As of today, I still think there is a
25% chance we will get superintelligence by 2030.
There's at least a 10% chance literally every person on Earth
will die by 2030. There's at least
a 10% chance we will get a permanent dictatorship.
A handful of people will run the entire world for centuries
together, with no ability to, you know, overthrow them.
At least 10% chance of this by 2030.
The only way all these numbers change is if there is a
political movement to pause AI research that succeeds within the next five years.
Uh, I'm going to now describe my technical
I think this.
(electronic music)
Creation of artificial superintelligence is likely the most important event
of approximately 10,000 years of human history.
It is more important than Industrial
Revolution/Newton/French
Revolution/printing press.
It is more important than invention of nuclear weapons, as an
ASI will accelerate creation of weapons more dangerous
than nukes.
It is definitely more important than the creation of the internet and all
Silicon Valley startups.
If the takeoff is fast enough, creation of superintelligence
may be the most important event in the approximately 14
billion year history of the universe.
It could end up more important than the evolutionary history of all
other life forms on Earth. And to the best of our current
knowledge, Earth is the only place in the universe
intelligent life.
Scaling of RL compute still seems important and
unsolved.
We still seem to have spent less than $1 million per
inference, and can, in theory, spend at least $1
billion on it. In my head, it is an open
question whether the labs tried it and got bad results,
or if there's some other fixable research or infra
bottleneck that is preventing them from trying it.
Meta-graph on this still seems useful, even though it is making
simplifications.
This is still important.
If we have one AI that is superhuman, we can
probably run at least 100,000 copies of it at
100 times the speed of human thinking.
This means that once the first slightly superhuman AI is
invented, we will go from slightly superhuman to
vastly superhuman in a very short span of time.
I would nowadays more emphasize the meta-learning point below,
though, because that decides what happens until we
superhuman AI.
Deferring to experts is obviously still important.
I value my inside view a lot, but I value expert
opinion some amount too.
Many researchers who pretend not to be doomer also have
timelines in the next five to 10 years.
Examples, Rich Sutton, Ilia Sutskever,
Andrej Karpathy, et cetera.
There is the obvious list of doomer researchers, example,
Geoffrey Hinton, Yoshua Bengio, Stuart Russell,
et cetera.
Heads of all frontier labs agree that their own tech could
cause human extinction.
Example, Elon Musk, Sam Altman,
Dario Amodei, Demis Hassabis, et cetera.
Generalization and meta-learning are related.
Newer models aren't just memorizing more skills, but are more
capable of learning new skills on their own without human
data guiding them.
There is a difference between training AI such that it learns skills that
humans have already have, versus training AI to know
how to learn new skills on its own.
The difference between these two is not binary.
There are levels to generalization.
Example of low level of generalization,
seeing lots of English to French translations and learning to
become good at English to French translation.
Example of medium level of generalization,
seeing many solved competitive programming puzzles and solving
another puzzle with an algo similar to one it has seen before for
a different puzzle.
Seeing lots of English text and a little Hindi text,
inferring the grammar similarities and differences across human languages,
and then learning to speak fluent Hindi as a result.
Yeah, lol. This is a real result, and an old
one.
Example of high level of generalization,
seeing a theorem from economics and realizing an analogous
version of it also applies to biology, thereby solving
an open problem in biology.
GPT-2 to GPT-5 has led to
immense progress in both learning new skills and
learning how to learn new skills. The latter
is more important though.
The best way to test the latter is to give AI problems that
a human has never seen and no human knows how to
solve. This could be simple tasks, like translation
in apocryphal ancient languages, or complex
tasks, like proposing novel molecular bio
run.
Continual learning is a hard problem, but my hunch
is one or two breakthroughs might solve it.
AI weights are currently static, and hence, AI
behaves as if it has amnesia.
Once a chain of thought is complete, the AI forgets it ever
did the task.Naive way to make sure AI
remembers its previous inferences is to fine-tune it
on this data. But fine-tuning LLMs is
very sample inefficient.
A slightly better approach is to put this data back into the chain of
thought itself. But there may be limits to how long
chains of thought can be. Open research question.
We might discover better approaches.
Operating in the real world can be expensive in domains like
biotech. Therefore, we must be sample efficient
in terms of how much data the AI requires from real-world
experiments before it learns useful insights from it.
Human language has features not present in animal language,
and this is likely an important part of why humans can build spacecraft and
colonize the Earth, but apes can't.
AI already has picked up all these features of human language.
Go read Hockett's views on what features separate human language from animal
language.
Humans use language for communication.
Humans might also use language as part of their reasoning process,
along with other modules, such as motor skills, spatial
visualization, et cetera.
Both of these are hypotheses for what separates humans from apes, and
I think there's a good chance they're true.
Other hypotheses include bigger birth canal and brain size
and evolutionary pressures to win social games.
These hypotheses seem compatible with the hypothesis that language is most
important.
Depending on how you measure it, AI may now be the second
most complex object in the observable universe.
More complex than ape brain, but less complex than human
brain.
Model scaling for text models might be saturating.
Open research question. But it is definitely not
saturated for images, video or robotics.
As of December 2025, text models intuitively feel
better than image models, and image models intuitively feel better
than video models, and video models intuitively feel better
than VLAs for robotics. I
think this is mostly just because of higher compute requirements for the latter
models.
Minor update as of January 6th, 2026.
Robotics has an additional bottleneck where big enough datasets are not
available.
Manually generating them is expensive, but probably affordable
given current AI R&D budgets. Most
people seem to IMO suck at forecasting AI
progress even one year into the future, let alone
five or 10.
Since 2022, I am used to watching people on Twitter
predictions of some specific benchmark or skill
X that will never get solved, only for it to get solved
one year later.
Your specific AI can't do XYZ task
that is trivial for humans is not impressive to me