YouSaid · the spoken record

Nikesh Arora

lines on the record: 94
first: 2026-06-22
most recent: 2026-06-22
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Then the question becomes all right, oh shit, the bad actor is in my infrastructure. How quickly can I find him and get rid of him? That becomes an AI task. That becomes the same conversation having so far is I need context, I need intelligence, I need to know what this means. So creating that context, that intelligence within the enterprise of what this intrusion means and how to protect against it becomes a challenge. This is the AI cybersecurity challenge, something, you know, again, this is not trying to hear to pitch my book, but we spent five years trying to build that capability inside enterprises. So in the net net, it ends up being accelerant. Does that mean I have everything I need? Not everything? Does that mean I need to get AI models to start helping me? Yes. So we're going to infuse more AI into our defense infrastructure.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, look, the solutions are there. The challenge is getting sometimes getting the attention and focus of the customer saying, listen, I got to go fix my stuff because it's important. What this has done is lit a fire under the security practitioners around the world saying this thing is not good. This is going to weaponize the bad actors. I better make sure my defenses are in place. Now remember, the way cyber defense is done is fundamentally just cybersecurity is two fundamental things, right? One thing is if it's bad and I'm at the gate, I'll stop it. Which means you have to have somebody at the gate. Now we have 150 million sensors in the world where we stand at the gate protecting our customers. If I can find a way of infusing AI at the gate and taking all these vulnerabilities and finding a way to protect you, I'm good. I don't have to chain the gatekeeper because there's no clawed endpoint agent that exists out there. There's no open AI endpoint agent that exists out there that I can replace Palo Alto Alto or the other people in the space with. The problem is not at the gate. What happens is despite all the perimeter defense you put in, things come in, things leak in, people make mistakes, people passwords get breached, there are vulnerabilities, people get in through.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Models against your infrastructure that's going to find security flaws, security vulnerabilities, misconfigurations, things that you've not been paying attention to. So it creates a bit of an urgency on the parts of the customers to improve their cybersecurity posture, which I think generally is a good thing for cybersecurity companies.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Somebody's left a WebSocket open, somebody's done some mess up in IP addressing, et cetera. So it finds the flaws outside in. That allows the bad actor to go figure out how can they daisy chain vulnerabilities and get into your infrastructure. It's not good enough from a defensive perspective because I can't use the model and say, go take every vulnerability you found and build a patch and go patch my system and protect me. So guess what? It's going to patch 30% of things which are not wrong. Who knows what that's going to do to blow up your infrastructure? So when Mythos came, we looked at it, retreated with respect. We ran it against our code. We discovered it finds bad stuff much faster than humans can. We found in six weeks what would have taken us five to six years. So we got it. We ran around, we patch it, but cloud code helps build the patch. But you'd have to run it through human eval, through testing, through production testing, to sandboxing to see, does this patch break anything in infrastructure? Only then, after six weeks, were we able to go patch everything? So what does this mean? This means that every enterprise better fix their stuff faster. Because if I point the next generation,”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I think ends up being an accelerant to cybersecurity. I think what happened when you saw Mitzos came out, it demonstrated that all the training we've been giving these models on how great code is written, the models were able to turn it around saying, well, I also know how to find bad code. So what happens is you point the gun the different way and the model says, oh my God, look at all this code that you have. There are so many flaws in it. As we talked about, the challenge is like every model, it also suffers from false positives. If you're an offensive actor,”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Than that, if you look at the consumer interaction that you have with your favorite frontier model, right, it's starting to remember, oh, you asked about this yesterday, you asked about that. Should I take the question just ask me in the context of everything I know about you? Or should I just limit the answer to as if I don't know anything about you? Now, having context of what I said to you over the last 30 days or the last 60 or 90 days requires you to store a lot of information It requires a lot of personalized interaction that needs to have. Now, if you want to maintain your moat with Harry and Nikesh in the future, the more context you have about me, the easier it becomes for you to give me the answers in the future. And as you start building context on a user basis, you create stickiness, and that becomes your mode.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“You want what I want? You're seeing that in the consumer space. That has not yet come to the enterprise space. Funnily enough, I think that shows up in the enterprise space, which means the demand for computer memory goes up on the enterprise side. Like how many, I don't haven't used all the coding models myself, but over time these coding models have to get really smart about understanding individual context of enterprises and humans. That's how they'll be more effective and more efficient. For that, we're going to still need more compute and more memory. So I think that will start defining where the value accrues. I think the value gets shared between frontier models and the context that gets created in enterprise play. I think the frontier models are fully understanding that this is where the gap is. I suspect the frontier AI models as a crystal ball, they will spend a lot more time the next year or two building memory around consumption.