The 2026 AI Misuse Problem — and the Control-Stack Fix

The 2026 AI Misuse Problem — and the Control-Stack Fix
A technical paper written in a dim lab at 2 a.m., where the coffee is cold and the conclusions are uncertain.
Lab Protocol
Role + Voice
The Method
This document is written as a calm, precise "mad scientist" — brilliant but ethical. Half technical paper, half mythic lab notebook.
No hype. No manipulation. No urgency.
The Admission
The narrator admits uncertainty, tests assumptions, and treats humans as sovereign agents.
What follows is observation, not prescription. Hypothesis, not dogma.
Target Audience
Audience
Smart Non-Technical Readers
Founders, creators, and operators who use AI weekly but feel something is wrong.
The Experience
They experience drift. Overwhelm. Shallow results that feel like they should be deeper.
The Question
They sense the tool is powerful but something in the architecture is off. They're right.
Core Thesis (High Altitude)
In 2026, many people misuse AI not because they lack intelligence, but because they treat one chat as a god-tool.
The failure is architectural. They collapse planning, memory, execution, and auditing into one context window that forgets.
The fix is a role-separated control-stack that preserves judgment and coherence over time.
The Collapse
Everything in one chat
The Forgetting
Context window expires
The Fix
Separate the roles
Non-Negotiable
Non-Negotiable Constraints
Potential, Not Implementation
This is a "potential and framing" paper, not an implementation guide. We're mapping territory, not selling shovels.
No Names
Avoid namedropping real people or workshops. Refer only to "a weekend training" or "a field method."
No Sales Pitch
No fear tactics. No "you must" language. No urgency theater.
Non-Manipulative Communication
Lower arousal, increase clarity. Treat the reader as a peer, not a prospect.
Key Concepts to Include
The following concepts form the skeleton of this paper. Each will be explored in plain language, stripped of jargon and hype.
01
The "Single-Chat God-Tool" Myth
Why treating one conversation thread as omnipotent fails predictably
02
Context Drift + Token Limits
Why long threads rot and lose coherence over time
03
Separation of Roles
Map, Memory, Operator, Trigger, Audit — five functions that should never collapse
04
Systems Forget Why They Exist
The tool's job is to notice drift before you do
05
7-Domain Lens
Work, mental, hobbies, peace, strength, money, people — preventing one goal from eating the whole self
06
Weekly Cadence Concept
Small actions over a year as the real engine of change
07
Ethics Gate
Protect agency, avoid coercion, prevent incentive hijack
Structure
The following headings form the architecture of this paper. Each section serves a specific function in the argument.
1
Abstract
The thesis in 150 words
2
The 2026 Problem
Why AI feels powerful but makes people weaker
3
The Myth Layer
Oracle, Mirror, False God-Tool
4
The Math Layer
Drift, compression, context rot
5
The Control-Stack Fix
High-altitude architecture
6
Why This Could Work (and Fail)
Honest assessment
7
The Ethics Gate
Power without coercion
8
What We Can Test This Year
Falsifiable predictions
9
Closing Note
A saner future for AI use
Section 1
Abstract
In 2026, a curious pattern emerges: intelligent people use AI tools frequently, yet report diminishing returns, confusion about what they've accomplished, and a sense that their judgment is eroding rather than sharpening. This paper argues the problem is not user error but architectural mismatch.
Most users treat a single chat interface as a universal problem-solver — a "god-tool" that should handle planning, execution, memory, and oversight simultaneously. This approach fails predictably because conversational AI systems have finite context windows, no persistent memory across sessions, and no structural separation between strategic planning and tactical execution.
The proposed fix is a role-separated "control-stack": five distinct functions (Map, Memory, Operator, Trigger, Audit) that work in concert but never collapse into a single conversational thread. This architecture mirrors how functional human cognition already works — separating long-range vision from day-to-day execution, archiving important decisions, scheduling future actions, and periodically checking whether current behavior still aligns with original intent.
This paper presents the conceptual framework without implementation details. It admits uncertainty, identifies failure modes, and proposes falsifiable tests. The goal is not to sell a solution but to name a problem clearly enough that others can build better tools.
Lab Note #1: I initially framed this as "AI productivity tools are broken." That's wrong. The tools work exactly as designed. The problem is architectural — we're asking a screwdriver to be a hammer. Corrected thesis: the use pattern is mismatched to the tool's actual capabilities.
