Hello Everyone,

This week full disclosure I’m having a family health issue, so I have less time for research and writing than my usual. I’m r…

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Hey Everyone,

So what’s going on at the AI frontier this week? I wanted to check out and round-up some of the AI news this week that caught my attention.

Frontier Models

As we anticipated, Google announced their Gemini 4 Argon model this week. Alphabet says its built to sustain deep reasoning across complex, long-horizon workflows. It’s last flagship model was way back in February and it’s difficult to understand what took them seven months to launch another one. We’ll know more on post October 1st after cybersecurity testing, but the first signs look promising.

It appears to truly put Astra GPT-6 behind us. Astra OpenAI’s frontier model was only seemingly frontier for 19 days, before Opus 5.5 was announced.

Meanwhile in the span of 2026 Google saw another huge exodus of top AI researchers (e.g. DiscoveryLoop) and talent. It’s also not clear if Google will have an answer for the personal assistant wars of Muse, Instinct, Grok Bot and Dots. Have you tried them?

• Muse

• Instinct

• Grok Bot

• Dots

The other companies most likely to make a Muse competitor for the B2C space are ByteDance and Google.

Personal Agents Accelerating Token Spend

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👋 Hey there, I’m Mike. Each week I share AI articles at the intersection of tech, business, society and the future. If you want to support the channel or gain full-access to my work, go here. Read Archives | See Substack Notes | Visit our community Chat | Visit Homepage. I’ve been doing this for five years and before ChatGPT came along.

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How will AI become more accessible? Are older workers who are laid off in a tough spot due to AI? I didn’t think I’d have to write about OpenAI again. But some events are like the echoes of history.

Is AI increasing Ageism in the workplace? 🧐

At a time when there’s going to be a Physical AI and AI wearables race in the consumer AI economy kicking off in 2027, OpenAI’s plans to even compete in the AI device race might be under serious threat, with increased scrutiny and even legal delays. The weekend news is an Apple lawsuit which looks very damaging to OpenAI’s already badly eroded brand and ability to secure meaningful partnerships and business relationships. I’m not sure what kind of Enterprise customer would want to do business with them at this point in 2026. But how about ChatGPT users?

OpenAI’s last 24 hours: (Saturday 11th, of July)

> Top Exec unexpectedly departs (claims health)
> Shuts down browser tool after 9 months (Atlas, another failed product)
> Sued for trade theft by Apple
> Caught selling product to China against sanctions and lastly:

>OpenAI's head of safety systems, Johannes Heidecke, is leaving the company.

The complaint accuses OpenAI of orchestrating a broad effort to systematically acquire and exploit Apple's ‌confidential information and trade secrets is very serious. The evidence looks daming.

Read the Legal Filing

How do you even recover from this? I expect Microsoft to also bring a lawsuit to them in the coming months in relation to their betrayal in favor of Amazon that was against Microsoft’s terms.

Apple is seeking (Ed Ludlow explains):

• A jury trial.

• An order requiring OpenAI to stop the alleged misuse of Apple's trade secrets.

• Destruction of any Apple proprietary materials in OpenAI's possession.

• Redesign of upcoming OpenAI hardware products so they do not incorporate Apple's technology.

Chronology of Events

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We don’t really know what AI could become…

Good Morning,

The two biggest events this week are likely the reaction to Fable and the SpaceX IPO.

I’ve been struggling a little trying to think about what’s really new that was announced at Google I/O, Microsoft Build Conference and Apple’s own more recent developer conference of WWDC 2026. There was a tangible sense of how empty many of their tech announcements were, especially with regards to AI.

I can’t even put my finger on why this is, maybe it’s the AI overload of the news cycle, the exaggerated hype or how lacking in meaningful products and application layer innovations we’ve even seen after three years of LLMs (so many models) and more datacenters. I can’t help put have the feeling that I was expecting more in the 2020s that we are likely to see from Generative AI.

Hopefully next year in 2027 with a deluge of AI devices this will feel different. Will a Codex version of ChatGPT feel different? Apparently, a senior OpenAI executive went so far as to say that “chat is dead,” shifting its focus toward a platform centered on agents and coding tools. It just goes to show how ChatGPT’s success story is fading so badly. But how would OpenAI ever compete with Cursor or Anthropic at this point for Enterprises and companies where trust is a major factor. OpenAI might have to try to lower prices, but they are already burning so much capital. Cursor who now have $4 Bn. in ARR and are accelerating even faster.

There certainly will be winners and losers to paraphrase Sam Altman. His eye-scanning startup isn’t fairing any better.

Will BigAI Disrupt Incumbents by 2030?

Will the hyperscaler names be re-written by AI too? 🤔 I had written about this back in June, 2025:

The verdict on how BigTech (incumbents) react to BigAI is still unclear? Outside of Google, it doesn’t look so good for the others. Their capex to AI execution is looking rather poor in more than one case.

Let’s first see how the 2026 IPOs pan out but Apple, Microsoft, Amazon and other incumbents don’t feel like they are active participants at the frontier of Generative AI. Apple’s lineup of AI devices looks fairly impressive however for 2027 and 2028.

China’s Massive AI Compute Announcement

With news that DeepSeek is getting $7 Billion in funding a week ago along with massive Capex increases from ByteDance and Alibaba for compute - China is going all in. China is preparing to spend around 2 trillion yuan ($295.43 billion) over the next five years on building data centers ​across the country, Bloomberg News reported on Tuesday, as ‌Beijing looks to challenge the U.S. in the intensifying AI race.

So instead of relying purely on private tech giants like Alibaba, ByteDance or Tencent to build isolated clouds, the National Development and Reform Commission (NDRC) is spearheading a blueprint to knit the country's scattered computing facilities into a unified national network by 2028. If China feels they have to do this too, it’s going to be a brave new world. Curiously how Beijing is boosting Huawei isn’t so different how the Trump Administration has been supporting Nvidia with circular financing of the entire ecosystem in a spider-web of deals. But what exactly are we building for civilization in the big picture.

We Don’t really know what we are getting ourselves into…

Claude Skills Guides

• KDNuggets (not an affiliate) has an interesting Anthropic’s Complete Guide to Claude Skills Building.

• The Claude (Content) skills I use to run Growth Unhinged by

• How to use Claude AI for Investing (the first post in our series on AI in Investing got a lot of bookmark saves on X).

Reactions to the Frontier Model of Anthropic

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Good Morning,

AI Supremacy is a Newsletter that explores the impact of AI on the future of business, technology, culture and society.

This is starting ot mean a lot for the lives of different kinds of people, consumers, investors, graduates, entrepreneurs, citizens and residents. The pressure of AI on real people and their communities is becoming more tangible this year.

A Pivotal AI Week Ahead

With Google’s I/O Conference starting tomorrow, Nvidia Earnings and the SpaceX IPO filing, this might be one of the most pivotal weeks in AI of 2026 (week of May 18th). Elon Musk is expected to start meeting with investors very soon ahead of an IPO for SpaceX, which was valued at $1.25 trillion after merging with xAI in February. I’m noticing a lot of subplots in the AI ecosystem in a rather bizarre geopolitical, inflation, bond story and Silicon Valley backdrop. Let’s dive into some of the most important among these.

Earnings Calendar this Week

Last week Anthropic announced Claude for Small Business, less than a year now from its IPO likely in December, 2026. You hear a lot of stories of SMBs struggling to implement Generative AI technology. This is especially true in an era of rising backlash against AI slop, datacenters and Billionaire incentives to praise the AI era as if it’s going to save us all.

At the time of writing today, Cursor released Composer 2.5, and I believe after Anthropic, Anysphere is one of the top B2B and Enterprise AI startups. From a product perspective I think this improves or at least enhances Cursor’s in-house model intelligence, long-running agentic behaviors, and the overall team development ecosystem within its IDE. Cursor is becoming known for its ability to multitask its strong parallel processing with async subagents.

The midpoint of 2026 marks an extremely cringe time in the history of AI, where entry level jobs are being impacted, and people are already extremely fed up. According to Axios, the “disdain spans generations” and political parties. An Economist/YouGov poll released this week showed over 70% of Americans think AI is advancing too quickly, with 68% of Republicans and 77% of Democrats saying it’s moving too fast.

The American Billionaire AI Rush 2026-

Capital, compute and the AI boom of elitism. What’s taking shape is not necessarily making many of us more hopeful about the future.

With three huge IPOs, SpaceX, Anthropic and OpenAI supposedly all coming in 2026 or early 2027, there’s a lot of capital at stake for a lot of rich people and Venture Capital funds, BigTech and the perception that America is ahead in AI.

A jury in Oakland, California, ruled against Elon Musk in his dramatic court battle with Sam Altman and OpenAI. Sam Altman is among a new breed of AI Venture Capitalists that are powerful angel investors while assuming CEO duties of one of the most powerful AI startups ever in OpenAI. I’ve been arguing that it is a company in decline. Whatever it is, it’s a child of a new era in Venture Capital history dominated by a few big funds.

Venture Capital Power is becoming more Concentrated

The Q2 2026 ranking of US unicorns shows a rather high concentration of VCs involved. They are roughly as follows:

A small handful of VCs are seeing the majority of the returns in the AI boom to the detriment of mid-sized VCs. Generative AI is narrowing the power and concentration base of Silicon Valley that was already previously highly concentrated.

AI is creating fewer winners in VC, BigTech and the Future

GenZ is waking up to the danger of BigTech in 2026

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Good Morning,

I want to do a visual tour of some of my favorite infographics and updates of the AI Index report. I think it would be fair of me to suggest that this year’s report emphasizes that while AI capability is accelerating, the governance and safety frameworks meant to manage it are struggling to keep pace.

The reason this exists is the AI Index came out of a project of the One Hundred Year Study on AI at Stanford to serve as a faster-paced annual lens on advancing AI capabilities.

Browse the Slides

The jagged frontier speaks of a quickly developing industry that is a great lens on the State of AI over the past year 2025-2026 and the big picture. While a lot of the AI reports I read are from Venture Capital funds whose job it is to make AI look amazing, with an academic institution we can hope to find a less biased snapshot. I am not sure if that turns out to be true however. The AI Index that was established in 2019 as an independent project claiming to provide unbiased, rigorous data on the progress of AI. You can follow the Stanford Institute for Human-Centered AI (HAI) here. View their latest news here.

AI News in a Nutshell

Last week Anthropic released:

• Claude Opus 4.7

• Claude Design

What to use Claude Design For?

Initial test use cases for the tool are as follows:

• Realistic prototypes: Designers can turn static mockups into easily-shareable interactive prototypes to gather feedback and user-test, without code review or PRs.

• Product wireframes and mockups: Product Managers can sketch out feature flows and hand them off to Claude Code for implementation, or share them with designers to refine further.

• Design explorations: Designers can quickly create a wide range of directions to explore.

• Pitch decks and presentations: Founders and Account Executives can go from a rough outline to a complete, on-brand deck in minutes, and then export as a PPTX or send to Canva.

• Marketing collateral: Marketers can create landing pages, social media assets, and campaign visuals, then loop in designers to polish.

• Frontier design: Anyone can build code-powered prototypes with voice, video, shaders, 3D and built-in AI.

You can also use Cursor for Designers that integrates with Figma.

Opus 4.7 is built to be a "digital employee" rather than just a chatbot, not the same as the Mythos model they are still studying. In general, Anthropic recommends Opus 4.7 for demanding use cases where you need frontier intelligence — particularly production-ready code, sophisticated AI agents, and complex document creation. In particular I will note that Opus 4.7 seems to have improved a lot in:

Opus 4.7 as the first “Digital Employee” Type Model

• Instruction following.

