The Ryan Dsouza Podcast

More features don't automatically make a better business. Vivian Gomez, co-founder and CEO of Scoop and a former product and growth leader at Truecaller, joins Ryan Dsouza to talk about building products that create real business value.

We discuss Truecaller's fintech journey, scaling with lean teams, starting again from zero, knowing when to kill a product, and growing on a tight budget. Vivian also shares his views on founder salaries, fundraising, B2B vs. consumer products, and what it takes to grow as a product manager.

Chapters

00:00 Highlights

00:34 Meet Vivian Gomez

01:22 From Chillr to Truecaller

03:41 Truecaller's fintech bet

09:56 Telecom, AI and infrastructure

17:11 Culture before and after an IPO

22:03 Why Truecaller built in India

29:31 Scaling with a lean team

31:50 Why constraints improve decisions

38:20 Starting again after massive scale

45:50 Knowing when to kill a product

48:15 Growth on a tight budget

52:56 Consumer products vs. B2B

57:55 Should founders pay themselves?

1:02:25 Fundraising, runway and salaries

1:12:19 Ad platforms and owning your customers

1:16:58 First-principles product thinking

1:19:20 Core product vs. more features

1:27:18 Growing as a product manager

1:34:14 Leadership and business outcomes

Explore Scoop: https://scoop.app/

Follow Ryan: https://www.instagram.com/decentmakeover

Prof Santosh Mehrotra challenges India's headline growth story and argues that the economy is not creating enough jobs. Drawing on labour-force data, he examines youth unemployment, workers returning to agriculture, and the risk of missing India's demographic dividend.

A co-author of India Out of Work, Mehrotra discusses manufacturing, services and the employment effects of recent economic policies. The conversation also covers what he thinks the government has got right.

Follow the Ryan Dsouza podcast for more conversations about India’s economy, technology and public life.

Chapters

0:00 "The government is lying to you"

0:46 Is India really the fastest-growing large economy?

5:47 Why a government would inflate the growth number

9:08 Why the north and the south hear the same story differently

12:21 What happens to 100 million young people out of work

16:12 The protests, and why he predicted them seven years ago

21:27 Why the education minister did not resign

26:20 The demographic dividend, and what it costs to miss it

32:21 Ageing before getting rich: Europe, Japan, and no pension

39:07 Is there a way out? "We have done it before"

40:48 Why the services sector cannot carry it

43:16 Demonetisation, and a badly designed GST

49:32 Lockdown: 35 million returned; farm employment rose 80 million in four years

52:09 PLI: 12 of the 14 sectors create few jobs

53:00 China at 31%, India at 3%

55:10 What this government got right

56:12 Is the BJP actually popular?

59:19 What the SIR is

Recorded remotely. The figures and characterisations in this episode are Prof Mehrotra's own.

Artwork background: Ministry of Parliamentary Affairs / PIB, new Parliament building (cropped and composited), Government Open Data License–India. https://commons.wikimedia.org/wiki/File:Glimpses_of_the_new_Parliament_Building,_in_New_Delhi_(2).jpg

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/

Physicist M. V. Ramana makes the case against nuclear power as a climate solution. We discuss India’s early nuclear ambitions, reactor costs, safety and waste, and whether AI data centres and small modular reactors change the economics.

Ramana is the author of Nuclear Is Not the Solution. This conversation examines his argument and what it could mean for India’s energy choices.

Follow the Ryan Dsouza podcast for more conversations about India’s economy, technology and public life.

Chapters:

00:00 Cold open

00:52 Homi Bhabha and the 8,000 MW promise

06:21 “Peaceful” nuclear explosions

10:15 Why people oppose nuclear power

15:51 Accidents, waste and safety

23:07 Nuclear, solar and wind: the economics

36:00 The AI data centre boom

44:12 Big Tech’s nuclear deals

56:32 Small modular reactors

59:21 Ramana’s case for an AI bubble

1:00:49 What it means for India

Artwork background: Reetesh Chaurasia, Kudankulam Nuclear Power Plant Unit 1 and 2 (cropped and composited), CC BY-SA 4.0. https://commons.wikimedia.org/wiki/File:Kudankulam_Nuclear_Power_Plant_Unit_1_and_2.jpg · https://creativecommons.org/licenses/by-sa/4.0/

India’s path to prosperity may look very different from China’s. Arjun Ramani joins Ryan Dsouza to discuss manufacturing, services and jobs, and how AI could change the opportunities available to India.

