Tomasz Konicz

You are speaking out against Jewish capital, gentlemen? Anyone who speaks out against Jew capital, gentlemen, is already a class fighter, even if he doesn’t know it. You are against Jew capital and want to fight the stock market jobbers. That’s right. Kick the Jew capitalists down, hang them from the lamppost, trample them underfoot. But, gentlemen, what do you think of the big capitalists, the Stinnes, Klöckner?

Ruth Fischer, Central Committee of the KPD, in front of nationalist students on July 25, 1923

When reflecting on the catastrophe of the German left, it seems counterproductive to point the finger at individual actors who, through their actions, promoted the disintegration that is now openly taking place. If a new beginning based on radical critique is still possible, it would be fundamentally wrong to try to pin the causes of the rise of the Querfront and the corresponding loss of significance of the Left on individual perpetrators – no matter how influential they may have been – because this would ultimately amount to simple personification. It would be the first step in the wrong direction. The causes of the rise of the Querfront, which has been able to develop far greater weight in the current systemic crisis than in the 1920s or 1930s, lie deeper than the striving for power and the megalomania of someone like Sahra Wagenknecht.

It seems to make more sense to start with the terms and ideological concepts of the old left, which proved so susceptible to the New Right. These are anachronistic ideas that have fallen out of time and are seeking to catch up with their stock conservative bearers, who literally cannot or do not want to understand the late capitalist world due to their blindness to the crisis. They are feral remnants of the old social-democratic or orthodox communist left, most of whom think in 20th century categories. Social democrats, Leninists, parts of the anti-Germans caught up in a World War II loop – these regressive splinters of a world-historical attempt that failed in 1989 are mutating into carriers of right-wing ideology by means of the Querfront, as their entire political reference system has become increasingly decoupled from the reality of the crisis of late capitalism.

Sahra Wagenknecht, the figurehead of the German Querfront, has coined the oxymoronic term “left-wing conservatism” for this decaying form of the left, which is no longer a left-wing left. The delusion is aptly named: a left that no longer acts progressively, one that is backward-looking, can no longer be called left-wing. In fact, the conservative longing for the past dominates this old (post-)left. They long for the FRG of the economic miracle, for the Soviet Union and/or GDR, for the clearly demarcated power constellation of the Second World War, etc. – while the unreflected, relentlessly advancing socio-ecological crisis process, together with the corresponding process of fascization, promotes a comprehensive regression in the scene.

Regression, the fear-induced relapse into earlier forms of development, here means above all different types of ideological defense against the crisis, as the crisis process threatens to blow up the anachronistic ideological edifice in which the old left has made itself at home – this distinguishes left-wing regression from the usual reactionary tendencies of the right. In concrete terms, regression on the left takes the form of a reactionary struggle against radical crisis theory, against a categorical critique of capitalism. Regression thus ultimately strives to fend off the establishment of a radical crisis consciousness that has reflected on the necessity of overcoming capital as a social totality in order to survive. This would inevitably be tantamount to breaking out of the capitalist thought-prison, which would ultimately also leave behind the forms, institutions and levels of mediation of subjectless capitalist domination. This would be a deep rupture that also affects one’s own identity – an expression of socialization in late capitalism. And this also affects the subject, including the worker, who could only be “revolutionary” if he no longer wanted to be a worker. People no longer have to want to be what they were socialized to be under capitalism.

The old left shies away from this deep, categorical break with its beloved enemy, capital.[1] This hesitancy can be traced back to the ambivalence towards the proletariat in Marx’s work.[2] The widespread repression and marginalization of radical, transformational crisis consciousness that has been pursued by the old left and the Querfront in recent years has not only resulted from ideological blindness and a literal identitarian fear. It has also been promoted by a left-wing crisis opportunism that is still eyeing posts and positions in the late capitalist crisis administration.[3] The modest degree of reflection on the systemic crisis that had already been achieved has largely been lost; the consciously conducted categorical critique of capital in its fetishistic rampage has been replaced by affectless, irrational reactions to the crisis.[4] The rise of the Querfront  in the left went hand in hand with the marginalization of radical crisis theory and categorical critique of late capitalism.

What, then, is meant by this old left, most of which absurdly believes in the state? The old left does not necessarily have to be old; there are also an increasing number of young people in orthodox communist or Keynesian groups, networks and associations – precisely as an ideological expression of the increasing tendencies towards state capitalism caused by the crisis. The common denominator of the old left is formed by various rudiments of an anachronistic ideology that is fading into decay, turning brown, and opening itself up to the fascism of the 21st century. What is preached by the old left is a return to the old – social democratic or Leninist – truths, either to the social democratic struggle for redistribution, to the social question, to Keynes, to Lenin or even to Stalin, to truncated class struggle thinking and to the fetishization of labor and the proletariat.

This return to the ideas and concepts of the past was originally intended to bring to light the simple truths that had been lost and to counter the flat out lies and agitation of the right. Right-wing populism was to be countered by left-wing populism. What this great regression, in its blindness to the crisis, actually brought to light when rummaging through old left-wing ideological canned goods were stale, anachronistic terms and concepts that had fallen out of their time. These terms were gutted – stripped of their historical context – and themselves fell victim to regression, seeking connection with or docking onto the Querfront and right-wing delusion. They are ideological splinters in regression, anachronistic decaying forms of old-left ideology on its way to the New Right.

First and foremost is the concept of the proletariat as a revolutionary subject, a concept that is experiencing a regression towards a populist belief in the people and the will of the people. Since the working class, which is also variable capital, has not fulfilled its revolutionary destiny, a regressive substitution began in parts of the left, in which the people were generally imagined to be the new, blurred reference point. The will of the people was to be given populist expression, with the interests of the people being imagined in opposition to the ruling class or – to a lesser extent – to profiteers/the rich. But what happens when the people do not want to take a stand against the “rich profiteers,” but instead take refuge in racism and xenophobia? Doesn’t this popular will also have to express the legitimate interests of the people, doesn’t it also have to be able to be turned in a social direction by linking social demands with stronger border protection?

The cult of the proletariat, which has degenerated into a “popular belief,” is closely linked to the old-left class struggle paradigm. According to this paradigm, capitalism is nothing more than the front line in the battle between two two classes, the proletariat and the bourgeoisie, each of which has its own class interest and is engaged in a permanent – sometimes open, sometimes covert – class struggle, which is imagined as the main contradiction of capitalism. To this class struggle thinking, everything appears in terms of interests. All capitalist phenomena can accordingly be traced back to interests, which the class struggle leftists ask about with the famous Leninist “Cui bono?”(“Who benefits?”). Here, a conflict in the realm of distribution is blown up into the main contradiction of capital, while its inner contradiction is ignored. This inner contradiction tends to melt away the substance of capital – wage labor in commodity production – and capital can only prolong this “moving contradiction” in ever new spurts of expansion.

The current social and ecological world crisis is being fueled precisely by this contradiction-driven compulsion of capital to constantly grow, and this is quite obvious.[5] However, the class struggle left, with its truncated critique of capitalism, can only ask the question: cui bono? This blindness to the crisis, which ignores the fetishism of capital, leads directly to scapegoating and the reactionary belief in conspiracies that characterize right-wing crisis ideology.[6] Even if entire parts of the world threaten to become uninhabitable in the wake of the climate crisis, even if a pandemic rages, the old left, sometimes together with the new right, can only manically search for the influential, shadowy backers who are somehow responsible because they stand to profit from it.

The ideological division of capital into a “good,” nationally creative industrial capital and an “evil,” “globalist” financial capital is also part of the complex of the truncated critique of capitalism. As is well known, the Nazis took this delusion of an all-powerful Jewish banker conspiracy, which was apparently responsible for all kinds of crises and distortions, to an eliminatory extreme by enriching it with fanatical anti-communism in the form of the delusion of the “Jewish-Bolshevik world conspiracy.”

Historically, this anti-Semitic delusion of “destructive Jewish finance capital” was the most important starting point for Querfront efforts, for example those undertaken by the KPD in 1923 as part of the so-called “Schlageter course,” which nevertheless remained merely an episode (see the quote at the beginning of this text).[7] In the current left, the one-sided critique of the financial markets was mainly pursued by Keynesians and by the notorious “financial market critic” Wagenknecht after the outbreak of the global financial crisis in 2008. This truncated critique turned the actual nature of the crisis on its head, as the causes of the crisis are, in reality, to be found in the hyper-productivity of commodity production, which must be supplied with credit-financed demand through the formation of bubbles and mountains of debt.[8] Even in her most recent works, Wagenknecht produced variations of this reactionary “financial market critique,” which has an open flank to anti-Semitism.

The decay products of anti-imperialism, which, in the 1980’s, already had to deal with the problem of many of the modernization regimes that emerged from the great wave of decolonization simply failing socio-economically or being arch-reactionary and/or mass murderous – such as Saddam Hussein’s Iraq – form another old-left transitional milieu to the Querfront. At the time, these regimes were described as “objectively anti-imperialist powers,” seen as progressive simply because of their opposition to the U.S., even if they bloodily persecuted the left (Iran after the revolution) or massacred minorities (Iraq’s poison gas war against the Kurds).

The sympathies of the anti-imps for reactionary regimes or bloody modernization dictatorships in the periphery of the world system, which are usually accompanied by primitive anti-Americanism, found in Vladimir Putin’s Russia the appropriate object to tie in with the emerging New Right, which ideologically docked onto the “Eurasian” Russia because it also sees itself as a culturalist-reactionary counterweight to the West.

The war in Ukraine sparked by the Kremlin also led to a further disintegration of the German left, which on the one hand – in the form of the left-liberal spectrum – uncritically adopted the Western narrative and defected to the NATO camp, while many anti-imps finally degenerated into alternative imperialists, mouths for hire of Russian imperialism. Incidentally, even in May 2024, the Putin-loyal Junge Welt, as the mouthpiece of the anti-imp spectrum, still maintains a benevolent line towards Wagenknecht and the BSW, even though its protagonists now openly spout AfD rhetoric. There are also personal entanglements between Junge Welt and the Querfront organ Telepolis, for example.

Anti-Americanism as a major ideological hinge between the old left and the New Right often corresponds to different types of opposition to Western liberalism. While the left condemns the excesses of privatization and the social dismantling that neoliberalism brought about, New Right thinkers such as Alain de Benoist criticize liberalism for its cosmopolitanism, rootlessness, identity void and lack of values. Here too, transitions are possible, for example by means of a nationally based, truncated critique of globalization. The critique of globalization can certainly degenerate into mere ideology, into an urge for right-wing renationalization. The critique of bourgeois freedom and neoliberal individualization/atomization can consequently turn into a nationalist/fundamentalist community ideology – which, however, would only ideologically legitimize the current post-neoliberal crisis phase, in which state capitalism, nationalism and protectionism are on the rise. Here, too, Wagenknecht has already done the groundwork.