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So I think there may be a digestion period at some point in time once we think the demand for compute is there, but the capacity to execute is now limited by physics, and the infrastructure players have built up too much capacity for this demand. I don't know when that rationalizes. Maybe it rationalizes and causes us to go think about a different sort of timeframe for putting all this compute out. That doesn't take away for the need of compute. We'll still want as much compute as we can deliver as fast as we can deliver. I think some of the model companies have outstripped anybody else's ability to build frontier AI models of that capacity at that speed in the trainings. I think you are seeing perhaps a settling down of who's going to be the frontier model player in the future. The question becomes, in the economics, what value accrues to the model, what value accrues to the application layer, as you said it. And I think the application layer is probably is a simplistic term because for the first time, you have memory and applications. Applications understand context. They understand context as specifically as to what...”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“The question which I don't know the answer to, and you can tell me so you spend more time with people here, I have to go to my day job at what point in time does physics kick in and we just can't produce the compute as fast as we want to. Like infrastructure people are gearing up for large amounts of capacity, large amounts of demand on the infrastructure side, and you come to a point where it says, you know what, it's only so many data centers we can build. There's only so much energy we have.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes. If he's making money, Info is more expensive than it's ever been. That's who you're seeing trillion dollar market caps in the info space because of the scarcity of compute and this need for speed.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Queries that we all understand. That's right. The problem is they were inefficient from a computer perspective. So you're seeing the efficiency come in. The problem is the cost of RD is now being spent in terms of what the tokens have to pay for. So I think token prices come down. I think the amount of compute that we need is going to be huge to the next 10 years. I think the frontier AI models are in a position to capture a significant amount of the future economic value of the use of AI.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, right now they're figuring out that all your frontier model companies are value maxing, not token maxing. They're raising money at a trillion dollars. At some point in time, they realize, oh my God, where's the next $100 billion of compute going to come from? Financial markets are not going to bear the cost of another $100 billion at a trillion dollars or trillion and a half because I'm going to need again the following year. So you say, well, I need to build a sustainable business model which starts showing some degree of gross margin profitability. The only lever they have is to take the fastest growing thing that they have on their portfolio from an economic perspective and charge us more for it. That's where you get the price of tokens from. I think the price of tokens are high. Now, you can imagine if the price of tokens high, every technologist is trying to figure out how do I make my compute more efficient, right, in the future. So I'm sure we'll see a whole bunch of advances in the world where memory and computer start getting used more efficiently from modeling perspective. I still believe I don't need Fable 5 or Mitsos 5 to do 90% of what people do with the ID.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“It's highly inefficient. So you could imagine a world where AI makes marketing really efficient and you get more dollars coming from traditional marketing into the online world because coming in the form of transactions.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“That's fine. The thing the best of breed is 7 to 10 percent. The average probably one and a half to 2%, which means 85 to 90 percent of your marketing is wasted. Now, if you get really smart, you have memory, you have context, and you get smarter in targeting Harry when he's trying to buy something out there in the world, then your conversion rate goes up. If you look at the entire value from the time you decide to buy something till the end, you get the product, you take the case of consumer goods. Today I want to say the cost of consumer goods is probably in the 5% to 8% of total list price. The 92% is distribution and marketing.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, think about the marketing chain, right? We do advertising, advertising is inherently efficient. What's the best conversion rate you get in online advertising, you think?”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Know that's an interesting question. It has to come from somewhere. When I started Google in 2004, we were 2% of the global advertising revenue and global advertising revenue was estimated between $500 or $600 billion. I think online is about 70% by lost count of total advertising revenue. And I don't think the overall number has changed by more than 3% a year or 5% a year. So I don't think the total advertising pie is going to increase. We've already taken away 60, 70% of the advertising pie in the online world. Unless you tell me there's going to be explosion atop where more people are going to spend more money in marketing, that money that you're hoping to fund consumer AI from advertising will have to come from current advertising revenues. So I don't think that changes the equation drastically to make the consumer profitable. I do think there's an opportunity that AI ends up taking more transaction revenue, which has not been into the purview of AI.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I think so. I think in the next three to five years, we will see a reduction in token pricing. I think at some point in time that consumer use of AI will get constrained by these frontier AI companies because they have enough post-training data.