# Short-Half-Life AI Tools: Edge Harvesting as the New Software Business Model

A research brief on monetizing fast-decaying AI features through rapid payback pricing, distribution velocity, and an agent harness core that compounds as models improve.

- Canonical URL: https://buildooor.com/research/edge-harvesting-ai-tools
- Author: Rob Baratta
- Published: 2026-03-05
- Version: Working Paper v1.0
- Keywords: short-half-life software, AI cannibalization, edge harvesting, micro SaaS monetization, agent harness architecture, platform risk, model deprecations, AI pricing compression, distribution velocity, software portfolio strategy

---

<ResearchAbstract>
  Software defensibility has shifted from a multi-year moat game to a repeated
  edge extraction game. In 2024 alone, U.S. private AI investment reached
  $109.1B, organizational AI adoption hit 78%, and inference costs for GPT-3.5-class
  workloads fell by 280x from late 2022 to late 2024. Those numbers imply two
  simultaneous truths: building is cheaper than ever, and feature-level advantage
  decays faster than ever. This paper argues that short-half-life AI tools are not
  a pathological business category; they are a rational category with a different
  operating model. The correct frame is not traditional SaaS compounding but
  volatility trading: find temporary inefficiencies, monetize quickly, and recycle
  gains into an agent harness layer that compounds as models improve. We define
  practical monetization archetypes, pricing envelopes, deprecation-aware
  product operations, and a barbell portfolio strategy that combines 30-180 day
  utility waves with durable context/memory/policy infrastructure.
</ResearchAbstract>

<ResearchSection number={1} title="From SaaS Moats to Edge Half-Lives">

The dominant software question of the 2010s was:
*can this product defend an ARR stream for a decade?*
The dominant software question of the late 2020s is different:
*can this product recover build cost before platform integration catches up?*
This is not rhetorical flourish. It is a structural consequence of cheaper
intelligence, higher baseline capability in consumer products, and faster
release cycles by model providers who increasingly ship direct-to-user
surfaces.

In practical terms, many categories that previously supported venture-scale
SaaS now behave like short-dated instruments. A utility can produce immediate
cashflow for 3-12 months, then flatten when an upstream model, operating
system, or distribution platform absorbs the core feature. The classic
flashlight-app dynamic has moved from hardware toggles to cognition-layer
workflows: summarize, route, prioritize, nudge, draft, reconcile, and plan.

The critical strategic error is treating this as a temporary anomaly. The more
accurate reading is that software production and software consumption have both
moved into a higher-frequency regime. Builders can launch in days. Users can
switch in minutes. Platforms can clone in weeks. Under those conditions,
persistence is no longer the default objective for every product line.
Rapid payback and portfolio recycling become first-class objectives.

This does not eliminate durable businesses. It changes where durability sits.
Durable value migrates from isolated feature execution to integrated memory,
policy, trust, and distribution systems. If the user can reproduce your value
with one new model release and a weekend project, you are in the feature layer.
If better models improve your throughput while your proprietary context graph,
decision policy, and trust loop stay unique, you are in the harness layer.

</ResearchSection>

<ResearchSection number={2} title="Edge Compression Is Measurable, Not Anecdotal">

The edge compression thesis can be grounded in hard macro data rather than
founder sentiment. Stanford's 2025 AI Index documents both rapid adoption and
rapid cost compression: U.S. private AI investment reached $109.1B in 2024,
global private investment rose to $252.3B (+26% YoY), organizational AI usage
climbed to 78%, and inference costs for GPT-3.5-equivalent performance fell
by roughly 280x between November 2022 and October 2024.