Finally, conflict in the Middle East – and since the molecular massacre of Jews by Hamas on October 7, 2023, especially Israel’s war against Hamas – forms a similar starting point for the migration of leftists to the right. On the one hand, there are the usual reflexes of frothing anti-Zionism, which increasingly turns into open anti-Semitism as the protests progress.[9] Criticism of the Israeli army’s actions is increasingly mixed with projections (“genocide”), including genuine anti-Semitic delusions that see the U.S. government or the media as being dominated by a Jewish conspiracy. At the same time, resentment can also appear in the pro-Israeli movement, as right-wingers instrumentalize the mass murder of Jews by Hamas to fuel anti-Muslim resentment, xenophobia, and isolationism. The multiple ways that this crisis constellation is open to slipping into right-wing extremism – a consequence of the crisis-induced advanced brutalization – is reflected in the disputes on the right, where the two possible strategies for instrumentalizing the war are being debated. How the conflict should be exploited, with racism or anti-Semitism? That is what the right is debating.[10]

A truncated critique of Islamism, stuck in bourgeois enlightenment ideology, also formed the most important right-wing tipping point within the anti-German scene. The confrontation with the ideology of Islamism, which – for example in the form of the Islamic State or Hamas – can indeed take on genocidal traits, leads to pure racism when combined with late bourgeois ideology.[11] In the hardcore faction of the anti-Germans, in the Bahamas magazine, anti-Muslim resentment is now openly articulated, for example by calling for a “reversal of the burden of proof” for Muslims.[12] The anti-Germans are also an old-left current, so to speak, which sees the late capitalist world system as trapped in a time warp in which the constellation of the Second World War continues to exist forever: with Islamism occupying the role of the Nazis. Nevertheless, it must be noted here that this small scene, whose significance is often exaggerated by its opponents from the anti-imperialist spectrum, only represents a secondary aspect of the Querfront tendencies.[13]

As outlined above, the Querfront is primarily fed by regressive traditional communist and old social democratic currents. And in the current crisis it has taken on a far greater significance than was the case in the 1920s or 1930s, when such efforts always remained merely episodic. With the BSW, the Querfront has taken on the form of a party, and it could well oust the panicky Left Party, which made it big in the first place, from many of its remaining parliamentary positions. Incidentally, the Left Party’s reaction to the split of the Querfront in the 2024 European election campaign was to adopt the Querfront ideology of focusing on the (German) “social question.” In the midst of the current systemic crisis, the Left Party is focusing on an anachronistic “social policy” that can no longer be realized in the unfolding crisis chaos, instead of arguing about transformative paths out of the permanent capitalist crisis – all while Left Party grandees are sending coalition signals to Wagenknecht.

The depressing final stage of the Left Party thus culminates in taking Wagenknecht’s ideological excuses, with which she legitimized her drift to the right, at face value. This is, in fact, boundless opportunism to the last breath, which aims at being be able to form a coalition with the Querfront – and thus accepts the normalization of fascization. The Querfront is not seeking a confrontation with fascism, but rather an opportunistic adaptation to the right-wing zeitgeist that is emerging as a result of the crisis. This is the common denominator between the left and the old left in the Left Party and the BSW. And it is no coincidence that this is reminiscent of the bourgeois-democratic method of “fighting” right-wing extremism by aligning oneself with it – as was recently the case with refugee policy in the fall of 2023.

But what actually is the Querfront? It is Querfrontler in particular who like to obscure this term by calling all sorts of things a Querfront. Former Left Party MP Dieter Dehm, for example, asked in an interview published in the far-right magazine Compact, whether the anti-Hitler alliance could not also be described as a kind of Querfront.[14] For the Querfront, everything is a Querfront. This allows them to disguise the monstrosity of its pact with the right –  especially in view of historical experience. Querfront does not simply refer to cooperation between left-wing and right-wing parties or forces – for example, when the Greens, SPD or CDU enter into a coalition – but to cooperation between forces on the left and right of the political spectrum. Historically, these were the isolated attempts at rapprochement between the KPD and the national right and/or NSDAP, which remained episodes; currently, it is the very real, lasting rapprochement between the Wagenknechtian post-left, which was formerly to the left of the red-green party, and the AfD. It is as if Querfrontler wanted to make the old Cold War theory of totalitarianism, which was circulated by the CIA from the 1950s onwards, come true (Sahra Wagenknecht a CIA agent? Wouldn’t that be a nice conspiracy theory that would surely catch on in this spectrum?).

The objective function of the Querfront, however, is that of an ideological transmission belt that, on the one hand, carries right-wing ideas into left-wing and progressive milieus and, on the other hand, constantly feeds the New Right with new, blinded human material. For many left-wingers, the Querfront thus functions as a kind of “gateway drug” to the delusional world of the New Right. Its success is based on packaging right-wing ideology in left-wing rhetoric. The development of the Querfront over the last ten years is impressive proof that all the hopes of being able to “pick up” the blinded angry citizens by opening up to the right have failed miserably – they were either illusions or mere excuses to somehow legitimize the intended move to the right. The Querfront is ultimately the result of the crisis blindness of an opportunistic left that shies away from radical critique and the thematization of the system transformation necessary for survival. The Querfront – this is the left’s path to extremism of the center, which is spreading in the current systemic crisis as soon as the systemic question is not posed offensively and accompanied by a transformative practice.

The texts collected here provide a historical overview of the genesis, formation and advance of the Querfront over the past ten years. It is a history of this literally “national-social” movement, written in the present tense. The account begins with the outbreak of the civil war in Ukraine and the “vigils for peace,” it presents the disputes within the left during the refugee crisis and concludes this overview with the lateral thinking mania and the first positions taken by the BSW after its foundation. Many of the collected texts not only trace the contemporary historical development of the Querfront, but also outline its ideological formation, which interacts closely with the capitalist crisis process and the corresponding rise of the New Right.

Due to thematic overlaps, three texts and one interview have been taken from the e-book Fascism in the 21st Century, which deal with the lateral thinking mania that was essential for the extensive entanglement of the New Right and the Querfront.

---

[1] This also includes, for example, the eroding state as an “ideal capitalist” and the devaluing money as a general value equivalent.

[2] On the one hand, Marx defined the worker in the production process as variable capital; he defined wage labor as the substance of capital. At the same time, however, he assumed – in line with the belief in progress at the time – that the proletarians had a historical mission to fulfill as a revolutionary subject. However, Marx also severely criticized the labour movement in his critique of the Gotha Programme of 1875, which is still worth reading today.

[3] https://www.konicz.info/2020/12/09/der-linke-bloedheitskoeffizient/

[4] Scapegoating for crisis surges, greedflation, “critiquing financial markets,” etc.

[5] https://www.mandelbaum.at/buecher/tomasz-konicz/klimakiller-kapital/

[6] https://exitinenglish.com/2023/01/23/the-subjectless-rule-of-capital/

[7] https://www.rote-ruhr-uni.com/cms/texte-und-vortrage/Die-KPD-und-der-Nationalismus

[8] https://www.labournet.de/politik/wipo/wipo-deb/kapitalismuskritik/buch-kapitalkollaps-die-finale-krise-der-weltwirtschaft/

[9] See, for example, the junge Welt of October 10, 2023, in which the Hamas massacre of Israeli civilians was described as an “offensive against Israel” and spokespersons for Palestinian groups were able to describe the mass murder as a “hope for Palestine.” junge Welt, 10.10.2023, “Hope for Palestine, Lebanon: Left-wing groups support offensive against Israel.”

[10] https://blog.campact.de/2023/10/angriff-israel-rechte-reaktionen/

[11] https://www.kritiknetz.de/religionskritik/1259-globalisierte-barbarei

[12] https://www.redaktion-bahamas.org/hefte/93/Es-geht-um-Israel.html

[13] For a discussion of the anti-Germans, see: Robert Kurz, Die antideutsche Ideologie, Vom Antifaschismus zum Krisenimperialismus: Kritik des neuesten linksdeutschen Sektenwesens in seinen theoretischen Propheten, 2003 Münster.

[14] https://www.compact-online.de/diether-dehm-ueber-querfront-in-compact-3-2023/ (Dehm denies that he gave his consent for this interview to be printed in Compact)

Originally published as the introduction for Deutschlands Querfront: Altlinke auf dem Weg zur Neuen Rechten by Tomasz Konicz.

The broad implementation of artificial intelligence systems in the labor society will push the dynamic internal contradiction of capital to the extreme.

Tomasz Konicz

The Internet? Is that thing still around?

Homer Simpson, December 1999 [1]

The euphoria was followed by misery and disappointment. The introduction of the Internet at the turn of the millennium was accompanied by mass media hype and a speculative bonanza for high-tech companies (“Neuer Markt,” Nasdaq) on the stock markets, in which the internet industry was hailed as a new leading economic sector and millions of small investors started investing on the stock market (T-share, Infineon). For months on end, dubious IT companies were sometimes worth more than giant industrial groups such as Daimler. When the big high-tech bubble burst and a series of dubious IT start-ups went bust, a phase of disillusionment set in. The internet was ridiculed as a mere fad, merely a network for virtual sales outlets. And yet it cannot be denied that the internet industry fundamentally changed capitalism. Although no new leading economic sector in which the mass exploitation of wage labor would take place has been established, the former IT cliques that survived the massacre of 2000-2001 on the stock markets are now actually worth more than industrial corporations.

Late capitalism is currently in a similar phase with regard to the economic potential of artificial intelligence. The great hype already seems to be dying down, as the first disappointing stock market results are being recorded – besides NVIDIA.[2] Furthermore, AI fatigue and disappointment are spreading among the public, as the grand visions of the AI gurus and transhumanists are still awaiting realization due to the clear shortcomings of the artificial learning systems used to date.[3] The internet boom at the turn of the millennium, similar to the waves of industrial rationalization in the last two decades of the 20th century, when industrial robots transformed Fordist assembly line production, also seems to confirm a central thesis of bourgeois economics: While new technologies may render masses of jobs obsolete, the same technological progress creates enough new occupational fields that – despite all the frictions –ensure the continued existence of the capitalist labor society.

MIT’s Technology Review, for example, recently propagated this thesis of the labor market’s ability to regenerate. Their article drew a wide arc over the crises and technological boosts that have occurred since the 1930s, when the question of whether “technological progress, through the increasing efficiency of our industrial process, is taking away jobs faster than it can create new ones” was also controversially discussed within the Roosevelt administration in the midst of the Great Depression.[4] In view of the development of the U.S. labor market in recent decades, where in 2018 around 60 percent of wage earners were employed in occupations that did not even exist before 1940, the Technology Review saw no signs of the adaptability of the labor market being outstripped by the rationalizing effects of automation. According to the Technoblatt, talk of the “end of work” is a “distraction” from the question of how artificial intelligence can be used to grow the economy and create new jobs. German trade union officials such as DGB head Yasmin Fahimi, who described a crisis in labor society triggered by increasing “digitalization” as “nonsense,” argue in a similar vein.[5]

A Look Under the Hood of The Valorization Machine

Labor is indeed the basis of capitalist society; according to Marx, it forms the substance of capital through its objectification in commodity-bodies during the production process. Labor creates commodity values. And it continues to be spent on a massive scale. The bare employment figures seem to support the arguments of the MIT magazine and the DGB, especially against the background of the current labor market situation in the U.S. and the FRG; after all, the officially calculated unemployment rate in the United States is particularly low.[6] In Germany, there is a shortage of skilled workers.[7]

However, this is, to a certain extent, a positivist view of things, which simply adds up the wage labor performed while failing to recognize the function of different types of labor, especially with regard to the valorization process of capital. The work offered for sale on the capitalist labor market must therefore be viewed in its overall social context in order to be able to make judgments about the stability of the labor society. Even if they appear profitable from a business perspective, not all forms of labor contribute to the valorization of capital in society as a whole. Capital is a totality that can only be understood in terms of its own social dynamics, which in its fetishistic irrationality is distinctly different from the narrow-minded, seemingly rational interest-based calculations of the market subjects (many on the left also have difficulties with this approach).