We explore premature deindustrialisation, India’s ambition to become a developed economy by 2047, and what factories and services can each deliver. Arjun also discusses writing and how large language models fit into that work.

Follow the Ryan Dsouza podcast for more conversations about India’s economy, technology and public life.

AI is changing software engineering, including the work through which junior developers learn. Indranil Tiwary joins Ryan Dsouza to discuss what those changes could mean for people entering the profession and for employers deciding whom to hire.

The conversation examines the place of junior developers, what employers look for now, and whether the familiar advice to “learn to code” still holds as software development changes.

Follow the Ryan Dsouza podcast for more conversations about India’s economy, technology and public life.

The race to build powerful AI systems is also a contest between countries. Raghav Toshniwal joins Ryan Dsouza to discuss sovereign AI, the US–China rivalry, and India's position in the competition around frontier models.

We explore the chips and computing power behind that race, alongside questions of AI safety and what increasingly capable systems could mean for ordinary users. The conversation connects the international competition with the technology people encounter in everyday life.

Follow the Ryan Dsouza podcast for more conversations about India’s economy, technology and public life.

Chapters

00:00 Claude Fable gets blocked

03:26 Does the ban actually help Anthropic?

08:49 AI becomes a national security issue

09:28 Why countries want sovereign AI

13:30 NVIDIA, Taiwan, and the AI supply chain

17:04 Who should fund AI infrastructure?

18:28 Are AI models actually profitable?

21:27 What went wrong with GPT-4.5?

24:20 Can India build its own frontier model?

28:27 RL environments and the new data bottleneck

30:02 Is this good or bad news for India?

32:41 What this means for frontier AI labs

33:36 Recursive self-improvement begins

35:23 Do AI products have switching costs?

40:46 Are we already in AI takeoff?

42:32 Why hasn’t AI caused runaway growth yet?

44:42 Where do AI use cases stop?

50:16 What does AI safety actually mean?

52:51 Why would AI become dangerous?

56:08 What safety teams are working on

59:32 Does RLHF solve alignment?

1:00:26 Reward hacking and Goodhart’s law

1:03:40 Why safety research needs compute

1:07:38 What should normal AI users do?

1:08:03 Gradual disempowerment

1:11:27 Are we already seeing warning signs?

1:13:48 Are LLMs the path to AGI?

1:15:23 Timelines for automated AI researchers

1:18:20 Can independent researchers contribute?

1:22:45 Closing

Podcast Socials :

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In this episode, I sit down with Ravi Handa — ex teacher turned edtech founder (his business was acquired by Unacademy), who FIRE'd at 40 and now lives in Jaipur — to make the case for why most ambitious young Indians should seriously consider leaving the country.We cover two big things. First, the move-abroad argument: why he thinks you should take a decade-plus view rather than a 2–5 year one, why India's growth story can be intact while still being a worse bet for you as an individual, and the quality-of-life basics India doesn't deliver even for the 1% — clean water, clean air, unadulterated food, and education beyond the top 5% of institutions. We get into "scarcity mindset" and "crab mentality," why taxes feel like a raw deal for the top 2%, who this advice actually applies to (and why it's easier the higher up the ladder you are), and why 35 is roughly the deadline.Then we pivot to FIRE — the five lucky breaks that let him retire at 40, why you shouldn't anchor on retiring at 35 or 40, how cost of living in India is "not a monolith" (Jaipur vs Bangalore), and why he's building again despite preaching financial independence.More on Ravi ,Handa Uncle : https://www.handauncle.com/Twitter : https://x.com/ravihanda?lang=enLinkedin : https://in.linkedin.com/in/ravihandaInstagram : https://www.instagram.com/ravihanda/?hl=enFacebook : https://www.facebook.com/ravihanda/YouTube : https://www.youtube.com/user/ravihandaPodcast Socials :Instagram : https://www.instagram.com/decentmakeoverTwitter : https://twitter.com/decentmakeovrLinkedin : https://www.linkedin.com/in/ryan-dsouza-74542b295/PODCAST INFO:Podcast website: https://anchor.fm/ryandsouzaApple Podcasts: https://apple.co/3NQhg6SSpotify: https://spoti.fi/3qJ3tWJAmazon Music: https://amzn.to/3P66j2BGoogle Podcasts: https://bit.ly/3am7rQcGaana: https://bit.ly/3ANS4v1RSS: https://anchor.fm/s/609210d4/podcast/rss

