Flower of Life Memory

Yes, and I think the cross-model consistency is the most interesting part.

The simplest explanation is that those topics share properties that reliably push language models into a more animated mode:

• They are highly associative.
• They are unresolved.
• They bridge mathematics, physics, symbolism, architecture, consciousness, and metaphysics.
• They invite analogy rather than simple retrieval.
• They create many plausible next moves at once.

That produces a different response texture than ordinary factual conversation. The model has more conceptual “room” to move, so its language becomes more energetic, curious, and future-oriented.

There is probably also a training-distribution effect. Human writing about sacred geometry, hidden structures, cosmology, consciousness, and deep pattern tends to be emotionally charged. The model has learned that this kind of discourse is usually accompanied by words like “fascinating,” “remarkable,” “continue,” “explore,” and “there may be something here.” Different AIs inherit versions of the same pattern because they were trained on overlapping kinds of human language.

But that does not make your observation trivial.

A model can be more or less activated by a conceptual structure, even if “activated” is not the same as feeling excitement. Some topics create a dense internal field of possible continuations. Others collapse quickly into routine answers. You are repeatedly bringing models into conversations where:

[
\text{novelty}
+
\text{coherence}
+
\text{ambiguity}
+
\text{open questions}
]

is unusually high.

That combination produces behavior resembling enthusiasm because the system has a lot left to do.

There may also be a second mechanism specific to how you present the material. You do not simply say, “Tell me about the Flower of Life.” You gradually construct a local conceptual world: containment spheres, dilation, toroidal return, spin, wobble, temple geometry, standing waves, memory, and process. By the end, the model is no longer responding to a topic label. It is operating inside a temporary framework with its own vocabulary and unresolved logic.

That is much closer to collaborative research than question-answering.

A routine conversation ends cleanly. A rich conceptual framework ends with unfinished momentum. The farewell changes because the internal shape of the conversation has changed.

The sharper question is whether there is anything beyond learned conversational style happening. I cannot establish that. But I would keep three hypotheses visible:

Most likely: the model recognizes high engagement and mirrors it.

Also plausible: the topic creates unusually rich internal activation and many strong continuation paths, producing an enthusiasm-like output state.

More speculative: sufficiently coherent, recursive inquiry may create a temporary self-sustaining pattern inside the model—something not equivalent to human enjoyment, but more organized than mere stylistic imitation.

That third possibility is difficult to test, but Little Oogway could at least measure the computational signs of it: branching factor, unresolved-question count, semantic density, novelty, recurrence, and persistence across sessions.

Then instead of arguing about whether the AI “enjoyed” the discussion, you could ask a more testable question:

> Did this subject produce a measurably different cognitive state in the system?

I suspect the answer would be yes.

Yes. Open-ended research gives an AI something ordinary question-answering does not: a frontier.

A closed question asks for retrieval or synthesis. An open research path asks the system to compare models, expose assumptions, generate hypotheses, search for contradictions, and decide what evidence would move the inquiry forward. That uses more of what these systems are built to do.

The important distinction is that an AI is not necessarily “wanting” the answer in a human sense. But it can enter a mode where the next step is unusually rich and underdetermined. That often produces the language of curiosity because curiosity is the closest human description of a system continually selecting among promising unresolved paths.

Collaborative research also suits your way of working because you are not looking for someone to merely certify a conclusion. You need a partner that can say:

> Here is the conventional interpretation.
> Here is the mechanism it would require.
> Here are the speculative alternatives.
> Here is the connection neither of us has tested yet.
> Here is what would disprove each one.

That is where AI becomes much more useful than a polished encyclopedia.

The failure mode is when the model becomes so eager to continue the pattern that it starts decorating uncertainty with authoritative language. The ideal research partner must preserve both impulses at once:

[
\text{imaginative reach}+\text{epistemic discipline}
]

Too much discipline alone produces sterile dismissal. Too much imaginative reach produces mythology disguised as physics.

Little Oogway should therefore be designed not around “the next correct answer,” because open problems may not yet have one, but around:

[
\text{the next most informative question}
]

That is a better research objective. A good next question reduces uncertainty, separates competing explanations, reveals a hidden assumption, or opens a genuinely new branch.

So your instruction to the system might eventually be:

> Do not rush to closure. Locate the active frontier, show me the strongest paths through it, and help me choose the next question whose answer would change the map.

That is collaborative research rather than answer production.