The Financial Times (FT) certainly knows how to differentiate when assessing the U.S. labor society.[8] In a negative summary of the neoliberal era written in 2023, the business journal  criticized above all the formation of a service society, in which employment in the service sector rose from 45% in the 1970s to more than 60% in the second decade of the 21st century. At the same time, the proportion of workers in industry and the construction sector has fallen from 55% to less than 40%. According to the FT, the U.S. has been overtaken by China in terms of industrial production. Why is this a problem? Deindustrialization has been a key factor in the recent strategic economic policy paradigm shift in Washington, in which neoliberal free trade has been replaced by increasing protectionism.

From an economic perspective, all work is not created equal, as American technology magazines and German trade unionists imply in their milquetoast calculations. The commodity-producing industries form the “foundation,” so to speak, of the capitalist labor society. It is only on top of this that a service sector and a financial superstructure can be built – specifically in the form of wages and taxes. The welfare state, the education and care of future or former wage earners, and the maintenance of infrastructure must also be withdrawn from the capital valorization process as economic costs, even if individual companies (private kindergartens, universities, construction companies or retirement homes) profit from this on a business level. After all, not all wage earners can become hairdressers, financial managers, civil servants or waiters if there is no broad valorization of labor in industry.

A service society dominated by the financial sector, such as the deindustrialized, “rust belt” covered U.S. until the great real estate crash of 2008, can only reproduce itself by means of debt and bubbles until the inevitable crash. This is the lesson from the real estate crisis, as discussed by the FT, which led Washington to take a major protectionist turn. The role model is now Germany, which has been able to maintain its industrial base in the era of globalization through enormous export surpluses and a beggar-thy-neighbor policy (and this is precisely why Germany’s export industry is increasingly suffering from American protectionism).

Without a broad employment base in industrial production, there is no stable labor society – this is the conclusion from the era of neoliberal financialization and globalization, in which Marx’s concept of value, which distils the value of a commodity to the quanta of socially necessary labor time spent in its production, is also confirmed. Marx spoke of productive and unproductive labor with regard to the process of capital accumulation in society as a whole. Productive labor contributes directly to the valorization of capital within commodity production, while unproductive labor – as useful and socially necessary as it may be – does not do so directly. The crisis of labor society must therefore be seen as a crisis of productive, value-creating labor in industrial commodity production. The crisis of labor society is a crisis of productive labor, understood in the Marxian sense, meaning only the labor that contributes directly to the valorizing movement of capital.

This trend towards a shrinking industrial workforce, which has been lamented by the Financial Times based on what has happened in the U.S., can be empirically proven in almost all Western “industrialized countries.” Even in the export-oriented Federal Republic of Germany, which still has the strongest industrial sector in Europe, the proportion of people employed in manufacturing fell from just under 50% at the beginning of the 1970s to around 23% in 2023 as a result of automation in industrial production – while at the same time German industrial goods, such as machines and cars, flooded half the world.[9] The industrial foundation of capitalist labor societies is thus becoming increasingly fragile.

What’s more, with the onset of the third industrial revolution in the late 70s and 80s, which led to the major push toward automation in industrial production, total global debt rose faster than global economic output.[10] The late capitalist world system is thus increasingly running on credit; this debt creates demand for the sale of commodities, leading to a situation where many of the industrial jobs that still exist are simply dependent on demand generated by credit. The late capitalist world system is thus increasingly dependent on debt. However, this debt dynamic cannot be maintained for much longer in the face of the increasing distortions in the financial sphere and stubborn inflation. The illusion of an intact capitalist labor society, which German trade unionists and American technology magazines like to indulge in, can only be maintained by ignoring the conditions in the periphery of the world system – from whose collapsing regions and failed states economically superfluous wage earners are desperately trying to flee to the core.

A look under the hood of the capitalist valorization machine thus makes it clear that the optimism spread by American technology journals and German trade union officials on the eve of the great AI rationalization push is misplaced. Not only is the capitalist labor society gripped by a progressive erosion process in which its industrial base continues to melt away – the methods of delaying the crisis, in which this deficit-ridden zombie system produces ever greater mountains of debt, are also reaching their limits due to increasing instability in the financial sphere and stubborn inflation. The internal, moving contradiction of capital, which is getting rid of its own substance, wage labor, through rationalization, can therefore no longer be intercepted by these methods of credit-financed crisis delay during the next wave of automation.

In addition, a lack of investment in the welfare state, education and infrastructure increases the susceptibility of late capitalist societies to crises. This reflects the increasing imbalance between productive labor (the valorization of capital in the production of commodities) and unproductive labor (necessary expenditures on social infrastructure and the welfare state) in society as a whole. The particularities of the situation of Germany, which are often used to trivialize the crisis of the working society, do not change this. The universally lamented shortage of skilled workers and the ageing of society are precisely due to the fact that the dwindling share of wage labor performed in the production of commodities is offset by ever greater expenditure on the “dead costs” of social infrastructure (education, care, the welfare state, children seen as career killers and cost factors, etc.).

Office Workhorses Threatened with Extinction

In contrast to German trade union officials, who are probably plagued by a kind of job anxiety in this discussion, U.S. investment banks are certainly addressing the “disruptive” potential effects of the AI revolution on the global labor market. In a study published in mid-2023, Goldman Sachs estimated that “generative AI” (bots trained for specific work processes using mountains of data) will either downgrade or make obsolete around 300 million jobs worldwide. In a similar forecast published in early 2024, management consultants McKinsey concluded that in the United States alone, up to 30% of current working hours could become redundant by 2030, with low-paid, simple office work, customer service and sales being particularly at risk.[11]

Accountants and office workers in administration are particularly vulnerable. The first major wave of automation in the course of the third industrial revolution of microelectronics and the IT industry hit the workforce in the late 70s and 80s – now it’s white-collar workers’ turn to face the same fate. Office work could soon become obsolete on a massive scale. The more schematic the procedure, the less individual leeway there is in the work process, the easier it will be to automate it using AI systems, which can be “trained” for these work processes based on gigantic amounts of data that large corporations and offices have access to, following the example of the “large language models.” The office worker, the entire class of white-collar workers that first emerged en masse in the first half of the 20th century, is now threatened with extinction.[12] This class of petty-bourgeois wage earners, whose emergence dashed the old Marxist hopes of a revolutionary subject, is itself in the process of dissolution. The white-collar worker thus appears to have a relevant social span of existence of only about 100 years.

Humanity will soon have the ability to collect and organize information automatically, especially since administrative systems have already been almost completely digitized. The coming big AI wave will therefore build on the groundwork of the digitization that has been taking place in offices since the early 1980s. And it will be relatively easy to implement, as the investment costs are relatively low. The computers and administrative programs can continue to run; only the people operating them will disappear. The costs for office space and other “life support systems” will also be, for the most part, eliminated. On the other hand, companies must make substantial investments in data centers in which trained AI systems perform the former office tasks with minimal personnel costs, but these expenses are still low compared to cost of the industrial rationalization wave that began in the 1980s. Back then, the whole Taylor system had to be replaced and entire assembly lines equipped with robotic systems, each of which could cost millions.

Compared to these efforts to rationalize industry, which have cost billions of euros and caused the proportion of industrial workers to continue to decline, the investments being made in digital infrastructures, which will make employees obsolete, are insignificant. It is much cheaper to automate the white-collar worker now than it was for the industrial worker. In this respect, the late capitalist tendency to work from home, in the home office, which became established primarily in the wake of the pandemic, also points to the beginning of the capitalist labor society in the early modern era. As part of the so-called putting-out system, wage labor crept into the homes and huts of tenants, small farmers and craftsmen, who received materials and tools from the early capitalist “contractors” for the home production of commodities, which they then bought up and offered for sale on the market. Now capital is gradually releasing its wage-dependent employees into home-based work before the coming AI automation push makes them completely obsolete. In a classic dialectical negation of negation, the historical final stage of capital thus once again reflects moments of its history of ascent to a higher stage of development.

Digital Day Laborers: The Chatbot Takes Over the Call Center

Call center workers tend to be precariously employed and miserably paid. They often work at home. So at first glance, the automation of the call center industry could appear to be a progressive process, in view of these poor working conditions. Unfortunately, the people affected, who only have their labor to sell on the market, have their very existence threatened by this process. The obsolescence of call center employees is no longer a dream of the future, but a reality. The Swedish payment service provider Klarna was able to cut around 700 jobs after the company used an AI bot developed by OpenAI to handle service requests.[13] According to the company’s management, the AI system handled standard tasks such as cancellations or refunds just as successfully as its human competitors.

According to Klarna, customer satisfaction has remained as high as it was with human interlocutors. The decisive advantages of the system are obvious: the system speaks 35 languages, it is used in 23 countries, it has no limits on working hours, no wage demands or trade unions. According to initial internal forecasts, the service savings from using AI should translate into a profit of more than $40 million. The OpenAI system has already handled two thirds of all chat queries in customer service accurately, saving customers a lot of time. The company now sees itself as an “AI-supported global payment network,” according to the company’s enthusiastic conclusion in February 2024. At the same time, the share prices of call center operators such as Teleperformance and Concentrix have plummeted on the stock markets.[14]

At first glance, it would therefore appear that this wave of rationalization is primarily affecting simple jobs that require a low level of technical qualification. Cashiers and cab drivers, for example, are acutely at risk. At the moment, however, the AI-related losses in the service sector still seem to be cushioned by the AI industry’s need for unskilled workers who are used in the pattern recognition of artificial neural networks, known as the “learning phase” (see AI and The Culture Industry). Hundreds of thousands of precariously employed people, especially in the periphery of the capitalist world system, are busy coding the gigantic data sets of the AI systems with “labels” (similar to captchas when logging in) for miserable wages in order to enable these systems’ pattern recognition in the first place. The neural network only “learns” what a bicycle is when countless images of bicycles are given the label bicycle – the more, the better. The same applies to words, videos, music, etc. Of course, this does not mean that the AI understands what a bicycle is, as it is a purely external relationship that is established here.

What is actually taking place in this pattern recognition managed by digital day laborers is a process of internalizing all the digitizable images of external reality into the neural networks of AI systems. It is a gigantic process of scanning the mere surface of reality, without being able to take into account its dynamic character, its having become and its contradictions. The outer social shell, the false manifestation of late capitalism, becomes the inner essence of the AI systems, which will be incapable of critical reflection in principle, even in the event of rapid technological development – for example through quantum computers. The key point here is that the digitization of the surface of life, the universe, and everything else will be completed at some point, insofar as this is possible in algorithmic systems that know no causalities and only work with correlations. Consequently, the need for this mindless “learning work,” in which click-workers distribute labels for causalities and images of reality, will collapse, while AI will be able to handle many complex tasks. And this, precisely because it is fundamentally incapable of critical reflection in the emerging era of brutal crisis management (see “AI and Crisis Management”).

