Explore evidence-led notes on where AI should run, what agents need after the demonstration, and how technical mechanisms change workflow economics. Claims are bounded to their workload and evidence.
Stress-test enterprise AI capital across five scoped causal worlds without assigning probabilities or a hidden base case. The scenario wind tunnel separates commodity intelligence, differentiated systems, sovereign stacks, agentic operations, and permission ceilings, then connects signposts, precedence, falsifiers, and no-regret moves to named decisions.
Read MoreUse a 20-question screen to evaluate claims about scaling, benchmarks, context, retrieval, tools, quantization, serving, work effects, adoption, security, governance, and frontier opacity. Every question carries an evidence class, countercondition, local test, owner, and expiry so a dated result stays inside its decision boundary.
Read MoreDiagnose LLM serving by separating prefill, decode, queueing, batching, speculative decoding, and external work. The inference measurement record keeps phase metrics tied to representative arrival patterns, tail latency, failures, and accepted workflow goodput so a local optimization cannot masquerade as an operating improvement.
Read MoreCompare managed API, self-hosted, and hybrid AI in currency per accepted completed workflow, not token price alone. This transparent ledger incorporates acceptance, retries, exceptions, human review, latency, caching, idle capacity, operating labor, recovery, and demand sensitivity while keeping savings, payback, and business value as unproven hypotheses.
Read MoreDecide whether an AI pilot should advance, remain limited, or stop by using a failure-control-evidence ledger. The gate connects evaluations, permissions, observability, injection and leakage tests, human checkpoints, recovery, agency, and cost controls to named owners, dated evidence, and hard failure conditions.
Read MoreUse a benchmark card to decide when measurements from a private 27B language-model environment are complete, comparable, reproducible, and relevant to architecture. The method records workload, hardware, model representation, cache state, concurrency, acceptance, units, matched baselines, raw outputs, and failures without publishing unsupported performance claims.
Read MoreUse this nine-row workload-placement matrix to decide when one enterprise AI workload belongs on a managed API, self-hosted infrastructure, or a deliberately bounded hybrid path. It turns data, capability, latency, demand, operations, exit cost, and control evidence into hard gates instead of a single weighted score.
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