts working together,
well-coordinated, for an entire decade
on a single problem. Think about that
for a second. We're not talking about
competing with one person or even one
company. We're talking about matching
the output of an entire professional
research field or a massive corporation
going allin for 10 years. And this needs
to happen across virtually every domain.
That's the bar for ASI. There's also
this third level they mention called
universal AI or AIXI, which is basically
the theoretical absolute ceiling of
intelligence. It's mathematically proven
but uncomputable, meaning we can only
approach it from below, never actually
reach it. Kind of like the speed of
light in physics. Now, here's where the
paper gets really interesting. They lay
out four main pathways from AGI to ASI.
And honestly, each one is kind of
terrifying in its own way. The first
pathway is just pure scaling. More
compute, bigger models, more data. This
is basically what got us here in the
first place. Over the last decade, the
compute used for the largest machine
learning training runs has been growing
exponentially, and algorithmic
efficiency has been improving at the
same time. So, we're not just throwing
more hardware at the problem. We're
getting better at using the hardware we
already have. The paper actually runs
this thought experiment. Let's say when
AGI first arrives, it's super expensive
and only a,000 instances can run
globally. But with a 10 times annual
growth rate, you'd have 10,000 instances
after one year. And after 5 years, you'd
have a 100 million instances. And here's
the kicker. A 100 million AGIS at human
level isn't just 100 million separate
workers. These things can share
knowledge instantly, communicate at
incredibly high bandwidth, copy
themselves perfectly, and coordinate in
ways humans simply can't. They don't
need meetings or emails or time to
explain concepts to each other. one
instance figures something out and
potentially all hundred million know it
immediately. That collective
intelligence could easily qualify as ASI
even if each individual unit is still at
the human level. You're basically
looking at a digital civilization that
thinks hundreds of times faster than us.
But then there's the data wall problem.
Current AI systems learn from human
generated content, text, code, images,
videos, scientific papers, all of it.
But we're not producing highquality
human data at the same exponential rate
that AI models are growing. Eventually,
you run out of good stuff to train on.
The paper doesn't say this will
definitely stop progress, though. There
are workarounds like synthetic data,
simulations self-play reinforcement
learning, and having AI systems improve
their own outputs through search and
then training on those improved results.
The question is whether that generated
data is good enough. Because if you
naively train on AI generated content,
things can degrade fast. The second
pathway is algorithmic paradigm shifts.
This is where AI doesn't just get
bigger, it gets fundamentally different.
Right now, we're dominated by
transformer-based models trained on
massive data sets, then refined with
instruction tuning and reinforcement
learning. That's taken us incredibly
far, but a lot of researchers think it's
still missing key ingredients for true
AGI. things like robust long-term
planning, continual learning, persistent
memory, better world models, and the
ability to operate reliably in
completely open-ended environments. A
real paradigm shift could mean new
architectures entirely, new training
methods, new memory systems, new forms
of reasoning, maybe even new hardware
like neuromorphic chips or analog
computing. The problem is that paradigm
shifts are basically impossible to
predict before they happen. If we knew
exactly what the next breakthrough would
be, it wouldn't really be a surprise
anymore. But if it does happen, all the
forecasts based purely on scaling
current systems would become wrong
almost overnight. Then there's the third
pathway, and this one gets closest to
the classic intelligence explosion idea,
recursive self-improvement. The loop is
simple. AI helps improve AI research,
which produces better AI, which then
helps even more with AI research. This
doesn't have to mean one dramatic moment
where a model rewrites its own code. It
can be much more gradual and
distributed. AI systems could help write
better algorithms, discover better
architectures, design more efficient
chips, improve manufacturing processes,
curate better data sets, generate better
synthetic examples, create better
simulations, and generally improve all
the infrastructure around AI
development. The paper makes this
interesting comparison to human
evolution. We didn't just improve
through individual intelligence. We
built language, writing, institutions,
science, markets, education systems,
entire civilizations. A single human
isn't that impressive, but a
civilization is. The question is whether
AI systems can build their own version
of that, but way faster. Because code
can be edited faster than DNA changes.
Data can be copied faster than books can
be printed. Specialists can be spawned
and trained faster than humans can be
educated. But recursive self-improvement
is also one of the least understood
pathways. Maybe it explodes
exponentially. Maybe it fizzles out.
Maybe AI helps researchers a lot, but
progress still slows because experiments
are expensive. Hardware takes time to
manufacture. Energy becomes a
constraint. Or the next breakthrough
ideas are just genuinely hard to find.
