By Alan Jacobson, Systems Architect & Analyst Definition. Governed, pre-execution provisioning (G-PEP) optimizes LLMs to reduce inference cost and capacity requirements up to 90 percent. It is an architectural control layer that authorizes, constrains, routes, defers, or denies AI execution before inference based on policy, entitlement, and cost
By Alan Jacobson, Systems Architect & Analyst * LLMs do not disclose active user growth or retention. (Analysis of each company suggests why, below) * Independent reporting by five data sources on web traffic and app downloads indicates adoption flattened months ago. * Chip sales are reported, but end-user adoption is not.
The AI Cost-Revenue Divergence (ACRD) detection framework compares revenue growth, cost-of-revenue growth, and gross-margin change to identify early signs of AI workload margin pressure. * 9 public cloud companies where AI costs are outpacing revenue and compressing margins * 25 public cloud companies where AI costs are beginning
Across 25 public cloud companies, AI costs are beginning to outpace revenue following AI rollout — with divergence ranging from +300 to +3,700 basis points in some cases, while in others, cost growth is 2–5x revenue growth, indicating AI workloads are scaling cost before monetization. The AI Cost-Revenue
AI Cost-Revenue Divergence (ACRD) compares revenue growth, cost-of-revenue growth, and gross-margin change to identify early signs of AI workload margin pressure. * More than 10 companies signal “Active,” almost 20 indicate “Emerging.” * ACRD Watchlist: 25 companies most exposed to AI workload margin pressure Signals are classified by
By Alan Jacobson, Systems Architect & Analyst Before AI can be adopted, it must work as a business. Today it doesn’t. The economics are broken on both sides of the ledger: revenue is mispriced and costs are uncontrolled. While chipmakers may prop up the NASDAQ by selling infra to
By Alan Jacobson, Systems Architect & Analyst The railroad crash of 1873 corrected capital excess without changing the product. The AI crash will correct capital excess by forcing the product itself to change. Why the 1873 railroad analogy is attractive — and incomplete The surface analogy is strong: * massive capital inflows
By Alan Jacobson, Systems Architect & Analyst There are three markets for AI. They are routinely conflated, incorrectly marketed as one, and that mistake explains why AI adoption, trust and monetization are breaking down at the same time. 1) Consumers: the mass market Consumers don’t want to think better.
By Alan Jacobson, Systems Architect & Analyst AI companies are shipping features at full speed while the fundamentals that can drive profitability — adoption, trust, governance, cost and revenue — remain unresolved. Feature Impact on bottom line Multimodal inputs and outputs Text, image, voice, and mixed-media interaction improvements. Improves demos and
By Alan Jacobson, Systems Architect & Analyst Is AI going to work out? Not without structural changes. What says AI must change? Data. Rates of adoption went flat almost a year ago for LLMs across the board. This is true at both at the consumer level (ChatGPT) and at the
Download PDF then use Share to send it to your own email for retrieval What to listen for What this means 1. AI revenue Are AI revenues discussed in concrete dollar terms, or described using words like “strong,” “early,” “encouraging,” or “meaningful”? When revenue is material, it is quantified. Continued
By Alan Jacobson, Systems Architect & Analyst AI 3.0 is three weeks away. Wait. What? It's as simple as 1-2-3: AI 1.0 has never made money — and never will Ignore the hype, the demos and the endless talk about “agentic AI.” Just follow the