Holidays are over, and we are preparing the Futurebraining Workbook, a field guide for building a human-centric system for working with AI.
The ambition is practical: simple frameworks, lived examples, and exercises that push you to apply ideas that matter immediately, not someday.
The three of us, Martin Jensen Methlie, Luciano Pollastri, and I, are not starting from scratch as founders; instead, this is more of a joint exercise in synthesis. The project combines our blog posts, interviews, conferences, webinars, and workshop experiences into a comprehensive resource that you can pick up and read on/offline. All in service of one thing: moving from simple automation to genuine AI co‑intelligence, what we call “MEAI co-intelligence”.
With the same speed AI had remixed an undrinkable summer cocktail, we lost our trust in AI as a serious ghostwriter. Reviewing a 30,000-word manuscript for structural coherence, factual integrity, and tone alignment is not just tedious; it’s unworkable. It would have taken longer to check and correct than to write a clean draft ourselves from the ground up.
Missing Backbone. Even with the suggested (cheat) index and sample flow, the output completely lacked a reliable internal architecture. Sections followed each other, but without logic. Building a hierarchy of ideas and arc is hard work for human intelligence; forget about synthetic forces at play.
Context Loss – Crucial decisions and references blurred or disappeared. Key through-lines broke down, undermining both logic and trust. While limited context windows are a known constraint of current models and not something to blame them for, this failure went beyond that. The models failed to prioritize, sustain arguments, or preserve what gave the text direction and weight.
Voice Drift – Despite spending time on training style, way too much input text returned as motivational one-liners. Our tone—meant to be grounded and instructive—was replaced by AI language.
Version Gaps – Fixing one chapter left earlier or leaving the later chapters untouched. There was no memory, no ripple effect, no cumulative intelligence.
Each of these issues multiplied under the weight of complexity. For simple topics, the output might have been ok. But once we layered in arguments, iterative structure, and the messiness of real collaborative writing, the cracks widened. And we hadn’t even started our complete review rounds yet. Imagine the sarcastic feedback still to come, the layered suggestions, and the intense back-and-forth between us in three colored comments.
Just one dry run, and the system was struggling to keep up, so we aborted the experiment and went back to the third review round of a book we were starting to dislike, with hate just around the corner.
We found that AI works best when asked to operate at the sentence or paragraph level. At most, we trust it with a single chapter focused on one clear topic or concept with an obvious internal hierarchy. Even then, we stay alert. Anything broader, with layered argumentation or cross-referencing, and the cracks start to show. AI excels at highlighting redundancies, eliminating unnecessary filler, and suggesting more precise wording. AI can offer excellent metaphors or analogies that sharpen a point. Used this way, it becomes a reliable line editor or writing coach, sound, but not overpowering or creatively stunning.
AI is genuinely helpful in short, directed research bursts. When pointed at a few academic sources and asked for comparative insights, it can surface patterns, contradictions, and useful references almost instantly. Tools like ChatGPT and Gemini (Google LM) have been especially valuable for this kind of focused investigation. They help clarify what we’re talking about, challenge our blind spots, and surface assumptions we may not realize we're making. Even then, we never take results at face value, but instead use them as the basis for analysis.
Because memory and context windows in most models can be fragile, we set boundaries. Exporting sections of ready chapters from Google Docs (or MS Word) into PDFs and then uploading them into AI when working on new chapters can help anchor the model and reduce hallucinations. It’s not foolproof, but it gives the model a tighter box to work inside.
It's sooooo tempting to automate parts of the writing process. We found that creating GPTs or agents that check for style or voice can be effective, but poses the same danger as a regular chat due to the content's large and complex nature. You might win back time, but that convenience comes at a cost. A slight inconsistency, a disappeared section—these are easy to spot in an article. In a 200-page draft, especially one you've already reviewed several times, it's like searching for a needle in a haystack.
For smaller chapters or sections, we deliberately built in what we call a "friction loop"—three steps that keep us on the AI ball:
Pause – Don’t accept any suggestion on autopilot.
Probe – Ask the model why it made that change or where the claim came from. Be specific in your prompt: identify exactly which sentence or paragraph you're referring to.
Fix – Clearly state what must remain unchanged, and what (if anything) is open to suggestion. You can ask for alternatives or critiques, but do not approve changes until you've reviewed and accepted them yourself.
Proof – ALWAYS Cross-reference suggestions with sources or intent.
This friction is deliberate. The more we stay alert—verifying, questioning, adjusting—the more potent the final output becomes. This isn’t about slowing down for its own sake. It’s about staying in control of the signal.
Writing a book is not just filling pages. It’s about holding shape, returning to hard ideas, navigating contradictions, and building something coherent across time. That’s human work—collaborative, effortful, and unpredictable. Depending on your personality, it is an “embrace the suck” activity.
When we gave AI the lead, structure evaporated, voice drifted, and logic broke down. Unsurprisingly, the illusion of fluency was strong, but depth and discipline were missing. That may sound harsh, but it's not a complaint; it's a boundary we must learn to work within.
That’s where the human premium lives: the felt sense that a real person is behind the work. We pay more for handmade items because we like the idea that human hands were part of the production process. We imagine the seasoned watchmaker, couturier, or writer in the flow, carefully crafting something with intention and attention. That emotional connection matters.
Could we have missed a tool, a more brilliant prompting technique, or a better way to load our context? Absolutely. If you know something we don’t, we’d love to hear it. But until then, we’ll keep the responsibility.