‘Greedy’ is a class of algorithms, making a locally optimal choice at each step, hoping it leads to a globally optimal solution.
Intro: Failed AI dreams
Growing with passion in mathematics and computers, I had this question in early 2018 when starting my studies at university: what’s next? Well, I was going to become a software engineer. But I specifically wanted to go into a subfield that blends with mathematics. I believe that the closer a subject is to mathematics, the closer it is to life. Web Development, which was popular at the time, did not align with this view.
There was already a hype around AI/ML, so I learned that’s probably where I wanted to go, both engineering and mathematics combined. The problem was that at the time, there were close to 0 job opportunities, at least for starters. In late 2018, somewhat “luckily”, a company had a guest talk at the university. They said they were creating an AI division in Uzbekistan, headed by a Swiss AI expert, and would start accepting applications for an internship program. That felt like a perfect opportunity. I passed the interviews and was accepted to their “internship preparation” program, which lasted for 6 months. They just told us to study some AI/ML courses on Coursera. At the end, they conducted a final exam with questions on mathematics and ML foundations. I waited for 1 month and was told I scored high. I was among the 3 out of 10 who passed. That felt like a dream coming true, and I was waiting for the next steps…silence. Another month passed…
However, and maybe not unexpectedly, they sent an email, saying they no longer needed AI interns, as “there is not enough AI work to do” in the project. With that, I had spent my 9 months, and this email was the dead end of my dreams for a career in AI.
Pivot
That closed door was a good learning to open the right next door for my career. I had 2 paths that I had considered next:
Path 1: continue with AI, thinking long term with the hope that AI will take off one day, where I could be part of the ship, with the caveat of having 0 real opportunities at that moment.
Path 2: pivot to Web Development, hundreds of immediate opportunities, with peers (as students) already earning 2-3x more than country’s average salaries, with the caveat that this work seemed automatable in the quite near future and eventually a saturated market.
I applied what we call in Computer Science a greedy algorithm, optimizing for the immediate best option at that moment: Web Development. Fortunately, a close friend from university, who had already been successful as a Web Developer, referred me for my first Web Development job at the company he was working at. From there, things started moving fast.
Looking back, that locally optimal choice opened doors and a career path I couldn’t have imagined, that eventually brought me to Amazon as a Software Engineer. You can read more about my path to Amazon here.
But did the greedy algorithm actually work? Or did I just get lucky?
Greedy algorithm worked
Coming back to the question: “Did the greedy algorithm actually work? Or did I just get lucky?” Answer: it wasn’t luck; the greedy algorithm worked, but in ways I never expected.
If you still remember, I was rejected from an AI internship in 2018, because “there was no AI work to do”. Fast forward to 2026, I’m at AWS, and we are being told “if you are not using AI for your job, you literally don’t belong here”. If we are not using AI, we are under-performing. Not only that, over the last year me and my team built an agentic AI pipeline that collects data, generates artifacts, and evaluates their quality, completely automating a big piece of manual work. Basically, we built a team of agents working for us overnight, and lets us focus on novel problems.
The path I walked away from a few years ago suddenly became my daily work. I couldn’t have gotten here by waiting for AI in 2018. In 2018, I optimized for what was visible: job opportunities, immediate growth, tangible skills. Web Development taught me to deliver business value. It taught me how systems work at scale. It brought me to Amazon. And Amazon put me right in the center of AI opportunities at scale. The 2018 version of me couldn’t have planned this path. He could only take the best step available at that moment.
If you’re facing a “passion vs. pragmatic” decision, just know that you are not abandoning your goals by applying a greedy algorithm, and it can eventually lead to the global optimum.
The algorithm worked once. Maybe it’s time to run it again: switch to AI research?
This was originally a series of posts in my Telegram channel.