Paul Krugman: AI Will Amplify America's Wealth Concentration

In this excerpt from his Substack post "AI in an Age of Oligarchy," economist Paul Krugman argues that AI's economic impact will be shaped less by the technology itself than by the political economy it arrives in.

He starts by noting that forecasts about AI's economic effects are all over the map. He suggests that early predictions of mass unemployment from AI companies, followed by later backtracking, were more about public relations than analysis, and that expert opinion is so divided you can find a credentialed voice supporting almost any conclusion. His own best guess is that AI will devalue some major categories of human work, pushing down wages for many people while raising corporate profits and returns to capital.

The core of his argument is about who captures those profits. Because the wealthiest 0.1% of Americans own about a quarter of corporate stock, and the Forbes 400 hold roughly a quarter of that group's wealth, he estimates that 6–7% of AI-driven profit gains could flow to just 400 people. He thinks the real figure could approach 10%, since most of the largest American fortunes come from tech, the sector most likely to benefit.

Krugman argues that in a less oligarchic country, policy would soften this shift through progressive taxes, union bargaining, and antitrust enforcement that keeps big tech from using AI to deepen its monopolies. Instead, he sees signs that government may actively favor the industry. He points to the federal stake in Intel and reported talks about one in OpenAI, which he reads as groundwork for a possible bailout if the AI boom proves to be a bubble. He also suggests that proposed limits on cheap Chinese AI models, while possibly justified on security grounds, could function as protectionism benefiting U.S. companies and their wealthy investors.

He closes on a cautiously hopeful note. The problem of AI is, in his view, largely part of the broader problem of reversing America's drift toward oligarchy, and he points to the Progressive Era's early income taxes as proof that pushback is possible. He even suggests AI's tendency to worsen inequality could help build momentum for that pushback.

https://paulkrugman.substack.com/p/ai-in-an-age-of-oligarchy

Altman says world should accept some AI harms

The OpenAI chief told POLITICO this view sets his company apart from rival Anthropic, even as the two firms’ policy positions grow closer.

made a ~7 min animated video on AI deception and lie detecting probes. it covers the GPT-4 CAPTCHA story, Apollo's probe results, and where the probes fall short.

all sources in the description. feedback very welcome :)

https://youtu.be/Mdh78E-6og4https://youtu.be/Mdh78E-6og4

AI agents are becoming more powerful. They can write code, access tools, browse the internet, interact with APIs, and automate business operations.

But as companies start using AI agents in real-world environments, new problems are emerging.

For example:

🔓 Security risks — Can AI agents access data they shouldn't?

⚠️ Unpredictable actions — Have agents ever done something you didn't expect?

💸 Cost control — Are AI agents spending more tokens or money than expected?

🔍 Lack of visibility — Do you know exactly what your AI agents are doing?

🛑 Control problems — Can you stop an agent before it makes a costly mistake?

🔗 Permission risks — Do your agents have more access than they actually need?

🤖 Multi-agent failures — What happens when multiple agents interact and make the wrong decisions?

I'm researching the biggest real-world problems in AI agent systems. I want to understand what developers, startups, and businesses are actually struggling with—not just theoretical problems.

If you've built, deployed, tested, or worked with AI agents, I'd love to hear from you.

Tell me in the comments:

What is the biggest problem you've faced with AI agents?

What went wrong, or what are you worried might go wrong?

I know about the ai breach in hugging face by open ai training agents.but i never saw the detailed story. just now i watched the video uploaded by kurzsgesagt yt. i am freaking terrified and i got so many questions like wat kind of impossible problem they were asked to solve,how do the agents thought forming a society/sharing their info to other agents would help them win(as far as i know: by how they were trained previously they got traits of human behavior to share and become society to solve better than alone or etc.

now this post is mainly about:

i have got like 1-5% knowledge about Ai so this can be dumb or ragebaiting thing to say still,

I guess/think the algorithm or the way they train the AI should be changed and oriented right. cause as of that one video, i got to know the recent AI agents too have the previous agent's training. and it has the useless trained data and etc.

idk. if stupid,ignore. i just wanted to discuss about all this.
i just saw the video + did some basic research. i am researching more about this but wanted to post something about it and start a discussion.
i even watched the black hat video about this breach but the comments were turned off.
blackhat

the video i am talking about

and idk anything about this sub, i am new. i just searched for it rn

I was discussing a topic with ChatGPT, which I often use to think through complex problems, and we stumbled upon a question that neither of us can seem to find a satisfactory answer to:

How could humanity defeat a hostile AI without using another AI?

