เกี่ยวกับตอนนี้
Bhavik Vasa argues that India's AI opportunity lies in specialised models built around proprietary data and practical business problems. The conversation explores where he sees value in applied AI, why a digital footprint is not the same as a credit decision, and what faster underwriting could mean for small businesses.
We discuss UPI, GST and the India Stack, the economics of large language models, and Vasa's view that financing will become increasingly embedded in everyday business tools.
Follow the Ryan Dsouza podcast for more conversations about India’s economy, technology and public life.
Chapters:
0:00 Cold open
0:46 Has the AI revolution reached B2B?
2:32 We've seen this hype cycle before
3:53 The value shifts to applied AI
6:32 Why India should build SLMs, not another frontier model
9:03 Instant underwriting on proprietary Indian data
10:33 What LLMs changed that machine learning couldn't
12:45 UPI, GST and the India Stack
13:54 A digital footprint is not a credit decision
17:51 If the rails are ready, where is the lending explosion?
21:53 Does regulation slow this down?
25:20 Squaring SLMs with OpenAI and Anthropic's growth
26:38 "You're only looking at revenue, not the cost of operations"
29:02 Is enterprise data actually ready?
30:48 GeM Sahay: instant credit for ten lakh MSMEs
34:30 The future of financing is invisible
35:40 The AI tools Bhavik actually uses
38:18 Embedding credit inside Tally and QuickBooks
40:24 Closing thoughts
Artwork background: Carl Lender, Datacenter Server Racks (cropped and composited), CC BY 2.0. https://commons.wikimedia.org/wiki/File:Datacenter_Server_Racks_(22370909788).jpg · https://creativecommons.org/licenses/by/2.0/
