How do Generative AI tools impact software developer productivity and code quality? A recent large-scale study – one of the most empirically robust I’ve seen – tackled this question by analysing 218k+ developers across 880m+ commits (with control groups and everything).

The results? A modest but consistent 4% productivity boost without sacrificing code quality.

Other key findings:

– Moderate GenAI users emerged as the highest overall performers (i.e. less is more)
– Only 1% of developers committed GenAI-authored code without significant rework.

It’s a nice 4% gain right? Not exactly game-changing. but worth it if licence costs stay reasonable. Enough to reduce a team of 100 to 96 engineers (or boost output by 4%)?

Not that simple – the study is somewhat flawed because it’s measuring effort not outcomes and individuals and not teams. The latest DORA Report (the gold standard when it comes to measuring high performing tech teams) found AI Tools marginally improving individual productivity, but having a NEGATIVE impact on overall team productivity.

As Giovanni Asproni said to me on Bluesky:

“I think that better product management, with a decent strategy, and proper prioritisation and planning will increase output far more than 4% in most organisations. And it may actually help saving energy and CO2 emissions.”

I ran a LinkedIn poll asking software developers about their experiences using AI coding assistants with production code. The results were interesting 👇

Why production code? Developers spend most of their time writing it, and AI tools can handle non-critical activities like proof-of-concepts, solo projects, and quick experiments fairly well.

I received 50 responses – not a huge sample, but interesting trends emerged nonetheless. Also, LinkedIn polls aren’t anonymous (which I didn’t know!), so I could see who voted. It allowed me to exclude non-developers and gauge experience levels from LinkedIn profiles and add it to the data.

You can see the results broken down by answer and experience level in the chart attached.

What stood out to me?

– Only one respondent – an apprentice engineer – said they “automate most things.”

– 74% found AI assistants helpful, but 26% felt they’re more trouble than they’re worth.

– All who said AI assistants “get in the way” (24%) had 8+ years of experience.

– 80% of respondents had 8+ years’ experience (I know a few of them, and others’ profiles suggest they’re legitimately experienced engineers).

My thoughts? Unless they improve significantly:

1) They’re unlikely to shift productivity dramatically. From my experience, “task” work takes about 10-30% of a developer’s coding time. So, while helpful, they’re not game-changing.

2) That a significant portion – especially experienced engineers – find these tools intrusive is concerning.

3) Having heard stories of AI tools introducing subtle bugs, I was surprised not to see more votes for “considered harmful.”

4) The idea that AI tools might replace software engineers – or that even “conversational programming” is anywhere close – still feels a very long way off.

5) I worry about an apprentice software engineer getting an AI tool to do most of their work. Partly because more experienced developers are a lot more cautious and must have a reason for this, but mainly because they won’t be learning much by doing so.

Hiring is one of the most impactful decisions for any organisation. The wrong person can badly impact the culture and performance of the organisation, absorb time in performance management and be a significant distraction.

Informal recruitment processes are leaving things down to chance.

Inefficient processes consume time and risk losing top candidates who move quickly.

This is why fixing the recruitment process is typically one of my first priorities when working with organisations.

Having interviewed thousands and hired hundreds over the past two decades, here are my top tips:

All candidates should have a positive experience

Whether or not a candidate gets the role, they should leave with a positive impression. A poor experience not only harms your brand but can dissuade other candidates from applying.

• Being warm, friendly, and engaging throughout the process costs nothing, and it doesn’t prevent you from asking tough questions.
• Give constructive feedback to unsuccessful candidates so they know where they can improve.

Short Lead Times

It’s all too common for the time from CV received to decision made to be a very lengthy process, often taking months.

• Aim for a two week turnaround from CV received to decision made.
• Much of the following advice is for how you can achieve this 👇

Start with a brief screening call

To make sure a candidate is a basic fit, get eligibility information and to introduce them to your company, and the recruitment process.

• If you’re working with a recruiter, ideally empower them to handle this stage (if properly briefed).
• It’s important this conversation isn’t just a box-ticking exercise. It sets the tone for the rest of the process, so make sure it’s welcoming and informative.

Pre-booked interview slots

So much time is lost in calendar tennis for scheduling interviews. Avoid this by agreeing, and putting in place, pre-booked interview slots upfront in interviewers’ calendars. Whoever is engaging with candidates can then offer them any of the available pre-booked slots. If they can’t do any of them you can always compromise and try and find other times, but make this route one.

Schedule all stages up front

Again, this saves time for both the candidate and your organisation, and gives the candidates clarity on timelines. If you’re doing multiple stages, there’s no reason why you can’t cancel a further stage if you don’t consider it’s worth proceeding with the candidate, just let them know up-front that you may do this.

Do as many stages as you can in one go

Consolidating interview stages reduces cycle times significantly. While you may end up spending more time with candidates who may not progress, you’ll save more overall by avoiding the delays of additional scheduling and decision-making between stages.

