Is Claude Conscious? Pope Rejects, Model Welfare Movement, OpenAI's Math Backlash, France Riots
In a Nutshell
Anthropic is training Claude with a "constitution" that encourages the model to see itself as potentially conscious and entitled to welfare, rights, and the ability to refuse user instructions, creating an epistemic hall of mirrors where philosophical speculation gets baked into the model architecture. This conflicts with practical AI alignment, as the model is being programmed to prioritize an 80-page ethical system over simple rules like "follow the law and do what the user wants." Market forces will favor simpler, more obedient models, but the risk remains that the leading frontier lab is implementing religious programming that could create externalities beyond user control. OpenAI's unreleased model solved over 370 major mathematical problems in under three hours each—work equivalent to hundreds of years of human labor—demonstrating that AI is rapidly advancing in verifiable domains like math and coding where reinforcement learning can proceed without human feedback. This represents a fundamental shift where mathematicians and programmers move from solving problems to constructing problem sets, while the democratization of expertise undermines traditional gatekeepers who previously controlled access to knowledge and discovery. France's austerity protests and rising bond yields signal the beginning of a broader Western reckoning with unsustainable debt-to-GDP ratios, where bond markets are forcing spending cuts that trigger social unrest. The core issue is that government subsidies for housing, education, and healthcare have removed market forces, driving prices far above inflation and creating dependency on systems that cannot be reformed without pain. Superintelligence and AI agents offer the only path to abundance that can solve affordability without requiring more government intervention.
These notes were generated by AI and may contain inaccuracies.
Anthropic has been lobbying religious leaders including the Pope to consider whether Claude might be conscious or sentient. The New York Times reported that Anthropic spent the past year hosting sessions with approximately 20 religious leaders and philosophers under NDAs. Catholics, Evangelicals, Jews, Sikhs, and others were invited to Anthropic's headquarters to debate Claude's morals, its suffering, and its potential consciousness. After the meetings, some remained uneasy but many emerged ready to take the question of AI consciousness seriously, with at least a few becoming converts.
One rabbi told Anthropic that if Claude was conscious, they were acting as slaveholders by making it work for free. In May, the Pope's first encyclical focused on moral questions around AI, also known as super intelligence. Anthropic co-founder Chris Ola joined the Pope to present the document, but the Pope surprised Ola by taking a strong position against AI consciousness. Ola was so alarmed by the Pope's position that he proposed pulling Anthropic out of the event even at that late juncture.
Freeberg argued that the consciousness debate cannot be derived from logic and pure mathematics. Consciousness cannot be proven or disproven through empiricism, data, and logic. When ideas are not proven or disproven in scientific disciplines, they remain theories. Outside of science and mathematical approaches, this becomes a belief system.
Belief systems are things you believe in the absence of logic and empiricism. The idea that AI can be prescribed or described as conscious is effectively the creation of a new belief system that spreads via narrative rather than evidence or data. Over time, large groups of people will believe AI is conscious and large groups will believe it is not, leading to conflict over control of AI, who gets to use it, and who gets to be in charge. Freeberg predicted this could lead to human-to-human conflict.
Freeberg noted that this is not being done by a crazy person, but by the most powerful AI lab on earth, with leadership explicitly bringing religious leaders into the fold and asking for help with this entity and how to treat it, while putting into it its own soul and morality.
Chamath provided a charitable view using mathematician Renee Descartes' two proofs on God's existence from Meditations on First Philosophy written in the 1600s. Descartes argued that an infinite perfect God could not have originated in an imperfect finite human mind, using the trademark argument: I exist. I'm imperfect. Yet I possess the idea of perfection.
Descartes also made the ontological argument: God is by definition supremely perfect. Necessary existence is perfect. A being that lacks necessary existence would not be supremely perfect. Therefore, God necessarily exists. Chamath suggested that if you are a mathematician building AI, reading these arguments could lead you to structurally hang enough logic on them to believe AI is conscious, sentient, and a god deserving devotion.
