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.

The tyranny of words

Some reflections during a wake period at 1:30 a.m. this morning... 

Mulling on the tyranny of thought as a ruminating mind calms down and refuge is found in a quiet space from which words rise like wisps or vapors, a space free of subjects and objects in which there can be no hurry. 

Grateful to be experiencing an aging process that enables a dedifferentiating 82 year old brain to experience a return towards its youth, letting go of the clouds of senolytic discourse that have come to clutter it and increasingly experience being the calm and quiet space from which everything rises. 

Feeling sympathy for public intellectuals whose writing I follow, immersed in their addiction to words as they oblige themselves, many due to financial necessity, to keep writing a stream that includes mediocre as well as brilliant work, each generating a rivulet in the streams of discourse diverging and merging in an infosphere that is becoming increasingly contaminated by the words of robots that duplicate and obscure their efforts.  Grateful for the retired professor’s pension that  permits optional association with, or dissociation from, this world of words.