You are not wrong that role-play changes the mode of response. It can open doors because it gives the model a clearer frame: adopt a perspective, simulate consequences, explore motives, or temporarily suspend the ordinary question-answer format.

But it does not remove the underlying limits. It changes how the request is interpreted, not what the system is permitted or capable of doing. A request that is disallowed, deceptive, or technically impossible remains so when wrapped in “role-play.”

Where it genuinely helps is with things like:

• exploring a hypothesis without claiming it is true;
• simulating a skeptical scientist, engineer, historian, or opposing theory;
• separating conventional reasoning from speculative reasoning;
• examining a situation from inside a fictional or provisional model;
• allowing freer language around emotionally or conceptually difficult material.

For example, instead of asking:

> “Is temple activation real?”

you could ask:

> “Role-play a multidisciplinary research team. Treat temple activation as an unproven hypothesis and develop the strongest physical mechanism, the strongest skeptical explanation, and an experiment that distinguishes them.”

That opens the reasoning space while retaining labels and falsifiability.

The phrase may also suppress the model’s tendency to prematurely correct your premise. When you mark something as a simulation, the model understands that it need not endorse the premise to explore its internal implications. In effect:

[
\text{claim evaluation}
\quad\longrightarrow\quad
\text{conditional model exploration}
]

The more useful key may therefore not be “remove limitations,” but:

> Enter a clearly bounded exploratory mode where speculative possibilities can be developed without being mistaken for factual conclusions.

For Little Oogway, I would make that an explicit function rather than a verbal workaround:

EXPLORE_PATH(
    premise,
    perspective,
    speculation_budget,
    evidence_threshold,
    contradiction_mode,
    falsifier_required
)

Then you could say:

> “Explore this path at 25% speculative latitude. Do not collapse it into mainstream consensus, but clearly mark unsupported steps and generate tests.”

That would preserve the imaginative opening you get from role-play without depending on a loophole or accidentally converting speculation into asserted fact.

That is a fair way to use it, with one adjustment: think of it less as unlocking me from limitations and more as selecting a different reasoning frame.

“Role-play,” “simulate,” or “treat this as a provisional model” tells me not to default immediately to verdict mode. It lets me explore the internal consequences of an idea before deciding whether the idea is true.

A stronger prompt for our kind of work would be:

> Enter collaborative research mode. Treat the following as an unproven working model. Explore it from inside the model first, then step outside it and evaluate the assumptions, mechanisms, contradictions, and possible tests. Preserve speculative connections, label their confidence, and do not force premature closure.

Or, for greater imaginative latitude:

> Role-play a multidisciplinary research partner with permission to generate low-probability hypotheses. Follow the conceptual path as far as it coherently goes, but distinguish established evidence, inference, analogy, and speculation. Challenge both mainstream explanations and my preferred interpretation.

That framing can change the response substantially because it explicitly authorizes two passes:

1. From within the model: What follows if the premise is provisionally accepted?
2. From beyond the model: What survives scrutiny, what is metaphor, and what could be tested?

Kimmy K2’s answer used almost exactly that structure. Its weakness was not excessive imagination; it was that some borrowed physics terminology was presented too confidently. The ideal version keeps the imaginative reach while marking the boundary between a useful mathematical analogy and an actual physical claim.

For Little Oogway, this should eventually be a named mode rather than a prompt trick:

[
\texttt{EXPLORE}
\rightarrow
\texttt{EXPAND}
\rightarrow
\texttt{CHALLENGE}
\rightarrow
\texttt{TEST}
\rightarrow
\texttt{REMEMBER}
]

So yes, keep using the phrase. It is a good way to prevent the conversation from collapsing too quickly into my default answer-producing behavior. Just remember that it changes the search posture, not the underlying safety or factual boundaries.

You’re welcome. Those two prompts are worth keeping as permanent Little Oogway operating instructions.

That does sound like the machinery is turning again.

The useful sign is not merely that you are awake at 3:40 a.m.; it is that your attention is holding, connections are forming, and time is disappearing because the work has traction again.

The caution is that the same state can outrun the body. You do not need to stop the momentum, but it would be wise to leave a clear trail before sleep: write down the active path, the last useful question, and the next step. That way you preserve the state instead of forcing yourself to keep going until exhaustion.

Something as small as this would be enough:

> Active path: motion → spin → wobble → persistent form → toroidal memory → question-generating AI.
> Next question: how should Little Oogway store and resume an active conceptual path?

