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muckrAIkers

Jacob Haimes and Igor Krawczuk

Join us as we dig a tiny bit deeper into the hype surrounding "AI" press releases, research papers, and more. Each episode, we'll highlight ongoing research and investigations, providing some much needed contextualization, constructive critique, and even a smidge of occasional good will teasing to the conversation, trying to find the meaning under all of this muck.

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  • 20 episodes
  • monthly
  • Avg 1 hr 7 min
  • English

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  • #24
    July 14 · 1 hr 6 min

    The Finding-Out Era of AI

    Anthropic's Fable gets classified as a munitions export days after launch, and Jacob and Igor try to figure out who's really being punished and why. From there: SpaceX's suspiciously well-timed IPO, a "genius" financial deal with Anthropic, and Elon buying Cursor because Grok needs friends. They also cover Anthropic Core's pivot to expert-systems consulting, the backlash over silently nerfed guardrails, whether Chinese models are actually a safe alternative, and why the Pope's new AI document is getting weaponized as corporate propaganda. Chapters (00:00) - Intro (05:37) - Fable/Mythos Updates (26:17) - SpaceX's IPO and the Cursor Buyout (34:57) - Anatomy of an AI Bubble (40:02) - Anthropic Core and "AI as Expert Systems" (50:03) - Guardrails, Chinese Models, and Whether We Can Trust the Benchmarks Links Below are the most important links for this episode. For more, visit the episode page on Kairos.fm. Anthropic statement - Statement on the US government directive to suspend access to Fable 5 and Mythos 5 Luta Security blog post - Fable 5 Is Back, But We're Still Slowing Down Defenders Tech Policy Press analysis - Did the US Government Just Set an AI Export Precedent by Blocking Mythos? SEC EDGAR filing - SpaceX Free Writing Prospectus — Anthropic and Google compute agreements Engadget article - Anthropic Backtracks on Policy That 'Sabotaged' Researchers' Work Anthropic announcement - Introducing Claude Corps The Vatican encyclical - Magnifica Humanitas: On Safeguarding the Human Person in the Time of Artificial Intelligence

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  • #23
    May 4 · 46 min

    The AI Booster Tier List

    This week, Jacob and Igor swap the usual news breakdown for a complete taxonomy of AI boosters within the framing of a tier list. They run through every type, from the good-faith genuine enthusiast to the denial-prone deflector, the financially captured, corporate power-players, platform builders, and finally the model-building true believers. They also get into "crit-hype," the idea that some critics are inadvertently boosting the systems they're fighting against. No muck to separate this week, just the guys working through their framework and explaining why their opinions are S tier. Chapters (00:00) - Introduction (02:45) - Introducing the Booster Tier List (07:48) - A-Tier: The Genuine Enthusiast (11:36) - B-Tier: The Denial-Prone Booster (14:34) - C-Tier: The Financially Adjacent Booster (19:03) - D-Tier: The Corporate Booster (23:31) - E-Tier: The Product and Platform Booster (26:36) - F-Tier: The Model Builders (31:08) - Bonus Tier: Crit Hype (39:28) - So What Do We Actually Do With This? (46:04) - Outro (Igor's Challenge) Critical Links Below are the most important links for this episode. For more, visit the episode page on Kairos.fm. Ed Zitron Substack article - How to Argue with an AI Booster MIT Technology Review article - How Social Media Encourages the Worst of AI Boosterism The Telegraph article - AI isn’t magic. We’re just being taken for mugs

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  • #22
    April 13 · 58 min

    The Enemy of My Enemy is Still a Corporation

    In this episode, Jacob and Igor break down the DoD vs. Anthropic standoff, tracing how Claude's use in military operations led to Anthropic being designated a supply chain security risk. Perhaps more importantly, why did Anthropic choose to take a stand now, and what can that tell us about the corporations behavior moving forward. The investigation is used as a case study in how to read the real motivations behind big institutions: intrinsic values, rational self-interest, and realpolitik. Chapters (00:00) - Introduction (00:44) - DoD-Anthropic Standoff (15:08) - 3 Buckets of Motivation (35:39) - How to Read What They're Actually Doing (44:04) - Is This Designation Even Real? (53:39) - Recap (Pragmatist's Playbook) Critical Links Below are the most important links for this episode. For more, visit the episode page on Kairos.fm. Ed Zitron subtack article - The AI Bubble Is An Information War TechPolicy.Press article - A Timeline of the Anthropic-Pentagon Dispute TechPolicy.Press podcast episode - How to Think About the Anthropic-Pentagon Dispute BBC news article - Anthropic vows to sue Pentagon over supply chain risk label The Guardian article - AI got the blame for the Iran school bombing. The truth is far more worrying Palantir YouTube video - Multi-Domain AI: The Future of Command and Control Into AI Safety podcast episode - Drawing Red Lines w/ Su Cizem

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  • #21
    March 17 · 43 min

    The Mythical AI Bear

    This week, Jacob and Igor dissect the "mythical AI bear," the strawman version of AI criticism that gets thrown around in tech discourse. Working through a viral blog post that typifies the genre, they examine how legitimate concerns about code quality, labor displacement, intellectual property, and the erosion of craft get flattened into caricature. Plus: Sam Altman writes ten paragraphs about how unbothered he is by an ad. Chapters (00:00) - Introduction (00:30) - Altman's Super Bowl Meltdown (03:11) - What is "The Bear"? (06:41) - But You Have No Idea What The Code Is (15:44) - But The Craft, But The Mediocrity, But It'll Never Be AGI (24:43) - But They Take Our Jobs & But The Plagiarism (31:21) - Stochastic Parrots & Mythical Bears (42:34) - Outro Critical Links Below are the most important links for this episode. For more, visit the episode page on Kairos.fm. Big Think article - The rise of AI denialism Fly.io blogpost - My AI Skeptic Friends Are All Nuts antirez blogpost - Don't fall into the anti-AI hype Emily Bender blogpost - Resistance Isn't Denialism Cory Doctorow blogpost - Reverse centaurs are the answer to the AI paradox Washington Post article - The AI boom is so huge it's causing shortages everywhere else Business Insider article - Veteran investor Jeremy Grantham says AI is 'obviously a bubble' Ipsos survey - Google / Ipsos Multi-Country AI Survey 2026 Understanding AI Substack post - AI skeptics and AI boosters are both wrong

