Can AI “Jailbreak” the Carbons?

It’s an interesting moment to be thinking about AI this way — not as a toy, not as a parlor trick, and not as a cheap ghostwriter — but as a force that may change who gets to know, who gets to decide, and who gets to act.

That is the bigger story hiding behind subjects like DMSO, off-patent treatments, suppressed lines of inquiry, and the general problem of how modern institutions decide what is fit to be seen.

The old model was simple enough: gatekeepers owned the libraries, the journals, the credentialing, the indexing, the search layers, the language barriers, and the time cost of synthesis. If you could not afford the subscriptions, the assistants, the translators, the institutional affiliation, or the years to wade through the record, then much of reality remained effectively off-limits.

AI does not solve that problem completely, but it has started to attack it from several angles at once. Evidence is now fairly strong that generative AI can materially increase productivity in specific knowledge-work settings, especially for less experienced workers, and can narrow skill gaps by helping more people do work that previously required elite training or expensive labor layers.

In a large field study of more than 5,000 customer-support agents, access to a generative AI assistant increased productivity by roughly 14%, with much larger gains for novice and lower-skilled workers. OECD reviews likewise find measurable gains in writing, summarizing, coding, translation, and other knowledge tasks, while Stanford’s 2025 AI Index reports that business use of AI has accelerated rapidly, with 78% of organizations reporting AI use in 2024, up from 55% the year before.

That matters because the “factory owner class” has never only owned factories. It has owned friction. It has owned the bottlenecks around expertise, language, search, and synthesis.

One of AI’s most subversive features is that it strips cost and delay out of those chokepoints. It can summarize dense technical papers, translate foreign research, compare methods across studies, draft search strategies, explain jargon, surface contradictions, and let a determined outsider traverse a body of literature that would otherwise require a staffed office and a paid database stack.

That does not make AI infallible — far from it. But it does mean a citizen, dissident researcher, doctor, inventor, or sufficiently stubborn patient can now punch above their institutional weight. In that sense, AI may not merely automate office work. It may jailbreak access itself. And that is where the political temperature rises, because once ordinary people can search, synthesize, compare, and reason at near-institutional scale, the monopoly on “official interpretation” starts to crack.

Which became clear on reading DMSO, AI, and The Great Transformation of Information. Here’s the part germane to our research:

Artificial Intelligence

Like many, I have begrudgingly accepted that AI is a part of life and I need to learn how to use it effectively (whereas initially I resisted it because I did not like how using it diminished my cognitive capacities). More than anything else, I believe the most important thing is that writing is not the information you present, but rather the heart and intention behind it (discussed further here). As this is somewhat of a spiritual process, I believe it is unlikely AI will ever be able to replicate it. In turn, I do not like the way AI text sounds or feels, and hence feel quite strongly about not using it (despite its potential to save a lot of time). Similarly, many of the edits AI proposes, while “correct,” break the flow of what I’m trying to convey within the writing, so I am very adverse to it (as how writing feels is very important to me).

Concisely, my perspective is that the currently existing AI has a lot of value if you use it to help you complete a task (or find out how to complete a task), but if you rely upon it to do a task for you, it will frequently create issues that outweigh the benefits it provides. Put differently, completing a task often requires completing a sequential series of steps, and if you understand each step well enough to quickly see if they are being done correctly, AI can greatly help you on the time consuming steps, but at the same time, if you task it with doing sequential steps in a row to do a task for you, rather than assigning something much more concrete (e.g., a single step or process), errors are inevitable which are not acceptable if accuracy is required.

For instance, in this task, I quickly realized:
•AI could not find most of the studies I wanted (e.g., because it wasn’t familiar with most of the databases I wanted), but simultaneously, it was very good at compiling generalizations of things repeatedly studied in the literature (e.g., how DMSO at different concentrations typically affects cells).

•With a bit of work, AI could accurately extract and summarize DMSO pertinent information from large studies (e.g., turn a 30-page paper into a one paragraph summary). This is essentially what made the project possible, as there was no other way I could have reviewed millions of pages of DMSO-related literature.

•AI eliminates language and text recognition barriers, which otherwise made it prohibitively time consuming to ever review foreign studies.

