Patterns of light through a glass

The diminishing status of science and the academy

Jessica Hullman, What stories should we tell about science now? Statistical Modeling, Causal Inference, and Social Science, August 28, 2026.

The opening paragraphs:

This is Jessica. Like many academics, I am concerned about what sort of new steady state U.S. universities will find themselves in after the dust settles on recent transitions. Namely, the last few years have brought funding cuts, targeted visa policy, reduced demand for grad degrees, and a general brain drain to industry (particularly noticeable in AI and computer science). It’s disorienting to think that academia has already peaked, and that the prestige ranking of the R1 faculty job over the top industry research positions (at least in computer science) might be inverting. But things feel very different than they did even a year ago. The reality of there being less money available to pay for basic aspects of research really started to hit me in the last six months. Post-covid, working on campus became less lively, but now it also feels like our collective attention is anxiously focused on Silicon Valley or Washington D.C. We hold faculty meetings where we discuss things like, Is there any way we can help local faculty members who were laid off from tenure track jobs? How will we ensure we can fund all of our own PhDs, given that TA quotas stay fixed but faculty are running out of funding runway?

To some, this is an overdue rebalancing. Nate Silver, for example, calls getting a PhD a “much worse value proposition than 20 years ago”, and predicts that elite higher ed will become “~50% less relevant in the new steady state,” which is in his eyes a good recalibration.

But it’s worth reflecting on what is lost exactly, if this dwindling of minds and resources continues. How should we think about the value of what universities provide over industry, like intellectual autonomy, or training on how to think scientifically? As a professor, I could make a list of the things that have kept me in academia–being free to work on the problems I find most important, the diversity of topics I can work on at any given time, grad students who care about doing deep work, having time to think about the best solution to a problem. But at an aggregate level, it’s less clear what the equation is.

The article then goes on to sketch out a number of specific issues. The first: “In search of the mysterious fruits of basic science.” Thus:

I’ve been reading the work of philosopher Heather Douglas, who has traced and critiqued the basic versus applied science distinction, the linear model as justification, and the idea of scientific freedom as limited social responsibility (see, e.g., here and here, or her book on the value-free ideal). Popularized by Vannevar Bush after WWII, in a report prepared for President Roosevelt, basic science is a reframing of pure science, presented as “scientific capital,” providing the principles and conceptions to power new products and processes years into the future. Bush called for deliberate policy to guard against the otherwise inevitable scenario where applied science drives out the pure. One of the eventual outcomes of his report was the creation of the NSF.

But despite the pragmatic nature of basic science espoused by Bush, as a derivation of pure science, it is hard to separate from less tangible values. One is that scientific understanding is a good outside of practical application, at both the individual and societal level. The earliest advocates of pure science associated it with being closer to God. Post-Enlightenment, this view gave way to a more secular superiority complex, which implied the strong character of the pure scientist, who chose to eschew wealth. “The highest occupation of mankind”, Henry Rowland called it in his Gilded Age era essay, “A Plea for Pure Science,” which bemoaned the vulgarity of attributing scientific greatness to the applied scientist rather than the pure.

One specific case: AI:

If we take our intuitions from the linear model, we might protest that innovation will suffer if universities’ research purposes are deprioritized. The post WWII science-industrial complex expanded the presence of basic research in industry, but studies suggest that the knowledge generating role of corporate R&D has been on the decline for years. To the extent that basic research is the supplier of downstream applications, it would seem we need universities more than ever.

But the distinction between basic and applied science that’s become synonymous with how we envision science has never been airtight. Critics questioned how an institution could be built around a distinction that seemed to amount to little more than a difference in intention, since applied research sometimes produced important new general knowledge, and pure science contributions sometimes had direct applicability.

AI research is a recent example. Not only is serious money being made without necessarily requiring advanced degrees, research positions do not require PhDs. By some accounts, passing 30 years old puts one in the older demographic of researchers at frontier AI companies. Yet much of the visible innovation in frontier model development has been heavily concentrated in industry labs, including transformer models, scaling laws, and AlphaFold.

Of course, AI owes much to academia. The amazing thing about deep learning and LLMs, to anyone who was paying attention to NLP before these developments, is that after many years of AI research contributing interesting questions but lackluster results, the technology finally seemed to work. Would we have had the foundations for deep learning if perceptrons had not been stubbornly pursued by academics like Frank Rosenblatt at Cornell early on, picked up again in the 1980s by Rumelhart and McClelland’s Parallel Distributed Processing group, despite multiple periods during which consensus said connectionist approaches were unlikely to pay off?

Other issues taken up: “Indulgence, autonomy, and social responsibility,” and “The problem with defining progress as prediction and control.” There’s much more at the link, including fairly extensive commentary.

