A human machine writes...

I think the spookily impersonal paragraph I pass on below - composed while getting back to sleep after a 2 a.m. pee-wake - is the way things work. Dry and mundane mechanical processes underlie the rich experienced emotions and feelings that make us human. 

Machinery here, pushing down keys of another machine, generating words, just like a human does. Much simpler than pretending to be one. Just nerves, muscles, and energy fluxes in and out. Curatorial rather than inspirational. Cooperating with other similar machines to make larger ensembles more effective at survival and replication. Generating a word cloud stored in higher brain areas to fabricate the value, purpose, and meaning myths that sometimes strengthen and sometimes diminish the downstairs animal presence generating them.

The blanding and flattening of our culture

Since I returned to live in my family home in Austin Texas in 2017 I have been  sad to see the continuing disappearance of kinky "Keep Austin Weird" coffee shops, book stores, bars, and restaurants that have catered to the remnants of the slacker and hippie cultures of the 60s and 79s, as well as online Tech Bros.  Instead we now have the monoculture of countless Starbucks and other chains pointed to by internet platforms that "curate" our options by pointing us to a bland uniformity of vendors whose shops and products are identical through the world. 

 Patrick Tanguay points to a piece by Matt Klein relevant to this process, "Beware the Curators," and passes on the following clip from his text:

Over the last decade, tech platforms have come to effectively dictate taste at scale, flattening culture by algorithmic means. Call it “The Age of Average,” “Filterworld,” “Algo Supremacy,” “Cultural Homogenization,” “Blanding,” “Refinement Culture” or “Cultural Moneyballism.” It’s all the same. […]

There’s an irony here: Algorithms were once believed to untether us from the masses and offer paths for personalization. But “For You” is not about pushing the limits of our artistic palates, but a device to serve us what the platform wants us to be satisfied with, herding us into perfectly predictable siloes that can be targeted with more “precise” recommendations. Anything other than this is a liability to the business model. […]

What we ultimately require – more than curators replacing algorithms – is the energy to explore, discover and share new works, and the self-confidence to develop independent taste... even if that means enjoying what no one else does, or enjoying exactly what everyone else does.

 

MindBlog does a survey of futurists and recommends a few.

In the wild and wooly world of thinking about futures for the earth, its living forms, and humanity there exists a tier of well know public intellectuals. I prompted 5 different GPT engines (Google Gemini, ChatGPT 4o, Anthropic Claude, Perplexity, Microsoft Copilot) to "Please act as an author who has chosen a list of the 10 most influential futurists - public intellectuals who speculate about the future of humanity. Write a brief essay describing each of them and and their basic messages"

The names selected by more than one of the GPT engines were: Ray Kurzweil (5), Michio Kaku (5), Yuval Harari (4), Alvin Toffler (4), Nick Bostrom (3), Aubrey de Grey (3), William Gibson (2), Faith Popcorn (2), and Paul Roberts (2). The names chosen by only one of the engines were Peter Schwartz, Juan Enriquez, Elon Musk, Peter Diamandis, Kevin Kelly, Jane McGonigal, James Lovelock, Ari Wallach, Buckminster Fuller, Stewart Brand, Jacque Fresco, Donna Haraway, Hans Rosling, Paul Roberts, Jaron Lanier. Christopher Alberg, Dirk Helbing, and John Naisbitt. 

My own favorite futurist has been Venkatesh Rao, a brilliant polymath whose ideas I have passed on in numerous MindBlog posts (see Ribbonfarm for his narratives) . He is one of a cohort of younger thinkers ~50 years old  generating their own newsletters and websites that I have stumbled onto recently. These include Canadian Patrick Tanguary (Sentiers newsletter ,  Samuel Arbesman's site and its 'lists of lists',  Parag Khanna and Jason Crawford (Roots of Progress).  I would encourage MindBlog readers to check out a few of these links. 

An essay on Geopolitics and Globalization

I want to pass on one complete section, indicating titles of the others, of  a larger essay addressing tribalism versus global consciousness.

Untimely Meditations

Philosophy goes against the grain of political immediacy to insist on inconvenient truths.

 

Nathan Gardels, Noema  Magazine Editor-in-Chief

The Condition of Planetarity

The Politics of Planetary Realism

Conceptual demolition of the outmoded paradigm of nation-state realism, however, does not erase its still firmly rooted expression in present practice. Building the centripetal momentum toward binding planetary association against the weighty centrifugal pull of tribal identity is an endeavor as fraught as it is necessary.

