AI and Agentic: Promise, Peril, and Predictability

Some filmmakers have this uncanny ability to see what's coming before the rest of us do. Eagle Eye (2008) was one of those films. In it, a government hijacked by an AI platform experienced chaos across an entire system. Then there is J.A.R.V.I.S. from Iron Man, a simulated assistant that feels disturbingly real. Both were fiction. Neither feels fictional anymore.

We are in the era of AI and agentic AI now. And down the road, that likely gives way to Artificial General Intelligence, aka AGI. Demis Hassabis of Google DeepMind has suggested it could arrive as early as the early 2030s. If that really happens, are we actually prepared?

The Security Wake-Up Call

The Mythos incident gave everyone in the digital world a genuine goosebump moment about what lies ahead for security and safety. It provided insights into how hackers could exploit zero-day and unknown vulnerabilities inside live systems. Banks especially are rattled. They are asking one very pointed question: how closely can we simulate the future so that fixes get applied before a model ever goes public? This is not academic anymore.

The Peril Nobody is Talking About Enough

Most of the conversation around AI is about what value it brings. Very few people are talking about its peril, and that silence worries me.

Comparing this to Oppenheimer's invention of the atomic bomb is not being dramatic. It is a genuine parallel. A transformational technology, in the wrong hands or in the wrong countries, can shift the world from a relatively safe place to something far darker, and fast. The answer back then was the Nuclear Non-Proliferation Treaty; an international agreement built specifically to protect against misuse and ownership control. We need something like that for AI. Not guidelines. Not ethics committees inside individual companies. A real international alignment on this for maintaining 3Cs, control, contradictions, and clarity, in an AI-led economy for the future of humanity.

Pope Leo XIV, in his recent encyclical, put it plainly: "technology should not be considered, in itself, as a force antagonistic to humanity," but equally, "the pursuit of greater profits cannot justify choices that systematically sacrifice jobs." He also made clear that human beings must retain decision-making authority over any military use of AI. These are not small statements, where they come from matters. It tells you this conversation has reached the highest levels of moral leadership in the world.

If Davos can hold countries accountable for sustainability commitments and economic prosperity, why can't the AI world follow the same path of building a regulatory structure and worldwide governance committee?

Is the Return on Investment Actually Worth It for Humanity?

Investment in AI will only grow; that much is certain. But a bigger and quieter question sits behind all the hype: is there enough return on investment, not just commercially, but societally? Is AI genuinely benefiting humanity at scale at this moment? Very few companies are actually working on value beyond their own commercial interest. Google DeepMind stands out in that sense, but that thinking needs to spread, not stay concentrated.

The New Currencies: Time and Tokens

Tokenomics became a real term similar to Bitcoin, a revolutionary movement that changed how we think about money entirely. Tokens are the new goldmine, and some filmmaker will probably build an entire storyline around them as a future currency before this decade ends. In this world, nothing is off the table. The next question is how to build a frugal token architecture. What harnesses and guardrails are needed? What are model providers doing to optimize use of tokens but provide an equal capability in terms of the outputs?

Now think about this: if AI genuinely saves 20% of people's time across every profession, where does that time actually go? Back to individuals? To family, to life outside work? Or do organizations simply absorb it back into more output? Will some countries eventually shift to a 6–7 hour working day, or will they use those productivity gains to squeeze more from the same workforce? What will happen to labor laws? This means time itself becomes a new currency to trade. And tokens become a new investment vehicle. Does the world become a less frantic place, or just a more energy-hungry one?

What Happens to Education?

Someone recently told me people are earning PhDs using ChatGPT. Does that mean OpenAI becomes a university in the not-too-distant future?

Will there still be a craze for computer science and IT as we know it today? What will new degree programmers even look like? Why would a student bother memorizing history or geography when ChatGPT, Claude, or Gemini can find it, summarize it, verify it, and present it in seconds? What effort remains in learning if the answers are always a prompt away?

And what happens to book publishers, to authors? Does that industry contract, or does it find a new form? The flip side is that students could do far more meaningful research by engaging deeply with LLMs. But what is the real risk of exploitation? That question is very much alive. The future will be a place for reviewers and orchestrators rather than programmers. 

Will the classic use of "pair programming or Xtreme programming" evolve in a new way making humans and agents work together? The importance of computer science will not be reduced, but it will bring a new perspective where humans will learn how to work and coexist with AI. The students need to learn core concepts and assess practical examples to validate and verify what AI will be creating in the future, along with building advanced models. This will be part of the newer education system.

More Orchestrators Than Creators

We will see more reviewers and orchestrators than original creators in the future. Is that actually the right direction? If so, what does it mean for original thought, creativity, and the ownership of knowledge and wisdom?

And what about the people who have spent decades building genuine expertise, earning their knowledge through experience, through failure, through years of slow and hard work? How do they compete with a generation that accesses and manages knowledge through AI systems from day one? That tension is not going away.

The Question of the Moment: Models vs. Harness 

This is the conversation happening everywhere right now. Frontier models are growing and competing hard with each other. However, the harness, meaning the orchestration layer, the integrations, the compliance controls, is what becomes the real intellectual property of any serious organization. SWE-bench and other evaluation techniques will become standard tools for testing model effectiveness. And as world models, the systems trained to simulate real-world physics and causality, develop, how they actually get deployed and applied is a question nobody has fully answered yet.

Energy is the Problem That Sits Behind AI Ethics

Every country that wants to lead in AI has to solve an energy problem first. Alternative power sources, more efficient data centers, better cooling, newer chip and GPU architectures. All of these are not nice-to-haves. They are the foundation. Without solving energy, everything else stalls. The solution is a well-written problem to solve by scientists and industries. The new way of generating energy should be explored beyond conventional processes. New energy-efficient materials must be discovered, new grid and distribution systems must be built, ways to converse and save energies across the world must be agreed upon, if we want to build AI to enhance human potentials and provide benefits to a larger biological ecosystem.

Governance: The Harness the World Actually Needs

When world models start simulating reality, and AGI begins genuinely mimicking human intelligence, governance stops being a regulatory question and becomes an existential one. Human beings will need to be in the loop from the start. But do we actually know how far that loop extends? Do we know when to step in and take control back from AI, and how those guardrails will be monitored in real time?

Country laws will have to change. Major model providers will have to think beyond their own interests. And there must be reliable and tested recovery paths for when AI fails or does something it was never designed to do. This is where harness engineering becomes a critical part of the AI-led supply-chain system.

The Motif model, where agents make decisions and act like humans to post and moderate within social networks, gives a real and sobering example of just how powerful and autonomous these systems can become. And the Mythos incident was another sharp practical reminder of why an AI proliferation committee is urgently needed, one that requires consent before any major model is released, and that sets shared goals across providers beyond competitive self-interest, for the greater human good.

A Final Point

It is actually simple when you lay it out. We need intelligence, which means models need more predictability. More predictability means faster and more efficient tokens. Faster tokens demand more power. More power needs water. All of it needs governance. Governance needs compliance as its harness. And none of it matters without skill transformation. So, it's a circular loop on how AI-led economics needs to be thought through beyond just autonomy and intelligence.

The education system has to change. It must blend AI-powered teaching into curricula, revising college degrees, building demand for material science, chemistry, biology, and computer science alongside AI. Every country that wants to stay ahead needs to build its skills economy, grow R&D budgets, and establish its own data center capacity.

The future is not something that happens to us. It is something we build, or fail to.