Research

Dispatch Studios maps the UK cultural production system using Donella Meadows' framework for understanding how complex systems generate patterns of behaviour. The research is designed to develop over time, deepening as evidence accumulates and testimony builds.

The research question: How is the UK Cultural Production System currently structured, what patterns of behaviour is it producing under platform-era and AI-accelerated conditions, and what structural changes could lead to different system behaviour for creative depth, cultural coherence, worker wellbeing, and public trust?

Put simply, how is this system structured, what behaviour is it producing, and where might change be possible?

Why "cultural production system"

"Creative industries" is a policy and economic construct. It was designed in 1998 when the Department for Culture, Media and Sport published the Creative Industries Mapping Document, reframing "the arts" as economic assets for the purposes of measurement, funding and strategy. That framework is useful for what it does. It centres economics over process and limits the questions that can be asked.

"Cultural production system" describes the machinery through which meaning gets made, mediated and circulated. It centres how culture is produced, under what conditions, by whom, and what happens to those people in the process. This framing allows questions the policy lens cannot ask. Questions about depth, creative conditions and what's being lost when the system optimises for speed and volume. About whether the measurement framework itself is part of the problem.

The core observation

The system is producing thinner, faster, more homogenised cultural output while depleting the people and capacities that could produce depth, and AI is intensifying this.

This is a structural observation, not a claim about individual failures. The model exists to explain why this behaviour occurs and where leverage for change might exist. Evidence may confirm, complicate, or contradict elements of this observation. The model evolves accordingly.

What the model maps

The research identifies six subsystems, each playing a distinct role in producing the observed behaviour:

• Culture (the raw material): where meaning, lived experience, and subcultural energy originate. Both the source and the destination of cultural output. The investigation generates cultural insights from this subsystem: understanding how meaning is being made and where subcultural energy is being sustained or extracted.
• Media (the translation layer): turns lived experience into stories, images, narratives. Decides what becomes visible and how it's framed. Gatekeeping has shifted from editorial to algorithmic, and advertising and marketing professionals have joined the media ecosystem as new gatekeepers.
• Work (the engine room): where cultural meaning becomes actual output. Where creative labour happens and human capacity is built or depleted.
• Infrastructure (the rules and constraints): sets rules, incentives, and constraints. Where power concentrates. Physical spaces, funding flows, measurement systems, and platform dynamics all sit here.
• Education (the pipeline): shapes who enters the system and with what capacities. Where beliefs about what "good work" means are transmitted.
• AI (the accelerant): not a discrete subsystem but a catalytic layer running through all others. Compresses timelines, enables volume, homogenises through pattern-matching, reshapes what creative work means.

The model also maps edge conditions. The external forces shaping what's possible inside the system. Global AI development, macroeconomic pressure, international regulation, global cultural flows, geopolitical shocks, ecological constraints. These sit outside the system boundary but influence everything within it. They explain why some pressures intensify regardless of domestic policy, and they open editorial tracks into how global dynamics land on UK creative workers.

AI gets its own position in the model because it is the current accelerant. The pressures on creative work predate AI by decades.

The attention economy's extraction logic goes back over 150 years. AI is intensifying all of it at once: collapsing the distance between idea and output, enabling volume without proportional effort, homogenising through pattern-matching, reshaping how people understand their own role and value. Understanding what AI is doing to the system means understanding what the system was already doing. The model identifies two paths: automation, where AI replaces human work, and augmentation, where AI expands human capacity. A third dimension is emerging from the research: what tools actually get built and whether they serve the people doing creative work or the priorities of the companies building them.

Critically, the model maps the distance between stated goals and operational goals. The Sector Plan talks about meaningful culture and creative excellence. What gets measured is volume, speed, and economic contribution. That gap is where much of the pressure people feel actually originates.

The research identifies paradigms, the deep assumptions holding the system in place. "Faster is better." "Creativity is infinite." "The individual is responsible for systemic failure." And it maps leverage points: places where intervention might shift system behaviour rather than being absorbed by it. These are the foundations for understanding where change is actually possible.

