Micky D's and out

AI in Africa

Paul Mozur, Adam Satariano, and Aaron Krolik, China’s A.I. Is Surging Across Africa. That Should Worry Silicon Valley. NYTimes, Aug. 5, 2026.

When Ernest Mwebaze, a tech developer in Uganda, was building an artificial intelligence system last year tailored for his country’s many languages, he tested American and Chinese tools to see which one could help.

China’s won.

The A.I. model from the Chinese internet giant Alibaba handled Uganda’s dozens of languages better than anything from Meta or Google, Mr. Mwebaze said. It was also inexpensive, and he could customize the model with his own data.

“We want to build things as cheap as possible, yet have them work really well,” said Mr. Mwebaze, a former research scientist at Google. His system, called Sunflower, is now used across Uganda, including by farmers to receive weather information and crop advice in local dialects.

Mr. Mwebaze, 47, is one of thousands of developers across Africa who have turned to Chinese A.I. models in the past year. In Kenya, entrepreneurs are using the models to streamline legal and business services. In Nigeria, they have made educational tools that teach high school students. In Ghana, developers are building local chatbots.

China’s A.I. is surging, especially in developing countries, as people look for the best possible system at the lowest possible cost. Unlike models made by the leading American A.I. companies OpenAI and Anthropic — which are closed and charge fees — the Chinese systems are publicly available to download and modify without payment or approval.

Chinese softpower:

Xi Jinping, China’s leader, is using A.I. as a form of soft power. At a July conference in Shanghai, he cast Chinese models as more reliable and cheaper and warned that A.I.’s benefits must be shared or they would create “new historical injustices.” Kenya, Ethiopia, South Africa and seven other African countries signed an A.I. pact with China at the event to promote international cooperation.

Chinese firms are also courting African developers with free computing and hands-on engineering help, developers said. Many Chinese-made smartphones in Africa come with Chinese A.I. tools preinstalled.

At the same time, China’s A.I. industry faces challenges. Companies have struggled to make money from their users and face security questions about data and links to Beijing. Many African developers still pay for American A.I. models, especially for technical tasks like coding.

Yet Africa’s experience shows the global A.I. competition is now a two-horse race.

China in Kenya's "Silicon Valley";

Konza Technopolis, a planned city, was announced 18 years ago as Kenya’s answer to Silicon Valley. Hopefully branded the Silicon Savannah, it is now a grid of empty boulevards and skeletal buildings, watched over by a powerful Chinese surveillance system.

It would look like a failed experiment but for one slate-gray building behind an electric fence. Financed by Chinese loans and built by Huawei, the site is a data center that runs computing for Kenya’s government.

For two decades, Chinese firms have wired Africa’s telecommunications networks and paved its roads. M-Pesa, Kenya’s celebrated mobile-money system, runs on Huawei technology.

These tech ties gave China a foothold in the continent, which it is using to sell A.I. One project advertised at Konza will use technology from the Chinese A.I. firm DeepSeek to combat telecom fraud.

Meanwhile, the American environment is risky:

In a Nairobi high-rise in June, a top Kenyan civil servant for technology said he was surprised when Anthropic pulled access to Fable, its powerful model, at the Trump administration’s behest.

It was a “wake up call” to the world, said John Tanui, the principal secretary in Kenya’s Ministry of Information, Communications and the Digital Economy. The abrupt move was a warning that relying on U.S. models could pose risks, he said.

Still, America remains in the game:

For all of China’s momentum, much of the A.I. money in Kenya still flows to U.S. companies.

Everyday users are on U.S. systems like ChatGPT and Claude. One 2025 survey found that 42 percent of Kenya’s 23 million internet users had used ChatGPT in the previous month, among the highest rate in the world.

Even Chinese models earn Americans money. Kenyan companies often run Chinese open-source models on cloud services from Amazon and Microsoft, so the U.S. giants make money from their data centers that deliver the technology to customers.

There's more at the link.

