The State of Gen AI insiders Report FINAL 0724 - Flipbook - Page 22
NEXT STAGE OF GENERATIVE AI INVESTMENT:
DATA
TOOLS
HORIZONTAL APPS
VERTICAL APPS
Critical for generative AI, is the
ability for developers to access
domain-specific information that
is not available on the internet or
in traditional databases, and to
update it in real time. This way,
they can provide better context
and accuracy for generative AI
models such as ChatGPT or
GPT-4, which are often trained on
outdated or incomplete data
scraped from the web. Investors
are seeking data platform
technologies, such as vector
databases, clean rooms, synthetic
data and focused LLMs, to address
this rapidly growing need.
The rise of LLMs has created many
gaps in today’s data infrastructure
stack. Investor interest centers
around LLM design simplification
tools for data scientists. Chief
data stack challenges include
unstructured image and video
data, the static nature of existing
LLM interfaces, the fragmentation
of different models and effective
product version governance.
Repetitive and pre-coded task sets
will be reshaped by generative
AI, beginning with immediate
functional transitions in marketing
content, code democratization,
CRM and virtual customer agent
support.
Investors are chiefly interested in
generative applications that can
embed far deeper into customers’
business workflows in large markets
with both complex, regulationheavy data management processes
and labor shortage. The main target
verticals include clinical healthcare,
drug development, and logistics.
THE STATE OF GENERATIVE AI INSIDERS REPORT
The next two key investment
areas are developer productivity
and data security. Productivity
benefits are primarily achieved via
code development acceleration
and rapid alpha testing/failure
detection cycles. Security
applications can predict and
effectively address outside
vulnerabilities, particularly for
apps operating with non-clean
room managed data sources.
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