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Agents' next frontier: agent-to-agent and network effects — Jean-Denis Greze, Town

Sep 3, 2026 · 21:17

Jean-Denis Greze, CTO of Town and former Plaid CTO, argues agent-to-agent is not a useful concept: every LLM system is a search problem, and what matters is whether the right information sits in the context window at the moment of a tool call. The ideal is a single agent with access to all the world's information; privacy, not context length, blocks it — his Coase framing: privacy is the transaction cost. He grades five strategies by how closely each approximates that impossible agent: a shared trust boundary like an HR agent scoped to the most junior access, custom tools that read everyone's mail but return only a connection score, shared silos fed by a sweeper agent, humans as the conduit, and a black box that searches every silo unasked, seeking approval only from the information's owner.

Leadership in AI Assisted Engineering – Justin Reock, DX (acq. Atlassian)

Dec 19, 2025 · 18:11

Justin Reock, Deputy CTO at DX (acquired by Atlassian), argues that AI's impact on engineering productivity varies wildly and that leaders must move beyond top-down mandates to focus on psychological safety, measurement of actual outcomes, and targeted integration across the SDLC. He presents data showing a 2.6% average increase in change confidence but extreme variability across companies, with some seeing 20% drops. Emphasizes that writing code is rarely the bottleneck; instead, leaders should identify and fix bottlenecks like context switching, citing Morgan Stanley's DevGenAI saving 300,000 hours annually by converting legacy code specs and Zapier reducing engineer onboarding to two weeks via AI agents. Introduces DX's AI Measurement Framework covering utilization, impact, and cost, and stresses trust-building through system prompt feedback loops and temperature settings. The episode delivers actionable guidance on measuring AI's true impact and enabling engineers through education, time to learn, and creative unblocking of usage.

Open Challenges for AI Engineering: Simon Willison

Jul 17, 2024 · 18:49

Simon Willison argues the GPT-4 barrier has been broken as GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, and open models like LLaMA 3 70B now compete, making GPT-4-class models a commodity. He highlights the AI trust crisis with examples of Dropbox and Slack being falsely accused of training on user data, and notes Anthropic trained Claude 3.5 Sonnet without customer data. Willison warns about prompt injection vulnerabilities, citing the Markdown image exfiltration bug affecting six major chatbots, and defines slop as unreviewed AI-generated content, calling for accountability and responsible use patterns.