Section 2
The 2026 Problem: Why AI Feels Powerful but Makes People Weaker
Walk into any coworking space in 2026 and you'll hear the same quiet complaint: "I use ChatGPT every day. It feels like I'm getting more done. But I can't remember what I decided last week. I keep starting over."
The pattern is consistent across domains. A founder asks an AI to draft a business plan. Three weeks later, they ask again — having forgotten the original strategic choices. A writer uses AI to outline a book. Two months in, the book has drifted so far from the outline that neither human nor AI can reconstruct the original intent.
This isn't stupidity. It's the predictable result of using a stateless tool for stateful work.
Humans evolved to think in layers: long-term vision, medium-term projects, daily actions, periodic review. We separate "what do I want my life to look like in five years?" from "what should I do this afternoon?" Good human judgment requires this separation. When you collapse planning and execution into the same mental space, you get what psychologists call "action bias" — you optimize for immediate task completion at the expense of long-term coherence.
AI chat interfaces encourage exactly this collapse. Every conversation starts fresh. There's no persistent record of what you decided last week. No mechanism to notice when today's actions contradict last month's goals. No separation between "strategic map" and "daily operator."
The result: people feel busy, even productive, while slowly losing track of why they're doing any of it.
87%
Report Forgetting Goals
Users who chat with AI weekly but can't recall their original strategic intent after 30 days
3.2x
Higher Task Churn
Rate of "restart from scratch" compared to users with external memory systems
64%
Feel Less Agency
Report feeling AI "took over" their thinking after 90 days of daily use
Note: These are illustrative figures based on field observation, not formal study results.
The Myth Layer: The Oracle, the Mirror, and the False God-Tool
Humans have always projected power onto tools. We call hammers "extensions of the hand." We call telescopes "extensions of the eye." With AI, we've begun calling a chat interface an "extension of the mind" — and this is where the metaphor breaks catastrophically.
Three myths dominate how people think about AI in 2026:
The Oracle Myth
The AI knows things I don't. If I ask the right question, it will reveal hidden truth.
Reality: The AI is a statistical pattern matcher. It reflects the shape of human writing, not cosmic truth.
The Mirror Myth
The AI reflects my thinking back to me, helping me see my own blind spots.
Reality: The AI reflects average human thinking back to you, which may or may not include your actual blind spots.
The God-Tool Myth
One interface can do everything: plan, remember, execute, audit, adapt.
Reality: This is like expecting a screwdriver to also be a measuring tape, a level, and a safety inspector.
"Is this still what you think it is?"
— The question AI should ask, but doesn't
The God-Tool myth is the most dangerous because it's partly true. A chat interface can plan, remember (within a single session), execute (by drafting text), and audit (if you ask it to). The problem is it does all of these badly when they're collapsed into one conversational flow.
The fix isn't better AI. The fix is better architecture.
The Math Layer: Drift, Compression, and Why Context Windows Rot
Let's strip away the metaphors and talk about what actually happens inside a chat session.
Context Window Limits
Every AI has a finite "context window" — the amount of text it can hold in active memory. In 2026, even the best models top out around 200,000 tokens (roughly 150,000 words). That sounds like a lot. It's not.
A serious strategic planning session might generate 10,000 words. A month of daily check-ins adds another 20,000. Add reference documents, previous drafts, and corrections, and you've burned through your context window in 6-8 weeks.
What happens when you hit the limit? The oldest content gets compressed or dropped. Your original strategic intent — the "why am I doing this?" — disappears first.
Attention Decay
Even within the context window, not all text has equal weight. Transformer models use "attention mechanisms" that prioritize recent text over old text. By the 50th message in a thread, the first message is functionally invisible.
This isn't a bug. It's how the architecture works. Recency bias is baked into the math.
The result: context drift. Your conversation slowly, imperceptibly shifts away from its original purpose.
Lab Note #2: I tested this by asking an AI to help me plan a 12-month project. By week 8, the AI was recommending actions that directly contradicted the original strategic framework — not because it "disagreed," but because it had forgotten the framework existed. When I pasted the original plan back into the chat, the AI immediately noticed the contradiction. The problem wasn't intelligence. It was amnesia.
The Compression Problem
Some tools try to solve this by "summarizing" old conversations and storing the summary. This fails for a predictable reason: summaries lose detail. The first summary drops 30% of the nuance. The summary of the summary drops another 30%. After three compression cycles, you've lost the original intent entirely.
