Authors: Sebastian Nicolas Müller (snimu), Claude 3.5 Sonnet (new)

Large Language Model development has historically followed a pattern where organizations push for scale while publishing innovative improvements separately. Major model releases (like Meta’s Llama series) often stick to proven approaches, while the same organizations publish creative innovations in their research. Drawing inspiration from Intel’s famous “tick-tock” model for semiconductor development, we can envision a more structured approach: the “tick-tock-boom” cycle.

The Three Phases

  1. Tick (Distill): During the Tick phase, organizations focus on distilling their previous largest model into a more efficient form. The goal is straightforward: create the most economical version of your powerful model while retaining as much capability as possible.

  2. Tock (Validate): The Tock phase represents the key innovation in this development pattern. While AI labs are constantly innovating across multiple dimensions - architectures, data strategies, and infrastructure improvements - there hasn’t been a systematic way to validate which innovations will work at larger scales. During Tock, you take these innovations (both from your lab and others) and validate them at a scale approaching your previous largest model. This creates a unique opportunity to verify which innovations truly work at scale, and critically, how they interact with each other.

    Innovations to validate span the entire technical stack:

    • Architecture: Novel attention mechanisms, activation functions, normalization strategies
    • Data: Mixing strategies, cleaning approaches, curation techniques, synthetic data generation
    • Infrastructure: Specialized kernels, precision techniques, memory optimization, training code efficiency

    The goal isn’t to generate new innovations (those happen continuously), but to rigorously validate which ones and which combinations are viable for your next scale-up.

  3. Boom (Scale Up): The Boom phase is where everything comes together. Using insights from the Tock phase about which innovations and combinations actually work at scale, you can now push to a dramatically larger model size while confidently incorporating improvements across your entire stack. Instead of having to play it safe with proven approaches (as many labs currently do), you can include validated innovations from both your lab and the broader field, potentially achieving better results than scale alone would provide.

Why This Pattern Works

The tick-tock-boom cycle aligns organizational resources and research in several key ways:

  1. Risk Management: By validating innovations at large (but not maximum) scale during Tock, organizations can be more adventurous in their flagship models while managing risk. The validation phase provides confidence about which improvements will translate to even larger scales. This is especially crucial when combining multiple innovations across architecture, data, and infrastructure.

  2. Real-World Validation: While traditional evaluations provide limited insight into model capabilities, deploying Tock-phase models through APIs (and optionally open-source releases) provides crucial real-world feedback. This user engagement reveals how different innovations actually perform in production environments and how they impact real use cases. By the time you reach the Boom phase, you have not just internal validation data but concrete user experiences to inform your architectural decisions.

  3. Continuous Innovation, Systematic Validation: The cycle acknowledges that innovation happens continuously in AI labs. Instead of trying to control when innovations happen, it creates a structured process for validating them at scale and incorporating them into major models.

  4. Field-Wide Leverage: The Tock phase can validate not just internal innovations, but promising ideas from the entire field. This allows organizations to leverage the complete landscape of public research while validating it at scales beyond what most labs can access.

  5. Resource Alignment: The natural time delays between phases create perfect alignment with organizational growth. After a Boom, you use your compute resources to create efficient models during Tick while acquiring more GPUs. By the time you reach Tock several months later, you have substantially more resources available to validate multiple innovation combinations at large scale. This validation period provides additional time for infrastructure growth, ensuring you’re ready for the next Boom phase.

Advancing the Field Through Publication

Publishing validated innovations, models, and code from the Tock phase is crucial for advancing the field as a whole. This comprehensive validation of what works at scale represents invaluable knowledge for the entire AI community. Open-sourcing these contributions accelerates progress across the industry and helps establish best practices for scaling AI systems.

These contributions naturally benefit the publishing organization as well:

  1. Talent Attraction: This pattern of systematic innovation and open contribution makes an organization extremely attractive to top talent. It demonstrates both technical sophistication and a commitment to advancing the field.
  2. Innovation Leadership: The comprehensive validation from the Tock phase showcases not just individual improvements, but systematic innovation capabilities across the entire technical stack.
  3. Natural Marketing: The validated innovations create compelling technical content that establishes genuine technical leadership rather than relying on traditional marketing approaches.

For organizations concerned about maintaining their competitive advantage, the cycle’s natural timing provides a solution: by the time others can implement the validated innovations, you’re already operating at a larger scale with newer improvements.

Citation

@misc{snimu2025ticktockboom,
    title={The Tick-Tock-Boom Cycle: A Strategic Pattern for LLM Development},
    author={Sebastian Nicolas Müller and Claude 3.5 Sonnet},
    year={2025},
    month={Feb},
    url={https://github.com/snimu/blog/blob/main/contents/tick-tock-boom/article.md}
}