Yes, but only if regulation becomes adaptive, risk‑based, and globally coordinated —and even then, it will always lag behind in some areas.

Why AI outpaces today’s regulation

  • Speed mismatch : AI models and use‑cases evolve on timescales of months or weeks, while laws and treaties often take years to draft, negotiate, and implement.
  • Global, fragmented landscape : AI is developed and deployed across borders, but regulation is still largely national or regional (EU AI Act, U.S. executive orders, sector‑specific rules), creating gaps and loopholes.
  • Technical complexity : Many lawmakers and regulators lack deep technical expertise, making it hard to write precise, future‑proof rules without accidentally stifling innovation or missing emerging risks.

What regulators are trying now

Several jurisdictions are experimenting with approaches meant to “keep pace”:

  • Risk‑tiered frameworks (e.g., EU AI Act):
    • High‑risk systems (health, transport, critical infrastructure) face strict obligations (transparency, safety, human oversight).
    • Lower‑risk or general‑purpose models get lighter rules, mainly around transparency and copyright.
  • Adaptive and iterative regulation :
    • Some proposals call for “living” rules that can be updated via technical standards or delegated acts, rather than waiting for full legislative cycles.
* Sector‑specific rules (finance, healthcare, defense) are emerging faster than comprehensive AI‑wide statutes.
  • Soft law and self‑regulation :
    • Industry codes of conduct, voluntary audits, and safety benchmarks (e.g., model‑card disclosures, red‑teaming) act as interim guardrails while formal laws catch up.

Arguments that regulation can keep up

Proponents of optimistic views stress:

  • Public and corporate demand : Polls show strong public support for AI regulation, and many big tech firms say they “welcome clear rules,” which can speed up political action.
  • Learning from past tech : Regulators can borrow lessons from internet, data‑privacy (GDPR‑style), and financial‑tech frameworks to design more agile AI‑governance structures.
  • Hybrid models : Emerging‑country approaches that mix international standards with local experimentation can create flexible, context‑sensitive rules that evolve alongside deployment.

Arguments that it can’t keep up

Critics and skeptics point out:

  • Inherent time lag : By the time a law passes, the dominant AI paradigm may have already shifted (e.g., from narrow‑task models to general‑purpose agents).
  • Global arbitrage : Companies can move development or deployment to jurisdictions with lighter rules, undermining stricter regimes.
  • Unforeseeable risks : As AI systems become more autonomous and capable, some risks (manipulation, emergent behavior, misuse in warfare) may be hard to anticipate and codify in advance.

A practical middle‑ground view

Most analysts now argue that regulation will never fully “catch up” in a static sense , but it can still be effective if:

  • It focuses on outcomes and risks (safety, fairness, transparency) rather than fixed technical specs.
  • It builds multi‑stakeholder governance (governments, industry, academia, civil society) with fast‑track review mechanisms.
  • It accepts that some domains will remain under‑regulated or over‑regulated for periods , and adjusts iteratively as evidence of harm or benefit accumulates.

In short: AI regulation can stay “close enough” to the frontier to manage major harms, but it will always be playing a kind of continuous catch‑up game rather than running side‑by‑side with the technology.

Information gathered from public forums, policy analyses, and academic sources available on the internet and portrayed here.