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