The Labs team intentionally builds products that are non-functional or unsafe with current AI models to serve as future benchmarks. This 'bad' product acts as a consistent testbed to measure progress and signal to the research team when a new model has finally crossed a critical capability threshold, making the product viable.

Anthropic's product managers on the research team spec out requirements for each new AI model, defining what it should be good at (e.g., coding, knowledge work). This product development discipline is applied to the inherently unpredictable process of "growing" a model, bridging the gap between research and user needs.

While mainly a horizontal platform, Anthropic strategically builds vertical applications. This isn't to compete with their ecosystem, but to build ahead of current model capabilities and demonstrate to the market what will be possible on their platform in the near future, accelerating adoption.

While traditionally creating cultural friction, separate innovation teams are now more viable thanks to AI. The ability to go from idea to prototype extremely fast and leanly allows a small team to explore the "next frontier" without derailing the core product org, provided clear handoff rules exist.

To avoid choosing between deep research and product development, ElevenLabs organizes teams into problem-focused "labs." Each lab, a mix of researchers, engineers, and operators, tackles a specific problem (e.g., voice or agents), sequencing deep research first before building a product layer on top. This structure allows for both foundational breakthroughs and market-facing execution.

Anthropic's intense focus on AI for coding wasn't just a market strategy. The core belief, held since 2021, was that creating the best coding models would accelerate their internal researchers' work, creating a powerful flywheel that improves their foundational models faster than competitors.

For AI-first products, future value is exponentially greater (e.g., 1000x in 2 years). Therefore, Anthropic's growth team flips the typical 70/30 optimization/big-bet ratio, focusing on larger swings that unlock new markets because small optimizations can't capture the massive potential value created by model improvements.

A key strategy for labs like Anthropic is automating AI research itself. By building models that can perform the tasks of AI researchers, they aim to create a feedback loop that dramatically accelerates the pace of innovation.

The innovation team operates on two principles. First, they identify and close the gap between what current AI models can do and how people actually use them. Second, they imagine what models will be good at in six months and start building the products for that future state today.