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# AI Lets Us Own More of the Outcome
- URL: https://abundance.alloconomy.com/alloconomy/ai-lets-us-own-more-of-the-outcome/
- Published: 2026-10-19T07:37:05.000Z
- Updated: 2026-10-19T07:37:05.000Z
- Description: A new layer of capability can expand what we imagine, learn, and deliver. Connecting deep expertise to a larger customer and business outcome.
- Author: Sathya Narayanan
- Tags: Alloconomy, AI Tsunami, Foundations, #Import 2026-10-18 19:15

**What could we build if more of the journey from an idea to a useful outcome were within our reach?**

That question connects almost everything I am doing with Alloconomy.

It is why I published [*From Tokens to Atoms*](https://www.amazon.com/Tokens-Atoms-Ownership-Economics-Abundance-ebook/dp/B0H9S4X1TG?ref=abundance.alloconomy.com) and [*When Scarcity Breaks*](https://www.amazon.com/gp/product/B0HBM5W6LM?ref=abundance.alloconomy.com). It is why I write about [advertising](https://abundance.alloconomy.com/alloconomy/ad-behind-the-page-who-gets-the-advertising-dollar/), [money](https://abundance.alloconomy.com/alloconomy/why-cheat-sheets-still-matter-in-the-agentic-age/), [allocations](https://abundance.alloconomy.com/alloconomy/allocation-is-life/), and [the work behind a customer promise](https://abundance.alloconomy.com/alloconomy/activity-based-costing/). It is why I am exploring [supply chains and enterprise systems](https://abundance.alloconomy.com/alloconomy/before-the-shelf/) through the [Supply Chain Management collection](https://abundance.alloconomy.com/supply-chain/). And it is why [understanding how we learn](https://abundance.alloconomy.com/alloconomy/how-understanding-happens/) matters so much to me.

I am a software development manager at Amazon. It would be natural to expect most of my writing to concern engineering teams, software delivery, and organizational leadership. Those are valuable subjects. My curiosity currently reaches across the larger system those teams help build: how a business creates something useful, gets it to a customer, and makes the economics work.

AI makes that exploration feel unusually consequential—and unusually possible.

**My belief is that, as AI makes more digital execution accessible, many of us will be able to own a broader stretch of the path from a customer’s problem to a valuable outcome. Our roles can grow around that opportunity.**

The invitation is to build on our expertise, learn across the connections, and attempt things we previously might never have considered.

*These are my personal views, not Amazon’s position. The business examples below are fictional; they do not describe my employer’s internal systems or practices.*

## The question behind [my two books](https://abundance.alloconomy.com/topics/books/)

In [*From Tokens to Atoms*](https://www.amazon.com/Tokens-Atoms-Ownership-Economics-Abundance-ebook/dp/B0H9S4X1TG?ref=abundance.alloconomy.com), I explored the distance between digital intelligence and useful change in the physical world. An answer can help us design something, plan it, or operate it. Getting the result into someone’s life also involves energy, materials, machines, infrastructure, economics, and institutions. Read the [newsletter introduction to the book](https://abundance.alloconomy.com/alloconomy/ai-can-make-intelligence-cheap-but-can-it-make-life-abundant/) for that starting point.

[*When Scarcity Breaks*](https://www.amazon.com/gp/product/B0HBM5W6LM?ref=abundance.alloconomy.com) carries that inquiry across a larger landscape: what might become possible as capabilities improve across those layers, and how could their benefits reach ordinary people? Its [newsletter introduction](https://abundance.alloconomy.com/alloconomy/when-scarcity-breaks/) lays out the larger question.

The reason I published [these books](https://abundance.alloconomy.com/topics/books/) begins with a positive belief. **AI gives us a new world of abstractions to build on. That can expand both what we are capable of doing and what we are capable of imagining.**

When less of our attention is consumed by assembling every underlying piece, we can ask larger questions. Could a small team build a useful service for customers who were previously too expensive to serve? Could a teacher develop a learning experience around difficulties that a standard lesson misses? Could an operator turn years of practical experience into a tool other people can use?

At a different scale, could better intelligence help us coordinate the technologies and institutions needed for more dependable energy, more accessible housing, or less wasteful production?

These are possibilities to investigate. Each needs evidence, expertise, resources, and a viable route into the world. But the ability to investigate more of them is itself exciting.

