All speakers

Wes McKinney

Principal Architect, Posit

Wes is an entrepreneur and open source developer focusing on analytical computing and new AI engineering systems. He is a Principal Architect at Posit and General Partner at Composed Ventures, an early stage angel fund. He created the Python pandas and Ibis projects, co-created Apache Arrow, and wrote Python for Data Analysis. He was a founder of Voltron Data, Ursa Labs, and DataPad. His current projects include: roborev (code review for AI agents), agentsview (search and analyze agent sessions), and msgvault (archive, search, and analyze email with AI).

Wes McKinney

Sessions / 2026 / 1 talk

  • The Mythical Agent-Month

    Code generation is converging to free. Agents run in parallel. A single developer can burn 10 billion tokens a month. So why isn't the software getting better? Drawing on Fred Brooks's fifty-year-old Mythical Man-Month, this talk argues agentic engineering is repeating software's oldest mistakes at machine speed: generating technical debt at unprecedented scale while making scope creep unstoppable. Agents can demolish accidental complexity but struggle to distinguish this from essential complexity, and they produce new accidental complexity if not kept in check. The bottleneck was never typing speed. Design taste and knowing when to say "no" were always the hard part. Agents are making this paradoxically even harder.

Sessions / 2024 / 1 talk

  • The Future Roadmap for the Composable Data Stack

    In this talk, I plan to review the progress we have made in the last 10 years developing composable, interoperable open standards for the data processing stack, from such infrastructure projects as Parquet and Arrow to user-facing interface libraries like Ibis for Python and the tidyverse for R. In discussing the current landscape of projects, I will dig into the different areas where more innovation and growth is needed, and where we would ideally like to end up in the coming years.

Sessions / 2018 / 1 talk

  • Apache Arrow: A Cross-language Development Platform for In-memory Data

    This talk discusses Apache Arrow project and its uses for high performance analytics and system interoperability. Data processing systems have historically been full-stack systems features memory management, IO, file format adapters, runtime memory format, in-memory query engine, and front-end user interfaces. Many of these components are fully "bespoke" or "custom", in part due to a lack of open standards for many of the pieces. Apache Arrow was created by a diverse group of open source data system developers to define open standards and community-maintained libraries for high performance in-memory data processing. Since the beginning of 2016, we have been building a cross-language development platform for data processing to help create systems that are faster, more scalable, and more interoperable. I discuss the current development initiative and future roadmap as it relates to the data science and data engineering worlds.

Sessions / 2016 / 1 talk

  • Python Data Wrangling: Preparing for the Future

    In this talk, I'll discuss the current status of the pandas project and where we are planning to take it in the near future. I'll also talk about related work in data interoperability, such as Apache Arrow, designed to bring together the Python and Big Data / Hadoop worlds.

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