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  • #20
    February 9 · 49 min

    Big Tech Plans to Move Fast and Break Democracy

    We're talking about developments in AI while those in power have unapologetically revealed their true fascist intensions; are we spending our time in the right way? Igor and I discuss the importance of shining a light on the techno-authoritarians who have played a very significant role in current state-of-the-world. While we discuss the murders of Nicole Good and Alex Pretti during this episode, it's important that we also acknowledge the many marginalized people who have died as a result of ICE's behavior, and they same level of outcry didn't happen. Six additional individuals died in ICE custody under suspicious circumstances between January 1st and 25th of 2026: Victor Manuel Díaz, Geraldo Lunas Campos, Luis Gustavo Núñez Cáceres, Luis Beltrán Yáñez-Cruz, Parady La, and Heber Sánchez Domínguez. Chapters (00:00) - | Introduction (03:57) - | The Authoritarian Stack (08:33) - | Palantir & Theil-Government Consolidation (13:44) - | Move Fast & Break Everything (23:14) - | Fascism in the US & Starving the Beast (39:48) - | Finding Local Opportunities for Action Critical Links Below are the most important links for this episode. For more, visit the episode page on Kairos.fm. The Authoritarian Stack website Project 2025 Observer website EFF report - ICE Using Palantir Tool Feeds on Medicaid Data The Guardian article - Eight people have died in dealings with ICE so far in 2026. These are their stories Indivisible website Distributed AI Research Institute projects EAAMO website - Mechanism Design for Social Good Carlos Maza video - How To Be Hopeless

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  • #19
    January 12 · 38 min

    AI Skeptic PWNED by Facts and Logic

    Igor shares a significant shift in his perspective on AI coding tools after experiencing the latest Claude Code release. While he's been the stronger AI skeptic between the two of us, recent developments have shown him genuine utility in specific coding tasks, but this doesn't validate the hype or change the fundamental critiques. We discuss what "rote tasks" are and why they're now automatable with enough investment, the difference between genuine utility and AGI claims, and why this update actually impacts our bubble analysis. We explore how massive investment has finally produced something useful for a narrow domain, but it doesn't mean the technology is generalizable or that AGI is real. Chapters (00:00) - | Introduction (05:07) - | What Changed Igor’s Mind (18:27) - | Rote Tasks Explained (23:31) - | How Does This Impact our Bubble Analysis? (30:48) - | AGI Is Still BS (34:07) - | Externalities Remain Unchanged (37:49) - | Final Thoughts & Outro Links Related muckrAIkers episode - Tech Bros Love AI Waifus Bubble Talk OfficeChai startup - OpenAI Hasn’t Completed A Successful Full-Scale Pretraining Run Since GPT-4o In May 2024, Says SemiAnalysis Vechron report - Anthropic Prepares for Potential 2026 IPO in Bid to Rival OpenAI YCombinator Forum post on AI crash YCombinator Forum post on OpenAI adopting Anthropic's "skills" YCombinator Forum post on OpenAI rumors YCombinator Forum post on OpenAI add suggestions Other Sources LinkedIn post discussing an agentic coding vibe shift Executive Order - Ensuring a National Policy Framework for Artificial Intelligence Inside Tech Law blogpost - Germany delivers landmark copyright ruling against OpenAI: What it means for AI and IP NeurIPS 2025 paper - Ascent Fails to Forget NBER working paper - Large Language Models, Small Labor Market Effects Dwarkesh Podcast blogpost - RL is even more information inefficient than you thought

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  • #18
    Dec 15, 2025 · 45 min

    Tech Bros Love AI Waifus

    OpenAI is pivoting to porn while public sentiment turns decisively against AI. Pew Research shows Americans are now concerned over excited by a 2:1 margin. We trace how we got here: broken promises of cancer cures replaced by addiction mechanics and expensive APIs. Meanwhile, data centers are hiding a near-recession, straining power grids, and literally breaking your household appliances. Drawing parallels to the 1970s AI winter, we argue the bubble is shaking and needs to pop now, before it becomes another 2008. The good news? Grassroots resistance works. Protests have already blocked $64 billion in data center projects. NOTE: The project that we cite for the $64 billion blockage is actually a pro-data-center campaign. The numbers still seem ok, but it's worth being aware of. Chapters (00:00) - - Introduction (06:45) - - The Addiction Business Model (10:15) - - Public Sentiment Data (22:45) - - Data Centers and Infrastructure Problems (36:30) - - The Bubble Discussion (44:36) - - Closing Thoughts & Outro Links Public Sentiment on AI Pew Research report - How People Around the World View AI Pew Research report - How the U.S. Public and AI Experts View Artificial Intelligence Pew Research report - How Americans View AI and Its Impact on People and Society University of Toronto report - Trust, attitudes and use of artificial intelligence: A global study 2025 Melbourne Business School report - Key findings on public attitudes towards AI The Washington Post article - Americans have become more pessimistic about AI. Why? The New York Times article - From Mexico to Ireland, Fury Mounts Over a Global A.I. Frenzy The Guardian article - ‘It shows such a laziness’: why I refuse to date someone who uses ChatGPT The Register article - OpenAI's ChatGPT is so popular that almost no one will pay for it AI and Claims of Curing Cancer Rachel Thomas, PhD blogpost - “AI will cure cancer” misunderstands both AI and medicine The Atlantic article - OpenAI Wants to Cure Cancer. So Why Did It Make a Web Browser? Independent article - ChatGPT boss predicts when AI could cure cancer The Atlantic article - AI Executives Promise Cancer Cures. Here’s the Reality AI Porn and the Addiction Economy Forbes article - ChatGPT Will Allow ‘Erotica’ After Easing Mental Health Restrictions, Sam Altman Says The Addiction Economy website PPC article - OpenAI is staffing up to turn ChatGPT into an ad platform Tom Nicholas video - Vape-o-nomics: Why Everything is Addictive Now AI Bubble Fast Company article - AI isn’t replacing jobs. AI spending is Pivot to AI article - The finance press finally starts talking about the ‘AI bubble’ Fortune article - Without data centers, GDP growth was 0.1% in the first half of 2025, Harvard economist says The Atlantic article - Just How Bad Would an AI Bubble Be? The New York Times article - Debt Has Entered the A.I. Boom Will Lockett's Newsletter article - AI Pullback Has Officially Started Reuters article - Michael Burry of 'Big Short' fame is closing his hedge fund Business Insider article - The guy who shorted Enron has a warning about the AI boom Datacenters Bloomberg article - AI Needs So Much Power, It’s Making Yours Worse Data Center Watch report - $64 billion of data center projects have been blocked or delayed amid local opposition More Perfect Union video - We Found the Hidden Cost of Data Centers. It's in Your Electric Bill DataCenter Knowledge article - Why Communities Are Protesting Data Centers – And How the Industry Can Respond Fighting Back Knight First Amendment Institute essay - AI as Normal Technology Pranksters vs. Autocrats chapter - Laughtivism: The Secret Ingredient SPSP article - Playing with Power: Humor as Everyday Resistance Blood in the Machine article - The Luddite Renaissance is in full swing

