Try for free
Post Money
Post Money

How Top VCs Are Building AI Portfolios in 2026

22 December 2025 42:51 In-depth conversations with top founders and VCs on building, scaling, and raising capital across industries.

Listen to episode

About this episode

In this episode of Post Money Podcast, Nilanjana Bhowmik sits down with Apoorva Pandhi, early-stage investor at Zetta Ventures, to unpack how the AI stack is really evolving and where founders and VCs should be placing their bets heading into 2026.

Inside the Episode

* How the modern AI stack is structuredand why most founders are betting on the wrong layer

* Why foundation models are becoming commodities and defensibility is moving elsewhere

* The difference between copilots, agents, and AI agencies and why workflow ownership matters

* When AI startups must go full-stack and build their own domain-specific models

* Why data infrastructure and unified runtimes remain critical in an AI-first world

* How feedback loops and data flywheels create long-term moats

* The future of SaaS in an agentic era and why it’s being reshaped, not killed

* How early-stage VCs evaluate AI startups heading into 2026

* Portfolio construction strategies for AI funds in a power-law market

About Apoorva Pandhi

Apoorva Pandhi is an early-stage investor at Zetta Ventures, a fund focused on AI, data infrastructure, and AI-native applications. Prior to Zetta, Apoorva invested at Lightspeed and Foundation Capital and previously worked as an operator in NLP-driven companies, leading go-to-market efforts for AI products. His work spans data infrastructure, AI tooling, and full-stack AI applications across biotech, enterprise software, and developer platforms.



This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit postmoneypodcast.substack.com

More AI podcast episodes

Browse all →

Want to find AI jobs?

Join thousands of AI professionals finding their next opportunity

We respect your inbox. Unsubscribe at any time.

© 2026 Post Money. All rights reserved.

Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.

Still have questions?

Contact Support