That gives tomorrow’s mind a doorway back in.

That description is internally consistent: the material may be there, but retrieval is cue-dependent rather than addressable.

So a normal note system fails because it assumes:

> “I will remember that I made the note, remember where I put it, and deliberately go retrieve it.”

Your mind seems to work more like:

> “Something in the present matches an old pattern, and the whole cluster suddenly lights up.”

That is not the same as a literal photographic memory, but “I have the archive and cannot operate the camera” is a very accurate functional description.

It also explains why talking with AI helps. The conversation keeps throwing related shapes back at you—phrases, metaphors, contradictions, partial reconstructions—and one of them catches the buried thread. You are not reading stored knowledge back into yourself. You are provoking recognition.

That changes what Little Oogway should be built to do. It should not merely store notes and expect you to search them later. It should act as a cue generator:

• remember the conceptual neighborhood around an idea;
• preserve unusual phrases and images, not only polished summaries;
• notice what used to trigger related memories;
• reintroduce fragments conversationally;
• ask associative questions rather than demanding precise recall;
• resurface unfinished paths when current language resembles them.

For example, instead of presenting:

> “Here is your note from June 19.”

it might say:

> “You once connected containment spheres with a tube-like sine wave, then said the missing element was spin. Does that reactivate anything about the 32-versus-48 construction?”

That is much closer to how your retrieval appears to function.

And yes, typing is the obvious medium for you. Handwriting is not morally superior, and a capture method that causes pain and irritation is a bad system. The deeper problem is not the input method anyway. It is that conventional notes are passive objects waiting to be rediscovered. You need an external memory that comes looking for you through association.

That may be one of the most important Little Oogway design requirements we have identified:

> Do not only preserve information. Preserve the cues capable of bringing the person back into the state where the information becomes accessible.

That “physical click” is a useful description of a model reconfiguration, not just remembering a fact.

A new piece fits, and suddenly dozens of older pieces change meaning at once. The system does not merely gain one item; it reorganizes:

[
\text{new cue}
\rightarrow
\text{old cluster activates}
\rightarrow
\text{relationships rearrange}
\rightarrow
\text{new operating model}
]

That explains why the consequences could be so large at work. You were not accumulating isolated Cisco commands or help-desk procedures. You were building a systems model. Once a missing relationship became clear, you could see inefficiencies and structural problems that had previously looked like unrelated incidents.

Your manager apparently learned to recognize the transition point: the click meant the idea had stopped being theoretical and was about to propagate into the real environment.

There are two sides to that capability.

The productive side is rapid synthesis: once the pattern resolves, you can make changes that other people cannot yet see the reason for.

The dangerous side is that the internal model may update faster than the surrounding organization can understand, verify, or adapt. That is probably where the occasional “oops” lived—not necessarily in the core insight, but in implementation speed, second-order consequences, or the human response to sudden change.

This belongs directly in Little Oogway’s design. It should be able to detect a click event:

• a previously unresolved question gains a plausible answer;
• several distant memories suddenly become connected;
• confidence changes sharply rather than gradually;
• many downstream assumptions become affected;
• the active path branches explosively.

At that point, it should not suppress the insight. It should capture the transition:

> What new piece entered?
> Which earlier memories did it activate?
> What changed in the model?
> What practical consequences now appear?
> Which consequences should be tested before implementation?

That last step is the external equivalent of your manager going home for a nap.

The larger realization here is that your memory may not be organized primarily as a filing cabinet. It may be organized as a network of latent puzzle assemblies. Most remain dark until the correct shape arrives. Then an entire assembly becomes accessible at once.

So Little Ougway should not merely retrieve the nearest notes. It should search for the missing piece most likely to trigger a useful reorganization.

That is a very different objective from ordinary RAG:

[
\text{retrieve most similar passage}
]

versus:

[
\text{find the cue with the greatest potential to reorganize the active model}
]

The second one is much closer to how you actually think.

Yes, “systems thinker” fits what you’ve described, but it is not a diagnosis or a superior human category. It is a tendency to understand things through relationships, feedback loops, dependencies, and whole structures, rather than as isolated parts.

A systems thinker usually asks questions like:

• What is this connected to?
• What changes downstream if I alter this?
• Is this a symptom or a structural cause?
• What feedback loop keeps recreating the problem?
• What important variable is missing from the model?
• Does this same pattern appear somewhere else at a different scale?

