Cheap technology is not a cheap task
Model fees are one component. Preparation, review, permissions and downstream correction may remain human work. Without changes there, tool efficiency may not become an operating result.
INVEST IN THE WORK
AI strategy and use-case portfolios.
As models become cheaper, worthwhile problems become the scarce resource.
A SIGNAL WORTH EXAMINING>280×
reduction in inference cost at a specified capability level1
2025 · Nov 2022–Oct 2024 · GPT-3.5-level performanceTHE ZHICE INTERPRETATION
As models become cheaper, worthwhile problems become the scarce resource.
Falling inference prices repeatedly redraw the economic boundary of tasks. They do not remove the costs of process integration, human review or error handling at the same pace. We believe AI investment should move from a fixed tool list to a revisable task portfolio, reassessing automation and human control as capability, prices and consequences change.
TWO PERSPECTIVES. ONE BUSINESS QUESTION.
Explore the choices behind the judgement.
01AI task-value discovery and use-case selectionStart AI investment with what has changed in task economics.
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Start AI investment with what has changed in task economics.
Cheaper inference makes more tasks feasible to try. Feasible and worthwhile remain different. We believe the enterprise must compare the value of the whole job: whether saved time can be used, how errors affect operations and whether the new method moves a workflow bottleneck.
Start AI investment with what has changed in task economics.
EXAMINING AI THROUGH THE TASK
THE REASONINGThe business conditions behind the view
Value often sits in the adjacent step
Faster drafting does not necessarily shorten approval; better prediction does not necessarily improve trading. The connection between a task and subsequent action determines whether an improvement is usable.
Sequence changes the value of learning
Related tasks can reuse knowledge, evaluations and interfaces. Selecting isolated demonstrations can miss clusters that accumulate enduring capability.
WHAT THIS MEANS FOR THE BUSINESSConnect the view to resources, ownership and action
01Compare the complete task chainRead the choiceCollapse
Examine preparation, processing, review and execution together. Identify where time is consumed and outcomes constrained before assigning work to AI.
02Prioritise verifiable business improvementRead the choiceCollapse
Consider frequency, consequences of error, assessability and value together. Valuable but hard-to-verify tasks require a different investment rhythm.
03Keep the portfolio revisableRead the choiceCollapse
Update priorities as models, prices and constraints change. Previously uneconomic tasks may become attractive; deployed tasks may warrant narrower scope.
What would make us revise this judgement?
Where the binding constraint is regulation, physical capacity or an immovable approval policy, AI may not be the next best investment. Establish what technology can actually change.
A QUESTION FOR THE NEXT BUSINESS DISCUSSION
What action will the organisation take differently after this task becomes faster, and which outcome should improve?
AI task-value discovery and use-case selectionFull perspective and practical approach
02AI transformation roadmap and value operationsAn AI roadmap earns its value by preserving the next choice.
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An AI roadmap earns its value by preserving the next choice.
When capability and prices change quickly, a multi-year blueprint centred on one model or platform can lose relevance. We believe transformation should develop three assets together: a revisable task portfolio, shared capability with genuine reuse and organisational learning that survives supplier changes.
An AI roadmap earns its value by preserving the next choice.
EXAMINING AI THROUGH THE TASK
THE REASONINGThe business conditions behind the view
A fixed tool list can lock the path too early
Selection before problem definition pushes future tasks into a predetermined platform. A resilient roadmap separates enduring capabilities from those that can be purchased or replaced as the market changes.
Isolated pilots accumulate hidden debt
Separate approaches to knowledge, permissions and evaluation can look fast initially but create repeated maintenance at scale. Shared capability should emerge alongside demonstrated reuse.
Organisational learning compounds
Failure patterns, operational feedback and acceptance rules improve the next experiment. Keeping only demonstrations and closure documents forces teams to relearn the same problems.
WHAT THIS MEANS FOR THE BUSINESSConnect the view to resources, ownership and action
01Sequence around dependenciesRead the choiceCollapse
Identify what tasks share, then order pilots, common capability and organisational adjustments. Avoid placing every foundation investment ahead of value testing.
02Give each investment stage an optionRead the choiceCollapse
Preserve the ability to expand, revise or stop, with evidence requirements for the next commitment. Early enthusiasm should not automatically become enduring cost.
03Invest in transferable organisational assetsRead the choiceCollapse
Maintain business evaluations, knowledge standards, interface contracts and operating experience as assets that continue to matter when models change.
What would make us revise this judgement?
For stable tasks and well-validated solutions, chasing every new model may add risk. Replaceability preserves choice; it does not justify constant switching.
A QUESTION FOR THE NEXT BUSINESS DISCUSSION
If the core model becomes stronger, cheaper or unavailable tomorrow, which capabilities still belong to the enterprise?
AI transformation roadmap and value operationsFull perspective and practical approach
Look beyond capability to judge what comes next in AI.
We track technical progress and test whether organisations can absorb it. The business boundary of AI changes with task economics, failure modes and accountability; one procurement decision cannot define it permanently.
- 01
Cost per valid task, not price per call
What we watch
Track calls, review time and exception handling needed for a usable result. Separate cheaper inference from expanding consumption.
How it changes the judgement
Falling review effort can justify revisiting the task portfolio. Cheaper calls with more exception work point to a need to redraw task boundaries.
- 02
Recovery across longer task chains
What we watch
Watch whether agents recognise deviation, stop inappropriate action and recover from interruption to an acceptable business state.
How it changes the judgement
Maturing recovery supports discussion of longer delegation. Failures that are hard to locate warrant narrower authority.
- 03
Business owners take responsibility for AI
What we watch
Look for owners who can explain outcome changes, adjust scope and jointly decide model and knowledge updates with technical teams.
How it changes the judgement
Sustained business ownership supports scale. Accountability left with a temporary project team signals unfinished operating arrangements.
A VIEW THAT CAN BE PUT TO WORK
The ZHICE Trusted AI Value MethodExplore the framework and implementationA perspective must stand up in practice.
Five stages—task analysis, portfolio choices, controlled design, evidence-based validation and scaled operations—connect business investment decisions with governance across the AI lifecycle. Drawing on published management research, the NIST risk management framework and agent engineering practice, the method brings evaluation datasets, permissions, human review and exit mechanisms into the design stage. Task requirements guide model selection; validation evidence determines deployment scope. Each capability upgrade prompts a fresh review of business outcomes and risk boundaries.
Data notes and research foundationsExpandCollapse
- 1Stanford HAI · AI Index 2025
Comparison of inference cost at GPT-3.5-level MMLU performance; not a reduction in total enterprise AI or project costs.
Stanford 2025 · Nov 2022–Oct 2024 · GPT-3.5-level performance - 2Stanford HAI · AI Index 2026
OSWorld is a structured computer-use benchmark; the result is not an energy-production success rate or a deployment threshold.
Stanford AI Index 2026 · OSWorld benchmark - 3IBM Institute for Business Value · 2025 CDO Study
Self-reported status in a cross-industry, cross-geography survey, not a measured maturity score for energy enterprises.
IBM 2025 · 1,700 senior data and analytics leaders globally
THE NEXT CONVERSATIONThe question worth discussing:
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