Walid Boulanouar, building more agents than you can count and serving as aiCTO ay automate & humanoidz, recently posted something that made me stop scrolling:

gpt-5.3-codex-ultra-high-max-very-fast 🙃

$1/token

In two short lines, he hinted at an absurdly powerful, ultra-fast model with an equally absurd price: one dollar per token. There is no actual product named gpt-5.3-codex-ultra-high-max-very-fast (at least not yet), and nobody is really charging $1 per token. But the joke lands because it captures a real tension in AI right now: how we think about model branding, capability, and cost.

Reading Walid's post, I saw more than a meme. I saw a compact critique of how we talk about large language models (LLMs) and how developers, founders, and buyers are starting to think about pricing.

The joke behind the name

The made-up model name "gpt-5.3-codex-ultra-high-max-very-fast" feels familiar on purpose. It exaggerates the naming trends we already see:

Incremental version bumps (3.5, 4, 4.1, 5.0…)
Product suffixes like "turbo", "mini", "pro", or "codex"
Vague performance claims like "ultra" and "max"
Speed branding such as "very-fast" or "turbo"

Walid is essentially holding up a mirror to the industry. When every release promises higher context windows, better reasoning, more tools, and faster responses, it becomes hard for non-experts to tell what actually matters. So we default to buzzwords.

As someone who is literally "building more agents than you can count" and wiring together tools like n8n, a2a, and cursor, Walid lives inside this stack every day. He knows that under the marketing names, what builders actually care about is much more concrete: latency, reliability, token limits, function-calling quality, and cost.

$1 per token: a pricing absurdity that makes a point

Then there is the punchline: $1/token.

In the real world, LLM pricing is racing downward. We discuss fractions of a cent per thousand tokens, specialized inference hardware, batching, and context compression. The idea of paying one US dollar for a single token is obviously ridiculous.

But that is exactly why the line works. By pushing the number into absurdity, Walid surfaces a question many teams quietly ask: what is a token of AI output actually worth to you?

If a model drafts a contract that would have taken your lawyer three hours, what was that worth?
If an agent handles 1,000 customer queries per day without burning out, what is the value per interaction?
If a coding assistant ships a feature a week earlier, how does that translate into revenue or saved salaries?

We are still terrible at answering these questions. Most organizations buy AI the way they buy cloud compute: by the unit, not by the outcome. Walid flips that on its head by throwing out a number so high it forces you to think in terms of value, not cost.

From meme to mental model for AI buyers

I read Walid's post as a compact mental model for anyone evaluating AI tools:

Do not get hypnotized by version numbers and adjectives. Focus on outcomes and the real economic value per token.

Those outcomes differ by role:

For developers, a "very-fast" model is not about bragging rights. It is about tight feedback loops, fewer context window errors, and better tool-calling. Time-to-first-correct-response is the real metric.
For product leaders, a "max" model is not about the biggest context window. It is about whether AI actually improves activation, retention, and expansion.
For operations and support, "ultra" is not about benchmarks. It is about whether you can safely hand off more workflows to agents without drowning your team in edge cases.

Seen this way, Walid's fictional $1 token is a thought experiment: if each token were incredibly expensive, which use cases would still make sense? Those are probably the ones where AI is already underpriced relative to the value created.

What this means for AI builders and agent architects

Walid works at the frontier of AI agents: connecting tools, orchestrating workflows, and turning LLM calls into systems that actually get work done. That perspective reveals another layer in his post.

Agents do not just consume tokens; they turn tokens into actions, database writes, API calls, and business results. A naive pricing mindset—"cheapest tokens win"—ignores the compounding value of better reasoning, fewer hallucinations, and smarter tool use.

An agent that costs 3x more per token but makes 10x fewer critical mistakes can be the cheaper system overall. You pay more for the model but less for human review, refunds, downtime, and rework.

So when Walid jokingly names an impossible model and gives it an impossible price, he is poking at our obsession with raw token cost instead of system-level economics.

How to read the next AI model announcement

Walid's tiny post is also a practical guide for surviving the next wave of AI launch marketing. The next time you see a model called something like "gpt-X-ultra-max-pro-very-fast" with eye-catching pricing, ask a few grounded questions:

What concrete tasks does this make cheaper, faster, or better?
How does this change my unit economics? Not just tokens, but full workflow cost.
Where does reliability matter more than raw speed or price?
Can simpler or smaller models, combined with good tooling, beat the flagship model for my use case?

This is exactly how practitioners like Walid evaluate new tools. The name and headline price get a glance; the real decision is about architecture, failure modes, and long-term maintainability.

Content that speaks to builders, not just algorithms

There is another lesson hiding here for anyone posting on LinkedIn or other platforms: technical audiences crave content that respects their intelligence.

Walid did not write a long thread, publish benchmarks, or drop a polished case study. He posted a single playful line and a fake price. Yet it resonated because:

It assumes the reader understands models, tokens, and pricing.
It invites interpretation instead of over-explaining.
It feels like an inside joke from someone who actually ships agent systems.

If you are trying to reach developers, AI engineers, or technical founders, this is a signal: you do not always need a 20-slide carousel. Sometimes a sharp, well-aimed meme plus real-world credibility is enough.

Bringing it back to your own AI strategy

So where does this leave you?

If you are building or buying AI systems, you can use Walid Boulanouar's post as a simple checklist:

Am I obsessing over version numbers and adjectives instead of measurable outcomes?
Do I understand the value per token in my key workflows, not just the cost per token on my cloud bill?
Am I designing systems where smarter, slightly more expensive models actually lower total cost and risk?

And if you are creating content around AI, ask yourself whether you are speaking to practitioners the way Walid does: with wit, clarity, and an assumption that they know what a token is and why pricing games matter.

Because beneath the joke of "gpt-5.3-codex-ultra-high-max-very-fast 🙃 $1/token" lies a serious point: the future of AI will not be won by the catchiest model name or the lowest price tag, but by the teams who deeply understand how each token maps to real-world value.

This blog post expands on a viral LinkedIn post by Walid Boulanouar, building more agents than you can count | aiCTO ay automate & humanoidz | building with n8n, a2a, cursor & ☕ | advisor | first ai agents talent recruiter. View the original LinkedIn post →