In this video, I am going to talk about four technical
why I believe super intelligence may be possible by 2030.
The first reason is super intelligence via speed.
Then we have model scaling, then we have RL scaling on top of
transformers. Lastly, we have RL scaling, uh, outside of
transformers. The
third reason you might be convinced is more of a speculative argument.
There isn't a lot of actual data backing this, but this is something I
believe. Uh, so today's, uh, AI models
can do tasks that can be done quickly.
Like, if you can do a task that will take, you know, one minute or 10 minutes, you
know the AI can do it for you. If you want to do a task
know, one hour of your time and lots of failed attempts,
probably not do it for you. Uh, however, the tasks
which it can do, those it can do way faster than you.
Like, if it will take, you know, 10 minutes, the AI will do it in,
seconds. Uh, AIs operate, at least as
of today, between 10 to, you know, 100 times the speed at which you are
operating.
Also, the time horizon of the tasks
is increasing. So, you know, AI six months ago could do tasks
that were
maybe one minute long and the AI today can do tasks that are,
long. There is a trend that is increasing in that direction.
The technical reason behind this is, uh, increasing
compute being given to reinforcement learning on top of transformers.
And that's something I'll get to later, but this is specific technical reason why
this is happening. Uh, my view of intelligence, at
least
to some extent, is that you get better at a thing by
doing it a lot of times. You know, you get better at giving speeches by
lot of speeches. You get better at writing code by writing
code. You get better
at, you know, producing movies by producing lots of
And if you can run faster, you can run more
iterations. Uh, all scientific discovery, in my
view, works like this. Uh, every lab
experiment you run is yet another iteration
system. You know, it could be microscopes
biology, it could be, uh, you know,
telescopes that help you study stars.
It could be any field. And if you can run more iterations,
you can make progress faster. Uh, there are some people who
think that the amount of AI-driven scientific progress
we can get is limited because
the physical world is slow. You know, labs are slow, labs are expensive, running
experiments takes time. And this is true.
However, A, there are domains where the labs are cheap.
Uh,
learning how to get good speeches is very cheap.
Learning how to get good at math and software is cheap.
Uh, like, it does not cost a lot of money.
And I think
that is partly why we have gotten a lot of good capabilities
from reinforcement learning in math and software.
Like, AI is extremely good at math and software in particular as of
2025. I think this also carries over to
other fields of scientific discovery.
Maybe the speed up is not that much, but there will be a
speed up. Uh, you can still get
super intelligence this way. If, you know, humanity would
otherwise have taken, you know, 100 years to, you know, invent a certain
technology, if AI can
do that in 10 years, that is still super intelligence.
Like, imagine if you were sitting in 1800 and you could get
entire 1800 to 1900, you know, technological inventions to you in
10 years. Or imagine you were in 1900, you know,
before World War I, you know, people used sticks and stones to literally
fight. And by the end of the century people used,
and, you know, space satellites and,
uh, bomber planes and so on.
If you can speed that up, you can compress the amount of time
requires to invent things, that gets you super
intelligence, and that is my third
reason. The fourth
reason I believe we might get super intelligence in the next five years
is, uh, model scaling. This is a well-known trend among
machine learning researchers. However, if you are not very in this space, you may
not be aware of it. Uh, most of the AI progress in the last
six years from 2019 to 2025 has been driven by one trend and
one trend only, which is you take a transformer, you give it
more data, more computation, and it will produce better
results. This is magic. People don't know how this happens,
This predictability repeatedly happens.
Uh, the two ingredients are compute and data.
Uh, data is just the entire internet's data
model. Compute is just more computers that people have
purchased. Uh, there's a special type of computer used.
They're called graphics processing units.
They are good at this operation of matrix multiplication in
particular. But basically, it's just more computers.
We have gone from
spending, you know, like,
$100,000, which could be literally just one machine
person's room, to, you know, $100 billion
worth of compute to train a single AI model that is, you know, an
entire data center spanning, you know, thousands of
acres.
This scale up in, you know, six orders of magnitude
has just been driven, because every time we notice we use more
more data, we get more AI performance.
We can now even predict this up to three decimal places.
There is a law called the Chinchilla Scaling Law.
Uh, I'll put that on the screen right now....
that N is the number of parameters, like how big the model
is, and D is how much compute we give to it.
Assume
we s- show the model every token in the data
exactly once. This L gives you the training loss.
This is a proxy for how good the model
performs.
There is one important metric here that's really important, which is
that the training loss correlates with your actual performance
that you care about, but it is not the performance itself.
It is possible for training loss to reduce and for capabilities to not increase
that much. It is possible for training loss to reduce
capabilities to increase a lot. Predicting this
relationship is hard between training loss and capabilities,
but there is a relationship. More training loss does mean more
capabilities. Most AI researchers across the field have
failed to predict exactly what training loss leads to exactly
what capabilities. There are few people who have predicted it,
and these people are, for example, Ilya Sutskever at OpenAI was very
bullish on this, and he predicts, you know, we will get
super intelligence in the next 5 to 10 years.
