Posts Tagged ‘AI’
“Common sense is not so common”*…
The Enlightenment is under attack by the Left and the Right. It can only be “saved,” Eliane Glaser argues, through use of its greatest legacy: permanent critique. And then there’s AI. After summarizing the critiques from both sides, she continues…
In consequence of this pincer-movement attack, the Enlightenment’s legacy is existentially vulnerable. It makes me deeply worried as someone whose entire career has been built on trying to understand and analyse the world around me – especially a world that still tries to confine thinking women to the realms of emotion and ‘personal experience’.
I believe that Enlightenment values are essential, but that we have largely forgotten how to make a good case for them: we need to rely on shared facts, tested by experiment; a public sphere where open discussion can take place; and the belief that discussion should be founded on reasoned argument. We need, moreover, to cherish the more political values of tolerance, freedom, human rights and the common good. Advocates for artificial intelligence have the temerity to claim that large language models are ushering in a ‘second Enlightenment’ (a claim that was uncritically echoed in a paper published by the World Economic Forum last year) when what we are in fact seeing is the destruction of the Enlightenment legacy under the false banner of its name. As the historian David Bell argued in The New York Times in 2025, AI is actually ‘shedding Enlightenment values’ by simply reinforcing ‘what we already think we know.’ In The Guardian,the journalist and geopolitical risk consultant Joseph de Weck warned that ‘AI is taking us back to the dark ages’, making us lazy, and stymying independent thinking.
The evidence suggests that we are going through a rapid de-enlightenment. Newspaper circulations, attention spans, and trust in forms of agreed knowledge are in freefall. Misinformation, disinformation and deepfakes are gaining ground. If we let go of the valuable aspects of the Enlightenment project, we open ourselves up to a world of AI blather, ‘my truth’ pronouncements, wobbly sentiment and unchecked power.
My unease with this parlous state of affairs has provoked me to go back and rethink the Enlightenment and what it has to offer. But, rather than unthinkingly recouping it as a mission, I want instead to tease out and weigh up its merits, to discern with nuance what is still fit for our times. I want to ask if it is possible to rescue the Enlightenment’s rallying power, and if it’s worth defending what the combined forces of Left and Right are coming together to attack. Are the Enlightenment’s deficiencies barnacles on an old ship, or integral to its design?…
And so she does. Do read on: “Flickering Enlightenment,” from @elianeglaser.bsky.social in @aeon.co.
* Voltaire
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As we reclaim reason, we might spare a thought for a glorious product of the Enlightenment, Joseph Haydn; he died on this date in 1809. A composer of the Classical period, he was pivotal in the evolution of chamber music forms like the string quartet and piano trio, and is known as the “Father” of both the symphony and sonata forms. Haydn was a friend and mentor of Mozart, and a teacher of Beethoven; indeed, the Haydn, Mozart and Beethoven trio are sometimes referred to as the “First Viennese School.”
Schonberg wrote that Haydn “was the Classic performer par excellence, and in his long life, from 1732 to 1809, he grew up with the new musical ideas and, more than any one man, shaped them.”
“Gambling is a tax on ignorance”*…
And as Einstein observed, “two things are infinite: the universe and human stupidity; and I’m not sure about the universe.”
Gambling– and related specualtive investments– have always been, for the vast majority of punters, a sucker’s bet. But, as Paul Kedrosky explains, the growing prevalence of AI and the emergence of prediction markets have amplified that painful reality…
The return skew in prediction markets’ returns is startling. It is partly a function of their nature, but also of vibe-coding script kiddies attacking every market anomaly as quickly as it arises. Check a recent WSJ article for examples.
The same dynamic is now spreading across retail-dominated markets. A driver is how AI lowers the cost of systematic exploitation and exploration to near zero. What used to require infrastructure, data pipelines, and bearded quants is now accessible via off-the-shelf models, APIs, and loosely stitched “agent” workflows doing … stuff that even their users don’t fully understand.
The result isn’t democratization of returns. It is wider participation, of a sort, alongside the rapid re-concentration of profits. A small subset of users—those willing to iterate fastest, monitor continuously, and deploy capital programmatically—capture gains, with everyone else just liquidity.
They scrape sentiment, parse new information, and reprice positions in seconds, compressing the half-life of mispricings. That doesn’t eliminate inefficiency, but changes who harvests it. The edge shifts from insight to speed, coverage, and execution discipline—areas where even modest automation compounds quickly, and edges disappear overnight.