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Now you're saying enterprise applications coding have to pay until we build transaction models or advertising models on the consumer side because they're not ready. Now, you can say, well, that happened in Search too, right? Google search was around, that happened YouTube. People used a lot of YouTube, a lot of compute, but it wasn't paying for itself. The problem is the computer requirements and the cost is now 10x of that when we were in that era or assist today. That's forcing token prices to go up. I think the long-term token pricing should be one-tenth of what it is today. What that happens, you will see that people will consume more. You can decide if 3.8 or 15.8 is not sure we can tell the answer right now because pricing will move very drastically in the next three to five years.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“That's still a narrow lens for me. If I abstract, if I step back today, there's not enough compute for what the world is demanding. Unequivocally, not enough compute. You can't buy compute. Compute is costing two to three x or 4x more than it used to cost two years ago. There's not enough compute. That scarcity of compute and that excess cost required to build and deliver compute, which allows us to go make AI useful, is causing the constraint and forcing pricing. And interestingly, more than half of the compute is going to feed the consumer, which is a fundamentally loss-making entity right now. I don't think any of the frontier models make any money in trying to get you and me to use chatGPD or Claude or Gemini every day. It's free. That's a lot of compute. Imagine there's billions of people around the world using for all kinds of queries every day. That's sucking away half the compute, which is making no return. Guess where the pressure goes? The pressure goes on the other half of compute, which is being used for coding in enterprise applications.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“We have a used judiciously model for tokens. It's not a free for all. Free for all sounds like it can go token max the hell out of it. We have to use it judiciously and we keep track of it to see what people are doing. And if we find somebody who's using it well, we won't constrain them. If we find somebody who's gone a little over the top, we'll find a way to.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, possibly, but I think part of the challenge is right now, everybody's experimenting on everything, and you have to figure out what is it that I need to build as an enterprise and what can I get off the shelf. If I can get an AI-based thinking application that does marketing for me, I don't need to build it. It's a generic problem everybody needs to solve. I can tweak it, I can customize it just the way I did with SaaS applications, but I don't need to build mine from scratch. So I've made sure that everything my team is building is proprietary to us. Where do we have unique, distinguished knowledge that we bring to bear, which nobody else can do on the outside? Let's put that, let's package it, let's use it. Where it's going to be a generic AI application 12 months, 24 months from now, let's just wait.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Give me three years, I'll have hopefully enough AI savvy people working at Palto. So there are two different ways to get there. People are experimenting. The risk is your smartest employee who knows how to use AI really well could be using 20 times the tokens that an average employee uses. And if you get into this whack-a-mole moment saying, oh my God, I'm going to stop people spending too many tokens, you actually will hurt the best AI savvy people more than you will hurt the average employee.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I know this whole world of Duncan Max Singh has got topsy turvy with the whole conversation around how many tokens people are using. The challenge right now is 90% of the enterprise employees are not AI savvy. They're not. They have to learn. I can't send them to university. There's no course you can take at any school anywhere. They have to be able to learn of their own. I think we're back to a Darwinian moment where everybody has to figure out who's really good. Now you've seen people like Brian Armstrong and Jack Dorsey go out and say, I'm going to decimate my organization and I'm going to start building from scratch and they've gone to some version of 30, 40% less people because they figured out there is no redemption. I can't train these people. I'm going to just find the people who are going to come in and help me do this stuff. That's one model. The other model is sort of gradual. We've been hiring people only through hackathons now, right? We see natural attrition of 2% give or take a month and we just replace them with people who actually are AI savvy people we hire from hackathons. Give me 12 months. I've sort of transformed 20-25% of my team.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Them everything we do. And the problem is not enough time in a day because I'm too busy dealing with some arcane piece of software at work, which I have to go feed. Well, if I had that software be really intelligent, it'd tell me what to do.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I think the places where people could be wrong is how many technical resources we need in the future. I think we need more, not less. I think there's this fallacy people believe we're going to have less people working because AI is going to take over our jobs. I don't believe that. I think what's going to happen is, you can't imagine the number of people on my team who want more technical resources, more AI savvy resources because they want to do exactly these things. Saying, oh, I've got an amazing project to transform marketing. I've got an amazing project to transform HR. What do you need? Oh, I need more people who understand how to prompt frontier models, build harnesses, bring proprietary data into play, bring modes. I need more compute, more storage because I want to learn everything. So I think we're going to need more technical resources. I think we're going to need more sales resources because if your product's really good, you need more people to go out there and cover the universe because not enough people know about it. I'm in Europe. I met 20 customers last week. I still see half them don't know all the stuff we have. I'm like, dude, we've been around for 20 years. Why is my team not out there pounding the pavement?”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Everything, right? The scholar, whether you want to call it an AI assistant, AI marketing assistant, AIHR assistant is going to say, I looked at your copy, it sucks. That's not good enough. It's not consistent with the tone of voice. Here's what I would recommend. This has an opinion. That will make my average employee much smarter than they were today. Then I don't need so many of them because they're doing most of the work for you.