Indian Generative AI Landscape: The next Gold rush

Trends, opportunities and challenges for Generative AI startups based out of India

The AI gold rush is upon us. In past couple of months, there has been a surge of interest as well as euphoria in AI applications because of the potential shown by LLMs like ChatGPT and Dall-E. Timing for building these applications wouldn’t have been better. We have compute, storage, data, talent and, most importantly, funding to build these models as well as applications over it.

At the same time, there is a lot of huff and bubble around the same. It is important to know that use of AI technology isn’t anyone’s moat any longer. The real moats would be scale, network, positioning, brand, switching costs, etc.

In 2022, funding soared to $2.65B across a record-high 110 deals. But unfortunately, a very small percentage came to here in India. However, based on interactions with various people who are building exciting stuff in this space, I am very sure that there would be many more startups making noises for all the right reasons in next 5 years.

The generative AI applications can be split into 3 pieces:

1. AI in open-ended creative workflow: Concept Art, Video, and Copywriting

2. AI in specialized knowledge: Like in Engineering, Medicine, Law, etc.

3. AI-embedded into enterprise workflows

Here, the 2nd and 3rd areas are tough-to-crack but, are also promising spaces with the most RoI as organizations will pay for the value generated.

Attached is the landscape in the generative AI space, where I have tried to map some startups which are building in this space from India for the world.

Some of the trends, opportunities and challenges in India which makes this space exciting are:

Trends and opportunities:

• AI Hardware And Software Costs Should Continue To Decline 70% At An Annual Rate (Wright’s Law)

• AI Training Costs Continue To Plummet

• High-quality domain-specific AI training data could result in winner-takes-most outcomes across vertical niche applications where big tech aren’t looking

• Increased appetite of individuals and enterprises to pay, due to clear increase in productivity and creativity

Challenges:

1. Even though costs are decreasing, there is still a need of huge data and expensive computing infrastructure along with good-quality of AI talent

2. Generative AI systems may not always produce high-quality outputs, and the generated outputs may contain errors or artifacts.

3. BigTech is aggressively investing in this space and most of the startups in application areas are going to be dependent on their models.

4. AI/usage of foundation models are not a long term moat as the tech would get cheaper and commoditized in the long-run.

To end the article, AI-powered Applications are mostly not about AI but about solving problems, building great product and team, and refining GTM.

Overall, it is an exciting space with it’s own fair share of challenges and it would be interesting to see how it evolves in future.

Feel free to DM me on LinkedIn if you have any thoughts on the piece or are working on anything in the space