What BIO 2025 Told Us About the Future of AI in Life Sciences
One thing was clear at the 2025 BIO International Convention (BIO 2025): the AI conversation in biotech has matured. We’re no longer asking if AI will transform life sciences – or even when. The question now is: how fast is it happening, who’s actually ready, and what’s delivering real value?
Here are our five key takeaways from the AI Summit and surrounding sessions – insights that aren’t just about technical breakthroughs, but about the infrastructure, data strategies, and day-to-day workflows that will define the next era of biotech.
AI Is Starting to Reason Like Scientists
One of the clearest signals that AI is evolving came from Iya Khalil, Ph.D., Vice President and Head of Data, A.I. & Genome Sciences at Merck & Co:
“We’re at the precipice of understanding disease biologies in ways we could not before, [AI] is going to hopefully bring a host of drugs that are working in different ways, ways we hadn’t imagined and anticipated, ways that are better for patients. Instead of a blunt instrument, you can target the disease itself. And then as we fold in our genetic and genomic data, that will make it more precise for that patient.”
This marks a major shift from tools that process data to systems that help generate hypotheses.
For bioinformatics teams, this changes the mission. It’s not just about building pipelines, it’s about enabling discovery. Infrastructure must now support iterative reasoning, not just reproducible analysis.
Multi-Agent AI Will Reshape Bioinformatics
Fauna Bio’s launch of Fauna Brain introduced a bold new model: a multi-agent AI system trained across cross-species omics data to independently execute research workflows. Their announcement summarized it simply:
“Fauna Brain is a multi-agent AI system that autonomously executes complex research tasks traditionally requiring expert teams.”
This has deep implications for bioinformatics. These systems are not compatible with siloed dashboards or single-omics views. They demand cross-modal, real-time, and continuously evolving infrastructure. If your data systems aren’t interoperable, you’re already behind.
Digital Twins Are Becoming Infrastructure
Digital twins, virtual models of biological systems, used to be a futuristic concept. At BIO 2025, it became clear they’re now a practical tool in biotech R&D. Companies like NVIDIA and GSK showed how they’re using AI to simulate cellular behavior, drug responses, and disease progression – in silico – before running wet-lab experiments.
As Ashley Zehnder, CEO of Fauna Bio, put it:
“It allows us to bring the power of AI to bear on nature’s most resilient biology and do it at a scale and speed that wasn’t previously possible.”
But here’s the catch: running simulations is only half the battle. The real challenge is managing all that synthetic data – validating it, comparing it to real-world results, and integrating it into existing workflows. That demands a new level of data infrastructure.
This is where bioinformatics needs to level up – from generating reports after the fact to actively coordinating and verifying complex, hybrid datasets. Digital twins aren’t just another tool. They’re changing the way discovery happens, and your infrastructure needs to catch up.
The Threat to Scientists Isn’t AI — It’s Infrastructure
Across sessions, a common refrain emerged: the biggest risk to life science innovation isn’t AI replacing scientists. It’s bad infrastructure holding them back.
As one panelist bluntly summarized:
“You can’t unlock AI’s potential with duct-taped Excel sheets and a Dropbox folder full of FASTQ files.”
Organizations don’t fall behind because they lack vision. They fall behind because their data systems can’t scale with it.
AI Funding Is Accelerating — But Execution Still Wins
The VC presence at BIO 2025 made one thing clear: funding for AI in biotech is accelerating. Investment in GenAI and healthcare-specific models has outpaced broader tech sectors this year. But interest doesn’t guarantee impact.
As Luba Greenwood, CEO of Gallop Oncology, noted:
“We can use AI to find inefficiencies and leverage them as a biotech company, to get to places like clinical trials faster.”
Pharma buyers aren’t just looking for innovation – they want domain-specific models, clean infrastructure, and teams that can deliver. Hype won’t cut it. Execution will.
Final Thoughts
BIO 2025 showed that AI is no longer an experiment – it’s becoming the core infrastructure of biotech. The companies that win won’t necessarily build the biggest models. They’ll build the most usable ones. The most interpretable ones. And the ones embedded into workflows that actually help scientists move faster.
That’s the mission we’re on at Bridge Informatics. If you’re rethinking how to future-proof your pipelines, lab workflows, or bioinformatics infrastructure – let’s talk.
Click here to schedule a free introductory call with a member of our team.
