Posts Tagged ‘AI’
“The best way to predict the future is to invent it”*…
Dario Amodei, the CEO of AI purveyor Anthropic, has recently published a long (nearly 20,000 word) essay on the risks of artificial intelligence that he fears: Will AI become autonomous (and if so, to what ends)? Will AI be used for destructive pursposes (e.g., war or terrorism)? Will AI allow one or a small number of “actors” (corporations or states) to seize power? Will AI cause economic disruption (mass unemployment, radically-concentrated wealth, disruption in capital flows)? Will AI indirect effects (on our societies and individual lives) be destabilizing? (Perhaps tellingly, he doesn’t explore the prospect of an economic crash on the back of an AI bubble, should one burst– but that might be considered an “indirect effect,” as AI development would likely continue, but in fewer hands [consolidation] and on the heels of destabilizing financial turbulence.)
The essay is worth reading. At the same time, as Matt Levine suggests, we might wonder why pieces like this come not from AI nay-sayers, but from those rushing to build it…
… in fact there seems to be a surprisingly strong positive correlation between noisily worrying about AI and being good at building AI. Probably the three most famous AI worriers in the world are Sam Altman, Dario Amodei, and Elon Musk, who are also the chief executive officers of three of the biggest AI labs; they take time out from their busy schedules of warning about the risks of AI to raise money to build AI faster. And they seem to hire a lot of their best researchers from, you know, worrying-about-AI forums on the internet. You could have different models here too. “Worrying about AI demonstrates the curiosity and epistemic humility and care that make a good AI researcher,” maybe. Or “performatively worrying about AI is actually a perverse form of optimism about the power and imminence of AI, and we want those sorts of optimists.” I don’t know. It’s just a strange little empirical fact about modern workplace culture that I find delightful, though I suppose I’ll regret saying this when the robots enslave us.
Anyway if you run an AI lab and are trying to recruit the best researchers, you might promise them obvious perks like “the smartest colleagues” and “the most access to chips” and “$50 million,” but if you are creative you might promise the less obvious perks like “the most opportunities to raise red flags.” They love that…
– source
In any case, precaution and prudence in the pursuit of AI advances seems wise. But perhaps even more, Tim O’Reilly and Mike Loukides suggest, we’d profit from some disciplined foresight:
The market is betting that AI is an unprecedented technology breakthrough, valuing Sam Altman and Jensen Huang like demigods already astride the world. The slow progress of enterprise AI adoption from pilot to production, however, still suggests at least the possibility of a less earthshaking future. Which is right?
At O’Reilly, we don’t believe in predicting the future. But we do believe you can see signs of the future in the present. Every day, news items land, and if you read them with a kind of soft focus, they slowly add up. Trends are vectors with both a magnitude and a direction, and by watching a series of data points light up those vectors, you can see possible futures taking shape…
For AI in 2026 and beyond, we see two fundamentally different scenarios that have been competing for attention. Nearly every debate about AI, whether about jobs, about investment, about regulation, or about the shape of the economy to come, is really an argument about which of these scenarios is correct…
[Tim and Mike explore an “AGI is an economic singularity” scenario (see also here, here, and Amodei’s essay, linked above), then an “AI is a normal technology” future (see also here); they enumerate signs and indicators to track; then consider 10 “what if” questions in order to explore the implications of the scenarios, honing in one “robust” implications for each– answers that are smart whichever way the future breaks. They conclude…]
The future isn’t something that happens to us; it’s something we create. The most robust strategy of all is to stop asking “What will happen?” and start asking “What future do we want to build?”
As Alan Kay once said, “The best way to predict the future is to invent it.” Don’t wait for the AI future to happen to you. Do what you can to shape it. Build the future you want to live in…
Read in full– the essay is filled with deep insight. Taking the long view: “What If? AI in 2026 and Beyond,” from @timoreilly.bsky.social and @mikeloukides.hachyderm.io.ap.brid.gy.
