September 1, 2024 4 min read

The Future of Reasoning in AI: What's Coming Next

The AI landscape continues to evolve rapidly. After the release of GPT-4o earlier this year, the community is buzzing about what’s next. One area of intense focus is improving AI reasoning capabilities. Let’s explore where the field is heading.

Current State: GPT-4o and Claude 3.5

Today’s frontier models are impressive but have limitations when it comes to complex reasoning:

from openai import OpenAI

client = OpenAI()

# GPT-4o handles many tasks well
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": """
            A farmer has 17 sheep. All but 9 run away.
            How many sheep does the farmer have left?
            """
        }
    ]
)

print(response.choices[0].message.content)
# Most models get this right, but more complex problems can trip them up

The Reasoning Challenge

Current LLMs generate responses token by token without explicit “thinking time.” This leads to issues with:

Multi-step mathematical proofs
Complex logical deduction
Problems requiring systematic exploration
Tasks where initial intuitions are misleading

Chain-of-Thought Prompting

Today, we can improve reasoning with explicit prompting:

# Traditional approach - encourage step-by-step reasoning
cot_prompt = """
Solve this problem step by step:

A store sells apples for $2 each and oranges for $3 each.
If someone buys 5 fruits and spends exactly $12,
how many of each fruit did they buy?

Let's think through this step by step:
1. First, identify the variables
2. Set up the equations
3. Solve the system
4. Verify the answer
"""

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": cot_prompt}]
)

The Future: Native Reasoning Models

The AI research community is exploring models that reason internally before responding. The idea is:

Traditional: Input -> Generate tokens immediately
Future: Input -> Internal reasoning phase -> Output based on reasoning

This could dramatically improve performance on complex tasks.

What to Watch For

OpenAI and other labs are likely working on:

Extended thinking time: Models that can “think” longer for harder problems
Self-correction: Catching mistakes during the reasoning process
Better mathematical reasoning: Improved performance on proofs and calculations
Verifiable reasoning chains: Making the thinking process more transparent

Preparing Your Applications

Build flexibility into your systems:

class ReasoningAwareClient:
    """Client that can adapt to new reasoning capabilities"""

    def __init__(self):
        self.client = OpenAI()
        self.default_model = "gpt-4o"

    def solve_problem(self, problem: str, complexity: str = "standard") -> str:
        """
        Solve a problem with appropriate reasoning approach.
        Ready to upgrade when new models arrive.
        """
        if complexity == "complex":
            # Use chain-of-thought for now
            # Ready to switch to reasoning models when available
            prompt = f"Solve this step by step:\n{problem}\n\nThink through this carefully:"
        else:
            prompt = problem

        response = self.client.chat.completions.create(
            model=self.default_model,
            messages=[{"role": "user", "content": prompt}]
        )

        return response.choices[0].message.content

    def upgrade_model(self, new_model: str):
        """Ready for next-generation models"""
        self.default_model = new_model

Current Best Practices

While we wait for reasoning-native models:

Use chain-of-thought prompting for complex tasks
Break problems down into smaller steps
Ask for verification of answers
Use multiple attempts and compare results

def solve_with_verification(problem: str) -> dict:
    """Solve a problem and verify the answer"""

    # First solve
    solution = client.chat.completions.create(
        model="gpt-4o",
        messages=[{
            "role": "user",
            "content": f"Solve step by step: {problem}"
        }]
    ).choices[0].message.content

    # Then verify
    verification = client.chat.completions.create(
        model="gpt-4o",
        messages=[{
            "role": "user",
            "content": f"""
            Problem: {problem}
            Proposed solution: {solution}

            Check this solution for errors. Is it correct?
            """
        }]
    ).choices[0].message.content

    return {
        "solution": solution,
        "verification": verification
    }

The Takeaway

The next breakthrough in AI capabilities is likely to come from better reasoning, not just larger models. Keep your architectures flexible and watch for announcements from OpenAI and other labs.

The AI landscape evolves quickly. Stay curious, keep building, and be ready to adapt.