Genspark’s Wen Sang: Super Agents, PLG, and Token Economics

Host Hash Pakbaz (SF Chapter Lead at The AI Collective) sits down with Wen Sang, Co-Founder and COO at Genspark, for a fast, candid chat on why AI agents change how knowledge work gets done. Wen shares the founder journey, the thinking behind “work done, not tools,” how PLG met enterprise needs, and why model orchestration and cost control matter.

🔥 Key Highlights:
– From MIT PhD to 10-year founder to Genspark COO – Wen’s path and timing for jumping into agents.
– “Pick the right problem” – how they sized the market and why knowledge workers came next after dev tools.
– From search to “work done” – the shift from apps to agents and a token-based cost model.
– PLG to enterprise pull – word of mouth growth, then SOC 2, ISO 27001, and GDPR requests from big customers.
– Model orchestration in practice – using the right model for the job to balance quality and cost.
– Shipping fast with AI-native development – 80% of code generated by AI, humans own taste and architecture.
– Agentic memory and integrations – Slack, Google, Microsoft, Notion, and hundreds of MCP tools to cut context switching.

🔗 Helpful Resources:
– Genspark: https://www.genspark.ai/
– The AI Collective:   / aicollective  
– The AI Collective Events Calendar: https://luma.com/genai-collective

⏱️ Timestamps/Chapters (optional):
00:00 - Intro and guest background
01:10 - Founder journey and previous exit
02:30 - Why agents now and picking the right problem
04:40 - From search to “work done” and the token model
07:50 - Microsoft 365 baseline and how AI changes the business model
09:55 - Pricing, credits, and COGS with LLM APIs
11:50 - Adoption outside Silicon Valley and global anecdotes
13:05 - B2C to B2B pull, compliance, and enterprise asks
15:30 - Government and global interest, real user stories
18:30 - Competitive landscape and “no moat, ship weekly” mindset
21:45 - Tech stack layers and model selection for tasks
23:10 - Product prioritization and retention as a north star
25:00 - Early career advice in an AI-first world
26:50 - What success looks like at Genspark
27:20 - Humans as directors, AI as actors – long-term view of work
29:55 - Agentic memory and deep integrations
31:35 - Cost vs performance, picking the right model for the job
33:40 - Closing thanks

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