Duolingo’s AI-first announcement, the backlash, and the backtrack reminded me of how Gerald Ratner destroyed his business overnight.

In April, Duolingo’s CEO, Luis von Ahn, announced a bold shift: the company would become “AI-first,” aiming to replace contractors with AI and making AI proficiency a key performance metric.

The announcement sparked immediate customer backlash. Duolingo’s social media feeds lit up with criticism, as users pushed back against job losses and what they saw as a decline in the quality of the product.

One thing Duolingo had been particularly good at was social media. Their accounts have massive followings, and the Duolingo Owl has become a well-known meme and a much-loved character.

Amid the backlash, they wiped their TikTok and Instagram feeds, replacing everything with cryptic messages. A core brand strength – suddenly gone. The content has since returned, but the damage to the brand was done. It only reinforced the sense that things were unravelling.

Not long after Luis issued a very public backtrack.

It immediately reminded me of the Gerald Ratner story. In 1991, Ratner, then CEO of a successful UK jewellery chain (also called Ratners), famously joked that his products were “total crap”. The comment destroyed consumer confidence overnight. The business collapsed, and so did his career.

Similarly, Duolingo’s announcement has significantly shifted public perception. Since the AI-first statement, I’ve seen just as many articles and comments claiming Duolingo was never a good tool for learning languages in the first place as I have about the announcement itself (and the subsequent backtrack).

Users are also calling the new AI-generated courses “AI slop” and complaining about the synthetic voices. Maybe some of that is true – but I’d wager it’s being projected onto the old content too.

The key point here is the customer perception has shifted, and potentially, like Ratners, irreversibly.

It also didn’t help that, around the same time, CEO Luis von Ahn suggested in a podcast that schools might eventually serve primarily as childcare centres, with AI doing the teaching. One thing you don’t do is dunk on teachers – a group held in consistently high regard by the public.

Only last week I posted an article on the pitfalls of headcount-first transformations. I didn’t expect it to be so relevant so soon.

This is exactly the kind of outcome you get when you don’t put customers at the heart of your strategy. And when you treat technology as the strategy, rather than a tool to support it, you risk compounding the problem. If you don’t start with purpose, people, and the system around them – AI won’t fix it. It’ll just as likely make things worse.

Software developers are not against more automation in their work – quite the opposite.

This image is from the “Tech Manifesto” I put together when I was at 7digital, 12 years ago. One of the principles was: “We prefer not to do the same thing twice”.

The best engineers and teams automate everything that moves – tests, build and deployment, monitoring, alerting, infrastructure provisioning. They use rich IDEs with refactoring tools, code formatters, linters, and even, dare I say it, code generation (which has been around long before GenAI, by the way).

It’s about reducing toil, eliminating waste, getting fast feedback, and making space to focus on the more meaningful and enjoyable parts of the job.

Things like understanding and solving real-world problems, turning ideas into working software, building useful things. Creating.

Exactly the parts GenAI still isn’t any good at.

All the fear-mongering about AI taking jobs reminds me of something I’ve seen too often: when organisations go into org change with the goal to reduce headcount, it rarely ends well.

I’ve been part of these exercises. You cut people, but the costs come back in other forms – lost sales, reduced capacity, expensive contractors to plug the gaps. The result? Often a rapid series of transformations, each one trying to fix the damage caused by the last. Org transformation whack-a-mole.

A good industry wide example was the trend to offshore software development a decade or so back. Sold as a way to cut costs, it often ended up costing more due to hidden overheads, coordination challenges, slow delivery and quality issues. Many quietly reversed course over the next few years.

The reason it doesn’t work? Yes, organisations can be bloated – but that’s usually a *symptom of deeper inefficiencies, not the root cause*.
If you cut people without addressing those inefficiencies, the problems persist – or get worse, because now fewer people are left to deal with the same issues.

The best transformations I’ve seen start with the outcome.

Why do we exist? What are we here to do?

Then look at the system end to end – people, culture, process, communication, technology – and identify the pain points and bottlenecks.

Optimise systematically.

Yes, this can lead to restructuring. Roles change. Some may no longer be needed. But that happens as a consequence of tackling the root causes.

