Opening Scene
The Shift Begins
In mid-2025, a global retail marketing team ran a routine experiment. They asked their AI content engine to generate 400 personalised product descriptions — the kind of micro-copy work that used to take days. The model performed flawlessly. But when the finance team looked at the usage bill, something didn't add up.
The campaign cost nearly 40% more than expected.
There were no additional requests. No last-minute changes to prompts. No unusual load. The culprit was something nobody had questioned: the format used to structure the data. Every request, every response, every intermediate step had been wrapped in precise, machine-friendly — but extremely token-heavy — JSON.
A format chosen for its familiarity had quietly inflated their AI spend.
That moment captured a shift spreading across marketing, product, and content operations: as businesses scale their AI use, the cost of structure itself becomes a strategic concern. The formatting choices that sit beneath the surface — invisible to most teams — are now shaping budgets, workflows, and the limits of creative experimentation.
And this is where JSON, once the hero of structured data, is becoming an unexpected liability.
The Insight
What's Really Happening
For two decades, JSON has been the default language of structured information. It's clean, readable, and supported almost everywhere. But what made JSON elegant for APIs makes it inefficient for AI systems: it repeats information constantly.
Every key. Every bracket. Every nested value. Every duplicated field name.
In an AI context, every character becomes a token, and every token becomes a cost.
The deep research shows that in typical marketing prompts — product catalogues, content briefs, brand guidelines, tone frameworks — JSON can consume up to 60% of the total token budget, often before the model has even begun generating content.
This isn't theoretical. It's structural. And it's the reason marketing teams are beginning to feel the effect in:
- Higher API costs
- Slower model responses
- Shorter context windows
- Reduced scale for content generation
- Constrained experimentation
Because every unnecessary token pushes teams closer to usage limits — and raises the cost of every idea.
TOON — a more compact, semantically structured alternative emerging inside AI tooling ecosystems — changes the equation. Designed for model efficiency rather than human readability, TOON cuts out repetitive key names, removes syntactic overhead, and compresses structure dramatically.
The research shows 30-60% token reduction across real-world marketing use cases.
JSON tells the model what you meant. TOON tells the model what it needs.
That difference is becoming strategic.
Why Token Inefficiency Matters Now
Token efficiency wasn't a conversation when teams were experimenting with small pilots — a few product descriptions, a handful of social posts, a draft campaign framework.
But as organisations scale:
- 500 briefs per week
- Training datasets for autonomous agents
- Personalised email variants
- Localization across dozens of markets
- Product catalogue ingestion
- Multi-step workflow chains
...the underlying cost structure becomes visible.
A 40% token inefficiency doesn't just increase spend. It restricts ambition.
It forces teams to choose between:
- generating 1,000 creative variations or generating 1,600
- analysing a full customer data sample or analysing half of it
- building richer training data or shrinking scope to save money
Token inefficiency is no longer a technical detail. It is an operational constraint. And as AI becomes the backbone of marketing execution, the cost of structure becomes the cost of creativity.
The Strategic Shift
Why This Matters for Business
AI-native organisations are discovering that efficiency is a competitive advantage.
The agentic era — where workflows are automated end-to-end and models perform multi-step reasoning — amplifies every inefficiency in a system. When an autonomous agent executes 20 steps, and each step contains JSON payloads, the token costs compound exponentially.
This is where TOON adoption aligns with broader strategic trends:
-
The move from “AI usage” to “AI infrastructure”
Companies aren't just prompting models anymore. They are building pipelines.
Pipelines multiply cost. Token inefficiency becomes architectural debt.
-
The emergence of AI-native data formats
Just as GPUs required new software frameworks, AI requires new data formats designed for model consumption, not human parsing.
JSON was created for developers. TOON is created for transformers.
-
Budget allocation shifting toward AI operations
Marketing budgets now include:
- prompt libraries
- model calls
- vector storage
- automated content workflows
Reducing token overhead increases operational headroom.
-
Scale makes inefficiency unsustainable
You can absorb a 40% overhead at 100 requests. You cannot absorb it at 10 million.
Token efficiency becomes a budget multiplier.
-
Experimentation becomes a strategic asset
Lower token usage means:
- running more variations
- exploring more directions
- testing more creative ideas
- training more capable agents
This increases marketing agility — the real differentiator in fast-moving markets.
In short: format choice now shapes your ability to innovate.
The Human Dimension
Reframing the Relationship
If you're a marketing leader, this shift affects you personally — not because you need to understand token compression algorithms, but because the economics change what your team can create.
You want more room for experimentation. You want faster iteration loops. You want intelligent agents that understand your brand. You want rich datasets powering insights. You want scalable personalisation without cost surprises. But JSON quietly limits all of that.
You feel the symptoms even if you've never seen the cause:
- campaigns that can't include all products
- insights that can't analyse full datasets
- content engines that time out or slow down
- workflows that break because context limits are exceeded
- creative idea exploration restricted by budget
And when your team asks for bigger AI budgets, it may not be because they need more power — but because the format you're using is wasting it.
The research makes this clear: When teams switch from JSON to TOON, they reclaim 30-60% of their AI budget — without changing models, prompts, or workflows.
It's like removing sandbags from the wings of a plane. The plane was always capable. The structure was holding it back.
The Takeaway
What Happens Next
JSON won't vanish overnight. It will continue powering APIs, back-end systems, and legacy workflows.
But in AI-driven marketing operations, the cost is becoming impossible to ignore.
The next generation of marketing infrastructure — agentic pipelines, dynamic content engines, automated campaign orchestration — will depend on formats that make computation cheaper, not more expensive.
TOON is the first step toward that future: a compact, AI-native approach to structure that frees room for scale, creativity, and experimentation.
The message is simple: Your data format is now a budget line. Your efficiency is now a strategic choice. And your marketing potential is shaped by the tokens you don't waste.