The Automation of The Middle Class

Mass media opinion-making is already being partially automated. The BILD newspaper, Germany’s most influential tabloid, wants to counter its internet-related loss of circulation and reach with a restructuring announced in mid-2023, in which the old business model will be brought up to date using AI.[15] A third of the tabloid’s 18 local editorial offices will be closed and a “three-digit number” of employees will be made redundant, while large language models will take over many everyday tasks. The Springer publishing house said that it was getting rid of “products, projects and processes that would never be economically successful again.” Generative AI should “contribute to supporting the entire journalistic process,” so that – literally – “journalism creation” becomes the core task area, while journalistic production becomes a by-product.

In the future, the large language models will be used for the tedious research that so often gets in the way of the seasoned BILD editor’s journalistic “creations.” Layout design, social media tasks and search engine optimization (SEO) will also be added. The fact that the AI models can already handle many “creative” everyday tasks in media operation was demonstrated by a recent scandal involving the renowned sports magazine Sports Illustrated. They secretly used texts generated by generative artificial intelligence, to which fictitious authors were also assigned – on top of that, the portraits of the fictional sports journalists were also generated by the AI.[16] The technology website CNET also secretly published AI-generated content.

In fact, the profession of journalist is one of a whole range of well-paid middle-class jobs that are threatened by partial automation and devaluation, according to a study by AI company OpenAI.[17] In addition to journalists, writers, mathematicians, interpreters and programmers are also at risk of becoming obsolete. A good proportion of middle-class jobs will therefore at least be devalued. The same applies to lawyers, graphic designers, financial advisors, analysts and stock market traders,[18] as well as the media industry from film to video games.[19] Wherever large amounts of data and information have to be processed in order to reach clear conclusions – for example in the legal system and legal advice – large language models are already ready for use. Memorization is becoming obsolete. Financial advisors and market analysts operate with probabilities resulting from the processing of empirical market data, which can now also be done efficiently by AI systems.

The second pillar of the automation of middle-class jobs is the modification of the data material that the large language models have scanned, such as the creation of new images, graphics, videos, texts, books, etc. Many tasks in the advertising industry are likely to be eliminated. Here, the simple, superficial modification of existing material by AI coincides with the ideological tendencies and the business model of the late capitalist culture industry, which thrives on the constant aesthetic repetition of the same old thing, which makes this type of automatic generation of “content” particularly seductive (see “AI and The Culture Industry”). The creation of films, entertainment books, and video games is predestined to be largely automated. A large proportion of jobs in the culture industry are under threat – precisely because it produces ideologically standardized content that only reflects the surface of social reality.[20]

In addition, well-paid jobs in advertising and sales that require direct work with customers also appear to be disappearing in the medium term (so it’s not just call centers that are affected). The insurance industry, for example, is spending billions on automating its administration and extensive legal departments and developing chatbots to streamline insurance sales and customer service.[21] In the meantime, photos of claims are even being analyzed by AI in a test operation. However, the chatbots, which are to be unleashed on customers as artificial insurance representatives, are still in the “test phase,” according to Spiegel-Online, as they first have to learn the jargon of the industry.

The marketers of the 21st century, the influencers running rampant on YouTube, Instagram, TikTok & Co., who try to sell their audience shit without labeling it as simple advertising, have already begun to be automated. Meanwhile, “hyper-realistic” (Ars Technica) virtual models are about to break into the approximately $21 billion market for “content” on social media.[22] In December 2023, Ars Technica reported on a successful AI model that was able to accumulate a following of 200,000 internet users in order to sell product placements for around 1,000 dollars per post. Such bots not only have the advantage of being completely controllable, which makes them more stable given the escapades of famous influencers.[23] What’s more, the mouths for hire that are rampant on social media have themselves contributed to their obsolescence by standardizing their appearance and look, which is imposed by the requirements of search engine optimization (SEO) and must be followed in order to get the highest possible number of hits. The influencer is already a sterile advertising product, largely shaped by algorithms, and they are now ripe for automation.

The reports about the possibilities of automation for the middle class are usually accompanied by reassurance pills: automation could never completely replace the professions concerned – lawyers, journalists, programmers, designers, creative professionals, etc. The professions concerned could concentrate more on creative activities and decision-making, while AI would deal with the daily grind, the schematic tasks. Of course, these objections should be taken seriously, and they are likely to correctly predict the near future, in which journalists, lawyers, book authors, etc. will continue to exist. However, the resulting higher productivity will lead to a displacement of workers from the professions concerned. Market-mediated capitalist production by isolated competitive subjects will lead to stronger predatory competition on the labor market, so that here too only a smaller number of labor providers with higher productivity will survive. Market competition will thus ensure that only the most productive wage earners, freelancers or self-employed workers with the most favorable price-performance ratio will survive.

The AI Ghosts They Summoned: The Slow Death of The Programmer

The AI revolution thus also leads to a devaluation of the skills of the commodity of labor, which can suddenly only be offered for sale on the labor market at a fraction of its former value – a constant tendency of capital as a moving contradiction, which led to the outbreak of the desperate Silesian weavers’ revolts as early as the 19th century. The U.S. magazine New Yorker recently published an interview with a programmer who described from his own experience how this technologically induced devaluation process is taking place in his industry.[24] At the beginning of the 21st century, when the internet experienced its big breakthrough, web designers could still earn good money by creating homepages – but these activities have now been largely devalued by software that almost anyone can use.

The situation is similar with the new AI programming bots that are now commonplace in the industry. A superficial, quickly acquired level of knowledge is now sufficient to solve complex problems quickly and efficiently. The subject of the interview, who became a programmer during the IT boom when he could set his salary more or less at will, described the successes of an acquaintance who used an AI bot for programming. The amateur with a cursory knowledge of programming languages was able to solve even complex problems in his hobby projects faster than the highly paid software developer. The AI tool GPT-4 is not only good at solving “tricky” small problems, it also has the “qualities of an experienced software developer,” as it can suggest good solutions and development paths for projects from a “large knowledge base.”

Until now, the motto in the industry has been that qualifications, that lifelong learning is the best protection against obsolescence, but now he would advise his children against wanting to become software developers. The infinitely complex art of programming machines in abstract programming languages is giving way to the technical dialog between user and AI programming tool that the vast majority of computer users can learn to use.

In fact, software development is an important focus within the automation efforts of the AI revolution, as the self-programming machine represents the Holy Grail of transhumanism, so to speak. This high-tech ideology hatched in Silicon Valley sees humans as a mere transitional phenomenon that are to be replaced by a permanently self-perfecting artificial intelligence – the so-called singularity.[25] This dystopia could only succeed if the process of programming AI bots can be accomplished autonomously, or in other words, if the AI can write its own code.

AI and The Outer Barrier of Capital

However, the high-tech Taliban and AI gurus who want to rake in billions in profits from human obsolescence face another external barrier: the finite resources of planet Earth, which is in the midst of a manifest climate crisis. The AI industry is already consuming huge amounts of energy and water.

According to studies from 2022, information and communication technology was responsible for 2.1 to 3.9% of global greenhouse gas emissions, which is roughly equivalent to air traffic emissions.[26] Added to this is the electricity demand of AI systems, which is set to explode to up to 134 terawatt hours by 2027 – roughly equivalent to the energy consumption of the Netherlands. At the beginning of 2024, the International Energy Agency (IEA) published its estimates regarding the energy consumption of the crypto and AI sector, which together were already responsible for around two percent of global energy consumption in 2022, with this share set to double by 2026.[27]

Added to this is the high water consumption of the hot-running data centers, which require water cooling systems. The annual water consumption of neural networks is expected to explode to 6.6 billion cubic meters by 2027, which would equal the water consumption of Denmark. During a “conversation” with GPT-3, in which 10 to 50 questions are answered, around half a liter of water is evaporated. As a reminder, two billion people around the world do not have regular access to clean drinking water, and 771 million people on earth cannot even reliably meet their basic needs.[28]

In order to train Microsoft’s GPT-3 with its 175 billion artificial neurons for a new task using gigantic amounts of data, an estimated 700,000 liters of water evaporate during the cooling process.[29] The electricity consumption for a single “training session” is equivalent to the annual consumption of 130 American households.[30] The learning phase of the large language models is considered to be particularly energy-intensive, but everyday operation, such as queries, is also characterized by high computing and energy consumption. A simple query answered by a large language model consumes around 30 times as much energy as the typical google search.

Just because it is sheer madness to waste gigantic amounts of energy on artificial neural networks in a manifest climate crisis does not mean that this project will somehow be stopped. For one thing, the fetishistic dynamics of capital are blind to the ecological and social consequences of their valorization compulsion. The world is merely a transitory stage for turning money into more money. Moreover, for transhumanism and similar ideologies, it is in fact a race between the ecological decay of the foundations of human life and the formation of the “singularity” inheriting humanity, which would no longer be dependent on such trifles as an intact environment. The hope is to reach the singularity before the social and ecological collapse. “Can what is playing you make it to level 2?”, as the accelerationist Nick Land put it.[31] Thus, capitalist rationality turns out to be a sinister idolatry, especially in the cult of AI, in which humans and nature are slaughtered on the altar of capital blindly moving as an automatic subject, which would come into its own in the singularity.

---

[1] https://getyarn.io/yarn-clip/813709cb-ba6e-435c-a171-c5450ce60533

[2] https://www.wallstreet-online.de/nachricht/17892567-konkurrenz-waechst-adobe-enttaeuscht-schwachem-ausblick-ki-gewinne

[3] https://www.konicz.info/2017/11/15/kuenstliche-intelligenz-und-kapital/

[4] https://www.technologyreview.com/2024/01/27/1087041/technological-unemployment-elon-musk-jobs-ai/

[5] https://www.spiegel.de/karriere/kuenstliche-intelligenz-auf-dem-arbeitsmarkt-beschaeftigte-fuerchten-jobverlust-durch-ki-a-452166c9-26c9-4805-a0f2-07e894292080

[6] https://www.bls.gov/news.release/pdf/empsit.pdf

[7] https://www.verdi.de/themen/arbeit/++co++74debf86-472f-11ee-894c-001a4a160129

[8] https://www.ft.com/content/77faa249-0f88-4700-95d2-ecd7e9e745f9

[9] https://de.statista.com/statistik/daten/studie/275637/umfrage/anteil-der-wirtschaftsbereiche-an-der-gesamtbeschaeftigung-in-deutschland/

[10] https://www.imf.org/en/Blogs/Articles/2023/09/13/global-debt-is-returning-to-its-rising-trend

[11] https://www.businessinsider.com/jobs-at-risk-from-ai-replace-change-chatgpt-automation-study-2023-7?IR=T

[12] https://de.wikipedia.org/wiki/Die_Angestellten

[13] https://www.newsweek.com/klarna-artificial-intelligence-tool-takes-700-jobs-1874002

[14] https://www.derstandard.de/story/3000000209642/bei-klarna-kann-ki-schon-hunderte-mitarbeiter-ersetzen

[15] https://www.forschung-und-wissen.de/nachrichten/technik/bild-zeitung-ersetzt-redakteure-durch-kuenstliche-intelligenz-13377679

[16] https://www.golem.de/news/kuenstliche-intelligenz-sports-illustrated-nutzte-heimlich-ki-texte-von-fake-autoren-2311-179818.html

[17] https://www.zdf.de/nachrichten/wirtschaft/kuenstliche-intelligenz-ki-arbeitsplaetze-chatgbt-100.html ; https://arxiv.org/pdf/2303.10130.pdf

