Johannes Klingebiel

@klingebeil.bsky.social

1076 Followers 563 Following

Designer and researcher. STS & media stuff. Researching Hype. Doing mischief @media-lab.de. Forward deployed zine maker. Optimist aus Notwehr. ✨ 🌐 johannesklingebiel.de

But there is a simple way of exposing the fatuity of these generalisations. Suppose a manager announces one morning that there will be a 50% (or 10%, or even 1%) pay raise all round. Can we expect a cycle of denial- defence-discarding-adaptationinternalisation? Isn't it more likely that, other things being equal, people will be pleased and acceptant of the change?
The point is an obvious one: by and large people resist change because the change is damaging to them. And damaging not for psychological reasons of fear and uncertainty, but for quite straightforward reasons.
It is not just that resistance to change may be well-founded in terms of its effects on those affected; it may also reflect the ill-thought out nature of the change itself. To see resistance as a defensive response to necessary change is to indulge in a fantasy of managerial omnicompetence. Yet, if the problem is often one of in-adequate implementation, isn't it possible that some of the resisters knew what they were talking about, and were not simply defending their own economic and psychological interests?

Notion surveyed more than 6,000 AI Decision Makers and everyday AI Users globally to cut through the noise and map the real state of AI transformation—from early experimentation to AI-native operations.

The findings may surprise you: 88% of organizations are still in the early stages. Leaders are twice as confident as the workers using AI day-to-day. And the gap between investing in AI and being ready to deploy it well it is widening, not narrowing, as companies advance.

They advocate a work culture in which you always have too many interesting things to do. Hyper-productivity, living in the fast lane, is valorized as success. Rest is merely a means to excel at the office.
(....)
High-tech workers devote extremely long hours to achieve peak performance, but also make a great show of working long hours to demonstrate dedication to work. In this culture, work defines worth and is based on producing technology and embracing a fast pace and open attitude.
Daily life is colonized by work concerns and is saturated with technology. The sheer quantity of communications like email means that devices need frequent attention.
Determining the timing of access becomes a major part of the working day.

TOGETHER, WE MOURN DESIGN- KILLED, ACCORDING To X, APPROXIMATELY 847 TIMES THIS YEAR ALONE.
AND YET. NO MATTER HOW MANY TIMES WE DECLARE DESIGN IS DEAD, IT IS ALIVE.
DESIGN IS EVERYWHERE, QUIETLY THRIVING IN THE CLARITY OF THE SUBWAY MAP WHEN YOU FEAR YOU HAVE MISSED YOUR STOP, IN THE LEGIBILITY OF THE DINNER MENU EVEN WHEN YOU AREN'T SURE IF SORREL IS A VEGETABLE OR A NEW PASTA SHAPE, AND IN THE DOPAMINE-RELEASING
NOTIFICATION THAT A MEETING HAS BEEN CANGELLED. DESIGN DOESN'T DIE, IT IS NOT A TREND TO BE DISRUPTED OR A TECHNOLOGY TO BE REPLACED. IT'S THE DIFFERENCE BETWEEN CARING ABOUT HOW SOMETHING MAKES YOU FEEL, NOT JUST IF IT WORKS.
SO HERE'S TO DESIGN - ENDLESSLY DECLARED GONE, ENDLESSLY PROVING EVERYONE WRONG.

Facts might not be a great bet in a news ecosystem increasingly mediated by AI either, Daudens said, as tech companies could get them by striking a deal with a single newswire.

This thought was echoed by Fang: there’s so much free information online such as research and press releases that AI companies could draw from, and that, for some, may be indistinguishable from journalism by news organisations.

Nikita Roy suggested we are at a turning point that, if missed, could spell disaster for the news industry. “We are in our Nokia and Kodak moment, where we are looking at a new product but thinking with the metrics of an old product,” she warned. Instead of worrying about how to recover lost web traffic and otherwise defend the status quo, we need to leave behind old assumptions.

AI is disrupting something more fundamental, Roy said: how information moves, how value is captured, what it means to be a media company.

