AI is great at pattern matching — but what happens when the most valuable insights don’t fit the pattern?
In this episode of DataStorage.com, we sit down with Abhishek "AJ" Jha "(AJ), Founder & CEO of Elucidata, to break down why traditional AI approaches fail in drug discovery — and how data-centric AI is reshaping the future of pharma, healthcare, and beyond.
AJ shares Elucidata’s journey from resisting the “AI” label to fully embracing it in the post-LLM era — not by building bigger models, but by focusing on data quality, governance, and out-of-distribution problems that actually matter in regulated industries.
🔍 In this conversation, we cover:
• Why pattern-matching AI breaks down in drug discovery and healthcare
• What “out-of-distribution” problems are — and why they’re so valuable
• How data-centric AI differs from model-centric AI
• The role of human-in-the-loop AI in high-stakes industries
• Preparing multimodal data (text, tabular, imaging) for AI-ready use cases
• Why starting with the use case beats “cleaning all the data”
• AI infrastructure decisions, cloud costs, GPUs, and egress challenges
• Deploying AI securely in regulated environments (HIPAA, SOC 2, GDPR)
• Whether companies should build their own models or fine-tune existing ones
This episode is a must-watch for:
• Pharma & biotech leaders
• AI and data infrastructure teams
• Founders building AI products in regulated industries
• Anyone questioning whether bigger models = better AI
⸻
🎧 Subscribe to DataStorage.com for deep conversations on AI, cloud infrastructure, data storage, and the real systems powering the AI economy.
🔗 Learn more about Elucidata: https://elucidata.io
🔗 More episodes: https://datastorage.com
What is The Datastorage.com Podcast: Building for Tomorrow's Cloud Infrastructure?
The Data Storage Podcast explores the infrastructure powering AI, cloud computing, and the next generation of data-driven companies.
Each episode features deep conversations with founders, engineers, investors, and infrastructure leaders building the future of AI workloads, multi-cloud architecture, synthetic data, storage economics, and distributed systems.
We go beyond surface-level cloud talk to examine:
• AI infrastructure strategy
• Multi-cloud architecture decisions
• Cloud cost optimization
• Data storage economics
• Synthetic data & robotics
• Emerging “neocloud” providers
• Enterprise storage modernization
If you’re a technical founder, infrastructure leader, system architect, or investor tracking the evolution of cloud and AI, this podcast is built for you.
Produced by Datastorage.com.