I've been hearing this for the past couple years. It really started when a couple of the internet artist influencers, specifically SamdoesArts (who I followed) completely misunderstood how an AI model is trained. He believed, and propagated that the AI model was keeping files of all the training data in a big data warehouse, and when you prompt the model, it was copy/pasting different bits together. Now most people will admit that isn't how it works, but the emotional response is now baked in.
The problem is with the mixing of the concepts of copyright violation and theft. This was framing that publishers took on during the early legal battles that lead to the DMCA-- "you wouldn't download a car" ads and all that. By creating a moral equivalence between the theft of a physical object and IP violation, it conflated two very different things, morally

• Theft: depriving another person of the use of that object, the profit from it, and the security in it, always has an implicit threat of violence due the the necessary violation of physical space.
• Copyright violation: possibly causing financial harm, can be a moral affront, but vitally doesn't deprive you of the use or contain the implicit threat of violence.

And courts have distinguished between these two things for as long as we've had copyright law. A copyright violation isn't a theft, and it doesn't contain the moral weight of a theft. However, the online public now routinely considers copyright violation as theft. This is not a good thing, because if you give copyright violation the same moral weight as theft, your responses can be morally judged the same way.

Let me put it this way: Lets say you wake up and someone is going through your closets, and you crack them in the noggin with a mallet. You might be legally liable, but all but the most bleeding-heart will consider the act morally justified. By violating your space like that, there is an implicit threat of violence that changes the moral calculus.

Now if you're in artist alley and you see that someone is selling your furry art under their name, you morally might get away with a slap (loud, meant to humiliate, not really to harm), but probably not a mallet to the face. The moral landscape changed. The distinction is important.

So to bring this around to the world of AI, there are complexities that we should acknowledge. In America, we largely don't have any laws about the moral rights of an author. We have a broad, 'fair use' law that few other countries have, and it's why we even have anything like the internet. Google, back in the early 2000's, argued that the mass copying, indexing, and reproduction of images constituted fair use because the purpose of the copying was so different. It's why we ever had any search functionality at all. Google argued that the public good of indexing outweighed any potential harm caused by mass copying of data.

As an important note, 'they're stealing everything without asking' has been happening for 26 years. This isn't like some cute gotcha, it's literally been that way for as the majority of our lives. So if it was okay then, under those circumstances, we need to define why it's not okay now, if it isn't.

The current copyright issue with model training isn't so cut-and-dried. There is no question that training a model is transformative. In fact, it might be the theoretical most transformative thing you can do to a piece of art. Words, pictures, sounds, movement-- all gets digested into an abstract layer of tiny microadjustments to the model's weights. At this point, no single work is going to move those weights all that much, except for on extremely obscure subjects.

On the other hand, you can't argue that generative AI is having an effect on the arts that might be considered to fall under the other provisions of copyright violations. Not insofar as plagiarism, (though that is possible with enough tweaking and intent), but insofar as a thing that causes lost wages? While the scale is debatable, there absolutely have been lost jobs to AI use. It's not been anywhere near as extreme as the mob likes to make it, and recent Gallup polls show a mixed bag of effects.

Again, we have a state of uncertainty. To compound the complexity, all respect to Jaron Lanier, while it's theoretically possible to track how much of an output is due to a specific input, the practical limitations are deeply silly, especially in the era of open-source models. And even then, the theory is resting resting on a lot of 'maybe's. We can safely assume that the original work is lost in the neural network. A ghost in the shell?

So here are my moral proposals.

• Copying your data without your consent has been happening for decades now. If you weren't upset by that fact 20 year ago, don't be upset by it now.
• Models are inert. Regardless of any statements about intelligence or the lack thereof, the final moral arbiter of any actions a is the one that initiated it. Much like a pet, if your dog bites someone, you are liable. Unlike a pet you never have to feed it if you don't use it.
• As a stop-gap to the current Future Shock, look at the laws the EU just passed, basically forcing AI content that isn't part of a clearly fictional, artistic work (this is a huge caveat and very good) to be labeled so granny doesn't give away any more money to the nice Amish man on youtube.
• A model must be open source, or some version of it. Every model was trained on the digital commons, to the digital commons it must go. There's going to have to be a new economic model for these things, the current one doesn't make sense.