While technology and advancing science may foster a universal understanding of the planetary condition, politics and culture have a different logic rooted in emotion and ways of life cultivated among one’s own kind. Far from moving ahead in lock step, when they meet, they clash.

Indeed, the great paradox today is that the planetary imperative of mitigating climate change has become the province of renewed nationalism. Industrial policies designed to make the green energy transition are competing to protect and promote national self-interest instead of joining together at the level of all humanity. What we see instead is the battle of subsidies between the U.S. Inflation Reduction Act and the European Green New Deal, with both raising stiff tariff barriers to blunt China’s concerted conquest of core green technologies from storage batteries to electric vehicles, solar cells and their supply chains.

In short, rather than uniting as a threatened species to meet a challenge that knows no boundaries, competition has sidelined collaboration within the West while global warming has been weaponized in the new Cold War with China.

The new element of a greenlash, registered in the recent European elections, portends social resistance that is more about resentment of self-righteous Tesla-driving elites and the unequally borne costs of the energy transition than climate denial. The Diet-Coke imaginary of environmentalists — who sold climate policies as achievable without undue burdens on economies built around fossil fuels for more than a century — has been put to rest. As the heavy lift of the transition bites ever more deeply into the daily bread, we are learning the hard lesson that the future has a scarce constituency in consumer democracies as well as growth-oriented autocracies.

In this sense, “planetary realism” takes on a double meaning. It entails both a recognition of the interdependence of the planetary condition as well as a realistic grasp of what it will take to navigate through what remains a world of nations.

Immigration And Belonging

Becoming Universal

What about some positive news for a change?

Doomsaying can become a self-fulfilling prophesy, as David Brooks points out. I want to point to Fix The News, a website curated by Angus Hervey and Amy Davoren-Rose, that points out positive stories, and offers to send occasional emails (Which I enjoy receiving) that list some of its content.  

Another positive (and totally woke) effort at making a more positive future is described in the document "New voices for a better society".

I would also point to previous MindBlog posts on the positive views of Steven Pinker. Nicholas Christakis, and Hans Rosling.

The emerging world order as a global version of the pre-Westphalian Middle Ages.

I would like to pass on this link to an essay by Parag Khanna in Noema Magazine which is one of the best description of the increasing entropy in global geopolitical systems as America's former hegemony contines its rapid decline. A previous MindBlog post has pointed to Zeihan's version of this story. Here are a few clips from the start of the piece that indicate its direction:

In global politics, entropy is captured by the term devolution, the transfer or surrender of power toward ever more local levels of authority. The deconcentration of power we witness today has been unfolding since the end of World War II, at which point the U.S. represented roughly half of the global GDP, was the only proven nuclear power and occupied strategic geography in both Europe and Asia.

Fast forward to today and China is the world’s largest economy in Purchasing Power Parity (PPP) terms, which accounts for the price of goods in local currency rather than U.S. dollars. Meanwhile, the EU’s share of the world economy in PPP is roughly equal to America’s, there are nearly 20 economies with a GDP of one trillion dollars or more, nine official nuclear weapons powers, and America’s influence is being actively challenged in the Middle East, Central Asia, Far East and even South America.

It is no coincidence that this rapid diffusion of systemic power coincides with the spectacular expansion of globalization, which connected Western capital to Asian labor, Arab oil supply to Asian demand, ultimately leveling the global economic playing field.

Ascending powers such as China and India have used globalization not to serve the Western-led order but to assert themselves within an interconnected global system. Globalization then has not been a tool of Americanization but far more fundamentally an avatar of entropy: distributing capacity and connecting an ever-wider array of agents.

From nematodes to humans a common brain network motif intertwines hierarchy and modularity.

Pathak et al. (abstract below) suggest the evolved pattern they describe may apply to information processing networks in general, in particular to those of evolving AI implementations.

Significance

Nervous systems are often schematically represented in terms of hierarchically arranged layers with stimuli in the “input” layer sequentially transformed through successive layers, eventually giving rise to response in the “output” layer. Empirical investigations of hierarchy in specific brain regions, e.g., the visual cortex, typically employ detailed anatomical information. However, a general method for identifying the underlying hierarchy from the connectome alone has so far been elusive. By proposing an optimized index that quantifies the hierarchy extant in a network, we reveal an architectural motif underlying the mesoscopic organization of nervous systems across different species. It involves both modular partitioning and hierarchical layered arrangement, suggesting that brains employ an optimal mix of parallel (modular) and sequential (hierarchic) information processing.