<ResearchTable
  caption="Table 1. Edge Compression Indicators (2024-2025)"
  columns={[
    { label: 'Signal' },
    { label: 'Latest Reading', mono: true },
    { label: 'Implication', muted: true },
  ]}
  rows={[
    ['U.S. private AI investment (2024)', '$109.1B', 'Capital concentrates where platform power already lives'],
    ['Global private AI investment growth (2024)', '+26% YoY to $252.3B', 'More capital chases faster cycles, not longer durability'],
    ['Organizations using AI (2024)', '78% (up from 55%)', 'Adoption is now mainstream, so novelty windows close faster'],
    ['Industry share of notable models (2024)', '90.16%', 'Roadmaps are controlled by a few labs and hyperscalers'],
    ['Top-vs-10th model performance gap', '5.4% (down from 11.9%)', 'Feature advantages are shorter-lived and easier to copy'],
    ['Inference cost for GPT-3.5-class tasks', '280x decline (Nov 2022 to Oct 2024)', 'MVP cost collapses, but pricing power collapses too'],
  ]}
  footnote="Sources: Stanford HAI AI Index 2025 summary and chapter PDFs; figures rounded where needed for readability."
/>

Each metric pushes in the same direction. More capital and broader adoption
attract more builders, which increases competition intensity in the exact layers
where build cost is falling fastest. Simultaneously, model quality convergence
reduces the duration of quality-based differentiation: when the performance
spread between the best and the tenth-best model narrows to single digits,
feature-level advantages are increasingly packaging and timing effects,
not enduring technical moats.

A second-order effect matters even more. Industry produced 90.16% of notable
models in 2024. That concentration means a small number of platform actors can
reset feature markets on short notice. You are no longer competing in a market
with many independent innovation trajectories; you are operating downstream of
a few release calendars.

<ResearchCallout>
  The new default is not "build once, rent forever." It is
  "ship fast, collect value quickly, and assume the baseline will
  move under you."
  Builders who price and operate for that reality win even when individual
  products have short shelf lives.
</ResearchCallout>

</ResearchSection>

<ResearchSection number={3} title="Monetization Still Works Because Demand Is Explosive During Window Periods">

Fast decay does not imply no revenue. It implies a different revenue curve.
Consumer demand around AI utilities has been strong enough to support meaningful
short-cycle monetization. Reporting that cites Sensor Tower shows AI app
spending exceeded $1.0B in 2024 with >200% YoY growth, and State of Mobile 2025
placed GenAI app spending at $1.49B (+169% YoY). Additional reporting on the
first half of 2025 cited $1.87B in GenAI app revenue and 1.7B downloads.

<ResearchTable
  caption="Table 2. Demand Momentum for AI Utility Surfaces"
  columns={[
    { label: 'Demand Metric' },
    { label: 'Observed Value', align: 'right', mono: true },
    { label: 'Commercial Meaning', muted: true },
  ]}
  rows={[
    ['AI app consumer spending in 2024', '>$1.0B', 'Users pay for immediate utility despite free alternatives'],
    ['YoY growth in AI app spending (2024)', '>200%', 'Willingness-to-pay can spike before platform absorption'],
    ['GenAI app spending in 2024 (State of Mobile 2025)', '$1.49B', 'Category has moved from experiment to recurring spend'],
    ['GenAI app spending growth in 2024 (State of Mobile 2025)', '+169% YoY', 'Revenue windows are short but large when timing is right'],
    ['GenAI app revenue in H1 2025', '$1.87B', 'Short cycles can still produce meaningful cashflow'],
    ['GenAI app downloads in H1 2025', '1.7B', 'Distribution velocity remains available to fast movers'],
  ]}
  footnote="Sources: TechCrunch reporting on Sensor Tower / State of Mobile data (2025), January-August 2025 coverage."
/>

Two observations matter for builders. First, willingness-to-pay exists even when
free model chat surfaces are available. Users pay for speed, fit, and convenience
in context, not raw model access. Second, category growth can be nonlinear for
brief windows when model capability crosses a threshold and UX has not yet
normalized across major platforms. Those windows are monetizable if onboarding,
distribution, and pricing are designed for immediate conversion.