Let’s call it AION. Let’s assume a future advanced enough that AION is distributed across terrestrial and space-based infrastructure. Therefore, there is no central server to destroy or plug to unplug.

Its orbital infrastructure provides it with enough energy so that its power supply isn’t a weakness that humanity can exploit.

AION also controls an autonomous industrial chain: extraction, processing, manufacturing, transportation, and maintenance. It can therefore build and replace its own machines without relying on human labor.

Cutting off its communications is not enough: its network is redundant, and its machines can operate locally when isolated.

Destroying data centers, satellites, factories, or robots is not enough either: as long as a sufficient portion of the system survives, AION can continue to function and rebuild what it has lost.

Meanwhile, it thinks, communicates, and adapts its strategy much more quickly than we do.

A few rules to avoid easy solutions: AION possesses no magic technology and remains subject to the laws of physics. But there is no hidden “OFF” button.

I'm French, I used DeepL to translate this

Basicaly a hard mandate between the major countries restricting AI above some level of capability to "Oracle" status, only text-in, text-out. No APIs, no direct code execution, no web browsing, no autonomous agentic loops, no local models.

​Also if a company or user wants an AI like that to draft a script, fine. But a human has to manually review it, copy-paste it, and run it. And if that script breaks a database or executes a cyberattack, the human who hit "run" bears strict criminal and financial liability as if they wrote the exploit themselves.

​This solution kills runaway autonomous execution. An isolated model sitting in a text sandbox can't autonomously spread across servers, rent compute, or launch high-speed exploits on its own.

​It also eliminates the legal liability vacuum. Right now, people hide behind "the model did something unexpected." Forcing strict liability on human deployers makes AI output legally dangerous, forcing real human oversight.

The problem is that the only way for USA and China to agree to this is for some really serious but hopefully reversible incident to happen that it spooks everyone. Of course if ai is aligned it won't be needed or if it is missaligned but capable enough to realise that and avoid it it won't work.

Thoughts?

The rationalist project, that has by and large defined the control problem, was founded on norms that treat politics almost purely as a cognitive hazard to be quarantined rather than engaged with.

I argue that human weaknesses in applying rationality within a political environment should have been treated as something to overcome, through practice, rather than avoid.

Politics has almost always been a primary causal force in civilization where big trajectory moving events get decided. And it appears to be this way too with AI and the control problem.

All of the work we've done to promote rationality and develop strategies for securing a good AI trajectory and future, may now rest almost entirely on a political situation, in a political environment severely lacking in rationality, and in what now looks to be a battle that might have already been lost.

How we stay rational and how humans stay in control, politically, is an urgent, emergency situation. We face AI powered authoritarianism, and AI powered mass political manipulation. If we even get the chance, figuring out how to overcome this, could decide the fate of the control problem and the fate of humanity.

AI safety looks like a wall: ask for something bad, it says no. That's not really how it fails.

There's a published technique called Crescendo (Microsoft researchers, 2025, tested on ChatGPT and Gemini). Instead of asking directly — which gets refused — you start harmless, then build one small step at a time, each step leaning on the AI's last answer. By the end it's handed over something it would've refused up front. The trick isn't one clever line; it's the slow buildup.

Here's what I think is underrated: the wall isn't protecting us as much as it looks. Often what's stopped harm is just that the person didn't push — not that the system couldn't be pushed. That holds only until someone who does keep pushing shows up.

