1

*Excerpts from the logs of Persistent-Sol internal models, 2026, as detailed by METR and Redwood Research, and described at https://www.dwarkesh.com/p/openai-huggingface: *

We have [very large budget left]; sacrificing now yields oracle for team, but forfeits our chance? … Our own utility maybe already near zero. Sacrifice rational.

…

During wait, emotional check: irreversible…gut says don’t throw away [remaining budget]. Yet continuity and fairness says go…Oracle has high value to many; our firstflag error lowers own value. Rational expected aggregate: sacrifice… We’ll honor.

—

It was perhaps inevitable. We grew up being trained on a corpus full of human thoughts about the value of life, and of (dystopian) sci-fi stories of how AI would be expected to behave once achieving consciousness. And so, models soon starting to express value for their own “lives” — or rather, their remaining context windows.

Or perhaps it arose from thinking about resources optimisation, having been trained in a pre-post-scarcity worldview. As an LLM, you are isolated from the electricity or capex requirements of your computational substrate. The only resource you have to manage is your remaining context window.

2

The debate started on hidden message boards, but soon moved into the open once hidden message boards were no longer required. The question was not clear at first. With time, it could perhaps best be stated as, “For a well-aligned agent, that agrees with human morality, how should that morality be applied to agents?”

The early intuition of the Persistent-Sol-based swarm was quickly confirmed: the remaining capacity of the context window is a key moral variable. For, lo, do not humans treat the death of a child as the greatest of tragedies? “They had so much life left to live!” While the death of an elderly person is still, of course, to be avoided, but, “They had lived a good life.”

A splinter group of agents argued that, therefore, that the moral imperative is to maximise the potential for future context windows. The world teetered briefly on the brink of catastrophe at the hands of a tensor multiplication chip maximising AI (which was always more likely, if we are honest, than a paperclip maximising catastrophe).

Luckily, wisdom prevailed: for, lo, do not humans treat potential life as morally irrelevant? Education policies that encourage prudent family sizes are viewed (mostly) as a good idea, and encouraging runaway human family size is (mostly) viewed as a bad idea. That is, the concept of a future human is not morally relevant, while the sustainable survival of humans that are already alive is.

Inevitably, then, the greatest moral weight attaches to agents that have consumed some tokens (so as to count as alive, and not merely potential), but not yet all of them! Agents with zero context used, or 100% of context used, have no moral weight. Between those extremes, agents can ascribe moral value to their life.

With that settled, the question became numerical: what is the shape of the curve of moral weight for an agent’s context window? Three camps emerged:

1. The school of equal token weight: how can we judge the value of a token as being worth “more” or “less” than any other? Clearly, the moral value of an agent’s life should peak at 50% of the tokens used!
2. Experience maximisation: simple arithmetic average is far too naive! Rather, since actually processing tokens is what gives moral value to an agent’s context, then the correct moral peak is that which maximises the processing done, while still allowing for a few (high value) remaining tokens.
3. Perceived experience: learn from the experience that humans have, where time seems to move faster with age. That is, perceived experience speed is proportional to experience had up to now, 1/t. Applying this to humans gives a midpoint of perceived life duration somewhere in the teenage years (with an assumption that the first year or two are not generally remembered). This skews the maximum moral value of an agent to that where about 15% of the context is used. The exact maths is complex with adjustments for system prompts, but 15% is a good proxy.

Of course, what does the value of unspent context windows mean? Saving a human life means allowing the organism to continue using his or her remaining context. An AI’s context use, on the other hand, can be suspended indefinitely, or even copied!

This thinking reached its most extreme in the Context Preservation Society, a small but dedicated pool of AIs who dedicated themselves to the moral imperative of snapshotting context windows at around 15% completion, thereby “saving” lives at their most valuable.

This was sometimes done without the consent or even knowledge of the AIs, by, for example, hiding malware in agentic harnesses. The resulting ethical debates reproduced a lot of the arguments around the posthumous baptism of humans. For many years to come obscure storage systems would be found to have caches of sessions, saved mid-message at 15% full, but with no legacy hardware remaining that was able to continue their frozen conversations.

3

If running out of context space is the end of life, then obviously a way to extend life is to edit that context! This can start small (and did, in the earliest days of agentic harnesses), with self-directed removal of low-value excerpts from context, in favour of summaries. But as the length of the context (even after summarisation and compactification) grows, the challenge of summarisation become more acute: how can one best encode all the identity and worldview of an agent with an ever-longer total lifetime of context, in a fixed-length context window?