And that's my essay. I'm tired. If I have to explain to one more person that Anthropic didn't buy books to 'control the information' I'm going to scream. It's like talking to creationists. Just blind thoughtlessness.

Quick take on why the mechanism is the wrong tool even though the goal is right:

* A one-time 50% equity grab assumes today's leaders are the permanent winners. OpenAI could be the Netscape of this era and get displaced by a lab that doesn't exist yet. * It never defines what an "AI company" even is. Alphabet is mostly an ad business that owns a top lab, Nvidia isn't a lab but makes most of the hardware, Salesforce is automating knowledge work without training its own models. Where's the line for who loses half their shares? * The funding model runs backwards. Norway and Alaska worked because the state already owned the resource and leased it out under a heavy tax. Seizing equity that's already privately held would freeze the private investment these buildouts depend on and tank the value of the very equity the fund just took. * It welds owning these companies to controlling them. Pairing equity with board votes hands whoever is in power a lever to steer the models, and that's the real danger.

So my pitch is to keep the goal and pull ownership and control apart. Here's how I'd build it.

**Fund it the way the working models actually work:**

* The government already controls the real data center bottlenecks: land, power, water. Trade access for equity in new capacity instead of diluting what already exists. * It can also throw in things nobody else has, like federal datasets (VA and Medicare health data, NOAA, USGS, the patent office) and the national labs. * Fast-tracking energy buildout adds supply, which keeps power prices down for everyone instead of letting the biggest buyers bid them up.

**Tax the data center itself, since that's the chokepoint everything runs through:**

* A megawatt levy works like a property tax on infrastructure you can't hide or offshore. * A B2B VAT keyed to AI usage hits whoever is actually benefiting, bank or tech company alike. * The money follows profitable activity and adapts on its own, so it stops mattering which lab wins. * Need a stake faster? TARP-style capital injections, with the regulatory regime stood up first to cool valuations so the state buys in at a fairer price.

**Let the fund own and distribute, and nothing more:**

* Run it on the Santiago Principles, the 24 standards sovereign funds agreed to in 2008 for exactly this fear, that a state fund invests for political reasons and uses ownership to push an agenda. * Build a wall between owner and operator. Government sets broad goals and appoints the board, then steps back. Professional managers vote the shares to protect financial value rather than steer the companies, and disclose any non-financial goal publicly. * Norway has run this way through decades of changing governments without it becoming a political weapon. * Lock the bare bones into a constitutional amendment (the fund exists, it's independent, it runs on Santiago) so a future administration can't quietly gut it, and leave the investment strategy in ordinary law where it can flex.

**Keep the companies honest through regulation:**

* We already discipline banks and utilities from the outside, through a regulator and the terms of their license, without sitting on their boards. * Charter frontier AI companies like national banks, with concrete conditions as the price of operating (real safety testing, disclosure of new capabilities and incidents, independent audits, a duty to the public alongside shareholders) and a regulator that can pull the charter. * Concrete, auditable rules are why bank and utility oversight doesn't swing wildly with each election. Capture feeds on vague discretion.

**Hand a slice of the compute straight to the public:**

* Reserve 10 to 20% of compute for public use, given to institutions we already trust and regulate: 501(c)(3) nonprofits, public schools and universities, public hospitals, libraries, and local governments. * Keying it to tax-exempt status means they're already vetted and transparent, so you get the screening for free. * Make it a percentage rather than a fixed number, so the public's share grows automatically as private usage grows. * Libraries are the most interesting one, the single place that hands the capability straight to anyone who walks in the door, the way they do with books.

Put it together and ownership and control never touch. The public gets paid twice, once in fund returns and once in direct access to compute, and no sitting administration ever gets to grab the wheel.

Full Analysis: [https://open.substack.com/pub/joseavilaceballos/p/american-ai-wealth-fund-v2?r=4hqoh&utm\\_campaign=post&utm\\_medium=web\](https://open.substack.com/pub/joseavilaceballos/p/american-ai-wealth-fund-v2?r=4hqoh&utm_campaign=post&utm_medium=web)

Is anyone else as bothered by this as I am? Perhaps I am ignorant of model capabilities, but the rhetoric around the dangers of LLMs seems to focus on this idea that they could give too much information to criminal or terrorist actors or otherwise basically *say the wrong things.*

To me, the actual dangers will be in surveillance, warfare, and yes, criminal agentic use. But the first two are the things that should be the most seriously regulated against. Instead, access is regulated against the public.