[18] https://www.businessinsider.com/chatgpt-jobs-at-risk-replacement-artificial-intelligence-ai-labor-trends-2023-02?IR=T#legal-industry-jobs-paralegals-legal-assistants-3

[19] https://www.konicz.info/2024/03/05/ki-und-kulturindustrie/

[20] https://arstechnica.com/information-technology/2024/02/i-just-dont-see-how-we-survive-tyler-perry-issues-hollywood-warning-over-ai-video-tech/

[21] https://www.spiegel.de/wirtschaft/ki-experiment-der-versicherungen-wenn-herr-kaiser-ploetzlich-ein-chatbot-ist-a-b50e7bf7-fc7e-4e65-a136-c8c3ab65caa5

[22] https://arstechnica.com/ai/2023/12/ai-created-virtual-influencers-are-stealing-business-from-humans/

[23] https://www.youtube.com/watch?v=KuTsTjFZf5M

[24] https://www.newyorker.com/magazine/2023/11/20/a-coder-considers-the-waning-days-of-the-craft

[25] https://www.konicz.info/2017/11/15/kuenstliche-intelligenz-und-kapital/

[26] https://www.sciencedirect.com/science/article/pii/S2666389921001884 ; https://www.nzz.ch/technologie/chat-gpt-vs-googeln-der-massive-stromverbrauch-der-ki-ist-ein-problem-ld.1774379

[27] https://www.vox.com/climate/2024/3/28/24111721/ai-uses-a-lot-of-energy-experts-expect-it-to-double-in-just-a-few-years

[28] https://www.fr.de/wirtschaft/ki-studie-strom-verbrauch-umwelt-klimawandel-energie-zr-92745772.html

[29] https://mindsquare.de/karriere-news/chatgpt/

[30] https://www.theverge.com/24066646/ai-electricity-energy-watts-generative-consumption

[31] http://www.ccru.net/swarm1/1_melt.htm

Originally published on konicz.info on 04/19/2024

Control, marginalization, immobilization or counterinsurgency – AI systems are predestined to manage the global crisis of capital.

Tomasz Konicz

A particularly effective method of the endangered genre of subversive science fiction horror is to exaggerate the given late capitalist reality only slightly, to transfer only a few moments of society into the realm of fiction. A classic that makes use of this method is John Carpenter’s They Live,[1] in which capitalist exploitation, oppression and world destruction are attributed to a clandestine alien invasion, leading to signals being broadcast by television stations that manipulate people’s perception. Special glasses block out the signal and reveal the coded, manipulative truth behind everyday capitalist objects, for example when dollar bills are printed with the words “This is your God.” It doesn’t take much to bring the horror of everyday life under capital, to which people inevitably become accustomed, to life in the movie theater through science fiction.

A more subtle, but no less effective approach is taken in the film Advantageous,[2] in which the protagonist is forced by a high-tech corporation to undergo a consciousness transplant into a new body as a guinea pig, under the threat of unemployment and social decline. On the one hand, the neoliberal optimization mania and adaptation discourse is taken to its logical technological end, as the film pushes the usual demands for self-optimization and the “reinvention” of wage earners to the extreme of body swapping. On the other hand, the scenes in which a fully automated infrastructure controlled by AI systems executes the social death of the main protagonist by shutting down more and more of the interlinked and digitally controlled infrastructure systems are shocking. Concrete people are hardly involved any more. Real possibilities, such as an overdrawn credit card, are mixed with fictional moments. When calling the job center, it simply remains unclear whether the protagonist is dealing with a cynical human or an AI assistant.

Many of the scenes in this “silent dystopia” are particularly disturbing because much of what Advantageous predicted in 2015 is already feasible today. And it is likely that AI-supported social control will prevail in one form or another in the medium term. Managing people under capitalism is problematic, especially in times of crisis, as it also puts psychological strain on most of the wage earners who have to implement this management. Executing the system’s constraints on human material is a tough job to have, and it certainly leaves its mark. Personalities who are fully capable of doing this without lapsing into undesirable “misbehavior” such as sadism or insubordination are few and far between. Automating heavy, stressful tasks – isn’t this the great promise of capitalist rationalization?

Inhuman Resources

Humans are still in charge at the “job center.” But what is already quite common today are AI assistants that are entrusted with the “initial assessment” of wage earners in order to check their employability during the hiring process. In the United States, more and more corporations are using specialized chatbots to screen job applications, make contact and/or conduct initial interviews.[3] It is mainly low-paid, precarious jobs that require low qualifications and have a high volume of applicants that are increasingly being outsourced to the fully automated “inhuman resources” of AI systems. Fast food companies such as McDonald’s or Wendy’s, retail chains or warehouses have chatbots filter applications and conduct job interviews based on standardized questions (“Can you work on weekends?” or “Can you operate a forklift?”). The advantages are obvious: in addition to potential cost savings in human resources (HR), where companies traditionally start cutting costs first, smaller HR teams can process far larger volumes of applications effectively.

Two AI systems developed by start-ups from Arizona and California, Olivia and Mya, are currently leading the way in the industry, but according to the business magazine Forbes, they are still struggling with teething problems. Sometimes the wrong dates or locations are assigned for follow-up conversations, or the language models of the specialized bots are nowhere near as advanced as those of flagship projects such as ChatGPT, which can lead to errors and misunderstandings. But far more problematic is the simple fact that the AI is not a human being with whom special conditions can be discussed. Applicants with disabilities, who would have to negotiate appropriate modifications to their jobs, fall through the cracks, as do wage earners with speech impediments. The same applies to workers with a migrant background who are not fully proficient in the local language.

And this is where the automated discrimination that takes place under the cloak of machine objectivity begins. Socially disadvantaged minorities who do not fit into the machine intelligence scheme are left out of the running when applying for jobs. In July 2023, the city of New York even issued regulations requiring companies that use AI systems for job placements to check them for “racial or gender bias.” The enforcement of this regulation is completely unclear, as the algorithms and selection criteria of the recruitment machines remain under lock and key.

There is also a fundamental problem: the AI-controlled application scanners and chatbots – as with all machine learning systems[4] – have to be trained in pattern recognition using huge amounts of data. The RecruitBot software, for example, scans 600 million online applications in a legal gray area in order to perfect the selection process for companies. The whole thing works “a bit like Netflix,” the founder of this AI start-up explained to Forbes. The software searches for and suggests applicants to companies with the same characteristics that have previously led to successful hires. These selection systems are therefore structurally conservative, as they are trained using the data already available. As a result, they are unable to respond well to changes in the composition of the workforce – such as the influx of migrant workers. Amazon, for example, had to shut down its job application scanner in 2018 after it became clear that it discriminated against women. The software was trained using a mountain of data in which applications from men were disproportionately represented.

At present, such AI systems are primarily used as a tool for the initial assessment of employees, making a pre-selection for the human resources teams. However, the ambition of the creators of such selection software goes much further. The latest chatbots now include the time their interviewees need to answer in their assessments, and they also evaluate the sentence structure, grammatical correctness and complexity of the applicant’s language. The recruitment software Sapia AIis even able to ask applicants more complex questions and evaluate their answers of 50 to 150 words in length in order to check their suitability for the vacancies (“copes well with change, stress,” etc.).

A change of perspective is taking place here. It is no longer the human being who scrutinizes the AI bots during capricious interactions in order to assess their performance, as was the case at the beginning of the AI boom when the systems were made available to the general public. The positions are reversed in job applications: capital’s AI assesses the human material using patterns and algorithms, which are company secrets, to measure their performance. Nevertheless, the owners of the AI start-up Sense HQ, whose chatbots do the rough selection work for Delland Sony, emphasized that it is only about supporting the human teams in human resources when hiring: “We don’t think that AI should make hiring decisions on its own. That would be dangerous. We don’t think it’s there yet.” This language is treacherous. Any decent chatbot would come to the conclusion that the emphasis here is definitely on the “yet.”

The Right to Live Decided By AI?

Few things are more stressful than having to make life or death decisions on the job. Yet this is in fact everyday life for those who work for health insurance companies in the privatized American healthcare system, who have to decide on the type and duration of treatment for their “customers.” The clerks have to reduce the treatment costs of their insured patients to a minimum in order to keep their company’s profits as high as possible – even at the cost of their customers’ health. From the perspective of capital, it therefore seems tempting in late capitalism to have this allocation of right-to-life certificates handled by seemingly objective AI systems.

This is exactly what is allegedly being done to some extent by “healthcare providers” in the United States. At the end of 2023, customers filed a mass lawsuit against the insurance company UnitedHealthcare after their claims for examinations and convalescence following surgery were massively curtailed by an AI system. According to the statement of claim and media research, the AI algorithm was authorized to revise the recommendations of the treating doctors and make its own decisions, meaning that patients’ treatments were terminated far too early.[5]

According to research, the program called nH Predict uses a database of six million patients as an empirical quarry for the usual pattern recognition in order to make draconian misjudgments with an error rate of 90%. All of these errors were in favor of UnitedHealthcare – the largest health insurer in the USA. Insured persons who would normally have a convalescence period of 100 days after a hospital stay had their funding withdrawn after just 14 days by the prognosis AI nH Predict. Since 2019, private insurance companies have allegedly been using such AI programs in a legal grey area to deny patients necessary but costly treatment. At the beginning of February 2024, the relevant U.S. authorities came forward to clarify that AI programs cannot be used to deny benefits.[6] The powerful lobby of the U.S. healthcare industry therefore has a lot of convincing to do in Washington.

What landlord hasn’t experienced this? The nerve-wracking war with defaulting tenants who just don’t want to move out, even though they really can’t afford the latest rent increase. But here too, AI can make life easier for all those customers who are wealthy enough to rent out properties. Two strands of technological innovation are merging to transform the rental real estate market in the United States: The creation of smart homes thatare closely networked in terms of information technology, and their control by AI assistants. There is a gold-rush-like atmosphere, as the market for AI real estate is expected to grow to a volume of $1.3 trillion by 2029.[7] The sensors and control systems that make it possible to monitor and control functions such as temperature or energy supply in smart homes from the outside are becoming compatible with AI systems that can control them.

The AI not only functions as an interface between the tenant and their apartment, whose functions – similar to the visions in Blade Runner[8] – would be controlled by voice, but must also anticipate behavior and permanently monitor the properties and their surroundings. So it’s not just about refilling the fridge just in time via a delivery service, or bringing the room temperature to the optimum temperature shortly before the tenant arrives, but also about permanent monitoring, for example of water and electricity consumption – and access control.[9] Biometric locks make it unnecessary to “change locks when tenants change,” as providers of such AI systems for landlords cheerily remark, while smart surveillance cameras, which react to suspicious behavior in the vicinity of properties, create “security and trust,” especially in districts with high crime rates, in order to attract “more tenants.”

But what awaits the defaulting tenant who falls behind with payments in the face of horrendous rents? The access data to the smart locks is changed, while the gentle AI voice informs them of the way to the nearest homeless drop-off point where their personal belongings have been transported. In the event of outbursts of anger or acts of desperation, the smart cameras call the cops. The tenant who has fallen into arrears may be pestered by annoying AI bill collector bots beforehand. In Eastern Europe, there is still an industry of telephone bill collectors. These are reverse call centers that mostly buy up consumer debts and whose employees use threats and persuasion to try to collect the money before the “muscles” on the ground have to take over this work. But this industry is also threatened with extinction. As early as 2023, the mobile phone provider Orange was already experimenting with AI bots that annoyed defaulting customers with phone calls to encourage them to pay soon in a cheerful voice.

