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Which are more dangerous: slides, or sticky notes?
In the story of the three billy goats gruff, the goats want to cross a bridge guarded by a troll. They manage this by each telling the troll that there is a bigger goat just behind them until (spoiler alert!) the biggest goat comes along and butts the troll into the sky.
Sometimes, when we are trying to make the case for enterprise technology capabilities, it feels like we are the trolls, and that we are so scared of the biggest billy goat that we won’t tackle the smaller goats. When we look across our technology landscapes, we see mess, waste and mayhem, and wish that we had some of the foundational capabilities that would help clean things up. Yet we hesitate, because we know that every time we build something we will uncover another problem, and another problem, and another problem, until we get to problems that are so big that we cannot imagine how to solve them.
The language illusion, doubled
Is programming a computer more like language or more like maths?
Neither, it turns out. In recent research, neuroscientists at MIT conducted brain scans of programmers while they were trying to solve problems, and discovered that, rather than engaging the language centres of the brain, they engaged a system known as the multiple demand network, usually used for complex problem solving.
Programming languages, it seems, are not the same as ordinary languages. This is not new news. In the earliest days of programming, when Grace Hopper was inventing high level languages, she and her team sent versions of their code to their bosses in French and German. The bosses sat up and paid attention: was it possible that their computers had suddenly learnt to speak foreign languages?
Technologists are always crying wolf (because of all the wolves)
The computer had failed. Unfortunately, it was the Apollo Guidance Computer (AGC), the machine that controlled the flight of a small, fragile spacecraft to the Moon and back. Fortunately, it wasn’t in space: it was on the ground, in a simulator.
Margaret Hamilton, the leader of the MIT team programming the AGC, often had to work weekends to meet the urgent schedule of the Apollo programme, and sometimes brought her daughter, Lauren, to work with her. Lauren liked to play in the simulator.
Coping with volatility: don't panic; seek truth; release frequently
If you’re in the last stages of a multi-year digital delivery programme, then you probably feel frazzled. That’s the normal condition of late stage programme teams. If your programme has coincided with the last five years (five year digital delivery programmes are still a thing) then you must feel frazzled to a historic degree.
It’s more complicated on the inside than it is on the outside
We don’t need time machines to create paradoxes in technology: they are built into the way we work. One of these paradoxes is that the simpler technology appears on the outside, the more complicated it is on the inside.
I was reminded of this recently when talking to someone who confidently told me that the more sophisticated AI models get, the easier they will be to use, for technologists as well as end users. AI would solve its own skills problem. I was surprised by this because, to me (and, I expect to most other technologists), while we understand how natural language interfaces can radically simplify the experience for end users, the introduction of the current wave of AI into our architecture makes it more complicated.
Learn to fail fast? Technologists fail all the time
From time to time, organisations attempt to learn new ways of working. They attempt to become digital or agile or data-driven or innovative. These attempts come with some familiar ideas: that we should execute through cross-functional teams who are empowered to experiment. One of these ideas is that we should not be scared of failure, and that we should learn to fail fast.
These attempts sometimes elicit eye rolls from the technology teams, especially the idea that we should embrace failure. This is not because these ideas are invalid: in fact, they are welcome to technology teams, and reflect their preferred ways of working. However, technologists have a different relationship with failure than non-technologists.
Are LLMs the air fryers of AI?
Do you know someone who got an air fryer for Christmas? Or did you get one yourself?
If you know someone who got an air fryer, then there’s a high chance that you have heard all about it, and how it has been a complete game changer. They can cook things in a fraction of the time it used to take! And it’s not a fryer at all - it’s really a mini-oven! If you got an air fryer yourself, then there’s a chance that you’ve used it for everything, and that, even now, you are thinking about what you could use it to cook next.
I don’t have an air fryer myself, but am old enough to remember when my family first got a microwave. We lived off jacket potatoes for at least a week, and tried microwaving many things that should not be microwaved (there’s a reason that roasts are called roasts). Eventually, we found, just as my friends with air fryers seem to be finding in the weeks after Christmas, that, while the microwave is a useful tool to have in the kitchen, it’s not the only answer, and certainly not the best answer for everything.
