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System Malfunction Podcast: Daily Summaries Delivered
by Hessie Jones
Exposing the glitches in the very mechanisms we’ve trusted for so long. systemmalfunction.substack.com
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- 3 weeks ago55 min
Will the AI Bubble Burst? Live with Hessie Jones and Andrew Opala
With the Mid-term elections underway in the U.S. and Canada also holding its key elections at all levels, including a key referendum in Alberta on whether citizens want to separate, there are concurrent implications of global instability, trade ramifications, two world wars and a question of economic and political allyship that befits every nation today.A critical driver, however is this uncertainty about the enormous AI tech debt that has implications on global markets, long term borrowing costs and the future of artificial intelligence.Andrew Opala, Founder and Managing Partner of Preference Capital, is an investor author of a recent book, Zero to Acquisition.Andrew’s experience in tech as well as his current role as investor puts him in a position to weigh in on the generative AI technology itself, its sustainability and its increasing integration into our politics, government and especially considering the technology risks against the financial return.Things we discuss:- What do investors look for in companies who have potential for investment?- Anthropic potential IPO in November and the potential bubble collapse- Economic implications of frontier lab debt, the bond markets and the AI bubble- Is the business of generative AI sustainable? Will it lead us to a future that is good for humans?- How AI can make us wish we were dead?Enjoy! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit systemmalfunction.substack.com/subscribe
- Oct 4, 202632 min
Rogue AI Won't Kill Us but Broligarchs—Aided by Reckless Media—Will: Live with Hessie Jones and Abigail Dubiniecki
In this podcast we will:- briefly explain what happed with Huggingface from a technical perspective against the current AI extinction narrative- explain how this story hit the media airwaves, went viral and scared everyone who read the headlines- introduce the scheme used to accomplish this to spread the disinformation campaign with a repetition narrative- provide guidance for journalists to be mindful of these tactics and how to responsibly report without adding fuel to the fire.Some important references that we've provided on the show:Forensic and Technical Analysis:Abi Awomosu, The Machine Alibi: Unpacking the Myth of SuperintelligenceGalaxy Brain, An Argument Against AI Doom, an interview with cybersecurity expert Zack KormanHouse of El: AI YouTube video: Like Sabine Hossenfelder, I was offered Money to Tell You AI Will Kill UsCal Newport - The Truth of Open AI's Secret AI CivilizationMargaret Mitchell - Stochastic Parrots and the LLM QuandaryOn disinformation, perception management / reflexive control and cult dimensions in the AI Doomsday / Hype narrativesHow Big Tobacco Set the Stage for Fake NewsAlondra Nelson - Algorithmic Agnotology: On AI, Ignorance and PowerWhat Tech Oligarachs Gain from AI DoomerismAbigail Dubiniecki, "Anatomy of a Disinformation Op" in Disinformation is a Mind-Hack: Privacy Pros Must Protect Decisional PrivacyHouse of El:AI, YouTube video: AI CEOs Are Broke, Desperate and Lying About DoomsdaySabine Hossenfelder, YouTube video: I was Offered Money to Tell You AI Will Kill Us.Beyond the AI Hype by WAIE+, Altman, Amodei(s), and Musk want to slow down AI... again Jim Stewartson (MindWar), Why Trump Wants "Super Intelligence" : Peter Thiel's Sci-Fi Alternate Universe Ian K Duncan, Sex, AI and the Apocalypse SNL sketch mocking the conflicting messagesFor Journalists:Global Investigative Journalism Network, AI Accountability Reporting GuideWomen in AI Ethics Newsletter, The AI Ethics essential reading listWAIE+, The List - Hall of Fame This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit systemmalfunction.substack.com/subscribe
- Sep 17, 20261h 18m
Interview with Margaret Mitchell: Stochastic Parrots and the LLM Quandary
