Opening Scene
A Subtle Break Point
In 2024, something quiet but consequential started happening across AI engineering teams. After years of treating JSON as an unquestioned default — a structural glue for APIs, logging, and data interchange — developers noticed an uncomfortable pattern. The more AI they added, the worse JSON behaved. Token bills ballooned. Retrieval pipelines slowed. Agents got stuck in loops because a brace was missing or a key drifted by a single character.
What had always been “good enough” was suddenly a bottleneck.
It was a reminder that AI doesn't look at data the way humans do. JSON was built for us: readable keys, curly braces, descriptive labels. But AI doesn't need any of that. In fact, it struggles with it. And in that mismatch — between human-friendly design and machine-efficient reality — a new class of formats began to emerge. One of them has quickly become the frontrunner: TOON, Token-Oriented Object Notation.
This is the story of why TOON is poised to replace JSON at the LLM boundary — not because JSON is bad, but because the AI era demands something better.
Framing Line
The first data format built for machines, not humans.
The Insight
What's Really Happening
The shift starts with a simple, uncomfortable truth: JSON is stunningly inefficient for LLMs.
Every key, every brace, every symbol counts as a token. Tokens are the atomic currency of AI models — they determine cost, latency, and how much context you can fit into a single prompt. Research shows JSON often uses 2× the tokens of more compact structures, sometimes more. In one benchmark, something as trivial as a product listing ballooned from 23 tokens in TOON to 42 in JSON, with no increase in meaning.
This inefficiency isn't cosmetic. It influences how AI behaves.
Token Bloat > Higher Costs and Slower Models
For companies running millions of LLM calls, JSON's repetition becomes a tax. A workflow that consumes 1M JSON tokens might drop to 550k with TOON, effectively halving costs while improving throughput.
Structural Ambiguity > Unreliable Agents
LLMs often generate JSON that is almost valid — which is the worst kind. A stray quote, a missing comma, an unexpected prefix like “Here's your JSON” — all common failure modes — can break entire pipelines. Agent frameworks learned this the hard way: early experiments like Auto-GPT would loop endlessly because JSON structure slipped by a millimetre.
No Guaranteed Schema > Drift Over Time
JSON syntax can be guaranteed. JSON meaning cannot. Ask for phone_number and the model may return phone, mobile, or contact. Ask for a number and it may decide today it's a string. These are not bugs; they're artefacts of a format designed for humans, interpreted by systems trained on probability.
Meanwhile, AI-native structured approaches — from OpenAI's structured outputs to Guardrails and Pydantic validation — all reveal the same underlying truth: the industry is scrambling to add rules, schemas, and determinism around a format that never had them.
TOON approaches the problem differently.
Instead of wrapping JSON in validators and hoping for the best, TOON rebuilds the very idea of machine-readable structure from the ground up. It removes repetition. It enforces order. It's designed so models always know what comes next.
In internal benchmarks, this has resulted not just in efficiency gains, but a ~5% improvement in task accuracy — not because the model is smarter, but because it's working with cleaner, less distracting input.
AI systems don't just consume structure; they depend on it.
The Strategic Shift
Why It Matters for Business
The rise of TOON signals something bigger than a new format. It signals the start of AI-native engineering—a shift from building systems that support AI to building systems around AI.
AI is no longer a feature; it's an interpreter.
Everything we pass into a model becomes the substrate for reasoning and retrieval. If that substrate is noisy, verbose, ambiguous, or expensive, we pay for it — financially and operationally.
This matters because:
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Context is the new computing resource.
Teams now spend more time optimising prompts than CPU cycles. If TOON gives you 40-70% more room in each prompt, you can pass in more documents, longer memories, richer instructions. That changes the ceiling of what is possible in RAG systems, agentic workflows, and multi-step analysis.
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Accuracy is now a competitive differentiator.
Every improvement in structural clarity — stable fields, explicit counts, fewer symbols — nudges models toward better performance. When LLM accuracy jumps from 65% to 70% simply because the format changed, not the model, leaders pay attention.
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Reliability is the foundation of AI automation.
Agent systems break for boring reasons: A missing comma. An unexpected enum. An out-of-place string.
TOON eliminates entire classes of failure by design. Where JSON creates variability, TOON delivers determinism. For enterprises building AI-powered workflows — whether operations, marketing, product, or logistics — this is the difference between experimentation and production.
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Cost matters more now than ever.
Running LLMs at scale is expensive. Token-heavy formats are a tax on innovation. TOON is a direct lever: fewer tokens, smaller bills, faster results.
The strategic takeaway? AI-native formats aren't a developer preference — they're a business advantage.
The Human Dimension
How This Changes Us
There's a human story here too, because every technology shift alters how people think, design, and work.
JSON shaped a generation of developers. It taught us that readability mattered. It let humans inspect, copy, paste, debug, and understand data structures at a glance.
But LLMs don't need any of that. They need consistency, not clarity. Schema, not prose.
This creates a subtle but important reframing: when you communicate with an AI, you're not the audience anymore.
You're designing for a machine partner with its own language preferences and performance characteristics. TOON's rise is one step in this broader realignment—away from human-first abstractions and towards formats optimised for the ways machines think.
And, increasingly, when you design workflows, build prompts, or structure agent communications, you'll find yourself choosing between two paths:
- the way humans prefer to view data, or
- the way AI prefers to receive it.
TOON lives firmly in the second category. It's what you choose when precision matters more than readability.
In that sense, adopting TOON isn't just a technical shift; it's a cultural one.
The Takeaway
What Happens Next
JSON isn't going anywhere. It will continue powering APIs, logs, and dashboards for decades. But at the LLM boundary — where token cost, accuracy, reliability, and context depth truly matter — a new standard is forming.
TOON is not a trend. It's a response to physics: the physics of tokens, of context, of statistical models that prefer structure over syntax.
We are entering a world where the interfaces between humans and machines bifurcate. Readable formats for us. Efficient, deterministic formats for them.
And when companies realise they can double throughput, halve cost, and improve model accuracy simply by changing the format — not the model — the shift becomes inevitable.
The future belongs to formats built for AI, not inherited from the pre-AI web. TOON is simply the first to show us what that looks like.