The impact of Google AI on queries for Wikipedia

Khosravi, Mehrzad and Yoganarasimhan, Hema, Impact of AI Search Summaries on Website Traffic: Evidence from Google AI Overviews and Wikipedia (January 30, 2026). Available at SSRN: https://ssrn.com/abstract=6164926

Abstract: Search engines increasingly display AI-generated answers above organic links, potentially displacing traffic to upstream publishers. We estimate the impact of Google’s AI Overviews (AIO) on Wikipedia’s search traffic using AIO’s staggered geographic rollout and Wikipedia’s multilingual structure. Our difference-in-differences design compares monthly external-search referrals to English Wikipedia articles with referrals to the same articles in German and French, and finds that default AIO availability reduced English search traffic by 5.45% and 4.82%, respectively. Our results suggest that answer-producing digital intermediaries can materially reallocate attention away from informational publishers, with implications for content monetization, search platform design, and policy.

Face Masks Required

The Umwelt Representation Hypothesis: Rethinking Universality

Bosch, Victoria, Sommers, Rowan P., Doerig, Adrien, Kietzmann, Tim C., The Umwelt Representation Hypothesis: rethinking Universality, Trends in Cognitive Sciences, Aug. 8 ,2026, doi: 10.1016/j.tics.2026.07.004

Highlights: Artificial neural networks are increasingly used to study neural representations.

Recent studies have found a surprising degree of representational alignment between artificial neural networks and biological brains. In addition, broad alignment among ANNs of different modalities and architectures has been observed.

These findings have been interpreted as evidence for Universality: the idea that sufficiently capable systems all converge on a single shared representation of reality.

This interpretation has large implications for model comparisons that require meaningful differences between artificial neural networks: if all artificial neural networks eventually align with the brain, then creating new model classes leads to no new insights.

This opinion article argues that this inference is too strong and proposes the Umwelt Representation Hypothesis: representational alignment emerges from overlap in ecological constraints, not convergence to one privileged world model.

Abstract: Artificial neural networks are increasingly used to study neural representations.Recent studies have found a surprising degree of representational alignment between artificial neural networks and biological brains. In addition, broad alignment among ANNs of different modalities and architectures has been observed.These findings have been interpreted as evidence for Universality: the idea that sufficiently capable systems all converge on a single shared representation of reality.This interpretation has large implications for model comparisons that require meaningful differences between artificial neural networks: if all artificial neural networks eventually align with the brain, then creating new model classes leads to no new insights.This opinion article argues that this inference is too strong and proposes the Umwelt Representation Hypothesis: representational alignment emerges from overlap in ecological constraints, not convergence to one privileged world model.

AIs are not very good at long horizon tasks

This paper is a brutal reality check for long-horizon AI. Give an agent a year of interconnected decisions, delayed feedback, and consequences from its own past actions, and its performance collapses relative to humans.

The researchers tested eight leading models, including… https://t.co/rGBfuPZEtv pic.twitter.com/6CPvVIQCQX

— Rohan Paul (@rohanpaul_ai) August 28, 2026

Friday Fotos: Newport Mall

Emergent Multiscale Organisation of Neural Dynamics

Milinkovic, B., Seth, A.K., Barnett, L., Carter, O., & Andrillon, T. (2026). Emergent Multiscale Organisation of Neural Dynamics Fragments in Anaesthesia. Imaging Neuroscience, Advance Publication. https://doi.org/10.1162/IMAG.a.1364

Abstract: Conscious experience depends on the coordinated activity of neural processes that span multiple scales: from synapses to whole-brain dynamics. A recently introduced measure, dynamical independence (DI), identifies, characterises, and quantifies these multi-scale relationships using an information-theoretic dimensionality reduction approach. Here, we use DI to examine changes in the emergent dynamical organisation in the human brain under three pharmacologically-distinct anaesthetic interventions (propofol, xenon, ketamine). Applied to source-reconstructed electroencephalography (EEG), our analysis reveals that propofol and xenon, anaesthetics that abolish conscious report, exhibit more emergent but highly variable dynamic structure, indicating fragmented macroscopic dynamical organisation. Ketamine, which preserves dream-like phenomenology, shows a different pattern relative to wakefulness: reduced overall emergence yet a partial preservation of the macroscopic structure. Further exploratory analyses revealed spatially localised source-level contributions to emergent dynamical structure, highlighting regional variations. Together, our results highlight drug-induced reconfigurations of emergent dynamical structure relative to wakefulness, dissociate the amount of emergence from the organisation of emergent dynamics, and caution against equating emergence with level of consciousness. Consequently, we suggest that wakeful conscious processing depends not only on integration between neural components within a single scale, but also on integration across scales, broadening currently held assumptions of putative signatures of consciousness.

Dan Everett on Joe Rogan

The sun blazes in, and then weeds

Fuse Powder BOOM! How California technocults created opportunties for capital seeking investment vehicles

David Hoyt, AI is the Latest and Best-Financed Cult to Emerge from California, 3 Quarks Daily, Aug. 23, 2026.

The article begins by discussing the cultish behavior surrounding AI in Silicon Valley; I discussed similar material in a 3QD article from 2022, On the Cult of AI Doom. The article pivots toward finances with this paragraph:

What makes the cult of AI different in tone and in worldview is its origin within a tech industry which sits at an epicenter of unregulated flows of global capital, its profound, longstanding, and growing links to the US security state, and the certainty that on the basis of these pillars it can build the future it wants here on earth right now, and that no one should be able to stop them.