The same question runs from [the books](https://abundance.alloconomy.com/topics/books/) into everyday work: **what can I now build on top of what others have made possible?**

[![A conceptual path from reusable capabilities through new questions and coordinated work to useful outcomes, linking From Tokens to Atoms and When Scarcity Breaks to the opportunity to imagine and build more.](https://storage.ghost.io/c/f5/a9/f5a9f801-5a10-44d6-b07c-6658660be1a9/content/images/ai-lets-us-own-more-of-the-outcome/new-possibilities.png)](https://storage.ghost.io/c/f5/a9/f5a9f801-5a10-44d6-b07c-6658660be1a9/content/images/ai-lets-us-own-more-of-the-outcome/new-possibilities.png?ref=abundance.alloconomy.com)

Conceptual illustration · [Open full-size image](https://storage.ghost.io/c/f5/a9/f5a9f801-5a10-44d6-b07c-6658660be1a9/content/images/ai-lets-us-own-more-of-the-outcome/new-possibilities.png?ref=abundance.alloconomy.com)

*[The books](https://abundance.alloconomy.com/topics/books/) explore this possibility across the economy. This essay brings it back to the work each of us can take on.*

**Explore the books:** [*From Tokens to Atoms* on Amazon](https://www.amazon.com/Tokens-Atoms-Ownership-Economics-Abundance-ebook/dp/B0H9S4X1TG?ref=abundance.alloconomy.com) · [Read its newsletter introduction](https://abundance.alloconomy.com/alloconomy/ai-can-make-intelligence-cheap-but-can-it-make-life-abundant/) · [*When Scarcity Breaks* on Amazon](https://www.amazon.com/gp/product/B0HBM5W6LM?ref=abundance.alloconomy.com) · [Read its newsletter introduction](https://abundance.alloconomy.com/alloconomy/when-scarcity-breaks/).

## Two ways we have always helped one another reach further

Consider a simple thought experiment. Imagine a Stone Age community in which one person becomes particularly good at shaping a cutting tool. Another person uses that tool to prepare food or work a material without having to reproduce the maker’s entire learning process.

Two ideas are already visible.

**Specialization** means developing depth in a particular capability. **Abstraction**, as I am using the word here, means making a capability usable without requiring its user to master every detail of how it is produced.

The tool carries work forward. Its user can begin from an achievement rather than from the beginning.

That is a useful lens on economic development, not a claim that two ideas explain all of human history. Energy, science, institutions, trade, and many other forces matter. Adam Smith’s discussion of the [division of labour](https://www.econlib.org/library/Smith/smWN.html?chapter%5Fnum=4&ref=abundance.alloconomy.com#book-reader) is one classic account of the gains from specializing and coordinating work.

A modern example makes the distinction clearer. A doctor can prescribe a medicine without knowing how to run the factory that manufactures it. The doctor needs appropriate knowledge of its use, evidence, interactions, and limitations. The manufacturer needs a different depth of expertise. Their work connects through a product and the standards and institutions around it.

Neither has to become the other. Together, they make an outcome possible.

Software works this way too. A developer can use a database without designing its storage engine. A business can use a payment service without building the whole payment network. Each capability becomes a starting point for more work.

Abstraction and specialization reinforce each other. Specialists create capabilities others can use. Those usable capabilities let other people concentrate on a different problem and develop another specialty.

**The extraordinary part is that the next person can build something the original specialist never imagined.**

[![Specialization develops a capability deeply; abstraction makes it usable by others. A reinforcing cycle opens new possibilities. A doctor uses medicine without running its factory, while understanding its promise and limits.](https://storage.ghost.io/c/f5/a9/f5a9f801-5a10-44d6-b07c-6658660be1a9/content/images/ai-lets-us-own-more-of-the-outcome/abstraction-and-specialization.png)](https://storage.ghost.io/c/f5/a9/f5a9f801-5a10-44d6-b07c-6658660be1a9/content/images/ai-lets-us-own-more-of-the-outcome/abstraction-and-specialization.png?ref=abundance.alloconomy.com)

Conceptual illustration · [Open full-size image](https://storage.ghost.io/c/f5/a9/f5a9f801-5a10-44d6-b07c-6658660be1a9/content/images/ai-lets-us-own-more-of-the-outcome/abstraction-and-specialization.png?ref=abundance.alloconomy.com)

## AI adds another layer to build on

Now consider a person with a deep understanding of a business problem.

