We should be precise about what we mean by AI. Most systems currently described as intelligent or autonomous are large-scale probabilistic models connected to conventional software, data stores and APIs.
The same way that memory is prescribed as human-like is just a vector database.
It can generate useful outputs, select from available tools and execute bounded workflows, but they do not possess consciousness, intrinsic goals or independent moral agency.
Current AI systems are non-conscious statistical and computational systems that can become operationally powerful when embedded in software, organisations and authority structures.
Their risks come primarily from human deployment decisions, economic incentives, system design, misuse and over-trust not from demonstrated independent intention.
That does not make them harmless.
The real risks arise when humans give unreliable systems excessive authority, deploy them without adequate testing, or encourage people to trust fluent outputs as if they were judgments from a sentient expert.
“AI will destroy us” is too vague to be useful. “AI will take everyone’s jobs” is equally incomplete.
The more immediate questions are, which tasks will be automated, who controls the systems, what permissions have been granted, how are failures detected, and who remains accountable?
Remember: Board concerns dismissed in favor of speed and market dominance.
And most importantly, what can we as citizens do to collaborate, since in the end it will be us most impacted by what AI technologies can do for us or not.
Companies should also be accountable, and legislated, about what they have built. Integrating a foundation model into a product can create substantial engineering value, but it is not the same as inventing the underlying intelligence.
Remember: We need federal regulations rather than trusting industry self-policing.
Marketing that blurs this distinction encourages fear, dependency and misplaced trust.
We need less anthropomorphism, less passive-aggressive futurism and more engineering clarity: defined capabilities, bounded authority, measurable reliability, transparent limitations and accountable human ownership.
Remember: History does not offer examples where concentrating enormous power in a few hands, without robust institutional constraints, produced sustainably beneficial outcomes for societies at large. The Roman emperors, absolute monarchs, 20th-century dictators, and modern autocrats all followed the same trajectory, initial consolidation, erosion of accountability, corruption, suppression, and eventual collapse or catastrophic harm.
About the Author
Diamantino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.
Good reframe. The question isn't whether the model is intelligent — it's what we let it decide.
Most of the failures I see aren't model failures. Someone handed a system decision rights nobody wrote down, and nobody owns. That's a design problem, not an AI problem.
Bounded permissions. Clear failure detection. A named human on anything that matters. Not exciting, but that's where it's actually won or lost.
Timely article as many AI fears are being tossed about. I totally agree this technology requires governance and legal guidelines put in place by governments.