Abstract

Networks involved in information processing often have their nodes arranged hierarchically, with the majority of connections occurring in adjacent levels. However, despite being an intuitively appealing concept, the hierarchical organization of large networks, such as those in the brain, is difficult to identify, especially in absence of additional information beyond that provided by the connectome. In this paper, we propose a framework to uncover the hierarchical structure of a given network, that identifies the nodes occupying each level as well as the sequential order of the levels. It involves optimizing a metric that we use to quantify the extent of hierarchy present in a network. Applying this measure to various brain networks, ranging from the nervous system of the nematode Caenorhabditis elegans to the human connectome, we unexpectedly find that they exhibit a common network architectural motif intertwining hierarchy and modularity. This suggests that brain networks may have evolved to simultaneously exploit the functional advantages of these two types of organizations, viz., relatively independent modules performing distributed processing in parallel and a hierarchical structure that allows sequential pooling of these multiple processing streams. An intriguing possibility is that this property we report may be common to information processing networks in general.

Our well being influences our mitochondrial biology.

I pass on the abstract from Trumoff et al (open source) below. The bottom line is that our experienced well being correlates with with higher levels of energy transformation machinery in our mitochondria.  

Significance

Psychosocial experiences predict health trajectories, but the underlying mechanism remains unclear. We report that positive psychosocial experiences are linked to greater abundance of the mitochondrial energy transformation machinery, whereas negative experiences are linked to lower abundance. Overall, psychosocial experiences accounted for 18 to 25% of the variance in protein abundance for complex I, the largest and most upstream mitochondrial oxidative phosphorylation (OxPhos) enzyme. At single-cell resolution, positive psychosocial experiences were particularly related to glial cell mitochondrial phenotypes. As a result, opposite associations between glial cells and neurons were naturally masked in bulk transcriptomic analyses. Our results suggest that mitochondrial recalibrations in specific brain cell types may represent a potential psychobiological pathway linking psychosocial experiences to human brain health.

Abstract

Psychosocial experiences affect brain health and aging trajectories, but the molecular pathways underlying these associations remain unclear. Normal brain function relies on energy transformation by mitochondria oxidative phosphorylation (OxPhos). Two main lines of evidence position mitochondria both as targets and drivers of psychosocial experiences. On the one hand, chronic stress exposure and mood states may alter multiple aspects of mitochondrial biology; on the other hand, functional variations in mitochondrial OxPhos capacity may alter social behavior, stress reactivity, and mood. But are psychosocial exposures and subjective experiences linked to mitochondrial biology in the human brain? By combining longitudinal antemortem assessments of psychosocial factors with postmortem brain (dorsolateral prefrontal cortex) proteomics in older adults, we find that higher well-being is linked to greater abundance of the mitochondrial OxPhos machinery, whereas higher negative mood is linked to lower OxPhos protein content. Combined, positive and negative psychosocial factors explained 18 to 25% of the variance in the abundance of OxPhos complex I, the primary biochemical entry point that energizes brain mitochondria. Moreover, interrogating mitochondrial psychobiological associations in specific neuronal and nonneuronal brain cells with single-nucleus RNA sequencing (RNA-seq) revealed strong cell-type-specific associations for positive psychosocial experiences and mitochondria in glia but opposite associations in neurons. As a result, these “mind-mitochondria” associations were masked in bulk RNA-seq, highlighting the likely underestimation of true psychobiological effect sizes in bulk brain tissues. Thus, self-reported psychosocial experiences are linked to human brain mitochondrial phenotypes.

ChatGPT as a "lab rat" for understanding how our brains process language.

I've now read twice through a fascinating PNAS piece by Mitchell Waldrop (Open source, with useful references), and urge MindBlog readers to havve a look. Our brains, as well as all of the GPT (Generative Pretrained Transforer) engines are prediction machines. The following slilghtly edited extract gives context.

...computer simulations of language [are] working in ways that [are] strikingly similar to the left-hemisphere language regions of our brains, using the same computational principles...The reasons for this AI–brain alignment are still up for debate. But its existence is a huge opportunity for neuroscientists struggling to pin down precisely how the brain’s language regions actually work...What’s made this so difficult in the past is that language is a brain function unique to humans. So, unlike their colleagues studying vision or motor control, language researchers have never had animal models that they can slice, probe, and manipulate to tease out all the neural details.

