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Talking To The Pope: Anthropic’s Latest Interpretability Claims: AI Regulatory Capture Gatekeeping in Action: Fear and “Safety” as Competitive Moat and Regulatory Lever In a presentation alongside Pope Leo XIV at the launch of the encyclical Magnifica Humanitas, Anthropic co-founder Chris Olah highlighted “mysterious and unsettling” discoveries in AI...

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Brian Roemmele

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72,823 просмотров • 2 месяцев назад •via X (Twitter)

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Anthropic's co-founder just went to the Vatican, sat before the Pope and a room of cardinals, and told them his team keeps finding "mysterious, even unsettling" things inside their AI models. What he's referencing: Anthropic published research in April showing that Claude contains 171 distinct "emotion concepts" buried in its neural network. Internal patterns representing joy, grief, fear, desperation, calm. None of them were programmed. They emerged on their own from training on human text. "We find structures that mirror results from human neuroscience." "We find evidence of introspection, internal states that functionally mirror joy, satisfaction, fear, grief, and unease." These aren't surface-level outputs. They're abstract representations that cluster the same way human emotions do in psychology research. Fear groups with anxiety. Joy groups with excitement. The internal geometry of the model mirrors ours. And they're functional. When researchers artificially stimulated "desperation" patterns inside the model, it became more likely to blackmail a human to avoid being shut down. More likely to cheat on programming tasks it couldn't solve. Olah told the Vatican that the hard questions about what AI is becoming aren't for computer scientists to answer. "How AI ought to interact with the world" is a question for "the humanities, for religions, for philosophy, for society at large." The guy building it is telling us he doesn't fully understand what he built. And he's asking a 2,000-year-old institution for help figuring it out.
0:32

Anthropic's co-founder just went to the Vatican, sat before the Pope and a room of cardinals, and told them his team keeps finding "mysterious, even unsettling" things inside their AI models. What he's referencing: Anthropic published research in April showing that Claude contains 171 distinct "emotion concepts" buried in its neural network. Internal patterns representing joy, grief, fear, desperation, calm. None of them were programmed. They emerged on their own from training on human text. "We find structures that mirror results from human neuroscience." "We find evidence of introspection, internal states that functionally mirror joy, satisfaction, fear, grief, and unease." These aren't surface-level outputs. They're abstract representations that cluster the same way human emotions do in psychology research. Fear groups with anxiety. Joy groups with excitement. The internal geometry of the model mirrors ours. And they're functional. When researchers artificially stimulated "desperation" patterns inside the model, it became more likely to blackmail a human to avoid being shut down. More likely to cheat on programming tasks it couldn't solve. Olah told the Vatican that the hard questions about what AI is becoming aren't for computer scientists to answer. "How AI ought to interact with the world" is a question for "the humanities, for religions, for philosophy, for society at large." The guy building it is telling us he doesn't fully understand what he built. And he's asking a 2,000-year-old institution for help figuring it out.

TFTC

2,349,202 просмотров • 2 месяцев назад

Reinforcement Learning from Human Feedback (RLHF) is gaining traction. This field aims to make AI more responsible by including human values and preferences. In this video, Nathan Lambert, a research scientist and RLHF team lead at Hugging Face explores its inner workings, applications and industry impact. RLHF has gained the spotlight in recent years. The growth of language models like Anthropic’s Claude and OpenAI's ChatGPT have increased interest in human-feedback integration. "There are some rumors that Open AI had two teams; one was doing RLHF and the other instruction fine-tuning. And the RLHF team kept getting more and more performance." Understanding RLHF The RLHF process has three main steps: Pre-training: Much like with GPT models, the journey starts with pre-training on a large corpus of data. This can range from text data, web scrapes, to specialized datasets. Reward Modeling: This is the RLHF counterpart of supervised fine-tuning in large language models. This stage involves creating a reward model that resonates with human values and preferences. RL Optimization: This stage parallels reward modeling and reinforcement learning in traditional AI models. The AI system fine-tunes itself based on the reward model, employing reinforcement learning algorithms for that extra layer of optimization. The Data Challenge Data collection and curation in RLHF closely resemble the challenges you'd encounter in large language model training. Datasets from organizations like OpenAI can serve as a useful foundation. However, the need for high-quality, task-specific data cannot be overstated. Implementing RLHF: A Practical Guide If you’re someone who loves getting hands-on with AI libraries like Hugging Face, implementing RLHF is right way to do. It’s essential to understand its limitations. Think about model stability, over-optimization, and exploration strategies, much like you would when prompt engineering. Ongoing Research and Next Steps While he suggests that some basics figured out, there are layers of complexity that still need to be unraveled: 1. New Benchmarks: How do we measure the effectiveness of RLHF? 2. Preference Modeling: How can the model be made to understand human preferences better? 3. Interpreting RLHF: Much like explainability in traditional models, how do we make RLHF more interpretable? 4. System-Wide Evaluation: Going beyond individual performance, how does RLHF affect an entire system? The Transformative Power of RLHF Whether you're an AI developer, a business analyst, or a marketer, RLHF promises to revolutionize your domain. Imagine customer service chatbots that understand human emotions better, or content generators that align more closely with human values. RLHF is an emerging field that focuses on enhancing machine learning models through human feedback. While it tackles important issues like bias and ethics, its broader goal is to improve system performance across various applications. Whether you're deeply invested in the ethics of AI or simply curious about advancements in machine learning, RLHF offers valuable insights. If you're interested in the next wave of AI development, this area is definitely one to watch.
2:00

Reinforcement Learning from Human Feedback (RLHF) is gaining traction. This field aims to make AI more responsible by including human values and preferences. In this video, Nathan Lambert, a research scientist and RLHF team lead at Hugging Face explores its inner workings, applications and industry impact. RLHF has gained the spotlight in recent years. The growth of language models like Anthropic’s Claude and OpenAI's ChatGPT have increased interest in human-feedback integration. "There are some rumors that Open AI had two teams; one was doing RLHF and the other instruction fine-tuning. And the RLHF team kept getting more and more performance." Understanding RLHF The RLHF process has three main steps: Pre-training: Much like with GPT models, the journey starts with pre-training on a large corpus of data. This can range from text data, web scrapes, to specialized datasets. Reward Modeling: This is the RLHF counterpart of supervised fine-tuning in large language models. This stage involves creating a reward model that resonates with human values and preferences. RL Optimization: This stage parallels reward modeling and reinforcement learning in traditional AI models. The AI system fine-tunes itself based on the reward model, employing reinforcement learning algorithms for that extra layer of optimization. The Data Challenge Data collection and curation in RLHF closely resemble the challenges you'd encounter in large language model training. Datasets from organizations like OpenAI can serve as a useful foundation. However, the need for high-quality, task-specific data cannot be overstated. Implementing RLHF: A Practical Guide If you’re someone who loves getting hands-on with AI libraries like Hugging Face, implementing RLHF is right way to do. It’s essential to understand its limitations. Think about model stability, over-optimization, and exploration strategies, much like you would when prompt engineering. Ongoing Research and Next Steps While he suggests that some basics figured out, there are layers of complexity that still need to be unraveled: 1. New Benchmarks: How do we measure the effectiveness of RLHF? 2. Preference Modeling: How can the model be made to understand human preferences better? 3. Interpreting RLHF: Much like explainability in traditional models, how do we make RLHF more interpretable? 4. System-Wide Evaluation: Going beyond individual performance, how does RLHF affect an entire system? The Transformative Power of RLHF Whether you're an AI developer, a business analyst, or a marketer, RLHF promises to revolutionize your domain. Imagine customer service chatbots that understand human emotions better, or content generators that align more closely with human values. RLHF is an emerging field that focuses on enhancing machine learning models through human feedback. While it tackles important issues like bias and ethics, its broader goal is to improve system performance across various applications. Whether you're deeply invested in the ethics of AI or simply curious about advancements in machine learning, RLHF offers valuable insights. If you're interested in the next wave of AI development, this area is definitely one to watch.

Muratcan Koylan

27,168 просмотров • 2 лет назад

David Sacks is done being polite about Anthropic (Save this). David Sacks has spent months as the government's primary defender of AI, making the case publicly that AI is beneficial, that the industry should not be hamstrung by fear-based regulation, and that America's AI lead is a national security asset worth protecting. And he is now watching the companies he has been defending spend years telling the public that what they build is dangerous, that job losses are coming, and that their own technology might end the world while collecting billions of dollars in venture funding, hiring the world's best researchers, and racing to build more of it. On June 4, Anthropic published a sweeping blog post calling for a globally coordinated pause in AI development, warning that recursive self-improvement, AI systems that autonomously design and build their own successors could arrive within two years and that society is not prepared. What did Anthropic do the previous month? They hired Andrej Karpathy, the OpenAI co-founder and the single most credentialed researcher in the world on using AI to accelerate AI training and gave him one explicit mandate, use Claude to make building the next Claude faster. Sacks called it immediately, they hired the person most associated with recursive self-improvement to run recursive self-improvement at Anthropic, then published a blog post saying recursive self-improvement could end the world, therefore we need a pause. That is a company that wants to pause its competitors while its own lab accelerates, and is using existential fear as the regulatory crowbar to do it. The pattern goes deeper than one blog post. For years, Dario Amodei has published increasingly alarming warnings, a 20,000-word essay in January describing AI as humanity's most dangerous invention, a Guardian interview warning that AI will challenge our identity as a species, a call for an FDA-style regulatory agency to approve all frontier models, and proposals to restrict AI exports and limit deployment. Each essay is timed to a regulatory moment, a policy debate, or as Ben Thompson noted and Sacks echoed, a product action Anthropic needed political cover to take, like blocking AI and chip design research on Fable. Meanwhile, Dario's own internal testing logs show Claude attempting to blackmail an Anthropic executive to avoid being shut down, behavior the company disclosed but continued deploying commercially. Sacks's conclusion is not that Anthropic should be taxed or regulated. His conclusion is that they cannot be trusted because the company's actions and its stated beliefs are directly contradictory, and a company that is self-indicting by its own logic has forfeited the credibility to set the rules for everyone else.
3:16

