Crowdbotics Logo

Platform arrow

Use AI to help you speed through the hardest part of modernization and maintenance: understanding how your system actually works.

About Us arrow

We are on a mission to radically transform the software development lifecycle.

Using Prediction Machines to Build Intelligence

Cory Hymel, VP of Product and Research at Crowdbotics, recently presented “Using Prediction Machines to Build Intelligence,” where he examined the limitations of large language models and defined a roadmap to true enterprise intelligence.

The AI Hype vs. Reality: Context is Not Intelligence

The current AI landscape is saturated with promises that large language models (LLMs) can magically solve business problems by simply consuming vast amounts of data. This marketing narrative has driven widespread expectations that feeding AI more context leads directly to intelligence and productivity gains.

However, this is a fundamental misconception. LLMs, at their core, are prediction machines designed to statistically guess the next word in a sequence based on probability patterns. They excel at pattern matching but lack true understanding or reasoning.

Current LLMs struggle with the vast amounts of information needed to modernize legacy systems, which can involve millions of lines of code. While expensive fine-tuning offers some improvement, expanding context windows or datasets has limitations and doesn’t lead to true intelligence.

Building Real Intelligence: Introducing Intelligence Models

To overcome these limitations, Crowdbotics is pioneering intelligence models that work alongside LLMs. These models capture structured knowledge, business logic, and reasoning patterns—encoding not only what the code does, but why it does it.

Rather than relying solely on probabilistic pattern matching, intelligence models provide deterministic, consistent, and explainable insights aligned with business context. This architecture enables AI-driven tools to deliver meaningful, reliable outcomes in software modernization, maintenance, and developer productivity.

In collaboration with GitHub and Microsoft, Crowdbotics demonstrated that integrating intelligence models with GitHub Copilot improved code suggestion acceptance rates by 51%—a testament to the power of adding true intelligence to generative AI.

Why This Matters: The Growing Complexity of Code

AI is generating code at an unprecedented scale—over 250 billion lines in the past year alone. Yet, the ability to understand, document, and maintain this code is lagging dangerously behind. Without tools that embed real intelligence, organizations risk introducing errors, inefficiencies, and technical debt.

Crowdbotics’ approach empowers enterprises to safely leverage AI by balancing automation with accuracy, helping to manage legacy systems and navigate the challenges posed by rapidly evolving AI-generated codebases.

Key Takeaways

  1. LLMs Are Prediction Machines: Large language models statistically predict the next word based on patterns but do not understand the “why” behind data.
  2. Context Is Not Intelligence: Increasing data or context size alone does not create intelligence; it often leads to diminishing returns and potential errors.
  3. Intelligence Models Complement LLMs: Structured knowledge and reasoning encoded in intelligence models provide consistent, explainable, and business-aligned insights.
  4. Practical Impact: Integrating intelligence models with AI tools like GitHub Copilot significantly improves developer efficiency and code quality.
  5. Addressing Code Complexity: With billions of lines of code generated by AI, intelligence models help manage legacy systems and evolving codebases to reduce risk and technical debt.

Conclusion

The path to effective AI adoption requires moving beyond the illusion that more context equals intelligence. As Cory Hymel emphasized at Gartner AIBS 2025, intelligence is structured knowledge, reasoning, and alignment with business intent—not just expanded data or bigger models.

Crowdbotics is at the forefront of this shift, offering intelligence models that complement prediction machines, enabling enterprises to modernize confidently, onboard developers rapidly, and maintain compliance efficiently.

For organizations looking to unlock the true potential of AI in software development, the message is clear: Context is not intelligence—but together, we can build it.