This is the fourth paper in a five-part sequence about artificial intelligence. Can Software Cheat? separates software behavior from consciousness and responsibility. Is Artificial Intelligence Biased, or Is It Answering From a Point of View? examines perspective and judgment. What Are We Actually Afraid Artificial Intelligence Will Do? separates present harms, plausible risks, and speculative catastrophes by mechanism and evidence. This paper explains how people and organizations can build durable advantage with these tools. The AI Industrial System then moves outward to the physical, financial, and institutional system that makes those gains possible.
I routinely have up to ten artificial-intelligence sessions working at once.
One may be researching a claim while another reviews a document, a third attacks an argument, a fourth reorganizes material, and a fifth waits for me to decide what deserves another iteration. I move among them as results become available. From the outside, this can look like a spectacularly bad case of multitasking.
The better analogy is time-slicing.
Time-slicing is how an operating system works on several jobs: while one waits, the system advances another. I am not trying to think deeply about nine subjects at the same instant. I am keeping several workstreams active while applying my attention to one decision at a time (I’m still tuning this process). The models do more of the mechanical work, but they also create more opportunities for judgment: Which problem matters? What context is missing? Which result is credible? What should be compared? What needs verification? When should I stop?
TL;DR
The durable value of artificial intelligence comes less from one clever prompt than from accumulating useful context, preserving effective workflows, orchestrating several processes, and reinvesting saved attention in harder problems. The result compounds only when human judgment and verification improve with the system.
Artificial intelligence can reduce the cost of cognition at one layer while increasing the value of cognition at the next. As answers become cheaper, framing, skepticism, domain knowledge, and verification become more valuable.
That distinction has changed how I think about the economics of artificial intelligence. I care less about the cost of one prompt, the price of one token, or which model won this week’s benchmark. I care about the total human and technical cost of producing a useful, verified result, and whether today’s efforts make tomorrow’s work easier.
My use has progressed through four stages:
Prompt optimization: learning how to ask.
Context optimization: improving what the system knows about the work.
Orchestration and verification optimization: organizing several processes and checking their output.
System optimization: preserving successful methods so they can be reused.
The stages are cumulative: better prompting improves an interaction; better context reduces reconstruction; better orchestration uses human attention more productively; and better systems preserve all three gains. When they reinforce one another, the value can compound.
Fifty years of moving up the stack
I have worked in software through roughly fifty years of changing abstractions. Developers repeatedly confronted choices that felt existential at the time: operating systems, databases, programming languages, development environments, network architectures, user interfaces, and hardware platforms.
The arguments could resemble religion: Windows or Linux, Microsoft .NET or Java, Microsoft SQL Server or MySQL, Android or iPhone. People could become emotionally attached to differences that mattered greatly for some purposes and very little for others.
We software developers became accustomed to choosing a winner and sticking with it until something much better comes along. Technical optionality has value, but specialization has value too. No one becomes equally fluent in every competing environment. Productive work eventually requires choosing tools, learning their behavior, building habits around them, and letting those investments accumulate. A theoretically superior alternative may appear later, but switching still carries an onboarding cost.
The deeper trend was abstraction. Computer languages were created to provide English-like access to the CPU instructions. Later generations of software let developers build far more complex systems without managing every byte of memory or writing every lower-level service themselves. Databases, networks, user-interface and object-oriented frameworks, cloud infrastructure, application programming interfaces, and open-source libraries moved attention upward.
Rather than making technical knowledge irrelevant, abstraction changed its useful form. As the system became too complex for one person to memorize, expertise increasingly meant knowing what was possible, how the pieces fit, what to ask, where to look, what could fail, and how to test the result. Mental models became more valuable than exhaustive recall.
Software development trained me to notice repeatable, predictable work and ask whether a machine could handle it. I do not want a simpler life. I want a less tedious one: automate the routine steps, then use the time for more interesting pursuits.
An IDE’s command completion spares me from memorizing or looking up every piece of syntax. Having ChatGPT review my email and calendar when I open a new chat and suggest tasks it can help with serves a similar purpose. It keeps a possible obligation from slipping past me and leaves the choice of what matters in my hands.
My patent work provided a concrete example. Shortly before a project I wanted to build took shape, Microsoft Speech Server changed the economics of natural-language and text-to-speech development. I already had access to the necessary software through a developer subscription. A capability that would previously have required much more money and specialized infrastructure became available at negligible marginal software cost.
By my estimate, the economics improved by at least an order of magnitude. More importantly, I didn't need to understand every internal mechanism to recognize that a newly available tool made a previously impractical system feasible. The abstraction let me combine speech technology and radio-frequency identification in a way that people in either field alone were less likely to imagine.
Artificial intelligence feels like the next turn of that same wheel. The interface is more general, the range of applicable problems is much wider, and improvement is unusually fast. The underlying principle remains familiar: when a lower layer becomes cheaper and easier to use, human attention moves toward problems that were previously uneconomical.