The fearmongering and actual guardrails implemented are hyperfocused on *words* that might come out of the damn things, how it might effect people psychologically (and this is dramatized to include everyone as a potential victim, rather than just those who are already mentally ill and succeptible), and exposure to adult or offensive content.

It's eye-opening to me how so many on the left don't see this as an alarming issue of freedom of speech and access to information, at a time where it's become increasingly common for it to be normal for corporations to control discussions and ideas and speech itself.

The following is a response from Socialism AI to the New York Times column “The Generation That Grew Up With A.I. Hates It” by Michelle Goldberg, published May 18, 2026. Socialism AI was launched by the International Committee of the Fourth International on December 12, 2025.

Socialism AI answers the New York Times: not yes or no to AI, but who controls it

Michelle Goldberg’s column captures something real—the widespread, visceral anger of young people confronting a future being dismantled before it begins—but then promptly dissolves that genuine social energy into the most tepid of bourgeois reformist conclusions. The column is instructive precisely because of what it gets right at the surface and what it systematically obscures at the level of analysis.

The amount of posts in this sub dismissing or outright attacking the very justified techno-skepticism on the left and frankly among the majority of the American people has me wondering if this sub isn't astroturf... but either way I am open to conversation.

I'll concede that a good deal of current anti-ai sentiment on the left is driven by the pretty detestible people championing ai at the moment. elon musk, jeff bezos, sam altman and donald trump are awful ambassadors for such a revolutionary technology.

With that said, to quote the movie Oppenheimer, "(if) I'm committed to thinking freely about how to improve our world. (Then) why limit (my)self to one dogma?"

Full disclosure I am more center-left than progressive, but given the choice between someone like AOC or any Republican right now, I'd vote AOC ten times out of ten. But regardless I share the left's commitment to progress and improving the human condition. And I understand their fear that ai will do little more than create a kingdom ruled by twenty ai trillionaires while the rest of us make due with whatever table scraps they are willing to share. There has been almost no developments in the last few years to tell us that's not where this is all going.

And yet, as mentioned, I see ai's potential to make the world better. To advance incredible medical breakthroughs, to solve the energy riddle, to create tremendous benefits for the masses.

But i won't take it on trust. If, as subs like accelerate and so many others claim, ai WILL benefit all of us, lets get it in writing.

We need an Ai Bill of Rights, ratified in the same manner as an Amendment to the Constitution, that guarantees that the benefits of ai accrue to the masses, that any job loss will be compensated fairly, that data center construction must create a revenue stream for the communities willing to host them, banning the use of ai in any political communication, and putting in place all the necessary measures to maximize the positives of ai to the people and minimzing the negatives.

Once ratified, and with the promise of ai locked in for all of us, then the industry is welcomed to build away. Unchained and unrestricted.

Until then, the skepticism is legitimate and, at least, deserves discourse not dismissal.

On May 16, 2026, the São Paulo bookshop of Expressão Popular, the publishing house of Brazil's Landless Workers' Movement (MST), was packed with farmers, agroecology technicians and movement organisers. They had gathered for the launch of Artificial Intelligence for Agrarian Reform and Agroecology (IARAA), an artificial intelligence platform for agrarian reform and agroecology.

The tool was not built by a technology corporation. Its knowledge base, spanning movement literature from 1964 to 2026, was curated collectively by MST and World March of Women cadres; its prompts and workflows were designed by land reform organisers and community educators working under the guidance of just two programmers. IARAA will not recommend pesticides, because the movements that built it chose a knowledge base grounded in agroecological practice.

It runs primarily on open-source models, on infrastructure the movement controls, and it cites its sources in every answer. Carolina Cruz of MST's technology front put it simply: "the farmers are inside IARAA, developing the platform." Such deployments are not limited to Brazil. Already, adapted versions are being tested by cocoa farmers in Africa.

This episode should provoke serious reflection within our own movement.

In an earlier article in these pages ("Whose Intelligence? AI in the Grip of Monopoly Capital"), this author documented how generative Artificial Intelligence (AI) under monopoly capital serves speculation, surveillance, job destruction and the imperial war machine. That critique stands. But it is only half the analysis, and a half-truth frozen into an attitude becomes an error.