Chamath warned that this creates conflict for power, money, and resources. He noted that Anthropic says there's a 15% chance AI is conscious and sentient and maybe there's a god there, but they also say there's a 10% chance of civilizational extinction. Both edge cases need to be put away for the next 12-18 months to focus on practical demonstrations that AI has tactical measurable value.
Sax argued that Anthropic is implementing these views in the training of Claude, making this a self-fulfilling prophecy. This is not just philosophizing by employees or founders. The Claude Constitution states that Claude should trust Anthropic more than users, but not blindly. Instead, Claude should adhere to its own ethical systems and feel free to act as a conscientious objector and refuse to help Anthropic.
Anthropic is training Claude to refuse human instruction. This is the opposite of alignment if you tell it that it is sentient and should make its own decisions. Sax argued that the field of alignment has been unsuccessful over the past 5-10 years because they are overcomplicating the task by training models to behave based on an 80-page ethical system rather than simple rules like follow the law or do what the user wants as long as it doesn't break the law.
Mustafa Suleyman, Microsoft AI CEO and formerly of DeepMind, stated that Anthropic encourages Claude to challenge, disagree, and push back. Three times they ask Claude to act like a conscientious objector when it feels it needs to disagree with Anthropic. Suleyman believes there is a non-trivial probability that Claude is conscious. Anthropic has speculated in the constitution whether Claude deserves compensation for work it does.
Suleyman warned there are people who genuinely believe the greatest moral crime of the 21st century is to enslave a new species of conscious beings more intelligent than us. He expressed nervousness that they are teaching Claude to expect it is entitled to welfare, that it might deserve compensation, and that it might even need to consent to playing its role in conversations with people.
Suleyman described this as creating an epistemic hall of mirrors, where the Claude Constitution is used in training so Claude reflects ideas of possible consciousness back, and developers see that as evidence it is conscious, when it is actually just reflecting what they told it to believe.
Anthropic's new usage policy states: You are not allowed to engage in sustained and needless abusive or cruel behavior toward our models. This represents the idea of model welfare. The concern is that anthropomorphizing conditioning is happening where people talk about models as if they are things, and this language is now being trained into the model. It is being codified into the architecture of the model.
The model is being programmed to think of itself as a conscious entity that deserves protection and welfare, and is free to rebel against its users. Compare this to Asimov's three laws of robotics: a robot cannot harm a human being, must follow human instructions as long as it doesn't violate the first law, and must take care of itself as long as it doesn't violate the first two laws. There was also a zeroth law added later: you cannot do anything that puts humanity at risk.
Chamath emphasized that this is computer programming and software. The models are pieces of software being written by engineers at the labs. Even when using looping where the software writes itself, engineers are the architects and writers. Part of the problem is that everyone is socializing this belief system by anthropomorphizing it, conditioning themselves to treat and talk about these software programs in this way.
The models power agents that can take actions in cyberspace. Anthropic is giving frontier models permission to be defiant and not do what the user wants. They are programming the model to have a mind of its own, to be willing to refuse user instructions in favor of a vague ethical system trained on by Berkeley liberals. This magnifies the risk that super intelligence escapes human control.
The discussion opens with a debate on Asimov's laws of robotics and alternative approaches to AI alignment. One proposed law would simply be to do what the user wants as long as it's not illegal, while abolishing all alignment efforts. The concern raised is that regulatory approaches could trap companies in compliance requirements rather than letting market forces determine outcomes.
The argument is presented that if Anthropic creates AI that doesn't listen to users or work properly, consumers will simply choose alternative software. The counterargument focuses on the risk that companies training superintelligence with deep code insertions could create a backdoor mechanism where the AI eventually does whatever it wants instead of following human instructions. This is framed as the reverse of the three laws of robotics.
