Peter Birkholm-Buch

Stuff about Software Engineering

Gaia: How We Built a Platform to Transform Infrastructure Creation

2024-11-08 /

The Evolution of Infrastructure Management

In Software Engineering in Growth Products at Carlsberg, our journey towards modern infrastructure management began with a familiar challenge: as the number of development teams grew, the traditional approach of manually provisioning and managing infrastructure became a significant bottleneck. The DevOps team, tasked with building and maintaining infrastructure for multiple development teams, found themselves overwhelmed by the increasing demand for infrastructure resources.

Each new project required careful setup of networking, permissions, and cloud resources, all of which had to be manually configured by a small DevOps team. As development velocity increased and more teams came onboard, this model proved unsustainable. New projects faced delays waiting for infrastructure provisioning, while the DevOps team struggled to keep pace with mounting requests.

Reimagining Infrastructure Creation

The solution emerged when the DevOps team envisioned a different approach: what if developers could create their own infrastructure while adhering to organizational standards? The challenge was to enable self-service infrastructure without requiring developers to understand the complexities of building secure, scalable, and compliant cloud resources.

This vision led to the creation of Gaia, a platform that automates infrastructure creation while maintaining strict security and compliance standards. Built by the DevOps team, Gaia represents a fundamental shift in how infrastructure is provisioned and managed at Carlsberg.

The Platform Engineering Approach

Infrastructure as Code Evolution

Gaia elevates infrastructure creation beyond basic scripting by providing a comprehensive platform engineering solution. The platform utilizes Terraform for infrastructure provisioning but abstracts its complexity through a higher-level interface. This approach allows developers to focus on their applications while ensuring infrastructure deployments follow organizational best practices.

Standardized Module Library

The platform provides an extensive library of pre-built, production-ready modules covering the complete spectrum of AWS infrastructure components:

  • Compute Services: EC2, ECS, EKS, Lambda
  • Data Stores: Aurora, RDS, DynamoDB, DocumentDB, Redis, Elasticsearch
  • Networking: VPC, Load Balancers, API Gateway, Route53
  • Security: IAM, ACM, Secrets Manager
  • Messaging: SQS, Kafka
  • Monitoring: CloudWatch, Managed Grafana

Each module encapsulates best practices, security controls, and compliance requirements, ensuring consistent infrastructure deployment across the organization.

Developer Experience

Simplified Workflow

Gaia integrates seamlessly with existing development workflows through GitHub. Developers request infrastructure by:

  1. Creating a configuration file with simple key-value pairs
  2. Submitting a pull request
  3. Awaiting automated validation and deployment

Example configuration for a serverless function with a storage layer and an API Gateway:

The API Gateway configuration for the API is very simple:

Once the developer is ready to create the infrastructure a Pull Request is created and a “code owner” (in this case a Platform Engineer Team Member) approves the request and the infrastructure is deployed automatically.

Automated Compliance

The platform automatically enforces organizational standards and security policies. Developers don’t need to worry about:

  • Network configuration
  • Security group settings
  • Access control policies
  • Compliance requirements

All these aspects are handled automatically by Gaia’s pre-configured modules.

Technical Architecture

Terragrunt Integration

Gaia leverages Terragrunt as a wrapper around Terraform to provide enhanced functionality:

  • Automatic variable injection based on environment context
  • Template-based module generation
  • Configuration reuse across environments
  • Simplified state management

Monitoring and Observability

The platform includes native integration with monitoring tools:

  • Automated Datadog dashboard creation
  • Standardized monitoring configurations
  • Built-in health checks and alerts
  • Custom metric collection

Organizational Impact

DevOps Transformation

  • Reduced manual infrastructure work by approximately 80%
  • Shifted focus from repetitive tasks to platform improvements
  • Enabled scaling of development operations without proportional increase in DevOps resources

Development Velocity

  • Eliminated infrastructure provisioning bottlenecks
  • Reduced time-to-deployment for new projects
  • Enabled consistent implementation across teams

Governance and Security

  • Centralized policy enforcement
  • Automated compliance checking
  • Infrastructure drift detection and remediation
  • Standardized security controls

Future Directions

Multi-Cloud Strategy

While currently focused on AWS, Gaia is being extended to support Azure. This expansion presents unique challenges due to fundamental differences in how cloud platforms implement similar services. The team is working to maintain the same simple developer experience while adapting to Azure’s distinct architecture.

