Nov 15, 2025
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AI History
Aakanksha Chowdhery: A lesser-known titan of the world of AI
Why You Haven’t Heard Her Name Yet
Chowdhery is not a household founder or brand face. According to her personal site, she was technical lead on the 540 billion-parameter model PaLM, and contributed to the infrastructure project Pathways at Google (Google Research) Her role: designing, training, scaling, stabilising large models and supporting the unseen plumbing that makes them work.
What She Built: The Infrastructure Behind The Buzz
1. Pathways: coordination at massive scale
Pathways is Google’s “single system” vision to train a unified model capable of thousands of tasks and modalities. A Silicon Valley Insider The paper behind PaLM describes how Pathways enabled a 6144-chip TPU v4 setup and achieved high hardware utilisation. Google Research
Chowdhery is listed among the authors for the Pathways system. Journal of Machine Learning Research
2. PaLM: from research paper to real capability
The PaLM model doesn’t just represent size. It set benchmarks across reasoning, code generation, multilingual tasks, and few-shot learning. Google Research
Chowdhery co-authored the PaLM paper and, per sources, handled infrastructure/training strategy for it. Journal of Machine Learning Research
3. Multimodal systems & beyond
Her profile lists work on PaLM-E (an embodied, multimodal model) and other cross-modality systems. Aakanksha Chowdhery That means bringing together text, image, code, etc — not just language models in isolation.
Why This Matters
Infrastructure matters: Most public AI discourse is about new algorithms or models. But without systems that can train, serve, cost-effectively scale, it remains research. She focused on the “boring but vital” work of engineering rather than just architecture.
Scale unlocks new capabilities: The PaLM paper shows that bigger isn’t just “more of the same” — performance on complex reasoning and code tasks jumped. arXiv
Representation matters: Engineering leadership in AI still skews towards a few names. Highlighting those who build the infrastructure widens the narrative.
Product vs demo gap: Many high-end demos don’t translate to real systems. She worked on making models that run not just look good.
Transparent research ethic: Google published detailed papers on Pathways/PaLM. This stands in contrast to some models where core specs weren’t disclosed. The difference isn’t trivial in responsible AI debates.
Key Takeaways for Professionals & Leaders
If you’re in AI or data leadership: ask not only what the model does, but how it’s trained, where it will run, how it scales.
For non-tech leaders: recognise that “data + model” is only part of the value. Infrastructure cost, serving latency, and reliability are equally strategic.
For anyone building or joining AI teams: there’s huge value in roles that focus on “make it real” (engineering, scaling, inference, tooling) rather than just “make it novel”.
Final Word
When you hear names like “GPT”, “Gemini”, “multimodal models”, remember there are people behind the scenes making them work at scale. Aakanksha Chowdhery’s work is a prime example of that. Not just in concept, but in systems that deliver. If you’d like, I can pull together a timeline of her major publications + talks, or analyse how Pathways/PaLM architecture informed later models.
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