The market therefore rewards a barbell stance: treat feature products as
intentionally time-bounded cashflow vehicles, while channeling earnings and
telemetry into a longer-lived harness substrate. The failure mode is trying to
force every short-wave product into a perpetual SaaS story with long sales
cycles, heavy roadmap promises, and cost structures that assume multi-year
retention.

</ResearchSection>

<ResearchSection number={4} title="Price for Payback, Not for Theoretical Lifetime Value">

If half-life is short, monetization design must prioritize fast payback over
elegant annual plans. In this regime, a 30-day cash recovery target often
dominates a 24-month LTV narrative. The objective is to convert novelty and
immediate task-value before integration pressure erodes differentiation.

<ResearchTable
  caption="Table 3. Monetization Archetypes for Short-Half-Life Tools"
  columns={[
    { label: 'Archetype' },
    { label: 'Price Envelope', mono: true },
    { label: 'Half-Life', mono: true },
    { label: 'Best Use Case', muted: true },
  ]}
  rows={[
    ['Single-job utility (lifetime)', '$19-$99 one-time', '1-6 months', 'Fast novelty capture, low support load'],
    ['Workflow accelerator subscription', '$9-$29 / month', '3-12 months', 'Daily operators who value speed over perfection'],
    ['Team micro-agent seat', '$49-$199 / seat / month', '6-18 months', 'Domain teams with repeated high-value tasks'],
    ['Template + automation bundle', '$79-$299 bundle', '2-9 months', 'Buyers who need immediate implementation'],
    ['Outcome-priced execution service', '$200-$2,000 / outcome', '6-24 months', 'When confidence in value capture is high'],
    ['Tool-led advisory retainer', '$1k-$10k / month', '12-36 months', 'Convert tool demand into longer-cycle services'],
  ]}
  footnote="Price ranges are operator heuristics based on current AI utility market behavior and observed conversion norms in low-friction digital tools."
/>

This table highlights a non-obvious point: "short-lived" and
"low quality" are not equivalent. A tool can be excellent,
save users hours weekly, and still be structurally transient because a platform
eventually internalizes the workflow. Monetization strategy should therefore
encode temporal realism directly in pricing and packaging decisions.

Outcome framing usually outperforms feature framing under cannibalization risk.
Features are copyable and often become default controls in upstream interfaces.
Outcome claims tied to specific user jobs retain persuasive force longer,
especially when backed by transparent before/after data. When possible, package
around a completed job unit (e.g., reconciled lead list, routed task queue,
finalized memo packet) rather than around the prompt or model selection UI.

<ResearchTable
  caption="Table 4. Offer Design Defaults Under Fast Edge Decay"
  columns={[
    { label: 'Design Variable' },
    { label: 'Fast-Cycle Default', muted: true },
    { label: 'Reason', muted: true },
  ]}
  rows={[
    ['Time-to-first-value', '<10 minutes', 'Novelty monetization fails if activation requires onboarding'],
    ['Trial design', 'No trial or 3-day max', 'Short windows punish long conversion funnels'],
    ['Payment trigger', 'Front-load at first successful output', 'Capture value before churn spike'],
    ['Packaging', 'Outcome unit, not feature list', 'Features are easiest for platforms to clone'],
    ['Refund policy', 'Tight but clear guarantee', 'Preserves trust while reducing abuse in low-ticket offers'],
    ['Upsell path', 'From tool to workflow to advisory', 'Extends LTV beyond feature half-life'],
  ]}
  footnote="Heuristics for maximizing conversion speed and reducing monetization lag in high-change model markets."
  compact
/>

The tactical implication is straightforward: long free trials, delayed value
realization, and complex tiering are often anti-patterns in this category.
You are not optimizing a mature procurement process. You are optimizing rapid,
trust-preserving value capture in a moving baseline.

</ResearchSection>

<ResearchSection number={5} title="Cost Curves Now Favor High Gross Margin Even at Low Ticket Prices">

Falling inference costs radically improve short-cycle economics. OpenAI
and Anthropic pricing surfaces in 2026 imply that substantial end-user
utility can often be delivered for cents per active session at low-to-mid
model tiers. This means a $19-$49 product can maintain strong gross margin if
orchestration is disciplined and context handling is efficient.