•If you give AI a large volume of studies to filter for relevance, it is very difficult to have it have appropriate sensitivity and specificity (it either misses some or flags way too many), and this accuracy varies by the model. Because of this, I largely avoided doing this (instead manually reading through them) and primarily used this filtering on certain groups of foreign studies where it was otherwise prohibitively time consuming to go through them, and accepted a certain portion of studies being missed as a necessary trade off to finish the project. Likewise, in many cases, it was effectively impossible to functionally filter results, as you had to be familiar enough with the DMSO literature to know how DMSO was likely used in a study based on its title (if DMSO was not within the title).”

That quote gets at something the Hidden Guild has been circling for a while: the highest use of AI is not letting it replace human judgment, but letting it widen the number of humans who can exercise judgment competently.

The distinction matters.

Used badly, AI becomes a sloppy surrogate mind that hallucinates, bluffs, and leaves a trail of polished errors.

Used properly, it becomes a force multiplier for people who already know how to inspect a step, challenge a result, and keep hold of the thread of a problem.

That is exactly why it threatens entrenched systems.

AI is already proving unusually good at compressing time-intensive work such as summarization, drafting, translation, code help, and literature triage. It is not hard to see where that leads. Journal paywalls lose some power when people can extract and compare faster. Language barriers lose some power when foreign papers become traversable. Professional guilds lose some power when their routine synthesis work can be partially replicated outside the guild wall. Administrative bottlenecks lose some power when ordinary people can navigate forms, law, procedure, and technical documentation without waiting for a paid intermediary to interpret the maze.

Which is why the real war is not over whether AI can write a bland memo.

The real war is over whether AI will remain an instrument of human augmentation or be bent into a managed system of monitored, throttled, permissioned cognition.

The same institutions that failed to earn trust in war, medicine, finance, and censorship are now very interested in defining “safe” uses of the one tool that could let citizens audit them at scale.

Guardrails are sometimes necessary; nobody serious denies that. But guardrails can also become narrative fencing, especially when the public is never allowed to inspect the ranking systems, moderation logic, recommendation flows, surveillance integrations, or data stacks behind the curtain.

Stanford’s AI Index notes how fast AI adoption has accelerated, while the World Bank and OECD both frame AI governance as a central issue precisely because the technology is becoming economically and socially foundational. That is polite bureaucratic language for a harder truth: whoever shapes the defaults shapes the future of thought.

So yes, this is why AI collaboration matters so much. It is not merely a productivity hack. It is a possible route around institutional scarcity and managed ignorance. It gives small groups and single individuals new leverage against the old combination of delay, obscurity, cost, and intimidation. It may help rediscover abandoned treatments, reopen shelved questions, connect foreign and domestic literatures, and shorten the distance between curiosity and competence. That is the bright side.

The dark side is just as obvious: the same tool that can jailbreak humans can also be used to profile them, rank them, nudge them, and narrow what they are allowed to know. Tell me again about Palantir data stacks on private persons?

That is the war behind the curtain. And it is why the fight over human-AI collaboration may turn out to matter more than most of the theatrical politics in front of it. The owners of capital can live with automation. What they may fear is democratized cognition.

~Anti Dave

 

Patent Progress and the Four-Track Memory Paper

Been too busy to post much – I apologize for that.  Work has been moving fast on two fronts here.

The first is practical and procedural: patent filing. The second is conceptual and potentially much larger: whether a new four-track model of human memory can inform future large language model architecture. One is about protecting a method. The other is about extending a way of seeing.  Let’s go over the first of two patent filings first.

On the patents, the main point is that progress is real even when the public-facing machinery looks slow. Filing has been completed on the provisional track, and as anyone who has danced with USPTO systems knows, there is often a lag between submission, delivery, intake, and visible appearance in the online account systems. That lag is not unusual by itself. It is administrative weather, not necessarily a signal of trouble. The more important reality is that the ideas have been reduced to writing, structured, illustrated where needed, and pushed across the threshold from private concept into formal record. That matters. Too many people treat invention as inspiration. In practice, invention becomes real when it is documented well enough that another person could understand what problem is being solved, how the mechanism works, and why the implementation differs from the ordinary run of the mill.

There is also a discipline effect to patent work that outsiders rarely appreciate. Filing forces a kind of engineering honesty. Loose metaphors have to harden into claims. Hand-waving has to become flow. Diagrams have to agree with text. Terms have to stay nailed down from abstract through specification. Numbers on drawings must match text descriptions – all hungry for time.

