# Engineering the AI Slowdown: Technical Hurdles in the 'Plan A' Governance Roadmap > Moving AI safety from philosophical discourse to hard engineering requires solving massive verification and economic modeling challenges. **Published:** July 10, 2026 **Author:** PSEEDR Editorial **Category:** risk **Content tier:** free **Accessible for free:** true **Editorial format:** analysis **News quality eligible:** true **Source count:** 1 **Word count:** 1005 **Tags:** AI Governance, Compute Verification, Post-AGI Economics, Hardware Compliance, Defensive Accelerationism **Canonical URL:** https://pseedr.com/risk/engineering-the-ai-slowdown-technical-hurdles-in-the-plan-a-governance-roadmap --- The discourse surrounding artificial intelligence governance is shifting from abstract philosophical alignment to concrete engineering and economic modeling. In a recent outline of research gaps for the "AI 2040: Plan A" proposal, [LessWrong contributor Thomas Larsen](https://www.lesswrong.com/posts/B4qMZnB4a5YgKiLNo/plan-a-suggestions-for-further-work) details the massive technical hurdles required to implement a global AI slowdown. PSEEDR analyzes this transition, highlighting how verifying hardware compliance and modeling post-AGI industrial scaling are now the critical bottlenecks for global regulators managing frontier AI risks. ## The Shift to Concrete Scenario Modeling For years, AI safety proposals have operated at an abstract level, attempting to remain consistent across a wide variance of future timelines. The AI Futures Project argues that this lack of concrete, gamed-out scenario formatting makes it impossible to analyze the actual tradeoffs of global governance. Without specific operational parameters, hidden contradictions in policy frameworks remain undetected. The source text advocates for rigorous modeling of competing prescriptive scenarios, such as "Plan S" (an indefinite halt on frontier capabilities until specific alignment milestones are met), "GPU Arms Control" (international limits on hardware stock and flow), and a "CERN for AI" (a single, highly regulated international frontier project). By forcing these proposals into concrete timelines, researchers can identify the exact points where regulatory regimes might fail, such as the accumulation of "dry tinder"-excess compute capacity that is restricted from capabilities research but could be rapidly redirected if international agreements collapse. ## The Mathematics of Compute Reduction At the core of any governance strategy aimed at slowing an intelligence explosion is the relationship between computational resources and capabilities scaling. The AI Futures Project currently estimates that a 10x reduction in research and development compute slows the intelligence explosion by a factor of 6x. However, this estimate carries an 80% confidence interval ranging from 3.5 to 8. This wide variance represents a significant vulnerability for policy design. Global regulators require precise impact assessments to justify interventions that could stall trillions of dollars in economic activity. The uncertainty stems largely from unknown future rates of algorithmic and software progress. If software efficiencies outpace hardware restrictions, compute caps may fail to achieve the desired slowdown. Refining this mathematical relationship is essential for establishing defensible, operational thresholds for international compute governance. ## Engineering Hardware Verification and Compliance The transition from policy ambition to enforceable regulation relies entirely on verification engineering. The source text highlights the necessity of accelerating "AI Silver Bullet" technologies to ensure compliance without requiring invasive, politically unfeasible oversight. A primary technical hurdle is the development of "inference-only retrofit capacity." This involves architecting hardware or cryptographic solutions that physically or mathematically prevent chips from being used for large-scale training runs while preserving their utility for commercial inference. Furthermore, the framework calls for privacy-preserving AI auditing and advanced lie detection mechanisms to monitor covert projects. These are no longer theoretical concepts; they are hard engineering requirements. If a global consortium cannot cryptographically verify that a massive data center is restricted to inference, any cap-and-trade policy on compute buildout becomes unenforceable, leaving the door open for state-backed or private covert scaling. ## Post-AGI Economic Modeling and Industrial Scaling Standard economic models are fundamentally ill-equipped to handle the industrial dynamics of a post-AGI world. Mainstream economics projects a continuation of historical 3% annual GDP growth, a baseline that fails to account for the explosive productivity gains of superhuman AI systems. The "Plan A" framework necessitates entirely new economic models that calculate "robot doubling times" and project industry valuations under extreme regulatory constraints. The source text introduces the concept of a $100 trillion defense budget for the mid-2030s, categorized under "D/acc" (defensive accelerationism), designed to mitigate biological, cyber, and persuasive AI threats. Allocating capital at this scale requires a granular understanding of how an industrial explosion will alter global supply chains, resource extraction, and manufacturing. Without robust models for how capital flows during a managed intelligence explosion, regulators cannot accurately predict the economic pressures that will inevitably attempt to fracture the governance regime. ## Structural Unknowns and Policy Limitations Despite the push for concrete modeling, significant structural unknowns limit the immediate applicability of the "Plan A" framework. The technical architecture required for inference-only hardware retrofits remains undefined in the public domain, leaving a critical gap in the verification chain. Additionally, while compute governance for digital systems can rely on quantifiable metrics like FLOP/s for GPUs, the framework lacks a viable metric to operationalize cap-and-trade policies for physical robotics. You cannot easily quantify the "performance" of a general-purpose robot in a way that allows for strict international capping. Furthermore, the methodology and specific allocation of the proposed $100 trillion D/acc budget remain highly speculative. The political feasibility of securing such a budget, alongside the geopolitical friction of enforcing military arms control over AI superweapons, presents a massive barrier to implementation. The base rates for the decline of historical international agreements suggest that maintaining a coalition under the intense economic pressure of an intelligence explosion will be exceptionally difficult. The viability of global AI governance now hinges on the ability of researchers and engineers to solve highly specific technical and economic problems. Abstract alignment theory must be translated into cryptographically secure hardware locks, precise compute-to-capabilities ratios, and macroeconomic models capable of mapping an industrial explosion. Until these engineering and modeling bottlenecks are resolved, comprehensive regulatory frameworks will remain vulnerable to covert defection and economic circumvention. ### Key Takeaways * Existing AI governance proposals require concrete, gamed-out scenario modeling to expose hidden policy contradictions and economic tradeoffs. * Current estimates suggest a 10x reduction in R&D compute slows capabilities scaling by 6x, but the wide confidence interval (3.5 to 8) complicates precise regulatory targeting. * Enforcing global compute caps requires hard engineering solutions, specifically inference-only hardware retrofits and privacy-preserving auditing mechanisms. * Standard economic models fail to account for post-AGI growth; new frameworks are needed to model robot doubling times and manage a proposed $100 trillion defensive accelerationism (D/acc) budget. * A critical limitation in current industrial policy proposals is the lack of a quantifiable metric for physical robotics, making cap-and-trade enforcement highly problematic compared to GPU FLOP/s. --- ## Sources - https://www.lesswrong.com/posts/B4qMZnB4a5YgKiLNo/plan-a-suggestions-for-further-work