AI? It’s just a tool. It could help. It could just as easily get in the way. Technology is a *fourth-order concern* – purpose, people, and process come first.

If you don’t understand the root causes, if you don’t work from first principles, AI won’t save you. It’ll amplify your dysfunction.




Footnote: There are situations where a headcount-first approach is justified – but these are typically extreme, when an organisation is fighting for immediate survival.

All the evidence I see continues to suggest that good engineering discipline is not just desirable, but essential when using GenAI for coding. But that’s exactly what the vast majority of software engineers – and teams – lack.

Take Test-Driven Development (TDD) for example. I keep hearing that one of the most effective ways to stay in control of GenAI output is to take a test-first approach (“Test-Driven Generation” or TDG as its becoming known) – and I agree based on experience. On one hand, I’m excited by the idea of a TDD renaissance. However, I saw something recently suggesting only around 1% of code is written that way. Anecdotally, most developers I speak to who say they know TDD, don’t actually understand what it is. It’s a clear example of the skills gap we’re dealing with.

Let alone TDD, again, everything I see and hear on the ground suggests effective GenAI-assisted development also relies on having comprehensive automated tests and the ability to release frequently in small batches. Many teams have neither. Some have a few tests. Most can only release a few times a month because the rely on long, manual regression cycles due to their lack of automated test coverage.

The DORA research project suggests only ~19% of software teams globally have the kind of engineering practices in place to potentially capitalise on GenAI coding (their latest report suggests a downward negative pressure on overall delivery due to GenAI coding, but that’s another thing…)

I’m not convinced by arguments that GenAI will improve code quality (vs experienced engineers not using GenAI). The skills gap is part of the problem – but also, studies like GitClear’s earlier this year already show a significant drop in code quality linked to GenAI use.

At the very least, good practices will act as damage limitation.

GenAI coding could be a turning point. But most teams simply aren’t equipped to handle it. And unless that changes – quickly – which seems unlikely given how long these practices have existed without widespread adoption, we’re likely heading for a wave of poor-quality code, delivered at speed.

GenAI is the hypiest tech I’ve seen in my career – and that’s saying something. Because of all the noise it generates, we need to hear from more grounded, pragmatic voices.

Social media is dominated by extremes: Those who see tech as the solution to everything, often without really understanding it – and those whose negativity leads them to dismiss it.

It’s great for engagement, but real progress will come from those in the middle – curious, thoughtful, and focused on outcomes.

In my mind, a techno-realist:

• Is open-minded, but not easily sold
• Is curious enough to dig in and understand how things actually work
• Is conscious of their biases
• Applies critical thinking
• Works from evidence
• Proves by doing
• Understands that every decision involves trade-offs
• Takes a systemic view – steps back to see the bigger picture and how things connect
• Understands that tech is powerful – but not always the answer
• Sees technology as a means to an end – never the end itself

Social platforms reward loud certainty, not nuanced thoughtfulness.

But these voices – the thoughtful ones – matter more than ever.

If this sounds like you, here’s how I suggest showing up as a techno-realist online:

• Be polite and constructive – even when you strongly disagree
• Call out the hype when you see it (but see point above)
• Amplify grounded voices – like, repost, and comment on thoughtful posts and replies
• Ask questions – seek to understand, not just to respond
• Share what you’re learning – especially from real-world experience
• Connect with and follow others who bring thoughtful, balanced perspectives

Let’s find each other – and make this mindset more visible 🙌

I’ve even added techno-realist to my LinkedIn profile 🫡

You might think your startup is too small to be a target and it’s only larger organisations at risk. But attackers don’t work like that. They behave more like drive by opportunists than trained assassins. They scan the internet to see what comes back, then probe for weaknesses. They spray phishing emails to see who bites. If your defences are weak, you’re low-hanging fruit.

One of the biggest threats today is ransomware – where attackers lock you out of your own systems and demand payment to unlock them. These attacks are widespread and often hit smaller companies simply because they’re easier targets.

Here are some practical, low-cost steps every founder should take – no deep tech knowledge needed:

🔐 Turn on two-factor authentication for all key accounts – (email, cloud services etc).

🔑 Use a password manager like 1Password or Bitwarden – never share passwords via Slack, email, or docs.

