Aryan

We've been investors in @micro1_ai since day 1 -- "staffing", "race to bottom", "sell asap' -- we'll just keep buying as much as we can. @RichardDreamVC @jonahvella

@kseniam0s term-sheet-received

One word for the VC who ghosted you

@andrew_n_carr beast

I probably should do more coffee chats, but we have been out of stealth - building motion foundation models - for 3 years now!

Why is every founder in "stealth" now? we all already know what you're building you've done more VC coffee chats than an Insight Partners summer analyst the only people you're in stealth from are your customers

@hosseeb will 1v1 anyone in fifa right now - weekend league grinder

If a founder was never good at video games, that's a red flag.

Dear PE/IB Associates in NYC, Stop letting the engineering bros mog you They're literally raising $10M to buy HVAC businesses with ChatGPT You're still color-coding Excel tabs at 2 am for your balding, divorced MD who's dating your ex Time to leave Paul's Casablanca behind

@ephraim888sun @ashwinkodib @kenanhsaleh @tkexpress11 beast - congrats bro, putting SMU on the map

Super excited to be building Infragrid alongside my longtime best friend @ashwinkodib and our incredible a16z Speedrun partners @kenanhsaleh @tkexpress11

@AlmostMedia not as savage as ur first check into valar !!

@AryanBha22 Lmao savage

can't stand the pure arrogance of some VC "juniors" at large funds who think they are the shit w/no track record at all You've never negotiated a term sheet, struggled to raise from LPs, or dealt with any governance bullshit post-investment you probably haven't even read your fund's LPA

@wadecapital @opinioncasino ahhahah im dead

@AryanBha22 @opinioncasino Nope, need more

She has gone private. Which is not good for Scott Evett’s marriage. Because flexing wealth, status and her looks on the Internet, to complete strangers, is a really important part of her life. Without the ability to flex all of this useless crap no one literally cares about, she will grow weary. And then she will grow depressed. And finally, she will find another man, who will allow her to flex her wealth. It’s really not great.

Returns

@geoffwoo great point

@AryanBha22 ya, junior vc has to remember it’s a service business to both founders and lp’s

"I took a fairly untraditional path into venture capital" - Kyle, 32, Harvard undergrad, ex-Uber growth team, Stanford MBA

@geoffwoo bros got his twitter on a claude loop ghost writer

america did not forget how to build. we just made the builder report to the powerpoint person for 30 years. the re-industrialization trade is status reversal: welders, process engineers, test stands, toolmakers, and people who can smell bad tolerances.

@markvalorian brother - you think as an 8 year old going into a professional development academy (like they do in europe) as a kid you are thinking about the "A list" characteristics of a sport?

Soccer is weird. There’s no shortage of optimistic takes about how “bright” the future is for soccer due to the World Cup exposure…but nobody is really acknowledging the obvious core limiting issue: soccer is just a weird sport here. If you choose to play soccer over football, basketball, or baseball (or hockey in certain areas), it’s just a weird decision. You’re voluntarily choosing a smaller market with less attention and lower earning potential. Unless you have some sort of outside factor motivating you to choose soccer over something else, it is simply not a rational choice to make. The opportunity cost to a promising young American athlete choosing to play soccer is tremendous. If you play football or basketball at a top-tier US college, you basically have the best life possible. You can earn millions from NIL, you play in front of tens of thousands, you’re a celebrity, you have groupies, you get a degree without actually having to do any school work…you are living the best life possible for that stage of life. If you play soccer, you have a fun fact to throw out at parties to introduce yourself to people who have no idea who you are. Of course, the obvious exception here is if you forgo college to go try and make some European soccer team…which carries a risk:reward profile that is untenable for most American families. If you play a college sport, you get a degree whether the sport pans out or not. If you fly out to Europe for a few years and then it doesn’t work, you are left empty handed. So for most Americans, saying yes to soccer means saying no to the tremendous advantages of more prominent sports; it’s simply not a decision most rational top-performing athletes make. You end up with a situation where the only people who play soccer are the people who either have some kind of unique external variable pushing them toward it (like a niche interest or a foreign family applying pressure), or those who have simply fallen back to soccer after not succeeding in a more prominent sport. This leads to a scenario where “soccer player” does not exactly carry an “A-list” connotation, which makes it less socially advantageous to play the sport, and ultimately creates the overall sentiment where soccer just feels kind of “weird.” As an observer, I wish this wasn’t the case; I wish we had as strong a presence in soccer as we do in the other sports to really see how we stack up against global competition—but it’s simply not realistic. In order for that to happen, it would require massive foundational changes to the social structures and incentives that funnel talent to different sports in our system—essentially amounting to reversing the flow of Niagara Falls. So as much as I would love to be a soccer optimist with an eye toward “revenge” in 2030, I think our time is much better served accepting US soccer for what it is and leaning instead into what we do best.

