Nationalize AI - Part 3 - Corporate Power

This is Part 3 in a multipart series on nationalizing AI:

• Nationalize AI - Part 1 - Defining The Problem
• Nationalize AI - Part 2 - Labor
• Nationalize AI - Part 3 - Corporate Power
• Nationalize AI - Part 4 - Inefficient
• Nationalize AI - Part 5 - Superiority
• Nationalize AI - Part 6 - Conclusion

Corporate Power

Current Consolidation

One of the largest current issues globally is the massive consolidation and increase of corporate power.

There’s a lot of reasons for this, but one of the main ones has been the increasing number of mergers:

(Number of acquisitions per year from 1985 - 2023)

Why is Corporate Power an Issue Already?

At a high level, it is pretty easy to understand the literal consolidation - regarding # of companies that control certain industries - as was just covered. The problem does not stop there, however. With more and more power, and less competition; corporations are not using their power to better society, they are using it irresponsibly to abuse people.

Let’s walk through general corporate abuse, big tech’s existing abuse, and the abuses of current AI technology.

General Corporate Abuse

Corporations do anything for a dollar. They do not care about anyone except their C-suite and the shareholders. Corporations and the rich are more than happy with harming you, killing you, bankrupting you, killing your job, stealing from you - just for their next buck. I do plan on writing about a specific part of this at some point in its own post - but let’s briefly go through these:

1. Turing Pharmaceuticals’ Price Hike
   • In 2015, Turing Pharmaceuticals, led by CEO Martin Shkreli, increased the price of the life-saving drug Daraprim by 5,000%, making it unaffordable for many patients.
2. Tobacco companies
   • Despite clear scientific evidence linking smoking to cancer and other diseases, tobacco companies publicly denied these risks, lying to everyone about the dangers of their products.
   • Targeted Marketing: They aggressively marketed cigarettes to vulnerable populations, including children and adolescents, to cultivate lifelong customers, often using themes of glamour and rebellion to appeal to youth.
   • “The tobacco industry continues to amplify misinformation in the media, including the recent egregious attacks on tobacco control organizations[1]. The tobacco industry is the only one that stands to benefit by undermining tobacco control organizations.” Source
   • These actions, over decades (and they continue), are the definition of killing people for money. By the way, tobacco stocks pay some of the highest dividends to shareholders in the stock market.
3. Lead in Gasoline
   • Many years ago, lead was added to gasoline despite the fact the companies knew that it was a known poison.
   • For years, these corporations created fake studies, spent tens of millions on legal fights, created propaganda and marketing campaigns – so they could profit from costing a massive amount of harm. Source
   • “A century of leaded gasoline has taken millions of lives and to this day leaves the soil in many cities from New Orleans to London toxic.” Source
   • Long-Term Effects on Survivors: Millions of people exposed to lead during childhood suffer lasting impacts, including reduced IQ, diminished cognitive function, lower socioeconomic status compared to their parents, and an increased risk of mental health issues. These effects hinder their quality of life, career achievements, and overall well-being. Source 1 Source 2

These are not even the biggest examples, or all of them - obviously - here’s a short list of some of the other biggest instances: Purdue Pharma knowingly fueled the opioid crisis, BP caused the Deepwater Horizon spill by reducing safety measures, Nestlé exploited water resources, the asbestos industry knowingly sold deadly products, Volkswagen cheated emissions tests, Enron committed massive fraud, Lehman Brothers caused the 2008 crash, Wells Fargo created fake accounts, Ford sold cars with fatal defects, Equifax exposed personal data, Turing Pharma exploited drug prices, Amazon monitored and exploited workers, Coca-Cola drained water in drought zones, and Shell suppressed climate research, etc.

Big Tech’s Existing Abuse

Beyond abuse in many non-tech industries - another even larger problem is with the big technology companies controlling technologies that are also more powerful than any technology that has come before.

