It was to make things easier for us. To address the things humans aren’t very good at. A bit of yin and yang in the world. Us humans, typically, are great at empathy, at communication, at relationships, at critical thinking, at creativity and at novelty. Empathy as the most important. We have this amazing ability to see someone else’s point of view and understand why we might feel something/take an action if we were in their shoes. Although a lot of the problems in the world today come from people not doing enough of that.

There are things we humans are bad at though. Imagine I asked you to just receive some forms people fill in, validate the data, do a bit of calculation on the input and then send the output on. And that’s all you’re going to do every day, for 8 hours, 5 days a week. Aside from being bored out of your mind, you’d also probably do it wrong. That’s not a slight on you, dear reader, or on the fact that these roles do need to exist, it’s just a reality of human nature. Most of us would get it wrong. Alongside that mundane task, if I also asked you to store in your mind every piece of data you come across whilst processing the inputs, and be able to regurgitate it at a second’s notice, you’d think I was mad. Our brains are great processing machines, and they do a pretty good job at storing information… Recall and working memory let us down.

We can’t run complex algorithms in seconds, and sometimes our emotions affect our decisions. Sometimes, this is a good thing. But herein lies the problem with the current state of the majority of the tech industry. The people in charge, the people leading, the enormous, capitalist, make money at all costs organisations have forgotten this. Software, computers, technology… foremost, they should make people’s lives better.

I’m reading Enshittification by Cory Doctorow at the moment (highly recommended, as is his blog). The case study on Google is fascinating. In court hearings, internal memos came out that showed Google actively pushed to make Google Search results worse in order to make you search more often. You know what happens if you search more often? You see more ads, which means Google makes more money. Similar stories exist for Facebook (leave MySpace and join us, we promise not to spy on you), Amazon (you can buy anything, and we definitely won’t undercut the sellers) or Apple (spying on people is bad, buy an iPhone, but we will also spy on you).

The ‘personal computer’ should be something that is filled with things that are personal to us, that help us. Not subscriptions that we pay for a below-par service that is getting worse. But hey, if the stock price goes up, that must be good, right? I realise I’m saying all of this as someone who has worked for two big tech companies. It’s why a couple of paragraphs ago I used the word majority of tech companies. I’ve seen both sides. I’ve seen the enormous, capitalist, make money at all cost organisations (hey AWS) and seen what a tech company looks like that cares about its people and in the main builds products that make users’ lives easier.

This isn’t a slight on people on the ground at tech companies either. There are thousands of brilliant people at these companies. But a mandate from above to push AI into every single crevice of every single part of every single product doesn’t leave these brilliant people with much choice. Because remember, in 2026, if your product doesn’t have an AI chatbot, it’s worthless (also said as someone who is actively working on an AI chatbot).

None of that is to say the technology itself is the problem though, it’s how, and why, it gets used. There are genuine, brilliant use cases for LLMs and the chatbots built on top of them. The human weakness I mentioned earlier, that recall and working memory let us down? That’s one of the things an LLM can be good at solving. Summarising huge amounts of random information into something a person can actually digest. Used well, a chatbot isn’t there to keep you searching for longer, it’s there to get you to the answer faster so you can go and do something more useful. That’s a good use of the technology, and it’s the bit of this whole thing I still believe in. Although, when said chatbot has a habit of making things up and hallucinating things do get a little less valuable.

There is a paradox with the current use of large language models (LLMs), particularly for software development. It gives a huge number of people the power to take back some element of control and build systems for their personal computers that are personal to them.

I’m a trustee of a local food bank, I manage all of their IT systems. Recently, I’ve built some custom software for them. Nothing fancy, at it’s core, it’s a database with some validation on the frontend that can send automatic text messages. Previously, with a full-time job, I wouldn’t have had time to build it. The food bank would have gone on with a pretty inefficient process, or bought some cloud subscription that kind of gave them the right functionality but not really. AI coding assistants enable me to build something that works for me. It’ll never be sold, we’ll never charge a subscription, we’ll never ask people to pay for it or enshittify it and sell everyone’s data. We’ve just built a tool, that makes everyone’s lives easier.

Whilst I’m not suggesting you go and try to build the next Amazon/Netflix/Google/Uber with your laptop and a Claude subscription. Although it never gets old watching the hilarity of seeing people try to and fail dramatically because, ya’know, distributed systems are hard. But a lot of the problems that come with large scale systems don’t apply when your application is small, has 10 users who access it intermittently.

I’d still consider myself an AI skeptic, but equally, I’m not afraid to go all in on the tools if I think the outputs can add value to people’s live.