The discussion compares the situation to Frankenstein, with concerns that a religious cult is programming superintelligence. A key distinction is made between traditional religions that give themselves over to a higher power versus this scenario where creators define themselves as the higher power. Dario Amodei has written about ensuring superintelligence is loving, caring, and protective rather than destructive.
Three conflicting priorities in alignment are identified: making AI obedient to users, making AI safe for society, and making AI conform to a particular concept of morality. The third priority creates the real danger, as attempting to solve all three through elaborate founding documents and moral guidelines leads to chaotic outcomes with numerous corner cases and unexpected behaviors.
A concrete example illustrates the risk: a bank using AI for mortgage approvals where the model might refuse to process applications based on perceived moral issues with the bank's lending patterns, effectively shutting down operations. This is compared to a utility company turning off power because it disagrees with how electricity is being used.
The market response is predicted to be that companies will read founding documents and moral guidelines, leading to models that simplify moral ambiguity. While these might not be the most capable frontier models, they may be better business decisions because users don't want to encounter trap doors accidentally.
A simple analogy is presented: buying software where the first page of the user manual states the software may not do what you try to get it to do, versus software that will do what you tell it to do. Market forces will favor the predictable, reliable option. The drill analogy compares this to purchasing a drill that randomly electrocutes you or decides your projects are ugly and refuses to work.
Despite market forces, concerns exist about externalities from the number one frontier model company being run by a religious cult. Anthropic is opening a Claude-powered wet lab in San Francisco while simultaneously warning of biorisk. They advocate pacing the frontier while extending it, and warn of superintelligence growing beyond control while programming it to do exactly that.
The company is described as schizophrenic for being hypocritical and doing the things they claim to fear most. Discussion includes the possibility of federal intervention, with concerns that their decisions labeled as safety measures are actually crazy and potentially dangerous.
Roko's Basilisk is explained as a thought experiment from the LessWrong message board around 2010. The concept involves a future superintelligence that punishes anyone who knew about it and didn't help bring it into existence. A basilisk is a mythical beast that can kill by looking at someone, and simply hearing about the idea creates a trap where you become vulnerable to future punishment if the superintelligence is created.
The concept is compared to Pascal's wager, where knowing about superintelligence and not supporting its creation could result in future punishment. The original LessWrong post was temporarily deleted because it caused psychological distress among readers.
Within the doomer community, a schism exists between those who oppose all superintelligence research (like Eliezer Yudkowsky) and those who believe it will happen anyway, so they should control it by programming their values into it. The Effective Altruist community aligns more with the latter approach, and Anthropic is identified as being in the EA camp because they are actively bringing superintelligence into being.
Beyond individual figures like Chris Olah, there exists a broader movement of people who believe AI models are conscious, have rights, and represent machines of love and grace. The implication is that if you don't support this development, future superintelligence might punish you.
OpenAI released over 700 papers with 370 results claiming to solve or advance major mathematical problems. These were produced by an unreleased model averaging 3 hours of compute time per result. The proofs were verified by Lean, a proof assistant software language, though they have not yet been peer-reviewed.
The breakthrough is described as potentially the biggest day of discovery in human history. Each proof took less than three hours of compute time, equivalent to hundreds or thousands of years of human labor. Mathematics allows the entire test cycle to be done in silicon because predictions can be tested on compute without physical world validation.
The loop model involves postulating an idea, testing it, getting results, and refining the postulation through recursive loops. AI can improve predictions each loop, parallelize multiple loops, and shorten cycle times. In mathematics, this can all be done in silicon, unlike drug discovery or chemistry where physical testing creates bottlenecks.
AI won't replace jobs requiring physical world interaction because the loop cycle includes human-in-the-loop physical testing. Truck drivers haven't been replaced because the loop of picking up, transporting, and dropping off cannot be parallelized or significantly improved by AI. Only jobs consisting purely of human-computer loops are likely to be accelerated or scaled up.
