Benchma(R)king: Making Chairs with LLMs

Listen to this Story Narrated by James Bridle

Contributor Bios

Writer

James Bridle is a writer, artist, and technologist whose artworks have been commissioned by galleries and institutions worldwide, including the Victoria & Albert Museum, the Barbican, Artangel, the Oslo Architecture Triennale, and the Istanbul Design Biennial. James is the author of New Dark Age, which has been translated into over a dozen languages; and Ways of Being. Their writing on literature, culture, and networks has appeared in Wired, The Atlantic, The New Statesman, The Guardian, and The Financial Times, among others. For BBC Radio 4, they wrote and presented the four-part series New Ways of Seeing.

James Bridle experiments with combining their practical carpentry skills and the abundant theoretical knowledge of AI to build a chair.

IN 1840, A SURVEYOR took a chisel to one of the outer walls of St. John’s Church, on Old Haymarket in Liverpool, England. They carved a horizontal line into the stone, a couple of inches long, and below this, three radiating lines, getting wider at the base: a broad arrow. The broad arrow had been used in England since the fourteenth century, and later across Britain and its colonies, to mark government property, but the horizontal line was a relatively new addition. Into this crevice the surveyor would have placed an angle iron, or “bench,” used to support a leveling rod, from which elevations could be taken. Thus, the broad arrow and the line were known as a “benchmark.”

The benchmark on St. John’s Church, recorded in the records of the government’s Ordnance Survey, its official mapping agency, marked one hundred feet above sea level. Chiseling it into the stone meant that someone else could come along later and set up a leveling rod in exactly the same position and take their own reading, transforming it into a datum line: a reference point for further, and repeatable, surveying.

For a few years, the benchmark on St. John’s was the reference datum for the whole country, until it was realized that more accurate measurements should begin at the mean tide level. A pole was set up in the Victoria Docks, also in Liverpool, and the fluctuations of the tide observed for nine days. The resulting mark was used as the fundamental reference until the 1920s, when it was shifted again, to Newlyn, in Cornwall. Benchmarks continued to be carved into churches, civic buildings, milestones, and country walls across the country, all referencing the tidal datum at Newlyn.

“Sea level” is actually different everywhere, but in Britain, it remains a function, plus or minus, of ODN: Ordnance Datum Newlyn. This is the level measured within the town’s tidal observatory, constructed on the South Pier in 1915. The observatory takes the form of a hollow tower in which several floats are suspended, connected to a recording device. An inlet pipe allows the tower’s well to fill and empty with the Atlantic tides. Between 1915 and 2015 the mean sea level at Newlyn increased by 1.83 mm/year—and by 3.8 mm/year between 1993 and 2014. Each change in sea level is reflected in an incremental adjustment to elevations across the land.

The last official benchmark was carved on a milestone located outside a pub in Loughton, Essex, in 1993, before satellite-based systems took over the datum (today, a GPS antenna sprouts from the top of the tide tower in Newlyn). The Ordnance Survey still maintains a record of over half a million benchmarks, although many more have disappeared. Hiking clubs lead expeditions to find them, and hobbyist benchmarkers check them off their lists. When you start looking for them, you see them everywhere.

“Benchmarking” remains in use today as a term for establishing a baseline against which other data can be measured and judged: software performance, product quality, profitability, growth; quality, time, cost. But it’s worth remembering its origins not as a tool for establishing hierarchies, but for leveling; for connecting, rather than separating; and for understanding the whole earth, and everything that happens upon it, as a continuous, indivisible field, stretched across cities, forests, and mountains, and ever susceptible to the rhythm of the tides.

Ordnance Survey benchmark at St. Martin’s Church, Canterbury, UK.

A COUPLE OF YEARS AGO, I found myself stuck in an apartment in Athens, and in desperate need of distraction. I’d been living with my family on a Greek island, where we had space, and time, and had started to build a house; but at the end of 2023 my partner, Navine, became severely ill, and we had to return to the city we’d lived in for five years previously. She spent nine months in hospital; I visited every day, while also looking after our three-year-old son between nursery school and sleep and school again. I had less space, and a lot more stress, but I also had time to fill, and was itching to use my nascent carpentry skills. I needed to make something, with the limited tools and materials I had to hand—anything would do, it just needed to keep my body, and my mind, busy.

