How Creative Work Becomes Deskilled (And What To Do About It)

Deskilling takes once fulfilling and meaningful work and turns it into routinized, repetitive tasks to disempower workers and normalize precarity.

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Photo by Miquel Parera on Unsplash

"Our work is made routine, is deskilled, becomes precarious and casualized."
— McKenzie Wark, Capital Is Dead: Is This Something Worse?

I love Canva. Love it.

I use Canva to create visual content to support my writing and podcasting—slide decks, social media content, videos, etc. Despite the fact that I would have once hired a graphic designer to do that work and can now do it myself, I would never call myself a graphic designer. This highlights a broader trend: Canva simplifies graphic design, enabling those without formal training to perform design tasks, which reflects the steady march of deskilling across more and more sectors of work.

Deskilling Work

Deskilling simplifies complex tasks into smaller, repetitive tasks that require few skills—a labor process critical to mass production and consumer capitalism. For instance, rather than a single carpenter building a table, management divides the project among several workers, each tasked with one small part of the process (e.g., milling, assembly, finishing, etc.). What was once a product created by a highly-skilled craftsman is now a routinized set of discrete tasks that can be done by low-skill workers on an assembly line—that's deskilling.

Deskilling has several upsides in this scenario. It increases the speed of production, reduces the cost of production, and makes the production process more legible to management. For the consumer, deskilling can lower prices and iron out variations in products and services.

Deskilling has significant downsides, too.

Management leverages deskilling as a control mechanism. Instead of one worker with extensive knowledge of the production process who can demand higher wages and more job security, management gains several workers with little knowledge of the full production process. They're easily replaceable and subject to ever-increasing work intensity. On their own, they have no leverage to ask for better working conditions or higher wages.

In this way, deskilling increases precarity and decreases job security—it gives management all of the power.

Deskilling also fosters a profound sense of alienation. A highly skilled worker who completes a complex task experiences a deeper connection to the product of their labor. They can step back from the table they've been working on with a sense of fulfillment and achievement. In contrast, a worker assigned to a single, repetitive task derives no satisfaction from the final product. They may work hard day in and day out without ever laying eyes on the end result.

It's crucial here to dispel the notion that workers in deskilled jobs are inherently 'low-skilled.' A highly skilled worker can be compelled to take on a lower-skill job when it's the only available option—a common scenario that often leaves individuals feeling underutilized, undervalued, and demoralized.

When it comes to manufacturing work, we take deskilling for granted.

We've so normalized mass production and assembly line work that they're still the dominant metaphors for work today. Over the last quarter century, companies have increasingly deskilled retail, customer service, healthcare, hospitality, and other types of human-to-human work. Consumers (and patients) begrudgingly accepted this change. We don't love the results, but more than a century of labor optimization made the shift feel inevitable.

We—the creatives, the professionals, the college-educated—might have thought our work safe from this trend. If we're self-employed or own our own businesses, we might feel more insulated still. After all, with no boss to manage our productivity to the minute and no corporate overlords to provide inane scripts or procedures, we're free to approach our work with care and creativity, right?

Unfortunately, the march towards a deskilled future continues unabated—threatening to undermine the value of our skills and the autonomy of our work.

This is the "paradox" of work in the 21st century, according to writer and researcher Amelia Horgan. While more and more types of work appear decentralized and self-managed, behind that façade is a "hidden deeper concentration of power within."

Horgan tells the story of some London subway workers who began posting inspirational quotes at their station, which passengers shared widely online. Once management noticed, they standardized (a form of deskilling) this practice, selecting quotes themselves and controlling the creative process. What was once a spontaneous creative act became a bureaucratized routine.

I think of this story often. Not only does it feel familiar from my time in retail management, but it also feels familiar to my experience every time I open Canva. Not only does Canva help me make up for my lack of skill with its ease of use and multitude of templates, but it also facilitates the speed of production required by social media distribution algorithms. Put another way, Canva helps me to meet the demands imposed by algorithmic management. The process of creating media is deskilled so that I can increase my output, therefore generating additional "profit" from my labor power for the platforms I post on.

Algorithmic management

Deskilling as part of capitalist labor management is almost as old as capitalism itself. Algorithmic management, on the other hand, is a much newer concept. Originally coined in 2015, algorithmic management encompasses the technologies and techniques used to manage workers within platform economies (e.g., the way Uber's algorithmically-driven systems can encourage drivers to concentrate in an area of high demand). Today, algorithmic management also describes the various technologies used to monitor and manage remote workers of all kinds.

Workers don't tend to fare well in algorithmic management systems. Researchers identified three common consequences of algorithmic management for workers: (1) "less task variety and skill use," (2) "reduced job autonomy," (3) "greater uncertainty and insecurity." Because algorithms are complex, opaque computational systems, workers do not and can not know whether what they input (i.e., their work) will result in the output they hope for. Workers feel out of control, insecure, and uncertain—at the whim of an algorithm.

Again, algorithmic management is most often studied as it applies to gig work. But we can also view other types of platform-mediated labor through this lens.

Communication is increasingly controlled by external forces. It seems to be guided by an automatic, mechanical process that is directed by algorithms, a process of which we are, however, unaware. We are at the mercy of the algorithmic black box. Human beings are reduced to data sets that can be controlled and exploited.
— Byung-Chul Han, The Crisis of Narration

Platforms—from Instagram to TikTok to Google Search—celebrate their recommendation algorithms as triumphs of consumer experience. They purport to fill your feed with a perfectly customized selection of content you actually want to see. Of course, that assumes the process starts with the user who asks to see types of content based on their preferences and the accounts they choose to follow. We 'train' the algorithm to show us what we want to see.

