Reasoning Engine
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Research Telemetry
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"Identify risks" is defined right now by three forces: rapid capability gains, tightening regulation, and a constrained compute supply. Open and closed systems are converging — differentiation is moving up the stack.
Key Points
Capability convergence between open and closed models is accelerating.
Reports disagree on whether inference cost is rising or falling — methodology differs by 3×.
Productivity gains are concentrated in software engineering and support workflows.
Hyperscaler capex growth outpacing data-center power availability.
Enterprise GenAI adoption
78%
34% YoYof Fortune 500 firms in production
Open-model capability gap
30%
45% YoYvs. leading closed frontier model
Avg. inference cost
$0.42 / 1M tok
62% YoYblended across top providers
Frontier training run cost
$1.4B
180% YoYestimated for next-gen models
Adoption of generative AI tools across enterprise functions has more than doubled year over year, with productivity gains concentrated in software engineering and customer operations.
We present empirical evidence that test-time compute scaling produces predictable improvements in reasoning benchmarks, complementing traditional pre-training scaling laws.
The next competitive frontier in AI is no longer raw model capability but the orchestration layer — purpose-built agents that compress entire workflows into a single API call.
Regulators clarified key obligations for general-purpose AI providers, with enforcement expected to ramp through 2026. Foundation model audits remain a contested area.
Recent open-weight releases have closed roughly 70% of the gap to leading closed-source frontier models on standard reasoning and coding benchmarks.
Hyperscaler capex continues to outpace data-center power availability, creating a structural bottleneck that could persist into 2027 absent grid reform.
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