The hottest Platform Engineering Substack posts right now

Change typically begins with a focus on delivery predictability and reducing work-in-progress, where throughput is treated as the main measure of value.
Introducing goals or OKRs shifts attention toward outcomes, but real outcome orientation only sticks when teams, architecture, funding, and ways of working are redesigned so objectives guide work as testable hypotheses.
The healthiest state is when value models underpin org design, goals, funding, and architecture so technology is inseparable from the business, but there is no final destination—models keep evolving and organizations can regress.

Productivity is a sociotechnical problem. You need to invest in reliable systems and tooling while also changing culture, meeting structures, and leadership alignment so engineers can do deep, uninterrupted work.
Roll out AI alongside developer experience work and make sure build, test, and telemetry systems are strong so developers trust AI-assisted workflows. Use exec-level signals to accelerate adoption, enable fast experiments, offer multiple tools, and build internal platforms when third-party tools don’t scale.
The big unsolved challenge is linking productivity gains to business outcomes. AI frees capacity that often goes to migrations and tech debt, but companies lack the instrumentation to show how that work turns into revenue or faster customer value.

Hiring is shifting toward AI‑fluent roles like “AI Engineer,” and companies are putting much more emphasis on code quality because AI makes writing code easier but often produces sloppy output that reviewers must catch.
Early, fragmented AI experiments are being centralized into platform-level models (AI Centers of Excellence or hub-and-spoke), so platform teams now own governance, orchestration, and making AI a standard developer tool.
A new operational layer—LLMOps—is emerging to run models, ship integrations, and create reusable prompts, while human challenges like security training, unclear ROI, and uncontrolled developer experimentation remain the biggest risks.

Standardizing build and deployment pipelines and automating SRE tasks removes repetitive work so large engineering teams can move like startups and focus on high‑value problems.
AI in 2026 shifts from demos to real procurement: organizations will budget heavily for AI and should prioritize applying models to new workflows while enforcing strong security and governance.
Pausing deploys (like Friday freezes) often increases risk by accumulating untested changes; regular, practiced deployments build resilience and reduce surprise failures.

GCP favors consistency and global networking primitives and is stronger in data, analytics, and ML. It uses a project-based organization that makes builds faster but more opinionated than AWS.
Platform teams now sit between security, compliance, finance, and application groups and need clearer ownership and decision authority to avoid an accountability gap.
A sophisticated, modular Linux malware framework is targeting cloud servers and containers for credential theft and stealthy persistence, so organisations should assume such threats are coming and tighten access controls, monitoring, patching, and Linux/cloud EDR.

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Build lightweight AI agents to remove coordination and repetitive overhead so engineers can focus on the work only they can do.
As AI cuts administrative work, each hire becomes more productive. That makes adding headcount more attractive than reducing it.
Deploy agents iteratively: start with real bottlenecks like standups and onboarding, test in safe channels, and maintain observability and governance to measure and scale what actually improves outcomes.

Companies are using AI tools to help engineers work faster, with data showing that these tools can significantly improve productivity. For example, tasks were completed 40% faster in some studies.
Understanding the differences between platform engineering and developer experience teams is important for improving how developers work. Companies are putting focus on their developer productivity teams to ensure that their developers have what they need.
New frameworks are being introduced to measure developer productivity more effectively. These frameworks help identify inefficiencies and understand how developers feel about their working conditions.

Platform Engineering teams help improve developer efficiency by unifying tooling and documentation.
Enterprises need to focus on making developers more efficient due to a predicted shortage of 4M developers by 2025.
Platform Engineering is gaining more budget allocation as it reduces costs through efficiency and improves velocity.

Using serverless computing on AWS Lambda has potential risks that need to be mitigated
Updating the Linux kernel on Ubuntu can be done for parts not available to install
Migrating to Ubuntu LTS provides insights for CentOS users

The industry has mainly focused on Kubernetes as the go-to cloud solution, but serverless options like Cloud Run can be effective for certain use cases and offer cost-efficiency.
Cloud Run offers a simplified platform for businesses with cyclical traffic patterns and minimal need for Kubernetes-level complexity, allowing developers to focus on delivering value rather than managing infrastructure.
Adopting Cloud Run can provide a flexible, cost-effective cloud solution that maintains the option to evolve to a more complex platform as needed, catering well to 'normal' businesses outside of internet-scale operations.

Specialists like doctors and lawyers often hesitate to provide clear recommendations to avoid legal issues, leaving people to make decisions on their own.
Cloud platforms like AWS and GCP offer numerous options but lack clear guidance, leading to decision paralysis for users.
An opinionated platform, like Konfig, can save engineering resources by providing pre-configured solutions based on best practices, allowing teams to focus on innovation.