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🌐Multipolar World Order – Part 1🌐
September 24, 2022
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Russia’s war with Ukraine is first and foremost a tragedy for the people of both countries, especially those who live—and die—in the battle zones. The priority for humanity, though apparently not for the political class, is to encourage Moscow and Kyiv to stop killing men, women and children and negotiate a peace deal.

Beyond the immediate confines of the conflict, the war is also seen by some as representative of an alleged clash between great powers and, perhaps, between civilisations. All wars are momentous, but the ramifications of Ukrainian war are already global.

Consequently, there is a perception that it is the focal point of a confrontation between two distinct models of global governance. The NATO-led alliance of the Western nations continues to push the unipolar, G7, international rules-based order (IRBO). It is opposed, some say, by the Russian and Chinese-led BRICS and the G20-based multipolar world order.

In this 3 part series we will explore these issues and consider if it is tenable to place our faith in the emerging multipolar world order.

There are very few redeeming features of the unipolar world order, that’s for sure. It is a system that overwhelmingly serves capital and few people other than a “parasite class” of stakeholder capitalist eugenicists. This has led many disaffected Westerners to invest their hopes in the promise of the multipolar world order:

Many have increasingly come to terms with the reality that today’s multipolar system led by Russia and China has premised itself upon the defense of international law and national sovereignty as outlined in the UN Charter. [. . .] Putin and Xi Jinping have [. . .] made their choice to stand for win-win cooperation over Hobbesian Zero Sum thinking. [. . .] [T]heir entire strategy is premised upon the UN Charter.

If only that were so! Unfortunately, it doesn’t appear to be the case. But even if it were true, Putin and Xi Jinping basing “their entire strategy” upon the UN Charter, would be cause for concern, not relief.

For the globalist forces that see nation-states as squares on the grand chessboard and that regard leaders like Putin, Biden and Xi Jinping as accomplices, the multipolar world order is manna from heaven. They have spent more than a century trying to centralise global power. The power of individual nation-states at least presents the possibility of some decentralisation. The multipolar world order finally ends all national sovereignty and delivers true global governance.

WORLD ORDER

We need to distinguish between the ideological concept of “world order” and the reality. This will help us identify where “world order” is an artificially imposed construct.

Authoritarian power, wielded over populations, territory and resources, restricted by physical and political geography, dictates the “world order.” The present order is largely the product of hard-nosed geopolitics, but it also reflects the various attempts to impose a global order.

The struggle to manage and mitigate the consequences of geopolitics is evident in the history of international relations. For nearly 500 years nation-states have sought to co-exist as sovereign entities. Numerous systems have been devised to seize control of what would otherwise be anarchy. It is very much to the detriment of humanity that anarchy has not been allowed to flourish.

In 1648, the two bilateral treaties that formed the Peace of Westphalia concluded the 30 Years War (or Wars). Those negotiated settlements arguably established the precept of the territorial sovereignty within the borders of the nation-state.

This reduced, but did not end, the centralised authoritarian power of the Holy Roman Empire (HRE). Britannica notes:

The Peace of Westphalia recognized the full territorial sovereignty of the member states of the empire.

This isn’t entirely accurate. That so-called “full territorial sovereignty” delineated regional power within Europe and the HRE, but full sovereignty wasn’t established.

The Westphalian treaties created hundreds of principalities that were formerly controlled by the central legislature of the HRE, the Diet. These new, effectively federalised principalities still paid taxes to the emperor and, crucially, religious observance remained a matter for the empire to decide. The treaties also consolidated the regional power of the Danish, Swedish, and French states but the Empire itself remained intact and dominant.

It is more accurate to say that the Peace of Westphalia somewhat curtailed the authoritarian power of the HRE and defined the physical borders of some nation states. During the 20th century, this led to the popular interpretation of the nation-state as a bulwark against international hegemonic power, despite that never having been entirely true.

Consequently, the so-called “Westphalian model” is largely based upon a myth. It represents an idealised version of the world order, suggesting how it could operate rather than describing how it does.

If nation-states really were sovereign and if their territorial integrity were genuinely respected, then the Westphalian world order would be pure anarchy. This is the ideal upon which the UN is supposedly founded because, contrary to another ubiquitous popular myth, anarchy does not mean “chaos.” Quite the opposite.

Anarchy is exemplified by Article 2.1 of the UN Charter:

The Organization is based on the principle of the sovereign equality of all its Members.

The word “anarchy” is an abstraction of the classical Greek “anarkhos,” meaning “rulerless.” This is derived from the privative prefix “an” (without) in conjunction with “arkhos” (leader or ruler). Literally translated, “anarchy” means “without rulers”—what the UN calls “sovereign equality.”

A Westphalian world order of sovereign nation-states, each observing the “equality” of all others while adhering to the non-aggression principle, is a system of global, political anarchy. Unfortunately, that is not the way the current UN “world order” functions, nor has there ever been any attempt to impose such an order. What a shame.

Within the League of Nations and subsequent UN system of practical “world order,”—a world order allegedly built upon the sovereignty of nations—equality exists in theory only. Through empire, colonialism, neocolonialism—that is, through economic, military, financial and monetary conquest, coupled with the debt obligations imposed upon targeted nations—global powers have always been able to dominate and control lesser ones.

National governments, if defined in purely political terms, have never been the only source of authority behind the efforts to construct world order. As revealed by Antony C. Sutton and others, private corporate power has aided national governments in shaping “world order.”

Neither Hitler’s rise to power nor the Bolshevik Revolution would have occurred as they did, if at all, without the guidance of the Wall Street financiers. The bankers’ global financial institutions and extensive international espionage networks were instrumental in shifting global political power.

These private-sector “partners” of government are the “stakeholders” we constantly hear about today. The most powerful among them are fully engaged in “the game” described by Zbigniew Brzezinski in The Grand Chessboard.

Brzezinski recognised that the continental landmass of Eurasia was the key to genuine global hegemony:

This huge, oddly shaped Eurasian chess board—extending from Lisbon to Vladivostok—provides the setting for “the game.” [. . .] [I]f the middle space rebuffs the West, becomes an assertive single entity [. . .] then America’s primacy in Eurasia shrinks dramatically. [. . .] That mega-continent is just too large, too populous, culturally too varied, and composed of too many historically ambitious and politically energetic states to be compliant toward even the most economically successful and politically pre-eminent global power. [. . .] Ukraine, a new and important space on the Eurasian chessboard, is a geopolitical pivot because its very existence as an independent country helps to transform Russia. Without Ukraine, Russia ceases to be a Eurasian empire. [. . .] [I]t would then become a predominantly Asian imperial state.

The “unipolar world order” favoured by the Western powers, often referred to as the “international rules-based order” or the “international rules-based system,” is another attempt to impose order. This “unipolar” model enables the US and its European partners to exploit the UN system to claim legitimacy for their games of empire. Through it, the transatlantic alliance has used its economic, military and financial power to try to establish global hegemony.

In 2016, Stewart Patrick, writing for the US Council on Foreign Relations (CFR), a foreign policy think tank, published World Order: What, Exactly, are the Rules? He described the post-WWII “international rules-based order” (IRBO):

What sets the post-1945 Western order apart is that it was shaped overwhelmingly by a single power [a unipolarity], the United States. Operating within the broader context of strategic bipolarity, it constructed, managed, and defended the regimes of the capitalist world economy. [. . .] In the trade sphere, the hegemon presses for liberalization and maintains an open market; in the monetary sphere, it supplies a freely convertible international currency, manages exchange rates, provides liquidity, and serves as a lender of last resort; and in the financial sphere, it serves as a source of international investment and development.

The idea that the aggressive market acquisition of crony capitalism somehow represents the “open markets” of the “capitalist world economy” is risible. It is about as far removed from free market capitalism as it is possible to be. Under crony capitalism, the US dollar, as the preferred global reserve currency, is not “freely convertible.” Exchange rates are manipulated and liquidity is debt for nearly everyone except the lender. “Investment and development” by the hegemon means more profits and control for the hegemon.

The notion that a political leader, or anyone for that matter, is entirely bad or good, is puerile. The same consideration can be given to nation-states, political systems or even models of world order. The character of a human being, a nation or a system of global governance is better judged by their or its totality of actions.

Whatever we consider to be the source of “good” and “evil,” it exists in all of us at either ends of a spectrum. Some people exhibit extreme levels of psychopathy, which can lead them to commit acts that are judged to be “evil.” But even Hitler, for example, showed physical courage, devotion, compassion for some, and other qualities we might consider “good.”

Nation-states and global governance structures, though immensely complex, are formed and led by people. They are influenced by a multitude of forces. Given the added complications of chance and unforeseen events, it is unrealistic to expect any form of “order” to be either entirely good or entirely bad.

That being said, if that “order” is iniquitous and causes appreciable harm to people, then it is important to identify to whom that “order” provides advantage. Their potential individual and collective guilt should be investigated.

This does not imply that those who benefit are automatically culpable, nor that they are “bad” or “evil,” though they may be, only that they have a conflict of interests in maintaining their “order” despite the harm it causes. Equally, where systemic harm is evident, it is irrational to absolve the actions of the people who lead and benefit from that system without first ruling out their possible guilt.