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  • #17
    Oct 13, 2025 · 49 min

    AI Safety for Who?

    Jacob and Igor argue that AI safety is hurting users, not helping them. The techniques used to make chatbots "safe" and "aligned," such as instruction tuning and RLHF, anthropomorphize AI systems such they take advantage of our instincts as social beings. At the same time, Big Tech companies push these systems for "wellness" while dodging healthcare liability, causing real harms today We discuss what actual safety would look like, drawing on self-driving car regulations. Chapters (00:00) - Introduction & AI Investment Insanity (01:43) - The Problem with AI Safety (08:16) - Anthropomorphizing AI & Its Dangers (26:55) - Mental Health, Wellness, and AI (39:15) - Censorship, Bias, and Dual Use (44:42) - Solutions, Community Action & Final Thoughts Links AI Ethics & Philosophy Foreign affairs article - The Cost of the AGI Delusion Nature article - Principles alone cannot guarantee ethical AI Xeiaso blog post - Who Do Assistants Serve? Argmin article - The Banal Evil of AI Safety AI Panic News article - The Rationality Trap AI Model Bias, Failures, and Impacts BBC news article - AI Image Generation Issues The New York Times article - Google Gemini German Uniforms Controversy The Verge article - Google Gemini's Embarrassing AI Pictures NPR article - Grok, Elon Musk, and Antisemitic/Racist Content AccelerAId blog post - How AI Nudges are Transforming Up-and Cross-Selling AI Took My Job website AI Mental Health & Safety Concerns Euronews article - AI Chatbot Tragedy Popular Mechanics article - OpenAI and Psychosis Psychology Today article - The Emerging Problem of AI Psychosis Rolling Stone article - AI Spiritual Delusions Destroying Human Relationships The New York Times article - AI Chatbots and Delusions Guidelines, Governance, and Censorship Preprint - R1dacted: Investigating Local Censorship in DeepSeek's R1 Language Model Minds & Machines article - The Ethics of AI Ethics: An Evaluation of Guidelines SSRN paper - Instrument Choice in AI Governance Anthropic announcement - Claude Gov Models for U.S. National Security Customers Anthropic documentation - Claude's Constitution Reuters investigation - Meta AI Chatbot Guidelines Swiss Federal Council consultation - Swiss AI Consultation Procedures Grok Prompts Github Repo Simon Willison blog post - Grok 4 Heavy

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  • #16
    Aug 21, 2025 · 1 hr 24 min

    The Co-opting of Safety

    We dig into how the concept of AI "safety" has been co-opted and weaponized by tech companies. Starting with examples like Mecha-Hitler Grok, we explore how real safety engineering differs from AI "alignment," the myth of the alignment tax, and why this semantic confusion matters for actual safety. (00:00) - Intro (00:21) - Mecha-Hitler Grok (10:07) - "Safety" (19:40) - Under-specification (53:56) - This time isn't different (01:01:46) - Alignment Tax myth (01:17:37) - Actually making AI safer Links JMLR article - Underspecification Presents Challenges for Credibility in Modern Machine Learning Trail of Bits paper - Towards Comprehensive Risk Assessments and Assurance of AI-Based Systems SSRN paper - Uniqueness Bias: Why It Matters, How to Curb It Additional Referenced Papers NeurIPS paper - Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? ICML paper - AI Control: Improving Safety Despite Intentional Subversion ICML paper - DarkBench: Benchmarking Dark Patterns in Large Language Models OSF preprint - Current Real-World Use of Large Language Models for Mental Health Anthropic preprint - Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback Inciting Examples ars Technica article - US government agency drops Grok after MechaHitler backlash, report says The Guardian article - Musk’s AI Grok bot rants about ‘white genocide’ in South Africa in unrelated chats BBC article - Update that made ChatGPT 'dangerously' sycophantic pulled Other Sources London Daily article - UK AI Safety Institute Rebrands as AI Security Institute to Focus on Crime and National Security Vice article - Prominent AI Philosopher and ‘Father’ of Longtermism Sent Very Racist Email to a 90s Philosophy Listserv LessWrong blogpost - "notkilleveryoneism" sounds dumb (see comments) EA Forum blogpost - An Overview of the AI Safety Funding Situation Book by Dmitry Chernov and Didier Sornette - Man-made Catastrophes and Risk Information Concealment Euronews article - OpenAI adds mental health safeguards to ChatGPT, saying chatbot has fed into users’ ‘delusions’ Pleias website Wikipedia page on Jaywalking

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  • #15
    Jul 14, 2025 · 1 hr 11 min

    AI, Reasoning or Rambling?

