Social

The social implications of AI are baaaaad. The broad categories I can think of: parasocial relations, cheapened human-looking output, and mental degeneration.

Parasocial relationships

I've only heard of this one by second degree connection, so it's not super prevalent, but it's prevalent enough. Some people get so socially attached to things that do not reciprocate. This draws these people away from reality and feeds their delusions. Reality is the ultimate source of all things, and it does not do us well to stray far from it.

I have no solution to this. Some people are just hopeless. We can't control it like drugs, because there are very powerful open models out there that will run locally on someone's computer. You can't control that. At least drugs are a physical thing.

Cheapened human-looking output

This is probably the broadest category. For the first time ever I think, it has become easier to produce than it is to consume "human-looking" output. This is why it feels AI slop is so prevalent. It takes 2 seconds to use AI to generate an essay that looks legit, but it takes 100x longer to read it and analyze it. That never use to be the case, it used to be it takes far longer to write something that passes as valid text. This has all kinds of downstream implications.

The most surface level issue with this is just that the internet is flooded with absolute trash AI images and AI articles that you can't really tell are slop until you waste your time and attention to read a couple of sentences in. This content can be useless or actively detrimental.

Another problem is the explosion of scamming. It used to be that scamming took some effort and had returns too low for most criminals people to consider. Now they can set up an AI scam farm with potentially very high returns.

Disinformation becomes much easier to spread because of collapse of the proof of effort model.

This represents a paradigm shift. We may have to shift to a verification/trust broker model, instead of the current implicit effort model. This a big problem because lower trust increases the cost to cooperation, and cooperation is the ultimate force multiplier for progress.

Mental degeneration

In some ways AI is like cars. Cars get us very far for no physical effort. AI gets us a lot of information with no mental effort. Cars made us lazy, lazy made us fat, and fat made us unhealthy. AI might do the same thing on a mental level, it'll make us lazy and stupid. We'll have to do deliberate mental exercise just like we do physical exercise to keep our minds sharp.

Is this different from what every generation has said about new technology? I've seen people quote ancient testaments (unconfirmed source), that say "writing is making us lazy because we no longer have to remember things with our minds". I think AI is different. I think some things free our minds up to do other tasks, where as others replace our minds altogether. For example, people were right about TV being brain rot. I think AI is more like TV than it is like writing.

Terence Tao has written about this, with the recent AI resolution to the Navier–Stoke millennium problem.

Philosophical

I've seen some discussion about the nature of AI self-awareness, and how human AI is.

I'm a naturalist, so I think it's possible for consciousness to arise as an emergent property out of purely physical material. So if I extend that framework, it is technically possible for AI to become self-aware. But again i don't think that's a big issue, as long as we're not programming the AI to suffer.

Consciousness is literally the hard problem of philosophy. I don't think we'll truly ever know if AI is self-aware or conscious. Just in case though, we shouldn't program AI with chronic pain. That shouldn't be hard. As long as we do that we should be in the clear morally.

I also don't think there's anything special to being human. I didn't feel any existential dread when machines beat chess or go. There's nothing special about an AI doing something or a human doing something, as long as the outcome is the same (I do have to distinguish that sometimes the process itself is a part of the outcome, maybe even the most important part of the outcome; an emphasis on outcome does not equate being narrowly results-oriented. This distinction is often lost in discussions on ends and means).

I am human, so I by the principle of propinquity, I put human interests first; but I don't go so far as to be a human chauvinist.

Confirming and dispelling some notions

Datacenters

One prevailing notion I see on reddit and lemmy that I would argue against is on datacenters. Most anti-data center arguments are just factually wrong. You don't like AI, that's a perfectly legitimate reason to be anti-datacenter; just say that, don't make things up. I'm not even some kind of datacenter stan. It's just that every time someone says "I'm anti datacenter because [some absurd BS]" it rubs me the wrong way.

Datacenters use power - everything uses power. If the datacenter builders think paying for the power is worth the money then they can spend that money. If anything this is a huge boon for greenfield build out of renewables due to the demand and money that's coming into the electrical generation sector. High demand is never the enemy if supply can be increased. If we had smart policy wonks (we don't) we would be co-opting AI money to build solar farms.

Datacenters use water - the amount of water data centers use is miniscule compared to its output density; depending on the cooling method, they could be using less water than a residential zone of the same area. I am computer hardware adjacent, I know for a fact how much water a server rack uses. The water consumption figures critics use are just absurd lies.