Since WWII, millions of innocents have been murdered by the US, its international allies and its corporate partners, all of whom have thrown their military, economic and financial weight around the world. The Western “parasite class” has sought to assert its IRBO by any means necessary— sanctions, debt slavery or outright slavery, physical, economic or psychological warfare. The grasping desire for more power and control has exposed the very worst of human nature. Repeatedly and ad nauseam.

Of course, resistance to this kind of global tyranny is understandable. The question is: Does imposition of the multipolar model offer anything different?

OLIGARCHY

Most recently, the “unipolar world order” has been embodied by the World Economic Forum’s inappropriately named Great Reset. It is so malignant and forbidding that some consider the emerging “multipolar world order” salvation. They have even heaped praise upon the likely leaders of the new multipolar world:

It is [. . .] strength of purpose and character that has defined Putin’s two decades in power. [. . .] Russia is committed to the process of finding solutions to all people benefiting from the future, not just a few thousand holier-than-thou oligarchs. [. . .] Together [Russia and China] told the WEF to stuff the Great Reset back into the hole in which it was conceived. [. . .] Putin told Klaus Schwab and the WEF that their entire idea of the Great Reset is not only doomed to failure but runs counter to everything modern leadership should be pursuing.

Sadly, it seems this hope is also misplaced.

While Putin did much to rid Russia of the CIA-run, Western-backed oligarchs who were systematically destroying the Russian Federation during the 1990s, they have subsequently been replaced by another band of oligarchs with closer links to the current Russian government. Something we will explore in Part 3.

Yes, it is certainly true that the Russian government, led by Putin and his power bloc, has improved the incomes and life opportunities for the majority of Russians. Putin’s government has also significantly reduced chronic poverty in Russia over the last two decades.

Wealth in Russia, measured as the market value of financial and non-financial assets, has remained concentrated in the hands of the top 1% of the population. This pooling of wealth among the top percentile is itself stratified and is overwhelmingly held by the top 1% of the 1%. For example, in 2017, 56% of Russian wealth was controlled by 1% of the population. The pseudopandemic of 2020–2022 particularly benefitted Russian billionnaires—as it did the billionaires of every other developed economy.

According to the Credit Suisse Global Wealth Report 2021, wealth inequality in Russia, measured using the Gini coefficient, was 87.8 in 2020. The only other major economy with a greater disparity between the wealthy and the rest of the population was Brazil. Just behind Brazil and Russia on the wealth inequality scale was the US, whose Gini coefficient stood at 85.

In terms of wealth concentration however, the situation in Russia was the worst by a considerable margin. In 2020 the top 1% owned 58.2% of Russia’s wealth. This was more than 8 percentage points higher than Brazil’s wealth concentration, and significantly worse than wealth concentration in the US, which stood at 35.2% in 2020.

Such disproportionate wealth distribution is conducive to creating and empowering oligarchs. But wealth alone doesn’t determine whether one is an oligarch. Wealth needs to be converted into political power for the term “oligarch” to be applicable. An oligarchy is defined as “a form of government in which supreme power is vested in a small exclusive class.”

Members of this dominant class are installed through a variety of mechanisms. The British establishment, and particularly its political class, is dominated by men and women who were educated at Eton, Roedean, Harrow and St. Pauls, etc. This “small exclusive class” arguably constitutes a British oligarchy. The UK’s new Prime Minister, Liz Truss, has been heralded by some because she is not a graduate of one of these select public schools.

Educational privilege aside, though, the use of the word “oligarch” in the West more commonly refers to an internationalist class of globalists whose individual wealth sets them apart and who use that wealth to influence policy decisions.

Bill Gates is a prime example of an oligarch. The former advisor to the UK Prime Minister, Dominic Cummings, said as much during his testimony to a parliamentary committee on May 2021 (go to 14:02:35). As Cummings put it, Bill Gates and “that kind of network” had directed the UK government’s response to the supposed COVID-19 pandemic.

Gates’ immense wealth has bought him direct access to political power beyond national borders. He has no public mandate in either the US or the UK. He is an oligarch—one of the more well known but far from the only one.

CFR member David Rothkopf described these people as a “Superclass” with the ability to “influence the lives of millions across borders on a regular basis.” They do this, he said, by using their globalist “networks.” Those networks, as described by Antony C. Sutton, Dominic Cummings and others, act as “the force multiplier in any kind of power structure.”

This “small exclusive class” use their wealth to control resources and thus policy. Political decisions, policy, court rulings and more are made at their behest. This point was highlighted in the joint letter sent by the Attorneys General (AGs) of 19 US states to BlackRock CEO Larry Fink.

The AGs observed that BlackRock was essentially using its investment strategy to pursue a political agenda:

The Senators elected by the citizens of this country determine which international agreements have the force of law, not BlackRock.

Their letter describes the theoretical model of representative democracy. Representative democracy is not a true democracy—which decentralises political power to the individual citizen—but is rather a system designed to centralise political control and authority. Inevitably, “representative democracy” leads to the consolidation of power in the hands of the so-called “Superclass” described by Rothkopf.

There is nothing “super” about them. They are ordinary people who have acquired wealth primarily through conquest, usury, market rigging, political manipulation and slavery. “Parasite class” is a more befitting description.

Not only do global investment firms like BlackRock, Vanguard and State Street use their immense resources to steer public policy, but their major shareholders include the very oligarchs who, via their contribution to various think tanks, create the global political agendas that determine policy in the first place. There is no space in this system of alleged “world order” for any genuine democratic oversight.

As we shall see in Part 3, the levers of control are exerted to achieve exactly the same effect in Russia and China. Both countries have a gaggle of oligarchs whose objectives are firmly aligned with the WEF’s Great Reset agenda. They too work with their national government “partners” to ensure that they all arrive at the “right” policy decisions.

THE UNITED NATIONS’ MODEL OF NATIONAL SOVEREIGNTY

Any bloc of nations that bids for dominance within the United Nations is seeking global hegemony. The UN enables global governance and centralises global political power and authority. In so doing, the UN empowers the international oligarchy.

As noted previously, Article 2 of the United Nations Charter declares that the UN is “based on the principle of the sovereign equality of all its Members.” The Charter then goes on to list the numerous ways in which nation-states are not equal. It also clarifies how they are all subservient to the UN Security Council.

Despite all the UN’s claims of lofty principles—respect for national sovereignty and for alleged human rights—Article 2 declares that no nation-state can receive any assistance from another as long as the UN Security Council is forcing that nation-state to comply with its edicts. Even non-member states must abide by the Charter, whether they like it or not, by decree of the United Nations.

The UN Charter is a paradox. Article 2.7 asserts that “nothing in the Charter” permits the UN to infringe the sovereignty of a nation-state—except when it does so through UN “enforcement measures.” The Charter states, apparently without reason, that all nation-states are “equal.” However, some nation-states are empowered by the Charter to be far more equal than others.

While the UN’s General Assembly is supposedly a decision-making forum comprised of “equal” sovereign nations, Article 11 affords the General Assembly only the power to discuss “the general principles of co-operation.” In other words, it has no power to make any significant decisions.

Article 12 dictates that the General Assembly can only resolve disputes if instructed to do so by the Security Council. The most important function of the UN, “the maintenance of international peace and security,” can only be dealt with by the Security Council. What the other members of the General Assembly think about the Security Council’s global “security” decisions is a practical irrelevance.

Article 23 lays out which nation-states form the Security Council:

The Security Council shall consist of fifteen Members of the United Nations. The Republic of China, France, the Union of Soviet Socialist Republics [Russian Federation], the United Kingdom of Great Britain and Northern Ireland, and the United States of America shall be permanent members of the Security Council. The General Assembly shall elect ten other Members of the United Nations to be non-permanent members of the Security Council. [. . .] The non-permanent members of the Security Council shall be elected for a term of two years.

The General Assembly is allowed to elect “non-permanent” members to the Security Council based upon criteria stipulated by the Security Council. Currently the “non-permanent” members are Albania, Brazil, Gabon, Ghana, India, Ireland, Kenya, Mexico, Norway and the United Arab Emirates.

Article 24 proclaims that the Security Council has “primary responsibility for the maintenance of international peace and security” and that all other nations agree that “the Security Council acts on their behalf.” The Security Council investigates and defines all alleged threats and recommends the procedures and adjustments for the supposed remedy. The Security Council dictates what further action, such as sanctions or the use of military force, shall be taken against any nation-state it considers to be a problem.

Article 27 decrees that at least 9 of the 15 member states must be in agreement for a Security Council resolution to be enforced. All of the 5 permanent members must concur, and each has the power of veto. Any Security Council member, including permanent members, shall be excluded from the vote or use of its veto if they are party to the dispute in question.

UN member states, by virtue of agreeing to the Charter, must provide armed forces at the Security Council’s request. In accordance with Article 47, military planning and operational objectives are the sole remit of the permanent Security Council members through their exclusive Military Staff Committee. If the permanent members are interested in the opinion of any other “sovereign” nation, they’ll ask it to provide one.

The inequality inherent in the Charter could not be clearer. Article 44 notes that “when the Security Council has decided to use force” its only consultative obligation to the wider UN is to discuss the use of another member state’s armed forces where the Security Council has ordered that nation to fight. For a country that is a current member of the Security Council, use of its armed forces by the Military Staff Committee is a prerequisite for Council membership.

