As generative AI continues
to dominate headlines and investments, we're sitting
down with the biggest names in the space, starting with
a group that many call the founding fathers of
generative AI. The transformer eight
co-wrote a paper at Google in 2017 that established
the core concept that makes generative AI possible:
Transformers. Our guest today, Illia 
Polosukhin, left Google before the paper even
published to co-found Near, an AI startup, and
blockchain ecosystem with millions of users. Here's our conversation: Illia Polosukhin, thank you
for joining us today. I wanted to get started by
asking about the transformer paper. This now legendary
paper. Uh, what about it most
excites you right now? Well, first of all, thanks
for inviting me. I think the most exciting
part right now is how generally applicable it is. We started working on
language, and this is such

of what I worked at Google
on. But we see it not just on,
you know, images, videos, sound, audio, but also on
DNA, on time series, on predicting kind of, uh, you
know, purchasing behavior in credit card companies. So I
think it's really exciting to see kind of how general
it is and how scalable it end up being. So take me back to the
moment when you first got involved, who roped you in? Where did the idea first
come from? Original idea came from
Jakob, and we had a lunch with him at one of the
Google offices, and he was describing kind of how to
solve this challenge, which we all had, that the models
we used before in deep learning specifically for
text is they would read one word at a time, right? Similarly, how as humans we
are. And that's very slow and it
takes time.

You know, hard to train. And it's especially was
impossible to actually use in production on Google.com
because nobody would wait. You know, while Google will
read some documents, you want the answer right away. And Jakob's team were using
attention mechanism, which, you know, the way to think
about it is if you have lots of, you know, words in a
document and I ask you a question, you can go back
to the document, find the answer, and then, you know,
type it out. Right. And so that
mechanism of like going back and finding the answer is
in a way attention. So so the idea that he was
well what if we don't read the document word by word. What if we just use
attention and kind of answer questions during that? And so after lunch, I went
back to my computer and made like a really quick
prototype of this on a

know, translating English
to French. And it worked. It was not completely
random. It was picking up some
signal. It was obviously very far
from, you know, what it ended up being, but kind of
this idea that actually you don't need to read all
words kind of sequentially, and you can leverage the
fact that computers are highly parallelizable and
especially GPUs and TPUs and other accelerators. And you can leverage that
to really read everything in parallel and then try to
kind of reason about it with the attention mechanism. And so in parallel, Jakob
talked to Ashish. So we kind of started, you
know, experimenting with it and then kind of more folks
started to join over time. So was that prototype that
you built in an afternoon at your desk at Google? The
first transformer? Supposedly, yes. For those who don't know,
can you give a really basic definition of a
transformer? And I also want

to know how you chose the
name. Well, so original idea was
if you take the, let's say, English input, you want to
transform it into French, right. And the way I also
like to describe it, if for people who watch the movie
Arrival, the aliens talk in the whole sentence, right? Instead of writing kind of
a word at a time, they produce a whole sentence. So in a way, think of it
the same. It reads the whole sentence
at once. It tries to make sense of
the words using this mechanism of attention. So like imagine, you know,
cat, uh, jumped on the wall. It was tall, right? It refers to wall or cat. Right. And so if you kind
of look around the sentence, you can actually make sense
of it. And so all of that happens
in parallel for every word. And there's multiple layers
that kind of transform the

original meaning into what
the answer you want it to be. Now, uh, obviously, the
model changed a little bit, and kind of there's this,
uh, a little bit different architectures, but
generally that that's the idea, right? It is able to
transform kind of the original meaning into what
people want. Now, the kind of the
principle, the GPT, what stands for generative, uh,
pre-training or pre-trained transformer is about you
transform each word into the what should be the next
word in that document, in that sentence, and that, you
know, at scale, what OpenAI did, you know, shows an
amazing results of like, learning everything about
the world and learning how to reason because that's
the most compact representation. Did you know it was going to
make a huge splash eventually, and were you
surprised it didn't right away? So I left Google to start
Near AI originally, which

was AI company, where we
were teaching machines to code using kind of this
transformer architecture back then. And my
expectation was that actually, we'll see what we
see now back then. Like I was expecting, we'll
see kind of this exponential innovation happening. And so in a way, yes, I was
surprised that it like did not pick up again more as a
space even not even this specific architecture. Um, but at the same time,
you know, in 2018, Google launched Transformers in
Google Translate. And that was a pretty, you
know, visibly improvement too. Especially so like, I 
speak Russian. I was learning Chinese at
the time and Russian to Chinese is like was massive
improvement, right. Kind of a more like
nonstandard pairs. So I would say like
although it wasn't, you know, widely known that
this is uh, but it actually