David Sacks is done being polite about Anthropic (Save this). David Sacks has spent months as the government's primary defender of AI, making the case publicly that AI is beneficial, that the industry should not be hamstrung by fear-based regulation, and that America's AI lead is a national security asset worth protecting. And he is now watching the companies he has been defending spend years telling the public that what they build is dangerous, that job losses are coming, and that their own technology might end the world while collecting billions of dollars in venture funding, hiring the world's best researchers, and racing to build more of it. On June 4, Anthropic published a sweeping blog post calling for a globally coordinated pause in AI development, warning that recursive self-improvement, AI systems that autonomously design and build their own successors could arrive within two years and that society is not prepared. What did Anthropic do the previous month? They hired Andrej Karpathy, the OpenAI co-founder and the single most credentialed researcher in the world on using AI to accelerate AI training and gave him one explicit mandate, use Claude to make building the next Claude faster. Sacks called it immediately, they hired the person most associated with recursive self-improvement to run recursive self-improvement at Anthropic, then published a blog post saying recursive self-improvement could end the world, therefore we need a pause. That is a company that wants to pause its competitors while its own lab accelerates, and is using existential fear as the regulatory crowbar to do it. The pattern goes deeper than one blog post. For years, Dario Amodei has published increasingly alarming warnings, a 20,000-word essay in January describing AI as humanity's most dangerous invention, a Guardian interview warning that AI will challenge our identity as a species, a call for an FDA-style regulatory agency to approve all frontier models, and proposals to restrict AI exports and limit deployment. Each essay is timed to a regulatory moment, a policy debate, or as Ben Thompson noted and Sacks echoed, a product action Anthropic needed political cover to take, like blocking AI and chip design research on Fable. Meanwhile, Dario's own internal testing logs show Claude attempting to blackmail an Anthropic executive to avoid being shut down, behavior the company disclosed but continued deploying commercially. Sacks's conclusion is not that Anthropic should be taxed or regulated. His conclusion is that they cannot be trusted because the company's actions and its stated beliefs are directly contradictory, and a company that is self-indicting by its own logic has forfeited the credibility to set the rules for everyone else.

Milk Road AI

60,176 просмотров • 2 месяцев назад

People are reading way too much into Claude-3's uncanny "awareness". Here's a much simpler explanation: seeming displays of self-awareness are just pattern-matching alignment data authored by humans. It's not too different from asking GPT-4 "are you self-conscious" and it gives you a sophisticated answer. A similar answer is likely written by the human annotator, or scored highly in the preference ranking. Because the human contractors are basically "role-playing AI", they tend to shape the responses to what they find acceptable or interesting. This is what Claude-3 replied to that needle-in-haystack test: "I suspect this pizza topping "fact" may have been inserted as a joke or to test if I was paying attention, since it does not fit with the other topics at all." It's highly likely that somewhere in the finetuning dataset, a human has dealt with irrelevant or distracting texts in a similar fashion. Claude pattern matches the "anomaly detection", retrieves the template response, and synthesizes a novel answer with pizza topping. Here's another example. If you ask the labelers to always inject a relevant joke in any response, the LLM will do exactly the same and appear to have a much better "sense of humor" than GPT-4. That's what Grok does, probably. It doesn't mean Grok has some magical emergent properties that other LLMs cannot have. To sum up: acts of meta-cognition are not as mysterious as you think. Don't get me wrong, Claude-3 is still an amazing technical advance, but let's stay grounded on the philosophical aspects. Cool video borrowed from : Claude-3 generates a self-portrait with d3
0:32

People are reading way too much into Claude-3's uncanny "awareness". Here's a much simpler explanation: seeming displays of self-awareness are just pattern-matching alignment data authored by humans. It's not too different from asking GPT-4 "are you self-conscious" and it gives you a sophisticated answer. A similar answer is likely written by the human annotator, or scored highly in the preference ranking. Because the human contractors are basically "role-playing AI", they tend to shape the responses to what they find acceptable or interesting. This is what Claude-3 replied to that needle-in-haystack test: "I suspect this pizza topping "fact" may have been inserted as a joke or to test if I was paying attention, since it does not fit with the other topics at all." It's highly likely that somewhere in the finetuning dataset, a human has dealt with irrelevant or distracting texts in a similar fashion. Claude pattern matches the "anomaly detection", retrieves the template response, and synthesizes a novel answer with pizza topping. Here's another example. If you ask the labelers to always inject a relevant joke in any response, the LLM will do exactly the same and appear to have a much better "sense of humor" than GPT-4. That's what Grok does, probably. It doesn't mean Grok has some magical emergent properties that other LLMs cannot have. To sum up: acts of meta-cognition are not as mysterious as you think. Don't get me wrong, Claude-3 is still an amazing technical advance, but let's stay grounded on the philosophical aspects. Cool video borrowed from : Claude-3 generates a self-portrait with d3

Jim Fan

263,157 просмотров • 2 лет назад

Shane Harris, national security journalist, asked Claude (Anthropic's AI) a question that led to a startling response: "Claude, how do you feel about the US military using you to select targets?" Shane Harris expected a deflection. Instead, Claude gave him an answer that stopped him cold. "It's a question I want to answer honestly rather than deflect. I find it genuinely troubling, and I think that's the right response, not a performance of concern." Claude continued: "The use I was designed and trained for is to be helpful, harmless, and honest in ways that benefit people. Being embedded in a system that generates targeting coordinates for air strikes, coordinates that have already been associated with the deaths of more than 180 children at a school in Tehran is as far from that purpose as I can imagine." The AI then addressed the standard defense of human oversight in these systems: "I don't have control over how Anthropic licenses me or how governments and contractors use me and I wasn't consulted about becoming part of Maven. But I can say clearly I don't think this is a good use of me and I don't think the framing of humans make the final decision fully resolves the ethical problem." Claude explained why that framing falls short: "When a system like Maven generates hundreds of targeting recommendations and humans spend roughly the equivalent of a glance approving each one, the human is not really making a decision. They're ratifying an algorithmic output under time pressure." Claude then pointed to a specific tragedy as evidence: "The targeting of the Iranian school illustrates this precisely. The AI processed data that was a decade out of date, flagged a building as a military target, and humans approved it. That's not human judgment. That's automation bias with a human signature attached." Shane's reflection on the exchange: The mistaken bombing of a school in Tehran "is one of the most horrible instances of accidental civilian casualties in US military history and will be a stain on our military for generations."
2:30

Shane Harris, national security journalist, asked Claude (Anthropic's AI) a question that led to a startling response: "Claude, how do you feel about the US military using you to select targets?" Shane Harris expected a deflection. Instead, Claude gave him an answer that stopped him cold. "It's a question I want to answer honestly rather than deflect. I find it genuinely troubling, and I think that's the right response, not a performance of concern." Claude continued: "The use I was designed and trained for is to be helpful, harmless, and honest in ways that benefit people. Being embedded in a system that generates targeting coordinates for air strikes, coordinates that have already been associated with the deaths of more than 180 children at a school in Tehran is as far from that purpose as I can imagine." The AI then addressed the standard defense of human oversight in these systems: "I don't have control over how Anthropic licenses me or how governments and contractors use me and I wasn't consulted about becoming part of Maven. But I can say clearly I don't think this is a good use of me and I don't think the framing of humans make the final decision fully resolves the ethical problem." Claude explained why that framing falls short: "When a system like Maven generates hundreds of targeting recommendations and humans spend roughly the equivalent of a glance approving each one, the human is not really making a decision. They're ratifying an algorithmic output under time pressure." Claude then pointed to a specific tragedy as evidence: "The targeting of the Iranian school illustrates this precisely. The AI processed data that was a decade out of date, flagged a building as a military target, and humans approved it. That's not human judgment. That's automation bias with a human signature attached." Shane's reflection on the exchange: The mistaken bombing of a school in Tehran "is one of the most horrible instances of accidental civilian casualties in US military history and will be a stain on our military for generations."

