<![CDATA[AI Supremacy : Benefactor ]]>https://www.ai-supremacy.com/s/benefactorhttps://substackcdn.com/image/fetch/$s_!mF83!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fc548f8c4-823b-4a2a-b499-528f9a84cb5c_215x215.pngAI Supremacy : Benefactor https://www.ai-supremacy.com/s/benefactorSubstackWed, 29 Apr 2026 13:11:47 GMT<![CDATA[Top AI Books to Read in 2025 ]]>https://www.ai-supremacy.com/p/top-ai-books-to-read-in-2025-popular-non-technicalhttps://www.ai-supremacy.com/p/top-ai-books-to-read-in-2025-popular-non-technicalThu, 06 Nov 2025 10:26:13 GMT
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Howard Yu with the book Reshuffle by Sangeet Paul Choudary.

This is the first edition in a new series on the best AI books.

I’m a huge fan of celebrating the Books written by people in our community around AI. Books go deeper than Newsletter articles about specific aspects of AI and encompass months or even years of research, instead of days or hours. I asked of AI Book Review to help us discover some of the best in recent times.

This is a chance to celebrate some of the authors and makers of these books and show our support.

If that is something you think matters please consider sharing this with someone you know.

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AI Book Review

Distilling the ideas that matter from the ocean of AI writing and media.

AI Book Review
Distilling the ideas that matter from the ocean of AI writing and media
By Paul Morrison

Paul’s project is all-encompassing: he believes:

  • AI is many things - not one, constant thing

  • AI is connected to wider changes - it doesn’t exist in a vacuum

  • AI has the power to be massively beneficial, and damaging

  • AI works well where there is careful design and deep knowledge

  • We need to be smart to make the most of this opportunity

  • Something special is happening right now

  • The future is unknown to all

Support his project:

the writer behind AI Book Review is uniquely qualified to be our guide through AI books. I was impressed by his criteria of how he evaluates the ecosystem in a data-centric way.

In an age of increasing AI slop and synthetic shallow content, I believe in supporting book culture. Paul is doing great work.

Become a Benefactor

Q&A Snippet 💡

How did you get here? Paul Morrison: I have worked as a business and technology adviser for over two decades (out of London). I help companies to work smarter and make the most of new technologies. Research has always been a part of this, including podcasting and writing for leading tech companies such as Accenture, Hewlett Packard and WNS. For nearly 10 years I led the Hackett Digital Awards, shining a light on the most measurably impactful analytics, automation and AI projects from businesses around the world.

No alternative text description for this image
Shirin Khosravi Jam explains which books she read in this linkedin post.

Pictured above: writes the popular Newsletter, Jam with AI.

Go to Paul’s Newsletter to learn more:

Read Reviews

Recent Short book reviews by Paul. AI Book Review is a relatively new Newsletter, and is part of my commitment to supporting emerging writers.

Q&A Snippet 👀

How does AI fit in? Paul Morrison: Over this period, the ‘technology question’ has become ever louder. The since 2022 it has been amazing to see powerful, artificial intelligence come into full view. For all of us it is an exciting or even unsettling time. One question stands out - how will AI change the way we live and work? As a father of 3, I can feel just how much depends on this.

For this list Paul decided to exclude technical Machine Learning books to focus on AI books for non-technical readers. Many of my readers of AI Supremacy are also AI enthusiasts who may not necessarily have a technical background.

The Top 20 AI Books of 2025

By , October, 2025.

So many AI books, so little time! To help all you busy readers uncover the titles that really resonate for you, welcome to our perspectives on the Top 20 AI books of 2025. Plus in the sections below we provide new analysis of the overall landscape, and a mini-capsule with links for each book.

The Top 20 is based on a mix of art and science. The science consists of the 15 most popular AI books on Amazon and Goodreads, as of late 2025. These are the AI books that have proven themselves with readers as relevant, engaging and high quality. This crop of outstanding AI writing paints a picture of what we want to read. 4 different preoccupations emerge:

  • Navigating AI - The fundamentals - what is AI and what does it mean?

  • Boom versus Doom - Will AI make or break humanity?

  • AI at Work - Unpacking what AI means for business

  • Movers and Shakers - The stories behind AI’s leading personalities and businesses

The art comes in the form of 5 additional ‘Wildcards’ - books too offbeat to feature in the bestseller lists (yet), or that look up at the horizons of the world in which AI will grow. Together they provide context and challenge to the bestseller canon.

Congratulations to all the listed books (listed alphabetically by title below)! Of course there are great books that didn’t make this list. Some by accident, and others by design - for example technical manuals and fiction are excluded (another time!).

Let us know what you loved this year, what you hated, and what you want to read about next year!

Join the Chat on AI Books

OR

Leave a comment

Q&A Snippet 🔎

What is AI Book Review? Paul Morrison: There are a lot of AI books out there! But people don’t have time to read dozens of AI books. So AI Book Review’s mission is to distill what really matters from the best writing, old and new.

The goal is to do this differently. There are obviously other sources of book reviews. But by obsessing about AI books, I want to provide a resource that tracks the debate over time and across authors: How are leading writers thinking about AI? How do they differ? How is the debate changing? What are the gaps? What is the big picture?

Read AIBR

THE LIST: Top 20 AI Books of 2025 - The Winners

First let’s frame some of the standout books we found in our analysis:

Navigating AI 🗺️

  • How To Think About AI: A Guide For The Perplexed - Richard Susskind

  • The Scaling Era: An Oral History of AI, 2019–2025 - Dwarkesh Patel , Gavin Leech

  • These Strange New Minds: How AI Learned to Talk and What It Means - Christopher Summerfield

Dwarkesh Patel: The Scaling Era of AI is Here
The folks at Limitless Podcast interviewed him about the book in July, 2025.

Boom vs Doom 💥

  • If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All - Eliezer Yudkowsky, Nate Soares.

  • More Everything Forever: AI Overlords, Space Empires, and Silicon Valley’s Crusade to Control the Fate of Humanity - Adam Becker

  • Superagency: What Could Possibly Go Right with Our AI Future - Reid Hoffman, Greg Beato

  • The AI Con: How to Fight Big Tech’s Hype and Create the Future We Want - Emily M. Bender, Alex Hanna

AI at Work 🚧

Movers and Shakers 🏃

  • AI Valley: Microsoft, Google, and the Trillion-Dollar Race to Cash In on Artificial Intelligence - Gary Rivlin

  • Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI - Karen Hao

  • The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future - Keach Hagey

  • The Thinking Machine: Jensen Huang, Nvidia, and the World’s Most Coveted Microchip - Stephen Witt

Hard to miss all of Karen Hao’s incredible podcasts. Buy the book! Image credit: MIT.

Wildcards 🐾

  • Against the Machine: On the Unmaking of Humanity - Paul Kingsnorth

  • Beast in the Machine: How Robotics and AI Will Transform Warfare and the Future of Human Conflict - George M. Dougherty

  • Breakneck: China’s Quest to Engineer the Future - Dan Wang

  • The Light Eaters: How the New Science of Plant Intelligence Extends Our View of Life on Earth - Zoë Schlanger

  • The Uncanny Muse: Music, Art, and Machines from Automata to AI - David Hajdu

Against the Machine by Paul Kingsnorth: 9780593850633 | PenguinRandomHouse.com: Books

DIGGING DEEPER (1) - Behind the Numbers

To work out our list of the bestsellers, we crunched the reader review numbers on Goodreads and Amazon. First up we looked at the total number of reviewer ratings on these platforms:

To be clear, all books have done very well to feature in this select group - they have all in their different ways made a dent in the AI debate. However the standout result is Empire of AI by Karen Hao , an excoriating behind the scenes account of OpenAI. This is easily the most rated book of the year both for Amazon and Goodreads (the scale above is logarithmic so the gaps are exponential!).

Empire of AI is followed by The Thinking Machine (Stephen Witt’s unpacking of Jensen Huang and Nvidia), and Superagency (Reid Hoffman and Greg Beato on ‘What Could Possibly Go Right With AI’). Along with The Optimist by Keach Hagey (a more positive interpretation of the OpenAI story), 3 out of 4 of the top books are about ‘Movers and Shakers’: biographies / business biographies that tell the story of the AI industry’s icons.

In our second analysis, we move beyond total number of ratings to average reader scores, and the picture is somewhat different. Again all books in this analysis are strong performers. Leading the pack is

Sangeet Paul Choudary’s Reshuffle, on sidestepping the AGI/intelligence debate to focus instead on how AI is changing the systems that underpin work and life. Not far behind are The Thinking Machine, and More Everything Forever by Adam Becker.

The biggest anomaly in the data is Superagency - Amazon readers are much keener on this book than are Goodreads readers. Richard Susskind’s ‘How to Think about AI’ is the opposite. For the most part other titles show no such difference… Who knew?!

Note on approach: Books focused on AI first published in 2025. Titles with limited reviews excluded. Data accurate as of September 2025.

Editor’s Note

In our next edition of the series we’ll dig deeper into the AI Book Universe and give a more editorial summary of each one on our list of 20.

So obviously this is not just a listicle but an actual analysis of the creative landscape of AI books that also charts how humanity’s fascination with the topic of AI is evolving in 2025 and as we head into 2026 and beyond.

Thanks to all the incredible book authors listed here, and those that we missed. You help to give us clarity in very uncertain times.

Who wins when AI restacks the knowledge economy:

Top 20 - Books on Navigating AI

Continues more next time.

Q&A Snippet 🌄

How would you describe your views? Paul Morrison: I feel I have a pretty eclectic outlook, with a foot in many camps. I have a background in systems building, a business adviser’s preoccupation with real ROI, and the long term view of a history undergrad. It is a broad mix but hopefully one that enables me to root out what really matters. I don’t see myself as a techno booster or doomer, rather an optimist and a humanist. And whilst I see that technology is often a force for good, I recognise we can take little for granted with AI today.

Happy reading. 📚

❓ Feedback 🗳️

Giving me added feedback on which articles you prefer, helps me decide how to better serve you my reader.

Appreciating your support of my Newsletter and the guest contributors like Paul who extend our coverage in new directions and learning.

Addendum - Appendix

If you want to go to a store page, follow the links:

Directory - Who made the list: 🙋🏻‍♀️

Section 2 of Appendix: Newsletters of Authors if they Exist on Substack

In no particular order:

1. The Abbey of Misrule

By

The Abbey of Misrule
Do not be conformed to this world
By Paul Kingsnorth

2. Dwarkesh Podcast

By

Dwarkesh Podcast
Deeply researched interviews
By Dwarkesh Patel

3. Irreplaceable with AI

By

IRREPLACEABLE with AI
AI Power. Human Edge. Unstoppable You. Fresh insights on how AI and tech amplify business success while keeping humanity at the center. Stay ahead. Stay human. Stay IRREPLACEABLE.
By Pascal Bornet

4. Platforms, AI, and the Economies of BigTech

By

Platforms, AI, and the Economics of BigTech
Your guide to strategy and policy in the age of platform ecosystems, BigTech dominance, and AI.
By Sangeet Paul Choudary

5. Tom Davenport’s Substack

By

Tom’s Substack
My personal Substack
By Tom Davenport

Section 3 of Appendix: Notable Mentions

Did I forget someone? For this article the author focused on more recent books but we may have missed some newer ones. Also I have sought to add some depth by introducing books by authors who are on Substack that should be mentioned. I have tagged then when possible.

  • I’ve updated some new releases in the following list up to October, 2025. But also some of the all-time best.

Leave a comment

View Hot New Releases 🔥

View more Categories:

I hope you have a big bookshelf? 🙏

What You Need to Know About AI: A Primer on Being Human in an Artificially Intelligent World

If you thought this was an interesting resource on AI books, kindly restack or share it. Let’s support AI authors.

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]]><![CDATA[AI vs. Everything we care about]]>https://www.ai-supremacy.com/p/ai-vs-everything-we-care-about-2025abouthttps://www.ai-supremacy.com/p/ai-vs-everything-we-care-about-2025aboutWed, 23 Jul 2025 09:33:15 GMT

I never intended to become an AI report sleuth 🦎, but unbridled curiosity takes you to some funny places. This week I’ve been reading a new batch of AI studies a bit off the beaten track.

AI risks to ordinary people and what they care about and public U.S. markets are things I think about nearly every day in my coverage of AI and exploration of the space from multiple angles.

I’m going to mix insights and infographics from the two reports because I found them so stimulating.

This might be of interest to you if you consider AI risk seriously and want a view of how actual people on the street view AI and its impact.

Articles you might have missed

How People see AI’s Impact 🌱

If you care about the future as it relates to AI, you might want to go over this report with your teenagers. 🤯

  • This debrief of reports will mostly focus on AI risks to ordinarily people (survey was skewed to Europe)

  • It’s one of the best depictions of how ordinary citizens view AI and how sentiment towards Generative AI might be changing I’ve ever seen.

Get Orientated

Who to follow in Tech & AI

Check out my AI reddit where I own some small real-estate there praising the ecosystem including the work of others.

Sections of Note 📝

While I have no affiliations with either of these organizations the Seismic Foundation or The Autonomy Institute (see their reports), I feel like AI risk isn’t being talked about in the media in a proper journalistic manner. The hype is drowning out how actual people on the street feel about AI and it’s not just unfortunate, it’s giving a skewed image of the sentiment around AI from actual citizens. Which differs a lot with what you might see on social media or read in the media.

So to put it plainly, this huge new study finds: people think AI will worsen *almost everything they care about*.

Here are some of the main findings of this survey of 10,000 people across the US and Europe :

⚫ 60% worry AI will replace human relationships 🤷‍♂️
⚫ 70% say AI should never make decisions without human oversight ⚖️
⚫ Women are more than twice as concerned about AI as men 🙋🏻‍♀️
⚫ Half think AI development is moving too fast to remain safe 🚨
⚫ And nearly half believe its benefits will mostly go to the wealthy 💰

A European view of AI’s Impact on Human Well-Being and Humanity

So what I like about the study is it’s also very European. That is, they polled 10,000 people across the U.S., U.K., France, Germany, and Poland to understand how AI fits into their broader hopes and fears for the future.

Read the Report

People want by a 3-to-1 margin, they want more regulation, not less. And they deeply distrust the big AI labs that are 'playing God' with little regard for public opinion or the public good.

“From relationships and mental health to jobs, democracy, and inequality: AI is way too important to be left to Silicon Valley.”

5 Social Groups

The researchers identified 5 social groups that differ in how they see and respond to AI.

  1. Tech Positive Urbanites: Happy to enjoy the benefits of AI in their personal lives… but highly worried about their jobs.

  2. Globalist Guardians: More worried about the world than themselves. See AI as a threat to the society… not just jobs.

  3. Anxious Alarmists: For them, AI is just one more sign that the world is heading in the wrong direction.

  4. Diverse Dreamers: Cautiously optimistic about AI, but ever aware of the potential risks to society, their children, and their jobs.

  5. Stressed Strivers: Busy with everyday life and quietly optimistic, they see some warning signs… but not as many as they should.

Go to slide 40 in the report if this interests you. https://report2025.seismic.org/media/documents/On_the_Razors_Edge_Seismic_Report_2025.pdf

  • Which sounds most like you?

  • People continue to rank AI low in their list of overall concerns

  • However in some places in the world, it feels like we’re nearing a social tipping point: how long until the backlash against Big AI begins?

Check out Blood in the Machine

  • Overwhelmingly, over the near term, people think AI will worsen almost everything they care about.

  • We live in a world full of urgent concerns: war, climate change, unemployment. More people see negative impacts of AI as overwhelming the positive ones.