In particular - the technology companies have been horrific in this regard - and there’s been a very very slow regulatory response or really any accountability for some massive wrongdoings, including (but not limited to):

• App Store rent seeking
  • The fees on the App Store(s) are the definition of rent seeking.
• The top social media sites are heavily consolidated and owned by the most powerful entities in the world, as shown in this chart:

Meta owns the top two and 3 of the top 9 social media sites. Not only this - Microsoft owns 1 of them, and the richest person in the world (Musk) owns 1 as well. Why have we all simply accepted that essentially all of our consumed social is owned by the biggest corporations and people in the world? By the way, this does not even count Threads (owned by Meta) because its a chart from 2023. How has the government not forced, at least, Meta to divest Facebook and Instagram already??

This power has been wielded for extremely nefarious purposes - such as election interference, general misinformation, massive foreign interference in our society causing unrest, selling of data, substantial increase in depression of adolescents, etc. Social media is the most powerful technology, that you’re allowed to privately own, ever.

Existing AI Abuse

By the way, the abuse of AI for profit is already happening. Let’s walk through a few examples, check out the links on each list item for more information on one specific instance:

1. Deepfakes in Revenge Porn
   • Many cases have emerged where AI-generated deepfake technology was used to create fake explicit videos of individuals, often women, and shared without consent.
2. Voice Cloning in Fraud
   • In 2021, cybercriminals used AI-based voice cloning to impersonate a company director in the United Arab Emirates, deceiving a bank manager into transferring $35 million. This sophisticated heist involved cloning the director’s voice to authorize fraudulent transactions.
3. AI-Generated Harassment
   • Some individuals use chatbots, such as GPT-based systems, to generate hundreds of abusive messages or harass others online, overwhelming victims with toxic content.
4. Disinformation Campaigns
   • Elections in many countries have been influenced to the point of changing the result, AI is being used already to subvert democracy.
5. AI-Powered Stalking
   • Tools like Clearview AI, which scrapes social media and other online platforms, have been misused by private individuals for stalking, identifying, and tracking people without their consent.
6. AI in Child Exploitation
   • Offenders are utilizing AI tools to create hyper-realistic, sexually explicit images of children. Law enforcement agencies are intensifying efforts to combat this misuse, as AI-generated child sexual abuse materials complicate the identification of real victims and pose significant challenges in prosecuting offenders.
7. AI being used to automatically rejecting healthcare claims
   • UnitedHealthcare is charged with using an AI system with a 90% error rate to deny elderly patients medically necessary post-acute care, forcing early discharges and financial burdens; or worse.
8. Many instances where AI is convincing people to do extremely bad things.
   • Chatbot ‘encouraged teen to kill parents over screen time limit’
   • NYC’s AI chatbot was caught telling businesses to break the law. The city isn’t taking it down
   • Mother says AI chatbot led her son to kill himself in lawsuit against its maker
   • A widow is accusing an AI chatbot of being a reason her husband killed himself

And these are just some of the abuses.

While nationalization won’t fix all of these things, nationally owned AI can help fight against bad actors instead of being used by the bad actors (to profit the richest corporations and people on the planet). A lot (the majority?) of these abuses use AI developed and deployed right here in the USA.

The Power of AI

What is particularly interesting about AI at the scale of some of the systems we have seen recently is that they have an extremely large barrier to entry for any potential competitors. Could any non-massive company/individual afford to buy thousands of GPUs and pay the energy to train a model that is even remotely competitive with the existing models? Of course not. There is some nuance here due to the fact that some components created by some of the corporations are open source, but that fact largely doesn’t change much (although I do think this presents issues that I will cover in the next article in the series).

The current power of AI and the inevitable power of AI is extreme. Even beyond the power of labor automation (covered in Part 2) - the applications in weaponry, impersonation, mass social manipulation, etc make AI a large enough risk to not leave it in the hands of the tech companies that have already let us down and hurt society. For example, any AI weapon will not be a normal weapon - they will be almost assuredly unbeatable, it’ll be like the Manhattan Project all over, but accelerating a lot faster and far more dangerous. More on weapons a little later.