Note: I've been watching Africa ever since I learned that Nollywood was the 3rd largest film industry in the world by number of titles (after Hollywood and Bollywood). Africa pretty much leap-frogged over analog cinema direct to digital. Now, of course, they don't have to develop and manufacture the digital video equipment to produce films, they just have to use it. But they do have to service and maintain it. Things are a bit different with AI. But with Chinese open-weight models, who knows?

Norway has a museum dedicated to whales

need you all to know that in june 2027 The Whale by Dorte Mandrup opens in Andenes, Norway. it’ll be an immersive museum dedicated to whales and the ocean, and the building is designed to look like it’s emerging from the Arctic coastline, like a whale. also, the curved roof will… pic.twitter.com/05an2bBUtS

— Holly Blake (@hollycurates) August 6, 2026

François Chollet sees Large Reasoning Models (LRMs) in the future

In the era of base LLM scaling (2022-2024), I believed the LLM line of research would reach a capability plateau (as later seen with base LLMs).

In late 2024, after the o3 test-time compute demo, I changed my views: the new models were showing genuine fluid intelligence, and… https://t.co/Rc99Fcr6NM

— François Chollet (@fchollet) August 7, 2026

What are large reasoning models?

Red flower

pic.twitter.com/JpsrlPHbkS

— wongsusie (@withnomoney) August 6, 2026

The expression of nuance and high dimensionality in LLMs

Jonathan Falk quoted over at Statistical Modeling, Causal Inference, and Social Science in a post by Andrew:

I have spent 50 years fighting the Curse of Dimensionality. I know this curse in my marrow. Brilliant inferences await me, but the space in which these insights are found is simply too vast to explore. So we simplify, reducing the dimensionality to something that while still vast, is confined to a hyperplane where we can, like Plato, see the projections of truth, not the truth itself.

But then what LLMs and their generation have taught me is that nuance, which is really just the inverse of inference (in that it’s the vast set of all things consistent with some inference) has an amazing boon of dimensionality. There appears to be no thought that can’t be described by a 14,000 dimension or so vector whose tuning has the huge advantage that 14,000-dimensional space is so empty that tiny nuances can be readily distinguished in such a space, so that you can hide uniqueness in the vastness of 14000-dimensional space that you couldn’t recover in a raw search in that same space.

Friday Fotos: Going to Mojo in Hoboken

Alcohol in America: Is it really health vs. pleasure?

Ginia Bellafante, What Else Do We Lose When People Give Up Booze?NYTimes, Aug. 1, 2026.

What happens when Americans stop drinking?

Across the country, sales of wine, beer and liquor, by volume, have been steadily decreasing: in 2023 falling 3 percent over the previous year; in 2025, 5 percent. I.W.S.R., the British research firm that ran these numbers, anticipates an 18 percent drop over the next decade. Data from the beer industry show the craft brewing work force in decline.

Employment metrics are just part of a calculus that counts consequences to ecosystems that are dependent on the habits that have fueled human fellowship for most of recorded time. From the birth of the public house in the 16th century to the revolution for gay rights, which began at the Stonewall Inn, alcohol has shaped the social politics of urban life. In tandem with new restaurants over the past 15 to 20 years, taprooms have drawn people to once sparsely populated downtowns. This was true of Angel City Brewery in the Los Angeles Arts District, which closed in April. Regulars mourned the loss of community it signaled. On the Fourth of July, Trim Tab Brewery in a revitalized section of Birmingham, Ala., closed after 13 years, ending its tenure as an exhibition and performance space. Many cities have had similar closures.

Health vs. Fellowship?

The realignment is testament to the power of messaging coming from various corners of the medical establishment, messaging that has convinced many Americans that even a few glasses of Sancerre a week can lead to serious illness and death. Three years ago, the World Health Organization declared that no amount of alcohol is safe. Across the country, sales of wine, beer and liquor, by volume, have been steadily decreasing: in 2023 falling 3 percent over the previous year; in 2025, 5 percent. I.W.S.R., the British research firm that ran these numbers, anticipates an 18 percent drop over the next decade. Data from the beer industry show the craft brewing work force in decline.