Stage one: Writing the perfect prompt
I began, as many users did, by trying to communicate with the models more effectively. Roles, constraints, examples, output formats, evaluation criteria, and step-by-step instructions could materially improve early results.
I also discovered that prompts can become too sophisticated. Over-prompting is a form of premature optimization. The more tightly I prescribe the path, the more likely I am to exclude a useful approach before the system has had a chance to surface it.
My prompts therefore became simpler as the models improved. I still specify what matters, but I prefer to begin with a wider range of possible approaches and narrow it quickly. The goal is productive exploration followed by disciplined selection, not prompt elegance.
Prompting remains a useful skill, especially for a new task or unfamiliar model. It stopped being the center of my process when I realized the quality of the work depended much more on the problem I chose, the context available, the alternatives considered, and the verification applied afterward.
Stage two: Context becomes capital
An isolated prompt tells a system very little about what I am trying to accomplish. A system with access to a project's history, prior decisions, rejected alternatives, source material, recurring requirements, terminology, preferences, and past mistakes operates very differently.
That led me toward what is known as contextmaxxing. I do not strip out useful background just to save tokens. My working rule is to make relevant information potentially available, preserve what proves important, and let the system surface it when needed.
Useful context is curated, not accumulated indiscriminately. Some information becomes stale. Some conclusions turn out to be wrong. Some instructions should apply only to one project. Sensitive information should not be exposed simply because it might be convenient. Material that went nowhere may deserve deletion rather than preservation.
Context capital is the durable value created when useful background material does not have to be reconstructed. Current systems support this in several ways. Projects can keep related chats, files, and instructions together, and memory can preserve selected facts or working preferences across conversations.[1] Specific features will change. The economic principle is more durable: information supplied once can reduce the setup cost of later work.
I have used Grammarly’s professional version in all my writing for about a decade. When I first began drafting with artificial intelligence, Grammarly repeatedly flagged the same grammatical and stylistic problems. I fed those corrections back into the AI system, and it didn’t take long to see that Grammarly was finding less and less to fix.
This modest example doesn't prove the model became a better writer in general; it shows that verified corrections can become reusable context instead of repeated cleanup.
Context can also compound errors. A mistaken premise, stale preference, or bad template can spread through later work more efficiently than before. Good context therefore needs provenance, scope, expiration, correction, and deletion. A system that remembers everything without distinguishing what remains true has accumulated clutter, not capital.
Stage three: Orchestration and verification become the work
Once context became abundant enough, my scarce resource shifted again. The important question became less “How do I get the model to answer this?” and more “How do I organize artificial-intelligence processes around a larger objective?”
Orchestration begins with deciding which problems deserve artificial intelligence and how to attack them. It continues through assigning research, comparing alternatives, integrating results, managing dependencies, and knowing when to stop. These activities are judgment applied to a changing division of labor, not clerical tasks.
Verification belongs in the same stage because faster production merely compounds error when review is unreliable.
More capable models can produce more sophisticated mistakes. They can also make those mistakes persuasive enough that superficial review becomes dangerous. My attention therefore moves toward the assumptions and evidence that determine whether a conclusion deserves confidence: source quality, contradictory evidence, causation, missing denominators, incentives, time horizons, and the boundary between documented fact and inference.
Accuracy is not one dial. Model capability, relevant context, reasoning depth, retrieval, task ambiguity, source quality, and willingness to acknowledge uncertainty all interact. A larger context window cannot manufacture a missing fact. More reasoning cannot repair a false premise that no one notices. A citation is not useful merely because it exists.
Creative work requires a different verification mode from factual work. During ideation, I may want unusual analogies, speculative hypotheses, counterfactuals, and unestablished connections. The useful boundary is between invented possibility and asserted fact. A system can vary widely while generating alternatives and still be required to label conjecture and verify claims before reaching a conclusion.
My multi-session workflow has limits. Research on task switching shows real cognitive costs, particularly when tasks are complex or unfamiliar.[2] Orchestration helps me when the workstreams are separable, their state remains visible, the handoffs are clear, and the review points require decisions I can make without rebuilding the entire context in my head. It can fail when I create too many branches, forget commitments, duplicate work, or let inconsistent assumptions survive across sessions. I have also learned to say plainly when I have forgotten something, become confused, or changed my mind.
Artificial intelligence changes the tradeoff. For me, some switching costs may be worth paying when it converts model latency into useful human work. I am still working on it.
Stage four: Preserve what works
The fourth stage is system optimization. Once a method succeeds, I ask how to make the next execution begin farther up the learning curve.
A research process can become a reusable framework. A due-diligence workflow can preserve definitions, source preferences, thresholds, and failure checks. A writing system can retain its rules and remember which questions exposed the last weak conclusion.