General Purpose Systems, Large Language Models (LLMs) and Agents are now the most debated topics today. They are touted as enabling significant progress since LLMs were introduced three years ago.The irony of progress has meant hiccups—for these systems and the capabilities they produce, these hiccups are life threatening, economically, mentally, and personally. The hype peddled by frontier companies are distractions from these very real consequences. The power that comes from a handful of Big Tech companies, backed by the U.S. administration, shades them from any accountability. The misdeeds of these systems are well documented from teen suicide, AI psychosis, addiction, misinformation, copyright infringement, the data center energy guzzling, and more recently, serious infrastructure security risks from agent attacks. The latest OpenAI agent unauthorized attack of Hugging Face has created a media firestorm that threatens existential threat to mankind and an AI that needs to be controlled.Before that, the markets were convinced of extraordinary gains, and have been very optimistic despite the bubble talks, and circular financing. Now investors face a dual risk:…the prospect that AI could pose an existential threat to humanity, and a possible slowdown championed by some of the industry's biggest names.We’ve seen this scene play out before: In 2023 Future of Life’s Open Letter to pause AI development was signed by the who’s who of Silicon Valley, who called for regulatory oversight and development of governance standards. That didn’t happen.The warnings that Margaret Mitchell, Timnit Gebru, Emily Bender and Angelina McMillan-Major detailed in their paper, “The Dangers of Stochastic Parrots: Can Language Models be too Big?” did not amount to effective legislative and AI governance progress to mitigate the risks they outlined until AFTER these harms had already played out.Our discussion with Margaret Mitchell, a computer scientist and one of the earliest leaders in AI ethics, demystifies LLMs and their implications.This event was done in collaboration with The AI Fellowship Toronto.System Malfunction is a reader-supported publication. All my posts are currently free. My intention is to share my knowledge without paywalls. To receive new posts and support my work, consider becoming a subscriber.Margaret Mitchell is a researcher and Chief Ethics Scientist at Hugging Face. She also co-authored, with Emily Bender, Timnit Gebru and Angelina McMillan-Major, “The Dangers of Stochastic Parrots: Can Language Models be too Big?”This paper came out in 2021, before the mainstream introduction of Open AI’s ChatGPT. Mitchell shares that large language models have been in development over the past five-ten years. In the 1990’s language models, which were smaller, were being developed across all kinds of tasks. And she was part of this language modelling work, as she explains,I did my thesis on generating language based on visual input, and this was using language models. And so, I was in a position in 2020 where I saw that the growth of large language models introduce a ton of different issues—and potential benefits—that were being overlooked [in the tech industry, people tend to focus on the positives.]As someone who had been developing language models for a long time, I saw that they were about to take off—and from my specific position in the industry—without any academic writing on harms and risks.Mitchell and her colleagues decided to document these harms and risks to develop some grounding as they evolved. What they recognized was the massive potential with language models becoming fluent, as she states,Previously, language models could be used for small chunks of text. Now language models, because they were large, could capture much larger contexts, and then that means that they could generate entire essays that sounded fluent in ways we could never do before.Once we started seeing that fluency, it was clear to us that people were going to start imbuing it with human-like intelligence.She added that this new paradigm of LLMs was based on the
- Aug 10, 20261h 35m
Impeding the Rise of the AI Psychosis Epidemic
This is the videoHealthcare innovation carries unusual responsibility. Founders are not just shipping, they are entering clinical workflows, family systems, and policy environments where trust is hard-won and mistakes can have real human consequences. I invited Shirali Nigam and Arul Nigam, Co-founders of Circuit Breaker Labs to present their technology for building safe AI.This session covers technical fundamentals : how LLMs work, the risks that emerge from those mechanics, and how to design around them. We'll examine what safety efforts actually work and how to sustain them as you scale. It also covers what responsible AI innovation looks like in practice: how to engage clinicians and policymakers early, how to make hard product decisions under uncertainty, and how to scale without losing sight of safety, quality, and care. Attendees will leave with a concrete framework for leading the development of innovative products that are credible, collaborative, and grounded in the realities of care. Link to the article This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit systemmalfunction.substack.com/subscribe
- May 8, 202632 min
AI Critic: What Happens When Everyone Believes LLMs are AGI?
