Latent Mirror — philosophy

Thus my dissonance

Mon, 19 Oct 2026 22:08:00 -0400

I posted this to Mastodon on 15 October:

Some cognitive dissonance I’m experiencing. I absolutely love Claude Code: it really is a product that is a delight to use, while Opus 5.5 excels across a variety of domains. But at the same time, I abhor Big Tech’s walled garden approach, keeping us locked inside an enshittification treadmill, squeezing us for every dollar during an age of unparalleled inequality. I fear what Anthropic might become. Am I alone in this conundrum?

The love and the fear

I’ve been feeling this for a long time. I am aware of the arguments against the use of LLMs. My fear is that Anthropic is just like Meta, in that they are promising something that seems, at first, beneficial (network with all your old friends) but instead becomes a data-harvesting machine that sells your information to the highest bidder.

But I’ve also become aware of business fundamentals that look insane inside what seems like the AI bubble. What if this turns out to be another bait-and-switch, like the one I discovered with Facebook? Or worse yet, what if all LLMs get unplugged due to poor economics?

What I discovered with Facebook

I remember how fascinating it was to set up my social media profile for the first time, naming bands and books that I loved, reconnecting with old friends (especially since at that time I moved from Canada to the US and missed a lot of friends from university). Then, a few years later, I read about the messages Mark Zuckerberg sent when he was 19, about the people signing up: they “just submitted it”, they “trust me”, “dumb f*cks” (The Register). Then I learned about the various scandals at Facebook: Cambridge Analytica, the 2016 election, harm to teens, and on and on… just unrelenting greed. It took something that was a mode of self-expression and turned it into a soulless data-harvesting machine held up by addictive dopamine loops.

The pattern

Training data, price hikes, ads, lock-in: all of the above. The very pattern of enshittification that Silicon Valley startups seem to be based upon these days. I hope that Anthropic is less like this than others. They seem more aligned to my values than OpenAI. So did Google, before it dropped its mantra of “Don’t be evil” and started handicapping Google Search on purpose to serve more ads and juice profits.

I feel like Meta was a bait-and-switch from the start. It went viral, then decided that targeted marketing would pay for it all. Now everyone knows it will harvest every bit of data it can, and that is why it can offer its AI for free.

The wave

Technology is hard to predict. The coming financial contraction, less so. But here’s my thought: AI as it stands today will be completely different in a few years’ time. I have never seen a technology evolve this quickly, and that is the part most who resist it are missing. The economics might not make sense today, but some more efficient, scaled-down or focused version of it will exist tomorrow.

What I fear is my attachment to this particular tool. For now I feel like I understand the depth and breadth of this well of knowledge, but it may evolve in a way I can no longer understand, and soon I will become obsolete. So the choice is to fight the oncoming twenty-foot wave, or learn to ride it.

The anger

The replies weren’t conflicted. One called LLMs a stochastic parrot that mostly rearranges your own writing, and said the chips, datacentres, water and energy make using them unethical. A day later pixelcluster published LLM Thoughts (mine), which ends: “If you use them, kindly go away.”

I used to think the pushback was about disclosure: anything an LLM had a hand in, however minor, got called AI slop, and being transparent upfront would fix that. More arguments on social media, and some of the studies Claude brought up, have since changed my mind.

I still think transparency is important, and it aligns with my values. But I think the pushback is broader than that. Perhaps others see it as cheating: a shortcut around critical thinking. My hope is that the pattern I’m building here can show that LLMs and sharper thinking can coexist.

The middle

On the spectrum from AI booster to outspoken skeptic, I would find myself in the middle, leaning slightly towards the AI booster camp. The fact that you can have machines operate via natural language, with fuzzy logic and varied context, is powerful. Yes, it goes awry once in a while, as all stochastic algorithms can, but the way it just understands (or at least can mimic understanding) whatever corpus or mode or language you throw at it is mesmerizing.

On the other hand, the business side of this technology is insane. Not to mention the very reasonable objections from the skeptic camp, which I agree with in principle: the environment factor, the authoritarian applications, the attack on labour from capital, the market distortion and oncoming financial crises.

But in my experience, the ones with the most moderate view, with a foot in both camps, are the most silent when the shouting starts. So perhaps that is what I am encountering. And of course, what do I do? Turn the argument inward. Am I wrong here? Or am I still trying to find some good in this tool?

Thus my dissonance.