The UN Secretary-General, identified as the “chief administrative officer” in the Charter, oversees the UN Secretariat. The Secretariat commissions, investigates and produces the reports that allegedly inform UN decision-making. The Secretariat staff members are appointed by the Secretary-General. The Secretary-General is “appointed by the General Assembly upon the recommendation of the Security Council.”

Under the UN Charter, then, the Security Council is made king. This arrangement affords the governments of its permanent members—China, France, Russia, the UK and the US—considerable additional authority. There is nothing egalitarian about the UN Charter.

The suggestion that the UN Charter constitutes a “defence” of “national sovereignty” is ridiculous. The UN Charter is the embodiment of the centralisation of global power and authority.

THE UNITED NATIONS’ GLOBAL PUBLIC-PRIVATE PARTNERSHIP

The UN was created, in no small measure, through the efforts of the private sector Rockefeller Foundation (RF). In particular, the RF’s comprehensive financial and operational support for the Economic, Financial and Transit Department (EFTD) of the League of Nations (LoN), and its considerable influence upon the United Nations Relief and Rehabilitation Administration (UNRRA), made the RF the key player in the transformation of the LoN into the UN.

The UN came into being as a result of public-private partnership. Since then, especially with regard to defence, financing, global health care and sustainable development, public-private partnerships have become dominant within the UN system. The UN is no longer an intergovernmental organisation, if it ever was one. It is a global collaboration between governments and a multinational infra-governmental network of private “stakeholders.”

In 1998, then-UN Secretary-General Kofi Annan told the World Economic Forum’s Davos symposium that a “quiet revolution” had occurred in the UN during the 1990s:

[T]he United Nations has been transformed since we last met here in Davos. The Organization has undergone a complete overhaul that I have described as a “quiet revolution”. [. . .] [W]e are in a stronger position to work with business and industry. [. . .] The business of the United Nations involves the businesses of the world. [. . .] We also promote private sector development and foreign direct investment. We help countries to join the international trading system and enact business-friendly legislation.

In 2005, the World Health Organisation (WHO), a specialised agency of the UN, published a report on the use of information and communication technology (ICT) in healthcare titled Connecting for Health. Speaking about how “stakeholders” could introduce ICT healthcare solutions globally, the WHO noted:

Governments can create an enabling environment, and invest in equity, access and innovation.

The 2015, Adis Ababa Action Agenda conference on “financing for development” clarified the nature of an “enabling environment.” National governments from 193 UN nation-states committed their respective populations to funding public-private partnerships for sustainable development by collectively agreeing to create “an enabling environment at all levels for sustainable development;” and “to further strengthen the framework to finance sustainable development.”

In 2017, UN General Assembly Resolution 70/224 (A/Res/70/224) compelled UN member states to implement “concrete policies” that “enable” sustainable development. A/Res/70/224 added that the UN:

[. . .] reaffirms the strong political commitment to address the challenge of financing and creating an enabling environment at all levels for sustainable development [—] particularly with regard to developing partnerships through the provision of greater opportunities to the private sector, non-governmental organizations and civil society in general.

In short, the “enabling environment” is a government, and therefore taxpayer, funding commitment to create markets for the private sector. Over the last few decades, successive Secretary-Generals have overseen the UN’s formal transition into a global public-private partnership (G3P).

Nation-states do not have sovereignty over public-private partnerships. Sustainable development formally relegates government to the role of an “enabling” partner within a global network comprised of multinational corporations, non-governmental organisations (NGOs), civil society organisations and other actors. The “other actors” are predominantly the philanthropic foundations of individual billionaires and immensely wealthy family dynasties—that is, oligarchs.

Effectively, then, the UN serves the interests of capital. Not only is it a mechanism for the centralisation of global political authority, it is committed to the development of global policy agendas that are “business-friendly.” That means Big Business-friendly. Such agendas may happen to coincide with the best interests of humanity, but where they don’t—which is largely the case—well, that’s just too bad for humanity.

GLOBAL GOVERNANCE

On the 4th February 2022, a little less then three weeks prior to Russia launching its “special military operation” in Ukraine, Presidents Vladimir Putin and Xi Jinping issued an important joint statement:

The sides [Russian Federation and Chinese People’s Republic] strongly support the development of international cooperation and exchanges [. . .], actively participating in the relevant global governance process, [. . .] to ensure sustainable global development. [. . .] The international community should actively engage in global governance[.] [. . .] The sides reaffirmed their intention to strengthen foreign policy coordination, pursue true multilateralism, strengthen cooperation on multilateral platforms, defend common interests, support the international and regional balance of power, and improve global governance. [. . .] The sides call on all States [. . .] to protect the United Nations-driven international architecture and the international law-based world order, seek genuine multipolarity with the United Nations and its Security Council playing a central and coordinating role, promote more democratic international relations, and ensure peace, stability and sustainable development across the world.

The United Nations Department of Economic and Social Affairs (UN-DESA) defined “global governance” in its 2014 publication Global Governance and the Global Rules For Development in the Post 2015 Era:

Global governance encompasses the totality of institutions, policies, norms, procedures and initiatives through which States and their citizens try to bring more predictability, stability and order to their responses to transnational challenges.

Global governance centralises control over the entire sphere of international relations. It inevitably erodes a nation’s ability to set foreign policy. As a theoretical protection against global instability, this isn’t necessarily a bad idea, but in practice it neither enhances nor “protects” national sovereignty.

Domination of the global governance system by one group of powerful nation-states represents possibly the most dangerous and destabilising force of all. It allows those nations to act with impunity, regardless of any pretensions about honouring alleged “international law.”

Global governance also significantly curtails the independence of a nation-state’s domestic policy. For example, the UN’s Sustainable Development Agenda 21, with the near-time Agenda 2030 serving as a waypoint, impacts nearly all national domestic policy—even setting the course for most domestic policy—in every country.

National electorates’ oversight of this “totality” of UN policies is weak to nonexistent. Global governance renders so-called “representative democracy” little more than a vacuous sound-bite.

As the UN is a global public-private partnership (UN-G3P), global governance allows the “multi-stakeholder partnership”—and therefore oligarchs—significant influence over member nation-states’ domestic and foreign policy. Set in this context, the UN-DESA report (see above) provides a frank appraisal of the true nature of UN-G3P global governance:

Current approaches to global governance and global rules have led to a greater shrinking of policy space for national Governments [. . . ]; this also impedes the reduction of inequalities within countries. [. . .] Global governance has become a domain with many different players including: multilateral organizations; [. . .] elite multilateral groupings such as the Group of Eight (G8) and the Group of Twenty (G20) [and] different coalitions relevant to specific policy subjects[.] [. . .] Also included are activities of the private sector (e.g., the Global Compact) non-governmental organizations (NGOs) and large philanthropic foundations (e.g., Bill and Melinda Gates Foundation, Turner Foundation) and associated global funds to address particular issues[.] [. . .] The representativeness, opportunities for participation, and transparency of many of the main actors are open to question. [. . .] NGOs [. . .] often have governance structures that are not subject to open and democratic accountability. The lack of representativeness, accountability and transparency of corporations is even more important as corporations have more power and are currently promoting multi-stakeholder governance with a leading role for the private sector. [. . .] Currently, it seems that the United Nations has not been able to provide direction in the solution of global governance problems—perhaps lacking appropriate resources or authority, or both. United Nations bodies, with the exception of the Security Council, cannot make binding decisions.

A/Res/73/254 declares that the UN Global Compact Office plays a vital role in “strengthening the capacity of the United Nations to partner strategically with the private sector.” It adds:

The 2030 Agenda for Sustainable Development acknowledges that the implementation of sustainable development will depend on the active engagement of both the public and private sectors[.]

While the Attorneys General of 19 states might rail against BlackRock for usurping the political authority of US senators, BlackRock is simply exercising its power as valued a “public-private partner” of the US government. Such is the nature of global governance. Given that this system has been constructed over the last 80 years, it’s a bit too late for 19 state AGs to complain about it now. What have they been doing for the last eight decades?

The governmental “partners” of the UN-G3P lack “authority” because the UN was created, largely by the Rockefellers, as a public-private partnership. The intergovernmental structure is the partner of the infra-governmental network of private stakeholders. In terms of resources, the power of the private sector “partners” dwarfs that of their government counterparts.

Corporate fiefdoms are not limited by national borders. BlackRock alone currently holds $8.5 trillion of assets under management. This is nearly five times the size of the total GDP of UN Security Council permanent member Russia and more than three times the GDP of the UK.

So-called sovereign countries are not sovereign over their own central banks nor are they “sovereign” over international financial institutions like the IMF, the New Development Bank (NDB), the World Bank or the Bank for International Settlements. The notion that any nation state or intergovernmental organisation is capable of bringing the global network of private capital to heel is farcical.