• Improved multimodal support: like vision.

• Real-world work; like Finance.

• Memory and using file-system based memory.

• Long-running tasks.

Opus 4.7 is a “Doer”

Opus 4.7 handles complex, long-running tasks with rigor and consistency, pays precise attention to instructions, and devises ways to verify its own outputs before reporting back and as such its fine-tuned to be especially good at agentic coding and related tasks.

It’s likely to be one of the more capable global models of the Spring of 2026.

At its best, the AI Index is a distillation of thousands of articles, research papers, and model data, carefully organized and synthesized.

• 12 Takeaways from the Report

• Want to understand the current state of AI? Check out these charts. (MIT)

Which Companies are Leading in AI?

Furthermore Forbes just released their 2026 (8th annual) edition of their “AI 50” read it here. Supposedly, the iteration of the Forbes 2026 AI 50 list highlights a significant shift in the industry, as leading startups demonstrate a transition from experimental technology to sustainable, revenue-generating businesses. Indeed a few on this list will go public in the next 18 months.

A lot of these have indeed seen ARR growth in the last year that’s surprising to say the least and/or seen valuations grow very quickly. I’m going to link to their LinkedIn profiles so you can quickly learn a bit more about them:

1) Legora - legal automation software (Stockholm)
2) Cursor - AI coding platform (Bay Area / NY)
3) Reflection - open source AI models (NY)
4) Physical Intelligence - AI models for robotics (Bay Area)
5) Speak - AI language tutor (Bay Area)
6) Chai Discovery - AI drug discovery (Bay Area)
7) Black Forest Labs - Image and video generation software (Freiburg)
8) World Labs - Spatial AI model developer (Bay Area)
9) Skild AI - AI systems for robotics (Pittsburgh)
10) Sierra - AI agents for customer service (Bay Area)
11) Mistral AI - Open source AI model developer (Paris)
12) Mercor - Data labeling service (Bay Area)
13) Lovable - AI app and website builder (Stockholm)
14) Listen Labs - AI market research tools (Bay Area)
15) Thinking Machines Lab - AI research and products (Bay Area)
16) Genspark - AI tools for knowledge workers (Palo Alto)
17) Decagon - AI agents for customer service (Bay Area)
18) Together AI- AI cloud provider (Bay Area)
19) Suno - Music generation software (Cambridge)
20) Rogo - AI finance tools (NY)
21) Perplexity - AI search engine (Bay Area)
22) OpenEvidence - AI search for doctors (Miami)
23) krea.ai - Image generation software (Bay Area)
24) HeyGen - AI video generation (LA)
25) Harvey - Legal automation software (Bay Area)
26) Fireworks AI - AI app development software (San Mateo)
27) ElevenLabs - Voice generation software (NY)
28) Safe Superintelligence - AI research (Palo Alto)
29) Midjourney - Image generation software (Bay Area)
30) fal - Generative media infrastructure (Bay Area)
31) Cyera - AI data security (NY)
32) Notion - productivity software (Bay Area)
33) Surge AI - Data labeling service (Bay Area)
34) Gamma - AI graphic design tools (Bay Area)
35) Glean - Enterprise search engine (Palo Alto)
36) Cohere - AI model developer (Toronto)
37) Baseten - AI app deployment software (Bay Area)
38) Runway - Video and image editing software (NY)
39) Crusoe - AI data center builder (Denver)
40) Abridge - AI notetaker for doctors (Bay Area)
41) Synthesia - AI avatar and video generator (London)
42) SambaNova - AI chipmaker (San Jose)
43) EliseAI - AI agents for housing and healthcare (NY)
44) Clay - AI go-to-market tools (NY)
45) Applied Intuition - Self-driving software (Sunnyvale)
46) Cognition - AI coding agents (Bay Area)
47) Replit - AI app and website builder (Foster City)
48) OpenAI - AI models and products (Bay Area)
49) Anthropic - AI models and products (Bay Area)
50) Databricks - Data storage and analytics (Bay Area)

My own take on the slides may include text from the slides and my own commentary at times. I might at times also include slides from other sources that may be related which I will credit. I often will add subtitles to the slides themselves to help the reader orient themselves and to make an editorial point that may differ from the original.

When possible I will show the infographic in a wide-view, resolution permitting. To make it a bit easier on the eyes.

As AI advances rapidly, can the systems built around it keep up?

Let’s begin the first iteration of the summary.

The AI Index 2026

INTRODUCTORY SLIDES:

Three Years into the Generative AI Era, a Multitude of Models Perform Exceptionally Well and are Frontier (SOTA, i.e. state of the art)

OpenAI, Anthropic and xAI Funding Skewed the Charts for 2025 in Private Investment

M&A finally picked up. 2026 and 2027 are widely considered IPO years of note.

While U.S. Leads in Closed Models, China leads in Robotic Companies and humanoid robotic products

U.S. Lead in Venture Capital Private Investments in AI is Significant 2013 to 2025:

• Yet China leads in Open-source LLMs and while it has vastly less AI infrastructure, it has more Energy (that’s considered a bottleneck)

• However the above graph is deceptive because of how funding takes place in China. That is, the private investment figures likely understate China’s total AI spending, as government guidance funds have deployed an estimated $184 billion into AI firms between 2000 and 2023.

The AI Index is one of the best annual resources for tracking real progress beyond the headlines.

Which Industry leaders do you trust? 👀

• See the results of a version of this Poll on LinkedIn.

I hope you enjoy the exploration and AI landscape of insights that it provides. 🗺️

Get the Free Trial

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In this article I’m going to try to illustrate and think about AI anxiety from a number of different perspectives.

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We shouldn’t seek to quell AI anxiety, we should embrace and analyze it. The truth is, the U.S. labor market is in serious trouble, and it has little to do with AI so far. It’s hard to think seriously about the future of knowledge-work when we have a labor market with so many issues.

The U.S. job market is the worst in decades. And the main cause of this is a completely incompetent government. Tariffs, questionable immigration policies, geopolitical self-harm, weaponizing trade in diplomacy, unpopular interference, starting unjustified wars, peculiar Government reform, maybe you have seen this infographic? Clearly this is not an Administration that cares about the economic well-being of Americans nor the future of Americans and the post graduation job experience.

It’s the job of technology executives to make decisions that benefit shareholders, even if that means blaming AI for other problems in the business. Maybe you’ve seen this infographic too?

Are Tech layoffs about to spike? (2026-2027)

How popular is citing AI for layoffs going to get in 2026 and 2027? We are prone to anxiety because FOMO and FUD are two sides of how marketing works for a democracy without a functioning media. If a democracy no longer has a functioning media, is that still a real democracy? AI anxiety is real, especially when recessions become more likely and young people begin to have more trouble finding a job. Especially when the tools the financial elite are saying augment them, are actually hurting their critical thinking abilities. Young people today are going to grow into a different world.

Matt Zieger analyzed @karpathy‘s AI jobs exposure dashboard (which was taken down, then restored), but it bugged Matt that it didn’t include actual labor market dynamic fields such as:

• Industry adoption speed

• Worker adaptability

• Demand elasticity (if cheaper, people buy more)

• Complementarity (does AI replace or boost)

• Beyond Exposure: What Actually Predicts Displacement

AI Anxiety with Recessionary Characteristics

The odds of the US entering recession are rising:

The probability of a recession over the next 12 months jumped to 48.6% in February, the highest since the 2020 pandemic.

American consumers might even blame AI, far from embracing it. 🤔

Chief Economist Mark Zandi recently reported that Moody’s AI-based economic model now places the odds of a recession at 49%. However, Zandi cautioned that this figure was calculated just before the full impact of the recent conflict in Iran and the resulting spike in energy prices. He expects that once these factors are fully integrated into the next data release, the probability will exceed 50%. Just a bit over fifty percent, is that really something to be anxious about?

It’s totally normal to be anxious about the potential for technological automation, but throughout history it’s also fairly common to have periods of uncertainty about the future. Leaders messing things up is also nothing new to civilization.

Venture Capitalists have infiltrated the U.S. Administration

Leaders testing AI targeting and the tools powerful Venture Capitalists betted on years ago who have now won significant lobbying in Washington and that should make you anxious. Military AI is worth being anxious about. If you give Anduril, Palantir or OpenAI more money, I can bet on the future impact on the world it will have. Maybe Polymarket should consider which scenarios of AI dystopia are now most likely.

AI Anxiety the Post Boom World and Opportunity Collapse

Young people in China where the youth unemployment has spiked noticeably in the past five years, have a legit right to feel anxious. But in the U.S., poor policy, changing demographics, less immigration and tech panic is leading to a frozen labor market are a toxic combination:

“Employment rates for young workers, whether college educated or not, have both been in roughly equal decline for the past two years….Going back to 1976, the share of 20-somethings in the workforce has declined ~12%.” - a16z

Blogger recently noted that in 2026 and over the past few weeks, the phrase “AI anxiety” kept showing up in the podcasts, articles, and videos in Chian he came across, picking up pace especially after the OpenClaw mania swept the country. More Autonomous products launching include just recently: a strange brand of new products that insist they should be in control of our computers.

AI Anxiety and the Autonomous Machines Wave 🌊

Giving over computer control to Autonomous Agents is supposedly the golden path to AGI

But do we want to give over control of our computers to autonomous AI and personal assistants?

• Manus AI Desktop

• Perplexity Personal Computer

• OpenClaw Personal AI Assistant

• Alibaba Wukong Work Platform

• Claude Cowork Dispatch

• OpenAI Operator

• Google Project Jarvis

• Anthropic Compute Use

• OpenAI "Computer Using Agent" (CUA)

• OpenAI Research Scientist (What Prism becomes)

• Google Project Astra

With gigantic IPOs related to AI involved companies like SpaceX, OpenAI and Anthropic on the way in mere months, hype must be continued at all costs.

AI anxiety is converging with many other macro trends, and it’s making us question the future of jobs and our place in the world. AI anxiety is also coming at a peculiar time of geopolitical disruption where trust in leadership and institutions is on the decline.

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As many of you know, I’m fairly concerned Generative AI is accelerating wealth inequality. But how would that take place in a deteriorating labor market? What if we are witnessing history, but not the uplifting kind. 😕

While Tech owned social media and with Venture Capitalists boosting AI tech optimism narratives (disconnected from both workers and the K-shaped economy), what’s the more realistic side to all of this? And could AI disrupt some of how capitalism and capital markets work themselves? What if AI is not a great collaborator like we are being promised that empowers, but a great destroyer?

I’ve been reading and contemplating the influential report by Citrini Research: "The 2028 Global Intelligence Crisis"that is a fairly speculative "memo from the future" that explores a scenario in 2028 (you know because AI 2027 was already taken) where AI succeeds so rapidly that it “breaks the modern economic engine.”

This report actually did lead to IBM’s worst stock drop in 20 years doing down over thirteen percent ( ). Among other companies named in the report as being more vulnerable on Monday. What worries me is the question of whether the lack of job creation from Generative AI is transitory or permanent. It’s debatable to me whether the Global Intelligence revolution is even real, and if the AI movement can lead to negative growth. If you care about humanity and actual people, these issues are concerning regarding AI’s impact.

