Ответь на вопрос (только отвечай на русском):
Why OpenAI o1 Is a Game-Changer for AI Agent Developers

the new 01 model is a total game changer
for all e agent developers to really
understand why this is such a game
changer we need to first talk about
systems one and systems 2 thinking as
described in one of my favorite books by
Daniel kimman thinking fast and slow so
you know that feeling when you're
talking to someone and it feels almost
effortless like you've been in this
situation before that systems one
thinking at work it's fast and intuitive
type of thinking with little to no
effort and almost no sense of control it
uses our subconscious mind to operate
based on patterns we already recognize
for example it is used when we are
speaking on a spot like in a
conversation with a friend systems to
thinking on the other hand is more
deliberate it requires a lot more mental
effort consideration and reasoning this
is something we use when we are
preparing for a speech or problem
solving but how does this relate to AI
well as Andre Kathy one of the engineers
behind chat GPT said a few months ago
llms currently support only systems one
thinking they simply sped out text based
on familiar patterns that they've
learned before without any consideration
this is why they can't come up with
anything original or create anything new
or they could couldn't before because
what openi has just done is introduced
some aspects of systems to thinking into
large language models previously if nii
agent couldn't find the solution it
would just keep looping on the task
forever or it would have to ask a human
for help for example sometimes the
browsing agent might get stuck on a
research task because it encounters a
pay wall and then it just keeps looping
forever trying to avoid that pay wall
when much simpler approach is to go back
a page and do another search this is why
for all complex nonlinear multi-step
workflows in our EI agency we always add
a way for users to supervise these
agents there are also some other ways
you can use to avoid these
hallucinations like you can add
hardcoded validation logic however the
real game Cher is that now with these
new models these agents can finally stop
and think for themselves so we don't
have to worry about this anymore this
has huge implications for all EI agent
developers first as highlighted in this
McKinzie study the state of AI in 2024
the primary reason Enterprises are still
barely using any AI in their operations
is inaccuracy and obviously the fear of
inaccuracy comes from incorrect
reasoning not from AI just outputting
undesirable text you can always add a
disclaimer that AI might be wrong and
with Gro 2 I think people have finally
started to distrust things on the
internet not just AI models the biggest
consequences of hallucinations occur
when an AI model misuses in action not
just outputs on text for example if it
refunds a customer when the refund
shouldn't have been made this will cause
a lot more trouble this is why this new
model is not just smarter but as open
say it's also safer and secondly we can
now cover significantly more complex
open-ended tasks from our experience at
our AI agency the 01 model essentially
does what we do with my agent framework
it adds planning breaking down tasks
exploring various options and so on
however now instead of you having to
create and prompt multiple agents
yourself add a Chain of Thought
parameters and so on this new model does
it for you out of the box and it does it
much better so the agent creation
process is not only more reliable but
also more streamlined you need fewer
agents less prompting and less
fine-tuning in order to accomplish a
task now how did they do this well in
theory it's quite simple they just
trained an llm on thoughts literally
they just show these models how to think
before speaking that's it in practice
however it's definitely a lot more
advanced than that from what we know so
far open the eye combined reinforcement
learning with Chain of Thought synthetic
data which allows it to evaluate its own
thinking with reasoning so it doesn't
just output thoughts it actually learns
to recognize and correct its own
mistakes break down tricky steps into
simpler ones and try different
approaches what we know for a fact is
that the Chain of Thought training data
is the secret Source here this is why
open AI is currently keeping it private
so how exactly they process this data is
still unclear but I believe it's not all
synthetic data because from the few
examples they shared it actually
includes words like H and interesting so
this is not just another technique like
structured outputs I talked about
earlier this is a whole new paradigm in
eii you see before 99% of compute in all
EI models was being spent on
pre-training and posttraining with less
than 1% spent on inference now open hii
has significantly shifted this Paradigm
by dramatically increasing the inference
time compute which is now spent on
reasoning tokens this has been shown to
significantly improve results not just
by 2 to 5% like we've seen with all
other models this year on some
benchmarks which involve freezing it's
been shown to improve performance eight
times compared to previous models and
it's not even GPT 5 or some new breed of