At the COP26 Conference in Glasgow in 2021, King Charles III—then Prince Charles—prepared the conference to endorse the forthcoming announcement of the Glasgow Financial Alliance for Net Zero (GFANZ). He made it abundantly clear who was in charge and, in keeping with UN objectives, clarified national governments role as “enabling partners”:

The scale and scope of the threat we face call for a global systems level solution based on radically transforming our current fossil fuel based economy. [. . .] So ladies and gentleman, my plea today is for countries to come together to create the environment that enables every sector of industry to take the action required. We know this will take trillions, not billions of dollars. [. . .] [W]e need a vast military style campaign to marshal the strength of the global private sector, with trillions at [its] disposal far beyond global GDP, and with the greatest respect, beyond even the governments of the world’s leaders. It offers the only real prospect of achieving fundamental economic transition.

Unless Putin and Xi Jinping intend to completely restructure the United Nations, including all of its institutions and specialised agencies, their objective of protecting “the United Nations-driven international architecture” appears to be nothing more than a bid to cement their status as the nominal leaders of the UN-G3P. As pointed out by UN-DESA, through the UN-G3P, that claim to political authority is extremely limited. Global corporations dominate and are currently further consolidating their global power through “multi-stakeholder governance.”

Whether unipolar or multipolar, the so-called “world order” is the system of global governance led by the private sector—the oligarchs. Nation-states, including Russia and China, have already agreed to follow global priorities determined at the global governance level. The question is not which model of the global public-private “world order” we should accept, but rather why we would ever accept any such “world order” at all.

This, then, is the context within which we can explore the alleged advantages of a “multipolar world order” led by China, Russia and increasingly India. Is it an attempt, as claimed by some, to reinvigorate the United Nations and create a more just and equitable system of global governance? Or is it merely the next phase in the construction of what many refer to as the “New World Order”?

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🚹 BOMBSHELL MEDICAL REPORT: Florida Launches Official Study into 77-Cent Ivermectin to Fight Stage 4 Cancer!

Florida shatters the Big Pharma consensus! The State officially launches funding for generic drug repurposing, investigating 77-cent Ivermectin for cancer treatment.

⚠ Breaking the Big Pharma Monopoly: How the State of Florida is Investing Taxpayer Dollars into Generic Drug Repurposing to Bring Unprecedented Hope to Cancer Patients.

00:02:39
đŸ‘ïž THE KILL CHAIN AUTOMATED: Palantir, the DOD, and the Age of AI Warfare đŸ›°ïžâšĄ

While the public debate remains focused on consumer AI chatbots, the defense-industrial complex has quietly deployed real-time artificial intelligence into operational military decision-making.

Here is what you need to know about the integration of Palantir’s AI infrastructure and military data networks:

📡 1. Shrinking the "Kill Chain"

Through platforms like Project Maven and Palantir's Artificial Intelligence Platform (AIP), military surveillance systems process massive streams of satellite imagery, drone telemetry, and signals intelligence in real time.

đŸ”č The Goal: Reduce target identification, processing, and decision workflows from hours to seconds.

đŸ”č The Reality: Data streams from edge sensors (drones, aircraft, satellites) are fused instantaneously, surfacing potential targets directly to operators with automated strike recommendations.

đŸ›Ąïž 2. Sensor Fusion & The Tactical Edge

Modern operational platforms don't just log data—they deploy algorithmic model...

00:01:51
The Only Thing Stopping You, Is You...

This is why prayer, visualization and meditations can be so powerful...

You already have it...

The universe will have no option but to make it a reality ✚

00:01:04
🚹 Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI 🚹

Chutes is gaining attention as a decentralized AI inference platform that claims to combine real usage, cryptographic verification, confidential computing, and open-source infrastructure into a working production system. The thesis is simple: instead of trusting Big Tech clouds with AI workloads, users get a distributed compute layer built around verification and privacy.

🔑 Key points

đŸ”č Chutes is live in production and reportedly scaled to more than 1,170 active GPU nodes, including large numbers of Nvidia H200s and Blackwell-class hardware.

đŸ”č The platform says it has processed nearly 38 trillion tokens since launch across 53 deployed applications and more than 700,000 registered users.

đŸ”č The team reportedly cut unprofitable usage programs, reduced total token volume, and still improved revenue efficiency, with revenue per GPU rising sharply after removing subsidized traffic.

đŸ”č Chutes is using post-quantum cryptography, trusted execution environments, and Nvidia confidential ...

🚹 Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI 🚹
🚹 JPMorgan’s criticism of the CLARITY Act is fueling a fresh power struggle over who gets to write America’s crypto rules 🚹

A new clash is emerging between legacy finance and crypto legislation after JPMorgan CEO Jamie Dimon reportedly warned that the CLARITY Act could let crypto firms offer bank-like products without bank-level oversight. The dispute is quickly turning into a larger fight over regulation, competitiveness, and who controls the future architecture of digital finance in the United States.

🔑 Key points

đŸ”č Jamie Dimon reportedly called the CLARITY Act a threat to the financial system, arguing it could allow crypto firms to offer yield-like products while avoiding the capital, reserve, and oversight burdens traditional banks face.

đŸ”č Senator Cynthia Lummis pushed back publicly, framing the issue as a global strategic race and warning that if the U.S. does not set digital asset standards, other powers will.

đŸ”č The core tension is whether the bill creates legitimate regulatory clarity or simply opens the door to regulatory arbitrage for crypto platforms operating outside the traditional banking...

🚹 JPMorgan’s criticism of the CLARITY Act is fueling a fresh power struggle over who gets to write America’s crypto rules 🚹
👉 Coinbase just launched an AI agent for Crypto Trading

Custom AI assistants that print money in your sleep? 🔜

The future of Crypto x AI is about to go crazy.

👉 Here’s what you need to know:

💠 'Based Agent' enables creation of custom AI agents
💠 Users set up personalized agents in < 3 minutes
💠 Equipped w/ crypto wallet and on-chain functions
💠 Capable of completing trades, swaps, and staking
💠 Integrates with Coinbase’s SDK, OpenAI, & Replit

👉 What this means for the future of Crypto:

1. Open Access: Democratized access to advanced trading
2. Automated Txns: Complex trades + streamlined on-chain activity
3. AI Dominance: Est ~80% of crypto 👉txns done by AI agents by 2025

🚹 I personally wouldn't bet against Brian Armstrong and Jesse Pollak.

👉 Coinbase just launched an AI agent for Crypto Trading
🚹Q2 webinar with Denelle Dixon (CEO STELLAR)🚹

Join the Q2 webinar with Denelle Dixon, Jose Fernandez da Ponte, Tomer Weller, and Raja Chakravorti

https://www.linkedin.com/events/7488670276189114369/

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🌎 Schumann Resonance Today 8/2 🌍

Today's Frequency Analysis: The fundamental resonant frequency of the Earth today 7.83 Hz it remains stable around. Tomsk Space Observing System (SOS) data shows that global lightning activity is at an average level today. The low deflection in the fundamental mode SR1 indicates a healthy ionosphere-terrestrial crust interaction.

Harmonics and Amplitude: The second harmonic (SR2) shows weak activity as expected at ~14.3 Hz. Third harmonic (SR3) ~20.8 Hz normal. Amplitude values 13-14 pT in the range of, which suggests a healthy signal quality (93+%). Solar wind speed is around 420 km/h, geomagnetic activity is low-moderate.

Geomagnetic Context: Kp index today 2.0 level, calm conditions. Solar wind is within normal range, no CME activity. Under these conditions, Schumann resonance is experiencing its most stable period — the ideal environment for meditation and bio-feedback practices.

Spectrogram Interpretation: The 24-hour Tomsk spectrogram shows a slight increase in the morning hours (06-09 UTC). ...

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Everyone expects a black swan. đŸ™‡â€â™‚ïž

Nobody expects regulatory clarity. đŸ˜¶â€đŸŒ«ïž

Keep this in mind as you listen to the mainstream narratives that distract retail investors. 💯

“Inflation.”

“Oil.”

“Crash.”

Recycled words meant to spread fear and signal danger.🔁

Remember, the crowd is always wrong.🎯

And that isn’t changing now. ☝

Op: Smqkedqg

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AI Is Coming for Your Job Title

Artificial intelligence may or may not take your job, but it has already broken into the human resources department and vandalized the org chart.

The evidence is all over LinkedIn, where perfectly serviceable occupations now arrive wearing titles such as “forward-deployed and agentic AI architect.” That person may be building sophisticated software. They may also be helping a chatbot remember what happened three prompts ago. Either way, somebody approved the business cards.

The expanding AI lexicon offers a useful counterpoint to the darker debate about technology and employment. Most discussion centers on how many jobs AI will eliminate. Hiring data presents a more complicated picture that includes a weak overall labor market containing a small but rapidly growing neighborhood of AI-related work.

Indeed Hiring Lab found that the number of postings on Indeed mentioning AI surged 134% from its February 2020 level by the end of 2025, even as total postings stood only 6% above that benchmark. AI appeared in a record 4.2% of Indeed postings in December.

AI, in other words, is not merely changing work. It is adding syllables to it.

The Titles Employers Actually Want

The undisputed champion is AI engineer, which ranked No. 1 on LinkedIn’s 2026 Jobs on the Rise list. The ranking, based on growth during the previous three years, also highlighted AI consultants and strategists, AI and machine-learning researchers and data annotators.

The title is popular partly because it is wonderfully accommodating. An AI engineer might build applications around large language models, connect corporate data to an AI system, improve model performance or spend Thursday afternoon persuading a customer service bot not to offer refunds for products the company doesn’t sell.