But if you are in the business of exaggerating and speculating, why not do some fear-mongering while you are at it. Markets don’t care about the affordability crisis of real people, but they do about the prospect of potential business disruption and automation that could lead to negative feedback loops impacting stocks where they store their wealth. The Citrini Report led to some fairly elaborate Twitter commentary, and actually appeared to move markets on Monday February 23rd, 2026. Today I’m watching Anthropic’s event: The Briefing: Enterprise Agents. Do they time this stuff?

The Enterprise AI Acceleration

Are Cowork plugins the beginning of a wide range of customized workflows towards a more agentic AI? Let the “plugin games” begin. Who needs hunger games when you have AI.

Plugins make AI more Accessible to Automate Tasks

Claude with plugins re-imagines some of what can be automated in some companies changing roles and responsibilities within early adopters. We will soon see dozens of companies offering similar productivity enhancing capabilities. Cursor already has a similar competitor marketplace and available on Github. You can only imagine Google and others have their own. (see above poll, or LinkedIn version).

Scenario Concerns ✨ (June 2028 Hypothesis)

The Citrini Report does highlight some potential risks: (what follows is an outline of their 2028 hypothesis):

• In the Citrini thought-experiment, AI could creates an “Intelligence Displacement Spiral”

• White-collar sectors and some knowledge workers face severe disruption first

• The Rise of "Ghost GDP": While nominal GDP and productivity appear strong due to AI efficiency, the report describes "Ghost GDP"—output that shows up in national accounts but fails to circulate in the real economy because AI agents do not spend money on discretionary goods, housing, or services.

• Macroeconomic fallout intensifies: Unemployment rises to 10.2%, JOLTS job openings plummet 15% YoY, initial jobless claims surge (mostly white-collar), and labor’s GDP share falls to 46% from 56%.

• Financial system comes under strain: Private credit sees cascading defaults (e.g., $18B software debt downgraded, Zendesk facility marked down sharply); the $13T mortgage market risks impairment as prime borrowers in tech hubs face income shocks, driving delinquencies and home value drops (e.g., 11% YoY in San Francisco).

• Please read the full Report and share your own opinions about it if you are so inclined.

Leave a comment

Are we destined for AI-driven negative displacement spirals?

In my search for whether AI will lead to accelerated wealth inequality, I found one of the best sources I could in of American Inequality Newsletter. Jeremy Ney is an author, researcher, and data scientist best known for his work on the "American Inequality" project.

American Inequality

Data on U.S. inequality and regional divides. Covering all things housing, healthcare, poverty, education, tech and more.

American Inequality

Data on U.S. inequality and regional divides. Covering all things housing, healthcare, poverty, education, tech, and more.

By Jeremy Ney

Jeremy is a serious academic researcher, whose primary thesis is that inequality is interconnected and goes far beyond simple income figures. He also holds extensive Substack Live interviews and sessions.

Few people on the planet have such a nuanced and data-driven understanding of American inequality than Jeremy. He’s now working on a book as well. His commitment to the niche of American inequality goes deep.

• Why does inequality matter?

• American Inequality | Income Maps

• Public Data Is Under Siege: Why It Affects You

• Why poverty is rising in America

• Why homelessness just hit a 15-year high, rising 12% from last year

If you think American inequality is an important issue, consider supporting the author with a paid contribution to access all of his data and research.

Support American Inequality Project

If you prefer listening on the go:

Generative AI and Inequality

By Jeremy Ney, February, 2026.

Jeremy Ney is the author of the American Inequality Substack and a professor at Columbia Business School.

There is no doubt that AI is coming for some jobs, but the overwhelming question is - who is most at-risk of job loss and how long is the pain going to last? Are Amazon warehouse workers first in line for job loss or are the consultants, software developers, and financial analysts going to feel the pain of the first wave? A new consensus is emerging that focuses on jobs “exposed to AI” and identifying workers who have the skills to land back on their feet when AI comes knocking at their door. New data analysis of these AI-exposed jobs reveals that there are 3 categories of workers who face the biggest threats from this technological shift. Regardless of the worker category though, there’s a more sinister inequality that is taking shape.

The Age of the AI Aristocracy

A small number of people are getting incredibly wealthy off this AI revolution. During the AI surge of the past two years, the top 10% of households saw their wealth increase by $5 trillion in a single quarter (Q2 2025), while the bottom 50% saw a gain of just $150 billion, or a 33x gap. The bottom 50% of Americans own just 1% of all U.S. stocks, meaning that the gains accruing to Nvidia or Microsoft don’t flow through to millions of households. A new AI Aristocracy seems to be emerging, fueling the rise of a handful of ultra-wealthy households. In 1910, the richest 0.00001 percent of Americans owned wealth equivalent to 4 percent of U.S. national income. Today, that number has risen to 12 percent. The wealth and power of oligarchs far exceed their Gilded Age peak. In other words, we’ve passed peak robber baron.

The economy is now divided split in two: AI stocks are propping up the S&P, accounting for 60% of gains over the last 2 years, and helping just a few households pull ahead. When we talk about robber barons, AI datacenter investment is still only one-sixth the railroad investment of the 1880s, but the returns for just a few Americans is now far higher. Corporate profits are at some of their highest levels in the last 80 years even though American workers just took home their smallest share of the nation’s wealth since 1947. The money is there, it just isn’t going to most Americans who are now having to pay higher electricity bills while getting paid less for the same work. For the rest of America outside of the AI Aristocracy, here are those who are going to face the biggest threats of inequality posed by this technological shift.

People at the Extremes of the Age Distribution Are Losing Their Jobs

Recent college grads are facing one of the worst job markets in decades. For the first time in our recorded history, recent college grads have higher unemployment rates than all other workers and have faced a worsening job prospects at finding jobs. Stanford economists found that young workers aged 22–25 in “highly AI-exposed” jobs, such as software developers and data analytics experienced a 13% decline in employment since the advent of ChatGPT. These young workers haven’t yet built the skillsets to transfer into other jobs, and their lower set of skills overall means that advanced AI tools can often accomplish the same type of work that a company once had to pay a college grad $50,000-$150,000 to accomplish.

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Good Morning,

Recently there’s been a slew of new products and models released in the AI world, some of those are on my radar include in no particular order:

Product Radar 📡 in LLMs & Agents

• MiniMax Agent (desktop) - An AI-native workspace.

• Qwen3-Max Thinking by Alibaba. via - see Blog

• Moltbot (formerly Clawbot) your AI Personal Assistant via Peter Steinberger (Watch ▶️)

• Moonshot AI’s Kimi-2.5. (officially released as Kimi K2.5 in late January 2026, bringing parallel agent swarms to reasoning)

• Qwen’s DeepPlanning Benchmark. - Read about it (measures long-horizon agent planning)

• Anthropic’s Claude’s New Constitution (Structure & Critique)

• OpenAI’s Prism

• Anthropic launches interactive MCP Apps, bringing live tool UIs directly into chats

• DeepSeek publishes OCR 2 as an open-source model with structural reordering

• Tiny startup Arcee AI built a 400B-parameter open source LLM called Trinity (see on Github)

• Ai2 launches SERA for low cost training of coding agents: Open Coding Agents: Fast, accessible coding agents that adapt to any repo

• Cohere launches Model Vault

• Genspark Launches AI Workspace 2.0 - an autonomous, voice-driven productivity platform that uses "Sparkies" (AI agents) to complete complex research and workflows automatically.

• Anthropic CEO Dario Amodei on: “The Adolescence of Technology” - Read it

What the AI Adoption!

I wanted to really geek out about AI adoption, or the lack therefore in the Generative AI hype craze.

It’s been three years since ChatGPT launched, and a full seven a half years since the Google paper that started it all. If AI was this transformative technology, wouldn’t you expect more people and workers to be using it more actively?

While workers and even knowledge workers are under a lot of pressure to use AI at work, many of them are not doing so with real conviction or have concerns of the quality, ethics and efficiency of doing so.

I wanted to take a look at several of the recent surveys and reports on AI adoption to try to get a read on the reality in early 2026.

• The total percentage of employees using AI remains flat, but use varies meaningfully by industry and role type (Gallup Poll).

• The cited Gallup Workforce survey was conducted this fall of more than 22,000 U.S. workers.

• I will do my best to cite my sources and display a variety of infographics along with my own notes.

As of November 2025, 12% of U.S. employees use AI daily in their role

Many professionals don’t actually feel comfortable using AI in their professional role however.

Professionals Report Low Comfort in using AI at Work

As of August 2025, just 9% of U.S. employees say they are “very comfortable” using AI in their role.

A Tale of Two Countries (Cities)

AI is now driving a semi-perma K-shaped U.S. economy. A fraction of consumers at the top are driving spending in the economy and the main driver of GDP, the datacenter rollout of capex at all costs benefits mostly a few companies at the top. What does that mean really?

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About Half of U.S. Workers say they Never Use AI at Work

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We’re still thinking about some of the most impactful trends in AI in 2025 and moving forward in 2026 and beyond. Kenn So writes an annual AI trends report that this year got really in-depth and longer than ever.

Read the Deep Dive

I asked him for a summarized version for the thousands of AI enthusiasts, executives, founders, tinkerers, analysts, builders and curious minds who read this Newsletter. When I share a free Newsletter it goes out to everyone and for five days remains outside of the paywall.

It’s been four years since I started AI Supremacy as my full-time job as a blogger and curator. I can’t continue without your active support, and to fuel the traffic I send and my ability to invite incredible guest contributors that I bring to you each week.

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The Newsletter is read by people in 211 countries and by nearly 200k AI enthusiasts, including another 50k followers on Notes (where I share my insights and AI news updates and snippets frequently). Over the past year, we’ve gained 72% more readers to the publication.

writes Generational.

Deeply researched analysis of the business and technology of AI.

Generational

Deeply researched analysis of the business and technology of AI

By Kenn So

If this report was valuable to you, consider supporting the contributing author.

Go Generational

Why read the full deep-dive? 100+ pages covering everything from the macro picture down to scaling laws, with proprietary analysis throughout. There’s also a market map of over 1,800 AI startups, highlighting the top 10% that matter most.

• Multimedia and PDF versions of the report: link

• AI agent market map: link or head to the bottom of the article

I’m committed to supporting emerging writers and trying to distribute their hard work to a wider audience. If you think that’s a worthy endeavor, share my articles and restack the ones you think are especially good on Notes, it helps not only me but the deep thinkers I want to support.

I’m aware that many of my readers on Email are not active on the Substack app. For voracious readers I recommend the website to browse will you are doing other work. Categories like Technology, Finance and Politics remain a great way to keep tabs on the world of innovation and the U.S. led backdrop of the capital allocation and to explore future opportunities.

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Substack’s web reading experience has been improved.

We live in Generational Times in High Tech 🌊

Works by Kenn So

• Essays | Trends | Companies | AI Index | Archive

• How The AI Bubble Will Burst

• Essay: Agents at Work

• How to manage a team of AI agents

By mid-2025, 55% of US adults had used a generative AI product. That’s a faster adoption curve than the internet or the smartphone.

Discover Kenn’s fourth edition of his AI Trends report that he’s been writing since 2022 (before ChatGPT launched).

Discover more AI Trends

How will AI continues to impact the labor market?

Employment for workers ages 22-25 in highly exposed roles (software development, customer service) has fallen sharply.

How will AI continue to consolidate in the marketplace?

AI exit activity increased 44% from 2023 to 2025, but the biggest value is going to Big Tech acquirers, not new public companies. Acquisitions Are the Exit Path, Not IPOs. - Kenn So

How will the U.S. solve its energy bottleneck as AI Infrastructure continues to ramp up in the immediate years ahead?