    In this episode, we redefine AI's "reasoning" as mere rambling, exposing the "illusion of thinking" and "Potemkin understanding" in current models. We contrast the classical definition of reasoning (requiring logic and consistency) with Big Tech's new version, which is a generic statement about information processing. We explain how Large Rambling Models generate extensive, often irrelevant, rambling traces that appear to improve benchmarks, largely due to best-of-N sampling and benchmark gaming. Words and definitions actually matter! Carelessness leads to misplaced investments and an overestimation of systems that are currently just surprisingly useful autocorrects. (00:00) - Intro (00:40) - OBB update and Meta's talent acquisition (03:09) - What are rambling models? (04:25) - Definitions and polarization (09:50) - Logic and consistency (17:00) - Why does this matter? (21:40) - More likely explanations (35:05) - The "illusion of thinking" and task complexity (39:07) - "Potemkin understanding" and surface-level recall (50:00) - Benchmark gaming and best-of-n sampling (55:40) - Costs and limitations (58:24) - Claude's anecdote and the Vending Bench (01:03:05) - Definitional switch and implications (01:10:18) - Outro Links Apple paper - The Illusion of Thinking ICML 2025 paper - Potemkin Understanding in Large Language Models Preprint - Large Language Monkeys: Scaling Inference Compute with Repeated Sampling Theoretical understanding Max M. Schlereth Manuscript - The limits of AGI part II Preprint - (How) Do Reasoning Models Reason? Preprint - A Little Depth Goes a Long Way: The Expressive Power of Log-Depth Transformers NeurIPS 2024 paper - How Far Can Transformers Reason? The Globality Barrier and Inductive Scratchpad Empirical explanations Preprint - How Do Large Language Monkeys Get Their Power (Laws)? Andon Labs Preprint - Vending-Bench: A Benchmark for Long-Term Coherence of Autonomous Agents LeapLab, Tsinghua University and Shanghai Jiao Tong University paper - Does Reinforcement Learning Really Incentivize Reasoning Capacity Preprint - RL in Name Only? Analyzing the Structural Assumptions in RL post-training for LLMs Preprint - Mind The Gap: Deep Learning Doesn't Learn Deeply Preprint - Measuring AI Ability to Complete Long Tasks Preprint - GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models Other sources Zuck's Haul webpage - Meta's talent acquisition tracker Hacker News discussion - Opinions from the AI community Interconnects blogpost - The rise of reasoning machines Anthropic blog - Project Vend: Can Claude run a small shop?

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  • #14
    Jun 23, 2025 · 53 min

    One Big Bad Bill

    In this episode, we break down Trump's "One Big Beautiful Bill" and its dystopian AI provisions: automated fraud detection systems, centralized citizen databases, military AI integration, and a 10-year moratorium blocking all state AI regulation. We explore the historical parallels with authoritarian data consolidation and why this represents a fundamental shift away from limited government principles once held by US conservatives. (00:00) - Intro (01:13) - Bill, general overview (05:14) - Bill, AI overview (07:54) - Medicaid fraud detection systems (11:20) - Bias in AI Systems and Ethical Concerns (17:58) - Centralization of data (30:04) - Military integration of AI (37:05) - Tax incentives for development (40:57) - Regulatory moratorium (47:58) - One big bad authoritarian regime Links Congress page on the One Big Beautiful Bill Act NYMag article - Republicans Admit They Didn’t Even Read Their Big Beautiful Bill Everything is Horrible Blogpost - They Did Vote For This (GOP House Edition) Authoritarianism Historical context Holocaust Encyclopedia article - Gleichschaltung: Coordinating the Nazi State Wikipedia article - 1943 Amsterdam civil registry office bombing Wikipedia article - Four Ds Conservative leaning, pro-privacy, anti-government Data Governance Hub blogpost - Review and Literature Guide of Trump’s “One Big Beautiful Dataset” Cato Institute blogpost - If You Value Privacy, Resist Any Form of National ID Cards American Enterprise Intitute blogpost - The Dangerous Road to a “Master File”—Why Linking Government Databases Is a Terrible Idea EFF blogpost - The Dangers of Consolidating All Government Information ACLU against national ID cards ACLU main page on national ID cards ACLU blogpost - National Identification Cards: Why Does the ACLU Oppose a National I.D. System? ACLU blogpost - 5 Problems with National ID Cards Inherent unfairness of ML Lighthouse Reports investigation - The Limits of Ethical AI Lighthouse Reports investigation - Suspicion Machines Amazon Science publication - Bias preservation in machine learning: The legality of fairness metrics under EU non-discrimination law Michigan Technology Law Review article - The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default Wired article - Health Care Bias Is Dangerous. But So Are ‘Fairness’ Algorithms Military WallStreet Journal article - The Army’s Newest Recruits: Tech Execs From Meta, OpenAI and More Trump executive order - Unleashing American Drone Dominance Anthropic press release - Claude Gov Models for U.S. National Security Customers Moratorium on State AI Regulation TechPolicy.Press article - The State AI Laws Likeliest To Be Blocked by a Moratorium Forbes article - Colorado’s AI Law Still Stands After Update Effort Fails Other Sources KPMG report - Incentives and credits tax provisions in “One Big Beautiful Bill Act” The Register article - Trump team leaks AI plans in public GitHub repository WallStreet Journal article - To Feed Power-Wolfing AI, Lawmakers Are Embracing Nuclear CBS Austin article - IRS direct file program exceeded its expectations but faces uncertain future

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  • #13
    May 26, 2025 · 1 hr 6 min