But what if the recommendation algorithm is training us?

Let's take a closer look at TikTok's For You Page.

When you create a TikTok account, you're prompted to select some broad interests for your feed. However, once you land on your For You Page, you're greeted with a continuous stream of content. If your initial preferences truly dictated its recommendations, you'd be asked for more specific details—accounts, hashtags, or even subtopics you wish to follow. Yet, TikTok seems to know us better than we know ourselves. We may 'train' the TikTok algorithm, but this training almost always begins with the content that TikTok itself provides for training.

The algorithm is programmed to manage users (what Dallas Smythe called 'audience power') by learning what will keep them on the platform longer so that they can view more ads and produce more activity. Content becomes a tool capital uses to extract 'work' from the viewer. On the flip side, the algorithm also trains and manages content creators to produce the kind of content optimized for extracting audience work.

To "make" a feed filled with content people want to see, someone has to produce that content. We call those people creators, and they (we) are responsible for a huge share of the capital generated by platform companies (i.e., we are workers). In this way, the algorithm creates management effects through the design, incentives, and features that act on creators.

Content creators, much like Uber drivers, DoorDashers, or TaskRabbit workers, find themselves under the sway of algorithmic management. Yet, there's a crucial difference. The drivers, Dashers, and Taskers are acutely aware that they're responding directly to algorithmically generated gigs.

In contrast, creators often believe they're acting with autonomy and agency and that the media they create is self-directed. They understand the need to please the algorithm for views, but the platform's rhetoric masks this reality, framing their actions as entrepreneurial choices. The algorithm isn't managing us, we're choosing to create what the algorithm wants in the pursuit of results and growth. This struggle, often unnoticed, is a constant battle for creators.

Algorithms compute. Someone inputs data, the algorithm runs it through a series of complex computations, and then it outputs some result. What this means for the algorithmic management of creators is that producing a work that contains the right data is key to getting the result they want. However, this focus on 'right data' often comes at the expense of creativity and craft. Because producing a work with the right data doesn't often mean producing something remarkable, original, or useful, we start to lose our knack for the remarkable, original, and useful.

To produce work with the right data at the frequency preferred by platform companies, we turn to deskilling ourselves.

We might call our images, videos, or writing content, but it's often contentless—all the better to appear legible to the code computing its fate.

Just as algorithmic management works on both creators and consumers, deskilling impacts consumers at the same time it impacts creators. Instead of engaging with complex media that requires a high level of cultural skill (even if it's 'just' laughing at a finely crafted joke), our task is simplified, made repetitive.

We're presented with a constant stream of algorithmically managed content, often lacking depth or originality, to momentarily "engage" with before the next one comes down the assembly line. This simplified, repetitive consumption can leave us feeling unsatisfied and craving something more meaningful.

What to do about deskilling

I believe we have a cultural, social, personal, and ethical responsibility to resist deskilling and algorithmic management as workers and consumers. What does that look like? I have two recommendations (for now).

Use open distribution systems whenever possible

If you want to view an Instagram post, you go to Instagram. If you want to see a LinkedIn post, you go to LinkedIn. These are closed distribution systems—and they're the hallmark of social media platforms. But closed distribution isn't the only way to distribute media.

Podcasts are distributed in an open system. They're built on (what feels like) an ancient technology: RSS. Each podcast has its own URL where a file with its RSS code lives. Podcast apps only act as a convenient interface for that code. I upload my MP3 file once and hit publish. My episode is now available "anywhere you get podcasts," which as Anil Dash recently pointed out, is a radical statement in the world of walled garden social media platforms.

Email is also an open distribution system. When I hit send on a new essay, I know that it'll land in approximately 10,000 inboxes. Email, more than podcasts, is subject to some algorithmic control (e.g., Gmail's auto-sorting of promotional mail or Substack's ability to route your subscriptions to your inbox on the app). But it's still radically open relative to Instagram or TikTok.

I'll also note that the emerging "fediverse" promises to bring open distribution to social media systems. And Bluesky allows (and is continuing to develop) users to create their own algorithmic curation systems.

By using an open distribution system, creators can focus on quality over quantity and consumer input over algorithm-mediated input while reducing the stress over whether their communication will actually reach the people who want to see it.

Upskill yourself

As I mentioned at the very top, I love Canva. And while Canva does have deskilling effects, it can also have upskilling effects. Using Canva challenges me to communicate more clearly, produce more engaging content, and translate my ideas for a variety of audiences. The “trick” I’ve learned when it comes to continually upskilling my approach to Canva is that I have to solve for craft and quality rather than quantity.

Technology, including automation and artificial intelligence, can be a real benefit to creative workers. Technology becomes a problem when we’re unaware of its logic and incentives. We get swept up in capital’s interests rather than embracing our own standards and goals.

It’s worth returning to one of my favorite questions: Do I have what I need to do this well?

The technology we use and the economic system we live in exert pressure on us to produce more. Upskilling—or simply adopting a personal policy to challenge yourself—is a way we can resist.

I have a working theory that underutilization—not having a way to exercise our full skill set—is a defining characteristic of the worker experience in the 21st-century economy. Deskilling is a prime driver of this. By turning our focus, whenever possible, to quality, craft, and creative expression, we can reclaim our potential and put it to good use.

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New Workshop: World-Building for Business Owners

When: May 15 from 1-3:30 pm EDT (New York City) and May 16 from 1-4 pm EDT
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Where: Crowdcast
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Price: $150-$400

Many small businesses result from combining disparate tactics, structures, and procedures. But a more effective and rewarding business design is one in which all of its parts make sense together—it’s a unified system.

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