Fei Fei Li is the Trail Blazer of AI no doubt.
Fei-Fei Li spent years hand-labeling 14 million images, gave the entire thing away for free, and watched it create a multi-trillion dollar industry that paid her nothing. Sixteen years later, AMD is finally paying her. $8.2 billion.
Rewind to 2007. She's a junior professor at Princeton, and the field's consensus is that progress comes from better algorithms, with data as an afterthought. Colleagues warn her that building a giant image dataset will kill her career. Money gets so tight she considers reopening her family's dry cleaning business in New Jersey, the same one she ran on weekends as a Princeton undergrad, to fund the project.
She builds ImageNet anyway and releases it free in 2009.
For three years, almost nothing happens. Then in 2012, two of Geoffrey Hinton's students train a neural net on a pair of $500 gaming GPUs and enter her competition. AlexNet drops the error rate from 26% to 15%, and that single result convinces the whole field that deep learning works.
Nvidia was a gaming chip company worth about $8 billion that day. It's worth over $5 trillion now, and the run started with two of its consumer cards winning a contest built on Fei-Fei's free dataset.
The entire industry monetized the wave except the person who started it. She stayed a professor.
Then in April 2024, at 47, she finally starts a company. Paul's math above puts the four co-founders' share at roughly $3.4 billion after just 2.5 years, call it $850 million each. And she walks into AMD as Chief Scientist reporting to Lisa Su, which puts the two most important women in AI inside the same company.
ImageNet made everybody in this business rich. It just took sixteen years to get around to her.
Abigail retweeted
The future of transport
I've now done a total of 3 hours riding around in @Tesla’s Cybercab and spent a total of $92.51. That travel time would have cost me $200 with Ubers.
My longest ride has been ~40 minutes. I legitimately can't come up with any major complaints for FSD V15. None. I’ve spoken to a handful of other people here who say the same.
I didn't need to call rider support once. No weird maneuvers, jitteriness, or hesitation. I do think pickup and drop-off locations could use some tweaking (same for Model Y) as you need to walk a bit sometimes, but I think that will get better as V15 progresses.
Two things you surprisingly get used to very quickly are having no rearview mirror and no side mirrors.
For anyone else trying Cybercab for the first time, I encourage you to recline your seat all the way back and just close your eyes for a minute. Realizing that you can nap in the spot where a driver traditionally sat while the car drives you around with no steering wheel or pedals is a pretty surreal feeling.
Abigail retweeted
SpaceXAI engineer, Lauren Tan:
"at SpaceXAI, 90% of engineers running 10-20 GrokBot agents in a loop. We have Chief of Staff agents, they are managing all other bots
GrokBot has already 1M+ users, but less than 1k people use it at 100% of its potential as our team"
in a 1-hour talk, a SpaceXAI engineer reveals how to build agentic systems like GrokBot from scratch
worth more than a $500 agentic course on the internet
watch today, then read how to build a team of GrokBot agents in the article below
Abigail retweeted
One of the best recent podcasts on AI. Neil has a gift for explaining all of the jargon and insights simply.
(I’m not involved, just found it unusually educational).
Neil Movva (@neilmovva) started his career at Nvidia, working on GPUs and kernels, and has an unusually deep understanding of inference, from software to chips to power.
We spend a lot of time on each of those layers, how they connect, and where the important tradeoffs are.
What makes this conversation special is how detailed it is (like a 401-level class), yet Neil makes it remarkably clear and easy to follow.
Today he runs Sail Research, a company building infrastructure for agents to make tokens as cheap as possible.
We discuss:
- Latency versus throughput
- Why there are no bad chips, only bad pricing
- The end of kernel engineering
- Buying chips and power no one else wants
- New chip architectures
- Nvidia lore + his contrarian view of the company
- Open source and the frontier labs
I learned a ton. Enjoy!
TIMESTAMPS
0:00 Intro
0:38 Building a “Token Factory”
4:21 The Future of Background Agents
13:09 Nvidia and the GPU Stack
23:27 Chips, Memory, and Transformers
36:14 The Future of AI Training Data
44:32 Chip Scarcity and Compute Arbitrage
52:44 Reinventing the AI Data Center
59:01 Power and the “Scavenger Strategy”
1:10:10 Open vs. Closed AI
Abigail retweeted
Mark Zuckerberg gets it right:
“The defining question of our age isn’t whether superintelligence will exist, but who will have access to it. Will it be centralized and restricted to a few institutions, or will it be a tool that empowers everyone?”