Prediction markets are simply the cleanest expression of this trend because they combine thin liquidity, discrete outcomes, and high retail participation. But the same pattern is visible in options flow, single-stock volatility events, and even online poker, which AI increasingly dominates.
As AI tools continue to scale, expect this to get worse: a small cohort running semi-automated strategies extracting semi-consistent edge, and a much larger base supplying them returns. Under the pressure of AI prevalance, markets don’t flatten, the return gradient steepens to a cliff…
Fewer and fewer winners take more and more of the pot. The mechanics of concentration: “AI is Eating Markets” from @paulkedrosky.com.
* Warren Buffett
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As we contemplate concentration, we might note that today is Mother’s Day. As noted yesterday, the observance became official on that date in 1914. But the quest to honor moms began a good bit earlier. On this date in 1908, Anna Jarvis held a memorial for her mother at St. Andrew’s Methodist Church in Grafton, West Virginia, the location of the International Mother’s Day Shrine. But her quest to create Mother’s Day had begun three years earlier when her mother Ann, a lifelong activist, died.
Ann had tried to start a “Mother’s Remembrance Day” in the mid-19th century. On her passing, Anna enlisted the support of retailer extraordinaire John Wanamaker, who knew a merchandising opportunity when he saw one, and who hosted the first Mother’s Day ceremonies in his Philadelphia emporium’s auditorium. In 1912, Anna trademarked the phrases “second Sunday in May” and “Mother’s Day”, and created the Mother’s Day International Association. By 1914, she and Wanamaker had built sufficient support in Congress to score the Congressional Resolution noted yesterday. (President Wilson, who was by current accounts uninterested in the move– distracted as he was by the beginnings of his ultimately unsuccessful effort to keep the U.S. out of the troubles in Europe that became World War I– nonetheless knew better than to take a stand against moms.)
“Everything is ephemeral, both that which remembers and that which is remembered”*…
Sally O’Reilly on “gray literature,” why it fascinates her… and how an early “AI” attempt to harvest it misses the mark…
In my modest collection of gray literature, the specialist title that comes closest to a blockbuster is Jean Aspin’s Vaginal Examination: A Unique Pocket Guide (ca. 1980s). Or perhaps it’s Dovea Genetics’s Beef Directory (2014).
Aspin was a community midwife in Luton and Dunstable University Hospital’s maternity wing. Her pocket guide is a well-produced, ring-bound, wipe-clean, tongue-shaped booklet, published by the baby milk company Cow & Gate. Its Latinate lists, labeled diagrams, and die-cut holes of increasing diameters, representing vaginal dilation, step a midwife through the assessment of fetal skull position during labor. The Beef Directory promises “Rock Solid Beef Genetics.” It peddles not anonymous meat but the sperm of individual bulls with names that sound like variety acts: Tonroe Lord Ian! Utile Ben! Virginia Andy! Vagabond! Mornity Handyman! Pinocchio! Seaview Tommy! Atok Socrates! Kilowatt D’Ochain! Immense D’Yvoir! It is richly illustrated, suitably glossy, and a chilling ode to muscle. (Behold the bulging rumps of Belgian Blues!)
Among the most niche in my collection of niche titles is the UK Ministry of Defence’s Corrosion: R.A.F. and A.A.C. Aircraft (1966), a bone-dry primer on the control, rectification, and treatment of nine types of corrosion. The Kent County Constabulary’s booklet Special Constabulary Inter-Divisional Competition (1971) is possibly the least read of all. Copied from typewritten documents, with hand-drawn diagrams, and stapled between two pieces of medium-weight red card, the booklet was produced “to enable officials and spectators to follow the progress of the Competition and the fortunes of the teams” during a public event at a Kent police station.3 Fun-seekers watched on as teams, comprising police officers from different divisions within the county, underwent an inspection of uniforms and accoutrements, competed in a quiz, and responded to a hypothetical incident at a demonstration involving a vicar, an unconscious policeman, a drug-addled youth, and an old man with a loaded shotgun.