They may know why customers struggle, which exceptions consume the team’s day, and what a better experience would look like. Turning that knowledge into a working product can still require research, analysis, design, software, testing, documentation, and ongoing operation.

AI can help across more of those activities. A person can explore an unfamiliar concept, examine alternatives, build an initial interface, analyze a dataset, and test a proposed workflow with much shorter distances between the steps.

That does not make every output dependable. It makes more attempts available.

There is evidence for useful gains in particular settings. A study of 5,172 customer-support agents reported a 15% average increase in issues resolved per hour with AI assistance, with substantial differences across workers. A separate experiment with consultants found better performance on tasks within the model’s capabilities and emphasized that those capabilities have an uneven boundary. These are findings about particular tools and tasks, not a universal productivity multiplier. [Brynjolfsson, Li and Raymond, revised 2024](https://arxiv.org/abs/2304.11771v2?ref=abundance.alloconomy.com); [Harvard Business School, 2023 experiment](https://aiinstitute.hbs.edu/navigating-the-jagged-technological-frontier/?ref=abundance.alloconomy.com).

My inference is that, where these capabilities become dependable, the practical boundary of what an individual or small team can deliver can expand.

I care especially about [**applied AI**](https://abundance.alloconomy.com/alloconomy/new-ai-labor-stack/): using available models, tools, knowledge, and expertise to solve a real problem. The research and infrastructure needed to build frontier models are a different undertaking, and I am less certain about how those roles evolve.

An AI service is also a different kind of abstraction from a well-specified calculator. Its outputs may vary and require checking. Part of using this new layer well is learning what it can be trusted to do in a particular setting—and building the tests and support that make that trust warranted.

That is work we can get better at. Every dependable capability gives us another place from which to begin.

## Our specialty can become the starting point for a larger contribution

For many of us, a job title is a compact description of our expertise: database engineer, Java developer, financial analyst, product manager, systems administrator, engineering manager.

Those descriptions remain useful. They tell colleagues something about where our depth lies. The opportunity I see is to connect that depth more directly to the outcome it helps produce.

A commercial organization pays for work because it expects that work to contribute to something worthwhile: serving customers, earning revenue, improving costs, maintaining reliability, managing risk, or building future capability. The connection may be indirect, shared across teams, or visible only over time. Individual compensation is not a precise meter of individual value.

Still, it is a powerful question to ask: **whose situation becomes better because this work succeeds, and how does that improvement sustain the business?**

A data engineer might begin with a pipeline and help carry the work through to a decision that improves stock availability. An analyst might begin with a variance report and help build a workflow that investigates causes and tests a response. A product manager might carry customer insight further into a working prototype, an operating process, and evidence of adoption.

I believe routine, reproducible digital tasks will become more widely available and less differentiating on their own. That is my directional judgment, not a declaration that every digital profession is becoming interchangeable. Deep expertise, trusted relationships, difficult context, and sound execution remain substantial contributions.

The encouraging implication is that we can apply those contributions across more of the journey. As that becomes practical, I expect organizations increasingly to define applied digital roles around a customer or business outcome. The expectation of broader ownership can grow because our ability to deliver it is growing too.

**End-to-end ownership means helping carry a defined outcome from problem to evidence, with the people and capabilities it needs.** It may belong to a team. It needs clear decision rights and boundaries. It does not require one person to perform every task.

## A useful product begins with the customer’s promise

Imagine Maya, who runs the fictional publisher Stories Inc. A school wants to know whether it can receive 600 copies of a book before the new term.

Maya sees an opportunity: help schools obtain dependable answers quickly enough to make their plans. AI could help her team interpret an inquiry, find relevant records, prepare a quote, and explain the options.

A narrow project might end when the reply is generated. A broader outcome starts with the school’s actual need: the right books, an affordable order, and a date it can rely on.

That creates a connected path:

One promise · A connected outcome

1. **Understand:** Which book, how many copies, which destination, and what deadline?
2. **Establish:** Which inventory and production records can support that promise?
3. **Design:** What can the system answer, what requires review, and how can the school correct it?
4. **Deliver:** Can printing, dispatch, payment terms, and customer support work together?
5. **Learn:** Did the school get what it needed, and did the order create a worthwhile contribution?

