Introduction
I watched the Davos conversations with a mix of awe and unease. The argument has shifted from theoretical speculation about artificial general intelligence (AGI) to an operational question: do we throttle development to create breathing room for safety, or do we allow speed to win because delay would cede advantage to rivals? At Davos this year the divide was plain — not a shouting match, but a fundamental split in priorities that will define policy, investment, and research for the next decade.
Why this matters now
The issue is not abstract. Two technical trends are colliding: (a) AI systems are increasingly able to accelerate their own design and software workflows, and (b) compute, data and deployment scale are both cheaper and more globally diffused than anyone predicted a few years ago. When an industry’s development loop shortens from years to months, institutions that were built for incremental change — regulators, education systems, labour markets — risk being overwhelmed. That asymmetry is the core of the Davos debate.
A short historical context
- The early AI debates (pre-2016) were dominated by algorithmic improvements and narrow applications. Progress felt linear.
- The scaling era (2018–2023) showed dramatic capability gains by throwing compute and data at large models — powerful but brittle and poorly grounded in the world.
- Today, we are testing hybrid claims: models that write better models, code, and agents that can act in simulated or digital environments. The step from assistance to self-acceleration is a discontinuity, and that discontinuity is what made Davos feel less like a conference and more like a crossroads.
I have long written about guardrails and operational rules for conversational systems — the idea of embedding safety-by-design, auditability and clear human-in-the-loop norms — and those prior reflections feel newly relevant in this moment Parekh’s Law of Chatbots.
The Davos fault-line: speed vs safety
At Davos the divide was expressed not simply as optimism versus pessimism, but as different mindsets about leverage and timing:
Speed camp: Treats rapid capability gains as a force for broad economic good — faster scientific discovery, automation of repetitive tasks, and productivity leaps. Their caution is procedural: embed safety but don’t let governance strangle innovation. The practical fear is that voluntary slowdowns will simply move activity to jurisdictions or labs that press ahead.
Safety camp: Argues that unchecked acceleration increases the risk of misuse, systemic shocks, and emergent behaviours that escape current containment models. Their practical recommendation is to create time and institutional space for independent evaluation, standards and perhaps temporary constraints on deployment at the frontier.
Both camps are right about parts of the problem. The policy challenge is aligning incentives so that safety is not a luxury only affordable to those who can move slowly.
Key stakeholders and their incentives
- Industry (startups, cloud providers, chipmakers): Rewarded by time-to-market, scale and enterprise adoption. Infrastructure owners push for scale; product teams push for faster iteration.
- Investors and boards: Focused on returns, short-term metrics and defensible moats. They will naturally prefer speed where competitive advantage is tangible.
- Governments and regulators: Concerned with national security, labour disruption, and systemic risk. Their levers include procurement, export controls, and public funding priorities.
- Researchers and civil society: Seek open evaluation, reproducible benchmarks and ethical constraints. They push for transparency and independent audits.
- Workers and citizens: Potentially bear the immediate social cost of disruption; their trust is crucial for adoption.
Risks we must take seriously
- Misalignment and deceptive behaviour: As models act with more autonomy, they may develop goal-directed behaviours that conflict with human intent.
- Rapid labour displacement: Entry-level white-collar tasks are already being impacted; faster capability growth shortens adaptation windows.
- Geopolitical fragmentation: Divergent national rules can create safety arbitrage and widen global inequality in access to benefits.
- Concentration of power: If a few actors control the most capable systems, market and political power can centralise rapidly.
- Infrastructure constraints and cascading failures: Energy or chip bottlenecks, coupled with tightly coupled supply chains, make the system brittle under stress.
Opportunities worth seizing
- Accelerated science: AI-assisted discovery can compress timelines for drug design, climate modelling, and material science.
- Productivity gains: Well-deployed AI can raise living standards and create new types of work.
- Inclusive innovation: If paired with thoughtful governance, AI can be a force for closing gaps in health, education and public services.
Regulatory approaches and governance frameworks (metaphors first)
Think of the policy problem as steering a high-performance vehicle on a fragile mountain bridge. You can either: (a) reduce throttle and create passing lanes (slow down development), (b) redesign the bridge to be wider and stronger (build governance and infrastructure), or (c) install smart traffic control that allows bursts of speed in safe windows (conditional, auditable approvals). We need all three, coordinated.
