The Pivot to Anticipatory Control: The EU AI Act, General Purpose AI, and the Architecture of Systemic Oversight

Executive Summary

The global governance of Artificial Intelligence (AI) has transitioned from a period of theoretical debate to one of intense operationalization and contestation, characterized by a fundamental tension between the rigidity of legislative “hard law” and the fluid demands of technological acceleration. As humanity navigates what I term the “Cognitive Revolution”, a transformation in which intelligence itself becomes the primary engine of economic value, regulatory frameworks are being forced to adapt at unprecedented speed. This report provides an exhaustive analysis of the European Union’s evolving AI governance regime as of late 2025, specifically focusing on the regulation of General Purpose AI (GPAI), the classification of systemic risks, and the institutional empowerment of the newly formed European AI Office.

This analysis synthesizes the theoretical imperatives outlined in my book, The Cognitive Revolution: Navigating the Algorithmic Age of Artificial Intelligence, with the concrete legislative reality of the EU AI Act’s implementation and its significant amendments proposed in November 2025. We examine the transition from the Act’s initial adoption to the complex operationalization of the “Digital Simplification Package,” a pivot that reflects the “Collingridge Dilemma” in real-time: the struggle to control a technology before its impacts are entrenched, without stifling the innovation necessary for economic survival.

The report argues that while the EU initially pursued a static, rules-based approach, the reality of “exponential fog”, the inability to predict AI trajectories, has forced a shift toward what I define as “Anticipatory Governance.” This shift is manifest in the centralization of oversight within the AI Office, the reliance on a dynamic Scientific Panel for foresight, and the controversial delay of high-risk obligations to align with technical standards. We explore the specific mechanisms of systemic risk classification (centered on the FLOPs threshold), the tiered obligations for GPAI models, and the geopolitical pressures—from the “Brussels Effect” to transatlantic friction—that are reshaping the European digital rulebook.

Part I: The Theoretical Imperative – The Governance Gap and the Demand for Anticipatory Steering

To understand the specific legal mechanisms of the EU AI Act, one must first contextualize them within the broader systemic challenge of governing exponential technology. The current era is not merely an extension of the digital age but a fundamental rupture, a “higher-order disruption” where the automation of cognition creates a recursive loop of self-accelerating progress.

1.1 The Pacing Problem and the Governance Gap

The central challenge facing regulators today is the “Governance Gap,” a widening chasm between the exponential pace of AI development and the linear, reactive cadence of traditional legislative institutions. As noted in The Cognitive Revolution, technology operates on a curve of accelerating returns, where “AI makes stronger AI,” effectively compressing decades of innovation into months. In contrast, democratic governance relies on balancing feedback loops—deliberation, consensus, and precedent—which inherently introduce time delays to ensure stability.

This mismatch creates a “pacing problem” where regulations are often obsolete by the time they are enacted. A static law written in 2021 to regulate “chatbots” may be woefully inadequate to govern the agentic, multi-modal systems of 2025. This dynamic exacerbates the “Collingridge Dilemma”: in the early stages of AI development, control is difficult because impacts are unforeseen; by the time impacts are clear (e.g., systemic bias, epistemic collapse), the technology is so entrenched that control becomes prohibitively expensive.

1.2 The Four Lenses of Navigation

To bridge this gap, a new cognitive toolkit is required, moving beyond simple technical fixes to a holistic governance strategy. I propose four interconnected lenses that are essential for analyzing the EU’s approach:

1. Systems Thinking: Governance must move beyond analyzing isolated AI models to comprehending the “AI Ecosystem” as a complex adaptive system. This involves mapping the interconnections between data flows, compute infrastructure, and economic incentives (the “Inference Economy”). A systems view reveals how small biases in training data can amplify into systemic discrimination through reinforcing feedback loops.

2. Emotional Intelligence (EQ): Often overlooked in technocratic policy, EQ is the “Human Compass for Governance”. It is essential for maintaining public trust and ensuring that the values encoded into AI systems, and the laws that govern them, reflect genuine human empathy and ethical judgment, which machines inherently lack.

3. Strategic Foresight: This is the “Engine of Anticipation.” It rejects the notion of a single predictable future in favor of exploring multiple plausible scenarios. Governance bodies must institutionalize foresight to “stress-test” policies against potential future capabilities (e.g., AGI, autonomous weapons) rather than reacting only to past harms.

