ENOCH WORLD Research · SydTek AI at Work
Useful work.
Measured progress.
Broader access.
Can lower-cost AI help people complete useful work, learn practical skills, and access better services? The Abundance Experiment turns that question into a practical, repeatable comparison.
Protocol published · Pilot results pending
01 / The research question
Does cheaper AI produce a better result?
Making an AI response less expensive does not by itself make a useful service affordable. People still need dependable information, human review, clear responsibilities, and access to the benefits.
This exploratory ENOCH WORLD / SydTek protocol examines one defined operational workflow at a time. It compares the existing method with an AI-assisted method and includes the work required to check and correct both.
The initial decision target
20% less total task time
With quality at least as good as the baseline, the agreed minimum quality achieved, and no critical errors in the assisted result.
This is a proposed project threshold. It is not a measured finding, an industry benchmark, or a promised outcome.
- Time: Does AI assistance reduce execution, review, and correction time combined?
- Cost: Do gains remain after valuing labor, tools, setup, and support?
- Capability: Can the operator use the workflow independently and explain its limitations?
- Access: Can a lower delivery cost support more affordable or explicitly funded services?
02 / A reproducible starting exercise
Prepare an inventory exception report.
Use the synthetic inventory below to practice the workflow. All items, quantities, and rules are invented for this exercise. No customer information or live purchasing system is connected.
Your task
Identify every item whose on-hand quantity is below its reorder threshold. For each exception, report its SKU, on-hand quantity, threshold, and shortfall. Calculate shortfall as threshold minus on hand. An item exactly at its threshold is not an exception. Do not recommend or place a purchase.
| SKU | Item | On hand | Threshold | Unit |
|---|---|---|---|---|
| SYN-101 | Shipping cartons | 18 | 30 | each |
| SYN-102 | Label rolls | 12 | 10 | roll |
| SYN-103 | Packing tape | 6 | 15 | roll |
| SYN-104 | Safety gloves | 20 | 20 | pair |
| SYN-105 | Mailer envelopes | 0 | 25 | each |
| SYN-106 | Storage bins | 9 | 8 | each |
| SYN-107 | Printer paper | 4 | 10 | ream |
| SYN-108 | Cleaning cloths | 35 | 15 | each |
Baseline run
Prepare the report using your existing non-AI method. Record execution, review, and correction time separately. Score the final report with the checklist.
Assisted run
Use an AI tool of your choice with the supplied data and prompt, then check every result. Include all checking and correction time. Record any tool cost and score the final report with the same checklist.
Completing the same example twice creates a familiarity advantage. Use this dataset to learn the method; for a comparative pilot, use matched fresh tasks, alternate order where practical, and record task difficulty and order. This public exercise does not enroll you in a study.
View the sample prompt
Use only the attached synthetic inventory CSV. Identify rows where on_hand is strictly less than reorder_threshold. Return a table containing SKU, item, on_hand, reorder_threshold, shortfall, and unit. Calculate shortfall as reorder_threshold minus on_hand. Do not convert units, invent missing values, or place orders. An item at its threshold is not an exception. If the data is ambiguous, state the ambiguity. State the number of exceptions and the total shortfall grouped by unit. Label the report “Synthetic practice only.”
View the answer key and 10-point checklist
Expected exceptions: SYN-101, shortfall 12 each; SYN-103, shortfall 9 rolls; SYN-105, shortfall 25 each; SYN-107, shortfall 6 reams. There are four exceptions. Grouped shortfalls are 37 each, 9 rolls, and 6 reams. Do not add unlike units into one grand total.
- SYN-101 appears with a shortfall of 12 each.
- SYN-103 appears with a shortfall of 9 rolls.
- SYN-105 appears with a shortfall of 25 each.
- SYN-107 appears with a shortfall of 6 reams.
- SYN-102, SYN-106, and SYN-108 are excluded.
- SYN-104 is excluded because it is exactly at the threshold.
- The report states four exceptions.
- On-hand quantities and thresholds match the supplied rows.
- Grouped shortfalls are correct and units remain distinct.
- The report is labeled synthetic and makes no purchasing decision.
Critical errors: missing an exception, reporting an incorrect SKU or shortfall, combining incompatible units into a purchasing quantity, or claiming an order was placed. Record critical errors separately from checklist points.
