Code and AI: Emerging Patterns

The world of computing stands at a fascinating crossroads where traditional programming meets artificial intelligence, creating a new landscape of possibilities and challenges. At its core, this transformation represents a shift from rigid, predictable systems, to adaptive, human-like problem-solving ones.

The Old Guard: Classical Programming's DNA

Classical programming has, until recently, always been the only option for harnessing digital logic. It's like a well-organized recipe book - every instruction must be precise, every ingredient (input) must be exactly as specified, and if you follow the steps correctly, you'll get the same cake every time. This code operates in a world of strict rules and expectations, where deviation from the prescribed format typically leads to errors or unexpected results.

AI: The Adaptive Newcomer

In contrast, AI models, and in particular Large Language Models (LLMs), operate more like an experienced chef who can work with whatever ingredients are available and still create something delicious. They can handle natural language instructions and unstructured inputs with remarkable flexibility. Let's look at a practical example:

Imagine you need to extract appointment details from emails. A classical program would need carefully formatted input like:

```

Date: 2025-03-25

Time: 14:00

Location: Office

```

If the inputs are slightly off, you might be able to apply some regex expressions to format it, but this approach will have its limits.

In contrast, an AI can handle something as casual as:

"Hey, let's meet next Tuesday afternoon around 2-ish at the office to discuss the project"

The Human Connection

It's no coincidence that AI's approach feels more natural to us. Human intelligence operates similarly - we're naturally equipped to handle ambiguity, interpret context, and adapt to unexpected situations. We don't crash when someone makes a typo or speaks in broken English; we adapt and understand.

Complementary Strengths

It would be wrong, however, to assume that the AI approach is an unmitigated improvement on classical code. Both have their strengths and weaknesses so tt is important to consider where and when to use AI versus code.

Classical Code's Superpower:

1. Transparency: Like a glass-walled machine, you can see every gear turning

2. Predictability: It follows rules religiously - no surprises

3. Security: Every action can be audited and controlled

4. Performance: Lightning-fast execution and far lower energy coinsumption compared to AI inference

AI's Magic Touch:

1. Natural Language Processing: It speaks human

2. Capturing Meaning: It understands the meaning of code, text and other content

2. Flexibility: Handles messy, real-world inputs gracefully and effortlessly

3. Multilingual Capabilities: Understands and translates between multiple languages

4. Problem-Solving: Can reason, ideate, and evaluate solutions on its own

6. Multi-modal Processing: It can interpret, create and transform all manner of content

The Future is Hybrid: Emerging Patterns of Integration

The fusion of classical programming and AI is giving rise to sophisticated hybrid architectures that leverage the strengths of both paradigms. This isn't just about using both approaches side by side. It is about identifying which paradigm is best suited for a particular task. How important is transparency? What are the desired outputs? Is creativity desired? Does the task involve natural language? How fast or energy efficient is the task at hand and what hardware is available to perform it? Often it makes sense to combine the two approaches at a much more granular level, so that AI and classical code work together, creating synergistic systems where each component enhances the other's capabilities.

Here are some common high-level patterns at the crossroad of AI and classical code:

1. Using AI to work on classical code

At the moment, one of the hottest AI use cases is performing classical code development tasks. It is already possible to develop powerful classically-coded applications in a fraction of the time, using AI Models trained on code from any number of different programming languages. This approach combines:

• AI's ability to understand natural language requirements, as well as the code that can fulfill them

• The reliability and performance of classical programming

• Automatic code generation with human oversight

• Built-in documentation and explanation capabilities

2. AI Agents using classical code

AI agents can accept natural language instructions from humans, reason about how appropriate solutions could best be delivered, and then coordinate the performance of those solutions, in whole or in part by employing classic code. For example, the Model Context Protocol (MCP), recently developed by Anthropic, represents a crucial bridge between AI and classical systems. It provides:

• Standardized communication protocols between AI and external tools

• Real-time data access for context-aware decisions

• Bidirectional communication enabling AI to both gather information and trigger actions

• A framework for adding new tools and capabilities

3. AI sandboxed by classical code

Because AI, for all its magic, has the potential to "go off the rails", behaving in undesired or unexpected ways, it can be very useful to limit its range of possible outputs by using classical code. For instance, using type-safe programming to ensure AI-generated responses conform to predefined structures, or implementing validation functions that check if AI outputs meet specific criteria before being used in production. This combines the flexibility of AI with the reliability of classical programming safeguards.

AIs access to resources can also be controlled by making the AI resource access go through classically coded gateways that employ classic authentication and authorization, cryptographic proofs, etc.

4. AI agents as a participants in a consensus

Extending the previous point, the cooperation of multiple agents (AI and human), possibly from different organizations and representing different interests, can be facilitated by using classically-coded consensus protocols.

Recent developments show DAOs (Distributed Automous Organizations) incorporating AI agents as representatives of different stakeholders, where agents participate in governance through smart contracts and automated consensus mechanisms. These AI-driven systems can negotiate, vote, and execute decisions while representing diverse interests, with blockchain ensuring transparency and accountability. Some platforms like ChaosChain are already implementing AI-driven agent governance where multiple AI models represent competing interests in decision-making processes.