A pivot towards test-time compute and open-weight reasoning architectures is narrowing the gap between proprietary frontier labs and accessible developer models.
Frontier AI development is moving away from brute-force pre-training as researchers find giving models time to think yields sharper results at lower cost.
The race to build bigger base models has stalled against physical and data limits, forcing developers to spend their computing budgets on thinking time instead.
The startup’s new Bonsai 2 model compresses Alibaba’s 27-billion-parameter LLM by nearly 90 per cent while keeping its performance almost fully intact.
As traditional scaling laws hit a wall, the industry is pivoting to models that 'think' before they speak—and the open-source community is leading the charge.