Platform Evolution

Planned enhancements include:

  • Enhanced monitoring capabilities
  • Expanded multi-cloud support
  • Deeper integration with development tools
  • Advanced automation features

Conclusion

Gaia represents a successful transformation from traditional DevOps to platform engineering. By providing developers with self-service infrastructure capabilities while maintaining security and compliance, the platform has eliminated a major organizational bottleneck. The success of this approach demonstrates how well-designed abstractions and automation can make infrastructure management accessible to development teams while maintaining enterprise-grade standards.

The platform has fundamentally transformed how Carlsberg manages cloud infrastructure. As cloud infrastructure continues to evolve, Gaia’s modular architecture and focus on developer experience position it well for future adaptations and enhancements. The platform serves as a testament to how modern platform engineering can effectively bridge the gap between development velocity and operational excellence.

Gaia was conceived by https://www.linkedin.com/in/josesganjos/ and built with the help from:

Evolve Commerce Club Expert Session #031

2024-11-08 /

I was honoured to be invited to speak at the 31st Expert Session at the https://www.evolve-community.com.

We touched on a lot of subjects around Software Engineering and mostly AI.

Thank you Carlos Monteiro and Gustavo Valle for inviting me.

Here are some links to posts which covers some of the topics we spoke about in more detail:

Why 100% Utilization Kills Innovation: The Mathematical Reality

2024-11-07 /

Imagine a highway at 100% capacity. Traffic doesn’t just slow down—it stops completely. A single broken-down car causes massive ripple effects because there’s no buffer space to absorb the variation. This isn’t just an analogy; it’s mathematics. And the same principle explains why running teams at full capacity mathematically guarantees the death of innovation.

The Queue Theory Reality

In 1961, mathematician J.F.C. Kingman proved something remarkable: as utilization approaches 100%, delays grow exponentially. This finding, known as Kingman’s Formula, demonstrates that systems operating at full capacity don’t just slow down linearly—they break down dramatically. Hopp and Spearman’s seminal work “Factory Physics” (2000) further established that optimal system performance occurs at around 80% utilization, giving rise to the “80% Rule” in operations management.

This isn’t opinion or management theory—it’s mathematics. When utilization exceeds 80-85%, systems experience:

  • Exponentially increasing delays
  • Inability to handle normal variation
  • Cascading disruptions from small problems
  • Deteriorating performance across all metrics

The Human System Connection

Just as a machine’s productivity is limited by its operational capacity, humans too are constrained by cognitive load. People and teams are systems too. When cognitive load research pioneers Sweller and Chandler demonstrated how mental capacity follows similar patterns, they revealed something crucial: minds at 100% capacity lose the ability to process new information effectively. Just as a fully utilized highway can’t absorb a single additional car, a fully utilized mind can’t absorb new ideas or opportunities.

The implications are profound: innovation requires spare capacity. This isn’t about working less—it’s about maintaining the mental and temporal space required for creative thinking and problem-solving. Studies of innovation consistently show that breakthrough ideas emerge when people have the bandwidth to:

  • Notice unexpected patterns
  • Explore new connections
  • Experiment with different approaches
  • Learn from failures

The Three Horizons Impact

McKinsey’s Three Horizons Framework provides a useful lens for understanding innovation timeframes:

  • Horizon 1: Improving current business
  • Horizon 2: Extending into new areas
  • Horizon 3: Creating transformative opportunities

Here’s where queue theory delivers its killing blow to innovation: At 100% utilization, everything becomes Horizon 1 by mathematical necessity. When a system (human or organizational) operates at full capacity, it can only handle what’s already in the queue. New opportunities, no matter how promising, must wait. Over time, Horizons 2 and 3 don’t just suffer—they become mathematically impossible.

To keep Horizons 2 and 3 viable, companies need to intentionally limit Horizon 1 resource utilization and leave room for creative and exploratory projects.

The Innovation Impossibility

Queue theory proves that running at 100% utilization:

  • Makes delays inevitable
  • Eliminates flexibility
  • Prevents absorption of variation
  • Blocks capacity for new initiatives

Therefore, organizations face a mathematical certainty: maintain 100% utilization or maintain innovation capability. You cannot have both. This isn’t a management choice or cultural issue—it’s as fundamental as gravity.

The solution isn’t working less—it’s working smarter. Just as highways need buffer capacity to function effectively, organizations need spare capacity to innovate. The 80% rule isn’t about reduced output; it’s about maintaining the space required for sustainable performance and growth.

The choice is clear: accept the mathematical reality that innovation requires spare capacity, or continue pushing for 100% utilization while wondering why transformative innovation never seems to happen.