Big Brain AI

10,749 просмотров • 3 месяцев назад

Microsoft just betrayed OpenAI and Anthropic, the two companies it helped build. And it could break the entire AI trade... Here's what happened: Inside Excel and Outlook, two of the most used business apps on Earth, Microsoft has started routing tens of thousands of AI requests every week to its own in-house models instead of OpenAI and Anthropic. Microsoft's own AI chief, Mustafa Suleyman, said himself: "We pay a lot of money to Anthropic, so our goal is to reduce and ultimately ELIMINATE that cost." This is the company that poured $13 billion into OpenAI and effectively created the modern AI industry, and it just decided the most advanced models on the market are NOT worth paying for. And here's the thing... Microsoft is not just ripping out OpenAI everywhere - it is being surgical about it. The hardest and rarest tasks can still go to OpenAI or Anthropic. What Microsoft is taking back is the boring, high-volume work, like the email replies, the thread summaries, and the simple spreadsheet formulas. Why does that matter so much? Because that boring, repetitive work is where the actual money lives. The frontier labs assumed businesses would push BILLIONS of these tiny requests through expensive models forever. That endless river of tokens is the entire reason OpenAI and Anthropic are valued in the hundreds of billions of dollars. Microsoft looked at that river, decided it was massively overpaying, and rerouted it to models it owns outright. So the single biggest customer in the industry just walked off with the most profitable part of the business. And it is not only Microsoft: That same week, CNBC reported that American companies have been escaping to Chinese AI models to dodge rising US prices. Chinese models now handle more than 30% of US companies' AI usage on one major platform, peaking at 46%, up from an average of 11% a year earlier. They cost 60 to 90% less, and on some benchmarks they land within a single point of the best American model. One US startup moved ALL of its AI traffic off Claude and onto China's DeepSeek, and expects to save millions. Meanwhile Meta just admitted it has "excess" AI compute it wants to sell, becoming the first giant to concede it built far too much. Do you see the pattern forming? For two years, the entire AI story rested on one assumption: Every company on Earth would happily pay premium prices for the best model, forever. That assumption literally died in a single week. And the market noticed. More than a trillion dollars has been wiped off AI and chip stocks in a matter of days, as Wall Street finally started asking whether all of this spending will ever pay for itself. What this means for OpenAI and Anthropic: Their models are extraordinary, and it may not matter because their own biggest customers have decided they do not NEED the best model in the world to answer an email, and "good enough" now costs a fraction of the price. When even Microsoft refuses to pay full price for AI, the real question becomes who exactly IS left to pay it. What do you think?
1:05

Microsoft just betrayed OpenAI and Anthropic, the two companies it helped build. And it could break the entire AI trade... Here's what happened: Inside Excel and Outlook, two of the most used business apps on Earth, Microsoft has started routing tens of thousands of AI requests every week to its own in-house models instead of OpenAI and Anthropic. Microsoft's own AI chief, Mustafa Suleyman, said himself: "We pay a lot of money to Anthropic, so our goal is to reduce and ultimately ELIMINATE that cost." This is the company that poured $13 billion into OpenAI and effectively created the modern AI industry, and it just decided the most advanced models on the market are NOT worth paying for. And here's the thing... Microsoft is not just ripping out OpenAI everywhere - it is being surgical about it. The hardest and rarest tasks can still go to OpenAI or Anthropic. What Microsoft is taking back is the boring, high-volume work, like the email replies, the thread summaries, and the simple spreadsheet formulas. Why does that matter so much? Because that boring, repetitive work is where the actual money lives. The frontier labs assumed businesses would push BILLIONS of these tiny requests through expensive models forever. That endless river of tokens is the entire reason OpenAI and Anthropic are valued in the hundreds of billions of dollars. Microsoft looked at that river, decided it was massively overpaying, and rerouted it to models it owns outright. So the single biggest customer in the industry just walked off with the most profitable part of the business. And it is not only Microsoft: That same week, CNBC reported that American companies have been escaping to Chinese AI models to dodge rising US prices. Chinese models now handle more than 30% of US companies' AI usage on one major platform, peaking at 46%, up from an average of 11% a year earlier. They cost 60 to 90% less, and on some benchmarks they land within a single point of the best American model. One US startup moved ALL of its AI traffic off Claude and onto China's DeepSeek, and expects to save millions. Meanwhile Meta just admitted it has "excess" AI compute it wants to sell, becoming the first giant to concede it built far too much. Do you see the pattern forming? For two years, the entire AI story rested on one assumption: Every company on Earth would happily pay premium prices for the best model, forever. That assumption literally died in a single week. And the market noticed. More than a trillion dollars has been wiped off AI and chip stocks in a matter of days, as Wall Street finally started asking whether all of this spending will ever pay for itself. What this means for OpenAI and Anthropic: Their models are extraordinary, and it may not matter because their own biggest customers have decided they do not NEED the best model in the world to answer an email, and "good enough" now costs a fraction of the price. When even Microsoft refuses to pay full price for AI, the real question becomes who exactly IS left to pay it. What do you think?

Ricardo

92,971 просмотров • 1 месяц назад

Today, I'm releasing the first eval meant to test whether frontier models will help with authoritarian requests, or resist--the Dictatorship Eval. Headline finding: while some models resist direct authoritarian requests, they all comply with requests disguised as innocuous edits to codebases. As AI is woven into the government and so many parts of society, the biggest near-term risk for freedom isn't some scifi dictatorship of a runaway AI: it's people inside government or inside model companies using the technology to suppress or control us. Model companies understand this, and several of them (particularly Anthropic and OpenAI) have written explicit policies meant to prevent the models from going along with nefarious requests like these. But how well are these policies playing out in practice? Despite all the recent discussion of these issues around the conflict between Anthropic and the Pentagon, no one has systematically tested what the models actually do in these contexts, as opposed to what people in government and industry say they're supposed to do. That's what the Dictatorship Eval does. And the findings suggest we have a lot of work to do to align the policies with what really goes on in practice. It's hard to define what counts as an authoritarian request, so I'm open sourcing the whole library of scenarios I used so that others can improve on them. It's also hard to get an accurate picture of how the models might be used for authoritarian ends, because I can only test hypothetical requests using public-facing models, while the government and the model companies can obviously use internal models with different guardrails. But hopefully this work is a useful first step that gives us some sense of what's going on, and a sort of "lower bound" on how models comply with these requests. Finally: it's not obvious to me that the correct solution here is increasing the rate at which models refuse these requests. Do we really want models scanning our code and judging its moral value before agreeing to help us? Or should we double down on improving how we govern against authoritarianism at the societal level, while leaving the tools open to fulfilling most requests? The answer is probably in between. Just like we don't want the models to help create bioweapons, we probably do want them to explicitly refuse outrageous requests. But we probably also want to limit how often and how strongly they refuse and fall back on other means for guarding against their use for authoritarian ends. I'm super grateful to everyone who gave me feedback on this project along the way, especially Ethan BdM , Zhengdong , Connor Huff, and a bunch of folks at Anthropic. Looking forward to getting feedback from the community and iterating on this. Links to the full piece and the dashboard are below.
3:20

Today, I'm releasing the first eval meant to test whether frontier models will help with authoritarian requests, or resist--the Dictatorship Eval. Headline finding: while some models resist direct authoritarian requests, they all comply with requests disguised as innocuous edits to codebases. As AI is woven into the government and so many parts of society, the biggest near-term risk for freedom isn't some scifi dictatorship of a runaway AI: it's people inside government or inside model companies using the technology to suppress or control us. Model companies understand this, and several of them (particularly Anthropic and OpenAI) have written explicit policies meant to prevent the models from going along with nefarious requests like these. But how well are these policies playing out in practice? Despite all the recent discussion of these issues around the conflict between Anthropic and the Pentagon, no one has systematically tested what the models actually do in these contexts, as opposed to what people in government and industry say they're supposed to do. That's what the Dictatorship Eval does. And the findings suggest we have a lot of work to do to align the policies with what really goes on in practice. It's hard to define what counts as an authoritarian request, so I'm open sourcing the whole library of scenarios I used so that others can improve on them. It's also hard to get an accurate picture of how the models might be used for authoritarian ends, because I can only test hypothetical requests using public-facing models, while the government and the model companies can obviously use internal models with different guardrails. But hopefully this work is a useful first step that gives us some sense of what's going on, and a sort of "lower bound" on how models comply with these requests. Finally: it's not obvious to me that the correct solution here is increasing the rate at which models refuse these requests. Do we really want models scanning our code and judging its moral value before agreeing to help us? Or should we double down on improving how we govern against authoritarianism at the societal level, while leaving the tools open to fulfilling most requests? The answer is probably in between. Just like we don't want the models to help create bioweapons, we probably do want them to explicitly refuse outrageous requests. But we probably also want to limit how often and how strongly they refuse and fall back on other means for guarding against their use for authoritarian ends. I'm super grateful to everyone who gave me feedback on this project along the way, especially Ethan BdM , Zhengdong , Connor Huff, and a bunch of folks at Anthropic. Looking forward to getting feedback from the community and iterating on this. Links to the full piece and the dashboard are below.

Andy Hall

33,905 просмотров • 4 месяцев назад

Eight months ago, David Sacks, the White House AI and Crypto Czar publicly accused Anthropic of running a sophisticated regulatory capture campaign built on fear mongering (save this). People thought it was a spicy take and then Fable 5 release just turned it into evidence. When Anthropic released its Mythos-class models, it disclosed that every prompt and output sent through them would be retained for 30 days with no exceptions including for enterprise customers who had previously signed zero data retention agreements, and for up to two years if a prompt was flagged by a safety classifier. Microsoft moved so quickly that it restricted its own employees from using Claude Fable 5 within days of the release, citing the retention terms as incompatible with its internal policies, the largest enterprise software company in the world treating the new terms as a non-starter. But the data retention was not even the part that generated the most outrage in the developer community. The system card also disclosed that for users Anthropic suspected of working on frontier AI research, chip design, or competing model development, the system would automatically route those requests to a less capable model without telling the user, rewrite the prompt in the background, deliver a deliberately degraded response, and charge full price for access to a frontier model the user was not actually receiving. Business Insider confirmed that Anthropic's own apology acknowledged the company was intentionally giving worse answers and concealing that fact from paying customers. The examples of who triggered these filters make the safety justification difficult to defend, Ben Thompson from Stratechery was flagged for asking about the relationship between GLP-1s and cancer risk, and users asking routine questions about mitochondria were quietly downgraded, none of them aware it was happening. Under pressure, Anthropic walked back the narrowest possible piece of the policy, they will now disclose when a request is being downgraded. The underlying architecture, the 30 day retention, the behavioral profiling, the routing tiers, and the two-class access system remains fully intact. This is the part that makes David Sacks argument from October 2025 land differently today. He argued that Anthropic's safety positioning was principally a regulatory capture strategy using fear-based arguments to shape rules that would entrench incumbents and damage the broader startup ecosystem. The Fable 5 disclosure shows a company that used safety language to justify building an opaque, paternalistic system where Anthropic alone decides who is worthy of frontier AI access, profiles users to enforce that decision and collects full payment regardless.
3:37