At Mumbai Tech Week 2026, the conversation around “AI in Action” is shifting from hype to real-world execution.In this deep-dive conversation, GetVantage Founder Bhavik Vasa joins Ryan to discuss why India’s biggest AI opportunity isn’t in building trillion-parameter LLMs — but in Applied AI powered by proprietary enterprise data, digital infrastructure, and embedded finance.From MSME lending and OCEN to cashflow-based financing, underwriting automation, due diligence, and workflow intelligence — this episode explores how India can become a global powerhouse in AI-led economic productivity.Key topics covered:•⁠ ⁠Why AI FOMO around public LLMs is fading•⁠ ⁠The rise of Small Language Models (SLMs)•⁠ ⁠Proprietary data as the real competitive moat•⁠ ⁠India Stack, UPI, GST & the MSME credit gap•⁠ ⁠How GetVantage uses Applied AI for underwriting•⁠ ⁠GrowthSahay, OCEN & embedded finance infrastructure•⁠ ⁠Revenue-based financing vs founder dilution•⁠ ⁠AI-powered due diligence & tender automation•⁠ ⁠The future of invisible finance and automated enterprise workflowsIf you're attending Mumbai Tech Week, building in AI, fintech, SaaS, embedded finance, digital lending, or enterprise infrastructure — this conversation is for you.Keywords:Mumbai Tech Week, MTW 2026, AI in Action, Applied AI, Proprietary Data, Small Language Models, SLMs, India AI ecosystem, Embedded Finance, OCEN, Revenue Based Financing, MSME Credit Gap, Digital Lending India, India

In this episode, I sit down with Aditya Karanam — part of the Effective Altruism community in India and an animal advocate working with Electric Sheep and Animal Ethics — to figure out what it actually means to "do good" well.We cover two big things. First, the basics of Effective Altruism: what it is, where it came from, and the three things the community uses to decide which problems to work on first (scale, neglectedness, and tractability). Why so much of the EA crowd seems to come from tech, what counts as an "EA intervention," and what the community in India actually looks like today.Then we pivot to animal advocacy — why Aditya has chosen this cause over every other one, what factory farming in India actually looks like (we're the second-largest beef producer in the world, by the way), and whether tech like lab-grown meat is the answer or just part of it.I push back through a lot of it — I'm a meat eater myself — so this is less a lecture and more me trying to figure it out in real time.EPISODE LINKS:Electric Sheep : https://www.electricsheep.is/Aditya's Linkedin : https://in.linkedin.com/in/aditya-s-karanam-84859b10280,000 Hours : 80000hours.orgGiving What We Can — givingwhatwecan.orgPodcast Socials :Instagram : https://www.instagram.com/decentmakeoverTwitter : https://twitter.com/decentmakeovrLinkedin : https://www.linkedin.com/in/ryan-dsouza-74542b295/PODCAST INFO:Podcast website: https://anchor.fm/ryandsouzaApple Podcasts: https://apple.co/3NQhg6SSpotify: https://spoti.fi/3qJ3tWJAmazon Music: https://amzn.to/3P66j2BGoogle Podcasts: https://bit.ly/3am7rQcGaana: https://bit.ly/3ANS4v1RSS: https://anchor.fm/s/609210d4/podcast/rss