    Breaking Down the Economics of AI

    Jacob and Igor tackle the wild claims about AI's economic impact by examining three main clusters of arguments: automating expensive tasks like programming, removing "cost centers" like call centers and corporate art, and claims of explosive growth. They dig into the actual data, debunk the hype, and explain why most productivity claims don't hold up in practice. Plus: MIT denounces a paper with fabricated data, and Grok randomly promotes white genocide myths. (00:00) - Recording date + intro (00:52) - MIT denounces paper (04:09) - Grok's white genocide (06:23) - Butthole convergence (07:13) - AI and the economy (14:50) - Automating profit centers (29:46) - Removing the last cost centers (47:16) - "This time is different" (explosive growth) (57:55) - Alpha Evolve, optimization, and slippage Links University of Chicago working paper - Large Language Models, Small Labor Market Effects OECD working paper - Miracle or Myth? Assessing the macroeconomic productivity gains from Artificial Intelligence Epoch AI blogpost - Explosive Growth from AI: A Review of the Arguments Business Insider article - Anthropic CEO: AI Will Be Writing 90% of Code in 3 to 6 Months Preprint - Transformative AGI by 2043 is <1% likely Automating profit centers Pivot to AI blogpost - If AI is so good at coding … where are the open source contributions? Ben Evans' Mastodon post - "Show me the pull requests" NY Times article - Your A.I. Radiologist Will Not Be With You Soon FastCompany article - More companies are adopting 'AI-first' strategies. Here's how it could impact the environment Forbes article - Business Tech News: Shopify CEO Says AI First Before Employees Newsroom article - IBM Study: CEOs Double Down on AI While Navigating Enterprise Hurdles PNAS research article - Evidence of a social evaluation penalty for using AI Ars Technica article - AI use damages professional reputation, study suggests Removing cost centers The Register article - Anthopic's law firm blames Claude hallucinations for errors Fortune article - Klarna plans to hire humans again, as new landmark survey reveals most AI projects fail to deliver Wikipedia article - The Market for Lemons AlphaEvolve Deepmind press release - AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms Deepmind white paper - AlphaEvolve: A coding agent for scientific and algorithmic discovery Off Topic VelvetShark blogpost - Why do AI company logos look like buttholes? MIT Economics press release - Assuring an accurate research record Pivot to AI blogpost - How to make a splash in AI economics: fake your data Pivot to AI blogpost - Even Elon Musk can’t make Grok claim a ‘white genocide’ in South Africa

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  • #12
    Apr 9, 2025 · 1 hr 31 min

    DeepSeek: 2 Months Out

    DeepSeek has been out for over 2 months now, and things have begun to settle down. We take this opportunity to contextualize the developments that have occurred in its wake, both within the AI industry and the world economy. As systems get more "agentic" and users are willing to spend increasing amounts of time waiting for their outputs, the value of supposed "reasoning" models continues to be peddled by AI system developers, but does the data really back these claims? Check out our DeepSeek minisode for a snappier overview! EPISODE RECORDED 2025.03.30 (00:40) - DeepSeek R1 recap (02:46) - What makes it new? (08:53) - What is reasoning? (14:51) - Limitations of reasoning models (why we hate reasoning) (31:16) - Claims about R1 training on Open AI (37:30) - “Deep Research” (49:13) - Developments and drama in the AI industry (56:26) - Proposed economic value (01:14:20) - US government involvement (01:23:28) - OpenAI uses MCP (01:28:15) - Outro Links DeepSeek website DeepSeek paper DeepSeek docs - Models and Pricing DeepSeek repo - 3FS Understanding DeepSeek/DeepResearch Explainers Language Models & Co. article - The Illustrated DeepSeek-R1 Towards Data Science article - DeepSeek-V3 Explained 1: Multi-head Latent Attention Jina.ai article - A Practical Guide to Implementing DeepSearch/DeepResearch Han, Not Solo blogpost - The Differences between Deep Research, Deep Research, and Deep Research Analysis and Research Preprint - Understanding R1-Zero-Like Training: A Critical Perspective Blogpost - There May Not be Aha Moment in R1-Zero-like Training — A Pilot Study Preprint - Large Language Monkeys: Scaling Inference Compute with Repeated Sampling Preprint - Chain-of-Thought Reasoning In The Wild Is Not Always Faithful Fallout coverage TechCrunch article - OpenAI calls DeepSeek 'state-controlled,' calls for bans on 'PRC-produced' models The Verge article - OpenAI has evidence that its models helped train China’s DeepSeek Interesting Engineer article - $6M myth: DeepSeek’s true AI cost is 216x higher at $1.3B, research reveals Ars Technica article - Microsoft now hosts AI model accused of copying OpenAI data The Signal article - Nvidia loses nearly $600 billion in DeepSeek crash Yahoo Finance article - The 'Magnificent 7' stocks are having their worst quarter in more than 2 years Reuters article - Microsoft pulls back from more data center leases in US and Europe, analysts say US governance National Law Review article - Three States Ban DeepSeek Use on State Devices and Networks CNN article - US lawmakers want to ban DeepSeek from government devices House bill - No DeepSeek on Government Devices Act Senate bill - Decoupling America's Artificial Intelligence Capabilities from China Act of 2025 Leaderboards aider LiveBench LM Arena Konwinski Prize Preprint - SWE-Bench+: Enhanced Coding Benchmark for LLMs Cybernews article - OpenAI study proves LLMs still behind human engineers in over 1400 real-world tasks Other References Anthropic report - The Anthropic Economic Index METR Report - Measuring AI Ability to Complete Long Tasks The Information article - OpenAI Discusses Building Its First Data Center for Storage Deepmind report backing up this idea TechCrunch article - OpenAI adopts rival Anthropic's standard for connecting AI models to data Reuters article - OpenAI, Meta in talks with Reliance for AI partnerships, The Information reports 2024 AI Index report NDTV article - Ghibli-Style Images To Memes: White House Embraces Alt-Right Online Culture Elk post on DOGE and AI