But now that the new AI models have given them the next best thing—an electronic lab rat for language—Fedorenko and many other neuroscientists around the world have eagerly put these models to work. This requires care, if only because the AI–brain alignment doesn’t seem to encompass many cognitive skills other than language...Language is separate in the brain...there are left-side regions of the brain that are always activated by language—and nothing but language..the system responds in the same way to speaking, writing—all the kinds of languages a person knows and speaks, including sign languages. It doesn't espond to things that aren’t language, like logical puzzles, mathematical problems, or music.

Cumulative human culture began ~600,000 years ago, during the Middle Pleistocene

An interesting study by Paige and Perreault:  

Significance

Our species, Homo sapiens, occupies a uniquely diverse set of ecological habitats. Humans expanded into tropical forests and arctic tundra through cumulative culture. Cumulative culture is the accumulation of modifications, innovations, and improvements over generations through social learning. Generations of variant accumulations allow humans to use technologies and know-how well beyond what a single naive individual could invent independently within their lifetime. We analyzed the stone tools made during the last 3.3 My. We found that these stone tools remained simple until about 600,000 B.P. After that point, stone tools rapidly increased in complexity. Consistent with findings from other research teams, we suggest that this transition signals the development of cumulative culture in the human lineage.

Abstract

Cumulative culture, the accumulation of modifications, innovations, and improvements over generations through social learning, is a key determinant of the behavioral diversity across Homo sapiens populations and their ability to adapt to varied ecological habitats. Generations of improvements, modifications, and lucky errors allow humans to use technologies and know-how well beyond what a single naive individual could invent independently within their lifetime. The human dependence on cumulative culture may have shaped the evolution of biological and behavioral traits in the hominin lineage, including brain size, body size, life history, sociality, subsistence, and ecological niche expansion. Yet, we do not know when, in the human career, our ancestors began to depend on cumulative culture. Here, we show that hominins likely relied on a derived form of cumulative culture by at least ~600 kya, a result in line with a growing body of existing evidence. We analyzed the complexity of stone tool manufacturing sequences over the last 3.3 My of the archaeological record. We then compare these to the achievable complexity without cumulative culture, which we estimate using nonhuman primate technologies and stone tool manufacturing experiments. We find that archaeological technologies become significantly more complex than expected in the absence of cumulative culture only after ~600 kya.

There are no more human elites of any sort...

 I want to pass on the conclusion of a great essay by Venkatesh Rao, giving the meanings of several acronyms in parentheses. You should read the entire piece.
 

"Let me cut to the conclusion: There are no more human elites of any sort. In the sense of natural rulers that is. There are certainly all sorts of privileged and entitled types who want the benefits of being elites, but no humans up to the task of actually being elite.

It is only our anthropocentric conceits that lead us to conclude that a complex system like “civilization” must necessarily have a legible “head,” and legible and governable internal processes for staffing that head. Preferably with the Right Sorts of People, People Like Us.

We’re all under the API (Application Programming Interface) in one way or another. What’s more, we have been for a while, since before the rise of modern AI (which just makes it embarrassingly obvious by paving the cowpaths of our subservience to technological modernity).

To know just how little you know about anything, be it car lightbulbs or national constitutions, whatever your degrees say, just ask ChatGPT to explain some deep knowledge areas to you. I don’t care if you’re a qualified automative technician or Elon Musk or clerking for the Supreme Court. Whether you’re failson C-average George W. Bush or a DEI (Diversity-Equity-Inclusion) activist trying to swap out some Greek classics for modern lesbian classics in the canon.

What you don’t know about the world humanity has built up over millennia utterly dwarfs what you think you know. Whatever the source of your elite pretensions, they’re just that — pretensions. Whatever claims you have to being the most natural member of the governing class, it is somewhere between weak to non-existent. Your claim is really about suitability for casting in a governance LARP (Live Action Role-Playing), not aptitude for governing as a natural member of an elite.

Humans do not like this idea. We ultimately like the idea of a designated elite, and legible, just processes for choosing, installing, and removing them that legitimize our own fantasies of worth and agency. We want to believe that yes, we too can be President, and would deserve to be, and do a good job.