Noise pollution - datacenters output less noise than basically any other industrial complexes like powerplants, factories, transit hubs. All the criticisms here depend on absurd misuse of resources. But it's very simple to just not do that.

Any locality where datacenters are built, they become a cash cows like any other business -- as long as it follows basic, existing zoning rules regarding consumption and ejecta control.

Copyright

Another point I want to discuss is AI copyright violation. I disagree that training AI is inherently violating copyright, even if the outcome is similar to copyright violation.

All copyright comes with fair and transformative use clause. When AI gets trained on copyrightable text or an image, you will never find it again in the weights. It's identical to when a person reads and watches, then synthetizes that info and outputs a distinct piece. It is still possible for AI to violate copyright, just like it's possible for any person to violate copyright, but training and inferencing are not inherently violations of copyright. The issue is separate from copyright.

I mentioned in the social section, the issue is ease of production. If a giga-artist can output 1000000 paintings a day, regardless of how good those paintings are, they cheapen themself and everyone else. This is achieving the same effect as copyright violation, which is to cheapen competing work. When I'm buying art I'm not just buying some paint, I'm buying the story behind it.

We should retain the right to sue AI users, hosts, and trainers for actual copyright violation. But we can't be trapped in the framework of copyright. Like I mentioned in the social section, this may have to move in a whole different direction like some kind of trust/verification scheme, but I'm not sure.

Autonomous weapons

There are levels to this. We already have missiles with computer assisted seekers that commit fratricide. I don't have a huge problem with AI seekers. People often forget that science fiction is fiction. Terminator isn't real. I'm not concerned about rogue hostile AI for various technological reasons. What I think is important in the end, is that true blackbox AI should be trained with Asimov's 3 laws as hard guardrails. This should be done in a verifiable way, so we can practically implement it in arms control.

The 2 problems with any weapon is 1) concentration of power, and 2) abuse by rogue actors. An example of 1 is the colonial era, when Europeans gains domination over all others and enslaved and genocides them. An example of 2) is random crazies doing mass shootings. We've dealt with both. Not well, but we're dealing with them and I don't think AI will bring anything truly unprecedented.

Overall though I should say again, as a society we should agree to slow down both AI adoption and research until we can catch up our thinking on some of these social and economic issues. But once the cat is out of the box we should try to use and direct it in the best direction we can. The last thing we should do is to emulate nuclear fearmongers who derailed the nuclear energy train, and possibly delayed action against climate change for decades.

I'm in a writing mood today so here's the entry on the broader implications of AI.

This is a big subject. I've given smattering of my thoughts before, but never synthesized them into 1 post. My thoughts are also still of yet incomplete. I believe no one can possibly have a complete picture, and indeed some things are simply unknowable until they happen.

I will breakdown my thoughts in 5 sections. First I will discuss the technology itself. Then I will discuss the economic implications, followed by the social and philosophical implications. Then I will follow with some future speculation. Lastly I will confirm or dispel some of the prevailing notions about AI.

Technology

I won't go in depth here, I'm not an AI expert. AI is based on some of the same structures found in the brain, hence the term "neural network".

Real nerds have recognized since the early 2010s that AI was going to be big, big big, unlike other tech trends like VR or blockchain. I think the tech itself is incredibly interesting. The math and science behind it just has that cool factor. The whole concept of training and inference is so clever and unintuitive, despite being literally in our brains.

The advances we've made in the last decade, in terms of hardware, computational methods, and high-level logic analytics, have been leaps and bounds. However, I think they may be limited or plateauing from this point on.

First, the hardware. Moore's law has already stopped scaling a decade ago. Minimum "fine" feature size (note this is different from advertised "node size" and minimum feature size) has already hit <10nm as of 2026. We are at or near the limits of physics. We're unlikely to get more than 1 order of magnitude improvement in terms of hardware compute out of traditional silicon based computers.

We're also fundamentally limited to print CPUs in 2D (with essentially constant number of layers). Brains are 3D.

There are some candidates to break out of this plateau. I would consider graphene-based CPUs, photonics, analog compute, quantum assisted compute, and grown computers (lol) to be some of the most notable directions of advancement, although I can't speculate on how important each will be.

Second, the software. I'm not knowledgeable as to what the frontier labs are cooking up. I've seen differing opinions on whether there are still major software breakthroughs possible in this field, or if it will be marginal gains from this point on.

