Asynchronous & Unreliable - Tech & AI Podcast

Navigating the Evolution of Enterprise Tech: Cloud Native, AI, and Beyond

In this episode, Anne Currie and Sarah Wells, ex Financial Times engineering leader and O'Reilly author, explore how enterprises survive rapid technological shifts from cloud native transformations to the rise of AI. They discuss lessons learned from the Financial Times’ cloud journey, the comparative impact of AI on software development, security concerns, and the importance of fostering resilient, adaptable teams in the ever-changing tech landscape.

Key Topics:

• Lessons from the Financial Times’ migration from on-premise to cloud, emphasizing gradual transition and cultural change.
• Insights into cloud native adoption and what enterprises learn through trial and error in cloud journeys.
• How AI is transforming development practices, including benefits, pitfalls, and the critical need for asking the right questions.
• The widening gaps in AI governance, security risks, and the importance of automating security in code reviews and deployment pipelines.
• Comparing the industry’s early days of internet boom with current AI evolution and understanding the long arc of technological adoption.
• The enduring necessity of software design, architecture, and human oversight in an AI-enhanced environment.
• Challenges around junior engineers working with AI tools, the risk of over-reliance, and the importance of effective collaboration.
• The legal, security, and ethical implications of AI, including managing vulnerabilities and maintaining trust.
• Reflecting on how past technological disruptions, like the decline of physical newspapers and the rise of digital, inform current industry shifts.

Transcript

When we make AI data centres expensive and unsustainable that's a choice because they don't have to be.

In this episode of Asynchronous and Unreliable, host Anne Currie speaks with Chad Gibson, ex Microsoft co-founder and CEO of AI host and infrastructure company Neuralwatt, to discuss innovative approaches to reducing the energy footprint of AI.

Discover how layered technologies, transparency, and customer-driven model choices can help to shape greener, cheaper, and more efficient AI.

Key topics

• NeuralWatt’s multi-layered approach to AI energy efficiency, from GPUs to inference cloud services
• The key role of software optimization in reducing power consumption and improving model efficiency
• How the demand for open source and Chinese models influences energy efficiency strategies
• Challenges in serving large, long-context models like GLM 5.2 and their operational impacts
• The importance of transparency and customer demand in achieving sustainable AI practices
• The potential of autonomous agents for further optimizing energy consumption
• Industry trends in hardware, model size, and software innovation pushing AI toward scalability
• Real-world examples of data center practices that either hinder or help sustainability goals

Transcript

Neuralwatt

Exploring AI, Software Development, and Future Possibilities with Martin Davidson for a discussion on #Rustlang, the AI SDLC,

Join us for a deep conversation on the latest in AI experimentation, software engineering, and reimagining hardware and UI design. Martin Davidson returns to share insights from his ongoing projects, exploring how AI models like Claude and Codex are transforming the way we build and understand software. #rustlang

Main Topics Covered:

• Martin’s current AI experimentation setup leveraging multiple subscriptions from Anthropic and OpenAI to write complex applications in Rust
• The shifting landscape of AI models, including the rise of smaller, more capable models like Sol
• The concept of "software on demand" — creating applications and systems dynamically with AI
• Reimagining operating systems and hardware on demand, inspired by edge computing and unikernels
• Challenges and opportunities with AI-generated code and system designs, including over-engineering tendencies
• The evolving infrastructure for software development, including automations, bug tracking, and CI/CD systems
• The philosophical implications of interacting with AI models as collective "we," and their increasing personality and utility
• The future of AI’s role in hardware design, programming languages, and the potential for safer, more efficient systems

This episode provides a fascinating glimpse into the future of AI-assisted software creation, hardware design, and the evolving relationship humans have with intelligent systems.

Martin's great substack

Transcript

This is the temperature/cognition site Martin mentioned: https://0x4d44.github.io/thermal-mind/
- And this is about getting Win2K working: https://0x4d44.github.io/win2k/
- This is Colonization & Civ: https://0x4d44.github.io/world-in-640k/
- This is Uno, sorry, Onu: https://0x4d44.github.io/onu/
- A broadband speedchecker: https://0x4d44.github.io/broadband-speed-checker/
- Ocean currents: https://0x4d44.github.io/ocean-currents/

What Happens When You're Not in the Room? - Leadership, Media, and Writing Insights #leadership #management #software

Book reveal - Jon Berger's new book on leadership and management, What Happens When You Are Not In The Room? comes out on the 1st September 2026.