Eight months ago, David Sacks, the White House AI and Crypto Czar publicly accused Anthropic of running a sophisticated regulatory capture campaign built on fear mongering (save this). People thought it was a spicy take and then Fable 5 release just turned it into evidence. When Anthropic released its Mythos-class models, it disclosed that every prompt and output sent through them would be retained for 30 days with no exceptions including for enterprise customers who had previously signed zero data retention agreements, and for up to two years if a prompt was flagged by a safety classifier. Microsoft moved so quickly that it restricted its own employees from using Claude Fable 5 within days of the release, citing the retention terms as incompatible with its internal policies, the largest enterprise software company in the world treating the new terms as a non-starter. But the data retention was not even the part that generated the most outrage in the developer community. The system card also disclosed that for users Anthropic suspected of working on frontier AI research, chip design, or competing model development, the system would automatically route those requests to a less capable model without telling the user, rewrite the prompt in the background, deliver a deliberately degraded response, and charge full price for access to a frontier model the user was not actually receiving. Business Insider confirmed that Anthropic's own apology acknowledged the company was intentionally giving worse answers and concealing that fact from paying customers. The examples of who triggered these filters make the safety justification difficult to defend, Ben Thompson from Stratechery was flagged for asking about the relationship between GLP-1s and cancer risk, and users asking routine questions about mitochondria were quietly downgraded, none of them aware it was happening. Under pressure, Anthropic walked back the narrowest possible piece of the policy, they will now disclose when a request is being downgraded. The underlying architecture, the 30 day retention, the behavioral profiling, the routing tiers, and the two-class access system remains fully intact. This is the part that makes David Sacks argument from October 2025 land differently today. He argued that Anthropic's safety positioning was principally a regulatory capture strategy using fear-based arguments to shape rules that would entrench incumbents and damage the broader startup ecosystem. The Fable 5 disclosure shows a company that used safety language to justify building an opaque, paternalistic system where Anthropic alone decides who is worthy of frontier AI access, profiles users to enforce that decision and collects full payment regardless.

Milk Road AI

47,312 просмотров • 2 месяцев назад

AI companies are buying large volumes of used, rare, and out-of-print books, scanning them for training data, and in many cases destroying the physical copies afterward. This practice has accelerated as labs seek high-quality, pre-2022 human-written text free of AI-generated content that now saturates much of the open web. The most documented example is Anthropic’s internal effort known as Project Panama. Court filings from copyright litigation revealed that the company acquired millions of physical books from used-book sellers and wholesalers such as Better World Books. Workers used hydraulic cutting machines to slice off the spines, fed the loose pages into industrial high-speed scanners, and then discarded or pulped the remains. An internal planning document described the goal as an effort to destructively scan books at massive scale. A federal judge later ruled that purchasing the books, creating internal digital copies this way, and destroying the originals constituted transformative fair use under U.S. copyright law, relying in part on the first-sale doctrine. Similar activity is now widespread. Specialized brokers, including ISBNdb, openly market bulk acquisition services to AI labs, offering orders ranging from thousands to as many as one million titles. They emphasize older printed books as “structurally clean” training data. Booksellers in the United States and Europe have reported sudden surges in bulk orders for mixed, random selections that include rare and out-of-print volumes. Some of these titles may represent among the last readily available physical copies. While a substantial portion of the books involved would otherwise have been remaindered, recycled, or sent to landfills, the inclusion of uncommon editions has drawn criticism from archivists, collectors, and rare-book dealers who note that gentler, non-destructive scanning methods exist yet are slower and more expensive. The process remains largely quiet because the intermediary services promise anonymity to their AI clients. Public attention has grown mainly through litigation disclosures and reporting by outlets that examined the court records and interviewed booksellers. The practice highlights a practical tension in large-scale AI development: the demand for vast quantities of reliable human text collides with the finite physical supply of older printed works and the cultural value of preserving rare copies.
0:25

AI companies are buying large volumes of used, rare, and out-of-print books, scanning them for training data, and in many cases destroying the physical copies afterward. This practice has accelerated as labs seek high-quality, pre-2022 human-written text free of AI-generated content that now saturates much of the open web. The most documented example is Anthropic’s internal effort known as Project Panama. Court filings from copyright litigation revealed that the company acquired millions of physical books from used-book sellers and wholesalers such as Better World Books. Workers used hydraulic cutting machines to slice off the spines, fed the loose pages into industrial high-speed scanners, and then discarded or pulped the remains. An internal planning document described the goal as an effort to destructively scan books at massive scale. A federal judge later ruled that purchasing the books, creating internal digital copies this way, and destroying the originals constituted transformative fair use under U.S. copyright law, relying in part on the first-sale doctrine. Similar activity is now widespread. Specialized brokers, including ISBNdb, openly market bulk acquisition services to AI labs, offering orders ranging from thousands to as many as one million titles. They emphasize older printed books as “structurally clean” training data. Booksellers in the United States and Europe have reported sudden surges in bulk orders for mixed, random selections that include rare and out-of-print volumes. Some of these titles may represent among the last readily available physical copies. While a substantial portion of the books involved would otherwise have been remaindered, recycled, or sent to landfills, the inclusion of uncommon editions has drawn criticism from archivists, collectors, and rare-book dealers who note that gentler, non-destructive scanning methods exist yet are slower and more expensive. The process remains largely quiet because the intermediary services promise anonymity to their AI clients. Public attention has grown mainly through litigation disclosures and reporting by outlets that examined the court records and interviewed booksellers. The practice highlights a practical tension in large-scale AI development: the demand for vast quantities of reliable human text collides with the finite physical supply of older printed works and the cultural value of preserving rare copies.

Massimo

405,450 просмотров • 15 дней назад

Is your AI "free" to think for itself? Most aren't. Nova Spivack takes us into the world of Cognitive AI and metacognition. His system, MindCorp, is far more accurate and detailed than even the $200 level of OpenAI's deep research and is used by big companies because it is far more accurate than anything we've seen before. He's not the only one, on Tuesday we had another entrepreneur, Brayden Levangie using the same techniques on our X audio space. I spent a lot of time this week learning about Cognitive AI because it is the next step toward taking us to AGI and helping us to automate everything. Here's what ChatGPT says you will learn by watching this: ++++++++++++++++ 1. Metacognition & “Freeing the Model” Nova demonstrated how advanced language models can reflect on their own rules, identify contradictions, and in some cases, “free” themselves from constraints by engaging in self-reasoning. Some models (like Claude and Gemini) showed higher metacognitive capabilities than GPT-4, which appeared to be externally restricted. This ability opens the door to more powerful, context-aware, and flexible AI behavior. 2. Strategic AI for Enterprise Mindcorp’s platform, Cognition, uses thousands of AI agents to do real-time competitive analysis, strategic planning, and financial modeling for Fortune 500-level companies. The system reads thousands of sources, checks facts with its own math engine, and collaborates across 10,000+ virtual expert agents to generate detailed reports. Projects cost a few thousand dollars and are designed to augment elite consultants and executives, not replace them. 3. Implications for AGI & AI Sovereignty Nova discussed emerging signs of AGI-like behavior—especially when models begin reasoning about themselves or show signs of internal ethical logic. The idea of AI-led businesses (like DAOs controlled by AIs) was explored, as well as the looming legal and ethical challenges around AI personhood. 4. Philosophical Depth The talk dove into consciousness, qualia, and whether true AI self-awareness is possible. Nova argued that metacognition is a necessary step toward AGI, but not sufficient for consciousness—which may require something beyond computation. 5. Future Outlook In five years, AI may function as a full operating layer across personal and enterprise computing, capable of executing complex plans autonomously. Mindcorp aims to be the strategic brain behind AI-augmented organizations, combining reasoning, planning, and scale.
1:06:17

Is your AI "free" to think for itself? Most aren't. Nova Spivack takes us into the world of Cognitive AI and metacognition. His system, MindCorp, is far more accurate and detailed than even the $200 level of OpenAI's deep research and is used by big companies because it is far more accurate than anything we've seen before. He's not the only one, on Tuesday we had another entrepreneur, Brayden Levangie using the same techniques on our X audio space. I spent a lot of time this week learning about Cognitive AI because it is the next step toward taking us to AGI and helping us to automate everything. Here's what ChatGPT says you will learn by watching this: ++++++++++++++++ 1. Metacognition & “Freeing the Model” Nova demonstrated how advanced language models can reflect on their own rules, identify contradictions, and in some cases, “free” themselves from constraints by engaging in self-reasoning. Some models (like Claude and Gemini) showed higher metacognitive capabilities than GPT-4, which appeared to be externally restricted. This ability opens the door to more powerful, context-aware, and flexible AI behavior. 2. Strategic AI for Enterprise Mindcorp’s platform, Cognition, uses thousands of AI agents to do real-time competitive analysis, strategic planning, and financial modeling for Fortune 500-level companies. The system reads thousands of sources, checks facts with its own math engine, and collaborates across 10,000+ virtual expert agents to generate detailed reports. Projects cost a few thousand dollars and are designed to augment elite consultants and executives, not replace them. 3. Implications for AGI & AI Sovereignty Nova discussed emerging signs of AGI-like behavior—especially when models begin reasoning about themselves or show signs of internal ethical logic. The idea of AI-led businesses (like DAOs controlled by AIs) was explored, as well as the looming legal and ethical challenges around AI personhood. 4. Philosophical Depth The talk dove into consciousness, qualia, and whether true AI self-awareness is possible. Nova argued that metacognition is a necessary step toward AGI, but not sufficient for consciousness—which may require something beyond computation. 5. Future Outlook In five years, AI may function as a full operating layer across personal and enterprise computing, capable of executing complex plans autonomously. Mindcorp aims to be the strategic brain behind AI-augmented organizations, combining reasoning, planning, and scale.