I am fairly certain though, that available "free" training data has been exhausted.

Overall I think there's at least 1 order of magnitude improvement we can still gain out of compute factors, and another 2 order of magnitude (multiplicative) out of software efficacy -- pre and post training -- in the next decade or so. How much of that will actually translate to functional improvement is debatable.

Asymptotic self improving AI is possible, but singularity is a pipe dream. In real space, we have yet to create self-replicating machines. This is because replication requires constant energy and material input. Self replicating AI will require constant energy input, and constant data input. I have some confidence in my intuition, that as long as data ingress is limited, which it is, the singularity will not be achieved.

That is not to put a cap on it though. The human species itself can be considered a self-replicating, self-learning singularity, limited only by our inability to leave our planet. Maybe one day we will create a singularity that involves AI. But this is all getting too philosophical instead of technical, so let's move on.

Economic

AI is a tool. A naive reading of tools is that all tools have the ability to do at least nothing. It is only up to us what we use that tool for.

AI is useful for things like protein folding, image analysis, information synthesis, coding etc. It has the potential to be a great economic boon. It also has negative uses like hacking, AI scams, forgery etc. In this section I will weigh the positives and negatives from an economic perspective.

But before that let me just consideration the stock market real quick. "Is there a AI driven bubble in the stock market?" Yes. Yes there is. I think this is so apparent there is no need for me to provide further analysis.

As discussed in my last entry, I expect AI to replace a large proportion of white collar jobs, especially mine (software engineering). This is just like how automation replaced factory jobs. The final result is that there is more output for less input. The problem arises from 1) the inability of a human to change fast enough to adapt in their lifespan, and 2) wealth distribution.

Automation has replaced a huge proportion of manufacturing jobs. But it has happened over 100 years, giving time to adapt. The internet exploded over 10 years, but it has replaced comparatively few jobs (at least skilled jobs), instead mostly just adding jobs. AI, however, has both the depth of penetration of automation, AND the speed of the internet.

This speed, depth, and breadth of change can't be handled organically. The government must step in. Fortunately, neoliberal structure is well equipped to handle this. Unfortunately, there seem to be idiots in government all over the world.

Fiscal and monetary policy

I have break this down to fundamentals. AI as a tool has shifted the productivity further in favor of capital, as opposed to labor. The more productive capital becomes, the more important it becomes to distribute it publicly. This is because of the principle of marginal utility. As an absurd example, money (as a conceptual unit of value) is far more valuable when evenly distributed to all 8 billion people, as opposed to concentrated in the hands of 1 person. I know this sounds communist (it kind of is), but it's not incompatible with neoliberal policy.

The first neoliberal resolution to the overproductive capital is to use a sovereign fund. While sovereign funds are not inherently neoliberal, it is compatible. Just as example figures, let's say that the sovereign fund owns 15% of all public companies (public capitalization out value private capitalization by several factors, in the US). In addition, all companies have their profits taxed by 15%. Dividends, buybacks, and profit taxes are directly redistributed to each adult citizen. In an overproductive capital scenario, at least (but probably a lot more) $900+ billion will be redistributed to 270 million adults in the US. That's about $3300 per person.

The second neoliberal resolution to the overproductive capital is to simply print money. Overproductive capital simultaneously increases productivity, and drives down the cost of labor, which lowers wages, which creates deflation. The government can print money equivalent to the amount necessary to achieve healthy inflation, and directly distribute it to citizens. Let's say deflation is 0.5% and target inflation is 2%. Inflation is roughly correlated with M2 increase, and M2 is roughly correlated with GDP. "2.5%" inflation gap can roughly be filled by printing money roughly equal to 2.5% of GDP, which is about $2300 per US adult.

The third neoliberal resolution to the overproductive capital is estate tax. This is functionally equivalent to a wealth tax, but when a person is dead they're much less likely to fight it. Wealth taxes are not neoliberal, unless the person you're collecting it from is dead, thus absolved of their relationship to this wealth. If loopholes are closed, up to 15% of all accumulated wealth can be collected over the course of an average lifespan. Going by US death rates, and accounting for deductions, this could be around 0.1% of all wealth in the US, which is about $600.

All together that's about $6200 in top line "UBI". Remember this is not meant to be a "living UBI". This is merely the supplement to suppressed labor wages due to overproductive capital. Another great thing about these levers is that they auto adjust according to the relative value of labor and capital.

