Video summary
The central question regarding how organizations should adapt their data strategies for the age of AI is whether they need to fundamentally alter how they store information or if current methods suffice. The speaker argues that while storage mechanics remain relevant, the most critical evolution lies in enhancing search capabilities rather than just changing infrastructure types like vector databases. Although there was significant hype surrounding specific database technologies, the ultimate goal is not merely storing data but ensuring it is indexed and structured so that AI agents can efficiently discover and retrieve accurate information first. This shift emphasizes that as models evolve over time, their ability to remain relevant depends entirely on injecting high-quality, searchable data into them.
Looking forward, a new specialized data layer will inevitably emerge between enterprise applications and AI systems, though this development is expected sooner rather than later. The primary function of this emerging layer will be to inject intelligence that guides models in understanding connections across different records and silos within an organization. This approach mirrors the historical concept of semantic layers used for data warehouses but adds a crucial dimension: while human guidance remains important, these systems must also possess self-improving capabilities based on user interactions. By analyzing which answers are most relevant to users, the system can automatically enrich its internal connections and memory without constant manual intervention.
The importance of this new layer is underscored by the necessity for AI agents to understand company knowledge over time; if an agent lacks context or historical data, it cannot truly comprehend current information. Consequently, there must be a mechanism to encode corporate knowledge into a novel data layer that functions effectively as memory for these systems. Ideally, this process should happen automatically, allowing the system to continuously learn and adapt based on how users engage with its outputs. This ensures that as AI models change architectures or capabilities in the future, they will always have access to a rich, searchable foundation of contextualized knowledge derived from years of accumulated organizational data.
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As you were also talking about, you
know, organizations, they have cemented
data, they have to look at it. Because
of AI,
uh
should they change the way they store
data? Or because of AI, they really
don't have to worry too much because AI
can really,
you know, filter, sniff through all the
data, and pick the data that it needs.
Uh what I'm trying to ask you is that
should organizations change their
approach to how they generate, create,
and store data? Or it really doesn't
matter, they can continue as they are
doing.
>> I would say today, the thing that is the
most important is the ability to search.
So, if I'm thinking about
new evolution of
data infrastructure, I think a lot more
is going to be put on search.
Uh you know, we had this whole
um
hype around vector databases. At the end
of the day, vector databases is an
ability to search around unstructured
data because suddenly we realized, "Oh,
yeah, we can do something with that
unstructured data." But at the end of
the day,
an agent will need to have a very, very
strong ability to search data, to
discover what is available to it. And
that's where I see a lot of the
infrastructure moving towards is it's
not so much about like how do you store
it, it's more like how do you store it
so that it is searchable, so that it is
indexed in a way that always provides
the most accurate and the most valuable
uh information first. Where do you see
this is going? Are we heading towards
a new data layer that sits specifically
between AI systems and enterprise
application? Or this is too early, we'll
see how things will pan out.
No, there there is definitely something
that's going to happen, uh and sooner
rather than later. Like,
the models might change.
The
the the architectures of the models will
change over time like that is a
certainty as something that will not
change is the fact that to make these
models relevant you need to inject data.
So what I think is going to happen on
this data
layer
is how do you bring more intelligence to
guide the model into or like to get the
model to actually populate more
information about what kind of
connection is being able to do between
different records between different
silos
and that to me is the
you know a few years ago everyone was
talking about like the semantic layer
for for data warehouses. Like that was a
way of like encoding
some information in a way some type of
of memory into the warehouse to make
sure that everyone is is talking the
same language and I think that's going
to be the same pattern that's going to
apply to data except that yes it might
be guided by humans but there will
always also be like a a self-improvement
based on how users are going the
relevance of the answers that has been
provided by the model to just enrich
this connection and that's where you
know you were talking about memory a lot
like this is where memory is going to
become very important like
Airbyte has
a fiscal year that starts
in February. Well if the agent doesn't
know it cannot understand the data. So
how do you make sure that over time all
these company knowledge gets encoded
as a novel layer of the data and ideally
automatically.