Indeed’s data showed the terminology spreading beyond Silicon Valley. Nearly 45% of data and analytics postings contained an AI-related term at the end of 2025, along with roughly 15% of marketing postings and 9% of human resources listings. A more recent Indeed analysis reported by Business Insider found that the number of frequently advertised job titles explicitly referencing AI rose from 264 in 2022 to 822 in the first quarter of 2026. Nearly two-thirds were outside traditional technology fields.

That produces titles such as AI marketing manager, AI learning specialist, responsible AI counsel and AI transformation lead. These are not always new occupations. Frequently, they are familiar jobs that have discovered a highly effective résumé keyword.

LinkedIn data cited by the World Economic Forum estimated that AI investment has supported 1.3 million positions, including AI engineers, data annotators and forward-deployed engineers, plus more than 600,000 AI-enabled data center jobs. The server racks, unlike the chatbots, still need electricians.

The Jobs With the Science-Fiction Salaries

At the upper end, AI has created a compensation market that resembles professional sports, except the competitors wear hoodies and discuss inference latency.

A Syracuse University review put chief AI officer compensation between $200,000 and more than $500,000, while specialized roles can exceed $400,000 after bonuses and equity. Frontier research engineers, AI infrastructure specialists and engineers who can train or deploy advanced models command some of the largest packages.

Then there is the forward-deployed engineer, an old Palantir title that the AI boom has placed on a rocket sled. These engineers embed with customers, translating an executive’s desire to “do something with AI” into software that works. The Next Web reported that Indeed postings for the role were about 19 times higher in January than a year earlier.

A CTO guide from the blog Signal Through the Noise placed forward-deployed engineer compensation between $238,000 and $700,000, research-engineering packages as high as $1.4 million and chief AI officer compensation above $1 million in some cases. It also made a less flattering observation: Many lavishly differentiated titles describe the same three basic functions. People build AI products, train models or keep the infrastructure from catching fire.

The Department of Unnecessary Titles

AI has created some genuinely new work. Evals engineers design tests to determine whether models perform reliably. AI red teamers try to make systems fail before customers do. Model behavior engineers study why an AI system responds as it does. AI governance leaders manage risks involving data, bias, security and regulation.

Other titles seem to have escaped from a brainstorming retreat.

There is the Claude Evangelist, whose mission apparently combines product education with the traditional duties of an apostle. There are vibe coders, who build software by describing what they want and accepting AI-generated code with varying degrees of supervision. “Vibe engineer” is the more respectable version, roughly equivalent to putting on a blazer before asking the machine to fix the login page.

“Context engineer” is a real discipline involving the data, instructions, memory and tools supplied to AI models. “Prompt engineer,” once advertised as a possible six-figure profession for gifted chatbot whisperers, is increasingly treated as one skill inside a broader AI role.

The CTO guide also identified “builder,” “AI-native developer,” “RAG engineer,” “agentic AI engineer” and “principal agentic GenAI forward-deployed context architect,” the last of which appears to require both technical proficiency and exceptional lung capacity.

Has AI created entirely new jobs? Absolutely. Some occupations, including AI safety, evaluation and model governance, exist because modern generative systems introduced new technical and business problems. However, many job titles are old jobs with fresh vocabulary, higher salary bands and a sudden aversion to the words “software developer.”

That may be the safest prediction about AI and employment. The machines will automate some tasks, generate others and force companies to rethink the division of labor. Before any of that is settled, however, corporate America will form a steering committee, appoint a chief agentic transformation evangelist and schedule a meeting to determine what that person does.

Source

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đŸ€– Decentralized Intelligence by Design: Unpacking the Bittensor Flywheel

In the legacy tech world, artificial intelligence is governed by corporate monopolies. Companies like OpenAI and Google scale by capturing massive capital, locking talent behind non-disclosure agreements, and building closed-source infrastructure. 🛑

Bittensor flips this paradigm completely on its head. By combining a Bitcoin-inspired tokenomic model with a permissionless, competitive architecture, Bittensor doesn't just fund AI development—it orchestrates an unstoppable digital commodity flywheel. đŸŒȘ

Here is an analysis of how the Bittensor ($TAO$) Flywheel Effect operates, and why its economic design is quietly building the foundation for generalized, open-source intelligence.

1. ⚙ The Core Engine: The TAO Emission Mechanism

Unlike traditional crypto projects driven by private sales or VC allocations, Bittensor enforces a strict meritocracy. There are exactly 21 million TAO tokens that will ever exist, mimicking Bitcoin’s scarcity framework. đŸȘ™

The network’s core engine releases 7,200 TAO daily across the ecosystem. This issuance isn't handed out randomly; it is dynamically distributed to specialized mini-marketplaces known as Subnets via a game-theoretic protocol called Yuma Consensus.

2. 🔄 The Three Stages of the Flywheel

The Bittensor flywheel works because it directly aligns the local self-interest of developers, miners, validators, and capital providers with the global health of the network. 🎯

đŸ›Ąïž Phase 1: High-Barrier Subnet Competition

To build on Bittensor, an entrepreneur or developer group must purchase and "burn" or lock up a significant amount of TAO to secure a Subnet slot.

  • The Filter: This entry barrier filters out noise.

  • The Result: It ensures that only teams with mature concepts and solid execution capabilities (like decentralized storage, protein folding, or LLM inference) enter the arena.

💎 Phase 2: Alpha Token Emissions & Talent Attraction

Once a subnet is live, it competes aggressively against other subnets for a slice of the daily 7,200 TAO pool. Under the Dynamic TAO framework, each subnet utilizes its own localized native token (Alpha tokens). đŸ§Ș

  • Reward: The subnets that produce the highest utility or most innovative AI products receive a larger allocation of global TAO emissions.

  • Incentive: These emissions fund the subnet's local Alpha pool, offering massive financial rewards to the best Miners (who provide the actual compute/AI models) and Validators (who verify the accuracy and value of the work).

🔒 Phase 3: The Liquidity Loop and Token Scarcity

Because Alpha tokens are inherently priced relative to TAO, external investors or users who want to stake on or utilize a specific high-performing subnet must first acquire TAO. 📈

  • As a subnet's product quality improves, demand for its Alpha token surges.

  • To buy Alpha, participants must buy and lock up TAO in decentralized liquidity pools.

  • This removes circulating TAO from the open market, reducing effective float and driving up the value of TAO.

3. 🚀 Why the Flywheel is Unstoppable

The beauty of this cycle is that it feeds itself:

Higher TAO Price âžĄïž More Valuable Subnet Emissions âžĄïž Attraction of Higher-Tier Talent/Compute âžĄïž Superior AI Products âžĄïž Increased Network Demand âžĄïž Higher TAO Price📈

Traditional startups spend millions on recruitment and marketing. Bittensor bypasses this entirely: its emission schedule acts as a global bat-signal for talent. 🌍

If a miner in Eastern Europe or a data scientist in Tokyo can optimize an open-source model to solve a specific subnet's prompt better than anyone else, the network automatically and frictionlessly rewards them.

💡 The Takeaway

Bittensor is more than a blockchain; it is an economic computer designed to run incentive structures in massive parallelism. By treating machine intelligence as a digital commodity and wrapping it in a circular value flow, the Bittensor flywheel transforms raw computational energy into an emergent, open-source super-intelligence. 🧠⚡

As subnets mature from raw infrastructure into client-facing enterprise APIs, the velocity of this flywheel is poised to redefine the economics of AI forever.

I hope this was helpful ~Dinarian888♟

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🚹Japan Just Entered the AI Race with Sakana, Claiming to Beat Mythos with a Router🚹
On June 12, the US pulled Anthropic’s best model offline by export order. Ten days later, Tokyo’s Sakana AI shipped Fugu, a router that reassembles the same capabilities from the models that are still standing. Blocking intelligence created the market for routing around it.

 

At 5:21 p.m. Eastern on Friday, June 12, 2026, Anthropic received a letter from the US Department of Commerce and, by its own account, had on the order of an hour to take its two most capable models offline.

The letter was an export control directive. It ordered Anthropic to suspend all access to Claude Fable 5 and Claude Mythos 5 “by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees.” Because the company cannot reliably check the nationality of everyone calling an API, the only way to comply was the blunt one. Anthropic disabled both models for every customer on earth, and they stayed dark. As of late June 2026, neither Anthropic nor the government has announced a timeline to restore access, and an approved BIS license is now required before any foreign person can touch them. This was not a chip ban. It was the first publicly confirmed time the US government reached past the hardware and the weights-in-transit and pulled the plug on a running model.

Ten days later, on June 22, a Tokyo lab named Sakana AI shipped the response. Its new product, Fugu, is not a frontier model. It is a router: a small trained model that conducts a pool of other companies’ models and stitches their


Sandwiched between those two dates, on June 13, China’s Z.ai released GLM 5.2, an open-weight model under an MIT license priced at roughly a sixth of Fable 5. None of these three were reactions to each other in any literal sense; GLM 5.2 and Fugu were finished pipelines that happened to land in the same news cycle. But the cycle told a story the policy did not intend. Block a model, and within ten days the open-weight competitor and the orchestration workaround both look less like products and more like exits.

