The disgustingly limited truth of today’s so-called AI is that it is nowhere near intelligent, much less near AGI or GenAI—which I discussed in my article of another platform (“Hard Truth Report: Today’s Abysmal State of AI Demands A Mandatory Human Cognition Revamp”). Ergo, to at least quickly remedy the countless revenue losses in compliance, this piece screams for engineers to immediately halt all nonsense and incorporate a simple, grand cognitive schema in all AI-training models that (as best as possible) understands, foresees, devises, and strategizes for all current and future compliance, governance, regulation frameworks. Such yields tremendous savings of time and mentality for engineers, money for investors and shareholders, and reputation for tech companies. This, respectfully, is a call-out to you Sam Altman (OpenAI), Erik Voorhees (Venice.ai), Eric Hartford (Dolphin Llama), Google/Alphabet (Gemini), Bill Gates (Microsoft), Thomas Dohmke (GitHub Copilot), Mark Zuckerberg (Meta/Facebook), Dario Amodei (Anthropic), Elon Musk (xAI/Grok), Jeff Bezos and Andy Jassy (Amazon), Demis Hassabis (DeepMind), Mustafa Suleyman (Microsoft AI / Inflection AI), and all AI advisors, board members, compliance officers, consultants, programmers, researchers, scientists, etc.
This mandate for such a revamp—based on simplicity and holism—follows the foundation behind history’s greatest thinkers, Occam’s Razor, WIRED Magazine’s show “Five Levels”, the Einstein-attributed phrase “If you cannot explain something simply you do not fully understand it”, as well as the discussion amongst the psychological/dream-invading experts of the 2010 movie “Inception” where Eames (Tom Hardy) tells Dom Cobb (Leonardo DiCaprio) that the secret to conducting inception (i.e. manipulating targets in the most fundamental, powerful way) is not just about going deep into targets’ minds, but implanting the simplest form, basic level of the cognitive redirect. This get-to-the-lowest-and-simplest-foundation maxim is seen in all universal tools across industries, including tree forests/diagrams, syntax trees, hierarchy/taxonomy maps, Venn diagrams, mind-maps, brainstorming maps, meta-analyses, Systems Science, interconnectivity, holism, and many others. I also attest to the power of this non-linear, holistic, and universal thinking style after using such to attain four degrees in parallel, helping me acquire six total. Another attestation after using such to publish 12 books relatively fast.
However, pathetically, AI engineers’ accepted practice of making AI compliant is secondary to making AI bigger, better, faster, and more efficient. Whatsmore, their best practice involves merely haphazardly instilling the minimal, superficial compliance variables in AI systems (e.g. rules to not harm or arm criminals). Their more terrible practice is retrofitting AI systems after deployment, which then incurs more cost than tending to things properly at the outset of design.
Most AI regulation efforts depend on *external governance layers*—checklists, audit frameworks, and model cards. Foundation-model creators (OpenAI, Anthropic, Google DeepMind, Meta, etc.) add policy filters and alignment layers, not predictive governance cognition. Their systems react to regulation, not pre-empt it. There is no known architecture in industry or academia that predictively models future legality or constructs adaptive cognition systems.
Major labs add alignment layers, RLHF, rule-based reasoning, and constitutional layers to steer behaviour. They teach models safety specs and filters. That is reactive alignment which introduces brittleness, mode collapse, and overconfidence—weaknesses that make forever-ahead compliance cognition impossible.
While RegTech (Regulatory Technology) and predictive-compliance products exist, their models are grossly limited and typically utilized in finance and reporting, as they merely use ML to flag likely violations and streamline reporting. Deloitte’s 2018 report explores how RegTech can help organizations reduce compliance costs by automating processes, improving accuracy, and enhancing efficiency. While not AI-specific, the principles discussed are applicable to AI systems and highlight the potential for cost savings through improved compliance practices. While such is useful predictive analytics, it is not the same as a continuous, supra-jurisdictional cognitive jurisprudence I propose that anticipates novel law.