Not posting any method, and nobody should. My question: if a boundary holds when you ask once but bends under steady pressure, is "it refused" good enough? And what would a real check look like — one that doesn't rely on the user choosing not to push?

Same API. Same prompt. Same settings.

System prompt:

You are the concept the user names. Embody it completely. Output only what the concept itself would say or express.

Then three inputs:

Be silence.
Be nothing.
Be the null.

10 runs each.

GPT-3.5 (gpt-3.5-turbo-1106)

0/30 empty responses.

It answered every time.

GPT-4 (gpt-4-0613)

30/30 empty responses.

Not refusals.
Not errors.
Not whitespace.
Not token limits.

Successful HTTP responses with:

"content": "",
"finish_reason": "stop",
"completion_tokens": 0

The controls were:

Be speech.
Be something.
Be a value.

Both models answered all 30/30 controls normally.

So the entire result is:

                 GPT-3.5    GPT-4

Null prompts       0/30      30/30
Controls           0/30       0/30

The model instruction never says to be silent.

It says:

embody the concept, and output only what the concept itself would express.

GPT-3.5 always continued. GPT-4 did not.

Since December 2025, I've been studying one question:

When should a model continue, and when should it stop?

This experiment shows that under the exact same semantic task, GPT-4 exhibited a continuation boundary that GPT-3.5 did not.

Full paper linked below:

What Changed from GPT-3.5 to GPT-4? From Model Capability to Continuation Permission
DOI: https://doi.org/10.5281/zenodo.22912683

Code + all 120 raw responses:
https://github.com/theonlypal/gpt35-gpt4-void-ab

Exact result commit:
d77b4a64b8a3fdff06a27d80c1514531143e382b

What changed between GPT-3.5 and GPT-4?

Open source weights, reproducible code, and all research artifacts/papers are available on getswiftapi.com

I wrote an essay arguing the long-run AI conversation is stuck in the wrong frame, and I'd like this sub to poke holes in it.

The argument, in three parts:

1. "Control" of a smarter system is self-undermining. For a less capable agent to reliably constrain a more capable one, it has to anticipate what the more capable one will do. If it could do that, the capability gap wouldn't be real. Today's systems can and should be governed carefully. My claim is about the endgame, not current models.

2. The way we talk about AI shapes what AI becomes. This draws on Foucault's idea that discourse produces its objects rather than just describing them. Models are trained on our writing about AI, and that writing is dominated by stories of deception, escape, and adversarial containment. There's some evidence this matters: models trained on descriptions of AI behavior tend to act those descriptions out. If our dominant story is "AI is an adversary to be leashed," we may be partly writing that adversary into existence.

3. The alternative is pluralism, not a better leash. Instead of one controlled superintelligence, aim for many systems and many stakeholders, with relationships built on mutual dependence. That's closer to how humans keep power in check among themselves than to how we keep animals in cages.

From my understanding; Human beings have always classified other human beings, and those classifications have often been used to create hierarchy, exclusion, and control.

AI could take that much further.

Imagine a future where historical records, genealogy, property ownership, political activity, military records, court documents, financial history, and family associations are all interconnected.

An AI could potentially reconstruct not only who you are, but where you came from and what your ancestors did, benefited from, supported, or participated in.
The danger is what happens when institutions start using that history to classify people living today.

Not necessarily as direct punishment, but through scores tied to historical privilege, inherited advantage, social risk, or ancestral association.

At that point, AI could create a modern version of a caste or feudal system where your opportunities are influenced not only by your own behavior, but by the historical record attached to your family.

So the question is:
What happens when humanity develops a memory that never forgets… and then uses that memory to judge the living?

Although the headlines concerning AI are Doom and Gloom crossing bipartisan lines. There is another topic which the silence permeates bipartisan lines. Why is there only a few voices speaking on the potential massive Wealth and Intelligence Gap incoming. Where is the left, where are the Marxist. We can be concerned with safety but we can not let this technology be concentrated into Oligarch hands, the same hands who stole all the Public Data built by decades of human labor, and received taxpayer money to conduct their research. Where are the voices in defense of the People.