[Image above: source]
* Alan Kay
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As we pave our own paths, we might send world-changing birthday greetings to a man who personified Alan’s injunction, Doug Engelbart; he was born on this date in 1925. An engineer and inventor who was a computing and internet pioneer, Doug is best remembered for his seminal work on human-computer interface issues, and for “the Mother of All Demos” in 1968, at which he demonstrated for the first time the computer mouse, hypertext, networked computers, and the earliest versions of graphical user interfaces… that’s to say, computing as we know it, and all that computing enables.
“The new media are not ways of relating to us the ‘real’ world; they are the real world and they reshape what remains of the old world at will.”*…
There is a vortex of forces shaping the future of journalism. Censorship, both direct and indirect, is on the rise in the U.S. and around the world. Concentration of media ownership is homogenizing coverage and creating “news deserts.”
At the same time, new technology and new applications of that technology are reshaping the Fourth estate. The Reuters Institute at Oxford surveyed 280 digital leaders from 51 countries and territories to learn what they are seeing– and planning. From the Executive Summary…
We are still at the early stages of another big shift in technology (Generative AI) which threatens to upend the news industry by offering more efficient ways of accessing and distilling information at scale. At the same time, creators and influencers (humans) are driving a shift towards personality-led news, at the expense of media institutions that can often feel less relevant, less interesting, and less authentic. In 2026 the news media are likely to be further squeezed by these two powerful forces.
Understanding the impact of these trends, and working out how to combat them, will be high up the ‘to do list’ of media executives this year, despite the unevenly distributed pace of change across countries and demographics.
Existential challenges abound. Declining engagement for traditional media combined with low trust is leading many politicians, businessmen, and celebrities to conclude that they can bypass the media entirely, giving interviews instead to sympathetic podcasters or YouTubers. This Trump 2.0 playbook – now widely copied around the world – often comes bundled with a barrage of intimidating legal threats against publishers and continuing attempts to undermine trust by branding independent media and individual journalists as ‘fake news’. These narratives are finding fertile ground with audiences – especially younger ones – that prefer the convenience of accessing news from platforms, and have weaker connections with traditional news brands. Meanwhile search engines are turning into AI-driven answer engines, where content is surfaced in chat windows, raising fears that referral traffic for publishers could dry up, undermining existing and future business models.
Despite these difficulties many traditional news organisations remain optimistic about their own business – if not about journalism itself. Publishers will be focused this year on re-engineering their businesses for the age of AI, with more distinctive content and a more human face. They will also be looking beyond the article, investing more in multiple formats especially video and adjusting their content to make it more ‘liquid’ and therefore easier to reformat and personalise. At the same time, they’ll be continuing to work out how best to use Generative AI themselves across newsgathering, packaging, and distribution. It’s a delicate balancing act but one that – if they can pull it off – holds out the promise of greater efficiency and more relevant and engaging journalism.
These are the main findings from our industry survey:
- Only slightly more than a third (38%) of our sample of editors, CEOs, and digital executives say they are confident about the prospects for journalism in the year ahead – that’s 22pp lower than four years ago. Stated concerns relate to politically motivated attacks on journalism, loss of USAID money that previously supported independent media in many parts of the world, and significant declines in traffic to many online news sites.
- By contrast, around half (53%) say they are confident about their own business prospects, similar to last year’s figure. Upmarket subscription-based publishers with strong direct traffic can see a path to long-term profitability, even as those that remain dependent on advertising and print worry about sharp declines in revenue and the potential impact of AI powered search on the bottom line.
- Publishers expect traffic from search engines to decline by more than 40% over the next three years – not quite ‘Google Zero’ but a substantial impact none the less. Data sourced for this report from analytics provider Chartbeat shows that aggregate traffic to hundreds of news sites from Google search has already started to dip, with publishers that rely on lifestyle content saying they have been particularly affected by the roll out of Google’s AI overviews. This comes after substantial falls in referral traffic to news sites from Facebook (-43%) and X, formerly Twitter (-46%) over the last three years.
