
Co-founder @FabHotels @TravelPlusHQ | Previously FabFurnish, Groupon, Bain & Co | Wharton, IIT
Joined March 2009
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Jensen reveals he first met the Founder of TSMC by cold calling their number off Yellow Pages
"I heard about this company in Taiwan, TSMC and UMC at the time. They were of course quite poor companies themselves, and they were barely making it."
"TSMC was probably about $300 million, something like that. It's a tiny little company. I heard about Morris Chang. And so I tried to call their US office."
"Back in the old days there were yellow pages. I looked it up, TSMC. There's a number there. I called their number and it's a recording. I left my name and my number and said a few things about NVIDIA: we're a new startup company building computer graphics chips. I called several times. Nobody called me back."
"And so I finally wrote a letter to Morris Chang. Didn't hear anything for a long time, and then one day it was 6 o'clock or something, this person called and said 'this is Morris Chang from TSMC.'"
"And meanwhile, everybody outside my office was playing video games and they're all laughing and screaming. 'Hey shut up! It's Morris!' I was so happy that the founder would actually call us back, because hey, maybe we won't have to die."
"And so we talked and he invited me to Taiwan. I came back to Taiwan, and that was the start of everything. Not just that partnership, but just the whole industry."
Vaibhav Aggarwal retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
Vaibhav Aggarwal retweeted
Assorted things I've been mulling over regarding agents in the economy:
• Companies are in some sense the original superintelligence. It's often the case that they can use their amortized cognitive surplus to advance their interests against those of certain consumers: relative to the optimization capacity of companies, consumers can be inattentive, underinformed, irrational, etc. (Think, say, of some of the classic insurance upsells at the end of a booking process, which is typically priced substantially above what a regular policy would cost.) Herbert Simon introduced the idea of bounded rationality, and firms often end up benefiting from it. Thanks to personal agents, we will probably get somewhat more rational in how we engage with companies.
• How exactly will outcomes change? Well, certain kinds of price discrimination will work less well (how do coupons work if every agent hunts fastidiously for them?), and certain kinds of customer cross-subsidy will change (today, people who forget to cancel subscriptions subsidize those who don't). Taken across the economy as a whole, I'm not sure what the incidence of all of this will be.
• In corporate strategy, people have long debated the relative importance of distribution and product quality. Perhaps you've made something better, but does anyone know about it? Won't people just continue to buy the ACME Corp Mousetraps? Personal agents will, I think, act as a kind of structural subsidy for product quality. How would markets look different if individuals always spent at least 10 hours researching their purchases? On the margin, focusing on making a product better is, I think, going to become a more effective strategy for companies. This is good!
• It true, a corollary is that information about product quality will become more important and impactful. Today, most people have very limited ability to aggregate and integrate such data, but agents will have insatiable thirst for such signals. (It's often said that data is becoming more valuable because it helps in the training of models. That's obviously true, but I think there's a secondary way in which it's more valuable simply as a complement to cheaper intelligence.)
• A big question is whether agents will increase superstar effects (rational agents all settle on the same product) or increase dispersion (because everyone has slightly different tastes and preferences, which agents are good at eliciting, finally overcoming the artificial clustering we've been subjected to).
• Many internet companies have monetized demand routing, which is in turn often predicated on short attention spans. "I'll click on the first thing I see." This has led to some amount of inefficiency, as companies try to propitiate capricious ranking algorithms and incur various placement fees. It's not all deadweight loss (willingness to pay is itself a kind of signal, which is valuable to buyers), but it's not great. How will the nature and value of this routing change in a world of personal agents? I don't know the answer, but I think that the effects might be pretty big.
• Much of what I'm writing hinges on the assumption that personal agents will be on the consumer's side. I suspect that that will be the winning strategy, but whether it's true, and what it means to be true, involves a lot of other questions.
Taken as a whole, the market is a massively parallelized, decentralized, and highly imperfect value attribution machine, and, taking stock of all of the effects, it seems to me that the quality point may well be the biggest: that agents will change the nature of the backpropagation such that the rewards for better products increase. Maybe this is too optimistic, but I suspect (and hope) that companies that make things that they're proud of will feel that the universe is a little more partisan in their favor.