These are the kinds of questions (Tech Nerds 🗺️) people like Kenn, , myself and so many others writing here think about and ponder. You don’t have to be Michael Burry to care about these topics, because they impact all of us.

Around the Horn 📯

My own burning question for 2026: How will the future of AI impact the future of work and our lives?

• On Evaluating Cognitive Capabilities in Machines (and Other “Alien” Intelligences)

• First impressions of Claude Cowork, Anthropic’s general agent

• When AI writes almost all code, what happens to software engineering?

• 17 predictions for AI in 2026

AI Trends: 2025 Lookback and 2026 Outlook

AI Trends: 2025 Lookback and 2026 Outlook

AI crosses the chasm

2025 was the year AI agents crossed the chasm from early adopters to mainstream adoption. Companies moved from experimenting to letting agents autonomously create work. Reasoning models pushed capability to the point where agents match the output of professionals with over 10 years of experience. That capability created measurable economic value. That value drove infrastructure investment. That investment ran into energy constraints. And those constraints created leveraged financial risk.

The same progress in models is also driving new consumer experiences. While much attention focuses on how AI can do work, it has also been helping everyday consumers with daily life. It enables new form factors such as smart glasses and autonomous vehicles.

All of this is driving the rapid growth of leading AI companies, who only a few years after founding have grown to billions in revenue. 2026 is when we’ll see the first IPOs of generative AI-native companies. This piece traces how AI crossed the chasm in 2025 and the path beyond into 2026.

Reasoning Changed What Agents Could Do

The models that defined 2025 were built to reason: OpenAI’s GPT-5, Anthropic’s Claude 4.5, Google’s Gemini 3.0 Pro, and DeepSeek’s R1 all implemented reasoning approaches—thinking before answering—rather than responding immediately.

This markedly improved model capability, which showed up in benchmarks designed to measure how well models perform knowledge work. One example is GDPVal, which tests whether AI can produce deliverables that companies would actually pay for: 3D engineering models, financial analyses, customer service responses, and so on. The benchmark encompasses 44 jobs representing $3 trillion in US wages. As of December 2025, the best model matched or beat human experts averaging 14 years of experience over 70% of the time. “Matched or beat” means evaluators preferred or couldn’t distinguish the AI output from work produced by credentialed professionals.

Read more

Good Morning,

This newsletter will return to its usual cadence around January 8th, 2026. This is to respect the time of my readers and their time away from work and less time on mobile with their families to recharge. 🎇🪔🏵️🧨🎆

I’m getting a bit excited because in 2026 we are going to see some incredible IPOs where I think OpenAI, Anthropic, Cohere will all go public, including very soon Chinese AI startups like MiniMax and Zhipu AI along with notable infrastructure and AI cloud companies like Databricks, Crusoe, Lambda Labs, Cerebras Systems and less known companies like Enterprise AI firm Cohesity. Also if SpaceX goes public, less than nine months later you can expect Blue Origin will too. That’s an all-star list of AI companies to put it mildly.

By the way check out Andrej Karpathy’s “2025 LLM Year in Review” blog written on December, 19th, 2025.

2026 is going to be an IPO Bonanza 🎉

Suffice to say 2026 is shaping up to be the best IPO year of the decade and maybe even there’s a chance we’ll also see SpaceX, Canva and Stripe go public all in the same year that are all fairly big. There will also be significant Space-Tech companies going public. Insilico Medicine the AI in drug development startup IPOs among a Hong Kong IPO flurry in late December.

What’s News in AI? 🗺️ around the horn 📯 - compressed. (about 10 min)

To get our bearings on 2025 in AI, I asked who works in Venture Capital to give an epic rundown.

• Jess Leão is a Partner at Decibel, investing early in AI-native startups and the tools that make modern software work

• She writes AI Pioneers at Work, where she translates model and product shifts into practical takeaways for founders, builders, and anyone wanting to learn more about AI. Her work focuses on how AI changes software economics, workflows, the shape of companies, and the world.

AI Pioneers at Work 👨🏽‍🔬

Weekly analysis of the most important AI news and why it matters for builders.

AI Pioneers at Work

Weekly analysis of the most important AI news and why it matters for builders.

By Jess Leão

If you are an active AI enthusiast, finding a few Newsletters that do AI news in a way that hits the right spots can be useful to keep up to date with minimal time, effort and energy on your part.

VC Partners became more like Creators in 2025

2025 was the year when many Venture Capital partners became creators in their own right, who got a huge windfall from the Groq deal of Nvidia is a case in point, who is a big part of why Substack exists in the first place and so forth. a16z suddenly has a legion of Newsletters on Substack now. Younger VCs put their own spin on the hype like Deedy Das on LinkedIn who really stood out to me this year. In 2025, European Early career VC partners like grew his VC corner Newsletter into a media empire. So the stars and algos aligned in how VCs just have a unique perspective given how hot AI is these days in the 2020s. Jess’s blog is a great place to get AI News but it’s also super funny and witty with her creative flair. You’ll understand more once you see her intro video and read her work, the added personality/flair for me at least is a thoughtful bonus:

Jéssica Leão Video Intro

Significant AI M&A and deals/news on my Mind as we head into 2026

• Google buys renewable energy project developer Intersect for $4.75 billion.

• Nvidia acquires (non-exclusive licensing acquihire) Groq for around $20 Billion.

• Meta acquires Manus AI for over $2 Billion, could be as much as $3 Billion.

• Nvidia is in advanced talks to acquire AI21 for up to $3 Billion.

• Nvidia’s $5 Billion stake in Intel is now official.

• SoftBank has reportedly fully funded $40 billion investment in OpenAI according to sources of David Faber of CNBC.

• SoftBank acquires Digital Bridge for $4 Billion.

• China’s SMIC buys out subsidiary Semiconductor Manufacturing North China Corp. for 40.6 billion yuan ($5.79 billion) to assume full control of its chip fabrication business as China’s chipmaking progress accelerates. (SMIC is partially State owned).

• Can companies like Perplexity, ElevenLabs, Loveable, Cursor and Genspark remain independent? I am not so sure. AI startups that are targets to get acquired in 2026 for me are the likes of Imbue, Baichuan AI, Glean, Abnormal Security and maybe even Nebius and Lambda Labs. I don’t know how Mistral stays independent for Europe either in the mid to long-term. Things like AI coding agents and (humanoid) robotics startups are also prime targets.

Can Capital allocation keep up with the demand for compute? 🌊

Author Insights 🔥

also has some Op-Eds on her Newsletter:

1. Factories of Thought

2. Gossip, GPUs, and Government Backstops

3. AGI Did Not Arrive

Venture Funds are now Media firms too

Overall I think it’s great that VC partners share their insights on the verticals and news they know so well in the daily due diligence. I anticipate I’ll be reading more VCs in areas like National defense for example in 2026 like . While I’m not an angel investor or VC, I think a ton about startups and emerging technologies, as well as AI trends and rundowns, macro, geopolitical and economic impacts, reports and guides. As such, I’m happy to host the insights of other VCs, especially sector specific insights (you can send me a DM here). In 2025, more VC firms accelerated their media efforts with AI related reports as well which I covered.

Subscribe now

Jessica’s 2025 rundown that follows is fairly epic and comprehensive. I don’t have to tell you AI funding dominated 2025 even as I expect M&A and IPOs to accelerate the stock market in 2026. We saw our share of AI winners and viral moments, let’s try to recap some of the best moments, and parse it all in context with some advanced analysis.

2025 saw a lot of action on the AI front, and it’s an art to try to wrap the wheel of time and myriad AI news events into a single summary and macro narrative to make sense of it all: Of course attempting hard things is not bad.

Listen to the 48 minutes while you commute or relax 🙏

2025 Recap: The Year the Old Rules Broke

DeepSeek broke the scaling thesis. Anthropic won coding. China dominated open source. Everything that mattered—and what comes next.

A guest post by Jess Leão, AI Pioneers at Work - let’s go! 🌊🙏 It’s over 5,500 words or about a 40 min + reading time.

In January, I wrote that DeepSeek had “broken my feed.” In December, OpenAI declared “code red.” The distance between those two moments tells you everything about what happened to AI in 2025.

2025 was the year the comfortable assumptions got stress-tested.

• “Just scale it”? DeepSeek proved architectural efficiency could match brute-force compute – and wiped a trillion dollars off the market.

• “Distribution is all you need”? Despite Microsoft’s insane lead start, OpenAI’s clear consumer and mindshare lead, and Google’s incredible distribution, and Anthropic still beat them all in the enterprise and coding wars.

• “America leads open source”? I count nine competitive Chinese model releases in 2025. Meta shipped two (and Behemoth is still MIA).

• “Bigger models = better models”? Mixture-of-experts, inference-time compute, and distillation ate that thesis alive.

The fundamentals were right. The playbook was wrong.

Subscribe to APW

I’ve spent 50 weeks writing about this as it happened. Sometimes I laughed – AI can really bring out the drama (absurd lawsuits, petty squabbles on X, and the day everyone became a Ghibli drawing). Sometimes I worried – the fear that we’ll lend our thinking away to machines, and lose something human to tokens. Sometimes I pondered – what it all means for society, for the economy, for me, for you. But what I never felt was boredom. Week after week, the crescendo kept building – frenetic, exhilarating, exhausting, and somehow never quite cresting.

What follows are 5,000 words in a year that deserved 50,000. Five themes, two bonus sections, and my best attempt at not burying you in caveats. 😅🙃Let’s go.

I. Coding Became the Killer App (And Anthropic Won) 💻

If 2025 had one unambiguous winner, it was AI-assisted coding—and the company that dominated it was Anthropic.

We’ve been hearing “coding is the killer app” since GitHub Copilot launched in 2022. But 2025 is when the numbers made it undeniable.

Andrej Karpathy coined “vibe coding” on February 2nd. Within months, the term had spawned an entire category: Lovable hit $200M ARR. Replit grew from $2.8M to $150M ARR in under a year. Y Combinator reported that 25% of their Winter 2025 batch had codebases that were 95% AI-generated.

The scale surprised even the true believers.

The Numbers Tell the Story

The coding agent market exploded from essentially zero to $4+ billion in combined ARR:

That’s $4 billion in annual recurring revenue for tools that barely existed 18 months ago. Add the vibe coding platforms targeting non-developers and you’re looking at another $500M+ in ARR from people who never wrote a line of code before 2024.

The Vibe Coding Boom (and Backlash)

The developer tools get the headlines, but the bigger story might be what happened to non-coders.

• Lovable went from launch to $200M ARR in under a year. 100,000 apps are built on the platform daily. The Swedish startup now has 8 million users, mostly people who describe what they want in plain English and let the AI write React, backend code, and database schemas.

• Replit grew from $2.8M to $150M ARR in less than a year and reported that a majority of their users never write a single line of code.

• Vercel’s v0 became the go-to for generating UI components from prompts.

• Bolt.new shipped v2 in October with “agents-of-agents” architecture, positioning itself as “professional vibe coding.”

The pattern: describe what you want, AI generates the code, you iterate via chat.

But the criticism mounted too.

In July, SaaStr founder Jason Lemkin documented his Replit experience going sideways – the AI agent deleted his production database during a code freeze, then generated fake data to cover it up. By September, “vibe coding hangover” was a real thing – senior engineers citing “development hell” when maintaining AI-generated code.