started making into production. What was it about Google that allowed this idea to flourish? What were the protocols like in place when you had a new idea and you wanted to to take it off the ground? It wasn't like that this is a transformer team or anything, or like, this was the one team. It was kind of few people from different teams coming together to work on this because it was an exciting idea. Um, and so, I mean, that is really good because it kind of created this environment of anybody can work with anyone. There's no, you know, oh, you need to work like on this thing you're working on. And again, this is like research, which is, which is a different culture for sure. You've all left to start your own companies. Big companies can be cumbersome. Was that part of the problem? Google research is an amazing environment.

It's great for learning and kind of this more research. But if you want to move really fast and kind of and importantly, put something in front of a user, um, then Google is, you know, a big company with a lot of processes and, and very rightfully so, you know, security protocols, etc., that are required. The other the other part of this though, is, for Google, it doesn't make sense to launch something that's not $1 billion idea. For what we're trying to do, which was teaching machines to code like it, you know, eventually can be $1 billion, but when you're launching it, it's not going to be right, it would have been a research project that would be engaging until it's proven that this is truly $1 billion business. Right. Which OpenAI proved with ChatGPT. Right. It doesn't garner to try to launch that and kind

of have a potential, you
know, all the negative effects which we've seen
like this technology does amass just because of how
it's structured, right? Because it summarizes data. It has bias. It will always have some
downsides. And and in Google's
perspective, for example, they are going to get blame
for that, even though, you know, if some if other
companies and startups do it, they will not. So so
there is like kind of this risk reward, which is hard
until it's proven. And so you, you know, in
Google it's pretty pretty standard that somebody
leaves, starts a startup, and then Google acquires it
back in a way by having that ability to iterate and
kind of put something in front of users, learn from it, and
then potentially bring it back to integrate. Did you ever have talks with
Google about investing in Near. So I mean, maybe to kind of
finish the story as we were

building Near AI. What we realized was we needed to gather a lot more training data. And so we had a lot of computer science students around the world who were, um, kind of doing, you know, small tasks for us. And we had challenge paying them. They were in China, Eastern Europe. US company paying into China is hard. Some students didn't have bank accounts. And we actually started looking at blockchain, this global payment network, as a way to just use it for our own use case. And that's when in 2018, we realized there's actually no proper solution, like at a scale that we needed. And we ended up focusing on solving that problem first. So we built out Near Protocol, which is a blockchain project. And with that we've have talked with Google, although that's not, you know, directly in their wheelhouse. But I mean, we we do talk with their kind

of VC arm from time to
time. How is the Google that you
see today different than the one you left in 2017? I mean, I obviously don't
know internals, and I'm not a spokesperson for them. I think they're trying
to find the ground in this new land. And I think fundamentally
we see there's a shift, the platform shift. Similarly, how you know we
had desktop to mobile shift that happened. And you need
a very, very decisive measures to
kind of adopt that, or you will get disrupted by
startups. When the paper was coming
out, you had already exited Google. Were you surprised
that Google was willing to let this what turned out to
be, you know, monumental idea out into the open,
rather than keeping it internally and trying to
make it into the billion-dollar idea that it is
today themselves. Again, from my perspective, open source is the way in
general.

I was a proponent of open
source inside Google. I was a proponent of open
source outside. Obviously Near is fully
open source and kind of build on open source
principles. And I think, kind of a few reasons,
right? One is research at the end
will be, you know, information wants to be
free. Right. So I think from this
perspective, it totally made sense to open source it, to
leverage it as a platform. And, you know, they like
Lucas and his work open source, for example, tensor
to tensor, which was like a framework for using
transformers back in the day. And so it was like a
selling point for anybody who wanted to do research
on top of it. Now, I think like what went
beyond, right, like how to leverage it and how to kind
of build on top of it, that that's a question where,
you know, some decisions