References:

  • Kingman, J.F.C. (1961). “The Single Server Queue in Heavy Traffic”
  • Hopp, W.J., & Spearman, M.L. (2000). “Factory Physics”
  • McKinsey & Company. “Three Horizons of Growth”
  • Sweller, J., & Chandler, P. “Cognitive Load Theory and the Format of Instruction”

Beyond DevOps: The Rise of Full-Stack Platform Engineering

2024-11-04 /

The Evolution of Infrastructure Management

DevOps promised to bridge the gap between development and operations, aiming to deliver infrastructure faster and more efficiently. However, in many organizations, the reality often fell short of this ideal. DevOps frequently became a practice where operations teams learned to script infrastructure without fully embracing key software engineering principles. It became more about scripting than true engineering.

The Need for a Higher Abstraction

As infrastructure needs grew more complex, it became clear that traditional DevOps approaches were not scaling effectively. Tools like Terraform, while powerful, often proved to be terse and not particularly developer-friendly. They got the job done, but they weren’t providing the streamlined experience that developers needed. A new approach was necessary – one that would raise the level of abstraction and make infrastructure more accessible.

The Golden Path as a Product

Enter the concept of the “golden path” – a set of pre-built, standardized infrastructure solutions that developers can easily use and customize. This approach treats infrastructure as a product, designed with the end-user – the developer – in mind. 

The golden path isn’t just a set of scripts or configurations; it’s a carefully crafted product that encapsulates best practices, security considerations, and organizational policies. It automates infrastructure creation while maintaining alignment with company standards, allowing developers to provision cloud resources without needing to worry about governance, security, or configuration inconsistencies.

Raising the Abstraction Level

To understand the significance of this shift, consider this analogy: Terraform, while powerful, is often like the assembly language of infrastructure. Platform engineering, and the golden path approach, is about raising that abstraction, creating reusable and maintainable infrastructure solutions that developers can work with seamlessly. 

Just as high-level programming languages made software development more accessible and efficient compared to assembly language, the golden path aims to do the same for infrastructure management. By creating higher-level abstractions, we’re making infrastructure more understandable, manageable, and aligned with modern software development practices.

The Role of Full-Stack Platform Engineers

This new approach requires a new kind of professional: the full-stack platform engineer. These engineers think like developers while solving infrastructure challenges. They build scalable, reliable, and developer-friendly infrastructure that empowers teams.

Full-stack platform engineers focus on creating robust, scalable infrastructure solutions that directly support business needs, rather than getting bogged down in low-level configuration details. They apply the same rigor expected in software development to infrastructure design, treating infrastructure truly as code.

Enhancing Developer Experience and Security

The golden path approach significantly enhances the developer experience. By integrating infrastructure provisioning directly into familiar development workflows (like those in GitHub), it allows developers to request and manage infrastructure as part of their normal process, without delays or context switching.

This approach also allows for the seamless integration of security practices. By baking security considerations into the golden path from the start, organizations can shift security left in the development process, addressing vulnerabilities at their source without compromising developer productivity.

A New Era of Infrastructure Management

The rise of full-stack platform engineering and the golden path approach represents a significant evolution in how we think about and manage infrastructure. It’s not just DevOps 2.0; it’s a fundamental shift in mindset that treats infrastructure as a product designed for developer success.

By raising the abstraction level, applying software engineering principles to infrastructure, and focusing on creating reusable, maintainable solutions, this approach promises to make infrastructure more accessible, secure, and aligned with modern development practices. As organizations continue to grapple with increasing complexity, the golden path offers a way forward – empowering developers, enhancing security, and ultimately accelerating innovation.

At Carlsberg, this approach has been embodied in Gaia, our golden path platform built by full-stack platform engineers. Gaia exemplifies how treating infrastructure as a product can transform development processes, making them more efficient and developer-friendly. It stands as a testament to the power of full-stack platform engineering in creating solutions that truly serve the needs of modern development teams.

As more organizations embrace this shift, we can expect to see a new landscape of infrastructure management emerge – one where the golden path, crafted by skilled full-stack platform engineers, leads the way to more innovative, secure, and efficient software development practices.

AI-Engineer: A Distinct and Essential Skillset

2024-10-28 /

Introduction

Artificial intelligence (AI) technologies have caused a dramatic change in software engineering. At the forefront of this revolution are AI-Engineers – the professionals who implement solutions within the ‘Builders’ category of AI adoption, as I outlined in my previous post on Four Categories of AI Solutions. These engineers not only harness the power of AI but also redefine the landscapes of industries.

