Indian Gen AI Investment report

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Building production-ready AI apps is a challenging task that involves various steps. Starting with infrastructure setup, model hosting, database configuration, data collection and preparation tools, model selection, training or fine-tuning with your data, orchestrating different models based on use cases, integrating with your systems, deployment and maintenance of models, monitoring performance and cost of these models.
Enablers perform one or more of these tasks taking the load off of the team to build auxiliary tools & frameworks to perform these tasks and focus on their core offering. They also take off the heavy engineering effort making AI apps easy to build with beginner to moderate development skills.
Enablers can be distinguished in three broad categories:-
1. Data Processing Frameworks - Before data can be used in AI applications, it often needs to be processed - cleaned, transformed, and structured. Data processing frameworks can handle large datasets and perform complex transformations. They also allow for distributed processing, significantly speeding up data processing tasks.
2. Machine Learning Frameworks - Machine learning frameworks provide tools and libraries for designing, training, and validating machine learning models. They often support GPU acceleration for faster computations and provide functionalities for automatic differentiation, optimization, and neural network layers.
3. MLOps Platforms - MLOps involves the principles and practices of automating and streamlining the machine learning lifecycle, from data collection and model training to deployment and monitoring. MLOps platforms help manage this lifecycle, including version control for models, automated training and deployment pipelines, model performance tracking, and facilitating collaboration between different roles (data scientists, ML engineers, operations, etc.).
Understanding Enablers
Underlying technology
High technological effort for most of the tooling solution
Others in the category of data collection take advantage of huge established network.
Difficult to replicate/ Proprietary IP
Engineering effort
High effort engineering for Industrywide application
Continuous upgrades, dependence on the foundational model upgrades
High engineering effort
Business use cases
Huge business opportunity. They act as picks & shovels for the AI apps.
With boom of AI apps, these tools are going to become essential in the development of this ecosystem.
Some of these are ecosystem specific; a tool for MLOps for LLama might not be as efficient for Chat GPT based system
Important to build developer community trust; high stickiness
Growth by word of mouth in dev communities
Huge business opportunity
Ecosystem specificity can be a concern
Capital requirement
Initial cash infusion; can be started with seed of upto 500k to 2 mill
Quick to revenue generation & sustainability
Investment size in scope
There are no rows in this table

Conclusion of Investment thesis for Enablers

Drivers & Challenges
These are the picks and shovels of the AI gold digging revolution
With the increasing number of AI apps in both vertical & horizontal category, each of them will require these to build efficiently by outsourcing number of engineering heavy repetitive tasks
A must have for any AI ecosystem to thrive
In some cases, highly dependent on ecosystem it’s being built for. So success is tied to the underlying foundational layer
Region agnostic, so possibility of Apps ‘Built in India for the Globe’
This also increases competition
What to look for (must haves):-
Strong team with proven engineering capabilities
If ecosystem specific, strong foundational layer success probability
Strong Developer community acceptance - this is the distribution and way to PMF

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