<ResearchTable
  caption="Table 5. Current API Pricing Surface (Selected Models)"
  columns={[
    { label: 'Provider / Model' },
    { label: 'Input ($ / 1M tokens)', align: 'right', mono: true },
    { label: 'Output ($ / 1M tokens)', align: 'right', mono: true },
    { label: 'Strategic Read', muted: true },
  ]}
  rows={[
    ['Anthropic Claude Opus 4.6', '$5.00', '$25.00', 'Frontier-tier reasoning at materially lower cost than prior Opus pricing bands'],
    ['OpenAI GPT-5.3-Codex', '$1.75', '$14.00', 'High-capability coding agent economics that still support sub-$100 offers'],
    ['Anthropic Claude Sonnet 4.6', '$3.00', '$15.00', 'Production baseline for agent workflows where speed-cost-quality balance matters'],
  ]}
  footnote="Sources: OpenAI model page for GPT-5.3-Codex; Anthropic Claude Opus 4.6 and Sonnet 4.6 model pages, accessed March 5, 2026."
/>

Margin discipline still matters. The fastest way to destroy a viable short-cycle
product is uncontrolled context bloat, redundant model calls, and no routing
policy. The right architecture routes most calls to the cheapest adequate model,
escalates only when confidence thresholds fail, and aggressively caches reusable
context. Providers themselves now expose cost controls (e.g., Anthropic Batch API
discounts), reinforcing the feasibility of low-ticket monetization with healthy
gross margin.

<ResearchTable
  caption="Table 6. Illustrative Unit Economics for Fast-Cycle AI Products"
  columns={[
    { label: 'Scenario' },
    { label: 'Users / Month', align: 'right', mono: true },
    { label: 'ARPU', align: 'right', mono: true },
    { label: 'Gross Revenue', align: 'right', mono: true },
    { label: 'Illustrative Inference Cost', align: 'right', mono: true },
    { label: 'Gross Margin', align: 'right', mono: true },
  ]}
  rows={[
    ['Niche solo utility', '400', '$19', '$7,600', '$300', '~96%'],
    ['Power-user workflow app', '1,200', '$24', '$28,800', '$1,450', '~95%'],
    ['Team micro-agent', '250 seats', '$149', '$37,250', '$3,800', '~90%'],
    ['Outcome-priced agent service', '140 outcomes', '$450', '$63,000', '$6,900', '~89%'],
  ]}
  footnote="Illustrative scenarios, not audited financials. Inference assumptions reflect blended low/mid-tier routing and moderate context sizes."
  compact
/>

These economics reframe the problem. You do not need a monopoly category winner
to produce meaningful cashflow. You need disciplined scope, rapid iteration,
and predictable conversion behavior in a well-defined niche. In other words,
monetization viability no longer requires defending the entire category over
many years; it requires operational precision during the useful window.

</ResearchSection>

<ResearchSection number={6} title="Deprecation and Integration Are Product Variables, Not Surprises">

Builders who treat platform changes as random shocks are repeatedly punished.
Deprecations and migrations are now routine. OpenAI, Anthropic, and Google all
publish model lifecycle changes with explicit dates, and consumer-facing products
can shift defaults quickly (as seen when GPT-4 was retired from ChatGPT).
A robust short-cycle business therefore includes an explicit deprecation clock.

