AI sector pivots to agent scaling

The transition to agentic self-iteration is a pragmatic wake-up call for an industry obsessed with raw frontier model updates. While raw model capability growth has normalized, the real gains are shifting to the developer's harness, custom feedback loops, and highly optimized token spend.

• –Feedback-driven flywheels: Software engineering is the first vertical achieving AGI-level automation because code execution provides a deterministic, testable sandbox for agent self-iteration.
• –From token maxxing to token budgeting: The era of unconstrained AI spending is ending, forcing enterprises to treat tokens as a metered resource subject to strict cost-benefit analysis.
• –Duopoly control: OpenAI and Anthropic continue to dominate the developer space, but the battlefield is shifting from foundational pre-training to post-training reinforcement learning and runtime execution.
• –Infrastructure bottlenecks: The "Agent Scaling Law" changes hardware demands, making KV cache management, communication bandwidth, and low-latency inference more critical than raw FLOPS.