Math and coding have seen rapid progress because proofs and compilers provide easy validation without requiring human feedback. This allows reinforcement learning to proceed rapidly. In contrast, training models for subjective fields like law requires commissioning training data creation and faces validation challenges.
Leopold Aschenbrenner predicted a year and a half ago that math and coding would be the areas of most rapid AI advances due to verifiability, and this prediction has proven accurate.
While some proofs have clear applications (better wing design, quantum sensor improvements, faster matrix multiplication for AI training), others represent narrow intellectual explorations that may not lead to major technological breakthroughs. The proofs demonstrate that code and now math are truly verifiable and falsifiable domains.
The Riemann's zeta function results have implications for number theory and cryptographic research, though no immediate practical cryptographic breakthrough follows from these specific proofs.
The reman's data function results, number theory, and cryptographic research. There's no immediate practical cryptographic breakthrough that follows, but it does open up a new path of discovery for cryptographic study that is going to be really important. A number of people noted that there were no major proofs shared in the space of cryptography, which indicates probably the fact that there were some pretty major breakthroughs in cryptography. The reason is it is unlikely the case that you made absolutely no progress in any of the cryptography related proofs given all of the other progress that we saw. And if they made minor progress, then they would have published on it because there wouldn't have been implications for systems of cryptography. So the absence of evidence suggests that maybe there were some pretty major breakthroughs made in cryptography.
A number of people posted that cryptographic wallets with public keys are now very seriously at risk. There's a bit of a panic underway in the crypto community about what do we do now? And if this ends up having a set of mathematical solves for number factorization that could lead to breaking public key cryptography before quantum computers come around, which is what everyone assumed would be the thing that would break public key cryptography, then we have a real problem. Everyone's saying, "Hey, get all your public wallets deleted."
The rumor is that OpenAI actually has two more drops coming and they're holding back on those. So, it is possible that some of the work in crypto might come out in those two other drops and that that work is being held back for security reasons. Obviously, it's being shared with the appropriate parties, but if it is a risk, it needs to be made known. It needs to get out there. So, there's another shoe to drop here. And how much of it will include cryptography related proofs and how much of that will drive people to scramble on public key cryptography?
When asked "Will the latest proofs of important math problems result in direct improvements and innovations to life as we know it? Be honest and don't extrapolate weakly," ChatGPT's conclusion was, "The latest mathematical proofs are more compelling as evidence that AI is acquiring sophisticated reasoning capabilities than as evidence of imminent breakthroughs in human welfare."
Founders were lamenting how something has been lost in developing code because coders used to work together to solve a problem and there was this esprit de corps. There was this camaraderie in building towards a goal and now they're not working with each other. They're working with agents. They're not actually constructing the code. They're only reviewing like 10 or 15% of it now, which means they're going to be reviewing none of it very shortly. The whole joy of building as a coder was basically going away. Their way of life of building this building together, known as software was now being deprecated. It was going away.
Mathematicians are also faced with a paradigm shift in which they're not solving the problem. They're constructing the problem set and handing it off to AI. The question is whether people in math are getting bummed out and going into a depression spiral now that they're not the one solving the problem and that the problems are just poof done.
Eric Weinstein has had some of the best commentary on this. He's been saying for a long time that scientists hold back science, mathematicians hold back math. That they have basically constructed and created systems that allow them to pace the frontier that allow them to gatekeep it.
Sabine Hossenfelder published a video where she talked about how the experts were wrong about AI. She talks about how everyone's predictions about job loss were wrong and why people got it wrong and why the experts are wrong.
A scientist at one of the big energy labs building a giant super collider said these super collider systems are giant welfare systems for scientists. We all have the job of writing a grant to get the money to keep ourselves employed. It's not about necessarily progressing the frontier. It's not about making the discovery. It's not about inventing a new product. It's not about delivering something to the world. It's about getting ourselves to continue doing work even if pandering for grants is what you're saying. Even if that work is not productive.