**AI and specialists contribute throughout. An owner keeps the customer promise and business result connected.**

Someone needs to understand why “in stock” might include copies already promised elsewhere, why a printing slot is not a delivery date, and why [a large order can still be unattractive if it creates enough extra work](https://abundance.alloconomy.com/alloconomy/activity-based-costing/).

Those details are where broader understanding becomes useful judgment. They tell the team what to ask, which evidence to check, and when to bring in a specialist.

The upside is larger than faster replies. Maya might be able to serve schools she previously could not support economically, offer more useful choices, or turn an unreliable inquiry process into a dependable service.

That is the direction that most interests me: **new capability, new customers, and new ways to create value**. Reducing waste is worthwhile too. But [saved minutes become a business benefit](https://abundance.alloconomy.com/alloconomy/activity-based-costing/) only when they release a real expense, improve a service, or create capacity that gets used.

For this example, a useful review would examine fulfilled orders, delivery reliability, customer experience, and contribution after relevant production, fulfillment, AI, and review costs. The comparison needs a credible baseline; extra orders cannot automatically be credited to the new tool. There are no assumed savings or revenue figures here. Maya would have to measure them.

## Understand the system; deepen where the decision requires it

If abstractions let us build without mastering every detail, why am I arguing for broader understanding?

Because the understanding that makes a capability useful is different from the knowledge required to manufacture it.

Maya need not design a database engine. She does need to know what an inventory number means. A product manager need not memorize every software framework. They do need to understand the user’s task, the information the product relies on, its important failure modes, its economics, and how to tell whether it helps.

The useful questions sit at the connections: what goes in, what comes out, what is promised, what is assumed, and what happens when the promise cannot be kept?

We can deepen selectively. If the decision turns on data freshness, investigate data freshness. If it turns on freight economics, involve someone who knows freight. If a financial or safety judgment requires qualified expertise, build that expertise into the process.

AI can help us learn the landscape and prepare better questions for those experts. Specialists, in turn, can use AI to make their expertise useful across a wider range of problems.

This is why I see broader ownership as an opportunity. We can participate meaningfully in more of the outcome while continuing to build on one another’s depth.

## Learning how to learn makes that opportunity usable

Broader access to tools expands what we can attempt. **Learning well expands what we can understand, judge, and carry through.**

That makes [*How Understanding Happens*](https://abundance.alloconomy.com/alloconomy/how-understanding-happens/) central to this whole project.

Reading an explanation and recognizing its vocabulary can feel like understanding. A better test is whether we can [explain the mechanism ourselves](https://abundance.alloconomy.com/alloconomy/how-understanding-happens-self-explanation/), [predict what a change would do](https://abundance.alloconomy.com/alloconomy/how-understanding-happens-evidence-of-understanding/), and [use the idea in a different situation](https://abundance.alloconomy.com/alloconomy/how-understanding-happens-transfer/).

In Maya’s case, suppose an agent explains the difference between physical stock and stock available to promise. She can ask herself to [explain it without looking](https://abundance.alloconomy.com/alloconomy/how-understanding-happens-retrieval/). Then she can test an example: there are 700 copies in the building, but 200 are committed to another customer. Can she promise 600? What changes if a reliable print run finishes tomorrow but the carrier has already left?

The example exposes where the mental model needs work. [Feedback](https://abundance.alloconomy.com/alloconomy/how-understanding-happens-feedback/) gives her something specific to improve. [Returning to the idea later](https://abundance.alloconomy.com/alloconomy/how-understanding-happens-spacing-and-interleaving/) and [applying it in another context](https://abundance.alloconomy.com/alloconomy/how-understanding-happens-transfer/) helps test whether it has become usable knowledge.

A practical learning cycle is:

**[Ask](https://abundance.alloconomy.com/alloconomy/how-understanding-happens-outcomes/) → [explain in your own words](https://abundance.alloconomy.com/alloconomy/how-understanding-happens-self-explanation/) → [apply](https://abundance.alloconomy.com/alloconomy/how-understanding-happens-transfer/) → [check the result](https://abundance.alloconomy.com/alloconomy/how-understanding-happens-feedback/) → [revise](https://abundance.alloconomy.com/alloconomy/how-understanding-happens-metacognition/) → [try again in a new situation](https://abundance.alloconomy.com/alloconomy/how-understanding-happens-transfer/).**

Agents can support each step. They can propose counterexamples, vary a scenario, and help us practice. We should also compare important answers with records, sources, observed outcomes, or people who know the subject. One agent agreeing with another is not independent confirmation.

