By Oleh Polishchuk (Senior Subject Matter Expert in AI Training & Evaluation)
Intelligence is not measured by terawatts. Stop paying billions for illusions, litigating over copyrights, and burning the planet's resources for random errors.
If you want to get a high-efficiency AI product that solves problems with the accuracy of an expert rather than a random probabilistic text generator, this Manifesto is for you.
If you once and for all want to solve the problem of project payback, stopping the burial of billions in infrastructural "black holes", this Manifesto is for you.
If you want to build a system with absolute security, protection against legal risks, and copyright lawsuits, this Manifesto is for you.
If you want to reduce system training and deployment time to mere days instead of months, this Manifesto is for you.
If you want data centers to stop devouring natural resources, whole states' megawatts of energy, and destroying ecology, this Manifesto is for you.
If you are tired of blind faith that "more parameters and more electricity" automatically solves any task, welcome to the Concept of Reasonable Sufficiency.
The End of the "Universal Combines" Era: A financier does not need an AI capable of writing poetry or composing music. An engineer, doctor, or lawyer does not need a general-purpose probabilistic text generator. Business needs a highly specialized, professional tool in a specific subject domain, protected by rigorous competency and security modules.
Attempts to create an all-knowing artificial intelligence by scaling parameters have driven the market into a financial dead end. The four main cloud giants (Amazon, Alphabet, Microsoft, Meta) within the current investment cycle have brought cumulative capital expenditures (CapEx) to exceed the $1.1 trillion mark (cumulative total since 2023), and their planned infrastructure spending in 2026 has reached the range of $720–$745 billion [according to official corporate quarterly reports and investment forecasts: Amazon up to $200–$220B, Microsoft around $175–$190B, Alphabet $175–$205B, Meta $130–$145B) [MLQ.ai / Value Add VC, July–August 2026].
Moreover, the sector's fundamental economic model is undergoing a critical phase of imbalance, which can no longer be masked by "victory" slogans : revenue from AI services and subscriptions today catastrophically fails to cover hardware expenses. According to specialized financial and technology analysts, the tech giants' current revenues from artificial intelligence constitute only a small fraction of the hundreds of billions of dollars invested in infrastructure. Companies are forced to subsidize the resulting colossal gap—a net loss from inference and supercluster operation—at the expense of other businesses, turning the AI direction into a financial black hole on corporate balance sheets [Wall Street Journal / Bloomberg, 2025–2026].
: The introduction of an independent external verification mechanism (logical gateways and checking algorithms) makes it possible to completely eliminate "hallucinations" and structural model drift without changing its baseline architecture.
The transition from total down of the entire internet to targeted, deductive information retrieval (the "Sherlock Holmes" principle) reduces system load multiple times and automatically resolves the problem of copyright lawsuits.
Rejection of redundant super data centers protects natural resources, restores investment profitability, and makes the entry threshold to high technologies accessible to the real sector without multi-billion-dollar losses.
Modern technological corporations impose an illusion of universality on the market, masking monstrous engineering inefficiency. When a general-purpose model receives a narrow financial or technical request, the system engages trillions of parameters trained on entertainment content and random texts. This is equivalent to engaging an institute of academicians to check a single accounting entry. At the same time, 90% of business tasks are simple, repetitive, and algorithmizable. Forcing such models to query terabytes for a basic check is economic absurdity. Hence the high-tech industry's record quarterly reports, where colossal revenue growth is instantly offset by falling operating margins (for instance, an 8% year-on-year drop in Meta's operating profit against the background of large-scale restructuring for AI) due to the astronomical cost of inference and hardware base appreciation [Global Banking & Finance Review / Reuters, July 2026].
Large models operate on the principle of a "digital vacuum cleaner" and probabilistic guessing. They do not verify facts—they generate text that looks convincing. In the corporate sector, finance, or law, such "confident lying" turns into million-dollar losses, legal vulnerability, and lawsuits for copyright infringement when collecting unlicensed datasets.