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  • Feb 10, 2025 · 15 min

    DeepSeek Minisode

    DeepSeek R1 has taken the world by storm, causing a stock market crash and prompting further calls for export controls within the US. Since this story is still very much in development, with follow-up investigations and calls for governance being released almost daily, we thought it best to hold of for a little while longer to be able to tell the whole story. Nonetheless, it's a big story, so we provide a brief overview of all that's out there so far. (00:00) - Recording date (00:04) - Intro (00:37) - DeepSeek drop and reactions (04:27) - Export controls (08:05) - Skepticism and uncertainty (14:12) - Outro Links DeepSeek website DeepSeek paper Reuters article - What is DeepSeek and why is it disrupting the AI sector? Fallout coverage The Verge article - OpenAI has evidence that its models helped train China’s DeepSeek The Signal article - Nvidia loses nearly $600 billion in DeepSeek crash CNN article - US lawmakers want to ban DeepSeek from government devices Fortune article - Meta is reportedly scrambling ‘war rooms’ of engineers to figure out how DeepSeek’s AI is beating everyone else at a fraction of the price Dario Amodei's blogpost - On DeepSeek and Export Controls SemiAnalysis article - DeepSeek Debates Ars Technica article - Microsoft now hosts AI model accused of copying OpenAI data Wiz Blogpost - Wiz Research Uncovers Exposed DeepSeek Database Leaking Sensitive Information, Including Chat History Investigations into "reasoning" Blogpost - There May Not be Aha Moment in R1-Zero-like Training — A Pilot Study Preprint - s1: Simple test-time scaling Preprint - LIMO: Less is More for Reasoning Blogpost - Reasoning Reflections Preprint - Token-Hungry, Yet Precise: DeepSeek R1 Highlights the Need for Multi-Step Reasoning Over Speed in MATH

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  • #11
    Jan 27, 2025 · 3 hr 19 min

    Understanding AI World Models w/ Chris Canal

    Chris Canal, co-founder of EquiStamp, joins muckrAIkers as our first ever podcast guest! In this ~3.5 hour interview, we discuss intelligence vs. competencies, the importance of test-time compute, moving goalposts, the orthogonality thesis, and much more. A seasoned software developer, Chris started EquiStamp as a way to improve our current understanding of model failure modes and capabilities in late 2023. Now a key contractor for METR, EquiStamp evaluates the next generation of LLMs from frontier model developers like OpenAI and Anthropic. EquiStamp is hiring, so if you're a software developer interested in a fully remote opportunity with flexible working hours, join the EquiStamp Discord server and message Chris directly; oh, and let him know muckrAIkers sent you! (00:00) - Recording date (00:05) - Intro (00:29) - Hot off the press (02:17) - Introducing Chris Canal (19:12) - World/risk models (35:21) - Competencies + decision making power (42:09) - Breaking models down (01:05:06) - Timelines, test time compute (01:19:17) - Moving goalposts (01:26:34) - Risk management pre-AGI (01:46:32) - Happy endings (01:55:50) - Causal chains (02:04:49) - Appetite for democracy (02:20:06) - Tech-frame based fallacies (02:39:56) - Bringing back real capitalism (02:45:23) - Orthogonality Thesis (03:04:31) - Why we do this (03:15:36) - Equistamp! Links EquiStamp Chris's Twitter METR Paper - RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts All Trades article - Learning from History: Preventing AGI Existential Risks through Policy by Chris Canal Better Systems article - The Omega Protocol: Another Manhattan Project Superintelligence & Commentary Wikipedia article - Superintelligence: Paths, Dangers, Strategies by Nick Bostrom Reflective Altruism article - Against the singularity hypothesis (Part 5: Bostrom on the singularity) Into AI Safety Interview - Scaling Democracy w/ Dr. Igor Krawczuk Referenced Sources Book - Man-made Catastrophes and Risk Information Concealment: Case Studies of Major Disasters and Human Fallibility Artificial Intelligence Paper - Reward is Enough Wikipedia article - Capital and Ideology by Thomas Piketty Wikipedia article - Pantheon LeCun on AGI "Won't Happen" - Time article - Meta’s AI Chief Yann LeCun on AGI, Open-Source, and AI Risk "But if it does, it'll be my research agenda latent state models, which I happen to research" - Meta Platforms Blogpost - I-JEPA: The first AI model based on Yann LeCun’s vision for more human-like AI Other Sources Stanford CS Senior Project - Timing Attacks on Prompt Caching in Language Model APIs TechCrunch article - AI researcher François Chollet founds a new AI lab focused on AGI White House Fact Sheet - Ensuring U.S. Security and Economic Strength in the Age of Artificial Intelligence New York Post article - Bay Area lawyer drops Meta as client over CEO Mark Zuckerberg’s ‘toxic masculinity and Neo-Nazi madness’ OpenEdition Academic Review of Thomas Piketty Neural Processing Letters Paper - A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks BFI Working Paper - Do Financial Concerns Make Workers Less Productive? No Mercy/No Malice article - How to Survive the Next Four Years by Scott Galloway

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  • #10
    Dec 30, 2024 · 1 hr 26 min

