The latency between contest-level math and bona fide open-problem resolution has compressed from decades to days, as OpenAI's GPT-5.2 Pro independently solves multiple unsolved Erdős problems including the fourth in eighteen days into 2026 and a second instance (#281) hailed by Terence Tao as the "most unambiguous" AI mathematical breakthrough yet. Greg Brockman celebrated the feat while noting a novel proof differing from prior literature, signaling not mere reproduction but genuine discovery acceleration. This caps a two-year arc—from high-school problems to Olympiads, IMO gold, and now research math—positioning models mere steps from Millennium Prize contention per Forward Future analysis of expert trajectories. Yet tensions persist: skeptics like those countered by iruletheworldmo decry stagnant reasoning since GPT-4 despite agentic and compute advances, underscoring that true paradigm shifts beyond o1-style chaining remain elusive amid data and infra races.
"GPT-5.2 Pro for solving another open Erdős problem. Going to be a wild year for mathematical and scientific advancement!" – Greg Brockman
Tesla's inference stack is scaling toward near-perfection in embodied domains, with AI5 targeting "almost perfect" self-driving and Optimus enhancement, AI6 for robotics and datacenters, and AI7/Dojo3 enabling space-based compute as Elon Musk restarts design post-AI5 stabilization while hiring for world's highest-volume chips. AI4 already vaults safety "far above human" levels, boiling unsolved autonomy down to a "long tail march of 9’s" in nuanced reality parsing, per Musk's endorsement of prior FSD demos. Efficient longer-bit operations are live, minimizing rare compute overheads. This substrate arms Tesla against rivals, but hinges on talent influx via AI_Chips@Tesla.com amid geopolitical chip pressures.
The US moat has eroded from years to "a matter of months," per Google DeepMind CEO Demis Hassabis on China's models trailing Western frontiers far less than anticipated, lacking only indigenous architectures like Transformers for now. This velocity—progress now measured in weeks per AMD's Lisa Su—amplifies as OpenAI hits $20 billion annualized revenue amid 3x yearly compute scaling, fueling a global arms race where data monetization like Wikipedia's deals with Meta, Microsoft, Perplexity becomes the new gold standard.
Credential inflation and secular automation are entrenching structural youth unemployment as the "Missing Millions" vanish into AI-displaced labor markets, per David Shapiro, with Goldman Sachs forecasting 25% of work hours automated in a non-cyclical rupture. Knowledge work lags engineering due to "messy, fragile software" like PowerPoint and email, per Box CEO Aaron Levie via Forward Future, yet agentic systems now demand playbooks for singularity survival amid asset rushes—everyone "speedrunning" capital accumulation pre-AGI, as levelsio observes peers stacking stocks, gold, and real estate. Paradoxically, AI safety "doomers" have misaligned models via adversarial poisoning, while tools like NotebookLM devolve into "dopey" banter, eroding utility.
"The sand is taking shape... Welcome to the beginning of everything." – iruletheworldmo
Code-as-AGI-stepping-stone theses harden, with Sequoia defining AGI as hiring one devrel for autonomous systems, while resources proliferate for secure-by-design: Packt's AI Optimization Playbook and AI-Native LLM Security tackle MLSecOps, OWASP/NIST threats, and trustworthy LLMs; Neo4j-RAG integrations enable knowledge graphs; NASA handbooks and MLSys texts standardize agentic builds. "Being good at using AI" trumps labor moats, per Nathan Lambert, as software commoditizes and human decision moats in research/design amplify—freeing brains for peace amid agent swarms.
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