Gray literature is a diffuse genre. Informational at base, its tone might tend toward bouncy sales patter or flinty authoritativeness. Visually, it ranges between perfunctory pragmatics, rickety flamboyant amateurism, and the polish of corporate comms. The most reliable way to identify an item’s grayness is by its function and milieu. According to the 2010 Prague definition, established at the 12th Annual Conference on Grey Literature and Repositories,
Grey literature stands for manifold document types produced on all levels of government, academics, business and industry in print and electronic formats that are protected by intellectual property rights, of sufficient quality to be collected and preserved by library holdings or institutional repositories, but not controlled by commercial publishers i.e., where publishing is not the primary activity of the producing body.
Gray literature does not have the market or cultural value of a novel or textbook. It is not an end in itself, but facilitative paraphernalia of some other endeavor—midwifery, policing, animal husbandry, war. This vicariousness, and its heterogenous forms, makes it notoriously difficult to place in library catalogues. Should a practical primer on the mitigation of corrosion in airplanes be placed under Dewey Decimal class “671: Metalworking & primary metal products,” “387: Water, air & space transportation,” or “358: Air and other specialized forces”? When a bull sperm directory is a matter of genetics, food production, and commerce, which can it be said to be about? Gray literature isn’t made with libraries or bookshops in mind. It strides out into the world to do an honest day’s work. None of this hanging around on hushed shelves waiting to impart knowledge in the abstract. It’s got sperm to tout, babies to birth, aircraft to maintain, a policed public to mollify.
I find these publications compelling by their very existence and, for the most part, unreadable. Their content slides off my mind. Gray literature’s high and narrow window onto specialist processes is anathema to traditional general-interest non-fiction publishing, which delivers information like a tap dispenses safely managed water—filtered, chlorinated, and piped into your very own quarters. Gray literature is a sploshing bucket of someone else’s water, murky with unfamiliar vocabulary, its means of application not always entirely obvious. Each publication is an invitation to speculate on a sector’s operations, to marvel at the specificity of other people’s knowledge and the focus of their working lives. My paltry library gestures toward the infinite complicatedness of human activity and the vast, disorganized array of murky buckets out of which the materiality of our lives somehow continues to emerge.
I have recently acquired some items that confuse the already untidy category of grayness. While seeking out books on theatrical quick-change (more on that another time), I came across the Webster’s Timeline History series and, out of curiosity, bought the three cheapest of the second-hand editions available: Wallpaper, 1768–2007; Secrecy, 393 BC–2007; and Bristol, 1000–1893. They are collations of excerpts, references, and citations that feature their titular word or phrase, and there are thousands of them. The series’s aggregate subject matter reads like the archest of list poems, the word associations of a disheveled mind, or dying humanity’s life flashing before its eyes…
Do read on for a fascinating/horrifying/illuminating tale all-too-relevant to our times– the story of the Webster’s Timeline History series…
On gray literature and Webster’s Timeline History books: “The First Tomato to Know Everything,” from @sosallyo.bsky.social in @cabinetmagazine.bsky.social.
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As we hold onto the human, we might (in preparation for tomorrow) remind ourselves that it was on this date in 1914 that President Woodrow Wilson issued a proclamation declaring the first national Mother’s Day. The previous day, May 8, Congress had designated the 2nd Sunday in May as Mother’s Day and had requested the proclamation.
“Always look on the bright side of life”*…
The estimable economic historian Louis Hyman has been engaged in an on-going “friendly debate” with his equally-estimable friend and Johns Hopkins colleague Rama Chellappa on “what AI means”…
… As I see this debate, this question of our age, there are two main questions that history can shed some light on.
- Is AI a complement or a substitute for labor? That is, will it increase demand for and the productivity of workers, or decrease it?
- Will AI be controlled by the few or be accessible to the many?
A Complement or a Substitute?
Consider a some of the most important technologies of the past 200 years.
When I am asked about what automation might look like, I inevitably discuss agriculture. Roughly all of our ancestors were farmers and approximately none of us today are. Yet we still eat bread made from wheat. That shift is possible because of automation.
The mechanical thresher, used to process wheat, was a substitute for the most backbreaking work of the harvest. But it also enabled more land to be cultivated, and that land was cultivated more efficiently, allowing for greater harvests. Mechanization of the farm, like the thresher, turned the American Midwest into the breadbasket of the world.
Those displaced farmers found work on railroads, moving all that. And those jobs, according to people at the time, were a kind of liberation from the raw animal labor of threshing. On net, it created demand for more workers at better wages in work more fit for people than beasts. For those that remained farmers, they found other higher-value work to be done. On a farm, there is always more work to do.