4. Anticipatory Governance: The synthesis of the above, this framework prioritizes “proactive steering” over “reactive regulation”. It advocates for flexible, adaptive mechanisms, such as regulatory sandboxes, tiered risk classifications, and continuous monitoring, that allow governance to co-evolve with technology.

1.3 From Hard Law to Agile Oversight

The theoretical imperative, therefore, is a shift from “hard law”—rigid, prescriptive rules that risk brittleness—to “agile oversight” or “soft law” mechanisms that can adapt to new information. While the EU AI Act is fundamentally a piece of hard legislation, its implementation structure (via the AI Office and Codes of Practice) represents an attempt to inject anticipatory agility into a rigid legal container. The tension between these two modes, the certainty of law vs. the flexibility of foresight, defines the current struggle in European AI policy.

Part II: The Architecture of Control – Regulating General Purpose AI (GPAI)

The EU AI Act, particularly following its finalization and the operational shifts in late 2024 and 2025, creates a specific regime for “General Purpose AI” (GPAI). This classification acknowledges that powerful foundation models represent a distinct category of risk compared to narrow AI systems, necessitating a bespoke regulatory approach.

2.1 Defining General Purpose AI (GPAI)

The Act distinguishes between standard AI systems and GPAI models based on “significant generality” and competence across a wide range of distinct tasks. A GPAI model is defined not just by its intended use but by its inherent capabilities, specifically, models trained on vast amounts of data using self-supervision at scale. This definition captures the “Digital Substrate” of foundation models (such as GPT-4, Claude, or Gemini), which serve as the base layer for downstream applications.

Key thresholds have been established to operationalize this definition:

• Compute Threshold: The Commission’s guidelines indicate that a model is presumed to be GPAI if its training compute exceeds FLOPs (floating point operations per second).

• Generality: The model must be capable of generating diverse outputs (text, image, video) and integrating into a variety of downstream systems.

2.2 The Two-Tiered System: Standard vs. Systemic Risk

The Act introduces a bifurcated regulatory structure for GPAI models, distinguishing between those that pose “systemic risk” and those that do not. This tiered approach is a direct application of systems thinking, allocating regulatory resources to the network nodes with the highest potential for cascading impact.

2.2.1 Standard GPAI Obligations

Providers of non-systemic GPAI models are subject to “transparency” and “copyright” obligations, regardless of whether they are open or closed source (though open-source models have some exemptions). These obligations include:

• Technical Documentation: Maintaining detailed records of the model’s training process and architecture to facilitate downstream compliance.

• Downstream Information: Providing clear instructions and capability summaries to companies integrating the model into their own systems.

• Copyright Compliance: Implementing a policy to respect EU copyright law, including honoring opt-outs (e.g., TDM reservations).

• Training Data Summary: Publishing a sufficiently detailed summary of the content used for training, according to a template provided by the AI Office.

2.2.2 Systemic Risk Classification

The concept of “systemic risk” is the linchpin of the Act’s approach to advanced AI. It is defined as a risk specific to the “high-impact capabilities” of GPAI models that can have a significant impact on the Union market or cause foreseeable adverse effects on public health, safety, fundamental rights, or society as a whole.

The FLOPs Threshold: The primary metric for presuming systemic risk is cumulative training compute. Any model trained with more than FLOPs is automatically assumed to possess high-impact capabilities and is classified as posing systemic risk. This quantitative threshold serves as a proxy for capability, reflecting the empirical observation that model performance scales predictably with compute (the “scaling laws” discussed in the AI research community and alluded to in The Cognitive Revolution’s discussion of exponential growth).

However, the classification is not purely arithmetic. The Commission can also designate a model as systemic based on qualitative criteria, such as the number of business users, the model’s reach (e.g., 45 million monthly active users in the EU), or specific high-risk capabilities. This flexibility allows the AI Office to capture efficient but powerful models that might fall below the compute threshold, an example of anticipatory agility.

2.2.3 Obligations for Systemic Risk Providers

Providers of GPAI models with systemic risk (e.g., OpenAI, Google DeepMind, Anthropic, Mistral) face a stringent “ex-ante” compliance regime designed to govern the “Pandora’s Box” of proliferation. In addition to standard transparency rules, they must do the following:

• Model Evaluation & Adversarial Testing: Perform state-of-the-art evaluations (”red-teaming”) to identify vulnerabilities and risks before deployment.

