Outrage over AI is pointless if we’re clueless about AI models

Yet, too often, they stop short of confronting the hard questions. It is easier to talk about a single high-profile lapse than to ask why such failures recur and what that reveals about the design of this technology and challenge of governing it.

Take the case of xAI’s Grok chatbot, which recently hit the news for generating deeply offensive and antisemitic output. As expected, the firm issued an apology and pledged reforms. Such gestures have become a ritual across the AI industry.

The immediate response to the latest scandal was a chorus demanding fines, tougher deterrents and stricter oversight. All of these are understandable and even justified. Yet, they risk treating symptoms while leaving the underlying ailment untouched.

What is frequently missed is that artificial intelligence remains an evolving field. The models drawing alarm are built through rapid iterations, with techniques, safeguards and deployment strategies shifting almost as quickly as they appear.

Historically, regulation has always trailed innovation. From early aviation to financial derivatives and digital privacy, lawmakers have struggled to keep pace with the speed and complexity of any evolving technology. It is wishful to assume AI would be any different.

Recent debates over water-marking, alignment methods and open-source risks show that even within the field, consensus is elusive and best practices are in flux.

At the same time, arguing for careful regulation does not diminish the real risks of AI. The question is not whether regulation is needed, but how to design it such that it rests on a genuine technical understanding, keeps pace with fast-moving systems and avoids becoming a reactive set of penalties imposed only after harm has been done.

The uncomfortable truth is that the very power of generative models lies in their unpredictability. These systems do not fetch fixed answers, but create new responses from complex probabilistic patterns in their training data.

Harmful or shocking outputs are not mere accidents or lapses in corporate discipline. They stem from how these models work. Calling for harsh penalties without grappling with this design paradox offers the illusion of certainty in a space where certainty cannot be guaranteed.

That said, accountability cannot be set aside just because the technology is complex. The real test is whether it changes incentives rather than merely making headlines. Financial liability has its place.

Yet, it is often overlooks the fact that strict penalties could slow innovation and entrench the dominance of a few large firms that are able to bear compliance costs.

We have seen this before. Over decades, regulatory fines have barely dented the profits of Big Tech giants like Microsoft, Google and Meta. Their ability to hire expensive legal teams and absorb penalties has meant such ‘deterrents’ have done little to curb their market power, while consumer dependence on their products has only deepened.

Such measures may end up reducing competition and diversity without tackling the technology’s real risks.

Deeper still lies a question rarely asked amid calls for AI regulation. Does the competence to supervise models and enforce rules exist? Across countries, AI oversight remains nascent. In many places, including India, legal frameworks for AI are yet to take shape.

Policymakers speak confidently of alignment, water-marking and output explainability, but usually do so from a position that is reliant on borrowed expertise. Turning ambition into technically grounded and enforceable regulation is only just beginning.

Beyond regulators, courts too will need special training to handle the nuances of AI disputes. Without the requisite competence, regulation risks serving institutional pride more than user protection. This gap matters because AI oversight must keep up with digital systems whose capabilities and risks evolve quickly.

Without steady investment in institutional knowledge, regulation would become reactive and symbolic, driven more by outrage than informed judgement. Regulation, however, must look past appearances to truly serve the public good.

Beyond the instinct to punish AI firms, a more effective path lies in radical transparency. Today, the largest AI companies reveal little about the data that trains their models, the safeguards they apply or the trade-offs they make between capability and control.

Independent audits, systematic red-teaming and detailed reporting of failures could align private incentives with the public good far better than fines imposed after an event.

Like in financial markets, it is disclosure and scrutiny that discipline complex systems. This is also why India must remain open to supporting open-source AI development for greater robustness, rather than give in to industrial lobbies eager to lock in closed models. Regulation must aim to align the public interest with private innovation.

It is tempting to reach for the regulatory hammer. Yet, what we need first is the clarity of better lenses to see what these models truly are. Without that, we will stay caught in a cycle of outrage and apology, while the real questions remain unanswered.

The author is a corporate advisor and author of ‘Family and Dhanda’.