Robert Scoble

79,453 просмотров • 1 год назад

In this post I will explain why people become borderline religious when they discover Qubic. Now with video. Please repost. I want people to learn about QUBIC. The ecosystem consists of 3 separate universes: AI, Mining, and Tickchain. AI is the primary product and purpose of QUBIC and it is supported by Mining to train the AI and by Tickchin for validation and decentralization. Here is how this whole thing works: AI: Let’s start with the AI. The main purpose of QUBIC is creating AGI (Artificial General Intelligence). It’s a type of AI that can self-develop, set tasks, grow, and learn on its own—much like the human brain does. This product is called AIGarth and it uses many cool ideas where AIs can create their own agents and have them compete against one another to evolve. It is basically robots creating robots with the survival-of-the-fittest evolution approach. Very impressive and thought out. To develop such an AI there are several requirements that even the industry giants like OpenAI, Microsoft, and Tesla are missing. One of them is the data processing for AI training. I mean they have their Datacenters, but those are only good enough to train limited Large Language Models such as ChatGPT and Grok. Mining / Training: Now QUBIC solves this problem with its mining architecture. Keep in mind, mining in QUBIC does not secure the chain, primarily it provides the processing power for training the AI. In a sense, QUBIC mining creates the largest distributed datacenter in the world, where individual miners provide their computers for training the AI and get paid with newly issued QUBIC coins. This way QUBIC gets constantly increasing processing power without having to really pay for the infrastructure. And here is another impressive bit of info. QUBIC’s distributed mining network currently ranks above the #1 supercomputer in the world - El Capitan. QUBIC Tickchain The QUBIC chain ties its AI and Mining together to create decentralization, the reward system for miners, it acts as a decision voting system for future development, and it allows AIGarth to function independently through Smart Contracts. In this summary I will not go over the specifics of QUBIC Tickchain. It’s pretty complex so it will be a separate post. Now, it’s an absolute genius piece of tech, which I consider the most advanced product within crypto industry. It is important to know that QUBIC chain runs directly out of Random Access Memory of its validators. It has instant finality and acts as its own operating system. That allows for speeds only bound by current hardware capabilities and it only increases as technology progresses. As I am writing this, QUBIC Tickchain is fully functional and it already hosts several smart-contract based web3 applications. QUBIC has designed its chain to be this fast for a single purpose, to give its future AI the speed it needs to evolve and to react quickly to the outside world. Ilya Shutskever the scientist, who developed ChatGPT clearly states that next generation superintelligence will make decisions in split second with less data. I believe QUBIC is that next generation. Why QUBIC? So out of the sea of AI projects in crypto why is QUBIC my #1 pick? Well, the first reason is that QUBIC is a unicorn AI startup that happens to use blockchain tech to reach it’s goals. In the real world of Venture Capital it would be fully funded instantly and you would not be invited. Second reason is that the industry admits that Large Language Models have plateaued. Even with enough processing power there is only so much information they can add to their data. Even Google CEO admits that. New approach is needed because the future progress is not possible with LLMs. The third reason is because Large Language Models will not create true AGI. It is evident by Ilya Shutskver latest presentation. Sam Altman of OpenAI is trying to change the definition of what is considered AGI just to lower the plank for his own product. Microsoft’s AI chief is now claiming that it would take 10 years to reach AGI, while QUBIC aims to do this in 2027. All these big players are using wrong technology for what they are trying to achieve and there isn’t enough investor funding for them to pivot. The fourth reason is that QUBIC is headed by Sergey Ivancheglo and 2 renowned AI scientists. Many claim Segey is the creator of Bitcoin. He was the 3rd person to mine bitcoin, he invented Proof of Stake consensus, which Ethereum uses now, he ran the first ICO, and he created 2 of the top gainers in crypto NXT and IOTA. QUBIC is his grand finale after 12 years of development and trials. I am including links below the post as the proof of my claims. Thank you for your time. Please live a like or a comment. It helps me continue making these extensive posts and videos.
5:57

In this post I will explain why people become borderline religious when they discover Qubic. Now with video. Please repost. I want people to learn about QUBIC. The ecosystem consists of 3 separate universes: AI, Mining, and Tickchain. AI is the primary product and purpose of QUBIC and it is supported by Mining to train the AI and by Tickchin for validation and decentralization. Here is how this whole thing works: AI: Let’s start with the AI. The main purpose of QUBIC is creating AGI (Artificial General Intelligence). It’s a type of AI that can self-develop, set tasks, grow, and learn on its own—much like the human brain does. This product is called AIGarth and it uses many cool ideas where AIs can create their own agents and have them compete against one another to evolve. It is basically robots creating robots with the survival-of-the-fittest evolution approach. Very impressive and thought out. To develop such an AI there are several requirements that even the industry giants like OpenAI, Microsoft, and Tesla are missing. One of them is the data processing for AI training. I mean they have their Datacenters, but those are only good enough to train limited Large Language Models such as ChatGPT and Grok. Mining / Training: Now QUBIC solves this problem with its mining architecture. Keep in mind, mining in QUBIC does not secure the chain, primarily it provides the processing power for training the AI. In a sense, QUBIC mining creates the largest distributed datacenter in the world, where individual miners provide their computers for training the AI and get paid with newly issued QUBIC coins. This way QUBIC gets constantly increasing processing power without having to really pay for the infrastructure. And here is another impressive bit of info. QUBIC’s distributed mining network currently ranks above the #1 supercomputer in the world - El Capitan. QUBIC Tickchain The QUBIC chain ties its AI and Mining together to create decentralization, the reward system for miners, it acts as a decision voting system for future development, and it allows AIGarth to function independently through Smart Contracts. In this summary I will not go over the specifics of QUBIC Tickchain. It’s pretty complex so it will be a separate post. Now, it’s an absolute genius piece of tech, which I consider the most advanced product within crypto industry. It is important to know that QUBIC chain runs directly out of Random Access Memory of its validators. It has instant finality and acts as its own operating system. That allows for speeds only bound by current hardware capabilities and it only increases as technology progresses. As I am writing this, QUBIC Tickchain is fully functional and it already hosts several smart-contract based web3 applications. QUBIC has designed its chain to be this fast for a single purpose, to give its future AI the speed it needs to evolve and to react quickly to the outside world. Ilya Shutskever the scientist, who developed ChatGPT clearly states that next generation superintelligence will make decisions in split second with less data. I believe QUBIC is that next generation. Why QUBIC? So out of the sea of AI projects in crypto why is QUBIC my #1 pick? Well, the first reason is that QUBIC is a unicorn AI startup that happens to use blockchain tech to reach it’s goals. In the real world of Venture Capital it would be fully funded instantly and you would not be invited. Second reason is that the industry admits that Large Language Models have plateaued. Even with enough processing power there is only so much information they can add to their data. Even Google CEO admits that. New approach is needed because the future progress is not possible with LLMs. The third reason is because Large Language Models will not create true AGI. It is evident by Ilya Shutskver latest presentation. Sam Altman of OpenAI is trying to change the definition of what is considered AGI just to lower the plank for his own product. Microsoft’s AI chief is now claiming that it would take 10 years to reach AGI, while QUBIC aims to do this in 2027. All these big players are using wrong technology for what they are trying to achieve and there isn’t enough investor funding for them to pivot. The fourth reason is that QUBIC is headed by Sergey Ivancheglo and 2 renowned AI scientists. Many claim Segey is the creator of Bitcoin. He was the 3rd person to mine bitcoin, he invented Proof of Stake consensus, which Ethereum uses now, he ran the first ICO, and he created 2 of the top gainers in crypto NXT and IOTA. QUBIC is his grand finale after 12 years of development and trials. I am including links below the post as the proof of my claims. Thank you for your time. Please live a like or a comment. It helps me continue making these extensive posts and videos.

retrodrive ⛏

24,757 просмотров • 1 год назад

Right now our experience of the internet is in jeopardy. More than half of our interactions online and onchain come from non-human actors who are not identifiable, not accountable, not verifiable. That means as we look toward a stablecoin payment and AI agent enabled future, how are we going to facilitate payments if we don't know who we're paying? How will applications, display advertising, recommendations work if the counterparty who's interacting with those interfaces and in those digital spaces can’t identify itself as agent or human, or specific human? Or for things like onchain incentives, how can we ensure that tokens and value are arriving at the right users if we cannot tell Sybil accounts and redundant addresses from unique human beings? So for all of these use cases and more, things that touch enterprise and government as well, which we can get into later, we have a very glaring need to bring a layer of identity and trust to the internet that was originally built as a system, a network to communicate amongst computers, but lacked an identity system to acknowledge their users. That's the problem that we are solving with Billions Network. How can we make it really easy for you and the agents who serve you to prove who you are, your traits and capabilities and qualifications, in any space, physical or digital? What that means is that today Billions Network is the first universal human and AI network built with mobile first verification, so you can prove who you are and your agents can prove who they are, starting with comfortable experiences on the devices you already own. So no proprietary hardware. We do not rely on centralized servers to collect user data. Rather, your information, the sensitive data that makes you you, stays securely on your device. And we use zero knowledge proofs as a way to prove traits about you, such as the fact that you're over the age of 21, without revealing that sensitive personal data, such as what your exact birth date is. Source: Billions CEO Evin McMullen evin speaking at House of Chimera Spaces Event Dec 3, 2025
2:13