<ResearchTable
  caption="Table 7. Model Lifecycle Events That Define the Product Clock"
  columns={[
    { label: 'Platform Event' },
    { label: 'Date', mono: true },
    { label: 'What It Signals', muted: true },
  ]}
  rows={[
    ['OpenAI retired GPT-4 from ChatGPT interface', '2025-04-30', 'Consumer-facing model shelf life is now short'],
    ['OpenAI scheduled chatgpt-4o-latest shutdown', '2026-02-17', 'Alias stability cannot be assumed in product architecture'],
    ['OpenAI scheduled gpt-4-32k deprecation', '2025-06-06', 'Legacy premium tiers can disappear rapidly'],
    ['Anthropic scheduled Claude 3.5 Sonnet (20240620) retirement', '2025-10-22', 'Provider-led migrations are recurring, not exceptional'],
    ['Google sunset gemini-2.5-flash-image-preview', '2026-01-15', 'Preview capabilities are explicitly temporary monetization windows'],
  ]}
  footnote="Sources: OpenAI deprecations docs and ChatGPT release notes; Anthropic model deprecations page; Google Gemini API changelog."
/>

The operational pattern is to convert lifecycle announcements into backlog events.
Every deprecation notice should trigger three parallel tracks: migration
implementation, pricing review, and customer communication. If your margin model
depends on a soon-to-retire endpoint, that is not a technical issue alone; it is
a pricing and positioning issue. If your core UX mirrors a new platform release,
that is not a roadmap coincidence; it is a revenue risk signal.

This is where many operators misread the market. They spend months refining
features while ignoring upstream lifecycle telemetry, then experience sudden churn
as users can get 80-90% of the value natively. The correct stance is proactive:
design migration-ready abstractions, measure feature substitutability continuously,
and pre-announce upgrades that reposition the offer around outcomes rather than
around specific model hooks.

</ResearchSection>

<ResearchSection number={7} title="The Agent Harness Layer Is Where Durability Reappears">

The feature layer is where edge decays; the harness layer is where edge can
compound. A harness is not simply an LLM wrapper. It is an integrated system that
owns context ingestion, memory, policy, routing, tool execution, and human
correction loops. Better base models increase the harness's effectiveness,
because the harness owns the problem framing and decision boundary, not just
one model call.

<ResearchTable
  caption="Table 8. Harness Layer Components and Compounding Mechanics"
  columns={[
    { label: 'Layer' },
    { label: 'What You Own', muted: true },
    { label: 'Why It Improves with Better Models', muted: true },
  ]}
  rows={[
    ['Context ingestion', 'User events, docs, calendar, location, messages metadata', 'Richer models classify and prioritize context better'],
    ['Memory model', 'Entity graph, preference graph, historical outcomes', 'Reasoning upgrades increase memory retrieval quality'],
    ['Policy/routing engine', 'Task policy, risk thresholds, escalation logic', 'Better models reduce false positives and improve routing'],
    ['Tool orchestration', 'API actions, retries, fallback providers', 'Model quality boosts tool-call success and recovery handling'],
    ['Human override loop', 'Approval states, correction logs, confidence gates', 'Correction data compounds into higher precision over time'],
    ['Feedback telemetry', 'Error classes, save-time metrics, intervention rates', 'Continuous tuning benefits from stronger base cognition'],
    ['Distribution identity', 'Audience trust, niche positioning, voice', 'Brand and trust remain outside raw model commoditization'],
  ]}
  footnote="Conceptual architecture for agent-layer products that benefit from model improvement instead of being replaced by it."
/>

This distinction aligns with your claim that builders should feel excited on
model release day. That emotional test is strategically useful: if a stronger
model makes your product better, your architecture likely sits above the feature
blast radius. If a stronger model makes your product unnecessary, your architecture
is probably too close to raw inference and too far from durable workflow ownership.

<ResearchCallout>
  Durable AI businesses increasingly resemble operating systems for decisions,
  not chat interfaces for prompts. The model is the engine; the harness is the
  vehicle. New engines should increase your speed, not destroy your business.
</ResearchCallout>

In enterprise and prosumer settings, this is also where trust economics emerge.
Auditability, override controls, escalation policies, and correction histories
are difficult for generalized platform features to replicate at domain depth.
These assets are dull from a demo perspective, but they are exactly what converts
intermittent utility spend into repeat organizational spend.