Concentration of power is the biggest risk of AI. When a small number of labs (working hand-in-glove with the administrative state) decide who has access to which model capabilities, they inevitably shape what can be said, known, and built. That’s not “safety.” It’s control.
As Mark points out, the history of open source shows that broad access and transparency are usually the best path to actual security and resilience. Decentralization creates checks and balances on power. By contrast, centralized alternatives, like bureaucratic approval regimes and mandatory gatekeeping, typically produce regulatory capture and reinforce cartels.
Personal superintelligence in everyone’s hands, with competing models and real data sovereignty, is a far better check on a dystopian future than self-appointed guardians who claim to be “aligned” with all of humanity.
Abigail retweeted
btw anthropic's internal document on this literally said "we don't want it to be known that we are working on this.”
it was called project panama.
here's exactly what happened:
1: anthropic concluded that books were the cheapest way to build a world-class model because they gave claude curated facts, structured arguments, compelling stories, and writing “an editor would approve of.”
2: once anthropic decided it needed books at enormous scale, its first solution was piracy.
it downloaded 7m+ books from online libraries including libgen. the judge later wrote that although anthropic had legal ways to buy them, it chose piracy to avoid what dario amodei called the “legal/practice/business slog.”
3: that piracy created a massive legal risk.
so in february 2024, anthropic hired tom turvey, the former head of partnerships for google books, to find a legally safer way of obtaining “all the books in the world.”
4: turvey first contacted major publishers about licensing their catalogs.
those attempts didn’t produce agreements, so anthropic chose a route that required no publisher permission: buying millions of physical books through distributors and used-book retailers.
5: within about a year, anthropic spent tens of millions acquiring and scanning millions of books, including many rare and 1/1 titles. one vendor proposal targeted 500,000 to 2 million books in six months.
6: to scan that many books within months, the vendors physically dismantled them.
a hydraulic cutter removed each spine. the pages were trimmed to size, fed as loose sheets through high-speed industrial scanners, and converted into searchable PDFs. the paper remains were then sent for recycling.
7: these PDFs were fed into claude as training data.
the complete collection became a private, searchable anthropic library that the company planned to “store forever.” the scans aren’t available to the public and were never open-sourced.
🦔AI companies are bulk-buying rare books, scanning them through high-speed machines that cut the spines off, and shredding the originals. A service called ISBNdb facilitates orders of up to a million books and keeps buyers anonymous. Pre-2022 books are premium because they're free of AI-generated text. A federal judge ruled the practice is fair use because eliminating the original means only one copy exists at a time. Anthropic hired the former head of Google Books partnerships to obtain "all the books in the world."
My Take
This got to me. A bookseller told 404 Media that rare books with almost no surviving copies are being fed into this pipeline. Books that survived wars, fires, and centuries of handling are being shredded so an AI can learn to write a better marketing email.
ISBNdb's website literally says "'AI company destroys two million books' is not a headline that generates sympathy," and they still built an entire business around making it happen quietly. They offer NDAs as a feature. They coach clients to call it "digital preservation."
I've covered AI companies scraping the internet, torrenting libraries, and stealing music. This is worse because it's irreversible. You can re-upload a website. You can reprint a bestseller. You can't replace the last three copies of an 18th-century botanical text once someone shreds them for training data. And the judge said it's legal. So it's going to accelerate.
"We shred rare books and offer NDAs so nobody finds out" is a legitimate business model in 2026. What a timeline.
Hedgie🤗
Abigail retweeted
Ninety-nine percent of people in the world are convinced they are incapable of achieving great things, so they aim for the mediocre.
The level of competition is thus fiercest for “realistic” goals, paradoxically making them the most time- and energy-consuming.
If you are insecure, guess what? The rest of the world is, too.
Do not overestimate the competition and underestimate yourself. You are better than you think.
Unreasonable and unrealistic goals are easier to achieve for yet another reason.
Having an unusually large goal is an adrenaline infusion that provides the endurance to overcome the inevitable trials and tribulations that go along with any goal. Realistic goals, goals restricted to the average ambition level, are uninspiring and will only fuel you through the first or second problem, at which point you throw in the towel.
If the potential payoff is mediocre or average, so is your effort.
The fishing is best where the fewest go, and the collective insecurity of the world makes it easy for people to hit home runs while everyone else is aiming for base hits.
There is just less competition for bigger goals.