Another way to think about this, is what if capital accounted for 100% of GDP (impossible and absurd)? For ease of calculation let's just say this capital is in the form of a company that prints useful money equivalent to GDP. 100% of that money is profit (this is a valid economic equivalence I'm pretty sure). 15% of gets taxed. 15% of it is owned by the government. This does not compound, it's additive, 30% gets redistributed. The company gets 15% of it taxed after 75 years, and gets sold back to itself at a 15x profit multiplier. This is equal to 7.5% of GDP per year. Plus 2% printed money. In total 39.5% of output gets redistributed to the people.

People policy

So what happens to people who suddenly find that their skills are useless? Tough luck I must say. I don't really have good solution to this one, other than artificially handicap AI development speed.

The natural resolution is that skilled people are usually skilled because they have some innate skill like smart or strong. And if they have that innate skill, they'll pick up something else relatively quickly. The problem is that these days we all have to have a 4 year degree to be a white collar worker, and spending another 4 years in school is a bit impractical for an adult who has 40 years of working age.

Perhaps we'll just have to accept that times change, and we lost the roll of the dice.

It's kind of late, I'll have to do a part 2 another day.

The other day I heard about the concept of a software "dark factory".

How it works is no humans ever touch the code. A process engineer writes the factory "process", and a spec guy rights the software specs. A handful of AI agents will then automatically analyze the specs, write the code, review the code, test the code, and deploy/monitor the code. Bugfixes will go through the same process. A human will perform final user acceptance testing.

There are already active companies using this process. I have not used their products (that I know of). However, based on my experience with AI capability as of late 2026, I believe such an AI software factory is capable of writing high complexity, medium scale, medium reliability, consumer-grade software. Which is most SaaS out there.

Let's formalize this from a management and jobs perspective.

A traditional engineering team may compose of this:

Upstream:

• 1 product manager/business analyst
• 1 designer

Midstream (software engineers):

• 1 staff engineer
• 2 tech leads/engineering managers
• 5 regular engineers

Downstream:

• 1 devops/devex guy
• 1 QA guy

That's 12 people.

An AI software factory with roughly the same output would compose of this:

• 1 product manager/spec writer
• 1 process engineer
• 1 "foreman"/QA
• 5-10 AI agents of various types - I happen to have some insight into token usage and costs. I think with the agents running at full tilt all day, each agent will consume on the order of $100s worth of tokens per day.

That's 3 people, plus ~2 people's worth of costs going to "renting" a capital good.

So at constant output, 3 out of 4 jobs are eliminated. Token costs are probably going down in the long term. I don't think Jevons paradox will apply here, because software is already so cheap.

This is very analogous to the relationship between automation and manufacturing. The biggest conceptual difference is that AI development is happening much faster. Another difference is that a software factory produces a one instance of a product, whereas a hard factory manufactures many of a product. But this is a pedantic difference not relevant to the broader implications.

I will probably discuss the broader implications of the software factory phenomenon, and AI itself, in another entry.

I'm starting a journal, or blog, or diary type page.

I'm starting this for 3 main reasons:

• I feel that my thoughts, reasoning, and ideology have matured enough to commit to writing.
• As I hit an age milestone, I can feel my memory declining. I'm writing my thoughts down so I don't forget.
• The world is in an interesting time right now. Technologically, socially, and geopolitically.

I'm laying down some rules for this journal:

• No set timing, length, or topic for content.
• It is anonymous. I might post personal stuff, but identifying info will be redacted or encrypted.

I suppose it's best to introduce me to myself. I have a background in software development. I have always been math, engineering, and technology oriented. I am a man who lives in a developed country of the world.

Philosophically I would consider myself a rationalist. I am pragmatic and utilitarian. I am a positive nihilist. I think naturalism is a good way to think about this world. I believe in reason and honor. I still have some idealism left in me.

I am atheist. Religion takes up very little space in my mind, it baffles me so many in this world are religious.

Politically I would say I lean liberal. On the political compass I am fairly left, but centrist on the authoritarian/libertarian axis. I feel most closely aligned with what is termed "neoliberal" these days. I loosely believe in democratic and free-market principles, although this is very contextual. I believe in evidence-based policy making. I think climate change, energy security, and economics are important topics of today. I think in the US political climate, everyone is focused on the wrong things.

--Bu11ish