A year ago, AI was exciting but unproven. I was experimenting, trying tools, figuring out what worked. Now, at the end of 2024, AI is embedded in every part of my development workflow. Not as a novelty, but as infrastructure. Here's my honest retrospective on what happened this year.
The biggest wins
AI for code review became my secret weapon. I started pasting every PR into Claude for review in February. By December, this habit has caught probably 50+ bugs that would have shipped to production. Not major catastrophes, but subtle issues. Off-by-one errors, missing null checks, race conditions in async code, SQL queries that would be slow at scale. The ROI on this single practice is enormous.
My testing quality improved dramatically. Using AI to brainstorm edge cases before writing tests changed how I think about testing. I catch more bugs during development now, which means fewer production incidents. My test suites are smaller but more effective because each test verifies something meaningful.
Architecture planning got faster and better. Having Claude as a sparring partner for design decisions means I explore more options before committing. Previously, I'd go with my first instinct and discover the flaws later. Now I stress-test designs in conversation before writing any code. This has saved me from at least three significant rewrites that would have been necessary later.
I shipped more, faster. This is the headline metric. I shipped roughly 40% more features this year compared to last year, with the same or fewer hours worked. The time saved on boilerplate, testing, and research went directly into building more things. AI didn't replace my work. It compressed the boring parts so I could spend more time on the interesting parts.
The biggest fails
I over-relied on AI for three months and hit burnout. I wrote about this in October. Trying to AI-everything is a trap. The cognitive overhead of managing multiple AI tools, providing context, and evaluating output is real and cumulative. The sustainable approach is targeted AI use, not maximum AI use.
I shipped an AI-generated bug to production. In April, an AI-generated database migration had a subtle error that corrupted timestamps for users in certain timezones. It looked correct in review. It passed tests (which were also AI-generated and didn't cover timezone edge cases). It took two days to identify and fix. This taught me that AI-generated code needs more scrutiny than hand-written code, not less, especially for data-touching operations.
I wasted time chasing tool hype. I spent probably 30 hours evaluating AI tools that I ended up not using. Every new launch, every "this changes everything" tweet, I'd install it and try it out. Most of the time, my existing tools were already good enough. I should have been building instead of evaluating.
I neglected fundamentals for a while. When AI can write code for you, it's tempting to stop understanding the code deeply. I caught myself blindly accepting AI suggestions for database queries without understanding the execution plan. I had to deliberately re-engage with the fundamentals: reading documentation, understanding internals, knowing why code works, not just that it works.
The state of AI at end of 2024
The landscape consolidated this year. At the start of 2024, there were dozens of AI coding tools competing. By December, the winners are clear: Claude for reasoning (I shared my first impressions of Claude when I switched), Cursor for coding, Perplexity for research. Everything else is either a niche tool or a worse version of one of these three.
Model quality improved but the gains were incremental. GPT-4 to GPT-4o was a speed improvement, not a capability leap. Claude 3 to 3.5 was similar: faster and cheaper, marginally smarter. The exception was o1, which showed that reasoning-focused models are a genuinely new capability. But o1 is too slow and expensive for daily use.
The hype cooled, and that's healthy. Earlier this year, every startup was "AI-powered." By November, people started asking "does the AI actually help, or is it just a chatbot wrapper?" I did a roundup of the best AI tools as of November 2024 to separate signal from noise. The market is maturing, and the tools that provide real value are separating from the ones that are just riding the trend.
What I'm excited about for 2025
AI agents that actually work. The current generation of AI tools is reactive: you ask, it answers. The next generation will be proactive: you describe a goal, it breaks it into steps and executes them. Early versions of this exist (Devin, various coding agents), but they're not reliable enough for production use yet. When they get there, the productivity implications are massive.
Reasoning models getting faster. o1 proved that "thinking before answering" produces better results. If the next generation can think at GPT-4 speeds, that's a step-change in capability across the board.
Local models closing the gap. Llama 3 was a big jump. If Meta keeps pushing, we might get local models that are 80-90% as good as the best API models by end of 2025. That changes the economics and privacy calculus completely.
Better integration, less tool-switching. The biggest friction in my AI workflow is switching between tools. I want one environment where I can think, code, test, research, and deploy, with AI seamlessly woven into each stage. Cursor is closest to this vision, but there's still a long way to go.
My advice for developers going into 2025
Learn to use AI effectively, but don't stop learning to code without it. The developers who will thrive are the ones who can use AI to move fast and still understand deeply what the AI is producing. Be the developer who uses AI as leverage, not the one who depends on it as a crutch. There's a meaningful difference, and it shows up the moment something goes wrong that the AI can't fix.
2024 was the year AI became a real tool for real work. 2025 will be the year we figure out exactly how much it can do and, equally important, how much it can't. I'm looking forward to finding out.