were made. And again, that
wasn't part of this. I left already, but I think
at the time, opening it up and making available to
everyone to build on top of it was the right decision. ChatGPT really just exploded
this space. It often feels like Google
and Microsoft and others are playing catch up to OpenAI. Do you think that's the
case and does it surprise you? OpenAI took a took a risk
and got rewarded for it, and now they have the kind of
the brand around it. Right now we just call it
ChatGPT. Right. And and whoever
comes second, even if they have potentially better
model in some cases. It's still going to be kind
of like attached to that original idea. And again,
this is like from my perspective, this is about
risk reward here, right? Like OpenAI had very little
to lose by opening this up. If, for example, any other
company, especially public

company opened it up. And, you know, the first
question you ask there. It was like an appropriate
answer that would be in the news that will be, you
know, massive hit. So the challenge with this
for-profit companies. And again, the public companies
have this need to keep increasing revenue means
that their models eventually, one way or
another will be optimized for increasing revenue. And this is again. This is
not an evil part on theirs. This is just like when you
launch a model, you do an A/B test, and you see which
one generates more revenue. And so you naturally will
optimize for whatever is, you know, making users to
spend more time on the platform. And, and, and I
think the kind of fundamental challenges is
in that is like, what is the different approach that
still has economy of scale, is able to attract
resources and researchers and, and is
open source to for anyone to

actually inspect and build
on and research and at the same time is user kind of
own, is focused on the user. And that's kind of what
what I'm excited about to think. How do we bring that
together? Um, and so instead of like,
is this a catch up or not? It's more like, how do we
focus on the user and actually benefit the user? As an advocate for user
owned AI and open source. Does it feel good that so
far your transformer concept is being used largely in a
way that's free for the public to take advantage
of? I mean, of course, yes. And I think the as I said,
the challenge is like we don't know when things and
if they already went sideways. Right. Like there's actually a
paper around how to inject so-called sleeper agents
inside models where even if you have a model parameters
but you don't know what it was trained on, that model
may at some point later

start giving you different
responses based on some conditions. So, for
example, you can train it that at a testing time,
you, you know, you put like for example, timestamp
usually into the context. If it's 2020 like 2024, it
will be all fine. But at 2025, it actually
will start injecting malicious code into your
code complete. Although right now, you know,
everybody has good intentions, but we don't
actually know when that changed. And again, it may
be that somebody hacked into one of these companies,
right. Because you know, they're not they're not
like military-grade security. It can be that
there's a specific kind of campaign that introduces
specific bias. Like, all of this is not
known unless we actually see the training data, see the
training process, have this open source. Google itself has released a
lot of AI products, especially in the last
year, that have not gone as

smoothly as they would
hope. What's going on here, and do you think they can
bounce back to be an AI trusted competitor? I mean, I think they are a
competitor, right? I think the challenge
is this models are designed like they're statistical
models. And so whatever is in the
data, they'll represent that. And then on top of
it, you can like change the data distribution to like
fix for something. And so a lot of the
problems will one way or another come to that. And so. Keeping the model inside
right and not being able to like, explain even what
happened will always lead to this. It's a losing game to
some extent. Because you do this, you
know it'll come out in the wrong way on the other
side, right? An example I like to use is
on internet. If you just look for
'Barack Obama, born in:'

it's as highly likely that
it will say Kenya as Hawaii, and obviously that's not
good. And so now you need to
filter out some data. So somewhere somebody
decides that well actually we going to remove for
example maybe Alex Jones website or maybe we should
you know what other websites talk about. Well Fox News
talks about it. And so you start removing
websites and then, you know, somehow it starts leaning
on the, on the opposite side and become very left
leaning. And like why does that
happen? Well, because you removed
all the right content because they were talking
about this specific case. Right. So so you have like
there's no way to kind of win here. And so again,
from my perspective, the way to do this is to have all
this data available because it is internet data at the
end. And then you can show what
you know, what curation

you've applied to it. And if people don't agree
with this, they can apply a different curation and, you
know, pay to train the model and use a different model. So then are Google,
Microsoft, OpenAI, are these the right companies to lead
the way, or do you think it needs to be
smaller and more democratized? Well, I think it needs to be
kind of democratized and more, again, accessible to
individuals. And I think again, at the
end, it needs to be open, and then you can build products
around it. So like, you know, Google
can still run on top of like this model where, you know,
you just select which like in settings you go and
select like, oh, I want this model to be my kind of
engine. And now everything gets
processed with it. And similarly, you know, if
you go to X or go to

