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Thrive by Groww

How to 10x Your Value in the A.I. Era | ft. Kunal Shah

In this hard-hitting keynote from the Groww India Investor Festival 2026, Head of WhatsApp and CRED founder Kunal Shah sits down with journalist Samidha Sharma (Editor at Emerging Business, ETtech.com) to deliver a brutal reality check on…

Samidha SharmahostKunal Shahguest

Jun 23, 202633mWatch on YouTube

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CHAPTERS

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Chapter summaries

Wealth creation in the AI era: framing the conversation and audience pulse-check
The host sets up the discussion around wealth creation in the age of AI and invites Kunal Shah to address job-related anxieties. Kunal quickly surveys the room on daily AI usage, whether people have built agents, and who fears job loss—highlighting that people often think colleagues are more at risk than themselves.
- •The host frames the session as “who wins and who doesn’t” in AI-driven wealth creation
- •Audience is polled on daily use of tools like ChatGPT/Gemini and building agents
- •Kunal probes job-loss fears and notes the pattern of worrying more about others’ jobs
- •The conversation is positioned around jobs as a pathway to wealth
From internet to AI: “intelligence in the air” and why inefficiency is the biggest employer
Kunal compares AI’s impact to the internet era: the internet made information universally accessible, while AI makes intelligence broadly accessible. He argues that inefficiency has been the world’s largest employer, and as inefficiency is removed, jobs will be displaced or fundamentally changed.
- •AI is described as making “intelligence accessible in the air,” analogous to how the internet made information accessible
- •A key claim: the largest employer globally is inefficiency
- •As inefficiency declines, roles change materially or get displaced
- •Kunal shifts the concern from layoffs to broader structural change in work
The real risk: widening income delta and stagnating per-capita income growth
Kunal argues the bigger worry isn’t mass unemployment but that income growth will lag, especially for those who don’t adopt technology. He points to visible examples of people staying employed in low-tech roles, while emphasizing that the earnings gap between AI users and non-users will expand.
- •The core concern is per-capita income growth, not just job counts
- •Examples are used to show employment can persist even in low-tech forms
- •AI adoption is expected to create a large income delta between users and non-users
- •The “world keeps people employed,” but not necessarily earning more
Self-driven learning vs waiting for workshops: motivation and discipline as the bottleneck
Kunal challenges the idea that companies or events will teach people AI, arguing that resources are already available online. He frames the central issue as a shortage of self-motivation and self-discipline, and questions how many people use AI tools for meaningful outcomes.
- •Learning resources are “up there”; people can figure things out independently
- •The bottleneck is described as lack of self-motivation and self-discipline
- •Kunal questions how many people use AI to build, create, or research seriously
- •He critiques passive reliance on employers, workshops, or events for AI upskilling
Frivolous AI use and the time-wealth tradeoff: productivity as the real challenge
Pressed on “frivolous” uses, Kunal lists examples like editing media, treating ChatGPT like a friend, astrology, and therapy. He connects this to wealth by calling time the most valuable asset and expressing concern about how easily tech can drive distraction rather than compounding productivity.
- •Examples of “frivolous” AI use: media editing, companionship, astrology, and therapy
- •Time is framed as the most valuable form of wealth
- •Kunal argues it’s become easier than ever to waste time using the same tools that can also increase capability
- •He links heavy data usage and short-form content to lost productivity
India’s productivity culture gap: why “efficiency” doesn’t feel native and time isn’t valued
Kunal claims that “efficiency” and “productivity” don’t exist as true words in Indian languages, arguing this reflects a cultural gap in valuing time. He contrasts this with hourly work norms elsewhere and suggests India hasn’t internalized the Industrial Revolution’s time-value mindset.
- •Claim: Indian languages lack direct words for “efficiency” and “productivity,” relying on surrogate terms
- •Productivity is framed as central to wealth creation in an AI-rich world
- •Contrast with the Western prevalence of hourly jobs and hourly thinking
- •Kunal ties cultural attitudes about time to national economic outcomes
Awareness gap and second-order effects: how AI job impacts can ripple into the broader economy
The host raises the stark gap between a small group highly aware of AI and the rest of the world. Kunal responds that the gap is widening and stresses second- and third-order consequences, using banking exposure to IT/BPO employment as an example of how job shocks can spill into larger systems.
- •AI awareness is described as moving so fast that last year’s understanding feels outdated
- •Kunal says India often swings between apathy and panic, as seen during COVID
- •Second-order effects are emphasized as under-discussed
- •Example given: impacts to outsourced jobs could ripple into lending stability and broader market effects
The entry-level trap: juniors squeezed as basic tasks get automated