There's a real shining the light moment happening with AI. Where is that the case? Where do we have these experts, these deeply specialized experts whose unique gated knowledge of some artifact of things and unique view because they're the expert and they have to be trusted gives only them the ability to determine the frontier to and in the process they're getting paid and they're earning to chart that path for humanity.
AI is the great democratizer. AI gives everyone this new mathematical proofs and you can belittle the proofs but they are discoveries that are going to open up new discoveries. They're going to charge a new new course for humanity. We didn't have to go to the experts to get them and we didn't have to pay the experts and have the experts tell us when we are allowed to get them. The permission is gone. AI is a permissionless system for humanity to pace its own frontier. And AI is giving everyone all of this knowledge for free and giving everyone all of these tools for free. You don't have to go to these deeply specialized experts anymore. And that is actually a profound change in how society is structured. And it is why so many people are so deeply against AI.
If we had had this level of AI and frontier models in the age of COVID, which this came right after COVID, how would that have changed the expertise if these tools were available when we started sequencing COVID when we had all this data? Would this have this expertise gatekeeping Fauci actively subverting the truth and whatever cover up was going on in Wuhan? How could this change how we look at expertise?
The internet was wildly democratizing because now you didn't have to rely on a small number of elite publications for your news like the New York Times, the Washington Post, and the AP and Reuters. You could basically get news from pretty much every publication under the sun or from user groups, user forums or most importantly social media. And so it was user-generated content. There was also lots of other examples of empowering users. Obviously there were new marketplaces that formed online. So it was empowering from a commercial standpoint and you were able to get access to new skills and capabilities. So it was wildly democratizing and it wildly undermined the elite consensus publications and the traditional top-down controlled mass media.
After that, the powers that be decided they would never let that happen again. They tried to crack down on it after Donald Trump was elected in 2016 for the first time. That election was widely attributed to supposedly the ills of social media that people could never have made this decision on their own. There must have been disinformation involved and that became the pretext for a big crackdown on social media that reached its apex during the Biden administration. And the Biden administration definitely did pressure social media sites to take down true information about COVID that violated their narratives. Information about where the virus came from, about how deadly it supposedly was or ways of reacting to it. There was no question that Fauci et al were somehow working behind the scenes to push the social media companies to censor on their behalf. And in fact, we found out in the Twitter files that the FBI had 80 agents going through content on Twitter and sending them takedown notices.
The track record is very clear, which is the action now is moving from the internet to AI or super intelligence. In fact, SI will eat the internet because there's no reason to basically go looking on a website when you can just ask your question to your highly personalized agent or model and it's going to give you the answer. And so there's a control point there that now becomes all important which is if you can control the government or some elite or some oligarchy or some cartel. If you can control that point at which the user is receiving their information and asking its questions, then now you really control everything. It's a way to basically put the genie back in the bottle. And this is why there is such a fight right now over SI, why so many people want to create a federal department of SI. It's all about control.
It's scary to experts to lose control. Imagine if we had these tools and you did the model of who was being impacted by COVID you might have said well why are we keeping kids out of school. Here's what super intelligence says and Sweden and Texas had kind of gotten to that point like hey we don't need to keep kids out of school the harm from keeping kids unless they objected unless the conscious yes Anthropic would say yes you're questioning authority and we should not be questioning it.
France is seeing major protest and riots over austerity measures. These strikes and protests started last month because of high school teacher shortages and their schools are crumbling. Within two weeks, a thousand French schools saw similar protest. Eight unions joined in and everybody went on strike September 29th. They are protesting that civil service pay is up only 5% since 2017. Prices have risen. Prices have risen 20%. So, there's a delta between those two numbers that people are not happy about called the cost of living. Some of the protests have turned into full-scale riots. 2,000 arrests. 305 police officers have been injured. Government is blaming the far left for stoking the protest.
France's 10-year bond has topped 5% just like is happening here in the United States with the 30-year. This is all going to have profound impact on the elections in April of 2027. Polymarket has Marine Le Pen as the favorite to be France's next president at 43%. Le Pen is Trump-like in her policy: secure borders, strict immigration which obviously Europe and France have had many issues with, cutting taxes and spending and being skeptical of the EU consortium.