The aim is to accelerate the development of our own understanding. A smooth demonstration is encouraging; being able to explain and improve what happens underneath it gives us more freedom to act.

Learning therefore belongs inside the work. We learn enough to attempt a bounded outcome, use the result to discover what we missed, and become capable of taking the next step.

## Why these subjects belong together

This is the thread through my writing. Each subject reveals a different connection between a capability and the result someone experiences.

[**Advertising Alloconomy**](https://abundance.alloconomy.com/alloconomy/ad-behind-the-page-who-gets-the-advertising-dollar/) follows who does the work, who receives the budget, and what would establish that advertising created value. It helps connect a technical delivery to the business paying for it.

[**Activity-Based Costing**](https://abundance.alloconomy.com/alloconomy/activity-based-costing/) follows the work behind a promise. It helps distinguish a convenient allocation from the resources an activity actually consumes—and a reported saving from an expense that can really change.

[**Allocation Is Life**](https://abundance.alloconomy.com/alloconomy/allocation-is-life/) and [**Abundance Is an Allocation Problem**](https://abundance.alloconomy.com/alloconomy/abundance-is-an-allocation-problem/) ask what we direct our time, money, attention, and capabilities toward as possibilities expand.

[**Money Cheat Sheets for Humans Working with AI**](https://abundance.alloconomy.com/alloconomy/why-cheat-sheets-still-matter-in-the-agentic-age/) and [**How central banks work**](https://abundance.alloconomy.com/alloconomy/your-financial-life-has-two-central-banks/) build the context for understanding funding, obligations, timing, and the financial conditions around a business.

My supply-chain work—[*Behind the Buy Button*](https://abundance.alloconomy.com/alloconomy/behind-the-buy-button/), [*Before the Shelf*](https://abundance.alloconomy.com/alloconomy/before-the-shelf/), and [*From Click to Doorstep*](https://abundance.alloconomy.com/alloconomy/from-click-to-doorstep/)—follows commercial promises, inbound movement, and delivery to a customer. Enterprise resource planning, or ERP, belongs in that inquiry because records of orders, receipts, invoices, and obligations help people coordinate the work. These offer three connected perspectives within the [Supply Chain Management collection](https://abundance.alloconomy.com/supply-chain/).

[**The AI Labor Stack**](https://abundance.alloconomy.com/alloconomy/new-ai-labor-stack/) maps the functions between a model and an outcome. This essay asks how our own contribution can stretch across more of that map. [**How Understanding Happens**](https://abundance.alloconomy.com/alloconomy/how-understanding-happens/) supplies the learning practices for making that stretch meaningful.

These are not subjects everyone must master before building anything. They are routes into the particular system a person wants to improve. Start with a real question; follow the connections it requires.

## Why Alloconomy matters when you can ask an agent yourself

A fair question follows: if AI makes all this exploration accessible, why read Alloconomy at all?

You can ask an agent directly. I encourage it. For a specific situation, a conversation adapted to your needs may be more useful than a general essay.

What I am trying to make is a connected starting point: worthwhile questions selected and developed, mechanisms explained in a useful order, examples that expose important distinctions, and sources and assumptions you can inspect.

A retailer creates a useful experience by selecting and arranging products made by others and doing the work to make them available. A software product can combine existing infrastructure into something a customer can use. In the same way, I am trying to turn available knowledge and AI capabilities into a learning experience. The comparison concerns the function of adding value, not equal scale or economics.

An agent can define [activity-based costing](https://abundance.alloconomy.com/alloconomy/activity-based-costing/) on request. My contribution should be to help a reader see why two equal-sized orders create different work, why an allocated cost may not disappear, and how those distinctions change a decision. Across articles, the same ideas can reappear in advertising, logistics, software, and money, giving the reader more opportunities to apply them.

That continuity matters. A reader may arrive with one question and discover another they did not yet know to ask. They can bring the explanation to their own agent, challenge it, adapt the example, and take it further.

