AI Perspectives #9: Executives Are Flying Blind
A call for urgent action to bridge the divide between hype and understanding
For a concise summary, please refer to the TL;DR section at the end of this document.
The AI Illusion: Beyond Hype—The Perils of Incomplete Understanding
Recently, I’ve been engaged in discussions with executives who, on the surface, exude genuine enthusiasm for AI. There’s an unmistakable desire to harness its transformative potential, yet beneath this energy lies a critical gap in understanding. Many well-intentioned leaders confuse the underlying AI models—the intricate algorithms and data engines—with the outward-facing applications built upon them.
This confusion has far-reaching consequences. On one hand, decision-makers might mistakenly assume that a state-of-the-art model automatically guarantees a secure, ethical, or effective application. On the other hand, they may overreact to a problematic application by banning an entire technology suite—even when the core model is safe and efficient. For instance, consider the case of Deepseek: while the Deepseek application has been deemed questionable and banned in many workplaces, its underlying model is both robust and effectively employed by trusted platforms like Perplexity. Such blanket decisions arise from a failure to distinguish between the model and its implementation, resulting in misguided policies that penalize valuable technology.
Another common misconception is the belief that feeding AI models with fake data can easily change their behavior. In reality, altering a sophisticated model isn’t as simple or cost-effective as it may seem—it often requires millions of dollars and extensive retraining. This misunderstanding only deepens the knowledge gap, as many executives fail to grasp the inherent complexity and financial implications of modifying AI systems.
In essence, this lack of nuanced understanding creates a dangerous environment for decision-making. Leaders risk making misinformed choices—whether by over-hyping safe technologies or by overreacting to flawed applications—that can jeopardize the future of their companies and teams. In our fast-paced, AI-driven world, the ability to differentiate between safe and risky scenarios isn’t just an academic exercise; it’s a vital skill that can determine long-term success or failure.
Models vs. Applications: Unmasking the Mystery
Note: This section is a bit technical—feel free to skip ahead and come back later if you'd like to dive deeper.To truly appreciate the nuances of AI, it’s vital to differentiate between three key components: the model, the application, and what we refer to as RAG—a term we use for a dynamic knowledge base.
The Model
Think of the AI model as a massive, intricate database—a large file filled with patterns, statistical relationships, and learned information gathered during its training. The model represents the core intelligence of the system, containing all the “knowledge” it has acquired. However, it’s important to understand that the model itself is static. It doesn’t know who you are, where you are, or the context of your recent interactions. It simply generates responses based on its pre-existing information.
The Application
In contrast, the AI application is like the conductor of an orchestra. It collects your input and, crucially, has the ability to integrate additional context. This could include supplementary data such as company-specific details, recent web search results, Wikipedia entries, or even files you’ve uploaded. The application then compiles all this information—including the history of your previous interactions—into one comprehensive prompt that is sent to the model. In essence, while the model processes and generates the response, the application provides the necessary context and direction, making sure that the output is relevant and tailored to your needs.
RAG – The Knowledge Base Approach
RAG, which stands for Retrieval-Augmented Generation, acts as a dynamic knowledge base. It retrieves up-to-date or domain-specific information from external sources and feeds it into the application. This means that beyond the static knowledge embedded in the model, RAG enriches the interaction with fresh data, ensuring that the responses are not only informed by past training but are also augmented with the latest available information.
Understanding these distinctions is crucial. For example, consider the Deepseek scenario: although the Deepseek application has raised concerns and been banned in many workplaces, its underlying model is both safe and efficient—evidenced by its use in platforms like Perplexity. Here, the error lies not with the model itself but with the application that failed to properly contextualize and secure its deployment. Similarly, many mistakenly believe that simply feeding fake information into an AI can alter the model’s behavior. In reality, altering a model is an immensely complex and costly endeavor, often requiring millions of dollars for retraining.
Ultimately, it’s the application’s role to aggregate and structure diverse sources of input into a single, coherent query. Without this crucial intermediary, even the most robust models would be unable to deliver meaningful, context-aware responses. Recognizing the interplay between models, applications, and RAG is essential for making informed decisions that truly harness AI’s potential without falling prey to oversimplified or misguided assumptions.
The Dangers of AI Ignorance: Real-World Risks
The gap between AI’s promise and its real-world application can have far-reaching and detrimental consequences. When decision-makers fail to grasp the nuances of AI, they expose their organizations to multiple risks, including:
Poor Investment Decisions: Misunderstanding AI can lead to investments in technologies that either over-promise or under-deliver. Companies may pour resources into flashy applications without the robust underlying models, or vice versa, missing the opportunity to harness genuine value.
Reputational Damage: Relying on AI products without a proper understanding of their strengths and limitations can result in public missteps. A failure to anticipate or mitigate risks—be it through biased outputs, security flaws, or operational failures—can harm a company’s image and erode trust among stakeholders.
Ethical Breaches: Without a deep understanding of AI’s inner workings, organizations risk deploying systems that inadvertently perpetuate bias or compromise privacy. Such ethical lapses not only lead to regulatory scrutiny but can also damage the company’s long-term credibility.