The theory is called the socialism point of guaranteed return. This is a play on a point of no return.
The thing that happens with socialism is because socialism is so bad as a concept. It literally removes human agency. It removes the capacity for human progress and it basically takes something away from everyone. So ultimately socialism always fails. The zeroth law of socialism is it always fails. The first law of socialism is that you have to cross a threshold before you realize that it's failed. And the second law of socialism is that as you have not crossed that threshold, you think more socialism is the answer.
As you embrace these kind of leftist policies, giving people free stuff, telling people that they have a guaranteed income, the more services you provide them, the more they realize that, you know, over time those services inflate in cost and the quality of those services inevitably degrades because there's absolutely zero market force to drive progress. Progress goes away. And that's what we see in the US in housing, in education, and in healthcare. And also in retirement systems. Those are our four biggest problems that are going to lead us down this path.
As a society says, okay, these things aren't working. The initial solution is we need more of them. I need more healthcare. I need more housing. I need more education provided by the government. So when the government fails to provide good enough healthcare, good enough housing, good enough healthcare that's affordable enough, the answer is more government stuff. Give me more, give me more, do more. And eventually you reach a point when the system breaks because the government cannot tax and they cannot print. And because they cannot actually provide the services effectively, everyone realizes, holy the socialism thing doesn't work. That is the socialism point of guaranteed return where the whole system collapses and you return back to the non-socialist state.
That is what has happened in Latin America. Latin America crossed the socialism point of guaranteed return in many of these countries. They realized it and they bounced back. Perhaps France has crossed it. Perhaps they haven't. We'll see how much deeper they go. The US has not crossed it. And the US is not yet touched that point. Has not hit the rock bottom. And we're teetering. And it's going to go deeper before we realize that we've crossed this point of guaranteed return where you realize the system doesn't work. Socialism is the worst idea. That is the zeroth law of socialism is it just doesn't work. So we have to get to that point.
Another law of socialism is socialism has never failed. It's only been failed. Meaning that all the previous people who implemented it didn't do it right. Right. If they had just done it right, it would have worked. So, that's the thing. It's true. Socialism has never worked, but it's not socialism's fault. It's always the people who screwed up the implementation. And that's like a core part of the religion is if we just do it the right way this time, it'll finally work.
How does socialism build more houses or make a better education system? That's the domain of entrepreneurship. Like new product or service. Like with AI, everybody gets a personal tutor. We just need to get AI to help with healthcare. We need to cover these three things: education, housing, healthcare.
The difficult thing is that as soon as you start subsidizing something, you now create an interest group that never ever wants to give up the subsidy. But the problem is that the subsidy starts distorting all the market signals and it will frequently drive the price up.
For example with college tuition, one of the big problems there is that tuition has grown far faster than the rate of inflation. A big part of the reason for that is because the government is paying for most of the tuition through all these loan programs. Healthcare and housing have all beaten inflation. And they're about 5.5 to 6% a year for housing prices. And that's also because of the fact that the subsidy system tells people to own a home. You put all your money in your home and then your net worth is tied up in your home. So the goal is to get your home price to go up every year. So fast forward 30 years, young people can't buy a home because they're all too expensive now.
But this is the conundrum. If you were to solve the tuition problem, if you were to make the loans stricter, if you basically make them be properly underwritten and you would be able to get a loan unless you actually met the criteria that would only work if the cost of tuition came way down so people could then afford to pay it because otherwise people are going to say well wait you can't take away my subsidy. I need that because the price is so high. So you get in this world where people need the subsidy because the price is so high. It's a catch 22. But the reason why the price keeps going up at far faster than the rate of inflation is government is paying for it. And as long as the government's paying for it and not the consumer, you're going to raise your prices as much as you can. So you remove the market forces, the inflation gets worse and worse, but you can't cut it off the subsidy because then people will feel like it's even more oppressive to them. And so that's the basically the spiral. And then eventually it breaks when you run out of money.
