Technology should not be viewed as a silver bullet to eliminate the human workforce. Instead, the golden rule of technological advancement should always be to amplify human potential and elevate human dignity. #robotics#AI#futureofwork
The 2026 FIFA World Cup kicks off today and the SU community spans both sides of the opening match. Our partners @SingularityUMX and @SUSouthAfrica are repping their nations. Where are you watching from?
The energy grid is now a strategic constraint for every business leader.
Are you factoring this into your planning? Join the Discussion Series tomorrow and let's talk. Register: https://t.co/XmXijAB2ky
AI hyperscalers pay 8โ80x more for compute capacity than for the electricity to generate it.
The result: they're outbidding regular consumers for power. 30+ data centers can't break ground because there's no energy left to permit them.
Data centers are the hidden backbone of the digital economy & their proliferation is accelerating in ways that have serious implications for business, geopolitics, & democratic governance. Let's talk about it. Register the Discussion Series for live Q&A and recording: https://t.co/a7raeSTGW4
Data centers now consume 6 percent of all electricity in the United States. That number crossed a threshold identified by researchers as the point where serious political and community backlash begins. It has. ๐งต
AI literacy starts long before the model is deployed. It starts with the data, and with the leaders willing to ask uncomfortable questions about it. What question from this list would be hardest for your organization to answer honestly?
Do you know what biases live in the data your AI was trained on? Do you know who does? Do you know where to find out? Alix Rรผbsaam argues these are now core leadership questions, and most organizations aren't ready to answer them. ๐งต
Rรผbsaam's framework for leaders: "How would the data we're using influence the outcome?" "Who or what is included, and who or what isn't, and why?" "How does my own background influence how I interpret the outcome?"
Most people are treating AI like a database. It's not. LLMs are probabilistic, not deterministic. They predict. They don't retrieve. That distinction matters for every decision you make with AI.
This exercise forces teams to break out of mechanistic, cause-and-effect thinking, allowing them to spot potential disruptions and identify net-new category opportunities before they become mainstream.
Traditional business education trains leaders to analyze their competitive landscape within a very fixed, narrow industry vertical, which leaves them highly vulnerable to disruptive changes coming from unexpected places.
Enter The Futures Wheel: a strategic foresight tool used to systematically map out the non-obvious first-, second-, and third-order consequences of a new technology or trend.
This is what reactive AI safety looks like when it fails.
Guardrails bolted on after deployment, reviewed after the fact, by humans who can't keep up with the output.
@NellWatson has been saying this for a while: the future of AI safety isn't auditing what the AI did. It's embedding strict policies directly into the system as it works and investing in machine-to-machine verification to catch critical errors automatically before they become headlines.
It doesn't matter how your LLM performed. If you're the one that deployed it, you'll be on hook for its actions.
Introduction to AI: Here, Real, and Yours to Lead with @KellieNuttall
Agentic AI isn't waiting for your governance framework to catch up. It's already making decisions, acting autonomously, and leaving a trail your legal team may not be able to follow.