@landondonovan what? center backs are incredible ball strikers. end of story?

Can somebody please explain to me why centerbacks are taking penalty kicks when there are other options. Am I missing something?

A Stargate for Data Labs are on a trajectory towards >$100B/year of data spend by 2030. As we begin the trillion-dollar compute project, we need to think about the equivalent civilizational-scale effort for the other core ingredient: data. At the foundation of the scaling revolution is a simple empirical law: deep neural networks improve smoothly, near magically, as you scale two things in proportion — (1) the size of the model and (2) the amount of data you train on. And despite the scaling laws being brutally diminishing, we’ve successfully bitten the bullet of logarithmic scaling with exponentially larger clusters and datasets, and received incredible new capabilities in return. But this exponential scaling is bound to hit some limits. Oddly enough, compute has compounded fairly smoothly without limit, with trillions flowing into hypercluster buildout. Instead, we’re starting to hit the limits of an exponential demand for data. Gone are the days of being purely in the compute-limited regime, where we had effectively infinite internet data but never enough GPUs, we’re now entering a data-limited regime. Luckily, this limitation is coinciding with staggering improvements in AI capabilities. Incredibly, we seem to have a real line of sight towards automating a majority of knowledge work with the methods we have today. RL + pretraining, and the data for each, will be generally sufficient to achieve most economically valuable tasks, given some minimal algorithmic progress and continued compute scaling. In a data-limited world, economic progress & scientific acceleration will be directly bottlenecked by our coverage in each domain. We need to see data collection as imperative, deserving the same civilizational ambition we’ve given compute. The internet as a one-time subsidy It’s underrated how much all progress in AI owes everything to the blessing of the internet, this one-time civilizational subsidy to deep learning, decades of unintentional accumulation of a perfect dataset: every book, blog post, image, video, paper, discussion, etc. all digitized and freely available. Without the internet, we’d likely see comparably minimal progress in AI today, and in fact, if you notice where systems currently underperform, it’s almost always a domain where web coverage is limited and data is private, expensive, non-digitized, or non-existent. But we’re running out of it. There are only about 300 trillion tokens of useful public human text, and the internet doesn’t produce nearly enough new high-quality data to match what scaling demands — we’re soon to hit the limits of public data for pretraining. And though the advent of RL bought us reprieve — chain-of-thought RL needed a new form of untapped data, gradable math & coding tasks, also available online — we’re quickly running dry of hard tasks for RL as well. Why do we need so much data anyways? Humans learn comparably in far less time, needing just one textbook where language models might need the equivalent of hundreds to learn a new topic. It’s possible we discover methods that are massively more data efficient — synthetic data, data efficient architectures, other exotic algorithms — but fundamental progress is slow and highly unpredictable, and the recipe we have just works today. And, while I’m wary of getting too deep here, even arbitrary data efficiency can’t replace data that just doesn’t exist in the first place. There’s a massive amount of missing information on the web: the dark matter of the internet — tacit knowledge, undocumented processes, etc. — most of which was never published and lives only inside organizations, the physical world, or just in people’s heads. I’ll leave it here and say, for reasons far longer than I can fit in this post [1], it’s best to operate on the assumption that our insatiable desire for data will continue as it has for the last decade. There will be >$100B/year in data spend by 2030 We’re not screwed yet, of course. Only a fraction of useful data in the world is on the public internet, the rest is stored inside private datasets, corporations, personal archives, universities, governments, and otherwise. Labs can and will continue to license these private datasets, or create them from scratch, like Anthropic’s book scanning project. And we’ll increasingly task human experts to manufacture new high-quality data, with a large fraction of hard RL training tasks already being sourced this way. But collecting this data, unlike before, will be expensive. As the free internet dries up and demand for data rises, we should see labs investing equally in data as compute, likely spending a significant fraction of their compute budgets on data. As we see trillions spent on compute, we should also expect hundreds of billions spent on data (human data & collection budgets), given their equivalent importance. And, notably, data spend is already tracking this way: total data spend across vendors, not counting internal lab efforts, is already roughly $7 billion per year. It’s quite reasonable we’ll see >10x by 2030. Data is the moat Data becoming increasingly private will also majorly shift the competitive landscape. While compute is a commodity — everyone buys the same chips and builds the same clusters — data really isn’t. The big reason why frontier models have felt eerily similar to one another, until now, is they were trained on substantially the same internet (pretraining data variability across labs seems pretty low). As labs diverge onto more exclusive, manually collected corpora, I think models will begin to increasingly diverge. OpenAI pulling ahead in mathematics and Anthropic in cybersecurity isn’t an accident. I really think laser-focused collection of high-quality midtraining tokens, custom RL tasks, environments, with dedicated research effort, has driven much of the visible progress in the last year. James Betker has an excellent blog about “the ‘it’ in a model is the dataset”: model architecture and compute buy you efficiency and order-of-magnitude performance, but ultimately, models, of any architecture, are such incredible approximators of their dataset that the core meat of a model boils down to just that, nothing else. Data is a major moat. AGI long, ASI short As I’ve tweeted before, I’m confident that, despite the narrative, the