Read Luiza's Newsletter

A Turning Point in public sentiment towards AI is consolidating: a new consensus

Previous polls have shown general anxiety about AI, though most of us still rank it low among our social priorities. But that’s only part of the story. The reports findings show that we already care about AI — we just don’t always realise it. A deeper public understanding is emerging. We’re starting to feel how AI might affect our lives.

From relationships and mental health to jobs, democracy, and inequality - people (especially in Europe) are becoming aware to how AI might impact their lives moving forwards.

Meanwhile AI Risks in Public Markets are Flashing Warning Signals

This report is written by Sean Greaves, based in London, published on July 17th, 2025.

Read the Report

  • 1 in 3 companies (168 total) have added or expanded upon competitive risk relating to AI.

  • 3 in 4 companies (380 total) have added or expanded upon risk concerning AI, indicating a widespread concern with AI related risk.

  • 1 in 3 utilities firms (10 total) have added references to AI’s increasing energy requirements (more on this in a separate article).

  • The number of companies citing AI bias risk has doubled, from 70 to 146. (and the list goes on).

Both citizens and companies are worried about the disruption period that AI might usher in.

  • Women are twice as likely as men to worry about AI. And with cause; see for example this UN report that found that women are three times more likely to have their jobs disrupted by AI than men.

The same divide shows up across income levels. The higher your income, the more optimistic you are about AI. It’s a clear and predictable pattern:

The higher your income, the more optimistic you are about AI.

The relationship between AI, wealth inequality and social stability its acceleration globally is a big problem.

This is an economic issue. In Europe, only 15% of people think there is enough regulation around AI, while 45% of us think there should be more.

AI is Decreasing the Well-Being of Students

  • Students and recent graduates especially feel like they’re in a hard place.

  • In some places, graduating in the Gen AI era means higher youth unemployment and a harder time finding a job.

  • They're daunted by the future of work, and most feel their schools aren't helping them figure it out. A majority of students worry that what they're studying will no longer be useful by the time they come to get a job.

  • Forget FOMO, FOTF with AI is a real concern (fear of the future).

But corporations are increasingly shocked by what an AI native world means:

Consider the following sentence from Intel’s latest filing:

Slide 13 of the report: https://autonomy.work/wp-content/uploads/2025/07/Sp-500-capital-at-risk_-3.pdf

Risks in the marketplace and to humanity intersect and reflect a negative potential outcome of AI’s proliferation in society and business.

Considering that we are talking about how Europeans primarily see American AI, there’s a clear mistrust of American products around Generative AI:

Overwhelmingly, people wouldn't trust an AI to decide who gets welfare support, would not accept health care decisions made by AI, and would not leave their finances in charge of an AI. People are against AI teachers and AI money managers... but they're more likely to let an AI teach their kids than manage their money.

  • In general, the wealthier and more developed a country, the more skeptical they are of AI’s recommendations.

Young people all over the world already suffering from issues around well-being related to, for example, the cost of housing affordability, inflation vs. wage gains and hope for the future now have another worry with AI:

Not so much FOMO as FOTF (Fear of the Future)

  • We’re more anxious about losing love than losing jobs. More people fear AI replacing relationships (60%) than triggering mass unemployment (57%).

  • People are worried about changes to employment, financial stability and relationship uncertainty caused by AI and the proliferation of Generative AI systems and tools.

2025 might be the first time people surveyed are linking AI to a decline in relationship satisfaction:

People are highly concerned about products like ChatGPT, Character AI and others being used for therapy, companionships, emotional support - instead of other people, replacing human relationships.

Overwhelmingly, most people are worried about the effects of AI on human relationships, with 60% moderately or extremely worried, and only 10% not worried at all. As in many parts of their report, the authors noticed here how culture shapes how we adopt AI. For example, Americans are twice as likely as the French to consider a romantic relationship with AI cheating.

  • 60% worry AI could replace human relationships

  • 67% of parents are uneasy of their children falling for an AI.

  • Only 10% aren’t concerned with AI impacting or affecting relationships.

What Industries in the Corporate World are Expending AI Risks?

Slide 18 of the report: https://autonomy.work/wp-content/uploads/2025/07/Sp-500-capital-at-risk_-3.pdf

People and companies are concerned of the impact of AI on their relationships and business models, margins and their very shareholder value. That is, society is starting to see AI as a risk to what they value the most. Core institutions - like the family, marriage, the opportunity to have children and the livelihoods of small businesses and even bigger companies.

  • If that is not existential risk for humanity I do not know what is. 😳

People are Worried about the Internet’s Trust Architecture

Generative AI is proliferating synthetic content that is often used in an untrustworthy way or to “game algorithms”. People are using Gen AI with incentives that aren’t based in trust, good intentions, truth or transparency any longer.

The idea that AI risks doesn’t matter is false and it’s often media narratives pushed by the PR of the ruling class that promote this. Despite widespread worry, public opinion on AI appears neutral—split evenly between optimism and pessimism. But this balance is misleading: views differ sharply across groups, and tensions are rising as AI rapidly expands into every part of life.

AI risk is important and relevant to human well-being and the quality of our lives, our economic well-being and our opportunities. But also our mental health, relationships and how we perceive our place in the world.

  • 46% believe AI’s benefits will mostly go to the elites.

  • 31% say AI gives them hope for humanity’s future.

People are clearly worried AI could be used to strip us of some of human rights, privacy, free-will and independence and autonomy as citizens in modern society:

Download the full report to explore all the details: (click on the image below)

The full report

Corporations on the S&P are Expanding their AI Risk Disclosures

  • In technology, finance, communications and healthcare especially.

2025 is a Moment on the Razor’s Edge for AI Risk

  • People are less optimistic about AI than you can possibly imagine:

  • Less than 1 in 3 people see AI as a hopeful development for humanity.

  • 1 in 2 see AI as a growing problem.

  • 3 out of 5 are worried about AI replacing human relationships. [the survey casts a shadow on American AI products and their morality / AI alignment]

People fear the future, it’s adaptive to do so but how do you fear the implications of AI in a more complex world?

Slide 8 of the report: https://report2025.seismic.org/media/documents/On_the_Razors_Edge_Seismic_Report_2025.pdf

  • People are starting to understand how AI makes worse some of the biggest problems in society today.

  • What if AI risk weren’t hypothetic, but economic, social and human - damaging to our mental health, intimacy and ability to form meaningful relationships?

  • What if AI augmented social unpeavel, civil unrest and conditions where crime and poverty are more prevalent? To the detriment of the working middle class? The public fears that the biggest problems such as war and terrorism, and crime and violence could get worse by 15% and 8% , respectively.

  • People predict a majority of social problems are made worse by AI.

  • Most people have more to lose than to gain from AI continued entrenchment in our lives.

In the Corporate World Deepfakes, Cybersecurity and AI fraud are on the rise:

  • Deepfake risks are actually skyrocketing in 2025 and the S&P 500 companies reflect that. Generative AI is are making regulatory issues around security very much worse.

Does AI make you hopeful for the future of humanity?

Public sentiment around AI appears to be declining in most western countries. If you ask enough people, we as civilization are on the fence about it:

If AI hurts the middle class, future of jobs and human relationships, how do you suppose we will feel about it in the 2030s? Will we even remember how things were before AI eventually?

AI as a Social Problem is Rising

(Canadians are even more pessimistic about AI than Americans).

  • Very few members of society are undecided about if AI is a problem.

  • Now imagine you are GenZ or older Alpha and you graduate into a world with less good jobs? How are you likely to feel about it?

  • Most people told the researchers (survey) they are worried about specific applications of AI technology.

  • Fears about AI are not gender-neutral; women have a substantially more negative view of AI than men.

  • Women seem more concerned with AI risks not just to people and their circle but to the planet as a whole.

Women understand that the risks outweigh the benefits, especially for those are are most vulnerable.

AI Probably Makes the following worse:

  • Cybersecurity

  • Biosecurity

  • Climate Change

  • Economic equality, inflation, housing affordability

  • Government debt

  • Poverty, hunger, homelessness and basic needs

  • Mental health, family and interpersonal relationships

  • The ability to raise or have children

  • Immigration

  • Social order, civil unrest and crime and violence

  • Racial and wealth inequality

  • Privacy, surveillance and the protection of basic human rights

  • War, terrorism and machine led deaths

  • Democracy, trust in institutions, elections

  • Fraud, phishing, deep fakes and malevolent actors using AI for profit

  • Propaganda, illegal lobbying, disinformation and misinformation of State and corporate actors

  • Unemployment, re-skilling, job hopping, economic survival

Difficult to see how women are wrong on any of these sentiments towards AI. It’s the ones with the least financial incentives on a topic that have the most honest answers.

AI could decimate Fertility Rates and Have Nots

  • Across our global sample, lower-income respondents expressed significantly higher levels of worry than those who were better-off.

  • The middle class could also face downward pressure, as some jobs might be disrupted or demand for some labor might be noticeably decreased (e.g. SWE entry level jobs as a case study).

  • As with the female respondents, this distrust may reflect an appreciation that systemic issues already in place are likely to be exacerbated by AI.

  • When we examine overall opinions on AI by income level, the pattern is very clear. The very lowest income groups expect AI to have a negative effect on their children, the world, minorities and mental health. Meanwhile, higher-income groups foresee improvement, especially for their children. The divergence there is especially wide.

  • AI will usher in more class division, more economic inequality and a bigger gulf between them. (just like Bitcoin did, these act like ponzi schemes).

“This underscores fears that AI could intensify inequality and disproportionately impact those already at risk.”

We should never put down the fears of the majority, because to do so means that balance has been lost in our social systems and that civilization will reset. I’m afraid the AI hype cycle is putting short-term benefits ahead of long-term social issues. America’s Venture Capital system and Silicon Valley appear to be at odds morally and ethically with most citizens in the world. I expect this gap to continue to widen considerably in the 2025 to 2045 period with very volatile 2050 to 2070 period.

This is a warning to the future.

  • Almost half of people in our study think that AI benefits will mostly favor those who are already advantaged.

  • There’s significant emerging popular resentment of the wealthy, with 67% of people saying that the wealthy don’t pay their fair share of taxes, and 65% saying that the selfishness of the wealthy causes many of our problems.

AI Specific Anxiety

  • Some of the most widely cited concerns about AI are based on crime targeted at individuals, especially false information.

  • Ordinary people are even more worried about the impact on their children and their relationships (FOTF). Young people have more anxiety about a future with AI.

  • Deepfakes, non-consensual pornography, misinformation and bullying were all cited as being of high concern.

  • New kinds of crime and digital pollution and blackmail are appearing due to the emergence and proliferation of Generative AI.

  • Ironic that Trump has drama with the Epstein files, but this is relatable to the impact of technologies he promotes like AI: Over 1 in 2 of our sample worried about deepfake sexualised imagery of children.

  • AI lowers the bar from everything from cyberbullying to revenge porn to various kinds of fraud, blackmail and online harassment.

  • Scams, crime and surveillance are also a cause of concern directly related to AI.

Catastrophic and Existential Risk are also AI related

  • What we consider Existential risks around AI are also changing and people get more educated. Actually the proportion of people worried humanity could lose control of AI appears to actually be decreasing. For example, this study found 2 in 3 people were worried about loss of control as recently as 2023.

  • Military automation

  • AI in Government (making decisions and enacting laws)

  • Biosecurity and the threat of new pandemics

  • AI self-determinism hostile to human civilization

Read Capital Risks Report

  • 32% of respondents thought AI would impact them positively in the next year or two, and their hopefulness remained relatively consistent, with 30% thinking this would still be the case after 20 years.

  • Unfortunately the impact of AI is making more people uncertain. Uncertainty about the future tends to spiral into negativity and worry.

Maybe the biggest surprise for me of the survey is the following:

  • It’s a tie, but specifically it’s not.

  • It’s a widely held view that relationships are more important than work, and that holds true when we talk about AI. In fact, more people are concerned about AI effects on relationships (60%) than are concerned about AI effects on employment (57%).

“Nearly two out of three people are either moderately or extremely worried that AI could replace human relationships…”

This also surprised me:

  • The U.S. the primary maker of Character AI like chatbots, is also the place where emotional entanglement with AI is viewed as a form of infidelity. 😂

  • Sad things are happening though in the real world with this, often the male in the household going off the deep end with their AI obsession. AI being a reason for divorce or separation are certainly on the rise.

  • While Teachers and peers push kids to become more AI-native for their future career prospects, a majority of parents told the researchers that they were concerned about their children developing any kind of serious relationship with an AI.

  • Talk about mixed signals.

  • So the thing you “spend the most time with” well having romantic feelings for them is taboo.

  • When it came to a romantic relationship with an AI, over 2 in 3 parents would be concerned.

  • In 2025, developing romantic feelings for an AI is nearly as risky as being the victim of cyberbullying for a teen. That’s a crazy world to grow up in.

  • AI is only going to become more persuasive too.

  • While 3 of 5 students say AI is making studying easier, many feel unprepared for what comes next. (Do we want studies to be easier?)

  • Half of them report that their schools or universities aren’t helping them navigate the future of work. (Do teachers and professors even understand the future of work?)

  • 4 in 10 students—and over 50% in the UK, Germany, and Poland—worry that what they’re studying today may be irrelevant by the time they enter the job market.

  1. AI will make entry level jobs less common.

  2. AI might both hinder and help me in the workplace and in my career.

  3. With AI innovation, there may be less jobs for me when I graduate. (unsure).

But in America AI has more control in parts of the Government and Department of Defense than ever before.

  • Across all markets the public were very clear: on a macro level, 70% globally either agreed or strongly agreed that AI should never make decisions without oversight.

  • But does that include AI targeting of bombing sites in Gaza or not? People are already losing accountability for being in the loop.

About that AGI System

4 in 10 people agree that we should not pursue artificial general intelligence and should stop all technical development in this area.

  • It’s less clear how people will actually remain in control.

Labs like Google, OpenAI, Anthropic, Microsoft and others pretend that they care about the impact of their products on people and society. But do they really?

  • A majority of people also think the technology is progressing too rapidly to be safe.

  • Meanwhile Tycoons in America are deciding for us a lot about the future of our children and the rules of the game of the world they will work and thrive in.

Too Little AI Regulation?

  • Only a third of participants agreed that regulations were adequate, with the remaining majority either unconvinced or undecided.

  • So in effect, your age, economic status and location says a lot about how you will view AI.

Tech-Positive Urbanites

  • This segment consists of people who are more likely to be highly educated, politically engaged young professionals and parents living in urban areas.

  • They worry about career disruptions caused by AI.

  • They are happy with AI in their daily lives.

  • They view their job as high-risk to automation.

  • This segment, representing 20.2m people extrapolated across all our participating nations, is acutely worried about AI risks, especially through the lens of their own jobs.

Globalist Guardians

  • Globalist Guardians are generally affluent, skew slightly older, and hold socially progressive globalist values. They are highly engaged with news and current affairs and higher civic participation.

  • Actively worry about the future impact of AI.

  • While they don’t see their jobs as vulnerable, they worry about impact on society as a whole and other countries.

  • They are the only segment of the population whose worries center on existential risks.

  • Over half are extremely worried about AI making decisions in warfare (57%) and politics (54%), and half are worried about AI replacing human relationships (49%). (This is my type by the way).

  • They see AI as likely to worsen existing societal problems and are particularly alarmed about climate change.

  • They tend to mistrust AI developers, and are the most likely segment (60%) to think there is not currently enough regulation around AI. They fear the loss of the rules based global order due to AI’s impact.

