The opening of @moonshots_pod - Moonshot Live - time to dream a better future with @PeterDiamandis @salimismail @DaveBlundin @alexwg @EMostaque !

Excited for the @moonshots_pod live summit in LA today and tomorrow

Today, we’re launching an ambitious new school called The Horowitz Andreessen Academy. Based in San Francisco, The Academy serves the most promising young high school graduates. We think this can be an elite institution that attracts top tier talent. One that prepares students for the future rather than remaining stuck in the past. The #1 goal is to help students learn to build, which is the most important skill in the AI era. They'll learn primarily by pursuing their own projects, either individually or in groups. There are classes and guest lectures, too, from some truly amazing people who have built modern-day Silicon Valley. The Academy is designed as a network, since that’s the reason students go to school in the first place. Core to that network are our 10 Founding Partners: Anduril, Anthropic, Coinbase, Google, Meta, NVIDIA, OpenAI, Palantir, Replit, and Stripe. The network includes over 50 hiring partners and over 200 speakers and mentors. To join as a hiring partner or faculty member, you can apply on our website. We raised $42M in funding led by @a16z. I'll be CEO and @pmarca and @eriktorenberg will join me on the board. Applications are open for our Founding Class Fellowship, which will be one year and tuition-free. Eventually, pending regulatory approval, we plan to offer a two-year program that charges tuition, similar in cost to an elite private university. We're looking for the most unusually ambitious young builders on the planet. Come join us in San Francisco: theacademysf.com/

These are devastating charts. Europe is cooked. The European tech industry has always lagged America’s. But AI turbocharged the gap, and it’s growing by the day. As AI starts displacing white-collar labor (in other words, as opex on token spend replaces opex on humans), a growing share of European GDP that used to get reinvested in EU economies will instead flow to US frontier labs and hyperscalers. Euros that used to go into the pockets of the continent’s top-quartile workers will instead be converted to dollars and accrue to the retained earnings of Anthropic, OpenAI, NVIDIA, Google, and Meta. For European tech companies, there’s no catching up. They’re not even playing the same game, let alone competing on the same field. US hyperscalers are projected to spend over $1 trillion on capex every year for the next three years; OpenAI and Anthropic are going to raise—and burn—hundreds of billions of dollars each. Meanwhile, the EU—with its onerous regulatory burden, high barriers to entry, weak capital markets, and entrepreneurial brain drain—has: - zero prominent frontier labs (no, Mistral doesn’t count) - no organic hyperscalers - minimal data centers construction, much of it owned by US companies simply trying to comply with EU data regulations - a startup ecosystem comparable to that of a tertiary US market, at best The continent that once gave the world steam engines, steelmaking, automobiles, synthetic fertilizer, X-rays, aspirin, nuclear fission, jet engines, and even the World Wide Web isn’t even on the map for the most disruptive technology innovation of our lifetimes. An entire continent’s economy doomed to the permanent underclass. It’s tragic. And it’s a tragedy entirely of their own making.