</ResearchSection>

<ResearchSection number={8} title="Portfolio Construction: Trade Short Waves, Compound Long Assets">

Treating all products as identical long-duration bets is no longer efficient.
A portfolio approach is more robust: dedicate part of build capacity to
short-wave edge extraction, while reserving explicit allocation for harness,
distribution, and data assets that compound across waves.

<ResearchTable
  caption="Table 9. Barbell Portfolio for the Cannibalization Era"
  columns={[
    { label: 'Portfolio Sleeve' },
    { label: 'Allocation', align: 'right', mono: true },
    { label: 'Primary Objective', muted: true },
    { label: 'Expected Horizon', mono: true },
  ]}
  rows={[
    ['Flash utilities', '30%', 'Exploit model or UX discontinuities quickly', '30-180 days'],
    ['Workflow products', '30%', 'Capture recurring payments from repeat tasks', '6-18 months'],
    ['Agent harness core', '25%', 'Compound proprietary memory and policy assets', '2-5 years'],
    ['Distribution/media moat', '10%', 'Lower launch CAC for each new wave', 'Persistent'],
    ['R&D optionality', '5%', 'Prototype frontier ideas before market consensus', 'Always-on'],
  ]}
  footnote="Illustrative allocation for solo operators or small teams balancing immediate cashflow with durable capability building."
/>

The portfolio view changes decision quality in three ways. First, it normalizes
product sunsetting as a healthy outcome when expected payback has already been
captured. Second, it prevents over-investment in defensive roadmaps for products
with structurally limited duration. Third, it creates an explicit reinvestment
path from short-cycle profits into long-cycle assets.

This is analogous to a trading desk funding a long-term strategy book: short-term
volatility capture provides operating cash and market intelligence, while a smaller
set of durable positions absorbs most compounding gains. In software terms,
the durable book includes memory infrastructure, domain datasets, correction logs,
and trusted distribution channels.

<ResearchTable
  caption="Table 10. Build Decision Matrix: Speed, Risk, and Durability"
  columns={[
    { label: 'Build Choice' },
    { label: 'Cannibalization Risk' },
    { label: 'Monetization Speed', mono: true },
    { label: 'Durability', mono: true },
    { label: 'Recommendation', muted: true },
  ]}
  rows={[
    ['Single-model UI wrapper', 'Critical', 'Fast', 'Low', 'Ship only if payback target is <30 days'],
    ['Prompt-packaged assistant', 'High', 'Fast', 'Low-Medium', 'Sell as bundle and collect upfront'],
    ['Domain workflow automation', 'Medium', 'Moderate', 'Medium', 'Good bridge product if tied to outcome metrics'],
    ['Agent harness with memory and policy', 'Low-Medium', 'Moderate', 'High', 'Best long-cycle compounding path'],
    ['Data network + expert feedback loop', 'Low', 'Slow', 'Very High', 'Durable moat; fund via short-cycle tools'],
  ]}
  footnote="Use this matrix to classify each new idea before development begins."
  compact
/>

The matrix also clarifies investor communication and self-governance. If you know
a concept sits in "critical cannibalization risk / fast monetization",
you can set explicit guardrails: limited engineering investment, hard payback
thresholds, and clear sunset criteria. Conversely, if a concept sits in
"low risk / slow monetization / high durability," you should expect
longer payback and evaluate by compounding telemetry quality rather than immediate
MRR.

</ResearchSection>

<ResearchSection number={9} title="Release-Day Operations: Converting Model Shocks into Revenue">

In a fast-cycle market, model launch days are not merely technical events.
They are commercial events. Teams that process these shocks quickly can capture
outsized demand before market messaging and platform UX normalize. This is where
a lightweight but disciplined operating cadence outperforms both ad hoc shipping
and heavyweight planning.