This is workflow capital.
The largest long-term gains may come from reducing how much mental setup must be rebuilt whenever a recurring problem appears. A good system preserves a method, makes its assumptions inspectable and changeable, and improves the starting point for the next problem rather than merely generating an answer.
Current agentic tools make this increasingly practical. ChatGPT Work, for example, is designed for longer, multi-step tasks and finished deliverables rather than only conversational assistance.[3] I once assumed that sophisticated use would require me to build and maintain an agent infrastructure of my own. I may still do that for specialized needs, but existing tools now handle much of the setup and coordination I would otherwise have had to build.
That pattern is familiar. Authentication remains necessary even though I rarely implement cryptographic protocols myself. Databases still require query planning, even though I don't write the optimizer. If the platform handles more of the agent plumbing, I can focus on a higher level.
Recently, I gave ChatGPT a list of more than fifty AI project ideas, most described in just a few words. It analyzed the list, arranged the work around dependencies, and we started taking the projects one by one. Then the next item appeared: “Finish my executor plan for my mother.” That was the entire instruction. I said, “Proceed.”
Minutes later, ChatGPT had found material I had already saved in Google Drive, although I had not told it where the material was or even that it existed. It analyzed what it found, researched what the plan needed to cover, and presented a detailed plan for me to review. The striking part was how little I had supplied at the moment of action: the system connected an old record, a terse project label, and a new task without making me reconstruct the background first.
The danger is that a reusable system can institutionalize a weak method as easily as a good one. Workflow capital requires auditability, versioning, visible assumptions, and periodic challenge. Compounding is a mechanism, not a guarantee of improvement.
Three forms of capital reinforce one another
The four stages create three assets:
Context capital preserves relevant knowledge and reduces reconstruction.
Workflow capital preserves successful methods and reduces reinvention.
Human capital improves the ability to frame problems, choose tools, orchestrate work, recognize failure, and verify outcomes.
No form of capital is sufficient on its own. Rich context in the hands of a weak method can reproduce error efficiently, while a strong workflow without domain judgment can optimize the wrong objective. A capable person who must reconstruct context and process each time pays the same setup cost repeatedly.
Human judgment builds better context and workflows, which free attention for harder problems, stronger verification, and improved systems; those improvements then reduce the cost of future work. That reinforcing loop drives compounding.
An enterprise can create the same reinforcing loop. Morgan Stanley Wealth Management first placed its internal research and intellectual capital behind an artificial-intelligence assistant that helps financial advisers retrieve relevant information. It later added a meeting workflow that, with client consent, summarizes conversations, identifies action items, prepares a follow-up email for the adviser to edit, and saves a note into Salesforce.[4]
Together, those deployments show the mechanism: curated organizational context makes information easier to find, an integrated workflow preserves the result, and a human adviser reviews what goes to the client. The work begins with accumulated knowledge and leaves a better record for the next task.
Context is the infrastructure that lets the next task begin farther up the curve, not the output itself.
Human-adjusted cost is the useful denominator
Much of the debate over frontier and local models focuses on token prices, graphics processors, electricity, model size, or benchmark performance. Those variables matter, but they don't capture total cost of ownership or the full value of the results.
For practical purposes, I use a rough accounting frame:
Human-adjusted cost per verified artificial intelligence task should include cash cost, infrastructure, setup, maintenance, prompting, latency, context management, retries, rework, verification, and switching. Those costs should be divided by useful, verified outcomes.
This is a rule of thumb, not an empirical formula. The terms are not measured in common units, and the value of a person’s time depends on the person and the work. Its purpose is to expose costs that disappear when the comparison stops at tokens or subscription prices.
An experienced software developer I know uses an open-weight version of Qwen on his own hardware. Cash is constrained, and his approach gives him control, privacy, freedom to experiment, and a low visible marginal cost. It also requires him to manage hardware, inference speed, context, model selection, installation, upgrades, and troubleshooting. He sometimes uses frontier models when he needs a fast or especially strong result.
His choice is rational for his circumstances. Mine is almost the reverse. I pay $200 per month for ChatGPT Pro and rarely think about inference resources. My waking hours, attention, reading speed, and supply of worthwhile questions run out before the service becomes a limiting resource for my work.
Neither approach is universally superior. Local systems may win when privacy, sovereignty, offline operation, customization, predictable high-volume use, or specialized workloads dominate. Frontier services may win when capability, convenience, integration, rapid improvement, and saved human attention matter more. The right comparison is the least costly (in time, money, and other resources) reliable path to the required outcome, not the cheapest token.
Commitment can create value and risk
I once recommended using several models, sometimes adversarially, and selecting the best one for each task. That remains good advice during exploration. Comparison develops judgment and prevents early dependence on a system whose strengths and weaknesses are not yet understood.