In this episode, Anne Currie and mission critical software and leadership veteran Jon Berger explore the nuances of leadership, the art of writing books, humour in professional communication, and media representations of leadership. They delve into the importance of leading through influence rather than presence, the joys and challenges of authoring, and the relevance of leadership in TV, film, and fiction.

Key Topics:

• The core idea that leadership occurs primarily when you're not in the room
• The significance of influencing others to achieve results
• The concept of leadership at scale versus micro-managing
• Jon Berger’s new book: What Happens When You Are Not In the Room?
• The value of humor and storytelling in professional writing
• Personal reflections on mistakes and learning as a leader
• Leadership as a collective effort, akin to assembling a team or a story
• Leadership lessons drawn from TV shows, films, and books
• The importance of encouraging everyone in an organization to be a leader
• The process and motivation behind writing a book

We'll send you a bonus chapter of the book by signing up at JonBergerLeadership.com

Episode transcript

The Future of Software: Insights from Experience

Discover how seasoned experts (aka old people in computer years) predict the evolution of software development over the next decade, focusing on #AI's transformative role, hardware implications, and the potential for machine-only software.

In this episode:

• Reflection on recent episodes discussing AI and code generation and what they imply for the future
• The challenge of maintaining software
• The shift from human to AI-centric code creation and the implications for trust and reliability
• How hardware limitations and technological advances will shape software design and optimization
• The concept of "wet code" — intentionally un-Dry, performance-optimized, AI-generated code that forgoes human maintainability
• The potential for machine code to replace high-level programming languages entirely
• The evolution from microservices as team management tools to pure operational resilience components
• The revolutionary impact of AI training on code creation, emphasizing self-learning models akin to AlphaGo
• The concept of unikernels and the radical idea of single-kernel systems replacing traditional OS structures
• The importance of shifting from maintainable code by humans to trust-based, AI-driven code systems

This episode explores paradigm-shifting ideas about the core foundation of software and hardware in a future where AI rewrites the very fabric of code creation and system design.

Transcript

A wild ride through Justin Cormack's attempts to build really hard stuff using AI. Did he recreate Amazon s3 in his back bedroom in a couple of weeks?

A fun dive into the fascinating world of AI-assisted software development, Unikernels, and the future of programming languages, with Justin Cormack.

Justin was the CTO of Docker for a decade, is one of the founders of Unikernels and is a leading figure in cloud-native and systems programming. In this episode, Anne hears all about his current experiments with building big, proper systems code using AI.

We also hear about his thoughts on the future of languages. Does Rust have it nailed or might there be something better coming along? Should we really have coding languages at all anymore?

In this episode we hear about:

• Justin's recent experiments with AI-generated code and how it accelerates complex software creation
• The concept of unikernels and how they differ from traditional operating systems
• Challenges and opportunities in building cloud-native and AI-optimized software systems - good, hard, scaleable software
• The role of programming languages like Rust, Zig, and emerging trends in AI-friendly languages

Justin's newsletter 

#Nix, #RustLang, #AI

In another friendly conversation, Anne and Holly Cummins of IBM discuss IBM's cloud native platform Quarkus and why it is so, so important right now. You've got to be able to turn it off and on again for operatioal efficiency, to take any advantage of the cloud and - vitally - for security because in the new world, patches have to be applied rapidly.

Anne and Holly, Java champion, explore the evolution of Java from its initial flexible, runtime-centric design to a modern cloud-native, operationally efficient platform. Holly shares insights into how the shift to immutable, start-up friendly environments like Quarkus is transforming how we build and deploy Java applications in the era of cloud computing, AI, and sustainability.