Missed Opportunities:
Internal AI Deployment: Companies that misunderstand AI may overlook the chance to implement internal solutions, such as customized large language models (LLMs) tailored to their specific operations. Instead of investing in proprietary systems that could streamline processes and foster innovation, they might rely solely on external applications, losing a competitive edge.
Leveraging Emerging Platforms: Many organizations still underestimate the potential of integrating AI within existing productivity ecosystems. For instance, modern platforms like Google Workspaces and Microsoft environments are evolving—introducing AI tools like Gemini and Copilot—to automate routine tasks, enhance collaboration, and drive strategic decision-making. Overlooking these opportunities means not only missing out on efficiency gains but also falling behind competitors who are quick to adapt to these transformative tools.
In essence, the inability to distinguish safe from risky AI scenarios isn’t just a theoretical concern—it has tangible impacts on strategic decision-making. Misinformed leaders risk not only wasting resources on ineffective or unsafe technologies but also neglecting critical opportunities to innovate and lead in an increasingly AI-driven world. Recognizing and addressing these gaps in understanding is paramount to ensuring that AI serves as a powerful asset rather than a potential liability for any organization.
The Deepseek Dilemma: A Cautionary Tale
DeepSeek’s AI models have proven their worth across multiple sectors by powering applications that adhere to strict safety and ethical standards. For instance, Amazon Web Services has integrated DeepSeek-R1 models into its Amazon Bedrock platform. This integration leverages enhanced security measures, comprehensive compliance support, and built-in guardrails that ensure content filtering and the protection of sensitive data. In healthcare, DeepSeek models contribute to diagnostic tools by offering explainable insights and mitigating bias through careful curation of diverse training data. Additionally, organizations using DeepSeek are actively engaged in ethical AI development practices—conducting regular bias audits, employing diverse datasets, and maintaining clear communication about how these systems function, often backed by dedicated ethics committees.
Yet, the controversy—what we call the Deepseek dilemma—arises from a critical misunderstanding. While the core DeepSeek models are robust and safe when deployed responsibly, the application layer that wraps these models is where problems often emerge. In many cases, a problematic or questionable application has led to sweeping bans, even though the underlying model remains effective and secure. For example, despite the Deepseek application being banned in several companies, its model is still being safely used by trusted platforms like Perplexity. This conflation of model and application risks penalizing a technology for issues that are, in fact, a matter of implementation rather than intrinsic capability.
Furthermore, this dilemma is compounded by the misconception that AI behavior can be easily altered by merely feeding it fake information. In reality, changing a model's behavior is an immensely costly and complex endeavor—often requiring millions of dollars for retraining. Such misunderstandings underscore the necessity of distinguishing between the inherent qualities of an AI model and the varied ways in which its applications can be engineered, secured, and governed.
The Deepseek case is a powerful reminder that responsible AI deployment depends on a clear separation between core technology and its application. Rigorous evaluation and continuous ethical oversight are essential not only to guard against risks but also to ensure that organizations can fully benefit from transformative AI capabilities. As technology evolves, maintaining this critical distinction will be paramount to harnessing AI safely and effectively.
A Global Imperative: Bridging the AI Knowledge Gap
The call to action is unequivocal: executives and leaders worldwide must assume responsibility for deepening their understanding of AI. In an era marked by rapid technological shifts, informed decision-making transcends being merely beneficial—it is essential for protecting business interests and upholding societal values. By bridging the AI knowledge gap, organizations not only shield themselves from unforeseen risks but also build the foundation for an AI-powered future that is ethical, sustainable, and transformative.
Global leadership in AI requires embracing a culture of critical inquiry and continuous learning. Leaders must actively seek to understand both the capabilities and limitations of AI technologies, recognizing that a nuanced grasp of these systems is key to leveraging their full potential. As AI becomes increasingly integrated into every facet of our lives—from internal operations to customer interactions—the distinction between robust models and their myriad applications grows ever more critical. Only by demystifying these complexities can organizations avoid the pitfalls of hasty, ill-informed decisions that might jeopardize their future.
Conclusion
The AI landscape is evolving at a breathtaking pace, and the stakes have never been higher. From confusing models with applications to misjudging the cost and complexity of altering AI behavior, the current knowledge gap poses significant risks. The DeepSeek dilemma exemplifies how misinterpretations can lead to overreactions and missed opportunities. Now more than ever, it is imperative that leaders develop a profound and nuanced understanding of AI—not just to harness its potential, but to safeguard their organizations from its inherent risks. Equally important is the need for continuous, role-specific education: short, targeted annual courses designed specifically for managers and executives can provide critical updates and insights. These bite-sized, industry-tailored learning opportunities ensure that leaders remain agile and informed, ready to navigate the rapid digital transformation of the AI age.
TLDR
Executives must bridge the AI knowledge gap to avoid misinformed decisions that risk business interests and societal values. Understanding the differences between AI models, applications, and knowledge bases (RAG) is crucial. The DeepSeek example shows how misinterpretations can lead to unnecessary bans or missed opportunities. Regular, short, role-specific training sessions—such as annual courses tailored to industry needs—are essential for keeping leaders updated in this fast-evolving digital transformation of the AI age.