Right now our experience of the internet is in jeopardy. More than half of our interactions online and onchain come from non-human actors who are not identifiable, not accountable, not verifiable. That means as we look toward a stablecoin payment and AI agent enabled future, how are we going to facilitate payments if we don't know who we're paying? How will applications, display advertising, recommendations work if the counterparty who's interacting with those interfaces and in those digital spaces can’t identify itself as agent or human, or specific human? Or for things like onchain incentives, how can we ensure that tokens and value are arriving at the right users if we cannot tell Sybil accounts and redundant addresses from unique human beings? So for all of these use cases and more, things that touch enterprise and government as well, which we can get into later, we have a very glaring need to bring a layer of identity and trust to the internet that was originally built as a system, a network to communicate amongst computers, but lacked an identity system to acknowledge their users. That's the problem that we are solving with Billions Network. How can we make it really easy for you and the agents who serve you to prove who you are, your traits and capabilities and qualifications, in any space, physical or digital? What that means is that today Billions Network is the first universal human and AI network built with mobile first verification, so you can prove who you are and your agents can prove who they are, starting with comfortable experiences on the devices you already own. So no proprietary hardware. We do not rely on centralized servers to collect user data. Rather, your information, the sensitive data that makes you you, stays securely on your device. And we use zero knowledge proofs as a way to prove traits about you, such as the fact that you're over the age of 21, without revealing that sensitive personal data, such as what your exact birth date is. Source: Billions CEO Evin McMullen evin speaking at House of Chimera Spaces Event Dec 3, 2025

Billions Network

30,898 просмотров • 8 месяцев назад

Introduction to Artificial Intelligence 101 For Jonathan Greenblatt I am not an expert on artificial intelligence or machine learning by any stretch of the imagination, but I am familiar with the very basic principles behind how these systems are built and developed. Two things to consider on AI bias: The responses generated by an AI system after being prompted reflect the corpus of data on which it was trained and the objectives used during its fine-tuning phase. (This is intended only as a high-level overview.) First, think of an AI model as a kind of blank slate, there is a pre-training stage that uses extremely large amounts of data, often measured in petabytes. This represents vast quantities of text, far more than any individual, like you or I, could read in a lifetime. After this initial training phase, developers conduct a fine-tuning process designed to shape how the system formulates its responses. While this description is over simplified, it captures the general idea behind how modern natural language processing AI models are created. If an AI model is trained and refined using material that reflects a particular viewpoint or advocacy perspective like Hasbara 8th-Front propaganda, its outputs will tend to reproduce that very subjective perspective. Conversely, if the training and fine-tuning emphasize a broader range of sources and methodologies, the resulting outputs will reflect that wider balance of information. Most people don't want to be brainwashed by the ADL, SPLC, Bnai Brith or other related activist groups. I personally would prefer to have a broader range of viewpoints so that they can each be individually fact checked based on past and current events in our reality. Hopefully nobody else wants to be told what to think, but will be given the option to explore perspectives with a skeptical, trust but verify attitude. My goal in studying the Leo Frank case for the past 57 years has been to learn both the prosecution and defense side with equal depth. If the goal is to encourage understanding and inquiry, it is important to present a full spectrum of documented perspectives so that readers and researchers can evaluate the material and reach their own logical and fair-minded conclusions based on evidence and reason. Shoving hasbara 8th-Front propaganda down the throats of younger folks is only going to cause them to explore other AI models and viewpoints more intensely. This younger generation is mentally vaccinated by nature from your organizations propaganda (and the propaganda of your fellow travelers), they have developed a herd immunity. And you are going to start seeing the results of that once your Golem army of baby boomers start passing away. We can already see that most AI models are Hasbara 8th-Front biased, we prompt them everyday, we are well aware, which means we will start to pursue other options. You might be able to manipulate the leading models with Hasbara 8th-Front, but you won't manipulate them all. People are naturally going to gravitate toward objective and truth seeking AI models. I pray that Elon Musk and XAI will pursue objective truth seeking with a burning fanaticism. When your cohorts forced the sale of TikTok USA, multitudes simply moved on to other social media. In other words, or to summarize: AI systems do not originate positions independently. They reflect the structure, assumptions, and emphases present in their training environment as designed by their developers. For those who are interested in genuine truth-seeking applications of AI, the expectation is that such systems strive for breadth of sourcing, transparency of method, share a wide variety of viewpoints, and openness to examination. Many observers have raised questions about how leading AI platforms handle controversial historical topics, including discussion of the Leo Frank case and the rape-murder of little Mary Phagan, and have called for continued improvement in neutrality, documentation, and analytical rigor. This issue is not going away and there is a world wide growing consensus against Zionist propaganda. The Palestinian Holocaust is not going away, it was live streamed genocide and petabytes of footage have been captured of its atrocities, these crimes against humanity and war crimes will be talked about everyday for the next millennia. The hope is that advancing technology will support open inquiry, truth seeking, logic, reason, objectivity, empiricism, the scientific method, responsible scholarship, and respectful dialogue, allowing people to review historical records carefully and draw thoughtful, fair-minded conclusions. We the people want Truth seeking AI. We want the objective truth (which is a process, not a destination), not the subjective truth of a group of people with burning anti-Christian fanaticism (many read the website) or anti-Gentilism in their hearts. You have some soul searching to do Jonathan Greenblatt, and I will continue to pray for you. I pray to God that you will give up your anti-Gentile, anti-American, treacherous, wicked and control freak ways, and allow the human race to review all perspectives so they might draw their own logical, and fair-minded conclusions. People reject all calls to be brainwashed. Most people want to learn how to sift the evidence, not brainwashed into thinking a certain way. In the first edition of my book from the 1980s I presented both the prosecution and defense perspectives. I'm Praying that you will turn away from the tribal extremist darkness in your soul and stop spreading false accusations that Leo Frank's trial was driven by anti-Semitism or that he was wrongfully convicted. Or the disingenuous claims you make every year on August 17, that Leo Frank was extra-judicially hanged because of his religion, when the real reason was that he was a convicted murderer, pervert, and a homicidal rapist-pedophile, who brutal beat, sexually assaulted, and strangled my great aunt, little Mary Anne Phagan to death. Leo Frank was not lynched because of anti-religious bigotry, but because he was a lethal child molester. Leo Frank also tried to racistly frame two black employees for the crime. I also pray that executives of the leading AI models will put a stop to spreading false information about the Mary Phagan case, it is a great injustice when their AI models falsify the history of the case, especially the facts of the Leo Frank trial which was extremely well documented. In the mean time, I politely encourage all members and supporters of the ADL Anti-Defamation League of B'nai B'rith to acquire a freshly printed book of my new 2025 revised edition of The Murder of Little Mary Phagan. It will provide you with insights you won't learn from the pro-Frank literature. See my pinned-post for more information.
2:57

Introduction to Artificial Intelligence 101 For Jonathan Greenblatt I am not an expert on artificial intelligence or machine learning by any stretch of the imagination, but I am familiar with the very basic principles behind how these systems are built and developed. Two things to consider on AI bias: The responses generated by an AI system after being prompted reflect the corpus of data on which it was trained and the objectives used during its fine-tuning phase. (This is intended only as a high-level overview.) First, think of an AI model as a kind of blank slate, there is a pre-training stage that uses extremely large amounts of data, often measured in petabytes. This represents vast quantities of text, far more than any individual, like you or I, could read in a lifetime. After this initial training phase, developers conduct a fine-tuning process designed to shape how the system formulates its responses. While this description is over simplified, it captures the general idea behind how modern natural language processing AI models are created. If an AI model is trained and refined using material that reflects a particular viewpoint or advocacy perspective like Hasbara 8th-Front propaganda, its outputs will tend to reproduce that very subjective perspective. Conversely, if the training and fine-tuning emphasize a broader range of sources and methodologies, the resulting outputs will reflect that wider balance of information. Most people don't want to be brainwashed by the ADL, SPLC, Bnai Brith or other related activist groups. I personally would prefer to have a broader range of viewpoints so that they can each be individually fact checked based on past and current events in our reality. Hopefully nobody else wants to be told what to think, but will be given the option to explore perspectives with a skeptical, trust but verify attitude. My goal in studying the Leo Frank case for the past 57 years has been to learn both the prosecution and defense side with equal depth. If the goal is to encourage understanding and inquiry, it is important to present a full spectrum of documented perspectives so that readers and researchers can evaluate the material and reach their own logical and fair-minded conclusions based on evidence and reason. Shoving hasbara 8th-Front propaganda down the throats of younger folks is only going to cause them to explore other AI models and viewpoints more intensely. This younger generation is mentally vaccinated by nature from your organizations propaganda (and the propaganda of your fellow travelers), they have developed a herd immunity. And you are going to start seeing the results of that once your Golem army of baby boomers start passing away. We can already see that most AI models are Hasbara 8th-Front biased, we prompt them everyday, we are well aware, which means we will start to pursue other options. You might be able to manipulate the leading models with Hasbara 8th-Front, but you won't manipulate them all. People are naturally going to gravitate toward objective and truth seeking AI models. I pray that Elon Musk and XAI will pursue objective truth seeking with a burning fanaticism. When your cohorts forced the sale of TikTok USA, multitudes simply moved on to other social media. In other words, or to summarize: AI systems do not originate positions independently. They reflect the structure, assumptions, and emphases present in their training environment as designed by their developers. For those who are interested in genuine truth-seeking applications of AI, the expectation is that such systems strive for breadth of sourcing, transparency of method, share a wide variety of viewpoints, and openness to examination. Many observers have raised questions about how leading AI platforms handle controversial historical topics, including discussion of the Leo Frank case and the rape-murder of little Mary Phagan, and have called for continued improvement in neutrality, documentation, and analytical rigor. This issue is not going away and there is a world wide growing consensus against Zionist propaganda. The Palestinian Holocaust is not going away, it was live streamed genocide and petabytes of footage have been captured of its atrocities, these crimes against humanity and war crimes will be talked about everyday for the next millennia. The hope is that advancing technology will support open inquiry, truth seeking, logic, reason, objectivity, empiricism, the scientific method, responsible scholarship, and respectful dialogue, allowing people to review historical records carefully and draw thoughtful, fair-minded conclusions. We the people want Truth seeking AI. We want the objective truth (which is a process, not a destination), not the subjective truth of a group of people with burning anti-Christian fanaticism (many read the website) or anti-Gentilism in their hearts. You have some soul searching to do Jonathan Greenblatt, and I will continue to pray for you. I pray to God that you will give up your anti-Gentile, anti-American, treacherous, wicked and control freak ways, and allow the human race to review all perspectives so they might draw their own logical, and fair-minded conclusions. People reject all calls to be brainwashed. Most people want to learn how to sift the evidence, not brainwashed into thinking a certain way. In the first edition of my book from the 1980s I presented both the prosecution and defense perspectives. I'm Praying that you will turn away from the tribal extremist darkness in your soul and stop spreading false accusations that Leo Frank's trial was driven by anti-Semitism or that he was wrongfully convicted. Or the disingenuous claims you make every year on August 17, that Leo Frank was extra-judicially hanged because of his religion, when the real reason was that he was a convicted murderer, pervert, and a homicidal rapist-pedophile, who brutal beat, sexually assaulted, and strangled my great aunt, little Mary Anne Phagan to death. Leo Frank was not lynched because of anti-religious bigotry, but because he was a lethal child molester. Leo Frank also tried to racistly frame two black employees for the crime. I also pray that executives of the leading AI models will put a stop to spreading false information about the Mary Phagan case, it is a great injustice when their AI models falsify the history of the case, especially the facts of the Leo Frank trial which was extremely well documented. In the mean time, I politely encourage all members and supporters of the ADL Anti-Defamation League of B'nai B'rith to acquire a freshly printed book of my new 2025 revised edition of The Murder of Little Mary Phagan. It will provide you with insights you won't learn from the pro-Frank literature. See my pinned-post for more information.

