AI startups should focus on solving real problems.
Aim to solve the problem 10x better with AI—whether it's through generating AI, computer vision, or NLP.
Example: InVideo reduced the time to create a video from 4 hours to 2 minutes.
Importance of not being outdone by rapid advancements (e.g., OpenAI).
The Indian Ecosystem
India as an AI use-case capital due to its specific needs and diverse applications.
AI in India is broad-based across sectors: Consumer Brands, Gaming, Financial Services, Healthcare, Education, Deep Tech, etc.
Surge cohorts include a mix of Indian and Southeast Asian startups.
AI Business Models in India
Vertical AI: Focus on specific industries like healthcare and retail.
Prosumers: Targeting consumers who are also producers (large user bases, rapid adoption).
Enterprise AI: Applications in customer support, sales, and marketing.
AI's impact varies: consumer adoption is quick, enterprise adoption has more friction but offers significant benefits (e.g., coding, customer support).
Insights for Entrepreneurs
Clearly articulate the problem you're solving. If you can't explain the problem compellingly, the solution won't matter.
Contextual Examples: Having deep insights and specific examples is crucial.
Focus on delivering a 10x better solution.
Validate assumptions with potential users before building extensively.
Design Partners: Early stage users to co-build solutions with.
Challenges and Solutions
Consumer vs. Enterprise Adoption: Consumers adopt quicker but enterprises worry about issues like hallucination, data privacy, and governance.
Moving from interesting prototypes to production-ready solutions remains a challenge for many AI startups.
Role of AI Models and Tools
Forward Compatibility: Ensure your solutions improve as underlying models (e.g., LLMs) get better.
Avoid depending on present gaps in models; such approaches are unsustainable long-term.
Growth and Potential
The importance of Missionary Mindset: Building solutions for the long-term, aiming to solve significant problems comprehensively.
Team Composition: Starting small (~8 people) and focusing on achieving product-market fit (PMF) before scaling.
Companies should aim for profitability and sustainable scaling.
Data and Insights: Specific metrics like customer retention, repeat rates, and unit economics should guide initial growth phases.
Surge Program Overview
Surge focuses on end-to-end support for early-stage startups, including capital, capability support, community, and guidance.
Program has a mix of in-person and remote sessions and offers coaching on storytelling, pitching, and company values.
Surge-backed startups should instill practices like weekly all-hands and regular updates to investors.
Deep Tech and Future Outlook
Rise in Deep Tech: Increase in startups focusing on solving technical challenges (e.g., semiconductors, quantum computing, defense tech).
Talent and infrastructure support is crucial (e.g., IIT Madras Research Park, ISRO).
Growth in deep tech facilitated by regulatory changes and supportive ecosystem.
Angel Investing
Founders should choose angel investors who add value beyond capital—advice, connections, and understanding of the startup journey.
Angel investors should not look at this as a traditional asset class but rather a way to support innovation and stay connected.
Startup ecosystem has matured significantly; quality and approach have evolved drastically over the past decade.
Conclusion
India poised to become a significant player in AI and Deep Tech with the right mix of talent, infrastructure, and regulatory support.
Early-stage startups should focus on building 10x better solutions to real problems.
The startup ecosystem continues to mature, offering more opportunities for substantial impact.