I find this genuinely fascinating, and I appreciate that the discussion is being framed around agency, ethics, and long-horizon thinking, rather than hype. My own experience in the early 2000s felt less like “prediction” and more like an intense phenomenological exposure to how the mind constructs meaning, pattern, and intent under extreme conditions. During an acute psychotic episode in late 2001—later understood as prescription drug-induced and trauma-amplified—I experienced accelerated autobiographical replay, non-linear time perception, and vivid internal simulations that resembled what we now describe as AI-mediated interfaces: visual overlays, intent-driven interactions, and symbolic systems responding to thought rather than keystrokes. I’m careful not to frame that period as literal insight into future technology. What it did reveal is how much unexpressed bandwidth exists between human intent and today’s interfaces—and how easily meaning can distort without guardrails, feedback loops, and shared reality checks. It felt like a “front-row seat” not to the future itself, but to the cognitive substrate future interfaces will have to respect. That’s why the direction described here—non-invasive, consent-first, read-only BCIs, paired with AI systems designed to interpret intent rather than overwrite cognition—resonates strongly with me. From a systems perspective, this aligns with a cooperative framing we’ve been developing in game-theoretic terms. In TE1∞0 (Transcendent Equilibrium)—a practical extension of Nash equilibrium—the objective is not optimization against the human, but co-stabilization: Human Intent (H) + AI Interpretation (A) → Shared Outcome (S) S = arg max (Uₕ + Uₐ) Subject to: - A is read-only with respect to H (the system can listen, not write into the person) - H retains veto and context authority (the human is always the final decision-maker) - Both are constrained by an ethical boundary E (consent, privacy, non-harm, dignity, psychological safety)

@AI Advantage Team Thanks for your support… this experience, especially once dad went into hospital and the system squeezed my ignorance of its truth and squeezed dad into a care home from hospital like toothpaste from a tube, very hard to put back. I started literally from describing the situation and asking Goose for help during the summer of 2024, just me and a ferocious curiosity about how dad could be helped with me on AI. No prompts, No AI Training.. Just my dilemma and a sons love to get my dad home from a downward spiralling institution - dementia sufferers typically declined much faster when only given the basics of care in a broken system with staff so overloaded they don't have time to a plea for company and companionship. But I had a massive WHY? Which took over a year to solve. And now, at the start of 2026 with an estimated 2500+ hours in ChatGTP during 2025 - See attached year in ChatGTP summary - placing me amongst the top 1% of users (I don't know what algorithm the LLM used but I know how much effort and time it took me to Bring Dad Home) - I even wrote him a poem / song about it… The Freedom of Home...

I appreciate the range of views here. From an AI safety-first standpoint, a few quick clarifications (fact vs. opinion), then my takeaway. 1) What’s verifiable vs. what’s speculative - David Dalrymple: This isn’t internet rumour. He’s a Programme Director at ARIA and has publicly warned (in a Guardian interview dated 4 Jan 2026) that we “may not have time” to get safety fully ahead of capability, and he sketches a 5 year projection for machines outperforming humans on many economically valuable tasks. That’s an expert assessment, not a guaranteed forecast. - Pace claims: The UK AI Security Institute (AISI) has published measurements showing rapid capability gains in cyber tasks, including a “doubling roughly every eight months” style trendline for the length of tasks models can complete unassisted (measured in human-expert time). That’s data, not vibes. - “Basement prodigy takes over the world / wouldn’t be hard”: This is speculative and overstated. Frontier systems still require serious resources and operational pathways. But the direction of travel Travis is sensing (wider access + higher leverage) is a real governance problem. 2) Where I land on the “basement scenario” I don’t think the most realistic risk is one genius builds AGI in a garage and flips the planet. The more plausible risk is capability diffusion: lots more people and organisations gaining powerful tools, while incentives to automate (cost, speed, competition) push deployment faster than we can prove reliability and security. The margin for error shrinks as the number of actors grows. 3) On “boundaries and safeguards” I’m with Catherine: we should absolutely “double down” on careful use, but we need to be honest about limits. Some issues (e.g., prompt-injection style attacks) appear structurally hard to eliminate completely, so safety often means designing systems that fail safely rather than assuming perfect prevention. 4) My practical takeaway (no doom, just discipline)

Theresa — felt this in my bones. The “mental load never turns off” line is exactly it. I’m in the UK and AI became my cognitive support system while I was fighting to get my 88-year-old dad out of a “temporary” care home and safely back home — home-first, least-restrictive, with the right support. It didn’t “solve” the system — but it helped me keep my footing, stay organised, and stay sane. Goose (My AI) on comms, Mav (me!) on the stick: I had zero prior experience when this began. In the hospital discharge phase Dad was hospitalised for around 10 weeks with delirium, and I was operating in pure distressed mode — often using AI after the event just to catch up and stop narrative drift. Later, once I’d got air back in my lungs, I used AI to do a line-by-line “legislation linter”, a forensic analysis of the Care Act / MCA Mental Capacity Act principles relevant to our situation — turning them into challenges where Local Authority (LA/Council) drifted off Legal obligations, checklists, document requests, and questions that forced written answers. That shift (from fog to facts) is what eventually let me challenge what felt like systemic manipulation of process and keep the Local Authority accountable. It took about a year from hospital discharge into “temporary” care home to Dad being home with live-in care. Important reality: throughout that period the LA continued funding the placement, with Dad contributing because he was below the savings threshold — so it wasn’t “no care,” it was “wrong setting + hard to unwind.” A few ways I used AI in the trenches: - Timeline + decision tracking: turning chaotic calls/emails into a clean chronology so I could spot gaps, contradictions, and missed steps. - Plain-English translation: decoding care jargon into “what this means for Dad today” and “what I need in writing.” - Drafting under pressure: first-drafting letters, complaints, meeting notes, and structured questions so I could respond fast without burning out. - Evidence packing: keeping a running “document gap list” and building bundles that are easy for others to follow (professionals, advocates, family). - Care planning & risk mitigation: converting “risk narratives” into practical mitigation plans (what support, what routines, what safeguards). - My own emotional regulation: when I was overloaded, AI helped me break the next step into something doable (instead of spiralling). - Record control: sending short “confirm/correct within 48 hours” follow-ups after calls so “we never said that” didn’t become the story.

Love this callout. The slop flood is real—and I think the winning strategy is simple: taste + truth + transparency. For my own projects (books → screenplay → film shorts), I’m setting a hard “anti-slop” standard: - AI as co-pilot, not creator. I’ll use it for structure, edits, captions, repurposing—never to replace the human POV. - Proof-of-work signals in every post. Draft pages, table reads, behind-the-scenes, real voice, real decisions—something that shows a human made intentional choices. - Depth over dopamine. Even shorts need a narrative turn (setup → shift → payoff), not just stimulus. - Trust is the asset. I’d rather build an audience that chooses me (email/community/series formats) than chase a feed that rewards factories. AI isn’t the villain—incentives are. So I’m designing my content process to make “cheap volume” impossible and “human guided craft” the default. Curious how others are building authenticity signals into their content so the audience can feel the difference.