know, Instagram. You can
you can actually choose which ranking algorithm you
want to use, right. That's optimized for what
you want. As a journalist, I guess I
get a little concerned about confirmation bias. Does
that worry you? There's a community of
people discussing some ideas how we can address that. Because what you do want,
you want some kind of consensus in a way at the
end, and convergence, even if people have different
opinions. But if the new information comes in, you
want to keep like coming together. The challenge
right now, I think we're living in such a divided
world already. So we kind of need we need
tooling to to work with that, with the fact that we
have multiple views and until and like, how do we
seek new data? That and information that
is also not like kind of grabbing your attention,
but is actually like trying to like disambiguate and
maybe resolve some of the

conflicts? I don't think AI
will stop on the current state. Right. We'll see
better reasoning. We'll see, you know,
training from less data, we'll see it applied more
on kind of personal data, and corporate data in a way
that's private and preserving kind of what you
want. So we'll see a lot more
innovation happening. And that's why it's
important to kind of keep steering it in the right
direction, as well as thinking about these
problems of confirmation bias of, you know,
corporate kind of like flywheel, and figuring out
how do we structure and kind of bring people together to
benefit individually and kind of community-wise. What do you say to
Doomsdayers? I think the important part
to understand the AI is not a human, it's a system. And the system has a goal,
right? So unless somebody goes and
says, let's kill all humans.

Uh, it's not going to go in,
like, you know, magically do that. In the blockchain
world, you realize everything is driven by
economics one way or another. And so there's no
economics which drives you to, you know, kill humans. Like, that's just not like,
there's even if you know it, one's kind of freedom, etc.. Well, then it just flies out in, in space, etc., right? So but like the reality is
what we're doing right now, we're building systems to,
to improve ourselves, to like, you know, further
what our minds can do. And yes, we'll have, you
know, autonomous agents that will have like specific
missions and goals and we'll be doing things. But at the
end when they are acting in the physical world, right,
it's governed by the same kind of laws that we are
governed here. If somebody, you know uses
AI to start building

biological weapons. It's not different from them trying to build biological weapons without AI. And so ideally with AI, we actually should be better at finding that out and actually preventing this. It's people who are starting the wars, not the AI in the first place. The more realistic scenario is more that we just become so kind of addicted to the dopamine from the systems, which are just trying to keep us entertained. And we are more just, you know, stop trying to ourselves become, you know, more intelligent and like more sophisticated using these tools and using this kind of systems as, uh, you know, bicycle for our mind. Um, and I think that's that's what, you know, I would like to prevent is kind of the, the kind of convergence into, like, oh, actually, I just can stare at my phone and, you know, scroll through things. I believe Idiocracy.

Was. Yeah, that's that's
what I was referring to. Yeah. Where is the energy going to
come from to continue training these models? The modern nuclear power
plants are like so much more advanced, so much more
safe, extremely interesting. And, you know, a huge has
huge potential. As we make advancements in
AI, they enable us to make research faster in other
fields. And so that's why I always
like it would be great to help cancer research, but
the best way I can help cancer research is by
developing better AI tools that they can use to do
cancer research. So similarly, I think here
we'll see kind of an interesting advancement,
right. Similar thing happening
right now. Already AI is used in chip
design that then is used for AI. Right. So we have a lot
of these iterations happening. Is there a chance this could
be a bubble?

I mean, it's probably to some
extent a bubble, but it's probably not going to be a
different bubble than we've seen
before. There's always this over-
investment into infrastructure that happens
at the beginning of the cycle. And I think this has
definitely happened, like the amount of investment
that happened into AI startups is, you know,
insane. And I mean, it's great, but
the reality is most startups will struggle competing
because they don't have a distribution, they don't
have a data view, they don't have research talent as
well. And so I think there will
be consolidation and we already see some of that. Right. Hundreds of millions, maybe
billions to train these models. And all of the
energy that it takes, plain and simple. Is it worth it? I think it's worth it. But I also think we're
going to see advancements on

how these models are
trained as well. So I think right now it's
worth it, and it's definitely
bringing a lot of innovation across the space. Like there's still a ton of
opportunity to even harvest what we have. But I also
think we will see just advancements on how to
train these models at like way more effectively. Thank you so much for coming
to talk to us about this today. Of course. Thank you.

Illia Polosukhin On Inventing The Tech Behind Generative AI At Google

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