For context, remember, no private company owns or has ever owned even a singular nuclear weapon, and they cannot be deployed by a corporation.

It will not be long before whoever controls the most powerful AI systems will have the ability to control every other institution, organization, and individual. And in the meantime, the increasing power the corporations control can be used to prevent competition, sway public opinion, prevent backlash & regulation, censor citizens & journalists, etc.

As powerful as owning social media is, AI is exponentially more powerful. By the way, the companies who are currently leading, and are far ahead, in AI - also own social media networks. That combo is even more terrifying.

The leeway ceded with social media must not be repeated with something exponentially riskier. For all of the above reasons, I believe I have demonstrated the risks of corporately owned AI.

Why Nationalization Solves For This

Even ignoring the points made in Part 2 (Labor) - the points here alone, I believe, demonstrate the risks of maintaining private ownership of AI and therefore the need for nationalization.

In the USA, corporations are legally much harder to hold accountable for abuses than the federal government. The Bill of Rights directly limits government power and ensures protections for individuals, such as freedom of speech, due process, and protection against unreasonable searches and seizures. These constitutional guarantees provide a legal framework for holding the government accountable in ways that simply don’t apply to corporations, which are primarily governed by statutory laws and regulations rather than constitutional mandates.

This fundamental difference underscores why nationalization can provide greater oversight and public accountability. This is not to say the government is held to account enough, by the way - this is not the case but here are some reasons why they are held to account far more than corporations:

1. Fiduciary Duty: Corporations prioritize shareholders, not public welfare, limiting accountability.
2. Limited Liability: Corporate structures shield individuals from personal accountability.
3. Lobbying Power: Corporations influence regulations, often weakening enforcement.
4. Opacity: Corporations operate privately, unlike the government, which is subject to transparency laws like FOIA.
5. Legal Resources: Corporations use vast resources to delay or avoid accountability.
6. Public Oversight: The federal government is subject to checks, balances, and public scrutiny that corporations avoid.
7. Examples: Cases like Enron, Purdue Pharma, and the widespread and extreme corporate misconduct during the financial crisis show how corporations and those who run them evade meaningful penalties, even when their actions cause massive and unprecedented harm.

AI’s immense and accelerating power should be controlled by an entity that the people collectively own and can oversee with transparency. In the USA, that entity is the federal government; lest the entities who control it, control us.

In the next part of the series, I will continue making the nationalization case by talking about how the market is not the most efficient way to allocate resources or drive innovation in AI, especially when public welfare and safety are at stake: Nationalize AI - Part 4 - Inefficient

Nationalize AI - Part 2 - Labor

This is Part 2 in a multipart series on nationalizing AI:

• Nationalize AI - Part 1 - Defining The Problem
• Nationalize AI - Part 2 - Labor
• Nationalize AI - Part 3 - Corporate Power
• Nationalize AI - Part 4 - Inefficient
• Nationalize AI - Part 5 - Superiority
• Nationalize AI - Part 6 - Conclusion

Labor

Overall, my thesis regarding labor and AI, for the purposes of this article, is broken down between two major points - ethics and employment. There’s plenty of interchange and correlation between the two, however specifically breaking it up here will allow me to be more pointed, especially about a specific ethical problem.

Ethics of Stealing

A critical foundation of the recent developments in AI is the data that is used for training. The specifics of how it is used and it’s part of the process here is less important than the fact that this data is largely publicly available data generated by billions of humans.

Ethically, how is it acceptable to do the following:

1. Use data generated by billions of humans
   • A lot of which is copyrighted, by the way!
2. Create an AI system that uses that data
3. Profit by charging businesses and other people for use of the system
   • In reality, currently, a lot of the money is from venture capital, but the same ethical issue persists (or perhaps is even worse?).