The industry has entered a zone of extreme risks. In addition to direct capital investments, tech giants' balance sheets are burdened with "hidden debt" of about $1.65 trillion (generated through long-term energy procurement contracts, equipment leasing, and data center construction obligations) [Financial Times / Tom's Hardware, July 2026]. The stock market reacted with severe cooling: investors began massively selling off the giants' shares after the publication of astronomical expenses, wiping billions of dollars of capitalization off stock markets in a matter of days (the market demonstrated an acute negative response in the form of instant 5–9% stock corrections following reports from companies with bloated CapEx forecasts) [Investing.com / MLQ.ai, 2026]. Venture capital is rapidly fleeing from the sector of mindless resource burning on general models. Investor funds are reorienting toward applied tasks, real payback, sovereign security, and local architectures.
Unlike today's model of AI usage, where a potential failure of, say, an AWS or Microsoft central data center in Virginia or Frankfurt paralyzes millions of businesses worldwide, the Concept of Reasonable Sufficiency relies on three-level protection (Fail-Safe):
Due to their optimization, highly specialized models operate on the company's local hardware or secure servers. If the internet goes down, the internal closed loop continues to function.
An agent does not require constant polling of the entire internet; its databases and logic are cached locally.
Instead of one energy-intensive data center per region, the network consists of compact independent micro-modules. The failure of a single node is instantly compensated by load redistribution to neighboring elements.
We can no longer sit back and wait for tech monopolists to crash the market or offer another expensive bug. Transitioning to the Concept of Reasonable Sufficiency is our common task. Every market participant must act right now:
Stop sponsoring infrastructural "black holes." Make the creation of highly specialized AI agents with forensic security and strict payback a non-alternative condition for financing.
Do not wait for commands from above. Stop scaling trillions of parameters and design compact, sovereign AI systems with rigid logical gateways. Create autonomous micro-models with forensic verification algorithms capable of solving narrow-profile tasks with zero hallucinations directly on local hardware.
Join forces to calculate precise estimates and financial models for local micro data centers. Prove with figures the real payback and profitability of compact sovereign systems, freeing businesses from multi-billion-dollar capital expenditures on energy-intensive cloud giants.
Stop deploying "universal toys" for the sake of fashion. Give developers a strict technical assignment to create narrow-profile agent systems for your specific business processes, protect them with rigid logical gateways, and provide them with high-quality, legally clean, verified internal data.
Stop chasing giants' mega-data centers that deplete local electrical grids, consume all cooling water, and leave the region with nothing but ecological burden. Switch to creating local, compact AI infrastructure. In terms of costs and capital investments, such projects are well within the budgets of local budgets and regional investors—by their essence and payback model, this is comparable to understandable businesses at the level of waste sorting or recycling: local demand, understandable infrastructure, stable income, and complete transparency for the territory.
Stop serving the interests of tech monopolies. Form a rigid general political agenda, bring standards of technological sovereignty, strict decentralization, and strict energy consumption limits to state and international platforms. Push through legislative initiatives introducing a direct ban on the monopolistic depletion of state resources, environmental sanctions for inefficient energy burning, and tax preferences for creators of local, secure AI systems.
Engage in monitoring energy consumption and developing local territories. Demand transparency from municipalities and businesses, protection of natural resources from depletion by giant superclusters, and a transition to environmentally friendly, local solutions.
Stop trailing behind technological progress and reacting post-factum to data leaks, copyright lawsuits, and multi-billion-dollar business losses. Develop strict proactive standards and legislative frameworks that will mandate logical security audits, hallucination checks, and the legal purity of training datasets. Introduce strict certification for new-generation AI agents, closing the market for opaque "black boxes" that violate the rights of citizens and corporations.
The era of blind worship of "digital giants" burning trillions of dollars and planetary resources for random answers has come to an end. The time of engineering reason and economic pragmatism has arrived.
No one will come and do this for us. It is time to take our digital fate into our own hands.
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