This told essay is based on a conversation with Manoj Aggarwal, a principal software engineer at a major software company who previously worked at Microsoft, Twitter, and Stripe. He is in his 30s and lives in California. The following has been edited for length and clarity.
I’ve worked as a software engineer for 14 years at companies like Microsoft, Twitter, and Stripe. And we’ve seen how that work has changed.
In a way, I think it’s a fun time to be a software engineer. With so many tools available, you can build almost anything from scratch without your skills becoming a bottleneck.
It’s also a difficult time for many engineers. AI tools have made coding easier, but engineers, not AI, are ultimately responsible for the code that ships. Even though AI speeds up many parts of the code review process and makes it more targeted, the added responsibility of reviewing AI-generated code can lead to burnout.
There are also concerns about AI replacing engineers. If you needed 10 engineers to ship a project two years ago, you probably only need five today.
I think young engineers may be most affected. During job hunting last year, it seemed like there were more job openings for senior engineers than for junior engineers. I think companies will continue to rely on experienced engineers working with AI tools.
That’s one of the reasons I try to keep these tools up to date.
Engage with AI outside of work hours without sacrificing your work-life balance
My current job is in an engineering role at a large software company, and AI has completely changed my workflow over the past year. My coding time has basically been reduced to writing prompts for AI tools, allowing me to work at a pace that feels 10x faster in some cases.
You can learn a lot about AI through your work. Still, I think it’s important to spend a few hours a week experimenting with AI outside of work to stay up to date with the industry. That way, you’ll have a busy day full of office tasks and free up time to work on great projects that you might not be able to create during your work hours.
Taking the time to experiment with AI outside of work hasn’t had a big impact on my work-life balance. A big reason is that I have a toddler at home so I can’t even think about opening my laptop while she’s awake. When I work on personal projects or read about AI, it’s usually after she goes to bed or on the weekends, which is also when she’s asleep.
Spend less than $100 per month on AI tools and build your own chatbot
Outside of work, I often use Claude Code, Microsoft Copilot, and Lovable. I spend about $50-$60 a month on Claude Code, depending on the number of tokens I use and the subscription fee. I recently tried out Anthropic’s Fable model for a personal project, and it’s amazingly fast.
One of the side projects I built is an open source tax and financial assistant. This allows users to connect to AI models such as Gemini and ChatGPT and upload financial documents such as credit card statements, investment portfolios, and tax forms. The tool analyzes these documents and points out things like overspending in certain categories, expensive subscriptions, and whether your investment portfolio is too aggressive or too conservative.
I used this tool to prepare my 2025 taxes and it helped my CPA make sure I was doing everything correctly. I didn’t want to upload my personal financial information to the AI model over the internet, so I built it to run locally on my computer.
Read more about people at corporate crossroads
Engineers should learn knowledge in areas that AI does not have.
One of the biggest challenges for engineers is adapting to a job that can change rapidly due to changes in technology and other factors beyond their control. This was true even before the AI boom.
In recent years, generative AI has raised new questions about job security for engineers. It’s important to keep up with AI, but I also don’t think AI has the full picture of what systems should be and why they’re designed the way they are. I think that’s where engineers still add value. I recommend relying on your experience, both your engineering skills and the organizational knowledge you’ve built if you’ve been with the company for a while.
Depending on the project, the number of engineers needed may decrease in the future, but I think the responsibilities of the remaining engineers will become extremely important. AI also makes mistakes, so humans need to be involved.
My advice to other engineers is to not be afraid to keep up with AI. Regardless of how you feel about AI, there is no doubt that other engineers are more productive when compared to other engineers using AI. So, experiment with different AI tools and find what works best for you.
Do you have a story to share about learning AI or working in the tech industry? Email the reporter: jzikula@businessinsider.comor via Signal at jzikula.29.