Golden tokens
Four prompts. That was all it took to hit the ceiling.
I am currently staring at a lockout screen that informs me I have exhausted my credits and am barred from the system for the next five hours. In an attempt to bypass the bottleneck, I tried switching to Opus 4.8, only to find the same wall. For a developer in the heat of a build, this isn’t just a minor inconvenience; it is a major structural failure in the workflow. It brings a simmering realization to the surface—one I have feared for some time: we have officially entered the era of “Two-Speed AI.”
The landscape of artificial intelligence has fractured. On one side, we have “AI for the wealthy”—those capable of spending astronomical, uncounted sums to access models with unprecedented intelligence, safety, and multiplicative power. On the other side, we find the rest of the population, relegated to models that, while capable, are increasingly distinguished by a widening chasm in ROI and capability.
This is a dispatch from that frontline, a reflection on the professional necessity of high-end inference, and a technical deep dive into my first experiences with Anthropic’s latest behemoth: Fable 5.
The Stratification of Intelligence
For a long time, we viewed AI through a somewhat democratic lens. However, the classification between those who pay for standard plans and those who can afford “max plans” has become a defining feature of the modern economy. We are seeing ROI factors of 5x versus 20x depending entirely on the subscription tier.
In the professional IT world and within the enterprise, the standard €20-a-month plan is no longer a viable toolkit; it is a toy. To actually perform at the level required by today’s industry standards, a €100 base plan is the bare minimum requirement. But even that is just the entry fee. This creates a fundamental problem. AI is no longer a “nice-to-have” option; it has transitioned into a strict professional obligation.
To be a professional today—whether independent or corporate—and to forgo the use of advanced AI is to choose a path of progressive non-competitiveness. Models like Anthropic’s Fable series (and its equivalents) have introduced a level of “sur-classification” that is now impossible to ignore. This multi-speed AI environment inherently favors the giants—those who can put hundreds of thousands, or even millions, of euros on the table to accelerate their productivity cadence. While some firms are practicing “token maxing” and dominating leaderboards, others are left counting credits like spare change.
Anthropic, intentionally or not, has created a hierarchy: the AI of the elite versus the AI of the masses. While I hope that OpenAI and Google do not follow this exact path—OpenAI has at least toyed with ad-supported GPT models to keep access open—I am increasingly convinced that AI access should be treated as a fundamental citizen’s right. Much like the internet, the state must eventually invest in collective AI infrastructure to ensure every citizen has the capacity to compete in this new world.
Testing the Mythos: A First Look at Fable 5
Despite the frustrations of the credit system, the actual performance of the models is, frankly, staggering. I recently gained access to Anthropic Fable 5—the public-facing version of their “Mythos” engine. As of June 2026, this is arguably the most intelligent and expansive model known to man.
To put it bluntly: Fable 5 represents a quantum leap in machine intelligence.
I integrated Fable 5 into my workflow via the Claude Code application. For those looking to replicate this, the process is straightforward: once in the Claude Code environment, you use the /model command followed by claude-fable-5.
The first thing you notice is the speed—or rather, the lack of it. It is slow. In fact, it feels noticeably more sluggish than Opus 4.8. However, this latency is the price of a massive increase in autonomous reasoning. What shocked me immediately was how quickly it “found its feet” without my intervention.
The SVG Framework Case Study: Surgical Autonomy
To test the limits of Fable 5, I ran it against a long-term project of mine: a custom UI framework built entirely on pure SVG. It’s a specialized, high-difficulty challenge I set for myself. The repository was initially bootstrapped using Gemini 3.5 Flash to get the foundation laid, but I wanted to see if Fable 5 could take the project to a production-ready state.
I asked for a comprehensive code review. The resulting report was unlike any I had received from a model before. The technical complexity of its explanations was remarkably high; it identified “high-level” architectural nuances that made sense in theory but were incredibly dense to parse. It pinpointed exactly why the current iteration wasn’t meeting the production standards I had set, and it did so with a level of detail that felt like talking to a principal architect.
The model proposed a list of fixes, identifying three specific areas that required what it termed “surgical intervention.” I didn’t even have to prompt it to begin the work. For over 40 minutes, Fable 5 labored autonomously. It only stopped twice: once to ask for permission to run a specific system command and once to execute the test suite.
The results were striking. Usually, with even the best models, a battery of tests will return one or two failures that require manual debugging. Fable 5 passed the entire suite on the first attempt. The execution was flawless, concluding a complex refactor that would have taken a human developer hours, if not days, of concentrated effort.
The Price of Perfection
However, this brilliance comes with a “negative” that cannot be ignored. After only three prompts and that single 40-minute autonomous session, I had consumed 66% of my context tokens. The cost is, quite simply, prohibitive.
This brings us back to the central dilemma. If a model like Fable 5 can solve in 40 minutes what used to take 10 hours, but it costs a significant portion of a monthly budget to do so, we are looking at a radical shift in how we value “work.”
Furthermore, there is the issue of security. We are now seeing “multi-level security” where models of the “Fable class” provide substantial protection and oversight that lower-tier models simply cannot replicate. This means that those without access to these premium models are not just less productive—they are more at risk.
Conclusion: AI as the New Public Utility
My experience with Fable 5 confirms two things: we have reached a level of AI capability that can truly handle “surgical” technical tasks autonomously, and we are dangerously close to a society where “intelligence” is a resource only accessible to the highest bidder.
If we allow AI to remain a tiered luxury, we are essentially legislating inequality into the future of work. The productivity gains are too great, and the competitive advantages too steep, for this to be left solely to the market. We need to start discussing AI as a collective investment—a public utility that ensures every citizen has the “intellectual bandwidth” to thrive.
Until then, I will be here, waiting for my five-hour lockout to expire, wondering how much further the framework could have gone if I just had a few more credits.
As we navigate this new era, I encourage fellow developers and policy-makers to advocate for more transparent and accessible AI tiers. The gap between “Standard” and “Elite” AI is no longer just a matter of convenience—it is a matter of professional survival.