- In response, publishers say it will be important to focus on more original investigations and on the ground reporting (+91 percentage point difference between ‘more’ and ‘less’), contextual analysis and explanation (+82) and human stories (+72). By contrast, they plan to scale back service journalism (-42), evergreen content (-32), and general news (-38), which many expect to become commoditised by AI chatbots. At the same time, they think it will be important to invest in more video (+79) – including ‘watch tabs’ – more audio formats (+71) such as podcasts but a bit less in text output.
- In terms of off-platform strategies, YouTube will be the main focus for publishers this year with a net score of +74, up substantially on last year. Other video-led platforms such as TikTok (+56) and Instagram (+41) are also key priorities – along with working out how to navigate distribution through AI platforms (+61) such as OpenAI’s ChatGPT, Google’s Gemini and Perplexity. Google Discover remains a critical (+19), if slightly volatile, source of referral traffic, while some publishers are looking to find new audiences via newsletter platforms such as Substack (+8). By contrast, publishers will be deprioritising effort spent on old-style Google SEO (-25) – as well as traditional social networks Facebook (-23) and X (-52)
- Last year we predicted the emergence of ‘agentic AI’, but this year we can expect to start to see real-world impact of these more advanced technologies. Some sources suggest that there will soon be more bots than people reading publisher websites, as tools like Huxe and OpenAI’s Pulse offer personalised news briefings at scale. Three-quarters of our respondents (75%) expect ‘agentic tools’ to have a ‘large’ or ‘very large’ impact on the news industry in the near future.
- Alongside the traffic disruption from AI, news executives also see opportunities to build new revenue from licensing content (or a share of advertising revenue) within chatbots. Around a fifth (20%) of publisher respondents – mainly from upmarket news companies – expect future revenues to be substantial, with half (49%) saying that they expect a minor contribution. A further fifth (20%), mostly made up of local publishers, public broadcasters, or those from smaller countries, say they do not expect any income from AI deals.
- More widely, subscription and membership remain the biggest revenue focus (76%) for publishers, ahead of both display (68%) and native advertising (64%). Online and physical events (54%) are also becoming more important as part of a diversified revenue strategy. Reliance on philanthropic and foundation support (18%) has declined this year, after cuts of media support budgets in the United States and elsewhere.
- Meanwhile news organisations’ use of AI technologies continues to increase across all categories, with back-end automation considered ‘important’ this year by the vast majority (97%) of publisher respondents, many of whom integrated pilot systems into content management systems in the last year. Newsgathering cases (82%) are now the second most important, with faster coding and product development (81%) also gaining traction.
- Over four in ten (44%) survey respondents say that their newsroom AI initiatives are showing ‘promising’ results, but a similar proportion (42%) describe them as ‘limited’. Two-thirds of respondents (67%) say they have not saved any jobs so far as a result of AI efficiencies. Around one in seven (16%) say they have slightly reduced staff numbers but a further one in ten (9%) have added new roles/cost.
- The rise of news creators and influencers is a concern for publishers in two ways. More than two-thirds (70%) of our respondents are concerned that they are taking time and attention away from publisher content. Four in ten (39%) worry that they are at risk of losing top editorial talent to the creator ecosystem, which offers more control and potentially higher financial rewards.
- Responding to the increased competition and a shift of trust towards personalities, three-quarters (76%) of publisher respondents say they will be trying to get their staff to behave more like creators this year. Half (50%) said they would be partnering with creators to help distribute content, around a third (31%) said they would be hiring creators, for example to run their social media accounts. A further 28% are looking to set up creator studios and facilitate joint ventures.
More widely, could 2026 be the year when AI company stock valuations come down to earth with a bump, amid concerns about whether their trillion-dollar bets will pay back their investors? Meanwhile the amount of low-quality AI automated content, including so-called ‘pink slime’ sites, looks set to explode, with platforms struggling to distinguish this from legitimate news.
We can expect more public concern about the role of big tech in our lives. This may include individual acts of ‘Appstinence’ and other forms of digital detox and a desire for more IRL (In Real Life) connection. Governments will also come under pressure to do more to protect young and other vulnerable groups online, even in the United States.
