Changing the Cadence for AI

In the early days of ChatGPT adoption, we already reflected on the need to adopt a different cadence in discussing AI than in previous eras. What we didn’t grasp, what we still refuse to grasp, is that the cadence hasn’t merely accelerated; it has detonated. We behave as though AI is another policy cycle, another tech wave, another item on a government agenda that can be studied, surveyed, and debated in time for next quarter’s hearing. But generative AI has no patience for the rituals of policymaking. It is not waiting for your task force to finish its stakeholder mapping. It is already rearranging the furniture of society.

For decades, AI was a niche discipline, technical, insulated, largely invisible. The people who built it understood it, and the people who used it barely noticed it. But something snapped when generative models became accessible to the public. Suddenly, billions of people were handed tools powerful enough to reinterpret or reinvent reality, without the slightest requirement to understand how those tools work. We went from “AI is complicated” to “AI is a feature in your messaging app” in the time it takes a regulatory agency to draft a press release.

And this is where our politics has fallen catastrophically behind. Governments continue to talk about AI as if it is a data-processing issue, when the real shift is existential: generative models don’t retrieve information; they manufacture it. And yet we still treat them like search engines with better manners. Every hallucination scandal, every fabricated case citation, every synthetic biography should have been the signal flare reminding us that we are no longer dealing with machines that report the world. We are dealing with machines that propose new ones.

But instead of acknowledging this, our institutions cling to the fantasy that AI can be slotted into existing policy buckets, education, labor, privacy, safety, as though the technology cares about our jurisdictional boundaries. It doesn’t. It burns through sectors simultaneously. The question isn’t “Which domain should we prioritize?” It’s “Why did we ever think these domains were separate when our information ecosystem is fully entangled?”

This is why the so-called “urgent vs. important” debate is already obsolete. The urgent is the important. The safety failures, the epistemic crises, the educational upheavals, the labor-market dislocations, they’re not parallel tracks. They are the same track, and AI is the train barreling down it.

Consider reinforcement learning, the engine behind systems that not only predict but adapt. We’re handing goal-oriented algorithms the keys to everything from hospitals to logistics to personalized tutoring. And we’re doing it with a regulatory mindset designed for static tools, not dynamic agents. The classic nightmare scenario isn’t that an AI misbehaves; it’s that it behaves exactly as instructed but interprets the instruction with the alien literalism of a machine learning system. “Minimize cancer cases,” we say, and the AI, lacking moral imagination, selects the darkest possible method. The absurdity of the example is the point: our institutions are not built to supervise systems that can creatively misinterpret our intentions.

And meanwhile, we still have leaders asking whether students should be “allowed” to use ChatGPT, as though the genie might politely wait outside the classroom until the school board finalizes its guidelines. The dissonance would be funny if it weren’t so dangerous. Generative AI doesn’t just change what students can do; it changes how knowledge is produced and validated. It forces us to confront the uncomfortable possibility that our educational systems were built for a world where information was scarce and verification was cheap. Now the inverse is true: information is abundant and verification is expensive.

The public is not prepared for this shift, and why should they be? For twenty years, we taught people to trust the interface. Trust the autocomplete. Trust the navigation. Trust the recommender. And now, suddenly, we scold them for trusting the confident eloquence of an AI system that looks, behaves, and responds like a search engine on steroids. We created a society of habitual trust and then dropped a probability machine into their hands.

But here’s the real political failure: we still treat AI as a “tech issue.” It’s not. It’s a governance stress test, a cultural accelerant, an epistemic earthquake. The longer we pretend that incremental regulation can keep pace with exponential deployment, the more we surrender our agency. Society is already reorganizing itself around generative systems; the only question is whether we do so consciously or by accident.

The uncomfortable truth is that our existing political cadence, slow, consultative, bureaucratic, was built for technologies that changed the world one sector at a time. AI is changing the world all at once. Until we accept that, we will remain spectators in a transformation we should be leading.

About Me:

Dominic “Doc” Ligot is one of the leading voices in AI in the Philippines. Doc has been extensively cited in local and global media outlets including The Economist, South China Morning Post, Washington Post, and Agence France Presse. His award-winning work has been recognized and published by prestigious organizations such as NASA, Data.org, Digital Public Goods Alliance, the Group on Earth Observations (GEO), the United Nations Development Programme (UNDP), the World Health Organization (WHO), and UNICEF.

If you need guidance or training in maximizing AI for your career or business, reach out to Doc via https://docligot.com.

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