Vaibhav Aggarwal retweeted
My best career advice:
At every job you should either learn or earn. Either is fine. Both is best. But if it's neither, quit.
Vaibhav Aggarwal retweeted
I've been talking to a YC founder who's 15 years in and still doing big new things. One of the biggest popular misconceptions about startups is that founders have a brilliant idea, then implement it. In fact more often than not the biggest idea comes later.
Vaibhav Aggarwal retweeted
the purest form of motivation is curiousity. hopefully in something that is unclear or boring to others but super interesting to you.
curiosity-driven motivation also prevents against BUI (building under the influence…of headlines, buzzwords, what VCs are chasing, and other signs that you’re following, not leading).
curiosity also summons energy you didn’t know you had, and it’s infections with the type of people you’ll need for your journey.
Vaibhav Aggarwal retweeted
Most successful startups consist of a wonderfully popular product more than compensating for many mistakes by the founders. You have to have that wonderfully popular product or there is too little margin for error. But if you do, it will buy your way out of all kinds of trouble.
Vaibhav Aggarwal retweeted
I tried asking myself what I'd learned after 40, and it turned out to be a lot of the most important stuff I know. At least 90% of what I know about startups, because I was 40 when we started YC. Plus everything I know about parenting and most of what I know about writing.
Vaibhav Aggarwal retweeted
Earnest builders win.
So interesting to overhear and speak to a bunch of young talented kids these weekend. Frankly, you can tell who’s gonna make it and who’s not, very quickly.
The earnest, excited young builders? Gonna do so well. You can just feel it. For the rest, I just really really hope they get some good guidance and figure it out, because being earnest and helpful and optimistic is gonna take them so so much farther than anything else in life and I know they have it in them. Speaking from experience.
Vaibhav Aggarwal retweeted
For my first post, I’m sharing a letter @NVIDIA signed on why open models matter.
AI will transform every industry, power every company, and be built by every country.
Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty.
The world needs both frontier closed models and frontier open models.
images.nvidia.com/pdf/Open-W…
AI Labs, 2024: We didn't steal your content; we just trained our model to learn! 😂
AI Labs, 2026: the Chinese are stealing our IP!!! Daddy Trump protect us!!!! 😂😂
Also, AI Labs, 2026: yeah, we stole your content — here’s $1.5b / < 1% of our market cap. 😂😂😂
AI Labs 2027: We're going open source, open weights and selling tokens for $1 per one billion
Vaibhav Aggarwal retweeted
One pattern I find useful for working with LLMs is a nice long ramble session. Sometimes the LLM needs more bits to understand what you're trying to achieve, but you're too lazy to type them. In these cases I like to lean back, switch to /voice and just ramble for like 10 minutes, total mess, anything goes, full stream of consciousness. Sometimes I declare it up top, something like "switching to speech recognition sorry for any typos...". Sometimes I turn it into a small interview of a few turns. But I find that the LLMs are somehow very good at reconstructing long incoherent rambles and often their echo of your own tangle of thoughts comes out quite a bit cleaner than what you started with. The result is that you improve the mind meld and have to correct things less from that point on.
Replying to @yacineMTB
I was clearly wrong about Anthropic. They are obviously currently the leader in AI. No company has released a model as good as Mythos/Fable and they will undoubtedly have Mythos 2 ready soon.
And I would never cut them off in a way that hurt them badly, even as a competitor. That’s not my style.
Tesla open sourced its patents and we made the Supercharger network available to all competitors, even though we could have made it a walled garden.
SpaceX launches competing satellite systems with no increase in price or use of unfair terms.
Even my worst enemies can attack me on this platform.
…
Vaibhav Aggarwal retweeted
The best predictor of success for tech companies, at every stage from during the YC batch to public company with billions in revenue, is the rate of shipping new stuff.
RoW would also need a neutral global hyperscaler to reliably serve open Chinese AI models. Current top-3 hosting is namesake and very unreliable (for obvious reasons).
A new @bgurley blog post!
I have been thinking about how sophisticated executives are using open source in super creative ways. Started writing this three years ago. Excited to finish it up and publish it! And with the new @p3institute brand.
substack.com/home/post/p-197…
@bgurley also, which IP holder has the economic motivation to be the donor in AI and AV?




