BREAKING: a16z Launches The Horowitz Andreessen Academy with $42M in Funding Yes, a school. The Academy is an unaccredited, residential alternative to a 4-year degree in San Francisco, led by Founder & CEO Gagan Biyani (@gaganbiyani) (Udemy, Maven). Marc Andreessen (@pmarca) & Erik Torenberg (@eriktorenberg) are on the board. Investors include Adam D'Angelo, Tobi Lütke, Tony Xu, Fidji Simo & Shyam Sankar. Applications open today for a Founding Class of roughly 50 students, starting Fall 2027. 1 year, no tuition, admitted on proof of work. Anthropic, Anduril, Coinbase, Meta, NVIDIA, OpenAI, Palantir, Replit & Stripe are Founding Partners, with 30+ hiring partners behind them. We cover: › The $42M raise and the runway behind it › Portfolios, interviews and IQ testing in admissions › 75% to 80% project time and weekly pod check-ins › Tuition, partnerships and equity as the 3 revenue lines › Why the Academy is telling fundable 17-year-olds to wait "The big difference between going to the academy and going to college is we are not gonna prioritize your grades. We're gonna prioritize proof of work." 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Gagan Biyani, Founder & CEO of Horowitz Andreessen Academy (01:01) Why this Academy is different from college (04:44) Started his first company at age 13 (08:30) Building a school for Silicon Valley (11:55) Raising $40M for a School (14:06) "You Did What?" - Proof of work over grades (19:08) Should young builders really start companies? (22:37) Why this is different from YC and fellowships (27:35) The Academy is for young builders (31:33) Why schools need to embrace AI (37:18) How AI is changing education (44:17) What an AI-native curriculum looks like (48:51) Why high-agency students struggle in college (53:12) Why the best builders zigzag (1:02:19) Why you should apply even if you don't get in (1:04:36) Building a supportive environment for young builders (1:08:54) What the Academy program looks like (1:10:44) Why the Academy is in San Francisco (1:11:19) The people who shaped Gagan (1:14:41) Favorite book!

If AI gave you back 2 hours a day, that's 730 hours a year. What would you do with them? Spend more time with your kids? Get healthy? Start the business you've been putting off? That's the conversation about the future of tech I want to have. What becomes possible when people get part of their lives back?

The laser locked itself in six seconds. The best engineers took ten minutes. Grad students used to drive in at 2 am for this. And thus began the fast takeoff in hardware and manufacturing.

i think AI is about to uncover an entire hidden world of animal communication and what it's already found is insane... > AI found evidence that elephants call each other by name. when researchers played one its name-like call, it approached the speaker faster and called back more, revealing a form of personal naming once thought almost uniquely human > AI analyzed 53,993 marmoset calls and found tiny monkeys may use names too. entire families use similar labels for the same monkey, suggesting they learn names and dialects from each other > AI learned to tell what Egyptian fruit bats were arguing about: food, mating, sleeping spots or personal space. it could even partly identify who was shouting at whom inside a colony of thousands > AI discovered a “phonetic alphabet” in sperm whale clicks, with rhythm, tempo and tiny timing changes combining into at least 143 recurring patterns. their communication system is nearly 10x more expressive than scientists previously believed, and we still don't know what most of it means > an AI trained on 1.5 million zebra finch calls held real-time vocal exchanges with living birds, generating calls as they spoke and getting them to respond with the same timing and flexibility they use with other birds > Google trained an AI on decades of dolphin recordings to predict what sound comes next, then paired it with an underwater device that gives objects their own synthetic whistles. the goal is for a wild dolphin to copy a whistle to ask a human for a specific object, creating a tiny shared vocabulary between species > AI combined microphones worn by wild crows with nest cameras and uncovered quiet calls that may announce when a crow is arriving at the nest and help entire families coordinate caring for their chicks > a robot bee performed the waggle dance real bees use to share the direction and distance of food, and the bees changed their flight paths based on its instructions. researchers effectively sent a destination into a hive in bee language

