Careers
Why Most Software Developers Are About to Become Business Analysts and QA Engineers
Here's an observation that I think most developers aren't ready to hear: the job you trained for is becoming two other jobs — and neither of them is writing code. As AI takes over the act of code generation — and it is, rapidly — the work that remains for human developers looks remarkably like two roles that have existed for decades: the business...
Here's an observation that I think most developers aren't ready to hear: the job you trained for is becoming two other jobs — and neither of them is writing code. As AI takes over the act of code generation — and it is, rapidly — the work that remains for human developers looks remarkably like two roles that have existed for decades: the business analyst and the QA engineer. Understanding what to build. Verifying that what was built is correct. The bit in the middle — the actual writing of code — is increasingly handled by AI. This isn't a distant prediction. It's happening right now in every team that uses IDE-based AI tools seriously. And it has profound implications for how developers think about their careers, how companies hire, and how software gets built. Software development has always been a three-phase process: Understand the problem — figure out what needs to be built and why Write the solution — translate that understanding into working code Verify the output — confirm...
AI Strategy
AI Is Not Faster and Cheaper. It's Better and Different.
There's a narrative about AI that goes something like this: "AI makes things faster and cheaper." It's not wrong. But it's completely superficial. And if that's how you're thinking about AI, you're...
There's a narrative about AI that goes something like this: "AI makes things faster and cheaper." It's not wrong. But it's completely superficial. And if that's how you're thinking about AI, you're going to make bad decisions about where to invest your time and money. AI is not just faster and cheaper. It's better and different. And the gap between those two framings is where all the real opportunity sits. When people think of AI as "faster and cheaper," they look at their existing processes and ask: "How can AI do this quicker?" That leads to predictable moves. Automate the email replies. Generate the blog posts faster. Speed up the data entry. Fine. You'll save some time. But you're still doing the same things you were doing before — just with a slightly faster engine. This is like getting a Ferrari and using it to do your weekly grocery run. Yes, you'll get there quicker. But you're missing the point entirely. Here's what "better" actually means. AI can process hundreds of data...
AI Strategy
The Maths on AI Is Wrong
Everyone's talking about AI replacing jobs. The headlines write themselves: millions of roles automated, entire industries disrupted, mass unemployment on the horizon. But the maths is wrong. Not...
Everyone's talking about AI replacing jobs. The headlines write themselves: millions of roles automated, entire industries disrupted, mass unemployment on the horizon. But the maths is wrong. Not completely wrong — AI will replace some jobs. If your work is routine, repeatable, and well-documented, then yes, a model can probably do it faster and cheaper than you can. That's real, and it's already happening. But here's what the doom-and-gloom predictions miss: if your job is highly skilled or creative, AI isn't replacing you. It's actually creating more work. Since I started using AI seriously, I've launched over twenty projects to explore what it's capable of. Not toy demos — real work. Books, apps, reports, marketing strategies, data analysis. Here's what I've found: my output is 10–100x what it would otherwise be as a single human. I can write a 30,000-word book. I can produce a 10,000-word report. I can build apps, create marketing personas, summarise thousands of notes and analyse...
Founder Lessons
AI Burnout Is Real
Since really digging into the latest AI tools in 2026, I've started more projects than in the previous five years combined. Ideas that would have taken months now take days. Things I never dreamed I could build are suddenly within reach. And I'm burning out. Not from the work itself. From the possibilities. Every time I finish something, three new...
Since really digging into the latest AI tools in 2026, I've started more projects than in the previous five years combined. Ideas that would have taken months now take days. Things I never dreamed I could build are suddenly within reach. And I'm burning out. Not from the work itself. From the possibilities. Every time I finish something, three new ideas appear. Every new feature release opens another door. Every capability improvement makes something else feel achievable. The project list grows faster than I can work through it. The ideas compound. The half-finished experiments pile up. And the nagging feeling that I should be doing more — building more, trying more, shipping more — never goes away. This isn't traditional burnout. Traditional burnout comes from doing too much of the same thing for too long. AI burnout is different. It comes from too much novelty. Too many possibilities. Too many directions that all feel urgent and exciting and achievable. The overwhelm isn't from...
AI Implementation
Do Your Staff Send Confidential Data to AI Servers?
I need you to sit with an uncomfortable truth for a moment: your staff are almost certainly sending confidential business data to third-party AI servers. Right now. Today. They're pasting client...