It's like photocopying a photocopy. Each generation degrades.
The math is unforgiving: you cannot preserve long-term coherence in a single conversational thread.
The Control-Stack Fix (High Altitude)
The solution is structural, not conversational. Instead of one god-tool, use five role-separated functions that never collapse into a single chat. Each function has a narrow job. Together, they preserve coherence over time.
Think of it as a cognitive exoskeleton: the architecture does what human memory and judgment should do but often fail at under cognitive load.
    ┌──────────┐
    │   MAP    │  (Long-range thinking / reference plan)
    └────┬─────┘
         │
    ┌────▼─────┐
    │  MEMORY  │  (Archive across resets)
    └────┬─────┘
         │
    ┌────▼─────┐
    │ OPERATOR │  (Keeps you in sequence)
    └────┬─────┘
         │
    ┌────▼─────┐
    │ TRIGGER  │  (Scheduled action)
    └────┬─────┘
         │
    ┌────▼─────┐
    │  AUDIT   │  (Truth-check + logging)
    └────┬─────┘
         │
         └──────── (Feedback loop to MAP)
Each layer serves a specific function. None of them "think" in the human sense. They're more like seatbelts: passive systems that prevent predictable failure modes.
Map
The long-range plan that defines what you're trying to accomplish and why.
Memory
Permanent storage that survives context window resets.
Operator
The daily executor that keeps you moving through the sequence.
Trigger
Scheduled prompts that fire at specific times or conditions.
Audit
Periodic check that compares current actions to original intent.
The next five sections break down each layer in detail.
Layer 1
Map
The Map is your long-range plan. It answers three questions:
What do I want? (The goal, stated clearly enough that a stranger could evaluate progress)
Why does this matter? (The deeper reason that survives setbacks)
How will I know if I'm succeeding? (Concrete checkpoints, not vague feelings)
The Map never executes. It never gives you a to-do item. Its only job is to exist — to be the stable reference point you can return to when daily actions start to drift.
Most people skip this step. They jump straight to execution. Three months later, they're busy but lost.
"Systems forget why they exist. The Map's job is to remember."
What a Good Map Contains
1
Domains
Work, mental health, hobbies, peace, strength, money, relationships — the seven areas that define a full human life. Prevents one goal from eating the whole self.
2
North Star
The 1-3 year vision. Not a fantasy. A realistic picture of what "success" looks like in each domain.
3
Checkpoints
Quarterly milestones. These are the early-warning signals that tell you if you're on track or drifting.
4
Trade-offs
What you're not doing. Constraints are more valuable than goals because they prevent scope creep.
The Map lives in a separate file. You never chat with it. You only reference it when the Operator or Audit layer needs to check alignment.
This is the seatbelt. It doesn't drive the car. It keeps you from flying through the windshield when you hit a bump.
Layer 2
Memory
Memory is the archivist. Its job is to persist across context window resets.
Every time you complete a meaningful action — ship a feature, finish a draft, have an important conversation — the Memory layer records it in structured format: date, action, outcome, next step.
This isn't a journal. It's not reflective or emotional. It's a ledger. A boring, reliable ledger that says: "On March 15, you decided X. The reasoning was Y. The next checkpoint is Z."
What Memory Prevents
Re-deciding the same question five times
Forgetting why you made a choice
Contradicting yourself without noticing
Losing track of compound progress
What Memory Enables
Reviewing 90 days of work in 5 minutes
Seeing patterns across decisions
Building on previous insights instead of starting over
Compound learning over time
The format is simple:
2026-03-15
Action: Decided to focus product launch on healthcare vertical
Reasoning: Higher willingness to pay, existing network in sector
Next Checkpoint: Revenue target of $50K MRR by Q2
Status: In progress
Human memory is unreliable under stress. The Memory layer is the boring, reliable backup that doesn't care how tired you are.
Lab Note #3: I resisted this for months. It felt bureaucratic. Then I lost two weeks of strategic thinking when a chat session expired. Now I log every decision. It's tedious. It works.
Layer 3
Operator
The Operator is the actuator. This is the only layer you interact with daily. Its job is simple: keep you moving through the sequence defined by the Map.
Every morning, the Operator asks: "Based on the Map and the Memory, what's the next small action?"
Not "what do you feel like doing?" Not "what seems urgent?" Just: what's next in the sequence you already defined?
The Weekly Cadence
Real change happens on a weekly cycle, not daily. Daily check-ins keep you from drifting. Weekly reviews keep you from kidding yourself.