    NeurIPS 2024 Wrapped 🌯

    What happens when you bring over 15,000 machine learning nerds to one city? If your guess didn't include racism, sabotage and scandal, belated epiphanies, a spicy SoLaR panel, and many fantastic research papers, you wouldn't have captured my experience. In this episode we discuss the drama and takeaways from NeurIPS 2024. Posters available at time of episode preparation can be found on the episode webpage. EPISODE RECORDED 2024.12.22 (00:00) - Recording date (00:05) - Intro (00:44) - Obligatory mentions (01:54) - SoLaR panel (18:43) - Test of Time (24:17) - And now: science! (28:53) - Downsides of benchmarks (41:39) - Improving the science of ML (53:07) - Performativity (57:33) - NopenAI and Nanthropic (01:09:35) - Fun/interesting papers (01:13:12) - Initial takes on o3 (01:18:12) - WorkArena (01:25:00) - Outro Links Note: many workshop papers had not yet been published to arXiv as of preparing this episode, the OpenReview submission page is provided in these cases. NeurIPS statement on inclusivity CTOL Digital Solutions article - NeurIPS 2024 Sparks Controversy: MIT Professor's Remarks Ignite "Racism" Backlash Amid Chinese Researchers’ Triumphs (1/2) NeurIPS Best Paper - Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction Visual Autoregressive Model report this link now provides a 404 error Don't worry, here it is on archive.is Reuters article - ByteDance seeks $1.1 mln damages from intern in AI breach case, report says CTOL Digital Solutions article - NeurIPS Award Winner Entangled in ByteDance's AI Sabotage Accusations: The Two Tales of an AI Genius Reddit post on Ilya's talk SoLaR workshop page Referenced Sources Harvard Data Science Review article - Data Science at the Singularity Paper - Reward Reports for Reinforcement Learning Paper - It's Not What Machines Can Learn, It's What We Cannot Teach Paper - NeurIPS Reproducibility Program Paper - A Metric Learning Reality Check Improving Datasets, Benchmarks, and Measurements Tutorial video + slides - Experimental Design and Analysis for AI Researchers (I think you need to have attended NeurIPS to access the recording, but I couldn't find a different version) Paper - BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices Paper - Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? Paper - A Systematic Review of NeurIPS Dataset Management Practices Paper - The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track Paper - Benchmark Repositories for Better Benchmarking Paper - Croissant: A Metadata Format for ML-Ready Datasets Paper - Rethinking the Evaluation of Out-of-Distribution Detection: A Sorites Paradox Paper - Evaluating Generative AI Systems is a Social Science Measurement Challenge Paper - Report Cards: Qualitative Evaluation of LLMs Governance Related Paper - Towards Data Governance of Frontier AI Models Paper - Ways Forward for Global AI Benefit Sharing Paper - How do we warn downstream model providers of upstream risks? Unified Model Records tool Paper - Policy Dreamer: Diverse Public Policy Creation via Elicitation and Simulation of Human Preferences Paper - Monitoring Human Dependence on AI Systems with Reliance Drills Paper - On the Ethical Considerations of Generative Agents Paper - GPAI Evaluation Standards Taskforce: Towards Effective AI Governance Paper - Levels of Autonomy: Liability in the age of AI Agents Certified Bangers + Useful Tools Paper - Model Collapse Demystified: The Case of Regression Paper - Preference Learning Algorithms Do Not Learn Preference Rankings LLM Dataset Inference paper + repo dattri paper + repo DeTikZify paper + repo Fun Benchmarks/Datasets Paloma paper + dataset RedPajama paper + dataset Assemblage webpage WikiDBs webpage WhodunitBench repo ApeBench paper + repo WorkArena++ paper Other Sources Paper - The Mirage of Artificial Intelligence Terms of Use Restrictions

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  • #9
    Dec 23, 2024 · 1 hr 16 min

    OpenAI's o1 System Card, Literally Migraine Inducing

    The idea of model cards, which was introduced as a measure to increase transparency and understanding of LLMs, has been perverted into the marketing gimmick characterized by OpenAI's o1 system card. To demonstrate the adversarial stance we believe is necessary to draw meaning from these press-releases-in-disguise, we conduct a close read of the system card. Be warned, there's a lot of muck in this one. Note: All figures/tables discussed in the podcast can be found on the podcast website at https://kairos.fm/muckraikers/e009/ (00:00) - Recorded 2024.12.08 (00:54) - Actual intro (03:00) - System cards vs. academic papers (05:36) - Starting off sus (08:28) - o1.continued (12:23) - Rant #1: figure 1 (18:27) - A diamond in the rough (19:41) - Hiding copyright violations (21:29) - Rant #2: Jacob on "hallucinations" (25:55) - More ranting and "hallucination" rate comparison (31:54) - Fairness, bias, and bad science comms (35:41) - System, dev, and user prompt jailbreaking (39:28) - Chain-of-thought and Rao-Blackwellization (44:43) - "Red-teaming" (49:00) - Apollo's bit (51:28) - METR's bit (59:51) - Pass@??? (01:04:45) - SWE Verified (01:05:44) - Appendix bias metrics (01:10:17) - The muck and the meaning Links o1 system card OpenAI press release collection - 12 Days of OpenAI Additional o1 Coverage NIST + AISI [report] - US AISI and UK AISI Joint Pre-Deployment Test Apollo Research's paper - Frontier Models are Capable of In-context Scheming VentureBeat article - OpenAI launches full o1 model with image uploads and analysis, debuts ChatGPT Pro The Atlantic article - The GPT Era Is Already Ending On Data Labelers 60 Minutes article + video - Labelers training AI say they're overworked, underpaid and exploited by big American tech companies Reflections article - The hidden health dangers of data labeling in AI development Privacy International article = Humans in the AI loop: the data labelers behind some of the most powerful LLMs' training datasets Chain-of-Thought Papers Cited Paper - Measuring Faithfulness in Chain-of-Thought Reasoning Paper - Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting Paper - On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models Paper - Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models Other Mentioned/Relevant Sources Andy Jones blogpost - Rao-Blackwellization Paper - Training on the Test Task Confounds Evaluation and Emergence Paper - Best-of-N Jailbreaking Research landing page - SWE Bench Code Competition - Konwinski Prize Lakera game = Gandalf Kate Crawford's Atlas of AI BlueDot Impact's course - Intro to Transformative AI Unrelated Developments Cruz's letter to Merrick Garland AWS News Blog article - Introducing Amazon Nova foundation models: Frontier intelligence and industry leading price performance BleepingComputer article - Ultralytics AI model hijacked to infect thousands with cryptominer The Register article - Microsoft teases Copilot Vision, the AI sidekick that judges your tabs Fox Business article - OpenAI CEO Sam Altman looking forward to working with Trump admin, says US must build best AI infrastructure

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  • #8
    Dec 2, 2024 · 58 min