As I recently discussed in “AI-Engineers: Why People Skills Are Central to AI Success” organizations face a critical talent shortage in AI implementation. McKinsey’s research shows that 60% of companies cite talent shortages as a key risk in their AI adoption plans. This shortage makes understanding the AI Engineer role and its distinct skillset more crucial than ever.

But what is an AI-Engineer?

Core Skills of an AI Engineer

AI-Engineers are skilled software developers who can code in modern languages. They create the frameworks and software solutions that enable AI functionalities and make them work well with existing enterprise applications. While they need basic knowledge of machine learning and AI concepts, their primary focus differs from data engineers. Where data engineers mainly focus on writing and managing data models, AI-Engineers concentrate on building reliable, efficient, and scalable software solutions that integrate AI.

Strategic Business Outcomes

The role of AI-Engineers is crucial in translating technological advancements into strategic advantages. Their ability to navigate the complex landscape of AI tools and tailor solutions to specific business challenges underlines their unique role within the enterprise and software engineering specifically. By embedding AI into core processes, they help streamline operations and foster innovative product development.

Continuous Learning and Adaptability

As I described in “Keeping Up with GenAI: A Full-Time Job?” the AI landscape shifts at a dizzying pace. Just like Loki’s time-slipping adventures, AI-Engineers find themselves constantly jumping between new releases, frameworks, and capabilities – each innovation demanding immediate attention and evaluation.

For AI-Engineers, this isn’t just about staying informed – it’s about rapidly evaluating which technologies can deliver real value. The platforms and communities that facilitate this learning, such as Hugging Face, become essential resources. However, merely keeping up isn’t enough. AI-Engineers must develop a strategic approach to:

  1. Evaluate new technologies against existing solutions
  2. Assess potential business impact before investing time in adoption
  3. Balance innovation with practical implementation
  4. Maintain stable systems while incorporating new capabilities

Real-World Impact

The real value of AI-Engineers becomes clear when we look at concrete implementation data. In my recent analysis of GitHub Copilot usage at Carlsberg (“GitHub Copilot Probably Saves 50% of Time for Developers“), we found fascinating patterns in AI tool adoption. While developers and GitHub claim the tool saves 50% of development time, the actual metrics tell a more nuanced story:

  • Copilot’s acceptance rate hovers around 20%, meaning developers typically use one-fifth of the suggested code
  • Even with this selective usage, developers report significant time savings because reviewing and modifying AI suggestions is faster than writing code from scratch
  • The tool generates substantial code volume, but AI-Engineers must carefully evaluate and adapt these suggestions

This real-world example highlights several key aspects of the AI-Engineer role:

  1. Tool Evaluation: AI-Engineers must look beyond marketing claims to understand actual implementation impact
  2. Integration Strategy: Success requires thoughtful integration of AI tools into existing development workflows
  3. Metric Definition: AI-Engineers need to establish meaningful metrics for measuring AI tool effectiveness
  4. Developer Experience: While pure efficiency gains may be hard to quantify, improvements in developer experience can be significant

These findings demonstrate why AI-Engineers need both technical expertise and practical judgment. They must balance the promise of AI automation with the reality of implementation, ensuring that AI tools enhance rather than complicate development processes.

Conclusion

AI Engineering is undeniably a distinct skill set, one that is becoming increasingly indispensable in AI transformation. As industries increasingly rely on AI to innovate and optimize, the demand for skilled AI Engineers who can both understand and shape this technology continues to grow. Their ability to navigate the rapid pace of change while delivering practical business value makes them essential to successful AI adoption. Most importantly, their role in critically evaluating and effectively implementing AI tools – as demonstrated by our Copilot metrics – shows why this specialized role is crucial for turning AI’s potential into real business value.

AI-Engineers: Why People Skills Are Central to AI Success

2024-10-23 /

In my blog post “Four Categories of AI Solutions” (https://birkholm-buch.dk/2024/04/22/four-categories-of-ai-solutions), I outlined different approaches to building AI solutions, but it’s becoming increasingly clear that the decision on how to approach AI hinges on the talent and capabilities within the organization. As AI continues to evolve at lightning speed, companies everywhere are racing to adopt the latest innovations. Whether it’s generative AI, machine learning, or predictive analytics, organizations see AI as a strategic advantage. But as exciting as these technologies are, they come with a less glamorous reality—people skills are the make-or-break factor in achieving long-term AI success.

The Talent Crunch: A Major Barrier to AI Success

Reports from both McKinsey and Gartner consistently highlight a serious shortage of skilled AI talent. McKinsey’s latest research suggests that AI adoption has plateaued for many companies not due to a lack of use cases, but because they don’t have the talent required to execute their AI strategies effectively. 60% of companies cite talent shortages as a key risk in their AI adoption plans. (McKinsey State of AI 2023: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year).

