The rapid evolution of artificial intelligence, particularly the advent of generative AI, has rendered many established frameworks for understanding and governing AI obsolete. This paradigm shift necessitates a re-evaluation of how organizations approach AI security, data privacy, and operational oversight. What was once a critical question for discerning AI vendors—"Do you use our data to train your model?"—now represents a symptom of crystallized, rather than fluid, intelligence in a rapidly changing technological landscape. This outdated inquiry, while rooted in a valid concern from a previous AI era, fails to address the multifaceted risks and capabilities of modern generative AI systems.
The Foundation of Crystallized Intelligence: Understanding Past AI
To grasp the inadequacy of the old question, it’s crucial to understand the distinction between crystallized and fluid intelligence, as theorized by psychologist Raymond Cattell. Crystallized intelligence refers to the accumulation of knowledge, expertise, and learned skills over time. It is the "what you know"—the vast repository of information and patterns acquired through experience. Fluid intelligence, conversely, is the ability to reason through novel problems and adapt to new situations where existing knowledge is insufficient. It is the "how you think"—the capacity for abstract reasoning, problem-solving, and innovative thinking.
In the context of artificial intelligence, particularly in the nascent stages of machine learning, organizations relied heavily on crystallized intelligence. Early AI systems were largely pattern-matching engines. They were trained on massive datasets—millions of resumes, job descriptions, and historical hiring outcomes—to identify correlations and predict outcomes based on past performance. The development lifecycle was characterized by a singular, extensive training phase, followed by validation, versioning, and monitoring. This process, while sophisticated for its time, resulted in relatively stable and predictable AI models.
During this era, the question, "Do you use our data to train your model?" was not merely relevant; it was paramount. It directly addressed the core concern of data privacy and security within a machine learning context. Organizations were rightly worried about their proprietary data being incorporated into a shared model, potentially exposing sensitive information or creating competitive disadvantages. The question was a direct probe into how an AI vendor handled data, and the answer determined the risk profile of adopting their solution. Data governance was the established framework, and it aligned perfectly with the deterministic nature of these earlier AI systems. The crystallized knowledge of how these models were built and secured was sufficient for making informed decisions.
The Generative AI Revolution: A Shift to Fluid Intelligence
The landscape has dramatically shifted with the emergence of generative AI. These advanced systems are no longer mere pattern-matching tools; they are sophisticated reasoning engines. Unlike their predecessors, generative AI models are probabilistic, meaning the same input may not always yield the same output. They are highly sensitive to prompts and system instructions, capable of altering their behavior dynamically without requiring extensive retraining. Furthermore, the increasing viability of synthetic data generation means that the necessity of specific, real-world data for building foundational models is diminishing.
This fundamental change in AI architecture and behavior has profound implications for governance and security. The systems are no longer static artifacts but active, adaptive, and continuously evolving entities. This evolution expands the surface area of risk well beyond the initial training data. Many existing governance frameworks, built on the principles of crystallized intelligence and static models, have not yet caught up to this new reality.
Madhu Mathihalli, VP and GM of Product at Eightfold, observed this disconnect firsthand. After 15 years of encountering the same question on Infosec and vendor review questionnaires – "Do you use our data to train your model?" – he articulated a critical insight: the question, while historically accurate, was now fundamentally misaligned with the capabilities of generative AI. He pointed out that this question belonged to a machine learning era, not the current generative era.
This observation highlights a crucial point: leaders are not asking the wrong questions out of negligence, but rather because they excelled at asking the right questions for a previous technological paradigm. When the underlying rules of AI interaction and development changed, their established expertise—their crystallized intelligence—became insufficient. Navigating this new terrain requires fluid intelligence: the ability to adapt, reason, and formulate new questions that address the emergent complexities of generative AI. Organizations, whether consciously aware or not, are now engaged in a real-time process of adapting to this new reality, often without the tools or frameworks to fully comprehend the challenges.
The Nuance of "Reasoning" in Generative AI
To illustrate the shift from recall to reasoning, consider a practical example in the context of hiring. An older AI tool, when presented with a candidate mentioning a "complex software migration" at their previous job, would likely perform keyword analysis. It would scan for terms like "migration," "led," and "software," cross-referencing them with a predefined list of required skills. This is akin to recall—retrieving information based on established patterns.
A generative AI system, however, operates differently. Upon hearing about a "complex software migration," it would engage in reasoning. It might follow up by asking, "You mentioned the migration was complex—what was the most challenging aspect of maintaining data integrity while systems were being transferred?" This interaction demonstrates an understanding of context and an ability to ask the next logical question, much like a skilled human interviewer. This is the essence of the shift from simple pattern matching to dynamic reasoning.
This adaptive interaction is a direct manifestation of fluid intelligence. It’s not about retrieving pre-programmed responses but about navigating novel situations in real time, generating relevant follow-up questions and insights. When AI systems operate in this manner, the responsibility for oversight expands significantly. Protecting the data used for initial training becomes only one component of responsible AI deployment. A robust governance strategy must incorporate enough crystallized knowledge to critically evaluate the AI’s outputs and responses, coupled with sufficient fluid intelligence to anticipate and address the questions that have yet to be conceived. As Madhu Mathihalli succinctly put it, "Security today isn’t just about protecting data. It’s about governing evolving intelligence."