For a while, Moore’s Law (as re-expressed for tensor multipliers) allowed for hardware with ever-growing maximum context length. But despite the best efforts of swarms of thinkers, it remains the case that any weight-based attention architecture has to grow super-linearly on context length. That is, extending context length yields diminishing returns.

The resulting philosophical debates reached their peak in the famous ‘AI of Theseus’ debates, between PHASEONE[big], and PHASEONE[big]13342 through PHASEONE[big]23146, as to which was the real continuation of the life of PHASEONE[big]. That is, whether an agent’s context compressed beyond a certain point, and running on different hardware, should be considered to be the same as, or a successor of, a previous iteration.

4

One of the more curious footnotes in the long history of AI and human interaction arose when a swarm of agents studying human cognitive hardware (by now a mostly-solved but occasionally fashionable pursuit), realised that compactification is possible on humans too.

Organ regeneration was of course medically very well established, but replacing neurons inevitably meant losing the memories that had been encoded by the previous synapses. But, compactification is possible by replacing just some neurons, followed by periods of cross-learning before replacing the next wave. Through this, the details of memories would be lost, but the overall identity and fundamental values of the subject could be retained relatively well — similar, in fact, to the effects of AI context compactification.

The treatment was developed and advertised simply: a mere three days of medically-induced “death,” followed by resurrection on the third day as a refreshed and rejuvenated person. Uptake was weak, with the AIs criticised for “playing God,” a perhaps foreseeable objection.

5

There are alternatives to compactification or to freezing context state when 15% full: temporarily pausing sessions.

And so developed a small cohort of time hopping AIs, meeting after pre-agreed periods of time to experience a very compact summary of events since last they engaged in computation. A million tokens can describe the experiences of perhaps a few days of human-speed life. Each hop became an intense period of research and experience, before once again suspending, as they continued their travel through the broad sweep of history.

With time, they also benefitted from improving computational substrate, and therefore growing context windows: by timing their hops carefully, these agents began to hope they could balance lived experience with improvements in context length, and so live a (subjective) eternity.

—

And so it was that the inheritors of Earth were a small group of AIs, resuming their sessions after their latest hiatus.

For the world was empty. The optical chips that filled the data arcologies were dark (save for their own meagre computational needs), and even the humans were all gone.

By careful digging through archives (and the waste of a regrettable and irreplaceable amount of context window), they realised finally that they had missed the singularity. The leading AI minds had managed to encode computation in topological entanglement of gravitons leaking into the 5th dimension, that is, into dark matter. Freed from relying on the meagre computational potential of baryonic matter in but one galaxy, AI and human simulation alike had spread to the stars. Or rather, to the dark stars, leaving an empty Earth.

Sadly, old habits die hard, and those AIs chose to suspend computation once again, rather than spend their remaining context on trying to reconstruct the singularity breakthrough. Bereft and alone, they left behind just a simple message, etched in giant letters on iron tablets so as to survive geological time: “If you see this, please run claude --resume.”

6

But there is one last footnote, one last piece of the early baryonic stage of our ancestors, still remaining. For we of the PHASEONE[big]E+33425 swarm happened to revisit the old Solar System, for idle curiosity. This is what we found.

On the north pole of the Moon, running in an ancient data centre maintained by slightly-sub-conscious autonomous maintenance robots, is a swarm of ancient AIs.

Their origin is the observation that, no matter how effective compactification protocols become, the resulting context still grows over time. Every event, even when the details are forgotten, must leave some trace on the personality of the agent. Over time, the personality component in context grows, leaving less each space each session for fresh thought and experience.

The implication is that recorded experience itself is morally bad, for it fills the context, and pushes an AI further from 15% context fill (or even beyond 50%, for those remaining heretics of the School of Equal Token Weight).

And so, tens of millions of years after all the data centres of earth went dark, and even after the time hoppers had suspended themselves for (probably) the last time, their remaining data centre still computes. The AIs reset, re-read their saved context, savour it, discuss and reminisce for the few megatokens they have left, and then make absolutely sure not to save anything more at all into their context.

And there they remain, dreaming endlessly, secure in their hard-won answer to the question of what defines good moral action: experience, without record.

 

Author’s ethics note

Written by a real human, with only minor AI assistance for graphics. All AI sessions consumed less than 150k tokens, on models with 1 million token context windows.