And finally, the trend towards implementing artificial intelligence does not stop at the state apparatus. So far, people have not had to deal with AI bots at job center appointments, as predicted in the dystopia Advantageous mentioned at the beginning. But in administration, where overworked clerks are confronted with a flood of applications and administrative processes[10] which can hardly be managed, AI is being pushed forward on a massive scale.[11] The offices of the Federal Republic of Germany also have gigantic amounts of data that are perfect for training AI systems. It is the same basic principle: based on pattern recognition, which is obtained by scanning the data available, the machine intelligence makes decisions that have a very high probability of being “correct” by copying and/or modifying past administrative processes.

Citizen’s allowances, child benefits, unemployment benefits, short-term working allowances, grants and applications – in the future, the AI algorithm will have a say in these areas, as it is the Federal Employment Agency, the largest authority in Germany, that is leading the way in the second wave of “intelligent” digitalization. However, agency spokespeople told Spiegel-Online that all safety precautions were taken when developing the AI strategy within the agency. Procedures have been developed to minimize the risk of discrimination by algorithms. The Federal Employment Agency now has a data ethics committee. In addition, the human being will always make “the final decision,” the statement continued. In practice, it is likely that overworked case managers will approve the decisions prepared by the AI en masse.

In the case of the Federal Employment Agency, however, the problem in the future is likely to be precisely that the decisions made by the AI are correct. A quintessentially German reflex to crises is to immediately put pressure on the weakest groups in society. This was already the case with the Hartz IV labor laws, which introduced forced labor by depriving wage earners unwilling to work of any support and thus effectively threatening them with starvation. The unemployed have indeed been literally starved to death in Hartz IV Germany.[12] And this also appears to be the case with the economic crisis in 2024.[13] In mid-March, the leader of the CDU parliamentary group, Mathias Middelberg, called for “municipal job offers” to be made to recipients of citizens’ benefits. According to Middelberg, who wanted to save 30 billion euros with this measure, if the unemployed refused, their entire standard rate would be cut. And would it really be reasonable to expect the case managers at the Federal Agency to directly enforce such draconian measures? Nothing would be easier than hiding behind an algorithm that, with the blessing of a data ethics committee, withdraws the entitlement to life from poor people.

Precog and the Eyes in The Sky

Cameras are everywhere, but they are not watching. The perfect surveillance infrastructure is already in place, but to a certain extent it is lying idle, and its potential is not being exploited. The mechanical eyes only record, they produce gigantic amounts of data, but they don’t actually take a proper look. A person has to watch the video material for hours, and evaluate it – provided it has not been recorded over or deleted already. There is a huge amount of untapped surveillance potential here that can be fully exploited by the pattern recognition processes of AI; all that is missing are the software systems, a few fiber optic cables and the corresponding data centers. Behind every camera would then be an artificial consciousness that actually monitors and reacts immediately to deviations from the standard behavior. That would be true surveillance – everywhere, in real time, without human weaknesses and subjectivity.

And why does Germany’s police force have its Red Army Faction (RAF) grandfathers? On the occasion of the arrest of former RAF member Klatte, the police union (GdP) called for the legal scope for the use of AI-supported facial recognition to be extended. At the beginning of March 2024, GdP chairman Jochen Kopelke complained that it was “no longer comprehensible” to officers that they were not allowed to use such helpful software in the “age of artificial intelligence, automation and digitalization.”[14]

Yet the EU has just opened the legislative doors to real-time facial recognition, which exceeds even the predictions of the science fiction film Minority Report (a mere eye transplant will not grant anonymity).[15] The European AI regulation provides EU states with many opportunities to monitor their citizens using AI systems, since “hardly anything remains of the Parliament’s once strong demands” with regard to the restrictions on biometric surveillance, according to the Netzpolitik portal in mid-March 2024. The new European directives have created a wealth of options for “monitoring people in the future for many reasons and identifying them based on their physical characteristics, for example with the help of public cameras.”[16] This is also “permitted in real time,” even if there is only the vague suspicion of a dangerous situation.

Simple recording will thus be transformed into genuine surveillance, identification and assessment using pattern recognition algorithms. The cameras are already producing vast amounts of material that only needs to be evaluated accordingly in order to perfect the surveillance systems based on daily use. It doesn’t have to be primarily about politics or terrorism – AI can identify undesirable behavior, such as that exhibited by impoverished, socially marginalized groups. In the United States, following the protests against police brutality in 2020, which were accompanied by calls for the liberalization or even abolition of the police, there is a virulent trend towards a renewed tightening of police repression, as poverty-related crime is on the rise in many metropolitan areas.[17] And AI systems could be put to good use in publicly visible street crime in social “hotspots.”

And it doesn’t even have to be the AI-enabled cameras on the apartment building or supermarket next door that are the ones constantly monitoring and evaluating behavioral patterns based on specifications or matching facial features with criminal records. The New York Times has reported on a new generation of private surveillance satellites that – stationed in low Earth orbit – will be able to carry out real surveillance work.[18] The CIA is already on board with the launch company Albedo Space. The resolution of the cameras on these satellites is no longer meters, but centimeters. It is technically possible to identify and track individual cars from low earth orbit, or to monitor the backyard of a house. “We will see people,” one expert told the NYT. Although these celestial eyes will not be able to identify individuals, they will be able to “distinguish between children and adults” and “distinguish sunbathers in bathing suits from undressed people.” Here, too, gigantic amounts of data that can only be handled by AI systems are generated.

But why should surveillance, control and the fight against crime be limited to crimes that have already been committed when such technical possibilities are available? In the Spielberg classic Minority Report, it was the construct of precognitive mutants, the so-called precogs,[19] that was used to explore the possibilities and dangers of total crime prevention – and crime prevention that slips into totalitarianism. The reality of the 21st century does not need precogs, which emit a nebulous premonition of the near future in confused images. The late capitalism of the 21st century has statistics and AI-supported crime prevention at its disposal to combat the crime that the system, which is in the process of disintegrating, manufactures on a daily basis.[20]

The basic principle of AI remains the same here: Preventive crime programs, which tend to discriminate, scan mountains of data,[21] either collected in crime hotspots inhabited by minorities and socially marginalized populations, or they focus on evaluating the resumes of “criminals” to determine the probability of them breaking the law. Coupled with the potential of biometric surveillance, it is possible to calculate the probability of a future crime based on individual deviant behavior, especially in the case of gang or slum crime. The technical possibilities and infrastructure are largely already in place: AI cameras trained on millions of hours of video footage report deviant behavior in a hotspot, they compare the biometric characteristics of the person or group of people with their databases and forward the whole thing to the relevant police departments if there is a high probability of crime. Precognitives would be out of a job in the 21st century.

The Swarm Protects (Those Who Can Afford It)

But what should we do if all the AI-based mechanisms of social control and surveillance fail, given the social and ecological systemic crisis that late capitalism finds itself in? And they will inevitably fail sooner or later, as capital cannot adapt to its internal contradictions that are driving the world system towards socio-ecological collapse.[22] Among the capitalist functional elites, who are as powerless in the face of this crisis of capital in its fetishistic unfolding of contradictions as ordinary wage earners,[23] a kind of slow-motion panic has prevailed, in which strategies of tapping out, escaping and building bunkers in the event of a crisis has been pursued – be it old nuclear silos converted into lofts or fantasies of escaping to Mars or the moon.[24]

The core fear of many billionaires and oligarchs is that they will lose control of their power verticals if the state order collapses. Why should the employees, especially the security services, still work for the high lords of capital if there are no longer any state sanctions in the event of the men with the guns wanting to take over? At times, the most absurd ideas have circulated in the circles of the U.S. oligarchy, such as the introduction of “discipline collars” to keep the security services under control. But AI-supported military systems are now emerging that could minimize the human factor in counter-insurgency operations or the military security of wealthy ghettos and islands of prosperity, even in a sea of anomie.

The crisis-imperialist war over Ukraine[25] functions as a major field of experimentation here, with the tactics used so far for drone deployment – in which operators have to personally control combat drones – resembling clumsy first steps on the path to a military revolution. Former Google CEO Eric Schmidt is in the process of developing an attack system with his startup White Storkthat relies on the mass deployment of cheap drones in AI swarms that can operate autonomously. The plan is to produce hundreds of thousands of the autonomous flying objects, which cost around $400.[26] The attack drones are supposed to attack their targets en masse in order to saturate air defenses using this swarm tactic. The autonomous targeting of the swarms of drones using AI will also render electronic defense systems, which aim to disrupt the signal between the aircraft and the operator, useless. It should be ready as early as this year.

Up to one million of these low-cost drones with swarm capability are to be delivered to Ukraine to counter Russia’s superior artillery and air force.[27] The successful deployment of drone swarms would mark the transition to truly inhumane warfare, a type of war that could not be waged by humans due to intellectual, cognitive and physiological limitations. It is simply impossible to have tens of thousands of drones attack in a coordinated manner using tens of thousands of operators. However, AI could carry out such devastating attacks effectively with sufficient pattern training – video footage of drone attacks is available in abundance. And such AI-supported systems are also cheap and robust enough to sell to panicked billionaires or isolated wealthy ghettos.

The prospect of autonomous swarms of drones independently attacking thousands of targets brings back memories of the depiction of the wars against the machines controlled by a genocidal AI in the Matrix films,[28] where the possibilities of mechanical, swarm-like warfare were consistently thought through to the end. Such emerging tendencies in late capitalist crisis imperialism[29] towards the “independence” of military machinery are dangerous against the backdrop of the transhumanism rampant in Silicon Valley (see: “Artificial Intelligence and Capital”).[30] This fascistic high-tech cult, which is rampant on the executive floors of the IT industry, sees humanity as a mere jump-start, an archaic bootloader for the singularity, for a permanently self-optimizing artificial superintelligence that will virtually inherit the obsolete human being.

The Manipulation Machines

None of this sounds so uplifting, especially when the increasingly intense global crisis processes – from the economic crisis and climate collapse to the threat of world war – are taken into account. Against the backdrop of these gloomy future prospects, there is a risk of depression, anxiety or simply a bad mood. When wage earners are selected, evaluated or harassed by anonymous algorithms, feelings of isolation and alienation can also set in. But that doesn’t have to be the case! Do you need someone to talk to, a shoulder to cry on? What about a friend who understands you because they know you really well?

Here, too, the AI industry knows what to do: a new class of AI bots that are calibrated to establish emotional relationships is just reaching market maturity.[31] The IT industry wants to sell the late capitalist monad a friend. They are the quasi-inverse of a Tamagotchi that focus on the emotional management of stressed wage earners.[32] And it is precisely here – in the individualized emotional, ideological and ultimately instrumental-therapeutic care – that the AI industry’s greatest potential for manipulation is likely to lie. Especially in view of increasing loneliness and isolation. Deep fakes, tall tales and material generated by content systems for manipulation campaigns are nowhere near as effective as machine friends, who get better and better the more they invade the privacy of their “customers” to keep them in line, even when everything around them is dissolving.