Also, legal AI research and tools exist to predict case outcomes and speed drafting, yet they remain domain-bound, evidence-driven, and constrained by existing statutes and precedent. They do not instantiate a universal regulatory cognition that universally preempts future statutes. Real-world adoption shows law teams adopting AI for efficiency (Thomson Reuters, 2024; Casetext CoCounsel, 2023). Such proves domain automation, not a universal jurisprudential cognition that anticipates novel statutes across jurisdictions.
Also, national and regional frameworks (NIST AI RMF, EU AI Act) create governance and compliance obligations. In April 2025, Reuters reported that the European Commission is seeking feedback to reduce the regulatory burden of the AI Act on startups, aiming to facilitate easier compliance, particularly for smaller innovators (Reuters, 2025). This initiative reflects a broader effort to address business concerns about excessive bureaucracy and high operational costs stemming from recent legislation. They standardize controls but do not define or produce architectures that self-generate contemplation of and strategy for future regulation (European Commission, 2025; NIST, 2023).
A 2025 OECD report professes how AI helps inspectors by detecting patterns indicative of potential non-compliance, for grander risk assessment (OECD, 2025). Such not only streamlines inspections but also enhances the efficacy of regulatory frameworks by ensuring resources are directed where most needed. However, this also is severely limited as it is not cognition, foresight and autonomous strategy.
This industry-wide asinine, inculcated practice furthers the truth behind the maxim attributed to Einstein, “Insanity is doing the same thing and expecting different results”, as well as the common-sense, collective premise behind basic problem-solving, root-level analysis, and input-equals-output maxims. The demand for a different path, novelty in thinking is also seen in the concept behind Executive Fellow of Harvard Business School Dr. Michael Skok (co-founder of Underscore.vc) in his “Designing A Customer-Centric Business Model” course of Harvard Innovation Labs when he said that when you are the creator of the game you can change the rules, and therefore have the greatest chance of winning. Relating to this issue of AI, the rules are the silo-minded, limited methods of AI development, and the winning is the materialization of real compliance and systems-foresight of genuine AI, AGI, GenAI (Skok, Harvard Innovation Labs, 2020).
Furthering this fact that AI experts merely try to perfect parroting are the many sources attesting to as much noted in that article “Hard Truth Report: Today’s Abysmal State of AI Demands A Mandatory Human Cognition Revamp”—including the biggest names in the industry. In addition to that is the work of now-turned-social-activist Dr. Noam Chomsky (PhD)—who I had the pleasure of quoting (with his permission) in my third book—which proves that the dawn of real AI, AGI, GenAI is nowhere close to fruition since AI-reasoning algorithms differ profoundly from how humans reason and use language, immediately placing significant limitations on what today’s AI can do with their encoded ineradicable defects (Chomsky, Roberts, & Watumull, 2023). Dr. Chomsky also says “The core problem is not that we lack data or computation, but that we mistake imitation for intelligence. We keep refining the same illusion rather than seeking understanding” (Chomsky, MIT public lecture, 2022).
The only system that destroys today’s compliance-failing Industry perpetuated by AI’s brightest minds that hemorrhage investors’ money is a supra-regulatory cognition model built on holism and simplicity that conducts live, continual scans of all worldwide media (e.g. politics, tragedies, cybercrime, scams…) so to remain perpetually ahead of any governance humans can contemplate. Such encodes law and foresight as an evolving, self-updating intelligence field with continual anticipatory legal cognition, cross-jurisdictional synthesis, and self-updating jurisprudential models. This approach reflects decades of applied systems design and regulatory strategy at the highest levels across industries and countries—based on tested and refined systems by an expert who solved the impossible in similarly complex, high-stakes environments. Implementing this framework allows the industry to escape reactive cycles, prevent trillions in wasted capital, and finally align AI development with societal, investor, and worldwide needs.