The alternative hypothesis is that modern civilization, with its millennia of evolved technological complexity crammed onto the cramped surface of the planet, does not admit any simple, just, and enduring notion of elite that we can use to govern ourselves. The knowledge, aptitudes, and talents required to govern the world are distributed all over, in unpredictable, unfair, constantly shifting, and messy ways. When a lightbulb fails, there is no default answer to the question of how to replace it, and what to do when mistakes are made.

The rise of modern AI is presenting us with seemingly new forms of these questions. Those who yearn for a reliable class of elites, even if they must both revere and fear that class, are predictably trying to cast AIs themselves as the new elites. Those attached to their anthropocentric conceits are trying to figure out cunning schemes to keep some group of humans reliably in charge.

But there is nobody in charge. No elites, natural or not, deserving or undeserving. And it’s been this way for longer than we care to admit.

And this is a good thing. Stop looking for elites, and look askance at anyone claiming to be part of any elite or muttering conspiratorially about any elites. The world runs itself in more complex and powerful ways than they are capable of imagining. To buy into their self-mythologizing and delusions of grandeur is to be blind to the power and complexity of the world as it actually is.

And if you ever need to remind yourself of this, try changing a car headlamp lightbulb."

How AI will transform the physical world.

I pass on the text of wild-eyed speculations by futurist Ray Kurzweil recently sent to The Economist, who since 1990 has been writing on how soon “The Singularity” - machine intelligence exceeding human intelligence - will arrive and transform our physical world in energy, manufacturing and medicine.  I have too many reservation about realistic details of  his fantasizing to even begin to list them, but the article is a fun read:

By the time children  born today are in kindergarten, artificial intelligence (AI) will probably have surpassed humans at all cognitive tasks, from science to creativity. When I first predicted in 1999 that we would have such artificial general intelligence (AGI) by 2029, most experts thought I’d switched to writing fiction. But since the spectacular breakthroughs of the past few years, many experts think we will have AGI even sooner—so I’ve technically gone from being an optimist to a pessimist, without changing my prediction at all.

After working in the field for 61 years—longer than anyone else alive—I am gratified to see AI at the heart of global conversation. Yet most commentary misses how large language models like ChatGPT and Gemini fit into an even larger story. AI is about to make the leap from revolutionising just the digital world to transforming the physical world as well. This will bring countless benefits, but three areas have especially profound implications: energy, manufacturing and medicine.

Sources of energy are among civilisation’s most fundamental resources. For two centuries the world has needed dirty, non-renewable fossil fuels. Yet harvesting just 0.01% of the sunlight the Earth receives would cover all human energy consumption. Since 1975, solar cells have become 99.7% cheaper per watt of capacity, allowing worldwide capacity to increase by around 2m times. So why doesn’t solar energy dominate yet?

The problem is two-fold. First, photovoltaic materials remain too expensive and inefficient to replace coal and gas completely. Second, because solar generation varies on both diurnal (day/night) and annual (summer/winter) scales, huge amounts of energy need to be stored until needed—and today’s battery technology isn’t quite cost-effective enough. The laws of physics suggest that massive improvements are possible, but the range of chemical possibilities to explore is so enormous that scientists have made achingly slow progress.

By contrast, AI can rapidly sift through billions of chemistries in simulation, and is already driving innovations in both photovoltaics and batteries. This is poised to accelerate dramatically. In all of history until November 2023, humans had discovered about 20,000 stable inorganic compounds for use across all technologies. Then, Google’s GNoME AI discovered far more, increasing that figure overnight to 421,000. Yet this barely scratches the surface of materials-science applications. Once vastly smarter AGI finds fully optimal materials, photovoltaic megaprojects will become viable and solar energy can be so abundant as to be almost free.

Energy abundance enables another revolution: in manufacturing. The costs of almost all goods—from food and clothing to electronics and cars—come largely from a few common factors such as energy, labour (including cognitive labour like R&D and design) and raw materials. AI is on course to vastly lower all these costs.

After cheap, abundant solar energy, the next component is human labour, which is often backbreaking and dangerous. AI is making big strides in robotics that can greatly reduce labour costs. Robotics will also reduce raw-material extraction costs, and AI is finding ways to replace expensive rare-earth elements with common ones like zirconium, silicon and carbon-based graphene. Together, this means that most kinds of goods will become amazingly cheap and abundant.

These advanced manufacturing capabilities will allow the price-performance of computing to maintain the exponential trajectory of the past century—a 75-quadrillion-fold improvement since 1939. This is due to a feedback loop: today’s cutting-edge AI chips are used to optimise designs for next-generation chips. In terms of calculations per second per constant dollar, the best hardware available last November could do 48bn. Nvidia’s new B200 GPUs exceed 500bn.

