Tag: automation

Keep the tension: designing with generative AI

Slides from my keynote at the CLICKNL Design Innovation Sessions, Digital Futures theme session, Dutch Design Week, 21 October 2026, Natlab, Eindhoven.

CLICKNL invited me to give this keynote. I saw it as a great opportunity to intervene in the debate about design and generative AI. My take is informed by my past work on contestability. That work is all about approaching “AI” as an arena where various parties compete over how it is made and how it is used. My take is also informed by my commitments to practice theory and political realism. Practice theory means looking at what people and things do together, rather than at technologies in isolation. Political realism means looking at politics as it actually happens. It asks who does what to whom, for whose benefit, instead of starting from abstract moral principles.

In the talk I argue that neither wholesale rejection nor uncritical embrace of generative AI will get us very far. GenAI deskills designers and opens up new ways of designing at the same time. It is built on the past work of designers, which raises the question of whose work we build on, and who gets to call the results their own. And it automates work, but in the process work may get delegated more than replaced, transforming practices as a result. Rather than trying to resolve these tensions, I look for emerging design practices that hold them and use them as an engine for moving forward. I discuss three cases: Vera van der Burg’s Text-to-Clay, Blaze Type’s test of Monotype’s AI type tool, and The Call by Holly Herndon and Mat Dryhurst. They all go beyond consuming off-the-shelf models. They treat pipelines and workflows as an object of design. And in all three there is an awareness of the value of effort and friction. I end with an open question. If AI is not one thing, not an agent, and neither our savior nor our doom, what metaphor does your mind go to?

I hope this talk helps designers think in concrete terms about how we might “do AI” differently from the dominant ways. And I hope it helps them break free from the simplistic for-or-against debates that get us nowhere.

CLICKNL DDW 2026 DIS Digital Futures keynote slides – Kars Alfrink – Keep the tensionDownload

Works discussed

Studio Dumbar

From Text-to-Clay, Vera van der Burg

Blaze Type and Monotype’s AI type tool

The Call, Holly Herndon and Mat Dryhurst

CRTA visual identity, Bruketa&Žinić&Grey

Mud to Models and Soil to Servers workshops

  • Fieke Jansen, Sunjoo Lee, Kars Alfrink and Lena Trotereau, From Mud to Models: Regenerative Futures for Community AI (conference workshop), TH/NGS 2025, Amsterdam, 12 Dec 2025, and From Soil to Servers: Regenerative Futures for Community Computing (workshop), Creative Coding Utrecht, Utrecht, 27 Mar 2026; Kars Alfrink and Sunjoo Lee, with Advaita Jairam, From Soil to Servers: Pairing Material Practice with Speculative Design to Rethink Computing Infrastructure (conference workshop), Philosophy of Human-Technology Relations Conference (PHTR 2026), The Hague, 22 Sep 2026.
  • TU Delft, “From Mud to Models: A Workshop on Regenerative AI Futures”, 17 Dec 2025. https://www.tudelft.nl/2025/io/december/from-mud-to-models-a-workshop-on-regenerative-ai-futures
  • Kars Alfrink, Sunjoo Lee, Lena Trotereau and Fieke Jansen, “Mud and models: Does making matter for regenerative AI speculation?”, in Iskander Smit and Andrea Krajewski (eds.), The State of Responsible Technology – RESIZE<REMIX<REGEN (RIOT 2026), Stichting ThingsCon Amsterdam, June 2026, pp. 34–39. https://doi.org/10.5281/zenodo.20569440