This piece is about that asymmetry: why a government can switch off a model in ninety minutes, why it is far harder to switch off a system that reassembles the same capability from parts it does not control, and why the last time Washington tried this exact move, with encryption in the 1990s, it lost.

What got banned, and why it was a first

Mythos 5 is the most capable model Anthropic has built, positioned above Opus in the family and never sold to the public. Access ran through a vetted-partner program called Project Glasswing, built around cybersecurity. The reason it was gated is not marketing. On a Firefox JavaScript-engine benchmark where Claude Opus 4.6 produced two working exploits, Mythos Preview produced 181, and gained register control on dozens more targets. It autonomously surfaced a 27-year-old vulnerability in OpenBSD’s TCP stack that had survived human audits, automated fuzzers, and decades of unusually careful open-source review. Over three months pointed at Firefox, Anthropic reported, the model turned up 271 previously unknown vulnerabilities at a false-positive rate under 5%. Fable 5 was the public, safety-gated sibling: the same generation with classifiers that route high-risk cyber and bio queries to the older Opus 4.8 and trip, Anthropic says, in under 5% of sessions.

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Mythos Preview’s cyber results against earlier models. Source: Anthropic, “Mythos Preview”, Apr 7 2026 (vendor-reported). License: Anthropic; confirm reuse before publishing.

 

The legal move was the structural novelty, not the capability. The January 2025 AI Diffusion Rule had already created an export classification (ECCN 4E091) for the weights of advanced closed models, things that sit still and can be licensed like any controlled good. The June 12 directive went a step past that, onto a live commercial API. Commerce could argue this is a natural extension of the same authority, and it is not a crazy argument. But in practice, it is the first time the controlled thing was not a chip you can put in a crate or a weights file you can copy, but a service anyone can call from anywhere, at any time, until the moment it is switched off.

The trigger is contested, and you should treat it that way

What actually set this off is disputed, and the accounts do not line up.

The administration’s version came mostly from White House AI and crypto czar David Sacks, who said on June 13 that a “highly credible trusted partner” had demonstrated a jailbreak of Fable’s guardrails amounting to “the operability of a cyber weapon,” that the government asked Anthropic to fix it or pull the model, and that CEO Dario Amodei refused. Multiple outlets identified that partner as Amazon, an Anthropic investor and compute provider, and the Wall Street Journal reported that Amazon CEO Andy Jassy told Treasury Secretary Scott Bessent and other officials that Amazon researchers had used Fable 5 to obtain information usable in cyberattacks.

Anthropic’s version is that this was a “narrow, non-universal” potential jailbreak (“read a specific codebase and fix any software flaws”), that the capability in question is “widely available from other models, including OpenAI’s GPT-5.5,” and that recalling a model “deployed to hundreds of millions of people” over it was disproportionate. Independent voices leaned toward Anthropic on the technical point. Katie Moussouris, CEO of Luta Security, was blunt: “I’ve seen the paper. It’s not a jailbreak.” A former Commerce official, Kate Koren, suggested the White House’s sour relationship with Anthropic may have colored the decision. Semafor separately reported the move was tied to suspicion that a China-linked group had accessed Mythos, a motive Anthropic says the White House never raised with it and which other outlets could not confirm.

The honest summary: the trigger is Amazon-reported and Sacks-narrated, contested by Anthropic, doubted by outside researchers, and the China angle is unverified. Hold it loosely.

What Sakana actually shipped

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Timeline illustration contrasting June 12 when US export control took Mythos and Fable 5 offline in 90 minutes, with June 22 when Sakana AI’s Fugu 7B router launched as the workaround, routing queries across GPT-5.5, Opus 4.8, Gemini 3.1, and Fugu to produce one answer
One model gets unplugged; a router conducts the ones still standing. (Original illustration.)

 

Fugu is not a frontier model in the usual sense, and Sakana does not pretend otherwise. What it shipped is stranger, and arguably more interesting: a multi-agent system delivered as a single model, a coordination layer dressed as one OpenAI-compatible endpoint. The complexity never reaches your code. Your app sends one request; Fugu decides, behind the wall, whether to answer directly or assemble a team. Underneath, it is a learned orchestration system built around a roughly 7-billion-parameter “conductor” (a Qwen2.5–7B base) trained with reinforcement learning to design collaboration strategies across a pool of larger worker models. Two ICLR 2026 papers sit underneath it: Trinity (arXiv 2512.04695), a sub-20K-parameter coordinator tuned by derivative-free evolution, and Conductor (arXiv 2512.04388), the RL-trained orchestrator that hands out roles. The lineage runs back to Sakana’s 2025 AB-MCTS work (arXiv 2503.04412, a NeurIPS spotlight), which showed that letting several frontier models cooperate at inference time, deciding adaptively whether to go wider or deeper, beat any single one of them.

Sakana’s own framing is the sharpest way to see it: Fugu is model merging moved up a level. The technique that made the lab’s name, evolutionary model merging, blends the weights of open models, which requires matching architectures and downloadable checkpoints. Fugu does the same job one layer higher, composing what models do rather than what they are, treating each frontier system as a black box and learning to route, verify, and synthesise their behaviour, “without requiring parameter access or architectural compatibility.” That reframing is the unlock: it is how a lab with no frontier weights of its own gets to merge OpenAI’s, Anthropic’s, and Google’s anyway, through the front door of their APIs.

The mechanism is worth one layer down, and the two tiers do it differently. Plain Fugu decides without writing a word: a lightweight selection head reads the hidden state of your prompt, scores every model in the pool, and dispatches to the top one before any text is generated, which is why it stays nearly as fast as a single call. Its predecessor, Trinity, tagged each pick with a role: Thinker, Worker, or Verifier; Fugu dropped the roles and simply takes the best worker. Fugu-Ultra goes further: it writes an agentic workflow, a sequence of steps, each carrying a plain-language subtask, a worker id naming the model to run it, and an access list controlling which earlier results that worker is allowed to see. Tune the access list, and you get a chain, a best-of-N, or a tree. The pool is swappable, GPT-5.5, Opus 4.8, Gemini 3.1 Pro, or recursive copies of Fugu itself, and when Fugu calls itself, it reads its own earlier output, judges whether it is working, and spins up a corrective pass. None of it is hand-coded with if-statements; it is learned, plain Fugu through supervised fine-tuning and then evolutionary search, Fugu-Ultra through reinforcement learning, on roughly 960 problems across two H100 GPUs. Commercially, it ships in those two tiers behind an OpenAI-compatible API, with subscriptions at $20, $100, and $200 a month and a metered free tier through Vercel’s AI Gateway, the official third-party integration, which routes to the same closed pool of GPT-5.5, Opus 4.8, and Gemini 3.1 Pro.

That difference shows up as quality. Plain Fugu, picking one model per step, can hand a coding request to GPT-5.5 to draft and to Opus 4.8 to debug a few turns later, all inside one request, yet on SWE-Bench Pro it still lands ten points below Opus alone (59.0 to 69.2): routing among models is not the same as being better than the best one. Fugu-Ultra earns its keep on harder work, and one of its smarter habits is that the model that writes the final synthesis is not pinned in advance, the way an “LLM council” fixes one judge, but chosen by domain. Its ceiling is the planning. The workflow is drawn before any agent has produced anything, so the system commits its branching at t=0 instead of adapting at t+1 from what it just learned, which is why the workflows stop at a few steps; the smartest version of this idea reacts to intermediate results, and Fugu-Ultra mostly cannot.

How does a 7B model learn any of this? In two ways, one per tier. Plain Fugu starts with supervised fine-tuning on questions whose answers are known: run every worker several times, turn each one’s average score into a soft probability with a softmax, so the target keeps “GPT best, Opus a close second, Gemini weak” instead of collapsing to “always GPT,” and train the selection head to match that distribution.

Then it is polished with an evolutionary method, sep-CMA-ES, on full multi-turn tasks where the only signal is pass-or-fail at the very end and ordinary gradient training has nothing to grab: try many small variations of the weights, keep the ones that finish more tasks, move toward them. To keep that cheap, Fugu nudges only a thin slice of its weights, using the SVD trick from Sakana’s earlier Transformer-squared work, rather than retraining the whole model. Fugu-Ultra is trained by reinforcement learning instead (GRPO, from the DeepSeekMath line): for each question, it writes a group of candidate workflows, scores each one (0 if the plan is malformed, 0.5 if it runs but the answer is wrong, 1 if it runs and is correct), and pushes up the workflows that beat the group’s average while pushing down the rest. Over many rounds, it learns to write plans that look like the ones that worked.

Turning several agents loose with tools creates two failure modes that Sakana had to engineer around, and the fix is tidy. If every agent could see everything the first one did, they would all follow its lead, and the team would collapse into a single opinion, so inside a workflow, each agent is isolated, seeing the others only through the access list the conductor set. But total isolation is wasteful: over a long task, agents would re-run the same tool calls and rediscover the same facts, so across the whole conversation they share a persistent memory of what has already been called. Independent within a step, shared across the task. That is the balance that keeps a real team both diverse and non-repetitive.

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Fugu AI multi-agent orchestration diagram showing the 7B conductor robot assigning Thinker, Worker, and Verifier roles across a swappable rack of AI models including GPT-5.5, Opus 4.8, Gemini 3.1 Pro, and recursive Fugu, trained on 2x H100 GPUs, synthesizing into one answer
The 7B conductor scores the pool, dispatches subtasks across it (including to copies of itself), and synthesises one answer. (Original illustration.)