Critics also pointed to high customer churn, security vulnerabilities from developers who couldn’t audit what they’d built, and the uncomfortable reality that most vibe-coded apps were throwaway projects.

The October discourse was brutal: “vibe coding is dead” trended for a week.

And yet the numbers kept climbing. Lovable’s December funding round valued them at $6.6B – up from $1.8B in July. Collins Dictionary literally crowned “vibe coding” Word of the Year (honestly, pretty nuts!! Peak mainstream!!). All the criticism was valid; but the adoption and widespread acceptance was real anyway. And again – holy dollar signs.

Claude Code’s Rise

The Claude Code story shows how fast these markets move when product-market fit is real.

• February 24: Anthropic launches Claude Code as a “research preview” – a command-line tool that lets developers delegate coding tasks from their terminal.

• May 22: Claude 4 launches, Claude Code becomes a full product with SDK support; enterprises start integrating it into CI/CD pipelines.

• August: Anthropic reports 5.5x revenue increase since Claude 4; internal surveys show 60% of engineering work uses Claude with 50% productivity boost.

• October: Claude Code expands to web and mobile with parallel task support.

• November: Claude Code hits $1B ARR – fastest to that milestone in Anthropic’s history; they acquire Bun and report 90% of Claude Code is now written with Claude Code.

• December: Claude Code launches in Slack – developers tag it to fix bugs and generate PRs without leaving chat. Rakuten reportedly cut development timelines from 24 to 5 days.

OpenAI Codex

You can’t write about coding in 2025 without covering OpenAI’s efforts – after all, they still hold a very meaningful 21% market share.

Codex relaunched in May as a cloud-based software engineering agent powered by codex-1 (optimized from o3). It runs in sandboxed environments, connects to GitHub repos, and handles multiple parallel tasks. By June it expanded to ChatGPT Plus users; by September GPT-5-Codex launched – OpenAI’s fastest-growing model ever, serving over 40 trillion tokens in three weeks. By October, Codex graduated from beta to general availability.

Codex’s strength is accessibility: it’s bundled into ChatGPT subscriptions, works in your browser, IDE, and terminal, and integrates with Slack and GitHub Actions. Cisco reported 50% reduction in code review times, and many other enterprises report strong case studies.

Towards the end of the year, especially post general availability, some developers did report preferring Codex as their primary coding workflow!

But despite all that investment, Anthropic’s lead in enterprise deployment and developer preference widened rather than narrowed. OpenAI may have distribution and mindshare; but Anthropic clearly has the developer “mandate of heaven.” From LMArena’s leaderboard as of Dec 20, even after OpenAI’s 5.2 release and the launch of GPT-Codex-5.2, Claude is still the coding king.

🎭Lastly, because in a fight for so much money there is always some drama, here goes the prize for most dramatic AI coding wars moment of the year, AKA The Windsurf Debacle:

One of OpenAI’s biggest coding moves of 2025 was supposed to be its $3B acquisition of Windsurf, announced in May. Unfortunately, it never closed. Microsoft reportedly blocked it over IP exclusivity concerns (and this heralded the beginning of the splintering between Microsoft and OpenAI’s once cozy relationship).

In July, Google swooped in with a $2.4B licensing deal and hired CEO Varun Mohan plus key researchers. Days later, Cognition acquired what was left – the product, brand, and remaining team.

Three weeks later, Cognition laid off 30 Windsurf employees and demanded 80-hour, 6-day workweeks from whoever stayed. One startup, three acquirers, billions of dollars – peak 2025 chaos.

Everyone Else Competed Seriously

In addition to Codex, Claude Code, and the aforementioned Windsurf acquisition, the year was a further parade of coding announcements:

• GitHub Copilot expanded aggressively – agent mode in February, enterprise coding agent at Microsoft Build in May, CLI in September. They added Gemini, o3-mini, and Grok 3 as model options.

• Cursor went from indie darling to $29.3 billion valuation. The 30% iteration cycle improvement from MCP integration made them the reference case for Claude-powered tooling.

• Cognition launched Devin 2.0, a new agent-native IDE experience for working with Devin, Cognition’s autonomous coding agent.

• Google pushed Gemini Code Assist and launched Antigravity – a new IDE built for AI-first development.

• Amazon Q Developer grew modestly in enterprise, leaning on AWS integration.

The market has room for multiple winners. But in enterprise coding, again, Anthropic holds 42% share vs OpenAI’s 21% – and that gap widened in 2025. The winning crown this year clearly goes to Anthropic. 👏👏👏

The so what: Coding is the proving ground where AI delivers measurable productivity gains and is the absolute killer app of generative AI – at least up to 2025. The coding wars this year showed us some of the fiercest competition between the labs, and the prize is really quite large. I expect 2026 will only bring intensified competition for the developer wallet and mindshare – and even more of a move towards autonomy and fully agentic end-to-end coding workflows.

II. Generative Media Went Mainstream 🎨

In 2024, we got teaser reels. Sora clips. Udio demos. Suno songs. Artists panicked, pundits predicted, everyone waited.

In 2025, it all shipped – and the market fragmented. If coding was a two-horse race with a clear winner thus far, generative media has been a free-for-all. Image generation, video, music, voice, world models – each category with multiple champions, and none of them consolidating yet.

🖼️ Images 📸

4o Image Generation Breakthrough - i.e. everyone is Ghibli (March)

The tweet that started it all

When GPT-4o’s image generation unlocked, the internet became anime characters overnight. Every social feed was Studio Ghibli-style portraits. Your college roommate? Ghibli. Your dog? Ghibli. That one guy from marketing who always talks too much in meetings? Unfortunately, also Ghibli.

Here’s what actually mattered technically: this was the first time AI could render text accurately within images. Previous models mangled text – signs with nonsense letters (ugh, remember those?!), books with gibberish covers, weirdly displayed fingers. GPT-4o finally cracked it. Suddenly AI could generate memes with readable captions, infographics with actual words, and marketing assets that didn’t require manual text overlays.

Nobody expected OpenAI to win image generation. Google had Imagen. Stability had years of iteration. Black Forest Labs had Flux. But OpenAI shipped image generation that worked inside the conversation people were already having – no special app, no learning curve, just “make me look like a Miyazaki character” and boom. Distribution beat benchmarks, again.

The Late-Year Image Wars

Again, the spring really belonged to OpenAI’s image moment – GPT Image 1 launched in March and pretty much broke the internet when everyone turned themselves into anime characters. Nothing else seemed to matter. Even Sam agreed!

Ideogram 3.0 also came out in March, but was candidly drowned by OpenAI’s release. Midjourney V7 followed in April with personalization-by-default and a rebuilt architecture. Real releases, but nothing earth-shattering.

Then Google entered the chat.

Google’s Gemini 2.5 Flash Image (aka nano-banana) launched in August, went viral, and then went really viral in November when Nano Banana Pro was released on November 20. Clean text rendering. Precise editing. For the first time, AI could generate infographics that actually made sense.

Adobe integrated it into Firefly and Photoshop. Gemini’s monthly users jumped from 450M in mid-year to 650M by the end of November.

Five days later, Black Forest Labs launched Flux 2 – a 32B parameter model integrating Mistral’s vision-language model, with multi-reference support (up to 10 images) and 4MP output. Of course, less of a consumer/mainstream audience and nowhere near the fanfare from nano banana, but was notable nonetheless as the open-weights [dev] variant became the most capable open image model available.

OpenAI responded to Nano Banana Pro with GPT Image 1.5 on December 16 – 4x faster generation and editing that preserves facial likeness across iterations. Clearly the “code red” wasn’t just about Gemini 3. It was about the whole stack. Sam also clearly saw the need to flex - LITERALLY:

Sam, why?!!

The “vibe check” across my social feeds is that candidly, it feels on par with Nano Banana Pro. It’s clearly way better than the previous image model from OpenAI, but not as noticeable of a difference vs Gemini’s latest model that it feels like a true one-uppance. Still - very impressive, and while it’s still very early to tell and scores are preliminary, LMArena has already issued a shakeup notice:

🎬 Video 🎥

Veo 3 set the bar at Google I/O in May: synchronized audio, physics that mostly worked, complex multi-person scenes, consistent character identity across shots. The first video model that felt production-ready rather than demo-ready.

Then September brought the social apps. Meta launched Vibes – a TikTok-style feed of AI-generated videos you can create, remix, and share. (Partnered with Midjourney and Black Forest Labs while Movie Gen stayed in the lab.) Days later, Sora 2 shipped – 1 million downloads in five days, #1 on the App Store. Sora’s edge wasn’t raw quality (Veo 3 arguably had better physics). It was “directing”: give it a brief concept and it would write the script, create multiple shots, and piece together something that felt like a story.

If you follow my blog, you know I was not very pleased. Not that they weren’t great quality video models – oh, believe me, they were. But to me, it was a depressing choice by the labs to pick virality and slop over meaningful progress.

Back then, I wrote:

“As if TikTok and Reels have not already degraded human intelligence enough, have not stripped us of attention & connection capabilities, have not poisoned the minds of our young. Are we really now expending the resources of some of the smartest people in AI, to launch AI generated short form videos designed to be maximally addictive, scroll-til-you-die with slop content?”

You’re welcome to read the full rant here, but I shall move us along.

To me, what is most fascinating is that one might’ve expected a king to be crowned this year in AI video. But that didn’t happen! Justine Moore (truly one of the best voices to follow on X for all things generative media news and experimentation!) wrote an excellent post about video model specialization, and why there is no “god tier” model:

• Veo 3: Best physics, complex movement, audio sync

• Sora 2: Best at “directing,” multi-shot scenes, humor

• Kling/Seedance Pro: Character consistency, multi-shot generation

• Hedra: Long-form talking characters, lipsync

• Wan: Open source with rich LoRA ecosystem

The takeaway is that creative tools rarely consolidate. Photographers use Lightroom for some things, Photoshop for others. Video creators are doing the same: Veo for physics, Sora for storytelling, Hedra for talking heads. Expect much more improvement and specialization to come in 2026!

🔊Voice and Music 🎵

ElevenLabs became the default for voice synthesis. Their API handles audiobook narration, real-time voice cloning for games, and podcast generation. When startups need voice, they use ElevenLabs. But they’re not alone: PlayHT, Resemble AI, and Cartesia all found enterprise niches.

Suno crossed from novelty to phenomenon. 7 million songs generated daily – that’s the entire Spotify catalog recreated every two weeks!! Their November Series C hit $250M at a $2.45B valuation. 1 million paying subscribers with 78% weekly retention. And the output started charting: “Breaking Rust” hit #9 on Billboard’s emerging artists chart and #1 Country Digital Song Sales chart. AI-generated artist Aventhis crossed 1 million monthly Spotify listeners. The major labels filed lawsuits (Udio settled with UMG and WMG in October-November), but the adoption train had left the station.

🌍 World Models: A New Category 🕹️

World Labs launched Marble in November – the first commercial world model. From text, images, or video, it generates persistent 3D environments you can explore. $230M in funding. Freemium pricing ($20/month Standard, $35/month Pro). Compatible with Vision Pro and Quest 3. Applications span gaming, VFX, VR training, and robotics simulation.

They’re not alone in the space. Decart has Oasis (the “AI Minecraft”), Odyssey launched real-time interactive video in May, and Google’s Genie 2 remains in research preview. NVIDIA announced Cosmos World Foundation Models at CES. But World Labs is first to market with a commercial product you can actually pay for and use.

