It’s 2025, and I’m sitting in my home office surrounded by three monitors, a mechanical keyboard with just the right amount of clickiness, and a coffee mug that reads “It’s not a bug, it’s a feature.” I’m Briana Gonzalez, and I’ve spent the last seven years building a career as an AI developer. As I prepare for this time capsule project, I can’t help but reflect on how algorithms—once abstract concepts I studied in college—have become the foundation of practically everything in our digital world.
When I first graduated with my computer science degree in 2018, I had no idea how rapidly the algorithmic landscape would transform. Back then, we were excited about neural networks that could identify cats in photos. Now, I’m working with systems that can generate entire codebases, design websites, and even assist in medical diagnoses.
Algorithms – The Algorithm Revolution in Everyday Development
The most significant shift I’ve witnessed is how algorithms have moved from specialized applications to being embedded in nearly every aspect of software development. When I started, implementing even a basic machine learning algorithm required extensive knowledge and careful setup. Today, even junior developers can integrate sophisticated AI capabilities with just a few lines of code and an API call.
My team recently completed a project for a retail client where we implemented a recommendation engine that boosted their online sales by 32% in just three months. The algorithms powering this weren’t particularly revolutionary—collaborative filtering and association rule learning—but the way we could seamlessly deploy them into production was light-years ahead of what was possible when I started my career.
What fascinates me most is how algorithms have democratized certain aspects of programming. Take debugging, for instance. I used to spend hours tracing through code, setting breakpoints, and trying to identify issues. Now, we leverage algorithmic tools that can pinpoint bugs, suggest fixes, and even prevent errors before they happen. It’s like having a senior developer looking over your shoulder at all times, offering guidance.
Algorithms – The Double-Edged Sword of AI in Development
As an AI developer, I live in the interesting space between creator and user. I build AI systems, but I also increasingly rely on them for my own work. Last month, I used an AI code assistant to refactor a particularly gnarly section of legacy code. It completed in hours what would have taken me days, and the result was cleaner than what I might have produced myself.
This raises complex feelings. There’s the pride of crafting tools that genuinely make people’s lives better, mixed with the occasional unease about how these same tools might transform my own profession. I’ve seen junior colleagues learn at an accelerated pace by using AI assistants, but I’ve also witnessed the frustration when these tools generate seemingly perfect code that contains subtle flaws only detectable by someone with deeper understanding.
The AI hype cycle has been particularly intense in my field. I recall a meeting in early 2023 where our leadership announced we would be “AI-first” in everything we did. The promise was that algorithms would handle the tedious parts of coding, freeing us to focus on higher-level problems. The reality has been more nuanced. While algorithms have certainly streamlined many aspects of development, they’ve also introduced new complexities and dependencies that require specialized knowledge to navigate.
Ethical Algorithms: More Than Just a Buzzword
The ethical dimensions of my work have become increasingly prominent. Three years ago, I was assigned to a healthcare project developing an algorithm to assist radiologists in identifying potential anomalies in mammograms. The system showed promising results in testing, with accuracy rates that matched or exceeded human specialists in controlled environments.
However, when we moved to real-world testing, we discovered significant performance disparities across different demographic groups. The algorithm performed poorly on images from women with dense breast tissue, a characteristic more common in certain ethnic groups. This wasn’t malicious—it simply reflected biases in the training data, which contained fewer examples from these populations.
This experience fundamentally changed how I approach algorithm development. I now recognize that technical excellence alone isn’t enough; we need to consider the social contexts in which our work will be deployed. I’ve become an advocate for diverse training data and rigorous testing across different user populations.
The recent findings that AI may not be safe to implement as a solo reader for breast cancer screening exams confirmed what many of us in the field had already suspected: algorithms can augment human expertise, but replacing it entirely introduces unacceptable risks in critical domains. I’ve found that the most successful implementations are those that enhance rather than replace human judgment.
The Practical Reality of Algorithm Development
For anyone considering following my path into AI development, it’s worth understanding what the day-to-day reality looks like. Despite the exciting advances, much of my work still involves the fundamentals: cleaning data, tweaking parameters, debugging unexpected behaviors, and documenting processes.
A typical week might include:
- Monday: Reviewing performance metrics from our production algorithms, identifying any anomalies or areas for improvement
- Tuesday: Collaborating with data scientists to refine models based on new information or changing requirements
- Wednesday: Writing and testing code to implement new features or optimize existing ones
- Thursday: Participating in design reviews and planning sessions for upcoming projects
- Friday: Documenting work, sharing knowledge with the team, and exploring emerging technologies
The salary range for AI developers has grown substantially since I entered the field. Entry-level positions now commonly start around $90,000, while experienced developers with specialized knowledge can command $150,000 or more. However, the field remains highly competitive, with employers increasingly looking for candidates who combine technical expertise with domain knowledge in areas like healthcare, finance, or retail.
Learning to Learn: The Meta-Skill of Algorithm Development
Perhaps the most valuable skill I’ve developed isn’t any particular programming language or framework—it’s learning how to learn continuously. The algorithmic landscape changes so rapidly that what’s cutting-edge today may be obsolete in eighteen months.
When I started, TensorFlow was the dominant framework for machine learning. Today, my team uses a mix of PyTorch, JAX, and several specialized libraries that didn’t exist five years ago. The fundamentals of algorithm design remain constant, but the implementation details are in constant flux.
I’ve found that the most successful developers maintain a curious mindset and set aside time each week for exploration and learning. My personal practice includes dedicating Friday afternoons to experimenting with new tools or techniques, participating in online communities, and occasionally contributing to open-source projects.
Looking Forward: Algorithms in the Next Five Years
As I place this reflection into our digital time capsule, I wonder what algorithm development will look like five years from now. Based on current trends, I anticipate even greater integration between traditional software development and AI capabilities, with more sophisticated tools that can generate entire applications from high-level specifications.
I expect we’ll see increased focus on:
- Explainable AI: As algorithms penetrate more critical domains, the need to understand and explain their decisions will become paramount
- Edge computing: Moving sophisticated algorithms to run on local devices rather than in the cloud
- Multimodal learning: Systems that can seamlessly integrate and reason across text, images, audio, and other data types
- Personalized models: Algorithms that adapt to individual users’ needs and preferences rather than one-size-fits-all solutions
Whatever the future holds, I’m certain that the fundamental skills that have served me well so far—logical thinking, attention to detail, effective communication, and ethical awareness—will remain valuable. Algorithms may change how we code, but they won’t change the need for human creativity and judgment in solving complex problems.
To whoever reads this in the future: I hope you find these reflections interesting, perhaps quaint, or maybe surprisingly prescient. The tools and technologies we use may be dramatically different by the time you encounter this, but I suspect the core challenges and satisfactions of creating technology to improve human lives will remain much the same.
