Logan Matthew Napolitano

If you serve on a board of directors and believe your company is burning hundreds of thousands—or millions—of dollars on AI APIs and unnecessary compute, let’s have a conversation. I think your concern may be justified. I will help you show the board exactly where the money is going, what the company actually receives for it, and whether continuing with the same architecture is a responsible fiduciary decision. Too many companies have mistaken access to an AI API for an AI strategy. They are paying repeatedly for models they do not own, sending the same institutional knowledge across an external boundary, retransmitting enormous contexts, absorbing unpredictable price changes, and building critical operations around providers whose models, limits, policies, and product priorities can change at any time. The cost looks small during experimentation. Then the system enters production. Every employee begins using it. Agents make multiple calls. Failed calls retry. Context windows expand. Retrieval systems resend documents. Workflows use expensive models for tasks that smaller models could perform. A single business action may quietly require dozens of model calls. The organization pays for every token, every retry, every repeated instruction, and every piece of context the model must reread. At scale, this can become an enormous recurring expense without creating a corresponding company-owned asset. Meanwhile, local AI has changed dramatically. Open models are improving quickly. Quantization, sparse mixture-of-experts architectures, speculative decoding, optimized inference engines, CPU execution, system-memory inference, and modern agent harnesses have made serious private deployments practical. People are already reporting GLM 5.3 running on CPU-based local systems at roughly 50 tokens per second. That should end the assumption that useful local AI always requires renting access to a hyperscaler’s infrastructure. But cost is only one part of the problem. The privacy is often inadequate. The company’s prompts, customer information, internal research, source material, operational memory, and decision history may repeatedly leave its controlled environment. Even where providers offer strong contractual safeguards, the organization still accepts another dependency and another security boundary. The institutional knowledge is also being built in the wrong place. Years of prompts, corrections, evaluations, workflow design, retrieved documents, agent memory, and employee feedback should become intellectual infrastructure owned by the company. If the organization cannot move that capability away from its provider without rebuilding everything, it has created dependence rather than an asset. Quality is another problem. Sending every task to a famous model does not guarantee that the task is being completed well. Models hallucinate. Agents loop. Tools are called incorrectly. Retrieval supplies irrelevant context. Long prompts bury the real instruction. Large models are used where smaller specialized models would be faster and more reliable. Companies often measure API availability while failing to measure completed-task accuracy, unsupported claims, retry rates, human correction time, latency, or cost per successful outcome. The best modern harnesses can make local models reliable agents by combining: model routing; structured tool access; retrieval; durable memory; constrained permissions; answer verification; uncertainty detection; hidden-state monitoring; specialist adapters; human escalation; and continuous evaluation. The model is only one part of the system. The harness determines what it can do. The memory system determines what it retains. The evaluation layer determines whether the company recognizes failure. The routing system determines whether a task needs a small local model, a larger private model, or an external frontier API. The monitoring layer determines whether the company understands what the system is doing before a bad answer reaches a customer. This does not mean every company should abandon external APIs. It means every external call should earn its cost. A responsible architecture may be entirely local, on-premise, privately hosted, or hybrid. External frontier models can remain available for the narrow tasks where measured performance justifies the added cost and exposure. The rest can run on infrastructure the company owns and controls. A board evaluating its AI strategy should demand clear answers: What is our total AI cost by workflow? What is the cost per successful completed task? How much spending comes from retries, oversized contexts, and unnecessary agent calls? Which tasks demonstrably require a frontier model? Which tasks could run locally without reducing practical quality? What happens to our costs if usage increases tenfold? Where are prompts, memory, customer information, and proprietary research stored? Can we change providers without rebuilding our operating system? Have we benchmarked a local or hybrid architecture using our actual work? Are we accumulating permanent intellectual infrastructure, or paying rent indefinitely? If management cannot answer those questions, the organization does not yet have a serious AI strategy. Proprioceptive AI has spent years conducting independent AI research across model internals, hidden states, interpretability, probes, adapters, hallucination reduction, model evaluation, memory, agent control, and controlled steering. We do more than recommend a different model. We investigate what the system is doing internally, identify where it becomes unreliable, measure failure before it reaches the user, and design practical local or hybrid systems around the organization’s actual requirements. We can help a company determine: which workloads should remain on commercial APIs; which workloads should move locally; which models fit each workflow; where the current system is wasting compute; where hallucinations or unstable answers originate; how private memory and retrieval should be structured; how agents should be constrained and monitored; and how to turn AI spending into infrastructure the company owns. If you believe your company is burning capital on AI without building a durable capability, you may be right. Let’s measure it, put the evidence in front of the board, and determine the responsible path forward. I'll talk to your board or anyone who you think needs to hear this. Book a conversation: calendly.com/logan-proprioce…