This is why frontier labs place AI inside their own control architecture. Bound the states the AI can reach. Reduce the variety you are required to absorb. AI in the loop only amplifies. On its own it becomes an arms race you will lose.

This is an old argument dressed up in AI clothing. They weren't designed for AI. They were designed for humans. And there’s the rub. You need to start using AI more. You don't want to be left behind. And if AI isn't working — don't blame the technology. The problem is with you. AI needs to become ready for use — and that’s on the AI provider.

When we use AI in its most common form, we come to realize something about it. This is not a backdrop to AI development. It is its organizing goal. AI adoption versus applied AI AI adoption is the use of the general intelligence as it is delivered. You can adopt AI and let your knowledge feed a general intelligence that serves mostly the AI provider , or you can apply AI and build an intelligence that is your own.

This is starting to look like how AI behaves inside our organizations. It over-constructs. Most AI deployments have no equivalent: no clear conditions under which a model is retired, rolled back AI without this isn't intelligence. It's uncontrolled mimicry wearing the face of intelligence.

When it comes to compliance, labelling everything as AI might be a bad idea. statistical analysis, models, and prediction, and this practice should continue without any confusion with AI However, AI does have unique characteristics that, if not understood, could pose significant risks to Nevertheless, labelling all of this as AI might unnecessarily create regulatory uncertainty and complexity The need for defining AI is indeed crucial, not only to separate the boundaries of where new risks not

About a year ago, I heard an AI expert suggest that we might need AI to control AI. The real question isn't "should AI control AI instead of humans?" This is where AI has to regulate AI because humans lack the requisite variety. So yes, we need AI to regulate AI where speed and scale matter. But I'm not skeptical anymore about needing AI to regulate AI.

The AI will sort it out. The end by any means. This sounds like progress. Most organizations deploying AI agents have not done this. Not all AI agents require the same governance. This applies equally to subordinates who use AI agents as tools. This is what happens when organizations compress strata with AI.

AI will need to be held accountable. We will need different categories to distinguish between each AI capability: AI Machines - AI systems AI Agents - AI Machines with agency but without moral capacity and limited culpability AI Ethical Agents - AI Agents with moral capacity and full culpability AI Machines can still have agency (self-referencing Perhaps, this is what is meant by autonomous AI.

Is the collateral damage from AI worth it, and who should decide? When it comes to AI, we appear to be “hell-bent“ towards developing Artificial General Intelligence ( AGI) so as to consume all available energy, conduct uncontrolled AI experiments in the wild at scale, If we did, we would keep AI in the lab until we studied it carefully. Time will tell if the decisions surrounding AI will prove to be reckless, foolish, or wise.

When we talk about AI adoption, most people mean efficiency. Making existing processes faster. With what has been borrowed against AI, efficiency does not come close to settling the account. The data centres behind AI run on staggering amounts of energy and water. Measured in what we were told AI would deliver. Cancer. Hunger. Not process cycle times. That is the only way AI ever delivers the goods.

We are now about to make the same mistake, at scale, with AI. The Same Mistake, Now Applied to AI New regulation for AI is arriving, and the predictable response is Most businesses will not build safety into their AI systems. That difference has always mattered, and with AI it becomes decisive. Every organization adopting AI now faces the same choice.

Forward Assurance for AI Systems Audit looks backward. It verifies that something was done. The gap AI is adopted when it can be trusted with the work that matters. professional standards — applying the methods, technical standards, and disciplines that govern how AI Three disciplines, working together → Engineering Methodology — how AI systems are engineered for the Who this is for Data, software, and IT solutions firms putting AI into client work.