🔒 Limit access – only give people what they need. Avoid shared logins.

📬 Set up your email securely – Google Workspace and Microsoft 365 include spam and phishing protection, but you still need to enable sender validation to prevent attackers sending emails that pretend to be from your domain (SPF, DKIM, DMARC).

🛡️ Use a web application firewall (WAF) – Cloudflare or AWS WAF can block common attacks before they reach your app.

💾 Back up your databases – and test that you can actually restore them.

🧊 Encrypt your databases – easy to enable in platforms like AWS or Azure.

🧪 Scan your code – GitHub and GitLab offer built-in code vulnerability scanning tools, even on free plans.

🔄 Keep third-party libraries and frameworks up to date – tools like GitHub Dependabot or Snyk are free or cheap and help let you know when things need patching.

🧩 And finally: have a plan for what you’d do if a device is lost, an account is compromised, or your data is locked or leaked.

None of this is expensive or particularly complicated. But recovering from an attack will be.

Hopefully this is a useful analogy you can use if you’re struggling with a boss or manager who thinks the way to go faster is to push the team harder or cram in more work.

A few months ago, I took up running. At first, I improved steadily – each 5km a little quicker than the last. I assumed the way to keep getting faster was simple: run harder, push more.

But then recently, I hit a wall. My pace stopped improving. I finished every run exhausted. And no matter how much I tried to “dig deep”, I wasn’t getting anywhere.

So I did some research. It turns out running hard all the time doesn’t make you faster – it often slows you down. Improvement comes from running slower most of the time, staying in your “aerobic zone”, building endurance, recovering well, and only pushing occasionally.

Here’s the key point: it’s completely counterintuitive.

The analogy with running breaks down a bit here, but this counterintuitiveness is exactly why so many software teams – despite best intentions – end up underperforming.

The intuitive belief is that the path to delivering faster is to do more: write more code, skip meetings, avoid “distractions”, and stay heads-down. But just like me trying to sprint every run, it has the opposite effect.

Some common examples

• Not spending enough time on discovery or analysis to “get going” faster – but ending up building the wrong thing and wasting time on re-work.
• Skipping retrospectives or post-mortems – missing key opportunities to learn and improve, so mistakes get repeated.
• Worrying that developers spend too much time collaborating – and believing solo work is more efficient, but ending up with bottlenecks, siloed knowledge, and poor decisions.

These instincts feel productive, but they’re often the root cause of slow, ineffective delivery.

Improvement doesn’t come from pushing harder. It comes from pacing well, working sustainably, and continuously improving the system you’re running in.

It’s often counterintuitive. But it’s true. Agile software development best practices have been around for decades, and the principles they were founded on even longer. Yet they’re still not common – because they go against intuition.

Sometimes, the way to go faster… is (quite literally in the case of running) to slow down.

This is a highly opinionated view on best practices for running stand-up meetings. It’s based on the approach I’ve developed and refined through working with probably nearing a hundred product teams over the past 25 years.

Across all of them, one thing has held true: a good stand-up acts as the beating heart of a high-performing team.

Done well, they give the team focus, momentum, visibility, and a shared sense of purpose. Done badly – and sadly, that’s more common – it becomes a daily chore. A box-ticking exercise. Status update theatre. Or worse, a passive, rambling, soul-draining ritual no one looks forward to.

In this article, I’ll share the practices I’ve seen consistently work – and explain why they matter, not just what to do.

Focus on the work, not the people

I have a strong personal dislike for the classic Scrum-style format of “yesterday, today, blockers.” It reinforces the idea that the stand-up is about checking that everyone is doing something, rather than focusing on what truly matters: are we delivering? It encourages individual updates over team progress, and often results in only talking about the work people are actively doing – which means anything not being worked on, including stuck or neglected items, gets ignored.

Everything from here on is with the assumption that you are taking this approach.

Walk the board from right to left

The work closest to being in production is the most valuable because it’s closest to delivering impact. Until something is released, it delivers zero value – no customer benefit, no feedback, no outcome. It’s also where the most effort has already been invested, so leaving it unfinished carries the highest cost. By focusing on what’s nearly done first, you prioritise finishing over starting, reduce waste, and increase the chances of delivering real value sooner.