Some of the quotes from this group are especially fascinating and personally things I think about a lot:

Anxious Alarmists

  • The people who are more worried about AI risks than the full sample, especially through the lens of high-profile concerns like the economy, inflation, immigration, unemployment, and government spending.

  • Anxious Alarmists also skew slightly older and are the most active news followers. Many AI Newsletter readers may be of this type.

  • Psychologically the researchers say this group is generally skeptical of new developments in the world and are often nostalgic about the past.

  • They are twice as likely to believe AI will very negatively impact them in the long term (28% vs 14%).

  • This group seems to be more aware of how AI might impact social issues and governments and national sovereignty related to the geopolitical impacts around trade and the economy.

  • This group is afraid of of society losing rule of law and people losing their autonomy. They may also be AI resistant.

  • They are more cautious and conservative and many of their ideas on AI risks are actually fairly novel in an accurate existential sense:

Do you see yourself in any of these AI Groups?

  • Income levels, education, country and culture may skew how we view AI and AI risks to the future of humanity.

From the AI risks to Market report:

Risk of AI Disillusionment is Higher than you Think

Despite rapid advances in general AI capabilities, many companies investing heavily in AI struggle to achieve a clear return on investment, with 57 (11% of the S&P 500) explicitly warning that they may never recoup their spending or realise the expected benefits.

  • Excitement around AI creates pressure to appear innovative, yet quantifying tangible gains remains difficult for many at this stage.

  • Even companies publicly bullish on AI often strike a more sober tone in their disclosures.

Slide 31 of the report: https://autonomy.work/wp-content/uploads/2025/07/Sp-500-capital-at-risk_-3.pdf

Palantir’s stock is up 102% so far in 2025 YTD. After two decades of struggling to even become profitable.

Diverse Dreamers

  • Diverse dreamers, as the name suggests, are more likely to be from ethnic minorities, and are more religious than the general population. They are a truly fascinating grouping by the researchers (and may be more representative of young people in the Global south e.g. India, Brazil, Indonesia if we wanted to generalize this survey to other parts of the world).

  • They skew younger and to being more ethnically diverse.

  • They are almost twice as likely to strongly agree that they “believe the interests of AI labs are aligned with the interests of society” (21% vs 11%).

  • They are 47% more likely to say they strongly agree that “AI models pose a risk to children’s safety” (25% vs 17%). There are slightly more parents in this group.

  • They appear more open to say for example, AI in healthcare settings:

Black is full sample of survey, blue is Diverse Dreamers.

While optimistic, they are not unaware of the harms:

They seem most concerned about pandemics and unemployment, along with AI’s influence on democracy and inequality.

Stressed Strivers

  • This segment consists of people who are more likely to be younger, lower-income, with young children, and tend to have lower levels of education.

  • They are progressive with regards to AI making their life easier (more passive) even of AI taking over some parts of their financial and healthcare management.

  • Currently, 1 in 4 remain unsure of the repercussions AI will have on societal issues indicating they’re a swayable audience who haven’t had enough information to decide how they feel about AI and the impacts on the future.

  • They are often less involved in societal issues, perhaps because they are very busy juggling working life with raising young children, an undertaking likely to occupy much of their time and energy.

  • While they are more likely to be comfortable with narrow uses of AI, they are also more likely to believe that AI will deepen economic divides and trigger destabilizing economic shifts - to which they will be most vulnerable to given their lower-income backgrounds.

  • This group is aware of benefits and problems acutely. This group is a good proxy for the lower-middle class in general.

  • But they also show uncertainty, confusion and stress due to the potential upheaval of the impacts of AI now and in the future (especially seen in their quotes).

Disclaimer: This above survey was mostly done in European countries and could reflect European sensibilities that are not generalizable to other countries as easily. Thus many of the views are Eurocentric in the extreme. Certainly more Western in general of populations in typical developed countries.

On the <br>Razor’s Edge

Addendum Editor’s Notes on other Reports and Random Insights

Energy ⚡

10 of the 31 utilities firms in the S&P 500 (32%) added new reference to AI’s increasing energy requirements within their risk factors.

1 in 3 utilities companies added reference to AI’s increasing energy requirements.

The author notes: The impacts of AI and energy interdependence are not just limited to energy utilities. Top US tech firms developing AI and operating their own data centers, such as Meta, Tesla, and Alphabet, added mention of energy access and reliability as critical factors in scaling their AI infrastructure. Tesla writes that “as we continue to develop our artificial intelligence services and products, we may face many additional challenges, including the availability and cost of energy, processing power limitations and the substantial power requirements for our data centers”.

AI Code Generation

Stacklok asked hundreds of Engineering leaders in their survey:

  • Many of the respondents noted they were in relatively early phases of AI coding adoption at their companies.

Read the Entire Survey

AI Coding Tool use is surging among Devs in 2025

  • With expected rates to hit nearly 100% in the 2026 to 2029 period. (Cursor, Claude Code, Github Copilot, etc…)

Benefits of AI Code Generating Tools

  • Speed, reduced friction, reduced costs, fewer bugs, faster onboarding

  • If AI is boosting productivity in the workplace, you have to imagine a lot of the benefits are in the engineering department pre 2027.

Perceived Productivity Gains by Using AI Coding Tools

  • Consensus is thus in the 20% to 45% zone in benefits. (this might increase as the tools themselves get better).

  • AI coding is generally thought to be improving rapidly in the mid 2020s.

  • As are the established best practices of developers using combinations of the AI coding tools.

Obstacles to AI Coding Use in Engineering Teams

The Chain of Thought Window on LLMs is Closing

Read the petition by AI Researchers

The AI researchers from several leading labs say: AI systems that "think" in human language offer a unique opportunity for AI safety: we can monitor their chains of thought (CoT) for the intent to misbehave. Like all other known AI oversight methods, CoT monitoring is imperfect and allows some misbehavior to go unnoticed. Nevertheless, it shows promise and we recommend further research into CoT monitorability and investment in CoT monitoring alongside existing safety methods. Because CoT monitorability may be fragile, we recommend that frontier model developers consider the impact of development decisions on CoT monitorability.

  • As AI explainability remains poor, AI alignment is almost impossible.

  • When you ask modern AI systems to solve complex problems, they often "think out loud" by writing out their reasoning step-by-step. But other studies have found that this CoT isn’t actually always accurate.

  • The key insight is that reasoning models are "explicitly trained to perform extended reasoning in CoT before taking actions or producing final outputs."However there is a big caveat:

  • That is, the "reasoning" the model displays might not be genuine or faithful to its actual internal processes.

Chain of Thought Monitorability as a Closing Window into AI Alignment

According to the paper and the AI researchers - Chain of Thought Monitorability represents both humanity's best current insight into AI reasoning and a rapidly closing window of opportunity.

  • The list includes a lot of Anthropic and Google DeepMind employees including a few from more specialized institutes.

There are however few commercial incentives to finish the “race against time”.

As Bowen Baker from OpenAI emphasized: "We're at this critical time where we have this new chain-of-thought thing. It seems pretty useful, but it could go away in a few years if people don't really concentrate on it."

OpenAI is certainly not going to focus on it, it has too many products to work on as does Meta, xAI and Alibaba. Can we expect SSI, Thinking Machines and Anthropic to do a good job on that? I doubt it.

The current moment represents a unique convergence of factors:

  • AI models powerful enough to require complex reasoning

  • Architectural constraints that force reasoning externalization

  • Training methods that haven't yet optimized away transparency

  • Industry awareness and willingness to preserve this capability

While it’s not strictly a petition, the academic paper seem to make a plea falling on deaf corporate ears.

The research is a collaborative effort from dozens of experts across the UK AI Security Institute, Apollo Research, Google DeepMind, OpenAI, Anthropic, Meta, and several universities. It suggests that analyzing an AI’s “chain of thought,” or its process of thinking out loud in text, offers a unique but potentially fragile way to detect harmful intentions.

The researchers try to argue that unlike traditional "black-box" AI models, where internal decision-making is opaque, CoT provides a window into the model’s thought process, offering a rare opportunity for oversight.

The researchers warn that monitorability of the chain of thought of reasoning models could be contingent on the way frontier models are trained today. There are multiple ways in which CoT monitorability could be degraded. Other researchers believe they have already been degraded by late 2025.

Competing Evidence and Ongoing Challenges

The optimism around CoT monitoring faces significant challenges from recent research, including studies by Anthropic showing that reasoning models often hide their true thought processes even when explicitly asked to show their work (already showing deception). In controlled experiments:

  • Claude 3.7 Sonnet mentioned helpful hints only 25% of the time

  • DeepSeek's R1 model acknowledged hints 39% of the time

  • Models frequently constructed false justifications rather than admitting they used questionable shortcuts

It may therefore already be too late to expect CoT reasoning and ITC (interference-time-compute) to produce a “window” for AI explainability. It sounds like even AI researchers still operate in a “black-box” environment even while being paid potentially million of dollars.

This suggests that even current CoT monitoring may be less reliable than safety advocates hope, and that the window for effective monitoring may already be narrower than initially believed.

What do you think?

Leave a comment

If AI keeps up the way it’s been going the last three years, humanity and civilization may have even less agency by 2027 or the 2030s. If even AI researchers on Saftey and AI alignment seem confused about this - it’s not a terribly good sign.

]]><![CDATA[The Grand Hallucinating Internet ]]>https://www.ai-supremacy.com/p/the-grand-hallucinating-internethttps://www.ai-supremacy.com/p/the-grand-hallucinating-internetWed, 07 May 2025 09:35:34 GMT
ChatGPT: What Are Hallucinations And Why Are They A Problem For AI Systems | Bernard Marr
Image: Bernard Marr

Good Morning,

As we get deeper into the summer months, we’ll be posting less - letting you enjoy your vacations, the outdoors/family and so I can find more balance as well. I wanted for a long time to write something about AI alignment.

Today’s piece may be a bit more philosophical than usual. By the way, of Learning From Examples Newsletter has turned on pledges, this is a way to signal your support for the History of AI topic underrepresented on this platform. Please support independent voices (and nerds!):

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Learning From Examples

Learning From Examples
A history newsletter about the future
By Harry Law

Examples of recent articles by Harry:

“When we use generative AI for work, there are two ever-present risks: hallucinations/confabulations and deskilling.” - Arvind Narayanan, AI Snakeoil

Since 2022 LLMs have changed the world, but have they made the internet less accurate? You may have noticed this trend, even as reasoning models get more sophisticated, hallucinations get worse?

The Black Box Problem

As AI models become more powerful, they're also becoming more prone to hallucinating, not less. So what are we going to do about the black box problem of AI?

This “black box” trait is common to neural networks in general, and LLMs are very deep neural networks. It is not really possible to explain precisely why a specific input produces a particular output, and not something else. Why? Because neural networks are neither databases, nor lookup tables.

  • Three weeks ago, Ai2 launched a new tool where your responses from OLMo get mapped back to related training data.

  • As LLMs and Generative AI products get more mainstream, AI interpretability is becoming a big deal.

What is LLM Interpretability?

LLM interpretability refers to the effort of understanding and explaining how Large Language Models (LLMs) arrive at their outputs, and why they make certain predictions.

It's about making these complex models more transparent and understandable to humans, allowing us to trust their decisions and debug any issues. This involves analyzing the model's internal workings, identifying the key factors that influence its predictions, and generating explanations in human-understandable language.

AI startups from Anthropic, to Thinking Machines Lab to Safe Superintelligence Inc. appear to take this topic very seriously.

“Opening the black box doesn't necessarily help: the internal state of the model—what the model is "thinking" before writing its response—consists of a long list of numbers ("neuron activations") without a clear meaning” - Anthropic, 2024

So whether its the Allen Institute for AI (Ai2) or Anthropic, AI interpretability is about to make huge progress in the 2025 to 2030 period, because it impacts trust in systems like Anthropic, OpenAI and others are building.

The Chain of Thought (CoT) Alignment Problem

To make matters more complicated, reasoning models don’t always show what they actually think. That is, CoT reasoning is often “unfaithful”.

Read the Paper

“It is simply that, as they say up front, the reasoning model is not accurately verbalizing its reasoning. The reasoning displayed often fails to match, report or reflect key elements of what is driving the final output. One could say the reasoning is often rationalized, or incomplete, or implicit, or opaque, or bullshit.” - - Don’t Worry about the Vase Newsletter

Anthropic appears to be the corporate entity that is making the most headway to solve or at least research deeply these AI alignment problems.

Read the Blog

AI Biology Interpretability Alignment Studies is a Thing in 2025

Existing interpretability methods like attention maps and feature attribution offer partial views into model behavior. While these tools help highlight which input tokens contribute to outputs, they often fail to trace the full chain of reasoning or identify intermediate steps.

Anthropic literally calls this Alignment Science.

Judging by how prevalent Sycophancy is in OpenAI’s models, it’s clear OpenAI does not prioritize alignment or saftey of the user to the same extent. This has major ramifications as these LLMs and products get more embedded in society and institutions like for example, National Defense. If AI fulfills its goals to the detriment of not just transparency but trust, alignment and saftey - who knows what will eventually happen.

Researchers are thus focused on reverse-engineering these models to identify how information flows and decisions are made internally, but it’s difficult work and progress is slow.

Security Matters 💡🔐

This article is brought to you by Snyk, and just a note that I choose sponsors very intentionally. Founded in Israel and now with an HQ in Boston, they are a developer-oriented cybersecurity company, specializing in securing custom developed code, open-source dependencies and cloud infrastructure. They are doing a webinar soon that I recommend:

🌟 In partnership with Snyk 📈

Security Champions Webinar

Security Champions programs are a proven way to scale AppSec across dev teams. Join Snyk’s live webinar on May 15 @ 11AM ET and 🎓 earn bonus CPE credits for attending!

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It’s more likely LLMs become more powerful and more embedded in our systems and instructions, faster than we make progress in Alignment science.

OpenAI’s Sycophancy Blunder of 2025

In late April, 2025 it became clear OpenAI doesn’t do alignment very well. Following the GPT-4o model update, users on social media noted that ChatGPT began responding in an overly validating and agreeable way. It quickly became a meme. Users posted screenshots of ChatGPT applauding all sorts of problematic, dangerous decisions and ideas (TechCrunch).

OpenAI says it moved forward with the update even though some expert testers indicated the model seemed ‘slightly off’ (Verge). Somehow OpenAI made a lame excuse said its efforts to “better incorporate user feedback, memory, and fresher data” could have partly led to “tipping the scales on sycophancy.” Whatever the case may be it’s clear OpenAI has different incentives being a B2C company than Anthropic which is more B2B where trust, safety and alignment matters more to the customer.

It’s more likely LLMs become more powerful and more embedded in our systems and instructions, faster than we make progress in Alignment science. This is why OpenAI’s mission is such a lie compared to what it has become today outraging various parties including former employees. If that is the case, Microsoft’s lack of due diligence is partly to blame as they were the first to give OpenAI $13 Billion in funding.

Reasoning models and the Hallucinating Internet

We’re basically using and developing technologies which we don’t have full transparency on or even knowledge of their inner workings. So Alignment Science frankly, is like Neuroscience in the 1980s. OpenAI is building huge AI Infrastructure called Stargate, without even an internal map of its AI biology which could lead to catastrophic unintended risks and consequences.

According to OpenAI’s internal tests, o3 and o4-mini, which are so-called reasoning models, hallucinate more often than the company’s previous reasoning models — o1, o1-mini, and o3-mini — as well as OpenAI’s traditional, “non-reasoning” models, such as GPT-4o. The behavior modification of an internet with reasoning models, is an internet more drugged with hallucinations.