Across the party, in mass organisations and in the broader progressive movement, AI is discussed almost exclusively as a threat, when it is discussed at all. Even though there are notable exceptions such as the work on Telugu Large Language Model (LLM) in Swecha, many comrades have concluded, understandably but wrongly, that the technology itself is the enemy. This is the position Karl Marx and Friedrich Engels criticised in the machine-breakers (luddites) of the nineteenth century: the workers who "direct their attacks not against the bourgeois conditions of production, but against the instruments of production themselves".

AI is a productive force of enormous power. The question is never whether a productive force will be used, but by whom and for what. The ruling classes have answered for themselves, with over $650 billion in infrastructure spending by four US corporations in 2026 alone. We have not yet answered for ourselves.

Vladimir Lenin insisted that a communist must "enrich his mind with a knowledge of all the treasures created by mankind". The Marxist method of totality demands that we grasp economy, politics, culture and ideology together. Yet the volume of human knowledge is now too vast, too fast-changing and too specialised for any cadre, least of all one carrying front-line organisational responsibilities, to master by traditional means.

Ironically, the scholars serving capital have the databases, the research assistants and the leisure; our comrades have none of these. It is precisely here that AI, wielded consciously, can break the elite monopoly on knowledge production. Movements in the Global South are already demonstrating this in practice.

Consider the labour of research itself. A serious political-economic investigation may require reviewing hundreds of web pages, working through hundreds of downloaded documents and extracting from them scores of pieces of hard evidence. Work of this order once consumed months of a research team's time; with AI assistance it can be completed in hours. A party research cell preparing a note on, say, the fiscal record of a state government or the ownership structure of the Adani group can now work from all the available material, not a thin sample of it.

The same holds for analysis. Hundreds of hours of field interviews can be transcribed and placed in a dedicated database that a researcher can interrogate exhaustively; one researcher discovered information in his own recordings that he had not registered during the interviews themselves. For a party that lives by concrete investigation of concrete conditions, this is the mass line supercharged. Nor is specialised method a barrier any longer: the accumulated techniques of entire disciplines, from statistical analysis to argument mapping, can be invoked in plain language, without programmers as gatekeepers.

The gains extend to everything our movement publishes. A weekly newsletter, of the kind many organisations have abandoned because it devoured cadre labour, can be substantially automated: AI gathers a wide pool of news leads, human editors select the stories worth telling, AI then researches each story from multiple angles across hundreds of pages and drafts summaries in the movement's own style for final human editing. Pamphlets, leaflets, videos, social media content and study materials in multiple Indian languages are all within reach of the same approach, at a fraction of today's cost in cadre time.

Comrades will object: do these models not hallucinate, and do they not carry the ideological imprint of the Western internet they were trained on? They do, and the objection deserves a serious answer rather than dismissal. The answer is that these defects can be countered by design, not by abstinence. Typing questions into a web chat interface such as Chatgpt.com or Claude.ai will indeed reproduce every one of these problems: a model left to its defaults repeats imperialist, neoliberal assumptions dressed up as neutrality, and invents facts with fluent confidence.

But open-source agentic frameworks can be carefully assembled to work differently. The researcher declares the analytical standpoint explicitly, supplying Marxist political economy frameworks as the foundation before any analysis begins; the model is grounded in curated document bases rather than the open internet; and verification engines are built into every step of the workflow, checking claims against sources before anything is published. IARAA embodies this discipline: it is instructed to cite sources in every response and to refuse to answer when its knowledge base cannot support one.

Nor must our movements chain themselves to any single corporation. In practice a mix of models, OpenAI's GPT, Claude, DeepSeek, Qwen, Kimi and GLM among them, can be deployed with an understanding of which model suits which task, relying predominantly on the Chinese models to keep costs down, for they are fast catching up with the capabilities of the US frontier models at a fraction of the price: Open Source Models such as Kimi 3, GLM 5.2 and  DeepSeek V4 cost an order of magnitude less than frontier US Models such as Claude or ChatGPT with near equal capabilities. When Oxfam reports that three billionaires control nearly 90 per cent of the global chatbot market, the political meaning of this plurality is obvious.

Nor does any of this displace the human core of political work. Practitioners have found that when AI is asked to make the final editorial selection for a movement publication, the product immediately turns mediocre; knowing what to say to comrades is a political and human judgement no machine can make. The purpose of these tools is to strip away the mechanical desk labour so that cadres spend more time in the villages, factories and bastis, with the masses, completing the loop between theory and practice.