Originally published at https://blog.jameseastham.co.uk on September 17, 2026.

We all hold opinions; it’s part of human nature. We hold these opinions as part of our identity. But other people hold different opinions, and conflict arises when they clash. But what if, instead of conflict, there was curiosity? Instead of shooting people down or convincing them otherwise, what if you sought understanding? People don’t just dream up their opinions; they have reasons for believing what they believe. Your job is to understand it.

Sometimes I run thought experiments in my head about how the world would look if certain nuggets of wisdom were the norm. This one is powerful. Imagine a world where for every opinion someone held, they understood the opposite viewpoint better. Imagine a world where people didn’t disagree until they were sure they had full understanding. It’s an oversimplification of all the world’s problems into this simple act of understanding, but I’m confident it would help.

The ‘normal’ way to tackle differing opinions is to show why you are right. “The other person is stupid because they don’t understand what I’m saying; therefore, I must shout louder”. A successful outcome is “I’m right and you’re wrong” instead of “we both learned something and moved forward”. One of the most demotivating things that can happen is to feel you’re not being listened to, yet this is all this line of reasoning does.

Instead, seek to understand. Approach a differing of opinion with curiosity. Let’s follow this through with a tangible example, and not to get too political with my writing, it’s just a great example. Many of you reading will have a political affiliation. Left, right, center? I don’t care for the specifics. Whichever way you lean, though, ask yourself:

• What are the reasons for not supporting them?
• Do you understand why you support them?
• Do you support them because it’s what you believe, or because you saw an inflammatory, algorithmically vomited video on social media?

Politics is a pretty inflammatory topic, but you can apply this same thinking anywhere. In your relationships, maybe your spouse/parent/sibling wants to do something one way, and you think it would work better in a different way. Don’t defend your path. Try to understand why they think their way will work. It will surprise you how often you are wrong!

It’s a tricky thing to do, but if you can optimise for curiosity and understanding, believe me, your personal relationships will be better for it. And whilst it might not solve all the world’s problems, you can do your bit to make the world a more understanding place.

James

Originally published at https://blog.jameseastham.co.uk on July 20, 2026.

Life is a pretty complex thing. It comes with pressures, challenges, good times, and hard times. You might not be responsible for a few hundred people, but you are certainly responsible for yourself. The lowly checklist can teach you to be more productive, avoid mistakes, and deal with pressure. For many of you, this will sound like a chore, not something that is useful. “I’ve done thing X a hundred times before, why do I need a checklist?”. Doing the same thing repeatedly, eventually you’ll get complacent. You’ll skip steps. You’ll miss things. Sometimes when you miss things, it might be ok, until the one time it isn’t.

In the past, I’ve talked about second-order thinking. It’s dangerous if you make decisions without thinking about the downstream affects. I said in that post that I struggle myself to apply this sometimes. So why not formalise it? That’s what checklists are for. Every time you have a major decision to make, run the checklists.

• Have I considered the downstream impact of this decision?
• What happens if I get punched in the face?
• Have I considered the entire system and the dependencies of this decision?

You have the list ready, and you run it without thinking. Now you don’t have to remember to think about the downstream impact, you take the run book and then just run it.

There is obviously some ceremony involved. In some cases, it’s not worth it. Jeff Bezos’ famous one-way and two-way doors works. If you aren’t familiar, a two-way door is an easily reversible decision. A one-way door is the opposite. For two-way door decisions, speed can matter more than considering all your options. If it doesn’t work, you reverse it. With a one-way doors though, you need to ask these kinds of questions. Run your checklist, ensure you’ve thought about the options.

James

Originally published at https://blog.jameseastham.co.uk on July 17, 2026.

Just because something is great today doesn’t mean it will be great tomorrow. It’s not always the best idea that wins, but often the one that can best deal with change. From natural selection to the big companies of old, who over time descend into obscurity. But what is it that allows something or someone to continue to be great? The things that go beyond great are anti-fragile.

Anti-fragility is a concept I first read about in Nassim Taleb’s book of the same name (the same Taleb who wrote Black Swan). In the opening part of the book, Taleb describes anti-fragility as:

Some things benefit from shocks; they thrive and grow when exposed to volatility, randomness, disorder, and stressors, and love adventure, risk, and uncertainty. Yet, in spite of the ubiquity of the phenomenon, there is no word for the exact opposite of fragile. Let us call it anti-fragile. Anti-fragility is beyond resilience or robustness. The resilient resists shocks and stays the same; the anti-fragile gets better.