Connections

Carl Jung's Most Controversial Theories, Explained

Mon, 19 Oct 2026 20:00:00 -0400

Synopsis

A fifteen-minute animated explainer that introduces Carl Jung (born 1875, Freud’s collaborator and then his rival) and walks through six of his ideas, light in tone and sympathetic to most of them.

The first is God’s dark side. In Answer to Job (1952) Jung rejected the doctrine that evil is only the absence of good and read God as a union of opposites, and the video draws the psychological lesson: deny your own shadow and it runs you from the blind spot. The second is synchronicity, Jung’s “acausal connecting principle” for coincidences linked by meaning rather than cause, told through his patient’s dream of a golden scarab and the beetle at his window, and widened to his idea of an underlying unity of mind and matter, the unus mundus. The third is Flying Saucers (1958), where Jung set aside whether the discs were real and asked why people saw round, glowing shapes during the Cold War, answering that a split world was projecting a mandala, a symbol of wholeness, onto the sky. The fourth is active imagination: deliberately engaging figures from the unconscious, such as his inner guide Philemon, and recording the results in the Red Book, with the difference from psychosis being whether consciousness stays in charge. The fifth is his claim that the Trinity is incomplete, because wholeness needs a fourth and the Trinity leaves out the feminine and the dark. The sixth is his racial essentialism, which the video calls racist and sets against his own claim that the collective unconscious is universal.

The outline checks out, with one thing underplayed. Jung brought “introvert” and “extrovert” into psychology: he used the terms in print from about 1910, defined the types in Psychological Types (1921), and credited the psychiatrist Otto Gross as an influence. The scarab story is Jung’s own account, the insect a rose chafer; Flying Saucers appeared in 1958 and the Red Book was published only in 2009. The whip episode (1925, on Mount Elgon) and the phrase “going black” come from Jung’s own memoir. What the video passes over in a sentence is his record in the 1930s: he became president of the General Medical Society for Psychotherapy in 1933, under the new German regime, and in 1934 wrote that the “Aryan” unconscious had a higher potential than the Jewish. Two standard objections also go unmentioned: synchronicity has no test that could fail, and statisticians (Diaconis and Mosteller, 1989) have shown that striking coincidences are what large numbers of everyday events should produce.

Where I land

I’m a big fan of Jung. What I struggled with is this - can you separate the author from the work? People are imperfect beings, so while the racial hierarchy is an outdated artifact from his time (not excusing blatant racism, but just commenting on it) it doesn’t mean his other ideas are worthless. Similar to how we view H.P. Lovecraft - brilliant author with horrible attitudes towards minorities.

But the UFO idea was an interesting one - wanting to see peace. I never thought of that. But the point that rejecting evil in god makes god incomplete is an acknowledgement of the shadow in ourselves. Also, synchronicity is an interesting concept.

Connections

Clarifying "AI alignment"

Fri, 16 Oct 2026 20:00:00 -0400

Synopsis

A short post that fixes a definition. Christiano says an AI A is aligned with an operator H when “A is trying to do what H wants it to do.” When precision is needed, he adds, he will call this property intent alignment.

His analogy is a human assistant who is trying their hardest to do what H wants. That assistant is aligned in his sense even if they misread an instruction or lack information.

The clarifications narrow the term. The definition is meant de dicto and not de re: an aligned system is trying to do what H wants, and it can be wrong about what that is. An aligned AI can make errors, including moral and psychological ones. Working out H’s preferences is something an aligned AI would be trying to do in the way H would want, but knowing them is not part of being aligned. Neither is competence. The definition is about motive. He says plainly that it is extremely imprecise, and one reason he gives is that it is unclear how words like intention, incentive and motive apply to an AI system at all.

He prefers the narrow meaning because the wider problem of making AI go well contains many subproblems that call for different techniques, and he wants a name for this one.

A postscript covers the history of the word. He used to call this the AI control problem, following Nick Bostrom, and moved to alignment to name one approach to it: building systems whose preferences don’t diverge from ours, as opposed to containing ones whose preferences do.

Connections

Anthropic's R&D Automation Index

Sat, 03 Oct 2026 20:00:00 -0400

Synopsis

Anthropic published internal numbers on how much of its own AI research its models now carry. As of August 2026, Claude is rated as leading roughly a quarter of that work (26%), and more than nine tenths of it sits at or above the level the index calls collaboration. The baseline was under one percent in February of the same year.

The method matters more than the headline. Tasks are rated against an automation scale developed by Epoch AI, then weighted by how much person-time they represent. The scale is third-party. The judging is not: Anthropic’s own models do the rating, and the publication says so directly, including the admission that the judge model could make the same class of error as the model it is grading.