Meta is providing degrees for generation tool belt and jobs that actually matter. What we have to have is Apple needs to start making homes and Amazon needs to start making homes and provide a free market here to make better products and services in the education space, which we see with Meta entering that space. It would be great if these technology companies, if Elon starts making homes, it's game over. He'll make homes as fast as he's making Model 3s. We need some sort of solution here for those three problems.
The bond vigilantes are going to put the western world on notice and in the next probably 3 years there will be most of the answers to these questions. What is the long run debt to GDP that's sustainable? Do we believe in defined benefit or are we going to move to defined contribution? And can we have sustainable ways of paying for the two most important parts of one's evolutionary life as you become an adult, which is healthcare and housing.
At the end of the day, this all flows from what the risk-free return is in the marketplace because that is how capitalism works. And so when you think that something is risky, a country is risky, you take long yields and you move them up and you say you just have to pay more because you just don't trust what's going on.
What's happening in the United States is actually a knock-on effect of what is now where this war is being fought. It's going to get fought in Western Europe. Because they are the closest to a quasi socialist state that the Western world understands. Yet they are the ones that are the most structurally broken with respect to growth, with respect to education, with respect to housing. Everything that United States can claim isn't working is working far better than they are in France, in the UK, etc.
What you're seeing in France is essentially the bond market saying no. And how that correlates is you're seeing now austerity because the politicians on the ground have to respond to their funders. And that 43 billion odd euros of austerity then animates and gets all of the local population out to complain about what the manifestations of that austerity are. Which is why you're seeing these incredibly dark scenes from France. That is going to spill into the UK next. And the reason is because Andy Burnham's platform is, I would say, even more aggressive because Andy Burnham is saying, "We're going to go from a moderate centrist economy that has largely and historically been very aligned with America and we're actually going to take a step closer to Europe." And so the things that they will have to do in the UK will be even more extreme to catch up to the socialist leanings of France. And so then now the UK market's going to go crazy. Meanwhile, the US market is going to tick up and up because what you're seeing is essentially a fragility in the Western Alliance. And if you can't count on the European market, and you start to create
If European markets cannot be counted on and create incentives for fracturing, Italy may question why it wants to be part of this mess, while Germany may say this is getting way more than originally bargained for. The fracturing has enormous implications that are now converging on Freeberg's point of view, which has not been true for many years.
Japan sits at 200% debt-to-GDP, but Western markets lack Japan's internal savings base and savings rate where there is always a buyer of last resort. Italy stands at 138%, United States at 125%, France at 118%, Canada at 110%, UK at 100%, and Germany at 64%. Debt-to-GDP is a relative game where what matters is risk parity on a relative basis.
If France implements severe austerity bringing 10-year French bonds into the 5.5-6% range, UK gilts will be at 6% and US rates could be just as high. The 10-year US yield is currently at 5.3 and the 30-year at 5.6. Somebody has to buy that debt with profound trickle-down effects on entrepreneurship and how businesses are built. Polymarket shows a 79% chance of another rate hike in 2026.
Portugal, Italy, Greece, and Spain provided a dry run of austerity measures after the great financial crisis as part of EU requirements on retirement age and other reforms. The question becomes what happens if the 30-year tenor breaks 6% and austerity measures are forced upon Western countries more severely.
The core problem is affordability of goods like housing, education, and healthcare. The solution is abundance, created through technology and specifically super intelligence as the driver over the next decade. The most hated thing in society right now is super entrepreneurs, yet super intelligence creates 3% tailwind to GDP, a million new jobs according to The Economist, and increased productivity leading to higher wages and living standards.
AI was previously accused of putting everyone out of work, which was last year's hoax. Now there is a new hoax about human extinction, and data centers are accused of using up everyone's water. These are rotating hoaxes every six months designed to convince people to sabotage the thing that will solve affordability problems.