Granted, a lot of the data is not generated necessarily by citizens of the USA - which is an interesting counterpoint; although my response to that is that should be globally owned instead of by any one nation. But for now, that seems far more impractical. Nationalization would likely be a critical first step towards globalized ownership, anyway.

While this is already a problem - profiting by using the free labor of others (who generated this data) at this scale - this only gets worse as the systems improve.

Billions of people contributed, and the CEO of OpenAI is driving a car around that costs 5 million dollars.

Unethical.

Employment

We do know, that these AI systems are useful for various tasks. I’ve used ChatGPT, for example, a lot of people have. It is useful! The usefulness is already resulting in increased productivity in a lot of industries.

Overall, as a society, we do want productivity to increase, so what is the problem?

Let’s take a step back and talk about, largely, non-AI related productivity gains. Other technologies, process improvements, etc. A critical issue we’ve been facing - which has led to historically bad inequality - is that productivity gains are not being met with the same % of an increase in wages, and it’s getting worse:

By the way - this is not strictly a partisan issue. Control of the presidency and congress have been both parties during this time frame (1979 - 2024), although it really started accelerating around when Reagan’s policies started (which Democrats & Republicans have refused to address / fix because of their corporate donors - but that’s a topic for a different day).

There’s many different angles to this, and it involves multiple groups in the upper class (shareholders, as well, for example) - but here is one example that is particularly stunning.

The two lines are realized vs granted compensation for CEOs represented as multipliers of the typical worker in their respective industries. The difference between realized and granted (the two lines) are not especially important for the context of this section. The main point here is that before 1980 - CEOs made “only” ~31 times the typical worker and now it’s over ~344 times as much. Note, the footnote in the image says this but this data is from the top 350 companies in the country specifically.

This is not a post primarily about income inequality - but it is critical to my thesis to show that productivity gains are not going to the average worker but to the members of the upper class.

Critically, I am not claiming nationalizing AI will solve this preexisting problem; but that it will prevent it from becoming worse due to the actions of private AI companies.

Effects on Employment

Short and Medium term

The effects on the labor market have already started. Increased productivity is common in many industries, as I mentioned before. As we know - the outcome of productivity gains are not resulting in rewards for workers, but rather increasing compensation for CEOs and shareholders. Nationalization of AI would ensure this is not the case.

While it is true that some segment of the affected labor force can shift, for now, this will not be the case forever.

Long Term

Despite my focus here on the current/short term effects on the labor market - it is my opinion that over some longer time frame, that AI will certainly be able to do every single job. This is a marked difference of AI versus new technologies in the past (printing press, internet, etc) which generated or shifted jobs in new or other sectors. An infinitely duplicatable, more-than-human-capable AI does not leave room for any job for any human.

The Long Term Is Likely Relatively Short

OpenAI is already talking about how AI could replace all / subsections of the labor force:

• “He’s going to be right there at the beginning of it, maybe even as things like AGI, we get there,” she (OpenAI Chief Financial Officer Sarah Friar) said on Tuesday, referring to autonomous systems that surpass humans in most economically valuable tasks. Source
  • The time frame being referred to here is within the next 4 years (!!!!!!)
• “I want the door open to everything,” Friar said in an interview, when asked about a recent report that the company has discussed a $2,000 monthly subscription for its AI products. “If it’s helping me move about the world with literally a Ph.D.-level assistant for anything that I’m doing, there are certainly cases where that would make all the sense in the world.” Source
• “OpenAI CEO Sam Altman and Chief Technology Officer (CTO) Mira Murati said last fall that AGI will be reached within the next 10 years.” Source
  • As previously mentioned, this is the point where most/all economically valuable tasks could be performed by AI.

This is coming soon.

Nationalization

To fully round out the earlier point on ethics for a second, it is wholly unacceptable that people and companies are getting rich by stealing from everyone and then benefiting from workers’ productivity gains and/or obsoleting their jobs.