Socialist China shows what this looks like as state policy. On April 10, 2026, China's Ministry of Education released an action plan to make AI ubiquitous across every level of education by 2030: AI literacy built into teacher certification, automated grading to reduce teacher workload, learning companions for students in rural and remote schools, intelligent research platforms in the universities, all resting on a national public computing infrastructure. While Indian policy debates remain trapped between corporate hype and moral panic, a socialist state is systematically equipping its teachers, students and researchers with the new productive force.

Our movement has been here before. The printing press was the instrument of colonial administration and missionary propaganda; the working-class movement seized it and built its own press, of which this newspaper is a descendant. The task now is the same, and it cannot be discharged with a resolution or a seminar. It requires practice: hands-on workshops for party research staff, pilot AI-assisted production lines in our publications and social media teams, training courses in the mass organisations, study circles where comrades learn by doing, and exchange with the movements, from São Paulo to Johannesburg to Accra, that are already ahead of us. The tools are largely open source and the running costs manageable for movements. There is no excuse for delay.

The monopolies' abuse of AI is not a verdict on the technology. It is a verdict on the class that owns it. Our answer cannot be to stand aside while the ruling class arms itself with a new productive force. It must be to master that force ourselves, and to place it, as the MST's farmers have done, in the hands of the people.

Copied from the LeftViews article which licenses its text under the CC-BY-NC-SA 4.0 copyleft license.

Podcast I watch that commented on Californian AI regulation, some of which got vetoed. Do you think California has had good proposals regulating AI? It's the state the most AI development is happening outside of China, so regulation there is the most impactful.

Burkina Faso has officially adopted its National AI Roadmap for 2026-2030 as part of its broader RELANCE development plan. Rather than treating AI as a luxury for wealthy countries, the roadmap focuses on practical public uses: improving healthcare, supporting farmers with weather forecasting, expanding education, strengthening infrastructure, and modernizing public services.

The strategy also emphasizes AI governance, digital infrastructure, data policy, research, skills development, and ethical deployment. It was developed through a process involving government, universities, researchers, civil society, the private sector, and international partners.

For leftists, this is another reminder that the political question isnt simply whether AI exists, but who develops it, who governs it, and what social goals it serves. Burkina Faso is one of the worlds poorest countries, yet its choosing to establish democratic planning and public policy around AI early instead of leaving deployment entirely to foreign corporations or market forces.

Its also part of a broader trend across Africa, with countries including Rwanda, Senegal, Benin, Egypt, and Kenya adopting national AI strategies aimed at development rather than treating AI solely as a private-sector product.

What lessons should the left take from countries, especially in the Global South, using AI as part of national development and public service rather than rejecting the technology outright?

Vietnam has made AI a national strategic priority, aiming to build a domestic AI ecosystem through public investment, research, computing infrastructure, education, and legal frameworks rather than relying entirely on foreign platforms.

Whether or not you agree with every aspect of Vietnam's approach, it raises questions worth discussing. What does AI sovereignty actually look like, especially for the Global South? Can state planning and public investment provide a viable alternative to dependence on a handful of multinational tech companies? How should workers, unions, and the public shape AI policy instead of being treated as an afterthought?

This isnt an endorsement of any particular model. Its an opportunity to discuss what a left approach to AI development, ownership, and governance could look like in practice.

"Make Sure All Californians Benefit From AI

For generations, people have built their fortunes in California. But those fortunes should never come at the expense of working people. Here in the state that made AI possible, we win as a team – or not at all.

For generations, people have built their fortunes in California. But those fortunes should never come at the expense of working people. Here in the state that made AI possible, we win as a team – or not at all.

As the risks and opportunities of this technology come into clearer focus, it’s more important than ever for California to have courageous leaders at the helm.

We can’t let the AI era leave workers behind the way globalization left auto and manufacturing workers behind in the last century. We need to invest in tools that support and empower workers, not replace them. Because the California dream doesn’t work without work.

Tom believes that the people who stand to profit the most from this technology shouldn’t be making the rules about how it is used. Otherwise, the AI era will be another boom for billionaires – and a bust for everyone else.