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Know my rule of thumb is that in the next three years, we'll probably have half the people in GNA type activities in companies, things like marketing, things like finance, things like HR, because there's a lot of process management there. A lot of process management can be made more intelligent using some version of an adapted future AI application, for lack of a better word. So SaaS applications will give weight AI applications. The difference being SaaS applications have no opinion. AI applications will have opinions. That's a fundamental rethink we need from a workflow perspective.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“The things we've been talking about over the last 10 years, I think some people have done that, they've actually analyzed earning scripts for companies and said, the last 20 years, what has company X done, and when does the CEO start getting away from a topic because perhaps it's not going so well. So this is really smart. You can do that stuff. So it's the best marketing training database in the world, the frontier models. Why do I need 400, 600 people in marketing? Because my biggest problem in marketing is I have 600 people, but I'm not sure they all fully understand how to consistently deliver my tone of voice, my value proposition, and how not to break my brand by having different collateral in the public domain.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Orthogonal points. I think two different points. I think mass collection of data to inform AI is a little dangerous because people see the outcome of what that's going to happen, right? And I've heard of companies where people are using cameras to track people folding laundry and earning clothes because they want to be able to train physically in the future to do those things. So that part aside, I think on the enterprise side, we can actually run a business much more effectively and efficiently if we decide where we are willing to relinquish control to AI. Perfect example, marketing, right? Anything that is required to train a marketing model is already out in the public domain. By definition, marketing is public domain, right? If it didn't market it, it's not in the public domain. So I have the best training data in marketing. I don't need to train an AI model with more marketing content. I may need to train it for tonavoise and what my brand is. And I'm pretty sure an AI model, if I throw my marketing collateral into it, will tell me this is not consistent with your brand.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Saying interviewing this person asked the following 10 questions because your three other colleagues only asked the following five. We needed no false positives to the human being. So AI could be hugely helpful in informing and making the process more intelligent. But that requires us to give up human control and let AI do 80% of the thinking for us. That's not how we're doing it right now. All we're doing is let's take this invoice, let's scan it, abstract the data, put it into AI, and say, look at that, it's happening 20% faster.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“No, I think there's going to be perhaps two or three different categories. One category is let's take the workflows of today. We have workflows around ERP, we have workflows around sales, team management, we have workflows around human resource management. There are existing workflows which have been sassified, as in some software company decided we all have common processes. Let me build a container where these common processes can be marginally customized by enterprise. They can code their workflow into my SaaS application and we're off to the races. Now that workflow required a lot of human judgment and human interaction. The software is not intelligent. It's been coded, right? You define input, you define output, I do the input, I know what the output will get. The idea is imagine workflows where in the hiring process, most of your workflows are containers. Imagine where AI actually is helping you make judgments. If I put every CV into AI and says these are 20 people you interview, just look to the CV, you should ask this person these following questions, sends a note to have.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I think more than half the enterprises are still not getting it right on the use of AI perspective. I think we're still busy trying to incorporate AI into our current business practices. So how do I take what I do today, use a little bit of AI, get marginally more efficient because I don't want to do this the old way. I think the opportunity is to rethink your workflow fundamentally with AI. That's where the true benefits are going to come. I think the winners in the long term are people who actually rethink their companies with AI, not people who adapt their current workflows marginally with AI.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Constant tension, the frontier models want the consumer attention because that drives post training for models. It drives the consumer brand of the model. On the other hand, the real enterprise revenue is going to come from use cases that require a lot more context. The one sort of standout use case we all know is coding. Coding is a universal activity. Everybody does it. So everybody's data is helpful in training the model. And that becomes sort of a large enterprise application. Hopefully there's a few more out there. But I think that's the tension that I wrote about.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Now take Wemo. In my view, Wimo is the biggest age product that is out there because guess what? You've replaced a human being called a driver. All decisions are made by AI machine learning. It decides when to turn, when to stop, what to do. But think about the amount of edge case training it took to replace that human agent with effectively an AI-driven agent. I don't know, tens of billions of dollars. So that's what it takes to take one use case and train the hell out of it. And if you think about what happened there, they could have used equivalent of an AI model, but then they built so much context and intelligence and edge case training and proprietary data to make that happen. That data is not available on the internet. You can't stick the next model of anthropic into your Mercedes and say, okay, drive me home. It's not going to be able to do it. And that's the depth issue, right? Because you need the depth of the context and understanding and the intelligence around the model to make it useful for the truly agendic use case. So I just think there's this.