• Start from the right hand side of the board
• Focus on the work, ensure all work in progress has been discussed
• Conclude the stand up by asking
  • If there’s anyone that hasn’t spoken?
  • “Are we on track?” – A final call as an opportunity for anyone to raise any issues

Bias for action, delivery, and results

Stand-ups work best when they reinforce a culture of delivery. It’s not just about sharing what you’re doing – it’s about driving action, finishing work, and holding each other to a high standard. These behaviours help teams stay focused, accountable, and outcome-oriented.

• Focus on completion – what will it take to get this done?
• Use commitment language
• Take ownership
• Challenge one another to uphold best practices

Visible, present and engaged

Whether remote or in person, being visibly present and engaged is a basic sign of respect – especially for the person facilitating. It’s frustrating and disruptive when people appear distracted or disinterested, particularly in a short, focused meeting like a stand-up. Cameras off might be fine for a long company all-hands, but not for a 10-minute team check-in. The stand-up only works if everyone is paying attention and showing up fully.

• Bring your whole self, pay attention.
• Be on time
• Cameras on when remote
• Do not multi-task
• Gather together in person on office days, don’t stay at desks

Efficient and focused

Stand-ups are a tool for focus and momentum, not a catch-all meeting. When they drag or lose direction, they quickly become a waste of time – and people disengage. Keeping them brief and on-topic ensures they stay effective, energising, and sustainable. Updates should be concise and relevant to the team’s progress. Longer conversations needed can still happen – just not here.

• Keep it brief, aim for 10 minutes or less
• Talk less, be informative. Be as to-the-point as possible. Be on track and speak to what team needs to know
• Take conversations offline (agree how to follow up and who’s taking the action)
• Only team members contribute (i.e. not stakeholders, supporting roles, observers)
• Make sure the board is up to date before you start
• BUT, fun is good! A bit of informal chat, banter and jokes is ok 

Well facilitated

A well-run stand-up doesn’t happen by accident – it needs strong facilitation. The facilitator sets the tone, keeps the meeting on track, and reinforces good habits. Without that, it’s easy for bad habits and practices to creep back in.

• Have a clear agenda and stick to it
• Be the pace setter
• Be energised
• Ensure you’re sticking to the agenda
• Ensure you’re adhering to best practices

Rotate the facilitator

The stand-up is ultimately for the team, not for the facilitator. Rotating who leads it is a powerful way to build shared ownership and reinforce that principle. When the same person always runs them, it can start to feel like their meeting – which subtly encourages passive behaviour, status reporting, and a lack of collective responsibility.

By rotating the facilitator, you signal that everyone has a role in making the stand-up effective. It keeps people engaged, encourages investment, and helps the whole team develop a shared understanding of what ‘good’ looks like.

But there’s a big caveat: facilitation still needs to be good. Make sure everyone taking the role:

• Is confident and capable of running an effective stand up
• Can hold the line if things go off course
• Is open to feedback

Importantly, someone still needs to be ultimately accountable for ensuring your stand-ups remain effective.

A great stand-up should energise the team, not drain it. If yours isn’t doing that, fix it.

Appendix

Stand up health check

Use this to periodically assess whether your stand-up is working as it should:

✅ Was everyone present and on time?
✅ If in person, did the team gather together (not stay at desks)?
✅ If remote, did everyone have their camera on?
✅ Was the board fully updated before you started?
✅ Did it finish within 10 minutes?
✅ Was everyone engaged and paying attention?
✅ Did everyone in the team speak and confirm what they’re doing?
✅ Was all work in progress discussed?
✅ Were any follow-up conversations taken offline, with a clear owner?

Further reading

Martin Fowler – Its Not Just Standing Up – a comprehensive guide to patterns and practices for daily stand-ups.

Most people were taught OOP means organising code into classes, using inheritance to share behaviour, and exposing/manipulating state via getters and setters. This has led to bloated, brittle code and side-effect-ridden systems that are hard to change.

But that was never the intention!

Alan Kay, who coined the term in the late ’60s, had something very different in mind. He saw OOP as a way to build systems from independent, self-contained objects – like small computers – that communicate by sending messages.

So where did it all go wrong?