<ResearchTable
  caption="Table 11. T+Window Operating Playbook After Major Model Releases"
  columns={[
    { label: 'T+Window' },
    { label: 'Operating Action' },
    { label: 'Monetization Intent', muted: true },
  ]}
  rows={[
    ['T+0 to T+24h', 'Benchmark old vs new model on core user jobs', 'Detect instantly marketable quality/cost delta'],
    ['T+24h to T+72h', 'Ship upgraded prompts, routing, and pricing copy', 'Capture novelty while search/social attention is high'],
    ['T+3 to T+14d', 'Launch narrow use-case landing pages and micro-offers', 'Convert curiosity traffic into paid cohorts'],
    ['T+2 to T+6w', 'Collect correction telemetry and publish comparative outcomes', 'Build trust and reduce churn'],
    ['T+6 to T+12w', 'Decide: double down, bundle, or sunset', 'Reallocate to higher-edge opportunities'],
  ]}
  footnote="Operational sequence for turning model improvements into shipped value and near-term revenue."
/>

The first 72 hours are disproportionately important. Benchmarking determines
whether your current offer should reprice, reposition, or split into a cheaper
and a premium tier. Public artifacts during this period matter: comparative output
examples, latency and cost deltas, and concrete statements of what the update does
for user outcomes. Vague excitement posts waste the window.

The following 2-6 weeks are the telemetry phase. This is when you accumulate the
signals that convert one-off launch demand into repeatable decision intelligence:
where users still intervene, where output quality failed, what tasks retained
willingness-to-pay despite platform improvements, and which cohorts churned after
copying baseline improvements from free tools.

Critically, this cadence can coexist with a high quality bar. Rapid does not mean
sloppy. It means tight scopes, pre-defined instrumentation, and clear go/no-go
thresholds. In practice, this often leads to better product hygiene than a
speculative long roadmap because each cycle forces concrete evidence of value.

</ResearchSection>

<ResearchSection number={10} title="Conclusion: A Practical Doctrine for the AI Edge Economy">

Short-half-life AI tools are not a dead end; they are a different asset class.
The appropriate doctrine is neither naive permanence nor nihilistic churn. It is
structured edge harvesting: build fast, monetize quickly, instrument deeply,
and recycle gains into durable harness assets.

Five operating principles follow from the evidence. **First**, assume
edge decay by default and design offers for immediate payback. **Second**,
package outcomes rather than features because outcomes survive cloning longer.
**Third**, manage deprecation calendars as revenue variables.
**Fourth**, allocate portfolio capacity explicitly across short-wave
cashflow and long-wave compounding. **Fifth**, treat every major model
release as a deploy-and-sell event, not a spectator event.

For builders with strong execution velocity, this regime can be positive-sum.
Falling model costs and rising baseline capability reduce the capital required to
launch, test, and monetize. The bottleneck shifts to judgment: selecting the right
wedge, setting the right pricing clock, and building the right substrate beneath
each wave. In this sense, software abundance does not remove edge; it changes edge
from possession to process.

The deepest strategic inversion is psychological. In the old playbook, cannibalization
was treated as failure. In the new playbook, cannibalization can be evidence that you
correctly identified value early. If you captured cashflow, learning, and proprietary
telemetry before integration, you did not lose the game; you completed the cycle.
The compounding question is what your harness learned and what you can deploy next.

</ResearchSection>

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TechCrunch. (2025, January 22). *AI apps saw over $1 billion in consumer spending in 2024.*
https://techcrunch.com/2025/01/22/ai-apps-saw-over-1-billion-in-consumer-spending-in-2024/

TechCrunch. (2025, July 30). *GenAI apps doubled their revenue, grew to 1.7B downloads in first half of 2025.*
https://techcrunch.com/2025/07/30/gen-ai-apps-doubled-their-revenue-grew-to-1-7b-downloads-in-first-half-of-2025/

</ResearchReferences>

<ResearchColophon
  citation={`Baratta, R. (2026). \u201CShort-Half-Life AI Tools: Edge Harvesting as the New Software Business Model.\u201D Buildooor Research Brief, March 2026.`}
  email="buildooor@gmail.com"
/>