data labeling industry will continue to fuel great businesses and be an excellent AGI long, ASI short. The argument is just: By the time the AGI labs no longer need data, it’s probably over for everything else too [2]. In this frame, the last companies left should be the data companies, as the last speck of economically relevant data is sucked in. And these companies are already among some of the fastest-growing companies in history: Mercor, founded three years ago, is rumored to be doing $2 billion in revenue with something like a few million expert labelers under contract. While these businesses are very non-stationary, what type of data is needed shifts constantly, I don’t think that diminishes their value. The long-tail of the economy is long, and the value isn’t diminishing as you extend farther into more obscure information: as models get more capable, the value of the marginal dataset goes up, not down. Automating a full job means covering its full distribution of tasks, tools, edge-cases, and long-horizon loops. There’s some O-ring logic to it: a dataset that buys a 1% bump can justify a previously unjustifiable collection cost when it’s the difference between a system that does 99% of a job and one that does all of it [3]. The competitive dynamics of the data industry are still evolving but as demand for data is increasingly niche, ultra high-quality, expert-generated, I think we’ll see real consolidation. Again, contra-narrative, we’ll probably see true competitive differentiation built on brand, quality control of data (which, from personal experience, can vary massively), as well as in network effects from the talent networks themselves over time. We’ve already seen rapidly shifting data type demand work in favor of incumbents, benefiting those with early knowledge of where the market is headed. The binding constraint It’s truly remarkable that we seem to have the recipe — pretraining + RL — to absorb most economically valuable work, despite being far from a lot of what we expected from “AGI”. The same way chess engines revealed we never needed general intelligence to solve chess, as we originally thought, we’ll soon realize that software, mathematics, and the vast majority of the economy (including physical, just running ~3 years behind!) are the same. If recursive self-improvement or some other algorithmic breakthrough arrives, that’s wonderful, but we really don’t have to wait for it. The binding constraint between here and an automated economy isn’t that, it’s data coverage: every app, workflow, edge case, process, etc. sitting in private stores or someone’s head. Ultimately, while we make tremendous strides in more efficient model architectures, and clusters like Stargate equip us with zettaflop-scale compute, we really aren’t making rapid progress collecting the data we lack. We’ll soon live in a world where we have the methods & compute to accelerate scientific progress or economic growth, but not the data. And we’re already there today: frontier models would surely be as good at accounting/many medical tasks/legal advice as they are at software engineering if we only had the same pretraining & RL coverage as we did for code. I really want to drill this in: The speed at which we automate the economy is going to be directly rate-limited by our ability to collect data about it. Worth noting that under this assumption, with data as defensible and directly proportional to economic & scientific progress, data should also be considered a national strategic asset like compute. Imagine what we’d do in a world where we had a Manhattan Project-effort for AI and needed to mobilize data collection as a limiting factor. We should be concerned about China, with greater state capacity and authoritarian economic control, being capable of mobilizing data collection at national scale, potentially compounding their economy and scientific output faster than us down the line. A Stargate for data I’m leaving my complete ideas for a future post, as this one is already far too long, so I’d really like to pose the question here. Stargate exists because we organized trillions of dollars, international strategy, gigawatts around compute as a fundamental ingredient. What would equivalent ambition look like for data? Obviously, scaling data collection, a heterogeneous mass of information across the economy, isn’t going to be as clear as scaling compute, as a homogenous infrastructural effort. A core division will be first, coverage — all uncaptured knowledge sitting across the economy/science/physical world and all that simply isn’t recorded — and, secondly, sheer volume in the domains we already train on: more hard math tasks, more high-quality web text, way more coding data, more legal drafts, etc. I have a post coming soon which breaks down my proposals. There’s a lot of room for creativity. Quickly, we’ll probably want to start with a deep census of what we have and what we’re missing, predict what the 2030 model will still be bad at and work backward to what we should be collecting today. You can probably license a large amount, leveraging high lab valuations to buy datasets or companies altogether. There’s an adversarial nature to a lot of this collection with firms, so there’s lots of engineering to do this correctly. We should go convince important companies to turn off deletion policies, even if we’re not buying from them yet. Data flywheels in consumer products will be massive. Confidential training, government legislation for grant-funded research, running companies at a loss for their data, etc. We’re headed towards hundreds of billions in expenditure, national prioritization, and major data limitation on the horizon. We have a great opportunity to think creatively about what a megaproject for data would look like: How do we, deliberately this time, construct the next internet’s worth of data? Footnotes: [1]: I’ll probably soon publish my much longer post explaining my position on data efficiency and why the value of this data is still pretty high in most worlds regardless of new algorithms. [2]: The “AGI freeroll” bet: heads you win, tails ASI flips the world upside down anyways. [3]: We already see a glint of validation of this point, given the data market is strongly tilting towards ultra-high-quality agentic data, rather than unskilled labeling — niche expert workflows, live environments, and evaluations requiring increasingly obscure talent & knowledge — yet shows increasing, not decreasing, revenues.

