← All speakers

Bio, Work & Ideas

Greg Ceccarelli

Conference affiliation: SpecStory · 2025

Greg Ceccarelli is co-founder and chief product officer of SpecStory, which builds tools to capture the intentions, decisions, and conversations behind AI-generated software. As coding agents make implementation cheaper, his work addresses a more consequential bottleneck: defining what should be built and determining whether it actually works.

Ceccarelli held data leadership positions at Google, Dropbox, and GitHub, where he co-authored research into software-development patterns and his machine-learning team worked on the first GitHub Copilot technical preview. He later led Pluralsight Flow, an engineering-workflow analytics product, before becoming Pluralsight’s chief product officer in 2023.

In November 2024, he founded SpecStory with chief executive Jake Levirne and chief technology officer Sean Johnson. Its premise, intent is the new source code, makes AI coding sessions durable engineering artifacts: SpecStory’s open-source tools preserve context that otherwise disappears between prompts, projects, and collaborators. His more recent work includes Stoa, a collaborative environment for translating product intent into working software.

  • Specification-driven development: Ceccarelli argues that faster code generation does little to resolve ambiguous requirements, missing domain knowledge, or conflicting assumptions. Specifications should connect intent, implementation, architecture, and tests.
  • Product judgment before implementation: His product-thinking curriculum applies jobs-to-be-done thinking and structured specifications to ensure that cheap software production still serves genuine customer needs.
  • Goal engineering and verification: Through Hardcore Agentic Engineering, he teaches teams to give coding agents explicit objectives, repository context, clear definitions of done, and independently verifiable results.
  • AI strategy grounded in customer outcomes: With Hamel Husain, he co-authored AI Essentials for Tech Executives. Their AI Engineer Summit collaboration skewered organizational silos, unjustified infrastructure spending, inaccessible technical jargon, perpetual beta, and evaluation metrics disconnected from actual users.

Read the topics behind these talks

1 conference talk

Key ideas

Scroll to read ↓

AI projects become expensive failures when strategy, language, staffing, and evaluation separate the technology from the people who understand the work.

  • What would guarantee an AI strategy fails?
    0:18 ↗
  • Divide the company, then disconnect spending from value
    2:19 ↗
  • Replace strategy with declarations and a permanent backlog
    5:00 ↗
  • Use technical language to exclude the people who know the work
    7:31 ↗
  • Mobilize the wrong expertise and ship before testing
    8:59 ↗
  • Treat every failure as a purchasing decision
    10:28 ↗
  • Collect enough metrics to find a success story
    12:02 ↗
  • Avoid the data, then make sure nobody else can inspect it
    13:35 ↗
  • The advice is inverted; the experience is real
    15:55 ↗

References