The failure, then and now, is to think farmers were only threshers. That was one part of their jobs. Today, our work, for most people, is also a bundle of tasks. Workers then and now could and can focus on parts of their job that are of higher value. And in a new economy, new tasks in new industries will be created. Many of the jobs that we do today (web designer, UI expert) were simply unimaginable in 1850. That is a good thing.
Consider now the assembly line. I’m sure you all know about the staggering increases in productivity that come from the division of labor. If you take my class in industrial history, you would learn deeply about the story of the automobile. With the assembly line, and no other change in technology, car assembly went from 12 and a half hours to about 30 minutes (once they worked out the kinks). Did this reduce the demand for workers? No. It reduced the price of cars. And that increased the demand for workers, who eventually could demand even higher wages through unionization.
It is important here to realize that better tools don’t make us get paid worse. They generally make us get paid more. Why? Because the tool, without the person, is useless. Even for today’s most cutting-edge AIs, that is true. It can code, but it can only code what I imagine it to code. It can draw, but only what I imagine it to draw. That is true for AIs as it was true for the thresher.
So, I would offer that AI will create more growth, more abundance. In the long run, all growth comes from higher productivity.
I would add one more piece to this story. Economic inequality has worsened since roughly 1970. It has worsened, therefore, not in the industrial era, but the digital era. I have argued elsewhere that this happened because for decades we did not use computers as tools of automation but as glorified typewriters (and then as televisions). Our productivity did not increase, especially to justify the expense of computers. Economists have debated for decades now over the lack of increase in productivity that came with the “digital age” of computing, but it is simple. We don’t use them as computers. Now we can.
For the first time now, normal people with their normal problems can use their computers to solve and automate their problems. AI can write code. AI can automate their tedium. The digital age did not bring any gains because it had no yet arrived. We were living through the last gasp of the industrial economy.
It is now here.
This technology will unleash unimaginable productivity gains. It will level the playing field between coders and the rest of us. Coders will lose their jobs, to be sure, but for the rest of us, the bundle of workplace tasks will become much better.
And truthfully, the demand for real computer scientists will probably increase in the era of vibe-coding. Computer science itself is a bundle of skills, of which coding is just one. The more important skill – software and data architecture – will only increase in demand as the usefulness of software expands…
[Hyman goes on to explore the dangers of monopolization (which, for reasons he explains, he believes are overstated); the future of softward (which, he believes, will skew to open-sorce), and of hardware (which, he believes will not be a bottleneck). He concludes…]
… Put together we come to a very different picture of what the digital age will be. The industrial age required massive investments to build the factories to make the products that were in demand. In the digital age, in contrast, the factories to build digital products will be made by the AI on your laptop. That is not inequality. That is equality.
The physical products of the Fordist industrial age were made for the mass market. In contrast, the digital products of the post-fordist digital age will be long-tail products. I don’t need to make mass market products; I can make them for a small niche, or just for myself.
Rather than fostering inequality, AI, then, is a great equalizer. To make products for a global market you don’t need a billion-dollar factory. You just need a laptop. That is astonishing.
That said, it will not be all sunshine and rainbows. Will AI solve the inequities of capitalism or its reliance on externalities as a source of primitive accumulation? Probably not.
But at the same time, AI is not a normal technology in that it has the potential to radically undermine many of the tendencies to concentrate capital that we have seen in the industrial age. We have been automated out of work before, that is nothing new, but it has always concentrated capital in the hands of the few. For the first time, there is potentially an alternative path forward.
AI will bring the digital age out of the hands of the coders. AI will not widen the gap—it will bridge it. Its ubiquity will mean that AI will be a tool that nearly all of us will be able to use in our daily work, which will make ordinary people more productive and prosperous…
Eminently worth reading in full: “Hooray! Post-Fordism Is Finally Here!“
Even as Hyman’s message is reassuring in the context of the flood of jeremiads in which we’re awash, it’s worth remembering that eerily-similar points were made a couple of decades ago about the threat/promise of digital publishing/commerce. Given the then-current conditions and then-plausible futures, those predictions might have come true… but in the event, they didn’t pan out as projected. That said, things are changing, so maybe this time things are different?