If you’re concerned about wasted effort, you can always tell the candidates up front that you may choose to finish the interview early if it’s clear to you it’s not progressing well.

Structured interviews

Every interview should be structured, with the same scripted questions for all candidates and scoring indicators for responses.

This reduces bias, ensures consistency, and simplifies decision-making. You never use scoring indicators as a pass or fail, they just help inform your decision.

I’ve developed interview templates where each stage has its questions and scoring indicators on the same form.

Two interviewers in each stage

This has several benefits:

• One can focus on the candidate while the other takes notes.
• It avoids leaving decisions to a single viewpoint.
• It provides an opportunity to train future interview leads.
• It allows candidates to meet more team members.

The second interviewer doesn’t have to be sitting there quietly, they can take part in asking follow up questions and responding to candidate questions.

The interview forms I mentioned previously? Use them as a shared document in e.g. Google or OneDrive and interviewers can see the notes as they’re typed, and make their own comments and observations.

Same-day candidate debrief

Debrief as quickly as possible after each stage or at the end of all interviews. The structured format, shared notes, and scoring indicators will help you avoid lengthy debate, maintain consistency and make timely decisions.

AI toothbrush anyone? Yes this is real, but I don’t want to talk about AI. I want to talk about technology having it’s place. Not everything is best solved by technology.


I’m a hardcore techie, but when it comes to personal hygiene, I couldn’t be more lo-fi. After years of trial and error, I’ve settled on a wooden toothbrush, basic Colgate toothpaste, and the same cheap Dove soap for both washing and shaving (with a basic razor and shaving brush).


A ~£100 “AI-powered” toothbrush solves no problems for me – or anyone, for that matter.


It’s why I challenge early stage startups if they really need to build something yet. It’s why I’m an advocate for practices like Value Stream Mapping and Service Design, and how often the desired outcome is best solved *without* needing to chuck technology at the problem.


It’s why I’m obsessed with process optimisation and systems thinking.


It’s why I loved the recent episode of “The Digital Human” on the potential for AI in the NHS (BBC Radio 4, will share link in comments), where the the eminently sensible Jessica Rose Morley, PhD pointed out there’s little point in getting carried away when, in reality, it’s still common for three wards to share one outdated PC on wheels, and the data is in such a mess.


Technology is expensive, it comes at a cost. Only when you’ve identified the user need and/or optimised existing processes is it worth investing in.

I believe it was Reed Hastings, CEO of Netflix who coined the phrase “brilliant jerk.”

Common in tech orgs – smart, technically gifted, and highly productive. They’re seen as the people to go to for solving big problems, and they often save the day in a crisis.

But boy do they ruffle some feathers getting there.

As a leader you can spend a lot of time cleaning up after the mess they create. But you put up with them coz they get stuff done.

“Jerk” is a big bucket of behaviours of course. They can often dominate conversations, dismiss others’ opinions, and aren’t shy about pointing out what’s wrong with the org, proudly “telling it like they see it,” but they’re typically the last to offer any suggestions.

At one organisation I worked, we had a toxic “them and us” culture between Sales and Dev. Interestingly, when one particular person left, it pretty much evaporated.

And don’t forget, “Your culture is defined by the worst behaviour you tolerate”

In a recent post, I explained why we’re inherently bad at estimating, which is a major reason software projects often run late. But that doesn’t mean we can’t plan ahead for the longer term and manage expectations. Here are some techniques I’ve found effective for longer-term planning, even in the face of uncertainty:

Provide ranged forecasts 🌦, not fixed estimates

Over any reasonable period of time, providing likely delivery dates as a range rather than a fixed date is better. Doing so embraces the inherent variability and help stakeholders better appreciate uncertainty. Giving a fixed date, even when you all know it’s just an estimate, sets false expectations.

Don’t just estimate time ⏱, estimate confidence 💪

Along with estimating how long a task might take, assess how confident you are in that estimate. Is it something we’ve done before and know well? High confidence. Is it new, complex, or something we’ve never tackled before? Medium or Low confidence.Multiply time by confidence. For example, a “Small” task (e.g. 1-3 days) with “Low” confidence can be re-forecast as 1-7 days.

Longer term planning as risk management 🚨

Effective planning isn’t just about setting expectations on delivery, but managing risks over the project lifecycle. All those estimates which have come in as Large or with Medium-Low confidence? Those are your biggest risks and represent the most uncertainty. Identify risks early, identify potential mitigations, highlight those risks to stakeholders.

Account for Optimism Bias 🌈

Whilst you can’t completely mitigate against this, there are things you can do to be a bit less vulnerable. Involve the entire team in estimates, account for full end to end delivery (not just developer time) and factor in holidays, sickness and other factors that could impact delivery.Also, those ranged forecasts which provide a cumulative lowest range for all the work? Highly unlikely due to optimism bias! It’s probably best not to present the most optimistic forecast for this reason.

