The speed of AI progress makes my 2019 medical research look

If I tried to explain today’s frontier models to my 2019 self—back when I was grinding through an MD and building a custom ML algorithm just to detect pneumonia on chest X-rays—I would have been laughed out of the room. Back then, what we are seeing now wasn't just a "future goal"; it was a complete, unadulterated pipe dream. Trying to describe the current state of LLMs to someone in the early 2000s would be about as effective as trying to explain a smartphone to a guy tinkering with a vacuum tube computer.

The sheer delta in efficiency is what actually keeps me up at night. In 2019, I spent two full months of intense, manual development work just to get a specialized diagnostic model functioning. Today? I could probably knock out the same logic in about 10 minutes of prompting and orchestration. We aren't just talking about incremental improvements; we are talking about a complete collapse of the time-to-solution metric.

The death of the "manual grind"

The most jarring part of this evolution isn't just the intelligence of the models, but the sheer accessibility of high-end compute and the rise of smart routing. I’ve moved away from the old way of trying to build everything from scratch. Instead, my current AI workflow involves a "smart routing" strategy.

I don't just throw everything at the most expensive model in sight (though sometimes it's worth it). I dynamically switch between high-tier frontier models for the heavy reasoning and open-weight models for the more routine tasks. This kind of orchestration is what makes a modern LLM agent actually viable for real-world deployment without burning a hole through your bank account.

For anyone looking to scale their own deployment without going broke, I've been using a setup that lets me route between these different tiers. It’s a massive difference when you realize that a couple hundred bucks now buys you capabilities that used to require a dedicated research lab and a team of PhDs.

Why this feels like AGI (even if it technically isn't)

There is a massive debate about whether we have reached AGI, but from a practical, "get stuff done" perspective, the line is blurring. When a model can take a task that used to define a semester of research and execute it during a coffee break, the technical definitions start to feel a bit pedantic.

We have moved from:

• 2019: Spending months on hyperparameter tuning and data cleaning for a single niche task.
• 2024: Using a multi-model pipeline to automate complex reasoning, coding, and analysis in minutes.

If you are still trying to build everything "from scratch" using old-school ML paradigms, you are essentially trying to win a Formula 1 race on a bicycle. The era of the lone researcher spending months on a single algorithm is being replaced by the era of the architect who knows how to orchestrate existing intelligence. It’s a terrifyingly fast transition, but man, it’s a hell of a time to be building.