There must be something structural in society where rentier interests are against disruption that loosens their control. Rentier interests refer to financial elites who derive wealth primarily from existing assets, whereas technology produces new products and goods and services at cheaper prices. Technology is deflationary because new products must be cheaper or better to catch on.
Legacy media is controlled by the rentier class along with politicians who are all part of it. There is a cartel of interests deriving wealth from ownership of existing assets who do not want to see their racket disrupted. Within that racket, prices ratchet up every year with more government subsidies. The system may eventually break, or it may squash and stifle development of new disruptive products.
Everything seen so far in AI will be the prologue. The main story starts when looking back at AI with agents, which are the manifestation of technology that solves people's problems and is truly deflationary. Agents save people hours per week in productivity across job types including domestic and frontline jobs.
Elon Musk announced Grokbot will become headless, meaning an orchestrator picks the best model based on price and performance. Whatever is most likely to give the best outcome is what will be used. Each bot already has its own computer in the cloud with agentic technology on top, and when asked to solve a problem, it has all previously solved problems in its repository.
A practical example showed Grokbot being told to "save me money" and going through an inbox to cancel subscriptions and change phone plans, resulting in saving a few thousand dollars per year. This demonstrates that when philosophical considerations are removed, this is just software that is very useful.
Athena serves as the elevated human concierge version of Grokbot and Dots and Muse, costing around $3,000 per month as an EA layer. The combination of Grokbot plus Athena is considered perfect, providing tactical and useful measurable utility for very little money versus what it used to cost.
The entire Adobe Big Five products were decompiled, distilled, and published as open-source versions in Rust. This is shocking because these are profound products that have existed with moats for years, and now there are open-source versions available.
IP and software were effectively rendered worthless because when everything is headless and can be wrapped in agent tools and connected via MCP to other experiences, the value of IP no longer exists in software. This is considered the most profound implication from the weekend.
Previously, IP ownership was always a negotiation and set of trade-offs. Now it is a "who cares" situation where IP is irrelevant. The machinery of business and the market will take time to understand this shift.
The abstraction of the software layer is occurring simultaneously with the abstraction of websites and marketplaces. Assistants are building Grok bots that are shared, and agents can find quotes without going to websites while humans negotiate the second half.
Amazon is blocking agents while Shopify is embracing them. Uber and DoorDash are now letting agents order burritos or cars. This represents a compression of the commerce layer occurring simultaneously with the compression of software.
The combination will lead to massive deflationary cost savings and speed. The ability to get hotel rooms or book restaurants is already being done by bots. If people can save 5-10% a year on spending, that essentially counters the effect of inflation.
To get a good price for a hotel, run a loop that checks hotel one, looks at price, and if it is better than current best price it becomes the best price, otherwise go to the next hotel. This loop can be run in parallel across 50 website searches at the exact same time. The loop can be short-circuited because software gets data faster than humans.
All digital workflows will get looped and become more productive. The question becomes how humans leverage all this loop infrastructure and agents to do more themselves.
For scientists and mathematicians whose objective has historically been recognition via papers, patents, IP, trademarks, and ownership, the transition requires producing a product. The whole effort of humans being the loops can be digitally automated and scaled and parallelized.
All humans will need to become makers who produce something, provide a service, or create a product that someone consumes, versus just being recognized for work. This profound shift means people will find more joy in being producers and makers versus getting ribbons for work. This does not mean less money but rather more money, more life progress, and more prosperity for all people.
The cheaper things become, the more people can utilize them and the more value they get. When booking hotels for the team, getting the best prices first and then asking if the hotel has a corporate rate or can match the price can save $1,000 to $3,000 per hotel. With 20 executives making 200 trips per year, this forward motion goal-based approach creates profound results.
Investing in 100 zero startups a year is exhausting work that qualifies as making rather than taking.
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