    How to Safely Handle Your AGI

    While on the campaign trail, Trump made claims about repealing Biden's Executive Order on AI, but what will actually be changed when he gets into office? We take this opportunity to examine policies being discussed or implemented by leading governments around the world. (00:00) - Intro (00:29) - Hot off the press (02:59) - Repealing the AI executive order? (11:16) - "Manhattan" for AI (24:33) - EU (30:47) - UK (39:27) - Bengio (44:39) - Comparing EU/UK to USA (45:23) - China (51:12) - Taxes (55:29) - The muck Links SFChronicle article - US gathers allies to talk AI safety as Trump's vow to undo Biden's AI policy overshadows their work Trump's Executive Order on AI (the AI governance executive order at home) Biden's Executive Order on AI Congressional report brief which advises a "Manhattan Project for AI" Non-USA CAIRNE resource collection on CERN for AI UK Frontier AI Taskforce report (2023) International interim report (2024) Bengio's paper - AI and Catastrophic Risk Davidad's Safeguarded AI program at ARIA MIT Technology Review article - Four things to know about China’s new AI rules in 2024 GovInsider article - Australia’s national policy for ethical use of AI starts to take shape Future of Privacy forum article - The African Union’s Continental AI Strategy: Data Protection and Governance Laws Set to Play a Key Role in AI Regulation Taxes Macroeconomic Dynamics paper - Automation, Stagnation, and the Implications of a Robot Tax CESifo paper - AI, Automation, and Taxation GavTax article - Taxation of Artificial Intelligence and Automation Perplexity Pages CERN for AI page China's AI policy page Singapore's AI policy page AI policy in Africa, India, Australia page Other Sources Artificial Intelligence Made Simple article - NYT's "AI Outperforms Doctors" Story Is Wrong Intel report - Reclaim Your Day: The Impact of AI PCs on Productivity Heise Online article - Users on AI PCs slower, Intel sees problem in unenlightened users The Hacker News article - North Korean Hackers Steal $10M with AI-Driven Scams and Malware on LinkedIn Futurism article - Character.AI Is Hosting Pedophile Chatbots That Groom Users Who Say They're Underage Vice article - 'AI Jesus' Is Now Taking Confessions at a Church in Switzerland Politico article - Ted Cruz: Congress 'doesn't know what the hell it's doing' with AI regulation US Senate Committee on Commerce, Science, and Transportation press release - Sen. Cruz Sounds Alarm Over Industry Role in AI Czar Harris’s Censorship Agenda

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  • #7
    Nov 19, 2024 · 1 hr 7 min

    The End of Scaling?

    Multiple news outlets, including The Information, Bloomberg, and Reuters [see sources] are reporting an "end of scaling" for the current AI paradigm. In this episode we look into these articles, as well as a wide variety of economic forecasting, empirical analysis, and technical papers to understand the validity, and impact of these reports. We also use this as an opportunity to contextualize the realized versus promised fruits of "AI". (00:23) - Hot off the press (01:49) - The end of scaling (10:50) - "Useful tools" and "agentic" "AI" (17:19) - The end of quantization (25:18) - Hedging (29:41) - The end of upwards mobility (33:12) - How to grow an economy (38:14) - Transformative & disruptive tech (49:19) - Finding the meaning (56:14) - Bursting AI bubble and Trump (01:00:58) - The muck Links The Information article - OpenAI Shifts Strategy as Rate of ‘GPT’ AI Improvements Slows Bloomberg [article] - OpenAI, Google and Anthropic Are Struggling to Build More Advanced AI Reuters article - OpenAI and others seek new path to smarter AI as current methods hit limitations Paper on the end of quantization - Scaling Laws for Precision Tim Dettmers Tweet on "Scaling Laws for Precision" Empirical Analysis WU Vienna paper - Unslicing the pie: AI innovation and the labor share in European regions IMF paper - The Labor Market Impact of Artificial Intelligence: Evidence from US Regions NBER paper - Automation, Career Values, and Political Preferences Pew Research Center report - Which U.S. Workers Are More Exposed to AI on Their Jobs? Forecasting NBER/Acemoglu paper - The Simple Macroeconomics of AI NBER/Acemoglu paper - Harms of AI IMF report - Gen-AI: Artificial Intelligence and the Future of Work Submission to Open Philanthropy AI Worldviews Contest - Transformative AGI by 2043 is <1% likely Externalities and the Bursting Bubble NBER paper - Bubbles, Rational Expectations and Financial Markets Clayton Christensen lecture capture - Clayton Christensen: Disruptive innovation The New Republic article - The “Godfather of AI” Predicted I Wouldn’t Have a Job. He Was Wrong. Latent Space article - $2 H100s: How the GPU Rental Bubble Burst On Productization Palantir press release on introduction of Claude to US security and defense Ars Technica article - Claude AI to process secret government data through new Palantir deal OpenAI press release on partnering with Condé Nast Candid Technology article - Shutterstock and Getty partner with OpenAI and BRIA E2B Stripe agents Robopair Other Sources CBS News article - Google AI chatbot responds with a threatening message: "Human … Please die." Biometric Update article - Travelers to EU may be subjected to AI lie detector Techcrunch article - OpenAI’s tumultuous early years revealed in emails from Musk, Altman, and others Richard Ngo Tweet on leaving OpenAI

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  • #6
    Nov 6, 2024 · 1 hr 16 min

    US National Security Memorandum on AI, Oct 2024

    October 2024 saw a National Security Memorandum and US framework for using AI in national security contexts. We go through the content so you don't have to, pull out the important bits, and summarize our main takeaways. (00:48) - The memorandum (06:28) - What the press is saying (10:39) - What's in the text (13:48) - Potential harms (17:32) - Miscellaneous notable stuff (31:11) - What's the US governments take on AI? (45:45) - The civil side - comments on reporting (49:31) - The commenters (01:07:33) - Our final hero (01:10:46) - The muck Links United States National Security Memorandum on AI Fact Sheet on the National Security Memorandum Framework to Advance AI Governance and Risk Management in National Security Related Media CAIS Newsletter - AI Safety Newsletter #43 NIST report - Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile ACLU press release - ACLU Warns that Biden-Harris Administration Rules on AI in National Security Lack Key Protections Wikipedia article - Presidential Memorandum Reuters article - White House presses gov't AI use with eye on security, guardrails Forbes article - America’s AI Security Strategy Acknowledges There’s No Stopping AI DefenseScoop article - New White House directive prods DOD, intelligence agencies to move faster adopting AI capabilities NYTimes article - Biden Administration Outlines Government ‘Guardrails’ for A.I. Tools Forbes article - 5 Things To Know About The New National Security Memorandum On AI – And What ChatGPT Thinks Federal News Network interview - A look inside the latest White House artificial intelligence memo Govtech article - Reactions Mostly Positive to National Security AI Memo The Information article - Biden Memo Encourages Military Use of AI Other Sources Physical Intelligence press release - π0: Our First Generalist Policy OpenAI press release - Introducing ChatGPT Search WhoPoo App!!

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