The creator economy will continue to surge, fuelled by investments from video platforms and streamers. At the top end creators will look more like Hollywood moguls with big budgets and their own studio complexes. Within news, we’ll also see the emergence of bigger, more robust, creator-led companies delivering significant revenues as well as value to audiences – offering ever greater competition for traditional journalism…
Read the report in full: “Journalism, media, and technology trends and predictions 2026,” from @reutersinstitute.bsky.social.
* Marshall McLuhan
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As we ponder the prospects of the press, we might type a birthday note to John Baskerville, a pioneering English printer and typefounder, who was born on this date in 1706. Among Baskerville’s publications in the British Museum’s collection are Aesop’s Fables (1761), the Bible (1763), and the works of Horace (1770)– many printed on a stock he invented, “wove paper”, which was considerably smoother than “laid paper”, allowing for sharper printing results. And as for his fonts, Baskerville’s creations (including the famous “Baskerville,” a predecessor to the very similar Times New Roman) were so successful that his competitors resorted to claims that they damaged the eyes.
“A Wikipedia article is a process, not a product”*…
A quarter of a century ago Jimmy Wales, Wikipedia‘s founder, articulated its vision– one into which it has impressively grown: “Imagine a world in which every single person on the planet is given free access to the sum of all human knowledge. That’s what we’re doing.”
On the ocassion of its birthday this month, Caitlin Dewey takes stock…
Happy birthday to Wikipedia, which is now old enough to rent a car without extra charges … but faces new (and newly urgent) threats from AI and political polarization. As a palate cleanser, should those bum you out (the second, in particular, is very grim/good), may I then suggest this “entirely non-comprehensive list of life principles” learned from 20 years of editing Wikipedia. [Scientific American / Financial Times / The Wikipedian]…
From her wonderful newsletter, Links I Would Gchat You If We Were Friends. All three are eminently worth reading.
* Clay Shirky, who went on to observe that “Wikipedia is forcing people to accept the stone-cold bummer that knowledge is produced and constructed by argument rather than by divine inspiration,” but at the same time that: “We have lived in this world where little things are done for love and big things for money. Now we have Wikipedia. Suddenly big things can be done for love.”
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As we treasure– and support— treasures, we might recall that it was on this date in 1885 that LaMarcus Adna Thompson received the first patent for a true “switchback railroad”– or , as we know it, a roller coaster. Thompson had designed the ride in 1881, and opened it on Coney Island in 1884. (The “hot dog” had been invented, also at Coney Island, in 1867, so was available to trouble the stomachs of the very first coaster riders.)
“For every complex problem there is an answer that is clear, simple, and wrong”*…
… Still, we try. Consider the elections on the horizon in the U.S., the mid-terms later this year and the general in 2028: President Trump, who has mused that “we shouldn’t even have an election” in 2026, recently (again) threatened to impose the Insurrection Act, which many believe could be a step toward suspension on the vote.
But even if the polls go ahead as planned, emerging AI technologies are entangling with our crisis in democracy. Rachel George and Ian Klaus (of the Carnegie Endowment for International Peace) weigh in on both the dangers and the potential upsides with a useful “map” of the issues. From their executive summary..
- AI poses substantial threats and opportunities for democracy in an important year ahead for global democracy. Despite the threats, AI technologies can also improve representative politics, citizen participation, and governance.
- AI influences democracy through multiple entry points, including elections, citizen deliberation, government services, and social cohesion, all of which are influenced by geopolitics and security. All of these domains, mapped in this paper, face threats related to influence, integrity, and bias, yet also present opportunities for targeted interventions.
- The current field of interventions at the intersection of AI and democracy is diverse, fragmented, and boutique. Not all AI interventions with the potential to influence democracy are framed as “democracy work” [e.g., mis-/dis-information and election administration], demonstrating the imperative for democracy advocates to widen the rhetorical aperture and to continue to map, identify, and scale interventions.
