Yash Jain's journey inside a frontier AI lab in SF

Before ChatGPT became a household name, few outside the AI industry imagined what happened inside the research labs building the world’s most advanced models. The public sees benchmark scores, product launches and viral demonstrations. Behind those announcements, however, researchers spend months trying to answer deceptively simple questions: Why did a model fail? What capability is it missing? How do you teach it to reason better than it did yesterday?
That world is familiar to 27-year-old Yash Jain . An IIT Bombay graduate who later pursued a Master’s degree in computer science at Georgia Tech, Yash now works at San Francisco-based Essential AI , a frontier AI lab founded by Ashish Vaswani, one of the co-authors of the landmark 2017 paper ₹Attention is all you need’, which introduced the transformer architecture that went on to produce ChatGPT, Gemini and Claude. At Essential AI, Yash has contributed to Rnj-1, the lab’s open-source reasoning model, with his work focusing on coding and agentic capabilities.
Yash offers a researcher’s view of how frontier AI models are built, tested and improved. “The first thing we do is identify where our current model is lacking,” Yash says. “Then we try to pinpoint what fundamental ability it is missing which caused this shortcoming. We read research papers, understand what opensource models are doing, run small experiments and keep accumulating solutions. Once we have enough of them ready, we train the next model.”



It is an iterative process that resembles scientific research more than conventional software development. Researchers debate learning rates, training data, model architectures and reasoning abilities, searching for incremental improvements that could eventually translate into significant gains.
One of the breakthroughs Yash worked on was teaching the model to mentally execute code before it had finished writing it. Instead of relying on an external compiler, the model learns to predict how its own code will behave.
“The first time we saw it working was pretty spectacular,” he recalls. “The model was able to produce the output even before it had finished writing the code itself. It was thinking far ahead.”
That ability forms part of what researchers increasingly describe as the next stage of AI development: moving beyond simply generating text towards models that can reason, use tools and complete multi-step tasks more autonomously.
Despite the industry’s fascination with ever-larger models containing hundreds of billions, or even trillions, of parameters, Yash believes the next commercial battleground may lie elsewhere.
“I think the future is having an ensemble of smaller models, while a large model navigates and orchestrates them,” he says. “An 8-billion parameter model that’s finetuned for your repository can often write better code for that repository than a much larger model trained on everything.”
The attraction is economics. Running frontier models remains expensive, particularly for enterprises deploying AI at scale.
“The token costs are rising exponentially,” Yash says. “People cannot sustain them in the long run.”
Smaller specialised models running locally could dramatically reduce inference costs while also keeping sensitive corporate data inside an organisation’s own infrastructure.