Episodes
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The current impact of AI on engineering velocity: What 400 companies are seeing (Abi Noda & Brian Houck)
Most organizations see modest improvements (around 10-15%) in engineering throughput from AI, far below the industry hype of 10x gains.
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Beyond AI tools: Evolving software engineering organizations for the agentic era
AI-driven development is here and progressing rapidly; organizations must lead the change proactively.
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Designing the AI‑native engineering organization with 1Password, Microsoft and Atlassian
AI is reshaping engineering structures, promoting smaller, agile teams that focus on rapid learning and experimentation.
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Assumptions as code: SiriusXM’s approach to platform prioritization
SiriusXM developed a custom prioritization framework involving weighted impact factors such as developer speed, reliability, cost, and trust, which allows for more nuanced decision-making across multiple teams.
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Measuring AI impact, assessing readiness, and new data trends
AI is reshaping the Software Development Life Cycle (SDLC) by influencing more areas than just code generation.
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Scaling developer experience across 1,000 engineers at Dropbox
Productivity should be treated as a business problem requiring cross-functional alignment.
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AI and productivity: A year-in-review with Microsoft, Google, and GitHub researchers
Measuring AI's impact requires a nuanced, multidimensional approach that goes beyond traditional metrics.
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Running data-driven evaluations of AI engineering tools
Robust evaluation frameworks are crucial for selecting AI engineering tools.
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DORA’s 2025 research on the impact of AI
Relying solely on DORA's four key metrics is not sufficient to gauge AI impact in software delivery.
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How Monzo runs data-driven AI experimentation
Monzo adopts AI cautiously, emphasizing structured experimentation to maintain compliance and innovation in a regulated industry.
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Planning your 2026 AI tooling budget: guidance for engineering leaders
Anticipate rising costs in AI tooling as tools become more feature-rich and widely adopted.
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The evolving role of DevProd teams in the AI era
Not summarized yet
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Lessons from Twilio’s multi-year platform consolidation
Migration processes are complex and require a focus on both technical and human aspects.
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Driving enterprise-wide AI tool adoption
Cultural change is as important as technological change for successful AI adoption.
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Measuring AI code assistants and agents with the AI Measurement Framework
AI is a valuable tool for enhancing developer experiences, but it isn't a one-size-fits-all solution.
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How to cut through the hype and measure AI’s real impact (Live from LeadDev London)
Understanding the 'Disappointment Gap' is crucial for leaders to navigate the reality of AI in engineering.
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Unpacking METR’s findings: Does AI slow developers down?
Self-reflection is crucial for developers when integrating AI tools into their workflows.
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CarGurus’ journey building a developer portal and increasing AI adoption
CarGurus successfully transitioned from a monolithic architecture to microservices using an IDP framework that streamlined operations.
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Snowflake’s playbook for operational excellence
Operational excellence at Snowflake is defined by strong communication and trust among teams.
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The biggest obstacles preventing GenAI adoption — and how to overcome them
AI adoption is significantly affected by the hype and inflated expectations, leading to a disconnect between anticipated and actual results.
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