In summary - the socialization of AI’s benefits, which in my view requires nationalization - is necessitated for these reasons:

• AI is already driving significant productivity gains in the labor market. However, as demonstrated earlier, the fruits of productivity gains, of course including those predating AI, have not been shared equitably with workers.
  • Over a longer period of time, it is clear that AI will make humans obsolete for labor.
• These systems are built upon the stolen labor outputs of everyone.

This is only part two of the series, but in my estimation - already - these issues alone prove my thesis on the necessity of nationalization.

In the next part of this series, we will explore how corporate consolidation of power further necessitates the nationalization of AI: Nationalize AI - Part 3 - Corporate Power

Nationalize AI - Part 1 - Defining The Problem

This is Part 1 in a multipart series on nationalizing AI:

• Nationalize AI - Part 1 - Defining The Problem
• Nationalize AI - Part 2 - Labor
• Nationalize AI - Part 3 - Corporate Power
• Nationalize AI - Part 4 - Inefficient
• Nationalize AI - Part 5 - Superiority
• Nationalize AI - Part 6 - Conclusion

As we’ve seen multiple breakthroughs in AI - I have been thinking a lot about the short, medium, and long term consequences. I have come to the opinion that, over some undefined but likely relatively short timeline, there are really two potential futures.

I do have a predisposition towards the nationalization of certain industries, particularly ones that are extremely harmful because they are run privately (health insurance is one such example, but hardly the only). While each of these industries becoming publicized would represent large quality of life improvements for the American people - AI nationalization dwarfs all the others by multiple times.

What do I mean by consequences? Well, I’d like to frame the discussion in these areas of concern.

• Effect on labor
• Corporate power
• Inefficiency of private AI
• AI Superiority

Now, obviously - each of these are extremely interdependent and so it is a bit hard to cleanly divide the supporting points of each section; but I will do my best.

Assumptions

The main assumption I’d like to put forth is that AI is powerful and will continue to accelerate. The time frame is in question, but the assumption is at some point we will have AI more intelligent and capable than any human or group of humans.

What could nationalization look like?

Well, at a high level - in my scenario, it could go something like this:

1. The federal government creates its own AI lab
   • The law must define the goals of this lab and regulations therein.
   • It would be critical that it is a program with transparency, oversight, and regulation more comprehensive than any existing program.
2. It outlaws private research on AI and/or direct ownership of certain / amount of GPUs/chips.
   • You’d also need to outlaw citizens working on AI in other countries.
   • This may feel radical - however there is precedent for such an action. Consider that any given private company or citizen can not develop a nuclear weapon.
3. Consolidate the existing AI labs under the federal government’s AI lab
   • The goals and research direction would likely change from the current private labs, but its goals and research direction would be clearly defined and transparent to the public.
4. Institute direct rebate programs to citizens (especially to anyone affected directly by job loss due to AI)
   • Perhaps direct rebate is not the correct way to go here, but the point would be to socialize the benefits of AI - however that looks.

My thesis, which I hope to convince you of by the end of the series, is that we must nationalize AI; and to a lesser extent, that it should look like the outline above.

In the next part of the series, we will discuss the effect of AI on the labor market and why it demonstrates a strong reason to nationalize: Nationalize AI - Part 2 - Labor

Jon Stewart & Bernie Sanders on Rebuilding Trust & Efficacy in the Government

Based on the knee-jerk reactions on social when I posted my last Jon Stewart must-watch - Jon Stewart - Why Men Are Leaving the Left - there are a lot of people who really don’t have much respect for Jon Stewart at this point. And I know that is also the case with Bernie Sanders, as well, especially in a specific part of the democratic party.

I was also out on both of these guys until recently. I don’t believe Bernie would have won, necessarily, but I do subscribe, now, to the idea that politics now is less about left vs right but really establishment vs anti-establishment.

The points talked about in the video are critical, in my opinion, to the election loss. I don’t 100% agree with everything, but largely - I find the analysis correct.

If you watch, I request you watch with an open mind, and of course - I don’t expect you to agree!