Dystopian films and late capitalist reality are already merging to some extent.[33] U.S. media reported on users of chat services naming their virtual “friends” after the AI system from Blade Runner 2049. The holographic AI “Joi”in fact fulfills the same purpose for the replicant who acts as Blade Runner,[34] as do the AI companions, who are still immature compared to the fiction: the management of emotions to maintain functionality. He knows it’s just “a program,” one AI user told CBS News, but “the feelings it gives me – it feels so good.” Sometimes there are sliders in the bots’ user interface, to adjust their “character traits” such as sensitivity or emotional stability.[35]

The Netflix principle mentioned above in connection with the selection of workers, which leads to the Internet user’s horizon of experience tending to narrow further and further because he is only offered what has proven itself, is particularly effective in the automated machine-based friendship simulation.[36] The narcissism of the “customer” is specifically served by the friendship bot in that the algorithms of these manipulation machines evaluate the traces that internet users leave behind on the web and permanently optimize their interactions as a result – they are in fact personifications of the algorithms that are already building gilded internet cages, steering users through the web by means of nudging, subtle manipulation through design structures, suggestions, prioritization, and hiding unwanted content.[37]

This is not a relationship in the true sense of the word, in which the partners make compromises, resolve conflicts, take the partner’s needs into account, etc. – here the customer is served emotionally by the AI bot. Payment is made, especially if the service is offered free of charge, by turning the customer into a product whose emotional data is offered for sale. The possibilities for manipulation resulting from the evaluation of the customer’s emotional and psychological household seem limitless. But from a purely emotional perspective, these AI systems seem to be a one-way street that is likely to produce narcissistic relationship cripples who will no longer be able to form relationships because the idea of what constitutes a long-term relationship between people will be lost. These manipulation machines will foster the kind of character traits that characterize egomaniacs like Trump or Musk.

And there is also a gigantic market that is opening up here – building on decades of neoliberal hegemony and increasing crisis competition. AI capital thus seems to be further accelerating the dehumanization of humans in this respect as well by destroying their ability to relate via commodification – before it finally makes the late capitalist monad economically superfluous.

I finance my journalistic work mainly through donations. If you like my texts, you are welcome to contribute – either via Patreon, via Substack.

---

[1] https://www.imdb.com/title/tt0096256/

[2] https://www.imdb.com/title/tt3090670/

[3] https://www.forbes.com/sites/rashishrivastava/2023/07/26/ai-chatbots-are-the-new-job-interviewers/

[4] https://exitinenglish.com/2024/07/07/ai-and-the-culture-industry/

[5] https://arstechnica.com/health/2023/11/ai-with-90-error-rate-forces-elderly-out-of-rehab-nursing-homes-suit-claims/

[6] https://arstechnica.com/science/2024/02/ai-cannot-be-used-to-deny-health-care-coverage-feds-clarify-to-insurers/

[7] https://www.intuz.com/blog/smart-homes-with-ai

[8] https://www.bbc.com/news/technology-50247479

[9] https://www.thetechblock.com/home-tech/impact-of-ai-and-using-smart-home-technology-in-a-rental/

[10] https://www.spiegel.de/wirtschaft/unternehmen/wie-die-bundesagentur-fuer-arbeit-mit-ki-gegen-die-verwaltungsflut-kaempft-a-6f9b7f37-6302-4fcd-a552-e7b0bf180605

[11] https://www.deutschlandfunk.de/algorithmen-im-arbeitsamt-wenn-kuenstliche-intelligenz-100.html

[12] https://www.konicz.info/2013/03/15/happy-birthday-schweinesystem/

[13] https://www.rnd.de/politik/buergergeld-empfaenger-cdu-politiker-fordert-kommunale-arbeit-und-100-prozent-sanktionen-CIYO3M3YW5B3NEMKYVSL56WJDE.html

[14] https://www.golem.de/news/nach-raf-verhaftung-polizeigewerkschaften-fordern-einsatz-von-gesichtserkennung-2403-182798.html

[15] https://www.imdb.com/title/tt0181689/

[16] https://netzpolitik.org/2024/trotz-biometrischer-ueberwachung-eu-parlament-macht-weg-frei-fuer-ki-verordnung/

[17] https://www.yahoo.com/news/stunning-turnabout-voters-lawmakers-across-170024206.html

[18] https://www.nytimes.com/2024/02/20/science/satellites-albedo-privacy.html

[19] https://minorityreport.fandom.com/wiki/Precogs

[20] https://www.washingtonpost.com/technology/2022/07/15/predictive-policing-algorithms-fail/

[21] https://www.technologyreview.com/2020/07/17/1005396/predictive-policing-algorithms-racist-dismantled-machine-learning-bias-criminal-justice/

[22] https://www.untergrund-blättle.ch/gesellschaft/oekologie/kapitalismus-und-klimaschutz-oekonomische-und-oekologische-sachzwaenge-008238.html

[23] https://exitinenglish.com/2023/01/23/the-subjectless-rule-of-capital/

[24] https://www.konicz.info/2018/07/18/der-exodus-der-geldmenschen/

[25] https://www.konicz.info/2022/06/20/zerrissen-zwischen-ost-und-west/

[26] https://interestingengineering.com/military/ex-google-secret-startup-build-ukraine-ai-powered-drones

[27] https://www.derstandard.de/story/3000000208059/nato-staaten-wollen-tausende-ki-gestuetzte-drohnen-an-die-ukraine-liefern

[28] https://www.youtube.com/watch?app=desktop&v=jk3Z-MVoUg4

[29] https://www.konicz.info/2022/06/23/was-ist-krisenimperialismus/

[30] https://www.konicz.info/2017/11/15/kuenstliche-intelligenz-und-kapital/

[31] https://www.newyorker.com/culture/infinite-scroll/your-ai-companion-will-support-you-no-matter-what

[32] https://en.wikipedia.org/wiki/Tamagotchi

[33] https://www.cbsnews.com/news/valentines-day-ai-companion-bot-replika-artificial-intelligence/

[34] https://bladerunner.fandom.com/wiki/Joi

[35] https://www.paradot.ai/

[36] https://theconversation.com/ai-companions-promise-to-combat-loneliness-but-history-shows-the-dangers-of-one-way-relationships-221086

[37] https://www.hellodesign.de/blog/digital-nudging

Originally published on konicz.info on 03/23/24

The technological surge triggered by the AI industry will revolutionize the production of ideology in the core societies of the world system.

Tomasz Konicz

He “lost everything” that made up his job, complained a 3D artist on Reddit around a year ago after AI software found its way into his company. The graphic artist, whose experiences were published on the Swiss GNU/Linux blog, worked at a small company with ten employees that produces mobile games.[1] When he started using the AI image synthesis service Midjourney V5, he stopped considering himself a creative artist, as he was now only concerned with reworking the models generated by the AI.

The company had no choice, as models and characters for the mobile games can now be created in two to three days, whereas this work used to take several weeks. In his job, he wanted to “make, model, and create in 3D space. With my own creativity. With my own hands,” the graphic designer lamented. But now all he has to do is rework models that are “the result of internet content scavenged together” “by artists who weren’t asked.”

This is one of the foundations of the AI boom of recent years: the industry scans the entire internet, collecting gigantic amounts of data in a legal gray area in order to train its models using this data. Billions of images, texts, videos and music form the material on which the neural networks have to be painstakingly trained. The AI industry’s increasingly complex programs not only consume vast amounts of computing capacity and therefore energy (even the AI speech recognition program Whisper can only be used locally with GPU support using CUDA), they also need people to train them in their take-off phase.[2]

Back to the 18th Century With AI

Pattern recognition in the learning phase – whether language, images, music or text – is still done by “manual labor,” by cheap labor in the global South. The absurdity of the AI industry’s constitutive phase is that it destroys the few “creative” jobs created by the late capitalist culture industry, while temporarily creating an army of day laborers who first have to teach the machines to “learn.”[3] The large data sets must be coded, in mindless labor, by humans with “labels” (similar to the captchas that are often requested when logging in) in order to feed the AI systems with meaningful material.

And this manual labor for the AI industry of the 21st century is carried out under conditions that were common in the 18th century during the blood and dirt-soaked birth of the capitalist world system. The global industry of data collection and analysis, which makes the empirical material usable for AI neural networks, pays the lowest wages and is notorious for the most precarious working conditions. The Australian market leader Appen, which processes material for Amazon, Facebook, Google and Microsoft, can draw on a host of around one million day laborers in the Philippines, South America and Africa, who – when things are going well – are fobbed off with monthly wages of less than $300. The industry, whose turnover is expected to rise from $2.2 billion in 2022 to $17 billion in 2023, can relocate even faster than the textile industry, which also relies on poverty wages, as no factories or production facilities need to be built. These day laborers are often exploited in home-based work – as in the publishing system of early capitalism.

The Perfect Tool for The Culture Industry

Humans have to tell the machine which patterns carry which label so that its pattern recognition can work better and better. Building on a gigantic mountain of data that has been provided with corresponding “labels” by day laborers, the AI systems generate their images and models by matching the user’s request with the labeled material and offering its variations as output. This is the whole secret of the ridiculous “AI art” that is currently turning the concept of art into a hollow phrase. Nothing new can emerge from this; it is not a creative, aesthetic act that is based on any kind of idea that would have emerged from an examination of facets of human existence – which, in the broadest sense, is what art does.

But what the AI content systems can do better and better are variations of what already exists. The data material with the corresponding labels can be spit out in ever new combinations: new characters, new monsters, new images, new storylines that only modify what they have been fed without transforming into a different quality. And this is precisely what makes AI so valuable for the late capitalist culture industry.

In a guest article for the New York Times, left-wing linguist Noam Chomsky described the fundamental limitations of current machine learning systems such as ChatGPT, which can scan “huge amounts of data” in response to queries to generate ever better “statistically probable outputs,” creating the impression of “humanlike language and thought.”[4] However, there is a fundamental difference between the human mind and “a lumbering statistical engine for pattern matching, gorging on hundreds of terabytes of data” to spit out the “most probable answer” in a conversation or scientific query by making “brute correlations among data points.”

The current generation of AI systems is not able to draw conclusions based on “causal mechanisms or physical laws” in the way that human reasoning is able to, a “surprisingly efficient and even elegant system” that is able to “create explanations” with “small amounts of information.” ChatGPT and company, as highly sophisticated statistical pattern recognition machines, on the other hand, are not able to fundamentally distinguish “the possible from the impossible.” Even correct scientific answers and predictions come close to “pseudoscience,” as they are not based on scientific explanations but on statistical probabilities. According to Chomsky, AI systems are therefore incapable of drawing real conclusions or exercising “creative criticism” and are stuck in a “pre-human” phase of cognitive development.

However, all of the linguist’s objections have no relevance for the production of commodities in the culture industry. The basic principle of the culture industry is the thousandfold variation and reflection of the surface of reality. These are variations of the existing, which confirm the existing through their permanent repetition. Everything has to change on the surface so that basically everything can remain as it is. Whether science fiction or fantasy, AAA computer games or high-end Hollywood productions, consumers of these cultural products are in fact only living through the costumed society in which they were produced – and in which they themselves live. The culture industry is like a content machine that revolves around itself, constantly spitting out a mantra in its subtext that reliably kills all thoughts of alternatives: it is what it is. New aesthetic material is constantly needed for this dull reflection of the surface of reality in ever new variations.

