STRATEGYWeeks to result

Six Demand Elasticities Model

Map six demand elasticities to find where AI expands markets and creates net-new jobs

Problem it solves

Strategists and founders lack a rigorous way to identify where AI will expand demand rather than simply displace workers.

Best for

Strategists, investors, and founders analyzing a specific sector for net-new AI-enabled market opportunity.

Not ideal for

Teams seeking a quick operational checklist; this is an analytical lens for market-level thinking, not a personal productivity tool.

Overview

Why this framework exists

Most AI economic analysis treats labor as supply-side only, missing the demand-expansion effect. This model corrects that bias by mapping six elasticities—Price, Access, Complexity, Continuity, Personalization, and Relational—each describing a distinct reason demand was previously suppressed. When AI lowers the cost and friction of production, those suppressed demands unlock. Two unlock types emerge: the Affordability Unlock, where existing services reach new buyers at lower price points, and the Possibility Unlock, where entirely new service models become operationally viable for the first time. Applying the model to a sector reveals market size, dominant unlock type, and the new role categories needed to serve expanded demand.

Core principles

6 total
  1. Demand is not fixed; it expands as supply-side constraints fall
  2. AI changes cost, speed, and availability simultaneously, triggering multiple elasticities at once
  3. Affordability unlocks and Possibility unlocks are structurally different and require distinct go-to-market responses
  4. Historical precedent consistently shows demand absorbs new supply rather than shrinking
  5. Different sectors have different elasticity profiles; mapping them reveals where opportunity concentrates
  6. Correcting the lump-of-labor fallacy requires asking not just what work disappears but what new demand activates

Steps

6 steps
  1. Define the sector and document its current supply constraints
    Select a specific sector or service category. Write one sentence for each constraint currently suppressing latent demand: price, geographic access, navigational complexity, frequency of engagement, lack of personalization, or absence of human relationship. Focus on constraints felt by the median consumer, not the affluent one who can already buy their way past them.
    Pro tipIf a wealthy person can solve the constraint today by paying more, that is strong evidence the elasticity is real and large once cost falls.
  2. Score all six demand elasticities for the sector
    Rate Price, Access, Complexity, Continuity, Personalization, and Relational elasticity each on a 1–5 scale. A high score means a large pool of latent demand is suppressed by that constraint. Ask for each: if this constraint disappeared tomorrow, would significantly more people consume this good or service?
    Pro tipHealthcare scores high on all six elasticities simultaneously, which is why it is the most compelling AI opportunity sector in the near term.
    WarningDo not conflate elasticity strength with ease of unlocking. A high score signals potential, not certainty of activation.
  3. Classify each potential unlock as Affordability or Possibility
    Affordability unlocks mean the same service reaches new buyers at a lower price point. Possibility unlocks mean an entirely new service model becomes operationally viable that could not exist before. For each high-scoring elasticity, determine which unlock type AI enables and document the distinction explicitly.
    Pro tipPossibility unlocks create larger moats because you are building a new category with no pre-existing competitor to undercut rather than competing on price in an existing one.
    WarningMany strategists conflate the two. A cheaper version of an existing service is fundamentally different from a new model and requires different org design, pricing, and positioning.
  4. Estimate the new addressable market in three scenarios
    For each activated elasticity, model conservative, middle, and aggressive adoption rates using adjacent markets as anchors. Run the math in workforce terms as well as revenue: knowing that 40 million patients at a 150:1 care ratio creates 267,000 navigator roles makes the opportunity concrete and defensible to stakeholders.
    Pro tipAnchoring to existing comparable job categories—personal financial advisers, high school teachers—makes abstract job estimates legible to non-economists.
  5. Map the new role categories that emerge to serve expanded demand
    Identify the types of human roles the new demand tier will require. Common AI-enabled role families include Navigators, Continuous Support Workers, AI-Augmented Service Operators, Data and Ops Specialists, QA and Safety and Compliance roles, and Escalation Specialists. Assign each emerging role to the elasticity it primarily serves.
    Pro tipThese are not AI jobs in the narrow sense of prompt engineers. They are sector-specific service jobs made viable by the AI layer beneath them.
    WarningResist defining new roles around current job titles. New service paradigms create new job shapes that do not map cleanly onto existing descriptions.
  6. Stress-test each new role against AGI displacement risk
    For each proposed role, identify which of the seven Human Premium categories—Relationship, Embodied Presence, Trust, Accountability, Translation, Behavior Change, Provenance—protect it from eventual AI substitution. Roles with at least two strong structural premiums are likely durable; roles protected only by current capability gaps are transitional.
    Pro tipThe strongest critique of new-job optimism is 'won't AGI eat those roles too?' Your sector analysis is incomplete without an explicit, principled answer to that question.
    WarningSkip this step and the entire analysis is vulnerable to the most common and most reasonable objection to AI job optimism.

Checklist

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Examples

3 cases
Healthcare: Continuous Preventative Care as a Possibility Unlock

Healthcare scores high on all six elasticities. The most powerful is Continuity: most people operate under episodic reactive care, seeing a doctor only when something breaks. AI that continuously monitors wearables, labs, and patient-reported data makes an always-on preventative care model operationally viable for the first time. This is a Possibility Unlock—continuous preventative care did not exist as a scalable category before AI, so there is no prior demand baseline to compare it to.

OutcomeNew roles like Continuous Care Navigator emerge to serve the human layer above the AI monitoring system, with estimates ranging from 267,000 to 1.2 million such roles in the US alone.
Small Business Professional Services: Affordability Unlock

A typical small business owner cannot simultaneously budget a $5,000 design project, $3,000 marketing campaign, $2,000 legal review, and $1,500 analytics report. The demand is real; the price floor is the binding constraint. AI dropping each cost by roughly tenfold activates a long-tail market of millions of businesses that were never agency clients before. This is a textbook Affordability Unlock: the same menu at a radically lower price reaches an entirely new buyer tier.

OutcomeMillions of small businesses become first-time buyers of professional services, and a new category of AI-augmented service operators emerges to serve them.
Mental Health: Expanding the Support Layer

Mental health has strong Access and Complexity elasticities: most people who need support face long wait times, high costs, and stigma. AI enables a broader support layer between nothing and licensed therapy—peer facilitation, continuous check-ins, group support with clear escalation protocols. This activates both an Affordability Unlock (cheaper than therapy) and a Possibility Unlock (a continuous support tier that never existed as a scalable category).

OutcomeNew human roles for peer-support facilitators and escalation specialists emerge in a market tier that previously had no formal supply at all.

Common mistakes

3 traps
Assuming demand is fixed (lump of labor fallacy)
The core error in most AI jobs analysis is treating total work as a fixed quantity: if AI does more, humans do less. History consistently disproves this because demand expands to absorb new supply. Skipping demand-side analysis produces systematically pessimistic and incomplete forecasts.
Conflating affordability and possibility unlocks
An affordability unlock scales an existing service to new buyers; a possibility unlock creates a category that did not previously exist. Treating them identically leads to wrong competitive positioning, wrong org design, and wrong pricing strategy because each requires a fundamentally different go-to-market approach.
Stopping at capability without asking service design
Asking only whether AI can perform a task misses the actual market question: does AI-only delivery satisfy what the market wants? Many services have structural reasons why human involvement remains integral to the value. Skipping this service design question leads to systematically overestimating AI substitution speed.

Origin story

How this framework came to be

Extracted from The AI Daily Brief: Artificial Intelligence News.

Source

Traced to primary
Source · VIDEO
The New Jobs AI Will Create — The AI Daily Brief: Artificial Intelligence News
The AI Daily Brief: Artificial Intelligence News · 2026
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