10 Years to Today

It's 2026 and as I write this, the San Francisco Giants are currently playing their Opening Day game of the new Major League Baseball season against the New York Yankees.

Ten years ago, back in April 2016, I had travelled to California for a week. It was an important and exciting time in my life as I had just started a new role with Periscope, the live video streaming application owned and run by Twitter.

At the end of my week there, the Periscope team bought a bunch of tickets to the San Francisco Giants Opening Day game against the Los Angeles Dodgers, and I was fortunate enough to be in AT&T Park (as was) to witness it. It was an awesome experience.

The game itself was electric - a heated rivalry between two teams that really didn't like each other. Think United versus City, or Arsenal versus Tottenham. There were home runs, bad officiating calls, the LA manager being thrown out of the game for arguing. It had it all.

Looking back on the ten year anniversary, I got thinking about how these two events have similar themes for more recent topics in today's fast-moving and changing world: how much the game of baseball has changed over the years through the introduction of new technology and processes, and also how receptive I was at the time to change and the unknown when going in to my new role.

Baseball is America's past-time. A game that has been played professionally since 1871. A game that has also seen significant rule changes since 2016 that have had a material impact on how the game is played. Some argue that the modern game of baseball is nothing like the game of the past, with so many modern rules favouring more action, scoring and excitement, the strategy element is now being lost and it's all about numbers and stats.

To prepare for and manage a game ten years ago would have required thinking about strategy and approaches that are no longer relevant in today's baseball game - thinking about how to make effective use of the player pitching the ball, how to move players around the field to protect against teams scoring, when to have coaches and managers enter the field of play to talk with the players. All of these things have either disappeared from the game, or have changed significantly in how they work.

This has required managers and players alike to almost go back to the drawing board to re-imagine how the game should be played effectively and successfully.

This is not too dissimilar to the challenges that software engineers and managers are facing today with the introduction of AI tooling and workflows. What has been working well for many years now has to be re-imagined to ensure continued success and efficiency. The National League moved to Designated Hitters, Software Engineering is moving to Agentic Development.

When I was first approached with the opportunity to work with the Periscope team back in 2016, I had never met or spoken with anyone on the team before. We had perhaps exchanged glancing pleasantries through Twitter posts or comments in live streams, but nothing more beyond that. I had a brief video call with a Lead Engineer who explained what the role and expectations were, we got some paperwork signed, and within 2 weeks I was walking the streets of San Francisco as a Twitter employee.

To say that I felt a huge sense of imposter syndrome at that time was an huge understatement. I had just agreed to work on a piece of software used by tens of millions of people around the globe, written in a programming language I had never used before, whilst also working alongside a super-experienced team of engineers based on the other side of the world. I began to seriously doubt my decision making process and wondered what I had got myself in to.

For me to be able to take the role and progress, I had to accept that there were things that I did not know and new processes that I had to learn. It was not an easy position to be in. It took a lot of self-motivation and reassurance that I could do this. I wanted to learn. I wanted to put myself on this track of being an engineer in a world class organisation. I needed to adapt quickly and be receptive to everything that the role threw my way.

Thankfully, at the time, Twitter was open and supportive of new hires and did everything they could to help them learn and get adjusted. The team there did everything they could to get the information and knowledge I needed, and alongside fast-paced self-learning and this mentorship, I was able to pick up the new language fairly quickly and within a few weeks I had my first production change rolled out - adding a new button to the backend system to help moderators control account's active status.

I could have quite easily baulked at the opportunity presented and decided that learning the new language wasn't worth the effort, or the possibility of screwing up in front of that many users was too much for me, but I persevered. I was able to learn, grow and adapt in a way that has helped shape my career over the past 10 years, and got me to where I am today. Would I do it all again? Absolutely.

Today brings a similar set of questions about where AI fits in the development practices and workflows we have relied on for years. Engineers are the players in this new version of the game, adapting in real time while still being expected to perform. It's entirely reasonable to feel a mix of excitement, fear, and uncertainty about what this era could mean.

What feels most important to me is that we keep talking about it. No-one has the full answer yet, and the future is being shaped in real time, but shared understanding beats private assumptions. If we're going to change the way we build software, we need to do it with open dialogue, honest questions, and the confidence that it's OK not to know everything on day one.

And just like it was for me in 2016, support makes all the difference. Change is easier when everyone has access to the same guidance and context, when people can ask for help without feeling judged, and when leaders are willing to invest time in bringing others along. The aim isn't to force everyone to move at the same speed, but to make sure nobody is left trying to figure it out alone.

From there, the only real way forward is to experiment. Give something a go. Try it on a small problem, learn what it changes, keep what helps, and drop what doesn't. The goal isn't to predict the future perfectly - it's to stay curious, keep adapting, and make sure we're still playing the game well, even as the rules keep changing.