Inside Silicon Valley’s VC Playbook | WTF is Venture Capital? - 2025 Edition | Ep. 24
Nikhil Kamath (host), Deedy Das (guest), Nikunj Kothari (guest), Nikhil Kamath (host), Niko Bonatsos (guest), Deedy Das (guest), Nikunj Kothari (guest), Niko Bonatsos (guest), Deedy Das (guest), Nikunj Kothari (guest), Nikhil Kamath (host), Nikunj Kothari (guest)
In this episode of Nikhil Kamath, featuring Nikhil Kamath and Deedy Das, Inside Silicon Valley’s VC Playbook | WTF is Venture Capital? - 2025 Edition | Ep. 24 explores venture capital playbook: future sectors, AI shifts, and second-order effects The conversation starts with the guests’ operator-to-investor backgrounds and how early-stage investors think about timing, competition, and “hot” vs. overlooked categories.
Venture capital playbook: future sectors, AI shifts, and second-order effects
The conversation starts with the guests’ operator-to-investor backgrounds and how early-stage investors think about timing, competition, and “hot” vs. overlooked categories.
They discuss key AI shifts: data scarcity, reinforcement learning and evals, long-horizon reasoning/agents, and the race to capture physical-world data for robotics—plus the geopolitical reality of China’s strong models under constraints.
The panel then explores second-order impacts: declining birth rates, digital addiction, privacy erosion, the return of religion/meaning, and how abundance could reshape work, inequality, and leisure.
Finally, they rate sectors through a 2035 lens (beauty/luxury, vices/speculation, education, longevity/senior living, energy/climate, EVs, data centers, content, live events) and debate India’s strategic position in the global AI stack.
Key Takeaways
Avoid ‘too hot’ categories early—pattern-matching deals are often late.
Bonatsos argues early-stage investors should be wary when dozens of startups pitch near-identical ideas (AI receptionists, app builders, RL environments); differentiation and founder edge matter more than category hype.
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AI is turning ‘uninvestable’ legacy industries into buyers.
Kothari notes higher rates + cost pressure made efficiency urgent; factories, agriculture, and other slow-cycle sectors now actively seek AI/automation solutions, reversing historical long sales-cycle apathy.
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Model progress is shifting from “more data” to better training regimes and evaluation loops.
Das highlights public data exhaustion and the growing importance of reinforcement learning setups and reward design; Bonatsos adds “evals” as a durable market—capturing expert corner cases to refine systems.
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Long-horizon reasoning unlocks real ‘agentic’ workflows.
Kothari points to models running for hours with minimal direction as a step-change vs. ...
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Embodied intelligence will be constrained by physical-world data and manufacturing, not just software.
Bonatsos emphasizes the scarcity of high-quality real-world sensor datasets (AV fleets are rare sources) and argues robotics adoption may lag AGI because producing robots at scale is a supply-chain problem.
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Fertility decline is a slow-burn macro risk with major economic knock-ons.
Das frames shrinking populations as a demand and growth headwind; the group links it to smartphones/digital dopamine and suggests counterforces like longevity, artificial wombs, and cultural/religious narrative shifts.
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The biggest 2035 tailwinds they converge on are vanity and vices.
Their highest-rated buckets are beauty/luxury and speculation/gambling/prediction markets, driven by durable human behavior (status, dopamine, entertainment) and scalable business models when regulation permits.
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Content is becoming the primary go-to-market wedge as search and ads fragment.
They expect brand-building to rely more on storytelling and attention capture (TikTok-style distribution), but Kothari warns attention without product quality creates churn and “empty calories.”
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Aging creates opportunity in senior living—community is the product.
They cite evidence that affluent senior living can improve longevity and quality of life via social connection, echoing “blue zones” research; stigma is cultural, but demographics and cost of care push adoption.
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India’s best AI play is likely applications + domain advantages, not frontier foundation models (yet).
Das argues AI lacks “local moats” and India faces talent, compute, and capital gaps for frontier research; Kothari counters that India’s demographic dividend and sector strengths (manufacturing, healthcare delivery, apps) can still win globally above the model layer.
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Notable Quotes
“Nine out of ten inbound requests you receive, they all sound the same.”