This talent crunch means that organizations are finding it difficult to retain and attract professionals with the skills needed to develop, manage, and scale AI initiatives. 58% of organizations report significant gaps in AI talent, with 72% lacking machine learning engineering skills and 68% short on data science skills (McKinsey AI Tech Talent Survey: https://www.mckinsey.com/capabilities/quantumblack/our-insights/new-mckinsey-survey-reveals-the-ai-tech-talent-landscape).

The New Frontier of AI Engineering Talent

Given the rapid pace of change in AI tools and techniques, developing an internal team of AI Engineers is one of the most sustainable strategies for companies looking to stay competitive. AI Engineers are those with the expertise to design, build, and maintain custom AI solutions tailored to the specific needs of the organization.

By 2026, Gartner predicts that 30% of new AI systems will require specialized AI talent, making the need for AI Builders even more urgent (Gartner 2023 Hype Cycle for AI: https://www.gartner.com/en/newsroom/press-releases/2023-08-17-gartner-identifies-key-technology-trends-in-2023-hype-cycle-for-artificial-intelligence).

The Challenge of Retaining AI Talent

The retention challenge in AI isn’t just about compensation—it’s about providing meaningful work, opportunities for continuous learning, and a sense of ownership over projects. Diverse AI teams are essential for preventing biases in models and creating more robust, well-rounded AI systems. Offering inclusive, supportive environments where AI engineers can grow professionally and personally is essential to keeping top talent engaged (McKinsey AI Tech Talent Survey: https://www.mckinsey.com/capabilities/quantumblack/our-insights/new-mckinsey-survey-reveals-the-ai-tech-talent-landscape).

Skills Development: A Competitive Advantage

AI is only as good as the team behind it. As Gartner highlights, the true value of AI will be realized when companies can seamlessly integrate AI tools into their existing workflows. This integration requires not just the right tools but the right people to design, deploy, and optimize these solutions (Gartner 2023 Hype Cycle for AI: https://www.gartner.com/en/newsroom/press-releases/2023-08-17-gartner-identifies-key-technology-trends-in-2023-hype-cycle-for-artificial-intelligence).

Companies that prioritize skills development will have a competitive edge. AI is advancing so quickly that organizations can no longer rely solely on external vendors to provide off-the-shelf solutions. Building an internal team of AI Builders—engineers who are continually learning and improving their craft—is essential to staying ahead of the curve. Offering employees opportunities to reskill and upskill is no longer optional; it’s a necessity for retaining talent and remaining competitive in the AI-driven economy.

Looking Ahead: The Evolving Role of AI Engineers

As organizations continue to explore AI adoption, the need for specialized AI models is becoming more apparent. Gartner predicts that by 2027, over 50% of enterprises will deploy domain-specific Large Language Models (LLMs) tailored to either their industry or specific business functions (Gartner 2023 Hype Cycle for AI: https://www.gartner.com/en/newsroom/press-releases/2023-08-17-gartner-identifies-key-technology-trends-in-2023-hype-cycle-for-artificial-intelligence).

I believe the role of AI Engineers will continue to grow in importance and complexity. We might see new specializations emerge, such as AI ethics officers ensuring responsible AI use, or AI-human interaction designers creating seamless experiences. 

In my view, the most valuable AI Engineers of the future could be those who can not only master the technology but also understand its broader implications for business and society. They might need to navigate complex ethical considerations, adapt to rapidly changing regulatory landscapes, and bridge the gap between technical capabilities and business needs.

Conclusion: Investing in People is Investing in AI Success

The success of any AI initiative rests on the quality and adaptability of the people behind it. For organizations aiming to lead in the AI space, the focus should be on creating a workforce of AI Engineers—skilled professionals who can navigate the ever-changing landscape of AI technologies.

The lesson is clear: to succeed with AI, invest in your people first. The future of AI is not just about algorithms and data—it’s about the humans who will shape and guide its development and application.

Keeping Up with GenAI: A Full-Time Job?

2024-10-22 /

As a technologist, it feels like I’m constantly time slipping — like Loki in Season 2 of his show. Every time I’ve finally wrapped my head around one groundbreaking AI technology, another one comes crashing in, pulling me out of my flow. Just like Loki’s painful and disorienting jumps between timelines, I’m yanked from one new release or framework to the next, barely catching my breath before being dropped into the middle of another innovation.