The model is an editorial framework. It provides the research spine for all content, interviews and documentary work. The questions it generates shape what stories we tell and how we tell them.

For the full methodology and how the lens shapes our editorial work, take a look at our Editorial Approach page.

What the evidence is showing

The research draws from government sources, academic research, industry data, surveys, and testimony. The strength of the evidence is that multiple independent sources, produced by different bodies for different purposes, converge on the same picture.

In July 2025, Skills England published a sector assessment showing that creative industries employers are reporting shortages in "creative and innovative thinking" itself.

The system designed to produce creativity cannot find people who can think creatively. The finding requires context: creative industries have lower overall skills gap rates than other sectors, and the data comes from employer perceptions. Even so, the signal is significant. And Skills England's own data deepens the puzzle: 69% of the creative workforce holds degree-level qualifications, compared to 44% nationally. The most credentialed sector still reports skills gaps. Something is happening after people enter the workforce that depletes the capacity their qualifications should represent.

Creativity research helps explain what. Teresa Amabile's work identifies four conditions that creative thinking requires: time for incubation, freedom from surveillance, intrinsic motivation, and psychological safety. The evidence shows all four are being eroded. Speed pressure eliminates incubation time. Metrics and platforms create constant surveillance. Financial precarity undermines intrinsic motivation. Job insecurity destroys psychological safety.

Creative PEC's Good Work Review, after consulting over 120 organisations, found that no creative sub-sector performed well across all 40 measures of job quality.

• Between 23,000 and 35,000 older workers have dropped out of film and TV mid-career.
• Only one in ten freelancers receives any training.
• 65% of freelancers report difficulty finding work while employers simultaneously report skills shortages.
• 28% of freelancers cannot stay in the sector without a partner's steady income. Surplus and shortage coexist.

This is a structural mismatch between what the sector needs and the conditions it offers.

The infrastructure is contracting. 125 grassroots music venues closed in 2023, representing 16% of the UK's grassroots venues. The UK touring circuit has shrunk: artists playing 11 shows on average in 2024 compared to 22 in 1994. 85% of creative industries venture capital in 2023 went to IT, software and computer services. Film, music, and visual arts shared the remaining 15%. The equity finance gap across the cultural core may be as high as £1.4 billion.

The government states that "human creativity will be more valuable than ever."

The Creative Industries Sector Plan's monitoring framework tracks GVA, productivity, business investment, R&D, real wages, exports and skills training. All quantitative. All "increase." There are no metrics for depth, quality, the conditions required for creative thinking, or the relationship between human and synthetic output. Donella Meadows' leverage points framework explains why this matters: everything in the monitoring framework is optimised for economic growth. There is nothing designed to grow creative capacity or protect the conditions that produce it.

The Good Work Review recommended a freelance champion in 2023. The Sector Plan promised one in June 2025. As of February 2026, one has still not been appointed.

These findings come from different parts of the system, measure different things, and describe different dynamics. They point in the same direction. The conditions that enable creative work are eroding across the workforce, the physical spaces where practice happens, the education pipeline, and the funding structures that sustain production.

The research also generates intelligence about what AI tools the creative industries actually need. Government policy frames "Createch" as the sector's future, but the current approach is technology-led rather than informed by the people who do the work. The structural understanding of how creative work happens, what conditions it requires, and where pressures are most acute generates insight into what tools should be built, how they should function, and what problems they should solve.

How the research develops

The model is a hypothesis being tested. It develops through four activities:

• Evidence synthesis from government sources, academic research, and industry data, mapping findings to specific model elements.
• Interviews and testimony that develop the model through lived experience. People hold knowledge about how the system actually works that no document can capture. The model provides structure for conversation. The conversations deepen the model.
• Surveys that reach people across the creative industries at scale, generating data about conditions, pressures, and experiences that feed the map and the archive.
• Editorial output that tests the framework publicly, inviting challenge and contributing to wider discourse.