— Niko Bonatsos
“We’ve run out of public data.”
— Deedy Das
“AGI is kind of already here… in a capabilities perspective, it’s already better.”
— Nikunj Kothari
“We should all assume that we’re living our lives in public now.”
— Niko Bonatsos
“The tyrant of efficiency. Counts seconds like coins…”
— ChatGPT (read aloud by Nikhil Kamath)
Questions Answered in This Episode
You rated beauty/luxury and prediction markets highest—what specific business models (brand, platform, infra) do you think will capture the most value by 2035?
The conversation starts with the guests’ operator-to-investor backgrounds and how early-stage investors think about timing, competition, and “hot” vs. ...
Get the full analysis with uListen AI
On ‘running out of data’: what are the most credible new data sources (enterprise, synthetic, embodied) and who will own them?
They discuss key AI shifts: data scarcity, reinforcement learning and evals, long-horizon reasoning/agents, and the race to capture physical-world data for robotics—plus the geopolitical reality of China’s strong models under constraints.
Get the full analysis with uListen AI
You mention evals as a big opportunity—what does an ‘evals company’ look like in legal/medicine, and how do you defensibly scale expert feedback?
The panel then explores second-order impacts: declining birth rates, digital addiction, privacy erosion, the return of religion/meaning, and how abundance could reshape work, inequality, and leisure.
Get the full analysis with uListen AI
If long-horizon agents are now viable, what are the first 2–3 job functions where agents will replace teams (not tasks) within five years?
Finally, they rate sectors through a 2035 lens (beauty/luxury, vices/speculation, education, longevity/senior living, energy/climate, EVs, data centers, content, live events) and debate India’s strategic position in the global AI stack.
Get the full analysis with uListen AI
Dating apps: do you think “digital twins” create a privacy backlash, or will users trade intimacy for better matching outcomes?
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Transcript Preview
How you guys see different sectors evolving? Where can me, as either a investor or somebody looking to start my career as a young man or woman, have enough tailwinds that it makes sense to spend the next decade in? [upbeat music] Ready?
Yeah.
Yeah, start. [upbeat music] So maybe we start with small introductions. Maybe two minutes on who you are, how you got to be where you are, and what you're doing now. Would you like to go first?
Sure. Um, you know, I, I started-- Well, depending on how far back you want to go, I was, I was from India-
Why DeeDee?
Why DeeDee? My real name is Debarghya Das. In the city of Kolkata, that's not as uncommon a name as you'd think, but even in the city of Kolkata, where I'm, where I'm from, nobody could say that name. So very quickly in high school, we had to call me something else, and my initials are DD. But then that didn't solve the problem, because then you have to explain what my name actually is anyway. And so then I just came up with the moniker DeeDee, and not DeeDee, the Dexter's sister. Um, and so I spelt it a little bit differently, and that's, that's what stuck. So that stuck in high school, and it's been DeeDee ever since.
So what did you study? When did you come here? What do you do now?
Awesome. So twenty eleven, uh, got to America for the second time. I was here as a kid for a bit. Um, I went to Cornell. I just studied computer science. I did my undergrad and my, my master's. Uh, first job was Meta in New York. Did that for a year, then went to Google in New York, traveled a little bit. So I spent some time in Israel, Bangalore. Uh, and then I was on the founding team of a company called Glean, which is a GC-backed company as well, and spent a good four years there. Towards the end of my time at Glean, I went from doing engineering to managing to, uh, running their AI product line.
What did Glean do?
Uh, Glean started as an enterprise search business. Um, happy to dive into what that means. Um, and then it evolved into generally an enterprise assistant and knowledge-finding tool for bigger companies to figure out, hey, like, answers to questions like: What do I do for onboarding? Or what is our holiday policy? Or even very specific questions about: What does this person do at the company, and how can I find this doc? All of that stuff. Um, Glean is now, like, a seven billion dollar company. It's done fairly well so far. After four years there, I decided to try something else. I'd-- My, my friends would joke, I, in a span of two years, I went from engineering, management, product, and then venture. Um, but I was just looking for something fun to do, and so I joined Menlo about a year and a half ago, and, uh, I'm at Menlo right now.
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