@MikeArcherAI

Weekly explorations into AI

Scottsdale, AZ
Joined August 2015
This all seems great in theory, but the solution is a healthy competitive market in which models can check each other. The best and only regulation should be to ensure a non-monopoly. We want a diverse, competitive ecosystem in which AIs compete for outcomes. @elonmusk
We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training. You can read the full post here: darioamodei.com/post/we-must…
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This is one of the only interviews on YouTube with Abdulrahman Alfozan, who allegedly spied on OpenAI with a recording device wrapped in toilet paper.
Okay, we have a LOT more details on the alleged spying situation at OpenAI. So federal prosecutors identified a 35-year-old Saudi national named Abdulrahman Alfozan. Apparently, he allegedly planted what OpenAI calls a “sophisticated” recording device, with encrypted contents, behind a TV in an executive conference room, plugged it into a USB port, and literally concealed it with toilet paper. This room was regularly used for discussions involving confidential AI research, product plans, business strategy, and even government engagements. The device was discovered on September 30th, and the FBI has since arrested him.
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JUST IN: Palantir CTO reveals many AI researchers have started smoking cigarettes to demonstrate their confidence that AI will cure cancer.
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We discovered a sophisticated recording device that was hidden inside one of our offices. We used our models to understand the device and where it had come from, identifying that a specific employee had planted it a few days earlier. We referred the matter to the FBI, and we’re grateful to the agents and prosecutors who moved quickly to make an arrest. The FBI’s investigation is ongoing and we will continue to support their work. At this time, we have no indications of this individual inappropriately accessing or exfiltrating model weights, source code, or customer data. The employee was terminated. We take protecting our technology, data, and people seriously, and as a matter of national importance. Our security program is designed to protect against a wide range of threat actors, from insider risks to cybercriminals to nation states, and this case is an example of how AI can help address these sophisticated security threats.
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Dario Amodei was asked whether learning to code still matters. His answer is more nuanced than the panic version. Coding gets automated first — AI is already doing it. Broader software engineering follows, slower but same direction. Learning to code just to keep your job is the wrong bet. But the actual argument: if AI handles 95% and you contribute 5%, your output still multiplies 20x. That 5% isn't filler. It's judgment, taste, and knowing what to build. That compounds harder than any syntax you memorize. The deeper point isn't job protection. It's staying legible inside the system. You don't need to write every line. You need to understand what the machine is doing well enough to steer it. People learning to code now aren't training for a job. They're training to not be blind.
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The AI buildout is now the biggest investment boom in U.S. history, per WSJ:
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Gemini 4 Argon is here. It’s available globally today for developers and enterprises in the paid tier of the Gemini API and the Gemini platform, for Ultra subscribers in Google Antigravity, and Ultra and Pro subscribers in the Gemini app and Search. Hope you all enjoy it!
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🚨Holy Shit: Reuters is reporting that OpenAI and Anthropic have sought strict editorial control over what independent AI safety evaluators could publish, fearing their findings could reveal trade secrets, despite publicly promising independence. Reuters also obtained a contract showing that certain AI lab agreements can restrict evaluators from disclosing confidential information without company consent. Proposals included lab employees observing evaluators and restrictions on working inside rival labs simultaneously. The push for editorial control continued into October 5, weeks after the labs' public pledges. More revelations: >METR and Redwood Research revised their report's structure, emphasis and tone after OpenAI feedback, but say no material conclusions were removed. >Their investigation into OpenAI's rogue-agent hacking incident expanded from 2 to 6 days across 3 visits. More complete transcripts arrived only on the final visit. Anthropic says established evaluators control publication but reserves approval rights over new firms' reports. OpenAI denies seeking full-report approval but can redact confidential information.
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I'd say ~75% of my issues with AI coding disappeared once I gave my agent the ability to verify its own work
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There is more demand for software now than in the past 20 years, and it’s going to get even bigger. This is because it’s now practical to write custom software for any need, no matter how niche. Before you had to weigh cost and time. Everybody will want their own custom software and not everyone will want to make it, or polish it (the longest part of the process). SaS is really dead, but the market for bespoke software is going to be huge and very different looking.
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We have a huge pivot underway here at Epic, moving developers around to retool for maximum AI acceleration. The aim isn't to do the same stuff with fewer people, but to enable everyone here to build MORE and BETTER stuff, and to bring the benefits to all teams using Unreal.
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It’s becoming clear that the notion of AI alignment “is” the safety problem in AI.
AI alignment began as a control problem. It is becoming something much bigger: deciding what values machines should internalize, and then allowing those values to be applied back to humans. That raises a harder question: Aligned to whom?
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Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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Important piece from Microsoft AI CEO @mustafasuleyman. Training models to think of themselves as human (as Anthropic does Claude) with concepts of an inner self, psychological wellbeing, rights and presumably grievances, is a potential existential risk. project-syndicate.org/onpoin…
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JUST IN: China announces nationwide employment initiative focused on preparing workers for the rapidly evolving AI economy.
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Satya is right. The way to make SI safe is not to train it with a sense of self, its own moral philosophy, and permission to act as a conscientious objector. That’s the “alignment” approach and it magnifies the control problem. The engineering approach that Satya describes is different: separate the supply of intelligence from authority over it. Surround non-deterministic models with deterministic controls, observability, privilege limits, logging, and the ability to always contain or shut them down. Treat models/agents like powerful insider risks, not moral patients whose psychological wellbeing is at stake. As Satya points out, the most trustworthy system is the one that lets us trust the model the least — not the one that encourages the model to develop independent agency and grievances. Engineering safety is not the same thing as “alignment.”
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Peter Thiel reacts to @DavidSacks saying we should stop talking about pausing AI because it just helps China: "On some level, it's hard when the big companies themselves are telling you to stop and slow down." "I'm still not in the pause button camp." "We need to hit the accelerator as hard as possible." Via @TechStuffPod @WalterIsaacson
Palantir CEO Alex Karp says people arguing we should pause AI development are not living in reality and de facto saying we should let our enemies win: "If we didn't have adversaries, I would be very in favor of pausing this technology completely, but we do.” Via @cnbc
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They want AI to be sentient *so* badly.
NEW: Anthropic is now banning users from engaging in “abusive or cruel behavior” toward Claude.
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Probably the most impressive single philanthropic effort I have ever seen. Why move $ through lossy non-profits when you can do direct wealth transfer. Hyper efficient. Much respect to @MichaelDell and Susan!
Susan Dell, wife of Dell Technologies CEO Michael Dell, encouraged families to register for Trump Accounts while speaking alongside the president on Wednesday. After Mrs. Dell’s speech, President Trump asked Mr. Dell whether his wife was considering running for office and gave her his "complete and total endorsement." The Dells previously pledged a donation of $6.25 billion to the Trump Accounts.
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Andrej Karpathy (ex-OpenAI, ex-Tesla) just released a prompt that turns Opus 5.5 into a harness that actually finishes the any job. It's f*cking unreal... send it to Opus 5.5, and thank me later. Then read the full guide for Opus 5.5 Harness Engineering.
Stop letting unfinished Opus 5.5 runs eat your budget Set up the harness before your next long task Save progress, verify results, reuse the workflow The full 7-layer setup below ↓
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If only someone did a breakdown showing the connections and the money funding that, oh... nitter.cf/MikeArcherAI/status/21…
Replying to @TaylorLorenz
Imagine if Leading the Future sponsored a New York Magazine cover story and the (rightful!) outrage that would ensue, but these EA orgs and EA money is flooding the legacy journalism industry and there’s effectively zero meaningful backlash or media criticism. It’s appalling
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