People who have actually believed in this law have generally predicted that AI will
come sooner than what most other people in the field
predict.
Uh, we still have at least two orders of
up left. Uh, we have spent, like I said, you know, $10 billion has
already been spent and the next set of la- data centers we're making are being
made so that we spend $100 trillion on the training of a single
model. We might scale that up another order of magnitude,
trillion dollars on a single model.
It seems likely we will not go beyond a trillion dollars on a single model
unless we get, you know, really exceptional capabilities.
This alone means, you know, two more orders of magnitude from 10
trillion. Which you can compare to, you know, the six
orders of magnitude they have already gone through.
There are some people who say, you know, the latest model, which was
GPT-4.5, was not that
impressive, meaning, you know, this law might be weakening, and that
might be true, but just remember we still have two more
The fifth reason you might be convinced super intelligence might be coming in the
next five years is reinforcement learning scaling.
Reinforcement learning is an old technique in machine learning.
However, it has been applied to transformers in
Literally only one year ago we figured out how
to apply reinforcement learning on a transformer to get
even more capabilities. Uh, reinforcement
learning allows the model to try something, fail, try something, fail,
try again, and until it gets the correct answer.
Uh, this is different from the base model
know, cached answer which it gives out.
Reinforcement learning allows us to spend more compute on a single
task. Uh, whereas earlier it would just give the
answer for the task.
There are multiple, you know, tasks which people consider
difficult, which, you know, AI would never crack, you know, as of 2023
and 2024. Uh, researchers said that
certain benchmarks would not be cracked.
People said, you know,
humanity's last exam would not be cracked.
People said, you know, ARC AGI would not be cracked.
People said, you know, mathematics olympiads would not be
All three of these have been cracked.
Uh, humanity's last exam consists of lot of
answers by PhDs in various fields of both science
and social science. This includes biology, chemistry,
archeology, uh, journalism, law and so on.
Uh, all these PhDs agreed that, you know, if these questions get
cracked, that means the model is as good as a PhD in those fields,
seeing AI is already solved like almost half of that data
set. We have
started seeing a loss to predict, you know,
how much computation leads to how much performance.
Like where we had with model scaling where we have a very clear empirical law which
gives you up to three decimal places accuracy predictions.
Uh, we do not have as accurate predictions for
scaling of reinforcement learning because it is relatively new.
We have less data points, we have tried this for less longer.
However, people are already starting to make some
are starting to see that
every time we increase the compute by, you know, 10x, we get a
little bit of improvement. So if you spend, you know, $10 per task,
spend $100, then you spend $1,000 per task,
improvement, little improvement.
We have gone all the way, at least as per public data of OpenAI,
the way up to $500,000 per task, and we also know
that, uh, AI has been able to get, you know, the gold
medal on the International Mathematics Olympiad, which is like the olympiad
for mathematics worldwide. We still have many,
many, many more orders of magnitude to go from,
$100,000 and in theory what we can
spend is, which is at least a billion dollars per
Like if you were really solving, you know, novel physics, novel mathematics, you
know, novel chemistry. If you were, you know, trying to solve something like
cancer or trying to, you know, solve math theorems that have, you know,
unsolved for, you know, the past century, like, why would you not spend a billion
dollars on this task? So we have many more orders
of magnitude to go here. We have a lot more compute we're going to
and we don't know how far this law scales, but we are going to find out very
soon.
The sixth and final reason you might be convinced super intelligence might be
coming by 2030 is that reinforcement learning has worked
before. Like I said, this is a not new technique.
We are applying it to transformers newly because transformers can, you know,
understand English and generalize across so many,
tasks. Whereas earlier before transformers we
doing specific things. Even when we just had narrow models doing
specific things, like we had just one model and
(laughs)
... one task. Even back then we knew that reinforcement learning actually works
pretty well. Uh, we have, uh, an AI
model that is, you know, the world champion in chess
chess. Like no chess experts had to tell
it's the best at chess. Uh, this is obviously by
DeepMind. This is, uh, AlphaZero.
We
also have DeepMind's model that beat the best
Go player in the world. This happened again before transformers
Like this is very old news, but we have gotten superhuman
performance in narrow domains. You know, poker has been solved.
Dota has been solved. StarCraft has been solved.
People, AI researchers moved on from games because
They found it too easy to make an AI that will just completely obliterate all
human players in a game. And that is why we are now
working with general domains. So we know point number A
on narrow domains, reinforcement learning gets superhuman
Point number B, transformers can work on general domains.
Point number C, reinforcement learning plus transformers
domains actually works. Point number D, this thing scales up.
Point number E, we are going to scale it
up.