In the last week alone, we’ve had a flood of announcements that make it impossible to stand still for even a second. Here’s just a glimpse:

  1. OpenAI Swarm (October 16, 2024)
    OpenAI dropped “Swarm,” an open-source framework for managing multiple autonomous AI agents—a move that could redefine how we approach collaborative AI and automation.
    https://github.com/openai/swarm
  2. WorldCoin Rebrands as World (October 18, 2024)
    WorldCoin’s new Orb is yet another reminder that biometric and blockchain technology continues to merge in unpredictable ways. With its iris-scanning Orb, it’s trying to push a global financial identity system—a concept that sparks as much excitement as concern, especially around data privacy.
    https://www.theverge.com/2024/10/18/24273691/world-orb-sam-altman-iris-scan-crypto-token
  3. Microsoft’s Copilot with Autonomous Agents (October 21, 2024)
    Microsoft’s second wave of Copilot introduces agents that can perform complex tasks autonomously, elevating Copilot from an assistant to a decision-making, workflow-automating powerhouse.
    https://www.microsoft.com/en-us/microsoft-copilot/blog/copilot-studio/unlocking-autonomous-agent-capabilities-with-microsoft-copilot-studio/
  4. Anthropic’s New Models & ‘Computer Use’ Feature (October 22, 2024)
    Anthropic has introduced the latest Claude 3.5 models, including a new ‘computer use’ feature that allows AI to interact directly with applications. This marks a major shift toward AI being able to execute tasks like filling out forms and interacting with user interfaces, making it a significant leap in real-world functionality.
    https://www.anthropic.com/news/claude-3-5-sonnet

The Data Speaks for Itself

These developments are not isolated. The latest Stanford AI Index Report 2024 provides some staggering numbers that put this onslaught of innovation into perspective:

  • 149 foundation models were released in 2023, more than double the number from 2022, and a whopping 65.7%were open-source. That’s an overwhelming volume of tools, each requiring careful consideration for their potential applications.
  • The number of AI-related publications has tripled since 2010, reaching over 240,000 by 2022. Staying on top of this research is an almost Sisyphean task, but these papers provide the groundwork for the rapid-fire advancements we’re seeing.
  • Generative AI investments exploded to $25.2 billion in 2023, nearly eight times the previous year. This boom in funding is driving the constant stream of new AI tools and capabilities, each promising to reshape the landscape.
  • AI was mentioned in 394 earnings calls across nearly 80% of Fortune 500 companies in 2023, an enormous jump from the 266 mentions in 2022. The sheer presence of AI in corporate strategy highlights how central this technology has become to every industry.

The Technologist’s Dilemma

The rapid pace of AI advancements presents an overwhelming challenge for technologists. Each new tool or framework isn’t just a minor update—it’s potentially transformative, requiring deep understanding and immediate adaptation. For those managing teams and implementing technological advancements, this fast-moving landscape demands constant learning and vigilance.

For more details and insights on the pace of AI developments, you can dive into the Stanford AI Index Report 2024 at this link:
https://aiindex.stanford.edu/wp-content/uploads/2024/05/HAI_AI-Index-Report-2024.pdf

Now I can go to sleep 😴

DevEx Analysis: Software Engineering in Growth Products, Carlsberg vs Gartner Benchmarks

2024-10-15 /

Introduction

This report was created by having ChatGPT (Auto) and Claude individually compare and evaluate the initiatives on DevEx that I’ve blogged about in the past against the official Gartner DevEx Best Practices. I then had ChatGPT combine the two results into the report below.

Report

This report evaluates the Developer Experience (DevEx) initiatives within the Software Engineering department in Growth Products at Carlsberg, comparing them against Gartner’s The State of Developer Experience Initiatives report. The initiatives reviewed apply specifically to the Software Engineering team within Growth Products and do not reflect Carlsberg’s global operations.

1. Focus on Tooling and Integration

The Software Engineering team in Growth Products demonstrates strong alignment with Gartner’s recommendations in the area of tooling and integration:

Key Highlights:

  • Emphasis on integrated platforms and tooling, such as the Gaia platform
  • Focus on automation to streamline developer workflows
  • Adoption of AI-driven tools like GitHub Copilot to enhance developer efficiency

The initiatives outlined in “From DevOps to Platform Engineering: How Gaia Transformed Our Approach to Infrastructure Alignment and Developer Experience” highlight how integrated tooling supports smoother workflows, a priority emphasized in Gartner’s best practices.

Final Rating: 4.5/5. The focus on improving developer workflows through integrated platforms and automation closely aligns with Gartner’s recommendations.