Further reading

  • Alfrink, K. (2025). People’s compute: Design and the politics of AI infrastructures. OSF. https://doi.org/10/q8kp
  • Caplan, L. (2025). Introduction: Contesting models – art and democracy since the 1990s. In L. Caplan & K. Greaves (Eds.), Model collapse: European contemporary art in a time of democratic crisis (pp. 1–20). Manchester University Press. http://www.jstor.org/stable/jj.36233585.6
  • Geuss, R. (2008). Philosophy and real politics. Princeton University Press.
  • Kuutti, K., & Bannon, L. J. (2014). The turn to practice in HCI: Towards a research agenda. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 3543–3552). ACM. https://doi.org/10.1145/2556288.2557111
  • Löwgren, J., & Stolterman, E. (2004). Thoughtful interaction design: A design perspective on information technology. MIT Press.
  • McGowan, T. (2016). Capitalism and desire: The psychic cost of free markets. Columbia University Press.
  • McGowan, T. (2019). Emancipation after Hegel: Achieving a contradictory revolution. Columbia University Press.
  • Morozov, E. (2024, August). AI and the techno-utopian path not taken. Le Monde diplomatique (English edition). https://mondediplo.com/2024/08/07ai-cold-war
  • Parsons, P. C. (2026). Myths and ironies of AI-assisted design. In Designing Interactive Systems Conference (DIS ’26) (pp. 523–535). ACM. https://doi.org/10.1145/3800645.3813086
  • Porfirio, D., McDermott, I., Chen, H.-M., Satake, S., Kanda, T., & LaToza, T. D. (2026). Designing for robot wranglers: A synthesis of literature and practice. In Designing Interactive Systems Conference (DIS ’26) (pp. 4284–4298). ACM. https://doi.org/10.1145/3800645.3813070
  • Retkowsky, J., Hafermalz, E., & Huysman, M. (2026). Harnessing a “spirited technology”: How working with generative AI collapses the creative process. Academy of Management Discoveries, 12(3), 363–387. https://doi.org/10.5465/amd.2024.0280
  • Retkowsky, J., Hafermalz, E., Huysman, M., & Odijk, D. (2026, September 16). Research: Gen AI is collapsing creative processes. Harvard Business Review. https://hbr.org/2026/09/research-gen-ai-is-collapsing-creative-processes
  • Salvaggio, E. (2026, August 16). The system from nowhere. Cybernetic Forests. https://mail.cyberneticforests.com/the-system-from-nowhere/
  • Sayers, S. (2011). Alienation as a critical concept. International Critical Thought, 1(3), 287–304. https://doi.org/10.1080/21598282.2011.609265
  • Shumailov, I., Shumaylov, Z., Zhao, Y., Gal, Y., Papernot, N., & Anderson, R. (2024). The curse of recursion: Training on generated data makes models forget. arXiv. https://arxiv.org/abs/2305.17493
  • Stilgoe, J., Owen, R., & Macnaghten, P. (2013). Developing a framework for responsible innovation. Research Policy, 42(9), 1568–1580. https://www.sciencedirect.com/science/article/pii/S0048733313000930
  • Suchman, L. (2023). The uncontroversial ‘thingness’ of AI. Big Data & Society, 10(2), 1–5. https://doi.org/10.1177/20539517231206794
  • Unger, G., & Narayanan, A. (2026, October 14). Steering AI beyond automation: A research agenda and plan for action. AI as Normal Technology. https://www.normaltech.ai/p/steering-ai-beyond-automation
  • van Maanen, G. (2022). AI ethics, ethics washing, and the need to politicize data ethics. Digital Society, 1(2), Article 9. https://doi.org/10.1007/s44206-022-00013-3
  • Wright, E. O. (2010). Envisioning real utopias. Verso.

High-skill robots, low-skill workers

Some notes on what I think I understand about technology and inequality.

Let’s start with an obvious big question: is technology destroying jobs faster than they can be replaced? On the long term the evidence isn’t strong. Humans always appear to invent new things to do. There is no reason this time around should be any different.

But in the short term technology has contributed to an evaporation of mid-skilled jobs. Parts of these jobs are automated entirely, parts can be done by fewer people because of higher productivity gained from tech.

While productivity continues to grow, jobs are lagging behind. The year 2000 appears to have been a turning point. “Something” happened around that time. But no-one knows exactly what.

My hunch is that we’ve seen an emergence of a new class of pseudo-monopolies. Oligopolies. And this is compounded by a ‘winner takes all’ dynamic that technology seems to produce.

Others have pointed to globalisation but although this might be a contributing factor, the evidence does not support the idea that it is the major cause.

So what are we left with?

Historically, looking at previous technological upsets, it appears education makes a big difference. People negatively affected by technological progress should have access to good education so that they have options. In the US the access to high quality education is not equally divided.

Apparently family income is associated with educational achievement. So if your family is rich, you are more likely to become a high skilled individual. And high skilled individuals are privileged by the tech economy.

And if Piketty’s is right, we are approaching a reality in which money made from wealth rises faster than wages. So there is a feedback loop in place which only exacerbates the situation.

One more bullet: If you think trickle-down economics, increasing the size of the pie will help, you might be mistaken. It appears social mobility is helped more by decreasing inequality in the distribution of income growth.

So some preliminary conclusions: a progressive tax on wealth won’t solve the issue. The education system will require reform, too.

I think this is the central irony of the whole situation: we are working hard to teach machines how to learn. But we are neglecting to improve how people learn.