(Image above: source)
* song (by Eric Idle) from Monty Python’s Life Of Brian
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As we resolve to remain rosy, we might send productive birthday greetings to Andrew Meikle; he was born on this date in 1719. A Scottish millwright, he invented the threshing machine (for removing the husks from grain, as mentioned above). One of the key developments of the British Agricultural Revolution in the late 18th century., it was also one of the main causes of the Swing Riots— an 1830 uprising by English and Scottish agricultural workers protesting agricultural mechanization and harsh working conditions.
“The present is pregnant with the future”*…
The estimable Tim O’Reilly uses scenario planning to create an insightful look at AI, our futures, and the choices that will define them…
We all read it in the daily news. The New York Times reports that economists who once dismissed the AI job threat are now taking it seriously. In February, Jack Dorsey cut 40% of Block’s workforce, telling shareholders that “intelligence tools have changed what it means to build and run a company.” Block’s stock rose 20%. Salesforce has shed thousands of customer support workers, saying AI was already doing half the work. And a Stanford study found that software developers aged 22 to 25 saw employment drop nearly 20% from its peak, while developers over 26 were doing fine.
But how are we to square this news with a Vanguard study that found that the 100 occupations most exposed to AI were actually outperforming the rest of the labor market in both job growth and wages, and a rigorous NBER study of 25,000 Danish workers that found zero measurable effect of AI on earnings or hours?
Other studies could contribute to either side of the argument. For example, PwC’s 2025 Global AI Jobs Barometer, analyzing close to a billion job ads across six continents, found that workers with AI skills earn a 56% wage premium, and that productivity growth has nearly quadrupled in the industries most exposed to AI.
This is exactly the kind of contradictory, uncertain landscape that scenario planning was designed for. Scenario planning doesn’t ask you to predict what the future will be. It asks you to imagine divergent possible futures and to develop a strategy that improves your odds of success across all of them. I’ve used it many times at O’Reilly and have written about it before with COVID and climate change as illustrative examples. The argument between those who say AI will cause mass unemployment and those who insist technology always creates more jobs than it destroys is a debate that will only be resolved by time. Both sides have evidence. Both are probably right at some level. And both framings are not terribly helpful for anyone trying to figure out what to do next…
[O’Reilly explains the scenario approach, then applies it to our future with AI (see the image above), astutely assessing the conflicting signals that we’ve experiencing; he explores the “robust strategy” for our uncertian future (strategic choices that make sense regardless of which future unfolds); then he concludes…
… I’ll return to the theme that I sounded in my book WTF? What’s the Future and Why It’s Up To Us.
Every time a company uses AI to do what it was already doing with fewer people, it is making a choice for the lower half of the scenario grid. Every time a company uses AI to do something that wasn’t previously possible, to serve a customer who wasn’t previously served, to solve a problem that wasn’t previously solvable, it is making a choice for the upper half. These choices compound, for good or ill. An economy that uses AI primarily for efficiency will slowly hollow itself out.
Looking at the news from the future, both sets of signals are present. The question is which will dominate. AI will give us both the Augmentation Economy and the Displacement Crisis, in different measures in different places, depending on the choices we make.
Scenario planning teaches us that we don’t have to predict which future we’ll get. We do have to prepare for a very uncertain future. But the robust strategy, the one that works across every quadrant, is to focus on doing more, not just doing the same with less, and to find ways that human taste still matters in what is created. As long as there is unmet demand, as long as there are problems we haven’t solved and people we haven’t served, AI will augment human work rather than replacing it. It’s only when we stop looking for new things to do that the machines come for the jobs…
Eminently worth reading in full. Indeed, speaking as a long-time scenario planner, your correspondent can only wish that everyone who wields “scenarios” applies the approach as appropriately, adriotly, and acutely as Tim has: “Scenario Planning for AI and the ‘Jobless Future‘,” from @timoreilly.bsky.social.
* Voltaire
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As we take the long view, we might send formative birthday greetings to Mark Pinsker; he was born on this date in 1923. A mathematician, he made impoprtant contributions to the fields of information theory, probability theory, coding theory, ergodic theory, mathematical statistics, and communication networks. This work, which helped lay the foundation for AI-as-we-know-it, earned him the IEEE Claude E. Shannon Award in 1978, and the IEEE Richard W. Hamming Medal in 1996, among other honors.
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