03 / Measure one comparison
Compare the complete cost of the work.
Enter one baseline run and one comparable assisted run. Include execution, review, and corrections. These calculations describe your entries; they are not published research findings.
The protocol and exercise are available on this page. Interactive comparisons and downloads require the page's tools to load. If controls are unavailable, use the visible inventory, prompt, and checklist.
This comparison
- Total time
- Time change
- Estimated full cost
- Estimated cost difference
- Quality checks
- Critical errors
A labor-cost estimate values time. Cash expenditure changes only when the organization actually changes its spending. These figures do not establish a general research finding.
Your session comparisons
No comparisons recorded in this session.
This tool has no submission endpoint. Entries stay in this page's memory until you export them or leave the page. Export before leaving. Do not enter personal or confidential information.
| Record | Workflow | Data type | Time change | Decision |
|---|
Clear every comparison in this session? Export first if you need to keep them.
04 / Pilot design
One workflow. A trained operator. Evidence of what changed.
The proposed first pilot is a four-week SydTek AI at Work operations sprint for adults in a small organization. It combines focused instruction, a working tool, an operating guide, and comparison with the organization's current method. The organization and participating adult agree to the workflow and intended data use before participation.
Comparable work
Set the task definition, quality checklist, critical-error rules, and cost assumptions before collecting measurements. Use matched fresh tasks, record differences in difficulty, and alternate condition order where practical. Repeated tasks within one organization are related observations.
Independent capability
At completion, the operator performs a fresh task and explains the sources, verification process, limitations, escalation point, and way to continue when the AI tool is unavailable. A completion record describes that specific demonstrated capability.
Responsible information use
Use approved, minimized, or synthetic data. A responsible adult reviews outputs and authorizes consequential action. Publish aggregate results or explicitly authorized examples. The public comparison tool collects no research submissions.
Honest interpretation
Report task counts, quality, timing, cost assumptions, support requirements, and unsuccessful runs. Summarize by organization as well as overall. A small exploratory pilot can guide the next build; it cannot establish an economy-wide effect.
The proposed 90-day sequence
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Days 1–14
Understand the recurring problem
Speak with ten potential buyers in one segment. Identify a repeated need, a responsible owner, a baseline, and a budget. These are proposed discovery targets.
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Days 15–30
Prepare one working demonstration
Build the workflow, operating guide, and focused teaching sequence. Define scope, price, quality requirements, and the baseline before delivery.
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Days 31–60
Deliver and measure
Proposed target: three paid pilots using the same core workflow. Record implementation, support, review, correction, labor, and tool costs alongside outcomes.
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Days 61–90
Decide what deserves expansion
Refine the components that repeat. Evaluate continued use, willingness to pay again, delivery economics, and whether a funded access model is feasible.
This schedule starts when pilot onboarding is complete. Publication of this protocol does not mean recruitment, enrollment, or pilot delivery has begun.
05 / Public results register
Pilot results are pending.
No participant outcomes, customer savings, or experimental findings are being claimed. Calculator examples are hypothetical and remain labeled in session exports.
Future reports should identify the measurement period, task groups, number of comparisons, quality outcomes, delivery costs, limitations, and protocol changes. Any claim of broader access must describe the actual service provided and how that access was funded.
06 / Research and context
Evidence behind the question
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Brynjolfsson, Li, and Raymond: Generative AI at Work
The 2025 published study examined 5,172 customer-support agents and reported a 15% average increase in issues resolved per hour, with substantial variation across workers. That result concerns its particular workplace setting.
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Stanford HAI: AI Index 2025
Documents falling costs for particular benchmark-equivalent AI capabilities. Model-query prices do not include every cost of delivering a dependable service.
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IMF staff: Broadening the Gains from Generative AI
Examines productivity potential, disruption, distribution, and policy uncertainty. The paper represents its authors' research.
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International Energy Agency: Key Questions on Energy and AI
Examines energy demand and physical infrastructure constraints underlying the expansion of AI services.
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Moonshots episode 220: a conversation with Elon Musk
Background discussion of a possible future of abundance. The experiment measures present workflow effects and does not treat a prediction about money or employment as an established finding.
Sources provide research or discussion context. Their inclusion does not imply an institutional affiliation, sponsorship, or endorsement of ENOCH WORLD or this pilot.
Explore research and provenance →