Then we have:

Two macroeconomic factors are at play in the breakneck speed with which AI infrastructure has been financed and built out since the release of Chat GPT in November of 2022. They have little to do with the intrinsic commercial potential of AI-related products or services, nor any particular innovation in computer engineering. The first, to put it simply, is the availability of lots of money. More than ever before, giant pots of money are sloshing around the planet, motivated by historically low interest rates over a relatively long period to search out the Next Big Thing. This money is mostly exempt from national regimes of control or taxation, is managed outside the traditional banking system, and is therefore more free to move around. The second is the chronic and by now widely acknowledged slow-down of economic growth in the advanced economies, dating back nearly half a century. What the hype typically fails to register is that investors don’t seek out technology and AI firms because they are the best bets on future returns – they seek them out because they are the only bets around.

To understand just why this is, Nick Srnicek provides a useful capsule history of the sequence of financial crises that have followed the liberalization of capital since the later 20th century. It is a story that leads with a crescendo to the current boom in AI development. In the mid 1990’s, the hottest global market was not centered in Silicon Valley, but in the small economies of East and Southeast Asia, the “Asian Tigers.” Only when these blew up beginning in 1997 did newly mobile capital, recently freed from decades of local restrictions on its movement in and out of national economies, begin to pour into Silicon Valley in what would become known as the dot-com bubble. When this blew up, as all bubbles do, it led to a decade of low interest rates dictated by the Federal Reserve. This, in turn, led global investors to flood the US housing market, inflating a nearly decade-long bubble in real estate prices. When this bubble blew up in 2008 with even more severity, followed almost immediately by the Greek/European debt crisis of 2009-2012, the solvency of major financial institutions in the US and Europe and of the entire economic order was threatened. Only massive intervention from the public sector avoided a meltdown. At the same time, the year 2012 marked the year that growth rates in the People’s Republic of China dropped under 10% annually for the first time in over a decade. This twin shock to the global economy, in combination with austerity measures put in place in the aftermath of the 2008 crisis, led to the unraveling of the global economic architecture put in place over the previous quarter century.

This is the context of chronic low growth in which money has been pouring into AI, either invested in privately held firms such as Anthropic or OpenAI, or publically held tech firms listed in the US such as Google and Microsoft, or in overseas firms such as semiconductor giant SK Hynix in South Korea. The search for returns continues to drive valuations of AI majors to astronomical heights, bringing significant portions of the market along with them. In May, 2026 Anthropic, which only recently turned a modest profit on sales of its AI assistant chatbot Claude, came close to breaking the valuation threshold of $1 trillion USD. (There is no standard, objective methodology for obtaining such valuations for private companies). According to the OECD, artificial intelligence firms captured 61% of global venture capital in 2025. A Stanford report puts global VC investment in Silicon Valley firms alone at 75% of the total. Overall capital spending on AI in the United States is at roughly 2% of GDP, and expected to reach 3-4% in 2027. This is equal to and slightly exceeding the US defense budget.

This is a lot of investment, but in what, precisely? As it stands right now, AI is an all-purpose everything enhancer, like the patent cure-all medicines of the nineteenth century.

There's more at the link, including thumbnail accounts of recent events on Wall Street and among VCs.

Think of it like this: The techno cult launched ChatGPT in late November of 2022. That's the fuse. That large pile of money that's been floating around looking for investment vehicles, that's the powder. When ChatGPT became a surprise hit, the fuse was lit and the powder exploded: BOOM! That's where we are now.

Eye on the High Line

Dolly Parton, RIP

How LLMs work [in pictures]

We will use everyday language where possible and mark technical terms in purple. Key is that modern LLMs have a component of making answers (in blue, called "inference") and a component of improving answers (red, called "training"). pic.twitter.com/iUmGaNMKUz

— Kording Lab 🦖 (@KordingLab) August 25, 2026

The real work is done by a core machine, the neural network which is used to generate one word at a time based on its inputs. The probability of each word depends on prior words. And words are chosen one by one according to those probabilities. pic.twitter.com/7zWO87TThs

— Kording Lab 🦖 (@KordingLab) August 25, 2026

The result of this are what is sometimes called large-scale "stochastic parrots". Very impressive text but often somewhat superficial. But these are the old LLMs (think 2022). Innovation was super fast since.

— Kording Lab 🦖 (@KordingLab) August 25, 2026

A second idea (B) for making answers, tool use. Call programs like databases, internet search, calculators etc. pic.twitter.com/zyPU00vmV3

— Kording Lab 🦖 (@KordingLab) August 25, 2026

And all of this, is better with better “training“ goals. Wherever truth is defined, push towards this truth (D). And where it is not defined, use LLMs to check answers ("self evaluation", (E)) pic.twitter.com/vLOy2UCsYb

— Kording Lab 🦖 (@KordingLab) August 25, 2026

A great deal of the strengths and weaknesses of today's LLMs can be understood from these component ideas.

— Kording Lab 🦖 (@KordingLab) August 25, 2026

On the waterfront [uptown Hoboken]