2025 was a big year for AI. It dominated conversations across every industry, with new tools and platforms launching rapidly. It’s nearly impossible to turn on the TV or scroll online without seeing an ad or even a TikTok influencer promoting the latest AI product.
As organizations accelerate their adoption of AI, it is becoming deeply embedded in digital ecosystems across product recommendations, search experiences, workflow automation, content creation, personalization engines, and more. These systems increasingly influence business decisions, customer experiences, and operational outcomes, making transparency and reliability a critical concern for enterprise leaders. Yet, even as momentum grows, everyone is still wrestling with a fundamental question: how does this technology actually work?
The Core of AI
To better understand why this question is so difficult to answer, it helps to look at how AI learns. At its core, AI uses algorithms and models to learn from data, identify patterns, and make decisions in a way similar to humans. Within the broader world of AI, there are a few categories that explain how it functions and evolves:
- Machine Learning (ML) – ML focuses on systems that learn from experience rather than being programmed for specific tasks. One of the most common approaches is the neural network, a system modeled loosely after the structure of the human brain. It uses interconnected nodes and layers to process data, find patterns, and make predictions.
- Deep Learning – Deep learning builds on ML with larger and more complex neural networks that handle advanced tasks. This is what powers things like image recognition, speech recognition, and complex language models.
- Generative AI – Gen AI uses deep learning models to create new outputs such as text, images, or music. Rather than just classifying or predicting, it generates content that appears new but is fundamentally derivative of the data it was trained on. Large Language Models (LLMs) like ChatGPT, Claude, and Gemini fall into this category, trained on massive datasets to produce outputs that reflect patterns from the original data, which means they can reproduce biases, errors, or copyrighted material. Other emerging concepts include agentic AI, which can autonomously plan and carry out tasks, and Artificial General Intelligence (AGI), a theoretical form of human-level intelligence, representing the broader frontier of AI development.
We understand quite a bit about how AI functions, including the technologies that power its learning, the data that shapes it, and the structures that allow it to process information. What we still don’t have a firm grasp on is how it reasons.
The “Black Box Problem”
One of the biggest challenges in AI today is the “black box problem,” our inability to fully understand how AI systems make decisions.
Dario Amodei, the CEO of Anthropic, one of the leading AI startup companies, has openly admitted to not fully understanding the innerworkings of their most advanced AI models, despite its rapid innovation. In fact, no one fully understands how these models reason or make decisions. Even with progress in interpretability research, no one can explain the “why” behind AI making the choices it makes. That lack of transparency can be dangerous, and it makes unpredictable or biased behavior difficult to pinpoint.
We’re already starting to see this unfold. Chatbots have produced volatile or emotionally manipulative advice. Recommendation algorithms have amplified harmful content. AI image tools have been caught reinforcing stereotypes or spreading misinformation. Content floating around on the internet is becoming increasingly hard to distinguish between what is real and what is fake. These outcomes raise questions about whether this technology can be safely scaled without better transparency and oversight. Is there a world in which AI reaches a point of self-improvement that we are no longer able to control?
The Ethical Dilemma
If we can’t fully explain how AI reaches its conclusions, it becomes harder to evaluate its safety, reliability, or impact. For organizations, this uncertainty impacts more than just compliance and legal exposure, but also customer trust, platform performance, and the effectiveness of automated workflows. The black box problem raises questions about where AI should augment human judgment, what guardrails are necessary, and how companies can responsibly integrate AI into commerce, personalization, and operational decisions.
Legal systems are starting to grapple with this uncertainty. Major lawsuits have been filed against leading AI companies, ranging from claims that these models were trained on copyrighted books without permission, to allegations of negligence, to cases where chatbots encouraged unsafe or harmful behavior. These legal challenges reflect the growing concerns about how AI systems operate, how they are trained, how their outputs can affect people in unpredictable ways, and how far they might go in pursuing their perceived goals.
Recognizing the black box problem is now essential for organizations investing in AI. When leaders recognize these limits, they can make more informed decisions, introduce the right guardrails, and determine where human-in-the-loop oversight is essential. This awareness will be critical for deploying AI responsibly and ensuring it strengthens both customer experiences and operational outcomes.