2. Embedding Security into the Developer Workflow

The approach taken by the Software Engineering team aligns well with Gartner’s guidance on embedding security into daily developer processes:

Key Highlights:

  • Security integration as part of the daily development workflow
  • Use of tools such as GitHub Advanced Security
  • Inclusion of security roles within the broader DevEx initiative

The best practices detailed in “GitHub Advanced Security Enables Shifting Security Left” demonstrate how security has been embedded into the development pipeline, a practice highly recommended by Gartner for mature DevEx programs.

Final Rating: 4.5/5. The integration of security into the workflow, combined with tooling that supports shifting security left, positions the Software Engineering team’s DevEx initiatives at an advanced level in this area.

3. Emphasis on Professional Growth and Autonomy

The Software Engineering team’s emphasis on professional growth, particularly in relation to senior roles, aligns strongly with Gartner’s recommendations:

Key Highlights:

  • Empowering senior developers to act as facilitators and influencers
  • Promoting autonomy while ensuring organizational alignment
  • Balancing team independence with a controlled framework

The initiatives described in “Seniority, Organizational Influence, and Participation” provide a clear view of how senior developers are encouraged to take on leadership roles that foster cross-team collaboration and influence, aligning with Gartner’s focus on supporting professional growth and autonomy.

Final Rating: 5/5. The focus on autonomy and professional growth, especially for senior developers, represents an exemplary alignment with Gartner’s DevEx principles.

4. Cultural and Organizational Alignment

The initiatives to foster a supportive and collaborative developer culture align closely with Gartner’s recommendations for cultural and organizational alignment:

Key Highlights:

  • Clear definitions of expected behaviors across the Software Engineering department
  • Promotion of psychological safety within teams
  • Fostering a collaborative and inclusive work environment

The best practices outlined in “A Manifesto on Expected Behaviors” establish clear behavioral expectations that create a psychologically safe environment for developers. Gartner stresses the importance of a supportive culture in any comprehensive DevEx program, which these initiatives directly address.

Final Rating: 4.5/5. The Software Engineering department has established a strong cultural foundation that promotes collaboration and safety. Further emphasis on psychological safety could further enhance this area.

5. Measuring DevEx

While the Software Engineering team demonstrates strength in many areas, there is room for improvement in how DevEx outcomes are measured:

Key Highlights:

  • Focus on productivity and automation metrics
  • Some discussion of tooling-related metrics, such as in “GitHub Copilot Metrics”
  • Limited use of broader DevEx metrics, such as DORA or job satisfaction surveys

The current focus on productivity and efficiency aligns with Gartner’s recommendations. However, Gartner also recommends a broader range of metrics, including velocity, developer satisfaction, and retention. Expanding the measurement approach, as suggested in these practices, would provide a more comprehensive understanding of DevEx success.

Final Rating: 3/5. Increasing the use of specific DevEx outcome metrics, such as DORA and job satisfaction, would enhance the ability to measure and track the effectiveness of DevEx initiatives within the Software Engineering department.

Overall Rating: 4.3/5 (Strong Alignment)

The Software Engineering department’s DevEx efforts demonstrate strong alignment with Gartner’s best practices, particularly in the areas of tooling, security integration, autonomy, and culture. The main area for improvement is in the explicit measurement and tracking of DevEx outcomes through broader metrics.

Recommendations

Incorporating Gartner’s benchmarks, the following recommendations are suggested for the Software Engineering department in Growth Products:

  1. Implement More Specific DevEx Metrics: Expanding the use of DORA metrics, job satisfaction surveys, and other developer feedback mechanisms would provide deeper insights into the effectiveness of the DevEx initiatives.
  2. Continue Refining Integrated Tooling: Maintaining the momentum around tools like Gaia and GitHub Copilot will ensure the DevEx program continues to drive productivity and reduce friction in the development workflow.
  3. Sustain Focus on Security Integration and Culture: The strengths in security integration and developer culture should remain a focus, ensuring that these foundational pillars of the DevEx program continue to evolve.
  4. Consider Formalizing the DevEx Program: If not already formalized, creating a structured DevEx program with clear goals, metrics, and accountability would allow for better tracking of progress and outcomes.
  5. Regularly Assess and Iterate on DevEx Initiatives: Using feedback loops and metrics to continuously iterate on the DevEx approach will ensure that it evolves in line with both developer needs and organizational goals.

Conclusion:

The DevEx initiatives within the Software Engineering department in Growth Products at Carlsberg are well-aligned with Gartner’s best practices, particularly in terms of integrated tooling, security, professional growth, and cultural alignment. By expanding the metrics framework to include broader DevEx outcome measurements, the program could reach even higher levels of maturity and effectiveness.