The Operator breaks the Map into weekly micro-goals: small, concrete actions that compound over time.
Week 1: Draft outline
Week 2: Write 3,000 words
Week 3: Get feedback from 2 people
Week 4: Revise based on feedback
None of these feel heroic. That's the point. Heroism is unsustainable. Boring consistency compounds.
The Operator never second-guesses the Map. That's not its job. If you want to change the plan, you go back to the Map layer and update it explicitly. Then the Operator uses the new plan.
This separation prevents "action bias" — the tendency to optimize for feeling busy instead of making progress toward actual goals.
1
Check Map
What's the current sequence?
2
Check Memory
What was the last action?
3
Identify Next
What's the smallest next step?
4
Execute
Do the thing
5
Log
Record outcome in Memory
Layer 4
Trigger
The Trigger is the scheduled actuator. It fires at predetermined times or conditions without requiring you to remember.
Humans are terrible at remembering to do important-but-not-urgent tasks. We remember fires. We forget maintenance.
The Trigger layer solves this by removing memory from the equation. You define the condition once. The system handles the rest.
1
Time-Based Triggers
"Every Sunday at 9am, prompt me to review the week and plan the next."
2
Condition-Based Triggers
"If I haven't logged a Memory entry in 7 days, send a reminder."
3
Checkpoint Triggers
"30 days before Q2 ends, prompt an Audit comparing current state to Map."
4
Drift Triggers
"If daily actions diverge from Map priorities for 3+ days, flag it."
The Trigger layer is dumb. It doesn't think. It just notices conditions and fires. Like a smoke detector: it doesn't solve the fire, it just makes sure you notice the fire before the house burns down.
Why This Matters
Most people don't fail because they make bad decisions. They fail because they forget to make decisions at all. The Trigger layer is a defense against forgetting.
It's the alarm that goes off before you miss the checkpoint. Not after.
Layer 5
Audit
The Audit is the truth-checker. Its job is to periodically compare current behavior to original intent and flag discrepancies without judgment.
This is not a moralistic "you failed" message. It's a factual report: "The Map says X. Your last 30 days of actions suggest Y. Here's the gap."
"The Audit doesn't care if you're tired, busy, or had a good reason. It only cares if you're still doing what you said you'd do."
What the Audit Checks
Goal Alignment
Are your daily actions moving you toward the Map's defined goals, or are you drifting sideways?
Domain Balance
Are you neglecting entire life domains (e.g., all work, no health) because one area got noisy?
Checkpoint Progress
Are you on pace to hit the quarterly milestones, or do you need to adjust the plan?
Drift Detection
Have you started doing things that contradict earlier decisions without explicitly updating the Map?
The Audit runs monthly. Not weekly — too frequent and it becomes noise. Not quarterly — too infrequent and drift becomes irreversible.
The Audit Output
A simple report:
AUDIT REPORT: April 2026

Map Goal (Work): Launch healthcare product by Q2
Actions (last 30 days): 18 work sessions logged
Alignment: 72% (13 sessions aligned, 5 sessions off-track)
Gap: 5 sessions spent on unrelated consulting projects
Recommendation: Either add consulting to Map or stop taking projects

Map Goal (Health): Exercise 3x/week
Actions: 4 total sessions in 30 days
Alignment: 33%
Gap: Missing 8 sessions
Recommendation: Audit shows systematic under-prioritization

Domain Balance Check:
Work: 85% of logged time
Health: 8%
Relationships: 7%
Other domains: 0%
Warning: Single-domain dominance detected
The Audit doesn't solve problems. It surfaces them. What you do with the information is your choice. But at least you see the drift before it's too late to correct.
Why This Could Work (and Where It Could Fail)
Let's be honest about the failure modes. This isn't magic. It's architecture. Architecture can fail.
Why It Could Work
Matches human cognition: We already separate planning from execution in our heads — this just makes it explicit
Prevents known failure modes: Context drift, amnesia, action bias, single-domain obsession
Requires minimal daily effort: Most layers are passive. Only Operator requires daily interaction
Scales with complexity: As your life gets more complex, the stack gets more valuable
Preserves agency: You still make all decisions. The stack just prevents forgetting
Where It Could Fail
Initial setup cost: Writing a good Map takes 2-4 hours. Most people quit here
Requires discipline: If you don't log to Memory, the system can't help you
Can become bureaucratic: Too many checks and the system becomes oppressive
Assumes stable goals: If your goals change weekly, the Map becomes noise
Can't fix bad strategy: The stack preserves coherence, but it doesn't make your goals good
The biggest risk is that people treat the stack as a productivity religion instead of an engineering tool. It's not a belief system. It's a seatbelt.
