baselines
Beyond Market Intelligence keeps baselines in one place: 3 stories so far. The section currently leads with “12 KPIs to Track AI Adoption, Cost, and Performance in Your Organization”, “Smarter vision, smaller cost: 95% fewer tokens, same accuracy.”, and “Explore a new recurrent architecture built for deeper reasoning and memory tasks.”. Twelve KPIs can make or break how you track AI adoption, cost, and performance. A 95% reduction in token usage while holding accuracy steady is the kind of number that makes you look twice. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every baselines story on Beyond Market Intelligence, newest first.
12 KPIs to Track AI Adoption, Cost, and Performance in Your Organization
Twelve KPIs can make or break how you track AI adoption, cost, and performance. The worked example, 50 users with a $110,000 first-year cost, grounds every metric in reality, from baselines to ownership rules. That clarity matters because vague measurements create vague results. For deeper coverage of how AI agents can actually retain context, see our related piece on building persistent memory with a single JSON file. Measure first, improve second.
Smarter vision, smaller cost: 95% fewer tokens, same accuracy.
A 95% reduction in token usage while holding accuracy steady is the kind of number that makes you look twice. It suggests a meaningful shift in how image-based inference could be priced and scaled. The MOMA Graph benchmark is a solid start, but the real test is replication across broader datasets and stronger baselines. That is the evidence I would want before calling it significant.
Explore a new recurrent architecture built for deeper reasoning and memory tasks.
After months of independent work, DABSN (Dynamic Adaptive Bias State Network) is now public, with a preprint and code that includes PyTorch, C++, and Triton implementations. The architecture's behavior on reasoning and long-sequence benchmarks is compelling, but the 24M-parameter language model trained on 1B tokens is where the results get genuinely interesting. Now, the ask is for collaborators: independent reproduction, stronger evaluations, or GPU access to push scaling further. It's a practical, open invitation to build on something promising, not a promise of perfection.