Jevons Paradox in the AI Era: Why "More Efficient AI" Leads to More Software

If AI makes coding, content, and decisions cheaper and faster, shouldn't the need for development decrease? Right?

This is exactly where a 160-year-old principle comes into play: Jevons Paradox. The idea: efficiency gains lower the cost per unit – and thereby often increase total consumption. Result: we don't consume "less," but more (just differently).

For brands, marketing teams, and digital decision-makers, this holds a huge opportunity: when software becomes cheaper, it suddenly pays to build more software – especially where standard SaaS doesn't differentiate.

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1) Jevons Paradox in 90 Seconds (and Why It's Not Just About Energy)

Historically, William Stanley Jevons observed in the 19th century: more efficient steam engines lowered the cost per unit of output – yet led to higher coal consumption, because the technology spread faster and more broadly.

In modern economics, this is often discussed more broadly as the Rebound Effect:

• Direct: something becomes cheaper → you use it more often
• Indirect: saved costs flow into other forms of consumption
• Economy-wide: productivity rises → growth rises → usage rises

Important: The research is nuanced – how strong the effect is depends on context, and "backfire" (rebound > 100%) is empirically hard to prove cleanly.

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2) Why AI Is a Jevons Turbocharger

AI is efficiency in its purest form: it massively reduces marginal costs for

• Creation (code, copy, designs, assets)
• Orchestration (workflows, automations, agents)
• Decision-making (analysis, variants, simulation)

This doesn't automatically mean "everything gets more expensive." But it very often means: we suddenly do things that were previously too costly to attempt.

A few signals from different directions:

• In an enterprise context, WWT argues that falling training and inference costs enable more experimentation, more deployment, and deeper process integration – meaning more usage, not less.
• Proxify explicitly applies Jevons to software: when AI accelerates development and makes it more accessible, appetite for custom solutions, integrations, and extensions grows (precisely because "one-size-fits-all" hits its limits).
• Additionally, survey data shows: daily usage of AI coding tools reportedly rose significantly within one year (e.g., 47% → 77%), and many developers report noticeable time savings – classic fertile ground for "more output."

In short: AI shifts the question from "Can we build this?" to "Why haven't we built this yet?"

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3) The Decisive Shift for Brands: From "Tool-Buying" to "Software-Building"

For the last 10–15 years, the default was: buy, don't build. SaaS promised rapid implementation, best practices, predictable costs. That remains true – until the moment differentiation becomes relevant.

With AI, something new is happening: the gap between standard SaaS and your actual workflow is becoming cheaper to close. SaaS is introduced efficiently but often creates new "scale inefficiencies" in practice, which are then solved through customization, workarounds, and extensions.

Our observation: in many organizations, a "last-mile backlog" emerges:

• 20% of the workflow that SaaS maps perfectly
• 80% reality (data flows, approvals, brand compliance, country variants, asset handovers, reporting logic)

And precisely this 80% is your differentiation.

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4) What This Means Concretely: More Software – But Different

"Having more software built" in 2026 doesn't automatically mean "building a monolith." It means rather:

A) Micro-Software Instead of Mega-Suite

Small, modular tools that solve exactly one bottleneck:

• Briefing-to-production pipelines
• Translation & localization with tonality guardrails
• DAM-to-publishing automations
• QA checks (claims, brand wording, format rules)

B) Agentic Workflows as the New Operating System

AI agents become process colleagues: they coordinate tools, pull data, generate variants, document decisions. As efficiency increases, so does the number of workflows that become "agent-capable."

C) "Composable Brand Systems"

Brands that want speed AND consistency are increasingly building:

• Style and tonality layers
• Template & component libraries
• Prompt/policy sets
• Measurement and learning loops (Digital Farming)

This isn't luxury – it's production capability.

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5) The Creative Engineering Sweet Spot: Where Jevons Creates the Most Value for Our Clients

When efficiency gains increase demand, the question is: What do we invest the freed-up capacity in?

Here's where we see the biggest levers for Creative Engineering:

1) Industrialize the Content Supply Chain (Without Going "AI-Generic")

More variants, more channels, more markets – but with brand identity. This requires software layers that make creativity scalable: templates, QA, approvals, versioning, learning loops.

2) First-Party Data → Creative Intelligence

The more AI produces, the more important steering becomes: What performs? Why? What do we learn? The rebound effect from ICT research shows that efficiency gains often lead to more usage – making measurement a prerequisite for "more" also being "better."

3) Internal Tools Become Strategic Again

Not every team needs another platform. Many teams need 3–5 internal tools that exactly match their processes – and that connect the stack rather than extending it.

4) Differentiation Through "Proprietary Workflows"

When everyone has access to similar models, the advantage lies in:

• your data
• your process logic
• your creative standards
• your experimentation speed

More software then means: more ownership of value creation.

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6) The Flip Side: When "More" Becomes Uncontrolled

Jevons is both opportunity and warning signal: efficiency can lead to uncontrolled expansion.

Three typical risks:

• Cost rebound (tokens, infrastructure, license add-ons)
• Quality rebound (more output, but more errors/inconsistency)
• Governance rebound (shadow automations, unclear accountability)

The solution isn't "less AI" but engineering discipline:

• Clear workflows (owner, inputs/outputs)
• Guardrails (brand, legal, data)
• Observability (cost, quality, performance)
• Iteration (Digital Farming instead of Big Bang)

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7) A Practical Starting Point for Clients: The "Jevons Audit" (2 Weeks)

If you want to find out where "more software" makes immediate sense for you:

1. Write down your top 10 workflows in marketing/comms (including handovers).
2. Mark three bottlenecks that cost you the most time (or kill quality).
3. Per bottleneck, decide: Buy / Compose / Build / Agent
4. Build one pilot that delivers measurable output (time, quality, cost).
5. Then scale: component library + governance + learning loop.

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Conclusion

Jevons Paradox isn't a meme – it's a pattern: when production gets cheaper, ambition rises – and output grows.

For our clients, this means: not less software. More – but purposefully.

And for Creative Engineering, it means: we build the layers that make brands fast, consistent, and measurable in an agent-driven, variant-heavy world.

Further reading: How the SaaS-Pocalypse is reshaping the software market and why Vibe Coding is accelerating the democratization of software development.