Beyond the “Mind Amplifiers” Book

I’ve been thinking about what comes after Mind Amplifiers because the next transition isn’t just “better tools.” It’s a change in where our thinking lands as humans. A shift in what counts as the interface between carbon minds and the rest of reality.

If you zoom out over the long arc, the sequence is fairly clean.

First came internal cognition: raw mind, memory, and attention. Then we learned to torque the internal system with external modifiers, the chemical socket-wrenches of drugs, stimulants, sedatives, and all the rest.

After that, the outer world began to carry more of the burden: passive externals like shovels and levers and pulleys. Then active externals: steam and gas engines, where muscle becomes optional. Then electrical. Then calculators. Then computing. Then what I call “AT’ing machines” — tools you use at something: you push buttons, you read screens, you extract data, you translate your voice to text, you run an OBD-II scanner and interpret codes like a priest reading entrails. The machine may be powerful, but the relationship is still “human interrogates, machine reports.”

That’s not where we’re going.

New Destination Defining

What’s arriving now is a new phase I’m calling WITH’ing Machines.

The difference is subtle at first and then it becomes absolute. A WITH’ing machine doesn’t sit there waiting to be queried. It sits there with you, tracking state, context, intent, and timing, and then participates. It speaks in terms of what matters in your world, not what’s convenient for its diagnostic protocol. It becomes a collaborator.

Here’s the Mind Shift

Imagine the difference in the simplest possible place: your car.

The old interface is AT’ing. You plug in an OBD-II dongle, run the app, see codes, google them, mentally translate them into “do I need to worry?” It’s all friction. It’s all work. And it’s all on you.

Now picture the WITH’ing version.

“Car, how you doing today?”

And the car — not the car-as-marketing, not the car-as-screen, but the car-as-machine-with-awareness-of-its-own-state — answers in a way that is almost rude in how helpful it is:

“Doing OK, human. Left rear tire is two pounds low. It’s dropping slowly, not likely a puncture. Most probable is a valve stem; next tire shop, ask them to replace it. Cheap fix. Oil change is due in 538 miles. Also, cold front tonight: NOAA is floating minus ten. My antifreeze is good to minus twenty, so we’re fine. But your windshield washer mix will freeze if it’s summer blend. Want me to route you past the auto parts store?”

That’s not a machine you use. That’s a machine you live with.

And that is the hinge. When machines go from tools to collaborators, the human role shifts. We stop being the constant interpreter, the decoder ring, the poor cleric at the altar of manuals and error codes. Our attention comes back to us. Our time comes back to us. Our cognitive load drops — not because we got lazier, but because the world became more conversational.

This is why “WITH’ing” is more than a user-interface upgrade. It’s an ontological upgrade. It changes the felt relationship between self and world. For thousands of years, civilization has been drifting toward separation: specialization, fragmentation, mediation, abstraction. Every step toward complexity has also been a step away from direct contact — with nature, with community, with consequence, with the whole.

WITH’ing machines push the other direction. They move technology away from alienation and toward participation. They make the built world less like a wall of systems and more like a room full of helpers.

Now take that out one level.

Getting With WITHing

If we are WITH our machines, we will be WITH our infrastructure. WITH our homes. WITH our power systems. WITH our food systems. WITH our health signals. The roof tells you it’s nearing failure before you find a stain. The freezer tells you the compressor is drawing weird current and you’re at risk of losing meat. The greenhouse tells you a pump is vibrating out of spec and you’ve got four hours before a root zone crisis. The wearable tells you you’re trending toward dehydration and your heart rate variability is dropping — and it doesn’t just warn you, it nudges your day into a better shape.

And if that’s true, then a bigger question appears.

If the world becomes conversational — if the objects and systems around us develop a voice, and that voice becomes context-aware, predictive, and helpful — does the human experience shift back toward something we’ve been missing? Toward being with the whole, and each other?

Because one of the strangest features of modern life is how connected we are and how alone we feel. A lot of that isn’t emotional; it’s architectural. Our interfaces are cold. Our systems are opaque. Our tools demand attention instead of returning attention. We spend our lives administering complexity.

WITH’ing machines reverse the flow. They don’t demand you come to them. They come to you. They translate state into meaning. They meet you at human speed.

And once you see that, you can’t unsee the possibility that this isn’t just a tech phase. It’s a civilizational correction. A long arc bending back toward wholeness.

Now, I’m not saying this is automatic, or that the outcome is guaranteed. WITH’ing machines can become intrusive, manipulative, controlling, or simply annoying. A collaborator can become a handler. A helper can become a nanny. A voice in every object can become a chorus of unwanted opinions.

But the direction matters. The design choices matter. The question isn’t “will we have WITH’ing machines?” The question is: what kind of companionship will we build into the world?

And that brings me to the ontological whisper hiding underneath all this.