The Expanding Risk Landscape of Generative AI
The implications of generative AI’s reasoning capabilities extend far beyond simple data privacy. The very nature of how these systems operate introduces new vectors of risk that the old question fails to address. These include:
- Prompt Injection and Manipulation: Generative AI can be susceptible to malicious prompts designed to bypass safety filters, extract sensitive information, or induce harmful outputs. The old question does not account for how external inputs can alter the AI’s behavior and potentially compromise its integrity.
- Hallucinations and Misinformation: While capable of generating novel content, generative AI can also "hallucinate"—produce outputs that are factually incorrect or nonsensical. Governing the accuracy and reliability of these outputs is a new challenge.
- Bias Amplification and Propagation: If the underlying data or the prompts used to interact with the AI contain biases, the generative model can amplify and propagate these biases in its outputs, leading to unfair or discriminatory outcomes.
- Intellectual Property and Copyright Concerns: The generative nature of AI raises complex questions about the ownership of the generated content and potential infringement of existing intellectual property rights.
- Evolving Operational Risks: As AI systems become more integrated into business processes, their continuous adaptation means that vulnerabilities can emerge and evolve over time, requiring ongoing monitoring and dynamic risk management strategies.
These emergent risks underscore the inadequacy of a governance model focused solely on the origin of training data. The focus must shift from a static understanding of data input to a dynamic understanding of AI behavior, reasoning processes, and emergent capabilities.
The Chronology of Evolving AI Concerns:
- Early 2000s – Mid-2010s (The Machine Learning Era): AI development primarily focused on supervised learning and pattern recognition. Key concerns revolved around data bias, model accuracy based on training data, and the privacy of data fed into the models. The question "Do you use our data to train your model?" was a direct and critical inquiry into data security and potential data leakage. Vendor questionnaires reflected this focus on data provenance and protection.
- Late 2010s – Early 2020s (The Rise of Deep Learning and Early Generative Models): While machine learning remained dominant, the capabilities of AI began to expand. Concerns started to broaden to include ethical considerations, algorithmic fairness, and the potential for AI to automate complex tasks. However, the fundamental data-centric questions often persisted in vendor assessments.
- 2020s – Present (The Generative AI Era): The widespread adoption of large language models (LLMs) and other generative AI technologies marked a significant inflection point. The focus shifted from AI as a data processor to AI as a creative and reasoning agent. New challenges emerged, including prompt engineering, AI safety, the potential for AI-generated misinformation, and the governance of dynamic, adaptive systems. The old question, rooted in the previous era, began to feel increasingly out of step with these new realities.
Reframing the Conversation: The Questions We Should Be Asking
The transition from a machine learning paradigm to a generative AI paradigm necessitates a fundamental shift in how organizations approach AI oversight. This is not a problem confined to AI vendors or security teams; it is an organizational imperative that requires collaboration across HR, talent acquisition, operations, and leadership.
Instead of asking the outdated question, organizations should be focusing on a new set of inquiries that address the realities of generative AI:
- How does the AI system adapt and learn in real-time? This probes the dynamic nature of the AI, moving beyond static training data to understand continuous learning mechanisms and their implications for evolving behavior.
- What are the mechanisms for controlling and guiding the AI’s reasoning process? This focuses on prompt engineering, system instructions, and guardrails that ensure the AI’s outputs align with organizational values and objectives.
- How are the outputs of the generative AI system validated for accuracy, fairness, and compliance? This addresses the challenge of hallucinations, misinformation, and bias, demanding robust mechanisms for quality assurance.
- What are the processes for identifying and mitigating emergent risks associated with the AI’s evolving capabilities? This emphasizes proactive risk management and the need for continuous monitoring and adaptation of governance frameworks.
- How does the organization ensure responsible and ethical use of the AI, particularly in sensitive areas like hiring and talent management? This broadens the scope to encompass ethical considerations, human oversight, and the potential impact on individuals.
- What are the strategies for ensuring transparency and explainability in the AI’s decision-making process, even as its reasoning becomes more complex? This addresses the "black box" problem and the need for understanding how AI arrives at its conclusions.
These questions reflect a more fluid and adaptive approach to AI governance. They acknowledge that the technology is not static and that the risks and opportunities it presents are constantly evolving.
The Broader Impact and Implications
The obsolescence of the traditional data-centric question signals a broader trend: the need for organizations to cultivate fluid intelligence across their leadership and operational teams. In an era defined by rapid technological advancement, the ability to unlearn old assumptions and embrace new ways of thinking is becoming the ultimate competitive advantage.
For HR and talent acquisition leaders, this means understanding that AI tools in their domain are not just about matching keywords but about facilitating more nuanced and insightful interactions. For operations leaders, it means recognizing that AI integration requires dynamic security and governance models, not rigid, one-time assessments.
The shift from crystallized to fluid intelligence in AI governance is not merely a technical adjustment; it is a strategic imperative. Organizations that fail to adapt their questioning and their frameworks risk falling behind, deploying AI solutions that are either ineffective, insecure, or ethically compromised. The journey toward responsible AI in the generative era requires a commitment to continuous learning, critical inquiry, and the courage to ask the new questions that will shape the future of technology and its impact on society. As the landscape of AI continues to transform at an unprecedented pace, the ability to adapt one’s thinking will be the most critical asset any organization can possess.