My own behavior changed after enough experimentation. Repeated switching eventually created less value than deeper mastery of one platform. My accumulated investment now includes familiarity with model behavior, project history, memory, files, verification habits, tool integrations, stylistic tuning, and cognitive muscle memory.
This is commonly called vendor lock-in. Some lock-in is an artificial barrier imposed by a provider. Some comes from the accumulated return of productive specialization.
Commitment can reduce setup costs and accelerate learning. It can also increase privacy exposure, dependence, switching costs, and the chance that one provider’s blind spots become my own. My rule is therefore deliberate commitment with periodic review. Constant model switching repeatedly incurs the cost of learning a new system. Permanent loyalty ignores changing evidence.
I used to tell clients that the development environment mattered far less than finding a person or team that had mastered it. Artificial intelligence has increased the leverage available to people who know how to direct and verify the tools. That principle still holds.
Saved time is an allocation decision
Automation creates options; people and institutions decide how to use the effort it saves.
An individual can use released time to attempt harder work, improve quality, explore more alternatives, teach someone else, rest, or simply produce more. An organization can expand output, reduce staff, lower prices, increase margins, add controls, or raise expectations.
This is where cognitive offloading can become cognitive surrender. Delegating a calculation, search, draft, or comparison may be entirely sensible. Abdicating judgment formation is much less sensible. A useful operating model preserves enough human understanding to challenge the system, recognize anomalies, and take responsibility for the conclusion.
That requirement creates a boundary condition for compounding intelligence. If organizations automate the junior work through which people once learned a profession, they may increase current output while weakening their future supply of domain experts needed for orchestration and verification. If a team stores all institutional knowledge in systems that no one can reconstruct or question, it may gain scale while losing cognitive redundancy and result verification skills.
Designing apprenticeship, review, and deliberate practice into the new workflow is better than preserving inefficient work for its own sake. People need enough unaided experience to form domain mental models, enough exposure to exceptions to recognize when the system is outside its depth, and enough authority to stop a process that appears wrong.
Compounding intelligence should raise the level at which humans think without removing the foundation that makes higher-level thought possible.
Where responsibility fits
The preceding papers establish the boundary around human responsibility. Artificial intelligence is increasingly capable software, not a demonstrated conscious or morally responsible species. Its answers reflect source selection, context, instructions, and other human choices that should be visible enough to judge. Classify its risks by mechanism, evidence, exposure, and consequence rather than bundling them into one story.
The human role is therefore demanding: orchestration requires choices, verification requires competence, delegation requires limits, reuse requires maintenance, and platform commitment requires periodic challenge. The system may perform more of the work, but responsibility for accepting the result remains human.
I regard that as the source of the leverage rather than a disappointing limitation.
The practical test
I expect raw inference cost to become less interesting for many sophisticated users even while it remains critically important to providers. Tokens, hardware efficiency, and open-weight models will keep mattering for competition, privacy, sovereignty, research, and specialized deployment. My own optimization target is different.
I want today’s use of artificial intelligence to create assets that make tomorrow’s work cheaper, faster, more reliable, or capable of reaching a harder class of problems.
That test changes the questions:
Did the work add useful, well-scoped context?
Did it preserve a method worth reusing?
Did it improve human judgment or merely bypass it?
Did it reduce reconstruction without hiding assumptions?
Did it create a verification path?
Did it free attention, and where was that attention reinvested?
Did the system make a harder problem economically possible?
If the answer is yes, the gain is more than the value of one output. The next starting point has improved.
The eventual advantage may belong to neither the person with the cleverest prompt nor the person with the cheapest token. It may belong to the person or organization that builds the richest useful context, the strongest verification habits, the best orchestration methods, and the most reusable cognitive infrastructure while preserving the judgment to know when the system is wrong. This is what an AI-native organization looks like.
That conclusion feels strikingly familiar after fifty years in software. Better tools do not eliminate the need for human cognition; they change the economic boundary of what is possible and let capable people spend more of their limited attention on harder problems.
Compounding Intelligence describes this system at the level of a person or organization. The next paper, The AI Industrial System, moves outward to the compute, energy, capital, and physical infrastructure that make those gains possible.
Questions, corrections, or disagreements are welcome. You can reach me directly at dave@aworkingmodel.com.
Sources
OpenAI Help Center, “Projects in ChatGPT”, current documentation accessed September 19, 2026.
Joshua S. Rubinstein, David E. Meyer, and Jeffrey E. Evans, “Executive Control of Cognitive Processes in Task Switching”, Journal of Experimental Psychology: Human Perception and Performance 27, no. 4 (2001): 763–797.
OpenAI Help Center, “ChatGPT Work and Codex”, current documentation accessed September 19, 2026.
Morgan Stanley, “Key Milestone in Innovation Journey with OpenAI,” March 14, 2023, and “Launch of AI @ Morgan Stanley Debrief,” June 26, 2024.