the best part of venture is that there's no rules find a way to make your LPs money is the only thing that matters (scout fund, seed fund, SPVs, etc.) 1 company, 5 companies, 30 companies, whatever works More GPs will pivot towards to SPVs, because it's becoming a better product for family offices / non-institutional tier LPs. Very few want to be in specialized funds and if they do, they are positioning for co-invest access

@uselegion @rex_woodbury missed us - @RichardDreamVC @jonahvella micro1, valar, prizepicks, mezcla opportunistic capital

Emerging managers worth watching in 2026. Newer funds, still small enough to move fast and take the meeting: 1. Rex Woodbury (@rex_woodbury): Daybreak Ventures. Writes the Digital Native newsletter, left Index to raise his own fund. First checks into consumer and AI. 2. Alana Goyal (@alanaagoyal): basecase capital. Backs deeply technical founders before there's even a company. Early into Supabase, Vercel and Browserbase. 3. Michelle Volz (@MichelleVolz): Pax Ventures. Spent years on a16z's American Dynamism team, now runs her own $50M fund. Pre-seed defense, aerospace and nuclear. 4. Rex Salisbury (@rexsalisbury): Cambrian Ventures. Built a16z's fintech practice, now runs a solo fund and a 1,500 person fintech founder community. All fintech, all pre-seed. 5. Maria Rotilu (@mariarotilu): OpenseedVC. Ex-Octopus, now writing first checks into operator-led startups across Europe and Africa. 6. Sarah Drinkwater (@sarahdrinkwater): Common Magic. Ran Google's Campus London. Backs companies where community is the moat. 7. Mike Annunziata (@nunzi46): Also Capital. Pre-seed hard tech. Was into Varda Space, Radiant Nuclear and K2 Space before most people would touch that stuff. 8. Carles Reina (@Carles_Reina): Baobab Ventures. Was the first investor in ElevenLabs. Now backs AI, robotics and defense founders out of his own fund. 9. Ryan Hoover (@rrhoover): Weekend Fund. Founder of Product Hunt. Small early checks into consumer, AI and creator tools. 10. Todd Goldberg (@toddgoldberg) and Rahul Vohra (@rahulvohra) Todd and Rahul's Angel Fund. Rahul runs Superhuman. Together they've backed 120+ startups including Mercury and Supabase. 11. Sheel Mohnot (@pitdesi): Better Tomorrow Ventures. A whole team of ex-fintech founders backing nothing but fintech. 12. Jeff Morris Jr (@jmj): Chapter One. Ex-Tinder product lead. Backs product-obsessed founders in consumer and crypto. 13. Neil Murray (@neilswmurray): Nordic Web Ventures. Wrote the first check into Lovable before it was a unicorn. Very early, Nordic, AI-native. 13. Nathan Benaich (@nathanbenaich): Air Street Capital. Writes the State of AI Report. AI-first, earliest checks, Europe and the US. 14. Jenny Fielding (@jefielding): Everywhere Ventures. Ex-Techstars. Tiny first checks into pre-seed companies all over the world. Who did we miss? Tag them below.