Even digital researchers can't skip
every bottleneck. If you need to build a
new AI chip that happens in the physical
world, if you need to run a biology
experiment, it still takes real time.
The fourth pathway might actually be the
most underrated one in the whole paper,
ASI through multi-agent collectives.
Instead of asking whether one AI model
becomes super intelligent, ask whether a
massive group of AI agents can become
super intelligent together. Humans
already do this. A corporation solves
problems no individual employee could
solve alone. A scientific field produces
knowledge no single researcher could
produce. But human group intelligence is
slow and messy. Communication is
limited. Coordination is hard.
Organizations become bureaucratic.
Knowledge gets siloed. AI collectives
could be completely different. They
could share information at speeds we
can't even imagine. They could duplicate
specialists instantly. They could
coordinate through software. They could
run thousands of parallel experiments.
They could form temporary teams for
specific problems, then dissolve and
reconfigure. They could use marketlike
systems or centralized planning in ways
humans can't manage because our
communication bandwidth is way too low.
So ASI might not look like one giant
mind at all. It might look like a vast
digital organization, a swarm, a
self-organizing research ecosystem, or a
super company made of agents. Now, after
laying out these four pathways, the
paper gets into what they call
frictions. Basically, the things that
could slow everything down or stop it
entirely. There are six main ones. First
is the data wall we already talked
about. Not enough high-quality training
data to keep scaling forever. Second is
resource constraints, the physical stuff
like energy, chips, rare materials, data
centers, cooling systems, manufacturing
capacity. If capabilities require
exponentially larger infrastructure, the
world might just struggle to build it
fast enough. Third is the possibility
that the current neural network paradigm
simply isn't sufficient for AGI or ASI,
no matter how much you scale it. Fourth
is that research itself gets harder as
fields mature. Lowhanging fruit
disappears. Progress requires more
effort and more complex ideas. Fifth is
what they call the abstraction barrier.
Current AI systems learn mostly from
human abstractions. The concepts,
categories, language, and structure we
already use. But major scientific
breakthroughs often require inventing
completely new abstractions, new ways of
thinking about reality. The worry is
that AI trained mainly on human
representations might become excellent
at manipulating existing concepts but
weaker at discovering fundamentally new
ones from scratch. And sixth is
deliberate slowdown political and social
factors. If AI creates accidents,
enables misuse, destabilizes labor
markets, or triggers public backlash,
governments might slow development
through regulation, licensing
requirements, capability caps, or other
restrictions. The paper's point isn't
that any of these definitely stops ASI.
It's that we genuinely don't know. Each
bottleneck could be a minor speed bump
or it could be an absolute wall. Whether
it becomes one or the other depends on
how fast the counterforces improve.
There's also this important reality
check buried in the paper. ASI is not
omnipotent. Even a super intelligence
would still face fundamental limits.
Physics doesn't stop applying.
Information can't travel faster than
light. Computation costs energy.
Physical systems take time to
manipulate. Some problems are chaotic,
unpredictable, or fundamentally hard. No
matter how smart you are, complexity
theory still matters. Logic still has
limits. So people need to stop jumping
from ASI to magical thinking like
instant cures for everything or perfect
control over reality. ASI could be far
beyond human intelligence and still be
constrained by computation, energy,
uncertainty, time, and the physical
world. The deeper message here is
uncertainty. not ignorance but genuine
uncertainty about which pathway
dominates and where progress plateaus.
Maybe scaling continues and gets us
there. Maybe scaling hits limits but
paradigm shifts unlock the next jump.
Maybe recursive improvement becomes the
main driver. Maybe multi-agent
collectives turn human level systems
into superhuman organizations. Or maybe
all four happen simultaneously,
compounding each other. or maybe several
major bottlenecks hit at once and
progress becomes slower and more uneven
than current trends suggest. What makes
this paper significant is that it's
forcing a conversation shift. We need to
stop treating AGI as a single finish
line. If AGI arrives, the next question
won't be are we done? It'll be what does
this system make possible next? Because
a human level AI isn't just another
human. It's a digital intelligence that
can be copied, accelerated, coordinated,
specialized, connected to tools, placed
inside organizations, and potentially
used to build better versions of itself.
We might be entering a period where
intelligence itself becomes an
industrial process. And once that
happens, the pace of change may no
longer be limited by how fast humans can
learn, organize, or invent. AGI might
just be the moment the real race begins.
So, what do you think is the real wall
between AGI and ASI? Compute, energy,
data, or something deeper? Drop your
thoughts in the comments. Subscribe for
more. Thanks for watching, and I'll
catch you in the next one.