Software Innovation Lab

Scaling Engineering Teams: Lessons from Google, Facebook, and Netflix

After spending over a decade in engineering leadership roles at some of the world’s most chaotic innovation factories—Google, Facebook, and Netflix—I’ve learned one universal truth: scaling e…

The AI Roles Continuum: Blurring the Boundary Between Research and...

The rapid scaling of deep neural networks and large language models has collapsed the once-clear divide between "research" and "engineering" in AI organizations. Drawing on a qualitative synthesis...

Enosis Solutions - Software Development and Testing Services

We design, build, test, modernize, and scale software across the entire product lifecycle, combining deep technical expertise with an AI-accelerated build process.

Using X-Labs to Unleash AI-Driven Scientific Breakthroughs | IFP

How to adapt our science funding mechanisms to the unique infrastructure needs of large-scale AI projects

AI Innovation Workspace | Miro

Speed up product development from ideation to launch. Align teams, break tool silos and ship what customers need in one AI-powered visual platform.

Scaling engineering organizations

Lessons learned from scaling Stripe's engineering team

Impressions from visiting OpenAI, Anthropic, & Cursor

A peek into where software engineering is headed from inside the sector’s leading AI labs. Agents running in the cloud are a major trend, while coding harnesses are spreading beyond the craft

Microsoft for Startups | Microsoft

Build fast, scale smart, and sell more with the cutting-edge AI, global customer experience, and deep enterprise experience of Microsoft. Get started today.

This morning, I had the distinct honour of giving the opening keynote at #RSECon26: "Where does the rigour go? Research software and the future of trustworthy science" The core argument: AI is… | Arfon Smith

This morning, I had the distinct honour of giving the opening keynote at #RSECon26: "Where does the rigour go? Research software and the future of trustworthy science" The core argument: AI is making it dramatically cheaper to produce plausible scientific outputs, without necessarily making them cheaper to check. The scarce resources are shifting towards attention, judgment, and verification.  This is no doubt a disruptive time for research software engineers and research more generally. For Research Software Engineers specifically I think that creates some important new opportunities around: encoding domain judgment, building verification infrastructure, connecting agents to reality, and helping ensure that the emerging verification layer for science remains open. Thanks to everyone at RSECon26 for the thoughtful discussion, and especially to the many people whose work and ideas I that heavily influenced this talk. Link to slides in first comment

We are all Product Engineers now | Seldo.com

Agents are eating the software development lifecycle from the bottom up — code generation already gone, review and ops following fast. What remains is figuring out what people actually want, which can't be automated or scaled. That job is called product engineering, it pays $240k, and nobody's training for it.

The Beginning of the End of Big Tech

From politicians to VC firms, everyone is falling out of love with the massive, money-oriented, global technology titans. In their place, we have the chance to build something open and trustworthy.

Presentations — Benedict Evans

Every year, I produce a big presentation exploring macro and strategic trends in the tech industry. New in May 2025, ‘AI eats the world’.

Industrial Research Labs Were Invented In Europe But Made The U.S. A Tech Superpower

It's a small number of research labs inside tech giants that are driving the rapid rise of AI today. But this is not the first time such labs have taken center stage, a new study shows.

Value Pairs: A New Way of Approaching Product Development

How AI-enabled teams can change how we think about product development