For those still using ChatGPT, the latest advancements in large language models (LLMs) have been significant. Leading the way are models like DeepSeek’s r1, OpenAI’s o3 and Deep Research, and Google’s Gemini-pro. These models exhibit fewer hallucinations, handle more complex mathematical and reasoning tasks, and even excel at research-based activities.
What’s particularly remarkable is that these LLMs are not only far more advanced but also increasingly cost-effective to develop—and those costs continue to decline. This trend is driven by two key factors: hardware optimization and scaled inference. Modern GPUs and specialized AI chips have become vastly more efficient, with advancements in tensor cores, memory bandwidth and techniques like model quantization and distillation. These innovations have significantly reduced the time and energy required to train and deploy state-of-the-art models.
On the algorithmic side, the focus has shifted from pre-training—where many believe gains are slowing—to optimizing inference-time scaling. By refining how models process and generate responses in real time, researchers continue to unlock new efficiencies, suggesting that there is still considerable room for improvement.
Commoditization Of Language Models And Value Moving To The Application Layer
As the cost of language models continues to drop, we are seeing a rapid expansion of both general-purpose and specialized AI models. General-purpose LLMs like DeepSeek and GPT-4 are highly versatile, capable of handling a wide range of tasks. However, specialized LLMs—designed for specific industries such as healthcare or law—are proving to be more effective within their domains. At the same time, smaller language models (SLMs) are gaining traction for their efficiency, making them ideal for edge computing and cost-sensitive applications. This commoditization is fundamentally reshaping the AI landscape.
As LLMs become more widely available and less differentiated, the real value is shifting to the application layer—how these models are integrated, customized and deployed to solve real-world problems. The key differentiator will not be the model itself but the ability to apply AI to specific use cases with an exceptional user experience. Additionally, success will depend on how configurable these applications are—whether at the company, departmental or even individual level.
For example, AI-powered chatbots for customer support, content generation tools for marketing and AI-driven decision-making systems for businesses all illustrate the power of application-layer innovation. However, long-term adoption and engagement won’t just depend on raw technological capability—it will come from delivering a seamless user experience and fostering a strong connection between AI and human users.
The Dual Nature Of The Application Layer: Value And AI Risk
The true value of AI will be realized at the application layer, where attention is shifting toward agentic AI—systems capable of planning, executing tasks, deploying tools, verifying correctness and course-correcting as needed. As AI applications grow more sophisticated, they will increasingly rely on multi-agent systems, intricate optimizations and complex, often opaque workflows.
Modern AI systems frequently involve multiple specialized agents working together, making it difficult to trace decision-making processes or determine which models are being used. These agentic AI systems operate in dynamic, unpredictable environments, interacting with other AI systems while dynamically selecting models and tools based on factors like cost, performance or reliability. This creates a “black box” effect, where the decision-making process becomes harder to interpret, raising concerns about transparency, accountability and ethical use.
While the application layer of agentic AI holds the greatest potential for value creation, it also presents significant risks. These include:
• Misuse and misapplication
• Data privacy and security
• Lack of transparency and explainability
• Bias and errors
To navigate these challenges, AI governance will be the guiding force for organizations looking to maximize AI’s value while minimizing its risks.
How to reduce AI Risk Management with smarter AI Inventory management.
Learn more at https://fairnow.ai/platform/ai-registry-where-ai-governance-begins/
Good AI Governance Builds Trust, Trust Increases The Speed Of Adoption
Some firms will hesitate to invest in AI or impose strict limitations out of fear of potential risks. However, by doing so, they will replace AI risks with business risks, as they may fall behind competitors who leverage AI to innovate and scale. On the other hand, some firms will dive headfirst into AI without fully considering its risks, exposing themselves to potential challenges.
The differentiator between these extremes will be good AI governance—a structured approach that enables firms to harness AI’s potential while managing its risks. Effective AI governance includes the following key components:
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- Ethical Guardrails: Establishing clear policies on when and where AI can be deployed, along with enforcement mechanisms to ensure adherence and remediation when needed.
- Transparency: Tracking and documenting AI metadata, including details about data sources, models, inputs, outputs and intended use, while maintaining transparency with stakeholders.
- Risk Assessments: Categorizing AI applications by risk level, with governance processes that scale appropriately based on potential impact.
- Human Oversight And Accountability: Ensuring that humans play an active role in defining guardrails, monitoring AI systems, addressing issues and ultimately being accountable for AI-driven decisions.
- Legal Compliance: Keeping AI systems aligned with an evolving and complex web of regulations.
- Continuous Monitoring: Regularly evaluating AI systems for performance, data privacy, security, bias, safety and reliability.
In the end, organizations that can build and maintain a robust AI governance program will be better positioned to invest in AI, build trust with internal and external stakeholders, increase the speed of innovation and adoption, and emerge as winners in the era of AI.
NOTE: Originally Posted on April 17, 2025 in Forbes
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