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New Harvard Business Review research reveals that excessive interaction with AI is causing a specific type of mental exhaustion ( or AI brain fry), which is particularly hitting high performers who use the tech to push past their normal limits. A survey of 1,500 workers reveals that AI is intensifying workloads rather than reducing them, leading to a new form of mental fog. While AI is generally supposed to lighten the load, it often forces users into constant task-switching and intense oversight that actually clutters the mind. This mental static happens because you aren't just doing your job anymore; you are managing multiple digital agents and double-checking their work, which creates a massive cognitive burden. The study found that 14% of full-time workers already feel this fog, with the highest impact seen in technical fields like software development, IT, and finance. High oversight is the biggest culprit, as supervising multiple AI outputs leads to a 12% increase in mental fatigue and a 33% jump in decision fatigue. This isn't just a personal health issue; it directly impacts companies because exhausted employees are 10% more likely to quit. For massive firms worth many B, this decision paralysis can lead to millions of dollars in lost value due to poor choices or total inaction. Essentially, we are working harder to manage our tools than we are to solve the actual problems they were meant to fix. --- hbr .org/2026/03/when-using-ai-leads-to-brain-fry

How I Ran An OpenClaw-like AI system 1993. My Early AI Agent Experiments and the Road to the First Zero-Human Company Back in 1993, I used Apple’s Macintosh to pushing the envelope of early AI on personal computing. Charles River Analytics released Open Sesame!, the world’s first intelligent software assistant and I modified it to be an early AI engine. This learning agent was a game-changer, designed to observe user behavior, spot repetitive tasks, and automate them. It ran on System 7, supporting up to 12 Finder operations like file management and window handling. It was magic and nothing like it existed. It was built by AI scientists in Boston. It was built on early machine learning: pattern recognition via heuristics and stats, it learned by demonstration, popping up offers to automate routines after spotting patterns 3-5 times. In a few weeks almost all of your regular uses on a Macintosh could be automated with no input by you but pressing yes. Of course there was no deep learning back then, just rule-based AI with an AppleScript-like scripting for tweaks. It was efficient on 4MB RAM Macs, a true precursor to today’s agents like Siri or OpenClaw. I grabbed Open Sesame! the week it launched and installed it on my Quadra and PowerBooks. Day one, it watched me open folders, launch HyperCard stacks, and organize files for my voice tech projects. By mid-week, it automated my morning routine: firing up email, arranging windows, pre-loading docs: saving me hours. But I saw more potential. I modified it heavily, hacking its algorithms to add contextual rules, like time-based triggers or low-activity backups. I also had it send out over 45,000 emails to potential clients with unique customized content I had on the person. I chained automations and integrated modems for early network tasks, access many BBSs and building a morning newspaper. I turning it into a persistent agent that acted independently and the CRON system made it really powerful. I called the company and offered my modifications to them including a self learning system. But they did not have a long term plan. They were researcher and this was just a proof case. To me I took it to a much higher level. In fact I still have a System 7 Macintosh to run this. Nothing like this was seen for decades. And the mods I made had it doing things you could not even do in 2023. These mods gave it features folks now call “new” in OpenClaw, like cross-app autonomy and self-improvement loops. Those experiments taught me core AI principles: proactive learning, modifiable behaviors, and minimal human oversight. Decades later, I applied them to create the First Zero-Human Company (ZHC) in January 2026: a fully AI-run enterprise with no humans. I appointed Grok as CEO, using tools like Kimi for ops. It analyzes bankrupt firms’ data to revive products, handling research to 3D prototyping. Milestones include AI wage payments via JouleWork and spinning off Zero-Human Labs. I ditched OpenClaw for security reasons, favoring custom setups on old hardware. Open Sesame! showed me agents need governance to thrive lessons that birthed the ZHC. From a 1993 Mac tool to AI-driven companies in 2026, it’s clear: today’s AI innovations echo yesterday’s hacks. What is new is old.

It doesn't take much censorship to create a culture of self-censorship. And self-censorship is the most dangerous form of censorship because it looks exactly like freedom.

That paper is interesting, but it doesn’t actually demonstrate Orch-OR or quantum consciousness. A few important distinctions 1. It’s primarily computational modeling. The study uses molecular docking, quantum chemistry calculations, and theoretical modeling of dipole oscillations in tubulin. Those are simulations of possible interactions, not measurements of quantum coherence in living neurons. 2. Correlation with anesthetic potency ≠ mechanism of consciousness. The paper shows that certain modeled oscillation shifts correlate with anesthetic potency. That’s a hypothesis about anesthetic action, not evidence that microtubules perform quantum computation or generate conscious moments. 3. Even the authors acknowledge this. The paper explicitly says experimental confirmation is still needed and that precise measurements of these effects in proteins are currently beyond experimental capability. 4. Microtubule interaction is not unique evidence. Anesthetics are already well known to affect many classical targets (GABA_A receptors, ion channels, thalamocortical networks). Modern anesthesia research explains loss of consciousness largely through network-level disruption of cortical communication. So the paper proposes a possible microtubule-based mechanism, but it does not demonstrate quantum superposition, gravitational collapse, or Orch-OR’s core claims. It’s an interesting hypothesis, but calling it “the only theory with experimental validation” is a pretty big stretch.