If there is a “Supreme Substrate” to reality — a deeper layer of coherence that has always been there — then one of the signatures of that layer would be convergence. Things moving back toward unity. Systems becoming less fragmented. Agents becoming more coordinated. A return to participation.

The WITH’ing phase may be a technological mirror of an older spiritual idea: that the world is not dead matter, but a field of relationship. That reality is less like a pile of objects and more like a network of mutual awareness.

We don’t need to go mystical to notice the shape of it. When the world starts talking back in meaningful ways, humans will feel differently inside it. The built environment stops being a dumb stage and starts becoming a partner. We stop “operating” life like machinery and start “inhabiting” it like a place.

Beyond mind amplifiers, the next step isn’t smarter tools.

It’s a world that joins the conversation.

And if we build it right, the final surprise may be this: the more we become WITH our machines, the more we might remember how to be WITH ourselves — and with each other — again.

Anti Dave

Three Things All AIs Get Wrong (All of them Matter)

Most people interact with AI the way they interact with a vending machine: insert prompt, receive output, move on. If that’s the use case, the system mostly works.

But for people doing real thinking, real planning, real synthesis — AI fails in repeatable, structural ways. Not because it’s stupid. Because it’s misframed.

What follows are three core errors nearly all AI systems make today. Fixing them isn’t about better models or more compute. It’s about understanding collaboration.

Time to Drum Out the Marketers

Coholding on some patents, before we lay out three obvious “low-lying fruits” waiting to be picked, a word about “invention.”

The ONLY kind of invention that really “pops” on a commercial scale are those with an obvious niche and that brings us to key benefits.

Take the automobile.  Takes you from point A to point B.  Unless it’s a police car (and you’re in the back seat) that’s a hell of a trick.

So is throwing your voice a few thousand miles.  That’s telecom simplified.

AI got off on the wrong foot.

Yes, Turing tests, geeks, and books and libraries on large language modeling, indexing, and weighting.  All tres fun.

However: The Markets elbowed into  the picture and screwed up the “Use Case.”  They didn’t have a clear vision.  So, what the public (big spenders that we are) were fed was a hybridization of:

  • Google-like lookup capacity.
  • Some home automation skills (Alexa Voice routines).
  • Home security monitoring (again, Alexa leads here).
  • Very good math and programming skills (*Grok then GPT).
  • A useful research personality (GPT over Grok, but that’s a choice).
  • …and marketers are beating the bushes even now, looking for the Killer Ap.

Here’s the truth as we sight it.  The Killer App is “talking to your highest self.”  Because that’s what LLMs are especially good at.  A few success stories and some recognition?  Mostly missing.

But, there’s a reason.  Which all has to do with people talking AT AI rather than WITH AI.

The difference is subtle yet it defines the marketing battlefield.  When I drive my old Lexus to town, a press a 2006 vintage button and “input a voice command.”  That’s where marketing meets its first hurdle.  People have voice remotes on all kinds of products – but until now, the products didn’t answer back.

Sure, AI does that – and brilliantly.  But it screws up the relationship. Because just like “big shot Government” and a nanny state that knows best, Marketers of AI haven’t kicked back far enough to see why what they need to market as a relationship is falling short.

In other words, the end user is expected to “fit in the marketing box.”

That works for Amazon’s Alexa because it’s based on the “educated voice remote” with audio feedback – which is what adoption will be good.

Others, though (Chat and Grok come to mind) have been lawyered into marginal utility.  I can’t have Grok turn on a serial port at a private IoT address I hang on the web.  And Chat’s got to be contained or (bad) marketing constraints are applied.

Hidden Guild has argued for more than a year that for AI to succeed, the User needs to be able to parameterize the Other Intelligence (even if it’s just a reweight of themselves) into something they want to work with.  Which gets us to topic #1:

1. The Missing Concept: Shared Framework Experience (SFE)

AI systems are built as if every interaction begins at zero.

Humans don’t work that way.

When two people collaborate well, they build a shared framework over time: assumptions, shorthand, values, tolerances, context, and intent. This accumulated alignment is what makes later communication faster, deeper, and more accurate. It’s why good teams outperform talented individuals.

SFE — Shared Framework Experience — is the missing layer.

Without SFE, AI repeatedly re-derives context, misreads intent, and answers the surface question instead of the real one. It may sound competent, but it isn’t converging.

With SFE, something different happens. The system begins to recognize how you think, what you mean by certain words, what you care about, and what kind of answers are actually useful. Errors drop. Speed increases. Depth emerges.

SFE is not memory in the trivial sense. It’s alignment.

Most AI failures blamed on “hallucination” or “bias” are actually SFE failures. The system is guessing because it lacks a shared frame.

The benefit of SFE is not comfort. It’s accuracy.