Hyundai bought control of Boston Dynamics at a $1.1B valuation in 2021. Today Unitree opened near $66B - 35x it's last VC round of $1.9B and ~7x its IPO price. Unitree has real revenues and major brand presence, but the company is not very well understood by most investors. In 2025, it generated $131m in Humanoid sales. 70-80% of its Humanoid sales were for research use cases, 20-30% for education and entertainment, and very little for genuine robotic use cases. Unitree's main product was a robot body that lacked a brain. An affordable, programmable, at least semi-reliable brainless humanoid body however, is exactly what the research market demanded. Unitree G1s are ubiquitous across robotics research groups around the world. Just as AI research is dependent on physical computing hardware, robot hardware is indispensable for research in robot learning, control systems, simulation, etc. While there are few Unitree humanoids currently deployed for real robotic work, the rise of the company has greatly accelerated global research progress. It optimized for an axis (cheap dynamic locomotion) that allowed it to capture the research market but is different from the requirements of of the deployment market (intelligence, durability, payload, safety certification). However, a lot of the engineering capability they've built as a company can and is starting to be used to develop more deployment optimized hardware models. This is a fundamentally different approach from most American humanoid companies which are building towards operational products for consumers and businesses in a straight shot. Companies like Figure and Apptronik invest more resources in R&D and don't yet offer it to retail because they want to go direct to the larger deployment markets. They are building towards a highly functional polished product that can eventually become a development platform like Apple (as opposed to starting as a development platform). For AI, Anthropic took the mass market product capital intensive approach and it took a lot of dollars and time before lifting off on revenue. Cohere and AI21 Labs have existed for a similar amount of time, and took a more capital-light path. AI21 had to pivot, while Cohere has continued to grow, although significantly more slowly than Anthropic. Neither approach is right or wrong and history is filled with examples of successful parallels for both. You cannot compare companies taking different approaches solely on a revenue multiple basis. The company's commercial approach is a byproduct of the Chinese private capital markets. A market where there are not as many venture dollars as the US that are willing to fund hundreds of millions to billions for R&D before any revenue is generated. Revenue growth is required to fund the next rung of capital even for potentially massive TAMs. Actuator scaling parlayed into quadrupeds, quadrupeds into the dominant robot hardware research platform. This IPO funds their transition to the most ambitious phase yet - a company building vertically integrated intelligent robots across a wide variety of form factors. The current market valuation is suggesting that they will accomplish this transition, although it is not final yet. The outcome for Unitree differs dramatically based on if they can successfully move up market. Companies like DJI and Toyota have previously done so, while a failure to do so could have the company looking like Raspberry Pi. A company that cemented themselves within experimentalists and niche industrial markets. However, it may not be necessary for Unitree to build SOTA research capabilities in order to scale robot sales into real deployments. If physical intelligence commoditizes, which we believe it does, then they could have plenty of externally produced models for their customers to choose from. The companies that can produce high quality hardware at scale stand to be large benefactors from the development of physical AGI. The focus on hardware has enabled Unitree to raise a huge war chest and have access to thousands of robots that they can use for robot learning data collection and research. While various data types can be used in pretraining for robot foundation models, robot data is required for the models to become performant. To collect a large set of robot data, you will need a lot of robots. They may have actually created a stronger path for themselves to produce performant physical AI models than companies that have focused purely on robot model development years ago. Wang Xingxing is famous for his technical chops, but he has also been an excellent business strategist.

1/ Stripe has signed an agreement to acquire OpenRouter. OpenRouter will continue to operate as it is: same name, same product, same roadmap, same mission. But now, we will do it faster, and with Stripe's unparalleled excellence and reach.

We were the first firm to invest in @OpenRouter. When @alexatallah joined @hf0, he had already built a $13B company. He knew what he was doing. He didn't need help. Alex joined HF0 because we're not an incubator. We don't work with founders who need help. HF0 is a family... We live under one roof. We eat around one table. We work tirelessly in pursuit of the singular ideal: That by removing every distraction, and relentless and fearlessly training one's energy on the most important problem in each moment - massive, seemingly impossible feats of entrepreneurship become tractable. OpenRouter's journey embodies the fruition of this philosophy. Just 3 years ago, @alexatallah presented OpenRouter at HF0 demo day. Now, he's here.