Economists, I've found, are quite skeptical that mass joblessness is on the horizon. In "What Will Be Scarce?," Alex Imas, an economist at the University of Chicago, tries to clarify the mistake most A.I. discourse, in his view, makes. "The answer to any question about the future economics of advanced A.I. begins with identifying what becomes scarce," Imas writes.
For most of human history, calories were scarce. Our energy went into finding or growing food. Agriculture steadily made food more plentifur and goods became scarce. Then goods were scarce; hand-me-down clothes were common and tools were expensive. Innovations in technology and manufacturing made goods cheaper. Then, technical knowledge became scarce: Doctors, lawyers and software engineers are paid high salaries because of the rarity of what they know. The fear is that A.I. will make knowledge plentiful; that it will turn the fruits of learning into a commodity as surely as manufacturing turned clothing into a commodity and industrial agriculture made strawberries commonplace.

The Massachusetts General Court required in 1776 that in each community a person be appointed to collect rags. To conserve paper, it remained until 1818 a punishable offense in England to print newssheets or broadsides larger than 22 by 32 inches (roughly the current size of the New York Times). The search for a reliable, less expensive alternative to rag scrap as the raw material for paper had been unsuccessful - though not for want of effort. Several materials were tried experimentally, including hemp, pine cones, used shingles, potatoes, and asbestos. A particularly imaginative source for rag itself was found in the mid19th century in Egyptian mummies. Shiploads of mummies were sent from Egypt to paper companies in the United States, where their linen wrappings were taken off and recycled, so to speak, into paper. This continued for some time, apparently without the intervention of concerned health officials, outraged clergy or jealous archaeologists. The only competition the papermakers had for the mummies was from the new Egyptian railroad, which, it seems, used them as fueL

The hype literacy toolkit for journalists
What is hype?
Hype is characterized by the fascination with the future: Exaggerated and unrealistic promises of value development are coupled with a strident optimism that captures the attention of investors, technologists, politicians, journalists, and the general public.
What hype does:
Hype often feels like a natural force surrounding technological development, but it is not. Hype is made. Powerful actors generate promises to attract resources, prestige, shape agendas, and position themselves as stewards of technological futures, foreclosing alternatives and silencing dissenting voices.
Why do journalists need hype literacy?
Journalists play a central role in deciding which stories gain traction, whose voices appear authoritative, and which visions of the future become publicly plausible. Hype literacy is about strengthening democratic accountability. It helps societies understand how the future is negotiated and whose interests dominate that negotiation.

Heath is part of a growing contingent of tech reporters using Al to help write and edit their stories. The Al workflow is especially enticing for reporters who have gone independent, losing valuable resources like editors and fact-checkers that typically come with a traditional newsroom. Rather than just prompting ChatGPT to write stories, independent journalists say they are re-creating these resources with Al.

With the appearance of English-language compilers in the late 1950s, this vision expanded to include nontechnical business users. Stephanie Shirley, who started a contract programming company in the early 1960s (see chapter 4), later recalled,“When COBOL was introduced, we thought that would be the end of the company, that nobody would be buying software anymore—programming—because it was just so easy.”39 Computer manufacturers eagerly promised customers that they would soon be able to remove their programming staff from the payroll. A Remington Rand ad from 1955 crowed, “Now . . . Univac Tells Itself What to Do!” adding that “Univac now automatically produces complex coded routines when given a simple instruction” (figure 3.3).40 The accompanying image of an executive triumphantly tearing up a sheaf of coding sheets seems to be a thinly veiled metaphor for firing the programmers who would have used them.

In our in-progress research, we discovered that AI tools didn't reduce work, they consistently intensified it. In an eight- month study of how generative AI changed work habits at a U.S.-based technology company with about 200 employees, we found that employees worked at a faster pace, took on a broader scope of tasks, and extended work into more hours of the day, often without being asked to do so. Importantly, the company did not mandate AI use (though it did offer enterprise subscriptions to commercially available AI tools). On their own initiative workers did more because AI made "doing more" feel possible, accessible, and in many cases intrinsically rewarding.