My adventures with Fable How I spent $845 in 48 hours to solve problems that didn't exist Transcript of the narrated version (5 min). Narrated with a synthetic voice (Larry). The writing is Esteban Rey's — kilowatto.com. --- My adventures with Fable. How I spent $845 in 48 hours to solve problems that didn't exist. There's been a lot of talk about Fable, the new supermodel of AI, lately. Forums are debating its implications for cybersecurity, lawmakers are discussing its political impact, and financial analysts are going crazy over its operating costs. To put it into perspective, Fable's price is prohibitive, approximately 10 times more expensive than using Opus 4.8, a whopping 50 times more costly than Sonnet 5, and about 15 times more expensive than the previous generation of GPT, before Sol was released. Despite this high cost, which can be thought of as a "genius tax," I decided to give Fable a try without any biases. My goal was the same as any tech director's, to do more with less, and faster, and I wanted to see if the cost was justified by a significant return on investment. So, my team and I connected Fable directly to our workflows and daily analyses, and what happened over the next 48 hours was a masterclass in technical brilliance and commercial absurdity. The proactive genius and GitLab vulnerability. Just minutes after scanning our infrastructure, Fable raised its hand, having found that our GitLab environment had some vulnerable open-source libraries. For those who aren't tech-savvy, GitLab is the ultimate collaborative development and version control platform, used by over 100,000 organizations worldwide to store their source code and automate deployments, literally the heart of software operations. Fable not only alerted us to the problem but also took the initiative, and consumed tokens at a rapid pace, to find the source code of the libraries, analyze the patches, and deliver a complete remediation process. It's incredible to think that GitLab has been living with these issues for years without almost anyone noticing, and Fable broke it down in minutes. The untouchable database. We soon utilized Fable to tackle a historical problem, a technical debt in a very old MySQL database. For years, we had refused to touch it, arguing that updating it would break compatibility with old systems and disrupt operations. When we asked Fable to analyze why we couldn't migrate it, the model simply ignored our defeatist human premise, deciding instead that it was possible. Within hours, it created a master script that completely updated the MySQL engine and even programmed the intermediate process, a middleware, to ensure backward compatibility, solving a problem that we had filed away in the someday drawer. The reality check (and the bill). From a pure engineering perspective, Fable is magic, but from a business perspective, the story is very different. How much did this proactivity impact our real operations? Absolutely nothing. We didn't implement the GitLab patch, because after analyzing the vulnerability, we realized that in our specific context, the flaw didn't put our data, our clients' data, or our internal projects at risk, so we could live with it just fine. The MySQL database that Fable fixed is a legacy system that's already on its way out, and fixing it doesn't add value to our objectives for this quarter, since the entire system will be decommissioned soon. The first 48 hours of autonomous Fable use resulted in an API bill of $845, meaning we spent almost a thousand dollars for a supercomputer to solve two problems that, operationally, didn't exist. One swallow doesn't make a summer. To date, Fable hasn't found any other operational issues that justify its daily rate. In fact, our current workhorse, Sonnet 5, is finding 95% of the same problems at an incredibly lower cost. I don't doubt Fable's power, I'm sure that in offensive cybersecurity companies or biotech labs, they could get their money's worth. But I don't have evidence that it's worth it for the vast majority of traditional businesses today. These are anecdotal cases, and my team and I won't be ditching Fable, but we'll be relegating it to the break glass in case of emergency shelf, and we'll only use it for impossible projects. The true orchestrator. Our strategy is much more pragmatic today. We are using Sonnet 5 for the heavy lifting and migrating complex tasks to GPT Sol, OpenAI's new flagship model, which is focused on continuous reasoning and high-fidelity agents. However, our real bet for the future is on local AI. We are closely following Kimi K3, the powerful Chinese model, with the goal of putting it on our own servers. The current challenge with Kimi K3 is that it requires a huge amount of GPUs and VRAM to run, but we hope that with proper distillation and quantization in the coming months, Kimi can become our internal AI orchestrator, free and private. In 2026, intelligence is no longer scarce, what is scarce is common sense, such as knowing when to buy a Ferrari to go around the corner and when it is better to walk. I would love to hear about your experiences, whether you are paying the Fable tax or opting to optimize.