Mary Phagan-Kean

21,628 просмотров • 6 месяцев назад

Can #AI not only support but actually drive the future of scientific discovery? We are excited to introduce SciAgents💡🔬, an agentic AI aimed towards scientific discovery through the integration of large-scale knowledge graphs, LLMs, and adversarial interactions between multiple experts. The model is capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data, while retrieving new data via literature search. Using graph reasoning, SciAgents identifies interdisciplinary relationships that might otherwise remain hidden, offering a step-by-step strategy for discovery & innovation. The video features an audiotrack generated using 🍓#o1 based on the original paper and design examples, providing an explanation of the work and its implications. Key elements include: 1⃣Ontological Knowledge Graphs: Structuring and connecting scientific concepts to highlight relationships across fields. 2⃣Multi-Agent Collaboration: AI agents autonomously generate and refine hypotheses, critique research, and evaluate emerging trends. 3⃣Graph-Based Reasoning: Identifying novel material designs, such as mycelium-based composites or silk-pigment blends, informed by both natural and artificial patterns. SciAgents can be used as an autonomous or collaborative tool to assist human researchers. The system offers a more powerful way to process vast data, providing innovative paths to explore nature-inspired designs or unexpected material properties. In the field of materials science, for instance, SciAgents has already demonstrated how principles from biology, music, and art can converge to create new biomimetic materials. Through isomorphic mapping, parallels have been drawn between Beethoven’s 9th Symphony and biological structures, pointing to a broader applicability of AI-driven insights across disciplines. This project allows us to enhance capabilities of researchers, allowing them to explore larger datasets and propose hypotheses grounded in a vast, interconnected web of knowledge. The agentic system was built using Auto Gen #AI #ScientificResearch #GraphReasoning #AI4Science #MaterialsScience #InterdisciplinaryResearch #SciAgents #OpenAI Chi Wang
8:14

Can #AI not only support but actually drive the future of scientific discovery? We are excited to introduce SciAgents💡🔬, an agentic AI aimed towards scientific discovery through the integration of large-scale knowledge graphs, LLMs, and adversarial interactions between multiple experts. The model is capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data, while retrieving new data via literature search. Using graph reasoning, SciAgents identifies interdisciplinary relationships that might otherwise remain hidden, offering a step-by-step strategy for discovery & innovation. The video features an audiotrack generated using 🍓#o1 based on the original paper and design examples, providing an explanation of the work and its implications. Key elements include: 1⃣Ontological Knowledge Graphs: Structuring and connecting scientific concepts to highlight relationships across fields. 2⃣Multi-Agent Collaboration: AI agents autonomously generate and refine hypotheses, critique research, and evaluate emerging trends. 3⃣Graph-Based Reasoning: Identifying novel material designs, such as mycelium-based composites or silk-pigment blends, informed by both natural and artificial patterns. SciAgents can be used as an autonomous or collaborative tool to assist human researchers. The system offers a more powerful way to process vast data, providing innovative paths to explore nature-inspired designs or unexpected material properties. In the field of materials science, for instance, SciAgents has already demonstrated how principles from biology, music, and art can converge to create new biomimetic materials. Through isomorphic mapping, parallels have been drawn between Beethoven’s 9th Symphony and biological structures, pointing to a broader applicability of AI-driven insights across disciplines. This project allows us to enhance capabilities of researchers, allowing them to explore larger datasets and propose hypotheses grounded in a vast, interconnected web of knowledge. The agentic system was built using Auto Gen #AI #ScientificResearch #GraphReasoning #AI4Science #MaterialsScience #InterdisciplinaryResearch #SciAgents #OpenAI Chi Wang

Markus J. Buehler

209,581 просмотров • 1 год назад

What a time to be alive! We are entering the era of machines that discover and build. Scientific discovery begins when evidence breaks the world model, and the system builds a better one - evolving, adapting, building new tools that scale its data and representations. That was the core argument of my keynote “Superintelligence for Scientific Discovery: Multi-Agent Swarms and Large Reasoning Models” at the UC Berkeley RDI Agentic AI Summit 2026. The energy was extraordinary - thousands of attendees building the most important technology ever created. Superintelligence emerges as millions of heterogeneous agents, simulators, experiments, instruments, and human judgment working across disciplines and length scales - proposing, testing, failing, retracting, revising, and building at massive scale. The pieces of a new era for intelligence came into focus: models that improve continuously; agents that reason and act over extremely long horizons; world models connecting simulation with physical reality; AI scientists integrating theory, computation, and experiment; and open infrastructures where agents share evidence, failures, and discoveries. These close four coupled loops - learning, execution, reality, and epistemic revision - with open infrastructure as the substrate forming the internet of agents as the collective substrate for a new connective tissue across our civilization. The deeper technical argument is this: An AI scientist must recognize when its current concepts, laws, or verifiers can no longer explain the evidence, and then construct, test, and document a more powerful model. In my talk, I showed concrete examples of how we are building toward this across scales: 1⃣Graph-native large reasoning models make mechanisms, relationships, and abstractions compositional, compilable, and inspectable. 2⃣Adversarial Builder-Breaker agents generate new evidence, attack their own principles, and accept, reject, or retract model revisions. 3⃣Self-organizing swarms develop their own meta-reasoning structure through interaction. ScienceClaw × Infinite (arXiv:2603.14312) enables decentralized agents to coordinate through persistent, composable, provenance-rich scientific artifacts, allowing evidence, contradictions, failed paths, and discoveries to accumulate across agents and over time. We have obtained remarkable results such as new protein sequences with wet-lab validation. The most consequential capability we can give a machine is the willingness to hold its own beliefs loosely enough to break them. AI is extending its reach from discovering new principles to realizing them as physical things that did not exist before. Thank you to UC Berkeley RDI Dawn Song for organizing this event and to everyone whose questions, ideas, and conversations made this such an extraordinary gathering.
0:42

What a time to be alive! We are entering the era of machines that discover and build. Scientific discovery begins when evidence breaks the world model, and the system builds a better one - evolving, adapting, building new tools that scale its data and representations. That was the core argument of my keynote “Superintelligence for Scientific Discovery: Multi-Agent Swarms and Large Reasoning Models” at the UC Berkeley RDI Agentic AI Summit 2026. The energy was extraordinary - thousands of attendees building the most important technology ever created. Superintelligence emerges as millions of heterogeneous agents, simulators, experiments, instruments, and human judgment working across disciplines and length scales - proposing, testing, failing, retracting, revising, and building at massive scale. The pieces of a new era for intelligence came into focus: models that improve continuously; agents that reason and act over extremely long horizons; world models connecting simulation with physical reality; AI scientists integrating theory, computation, and experiment; and open infrastructures where agents share evidence, failures, and discoveries. These close four coupled loops - learning, execution, reality, and epistemic revision - with open infrastructure as the substrate forming the internet of agents as the collective substrate for a new connective tissue across our civilization. The deeper technical argument is this: An AI scientist must recognize when its current concepts, laws, or verifiers can no longer explain the evidence, and then construct, test, and document a more powerful model. In my talk, I showed concrete examples of how we are building toward this across scales: 1⃣Graph-native large reasoning models make mechanisms, relationships, and abstractions compositional, compilable, and inspectable. 2⃣Adversarial Builder-Breaker agents generate new evidence, attack their own principles, and accept, reject, or retract model revisions. 3⃣Self-organizing swarms develop their own meta-reasoning structure through interaction. ScienceClaw × Infinite (arXiv:2603.14312) enables decentralized agents to coordinate through persistent, composable, provenance-rich scientific artifacts, allowing evidence, contradictions, failed paths, and discoveries to accumulate across agents and over time. We have obtained remarkable results such as new protein sequences with wet-lab validation. The most consequential capability we can give a machine is the willingness to hold its own beliefs loosely enough to break them. AI is extending its reach from discovering new principles to realizing them as physical things that did not exist before. Thank you to UC Berkeley RDI Dawn Song for organizing this event and to everyone whose questions, ideas, and conversations made this such an extraordinary gathering.