• Systemic Risk Assessment: Assess and mitigate risks at the Union level, including the potential for misuse (e.g., cyberattacks, biological weapon design).

• Serious Incident Reporting: Track and report serious incidents to the AI Office without undue delay.

• Cybersecurity: Ensure adequate levels of cybersecurity protection for the model and its physical infrastructure (data centers) to prevent theft or tampering.

This tiered structure attempts to address the “governance gap” by placing the heaviest burden on actors with the most significant capacity to cause systemic harm, aligning with the “Leverage Points” concept in systems thinking.

Part III: The Institutional Engine – The AI Office and the Mandate for Foresight

To enforce these complex rules, the EU has established a new centralized governance architecture. The European AI Office, fully operational as of mid-2025, represents a significant centralization of power, moving oversight of the most powerful AI models from national authorities to Brussels.

3.1 Structure and Powers of the AI Office

The AI Office is not merely a bureaucratic appendage; it is designed as a “centre of AI expertise” with a mandate to act as the primary enforcer for GPAI. Its organizational structure reflects the specialized nature of its mission, consisting of six specific units:

1. Excellence in AI and Robotics: Focusing on R&D and innovation support.

2. Regulation and Compliance: The enforcement arm for the AI Act.

3. AI Safety: Dedicated to evaluating systemic risks and technical safety.

4. AI Innovation and Policy Coordination: Managing sandboxes and startups.

5. AI for Societal Good: Aligning AI with broader societal goals (SDGs).

6. AI in Health and Life Science: Addressing specific high-stakes vertical applications.

Centralized Oversight: Under the “Digital Simplification Package” proposed in November 2025, the AI Office’s powers were further reinforced. It now has centralized oversight not just of GPAI models, but of AI systems built on general-purpose models where the provider is the same (vertical integration). This reduces governance fragmentation, ensuring that a single, expert body in Brussels supervises entities such as ChatGPT and Gemini rather than 27 disparate national regulators.

3.2 The Scientific Panel and the Role of Foresight

A critical component of the AI Office’s anticipatory capability is the Scientific Panel of Independent Experts. Established to support enforcement, this panel comprises up to 60 experts selected for their technical expertise and independence.

The “Qualified Alert” Mechanism: The Scientific Panel holds a unique power: the ability to issue a “qualified alert” to the AI Office. If the panel identifies that a GPAI model poses systemic risk (even if it hasn’t met the FLOPs threshold), it can trigger an investigation or reclassification. This mechanism is a direct application of Strategic Foresight. It creates a sensor network within the governance structure, allowing the regulator to detect “weak signals” of emerging risk before they manifest as catastrophic failures.

Anticipatory Mandate: The AI Office is explicitly tasked with “monitoring the AI ecosystem, technological and market developments,” and the emergence of unexpected risks. This mandate institutionalizes the “Horizon Scanning” function described in The Cognitive Revolution. By continuously updating benchmarks and thresholds through delegated acts, the Office seeks to keep the regulatory perimeter fluid, thereby directly addressing the “Pacing Problem”.

3.3 The AI Board and Advisory Forum

Supporting the AI Office are the European Artificial Intelligence Board (composed of member-state representatives) and an Advisory Forum (stakeholders from industry, civil society, and academia). While the AI Office drives enforcement for GPAI, the Board ensures consistency in how national authorities regulate high-risk AI systems (e.g., in employment or policing). This multi-stakeholder architecture aims to balance centralized technical expertise with democratic legitimacy and diverse feedback loops.

Part IV: The Soft Law Bridge – The GPAI Code of Practice

Recognizing that “hard law” takes years to update, the EU AI Act incorporates “soft law” mechanisms to provide immediate, flexible guidance. The centerpiece of this strategy is the General-Purpose AI Code of Practice, finalized in July 2025.

4.1 The Role of the Code

The Code of Practice acts as a bridge between the abstract obligations of the AI Act (e.g., “assess systemic risk”) and the concrete engineering realities of AI development. While voluntary, the Code offers a “presumption of conformity”: providers who sign and adhere to the Code are deemed compliant with the Act, reducing their legal uncertainty and administrative burden. Conversely, providers who opt out must demonstrate compliance through “alternative adequate means,” a path that is subject to greater scrutiny and legal risk.