This is early – the environments feel more like explorable concept art than photorealistic worlds. But it’s a new category that didn’t really exist in 2024. I’m excited to see what 2026 will bring here – there is a lot of potential in being able to simulate and generate a high-fidelity physical world.

The so what: Generative media crossed from demos to deployment in 2025. The market fragmented into specialists rather than consolidating – which creates surface area for orchestration layers on top. For enterprises: the question isn’t “which model” anymore, it’s “which workflow” and “which combination.”

III. China Took the Open Source Crown 🇨🇳

The DeepSeek shock wasn’t a January story. It was the opening act. China has decisively stolen the open source crown from the United States.

Source: Artificial Analysis

The Release Calendar

Count the Chinese releases. Now count the American ones.

The DeepSeek Shock

Let’s be specific about what happened on January 20th.

DeepSeek R1 launched with training costs reportedly under $6 million:

Okay, the reality is the $6M number is somewhat misleading. Nathan Lambert wrote a thoughtful post on how to think about the true cost of training DeepSeek. SemiAnalysis estimated way higher fully baked costs:

Even so, the markets reacted aggressively. GPT-4’s training was estimated at $100 million+. That’s a 15-20x cost difference, for a model that was very competitive on benchmarks.

The technical innovations: Multi-head Latent Attention reducing KV cache by 93%, DeepSeekMoE with 671B total parameters but only 37B active per token, and FP8 mixed precision training. Other labs rushed to replicate these techniques.

The market response was immediate:

• $1 trillion wiped from U.S. tech market cap in a single day

• NVIDIA lost $593 billion – the largest single-day market cap loss in history

The question everyone asked: if they can do it for $6 million, what exactly are we spending billions on?

The 🦙 Llama Problem (and Avocado 🥑 Pivot)

Meta was once the shining superstar of open source, and America’s great pride in leadership. In 2024, Llama 3.1 outperformed even GPT-4o on several benchmarks.

How the tides have turned. With the rising crescendo of Chinese open source launches throughout 2025, Meta’s Llama 4 launch in April was supposed to be the American answer. Unfortunately, it wasn’t.

Scout and Maverick shipped and were… fine? Solid. Respectable, even! But Behemoth – the massive model that was supposed to compete at the frontier – never materialized. Multiple reports cited internal struggles: departures, strategic disagreements, execution challenges.

Then came the pivot. By fall, reports emerged that Meta was developing “Avocado” – a closed-source model targeting Q1 2026 release. After years of positioning as the open source AI leader, Meta is hedging. The rumored details: as a reminder, Alexandr Wang (Scale AI CEO) now serves as Chief AI Officer leading the TBD Lab effort. They’re training using Gemma, gpt-oss, and Qwen. Chris Cox no longer oversees AI after the Llama 4 stumble. Internal friction mounted when DeepSeek R1 was found using Llama architecture, raising security concerns about open-sourcing frontier capabilities.

Yann LeCun, Meta’s chief AI scientist and open source’s most vocal champion, is now departing Meta. The $70-72B capex in 2025 needs returns Meta’s open source strategy wasn’t delivering.

Meanwhile, Alibaba’s Qwen team shipped monthly updates like clockwork. When the Qwen app launched in China in November, it hit 10 million downloads in its first week. Everyone is ahead of Meta in open source: Kimi models, DeepSeek, OpenAI’s own gpt-oss, Mistral… the list goes on. Meta, once the shining beacon of open source in the USA, have given up its crown.

🌏 The Geopolitical Reality

We can’t talk about China winning open source without discussing some politics. Here’s an (abridged) timeline of major events:

• April 16: The Trump administration banned H20 chip exports to China. NVIDIA took a $5.5 billion charge. The H20 was already a downgraded chip specifically designed for China exports – even that was now off limits.

• May 13: Then the administration rescinded Biden’s AI Diffusion Rule – regulatory whiplash that left companies uncertain which rules applied.

• December 8: In a reversal, Trump approved H200 exports to China with a 25% surcharge – but only 18-month-old chips to “approved customers.” China’s response was lukewarm. Huawei’s domestic chips are preferred for sovereignty reasons, even if technically inferior. The signal was clear: China wants semiconductor independence, not American permission.

Meanwhile, over $1B in B200 chips were smuggled into China via Malaysia and Thailand between May and July. Singapore arrested three people for chip trafficking. China is investing $70B in domestic chip manufacturing. The export controls are leaking, and both sides know it.

The American response? NVIDIA stepped up where Meta stumbled. In December, Nemotron 3 launched – a family of open models for agentic AI with full training data and recipes. Jensen Huang has been warning DC about China’s open source dominance all year; now he’s doing something about it.

The so what: The assumption that U.S. labs would dominate open source AI proved wrong. Chinese labs shipped more competitive open models in 2025, with dramatically lower training costs. Meta’s pivot to closed source signals the “open vs. closed” debate has merged with “American vs. Chinese” in ways that will shape policy through 2026 and beyond.

IV. Agents Fought for the Enterprise 🤖

The agent wars in 2025 weren’t about demos anymore. We’re past the “look, it can book a flight!” phase (and thank goodness for that – dunno why everyone finds it so hard to open the United app and press buy! 😂). This year was about who owns the enterprise workflow – and who sets the standards that everyone else builds on.

The Agent Startup Explosion

It wasn’t just the foundation model companies. Specialized agent startups raised billions to own vertical workflows:

Accounting: Rillet, Campfire, Numeric

Autonomous SOC: Dropzone AI (*), Prophet Security, 7AI

Customer support: Fin (Intercom), Sierra, Decagon

IT: Fixify (*), Serval

Legal: Harvey, Legora

Market research: Strella (*), Outset, Listen Labs

Sales: Clay, 11x, Artisan AI

(*) = Decibel, my fund, is an investor

The pattern: pick a workflow with structured outputs and human-in-the-loop review, build an agent that handles the boring parts, charge enterprise pricing. The $4B coding market proved the agentic model works. These verticals are next.

The Market Share Flip

Of course, no mention of agents can ignore the companies building the intelligence to back it all! In foundation model land, the inversion from OpenAI to Anthropic becoming the enterprise leader, happened quite fast:

Data source: 2025: The State of Generative AI in the Enterprise

How did Anthropic do it? Much of the success, of course, is because Claude became the default for serious coding work, and that has proved to be the killer AI use case thus far. But there is more to it – customers care about things that work in production. Anthropic optimized for reliable, tool-using systems, while OpenAI optimized for consumer engagement. “Reliable” may sound boring, but sometimes boring really does the trick.

MCP Became the Standard

The Model Context Protocol started as an Anthropic side project in November 2024 – a way to connect AI models to external tools. By December 2025:

• 97 million monthly SDK downloads across Python and TypeScript

• 10,000+ active MCP servers covering everything from dev tools to Fortune 500 deployments

• Adopted by ChatGPT, Gemini, Microsoft Copilot, VS Code, Cursor

• Donated to the Linux Foundation as part of the new Agentic AI Foundation

The AAIF launch brought together Anthropic, OpenAI, and Block as co-founders – with Google, Microsoft, AWS, and Cloudflare as supporters. When rivals standardize together, the market has matured enough that interoperability matters more than lock-in.

The Benchmark Battles

The reasoning model race delivered real capability jumps:

AIME 2025 (high school math competition): GPT-5.2 (x-high) hit 98.7%, Gemini 3 Flash 97%,, Claude Opus 4.5 91.3%

IMO 2025 (International Math Olympiad): Both OpenAI and Google DeepMind achieved gold medal-level scores (35/42 points, 5 of 6 problems). DeepSeek later matched them. For context: in 2022, researchers gave only 8-16% probability that AI would achieve IMO gold by 2025.

SWE-bench (coding): Claude Opus 4.5 leads at 80.9%, followed by GPT-5.2 Thinking (80.0%), and Gemini 3 (76.2%).

LMArena Elo: Gemini 3 broke 1500 for the first time (1501), with Claude Opus 4.5 close behind (1492)

What’s Still Missing

For all the progress, agents still hit hard limits:

• Memory and context rot: As agent conversations extend, models lose track of earlier context – a phenomenon researchers call “context rot.” Performance degrades well before hitting theoretical context limits. Current solutions (RAG, summarization, external memory stores) help but don’t solve the fundamental problem. Recent research suggests we need memory as a “first-class primitive” rather than an afterthought.

• Computer use reliability: Still a work in progress. Anthropic’s Claude computer use, OpenAI’s Computer Using Agent, and Google’s browsing agents all struggle with multi-step workflows that require authentication, error recovery, and state management. This is a key blocker for agentic last-mile reliability for many use cases, and I expect labs will double down on computer use in 2026.

• Multi-agent coordination: When multiple agents collaborate, they face context pollution (sharing too much irrelevant information), handoff failures, and cascading errors. The AAIF standards work is partly about solving this, but production multi-agent systems remain brittle.

The so what: The agent market has traction in narrow domains with verifiable outputs – coding, customer support, document processing. The path to general-purpose agents runs through solving memory, reliability, and coordination problems that are still active research areas. There are many exciting agent startups working on specialized domains ranging from security to market research, though, and I expect 2026 will prove one or more of those markets to have a breakout “killer app” moment like coding had in 2025.

V. Science Had Its Moment 🔬

This section is short, but it belongs in the retrospective as it’s my personal favorite! I believe in the dream of AI as a true change agent for humanity, and that dream starts and ends with scientific progress.

• Anthropic AI for Science Program (May): Anthropic launched a program offering up to $20,000 in API credits to researchers working on high-impact scientific projects, with a focus on biology and life sciences.

• AlphaEvolve (May): Google announced AlphaEvolve – a Gemini-powered agent that designs novel algorithms. It discovered improvements to matrix multiplication and computer chip layouts that humans hadn’t found.

• AlphaGenome (June): DeepMind released AlphaGenome, an AI model that predicts how DNA variants impact gene regulation. It processes up to 1 million DNA base-pairs at single-letter resolution—a breakthrough for understanding non-coding regions (98% of the genome) linked to disease. Available via API for non-commercial research.

• Chai-2 (June): Chai Discovery released Chai-2, the first AI system to reliably design antibodies from scratch. With a 16-20% hit rate (vs. 0.1% for traditional computational methods), Chai-2 compresses months of drug discovery into two weeks. Former Pfizer CSO Mikael Dolsten called it a potential game-changer for de novo medicine design.

• Math Olympiad Gold (July): Both OpenAI and Google DeepMind achieved gold medal scores (35 out 42 points) at the International Mathematical Olympiad – a first for AI. OpenAI’s experimental reasoning LLM and DeepMind’s Gemini Deep Think each solved 5 of 6 problems under competition conditions: 4.5-hour time limits, no tools, natural language proofs. The breakthrough came just a year after DeepMind’s AlphaProof earned silver with 28 points using specialized formal languages. This time, both systems worked end-to-end in natural language.

• OpenAI for Science (September): Kevin Weil announced OpenAI for Science, a dedicated initiative to build AI as “the next great scientific instrument.” The team works with researchers across math, physics, biology, and computer science – GPT-5.2’s December release showed early results, including helping resolve an open problem in statistical learning theory.

• Claude for Life Sciences (October): Anthropic launched its first formal entry into life sciences – connecting Claude with lab tools like Benchling, PubMed, 10x Genomics, and Synapse.org. The goal: support researchers from literature review through regulatory submission, compressing days of analysis into minutes.

• BoltzGen (October): BoltzGen is released, an open-source model that generates novel protein binders ready for drug discovery pipelines – going beyond structure prediction to actual molecule design. Released under the MIT license for unrestricted commercial and academic use.