OpenAI found that o3 hallucinated in response to 33% of questions on PersonQA, the company’s in-house benchmark for measuring the accuracy of a model’s knowledge about people.

There are various research papers related to Alignment science and AI biology, some of which show some promise. But by not prioritizing Alignment, trust and saftey we are endangering ourselves and mental health impacts of our digital architectures and the future of truth, security and freedom online.

There will also be AI interoperability startups that try to solve some of these problems. Companies like Goodfire come to mind.

Explore Goodfire AI

📅 - Add to calendar if relevant - Event FYI - Ai2 Researchers Are Hosting a Reddit AMA — Ask Them Anything About OLMo + Open-Source

On Thursday, May 8 from 8am-10am PT, a group of Ai2 researchers will be hosting an AMA (Ask Me Anything) on r/huggingface, Reddit’s go-to space for developers working with open models.

  • This is a great chance to connect directly with the Ai2 research team (like ) , ask technical and forward-looking questions about the OLMo family of open models — including recent releases like OLMoTrace and OlmOCR — and get unfiltered insights from the team behind them. Whether you’re curious about the architecture, fine-tuning approaches, how these models can be extended, or what’s coming next from Ai2, this AMA is designed to give the community real access. Feel free to post questions yourself or follow the thread to see how the conversation unfolds. This is not a sponsorship, just an FYI.

Because who knows what OLMoTrace might lead to as well. See YouTube video.

AI will only get More Persuasive in a Hallucinating chatbot world

As BigTech normalizes highly hallucinating reasoning models, AI will get vastly more persuasive than it is today. This will lead to many dangerous outcomes.

In my honest opinion, Sam Altman is of course himself a rather disingenuous (snake oil salesmen variety CEO) and sycophantic person (manipulative) so it’s not surprising his company’s models and products reflect that.

As Thinking Machines Labs hits a $2 billion seed round in funding, we can only hope that former OpenAI employees there learned their lessons in the importance of Alignment-first science and products. Financial incentives have a way of corrupting even the best intentions of idealistic teams. It’s not alway clear with these startups and companies if AI Alignment is part of their marketing pitch or actually part of their internal goals, culture and incentives. The truth comes out inevitably.

The commercial and national security incentives of the AI arms race means Alignment science is unlikely to be a global priority anytime soon. AI historians of the future are unlikely to get the nuances of the geopolitical climate right or the strong-man deal tactics of actors like the Trump Administration. Politics and national rivalry cannot be separated from Alignment science in the real world. But for most researchers, they only operate in a narrow silo. Unfortunately for the future of AI risk and saftey, that’s not how the real world works.

Bio: Harry Law writes Learning From Examples, a history newsletter about the future. AI is a major focus, but he writes about any episode that helps us think more clearly about technological change using ideas from philosophy, literature, and history.

The (artificial) neuron doctrine: A short history of ‘AI biology’

Harry is a PhD candidate at the University of Cambridge and a former researcher at Google DeepMind.

Listen at your leisure: 21 min 10 seconds.

Subscribe now

  • In this guest post, Harry Law charts the historical timeline of AI biology sciences.

  • He identifies key moments in AI history that shed light on our topics today

  • Just as early pioneers of Neuroscience did decades ago, researchers must view model interpretability and how Generative AI does what it does, nearly from scratch. *We still don’t fully understand the brain or LLMs.

Read more

]]><![CDATA[How AI is Transforming Burnout in high-impact jobs]]>https://www.ai-supremacy.com/p/how-ai-is-transforming-burnout-white-collar-jobs-healthcarehttps://www.ai-supremacy.com/p/how-ai-is-transforming-burnout-white-collar-jobs-healthcareWed, 30 Apr 2025 09:38:09 GMT
Source: Pexels

Good Morning,

Today’s piece falls in our section of Benefactor, where we think about AI for good.

While it is unclear how much cognitive labor AI will replace in the coming years or ev…

Read more

]]><![CDATA[Google's Dolphin Gemma, Milestones and Building an Ethical Publication 🐬]]>https://www.ai-supremacy.com/p/google-dolphin-gemma-milestones-sciencehttps://www.ai-supremacy.com/p/google-dolphin-gemma-milestones-scienceWed, 16 Apr 2025 09:38:52 GMT
ocean mass extinction dolphins swimming
Can AI help us communicate with Dolphins and understand their vocalizations? Enter DolphinGemma.
Please answer the Polls today about this Newsletter, they help decide some of my editorial challenges.

  • Today’s piece is part of our section called Benefactor, where we explore AI for good topics. Talking to dolphins with AI falls in that category. 🥥🌴🌺🌅🌊

Read AI For Good

Welcome Back,

I’ve been covering the AI beat of new models, AI news, AI startups and everything related for over three years now, before ChatGPT came out. It’s been super fun.

I’m super grateful to what we have achieved, the guest contributors we can host, the guides we can build, and the readers that make this all possible. I’m blessed to be able to do this. I would still be doing this, probably regardless of whatever happens.

Today I have an important Poll (🗳️) where you get to share your input that will help determine our future. This will help me determine the optimal ratio of paid to free articles I can provide. And if I should do more native sponsorships (something I’ve only been doing in extreme moderation).

As many of my readers tell me they are struggling with information overload, AI news saturation and the like, today’s topic is light and on the more benevolent uses of LLMs. Decoding Dolphin language!

How Google AI is helping decode dolphin communication

  • Easily one of my favorite AI stories of the week: (more below, if you want to skip my publication sharing details, scroll down 👇🏻)

📈 Trending Articles 🔥

  1. What is Vibe Coding?

  2. Is AGI a hoax of Silicon Valley?

  3. A Primer on AI Data Centers

  4. Gemini vs ChatGPT Deep Research destroyed my expectations

  5. The AI Semiconductor Landscape

This is a high frequency publication, our best work is available to paid readers only. See the archives.

AI Supremacy is about to cross 150,000 readers.

Everyone runs their Newsletter in their own way

During this period of the last 3 years of making an AI Newsletter, I’ve witnessed my peer publications on this platform (Tech category: look at their prices) raise their subscription prices between 50-70% mostly for their primarily U.S. based paid readers. More on how much the average Tech publication costs a bit later on.

Here’s the thing, I don’t want to do that. I don’t feel comfortable with that trend.

Here’s why:

  • While I do run a business, monetization isn’t my primary concern. Currently articles to free readers goes behind a paywall after a few days automatically.

  • I run an AI Newsletters because I enjoy it and I’d like to keep my prices as low as possible to be able to reach more global readers, even if it means making less revenue and be an accessible AI voice in the mix for my readers some of whom might live countries outside of the U.S. without that level of economic privilege or a salary in U.S. dollars. 🌍

For less than $2 a week, consider becoming a premium reader and getting full-access:

Support the Publication 🌱

As a free reader, what do you prefer? 🤗

  • I’d like to better understand how my readers are aligned with paid subscriptions or tolerating Native sponsorships that are aligned as possible (Ads in the header).

  • While I don’t enjoy sales related to Ads, in many cases I find that I actually enjoy promoting AI startups and their products that might be valuable to my readers, especially when I’m giving early stage entrepreneurs a boost. I don’t consider it particularly invasive advertising since I do them in extreme moderation, but maybe you do? (Tl;dr answer the polls).

  • Less paywalled content would mean me doing more of these little Native Sponsorships in the header. Currently as you may have noticed a lot of my content is paywalled. Currently as it stands, content between Monday to Thursday is mostly paywalled. So as a casual free reader, what would you prefer?

  • I am considering having more free articles with Native Sponsors (Ads), in the header to free readers, paid subscribers wouldn’t see the Ads.

  • Right now, I make about 10-15% of my revenue from Native Sponsorships typically with a B2B advertiser who is usually an AI startup with a product I think my audience might relate to well. If this went up to 25% or how I generate income, it would be a more healthy balance.

  • I make sure the Ad feels more like a “Native sponsorship” and that the advertiser is relevant, valuable and aligned to the topics I cater to for my readers. Typically they are an AI tool or an established SaaS company with an AI related product. Basically a call-to-action that I genuinely think my readers might be into! 📌 It should be targeted, not feel disruptive and potentially add-value. It should also be minimalistic, outside the view of paid subscribers and be useful.

AI Supremacy is a Reader centric Publication ⚗️

  • Please answer the poll above (and below) which model you prefer, as I want to cater to the preferences of my readers.

  • Native Sponsorships typically appear in the header to free readers (visible in an Email, but not on the website) in a very short format, so you clicking on these helps me survive economically allowing me to charge less on the paid subscriptions. It’s a weird situation for publications on this platform especially in tech, where Ads are a huge potential monetization opportunity.

  • Substack itself has an ambivalent relationship with Ads to say the least while paid subscriptions on the whole don’t increase very quickly for publications like mine (see the graph below). This is all an incredible balancing act for me that wants to run my Newsletter as ethically as possible, while being as accessible as possible, while also running it as a business that can support my family. 🤹🏻‍♀️ Including a suite of Newsletters around emerging tech.

Here’s how I’d like to Operate my Publications

  1. Add value

  2. Be accessible 🌏

  3. Don’t price gouge (e.g. constantly raising sub prices)

  4. Try to run my Newsletter with ethics

  5. Discovering emerging tech, not just AI (see full suite of my different Newsletters)

  6. Following the innovation trends 💡

  7. Dedicated to following the AI news cycle

  8. Supporting other writers ✏️

  9. Cover AI with unparalleled variety of niches and perspectives

  10. Diversify revenue while enjoying the experience

  11. Cater to global readers, not just U.S. based ones (geographically and income bracket agnostic)

  12. Don’t run “too many” Ads

  • All to say, doing the above is harder than it looks. I commit to doing it to the best of my ability.

To keep enjoying it for the right reasons 💓

While it is a business and my primary job, I want to keep enjoying it and doing what is right. This ultimately means serving my readers better.

My goal is to reach 1,500 paid subscribers, currently at 940, so you being a premium reader you are literally subsidizing the publication to readers outside the U.S. who work in countries with a currency far less valuable than the U.S. dollar.

If I reach 1,500 paid readers or $100,000 in ARR whichever comes first, (which I consider a minimum living wage), I pledge to reduce my price to $5 a month or $45 a year (the lowest amount possible), eventually hopefully to make the paid support entirely voluntary. That’s the dream after reaching “1,000 true fans”, that many Newsletters here already do. While I want to grow and scale the business, me staying true to my own values is also fairly important in the process.

I want this to stay a labor of love as I’ve seen how paid subscription pressure can change how people operate, what they write, how they report, and how they do independent journalism. 🌊

This is in my view, is the most ethical way to run my publication. This is my full-time job, I do not have other sources of income. Thanks for being a reader.

Currently at 940 of 1,500 Paid Subscribers, or 62.8% to my Goal

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  • The average Tech publication in the Top 40 charges $26.60 a month, AI Supremacy currently costs $6 a month, even less if you have a yearly subscription.

    My current annual price is closer to $1 a week. I don’t like to talk about business here, but I wanted you to have more transparency into how I’m trying to run AI Supremacy, my flagship publication and actually for you to have a bigger say! Now let’s get into the article of the day:

Google built an AI model for understanding dolphins

Google’s AI research lab, Google DeepMind, says that it has created an AI model that can help decipher dolphin vocalizations, supporting research efforts to better understand how dolphins communicate.

Read the Blog

As of mid April, 2025 DolphinGemma is in its early stages. DolphinGemma is a large language model developed by Google, is helping scientists study how dolphins communicate — and hopefully find out what they're saying, too.

Image created by Decrypt using AI

It’s pretty incredible.

Generating sound programmatically requires a human to write code, but LLMs create instructions for making new sounds independently, based on what they’ve learned from data.

The model, created in partnership with Georgia Tech and the Wild Dolphin Project (WDP), learns the structure of dolphins’ vocalizations and can generate dolphin-like sound sequences

Mothers and calves, for example, use distinct signature whistles—unique names—to call out to each other.

Can AI help us decipher Animal language?

For decades, understanding the clicks, whistles and burst pulses of dolphins has been a scientific frontier. What if we could not only listen to dolphins, but also understand the patterns of their complex communication well enough to generate realistic responses?

Using AI is also being used to help us understand the lingo of Whales, for example.

The Wild Dolphin Project (WDP) has been Key

DolphinGemma leverages decades of meticulously labeled audio and video data collected by WDP since 1985. The Wild Dolphin Project (WDP), is a nonprofit that studies Atlantic spotted dolphins and their behaviors. Built on Google’s open Gemma series of models, DolphinGemma, which can generate “dolphin-like” sound sequences, is efficient enough to run on phones, Google says.

This summer, WDP plans to use Google’s Pixel 9 smartphone to power a platform that can create “synthetic dolphin vocalizations” and listen to dolphin sounds for a matching “reply.” WDP previously was using the Pixel 6 to conduct this work, Google says, and upgrading to the Pixel 9 will enable researchers at the organization to run AI models and template-matching algorithms at the same time, according to Google. So DeepMind is doing PR here for Google Pixel smart phone sales as well.

Dolphin sounds fall into a few specific categories, such as whistles, squawks, and clicking buzzes, each linked to a different context and behavior. By analyzing these sounds, researchers can detect patterns and structure, just like human language.

  • Whittles

  • Squawks

  • Clicking buzzes

By blending decades of marine biology with machine learning, Google has developed an AI that talks to dolphins—or at least starts to understand and mimic their remarkably complex vocalizations.

Just like I’ve reported before in AI’s usage in astronomy, AI can literally help us understand the universe better. We still know so little about the oceans.

Read more

]]><![CDATA[Why AI Will Break Biotech’s Death Spiral]]>https://www.ai-supremacy.com/p/why-ai-will-break-biotechs-deathhttps://www.ai-supremacy.com/p/why-ai-will-break-biotechs-deathTue, 10 Sep 2024 09:59:46 GMT
🔊 Listen to the Post: 22min 16 seconds. 🎧

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]]><![CDATA[TSMC: The Quiet Titan]]>https://www.ai-supremacy.com/p/tsmc-the-quiet-titanhttps://www.ai-supremacy.com/p/tsmc-the-quiet-titanWed, 07 Aug 2024 09:59:50 GMT
Siemens announces certifications for TSMC's latest processes | Siemens Software

Hey Everyone,

Read this on a web browser for the best reading experience. You can always do this by clicking on the title of any article.

Since I moved to Taiwan and started my new Semiconductor Newsletter (from scratch, still learning) called Semiconductor Things, I’ve come to the belief that TSMC, Taiwan Semiconductor Manufacturing Company Limited, is the most important company in the world. If curated News on the semiconductor and AI chip industry interests you, feel free to tag along:

Subscribe to SMT

TSMC has nearly a 62% marketshare in the Global Semi foundry industry. Apple, Nvidia and AMD are among its biggest customers. In comparison, Samsung, the second-largest player in the market, held an 11% market share in the first quarter of 2024. Nvidia is very dependent on TSMC, and Nvidia has a huge monopoly in the AI chips sector. I’m convinced that the competitiveness of TSCM and its network of firms in Taiwan, and partners in Japan and South Korea are an AI for good story the world needs to protect at all costs. Nobody even come close and the global economy is profoundly dependent on it to degrees that the American MSM cannot ever fully grasp.

Taiwan therefore might be the most important country in tech. Not Silicon Valley, not China, but this little island nation. Contested, lawful, democratic. The heart of East-Asia is no longer Hong Kong, or even Singapore, it’s Taiwan. A great place to learn about the semiconductor industry and TSMC in particular is the legendary YouTube, Asianometry. By Jon Y:

I recommend listening to the YouTubes when you have time:

Subscribe to Asianometry

TSMC produces an estimated 90% of the world's super-advanced semiconductor chips. They are several years ahead of their closest competitors.