4.2 The Three Pillars of the Code

The Code is structured into three distinct chapters, reflecting the different risk profiles of GPAI models:

1. Transparency: Applicable to all GPAI providers. It outlines the information that must be disclosed to downstream providers and the public. The final version emphasizes protecting trade secrets while ensuring sufficient utility for downstream compliance; it introduces a “14-day period” for responding to requests for additional information.

2. Copyright: Applicable to all GPAI providers. This chapter operationalizes the requirement to respect EU copyright law. It mandates “state-of-the-art” technical safeguards to prevent the generation of infringing content (e.g., exact reproduction of protected works). It requires providers to honor machine-readable opt-outs (like robots.txt). It moves beyond “best efforts” language to firmer commitments.

3. Safety and Security: Applicable only to providers of systemic risk models. This chapter outlines the “Safety and Security Framework” (SSF) that providers must adopt. It mandates the assessment of four specific risk categories: CBRN (Chemical, Biological, Radiological, Nuclear) threats, Loss of Control, Cyber Offense, and Harmful Manipulation.

4.3 Industry Dynamics and “Regulatory Capture”

The development of the Code involved a massive multi-stakeholder consultation with over 1,000 participants. However, the process has not been without controversy. Civil society groups and some smaller EU firms have argued that the drafting was dominated by major US tech giants (OpenAI, Google, Microsoft), leading to a “watered-down” text that favors incumbents. For instance, the “Safety and Security” chapter relies heavily on internal risk assessments by the companies themselves, which critics argue creates a “fox guarding the henhouse” dynamic, a form of corporate capture that my framework warns against by emphasizing independent oversight.

Despite this, the Code represents a functional pragmatic compromise. Major US players like Google and OpenAI have signaled support, while others like Meta have refused to sign, citing regulatory overreach. This divergence sets the stage for a confrontation in August 2026, when full enforcement and penalties (up to 3% of global turnover) kick in.

Part V: The “Digital Simplification” Pivot – 2025 Amendments and the Retreat to Agility

In November 2025, the European Commission unveiled the “Digital Simplification Package” (also referred to as the “Digital Omnibus”), a sweeping legislative proposal that significantly alters the trajectory of the AI Act. This development marks a pivotal moment in EU governance, reflecting intense pressure to boost competitiveness and address the “innovation gap” with the US and China.

5.1 The Rationale: Competitiveness and Draghi’s Warning

The impetus for this package stems mainly from the Draghi Report on European competitiveness, which warned that Europe was falling behind in the global AI race due to regulatory rigidity and fragmentation. Moreover, external pressure from the incoming US administration (Trump) and threats of tariffs on “anti-tech” regulations created a geopolitical forcing function. The narrative shifted from “regulation as a superpower” (the Brussels Effect) to “simplification for survival.”

5.2 Targeted Amendments to the AI Act

The Digital Omnibus proposes targeted amendments to the AI Act that effectively delay and soften some of its most stringent requirements:

• Delay of High-Risk Rules: The application of rules for high-risk AI systems (e.g., in HR, education, and critical infrastructure) is proposed to be delayed by up to 16 to 18 months. The rationale is to link the entry into force of these rules with the availability of harmonized technical standards. Essentially, companies won’t be forced to comply until the “how-to” manuals (standards) are written and approved.

• SME Simplifications: Extending simplified documentation requirements to “small mid-cap” companies (SMCs), not just SMEs, to reduce the compliance burden.

• Expansion of Sandboxes: Broadening the scope of regulatory sandboxes and “real-world testing” conditions. This allows companies to test high-risk AI systems in real-world environments (outside strict labs) with reduced regulatory friction, thereby accelerating product development.

5.3 The GDPR Overhaul

Perhaps most controversially, the package includes amendments to the GDPR to facilitate AI training.

• Legitimate Interest for Training: The proposal explicitly allows companies to use personal data for training AI models without obtaining prior consent, relying instead on “legitimate interest” as a legal basis, provided safeguards (like pseudonymization) are in place.

• Re-definition of Personal Data: Narrowing the definition of personal data to exclude cases where a data holder cannot reasonably identify the individual, effectively freeing up vast datasets for AI processing.

5.4 Analysis: Deregulation or Anticipatory Adaptation?

This pivot generates profound disagreement.

• The “Rollback” Critique: Civil society groups (EDRi, Access Now) and privacy advocates denounce the package as a “massive rollback” of digital rights. They argue that delaying high-risk rules and loosening GDPR protections prioritizes corporate profits over fundamental rights, potentially allowing unsafe systems to proliferate unchecked.