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“And the false positive doesn't matter. On the enterprise side, false positives matter a lot. They matter because if you imagine a future where an agent's going to make independent decisions and act on it, you have zero tolerance for false positives.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Brother, they believe the model or not, and somehow people get rid of some false positives. Somehow people don't care about the false positives. Sometimes people believe the false positives. So the consumer is highly tolerant on this notion of false positives and doesn't seem to distinguish and it just seems to get better and better. I literally had Gemini produce an investment memoranda for something I was looking at. I looked at it, it looked pretty accurate, give or take, I tweak a few things, but it seems passable. So on the consumer side, it's a breadth issue, right? It wrote an investment memorandum for me, which is cool. I would have had to hire a banker and a bunch of investment analysts that have taken me days and I did it in four minutes. So the breadth is there, which means it's my go-to place. And as you know, in consumer, if you become the go-to brand, talking about brands, it's hugely beneficial, right? Whether it's YouTube, that's the only place to go look for streaming video or Google. That's the only place to go do a search, it becomes hugely multiplicative from a distribution perspective. So our frontier model friends are chasing the consumer brand.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I've been listening to some of your podcasts and I've been paying attention to what happens to the market because I want to understand where all this settles down, not just because I want to understand it, but also impacts how I build my own business. So you got these phenomenal frontier models and they keep sort of leaffrogging each other. Every few days there is a net new model that's delivered by OpenAI or by Google or by our friends at Anthropic. And the question becomes, okay, if I'm models are moving at this pace, what do I need to build? What do I need to do with these models? And as we came with that mythos moment when everybody was busy chasing mythos and that was kind of important, realized even the best model has a high false positive rate. But for some reason, in the consumer space, we don't seem to care. I was talking to my sister this morning. So I just went to ChatGPT and asked all these questions. It was very helpful. So I guess what happens is the consumers are way more tolerant of false positives because just kind of always the person in the middle, right? There's always somebody who's understanding what the model says and making their own judgment.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Look, I used to speak against my own tissue. There's a spectrum. On one end of the spectrum, we have real differentiated product, in which case the product helps build the brand. Google search is now you Google something, right? On the other hand, it's a commodity. It's water. I don't know why this is called Avian, right? But this is a commodity. Here, only brand matters. So depends where you're on the spectrum. If all you are a commoditized product, yes, brand matters a lot. If you are a differentiated product, then you build the brand in the back of the differentiated product. And you can decide where you want to be in that spectrum.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“See, I spend many years at Google, as you know. Actually, I was Chief Marketing Officer for five years. And if you look historically in technology, there are companies which have died who had great brands. Remember Sun Microsystems? He's a darling 30 years ago. It doesn't exist. Why? Because their brand went dead or the product went to hell. What about Yahoo? Remember that company? It's a great brand. Amazing. It's way before Google. I think it's probably a fraction, probably even two decimal point number versus what Google is. So I think product helps make brands.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I think if you build a great product, a great company, people like your product, eventually your brand survives all of it. I think you can have a great brand, shitty product, shitty execution, and your brand goes to hell in a handbasket. So I flip it around.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Not for the lack of trying. All we can do is try. If it works out, it's great. And if you try a lot, you succeed more often than you think.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I'm He Actually, let's spin that a bit. How do I make it better? Change the outlook. How do I make it better? What can I do to make this better? That's why I run my day and run my life, my company. How can I make it incrementally better today? And how can I make it radically better in three years?”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, the good news is I told you earlier in the chatting, I have total memory loss. I have no recollection of what I said and what we talked about. So I have to go back to listen to it. I'm just happy to be here. It was great. We got on blissfully, Al. It was wonderful.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I think the long term token pricing should be one tenth of what it is today. Mythos ended up, I think ends up being an accelerant to cybersecurity. In technology, you miss one trick, you can survive, you miss two tricks, you're partly impaled, you miss three tricks, you could be obsolete. I came to the United States with two suitcases, $200, and I was willing to do anything, anything at all to make sure that I made a life for myself because there was no way to go back. When I came to the United States, I was a security guy. I took notes of the disabled. I flipped burgers at Burger King. I had $200. I had to find a way of paying my tuition.”
2026-06-22 · The Twenty Minute VC · 20VC: Nikesh Arora on the Frontier Model Problem: Breadth vs Depth | The Future of Token Costs | Memory Becoming the Moat | Where Value Accrues: Infra, Models, or Apps? | Why Enterprise AI is Not Ready & Systems of Record vs Systems of Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source