Languages like C++ and Java formalised classes and inheritance as core features. Academia followed, teaching the “4 pillars” of OOP – encapsulation, abstraction, inheritance, and polymorphism – often illustrated with real-world analogies like cats inheriting from animals or shapes extending other shapes 🤦‍♀️

This encouraged developers to focus on classification and hierarchy, rather than systems that emphasise behaviour, clear boundaries, and message-based interaction.

Kay later said:

“I’m sorry that I long ago coined the term ‘objects’ for this topic because it gets many people to focus on the lesser idea. The big idea is messaging.”

And:

“OOP to me means only messaging, local retention and protection and hiding of state-process, and extreme late-binding of all things.”

In other words, OOP was meant to be about communication, modularity, and flexibility – not rigid structures or class hierarchies.

Kay’s original ideas are still just as relevant today. They’re language-agnostic, and they apply just as well in JavaScript, Go, or Rust as they do in Java or C#.

If you’ve got a beef with OOP, aim it at what it became – not what it was meant to be.

What can we do instead?

If you want to align more closely with the original spirit of OOP – and build systems that are easier to understand, change, and scale – here are some heuristics worth considering. These aren’t hard rules. Like any design choice, they come with trade-offs. The key is to think deliberately and apply them where they bring value.

Design small, composable parts that can evolve

Avoid deep inheritance hierarchies. Instead, model systems using small, focused components that you can compose (“composition over inheritance”). This encourages flexibility and separation of concerns.

Let objects own their state and behaviour

Don’t pass state around or expose internals for others to manipulate. Instead, define clear behaviours and interact through messages. This reduces coupling and makes each part easier to reason about in isolation.

Reduce hidden side effects

Use immutable data and pure functions to limit surprises and make behaviour more predictable. This isn’t about functional purity – it’s about making change safer and debugging less painful.

Look to supporting architectural patterns

Approaches like Domain-Driven Design (DDD) and Hexagonal Architecture (aka Ports and Adaptors) both support a more Alan Kay style approach to OOP.

DDD encourages modelling your system around behaviour and intent, not just data structures. Entities and aggregates encapsulate state and logic, while external code interacts through clear, meaningful operations – not by poking at internal data. Bounded contexts also promote modularity and autonomy, aligning closely with the idea of self-contained, message-driven objects.

Hexagonal Architecture reinforces separation of concerns by placing the application’s core logic at the centre and isolating it from external systems like databases, user interfaces, or APIs. Communication happens through defined interfaces (“ports”), with specific technologies plugged in via adapters. This approach makes systems more modular, testable, and adaptable – supporting the kind of clear boundaries and message-based interaction that aligns closely with the original intent of OOP.

If you’ve not yet got into Systems Thinking, now is more important than ever.

AI brings the promise of big productivity gains – but so many optimisation efforts deliver nothing. Worse, they can take you in the wrong direction, faster – exacerbating the very issues you’re trying to solve.

Why? Often the things that look like the problem are just symptoms. They’re usually the most visible and measurable activities – and therefore appealing to focus on. At best, it’s like taking painkillers instead of treating the underlying cause of the illness.

I see it all the time – organisations attempting local optimisations to the wrong part of the system, blind to the knock-on effects. Fixes that make bottlenecks worse, and costs taken out in one place, only to reappear elsewhere – often bigger than the original saving.

GenAI makes it even more tempting to fix the wrong problems. It’s good at generating documents, writing more code, handling support queries. None of which are directly value-add activities, but they are visible and measurable.

There’s a real risk of just getting busier with busywork.

That’s why you need to step back and look at the system as a whole. Map end-to-end value streams – across people, process and technology. Identify pain points, bottlenecks, and constraints. Understand how the work flows, and what’s actually causing the outcomes you’re seeing.

That’s systems thinking in a nutshell.

A lot of the theory-heavy books make it sound more complex than it is. It’s why I’m a big fan of The Goal by Eliyahu M. Goldratt. Yes, it’s a corny business novel – but it’s one of the most practical intros to systems thinking you’ll find.

When you take a systematic approach, more often than not, you’ll find the real problems aren’t where the pain is, and rather than playing whack-a-mole patching problems, you’ll uncover opportunities to make changes that deliver real, lasting impact 🙌