AI development — Same problems but faster

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Twelve months ago I looked at the bold predictions of tech-CEOs. The most vocal predicted big changes in software development and the wholesale replacement of software engineers with AI. A revolution.

“In three to six months, AI is writing 90 percent of the code.” “Probably in 2025, we … are going to have an AI that can effectively be a sort of midlevel engineer that you have at your company that can write code.”

It didn’t happen. It looked like hype then and a year later it is confirmed as pure hype — from CEOs who needed a better Q2 forecast.

AI writes a great deal of code, quickly, but we haven’t seen the promised revolution. Organisations are not seeing the productivity increases the hype forecast. As I said at the time, “these tools aren’t (yet) replacing engineers, but they are redefining what engineers do”.

For myself, looking back over the last year, I don’t think AI has revolutionised anything in software development. Nothing. I think we’re seeing the same problems, just faster.
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Good Strategy/Bad Strategy: The Difference and Why It Matters by Richard Rumelt

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Good Strategy, Bad Strategy by Richard Rumelt is a well-known business and strategy book from 2011. Its central concept is focus — and more precisely, focus + asymmetry: finding where a small, well-directed push moves the system far more than a large, unfocused effort. The purpose of that push is value creation — producing the greatest positive impact for customers, stakeholders, and society with the least waste of time, capital, and attention.
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Squeeze to Sell: Inside the PE Value Creation Playbook

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If you’ve worked in or around private equity (PE) companies, you’ll know it’s all about value creation. PE firms make their money by buying businesses, increasing their value, and selling them at a premium. They’re experts at creating value under pressure — so their approach is worth understanding. In this post, we’ll look at the private equity lifecycle and take a deep dive into PE value creation sprints.
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Value Creation: The Engine Behind Sustainable Growth

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Private equity firms live and breathe value creation — it’s how they make their money. But they’re not the only ones who should care. Every business leader should be thinking about value creation. Yet, surprisingly, many aren’t.

So what is it? Why does it matter? The short answer: the most successful businesses understand how to build, scale, and sustain growth by deliberately creating value — for customers, employees, shareholders, and more. If you’re not focused on it, you’re leaving opportunity (and probably money) on the table.
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AI Isn’t Magic — It’s a Stack

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Artificial Intelligence. Two words that trigger everything from excited coffee chats with my mates to boardroom FOMO to outright apocalyptic fear. AI has dominated every conversation I’ve had for the last six months. Everyone wants a slice of it. It shows up in product roadmaps, vendor decks, job specs, and shareholder updates. But when I scratched beneath the surface, I found a messy pile of buzzwords and vague ambition.

I don’t know about you, but I like simple. So here is my attempt to make AI simple.

If you’re leading digital delivery, managing platforms, or just trying to figure out what AI means for your portfolio, this is your practical intro. There are buzzwords, but I explain them. There is no hype because I don’t have to impress anybody. It is just a clear look at how AI is structured, what matters now, and what’s coming.

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Bold Predictions or Hype? Tech CEOs on AI’s Impact on Software Development

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I’ve never seen a hype cycle like this one. Huge sound bites about AI — specifically, generative AI. Depending on who you ask, AI is either a helpful copilot or it’s gunning for your entire dev team’s jobs. As usual the truth is probably somewhere in between. But with so many CEOs talking up how much code AI is generating, it’s worth stepping back to ask: are they predicting the future, or selling it?
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Another Silver Bullet? What AI Tools Can Learn from the Last 50 Years of Developer Hype

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The emergence of AI coding assistants such as GitHub Copilot, Amazon CodeWhisperer, and Claude 3.5 Sonnet has stirred debate about their long-term impact on software development. I’ve been in this game a while, which means I approach software innovation with a healthy dose of scepticism. I have seen lots of fashion trends — waves of developer productivity tools — some impactful, most not. From 4GLs to IDEs to low-code — each one promising to change the game, then fading into the background. Sound familiar? Every year there is somebody claiming they have a tool that can write your code for you. Is AI just the latest wave? I wondered if looking back at those old patterns — the hype, the crashes, the survivors — might tell us where this one’s going. Do those patterns from history tell me anything about the current trajectory of AI coding tools? The answer? Yes and no. Which, if you’ve ever worked in software, is probably the only honest answer you’ll get.
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What Software Development Can Learn from Double-Entry Bookkeeping

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Double-entry bookkeeping revolutionised business by introducing a self-checking system of financial integrity. In the world of software, Behaviour-Driven Development (BDD) does something strikingly similar — pairing each feature with a clear, testable description of how it should behave. Double-entry bookkeeping and automated software testing might seem worlds apart – one emerged in Renaissance-era accounting and the other in modern software development. Yet conceptually, they serve a similar purpose: both introduce systems of checks and balances to ensure integrity and accuracy in their respective domains. In accounting, double-entry bookkeeping requires every transaction to be recorded in two accounts (debits and credits) that must balance, creating an internal self-checking mechanism. In software, automated tests act as a parallel record of expected behaviour, continuously verifying that the code produces the intended outcomes. This post explores how a 500-year-old accounting innovation revolutionised financial record-keeping and how its spirit lives on in the way we write and test software today.

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Disaster, rescue and recovery: Kickstarting delivery

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70% of projects fail to deliver — and the cost is more than financial. Few broken programmes crash in a spectacular fashion. Most just quietly stall. You’ve got a roadmap. You’ve got people. You’ve got the rituals. But… nothing’s really moving. Instead you have sceptical stakeholders, wincing delivery teams, endless meetings and no actual decisions. Everyone is busy, but no one is sure what they’re delivering or why. Sound familiar? You’ve stalled.
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