The Data Moat: A Google Veteran's Investment Thesis for AI/David Yakobovitch ~ Spark of Ages Ep 58

We chart how AI leapt from chat to code, why product is now the leverage point, and how startups can market to algorithms without losing trust. David Yakobovitch shares hard-won views on moats, data, defense tech, and the immigrant energy powering American dynamism.

• leaders and market share across Google, OpenAI, Anthropic
• vibe coding benefits, code quality risks, review loops
• prompt libraries, agent swarms, PRD automation
• weekly shipping pace and the SaaS squeeze
• marketing to algorithms, buyer agents, bot traffic control
• pilot to production gap, rise of forward-deployed engineers
• moats beyond models via domain, workflow, and proprietary data
• China’s progress, open source, and on-device AI bets
• defense tech, swarms, and physical AI opportunities
• endurance mindset, yoga discipline, and founder stamina
• personal workflows across Gemini, Claude, and OpenAI
• investing across seed and growth with outcome focus

The model wars aren’t theoretical anymore—they’re shaping how software gets built, shipped, and sold. We sit down with David Yakobovitch, GP at Data Power Capital and former global product lead at Google, to map where AI is actually working in 2026: vibe coding that shrinks teams, agent swarms that harden quality, and product-led moats that outlast model churn. David pulls back the curtain on how Claude, OpenAI, and Google now compete neck and neck on code and content, why prompt engineering as a job vanished while prompts became more valuable, and how forward-deployed engineers bridge the stubborn pilot-to-production gap that has haunted data projects for a decade.

We explore go-to-market in a world where buyer agents screen your pitch before a human blinks. That means structuring materials for machines, tuning sites for humans and crawlers, and building demos that agents can evaluate safely. We also go into what happens as models commoditize: the moat shifts to domain depth, proprietary offline data, secure connectors, and measurable workflow outcomes. From small language models running on CPUs in air‑gapped containers to Apple’s on-device bet, the edge is back—especially for Europe’s sovereignty demands and public sector buyers.

Then we widen the lens. Defense and “physical AI” blend hardware and autonomy: swarms, hypersonics, and resilient edge compute that must perform in the real world. David shares why he’s backing both the silicon and the software, and how American dynamism—powered by immigrants and impatient builders—remains a durable advantage. Along the way, we trade notes on multi-model workflows, open source momentum, China’s narrowed gap, and the endurance mindset that carries teams through the disappointment dip after the first shiny demo.


David Yakoboitch: https://www.linkedin.com/in/davidyakobovitch/

David Yakobovitch is a General Partner and Managing Director of DataPower Capital, a New York City-based venture capital firm investing across Applied AI, Inference Infrastructure, and DeepTech.  With a portfolio of over 36 companies, David is an investor in the most defining frontier technology firms of our era, including OpenAI, Anthropic, xAI, Neuralink, DataBricks, Groq, Cruesoe, Anduril and SpaceX. David is a leading voice as the host of HumAIn, a podcast focused on Applied and Responsible AI.  Previously, David served as a Global Product Lead a

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Rajiv Parikh: https://www.linkedin.com/in/rajivparikh/

Sandeep Parikh: https://www.instagram.com/sandeepparikh/

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