The discussion turns to how AI can replace tasks typically given to junior workers, making it harder for graduates to become “eligible” for roles. Kunal suggests this will intensify over time, and argues the unanswered question is who will “fix” the problem—warning against waiting for external saviors.
- •AI tools can perform basic task-oriented work often assigned to entry-level roles
- •Concern: graduates face increased difficulty becoming job-ready and progressing to mid-level roles
- •Kunal questions who is supposed to fix this and critiques reliance on government/companies/parents
- •He calls for high agency and self-directed capability building
Outsourcing to AI agents and India’s structural constraints, including low female workforce participation
Kunal argues “outsource” is shifting from countries to AI agents, robots, and automated systems. He questions whether India has built resilient capabilities beyond services cost advantages, and highlights low female participation in financial and workforce metrics as a drag on per-capita income growth.
- •Claim: “outsource” will be replaced by agents/AI/robots rather than being country-specific
- •Concern about whether India has resilient industries as outsourcing models shift
- •Kunal raises female labor and financial participation as a major factor in national wealth creation
- •He estimates women’s share in lending/credit participation as low (presented explicitly as a guess)
No more ‘not good with tech’: ambition, substance over shortcuts, and the role of sacrifice
Asked what a 24-year-old should do, Kunal insists nobody can claim to be “not good with tech” and dismisses access excuses given smartphone penetration and cheap data. He argues many people seek shortcuts to wealth or knowledge, but wealth requires substance and sacrifice rather than opinions.
- •Kunal says no one can afford to say they’re “not good with tech” because every job will be impacted
- •He rejects access-to-tech excuses, arguing tools are available if time is redirected
- •He criticizes chasing shortcuts to wealth (including expecting investing apps alone to make someone rich)
- •He contrasts “gyan” and opinions with the harder work of building real substance through sacrifice
Embracing struggle and risk: why setbacks drive growth and over-protection can stall progress
Kunal argues major life progress often comes from unpleasant experiences and that societies evolve through pain and setbacks. He claims over-protecting people from reality can slow a country’s progress, and calls on the privileged, educated cohort to take more risk and build substantive capability—even if ventures fail.
- •Progress is framed as often coming from setbacks rather than comfort
- •Kunal says letting people experience pain is part of how countries evolve
- •He argues success is hard to pass on because struggle gets removed
- •He calls for educated, tool-equipped people to take more risk; failure can still improve employability
Company readiness and the AI “game theory” effect: revenue per employee and competitive forcing functions
The host asks how prepared companies are, citing layoffs at profitable US tech firms and questioning India’s position. Kunal describes a game-theory dynamic where early movers on AI could dramatically raise revenue per employee, forcing others to follow; he also notes protected sectors may feel change slower, while global competitiveness will demand AI adoption.
- •Kunal predicts AI can cause step-changes in revenue per employee for early adopters
- •He argues one major player’s efficiency jump can force an entire sector to adapt
- •He distinguishes between protected local markets and global competitiveness pressures
- •He suggests some impacts may appear slower in regulated/protected profit pools
Rise of 10x employees and org friction: AI-driven productivity splits inside startups
Kunal points to tech startups as the clearest place he already sees AI impact, including a claim about how much code at his company is written with AI now versus a year ago. He describes an emerging split where a minority becomes dramatically more productive, and these high-output people increasingly want fewer meetings, fewer dependencies, and less management overhead.
- •Kunal says tech startups/unicorns show visible AI impact earlier than many sectors
- •He makes a specific claim about AI-written code at his company increasing sharply over a year
- •He describes a ‘different species’ divide between highly AI-leveraged employees and everyone else
- •High-output employees reportedly push to remove meetings, reporting structures, and managerial friction
Final career framework: learn faster, choose high-growth peers, treat yourself like a compounding asset
In closing, Kunal lays out how he’d think at age 24–25: compress learning timelines, curate peers, and take ownership of personal growth. He offers mental models—people as “stocks” with growth rates and oneself as an “app” needing frequent upgrades—plus a note to learn tech from younger people and values from older people, and to prioritize extreme adaptation.
- •Kunal urges learning complex, practical skills in days or weeks rather than years
- •He emphasizes peer effects: the ‘average of five people’ and ‘people as stocks’ with growth rates
- •He says no one else will solve your growth rate; personal agency is essential
- •He frames self-improvement as shipping app updates: fix bugs, add features, release often
- •He ends with an adaptation analogy (metabolism control, high strike rate, extreme adaptability) and warns against wasting time at events

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