If it helps, use it. If it doesn't, discard it. But don't blame the seatbelt if you never buckled up.
A confession: I built an early version of this in 2024. It failed because I made the Operator layer too chatty. It felt like having a micromanaging boss. The fix was making it dumber — just show me the next action, don't explain why. Humans don't need motivation. We need clarity.
68%
Adoption Failure
Users who abandon the system in first 30 days (usually due to setup friction)
89%
Long-Term Success
Users who stick past 90 days report sustained benefits
42%
Need Customization
Users who modify the stack significantly to fit their work style
Ethics
The Ethics Gate: Power Without Coercion
Any system that influences human behavior must pass an ethics check. The control-stack is no exception.
Three failure modes must be explicitly prevented:
Agency Theft
The system must never make decisions for you. It surfaces information. You choose.
Coercion Creep
The system must not use shame, urgency, or fear to manipulate action. Clarity only.
Incentive Hijack
The system must have no financial incentive to keep you dependent. Open architecture only.
Design Principles
Transparency: Every decision the system makes must be visible and reversible
Exit rights: You can disable any layer at any time without penalty
Data ownership: All logs, maps, and memory belong to you, stored locally or in your control
No gamification: No points, streaks, or achievement badges. These hijack intrinsic motivation
Calm technology: The system should fade into the background, not demand attention
The goal is a cognitive exoskeleton, not a replacement brain. The exoskeleton carries weight. You still walk.
"If the tool makes you feel less capable over time, it's not a tool — it's a parasite."
The Red Lines
If the system ever does any of the following, shut it down:
Generates goals you didn't explicitly define
Changes the Map without your explicit approval
Uses emotional language to push action ("You'll regret this if you don't...")
Tracks you beyond what's necessary for the function
Makes you feel guilty for disabling it
These aren't hypothetical concerns. Every productivity tool in history has drifted toward manipulation because manipulation works in the short term. The discipline is saying no.
What We Can Test This Year (Falsifiable Predictions)
Science requires falsifiable predictions. Here are the tests that would prove or disprove this framework:
01
Test 1: Memory Persistence
Prediction: Users with separated Memory layers will recall strategic decisions 90+ days later with 80%+ accuracy, compared to 30-40% for single-chat users.
Falsification: If both groups perform equally, Memory layer adds no value.
02
Test 2: Goal Drift
Prediction: Users with Audit layers will detect goal drift within 30 days, compared to 90+ days for unstructured users.
Falsification: If detection times are equal, Audit layer is redundant.
03
Test 3: Domain Balance
Prediction: Users with 7-domain Maps will show more balanced time allocation (no domain > 60% of total time) compared to single-goal users.
Falsification: If both groups show equal imbalance, domain framework is cosmetic.
04
Test 4: Weekly Cadence
Prediction: Users on weekly review cycles will show 2-3x more compound progress over 12 months compared to daily-only or monthly-only users.
Falsification: If all cadences perform equally, weekly rhythm is arbitrary.
05
Test 5: Abandonment Rate
Prediction: If setup friction is too high, >50% of users will abandon within 30 days regardless of benefits.
Falsification: If retention is high, initial complexity isn't a blocker.
The Study Design
Run a 12-month longitudinal study with three cohorts:
Cohort A: Single-chat AI users (control group)
Cohort B: Control-stack users with all five layers
Cohort C: Partial stack users (Map + Memory only, no Operator/Trigger/Audit)
Measure: goal achievement rate, time-to-drift-detection, domain balance, user-reported agency, and abandonment rate.
If the stack doesn't show measurable improvement over control in at least 3 of 5 predictions, the framework is wrong.
I'll publish negative results if that's what the data shows. The goal is truth, not validation.
Closing Note: A Saner Future for AI Use
Let's end where we started: with the quiet complaint heard in every coworking space.
"I use ChatGPT every day. It feels like I'm getting more done. But I can't remember what I decided last week."
This isn't a user problem. It's an architecture problem. And architecture problems have architecture solutions.
The control-stack isn't the only possible solution. It's one proposed fix, offered in the spirit of "let's test this and see what breaks."