On the 2025 to-do list: figure out AI agents
Recent years have seen waves of AI innovation breaking faster than we can figure out good practice. Organisations around the world are working hard, not only to find ways to put AI to work, but to do so safely and responsibly. The AI to-do list often seems to be growing longer faster than we can strike items off it - but the only route to good practice is practice.
The advent of AI agents promises to add more items to the to-do list. The AI agent wave started cresting in 2024, and will break in 2025. Several major technology vendors and platforms already offer their customers the ability to build, configure and operate AI agents in an enterprise context, and the ability for consumers to build agents or to subscribe to existing agents, cannot be far behind (indeed, it is likely that, by the time this article is published, it will already be happening).
Embrace the gift of boredom this Christmas
It’s Boxing Day today, which means that, if you live in the United Kingdom, you are entering the limbo period between Christmas Day and New Year’s Eve. (Other countries may have the same experience, but I have only ever celebrated Christmas in the UK, with our holiday timing and traditions.) The presents have been opened, and the Christmas dinner has been eaten. Batteries have been found for the toys that needed them, but they’re starting to run down. The board games have been played, and you are wondering when your relatives are going home. It’s dark outside and it’s possibly raining. Most of the shops are shut. In theory, tomorrow is a working day, but lots of people are still on holiday.
You might feel a bit aimless during this period. Possibly even a little . . . bored?
At this point, you may be tempted to reach for one of the distraction devices that most of us have to hand (you may even be reading this article on one of them). Perhaps what’s needed to brighten up these midwinter days is to scroll through a social media feed, to reply to all of the messages in the chat group, to scan the news, or have a quick go on a mobile game.
Or perhaps not.
All I want for Christmas is speed and reliability
What were your Christmas lists like as a child? Were they modest requests for improving books and educational toys? Or were they, like most Christmas lists, an extravaganza of wishes, containing toys and sweets and games . . . and possibly a dragon and a unicorn?
It sometimes feels like our requirements for software development are like a Christmas list written by a very small child that wants everything at once. We’d like the ERP package to have world class embedded processes, but we’d also like it to customise it to meet our every need. We’d like the cloud platform to give us capacity on demand and pay as we go, but we’d also like it to be on-premise because we’re worried about security. We’d like an AI system that predicts our customer’s needs, but we’d like to do it without using our data.
And being a project manager or a product owner can feel like a harried parent who can’t possibly afford everything on the list, and is worried that Christmas morning is going to be a disappointment. If I give them all the customisations they want, then we can never take the upgrade. I can give them security on cloud, but they’ve got to understand the shared responsibility model. I can build them an AI model, but not unless someone’s prepared to share the data.
Precision is not pedantry; clarity is not cynicism
The Nomenclature Committee of the Association of Computing Machinery might not sound very exciting. However, it got to decide the words that we use to describe computers, and words matter: naming is a powerful act. When the computing pioneer, Grace Hopper, chaired the committee in the 1950s, she steered them to avoid ‘words of the magic brain class’, and to use terms such as ‘storage’ instead of ‘memory’, and ‘processing’ instead of ‘thinking’.
This direction was needed in the 1950s. Computers were new, and to most people they seemed like magic. Even though the computation they performed was complex - since the early days, computers had been used for hard mathematical problems such as code breaking and navigation - they did far less than the computers we have today. Today, it would seem strange to describe a machine that was limited to mathematical operations (no speech, no graphics, no sound) as thinking. Yet, in those early days, it was astonishing that computers could compute at all: that they could do work previously reserved for the human brain and mind. It is unsurprising that they were described with breathless excitement.
Playing the triangle: breaking down technology risk
What’s more risky? Building a hotel on the side of a volcano, or trying to deliver a software project?
Years ago, while working for a bank, I heard a talk from a colleague in the Structured Finance team: this team created complicated lending structures for projects that carried high degrees of risk. He told a story about a loan for a hospitality business that was building a new resort on the side of a volcano. Unsurprisingly, that project required some complicated risk models.
I put up my hand and asked, ‘How do you model risk for IT projects?’
The banker smiled and shook his head.
‘We don’t,’ he said. ‘Far too risky. They fail all the time and we don’t know why.’