Opening — Why this matters now The fashionable version of AI strategy still sounds suspiciously like a gym membership pitch: bigger model, more parameters, more GPUs, more everything. The operational version is less glamorous and much more important: where does the computation happen, which parts of the model are actually used, how predictable is demand, and whether the system can turn those facts into lower latency, lower cost, or better decisions. ...
Opening — Why this matters now The first wave of enterprise AI adoption was obsessed with model choice. Which model is smarter? Which model writes better? Which model can reason, code, browse, call tools, summarize contracts, and politely pretend it enjoys quarterly planning? That was the easy part. The less glamorous question is now becoming more expensive: how do we serve all these model calls reliably, cheaply, and at scale? ...
Opening — Why this matters now AI agents are leaving the demo cage. They are no longer just politely completing prompts; they are planning workflows, calling tools, reading files, coordinating intermediate steps, and accumulating context like a bureaucrat hoarding PDFs. This is useful. It is also expensive. The paper “QuantClaw: Precision Where It Matters for OpenClaw” studies a problem that sounds technical but is really managerial: agent systems often run every task at a fixed numerical precision, even though not every task deserves the same computational budget.1 A safety-critical terminal command and a lightweight retrieval summary are not the same species of work. Treating them identically is the infrastructure equivalent of sending a limousine to deliver printer paper. ...
Opening — Why this matters now The industry has quietly reached an uncomfortable realization: throwing more tokens at a problem is no longer impressive—it’s expensive. Test-time scaling, once celebrated as a clever workaround to model limitations, is starting to look like an unhedged position. Generating 500–700× more tokens to approximate reasoning is not intelligence—it’s brute-force search with a rising cloud bill. ...
Opening — Why this matters now Large language models are getting cheaper to run, not because GPUs suddenly became charitable, but because we keep finding new ways to make models forget precision without forgetting intelligence. Post-training quantization (PTQ) is one of the most effective tricks in that playbook. And yet, despite years of algorithmic polish, PTQ still trips over something embarrassingly mundane: the calibration data. ...
Enhancing Privately Deployed AI Models: A Sampling-Based Search Approach Introduction Privately deployed AI models—used in secure enterprise environments or edge devices—face unique limitations. Unlike their cloud-based counterparts that benefit from extensive computational resources, these models often operate under tight constraints. As a result, they struggle with inference-time optimization, accurate self-verification, and scalable reasoning. These issues can diminish trust and reliability in critical domains like finance, law, and healthcare. How can we boost the accuracy and robustness of such models without fundamentally redesigning them or relying on cloud support? ...