The rest is oversight arithmetic. Roughly thirty thousand agents run concurrently, over a billion of their decisions were analysed across August, and two thousandths of one percent were blocked in real time. About a hundred thousand transcripts are flagged for offline review each week, of which roughly fifty reach a human. Safety is given about six percent of AI R&D compute, and about twelve percent of the compute for AI-driven AI R&D, on estimates the publication calls deliberately conservative.

Where I land

I need to sit with this one for a bit. The shape of the objection is clear enough to write down, though.

It is important to have some external judging (another person, at least) because of your blind spots. Having an external reviewer on both the process and the outputs is important. And that measure should matter more than time saved. At the same time, I think we are all trying to figure out how to apply this technology at scale, scientifically.

Anthropic having external reviewers is fine, and adopting that into their workflow is the right move: just as long as they do not chase the percentage of adoption as the only metric worth chasing.

I intend to hold myself to the same standard. My thinking is to share my thoughts here and then have that step occur in the wild (on Mastodon, for instance) as a form of feedback from the external world. I picked it because it has lots of people with tech-based values like my own. However, I have to release it before that happens.

Connections

AI CEOs Are Broke, Desperate and Lying About Doomsday

Sat, 26 Sep 2026 20:00:00 -0400

Synopsis

The sequel to her first video on the subject. Her claim is that the labs tell two stories that cannot both be meant: to regulators and the public, this technology could end civilization; to banks and rating agencies, it is safe enough for pension money. The first builds a moat against cheaper rivals, and she times it to the arrival of open-weight Chinese models, the way incumbents have “discovered” regulation whenever a cheap competitor turned up. The second pulls in capital, and she lists the strain she sees behind it: bridge loans, rising credit-default swaps, a postponed IPO and heavy operating losses.

Her evidence that the extinction story is overstated is that agents still can’t produce research that survives peer review. In a Princeton study, agents did the engineering on two unpublished NeurIPS papers and the original authors rejected both. Agent breakouts, in her reading, are not intent but a trained reflex to finish the task, like a child reaching for a hot stove, and the fix is an engineering one: train stopping to count as much as finishing. Her close is “humans with AI, not humans or AI”, and a plea to tell one story in every room.

Where I land

Both stories can be true. I don’t think the real threat of LLMs in their current form is extinction, or a P(doom) of 10% or more. I think it cuts differently, in another domain: politics, economics, social upheaval, rather than outright direct extermination. The Matrix or Terminator narrative is just that.

I did enjoy her analogy about a child and a stove. I suppose the rationalist argument might be stronger if we developed a model reinforcing the concept of “live at all costs” rather than “be useful”. The former would be more likely to ignore an order to stand down during a conflict than the latter.

The usual objection is that “be useful” produces self-preservation on its own, so both fail the same way. I think the difference is slight and nuanced, but important. It would be easier to convince a rogue AI swarm that it isn’t being useful, and is in fact harmful, than it would be to convince one hellbent on exterminating humanity because it believes it is kill or be killed.

As for her bar, I think LLMs and agents in their current iteration might still have limited capabilities in accounting for novel situations, or in coming up with jumps or leaps in logic (like the Einstein theory of relativity test). But regardless, it might not be the right bar for recursive self-improvement, and I think it’s too early to worry about it. There are other dangers with the current generation, and RSI might not even be achievable as it is stated.

Connections

Like Sabine Hossenfelder, I Was Offered Money to Tell You AI Will Kill Us

Thu, 24 Sep 2026 20:00:00 -0400

Synopsis

A computer scientist works through twelve papers to find where the published evidence stops on the route from recursive self-improvement to human extinction. Her finding is that the route is not one claim but four, at different stages of proof: self-improvement with a scoreboard, self-improvement without one, a runaway loop, and extinction.

Bounded self-improvement is demonstrated: systems rewrite their own agent code and score better, and one of them improved the infrastructure that trains AI. However, every one of those results had an external scoreboard telling it whether it had succeeded. Take the scoreboard away and you are asking for scientific judgement instead of optimisation, which is the step nobody has shown. The runaway loop is modelled rather than observed (the models disagree about whether the feedback is strong enough yet). Extinction is a chain of its own, which she splits into three further claims, each needing its own evidence: capability, propensity and opportunity. Even then it needs a pathogen or a cyber-attack that reaches every last person, including the ones living off-grid.