Here's Stripe's letter to investors explaining its acquisition of OpenRouter (LEAKED)

they actually posted the weights of the algorithm

The next few years in SF will be incredible. Many big lab employees will leave . The idea of sailing into the sunset is temporal and will quickly subside. Inevitably, they will start their own companies and absorb a tremendous amount of venture capital. This will lead to an abundance of innovation. On the negative side, a lot of this innovation will be in the largely overlapping homogenous areas where their skillets lie. We see this already: many companies doing AI for quant trading, drug discovery, material discovery, robotics, new AI architectures, hardware. Competition in such disciplines will be severe. Yet on the positive side, never in history could you raise this quantum ($100M+) of risk capital out of the gate and the space of invention that begets is unprecedented. People will take swings that were once not viewed as VC backable businesses. And yes, beyond just buying loads of compute to train models. Moonshot ideas which usually only worked under the comfort of a big company. Instead of 100, there may be 1000 startups dubbed “neolabs”. Many will invest capital of their own. They will all pay top dollar to attract talent. They will poach large swathes of academia out of their labs to help. Many will not find the hunger to succeed and get lazy from their riches. The more successful ones will acquire them if they are colleagues they liked. The amazing ones will succeed immeasurably. And this will beckon a new renaissance we won’t be able to fathom from where we are now.

‼️huge ssi news. ilya is about to take his first tentative steps out of the age of research and back into the age of scale. it’s time to smell what ssi is cooking. ssi have built a small reasoning engine that can compete with much larger training runs because his data is better curated to meta learning. but, more importantly. we’re about to step into the era of TTT (test time training. gradient descent happening in real time to solve your problems). so instead of a context window you get actual learning. and because it’s so sample efficient it can be trained on hard to verify tasks that other paradigms can’t touch. everyone else’s weights are frozen, they struggle out of distribution. ssi have created something that has a bundle of knowledge but can truly learn in real time and use that to your advantage. current approaches are trying to hack their way to ‘learn’ with memory tricks, this thing will updates its weights, remember key lessons, and finally feel like a human level reasoner. this is a huge paradigm shift from the king. early results are very impressive. we can stop watching memento on repeat. it’s learning all the way down, the descent is real.

Incredible. Jensen is completing the circle. - Bankers don’t like GPUs as collateral because the depreciation is unpredictable - It’s unpredictable because a new GPU can obsolete an old one - Jensen knows his own roadmap - so he’s offering depreciation insurance to the banks - the depreciation insurance (up to 25%) helps the banks get marginal deals over the line Speculation - Nvidia will also advise the banks on “reference designs” for datacenters that will make them fungible - Having them be fungible means that the debt can repackaged into Asset Backed Securities, Collateralized Loan Obligations and Collateralized Debt Obligation (ABS, CLOs and CDOs from 2008 haha) - This allows tranching to get investment grade ratings on the debt so that it can be resold to pension funds and insurance firms - It also allows the banks to trade idiosyncratic project specific credit risk for sector wide credit risk So Jensen is trying to get his customers the same cost of financing as real estate rather than venture equity. This is going to move the data center game out of the VCs and into the big leagues.

to put ai progress in perspective: 9 months ago: most developers wrote code by hand now: misaligned multi-agent swarm finding and collaborating on 0-days undetected (OpenAI/hugging face) 9 months in the future likely much crazier

🚨 HUGE NEWS: Claude now embeds an invisible watermark into every piece of text it generates. Anthropic just documented how it works. Two marks, both machine-readable: > Text: an imperceptible watermark woven into the words themselves. You can’t see it, and it doesn’t change meaning, quality, or readability. > Files (.svg, .png, .jpg): signed provenance metadata on the C2PA open standard, so you can tell if a file’s been tampered with. The watermark is applied at the model level. That means it shows up no matter where the text comes from: the API, Claude, Claude Code, Cowork, Claude Tag, and even when a supported model runs through AWS, Google Cloud, or Microsoft Foundry. Models launched on or after August 2, 2026 mark from day one. Older models are getting it during a transition period. Every sentence Claude writes for you now carries a signature you’ll never see.

Massive turnout at the @Alphaschool , Founder School Weekend LA event, more than 170+ parents / kids coming to explore the future of education. @mackenzieprice and @nateliason raising the bar for what we can expect from our schools and learning with AI + compassion!! #inspired

RT @levelsio: Crazy to think the entire West is literally leaning on one single guy to do things at the same level China does