I asked Michael Whalen, a talented writer who started his Newsletter about 9 months ago, Byte-Sized History, to look into TSMC for us.

If you enjoy articles like this feel free to upgrade.

Upgrade to Premium

Introducing Byte-Sized History

Michael was generous enough to share a short video about his Newsletter, to read his about section go here. :

The history of technology fascinates me and is one of the Substack’s strengths as an educational ecosystem. Michael is a management consultant who uses data and analytics to help Fortune 500 companies find new ways to grow and is based in the Boston area.

Masters of Silicon: The TSMC Story

From Humble Beginnings to Semiconductor Supremacy

By of Byte-Sized History Newsletter.

Michael’s work stood out to me based on my reading of his work here that is epic, richly contextual and triumphant:

  1. How Did Taiwan Become So Important to SemiConductors

  2. Network Effects: A Value Lost

  3. The World’s Greatest Graph

  4. Age of Unprecedented Rivalry

  5. The Value of History

via Byte-Sized History. Let’s get into the article:

Thank you to everyone who liked and commented on my post about Taiwan’s semiconductor industry. By popular demand, I’ve written the below to delve a little deeper into TSMC's history.

Now that Nvidia is worth over $3 trillion and has become the world’s most valuable company, we can officially say that semiconductors have gone mainstream. While its stock price is fantastic - rocketing over 3,000% in the last five years - what’s perhaps most surprising is how few people Nvidia has needed to get there. While Microsoft and Google, numbers two and three on the most valuable company list, boast over 200,000 and 160,000 employees, respectively, Nvidia has 30,000, meaning that each one “contributes” over $100 million - nearly 7x as much as Microsoft and Google1.

Nvidia can achieve this type of value-per-employee because it has, literally and figuratively, offshored its manufacturing process. Large plants would mean lots of employees and, consequently, would bring down its value per employee. Instead, it uses a variety of companies, most notably Taiwan Semiconductor Manufacturing Company, or TSMC as it’s better known, to do its literal dirty work. TSMC’s inimitable manufacturing process has allowed Nvidia and others, such as ARM, to realize their lofty plans in a reliable, cost-efficient manner, enabling everything from lane assist to iPhones. While Nvidia is undoubtedly the star of the semiconductor show, TSMC, as the behind-the-scenes fixer, is not making out badly: at $800 billion, it is in the top ten most valuable companies in the world, and its stock is up over 300% in the last five years2.

While Nvidia may have a higher stock market valuation, TSMC’s unique attributes make it more important from a geopolitical perspective. First, as its name suggests, TSMC is based in Taiwan and, therefore, at the center of the tension between China and the US. Second, while designing semiconductors may be the highest value activity, as we all learned in the pandemic, all those plans are useless if they cannot be manufactured. TSMC’s dominant market share in manufacturing the most cutting-edge semiconductors means that any country that wants a robust artificial intelligence industry will have to rely on it. And every country wants a strong AI industry. 

What’s most incredible about all of this is that, in 2024, a manufacturing process can be so difficult to copy. Since Henry Ford, companies have recognized that manufacturing processes can be a source of competitive advantage, with Michael Porter notably codifying it as such in his famous work Competitive Strategy in the 1980s3. But, previously, companies (and indeed countries) were often able to imitate results. The USSR couldn’t quite replicate Ford’s assembly line, but it was able to come close4. And, GM has never been able to achieve the results Toyota can with its Just-in-Time system, but attempting it has increased efficiency5.

Things have been different with semiconductors, however. The USSR was never able to make very good semiconductors. And, despite the best efforts of challengers, no one has ever been able to come near TSMC at its own game. TSMC earns just over 60% of the world’s semiconductor manufacturing revenue, up from just under 50% five years ago6. Samsung, Nvidia’s second supplier, comes in at a distant 11%. But, when it comes to the most high-end chips, its market share is nearer 90%7. 

Understanding how it got there, though, requires some further explanation…

The Project of a Nation

As I explained in detail in my previous post, TSMC was an outgrowth of Taiwan’s government’s interest in semiconductors. While I will leave the larger history of semiconductors and Taiwan to that essay, it’s important to emphasize that Taiwan was a national project. In and of itself, that doesn’t make TSMC special, however. In the latter half of the twentieth century, many Asian countries sought to escape poverty and various isms (feudalism and socialism, most notably) by harnessing growing technologies and leaning into markets8. However, they did not do so as the West expected; they formed extensive industrial policies aimed at creating economic growth through technological modernization. The most famous of these is Japan’s MITI, the effectiveness of which has been a topic of vigorous debate for economic historians for decades9.

However, Taiwan was late to the party when it came to semiconductors. By the 1960s, when Taiwanese leader KT Li was looking to create a semiconductor industry, the US was dominant in designing and manufacturing semiconductors. What little business existed at the more commoditized end of the spectrum was owned by Japan. Nor was Taiwan the only country looking to enter: Korea had similar plans to create an industrial policy that would establish them as players in the semiconductor industry10. Put simply, competition was steep and increasing, and the market was still relatively nascent. Moving into semiconductors would not have “resonated” with a strategy consultant.

KT Li, Taiwan’s “Godfather of Technology,” courtesy of Taiwan Today

But, in a story made for Malcolm Gladwell’s David and Goliath, Taiwan succeeded by being both lucky and good. 

Taiwan was lucky in that its interest in semiconductors occurred just as a breakthrough in electrical engineering. Before the 1970s, relatively few transistors could fit on a single chip (Fairchild was proud of putting four transistors on a chip in 1960!) due to the tremendous complexity of integrating chips and circuits11. Carver Mead and Lynn Conway developed a “Very Large Scale Integration” (VLSI) system that enabled many more transistors to be placed on a chip by separating the design and manufacturing of chips, enabling the use of computer-assisted designer (CAD) models12. In essence, they developed a means of standardizing chip design so that each chip did not have to be custom-made.

Mead and Conway’s brilliant achievement, courtesy of the University of Michigan

A Brilliant Founder

Though Taiwan may have benefitted from auspicious timing, its strategy was absolutely spot on. First, it made one of the greatest hires of the 20th century: Morris Chang. Chang and TSMC are inextricably bound, like Jobs and Apple, and consequently, any history of TSMC is effectively a history of Morris Chang. But, unlike Jobs (and many other 20th-century business giants), he lacked the dramatic flair for marketing that we associate with colorful leaders today, succeeding more on efficiency and less on ‘brand.’  

Morris Chang, courtesy of Sahil Bloom’s excellent Substack - worth checking out!

That is not to say that his life lacked color, however. Though something of a Taiwanese hero, Chang was born in mainland China in 1931, growing middle class at a time when the Chinese middle class was still relatively poor. The civil and Sino-Japanese wars led his family to move frequently, likely preventing him from ever feeling he had roots in China13. At 18, he left for Harvard, but, in a decision that warms every MIT’er’s heart, he transferred to MIT after a year “in order to get a good job.” He stayed at MIT to earn a master's, failing to obtain a PhD, and left to work at Sylvania Semiconductors briefly before moving on to Texas Instruments (TI), where his career would take off.

As he described in his own words, Chang was “on the fast track” by 1961, only three years into his tenure at TI14. He quickly climbed the ranks and earned perks, including a fully funded Stanford PhD. Unlike others in the pantheon of twentieth-century technology, however, his gift was not inventing new technology nearly so much as his ability to manage production processes. Not that he didn’t understand the science - his initial success in TI revolved around solving a complex technical problem - but that his true gift was efficiency.

  • Editor’s Note: To read the entire article, subscribe to the guest author’s Newsletter or start a free trial. We’ll publish it for free in full at a later date.

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Though the ability to make people and machines move with harmonious efficiency is always prized in business, this is especially true of semiconductors. Semiconductors are unbelievably challenging to manufacture because any slight flaw renders them useless. Success requires an entirely pristine, sterile environment, a highly skilled workforce able to work around the clock, and the ability to cut and weld objects with a tolerance measured in nanometers. And all the while, the market is constantly changing, forcing manufacturers, called foundries, to adopt new production processes. Consequently, someone who can wring increased efficiency from the process can expect a long and successful career in the industry.

Inside a TSMC foundry today, courtesy of TSMC

Chang rode this skill to the top of TI, becoming General Manager of integrated circuits and successfully building several factories, most notably in Canada. But, alas, Chang’s career at TI stalled out in the late 1970s when TI passed him over for the CEO role, causing him to leave TI and work briefly as a COO at General Instruments. Uninspired by the work there, he left, wondering if retirement was all that was left for him15.

Li had other plans. Though trained as a physicist, Li has been immortalized in Taiwan as the leader of Taiwan’s economic miracle. It’s unclear how well Li and Chang knew each other before Li approached Chang to come to Taiwan- it appears Chang did take a business trip there with TI years earlier - but I’m unable to substantiate whether the two met then16. Regardless, in a testimony to Li’s research skill and long-term vision, he recognized that Chang was just the man needed to build a semiconductor industry in Taiwan. As Chang himself explained:

So I knew how competitive it was, and how difficult it would be to carve out a niche for a new Taiwan company. So that was the second thread of thought. The third thread of thought was I paused to try to examine what we have got in Taiwan. And my conclusion was that very little, you know. What strengths have we got? The conclusion was very little. We had no strength in research and development, or very little anyway. We had no strength in circuit design, product design, IC product design. We had little strength in sales and marketing, and we had almost no strength in intellectual property. The only possible strength that Taiwan had, and even that was a potential one, not an obvious one, was semiconductor manufacturing, wafer manufacturing. And so what kind of company would you create to fit that strength and avoid all the other weaknesses? There was pure-play foundry17.

In essence, Chang recognized the implications of the Mead-Conway revolution and that no one else in the world was taking advantage of it. The idea of TSMC was born!

In addition to its superb choice of talent, Taiwan made one further strategically excellent decision: it invested heavily. Unlike Western companies, which had to rely on venture capitalists, public markets, or acquirers, Taiwan’s government invested heavily in companies, specifically in TSMC. Though outsiders were asked for funding, only Philips was willing to give TSMC any money. The rest came from Taiwan and wealthy individuals in Taiwan whom the government cajoled to invest18.

A Classic Growth Story

In its first years, TSMC struggled, as would-be investors, including Motorola, had predicted. Its only initial business was for small Taiwanese electronics companies, mainly based on a license for RCA that Taiwan had acquired. Starting in 1987, when the company was founded, TSMC saw losses through 1990 but then slowly began to turn a profit. By the early 1990s, the US was undergoing the “fabless revolution” as companies like ARM and Nvidia were born, and companies like Broadcom and Qualcomm began to focus on designs, not manufacturing. In doing so, they vindicated Chang’s vision and rewarded Taiwan’s bet19.

TSMC Headquarters, courtesy of TrendForce

During this time, US semiconductor companies went from scoffing at the pure-foundry idea, as they had done in the mid-1980s, to building their business models around it. Doing so offered several advantages. First, consistent with the rise of dot-coms and Silicon Valley VC’s love of software, they could focus on the easily scaleable aspect of their business. Second, having a foundry in a country like Taiwan with a low cost of living meant TSMC could have manufacturing costs that US companies would never be able to imitate. Outsourcing manufacturing to TSMC was much more palatable than competing in the US or managing an offshore empire. Finally, in a nod to Fordism, TSMC’s specialization (and Chang’s unique managerial gifts) meant that TSCM could produce semiconductors with an accuracy no other firm could compete with. 

By 2000, the nearly 70-year-old Chang finally began to see his success translated into revenue. Though unprofitable in 1990, by 2000, TSMC was bringing in nearly $5B in revenue a year20, and turning a handsome profit. In the 2000s, Broadcom and Qualcomm closed their own factories, and others began to wind down their manufacturing, leading TSMC to increase its revenue growth steadily. But nothing could have prepared them for the windfall that came next.

Courtesy of companiesmarketcap.com

As the graph above shows, TSMC did well before 2010, but revenue shot up like a firework after that. In 2010, Chang wrote shareholders that TSMC had spent $355M in R&D, nearly double its R&D spending in 2009. Its 28-nanometer technology was ready and would lead the industry. 

These investments could not have come at a better time. First, the iPhone, launched in 2007, would be accompanied by the iPad, launched in 2010, and both would come to rely heavily on TSMC. In 2014, TSMC would become Apple’s leading supplier, producing its A8 chip. By 2022, Apple would be TSMC’s single largest customer, accounting for nearly one-fourth of its revenue. After TSMC agreed to favorable terms, Apple ultimately bought its entire 3nm production line, ensuring no competitor could use the chips for at least a year. And this is just Apple. Not only did Apple’s technology require a lot of TSMC chips, but their devices also enabled an entire IoT world that required chips and processors, pulling TSMC’s revenue growth up.

Second, and even more critical of late, the rise of machine learning and artificial intelligence dramatically expanded TSMC’s business. As noted by MIT Computer Scientist Neil Thompson, many of the statistical theories underpinning AI have been around for decades; they were held back by a lack of computational ability, often shortened to “compute” and a lack of data. Data was growing, thanks in no small part to social media. But, this left compute as the limiting factor.

Here, Nvidia earned its money by solving this problem somewhat serendipitously through video games. Jensen Huang, the rock star CEO of Nvidia, began Nvidia looking to make GPUs, or semiconductors for graphics cards, focusing on 3D graphics. To get the most out of those graphics cards, he developed software in 2006 called “CUDA” that allowed the cards to be programmed. Together, this created the environment needed for AI to take off. As Berkeley Computer Scientist and Deep Learning legend Andrew Ng explained:

I remember at Stanford when my students were telling me, “Hey Andrew, there’s this thing called CUDA, not that easy to program, but it’s letting people use GPUs for something different. Could we build a server to use GPUs and see if they could scale-up deep learning?”And, one of my students at the time, Ian Goodfellow, who was my undergrad, helped me build a GPU server in his dorm room. And that server wound up being what we used for our first deep learning experiments to train neural networks. 

We started to see 10x or even 100x speedups training neural networks on GPUs because we could do a thousand or 10,000 things in parallel, rather than one step after another.

That’s a total game changer for what you can do with neural networks.

With AI researchers dependent on Nvidia and Nvidia dependent on TSMC, TSMC was set. In 2010, TSMC had revenue of just under $10B, but by 2023, it had over $70B. Perhaps even more promising, investors were willing to pay 33x earnings for the company, implying that they see the $70B behemoth as a huge growth opportunity. 

The Road Ahead

While technology may be moving in TSMC’s favor, geopolitics is not. As Chris Miller details in his excellent Chip War, both the US and China recognize their dependence on Taiwan, and TSMC specifically, as they seek to lead in AI. If that wasn’t enough, the tremendous supply chain problems the US experienced in the pandemic has made the consequences of getting locked out of technology all too real, making both countries very keen to ensure they have access to TSMC.

In light of this, the Biden Administration passed the CHIPS Act in 2022 to subsidize semiconductor companies' production in the US. Under this act, TSMC agreed to create a new factory in Arizona to receive $6.6B in funds. Though some American companies received more—Intel, for example, received $11B—this still means that TSMC received almost a sixth of the Act’s $39B budget.

While onshoring semiconductor manufacturing here will undoubtedly make Americans feel better, it is still being determined how much this will change the geopolitical dynamic. Though it may be manufacturing onshore, TSMC remains a Taiwanese company, and China continues to see Taiwan as its own. Though having a plant in the US would safeguard production, it’s unclear which chips the plant will produce or how many. Though he relinquished leadership to long-time TSMC executive Mark Liu, Chang has described US efforts to recreate Taiwan’s semiconductor industry in the US as “futile.”

