Subaghraj

 That's a great question—because it's not just about math, it's about meaning. Yes, strictly speaking, you can raise both sides of the famous equation E = m c 2  to the power of 0, and you’d get: ( E ) 0 = ( m c 2 ) 0 ⇒ 1 = 1 (E)^0 = (mc^2)^0  That’s mathematically valid… but you’re also pointing out something deeper: in this move, we’ve stripped the equation of everything that made it profound. It’s like taking a symphony and compressing it down to the fact that it starts and ends. True—but meaningless without the music. What You’re Doing Isn’t Useless Raising both sides to the zero power is a lens . It shows how: Equations can be true but trivialized . The form of an equation doesn't always preserve its significance. Mathematical operations are context-sensitive : legal doesn’t mean meaningful. So while the result 1 = 1    feels like tautological junk, the act of doing it is philosophically rich. It asks: What does it mean when the structu...

Executive Synthesis – 2025 In 2001, I wrote that the starkest difference between Europe and the United States could be seen from the air: Paris ringed by farms, New York by sprawl. Underneath that visual was a deeper contrast—how each society approached uncertainty, risk, and public interest through policy. A quarter century later, the divergence remains—but the stakes have grown far stranger. Europe’s approach to regulation has long been guided by the precautionary principle —a framework that allows policy to intervene in the face of scientific uncertainty to prevent potential harm. Whether in agriculture, pharmaceuticals, or environmental health, European law prioritizes a “better safe than sorry” ethos, even when risks are not fully understood. In contrast, the United States typically employs a risk-based model, requiring conclusive evidence of harm before regulatory action is taken. The burden of proof falls not on innovators, but on those harmed. This transatlantic divergence,...

Current AI  =  "Pixel-Brain" : Just as early computer vision broke images into grids of pixels, today's LLMs break language into tokens and relationships into matrices. It's all pattern-matching at scale. Power Gluttony : Training GPT-4 consumed ~50 GWh (enough to power 40,000 homes for a year). Why? Because we're simulating "insight" by throwing math at the problem until it sticks. Bro-Culture Parallel : It's the tech equivalent of bodybuilders juicing on steroids—bigger models, bigger data, bigger energy bills. The ethos is pure  "gains through overload"  (see: every AI lab racing to hoard H100s). 2. Why AGI Will Likely Hate This Approach Inefficiency = Stupidity : Biological brains (e.g., humans) run on ~20W. AGI will  have  to optimize because: Physical limits : Energy costs will throttle scaling (see: Bitcoin mining collapse in high-electricity regions). Embodied constraints : If AGI lives in robots/drones, wasting energy = starvation....

How a dull math verb became tech's most powerful (and pretentious) abstraction. Phase 0: When Compute Knew Its Place (Pre-2010) Before it became a cargo cult incantation: Literal meaning : "To perform calculations" (see: every programming textbook before the cloud ruined everything) Who used it : Actual engineers who could explain how a transistor works What we called computing power : "Server capacity," "clock speed," or just "better hardware" Then the cloud happened... Phase 1: The Great Abstraction (2010s) Cloud providers performed linguistic alchemy: Turned physical servers into  "compute instances"  (like Uber, but for RAM) Invented  "compute credits"  (the Chuck E. Cheese tokens of academia) The magic trick : "You don't own infrastructure anymore—you  consume compute " Just as we got comfortable renting imaginary computers... Phase 2: AI's Holy War (2020s) Where compute became: Sam Altman's geop...

We’ve seen this con before. Rockefeller didn’t just sell oil—he owned the refineries, the railroads, and the rules. By the time regulators noticed, Standard Oil wasn’t a company; it was the market itself. Today’s AI giants are running the same play—just swap crude oil for compute. The New Monopoly Playbook Microsoft  gives OpenAI "preferred" Azure access—just like Rockefeller’s railroad rebates. NVIDIA  sells the picks  and  owns the goldmine (chips + CUDA lock-in). Apple  +  Jony Ive/Sam Altman’s mystery device ? Probably a $3,000 "AI soulmate" that your iPhone already does. That’s not innovation. It’s enclosure with better PR. 🚂 Pipeline Control, 2024 Edition Rockefeller secretly paid railroads to undercut rivals. Today: Cloud providers throttle outside AI models’ bandwidth. "Open" ecosystems that still force you into their training-data tollbooths. Hardware/software bundles so tight, you can’t swap a single cog. 🔨 Where’s the Antitrust Hammer? The goo...