• The “Agility” Defense: The Commission and industry proponents frame this as “Anticipatory Governance” in action. They argue that enforcing high-risk rules without technical standards creates legal uncertainty, stifling innovation. By delaying enforcement until standards are ready, and by centralizing GPAI oversight, they claim to be building a more effective, not just stricter, regime.

This aligns with the productive tension identified in The Cognitive Revolution: the conflict between the “deliberative” pace of democracy and the “exponential” pace of AI. The EU is attempting to resolve this by making the regulation more “agile”—shifting the burden from rigid ex-ante rules to continuous, standards-based monitoring.

Part VI: Synthesis – The New Social Contract for the Algorithmic Age

The evolution of the EU AI Act through late 2025 reveals the emergence of a sophisticated, albeit contested, architecture for Systemic Oversight.

6.1 Resolving the Pacing Problem

The EU has recognized that it cannot solve the pacing problem with static text alone. The creation of the AI Office, the Scientific Panel, and the reliance on Codes of Practice creates a dynamic institutional layer capable of updating the “software” of regulation without rewriting the “hardware” of the Act itself. Suppose a new model emerges that poses a novel type of bio-risk and exceeds the FLOP thresholds. In that case, the Commission can update the thresholds, and the Scientific Panel can issue alerts immediately, without waiting for a new Parliament vote. This is the essence of Anticipatory Governance.

6.2 Centralization as a Strategy

The shift toward centralized oversight for GPAI represents a strategic recognition that systemic risks are inherently cross-border. A fragmented approach, where 27 national authorities interpret GPAI rules differently, would be catastrophic for both safety and the single market. By empowering the AI Office to serve as the sole supervisor of systemic models, the EU is attempting to align the scale of regulators with that of the regulated entities (the “Tech Giants”).

6.3 The Fragility of the Model

However, the “Digital Simplification Package” exposes the fragility of this model. The willingness to delay high-risk rules and water down GDPR in the face of economic stagnation and US pressure suggests that the “Brussels Effect” has limits. When the economic costs of “hard law” become too high, the political will for strict enforcement wavers. This confirms my warning about the “Human Factor”, the unpredictable logic of competition and geopolitics that can undermine rational governance frameworks.

Conclusion

The EU AI Act, as it stands in late 2025, is no longer just a “product safety” law. It has morphed into a hybrid system of Anticipatory Control. It combines the hard floor of “unacceptable risk” bans with the agile ceiling of “systemic risk” oversight, bridged by the soft law of Codes of Practice.

For providers of General Purpose AI, the message is clear: the era of “permissionless innovation” is over in Europe, replaced by a regime of “demonstrable trustworthiness.” Providers must now build the institutional capacity for foresight, risk assessment, and transparency not just as a compliance exercise, but as a license to operate in the Cognitive Age. The success of this regime will depend not on the law’s text, but on the AI Office’s capacity to wield its new powers with wisdom, resisting capture while fostering the innovation needed to prevent Europe from becoming a digital colony.

As I conclude, the choice is between a “virtuous cycle” of managed progress and a “vicious cycle” of unmanaged risk. The EU’s 2025 reforms represent a high-stakes bet that it can engineer the former by bending the arc of the AI revolution toward human values, one regulatory amendment at a time.

List of Key Acronyms

• GPAI: General Purpose AI

• FLOPs: Floating Point Operations per Second (measure of compute)

• GDPR: General Data Protection Regulation

• SME: Small and Medium-sized Enterprises

• SMC: Small Mid-Cap companies

• CBRN: Chemical, Biological, Radiological, Nuclear

• CoP: Code of Practice

• EAIO: European AI Office

References

Diallo, O. (2025). The Cognitive Revolution: Navigating the Algorithmic Age of Artificial Intelligence. Independently Published.

Diallo, O. (2025, November 21). The EU’s AI Pivot: Why the Cognitive Revolution Demands More Than Static Regulation. Your Compass.

European Commission. (2025, November 19). Proposal for a Regulation of the European Parliament and of the Council amending Regulation (EU) 2024/1689 (Digital Omnibus on AI). Brussels: European Commission.

Bertuzzi, L. (2025, November 19). EU Commission pitches delays, changes to AI Act’s key duties. MLex Market Insight.

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