Don't copy India's AI playbook; tailor for AU and NZ

Claiming India’s “middle path” is a ready-made template for Canberra and Wellington misses the point. Policy transfer is about incentives and institutions — not metaphors that fit neatly in a headline.

Tech Policy Press is right on one thing: a hybrid approach to AI governance beats the false choice between unregulated chaos and lock-it-all-down precaution. Treating AI as either an economic engine or a public menace is lazy policymaking. A “middle path” that mixes experimentation with guardrails is, conceptually, the adult in the room.

The trouble starts when that concept gets turned into an export model.

Where the model actually travels

There are at least two practices from India that Australia and New Zealand can copy without pretending they share India’s structural conditions.

First, iterative rule-making. Try rules in sandboxes or narrow sectors, adjust based on empirical outcomes, then scale. That’s not exotic; it’s just disciplined version control for policy. The UK has done this in fintech; Singapore has done it in payments. Australia and New Zealand can do the same in AI, sector by sector, instead of arguing abstract principles in 200-page consultation papers.

Second, multi-stakeholder forums that are more than theater. Put government, industry, and civil society in the same room, with real timelines and real consequence for deadlock. You hash out norms — disclosure, audit expectations, red lines — then let regulators translate those into rules and guidance. That approach fits both countries’ political cultures.

Those are the parts worth borrowing.

Now to what the article flattens.

Scale isn’t a rounding error

The Tech Policy Press piece leans on India’s balance between rapid innovation and governance as if scale were incidental. It isn’t.

India’s vast user base and data pool let it run permissive experiments while firms spread risk across huge populations. That changes the payoff structure. You can be wrong on design and still have enough market depth to correct course without killing an entire sector.

Australia and New Zealand don’t get that luxury. When your domestic market is smaller, “move fast and fix later” means each policy misstep hits a higher share of citizens and firms. Let’s be real: loosening guardrails in a small market amplifies exposure per capita and concentrates political blowback. That makes risk more asymmetric, not less.

Institutions, not vibes

The article also underplays political structure and state capacity.

India’s federal system, with its center–state bargaining, allows uneven, iterative rule-making. One state pushes ahead; another drags; the center arbitrages. What looks like messy pragmatism from the outside is, internally, a way to test and negotiate policy across jurisdictions.

Australia’s federal–state mix works differently, with distinct regulators and sharper lines of responsibility. New Zealand’s unitary system moves faster, but with fewer institutional counterbalances. Policy tinkering that reads as flexibility in New Delhi can look like inconsistency or even regulatory drift in Canberra or Wellington.

From my Goldman days, we treated every “new strategy” as guilty until the backtesting cleared it. Governance experiments deserve the same skepticism. You don’t copy a structure built on one set of political bargains and expect it to run smoothly in a completely different regulatory culture.

Data, privacy, and who actually holds the chips

Then there’s geopolitics and infrastructure.

India’s AI and data choices sit inside a broader strategy: balancing major powers, nudging domestic champions, and bargaining with global platforms from a position of scale. Its “middle path” is wrapped in industrial policy and security calculus.

Australia and New Zealand are more import-dependent on critical cloud, chip, and AI infrastructure. They sit inside alliance structures that shape their room to maneuver on data flows, surveillance concerns, and vendor concentration. That changes what any “middle path” can achieve in practice, because a lot of the stack is literally offshore and owned by someone else.

The article also skims past privacy and data sovereignty. India’s compromises on data localisation and cross-border flows reflect its own political and social trade-offs. Australia and New Zealand have different privacy cultures and legal precedents; their citizens may tolerate far less ambiguity around data use. A “balanced” model in one context can look like a rights rollback in another.

Sectors are not interchangeable

Another blind spot: sectoral heterogeneity.

AI in consumer finance is not AI in healthcare, defence, or election-adjacent platforms. A relaxed, innovation-first stance that might be acceptable for recommendation engines can be catastrophic in clinical decision tools or systems moderating political speech.

A credible middle path needs sector-specific risk calibration: tighter ex-ante controls and audits where harms are irreversible, more breathing room where harms are reversible and contestable. The Tech Policy Press piece gestures in this direction but doesn’t do the hard sorting.

If you want a cautionary tale, look at how social media regulation was treated as a generic “innovation issue” in multiple democracies. The bill for that shortcut is still arriving.

The real mechanics, not just the mood

To be fair, defenders of the article could argue that Australia and New Zealand are sophisticated governance systems that would, of course, adapt the Indian approach rather than photocopy it. So, yes, you can absolutely borrow the spirit: iterative regulation, public–private pilots, targeted exemptions.

The risk is stopping at “spirit” and skipping mechanics.

That’s where you get regulatory whiplash: loosen rules to chase innovation, watch harms pile up, then slam on the brakes with rushed bans or overcorrections. The fix isn’t poetic references to a middle path. It’s unglamorous scaffolding: pilot programs with explicit exit conditions, sector-specific impact assessments, and funded, independent audit capacity with teeth.

The Tech Policy Press piece nudges in this direction, but it doesn’t fully map the operational steps — the budgeting, institutional design, and political trade-offs — that make or break any imported model.

India’s “middle path” is useful for Australia and New Zealand as a stress test, not a stencil. If policymakers treat it that way, the next wave of AI rules will say less about Delhi’s metaphors and more about Canberra’s and Wellington’s actual risk appetite.