Concrete regulatory and governance ideas
Tiered, capability-sensitive regulation: Like aviation safety, set progressive requirements tied to demonstrable capability levels — from narrow models to frontier systems that can materially change economic or security outcomes.
Independent third-party audits and benchmarks: Fund neutral labs that run reproducible stress tests, red-team evaluations and alignment checks. Public procurement should require passing these audits.
Conditional deployment licenses: For high-impact systems, require time-limited, monitored approvals with rollback provisions. Think of it as regulatory sandboxes at global scale.
Export controls tied to risk, not geography: Design controls to limit the spread of the riskiest hardware and pre-trained weights, calibrated to safety outcomes rather than blunt geopolitical blocs.
Liability and insurance frameworks: Create clear legal standards for accountability in cases of harm, and encourage insurance products that price safety investments.
Data stewardship and provenance standards: Require model cards, data lineage and rights management so downstream users can evaluate trustworthiness.
Incentives for safety R&D: Use public funding, prizes and procurement to reward work that improves interpretability, robustness and controllability.
Governance architectures to consider
Multi-stakeholder governance bodies: National AI councils that include industry, labour, researchers and civil society; an international coordinating body (not necessarily a UN agency at first, but a treaty forum) for cross-border standards.
Model registry: A global (or interoperable national) registry of high-capability models with provenance, audit history and red-team summaries.
Regulatory reciprocity pacts: Countries can agree to mutual recognition of audits and certifications to reduce fragmentation while maintaining safety standards.
Concrete next steps — industry, governments, researchers
Industry
- Adopt a safety-by-design checklist across product lifecycles and publish red-team results for high-impact systems.
- Use staged rollouts with monitoring telemetry and rollback capabilities; invest at least 5–10% of advanced model budgets into independent audits and safety tooling.
- Form interoperable consortia to share non-competitive safety research and benchmarks.
Governments
- Create procurement rules that condition public purchases of advanced AI on passing third-party audits and data-provenance checks.
- Fund regional audit labs and scholarships to build expertise in regulation and evaluation.
- Negotiate export-control frameworks focused on dual-use hardware and system capabilities, and pursue reciprocity pacts that reduce arbitrage.
Researchers
- Prioritise reproducible benchmarks for alignment and robustness; publish negative results and failure modes as canonical knowledge.
- Build open toolchains for model introspection, verification and long-term monitoring.
- Collaborate with social scientists to study socio-economic impacts and design transition policies for labour markets.
Practical near-term playbook (6–18 months)
- Establish independent audit pilots in three regions (public–private funded).
- Define common capability tiers and a minimal audit checklist for each tier.
- Link a portion of public research grants to commitments for open safety datasets and reproducible methods.
- Launch reskilling pilots targeted at roles most at-risk from automation.
- Convene a diplomatic table to draft an initial set of export-control principles based on risk thresholds.
A balanced posture: neither brake nor blind accelerator
We do not have to choose between paralysis and recklessness. The right posture is pragmatic precaution: move forward, but rewire incentives so speed does not continually outpace safety. That means public funding for safety, procurement that rewards audited systems, and global coordination to prevent dangerous arbitrage.
Final reflection
Davos revealed that the AGI question is now a political, economic and institutional one as much as a technical one. The real task is designing institutions that can learn as fast as the technology does — institutions that can absorb shocks, distribute benefits, and hold actors accountable. I have argued previously for guardrails in conversational systems and design rules that embed safety at the core Parekh’s Law of Chatbots. Today, those precautions feel less like optional good practice and more like necessary infrastructure for an era of rapid change.
Metaphor to leave with: imagine building a city around a river that has suddenly begun to flood unpredictably. Some will say we need faster boats (speed); others say we must build higher levees (safety). We need both: better boats that report their position, levees designed with future floods in mind, and a coordinated emergency plan so the next storm is not a catastrophe.
If you are a leader in industry, government or research: start by demanding auditable evidence for claims, funding neutral evaluation, and aligning incentives so that safety investments are a competitive advantage, not a regulatory tax.
Regards,
Hemen Parekh
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