Writing · 57 articles
Practical thinking on AI, engineering and leadership
A 2026 series on using AI in engineering companies, and earlier essays on strategy, technology leadership and my own frameworks. Follow by RSS
AI in engineering companies (2026)
Where the value is, where the risks are, and what has to be in place first. Written from hands-on work, not from the sidelines.
Start here · 13 min readAI is not my first rodeoWhat seven hype cycles taught me about this one Read itThe five levels of AI value
Find, structure, connect, act, learn: the ladder, and why you cannot skip steps.
- 1Beyond search: the five levels of AI value in an engineering company7 min
- 2Your company already knows the answer. It just can't find it.7 min
- 3From documents to data: building an engineering model you own7 min
- 4Where should your AI live? Cloud, UK-hosted or in the building11 min
- 5From what the manual says to what the product is doing6 min
- 6AI drafts, engineers decide6 min
- 7Closing the loop: letting field data change the product6 min
Agents
What agents are, when not to use one, and how to run them safely and affordably.
Security
How AI moves the attack surface, and how to defend your AI systems and your connected products.
Coding agents
Getting real value from AI coding tools: judgement, security, tokens, cost and privacy.
- 1Vibe coding is just another level of abstraction5 min
- 2Coding agents: strengths, weaknesses and opportunities5 min
- 3Coding agents need experienced engineers5 min
- 4The hobby system and the professional system7 min
- 5The security of AI-written code5 min
- 6What tokens are, and why they matter5 min
- 7Managing a token budget5 min
- 8Local, cloud and frontier: getting the most from your budget5 min
- 9Keeping your code local and private6 min
Essays
Technology leadership, connected products, strategy and the methods I use with clients: ideas I have tested in practice.
AI and technology leadership
- When AI removes the translation layer between thinking and building
- When AI decisions have physical consequences
- What a CTO should be hearing
- Not all CTOs are created equal
- Where technical debt becomes expensive
- Leading without pretending to know
- What vision means in a CTO
- What it takes to be an outstanding CTO
- The C-suite a startup actually needs
Connected products and strategy
- When the product is already in the field
- What the launch actually costs
- Connected data, disconnected decisions
- The OTA problem: firmware strategy for connected products
- The metrics that hide the failure
- What makes an embedded system reliable
- How to diagnose strategic deficit
- When a company has no strategy
- The cost of skipping product strategy
Organisations and people
My methods
Archive: 26 earlier LinkedIn essays
Written mostly in 2024 and kept here for reference.
- Bridging the Gap: Fostering Collaboration Across Departments and Organisations
- Competing Beyond Price
- Confidence, Ego, and the Cost of Not Listening
- Conscious Agility
- Conscious Collaboration
- Conscious Collaboration: The Future of Problem Solving and Innovation?
- Defined Strategy vs. Ambiguity for Pre-Profit Companies
- Embracing the Fourth Industrial Revolution: A 3-Minute Guide for Organisations
- From Silicon to Society
- How’s that going for you?
- If you only had a hammer
- Old Wisdom, Relearned at Cost
- Products the Market Actually Needs
- Revenue Forecasting: Strategies for Uncertain Times
- Strategic Communication Is Part of the Work
- Strategy on a Longer Clock
- The Cost of Endurance
- The Crucial Role of Trained Facilitation in Collaboration
- The Intersection of Idea Mining and Black Swan Theory: Predicting the Unpredictable
- The Silent Strategy: Harnessing 'Quietude' in Business Innovation
- The Unseen Beauty of Inclusion at the C-Suite Level
- Thriving in Uncertainty: Leveraging the Black Swan Theory and Cynefin Framework for Innovation
- Unlocking Potential: Exploring Cynefin, Conscious Collaboration, and Idea Mining
- What Resilience Actually Feels Like
- When a Larger Competitor Dumps Price
- When Ego Kills Innovation: The Case for Conscious Collaboration and Idea Mining
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I use AI tools in my writing, mainly to work faster and to bridge the gap between thinking and getting words on a page. The ideas, arguments, frameworks and experiences are mine, developed through direct practice over a lifetime at the bleeding edge, not generated by a prompt. Given what I advise on, it seems worth being clear about that.