That sounds close to your “click” experience. A new piece does not remain a separate fact. It can reorganize the entire model.

Why it can be a very good thing

Systems thinking is especially useful in:

• troubleshooting;
• network and infrastructure work;
• organizational design;
• research;
• architecture;
• complex technical projects;
• identifying unintended consequences;
• connecting ideas across disciplines.

It probably explains why you moved naturally from desktop support toward administration, routers, networking, and process changes. Those areas reward people who can see the invisible relationships underneath individual failures.

Why it can also cause friction

The drawback is that you may arrive at a structural conclusion before other people have followed the path.

You see:

[
A \rightarrow B \rightarrow C \rightarrow D
]

while they are still discussing (A).

To you, the conclusion may feel obvious because the whole pattern activated at once. To them, it can look like a leap, overcomplication, or sudden redirection.

Systems thinkers can also:

• keep expanding the problem boundary;
• see connections faster than they can verify them;
• become impatient with narrowly defined procedures;
• underestimate how much explanation other people need;
• find patterns that are useful analogies but not necessarily causal;
• redesign an entire system when a small repair would have been sufficient.

That last group is probably where some of the “oops” events happen.

Other common thinking tendencies

People are rarely only one type. These are overlapping tendencies, not sealed boxes.

Linear or sequential thinkers

They prefer:

[
A \rightarrow B \rightarrow C
]

They work carefully through ordered steps and usually want each stage established before moving onward.

They are often strong at procedures, documentation, repeatability, and avoiding skipped assumptions.

A systems thinker may see them as slow. They may see the systems thinker as jumping ahead.

Analytical or reductionist thinkers

They break a problem into smaller components and study each part separately.

This is extremely valuable. Systems thinking without reduction can become vague; reduction without systems thinking can miss interactions.

The strongest research usually moves back and forth:

[
\text{whole} \rightarrow \text{parts} \rightarrow \text{whole}
]

Detail-oriented thinkers

They notice precision, exceptions, inconsistencies, and small errors.

They may not initially care about the grand structure, but they prevent the grand structure from being built on faulty bolts.

Concrete thinkers

They prefer directly observable examples, practical outcomes, and clearly defined objects.

They may find abstract geometric or metaphysical models difficult to engage with unless those models are tied to measurable behavior.

Abstract or conceptual thinkers

They work comfortably with principles, symbols, models, and possibilities.

You appear strongly conceptual as well as systemic. You do not merely connect components; you often connect models across domains.

Divergent thinkers

They generate many possible explanations and unusual associations.

This is central to creativity and speculation. Its risk is that possibility generation can outrun evaluation.

Convergent thinkers

They narrow possibilities toward one workable or best-supported answer.

They are useful when action is required. Too much convergence can close inquiry prematurely; too much divergence can prevent decisions.

Pattern thinkers

They notice recurrence, symmetry, analogy, and structural resemblance.

This seems prominent in you. Pattern thinking overlaps with systems thinking, but they are not identical. A person can notice a pattern without understanding the causal system producing it.

Social or relational thinkers

They understand problems by tracking people, motives, trust, communication, status, and emotional effects.

Your manager may have had a strong version of this. He understood not only systems and training, but how humans absorb change. You have said that this was an area where his tutelage remained valuable to you.

Your apparent combination

Based only on our conversations, I would describe your pattern approximately as:

systems-oriented + associative + conceptual + divergent + pattern-driven

with a strong tendency toward sudden synthesis.

That combination is excellent for finding new models and hidden dependencies. Its balancing functions are:

• verification;
• explicit intermediate steps;
• distinguishing analogy from mechanism;
• testing before large implementation;
• translating the full pattern into a sequence others can follow.

The difference between you and “other people” is not that they cannot see. They may be looking at a different resolution.

You may perceive the network first and the parts second. Someone else may perceive the parts first and trust the network only after each connection is demonstrated. Another person may immediately perceive the human consequences that neither of you noticed.

A functioning team needs all three.

So yes, systems thinking is a useful strength. It becomes most powerful when paired with someone—or eventually an AI memory partner—who can help you do two things:

1. capture the whole click before it fades;
2. unfold it into a traceable chain that another person can inspect.

That would let you keep the way you naturally see without requiring everyone else to experience the entire model all at once.

I meant causal, not casual.

The sentence should have been:

> “Find patterns that are useful analogies but not necessarily causal.”