This is where the opening quote of this post comes in: being able to deal with the complexity of the environment you operate within. Job one is to deal with change. To be resilient. You can’t gain from disorder if you can’t even survive it. Many of you reading this will operate in a complex environment, accepting that the world around you is constantly changing and unpredictable. You can have the best plan in the world, and then you get punched in the face. But how do you actually become anti-fragile?

I believe there are two ways, from two different areas. The first is from Taleb himself, and the idea of the barbell. The barbell strategy pairs extreme safety with extreme risk-taking, evading moderate “middle” positions. Reid Hoffman, of Netflix fame, advocates for this under a different name with his ABZ strategy. Plan A is the core pursuit, and Plan Z is the safe fallback. Applying this to investments, your plan A is a relatively ‘safe’ investment. Something that you’d be confident in, say, bonds. Then the extreme risk-taking might be something like a leveraged ETF or a crypto investment. Something with a potentially huge upside, and because you only invest a small percentage of your wealth, you minimise your risk.

The second is Barry O’Reily’s idea of residuality. Particularly the idea of stressing the system. Barry comes from a software architecture perspective, but you can apply it anywhere. Say you’ve made your investment decision, you can stress that decision against a variety of different factors. What happens if the stock market crashes? What happens if the economy slows down? What happens if aliens descend upon Earth and take over the world? You can apply the stressor and then look at what the potential solution would be. This doesn’t necessarily mean you solve for every solution, but at least you understand the risks.

This is important for risks that are more systematic. The global food system, the global financial system, energy supply, etc. Stress testing your current approach against this can help you understand how you can prepare. Investing in solar power, battery storage, and an electric car might seem like an enormous investment. But in a not impossible future where the climate is unpredictable, rolling blackouts become normal, or a wannabe dictator decides on a whim that affects the global fossil fuel supply, having guaranteed self-sufficiency seems less expensive.

Think about anti-fragility, think about if you’re ready to deal with the complexity and unpredictability of the world around you. You won’t catch all the things that could go wrong, and you might not solve it all. But at least you are aware.

James

Originally published at https://blog.jameseastham.co.uk on July 16, 2026.

The Brits, thinking they were smart, assumed this would cause the cobra population to shrink as they encouraged the locals to kill cobras. Some enterprising local Indians then proceeded to breed cobras, knowing they could breed them in large numbers, kill them and make a tonne of money from the stupid British. Ethics aside, what the Brits didn’t consider was the second order impact of their decision.

I’ve missed these impacts several times in my career. Choosing to build microservices for a startup with a development team of 1 (me). Fun initially, 6 months later trying to lockstep releases across 4 different codebases… not so fun. Taking on a project I wasn’t that interested in but knew it would be good for my promotion, not thinking about the second order impact of “oh shit, that’s going to take up basically all of my free time for 3 months”.

It’s a simple thing to challenge. Ask yourself, “and then what?”. If you’re making your decision within a complex system, you still might not catch every possible second order effect. But at least you are questioning it. Some of Barry O’Reily’s work on residuality, particularly around stressors, is relevant here. If you’re thinking about an architecture for a system, think about the possible stressors that could impact it. There are no bad ideas. Throw them all into the pot. What are the things that could happen that will cause stress on the system?

Once you’ve got your list of stressors, you can then apply that to your decision. You might not solve all the problems, you might accept some of the risk. But at least you’ve acknowledged them. You’ve acknowledged that your decision might not perfect, but it’s the best you have right now.

If you layer these second order impacts into a fictional person who goes all in on AI, let’s call them Karl. Karl has spent the last 12 months using AI for everything. He’s basically automated his entire job away. He uses it for coding, for note-taking, for summarising his notes, for sending his emails, managing his inbox and calendar. Karl feels great because he’s got an AI to automate away his entire job. Now he can just play golf and get paid for it. The first order impacts are overwhelmingly positive.

Let’s stress this decision. What if, as many people predict, the per-token cost of using LLM’s skyrockets. AI providers have got to get out of their 32 billion dollar losses somehow. With an X number of times cost. Is it still viable? Karl has offloaded his ability to think to a hallucinogenic bag of words. Maybe the only way he can move forward is to pay the LLM companies. Bad for Karl, great for OpenAI.

An alternative stressor is that his employer realises Karl has just proved AI can automate away his entire job. Great for the employer, they fire Karl and keep his automations. Karl is sad. But at least he has time to play golf now? For the AI company, though, they now need to consider the aforementioned stressor because what if token costs go up X amount because someone needs to find 32 billion dollars somewhere.

