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Scientists connected three human mini-brains. Together, they learned something the individual organoids could not.
This is part of an emerging field called organoid intelligence: using lab-grown neural tissue as a form of biological computing. Instead of trying to make silicon behave more like a brain, OI asks a different question: what if we used actual living neural tissue to process information? In this experiment, connected brain organoids began to differentiate functionally through repeated stimulation. The network adapted.
That matters because biological brains are extraordinarily energy efficient, adaptive, and capable of learning from relatively little data compared with today’s AI systems. If OI develops, the future of computing may not be purely digital. We could see hybrid systems combining silicon, AI, and living neural tissue. That creates extraordinary possibilities in computing, drug discovery, and neuroscience, alongside equally extraordinary ethical questions.
The next generation of intelligence may not be artificial. It may be biological.
The leadership question
If biological systems become part of the computing stack, what happens to your assumptions about AI, hardware and the limits of machine intelligence?
The other scenario prompts:
Why Edge AI Changes IoT Architecture
Instead of treating the wearable as a sensor that simply sends data elsewhere, Edge AI allows some analysis to happen directly on the device. That sounds like a small technical change, but it affects how data moves, how quickly a wearable can respond, how much information it needs to transmit, and how its limited battery and computing resources are used.
https://www.iotforall.com/edge-ai-iot-architecture
Why Data Sovereignty Is Becoming an Enterprise Architecture Question
Data sovereignty now covers who can access, operate, and govern enterprise data, along with where that data is stored. AI extends the sovereignty boundary to prompts, retrieval, inference, and model services that can cross jurisdictions
https://erp.today/data-sovereignty-enterprise-architecture/
The Pain Axis: LLMs Represent Self-Directed Harm and Act on It
LLMs sometimes behave in ways resembling human emotional responses, and recent work identified internal representations that may underlie these behaviors. We ask whether LLMs represent pain distinctly from fear, sadness, and generic negative valence, and whether this representation functions as pain would be expected to.
https://arxiv.org/abs/2609.16247
When AI recommendations become engineering decisions
Successful organizations provide the context needed to make AI recommendations explainable, traceable, and trustworthy.
https://www.engineering.com/when-ai-recommendations-become-engineering-decisions/
What Does ‘Delete My Data’ Mean When AI Has Already Learned From It?
Data Is No Longer Just Stored, It’s Transformed. For decades, enterprise data governance has centered on systems of record. Information enters the organization, moves between systems, gets copied and eventually deleted according to policy and individual rights
Is Overreliance on AI Causing Agency Decay?
Agency decay is the gradual erosion of a person’s ability and willingness to observe carefully, think independently, choose deliberately, and act responsibly.
https://knowledge.wharton.upenn.edu/article/is-overreliance-on-ai-causing-agency-decay/
AI Adoption Is Everywhere, But Transformation Is Not
AI adoption is accelerating at a remarkable pace . But many organizations are mistaking AI activity for AI transformation.
The infrastructure gamble you can’t afford to get wrong
Why structural hardware scarcity is forcing enterprises to rethink how they plan for capacity
https://www.ciodive.com/spons/the-infrastructure-gamble-you-cant-afford-to-get-wrong/831848/
The end of ERP software as we know it: Why agentic AI ERP is the most practical evolution
Intelligence, automation and adaptability are layered on top of the existing ERP system
What Five Years of Cloud Data Reveal About What Comes Next
While technologies have evolved rapidly, organizations are facing a more fundamental challenge: gaining the visibility needed to understand where technology investments are going, who owns them and whether they’re delivering meaningful business value.
