Arena

Foundation models

The companies building the core AI models. The performance gap is narrowing, so reliability, access, and rollout terms now matter more in practice.

Cloud & compute

The infrastructure layer that sets cost, speed, and availability. Hardware access and cloud contracts often shape what teams can ship faster than model headlines do.

Developer tooling

The tools teams use to build with AI, from coding assistants to agent frameworks. Real value comes from workflow fit and reliable outcomes, not flashy demos.

Data & evaluation

How teams test model quality and behavior before and after launch. Strong evaluation habits reduce surprises and improve trust in production.

Distribution

The channels that deliver models into real products and enterprise systems. Distribution paths often decide adoption speed and switching effort.

Adoption

Where AI is actually being used in day-to-day workflows. Lasting adoption depends on integration, governance, and budget discipline, not pilot buzz.

Partnerships & capital

The partnerships and funding deals that shift market power. These moves can quickly change model access, compute options, and negotiation leverage.

Governance & policy

The rules and control requirements around AI use. For many teams, policy readiness now affects launch timing as much as engineering readiness.