I need you to sit with an uncomfortable truth for a moment: your staff are almost certainly sending confidential business data to third-party AI servers. Right now. Today. They're pasting client contracts into ChatGPT to summarise them. They're uploading financial spreadsheets to Claude to analyse trends. They're feeding proprietary strategy documents into Gemini to get feedback. They're sharing customer data, internal communications, competitive intelligence, and trade secrets with AI services hosted on servers they don't control, in jurisdictions they haven't considered, under terms they haven't read. They're not doing this maliciously. They're doing it because these tools are genuinely useful, and nobody told them not to. The question isn't whether this is happening in your organisation. It's whether you know about it. Let me paint the picture concretely. In a typical mid-market business with 50 to 200 employees, I'd estimate: 70-80% of knowledge workers use AI tools at least...
AI Implementation
Data Model-Driven AI: Why Your Data Architecture Determines Your AI Ceiling
Every AI implementation that fails has the same root cause. It's not the wrong model. It's not the wrong tool. It's not even the wrong use case. It's bad data. Specifically, it's data that's...
Every AI implementation that fails has the same root cause. It's not the wrong model. It's not the wrong tool. It's not even the wrong use case. It's bad data. Specifically, it's data that's scattered, siloed, duplicated, inconsistent, incomplete, or structured in ways that don't reflect how the business actually operates. And no amount of AI sophistication can compensate for a broken data foundation. This is the conversation that almost nobody is having in the AI adoption space. Everyone talks about tools, models, and prompts. Almost nobody talks about data architecture. And that's why most AI implementations underperform. Think of your data architecture as a ceiling on what AI can do for your business. If your customer data lives in three different tools with no sync, AI can't give you a unified view of a customer. It doesn't matter how good the AI is — it can only see what you let it see. If your product catalogue is maintained in a spreadsheet that's updated manually, AI can't...
Careers
Why People Implementing AI Now May Be Working Themselves Out of a Job
I'm going to say something that might be uncomfortable for people in my line of work: if your primary value is implementing AI solutions, you're building a skillset with a limited shelf life. I say this as someone who runs an AI consultancy. I say it as someone who spends every day helping organisations adopt AI. And I say it because I think...
I'm going to say something that might be uncomfortable for people in my line of work: if your primary value is implementing AI solutions, you're building a skillset with a limited shelf life. I say this as someone who runs an AI consultancy. I say it as someone who spends every day helping organisations adopt AI. And I say it because I think honesty about the trajectory is more useful than pretending the current demand for AI implementation skills will last forever. It won't. And the sooner we're honest about that, the better positioned we'll all be. Right now, AI implementers are in extraordinary demand. Every business wants to get started with AI. They need people who can configure agents, build automations, design workflows, integrate systems, and deploy AI-powered solutions. The market for this skill is hot. Salaries are up. Consultancies are booked out months in advance. But here's the irony: the work of implementing AI is itself a pattern-based, structured activity that AI is...
Founder Lessons
The Golden Age of the Builder-Generalist
I've spent the last decade building things across at least five disciplines I was never formally trained in. Product management, UX design, no-code development, marketing, operations. I was a...
I've spent the last decade building things across at least five disciplines I was never formally trained in. Product management, UX design, no-code development, marketing, operations. I was a physiotherapist for 16 years before any of this. The traditional career advice would say that's a problem. "You need to specialise." "Jack of all trades, master of none." "Pick a lane." I disagree. And in 2026, I think the evidence is overwhelming: we're living in the golden age of the builder-generalist. A builder-generalist is someone with working-level competence across multiple domains — product, design, technology, marketing, operations — who uses that breadth to actually build and ship things. The emphasis is on "builder." This isn't about knowing a little about a lot. It's about being able to execute across multiple disciplines well enough to create something complete and valuable. A builder-generalist can: Define a product based on real user needs Design a user interface that's functional...
AI Strategy
Don't Underestimate the Inertia of the Public Sector
Every AI prediction follows the same script: massive disruption, entire industries transformed, everything changes overnight. And in some parts of the economy, that's roughly true. Startups move...
Every AI prediction follows the same script: massive disruption, entire industries transformed, everything changes overnight. And in some parts of the economy, that's roughly true. Startups move fast. Tech companies iterate weekly. Private sector organisations with strong leadership can pivot in months. But the public sector? For-purpose organisations? Government agencies? They were built to resist exactly this kind of change. And that's not a bug. It's the whole point. There's a reason the world's most successful societies have stable bureaucracies. Predictable institutions, consistent processes, and cautious decision-making create the foundation that everything else is built upon. Business can move fast because government moves slow. Startups can take risks because regulators provide guardrails. Innovation thrives precisely because there's a stable, boring, dependable layer underneath it. The public sector's conservatism isn't a weakness. It's load-bearing infrastructure. And AI —...