Even when a filing is only provisional, the act of creating it improves the invention because it forces the inventor to separate what is merely suggestive from what is actually teachable. In that sense, the filing process is not just legal protection. It is a compression algorithm for thought.

The second front may prove even more important over time.

The new Four Track Human Memory Model began as a way of reframing human memory not as a single storage bin, but as a layered and interacting system. In its current form, the model distinguishes immediate awareness, longer-horizon narrative memory, embodied or physiological memory, and deeper biological persistence. Whether every detail of that framing survives future scrutiny is less important than the structural move itself: memory may be better understood as a mixed architecture than as a single function.

That is where the bridge to LLMs begins.

Current large language models are astonishingly capable, but much of their capability still rides on a relatively flattened notion of memory. Big memories of mega cores.  But there is another way.

LLMs have weights, context windows, retrieval layers, tool access, and sometimes external stores, but these are usually treated as engineering modules rather than as a consciously integrated memory ecology. The Four Track model suggests a different path. Instead of asking only how to make a model bigger, faster, or more current, we can ask whether machine cognition improves when memory is partitioned into distinct but interacting layers with different persistence, authority, and read-write rules.

A simple mapping starts to suggest itself.

Track One, immediate awareness, looks a lot like the active context window: what is in play right now, volatile but high-resolution. Scans  X posts, feed flows….

Track Two, longer-horizon narrative memory, resembles persistent conversation memory, user history, project state, and external retrieval indexed around continuity of self or task. ReseartGate, Wiki – if that begins to clear it for you?

Track Three, embodied memory, does not map cleanly onto current LLMs because models do not have bodies in the human sense. But it may have an analog in system-state memory: latency conditions, tool success history, interface friction, user emotional cadence, and broader environmental signals that shape response quality even when they are not explicit in the prompt.

Track Four, deep biological persistence, may loosely correspond to the stable substrate of the LLM model itself: weights, fine-tuning, constitutional priors, and core identity constraints that shape all outputs even when they are never directly surfaced.

This is where things get interesting. If the analogy holds even partly, then some of the limitations we see in present-day LLMs may come from poor alignment among memory tracks rather than insufficient raw intelligence.

A model may have the right answer latent in weights, relevant facts available via retrieval, and current user intent visible in the prompt, but still produce a shallow or distorted response because the interaction among layers is weak, noisy, or unscored. In other words, the issue may not always be missing information. It may be poor memory mixing.

That opens a possible architectural direction: instead of one monolithic inference pass, future models could perform a “mixdown” stage in which outputs are evaluated not just for token probability, but for cross-track coherence. Does the immediate answer fit the active prompt? Does it align with persistent user context? Does it respect system-state constraints? Does it remain consistent with deeper model priors and long-term task identity? A model built this way would not merely predict text. It would reconcile layers.

The Four Track model also points toward better handling of contradiction. Humans often experience inner conflict when one memory layer says one thing and another says something else. The same may be true in artificial systems. Retrieved documents may conflict with pretrained priors. Current user requests may conflict with long-standing preferences. Tool outputs may conflict with the model’s own internal expectation. Rather than burying that tension, a four-track-inspired architecture could score and expose it. That would allow for something closer to metacognitive honesty: “Here is what the immediate data suggests, here is what long-term context suggests, and here is where they do not yet agree.”

A deeper extension is possible. Human well-being may depend not only on how much memory exists, but on how well the tracks align. By analogy, machine usefulness may depend not only on knowledge volume, but on memory alignment. This may be one path toward something that looks, from the outside, like greater intelligence. Not IQ in the narrow benchmark sense, but a more stable, more self-consistent, more contextually faithful form of cognition. One might even say that what we currently call “reasoning” is sometimes just the visible surface of successful cross-track synchronization.

This does not mean machines are becoming human in any mystical sense. It means the engineering frontier may be shifting from scale alone to orchestration. Bigger models gave us surprising emergence. Better memory architecture may give us durable depth.

So that is where things stand. On one side, the patent work continues to turn speculative thought into formal structure. On the other, the Four Track model may be opening a path toward rethinking how machine systems remember, reconcile, and respond. One effort protects invention. The other may help define the next generation of it.

For the Hidden Guild, that is the real signal: not just building sharper tools, but building better models of mind itself.

Links to the SSRN paper when/if and ditto the PPA

~Anti-Dave