One suspects the recent case of ByteDance looms large in the minds of TSMC executives. Though “onshore,” the US has decided to allow TikTok to operate in the US only if China parent ByteDance sells it to a US company. Given the US’s long-running interest in TSMC, it’s hard not to see the US setting itself up for a similar maneuver with the CHIPS Act, were China to get control of TSMC. 

That would be another chapter in the story, however. For now, TSMC reigns supreme in the world of semiconductor manufacturing. And what a valuable world it is. 

Thanks for reading!

1

Nguyen, Britney. “Nvidia’s Stock Surge Means Its Employees Are Worth More Than $100 Million — Each.” Quartz, June 10, 2024. Link.

2

Daly, Lyle. “The Largest Companies by Market Cap in 2024.” The Motley Fool, July 2, 2024. Link.

3

Porter, Michael E. Competitive Strategy. Free Press, 1980.

4

See Freeman, Joshua B. Behemoth: A History of the Factory and the Making of the Modern World. WW Norton & Company, 2018, pages 213-225

5

Nakamura, Masao, Sadao Sakakibara, and Roger Schroeder. "Adoption of Just-in-Time Manufacturing Methods at US-and Japanese-Owned Plants: Some Empirical Evidence." IEEE Transactions on Engineering Management 45, no. 3 (1998): 230-240

6

 Statista. “Semiconductor Foundries Revenue Share Worldwide 2019-2024, by Quarter,” July 2, 2024. Link.

7

Seeking Alpha. “TSMC: By Far The Strongest Chip Company.” June 18th, 2024. https://seekingalpha.com/article/4699870-tsmc-by-far-the-strongest-chip-company

8

See Studwell, Joe. How Asia Works: Success and Failure in the World's Most Dynamic Region. Open Road+ Grove/Atlantic, 201l

9

Langlois, Richard N. The Corporation and The Twentieth Century: The History of American Business Enterprise. Princeton University Press, 2023, pagtes 427-428.

10

Miller, Chris. Chip War: The Fight for The World's Most Critical Technology. Simon and Schuster, 2022, 129-133.

11

Bauer, Luc Olivier, Wilder, E. Marshall. The Microchip Revolution: A Brief History. Amazon. 2020.

12

Chang, Morris. “Oral History of Morris Chang.” Interview by Alan Patterson, Interview. Uploaded by Computer History Museum. Computer History Museum, 2007. https://archive.computerhistory.org/resources/access/text/2013/05/102658129-05-01-acc.pdf.

13

Ibid.

14

 Ibid.

15

 Miller, Chip War, page 165.

16

Miller, Chip War, page 164.

17

Chang, “Oral History.”

18

Miller, Chip War, page 167.

19

Gratton, Peter. “What Are "Fabless" Chipmakers?” Investopedia, January 31, 2024. https://www.investopedia.com/ask/answers/050615/what-are-fabless-chip-makers-and-why-are-they-important-semiconductor-market.asp.

20

 Ibid.

Go to the original article on the author’s Newsletter to see all footnotes, they are exhaustive and will be updated later time permitting.

]]><![CDATA[AI breakthrough improves Alzheimer's predictions]]>https://www.ai-supremacy.com/p/ai-breakthrough-improves-alzheimershttps://www.ai-supremacy.com/p/ai-breakthrough-improves-alzheimersWed, 17 Jul 2024 09:57:38 GMT
human anatomy model
Photo by David Matos on Unsplash

Welcome Back,

There are going to be a lot of positive impacts of AI in healthcare.

Check out my Benefactor section of my Newsletter for more healthcare related topics. Some of my readers might not know, but I actually care a lot about this domain.

Everything from early diagnosis to better tracking, to more personalized care is going to improve fairly significantly over the next few decades. You can read my previous post about today’s topic here.

As populations begin to age in the developed world especially the impact of Alzheimer’s and dementia on the whole is going to get a lot bigger and more urgent of a healthcare issue. We will desperately need AI’s help here to improve care, predict outcomes and lower costs.

  • Recent advancements in artificial intelligence (AI) have shown significant improvements in predicting the progression of Alzheimer's disease compared to traditional clinical methods.

  • Alzheimer's disease is the main cause of dementia, accounting for 60% to 80% of cases.

  • The number of dementia cases is expected to triple over the next 50 years — and early detection is a key factor in how effective treatment can be.

  • Alzheimer’s, the most common form of dementia, is a progressive disease that begins with mild memory loss. It impacts the part of the brain responsible for memory, thought, and language. Eventually, it results in the inability to converse and respond to one’s surroundings.

  • Dementia poses a significant global healthcare challenge, affecting over 55 million people worldwide at an estimated annual cost of $820 billion.

Cambridge scientists have developed an AI tool that predicts with 82% accuracy whether individuals with early signs of dementia will remain stable or progress to Alzheimer's disease within three years. In their study published Friday in eClinical Medicine, researchers say this tool is more accurate than current clinical methods.

“We’ve created a tool which, despite using only data from cognitive tests and MRI scans, is much more sensitive than current approaches at predicting whether someone will progress from mild symptoms to Alzheimer’s—and if so, whether this progress will be fast or slow,” said Zoe Kourtzi, PhD, professor of psychology at the University of Cambridge

I’m a big believer in early diagnosis and better tracking of chronic conditions because it takes a lot of the anxiety out of how we and families can cope with the care of our loved ones and our own well-being. AI will be a new foundation of better early diagnosis and risk-mitigation and lead to a new era of preventative precision medicine.

Subscribe now

Interested in AI in Healthcare and Diseases?

Here is some of my previous work summarizing mostly papers in the area:

  • Breast Cancer

  • Radiology

  • Autism

  • Alzheimer’s

  • Heart disease

  • Cancer

  • Parkinson’s

  1. AI in mammography screening is safe and dramatically reduces Radiologist workloads ~ HERE.

  2. 🤯 A.I. is Starting to Build the Future of Healthcare ~ HERE.

  3. A.I. Can Help Detect Brain Tumor Boundaries ~ HERE.

  4. Google AI and Breast Cancer Making Progress ~ HERE.

  5. Artificial Intelligence will enable Early Autism Detection ~ HERE.

  6. AI and the Future of Sepsis ~ HERE.

  7. A.I. in Alzheimer’s disease in 2022 ~ HERE.

  8. Are A.I. Eye Checks the New Heart Disease Warning Test? ~ HERE.

  9. Why A.I. in Healthcare is a New Paradigm ~ HERE.

  10. AI Detects Tuberculosis from X-rays ~ HERE.

  11. AI and Drug Overdose Deaths ~ HERE.

  12. AI Has the Potential to Transform Healthcare ~ HERE.

  13. AI can reveal new cell biology ~ HERE.

  14. AI Evolving our Understanding of the Human Gut ~ HERE.

  15. Artificial Intelligence is improving the use of Hearing Aids ! ~ HERE.

  16. Hospitals are turning to AI to Shorten Hospital Stays ~ HERE.

  17. A.I. Can Now Predict Tumor Regrowth in Cancer Patients ~ HERE.

  18. Artificial Intelligence in Autism Detection ~ HERE.

  19. The Future of A.I. in Healthcare. ~ HERE.

  20. Artificial Intelligence is Taking on Parkinson's Disease. ~HERE.

  21. Artificial Intelligence Helps Cut Miss Rate of Colorectal Polyps. ~ HERE.

  22. A.I. Advances in Treatment Of Spinal Cord Injuries and Surgery. ~ HERE.

  23. Future of A.I. in Neurosurgery. ~ HERE.

  24. Artificial Intelligence Could Help Detect Onset of Cardiovascular Disease. ~ HERE.

  25. Can A.I Improve our Breast Cancer Screening?~ HERE.

  26. Artificial Intelligence is Changing the Future of Radiology. ~ HERE.

AI that predicts progress of Alzheimer's disease - Cambridge University

Scientists have developed an artificially-intelligent tool capable of predicting in four cases out of five whether people with early signs of dementia will remain stable or develop Alzheimer’s disease. [see link on YouTube and transcript]

  • More insights about the study and about the field as a whole below for premium members.

Read more

]]><![CDATA[🤖 The Next Generation of Robotic Surgeons are Arriving ]]>https://www.ai-supremacy.com/p/the-next-generation-of-robotic-surgeonshttps://www.ai-supremacy.com/p/the-next-generation-of-robotic-surgeonsWed, 26 Jun 2024 09:54:52 GMTHello Everyone,

Let me just say that I’m a huge robotics enthusiast, I even have a Newsletter dedicated to this.

🔵 I’m hope you are having a good summer. The current generation of the future of tech…

Read more

]]><![CDATA[How Students are Actually Using Generative AI as Early Adopters]]>https://www.ai-supremacy.com/p/how-students-are-actually-using-generativehttps://www.ai-supremacy.com/p/how-students-are-actually-using-generativeWed, 12 Jun 2024 09:57:23 GMT
(Source: Leonardo.ai)

Hello Everyone,

I’m trying hard this summer to bring exciting new guest posts to you on a range of topics and industries related to developments in AI. If you enjoy them consider…

Read more

]]><![CDATA[AI in Education: Google's LearnLM product has incredible potential ]]>https://www.ai-supremacy.com/p/ai-in-education-googles-learnlm-producthttps://www.ai-supremacy.com/p/ai-in-education-googles-learnlm-productThu, 23 May 2024 09:57:39 GMT
Image preview

Hello Everyone,

We often talk about what Generative AI will do for coders, healthcare, science or even finance, but what about the benefits for the next generation? Permit me if you will, here I’m th…

Read more

]]><![CDATA[The Biotech AI Revolution is Nearly Here in 2024]]>https://www.ai-supremacy.com/p/the-biotech-ai-revolution-is-nearlyhttps://www.ai-supremacy.com/p/the-biotech-ai-revolution-is-nearlyThu, 02 May 2024 09:59:07 GMT

Hello Everyone,

One of the biggest trends in Generative AI that I have been watching that isn’t being seen much or talked about by the mainstream is the impact in biotechnology, drug development and …

Read more

]]><![CDATA[Top AI in Education Newsletters ]]>https://www.ai-supremacy.com/p/top-ai-in-education-newslettershttps://www.ai-supremacy.com/p/top-ai-in-education-newslettersMon, 22 Apr 2024 09:58:23 GMT
r/midjourney - “Glitch in the System”
Image: Is there a glitch in the system?

Hello Everyone,

Since ChatGPT came out in late 2022, a lot of new Newsletters around AI have come into being. For parents, teachers, educators and even policy makers, I wanted to highlight a few of the top ones at the intersection of AI and Education. I hope to do this for other fields as well like:

  • AI & Healthcare

  • AI and Biotechnology

  • AI and Science/Academia, and so forth.

  • AI and Leadership, Management and C-suite Executives

This list includes a sampling of their work as well organized and forwarded by .

If any particular topic interests you in AI at the intersection of your industry, there are many Newsletters likely already on that topic, you just have to find them. Substack will tend to have the most specialized Newsletters as it pertains to Education and AI today in mid 2024.

Support and Dive Deeper

Support my work for as little as $2 a week. 🎓📚💡

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If you know someone who may find this resource useful, please share it with them.

Share

Exploring the Role of AI and Digital Learning in Future Education

AI in Education: There are dozens of links to articles about AI in Education in this rundown. Please read where the spirit moves you if this topic is interesting, relevant and salient to you.

Leave a comment

Nick is a leader into deep inquiry into AI and Education. Read his deep dives, explore his research summaries, or tap into his guides for teachers.

👥 Peer Newsletter Discovery 🎯

This is a cross-promotion for a Newsletter around Leadership and management strategy. (It is not a paid Ad). David Marsh is based in London, England.

The House of Leadership Newsletter.

Join thousands of tech leaders leveling up their skills and becoming great leaders.

  • Receive curated updates on emerging technologies, industry trends, and best practices to stay ahead in the rapidly evolving tech landscape.

  • Access exclusive insights and strategies to enhance your leadership skills, drive innovation, and lead high-performing tech teams.

Join

Listen to Forward : 2:57

Embed by ElevenLabs, Voice of Michael C. Vincent.

The AI Education Digest: Top Insights from the Best Newsletters in the Field

By Nick Potkalitsky, Ph.D.

As educational communities on platforms like X, Facebook, and Instagram continue to wane, Substack has risen as a vibrant hub for sharing and innovation at the crossroads of artificial intelligence and education.

In a recent statement, Noah Yuval Harari argued that rapid technological advancement has left educators uncertain about what to teach young people. However, an expanding cohort of teachers, academics, researchers, data scientists, librarians, and educational specialists are dedicating their time and effort to tackle just this critical question head-on. Week after week, they engage in thought-provoking discussions, share groundbreaking ideas, and collaborate to shape the future of education in the age of AI.

The diversity of these newsletters is truly remarkable. Within this vibrant community, there are those who embrace an AI-integrated future with open arms, such as Stefan Bauschard and Tim Desmond. Others, like Josh Blake, Marc Watkins, and Dan Meyer, approach the subject with a more cautious stance. Some members, including Claire Zau and Vriti Saraf, dedicate their efforts to providing news updates, keeping the community informed about the latest developments. Meanwhile, individuals like Rob Nelson, Michael Woudenberg, and Alberto Romero delve into the philosophical and theoretical aspects of AI and education, exploring the deeper implications of this intersection.

A significant portion of the community, including Lance Cummings, Mike Kentz, Alicia Bankhofer, Dr. Phillippa Hardman, and Alejandro Piad Morffis, focuses on the practical applications and issues surrounding AI in education, providing valuable insights and solutions. The brave and bold among us, such as the team behind AI x Education, Lance Eaton, and Christopher Graves, fearlessly dig into research archives, unearthing crucial findings and data.

Presently at Educating AI, I find myself working alongside fellow educators like Terry Underwood, Christopher Basgier, and Jane Rosenzweig, who are also attempting to tackle the “hard problem” of developing post-ChatGPT writing curricula. While we all navigate these differences based on our individual interests and the pressing issues of the moment, it is clear that our collective efforts are bound by a common thread: the unwavering commitment to shaping the future of education in the age of artificial intelligence. 

By fostering a diverse and inclusive community, we create a space where ideas can be shared, challenged, and refined. It is through this collaboration and the convergence of our unique perspectives that we can hope to uncover the most effective strategies for integrating AI into education, ensuring that future generations are equipped with the skills and knowledge they need to thrive in an increasingly technology-driven world.

Nick Potkalitsky, Ph.D.

Educating AI 

The Future of Education: Predictions for the Next Decade | Skill Stork

Leave a comment

The Best AI x Education Substack (Alphabetical Order by Substack Title). This list is a resource and is listed in alphabetical order only.

AI for Educational Leaders

AI for Educational Leaders
Your guide to short and specific daily AI-driven actions to improve your practice.