It was around this time that the current wave of what we’re calling artificial intelligence started to excite, and demand, attention. I actually studied AI at university, twenty years ago: exactly the wrong time, it turned out, to be studying it. The history of AI is one of booms and busts: “AI winters,” they were called, when the field seemed to sputter out after a series of advancements, and twenty years ago it was decidedly chilly. We learned about expert systems and neural networks from aging professors, but all the sensible students were learning about classical logic and statistics as preparation for careers in digital finance. It was another decade before the field would regain its energy, and—this is a controversial take—it would have little to do with conceptual breakthroughs. Rather, the growth of the internet, with its accompanying boom in processing capacity and data storage, would allow those same ideas—primarily neural networks—to be supercharged by graphics cards, digitized text and artworks, surveillance and social media, and the wholesale transference of everyday life to the online world. The AI “revolution,” if that’s what it is, is not a technological breakthrough, but the overwhelming accumulation of power and data in the hands of a few companies: power and data which rightfully belong to, and have been stolen from, everyone. But I digress.

I’d been noodling with this idea for some time: What if we used AI for something actually useful? It feels like we’re applying this new tool to all the wrong things: beating us at the games we enjoy, supercharging predatory capitalism, extracting and burning fossil fuels, depleting the planet’s resources, conflating images with art, encouraging extremism, suppressing rights, hallucinating, and so on and so forth. Wouldn’t it be infinitely more interesting if AI helped us do useful things in new and better ways, ways which we could not have imagined alone? Intelligences which we engaged with more like colleagues and collaborators than slaves and masters.

The one thing computers have always been really good at, one of the few qualities which genuinely impress me, is optimization. They can take a human-designed thing, an object or an algorithm, and ruthlessly strip it to the bare essentials while maintaining its functionality. Give a clever machine some widget, like a structural bracket or a complex workflow, and it will produce some alien-looking thing, a third of the weight and twice as efficient, which a human mind would not have conceived. That’s cool, that’s interesting. Of course, applied to culture, society, and the wider environment, such an idea of efficiency is a disaster, but it’s pretty neat for objects, as long as a human is keeping an eye on things.

I thought I might be able to make use of this particular skill to create something with what I had to hand: By combining my rudimentary carpentry abilities with the machine’s knack for efficiency and strangeness, something interesting might happen. Specifically, I didn’t want to design a process which would ultimately replace me, or take over the rewarding bits of work, but something more like a smart carpenter’s mate, that could do some of the sums and make useful suggestions and leave me to do the fiddling and the sanding. A collaborator, not a boss.

I measured up the various pieces of scrap wood on my balcony, left over and saved, Dad-style, from past shelving and cabinetry projects. A sheet of plywood, some pine panels, an L-shaped piece of chipboard, and half a dozen bits of dowel and beam. I had a saw, a drill, assorted screws, and some wood glue. And I gave this information to OpenAI’s ChatGPT—then running the version known as GPT-4, its second publicly accessible iteration—and asked it to design me a chair.

What if we used AI for something actually useful?

I’D MADE A FEW CHAIRS BEFORE. Chairs are interesting, and trickier than you expect. They have the simplest of functions—keep your arse up off the ground—and infinite ways of achieving it. And as anyone who’s ever sat in one knows, they fail in all kinds of ways. Cheap chairs wobble and buckle, materials snap and wear out; some chairs produce a deep agony after only minutes of sitting in them. Making one that stands up, supports an adult human, and doesn’t hurt is actually quite a feat. I’d tried my hand at making a few chairs in the past, and my score was usually two out of three.

My main instructor in chair-making, and much else, is Enzo Mari. Mari was an Italian designer who came to fame in the 1950s and ’60s, in the postwar heyday of Italian industrial design, producing thousands of pieces of homeware and furnishings from the exciting new materials of the time: injection-molded plastics, tubular metal, and mass-produced modular cardboard and plywood forms. But Mari was also a contrarian: “When I design an object and people say: ‘oh, well done!,’ I unfailingly ask myself: Where did I go wrong?” he once told a magazine writer. “If everybody likes it, it means I have confirmed the existing reality and this is precisely what I don’t want.”

This “existing reality” was a problem he ran headfirst into when, in 1971, he designed a neat little sofa bed for the highly fashionable Italian furniture company of Maddalena De Padova. Mari aimed squarely at functionality, and his sofa bed resembled a plain padded bench with a backrest which dropped down to make room for sleep. Despite, or perhaps because of, this simplicity, his design was rejected, and when another company took it on, it failed to sell. Mari blamed this failure on the fact that it cost too little: Mass production, he believed, had made people careless of the true utility of things, and “design” had been debased to the mere production of status symbols, the visible representations of consumers’ wealth and position.

To repair this lost connection with the real value of things, Mari decided, it was necessary to involve the consumer—the user of things—in the design and making of objects. To do so, it was necessary to reject most of the techniques, processes, and materials of industrial design and go back to basics: “Everybody has at least tried to hammer in a nail,” he thought, and with that skill alone it was possible to produce much of what was sold in high-end boutiques. So, with the help of a few assistants, he set to work—directly, with little or no preliminary design—to make a series of chairs, tabl