Employment metrics are just part of a calculus that counts consequences to ecosystems that are dependent on the habits that have fueled human fellowship for most of recorded time. From the birth of the public house in the 16th century to the revolution for gay rights, which began at the Stonewall Inn, alcohol has shaped the social politics of urban life. In tandem with new restaurants over the past 15 to 20 years, taprooms have drawn people to once sparsely populated downtowns. This was true of Angel City Brewery in the Los Angeles Arts District, which closed in April. Regulars mourned the loss of community it signaled. On the Fourth of July, Trim Tab Brewery in a revitalized section of Birmingham, Ala., closed after 13 years, ending its tenure as an exhibition and performance space. Many cities have had similar closures.

The realignment is testament to the power of messaging coming from various corners of the medical establishment, messaging that has convinced many Americans that even a few glasses of Sancerre a week can lead to serious illness and death. Three years ago, the World Health Organization declared that no amount of alcohol is safe.

Some collateral effects:

For decades, the Brown-Forman company has sustained the city’s museums and performing arts through corporate giving and its affiliated foundation. As directors of local cultural organizations will tell you, there is virtually no institution or preservation effort in Louisville that does not bear its imprint. For example, Patrick Lewis, the president of the city’s Filson Historical Society, noted that when Breonna Taylor was fatally shot by a Louisville police officer six years ago, Brown-Forman provided the money for the society to hire a full-time specialist in Black history.

During the last fiscal year, according to the company and tax filings, Brown-Forman gave away more than $14 million, making it among the leaders in local philanthropy. For example, by contrast, Churchill Downs, the host of the Kentucky Derby, distributed less than a quarter of that.

But Brown-Forman is more than a company and a philanthropic wing; it is a web of extended family members, individual executives and friends of the company who hold stock that has lost more than half of its value in the past five years. What most worries the town’s fund-raising class is the fading away of annual donations in the four or five digit range. As one cultural leader explained, the success of bourbon tourism could not compensate for the pronounced downturn in alcohol consumption, which has affected a network critical to supporting the city’s cultural life.

What of pleasure?

It is not the purview of public health officials to think about the ways in which alcohol consumption might affect arts funding. But doctors, therapists and epidemiologists talk a lot about the health costs of loneliness and social isolation in an increasingly virtual age.

Last year, an academic paper published in the journal Addiction by a professor of psychology in Denmark and a public health lecturer in Scotland stood out for its contrarianism, arguing that alcohol research had a “problem with pleasure.” It had been negated, they wrote, naïvely considered an illusion manufactured by advertisers and notable merely for its oppositional relationship to health.

Maybe, they ventured, it was time for a reframing.

There's more at the link.

scaredketchup: November is Coming

Inside 1301

What we’ve got in frontier models is now neurosymbolic

I would have assumed it was fairly obvious, but in case it's not: a million-line codebase (also known as a "harness"), running at inference time, orchestrating thousands of calls to a neural network for any given task, is the exact definition of a "neurosymbolic architecture"

— François Chollet (@fchollet) August 6, 2026

For a very long time most high-performing AI models were end-to-end neural models; vector input -> vector output, with only ultra-thin symbolic preprocessing and postprocessing layers (e.g. label decoding). For many, it seemed that moving more and more logic to the end-to-end…

— François Chollet (@fchollet) August 6, 2026

Why Wall Street is Ignoring Big Tech's Debt

YouTube page:

Nikkei Asia recently reported that the five biggest US tech companies are carrying an estimated $1.65 trillion in "hidden," off-balance-sheet debt — and a lot of commentators have reached for the word "Enron" to describe the issue. In this video I look at whether that comparison holds up. It doesn't: unlike Enron, this debt isn't concealed through fraud — it's disclosed in the footnotes, it breaks no accounting rules, and much of what is going on is perfectly ordinary. But that raises a more interesting question than "will they get caught." If the aggressive stuff — the adjusted earnings, the leases, the stock-based compensation added back — is all sitting there in plain sight, does dressing up the numbers actually fool anyone? Drawing on the work of Aswath Damodaran, Richard Sloan, Robert Bloomfield and others, I dig into what the research says about whether markets reward clean accounting or aggressive accounting, why the debt isn't hidden so much as filed somewhere too tedious for most people to read — and why the real risk in the AI boom probably isn't the borrowing at all, but the enormous revenue it's all assuming will show up.

On Washington St.

Big Tech is about data politics. Time to bone up on it.

Tressie McMillan Cottom, Want to Fight Back Against Big Tech? Start Here. NYTimes, August 3, 2026. She begins:

In June, I wrote about how the data center revolts happening across the country get to the heart of our greatest political problems. Artificial intelligence is not just a market product or a scientific achievement. It is a political theory. And it is gobbling up our democracy.

The challenge this presents to a functioning democracy is immense. Democrats cannot just “win the midterms” and go back to life before Donald Trump and his army of tech bros upended our way of life. We can talk about reforming the Supreme Court, ending the filibuster, enshrining women’s autonomy in the Constitution and getting money out of elections. But those things won’t matter if we don’t change our laws and norms about what I am calling data politics.

All of the speculation and hype around A.I. is hiding the fact that the technology isn’t just large language models or agents or data centers. It is the backbone of a superstructure that merges regressive politics with unchecked economic power in the guise of technological innovation. Put simply, there is too much money, some of it too untraceable, giving a small group of unelected people too much power over the people. That’s data politics.

Data politics is the right’s answer to what’s next after Trump: more Trumpism, without the man who gave away the country’s store for personal gain. What should the left’s answer be?

She then goes on to list a bunch of books people should read.

AI's biggest illusion, that chess is a good model for intelligence in general

I have been saying in various times and places that it seems to me that AI has (implicitly) taken chess as its prototype for AI research. For one thing, we have John McCarthy's well-known article, “Chess as the Drosophila of AI” (1990). That is, however, a mistake, as I have pointed out in a recent working paper, Computation, Chess, and Language in Artificial Intelligence. Chess is well-defined, while natural language is not. As a consequence the search space for chess is simple in form, a tree, and well understood. That is not at all the case for natural language. Finally, chess is finite, very large, but finite. That is not at all the case for natural language. Consequently chess is not at all a good paradigm for intelligence in general. Intuitions thus gained from it are likely to be misleading for the general problem.

It's in that context that I offer the passage from a recent podcast by Dwarkesh Patel, Eric Jang – Building AlphaGo from scratch. Note the lede for the podcast, "AlphaGo is still the cleanest worked example of the primitives of intelligence: search, learning from experience, and self-play." Here's how Dwarkesh introduces the podcast:

Eric Jang walks through how to build AlphaGo from scratch, but with modern AI tools.

Sometimes you understand the future better by stepping backward. AlphaGo is still the cleanest worked example of the primitives of intelligence: search, learning from experience, and self-play. You have to go back to 2017 to get insight into how the more general AIs of the future might learn.

Once he explained how AlphaGo works, it gave us the context to have a discussion about how RL works in LLMs and how it could work better – naive policy gradient RL has to figure out which of the 100k+ tokens in your trajectory actually got you the right answer, while AlphaGo’s MCTS suggests a strictly better action every single move, giving you a training target that sidesteps the credit assignment problem. The way humans learn is surely closer to the second.

Note that MCTS (Monte Carlo tree search) is one of the oldest algorithm in the book. Trees are very well defined. How do you structure language as a tree? So human intelligence may well tend toward that second alternative, the one based on MCTS, but it is not at all clear how studying chess is going to help you figure out how human intelligence does it. MCTS is not available.