The Horizontal vs Vertical AI Debate: Why This Ex-Meta AI Researcher Is Betting Big on Horizontal Web Agents

Should you build narrow (vertical) or go broad (horizontal) in AI? This episode unpacks why one PhD researcher abandoned his working vertical product to chase a much riskier horizontal bet – and why VCs leaning heavily into vertical AI might be missing something.

Abhishek Das is the co-founder and co-CEO of Yutori, which has raised over $15 million from Radical Ventures, Felicis, and prominent angels including Ali Gil, Sarah Guo, Scott Belsky, and Guillermo Rauch. Previously a research scientist at Meta’s FAIR lab, Abhishek holds a PhD from Georgia Tech where he pioneered work on AI agents that can see, talk, and act starting in 2016.

In Today’s Episode We Discuss:
00:53 – Why how we interact with the web hasn’t changed in three decades and what will break that
02:27 – The coming shift from manual browsing to AI assistants performing tasks in the background
05:57 – What “agents” actually meant in ML research before the term became overloaded
06:14 – Why 90% accuracy per step creates catastrophic failure rates over multi-step workflows
08:46 – The behavior pattern humans nail intuitively that machines struggle with: backtracking from errors
10:11 – The DoorDash experiment: building an end-to-end food ordering agent that never shipped
12:58 – Why training on sinle websites leads to memorization instead of generalization
13:03 – The dopamine problem: some tasks users don’t want automated
15:08 – Why capability-scoped beats website-scoped: the pivot to read-only horizontal agents
18:05 – Three criteria that drove the horizontal decision: research, user value, and data strategy
24:18 – Scouts API launch: why different channels have different risk appetites for web agents
26:30 – Flying close to the sun: how Yutori competes with hyperscalers on horizontal AI
30:32 – What VCs should actually test for in horizontal AI teams beyond founder horsepower
32:10 – Why three-month roadmaps are the only reasonable planning horizon in AI today
33:05 – The dogfooding ritual: every team member rotates through user feedback weekly
34:50 – Why research and product can’t be siloed and how ideas flow both directions
36:03 – The uncomfortable truth: end users don’t care about your research breakthroughs
37:32 – The Nintendo Switch 2 problem: aggregating individual feedback into systemic fixes
39:35 – Reframing web agents as “buyer’s agents” that filter the internet on your behalf
40:59 – The simulation bet: training agents on cloned websites for high-stakes irreversible actions
43:05 – Why initial team skepticism about Scouts’ value proposition was completely wrong
45:01 – How scout reports contextualize results with reasoning and ingest feedback over time
47:52 – The core insight test: where does your instinct lie across research, market, and domain?
49:36 – The hiring trap: why preemptively hiring sales leadership to impress VCs backfires
51:18 – The 12-year-old advice that still guides him: “Be a sponge when entering a new space”
53:05 – Non-negotiables: walking the dog with podcasts and personally reading every user email
54:49 – What founders actually need from VCs: direct and timely feedback, not just capital