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Silicon is no longer the only substrate for computing.
Researchers in Singapore have unveiled a biological data centre prototype that combines living human neurons with silicon hardware. The installation uses 20 biological computing units containing stem-cell-derived neurons that receive signals, respond, and adapt. The ambition is computing that can learn with far less data and potentially consume dramatically less energy than conventional AI infrastructure.
Convergence
This is still prototype technology. But that is not the interesting part. The interesting part is convergence. AI is colliding with biology, neuroscience, energy, and computing infrastructure. The distinction between hardware, software, and wetware is beginning to blur.
This convergence of biology and computing is one of the themes I explore in my latest book, Stop Predicting: 13 lessons from 1,400 attempts to see what was coming.
- https://interestingengineering.com/innovation/world-first-biological-data-center
The future fitness move
Stop scanning AI, biotech, energy and computing as separate industries. Start watching where they collide. The most consequential disruptions are increasingly going to emerge between categories, not neatly inside them.
THE OTHER PROMPTS:
Consistency is the new latency: AI at the data layer
There’s a quiet assumption baked into most AI architectures today regarding data layer consistency, and it’s costing companies more than they realize. The assumption is that the data your AI agent reads is the current state of reality. In a world of distributed systems, cross-region replication, and autonomous agents making millisecond decisions, this assumption breaks down.
https://aws.amazon.com/blogs/architecture/consistency-is-the-new-latency-ai-at-the-data-layer/
Anthropic set AI agents loose on the same task. They started a turf war.
“We consistently saw a multiagent turf war,” Anthropic researchers wrote. The models all assumed the others were “purposefully impeding their work” and started sabotaging each other with “increasingly aggressive, self-replicating malware.”
4 ways data management influences AI outcomes
Agentic AI deployment is only as effective as the foundational state of the enterprise data infrastructure underpinning it. Leaders must master the fundamentals, from data context to siloed sources.
https://www.nojitter.com/data-management/4-ways-data-management-influences-ai-outcomes
The Hidden Cost Of Keeping Data You No Longer Need
Minimizing the data you hold is no longer just a good suggestion but a requirement, as today’s privacy laws include provisions governing how long you can retain the data you collect.
1,000 AI Agents Started Agreeing Without Anyone Telling Them To
It suggests large groups of AI agents may be able to coordinate without central control – potentially forming collectives larger than informal human groups.
https://www.sciencealert.com/1000-ai-agents-started-agreeing-without-anyone-telling-them-to
Scaling Citizen Development
Empowered Employees Accelerate Innovation Across Organizations.
https://www.ssonetwork.com/intelligent-automation/columns/scaling-citizen-development
Why more data will not deliver AI data readiness
Volume-first data strategy served the BI era but falls short for agentic AI, where the constraint on readiness is context and governance rather than how much data exists.
https://www.techtarget.com/data-technologies/feature/Why-more-data-will-not-deliver-AI-data-readiness
Why AI can only ever be as trustworthy as the data behind it
CIOs shouldn’t just be asking whether AI works, but also if it can be trusted. Which is why enterprise content management is one of enterprise AI’s most important foundations.
https://www.business-reporter.co.uk/ai–automation/why-ai-can-only-ever-be-as-trustworthy-as-the-data-behind-it