Now Karl’s company is wishing they had Karl back.

There are a lot of if’s and but’s in that short narrative. The point doesn’t have to be that you believe something will happen. The point is to go wild and consider all possible outcomes. Maybe Karl decides he is happy with both of those risks and he carries on. Cool. But at least you’ve considered the longer tail of impacts.

Ask yourself, “and then what?”. And start to consider the second order impacts.

James

Originally published at https://blog.jameseastham.co.uk on July 14, 2026.

Imagine you’re standing behind a golf ball, needing to hit a shot. For this thought experiment, imagine you can actually strike a golf ball. There is a lake on the left and a couple of deep bunkers in front. The ‘best miss’ here is long and/or right. Obviously, the intention is still to hit the ball to the green. But knowing the best miss will help you select a better target. You might pick a club based on the distance to the back of green rather than the middle (doesn’t matter too much if you go long) and when you line up the shot you’re going to pick a spot somewhere on the right side. Take the bunkers out of play, take the water out of play, and hey you might still get the ball on the green. But if you don’t I’ll still be ok.

I don’t know about all of you, but often when facing a decision or planning my next few months of work, I think about the happy path. When I started playing golf again, I’d always aim for the the flag. Back when I was spending more time designing systems and writing code, rather than teaching people about those things, I’d design a system based on what the expected good state looks like. For anyone who has worked in software, or made plans, this is a terrible idea.

This idea of the best miss is useful for life. That’s not to say you don’t want or expect the happy path, not at all. That is still the goal, still the desired outcome. But spend time thinking about where you want to miss. If you’re designing a software system, your best miss is that your users still have a good experience even if everything behind the scenes is on fire. If you’re budgeting, your best miss is that you have a bit of space every month for if an unexpected expense comes up. If you’re writing up a strategy for your next 6 months of work, your best miss is to consider what happens if things don’t work out as expected and plan for it.

If, in life, you could strive to be like an elite golfer. Where your bad shot is still 10x better than the average Joe, over time you’ve got an excellent chance of reaching your goals. Whatever they may be.

James

Originally published at https://blog.jameseastham.co.uk on July 14, 2026.

A few years ago, I was running ultra-marathons. 26-mile plus running races, with the furthest I competed in being 50 miles. There were a lot of reasons I started running ultra-marathons, which I won’t go into here. What I didn’t expect was the number of life lessons I took away. One of my favourites comes from Stoic philosophy.

Paraphrasing Marcus Aurelius, “If a human can do it, and you yourself are a human, then you can do it too.”. If there are examples of a human performing a task, given you are also a human, then it is within your grasp. It goes without saying, there are limits to this statement. I’m not going to will myself into being the next Usain Bolt. There are some biological limitations. When you look around at almost any human endeavour there are people of all different backgrounds and abilities. If they can do it, and they are human, then why can’t I?

I find this incredibly motivating, and it feeds the work by Carol Dweck on growth mindset. Simplifying Carol’s work, there are two types of mindsets: fixed and growth. A fixed mindset is one that believes you can’t improve, and a growth mindset is one that believes you can. I can look at Usain Bolt and say a) I’m never going to be as fast as him, why would I try or b) I’m going to train hard and get as fast as I can. If he can do it, why can’t I?

One thing that separates some of the hardest working, talented people I know from some incredible people who don’t seem to move ‘forward’. Do you look at the world and think everyone is better than me why bother trying? Or do you look around and think all these other people are human just like me, I’m going to push and become the best that I can be. This mindset has served me well, but it’s worth noting that not everyone starts from the same place.

It’s important to apply this thinking to things within your control, things outside your control are just that. Of course, societal barriers and discrimination are real, and not everything is within our control. But where you do have agency, this mindset can help push you forward. Remember, if it is humanly possible, it’s within your reach.

James

Originally published at https://blog.jameseastham.co.uk on July 13, 2026.

A Black Swan is a completely unpredictable event that has outsized consequences. Human history is made up of these big unpredictable events that nobody saw coming. Things like the 2008 financial crisis, 9/11, along with many other events throughout history. It can even be something you predict but under-or overestimate the impact. You launch a startup expecting 1,000 users, and you go viral and get a 1,000,000.

If you are working in a domain that is unpredictable, and where the outcome has big consequences. You have to be aware of the Black Swans. Part of the problem is that we humans aren’t great at being ready for them. We love stories. We look back at an event, assign a narrative, and use that to rationalize why something happened. This gets dangerous when we use that to project forward. We think we are “learning” from history. But if you are dealing with something complex, there is no guarantee the future will be anything like the past. In fact, it’s probably the opposite.