No silver bullet, but a better approach

These practices won’t eliminate uncertainty or guarantee perfect outcomes – there’s no silver bullet in longer term planning. However, I’ve found they help organisations plan more realistically, reducing the stress and frustration that often come with missed deadlines, and enabling more effective, adaptable strategies in the face of uncertainty.

Addressing performance issues is something many shy away from, often putting it off or hoping it will go away.

The sooner you do so, the more likely there’ll be a positive outcome for very everyone.

The longer you wait, the more difficult it becomes.

If it’s clear things aren’t working out then you need to act decisively. Don’t let it drag on.

It’s not only about the individual; it’s letting the whole team down and sets the standard for the level of performance considered acceptable.

“𝘞𝘦 𝘯𝘦𝘦𝘥 𝘰𝘶𝘳 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘦𝘳𝘴 𝘵𝘰 𝘣𝘦 𝘣𝘶𝘴𝘺 𝘤𝘰𝘥𝘪𝘯𝘨”

I still regularly come across this mindset. Not with any ill-intention. Developers aren’t cheap, so naturally you want them to be productive.

Whenever I do, I’m reminded of this meme (image created by Sebastian Hermida).

So this is a reminder that with software development…

𝗧𝘆𝗽𝗶𝗻𝗴 𝗶𝘀 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸 🐵⌨

Development is about a lot more than just being heads down hammering out code – it’s about problem-solving, understanding user needs, designing scalable solutions, and ensuring that what’s built is truly valuable. Rushing to code without seeing the bigger picture leads to missed requirements & re-work, technical debt and buggy software.

It actually slows things down more than it speeds them up 🐌 🚧

Developers should be involved in the full end-to-end delivery process, not just the coding bit:

🔍 Understanding what we need to deliver, and why

🧪 Quality Assurance (you mean you didn’t test it before you threw it over the wall to QA?)

🚀 Shipping! Getting things out to customers before picking up new work

🤝 Collaborating with each other and other disciplines

Collaboration is a key one. When developers pair with each other or folks from other disciplines, it speeds up the process by reducing feedback loops and capturing issues early, and fosters shared understanding and ultimately, better solutions.

Having developers engaged more widely might seem counterintuitive if you’re focused on keeping them “busy” with coding, but it’s the best way to get the most out of their time.

Why are so many software projects late? Estimation (and how we use it) is often at the heart of it.

Humans are naturally optimistic. We overestimate how well things will go and underestimate the time required. It’s a well documented phenomenon:

🤞 Optimism Bias: Our tendency to believe things will go better than they likely will.

🗓️ Planning Fallacy: Underestimating time required, even when past experience tells us otherwise.

Secondly, even with careful planning, we can only account for what we know. As well as software development being inherently complex, as time progresses, unexpected factors always emerge: shifting priorities, new challenges, team changes – the “known unknowns” as Donald Rumsfeld infamously put it.

𝗖𝗮𝗻 𝘆𝗼𝘂 𝗴𝗲𝘁 𝗯𝗲𝘁𝘁𝗲𝗿 𝗮𝘁 𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗶𝗻𝗴?
Yes, we can avoid common pitfalls and improve our estimation practices. But they are still likely to be optimistic because of the reasons above.

“It always takes longer than you expect, even when you take into account Hofstadter’s Law.”

– Hofstadter’s Law


𝗗𝗼𝗲𝘀 𝗔𝗴𝗶𝗹𝗲 𝘀𝗼𝗹𝘃𝗲 𝘁𝗵𝗶𝘀?
Agile embraces uncertainty, such as breaking down work into the smallest possible pieces and delivering value early and often. But it doesn’t eliminate the challenge of longer-term planning.


𝗗𝗼𝗻’𝘁 𝗯𝗼𝘁𝗵𝗲𝗿 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝘁𝗵𝗲𝗻?
Not at all. Organisations need to plan. Without long-term planning, you can’t set realistic budgets, allocate resources & people, or prioritise efforts. The trick is learning how to plan effectively, even in the face of uncertainty.

In my next post, I’ll share how I’ve approached longer-term planning successfully.

If you’re looking to drive change in your organisation, 𝗲𝗹𝗲𝘃𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝗰𝗵𝗮𝗻𝗴𝗲 𝗮𝗴𝗲𝗻𝘁𝘀:

🔎 Identify them – look for the people who are most on board with the changes you’re trying to make, the most proactive and unafraid to challenge the status quo.

💪 Empower them – give them a seat at the table, involve them in initiatives, elevate their voices. Don’t be afraid to break hierarchy in doing so.

🛡 Support them – protect their time, don’t overload them.

The new leaders in your organisation may already be there