This is the argument behind [*What Alloconomy Makes*](https://abundance.alloconomy.com/alloconomy/what-alloconomy-makes/), now brought into this larger picture. I combine the topics I choose, my experience, what I am trying to learn, and the work of research, checking, connection, and explanation. I use AI extensively in that process. The result still has to earn your time.

My effort or expenditure does not establish its value. A useful test does: **can you explain something more clearly, ask a better question, or make a better-supported decision after using it?**

I can provide a path and useful material. Understanding develops through what you do with them.

## A larger horizon for work

I expect this expansion to be particularly visible in work whose inputs and outputs are digital. In physical work, progress also depends on equipment, environments, safety, and the practical realities of moving and changing matter. A plumber’s or pilot’s expertise does not simply become a software interface. Physical work can change too; the constraints and pace differ.

Even within digital work, tasks and jobs are different things. The ILO’s 2025 assessment describes occupational exposure to generative AI and emphasizes transformation as the more likely overall impact; exposure is not a count of jobs that will disappear. My argument about broader ownership is a direction I believe worth preparing for, not a universal forecast about titles or employment. [ILO, *Generative AI and Jobs*](https://www.ilo.org/publications/generative-ai-and-jobs-refined-global-index-occupational-exposure?ref=abundance.alloconomy.com).

There is also an organizational choice here. People need access to information, time to learn, support from specialists, and authority proportionate to the outcomes they are asked to own. A team can share an outcome and preserve clear responsibilities within it. That is how the opportunity becomes practical.

For my own work, engineering leadership is one capability I bring to that larger task. I want to keep developing the ability to connect people, technology, customer needs, and business economics—and to learn what I do not yet understand.

A good place to begin is one bounded problem. Name the person who benefits. Describe what better would look like. Trace the essential steps and costs. Identify what you can build on, what you need to learn, and whose expertise you need. Try something small enough to assess, then follow it through to the result.

You do not need a new title to begin making those connections.

## What will you build on top of it?

[*From Tokens to Atoms*](https://www.amazon.com/Tokens-Atoms-Ownership-Economics-Abundance-ebook/dp/B0H9S4X1TG?ref=abundance.alloconomy.com) and [*When Scarcity Breaks*](https://www.amazon.com/gp/product/B0HBM5W6LM?ref=abundance.alloconomy.com) began with my belief that the world still contains extraordinary possibilities—and that better capabilities can help us reach them.

Alloconomy is one way I am acting on that belief. I am using the tools, broadening my understanding, making things, and putting the results where other people can question and use them. Learning is already a meaningful return for me and my family. Building a sustainable business remains a separate test I want to meet.

The [AI tsunami](https://abundance.alloconomy.com/alloconomy/new-ai-labor-stack/) gives us reason to reconsider how much of our work is defined by yesterday’s boundaries. It also gives us new ways to explore what comes next.

**Build on a deeper layer of capability. Learn enough to see a larger possibility. Carry more of it through to something useful.**

That is the connection between [my books](https://abundance.alloconomy.com/topics/books/), [my learning](https://abundance.alloconomy.com/alloconomy/how-understanding-happens/), and the work I want to do.

**What worthwhile outcome is now within your reach that you would not have attempted before?**

---

**A place to begin:** Explore [*From Tokens to Atoms*](https://www.amazon.com/Tokens-Atoms-Ownership-Economics-Abundance-ebook/dp/B0H9S4X1TG?ref=abundance.alloconomy.com) and [*When Scarcity Breaks*](https://www.amazon.com/gp/product/B0HBM5W6LM?ref=abundance.alloconomy.com) on Amazon, or their newsletter introductions—[the first book’s introduction](https://abundance.alloconomy.com/alloconomy/ai-can-make-intelligence-cheap-but-can-it-make-life-abundant/) and [the second book’s introduction](https://abundance.alloconomy.com/alloconomy/when-scarcity-breaks/)—for the larger horizon, [How Understanding Happens](https://abundance.alloconomy.com/alloconomy/how-understanding-happens/) for the learning, or [Activity-Based Costing](https://abundance.alloconomy.com/alloconomy/activity-based-costing/) for one practical way to connect work to business value. Choose the question closest to your own work and take it further.

*Written with AI assistance. The argument and forecasts are my own position for discussion; the cited studies support their stated, limited findings. The illustrations are conceptual, and the Stories Inc. example is fictional.*