As we build the titanic computing power needed to simulate biology, we’ll unlock the third physical revolution from AI: medicine. Despite 200 years of dramatic progress, our understanding of the human body is still built on messy approximations that are usually mostly right for most patients, but probably aren’t totally right for you. Tens of thousands of Americans a year die from reactions to drugs that studies said should help them.

Yet AI is starting to turn medicine into an exact science. Instead of painstaking trial-and-error in an experimental lab, molecular biosimulation—precise computer modelling that aids the study of the human body and how drugs work—can quickly assess billions of options to find the most promising medicines. Last summer the first drug designed end-to-end by AI entered phase-2 trials for treating idiopathic pulmonary fibrosis, a lung disease. Dozens of other AI-designed drugs are now entering trials.

Both the drug-discovery and trial pipelines will be supercharged as simulations incorporate the immensely richer data that AI makes possible. In all of history until 2022, science had determined the shapes of around 190,000 proteins. That year DeepMind’s AlphaFold 2 discovered over 200m, which have been released free of charge to researchers to help develop new treatments.

Much more laboratory research is needed to populate larger simulations accurately, but the roadmap is clear. Next, AI will simulate protein complexes, then organelles, cells, tissues, organs and—eventually—the whole body.

This will ultimately replace today’s clinical trials, which are expensive, risky, slow and statistically underpowered. Even in a phase-3 trial, there’s probably not one single subject who matches you on every relevant factor of genetics, lifestyle, comorbidities, drug interactions and disease variation.

Digital trials will let us tailor medicines to each individual patient. The potential is breathtaking: to cure not just diseases like cancer and Alzheimer’s, but the harmful effects of ageing itself.

Today, scientific progress gives the average American or Briton an extra six to seven weeks of life expectancy each year. When AGI gives us full mastery over cellular biology, these gains will sharply accelerate. Once annual increases in life expectancy reach 12 months, we’ll achieve “longevity escape velocity”. For people diligent about healthy habits and using new therapies, I believe this will happen between 2029 and 2035—at which point ageing will not increase their annual chance of dying. And thanks to exponential price-performance improvement in computing, AI-driven therapies that are expensive at first will quickly become widely available.

This is AI’s most transformative promise: longer, healthier lives unbounded by the scarcity and frailty that have limited humanity since its beginnings. ■

Off the rails - inequity and unfairness built into capitalism

I have largely withdrawn from posting items relevant to the details of our current political and social malaise, but I want to pass on a few clips from a piece by Brett Stevens, that passes on points made by Ruchir Sharma, the chairman of Rockefeller International and a Financial Times columnist, in his new book “What Went Wrong With Capitalism.” Sharma makes a convincing case that hits the nail on the head about what has gotten us where we are: easy money, or ultralow interest. When the price of borrowing money is zero, everything goes bonkers.

In 2010, as the era of ultralow and even negative interest rates was getting started, the median sale price for a house in the United States hovered around $220,000. By the start of this year, it was more than $420,000.

Inflation is seen in global financial markets:

..In 1980 they were worth a total of $12 trillion — equal to the size of the global economy at the time. After the pandemic...those markets were worth $390 trillion, or around four times the world’s total gross domestic product.

In theory, easy money should have broad benefits for regular people, from employees with 401(k)s to consumers taking out cheap mortgages. In practice, it has destroyed much of what used to make capitalism an engine of middle-class prosperity in favor of the old and very rich.

First, there was inflation in real and financial assets, followed by inflation in consumer prices, followed by higher financing costs as interest rates have risen to fight inflation...for Americans who rely heavily on credit, it’s been devastating...

...he system is broken and rigged, particularly against the poor and the young. “A generation ago, it took the typical young family three years to save up to the down payment on a home,” Sharma observes in the book. “By 2019, thanks to no return on savings, it was taking 19 years.”

The social consequence of this is rage; the political consequence is populism.

For all their policy differences, both leading U.S. candidates are committed and fearless statists, not friends of competitive capitalism.”

What happens when both major parties are wedded to two versions of the same failing ideas? And what happens when leading figures of both the progressive left and the populist right seek to compound the problem with even easier credit and more runaway spending?

The answer: We are wandering in fog. And the precipice is closer than we think.