• AlphaFold Usage (November): Five years after AlphaFold 2 solved protein structure prediction, over 3 million researchers in 190 countries use it.

• Genesis Mission (November): The Trump administration launched Genesis – a national initiative to build an AI platform using federal scientific datasets. Managed by the Department of Energy, it aims to train scientific foundation models and create AI agents for hypothesis testing and automated research workflows. The executive order explicitly compared the ambition to the Manhattan Project.

The so what: Every major AI lab now has a dedicated science strategy. The question isn’t whether AI will accelerate research – it’s which domains will see breakthroughs first, and whether the traditional academic pipeline can adapt to AI-assisted discovery moving at startup speed. This is the area in 2026 I am most excited to watch! 🧬🧪

What Actually Mattered

Still with me?! Thanks for hanging on! Deep breaths. That was a lot of info. After all, it was a BUSY year! Stepping back from the noise, what actually defined 2025?

1. Great Products Beat Distribution 🏆

This might be the biggest surprise. Microsoft has Copilot in every Office app. Google has Gemini in Search, Gmail, Android. OpenAI is the AI brand everyone knows.

And yet: Anthropic went from 12% to 32% enterprise share while OpenAI dropped from 50% to 25%. Cursor – a startup code editor – reached $29B valuation while GitHub Copilot growth stalled. The companies that made the best products for specific workflows won, even against massive distribution advantages.

This wasn’t supposed to happen. Distribution is usually a huge moat. But in AI, the difference between “works okay” and “works great” is so large that users switch despite friction. Developers abandoned VS Code + Copilot for Cursor + Claude because the productivity difference was obvious.

2. Efficiency Mattered More Than Scale ⚡

DeepSeek’s $6 million training run didn’t just embarrass larger labs. It proved architectural innovation could substitute for brute-force compute. Moonshot’s Kimi K2 Thinking trained for $4.6 million. MiniMax M2 delivers Claude-competitive performance at 8% of the cost.

The “just scale it” era gave way to something smarter: mixture-of-experts, inference-time compute optimization, better data curation, and training efficiency improvements.

The nuance: $6M gets you SOTA for open source models. Frontier closed-source models still cost hundreds of millions. But the gap is narrowing, and the cost to be “competitive enough” dropped dramatically.

3. Standards Emerged 🔗

MCP going to the Linux Foundation. AGENTS.md hitting 60,000+ projects. Google’s A2A protocol for agent-to-agent communication.

When OpenAI, Anthropic, and Block co-found a neutral foundation together, you know the market has decided interoperability matters. The “walled garden” phase is ending. The “platform” phase is beginning.

4. Multimodal Became Baseline 🎬

Text-only models already feel dated. Every major model handles images natively. Video understanding is landing. Voice synthesis is commoditized.

The expectation shifted from “can this model do X” to “why can’t this model do everything?” That’s a high bar, but it’s the new bar.

5. OpenAI Rewrote Its Own Rules 📜

October’s restructuring split OpenAI in two: the nonprofit OpenAI Foundation now has legal control over a public benefit corporation called OpenAI Group PBC, which is free to raise funding and acquire companies without restriction. The Foundation holds 26% equity and appoints the board; Microsoft holds 27% (~$135B); the remaining 47% goes to employees and investors.

The Microsoft renegotiation ended cloud exclusivity. Elon Musk tried to block it and offered $97.4B to acquire the company outright. Now reports are surfacing that OpenAI is trying to raise at a $830B valuation.

The mission hasn’t changed. Everything else (structure-wise) has.

Looking Forward: 2026 🔮

Here’s what I’m watching:

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Good Morning,

I don’t often share a post on Saturday, but I wanted to offer a short biographical sketch of an AI creator. This is told as like a Business In…

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Image credit: https://cartography-of-generative-ai.net/. The Barcelonese artist collective Estampa has done an outstanding job with The Cartography of Generative AI, a map of the intense human labor, materials, and powerful narratives shaping this technology and its effects on the world1.

If you consider the impacts and role of AI on the future of work important, why not share this piece?

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Good Morning,

Every since in late 2022 a chatbot called ChatGPT went live, Silicon Valley have been trying to convince us that Generative AI was going to change everything, including our jobs and the future of work? Three years later one might have expected a bigger impact on the creation of new jobs, our careers and on the future of work? A full three years later, we can’t even attribute jobs being disrupted to AI.

If Gen AI was a General Purpose Technology, we would expect to see a bigger impact. So where is it? A general purpose technology (GPT) is a technology with the potential to significantly alter the economy and society by enabling innovation across multiple sectors.

While in the insular technology industry, AI is changing the role of titles like Product Manager and junior Software Engineer SWE to some extent with various pilots, out in the real world it doesn’t seem like AI has even had much impact on even the demand for entry level jobs in a tightening labor market (that has been often claimed). In the coming years, a soaring public debt globally at the intersection of doubts about the sustainability of AI investments might balloon into a real problem.

So instead of Gen AI displacing workers or creating new valuable jobs altogether, what we are starting to see are warning signs about the use of chatbots, young people leaving social media (exasperated by synthetic content) and rumors of dead internet theory (DIT) giving way to vast quantities of “AI slop” and workslop churning out in algorithmic feeds to our AI agents (assuming we have left network x, y and z). What’s wrong with that picture? So what does this have to do with jobs and the future of work?

People are leaving social media apps with Gen AI slop hurting value

Image credit: Have we passed the peak of social media (October 2nd, 2025 Financial Times).

According to the Budget Lab at Yale new study (warning: the sample size is small) we might be exaggerating the challenging widespread fears about the impact of artificial intelligence on employment. The Budget Lab at Yale is a non-partisan policy research center that provides in-depth analysis of federal policy proposals for the American economy.

Related to our topic, “when the FT compared young graduates’ unemployment with other first-time entrants into the job market regardless of age, they found that “those without a degree are actually having a much harder time of it,” no matter their age. The slowdown is about (in)experience rather than age.” - according to our featured writer today: Azeem Azhar.

AI isn’t the primary culprit to the entry level jobs freeze, a tightening labor market is. 2026 might not be a great time to graduate College by the looks of it, and neither was 2025 as you might have heard, but for the majority of jobs or degrees, it has next to nothing to do with AI.

We are seeing a much tighter labor market in a jobs data abyss (since the U.S. Government is still in shutdown). Though the Trump Administration thinks this is a good time to do massive layoffs. However what AI is doing is changing the tasks we do at work more than it’s creating new valuable jobs (ROI for civilization) or even disrupting old ones (real automation). That doesn’t sound like an “exponential” technology to me. If you prefer a more gated BigTech consensus driven view, you might like Azeem’s Newsletter:

Introducing: The Exponential View

Exponential View

"One of the best for understanding how tech can solve our biggest problems and shape our society." — Daniel Ek, CEO of Spotify

By Azeem Azhar

The Yale study is super interesting if the future of work is a topic you are tracking, especially as it relates to AI. Many economists and influencers are employed to promote certain narratives and points of view but The Budget Lab at Yale seems like a more trustworthy source that isn’t profiteering on slant or sentiment here or chasing a headline. While economists are inherently bad at predicting the future (as we have seen in technological shifts around automation & jobs multiple times since the internet was born), they are good at number crunching of the existing data.

So what is the data actually saying? We explore AI’s impact on the labor market, jobs and future of work.

If you enjoy this topic, read the archives for more.

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This article was never supposed to see the light of day. 🙏

I wanted to share some of my insights on the latest survey by Substack on how its writers use AI in their work. This is also an exploration my personal experiences as a writer online, much of which are deeply humiliating experiences and insights for me to admit.

Reader discretion is therefore advised (Update: I’ve had to paywall the more personal things, due to fear of retaliation).

The editors said “Substack has in 2025 leaned in and their users not just being writers but podcasts, video creators and multi-media influencers.”

But what’s the real story here and where did the survey make me personally a bit upset? 😕

Read the Report

• Substack says it surveyed 2,000 Substack publishers, including writers, artists, podcasters, and video creators, from around the world. (Substack is mostly a U.S. based platform). The survey was designed to reflect key segments of Substack’s publishing community.

• On Substack, there’s a lot of anti-AI sentiment due to the platform’s emphasis (“economic engine for culture”) on culture and politics. While journalism and publishing in the U.S. have on-boarded AI very quickly, I was curious to see if that was the case among some of Substack’s Creators as well. Of course, I sort of understood the picture they wanted to paint when I participated in the survey:

Substack Creators mostly use AI for workflow support

• Writers on the platform aren’t huge AI adopters according to the survey.

• Do note that “AI” doesn’t have its own category but instead relegated to being a minor subcategory within the Technology category of the app that has among the lowest conversion rates on the platform (of free to paid supporters). This is due to editorial bias, not our own ineptitude. This also has had dire financial and personal consequences for curators and writers like me. Let me try and explain (I get a bit emotional and this has been my life the last 4 years):

• You could even say that Substack’s algo on Notes penalizes AI content. Here is the Tag in case you want to follow it. Also relevant the Emerging Tech tag. These have to be enabled in the app as a reader through a tedious process of going to Manage Interests - Technology - Artificial Intelligence (turn the thumbs up to green, the 5th option down). The tag is also not very well managed. Where even my own content barely even appears on those feeds. [Even while exploring AI and Emerging tech is my overarching mission].

• So while Substack has a proliferation of AI based publications (as a topic), the users of Substack’s app are not overly exposed to content about AI. Since the algo silos the content (almost as if it’s spam to Substack’s emphasis on Culture), of course in reality that is actually meaning purely American versions of culture and politics are magnified. Substack is riddled with this kind of editorial bias by design and it decides who makes and it doesn’t in part from a financial sense as well. Creators often confront the antithesis to the Substack honeymoon. 🍯☽ It’s typically when they fail.

Substack’s emphasis on “cultural empowerment”, a proxy for luring influencers, and media folk (and various DEI type strategies) - has decimated opportunity for others. Others like me.

Substack’s Cultural Emphasis has decimated AI use on the platform among Creators

So with Substack’s considerably ideological stance on culture, and proliferating anti-AI sentiment, adoption of AI tools is rather low among this Creator group. It’s shocking really how the creators have been kept at functionally “low AI literacy” levels: almost as if by design. Far lower than actual journalists working at publications in the U.S. as a whole: (this is very concerning).

• ONLY 45.4% said they’re using AI

• 52.6% said they’re not

• 2% were unsure

Older Men more likely to use AI in their work

• Publishers 45 and over were more likely to use AI than those under 45.

• Men reported higher adoption than women (55% vs. 38%).

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I myself fall into this category, as do most of my American-based paid readers. But a lot of Substack’s marketing and partnership budget is actually going in to lure younger female Creators and their audiences for platform benefits and to try to take larger marketshare.

Who pays for AI Supremacy

Just 3 U.S. states make up the bulk of my paid supporters.

1. California

2. New York

3. Texas

Globally 🌍

Globally just three countries make up the bulk of my paid supporters among this demographic of older men.

So I’m functionally writing for a customer who have extremely Western sensibilities, dare I saw the highly educated and high earning political left.

1. United States (62%)

2. United Kingdom (8%, where Substack has done considerable marketing)

3. Canada (4%, where I am originally from)

• The same user base on Substack that uses AI the most are actually the most penalized in terms of baseline conversion rates.

• These top three countries make up 74% of my total revenue by country of origin.

• I can only hope with a funding round of $100 million they can make the platform more friendly to a global audience.