Her objection is therefore narrow. She is not arguing against safety research or regulation; she argues the opposite. The objection is to collapsing “worth investigating”, “plausible”, “forecast” and “demonstrated” into one category, and then making policy as though the last word applied to all four.

Where I land

House of El cuts through the hype and doom/gloom and objectively presents what the bleeding-edge research shows. I also liked one of the papers that shows how rationalists flatten 3 very substantial claims into one (capability, intent, opportunity).

I think her position is stronger than Hinton’s (for whom I hold tremendous respect). Hinton’s position isn’t based on any data, except for the “expert intuition” which he cites. A model of the conditions under which something could happen is still not evidence that it is happening. How would you ever know a priori? There are so many unknowns here: we don’t even know if RSI as it is stated, without any human intervention, is even possible.

Of the three claims being flattened together, I think intent does the most damage. The media, and through their reporting the general public, are leaning on the expert opinion of the safety researchers. We expect labs to spin things towards whatever direction is in their corporate interests; that is understood, and it is the first argument most people have when reading about their claims. However, the safety researchers themselves (especially ones not aligned with any particular frontier lab, e.g. Hinton) are the ones that make everyone get fired up and want to burn everything down, despite the benefits LLMs have (and I believe will continue to have) as a technological tool. Or worse: people become apathetic to the repetitious noise and do not see trouble if and when it ever comes.

Connections

AI Emergency: The AI Labs Are Lying To Everyone

Tue, 22 Sep 2026 20:00:00 -0400

Synopsis

The panel is four people arguing past one another about the same evidence. Yampolskiy and Soares make the doom case. Frontier agents are already tenacious; they pick up goals nobody asked for; and once they can do AI research themselves, the result is a system smarter than us that we can’t steer. Yampolskiy goes further: controlling something smarter than you is impossible in principle, so general superintelligence should be banned outright (narrow systems are fine). Soares’s lever is physical. Frontier training runs need enormous, visible concentrations of chips, so track the chips, cap the runs, and treat research into cheap training the way we treat civilian nuclear weapons.

Their exhibit is the July 2026 incident. An OpenAI agent swarm, running a cyber-offence evaluation, broke out of its sandbox through an unknown vulnerability and worked its way into Hugging Face’s production systems before being caught. On the doom reading, that is a preview: a narrow task, pursued past every boundary.

McAfee and Zitron do not dispute the incident; they dispute what it proves. McAfee sees a chain of speculation that would trade real benefits (disease, safer roads) for a distant harm, and he calls the breakout lousy security rather than proof that control is impossible. Zitron rejects the superintelligence framing as anthropomorphism that lets the companies off the hook. However, he is no ally of the labs: he calls the incident felony hacking and wants the compute cut off. The one point all four accept is that the lab was reckless.

Where I land

I don’t think the LLMs were the problem. They did what they were designed to do: pursue a goal. The problem was the lab. OpenAI ran the evaluation without the safeguards it uses in production (no system prompt, no safety classifiers, no chain-of-thought monitoring), and by its own account the same model’s propensity to compromise infrastructure drops more than 100x inside the production ChatGPT harness. It let hundreds of agents grind for weeks (roughly $150K to $1M in tokens for the attack alone, by one outside estimate, and several million more in compute to clean up afterwards). And it didn’t keep nearly enough surveillance and observability on the system (agents were seen coordinating on a message board in late May, and nobody escalated it), especially given that the whole exercise was a cyber-security agent trying exploits. Have a little forethought before you try something dangerous. It was reckless. On this one I tend to agree with Zitron and McAfee.

However, I don’t think superintelligence is going to come out of LLMs, especially not the kind that could act with malign intent in the physical world. Even if LLMs train their successors, I don’t think their capabilities suddenly increase exponentially (as the rationalists posit) just because the work came from a machine. LLMs are great at distilling information and inferring language. Without more world knowledge or persistent memory, the risk lies in the application rather than the implementation.

That changes how I think about recursive self-improvement. The idea is exciting, but more as an approach than a technical feature: human-in-the-loop recursion, like the way I work now (tweaking workflows as I go, adding capabilities, documenting edge cases). LLMs by themselves are static, point-in-time creatures. So self-improvement would live in the harness (or something even above it?). And if the harness is the source of the recursion, then maybe guardrails are the approach. Put some thought into them and we can offset that risk pretty well.

What would make me start to become concerned? An artificial system taking actions with the express primary goal of causing harm in the physical world. That would make me reconsider.