a model is the same foundational model
as GPT 40 as far as we know only with
this new additional layer on top but the
best of all is that as we increase the
inference time compute it seems like the
performance just keeps on increasing as
well so this definitely tells us that
open EI is heading in the right
direction and that reasoning models is
the next big thing by the way if you're
using this model on chpt right now keep
in mind that it's only a preview version
with temperature set to one that's why
some people on Twitter are reposting
that it doesn't get the strawberry
question right in our agency we always
set the temperature to at least point3
when working on reasoning tasks so open
purposely released an imperfect preview
model first so it can make mistakes and
they can then use them to improve its
reasoning even further now of course
there are certain limitations the first
major limitation is that this model is a
lot more expensive it's significantly
more expensive because it generates a
ton of reasoning tokens before it
outputs a response and you are charged
for these tokens as completion tokens
not as input tokens not only that but
the output tokens themselves are also
four times more expensive than the
previous version so if you combine it
all together the total inference costs
for this model are around 10 times more
than GPT 40 many use cases with this
pricing simply don't make sense the
second limitation is that it takes much
longer to respond since it takes around
10 seconds to think realtime
applications like phone calls might not
be feasible and lastly evaluator still
prefers standard GPT models for certain
tasks like personal writing and editing
so this model is primarily designed for
complex problem solving and reasoning
not for Creative tasks overall it's
really interesting to see how the
limitations of these models are Evol in
over time to handle different types of
tasks much like how some people are
better at math While others excel in
Creative tasks so when deploying agents
we will soon also have to consider the
right type of the model for the right
role now the question is when should you
use it and when should you not my answer
is use it the same way you use systems
to thinking whenever your agent needs to
perform any kind of design work that
does not have significant time
constraints use this model think about
roles like managers or CEOs who are
typically more expensive because they
need to guide the rest of the team they
kind of disappear once in a while to
really think about the problem scope the
project and only then they come back
with the tasks that they assign to other
team members they don't execute the
tasks themselves because this would be
inefficient they only provide high level
supervision so only when a team member
is stuck or or needs any help they jump
in and provide more guidance this is
essentially how Devon did it if you
watch their demo video Dev breaks down
complex problems into manageable steps
and then executes them one at a time the
reasoning model determines the steps and
the approach while other smaller models
can implement the code so hopefully soon
with the release of this model in
assistant CPI everyone will be able to
create their own Deon for any kind of
role in a matter of days or or even
faster however that's not it you can
also use these models to assist you in
creating agents and process their
knowledge which I will show you in the
next video so what does this all mean
for the future well if you thought that
the 01 model was groundbreaking just
wait until Orion arrives I believe open
EI is moving towards EI models that can
take 10 to 20 minutes or even more maybe
even days to deeply consider a problem
conduct research with web browsing other
tools files as needed and then return to
you with a truly Noel solution for
example they could tackle a problem
which Humanity has struggled with for
decades this will ultimately lead to an
intelligence explosion as scientists
will use these models to significantly
accelerate their own
research oh my God it
ran now here's where it gets really
interesting for EI agents with this new
reasoning capability AI agents won't
just follow the same instructions they
will reflect on their own performance
identify areas for improvement and then
adjust themselves accordingly this kind
of self-awareness with this new model
opens up the door to exponentially
self-improving AI agents in the next few
years I'm sure that we will see some
businesses that were created just from
an idea and an initial set of
instructions solely using AI agents that
constantly self-improve and evolve over
time but that's not it in the meantime
AI agents will stop being just simple
assistants they will become strategic
partners for businesses in all important
decisions they will analyze all your
company's data all at once and then come
up with strategic insights that you
could not have possibly foreseen on your
own also this will of course lead to a
significant increase in Enterprise
adoption because companies that were
previously cautious due to inaccuracy
might finally find these new models
reliable enough more videos on the 01
model are definitely coming up soon so
if you don't want to miss out don't
forget to subsscribe