#Java #Javasoftware #security #operationalefficiency #ai #podcast

Transcript

In this episode, Anne and industry expert Adrian Mouat of security firm Chainguard and author of O'Reilly's Using Docker discuss the "crazy" rapid evolution of AI, its implications for cybersecurity, the future of software development, and the urgent need for adaptive security practices. They also discuss the brand new Athena coalition for accelerating patches against vulnerabilities in the face of AI-driven attacks

Main Topics:

• The accelerating pace of vulnerabilities and exploitations in cybersecurity
• The impact of AI on the entire Software Development Life Cycle (SDLC)
• Mythos and the potential existential threats posed by advanced language models
• The influence of science fiction themes on real-world AI development
• Practical security measures: CI/CD, sandboxing, API token management, and blast radius reduction
• Emerging industry responses: Project Athena and AI security coalitions

Transcript

#AI #cybersecurity #cloudnative #software

How Do We Make AI-Generated Code Good?

Anne and Yanqing Cheng, founder of AI startup Tollens.ai, explore the future of software quality, AI management, and organizational trust. Discover how holistic engineering management, trust, and organizational dynamics influence AI and software quality.

Key Topics

• The evolving definition of quality in AI-generated software
• Tollens.ai's approach as an engineering management consultant
• The importance of holistic management and decision-making
• How AI tools can aid managers and engineers in complex decisions
• Challenges of trust, psychological safety, and organizational dynamics in AI deployment
• The impact of AI on software productivity and the future of coding
• Managing bottlenecks in AI-powered engineering workflows
• Strategies for capturing and leveraging organizational knowledge
• The gradual transition to AI-native companies and the pace of change
• Practical tips for integrating AI into daily workflows to reduce friction

transcript

Blue Tuxedo

Music licensed through Soundstripe.

Code: 3XRM21XFMMGPO1PF

Title: Navigating AI's Impact on Software Development and Trust

O'Reilly author Anne Currie and Mission Critical software expert Jon Berger discuss the question of the moment: how can we entrust AI with the software that underpins everything?

Join Anne and Jon as they explore real experiments, strategic implications, and practical steps for leaders in a rapidly evolving tech landscape.

Main Topics:

• The future of AI in software engineering: rapid development and risk management
• Leadership strategies for integrating AI with team dynamics and trust
• Fundamental principles in software and life that AI may reshape
• Practical ways to start using AI safely in development cycles
• The importance of diversified oversight and multiple 'oracles' in AI deployment

In this episode:

• How AI is changing the long-term landscape of human versus machine-built software
• Why understanding your unique business context matters in adopting AI tools
• The significance of experimenting with low-risk AI integrations like code review and testing
• The evolving concept of trust in AI-generated code and processes
• Strategic leadership tips: balancing risks, risks awareness, and fostering innovation

Timestamps: 

00:00 - Welcome and episode overview on AI's influence on software creation

01:22 - Transition from human-only to AI-assisted software engineering

02:48 - The importance of understanding your context as a leader

03:46 - Considerations for teams experimenting with AI in development

04:19 - Risks and opportunities in AI-driven software processes

05:50 - How speed and scale shape business decisions in AI-enabled environments

07:12 - The role of leadership in managing AI adoption and team dynamics

08:37 - Critical thinking about AI's impact on team sizes and productivity

10:30 - Market-driven decisions: layoffs and strategic AI integration

12:15 - Choosing trustworthy sources and multiple perspectives ("oracles")

13:55 - Balancing risk-taking and risk mitigation among teams

15:22 - Protect the future versus change the future in tech strategies

16:02 - Assessing environment and risk: high-stakes vs low-stakes AI applications

17:11 - Supporting existing systems with AI: deployment, testing, and feedback loops

19:22 - Trust, testing, and automation in the software lifecycle

21:23 - The changing nature of AI models and managing their variability

22:26 - The importance of agility and adaptability in a fast-changing AI landscape

23:05 - Valuing diverse team roles, including skeptics and early adopters

27:28 - Embracing AI as a black box: focusing on outcomes rather than process transparency

30:56 - Revisiting core principles of system resilience in an AI world

31:52 - How AI shifts decision-making trade-offs and process scaling

32:24 - Moving forward with trust: aligning business goals with AI capabilities

33:22 - Starting small: low-risk AI applications in testing and review

36:19 - Building confidence with modular, trustable AI-driven processes

39:38 - Actionable strategies for leaders: experiment, assess, and iterate safely

Note: For further insights into managing AI in software development, stay tuned for upcoming episodes on security, testing, and team leadership adaptations.

Full transcript