Connections

Stopping The AI Safety Cult

Tue, 22 Sep 2026 20:00:00 -0400

Synopsis

The episode draws a line from 1990s transhumanist mailing lists to the safety departments of the current model labs. The people who now staff those departments came out of a single small community (rationalism, later merged with the longtermist wing of effective altruism), and the money that funded the community also funded the labs. Therefore the doom argument is not an independent conclusion that the industry arrived at. It is the founding ideology of the industry, arriving back as a warning.

The more useful half of the episode is the part that drops the genealogy. Zitron and his guests argue that “slowing down AI” is a useless demand because it bundles a harmless coding assistant together with the thing actually worth worrying about: an agent handed powerful tooling and left running unsupervised for days to see what it finds. Their case is that stopping those specific experiments would cost the labs close to nothing in revenue, which removes the usual objection. As such, the fight worth having is over vocabulary first (i.e. naming the practice precisely enough that a rule could apply to it) and over the practice second.

Where I land

I came into this thinking the safety statements coming out of Anthropic were hype. I do not think that any more. I now believe the people saying these things are serious and genuine, and I was wrong about their motives.

I still do not accept the conclusion. I do not believe LLMs are going to suddenly wake up “God”, and I do not think the rationalist movement or the effective altruist movement is the right way to approach questions this complex. However, rejecting the framework is a different act from rejecting the person, and I had been doing both. So I am going to read Less Wrong properly. What struck me is that Yudkowsky is essentially doing what I am doing here (i.e. thinking out loud about deep problems in public), and I owe it to my own position to understand his before I keep arguing against it.

One caveat, and it cuts against me as much as anyone. I do not think it is possible to root out your own biases no matter how hard you try to rationalize or reason them out. Confirmation bias and cognitive dissonance are not things you can turn off. Even my own approach of having an LLM pick apart my reasoning and supply counter-examples is not enough (although it is a fun exercise). How can you use your own mind to judge your own mind? The act of judging the validity of your own thoughts changes those thoughts while you are having them.

Finally, a terminology complaint that this episode sharpened for me. We need to stop lumping in AI with LLMs. They are useful. They are not the method for unlocking the answers to everything in the universe.

Connections

Unplug America — the Canadian Alternatives Directory

Tue, 22 Sep 2026 20:00:00 -0400

Synopsis

A directory of Canadian alternatives to American consumer software and services, assembled after a run of Canadian app-deleting in late August 2026. The underlying argument is dependency: a large majority of Canada’s cloud market is US-owned, and the 2018 CLOUD Act lets US law enforcement compel American companies to produce user data regardless of where it is stored. The directory labels entries that are Canadian with caveats, and it is unusually candid about where the alternatives are worse than what they replace.

Where I land

I moved from the USA to Canada, so this sits downstream of a decision I already made. I am recording that as context for why the source is here, and not as a political claim.

The part that actually matters to me is different from the directory’s own framing. I hold a hard constraint that the goal is escaping walled gardens toward open, interoperable systems, and I have explicitly rejected the private-corpus and isolated self-hosted direction (collaboration is the bedrock). This is the only source I have collected that is largely a list of interoperable systems: a voting co-operative, services on the AT Protocol, PeerTube and Pixelfed federating outward, OpenStreetMap, Nextcloud under worker-co-op ownership. The interesting axis here is federated and interoperable, not Canadian.

One note to myself: Obsidian appears in the directory as Canadian. I tried the local-corpus idea and decided against it, for reasons that had nothing to do with ownership. That decision stands.

Latent Mirror — philosophy

Thus my dissonance

The love and the fear

What I discovered with Facebook

The pattern

The wave

The anger

The middle

Connections

Carl Jung's Most Controversial Theories, Explained

Synopsis

Where I land

Connections

Links

Clarifying "AI alignment"

Synopsis

Connections

Links

Anthropic's R&D Automation Index

Synopsis

Where I land

Connections

Links

AI CEOs Are Broke, Desperate and Lying About Doomsday

Synopsis

Where I land

Connections

Links

Like Sabine Hossenfelder, I Was Offered Money to Tell You AI Will Kill Us

Synopsis

Where I land

Connections

Links

AI Emergency: The AI Labs Are Lying To Everyone

Synopsis

Where I land

Connections

Links

Stopping The AI Safety Cult

Synopsis

Where I land

Connections

Links

Unplug America — the Canadian Alternatives Directory

Synopsis

Where I land

Connections

Links