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.
About This Episode
AI is great at pattern matching, but what happens when the most valuable insights don’t fit the pattern? John Kosturos sits down with Abhishek “AJ” Jha, Founder and 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.
Abhishek “AJ” Jha
LinkedInFounder & CEO, Elucidata
AJ founded Elucidata to solve one of the hardest problems in life sciences: making biomedical data actually usable for AI. Starting from a deep conviction that data quality, not model size, is the real bottleneck in drug discovery, he built Elucidata into a data-centric AI platform trusted by leading pharma and biotech companies. AJ brings a rare combination of scientific rigor, product instinct, and infrastructure depth to every conversation about where AI in regulated industries is actually heading.
Why Pattern-Matching AI Breaks Down in Drug Discovery
Traditional AI excels at recognizing patterns in historical data, but biology is full of novel, out-of-distribution problems where that approach fails. AJ explains why this gap matters and where it costs pharma companies the most.
▶ Watch nowOut-of-Distribution Problems and Why They’re So Valuable
What out-of-distribution problems actually are in a biological context, why they represent the highest-value frontier in AI-driven science, and how Elucidata approaches solving them differently.
▶ Watch nowData-Centric AI vs. Model-Centric AI
Why the industry’s obsession with larger models misses the root cause of poor AI performance in life sciences, and how a data-centric approach, focused on curation, quality, and governance, changes the outcome.
▶ Watch nowThe Role of Human-in-the-Loop AI in High-Stakes Industries
Why human expert oversight is not a bottleneck but a quality multiplier in AI systems where errors have real-world consequences, and how Elucidata designs its platform around that principle.
▶ Watch nowPreparing Multimodal Data for AI-Ready Use Cases
How Elucidata handles the full spectrum of biomedical data: text, tabular, and imaging, and why multimodal readiness is still an early-innings problem that most pharma organizations are underestimating.
▶ Watch nowStart with the Use Case, Not the Data
Why the instinct to “clean all the data first” is one of the most expensive mistakes in enterprise AI, and how working backwards from a specific downstream use case accelerates value and reduces waste.
▶ Watch nowAI Infrastructure, Cloud Costs, GPUs, and Egress
The infrastructure realities behind preparing and moving large biomedical datasets: egress fees, unexpected cloud costs, GPU access, and the storage architecture decisions that quietly shape what is possible in production.
▶ Watch nowDeploying AI Securely in Regulated Environments
How Elucidata navigates HIPAA, SOC 2, and GDPR compliance in live deployments, what the vendor landscape for regulated AI infrastructure looks like today, and what enterprises should require before signing contracts.
▶ Watch nowBuild Your Own Model or Fine-Tune an Existing One?
With foundation models advancing rapidly, AJ makes the case for when it still makes sense to fine-tune or build domain-specific models, and when it definitively does not, with specific guidance for pharma and biotech teams.
▶ Watch nowPharma, Biotech, and Life Sciences Leaders
If you’re evaluating AI for drug discovery, clinical data analysis, or research automation, AJ delivers one of the most honest assessments of where AI actually works in biology today and where it still falls short.
AI and Data Infrastructure Teams
From multimodal data pipelines to egress cost management and GPU-optimized storage architecture, this episode covers the infrastructure decisions that determine whether an AI system in a regulated industry can scale or stalls.
Founders Building AI Products in Regulated Industries
AJ shares the full arc of Elucidata’s positioning journey, from resisting the AI label to embracing it strategically in the post-LLM era. It’s a blueprint for how domain-specific AI companies can build durable moats around data, not just models.
Enterprise Data and Compliance Officers
Navigating HIPAA, SOC 2, or GDPR while trying to unlock AI at scale? AJ breaks down how Elucidata approaches secure deployment in regulated environments and what the maturing compliance vendor landscape now offers.
Anyone Questioning Whether Bigger Models Equal Better AI
This episode is a direct challenge to the assumption that scaling model size is the answer. AJ makes a compelling, evidence-backed case for why data quality and use-case specificity outperform raw model scale in almost every domain that actually matters.
About Elucidata
Elucidata is a data-centric AI platform purpose-built for the life sciences. It helps pharma and biotech organizations harmonize, curate, and govern complex multimodal biomedical data, making it AI-ready for drug discovery, translational research, and clinical applications. Rather than chasing bigger models, Elucidata focuses on the quality, structure, and provenance of the data those models depend on, treating data infrastructure as the true competitive moat in regulated AI.
