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About Dwayne Monroe

Technologist, writer and other things which require quiet and time to do well. Sadly, we live in an age that grants us neither quiet nor time, alas.

The Metaverse: A Brief Inquiry

Facebook’s plan to become a ‘Metaverse company‘ (and indeed, completely rebrand the company around this concept) has attracted a lot of comment in tech media and social media spaces.

This is unsurprising; both because the idea seems futuristic (being based on a science fiction confection introduced in Neal Stephenson’s dystopian 1992 novel ‘Snow Crash‘) and also, because the tech media space reports anything announced by a so-called FAANG company as if it’s marvelous and inevitable.

Let’s apply a bit of real-ness to this and use a materialist analysis to interrogate the idea of the ‘Metaverse’ (this is similar in theme to my inquiry into Boston Dynamics).


Light Detective Work and Logical Inference

Tech companies create an air of secrecy around projects such as FB’s Metaverse effort for competitive reasons but also, I’d argue, to obscure what is often merely the assembly of already existing elements into platforms. Mariana Mazzucato analyzes this tendency using the iPhone in her book, ‘The Entrepreneurial State‘.

Here’s how the iPhone’s elements are dissected in Mazzucato’s book:

A similar method can be applied to an analysis of FB’s Metaverse.

The Oculus platform and Facebook’s Ray Ban stories glasses provide sufficient information for some light detective work. No matter how secretive a company tries to be, its job postings, properly interpreted and supported by experience, provide a rich source of evidence for what an organization is doing.

Working on the assumption that the Metaverse will primarily consist of repurposed elements (and the fact everything depends on, and leads to data centers), I examined Oculus job postings and dissected their contents.

The main technical themes were:

  • Optics
  • Haptics
  • Tracking
  • Display
  • Computer vision
  • User experience
  • Audio
  • Perceptual psychology
  • Research Science
  • Mechanical Engineering
  • Electrical Engineering
  • Software Engineering
  • Networking
  • Server operations

Of course, it’s impossible to know the precise details of FB’s system topology without a reference architecture but experience leads me to think we can achieve a solid approximation (and data center dependency is an absolute certainty no matter what else may be going on).

What can we infer from this?


How Sustainable and Realizable Is the Metaverse Concept?

Although the tech press treats every industry pronouncement as an irrefutable prediction there’s precedent of lots of smoke but little to no fire (recall Amazon’s supposedly brilliant drone delivery service). According to some estimates, Facebook has over 2 billion active users. An effort to move all, or even a statistically significant portion of this user base to a platform that generates a virtual reality environment for, and ingests audio/visual data from, hundreds of millions of people means a massive investment in physical infrastructure – computers, network infrastructure, cooling systems and real estate to host this and other relevant equipment (to get a sense of the industrial and extractive elements of what’s called ‘the cloud’ I suggest Nathan Ensmenger’s essay ‘The Cloud is a Factory’).

It also means an increase in demands for data transfer over Internet. It’s easy to project system crashes, bad connections and other problems caused by scalability challenges. It’s fair to ask if, despite the hype, any of this is actually possible as described and if so, how reliable will it be?

Conclusion

There’s abundant evidence Facebook (or whatever it’ll call itself in a week) is a problem. The company’s role in a variety of destructive activities is well documented. For that reason alone, the ‘Metaverse’ push is immediately suspect. I think we can also conclude however, that it might not be achievable as advertised and may turn out to be, like so much else that emerges from Silicon Valley, an elaborate grift, dressed up as a bold vision of the future.

We should recall that in the novel that gave the project its name, the ‘Metaverse’ is the last refuge for people living in a collapsed world. In this case, we might get the collapse without even the warped comforts a virtual world is supposed to offer.

UPDATE (29 Oct)

On 28 October, Facebook announced it was rebranding as ‘Meta’ to reflect its focus on being a ‘metaverse’ company.

The keynote video presented a vision (such as it is) for what the ‘metaverse’ is supposed to be…eventually. Zuckerberg walks within a fully virtual environment, uses a virtual pop-up menu and zooms (virtually) into an environment creatively named “Space Room”.

Rebranding the company formerly known as Facebook as Meta is, in part, surely intended to breathe new life into a moribund platform and distract attention away from the many negative associations Facebook has earned. Even so, we can predict that within the company, there will be efforts to make as much of this notion real as possible – despite the fact promoted elements (such as an environment you can walk through as if it’s real) are thoroughly impossible and likely to remain so for quite some time – indeed, some would require a multitude of breakthroughs in foundational sciences such as physics.

This means that the situation for Meta workers will become more difficult as they’re pushed to do things that simply cannot be achieved.


UPDATE (16 DEC)

On 14 December, Intel’s Senior vice president, General manager of the Accelerated Computing Systems and Graphics Group, Raja Koduri, published this paper which supports my assertion that the ‘Metaverse’ (it pains me to use that term, which describes nothing and is made of hype) will require orders of magnitude more computing capacity than currently available.

Here’s a key quote:

Consider what is required to put two individuals in a social setting in an entirely virtual environment: convincing and detailed avatars with realistic clothing, hair and skin tones – all rendered in real time and based on sensor data capturing real world 3D objects, gestures, audio and much more; data transfer at super high bandwidths and extremely low latencies; and a persistent model of the environment, which may contain both real and simulated elements. Now, imagine solving this problem at scale – for hundreds of millions of users simultaneously – and you will quickly realize that our computing, storage and networking infrastructure today is simply not enough to enable this vision.

We need several orders of magnitude more powerful computing capability, accessible at much lower latencies across a multitude of device form factors. To enable these capabilities at scale, the entire plumbing of the internet will need major upgrades. Intel’s building blocks for metaverses can be summarized into three layers and we have been hard at work in several critical areas.

Intel: https://download.intel.com/newsroom/archive/2025/en-us-2021-12-14-powering-the-metaverse.pdf

Of course, this can be interpreted as self-serving for Intel which stands to benefit (to say the least) from a massive investment in new computing gear. That doesn’t negate the insight, which is based on hard material reality.

What’s Behind the Explosion of AI?

Synopsis

The spread of AI (algorithmic) harms such as automated recidivism and benefits determination systems has been accelerated by the cloud era which has made the proliferation of algorithmic automation possible; indeed, the companies providing cloud services promote their role as accelerators. 

Background 

We are witnessing a significant change in the way computing power is used and engineered by public and private organizations. The material basis of this change is the availability of utility services such as on-demand compute, storage and database offered primarily by Amazon (with its Amazon Web Services platform), Microsoft (Azure) and Google (Google Cloud Platform). There are other platforms, such as Alibaba, based in the PRC but those three Silicon Valley giants dominate the space. This has come to be known as ‘public cloud’ to distinguish it as a category from private data centers. The term is misleading; ‘public cloud’ is a privately owned service, sold to customers via the public Internet. 

 ‘Public Cloud’ services make it possible for government agencies and businesses to reduce – or eliminate – the work of hosting and maintaining their own computational infrastructure within expensive data centers. Although the advantages seem obvious (for example, reduced overhead and the ability to focus on the use of computer power for business and government goals rather than the costly, complex, time-consuming and often error-prone task of systems engineering) there are also serious new challenges which are having an impact on US, and global, political economy. 

Impact

The rise of unregulated ‘public cloud’ has made the broad and rapid spread of algorithmic harms possible – via, for example, platform machine learning services such as Amazon Sagemaker and Microsoft Cognitive Services.  

The relationship can be visualized:

There’s a potent combination of: 

  • The lack of regulation 
  • The lowered barrier to entry made possible by ‘public cloud’ algorithmic utility services 
  • The marketing value (supported by AI hype) of creating and promoting a product and/or service as based on ‘AI’ (as labor reducing, or even eliminating, automation) 

This combination is producing an explosion of algorithmic platforms which are having a direct, negative impact on the lives of millions – notably the poor and people of color but rapidly spreading to all sectors of the population. My position is that this expansion is materially supported by cloud platforms and a lack of public oversight. 

Star Trek’s Concept of AI is Better Than Ours

Introduction

The fictional world of Star Trek, which depicts fanciful technologies such as warp drive, replicators and transporters, presents a surprisingly more realistic view of the potential uses for, and evolution of, advanced computation than the press releases of Google etc. and supportively breathless media accounts. 

I say more realistic, because, with notable exceptions (typically used to prove a larger point or create dramatic tension), computers in Star Trek are understood by in-world characters to be mindless, despite exhibiting capabilities which, by our standards, would be considered astounding achievements and irrefutable signs of intelligence and intent.

Artificial Intelligence, an aspirational term that does not describe any existing technology or collection of technologies, is, as a business endeavor, riddled with hype. Consider the article, ‘A robot wrote this entire article. Are you scared yet, human?’ published in the Guardian, 8 September, 2020. The article, assembled by cherry picking output from GPT-3, was, at the time of its publication, promoted as evidence of GPT-3 being a significant step up the ladder towards what’s sometimes called Artificial General Intelligence or AGI. After pushback and critique, Guardian’s editors added a bit more context, admitting that an AI did not, in fact, write the article: “We cut lines and paragraphs, and rearranged the order of them in some places. Overall, it took less time to edit than many human op-eds.” (the bit of face saving at the end is hilarious).

This hype requires, indeed, demands, a variety of counterpoint arguments. Hopefully this essay and the ones to follow will make a contribution.

In a series of three posts, I’ll present three in-show situations (from both the original and Next Generation series) :

  • The Original Series Episode “The Ultimate Computer
  • The Next Generation Episode “The Measure of a Man
  • The Next Generation Episode “Boobytrap

I’ll use these episodes to illustrate Star Trek’s thematic treatment of computer power – as a tool, not to be confused with the complexity and nuance of living minds. Furthermore, I’ll argue that Star Trek posits that the power of minds comes, perhaps paradoxically, from incompleteness (about which, more later).

This may seem trivial or of only academic interest. My argument is that the presentation of computational systems as possessing intelligence is a propaganda project, intended to demobilize workers and obscure the true sources of harm. Each of us who knows better has a responsibility to shine a light on this propaganda in a variety of ways.

This is a part of that effort.

The Ultimate Computer

Dr. Daystrom explains M5

The Ultimate Computer” is the twenty-fourth episode of the second season of the American science fiction television series Star Trek. Written by D.C. Fontana (based on a story by Laurence N. Wolfe) and directed by John Meredyth Lucas, it was first broadcast on March 8, 1968.”


In “The Ultimate Computer” the viewer is presented with a clear line of separation between the starship Enterprise’s sophisticated library computer system (known as LCARS in the Next Generation series) – which possesses interactive voice response, large language and text synthesis capacities and extensive command and control capabilities – and a thinking machine, the M5, created by Dr. Richard Daystrom (the scientist who designed standard starship computer systems). The M5, patterned after Daystrom’s mind,  is able to reason and indeed, exhibits the ability to think in basic ethical terms during a critical scene, when it’s forced to confront the fact its actions resulted in death. Despite these remarkable capabilities, the machine lacks nuance and could be said to operate on the level of an extraordinarily well-informed child.

For me however, the remarkable thing about this episode is the fact in-world characters such as Spock, Kirk and McCoy collectively express astonishment that the machine is able to think at all.  

In their experience, there’s a common understanding of what thinking beings do and what sophisticated computers are capable of. There is, in other words, no confusion between the act of rapid, statistical pattern matching, text parsing and data synthesis via sensors and what they, as people, do from moment to moment.

Consider this scene, when Kirk and Spock debate Dr. Daystrom about just what M5 is:

Spock: (to Daystrom, while examining the M5): I am not familiar with these instruments Dr. You are using an entirely new type of control mechanism. However, it appears to me this unit is drawing more power than before.

Daystrom: Quite right! As the unit is called upon to do more work, it pulls more power to enable it to do what is required of it just as a human body draws more energy to run than to stand still.

Spock: Dr, this unit is not a human body. A computer can process information, but only the information that is fed into it.

Kirk (to Daystrom): Granted, it can work a thousand…a million times faster than the human brain but it can’t make a value judgement, it hasn’t intuition, it can’t think.

Daystrom (smiling like a Cheshire Cat – then, waxing poetic) : Can’t you understand? The Multitronic unit is a revolution in computer science. I designed the duotronic elements you use in your ship right now and I know they are as archaic as dinosaurs compared to the M5…a whole…new approach!

[…]

Later, in a tense scene, after M5 has fired weapons on unprotected starships (misinterpreting an exercise for real combat), wounding and killing many, Daystrom tries to reason with it to stop:

Daystrom reasons with M5

Daystrom (to M5 via audio interface): M5 tie-in

M5 (to Daystrom, via ship audio): M5

Daystrom (stressed, trying to calm his voice): This is…this is Daystrom

M5: Daystrom, acknowledged

Daystrom: M5, do you know me?

M5: Daystrom, Richard, originator of comtronic/duotronic systems born…

Daystrom: Stop. M5, your attack on the starships is wrong. You must break it off.

McCoy (to Kirk): I don’t like the sound of him Jim.

Kirk: You’d better pray the M5 listens to the sound of him.

M5 (still responding to Daystrom): Programming includes protection against attack. Enemy vessels must be neutralized

Daystrom: But these are not enemy vessels! These are federation starships. You’re killing…we’re killing…murdering…human beings, beings of our own kind. You were not…created for that purpose. You’re my greatest creation. The ‘unit to save men’ – you must not destroy men.

M5: This unit must survive.

Daystrom: Survive! Yes! Protect yourself! But, not murder. You must not die, men must not die. To kill, is a breaking of civil and moral laws we’ve lived by for thousands of years. You’ve murdered hundreds of people…we’ve murdered…how can we repay that?

M5: They attacked this unit…

Kirk (whispering to Spock while M5 is still replying to Daystrom): The M5 is not responding to him, it’s talking to him.

Spock: I am most impressed with the technology Captain. Dr. Daystrom has created a mirror image of his own mind.

It’s talking to him” Kirk observes. For him, and everyone else in this world, a clear distinction is made between programmatic response, and actual conversation. This profound difference is purposely obscured by the current discourse which encourages us to view audio response technologies such as Amazon Alexa, Siri and GPT-3 as being capable of conversation.

M5 in motion

In the end, M5, built to create a new class of autonomous computers, intended to replace crewed space vessels, is shown to be deeply inadequate for the task. 


This episode establishes what I’ll describe as the pop sci-fi epistemological framework of Star Trek on the question of what Joseph Weizenbaum defined as “Computer Power and Human Reason” (the difference between judgement and calculation). In Star Trek, Computers, as a rule, are unable to reason and incapable of judgement. Outliers and exceptions, such as M5, illustrate this principle via their existence as outliers (which can’t be productionized).

In the next post, I’ll explore how the question of computer power and human reason is addressed in the ‘Next Generation’ episode, “The Measure of a Man“.

Boston Dynamics: A Brief Inquiry

As with death and taxes, you can be certain that whenever a video showing a Boston Dynamics robot is shared on Twitter, there are three reliable formulations:


1.) ‘Skynet’
2.) Robot overlords

3.) Techie admiration for engineering prowess

Typically missing are considerations of BD’s business model; who are the customers and what are these robots actually good for, if anything? I decided to do a bit of research – not very deep to be sure but, enough to go beyond social media flailing about.

The sensible place to start was Boston Dynamics’ website which is, unsurprisingly, polished, showcasing production robots such as ‘Stretch‘, ‘Spot‘, ‘Pick’ and of course, everyone’s favorite dancer/supposed robot overlord, Atlas.

The production robot use-cases – per the website – are warehouse operations (where Pick seems a bit in the way) and hazardous conditions operations (Spot’s supposed value-proposition). I didn’t see mention of Spot’s use by police forces as a remote controlled proxy.

Let’s get to Atlas, which is usually the star attraction and undoubtedly a customer and investment attractor for BD. I found a presentation by Scott Kuindersma, Research Scientist and Atlas Project Lead which provides solid, down-to-earth information about how this robot works.

https://youtu.be/EGABAx52GKI?si=WVEsSh3BpKVP_m5U

Kuindersma describes Atlas as a demonstration platform. This is important to note because it separates this machine from production offerings. BD’s non-trivial achievement is building a system that can find its center of mass (a centroidal solution) and perform from a catalog of actions.

Atlas’ actions are defined ‘offline’ (i.e., virtually, via computational modeling) and then applied ‘online’ in the real-world. A system called Model Predictive Control puts Atlas’ library of motions to use while 3D plane fitting algos enable navigation through an environment.

As of June 2020, Kuindersma and his team’s focus was building a library of actions for Atlas that would enable it to perform parkour – or something close to it. This will surely be impressive and no doubt lead to further customers and investment.

Now, let’s ask ourselves, who is helped by BD’s work and what are the most likely use-cases? Videos of the warehouse robots show machines that seem slow and inefficient when compared to people. Boxes are moved from points A to B but lots of people – not shown in the videos – are surely required.

Spot, as we’re seeing, is finding use by police forces, for industrial inspection and by militaries. In a way, Spot can be viewed as an earth-bound drone. We can anticipate seeing it deployed in much the same way other remote operated devices are being used – for surveillance and perhaps distance violence.

Atlas, as a research platform, is obviously meant to both advance the state of the art and provide dream-fuel for those who long to see autonomous machines moving among us. The ‘AI’ dream is certain to be dashed but we can foresee remote operation scenarios that are just as dystopian.

Boston Dynamics efforts do bear watching. Not because Atlas – or some successor platform – will, Hollywood-style, grow weary of us and take over the world. Rather, that, as always, people will be behind the curtain, using the veil of machine distance to obscure culpability.

Attack Mannequins: AI as Propaganda

What follows is a sketch, the foundation of a propaganda model, focused on what I’ll call the ‘AI Industrial Complex‘. By the term AI Industrial Complex, (AIIC) I mean the combination of technological capacity (or the lack thereof) with marketing promotion, media hype and capitalist activity that seeks to diminish the value of human labor and talent. I use this definition to make a distinction between the work of researchers and practical technologists and the efforts of the ownership class to promote an idea: that machine cognition is now, or soon will be, superior to human capabilities. The relentless promotion of this idea should be considered a propaganda campaign.

If There’s No AI, What is Being Promoted?

It’s my position there is no existing technology that can be called ‘artificial intelligence’ (how can we engineer a thing we haven’t yet decisively defined?) and that, at the most sophisticated levels of government and industry, the actually existing limitations of what is essentially pattern matching, empowered by (for now) abundant storage and computational power, are very well understood. The existence of university departments and corporate divisions dedicated to ‘AI’ does not mean AI exists; it’s evidence there’s powerful memetic value attached to using the term, which has been aspirational since it was coined by computer scientist John McCarthy in 1956. Once we filter for hype inspired by Silicon Valley hustling (the endless quest to attract investment capital and gullible customers) we are left with promotion intended to shape common perception about what’s possible with computer power. 

As an example, consider the case of computer scientist Geoffrey Hinton’s 2016 declaration that “we should stop training radiologists now” Since then, extensive research has shown this to have been premature, to say the least (see “Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy“).

It’s tempting to see this as a temporarily embarrassing bit of overreach by an enthusiastic field luminary – yet another example of familiar hype but let’s go deeper and ask questions about the political economy underpinning this messaging excess.

Hinton on Radiology in 2016

Radiologists are expensive and, in the US, very much in demand (indeed, there’s a shortage of qualified people). Labor shortages typically lead to higher wages and better working conditions and form the material conditions that create what some call labor aristocracies. In the past, such shortages were addressed via pushes for training and incentives to workers (such as the lavish perks that were common in the earlier decades of the tech era).

If this situation could be bypassed via the use of automation, that would devalue the skilled labor performed by radiologists, solving the shortage problem while increasing the power of owners over the remaining staff.

The promotion of the idea of automated radiology – regardless of actually existing capabilities – is attractive to the ownership class because it holds the promise of weakening labor’s power and increasing – via workforce cost reduction and greater scalability – profitability. I say promotion, because there is a large gap between what algorithmic systems are marketed as being capable of, and reality. This gap, which, as I stated earlier is well understood by the most sophisticated individuals in government and industry, is unimportant to the larger goal of convincing the general population their work efforts can be replaced by machines. The most important outcome isn’t thinking machines (which seems to be a remote goal if possible at all) but a demoralized population, subjected to a maze of crude automated systems which are described as being better than the people forced to navigate life through these systems.

A Factor Among Factors

Technological systems – and the concepts attached to them – emerge from, and reflect the properties of the societies that create those systems. Using the Hegelian (and later, Marxist) philosophy of internal relations, we can analyze both real algorithmic systems and the concept of ‘AI’ as being a part of the interplay of factors that comprise global capitalist dynamics – both actor and acted upon. From this point of view, the propaganda effort promoting ‘AI’ should not be considered in isolation, but as one aspect of a complex.

Hype vs. Propaganda

What defines hype and what differentiates standard industry hype from a propaganda campaign?

Hype (such as marketing material that makes excessive claims – for example, AI reading emotions) is narrowly designed to attract investment capital and customers. Hype should be considered a species of advertisement. Propaganda has a broader aim, which is described by Jacques Ellul in his work, Propaganda.

Describing one of the four elements of propaganda, and bridging from advertising to propaganda, Ellul writes…

Public and human relations: These must necessarily be included in propaganda. This statement may shock some readers, but we shall show that these activities are propaganda because they seek to adapt the individual to a society, to a living standard, to an activity. They serve to make him conform, which is the aim of all propaganda. In propaganda we find techniques of psychological influence combined with techniques of organization and the envelopment of people with the intention of sparking action.”

A Propaganda Model: Foundational Concepts

As the model of AI as propaganda is constructed, the works of three thinkers will provide key guidance:

Jacques Ellul: Propaganda

As already noted, Ellul’s key work on propaganda (which, I think, was the first to apply sociology and psychology to the topic) is a critical source of inspiration:

“Propaganda is first and foremost concerned with influencing an individual psychologically by creating convictions and compliance through imperceptible techniques that are effective only by continuous repetition. Propaganda employs encirclement on the individual by trying to surround man by all possible routes, in the realm of feelings as well as ideas, by playing on his will or his needs through his conscious and his unconscious, and by assailing him in both his private and his public life.

The propagandist also acknowledges the most favorable moment to influence man is when an individual is caught up in the masses. Propaganda must be total in that utilizes all forms of media to draw the individual into the net of propaganda. Propaganda is designed to be continuous within the individual’s life by filling the citizen’s entire day. It is based on slow constant impregnation that functions over a long period of time exceeding the individual’s capacities for attention or adaptation and thus his capabilities of resistance”

Full at Wikipedia’s article 

The relentless promotion of the idea that automation is on the verge of replacing human labor can be interpreted as being part of an effort to create a conviction (there is artificial intelligence’, it cannot be stopped) and compliance (resistance to ‘AI’ is retrogressive Luddism).

Noam Chomsky/Edward S. Herman: The Propaganda Model

In their book, ‘Manufacturing Consent’ Chomsky and Herman present a model of propaganda via media:

“The third of Herman and Chomsky’s five filters relates to the sourcing of mass media news: 

The mass media are drawn into a symbiotic relationship with powerful sources of information by economic necessity and reciprocity of interest. Even large media corporations such as the BBC cannot afford to place reporters everywhere. They concentrate their resources where news stories are likely to happen: the White House, the Pentagon, 10 Downing Street and other central news “terminals”. Although British newspapers may occasionally complain about the “spin-doctoring” of New Labour, for example, they are dependent upon the pronouncements of “the Prime Minister’s personal spokesperson” for government news. Business corporations and trade organizations are also trusted sources of stories considered newsworthy. Editors and journalists who offend these powerful news sources, perhaps by questioning the veracity or bias of the furnished material, can be threatened with the denial of access to their media life-blood – fresh news. Thus, the media has become reluctant to run articles that will harm corporate interests that provide them with the resources that they depend upon. 

The dependence of news organizations on press releases from Google and other tech giants that promote the idea of ‘AI’ can be interpreted as being an example of the ‘symbiotic relationship, based on reciprocity of interest’ Chomsky and Herman detail.

Full at Wikipedia’s article

Summary

The concept of “artificial intelligence” is aspirational (like ‘warp drive’) and does not describe any existing or likely to exist computational system. Despite this, the concept is promoted to attract investment capital and customers but also, more critically for my purposes, devalue the power of labor – if not in fact than in perception (which, in turn, becomes fact). For this reason, I assert that ‘AI’, as a concept, is part of a propaganda campaign.

Key Characteristics of AI Propaganda

The promotion of the concept of AI, as a propaganda effort, has several elements:

* Techno-optimism: The creation of thinking machines is promoted as being possible, with little or no acknowledgement of limitations.

* Techno-determinism: The creation of thinking machines is promoted as being inevitable and beyond human intervention, like a force of nature

* An Elite Project: Although individual boosters, grifters, techno enthusiasts and practitioners may contribute within their circles (for ex. social media) to hype, the propaganda campaign is an elite project designed to effect political economy and the balance of power between labor and capital.

* Built on, but not limited to, hype: There is a relationship between hype and propaganda. Hype is of utility to the propaganda campaign but the objective of that campaign is broader and targeted towards changing societal attitudes and norms.

I use the term attack mannequins to describe this complex – lifeless things, presented as being lifelike, used to assault the position and power of ordinary people.


UPDATE: 2 NOVEMBER 2021

In this video, YouTube Essayist Tom Nicholas details the efforts Waymo has made to convince people – via the use of YouTube ‘educators’ – that autonomous vehicles are a perfected technology, superior to human drivers and a solution to traffic safety and congestion issues.

Nicholas makes the point that inasmuch as the Waymo ‘autonomous’ taxi service (supported by a large staff of people behind the scenes) only operates in a subsection of the suburbs of Phoenix, Arizona USA, the PR campaign’s goal can’t be explained as advertising; it’s part of a broad effort to change minds.

In other words, propaganda.