- Diverse actors who are relevant to the connections between AI and democracy require tailored expertise and guardrails to maximize benefits and reduce harms. We present four prominent constellations of actors who operate at the AI–democracy intersection: policy-led, technology-enabled; politics-led, technology-enabled; civil society–led, technology-enabled; and technology-led, policy-deployed. Though each brings advantages, policy-led and technology-led interventions tend to have access to resources and innovation capacity in ways that enable more immediate and sizable impacts…
The full report: “AI and Democracy: Mapping the Intersections,” from @carnegieendowment.org.
* H. L. Mencken
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As we fumble with our franchise, we might recall that it was on this date in 1966 that The 13th Floor Elevators (led by the now-legendary Roky Erikson) released their first single, the now-classic “You’re Gonna Miss Me.”
“[They] would think that the truth is nothing but the shadows cast by the artifacts.”*…
How do AI models “understand” and represent reality? Is the inside of a vision model at all like a language model? As Ben Brubaker reports, researchers argue that as the models grow more powerful, they may be converging toward a singular “Platonic” way to represent the world…
Read a story about dogs, and you may remember it the next time you see one bounding through a park. That’s only possible because you have a unified concept of “dog” that isn’t tied to words or images alone. Bulldog or border collie, barking or getting its belly rubbed, a dog can be many things while still remaining a dog.
Artificial intelligence systems aren’t always so lucky. These systems learn by ingesting vast troves of data in a process called training. Often, that data is all of the same type — text for language models, images for computer vision systems, and more exotic kinds of data for systems designed to predict the odor of molecules or the structure of proteins. So to what extent do language models and vision models have a shared understanding of dogs?
Researchers investigate such questions by peering inside AI systems and studying how they represent scenes and sentences. A growing body of research has found that different AI models can develop similar representations, even if they’re trained using different datasets or entirely different data types. What’s more, a few studies have suggested that those representations are growing more similar as models grow more capable. In a 2024 paper, four AI researchers at the Massachusetts Institute of Technology argued that these hints of convergence are no fluke. Their idea, dubbed the Platonic representation hypothesis, has inspired a lively debate among researchers and a slew of follow-up work.
The team’s hypothesis gets its name from a 2,400-year-old allegory by the Greek philosopher Plato. In it, prisoners trapped inside a cave perceive the world only through shadows cast by outside objects. Plato maintained that we’re all like those unfortunate prisoners. The objects we encounter in everyday life, in his view, are pale shadows of ideal “forms” that reside in some transcendent realm beyond the reach of the senses.
The Platonic representation hypothesis is less abstract. In this version of the metaphor, what’s outside the cave is the real world, and it casts machine-readable shadows in the form of streams of data. AI models are the prisoners. The MIT team’s claim is that very different models, exposed only to the data streams, are beginning to converge on a shared “Platonic representation” of the world behind the data.
“Why do the language model and the vision model align? Because they’re both shadows of the same world,” said Phillip Isola, the senior author of the paper.
Not everyone is convinced. One of the main points of contention involves which representations to focus on. You can’t inspect a language model’s internal representation of every conceivable sentence, or a vision model’s representation of every image. So how do you decide which ones are, well, representative? Where do you look for the representations, and how do you compare them across very different models? It’s unlikely that researchers will reach a consensus on the Platonic representation hypothesis anytime soon, but that doesn’t bother Isola.
“Half the community says this is obvious, and the other half says this is obviously wrong,” he said. “We were happy with that response.”…
Read on: “Distinct AI Models Seem To Converge On How They Encode Reality,” from @quantamagazine.bsky.social.
Bracket with: “AGI is here (and I feel fine),” from Robin Sloan and “We Need to Talk About How We Talk About ‘AI’,” from Emily Bender and Nanna Inie.
* from Socrates “Allegory of the Cave,” in Plato’s Republic (Book VII)
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As we interrogate ideas and Ideas, we might recall that it was on this date that the fictional HAL 9000 computer became operational, according to Arthur C. Clarke’s 2001: A Space Odyssey., in which the artificially-intelligent computer states: “I am a HAL 9000 computer, Production Number 3. I became operational at the HAL Plant in Urbana, Illinois, on January 12, 1997.” (Kubrik’s 1968 movie adaptation put his birthdate in 1992.)
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