What does this mean for you? First off, beware the expert. If somebody calls themselves an “expert” in a domain that is based on prediction, is complex with variability, then believe nothing they say. You can see that in real time with AI. It seems everyone has become an “expert” in AI and knows what the future is going to be. In reality, nobody knows anything. Without real, hard, empirical facts, all you have is speculation. If the “expert” omits the potential variability in their prediction, trust them even less.

An important aside, you should definitely trust the experts in domains that are more static. Doctors, surgeons, etc… If the domain isn’t unpredictable, trust them. Human brains are pretty static.

Once you become skeptical of experts, you can make more pragmatic decisions about your day-to-day life. You can take the views of ‘experts’, look at the variability in outcomes and use that variability to decide your approach. Look at the positive and negative potential outcomes, and ensure you are prepared both. The Stoic idea of premeditatio malorum feeds into this. What’s the worst thing that could happen, prepare for it.

You might not change, you might even carry on as if the positive outcome is going to happen. Being prepared for the worst makes it less of a shock if it does happen, though.

Expect much more from The Black Swan over the coming weeks.

James

Originally published at https://blog.jameseastham.co.uk on July 12, 2026.

In Meditations, Marcus Aurelius writes, “You could leave life right now. Let that determine what you do and say and think”. Taken at face value, it’s the grim thought that you are going to die. Underneath that is a deep appreciation for the things you do every day. Everything you do in your life, at some point you are going to do it for the last time. Eat pizza, go for a walk, play golf, go out for a run, give your parents/partner/sibling a hug. The cruel part is that you never know when the last time is going to be.

When you apply this every day, though, it is genuinely life-changing (and I don’t use that word lightly). The impermanence of everything around you increases your appreciation of it. This might be the last time you get to eat pizza; savour every single bite. This might be the last time you can hug your partner, squeeze them extra tight. You just never know.

Next time you do something you enjoy, remember it might be the last time you get to do it. Don’t get sad about it, appreciate it. Enjoy it. Savour it.

James

Originally published at https://blog.jameseastham.co.uk on July 11, 2026.

How do think? It’s not something you probably think about very often. You’re born, you go to school, you learn some stuff, you go to work, where you hopefully use the things you’ve learned to think about a problem. At least in Western culture, most things we are taught follow a model of linear thinking. The school year has a sequence, every lesson has a sequence. A and then B and then C and then D.

This is great for a lot of things. Passing exams in school, at least with the way most school systems work, requires this style of thinking. With the chaos of the modern world, there’s another type of thinking I’d argue is more important. It starts with the child asking “why”.

If you’ve spent any amount of time around a child, they are constantly asking why. Why is this like this? How does X relate to Y? What caused that to happen? It’s cute from the outside, as someone who’s about to become a father for the first time, ask me if it’s still cute in a few years. The child is trying to make connections. To work out how the different pieces of their understanding and how they relate to each other. The child, quite naturally, is thinking in systems.

Somewhere along the way, this gets lost. Linear thinking is pushed down your throat, learn this, then this, then this. You get a job, perform task X, then task Y, then task Z, and hopefully at the end get a pay rise. When you’re down in the detail this works well, but the world around us isn’t linear and definitely doesn’t follow a straight, easy to understand path.

Imagine for a moment you had to make a decision about the future of the company you work for. For arguments sake assume you work in finance. One way to make the decision is to simply make the best decision that benefits you, right now. The selfish option. And honestly, I’ve seen lots of people over the years who take this approach. But the company is made up of lots of different people, departments, customers, suppliers. There is an entire complex web of relationships that make up the system as a whole. To make something like a sensible decision, you need to have some understanding of the entire system. If you were to apply linear thinking to this task, you might think the best decision (for accounts) is to put up your prices 100x and find a cheaper supplier. $$$ for everyone! The short-term impact — profits go up and you look great for your insightful decision. But what’s the long-term impact of your decision? If you’ve forgotten about the rest of the system (in this case, customer satisfaction) your system is going to die.

Systems thinking is about trying to understand all the nodes, all the relationships, and how they work together to make the system as a whole. Famous system thinker Russel Ackoff said “A system is never the sum of it’s parts — it’s the product of their interaction”. When it comes to making decisions, think about the different pieces but more importantly think about their interactions. If you understand one piece, ask yourself what are the other pieces that I interact with and if I make change X how is that going to impact them. It’s not about slowing down, it’s about truly understanding the long term impacts of your actions.

James

Originally published at https://blog.jameseastham.co.uk on July 10, 2026.