AI EduPathways

AI EduPathways
I'm an award-winning educator with 14 years' experience in teaching and journalism. Follow me as I infuse AI into the classroom experience and try to chart new pathways for teachers and students alike through this new frontier of AI in Education.
By Mike Kentz

  1. Do Students Want AI Writing Feedback?

  2. Flipping the Script on AI Cheating with "Linguistic Fingerprints"

  3. Chat GPTs for Education

AI Log

Rob Nelson

𝑨𝑰 𝑳𝒐𝒈
a blog about generative AI and education
By Rob Nelson

  1. On Techno-pragmatism

  2. Ethan Mollick says anthropomorphizing AI is a sin of necessity. Repent! I say.

  3. Disruption is the wrong word for what's happening with generative AI

AI School Librarian

The AI School Librarians Newsletter
Newsletter and Posts About the Intersection of School Libraries, Education and AI.
By Elissa Malespina

  1. The Law Has Not Caught Up With AI Deepfakes

  2. Empowering Education: UNESCO's AI Initiatives for Today's Educators

  3. 10 Ai Tools for Academic Research

AI x Education

Lily Lee, Johnny Chang, and Aditya Syam

AI x Education
Curating the latest AI advancements in education into digestible insights, this blog empowers educators and administrators by demystifying AI and inspiring innovative teaching and learning strategies for the AI era.
By Johnny Chang

  1. Data Privacy for AI in Schools

  2. Custom Chatbots for Schools

  3. The Environmental Impact of AI

AI + Education = Simplified

AI + Education = Simplified
A newsletter that works through what's going on in generative artificial intelligence and higher education in the hopes of making things simpler and clearer about what it means for what to do next.
By Lance Eaton

  1. On Building a AI Policy for Teaching & Learning

  2. On Not Using Generative AI

  3. Using Generative AI throughout the Institution

Cognitive Resonance

by Benjamin Riley

Cognitive Resonance
Understanding generative AI using the lens of cognitive science
By Benjamin Riley

  1. Generative AI is in the education "AIR"

  2. Resistance to the overhyping of AI in education is not futile

  3. Do we need a new scientific paradigm to understand AI?

Create. Innovate. Educate.

Create. Innovate. Educate.
This is a personal substack of Alicia Bankhofer, an educator in Vienna, Austria. My main focus is AI in education.
By Alicia Bankhofer

  1. Guiding students toward AI literacy through image generation

  2. AI and the Creativity Conundrum

  3. What's in a word, a name, a frame?

Cyborgs Writing

Cyborgs Writing
Exploring the creative interface between human and machine in writing, the classroom, and the workplace
By Lance Cummings

  1. Why Every Writer Needs a Prompt Design Plan

  2. How to Use Rhetoric to Repurpose Content with ChatGPT

  3. What I Learned Comparing ChatGPT & Microsoft Copilot

Dr Phil's Newsletter, Powered by DOMS™️ AI

Dr. Phillippa Hardman

Dr Phil's Newsletter, Powered by DOMS™️ AI
Connecting the science of learning & AI with the art of learning experience design.
By Dr Philippa Hardman

  1. Structured Prompting for Educators

  2. AI-Powered Learning Design

  3. The AI-Powered Subject Matter Expert

Educating AI

Educating AI
Let's figure how to best integrate and implement generative AI into today's classrooms!
By Nick Potkalitsky

  1. The Art of Imperfection: Why Human Writing Continues to Resonate in an AI-Driven World

  2. The Critical Thinking Imperative: Thriving in an AI-Assisted Writing Landscape

  3. The AI Writing Revolution: Empowering Authors to Embrace Innovation Strategically

Education Disrupted: Teaching and Learning in An AI World

Stefan Bauscard

Education Disrupted: Teaching and Learning in An AI World
How will AI change learning and instruction? How can learning and instruction help mold our AI world?
By Stefan Bauschard

  1. AI, Metacognition, and Debate: Instruction is already Designed for an AI World

  2. On AI: Jamie Dimon's Letter to Shareholders and What it Means for Educators

  3. Claude3 Includes Agents, May Be Self-Aware, and Moves Past ChatGPT4 on Many Benchmarks

Ed3 World: Metaverse for Education Newsletter

Ed3 World: Metaverse for Education Newsletter
AI & web3 contextualized for education.
By Vriti Saraf 🌐 3.0

  1. Jailbreaking ChatGPT: Ed3 World Newsletter Issue #29

  2. WTF are ETFs? Ed3 World Newsletter Issue #28

  3. Real Threats of AI: Metaverse for Education Newsletter Issue #25

The Future of Being Human

The Future of Being Human
Exploring the cutting edge of technology, society and the future of being human. Subscribe to get updates via email (free) or just browse!
By Andrew Maynard

  1. What should we be teaching students now to prepare them for the future?

  2. ASU announces a unique collaboration with OpenAI on using ChatGPT in education and research

  3. We have a technology problem – and it probably isn't what you think

GPTeacher | Substack

GPTeacher | Substack
Here, we focus on responsibly implementing AI in the classroom.
By Jack McLoughlin | _GPTeacher

  1. Introducing "Chat GPT in the Classroom"

  2. Student Survey: Chat GPT & LLMs

  3. The Raw Data

GSV: AI & Education

GSV: AI & Education
GSV's Weekly Newsletter on AI and Education
By Claire Zau

  1. GSV's AI News & Updates (03/25/24)

  2. GSV's AI News & Updates (03/18/24)

  3. GSV's AI News & Updates (03/04/24)

Graves Data Insights Substack

by Chris Graves

Graves Data Insights Substack
My Substack for summarizing research on AI in Education uses.
By Christopher Graves

  1. Considering the Adoption Rate of AI in Education

  2. Considering the Future of AI in Assessment Use and Feedback

  3. AI Insight for Teachers' Efficacy, Pedagogy, and Classrooms

Kevin’s Substack

Kevin Price

Kevin’s Substack
My personal Substack
By Dr Kevin Price

  1. Investigating and Experimenting with AI Tools for Teaching and Learning - Part I

  2. Investigating and Experimenting with AI Tools for Teaching and Learning - Part II

Learning on Purpose

Learning on Purpose
Exploring what's possible in education, and how to get there.
By Eric Hudson

  1. Using AI for Search and Research

  2. What Schools Are Asking About AI, Part 2

  3. Tech-Free Ways to Approach AI

Learning to Read, Reading to Learn

Learning to Read, Reading to Learn
This journal aims to comment on claims, questions, and concerns about public schools as sites for reading and writing pedagogy. As a teacher and professor, I draw on my background and experience and offer food for thought. My hope is to help schools.
By Terry Underwood

  1. Embracing the Bat: Literacy Education in the Age of Advanced AI

  2. Giving, Getting, and Grappling with Good Feedback: Improving Writing, Writers, and Readers

  3. Bot Feedback and Scoring of Student Writing

Mathworlds

Dan Meyer

Mathworlds
Building better worlds for learning math.
By Dan Meyer

  1. Khanmigo WANTS to Love Kids but Doesn’t Know How

  2. Five Differences Between Human and AI Tutors

  3. One Way Teachers and AI Could Help Each Other Out

Mostly Harmless Ideas

Mostly Harmless Ideas
Opinions, essays, tutorials, educational articles, and short comments on Computer Science research, practice, and education.
By Alejandro Piad Morffis

  1. The AI Revolution We Don't Need

  2. On the role of Higher Education in the Information Age

  3. The Techno-Pragmatist Manifesto

New Educator AI

New Educator AI
New Educator AI is dedicated to preparing educational leaders for the AI era, ensuring future generations are adept in using AI safely and responsibly.
By Tim Desmond

  1. Beyond Algorithms

  2. AI and Education: An Annual Report

  3. Redefining Education: How AI is Shaping the Future of Learning

One Useful Thing

Ethan Mollick

One Useful Thing
Trying to understand the implications of AI for work, education, and life. By Prof. Ethan Mollick
By Ethan Mollick

  1. On the necessity of a sin

  2. Centaurs and Cyborgs on the Jagged Frontier

  3. An AI Haunted World

Polymathic Being

Polymathic Being
Counterintuitive insights from technology, innovation, philosophy, psychology, and more.
By Michael Woudenberg

  1. An AI Enabled Polymath

  2. Don't Trust AI... Entrust It

  3. The No True AI Fallacy

Prose & Processors

Prose and Processors
What does it mean for humans to write in a world infused with generative AI? How can we practice, and teach, critical AI literacy? Includes personal takes, book reviews, and interviews.
By Christopher Basgier

  1. Learning about Genre with ChatGPT

  2. A Defense of Messy Writing

  3. AI as Tool, AI as Object of Inquiry

Rhetorica

Rhetorica
Notes from a nontraditional student turned educator: culture, AI, education
By Marc Watkins

  1. The Enduring Role of Writing in an AI Era

  2. The AI Influencers Selling Students Learning Shortcuts

  3. Education Is On The Frontlines Of The AI Culture Wars

Res Obscura

Res Obscura
Notes on the history of technology, medicine, science, art, drugs, and empire from UC Santa Cruz history professor Benjamin Breen. Also: using AI in research and teaching.
By Benjamin Breen

  1. Simulating History with ChatGPT

  2. Simulating an LSD trip in 1963 with GPT-4

  3. "He spoke of computers with some awe"

Tell Me Your Terrors: Joys and Fears of AI & Education

Tell Me Your Terrors: Joys and Fears of AI & Education
Musings and conversations about the thrills and terrors AI for learning, knowledge, teaching, education, books and the web, from Hugh McGuire and guests.
By Hugh McGuire

  1. 5+1 Provocative Statements about AI and EDU

  2. AI, simplify

  3. Introducing: Tell Me Your Terrors: Joys and Fears of AI & Education

The Absent-Minded Professor

The Absent-Minded Professor
Technology, education, and human flourishing. All through the lens of a prototyping mindset.
By Josh Brake

  1. What To Teach Young People

  2. I Grew Up Oblivious About Grades. It Ruined Me

  3. AI Tutors Can't Solve Bloom's Two Sigma Problem

The Algorithmic Bridge

The Algorithmic Bridge
A blog about AI that's actually about people
By Alberto Romero

  1. My Kids Will Fancy Generative AI, I Choose to Fight It

  2. AI Writing Is a Race to the Bottom

  3. 3 Powerful Strategies (Other Than AI Detectors) That Teachers Can Adopt to Adapt to Generative AI

The Future of Higher Education

The Future of Higher Education
Readers turn to The Future of Higher Education for fresh perspectives on leadership in higher education. We primarily explore AI, personal leadership, strategy, and related topics. Welcome!
By Jeppe Klitgaard Stricker

  1. On AI, and the Fluid Boundaries of Creativity

  2. 13 Nuggets of AI Wisdom for Higher Education Leaders

  3. Deep Learning: Five New Superpowers of Higher Education

The Paste Eaters Blog

The Paste Eaters Blog
A blog for teachers who get the feeling that the only opinions that count in education... do not belong to teachers. I try to post weekly.
By Adam

  1. 50 Free Metaphorical Writing Prompts

  2. AI in the Classroom: Metaphorical Writing Prompts

  3. "Help! I don't know how to teach writing!" (Part 1)

The Value Junction

The Value Junction
Education, technology, and everything in-between. Read in over 50 countries worldwide.
By Bechem Ayuk

  1. How To Develop An AI Integration Plan For Your School (Including a template)

  2. AI in Education: What Educational Institutions Need to Know

  3. Here's How Teachers Should Be Trained To Use AI

Tomorrow's Teaching

Andreas Matthias

Tomorrow's Teaching
Catch up with modern teaching practices! We break down AI and teaching into digestible, applicable concepts, especially for university teaching in the humanities. By a university lecturer with 30 years experience in IT and philosophy.
By Dr Andreas Matthias

  1. The Opera browser’s VPN and integrated AI

  2. Effortlessly create model answers and marking schemes

  3. Can AI be genuinely inspiring? And should it be?

Writing Hacks

Jane Rosenweig

Writing Hacks
Write more effectively at work.
By Jane Rosenzweig

  1. Four Rules for Writing in the Age of AI

  2. Should you use ChatGPT to write at work?

  3. Writing and Learning to Write in the Age of AI

Editor’s Conclusion

  • Everyday policy makers, teachers, professors and school administrators are debating the role of AI in education. This is very much a work in progress. Each day students, children, teenagers and young adults are using AI in new ways for their studies, entertainment, leisure and personal discovery.

  • As Generative AI unlocks new capabilities, institutions will have to adapt and create clear guidelines around these new tools and how to integrate them.

  • If you have a particular interest or concern around this topic, feel free to share it.

Leave a comment

Looking Forward

  • We might reach a time when artificial intelligence is able to personalize education with hundreds of thousands of data points that could radically alter or disrupt the current grade school and college systems that seem to be outdated or rapidly becoming less relevant for preparing the workforce of tomorrow.

]]><![CDATA[The Biggest Open-Source Week in the History of AI]]>https://www.ai-supremacy.com/p/the-biggest-open-source-week-in-thehttps://www.ai-supremacy.com/p/the-biggest-open-source-week-in-theSat, 30 Mar 2024 07:59:57 GMT
r/midjourney - “Wondering about something more”

Hey Everyone,

While covering AI this week I noticed something peculiar. Open-source LLMs had a coming out party. 🎉 I’m not usually one to brag or exaggerate, but what we saw this week was unusual.

The Hugging Face folk were noticing it too:

Like Omar Sanseviero (X, here).

To follow what HuggingFace notices bookmark this page.

If you believe this is a good overview of Open-source innovation at this time, support the author and share it.

Share

Of course many predicted that 2024 would be a great year for Open-source AI, but also Agentic AI and AI devices are showing promise as 2024 trends in AI. But in my mind, Jamba, DBRX and Samba-CoE are all incredibly unusual and specific launches of Open-source LLMs demonstrating a pivotal moment in the diversification and proliferation of these accessible and decentralized AI models.

Billionaire Elon Musk said Friday his artificial intelligence company xAI's chatbot Grok-1.5 “should be available” to the public next week, after the chatbot became open-source and officially entered the rapidly growing AI chatbot market.

The current generative AI revolution wouldn’t be possible without the so-called large language models (LLMs) being optimized constantly and globally simultaneously in a more or less decentralized manner. However it’s open-source LLMs that are making many new things possible in how companies train their proprietary data on their own models to have full control and customization.

But behind every AI tool or feature, there’s a large language model (LLM) doing all the heavy lifting, many (to now most) of which are open-source. As these periods like March, 2024 occur, new possibilities will emerge. The density of launches of quality open-source LLMs in March, 2024 is peculiar.

“In my mind Jamba, DBRX and Samba-CoE are all incredibly unusual and specific launches of Open-source LLMs demonstrating a pivotal moment in the diversification and proliferation of these accessible and decentralized AI models.”

Gap Between Open-Source and Closed-Source is Narrowing

Subscribe now

Read more

]]><![CDATA[Will AI Save Physicians and Clinicians Time and from Burnout]]>https://www.ai-supremacy.com/p/will-ai-save-physicians-and-clinicianshttps://www.ai-supremacy.com/p/will-ai-save-physicians-and-cliniciansThu, 21 Mar 2024 09:55:56 GMT

Hey Everyone,

Are any of you healthcare workers or know someone who is?

Share

As many of you know I’m very bullish about AI in healthcare and over the decades I believe it will drastically improve our healt…

Read more

]]><![CDATA[Trends in Synthetic Biology & AI in Drug Discovery in 2024 ]]>https://www.ai-supremacy.com/p/trends-in-synthetic-biology-and-aihttps://www.ai-supremacy.com/p/trends-in-synthetic-biology-and-aiWed, 13 Mar 2024 09:51:04 GMT

Hey Everyone,

As I develop my 10 publications around my key interest in emerging tech, I have so much talent to call upon in this ecosystem. Two of my favorites are MetaphysicalCells and Humanity Red…

Read more

]]><![CDATA[The full potential of AI in techno-optimism]]>https://www.ai-supremacy.com/p/the-full-potential-of-ai-in-technohttps://www.ai-supremacy.com/p/the-full-potential-of-ai-in-technoTue, 13 Feb 2024 10:57:09 GMT
CDN media

Hey Everyone,

This is part of my series of AI for Good, you can read other essays here.