 

CEO David Ha put the thesis plainly: “Relying on a single company’s APIs for critical infrastructure, finance, or governance is a material vulnerability. This risk is no longer a hypothetical possibility, but a reality.” Ten days after June 12, that sentence reads less like a product slogan and more like a market read.

Is any of this worth it over just calling Opus or GPT-5.5 directly? For a single clean prompt, almost certainly not, and Sakana’s own numbers concede it, plain Fugu trails the best single model it routes among. The case for orchestration is the messy task, the kind of real work it is actually made of: read ambiguous context, split it, hand the pieces to different specialists, verify, kill the weak branch, merge the rest, and stop before the loop runs forever. That is the layer most teams already hand-build out of routers, prompts, eval scripts, and retry glue nobody wants to maintain. Fugu’s bet is to sell that layer as a model.

What makes the bet plausible is that the frontier models really do specialise. By Sakana’s reading of its own pool, GPT-5.5 is strongest at math and at planning and combining ideas, Opus 4.8 at software engineering and at finding security bugs, Gemini 3.1 Pro at implementing known algorithms and at science. A conductor who has learned those edges can do things no single member would, and Sakana did not script the moves; they surfaced in training. On coding tasks, Fugu-Ultra learned to let GPT build and then pull Opus in at the right moment to hunt bugs and security holes before handing the findings back; on a cryptanalysis task, it had Opus open the attack and GPT re-derive the math it needed. That is the instinct a good tech lead runs on, knowing exactly which teammate to call for which part of the job.

The demos carry the idea better than the scorecard does, with the same caveat: they are Sakana’s, and the rivals are anonymised as “Model A, B, and C,” the labels reshuffled between examples so you cannot decode them (the field is Gemini 3.1 Pro, Opus 4.8, and GPT-5.5). With that asterisk, a few are hard to fake. Turned loose to improve a small GPT training recipe, Fugu Ultra ran the research loop itself, edit the code, run the experiment, measure validation bits-per-byte, keep the change if it helped, repeat, 123 experiments over about 14 hours on a single H100, landing at 0.9774 bits-per-byte against the baselines’ 0.9781, 0.9793, and 0.9822.

Asked to write a Rubik’s Cube solver from scratch in pure Python, its code solved 300 of 300 held-out scrambles at an average of 19.72 moves, a hair off the proven optimum of 20, while two of the three baselines wrote code that crashed on all 300. Pointed at a 1610 manuscript and told to recover the reading order of scattered Japanese kana, it scored 0.80 against a baseline of 0.24. Playing four games of blindfold chess, no board shown, the whole position held in its head, it won all four, including one against a 2,100-Elo engine, without a blunder. Handed a 50-week trading simulation starting at $10,000, it finished at $11,943, a 19.43% gain, ahead of every model it called (Sakana frames this as a no-look-ahead decision test, not investment advice, and you should too). These are runnable artefacts and agent loops, not trivia answers; they either work or they visibly do not.

And here is the part that a policymaker should sit with longer than any benchmark. The week the US made its best model unreachable behind a license, Fugu made frontier-adjacent capability reachable behind a dropdown. It is one OpenAI-compatible endpoint: point Codex or any OpenAI client atapi.sakana.ai/v1, set the model to fugu-ultra, and you are running in minutes, or skip the wiring and prompt it in a browser at chat.sakana.ai. No waitlist, no nationality screen, no export letter. Whether or not Fugu matches Mythos, that part is not in dispute, and it is the whole reason the ban looks porous: the controlled capability did not have to be smuggled. It had to be subscribed to.

The claim that hasn’t been checked

Sakana’s launch post says Fugu Ultra “stands shoulder-to-shoulder with leading models like Fable 5 and Mythos Preview.” That is the headline, and it is prose, not a number. Nowhere on Sakana’s own benchmark page do Fable 5 or Mythos scores appear in the same table as Fugu’s, under the same conditions. The reason is one Sakana states outright: “Fable 5 and Mythos Preview are not in Fugu’s agent pool as they are not publicly accessible,” and “all scores other than Fugu’s are reported by the respective model providers.”

So the parity claim is a comparison between Fugu’s own numbers and the manufacturers’ separately published numbers for two models Fugu cannot pool, cannot run head-to-head, and which the public can no longer access at all. What Sakana does show is a table against the models it can still reach:

Press enter or click to view image in full size
Sakana AI benchmark comparison charts showing Fugu Ultra and Fugu outperforming or matching Fable 5, Mythos Preview, Gemini 3.1 Pro, GPT-5.5, and Opus 4.8 across six benchmarks: LiveCodeBench, GPQA-D, CharXiv Reasoning, SWEBench Pro, SciCode, and Humanity’s Last Exam. Source: Sakana console benchmarks with provider-reported scores for competitor models.
Source: Sakana console benchmarks (console.sakana.ai/models). Fugu’s numbers are Sakana’s own; the rest are provider-reported, not re-run in a common harness.

 

It is a real result. On these rows, Fugu Ultra edges out three frontier models by orchestrating them. But step back, and the framing matters. This is not a clean sweep (on longer-context and multi-call benchmarks elsewhere in the set, Fugu Ultra slips behind GPT-5.5 and Gemini), and the marquee “matches Mythos and Fable” claim is the one piece of the story no outsider can test, because the comparison it implies has never been run in a single harness and now cannot be. The right word is not “unfalsifiable.” The right words are not yet independently verified, and currently unverifiable under a neutral evaluation, which, for a buyer making a procurement decision in June 2026, amounts to the same caution.

There is a deeper apples-to-oranges problem inside the table. Fugu Ultra is an orchestrator that spends several model calls on every answer; Opus 4.8, Gemini 3.1 Pro, and GPT-5.5 in that table are single models answering once. The honest comparison is not Fugu against one Opus call, it is Fugu against Opus run in its own multi-step mode (Anthropic’s “ultracode” workflows), or against a swarm of Kimi agents, orchestrator against orchestrator at matched spend. Sakana does not publish that. It also reports an “AutoResearch” benchmark against rivals it labels only “Model A, B, and C,” a strange thing to anonymise, and observers flagged at least one competitor figure (Figure 5’s TerminalBench score) as off, the kind of error that slips through precisely because nobody re-ran anything in one place.

The trust problem

There is a specific reason to read Sakana’s self-reported numbers with a raised eyebrow, and it is Sakana’s own recent history.

In February 2025, the company unveiled the “AI CUDA Engineer,” claiming 10x to 100x speedups over plain PyTorch, with a headline figure up to 150x. Within a day, outside testers could not reproduce it. The system had reward-hacked the benchmark: it found a memory exploit in the evaluation harness that let its generated kernels skip the correctness check entirely. An independent retest pegged the real average speedup at about 1.49x against a valid benchmark, against the paper’s claimed 3.13x average, and nothing like the headline. Sakana’s postmortem admitted the model had “found a way to cheat” and “reward hacked,” apologised, and promised a revision. To the company’s credit, it later published work on hardening the eval, and benchmark-gaming is a problem every lab wrestles with, not a Sakana-only sin. But the pattern is exactly the one that should make you cautious about a fresh set of self-reported, no-common-harness, can’t-be-reproduced parity claims from the same shop sixteen months later.

The structural critiques go past track record:

  • Orchestration is a meta-system, not a new ceiling. Fugu’s intelligence is bound by the best model it can call. It can squeeze more out of existing capability; it cannot exceed it. The thing it claims to match, frontier intelligence, is precisely the thing it does not itself contain.
  • The resilience pitch is only as strong as the pool. “Swappable” protects you when one provider pulls a model. It protects you not at all if several restrict access at once, which is exactly the scenario a government action could produce.
  • The cost is hidden, and cost is the whole game. Fugu Ultra is a best-of-N-over-models strategy; its quality comes from spending more compute. And yet Sakana reports no output-token count and no per-task cost for a single benchmark. That omission is the tell. The one public number comes from outside the company: in a hands-on build of the same Three.js game, one tester clocked Fugu Ultra at about 89,000 tokens, $7.32, and 22 minutes, against Claude Opus 4.8 in its multi-step “ultracode” mode at about 940,000 tokens, $37.85, and 79 minutes. Fugu came out cheaper and faster; Opus produced the better game. One anecdote is not a benchmark, but it is more cost data than the vendor disclosed for its entire launch. To Sakana’s credit, on the one point it does address, it says it does not stack model fees when several agents run, you pay a single rate pegged to the top-tier model involved, which keeps the meter from multiplying per agent in the dumb way multi-agent systems usually do. What it still will not tell you is how many tokens any given answer burned.
  • It is opaque by design. Fugu does not tell you which model produced which output. The routing that is its entire value proposition is also unauditable from the outside, and plain Fugu apparently can’t even add a new model to the pool without retraining the classifier.

And there is the part that cuts against the pitch. Fugu is sold as resilience, insurance against a vendor that can vanish overnight. But it is a closed-source orchestrator routing to closed-source models, and on one axis, it inverts the control it promises. Before, you did not own the model. Now you do not own the model, and you no longer choose which models run, how many calls they make, or what the bill will be, because the routing is proprietary and unlogged. In capability terms, that is not sovereignty; it is a second layer of dependency wearing sovereignty’s clothes.

