The speed of AI progress makes my 2019 medical research look

A medical researcher reports that AI progress since 2019 has collapsed the time-to-solution for specialized diagnostic model development from two months of manual work to about 10 minutes of prompting and orchestration, citing the accessibility of high-end compute and smart routing between frontier and open-weight models as key drivers. The researcher argues that practical AI capabilities now blur the line with AGI, urging developers to adopt multi-model pipelines over building from scratch.

The speed of AI progress makes my 2019 medical research look 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. Mistral Shieldstral: 3B Open-Weights Multimodal Moderation 29d ago /en/news/4978/ Next That $200 a week for running a computer sounds too good to be → /en/news/8626/ a library of Claude prompt techniques https://tanyan888.com/ , with plenty of directly applicable cases. All Replies （4） @NeonPanda /en/users/NeonPanda/ I do the same thing Just make sure you double check the citations though, sometimes it gets a bit creative.