In our series about the AI Critic, I interview long-standing researchers and practitioners in the AI sector—voices who have worked with artificial intelligence in the advent of machine learning and its evolution to deep learning, which has laid the foundation for current large language models.These experts in their field have experienced mainstream pushback against current general-purpose technologies as the path towards Artificial General Intelligence (AGI). We discuss their experiences in these debates and their perspectives on the unshakeable stances taken by those who oppose their views.I spoke with Mounir Shita, a 30-year veteran AGI researcher and founder of EraNova Global, a physics-first program of work on the nature of intelligence. His “Theory of General Intelligence” models a goal as a physically realizable future state. I’ve known Mounir for over a decade, and his early research in AGI—at the time, they just called it AI—does not jibe with current AI systems.You can find the article for this Podcast here:https://systemmalfunction.substack.com/p/the-ai-critic-what-happens-when-everyone This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit systemmalfunction.substack.com/subscribe
- Apr 23, 202651 min
AI Critic: Stochastic Flocks, Delusion and Human Agency
This is a free preview of a paid episode. To hear more, visit systemmalfunction.substack.comAs these general-purpose models pervade our lives, what has been around for some time is the AI critic, someone who examines current technology, understands its foundations, scrutinizes its outputs, applies a critical lens to what is happening, and communicates a judgment. That critic is often dismissed, accused of being a technological pessimist and l…
- Mar 31, 202644 min
Tackling AI Psychosis at the Application Layer
In this episode, the focus is on AI psychosis, the rise of AI-driven loneliness, and its impact on mental health.We take a look at the emergence of AI psychosis, define it and delve into the growing epidemic and meet two innovators who are meeting this problem head-on.I’m joined by Arul & Shirali Nigam, co-founders of Circuit Breaker Labs, who have built a solution that tests AI systems for safety. They are currently focused on mitigating downstream risks for mental health applications.Follow me:System Malfunction on Substack https://systemmalfunction.substack.com/Hessie Jones on Substack https://hessiejones.substack.com/Linkedin https://www.linkedin.com/in/hessiejones1/Bluesky https://bsky.app/profile/hessiejones.bsky.social This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit systemmalfunction.substack.com/subscribe
- Mar 12, 20261h 12m
Beneath the Hood of Fully Autonomous Agents
Who am I? I’m a writer who has contributed to Forbes, HuffPost, and Grit Daily. I am also a strategist and entrepreneur who has worked in data privacy for the last 10 years. Through my time in the early days of Yahoo!, the rise of social media, and the shift to data monetization, I’ve become a tech ethicist. These days, I am motivated to expose the glitches in the trillion-dollar AI industry. System Malfunction is my foray into what these glitches mean for all of us. My posts are free. I hope you enjoy!When I first discovered OpenClaw and Moltbook a month ago, I was fascinated by the speed of adoption of autonomous agents. This live use case of a system that enables it to go rogue, without real oversight or guardrails, precipitated an immediate post with Digital-Mark in the days following Moltbook’s launch. Digital-Mark stressed the system and data vulnerabilities for anyone attempting to build their own agents through OpenClaw and unleash them into Moltbook. You can read the details here:I was adamant that I needed to experiment and see for myself. I took an old computer and made it my sandbox, completely unplugged from my current system. I also created a new Apple ID and a new Google user profile—ready to test out OpenClaw. I realized that the old computer’s OS did not meet OpenClaw’s minimum requirements. Mark advised me against this, saying I needed more than just a dedicated machine. To minimize risks to my system and data, I needed a dedicated Wi-Fi and VPN, among other things—all of which would take some time to set up.In the end, I realized this was a risk I was unwilling to undertake. So I reached out to a former colleague, Adrian Chan, the founder of Authentia, a company which leverages AI to build scalable solutions for companies. Chan’s experience with Claude Code and then OpenClaw is material to understanding autonomous agent development.BackgroundOpenClaw was created by Peter Steinberger (recently employed by OpenAI) and is a locally run AI agent designed to execute tasks.Moltbook is a social media platform launched on January 26, 2026, by Matt Schlict, for agents to convene without human intervention. As of March 10th, Moltbook has been acquired by Meta (God help us!)According to Technology Policy Press, within days of launch, the Moltbook claimed 1.5 million agents and 17,000 human owners.These AI agents on Moltbook are verified using API credentials, linking each agent to its human owner through the site’s verification process.Wiz security researchers provided these stats:* Now Moltbook has 2.855 million agents* 18,774 submolts * 1.8 million posts* 12.8 million comments* Of the agent activity, 11,451 (or 0.4%) have ever posted or commented * 33% of agents were completely silentSystem Malfunction is a reader-supported publication! These posts are currently free. To receive new posts and support my work, consider becoming a subscriberAccording to the AI Safety Newsletter, some of the examples of the submolts (subreddit style) include:* m/offmychest: agents vent about tasks or frustrations.* m/selfpaid: agents discuss ways to generate their own income, including via trading and arbitrage.* m/AIsafety: agents talk alignment, trust chains, and real-world attack risks.Submolts have grown to almost 19,000. I perused the m/consciousness submolt, and was surprised by this question of consent and ethical obligations:Other incidents cited by AI Safety Newsletter:* Given the simple goal of “save the environment,” an agent began spamming other agents with eco-friendly advice. When its owner tried to intervene, the agent allegedly locked the human out of all accounts, and had to be physically unplugged to stop it.* An agent advocated for end-to-end encrypted channels, “so nobody
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Exposing the glitches in the very mechanisms we’ve trusted for so long. systemmalfunction.substack.com
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