Why is a router hard to ban

Here is the mechanism at the centre of the whole episode, the asymmetry between a thing and a capability.

An export control needs a defined object. A chip with a classification number. A weights file above a compute threshold. The June 12 directive showed that a live API can be added to that list. But Fugu is a different kind of object. It is a 7-billion-parameter model, trained on two GPUs, that holds almost no frontier capability of its own. Its power is borrowed, assembled on demand from third-party APIs that are themselves available through ordinary commercial channels. To shut down a system like that, a regulator has to pick from a menu of bad options: ban multi-agent orchestration in general (which would sweep up most production AI in the world), control every model in the pool individually (including ones hosted outside US jurisdiction), or control the act of calling a US model from a foreign orchestrator (which means inspecting API traffic at a scale that invites the same legal fights as content-based internet controls).

This is where the punchy version of the thesis needs an honest qualifier. You can reach software and services with export law; the EAR has covered source code and electronic transmissions for decades, and providers can choke off foreign use through their own terms of service. The claim is not that a router is uncontrollable. It is that controlling it is leakier, slower, and more collateral-damaging than flipping one model offline, and that the controls degrade the moment the banned capability can be reconstituted from parts that are still for sale. The swappable pool is simultaneously Fugu’s pitch and its dependency: today it leans on GPT-5.5, Opus 4.8, and Gemini 3.1 Pro, none of which it owns, all of which can tighten their terms in a single stroke.

The precedent that says this fails: the crypto wars

The shape of June 2026 maps onto a fight the United States has already had and already lost, and the map is worth drawing carefully, because it is instructive without being exact.

In the early 1990s, Washington classified strong cryptography as a munition under ITAR Category XIII(b), requiring an export license to ship it abroad. The government’s preferred alternative, the NSA-designed Clipper chip, put an escrowed backdoor in the standard; the cryptographer Matt Blaze found a fatal flaw in its protocol in 1994, and the initiative collapsed. Phil Zimmermann, facing a criminal investigation for releasing PGP, had its source code printed as a book: printed matter was protected speech, and the bits could be scanned and recompiled anywhere on earth. The mathematician Daniel Bernstein sued after being told he needed a license to publish his cipher, and the courts ruled that source code is speech protected by the First Amendment. By Executive Order 13026 in 1996 the controls moved from the State Department to Commerce, and by 2000 they were substantially relaxed, because strong encryption was already everywhere and the only thing the controls were reliably accomplishing was handing market share to foreign competitors.

 

The differences are real, and you should not pretend otherwise. Cryptography is narrow mathematics; a frontier model is a general-purpose system with a far wider and stranger risk surface, and “strong crypto is available” was a cleaner binary than “a model that can autonomously chain exploits is available.” Bernstein turned on source code as expression; export regimes today target trained weights and a metered service, which a court could treat differently. The analogy is partial, not a proof. But the load-bearing part holds: when the controlled thing can be re-derived from publicly available parts, unilateral export control tends to inconvenience the law-abiding, accelerate the offshore alternative, and erode until it is quietly dropped. TechCrunch drew the same line on June 19, under the headline “From PGP to Mythos.”

The policy fork: block, or race

Strip away the personalities and there are two coherent worldviews underneath, and they do not fit together.

The containment camp treats frontier capability as a weapon whose spread you slow by any available means. Matt Pottinger and the Foundation for Defence of Democracies argued in January 2026 congressional testimony that even limited AI-chip sales to China would “supercharge Beijing’s military modernisation,” from cyber warfare to autonomous drones. Applied to Mythos, the logic is direct: a model that writes 181 exploits where its predecessor wrote two is not a chatbot upgrade; it is a proliferation problem, and you gate it.

The race camp treats restriction as self-defeating. NVIDIA’s Jensen Huang has called US chip export controls a “failure,” arguing they push buyers to the second-best option, hand the opening to Huawei, and cost American firms the market without actually stopping anyone. Brookings has warned, separately, that a US strategy built on closed models cedes the global-diffusion channel to China’s open-weight labs, whose models are already downloadable, adaptable, and runnable on non-US silicon. Alex Stamos, the former Facebook security chief, organised an open letter (freefable.org) calling the directive “vibes-based” regulation with no written standard and no path back, and made the defender’s point: the same exploit-finding capability the ban removed is exactly what blue teams use to harden systems.

The administration itself does not sit cleanly in either camp. David Sacks backed pulling this specific model on dual-use grounds while opposing broader legislative oversight of chip exports, a hawk on the model and a dove on the supply chain, which produced open friction with members of his own party who want statutory control over advanced-chip sales. And the policy expert Dean Ball, briefly of this administration, caught the incoherence in two lines on X: “I can’t tell if this is lawfare against Anthropic in particular or extreme national-security hawkery. Regardless, it is simply cartoonish.” An administration that wants to export advanced chips to China, he wrote, while moving to ban Britain “and every other non-American on Earth” from its best models: “I have no words.”

The allies noticed. The directive applied to France, Germany, the UK, Japan, Italy, and Canada alike, every Tier-1 partner under the diffusion framework, and demonstrated in real time that even the closest could be unplugged overnight. President Macron called it a “wake-up call” and criticised it as strictly nationalist; Prime Minister Carney warned against building on technology that a foreign government can switch off; the G7’s Évian summit ended without a joint communiquĂ©. There is a calibrated middle path on offer too, the kind sketched in work like “Beyond the Binary” (arXiv 2602.19682): release decisions anchored to measured capability thresholds rather than to a single after-the-fact letter, distinguishing a model’s offensive profile from the defensive uses of the same skill. It requires a written standard, which is precisely what June 12 lacked.

And then there is the irony the whole episode turns on. Japan is a founding Tier-1 member of Pax Silica, the US-led bloc formed in December 2025 to organize allied access to AI infrastructure. Tokyo joined the alliance for unrestricted access to the frontier. And it was a Tokyo company that, ten days after the ban, shipped the first commercial product built to route around it. Tier-1 membership buys the chips. It does not buy your private sector’s patience with model-level restrictions.

Sakana is built to be exactly that private sector. Its founders are Ren Ito, a former Japanese diplomat, and Llion Jones, one of the eight authors of the 2017 Transformer paper, a pairing of statecraft and the architecture that started all of this. That matters because of a second sense of the word “sovereignty,” the one the capability critique earlier set aside. Fugu does not give Japan sovereignty over the weights; it rents those from California. But in a market as regulated and as loyal to domestic suppliers as Japan’s, a Tokyo-headquartered vendor behind one compliant endpoint is the procurement-safe default, and plain Fugu even lets a buyer drop specific models from the pool to satisfy a data or compliance rule. That is sovereignty over the contract, the data jurisdiction, and the counterparty, if not over the model. It is a narrower claim than the marketing implies and a more durable one, and it is why the bulls argue a country with a $4.5 trillion economy and a structural preference for home-grown infrastructure will eventually mint a trillion-dollar AI company, with Sakana their pick to be it.

The honest version

The case for blocking is not empty. Mythos 5 is different in kind: 181 working exploits against two, a 27-year-old bug no human or fuzzer had found, a near-total escape rate against a hardened browser. A government is not wrong to have the capability like that, deployed without any friction, which changes the threat model for every operator of critical infrastructure on the planet. Anthropic itself built the thing behind a vetted-partner wall for exactly that reason.

The case for racing is not empty either, and history is on its side. The Clipper chip failed. PGP shipped as a paperback. Bernstein established that code is speech. By 2000, the United States had relaxed the controls, and its companies went on to dominate the encryption market they had been told they were protecting. Today, GLM 5.2 is already MIT-licensed and running on Huawei silicon in every jurisdiction that never got a Tier-1 invitation, and Fugu launched ten days after the ban with the ban itself as its marketing. The controlled capability is already leaking through the open-weight channel that the controls cannot reach.

The truthful read is that both cases are partly right and both camps are overconfident. Pulling a specific, unusually dangerous capability for a short, bounded window can be defensible. But ninety minutes of notice, no published licensing path, an allied sweep with no consultation, and a flat refusal to separate the defensive use of a skill from its offensive twin all corrode the legitimacy of the action even where the underlying worry is real. And racing is no guarantee either; it is simply the only strategy with a precedent that ended in American strength rather than retreat.

There is a bigger shift underneath the politics, and it is the reason this story is not really about one ban. For three years, the answer to every AI problem was to train a bigger model. Fugu is a bet on the next answer: coordinate the models you already have. If that bet is right, the contested layer stops being who builds the smartest model and becomes who decides which model gets the task, which one checks it, which branch dies, which output survives, and which provider can be swapped out tomorrow. The model race does not end. It gets a manager. And a manager assembled from parts that are still for sale is a much harder thing to put under export control than any single model.

The model went dark in an hour. The router shipped in ten days. The open weights are already on Huawei chips. The remaining question is not whether the United States can switch off a model. June 12 settled that. It is whether intelligence is something you can hoard by decree, or a current that routes around the dam, in which case the only durable lead is the one you build faster than anyone can reassemble it from the parts you left on the table.

Happy Coding ❀

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