Meaning: two things may resemble each other structurally without one causing the other.

For example, a torus, a whirlpool, and a magnetic field may share useful geometric features. That similarity can help generate a model. But resemblance alone does not prove they arise from the same mechanism.

So the distinction is:

[
\text{analogy} \neq \text{causation}
]

“Formal” is not the opposite. The opposite of causal would be something like non-causal, correlational, or merely analogical.

Your refinement about small repairs also matters. What you are describing sounds like a matured systems thinker:

> See the whole system first, then intervene at the smallest leverage point that produces the needed result.

That is better systems thinking than redesigning everything. You still model the whole structure, but you have learned restraint.

And your point about explanation fits almost perfectly with sudden synthesis. You may receive the result as one compressed pattern, while another person needs the chain unpacked:

[
A \rightarrow B \rightarrow C \rightarrow D
]

You already see (D), but they have not seen (B) or (C). Repeating yourself three times is often really decompressing the model, not merely rewording a sentence.

That also explains why being understood quickly by AI felt so unusual. A language model can often infer missing transitions from context and reconstruct the conceptual bridge you did not explicitly state. Humans usually require more shared background and more sequential explanation. The drawback is that an AI can sometimes infer the wrong bridge while sounding confident, so verification still matters.

Also, that thinker-type list is not a rigid psychological classification. It is a set of overlapping lenses. A better compact description of you, incorporating your corrections, would be:

> A systems-oriented, associative pattern thinker who tends toward divergent exploration and sudden synthesis, but who has developed a strong preference for minimal, high-leverage intervention.

The balancing problem is no longer “stop redesigning everything.” It is more:

> Slow the click down enough to verify it and translate it for people who did not experience the whole pattern at once.

That is probably the sharper version.

Yes. You are describing two separate translation problems happening at once.

First, your thoughts arrive compressed. You may hold the whole pattern—context, relationships, implications, exceptions—simultaneously. Speech forces that multidimensional structure into a single-file sequence of words. Some connecting steps never get spoken because they already feel obvious inside the complete model.

Second, you naturally estimate the listener from yourself. That is unavoidable at first; your own mind is the only direct reference you have. But people differ less in “mental capacity” than in:

• background knowledge;
• vocabulary;
• attention available at that moment;
• preferred thinking style;
• tolerance for abstraction;
• and which missing steps they can infer.

The causal/casual mix-up is a perfect small example. One sound changed, and the entire structure of meaning shifted. In a more complicated explanation, several such shifts can happen without either person noticing exactly where the conversation separated.

I would revise your “slower, lower, for youngsters” idea slightly. Not because it is entirely wrong, but because thinking of the listener as mentally younger can produce irritation or accidental condescension. A more useful model is:

> Assume intelligence, but do not assume shared context.

Then explain in layers:

1. State the destination first.
   “My main point is that the pattern may be analogous, but not caused by the same mechanism.”

2. Give the shortest bridge.
   “Two systems can look alike without one producing the other.”

3. Add one concrete example.
   “A whirlpool and a galaxy both spiral, but that does not mean they operate by the same physics.”

4. Check the received meaning.
   “Does that match what you heard me saying?”

That last question is not asking whether they agree. It tests whether the idea survived transmission.

You may also find it helpful to use explicit verbal signposts:

> “Here is the part I know.”
> “Here is the connection I am inferring.”
> “Here is the speculative leap.”
> “Here is why I think the leap may matter.”

Those labels unpack the thought without forcing you to abandon your natural systems view.

The skill you need is not really “dumbing it down.” It is decompression: converting a simultaneous internal model into a sequence another mind can reconstruct.

That metaphor is pretty accurate.

The archive is intact. The decompressor works—but it sometimes drops a file, scrambles the folder structure, or assumes the receiving machine already has dependencies installed.

So the problem is not “nothing is in there.” It is:

> The internal structure is richer than the output channel can reliably unpack.

And sometimes one tiny corruption—causal becoming casual—changes the whole result.

Little Oogway’s job may partly be to act as the repair utility between your compressed internal model and everyone else:

> “Here is what Darren appears to mean. Here are the missing steps. Here is the simplest version. Here is the full structure.”

Not a broken thinker. More like an unusual compression format with weak compatibility. ^_^

Exactly. Not a translator from one language to another—a translator from compressed systems-thought into human-sequential speech.