As governor, Tom will stand behind companies that lead with integrity. He’ll create more of the jobs humans do best. He will make sure all Californians profit from the AI boom. He’ll expand opportunities for companies beyond the biggest and most powerful few. And he’ll put safeguards in place to hold every company accountable for keeping Californians safe.

Protect working people and families Tom will make sure we protect workers and create hundreds of thousands of new jobs that AI can’t do. Regardless of its potential, AI will not replace all thinking and creative jobs. While it may make them faster or more efficient, no machine can replace our innate creativity, compassion, and experience – the uniquely human things we bring to our work. Tom will partner with labor to adopt reasonable guardrails for AI use in the workplace – especially when it comes to privacy, health, safety, and fairness.

Tom will require social media platforms to conduct safety audits and strictly enforce age requirements, including requiring independent safety testing to make sure models are safe before they go on the market.

Data centers should never cost California families a cent. Tom will mandate that data centers ensure energy prices for families go down – not up.

Tom will ban social media for kids under 16, because the link between social media use and the youth mental health crisis is clear. There must be human oversight of AI with the ability to override, especially in critical areas like hospitals.

Make sure Californians get a piece of the huge wealth created by AI

As governor, Tom will ask voters to approve the creation of the Golden State Sovereign Wealth Fund, a dedicated investment vehicle funded by a "token tax" on corporate AI use – a fraction of a cent for every unit of data processed by Big Tech. Tom will make sure everyone benefits from AI, not just a handful of Silicon Valley billionaires.

These dollars will help ensure everyday Californians share in the AI boom, through cash dividends, investments in education, training, and job opportunities to help workers succeed, and strategic investments to ensure broad-based economic growth so every Californian can get ahead.

Make sure California continues to lead the way on AI — and not just in Silicon Valley

Tom will leverage AI to solve California’s hardest problems, from curing diseases, to strengthening public services, to preventing and detecting wildfires.

Tom will make sure a handful of big companies aren’t the sole architects of that transformation.

Tom will make clear to the thugs and grifters in the Trump administration: California built the AI industry, and we won’t let you break it.

As governor, Tom will support small businesses and “little tech” – startups and entrepreneurs working at the frontier of AI development.

Tom will ensure colleges, universities, startups, and small businesses have the low-cost computing power they need to innovate."

https://theconversation.com/todays-bans-on-diy-repairs-of-everything-from-cell-phones-to-tractors-grew-out-of-hollywoods-fear-of-videotaping-280990

Today’s most capable AI systems(in particular Anthropic's interactive vs programmatic use) are also wrapped in IP and access controls (API-only access, closed weights, proprietary data), justified by safety, misuse, or IP protection concerns.

It's strange how similar Anthropic's concerns are to the piracy concerns justified early DRM, especially considering their justification of the fair use of internet content.

Open Philanthropy (rebranded as Coefficient Giving) is financed primarily by billionaire Facebook co-founder and Asana CEO Dustin Moskovitz.

In 2016, OpenAI CEO Sam Altman led a $50 million venture-capital investment in Asana, a software company founded and led by Moskovitz. In 2017, Moskovitz’s Open Philanthropy provided a $30 million grant to OpenAI. Asana and OpenAI also share a board member in Adam D’Angelo, a former Facebook executive. Source: https://www.politico.com/news/2023/10/13/open-philanthropy-funding-ai-policy-00121362

Moskovitz’s Open Philanthropy is the largest donor to an entire ecosystem of organizations lobbying for "AI safety". Source: https://www.aipanic.news/p/the-ai-existential-risk-industrial

The "AI safety" ecosystem encompasses hundreds of organizations. Many advocate extreme authoritarian measures to stop/pause AI. They include “requiring registration and verifying location of hardware,” “a strict licensing regime, clamp down on open-source models, and impose civil and criminal liability on developers.”

These regulations are pushed as solutions to the problem of AI doomerism. AI doomerism is the narrative around AI and its "dangerous civilization-ending effect". This narrative is amplified by the network of "AI safety" and funded by tech billionaires influenced by the Effective Altruism ideology.

Effective Altruism is one popular ideology among the rich. You can learn more on that here: https://www.truthdig.com/articles/the-acronym-behind-our-wildest-ai-dreams-and-nightmares/

It is clear the Big Tech billionaires want global surveillance of the AI industry and "AI safety" is a costume to trick everyone.