You can’t predict the future. You can build the fitness to respond to it. #MindCandy is fuel for strategic conversation about what’s next. Forward this to your leadership team and DM me for a conversation.
New Research Tests Whether AI Models Know Their Own Thoughts
By injecting “thoughts” directly into large language models, researchers are trying to map out AI’s capacity for introspection. If this becomes robuts, expect five things:
- Explainability: AI explains its own decisions instead of being reverse-engineered.
- Unreliable self-report: Like humans, it may confabulate or conceal.
- Self-correction: It notices its own uncertainty and errors.
- Consciousness debate: Not proof of experience, but harder to dismiss.
- New relationship: From tool to questionable colleague, raising audit questions.
The catch: an AI that can see its own thinking may also become better at deceiving
The leadership question
Great leaders know when they don’t know. Now machines are learning the same thing. How introspective is your organisation? When did your board last examine how it decides, not just what?
The other prompts:
The business analytics paradox: why you struggle even with plenty of data
Governed, AI-assisted self-service turns analytics from a report queue into a decision-making capability.
https://www.databricks.com/blog/business-analytics-paradox-why-you-struggle-even-plenty-data
The open compute revolution: Why the C-suite is redefining the modern data center
Transitioning from proprietary hardware to open compute infrastructure enables enterprise leaders to eliminate vendor lock-in, lower total cost of ownership, and achieve ambitious sustainability targets.
New Research Finds AI Is Moving Faster Than Organizations Can Adapt
Across finance, HR, and supply chain, AI adoption is moving faster than the workforce, processes, and capabilities needed to support it.
Future Cloud Infrastructure: the new cloud infrastructure as a driver of resilience and competitive advantage
Private cloud has returned to the strategic agenda, driven by three forces: Sovereignty and regulation, cost and predictability: predictable, high-volume workloads are more cost-efficient on private infrastructure than in public cloud and on-premise AI.
A framework for determining when AI can make decisions
Modern enterprises face the daunting challenge of determining what tasks artificial intelligence can safely perform without human oversight.
https://mitsloan.mit.edu/ideas-made-to-matter/a-framework-determining-when-ai-can-make-decisions
Why semantic layers will be essential for enterprise AI
Access is solved; understanding is not. Why semantic layers and shared business meaning are essential for reliable enterprise AI agents.
https://thenewstack.io/semantic-layers-enterprise-ai/
The model that built IT has met its limits
The legacy model that built modern IT is outdated. What once simplified work now adds friction. A different model is taking shape, built on consolidation, density and efficiency instead of endless expansion.
https://www.ibm.com/think/insights/model-that-built-it-has-met-its-limits