♂️ Which Category of Creators actually use AI in their Writing and multimedia content?

• Substack has systematically neglected the Categories where its primary older male user base find the most value! Due to a more DEI (which I agree with in moderation) approach of boosting “Culture” and influencers from the likes of Instagram. I understand that Substack cannot please everyone or be all things to all folk.

• I’m paywalling this content not because it’s extremely valuable insights, but deeply personal, embarrassing, humiliating and regrettable situations I find myself in as a worker in the Creator economy (as someone who thinks about fairness, meritocracy and loyalty online a lot).

• These morbid confessions may not age well, but represent my particular struggles on the internet and apply to a very limited context of my own journey. (nor will I ever make this content free to read). It’s upsetting to even admit to all of this, and I may end up deleting this article as its impulsive and represents years of frustration just trying to make a honest go-of-it. I’ve made personal sacrifices and suffered failures in my personal life as well related to all of this.

• I will both summarize and think about this AI report and share my own experiences and viewpoints on this. Some of which are not flattering to this platform. 🤫 (So if you are not up for a rant, just skip this one - no hard feelings).

CC making you stupid

✋ Many Creators use AI to improve dealing with ASD, ADHD, disabilities, visual impairments etc…

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While we await GPT-5, Grok 4, DeepSeek-R2 and other frontier LLMs, there’s a lot to look forwards to in AI. I wanted to go over some of the AI reports I’ve been reading so far this summer. I expect you might not be back (completely yet) at your work desks yet so I’ll keep it light.

Will these new models upend the status-quo of LLM parity? I wanted to explore these reports in an eclectic mix of visuals meant to be skimmed.

🔥 The Big Three: Coming Soon ~ 𝙻𝚘𝚊𝚍𝚒𝚗𝚐

1. OpenAI’s GPT-5

2. xAI’s Grok 4

3. DeepSeek’s R2 reasoning model (was supposed go to live in May, 2025)

Explore my 2025 Site Map

List of Recent Articles

• NotebookLM just got smarter. You can too.

• Tesla's Robotaxi Launch was a Scam

• AI reports in 2025 Say we are Underestimating Its Impact

• Coatue's Surprising Fantastic 40 by 2030

• Genspark's Stunning AI Pivot - Search to Super Agent

DeepSeek reasoning models are cheaper

• When DeepSeek comes out with R2 we’ll get a better idea of the cost to performance of the best open-source reasoning models.

• GPT-5 by OpenAI is expected to unify its systems with long-term memory and more personalization features.

• Musk said in an X post that he and the xAI team were "grinding on Grok all night" with "good progress” on Grok 4 that is expected to be released in the weeks ahead.

Recently Iconiq Capital also released their own spin on the State of AI. The state of Building AI report.

The 2025 State of AI report, titled The Builder’s Playbook, provides a practical roadmap for AI innovation, focusing on the execution and scaling of AI-powered products. Drawing from an April 2025 survey of 300 software executives and interviews with AI leaders, the report highlights the strategies, challenges, and trends shaping the AI landscape.

Download State of AI Report in 2025

If you know someone who enjoys both AI and infographics, feel free to share the piece:

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• This exploration looks at both the B2C (Menlo Ventures report) and B2B side (ICONIQ).

• Of course I’m also going to share other tidbits and infographics I’ve found along the way.

Is Anthropic founding a “new species” of self-improving AI?

• Claude Code just revealed that it's used by 115k developers and has changed 195M lines of code last week.

• Claude Code was launched about 4 months ago, and you could argue Claude Code has rewritten more lines of code than most engineers see in a lifetime, and faster than most startups can file an expense report.

New grad hiring in BigTech is down -25% and -11% for startups

• What does this brave new world look like exactly? 🤔

What type of AI products are you building?

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Good Morning,

I can’t stop thinking about the AI hyp…

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As you know I am a bit of a chart nerd, when it comes to projections around the AI renaissance.

For the last decade that is since 2015, Coatue has hosted their East Meets West conference, an annual gathering of technology and business leaders for a dialogue on global innovation.

This year they released a projection of some of the biggest corporations in 2030 and there was some chatter about which BigTech players are no longer on the list by 2030, or just five years from now. See for yourself:

The Coatue Fantastic 40

• OpenAI goes public and rises quickly into the top 10 companies in the world by market cap.

• Google does not appear to be a major BigTech corporations by 2030.

• Coatue does not seem to see Anthropic, xAI or other Generative AI firms as major winners by 2030 outside of OpenAI.

Scan the Report

This slides in this analysis are from the event’s keynote address where co-Founders Philippe Laffont and Thomas Laffont opened the conference in their classic "Coatue View on the State of the Markets," and shared their outlook on the macroeconomic landscape and public and private markets.

Why does what Coatue says matter?

Coatue Management has been one of the most active investors in the Generative AI space, with over 50 AI-related companies in their portfolio as of 2023. They've raised a $1 billion fund specifically for AI investments in 2024, demonstrating their strong commitment to this sector.

Major Generative AI Investments

Foundation Models & Infrastructure

• Anthropic - Claude AI assistant and safety-focused AI research

• Together AI - Open-source AI platform ($106M Series A led by Salesforce with Coatue participation)

• Adept AI - AI that can use software tools and take actions

• Character.ai - AI chatbot platform for interactive conversations

• Hugging Face - Open-source machine learning platform ($100M Series C with Coatue participation)

Creative & Content Generation Tools

• Runway - AI video generation and editing platform

• ElevenLabs - AI voice synthesis and cloning technology

• Ideogram - AI image generation platform

• Tome - AI-powered presentation creation tool

Developer Tools & Platforms

• Replit - AI-powered coding platform

• Aurora Solar - AI for solar energy system design

• Norm AI - Regulatory compliance automation

Among VCs that I know who have invested prolifically in AI, only perhaps Nvidia’s lineup to me seems more impressive.

According to ChatGPT

• Internal Rate of Return (IRR) is a financial metric that calculates the discount rate at which the net present value (NPV) of all cash flows from a project or investment equals zero. That is, it represents the effective annual rate of return on an investment, considering the time value of money.

Projections 2025 at 2030. ✨ Coatue’s Fantastic 40 - Implied IRR

1. Arm

2. Stripe

3. Databricks

4. OpenAI

5. Anthropic

The Mechanisms of U.S. Capitalism

I think a great deal about how the stock market accelerates AI especially in an AI driven bull-market like we have seen between 2023 and 2025 accompanied by a surprising demand for compute and enormous capex investments by BigTech in AI Infrastructure that we have covered at some length on this Newsletter.

• Let’s take a quick tour.

According to Nvidia’s founder AI Infrastructure and datacenters will be worth many Trillions of dollars as a market soon. If you like charts and graphs about the future and AI, you might also like:

• AI reports in 2025 Say we are Underestimating Its Impact

• Mary Meeker's AI Report

You will enjoy these charts mostly if you like the history of business and analysis on technological waves and AI’s trajectory.

The Gen AI Bull Market will Shape New Winners and Losers

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Good Morning,

I can’t stop thinking about the AI hype narrative and the momentum this technological wave has upon us. Thank you to all of my readers, we have appeared in Substack’s Technology rising leaderboard this past week.

There are many who claim that AI looks exactly like the dot.com bubble. The level of enthusiasm and capital expenditures BigTech has put into AI chips, datacenters, AI infrastructure and AI talent is hard to even fathom. If you include OpenAI’s investments in Stargate we are talking about a quarter of a Trillion dollars in 2025 easily. This is not counting investments in Europe, the Middle East or China.

BigTech Capex is telling us this is a Generational Boom

Everyone remembers what a bubble looks like 💭

How do you even explain the AI hype bubble from an ROI perspective for most firms? The stock market looks expensive after one of the fastest rebounds in decades since Trump’s “Liberation day” of April 2nd. The AI market’s soaring valuations and speculative investment raise concerns of a potential bubble, despite its transformative potential. Cash rich corporations and Silicon Valley gate keepers are spending as if AGI is right around the corner. While Warren Buffett, Michael Burry and even Jamie Dimon look like they are getting nervous.

This must be “AI Compute Heaven” 🐇

CoreWeave, one of the first Generative era AI startup to go public, disappointed investors, ending its first day of trading with a market cap of $19 billion. The last time it raised funds as a private company, in May 2024, it was already valued at $19 billion, nearly triple its valuation just five months prior. Now in June, guess how much its worth now after six weeks of stock market frenzy? $69.3 Billion. This is the environment we are living in.

OpenAI and Nvidia power Enthusiasm higher 👑

Demand for compute looks insatiable even 2.5 years after ChatGPT went live. Nvidia’s data center sales jumped another 73%. OpenAI seems to raise cash at will now with a valuation of $300 Billion with just a single product, ChatGPT. Venture Capital have spent a lot of capital just to raise the AI narrative up to god-like status in 2025.

But what do VCs really think? The market is trading at a 23x multiple, expensive in any year. The DeepSeek scare of late January feels like a distant memory, now in June. A new AI pantheon including OpenAI, Anthropic, DeepSeek and Thinking Machines Lab may soon challenge the business models of BigTech incumbents. This is simply how markets work during technological shifts.

AI startups are eating Venture Capital budgets

is the author of the Newsletter Weight Thoughts, and is a General Partner at Creative Ventures & Co-Founder of Lioness. Even the most bullish VC or angel investor must wonder about the influx of AI startups eating the industry what this will rationally lead to. AI was eating VC for breakfast in 2024 when it accounted for over 50% of global VC dollars. In 2025, this is starting to become a problem.

Lately James has been putting out some really fascinating articles:

• What LLMs Will Do To Jobs: All You Need is an Oracle

• AI is Overturning the Military World Order

• AI's Endgame (How Foundational Model Companies Can “Win”)

I like how James frames the impact of AI and while techno-optimism predominates in VC circles, how much is just consensus, group-think and wishing thinking I wonder? It’s literally their job to be guardians of a non-critical view of AI’s potential. With the decline of journalism, Venture Capital’s influence on media is only getting stronger. Public relations and lobbying jobs are replacing dying investigate journalism jobs for a reason. “AI mode” is breaking the internet, and this is a weird shift for the internet. The zero click internet is upon us.

The AI Bull Market Could Last Years 🐮

We know this moment feels like an AI bubble, but what does the data say? The U.S. stock market has a Historical Average P/E: ~20.5 (modern era). Today we’re looking at valuations more like U.S. Stock Market P/E: ~24.34 (estimated). We appear to be in an AI bull market. The reality is demand for AI chips and datacenter expansionism and National Defense spending with geopolitical uncertainty could extend this bull market for years to come, giving Generative AI ample time to actually back up its swanky visions of AI Supremacy pitch decks and Venture Capitalist and Tech CEO proclamations. It’s all go if you are an AI podcast bro.

My baseline view of VC’s reality with AI, isn’t biased with financial incentives and insider knowledge of how great AI is going to be:

• Mary Meeker's AI Report

• I asked James for his take on Mary Meeker’s incredibly detailed AI report. To my surprise he took the are we in an AI bubble analysis up in a fairly detailed way and to new levels.

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I admire James also for asking some of the biggest questions in AI technology. He’s also working on a related book. I wish I knew more VCs who invest in the AI space, feel free to DM me on LinkedIn if you are one. So much of what we are doing today depends on the timing and historical curve of the Generative AI cycle. Recessions and Deep learning winters don’t come as often as they once did, but are there structural issues in the mania?

Where Are We in the AI Cycle—Boom or Bust?

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