Coté

Try using a platform to combat Conway’s Law and organizational friction caused by too many groups/silos. This matters because it removes the structural excuse for fragmentation. When a single platform surfaces all the controls a unified team needs, there is no longer a technical reason to keep five separate teams in five separate rooms. The organisational argument for siloes collapses alongside the technical one. Conway’s Law says that a system will be shaped - organization sub-divided - as a replica of the orgnonzatikn that built the system.

The [Wells Fargo] CEO named auditing, testing, legal, contracts, patent filings, pitchbooks in investment banking and credit memos as a handful of areas across the company executives see room for AI to improve processes. “How much of that actually results in pure margin or return expansion is to be seen.” Scharf said, since competitors will be chasing similar AI goals, but it is “a net positive” for the company’s future expense base.

From what I can tell, every core part of the software stack is stopping what they’re doing and taking care of the flood of new, AI-driven security issues. 🔗 Java Maintenance Engineering Shifts Focus on Quarterly Critical Patch Stabilization

Original: A Backstory from My Backrooms by Paige K. Bradley. Summarized by AI on June 3, 2026. {I love backrooms. One of the first things I did with AI image generator was make endless empty malls and backrooms. So good. -Coté} A stray 2019 4chan post about a bland, fluorescent-lit interior sparked the viral myth of the backrooms, a concept of endless, liminal spaces that feel familiar yet threatening. Its resonance lies in the idea of “no clipping” from reality—slipping into a hollow, game-like purgatory where meaning and orientation fail.

Original: Enterprise AI is still stuck at experimentation – Valiantys and Glean think they know why by diginomica. Summarized by AI on June 3, 2026. Most enterprise AI pilots stall, and the diagnosis from Nathan Chantrenne, Chief AI Officer at Valiantys, is that the field measures the wrong things and fragments its tooling. The dominant success metric - “employees save four hours a week” - tells you nothing, because nobody knows what those hours become; they might just mean more coffee.

Original: Is this sustainable? by Jamie Hurst. Summarized by AI on June 3, 2026. AI has collapsed the distance between idea and implementation. Senior engineers can now move from concept to working proof-of-concept in days, bypassing the old cycle of proposals, approvals, and sequential team work. This shift has replaced slide decks with demos, rewarding concrete experimentation over theoretical cases. Organizational alignment has become the new bottleneck. Multiple teams can quickly produce overlapping solutions, making coordination and convergence harder even as technical velocity rises.

I think the critical part of it was really realizing that we had built the original product presupposing that our customers had data integrated, that we could focus on the analytics that came subsequent to having your data integrated. I feel like that founding trauma was realizing that actually everyone claims that their data is integrated, but it is a complete mess and that actually the much more interesting and valuable part of our business was developing technologies that allowed us to productize data integration, instead of having it be like a five-year never ending consulting project, so that we could do the thing we actually started our business to do.

Original: How People Are Really Using AI in 2026 by Harvard Business Review. Summarized by AI on June 2, 2026. Generative AI has become deeply embedded in daily life, with 900 million regular ChatGPT users and Google Gemini close behind. A longitudinal study of 12,637 fresh use cases shows adoption expanding across personal, emotional, and work contexts, creating new dependencies and risks alongside efficiency gains. A key trend is “thinkslop”: the lazy outsourcing of cognitive labor to AI.

Here’s what I learned: container base images grew up as a developer convenience tool, not a security artifact. Installing extra packages from the command line is one of the first things any Docker tutorial teaches–Docker’s own Dockerfile guide includes apt-get install–and many of the most popular official images ship a full toolchain by default, with -slimand -alpine variants offered precisely because the defaults carry more than most workloads need, and changing them would have broken enough downstream workflows that it was never going to be a routine upstream decision.

Removing product as a bottleneck: The conversation around PaaS is urgent again, and AI is why. Code generation can speed up your development cycles, building and pushing features faster, but production delays will persist if you’re still deploying at the same speed as before. To avoid eroding the benefits of code generation, you need to deploy applications nearly as fast as they can be coded with AI Better, sustainable security: