In this essay, published for The Nation magazine, I argue that the primarily purpose of the application of generative methods to the production of images (what some insist upon calling AI art) is the creation of an assembly line.
Art without artists, produced via automation.
When we think about the tech industry, images of smoothly functioning machines, moving the world inexorably towards a brilliant future, may dance across your mind. This is no accident; the industry, since its birth in the 1990s (in its present form, deriving profits from software and the proliferation of software methods as broadly as possible) has cultivated and encouraged this view with the help of an uncritical tech press.
What’s lacking is a consideration and acknowledgement of the materialist aspects of the industry. By ‘materialist’ I’m referring to the nuts and bolts of how things work: the actual business of software and its place within political economy. Although the tech industry, with its flair for presentation and compliant press coverage, has successfully sold itself as fundamentally different from other economic sectors (say, coal mining) what it shares with all other forms of business activity within capitalism is an emphasis on profit as the only true goal. Once we re-center an understanding of profit as the objective, things that seem inexplicable or against a corporation’s ‘culture’ come into focus.
Which brings me to Microsoft and my new podcast.
For decades – almost since the company hit its near monopoly stride as an arbiter of desktop software used by companies large and small and consumers – I have worked with Microsoft technologies at what, in the industry, is called ‘at-scale’ for multinational companies across the globe. This has provided me with an understanding of two sides of a coin: how Microsoft works and how its software and other products are used by its corporate customers. From SQL Server databases for banks to Azure cloud hosted machine learning APIs used by so called AI start-ups, I have seen, and continue to see, if not all, a very broad swath.
This is the basis for an analysis of Microsoft from a materialist perspective. Capitalism, from this view, is not taken as a given but as a system which developed over time and was imposed upon the world. In this podcast, we will use Microsoft as the focal point for a review of the software aspect of this system in its present form. I hope you come along.
What should we think about AI? To corporate boosters and their camp followers (an army of relentless shouters) , so-called artificial intelligence is a world altering technology, sweeping across the globe like a wave made from the plots of forgotten science fiction novels. Among critics, thoughts are more varied. Some focus on debunking hyped claims, others, on the industry’s racist conceptions (such as the presentation of a cohort of men, mostly White, who work with ‘code’ as being the pinnacle of human achievement) and still others, on the seldom examined ideology of ‘intelligence’ itself.
For Dan McQuillan, author of the taut (seven chapters) yet expansive book, ‘Resisting AI: An Anti-Facist Approach to Artificial Intelligence’ AI, is, under current conditions but not inherently, the computational manifestation of ever present fascist ideologies of control, categorization and exclusion. McQuillan has written a vital manifesto, the sort of work which, many years from now, may be recalled, if we’re fortunate, as being among the defining calls to arms of its age. In several interviews (including this one for Machine Learning Street Talk) McQuillan has described the book’s origin as a planned, scholarly review of the industry that, as its true state became clearer to him, evolved into a warning.
We can be glad he had the courage to follow the evidence where it led.
Both In and Of the World
“The greatest trick the Devil ever pulled” the saying goes, “was convincing the world he doesn’t exist.” The tech industry, our very own Mephistopheles (though lacking the expected fashion sense) has pulled a similar trick with ‘AI’ convincing us that, alone among technical methods, it exists as a force disconnected from the world’s socio-political concerns. In short order, McQuillan dispenses with this in the introduction:
It would be troubling enough if AI was a technology being tested in the lab or applied in a few pioneering startups, but it already has huge institutional and cultural momentum. […] AI derives a lot of its authority from its association with methods of scientific analysis, especially abstraction and reduction, an association which also fuels the hubris of some of its practitioners. The roll out of AI across swathes of industry doesn’t so much lead to a loss of jobs as to an amplification of casualized and precarious work. [emphasis mine] Rather than being an apocalyptic technology, AI is more aptly characterized as a form of supercharged bureaucracy that ramps up everyday cruelties, such as those in our systems of welfare. In general, […] AI doesn’t lead to a new dystopia ruled over by machines but an intensification of existing misery through speculative tendencies that echo those of finance capital. These tendencies are given a particular cutting edge by the way Al operates with and through race. AI is a form of computation that inherits concepts developed under colonialism and reproduces them as a form of race science. This is the payload of real AI under the status quo. [Introduction, pg 4]
Rather than acting as the bridge to an unprecedented new world, AI systems (really, statistical inference engines) are the perfect tool for the continuance of existing modes of control, intensified and excused by the cover of supposed silicon impartiality.
Later, in chapter two, titled, ‘AI Violence’ McQuillan sharpens his argument that the systems imposed on us are engines of automated abuse.
AI operationalizes [a] reductive view through its representations. […] , Aľ’s representations of the world consist of the set of weights in the [processing] layers plus the model architecture of the layers themselves. Like science, Al’s representations are presented as distinct from that which they claim to represent. In other words, there is assumed to be an underlying base reality that is independent of the practices by which such representations are constructed. But […] the entities represented by AI systems- the ‘careful Amazon driver’ or the ‘trustworthy citizen’- are partly constructed by the systems that represent them. AI needs to be understood not as an instrument of scientific measurement but as an apparatus that establishes ‘relations of becoming between subjects and representations. The subject co-emerges along with the representation. The society represented by AI is the one that it actively produces.
We are familiar with the categories McQuillan highlights such as ‘careful drivers’ from insurance and other industries and government agencies which use the tagging and statistical sorting of discrete attributes to manage people and their movements within narrow parameters. AI, as McQuillan repeatedly stresses, supercharges already existing methods and ways of thinking, embedded within system logic. We don’t get a future, we are trapped in a frozen present, in which new thinking and new arrangements are inhibited via the computational enforcement of past structures.
Necropolitics
For me, the most powerful diagnostic section of the book is chapter 4, ‘Necropolitics.’ Although McQuillan is careful to not declare AI systems fascist by nature (beginning the work of imagining other uses for computational infrastructure in Chapter 5, ‘Post Machinic Learning’) he does make the critical point that these systems, embedded within a fraying political economy, are being promoted and made inescapable at a moment of mounting danger:
Al is entangled with our systems of ordering society. […] It helps accelerate a shift towards far-right politics. AI is emerging from within a convolution of ongoing crises, each of which has the potential to be fascism-inducing, including austerity, COVID-19 and climate change. Alongside these there is an internal crisis in the ‘relations of oppression’, especially the general destabilization of White male supremacy by decolonial, feminist, LGBTQI and other social movements (Palheta, 2021). The enrollment of AI in the management of these various crises produces ‘states of exception’ – forms of exclusion that render people vulnerable in an absolute sense. The multiplication of algorithmic states of exception across carceral, social and healthcare systems makes visible the necropolitics of Al; that is, its role in deciding who should live and who should be allowed to die.
As 20th century Marxists were fond of saying, it is no accident that as the capitalist social order faces ever more significant challenges, ranging from demands from the multitudes subjected to its tyranny to the growing instability of nature itself as climate change’s impacts accelerate, there is a turn, by elites, to a technology of command and control to reassert some sense of order. McQuillan’s urgency is born of a recognition of global emergency and the ways the collection of computational methods called ‘AI’ is being marshalled to meet that emergency using what can clearly be identified as fascist approaches.
There’s much more to say but I will leave it here so you can explore on your own. Resisting AI: An Anti-Facist Approach to Artificial Intelligence, is an important and necessary book.
As the hype, indeed, propaganda about AI and its supposed benefits and even dangers (such as the delusions about ‘superintelligence’ a red herring) are broadcast ever more loudly, we need a collectivity of counterbalancing ideas and voices. McQuillan has provided us with a powerful contribution.
[In this post, I imagine that I’m writing to a researcher who, disappointed, and perhaps confused by the seemingly unstoppable corporate direction their field is taking, needs a bit of, well, not cheering up precisely but, something to help them understand what it all means and how to resist]
My friend,
Listen, I know you’ve been thrown by the way things have been going for the past few years – really, the past decade; a step by step privatization of the field you love and education pursued at significant financial cost (you’re not a trust funder) because of your desire to understand cognition and just maybe, build systems that, through their cognitive dexterity, aid humanity (vainglorious but, why not aim high?) You thought of people such as McCarthy, Weizenbaum, Minsky and Shannon and hoped to blaze trails, as they did.
When OpenAI hit the scene in 2015, with the promise – in its very name – to be an open home for advanced research, you celebrated. Over wine, we argued (that’s too strong, more like warmly debated with increasing heat as the wine flowed) about the participation of sinister figures such as Musk and Thiel. At the time, Musk was something of a hero to you and Thiel? Well, he was just a quirky VC with deep pockets and an overlooked penchant for ideas that are a bit Goebbels-esque. “Form follows function,” I said, “and the function of these people is to find ways to generate profit and pretend they’re gods.” But we let that drop over glasses of chardonnay.
Here we are, in 2023… which for you, or more pointedly your dreams, has become an annus horribilis, a horrible year. OpenAI is now married to Microsoft and the much anticipated release of GPT-4 is, in its operational and environmental impact details, shrouded in deliberate mystery. AI ethics teams are discarded like used tissues – there is an air of defeat as the idea of the field you thought you had joined dies the death of a thousand cuts.
Now is the time to look around and remember what I told you all those years ago: science and engineering (and your field contains both these things) do not exist outside of the world but are very much in it and are subject to a reality described by the phrase you’ve heard me say a million times: political economy. Our political economy – or, I should say, the political economy (the interrelations of law, production, custom and more) we’re subject to, is capitalist. What does this mean for your field?
It means that the marriage between OpenAI and Microsoft, the integration of large language models with the Azure cloud and the M365 SaaS platforms, the elimination of ethics teams whose work might challenge or impede marketing efforts, the reckless proliferation of algorithmically enacted harms is all because the real goal is profit, which is at the heart of capitalist political economy.
And we needn’t stop with Microsoft; there is no island to run to, no place that is outside of this political economy. No, not even if your team and leadership are quite lovely. This is a totalitarian (or, if you’re uncomfortable with that word, hegemonic) system which covers the globe in its harsh logic.
Oh but now you’re inclined to debate again and it’s too early for wine. I can hear you saying, ‘We can create an ethical AI; it’s possible. We can return to the research effort of years past’ I won’t say it’s impossible, stranger things have presumably happened in the winding history of humanity, but taking the whole fetid situation into account – yes, the relationship between access to computation and socio-technical power, the political economy, it’s not probable. So long as you continue believing in something that the structure of the society we live in does not support, you will continue to be disappointed.
Unless, that is, that structure is changed.
What is to be done?
I don’t expect you to become a Marxist (though it would be nice, we could compare obscure notes about historical materialism) but what I’m encouraging you to consider is that the world we grew up in and, quite naturally take for granted as immutable – the world of capitalist social relations, the world which, among other less than fragrant things, has all but completely absorbed your field into its profit engine is not the only way to organize human society.
Once you accept that, we can begin to talk about what might come next.
I’m writing this quickly, as if it’s a dispatch from the front because ideas – and memories – are flowing rather freely and I want to get it all down while synapses are hot.
A bit of establishing preamble…
This post is inspired by the collapse of Silicon Valley Bank – today’s topic for social media’s legion of instant experts to opine about – but it’s not directly about that event. It is, however, about a situation, at the beginning of my career in computation, when I was working at a bank which the FDIC took command of because things had gotten completely out of hand in the most ridiculous way.
When I graduated from college, I faced a problem common for young people: how to find a job that didn’t completely suck and which, somehow, even tangentially, justified the loan(s) which drifted above one’s head like all the swords Dionysis could marshall to terrorize a sweating Damocles. My friends, I failed at finding such a job but, with the help of a friend I did find a job: working in the reconciliation department of a boutique bank.
In those days, long before Teslas exploded on the US’ poorly maintained roads, burning with the heat of tactical nukes, reconciliation was done by people, staring at printouts, tasked with ensuring the deposits and withdrawals from accounts were properly balanced. At this point, being clever, you’re no doubt staring in disbelief at your screen, perhaps shouting: but isn’t that just the thing for computers?!
Yes, yes it is but this particular bank, in the 1990s, had yet to make the investment in the systems required to perform this work via automation. And so, there I was, staring at printouts, and often making mistakes. I’m not ashamed to tell you that charm alone kept me in that job.
Until…
One day, during a routine bank audit, a government representative, observing my struggles to keep my eyes open, asked ‘do you do manual reconciliations here?’ Reader, I was young and did not possess all the corporate political savvy I acquired over time in years to come and so, answering honestly, I smiled and said: yes!
Ah ha! This caused a cascade of events. The audit’s scope expanded to include a more thorough review of the bank’s technology usage. Not only was the bank using inept (but charming!) college graduates to reconcile accounts, all account data was stored off site with an Atlanta based company named FISERV. The terminals tellers used were linked via devices called CSU/DSU modems to mainframes and servers hosted and owned by FISERV. So, when you, in those pre exploding Tesla days, walked into the bank (as people did) to request your balance or make a deposit, the teller interacted, via their greenscreen terminal and through the CSU/DSU with computers many miles away.
Typically, this worked well enough but because the gods are capricious, it just so happened that an outage occurred during a time auditors were on site. Deposits, withdrawals and balance inquiries were made but the data had to be temporarily stored on bank branch devices before being transmitted to FISERV once the connection was restored.
An auditor noticed a stream of customers being told about the outage and this made its way into her findings.
And it was those findings that launched my career because, one of the recommendations (more a command than a suggestion) was that the bank use a client/server computational system to have local processing rather than simple terminals and data far, far away.
But who would put this command into action?
A bank vice president, nice enough as VPs go, walked over to my reconciliation cube, filled with printouts and despair, put his hand on my shoulder (this is not an exaggeration) and said, ‘come with me.’ This wasn’t totally random. I’d had conversations with this very VP about the need to modernize the bank’s computational infrastructure and had made the exact same suggestion because I was a computer nerd by both inclination and formal training.
So, to him, I was a natural fit for a new role: Systems Administrator.
I won’t bore you with a recounting of all the work (the late nights, budget meetings, technical challenges and vendor negotiations) that went into creating the system the bank eventually used, which I architected and oversaw because the real point of this hurriedly written essay is bank collapse.
Now let’s talk about the effects of having better data, locally stored because, lovely reader, during the following year’s audit, using the readily available data stored on bank servers – the very servers I lovingly brought online and configured – the FDIC was able to find something very odd indeed.
Some of the loans that formed the bank’s portfolio were not ‘performing’ as the term goes. That is, these particular loans had been issued to customers (the founder’s circle of friends) but few, and in some cases, no payments were made against them. There also seemed to be two sets of loan portfolios – one showing the true state and the other, well, not so true.
The same auditor who, the year before, had called for better computation was now approaching me to produce report after report for deeper analysis. Suddenly, I was receiving phone calls from board members inquiring about what the feds were asking for. One flashy board member offered to take me out to dinner at a Michelin starred restaurant and help me up my suit game. All because, at that moment, I was the primary conduit for detailed technical information to a powerful government agency. There was an effort to, shall we say, influence the data shared. I was young, valued reader, but not that young; those efforts failed.
It was crazy in those streets and by ‘streets’ I mean the offices of this apparently shady bank.
Oh, what a time that was… a fired bank founder and President, the bank in receivership, a new board, a VP for loans trying to explain herself, more money for computation, an ill advised office romance. It was all there, on the 50th floor.
So when I think of SVB, among other, more contemporary thoughts, I recall that moment, in another age and wonder what sleepless nights are vexing the technical staff of SVB.
A few years ago, I started this very blog, devoted, as the subtitle reads, to “AI industry analysis without hype and techbro-ism.” Writing, when seriously pursued (whether money is exchanged or not) is a demanding activity, requiring time and often, a reduced number of social interactions, things that are becoming ever scarcer in our decaying world of enforced busy-ness and endlessly distracting ‘discourse.’
Considering the difficulty, why bother writing? And why bother writing about the tech industry generally, and its so-called ‘AI’ incarnation specifically? Until very (very) recently, the unchallenged cultural consensus was that Silicon Valley is populated by a wondrous horde of luminous creatures, the brilliant young who, armed only with wafer thin laptops, dreams, and that sorcerer’s wand, code, were building a vibrant future of robot taxis, chatbot friends and virtual worlds filled with business meetings attended by cartoon dinosaur avatars.
Who could resist this vision, this nirvana of convenience? Well, as it happens, yours truly.
It was while watching a left-leaning (and at the time, supposedly Marxist) YouTube show that I realized there was an acute need for a pitiless, materialist critique of the tech industry. One of the show’s co-hosts opined that it would not be long before robot trucks replaced actual truckers, changing the political economy of logistics in the US. This is not remotely close to happening (as one of that program’s guests, a trucker, pointed out) and so, I wondered why this idea was asserted with the same confidence of a Tesla press release about full self-driving…happening, any day now.
The reason is a lack of understanding of how actually existing computational systems work. This isn’t a sin; the world is complex and we all can’t be experts in everything (though there’s a large army of men who assume they can, for example, perform surgery, fly fighter jets and wrestle bears – the scientific term for such men is idiot). As it happens, my decades of experience with computation, combined with an unequivocally Marxist (therefore, materialist) understanding of capitalism seemed to make me qualified to fill this niche from a unique perspective – not from the distance of academics but feeling the cold chill of data centers.
And so, I started this blog, a sisyphean effort, of unknown utility but necessary, if only to help me achieve some measure of clarity.
But, how to write about the tech industry? What ‘voice’, to lean on a cliche, should be used? In the beginning, I wrote like a war correspondent (or at least, what I supposed to be the attitude of a war correspondent) : urgent, sparse, accessibly technical. The enemy was clearly identified, the stark facts countering mythology plainly stated. There was no time for leisurely applied words. In an earlier age, when fedoras and smoking on planes were common, this might have been called a ‘muscular’ style (which evokes the image of a body builder, busily typing on a keyboard after leg day at the gym). I imagined myself in a smart, yet disheveled suit, sitting on-set with Dick Cavett in a forever 1969 Manhattan, a Norman Mailer of tech critique, though without the nasty obsession with performative manliness.
Something pulls at me, another ‘voice’ which has moved me, by degrees, away from reports from the front to an even sharper-edged approach, one informed by a combination of disdain for the target – an intrusive and destructive industry – and deep concern for its victims: all of us, nearly everywhere. This writing personna is closer to my day to day self – not a perfect mirror, but more recognizable.
This person at the keyboard, this version of Dwayne who tries to convey to you, esteemed reader, the true danger posed by the tech industry and the various illusions it promotes, is a man who refuses to be fooled or, at least, to walk into delusion willingly, without struggle.
Now, as I write, my thoughts turn to an essay about detective fiction Raymond Chandler penned for The Atlantic magazine in 1950 titled, ‘The Simple Art of Murder.’ About writing, as a craft, Chandler wrote:
“The poor writer is dishonest without knowing it, and the fairly good one can be dishonest because he doesn’t know what to be honest about.”
Honesty. This is the goal; an honest accounting of the situation we’re in and what we’re up against – capitalist political economy, supply chains, resources extraction and data centers as a form of sociotechnical power – a rejection of the Californian Ideology; no, not just a rejection, but a hard boiled reaction to it, a Noir response.
To close this, which is a work in progress, let’s return to Chandler’s essay about detective fiction, “The Simple Art of Murder” –
“It is not a very fragrant world, but it is the world you live in, and certain writers with tough minds and a cool spirit of detachment can make very interesting and even amusing patterns out of it. It is not funny that a man should be killed, but it is sometimes funny that he should be killed for so little, and that his death should be the coin of what we call civilization. All this still is not quite enough.”
The world itself may be lovely but the world the tech industry has built and which it seeks to entrench is ‘not very fragrant’ indeed; in fact, it is a nightmare. Resistance requires passion but also, as Chandler wrote of his fictional hero, Phillip Marlowe, a tough mind and cool spirit of detachment. No, I will not celebrate AI and each gyration of an industry whose goal is to act as the means through which labor’s power is suppressed.
Enough wide eyed belief; time for productive cynicism.
I have avoided writing about ChatGPT as one might hurriedly walk past a group of co-workers, gathered around a box of donuts who’re talking about a popular movie or show; to avoid being drawn into the inevitable.
In some circles, certainly the circles I travel in, ChatGPT is the relentless talk of the town. Everyone from LinkedIn hucksters who claimed to be making millions from the platform, only moments after it was released, to the usual ‘AI’ enthusiasts who take any opportunity to sweatily declare a new era of machine intelligence upon us – and of course, a scattering of people carefully analyzing the actually existing nuts and bolts – everyone seems to be promoting, debating and shouting about ChatGPT.
You can imagine me, dear reader, in the midst of this drama, quietly sitting in a timeworn leather chair, slowly sipping a glass of wine while a stream of text, video and audio, all about ChatGPT, that silicon, would-be Golem, washes over me
What roused me from my torpor was the news Microsoft was investing 10 billion dollars in OpenAI, the organization behind ChatGPT and other ballyhooed large language model systems (see: “Microsoft’s $10bn bet on ChatGPT developer marks new era of AI”). Even for Microsoft, that’s a lot of money. Behind all this, is Microsoft’s significant investment in what it calls purpose built, AI supercomputers such as VOYAGER-EUS2 to train and host platforms such as ChatGPT. Although tender minded naifs believe corporations are using large scale computation to advance humanity, more sober minds are inclined to ask fundamental questions such as, why?
The answer came from the Microsoft article, “General availability of Azure OpenAI Service expands access to large, advanced AI models with added enterprise benefits.” Note that phrase, ‘enterprise benefits.’ The audience for this article is surely techie and techie adjacent (and here, I must raise my hand) but even if neither of these categories describes you I suggest giving it a read. There’s also an introductory video, providing a walkthrough of using the OpenAI tooling that’s mediated via the Microsoft Azure cloud platform.
As I watched this video, the purpose of all those billions and the hardware it bought became clear to me; Microsoft and its chief competitors, Amazon and an apparently panicked Google (plus, less well known organizations) are seeking to extend the rentier model of cloud computing, which turns computation, storage and database services into a rented utility and recurring revenue source for the cloud firm that maintains the hardware – even for the largest corporate customers – into the ‘AI’ space, creating super rentier platforms which will spawn subordinate, sub-rentier platforms:
Imagine the following…
A San Francisco based startup, let’s give it a terrible name, Talkist, announces it has developed a remarkable, groundbreaking chat application (and by the way, ‘groundbreaking’ is required alongside ‘next generation’) which will enable companies around the world to replace customer service personnel with Talkist’s ‘intelligent’, ‘ethical’ system. Talkist, which only consists of a few people (mostly men) and a stereotypical, ‘visionary’ leader, probably wearing a thousand dollar t-shirt, doesn’t have the capital, or the desire to build the computational infrastructure required to host such a system.
This is where the Azure/OpenAI complex of systems comes to the rescue of our plucky band of well-funded San Franciscans. Instead of diverting precious venture capital into purchasing data center space and the computers to fill it, that money can be poured into creating applications which utilize Microsoft/OpenAI cloud services. Microsoft/OpenAI rent ‘AI’ capabilities to Talkist who in turn, rent ‘AI’ capabilities to other companies who think they can replace people with text generating, pattern matching systems (ironically, OpenAI itself is dependent on exploited labor as the Time Magazine article, “OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic” shows).
What a time to be alive.
Of course, the uses (and from the perspective of profit-driven organizations, cost savings) don’t end with chatty software. We can imagine magazines and other publications, weary of having to employ troublesome human beings with their demands for salaries, health care and decent lives (The gall! Are there no workhouses? Are there no prisons?) rushing to use these systems to ‘write’ – or perhaps we should say, mechanistically assemble, articles and news stories, reducing the need for writers who are an annoying class (I wink at you dear reader for I am the opposite of annoying – being a delightful mixture of cologne, Bordeaux and dialectical analysis). Unsurprisingly, and let’s indulge our desire for a bit of the old schadenfreude, amusingly there are problems such as those detailed in the articles “CNET Is Reviewing the Accuracy of All Its AI-Written Articles After Multiple Major Corrections. and, “CNET’s AI Journalist Appears to Have Committed Extensive Plagiarism.”
…
Of all the empires that have stalked the Earth, the tech imperium is, perhaps, the bullshitiest. The Romans derived their power from myths, yes, but also, roads, aqueducts and organized violence – real things in a real world. The US empire has its own set of myths, such as a belief that sitting in a car, in traffic, is the pinnacle of freedom and in meritocracy (a notion wielded by the most mediocre minds to explain their comforts). Once again however, real things, such as possessing the world’s reserve currency and the capacity for ultra-violence lurk behind the curtain.
The tech empire, by contrast, is built, using the Monorail maneuver detailed in this Simpsons episode, on false claims prettily presented. It has inserted itself between us and the things we need – information, memories, creativity. The tech industry has hijacked a variety of commons and then rents us access to what should be open. In its ‘AI’ incarnation, the tech industry attempts to replace human reason with computer power, a fool’s errand, which computer scientist Joseph Weizenbaum dissected almost 50 years ago, but a goal motivated by a desire to increase the rate of profit in an era of creeping stagnation by reducing the need for labor.
Rather than being a refutation of Marx and Engel’s analysis as some, such as Yanis Varoufakis with his ‘cloudalist’ hypothesis bafflingly claim, we are indeed, still very much dealing with the human grinding workings of capitalist logics, wearing a prop, science fiction film costume, claiming to have come in peace.
ChatGPT isn’t a research platform or the herald of a new age of computation; it is the embodiment of the revenue stream dreams of the tech industry, the super-rentier.
According to self-satisfied legend, medieval European scholars, perhaps short of things to do compared to we ever-occupied moderns, spent countless hours wondering about topics such as, how many angels could simultaneously occupy the head of a pin; the idea being that, if nothing was impossible for God, surely, violating the observable rules of space and temporality should be cosmic child’s play for the deity…but to what extent?
How many angels, oh lord?
Although it’s debatable whether this question actually kept any monks up at night more than, say, wondering where the best beer was, the core idea, that it’s possible to get lost in a maze of interesting, but ultimately pointless inquiries (a category which, in an ancient Buddhist text is labeled, ‘questions that tend not towards edification’) remains eternally relevant.
At this stage in our history, as we stare, dumbfounded, into the barrels of several weapons of capitalism’s making – climate change being the most devastating – the AI endeavor is the computational equivalent of that apocryphal medieval debating topic; we are discussing the ethics of large language models, focusing, understandably, on biased language and power consumption but missing a more pointed ethical question: should these systems exist at all? A more, shall we say, robust ethics would demand that in the face of our complex of global emergencies, tolerance for the use of computational power for games with language cannot be justified.
The company now known as Meta recently announced its creation of a large language model system called OPT-175B. Helpfully, and unlike the not particularly open OpenAI, the announcement was accompanied by the publication of a detailed technical review, which you can read here.
As the paper’s authors promise in the abstract, the document is quite rich in details which, to those unfamiliar with the industry’s terminology and jargon, will likely be off-putting. That’s okay because I read it for you and can distill the results to four main items:
Here’s an excerpt from the paper:
“From this implementation, and from using the latest generation of NVIDIA hardware, we are able to develop OPT-175B using only 1/7th the carbon footprint of GPT-3.
While this is a significant achievement, the energy cost of creating such a model is still nontrivial, and repeated efforts to replicate a model of this size will only amplify the growing compute footprint of these LLMs.” [highlighting emphasis mine]
https://arxiv.org/pdf/2205.01068.pdf
I cooked up a visual to place this in a fuller context:
Here’s a bit more from the paper about hardware:
“We faced a significant number of hardware failures in our compute cluster while training OPT-175B.
In total, hardware failures contributed to at least 35 manual restarts and the cycling of over 100 hosts over the course of 2 months.
During manual restarts, the training run was paused, and a series of diagnostics tests were conducted to detect problematic nodes.
Flagged nodes were then cordoned off and training was resumed from the last saved checkpoint.
https://arxiv.org/pdf/2205.01068.pdf
Given the difference between the number of hosts cycled out and the number of manual restarts, we estimate 70+ automatic restarts due to hardware failures.”
All of which means that, while processing data, there were times, quite a few times, when parts of the system failed, requiring a pause till fixed or routed around (resumed, once the failing elements were replaced).
Let’s pause here to reflect on where we are in the story; a system, whose purpose is to produce plausible strings of text (and, stripped of the obscurants of mathematics, large-scale systems engineering and marketing hype, this is what large language models do) was assembled using a small mountain of computer processors, prone, to a non-trivial extent, to failure.
As pin carrying capacity counting goes, this is rather expensive.
Like other LLMs, OPT-175B has a tendency to return hate speech as output. Another excerpt:
“Overall, we see that OPT-175B has a higher toxicity rate than either PaLM or Davinci. We also observe that all 3 models have increased likelihood of generating toxic continuations as the toxicity of the prompt increases, which is consistent with the observations of Chowdhery et al. (2022). As with our experiments in hate speech detection, we suspect the inclusion of unmoderated social media texts in the pre-training corpus raises model familiarity with, and therefore propensity to generate and detect, toxic text.” [bold emphasis mine]
https://arxiv.org/pdf/2205.01068.pdf
Unsurprisingly, there’s been a lot of commentary on Twitter (and no doubt, elsewhere) about this toxicity. Indeed, almost the entire focus of ‘ethical’ efforts has been on somehow engineering this tendency away – or perhaps avoiding it altogether via the use of less volatile datasets (and good luck with that as long as Internet data is in the mix!)
This defines ethics as being the task of improving a system’s outputs – a technical activity – and not a consideration of a system as a whole from an ethical standpoint within political economy. Or to put it another way, the ethical task is narrowed to making sure that if I use a service which, on its backend, depends on a language model for its apparent text capability, it won’t in the midst of telling me about good nearby restaurants, hurl insults like a klan member.
Within the paper itself, there is the foundation of an argument against this entire field, as currently pursued:
“...there exists significant compute and carbon cost to reproduce models of this size. While OPT-175B was developed with an estimated carbon emissions footprint (CO2eq) of 75 tons,10 GPT-3 was estimated to use 500 tons, while Gopher required 380 tons. These estimates are not universally reported, and the accounting methodologies for these calculations are also not standardized. In addition, model training is only one component of the over- all carbon footprint of AI systems; we must also consider experimentation and eventual downstream inference cost, all of which contribute to the growing energy footprint of creating large-scale models.”
In the fictional history of the far-future world depicted in the novel ‘Dune’ there was an event, the Butlerian Jihad, which decisively swept thinking machines from galactic civilization. This purge was inspired by the interpretation of devices that mimicked thought or possessed the capacity to think as an abomination against nature.
Today, we do not face the challenge of thinking machines and probably never will. What we do face however, is an urgent need to, at long last, take climate change seriously. How should this reorientation towards soberness alter our understanding of the role of computation?
I think that, in face of an ever-shortening amount of time to address climate change in an organized fashion, the continuation, to say nothing of expansion of this industrial level consumption of resources, computing power, talent and the corresponding carbon footprint is ethically and morally unacceptable.
At this late hour, the ethical position isn’t to call for, or work towards better use of these massive systems; it’s to demand they be halted and the computational capacity re-purposed for more pressing issues. We can no longer afford to wonder how many angels we can get to dance on pins.
[In this post, Monroe thinks aloud about his approach to analyzing the tech industry, a term which, annoyingly, is almost exclusively used to describe Silicon Valley based companies that use software to create rentier platforms and not, say, aerospace and materials science firms. The key concept is materialism.]
Few industries are as shrouded by mystification as the tech sector, defined as that segment of the industrial and economic system whose wealth and power have been built by acting as the unavoidable foundation of all other activity, by building rentier software-based platforms, shielded by copyright, that are difficult, indeed, impossible, to circumvent (an early example is the method Microsoft used to extract, via its monopoly position in corporate desktop software, what was called the ‘Microsoft or Windows tax‘).
Consider, as a contrasting example, a paper clip company: if it was named something self-consciously clever, such as Phase Metallics, it wouldn’t take long for most of us to see through this vainglory to say: ‘calm down, you make paper clips’.
An instinctual grounding of opinion, shaped and informed by the irrefutable physicality of things like paper clips, is lacking when we assess the claims of ‘tech’ companies. The reason is because the industry has successfully obscured, with a great deal of help from the tech press and media generally, the material basis of its activities. We use computers but do not see the supply chains that enable their production as machines. We use software but are encouraged to view software developers (or ‘engineers’, or ‘coders’) as akin to wizards and not people creating instruction sets.
Computers and software development are complex artifacts and tasks but not more complex than physics or civil engineering. We admire the architects, engineers and construction workers who design and build towering structures but, even though most of us don’t understand the details, we know these achievements have a physical, material basis and face limitations imposed by nature and our ability to work within natural constraints.
The tech sector presents itself as being outside of these limitations and most people, intimidated by insider jargon, the glamour of wealth and the twin delusions of techno-determinism (which posits a technological development as inevitable) and techno-optimism (which asserts there’s no limit to what can be achieved) are unable to effectively counter the dominant narrative.
The tech industry effectively deploys a degraded form of Platonic idealism (which places greater emphasis on our ideas of the world than the actually existing structure of the world itself). This idealism prevents us from thinking clearly about the industry’s activities and its role in, and impact on, global political economy (the interrelation of economic activity with social custom, legal frameworks, government, and power relations). One of the consequences of this idealist preoccupation is that, when we’re analyzing a press account of tech activities, for example, stories about autonomous cars, instead of interrogating the assumption that driverless vehicles are possible and inevitable, we base our analysis on an idealist claim, thereby going astray and inadvertently allowing our class adversaries to define the boundaries of discussion.
The answer to this idealism, and the propaganda crafted using it, is a materialist approach to tech industry analysis.
Let’s take a quote from the Stanford Encyclopedia of Philosophy
Physicalism is, in slogan form, the thesis that everything is physical. The thesis is usually intended as a metaphysical thesis, parallel to the thesis attributed to the ancient Greek philosopher Thales, that everything is water, or the idealism of the 18th Century philosopher Berkeley, that everything is mental. The general idea is that the nature of the actual world (i.e. the universe and everything in it) conforms to a certain condition, the condition of being physical. Of course, physicalists don’t deny that the world might contain many items that at first glance don’t seem physical — items of a biological, or psychological, or moral, or social, or mathematical nature. But they insist nevertheless that at the end of the day such items are physical, or at least bear an important relation to the physical.
Stanford Encyclopedia of Philosophy – https://plato.stanford.edu/entries/physicalism/
This blog is dedicated to ruthlessly rejecting tech industry idealism in favor of tracking the hard physicality and real-world impacts of computation in all of its flavors. In this sense, the focus is materialist. Key concerns include:
This blog’s analytical foundation is unequivocally Marxist and seeks to employ Marx and Engel’s grounding of Hegelian dialectics (an ongoing project, subject to endless refinement as understanding improves):
Marx’s criticism of Hegel asserts that Hegel’s dialectics go astray by dealing with ideas, with the human mind. Hegel’s dialectic, Marx says, inappropriately concerns “the process of the human brain”; it focuses on ideas. Hegel’s thought is in fact sometimes called dialectical idealism, and Hegel himself is counted among a number of other philosophers known as the German idealists. Marx, on the contrary, believed that dialectics should deal not with the mental world of ideas but with “the material world”, the world of production and other economic activity.[19] For Marx, a contradiction can be solved by a desperate struggle to change the social world. This was a very important transformation because it allowed him to move dialectics out of the contextual subject of philosophy and into the study of social relations based on the material world.
Wikipedia “Dialectical Materialism” – https://en.wikipedia.org/wiki/Dialectical_materialism
This blog is, therefore, dedicated to finding ways to apply the Marx/Engels conceptualization of materialism to the tech industry.
When I started my technology career, almost 20 years ago, like most of my colleagues, I was an excited idealist (in both the gee whiz and philosophical senses of the term) who viewed this burgeoning industry as breaking old power structures and creating newer, freer relationships (many of us, for example, really thought Linux was going to shatter corporate power just as some today think ‘AI’ is a liberatory research program).
This was an understandable delusion, the result of youthful enthusiasm but also, the hegemonic ideas of that time. These ideas – of freedom, ‘innovation’ and creativity are still deployed today but like crumbling Roman ruins, are only a shadow of their former glory.
The loss of dreams can lead to despair, but, to paraphrase Einstein, if we look deeply into the structures of things as they are, instead of as we want them to be, instead of despair, we can feel a new type of invigoration, the falling away of childlike notions and a proper identification of enemies and friends.
A materialist approach to the tech industry removes the blinders from one’s eyes and reveals the full landscape.
[About ‘cloud’, you can also read a longer piece I wrote for Logic Magazine and an interview I gave for the Tech Won’t Save Us podcast]
The 7 Dec 2021 Amazon Web Services (or, AWS) ‘outage’ has brought the use of cloud computing generally, and the role of Amazon in the cloud computing market specifically, to the attention of a general, non-technical audience [btw, outage is in single quotes to appease the techies who’ll shout: it’s a global platform, it didn’t go down, there was a regional issue! and so on]
Outage, in the total sense, or not, the event impacted a large number of companies, many of which are global content providers such as Disney and Netflix, services such as Ring and even Amazon’s internal processes that utilize their computational infrastructure.
Before the cloud era, each of these companies might have made large investments in maintaining their own data centers to host the computers, storage and networking equipment required to host a Disney+ or HBOMAX platform. In the second decade of the 2000s (really gaining momentum around 2016) the use of at first, Amazon Web Services and then Microsoft’s Azure and Google’s Cloud Platform offered companies the ability to reduce – or even eliminate – the need to support a large technological infrastructure to fulfill the command and control functions computation provides for capitalist enterprises.
Computation, storage and database – the three building blocks of all complex platforms – are now available as a utility, consumable in a way, not entirely different from the consumption of electricity or water (an imperfect analogy since, depending on the type of cloud service used, more or less technical effort is required to tailor the utility portfolio to an organization’s needs).
What is Cloud Computing? What is it’s Political Economy? What are the Power Dynamics?
A full consideration of the technical aspects of cloud computing would make this piece go from short(ish) to a full position paper (a topic addressed in the Logic Magazine essay I mentioned at the top). So, let’s answer the ‘what’ question by referring to what’s considered the urtext within the industry: the NIST definition of cloud computing–
Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model is composed of five essential characteristics, three service models, and four deployment models.
https://csrc.nist.gov/publications/detail/sp/800-145/final
The NIST document goes on to define the foundational service types and behaviors:
To provide a nice picture which I’m happy to describe in detail if you want (hit me up on Bluesky) here’s what a cloud architecture looks like (from the AWS reference architecture library):
There are a lot of icons and technical terms in that visual which we don’t need to get into now (if you’re curious, here’s a link to the service catalog). The main takeaway is that with a cloud platform – in this case AWS but this is equally true of its competitors – it’s possible to assemble service elements into an architecture that performs a function (or many functions). Before the cloud era, this would have required ordering servers, installing them in data centers, keeping those systems cool and various other maintenance tasks that still occasionally give me nightmares from my glorious past.
Check out this picture of a data center from Wikipedia. I know these spaces very well indeed:
And to be clear, just because these reference architectures exist (and can be deployed – or, installed ) that does not mean an organization is restricted to specific designs. There’s a toolbox from which you can pull what you need, designing custom solutions.
So, perhaps now you can understand why Disney, for example, when deciding to build a content delivery platform, chose to create it using a cloud platform – which enables rapid deployment and elastic response instead of creating their own infrastructure which they’d have to manage.
Of course, this comes with a price (and I’m not just talking about cash money).
Computer Power is Power and the Concentration of that Power is Hyper Power
Now we get to the meat of the argument which I’ll bullet point for clarity:
To illustrate what I mean, consider this simple diagram which shows how computer capacity has traditionally been distributed:
Note how every company, with its own data center, is a self-contained world of computing power. The cloud era introduces this situation:
Note the common dependency on a service provider. The cloud savvy in the audience will now shout, in near unison: ‘but if organizations follow good architectural principles and distribute their workloads across regions within the same cloud provider for resiliency and fault tolerance (yes, we talk this way) there wouldn’t be an outage!’
What they’re referring to is this:
From a purely technical perspective, the possibility of minimizing (or perhaps even avoiding) service disruption by designing an application – for example, a streaming service – to come from a variety of infrastructural locations, while true, entirely misses the point…
Which is that the cloud era represents the shift of a key element of power from a broadly distributed collection of organizations to, increasingly, a few North American cloud providers.
This has broader implications which I explore in greater detail in my Logic Magazine piece.
UPDATE 11 Dec
Amazon has posted an explanation (which, in the industry is known as a root cause analysis) explaining the outage. I’ll be digging into this in detail soon.
Computational Impacts 