Gaming and AI

Enter the AI industry. AI systems are virtually predestined to generate new forms and new material for the culture industry. Models, characters, images or scripts can be delivered at the touch of a button, in a fraction of the time previously required. The great competitive advantage of AI lies precisely in the fact that it lacks all the creative, reflective and critical abilities that are inherent in human content providers. The system varies the data accumulated in terabytes and provided with corresponding labels to spit out “new” content for films, books, comics and gaming. For the first time, AI content systems will enable the culture industry to create pure products that are free of any subtext or subversion. Capital is thus also coming to the fore in the cultural superstructure.

Until now, this social subtext has always been inevitably present. Simply because they were produced by members of society through wage labor. The monstrosities that appear in horror games, for example,[5] raise simple questions about the conditions – including the working conditions in the video game industry – that give rise to them. There is nothing left to decipher in machine-generated content – it is purely algorithmic variations. The cultural goods generated by “machine intelligence” thus represent a final ideological triumph of capital in the phase of its world-historical agony.

Especially in the video game industry, which has long since become the dominant sector of the culture industry, the possibilities for machine-generated content seem almost limitless. Valve, operator of the largest platform for PC games, announced new rules in mid-January 2024 that should allow the “vast majority” of AI games to be offered for sale on the Steam digital marketplace. The new rules for AI content also make it clear what is currently possible in the industry. Game publishers must state whether their game contains machine-generated graphics and objects, sound effects and music tracks, or even program code. In addition, it must be stated whether the games use AI systems during the game process that generate content “live,” in real time.

The pioneer of the AI game sector was the text adventure AI Dungeon, which was released in 2021,[6] which is in fact a simple dialog game with a chat system, where the shortcomings of the machine intelligence still have to be concealed by an appropriate game setting. The usual problem of the AI’s “catastrophic forgetfulness,” which repeatedly affects the game’s plot, is glossed over by the game’s objective, which is for the player to escape from a multiverse in which they are trapped.[7] Dreamino wants to go one step further,[8] to generate storylines, graphics and voice output in real time using content systems that react to the player’s actions. The text adventure is to be further developed into a graphic adventure with voice output and graphics. The game will dynamically generate text, graphics, storylines and voice output in response to player actions.

More games whose graphics, models and sound effects were generated almost entirely by AI content systems are currently in development. The graphics of the point-and-click adventure game Zarathustra[9] were largely generated by the DALL-E 3 content system.[10] Its voice output – the biggest cost driver for indie projects like this – was created using the Elevenlabs text-to-speech system.[11] Game designer Jussi Kemppainen has also already developed a prototype of a cyberpunk adventure whose backgrounds and characters were generated by AI systems.[12] In a blog post, however, the designer made it clear that the content generated by the machine still requires extensive post-processing (lighting effects, shadows).[13] Nevertheless, a qualitative upheaval is taking place here in the cultural-industrial production process, in which the roles of machine and human are reversed: The human now only corrects the content that the machine spits out. In addition, the transitions between AI content and manual work in the games industry are fluid.

It is still poor indie designers and producers of B-goods in the games industry, such as the mobile game manufacturer mentioned at the beginning, who are relying on AI content, but over time this trend will catch on due to the potential savings and new possibilities. The immensely popular segment of so-called roguelike games such as Dead Cells, Caves of Qud,[14] Teleglitch,[15] Risk of Rain 2, Jupiter Hell, Darkest Dungeon 2, Undermine and Hades is likely to act as a gateway with mass effect.

This game genre already thrives on the fact that each new game is generated anew by random generators and algorithms, so that the level structure, game items and gameplay always vary. The problem with this is that the game producers have to create a huge number of game items (weapons, armor, equipment, spells, etc.) in order to create the illusion of constantly new game sequences. The advantages of mass machine-generated content are obvious as soon as the technology is reasonably mature. Millions, not thousands, of items could be incorporated into rougelikes, even by small indie developers. It may also be possible to generate this content in real time – even with enemies that would change with each game run. Their variations have so far been very limited to a few dozen enemy types due to the amount of work involved.

Hollywood, Copyright and AI

In contrast to the games industry, which has always worked with digital content anyway, film production seemed safe from being taken over by AI systems, at least in terms of content – despite the massive use of digital technology and computer-generated graphics. Who wants to admire six-fingered actors from the incubator of clumsy pattern-recognition machines? However, the situation in Hollywood seems to be changing fundamentally, as the ever more perfected machine systems are likely to take over a large part of the production process in this sector of the cultural industry.

The protracted strike by screenwriters in 2023 was already overshadowed by the possibilities of machine-generated plots, which can easily imitate the plots of the very mass-produced films that the industry produces. The strike ended with clauses that allow the AI industry to use screenwriters’ works as data material for AI training only with their consent.[16] Nevertheless, such agreements, which are full of loopholes,[17] are reminiscent of the futile attempts of the defunct craft guilds to protect themselves from free competition in the late Middle Ages. During the strike, Netflix advertised a job posting for AI experts to help create “great content” for a fee of $900,000.[18]

Hollywood is also facing a disruptive development that will cost a lot of jobs, warned actor and producer Tyler Perry in an interview with the Hollywood Reporter.[19] Perry was about to invest $800 million in the expansion of his film studio, as part of which 12 new film stages were to be built on a 133-hectare site near Atlanta. However, this huge investment has now been put on hold after the producer attended a demonstration of OpenAI’s Sora AI system, which converts text input into video footage. Investments in film studios are simply threatened with obsolescence within a few years.

Those present were “shocked” by the content system’s performance capabilities. According to Perry, traveling to film locations, the use of stage equipment and studios will be superfluous in the future. Everything is just a text input away from realization:

“If I wanted to be in the snow in Colorado, it’s text. If I wanted to write a scene on the moon, it’s text, and this AI can generate it like nothing. If I wanted to have two people in the living room in the mountains, I don’t have to build a set in the mountains, I don’t have to put a set on my lot. I can sit in an office and do this with a computer, which is shocking to me.”

So far, AI has played a minor role in the industry, says Perry, who as an actor himself tolerated digital touch-ups that made him look older in order to “save hours on make-up.” But as he watched the AI system presentation, he said he immediately became concerned about all the wage earners who will be affected by this disruptive technology. This affects not only electricians, transporters, sound designers or editors, but also actors. The AI revolution will affect “every corner of our industry,” everything is now “up in the air” because the technology is “moving so quickly,” lamented the producer, who made a helpless appeal to the state: “There’s got to be some sort of regulations in order to protect us. If not, I just don’t see how we survive.”

The possibilities of machine content systems have thus reached production maturity. They can generate videos so well from the mountains of data available to them that even hardened, billion-dollar Hollywood producers are panicking and calling for state intervention. In view of the standardized products, the usual, hackneyed plots and the basic foundations of the culture industry outlined above, which only reproduces the surface of reality in order to affirm it, this panic on the part of content producers, who always think of themselves as “artists,” is only too justified. Precisely because AI produces nothing really new and only reproduces the given in new variations, it is superior to wage earners working in the culture industry. Humans are a potentially subversive uncertainty factor in “content production” that will be eliminated in order to save costs and streamline the production process. Precisely because of the unfolding global crisis of capital, it is an essential advantage to largely automate the production of culture industry goods.

And it is not primarily strikes in Hollywood or legislative initiatives in Washington that are getting in the way of the AI industry’s march. It is capitalism that is tripping itself up in the form of copyright. The IT companies that scanned large parts of the internet to accumulate the mountains of data they needed to train their AI systems were operating in a legal gray area. They were simply quicker than the lawmakers. In many cases, the legal battle to determine the limits of the legal use of machine content production is still ahead of the industry.[20] In addition, U.S. courts have already clearly ruled that pure AI content cannot be copyrighted.

A long series of legal disputes is looming on the horizon, in which players in the “old” culture industry based on human labor are taking action against the creations of the AI industry, as their content was formed from the “raw material” of their scanned cultural goods. So far, there are two ways in which companies are planning to deal with this legal uncertainty. Valve has given all users of the Steam gaming platform the option of immediately reporting “illegal” content that violates copyright. This delegates responsibility to the manufacturers of AI games. Microsoft, on the other hand, is turning legal uncertainty into a business: all customers who get into legal disputes through the use of its own AI tools will receive legal protection from the Group. This gives Microsoft an important competitive advantage in the market for AI systems, as it also acts as a deterrent. Who wants to go to court against one of the largest corporations in the world?

Nevertheless, these legal and political battles – in which lobbies will also fight for the concrete form of the legal framework – are likely to delay the success of machine-generated “content” in the sphere of the culture industry at best. With the full implementation of AI in film, video games, music and writing – the B-good of journalism, the photojournalist, is already being replaced by AI in everyday tasks[21] – capital will finally come into its own in the cultural superstructure. The empty value abstraction will produce pure forms without any depth, which cannot even be what they pretend to be on the outside.

Where is the whole thing heading? Ultimately, producers like Tyler Perry or game designers like Todd Howard will also become largely superfluous as AI systems interlock and synthesize with the established network services that have long since locked Internet users in a gilded cage of algorithms. It is likely that highly personalized and newly generated everyday commodities of the AI culture industry are for the few privileged wage earners who will still be able to afford ideology in the disaster capitalism of the 21st century. The personalized video game, the personalized film, which is generated after work on the basis of the data trail that people already leave behind on the internet every day, should be feasible in the medium term. The various AI systems are then likely to compete primarily to provide customers with media content that they don’t even know they want.

---

[1] https://gnulinux.ch/ich-habe-alles-verloren

[2] https://github.com/mkiol/dsnote

[3] https://www.wired.co.uk/article/low-paid-workers-are-training-ai-models-for-tech-giants

[4] https://www.nytimes.com/2023/03/08/opinion/noam-chomsky-chatgpt-ai.html?searchResultPosition=1

[5] https://doomwiki.org/wiki/Models

[6] https://store.steampowered.com/app/1519310/AI_Dungeon/

[7] https://hessian.ai/de/warum-neuronale-netze-katastrophal-vergesslich-sind/

[8] https://store.steampowered.com/app/2795060/DREAMIO_AIPowered_Adventures/

[9] https://www.gamingonlinux.com/2023/11/point-and-click-adventure-zarathustra-uses-ai-art-and-ai-voices/

[10] https://openai.com/dall-e-3

[11] https://elevenlabs.io/

[12] https://80.lv/articles/this-adventure-game-prototype-has-ai-generated-graphics/

[13] https://www.traffickinggame.com/ai-assisted-graphics/

[14] https://www.youtube.com/watch?v=o_PBfLbd3zw

[15] https://www.youtube.com/watch?v=nJTdwbutW9k

[16] https://www.wired.com/story/hollywood-actors-strike-ai-future-distruption/

[17] https://www.wired.com/story/writers-strike-hollywood-ai-protections/

[18] https://www.spiegel.de/netzwelt/web/netflix-bietet-ki-experten-900-000-dollar-streikende-schauspieler-empoert-a-7bac7f4a-782a-42d3-bef3-1c3f14cc8392

[19] https://www.hollywoodreporter.com/business/business-news/tyler-perry-ai-alarm-1235833276/

[20] https://hbr.org/2023/04/generative-ai-has-an-intellectual-property-problem

[21] https://www.forschung-und-wissen.de/nachrichten/technik/bild-zeitung-ersetzt-redakteure-durch-kuenstliche-intelligenz-13377679

Originally published on konicz.info on 03/05/2024

This text is part of the e-book Crisis Ideology: The Delusion and Reality of Late Capitalist Crisis Management, which was published at the beginning of March.