Okay. So the first reason you might be convinced
is coming in the next five years is that a lot of existing people in the space
believe this. And I think some of them actually
genuinely believe this. Like, this is not entirely just hype or
marketing.
In between 5 and 20 years from now, there's a good chance, a
50% chance, we'll get AI smarter than us.
Oh, yeah. Yes. In fact, I have reduced my expectations for
when we will get to AGI or whatever, human
level. Uh, I used to think it would be 5 to 20
years, and now I think it could be, you know, 2 to 10 years.
And so as a result of that, I, I... You can find it on my blog
from about 2009, I think is the first time I publicly talked about
I predicted a 50% chance of, um, AGI by
2028.
How did-
I still, I still believe that today.
But still, if you, if you extrapolate this kind of progress,
very different place
in 5 years, let alone 10 years.
... has been added. If you just kind of, like, eyeball the rate at which these
capabilities are increasing, it does make you think that we'll
get there by 2026 or 2027.
In the essay, you actually say a really big
thing, which is ASI, superintelligence, is
actually thousands of days away.
Maybe. I mean, that's our hope, our guess.
Yeah.
Like, I think, I think we're quite close to digital
superintelligence. It, it may
happen this year, and if it doesn't happen this year, next year
for sure.
The second reason you might be convinced, uh,
is just by trying the models yourself.
Like, instead of trusting what, you know, other people say about the various AI
models, try the models yourself. And don't just try, you know,
one AI model. Try at least two or three.
Try the model, you know, 2019, try a model from 2022, try a
model from 2025. And then once you have, like, at least two or three data points
from, you know, three different time periods,
then you can use that to, you know, extrapolate, you know,
next. Uh, I think a common issue
is that a lot of people who are using AI have only been using it since
2023 or 2024. That simply does not give you enough data
about the past.
Like, AI has been a thing since at least 2019.
Uh, transformers in particular have been a
2019.
The third reason you might be convinced is more of a speculative
argument. There isn't a lot of actual data backing
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, you know,
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're
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 a specific technical reason
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,
know, invent a certain technology, if AI can do
that in ten 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
ten 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
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 the space, you may not be aware of it.
Uh, most of the AI progress in the last six
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.
People don't know how this happens, but this happens.
This predictability repeatedly happens.
Uh, the two ingredients are compute and data.
Uh, data is just the entire internet's data
the 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, 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 compute and 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,
where 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
nitpick 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 five to ten 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 ra- data centers we're making are being
made so that we spend $100 billion 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
gone through. There are some people who say, you know,
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 actually 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 could just give the
answer for the task. There are multiple, you
know, tasks which people consider difficult
would never crack, you know, as of 2023 and
2024. Uh, researchers said that
certain benchmarks would not be cracked.
People said, you know,
Humanities Last Exam would not be cracked.
People said, you know, ARC AGI would not be cracked.
People said, you know, mathematics would not be cracked.
All three of these have been cracked.
Uh, Humanities 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 has 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 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 $1000 a task, you get little improvement, little
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 top 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,
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 those tasks? So we have many more orders
of magnitude to go here. We have a lot more compute we
this, and we don't know how far this law scales,
find out very soon.
The sixth and final reason you might be convinced superintelligence might be
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 had, you know,
doing specific things. Even when we just had narrow models doing
specific things, like we had just one model
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 that knows nothing about chess.
Like, no chess experts had to tell that model how to get good at chess
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 were even
invented. 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 them too easy, they found it too easy to make an AI that
would just completely obliterate all the 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.