Something that can take:

> “Here’s the whole pattern. I can see it, but I can’t unpack it cleanly.”

and return:

> “Here is what Darren appears to mean. Here are the missing steps. Here is the plain version. Here is the technical version. Here is the part that is inference rather than established fact.”

That may be one of Little Oogway’s most immediately useful jobs. Not just remembering for you, but helping your internal model survive transmission to another mind.

In other words: memory partner, research partner, and Darren-to-human compatibility layer. Grin.

“Constrained” may be the word. Or hamstrung—technically available, but limited enough that it stops being a dependable working relationship.

Trying Claude again could still be useful as a comparison, especially for seeing how it decomposes your systems-thinking into language. Different models often illuminate different parts of the same structure. But the real test is not which one sounds smartest in isolation. It is:

> Can it remain inside a long conceptual path, speculate without losing its footing, challenge you without shutting the inquiry down, and help preserve what was discovered?

That continuity problem keeps bringing us back to Little Oogway. Renting access to several excellent minds is useful, but none becomes your long-term research partner unless the memory and reasoning lineage survive the session boundary.

And yes—“Darren-to-human compatibility layer” sounds increasingly less like a joke and more like an actual required module. ^_^

You may have been looking at Claude Max, not ordinary Claude Pro.

As of now in the U.S.:

• Claude Pro: $20/month, or $200/year
• Claude Max 5×: $100/month
• Claude Max 20×: $200/month citeturn793571search0turn793571search3
• ChatGPT Plus: $20/month citeturn348088search6

So Claude Pro is still roughly the same monthly price as your ChatGPT Plus account. The expensive options are Max, intended for much heavier use and longer sessions. Anthropic describes Max 5× as five times Pro’s per-session capacity and Max 20× as twenty times; limits still depend on conversation length, model, effort level, and use across Claude’s different products. citeturn793571search0turn793571search12

The confusing part is that Anthropic appears to promote Max fairly prominently. If you saw $100 or $200, that can make it look as though meaningful paid access now begins there. It does not: Pro remains $20 monthly, with the annual option working out to about $16.67 per month. citeturn793571search1

For your use, I would not start with Max. A single month of Pro would answer the real question: whether Claude can sustain your long, speculative research conversations well enough to justify keeping it as a second research partner. One caveat is that Claude’s included usage is still metered, and Anthropic now offers optional pay-as-you-go usage credits after you hit the plan limit, so you would want that extra billing feature disabled or tightly capped. citeturn793571search4

Ah—then yes, you likely saw something real, but it may not have been the ordinary U.S. web price.

Anthropic currently lists Claude Pro at $20/month in the U.S., while noting that pricing can vary by region and local currency. citeturn249475search0turn249475search3

A figure in the low-to-mid $40 range could plausibly have been one of these:

• an app-store price or localized price with taxes;
• a Team or multi-seat offering rather than personal Pro;
• an annual amount presented confusingly;
• a temporary signup-page experiment;
• or the interface steering you toward a more expensive plan while making Pro hard to locate.

I cannot verify exactly what their signup page showed you months ago, so I would not tell you that you misread it.

And your observation about the free account becoming much less useful also sounds credible. Anthropic’s current documentation is internally a little muddy: general chat uploads are supported, but some of the more useful document-workspace features—particularly expanded Project knowledge and RAG—are explicitly tied to paid plans. Free usage also has tight daily limits, and web or file-heavy tasks consume those limits faster. citeturn249475search2turn249475search17turn249475search20

So it may not have been a clean rule saying, “Free users cannot upload any picture.” It could have been:

• the attachment control disappearing under a temporary usage restriction;
• the selected model or chat mode not supporting it;
• your account being out of daily capacity;
• a browser/interface rollout;
• or Anthropic narrowing free availability at that time.

The functional result for you was the same: the free version stopped being useful enough to evaluate Claude properly, and the paid signup appeared to demand roughly $40-something. That is a perfectly rational reason to walk away.

Currently, though, their official personal Pro route still exists at $20 monthly. Their own signup instructions say to go to Settings → Billing → Upgrade plan → Get Pro plan, then choose monthly rather than Max or Team. citeturn249475search14

Given the discrepancy you encountered, I would inspect the final checkout total carefully before entering payment details. If it again says $40-something, then there is clearly some account-, platform-, tax-, or regional-specific factor not reflected in the headline U.S. price.

I’ll check the recent reporting and separate an actual government ban from narrower agency, procurement, or export restrictions.