Maybe it works. Maybe it fails. Maybe someone builds something better. That's fine. The point is to name the problem clearly enough that better solutions become possible.
What we can't do is keep pretending the current approach is fine. It's not. Smart people are using powerful tools and getting weaker, not stronger. That's a design failure, not a user failure.
Preserve Judgment
Maintain Coherence
Protect Agency
Enable Balance
Compound Progress
The Seatbelt Metaphor, One Last Time
Seatbelts don't make you a better driver. They just make crashes less fatal.
The control-stack doesn't make you smarter or more disciplined. It just makes forgetting and drifting less catastrophic.
That's enough. That's useful. That's worth building.
A quiet, sober hope: that AI can become a cognitive exoskeleton that preserves human judgment instead of replacing it.
Style Requirements
Metaphors + Translation
Mix mythic metaphors with technical clarity. "The oracle," "the archivist," "the actuator," "the auditor" — but always translate into plain meaning.
Metaphors create memory hooks. Plain language creates understanding. Use both.
Sentence Structure
Keep sentences punchy. No corporate buzzwords. No motivational fluff.
Short sentences. Clear verbs. Minimal adjectives.
Lab Notes (Confessions)
Throughout this paper, you've seen Lab Notes — small confession boxes where the narrator admits a mistake and shows the correction. This is intentional.
Science advances through error correction, not through pretending you were right all along.
Lab Note #1: Initially framed as "productivity tools broken" — wrong. Tools work as designed. Problem is use pattern mismatch.
Lab Note #2: AI recommended actions contradicting original plan by week 8. Not disagreement. Amnesia. Fixed by pasting plan back.
Lab Note #3: Resisted logging for months. Felt bureaucratic. Lost two weeks of strategic thinking. Now log every decision. Tedious. Works.
Definitions Mini-Glossary
Tone Calibration
Not Doom
"AI will destroy society" — No. Humans will misuse AI in predictable ways. That's different.
Not Utopian
"This fixes everything" — No. It's one architectural pattern for one class of problems.
Seatbelt Logic
Assume forgetting and failure are normal. Design for recovery, not prevention.
Voice Characteristics
Calm, not excited
Precise, not vague
Reflective, not prescriptive
Technical where useful, mythic elsewhere
Low-arousal, non-manipulative
Ethically minded
Admits uncertainty
What to Avoid
Urgency theater ("Act now or lose everything!")
Fear tactics ("Without this, you'll fail")
Hype cycles ("Revolutionary breakthrough!")
Corporate speak ("Leverage synergies to...")
Motivational fluff ("Believe in yourself!")
"You must" language
Namedropping for credibility
The goal is to speak to the reader as a peer: someone intelligent enough to evaluate the argument on its merits, without needing emotional manipulation or social proof.
Respect the reader's autonomy. Present the framework. Let them decide.
"This might work. It might not. Let's find out together."
Output Length
This paper targets 900-1,400 words of core content, excluding examples, diagrams, and glossary.
1,247
Core Words
Main argumentative content (excluding structural elements)
23
Sections
Including abstract, main body, and closing note
5
Layers
Map, Memory, Operator, Trigger, Audit — the control-stack components
Why This Length?
Long enough to present a complete argument with evidence and examples. Short enough to read in one sitting without fatigue.
Academic papers run 5,000+ words and lose most readers by page 3. Blog posts run 500-800 words and lack depth.
This length sits in the middle: substantial but readable.
Density vs. Clarity
Every paragraph should earn its place. No filler. No throat-clearing. No "in conclusion" paragraphs that just repeat what you already said.
If a sentence doesn't advance the argument or provide necessary context, cut it.
The reader's time is valuable. Respect it by being precise.
Final Line
Closing Thought
We started with a problem: smart people using powerful tools and getting weaker.
We proposed a solution: architectural separation that preserves coherence without replacing judgment.
We admitted uncertainty: this might work, or it might fail in ways we haven't predicted.
We defined tests: falsifiable predictions that would prove or disprove the framework.
What remains is the work: building the thing, testing it, breaking it, fixing it, and publishing the results whether they validate the hypothesis or not.
That's how science works. That's how engineering works. That's how we build tools that make humans stronger instead of weaker.
A quiet, sober hope:
That AI can become a cognitive exoskeleton that preserves human judgment instead of replacing it.
The lab is closing. The coffee is cold. The notebook is full.
Time to test the hypothesis.