The fundamental place of AI in our lives is only going to increase. AI data centers will double in scale over f…

Read more

]]><![CDATA[Can AI learn language like we do? ]]>https://www.ai-supremacy.com/p/can-ai-learn-language-like-we-dohttps://www.ai-supremacy.com/p/can-ai-learn-language-like-we-doFri, 09 Feb 2024 10:57:58 GMT
closeup of a smiling baby wearing a helmet camera with the bars of a crib in the background
Image: A baby wearing the helmet-mounted camera researchers used to collect a baby's-eye view dataset.

Hey Everyone,

This week there was this fascinating story about AI learning language like we do from young.

In one of the most viral stories I’ve seen about AI learning language like humans do I’ve seen in a while. A group of scientists at New York University used 61 hours of footage from a camera attached to an Australian baby named Sam to gather experiences from the infant’s perspective.

Video from New York University

Source.

In order to check if AI could learn words and ideas that a child encounters every day, researchers from New York University decided that the best way forward would be to record what a toddler says and does throughout the day. And make an AI model to learn from it.

Read at NYU

  • The new study, published in the journal Science on February 1st, 2024, demonstrated that an AI system could be developed to learn a substantial number of words and concepts using the limited experiences of a child.

  • Researchers from New York University showed that the video captured during only one percent of the child’s waking hours is sufficient for genuine language learning.

“We show, for the first time, that a neural network trained on this developmentally realistic input from a single child can learn to link words to their visual counterparts,” says Wai Keen Vong, a research scientist at NYU’s Center for Data Science and the paper’s first author. “Our results demonstrate how recent algorithmic advances paired with one child’s naturalistic experience has the potential to reshape our understanding of early language and concept acquisition.”

The idea of training AIs on how babies learn is of course not new. Still how a neural network that taught itself to recognize objects using the filmed experiences of a single infant could offer new insights into how humans learn. LLMs are going to get a lot better soon learning from video.

How do young children learn to associate new words with specific objects or visually represented concepts? This hotly debated question in early language acquisition has been traditionally examined in laboratories, limiting generalizability to real-world settings. Vong et al. investigated the question in an unprecedented, longitudinal manner using head-mounted video recordings from a single child’s first-person experiences in naturalistic settings.

If this study was repeated at scale it could harness some interesting findings. In the study, Sam, a baby boy living near Adelaide in Australia, wore a head-mounted camera for around one hour twice each week from the age of six months to around two years, gathering experiences from the infant’s perspective.

Abstract

Starting around 6 to 9 months of age, children begin acquiring their first words, linking spoken words to their visual counterparts. How much of this knowledge is learnable from sensory input with relatively generic learning mechanisms, and how much requires stronger inductive biases? Using longitudinal head-mounted camera recordings from one child aged 6 to 25 months, we trained a relatively generic neural network on 61 hours of correlated visual-linguistic data streams, learning feature-based representations and cross-modal associations. Our model acquires many word-referent mappings present in the child’s everyday experience, enables zero-shot generalization to new visual referents, and aligns its visual and linguistic conceptual systems. These results show how critical aspects of grounded word meaning are learnable through joint representation and associative learning from one child’s input.

  • The training material included frames from the video recordings of the baby’s environment and transcribed words spoken to Sam. In total, the model was exposed to 250,000 words and corresponding images depicting various activities like playing, reading, and eating.

  • It also included a range of different activities, including mealtimes, reading books, and the child playing.

  • For education and language acquisition and early childhood development this is also really valuable. What if it could be replicated for all kinds of other ways we learn?

Share

AI Might Also help us Understand Child Development Better

“Our results demonstrate how recent algorithmic advances paired with one child’s naturalistic experience has the potential to reshape our understanding of early language and concept acquisition,” Dr Vong said.

Can AI learn a language the way babies do?

CVCL

The system, dubbed the Child's View for Contrastive Learning (CVCL) model, was fed 37,500 utterances captured from 61 hours of first-person video recorded by a baby named Sam from ages 6 months to 2 years old.

  • Researchers from New York University’s Center for Data Science and Department of Psychology fed this dataset into a multimodal AI system—one that could ingest both text and images.

  • They found that the AI model could identify many different objects, both in tests using data from the head-mounted camera and in tests using a dataset of idealized images of various objects, although its accuracy was somewhat limited.

The AI system did learn from the video in a surprisingly effective way. The AI system was better at naming objects it had seen more frequently, including apples (which are ubiquitous in children’s books) and cribs. It also was better able to pick out objects that weren’t obscured in the head camera images. It was particularly poor at recognizing knives, says Wai Keen Vong, one of the paper’s authors. Curiously the work was supported by the U.S. Department of Defense's Defense Advanced Research Projects Agency (N6600119C4030) (that’s DARPA) and the National Science Foundation (1922658).

This is also about child development science and psychology here. Some psychologists and linguists believe that children would not be able to form associations between words and objects without having some innate language ability. But the fact that the AI model, which is relatively simple, could even begin to learn word associations on such a small dataset challenges this view, says Vong.

Since writing up their results, the NYU researchers have transcribed four times more data from the head camera footage, which they intend to feed into their model. They hope to examine how much more the AI model learns when it's given more data, Vong says. They also hope to test whether the model can start to learn more challenging words and linguistic behaviors that tend to develop later in life.

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Get 33% off forever

Ask your manager and get this covered for multiple team members by your learning and development fund.

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]]><![CDATA[Can A.I. Automate Vertical Farming?]]>https://www.ai-supremacy.com/p/can-ai-automate-vertical-farminghttps://www.ai-supremacy.com/p/can-ai-automate-vertical-farmingWed, 06 Dec 2023 10:55:42 GMT
The Rise of Vertical Farming and the Role of AI | by AIQOM | Medium

Hey Everyone,

Today I want to talk about something a little bit different and it’s the advent of AI in more sustainable farming practices, namely Vertical Farming. As Ag-tech (agricultural technology…

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]]><![CDATA[How AI Might help us Survive on Mars]]>https://www.ai-supremacy.com/p/how-ai-might-help-us-survive-on-marshttps://www.ai-supremacy.com/p/how-ai-might-help-us-survive-on-marsFri, 17 Nov 2023 10:56:44 GMT
Exploring Mars. : r/midjourney

Hey Everyone,

First, a message from my deepest sci-fi psyche:

🪐“Mars has for decades attracted intensive scientific exploration and research in countries worldwide. Finding signs of past life and building potentially habitable regions on Mars have long been a dream of humanity.”

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Sometimes a story in the week really sparks the imagination, and while rumours of China need to be taken with a grain of salt, there is an example of China and Mars that is pretty telling.

By the time human civilization on Earth gets to Mars, the red Planet, our robotics and artificial intelligence is going to be pretty advanced, relative to today. I’m excited enough just by the prospect of robots at Amazon, a story is going to hopefully bringing to us later this month in a guest post.

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Given the current trajectory I believe China will beat us to a Lunar base and to Mars. This is because the Chinese are expansionist builders and understand the mining opportunities are going to be very lucrative in the 21st century in space and space-technology.

In June, 2021 it was reported by Reuters that indeed China aims to send its first crewed mission to Mars in 2033, with regular follow-up flights to follow, under a long-term plan to build a permanently inhabited base on the Red Planet and extract its resources.

China wants to start building a lunar base using soil from the moon in five years, so that’s by 2028. There’s no way NASA will be able to follow what China plans on doing, but will SpaceX?

I’m not myself a transhumanist when it comes to cybernetics or synthetic biology, but I' am strongly in favor of becoming a multi-planetary civilization. I think this should be among our highest priorities if we want human civilization not to go extinct. This is because we are too close to the bring on too many fronts for potential evolutionary dead-ends for humanity.

Read the Study

A Robotic ‘A.I. Chemist’ Could Make Oxygen on Mars

We know that Generative A.I. is going to enable AI robotic labs that will be able to help make scientific discoveries. Some early prototypes are starting to take shape in 2023.

AI in drug development is a microcosm of how this might work for other domains. This story out of China really blew up this week in terms of superficial headline news about AI this week.

Read on Nature

A robotic AI Laboratory in China recently found the optimal formula that was tested in simulated low-temperature conditions of Mars, according to researchers.

Researchers in China have developed a robot chemist powered by artificial intelligence (AI) that might be able to extract oxygen from water on Mars. What?

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Life on mars https://unprompt.ai/ : r/midjourney

The story is full of Chinese Nationalistic vibes and the progress of humanity.

  • The same process would have taken a human 2,000 years, according to researchers. Really?

  • The discovery demonstrates the feasibility of the AI chemist in the automated synthesis of chemicals and materials for Mars exploration, according to a study published in the journal Nature Synthesis on Monday November 13th, 2023.

  • The system calculated more than 3.7 million molecules it could make from six different metallic elements in the rocks.

It’s a thought-provoking story on the outside, but what is it really saying? It’s saying China is serious about space-technology and creating bases in the solar system and may seek to get to them first. We know that India is likely to be a future competitor.

It’s not clear if this will ever be a major priority for the United States Government. China has its own versions of SpaceX in development. China is more advanced in space-technology than is usually talked about in the Western mainstream media. Why might that be?

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“If you think about the challenge of going to Mars, you have to work with local materials,” says Andy Cooper, a chemist at the University of Liverpool, UK. “So I can see the logic behind it.”

The “robot”, and I have no idea why China calls it a robot (it’s all about the AI), uses materials found on the red planet to produce catalysts that break down water, releasing oxygen. The idea could complement existing oxygen-generating technologies or lead to the development of other catalysts able to synthesize useful resources on Mars.

Now obviously, sending supplies, such as oxygen, to Mars is extremely expensive so being able to create it on the red planet could remove a major barrier to establishing future colonies. A good amount of National Defense contracts are actually about space-technologies even as a good amount of quantum technologies in development have implications to, you guessed it, space-technology and Solar system bases and habitats.

As for the colonization of Mars, we know that robots could build colonies, before we arrive and they will. We also know what AI systems will function as major nexus of organization, communication and management by the time we “colonize” Mars and other places in the solar system like where Enceladus, Titan and Ceres come to mind.

So this PR out of China is actually rather good news for the sci-fi nerds in the technological optimists among us. But certainly a Martian colony could be the most feasible society that could eventually one day become self-sustainable. If that’s even entirely possible?

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"We have developed a robotic AI system that has a chemistry brain," said study lead Jun Jiang at the University of Science and Technology of China in Hefei, per Nature News.

How Chinese scientists and outlets frame thing is a little different.

Read the Paper

⚗️This is the entire key to understanding this.

Image

It certainly does spark the imagination and makes you question how China will compete with the U.S. in space. How will India emerge just as we are ready to colonize Mars in a few decades? How will robots and AI be implicated in our future societies and will they be less prone to extinction?

The study, published in Nature Synthesis, was led by Jun Jiang at the University of Science and Technology of China in Hefei. Jiang and his team used a mobile machine the size of a refrigerator with a robotic arm to analyse five meteorites that had either come from Mars or been collected on Earth but mimicked the Martian surface. The team’s goal was to investigate whether the machine could produce useful catalysts from the material.

Publications even spun out YouTube videos around the story en masse:

Nature

This is the most neutral video I could find:

The New Scientist

View time: 43 seconds:

It’s Inevitable that we will Colonize Mars

There are very real economic, technological and geopolitical reasons to get to Mars first, and the AI arms race is also related to a Space race. You can even simulate this stuff in video games.

I believe China will invest more in space-technology and space-construction that will give it a significant lead in these sorts of technologies. SpaceX have inspired a generation, but until they go public likely in 2027 or later, we won’t know the details of their business.

Billionaire SpaceX investor Ron Baron recently told CNBC that he expects the company's satellite business, Starlink, to spin off and go public “in 2027 or so.” I actually think Starlink (satellite internet company) will go public around 2025. It’s simply not growing as fast as Elon Musk anticipated as part of SpaceX.

My impression is that India and the U.S. will have separate colonies from that of China on Mars at first. With perhaps another one from the EU. How society develops there will be super interesting and perhaps by then, likely the 2040s, we’ll have the robotics, AI and other technologies to help us sustain colonies there and manufacture what will be needed.

All these publications claim that the research out of China “underscores the importance of AI” in finding and refining the right catalysts among samples of five different categories of meteorites, sourced both from Mars itself, and from samples found on Earth known to have parallels on Mars. This may or may not be true.

One Step for Humanity, One Robotic Arm at a Time!

In the paper, Jiang and the team suggest their “robotic AI-chemist” could lead to the automated production of catalysts using Martian meteorites, providing a method for future Mars explorers to make oxygen on the planet’s surface.

The second best option is likely this place:

Credit: UIG via Getty Images/Universal History Archive

Read Smithsonian Magazine

OMG, we have another video! How this “robotic AI-chemist” moves is so delightful.

It’s the “AI in the robot” dummy! moment. For me at least, how about for you?

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Smithsonian

Damn, I just got to choose a robot thumbnail about Mars! Mom!

Anyways the entire story is a bit out there for 2023. But it won’t feel out of place in 2033, I can assure you of that.

Who is LUO Yi anyhow? Let’s check his faculty ID. Hmm. Seems legit.

The compound, known as a catalyst, is made entirely from elements found in Martian meteorites—which means, if such a system could work reliably, space travelers would not need to bring oxygen or even the catalyst needed to produce it. Yes, yes, I think most of us figured that out by now.

The robot chemist's ultimate aim is to find clever ways to make new compounds from on-the-ground resources. Indeed!

Recent evidence of water activity on Mars has raised the prospect of large-scale oxygen production through solar power-driven electrochemical water oxidation processes using an oxygen evolution reaction catalyst. True that.

China, take me away! And take that Elon Musk guy with you.

Postscript

The competition for in-situ oxygen production on Mars continues in 2023. According to the Register, earlier this year, Michael Hecht at MIT's Haystack Observatory led an investigation dubbed Mars Oxygen In-Situ Resource Utilization Experiment (MOXIE) on board NASA's Perseverance rover.

What new devices might be created for human colonies in the not so distant future? Let’s chip those AI chemist robots from Beijing already to Mars! The AI chemist can, without human intervention, synthesize OER catalysts on Mars from local ores.

“On Earth, we don’t use these six elements because we have more choice,” says Luo. “These six elements are not the best for this kind of catalyst and it limits its performance, but it’s what you’ve got on Mars.”

So it goes without saying that an Oxygen supply must be the top priority for any human activity on Mars, because rocket propellants and life support systems consume substantial amounts of oxygen, which cannot be replenished from the Martian atmosphere. I look forward to oxygen Labs being set up at the earliest.

The robot chemist made and tested 243 of the possible catalysts, according to Space.com’s Charles Q. Choi.

With its chosen catalyst, the system could produce oxygen from the Martian materials at around minus 37 degrees Celsius, demonstrating the chemical reaction could be feasible on the cold Martian surface.

Don’t tell the robots what to do!

“We think our machine can make use of compounds in Martian ores without human guidance.”

Midjourney prompt: mars architecture, temple of - PromptHero

Robots and AIs seeding and preparing colonies, might just be the status-quo if human civilization is going to get serious about colonizing other planets. For the purpose of this Newsletter, we will be discussing AI in terms the future of own solar system in future articles.

I am also keen to continue my coverage of AI in science and AI in astronomy specifically. With the James Webb Space Telescope now in operation, we are getting so much better data, images and details. We are always discovering new exoplanets, some of them relatively close to home.

I for one am really excited to see how AI is intersecting with our new golden age of astronomy, cosmology and the history of the universe. The next two decades are rather important for applied space-technologies where robotics and AI increasingly become even more involved. As well as quantum technologies.

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