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WorkThe story, in brief

Paving the road for AI agents: Interview with Factory CEO Matan Grinberg

Factory CEO: Scaling AI agents isn't about model power—it's about how your team actually works.

Reporting by McKinsey Insights 1 min brief
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AI agents and the coordination of work.AI illustration by KeyNews
The KeyNews take

Why it matters

As AI agents move from demos to production, the limiting factor isn't capability—it's organizational design. Grinberg's take challenges the industry's obsession with model releases and points to operating model changes as the real bottleneck for enterprise AI adoption.

The key facts

9 to know

  1. Factory CEO Matan Grinberg on AI agent scaling strategy

  2. Focus: operating model changes over model capability improvements

  3. Context: software engineering as primary deployment domain

  4. Implication: organizational structure, not LLM power, drives AI ROI

  5. Interview format suggests strategic commentary on agent-as-product maturation

  6. Factory CEO argues scaling AI agents requires operating model changes, not just model capability

  7. Interview emphasizes team workflow restructuring over 'flashy demos'

  8. Published by McKinsey (credible business intelligence outlet)

  9. Focus on practical deployment barriers in software engineering

Go to the source

McKinsey Insightsmckinsey.com

Publisher excerpt: Factory CEO and cofounder Matan Grinberg discusses why scaling AI in software engineering depends less on flashy demos and more on changes to the operating model and how teams work.
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