By Alan Jacobson, Systems Architect & Analyst What is the revenue model for AI? * It is not social media. * It is not “we will build it and they will come.” * It is not Facebook, Instagram, or YouTube, where one influencer reaches millions and advertising makes money. AI is a 1:
By Alan Jacobson, Systems Architect & Analyst For two years, the world fed on a fantasy: AI was exploding. Investor decks, media headlines, VC podcasts and earnings calls all pointed to the same kind of chart — the hockey stick. Growth was inevitable, exponential, unstoppable. AI would remake everything, on a
By Alan Jacobson, Systems Architect & Analyst For months, almost everyone in AI made the same mistake: They trusted the projections instead of the data. Every chart pointed up. Every forecast curved skyward. Even the “conservative” projections showed clean diagonal growth — not a hockey stick, but something close enough
By Alan Jacobson, Systems Architect & Analyst Everyone is trying to understand Sam Altman right now — the sudden crises, the shaky governance, the erratic memos, the fire drills, the overpromises, the leaks, the vibes. (MSFT has $135B invested in Sam Altman’s OpenAI) They’re looking in the wrong direction.
By Alan Jacobson, Systems Architect & Analyst For anyone who thinks AI will eventually “figure out” its revenue model once it reaches scale, let’s be clear about one thing: Advertising is a permanently closed door. It won’t just be difficult. It’s structurally incompatible with how AI works—
By Alan Jacobson, Systems Architect & Analyst Is this the Tom Brady Moment? The moment the backup steps in and everything changes? OpenAI was the starter. Gemini was on the bench. Suddenly, Gemini’s ahead. OpenAI’s leaking confidence. The whole market shifts its weight. So yes — it looks like
Modern societies depend on complex information systems to make decisions in finance, health care, law, education, transportation, logistics, defense, and everyday life. In all of these domains, there is a long-standing expectation that people and systems will acknowledge uncertainty, limits, or incapacity rather than guess. In human settings, phrases like
Large language models process language within a finite “context window,” also called context length. The context window is the amount of text, measured in tokens rather than characters or words, that the model can consider at one time when generating a response. Industry explainers routinely describe the context window as
The disclosure relates to the field of large-scale software systems, distributed digital services, and artificial intelligence systems that interact with users, enterprises, and regulated environments. Modern digital systems are increasingly complex, interconnected, and governed by overlapping operational, legal, and ethical requirements. As these systems scale, they exhibit failure modes that
Artificial intelligence (AI) and machine learning (ML) systems are increasingly embedded in critical domains including healthcare, finance, education, employment, law enforcement, national security, social media, and mental health support. These systems are no longer confined to low‑stakes recommendation tasks; they make or shape decisions that can affect liberty, livelihood,
Artificial intelligence systems, including large language models, recommendation engines, vision models, and hybrid multimodal architectures, increasingly operate as general-purpose platforms serving diverse workloads for many different users and organizations. These systems are typically deployed on shared compute infrastructure, such as cloud-based clusters of CPUs, GPUs, and specialized accelerators. As adoption
Modern artificial intelligence systems, particularly large language models (LLMs), remain fundamentally constrained by how they manage, retain, and retrieve information across time. Although these systems are capable of producing fluent, context-aware output within a single conversation, their ability to sustain continuity, accuracy, and coherence across extended interactions is limited by