I listened to The Artificial Intelligence Show, where the host proposed monetizing AI by selling single purpose agents at the cost of a full time employee, positioned as doing the work of ten. It is commercially elegant but philosophically familiar, narrowing broad, low cost intelligence into boxed roles because that is easier to price. We have done this for decades by segmenting software into constrained products and tiers. I believe this time is different. Rather than fragment AI into ten agents, the future belongs to one unified assistant that minimizes friction and respects the user’s cognitive space.

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I was listening this morning to The Artificial Intelligence Show, episode 196, forwarded to me by a good friend. In the episode, the host explored the possibility of selling an AI agent for roughly the cost of a full time employee, while positioning it as capable of doing the work of ten. From a revenue standpoint, it’s seductive because you can simply replace one salary line item with a slightly smaller software fee, and capture enormous margin along the way.

As I listened, it reminded me of everything I have historically disliked about how new technologies are commercialized. The host was essentially describing a deliberate narrowing of capability. But AI today is widely accessible, foundational models are available at extremely low marginal cost that are broad, flexible, and general. Yet his proposal implicitly kneecaps that breadth and forces the intelligence into a familiar box: one agent, one job description, one functional identity, one paid seat. Why? Because that structure maps cleanly to how companies have always purchased labor and software and it is easier to monetize something that resembles an employee than something that resembles open capacity.

This is monetization-first thinking, and to be fair this approach has historically worked. The software industry has long segmented capability into constrained packages in order to extract value. Microsoft Office was split into Word, Excel, and PowerPoint rather than offered as a single evolving work surface. Adobe separated Photoshop, Illustrator, and InDesign into distinct products in the same way. Enterprise vendors routinely created tiered plans where features were artificially limited so that customers would upgrade. Even hardware companies shipped devices with dormant capabilities enabled only through higher priced versions. In each case, the product was not necessarily designed around the full potential of the technology, but around clean monetization boundaries.

I believe this moment is different. Artificial intelligence is not a narrow feature set and when we slice it into ten branded agents, each with a different name, dashboard, billing model, and skill boundary, we are fighting its nature. We are constraining it into neat revenue templates and reintroducing friction into something that could be unified.

Imagine a company buying ten different agents from ten different vendors. A sales agent, a finance agent, a compliance agent, a hiring agent, a marketing agent, and so on. Each comes with its own interface, prompt style, configuration logic, data boundaries, rules and performance metrics. The organization now has to manage ten artificial personalities layered on top of its existing software sprawl. That’s more fragmentation, plain and simple.

The alternative is what I call the super secretary model. Instead of distributing intelligence across multiple branded bodies, we anchor it in one unified assistant with one interface and one cognitive surface. That assistant can be taught sales, finance, operations, hiring, analytics, and strategy as skills, not as separate identities. Under the hood, it may orchestrate multiple models or frameworks, but the human worker interacts with a single coherent presence.

This approach respects a simple truth: cognitive space is sacred. Every new interface we introduce occupies mental real estate inside our users’ heads. Every additional dashboard, configuration panel, and workflow demands their attention. Every action we force a user’s body to take, every click, every context switch, creates fatigue. Historically, vendors could get away with this because switching costs were high and alternatives were limited. But today, low cost AI is ubiquitous and all of our buyers know it. If we add friction in order to maximize monetization, users will move.

The shift now tilts toward the end user rather than the corporation because when intelligence is abundant, simplicity becomes the differentiator. The companies that win will assume that every bit of space in a user’s head is precious. They will treat attention as a constrained resource. They will design as if every unnecessary interaction is a tax. The agents everywhere model optimizes for revenue segmentation and taxes the user, but the super secretary model optimizes for ease of use and friction minimization among the user base.

In prior eras, it made sense to break products apart because distribution and infrastructure were expensive. Today, inference costs are falling and models are commoditizing. Artificial constraints will become more visible and users will increasingly recognize when capability has been artificially boxed for pricing convenience.

I believe we are entering a user-first phase of technology design where we begin by asking: how do we reduce mental overhead? How do we consolidate surfaces? How do we let intelligence integrate rather than fragment? For me, the answer is clear: I do not want ten agents with ten names from ten vendors, each optimized to do one thing well because that is easier to sell. I want one assistant that can do anything, growing in skill over time, anchored in a single, reliable unified system of record. That is the model that respects both the technology and the human using it.

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