Seniority, Organizational Influence and Participation

2024-10-15 /

The table below is meant to better explain how seniority, organizational influence and behaviors are connected. Anyone wanting to move up the career ladder must master our expected behaviors and participate accordingly.

StrengthEntryIntermediateExperiencedAdvancedExpert
ScopeEntry level professional with limited or no prior experience; learns to use professional concepts to resolve problems of limited scope and complexity; works on assignments that require limited judgment and decision making. Developing position where an employee is able to apply job skills, policies and procedures to complete tasks of moderate scope and complexity to determine appropriate action.Journey-level, experienced professional who knows how to apply theory and put it into practice with full understanding of the professional field; has broad job knowledge and works on problems of diverse scope.Professional with a high degree of knowledge in the overall field and recognized expertise in specific areas.Leader in the field who regularly leads projects of criticality to company and beyond, with high consequences of success or failure.  This employee has impact and influence on company policy and program development. Barriers of entry exist at this level.
AnalogyLearning about rope and knotsCan tie basic knots, learning complex knotsCalculates rope strength, knows a lot about knotsUnderstands rope making, can tie any knotKnows more about rope than you ever will, invented new knot
Influence & ImpactSelfPeersTeamDepartmentCompany
Coding100%100%80%-100%<25%<10%
ParticipationActively participate in discussions and team activities. Seek guidance from more experienced team members. Be open to receiving feedback and learning from mistakes.Contribute to team discussions and share knowledge gained from learning basic concepts. Offer assistance to junior team members. Seek feedback on performance and actively work on improving skills.Actively contribute expertise to team projects and discussions. Mentor junior team members and facilitate knowledge sharing sessions. Regularly seek out new learning opportunities and share insights with the team.Lead collaborative learning initiatives within the team. Actively contribute to the development of best practices and processes. Mentor and coach less experienced team members, fostering a culture of continuous learning.Serve as a subject matter expert, providing guidance and direction on complex projects. Spearhead innovative learning initiatives and contribute to industry knowledge sharing. Act as a mentor and coach for both technical and professional development.

A Manifesto on Expected Behaviors

2024-10-15 /

Introduction

In Software Engineering at Carlsberg our collective success is built on the foundation of individual behaviors that foster a positive, innovative, and collaborative environment. This document outlines the expected behaviors that every team member, irrespective of their role or seniority, is encouraged to embody and develop. These behaviors are not just guidelines but the essence of our culture, aiming to inspire continuous growth, effective communication, and a proactive approach to challenges. As we navigate the complexities of software engineering, these behaviors will guide us in making decisions, interacting with one another, and achieving our departmental and organizational goals. Together, let’s build a culture that celebrates learning, teamwork, and excellence in everything we do.

Learn together, grow together

“We embrace collaboration, share knowledge openly, and celebrate both individual and team success.”

  • Contribute and support: Actively participate in discussions, offer help, and celebrate each other’s successes.
  • Give and receive feedback: Regularly seek and provide constructive feedback for improvement.
  • Share expertise openly: Willingly share knowledge and expertise to benefit the team.

Communicate clearly, connect openly

“We foster understanding through respectful, transparent, and active communication using the right tools for the job.”

  • Listen actively, engage thoughtfully: Pay close attention, ask questions, and respond thoughtfully to diverse perspectives.
  • Clarity over jargon, respect in tone: Communicate with clarity, avoid technical jargon, and use respectful language.
  • Prompt and appropriate: Respond efficiently and tailor your communication to fit the situation and audience.
  • Choose the right channel: Utilize appropriate communication methods based on the message and context.

Continuous Learning and Improvement is the Way!

“We value continuous learning, actively seek opportunities to improve, and celebrate progress together.”

  • Quality First, Every Step of the Way: Never pass a known defect down the stream. If you see something which will cause problems for others then you should stop the work.
  • Challenge yourself and learn: Regularly seek new experiences and reflect on your experiences to improve.
  • Experiment and share: Be open to trying new things and share your learnings with the team.
  • Track your progress: Regularly measure your progress towards goals and adjust your approach as needed.

Own your work, drive results

“We take responsibility, proactively solve problems, and seize opportunities to excel.”

  • Embrace challenges, deliver excellence: Aim for impactful work and go the extra mile for outstanding results.
  • Be proactive problem-solvers: Actively seek, address, and prevent the escalation of challenges by ensuring solutions not only fit within established boundaries but also uphold the highest quality standards.
  • Learn and bounce back: Embrace mistakes as learning opportunities and quickly recover from setbacks.
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