Read the paper. Follow the economics.

Successful in-house inference chips can retain more infrastructure economics inside a cloud platform and change the mix of purchased GPUs.

More arithmetic requires effective memory and interconnect throughput; benchmark peak FLOPS alone misses these constraints.

Control of chips, software and the data center can improve utilization and reduce dependence on a single external platform.

As cluster scale rises, data movement and resilience can limit delivered compute even when chips are available.

Persistent paid workload growth could offset falling token prices; volume alone cannot establish this offset.

Routing across models can weaken a single model vendor's hold on customers while increasing the value of workflow ownership.

Longer reliable workflows could change seat demand and labor intensity, but supervision and verification remain economic costs.

Autonomous tasks can require more tool calls, retries and tokens even if each individual inference becomes cheaper.

Reasoning may increase tokens and server time per query; smaller models and better accuracy can offset the cost.

Accessible reasoning models may reduce model-level differentiation and shift value toward distribution, proprietary data and reliable workflows.

Cheaper useful inference can pressure price per token while making more applications economic. Total accelerator demand depends on the demand response.

Expert routing and large model weights can preserve memory and communication constraints despite lower active arithmetic.

Hard tasks can become more compute-intensive while easy tasks are routed cheaply, changing capacity planning and pricing.

Routing and verification software can capture value if it lowers cost per accepted answer without degrading reliability.

Better utilization can reduce required GPUs for a fixed workload or enable more demand at a lower price; the net hardware effect is conditional.

A strong compiler and kernel ecosystem can extend the economic usefulness of an accelerator platform.

If adopted at comparable quality, recurrent or hybrid designs can change the balance between cache memory, arithmetic and bandwidth per token.

Cheaper long-sequence processing could expand document, audio and other streaming applications; commercial demand still needs validation.

More concurrent requests per device can improve provider margins or be passed through as lower API prices.

A fixed workload can need fewer devices, while cheaper access can expand usage. Hardware spending follows the combined effect.