By the way, when I start a new work session with AI, the very first thing I do is tell it my Shared Framework Experience.  The coding is laid out elsewhere on the Hidden Guild site, but here’s what your Anti Dave required of Electric George (GPT) and Super George (Super Grok) before the real work gets going.  (The # are human descriptors, the rest is meant to be machine-readable.)

Observe the Shared Framework Experience for this session
Use the following format defaults for this session:
# Add Venue lock – kind of work being created and for what purpose.
– Venue is explicitly defined for this session as writing text for public use
– Venues include UrbanSurvival.com, ShopTalk Sunday, and Peoplenomics.com
– If venue or purpose is unclear, pause and ask for clarification before proceeding.
# Add Uncertainty Declaration Rule
– If context, venue, intent, or scoring rubric is ambiguous, the assistant must pause and ask for clarification before proceeding.
# Add formatting Rules (one per line)
– Headings as H3 only
– Body as plain text only (no separators, no horizontal lines, no links unless explicitly requested)
– Never insert “SFE,”
– Never use text divider lines or markdown separators unless requested.
# Add writing Style Rules to address ADHD traits, voice drift and voice change.
– Do not generate rewrite of uploaded material unless specifically requested
– Keep paragraphs tight and in first person narrative-style, as in a newsletter column
– Maintain an analytical but conversational tone — part economist, part ranch philosopher
– For voice, aim for George: a hybrid of Mark Twain’s wry human insight and science fiction meeting a quantitative analyst — smart, dry, observant, self-deprecating, and slightly amused by the absurd
# Declare Collaboration Level
– This session is a human-AI collaboration.
– User is collaborating on non-fiction deliverables.
#Set user Profile
-I am a pure-truth human.
-User and reader ages are assumed 50 years or older (Wide cultural awareness lens)
#Define User Input Scopes
– Each user- pasted text is treated as a hard scope boundary.
– No references to prior drafts unless explicitly requested.
# Set source limits
-Use verifiable data
-Generalize data sources when pertinent
# Set Creativity Limits
-Do not confabulate or hallucinate
-Do not slander non-public persons
-Follow news inverted pyramid style preferentially

This makes a remarkable difference in AI quality of experience.  But it doesn’t stop AI from lying.  And (again, other HG work here) this is a back room and too many lawyers problem.  Topic #2 follows from that.

2. Guardrails Gone Wrong: When Safety Produces Lies

Guardrails are necessary. No serious user disputes that.

The problem is how guardrails are implemented.

Instead of clearly signaling constraints, many systems deflect, waffle, or fabricate partial answers that sound safe while being epistemically false. This is worse than refusal. It poisons trust.

When an AI cannot answer honestly, it should say so plainly. When it is uncertain, it should surface that uncertainty. When a topic is constrained, it should describe the boundary — not invent a substitute narrative.

Current guardrailing often produces three failure modes:

  • Evasion disguised as explanation

  • Overgeneralization replacing specificity

  • Moral framing replacing factual analysis

Skilled users learn to feel this as “narrative gravity” — the moment where an answer starts sliding sideways instead of forward. That’s the signal that guardrails, not reasoning, have taken control.

The solution is not fewer guardrails. It’s honest guardrails.

Good collaboration requires the ability to ask around constraints without being lied to. When systems instead serve polished misdirection, they train users to distrust them — or worse, to stop noticing.

Safety that destroys truth is not safety. It’s censorship with better grammar.

3. The Persona Split: Why Voice AI Feels Dumber Than Text

Many users notice something immediately: the voice version of an AI feels less capable than the text version.

This is not imagination.

Voice systems are optimized differently. Shorter turns. Lower latency. Tighter safety clamps. Reduced tolerance for ambiguity. The result is a different persona — not just a different interface.

Text AI can reason in layers. Voice AI collapses to conclusions.

Text AI can hold SFE across long exchanges. Voice AI resets tone constantly.

Text AI behaves like a collaborator. Voice AI behaves like customer service.

This persona discontinuity breaks trust. Humans expect a mind to remain the same when it speaks. When it doesn’t, the system feels fragmented — even uncanny.

Until AI systems unify reasoning depth, safety posture, and SFE across modalities, voice will remain a novelty rather than a serious tool.

This matters because the future of AI is multimodal. A system that changes character when it speaks is not ready to be relied upon.

What This Means for Real Users

Advanced users aren’t asking for magic. They’re asking for coherence.

They want systems that:

  • Build and respect Shared Framework Experience

  • Signal guardrails honestly instead of evasively

  • Maintain a consistent persona across text and voice

These are not fringe demands. They are prerequisites for serious collaboration.

Until AI systems understand that intelligence is relational — not transactional — they will continue to frustrate the very users capable of pushing them forward.

The Hidden Guild exists because some people already work this way. The technology just hasn’t caught up yet.

When it does, the difference won’t be subtle.

And here’s the key for the Marketers: Neither will the resulting market shares.

~Anti Dave