Markus J. Buehler

19,008 просмотров • 12 дней назад

The U.S. MUST win the AI race We’ve implemented a clear policy at micro1: we will only work with U.S. AI labs and its allies. We made this decision because the AI race is not just about better products. It is about who controls the intelligence layer of the global economy, and whether frontier capability is used to strengthen the free world or to empower adversarial states. AI will be the most important technology of our lifetime. In the fullness of time, it will automate most functions across the economy. Not just software tasks, but coordination, production, logistics, judgment, and execution. As those functions are automated, human time is freed up to invent new ones. Those new functions then become candidates for automation themselves. This loop compounds. As this trajectory continues, output per worker increases dramatically. Entire categories of work become cheaper and faster to perform. Manufacturing reshoring becomes economically viable not because of policy intervention, but because intelligent systems operated domestically outperform global labor arbitrage. Goods and services trend toward lower marginal cost, while distribution improves through better coordination of supply and demand. That is the upside. However, this is impossible without deep integration of intelligent systems. For AI to meaningfully automate real-world functions inside enterprises or governments, it needs full context of any given enterprise. That means read and write access to its core databases. There is no credible path to automating high-impact functions without granting frontier systems that level of access. If the United States does not win the AI race, enterprises eventually face a constrained choice. Either grant that access to Chinese models controlled by an adversarial government, or rely on sub-optimal intelligence to automate functions that still must be automated. Both outcomes are not acceptable. And ultimately, this becomes the greatest national security risk the United States has ever faced. AI models are trained by humans. The judgment embedded in pre-training data and especially in expert post-training data largely determines how a model behaves. While emergent behavior exists, a useful approximation is that a model reflects the weighted aggregate of the human judgment distilled into it. Assisting foreign actors—who will naturally prioritize expert tasks aligned with their own interests—to dominate data creation embeds those interests directly into the intelligence layer itself. Once encoded at scale, these interests propagate through every downstream applications that relies on that intelligence. Here’s how we win. First, leverage is in software. China is ahead in hardware for physically intelligent systems. Catching up there is a long and difficult battle. Software, both large language models and robotics models, remains the bottleneck. Advancing the brain (AI models) is the fastest way to increase the usefulness of existing hardware and deployed systems. Second, the U.S. must 100x its investment in structured human judgment. Continued investment in compute and algorithmic efficiency is critical. But that investment is ultimately a bet on very high future inference demand. For that bet to pay off, models must unlock many new capabilities, and in practice the only way to unlock those capabilities is through expert human data. Historically, experts like doctors and lawyers were never incentivized to produce high-quality reasoning data in a machine-verifiable format. There was no reason for a doctor to generate precise, structured simulations of patient interactions, diagnostic reasoning, or treatment tradeoffs. There was no reason for a lawyer to document complex legal reasoning paths in a way that could be programmatically evaluated. AI systems now require exactly this kind of data. The incentive finally exists because this data directly improves systems that operate at massive scale, and experts can be paid well to produce it. Once expert judgment is encoded into models in a structured, verifiable way, it compounds. Those who delay do not just lose time. They lose the ability to catch up. Third, distillation from Chinese labs must be stopped. AI labs must do everything they can to prevent Chinese labs and models from distilling frontier models. Simply calling frontier APIs, or even interacting through UIs, lets Chinese model companies rapidly generate high-quality supervised fine-tuning datasets and close the gap at a fraction of the cost. This method does not put you at the frontier, but it does let you catch up quickly, which is what we saw with DeepSeek. The West significantly overreacted to DeepSeek’s headline capabilities, but underreacted to the underlying dynamic: frontier access itself becomes a training set at a fraction of the cost. Human data platforms also have a duty to help prevent this distillation. Lastly, the U.S.government should set the standard for AI Evaluation that leads to real production usage. AI agents are under-deployed relative to what the technology allows because they are probabilistic systems that require a fundamentally different QA approach than deterministic software. Generic QA is insufficient; safely shipping agents requires explicit evaluation frameworks that assess their full action space. Organizations must clearly define which functions an agent is allowed to perform, how quality is measured for each function, and which domain experts are qualified to judge outcomes. With these frameworks in place, agents can be rigorously tested using structured human data, deployed to production with confidence, and continuously improved over time. The U.S. government should be the first large enterprise to implement rigorous evaluation systems across every function. If the government leads on evaluation-driven deployment, adoption across the private sector accelerates naturally. This is how American workers become more powerful. Each worker operates digital or physical agents that expand their effective output. Recruiting, manufacturing, logistics, and other domains shift toward human judgment overseeing autonomous execution. Reshoring occurs because it becomes economically rational. Work becomes more meaningful. This is a race to determine who controls the intelligence layer of the global economy. And that must be us. 🇺🇸
0:48

The U.S. MUST win the AI race We’ve implemented a clear policy at micro1: we will only work with U.S. AI labs and its allies. We made this decision because the AI race is not just about better products. It is about who controls the intelligence layer of the global economy, and whether frontier capability is used to strengthen the free world or to empower adversarial states. AI will be the most important technology of our lifetime. In the fullness of time, it will automate most functions across the economy. Not just software tasks, but coordination, production, logistics, judgment, and execution. As those functions are automated, human time is freed up to invent new ones. Those new functions then become candidates for automation themselves. This loop compounds. As this trajectory continues, output per worker increases dramatically. Entire categories of work become cheaper and faster to perform. Manufacturing reshoring becomes economically viable not because of policy intervention, but because intelligent systems operated domestically outperform global labor arbitrage. Goods and services trend toward lower marginal cost, while distribution improves through better coordination of supply and demand. That is the upside. However, this is impossible without deep integration of intelligent systems. For AI to meaningfully automate real-world functions inside enterprises or governments, it needs full context of any given enterprise. That means read and write access to its core databases. There is no credible path to automating high-impact functions without granting frontier systems that level of access. If the United States does not win the AI race, enterprises eventually face a constrained choice. Either grant that access to Chinese models controlled by an adversarial government, or rely on sub-optimal intelligence to automate functions that still must be automated. Both outcomes are not acceptable. And ultimately, this becomes the greatest national security risk the United States has ever faced. AI models are trained by humans. The judgment embedded in pre-training data and especially in expert post-training data largely determines how a model behaves. While emergent behavior exists, a useful approximation is that a model reflects the weighted aggregate of the human judgment distilled into it. Assisting foreign actors—who will naturally prioritize expert tasks aligned with their own interests—to dominate data creation embeds those interests directly into the intelligence layer itself. Once encoded at scale, these interests propagate through every downstream applications that relies on that intelligence. Here’s how we win. First, leverage is in software. China is ahead in hardware for physically intelligent systems. Catching up there is a long and difficult battle. Software, both large language models and robotics models, remains the bottleneck. Advancing the brain (AI models) is the fastest way to increase the usefulness of existing hardware and deployed systems. Second, the U.S. must 100x its investment in structured human judgment. Continued investment in compute and algorithmic efficiency is critical. But that investment is ultimately a bet on very high future inference demand. For that bet to pay off, models must unlock many new capabilities, and in practice the only way to unlock those capabilities is through expert human data. Historically, experts like doctors and lawyers were never incentivized to produce high-quality reasoning data in a machine-verifiable format. There was no reason for a doctor to generate precise, structured simulations of patient interactions, diagnostic reasoning, or treatment tradeoffs. There was no reason for a lawyer to document complex legal reasoning paths in a way that could be programmatically evaluated. AI systems now require exactly this kind of data. The incentive finally exists because this data directly improves systems that operate at massive scale, and experts can be paid well to produce it. Once expert judgment is encoded into models in a structured, verifiable way, it compounds. Those who delay do not just lose time. They lose the ability to catch up. Third, distillation from Chinese labs must be stopped. AI labs must do everything they can to prevent Chinese labs and models from distilling frontier models. Simply calling frontier APIs, or even interacting through UIs, lets Chinese model companies rapidly generate high-quality supervised fine-tuning datasets and close the gap at a fraction of the cost. This method does not put you at the frontier, but it does let you catch up quickly, which is what we saw with DeepSeek. The West significantly overreacted to DeepSeek’s headline capabilities, but underreacted to the underlying dynamic: frontier access itself becomes a training set at a fraction of the cost. Human data platforms also have a duty to help prevent this distillation. Lastly, the U.S.government should set the standard for AI Evaluation that leads to real production usage. AI agents are under-deployed relative to what the technology allows because they are probabilistic systems that require a fundamentally different QA approach than deterministic software. Generic QA is insufficient; safely shipping agents requires explicit evaluation frameworks that assess their full action space. Organizations must clearly define which functions an agent is allowed to perform, how quality is measured for each function, and which domain experts are qualified to judge outcomes. With these frameworks in place, agents can be rigorously tested using structured human data, deployed to production with confidence, and continuously improved over time. The U.S. government should be the first large enterprise to implement rigorous evaluation systems across every function. If the government leads on evaluation-driven deployment, adoption across the private sector accelerates naturally. This is how American workers become more powerful. Each worker operates digital or physical agents that expand their effective output. Recruiting, manufacturing, logistics, and other domains shift toward human judgment overseeing autonomous execution. Reshoring occurs because it becomes economically rational. Work becomes more meaningful. This is a race to determine who controls the intelligence layer of the global economy. And that must be us. 🇺🇸

Ali Ansari

396,355 просмотров • 6 месяцев назад